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

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A Patient-Derived, Pan-Cancer EMT Signature Identifies Global Molecular Alterations and Immune Target Enrichment Following Epithelial-To-Mesenchymal Transition Published OnlineFirst September 29, 2015; DOI: 10.1158/1078-0432.CCR-15-0876 Cancer Therapy: Preclinical Clinical Cancer Research A Patient-Derived, Pan-Cancer EMT Signature Identifies Global Molecular Alterations and Immune Target Enrichment Following Epithelial-to-Mesenchymal Transition Milena P. Mak1,2, Pan Tong3, Lixia Diao3, Robert J. Cardnell1, Don L. Gibbons1,4, William N. William1, Ferdinandos Skoulidis1, Edwin R. Parra5, Jaime Rodriguez-Canales5, Ignacio I.Wistuba5, John V.Heymach1, John N.Weinstein3, Kevin R.Coombes6, Jing Wang3, and Lauren Averett Byers1 Abstract Purpose: We previously demonstrated the association between EMT markers across diverse tumor types and identifies differences epithelial-to-mesenchymal transition (EMT) and drug response in in drug sensitivity and global molecular alterations at the DNA, lung cancer using an EMT signature derived in cancer cell lines. RNA, and protein levels. Among those changes associated with Given the contribution of tumor microenvironments to EMT, we EMT, pathway analysis revealed a strong correlation between EMT extended our investigation of EMT to patient tumors from 11 and immune activation. Further supervised analysis demonstrat- cancer types to develop a pan-cancer EMT signature. ed high expression of immune checkpoints and other druggable Experimental Design: Using the pan-cancer EMT signature, we immune targets, such as PD1, PD-L1, CTLA4, OX40L, and PD-L2, conducted an integrated, global analysis of genomic and prote- in tumors with the most mesenchymal EMT scores. Elevated omic profiles associated with EMT across 1,934 tumors including PD-L1 protein expression in mesenchymal tumors was confirmed breast, lung, colon, ovarian, and bladder cancers. Differences in by IHC in an independent lung cancer cohort. outcome and in vitro drug response corresponding to expression Conclusions: This new signature provides a novel, patient- of the pan-cancer EMT signature were also investigated. based, histology-independent tool for the investigation of EMT Results: Compared with the lung cancer EMT signature, the and offers insights into potential novel therapeutic targets for patient-derived, pan-cancer EMT signature encompasses a set of mesenchymal tumors, independent of cancer type, including core EMT genes that correlate even more strongly with known immune checkpoints. Clin Cancer Res; 1–12. Ó2015 AACR. Introduction phenotype, a process known as "epithelial-to-mesenchymal tran- sition" (EMT). Studies have shown that EMT plays an important Over the past decade, multiple lines of evidence have suggested biologic role in cancer progression, metastasis, and drug resistance that epithelial cancers can transform into a more mesenchymal (1–3). Tools that facilitate the study of EMT, therefore, will provide new insights into molecular regulation and evolution 1Department of Thoracic/Head and Neck Medical Oncology, The Uni- during oncogenesis and may help to improve treatments for versity of Texas MD Anderson Cancer Center, Houston,Texas. 2Medical mesenchymal cancers. The development of EMT signatures or Oncology, Instituto do Cancer do Estado de Sao Paulo, Faculdade de other molecular markers to identify whether a cancer has under- Medicina da Universidade de Sao Paulo, Sao Paulo, Brazil. 3Depart- ment of Bioinformatics and Computational Biology, The University of gone EMT is an area of active research. Most studies in this area, Texas MD Anderson Cancer Center, Houston, Texas. 4Department of however, have focused on a single tumor type and/or on preclin- Molecular and Cellular Oncology,The University of Texas MDAnderson ical models (3–7). Cancer Center, Houston, Texas. 5Department of Translational and Molecular Pathology, The University of Texas MD Anderson Cancer We previously developed a robust, platform-independent EMT Center, Houston, Texas. 6Department of Biomedical Informatics, Ohio signature based on a set of 54 lung cancer cell lines (hereafter State University, Columbus, Ohio. called the lung cancer EMT signature). In vitro, this lung cancer Note: Supplementary data for this article are available at Clinical Cancer EMT signature predicted resistance to EGFR and PI3K/Akt inhi- Research Online (http://clincancerres.aacrjournals.org/). bitors and identified AXL as a potential therapeutic target for M.P. Mak and P. Tong are co-first authors. overcoming resistance to EGFR inhibitors [a common treatment – Corresponding Authors: Lauren Averett Byers, The University of Texas MD for non small cell lung cancer (NSCLC)]. We and others observed fi Anderson Cancer Center, 1515 Holcombe Blvd, Unit 0432, Houston, TX 77030. signi cantly greater resistance to EGFR inhibitors in lung cancers Phone: 713-792-6363; Fax: 713-792-1220; E-mail: [email protected]; and that had undergone EMT, as determined by either their baseline Jing Wang, Department of Bioinformatics and Computational Biology, The EMT signature (3) or the expression of specific EMT markers after University of Texas MD Anderson Cancer Center, Houston, TX. Phone: 713- the development of acquired EGFR inhibitor resistance (8). 794-4190; Fax: 713-563-4242; E-mail: [email protected] Although it is appealing to apply the lung cancer EMT signature doi: 10.1158/1078-0432.CCR-15-0876 to diverse tumor types, this approach may be limited by tumor Ó2015 American Association for Cancer Research. type-specific differences in EMT or discrepancies between cell line www.aacrjournals.org OF1 Downloaded from clincancerres.aacrjournals.org on September 26, 2021. © 2015 American Association for Cancer Research. Published OnlineFirst September 29, 2015; DOI: 10.1158/1078-0432.CCR-15-0876 Mak et al. CCLE by SNP fingerprinting at multiple steps, profiles were Translational Relevance compared with existing profiles (10, 11). We used level-3 TCGA Epithelial-to-mesenchymal transition (EMT) is associated pan-cancer data (12, 13), including RNAseqV2, reverse phase with resistance to many approved drugs and with tumor protein array (RPPA), miR, copy number, mutation, and clinical progression. Here, we sought to characterize the common data. A summary of the TCGA data can be found in Supplemen- biology of EMT across multiple tumor types and identify tary Table S1. The Profiling of Resistance Patterns and Oncogenic potential therapeutic vulnerabilities in mesenchymal tumors. Signaling Pathways in Evaluation of Cancers of the Thorax and A patient-derived, pan-cancer EMT signature was developed Therapeutic Target Identification (PROSPECT) dataset of surgi- using 11 distinct tumor types from The Cancer Genome Atlas. cally resected NSCLC has been previously described (9). Array- Mesenchymal tumors had similar patterns of gene, protein, based expression profiling of PROSPECT tumors was performed and miRNA expression independent of cancer type. Tumors using the Illumina Human WG-6 v3 BeadChip according to the with mesenchymal EMT scores not only had a higher expres- manufacturer's protocol and gene expression data have been sion of the receptor tyrosine kinase Axl (previously implicated previously deposited in the GEO repository (GSE42127). with EMT and associated with response to Axl inhibitors), but also expressed high levels of multiple immune checkpoints Developing the pan-cancer EMT signature including PD-L1, PD1, CTLA4, OX40L, and PD-L2. This novel We adopted an approach similar to that used by Byers and finding, which was validated in an independent patient colleagues (3) to derive the pan-cancer EMT signature. We used cohort, highlights the possibility of utilizing EMT status— four established EMT markers, namely, CDH1 (epithelial marker, independent of cancer type—as an additional selection tool to E type), CDH2 (mesenchymal marker, M type), VIM (M type), and select patients who may benefit from immune checkpoint FN1 (M type) as seeds to derive the pan-cancer EMT signature on blockade. the basis of TCGA pan-cancer RNAseq data. In particular, we computed correlations (Pearson correlation, r) between all mRNAs in the RNAseq data and each of the established EMT markers for each individual tumor type. models [the starting point of the lung cancer EMT signature (3)] fi and clinical patient samples—especially in regard to the interplay Identi cation of EMT-associated genomic features between EMT, tumor microenvironments, and immune response We computed Pearson correlation between the EMT score and at different disease sites (9). Therefore, we built upon our previous individual genomic features because these features are normally approach to derive a pan-cancer EMT signature by leveraging distributed. The genomic features per gene included the mRNA molecular datasets from 11 tumor types (n ¼ 1,934 tumors expression, RPPA expression (either total protein or phosphory- overall; see Supplementary Table S1) using clinical patient sam- lated protein), and miRNA expression levels (5p and 3p mature ples from The Cancer Genome Atlas (TCGA). The datasets were strands). obtained from primarily epithelial malignancies, such as breast, colorectal, and endometrial cancer, and two cohorts of lung Association between EMT score and other covariates cancer (adenocarcinoma and squamous cell carcinoma). We applied the log-rank test to assess the association between We tested the performance of the pan-cancer EMT signature by the dichotomized EMT score (with a cutoff of 0) and overall comparing its association with established, but independent, survival. We used the ANOVA test to assess the association EMT markers at the proteomic, microRNA (miRNA), and mRNA between EMT score and
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