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. Author manuscript; available in PMC: 2025 Feb 15.
Published in final edited form as: Cancer. 2023 Oct 27;130(4):576–587. doi: 10.1002/cncr.35071

Pathogenic Germline Variants in Patients with Endometrial Cancer of Diverse Ancestry

Ying L Liu 1,2, Sushmita Gordhandas 3, Kanika Arora 4, Eric Rios-Doria 3, Karen A Cadoo 5, Amanda Catchings 1, Anna Maio 6, Yelena Kemel 6, Margaret Sheehan 1, Erin Salo-Mullen 1, Qin Zhou 7, Alexia Iasonos 7, Jian Carrot-Zhang 8, Beryl Manning-Geist 3, Tiffany Sia 3, Pier Selenica 4, Chad Vanderbilt 4, Maksym Misyura 4, Alicia Latham 1,2, Chaitanya Bandlamudi 4, Michael F Berger 4, Jada G Hamilton 1,9,10, Vicky Makker 1,2, Nadeem R Abu-Rustum 3,11, Lora H Ellenson 4, Kenneth Offit 1,2, Diana L Mandelker 4, Zsofia Stadler 1,2, Britta Weigelt 4, Carol Aghajanian 1,2, Carol Brown 3,11
PMCID: PMC10922155  NIHMSID: NIHMS1939756  PMID: 37886874

Abstract

Background:

Racial disparities in outcomes exist in endometrial cancer (EC). The contribution of ancestry-based variations in germline pathogenic variants (gPVs) is unknown.

Methods:

Germline assessment of ≥76 cancer predisposition genes was performed in patients with EC undergoing tumor-normal MSK-IMPACT sequencing from 1/1/15–6/30/21. Self-reported race/ethnicity and Ashkenazi Jewish (AJ) ancestry data classified patients into groups. Genetic ancestry was inferred from MSK-IMPACT. Rates of gPV and genetic counseling were compared by ancestry.

Results:

Among 1,625 patients with EC, 216 (13%) had gPVs; 15 had >1 gPV. Rates of gPV varied by self-reported ancestry (AJ, 40/202 [20%]; Asian, 15/124 [12%]; Black/African American (AA), 12/171 [7.0%]; Hispanic, 15/124 [12%]; Non-Hispanic (NH)-White, 129/927 [14%]; missing, 5/77 [6.5%]; p=0.009], with similar findings by genetic ancestry (p<0.001). We observed a lower likelihood of gPVs in patients of Black/AA (OR, 0.44; 95% CI: 0.22–0.81) and African (AFR) ancestry (OR, 0.42; 95% CI: 0.18–0.85) and a higher likelihood in patients of ASJ genetic ancestry (OR, 1.62; 95% CI; 1.11–2.34) compared with patients of NH-White/European ancestry, even after adjustment for age and molecular subtype. Somatic landscape influenced gPVs with lower rates of microsatellite instability-high tumors in patients of Black/AA and AFR ancestry. Among those with newly identified gPVs (n=114), 102 (89%) were seen for genetic counseling, with lowest rates among Black/AA (75%) and AFR patients (67%).

Conclusions:

In those with EC, gPV and genetic counseling varied by ancestry, with lowest rates among Black/AA and AFR patients, potentially contributing to disparities in outcomes given implications for treatment and cancer prevention.

Keywords: genetic testing, endometrial cancer, ancestry, race, disparities, germline finding

Precis:

Among 1,625 patients with endometrial cancer, 216 (13%) had germline pathogenic variants (gPV), and rates of gPV and subsequent genetic counseling varied by both self-reported and inferred genetic ancestry, with lowest rates among Black/AA and African (AFR) patients. These differences could potentially contribute to disparities in outcomes given implications for treatment and cancer prevention and should be further investigated.

Plain Language Summary:

Black women with endometrial cancer do worse than white women, and there are many reasons for this disparity. Certain genetic changes from birth (mutations) can increase the risk of cancer, and it’s unknown if rates of these changes are different between different ancestry groups. We studied genetic mutations in 1,625 diverse women with endometrial cancer and found the lowest rates of mutations and genetic counseling in Black and African ancestry women. This could affect their treatment options as well as their families and may make disparities worse.

Introduction

Endometrial cancer (EC) is the most common gynecological malignancy in the US,1 with rising incidence after decades of decline.2 EC represents a heterogeneous group of diseases that can be stratified into four molecular subtypes based on genomic characteristics, with variable outcomes: POLE (polymerase epsilon) ultramutated, microsatellite instability-high (MSI-H), copy number-low (CN-L), and copy number-high (CN-H).3

Disparities in EC outcomes by self-reported race/ethnicity have been well described. Black/African American (AA) women have 2–3 times worse survival compared with White women,4 a trend that is worsening.5 Biological and social determinants of health may play a role in these disparities.6 Black/AA women are more likely to present with aggressive EC subtypes compared with White women.7 Ongoing studies are exploring genomic differences in tumors from patients of diverse ancestry. However, little is known about germline differences by ancestry, as well as subsequent genetic counseling, in patients with EC and their contribution to disparate survival outcomes.

Inherited predisposition is present in approximately 5–15% of ECs, with enrichment among certain molecular subtypes (MSI-H and CN-H).8–12 Lynch Syndrome (LS) is the most common inherited syndrome and is associated with mismatch repair-deficient (MMRd) and MSI-H ECs.13,14 In patients with serous and CN-H ECs, germline pathogenic variants (gPVs) in BRCA1/2 and other genes involved in homologous recombination (HR) have been identified.9,15,16 Additionally, accumulating data suggest a modest increased risk of serous/CN-H ECs in those with BRCA1/2 gPVs.17,18 However, the prevalence and distribution of germline findings in unselected patients with EC across diverse ancestry groups is unknown.

Disparities in referral/uptake for genetic counseling, completion of genetic testing, and subsequent utilization of cancer screening and prevention by race/ethnicity, which affect not only patients but their families, are well characterized in the hereditary breast and ovarian cancer literature.19-21 Across cancer types, in patients who undergo paired somatic/germline assessment as part of routine oncologic care (mainstreaming),22,23 self-identified Black/AA patients are less likely to undergo recommended counseling for germline findings compared with non-Hispanic (NH) White patients.24 However, there is a lack of data regarding racial/ethnic disparities in relation to genetic testing and follow-up specifically in patients with EC, a cancer type where racial disparities in outcomes are particularly stark.25

We sought to characterize the spectrum of gPVs in patients with EC by ancestry (self-reported and genetic inference), while correlating with tumor phenotype, and to determine disparities in subsequent genetic counseling.

Methods

Patient Selection

We included patients with histologically confirmed EC (excluding uterine sarcomas) who consented to clinical tumor-normal sequencing using Memorial Sloan Kettering Cancer Center (MSK)-Integrated Mutation Profiling of Actionable Cancer Targets (MSK-IMPACT) between 1/1/2015 and 6/30/2021,26,27 inclusive of germline analysis of 76–90 genes.28 MSK-IMPACT sequencing has been offered to all patients with EC as part of standard of care since 2015. For this study, molecular pathologists or clinical molecular geneticists reviewed sequencing results to define pathogenic/likely pathogenic variants.29 gPVs were classified as high (relative risk [RR] >4), moderate (RR, 2–4), low (RR<2), or uncertain penetrance, or recessive as previously described,30-32 and details of specific variants have been previously published.9 Variants of uncertain significance were not reported. This study was approved by the Institutional Review Board of MSK under IRB #12–245.

Clinicopathologic data and genetic counseling follow-up

Clinical information, including age at diagnosis, body mass index (BMI), disease stage, smoking history, race/ethnicity, preferred language, and self-reported Ashkenazi Jewish (AJ) ancestry, were abstracted from the medical record. Pathology reports were reviewed for histology, tumor grade, MMR immunohistochemistry, and MLH1 promoter hypermethylation status by expert gynecologic pathologists. Data on genetic counseling follow-up were obtained from clinic databases and analyzed up until 12/2019, at which time the clinic was converted to telemedicine given the escalating COVID-19 pandemic.

Molecular data and biallelic gPV inactivation

Genomic features, including MSI status based on MSIsensor score, were obtained from MSK-IMPACT, as previously described.33,34 Tumors with an MSIsensor score ≥10 were considered MSI-H, <10 and ≥3 MSI-indeterminate, and <3 microsatellite stable (MSS).35 ECs were classified into four molecular subtypes (POLE, MSI, CN-H and CN-L) using a surrogate model, as previously described.33,34,36 Loss of heterozygosity (LOH) in tumors at the gPV locus was assessed using the FACETS algorithm.37 Biallelic inactivation was defined as a loss of the wild-type allele in the tumor at the locus of a gPV, presence of a second somatic pathogenic alteration, or in the case of MLH1 gPV, promoter hypermethylation. Patients with >1 gPV and discordant LOH status were considered biallelic if LOH was present at the high-penetrance gPV.

Self-reported ancestry

Self-reported data on race/ethnicity and AJ ancestry were used to classify patients into mutually exclusive ancestry groups: NH-White, AJ, Non-White (Asian, Black/AA, Hispanic, or Other), or unknown. All patients self-identifying as AJ or Hispanic were classified as such, regardless of race, and all patients identifying as American Indian/Alaskan Native, Native Hawaiian or Other Pacific Islander were classified as Other, as previously described.24

Ancestry inference

Genetic ancestry was inferred from MSK-IMPACT clinical sequencing panel data, as previously described.38 Briefly, we ran ADMIXTURE v1.339 in supervised mode using the 1000 Genomes Project40 (1KGP) cohort as reference to infer ancestral proportions of African (AFR), European (EUR), East Asian (EAS), Native American (NAM), and South Asian (SAS) populations; genetic AJ ancestry (ASJ) was added recently. Patients who had an ancestral fraction of >0.8 for any single population were assigned that population label, otherwise they were considered admixed (Mixed).

Statistical analyses

Clinicopathologic variables, gPV rate, tumor characteristics, and genetic counseling follow-up were reported using summary statistics and stratified by ancestry, self-reported and genetic, at the patient level. Associations between continuous clinicopathologic variables and ancestry were performed using non-parametric tests. Univariate and multivariable logistic regression models were built to examine associations between presence of gPV and ancestry without/with clinical covariates. All tests were two-sided, and p<0.05 was considered statistically significant. All statistical analyses were performed using R 4.2.2 (https://www.R-project.org/).

Results

Patient characteristics and ancestry

Of 1,625 patients with EC who underwent MSK-IMPACT between 1/2015 and 6/2021, inclusive of germline assessment, 1,129 (69%) self-identified as NH-White (AJ, 202 [12%]; non-AJ, 927 [57%]), 419 (26%) as Non-White (Asian, 124 [8%]; Black/AA, 171 [10%]; and Hispanic, 124 [8%]), and 77 had missing data; no patients identified as “Other.” Genetic ancestry correlated well with self-reported ancestry. Most (84%) patients who self-reported Asian ancestry were of EAS (75/124; 60%) or SAS (29/124; 24%) descent, and most patients who identified as Hispanic were of Mixed ancestry (93/124; 75%). Genetic ancestry provided further insight into all but 2 patients with missing self-reported ancestry. Among the 171 patients who identified as Black/AA, 122 (71%) had ≥80% AFR ancestry, 45 (26%) were classified as Mixed, 4 (2.3%) had missing data, and none were classified as EUR. Of note, 2 patients who identified as NH-White were classified as AFR using inference methods (Figure 1/2).

Figure 1: Patient Selection by Self-Reported Ancestry and Comparison with Genetic Ancestry.

Figure 1:

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Figure depicts breakdown of endometrial cancer cohort by self-reported and genetic ancestry and overlap between the two measures.

AJ, self-reported Ashkenazi Jewish; NH, non-Hispanic; AA, African American; AFR, African; EUR, European; EAS, East Asian; NAM, Native American; SAS, South Asian; ASJ, genetic Ashkenazi Jewish

Figure 2: Correlation of Self-Reported and Genetic Ancestry and Flow of Patients with Endometrial Cancer with Newly Identified Germline Pathogenic Variants and Genetics Follow-up.

Figure 2:

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Sankey plot shows the flow of patients from self-reported ancestry groups into genetic ancestry groups and then identifies those with newly identified gPVs and available follow-up data and then depicts the proportion of patients who had subsequent genetic counseling. Note: Among the 102 patients with gPV and unavailable/missing CGS follow-up data due to the 2020 transition to telemedicine in response to the COVID-19 pandemic, 22 had previously known about their result, with 17 patients (77%) having gPV in a high penetrance gene, most commonly BRCA1/2 or Lynch Syndrome.

AJ – self-reported Ashkenazi Jewish, AA – African American, AFR – African, EUR – European, EAS – East Asian, NAM – Native American, SAS – South Asian, ASJ – genetic Ashkenazi Jewish, gPV – germline pathogenic variant, CGS – Clinical Genetics Service, EC – endometrial cancer, ADM – Admixed/Mixed

Patients who identified as Asian or Hispanic were least likely to select English as their preferred language (86% and 75%, respectively; p<0.001). Age at diagnosis, smoking, and BMI varied significantly by self-reported ancestry (p<0.001). Stage, histology, and molecular subtype varied significantly by self-reported ancestry (p<0.001). Patients who identified as Black/AA had the highest rates of stage IV disease at diagnosis (21%), serous/carcinosarcoma (33%/20%) histology, and CN-H tumors (67%), as well as the lowest rate of MSI-H tumors (15%) (Table 1). Findings were comparable by genetic ancestry, with some differences in BMI/obesity, smoking history, and preferred language between the EAS and SAS groups (Supplementary Table 1).

Table 1:

Clinical Characteristics of Patients with Endometrial Cancer with Germline Genetic Analyses by Self-Reported Ancestry

Characteristic Overall N=1,625 AJ n=202 Asian n=124 Black/AA n=171 Hispanic n=124 NH-White n=927 Missing n=77 p*
Preferred Language <0.001
 English 1,530 (95%) 198 (99%) 107 (86%) 168 (99%) 91 (75%) 895 (97%) 71
 Non-English 87 (5.4%) 3 (1.5%) 17 (14%) 2 (1.2%) 31 (25%) 31 (3.3%) 3
 Missing 8 1 0 1 2 1 3
Age at Diagnosis, years <0.001
 Median (range) 63 (25–96) 66 (29–93) 57 (26–84) 64 (35–87) 62 (27–90) 63 (25–96) 66 (35–89)
 Mean (SD) 63 (10) 65 (11) 56 (12) 64 (9) 61 (12) 63 (10) 63 (10)
Age at Diagnosis, years <0.001
 ≤50 180 (11%) 18 (8.9%) 34 (27%) 16 (9.4%) 20 (16%) 84 (9.1%) 8
 50+ 1,445 (89%) 184 (91%) 90 (73%) 155 (91%) 104 (84%) 843 (91%) 69
Smoking History <0.001
 Ever Smoker 535 (33%) 80 (40%) 23 (19%) 43 (25%) 20 (17%) 346 (38%) 23
 Never Smoker 1,067 (67%) 121 (60%) 99 (81%) 128 (75%) 101 (83%) 570 (62%) 48
 Missing 23 1 2 0 3 11 6
BMI, kg/m2 <0.001
 Median (range) 29.6 (15.3–67.6) 27.9 (16.5–52.8) 26.1 (15.3–42.8) 32.7 (18.4–53.8) 31.6 (16.9–56.1) 29.9 (16.9–67.6) 28.2 (16.3–48.9)
 Mean (SD) 31.1 (8.2) 29.5 (6.8) 26.6 (5.5) 33.4 (7.0) 32.2 (8.1) 31.5 (8.8) 29.8 (7.4)
 Missing 17 0 2 0 5 4 6
Obesity <0.001
 Obese (BMI ≥30 kg/m2) 778 (48%) 82 (41%) 29 (24%) 112 (65%) 67 (56%) 460 (50%) 28
 Not Obese (BMI <30 kg/m2) 830 (52%) 120 (59%) 93 (76%) 59 (35%) 52 (44%) 463 (50%) 43
 Missing 17 0 2 0 5 4 6
FIGO Stage <0.001
 I 972 (64%) 140 (73%) 68 (60%) 81 (52%) 71 (62%) 575 (66%) 37
 II 64 (4.2%) 3 (1.6%) 10 (8.8%) 9 (5.7%) 7 (6.1%) 30 (3.4%) 5
 III 283 (19%) 24 (12%) 19 (17%) 34 (22%) 23 (20%) 170 (19%) 13
 IV 198 (13%) 26 (13%) 16 (14%) 33 (21%) 13 (11%) 100 (11%) 10
 Missing 108 9 11 14 10 52 12
Histology <0.001
 Endometrioid grade 1/2 800 (52%) 95 (50%) 69 (59%) 34 (21%) 68 (58%) 503 (56%) 31
 Endometrioid grade 3 152 (9.8%) 26 (14%) 9 (7.8%) 9 (5.7%) 12 (10%) 94 (11%) 2
 Serous 218 (14%) 19 (9.9%) 17 (15%) 53 (33%) 11 (9.3%) 107 (12%) 11
 Clear Cell 46 (3.0%) 7 (3.7%) 3 (2.6%) 4 (2.5%) 6 (5.1%) 22 (2.5%) 4
 Carcinosarcoma 168 (11%) 21 (11%) 11 (9.5%) 32 (20%) 11 (9.3%) 83 (9.3%) 10
 Undiff/Dediff 33 (2.1%) 3 (1.6%) 1 (0.9%) 5 (3.1%) 5 (4.2%) 18 (2.0%) 1
 Mixed/High-grade NOS 131 (8.5%) 20 (10%) 6 (5.2%) 22 (14%) 5 (4.2%) 67 (7.5%) 11
 Missing 77 11 8 12 6 33 7
Molecular Subtype <0.001
 POLE 106 (6.6%) 17 (8.4%) 11 (8.9%) 2 (1.2%) 12 (9.7%) 58 (6.3%) 6
 MSI-H 413 (26%) 62 (31%) 24 (19%) 25 (15%) 31 (25%) 256 (28%) 15
 CN-H 542 (34%) 59 (29%) 33 (27%) 114 (67%) 34 (27%) 268 (29%) 34
 CN-L 546 (34%) 60 (30%) 54 (44%) 26 (15%) 47 (38%) 337 (36%) 22
 Unclassifiable 18 (1.1%) 4 (2.0%) 2 (1.6%) 4 (2.3%) 0 (0%) 8 (0.9%) 0
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AJ, self-reported Ashkenazi Jewish; AA, African American; NH, non-Hispanic; BMI, body mass index; Undiff, undifferentiated; Dediff, dedifferentiated; NOS, not otherwise specified; POLE, polymerase epsilon ultramutated; CN-H, copy number-high; CN-L, copy number-low; MSI-H, microsatellite instability-high

*

Kruskal-Wallis rank sum test; Fisher’s exact test for count data with simulated p-value (based on 2000 replicates). Missing values were excluded from p-value calculations

Germline landscape by ancestry

gPVs were observed in 216 (13%) of 1,625 patients (high, 73 (34%); moderate, 36 (17%); and low/uncertain/recessive penetrance, 107 (49%)); 15 patients had >1 gPV. Rates of gPV varied significantly by self-reported ancestry, with the lowest rate among Black/AA patients (AJ, 40 [20%]; Asian, 15 [12%]; Black/AA, 12 [7.0%]; Hispanic, 15 [12%]; and NH-White, 129 [14%]; p=0.009]. Rates of gPV also varied significantly by genetic ancestry, with the lowest rate among patients of AFR ancestry (AFR, 9 [6.4%]; ASJ, 53 [19%]; EAS, 9 [11%]; EUR, 112 [13%]; NAM, 2 [40%]; SAS, 7 [20%]; and Mixed, 17 [8.6%]; p<0.001; Figure 3/Supplementary Tables 2 and 3). Gene penetrance did not vary by self-reported (p=0.88) or genetic ancestry (p=0.47; Supplementary Tables 2 and 3). Among self-reported Black/AA patients, there were 13 gPVs among 12 patients; 5 gPVs had biallelic inactivation within the tumor (2 MSH2, 1 MSH6, 1 BRCA2, and 1 FANCA) (Supplementary Table 4).

Figure 3: Prevalence of Germline Pathogenic Variants and Subsequent Genetic Counseling by Ancestry, Self-Reported and Genetic.

Figure 3:

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The figure depicts the prevalence of gPVs overall and by self-reported (A) and genetic (B) ancestry, stratified by gene penetrance (high, moderate, low/uncertain/recessive), and rates of subsequent genetic counseling for patients with newly identified gPVs by ancestry, genetic and self-reported (C). Not depicted are 5 patients of NAM genetic ancestry, of whom 2/5 (20%) had 1 high and 1 low/uncertain/recessive gPV.

gPV – germline pathogenic variant, AFR – African, EUR – European, EAS – East Asian, NAM – Native American, SAS – South Asian, ASJ – genetic Ashkenazi Jewish, AA – African American, NH – Non-Hispanic, AJ – self-reported Ashkenazi Jewish

Logistic regression models of gPV

Patients with gPVs had a younger age at diagnosis and differential distribution of tumor histology and molecular subtype compared to those without gPVs (p<0.05; Supplementary Table 5). On univariate analyses, younger age (OR, 0.98; 95% CI: 0.97–0.99; p=0.005), grade 3 endometrioid (OR, 1.67; 95% CI: 1.04–2.61; p=0.03), clear cell histology (OR, 2.23; 95% CI: 1.05–4.39; p=0.03), and MSI-H molecular subtype (OR, 1.97; 95% CI: 1.36–2.85; p<0.001) were associated with increased likelihood of harboring a gPV (Supplementary Table 6).

On univariate analyses, self-reported Black/AA ancestry (OR, 0.47; 95% CI: 0.24–0.83; p=0.012) and AFR ancestry (OR, 0.45; 95% CI: 0.21–0.86; p=0.001) were associated with lower likelihood of harboring a gPV, and self-reported AJ ancestry (OR, 1.53; 95% CI: 1.02–2.25; p=0.012) and genetic ASJ ancestry (OR, 1.57; 95% CI: 1.09–2.23; p=0.001) were associated with increased likelihood of harboring a gPV compared with NH-White and EUR ancestry, respectively (Supplementary Table 6). On multivariable models, these findings persisted even after adjustment for age at diagnosis and molecular subtype, and Black/AA (OR, 0.44; 95% CI: 0.22–0.81; p=0.015) and AFR ancestry (OR, 0.42; 95% CI: 0.18–0.85; p=0.001) were associated with lower likelihood of harboring a gPV compared with NH-White and EUR ancestry, respectively. After adjustment for covariates, only ASJ genetic ancestry was associated with increased likelihood of harboring a gPV compared with EUR ancestry (OR, 1.62; 95% CI: 1.11–2.34; p=0.001; Table 2).

Table 2:

Multivariable Logistic Regression Models for Germline Pathogenic Variant by Self-Reported and Genetic Ancestry

Self-reported Ancestry
OR of gPV 95% CI p
Age at Diagnosis (continuous) 0.98 0.96, 0.99 0.007
Molecular Subtype 0.002
 CN-L 1.00 —
 POLE 0.90 0.43, 1.73
 MSI-H 1.99 1.37, 2.92
 CN-H 1.40 0.93, 2.11
Ancestry, Self-reported 0.015
 NH-White 1.00 —
 Black/AA 0.44 0.22, 0.81
 Asian 0.76 0.40, 1.35
 Hispanic 0.85 0.46, 1.47
 AJ 1.49 0.98, 2.23
Genetic Ancestry
OR of gPV 95% CI p
Age at Diagnosis (continuous) 0.97 0.96, 0.99 0.001
Molecular Subtype 0.001
 CN-L 1.00 —
 POLE 1.01 0.49, 1.92
 MSI-H 2.09 1.43, 3.07
 CN-H 1.40 0.92, 2.13
Ancestry, Genetic 0.001
 EUR 1.00 —
 AFR 0.42 0.18, 0.85
 ASJ 1.62 1.11, 2.34
 EAS 0.67 0.29, 1.38
 NAM 4.62 0.60, 28.6
 SAS 1.45 0.56, 3.28
 Mixed 0.61 0.34, 1.03
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OR, odds ratio; gPV, germline pathogenic variant; CI, confidence interval; CN-L, copy number-low; POLE, polymerase epsilon ultramutated; MSI-H, microsatellite instability-high; CN-H, copy number-high; NH, non-Hispanic; AA, African American; AJ, self-reported Ashkenazi Jewish; AFR, African; EUR, European; EAS, East Asian; NAM, Native American; SAS, South Asian; ASJ, genetic Ashkenazi Jewish

Germline-somatic interactions

Biallelic inactivation of the gPV within EC tumors was observed in 70 (32%) of the 216 patients with gPVs, with higher rates in high-penetrance genes (62%), mostly associated with LS and HR genes (Supplementary Table 2). Although limited in numbers, rates of biallelic inactivation within tumors varied significantly by genetic ancestry, most frequently present in patients of SAS and AFR ancestries (AFR, 6 [67%]; ASJ, 18 [34%]; EAS, 2 [22%]; EUR, 31 [28%]; NAM, 1 [50%]; SAS, 5 [71%]; Mixed, 4 [24%]; p=0.039], but not self-reported ancestry (p=0.16; Supplementary Figure 1/Supplementary Table 3).

When classified by molecular subtype, 106 (6.6%) tumors were POLE ultramutated, 413 (26%) MSI-H, 542 (33%) CN-H, 546 (34%) CN-L, and 18 (1%) unclassifiable (Table 1). Given differences in gPV by molecular subtype, with enrichment in MSI-H and possibly CN-H tumors, and variations in molecular subtype by ancestry, with more CN-H tumors and less MSI-H tumors in those of Black/AA and AFR ancestry (Supplementary Figure 2), we explored ancestry-based differences in germline landscape within tumor subtypes.

LS (gPV in MMR genes) was observed in 39 (2.4%) patients overall (Supplementary Table 7), with the highest rates among those of Asian (4.8%), specifically SAS (8.6%) ancestry, and the lowest rates among those of Black/AA (1.8%), Hispanic (1.6%), and EUR (1.7%) ancestry (Figure 4). As LS is associated with MSI-H tumors, and rates of MSI-H tumors varied by ancestry, we adjusted for this by exploring the prevalence of LS-associated MSI-H EC by ancestry. By doing so, we found significant variation by self-reported (p=0.019) and genetic (p<0.001) ancestry; 25% of MSI-H tumors were associated with LS in those of Asian ancestry (EAS, 25%; SAS, 30%), and only 5.4% and 3.8% of MSI-H tumors were associated with LS in those of NH-White and EUR ancestry, respectively, once those of AJ/ASJ ancestry were separated. Although Black/AA an AFR patients had the low rates of LS overall, they also had the lowest rates of MSI-H tumors. After adjusting for this, 8% and 16% of MSI-H tumors were associated with LS in those of Black/AA and AFR ancestry, respectively (Figure 4/Supplementary Tables 8 and 9).

Figure 4: Rates of Lynch Syndrome and BRCA1/2 Germline Pathogenic Variants by Ancestry, Self-Reported and Genetic: Overall and within MSI-H and CN-H Tumors.

Figure 4:

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The figure depicts rates of Lynch Syndrome (A) and BRCA1/2 gPV (B) overall as well as the proportion driving MSI-H tumors (C) and CN-H tumors (D) by self-reported and genetic ancestry. Not depicted are 5 patients of NAM genetic ancestry, among whom 1 patient had a BRCA2 gPV.

gPV – germline pathogenic variant, AA – African American, AFR – African, EUR – European, EAS – East Asian, NAM – Native American, SAS – South Asian, ASJ – genetic Ashkenazi Jewish, NH – Non-Hispanic, AJ – self-reported Ashkenazi Jewish, MSI-H – microsatellite instability-high, CN-H – copy number-high

BRCA1/2 gPVs were observed in 20 (1.2%) patients (Supplementary Table 10), with similar rates by self-reported (p=0.36) and variation by genetic ancestry due to the small number of patients in the NAM group (p=0.027; Figure 4). As gPVs in BRCA1/2 are associated with CN-H tumors, which are enriched in those of Black/AA and AFR ancestry, we explored rates of BRCA1/2-associated CN-H tumors by ancestry and found significant variation by both self-reported (p=0.045) and genetic ancestry (p<0.001). Rates of BRCA1/2-associated CN-H tumors were highest in patients of AJ (8.5%) and SAS (17%) ancestries and lowest in those of Black (0.9%), AFR (1.1%), and EAS (0%) ancestries (Figure 4/Supplementary Tables 8 and 9).

Clinical genetics follow-up

Of the 216 patients with gPVs, 114 (53%) had newly identified gPVs and follow-up data available, and among those patients, 102/114 (89%) were seen for genetics clinic counseling follow-up. Rates were not significantly different by self-reported (p=0.24) or genetic ancestry (p=0.54). However, patient numbers were limited, and the lowest rates were observed in patients of Black/AA (75%) and AFR (67%) ancestry, and the highest rates were observed in those of Asian (EAS and SAS) ancestry (100%). Among the 102 patients with gPV and unavailable/missing CGS follow-up data, 22 had previously known about their result, with 17 patients (77%) having gPV in a high penetrance gene, most commonly BRCA1/2 or Lynch Syndrome. The remaining 80 patients had unavailable/missing CGS follow-up data given the transition to telemedicine in 2020 due to the COVID-19 pandemic. (Figure 3/Supplementary Tables 2 and 3).

Discussion

In our study, 13% of patients with EC harbored a gPV, 51% of high or moderate penetrance. Rates varied significantly by ancestry, both self-reported and genetic, with the lowest rates in patients of Black/AA and AFR ancestry and the highest in those of AJ ancestry, even after adjustment for age and tumor subtype in multivariable models. Biallelic inactivation was observed within tumors across all ancestry groups, particularly in high-penetrance genes such as LS-associated genes and BRCA1/2. Germline landscape was strongly influenced by somatic molecular subtype given enrichment of CN-H tumors and lack of MSI-H tumors in those of Black/AA and AFR ancestry. Although limited by sample size, patients of Black/AA and AFR ancestry had the lowest rates of genetics follow-up. Ancestry-based differences in germline findings and subsequent genetic counseling may contribute to disparate outcomes for patients with EC and their families, given implications for treatment and cancer prevention, and warrant further investigation.

Other studies have reported similar rates of gPV in large cohorts of patients with EC,10,12,41 but they were limited by minimal patient diversity or biased in ascertainment, and all lacked comprehensive correlation with tumor molecular data, including important ancestry-based differences in molecular subtype. We found patients of self-reported Black/AA and AFR ancestry were less likely than those of NH-White and EUR ancestry to harbor gPVs, even after adjustments for other clinical factors, which may have implications for cancer prevention and treatment. This is heavily influenced by germline-somatic interactions, as those of Black/AA and AFR ancestry had the highest rates of CN-H tumors and the lowest rates of MSI-H tumors, which are associated with LS. Of note, the low rate of LS overall in those of Black/AA and AFR ancestry increased after adjusting for MSI-H tumors. However, a lower rate of BRCA1/2 gPVs persisted in CN-H tumors for those of Black/AA and AFR ancestry. In contrast, LS and BRCA1/2 gPVs were enriched in those of AJ and Asian, particularly SAS, ancestry within MSI-H and CN-H tumors. Our findings of high rates of LS and BRCA1/2 gPVs in those of Asian ancestry is novel and may have been influenced by the high rate of young patients in this group (27% diagnosed at age ≤50 years). Differential rates of gPV in these patients may influence outcomes by affecting utilization of therapies, including immune checkpoint blockade42 for LS and other targeted therapies, such as Poly (ADP-ribose) polymerase (PARP) inhibitors, which are being used in clinical trials for BRCA1/2-associated ECs.43 This is particularly concerning given existing racial disparities in clinical trial enrollment,44 which may be perpetuated by these germline differences.

Black/AA and AFR patients had the lowest rates of genetic counseling follow-up, which may affect rates of cascade counseling for at-risk relatives. This is consistent with our findings in pan-cancer patients, in which self-reported Black/AA patients had lower rates of genetics follow-up even after adjustment for other clinical confounders.24 Other studies have also demonstrated racial disparities in genetic testing/counseling.25,45 This is of particular importance in self-reported Black/AA patients and their families given potential implications for cancer risk reduction and prevention in light of ongoing disparities in screening for EC46,47 and other cancers.48

As more studies support the utility of germline assessment in EC,10,12,41 it is important to understand variations by ancestry measures, correlations with tumor phenotype, and downstream implications on treatment and cancer prevention and the potential contribution to racial disparities in outcomes. The increased use of genetic testing and counseling for patients and family of underserved populations may help counter these outcomes differences.

The strengths of our study include a large, unselected population, with 25% of patients identifying as non-White, paired tumor-normal sequencing that allows inference of germline-tumor interactions, and orthogonal ancestorial assessments using self-reported and inferred methods. Discrepancies between self-reported and genetic ancestry may suggest either technical error and misclassification in the medical record or the difference between genetic ancestry, which is a biological measurement, and self-reported race/ethnicity, which is a social construct.49 The use of both measures strengthens our findings. Although we found variations by ancestry, overall levels of genetic counseling are high (89%), likely reflecting the integration of tumor and germline sequencing in oncology clinics and our genetics workflows designed to maximize follow-up in this patient population.24

The limitations of our study include ascertainment bias, given our tertiary cancer center and predominantly White/European patients. Efforts to address this and increase diverse enrollment into tumor-normal sequencing and other clinical trials are ongoing. Although we examined rates of gPV and genetic counseling, other clinical variables and social determinants of health may influence uptake of genetic testing and counseling in patients with EC,50 and studies to explore these areas are ongoing. Genetics clinic follow-up was also limited to patients seen prior to 2020 given the conversion of our clinic to telemedicine due to the COVID-19 pandemic; studies to evaluate the effect of telemedicine on follow-up rates are ongoing. Additionally, although our overall population was large, the numbers of patients per ancestorial group were small, making comparisons exploratory. Future studies in larger, prospective settings are warranted to further explore specific genes including BRCA1 and BRCA2 as EC risk may vary,17,18 and dive deeper into germline-somatic interactions among molecular subgroups to investigate differences between MLH1 promoter hypermethylation and mechanisms of MSI-H tumors amongst ancestry groups.

Although the prevalence of gPVs in unselected patients with EC is high, there is variation across ancestry groups, with the lowest rates observed in patients identifying as Black/AA or of AFR ancestry, which may be influenced by differences in tumor molecular subtypes. Accordingly, there is a need to better understand the influence of these ancestry-based germline differences on cancer management as well as downstream genetics care and evaluation of at-risk family members to enhance the use of genetic assessment as a novel way to combat racial disparities in EC outcomes.

Supplementary Material

Supinfo

Acknowledgements

Outside the submitted work, YLL reports research funding from AstraZeneca, GSK, and Repare Therapeutics. BW reports research funding by Repare Theraputics. VM reports advisory board participation for Eisai, Merck, Clovis, Faeth, Duality, Morphyes, Karyopharm, Novartis, Lilly, and Immunocore. NAR reports grant funding from GRAIL paid to the institution. CA has received research grants from Abbvie, Clovis, Genentech, and Astra Zeneca and served on advisory boards for Abbvie, AstraZeneca/Merck, Eisai/Merck, Mersana Therapeutics, Repare Therapeutics, and Roche/Genentech. KAC reports grant funding from the Irish Cancer Society, MSD, and Immunogen; consulting fees from Nextcure, MJH Life Sciences, and GSK; payments/honoraria from GSK, AstraZeneca, and MSD; financial support to attend meetings from Roche, Pfizer, and MSD; advisory board participation at MSD, AstraZeneca, GSK, and Eisai; a voluntary advisory role at the National Cancer Control Programme Ireland; and a voluntary board member at ARC Cancer Support Centers. ZS reports an immediate family member who serves as a consultant in Ophthalmology for Adverum, Genentech, Gyroscope Therapeutics Limited, Neurogene, Optos Plc, Outlook Therapeutics, RegenexBio, and Regeneron.

Funding:

This work was supported by a Memorial Sloan Kettering Cancer Center Cycle for Survival Grant (PI: Brown) and in part by a National Cancer Institute/National Institutes of Health Cancer Center Support Grant (P30 CA008748) and the Robert and Kate Niehaus Center for Inherited Cancer Genomics.

Footnotes

Data Access, Responsibility, and Analysis: All authors had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Conflict of Interest Statement

All other authors have no potential conflicts of interest to disclose.

Data Sharing Statement:

Data will be made available upon reasonable request through institutional processes.

References

  • 1.Siegel RL, Miller KD, Wagle NS, Jemal A. Cancer statistics, 2023. CA: a cancer journal for clinicians. Jan 2023;73(1):17–48. doi: 10.3322/caac.21763 [DOI] [PubMed] [Google Scholar]
  • 2.Lortet-Tieulent J, Ferlay J, Bray F, Jemal A. International Patterns and Trends in Endometrial Cancer Incidence, 1978–2013. Journal of the National Cancer Institute. Apr 1 2018;110(4):354–361. doi: 10.1093/jnci/djx214 [DOI] [PubMed] [Google Scholar]
  • 3.Kandoth C, Schultz N, Cherniack AD, et al. Integrated genomic characterization of endometrial carcinoma. Nature. May 2 2013;497(7447):67–73. doi: 10.1038/nature12113 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4.Cote ML, Ruterbusch JJ, Olson SH, Lu K, Ali-Fehmi R. The Growing Burden of Endometrial Cancer: A Major Racial Disparity Affecting Black Women. Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology. Sep 2015;24(9):1407–15. doi: 10.1158/1055-9965.Epi-15-0316 [DOI] [PubMed] [Google Scholar]
  • 5.Gaber C, Meza R, Ruterbusch JJ, Cote ML. Endometrial Cancer Trends by Race and Histology in the USA: Projecting the Number of New Cases from 2015 to 2040. J Racial Ethn Health Disparities. Oct 17 2016;doi: 10.1007/s40615-016-0292-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6.Collins Y, Holcomb K, Chapman-Davis E, Khabele D, Farley JH. Gynecologic cancer disparities: a report from the Health Disparities Taskforce of the Society of Gynecologic Oncology. Gynecologic oncology. May 2014;133(2):353–61. doi: 10.1016/j.ygyno.2013.12.039 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7.Pinheiro PS, Medina HN, Koru-Sengul T, et al. Endometrial Cancer Type 2 Incidence and Survival Disparities Within Subsets of the US Black Population. Front Oncol. 2021;11:699577. doi: 10.3389/fonc.2021.699577 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Stadler ZK, Robson ME. Inherited predisposition to endometrial cancer: moving beyond Lynch syndrome. Cancer. Mar 1 2015;121(5):644–7. doi: 10.1002/cncr.29107 [DOI] [PubMed] [Google Scholar]
  • 9.Gordhandas S, Rios-Doria E, Cadoo KA, et al. Comprehensive Analysis of Germline Drivers in Endometrial Cancer. Journal of the National Cancer Institute. Feb 6 2023;doi: 10.1093/jnci/djad016 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10.Heald B, Mokhtary S, Nielsen SM, et al. Unexpected actionable genetic variants revealed by multigene panel testing of patients with uterine cancer. Gynecologic oncology. Jun 9 2022;doi: 10.1016/j.ygyno.2022.05.023 [DOI] [PubMed] [Google Scholar]
  • 11.Karpel HC, Chern JY, Smith JM, Smith AJ, Pothuri B. Utility of germline multi-gene panel testing in patients with endometrial cancer. Gynecologic oncology. Apr 25 2022;doi: 10.1016/j.ygyno.2022.04.003 [DOI] [PubMed] [Google Scholar]
  • 12.Levine MD, Pearlman R, Hampel H, et al. Up-Front Multigene Panel Testing for Cancer Susceptibility in Patients With Newly Diagnosed Endometrial Cancer: A Multicenter Prospective Study. JCO Precis Oncol. Nov 2021;5:1588–1602. doi: 10.1200/po.21.00249 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.Crosbie EJ, Ryan NAJ, Arends MJ, et al. The Manchester International Consensus Group recommendations for the management of gynecological cancers in Lynch syndrome. Genetics in medicine : official journal of the American College of Medical Genetics. Oct 2019;21(10):2390–2400. doi: 10.1038/s41436-019-0489-y [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.Dominguez-Valentin M, Sampson JR, Seppala TT, et al. Cancer risks by gene, age, and gender in 6350 carriers of pathogenic mismatch repair variants: findings from the Prospective Lynch Syndrome Database. Genetics in medicine : official journal of the American College of Medical Genetics. Jan 2020;22(1):15–25. doi: 10.1038/s41436-019-0596-9 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15.Pennington KP, Walsh T, Lee M, et al. BRCA1, TP53, and CHEK2 germline mutations in uterine serous carcinoma. Cancer. Jan 15 2013;119(2):332–8. doi: 10.1002/cncr.27720 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.Ring KL, Bruegl AS, Allen BA, et al. Germline multi-gene hereditary cancer panel testing in an unselected endometrial cancer cohort. Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc. Nov 2016;29(11):1381–1389. doi: 10.1038/modpathol.2016.135 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17.de Jonge MM, de Kroon CD, Jenner DJ, et al. Endometrial Cancer Risk in Women with Germline BRCA1 or BRCA2 Mutations: Multicenter Cohort Study. Journal of the National Cancer Institute. Mar 12 2021;doi: 10.1093/jnci/djab036 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.Shu CA, Pike MC, Jotwani AR, et al. Uterine Cancer After Risk-Reducing Salpingo-oophorectomy Without Hysterectomy in Women With BRCA Mutations. JAMA oncology. Nov 1 2016;2(11):1434–1440. doi: 10.1001/jamaoncol.2016.1820 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19.Chapman-Davis E, Zhou ZN, Fields JC, et al. Racial and Ethnic Disparities in Genetic Testing at a Hereditary Breast and Ovarian Cancer Center. J Gen Intern Med. Jan 2021;36(1):35–42. doi: 10.1007/s11606-020-06064-x [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20.Forman AD, Hall MJ. Influence of race/ethnicity on genetic counseling and testing for hereditary breast and ovarian cancer. Breast J. Sep-Oct 2009;15 Suppl 1:S56–62. doi: 10.1111/j.1524-4741.2009.00798.x [DOI] [PubMed] [Google Scholar]
  • 21.Reid S, Cadiz S, Pal T. Disparities in Genetic Testing and Care among Black women with Hereditary Breast Cancer. Curr Breast Cancer Rep. Sep 2020;12(3):125–131. doi: 10.1007/s12609-020-00364-1 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Piedimonte S, Power J, Foulkes WD, et al. BRCA testing in women with high-grade serous ovarian cancer: gynecologic oncologist-initiated testing compared with genetics referral. International journal of gynecological cancer : official journal of the International Gynecological Cancer Society. Nov 2020;30(11):1757–1761. doi: 10.1136/ijgc-2020-001261 [DOI] [PubMed] [Google Scholar]
  • 23.Czekalski MA, Huziak RC, Durst AL, Taylor S, Mai PL. Mainstreaming Genetic Testing for Epithelial Ovarian Cancer by Oncology Providers: A Survey of Current Practice. JCO Precis Oncol. Jan 2022;6:e2100409. doi: 10.1200/po.21.00409 [DOI] [PubMed] [Google Scholar]
  • 24.Liu YL, Maio A, Kemel Y, et al. Disparities in cancer genetics care by race/ethnicity among pan-cancer patients with pathogenic germline variants. Cancer. Nov 1 2022;128(21):3870–3879. doi: 10.1002/cncr.34434 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.Hinchcliff EM, Bednar EM, Lu KH, Rauh-Hain JA. Disparities in gynecologic cancer genetics evaluation. Gynecologic oncology. Apr 2019;153(1):184–191. doi: 10.1016/j.ygyno.2019.01.024 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Zehir A, Benayed R, Shah RH, et al. Mutational landscape of metastatic cancer revealed from prospective clinical sequencing of 10,000 patients. Nature Medicine. 2017–06-01 2017;23(6):703–713. doi: 10.1038/nm.4333 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27.Cheng DT, Mitchell TN, Zehir A, et al. Memorial Sloan Kettering-Integrated Mutation Profiling of Actionable Cancer Targets (MSK-IMPACT). The Journal of Molecular Diagnostics. 2015–05-01 2015;17(3):251–264. doi: 10.1016/j.jmoldx.2014.12.006 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28.Cheng DT, Prasad M, Chekaluk Y, et al. Comprehensive detection of germline variants by MSK-IMPACT, a clinical diagnostic platform for solid tumor molecular oncology and concurrent cancer predisposition testing. BMC medical genomics. May 19 2017;10(1):33. doi: 10.1186/s12920-017-0271-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29.Richards S, Aziz N, Bale S, et al. Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology. Genetics in medicine : official journal of the American College of Medical Genetics. May 2015;17(5):405–24. doi: 10.1038/gim.2015.30 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30.Easton DF, Pharoah PD, Antoniou AC, et al. Gene-panel sequencing and the prediction of breast-cancer risk. The New England journal of medicine. Jun 4 2015;372(23):2243–57. doi: 10.1056/NEJMsr1501341 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31.Mandelker D, Zhang L, Kemel Y, et al. Mutation Detection in Patients With Advanced Cancer by Universal Sequencing of Cancer-Related Genes in Tumor and Normal DNA vs Guideline-Based Germline Testing. Jama. Sep 5 2017;318(9):825–835. doi: 10.1001/jama.2017.11137 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32.Tung N, Domchek SM, Stadler Z, et al. Counselling framework for moderate-penetrance cancer-susceptibility mutations. Nature reviews Clinical oncology. Sep 2016;13(9):581–8. doi: 10.1038/nrclinonc.2016.90 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33.Momeni-Boroujeni A, Dahoud W, Vanderbilt CM, et al. Clinicopathologic and Genomic Analysis of TP53-Mutated Endometrial Carcinomas. Clin Cancer Res. May 1 2021;27(9):2613–2623. doi: 10.1158/1078-0432.Ccr-20-4436 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34.Momeni-Boroujeni A, Nguyen B, Vanderbilt CM, et al. Genomic landscape of endometrial carcinomas of no specific molecular profile. Mod Pathol. Apr 1 2022;doi: 10.1038/s41379-022-01066-y [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35.Niu B, Ye K, Zhang Q, et al. MSIsensor: microsatellite instability detection using paired tumor-normal sequence data. Bioinformatics. Apr 1 2014;30(7):1015–6. doi: 10.1093/bioinformatics/btt755 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36.Rios-Doria E Molecular Classification of Endometrial Carcinomas: A Single-Institution Review. 2022: [Google Scholar]
  • 37.Shen R, Seshan VE. FACETS: allele-specific copy number and clonal heterogeneity analysis tool for high-throughput DNA sequencing. Nucleic Acids Research. 2016–09-19 2016;44(16):e131–e131. doi: 10.1093/nar/gkw520 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 38.Arora K, Tran TN, Kemel Y, et al. Genetic Ancestry Correlates with Somatic Differences in a Real-World Clinical Cancer Sequencing Cohort. Cancer Discovery. 2022;doi: 10.1158/2159-8290.Cd-22-0312 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 39.Alexander DH, Novembre J, Lange K. Fast model-based estimation of ancestry in unrelated individuals. Genome Res. Sep 2009;19(9):1655–64. doi: 10.1101/gr.094052.109 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 40.Auton A, Brooks LD, Durbin RM, et al. A global reference for human genetic variation. Nature. Oct 1 2015;526(7571):68–74. doi: 10.1038/nature15393 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 41.Karpel HC, Chern JY, Smith JM, Smith AJ, Pothuri B. Utility of germline multi-gene panel testing in patients with endometrial cancer. Gynecologic oncology. Jun 2022;165(3):546–551. doi: 10.1016/j.ygyno.2022.04.003 [DOI] [PubMed] [Google Scholar]
  • 42.Le DT, Durham JN, Smith KN, et al. Mismatch repair deficiency predicts response of solid tumors to PD-1 blockade. Science (New York, NY). Jul 28 2017;357(6349):409–413. doi: 10.1126/science.aan6733 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 43.Musacchio L, Caruso G, Pisano C, et al. PARP Inhibitors in Endometrial Cancer: Current Status and Perspectives. Cancer management and research. 2020;12:6123–6135. doi: 10.2147/cmar.S221001 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 44.Green AK, Tabatabai SM, Aghajanian C, et al. Clinical Trial Participation Among Older Adult Medicare Fee-for-Service Beneficiaries With Cancer. JAMA oncology. Dec 1 2022;8(12):1786–1792. doi: 10.1001/jamaoncol.2022.5020 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 45.Williams CD, Bullard AJ, O’Leary M, Thomas R, Redding TSt, Goldstein K. Racial/Ethnic Disparities in BRCA Counseling and Testing: a Narrative Review. J Racial Ethn Health Disparities. Jun 2019;6(3):570–583. doi: 10.1007/s40615-018-00556-7 [DOI] [PubMed] [Google Scholar]
  • 46.Doll KM, Khor S, Odem-Davis K, et al. Role of bleeding recognition and evaluation in Black-White disparities in endometrial cancer. American journal of obstetrics and gynecology. Dec 2018;219(6):593.e1–593.e14. doi: 10.1016/j.ajog.2018.09.040 [DOI] [PubMed] [Google Scholar]
  • 47.Doll KM, Romano SS, Marsh EE, Robinson WR. Estimated Performance of Transvaginal Ultrasonography for Evaluation of Postmenopausal Bleeding in a Simulated Cohort of Black and White Women in the US. JAMA oncology. Aug 1 2021;7(8):1158–1165. doi: 10.1001/jamaoncol.2021.1700 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 48.Santiago-Rodríguez EJ, Shariff-Marco S, Gomez SL, Hiatt RA. Disparities in Colorectal Cancer Screening by Time in the U.S. and Race/Ethnicity, 2010–2018. Am J Prev Med. Feb 16 2023;doi: 10.1016/j.amepre.2023.01.033 [DOI] [PubMed] [Google Scholar]
  • 49.Borrell LN, Elhawary JR, Fuentes-Afflick E, et al. Race and Genetic Ancestry in Medicine - A Time for Reckoning with Racism. The New England journal of medicine. Feb 4 2021;384(5):474–480. doi: 10.1056/NEJMms2029562 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 50.Chapman-Davis E, Webster EM, Balogun OD, Frey MK, Holcomb K. Landmark Series on Disparities: Uterine Cancer and Strategies for Mitigation. Ann Surg Oncol. Jan 2023;30(1):48–57. doi: 10.1245/s10434-022-12765-w [DOI] [PubMed] [Google Scholar]

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Supplementary Materials

Supinfo
NIHMS1939756-supplement-Supinfo.docx (389.9KB, docx)

Data Availability Statement

Data will be made available upon reasonable request through institutional processes.

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