Format

Send to

Choose Destination
Cell Rep. 2018 Jul 3;24(1):238-251. doi: 10.1016/j.celrep.2018.06.006.

Integrative Bayesian Analysis Identifies Rhabdomyosarcoma Disease Genes.

Author information

1
Department of Pediatrics, Division of Hematology/Oncology, University of Texas Southwestern Medical Center, Dallas, TX; Department of Clinical Sciences, University of Texas Southwestern Medical Center, Dallas, TX.
2
Department of Pediatrics, Division of Hematology/Oncology, University of Texas Southwestern Medical Center, Dallas, TX. Electronic address: yanbin.zheng@utsouthwestern.edu.
3
Department of Pediatrics, Division of Hematology/Oncology, University of Texas Southwestern Medical Center, Dallas, TX.
4
Department of Pathology, University of Texas Southwestern Medical Center, Dallas, TX; Harold C. Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX.
5
Department of Pediatrics, Division of Hematology/Oncology, University of Texas Southwestern Medical Center, Dallas, TX; Harold C. Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX; Gill Center for Cancer and Blood Disorders, Children's Health, Children's Medical Center, Dallas, TX.
6
Pediatric Oncology Branch, Center for Cancer Research, National Cancer Institute, Bethesda, MD.
7
Oncogenomics Section, Genetic Branch, Center for Cancer Research, National Cancer Institute, Bethesda, MD.
8
Department of Pathology, Keck School of Medicine at USC, Los Angeles, CA; Department of Pediatrics, Keck School of Medicine at USC, Los Angeles, CA.
9
Division of Pediatric Hematology/Oncology, Seattle Children's Hospital, Seattle, WA; Fred Hutchinson Cancer Research Center, University of Washington, Seattle, WA.
10
Department of Pediatrics, Division of Hematology/Oncology, University of Texas Southwestern Medical Center, Dallas, TX; Department of Molecular Biology, University of Texas Southwestern Medical Center, Dallas, TX; Harold C. Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX; Gill Center for Cancer and Blood Disorders, Children's Health, Children's Medical Center, Dallas, TX.
11
Department of Pediatrics, Division of Hematology/Oncology, University of Texas Southwestern Medical Center, Dallas, TX; Harold C. Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX; Gill Center for Cancer and Blood Disorders, Children's Health, Children's Medical Center, Dallas, TX. Electronic address: stephen.skapek@utsouthwestern.edu.

Abstract

Identifying oncogenic drivers and tumor suppressors remains a challenge in many forms of cancer, including rhabdomyosarcoma. Anticipating gene expression alterations resulting from DNA copy-number variants to be particularly important, we developed a computational and experimental strategy incorporating a Bayesian algorithm and CRISPR/Cas9 "mini-pool" screen that enables both genome-scale assessment of disease genes and functional validation. The algorithm, called iExCN, identified 29 rhabdomyosarcoma drivers and suppressors enriched for cell-cycle and nucleic-acid-binding activities. Functional studies showed that many iExCN genes represent rhabdomyosarcoma line-specific or shared vulnerabilities. Complementary experiments addressed modes of action and demonstrated coordinated repression of multiple iExCN genes during skeletal muscle differentiation. Analysis of two separate cohorts revealed that the number of iExCN genes harboring copy-number alterations correlates with survival. Our findings highlight rhabdomyosarcoma as a cancer in which multiple drivers influence disease biology and demonstrate a generalizable capacity for iExCN to unmask disease genes in cancer.

KEYWORDS:

Bayesian algorithm; CRISPR/Cas9; childhood cancer; integrative genomic analysis; oncogene; rhabdomyosarcoma; tumor suppressor gene

PMID:
29972784
DOI:
10.1016/j.celrep.2018.06.006
Free full text

Supplemental Content

Full text links

Icon for Elsevier Science
Loading ...
Support Center