Endometrial Cancer Cohort Research Articles

Phase II basket study of disitamab vedotin among patients with previously-treated, locally advanced unresectable or metastatic solid tumors that express HER2: Ovarian and endometrial cancer cohorts (DV-005; Trial in Progress)

Phase II basket study of disitamab vedotin among patients with previously-treated, locally advanced unresectable or metastatic solid tumors that express HER2: Ovarian and endometrial cancer cohorts (DV-005; Trial in Progress)

Efficacy and safety of trastuzumab deruxtecan among patients with HER2-expressing solid tumors: Biomarker and subgroup analyses from the cervical, endometrial, and ovarian cancer cohorts of the DESTINY-PanTumor02 study

Efficacy and safety of trastuzumab deruxtecan among patients with HER2-expressing solid tumors: Biomarker and subgroup analyses from the cervical, endometrial, and ovarian cancer cohorts of the DESTINY-PanTumor02 study

The impact of environmental factors and social determinants of health on mismatch repair status in a single-center endometrial cancer cohort

The impact of environmental factors and social determinants of health on mismatch repair status in a single-center endometrial cancer cohort

Impact of the 2023 FIGO staging on a high-risk endometrial cancer cohort

Impact of the 2023 FIGO staging on a high-risk endometrial cancer cohort

Ensemble transformer-based multiple instance learning to predict pathological subtypes and tumor mutational burden from histopathological whole slide images of endometrial and colorectal cancer

In endometrial cancer (EC) and colorectal cancer (CRC), in addition to microsatellite instability, tumor mutational burden (TMB) has gradually gained attention as a genomic biomarker that can be used clinically to determine which patients may benefit from immune checkpoint inhibitors. High TMB is characterized by a large number of mutated genes, which encode aberrant tumor neoantigens, and implies a better response to immunotherapy. Hence, a part of EC and CRC patients associated with high TMB may have higher chances to receive immunotherapy. TMB measurement was mainly evaluated by whole-exome sequencing or next-generation sequencing, which was costly and difficult to be widely applied in all clinical cases. Therefore, an effective, efficient, low-cost and easily accessible tool is urgently needed to distinguish the TMB status of EC and CRC patients. In this study, we present a deep learning framework, namely Ensemble Transformer-based Multiple Instance Learning with Self-Supervised Learning Vision Transformer feature encoder (ETMIL-SSLViT), to predict pathological subtype and TMB status directly from the H&E stained whole slide images (WSIs) in EC and CRC patients, which is helpful for both pathological classification and cancer treatment planning. Our framework was evaluated on two different cancer cohorts, including an EC cohort with 918 histopathology WSIs from 529 patients and a CRC cohort with 1495 WSIs from 594 patients from The Cancer Genome Atlas. The experimental results show that the proposed methods achieved excellent performance and outperforming seven state-of-the-art (SOTA) methods in cancer subtype classification and TMB prediction on both cancer datasets. Fisher’s exact test further validated that the associations between the predictions of the proposed models and the actual cancer subtype or TMB status are both extremely strong (p<0.001). These promising findings show the potential of our proposed methods to guide personalized treatment decisions by accurately predicting the EC and CRC subtype and the TMB status for effective immunotherapy planning for EC and CRC patients.

Exploring metastasis and recurrence patterns in low-risk grade 3 endometrial cancer: A multicenter retrospective cohort study

ObjectiveFemales with low-risk endometrial cancer typically have low lymph node metastasis risk and promising prognosis without lymphadenectomy. However, the impact of grade 3 endometrial cancer on nodal involvement, recurrence, and prognosis within this specific subgroup remains unclear. Therefore, in this study, we aimed to investigate the prognosis, patterns of metastasis, and recurrence in a subgroup of females with grade 3 early-stage low-risk endometrioid endometrial cancer. MethodsWe identified patients from the endometrial cancer cohorts of seven institutional hospitals. The study included patients who underwent hysterectomy between January 2013 and December 2021 with preoperative endometrioid histological type, less than half myometrial invasion, no tumor spread outside the corpus on imaging, normal CA-125 level, and histological grade 3. The clinicopathological characteristics and survival outcomes of the patients were collected. Recurrence-free survival was estimated using the Kaplan–Meier method and compared using the log rank test. ResultsOverall, 36 patients were included in this analysis. Of the 33 patients who underwent lymphadenectomy, 1 (1/33, 3.0 %) had lymph node metastasis and 27 (75.0 %) received adjuvant therapy. At a median follow-up of 58 months, three females (8 %) had recurrence and all cases involved lymph nodes. The 5-year recurrence-free survival was 88.7 %. No significant difference was observed in the recurrence-free survival between females who did and did not undergo lymphadenectomy (p = 0.554). ConclusionFemales diagnosed with low-risk grade 3 endometrial cancer typically have favorable prognosis. However, lymph node metastasis and recurrence risks still exist, with all recorded instances of recurrence involving lymph nodes.

Efficacy and Safety of Sacituzumab Govitecan in Patients With Advanced Solid Tumors (TROPiCS-03): Analysis in Patients With Advanced Endometrial Cancer.

Patients with advanced endometrial cancer (EC) who progress on or after platinum-based therapy and immunotherapy have poor prognosis. We report efficacy and safety of sacituzumab govitecan (SG), a trophoblast cell-surface antigen 2 (Trop-2)-directed antibody-drug conjugate, in patients with advanced EC. TROPiCS-03 (ClinicalTrials.gov identifier: NCT03964727) is a multicohort, open-label, phase II basket study in patients with metastatic solid tumors. Eligible patients in the EC cohort received SG 10 mg/kg once on days 1 and 8 every 3 weeks. Primary end point was objective response rate (ORR) by investigator's assessment per RECIST v1.1. Secondary end points included clinical benefit rate (CBR; complete and partial response, and stable disease ≥6 months), duration of response (DOR), and progression-free survival (PFS) per investigator assessment, overall survival, and safety. Trop-2 expression of archival or baseline tumor specimens was analyzed by immunohistochemistry. At data extraction date, 41 patients were enrolled. Median follow-up was 5.8 months (range, 0.7-19.3); median previous therapies was three (range, 1-6); and 85% of patients received previous chemotherapy and immunotherapy. ORR was 22% (95% CI, 11 to 38); CBR was 32% (95% CI, 18 to 48). Median DOR was 8.8 months (95% CI, 2.8 to not estimable); median PFS was 4.8 months (95% CI, 2.8 to 9.8). Trop-2 exploratory analysis was conducted retrospectively for 39 patients. Tumor Trop-2 protein was highly expressed in EC, showing limited correlation with efficacy. Grade ≥3 treatment-related adverse events (TRAEs) occurred in 73% of patients. Study drug discontinuation due to TRAEs was 5%. Two deaths occurred, deemed unrelated to SG. Findings from TROPiCS-03 showed encouraging efficacy of SG with a manageable toxicity profile in a heavily pretreated population with advanced EC. Safety findings were consistent with the known SG safety profile.

A stemness-based signature with inspiring indications in discriminating the prognosis, immune response, and somatic mutation of endometrial cancer patients revealed by machine learning.

Endometrial cancer (EC) is a fatal gynecologic tumor. Bioinformatic tools are increasingly developed to screen out molecular targets related to EC. Our study aimed to identify stemness-related prognostic biomarkers for new therapeutic strategies in EC. In this study, we explored the prognostic value of cancer stem cells (CSCs), characterized by self-renewal and unlimited proliferation, and its correlation with immune infiltrates in EC. Transcriptome and somatic mutation profiles of EC were downloaded from TCGA database. Based on their stemness signature and DEGs, EC patients were divided into two subtypes via consensus clustering, and patients in Stemness Subtype I presented significantly better OS and DFS than Stemness Subtype II. Subtype I also displayed better clinicopathological features, and genomic variations demonstrated different somatic mutation from subtype II. Additionally, two stemness subtypes had distinct tumor immune microenvironment patterns. In the end, three machine learning algorithms were applied to construct a 7-gene stemness subtype risk model, which were further validated in an external independent EC cohort in our hospital. This novel stemness-based classification could provide a promising prognostic predictor for EC and may guide physicians in selecting potential responders for preferential use of immunotherapy. This novel stemness-dependent classification method has high value in predicting the prognosis, and also provides a reference for clinicians in selecting sensitive immunotherapy methods for EC patients.

A phase 1/2 study of ubamatamab (REGN4018), a MUC16×CD3 bispecific antibody, administered alone or in combination with cemiplimab (anti–PD-1) in patients with recurrent ovarian cancer or MUC16+ endometrial cancer.

TPS5632 Background: Mucin 16 (MUC16) is a cell surface glycoprotein that is overexpressed in ovarian cancer (OC) and endometrial cancer (EC). Ubamatamab is a MUC16 × cluster of differentiation 3 (MUC16×CD3) bispecific antibody that bridges MUC16 on tumor cells and CD3 on T cells to promote T-cell–mediated cytotoxicity. Cemiplimab, a monoclonal antibody targeting the programmed cell death-1 (PD-1) receptor, enhanced MUC16×CD3 activity in preclinical models. In Phase 1 of this first-in-human study (NCT03564340), ubamatamab alone and in combination with cemiplimab demonstrated an acceptable safety profile, durable clinical activity across a range of doses, and linear pharmacokinetics (PK) in patients (pts) with recurrent OC (Liu et al. IGCS 2023. Plenary 03). Phase 2 will assess the efficacy of ubamatamab alone and in combination with cemiplimab in pts with recurrent OC. Ubamatamab will also be evaluated for recurrent advanced or metastatic EC that is MUC16+. Methods: For individuals with recurrent OC, elevated serum cancer antigen (CA)-125 ≥2× the upper limit of normal, and up to four prior lines of cytotoxic therapy, weekly dosing of ubamatamab alone or in combination with cemiplimab will be evaluated in monotherapy and combination therapy expansion cohorts. A Phase 2 cohort will also be randomized 1:1:1 to receive ubamatamab 250 mg IV Q3W or 800 mg IV Q3W or ubamatamab 250 mg IV Q3W + cemiplimab 350 mg IV Q3W. For the EC cohort (≥25% of tumor cells MUC16+, as assessed by immunohistochemistry [IHC]) pts with advanced or metastatic EC that has progressed or is recurrent after prior anti–PD-1 therapy and prior platinum-based chemotherapy will be enrolled. The initial EC cohort will receive ubamatamab 250 mg IV Q3W. Based upon observed safety and activity, additional cohorts may be enrolled to receive ubamatamab 800 mg IV Q3W and/or ubamatamab 250 mg IV Q3W + cemiplimab 350 mg IV Q3W. To limit the risk of cytokine release syndrome, all pts will receive ubamatamab once-weekly step-up dosing prior to addition of cemiplimab and/or Q3W dosing. Expansion cohorts will use a Simon 2-stage study design, with an interim analysis after the first 20 pts. Any treatment arm with ≥3 objective responses may be expanded to 50 pts. In Phase 2, the primary endpoint will be the objective response rate for each arm (RECIST v1.1). Secondary endpoints include duration of response and progression-free survival, safety, PK, and change from baseline in quality of life. Exploratory objectives include clinical correlation with serum CA-125, MUC16 IHC, and immune markers. The study is currently enrolling to these OC and EC expansion cohorts. Clinical trial information: NCT03564340 .

Prognostic value of PORTEC-3 molecular markers by disease risk in a real-world early endometrial cancer cohort.

Prognostic value of PORTEC-3 molecular markers by disease risk in a real-world early endometrial cancer cohort.

Prediction of recurrence risk in endometrial cancer with multimodal deep learning

Predicting distant recurrence of endometrial cancer (EC) is crucial for personalized adjuvant treatment. The current gold standard of combined pathological and molecular profiling is costly, hampering implementation. Here we developed HECTOR (histopathology-based endometrial cancer tailored outcome risk), a multimodal deep learning prognostic model using hematoxylin and eosin-stained, whole-slide images and tumor stage as input, on 2,072 patients from eight EC cohorts including the PORTEC-1/-2/-3 randomized trials. HECTOR demonstrated C-indices in internal (n = 353) and two external (n = 160 and n = 151) test sets of 0.789, 0.828 and 0.815, respectively, outperforming the current gold standard, and identified patients with markedly different outcomes (10-year distant recurrence-free probabilities of 97.0%, 77.7% and 58.1% for HECTOR low-, intermediate- and high-risk groups, respectively, by Kaplan–Meier analysis). HECTOR also predicted adjuvant chemotherapy benefit better than current methods. Morphological and genomic feature extraction identified correlates of HECTOR risk groups, some with therapeutic potential. HECTOR improves on the current gold standard and may help delivery of personalized treatment in EC.

Low-dose lenvatinib and anti-programmed cell death protein-1 combination therapy in patients with heavily pre-treated recurrent ovarian and endometrial cancer: a pilot study

ObjectiveTreatment options for heavily pre-treated recurrent ovarian and endometrial cancer are limited. Lenvatinib plus anti-programmed cell death protein-1 (PD-1) combination therapy has been efficacious in advanced endometrial cancer, but at...

Abstract B023: The role of ribosomal protein L22 in regulating the immune landscape in mismatch repair deficient endometrial cancers

Abstract The purpose of this study is to identify novel mechanisms of immune evasion in mismatch repair deficient endometrial cancers. Despite having a hypermutated phenotype that is proposed to lead to the accumulation of many neoantigens for immune recognition, our group has found that mismatch repair deficient endometrial cancers characterized by MLH1 protein loss display large variations in the number of tumors infiltrating CD8+ T cells (TILs) across different tumors. To better understand how variations in TILs arise in mismatch repair deficient endometrial cancers, we utilized human endometrial cancer samples from the UNCseq cohort. Immunohistochemistry (IHC) was used to identify expression patterns in proteins of interest across mismatch repair deficient endometrial cancers. To determine how expression patterns of proteins of interest correlate with TILs in our patient samples, Definiens Tissue Studio was utilized to quantify immunofluorescent CD8+ TILs in our mismatch repair deficient endometrial cancer cohort. Although upregulation of programmed death ligand 1 (PD-L1) expression is often cited as an explanation for low TILs, analysis of human data by our group shows that PD-L1 expression does not correlate with TIL numbers in mismatch repair deficient endometrial cancers. Interestingly, The Cancer Genome Atlas (TCGA) data reported that ribosomal protein L22 (RPL22) is mutated in ~50% of mismatch repair deficient endometrial cancers. We analyzed RPL22 by IHC and found that it was lost in 52% of our human mismatch repair deficient endometrial cancer cohort. Loss of various ribosomal proteins can alter translation efficiency of proteins critical for promoting immunosurveillance and the immune response in cancer. In our cohort, mismatch repair deficient endometrial cancers that have lost expression of RPL22 have lower numbers of CD8+ TILs. Preliminary data also show that human endometrial cancer cell lines that do not express RPL22 also do not express beta-2-microglobulin (β2M), a component of the major histocompatibility complex I (MHC-I) molecules that is necessary for neoantigen presentation to CD8+ T cells. Thus, we hypothesize that RPL22 loss in mismatch repair deficient endometrial cancers results in decreased translation of β2M and CD8+ T cell evasion. Moving forward, we speculate that RPL22 could be utilized as a predictive biomarker for immunotherapy response in mismatch repair deficient endometrial cancers. Citation Format: Macy L. Osborne, Hannah Atkins, Andrew B. Gladden, Russell R. Broaddus. The role of ribosomal protein L22 in regulating the immune landscape in mismatch repair deficient endometrial cancers [abstract]. In: Proceedings of the AACR Special Conference on Endometrial Cancer: Transforming Care through Science; 2023 Nov 16-18; Boston, Massachusetts. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(5_Suppl):Abstract nr B023.

Modifiable Risk Factors and Risk of Colorectal and Endometrial Cancers in Lynch Syndrome: A Systematic Review and Meta-Analysis.

Lynch syndrome is the most common hereditary cause of colorectal and endometrial cancers. Modifiable risk factors, including obesity, physical activity, alcohol intake, and smoking, are well-established in sporadic cancers but are less studied in Lynch syndrome. Searches were conducted on MEDLINE, Embase, and Web of Science for cohort studies that investigated the association between modifiable risk factors and the risk of colorectal or endometrial cancer in people with Lynch syndrome. Adjusted hazard ratios (HRs) and 95% CIs for colorectal and endometrial cancers were pooled using a random effects model. The protocol was prospectively registered on PROSPERO (CRD 42022378462), and the meta-analysis was conducted in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses and Meta-Analysis of Observational Studies in Epidemiology reporting guidelines. A total of 770 citations were reviewed. Eighteen studies were identified for qualitative synthesis, with seven colorectal cancer (CRC) studies eligible for meta-analysis. Obesity (HR, 2.38 [95% CI, 1.52 to 3.73]) was associated with increased CRC risk. There was no increased CRC risk associated with smoking (HR, 1.04 [95% CI, 0.82 to 1.32]) or alcohol intake (HR, 1.32 [95% CI, 0.97 to 1.81]). Type 2 diabetes mellitus (T2DM) and some dietary factors might increase risk of CRC although more studies are needed. In a qualitative synthesis of three endometrial cancer cohort studies, female hormonal risk factors and T2DM may affect the risk of endometrial cancer, but obesity was not associated with an increased risk. Lifestyle recommendations related to weight and physical activity may also be relevant to cancer prevention for individuals with Lynch syndrome. Further high-quality prospective cohort studies, in particular, including endometrial cancer as an end point, are needed to inform evidence-based cancer prevention strategies in this high-risk population.

MiRNA levels are associated with body mass index in endometrial cancer and may have implications for therapy.

Endometrial cancer (EC) is the most prevalent gynecological cancer in high-income countries. Its incidence is skyrocketing due to the increase in risk factors such as obesity, which represents a true pandemic. This study aimed to evaluate microRNA (miRNA) expression in obesity-related EC to identify potential associations between this specific cancer type and obesity. miRNA levels were analyzed in 84 EC patients stratified based on body mass index (BMI; ≥30 or <30) and nine noncancer women with obesity. The data were further tested in The Cancer Genome Atlas (TCGA) cohort, including 384 EC patients, 235 with BMI ≥30 and 149 with BMI <30. Prediction of miRNA targets and analysis of their expression were also performed to identify the potential epigenetic networks involved in obesity modulation. In the EC cohort, BMI ≥30 was significantly associated with 11 deregulated miRNAs. The topmost deregulated miRNAs were first analyzed in 84 EC samples by single miRNA assay and then tested in the TCGA dataset. This independent validation provided further confirmation about the significant difference of three miRNAs (miR-199a-5p, miR-449a, miR-449b-5p) in normal-weight EC patients versus EC patients with obesity, resulting significantly higher expressed in the latter. Moreover, the three miRNAs were significantly correlated with grade, histological type, and overall survival. Analysis of their target genes revealed that these miRNAs may regulate obesity-related pathways. In conclusion, we identified specific miRNAs associated with BMI that are potentially involved in modulating obesity-related pathways and that may provide novel implications for the clinical management of obese EC patients.

Multi-omics Deep-learning Prediction of Homologous Recombination Deficiency-like Phenotype Improved Risk Stratification and Guided Therapeutic Decisions in Gynecological Cancers.

Homologous recombination deficiency (HRD) is a well-recognized important biomarker in determining the clinical benefits of platinum-based chemotherapy and PARP inhibitor therapy for patients diagnosed with gynecologic cancers. Accurate prediction of HRD phenotype remains challenging. Here, we proposed a novel Multi-Omics integrative Deep-learning framework named MODeepHRD for detecting HRD-positive phenotype. MODeepHRD utilizes a convolutional attention autoencoder that effectively leverages omics-specific and cross-omics complementary knowledge learning. We trained MODeepHRD on 351 ovarian cancer (OV) patients using transcriptomic, DNA methylation and mutation data, and validated it in 2133 OV samples of 22 datasets. The predicted HRD-positive tumors were significantly associated with improved survival (HR = 0.68; 95% CI, 0.60-0.77; log-rank p < 0.001 for meta-cohort; HR = 0.5; 95% CI, 0.29-0.86; log-rank p = 0.01 for ICGC-OV cohort) and higher responsetoplatinum-basedchemotherapy compared to predicted HRD-negative tumors. The translational potential of MODeepHRDs was further validated in multicenter breast and endometrial cancer cohorts. Furthermore, MODeepHRD outperforms conventional machine-learning methods and other similar task approaches. In conclusion, our study demonstrates the promising value of deep learning as a solution for HRD testing in the clinical setting. MODeepHRD holds potential clinical applicability in guiding patient risk stratification and therapeutic decisions, providing valuable insights for precision oncology and personalized treatment strategies.

Identification of key genes associated with endometriosis and endometrial cancer by bioinformatics analysis.

Endometriosis (EMS) is acknowledged as a risk factor for the development of endometrial cancer (EC), although the precise molecular mechanisms that underpin this association have yet to be fully elucidated. The primary objective of this investigation is to harness bioinformatics methodologies to identify pivotal genes and pathways that may be implicated in both EMS and EC, potentially offering novel therapeutic biomarkers for the management of endometriosis. We acquired four datasets pertaining to EMS and one dataset concerning EC from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) in EMS and EC cohorts, in comparison to controls, were ascertained utilizing the limma package. Subsequently, we conducted a series of bioinformatic analyses, including Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, and protein-protein interaction (PPI) analysis, to delineate pathways associated with the identified DEGs. Our bioinformatics analyses disclosed 141 shared DEGs between EMS and EC groups relative to the control cohort. GO analysis demonstrated that these genes are predominantly involved in the regulation of growth and development, as well as signal transduction pathways. KEGG analysis underscored the significance of these genes in relation to the JAK-STAT signaling pathway and leukocyte transendothelial migration. Furthermore, PPI analysis pinpointed ten central genes (APOE, FGF9, TIMP1, BGN, C1QB, MX1, SIGLEC1, BST2, ICAM1, MME) exhibiting high interconnectivity. Notably, the expression levels of APOE, BGN, C1QB, and BST2 were found to correlate with cancer genomic atlas data, and were implicated in tumor immune infiltration. Strikingly, only APOE and BGN demonstrated a significant correlation with patient prognosis. This comprehensive bioinformatics analysis has successfully identified key genes that may serve as potential biomarkers for EC. These findings significantly enhance our comprehension of the molecular underpinnings of EC pathogenesis and prognosis, and hold promise for the identification of novel drug targets.

Differential outcomes and immune checkpoint inhibitor response among endometrial cancer patients with MLH1 hypermethylation versus MLH1 “Lynch-like” mismatch repair gene mutation

ObjectivesTo identify differential survival outcomes and immune checkpoint inhibitor (ICI) response in MLH1 hypermethylated versus MLH1 mutated (“Lynch-like”) endometrial tumors and determine whether their molecular profiles can elucidate the differential outcomes. Methods1673 mismatch repair deficient endometrial tumors were analyzed by next-generation sequencing and whole transcriptome sequencing (Caris Life Sciences, Phoenix, AZ). PD-L1, ER, and PR were tested by immunohistochemistry and immune cell infiltrates were calculated using MCP-counter. Significance was determined using Chi-square and Mann-Whitney U tests and adjusted for multiple comparisons. Overall survival (OS) was depicted using Kaplan-Meier survival curves. ResultsThe endometrial cancer cohort comprised 89.2% patients with MLH1 hypermethylated tumors and 10.8% with MLH1 mutated tumors, with median ages of 67 and 60 years, respectively (p < 0.01). Patients with MLH1 hypermethylated tumors had significantly worse OS and trended toward worse OS following ICI treatment than patients with MLH1 mutated tumors. The immune microenvironment of MLH1 hypermethylated relative to MLH1 mutated was characterized by decreased PD-L1 positivity, immune checkpoint gene expression, immune cell infiltration, T cell inflamed scores, and interferon gamma (IFNγ) scores. MLH1 hypermethylation was also associated with decreased mutation rates in TP53 and DNA damage repair genes, but increased rates of JAK1, FGFR2, CCND1, and PTEN mutations, as well as increased ER and PR positivity. ConclusionsEndometrial cancer patients with MLH1 hypermethylation display significantly decreased survival and discrepant immunotherapy responses compared to patients with MLH1 mutated tumors, which was associated with differential mutational profiles, a more immune cold phenotype, and increased ER/PR expression in MLH1 hypermethylated tumors. Providers may consider early transition from single agent ICI to a multi-agent regimen or hormonal therapy for patients with MLH1 hypermethylated tumors.

Environmental exposures and mismatch repair status in a western Pennsylvania endometrial cancer cohort (155)

Environmental exposures and mismatch repair status in a western Pennsylvania endometrial cancer cohort (155)

Prospective cohort of pre- and post-diagnosis alcohol consumption and cigarette smoking on survival outcomes: an Alberta Endometrial Cancer Cohort Study.

To examine the independent and joint relationships between cigarette smoking and alcohol consumption with survival outcomes after endometrial cancer diagnosis. Pre- and post-diagnosis smoking and drinking histories were obtained from endometrial cancer survivors diagnosed between 2002 and 2006 during in-person interviews at-diagnosis and at ~ 3 years post-diagnosis. Participants were followed until death or January 2022. Multivariable-adjusted hazard ratios (HRs) and 95% confidence intervals (CIs) were estimated with Cox proportional hazards regression for associations with disease-free survival(DFS) and overall survival (OS). During a median 16.9 years of follow-up (IQR = 15.5-18.1 years), 152 of the 540 participants had a DFS event (recurrence: n = 73; deaths: n = 79) and 134 died overall. Most participants in this cohort were current drinkers (pre = 61.3%; post = 64.7%) while few were current cigarette smokers (pre = 12.8%; post = 11.5%). Pre-diagnosis alcohol consumption was not associated with survival, yet post-diagnosis alcohol intake ≥ 2 drinks/week was associated with worse OS compared with lifetime abstention (HR = 2.36, 95%CI = 1.00-5.54) as well as light intake (HR = 3.87, 95%CI = 1.67-8.96). Increased/consistently high alcohol intake patterns were associated with worse OS (HR = 2.91, 95%CI = 1.15-7.37) compared with patterns of decreased/ceased intake patterns after diagnosis. A harmful dose-response relationship per each additional pre-diagnosis smoking pack-year with OS was noted among ever smokers. In this cohort, smoking and alcohol individually were not associated with DFS and combined pre-diagnosis smoking and alcohol intakes were not associated with either outcome. Endometrial cancer survivors with higher alcohol intakes after diagnosis had poorer OS compared with women who had limited exposure. Larger studies powered to investigate the individual and joint impacts of cigarette smoking and alcohol use patterns are warranted to provide additional clarity on these modifiable prognostic factors.