Haploinsufficiency networks identify targetable patterns of allelic deficiency in low mutation ovarian cancer

Joe Ryan Delaney (), Chandni B. Patel, Katelyn McCabe Willis, Mina Haghighiabyaneh, Joshua Axelrod, Isabelle Tancioni, Dan Lu, Jaidev Bapat, Shanique Young, Octavia Cadassou, Alena Bartakova, Parthiv Sheth, Carley Haft, Sandra Hui, Cheryl Saenz, David D. Schlaepfer, Olivier Harismendy and Dwayne G. Stupack ()
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Joe Ryan Delaney: UCSD School of Medicine and UCSD Moores Cancer Center
Chandni B. Patel: UCSD School of Medicine and UCSD Moores Cancer Center
Katelyn McCabe Willis: UCSD School of Medicine and UCSD Moores Cancer Center
Mina Haghighiabyaneh: UCSD School of Medicine and UCSD Moores Cancer Center
Joshua Axelrod: UCSD School of Medicine and UCSD Moores Cancer Center
Isabelle Tancioni: UCSD School of Medicine and UCSD Moores Cancer Center
Dan Lu: UCSD School of Medicine and UCSD Moores Cancer Center
Jaidev Bapat: UCSD School of Medicine and UCSD Moores Cancer Center
Shanique Young: UCSD School of Medicine and UCSD Moores Cancer Center
Octavia Cadassou: UCSD School of Medicine and UCSD Moores Cancer Center
Alena Bartakova: UCSD School of Medicine and UCSD Moores Cancer Center
Parthiv Sheth: UCSD School of Medicine and UCSD Moores Cancer Center
Carley Haft: UCSD School of Medicine and UCSD Moores Cancer Center
Sandra Hui: UCSD School of Medicine and UCSD Moores Cancer Center
Cheryl Saenz: UCSD School of Medicine and UCSD Moores Cancer Center
David D. Schlaepfer: UCSD School of Medicine and UCSD Moores Cancer Center
Olivier Harismendy: UCSD School of Medicine and UCSD Moores Cancer Center
Dwayne G. Stupack: UCSD School of Medicine and UCSD Moores Cancer Center

Nature Communications, 2017, vol. 8, issue 1, 1-11

Abstract: Abstract Identification of specific oncogenic gene changes has enabled the modern generation of targeted cancer therapeutics. In high-grade serous ovarian cancer (OV), the bulk of genetic changes is not somatic point mutations, but rather somatic copy-number alterations (SCNAs). The impact of SCNAs on tumour biology remains poorly understood. Here we build haploinsufficiency network analyses to identify which SCNA patterns are most disruptive in OV. Of all KEGG pathways (N=187), autophagy is the most significantly disrupted by coincident gene deletions. Compared with 20 other cancer types, OV is most severely disrupted in autophagy and in compensatory proteostasis pathways. Network analysis prioritizes MAP1LC3B (LC3) and BECN1 as most impactful. Knockdown of LC3 and BECN1 expression confers sensitivity to cells undergoing autophagic stress independent of platinum resistance status. The results support the use of pathway network tools to evaluate how the copy-number landscape of a tumour may guide therapy.

Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:8:y:2017:i:1:d:10.1038_ncomms14423

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DOI: 10.1038/ncomms14423

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