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Background Risk-assessment of endometrial cancer (EC) is based on clinicopathological factors and molecular subgroup. It is unclear whether adding hormone receptor expression, L1CAM expression or CTNNB1 status yields prognostic refinement. Methods Paraffin-embedded tumour samples of women with high-risk EC (HR-EC) from the PORTEC-3 trial (n = 424), and a Dutch prospective clinical cohort called MST (n = 256), were used. All cases were molecularly classified. Expression of L1CAM, ER and PR were analysed by whole-slide immunohistochemistry and CTNNB1 mutations were assessed with a next-generation sequencing. Kaplan–Meier method, log-rank tests and Cox’s proportional hazard models were used for survival analysis. Results In total, 648 HR-EC were included. No independent prognostic value of ER, PR, L1CAM, and CTNNB1 was found, while age, stage, and adjuvant chemotherapy had an independent impact on risk of recurrence. Subgroup-analysis showed that only in NSMP HR-EC, ER-positivity was independently associated with a reduced risk of recurrence (HR 0.33, 95%CI 0.15–0.75). Conclusions We confirmed the prognostic impact of the molecular classification, age, stage, and adjuvant CTRT in a large cohort of high-risk EC. ER-positivity is a strong favourable prognostic factor in NSMP HR-EC and identifies a homogeneous subgroup of NSMP tumours. Assessment of ER status in high-risk NSMP EC is feasible in clinical practice and could improve risk stratification and treatment.

BackgroundThe endometrial cancer molecular classification has been integrated into the 2020 World Health Organization (WHO) diagnostic classification and European treatment guidelines, and provides direction towards more effective and less toxic adjuvant treatment strategies for women with endometrial cancer.Primary Objective(s)The RAINBO program of clinical trials will investigate four molecular class-directed adjuvant treatment strategies following surgical resection to either increase cure rates through the addition of novel targeted therapies or safely reduce toxicity and improve quality of life through treatment de-escalation.Study HypothesisMolecular-directed adjuvant treatment strategies will improve clinical outcomes and reduce toxicity of unwarranted therapies in women with endometrial cancer. The overarching and translational research RAINBO program will advance knowledge of predictive and prognostic (bio)markers that will improve prognostication and treatment allocation.Trial DesignThe RAINBO program is a platform of four international clinical trials and an overarching research program. The randomized phase III p53abn-RED trial for women with invasive stage I–III p53abn endometrial cancer compares adjuvant chemoradiation followed by olaparib for 2 years with adjuvant chemoradiation alone. The randomized phase III MMRd-GREEN trial for women with stage II (with lymphovascular space invasion (LVSI)) or stage III mismatch repair-deficient (MMRd) endometrial cancer compares adjuvant radiotherapy with concurrent and adjuvant durvalumab for 1 year to radiotherapy alone. The randomized phase III NSMP-ORANGE trial is a treatment de-escalation trial for women with estrogen receptor positive stage II (with LVSI) or stage III no specific molecular profile (NSMP) endometrial cancer comparing radiotherapy followed by progestin for 2 years to adjuvant chemoradiation. ThePOLEmut-BLUE trial is a phase II trial in which the safety of de-escalation of adjuvant therapy is investigated for women with stage I–IIIPOLEmut endometrial cancer: no adjuvant therapy for lower-risk disease and no adjuvant therapy or radiotherapy alone for higher-risk disease. The overarching RAINBO program will combine data and tumor material of all participants to perform translational research and evaluate molecular class-based adjuvant therapy in terms of efficacy, toxicity, quality of life, and cost-utility.Major Inclusion/Exclusion CriteriaInclusion criteria include a histologically confirmed diagnosis of endometrial cancer treated by hysterectomy and bilateral salpingo-oophorectomy with or without lymphadenectomy or sentinel lymph node biopsy, with no macroscopic residual disease after surgery and no distant metastases, and molecular classification according to the WHO 2020 algorithm.Primary Endpoint(s)Recurrence-free survival at 3 years in the p53abn-RED, MMRd-GREEN, and NSMP-ORANGE trials and pelvic recurrence at 3 years in thePOLEmut-BLUE trial.Sample SizeThe p53abn-RED trial will include 554 patients, the MMRd-GREEN trial 316, the NSMP-ORANGE trial 600, and thePOLEmut-BLUE trial 145 (120 for lower-risk disease and approximately 25 for higher-risk disease). The overarching research program will pool the four sub-trials resulting in a total sample size of around 1600.Estimated Dates for Completing Accrual and Presenting ResultsThe four clinical trials will have different completion dates; main results are expected from 2028.Trial Registration NumberThe RAINBO program is registered at clinicaltrials.gov (NCT05255653).

Introduction: The clinical role of the molecular endometrial cancer (EC) classification has not been fully explored in patients staged with lymphadenectomy or without adjuvant treatment, conditions that could potentially moderate the prognostic value of the classification. We aimed to evaluate the clinical outcome of the molecular subgroups in patients with high-grade EC staged by lymphadenectomy and those without adjuvant treatment.Methods: DNA-sequencing for the detection of pathogenic POLE-exonuclease domain mutations and immunohistochemistry for mismatch repair (MMR) proteins and p53 expression were performed on 412 high-grade EC from the Danish Gynaecological Cancer Database (2005-2012) to classify them as POLE-ultramutated (POLEmut), MMR-deficient (MMRd), p53-mutant (p53abn), or no specific molecular profile (NSMP). Patients with stage IV or residual disease after surgery were excluded. Kaplan-Meier method, log-rank test and Cox proportional hazard models were used for analysis.Results: Molecular analysis was successful in 367 EC; 251 patients had undergone lymphadenectomy. Five-year recurrence rates in this subgroup of patients was 36.7% for women with p53abn EC, 0.0% for POLEmut EC, 13.4% for MMRd EC and 42.9% for NSMP EC (p < 0.001). Similar results were observed among stage IA-IB patients. Among patients without adjuvant treatment (n = 264), none with POLEmut EC (n = 26) had a recurrence.Conclusion: The molecular EC classification has strong prognostic value, independent of clinicopathological factors, also among high-grade EC patients staged by lymphadenectomy and those without adjuvant treatment. The unfavourable prognosis of early-stage p53abn EC is not due to undetected lymph node metastasis, and the indolent behaviour of POLEmut EC is independent of adjuvant treatment.

Contains fulltext : 251987.pdf (Publisher’s version ) (Open Access) BACKGROUND: Patients with advanced endometrial cancer have a poor prognosis, and treatment options are limited. The investigator-initiated, multicenter, phase II DOMEC trial (NCT03951415) is the first trial to report data on efficacy and safety of combined treatment with PD-L1 and PARP inhibition for advanced endometrial cancer. PATIENTS AND METHODS: Patients with metastatic or recurrent endometrial cancer were enrolled. Patients received durvalumab 1500 mg intravenously q4w and olaparib 300 mg 2dd until disease progression, unacceptable toxicity, or patient withdrawal. Patients with at least 4 weeks of treatment were evaluable for analysis. The primary endpoint was progression-free survival at 6 months. Evidence for efficacy was defined as progression-free survival at 6 months in ≥50% of patients. Secondary endpoints included safety, objective response and overall survival. RESULTS: From July 2019, through November 2020, 55 patients were enrolled. At data cut-off (September 2021), 4 of the 50 evaluable patients were still on treatment. Seventeen patients (34%) were progression-free at 6 months. Objective response rate was 16% (95% CI, 8.3 to 28.5) with 1 complete and 7 partial responses. With a median follow-up of 17.6 months, median progression-free survival was 3.4 months (95% CI, 2.8 to 6.2) and median overall survival was 8.0 months (95% CI, 7.5 to 14.3). Grade 3 treatment-related adverse events occurred in 8 patients (16%), predominantly anemia. There were no grade 4 or 5 treatment-related adverse events. CONCLUSION: The combination of durvalumab and olaparib was well tolerated, but did not meet the prespecified 50% 6-month progression-free survival in this heterogeneous patient population with advanced endometrial cancer.

Endometrial cancer (EC) diagnostics is evolving into a system in which molecular aspects are increasingly important. The traditional histological subtype-driven classification has shifted to a molecular-based classification that stratifies EC into DNA polymerase epsilon mutated (POLEmut), mismatch repair deficient (MMRd), and p53 abnormal (p53abn), and the remaining EC as no specific molecular profile (NSMP). The molecular EC classification has been implemented in the World Health Organization 2020 classification and the 2021 European treatment guidelines, as it serves as a better basis for patient management. As a result, the integration of the molecular class with histopathological variables has become a critical focus of recent EC research. Pathologists have observed and described several morphological characteristics in association with specific genomic alterations, but these appear insufficient to accurately classify patients according to molecular subgroups. This requires pathologists to rely on molecular ancillary tests in routine workup. In this new era, it has become increasingly challenging to assign clinically relevant weights to histological and molecular features on an individual patient basis. Deep learning (DL) technology opens new options for the integrative analysis of multi-modal image and molecular datasets with clinical outcomes. Proof-of-concept studies in other cancers showed promising accuracy in predicting molecular alterations from H&E-stained tumor slide images. This suggests that some morphological characteristics that are associated with molecular alterations could be identified in EC, too, expanding the current understanding of the molecular-driven EC classification. Here in this review, we report the morphological characteristics of the molecular EC classification currently identified in the literature. Given the new challenges in EC diagnostics, this review discusses, therefore, the potential supportive role that DL could have, by providing an outlook on all relevant studies using DL on histopathology images in various cancer types with a focus on EC. Finally, we touch upon how DL might shape the management of future EC patients.

Item does not contain fulltext Open-domain large language models have progressed to generating natural-sounding and coherent text. Even though the generated texts appear human-like, the main stumbling block is that their output is never fully predictable, which runs the risk of resulting in harmful content such as false statements or inflammatory language. This makes it difficult to apply these models in highly sensitive domains including personal health counselling. Hence, most of the chatbots for highly sensitive domains are developed using pre-scripted approaches. Although pre-scripted approaches are highly controlled, they suffer from repetitiveness and scalability issues. In this paper, we explore the possibility of combining the best of both worlds. We propose and describe in detail a new, flexible expert-driven hybrid architecture for harnessing the benefits of large language models in a controlled manner for highly sensitive domains and discuss the expectations and challenges. ICAART 2022: The 14th International Conference on Agents and Artificial Intelligence (February 3-5, 2022)