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Functional impact bias reveals cancer drivers.

ABSTRACT: Identifying cancer driver genes and pathways among all somatic mutations detected in a cohort of tumors is a key challenge in cancer genomics. Traditionally, this is done by prioritizing genes according to the recurrence of alterations that they bear. However, this approach has some known limitations, such as the difficulty to correctly estimate the background mutation rate, and the fact that it cannot identify lowly recurrently mutated driver genes. Here we present a novel approach, Oncodrive-fm, to detect candidate cancer drivers which does not rely on recurrence. First, we hypothesized that any bias toward the accumulation of variants with high functional impact observed in a gene or group of genes may be an indication of positive selection and can thus be used to detect candidate driver genes or gene modules. Next, we developed a method to measure this bias (FM bias) and applied it to three datasets of tumor somatic variants. As a proof of concept of our hypothesis we show that most of the highly recurrent and well-known cancer genes exhibit a clear FM bias. Moreover, this novel approach avoids some known limitations of recurrence-based approaches, and can successfully identify lowly recurrent candidate cancer drivers.

SUBMITTER: Gonzalez-Perez A

PROVIDER: S-EPMC3505979 | biostudies-literature | 2012 Nov

REPOSITORIES: biostudies-literature

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Functional impact bias reveals cancer drivers.

Gonzalez-Perez Abel A Lopez-Bigas Nuria N

Nucleic acids research 20120816 21

Identifying cancer driver genes and pathways among all somatic mutations detected in a cohort of tumors is a key challenge in cancer genomics. Traditionally, this is done by prioritizing genes according to the recurrence of alterations that they bear. However, this approach has some known limitations, such as the difficulty to correctly estimate the background mutation rate, and the fact that it cannot identify lowly recurrently mutated driver genes. Here we present a novel approach, Oncodrive-f ...[more]

PMID: 22904074

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Project description:BackgroundMethylation of the p16 promoter resulting in epigenetic gene silencing-known as p16 epimutation-is frequently found in human colorectal cancer and is also common in normal-appearing colonic mucosa of aging individuals. Thus, to improve clinical care of colorectal cancer (CRC) patients, we explored the role of age-related p16 epimutation in intestinal tumorigenesis.MethodsWe established a mouse model that replicates two common genetic and epigenetic events observed in human CRCs: Apc mutation and p16 epimutation. We conducted long-term survival and histological analysis of tumor development and progression. Colonic epithelial cells and tumors were collected from mice and analyzed by RNA sequencing (RNA-seq), quantitative PCR, and flow cytometry. We performed single-cell RNA sequencing (scRNA-seq) to characterize tumor-infiltrating immune cells throughout tumor progression. We tested whether anti-PD-L1 immunotherapy affects overall survival of tumor-bearing mice and whether inhibition of both epigenetic regulation and immune checkpoint is more efficacious.ResultsMice carrying combined Apc mutation and p16 epimutation had significantly shortened survival and increased tumor growth compared to those with Apc mutation only. Intriguingly, colon tumors with p16 epimutation exhibited an activated interferon pathway, increased expression of programmed death-ligand 1 (Pdl1), and enhanced infiltration of immune cells. scRNA-seq further revealed the presence of Foxp3+ Tregs and γδT17 cells, which contribute to an immunosuppressive tumor microenvironment (TME). Furthermore, we showed that a combined therapy using an inhibitor of DNA methylation and a PD-L1 immune checkpoint inhibitor is more effective for improving survival in tumor-bearing mice than blockade of either pathway alone.ConclusionsOur study demonstrated that age-dependent p16 epimutation creates a permissive microenvironment for malignant transformation of polyps to colon cancer. Our findings provide a mechanistic rationale for future targeted therapy in patients with p16 epimutation.

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Functional impact bias reveals cancer drivers.

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Functional impact bias reveals cancer drivers.

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