A Patient-Derived, Pan-Cancer EMT Signature Identifies Global Molecular Alterations and Immune Target Enrichment Following Epithelial-to-Mesenchymal Transition

Abstract

Purpose We previously demonstrated the association between epithelial-to-mesenchymal transition (EMT) and drug response in lung cancer using an EMT signature derived in cancer cell lines. Given the contribution of tumor microenvironments to EMT, we extended our investigation of EMT to patient tumors from 11 cancer types to develop a pan-cancer EMT signature. Experimental Design Using the pan-cancer EMT signature, we conducted an integrated, global analysis of genomic and proteomic profiles associated with EMT across 1,934 tumors including breast, lung, colon, ovarian, and bladder cancers. Differences in outcome and in vitro drug response corresponding to expression of the pan-cancer EMT signature were also investigated. Results Compared to the lung cancer EMT signature, the patient-derived, pan-cancer EMT signature encompasses a set of core EMT genes that correlate even more strongly with known EMT markers across diverse tumor types and identifies differences in drug sensitivity and global molecular alterations at the DNA, RNA, and protein levels. Among those changes associated with EMT, pathway analysis revealed a strong correlation between EMT and immune activation. Further supervised analysis demonstrated high expression of immune checkpoints and other druggable immune targets such as PD1, PD-L1, CTLA4, OX40L, and PDL2, in tumors with the most mesenchymal EMT scores. Elevated PD-L1 protein expression in mesenchymal tumors was confirmed by immunohistochemistry in an independent lung cancer cohort. Conclusions This new signature provides a novel, patient-based, histology-independent tool for the investigation of EMT and offers insights into potential novel therapeutic targets for mesenchymal tumors, independent of cancer type, including immune checkpoints.

The immune checkpoint ligand PD-L1 is upregulated in EMT-activated human breast cancer cells by a mechanism involving ZEB-1 and miR-200

“…15 Subsequently, in a patient-derived mesenchymal tumor, a pan-cancer EMT signature was identified that showed high expression of multiple immune checkpoints including PD-L1. 16 Our data strongly demonstrate that among the different EMT-activated MCF7 clones, PD-L1 is differentially upregulated in MCF7 sh-WISP2 and MCF7-1001/2101 cells, but not in MCF7 SNAI1 and MCF7 SNAI1-6SA cells. Additionally, we provide new evidence here that not all EMT-activated breast cancer cells upregulate PD-L1 expression.…”

“…It would be of major interest to study whether metastatic mesenchymal tumors with increased PD-L1 expression 16 as compare with primary tumors will respond better to anti-PD-1/ anti-PDL1 immunotherapy. It would also be interesting to compare CD8 C T cell immunosuppression mediated by increased PD-L1 in various EMT-activated tumors driven by differentially expressed multiple EMT-TFs (ZEB, SNAIL, and TWIST families).…”

Growth and metastasis of lung adenocarcinoma is potentiated by BMP4-mediated immunosuppression

“…3,13,14,42,43 EMT is a critically important pathway during embryogenesis that is frequently reactivated during tumor progression. An increasing number of studies have examined the EMT state and intratumoral factors that lead to the development of an immunosuppressive tumor microenvironment, 13,43,44 suggesting that multiple parallel pathways are activated that may suppress an effective antitumor immune response. Although we have previously reported that PD-L1 is a direct target of miR-200, which contributes to immunosuppression in the primary tumor tissue, 13 the direct miR-200-PD-L1 axis likely does not entirely account for the immunosuppressive effects observed.…”

Immunological and classical subtypes of oral premalignant lesions

“…epithelial – vs. mesenchymal-like samples using a pancancer signature of epithelial-to-mesenchymal transition. 36 Unlike algorithm-based methods, they allow classifying new samples independently from previous ones, which is attractive in the clinical setting, especially in the context of the forthcoming clinical trials evaluating new prevention strategies. Our OPL classifier was built from a training subset of the discovery dataset, including a relative small number of samples.…”