The evolving role of morphology in endometrial cancer diagnostics: From histopathology and molecular testing towards integrative data analysis by deep learning.

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.

Endoscopic tumour morphology impacts survival in adenocarcinoma of the oesophagus.

BACKGROUND: Prognostication in oesophageal cancer on the basis of preoperative variables is challenging. Many of the accepted predictors of survival are only derived after surgical treatment and may be influenced by neoadjuvant therapy. This study aims to explore the relationship between pre-treatment endoscopic tumour morphology and postoperative survival. METHODS: Patients with endoscopic descriptions of tumours were identified from the prospectively managed databases including the OCCAMS database. Tumours were classified as exophytic, ulcerating or stenosing. Kaplan Meier survival analysis and multivariable Cox regression analyses were performed to determine hazard ratios (HR) with 95% confidence intervals. RESULTS: 262 patients with oesophageal adenocarcinoma undergoing potentially curative resection were pooled from St Thomas' Hospital (161) and the OCCAMS database (101). There were 70 ulcerating, 114 exophytic and 78 stenosing oesophageal adenocarcinomas. Initial tumour staging was similar across all groups (T3/4 tumours 71.4%, 70.2%, 74.4%). Median survival was 55 months, 51 months and 36 months respectively (p < 0.001). Rates of lymphovascular invasion (P = 0.0176), pathological nodal status (P = 0.0195) and pathological T stage (P = 0.0007) increased from ulcerating to exophytic to stenosing lesions. Resection margin positivity was 21.4% in ulcerating tumours compared to 54% in stenosing tumours (p < 0.001). When compared to stenosing lesions, exophytic and ulcerating lesions demonstrated a significant survival advantage on multivariable analysis (HR 0.56 95% CI 0.31-0.93, HR 0.42 95% CI 0.21-0.82). CONCLUSION: This study demonstrates that endoscopic morphology may be an important pre-treatment prognostic factor in oesophageal cancer. Ulcerating, exophytic and stenosing tumours may represent different pathological processes and tumour biology.

Effects of mutations and immunogenicity on outcomes of anti-cancer therapies for secondary lesions.

Cancer development is driven by mutations and selective forces, including the action of the immune system and interspecific competition. When administered to patients, anti-cancer therapies affect the development and dynamics of tumours, possibly with various degrees of resistance due to immunoediting and microenvironment. Tumours are able to express a variety of competing phenotypes with different attributes and thus respond differently to various anti-cancer therapies. In this paper, a mathematical framework incorporating a system of delay differential equations for the immune system activation cycle and an agent-based approach for tumour-immune interaction is presented. The focus is on those metastatic, secondary solid lesions that are still undetected and non-vascularised. By using available experimental data, we analyse the effects of combination therapies on these lesions and investigate the role of mutations on the rates of success of common treatments. Findings show that mutations, growth properties and immunoediting influence therapies' outcomes in nonlinear and complex ways, affecting cancer lesion morphologies, phenotypical compositions and overall proliferation patterns. Cascade effects on final outcomes for secondary lesions are also investigated, showing that actions on primary lesions could sometimes result in unexpected clearances of secondary tumours. This outcome is strongly dependent on the clonal composition of the primary and secondary masses and is shown to allow, in some cases, the control of the disease for years.

The biology and mathematical modelling of glioma invasion: a review.

Adult gliomas are aggressive brain tumours associated with low patient survival rates and limited life expectancy. The most important hallmark of this type of tumour is its invasive behaviour, characterized by a markedly phenotypic plasticity, infiltrative tumour morphologies and the ability of malignant progression from low- to high-grade tumour types. Indeed, the widespread infiltration of healthy brain tissue by glioma cells is largely responsible for poor prognosis and the difficulty of finding curative therapies. Meanwhile, mathematical models have been established to analyse potential mechanisms of glioma invasion. In this review, we start with a brief introduction to current biological knowledge about glioma invasion, and then critically review and highlight future challenges for mathematical models of glioma invasion.

Tumour stromal morphology impacts nanomedicine cytotoxicity in patient-derived xenografts.

It is challenging to evaluate how tumour pathophysiology influences nanomedicine therapeutic effect; however, this is a key question in drug delivery. An advanced analytical method was developed to quantify the spatial distribution of drug-induced effect in tumours with varied stromal morphologies. The analysis utilises standard immunohistochemistry images and quantifies the frequency of positive staining as a function of distance from the stroma. Two stromal morphologies - Estuary and Tumour Island - were classified in 28 tumours from a lung cancer explant model in mice treated with liposomal doxorubicin. Analysis demonstrated that Estuary-like tumours presented a highly convoluted tumour-stroma interface, with most tumour cells in close proximity to vessels; these tumours were 8.8-fold more responsive to liposomal doxorubicin than were Tumour Island-like tumours, which were nearly unresponsive to liposomal doxorubicin. SDARS analysis allows the relative treatment effect to be assessed in tumours individually, and enables investigation of nanomedicine delivery in complex tumour pathophysiologies. FROM THE CLINICAL EDITOR: Advances in nanotechnology have brought about many novel treatment modalities for cancer. Nonetheless, there is no standard evaluation technique for tumor cells' drug response. The authors here utilized patient-derived tumour xenograft (PDTX) models to have a more translatable pre-clinical evaluation platform for nanomedicine drugs. They then used advanced imaging acquisition technique to analyze tumor stromal morphology, which they named Spatial Distribution of Apoptosis Relative to Stroma (SDARS). The findings would have significant clinical impact as it would help predict the eventual clinical drug response.