Until recently the cancer research community was struggling to discover gene fusions in epithelial cancers. However, advances in Next Generation Sequencing (NGS) have revealed that structural variants (SVs), which can generate gene fusion transcripts, are a severely under-represented class of mutations that are prevalent across solid tumors. From an analysis perspective, we are faced with the critical challenge of ensuring that a casual gene fusion is not only detected, but can be prioritized accordingly amongst the increasing number of chimeric mutations in a cancer transcriptome. Furthermore, it would be of immediate clinical impact to understand the role of gene fusions during tumor progression and in response to treatment. Currently, NGS has provided a high-resolution snapshot of the oligoclonal composition and evolution within tumors. This can be exemplified by pioneering studies at The Genome Institute leveraging deep, targeted sequencing of single nucleotide variants (SNVs) discovered using whole genome sequencing (WGS) to calculate variant allele frequencies (VAFs) for studying clonal evolution over time. However, existing discoveries understanding clonal evolution focused solely on SNVs thereby missing SVs and their corresponding gene fusion products. The inability to incorporate gene fusions into clonal analysis of tumor cells represents a critical barrier in the field. Furthermore, unlike hematologicl malignancies where it is common to take repeat biopsies, it is not commonplace to serially biopsy patients with solid tumors thereby limiting our ability to monitor patients. To circumvent this issue, recent efforts have focused on detecting genomic aberrations from circulating tumor DNA (ctDNA) within the plasma. Given the clinical significance of gene fusions across human cancers, this proposal focuses on improving our ability to detect and monitor gene fusions within the context of clonal populations throughout tumor evolution through the following aims: (1) integrate SV-Seq and ChimeraScan to establish a robust computational method to detect high-confidence gene fusions from WGS and RNA-Seq data, (2) apply our integrated method across a cohort of colorectal cancer patients to identify gene fusions and calculate their VAFs, from their corresponding genomic breakpoints, to understand their role within (founder- or sub-)clones during the progression to metastatic disease, and (3) assess whether monitoring gene fusions (via their genomic breakpoints) in ctDNA can recapitulate what is observed in tumor biopsies. In this study we have chosen to focus on colorectal cancer given our unique patient cohort, the availability of WGS and RNA-Seq from primary and metastatic tumors, and the availability of plasma collected at the time of surgery and from a follow-up visit. While our focus on colorectal cancer may yield novel discoveries, this work is broadly applicable across human cancers. Just as understanding the role of SNVs in clonal evolution has led to groundbreaking discoveries, we envision that this proposal will lay the foundation for similar discoveries focusin on gene fusions that could impact patient care.

Public Health Relevance

Characterization of key genetic aberrations in cancers is essential for the development of early diagnostic markers and effective therapeutic targets. Therefore this proposal seeks to prioritize clinically relevant gene fusions by integrating whole genome and transcriptome sequencing of human tumors. Additionally, developing a method to place gene fusions in the context of (founder or sub-) clonal tumor cell populations will improve our understanding of their role in tumor progression and treatment response. To translate these findings into the clinic we will evaluate our ability to detect gene fusions, by their genomic breakpoints, within circulating tumor DNA (ctDNA), a non-invasive approach that could significantly impact clinical management.

National Institute of Health (NIH)
National Cancer Institute (NCI)
Exploratory/Developmental Grants (R21)
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Special Emphasis Panel (ZCA1-SRLB-4 (J1))
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Li, Jerry
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Washington University
Internal Medicine/Medicine
Schools of Medicine
Saint Louis
United States
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Griffith, Malachi; Griffith, Obi L; Krysiak, Kilannin et al. (2016) Comprehensive genomic analysis reveals FLT3 activation and a therapeutic strategy for a patient with relapsed adult B-lymphoblastic leukemia. Exp Hematol 44:603-13
Zhang, Jin; White, Nicole M; Schmidt, Heather K et al. (2016) INTEGRATE: gene fusion discovery using whole genome and transcriptome data. Genome Res 26:108-18
Griffith, Malachi; Miller, Christopher A; Griffith, Obi L et al. (2015) Optimizing cancer genome sequencing and analysis. Cell Syst 1:210-223