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Translational implications of tumor heterogeneity.

ABSTRACT: Advances in next-generation sequencing and bioinformatics have led to an unprecedented view of the cancer genome and its evolution. Genomic studies have demonstrated the complex and heterogeneous clonal landscape of tumors of different origins and the potential impact of intratumor heterogeneity on treatment response and resistance, cancer progression, and the risk of disease relapse. However, the significance of subclonal mutations, in particular mutations in driver genes, and their evolution through time and their dynamics in response to cancer therapies, is yet to be determined. The necessary tools are now available to prospectively determine whether clonal heterogeneity can be used as a biomarker of clinical outcome and to what extent subclonal somatic alterations might influence clinical outcome. Studies that use longitudinal tissue sampling, integrating both genomic and clinical data, have the potential to reveal the subclonal composition and track the evolution of tumors to address these questions and to begin to define the breadth of genetic diversity in different tumor types and its relevance to patient outcome. Such studies may provide further evidence for drug-resistance mechanisms informing combinatorial, adaptive, and tumor immune therapies placed within the context of tumor evolution.

SUBMITTER: Jamal-Hanjani M

PROVIDER: S-EPMC4374162 | biostudies-literature | 2015 Mar

REPOSITORIES: biostudies-literature

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Translational implications of tumor heterogeneity.

Jamal-Hanjani Mariam M Quezada Sergio A SA Larkin James J Swanton Charles C

Clinical cancer research : an official journal of the American Association for Cancer Research 20150301 6

Advances in next-generation sequencing and bioinformatics have led to an unprecedented view of the cancer genome and its evolution. Genomic studies have demonstrated the complex and heterogeneous clonal landscape of tumors of different origins and the potential impact of intratumor heterogeneity on treatment response and resistance, cancer progression, and the risk of disease relapse. However, the significance of subclonal mutations, in particular mutations in driver genes, and their evolution t ...[more]

PMID: 25770293

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Project description:BackgroundGlioblastoma (GBM) is a fatal brain tumor with no effective treatment. The specific GBM tumor immune microenvironment (TIME) may contribute to resistance to immunotherapy, a tumor therapy with great potential. Thus, an in-depth understanding of the characteristics of tumor-infiltrating immune cells is essential for exploring biomarkers in GBM pathogenesis and immunotherapy.MethodsWe estimated the relative abundances of 25 immune cell types in 796 GBM samples using single sample gene set enrichment analysis (ssGSEA). Unsupervised clustering was used to identify different GBM-associated TIME immune cell infiltration (GTMEI) patterns. The GTMEIscore system was constructed with principal component analysis (PCA) to determine the immune infiltration pattern of individual tumors.ResultsWe revealed three distinct GTMEI patterns with different clinical outcomes and modulated biological pathways. We developed a scoring system (GTMEIscore) to determine the immune infiltration pattern of individual tumors. We comprehensively analyzed the genomic characteristics, molecular subtypes and clinicopathological features as well as proteomic, phosphoproteomic, acetylomic, lipidomic and metabolomic properties associated with the GTMEIscore and revealed many novel dysregulated pathways and precise targets in GBM. Moreover, the GTMEIscore accurately quantified the immune status of many other cancer types. Clinically, the GTMEIscore was found to have significant potential therapeutic value for chemotherapy/radiotherapy, immune checkpoint inhibitor (ICI) therapy and targeted therapy.ConclusionsFor the first time, we employed a multilevel and multiplatform strategy to construct a multidimensional molecular map of tumors with different immune infiltration patterns. These results may provide theoretical basises for identifying more effective predictive biomarkers and developing more effective drug combination strategies or novel immunotherapeutic agents for GBM.

Dataset Information

Translational implications of tumor heterogeneity.

Publications

Translational implications of tumor heterogeneity.

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