VOLTA: an enVironment-aware cOntrastive ceLl represenTation leArning for histopathology.

In clinical oncology, many diagnostic tasks rely on the identification of cells in histopathology images. While supervised machine learning techniques necessitate the need for labels, providing manual cell annotations is time-consuming. In this paper, we propose a self-supervised framework (enVironment-aware cOntrastive cell represenTation learning: VOLTA) for cell representation learning in histopathology images using a technique that accounts for the cell's mutual relationship with its environment. We subject our model to extensive experiments on data collected from multiple institutions comprising over 800,000 cells and six cancer types. To showcase the potential of our proposed framework, we apply VOLTA to ovarian and endometrial cancers and demonstrate that our cell representations can be utilized to identify the known histotypes of ovarian cancer and provide insights that link histopathology and molecular subtypes of endometrial cancer. Unlike supervised models, we provide a framework that can empower discoveries without any annotation data, even in situations where sample sizes are limited.

Neither Tumor-Infiltrating Lymphocytes nor Cytotoxic T Cells Predict Enhanced Benefit from Chemotherapy in the DBCG77B Phase III Clinical Trial.

Recent studies have shown that immune infiltrates in the tumor microenvironment play a role in response to therapy, with some suggesting that patients with immunogenic tumors may receive increased benefit from chemotherapies. We evaluated this hypothesis in early breast cancer by testing the interaction between immune biomarkers and chemotherapy using materials from DBCG77B, a phase III clinical trial where high-risk premenopausal women were randomized to receive chemotherapy or no chemotherapy. Tissue microarrays were evaluated for tumor-infiltrating lymphocytes (TILs) assessed morphologically on hematoxylin and eosin-stained slides, and by immunohistochemistry for CD8, FOXP3, LAG-3, PD-1 and PD-L1. Following REMARK reporting guidelines, data analyses were performed according to a prespecified statistical plan, using 10-year invasive disease-free survival as the endpoint. Differences in survival probabilities between biomarker groups were evaluated by Kaplan-Meier and Cox proportional hazard ratio analyses and prediction for treatment benefit by an interaction test. Our results showed that stromal TILs were associated with an improved prognosis (HR = 0.93; p-value = 0.03), consistent with previous studies. However, none of the immune biomarkers predicted benefit from chemotherapy in the full study set nor within major breast cancer subtypes. Our study indicates that primary tumors with higher immune infiltration do not derive extra benefit from cyclophosphamide-based cytotoxic chemotherapy.

Sarcomas: Immune biomarker expression and checkpoint inhibitor trials.

Sarcomas are a heterogenous group of mesenchymal cancers comprising over 100 subtypes. Current chemotherapy for all but a very few subtypes has limited efficacy, resulting in 5-year relative survival rates of 16% for metastatic patients. While sarcomas have often been regarded as an "immune cold" tumor category, recent biomarker studies have confirmed a great deal of immune heterogeneity across sarcoma subtypes. Reports from the first generation of clinical trials treating sarcomas with immunotherapy demonstrate a few positive responses, supporting efforts to stratify patients to optimize response rates. This review summarizes recent advances in knowledge around immune biomarker expression in sarcomas, the potential use of new technologies to complement these study results, and clinical trials particularly of immune checkpoint inhibitor therapy in sarcomas. Each of the immune biomarkers assessed was reviewed for subtype-specific expression patterns and correlation with prognosis. Overall, there is extensive heterogeneity of immune biomarker presence across sarcoma subtypes, and no consensus on the prognostic effect of these biomarkers. New technologies such as multiplex immunohistochemistry and high plex in situ profiling may offer more insights into the sarcoma microenvironment. To date, clinical trials using immune checkpoint inhibitor monotherapy have not shown compelling clinical benefits. Combination therapy with dual checkpoint inhibitors or in combinations with other agents has yielded more promising results in dedifferentiated liposarcoma, undifferentiated pleomorphic sarcoma, angiosarcoma and alveolar soft-part sarcoma. Better understanding of the sarcoma immune status through biomarkers may help decipher the reasons behind differential responses to immunotherapy.