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MutComFocal: an integrative approach to identifying recurrent and focal genomic alterations in tumor samples
Vladimir Trifonov1, Laura Pasqualucci, Riccardo Dalla Favera
1Department of Biomedical Informatics, New York, NY 10032, USA. vt2184@c2b2.columbia.edu
Background:
Most tumors are the result of accumulated genomic alterations in somatic cells. The emerging spectrum of alterations in tumors is complex and the identification of relevant genes and pathways remains a challenge. Furthermore, key cancer genes are usually found amplified or deleted in chromosomal regions containing many other genes. Point mutations, on the other hand, provide exquisite information about amino acid changes that could be implicated in the oncogenic process. Current large-scale genomic projects provide high throughput genomic data in a large number of well-characterized tumor samples.
Methods:
We define a Bayesian approach designed to identify candidate cancer genes by integrating copy number and point mutation information. Our method exploits the concept that small and recurrent alterations in tumors are more informative in the search for cancer genes. Thus, the algorithm (Mutations with Common Focal Alterations, or MutComFocal) seeks focal copy number alterations and recurrent point mutations within high throughput data from large panels of tumor samples.
Results:
We apply MutComFocal to Diffuse Large B-cell Lymphoma (DLBCL) data from four different high throughput studies, totaling 78 samples assessed for copy number alterations by single nucleotide polymorphism (SNP) array analysis and 65 samples assayed for protein changing point mutations by whole exome/whole transcriptome sequencing. In addition to recapitulating known alterations, MutComFocal identifies ARID1B, ROBO2 and MRS1 as candidate tumor suppressors and KLHL6, IL31 and LRP1 as putative oncogenes in DLBCL.
Conclusions:
We present a Bayesian approach for the identification of candidate cancer genes by integrating data collected in large number of cancer patients, across different studies. When trained on a well-studied dataset, MutComFocal is able to identify most of the reported characterized alterations. The application of MutComFocal to large-scale cancer data provides the opportunity to pinpoint the key functional genomic alterations in tumors.
Insights
This study introduces a Bayesian method, Mutations with Common Focal Alterations (MutComFocal), to identify cancer genes by analyzing genomic alterations. The approach successfully pinpointed novel candidate tumor suppressors and oncogenes in Diffuse Large B-cell Lymphoma (DLBCL).
Area of Science:
- Genomics
- Cancer Biology
- Bioinformatics
Background:
- Tumors arise from accumulated somatic genomic alterations, presenting a complex challenge for identifying key cancer genes.
- Identifying driver mutations is difficult as key genes are often in large amplified or deleted chromosomal regions.
- High-throughput genomic data from large tumor sample collections are now available.
Purpose of the Study:
- To develop a Bayesian approach for identifying candidate cancer genes by integrating copy number and point mutation data.
- To create an algorithm, Mutations with Common Focal Alterations (MutComFocal), that prioritizes small, recurrent genomic alterations.
- To apply this method to large-scale cancer genomic data for pinpointing functional alterations.
Main Methods:
- Developed a Bayesian statistical framework to integrate copy number and point mutation data.
- Designed the Mutations with Common Focal Alterations (MutComFocal) algorithm to detect focal copy number alterations and recurrent point mutations.
- Applied MutComFocal to high-throughput genomic data from Diffuse Large B-cell Lymphoma (DLBCL) samples.
Main Results:
- MutComFocal was applied to DLBCL data from 78 samples (copy number) and 65 samples (point mutations).
- The algorithm successfully recapitulated known alterations in DLBCL.
- Identified ARID1B, ROBO2, and MRS1 as candidate tumor suppressors and KLHL6, IL31, and LRP1 as putative oncogenes in DLBCL.
Conclusions:
- Presented a novel Bayesian approach (MutComFocal) for identifying candidate cancer genes using integrated genomic data.
- Demonstrated the method's ability to identify known alterations when trained on well-characterized datasets.
- Highlighted the potential of MutComFocal for pinpointing key functional genomic alterations in large-scale cancer studies.
