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
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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
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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
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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
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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
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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 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. Samples were clustered based on principal
components into three groups noncancer control with 95% confidence interval by
hierarchical 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 samples were
processed in the same batches, thus additional batch-effect corrections were not
necessary (22).
Differentially expressed gene-analysis
Differentially expressed genes of tumor subtypes compared to noncancer control
expression in noncancer control cells were determined by the significance criteria
(adjusted P value < 0.05, |Log2 (fold change)| ≥ 1, and base Mean ≥ 100) as
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implemented in the R packages DESeq2and edgeR. The adjusted P value for
multiple testing was calculated by using 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 hierarchical clustering algorism
(Cluster 3.0) with uncentered correlation and average linkage (24). The gene
expression pattern was visualized 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 s (subtype A-UP and B-DOWN, subtype A-DOWN
and B-UP) were selected and enriched for Gene Ontology (GO) gene sets by
Gene Set Enrichment Analysis (GSEA) in order to determine genes enriched in
ranked gene lists.
Fragments Per Kilobase Million (FPKM) calculation and normalization
Raw reads (HTseqcounts) were normalized using FPKM as implemented in the R
package ’edgeR’ and the FPKM values were transformed to log2 values and adjusted
to the median centered gene expression values by subtracting the row-wise median
from the expression values in each row (Cluster 3.0). The centered and log2
transformed VSD and FPKM expression were used to illustrate gene expression
pattern in a heatmap.
GSEA and network analysis
GO analysis of the gene expression data was performed using GSEA (v2.24)
desktop tools (permutation = 1,000) and visualized by the Enrichment Map tool in
Cytoscape [P ≤ 0.05, FDR q-value ≤ 0.1, and similarity ≤ 0.5 (25)].
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Tumor microenvironment analysis
The fractions of stromal and immune cells in tumor samples were estimated by
Estimation of STromal and Immune cells in MAlignant Tumours using Expression
data (ESTIMATE) scores with predictions of tumor purity based on the absolute
method previously reported (26). The abundance of six infiltrating immune cell types
(B cells, CD4+ T cells, CD8+ T cells, neutrophils, macrophages, and dendritic cells) in
the two subtypes of LUSC was estimated with the Tumor IMmune Estimation
Resource (TIMER) algorithm (27).
Immune score, stromal score, tumor purity, and cell cycle score
Immune score, stromal score, and tumor purity were made using ESTIMATE. The
gene set for cell cycle score was used to calculate the cell cycle score from the
one in Davoli, T et al. (28).
Statistical analyses
Quantitative data are presented as mean ± standard deviation. We used R-3.2.3 to
perform the statistical analyses. The normality of the variables was tested by
Shapiro-Wilk normality test (29). For two groups, significance (P value) for normally
distributed variables was determined by unpaired Student t test, and non-normally
distributed variables were analyzed Mann-Whitney U test. For more than two groups,
Kruskal-Wallis and one-way ANOVA tests were used for non-parametric and
parametric method, respectively (30). Statistically significant differences were tested
at P values < 0.05. R value was computed by Pearson and distance correlation.
Survival graph was plotted by Kaplan Meier method, and comparison between the
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subtypes were analyzed with the log-rank test, and Cox’s proportional hazards
model was also used for survival analysis (SPSS for Windows, version 22; SPSS
Inc., Chicago, IL).
Somatic mutation detection using whole exome sequencing
To find somatic mutations, we performed whole exome sequencing (100X). Reads
were aligned to the NCBI human reference genome (hg19) using BWA (31). Picard
was applied to mark duplicates and we used Genome Analysis Toolkit (GATK Indel
Realigner) to improve alignment accuracy. Somatic single-nucleotide variants (SNVs)
from 101 LUSC samples with matched noncancer control samples were called using
MuTect (32). GATK’s Haplotype Caller was also used for indel detection. All variants
were annotated with information from several databases using ANNOVAR (33).
Somatic copy-number variant detection
To detect somatic copy-number variants from whole exome sequencing data,
EXCAVATOR analysis was applied (34). GISTIC analysis was used to identify
recurrent amplification and deletion peaks (35).
Calculation of SCNV levels
We obtained amplified and deleted copy-number variants using EXCAVATOR and
GISTIC analyses. Arm and focal SCNV levels of each patient were respectively
calculated by summing the copy number changes at each copy number event. Arm,
chromosome, and focal CNV level were normalized to the mean and standard
deviation among the samples (28).
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Total SCNV = ∑ | 퐶표푝푦 푁푢푚푏푒푟 푐ℎ푎푛푔푒 푎푡 푡표푡푎푙 푐표푝푦 푛푢푚푏푒푟 푟푒푔𝑖표푛|
Arm-level SCNV = ∑ | 퐶표푝푦 푁푢푚푏푒푟 푐ℎ푎푛푔푒 푎푡 푎푟푚 푐표푝푦 푛푢푚푏푒푟 푟푒푔𝑖표푛|
Focal-level SCNV = ∑ |퐶표푝푦 푁푢푚푏푒푟 푐ℎ푎푛푔푒 푎푡 푓표푐푎푙 푐표푝푦 푛푢푚푏푒푟 푟푒푔𝑖표푛|
Cell cycle related SCNV=∑|Copy Number change at copy number region of genes
used in Cell cycle score. |
Immune related SCNV=∑|Copy Number change at copy number region of genes
used in immune score. |
Prediction of neoantigens
We predicted neoantigens using the pVAC-Seq pipeline (36). We used non-
synonymous mutations to follow the pVAC-seq pipeline. Amino acid changes and
transcript sequences were annotated by variant effect predictor. Epitopes
predicted by HLAminer and were filtered by RNA expression (FPKM>1) and
coverage (tumor coverage >10X and noncancer control coverage>5X).
Availability of data and material
Whole exome and RNA sequencing data are available under the NCBI Sequence
Read Archive (SRA) study accession no. SRP114315
Results
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Identification of LUSC subtypes
In this study, 101 LUSC and matched noncancer control samples were used to
discover significant differential gene expression, and principal component analysis
(PCA) on entire samples was performed to identify distinctive clusters based on
gene variability between LUSC and noncancer control samples. PCA with the top
1,000 most variable genes and unsupervised hierarchical clustering with the k-
means algorithm were applied to RNA-seq data and distinguished noncancer
control from tumor samples. In contrast, 19 tumor samples overlapped and were
more closely associated with the noncancer control group with 95 % confidence
interval ellipsoids (Fig. 1A and Supplementary Fig. S1). Additional PCA with 101
tumor samples distinguished the 19 tumor samples (subtype B) from 82 other
major tumor samples (subtype A) at the 95 % confidence interval. Thus, PCA
revealed two molecular subtypes, A and B. These two subtypes were also
distinguishable in the TCGA LUSC cohort (n = 431) by PCA and unsupervised
hierarchical clustering at 95 % confidence interval (Supplementary Fig. S2).
To identify the pattern of genomic alterations in LUSC, we sequenced the exome
of independent noncancer control samples (n = 101) and tumor samples (n = 101).
The number of total mutations in subtype B was less than subtype A (P < 0.001 by
Mann-Whitney U test; Fig. 1B and Supplementary Fig. S3A). Mutations in genes
encoding TP53, NAV3, CDH10, KMT2D, NFE2L2, CTNNA3, KEAP1 NTRK3, RB1,
NOTCH1, PTEN, FGFR2 and EGFR were also identified in current study (Fig. 1C).
Mutational signature combinations in subtype A were significantly different from
those in subtype B (P < 0.01 by Mann-Whitney U test) showing more irregular
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patterns [Fig. 1D and Supplementary Fig. S3D, (32)]. We compared the burden of
somatic copy-number variation (SCNV) (28). Arm-level SCNVs (length > 98% of a
chromosome arm), focal-level SCNVs (length < 98% of a chromosome arm), and
total SCNVs (all of a chromosome), which comprise the burden of SCNV, were
mostly found in subtype A (Fig. 1E and F). To elucidate the effect of genomic
alteration on the tumor microenvironment, we also investigated major
histocompatibility complex (MHC) and tumor specific antigen (TSA) level, and found
that subtype A carried more deletions of MHC regions than did subtype B
(Supplementary Fig. S3C). TSA level was also higher in subtype A than in subtype B
(P < 0.001 by Mann Whitney U test; Supplementary Fig. S3D). Our transcriptome
analysis thus defined two LUSC subtypes, A and B, with different patterns of somatic
mutations.
Identification of differentially expressed genes in subtypes
We analyzed differential expression of 4,807 genes in subtype A and 1,471 genes
in subtype B compared to noncancer control expression, and matched the
significance criteria applied in this study (DESeq2: false discovery rate (FDR) < 0.1,
DESeq2: FDR < 0.05). We derived a heatmap depicting 5,387 differentially
expressed genes in subtype A and B by excluding the overlapped genes
(Supplementary Table S2). Differentially expressed genes were significantly
enriched in several pathways through Gene Ontology (GO) term enrichment
analysis. Genes for which expression was upregulated were mainly involved with
the cell cycle and DNA replication (n = 1,826) in subtype A and with immune and
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defense pathways (n = 1,876) in subtype B (Fig. 2A).
Further functional gene enrichment analysis using GSEA (Gene Set Enrichment
Analysis, v2.2.3; ref. 25) proved that these differentially expressed genes were
enriched in pathways related to cytoskeleton, mitosis, cell cycle, and chromatin
modification in subtype A and pathways related to defense response, immune
response, cytokine and metabolic and biosynthetic process in subtype B. Using
network analysis with Cytoscape (P ≤ 0.05, FDR q-value ≤ 0.1, and similarity ≤
0.5), we found that the increase in expression and activation of pathway-related
genes in subtype B was linked to regulation of immune cell differentiation and
apoptotic processes, which would increase immune cell abundance. Genes
characterizing subtype A, on the other hand, were linked to cell cycle and
microtubule pathways that would enhance cancer cell proliferation (Fig. 2B).
Consistent with gene set enrichment analysis, network analysis showed that cell
cycle and immune system gene sets were differentially correlated in each subtype.
Both immune response and cell cycle pathways were differently upregulated in the
subtypes, showing an interaction between immune response and somatic mutations.
The immune landscape of the microenvironment in LUSC
To identify the roles of immunity and distribution of infiltrating immune cells in tumor
and noncancer control samples, we computed stromal and immune scores along
with tumor purity based on the ESTIMATE method; ref. 26).The stromal and
immune scores in subtype B compared to subtype A were significantly increased
(stromal core P = 9.10 x 10-25, immune score P = 7.51 x 10-19 by unpaired Student
t test; Fig. 3A and Supplementary Fig. S4). The high immune scores suggested
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that recruitment of immune cells was more enhanced in subtype B than in A, which
implicates not only immune cells surrounding the tumor but also immune cells
infiltrating the tumor (26). This observation may reflect the fact that the different
portion of infiltrating immune and stromal cells was intermixed in a dissecting
tumor, and this can be useful for predicting the degree of tumor purity and tumor
microenvironment (tumor purity P = 9.80 x 10-20 by unpaired Student t test;
Supplementary Fig. S4). As subtype B had more tumor-infiltrating immune cells than
did subtype A, cytolytic activity of subtype B must have been higher. Indeed, the
cytolytic activity score was higher in subtype B than in subtype A (P = 2.07 x 10-6 by
unpaired Student t test; Supplementary Fig. S4). Cytolytic activity is a metric of
immune-mediated cell destruction, and immune infiltration predicts more cytolytic
activity in subtype B of LUSC (37). Similarly, TCGA LUSC cohort results identified
different microenvironmental factors in the two subtypes. Subtype B (n = 328) in the
TCGA LUSC cohort was consistent with the description of our LUSC subtype B (n =
19; higher immune and stromal scores, lower tumor purity, and higher CYT score;
Supplementary Fig. S5). The TCGA cohort contained more subtype B than subtype A
samples; the Korean and TCGA cohorts likely had different subtype compositions.
Immune cells are important in tumors. Stromal cells influence tumor proliferation
and inflammation. In order to evaluate tumor associated stroma, the effects of
activated and normal stroma on two tumor subtypes were described by 50 stromal
signature genes as previously reported (38). The heat map of LUSC and noncancer
control cells using active and normal stroma genes indicated three groups: activated
stromal-rich samples (subtype A), normal and activated stromal-rich samples
(subtype B), and normal stromal-rich samples (noncancer control). Mean normalized
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gene expression of the 25 exemplar activated and normal stromal genes was
higher in subtype B than in subtype A (activated stroma P = 0.0247; normal stroma
P < 0.01 by Mann-Whitney U test: Fig. 3A and Supplementary Figs. S6A and B).
The activated stromal signature genes were associated with carcinoma associated
fibroblasts (CAFs) and were overexpressed in subtype B as compared to subtype
A and noncancer control. Likewise, the normal stromal genes, which reflect the
composition of immune cells, were more highly expressed in subtype B than in
subtype A. Overexpression of growth factors and chemokines related to CAFs,
such as HGF, FGF7, CXCL12, MMP2, IL6, CCL2, NFkB, mediated tumor
promotion and aggressive invasion in subtype B [Supplementary Fig. S6C; (39)].
To analyze tumor microenvironment integration in subtypes A and B, we
assessed the correlation of stromal and immune scores as well as tumor purity.
There was a positive correlation between stromal and immune scores (subtype A,
Pearson’s r, 0.79; distance r, 0.78; subtype B Pearson’s r, 0.46; distance r, 0.54);
samples with high tumor purity showed low stromal and immune scores (Fig. 3B).
Subtypes A and B differed in their association with stromal and immune scores,
suggesting variation in microenvironments and in interactions with stromal and
immune cells.
We estimated (with use of the TIMER algorithm) the abundance of tumor-infiltrating
immune cells in six cell types (B cells, CD4+ T cells, CD8+ T cells, neutrophils,
macrophages, and dendritic cells) to predict immune cell profiling in subtypes A and
B. Noncancer control A and noncancer control B subtypes had no significant
differences. Dendritic cells were the most abundant cells among both tumor and
noncancer control cells. Although macrophages were not the most abundant cells in 15
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tumors and noncancer control samples, macrophages seemed to be the most
influential in a subtype-specific manner (P = 6.27 x 10-9 by Mann-Whitney U test;
Figs. 3C and D). The proportion of macrophages in subtype B was significantly
higher than in subtype A, supporting the previous observations that macrophages
are present in large numbers in the tumor microenvironment and promote tumor
progression and metastasis (40).
Intratumoral immune coordination was assessed by analyzing the correlation
between selected immune cell markers. Pairwise comparisons of immune cell
abundance levels were done by measuring Pearson correlation coefficients (r).
The relationships implied by these correlations were visualized as r values
(Supplementary Fig. S7). Correlation between immune cells was greater in tumor
cells than in noncancer control cells, and the degree of correlation was higher in
subtype B than in subtype A. Dendritic cells were more correlated with other
immune cells in subtype B than in subtype A, and the correlations of dendritic cells
with neutrophil and neutrophil with CD8+ T cells were increased in subtype B.
Dendritic cells and neutrophils seemed to have immediate connection with CD8+ T
cells, confirming previous findings that the crosstalk between neutrophils and
recruited dendritic cells accelerates antigen presentation to T cells and generates
an antigen-specific immune response (41,42). Elevated expression of macrophage
2 signature genes in subtype B, a previously validated gene set (43), suggests
that tumor-associated macrophages (TAMs) promote tumor growth in subtype B
cancer (Fig. 3D and Supplementary Figs. S8A and B). We examined the
reproducibility of the immune parameters by subtype in both our LUSC and TCGA
LUSC cohorts. All Immune scores, activated stromal gene expression, and
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macrophage 2 activity patterns by subtype were highly reproducible across both
cohorts. LUSC and TCGA LUSC cohort subtype B showed higher immune
infiltration (Supplementary Fig. S9). The immune microenvironment in subtype B
was converted to escape mode during the immunoediting process of LUSC in
order to help proliferation of resistant clones in an immunocompetent host. This
complexity of subtype-specific microenvironments should inform therapeutic
strategies for treatment of LUSC.
Impacts of somatic copy-number variants on tumor microenvironment
To identify the effect of genomic alterations on tumor microenvironments in LUSC,
we investigated the association between somatic genomic alterations and the
immune score. The SCNVs were significantly higher in subtype A (P < 0.001 by
Mann-Whitney test; Fig. 4A), and were negatively correlated with the immune
score (Pearson’s coefficient: -0.58) and with other immune related properties such
as the stromal score (stromal cell infiltration), CYT score (immune cytolytic activity),
CD4+ T-cell infiltration (Pearson’s coefficient: -0.42), macrophage infiltration
(Pearson’s coefficient: -0.39), and dendritic cell infiltration (Pearson’s coefficient: -
0.41; Figs. 4B and C). These results suggested that the negative correlation
between immune score and SCNVs in subtype A may influence the immune
response of subtype A. Focal-level SCNVs contributed more to immune scores
than arm-level SCNVs (Figs. 4D and E). In an association test, focal-level SCNVs
were more highly correlated with immune score (Fig. 4F). These findings indicate
that focal-level SCNVs are more influential on the tumor microenvironment, and
might be better targets for immunotherapy. Focal-level SCNVs had a stronger
correlation with LUSC immunity and may make it easier to determine the target 17
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genes for drug interventions than arm-level SCNVs, because arm-level SCNVs are
large genomic defects that may affect multiple targets (44,45). To assess the role of
focal-level SCNV in the tumor microenvironment, we investigated focal-level SCNVs
in immune related pathways via KEGG enrichment analysis. Subtype A had a high
prevalence of SCNVs on immune-related pathways (B-cell receptor signaling
pathway, chemokine signaling pathway, T-cell receptor signaling pathway and Toll-
like receptor signaling pathway of KEGG; Fig. 5). Genes harboring SCNV deletions,
which lead to loss of function, were enriched in subtype A only for immune related
pathways of immune system processes, immune responses and response to
cytokine (Supplementary Fig. S10). These data suggested that focal-level SCNVs
may drive the low immune response in tumor microenvironments and would be good
targets to control immunity.
By analyzing the tumor microenvironment with RNA sequencing and whole exome
sequencing, we defined two immune related subtypes: subtype A (immune defective)
and subtype B (immune competent). The log2 VSD expression pattern between
tumors and noncancer control tissue was consistent over the validation gene set with
previously studied subtypes [Supplementary Fig. S11; (46)]. The subtype A
expression pattern resembled that of the classical subtype, in which TP63, AKR1C3,
FOXE1, and TXN genes were overexpressed. Subtype B appeared to overexpress
basal-related genes (S100A7, MMP13, and SERPINB3) and secretory-related genes
(ARHGDIB and TNFRSF14). Except for the secretory-related genes, the other genes
were all overexpressed in subtype A relative to subtype B. The expression pattern of
previous validated genes differed between subtypes. Thus, our clustering method
with gene selection for subtyping achieved similar results to those of the previous
18
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study. Our immune subtyping approach categorized the LUSC into immune defective
and competent subtypes based on the pattern of infiltrating immune, stromal cells,
immune cell composition. Our data also suggest that genomic alterations, especially
SCNVs, decrease immune related activities and immune cells in cell proliferation
related subtypes of LUSC, and that activated stroma and TAM lead to the evolution
of cancer cells in the immune related subtype. Although the subtypes differed in
immune activation, mutation burden, and SCNV, the subtypes had no significant
differences in clinical features or overall survival in both cohorts (our LUSC cohort P
value = 0.223; TCGA LUSC cohort P value = 0.54; log-rank test; Supplementary Figs.
S12 and S13). It was difficult to evaluate to what extent clinical features were
affected by low immune activities due to the high SCNV in subtype A and the
increased activity of TAMs and activated stroma in subtype B.
We found that all immune checkpoint genes were more highly expressed in subtype
B than subtype A in both our LUSC samples and the TCGA LUSC cohort (Fig. 6A),
and two independent cohorts had similar expression patterns for immune
checkpoints (Fig.6B). Both PD-1 and its ligand PD-L1 as well as other immune
checkpoint genes are highly expressed in subtype B samples. PD-1 and PD-L1 are
involved in immune tolerance by preventing stimulation of an immune response and
inducing tumor immune escape (47), and might be valuable targets for new drugs in
subtype B. However, difficulty with inducibility of PD-L1 protein expression and with
accurate assays complicate their use. More reliable biomarkers are needed (48).
Discussion
19
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Although the study of PD1 expression for immunotherapy has produced benefit in
many clinical cases, some patients do not respond to checkpoint blockade (49). For
such nonresponders, tumor-specific antigens with appropriate MHC-binding affinity,
might be more useful for immunotherapy (36,50,51). We analyzed the MHC region
and TSA levels for both subtypes and found that subtype A had more TSA and more
deletions of MHC regions than did subtype B. However, these results did not explain
the role of the tumor microenvironment. Further study is needed to validate the
results. In particular TSA content was predicted but not validated in the clinical
environment.
Our genetic results thus provide evidence for an immune response to cancer in
humans and indicate a mechanism of tumor-intrinsic resistance to cytolytic activity in
tumors with a high burden of somatic mutations. Analysis of genomic alterations and
their impact on the tumor microenvironment in a subtype-specific manner might
identify patients who could benefit from cancer immunotherapies that boost the
immune system.
Acknowledgements
This work has been supported by Macrogen Inc. (grant no. MGR17-01) and the
National Research Foundation of Korea (NRF) grant funded by the Korea
government (MSIP) (No. NRF-2014R1A2A2A05003665).
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Table 1. Clinical data summary
Clinical data summary Korean (n=101) Patient characteristics Number of patients
Age at diagnosis, years Median 70 Range 35-83 Sex Male 95 Female 6 Smoking status Never-smoker 12 Former smoker 61 Current smoker 28 Median follow-up, months 45 Tumor stage Ⅰ 51 Ⅱ 29 Ⅲ 21 Ⅳ 0 T stage T1 27 T2 58 T3 15 T4 1 N stage N0 62 N1 24 N2 15 Recurrancy 31 Total LN Median 30 Range 5-66
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Figure Legend
Figure 1. Identification of the subtypes of LUSC. A, Principal component analysis
(PCA) of the top 1,000 most variable genes among the subtype A, B, and noncancer
control were performed across all samples. 3D PCA scores plots of top 1,000 of
most variable genes was drawn as meshes containing cancer and noncancer control
points (left) and subtype A and B points (right) based on K-means clustering (k-
means = 2) on the first three PCs with 95% confidence interval ellipsoids. B, Ratio of
somatic synonymous and nonsynonymous mutations in mutations per megabase
and subtypes were classified. C, Name of significantly mutated genes
(left),distribution of mutations across 101 LUSC, and frequency of significantly
mutated genes (right) were plotted. D, The mutational signature revealed by somatic
mutations in whole-exome sequencing. E, Arm level CNV, focal level CNV and total
level CNV of individual sample displayed in each column. F, Significant, focally
amplified (red), and deleted (blue) regions are plotted across chromosomal locations.
Figure 2. Molecular subtypes at transcriptomic expression level. A, Heatmap
depicted 5,387 differentially expressed genes in subtype A and B, and two distinct
clustered genes in differentially expressed genes were selected. Top 10 GO gene
sets in either clusters were determined based on the rank of enrichment –log10(P
value) of pathway and the matched significance criteria (P-value < 0.05 and FDR q
value < 0.1) B, Network visualization based on gene enrichment analysis. Nodes
represent subtype A (red) and B (blue) networks, and green edges represent genetic
overlap between networks using Gene Ontology (GO) enrichment analysis
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(permutation = 1,000). The size of each node reflects the number of genes included
in the network. Genes in significant networks were annotated and grouped with
simplified GO terms. Networks meeting the cut-off conditions detailed at the bottom
of the figure (right) were visualized with the Enrichment Map plugin for Cytoscape (P
≤ 0.05, FDR q value ≤ 0.1, and similarity ≤ 0.5).
Figure 3. The immune landscape of the microenvironment in LUSC. A, The heatmap
depicted the expression of stromal genes and the tumor microenvironmental factor
(cytolytic activity (CYT), purity, immune, stromal), which divided into three groups,
describing activated stroma-rich samples (subtype A), normal and activated stroma-
rich samples (subtype B), and normal stroma-rich samples (noncancer control) gene
expression. B, Scatterplots between stromal and immune scores with tumor purity
gradient were shown, and correlation coefficient indicated by each of the subtypes.
The color grading corresponds to the tumor purity, indexed as shown on the color
bar at the bottom right of the figure. C, The abundance of infiltrating B cells, CD4+ T
cells, CD8+ T cells, neutrophil, macrophages, and dendritic cells in two subtypes was
estimated, and each P value was indicated by each of the subtypes (Mann-Whitney
U test and Kruskal-Wallis test). Box represents the median (thick line) and the
quartiles (line). D, The median centered and log2 transformed expression level
(log2fpkm+1) of M1 and M2 signature genes in subtypes and noncancer control was
box-plotted with corresponding Mann-Whitney U and Kruskal-Wallis test results. Box
represents the median (thick line) and the quartiles (line).
Figure 4. Impacts of genomic alterations on tumor micro environments in LUSC. A,
The level of total CNVs in cell cycle related gene sets are displayed across classes
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(left) and the level of total CNVs in immune related gene sets are displayed across
classes (right). B, The association between genomic alteration load (the number of
mutations, the level of CNV) and cell cycle score is shown (left) and the association
between genomic alteration load and immune score is shown (right). C, The
correlations between genomic alterations (CNVs, mutations) and immune related
properties were displayed. D, The cell cycle scores are shown in each sample and
the high score densities and low score densities of cell cycle score are plotted on the
x axis and y axis. E, The immune scores are shown in each sample and the high
score densities and low score densities of immune score are plotted on the x axis
and y axis. F, The correlation between CNV level (arm, focal) and cell cycle score or
immune score are displayed across subtype.
Figure 5. SCNV in LUSC immune pathway. The diagrams show the genes with
SCNV in the four immune related pathways and the percentage of samples with
SCNV in immune related pathway across classes; copy number deletion (blue), copy
number amplification (pink), the percentage of samples with SCNV in subtype A (red
box), the percentage of samples with SCNV in subtype B (blue box)
Figure 6. The expression pattern of immune checkpoints in LUSC. A, The
expression of immune checkpoint genes was analyzed between subtypes in our
LUSC samples (n = 101) and TCGA LUSC cohort (n = 431), respectively. The
comparison of median centered and log2 transformed expression (log2fpkm) of
immune checkpoint genes was performed between subtypes, and P-value was
28
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indicated by Mann-Whitney U or unpaired Student t test based on the normality. A
two-color scale was used with blue indicating low expression values and red
representing highly expressed genes. B, The median centered and log2 transformed
expression level (log2fpkm) of immune checkpoint genes in both cohorts was box-
plotted with corresponding Kruskal-Wallis test results. Box represents the median
(thick line) and the quartiles (line).
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Whole exome and transcriptome analyses integrated with microenvironmental immune signatures of lung squamous cell carcinoma
Jeong-Sun Seo, Ji Won Lee, Ahreum Kim, et al.
Cancer Immunol Res Published OnlineFirst May 2, 2018.
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