Endometrial carcinoma represents the most common gynecological cancer in Europe and the USA. Histopathological classification based on tumor morphology and tumor grade has played a crucial role in the management of endometrial carcinoma, allowing a prognostic stratification into distinct risk categories, and guiding surgical and adjuvant therapy. In 2013, The Cancer Genome Atlas (TCGA) Research Network reported a large scale molecular analysis of 373 endometrial carcinomas which demonstrated four categories with distinct clinical, pathologic, and molecular features: POLE/ultramutated (7% of cases) microsatellite instability (MSI)/hypermutated (28%), copy-number low/endometrioid (39%), and copy-number high/serous-like (26%). In the present article, we report a detailed histological and molecular review of all endometrial carcinoma histotypes in light of the current ESGO/ESTRO/ESP guidelines. In particular, we focus on the distribution and prognostic value of the TCGA groups in each histotype.

The four TCGA-based molecular prognostic groups of endometrial carcinoma (EC), i.e., POLE-mutant, mismatch repair (MMR)-deficient, p53-abnormal, and “no specific molecular profile” (NSMP), have recently been integrated into ESGO-ESTRO-ESP guidelines. The POLE-mutant and MMR-deficient groups are associated with high mutational load, morphological heterogeneity, and inflammatory infiltration. These groups are frequent in high-grade endometrioid, undifferentiated/dedifferentiated, and mixed histotypes. POLE-mutant ECs show good prognosis and do not require adjuvant treatment, although the management of cases at stage >II is still undefined. MMR-deficient ECs show intermediate prognosis and are currently substratified based on clinicopathological variables, some of which might not have prognostic value. These groups may benefit from immunotherapy. P53-mutant ECs are typically high-grade and often morphologically ambiguous, accounting for virtually all serous ECs, most carcinosarcomas and mixed ECs, and half of clear-cell ECs. They show poor prognosis and are treated with chemoradiotherapy; a subset may benefit from HER2 inhibitors or PARP inhibitors. The NSMP group is the most frequent TCGA group; its prognosis is highly variable and affected by clinicopathological/molecular factors, most of which are still under evaluation. In conclusion, the TCGA classification has improved diagnosis, risk stratification, and management of EC. Further studies are needed to resolve the points of uncertainty that still exist.

Background: Several types of neuroendocrine neoplasms (NENs) have been described in the duodenal tract, from low-grade tumors (NETs) to high-grade neuroendocrine carcinomas (NECs). A comprehensive analysis of histology, hormonal profile and prognostic parameters of a sufficiently large duodenal NEN series to cover all main kinds of neoplasms is however lacking. Methods: We collected a retrospective series of 203 duodenal wall and ampullary region NENs, from six specialized endocrine pathology centers. All were characterized histopathologically and histochemically, and 190 were followed for a median of 9 years. Results: Twenty-seven poorly differentiated NECs, mostly from the ampullary region, were identified and shown to lead to patient demise in a median of 10 months. Among 176 NETs, four subtypes were characterized, including 20 gastrinomas, 37 ampullary-type somatostatin-producing NETs (ASTs), 12 gangliocytic paragangliomas (GPs) and 106 nonfunctioning NETs (nfNETs). ASTs and GPs were mostly localized in the ampullary/periampullary region, while gastrinomas and nfNETs were mainly from the proximal duodenum. ASTs and gastrinomas showed high rates of local infiltration (especially lymphoinvasion and deep duodenal wall/pancreatic tissue invasion) and lymph node metastasis, while nfNETs had significantly lower and more size-dependent local invasive potential. Disease-specific survival differed significantly between NETs and NECs, though not among NET subtypes. NET cases with distant metastases (n = 23) were significantly associated with larger size, higher proliferative grade, lymphovascular invasion, deep invasion and local lymph node metastasis. Conclusion: Our careful analysis of a large series of duodenal NENs identified five histologically and prognostically different histotypes of potential clinical relevance.

Background: The World Health Organization (WHO) and the American Joint Cancer Committee (AJCC) modified the grading of pancreatic neuroendocrine neoplasms from a three-tier (WHO-AJCC 2010) to a four-tier system by introducing the novel category of NET G3 (WHO-AJCC 2017). Objectives: This study aims at validating the WHO-AJCC 2017 and identifying the most effective grading system. Method: A total of 2,102 patients were enrolled; entry criteria were: (i) patient underwent surgery; (ii) at least 2 years of follow-up; (iii) observation time up to 2015. Data from 34 variables were collected; grading was assessed and compared for efficacy by statistical means including Kaplan-Meier method, Cox regression analysis, Harrell’s C statistics, and Royston’s explained variation in univariable and multivariable analyses. Results: In descriptive analysis, the two grading systems demonstrated statistically significant differences for the major category sex but not for age groups. In Cox regression analysis, both grading systems showed statistically significant differences between grades for OS and EFS; however, no statistically significant difference was observed between the two G3 classes of WHO-AJCC 2017. In multivariable analysis for the two models fitted to compare efficacy, the two grading systems performed equally well with substantially similar optimal discrimination and well-explained variation for both OS and EFS. The WHO-AJCC 2017 grading system retained statistically significant difference between the two G3 classes for OS but not for EFS. Conclusions: The WHO-AJCC 2017 grading system is at least equally performing as the WHO-AJCC 2010 but allows the successful identification of the most aggressive PanNET subgroup. Grading is confirmed as probably the most powerful tool for predicting patient survival.

BRCA 1/2 genes mutation status can already determine the therapeutic algorithm of high grade serous ovarian cancer patients. Nevertheless, its assessment is not sufficient to identify all patients with genomic instability, since BRCA 1/2 mutations are only the most well-known mechanisms of homologous recombination deficiency (HR-d) pathway, and patients displaying HR-d behave similarly to BRCA mutated patients. HRd assessment can be challenging and is progressively overcoming BRCA testing not only for prognostic information but more importantly for drugs prescriptions. However, HR testing is not already integrated in clinical practice, it is quite expensive and it is not refundable in many countries. Selecting patients who are more likely to benefit from this assessment (BRCA 1/2 WT patients) at an early stage of the diagnostic process, would allow an optimization of genomic profiling resources. In this study, we sought to explore whether somatic BRCA1/2 genes status can be predicted using computational pathology from standard hematoxylin and eosin histology. In detail, we adopted a publicly available, deep-learning-based weakly supervised method that uses attention-based learning to automatically identify sub regions of high diagnostic value to accurately classify the whole slide (CLAM). The same model was also tested for progression free survival (PFS) prediction. The model was tested on a cohort of 664 (training set: n = 464, testing set: n = 132) ovarian cancer patients, of whom 233 (35.1%) had a somatic BRCA 1/2 mutation. An area under the curve of 0.7 and 0.55 was achieved in the training and testing set respectively. The model was then further refined by manually identifying areas of interest in half of the cases. 198 images were used for training (126/72) and 87 images for validation (55/32). The model reached a zero classification error on the training set, but the performance was 0.59 in terms of validation ROC AUC, with a 0.57 validation accuracy. Finally, when applied to predict PFS, the model achieved an AUC of 0.71, with a negative predictive value of 0.69, and a positive predictive value of 0.75. Based on these analyses, we have planned further steps of development such as proving a reference classification performance, exploring the hyperparameters space for training optimization, eventually tweaking the learning algorithms and the neural networks architecture for better suiting this specific task. These actions may allow the model to improve performances for all the considered outcomes.

Among the four endometrial cancer (EC) TCGA molecular groups, the MSI/hypermutated group represents an important percentage of tumors (30%), including different histotypes, and generally confers an intermediate prognosis for affected women, also providing new immunotherapeutic strategies. Immunohistochemistry for MMR proteins (MLH1, MSH2, MSH6 and PMS2) has become the optimal diagnostic MSI surrogate worldwide. This review aims to provide state-of-the-art knowledge on MMR deficiency/MSI in EC and to clarify the pathological assessment, interpretation pitfalls and reporting of MMR status.