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Endometrial cancer risk stratification

ABSTRACT: Endometrial cancer (ENDOM) prevention remains challenging, creating an urgent need for better risk stratification tools. We developed a prompt-based bimodal multilevel endometrial cancer (2M-EC) predictive platform that integrates clinically accessible data with multi-biofluid molecular profiles through MALDI-TOF MS analysis. Our study established the MBF-ED cohort (n=531), collecting comprehensive clinical data and multi-dimensional body fluid samples. We processed these using a unique analytical pipeline: (1) simplifying clinical variables through empirical and data-driven methods, (2) extracting ENDOM-specific MS features using machine learning, and (3) developing an innovative bimodal AI architecture that fuses 2D MS omics matrices with 1D clinical vectors. The resulting prompt-based 2M-EC predictive platform provides real-time, interpretable risk stratification through an online interface. The advantages of it include overcoming single-marker limitations via multimodal integration, combining molecular depth with clinical practicality and scalable design adaptable to both resource-limited and advanced healthcare settings. This work demonstrates how AI can bridge cutting-edge molecular profiling with routine clinical practice, offering a new paradigm for task-oriented cancer risk assessment.

ORGANISM(S): Homo Sapiens

SUBMITTER: Liang Qiao

PROVIDER: PXD063211 | iProX | Tue Apr 22 00:00:00 BST 2025

REPOSITORIES: iProX

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Project description:Lynch syndrome (LS), caused by inherited mutations in DNA mismatch repair genes including MSH2, carries a 60% lifetime risk of endometrial cancer (EC). Beyond hypermutability, mechanisms driving LS-associated EC remain unclear. We investigated MSH2 loss in EC pathogenesis using a mouse model (PR-Cre Msh2(LoxP/LoxP), abbreviated Msh2KO), primary cell lines, human tissues, and human EC cells with isogenic MSH2 knockdown. By eight months, 58% of Msh2KO mice developed endometrial atypical hyperplasia (AH), a precancerous lesion. At 12-16 months, 47% exhibited either AH or ECs with histologic similarities to human LS-ECs. Transcriptomic profiling of EC from Msh2KO mice revealed a signature of mitochondrial dysfunction. Subsequent studies in vitro and in vivo revealed mitochondrial dysfunction based upon two mechanisms: reduced mitochondrial content and structural disruptions in retained mitochondria. Human LS-ECs also exhibited reduced mitochondrial content compared to non-LS-ECs. Functional studies demonstrated metabolic reprogramming of MSH2-deficient EC cells, including decreased oxidative phosphorylation and heightened susceptibility to glycolysis suppression. These findings identified mitochondrial dysfunction and metabolic disruption as consequences of MSH2 deficiency in EC. Mitochondrial and metabolic aberrations should be evaluated as biomarkers for endometrial carcinogenesis or risk stratification and represent potential targets for cancer interception in women with LS. To achieve Msh2 loss in the endometrium, we bred progesterone receptor Cre knock-in (PR-Cre) mice to Msh2(LoxP/LoxP) mice to produce PR-Cre+Msh2(LoxP/LoxP) mice with targeted loss of MSH2 expression in tissues that express PR, including the uterus (hereafter abbreviated Msh2KO). Control mice contain Msh2(LoxP/LoxP) without PR-Cre. We profiled Msh2KO endometrial tumors, Msh2KO benign endometrium, and control mouse benign endometrium using transcriptomic profiling of microdissected glands via microarray analysis (ClariomD, Affymetrix).

Project description:Background Bladder cancer represents a heterogeneous disease with distinct clinical challenges. Non-muscle invasive bladder cancer (NMIBC) typically presents as indolent and slow-growing, yet a critical clinical challenge remains: identifying which patients will progress to muscle-invasive disease requiring radical interventions. Early detection of progression propensity is essential, as once muscle invasion occurs, the risk of distant metastasis increases substantially, and treatment shifts from conservative TURBT (Transurethral Resection of Bladder Tumor) to aggressive surgical interventions with significant morbidity. Current risk stratification methods fail to adequately predict this transition in approximately 30% of cases, highlighting the urgent need for more accurate prognostic tools. Objective This retrospective study aimed to develop and validate a transcriptomics-based mRNA score for predicting early NMIBC recurrence, comparing its performance against traditional risk stratification methods. Methods We analyzed mRNA expression profiles from primary retrospective NMIBC tumor specimens (n=25) collected between [2018-2022]. Traditional risk stratification tools, including EORTC scoring, were applied alongside our novel mRNA-based risk score to evaluate predictive accuracy for recurrence. Results The transcriptomics-based mRNA score demonstrated a median prediction accuracy of 90% across 10,000 resampling iterations for predicting early NMIBC recurrence, significantly outperforming traditional EORTC risk scores. Our comprehensive gene set identified 435 differentially expressed genes associated with recurrence. Kaplan-Meier analysis showed significantly different recurrence-free survival between high and low mRNA risk score groups (Bonferroni corrected p-value<0.0001). Conclusions This retrospective analysis confirms that mRNA expression-based risk stratification provides superior predictive accuracy compared to conventional clinicopathologic risk tools. Implementation of this gene signature could potentially reduce over-investigation and improve surveillance cost-effectiveness after TURBT in patients with primary high-risk NMIBC. These findings may transform the clinical management paradigm by enabling more personalized follow-up protocols based on molecular risk assessment.

Project description:Endometrial cancer (EC) is the most common female genital malignancy and the fourth most common cancer in women in the developing world1. EC has been traditionally classified into two main groups with different clinical, pathological and molecular features2,3. Type I or endometrioid endometrial carcinomas (EECs) account for about 75% of the cases and are typically estrogen-related and low-grade tumors with good prognosis that coexist or are preceded by endometrial hyperplasia, mainly diagnosed in perimenopausal women. In contrast, type II or non-endometrioid endometrial carcinomas (NEECs) are high-grade aggressive tumors associated with endometrial atrophy and poor prognosis, unrelated to estrogen and diagnosed in older women. These comprise several histological subtypes, being the most common the serous carcinomas (SEC)4. In recent years numerous large-scale studies of primary endometrioid and serous tumors have been performed5, revealing new mutated genes and establishing a new molecular subclassification based on the results obtained by The Cancer Genome Atlas (TCGA) consortium6, which implies different clinical outcomes. More recently, the genomic evolution of EC has been analyzed through a comparative study of samples from endometrial atypical hyperplasia, primary tumors and paired metastases7, revealing the presence of intratumor heterogeneity as previously described in primary EC and other tumor types8,9. However an in-depth study considering multiple regions from primary tumor and paired metastases has not been performed up to now to our knowledge. Here we analyze by whole-exome sequencing (WES), massive parallel targeted sequencing and array comparative genomic hybridization (aCGH) the clonal evolution and intratumor heterogeneity of 7 endometrioid and 3 serous metastatic endometrial carcinomas. Different locations from the primary tumor as well as from their paired metastases were included in the study, allowing the reconstruction of the spatial and temporal phylogenetic evolution of the tumor. Different phylogenetic evolution patterns were identified, independently of the classical histological or molecular classification of the tumor, although similar patterns were found in ovarian metastasis and recurrent disease.

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Endometrial cancer risk stratification

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