br Herein we investigated factors that are associated with I
Herein, we investigated factors that are associated with ITH of prostate cancer through comprehensive genomic and transcriptomic analysis. We also investigated genomic alterations, altered pathways, and clinical features as the indicators of high ITH.
Materials and Methods
Genomic and transcriptomic alterations including somatic muta-tions, copy number alterations, and gene fusions were collected from The Cancer Genome Atlas Research Network (The Cancer Genome Atlas Research Network 2015) . Gene Dalbavancin data from RNA-seq were obtained from GDAC Firehose (http://gdac.broadinstitute. org). A total of 85 samples with clonality information were used to determine the association between genomic alteration and expression profile.
Gene Expression Analysis
Differentially expressed genes (DEGs) were identified using DESeq R package (www.huber.embl.de/users/anders/DESeq/). Sig-nificant DEGs by both ≥2-fold change and adjusted P value b.05 were chosen. In order to identify overrepresented functions of an interesting group, gene set enrichment test was performed using Gene Set Enrichment Analysis (software.broadinstitute.org/gsea/), based on REACTOME pathway database in the Molecular Signatures Database (MSigDB). To estimate the fractions of immune-associated cell types including CD8-positive T cells, CIBERSORT was applied using RNA-seq expression profiles . It can infer relative proportions of each immune cell types using gene expression profiles.
Clonality and Tumor Purity Information
Clonality information was obtained from a pan-cancer analysis of the ITH, measured using PyClone and EXPANDS tools . The number of subclones ranged from one to eight and represented the ITH level. In order to categorize high and low ITH, we defined a tumor as an oligoclone when there were one or two subclones; otherwise, the tumor was defined as a multiclone . Tumor purity information was collected from pan-cancer analysis of the tumor purity . In brief, the purity levels were arbitrarily chosen from multiple estimators. The consensus purity estimation method is the median value for estimators after normalization.
The significance of clinical outcomes of the selected genes was plotted using Kaplan-Meier survival analysis using the survival package in R (http://CRAN.R-project.org/package=survival). Log-rank test was used for survival analysis. Fisher's exact test was used for the statistical analysis of ITH and genomic mutations. P b .05 was considered statistically significant. Information gain (IG) was used to
select the informative features for discriminating cancers with high clonality. IG for tumor samples D and a feature a is defined as :
v∈value ða Þ D
where value(a) is the set of all possible values for feature a and Dv is the subset of D which the feature a has value v.
Genomic Profiles According to Degree of ITH
A total of 85 patients having clonality information with prostate cancer were evaluated. Patient characteristics according to clonality are shown in Table 1. Here, an oligoclone had one or two subclones, and a multiclone had more than two subclones. Prostate-specific antigen (PSA) is one of the major markers used to diagnose prostate cancer. In the prostate cancer cohort, PSA level (n = 472) was significantly correlated with clinical outcome (Supplementary Figure S1). Although the association between tumor heterogeneity and level of PSA is not prominent, high level of PSA (N1.5) was more frequently observed in the group with multiclone, indicating high ITH (Figures 1 and 2A). In the oligoclone group, only two patients had a high level of PSA, while there were seven such patients in the multiclone group. Furthermore, the average level of PSA was 0.28 in the oligoclone group and 1.67 in the multiclone group (Table 1). The analysis suggested that the level of tumor progression or invasiveness is substantially associated with ITH and PSA levels.
Several factors such as average PSA level, tumor mutation burden (TMB), and CD8 scores are associated with the number of clones (Figure 2). The data demonstrated that the level of PSA increased as the number of clones increased. TMB also increased slightly when the number of clones increased. On the other hand, the activation score of CD8 generally declined with the accumulation of clones. These results were not likely affected by tumor purity estimated by four different kinds of measurements including immunohistochemistry, as tumor purities of samples in PC were not different according to number of subclones (Supplementary Table S1).
Moreover, we measured the activation degree of immune cells adjacent to cancer cells using decomposition of RNA-sequencing data. When comparing the immune profiling based on tumor heterogeneity, the activation score of T cell (CD8+) showed slight differences (P = .05) between tumors with oligoclones and those with multiclones (Supplementary Figure S2). On average, patients who had high heterogeneity showed a lower immune activation score of T cell compared to those with low heterogeneity. Generally, cancer cells are known to develop immunosuppression or avoidance . Our analysis indicated that the immune avoidance mechanism works better by reducing the activation of T cells in multiclonal than oligoclonal prostate cancer.