Weakly supervised deep learning to predict recurrence in low-grade endometrial cancer from multiplexed immunofluorescence images.

Predicting recurrence in low-grade, early-stage endometrial cancer (EC) is both challenging and clinically relevant. We present a weakly-supervised deep learning framework, NaroNet, that can learn, without manual expert annotation, the complex tumor-immune interrelations at three levels: local phenotypes, cellular neighborhoods, and tissue areas. It uses multiplexed immunofluorescence for the simultaneous visualization and quantification of CD68 + macrophages, CD8 + T cells, FOXP3 + regulatory T cells, PD-L1/PD-1 protein expression, and tumor cells. We used 489 tumor cores from 250 patients to train a multilevel deep-learning model to predict tumor recurrence. Using a tenfold cross-validation strategy, our model achieved an area under the curve of 0.90 with a 95% confidence interval of 0.83-0.95. Our model predictions resulted in concordance for 96,8% of cases (κ = 0.88). This method could accurately assess the risk of recurrence in EC, outperforming current prognostic factors, including molecular subtyping.

The association between the tumor immune microenvironments and clinical outcome in low-grade, early-stage endometrial cancer patients.

Endometrial tumors show substantial heterogeneity in their immune microenvironment. This heterogeneity could be used to improve the accuracy of current outcome prediction tools. We assessed the immune microenvironment of 235 patients diagnosed with low-grade, early-stage endometrial cancer. Multiplex quantitative immunofluorescence was carried out to measure CD8, CD68, FOXP3, PD-1, and PD-L1 markers, as well as cytokeratin (CK), on tissue microarrays. Clustering results revealed five robust immune response patterns, each associated with specific immune populations, cell phenotypes, and cell spatial clustering. Most samples (69%) belonged to the immune-desert subtype, characterized by low immune cell densities. Tumor-infiltrating lymphocyte (TIL)-rich samples (4%) displayed high CD8+ T-cell infiltration, as well as a high percentage of CD8/PD-1+ cells. Immune-exclusion samples (19%) displayed the lowest CD8+ infiltration combined with high PD-L1 expression levels in CK+ tumor cells. In addition, they demonstrated high tumor cell spatial clustering as well as increased spatial proximity of CD8+ /PD-1+ and CK/PD-L1+ cells. FOXP3 and macrophage-rich phenotypes (3% and 4% of total samples) displayed relatively high levels of FOXP3+ regulatory T-cells and CD68+ macrophages, respectively. These phenotypes correlated with clinical outcomes, with immune-exclusion tumors showing an association with tumor relapse. When compared with prediction models built using routine pathological variables, models optimized with immune variables showed increased outcome prediction capacity (AUC = 0.89 versus 0.78) and stratification potential. The improved prediction capacity was independent of mismatch repair protein status and adjuvant radiotherapy treatment. Further, immunofluorescence results could be partially recapitulated using single-marker immunohistochemistry (IHC) performed on whole tissue sections. TIL-rich tumors demonstrated increased CD8+ T-cells by IHC, while immune-exclusion tumors displayed a lack of CD8+ T-cells and frequent expression of PD-L1 in tumor cells. Our results demonstrate the capability of the immune microenvironment to improve standard prediction tools in low-grade, early-stage endometrial carcinomas. © 2022 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Proteomic Analysis of Low-Grade, Early-Stage Endometrial Carcinoma Reveals New Dysregulated Pathways Associated with Cell Death and Cell Signaling.

Low-grade, early-stage endometrial carcinoma (EC) is the most frequent malignant tumor of the uterine corpus. However, the molecular alterations that underlie these tumors are far from being fully understood. The purpose of this study is to describe dysregulated molecular pathways from EC patients. Sixteen samples of tumor tissue and paired healthy controls were collected and both were subjected to mass spectrometry (MS)/MS proteomic analysis. Gene ontology and pathway analysis was performed to discover dysregulated pathways and/or proteins using different databases and bioinformatic tools. Dysregulated pathways were cross-validated in an independent external cohort. Cell signaling, immune response, and cell death-associated pathways were robustly identified. The SLIT/ROBO signaling pathway demonstrated dysregulation at the proteomic and transcriptomic level. Necroptosis and ferroptosis were cell death-associated processes aberrantly regulated, in addition to apoptosis. Immune response-associated pathways showed a dominance of innate immune responses. Tumor immune infiltrates measured by immunofluorescence demonstrated diverse lymphoid and myeloid populations. Our results suggest a role of SLIT/ROBO, necroptosis, and ferroptosis, as well as a prominent role of innate immune response in low-grade, early-stage EC. These results could guide future research in this group of tumors.