SCNA

Abstract: We describe methods with enhanced power and specificity to identify genes targeted by somatic copy-number alterations (SCNAs) that drive cancer growth. By separating SCNA profiles into underlying arm-level and focal alterations, we improve the estimation of background rates for each category. We additionally describe a probabilistic method for defining the boundaries of selected-for SCNA regions with user-defined confidence. Here we detail this revised computational approach, GISTIC2.0, and validate its performance in real and simulated datasets.

Abstract: Determining how somatic copy number alterations (SCNAs) promote cancer is an important goal. We characterized SCNA patterns in 4,934 cancers from The Cancer Genome Atlas Pan-Cancer data set. Whole-genome doubling, observed in 37% of cancers, was associated with higher rates of every other type of SCNA, TP53 mutations, CCNE1 amplifications and alterations of the PPP2R complex. SCNAs that were internal to chromosomes tended to be shorter than telomere-bounded SCNAs, suggesting different mechanisms underlying their generation. Significantly recurrent focal SCNAs were observed in 140 regions, including 102 without known oncogene or tumor suppressor gene targets and 50 with significantly mutated genes. Amplified regions without known oncogenes were enriched for genes involved in epigenetic regulation. When levels of genomic disruption were accounted for, 7% of region pairs were anticorrelated, and these regions tended to encompass genes whose proteins physically interact, suggesting related functions. These results provide insights into mechanisms of generation and functional consequences of cancer-related SCNAs.

Abstract: INTRODUCTION Aneuploidy, also known as somatic copy number alterations (SCNAs), is widespread in human cancers and has been proposed to drive tumorigenesis. The relationship between SCNAs and the characteristic functional features or “hallmarks” of cancer is not well understood. Among these cancer hallmarks is immune evasion, which is accomplished by neoantigen editing, defects in antigen presentation and inhibition of tumor infiltration, and/or cytotoxic activities of immune cells. Whether and how tumor SCNA levels influence immune evasion is of particular interest as this information could potentially be used to improve the efficacy of immune checkpoint blockade, a therapy that has produced durable responses in a subset of cancer patients. RATIONALE Understanding how SCNAs and mutation load affect tumor evolution, and through what mechanisms, is a key objective in cancer research. To explore the relationships between SCNA levels, tumor mutations, and cancer hallmarks, we examined data from 5255 tumor/normal samples representing 12 cancer types from The Cancer Genome Atlas project. We assigned each tumor an SCNA score and looked for correlations with the number and types of tumor mutations. We also compared the gene expression profiles of tumors with high versus low SCNA levels to identify differences in cellular signaling pathways. RESULTS First, we found that, for most tumors, there was a positive correlation between SCNA levels and the total number of mutations. Second, tumors harboring activating oncogenic mutations in the receptor tyrosine kinase–RAS–phosphatidylinositol 3-kinase pathway showed fewer SCNAs, a finding at odds with the hypothesis of oncogene-driven genomic instability. Third, we found that tumors with high levels of SCNAs showed elevated expression of cell cycle and cell proliferation markers (cell cycle signature) and reduced expression of markers for cytotoxic immune cell infiltrates (immune signature). The increased expression level of the cell cycle signature was primarily predicted by focal SCNAs, with a lesser contribution of arm and whole-chromosome SCNAs. In contrast, the lower expression level of the immune signature was primarily predicted by high levels of arm and whole-chromosome SCNAs. SCNA levels were a stronger predictor of markers of cytotoxic immune cell infiltration than tumor mutational load. Finally, through analysis of data from two published clinical trials of immunotherapy in melanoma patients, we found that high SCNA levels in tumors correlated with poorer survival of patients. The combination of the tumor SCNA score and the tumor mutational load was a better predictor of survival after immunotherapy than either biomarker alone. CONCLUSION We found that two hallmarks of cancer, cell proliferation and immune evasion, are predicted by distinct types of aneuploidy that likely act through distinct mechanisms. Proliferation markers mainly correlated with focal SCNAs, implying a mechanism related to the action of specific genes targeted by these SCNAs. Immune evasion markers mainly correlated with arm- and chromosome-level SCNAs, consistent with a mechanism related to general gene dosage imbalance rather than the action of specific genes. A retrospective analysis of melanoma patients treated with immune checkpoint blockade anti–CTLA-4 (cytotoxic T lymphocyte–associated protein 4) therapy revealed that high SCNA levels were associated with a poorer response, suggesting that tumor aneuploidy might be a useful biomarker for predicting which patients are most likely to benefit from this therapy.

Abstract: Purpose: The T-cell-inflamed phenotype correlates with efficacy of immune-checkpoint blockade, whereas non-T-cell-inflamed tumors infrequently benefit. Tumor-intrinsic WNT/β-catenin signaling mediates immune exclusion in melanoma, but association with the non-T-cell-inflamed tumor microenvironment in other tumor types is not well understood. Experimental Design: Using The Cancer Genome Atlas (TCGA), a T-cell-inflamed gene expression signature segregated samples within tumor types. Activation of WNT/β-catenin signaling was inferred using three approaches: somatic mutations or somatic copy number alterations (SCNA) in β-catenin signaling elements including CTNNB1, APC, APC2, AXIN1, and AXIN2; pathway prediction from RNA-sequencing gene expression; and inverse correlation of β-catenin protein levels with the T-cell-inflamed gene expression signature. Results: Across TCGA, 3,137/9,244 (33.9%) tumors were non-T-cell-inflamed, whereas 3,161/9,244 (34.2%) were T-cell-inflamed. Non-T-cell-inflamed tumors demonstrated significantly lower expression of T-cell inflammation genes relative to matched normal tissue, arguing for loss of a natural immune phenotype. Mutations of β-catenin signaling molecules in non-T-cell-inflamed tumors were enriched three-fold relative to T-cell-inflamed tumors. Across 31 tumors, 28 (90%) demonstrated activated β-catenin signaling in the non-T-cell-inflamed subset by at least one method. This included target molecule expression from somatic mutations and/or SCNAs of β-catenin signaling elements (19 tumors, 61%), pathway analysis (14 tumors, 45%), and increased β-catenin protein levels (20 tumors, 65%). Conclusions: Activation of tumor-intrinsic WNT/β-catenin signaling is enriched in non-T-cell-inflamed tumors. These data provide a strong rationale for development of pharmacologic inhibitors of this pathway with the aim of restoring immune cell infiltration and augmenting immunotherapy. See related commentary by Dangaj et al., p. 2943