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Breast cancer genome and transcriptome integration implicates specific mutational signatures with immune cell infiltration.

ABSTRACT: A recent comprehensive whole genome analysis of a large breast cancer cohort was used to link known and novel drivers and substitution signatures to the transcriptome of 266 cases. Here, we validate that subtype-specific aberrations show concordant expression changes for, for example, TP53, PIK3CA, PTEN, CCND1 and CDH1. We find that CCND3 expression levels do not correlate with amplification, while increased GATA3 expression in mutant GATA3 cancers suggests GATA3 is an oncogene. In luminal cases the total number of substitutions, irrespective of type, associates with cell cycle gene expression and adverse outcome, whereas the number of mutations of signatures 3 and 13 associates with immune-response specific gene expression, increased numbers of tumour-infiltrating lymphocytes and better outcome. Thus, while earlier reports imply that the sheer number of somatic aberrations could trigger an immune-response, our data suggests that substitutions of a particular type are more effective in doing so than others.

SUBMITTER: Smid M

PROVIDER: S-EPMC5052682 | biostudies-literature | 2016 Sep

REPOSITORIES: biostudies-literature

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Breast cancer genome and transcriptome integration implicates specific mutational signatures with immune cell infiltration.

Smid Marcel M Rodríguez-González F Germán FG Sieuwerts Anieta M AM Salgado Roberto R Prager-Van der Smissen Wendy J C WJ Vlugt-Daane Michelle van der MV van Galen Anne A Nik-Zainal Serena S Staaf Johan J Brinkman Arie B AB van de Vijver Marc J MJ Richardson Andrea L AL Fatima Aquila A Berentsen Kim K Butler Adam A Martin Sancha S Davies Helen R HR Debets Reno R Gelder Marion E Meijer-Van ME van Deurzen Carolien H M CH MacGrogan Gaëtan G Van den Eynden Gert G G M GG Purdie Colin C Thompson Alastair M AM Caldas Carlos C Span Paul N PN Simpson Peter T PT Lakhani Sunil R SR Van Laere Steven S Desmedt Christine C Ringnér Markus M Tommasi Stefania S Eyford Jorunn J Broeks Annegien A Vincent-Salomon Anne A Futreal P Andrew PA Knappskog Stian S King Tari T Thomas Gilles G Viari Alain A Langerød Anita A Børresen-Dale Anne-Lise AL Birney Ewan E Stunnenberg Hendrik G HG Stratton Mike M Foekens John A JA Martens John W M JW

Nature communications 20160926

A recent comprehensive whole genome analysis of a large breast cancer cohort was used to link known and novel drivers and substitution signatures to the transcriptome of 266 cases. Here, we validate that subtype-specific aberrations show concordant expression changes for, for example, TP53, PIK3CA, PTEN, CCND1 and CDH1. We find that CCND3 expression levels do not correlate with amplification, while increased GATA3 expression in mutant GATA3 cancers suggests GATA3 is an oncogene. In luminal cases ...[more]

PMID: 27666519

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Project description:In the present study, we aimed to investigate immune-related signatures and immune infiltration in melanoma. The transcriptome profiling and clinical data of melanoma were downloaded from The Cancer Genome Atlas database, and their matched normal samples were obtained from the Genotype-Tissue Expression database. After merging the genome expression data using Perl, the limma package was used for data normalization. We screened the differentially expressed genes (DEGs) and obtained immune signatures associated with melanoma by an immune-related signature list from the InnateDB database. Univariate Cox regression analysis was used to identify potential prognostic immune genes, and LASSO analysis was used to identify the hub genes. Next, based on the results of multivariate Cox regression analysis, we constructed a risk model for melanoma. We investigated the correlation between risk score and clinical characteristics and overall survival (OS) of patients. Based on the TIMER database, the association between selected immune signatures and immune cell distribution was evaluated. Next, the Wilcoxon rank-sum test was performed using CIBERSORT, which confirmed the differential distribution of immune-infiltrating cells between different risk groups. We obtained a list of 91 differentially expressed immune-related signatures. Functional enrichment analysis indicated that these immune-related DEGs participated in several areas of immune-related crosstalk, including cytokine-cytokine receptor interactions, JAK-STAT signaling pathway, chemokine signaling pathway, and Th17 cell differentiation pathway. A risk model was established based on multivariate Cox analysis results, and Kaplan-Meier analysis was performed. The Kruskal-Wallis test suggested that a high risk score indicated a poorer OS and correlated with higher American Joint Committee on Cancer-TNM (AJCC-TNM) stages and advanced pathological stages (P < 0.01). Furthermore, the association between hub immune signatures and immune cell distribution was evaluated in specific tumor samples. The Wilcoxon rank-sum test was used to estimate immune infiltration density in the two groups, and results showed that the high-risk group exhibited a lower infiltration density, and the dominant immune cells included M0 macrophages (P = 0.023) and activated mast cells (P = 0.005).

Dataset Information

Breast cancer genome and transcriptome integration implicates specific mutational signatures with immune cell infiltration.

Publications

Breast cancer genome and transcriptome integration implicates specific mutational signatures with immune cell infiltration.

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