Prioritizing rare variants associated with cancer using non-coding annotation

Recent progress made by the ENCODE consortium and the Epigenome Roadmap Project has provided detailed annotation of noncoding regions of the human genome. Whole-genome sequencing has identified large volumes of rare variants in such regions. Thus, this is an opportune time to study rare variants in noncoding regions. Despite these opportunities, little effort has been invested in leveraging these resources to tackle problems in cancer-risk variant prioritization. Here, we are developing strategies to use patterns of natural polymorphisms to prioritize the most impactful noncoding variants. To refine our approach, we are developing a tunable parameterization scheme in conjunction with iterative experimentation. With the support of this grant, we have made achievements in developing pipelines, computational tools, and experimental technologies.

The Gerstein lab has focused on pipeline and computational tool development. Specific outcomes include:

The Yu Lab has developed advanced biotechnology tools to conduct mutagenesis and sequencing of specific regions. Specific outcomes include:

The Rubin Lab has made progress on experimental validation. Specific outcomes include:

Whole-genome sequencing of phenotypically distinct inflammatory breast cancers reveals similar genomic alterations to non-inflammatory breast cancers.
X Li, S Kumar, A Harmanci, S Li, RR Kitchen, Y Zhang, VB Wali, SM Reddy, WA Woodward, JM Reuben, J Rozowsky, C Hatzis, NT Ueno, S Krishnamurthy, L Pusztai, M Gerstein (2021). Genome Med 13: 70.

Passenger Mutations in More Than 2,500 Cancer Genomes: Overall Molecular Functional Impact and Consequences.
S Kumar, J Warrell, S Li, PD McGillivray, W Meyerson, L Salichos, A Harmanci, A Martinez-Fundichely, CWY Chan, MM Nielsen, L Lochovsky, Y Zhang, X Li, S Lou, JS Pedersen, C Herrmann, G Getz, E Khurana, MB Gerstein (2020). Cell 180: 915-927e16.

Estimating growth patterns and driver effects in tumor evolution from individual samples.
L Salichos, W Meyerson, J Warrell, M Gerstein (2020). Nat Commun 11: 732.

GRAM: A GeneRAlized Model to predict the molecular effect of a non-coding variant in a cell-type specific manner.
S Lou, KA Cotter, T Li, J Liang, H Mohsen, J Liu, J Zhang, S Cohen, J Xu, H Yu, MA Rubin, M Gerstein (2019). PLoS Genet 15: e1007860.

A comprehensive catalog of predicted functional upstream open reading frames in humans.
P McGillivray, R Ault, M Pawashe, R Kitchen, S Balasubramanian, M Gerstein (2018). Nucleic Acids Res 46: 3326-3338.

Intensification: A Resource for Amplifying Population-Genetic Signals with Protein Repeats.
J Chen, B Wang, L Regan, M Gerstein (2016). J Mol Biol 429: 435-445.

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