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Whole Exome and Transcriptome Analyses Integrated With Author Manuscript Published OnlineFirst on May 2, 2018; DOI: 10.1158/2326-6066.CIR-17-0453 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Whole exome and transcriptome analyses integrated with microenvironmental immune signatures of lung squamous cell carcinoma Jeong-Sun Seo1,2,3,6,* , Ji Won Lee2,3,* , Ahreum Kim2,3,*, Jong-Yeon Shin2,6,*, Yoo Jin Jung4, Sae Bom Lee4, Yoon Ho Kim4, Samina Park5, Hyun Joo Lee5, In-Kyu Park5, Chang-Hyun Kang5, Ji-Young Yun2,6, Jihye Kim2,6 & Young Tae Kim2,4,5 1Gongwu Genomic Medicine Institute (G2MI), Medical Research Center, Seoul National University Bundang Hospital, Seongnamsi 13605, Korea 2Genomic Medicine Institute (GMI), Medical Research Center, Seoul National University, Seoul 03080, Republic of Korea 3Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul 03080, Republic of Korea 4Cancer Research Institute, Seoul National University College of Medicine, Seoul 03080, Korea 5Department of Thoracic and Cardiovascular Surgery, Seoul National University Hospital, Seoul 03080, Korea 6Macrogen Inc., Seoul 08511, Korea * These authors contributed equally to this work Running title Genomic landscape and microenvironmental immune signature Abbreviations LUSC: Lung squamous cell carcinoma SCNV: Somatic copy-number variation PCA: Principal component analysis GO: Gene ontology GSEA: Gene set enrichment analysis STAR: Spliced transcripts alignment to a reference VSD: Variance stabilizing data FPKM: Fragments per kilobase million DEG: Differentially expressed ESTIMATE: Estimation of stromal and immune cells in malignant tumours using 1 Downloaded from cancerimmunolres.aacrjournals.org on October 1, 2021. © 2018 American Association for Cancer Research. Author Manuscript Published OnlineFirst on May 2, 2018; DOI: 10.1158/2326-6066.CIR-17-0453 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. expression TIMER: Tumor immune estimation resource FDR: False discovery rate CYT: Cytolytic activity CAF: Carcinoma associated fibroblasts TAM: Tumor associated macrophage KEGG: Kyoto encyclopedia of genes and genomes. Corresponding authors Professor Jeong-Sun Seo, Gongwu Genomic Medicine Institute (G2MI), Medical Research Center, Seoul National University Bundang Hospital, Seongnamsi 13605, Korea. E-mail: [email protected] or Professor Young Tae Kim, Department of Thoracic and Cardiovascular Surgery, Seoul National University Hospital, Seoul 03080, Korea. E-mail: [email protected]. Conflicts of Interest No potential conflicts of interest relevant to this article were disclosed. Abstract The immune microenvironment in lung squamous cell carcinoma (LUSC) is not well understood, with interactions between the host immune system and the tumor, as well as the molecular pathogenesis of LUSC, awaiting better characterization. To date, no molecularly targeted agents have been developed for LUSC treatment. Identification of predictive and prognostic biomarkers for LUSC could help optimize therapy decisions. We sequenced whole exomes and RNA from 101 tumors and matched noncancer control Korean samples. We used the information to predict subtype-specific interactions within the LUSC microenvironment and to connect 2 Downloaded from cancerimmunolres.aacrjournals.org on October 1, 2021. © 2018 American Association for Cancer Research. Author Manuscript Published OnlineFirst on May 2, 2018; DOI: 10.1158/2326-6066.CIR-17-0453 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. genomic alterations with immune signatures. Hierarchical clustering based on gene expression and mutational profiling revealed subtypes that were either immune defective or immune competent. We analyzed infiltrating stromal and immune cells to further characterize the tumor microenvironment. Elevated expression of macrophage 2 signature genes in the immune competent subtype confirmed that tumor-associated macrophages (TAMs) linked inflammation and mutation-driven cancer. A negative correlation was evident between the immune score and the amount of somatic copy-number variation (SCNV) of immune genes (r = -0.58). The SCNVs showed a potential detrimental effect on immunity in the immune-deficient subtype. Knowledge of the genomic alterations in the tumor microenvironment could be used to guide design of immunotherapy options that are appropriate for patients with certain cancer subtypes. Introduction Lung cancer is the second leading cause of death in Korea. The most common type of primary lung cancer, lung adenocarcinoma, has been characterized at the molecular level (1,2). Lung squamous cell carcinoma, which accounts for 30 percent of all lung cancers (3), is not well characterized due to poor understanding of the cancer’s genomic evolution (4) and the antitumor activity of immune cells (5,6). Genomic alterations in the tumor characterize various stages of cancer progression. Immune defenses, on the other hand, are governed by tumor stroma, including basement membrane, extracellular matrix, vasculature, and cells of the immune system (7-9). Most cells in tumor stroma have some capacity to suppress a tumor, 3 Downloaded from cancerimmunolres.aacrjournals.org on October 1, 2021. © 2018 American Association for Cancer Research. Author Manuscript Published OnlineFirst on May 2, 2018; DOI: 10.1158/2326-6066.CIR-17-0453 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. although this capacity changes as the cancer progresses; invasion and metastasis can follow (10-13). Immune and stromal characteristics have emerged as prognostic and predictive factors that could be used to guide a personalized approach in cancer immunotherapy (14,15). Analyses of genomic alterations, especially somatic mutations, have been used to predict response to immunotherapy (16,17). Here, we used genomic and transcriptomic analysis to integrate molecular subtypes of tumors with and immune responses. We show that genomic alterations affect the tumor microenvironment and tumor development in a subtype-specific manner. The data show how genomic alterations and tumor microenvironment impact cancer proliferation and invasion, and how predicted roles of immune cells and their interactions with cancer cells in LUSC might affect cancer therapy and patient survival. Materials and Methods RNA and whole exome sequencing All protocols of this study were approved by the Institutional Review Board of Seoul National University Hospital (IRB No:1312-117-545). One hundred and one cases of lung squamous cell cancer samples, taken between 2011-2013, were included. Of these 101 patients, two patients were treated by neoadjuvant chemotherapy before surgery, and were subsequently excluded from 4 Downloaded from cancerimmunolres.aacrjournals.org on October 1, 2021. © 2018 American Association for Cancer Research. Author Manuscript Published OnlineFirst on May 2, 2018; DOI: 10.1158/2326-6066.CIR-17-0453 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. survival analysis. All the tumor and matched adjacent noncancer control tissue specimens were grossly dissected immediate after surgery and preserved in liquid nitrogen. Data on clinical features such as smoking history, pathologic TNM stage, tumor size and degree of differentiations were collected (Table 1 and Supplementary Table S1). For RNA-seq, we extracted RNA from tissue using RNAiso Plus (Takara Bio Inc.), followed by purification using RNeasy MinElute (Qiagen Inc.). RNA was assessed for quality and was quantified using an RNA 6000 Nano LabChip on a 2100 Bioanalyzer (Agilent Inc.). The RNA-seq libraries were prepared as previously described (18). For whole exome sequencing, genomic DNA was extracted and 3 μg from each sample was sheared and used for the construction of a paired-end sequencing library as described in the protocol provided by Illumina. Enrichment of exonic sequences was then performed for each library using the SureSelect Human All Exon 50Mb Kit (Agilent Inc.) following the manufacturer's instructions. Libraries for RNA and whole exome sequencing were sequenced with Illumina TruSeq SBS Kit v3 on a HiSeq 2000 sequencer (Illumina Inc.) to obtain 100-bp paired-end reads. The image analysis and base calling were performed using the Illumina pipeline (v1.8) with default settings. RNA-seq analysis To characterize the LUSC transcriptome profile in cancer and noncancer control cells, we performed RNA-Seq for 101 LUSC and matched noncancer control samples. Total RNA extracted from lung specimens and depleted of ribosomal RNA was sequenced at the desired depth (100X) on RNA-Seq (Illumina HiSeq). The 5 Downloaded from cancerimmunolres.aacrjournals.org on October 1, 2021. © 2018 American Association for Cancer Research. Author Manuscript Published OnlineFirst on May 2, 2018; DOI: 10.1158/2326-6066.CIR-17-0453 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. reads were aligned to the human genome (version GRCh37) with the Spliced Transcripts Alignment to a Reference (STAR) alignment software. The pre- processing pipeline on the GTAK website was followed (19). The raw read counts were generated using HTSeq-count for each annotated gene. Unsupervised subtype clustering With the Ensembl gene set, the number of raw reads aligned to each gene was computed by HT-seq count and was normalized by Variance Stabilizing
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