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      Transcriptomic Profiling of MSI-H/dMMR Gastrointestinal Tumors to Identify Determinants of Responsiveness to Anti–PD-1 Therapy

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          Abstract

          Purpose:

          Transcriptomic profiling was performed for microsatellite instability-high (MSI-H)/mismatch repair-deficient (dMMR) gastrointestinal tumors to determine the predictors of response to PD-1 blockade.

          Experimental Design:

          Thirty-six patients with MSI-H/dMMR gastrointestinal tumors, including gastric cancer, colorectal cancer, cholangiocarcinoma, small intestine cancer, and pancreatic cancer, being treated with PD-1 blockade were analyzed. We conducted the transcriptomic analysis of gastrointestinal tumors using RNA sequencing data, including the consensus molecular subtypes (CMS) of colorectal cancer.

          Results:

          Gene set enrichment analysis (GSEA) demonstrated that non-responders had upregulations of epithelial–mesenchymal transition, angiogenesis, hypoxia, mTORC1, TNF-α, KRAS, Wnt/β-catenin, TGF-β, and various metabolism-related signaling pathways. Meanwhile, the IFNγ pathway was enriched in responders. On the basis of the leading-edge analysis of GSEA, VEGF-A was significantly correlated with enriched pathways in non-responders. Patients with high VEGF-A expression, compared with those with low expression, had significantly shorter progression-free survival [PFS; median 4.8 months vs. not reached (NR), P = 0.032] and overall survival (median 11.1 months vs. NR, P = 0.045). Among 13 patients with colorectal cancer evaluable for CMS classification, the objective response rate was 100%, 0%, 0%, and 16.7% in CMS1, CMS2, CMS3, and CMS4, respectively. Patients with CMS1 had significantly longer PFS (NR vs. 4.8 months, P = 0.017) than those with CMS2, CMS3, or CMS4.

          Conclusions:

          Several transcriptomic features, including CMS classification and related genes, were associated with response to PD-1 blockade in MSI-H/dMMR gastrointestinal tumors. These findings can help develop predictive biomarkers or combination immunotherapies.

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          Most cited references43

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          Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

          Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.
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            GSVA: gene set variation analysis for microarray and RNA-Seq data

            Background Gene set enrichment (GSE) analysis is a popular framework for condensing information from gene expression profiles into a pathway or signature summary. The strengths of this approach over single gene analysis include noise and dimension reduction, as well as greater biological interpretability. As molecular profiling experiments move beyond simple case-control studies, robust and flexible GSE methodologies are needed that can model pathway activity within highly heterogeneous data sets. Results To address this challenge, we introduce Gene Set Variation Analysis (GSVA), a GSE method that estimates variation of pathway activity over a sample population in an unsupervised manner. We demonstrate the robustness of GSVA in a comparison with current state of the art sample-wise enrichment methods. Further, we provide examples of its utility in differential pathway activity and survival analysis. Lastly, we show how GSVA works analogously with data from both microarray and RNA-seq experiments. Conclusions GSVA provides increased power to detect subtle pathway activity changes over a sample population in comparison to corresponding methods. While GSE methods are generally regarded as end points of a bioinformatic analysis, GSVA constitutes a starting point to build pathway-centric models of biology. Moreover, GSVA contributes to the current need of GSE methods for RNA-seq data. GSVA is an open source software package for R which forms part of the Bioconductor project and can be downloaded at http://www.bioconductor.org.
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              Mismatch repair deficiency predicts response of solid tumors to PD-1 blockade

              The genomes of cancers deficient in mismatch repair contain exceptionally high numbers of somatic mutations. In a proof-of-concept study, we previously showed that colorectal cancers with mismatch repair deficiency were sensitive to immune checkpoint blockade with antibodies to programmed death receptor-1 (PD-1). We have now expanded this study to evaluate the efficacy of PD-1 blockade in patients with advanced mismatch repair-deficient cancers across 12 different tumor types. Objective radiographic responses were observed in 53% of patients, and complete responses were achieved in 21% of patients. Responses were durable, with median progression-free survival and overall survival still not reached. Functional analysis in a responding patient demonstrated rapid in vivo expansion of neoantigen-specific T cell clones that were reactive to mutant neopeptides found in the tumor. These data support the hypothesis that the large proportion of mutant neoantigens in mismatch repair-deficient cancers make them sensitive to immune checkpoint blockade, regardless of the cancers' tissue of origin.
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                Author and article information

                Journal
                Clin Cancer Res
                Clin Cancer Res
                Clinical Cancer Research
                American Association for Cancer Research
                1078-0432
                1557-3265
                13 May 2022
                07 March 2022
                : 28
                : 10
                : 2110-2117
                Affiliations
                [1 ]National Cancer Center Hospital East, Kashiwa, Chiba, Japan.
                [2 ]Division of Cancer Immunotherapy, Exploratory Oncology Research and Clinical Trial Center, National Cancer Center, Kashiwa, Japan.
                [3 ]Department of Gastroenterological Surgery, Yokohama City University Graduate School of Medicine, Yokohama, Japan.
                [4 ]General Medicinal Education and Research Center, Teikyo University, Tokyo, Japan.
                [5 ]Division of Cellular Signaling, National Cancer Center Research Institute, Tokyo, Japan.
                [6 ]Department of Pathology and Clinical Laboratories, National Cancer Center Hospital East, Kashiwa, Japan.
                Author notes
                [* ] Corresponding Author: Akihito Kawazoe, National Cancer Center Hospital East, 6-5-1 Kashiwanoha, Kashiwa, Chiba 277-8577, Japan. Phone: 81-47-133-1111; Fax: 81-47-134-6928; E-mail: akawazoe@ 123456east.ncc.go.jp
                Author information
                https://orcid.org/0000-0002-8377-1884
                https://orcid.org/0000-0001-8632-3748
                https://orcid.org/0000-0003-4146-3629
                https://orcid.org/0000-0002-7408-7298
                https://orcid.org/0000-0002-5241-6855
                https://orcid.org/0000-0002-4196-555X
                https://orcid.org/0000-0001-5041-2765
                https://orcid.org/0000-0002-5922-1487
                https://orcid.org/0000-0003-0044-953X
                https://orcid.org/0000-0001-6273-0189
                https://orcid.org/0000-0002-7187-3664
                https://orcid.org/0000-0003-4645-0181
                https://orcid.org/0000-0002-4050-2086
                https://orcid.org/0000-0001-5196-3630
                https://orcid.org/0000-0001-5520-8114
                https://orcid.org/0000-0002-0489-4756
                Article
                CCR-22-0041
                10.1158/1078-0432.CCR-22-0041
                9365358
                35254400
                31b011e3-bc56-42ee-85de-508d5c90fa79
                ©2022 The Authors; Published by the American Association for Cancer Research

                This open access article is distributed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) license.

                History
                : 06 January 2022
                : 07 February 2022
                : 03 March 2022
                Page count
                Pages: 8
                Funding
                Funded by: Japan Agency for Medical Research and Development, DOI https://doi.org/10.13039/100009619;
                Award ID: JP21cm0106502
                Funded by: Japan Society for the Promotion of Science, DOI https://doi.org/10.13039/501100001691;
                Award ID: 16K07143
                Award ID: 21H02772;
                Categories
                Translational Cancer Mechanisms and Therapy

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