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. Author manuscript; available in PMC: 2026 Jan 31.
Published in final edited form as: JCO Precis Oncol. 2026 Jan 29;10:e2500748. doi: 10.1200/PO-25-00748

Age-related germline landscape of endometrial cancer: focus on early-onset cases

Judy J Wang 2, Juliet Milani 1, Sarah Kane 1, Qin Zhou 6, Alexia Iasonos 6, Alicia Latham 1,2, Yelena Kemel 5, Maria Carlo 1,2, Mohammad Abbass 3, Lauren G Banaszak 1, Chimene Kesserwan 1,9, Yonina R Murciano-Goroff 1, Jennifer J Mueller 3,8, Nadeem R Abu-Rustum 3,8, Vicky Makker 1,2, Lora H Ellenson 4, Michael F Berger 7, Diana Mandelker 4, Kenneth Offit 1,2, Zsofia Stadler 1,2, Carol Aghajanian 1,2, Britta Weigelt 4, Ying L Liu 1,2
PMCID: PMC12857754  NIHMSID: NIHMS2131774  PMID: 41610375

Abstract

Purpose:

Early-onset endometrial cancer (eoEC) is increasing, and germline drivers may be enriched in younger patients. We sought to define germline pathogenic variants (gPV) in those with EC by age.

Patients and Methods:

We identified patients with EC who underwent clinical tumor-normal sequencing from 12/2014-6/2021 and collected clinical variables. Logistic regression models evaluated associations between age at EC diagnosis and presence of gPV, biallelic inactivation, and Lynch syndrome (LS). Age categories were defined as early-onset (eoEC, EC<50 years) and later-onset (EC ≥70 years) and were compared to those diagnosed ages 50-69 years.

Results:

Among 1625 patients with EC, median age at diagnosis was 63 (range 24-96) years. We observed gPV in 28/170 (16%) patients with eoEC, 152/1066 (14%) patients diagnosed 50-69 years, and 36/389 (9%) patients with later-onset EC (p=0.016). LS was enriched in eoEC, with 6.5% of patients diagnosed age <50 years having LS. In multivariable models compared to those with EC diagnosed 50-69 years, eoEC was more likely to exhibit biallelic inactivation (OR 3.34 95% CI 1.44-7.35) and be associated with LS (HR 3.49 95% CI 1.63-7.01). Among early-onset EC, 14/28 (50%) gPV were high penetrance and 14/28 (50%) exhibited biallelic inactivation. However, heterogeneity was observed, and rates of gPV were 8.9% and 19%, biallelic inactivation was 0% and 11%, and LS was 2.2% and 8% in those diagnosed <40 years and 40-49 years respectively.

Conclusion:

Rates of gPV, biallelic inactivation and LS differ across age groups for EC, with highly penetrant genes driving tumorigenesis enriched in younger patients. However, very early-onset EC may have different drivers and necessitates more research.

Keywords: endometrial cancer, age, genetics, early-onset cancer

INTRODUCTION

A rise in early-onset cancers has emerged as a concerning epidemiological phenomenon with multifactorial drivers1-3. Early-onset endometrial cancer (eoEC), defined as cancer diagnosis before the age of 50 years, has also been rising steadily1,4-6 with increases in incidence ranging from 1.83% to 2.63% depending on the age cohort. The highest rates observed are among individuals aged 30–39 years4,5. Mortality rates have also increased among eoEC cases, particularly within the 30–39 and 40–49 year-old cohorts4. Notably, the fastest rise in mortality was seen in the 70–79 year-old group4. These epidemiological trends highlight significant changes at both ends of the age spectrum in EC.

Proposed non-genetic factors for the rise in eoEC include increasing rates of obesity and metabolic syndrome, prolonged exposure to unopposed estrogen, and broader environmental and lifestyle shifts like earlier menarche and delayed childbearing7-10. In parallel, we have developed a better understanding of the germline landscape of EC and relevant pathogenic variants (gPVs). Germline mutations can predispose individuals to early tumorigenesis through mechanisms such as high microsatellite instability (MSI-H), seen commonly in Lynch Syndrome (LS) with gPV in mismatch repair (MMR) genes. However, more recent studies are highlighting gPV in other genes related to homologous recombination deficiency, and dysregulation of cell signaling pathways11-13. A genetic predisposition combined with environmental exposures can accelerate carcinogenesis in young carriers, particularly those with high penetrance gPV, and assessing biallelic inactivation within tumors can help delineate the role of germline variants in tumorigenesis.

Previous studies have explored the somatic mutation landscape of eoEC, revealing distinct molecular features compared to later-onset cases. EoEC tumors have increased odds of having non-silent mutations in CTNNB1 and BRCA2, while exhibiting lower odds of mutations in PIK3R1 and FGFR214-16. However, there are few modern studies evaluating the age-related germline landscape of EC. In a prospective, multicenter study of 100 women with eoEC, approximately 9% were found to have LS–associated mutations17. BRCA1 and BRCA2 gPV are more commonly seen in later-onset cases.

Besides LS and BRCA1/2 genes, little remains known of other gPV and their role in driving eoEC. Given emerging evidence suggesting that germline drivers may partly explain the rising incidence in younger populations, this study aims to characterize age-related variations in gPV among patients with EC with a particular focus on drivers of eoEC.

METHODS

Patient selection and data collection

We included patients with histologically confirmed EC treated at a single institution who underwent germline genetic testing between December 29, 2014 and July 22, 2021 using an FDA-authorized tumor-normal next-generation sequencing panel (MSK-IMPACT [Memorial Sloan Kettering Cancer Center – Integrated Mutation Profiling of Actionable Cancer Targets]), and includes germline analysis of 76-90 genes, as previously described18-20. This study was approved by the Institutional Review Board of MSK under IRB #12-245, and written informed consent was obtained from all patients included in this study.

Demographic information was collected, including primary language (English vs non-English), smoking status (never vs current/former smoker), self-reported race, and inferred genetic ancestry21. Specifically, ancestry groups included African (AFR), European (EUR), East Asian (EAS), Native American (NAM), South Asian (SAS), and Ashkenazi Jewish (ASJ) populations.

Clinicopathologic variables were also abstracted, including age at EC diagnosis, FIGO stage at diagnosis (per 2009 version), body mass index (BMI), tumor histology, and molecular subtype. All cases underwent review by expert GYN pathologists. For this study, we defined eoEC as EC diagnosed before age 50, average-onset as diagnosed between ages 50 and 69, and later-onset as diagnosed at age 70 or older. For both ends of the age distribution, we defined the very-early-onset EC group as diagnosed before age 40, and very-late-onset group as diagnosed after age 80. Molecular subtypes including POLE ultra-mutated (POLE), microsatellite instability-high (MSI-H), copy number-low (CN-L)/ no specific molecular profile (NSMP), and copy number high (CN-H)/ p53abnormal, were assigned as described22. For select cases, genetic counselors performed a more comprehensive analysis of family histories for inheritance patterns.

Germline pathogenic variants (gPV) and biallelic inactivation

Molecular pathologists characterized gPV as “pathogenic” or “likely pathogenic” based on the 2015 American College of Medical Genetics and Genomics and the Association for Molecular Pathology guidelines23. These gPV are additionally classified mode of inheritance as autosomal dominant or recessive, and by disease penetrance as high, moderate, low, or uncertain24,25. For this study, we grouped low, uncertain, and recessive into the same category for regression analysis, as we are primarily interested in high and moderate penetrance genes. Biallelic inactivation at the gPV locus was assessed, which was defined as either as a loss of the wild-type allele through deletion (loss of heterozygosity) or the presence of a somatic pathogenic alteration affecting the second allele, as detailed previously13.

Statistical analysis

Descriptive statistics were provided. Cochran-Armitage trend tests were applied to identify whether there were trends in rates of gPVs, biallelic inactivation events, and LS by decades of age (from <40 years to ≥80 years). The association between patients’ characteristics/molecular subtypes and age groups (<50 vs 50-69 vs ≥70) were tested using Fisher Exact test for categorical variable and Kruskal-Wallis rank sum test for continuous variable. Considering dichotomized outcomes such as presence of gPV, biallelic inactivation, and LS, logistic regression methodology was used. Firstly, the variables’ prediction abilities were examined in univariate logistic regression setting. All the variables showing statistical significance in univariate setting were considered as candidates in multivariate model building. Stepwise model selection method was applied to obtain the final multivariate logistic regression model. All analyses were performed in R 4.4.2 (https://www.R-project.org/). All tests are two-sided and p-value ≤0.05 was considered as statistically significant.

RESULTS

Patient characteristics

Of 1625 patients with EC who underwent germline testing, median age at diagnosis was 63 years (range 24-96 years). A total of 170 patients (10%) were diagnosed with EC under age 50, 1066 (66%) diagnosed between ages 50-69, and 389 (24%) diagnosed at age 70 or older (Table 1). Most patients were English-speaking (1530/1625, 95%), non-Hispanic white (927/1625, 60%), of EUR ancestry (843/1625, 53%), not obese (defined as BMI<30; 830/1625, 52%), and without any smoking history (1067/1625, 67%). Younger patients were more likely to have a lower BMI (p=0.004), never smoked (p=0.003), and be of East and South Asian genetic ancestry (p<0.001). Tumors in younger patients were more likely to be diagnosed at an earlier stage (FIGO 2009 stage I/II) (p=0.002), be of endometrioid histology (p<0.001), and be of CN-L/NSMP or POLE molecular subtypes (p<0.001) (Table 1).

Table 1:

Demographic and clinicopathologic characteristics by age of endometrial cancer diagnosis.

Characteristic Overall Age <50 Age 50-59 Age ≥70 p-value
TOTAL 1,625 (100%) 170 (10%) 1,066 (66%) 389 (24%)
Primary Language 0.054
English 1,530 (95%) 164 (96%) 1,011 (95%) 355 (92%)
Non-English 87 (5%) 6 (4%) 51 (5%) 30 (8%)
Race/Ethnicity <0.001
Non-Hispanic White 927 (60%) 79 (48%) 622 (61%) 226 (62%)
Non-Hispanic Black 171 (11%) 14 (9%) 120 (12%) 37 (10%)
Asian 124 (8%) 33 (20%) 72 (7%) 19 (5%)
Hispanic 124 (8%) 20 (12%) 80 (8%) 24 (7%)
Ashkenazi Jewish 202 (13%) 17 (10%) 124 (12%) 61 (17%)
Genetic Ancestry <0.001
EUR 843 (53%) 72 (43%) 564 (55%) 207 (55%)
AFR 140 (9%) 12 (7%) 98 (10%) 30 (8%)
ASJ 274 (17%) 22 (13%) 162 (16%) 90 (24%)
EAS 82 (5%) 23 (14%) 44 (4%) 15 (4%)
SAS 35 (2%) 7 (4%) 26 (3%) 2 (1%)
Mixed/NAM 202 (13%) 30 (18%) 138 (13%) 34 (9%)
BMI (kg/m2) 29.6 (15.3 – 67.6) 27.6 (15.3 – 63.1) 30.2 (16.5 – 66.2) 28.6 (16.3 – 67.6) 0.004
Obesity * 0.023
Yes 778 (48%) 73 (43%) 536 (51%) 169 (44%)
No 830 (52%) 97 (57%) 518 (49%) 215 (56%)
Smoking History 0.003
Never smoked 1,067 (67%) 128 (75%) 707 (67%) 232 (61%)
Current or history of smoking 535 (33%) 42 (25%) 344 (33%) 149 (39%)
FIGO 2009 Stage 0.002
I 972 (64%) 96 (65%) 667 (66%) 209 (57%)
II 64 (4%) 12 (8%) 42 (4%) 10 (3%)
III 283 (19%) 25 (17%) 174 (17%) 84 (23%)
IV 198 (13%) 15 (10%) 122 (12%) 61 (17%)
Tumor Histology <0.001
Endometrioid (G1 or G2) 800 (52%) 113 (72%) 546 (54%) 141 (38%)
Endometrioid (G3) 152 (10%) 24 (15%) 97 (10%) 31 (8%)
Serous 218 (14%) 0 (0%) 135 (13%) 83 (22%)
Clear cell 46 (3%) 4 (3%) 25 (3%) 17 (5%)
Carcinosarcoma 168 (11%) 4 (3%) 112 (11%) 52 (14%)
Undifferentiated/Dedifferentiated 33 (2%) 5 (3%) 22 (2%) 6 (2%)
Mixed 131 (9%) 8 (5%) 81 (8%) 42 (11%)
Molecular Subtype <0.001
CN-L/NSMP 546 (34%) 85 (51%) 361 (34%) 100 (26%)
POLE 106 (7%) 28 (17%) 73 (7%) 5 (1%)
MSI-H/MMRd 413 (26%) 38 (23%) 279 (27%) 96 (25%)
CN-H/p53abnormal 542 (34%) 16 (10%) 339 (32%) 187 (48%)
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*

Defined as BMI of 30 or higher

Abbreviations: EC – endometrial cancer, BMI – body mass index, CN-L – copy number low, MMRd, mismatch repair-deficient, MSI-H – microsatellite instability high, NSMP, no specific molecular profile, CN-H – copy number high

Distribution of gPVs by age decade

Of the 1625 patients, 216 (13%) had one or more gPVs. We observed significant differences in gPV rates across the age spectrum (p=0.006, Supplemental Table 1). From youngest to oldest decades, gPVs were found in: 4/45 (9%) patients diagnosed younger than 40 years, 24/125 (19%) between ages 40-49, 62/405 (15%) between ages 50-59, 90/661 (14%) between ages 60-69, 34/322 (11%) between ages 70-79, and 2/67 (3%) ages 80 or older (Figure 1). Statistical significance was observed comparing rates of gPV between early-onset, average-onset, and later-onset EC in univariate models (p=0.016, Supplemental Table 2).

Figure 1:

Figure 1:

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Rates of germline pathologic variant (gPV) and Lynch Syndrome in EC by age decade.

Figure shows age decade-specific rates of germline pathogenic variants, biallelic inactivation, and Lynch Syndrome. Dark colored bars represent presence of these characteristics, whereas transparent light bars represent the absence of these characteristics, respectively.

Abbreviations: gPV – germline pathologic variant, LS – Lynch Syndrome.

Predictors of gPV

Odds of gPV varied by age, even after adjustment for ancestry and molecular subtype in multivariable models. Patients over 70 years old had lower odds of harboring gPVs compared to the patients ages 50 to 69 (OR 0.56, 95% CI [0.37, 0.83]). Those with AFR genetic ancestry were less likely to have gPVs (OR 0.38, 95% CI [0.17, 0.76]) and those with ASJ ancestry were more likely to have gPVs (OR 1.61, 95% CI [1.11, 2.32]) compared to those of EUR genetic ancestry. Patients with tumors of MSI-H molecular subtype were more likely to have gPV (OR 2.02, 95% CI [1.39, 2.97]) compared to those with CN-L/NSMP ECs (Table 2).

Table 2:

Multivariable logistic regression models for (1) presence of gPV, (2) biallelic inactivation, (3) Lynch syndrome.

Characteristics gPV Biallelic Inactivation* LS genes**
OR 95% CI p-value OR 95% CI p-value OR 95% CI p-value
Age at Dx 0.003 <0.001 0.002
50-69 years 1.00 -- 1.00 -- 1.00 --
<50 years 1.36 0.84, 2.15 3.42 1.52, 7.30 3.49 1.63, 7.01
≥70 years 0.56 0.37, 0.83 0.36 0.14, 0.79 -- --
Genetic Ancestry <0.001 0.019
EUR 1.00 -- 1.00 --
AFR 0.38 0.17, 0.76 0.82 0.27, 2.10
AJ 1.61 1.11, 2.32 2.02 1.01, 3.90
EAS 0.76 0.34, 1.53 0.33 0.02, 1.69
SAS 1.48 0.58, 3.32 3.47 0.93, 10.3
Mixed/NAM 0.66 0.38, 1.09 0.45 0.13, 1.21
Molecular Subtype <0.001 <0.001
CN-L/NSMP 1.00 -- 1.00 --
POLE 1.01 0.50, 1.93 1.05 0.15, 4.92
MSI-H/MMRd 2.02 1.39, 2.97 5.49 2.28, 15.4
CN-H/p53abnormal 1.33 0.88, 2.00 2.86 1.02, 9.17
Histologic Type 0.007
Endometrioid (G1 or G2) 1.00 --
Others 2.66 1.30, 5.49
Obesity 0.046
No 1.00 --
Yes 0.50 0.24, 0.99
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*

% Biallelic inactivation is out of all gPV.

**

Due to the small sample size of patients 70 years and older with Lynch Syndrome genes, we combined this control group to include all patients ≥50 years.

Abbreviations:

Ancestry: EUR – European, AFR – African, AJ – Ashkenazi Jewish, EAS – East Asian, SAS – South Asian, NAM – Native American

Molecular: CN-L – copy number low, MMRd, mismatch repair-deficient, MSI-H – microsatellite instability high, NSMP, no specific molecular profile, CN-H – copy number high

Others: gPV – germline pathogenic variant, LOH – loss of heterozygosity, LS – Lynch Syndrome, OR – odds ratio, CI – confidence interval

Predictors of biallelic inactivation

Biallelic inactivation also differed by age groups, with enrichment in early-onset EC that decreased for each subsequent older age cohort (p<0.001). Of those with gPVs, biallelic inactivation was observed in 14/28 eoEC tumors (50%), 48/152 average-onset tumors (32%) and 8/36 later-onset tumors (22%). Early-onset EC has higher odds of biallelic inactivation (OR 3.42, CI [1.52, 7.30]), while later-onset EC has lower odds (OR 0.36, CI [0.14, 0.79]) in multivariable models. Compared to lower-grade ECs (grades I/II), other histology types are more likely to have biallelic inactivation (OR 2.66, CI [1.30, 5.49]). ASJ genetic ancestry was also a predictor of biallelic inactivation (OR 2.02, CI [1.01, 3.90]). MSI-H and CN-H/p53 abnormal were molecular subtypes more likely to have biallelic inactivation (OR 5.49, CI [2.28, 15.4]; OR 2.86, CI [1.02, 9.17] respectively, Table 2).

Predictors of Lynch Syndrome (LS)

We observed 39 patients with LS (5 with MLH1, 11 with MSH2, 19 with MSH6, 4 with PMS2, and 0 with EPCAM). LS was significantly higher in eoEC cohort (11/170, 7%), and no patients 70 years or older had LS. Early-onset EC had higher odds of LS (OR 3.49, CI [1.63, 7.01], p=0.002) in multivariable models. Obese patients were less likely to have LS compared to non-obese patients in models (OR 0.50, CI [0.24, 0.99], Table 2). All but 6 patients with LS had an MSI-H EC. Of these, 2/6 had a POLE phenotype and were diagnosed at age 51 and 50. The remaining 4 ECs occurred in patients with MSH6-LS with only one occurring age <50 (Supplemental Table 3).

Focused look: Early-onset endometrial cancer

The most common gPVs seen in eoEC were MSH2 (n=5), MSH6 (n=3), MLH1 (n=3), and CHEK2 (n=3) (Figure 2); however, heterogeneity was observed. In very-early-onset EC, 3 gPVs were observed (1 ATM, 2 CHEK2, 1 MLH1), compared to 15 different gPVs in those diagnosed ages 40-49 years (Supplemental Table 4). Among all patients with gPVs, those with eoEC were more likely to carry high penetrance gPV than older patients (p=0.024). LS occurred in 11 patients (5 MSH2, 3 MSH6, 3 MLH1), with 10 patients in the 40-49 age group and 1 patient in the <40 age group. However, there was heterogeneity in age distribution among patients with LS (Figure 3A), with MLH1 and MSH2 more commonly associated with early-onset tumors and PMS2 with later-onset tumors. Similarly, BRCA1 was associated with earlier-onset EC than BRCA2 (Figure 3B). The youngest patient with EC at age 31 had an MLH1 gPV, with at least 4 individuals with confirmed MLH1 gPV in the family all of which developed LS-associated cancers (endometrial or colorectal cancers) at young ages between 31 and 45, underscoring the high penetrance of this gene (Figure 3C). Other gPVs were in high-penetrance genes with either possible association with EC (1 BRCA1, 1 PALB2) or no known association with EC (4 CHEK2, 2 ATM, 1 RET). Ten gPVs were in genes with autosomal recessive mode of inheritance or genes with no known associated cancer risks in heterozygotes (Supplemental Table 4).

Figure 2:

Figure 2:

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Age-related Germline Landscape and Biallelic Inactivation

Figure shows specific gene variants observed in patients with endometrial cancer, grouped by age decade on the horizontal axis. They are further broken up by high/moderate/low penetrant variants for better visualization and comparison. Left-sided graph depicts incidence (in number of patients) of each variant, ranging from 0 to 10. Right-sided graph depicts presence of biallelic inactivation (shaded as number of biallelic inactivated genes) for each variant, ranging from all biallelic inactivated to none.

Abbreviations: gPV – germline pathologic variant, LS – Lynch Syndrome, Bi – biallelic, Mono – monoallelic.

Figure 3:

Figure 3:

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Age distribution of EC in Lynch Syndrome and BRCA1/2

Figures show age distribution (%) by (A) MMR genes (LS) and (B) BRCA1/2 genes. Pedigree (C) depicts family history of the youngest patients with LS (MLH1 gPV) and EC diagnosed at age 31. Pedigree (D) depicts family history of 47-year-old patient with EC and BRCA1 gPV who was proband of her family; family history of multiple breast, ovarian, and colorectal cancers.

Abbreviations: EC – endometrial cancer, CRC – colorectal cancer, BC – breast cancer, OC – ovarian cancer, gPV – germline pathogenic variant, LS – Lynch Syndrome

All cases of biallelic inactivation were observed in the 40-49 age group, compared to 0 cases the younger <40 age group. In particular, patients with BRCA1 and PALB2 gPVs all had biallelic inactivation. Of these 28 patients with gPV and biallelic inactivation, 21(75%) represented the familial proband, and 11 (39.3%) pts had family history of cancer including 3 with family history of eoEC. One proband with BRCA1 gPV, diagnosed at age 47, had multiple family members with breast and ovarian cancers. Although definitive genetic testing was not performed in these relatives, the clinical phenotype was consistent with hereditary breast and ovarian cancer syndrome (Figure 3D).

Focused look: Later-onset endometrial cancer

In later-onset EC, most common gPVs were MUTYH (heterozygous, n=7), CHEK2 (n=6), APC-I1307K (n=4), ERCC3 (heterozygous, n=4), and FANCA (heterozygous, n=4). In the very-late-onset EC (diagnosed ≥80 years), 2 gPVs were observed (1 FANCA, 1 CHEK2), compared to 34 patients with 16 different gPVs in the slightly younger 70-79 cohort. Most of these gPVs (12/16, 75%) were low or uncertain penetrance. Biallelic inactivation in tumor samples was seen in individuals with gPV in BRCA1, PMS2, and RAD51D, whereas biallelic inactivation was not present in examined tumors of individuals with gPV in BRIP1, ERCC3, and MUTYH. Notably, biallelic inactivation for CHEK2 was heterogeneous with no biallelic inactivation in those at the younger end of this range; however, the oldest patient at age 80 had biallelic inactivation (Figure 2).

Age of EC diagnosis by molecular and histologic subtypes

Median age at diagnosis was lowest for ECs of POLE molecular subtype at 55.5 years, and highest for CN-H/p53abnormal ECs at 66 years (Figure 4a). By histologic subtype, median age at diagnosis was lowest for low-grade (I & II) endometrioid cancer at 60 years, and highest for clear cell EC at 68 years (Figure 4b). The youngest patient in our study cohort had an EC of CN-L/NSMP molecular subtype of endometrioid histology, whereas the oldest EC patient had CN-H/p53abnormal carcinosarcoma. Amongst the eoEC cohort, 158 patients (out of 170) had unknown histology. Of those with known histology, 113/158 (72%) were G1/2 endometrioid tumors, 24/158 (15%) were G3 endometrioid tumors, and 21/158 (13%) were clear cell, carcinosarcoma, mixed, or undifferentiated tumors.

Figure 4:

Figure 4:

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Age Distribution by Molecular Subtype and Histology

Figure shows average and interquartile range for age of endometrial cancer diagnosis, grouped by (a) molecular subtypes, and (b) histology substyles. Outliers outside of the interquartile range are represented with individual data points.

Abbreviations: G1/G2/G3 – grade 1/2/3.

DISCUSSION

We demonstrate that gPV, biallelic inactivation, and LS were significantly enriched in eoEC. Compared to those diagnosed at ages 50–69, eoEC was independently associated with higher odds of biallelic inactivation in those with gPV. Within the eoEC group, we observed heterogeneity with patients diagnosed before age 40 having lower rates of gPVs and no observed biallelic inactivation, suggesting potential differences in the germline landscape relative to those diagnosed at ages 40–49. These findings suggest multifactorial drivers of tumorigenesis in eoEC and support further study of very-early-onset EC given its rarity and unique characteristics.

LS and high penetrance genes

Among prior studies evaluating early-onset cases, the most common gPV were those in MMR genes underlying LS, with up to 26% of eoEC patients harboring such a gPV13,26,27. We find a similar distribution, with 3 of the 4 most common gPVs in eoEC being MSH2, MLH1, and MSH6. Within LS, we see a clear age-related difference, with MLH1 appearing only in eoEC and PMS2 favoring older patients. Prior studies have shown that MLH1 and MSH2 are associated with higher EC risk, although these risks do not increase appreciably until after the age of 40 years28,29. Interestingly, MSH6-associated LS shows a later age of EC onset compared to MLH1/MSH2-associated LS in our cohort, despite similar lifetime cumulative risks. MSH6-LS also represented most of the discordant tumor cases, highlighting known limitations in MMR/MSI assessments in LS that may disproportionately affect MSH6. This further supports orthogonal methods of MMR/MSI tumor testing in LS, particularly in MSH6-LS. Notably, PMS2-associated EC was exclusively seen in post-menopausal women. This has implications for EC screening and timing of risk-reducing hysterectomy and potential coordination with delayed bilateral oophorectomy (BO) to closer to menopause in MSH6 and PMS2-associated LS.

BRCA1 gPV may also confer increased risk of EC. In a prospective study of almost 5000 patients with BRCA mutations, Kotsopoulos et al. showed higher risk of EC among BRCA1 carriers from age 40 to 70, although the absolute rates were low at 3.4% for BRCA1 carriers and 1.6% for BRCA230. In our study, however, we observed only one eoEC case in a patient with a BRCA1 gPV, which suggests that despite the increased risk, the age of onset is comparable to sporadic EC, which may be helpful in counseling patients around concurrent hysterectomy with BSO in those with BRCA1 gPV.

Other germline findings

Our study also revealed gPV in more moderate penetrance, non-LS/BRCA1/2 genes that are potentially actionable, including ATM and CHEK2. These genes are both associated with moderate cancer risk and implicated in several cancers, including breast, prostate, and pancreatic 31-34. Emerging work is also focused on uncovering novel associations in EC, such as potential associations with other genes involved in homologous recombination repair, RAD51D and PALB235. Furthermore, we did not observe gPV in PTEN and POLD1 that may be associated with increased EC risk, likely due to their low overall prevalence in populations unselected for strong family histories or syndromic features. Hence, despite the relatively large study size, we remain unable to comment on all germline genetics and individual driver patterns. Our findings underscore the importance of broad multigene panel germline testing in patients with EC, irrespective of age or family history, to improve identification of actionable variants that may inform surveillance and risk assessment strategies.

Heterogeneity in eoEC

Although eoEC is increasingly recognized as a biologically distinct subset of EC, little is known about the differences between the very-early-onset cohort (diagnosed <40 years) and the slightly older 40-49 years age group. In this study, several notable distinctions emerged. We observed a markedly lower prevalence of gPV in the very eoEC group (1/4, 25%) compared to those aged 40–49 (13/24, 54%), suggesting that heritable variants may play a less prominent role in tumorigenesis among the youngest patients. Biallelic inactivation was also enriched in the 40–49-year group (14/24, 58%) and absent in the <40 group (0/4, 0%), further suggesting that known germline contributors may play less of a role in very-eoEC. Existing studies suggest that very early-onset cases may be sporadic and driven by environmental exposures including estrogen excess and metabolic syndromes36-38.

One prior hypothesis is that rising obesity rates may be a driver of increased eoEC incidence, mechanistically mediated by increased peripheral estrogen production in adipose 39,40. Our findings challenge this association, as the patients with eoEC in our cohort had significantly lower body mass indices compared to older patients (average BMI of 27.6 for eoEC vs 30.2 for average-onset EC, p=0.004). Despite lower BMI, the molecular landscape of eoEC was enriched for POLE and CN-L/NSMP molecular subtypes, reflecting a predominance of sporadic, ultra-mutated, and molecularly heterogeneous tumors arising from non-hereditary molecular pathways. Epigenetic changes, such as promoter methylation and distinct methylation profiles, may also play a role. In younger patients, prolonged exposure to unopposed estrogen, whether due to reproductive factors such as early menarche, anovulation, or nulliparity, can additionally drive tumorigenesis especially in CN-L/NSMP cancers independent of germline predisposition, given this subtype’s association with endometroid histology, estrogen and progesterone receptor (ER/PR) positivity, and hormone-driven tumorigenesis41,42. Even in the absence of obesity-related metabolic disruption, hormonally-mediated pathways may still play a significant role in eoEC pathogenesis. This remains a hypothesis-generating exploratory finding that requires more study.

Furthermore, our ancestry-based findings are supported by existing literature showing that eoEC incidence has been increasing faster for women of color40,43. A prior study on over 900,000 patients found that eoEC was more likely to be found in Hispanic or NH Asian/Pacific Islander patients44. Another supported that East Asians with EC were diagnosed at a younger age and found to have a high rate of LS45. Taken together, these findings underscore the complex interplay between hormonal, genetic, and ancestral factors in eoEC development and support the need for tailored prevention strategies and risk stratification.

Strengths and limitations

A key strength of our study is the use of a large cohort from an urban academic center with a high germline testing uptake rate in unselected patients, which allows for more comprehensive and robust assessment of inherited cancer risk. Unlike prior studies that have typically grouped all patients under age 50 together, we provide a novel, age-stratified analysis of germline pathogenic variants across individual decades of life. This granular breakdown and associations with tumor phenotype enable a more precise understanding of the unique molecular and clinical features of the very young endometrial cancer population (<40 years), a group often underrepresented or homogenized in existing literature.

This study has several limitations. As a single-center analysis, our findings are subject to ascertainment bias and may not be fully generalizable to more diverse, community-based populations. Despite outreach to diverse populations, our cohort was predominantly White and European, which limits our ability to further explore ancestry-specific patterns in gPVs. The sample sizes of several less common but potentially actionable variants such as for PALB2 were small, which reduced the statistical power to draw definitive conclusions about their clinical significance. Similarly, the limited number of older patients with LS precluded a focused regression analysis on later-onset EC, despite observed age-related trends. Finally, while our study emphasizes germline findings, a more comprehensive analysis incorporating somatic alterations and their correlation with germline variant expression could provide deeper insights into tumor biology and variant pathogenicity.

CONCLUSION

Distinct differences in the germline landscape exist across early-onset, average-onset, and later-onset ECs with enrichment of gPV in eoEC suggesting varying drivers across age. While LS is more common in early-onset cases (particularly ages 40–49), there is heterogeneity by LS gene, supporting gene-specific management of EC risk. While gPVs were observed in all age groups, low rates at the extremes of age suggest other drivers that need further research.

Supplementary Material

PV Appendix Tables

CONTEXT SUMMARIES.

Key Objective:

To explore age-related differences in the prevalence and characteristics of germline pathogenic variants (gPV), biallelic inactivation, and Lynch syndrome in endometrial cancer (EC).

Knowledge Generated:

Among 1,625 patients with EC undergoing tumor-normal sequencing via MSK-IMPACT, gPV were observed in 16% of early-onset cases (age <50), 14% in patients aged 50–69, and 9% in those aged ≥70. Early-onset EC was more likely to exhibit biallelic inactivation and be associated with Lynch syndrome, particularly among patients aged 40–49. However, those diagnosed before age 40 had lower rates of gPVs, biallelic inactivation, and LS, suggesting heterogeneity in this early-onset group.

Relevance:

Our findings support germline evaluation in EC, particularly early-onset cases, to inform genetic counseling, risk-reducing strategies, and family testing. The observed molecular heterogeneity also underscores the need to identify additional drivers in very early-onset cases.

Funding:

This work was supported by a generous a donation from Carolyn Tastad and Robert Young and the Robert and Kate Niehaus Center for Inherited Cancer Genomics. MSK is supported by a National Cancer Institute/National Institutes of Health Cancer Center Support Grant (P30 CA008748). B. Weigelt is supported in part by Cycle for Survival and Breast Cancer Research Foundation grants.

Conflict of Interest (per ASCO disclosures and below)

B. Weigelt reports research grants from REPARE Therapeutics and SAGA Diagnostics paid to the institution, and employment of an immediate family member at AstraZeneca.

Y.R. Murciano-Goroff reports travel, accommodation, and expenses from AstraZeneca and Loxo Oncology/ Eli Lilly. She acknowledges honoraria from Virology Education and Projects in Knowledge (for a CME program funded by an educational grant from Amgen). She has been on an advisory board for Revolution Medicines, and consulted for AbbVie. She acknowledges associated research funding to the institution from Mirati Therapeutics, Bristol Myers Squibb/ E.R. Squibb & Sons, Loxo Oncology at Eli Lilly, Elucida Oncology, Taiho Oncology, Hengrui USA, Ltd/ Jiangsu Hengrui Pharmaceuticals, Luzsana Biotechnology, Endeavor Biomedicines, and AbbVie. She is an employee of Memorial Sloan Kettering Cancer Center, which has an institutional interest in Elucida. She acknowledges royalties from Rutgers University Press and Wolters Kluwer. She acknowledges food/beverages from Endeavor Biomedicines, AstraZeneca, and Eli Lilly, and other services from Amgen, Loxo Oncology/ Eli Lilly, and AbbVie. Y.R. Murciano-Goroff acknowledges receipt of training through an institutional K30 grant from the NIH (CTSA UL1TR00457). She has received funding from a Kristina M. Day Young Investigator Award from Conquer Cancer, the ASCO Foundation, endowed by Dr. Charles M. Baum and Carol A. Baum. She is also funded by the Fiona and Stanley Druckenmiller Center for Lung Cancer Research, the Andrew Sabin Family Foundation, the Society for MSK, the Squeri Grant for Drug Development, and a Paul Calabresi Career Development Award for Clinical Oncology (NIH/NCI K12 CA184746) as well as through NIH/NCI R01 CA279264.

Footnotes

A portion of this work was presented as a poster as the ASCO 2025 Annual Meeting

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Supplementary Materials

PV Appendix Tables
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