Cancers are typically composed of heterogeneous populations of tumors cells characterized by mutations that distinguish each cell subpopulation from one another. The parent grant leverages DNA sequencing and a suite of important new analytical algorithms and visualization tools to identify mutations in cancer patients and track the evolution of a patient?s tumor over the course of treatment. The tools we have developed, or are in the process of developing, form the foundation of a functional precision oncology approach we are implementing at the University of Utah with a combination of set-aside funding from the parent project, and institutional funding. The current Supplemental Request will adapt the tools funded by the parent project for effective use in pediatric cancers. The main objective of this Supplemental Proposal is to adapt our functional precision oncology approach to inform treatment selection in children with brain tumors, a patient cohort that experiences extremely dire prognoses in which rational treatment choice is difficult without precision guidance. There are compelling reasons to believe, however, that such adaptation will require algorithmic modifications. This is because key aspects of our approach rely on genomic mutations in the tumor, but previous studies show that pediatric tumors have a substantially lower mutation load than adult cancers. Therefore it is necessary to expand our functional approach in pediatric cases, and adapt them to lower mutation loads in pediatric tumors. To accomplish such adaptation, we will, first, perform functional drug screening and genomic/transcriptomic characterization in two pediatric brain cancer index patients, so we have appropriate test cases drivin our tool development and testing. We will then, second, analyze the pediatric brain tumor index patient datasets, and adapt our functional precision informatics methods for use in pediatric brain tumors with expected lower genomic mutation loads. We foresee two specific areas of development: because of the substantially lower number of mutations in the pediatric cases, we will integrate the ability to simultaneously analyze somatic copy number variation (CNV) mutations, together with somatic point mutations to (1) reconstruct the genomic subclones that make up the tumor; and (2) to carry out cell assignment to the genomically defined sublones from single-cell RNA sequencing data used to study tumor subclone-specific gene expression behavior. We have already shown that CNVs can be used both for tumor subclone reconstruction and cell assignment, and anticipate that by integrating CNV and point mutation- based analyses in a single tool will allow functional precision analysis of pediatric cancer patients at, or close to, the level achievable for adult patients. 1

Public Health Relevance

We are in the process of developing computational tools for dissecting tumor heterogeneity, and to understand tumor evolution at a subclonal level. We are currently applying these tools for precision analysis-guided treatment selection in adult patients with advanced/metastatic cancer. Here we will adapt these tools to inform treatment selection in children with brain tumors, a patient cohort that experiences extremely dire prognoses in which rational treatment choice is difficult without precision guidance. 1

National Institute of Health (NIH)
National Cancer Institute (NCI)
Resource-Related Research Projects--Cooperative Agreements (U24)
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Special Emphasis Panel (ZCA1)
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Li, Jerry
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University of Utah
Schools of Medicine
Salt Lake City
United States
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Belyeu, Jonathan R; Nicholas, Thomas J; Pedersen, Brent S et al. (2018) SV-plaudit: A cloud-based framework for manually curating thousands of structural variants. Gigascience 7:
Layer, Ryan M; Pedersen, Brent S; DiSera, Tonya et al. (2018) GIGGLE: a search engine for large-scale integrated genome analysis. Nat Methods 15:123-126
Than, Hein; Qiao, Yi; Huang, Xiaomeng et al. (2018) Ongoing clonal evolution in chronic myelomonocytic leukemia on hypomethylating agents: a computational perspective. Leukemia 32:2049-2054
Jain, Miten; Koren, Sergey; Miga, Karen H et al. (2018) Nanopore sequencing and assembly of a human genome with ultra-long reads. Nat Biotechnol 36:338-345
Ostrander, Betsy E P; Butterfield, Russell J; Pedersen, Brent S et al. (2018) Whole-genome analysis for effective clinical diagnosis and gene discovery in early infantile epileptic encephalopathy. NPJ Genom Med 3:22
Pedersen, Brent S; Collins, Ryan L; Talkowski, Michael E et al. (2017) Indexcov: fast coverage quality control for whole-genome sequencing. Gigascience 6:1-6
Brady, Samuel W; McQuerry, Jasmine A; Qiao, Yi et al. (2017) Combating subclonal evolution of resistant cancer phenotypes. Nat Commun 8:1231
Eilbeck, Karen; Quinlan, Aaron; Yandell, Mark (2017) Settling the score: variant prioritization and Mendelian disease. Nat Rev Genet 18:599-612
Khorashad, J S; Tantravahi, S K; Yan, D et al. (2016) Rapid conversion of chronic myeloid leukemia to chronic myelomonocytic leukemia in a patient on imatinib therapy. Leukemia 30:2275-2279