Systematic interrogation of mutation groupings reveals divergent downstream expression programs within key cancer genes
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Published on: April 11, 2016
Michal R Grzadkowski1, Hannah D Holly2, Julia Somers2
1Department of Molecular and Medical Genetics, Oregon Health & Science University, Portland, OR, USA. grzadkow@ohsu.edu.
Background:
Genes implicated in tumorigenesis often exhibit diverse sets of genomic variants in the tumor cohorts within which they are frequently mutated. For many genes, neither the transcriptomic effects of these variants nor their relationship to one another in cancer processes have been well-characterized. We sought to identify the downstream expression effects of these mutations and to determine whether this heterogeneity at the genomic level is reflected in a corresponding heterogeneity at the transcriptomic level.
Results:
By applying a novel hierarchical framework for organizing the mutations present in a cohort along with machine learning pipelines trained on samples' expression profiles we systematically interrogated the signatures associated with combinations of mutations recurrent in cancer. This allowed us to catalogue the mutations with discernible downstream expression effects across a number of tumor cohorts as well as to uncover and characterize over a hundred cases where subsets of a gene's mutations are clearly divergent in their function from the remaining mutations of the gene. These findings successfully replicated across a number of disease contexts and were found to have clear implications for the delineation of cancer processes and for clinical decisions.
Conclusions:
The results of cataloguing the downstream effects of mutation subgroupings across cancer cohorts underline the importance of incorporating the diversity present within oncogenes in models designed to capture the downstream effects of their mutations.
Insights
Cancer-driving genes have diverse mutations. This study reveals distinct transcriptomic effects for mutation subsets, impacting cancer process understanding and clinical decisions.
Area of Science:
- Genomics and transcriptomics in cancer research
- Computational biology and machine learning applications in oncology
Background:
- Genes involved in cancer often present varied genomic alterations within tumor populations.
- The transcriptomic consequences and interplay of these genetic variants are frequently not well understood.
- Understanding mutation heterogeneity is crucial for deciphering complex cancer mechanisms.
Purpose of the Study:
- To identify the downstream gene expression changes caused by specific mutations.
- To investigate if genomic heterogeneity translates to transcriptomic heterogeneity in cancer.
- To characterize functional divergence among mutation subsets within frequently mutated genes.
Main Methods:
- Developed a novel hierarchical framework to organize mutation data within tumor cohorts.
- Employed machine learning pipelines trained on sample expression profiles to analyze mutation signatures.
- Systematically interrogated mutation combinations recurrent in cancer to identify expression effects.
Main Results:
- Catalogued mutations with significant downstream expression effects across multiple tumor types.
- Identified over a hundred genes where distinct mutation subsets exhibit divergent functional impacts.
- Findings were robustly replicated across various disease contexts, demonstrating broad applicability.
Conclusions:
- Cataloguing mutation subgroup effects highlights the importance of considering oncogene diversity.
- Models of oncogene function must account for the heterogeneity of mutations within genes.
- These insights have implications for refining cancer process delineation and clinical decision-making.
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