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MLH1 expression predicts the response to preoperative therapy and is associated with PD-L1 expression in esophageal cancer.

ABSTRACT: Programmed death-ligand 1 (PD-1/PD-L1) inhibition therapy demonstrates potential as a future treatment for esophageal cancer. Mismatch repair status and tumor PD-L1 expression are the candidate predictive biomarkers for response to this therapy. In colorectal cancer, mismatch repair-deficient tumors are associated with improved survival, although they are not sensitive to 5-fluorouracil-based chemotherapy. The purpose of the present study was to investigate the association between MutL homolog 1 (MLH1) expression and prognosis, response to therapy and PD-L1 expression in esophageal cancer. Immunohistochemistry was used to evaluate MLH1 and PD-L1 expression in 251 resected specimens. Of the specimens, 30.3% exhibited low MLH1 expression and 15.5% exhibited high PD-L1 expression. The 5-year overall survival rates for the high MLH1 expression group and the low MLH1 expression group were 51.3 and 55.6%, respectively (P=0.5260). The responder ratio was 45.7% in the high MLH1 expression group and 15.4% in the low MLH1 expression group (P<0.0001). The frequency of high PD-L1 expression was 11.4% in the high MLH1 expression group (P=0.0064) and 25.0% in the low MLH1 expression group. MLH1 expression may be a predictive factor for the response to preoperative therapy in esophageal cancer, and esophageal cancer with low MLH1 expression may have a mechanism that assists in promoting tumor PD-L1 expression.

SUBMITTER: Momose K

PROVIDER: S-EPMC5494664 | biostudies-other | 2017 Jul

REPOSITORIES: biostudies-other

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Project description:Programmed death-ligand 1 (PD-L1) IHC is the most commonly used biomarker for immunotherapy response. However, quantification of PD-L1 status in pathology slides is challenging. Neither manual quantification nor a computer-based mimicking of manual readouts is perfectly reproducible, and the predictive performance of both approaches regarding immunotherapy response is limited. In this study, we developed a deep learning (DL) method to predict PD-L1 status directly from raw IHC image data, without explicit intermediary steps such as cell detection or pigment quantification. We trained the weakly supervised model on PD-L1-stained slides from the non-small cell lung cancer (NSCLC)-Memorial Sloan Kettering (MSK) cohort (N = 233) and validated it on the pan-cancer-Vall d'Hebron Institute of Oncology (VHIO) cohort (N = 108). We also investigated the performance of the model to predict response to immune checkpoint inhibitors (ICI) in terms of progression-free survival. In the pan-cancer-VHIO cohort, the performance was compared with tumor proportion score (TPS) and combined positive score (CPS). The DL model showed good performance in predicting PD-L1 expression (TPS ≥ 1%) in both NSCLC-MSK and pan-cancer-VHIO cohort (AUC 0.88 ± 0.06 and 0.80 ± 0.03, respectively). The predicted PD-L1 status showed an improved association with response to ICIs [HR: 1.5 (95% confidence interval: 1-2.3), P = 0.049] compared with TPS [HR: 1.4 (0.96-2.2), P = 0.082] and CPS [HR: 1.2 (0.79-1.9), P = 0.386]. Notably, our explainability analysis showed that the model does not just look at the amount of brown pigment in the IHC slides, but also considers morphologic factors such as lymphocyte conglomerates. Overall, end-to-end weakly supervised DL shows potential for improving patient stratification for cancer immunotherapy by analyzing PD-L1 IHC, holistically integrating morphology and PD-L1 staining intensity.SignificanceThe weakly supervised DL model to predict PD-L1 status from raw IHC data, integrating tumor staining intensity and morphology, enables enhanced patient stratification in cancer immunotherapy compared with traditional pathologist assessment.

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MLH1 expression predicts the response to preoperative therapy and is associated with PD-L1 expression in esophageal cancer.

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