University of Southern California Dissertations and Theses

Hormonal and genetic risk factors of endometrial cancer and trends in incidence and survival of adult acute lymphoblastic leukemia

(USC Thesis Other)

Hormonal and genetic risk factors of endometrial cancer and trends in incidence and survival of adult acute lymphoblastic leukemia

PDF

Download a page range

Download transcript

Copy asset link

Request this asset

Transcript (if available)

Content HORMONAL AND GENETIC RISK FACTORS OF ENDOMETRIAL CANCER AND TRENDS IN INCIDENCE AND SURVIVAL OF ADULT ACUTE LYMPHOBLASTIC LEUKEMIA by Pedram Razavi _______________________________________________________________________ A Dissertation Presented to the FACULTY OF THE USC GRADUATE SCHOOL UNIVERSITY OF SOUTHERN CALIFORNIA In Partial Fulfillment of the Requirements for the Degree DOCTOR OF PHILOSOPHY (EPIDEMIOLOGY) December 2009 Copyright 2009 Pedram Razavi ii DEDICATION In memory of my father who has always been my source of inspiration iii TABLE OF CONTENTS DEDICATION ii LIST OF TABLES v LIST OF FIGURES vii ABSTRACT viii Chapter 1: Long-Term Postmenopausal Hormone Therapy and Endometrial Cancer 1 1.1 Abstract 1 1.2 Introduction 2 1.3 Materials and Methods 4 1.3.1 Study Population 4 1.3.2 Data Collection 6 1.3.3 Hormone Use 7 1.3.4 Age at Menopause 7 1.3.5 Statistical Analysis 8 1.4 Results 9 1.5 Discussion 18 1.5.1 Sequential Estrogen-Progestin Therapy 18 1.5.2 Continuous-Combined EPT 19 1.6 Conclusion 25 Chapter 2: Genetic Variations in Sex Hormone Genes, Postmenopausal Hormone Therapy, and Risk of Endometrial Cancer 26 2.1 Introduction 26 2.2 Materials and Methods 27 2.2.1 Study Population 27 2.2.2 htSNP Selection and Genotyping 28 2.2.3 Postmenopausal Hormone Use 29 2.2.4 Statistical Analyses 29 2.3 Results 30 2.4 Discussion 39 2.4.1 CYP11A1 39 2.4.2 CYP19A1 40 2.4.3 CYP17A1 42 2.4.4 ESR2 43 2.4.5 Strengths and Weaknesses 44 iv 2.5 Conclusion 45 Chapter 3: Trends in Incidence and Survival of Adult Acute Lymphoblastic Leukemia 46 3.1 Abstract 46 3.2 Introduction 48 3.3 Materials and Methods 50 3.3.1 Race/Ethnicity 50 3.3.2 Study Population 51 3.3.3 Incidence Analysis 53 3.3.4 Survival Analysis 54 3.4 Results 54 3.4.1 Incidence 54 3.4.2 Survival 62 3.5 Discussion 68 3.5.1 Incidence 68 3.5.2 Survival 69 3.5.3 Strengths and Weaknesses 71 3.6 Conclusion 72 Bibliography 74 v LIST OF TABLES Table 1.1: Characteristics of 311 endometrial cancer cases and 10 570 controls (nested within the California Teachers Study) Table 1.2: Association between duration of use of postmenopausal 13 hormone therapy (HT) and endometrial cancer by HT type Table 1.3: Association between duration of postmenopausal 14 hormone therapy and endometrial cancer by type of HT and BMI Table 1.4: Association between lifetime duration of hormone therapy 15 use and endometrial cancer, by delay between menopause and start of hormone therapy Table 1.5: Association between current use of postmenopausal 17 hormone therapy and endometrial cancer by HT type among women who previously used ET/short-sequential EPT Table 1.6: Association of continuous-combined EPT use 24 (progestin ≥ 25 days/month) and endometrial cancer Table 2.1: The association of haplotypes in sex hormone 31 metabolism genes and risk of endometrial cancer Table 2.2: Association between duration of use of postmenopausal 34 hormone therapy (HT) and endometrial cancer by HT type Table 2.3: Interaction between postmenopausal estrogen therapy (ET) 35 use and sex hormone metabolism genes haplotypes Table 2.4: Interaction between postmenopausal estrogen-progestin 37 therapy (EPT) use and sex hormone metabolism genes haplotypes Table 3.1: Age-adjusted average incidence rates for adult acute 57 lymphoblastic leukemia (SEER 17: 2001-2005) Table 3.2: Characteristic of 6424 adult ALL cases 64 (SEER 17: 1975 – 2005) vi Table 3.3: Analysis of all-causes adult ALL survival 65 (SEER 17: 1975 - 2005) Table 3.4: Relative mortality rate for racial/ethnic groups by age 66 and ALL subtype vii LIST OF FIGURES Figure 3.1: Age-specific incidence rate of adult ALL subtypes 58 (SEER: 2001 - 2005) Figure 3.2: Average age-adjusted incidence rate of adult ALL 59 by race/ethnicity (SEER: 2001 - 2005) Figure 3.3: Adult ALL age-specific incidence rates by sex 60 (SEER: 1992 - 2005) Figure 3.4: Adult ALL age-specific incidence rates by race/ethnicity 61 (SEER: 1992 - 2005) Figure 3.5: Adjusted survival curves of adult ALL by race/ethnicity, 67 stratified by age and ALL subtype viii ABSTRACT This dissertation consists of three chapters: The first chapter examines the association between lifetime postmenopausal hormone therapy (HT) use and risk of endometrial cancer using data from a case-control study of invasive endometrial cancer nested within the California Teachers Study (CTS) cohort. In this chapter, I summarize the current literature describing the effect of HT on endometrial cancer. I then present the findings for an analysis of HT and endometrial cancer in the CTS, with new detail on the type of combination therapy. This manuscript has been accepted with minor changes for publication in Cancer Epidemiology, Biomarkers & Prevention. The second chapter describes the potential impact of genetic and environmental factors on endometrial cancer risk. The analysis focuses on genetic variants in a select number of genes in the sex hormone metabolism pathway and risk of endometrial cancer. I further examine the modifying effect of these variants on the risk of endometrial cancer associated with different HT regimens. The third chapter examines patterns of incidence and survival for adult acute lymphoblastic leukemia (ALL) by racial/ethnic groups. Data for these analyses came from the National Cancer Institute, Survival, Epidemiology, and End Results (SEER) database. Previous studies have suggested overall lower survival for some racial/ethnic ix groups compared to others. We further explore survival patterns by race, ALL subtype and age in the SEER data, which has not been well characterized in the literature. 1 Chapter 1: Long-term Postmenopausal Hormone Therapy and Endometrial Cancer 1.1 Abstract Estrogen alone therapy (ET) or estrogen and progestin (EPT) as menopausal hormone therapy (HT) has been commonly used to alleviate menopausal symptoms. Treatments containing ≥10 days/month (d/m) of progestin are considered relatively safe with respect to endometrial cancer risk. However, the endometrial safety of long-term EPT regimens is uncertain. We conducted a case-control study of 311 invasive endometrial cancer cases and 570 controls nested within the California Teachers Study cohort. We used unconditional logistic regression to obtain odds ratios (ORs) and 95% confidence intervals (95%CIs) for the association between long term HT use and endometrial cancer risk and to assess the modifying effect of body mass index (BMI). Long-term ( ≥ 10 years) use of ET, sequential EPT with < 10 d/m progestin, and continuous-combined EPT regimens ( ≥ 25 d/m progestin) were all associated with an elevated risk of endometrial cancer (OR: 4.5; 95%CI: 2.5–8.1, OR: 4.4, 95%CI: 1.7– 11.2, and OR: 2.1; 95%CI: 1.3–3.3, respectively; all P for trend < .0001). Risk 2 associated with short-term use was elevated only for ET preparations. The association for continuous-combined EPT was confined to thinner women (BMI < 25 kg/m 2 ) (P for interaction: 0.03). Among heavier women (BMI ≥ 25 kg/m 2 ), use of continuous- combined EPT was associated with a statistically nonsignificant reduction in risk. These findings confirm that long-term use of ET, sequential EPT, or, among non-obese women, continuous-combined EPT is associated with increased risk of endometrial cancer. 1.2 Introduction Menopausal estrogen therapy (ET) increases the risk of endometrial cancer in postmenopausal women (Brinton and Hoover 1993; Grady et al. 1995; Pike et al. 1997). Histologic studies, however, have reported significantly reduced endometrial hyperplasia when progestin was added to estrogen in a sequential manner (Campbell et al. 1978; Whitehead et al. 1978; Whitehead et al. 1979). Thus, to counteract the adverse effects of ET on the endometrium, combined estrogen and progestin therapy (EPT) was introduced in the early 1980s. Initially several different sequential EPT regimens ranging from 5 to 15 days (mostly 7 days) of progestin per month were prescribed (Stefanick 2005). By the late 1990s short-sequential EPT (<10 days/month [d/m] progestin) was found to be associated with an increased risk of endometrial cancer (Pike et al. 1997). Long- 3 sequential EPT regimens ( ≥10 d/m progestin) or continuous-combined EPT (estrogen and progestin daily) were not associated with such high risk (Beresford et al. 1997; Pike et al. 1997; Weiderpass et al. 1999). More recent studies, published from 2000 onwards, provide inconsistent results on the effect of long-sequential EPT and continuous-combined EPT on endometrial cancer risk. Although two case-control studies (Hill et al. 2000; Doherty et al. 2007), two randomized clinical trials (Hulley et al. 2002; Anderson et al. 2003), and a large cohort study (Beral et al. 2005) suggested a null or inverse association between use of continuous-combined EPT and the risk of endometrial cancer, two case-control studies (Jain et al. 2000; Newcomb and Trentham-Dietz 2003) and a cohort study (Lacey et al. 2005) found increased risk with long-term use of continuous-combined EPT. Thus the long term safety of these regimens with respect to the endometrium is not clear. Another remaining question is whether body mass modifies the effect of specific regimens of postmenopausal hormone therapy (HT) on endometrial cancer risk (Beral et al. 2005; Friedenreich et al. 2007; Pike et al. 2007; McCullough et al. 2008). One hypothesis is that the effect of ET would be minimal among women who already have elevated endogenous estrogen levels due to obesity, while combined EPT treatment 4 might be beneficial against endometrial cancer in obese women. We address these questions using data from the California Teachers Study (CTS). 1.3 Materials and Methods 1.3.1 Study Population The CTS has been described elsewhere in detail (Bernstein et al. 2002). Briefly, the CTS is an ongoing cohort study of current and former female public school teachers and administrators. The cohort was established in 1995-96 when 133,479 women completed a self-administered questionnaire related to women’s health. Women were eligible for the current case-control study if they maintained California residence after joining the cohort and had not been previously diagnosed with endometrial cancer or had a hysterectomy. Eligible cases were identified by linkage between the cohort files and the California Cancer Registry. Women were included in the case group if they were 50-85 years old when diagnosed with an incident first endometrial cancer (ICD-O-3 codes C54.1 and C54.9) between joining the cohort and December 31, 2004. Of 675 eligible cases approached for an interview and asked to provide a DNA specimen, 401 (59%) participated. We were unable to contact 48 (7%) women, 113 (17%) declined to participate, 95 (14%) had died before we were able to contact them, and 18 (3%) were 5 not interviewed for other reasons. Of the 401 interviewed cases we excluded seven women with in situ carcinomas and nine women with either endometrial sarcomas or mullerian mixed tumors (ICD-O-3 morphology codes: 8930-8933, 8950, 8980). Control selection was based on eligibility at pre-determined quarterly selection dates starting on March 31, 1996. Controls were frequency-matched to the expected distribution of cases with respect to age (five-year age groups through 80+), race/ethnicity (white, African American, Hispanic, Asian/Pacific Islander, Native American, and other/mixed), and broad geographic region within California (corresponding to the state’s 10 regional cancer registry regions) with interview dates spread out over the period of case selection. Of 1329 eligible controls selected, 682 (51%) were interviewed and provided a DNA sample. We were unable to contact 170 (13%) women, 359 (27%) declined to participate, 82 (6%) died before we were able to contact them, and 36 (3%) were not interviewed for other reasons, leaving 682 controls for the analyses. We compared the distribution of established endometrial cancer risk factors (reproductive factors, body mass index [BMI], and HT use) as assessed on the baseline CTS questionnaire between cases participating in this nested case-control study and CTS cases not participating in the case-control study (and a similar comparison among 6 controls). We found no evidence that cases (or controls) participating in the nested case- control study differed from non-participating cases (or women eligible to be controls) on any of these risk factors (data not shown). The case-control study was approved by the Institutional Review Boards of the Northern California Cancer Center and the University of Southern California, and all participants provided signed informed consent. 1.3.2 Data Collection In addition to the limited exposure data collected from mailed questionnaires from all members of the cohort at baseline, we obtained detailed menstrual, reproductive and hormone use histories from participants in this nested case-control study through in- person interviews using a structured questionnaire. Lifetime calendars were created during these interviews and used to facilitate the recall of important life-events including dates of HT use. The respondents were shown photographs of common HT formulations (Beresford and Coker 1989). For each episode of HT use we obtained detailed information on the: 1) date use started and ended; 2) brand and dosage; 3) number of days per month of usage; and 4) reasons for use. Exposure data were truncated at 12- months prior to diagnosis for cases or selection date for controls (hereafter referred to as the reference date). 7 1.3.3 Hormone Use We categorized each episode of postmenopausal HT use based on the number of days per month that the woman used progestin. Four types of HT use were defined: 1) ET: no progestin use; 2) short-sequential EPT: progestin < 10 d/m; 3) long-sequential EPT: progestin 10-24 d/m; and 4) continuous-combined EPT: progestin ≥ 25 d/m. We calculated total lifetime duration of use of each HT type and categorized the duration of use as no use (never users and users for < 6 months), 6-59 months, 5-9 years, and ≥ 10 years of use. 1.3.4 Age at Menopause Age at menopause was defined as the age at last menstrual period (LMP) for women who had not used oral contraceptives (OC) or HT within the 12 months prior to their LMP. For women who started using HT while still menstruating, age at menopause was defined as the age at which they started using HT. Eight women had used OCs through their last menstrual period, and their age at menopause was defined as their age at last OC use. Women were considered pre- and peri-menopausal if they reported their LMP within 3 months and 3-12 months of their assigned reference date, respectively. 8 1.3.5 Statistical Analysis For the present analysis, we excluded women with a prior history of breast cancer (37 cases and 59 controls); women who were pre- or peri-menopausal at reference date (28 cases and 45 controls); women with unknown menopausal status (5 cases and 4 controls) and women with unknown age at menopause (3 cases and 2 controls). We also excluded women (1 case and 2 controls) who reported having natural menopause before age 35 because it was not clear whether this was secondary amenorrhea or actual menopause. The final data set included 311 cases and 570 controls. We estimated odds ratios (OR) and 95% confidence intervals (95%CI) using unconditional logistic regression analysis with HT never users as the referent group. Twenty women, classified as never-users, reported using episodes of progestin-only pills in the postmenopausal period, but excluding these women from the referent group did not change the results. The regression models were adjusted for matching factors (age at reference date, race/ethnicity [White, African American, Hispanic], and geographic area of residence) and reference year. In addition, we adjusted our models for the following known or suspected confounders selected a priori: number of full-term pregnancies, age at the last full-term pregnancy, BMI at reference date, lifetime duration of OC use, and age at menopause. The categories for the covariates used in the statistical model are the same as the categories reported in Table 1.1 unless otherwise noted. Additional 9 adjustment for hypertension, other medication use, history of endometrial fibroadenoma, and number of previous dilatations and curettages (D&Cs) did not alter the results (not shown). We defined current use as taking HT for at least six consecutive months within one year of the reference date, and then further evaluated the effect of current use of the various HT types compared to never users. To evaluate the modifying effect of BMI, we performed separate analyses using 25 kg/m 2 as the cut point. Tests for trend and interaction were performed using likelihood ratio tests. The reported P-values are two-sided. SAS® 9.2 (SAS Institute, Cary, NC) was used for all analyses. 1.4 Results The characteristics of the study population are provided in Table 1.1. The distribution of known endometrial cancer risk factors followed the expected patterns. Compared to controls, cases were more likely to have later menopausal age and higher BMI, to be nulliparous, to have had their last full-term pregnancy at a younger age and to be less likely to have used oral contraceptives. 10 Table 1.1: Characteristics of 311 endometrial cancer cases and 570 controls (nested within the California Teachers Study) Characteristics Categories Cases N (%) Controls N (%) P Value* Age <55 28 (9.0) 54 (9.5) 55-59 58 (18.6) 100 (17.5) 60-64 63 (20.3) 101 (17.7) 65-69 54 (17.4) 110 (19.3) 70-74 55 (17.7) 108 (18.9) 75+ 53 (17.0) 97 (17.0) Race White 300 (96.5) 525 (92.1) African American 2 (0.6) 9 (1.6) Hispanic 3 (1.0) 17 (3.0) Other 6 (1.9) 19 (3.3) Menopause Type Natural 205(65.9) 391 (68.6) First HT Use 100 (32.2) 167 (29.3) Other 6 (1.9) 12 (2.1) 0.75 Menopause Age <47 55 (17.7) 102 (17.9) 47-49 57 (18.3) 125 (21.9) 50-52 91 (29.3) 192 (33.7) 53-55 74 (23.8) 115 (20.2) 56+ 34 (10.9) 36 (6.3) 0.033 Menarche Age <12 64 (20.6) 99 (17.4) 12 78 (25.1) 162 (28.4) 13 104 (33.4) 180 (31.6) 14+ 65 (20.9) 129 (22.6) 0.58 BMI <25 142 (45.7) 336 (58.9) 25-29 93 (29.9) 163 (28.6) 30-34 48 (15.4) 51 (8.9) 35+ 28 (9.0) 20 (3.5) <0.001 Parity Nulliparous 75 (24.1) 108(18.9) Parous 236(75.9) 462(81.1) 0.092 Number of Full-Term Pregnancies 0 75 (24.1) 108 (18.9) 1 40 (12.9) 70 (12.3) 2 95 (30.5) 173 (30.4) 3 72 (23.2) 145 (25.4) 4+ 29 (9.3) 74 (13.0) 0.19 11 Table 1.1, Continued Age at Last full-Term Pregnancy † <25 25 (10.6) 42 (9.1) 25-29 92 (39.0) 149 (32.3) 30-34 85 (36.0) 178 (38.5) 35+ 34 (14.4) 93 (20.1) 0.053 OC Use Never Use 187 (60.1) 298 (52.3) <5y 69 (22.2) 120 (21.1) 5-9y 37 (11.9) 70 (12.3) 10-14y 9 (2.9) 39 (6.8) 15+y 9 (2.9) 43 (7.5) <0.001 * Multivariate logistic regression models adjusted for matching factors (age, race, geographic area of residence, and reference year) † Additionally, adjusted for total number of full-term pregnancies Abbreviations: BMI: Body Mass Index; OC: Oral Contraceptive 12 A total of 211 cases (68%) and 347 controls (61%) reported using some type of HT (Table 1.2). Use of both ET and short-sequential EPT were strongly associated with increased risk of endometrial cancer, with increasing risk for longer duration of use. The ORs per 5-year use of ET and short-sequential EPT were 1.63 (95%CI: 1.38-1.95) and 1.70 (95%CI: 1.28-2.31), respectively. Because their effects were similar (p = 0.83), we combined ET and short-sequential EPT (ET/short-sequential EPT) in all further analyses. Use of long-sequential EPT was associated with a small increased risk (OR: 1.10 per 5-year use), but this result was not statistically significant. Women who used continuous-combined EPT for ≥10 years were twice (95%CI: 1.27–3.30) as likely to develop endometrial cancer as never users and the OR per 5-year use was 1.26 (95%CI: 1.11-1.44). Restricting the analysis to women who reported using only one type of HT did not change our results (data not shown). ET/short-sequential EPT use was associated with an increased risk both among women with low and high BMI, and the relative risks were of almost equal magnitude (ORs per 5-year use: 1.65 and 1.51 among women with BMI < 25 kg/m 2 and ≥ 25 kg/m 2 , respectively) (Table 1.3). Among the continuous-combined EPT users, we observed increased risk with longer duration of use for women with a BMI< 25 kg/m 2 (P for trend: 0.0001) but no effect among heavier women (P for trend: 0.49) (Table 1.3). 13 Table 1.2: Association between duration of use of postmenopausal hormone therapy (HT) and endometrial cancer by HT type HT Type Duration (Years) N Ca(Co) OR (95% CI)* P For Trend Never 100(223) 1 (Ref) ET < 5 53(58) 2.42 (1.56-3.77) 5-9 17(21) 2.48 (1.23-5.01) 10+ 34(29) 4.46 (2.46-8.11) <.0001 Per 5 y 1.63 (1.38-1.95) Short-Sequential EPT < 5 13(20) 1.41 (0.65-3.06) 5-9 10(11) 1.87 (0.74-4.73) 10+ 12(9) 4.35 (1.68-11.22) <.0001 Per 5 y 1.70 (1.28-2.31) Long-Sequential EPT < 5 4(12) 0.48 (0.14-1.68) 5-9 7(12) 1.30 (0.46-3.68) 10+ 4(16) 0.91 (0.29-2.91) 0.854 Per 5 y 1.10 (0.75-1.55) Continuous-Combined EPT < 5 44(87) 0.86 (0.55-1.35) 5-9 27(67) 0.81 (0.48-1.37) 10+ 47(69) 2.05 (1.27-3.30) 0.0006 Per 5 y 1.26 (1.11-1.44) * Mutually adjusted ORs: adjusted for age, race, reference year, geographic area of residence, menopause age, number of full-term pregnancies, age at the last full-term pregnancy, lifetime oral contraceptive use duration and body mass index † OR per 5-year use Abbreviations: ET: Estrogen Therapy; EPT: Estrogen-Progestin Therapy; OR: Odds Ratio; CI: Confidence Interval; Ca: Case; Co: Control; Ref: Reference Group; HT: Hormone Therapy 14 Table 1.3: Association between duration of postmenopausal hormone therapy and endometrial cancer by type of HT and BMI BMI < 25 kg/m 2 BMI ≥ 25 kg/m 2 HT Type Duration (Years) N Ca(Co) OR (95% CI)* N Ca(Co) OR (95% CI)* P for Interaction Never 26(126) 1 (Ref) 74(97) 1 (Ref) ET/Short- Sequential EPT < 5 27(45) 2.23 (1.24-3.99) 26(29) 1.61 (0.86-3.02) 5-9 16(17) 3.86 (1.77-8.42) 8(14) 1.01 (0.39-2.65) 10+ 30(30) 4.86 (2.51-9.41) 18(8) 4.66 (1.82-11.97) P trend < .0001 P trend = 0.001 Per 5 year 1.65 (1.37-2.02) 1.51 (1.19-1.96) 0.459 Continuous- Combined EPT < 5 19(56) 0.80 (0.42-1.52) 25(31) 0.81 (0.43-1.55) 5-9 13(35) 1.27 (0.59-2.71) 14(32) 0.48 (0.23-1.00) 10+ 28(40) 3.03 (1.60-5.73) 19(29) 1.13 (0.56-2.30) P trend = 0.0001 P trend = 0.49 Per 5 year 1.40 (1.18-1.67) 1.09 (0.89-1.32) 0.033 * Mutually adjusted ORs: adjusted for age, race, reference year, geographic area of residence, menopause age, number of full- term pregnancies, age at the last full-term pregnancy, lifetime oral contraceptive use duration and body mass index as a continuous variable Abbreviations: ET: Estrogen Therapy; EPT: Estrogen-Progestin Therapy; OR: Odds Ratio; CI: Confidence Interval; Ca: Case; Co: Control; Ref: Reference Group; HT: Hormone Therapy 15 Because it is conceivable that specific HT regimens would have different effects whether started around the time of menopause or after menopause, we stratified the data on time from menopause to start of HT use. Among the women who used only one type of HT, there was no difference in the effect of ET/short-sequential EPT between women who started HT within one year of menopause and those who started HT more than a year later (P for interaction = 0.47) (Table 1.4). Among women who had only used continuous-combined EPT, the risk was significantly elevated among women who started continuous-combined EPT within one year of menopause (OR per 5-year: 1.37; 95%CI: 1.16–1.63), but not among those who started continuous-combined EPT later (OR per 5-year: 1.08; 95%CI: 0.82–1.38). We further addressed the question of whether risk differed for ET/short-sequential EPT users who stopped taking hormones and ET/short-sequential EPT users who switched to continuous-combined EPT or long-sequential EPT (Table 1.5). Although there was some indication that both continuing ET/short-sequential EPT use or shifting to continuous- combined EPT was worse than stopping all hormone use, this finding was no longer statistical significant after we adjusted for duration of ET/short-sequential EPT use. 16 Table 1.4: Association between lifetime duration of hormone therapy use and endometrial cancer, by delay between menopause and start of hormone therapy Menopause–Hormone Therapy Delay < 1 year ≥ 1 year HT Category N Ca(Co) OR (95%CI)* per 5-year use N Ca(Co) OR (95%CI)* per 5-year use P for Interactio Never Use 100(223) 1 (Ref) 100(223) 1 (Ref) ET/Short-Sequential EPT 83(103) 1.63 (1.38-1.95 42(40) 2.00 (1.46-2.79) 0.259 Continuous-Combined EPT 76(136) 1.35 (1.16-1.58 42(87) 1.16 (0.93-1.44) 0.151 ET/Short-Sequential EPT (only) 53(61) 1.85 (1.50-2.32 26(31) 1.86 (1.35-2.65) 0.472 Continuous-Combined (only) 49(91) 1.37 (1.16-1.63 28(76) 1.08 (0.82-1.38) 0.076 * Mutually adjusted ORs per 5 year of use; adjusted for age, race, reference year, geographic area of residence, menopause age, number of full-term pregnancies, age at the last full-term pregnancy, lifetime oral contraceptive use duration and body mass index Abbreviations: ET: Estrogen Therapy; EPT: Estrogen-Progestin Therapy; OR: Odds Ratio; CI: Confidence Interval; Ca: Case; Co: Control; Ref: Reference Group; HT: Hormone Therapy 17 Table 1.5: Association between current use of postmenopausal hormone therapy and endometrial cancer by HT type among women who previously used ET/short- sequential EPT Current HT Type N Ca(Co) OR (95% CI)* OR (95% CI) † No Current (stopped use) 35(57) 1 (Ref) 1 (Ref) ET/ Short-Sequential EPT (continued use) 43(35) 2.23 (1.16-4.27) 1.77 (0.87-3.61) Continuous-Combined EPT (changed preparation type) 27(30) 1.65 (0.83-3.27) 1.47 (0.73-2.96) * Adjusted for, age, menopause age, and lifetime oral contraceptive use duration † Further adjusted for lifetime duration of ET/ Short-Sequential EPT use Abbreviations: ET: Estrogen Therapy; EPT: Estrogen-Progestin Therapy; OR: Odds Ratio; CI: Confidence Interval; Ca: Case; Co: Control; Ref: Reference Group; HT: Hormone Therapy 18 1.5 Discussion In this case-control study of endometrial cancer we observed a statistically significant increased risk associated with long-term use of continuous-combined EPT, which was limited to thinner (BMI < 25 kg/m 2 ) women. ET and long-term use of short-sequential EPT were associated with significant increased risks of endometrial cancer both in thinner and heavier women. 1.5.1 Sequential Estrogen-Progestin Therapy Our findings for both short-sequential EPT and long-sequential EPT are consistent with previous studies (Beresford et al. 1997; Pike et al. 1997; Weiderpass et al. 1999). The four-fold increased risk for ≥ 10 years of short-sequential EPT use is similar to that in a recent report by Doherty et al. (OR: 5.9; 95%CI: 2.9–12.0) for > 6 years of use (Doherty et al. 2007). Beral and colleagues (Beral et al. 2005) extensively reviewed the effects of long-sequential EPT (defined as progestin 10-15 d/m). Their meta-analysis, based on 456 cases from six published studies, reported a 1.14 elevation in risk (95%CI: 1.01– 1.28) associated with ever-use of long-sequential EPT. We observed a similar although not statistically significant elevation in risk per five years of long-sequential EPT use. 19 1.5.2 Continuous-Combined EPT Findings for the effects of continuous-combined EPT on the risk of endometrial cancer are inconsistent. In general early studies found no association, while several of the newer studies have reported inconsistent effects. Case-control studies conducted in Seattle (Hill et al. 2000; Doherty et al. 2007), and Sweden (Weiderpass et al. 1999) reported reduced risks with continuous-combined EPT of 0.77 (95%CI: 0.45–1.3) for > 6 years of use and 0.2 (95%CI: 0.1–0.8) for ≥ 5 years of use, respectively. On the other hand, Newcomb et al. reported a significant increased risk for women who used EPT with progestin for > 21 d/m (OR:2.6; 95%CI: 1.27–4.0) (Newcomb and Trentham-Dietz 2003). A Canadian case-control study (Jain et al. 2000) and one conducted in Los Angeles (Pike et al. 1997) both found an elevated but statistically non-significant increased risk associated with ever use and ≥ 2 years of continuous-combined EPT use, respectively. The results of two cohort studies were also inconsistent for continuous-combined EPT (defined as progestin > 15 d/m). The Million Women Study (Beral et al. 2005) reported a statistically significant reduced risk (RR: 0.71; 95%CI: 0.56–0.9) based on 73 cases who reported continuous-combined EPT as their last hormone therapy. However, in a smaller US cohort study based on 15 exposed cases, the risk for continuous-combined EPT use was significantly elevated (RR: 2.3; 95%CI: 1.3–4) (Lacey et al. 2005). 20 Clinical trials have reported a statistically non-significant reduced risk with use of continuous-combined EPT, perhaps because case numbers have been small. The Women's Health Initiative trial, found a decreased risk of postmenopausal endometrial cancer in the continuous-combined EPT arm with a relative risk of 0.81 (95%CI: 0.41– 1.22) after 5.6 years of follow-up (Anderson et al. 2003). After 6.8 years of follow-up in the smaller Heart and Estrogen/Progestin Replacement Therapy (HERS II) trial, only two women in the hormone group and eight women in the placebo group developed endometrial cancer (RR: 0.25; 95%CI: 0.05–1.18) (Hulley et al. 2002). However, it is possible that the duration of use in these trials was not sufficient to find the increased risks observed with long term use in several recent observational studies. In our study, continuous-combined EPT did not increase endometrial cancer risk during the first 10 years of use. However, using continuous-combined EPT ≥ 10 years was associated with a significantly increased risk of cancer (OR: 2.05; 95%CI: 1.27-3.30). It is generally believed that daily use of low-dose progestin opposes the effect of exogenous and endogenous estrogen on the endometrium, resulting in a lower risk of endometrial cancer with the use of a continuous-combined EPT regimen (Key and Pike 1988). Supporting this hypothesis, several histology studies and randomized trials have reported significantly lower rates of complex and atypical hyperplasia among continuous-combined EPT users when compared to HT never-users (Lethaby et al. 21 2004). However, there is some evidence for epithelial cell proliferation in the endometrium of women using continuous-combined EPT even in the absence of hyperplasia (Sturdee et al. 2000; Dahmoun et al. 2004; Langer et al. 2006; Archer et al. 2007; Magyar et al. 2007). Around 85% of continuous-combined EPT users in our study reported using 2.5 or 5 mg of medroxyprogesterone (MPA) as a separate pill. We hypothesize that on a long-term basis, this dose of MPA would not be sufficient to oppose the effect of exogenous estrogen on cell proliferation, and that this is why we observed an increased risk of endometrial cancer in women with long-term continuous- combined EPT use in our study. Consistent with this, we observed the same pattern of increased risk among women who used continuous-combined EPT for more than 10 years whether the women exclusively used MPA 2.5 mg or used MPA of 5mg (results not shown). Similar to our findings, in the Million Women Study and the National Institutes of Health American Association of Retired Persons (NIH-AARP) Diet and Health Study the risk of endometrial cancer associated with continuous-combined EPT use was lower for obese women (Beral et al. 2005; Chang et al. 2007). Obese women have higher levels of endogenous estrogen associated with greater aromatase activity and lower levels of sex-hormone-binding-globulin (SHBG). In these women, the addition of exogenous progestin associated with EPT may work to oppose the already-elevated 22 serum estrogen levels (MacDonald and Siiteri 1974; MacDonald et al. 1978; Siiteri 1981; Key and Pike 1988). Key and Pike have suggested that the endometrial response to estrogen has a ceiling beyond which additional estrogen exposure does not further increase the mitotic activity of the endometrial cells in the basal layer (Key and Pike 1988). This may explain the significantly lower risks associated with continuous- combined EPT in heavier women. This hypothesis could also explain the observed increase in risk among thin women. Given the low levels of endogenous estrogen among postmenopausal women with a lower BMI, it is possible that the risk associated with long-term use of exogenous estrogen exceeds the protective effect of daily progestin use. According to the model proposed by Pike et al (Pike 1987) aging of the endometrium occurs more slowly following menopause. In our study the elevated risk associated with continuous-combined EPT use was higher among women who started HT within one year of their menopause. One could speculate that among these women, the aging of the endometrium continues at the premenopausal rate and that their hormone use postpones the time at which the endometrium goes through estrogen-deprivation and postmenopausal changes. As in all case-control studies, there is the possibility of recall bias in our study, especially for long-term hormone therapy users and women who used several different 23 types or preparations of HT. To evaluate the role that such bias may have played in our findings, we compared the HT exposure information for women who reported long-term HT use as reported on the case-control questionnaire to that obtained from the same women on the CTS baseline questionnaire. A few cases had discrepancies in their hormone treatment histories between the two assessments. When we redid the case- control analyses excluding these cases, our results did not change, suggesting that it is unlikely that recall bias explained our findings. Also, 4 cases and 11 controls reported using continuous-combined EPT pills (Prempro) before these were available in the United States. However, when we either excluded these women or changed the episodes to ET alone, we found an even higher increased risk associated with combined EPT, indicating that this misclassification of exposure simply introduced a bias towards the null. In order to verify our findings for long-term continuous-combined EPT use in our study, we performed a nested case-control analysis within the CTS, based solely on the baseline questionnaire data, using an incidence-density sampling method. The results of this analysis confirmed our findings, although the information on HT use was less detailed and specific (Table 1.6). 24 Table 1.6: Association of continuous-combined EPT use (progestin ≥ 25 days/month) and endometrial cancer (case-control study, nested within a cohort of CTS’s continuous-combined EPT-only, based on baseline questionnaire data) HT Type Duration (Years) N Ca(Co) OR (95% CI)* P For Trend Never 50(22337) 1 (Ref) Continuous- Combined EPT <3 7(2423) 1.51 (0.64-3.58) 3-5 6(2870) 0.61 (0.19-1.95) 6-9 14(5614) 1.29 (0.65-2.57) 10-14 7(6440) 0.74 (0.32-1.74) 15-19 10(2674) 2.75 (1.26-5.98) 20+ 3(782) 3.83 (1.27-11.58) <.0001 *Adjusted for age, race, reference year, menopause age, number of full-term pregnancies, age at the last full- term pregnancy, lifetime oral contraceptive use duration and body mass index Abbreviations: EPT: Estrogen-Progestin Therapy; OR: Odds Ratio; CI: Confidence Interval; Ca: Case; Co: Control; Ref: Reference Group; HT: Hormone Therapy; CTS: California Teachers Study 25 Our study has several strengths. First, the percentage of postmenopausal CTS members who used HT at baseline (74%) was relatively high (Bernstein et al. 2002) providing additional statistical power for evaluating specific patterns of use. We observed a high proportion of continuous-combined EPT users in our population who tended to use this regimen for a longer duration than in other published studies. Second, identification of cancer diagnoses based on the high-quality and highly complete California Cancer Registry reduces the possibility of case-control misclassification in our study. Finally, all of the CTS participants have a college education; thus, they may more accurately report their past hormone-therapy use. 1.6 Conclusion In summary, the results of this study confirm that estrogen alone and sequential EPT increase endometrial cancer risk. Our results also suggest that long term use of continuous-combined EPT may be less safe for the endometrium than initially assumed in non-obese women. However, whether continuous-combined EPT may be beneficial with respect to endometrial cancer risk among obese women remains to be determined. 26 Chapter 2: Genetic Variations in Sex Hormone Genes, Postmenopausal Hormone Therapy, and Risk of Endometrial Cancer 2.1 Introduction Postmenopausal hormone therapy has been widely used in treatment of menopausal symptoms. While estrogen alone therapy (ET) is an established risk factor of endometrial cancer (Brinton and Hoover 1993; Grady et al. 1995; Pike et al. 1997), several more recent reports suggest that long-term use of estrogen-progestin therapy (EPT) may also increase the risk of endometrial cancer (Jain et al. 2000; Newcomb and Trentham-Dietz 2003; Lacey et al. 2005; Razavi et al. 2009). Given the role of hormones in endometrial cancer, it is possible that inherited variations in hormone receptors or in genes encoding hormone metabolism enzymes may affect the risk of endometrial cancer. It is also possible that such variants could modify the effect of hormone therapy (HT) on endometrial cancer risk. To answer these questions we performed a nested case-control study within the California Teachers Study (CTS) cohort. We report the results of the genes main effect and gene-environment interaction analyses. 27 2.2 Materials and Methods 2.2.1 Study Population This nested case-control study in the CTS has been described elsewhere in detail (Razavi et al. 2009). Briefly, the CTS is an ongoing cohort study of 133,479 current and former female public school teachers and administrators who were enrolled in the California State Teachers Retirement System and completed a self-administered questionnaire related to women’s health in 1995-1996 (Bernstein et al. 2002). Women were eligible for the current nested case-control study if they maintained California residence after joining the cohort and had not been previously diagnosed with endometrial and breast cancer, or had a hysterectomy. Cases were identified by linkage of cohort participant data to the California Cancer Registry. Cases were eligible for current study if they were 50-85 years old when diagnosed with invasive endometrial cancer (ICD-O-3 codes C54.1 and C54.9) after cohort enrollment and before December 31, 2004. Controls were frequency matched to cases based on age (5-year age groups through 80+), race/ethnicity (African American, Asian/Pacific Islander, Hispanic, Native American, white, and other/mixed), and geographic region of residence within California. The interviews and collection of blood/buccal samples from endometrial cancer cases and controls were performed between February 2002 and May 2007. 28 Since the number of non-white participants who completed the interview and provided DNA sample was small (< 9%), this analysis was restricted to white participants. We further excluded women who were pre- or peri-menopausal at the time of diagnosis or reference date or had incomplete covariate data. The final analysis included 296 cases and 501 controls. 2.2.2 htSNP Selection and Genotyping For each gene, haplotype blocks were constructed using the method described by Gabriel et al. (Gabriel et al. 2002) and haplotype frequencies were estimated within each ethnic group using the expectation-maximization algorithm of Excoffier and Slatkin (Excoffier and Slatkin 1995). The TagSNP software (Stram et al. 2003) was used to select the htSNPs in each haplotype block. Among the whites, the minimum R h 2 (the squared correlation between the true haplotypes and the estimated haplotypes) in each linkage-disequilibrium (LD) block was > 0.80 for all the genes included in this study. DNA was extracted using the QIAamp 96 DNA Blood Kit (Qiagen, Germantown, MD) and all genotyping was completed using the TaqMan assay (Applied Biosystems, Foster City, CA). 29 2.2.3 Postmenopausal Hormone Use The details of HT use as well as the main effects of ET and EPT on endometrial cancer risk are described in detail elsewhere (Razavi et al. 2009). For this analysis, we categorized HT use with <10 days/month of progestin as unopposed estrogen therapy (ET) and  10 days/month as estrogen-progestin therapy (EPT). Lifetime duration of HT use was calculated for each regimen. We categorized hormone use into three categories <1 year, 1-9 years, and 10+ years. We used <1 year category as the reference group in all the analyses. 2.2.4 Statistical Analyses We performed haplotype-based analyses using multivariable unconditional logistic regression models. We used the methods described by Zaykin et al. (Zaykin et al. 2002) to estimate odds ratios (OR) and 95% confidence intervals (95% CI) of each haplotype within an LD block using the most common haplotype as the reference group (global model) (Zaykin et al. 2002). We used likelihood ratio tests to calculate the p-values and to construct the 95% CIs and the Bonferroni method to adjust the calculated p-values for multiple testing. We also examined the effect of each single haplotype by comparing the effect of the haplotype with all the other haplotypes in the block (single model). We adjusted the models for age at the time of diagnosis/selection. Additional adjustment for HT use, oral contraceptive (OC) use, body mass index (BMI), parity, and type of 30 menopause did not change the results and thus we only present the results of the age- adjusted models. To examine the interaction between genetic variations and different HT use regimens, we fitted a single haplotype model for each LD block while including the ET and EPT duration variables. These interaction models were further adjusted for age at the time of diagnosis/selection (continuous), age at menopause (continuous), BMI (continuous) and lifetime OC use (continuous in months). We assessed the statistical significance of the interaction between each haplotype and respective HT variables by conducting likelihood ratio tests comparing the model with and without the ET and EPT interaction terms. We performed both 1 degrees of freedom (df) test using the HT variables as continuous variables and 2 df test using the HT variables as categorical variables (<1,1- 9,10+ years of use). 2.3 Results All the SNPs conformed to Hardy-Weinberg equilibrium (HWE) among controls (P > 0.01). After adjustment for multiple testing, none of the haplotypes were associated with risk of endometrial cancer (Table 2.1). 31 Table 2.1: The association of haplotypes in sex hormone metabolism genes and risk of endometrial cancer Haplotype frequency (%) Overall Effect* Gene Haplotype Cases Controls OR 95% CI CYP11A1 Block 1 1A - GGGT 61 60 1(Reference) 1B - GGCT 7 8 0.91 0.62 1.33 1C - GGCC 17 16 1.04 0.80 1.36 1D - GACC 1 2 0.60 0.25 1.44 1E - AACC 13 14 0.87 0.65 1.17 CYP17A1 Block 1 1A - CTAGG 37 36 1(Reference) 1B - CCAGG 2 2 0.92 0.45 1.86 1C - CCAGT 18 18 0.97 0.73 1.27 1D - TTGGG 10 10 1.03 0.73 1.46 1E - TTGAG 31 31 0.95 0.75 1.20 Block 2 2A - TAAA 29 30 1(Reference) 2B - CACA 23 22 1.11 0.85 1.46 2C - CACG 17 17 1.05 0.77 1.42 2D - CAAA 15 16 1.04 0.76 1.42 2E - CGCA 16 16 1.03 0.76 1.39 CYP19A1 Block 1 1A - AGTTGA 51 52 1(Reference) 1B - AGCTAA 4 5 0.79 0.48 1.30 1C - AGCCAA 23 22 1.09 0.85 1.39 1D - TCCTAT 8 7 1.22 0.82 1.82 1E - TCCCGA 5 5 1.08 0.69 1.67 Block 2 2A - AAGCG 77 75 1(Reference) 2B - GAGCG 9 10 0.86 0.61 1.22 Block 3 3A - GGG 41 44 1(Reference) 3B - GGA 13 13 1.05 0.77 1.44 3C - AAA 44 41 1.14 0.92 1.42 Block 4 4A - AAGGGAC 47 44 1(Reference) 4B - AGGGAGA 9 10 0.88 0.62 1.24 4C - CGGGGGC 18 18 0.935 0.711 1.23 4D - CGGGGGA 16 17 0.891 0.673 1.18 32 Table 2.1, Continued HSD17B1 Block 1 1A - CGAT 40 43 1(Reference) 1B - CAGC 27 26 1.08 0.84 1.38 1C - CAAC 0 0 - - - 1D - CGAC 5 5 1.01 0.64 1.62 1E - AAGC 27 25 1.14 0.89 1.45 HSD17B2 Block 1 1A - CAT 50 54 1(Reference) 1B - CAC 6 5 1.15 0.74 1.81 1C - CGC 5 7 0.72 0.46 1.14 1D - TGC 36 32 1.23 0.99 1.53 1E 3 3 1.27 0.70 2.32 Block 2 2A - GCAT 49 49 1(Reference) 2B - GCAC 12 9 1.36 0.98 1.90 2C - GCGC 7 8 0.88 0.60 1.31 2D - GTAC 0 0 - - - 2E - GTGC 27 29 0.91 0.72 1.14 2F - CCAC 3 4 0.85 0.49 1.48 2G - CCGC 1 1 - - - ESR2 Block 1 1A - CGCC 44 44 1(Reference) 1B - CACC 8 9 0.88 0.59 1.30 1C - CACT 8 9 0.83 0.56 1.22 1D - CATC 4 3 1.49 0.86 2.60 1E - TACC 7 6 1.27 0.81 1.99 1F - TACT 28 27 1.02 0.81 1.30 *Adjusted for age at the time of diagnosis/selection 33 As previously reported from this study (Razavi et al. 2009) both ET and EPT use were associated with risk of endometrial cancer. The ORs were 1.65 (95%CI: 1.41-1.93) per 5 years of ET use and 1.25 (95%CI: 1.10-1.42) per 5 years of EPT use (Table 2.2). We found some indication of an interaction between several of the genes and HT use (Tables 2.3 and 2.4). For ET use, there was some indication of an interaction with CYP11A. The strongest interaction was observed between duration of ET use and the most common haplotype of CYP11A (haplotype 1A; p= 0.0019). However, this interaction was not significant after adjustment for multiple testing (p=0.15). In the categorical HT model, when we compared the women who used ET for 10+ years with HT never-users, the interaction effect was 2.37 (OR per each copy of 1A for 10+ year ET use: 2.20; 95%CI: 1.16-4.34 vs. OR per each copy of 1A for <1 year ET use: 0.93; 95% CI: 0.71-1.21). For EPT use, the strongest indication of an interaction was for a haplotype on CYP19. We also observed a statistically non-significant (P 2df =0.048, P 1df =0.041) interaction between haplotype 3A and EPT use. Among the women who used EPT <1 year the OR for each copy of this haplotype was 1.37 (95%CI: 0.89-2.09) while among the long- term EPT users (10+ year of EPT use) the OR was 0.57 (95%CI: 0.24-1.26). 34 Table 2.2: Association between duration of use of postmenopausal hormone therapy (HT) and endometrial cancer by HT type HT type Duration N Ca(Co) OR (95% CI)* <1 y 181(382) 1 (Reference) ET 1-9y 64(81) 2.18 (1.46-3.24) 10+y 48(36) 4.93 (2.91-8.34) Per 5-year 1.65 (1.41-1.93) † EPT 1-9y 67(133) 0.89 (0.61-1.31) 10+y 49(80) 1.78 (1.14-2.79) Per 5-year 1.25 (1.10-1.42) † *Mutually adjusted ORs: adjusted for age, race, reference year, geographic area of residence, menopause age, number of full-term pregnancies, age at the last full-term pregnancy, lifetime oral contraceptive use duration and body mass index † OR per 5-year use Abbreviations: ET: Estrogen Therapy; EPT: Estrogen-Progestin Therapy; OR: Odds Ratio; CI: Confidence Interval; Ca: Case; Co: Control; HT: Hormone Therapy 35 Table 2.3: Interaction between postmenopausal estrogen therapy (ET) use and sex hormone metabolism genes haplotypes ET OR (95% CI)* <1 y 1-9 y 10+ y P 2-df † P 1-df ‡ CYP11A1 Block 1 1A GGGT 0.93 (0.71-1.21) 1.27 (0.78-2.10) 2.20 (1.16-4.34) 0.04 0.0019 1B GGCT 1.14 (0.66-1.93) 0.61 (0.25-1.40) 0.49 (0.15-1.54) 0.263 0.112 1C GGCC 1.19 (0.85-1.67) 0.89 (0.44-1.75) 0.90 (0.30-2.83) 0.699 0.475 1D GACC 0.89 (0.29-2.45) - - - 1E AACC 0.89 (0.59-1.31) 1.02 (0.52-1.95) 0.42 (0.15-1.12) 0.311 0.067 CYP17A1 Block 1 1A CTAGG 0.99 (0.75-1.29) 1.23 (0.75-2.03) 1.63 (0.83-3.37) 0.354 0.247 1B CCAGG 0.90 (0.63-1.28) 1.09 (0.61-1.93) 0.84 (0.35-2.09) 0.831 0.363 1C CCAGT 1.28 (0.81-1.99) 0.75 (0.35-1.54) 0.64 (0.23-1.83) 0.297 0.126 1D TTGGG 1.02 (0.76-1.35) 0.98 (0.59-1.63) 0.75 (0.38-1.46) 0.714 0.346 1E TTGAG 0.84 (0.42-1.61) 0.55 (0.15-1.67) 1.82 (0.37-12.72) 0.499 0.625 2A TAAA 0.93 (0.69-1.25) 1.00 (0.59-1.69) 0.74 (0.36-1.49) 0.791 0.617 Block 2 2B CACA 1.03 (0.74-1.42) 1.51 (0.82-2.80) 1.18 (0.58-2.52) 0.551 0.519 2C CACG 0.94 (0.64-1.37) 1.09 (0.63-1.91) 0.64 (0.25-1.61) 0.611 0.941 2D CAAA 1.19 (0.82-1.72) 0.76 (0.39-1.42) 0.77 (0.33-1.83) 0.377 0.238 2E CGCA 0.94 (0.66-1.34) 0.73 (0.36-1.43) 4.03 (1.28-15.93) 0.034 0.197 CYP19A1 Block 1 1A AGTTGA 0.88 (0.67-1.14) 0.93 (0.58-1.51) 1.03 (0.56-1.93) 0.884 0.839 1B AGCTAA - - - - - 1C AGCCAA 0.93 (0.67-1.29) 1.97 (1.02-3.89) 1.06 (0.50-2.30) 0.135 0.48 1D TCCTAT 1.43 (0.86-2.35) 0.75 (0.23-2.20) 1.17 (0.31-4.63) 0.569 0.569 1E TCCCGA 0.81 (0.41-1.53) 0.89 (0.30-2.54) 1.28 (0.45-4.14) 0.775 0.521 1F 1.37 (0.88-2.11) 0.82 (0.37-1.73) 0.69 (0.22-2.21) 0.346 0.266 Block 2 2A AAGCG 1.01 (0.73-1.40) 1.78 (1.01-3.21) 0.92 (0.45-1.85) 0.194 0.662 2B GAGCG 1.07 (0.66-1.68) 0.45 (0.17-1.06) 0.71 (0.21-2.33) 0.214 0.272 2C 0.95 (0.64-1.39) 0.73 (0.37-1.42) 1.39 (0.57-3.61) 0.528 0.791 Block 3 3A GGG 0.83 (0.63-1.09) 1.14 (0.69-1.89) 1.03 (0.53-1.99) 0.495 0.14 3B GGA 1.01 (0.67-1.50) 0.69 (0.32-1.44) 1.34 (0.58-3.34) 0.492 0.943 3C AAA 1.25 (0.95-1.65) 0.93 (0.57-1.52) 0.76 (0.39-1.47) 0.285 0.058 3D 0.62 (0.19-1.67) - - - - 36 Table 2.3, Continued Block 4 4A AAGGGAC 1.14 (0.87-1.50) 1.11 (0.69-1.78) 0.83 (0.43-1.59) 0.669 0.331 4B AGGGAGA 0.87 (0.55-1.37) 1.73 (0.66-4.69) 1.18 (0.48-3.02) 0.428 0.664 4C CGGGGGC 1.05 (0.74-1.47) 0.93 (0.48-1.74) 1.58 (0.70-3.76) 0.586 0.556 4D CGGGGGA 0.94 (0.65-1.36) 0.83 (0.43-1.59) 1.31 (0.54-3.46) 0.724 0.173 4E 0.81 (0.51-1.26) 0.76 (0.33-1.72) 0.37 (0.11-1.17) 0.465 0.202 HSD17B1 Block 1 1A CGAT 0.92 (0.71-1.21) 0.67 (0.40-1.10) 1.18 (0.60-2.35) 0.364 0.759 1B CAGC 1.05 (0.77-1.42) 1.54 (0.91-2.63) 0.57 (0.25-1.27) 0.119 0.386 1C CAAC 0.82 (0.44-1.48) 0.85 (0.28-2.43) 0.77 (0.16-3.66) 0.994 0.912 1D CGAC 1.11 (0.82-1.50) 1.08 (0.60-1.96) 1.23 (0.55-2.86) 0.965 0.44 1E AAGC 0.96 (0.26-2.99) - - - - ESR2 Block 1 1A CGCC 0.94 (0.72-1.24) 1.02 (0.64-1.62) 1.05 (0.56-1.98) 0.934 0.335 1B CACC 0.72 (0.41-1.21) 0.83 (0.31-2.18) 3.32 (1.06-12.53) 0.054 0.15 1C CACT 0.92 (0.55-1.50) 0.49 (0.16-1.30) 0.53 (0.15-1.65) 0.431 0.059 1D CATC 1.18 (0.63-2.16) 0.83 (0.30-2.16) - 0.326 0.861 1E TACC 1.08 (0.80-1.45) 1.44 (0.78-2.65) 0.62 (0.29-1.29) 0.217 0.517 1F TACT 1.53 (0.80-2.88) 1.15 (0.27-4.88) 0.95 (0.24-4.17) 0.805 0.689 *Adjusted for age at diagnosis/selection, menopause age, lifetime oral contraceptive use duration and body mass index † P for interaction based on 2-df likelihood ratio tests for interaction term using categorical HT variables ‡ P for interaction based on 1-df likelihood ratio tests using continuous HT variables in months 37 Table 2.4: Interaction between postmenopausal estrogen-progestin therapy (EPT) use and sex hormone metabolism genes haplotypes EPT OR (95% CI)* <1 y 1-9 y 10+ y P 2-df † P 1-df ‡ CYP11A1 Block 1 1A GGGT 1.18 (0.88-1.58) 1.09 (0.72-1.66) 0.87 (0.51-1.51) 0.641 0.235 1B GGCT 0.93 (0.52-1.65) 0.71 (0.32-1.51) 1.03 (0.29-3.35) 0.816 0.766 1C GGCC 1.06 (0.71-1.57) 1.18 (0.67-2.01) 1.16 (0.56-2.43) 0.947 0.898 1D GACC 1.03 (0.36-2.76) - - - 1E AACC 0.72 (0.47-1.07) 1.06 (0.53-2.11) 1.23 (0.53-2.78) 0.401 0.048 CYP17A1 Block 1 1A CTAGG 1.00 (0.74-1.35) 1.47 (0.94-2.31) 0.97 (0.59-1.59) 0.325 0.485 1B CCAGG 1.00 (0.69-1.43) 0.80 (0.42-1.50) 0.92 (0.46-1.82) 0.844 0.737 1C CCAGT 1.00 (0.62-1.59) 0.83 (0.41-1.63) 2.16 (0.72-6.62) 0.343 0.508 1D TTGGG 1.04 (0.77-1.39) 0.86 (0.51-1.44) 0.88 (0.48-1.59) 0.779 0.905 1E TTGAG 0.84 (0.39-1.73) 0.74 (0.28-1.80) 1.13 (0.24-4.60) 0.886 0.910 2A TAAA 0.96 (0.71-1.30) 0.96 (0.56-1.63) 0.72 (0.37-1.34) 0.708 0.961 Block 2 2B CACA 1.05 (0.73-1.50) 1.44 (0.86-2.40) 1.00 (0.54-1.83) 0.562 0.990 2C CACG 0.93 (0.63-1.37) 0.88 (0.47-1.62) 1.07 (0.53-2.16) 0.912 0.393 2D CAAA 1.04 (0.70-1.53) 0.80 (0.44-1.42) 1.63 (0.67-3.99) 0.423 0.821 2E CGCA 1.02 (0.70-1.47) 0.90 (0.43-1.82) 1.04 (0.51-2.10) 0.950 0.341 CYP19A1 Block 1 1A AGTTGA 0.94 (0.70-1.24) 1.13 (0.72-1.77) 0.61 (0.36-1.02) 0.197 0.277 1B AGCTAA 0.74 (0.33-1.56) 0.79 (0.23-2.34) 1.13 (0.24-4.96) 0.882 0.712 1C AGCCAA 1.12 (0.79-1.59) 1.08 (0.62-1.86) 0.91 (0.47-1.76) 0.861 0.358 1D TCCTAT 1.57 (0.86-2.85) 0.93 (0.40-2.04) 1.08 (0.36-3.08) 0.556 0.593 1E TCCCGA 0.87 (0.46-1.63) 0.49 (0.15-1.29) 4.30 (1.03-22.75) 0.049 0.115 1F 0.90 (0.57-1.42) 1.15 (0.49-2.61) 3.17 (1.21-9.00) 0.070 0.028 Block 2 2A AAGCG 0.99 (0.70-1.38) 1.27 (0.74-2.22) 1.53 (0.81-2.96) 0.432 0.144 2B GAGCG 0.72 (0.41-1.21) 1.65 (0.76-3.59) 0.59 (0.22-1.40) 0.144 0.683 2C 1.26 (0.83-1.89) 0.48 (0.24-0.95) 0.82 (0.38-1.72) 0.055 0.187 Block 3 3A GGG 0.92 (0.69-1.24) 0.91 (0.57-1.43) 0.85 (0.48-1.49) 0.967 0.567 3B GGA 1.37 (0.89-2.09) 0.59 (0.28-1.20) 0.57 (0.24-1.26) 0.048 0.041 3C AAA 0.90 (0.67-1.21) 1.54 (0.98-2.44) 1.45 (0.82-2.61) 0.096 0.045 3D 1.76 (0.53-5.70) 0.22 (0.01-1.24) 3.04 (0.44-27.10) 0.083 0.844 38 Table 2.4, Continued Block 4 4A AAGGGAC 0.87 (0.65-1.16) 1.68 (1.07-2.69) 1.32 (0.75-2.34) 0.045 0.147 4B AGGGAGA 1.09 (0.67-1.75) 0.90 (0.40-1.89) 0.96 (0.36-2.41) 0.904 0.875 4C CGGGGGC 1.04 (0.71-1.51) 1.26 (0.71-2.24) 0.95 (0.50-1.79) 0.791 0.724 4D CGGGGGA 1.15 (0.78-1.69) 0.66 (0.32-1.29) 0.78 (0.37-1.54) 0.305 0.166 4E 1.02 (0.61-1.68) 0.34 (0.15-0.72) 0.89 (0.36-2.13) 0.057 0.761 HSD17B1 Block 1 1A CGAT 0.91 (0.68-1.21) 0.64 (0.40-1.01) 1.34 (0.77-2.37) 0.129 0.217 1B CAGC 1.05 (0.75-1.47) 1.25 (0.75-2.05) 0.96 (0.54-1.71) 0.787 0.590 1C CAAC 0.81 (0.41-1.53) 1.50 (0.60-3.74) 0.14 (0.01-0.80) 0.060 0.099 1D CGAC 1.14 (0.82-1.59) 1.18 (0.71-1.95) 0.95 (0.50-1.79) 0.852 0.546 1E AAGC 0.96 (0.18-3.65) 158.35 (0.75--) 1.26 (0.23-7.08) 0.205 0.338 ESR2 Block 1 1A CGCC 1.06 (0.79-1.43) 0.87 (0.57-1.32) 0.87 (0.50-1.51) 0.689 0.497 1B CACC 0.93 (0.52-1.62) 1.38 (0.68-2.77) 0.18 (0.02-0.81) 0.052 0.310 1C CACT 0.70 (0.39-1.20) 0.86 (0.37-1.83) 0.87 (0.28-2.57) 0.886 0.794 1D CATC 1.63 (0.82-3.26) 0.66 (0.21-1.74) 0.95 (0.29-2.99) 0.314 0.528 1E TACC 0.94 (0.67-1.31) 1.23 (0.75-2.01) 1.31 (0.71-2.42) 0.512 0.669 1F TACT 1.17 (0.59-2.29) 0.90 (0.16-4.47) 2.72 (0.85-9.27) 0.404 0.078 1G *Adjusted for age at diagnosis/selection, menopause age, lifetime oral contraceptive use duration and body mass index † P for interaction based on 2-df likelihood ratio tests for interaction term using categorical HT variables ‡ P for interaction based on 1-df likelihood ratio tests using continuous HT variables in months 39 2.4 Discussion In this nested case-control study, using a haplotype-based approach, we evaluated the genetic variations of important sex hormone metabolism genes, use of postmenopausal HT, and possible interactions with risk of endometrial cancer. None of the variants we studied in genes encoding for enzymes involved in sex hormones metabolism was associated with endometrial cancer. We found some indications for an interaction between ET use and CYP11A (haplotype 1A) and an interaction between EPT use and CYP19 (haplotype 3A). Our results are for the most part consistent with previous literature. In the following we discuss our findings in light of what others have found on the main effects and interactions for each gene. 2.4.1 CYP11A1 CYP11A1 is a rate-limiting enzyme located upstream in sex hormone metabolism pathways, which converts cholesterol to pregnenolone (Gasteiger et al. 2003). Early studies on variations of CYP11A1 were mainly focused on a number of (TTTTA) n repeats located in the 5’ region in block 1. Setiawan et al. reported that haplotype 1A was associated with a smaller number of (TTTTA) n repeats in data from the Multiethnic Cohort study (Setiawan et al. 2006).The number of repeats has been shown to be associated with polycystic ovarian syndrome (Franks et al. 1997; Gharani et al. 1997; Diamanti-Kandarakis et al. 2000; Gaasenbeek et al. 2004), and breast and prostate 40 cancer (Kumazawa et al. 2004; Zheng et al. 2004; Setiawan et al. 2006). To our knowledge only one study has been reported on the effect of CYP11A1 on risk of endometrial cancer. Olson et al. found no association between the number of (TTTTA) n repeats and risk of endometrial cancer in a study of 417 cases and 402 controls (Olson et al. 2008). In our study none of the haplotypes in CYP11A1 was associated with risk of endometrial cancer. However, we found a possible interaction between ET use and haplotype 1A (Gasteiger et al. 2003). There has been no evidence to support the association of the number of repeats with levels of progesterone, androgens, or estrogens among premenopausal women (Olson et al. 2007). However, a single study reported marginal decrease in serum estradiol levels among postmenopausal women who had high number of repeats (Zheng et al. 2004). Therefore, if haplotype 1A (as a marker of low number of repeats) is associated with lower activity of the enzyme and consequent lower endogenous progesterone level then it is possible that the effect of exogenous estrogen is exaggerated among the carriers of this gene. 2.4.2 CYP19A1 CYP19A1, also known as aromatase, is a rate-limiting enzyme involved in the conversion of androgens to estrogen. Three common genetic polymorphisms in 41 CYP19A1 have been previously studied and have been reviewed recently (Olson et al. 2007). All three studied variants seem to be in LD (block 4) and include: 1) (TTTA) n repeat in intron 4, where the number of repeats varies between 7- to 13-repeats, with seven the most common; 2) A three base pair (TCT) deletion in close vicinity of (TTTA) n repeats, which has been found only with the 7-repeat allele (Baxter et al. 2001); 3) a T/C change in exon 10, (rs10046) which has been associated with an 8- repeat TTTA allele (Haiman et al. 2002). There has been no association between the number of repeats and serum progesterone and androgen levels. The results on the effect of repeats on serum estrogens are inconsistent (Haiman et al. 2003; Tworoger et al. 2004). Longer TTTA repeats (>7) were found to be associated with increased risk of endometrial cancer. In a small Russian case-control study >7 repeat alleles were more common (p=0.064) among the endometrial cancer patients (40.4%) compared to hospital-based controls (28.8%) (Berstein et al. 2001). In a nested case-control study within Nurse’s Health Study (NHS), Paynter et al. reported a significantly increased risk associated with >7 repeat alleles (OR for both >7 alleles: 1.92; 95% CI: 1.17-3.14) (Paynter et al. 2005). In the same study, having T allele of rs10046 (haplotype 4A) was associated with increased risk. Our results were in the same direction as these previous studies; we found an elevated, but statistically non-significant risk associated with the most common haplotype in block 4, which includes the previously studied variants (OR for haplotype 4A compared to all other haplotypes: 1.14; 95% CI: 0.93-1.39). 42 In a recent pooled analysis in the Epidemiology of Endometrial Cancer Consortium (Setiawan et al. 2009), carrying the A allele of rs749292 or rs727479 in CYP19A1 was associated with both increased levels of serum estrogens and increased risk of endometrial cancer (OR per copy of A-A haplotype: 1.15; 95% CI: 1.09-1.21). This finding may explain the increased but non-significant risk associated with haplotypes 3C and 4A in our study, which included the A allele of rs749292 and rs727479, respectively. We observed no interaction between ET use and CYP19A1 variations. However, we found two suggestive interactions between duration of EPT use and haplotypes 4A and 3B. We have no explanations for these interactions, and cannot exclude the possibility that they are due to chance. 2.4.3 CYP17A1 CYP17A1 encodes the enzyme, which converts pregnenolone to 17- hydroxypregnenolone and dehydroepiandrosterone (DHEA) and also converts progesterone to 17-hydroxyprogesterone and androstenedione. The most studied polymorphism in CYP17A1 is rs743572 [a T/C (A1/A2) SNP in the 5'UTR]. Most of the previous studies consistently reported a decreased risk of endometrial cancer associated with the A2 (C) allele (Haiman et al. 2001; McKean-Cowdin et al. 2001; Berstein et al. 2004; Aban et al. 2006). In contrast, in a large population-based study of 497 endometrial cancer cases and 1,024 controls in Poland, no association was observed for 43 A2 allele (p=0.15) or haplotypes in CYP17A1’s two LD blocks. Our results were consistent with the Polish study and were in contrast to the previous studies; we found no association between haplotypes 1D, and 1E, which included the A2 (C) allele and endometrial cancer. We also did not observe any significant interaction between CYP17A1 haplotypes and EPT use. However, we found a possible interaction between ET use and haplotype 2E. To our knowledge there has been no study on the effect of variations in block 2 of CYP17A1 and serum sex hormone levels. This possible interaction may be explained by lower serum progesterone levels in carriers of haplotype 2E due to a possible higher activity of enzyme among 2E carriers. 2.4.4 ESR2 Only two studies have examined the association of ESR2 polymorphisms and risk endometrial cancer. In a study of 222 endometrial cancer cases nested within NHS, Setiawan et al. found no association between rs1256049 (a C/T synonymous SNP in exon 5) or rs1271572 (a common C/A SNP in the promoter region) and endometrial cancer risk (Setiawan et al. 2004). A recent Australian study of 191 endometrial cancer cases also found no association with (Ashton et al. 2009). However, in the Australian study, G allele of two SNPs (rs1255998 and rs4986938) located in the 3’ region of the gene were associated with elevated risk (OR for rs1255998: 1.80; 95% CI: 1.17-2.79, OR for rs4986938: 1.99; 95% CI: 1.02-3.89). In our study, none of the ESR2 haplotypes 44 were associated with risk of endometrial cancer. We also did not observe any significant effect modification for ET or EPT use and ESR2 haplotypes. 2.4.5 Strengths and Weaknesses We cannot exclude the possibility of selection bias, as the participation rate was relatively low (around 50%) among both cases and controls. However, we have no reason to believe that the genetic variations in sex hormones may differentially effect participation of the women in our study. Also, the frequencies of alleles among the controls were similar to those reported in previous studies. As previously described (Razavi et al. 2009), we compared the distribution of established endometrial cancer risk factors as assessed on the baseline CTS questionnaire between cases participating in this nested case-control study and CTS cases not participating in the case-control study (and a similar comparison among controls). We found no evidence that cases (or controls) participating in the nested case-control study differed from non-participating cases (or women eligible to be controls) on any of these risk factors. Our study has several strengths. Compared to the previous studies, our study included a relatively large sample size, however, we still had modest power to study gene environment interactions. In this study, assuming a log-additive model, two sided test, OR ET =3.0, OR EPT =2.0, OR G =1.2, and α=0.05, when the minimum allele frequency is 45 0.4-0.2, we had 80% power to detect interaction effects of 1.88-2.02 for EPT use and 2.18-2.25 for ET use. For the genes main effect, we had 80% power to detect ORs ranging 1.33 to 1.40 for alleles with minor allele frequency of 0.4 to 0.2. 2.5 Conclusion In this nested study, using a haplotype-based approach, we examined the association between variations in sex hormone metabolism pathway genes and risk of endometrial cancer. None of the genes included in this study was associated with risk of endometrial cancer. We found some suggestions for possible interactions between variations in these genes and the effect of postmenopausal HT use on risk of endometrial cancer. 46 Chapter 3: Trends in Incidence and Survival of Adult Acute Lymphoblastic Leukemia 3.1 Abstract Background: Acute lymphoblastic leukemia (ALL) is an aggressive malignancy whose incidence declines through adolescence and then increases steadily with age. Prognosis is inversely related to age among adults. In the current analysis, we explore the impact of race/ethnicity on incidence and survival among adults with ALL in the United States (US). Methods: We examined trends in incidence and survival among adults with ALL in the US using the National Cancer Institute’s Surveillance, Epidemiology and End Results (SEER) program, which includes data from 17 SEER registries. We calculated incidence rates using data for the years 2001-2005, a time period reflecting the most current and complete classification of ALL subtypes. For the survival analysis we used comprehensive data collected between 1975 and 2005. We categorized race/ethnicity into 5 mutually exclusive categories: African-Americans (AA), Asian/Pacific Islanders (A/PI), Hispanic whites (HW), and non-Hispanic whites (NHW). We used multivariate 47 Cox hazard models to estimate the hazard ratios (HR) and 95% confidence intervals (95% CI) for adult ALL by race/ethnicity, sex, and cell of origin (B- or T-cell). Results: The highest age-adjusted incidence rate (AAIR) of ALL was observed for HW (AAIR: 1.60; 95% CI: 1.43-1.79). HW had a significantly higher AAIR across all age categories as compared to the other racial/ethnic groups, while AA had the lowest AAIR. In particular, the observed rate of B-cell ALL among HW (AAIR 0.77; 95% CI 0.69-0.87) was more than twice that of NHW (AAIR: 0.29; 95% CI: 0.27-0.32) and more than three times the rate observed among AA (AAIR: 0.20; 95% CI: 0.15-0.26). In contrast, we did not observe statistically significant variability in the rates of T-cell ALL across racial/ethnic groups (overall AAIR: 0.12; 95% CI: 0.11-0.14). Survival was significantly poorer among AA (HR: 1.26; 95% CI: 1.09-1.46), HW (HR: 1.21; 95% CI: 1.09-1.46), and A/PI (HR: 1.18; 95% CI: 1.06-1.32) compared to NHW for all subtypes of ALL. For both B- and T-cell ALL, survival differences between racial/ethnic groups existed only among young adults (age < 40 years). No statistically significant difference was observed between racial/ethnic groups for adults ≥ 40 years. Conclusions: The incidence rate of B-cell ALL among adults in the US is higher among HW than other racial/ethnic groups. Survival is significantly poorer among AA and HW than among NHW with B-cell ALL and poorer among AA, HW and A/PI than 48 among NHW with T-cell ALL in adults up to the age of 40. Survival trends appear to converge thereafter among all racial/ethnic groups. 3.2 Introduction Acute lymphoblastic leukemia (ALL) is a malignant disorder of B- or T-lymphocyte progenitors, which represents a biologically and clinically heterogeneous group of diseases. ALL is the most common malignancy in children. However, almost one third of ALL cases occur in adults, which accounts for less than 1% of adult malignancies (Pui and Evans 1998). The prognosis and treatment of ALL differs between ALL subtypes categorized on the neoplasm’s B-cell or T-cell lineage. Thus, although other more detailed classifications have been used in the past (Yeoh et al. 2002; Mancini et al. 2005; Pui 2005), in the most recent WHO classification for hematologic diseases, ALL was divided into two main categories (B-precursor and T-precursor subtype) based on leukemic cell lineage (Harris et al. 2000). The overall and age-specific incidence of adult ALL and its proportion of B-cell and T-cell subtypes among different racial/ethnic groups have not been described and the data is sparse. 49 While there has been a significant improvement in the treatment and survival of childhood ALL in recent years (Silverman et al. 2001; Pui et al. 2008), the prognosis for adults diagnosed with ALL remains unsatisfactory despite the implementation of modern treatments, including hematopoietic stem cell transplantation (Mengarelli et al. 2002; Thomas et al. 2004; Pui and Evans 2006; Fielding et al. 2007; Rowe and Goldstone 2007). Several factors are believed to contribute to the poor prognosis of adults, including increased frequency of drug-resistant leukemia, poor compliance and tolerance of adult patients to the treatment, and higher rates of treatment-related mortality (Pui and Evans 2006). Several reports, mostly from pediatric studies, suggested that race/ethnicity affect the outcome of ALL patients (Walters et al. 1972; Kalwinsky et al. 1985; Pinkel 1993; Pinkel 1995). In a population-based study of childhood ALL based on SEER data from the years 1993 to 1999, African-Americans (AA) and Hispanic whites (HW) were reported to have lower survival when compared to Non-Hispanic Whites (NHW) (Kadan-Lottick et al. 2003). It was suggested that the poor survival in AA might be associated with a higher frequency of T-cell leukemia and chromosomal abnormalities among AA cases. In a study of leukemia, patients treated with allogeneic hematopoietic stem-cell transplantation, mortality was higher among HW than NHW (Serna et al. 2003), which was attributed to a higher rate of relapse despite successful completion of therapy (Baker et al. 2005). In a re-analysis of Pediatric Oncology Group multicenter randomized clinical trials of B-cell childhood 50 ALL, Pollock et al. concluded that clinical presentation, tumor biology, and deviations from prescribed therapy did not explain the differences in survival among the racial/ethnic groups. Further these authors reported that the worse outcome in the AA and HW was most likely related to variations in chemotherapeutic response to treatment and not to compliance (Pollock et al. 2000). In the same clinical trials, the difference in the survival of ALL by racial/ethnic groups was only observed among patients with B- cell ALL; there was no difference observed in the prognosis of T-cell ALL among racial/ethnic groups (Pullen et al. 1999). 3.3 Materials and Methods 3.3.1 Race/Ethnicity We categorized race/ethnicity into 5 mutually exclusive categories: African-Americans, American Indians/Alaska Natives (AI/NA), Asians/Pacific Islanders (A/PI), Hispanic whites, and Non-Hispanic whites. To define Hispanic ethnicity, we used SEER’s Hispanic-origin variable, which is based on the NAACCR Hispanic Identification Algorithm (NHIA). This algorithm uses a combination of NAACCR variables and the 1990 Spanish Surname List to directly or indirectly classify cases as Hispanic origin. The algorithm uses a set of NAACCR standard variables including: self defined Spanish/Hispanic Origin based on medical record, death certificates, other available 51 sources, and considers last name, maiden name, birthplace, race, and sex. We included 11 cases dually coded as black and Hispanic in the AA group for our analyses. 3.3.2 Study Population Our analysis was completed using population-based data from the National Cancer Institute’s Surveillance, Epidemiology, and End Results (SEER) program. For this analysis, we used data from SEER 17 database (SEER 2008), which includes information from 17 cancer registries and represents 26 percent of the US population. The participant registries include: Atlanta, Connecticut, Detroit, Hawaii, Iowa, New Mexico, San Francisco-Oakland, Seattle, and Utah (with data from 1975 forward); Alaska, Los Angeles, San Jose-Monterey, and Rural Georgia (data from 1992 forward); and Greater California, Kentucky, Louisiana, and New Jersey (data from 2000 forward). The specific years of SEER data used for analysis were selected based on the availability of information necessary for each analytic question. For long-term trend analysis of both incidence and survival (1975-2005) we used data from the original nine SEER registries that contributed incidence data over the entire 30 year interval. For race/ethnicity specific trend analysis we restricted the analysis to the time period 1992- 2005. While the population data have been available for AA and NHW populations since 1975, data for other racial/ethnic populations have become available only since 52 1992. We calculated ALL cell type specific incidence rates using data for the years 2001-2005, a time period reflecting the most current and complete classification of ALL subtypes. Cases of ALL were defined based on the recent WHO classification using the third edition of the International Classification of Diseases for Oncology (ICD-O-3) (Fritz 2000) codes 9827-9829 and 9835-9837. A total of 6,991 cases of adult ALL were recorded in all SEER registries between the years 1975 and 2005. We excluded 87 cases who were AI/NA (n=54) or from the Rural Georgia and Alaska registries (n=13) because there were few individuals in these categories. We further excluded cases that were not confirmed through microscopic or cytology tests (n=130) and cases of unknown race/ethnicity (n=20). For the survival analysis we further excluded cases whose reporting sources were coded as autopsy- or death-certificate-only (n=3), and cases with a history of previous malignancy at the time of ALL diagnosis (n= 347). These exclusions resulted in a total of 6,774 cases in the trend analysis, 6,424 cases in the final survival analysis, and 3,465 cases diagnosed between 2001 and 2005 for the incidence analysis. 53 3.3.3 Incidence Analysis We calculated the age-adjusted annual incidence rates (AAIR) per 100,000, age- adjusted to the 2000 US standard population. Calculations were completed using SEER*Stat Version 6.4.4 statistical software (Surveillance Research Program, NCI, seer.cancer.gov/seerstat). Age-specific trends in ALL incidence rates were analyzed using joinpoint regression models (Harris 1990). Joinpoint model determines the best fitting line or set of lines to describe a data series when more than one slope is required to describe the latter. In this model, a joinpoint is a point in time at which the trend changes significantly. Each segment is a straight line in log scale that connects two joinpoints (Kleinbaum et al. 1988). We estimated the trends using Joinpoint version 3.3.1 regression software (Statistical Research and Applications Branch, NCI). The test of significance uses a Monte Carlo permutation method (Edgington 1987; Kim et al. 2000). SEER registries adopted the ICD-O-3 classification (which specifies cell of origin), in 2001 and consequently ALL cases before this year that were missing cell of origin were categorized as NOS (ICD-O-3 codes 9835 and 9827). However, ALL subtypes were identifiable for some ALL cases diagnosed from 1992 through 2000, because cell of origin was usually recorded under the “grade” variable in SEER. 54 3.3.4 Survival Analysis We used multivariate Cox proportional hazard models (Cox 1972) to estimate the hazard ratios (HR) and 95% confidence intervals (95% CI) for adult ALL all-causes mortality by race/ethnicity, sex, and ALL sub-type. All models were stratified by SEER registry and were adjusted for age, diagnosis era, and use of non-CNS radiation (radiation vs. none). We used the corrected group prognosis method described by Ghali et al. to construct weighted average adjusted survival curves based on multivariate Cox proportional hazard models (Ghali et al. 2001). We used likelihood ratio tests to calculate the P-values for trends and interactions. The reported P-values are two-sided. SAS® 9.2 (SAS Institute, Cary, NC) was used for all analyses. 3.4 Results 3.4.1 Incidence The AAIRs for each of the major racial/ethnic groups by age, sex, and ALL sub-type are presented in Table 3.1 and in Figure 3.1. Across all age categories, HW had significantly higher age-specific incidence rates compared to the other racial/ethnic groups, while AA had the lowest rates. Males have a significantly higher rate of ALL than females (AAIR male : 1.12; 95%CI: 1.07-1.17, AAIR female : 0.76; 95% CI: 0.72-0.80). 55 The highest AAIR was observed among HW males (AAIR: 1.60; 95% CI: 1.43-1.79). Among females, the AAIR in HW (AAIR: 1.28; 95% CI: 1.13-1.44) was almost two times the rate in all other racial/ethnic groups. When we examined AAIRs by ALL sub-type, the observed rate for B-cell ALL in HW (AAIR: 0.77; 95% CI: 0.69-0.87) was almost three times that of NHW (AAIR: 0.29; 95% CI: 0.27-0.32) for men and women combined. In contrast, we did not observe a statistically significant difference in the rates of T-cell ALL across racial/ethnic categories. HW had the highest frequency of B-cell sub-type (83%) among all ALL cases, while AA had the highest percentage of T-cell ALL (46%) (Figure 3.2). We further examined the age-specific rates for males and females (Figure 3.3). For both genders, the peak incidence rate of ALL was observed in the first 3-5 years of life (data not shown), which is indicated in our <20 year of age group in Figure 3.3. After this peak in early childhood, the incidence rate of ALL dropped by 3.4% per two years of age for males and 3.9% per two years for females, using data from 2001-2005. However, the age-specific incidence rate for the <20 year age group was lower in females, and their incidence rate reached its lowest point approximately 6 years earlier (age = 34) than that of the males’ (age = 40). Among males 40 years of age and older at 56 diagnosis, we observed a 3.02% increase per two years. In females, the biannual change was 2.69% for women diagnosed at age 34 and older. The lowest age-specific incidence rate for men and women combined was observed at approximately 35 years of age for each racial/ethnic group (Figure 3.4) with the exception of AA, whose lowest age- specific rate was observed at age 28. Table 3.1: Age-adjusted average incidence rates for adult acute lymphoblastic leukemia (SEER 17: 2001-2005) Total NHW AA HW A/PI N AAIR* (95%CI) N AAIR* (95%CI) N AAIR* (95%CI) N AAIR* (95%CI) N AAIR* (95%CI) Overall 346 5 .93 (.90-.96) 206 0 .87 (.83-.91) 253 .68 (.59-.77) 786 1.44 (1.33-1.56) 323 .81 (.72-.91) Age 20-29 711 .92 (.86-.99) 328 .84 (.75-.93) 59 .65 (.49-.83) 241 1.32 (1.15-1.49) 67 .74 (.57-.94) 30-39 584 .65 (.60-.71) 289 .57 (.51-.64) 42 .40 (.29-.54) 186 1.09 (.94-1.26) 60 .58 (.44-.75) 40-49 529 .67 (.61-.72) 289 .57 (.51-.64) 59 .71 (.54-.91) 124 1.14 (.95-1.36) 51 .58 (.43-.77) 50-59 522 .94 (.86-1.02) 326 .87 (.78-.97) 40 .77 (.55-1.05) 101 1.64 (1.33-1.99) 48 .80 (.59-1.06) 60-69 423 1.17 (1.06-1.29) 281 1.12 (.99-1.26) 25 .78 (.50-1.15) 75 2.09 (1.64-2.62) 40 1.03 (.73-1.40) 70-79 406 1.51 (1.36-1.66) 308 1.54 (1.37-1.72) 18 .91 (.54-1.43) 38 1.79 (1.26-2.46) 38 1.47 (1.04-2.02) 80+ 290 1.86 (1.65-2.09) 239 1.94 (1.70-2.20) 10 .99 (.47-1.81) 21 2.18 (1.35-3.33) 19 1.61 (.97-2.51) Sex Female 1490 .76 (.72-.80) 870 .68 (.64-.73) 124 .63 (.52-.75) 330 1.28 (1.13-1.44) 146 .69 (.58-.81) Male 197 5 1.12 (1.07-1.17) 119 0 1.07 (1.01-1.13) 129 .71 (.59-.86) 456 1.60 (1.43-1.79) 177 .96 (.81-1.11) Type B-Cell 979 .37 (.34-.39) 500 .29 (.27-.32) 54 .20 (.15-.26) 332 .77 (.69-.87) 88 .36 (.28-.44) T-Cell 337 .12 (.11-.14) 191 .12 (.10-.14) 39 .14 (.10-.19) 66 .14 (.10-.18) 38 .15 (.10-.20) NOS 132 0 .50 (.47-.53) 814 .47 (.44-.51) 112 .43 (.35-.52) 292 .73 (.64-.83) 84 .35 (.28-.43) * Incidence rates per 100,000 adjusted to US 2000 standard population, † Based on SEER 17: 2001-2005, Abbreviations: AAIR: Age-adjusted annual incidence rate, NHW: non-Hispanic white, AA: African-American, HW: Hispanic White, A/PI: Asian/Pacific Islander 58 Figure 3.1: Age-specific incidence rate of adult ALL subtypes (SEER: 2001-2005) 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 20 24 28 32 36 40 44 48 52 56 60 64 68 72 76 80 84 Age ‐Specific Incidense Rate B‐Cell Rate B‐Cell Trend T‐Cell Rate T‐Cell Trend F 2 Figure 3.2: A 2001-2005) 0 0.2 0.4 0.6 0.8 1 1.2 1.4 Age‐Adjusted Rate Average age e-adjusted in B‐Cell AL ncidence rate LL Subtype e of adult AL T‐Cell LL by race/ethnicity (SE NHW Black HW Asian/A 59 EER: PI F Figure 3.3: A Adult ALL a age-specific incidence ra ates by sex ( (SEER: 1992 2-2005) 60 61 Figure 3.4: Adult ALL age-specific incidence rates by race/ethnicity (SEER: 1992-2005) 62 3.4.2 Survival The characteristics of adult ALL cases included in the analysis are described in Table 3.2. Compared to other racial/ethnic groups, HW tends to be younger, diagnosed in more recent era and had a higher rate of B-ALL. Table 3.3 shows the overall and 1-year survival rates by characteristics of the SEER population including race/ethnicity, sex, and years of diagnosis. Survival was significantly poorer among AA (HR: 1.26; 95% CI: 1.09-1.46), HW (HR: 1.21; 95% CI: 1.09-1.46), and A/PI (HR: 1.18; 95% CI: 1.06- 1.32) compared to NHW for all subtypes of ALL. The 1-year and overall survival of adult ALL patients improved significantly from 1975 to 2005 (P for trend < 0.0001). The relative mortality risk was 75% higher for the period of 1975-1980 compared to 2001-2005 (HR: 1.75; 95% CI: 1.52-2.01). After adjustment for possible confounders, T-cell ALL had significantly better survival compared to B-cell ALL (HR: 0.88; 95 CI: 0.80-0.97). We further examined mortality relative risks in two age groups (Figure 3.5). Because survival is known to vary by age, we performed a survival analysis of adults by age less than 40 years at diagnosis (younger adults) or greater than 40 years at diagnosis (older adults). We picked 40 years of age as our cut point based on the observed trend in age-specific incidence rates as described above. Among adults younger than 40 years of age at diagnosis with B-cell ALL, survival was significantly poorer for AA (HR: 1.60; 95% CI: 1.02-2.43) and HW (HR: 1.53; 95% CI: 1.20-1.94) males and females compared to NHW. For adults 40 years of age and older at diagnosis, survival 63 differences between the different racial/ethnic groups were no longer statistically significant for B-cell ALL. For T-cell ALL, survival was significantly poorer for AA (HR: 1.61; 95% CI: 1.22-2.10), HW (HR: 1.49; 95% CI: 1.14-1.93) and A/PI (HR: 1.57; 95% CI: 1.13-2.13) adults as compared to NHW of all ages. A similar survival pattern by age (adults above and below age 40 years) was observed for T-cell as described for B-cell. 64 Table 3.2: Characteristic of 6424 adult ALL cases (SEER 17: 1975 – 2005) Total AA A/PI HW NHW Characteristics Category N N (%) N (%) N (%) N (%) Status Alive 1762 101 (23.0) 152 (30.7) 461 (36.6) 1048 (24.8) Dead: ALL 3960 285 (64.9) 282 (57.0) 713 (56.5) 2680 (63.4) Dead: Infection 116 10 (2.3) 8 (1.6) 13 (1.0) 85 (2.0) Dead: Other 243 20 (4.6) 16 (3.2) 20 (1.6) 187 (4.4) Dead: Unknown 222 15 (3.4) 30 (6.1) 42 (3.3) 135 (3.2) Multiple Primaries 121 8 (1.8) 7 (1.4) 12 (1.0) 94 (2.2) Age 20-29 1352 112 (25.5) 95 (19.2) 384 (30.5) 761 (18.0) 30-39 1106 79 (18.0) 99 (20.0) 293 (23.2) 635 (15.0) 40-49 947 83 (18.9) 91 (18.4) 200 (15.9) 573 (13.6) 50-59 902 59 (13.4) 67 (13.5) 157 (12.5) 619 (14.6) 60-69 836 51 (11.6) 63 (12.7) 122 (9.7) 600 (14.2) 70-79 768 37 (8.4) 55 (11.1) 78 (6.2) 598 (14.1) 80+ 513 18 (4.1) 25 (5.1) 27 (2.1) 443 (10.5) Sex Female 2678 201 (45.8) 215 (43.4) 531 (42.1) 1731 (40.9) Male 3746 238 (54.2) 280 (56.6) 730 (57.9) 2498 (59.1) Diagnosis Era 2001-2005 2451 186 (42.4) 199 (40.2) 674 (53.5) 1392 (32.9) 1996-2000 1248 79 (18.0) 105 (21.2) 303 (24.0) 761 (18.0) 1991-1995 998 68 (15.5) 75 (15.2) 174 (13.8) 681 (16.1) 1986-1990 726 49 (11.2) 52 (10.5) 58 (4.6) 567 (13.4) 1981-1985 582 33 (7.5) 41 (8.3) 31 (2.5) 477 (11.3) 1975-1980 419 24 (5.5) 23 (4.7) 21 (1.7) 351 (8.3) ALL Subtype B-ALL 2556 140 (31.9) 217 (43.8) 751 (59.6) 1448 (34.2) T-ALL 949 107 (24.4) 91 (18.4) 169 (13.4) 582 (13.8) NOS 2919 192 (43.7) 187 (37.8) 341 (27.0) 2199 (52.0) Radiation Beam radiation 1456 84 (19.1) 128 (25.9) 310 (24.6) 934 (22.1) None 4904 353 (80.4) 361 (72.9) 937 (74.3) 3253 (76.9) Unknown 64 2 (.5) 6 (1.2) 14 (1.1) 42 (1.0) Abbreviations: NHW: non-Hispanic white, AA: African-American, HW: Hispanic White, A/PI: Asian/Pacific Islander, 65 Table 3.3: Analysis of all-causes adult ALL survival (SEER 17: 1975-2005) 1-Year Survival Overall Survival Characteristics Category N (%) HR (95%CI)* HR (95%CI)* P-Value ALL Type B-Cell 2556 (39.8) 1 (Reference) 1 (Reference) T-Cell 949 (14.8) 0.92 (0.80-1.05) 0.88 (0.80-0.97) 0.013 NOS 2919 (45.4) 1.13 (1.03-1.24) 1.05 (0.97-1.13) 0.261 Race/Ethnicity NHW 4229 (65.8) 1 (Reference) 1 (Reference) AA 439 (6.8) 1.26 (1.09-1.46) 1.34 (1.19-1.50) < .0001 HW 1261 (19.6) 1.21 (1.04-1.40) 1.21 (1.10-1.32) < .0001 A/PI 495 (7.7) 1.18 (1.06-1.32) 1.16 (1.03-1.32) 0.015 Gender Female 2678 (41.7) 1 (Reference) 1 (Reference) Male 3746 (58.3) 1.09 (1.02-1.18) 1.12 (1.06-1.19) 0.0001 Year of diagnosis 2001-2005 2451 (38.2) 1 (Reference) 1 (Reference) 1996-2000 1248 (19.4) 1.18 (1.05-1.32) 1.09 (1.00-1.20) 1991-1995 998 (15.5) 1.30 (1.13-1.48) 1.22 (1.10-1.35) 1986-1990 726 (11.3) 1.40 (1.20-1.63) 1.36 (1.20-1.53) 1981-1985 582 (9.1) 1.34 (1.12-1.59) 1.40 (1.23-1.59) 1975-1980 419 (6.5) 1.81 (1.51-2.16) 1.75 (1.52-2.01) < .0001 * Variables simultaneously fitted in the multivariate Cox proportional hazard models. Models were stratified by SEER registry and were adjusted for diagnosis age (continuous), radiation therapy (yes, no) and interaction term for age and year of diagnosis Abbreviations: HR: Hazard Ratio; 95%CI: 95% Confidence Interval; AA: African-American, A/PI: Asian/Pacific Islander, HW: Hispanic White, NHW: Non-Hispanic White, 66 Table 3.4: Relative mortality rate for racial/ethnic groups by age and ALL subtype Age HR (95% CI)* ALL –Type HR (95% CI) † Race/Ethnicity <40 >40 B-Cell T-Cell NHW 1 (Reference) 1 (Reference) 1 (Reference) 1 (Reference) AA 1.80 (1.48-2.18) 1.12 (0.97-1.30) 1.40 (1.12-1.74) 1.61 (1.22-2.10) A/PI 1.32 (1.05-1.64) 1.10 (0.95-1.27) 1.11 (0.91-1.35) 1.57 (1.13-2.13) HW 1.46 (1.26-1.69) 1.06 (0.95-1.20) 1.18 (1.04-1.35) 1.49 (1.14-1.93) * Adjusted for age (continuous), sex, year of diagnosis, radiation therapy and ALL type; stratified by SEER registry † Adjusted for above variables except for ALL type Abbreviations: HR: Hazard Ratio; 95%CI: 95% Confidence Interval; AA: African-American, A/PI: Asian/Pacific Islander, HW: Hispanic White, NHW: Non-Hispanic White 67 Figure 3.5: Adjusted survival curves of adult ALL by race/ethnicity, stratified by age and ALL subtype 68 3.5 Discussion 3.5.1 Incidence In this large population-based study of adult patients with ALL, we found significantly higher AAIRs of ALL for HW than for any other racial/ethnic group. In particular, the rate of B-cell ALL was twice as high for HW adults than NHW and three times as high as AA adults. Little data is available in the literature on the variation in ALL incidence rates for adults by race/ethnicity. However, age-specific incidence rates of ALL have been previously reported as higher among HW children compared to other racial/ethnic groups among the pediatric population (McNeil et al. 2002). Our finding is consistent with these findings. The reason HW might be more susceptible to ALL remains obscure. Gallego- Arreola et al. hypothesized that a polymorphism in the CYP1A1 enzyme, which activates certain procarcinogens, may enhance susceptibility to ALL. The allele associated with higher CYP1A1 activity has been found more frequently in Mexican ALL patients compared to matched controls (Gallegos-Arreola et al. 2004; Gallegos- Arreola et al. 2008). In contrast, AA appear to be much less susceptible to ALL than other racial/ethnic groups (Landgren and Weiss 2009). The higher rates of ALL in HW in both childhood and adulthood suggest that genetic factors or early life exposures (to 69 which HW may be more frequently exposed or have greater susceptibility) play a major role in the etiology of the disease. 3.5.2 Survival Survival from ALL for all ages combined was better for NHW than all other racial/ethnic groups including AA and HW. The significant differences in survival by race/ethnicity were observed for both B-cell and T-cell ALL. We observed racial/ethnic differences in survival among younger adults (< 40 years of age at diagnosis), but found no differences in survival by race/ethnicity among the older age group. This observation suggests that there may be a fundamental difference may indeed exist between the characteristics of ALL for those diagnosed at younger and older ages (here stratified by age 40); for individuals diagnosed early, there appears to be important differences in survival by race/ethnicity, which may correspond to ALL characteristics, treatment strategy, treatment response, or compliance. In the pediatric literature, most studies suggest that AA children have poorer outcomes even when controlling for important prognostic variables such as age, white blood cell (WBC) count at presentation, and treatment era (Pui et al. 1995; Pollock et al. 2000). In a re-analysis of several Pediatric Oncology Group multicenter randomized clinical trials of B-cell childhood ALL, Pollock et al. concluded that clinical presentation, tumor 70 biology, and deviations from prescribed therapy did not explain the observed differences in survival by racial/ethnic group. Further these authors reported that the worse survival in the AA and HW children was most likely related to biological differences in chemotherapeutic response to treatment and not to compliance (Pollock et al. 2000). However, in a recent pooled analysis of multiple clinical trials conducted at St. Jude hospital, Pui et al found no difference in outcome between AA children and NHW children treated on the same regimens (Pui et al. 2003). The determinants of leukemia outcomes in the modern era are increasingly incorporated with cytogenetic information (Pui and Evans 1999; Mancini et al. 2005; Vitale et al. 2006; Moorman et al. 2007; Rowe and Goldstone 2007; Pullarkat et al. 2008). The SEER database does not include this important information and we have no data on the prevalence of adverse prognostic markers such as the bcr-abl chromosomal translocation among adults (SEER 2008), overall or by race/ethnicity. Aldrich et al. analyzed a variety of cytogenetic abnormalities among 389 HW and NHW children in Northern California and found that only TEL-AML1 translocations were significantly less common among HW than among NHW (Aldrich et al. 2006). Additional cytogenetic studies are necessary to establish whether such markers explain the variability in prognosis across racial/ethnic groups among adults. 71 3.5.3 Strengths and Weaknesses Our study is based on SEER data, a population-based registry that includes 17 geographical sites identified to represent the various ethnic, urban, rural, and socioeconomic populations in the US. The correct identification and classification of cases of Hispanic ethnicity has been an issue for SEER. Clegg et al. recently evaluated the quality of the ethnicity variable by comparing SEER data with self-reported data from 13,538 cancer patients diagnosed between 1973–2001 in the SEER—National Longitudinal Mortality Study (SEER-NLMS) linked database (Clegg et al. 2007). The overall agreement for race was excellent ( = 0.90, 95% CI = 0.88–0.91), while agreement was moderate for Hispanic ethnicity (  = 0.61, 95% CI = 0.58–0.64). The data suggested that individuals of Hispanic ethnicity were likely to be under identified in SEER. In our study if underestimation of HW in the source population was the result of higher AAIR of ALL among HW, then we would expect to observe this differential pattern of rates for both T-cell and B-cell ALL and not just B-cell ALL. In the SEER- NLMS analysis, the survival rates of HW patients were not different whether self- identification or SEER classification was used for the analysis (Clegg et al. 2007). We adjusted our survival models for potential confounders including age, gender, treatment era, ALL subtype, and SEER registry. However, other potential treatment options that may be associated with both race/ethnicity and ALL survival (e.g. 72 allogeneic stem cell transplant) are not readily available in the SEER data. We did control for history of non-CNS radiation therapy among ALL patients in our models, assuming that this variable is a marker for patients who received transplantation. Another concern was the possibility that some HW or A/PI immigrants might return to their native countries to die and thus would not be recorded as deceased in the SEER data. We could not use the place of birth to explore this issue because a high proportion of cases missed this information. As described recently (Gomez et al. 2004), we found that almost 50% of the participants had unknown places of birth. In addition, having an unknown birthplace was highly associated with being alive at the end of follow-up and was inversely associated with the year of diagnosis (data not shown), which indicates that this variable may have been mostly extracted from death certificates rather than medical records. However, this bias, if significant, would result in an underestimation of relative mortality risks in our analysis because these patients would have been reported as having longer than actual survival times. 3.6 Conclusion In this study, we found that the incidence rate of ALL among adults was higher for HW in all age groups compared to all other racial/ethnic groups. The higher incidence rates among HW were driven by the higher rates of B-cell ALL in this population compared to other racial/ethnic groups. Survival was significantly worse for AA, A/PI, and HW 73 diagnosed with ALL before the age of 40 compared to NHW; this association was true for both B-and T-cell ALL. ALL survival was similar by race/ethnicity for adults diagnosed at 40 years of age and older. This age-specific survival pattern along with the very different patterns of age-specific incidence rates, above and below 40 years of age at diagnosis, suggests that ALL in young adults may be a residual of childhood ALL and may be biologically different from ALL in older adults. 74 Bibliography Aban, M., Arslan, M., Tok, E., Tekes, S., Budak, T. and Altintas, A. (2006). "Cyp17 Genetic Polymorphism in Patients with Endometrial Hyperplasia and Cancer." Int J Gynecol Cancer 16 Suppl 1: 448-451. Aldrich, M.C., Zhang, L., Wiemels, J.L., et al. (2006). "Cytogenetics of Hispanic and White Children with Acute Lymphoblastic Leukemia in California." Cancer Epidemiol Biomarkers Prev 15(3): 578-581. Anderson, G.L., Judd, H.L., Kaunitz, A.M., et al. (2003). "Effects of Estrogen Plus Progestin on Gynecologic Cancers and Associated Diagnostic Procedures: The Women's Health Initiative Randomized Trial." JAMA 290(13): 1739-1748. Archer, D.F., Hendrix, S., Gallagher, J.C., et al. (2007). "Endometrial Effects of Tibolone." J Clin Endocrinol Metab 92(3): 911-918. Ashton, K.A., Proietto, A., Otton, G., et al. (2009). "Estrogen Receptor Polymorphisms and the Risk of Endometrial Cancer." BJOG 116(8): 1053-1061. Baker, K.S., Loberiza, F.R., Jr., Yu, H., et al. (2005). "Outcome of Ethnic Minorities with Acute or Chronic Leukemia Treated with Hematopoietic Stem-Cell Transplantation in the United States." J Clin Oncol 23(28): 7032-7042. Baxter, S.W., Choong, D.Y., Eccles, D.M. and Campbell, I.G. (2001). "Polymorphic Variation in Cyp19 and the Risk of Breast Cancer." Carcinogenesis 22(2): 347- 349. Beral, V., Bull, D. and Reeves, G. (2005). "Endometrial Cancer and Hormone- Replacement Therapy in the Million Women Study." Lancet 365(9470): 1543- 1551. Beresford, S.A. and Coker, A.L. (1989). "Pictorially Assisted Recall of Past Hormone Use in Case-Control Studies." Am J Epidemiol 130(1): 202-205. Beresford, S.A., Weiss, N.S., Voigt, L.F. and McKnight, B. (1997). "Risk of Endometrial Cancer in Relation to Use of Oestrogen Combined with Cyclic Progestagen Therapy in Postmenopausal Women." Lancet 349(9050): 458-461. 75 Bernstein, L., Allen, M., Anton-Culver, H., et al. (2002). "High Breast Cancer Incidence Rates among California Teachers: Results from the California Teachers Study (United States)." Cancer Causes Control 13(7): 625-635 . Berstein, L.M., Imyanitov, E.N., Kovalevskij, A.J., et al. (2004). "Cyp17 and Cyp19 Genetic Polymorphisms in Endometrial Cancer: Association with Intratumoral Aromatase Activity." Cancer Lett 207(2): 191-196. Berstein, L.M., Imyanitov, E.N., Suspitsin, E.N., et al. (2001). "Cyp19 Gene Polymorphism in Endometrial Cancer Patients." J Cancer Res Clin Oncol 127(2): 135-138. Brinton, L.A. and Hoover, R.N. (1993). "Estrogen Replacement Therapy and Endometrial Cancer Risk: Unresolved Issues. The Endometrial Cancer Collaborative Group." Obstet Gynecol 81(2): 265-271. Campbell, S., McQueen, J., Minardi, J. and Whitehead, M.I. (1978). "The Modifying Effect of Progestogen on the Response of the Post-Menopausal Endometrium to Exogenous Oestrogens." Postgrad Med J 54 Suppl 2: 59-64. Chang, S.C., Lacey, J.V., Jr., Brinton, L.A., et al. (2007). "Lifetime Weight History and Endometrial Cancer Risk by Type of Menopausal Hormone Use in the Nih-Aarp Diet and Health Study." Cancer Epidemiol Biomarkers Prev 16(4): 723-730. Clegg, L.X., Reichman, M.E., Hankey, B.F., et al. (2007). "Quality of Race, Hispanic Ethnicity, and Immigrant Status in Population-Based Cancer Registry Data: Implications for Health Disparity Studies." Cancer Causes Control 18(2): 177- 187. Cox, D.R. (1972). "Regression Models and Life-Tables." J Roy Stat Soc B 34(2): 187- &. Dahmoun, M., Odmark, I.S., Risberg, B., Karlsson, M.G., Pavlenko, T. and Backstrom, T. (2004). "Apoptosis, Proliferation, and Sex Steroid Receptors in Postmenopausal Endometrium before and During Hrt." Maturitas 49(2): 114- 123. 76 Diamanti-Kandarakis, E., Bartzis, M.I., Bergiele, A.T., Tsianateli, T.C. and Kouli, C.R. (2000). "Microsatellite Polymorphism (Tttta)(N) at -528 Base Pairs of Gene Cyp11alpha Influences Hyperandrogenemia in Patients with Polycystic Ovary Syndrome." Fertil Steril 73(4): 735-741. Doherty, J.A., Cushing-Haugen, K.L., Saltzman, B.S., et al. (2007). "Long-Term Use of Postmenopausal Estrogen and Progestin Hormone Therapies and the Risk of Endometrial Cancer." Am J Obstet Gynecol 197(2): 139 e131-137. Edgington, E.S. (1987). Randomization Tests. New York, M. Dekker. Excoffier, L. and Slatkin, M. (1995). "Maximum-Likelihood Estimation of Molecular Haplotype Frequencies in a Diploid Population." Mol Biol Evol 12(5): 921-927. Fielding, A.K., Richards, S.M., Chopra, R., et al. (2007). "Outcome of 609 Adults after Relapse of Acute Lymphoblastic Leukemia (All); an Mrc Ukall12/Ecog 2993 Study." Blood 109(3): 944-950. Franks, S., Gharani, N., Waterworth, D., et al. (1997). "The Genetic Basis of Polycystic Ovary Syndrome." Hum Reprod 12(12): 2641-2648. Friedenreich, C., Cust, A., Lahmann, P.H., et al. (2007). "Anthropometric Factors and Risk of Endometrial Cancer: The European Prospective Investigation into Cancer and Nutrition." Cancer Causes Control 18(4): 399-413. Fritz, A.G. (2000). International Classification of Diseases for Oncology : Icd-O. Geneva, World Health Organization. Gaasenbeek, M., Powell, B.L., Sovio, U., et al. (2004). "Large-Scale Analysis of the Relationship between Cyp11a Promoter Variation, Polycystic Ovarian Syndrome, and Serum Testosterone." J Clin Endocrinol Metab 89(5): 2408- 2413. Gabriel, S.B., Schaffner, S.F., Nguyen, H., et al. (2002). "The Structure of Haplotype Blocks in the Human Genome." Science 296(5576): 2225-2229. Gallegos-Arreola, M.P., Batista-Gonzalez, C.M., Delgado-Lamas, J.L., et al. (2004). "Cytochrome P4501a1 Polymorphism Is Associated with Susceptibility to Acute Lymphoblastic Leukemia in Adult Mexican Patients." Blood Cells Mol Dis 33(3): 326-329. 77 Gallegos-Arreola, M.P., Gonzalez-Garcia, J.R., Figuera, L.E., Puebla-Perez, A.M., Delgado-Lamas, J.L. and Zuniga-Gonzalez, G.M. (2008). "Distribution of Cyp1a1*2a Polymorphism in Adult Patients with Acute Lymphoblastic Leukemia in a Mexican Population." Blood Cells Mol Dis 41(1): 91-94. Gasteiger, E., Gattiker, A., Hoogland, C., Ivanyi, I., Appel, R.D. and Bairoch, A. (2003). "Expasy: The Proteomics Server for in-Depth Protein Knowledge and Analysis." Nucleic Acids Res 31(13): 3784-3788. Ghali, W.A., Quan, H., Brant, R., et al. (2001). "Comparison of 2 Methods for Calculating Adjusted Survival Curves from Proportional Hazards Models." JAMA 286(12): 1494-1497. Gharani, N., Waterworth, D.M., Batty, S., et al. (1997). "Association of the Steroid Synthesis Gene Cyp11a with Polycystic Ovary Syndrome and Hyperandrogenism." Hum Mol Genet 6(3): 397-402. Gomez, S.L., Glaser, S.L., Kelsey, J.L. and Lee, M.M. (2004). "Bias in Completeness of Birthplace Data for Asian Groups in a Population-Based Cancer Registry (United States)." Cancer Causes Control 15(3): 243-253. Grady, D., Gebretsadik, T., Kerlikowske, K., Ernster, V. and Petitti, D. (1995). "Hormone Replacement Therapy and Endometrial Cancer Risk: A Meta- Analysis." Obstet Gynecol 85(2): 304-313. Haiman, C.A., Hankinson, S.E., Colditz, G.A., Hunter, D.J. and De Vivo, I. (2001). "A Polymorphism in Cyp17 and Endometrial Cancer Risk." Cancer Res 61(10): 3955-3960. Haiman, C.A., Hankinson, S.E., Spiegelman, D., Brown, M. and Hunter, D.J. (2002). "No Association between a Single Nucleotide Polymorphism in Cyp19 and Breast Cancer Risk." Cancer Epidemiol Biomarkers Prev 11(2): 215-216. Haiman, C.A., Stram, D.O., Pike, M.C., et al. (2003). "A Comprehensive Haplotype Analysis of Cyp19 and Breast Cancer Risk: The Multiethnic Cohort." Hum Mol Genet 12(20): 2679-2692. Harris, J.E. (1990). "Reporting Delays and the Incidence of Aids." Journal of the American Statistical Association 85: 915-924. 78 Harris, N.L., Jaffe, E.S., Diebold, J., et al. (2000). "The World Health Organization Classification of Hematological Malignancies Report of the Clinical Advisory Committee Meeting, Airlie House, Virginia, November 1997." Mod Pathol 13(2): 193-207. Hill, D.A., Weiss, N.S., Beresford, S.A., et al. (2000). "Continuous Combined Hormone Replacement Therapy and Risk of Endometrial Cancer." Am J Obstet Gynecol 183(6): 1456-1461. Hulley, S., Furberg, C., Barrett-Connor, E., et al. (2002). "Noncardiovascular Disease Outcomes During 6.8 Years of Hormone Therapy: Heart and Estrogen/Progestin Replacement Study Follow-up (Hers Ii)." JAMA 288(1): 58-66. Jain, M.G., Rohan, T.E. and Howe, G.R. (2000). "Hormone Replacement Therapy and Endometrial Cancer in Ontario, Canada." J Clin Epidemiol 53(4): 385-391. Kadan-Lottick, N.S., Ness, K.K., Bhatia, S. and Gurney, J.G. (2003). "Survival Variability by Race and Ethnicity in Childhood Acute Lymphoblastic Leukemia." JAMA 290(15): 2008-2014. Kalwinsky, D.K., Rivera, G., Dahl, G.V., et al. (1985). "Variation by Race in Presenting Clinical and Biologic Features of Childhood Acute Lymphoblastic Leukaemia: Implications for Treatment Outcome." Leuk Res 9(6): 817-823. Key, T.J. and Pike, M.C. (1988). "The Dose-Effect Relationship between 'Unopposed' Oestrogens and Endometrial Mitotic Rate: Its Central Role in Explaining and Predicting Endometrial Cancer Risk." Br J Cancer 57(2): 205-212. Kim, H.J., Fay, M.P., Feuer, E.J. and Midthune, D.N. (2000). "Permutation Tests for Joinpoint Regression with Applications to Cancer Rates." Stat Med 19(3): 335- 351. Kleinbaum, D.G., Kupper, L.L. and Muller, K.E. (1988). Applied Regression Analysis and Other Multivariable Methods. Boston, Mass., PWS-Kent Pub. Co. Kumazawa, T., Tsuchiya, N., Wang, L., et al. (2004). "Microsatellite Polymorphism of Steroid Hormone Synthesis Gene Cyp11a1 Is Associated with Advanced Prostate Cancer." Int J Cancer 110(1): 140-144. 79 Lacey, J.V., Jr., Brinton, L.A., Lubin, J.H., Sherman, M.E., Schatzkin, A. and Schairer, C. (2005). "Endometrial Carcinoma Risks among Menopausal Estrogen Plus Progestin and Unopposed Estrogen Users in a Cohort of Postmenopausal Women." Cancer Epidemiol Biomarkers Prev 14(7): 1724-1731. Landgren, O. and Weiss, B.M. (2009). "Patterns of Monoclonal Gammopathy of Undetermined Significance and Multiple Myeloma in Various Ethnic/Racial Groups: Support for Genetic Factors in Pathogenesis." Leukemia. Langer, R.D., Landgren, B.M., Rymer, J. and Helmond, F.A. (2006). "Effects of Tibolone and Continuous Combined Conjugated Equine Estrogen/Medroxyprogesterone Acetate on the Endometrium and Vaginal Bleeding: Results of the Opal Study." Am J Obstet Gynecol 195(5): 1320-1327. Lethaby, A., Suckling, J., Barlow, D., Farquhar, C.M., Jepson, R.G. and Roberts, H. (2004). "Hormone Replacement Therapy in Postmenopausal Women: Endometrial Hyperplasia and Irregular Bleeding." Cochrane Database Syst Rev(3): CD000402. MacDonald, P.C., Edman, C.D., Hemsell, D.L., Porter, J.C. and Siiteri, P.K. (1978). "Effect of Obesity on Conversion of Plasma Androstenedione to Estrone in Postmenopausal Women with and without Endometrial Cancer." Am J Obstet Gynecol 130(4): 448-455. MacDonald, P.C. and Siiteri, P.K. (1974). "The Relationship between the Extraglandular Production of Estrone and the Occurrence of Endometrial Neoplasia." Gynecol Oncol 2(2-3): 259-263. Magyar, Z., Berkes, E., Csapo, Z. and Papp, Z. (2007). "Effect of Hormone Replacement Therapy on Postmenopausal Endometrial Bleeding." Pathol Oncol Res 13(4): 351-359. Mancini, M., Scappaticci, D., Cimino, G., et al. (2005). "A Comprehensive Genetic Classification of Adult Acute Lymphoblastic Leukemia (All): Analysis of the Gimema 0496 Protocol." Blood 105(9): 3434-3441. McCullough, M.L., Patel, A.V., Patel, R., et al. (2008). "Body Mass and Endometrial Cancer Risk by Hormone Replacement Therapy and Cancer Subtype." Cancer Epidemiol Biomarkers Prev 17(1): 73-79. 80 McKean-Cowdin, R., Feigelson, H.S., Pike, M.C., Coetzee, G.A., Kolonel, L.N. and Henderson, B.E. (2001). "Risk of Endometrial Cancer and Estrogen Replacement Therapy History by Cyp17 Genotype." Cancer Res 61(3): 848-849. McNeil, D.E., Cote, T.R., Clegg, L. and Mauer, A. (2002). "Seer Update of Incidence and Trends in Pediatric Malignancies: Acute Lymphoblastic Leukemia." Med Pediatr Oncol 39(6): 554-557; discussion 552-553. Mengarelli, A., Iori, A.P., Cerretti, R., Cerilli, L., Romano, A. and Arcese, W. (2002). "Increasing Risk of Relapse after Allogeneic Stem Cell Transplant for Adult Acute Lymphoblastic Leukemia in > or = 2nd Complete Remission Induced by Highly Intensive Chemotherapy." Haematologica 87(7): 782-784. Moorman, A.V., Harrison, C.J., Buck, G.A., et al. (2007). "Karyotype Is an Independent Prognostic Factor in Adult Acute Lymphoblastic Leukemia (All): Analysis of Cytogenetic Data from Patients Treated on the Medical Research Council (Mrc) Ukallxii/Eastern Cooperative Oncology Group (Ecog) 2993 Trial." Blood 109(8): 3189-3197. Newcomb, P.A. and Trentham-Dietz, A. (2003). "Patterns of Postmenopausal Progestin Use with Estrogen in Relation to Endometrial Cancer (United States)." Cancer Causes Control 14(2): 195-201. Olson, S.H., Bandera, E.V. and Orlow, I. (2007). "Variants in Estrogen Biosynthesis Genes, Sex Steroid Hormone Levels, and Endometrial Cancer: A Huge Review." Am J Epidemiol 165(3): 235-245. Olson, S.H., Orlow, I., Bayuga, S., et al. (2008). "Variants in Hormone Biosynthesis Genes and Risk of Endometrial Cancer." Cancer Causes Control. Paynter, R.A., Hankinson, S.E., Colditz, G.A., Kraft, P., Hunter, D.J. and De Vivo, I. (2005). "Cyp19 (Aromatase) Haplotypes and Endometrial Cancer Risk." Int J Cancer 116(2): 267-274. Pike, M.C. (1987). "Age-Related Factors in Cancers of the Breast, Ovary, and Endometrium." J Chronic Dis 40 Suppl 2: 59S-69S. Pike, M.C., Peters, R.K., Cozen, W., et al. (1997). "Estrogen-Progestin Replacement Therapy and Endometrial Cancer." J Natl Cancer Inst 89(15): 1110-1116. 81 Pike, M.C., Wu, A.H., Spicer, D.V., Lee, S. and Pearce, C.L. (2007). "Estrogens, Progestins, and Risk of Breast Cancer." Ernst Schering Found Symp Proc(1): 127-150. Pinkel, D. (1993). "Ethnicity and Survival in Children with Acute Lymphoid Leukemia." Leukemia 7 Suppl 2: S146-147. Pinkel, D. (1995). "Acute Lymphoid Leukemia Outcomes in Black and White Children." JAMA 274(5): 379-380. Pollock, B.H., DeBaun, M.R., Camitta, B.M., et al. (2000). "Racial Differences in the Survival of Childhood B-Precursor Acute Lymphoblastic Leukemia: A Pediatric Oncology Group Study." J Clin Oncol 18(4): 813-823. Pui, C.H. (2005). "Impact of Molecular Profiling and Cytogenetics in Acute Lymphoblastic Leukemia." Hematology 10 Suppl 1: 176-177. Pui, C.H., Boyett, J.M., Hancock, M.L., Pratt, C.B., Meyer, W.H. and Crist, W.M. (1995). "Outcome of Treatment for Childhood Cancer in Black as Compared with White Children. The St Jude Children's Research Hospital Experience, 1962 through 1992." JAMA 273(8): 633-637. Pui, C.H. and Evans, W.E. (1998). "Acute Lymphoblastic Leukemia." N Engl J Med 339(9): 605-615. Pui, C.H. and Evans, W.E. (1999). "Genetic Abnormalities and Drug Resistance in Acute Lymphoblastic Leukemia." Adv Exp Med Biol 457: 383-389. Pui, C.H. and Evans, W.E. (2006). "Treatment of Acute Lymphoblastic Leukemia." N Engl J Med 354(2): 166-178. Pui, C.H., Robison, L.L. and Look, A.T. (2008). "Acute Lymphoblastic Leukaemia." Lancet 371(9617): 1030-1043. Pui, C.H., Sandlund, J.T., Pei, D., et al. (2003). "Results of Therapy for Acute Lymphoblastic Leukemia in Black and White Children." JAMA 290(15): 2001- 2007. 82 Pullarkat, V., Slovak, M.L., Kopecky, K.J., Forman, S.J. and Appelbaum, F.R. (2008). "Impact of Cytogenetics on the Outcome of Adult Acute Lymphoblastic Leukemia: Results of Southwest Oncology Group 9400 Study." Blood 111(5): 2563-2572. Pullen, J., Shuster, J.J., Link, M., et al. (1999). "Significance of Commonly Used Prognostic Factors Differs for Children with T Cell Acute Lymphocytic Leukemia (All), as Compared to Those with B-Precursor All. A Pediatric Oncology Group (Pog) Study." Leukemia 13(11): 1696-1707. Razavi, P., Pike, M.C., Horn-Ross, P.L., Templeman, C., Bernstein, L. and Ursin, G. (2009). "Long-Term Postmenopausal Hormone Therapy and Endometrial Cancer." Cancer Epidemiol Biomarkers Prev In Press. Rowe, J.M. and Goldstone, A.H. (2007). "How I Treat Acute Lymphocytic Leukemia in Adults." Blood 110(7): 2268-2275. SEER (2008). Surveillance, Epidemiology, and End Results (Seer) Program, Seer*Stat Database: Incidence - Seer 17 Regs Limited-Use, Nov 2007 Sub (1973-2005) <Katrina/Rita Population Adjustment> - Linked to County Attributes - Total U.S., 1969-2005 Counties, National Cancer Institute, Dccps, Surveillance Research Program, Cancer Statistics Branch, Released April 2008, Based on the November 2007 Submission. Serna, D.S., Lee, S.J., Zhang, M.J., et al. (2003). "Trends in Survival Rates after Allogeneic Hematopoietic Stem-Cell Transplantation for Acute and Chronic Leukemia by Ethnicity in the United States and Canada." J Clin Oncol 21(20): 3754-3760. Setiawan, V.W., Cheng, I., Stram, D.O., et al. (2006). "A Systematic Assessment of Common Genetic Variation in Cyp11a and Risk of Breast Cancer." Cancer Res 66(24): 12019-12025. Setiawan, V.W., Doherty, J.A., Shu, X.O., et al. (2009). "Two Estrogen-Related Variants in Cyp19a1 and Endometrial Cancer Risk: A Pooled Analysis in the Epidemiology of Endometrial Cancer Consortium." Cancer Epidemiol Biomarkers Prev 18(1): 242-247. 83 Setiawan, V.W., Hankinson, S.E., Colditz, G.A., Hunter, D.J. and De Vivo, I. (2004). "Estrogen Receptor Beta (Esr2 ) Polymorphisms and Endometrial Cancer (United States)." Cancer Causes Control 15(6): 627-633. Siiteri, P.K. (1981). "Extraglandular Oestrogen Formation and Serum Binding of Oestradiol: Relationship to Cancer." J Endocrinol 89 Suppl: 119P-129P. Silverman, L.B., Gelber, R.D., Dalton, V.K., et al. (2001). "Improved Outcome for Children with Acute Lymphoblastic Leukemia: Results of Dana-Farber Consortium Protocol 91-01." Blood 97(5): 1211-1218. Stefanick, M.L. (2005). "Estrogens and Progestins: Background and History, Trends in Use, and Guidelines and Regimens Approved by the Us Food and Drug Administration." Am J Med 118 Suppl 12B: 64-73. Stram, D.O., Haiman, C.A., Hirschhorn, J.N., et al. (2003). "Choosing Haplotype- Tagging Snps Based on Unphased Genotype Data Using a Preliminary Sample of Unrelated Subjects with an Example from the Multiethnic Cohort Study." Hum Hered 55(1): 27-36. Sturdee, D.W., Ulrich, L.G., Barlow, D.H., et al. (2000). "The Endometrial Response to Sequential and Continuous Combined Oestrogen-Progestogen Replacement Therapy." BJOG 107(11): 1392-1400. Thomas, X., Boiron, J.M., Huguet, F., et al. (2004). "Efficacy of Granulocyte and Granulocyte-Macrophage Colony-Stimulating Factors in the Induction Treatment of Adult Acute Lymphoblastic Leukemia: A Multicenter Randomized Study." Hematol J 5(5): 384-394. Tworoger, S.S., Chubak, J., Aiello, E.J., et al. (2004). "Association of Cyp17, Cyp19, Cyp1b1, and Comt Polymorphisms with Serum and Urinary Sex Hormone Concentrations in Postmenopausal Women." Cancer Epidemiol Biomarkers Prev 13(1): 94-101. Vitale, A., Guarini, A., Ariola, C., et al. (2006). "Adult T-Cell Acute Lymphoblastic Leukemia: Biologic Profile at Presentation and Correlation with Response to Induction Treatment in Patients Enrolled in the Gimema Lal 0496 Protocol." Blood 107(2): 473-479. 84 Walters, T.R., Bushore, M. and Simone, J. (1972). "Poor Prognosis in Negro Children with Acute Lymphocytic Leukemia." Cancer 29(1): 210-214. Weiderpass, E., Adami, H.O., Baron, J.A., et al. (1999). "Risk of Endometrial Cancer Following Estrogen Replacement with and without Progestins." J Natl Cancer Inst 91(13): 1131-1137. Whitehead, M.I., Campbell, S., Dyer, G., et al. (1978). "Progestogen Modification of Endometrial Histology in Menopasual Women." Br Med J 2(6152): 1643-1644. Whitehead, M.I., King, R.J., McQueen, J. and Campbell, S. (1979). "Endometrial Histology and Biochemistry in Climacteric Women During Oestrogen and Oestrogen/Progestogen Therapy." J R Soc Med 72(5): 322-327. Yeoh, E.J., Ross, M.E., Shurtleff, S.A., et al. (2002). "Classification, Subtype Discovery, and Prediction of Outcome in Pediatric Acute Lymphoblastic Leukemia by Gene Expression Profiling." Cancer Cell 1(2): 133-143. Zaykin, D.V., Westfall, P.H., Young, S.S., Karnoub, M.A., Wagner, M.J. and Ehm, M.G. (2002). "Testing Association of Statistically Inferred Haplotypes with Discrete and Continuous Traits in Samples of Unrelated Individuals." Hum Hered 53(2): 79-91. Zheng, W., Gao, Y.T., Shu, X.O., et al. (2004). "Population-Based Case-Control Study of Cyp11a Gene Polymorphism and Breast Cancer Risk." Cancer Epidemiol Biomarkers Prev 13(5): 709-714.

Abstract (if available)

Abstract This dissertation consists of three chapters:

Linked assets

University of Southern California Dissertations and Theses

Conceptually similar

PDF

Genes and hormonal factors involved in the development or recurrence of breast cancer

PDF

Genetic and environmental risk factors for childhood cancer

PDF

Genetic risk factors in multiple myeloma

PDF

Pre-B cell receptor signaling in acute lymphoblastic leukemia

PDF

DEXA measures of body fat percentage and acute phase proteins among breast cancer survivors

PDF

The role of steroid hormones in the etiology of urologic diseases

PDF

Associations between exercise and quality of life in adult survivors of non-Hodgkin's lymphoma and colorectal cancer

PDF

The role of survivin in drug resistant pediatric acute lymphoblastic leukemia

PDF

Tracking human acute lymphoblastic leukemia cell clones in xenograft mouse models

PDF

Randomized clinical trial generalizability and outcomes for children and adolescents with high-risk acute lymphoblastic leukemia

PDF

Molecular signatures underlying intercellular differences in leukemia progression and chemotherapy response

PDF

The role of adipocyte-derived free-fatty acids in acute lymphoblastic leukemia

PDF

Breast cancer in the multiethnic cohort study: Genetic (prolactin pathway genes) and environmental (hormone therapy) factors

PDF

The impact of visual field impairment on health related quality of life: a population-based study

PDF

Examining the relationship between common genetic variation, type 2 diabetes and prostate cancer risk in the multiethnic cohort

PDF

Factors that influence mammographic density: role of estrogen metabolism genes, biomarkers of inflammation, and lifestyle

PDF

The effects of tobacco exposure on hormone levels and breast cancer risk among young women

PDF

Postmenopausal hormone therapy, risk of heart disease and total mortality among women in the California Teachers Study

PDF

Role of integrin α4 in drug resistant acute lymphoblastic leukemia

PDF

The influence of DNA repair genes and prenatal tobacco exposure on childhood acute lymphoblastic leukemia risk: a gene-environment interaction study

Asset Metadata

Creator Razavi, Pedram (author)

Core Title Hormonal and genetic risk factors of endometrial cancer and trends in incidence and survival of adult acute lymphoblastic leukemia

School Keck School of Medicine

Degree Doctor of Philosophy

Degree Program Epidemiology

Publication Date 11/24/2009

Defense Date 09/17/2009

Publisher University of Southern California (original), University of Southern California. Libraries (digital)

Tag acute lymphoblastic leukemia,body mass index,endometrial cancer,Estrogen,Ethnicity,haplotype,Hispanic,incidence eate,OAI-PMH Harvest,postmenopausal hormone therapy,progestin,Race,single nucleotide polymorphism,Survival

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Ursin, Giske (committee chair), McKean-Cowdin, Roberta (committee member), Pike, Malcolm C. (committee member), Spicer, Darcy V. (committee member)

Creator Email prazavi@gmail.com,prazavi@usc.edu

Permanent Link (DOI) https://doi.org/10.25549/usctheses-m2763

Unique identifier UC1150184

Identifier etd-Razavi-3356 (filename),usctheses-m40 (legacy collection record id),usctheses-c127-278284 (legacy record id),usctheses-m2763 (legacy record id)

Legacy Identifier etd-Razavi-3356.pdf

Dmrecord 278284

Document Type Dissertation

Rights Razavi, Pedram

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