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Genomic Analysis of Immune Cell Infiltrates Across 11 Tumor Types.

ABSTRACT: Immune infiltration of the tumor microenvironment has been associated with improved survival for some patients with solid tumors. The precise makeup and prognostic relevance of immune infiltrates across a broad spectrum of tumors remain unclear.Using mRNA sequencing data from The Cancer Genome Atlas (TCGA) from 11 tumor types representing 3485 tumors, we evaluated lymphocyte and macrophage gene expression by tissue type and by genomic subtypes defined within and across tumor tissue of origin (Cox proportional hazards, Pearson correlation). We investigated clonal diversity of B-cell infiltrates through calculating B-cell receptor (BCR) repertoire sequence diversity. All statistical tests were two-sided.High expression of T-cell and B-cell signatures predicted improved overall survival across many tumor types including breast, lung, and melanoma (breast CD8_T_Cells hazard ratio [HR] = 0.36, 95% confidence interval [CI] = 0.16 to 0.81, P = .01; lung adenocarcinoma B_Cell_60gene HR?=?0.71, 95% CI?=?0.58 to 0.87, P = 7.80E-04; melanoma LCK HR?=?0.86, 95% CI?=?0.79 to 0.94, P = 6.75E-04). Macrophage signatures predicted worse survival in GBM, as did B-cell signatures in renal tumors (Glioblastoma Multiforme [GBM]: macrophages HR?=?1.62, 95% CI?=?1.17 to 2.26, P = .004; renal: B_Cell_60gene HR?=?1.17, 95% CI?=?1.04 to 1.32, P = .009). BCR diversity was associated with survival beyond gene segment expression in melanoma (HR?=?2.67, 95% CI?=?1.32 to 5.40, P = .02) and renal cell carcinoma (HR?=?0.36, 95% CI?=?0.15 to 0.87, P = .006).These data support existing studies suggesting that in diverse tissue types, heterogeneous immune infiltrates are present and typically portend an improved prognosis. In some tumor types, BCR diversity was also associated with survival. Quantitative genomic signatures of immune cells warrant further testing as prognostic markers and potential biomarkers of response to cancer immunotherapy.

SUBMITTER: Iglesia MD

PROVIDER: S-EPMC5241901 | biostudies-other | 2016 Nov

REPOSITORIES: biostudies-other

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Genomic Analysis of Immune Cell Infiltrates Across 11 Tumor Types.

Iglesia Michael D MD Parker Joel S JS Hoadley Katherine A KA Serody Jonathan S JS Perou Charles M CM Vincent Benjamin G BG

Journal of the National Cancer Institute 20160622 11

<h4>Background</h4>Immune infiltration of the tumor microenvironment has been associated with improved survival for some patients with solid tumors. The precise makeup and prognostic relevance of immune infiltrates across a broad spectrum of tumors remain unclear.<h4>Methods</h4>Using mRNA sequencing data from The Cancer Genome Atlas (TCGA) from 11 tumor types representing 3485 tumors, we evaluated lymphocyte and macrophage gene expression by tissue type and by genomic subtypes defined within an ...[more]

PMID: 27335052

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Project description:Single-cell analysis has the potential to provide us with a host of new knowledge about biological systems, but it comes with the challenge of correctly interpreting the biological information. While emerging techniques have made it possible to measure inter-cellular variability at the transcriptome level, no consensus yet exists on the most appropriate method of data analysis of such single cell data. Methods for analysis of transcriptional data at the population level are well established but are not well suited to single cell analysis due to their dependence on population averages. In order to address this question, we have systematically tested combinations of methods for primary data analysis on single cell transcription data generated from two types of primary immune cells, neutrophils and T lymphocytes. Cells were obtained from healthy individuals, and single cell transcript expression data was obtained by a combination of single cell sorting and nanoscale quantitative real time PCR (qRT-PCR) for markers of cell type, intracellular signaling, and immune functionality. Gene expression analysis was focused on hierarchical clustering to determine the existence of cellular subgroups within the populations. Nine combinations of criteria for data exclusion and normalization were tested and evaluated. Bimodality in gene expression indicated the presence of cellular subgroups which were also revealed by data clustering. We observed evidence for two clearly defined cellular subtypes in the neutrophil populations and at least two in the T lymphocyte populations. When normalizing the data by different methods, we observed varying outcomes with corresponding interpretations of the biological characteristics of the cell populations. Normalization of the data by linear standardization taking into account technical effects such as plate effects, resulted in interpretations that most closely matched biological expectations. Single cell transcription profiling provides evidence of cellular subclasses in neutrophils and leukocytes that may be independent of traditional classifications based on cell surface markers. The choice of primary data analysis method had a substantial effect on the interpretation of the data. Adjustment for technical effects is critical to prevent misinterpretation of single cell transcript data.

Dataset Information

Genomic Analysis of Immune Cell Infiltrates Across 11 Tumor Types.

Publications

Genomic Analysis of Immune Cell Infiltrates Across 11 Tumor Types.

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