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YTHDF1 and YTHDF2 are associated with better patient survival and an inflamed tumor-immune microenvironment in non-small-cell lung cancer.

ABSTRACT: The human YTH domain family (YTHDF) proteins are RNA-binding proteins that recognize N6-methyladenosine (m6A), facilitating various biological processes via m6A RNA modification. How these molecules associate with non-small-cell lung cancer (NSCLC) molecular mechanisms remain unclear. The protein expression levels of YTHDF1 and YTHDF2 in 603 cases of resected NSCLC were evaluated using immunohistochemistry. We analyzed the associations of these attributes with patient characteristics and survival. We also assessed four subsets of lymphocytes (PD-1+, CD8+, Foxp3+, and CD45RO+) as tumor-infiltrating lymphocytes (TILs) in the tumor nest and in the surrounding stroma separately. In addition, we investigated differentially expressed genes and the expression of PD-L1 in YTHDF1- and YTHDF2-deprived lung cancer cells. The expressions of both YTHDF1 and YTHDF2 were less in the advanced-stage tumors than in the early-stage tumors. The expressions of both YTHDF1 and YTHDF2 were also independent favorable prognostic factors for recurrence-free survival (HR, 0.745; 95% CI, 0.562-0.984 for YTHDF1; HR, 0.683; 95% CI, 0.503-0.928 for YTHDF2). The TIL densities of almost all four lymphocyte subsets in the stroma were significantly higher in the tumors with high YTHDF1 and YTHDF2 expression. In vitro, YTHDF1 and YTHDF2 knockdown in cells upregulated tumor PD-L1 expression and altered multiple immune-related genes. High expressions of both YTHDF1 and YTHDF2 are associated with a favorable prognostic outcome of NSCLC patients, a greater amount of TILs, and downregulation of PD-L1. YTHDF1 and YTHDF2 could be novel prognostic and druggable targets related to the tumor-immune microenvironment in lung cancers.

SUBMITTER: Tsuchiya K

PROVIDER: S-EPMC8366544 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundDespite the fact that tumor microenvironment (TME) and gene mutations are the main determinants of progression of the deadliest cancer in the world - lung cancer, their interrelations are not well understood. Digital pathology data provides a unique insight into the spatial composition of the TME. Various spatial metrics and machine learning approaches were proposed for prediction of either patient survival or gene mutations from this data. Still, these approaches are limited in the scope of analyzed features and in their explainability, and as such fail to transfer to clinical practice.MethodsHere, we generated 23,199 image patches from 26 hematoxylin-and-eosin (H&E)-stained lung cancer tissue sections and annotated them into 9 different tissue classes. Using this dataset, we trained a deep neural network ARA-CNN. Next, we applied the trained network to segment 467 lung cancer H&E images from The Cancer Genome Atlas (TCGA) database. We used the segmented images to compute human-interpretable features reflecting the heterogeneous composition of the TME, and successfully utilized them to predict patient survival and cancer gene mutations.ResultsWe achieved per-class AUC ranging from 0.72 to 0.99 for classifying tissue types in lung cancer with ARA-CNN. Machine learning models trained on the proposed human-interpretable features achieved a c-index of 0.723 in the task of survival prediction and AUC up to 73.5% for PDGFRB in the task of mutation classification.ConclusionsWe presented a framework that accurately predicted survival and gene mutations in lung adenocarcinoma patients based on human-interpretable features extracted from H&E slides. Our approach can provide important insights for designing novel cancer treatments, by linking the spatial structure of the TME in lung adenocarcinoma to gene mutations and patient survival. It can also expand our understanding of the effects that the TME has on tumor evolutionary processes. Our approach can be generalized to different cancer types to inform precision medicine strategies.

Dataset Information

YTHDF1 and YTHDF2 are associated with better patient survival and an inflamed tumor-immune microenvironment in non-small-cell lung cancer.

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