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Rationalized immunotherapy by immune signature characterization
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Rationalized immunotherapy by immune signature characterization
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Content
RATIONALIZED IMMUNOTHERAPY BY IMMUNE SIGNATURE CHARACTERIZATION
by
Rebecca Eli Sadun
A Dissertation Presented to the
FACULTY OF THE GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
DOCTOR OF PHILOSOPHY
(PATHOBIOLOGY)
August 2007
Copyright 2007 Rebecca Eli Sadun
ii
ACKNOWLEDGEMENTS
I would first like to thank my advisor and mentor, Alan Epstein, for his guidance,
patience, and continued commitment to my growth and development. For three
years he has gifted me his time, his expertise, and his kindness – thank you.
I would also like to express my sincere appreciation to the other members of my
committee – Florence Hofman, Martin Kast, Michael Stallcup, and Alan Yang –
each of whom provided scientific, professional, and personal wisdom, my best
interests always at heart.
I would of course also like to thank the members of my laboratory – Meg Flanagan,
Nan Zhang, Robyn Arias, Sam Kim, Ed Diaz, James Peng, Maggie Yun, Mandy
Han, Peisheng Hu, Leslie Khawli, Xiaoying Chen, Will Morris, Ryan Park, and
Scott Bergfeld – every one of you was able to provide much-needed help at a
much-needed time. Thank you for your loving support.
I would like to thank the MD/PhD Office for its continued support of me and all of my
colleagues. To Sandy Mosteller, Roland Rapanot, and Dr. Henderson, thank
you for helping to make the MD/PhD program feel like an extended family, with
everyone rooting for everyone. To Dr. Chow, than you for taking a personal
interest in the lives and work of each of your students; your mentorship and
support is more influential than you’ll ever know.
iii
I would like also to thank my favorite administrators, who have shed a new light on
that term, and all of the members of the KAM Team who have kept me excited
about medicine and humanism throughout my time at Keck. In particular, Drs.
Arias, Elliott, Katsufrakis, Quinn, Schaff, Schechter, and Taylor, who have
provided me with role models at every turn and who have reminded me of the
great things that come from people who never stop caring.
My deepest appreciation to those who personally invested themselves into their
mentorship…Drs. Elliott, Dixon, Katsufrakis, May, Meyerowitz, Nyquist, Schaff,
Topaz, and Tsoulas. To describe what you´ve taught me would be trivializing.
To repay you for your dedication of time would be impossible. And to emulate
the energy, integrity, and humanity that you have modeled would be
unachievable. Nevertheless, I thank you for providing me with heroes.
To the members of the ICM Office, who helped me realize many dreams: Suzanne,
thank you for your warm smiles; Moreen Logan, thank you for your endless
energy and dedication – and for making things happen; Dr. Woehrle, thank you
for kindling my love of clinical medicine; and Dr. Schaff, thank you for kindling a
deep commitment to humanism in medicine by providing both example and
endless encouragement.
I would also like to express my love and appreciation to my family. Unconditional
love is what makes PhDs possible.
iv
A great many friends have provided me with support, laughter, hugs, and gentle-
prodding throughout these years. In particular I would like to mention a few without
whom there would have been far less sunshine:
To Rachel, who quite simply is my other, better half;
To Brian, who was always just a phone call away, night or day, happy or sad, quick
questions or protracted philosophizing, across town or across the country (and
sometimes both on the same day);
To Parag Mallick, who changed me forever, almost always for the better, and who,
in his give and in his take, brilliantly blurred the lines of caring, love, and
friendship, in a way for which I will always be grateful;
To Karen Knee, who has already earned her stripes as my Maid of Honor;
To Mi-Mi Chen, whose consistent encouragement was a strong buoyant force;
To Michael Chang, who reminds me regularly that insta-friends also make the best
life-long friends;
To Sue Wang, who I definitively declare, in published writing, to be the gold
standard;
To Linda Bi, whose friendship is like looking into a mirror and remembering who I
want to be;
To Ezra Feldman, who provided me with my first sense of soul mate and who still
blesses me with his presence in every rainstorm;
To Christine Hyun, whose heart never stops giving and whose mere mention of
name brings a smile to my face;
To Corinne Yarbrough, whose gift of two years of friendship were strong enough to
carry me through my five years at Keck;
v
To Claudia Martinez, who constantly reminds me of what a beautiful human being
looks like, inside and out;
To Paul Chen, with whom friendship grows stronger, deeper, and truer by the year;
To Steven Wu, who reminds me of the joys of conversation and connection;
To Pei Chen, who walks in the ways of friendship and poetry;
To HuiLee Wong, whose love of science provided much-needed inspiration;
To Anirban Mitra, who is responsible for bringing this thesis into existence four times
over and who provided rarified support and laughter on a daily basis;
To my PhD support team, namely Sahar Bedrood, Julie Kang, and Peter Pyrko,
without whom I can’t imagine having survived the inherent causalities of
graduate school;
To Jody Chuang, Albert Tsai, and John Xie, who constituted the friendliest cohort for
which one could ever ask;
To Deborah Kimball, Amira Baker-Jud, Suzanne Sachsman, and Vanessa Lauzon,
who have each taken their turn at serving as best friend in locis and who have
repeatedly reminded me of the simple and complex beauties of true friendship;
To Kristin Woolley, Mary Moreno, Lily Siritara, and Alaina Steck, who put up with a
struggling PhD student, and who did so with smiles, hugs, and backrubs;
To Ruthann Chou, Lorain Wang, and Sabrina Hong, who remind me of the meaning
of golden friends, year in and year out;
To Stephen Burns, Flavio Casoy, Jack Rusley, and Makini Chisolm-Striker, who
have convinced me forever that MedEd people are the coolest – and the kindest;
To Albert Cho and Claudia Cyganowski, for their never-ending love and inspiration;
And to countless others I could not name, all of whom are part of why I love Keck.
vi
TABLE OF CONTENTS
ACKNOWLEDGEMENTS ii
LIST OF TABLES viii
LIST OF FIGURES ix
ABSTRACT x
CHAPTER I: INTRODUCTION
1.1. Immunotherapy’s Humble Beginnings 1
1.2. Tumor Antigenicity, Immunoginicity, and the Origins of
the Theory of Immunosurveillance 2
1.3. Early Attempts at Cancer Immunotherapy 6
1.4. Immunoediting, Immunosurveillance, and Tumor Escape in Modernity 9
1.4.1. Immunosurveillance 10
1.4.1.1. Innate Immune Responses 11
1.4.1.2. Adaptive Immune Responses 12
1.4.2. Tumor Escape 12
1.5. Current Strategies for Cancer Immunotherapy 13
1.5.1. Categories 14
1.5.1.1. Peptide-Specific Approaches 15
1.5.1.2. Broad-Spectrum Approaches 16
1.5.2. Limited Successes 18
1.6. Argument for Cancer-Specific or Case-Specific Assessment 19
1.7. New Answers to Old Questions 20
CHAPTER II: OX40L FUSION PROTEIN PRODUCES COMPLETE
REMISSION AND ENHANCES SURVIVAL IN TWO MURINE TUMOR
MODELS
2.1. Background and Significance 24
2.2. Research Design and Methods 27
2.3. Results 33
2.4. Discussion 43
CHAPTER III: IMMUNE SIGNATURES OF MURINE AND HUMAN
CANCERS REVEAL UNIQUE MECHANISMS OF TUMOR ESCAPE AND
NEW TARGETS FOR CANCER IMMUNOTHERAPY
3.1. Background and Significance 48
3.2. Research Design and Methods 52
3.3. Results 55
3.4. Discussion 63
CHAPTER IV: RATIONALIZED IMMUNOTHERAPEUTICS FOR CANCER
ON THE HORIZON
4.1. Background and Significance 73
4.2. Research Design and Methods 74
4.3. Results 75
4.4. Discussion 77
vii
CHAPTER V: COLLECTIVE CONCLUSIONS AND FUTURE DIRECTIONS
5.1. Summary 80
5.2. Potential Obstacles 81
5.2.1. Overcoming Tolerance 82
5.2.2. Overcoming Immunoediting 84
5.3. Future Directions 86
5.4. Concluding Remarks 89
BIBLIOGRAPHY 90
viii
LIST OF TABLES
Table 1.1: Clinical Results from Treatment with Coley’s Toxin 6
Table 2.1: Tumor-Infiltrating Leukocytes 40
Table 3.1: Murine Primers 53
Table 3.2: Human Primers 54
Table 4.1: FoxP3 Expression and Response to LEC/chTNT-3 + PC61 77
ix
LIST OF FIGURES
Figure 2.1: Construction and purification of Fc-mOX40L 33
Figure 2.2: In vivo demonstration of potency 34
Figure 2.3: Dosing curves in two murine tumor models 36
Figure 2.4: Fc-mOX40L treatment enhances survival 38
Figure 2.5: Depletion of CD8
+
population abrogates treatment effects 39
Figure 2.6: Immunohistochemical evidence of treatment-induced necrosis 42
Figure 3.1: Murine tumor immunomodulatory gene expression profiles 56
Figure 3.2: Human tumor immunomodulatory gene expression profiles 58
Figure 3.3: Temporal patterns of gene expression 60
Figure 3.4: Specific up- and down-regulation of immunomodulatory genes
in human breast and colorectal cancers 62
Figure 4.1: Intratumoral expression of immunomodulatory genes 76
x
ABSTRACT
Great strides in the understanding of cancer biology have provided a multitude of
approaches to the medical treatment of cancer. While chemotherapy alone has
succeeded in making many malignancies into treatable diseases, certain cancers
remain elusive despite the current arsenal, and for many patients the toxicities of
chemotherapies are intolerable or even fatal. One attractive alternative to
chemotherapy is cancer immunotherapy, an approach that seeks to harness the
power of the immune system to treat cancer. Despite achieving only modest clinical
successes to-date, immunotherapy offers the promise of a physiological treatment
as opposed to a treatment based on semi-selective toxicity. This manuscript begins
by describing a new murine immunotherapeutic reagent, constructed by genetically
linking the antibody Fc backbone to the molecule OX40L, which can provide a T cell
activating second-signal and simultaneously inhibit the effects of suppressor T cells
known as Tregs. The resultant Fc-mOX40L fusion protein has demonstrated
striking anti-tumor effects in two murine experimental tumor models. Though similar
fusion protein strategies have been described in the literature, this manuscript goes
on to describe a novel approach to delineating tumors’ “escape” strategies and
identifying the immunotherapy with the greatest capacity for impeding those
mechanisms of tumor escape. Using real-time PCR, it was possible to create
immune signatures that characterize the T cell activation profile of five murine tumor
models as well as human breast and colorectal tumor specimens. This technique
identified several cancer-induced immunotherapy targets in human ductal
adenocarcinoma of the breast, including high expression of immunoinhibitory B7-H4
xi
and low expression of immune activating CD28 and CD83. Finally, this manuscript
describes how our “immunotyping” approach was used to reveal the immunological
mechanisms of action of a combination immunotherapy regimen known to produce
differential responses in different murine tumor models. Ultimately, the degree of
Treg cell involvement was identified as the determining factor for the degree of
response to treatment. Though the age of “personalized medicine” has been
considered by many to be a pipedream with respect to complex diseases like
cancer, the technology and analysis herein described make a case for rationalized
immunotherapeutics being close on the horizon.
1
CHAPTER I: INTRODUCTION
1.1. Immunotherapy’s Humble Beginnings
Following cardiovascular events, cancer remains the second most common
cause of death in the United States. For more than 100 years, scientists have
endeavored to apply the principles of treating infectious disease to the treatment of
cancer (Coley 1991). Though the first immunotherapy capable of demonstrating a
statistically-significant cure rate dates as far back as the late 19
th
century (McCarthy
2006), immunotherapy was soon overshadowed by the successes of chemotherapy
that were seen in the mid-1900s (Papac 2001). In the past two decades, however,
immunotherapy has remerged as a burgeoning field with exciting therapeutic
prospects. This sudden resurgence can be attributed in part to the stagnation of
chemotherapy, which is still limited by severe toxicities, and in part to great leaps in
our understanding of molecular tumor immunology. Immunotherapy is now
outgrowing the stigma of “adjuvant therapy” and, having several advantages over
standard chemotherapy, has begun to emerge as a first-line therapy in certain
cancers, especially in those refractory to chemotherapy.
By way of an introduction to this dissertation on tumor immunology and
immunotherapy, we will begin by considering the history of approaching cancer as
an immunological dysfunction as well as the history of using immune modulation to
treat cancer. We will continue to discuss some of the challenges with which
immunotherapy has been confronted, paying special attention to the ways in which
tumors evade natural and augmented immune responses. Finally, we will discuss
2
some of the recent approaches taken to counteract these tumor escape
mechanisms and to bolster the immune response to cancer cells even in the face of
dynamic mechanisms of tumor escape.
By intertwining a historical perspective with a focus on recent developments
in tumor immunology, it should be possible to provide a context in which to
appreciate the following statement from a major players in tumor immunology:
During the last few years there as been an increasing level of interest in
immunological aspects of malignant disease. This is of course due in part to the
possibility that some method of cancer therapy might emerge from such
investigations. No method of [universal] therapy has been established, but there
are other aspects which are developing some practical importance in the form of
diagnostic tests, an understanding of the natural history of cancer, and a warning
on one of the effects of prolonged use of immunosuppressive drugs. (Burnet 1973)
The above quote was written in 1973 by the grandfather of
immunosurveillance, but it could just as easily have been written today in 2007. The
field of immunosurveillance and the study of tumor escape are still without a “magic
bullet” because they are, in essence, chasing a moving target. Nevertheless, as
tumor immunologists have begun to learn more about tumor-induced dysregulation
of immunological function, it has become increasingly realistic to think in terms of
devising immunotherapeutic interventions that can reverse the immunological
dysfunctions that we have begun to elucidate.
1.2. Tumor Antigenicity, Immunogenicity, and the Origins of the Theory of
Immunosurveillance
The manner in which the immune system succeeds in fighting off microbial
infection derives from the immune system’s ability to differentiate between cells
3
that are “self” and those that are, in part or in totality, “non-self” (Kohl 2006).
Though this function of the immune system long dominated immunology as the
central dogma, it is now recognized that the functions of the immune system are
considerably more nuanced, including the ability to recognize cells that have
exceeded their life expectancy or their teleological purpose, as seen with post-
infection Fas-ligand apoptosis of clonally-expanded lymphocytes (Yonehara et al.
1994) or splenic macrophages destruction of red blood cells after 120 days (Biondi
et al. 2002). Several cues signal the immune system to destroy “self” cells under
physiological conditions, and such mechanisms are likely to play a role in the
presumed ability of the immune system to recognize malignantly-transformed cells
as “aberrant” (Tesniere et al. 2006).
One such mechanism entails the tumor cell’s tendency to display tumor-
specific antigens (TSA) and/or tumor-associated antigens (TAA), largely due to the
genetic instability and the wide-spread gene activation that are characteristic of
cancer cells (Lengauer et al. 1998a; Lengauer et al. 1998b). Tumor-specific
antigens are recognized by the immune system as “non-self,” while tumor-
associated antigens can be recognized as “aberrant” by virtue of being expressed in
the wrong place, at the wrong time, in an altered form, or at altered levels. The
foreign or abnormal epitopes on these antigens that can be recognized by the
immune system are what make cancer cells classifiable as antigenic.
The immunogenicity of tumors, however, is more controversial as it entails
not just recognition of abnormal antigens, but purposeful immune response.
Evidence in favor of immunogenicity derives from several phenomena, the first of
which is the observation that in states of immunosuppression, certain cancers occur
with increased incidence (Zeier et al. 2002). The cervical and skin cancers that
4
often develop in immunosuppressed individuals are usually attributed to viral
transformation, which is the easiest to declare immunogenic on the basis of
expression of “non-self,” viral antigens. Nevertheless, lymphomas not virally-
induced also occur with increased frequency in patients suffering from
immunosuppression, arguing in favor of other routes to immunogenicity.
The spontaneous regression of non-virally-induced cancers, documented in
large meta-analyses, is further evidence in support of tumor immunogenicity (Challis
et al. 1990). One proposed theory for immune recognition and destruction of tumors
lacking viral proteins is the down-regulation of major histocompatibility (MHC) I
molecules, a widely-observed phenomenon that may initially confer a survival
advantage upon the tumor cells that can limit expression of recognizable TSAs and
TAAs; this act may, however, trigger an innate immune response dominated by the
natural killer cell (Pantel et al. 1991; Schreiber et al. 2002).
Additional empirical evidence pointing towards tumor immunogenicity
includes the preponderance of tumor-draining lymph nodes that show signs of
activation (Harada et al. 1996) and tumor infiltration of immune effector cells. In
many cancers, the density of intratumoral lymphocyte involvement correlates
positively with a favorable prognosis (Mapara et al. 2004; Zhang et al. 2003).
In 1964 FM Burnet first published a manuscript describing what he termed
“immune surveillance” and which later became shortened to “immunosurveillance.”
This theory suggests that a primary purpose of the immune system is the protection
of the organism from cancer (Burnet 1970). As connoted in the name “immune
surveillance,” Burnet believed that immune cells constantly patrol for malignant or
pre-malignant cells. He hypothesized that four conditions would be observable “at
the clinical level…if the concept of immunological surveillance is legitimate”:
5
1. The age incidence of malignant disease should reflect a greater emergence of
malignant change initiated at ages of relative immunological inefficiency, the
ante- and perinatal period[s] and old age.
2. Conditions associated with depression of the T-D [thymus-dependent] immune
system[,] whether genetic, induced by drugs, or of other origin[,] should
increase the likelihood of cancer.
3. Under certain circumstances spontaneous regression of cancer can occur, and
the results of therapy may be better than expected. Indications that immune
responses are playing a part in these phenomena should be detectable.
4. Large-scale[,] routine histology of common sites of cancer should show a
higher proportion of histolotically[-]diagnosable cancer than could be expected
to emerge clinically. (Burnet 1971)
Burnet continues to say that “a similar list can be given in relation to
laboratory studies on experimental cancer…”:
1. Immunosuppressive agents should facilitate the spontaneous appearance or
experimental transfer of cancer cells.
2. Neonatal thymectomy should also facilitate.
3. Foetal or perinatal tolerance should be able to annul the surveillance effect.
4. Immune paralysis by excess antigen from an active tumor may be
demonstrable. (Burnet 1971)
In 1971 Burnet stated, “At both clinical and experimental levels, most of these
expectations have been fulfilled,” though acknowledging that “it is probably
legitimate to claim that some of the clinical findings are rare.” It stands to note that
nearly 40 years later, the degree to which these conditions have been met is still
being debated. Indeed, while modern cancer biology has been able to substantiate
some of Burnet’s predictions, the field has also refuted others of his beliefs, such as
the belief that a “carcinogen combined with a cell antigen and in so doing gave rise
to new antigens” (Green 1954). More than a century since tumor immunology was
born, the notion of immunosurveillance remains hotly debated.
6
1.3. Early Attempts at Cancer Immunotherapy
Where laboratory experimentation has failed to rigorously prove all of
Burnet’s conditions for immunological surveillance, a century’s worth of primitive
clinical attempts to treat cancer by bolstering an immune response have provided
enough tantalizing evidence to keep the field afloat. Even before Burnet’s
formalization of the immunological theory of cancer, the powers of the immune
system were being harnessed for their ability to defeat malignancies. Indeed, within
decades of Edward Jenner’s smallpox vaccine entering mainstream medicine,
physicians began speculating about whether vaccines or passive serotherapy could
kill the cancer cells “infecting” their patients (Gold 1970). Xenogenetic serotherapy,
autologous and allogeneic tumor vaccines, and bacterial product immunotherapy
were all pursued in “human trials,” but only a few such attempts produced more than
anecdotal success (Gold 1970). The most promising of the approaches, William
Coley’s use of bacilli Calmette-Buerin (BCG) in “Coley’s toxin,” cured more than 90
of his cancer patients.
Coley’s results were later tabulated by his daughter, Helen Coley Nauts, who
documented a total of 894 cases treated with Coley’s toxin. She later published the
results from independent trials performed by Coley, such as those seen below
(Nauts et al. 1990):
Table 1.1. Clinical Results from Treatment with Coley’s Toxin
Tumor Type
Operable
patients: #
Alive after 5
years: # (%)
Inoperable
patients: #
Alive after 5
years: # (%)
Giant cell bone
tumor
38 33 (87%) 19 15 (79%)
Breast cancer 13 113 (100%) 20 13 (65%)
7
Other 5-year survival rates included: 67% in Hodgkin's disease, 67% in
inoperable ovarian cancer, and 60% in inoperable malignant melanoma (Nauts
1982). By metanalysis across tumor types, patients with inoperable tumor had 45%
five-year survival, while those with operable tumors had 50%. Studies later
performed by impartial researchers also showed encouraging results: In 1962
Barbara Johnston published a double-blind study on Coley’s toxins. In the control
group treated with fever-inducing placebo, only one patient of 37 showed any signs
of improvement, whereas of the 34 patients treated with Coley’s toxins, 18 showed
no improvement, seven noted decreased pain, and nine showed tumor necrosis,
apparent inhibition of metastases, shrinkage of lymph nodes, and/or disappearance
of tumors (Johnston 1962; Johnston et al. 1962). The first results of randomized
trials of Coley’s toxins revealed that advanced non-Hodgkin’s lymphoma patients
receiving Coley’s vaccine had a 93% remission rate, in comparison to 29% for
controls who received chemotherapy alone (Nauts & McLaren 1990).
Despite these successes, there were many failures. With immunotherapy
cures persistently elusive, the advent of chemotherapy, radiotherapy, and radical
surgical interventions diverted attention away from cancer immunotherapy for
several decades. By the late 1960s, however, it became apparent that the
limitations of combination chemotherapy and radiation were no longer a matter of
protocol optimization, but rather a matter of needing to balance aggressive
treatment with protection from extreme toxicities (Bagshawe 1989). Around the
same time there were dramatic improvements in laboratory methodologies such as
cancer cell line culture, immune cell isolation and modulation, and monoclonal
antibody production, which created an ideal environment for renewed exploration of
immunotherapy to treat cancer.
8
Early trials using monoclonal antibodies met with mixed successes. The first
trial, completed in 1988, showed three total remissions and 23 partial remissions of
the 184 patients treated (Catane et al. 1988). In some patients, the response
seemed to result from direct anti-tumor cytotoxicity, whereas in other patients the
response was determined to be antibody-dependent cellular cytotoxicity (ADCC). B
cell lymphomas treated with anti-idiotype antibodies showed good anti-tumor effects
and limited toxicities, with partial responses in more than 50% of treated cases
(Levy et al. 1991). Nevertheless, in most cancers immunotherapy success rates did
not exceed that of the other treatment modalities, relegating immunotherapy to the
realm of adjuvant therapy.
Cytokine treatments have become a commonly-employed adjuvant
approach, beginning with cytotoxic cytokines like IL-1 and TNF- α, and eventually
including cytokines with immune stimulating effects like IL-2 (Mihich 1994; Onozaki
et al. 1985; Thomson 1991). Other investigators began taking novel approaches,
including the collection of tumor-infiltrating lymphocytes (TILs), in vitro expansion of
the TIL population, and re-infusion of TILs into the cancer patient from whom they
were derived. Remarkably, these TILs were found to re-infiltrate the tumor. In
combination with IL-2 or cyclophosphamide administration, the technique produced
some encouraging results: 40% of patients showed a >50% decrease in tumor size
with such treatment, though the responses of these early TIL engineering
experiments were relatively short-lived (Mihich 1994). Long-term clinical responses,
unfortunately, remained disappointing throughout the 20
th
century.
Alas, the vast majority of the anti-cancer immunotherapies used from the late
1800s to the late 1900s repeatedly aimed to treat all cancers alike. Though it was
conceptually recognized that strong selection pressures enabled tumors to
9
develop ways to evade the immune system and that different tumors employed
different strategies for immune evasion, a lack of practical knowledge regarding the
molecular basis of tumor escape from immunosurveillance prevented researchers
from targeting immunotherapeutic strategies to specific tumor-induced immune
dysfunctions. The ability to create targeted immunotherapies that can counteract
specific tumor-induced immune dysfunctions required a deeper understanding of
immunology and cancer biology, a deeper understanding of the abilities and
limitations of immunosurveillance in the face of the selection pressures that promote
tumors’ dynamic escape from the immune system.
1.4. Immunoediting, Immunosurveillance, and Tumor Escape in Modernity
The concept of how the immune system interacts with developing tumors
has changed since the theory of immunosurveillance was first proffered, but
continued evidence of interplay between tumor cells and immune cells has helped in
the development of a modern understanding of tumor escape (Dunn et al. 2002;
Dunn et al. 2004a; Dunn et al. 2004b). A new term, “immunoediting,” has emerged
as a way of understanding how a tumor’s acquired genetic and epigenetic changes
are shaped by the selection pressures of immunosurveillance and how this gives
rise to cancer cells that have evolved to be unrecognizable – or “immune” – to the
immune system (Smyth et al. 2006). New insights into these adaptive changes
enable us to better understand the physiological conditions under which different
components of the immune system keep aberrant growth under control, as well as
precisely how things go wrong. Thus, teasing out the tumor escape processes that
result from immunoediting has enhanced and will continue to enhance our
10
understanding of immunosurveillance, immune evasion, and routes for using
immunotherapy to shift the balance in favor of immunosurveillance.
1.4.1. Immunosurveillance
The old assertions of immunosurveillance have been rigorously challenged
in recent years. For decades, skeptics harped on the lack of an animal model that
could demonstrate systemic immunosuppression causing an increase in the
incidence of non-virally-induced tumors (Stutman 1975). Immunosurveillance thus
gained substantial credibility with the discoveries of perforin
-/-
mice (Russell et al.
2002; van den Broek et al. 1996) and Rag-1
-/-
or Rag-2
-/-
mice (Shankaran et al.
2001; Shinkai et al. 1992), all of which show increased incidence of carcinogen-
induced tumor development, even in pathogen-free mouse facilities. These and
other, newer mouse models like IFNGR1
-/-
and STAT-1
-/-
mice (Shankaran et al.
2001) are similar in that their immune dysfunction affects both the innate and the
adaptive arms of the immune system. By inhibiting downstream events critical to
both arms, like the production of IFN- γ and perforin, these mice have no means of
mounting immunosurveillance and are thus highly susceptible to cancers. In mice, it
seems, either NK cells or cytotoxic T lymphocytes (CTLs) are enough to preserve
immunosurveillance, and only in the absence of both do we see evidence of
immunosurveillance being critical to the prevention of the many non-virally-induced
tumors (Dunn et al. 2002).
Our understanding of the cell types that constitute immunosurveillance has
grown significantly from the days of Burnet. Today it is clear that the immune
response to cancer hinges on far more than just the NK and CTL responses;
11
rather, many leukocyte subpopulations interact in complex ways to protect against
the growth of tumors via an intricate interplay of the innate and adaptive arms of the
immune system (Smyth et al. 2001; van den Broeke et al. 2003). Dysregulation, of
course, can occur at the level of any of these interacting subpopulations, creating
myriad opportunities for disruption of a precarious balance, pushing the immune
response towards tolerance to tumor.
1.4.1.1. Innate Immune Responses
The triggering of the innate immune response has been less well studied
than that of the adaptive immune response. Instead of relying on TSAs or TAAs,
the innate immune system responds to fundamental disturbances in cellular
behavior. The central role of NK cells in immunosurveillance is demonstrated by
mice with severe combined immunodeficiency (SCID), mice that lack T and B cells
but still produce NK cells. SCID mice do not demonstrate increased rates of non-
virally-induced cancers, presumably because of the anti-tumor activity of NK cells
(Wherry et al. 1991).
One postulated trigger for NK recognition of tumor cells relates to MHC I
expression levels. Because MHC I molecules present aberrant cancer proteins to
antigen presenting cells, those cancer cells that down-regulate MHC molecules are
more likely to avoid T cell lysis, creating a selection pressure that commonly leads
to MHC I down-regulation, a phenomenon observed in a great many cancers
(Whiteside et al. 1995). These MHC I-deficient cancer cells, however, are often
recognizable by NK cells by virtue of MHC I down-regulation. In addition to NK
cells, other innate immune cells such as NKT cells, γ- δ T cells, and macrophages,
12
all of which have been shown to recognize and respond to tumor cells (Bauer et al.
1999; Diefenbach et al. 2000; Groh et al. 1998).
1.4.1.2. Adaptive Immune Responses
Adaptive immune responses are antigen-specific and require the expression
and recognition of TSAs or TAAs, though these tumor antigens need not be either
characterized or universal. Because CD8
+
cytotoxic T cells are the primary effector
cells responsible for adaptive immune destruction of tumors, most adoptive transfer
research has focused on the CD8
+
population. Recent studies, however, have
shown that improved clinical outcomes can be achieved when CD4
+
T helper cells
are included in adoptive transfer procedures (Antony et al. 2005). Accordingly, it
has become clear that multiple components of the adaptive immune system
contribute to anti-tumor activity. Much as it has been recognized that the best
responses are achieved when T helper and T effector cells cooperate
synergistically, it has been postulated that combining innate and adaptive responses
in a single, coordinated response to tumor, might be the most effective approach to
immunotherapy.
1.4.2. Tumor Escape
The mechanisms through which the tumors evade the immune system can
reflect changes in the tumor cell (called tumor cell-associated immunosuppressive
factors) or changes in either immune cells or cells of the microenvironment
(considered tumor-induced immunoinhibitory factors) (Campoli et al. 2006).
13
Tumor cell-associated immunosuppressive factors are those that involve molecules
expressed or secreted by tumor cells, which can directly inhibit the immune
response, such as suppressive cytokines (IL-10, TGF- β, and IL-8), small molecules
of the inflammatory pathways (prostaglandin E2, histamine, epinephrine, free
radicals, and vascular endothelial growth factor), and inhibitory or apoptosis-
inducing tumor necrosis factor ligands (FasL and TRAIL) (Campoli & Ferrone 2006).
Tumor cell-induced immunosuppressive factors consist of lymphocyte signaling
defects (NF κB abnormalities and Ca
++
flux anomalies), Th2 polarization, tolerization
of T cells, inhibition of leukocyte migration, induction of T cell and dendritic cell (DC)
apoptosis, inhibition of DC maturation, and expansion of suppressive cell
populations, including T regulatory cells (Tregs), myeloid-derived suppressor cells
(MDSCs), and certain macrophage subpopulations (Campoli & Ferrone 2006).
New tumor escape mechanisms are being discovered with regularity, and
the interplay of these mechanisms is only beginning to be understood. For
immunotherapy to be universally successful, it must be able to overcome any and all
of these immunoinhibitory forces; for any one particular immunotherapy to be
successful in a given patient, the immunotherapy must be able to overcome the
tumor escape mechanism(s) being employed by that specific tumor.
1.5. Current Strategies for Cancer Immunotherapy
With knowledge of these many mechanisms by which tumors evade the
immune system, a number of immunotherapeutic approaches have been devised to
counteract or overwhelm tumor escape. Many of these approaches have shown
14
promising preclinical results, though the results of most clinical trials have been
disappointing by comparison (Antonia et al. 2004). Most therapies have been tested
on several different human cancers, often with a small percentage of responders in
each. No single therapy appears to have an overwhelming outcome, or even a
significantly-better outcome than any of the other therapies. Interestingly, though
most therapies do produce a statistically-significant improvement in clinical
outcomes for one or even multiple cancers, with the exception of a few antigen-
specific therapies – such as HER-2/neu monoclonal antibodies in breast cancer
(Slamon et al. 2001), gp100 vaccines in malignant melanoma (Markovic et al. 2006;
Sin 2006), or E6/E7 vaccines in cervical carcinoma (Markovic et al. 2006; Sin 2006)
– clinical data have failed to definitively establish which immunotherapeutic
approaches are most appropriate for which cancers. Thus, the quest continues for
“more effective immunotherapies.”
1.5.1. Categories
Clinical trials are studying, in parallel, a great number of different approaches
to the immunotherapy of cancer. The most common categories of immunotherapy
include vaccines, monoclonal antibodies, adoptive transfer of T cells, recombinant
cytokine therapy, and CpG oligonucleotides (Antonia et al. 2004). These
approaches can logically be divided into two categories on the basis of their
applicability: those that require significant knowledge about TSAs or TAAs and
those that are broader in application. We will now consider, individually, each of the
members of these two categories.
15
1.5.1.1. Peptide-Specific Approaches
Vaccines
Many approaches have been taken to the use of vaccines in the treatment of
cancer. Some of the most promising have been peptide-based vaccines, which
have been useful against melanoma antigen-3 (MAGE-3) (Marchand et al. 1999) as
well as gp100 (Jager et al. 1996) in malignant melanoma and against E7 in HPV-
induced cervical carcinoma (Muderspach et al. 2000). Additional peptide-specific
vaccinations have been DC-based, including DCs pulsed with known tumor antigens
(Celluzzi et al. 1996; Kono et al. 2002) such as gp100 or melanoma antigen
recognized by T cells 1 (MART-1), both for malignant melanoma. In the vast
majority of these vaccine-based immunotherapy studies, clinical outcome was
strongly correlated with certain HLA types (Antonia et al. 2004), suggesting that
individuals lacking key HLAs are unlikely to benefit from these peptide-specific
strategies. Furthermore, while the vaccine approach holds promise in cancers for
which there are many known tumor-specific antigens, for other cancers these
vaccines are unlikely to yield effective therapy in the near future.
Monoclonal antibodies
Many monoclonal antibodies, like Trastuzumab (anti-HER-2/neu) and
Rituximab (anti-CD20), have been designed to target antigens over-expressed in
specific cancers. For HER-2/neu-positive breast cancers, B-cell lymphomas, and
malignant melanomas expressing any of the many known TSAs, such monoclonal
antibodies represent a promising approach to targeted immunotherapy (Emens et
16
al. 2006). Ninety-five percent of B-cell lymphomas express CD20 (Olszewski et al.
2004), while only 30% of breast cancers express HER-2/neu (Emens et al. 2004),
making these monoclonal antibodies ideal for some patients, but inappropriate for
many others.
1.5.1.2. Broad-Spectrum Approaches
T cell adoptive transfer
Many approaches have been taken to ex vivo stimulation of T cells for
adoptive transfer, including cytokine stimulation, peptide stimulation, and genetic
engineering of T cells (Wrzesinski et al. 2007). Though most T cell adoptive transfer
protocols have focused on CD8
+
cytotoxic T cells, the success rates of adoptive
transfer trials have been enhanced by the addition of CD4
+
helper T cells (Antony et
al. 2005). Unfortunately, however, patients have been observed to fail to respond to
adoptive transfer protocols, even in the presence of persisting tumor-targeting,
antigen-specific T cells (Rosenberg et al. 2005), suggesting that
immunosuppressive factors may dominate. To combat this phenomenon, studies
have added lymphodepletion to the protocol, prior to adoptive transfer, and these
trials have met with notable success (Muranski et al. 2006).
Recombinant cytokines
Though recombinant IL-2 has been used in combination with several
vaccines (Overwijk et al. 2000; Rosenberg et al. 1998; Shimizu et al. 1999), reliance
on IL-2 alone is limited by toxicities associated with vascular leak syndrome (Siegel
et al. 1991). Recombinant IL-12 has also been used in clinical trials because of
17
its ability to polarize the T helper response towards Th1 populations (Trinchieri
2003). Again, however, the clinical utility of the cytokine is limited in human studies
by the extreme toxicity induced at high doses (Leonard et al. 1997). Recombinant
Flt3 ligand has been used to treat cancers as varied as colon, renal, and breast
cancers, where it has been shown to increase the DC numbers present in the
peripheral blood of patients with metastatic cancer; the activity of those DCs,
however, has not been fully established (Disis et al. 2002; Fong et al. 2001; Morse
et al. 2000; Rini et al. 2002).
CpG oligonucleotides
Working through the innate arm of the immune system, CpG
oligonucleotides can activate DCs through toll-like receptors (TLRs) (Krieg 2002;
Wooldridge et al. 2003), prompting DCs to increase surface expression of ligands
that provide co-stimulatory signals to T cells (Hartmann et al. 1999; Heeg et al.
1998). Several clinical trials are underway, testing the efficacy of CpG
oligonucleotides in the treatment of everything from leukemia to glioma, succeed
both with and without adjuvant immunotherapies.
Vaccines
In addition to the previously-mentioned vaccines, which are categorized as
tumor-specific by virtue of utilizing known TSAs and TAAs, there exist other
vaccines that can be applied to considerably more cancers because they utilize
activation strategies that do not rely on specific antigens. One example is the BCG
vaccine, based on Coley’s original discovery (Hsueh et al. 1998). Other approaches
include the use of heat-shock proteins (hsps) (Belli et al. 2002; Mazzaferro et al.
18
2003) and various techniques employing generically-stimulated DCs, matured via
cytokines (IL-4 and GM-CSF) and/or exposure to tumor lysates that provide tumor
antigens without requiring knowledge of those antigens (Nestle et al. 1998).
Monoclonal antibodies
While the monoclonal antibodies previously discussed were specific to
tumor-associated antibodies, other monoclonal antibody approaches are broad
spectrum, including those that are antagonist antibodies to growth factors
(Cetuximab, anti-EGFR) (Emens et al. 2006), to angiogenesis factors
(Bevacizumab, anti-VEGF) (Emens et al. 2006), or to immunoinhibitory molecules
(anti-CTLA-4, anti-PD-L2, or anti-PD-L2) (Curiel et al. 2003; Radhakrishnan et al.
2004; Strome et al. 2003) – as well as those that are agonist antibodies for
immunostimulatory molecules like members of the B7 or TNF families (anti-CD40,
anti-OX40, and anti-41BB) (Diehl et al. 2000; Melero et al. 1997; Pan et al. 2002;
Sotomayor et al. 1999). Though some of these monoclonal antibodies are just
entering clinical trials, anti-CTLA-4 has been in trials long enough to provide the
unfortunate clinical picture of a therapy that produces more cases of grade III
autoimmunity than it does cases of clinically-significant tumor regression (Phan et
al. 2003).
1.5.2. Limited Successes
Despite the long list of creative approaches to immunotherapy, and despite
the fact that each can tout cases that have undergone complete remissions, no trials
can claim complete responses greater than 15% or lasting partial responses
19
greater than 30% (Antonia et al. 2004). There are hundreds of flavors of
immunotherapies, each with demonstrable potential, but none with consistent
positive results.
1.6. Argument for Cancer-Specific or Case-Specific Assessment
The many different varieties of immunotherapies are both a blessing and a
curse. The curse comes from the unfortunate message that immunotherapy clinical
trials convey to clinical oncologists: The glass ceiling is firm, and with so few cured,
immunotherapy should be a last-resort, reserved for patients who have failed
chemotherapy, or as an adjuvant therapy to be combined with highly toxic drugs.
The blessing of so many varied approaches to immunotherapy is that while only a
small percentage of patients may be right for any one therapy, only one of these
many therapies needs to be right for any one patient. For many years oncologists
have been looking for a way to identify which patients are most appropriate for an
immunotherapy protocol – perhaps the question ought to be which immunotherapies
are most appropriate for a patient.
Now that tumor immunology has begun to elucidate the many mechanisms
by which tumors escape from the immune system, it becomes reasonable to
assume that these immunoinhibitory escape strategies must be addressed if an
immunotherapy is to be successful. By logical extension, it is also reasonable to
consider the possibility that the mechanism by which a tumor evades the immune
system could in fact be the long-awaited “prognostic marker” that will indicate
whether an immunotherapy regimen is likely to work in a patient: A Treg inhibitor is
not likely to work in a patient with high PD1 but low Treg counts. It thus becomes
20
possible to reason backwards, beginning first with the mechanism of tumor escape
that can serve as an “immunotherapy marker,” helping predict the most appropriate
regimen: Provide a Treg inhibitor to the patient with high Treg counts and a PD1
blockade to the patient with over-expressed PD-L1.
In the course of determining cancers’ immune signatures, it may be revealed
that certain cancers demonstrate characteristic tumor escape mechanisms; in these
cases, certain immunotherapies might be appropriate for many if not all patients with
that malignancy. Knowledge of consistent cancers would be profoundly helpful in
determining which immunotherapies to optimize for the treatment of a certain
organ’s malignancies. Other cancers, however, may not have a dominant tumor
escape mechanism, because cellular aberrations don’t necessarily reflect organ-
based classification schemes. Under such circumstances, it becomes necessary to
determine, for each patient, the predominating tumor escape mechanism(s), such
that the most appropriate thereby can be selected on a patient-by-patient basis.
In summary, many promising immunotherapies exist, each of which has had
a small but demonstrable success because it inhibits the right immune evasion in a
small number of patients, enabling it to reverse tolerance and cure those patients.
What the field of immunotherapy needs now is not the development of additional
strategies, but the development of solid criteria for the rational selection of the most
appropriate immunotherapy for each patient.
1.7. New Answers to Old Questions
Operating under the premise that the immune system is capable of
identifying and eliminating tumors under physiological conditions but loses that
ability when inhibited by malignancies, many immunotherapeutic strategies are
21
currently being investigated. Modest clinical successes can be attributed to the
failure to identify which patients will respond, which presumably relates to currently
uncharacterized tumor-induced immune dysfunctions. The continual discovery of
additional tumor immunosuppressive pathways provides increased opportunities for
reversing tolerance and treating cancer, but also reveals the challenges involved in
identifying the right drug for a given cancer patient. This manuscript describes the
development of a new immunotherapeutic reagent (Chapter 2) as well as a novel
and promising methodology that may permit the rationalized selection of the best
immunotherapeutic approach to each individual cancer patient (Chapters 3-4).
More specifically, Chapter 2 describes the use of a tumor necrosis factor
super family (TNFSF) ligand to provide a T cell activating second-signal while
simultaneously inhibiting the immune suppressing effects of Treg cells. The TNFSF
molecule OX40L was genetically linked to the Fc portion of an antibody to increase
its half-life, and the resultant fusion protein has demonstrated striking anti-tumor
effects in two murine experimental tumor models.
Though other co-stimulatory fusion proteins have been described in the
literature, Chapter 3 presents a novel approach to delineating tumors’ mechanisms
of escape and identifying the immunotherapy with the greatest capacity for impeding
those immune evasion strategies. Using real-time PCR, it was possible to create
immune signatures that characterize the T cell regualtion profile of five murine tumor
models as well as human breast and colorectal tumor specimens. In addition to the
identification of T cell-based tumor escape mechanisms for breast, colorectal, lung,
lymphoma, and renal cell murine tumors, this technique demonstrated that most, if
not all, human ductal adenocarcinomas of the breast are well characterized by an
immune signature showing high expression of immunoinhibitory B7-H4 and low
22
expression of immune activating CD28 and CD83, whereas human colorectal
adenocarcinomas defy characterization by a single T cell signature.
Next, this manuscript describes how the expansion of this “immunotyping”
approach makes it possible to describe the immunomodulatory effects of
immunotherapy. Chapter 4 describes how examining the perturbation of gene
expression immune signatures caused by an immunotherapy regimen permits
identification of molecular mechanism(s) of action. By treating three murine tumor
models with one immunotherapy regiment and by comparing the response to
treatment with the treatment-induced gene expression changes, it was possible to
determine that response to treatment could be predicted based upon expression
levels of FoxP3. Such a finding makes inherent sense given the fact that Tregs are
known to be immunosuppressive and that one of the elements of the
immunotherapy regimen has been shown to deplete Tregs.
Though this notion of “personalized medicine” has been considered by many
to be a pipedream with respect to complex diseases like cancer, the chapters of this
manuscript taken together suggest differently. If it is possible to characterize the
immunological physiology of cancer drugs and the immune escape pathologies of
individual cancers both cheaply and efficiently, then it is possible to determine,
based on a patient’s tumor’s immune signature, which therapy will most directly
reverse that tumor’s escape mechanisms. The technology and analysis herein
described thus make it possible to ascertain the most appropriate therapy for an
individual cancer patient. Because rationalized immunotherapy should
dramatically enhance response rates to immunotherapeutics, this strategy could
enable immunotherapy to provide potent, low toxicity, mainstream treatment for
cancer.
23
Finally, Chapter 5 considers the progress that has been made towards
rationalized immunotherapeutics in light of the two looming potential obstacles:
dominant tolerance and immunoediting. A proposal for future studies is discussed
in hopes that, through further exploration, it will be possible to first probe and then
address the potential obstacles to immunotherapy – enabling the field to see the
benefits of a rational approach to the use of promising therapies.
24
CHAPTER II: OX40L FUSION PROTEIN PRODUCES COMPLETE REMISSION
AND ENHANCES SURVIVAL IN TWO MURINE TUMOR MODELS
2.1. Background and Significance
OX40 (TNFRSF4, CD134) is a membrane-associated glycoprotein member
of the tumor necrosis factor receptor superfamily (TNFRSF). This receptor is
transiently expressed on the surface of T cells after T cell receptor (TCR) ligation
(Calderhead et al. 1993; Mallett et al. 1990; Paterson et al. 1987). Its natural ligand
OX40L (TNFSF4, CD134L) belongs to the tumor necrosis factor ligand superfamily
(TNFSF) (Godfrey et al. 1994; Miura et al. 1991) and is found primarily on antigen
presenting cells (APCs), including activated B cells, dendritic cells (DCs), and
macrophages. In addition, OX40L is expressed on activated endothelial cells (Imura
et al. 1996; Ohshima et al. 1997; Pippig et al. 1999; Stuber et al. 1995; Takasawa et
al. 2001; Weinberg et al. 1999) and has been reported to be up-regulated on CD4
+
T cells during antigen simulation (Soroosh et al. 2006; Stuber et al. 1995).
Early studies of primary T cell responses revealed OX40L to be a potent co-
stimulatory molecule for sustaining the CD4
+
T cell response (Gramaglia et al. 2000;
Gramaglia et al. 1998; Maxwell et al. 2000). OX40-OX40L signaling has been
shown to prolong T cell division following CD28 induction, acting as a second signal
that can enhance the survival of CD4
+
cells via increased expression of Bcl-xL and
Bcl-2 (Rogers et al. 2001). In vivo, OX40 signaling augments tumor-specific priming
by stimulating and expanding the natural repertoire of the host’s tumor-specific
CD4
+
T cells (Murata et al. 2000; Pippig et al. 1999; Rogers et al. 2001; Weinberg et
al. 1999). Additionally, OX40 signaling is believed to increase CD4
+
T cell
25
differentiation from effector to memory cells (Weinberg et al. 1998). Finally, studies
show that in a setting of viral infection, OX40L-stimulated CD4
+
T cell have an
enhanced ability to stimulate virus-specific CD8
+
T cells (Yu et al. 2006). Therefore,
it is believed that OX40-OX40L interactions are crucial for the generation and
survival of memory CD4
+
T cells.
In addition to OX40L’s effects on CD4
+
T cells, recent studies have shown
that OX40 signaling also directly co-stimulates CD8
+
T cells (Bansal-Pakala et al.
2004; Wang et al. 2001). OX40L stimulation results in the clonal expansion and
increased effector function of CD8
+
T cells, with additive and synergistic effects
when delivered in conjunction with B7.1 or 4-1BBL, respectively (Serghides et al.
2005). Anti-OX40L mAb suppressed IFN- γ expression and proliferation of CD8
+
T
cells in co-culture with DCs, suggesting that DC OX40L is largely responsible for
CD8
+
expression of IFN- γ (Fujita et al. 2006). In addition, in vivo administration of
the agonist anti-OX40 antibody OX86 was found to increase survival and potency of
antigen-specific CD8
+
memory T cells (Ruby et al. 2007). Most recently it was
discovered that IL-18, which increases antigen-specific CD4
+
and CD8
+
T cells, acts
through the OX40 pathway, by increasing DC expression of OX40L when
administered alone or in conjunction with peptide and by increasing antigen-specific
T cell expression of OX40 when delivered alone or in conjunction with peptide
(Maxwell et al. 2006).
Importantly, in addition to expression on CD4
+
and CD8
+
T cells, OX40 has
also been found to be expressed on both naïve and activated CD4
+
CD25
+
regulatory T cells (Tregs) (Gavin et al. 2002; McHugh et al. 2002). OX40 signaling
may provide an inhibitory signal that abrogates Treg-mediated suppression when
delivered to Ag-engaged naïve T cells (Takeda et al. 2004). The agonist antibody
26
OX86 was found to inhibit the suppressive effects of Tregs while restoring effector
T-cell proliferation and cytokine production (Valzasina et al. 2005). Using a graft-
versus-host disease model, it was confirmed that OX40 abrogation of Treg occurs in
vivo and is nearly as potent as GITRL abrogation of Tregs (Valzasina et al. 2005).
Because of the ability of the OX40-OX40L pathway to stimulate effector and
memory CD4
+
and CD8
+
T cells and to suppress CD4
+
CD25
+
Tregs, it was
postulated that OX40 signaling might provide significant anti-tumor activity. Indeed,
it was discovered that engagement of OX40 by OX86 or by OX40L during tumor
priming enhances a tumor-specific T cell response that can increase the percentage
of mice that become tumor-free (Weinberg et al. 2000). The observed tumor-specific
CD8
+
T cell response was sustained for several weeks and was sufficient to treat
established tumors, even under immunocompromising conditions (Lee et al. 2004).
In addition, mRNA-transfected DCs increased survival in a B16-F10 melanoma
model (Dannull et al. 2005) and enhanced the anti-tumor effects of a herpes simplex
virus- (HSV) based immunotherapy, DISC-HSC/mGM-CSF, on Colon 26 tumors,
bringing about complete tumor regression (Ali et al. 2004; Assudani et al. 2006).
Further studies have confirmed that OX86 has a synergistic effect in combination
with GM-CSF (Gri et al. 2003) or the 4-1BB (Pan et al. 2002) agonist antibody 2A in
the treatment of Colon 26. 2A antibody treatments enhanced the one-year survival
of mice bearing Colon 26 hepatic metastases (8x8 to 12x12 mm
2
in diameter),
permitting a 60% survival rate, in comparison to a 35% survival rate seen with 2A
treatment alone (Pan et al. 2002). Adenovirus vector-mediated gene transfer of
OX40L to tumor cells has also shown tumor regression and increased survival in the
Lewis lung carcinoma model as well as other murine models (Andarini et al. 2004).
Finally, in addition to its anti-tumor effects on solid tumors, transfection of OX40L
27
has been found to dramatically inhibit the development of lymphoma cell growth
(Kaneko et al. 2005).
The ability of OX40 to stimulate CD4
+
and CD8
+
T cells while suppressing
Treg cells makes the OX40 pathway appear ideal for exploitation in anti-tumor
therapies. Though other investigators have explored the use of the OX86 agonist
antibody, it has been postulated that the natural TNF ligand may produce fewer side
effects (Zhang et al. 2007), advocating in favor of the use of OX40L for the
treatment of cancer. Conjugation of TNF ligands to the Fc portion of antibodies has
been shown to extend half-life while providing added effector functions including
antibody-dependent cell cytotoxicity (ADCC) (Taylor et al. 2002). Zubairi et al have
reported on the successful construction of an OX40L-Fc with immunotherapeutic
efficacy in the treatment of Leishmania donovani infection (Zubairi et al. 2004), and
Morris et al have produced a Fc:OX40L reagent via coiled-coil trimerization that
demonstrated immune stimulation in vitro (Morris et al. 2007), but prior to this
publication, there has been no in vivo examination of an OX40L stimulating
antibody. We now describe the genetic engineering of a new C-terminal Fc-
mOX40L construct with in vitro potency and in vivo anti-tumor activity in two solid
tumor models of the mouse.
2.2. Research Design and Methods
Antibodies and cell lines
Anti-CD3 (145-11C clone), PE-anti CD4 (RM4-5 clone), PE-anti-
CD8 α (53-6.7 clone), PE-anti-CD25 (PC61 clone), PE-anti-CD11c (HL3 clone), and
28
PE-anti-CD49b (DX5 clone) mAbs,\ and HRP-streptavidin were purchased from BD
Pharmingen (San Diego, CA). Hybridomas, including rat anti-mouse CD4 (GK1.5),
anti-CD8 β (H35), and anti-CD25 (PC61) mAbs were purchased from American Type
Culture Collection (ATCC, Manassas, VA). Anti-OX40 (OX86) mAb-producing cell
line was obtained from the European Cell Culture Collection (Wiltshire, UK). For
immunohistochemical staining, both primary rabbit anti-mouse granzyme B and
secondary biotinylated goat anti-rabbit IgG polyclonal antibodies were purchased
from Abcam Inc. (Cambridge, MA).
The NS0 murine myeloma cell line was obtained form Lonza Biologics
(Slough, UK). The Colon 26 colorectal adenocarcinoma and RENCA renal
carcinoma cell lines were obtained from ATCC.
Reagents and mice
The Glutamine Synthetase Gene Amplification System with expression
plasmids pEE6/hCMV-B and pEE12 was purchased from Lonza Biologics (Slough,
U.K.). The murine OX40L cDNA was a generous gift from Dr. Sugamura from the
Dept. of Microbiology and Immunology, Tohoku University, Japan. Restriction
endonucleases, T4 DNA ligase, Vent polymerase, and other molecular biology
reagents were obtained from either New England Biolabs (Beverly, MA) or
Boehringer Mannheim (Indianapolis, IN). Characterized and dialyzed fetal calf sera
(FCS) were purchased from Hyclone Corp. (Logan, UT), and RPMI 1640 medium,
Hybridoma Selective Medium without L-glutamine, MEM non-essential amino acids
solution (100x), and phosphate-buffered saline (PBS) were purchased from GIBCO
LifeTechnologies (San Diego, CA). The murine IL-2 ELISA kit was purchased from
BD Biosciences (San Jose, CA).
29
Six-week-old female BALB/c mice were obtained from Harlan Sprague-
Dawley (Indianapolis, IN). All experiments were performed in accordance with
Institutional Animal Care and Use Committee (IACUC) protocols and institutional
guidelines for the proper humane care and use of animals in research.
Construction of Fc-mOX40L
Fc-mOX40L was cloned as previously described (Zhang et al. 2007). The
Fc-mOX40L cDNA extracellular domain was amplified using primers 5'-AAG GAA
AAA AGC GGC CGC CAA CTC TCT TCC TCT CCG GCA-3' and 5'-GGC GAA TTC
TCA CAG TGG TAC TTG GTT CAC AGT-3'. The PCR fragment then was inserted
into the pEE12 vector via Not I and EcoR I sites in the C-terminus of the Fc
sequence. The vector with the Fc-mOX40L fusion gene was transfected by
electroporation into NS0 cells.
Expression and purification of Fc-mOX40L
The Fc-mOX40L was expressed in NS0 murine myeloma cells for long-term
stable expression as per the manufacturer’s protocol (Lonza Biologics). The highest
producing clone, determined by indirect ELISA screening for murine Fc in a 24-hr
assay, was scaled-up for incubation in an aerated 3- or 8-L stir flask bioreactor
using 2.5% dialyzed fetal calf serum. The secreted fusion protein was purified from
the clarified cell culture supernatant by tandem protein-A affinity and ion-exchange
chromatography, as described previously (Li et al. 2004). The fusion protein was
confirmed to produce a single peak by HPLC analysis (data not shown). The fusion
protein was also analyzed by ELISA to verify the presence and proper folding of the
OX40L extracellular domain and by SDS-PAGE to ensure assembly and purity.
30
In vitro activity assay
The bioactivity of the OX40L moiety was determined by ELISA measurement
of IL-2 production from splenocytes aseptically isolated from six-week-old female
BALB/c mice. After the red blood cells were lysed using the BD Pharm Lyse
TM
Lysing buffer (BD Pharmingen), single cell suspensions were washed twice in PBS
and incubated in a 24-well plate (1.5 x 10
6
cells/well) pre-coated with 5 µg/ml anti-
CD3 (145-11C clone) in the presence of 2 µg/ml Fc-mOX40L or OX86. After 48 hr,
IL-2 production was determined by sandwich ELISA (BD Biosciences) for the above
culture supernatants according to the manufacture’s protocol. All assays were
performed in triplicate.
Murine Treg and CD8
+
T cell proliferation assays
In order to demonstrate the effect of the Fc-OX40L fusion protein on T cell
subsets, a panel of Fc fusion proteins produced in our laboratory was tested in
mouse Treg and CD8
+
cell proliferation assays. For these assays, splenocytes from
normal C57BL/6 mice were subjected to cell purification using either a Miltenyi
Biotec CD4
+
CD25
+
Regulatory T Cell Isolation Kit (Treg) or CD8a microbeads (CD8
+
cells). All wells contained irradiated APCs at a concentration of 1 x 10
5
/well and
anti-CD3 (1 µg/mL). The Fc fusion proteins were tested in triplicate using two
concentrations (1 and 10 µg/mL) in which CD8
+
cells alone (5 x 10
4
/well), Treg cells
alone (5 x 10
4
cells/well), and a 1:1 combination of both cell populations (5 x 10
4
of
each/well) were tested. In this way, background data of proliferation was obtained
since some of the Fc fusion proteins induced proliferation of reporter and Treg cells.
3
H-thymidine was added for the last 18 hr of culture. Triplicate wells were averaged
and percent suppression was calculated by the equation:
31
CD8
+
alone minus (CD8
+
/Treg combination minus Treg alone) X 100
CD8
+
alone
Immunotherapeutic studies
Six-week-old female BALB/c mice were injected subcutaneously in the left
flank with a 0.2-mL inoculum containing approximately 5 x 10
6
Colon 26 or RENCA
cells. When tumors reached 0.5 cm in diameter 5-7 days after tumor implantation,
groups of mice (n=5) were intravenously (i.v.) treated with a 0.1-mL inoculum
containing incrementally increasing equimolar concentrations of Fc-mOX40L or
OX86 (1 to 150 μg/dose). All groups of mice were treated daily ×5 and tumor growth
was monitored every other day by caliper measurement in three dimensions,
enabling tumor volumes to be calculated by the formula length × width × height. The
results were expressed as the mean ± SD. Significance levels (P values) were
determined using the Wilcoxon rank-sum test.
Survival study
Groups of BALB/c mice (n=5) were injected with Colon 26 or RENCA cells
as described above. Five days after tumor implantation, mice were treated with 1
nmol/dose Fc-mOX40L (100 µg dose level) or OX86 (200 µg dose level) daily ×5,
and survival of the mice was recorded for 120 days. Significance levels (P values)
were determined using the Wilcoxon rank-sum test.
Flow cytometry of tumor-infiltrating lymphocytes
Mice from the control group and Fc-mOX40L- and OX86-treated groups
injected with 500 pmole/dose (50 µg/dose and 75 µg/dose, respectively) were
32
sacrificed by sodium pentobarbital overdose 23 days following tumor implantation.
Tumors were weighed and tumor-infiltrating lymphocytes were isolated as described
previously (Sanderson et al. 2005). PE-labeled anti-CD4, anti-CD8, anti-CD11c,
anti-CD25, and anti-CD49b antibodies were used to stain the tumor-infiltrating
lymphocytes for flow cytometric analysis. After gating on lymphocytes, the
percentages of all lymphocyte populations were calculated.
Morphologic studies
Tumors from treated and control Colon 26-bearing mice were removed on
day 15 post-tumor implantation. Tumors were fixed in 10% neutral buffered formalin
(VWR Scientific, West Chester, PA) and paraffin-embedded sections from Colon 26
tumor-bearing mice were stained with Hematoxylin and Eosin (H&E).
Leukocyte subset depletion and combination studies
To deplete CD4
+
, CD8
+
, CD25
+
, or NK cells, mice were injected i.p. on days
0, 7, and 14 post-tumor implantation with 0.5 mg of purified anti-CD4 antibody
(GK1.5), anti-CD8 antibody (H35), anti-CD25 antibody (PC61), or anti-NK antibody
(rabbit anti-mouse/rat asialo GM1 polyclonal antibody, Cedarlane Laboratories) in a
1-mL inoculum in PBS. Depletion of specific T-cell subsets was confirmed by flow
cytometry analysis of lymph nodes of inoculated mice using antibody clones that
differ from those used for depletion (data not shown). Tumor volumes were
calculated three times per week by caliper measurement in three dimensions.
33
2.3. Results
Construction and expression of Fc-mOX40L fusion protein
Because the C-terminus of OX40L is extracellular and essential for the
ligand’s bioactivity, the N-terminus of the OX40L gene was fused to the C-terminus
of the immunoglobulin heavy chain gene (Figure 2.1A). The resulting fusion gene
was inserted into a pEE12 vector under an antibody leader sequence, transfected,
and expressed. Proper assembly of the Fc-mOX40L fusion protein was
demonstrated by 4-15% reducing SDS-PAGE (Figure 2.1B). The single resulting
band at ~55 KD corresponds to glycosylation of the uncleaved 44 KD Fc-mOX40L
fusion protein. After purification, the fusion protein appeared as a single peak by
HPLC analysis (data not shown).
A B
Figure 2.1. Construction and purification of Fc-mOX40L. (A) Schematic of the construction
and final assembly of the murine Fc-mOX40L fusion protein, in which the extracellular
portion of mOX40L was inserted at the C-terminus of the immunoglobulin heavy chain. (B)
Electrophoretic analysis of the purified Fc-mOX40L using Coomassie blue-stained 4-15%
reducing SDS-PAGE, showing Fc-mOX40L migration as a 50-60 KD protein, which is
indicative of glycosylation of the Fc-mOX40L protein that has a calculated molecular mass of
approximately 44 KD.
34
In Vitro IL-2 Production at 72 Hr
0
200
400
600
800
1000
1200
1400
1600
αCD3 alone αCD3 + αCD28 αCD3 + OX86 αCD3 + Fc-mOX40L
IL-2 Concentration (pg/mL) IL-2 Concentration (pg/mL)
Bioactivity of OX40L moiety
To determine whether the OX40L moiety retained its biological activity, an
IL-2 production assay was performed by ELISA (Figure 2.2). The Fc-mOX40L fusion
protein induced significant IL-2 production in the presence of bound anti-CD3
compared with anti-CD3 alone (P < 0.0002), suggesting that Fc-mOX40L provides a
potent co-stimulatory second signal. This in vitro bioactivity assay revealed that the
constructed Fc-mOX40L fusion protein has bioactivity equivalent to the previously-
available agonist antibody OX86 and that Fc-mOX40L induction of IL-2 production
nearly reaches the IL-2 production level induced by anti-CD3 and CD28 co-
stimulation, the canonical activating first and second signals. These data
demonstrate the potency of the OX40L moiety of the fusion protein.
Figure 2.2. In vivo demonstration of potency. Demonstration of the potency of Fc-mOX40L
in comparison to agonist antibody OX86, with CD3 alone serving as a negative control for
co-stimulation.
35
Murine Treg and CD8
+
T cell proliferation assays
As shown in Figure 2.3A, at 10 µg/mL, Fc-mOX40L markedly reversed Treg
suppression of reporter lymphocytes. Other Fc fusion proteins, including those
consisting of MIG, LEC, TNF α, and LIGHT did not show this activity CD137L and
IL-2 showed greater reversal than Fc-mOX40L.
In a parallel study, murine CD8
+
cells were incubated with the same Fc
fusion proteins to determine their effects on this critical effector cell population.
The results of these studies (Figure 2.3B) indicate that IL-2-Fc had the greatest
proliferative response on CD8
+
cells as expected, but that Fc-mOX40L also had
moderate proliferative effects on this subpopulation of T cells. As shown in Figure
2.3C, several of the fusion proteins directly caused the proliferation of Treg cells.
Since the suppressive Treg cells do not proliferate to anti-CD3 stimulation in vitro,
this is an indication that these cells are losing their suppressive phenotype. This
background proliferation was subtracted in the percent suppression calculations.
From these data, it is confirmed that Fc-mOX40L can reverse Treg suppression,
which may contribute to its anti-tumor capabilities.
36
Figure 2.3. Treg suppression by Fc-mOX40L. Effect of different Fc fusion proteins on (A) %
suppression of CD8
+
cells by Treg cells, (B) proliferation of CD8
+
T cells, and (C)
proliferation of Treg cells. Note that the Fc-mOX40 induced the proliferation of CD8
+
cells
but markedly inhibited Treg suppression of proliferating CD8
+
T cells.
37
Immunotherapeutic dosing studies
A dosing study was performed on Colon 26- and RENCA-bearing BALB/c
mice using i.v. equimolar doses of Fc-mOX40L (1-100 µg/dose) or OX86 (1.5-150
µg/dose), with i.v. injection of PBS serving as a negative control (Figure 2.4). At 1
µg/dose, Fc-mOX40L did not produce measurable tumor reduction in either
treatment model, while the molar equivalent of OX86, 1.5 µg/dose, Fc-mOX40L
produced minimal tumor regression in the Colon 26 model (Figure 2.4A). As the
concentration of the treatments increased, so did the rapidity with which Fc-mOX40L
produced complete remission in the Colon 26 model, while the partial remission
effects of OX86 plateaued and failed to show continued dose-dependence in Colon
26-bearing mice (Figure 2.4A). In the RENCA model, 15 µg/dose through 150
µg/dose produced identical results, with OX86 failing to bring about measurable
tumor regression, while Fc-mOX40L rapidly produced complete remission at
equivalent doses (Figure 2.4B).
Survival studies
Survival studies were performed using equimolar concentrations of Fc-
mOX40L (50 µg/dose) or of OX86 (75 µg/dose). All mice treated with Fc-mOX40L
survived for the full observation period of 113 days, whereas treatment with OX86
succeeded only in extending the median survival from 55 days to 70 days in Colon
26-bearing mice (Figure 2.4C) and from 45 days to 65 days in RENCA-bearing mice
(Figure 2.4D). In contrast to Fc-mOX40L-treated mice, no control mice or OX86-
treated mice survived beyond 80 days in either tumor model (P < 0.05).
38
500 pmole/dose
0
0.1
0.2
0.3
0.4
5 7 9 1214 1619 21 23
Days after tumor implantation
tumor volume (cm3)
Control
OX86
Fc-OX40L
10 pmole/dose
0
0.2
0.4
0.6
0.8
1
5 7 9 1214 1619 21 23
Days after tumor implantation
tumor volume (cm3)
Control
OX86
Fc-OX40L
100 pmole/dose
0
0.2
0.4
0.6
0.8
1
5 7 9 12 14 161921 23
Days after tumor implantation
tumor volume (cm3)
Control
OX86
Fc-OX40L
500 pmole/dose
0
0.2
0.4
0.6
0.8
1
5 7 9 12 14 16 19 21 23
Days after tumor implantation
tumor volume (cm3)
Control
OX86
Fc-OX40L
A
B
0.00
0.25
0.50
0.75
1.00
030 60 90
Days Following Tumor Implantation
Estimated Probability of Survival
p<0.001
Fc-OX40L
OX86
PBS Control
0.00
0.25
0.50
0.75
1.00
030 60 90
Days Following Tumor Implantation
p<0.001
Fc-OX40L
OX86
PBS Control
C Colon 26 Survival Plot D RENCA Survival Plot
Figure 2.4. Dosing and survival curves in two murine tumor models. Dose response of Fc-
mOX40L in (A) Colon 26-bearing and (B) RENCA-bearing BALB/c mice. Kaplan-Meier
survival analysis of Fc-mOX40 in (C) Colon-26-bearing and (D) RENCA-bearing BALB/c
mice.
39
0
100
200
300
400
500
600
700
800
5 7 9 12 14 16 19 21 23
days
tumor volume (mm3)
no treatment
anti-CD4 (days 0, 7, 14)
anti-CD8 (days 0, 7, 14)
anti-CD25 (day 0)
anti-NK (days 0, 7, 14)
TREATMENT
0
100
200
300
400
500
600
700
800
5 7 9 12 14 1619 2123
days
tu mo r v o lu me (mm3)
no treatment
Fc-mOX40L (30ug/dose +
anti-CD4 (days 0, 7, 14)
Fc-mOX40L 30ug/dose +
anti-CD8 (days 0, 7, 14)
Fc-mOX40L (30ug/dose)
+ anti-CD25 (day 0)
Fc-mOX40L(30ug/dose) +
anti-NK (days 0, 7, 14)
Fc-mOX40L (30ug/dose)
TREATMENT
Leukocyte subset depletion and combination studies
In order to assess the role of specific leukocyte populations in Fc-mOX40L-
mediated immunotherapy, individual immune cell populations were depleted by
monoclonal antibody administration. Depletion was confirmed by flow cytometry
analysis of splenic populations (data not shown). As seen in Figure 2.5A, anti-CD4
and anti-CD25 depletion produced, by themselves, significant reduction in tumor
size, while anti-NK depletion also brought about measurable reduction in tumor size.
When Fc-mOX40L-treated mice were depleted by these procedures (Figure 2.5B),
anti-CD4 and anti-NK treatment enhanced the anti-tumor effects of Fc-mOX40L
treatment, while anti-CD8 abrogated the effect of Fc-mOX40L treatment, indicating
that Fc-mOX40L is dependent upon CD8
+
T cells for its therapeutic effects.
A
B
Figure 2.5. Depletion of CD8+ population abrogates treatment effects. Depletion studies
demonstrating (A) immune cell population anti-seras’ effects on tumor growth, and (B) anti-
seras’ effects on the anti-tumor activity of Fc-mOX40L.
40
Characterization of tumor-infiltrating leukocytes
Using equimolar doses of Fc-mOX40L and OX86 (50
and 75 µg/dose, respectively), the relative composition of tumor-infiltrating
lymphocytes was determined by flow cytometry at day 23 (Table 2.1). In Colon 26-
bearing mice, both Fc-mOX40L and OX86 induced significant tumor infiltration of
CD11c
+
cells, whereas only in the Fc-mOX40L-treated mice was there a significant
increase in tumor infiltration of CD8
+
cells (6.73% of gated lymphocytes in
comparison to 0.7% in the control and 0.4% in OX86-treated mice). Both Fc-
mOX40L and OX86 treatment produced a decrease in the tumor-infiltrating CD25
+
and CD49
+
cells in the Colon 26 model, and in the tumor-draining lymph nodes of
Colon 26-bearing mice, there was a decrease in CD11c
+
and CD49
+
cells.
Table 2.1. Tumor-Infiltrating Leukocytes
RENCA
Tumor
Control Fc/OX40L OX86
RENCA
TDLN
Control Fc/OX40L OX86
CD4 0% 0.44% 0.55% CD4 23.28% 23.94% 15.83%
CD8 0.24% 0.87% 0.50% CD8 4.28% 10.15% 5.30%
CD11b 10.24% 6.46% 5.55% CD11b 5.81% 20.97% 8.20%
CD25 0.60% 0.56% 0.11% CD25 4.23% 17.06% 4.75%
CD49 4.25% 2.47% 3.79% CD49 14.76% 18.93% 7.73%
Colon 26
Tumor
Control Fc/OX40L OX86
Colon 26
TDLN
Control Fc/OX40L OX86
CD4 0.30% 0.15% 0.10% CD4 2.34% 2.94% 5.23%
CD8 0.70% 6.73%0.41% CD8 1.59% 2.38% 3.04%
CD11b 2.77% 8.98% 6.68% CD11b 1.68% 10.02% 5.85%
CD25 1.42% 0.42% 0.51% CD25 1.64% 2.00% 2.66%
CD49 23.32% 1.49% 9.99% CD49 3.67% 6.26% 9.27%
Dotted lines indicate significant decreases from control; bolded lines indicated significant increases.
41
In RENCA-bearing mice, few of the same patterns were observed. In
particular, there was no Fc-mOX40L treatment-induced increase in tumor-infiltrating
CD8
+
cells, the presumed effector cell for TNFSF-induced anti-tumor activity. Also
notable, RENCA tumor-draining lymph nodes had much greater percentages of
positive staining cells across the board, and while even untreated RENCA tumor-
draining lymph nodes had more lymphocytes than Colon 26-bearing mice, Fc-
mOX40L treatment was able to further increase the percentage of CD8
+
and
CD11b
+
cells, though CD25
+
cells were also increased in the tumor-draining lymph
nodes of RENCA-bearing mice.
Immunohistochemical studies
H&E slides were prepared from paraffin-embedded
tumor tissues removed from Colon 26- and RENCA-bearing mice six days after the
completion of treatment with daily ×5 injections of the Fc-OX40L fusion protein and
the OX86 agonist antibody at equal molar concentrations of 100 µg/dose and 150
µg/dose, respectively (Figure 2.6).
42
Figure 2.6. Immunohistochemical evidence of treatment-induced necrosis. H&E staining of
tumors from (A) control, (B) Fc-mOX40L- and (C) OX86-treated Colon 26-bearing mice.
A
B
C
43
Tumors from PBS-treated control mice showed a
necrotic core typical of such large tumors. Tumors from mice treated with either Fc-
mOX40L or OX86, although much smaller due to successful treatment, had
prominent necrosis. In Colon 26-bearing mice, Fc-OX40L-induced coagulative
necrosis throughout the tumor, with high magnification revealing infiltration of
mononuclear cells. OX86 treatment, however, produced necrosis concomitant with
infiltration of a polymorphonuclear population and a liquifactive pattern of tumor
lysis. In RENCA-bearing mice, the same pattern of necrosis was observed as with
Colon 26-bearing mice, though the tumors were larger and the necrosis less
pronounced (data not shown).
2.4. Discussion
Over the past several years, many co-stimulatory ligands belonging to the
TNFSF and B7 families have been investigated, both by our laboratory and by
others. While many of these co-stimulatory molecules have shown a degree of in
vivo anti-tumor activity, few molecules have been as promising as OX40L. Signaling
through OX40 stimulates both effector and responses and activates both CD4
+
and
CD8
+
effector cells. Remarkably, activation using OX86 has been shown to be
tumor specific and effector cell specific, also inhibiting the response of suppressor
Tregs. While OX86 has been shown by others to reduce tumor growth or synergize
with other immunotherapies, in our hands, Fc-mOX40L is far more potent than
OX86 and is capable of producing complete and lasting remissions.
The genetically-engineered Fc-mOX40L expressed by NS0 cells was shown
to remain intact (Figure 2.1B) and to retain its bioactivity (Figure 2.2). By IL-2
44
production ELISA, Fc-mOX40L appears to have bioactivity equivalent to OX86. This
degree of co-stimulation falls within 10% of the maximal IL-2 production induced by
CD3/CD28 stimulation. Indeed, Fc-mOX40L-induced IL-2 production exceeded that
produced by other co-stimulatory fusion proteins produced in our laboratory,
including Fc-B7.1 (Liu et al. 2005), Fc-CD137 (Zhang et al. 2007), and Fc-GITRL
(Hu et al. 2007). The fact that Fc-mOX40L’s potency exceeds that of Fc-B7.1, which
captures the co-stimulatory activity of the B7-CD28 signaling pathway, suggests that
Fc-mOX40L has an unparalleled capacity to activate CD8
+
effector T cells. As
important, Treg activity assays showed that Fc-mOX40L, like Fc-mGITRL (Hu et al.
2007) inhibits Treg suppression yet enhances CD8
+
T cell proliferation (Figure 2.3).
In vivo Fc-mOX40L was also found to be extremely potent. In Figure 2.4A it
can be seen that Fc-mOX40L is inactive at 1 µg/dose, but at 10 µg/dose and higher
produces complete remission in both Colon 26-bearing and RENCA-bearing mice.
At 50 µg/dose, Fc-mOX40L prevents appreciable tumor growth in both Colon 26
and RENCA models (Figure 2.4A and B). Fc-mOX40L was tested at up to 100
µg/dose (not shown), wherein it produced complete remission much as seen with 50
µg/dose. By comparison, OX86 agonist antibody produced only partial remission,
plateauing at 80% reduction in tumor volume, even at 150 µg/dose.
The therapeutic window of Fc-mOX40L is ideal for an immunotherapeutic
drug, because 10 µg/dose produced complete remission in all treated mice, but
mice treated with doses as great as 100 µg/dose did not show any signs of toxicity.
Toxicologic studies in rhesus monkeys with an anti-OX40 agonist antibody did not
reveal side effects, and at necropsy, monkeys were found to have only enlarged
splenic and gut-associated lymph nodes for up to 28 days post-treatment, a finding
consistent with T cell activation. Immunohistochemistry confirmed spleen and
45
lymph node lymphocyte hyperplasia and showed no signs of destruction due to
inflammation, suggesting OX40-OX40L pathway stimulation to be a relatively safe
immunotherapeutic response. Because Fc-mOX40L produces complete remission
at doses far lower than agonist antibody but has similar tolerability at very high
doses, Fc-mOX40L is an even safer anti-tumor therapy.
In survival studies, Fc-mOX40L again proved to be superior to the well-
known OX86 antibody. As seen in Figure 4A, the partial remission produced by 75
µg/dose OX86 in Colon 26-bearing mice was not a lasting response, with all mice
dying before the middle of the third month and a mean increase in life expectancy of
only two weeks. Fc-mOX40L at 50 µg/dose, however, produced lasting remission
and mice survived through the completion of the study, roughly four months later. In
Figure 4B the same trend was evident in RENCA-bearing mice, with OX86
increasing mean survival by roughly three weeks, but OX86-treated mice dying early
in the third month. Fc-mOX40L-treated mice, however, were able to sustain lasting
remission in the very aggressive RENCA model, with all surviving through the end of
the study. As shown Figure 2.5, depletion studies in treated mice demonstrated that
CD8
+
T cells were critical for Fc-mOX40L immunotherapy, but unlike other
immunotherapeutic ligands such as the chemokine LEC (Li et al. 2003a; Li et al.
2003b), NK cells did not appear to be required for tumor regression.
In Figure 6, treatment of Colon 26-bearing mice can be seen to induce tumor
necrosis in excess of that which would be expected for the size of the tumor.
Interestingly, while both Fc-mOX40L and OX86 induced tumor lysis, they did so with
a different pattern of necrosis. In particular, Fc-mOX40L produced massive central
necrosis and OX86 produced a peripheral ring of necrosis. While treatment-induced
necrosis was less pronounced in the RENCA model, it also followed the same
46
pattern (data not shown). The central necrosis induced by Fc-mOX40L is likely an
indicator of better prognosis because many treatments have been found to fail due
to the their inability to overcome the high oncotic pressure of tumors that makes
infiltration and deep permeation challenging (Di Paolo et al. 2007; Karakousis et al.
1979). The larger size and charge distribution of OX86 compared to the smaller
sized Fc-mOX40L may account for these differences in vivo.
By all measures, Fc-mOX40L succeeds in activating an anti-tumor response,
as would be expected of many of the ligand members of the TNF superfamily.
Though other receptor members of the TNFR superfamily have been studied for
their therapeutic potential with substantial but not total success, OX40 appears to
have several advantages over its superfamily sisters. In one study, investigators
identified a dichotomy between the co-stimulatory action of OX40 and 4-1BB, finding
that OX40 enhances early effector function as well as late accumulation survival,
whereas the early effects of 4-1BB may act in a negative, regulatory fashion and
serve to limit primary CD8
+
responses (Lee et al. 2006).
In summary, we provide the first description of an Fc-mOX40L fusion protein
with demonstrated in vitro immune stimulation and in vivo anti-tumor activity. In our
hands, Fc-mOX40L has proven more potent than the OX86 agonist antibody, both
in vitro and in vivo. Stimulation of the OX40-OX40L pathway is as potent as B7.1
stimulation (Serghides et al. 2005), but has the added benefits of producing an
antigen-specific immune response (Maxwell et al. 2006; Ruby et al. 2007) and Treg
inhibition. Additionally, stimulation of the OX40-OX40L pathway has been shown to
produce minimal to no toxicity in primates, even when significant immune activation
is induced (Weinberg et al. 2006). In light of the significant and lasting tumor
remission and increased survival brought about by Fc-mOX40L in both Colon 26-
47
and RENCA-bearing mice, this highly potent fusion protein appears to be a strong
candidate for immunotherapy of solid tumors.
48
CHAPTER III: IMMUNE SIGNATURES OF MURINE AND HUMAN CANCERS
REVEAL UNIQUE MECHANISMS OF TUMOR ESCAPE AND NEW TARGETS
FOR CANCER IMMUNOTHERAPY
3.1. Background and Significance
Immunosurveillance defines the process by which immune mediators are
able to recognize and attack tumors (Dunn et al. 2004a; Malmberg et al. 2006). The
ability of the immune system to recognize and destroy tumor cells is supported by
observations of spontaneous T cell responses in human malignancies (Boon et al.
2006), spontaneous CTL-induced tumor regression (Zorn et al. 1999), and the
involvement of innate immune effectors in the identification and elimination of tumor
cells (Hayakawa et al. 2006). Nevertheless, though the immune system may on
occasion succeed in eliminating nascent tumors, immunoediting places a strong
selection pressure on tumor cells. Since tumors are often genetically unstable (Ling
et al. 1985), immunoediting confers a survival advantage upon tumor cells, enabling
them to develop strategies for evading the immune system. Consistent with the
multihit model of oncogenesis (Owens et al. 1999), the acquisition of tumor escape
mechanisms may be a necessary “hit” for a malignancy and is hence considered by
many to be “the seventh hallmark of cancer” (Hanahan et al. 2000). Tumor escape
strategies include mechanisms as varied as clonal deletion (Lauritzsen et al. 1998),
induction of peripheral anergy (Rivoltini et al. 2002), and epigenetic modification
(Tomasi et al. 2006). In each instance, the tumor exploits immunological signaling
pathways to create a microenvironment favorable to tumor growth (Whiteside 2006).
All such mechanisms of tumor escape pose a great obstacle to the widespread
49
use of immunotherapy for the treatment of cancer (Pardoll 2003). The reliable
identification of the mechanisms being employed by a given tumor therefore
becomes important if immunotherapeutic treatments are to reverse tumor escape
pathways.
One of the first identified mechanisms of tumor escape came from the
observation that certain tumors secrete immunosuppressive cytokines like IL-10 and
TGF- β (Mocellin et al. 2001). In more recent years, an increasing number of tumors
have been observed to attract suppressor T cells, known as T regulatory cells
(Tregs), which may help establish tolerance at the tumor site (Wang 2006). Most
recently, the field has focused on the various mechanisms by which tumors prevent
effector T cell activation by impeding activating “second signals” and critical
“expansion signals” that are usually delivered through members of the B7/CD28
(Greenwald et al. 2005) and TNSF/TNSFR superfamilies (So et al. 2006; Watts
2005). Each of these superfamily pathways has immunostimulatory and
immunoinhibitory members, and it has been hypothesized that the overall ratio of
immunostimulatory to immunoinhibitory molecules determines whether T cells
become activated or anergic in response to an antigenic exposure (Taylor et al.
2006).
Though many potentially-relevant immunoinhibitory mechanisms have been
identified in recent years, most studies on tumor escape have explored individual
tolerogenic genes or small sets of genes, commenting on which cancers
demonstrate involvement of that mechanism. Alternatively, some studies have
investigated one specific tumor,
elucidating the array of immunological genes with
altered expression. These two disparate approaches paint a patchy picture of
50
immune escape mechanisms. Using immunotherapy to treat patients with cancer,
however, necessitates a more complete understanding of all of the major
mechanisms of tumor escape at play for a given tumor, as well as an understanding
of the differences in tumor escape between different cancer types.
Although DNA array-based “immunogenomics” has been proffered as a
theoretically-promising approach to this question (Mocellin et al. 2004), microarray
experiments thus far have failed to provide a clear story of tumor escape, in part
because of the great challenge involved in integrating the vast array of modulated
genes into coherent immunomodulatory pathways (Hyatt et al. 2006). Exceptions
include the occasional cancer-specific gene network, like the HMGB1/amphoterin
pathway in gastric cancer (Oue et al. 2005), but such information does not approach
providing a systematic delineation of the critical genes in each of the common
cancers. Consequently, the field still lacks the fundamental knowledge that would
enable the determination of the most appropriate immunotherapy regimen for a
given tumor. This becomes especially important as new single-molecule
immunotherapy reagents, including anti-CD25 antibody (Li et al. 2003a; Needham et
al. 2006), anti-CTLA-4 antibody (Abrams 2004), PD-L1 blockade (Curiel et al. 2003),
and PD1 blockade (Hirano et al. 2005) become available for clinical use.
Characterization of “important” escape mechanisms has been complicated
by the fact that tumors, even of the same organ, are notorious for demonstrating
significant genetic and biological variability. As a result, the employment of tumor
escape mechanisms may vary significantly from one type of cancer to another – or
within diagnostic categories of the same tumor. It thus becomes necessary to
investigate 1) whether universal tumor escape mechanisms exist, 2) whether
51
classic histopathologic diagnostic criteria divide tumors into classifications that
correlate with different tumor escape mechanisms, and 3) whether immune gene
signatures can further differentiate cancers that are grouped together by similar
histopathologic features but which fundamentally differ genetically and
immunologically.
To characterize patterns of tumor escape mechanisms and to determine the
extent of tumor escape heterogeneity, this study was designed to examine the
expression of key immunoinhibitory genes, which may be up-regulated to produce
tumor escape, and of key immunostimulatory genes, which may be down-regulated
to avoid immune regulation (Zou 2005). Real-time rtPCR was used to quantify the
differential expression of a panel of immunoinhibitory signals including B7-H4,
CTLA-4, FoxP3, IDO, IL-10, IL-10R, PD1, PD-L1, PD-L2, TGF-β, and TGF- βR,
along with a panel of immunostimulatory signals including B7-H3, CD28, CD80,
CD83, CD86, GITR, GITRL, OX40, OX40L, 4-1BB, 4-1BBL, Lag3, IL-6, and IL-6R.
Initially, five histologically-different BALB/c murine tumor models were
studied: 4TI breast cancer, A20 leukemia, C26 colon cancer, MAD109 lung cancer,
and RENCA renal cell carcinoma. To translate these studies to human cancers, two
of the murine tumor models showing similar immunomodulatory expression
patterns, colorectal C26 and breast 4TI, were then studied alongside corresponding
human tumors. The results of these real-time rtPCR studies provide a basis for
exploring the tumor immune signatures of human cancers in order to define
molecular targets for immunotherapy.
52
3.2. Research Design and Methods
Cell culture and reagents
4TI, A20, C26, MAD109, and RENCA murine tumor cell lines were
purchased from American Tissue Culture Collection (Manasus, VA) and maintained
in RPMI 1640 medium with 10% FBS, L-glutamine, and Pen/Strep (Li et al. 2004).
Animal experiments
Six-week-old female BALB/c female mice were purchased from Harlan
Sprague-Dawley (Indianapolis, IN). Institutional Animal Care and Use Committee
(IAUCUC) approved protocols and institutional guidelines for the proper humane
care and use of animals in research were followed in all experiments. Mice were
injected subcutaneously in the flank with a 0.2-mL inoculum of 5 x 10
6
viable cells of
the murine tumor cell lines. Tumors were allowed to develop, and mice bearing each
tumor type were sacrificed on days 2, 4, 7, 14, and 21 (n=2 of each tumor per day).
Control mice were sacrificed on day 10. Tumor and tumor-draining lymph nodes
were harvested and preserved in Qiagen RNAlater (Valencia, CA).
RNA isolation and reverse transcription
RNAlater-preserved tissues were processed with a homogenizer and
reverse transcribed with a Qiagen total RNA isolation kit (Valencia, CA). RNA was
subjected to DNase using Ambion’s RNAqueous-4PCR kit (Austin, TX) and reverse
transcribed using Invitrogen SuperScript III (Carlsbad, CA) with 4 µl RNA/reaction.
First-strand synthesis was verified by non-quantitative rtPCR with murine GAPDH
primer, and RT
-
controls were used to verify RNA purity.
53
Human specimens
Human samples, both cancerous biopsies and normal tissue controls, were
purchased as PCR-ready samples from OncoMatrix (San Marcos, CA). When
possible, matched lymph node specimens were also purchased. All human samples
were reverse transcribed and analyzed as described above using 4 μl
RNA/SuperScript III first-strand reaction. To control for age-related changes in the
intensity of immune response, all breast cancer specimens and breast tissue control
specimens were obtained from patients between the ages of 42 and 47. Colon
tumor specimens were obtained from patients between the ages of 63 and 67, and
control specimens were patient-matched. All tumor samples were graded by a
surgical pathologist as moderately differentiated.
Real-time primers
Real-time rtPCR primers were designed using Primer3 software
(http://frodo.wi.mit.edu/cgi-bin/primer3/primer3_www.cgi) or were purchased from
SuperArray (Frederick, MD):
Table 3.1. Murine Primers
Gene Forward Sequence Reverse Sequence
CTLA-4 5’-CAG GTG ACC CAA CCT TCA GT-3’ 5’-CAG TCC TTG GAT GGT GAG GT-3’
FoxP3 5’-GTG GTC AGC TGG ACA ATC AC-3’ 5’-CTG AGG CAC CTG TTT TAG GA-3’
GAPDH 5’-AAC TTT GGC ATT GTG GAA GG-3’ 5’-CAC ATT GGG GGT AGG AAC AC-3’
GITRL 5’-CAA GAC ATG CCA ACA ACA CC-3’ 5’-AAG GCC TAG GGG AAA GTT CA-3’
ICOS 5’-GCC ACC ATC TGT CCT CAT TT-3’ 5’-CAG GCA TCT AAG CCC TGA AG-3’
ICOSL 5’ AAG CCT CAA GAA CCC CAG AT-3’ 5’-GAA CCC GCT AGA AAC ATG GA-3’
IDO SuperArray PPM05363A: proprietary SuperArray PPM05363A: proprietary
IL-10 5’-CGG GAA GAC AAT AAC TG-3’ 5’-CAT TTC CGA TAA GGC TTG G-3’
PD1 5’-ATT CGT AGA CTG GGG GAC TG-3’ 5’-CAT GCA GAA GGA CAG CAG AT-3’
PD-L1 5’-CGA ATC ACG ATG AAA GTC AA-3’ 5’-GCT GGT CAC ATT GAG AAG CA-3’
PD-L2 5’-CGT GAC AGC CCC TAA AGA AG-3’ 5’-GAT GAC CAG GCA ACG GTA CT-3’
TGF- β 5’-TGC TTC AGC TCC ACA GAG AA-3’ 5’-TGG TTG TAG AGG GCA AGG AC-3’
54
Table 3.2. Human Primers
Gene Forward Sequence Reverse Sequence
4-1BB 5’-CAC TCT GTT GCT GGT CCT CA-3’ 5’-CAC AGG TCC TTT GTC CAC CT-3’
4-1BBL 5’-CTT CAC CGA GGT CGG AAT AA-3’ 5’-GTC CAA CTT GGG GAA GGA GT-3’
B7-H3 5’-GGT CAG CTT CTG TCC CTC TG-3’ 5’-GGA GTC CTT GAG GGA GGA AC-3’
B7-H4
SuperArray: PPH09043A: proprietary SuperArray: PPH09043A: proprietary
CD28 5’-CGT CAG GAC AAA GAT GCT CA-3’ 5’-ACC TGA AGC TGC TGG GAG TA-3’
CD80 5’-GGG AAA GTG TAC GCC CTG TA-3’ 5’-GCT ACT TCT GTG CCC ACC AT-3’
CD83 5’-GGA TGA GAG GGT GCT ATC CA-3’ 5’-CTT CGT GAA GTC CCT TCT GC-3’
CD86 5’-TGG AAC CAA CAC AAT GGA GA-3’ 5’-AAA AAG GTT GCC CAG GAA CT-3’
CTLA-4 5’-GCT CAG CTG AAC CTG GCT AC-3’ 5’-CTT CAG TCA CCT GGC TGT CA-3’
FoxP3 5’-CAA ATG GTG TCT GCA AGT GG-3’ 5’-CAC AGA TGA AGC CTT GGT CA-3’
GAPDH 5’-GAA GGT GAA GGT CGG AGT C-3’ 5’-GAA GAT GGT GAT GGG ATT TC-3’
GITR 5’-GAG TGG GAC TGC ATG TGT GT-3’ 5’-TCT GTC CAA GGT TTG CAG TG-3’
GITRL
SuperArray: PPH00824A: proprietary SuperArray: PPH00824A: proprietary
IDO 5’-GGC AAA GGT CAT GGA GAT GT-3’ 5’-CTG CAG TCT CCA TCA CGA AA-3’
IL-6 5’-AAA GAG GCA CTG GCA GAA AA-3’ 5’-AGC TCT GGC TTG TTC CTC AC-3’
IL-6R 5’-CTG AGG GTG AGT GGG TGA AT-3’ 5’-GCT TCT CAC CTG AGG TCC TG-3’
IL-10
SuperArray: PPH00572A: proprietary SuperArray: PPH00572A: proprietary
IL-10R
SuperArray: PPH00591A: proprietary SuperArray: PPH00591A: proprietary
Lag3 5’-TGG AGA AGA CAG TGG CGA C-3’ 5’-CTC AGC TCC AGG TCA GAG CT-3’
OX40
SuperArray: PPH00818A: proprietary SuperArray: PPH00818A: proprietary
OX40L 5’-CTC CAC CTT CCT TCC ATC AA-3’ 5’-CAA GAC CCA GAA ATG GGA AA-3’
PD1 5’-GTG TCA CAC AAC TGC CCA AC-3’ 5’-CTG CCC TTC TCT CTG TCA CC-3’
PD-L1 5’-TAT GGT GGT GCC GAC TAC AA-3’ 5’-TGC TTG TCC AGA TGA CTT CG-3’
PD-L2 5’-TGA CTT CAA ATA TGC CTT GTT AG
G-3’
5’-GAA GAG TTC TTA GTG TGG TTA TA
G-3’
TGF-β 5’-GGG ACT ATC CAC CTG CAA GA-3’ 5’-CCT CCT TGG CGT AGT AGT CG-3’
TGF-βR
SuperArray: PPH00237A: proprietary SuperArray: PPH00237A: proprietary
Real-time rtPCR
Real-time reactions were performed with 1 µl cDNA, 0.5 µl each forward and
reverse primers (25-40 pmol/µl), 12.5 µl Stratagene Brilliant SYBR Green Master
Mix (La Jolla, CA), and 10.5 μl millipore water. Reactions were run for 40 cycles on a
Stratagene Mx3000P cycler. Each sample was amplified in triplicate runs and
GAPDH served as the housekeeping gene control for normalization.
55
Quantitative analysis
For each sample, the triplicate Ct values from each primer were averaged.
cDNA concentration differences were normalized by the equation 2
gene mean Ct – GAPDH
mean Ct
. Fold increases and fold decreases in gene expression were calculated as the
ratio of normalized-case : normalized-control. For analysis by hierarchical clustering,
fold values were log transformed using Cluster 2.11 software (Eisen et al. 1998) and
exported for display in TreeView 1.60 software.
3.3. Results
Expression profiles of immunomodulatory genes in murine tumor models
Using real-time rtPCR, the gene expression of 11 immunomodulatory genes
was analyzed for each of the five murine tumor models. Standard deviation bars
were often too small to be visible in bar graphs, confirming minimal genetic
variability in the BALB/c mouse strain and verifying high precision of the
methodology. Of the eight immunoinhibitory genes, IDO, IL-10, TGF- β, PD-L1, and
PD-L2 were consistently and robustly up-regulated across all tested cancers (Figure
3.1). CTLA-4 and PD1 showed marked up-regulation in three of the five tumor
models (C26, A20, and 4TI), the same three models that respond to therapy with a
combination immunotherapy protocol consisting of anti-CD25 and a targeted liver
expression chemokine (LEC) fusion protein (Li et al. 2003a). In addition, the three
tested immunostimulatory molecules were all moderately decreased in the tumors of
C26-, MAD109-, A20-, and RENCA-bearing mice, but were increased in 4TI-bearing
mice.
56
Figure 3.1. Murine tumor immunomodulatory gene expression profiles. TreeView display of
log-transformed fold-change values in murine (A) tumors and (B) tumor-draining lymph
nodes. Increased expression is indicated by the intensity of red, while decreased expression
is indicated by the intensity of green. Black indicates no change while grey is a missing
data-point. Vertical lines separate tumor models. For each model, left to right represents
increasing days after tumor implantation. The horizontal line separates immunoinhibitory
genes (above) from immunostimulatory genes (below).
Tumor-draining lymph node expression patterns were markedly different
from the intratumoral expression described above. With a few noteworthy
exceptions, the panel of immunomodulatory genes showed generalized down-
regulation in the tumor-draining lymph nodes in comparison to control lymph nodes.
This is despite the fact that tumor-draining lymph nodes were visibly congested and
enlarged at the time of surgical excision. For a subset of molecules, those for which
antibodies for immunohistochemistry were commercially available (CD25, CD80,
CD83, CD86, PD1, and PD-L2), real-time rtPCR data was substantiated with
immunohistochemical analysis, confirming close correlation between mRNA
quantitation and protein expression (data not shown).
Expression profiles of immunomodulatory genes in human tumor models
To investigate the ability of murine intratumoral gene expression data to
predict immunotherapy targets for human tumors, two human tumor types were
57
analyzed using the same methods. In mice, colorectal C26 and breast 4TI tumor
models bore similar signatures, prompting us to choose human colorectal and ductal
breast adenocarcinomas to study alongside their murine counterparts. Tumor-
draining lymph nodes were not analyzed because of reduced tissue availability.
For these studies, an expanded panel of 25 immunomodulatory genes
was investigated. These genes were selected based upon their role in T cell
activation and tolerance induction. In the eight examined ductal adenocarcinomas
of the breast (Figure 3.2), three immunoinhibitory genes were consistently and
significantly increased: TGF-β receptor (P = 0.003), IL-10 receptor (P = 0.006),
and B7-H4 (P =0.008). Additionally, two immunostimulatory genes were
profoundly down-regulated in all breast cancer specimens: CD28 (P < 0.0001) and
CD83 (P < 0.0001). Interestingly, several purported immunoinhibitory genes, such
as FoxP3, IDO, PD1, PD-L1, PD-L2, and CTLA-4, showed significantly-decreased
expression, suggesting that they are not responsible for producing tumor escape in
human ductal adenocarcinoma of the breast. Remarkably, all targets showed
consistent expression patterns throughout the progression of breast cancer tumors,
indicating that gene expression was not stage dependent.
Immunomodulatory gene expression patterns were less consistent across
the 11 colorectal cases examined (Figure 3.2). Whereas breast cancer profiles
showed three universally-increased immunoinhibitory genes and two universally-
decreased genes, no change in gene expression was 100% consistent across all
colorectal cases. The immunostimulatory genes Lag-3 and IL-6 receptor showed
generalized down-regulation in most cases, and PD-L2 expression was significantly
increased in half of the tested cases. Nevertheless, these gene expression patterns
58
were not sufficiently generalized to make blanket predictions regarding appropriate
and inappropriate immunotherapeutic targets for the treatment of colorectal cancer.
Indeed, the only widespread result was decreased expression of immunoinhibitory
B7-H4, which notably, was one of the most profoundly-increased genes in the tested
breast cancer cases.
Breast Colorectal
Figure 3.2. Human breast and colorectal cancer immunomodulatory gene expression
profiles. TreeView display of log-transformed fold-change values from human (left)
adenocarcinoma of the breast and (right) colorectal adenocarcinoma. Increased expression
is indicated by the intensity of red, while decreased expression is indicated by the intensity of
green and black indicates no change. For each, tumor samples appear in order from left to
right in terms of progressive stage by pathological diagnosis. The horizontal line separates
immunoinhibitory genes (above) from immunostimulatory genes (below). Note that the
breast ductal adenocarcinoma cases show remarkable homogeneity in their immune
signatures, whereas the colorectal adenocarcinoma cases are fundamentally
heterogeneous. Clinically-significant targets are those that appear in red above the
horizontal line (e.g., TGF- βR, IL-10R, and B7-H4 in breast cancer) or in green below the line
(e.g., CD28 and CD83 in breast cancer).
59
Temporal patterns of gene expression
In addition to assessing overall trends in expression profiles, changes in the
expression of specific genes over time were also assessed. To direct a therapy at a
specific molecular target, it is important to know not just which molecules to target,
but also at what stage(s) of tumor growth it is appropriate to target them. In theory,
some targets have stage-independent expression patterns, whereas others show
stage-dependent dynamic changes in expression. To prove this point, Figure 3
demonstrates two important categories of up-regulation, namely consistently-
increased expression (Figure 3.3A), which is characterized by murine PD-L2, and
peaking and waning expression (Figure 3.3B), which is characteristic of molecules
like murine PD1. These expression patterns help establish the temporal window
during which the proposed therapy is most likely to produce significant clinical
results.
In human ductal adenocarcinoma of the breast, as with the murine cancers,
many genes did indeed fall into one of the two aforementioned categories. B7-H4
(Figure 3.3C) and the TGF- β receptor, two immunonhibitory genes, both showed
patterns of consistent increase throughout the stage progression of ductal tumors.
IDO expression, however, peaked and then fell (Figure 3.3D). Colorectal cancers,
in contrast, were heterogeneous both within and between stages and consequently
failed to demonstrate stage-dependent expression patterns.
60
Figure 3.3. Temporal patterns of gene expression. Gene expression was examined over the
course of the development of murine 4TI breast tumors (days 2-21) and over the progressive
stages of human ductal adenocarcinomas (stages I-IIIC). Patterns were categorized as
either consistently progressive (A and C) or peaking and waning (B and D) for purposes of
determining appropriate windows for therapeutic intervention.
Grouped cell analysis of immunomodulatory gene expression
Enhanced gene expression could indicate either increased
proliferation/infiltration of the expressing cell type, or specific up-regulation of the
gene by expressing cells. Similarly, decreased expression could be due to
decreased proliferation/attraction, or to specific down-regulation of the gene by one
or more of the expressing cell populations. To differentiate between changes in cell
number and changes in gene expression per cell, we grouped gene expression fold
61
changes by expressing cell type (Figure 3.4). Uniformly-increased or uniformly-
decreased gene expression across a cell type indicated a change in
proliferation/infiltration of that cell type, whereas gene-specific increases or
decreases, above or below the cell’s baseline, indicated specific up- or down-
regulation of that gene.
Grouped cell analysis revealed several immunoinhibitory molecules to be
increased as the result of specific up-regulation and several immunostimulatory
molecules to be decreased as the result of specific down-regulation (Figure 3.4). In
human breast cancer, the down-regulation of immunostimulatory CD28 and CD83
was especially notable. Additionally, all stages of ductal adenocarcinoma showed a
specific up-regulation of immunoinhibitory genes B7-H4, IL-10 receptor, and TGF-β
receptor. By contrast, in colorectal cancer grouped cell analysis, no
immunoinhibitory gene showed completely consistent specific up-regulation, though
a number of cases did show specific up-regulation of the immunosuppressive
cytokines IL-10 or TGF-β.
62
Figure 3.4. Specific up- and down-regulation of immunomodulatory genes in human breast
and colorectal cancers. Immunomodulatory genes were grouped by expressing cell type to
determine whether increased gene expression was attributable to increases in cell number
(consistent increase or decrease across cell markers) or specific up- or down-regulation of
gene expression (singular increase or decrease in specific cell markers) that stand out
above or below baseline expression changes attributable to cell number. Solid black arrows
indicate specifically up-regulated immunoinhibitory molecules, whereas open arrows indicate
specifically down-regulated immunostimulatory molecules.
63
In addition to the key targets identified via grouped cell analysis, Figure 4
also shows the specific up-regulation of certain immunostimulatory molecules (like
GITR in human breast cancer) and the specific down-regulation of certain
immunoinhibitory molecules (like B7-H4 in human colorectal cancer), changes
unlikely to contribute to the state of tolerance. Increased immunostimulatory genes
and decreased immunoinhibitory genes, while identifiable via this expression
analysis, are not designated rational targets for immunotherapy as their altered
gene expression is more likely the effect of a failed immune response than the
cause of the immune evasion.
To complement this deductive analysis, flow cytometry was used to examine
treatment-induced changes in murine cell populations (data not shown).
Specifically, treatment with the LEC fusion protein was found to decrease CD49b
+
cells by 70%, increase CD11b
+
cells by 20%, and nearly double the population of
CD4
+
CD25
-
cells. In addition, LEC treatment shifted the CD8
+
population to being
increasingly CD62L
+
, suggestive of a central memory T cell population (T
CM
).
3.4. Discussion
Immunotherapy has emerged as a promising approach to the treatment of
cancer, but still suffers under the weight of two substantial challenges: the need to
overcome established immunological tolerance and the need to avoid induction of
auto-immune disease. Both challenges can be satisfactorily overcome only through
knowledge of the mechanisms of tolerance employed by different tumors. Such
knowledge would permit the identification of therapies that can directly reverse
64
tumor escape, which would enable immunotherapy to combat tolerance without
creating the auto-immune complications characteristic of indirect, systemic
immunotherapeutic approaches.
Nevertheless, systematic analysis of the different escape mechanisms
employed by different tumors had been precluded to-date by several difficulties
inherent in such a study. These challenges include the questionable connection
between murine immune findings and human immune responses, the recent
identification of dozens of new immunomodulatory molecules, uncertainties
regarding the location of tolerance induction or potential locations for tolerance
reversal, and background differences in individual patients’ basal immune states.
While these are all indisputable challenges, the present study has attempted to
address each and is therein capable of offering novel insights into the tumor escape
mechanisms of different tumor systems. This study also takes the first step towards
demonstrating the feasibility of fingerprinting tumor escape by determining immune
gene expression levels, in order to guide the selection of immunotherapeutic
strategies.
The murine data presented in Figure 3.1 demonstrate that different tumors
do indeed employ different mechanisms for tumor escape. The range of
immunomodulatory gene expression within any given murine tumor type was <10%
because of the genetically-identical background of the inbred strain, demonstrating
the precision of the methodology. Nevertheless, the range of immunomodulatory
gene expression between murine tumor types did indeed differ, often more than
100-fold, demonstrating the discriminating capacity of the methodology.
Interestingly, some immunomodulatory genes were consistently up-regulated, like
65
PD-L2, whereas the expression of others varied considerably across tumor models,
like with CTLA-4. Additionally, the results displayed in Figure 3.1 prove that tumors
spanning a range of immunogenicity, from very low to very high, all induce changes
in the expression of immunological molecules, and in all cases such changes can be
discriminated by real-time rtPCR.
Though quantitative rtPCR data provides only a proxy of protein expression,
the technique has distinct advantages over immunohistochemistry,
immunofluorescence, or flow cytometry. Firstly, the real-time rtPCR approach
enables the simultaneous screening of a large number of genes in the same
sample. Importantly, quantitative rtPCR also has the sensitivity to quantify small
changes in the expression of candidate genes, even those with high baseline
expression levels. Quantitative rtPCR can therefore be used much like microarrays
to create a “gene signature” that has enhanced statistical power, while being an
inexpensive and relatively simple technology that can translated to the clinical
laboratory more easily than microarrays or flow cytometry.
The success of the methodology with murine tumor analysis was
complemented by analysis of human tumor biopsies of breast and colorectal
cancers. This enabled examination of the correlation between the tumor escape
profile of a given murine tumor model and the corresponding human counterpart.
For the human studies, an even larger panel of immunomodulatory genes was
employed than for the mouse studies, and Figure 3.2 highlights those of the 25
genes that reveal key tumor escape targets for human breast and colorectal
adenocarcinomas. Focusing first on breast cancer, several genes present
themselves as potential causes of tolerance and thus good candidates for target
66
by immunotherapy. Significant and specific down-regulation of CD28 and CD83
suggest routes for co-stimulatory or cytokine therapy. CD28 is the receptor through
which the activating second signal is delivered to the T cell (Bernard et al. 2002),
while CD83 is a marker of DC maturation (Lechmann et al. 2002). The decrease of
both these molecules suggests that tumor escape in human breast adenocarcinoma
may be attributed, at least in part, to T cell down-regulation of CD28 and DC
maturation block (with CD83 down-regulation being either the cause or the effect of
this block). In considering the potential of immunotherapy to reverse these escape
mechanisms, IL-12 is known to stimulate CD28 expression (Warrington et al. 2003),
while IL-4, GM-CSF, and TNF- α are known to promote DC maturation and
expression of CD83 (Brunner et al. 2000). These cytokines can be delivered to the
tumor site via common immunobiology techniques such as Fc-binding to immune
effector cells or antibody-targeting to the tumor, enabling tumor-specific stimulation
and reversal of tumor escape.
In addition to the down-regulation of these two immunostimulatory
molecules, all tested human breast cancer specimens showed significant and
specific up-regulation of the immunoinhibitory molecule B7-H4, a member of the B7
family. This finding is corroborated by previously-published investigations which
have shown, using immunohistochemistry, that B7-H4 is up-regulated and
expressed on the surface of many breast and ovarian tumor cells (Salceda et al.
2005; Tringler et al. 2005). Though the receptor for B7-H4 remains unknown,
experiments using a human B7-H4-Fc fusion protein (hB7-H4Ig) demonstrated the
ability of B7-H4 to suppress secretion of cytokines and T cell proliferation (Sica et al.
2003). These features make B7-H4 a good target for an antibody blockade that
67
would reverse the immunosuppressive function of this molecule, enhancing ongoing
immunotherapeutic strategies in breast cancer.
Remarkably, breast cancer results from Figure 3.2 show consistent
expression patterns throughout the progression of breast cancer tumors, rather than
demonstrating stage-dependent gene expression. This finding is critical in that it
suggests that patients with either early or late diagnoses can be treated with the
same immunotherapeutic regimen.
Unlike human breast cancer specimens, however, the sampled colorectal
tumors did not display stage-consistent gene expression, nor did they display
patient-to-patient consistency. Potential explanations for colorectal gene expression
variability include heterogeneity of patients’ gender (whereas all studied breast
cancer patients were female), age-dependent changes in patients’ immune
responses (breast cancer patients were aged 42-47, while colorectal cancer patients
were aged 63-67, a time when patient immune responses are known for showing
blunting) (Nikolich-Zugich 2005), or differences between tumors that arose in the
rectum from those that arose in the colon. None of these factors alone correlates
with the observed gene expression pattern variations, though a complex
combination of these factors could perhaps be explanatory.
Alternatively, colorectal adenocarcinoma may represent a diverse family of
tumors, histopathologically similar but genetically distinguishable, each tumor sub-
type inducing immunologic tolerance through a distinctive mechanism. These
studies thus present a possible means for teasing apart this immunologically-
heterogeneous group of colorectal tumors, enabling sub-classification that could
enhance prognostic precision and promote treatment response prediction.
68
Additionally, the finding of colorectal immune gene heterogeneity argues in favor of
analyzing individual patient’s tumors, rather than relying on a classic signature, in
order to achieve a basis for predictive judgment regarding treatment of colorectal
adenocarcinoma patients.
Figure 3.2 also demonstrates the ability of the described real-time rtPCR
panel to go beyond identifying a list of the differentially-expressed
immunomodulatory genes, but helping to guide the selection of which genes are the
most appropriate targets for immunotherapy. As an example, in human breast
cancer, several immunoinhibitory molecules showed decreased expression and
several immunostimulatory genes showed increased expression, perhaps a
reflection of the unsuccessful immune response that was mounted against the
tumor. Invariably, the decreased expression of immunoinhibitory CTLA-4 would
argue against the use of anti-CTLA-4 for these breast cancer patients, much as the
increase of immunostimulatory GITR in breast cancer would argue against the use
of GITR agonist treatment, a currently-proposed immunotherapy strategy (Ramirez-
Montagut et al. 2006). The potential therapies of anti-CTLA-4 and GITR-Fc fusion
protein thus stand in contrast to antagonistic antibodies to TGF-β and IL-10
receptors, to B7-H4 blockade, or to IDO chemical inhibition, all of which do derive
support from Figures 3.2 and 3.4 as potentially-beneficial treatments for breast
cancer patients.
Figure 3.3 provides important insights into the clinically-relevant time window
for each of these highlighted molecular targets. The examination of the temporal
patterns of immunomodulatory gene expression helps identify, for example, that
antibodies directed at B7-H4 would be appropriate therapy at any stage of ductal
69
adenocarcinoma of the breast, and would perhaps become an increasingly-useful
treatment strategy during later stages of cancer development. Treatment inhibiting
IDO, however, would be best if delivered during an intermediary phase of the
disease, while the expression of immunoinhibitory IDO is greatest; this
immunotherapy, therefore, would have a constraining window, and this window
should guide the decision to treat with the IDO chemical inhibitor 1-MT (Muller et al.
2005). These results emphasize that for certain markers, determination of the
immune signature is a critical prerequisite to identifying those patients for whom a
given immunotherapy will be of value.
Ultimately, the dramatic intratumoral immunomodulatory gene expression
changes that are seen in human breast and colorectal tumors are encouraging in
that they suggest that immune gene markers may be useful for both monitoring
immunotherapy treatment and directing new treatment strategies. Tumor-draining
lymph node immunomodulatory gene expression analysis may reveal additional
targets that are especially prominent in the lymph node, but there appears to be
sufficient tumor-induced immune dysregulation at the site of the tumor to justify
targeted therapy, which has the profound advantage of minimizing the auto-immune
sequelae that often result from systemic administration of immune-stimulating
therapies. Tumor-specific immunotherapies can be achieved by creating targeted
antibody fusion proteins, like B7.1/chTNT-3 (Liu et al. 2005), which have been
developed in our laboratory.
Given the existence of many already-developed immunotherapies, another
potential future application of this study entails probing currently-used cancer
immunotherapy reagents for their tolerance reversal mechanism of action. Using the
knowledge of a gene expression signature that is characteristic of a given murine
70
tumor, it should be possible to treat that tumor model with successful
immunotherapies and ask the question of which genes are modulated. One would
look specifically for which previously up-regulated immunoinhibitory genes are
down-regulated in a dose-dependent fashion and which previously down-regulated
immunostimulatory genes are up-regulated. Strong correlation between reversal of
tolerogenic gene expression and tumor regression would suggest a tolerance
reversal mechanism of action for the immunotherapy reagent.
Continuation of these studies could provide a three-fold impact on research
into cancer immunotherapy. First, as the Broad Institute’s “Connectivity Map”
becomes available, it will be possible to query the database for FDA-approved drugs
and small molecules that are known to induce a gene expression profile opposite of
that seen to be induced by developing tumors (Lamb et al. 2006). These treatments
are key in that they will be rapidly available for clinical trials. Second, the extension
of this line of investigation to examine the effects of known immunotherapy reagents
on immunomodulatory gene expression profiles will help determine the manner in
which already-developed immunotherapies work to reverse tolerance to tumor. As
such, the application of these already-developed immunotherapeutic reagents will
be greatly enhanced by facilitating their use in the most appropriate patient
populations. Third, for cancers that lack already-developed immunotherapies,
identification of a clear gene target will help prioritize the generation of new
immunotherapy reagents. This will also enable the privileging of targets that appear
to be involved in the tumor escape of multiple tumors and which can thus be used in
the treatment of multiple cancers.
In summary, this study demonstrates how tumor immune signatures can be
used to determine the genetic basis of a tumor’s escape mechanisms and to
71
select an appropriate immunotherapeutic strategy for treating that tumor. The
murine data establish the precision of real-time rtPCR analysis using a panel of
immunoinhibitory molecules. Human breast cancer data indicate that certain human
tumors show predicable patterns of gene expression corresponding to progressive
stages of disease, while specifically advocating blockade of B7-H4 and stimulation
of CD28/CD83 expression as treatment for patients with ductal adenocarcinoma of
the breast. Human colorectal data revealed that some cancers that are
histopathologically grouped together may in fact represent a genetically-diverse
group of cancers, each employing different tumor escape mechanisms and each
requiring a different strategy for immunotherapeutic treatment. Despite their
differences, each human breast and colorectal tumor appears to have specifically
up-regulated immunoinhibitory molecules and down-regulated immunostimulatory
molecules that make excellent targets for immunotherapy.
Taken together, these data indicate the potential of establishing tumor
immune gene expression levels for cancer patients in order to determine the
molecular basis for different tumors’ escape mechanisms. Real-time rtPCR
technology is inexpensive, rapid, reproducible, and capable of being performed on
equipment readily available at most medical centers. It thus represents a technique
highly translatable to the clinical laboratory. In addition to using an immunologic
signature to enhance diagnosis, this technique would enable clinicians to select the
most appropriate immunotherapy(s) and determine whether the treatment should be
targeted to the tumor or delivered systemically, depending on the degree to which
lymph node biopsies show evidence of local tolerance. Before such analysis
becomes a customary part of a cancer workup, laboratories must determine the
expression levels of immune genes for the common cancers of man in order to
72
describe classic immune signatures and to identify the most important molecular
targets for the immunotherapy of these tumors. Indeed, this study represents the
first step in a marathon, where the finish line holds the promise of patient-specific
immunotherapy for cancer.
73
CHAPTER IV: RATIONALIZED IMMUNOTHERAPEUTICS FOR CANCER ON
THE HORIZON
4.1. Background and Significance
Cancer immunotherapy is a burgeoning field that seeks to harness the
power of the immune system to treat cancer. Current research supports the idea
that immunotherapies bolster the natural anti-tumor immune response by providing
necessary immunostimulation and/or by inhibiting tolerance to tumors. However,
the precise mechanism(s) by which these drugs act are still being investigated.
Immunotherapy regimens have historically achieved complete remission in only 7-
10% of patients, primarily because tumors evoke potent methods to evade innate
and adaptive immunity (Antonia et al. 2004). In light of the little that is known about
the molecular pathways through which these therapies work, it is nearly impossible
at this time to predict which cancer patients are likely to respond to a given
immunotherapeutic agent. Here we describe a technique for elucidating the
mechanisms of action of immunotherapy drugs as well as a method for determining
the most appropriate immunotherapy for the treatment of a given cancer patient.
For clinical applicability, tumor escape profiles must be examined for
common cancers and the mechanisms of action of immunotherapeutic drugs and/or
vaccines need to be determined. Our data strongly advocate in favor of creating a
coalition of institutions to collaborate on the high throughput analysis of tumor
escape mechanisms and drug mechanisms of actions. Such efforts would
74
provide a foundation for the rationalized selection of immunotherapies for individual
cancer patients.
4.2. Research Design and Methods
As a first step towards this goal, the mechanisms of action of a newly-
developed murine immunotherapy protocol were examined by comparing the gene
expression signatures of treated and untreated murine tumors. First, C26 (colon),
4TI (breast), or MAD109 (lung) adenocarcinoma cells were implanted in BALB/c
mice. Real-time rtPCR was used to measure the expression levels of a panel of
genes involved in T cell activation or inhibition at multiple time points during tumor
development. The resulting gene signatures, revealing cancer-induced changes in
gene expression, differed between tumor types yet were consistent within each
tumor model. Next, to elucidate the genetic consequences of a given
immunotherapy, each of these tumor models was treated concomitantly with PC61,
a Treg-depleting rat anti-mouse-CD25 antibody (0.5 mg, i.p., day 0), and the tumor-
targeting chemokine fusion protein LEC/chTNT-3 (30 µg, i.v., days 6-10)
(Li et al. 2003a). This regimen produced variable therapeutic results depending on
the tumor model, characterized by complete remissions in C26 colon adenocarcinoma
tumor-bearing mice (good responder), 80% tumor regression for 4TI breast
adenocarcinoma-bearing mice (intermediate responder), and 60% tumor regression in
MAD109 lung adenocarcinoma-bearing mice (poor responder). Finally, gene
75
expression was evaluated in these treated mice as described above, using the same
panel of T cell stimulatory and inhibitory genes to permit comparisons between
treated and untreated mice.
4.3. Results
Comparison of the cancer-induced gene expression profiles in untreated
mice with the profiles of successfully- and unsuccessfully-treated mice revealed
important gene targets and provided insight into the mechanisms of action of the
tested immunotherapy (Figure 4.1A). To validate our data, FoxP3 up-regulation was
studied as a predictor for response to treatment with PC61 + Lec/cTNT-3.
Expression of the transcription factor FoxP3 is one of the most reliable markers of
Treg cells, and induction of Treg cells has emerged as one of the primary
immunoinhibitory mechanisms employed by many human cancers (Beyer et al.
2006). Because PC61 is known to deplete Treg cells, we would expect PC61 to be
therapeutic only in those cancers whose tumor escape strategy is Treg induced.
Consistent with this hypothesis, untreated C26-bearing mice showed
increased average expression of FoxP3 (120% increase; Figure 4.1B), while 4TI-
bearing mice showed a modest increase in FoxP3 expression (60% increase; data
not shown) and MAD109-bearing mice did not show any cancer-induced increase in
FoxP3 expression (40% decrease; Figure 4.1C). These data suggest that the tumor
escape strategy of C26 is due in large part to Tregs, explaining its response to the
Treg-depleting therapies PC61, while the tumor escape strategy of MAD109 is
independent of Treg cells, explaining its failure to respond to PC61.
76
A
C
Days (post tumor implantation)
0 2 4 7 14 21
B
024 7 14 21
0
50
100
150
200
250
300
350
0 2 4 7 14 21
untreated treated
MAD109 FoxP3 Expression
4TI FoxP3 Expression
0
50
100
150
200
250
300
350
02 47 14 21
untreated treated
Days (post tumor implantation) Expression (% of control)
0
50
100
150
200
250
300
350
C26 FoxP3 Expression
Expression (% of control)
0 2 4 7 14 21
024 7 14 21
B
C
Days (post tumor implantation)
Figure 4.1. Intratumoral expression of immunomodulatory genes. (A) An immune signature
on three murine tumor models, reflecting tumor-induced changes in gene expression (e.g.,
C26 day7) and treatment-induced changes in gene expression (e.g., C26 day7-tx). The first
eight listed genes (above the white line) are immunoinhibitory, while the final three listed
genes are immunostimulatory. Gene up-regulation is indicated by red, while gene down-
regulation is indicated by green (brighter colors are farther from basal expression); black
indicates no change in expression, while grey indicates a missing datapoint. The therapeutic
mechanisms of the treatment can be identified as those areas in which treatment induced
down-regulation of immunoinhibitory genes (yellow boxes) or those areas in which treatment
induced up-regulation of immunostimulatory genes (turquoise boxes). All data is log
transformed; accordingly, even slight changes in coloration reflect significant changes in
expression. Higher resolution assessment of individual genes can also be explored: (B)
Expression of Treg FoxP3 in C26-bearing mice and in (C) MAD109-bearing mice. PC61
treatment reduces the expression of FoxP3, inhibiting Treg-induced tumor escape in C26-
and 4TI-bearing mice, but offering no molecular or therapeutic value to MAD109-bearing
mice, which show no tumor-induced increase in FoxP3 expression.
It is important to note that LEC/chTNT-3 treatment alone produces
approximately 60% tumor regression in C26-bearing mice, similar to the therapeutic
response seen in MAD109-bearing mice; while C26 mice benefit significantly from
addition of PC61, MAD109 mice do not. The enhanced responses seen in C26 and
4TI mice upon addition of PC61 correspond well with the degree of tumor-induced
FoxP3 expression seen in these mice, which serves as a proxy for the involvement of
Treg cells (Table 4.1).
77
Table 4.1. FoxP3 Expression and Response to LEC/chTNT-3 + PC61
Tumor Model % Regression FoxP3 % Change
Lec/chTNT-3
alone
Le/chTNT-3 +
PC61
Colon 26 60% 100% 120% increase
4TI 60% 80% 60% increase
MAD109 60% 60% 40% decrease
4.4. Discussion
Though this panel of primers measures the genes of only T effector and T
regulatory cells, an expanded primer set would enable one to determine the
involvement of other cell types responsible for immunosurveillance or tumor escape,
including dendritic cells (DCs), natural killer (NK) and natural killer T (NKT) cells, and
myeloid-derived suppressor cells (MDSCs). Analysis of the involvement of these
other immune cell populations would permit the identification of immunotherapies
whose mechanisms of action work through these cells.
The “Connectivity Map” pioneered by Justin Lamb of the Broad Institute (Lamb
et al. 2006) demonstrates how new uses for old drugs can be discovered by
comparing gene signatures of disease processes and gene signatures associated
with the use of FDA-approved drugs. Since Lamb’s database currently contains gene
expression patterns obtained only from cancer cell lines, the database misses
expression changes in cancer-induced genes belonging to infiltrating immune cells or
cells of the tumor microenvironment. Thus, additional data will need to be obtained to
broaden its applicability to cancer immunity.
78
As an important complement to this murine data, our lab has shown that it is
possible to determine the gene expression fingerprints of mRNA extracted from
banked human tumor specimens (Sadun et al. 2007). From these data, one can
correlate the expression signatures with medical record data on patient treatment
protocols and patient outcome, enabling the determination of signatures that predict
responders and non-responders for specific therapy regimens. In essence, if 100
melanoma patients are treated with IL-2, and 15% respond, the discovery that those
15 patients share a distinct immune gene expression signature defining them as a
subpopulation would indicate that all future melanoma patients with that immune
signature should be treated with IL-2. Conversely, those patients with different
immune expression signatures should be treated with more appropriate
immunotherapeutic strategies in lieu of IL-2. Once characteristic signatures are
established, patient biopsies can be analyzed using established gene panels and
real-time PCR equipment available in most hospitals, in order to determine the
immune signature of individual patients.
In summary, these data demonstrate that analysis of intratumoral gene
expression can be used to determine the mechanisms by which tumors escape as
well as the mechanisms by which immunotherapies modulate tumor conditions.
Hence, by determining which immune signatures correlate best with a favorable
response to a given treatment, it becomes possible to begin to match therapy with
patient. In conjunction with previously-published data demonstrating our ability to
identify subpopulation gene expression signatures from analysis of patient tumor
79
biopsy specimens such as those seen in ductal adenocarcinoma of the breast
(Sadun et al. 2007), evidence points towards the feasibility of matching
mechanistically-characterized immunotherapies with their appropriate tumor
counterparts. On this basis, it should be possible to identify the immunotherapeutic
reagent(s) that would most directly reverse the tumor escape mechanism(s) being
employed by an individual patient’s cancer. Such data should bring us quickly to a
new frontier in medicine, one in which we will be able to rapidly and inexpensively
determine the most effective available treatment for each patient.
80
CHAPTER V: COLLECTIVE CONCLUSIONS AND FUTURE DIRECTIONS
5.1. Summary
Thus far we have discussed both historic and current approaches to treating
malignancies with stimulation of the immune system. Chapters 2 through 4
explored new approaches to immunotherapy, beginning first with the development
and characterization of a novel fusion protein for immunotherapy, detailed in
Chapter 2. This fusion protein exhibits many of the characteristics of the most
successful immunotherapies – it simultaneously inhibits Treg activity and provides
co-stimulation, it incites a coordinated immune attack by activating both CD4
+
helper
cells and CD8
+
cytotoxic cells, and it stimulates the development of both effector
and memory cells, enabling the clearance of a large bulky tumor as well as
protection against recurrence – but in the final analysis, even if Fc-mOX40L is a
truly promising immunotherapeutic reagent, it is just one more to add to the already-
long list of promising immunotherapies, just one more reagent that works beautifully
in mouse models but will likely provide cures for only a small subset of cancer
patients when brought to clinical trials.
The great breakthrough in cancer immunotherapy, however, is likely to come
not from a lengthening of the list of potential arsenal, but rather from enhanced
understanding of which arsenal is appropriate for which malignancy. Chapter 3
described the manner in which malignancies can be assessed for the genetic
signature that defines their pattern of tumor escape. In some cases, whole classes
of tumors may behave similarly in immune evasion, as seen with the
81
homogeneous immune signatures of all tested cases of human ductal breast
adenocarcinoma. These cases would likely all respond to the immunotherapy that
could reverse the tumor escape processes defined by this characteristic immune
signature.
Other malignancies, however, may be more strongly influenced by
immunoediting, resulting in a heterogeneous set of immune signatures. These
circumstances are reflected in the data derived from the human colorectal
adenocarcinoma cases examined. In all likelihood, these cases would require
different immunotherapeutic strategies, each targeted to the dominant tumor escape
process as defined by the immune signature.
The selection of the appropriate immunotherapy based on immune
signatures can be straightforward, as in the case of an immune signature that shows
significantly-increased CTLA-4 expression in the absence of other up- or down-
regulated genes. For such an individual, anti-CTLA-4 monoclonal antibody
immunotherapy (ipilimumab, MDX-010) would be the appropriate deductive choice.
For less clear signatures, however, where deductive matches are not immediately
evident, empiric matching can be used. Empiric matches for immune signatures
are made possible through the systematic examination of immunotherapies’
mechanisms of action. The appropriate immunotherapy can be selected for a
patient by comparing his tumor’s immune signature to the therapy signatures of all
available immunotherapies, permitting the selection of the therapy signature most
complementary to his immune signature. Chapter 4 described the process by which
immunotherapy mechanisms of action can be determined and complementary case-
therapy matching performed.
82
5.2. Potential Obstacles
Though the immune signature data generated thus far is encouraging and
the promise of personalized immunotherapy is tantalizing, there remain several
potential threats to the realization of rationalized immunotherapeutics. For this
research to translate to the clinic, we must address two looming immunological
obstacles: (1) lingering tolerance and (2) continuous immunoediting.
5.2.1. Overcoming Tolerance
By many measures, immune cells recognize and respond to cancers of both
viral and non-viral origin. Thus, failures of the immune response to eradicate tumors
are attributed to the establishment of tolerance, a process whose physiologic
purpose is to protect against autoimmunity from peripheral exposure to self-
antigens. Tolerance can develop in malignancies for several reasons. First,
tolerance occurs whenever an intricate set of checks and balances fails to take
place according to a prescribed pattern, a pattern that is designed to be triggered by
the circumstances characteristic of infections and to avoid being triggered by self-
antigens. These circumstances are colloquially referred to as “danger signals” and
occur in conjunction with the local tissue damage typical of infectious processes
(Matzinger 1994). In the absence of these danger signals, tolerance can develop.
Unfortunately for tumor immunology, tolerance has been shown to be a dominant
condition, and once tolerance is established, reversal is challenging. Thus, there
remains the fear that even if the culprit of tumor escape is identified and eliminated,
tolerance, once induced, may persist.
83
There are at least three ways to consider this potential reality: One way to
think about the threat of dominant immunosuppressing factors is that if Treg
depleting therapy in a Treg-associated tumor is insufficient to reverse tolerance and
cure the cancer, then identifying the source of the tolerance is irrelevant, suggesting
that immunotherapy will never stand up to entrenched tolerogenic conditions.
Indeed, there is certainly evidence to suggest that Treg elimination does not, by
itself cure cancer. Another way to think about the obstacle of tolerance is that if
genetically-engineered, ex vivo-activated TILs can home to tumor and persist at the
tumor site, but not by themselves cure cancer, then tolerance is an immunologically-
dominant condition that cannot be overcome in even the best of circumstances.
Again, there exists evidence to suggest that immunosuppressing factors prevent ex
vivo activated T cells from serving their effector function.
A third way of thinking about the obstacle of tolerance, however, is that
unaddressed it will surely dominate, but when addressed directly, can be overcome.
The evidence to support this assertion comes from the Rosenberg group, which
demonstrated the impact of lymphodepletion prior to adoptive transfer of T cells, a
technique that enhances the effector functions of those T cells (Muranski et al.
2006). Given the assumption that lymhodepletion will help only in those cases in
which the dominant mechanism of tumor escape is attributable to an
immunosuppressive subpopulation of lymphocytes (e.g., Tregs), the observed
increase in outcome seen in the Rosenberg study can be considered highly
significant, since it represents only those patients in whom immunosuppressive
lymphocytes are the cause of the tolerance.
The lack of universal response to lymphodepletion plus adoptive transfer
underscores the necessity to address not just a cause of tolerance, but the cause
84
of tolerance, as determined by examining a tumor’s mechanisms of tumor escape.
If these experiments were to be repeated and adoptive transfer were to be paired
with lymphodepletion in those patients for whom evidence suggested Treg-induced
tolerance, paired with DC vaccines for those patients demonstrating tolerance due
to immature DCs, paired with antagnostic anti-PD-L1 or PD1 blockade for those
patients in whom tumor cell over-expression of PD-L1 sustains tolerance, etc., one
would expect improved responses for each patient for whom the cause of tolerance
was addressed. As long as tolerance is not a permanent state, and evidence
suggests it is not (Ohlen et al. 2002; Teague et al. 2006), direct reversal of tumor
escape represents a means for overcoming tolerance.
5.2.2. Overcoming Immunoediting
Immunotherapy is a promising approach to cancer because it targets an
element fundamental to tumor survival, that of immune evasion. Though not one of
the “six hallmarks of cancer” originally described by Hanahan and Weinberg
(Hanahan & Weinberg 2000), tumor escape is identical to the original six identified
traits in that it is a necessary characteristic of any malignancy that can continue to
grow and thrive. Much like the other six hallmarks, tumor escape is a tempting trait
to attack, for the hindrance of any of these necessary characteristics would mean
the inevitable death of tumor cells. Unfortunately, however, designed attacks on
others of these hallmark traits has demonstrated that rapid evolution takes place on
a cellular level within tumors, enabling cells to survive strong selection pressures.
Anti-angiogenesis drugs are a prime example of how attacking one of the hallmarks
of cancer can lead to rapid tumor regression, but the ability of a small handful of
85
cancer cells to evolve alternate survival mechanisms can result in diminished
response to treatment and, ultimately, in relapse (Zondor et al. 2004).
The same obstacle plagues the field of immunotherapy, even targeted
immunotherapy as described in this manuscript. If immunoediting permits certain
cancer cells to evade the powerful forces of immunosurveillance, propagating and
spreading their capacity for tumor escape, targeting just that one tumor escape
mechanism may do little more than provide a second selective pressure that will
merely push the cancer cells towards other mechanisms of tumor escape.
While this concern is not grounds for dismissing rationalized
immunotherapeutics, it is critical that we anticipate and address this potential threat
to single-agent immunotherapy. Several strategies can be applied to compensating
for the tumor’s ability to evolve new tumor escape mechanisms. The first strategy
borrows from the study of infectious disease, which has learned that challenging
and rapidly-evolving microbes must be treated simultaneously with multiple
antibiotics. Long-gone are the days of treating tuberculosis (TB) patients with a
single antibiotic; instead, a minimum of four antimicrobial drugs are simultaneously
applied (Gleeson et al. 2006) to ensure that any bacilli with resistance traits towards
one or two of the drugs will be killed by the remaining drugs before bacilli with
resistance traits have the opportunity to propagate.
The same principle can be applied to the evolution and propagation of
resistance traits in cancer cells. By simultaneously targeting all of the tumor escape
mechanisms that show evidence of involvement in a given cancer, the powerful
force of immunoediting can be overcome. Similarly, immunotherapy drugs can be
applied in combination with pharmacologic agents that target other of cancer’s
hallmark traits, such that no one cell is liable to simultaneously posses resistance
86
to the angiogenesis inhibitor, the right growth factor inhibitor, the right matrix
metaloprotease (MMP) inhibitor (Heath et al. 2000), and the right tumor escape
inhibitor. It becomes critical in this model to identify which is the right drug for each
of these categories, but just as an immune signature can predict the most
appropriate tumor escape inhibitor, gene signatures can identify which MMP is
responsible for a given tumor’s invasion, and thereby predict the most appropriate
pharmacologic inhibitor.
The second strategy to overcoming immunoediting-attributable resistance to
targeted tumor escape immunotherapy entails a paradigm shift in cancer therapy
from the desire to find a “cure” to the willingness to treat the cancer like a chronic
disease (Spriggs 2001). Serial immune signature determination would enable
immunotherapy to stretch into a long game of cat and mouse: While treating the
dominant tumor escape mechanism(s) may lead to development of new escape
mechanisms, these new mechanisms will become evident in progressive immune
signature analyses, permitting the serial identification of and use of secondary,
tertiary, and even quaternary immunotherapy drugs. The same principle applies to
the valid concern that the immune signatures of some metastases may differ from
the signature of the primary tumor. In such an instance, serial treatments may be
applied as necessary, as immune signatures permit continual, dynamic illumination
of the tumor escape forces at play.
5.3. Future Directions
This research can be expanded along multiple dimensions, each bringing us
a little closer to using rationalized immunotherapeutics as mainstay treatment for
87
cancer patients. Five steps for future studies will be discussed: (1) retrospective
immune signature analysis for prediction of response and outcome, (2) prospective
analysis of immunotherapy selection based on immune signature predictions, (3) in
vitro analysis of human cancer immune signatures and known drug modulations of
immune signatures, (4) immunotyping of common human cancers and immune
signature determination of current cancer treatments, and (5) probing and
addressing the aforementioned potential obstacle of continued immunoediting.
The first and easiest step to testing the ability of an immune signature to
predict response to immunotherapy would entail a retrospective study. Because
malignant melanoma is one of the cancers most frequently treated with
immunotherapy and because there are several different ongoing immunotherapy
clinical trials for malignant melanoma, this would be an ideal place to begin. Such a
study would entail the identification of 25 patients treated with IL-2, 25 patients
treated with IFN- γ, 25 patients treated with anti-CTLA-4, and 25 control patients who
did not receive immunotherapy. For each of the treated groups, approximately ten
cases should have been considered “responders” in their respective clinical trials.
All 100 cases should be patients younger than 60 years old, so as to prevent the
confounder of immune blunting associated with advanced aged. Exclusion criteria
would include prior treatment with immunotherapy or chemotherapy, known
immunodeficiency or auto-immune diseases, or dependence on corticosteroids for
any other chronic disease process.
Blinded to case outcomes, the 35-gene panel described in Chapter 3 would
be used to examine the immunomodulatory gene expression of the biopsy
specimens from these 100 cases, and hierarchical clustering software would be
used to boot-strap individual immune signatures. Predictions would be made as
88
to which groups would respond to which therapy, after which patients’ response
statuses and outcomes would be revealed and analyzed for correspondence with
predictions. If insufficient correlations can be found, sample number would be
doubled and the gene panel expanded to include MDSC and NK cell genes.
If immune signature clustering is indeed found to be predictive of response
to specific immunotherapies, an appropriate prospective study would be designed,
in collaboration with ongoing clinical trials still recruiting patients. In such a manner
it should be possible to enhance the outcome of these clinical trials by helping to
guide which patients are treated with the most appropriate therapies. Ultimately, of
course, a double-blind prospective study would be designed, in which rationalized
immunotherapy is executed on the basis of immune signature evaluation prior to
treatment, first for malignant melanoma and next for a cancer like ductal
adenocarcinoma of the breast, which presents with homogenous gene signatures.
While prospective trials are distant future plans, retrospective analysis would
be relatively easy to set up and easy to conduct. Concurrent with retrospective
studies, one could build the human cancer immune signature fund of knowledge via
three approaches that would, collectively, produce a significant tumor escape
database. The first approach would entail a direct extension of the Broad Institute
Connectivity Map, using co-cultures that contain immune cells, rather than relying
exclusively on cancer cell lines (Lamb 2007). In phase I, known cancer treatments
– both chemotherapeutic and immunotherapeutic – would be used; in phase II, other
FDA drugs and small molecules would be tested.
The additional two components of building this immune signature database
would necessitate a multi-institution collaboration, whereupon gene signatures
would be determined for several hundred patients diagnosed with each of the ten
89
most common cancers. Simultaneously, the manner in which current cancer
treatments impact tumor escape – for better or for worse – should be captured by
immunotyping patient biopsies both before and after treatment. Such analysis
would be expected to reveal unknown tumor escape-targeting mechanisms of action
for these drugs, enabling their rationalized application for future patients.
Finally, in light of the anticipated challenge of immunoediting contributing to
continued escape from rationalized immunotherapeutics, the impact of this
challenge should be assessed, and the mechanisms with which it can be addressed
should be tested. To accomplish this, animal experiments should be performed in
which immunotherapy is provided at sub-therapeutic doses. Much like providing
antibiotics at sub-threshold doses, this would increase the likelihood that cancer
cells (primary or metastatic) would evolve additional tumor escape mechanisms. In
such a model, it would be possible to test the two aforementioned strategies for
overcoming immunoediting, so that the when rationalized immunotherapeutics is
brought to the clinic, it will be clear whether the infectious disease model or the
chronic disease model is the preferred approach.
5.4. Concluding Remarks
Tumor immunology has grown in leaps and bounds over the past 100 years,
and the first immunotherapy, Coley’s Toxin, has been complemented by nearly 100
additional immunotherapeutic strategies. Though the stagnant response rates of
clinical immunotherapy has made some believe immunotherapy is seeing the
beginning of the end, the newfound ability to rationally apply these immunotherapies
makes this era nothing less than the end of the beginning.
90
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Abstract (if available)
Abstract
Great strides in the understanding of cancer biology have provided a multitude of approaches to the medical treatment of cancer. While chemotherapy alone has succeeded in making many malignancies into treatable diseases, certain cancers remain elusive despite the current arsenal, and for many patients the toxicities of chemotherapies are intolerable or even fatal. One attractive alternative to chemotherapy is cancer immunotherapy, an approach that seeks to harness the power of the immune system to treat cancer. Despite achieving only modest clinical successes to-date, immunotherapy offers the promise of a physiological treatment as opposed to a treatment based on semi-selective toxicity. This manuscript begins by describing a new murine immunotherapeutic reagent, constructed by genetically linking the antibody Fc backbone to the molecule OX40L, which can provide a T cell activating second-signal and simultaneously inhibit the effects of suppressor T cells known as Tregs. The resultant Fc-mOX40L fusion protein has demonstrated striking anti-tumor effects in two murine experimental tumor models. Though similar fusion protein strategies have been described in the literature, this manuscript goes on to describe a novel approach to delineating tumors' "escape" strategies and identifying the immunotherapy with the greatest capacity for impeding those mechanisms of tumor escape. Using real-time PCR, it was possible to create immune signatures that characterize the T cell activation profile of five murine tumor models as well as human breast and colorectal tumor specimens. This technique identified several cancer-induced immunotherapy targets in human ductal adenocarcinoma of the breast, including high expression of immunoinhibitory B7-H4 and low expression of immune activating CD28 and CD83.
Linked assets
University of Southern California Dissertations and Theses
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Asset Metadata
Creator
Sadun, Rebecca Eli
(author)
Core Title
Rationalized immunotherapy by immune signature characterization
School
Keck School of Medicine
Degree
Doctor of Philosophy
Degree Program
Pathobiology
Publication Date
06/11/2007
Defense Date
04/18/2007
Publisher
University of Southern California
(original),
University of Southern California. Libraries
(digital)
Tag
biomarkers,cancer,immunoeditting,immunosurveillance,OAI-PMH Harvest,oncology,real-time pcr,tolerance,tumor escape
Language
English
Advisor
Epstein, Alan L. (
committee chair
), Hofman, Florence M. (
committee member
), Kast, W. Martin (
committee member
), Stallcup, Michael R. (
committee member
), Yang, Allen S. R. (
committee member
)
Creator Email
sadun@usc.edu
Permanent Link (DOI)
https://doi.org/10.25549/usctheses-m524
Unique identifier
UC1176948
Identifier
etd-Sadun-20070611 (filename),usctheses-m40 (legacy collection record id),usctheses-c127-501913 (legacy record id),usctheses-m524 (legacy record id)
Legacy Identifier
etd-Sadun-20070611.pdf
Dmrecord
501913
Document Type
Dissertation
Rights
Sadun, Rebecca Eli
Type
texts
Source
University of Southern California
(contributing entity),
University of Southern California Dissertations and Theses
(collection)
Repository Name
Libraries, University of Southern California
Repository Location
Los Angeles, California
Repository Email
cisadmin@lib.usc.edu
Tags
biomarkers
immunoeditting
immunosurveillance
oncology
real-time pcr
tumor escape