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Whole Exome and Transcriptome Analyses Integrated with Microenvironmental Immune Signatures of Lung Squamous Cell Carcinoma Jeong-Sun Seo1,2,3,4, Ji Won Lee2,3, Ahreum Kim2,3, Jong-Yeon Shin2,4, Yoo Jin Jung5, Sae Bom Lee5, Yoon Ho Kim5, Samina Park6, Hyun Joo Lee6, In-Kyu Park6, Chang-Hyun Kang6, Ji-Young Yun2,4, Jihye Kim2,4, and Young Tae Kim2,5,6
The immune microenvironment in lung squamous cell carci- noma (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 andRNA from101 tumors andmatchednoncancer control Korean samples. We used the information to predict subtype-specific interactions within the LUSCmicroenvironment and to connect 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 microenviron- ment. Elevated expression ofmacrophage 2 signature genes in the immune competent subtype confirmed that tumor-associated macrophages (TAM) 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 ¼ �0.58). The SCNVs showed a potential detrimental effect on immunity in the immune-deficient subtype. Knowledge of the genomic alterations in the tumor microenvi- ronment could be used to guide design of immunotherapy options that are appropriate for patients with certain cancer subtypes. Cancer Immunol Res; 6(7); 848–59. �2018 AACR.
Introduction Lung cancer is the second leading cause of death in Korea. The
most common type of primary lung cancer, lung adenocarcino- ma, has been characterized at the molecular level (1, 2). Lung squamous cell carcinoma, which accounts for 30% of all lung cancers (3), is notwell 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 charac- terize various stages of cancer progression. Immune defenses, on the other hand, are governed by tumor stroma, including base- mentmembrane, extracellularmatrix, vasculature, and cells of the
immune system (7–9). Most cells in tumor stroma have some capacity to suppress a tumor, although this capacity changes as the cancer progresses; invasion and metastasis can follow (10–13).
Immune and stromal characteristics have emerged as prognos- tic and predictive factors that could be used to guide a person- alized 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 define molecular subtypes of tumors with 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 lung squamous cell carcinoma (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 and 2013, were included. Of these 101 patients we excluded two patients, a patient treated with one cycle of weekly docetaxel 65 mg and cisplatin 48 mg regimen preoperatively, andanother patientwhodiedofmassivepulmonary embolism at 16 days after operation, from subsequent survival analysis. All the tumor and matched adjacent noncancer
1Precision Medicine Center, Seoul National University Bundang Hospital, Seongnamsi, Korea. 2Genomic Medicine Institute (GMI), Medical Research Center, Seoul National University, Seoul, Republic of Korea. 3Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul, Republic of Korea. 4Macrogen Inc., Seoul, Republic of Korea. 5Seoul National University Cancer Research Institute, Seoul, Republic of Korea. 6Department of Thoracic and Cardiovascular Surgery, Seoul National University Hospital, Seoul, Republic of Korea.
Note: Supplementary data for this article are available at Cancer Immunology Research Online (http://cancerimmunolres.aacrjournals.org/).
J.-S. Seo, J.W. Lee, A. Kim, and J.-Y. Shin contributed equally to this article.
Corresponding Authors: Jeong-Sun Seo, Precision Medicine Center, Seoul National University Bundang Hospital, Seongnamsi 13605, Korea. Phone: 82- 31-600-3001; E-mail: [email protected]; and Young Tae Kim, Department of Thoracic and Cardiovascular Surgery, Seoul National University Hospital, Seoul 03080, Republic of Korea. Phone: 82-22-072-3161; Email: [email protected]
�2018 American Association for Cancer Research.
Cancer Immunology Research
Cancer Immunol Res; 6(7) July 2018848
on June 29, 2021. © 2018 American Association for Cancer Research. cancerimmunolres.aacrjournals.org Downloaded from
Published OnlineFirst May 2, 2018; DOI: 10.1158/2326-6066.CIR-17-0453
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; Supplementary Table S1). For RNA-seq, we extracted RNA from tissue using RNAiso Plus (Takara Bio Inc.), followed by puri- fication using RNeasy MinElute (Qiagen). RNA was assessed for quality and was quantified using an RNA 6000 Nano LabChip on a 2100 Bioanalyzer (Agilent Technologies). The RNA-seq libraries were prepared as previously described (18).
For whole-exome sequencing, genomic DNAwas extracted and 3 mg 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 Technologies) 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 Illu- mina pipeline (v1.8) with default settings.
RNA-seq analysis To characterize the LUSC transcriptome profile in cancer and
noncancer control cells, we performedRNA-seq for 101 LUSC and matched noncancer control samples. Total RNA extracted from lung specimens and depleted of ribosomal RNAwas sequenced at the desired depth (100�) on RNA-Seq (Illumina HiSeq). The reads were aligned to the human genome (version GRCh37) with the Spliced Transcripts Alignment to a Reference (STAR) align-
ment software. The preprocessing 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 genewas computed byHT-seq count andwas normalized by the Variance Stabilizing Data (VSD) method with use of the R package DEseq2. The variance for each gene was calculated, and the top 1,000 genes by variance were selected for PCA analysis (20). PCA analysis using the 1,000 most variable genes was conducted with all tumor and noncancer control samples. Sam- ples were clustered based on principal components into three groups noncancer control with 95% confidence interval by hier- archical clustering methods as implemented in the R package rgl (21). When analyzing RNA sequencing data, batch effects should be considered if experimental conditions and library preparation varied. All of our sampleswere processed in the samebatches, thus additional batch-effect corrections were not necessary (22).
Differentially expressed gene analysis Differentially expressed genes of tumor subtypes comparedwith
noncancer control expression in noncancer control cells were determined by the significance criteria (adjusted P < 0.05, |Log2 (fold change)|� 1, and basemean� 100) as implemented in the R packages DESeq2 and edgeR. The adjusted P value for multiple testingwas calculated byusing the Benjamini–Hochberg correction from the computed P value (23). The centered VSD values of the differentially expressed gene list were applied to the array hierar- chical clustering algorism (Cluster 3.0)with uncentered correlation and average linkage (24). The gene expression pattern was visual- ized with use of JAVA treeview. The hierarchical tree by arrays was generated by the clustering process and two types of gene sets in differentially expressed genes (subtype A-UP and B-DOWN, sub- type A-DOWN and B-UP) were selected and enriched for Gene Ontology (GO) gene sets byGene Set Enrichment Analysis (GSEA) in order to determine genes enriched in ranked gene lists.
Fragments per kilobase m