Abstract

Precision medicine aims to tailor cancer therapies to target specific tumor-promoting aberrations. For tumors that lack actionable drivers, which occurs frequently in the clinic, extensive molecular characterization and pre-clinical drug efficacy studies will be required. A cell line maintained at low passage and a patient- derived xenograft model (PDX) were generated using a fresh biopsy from a patient with a poorly-differentiated neuroendocrine tumor of unknown primary origin. Next-generation sequencing, high throughput signaling network analysis, and drug efficacy trials were then conducted to identify actionable targets for therapeutic intervention. No actionable mutations were identified after whole exome sequencing of the patient’s DNA. However, whole genome sequencing revealed amplification of the 3q and 5p chromosomal arms, that include the PIK3CA and RICTOR genes, respectively. We then conducted pathway analysis, which revealed activation of the AKT pathway. Based on this analysis, efficacy of PIK3CA and AKT inhibitors were evaluated in the tumor biopsy-derived cell culture and PDX, and response to the AKT inhibitor AZD5363 was observed both in vitro and in vivo indicating the patient would benefit from targeted therapies directed against the serine/threonine kinase AKT. In conclusion, our study demonstrates that high throughput signaling pathway analysis will significantly aid in identifying actionable alterations in rare tumors and guide patient stratification into early-phase clinical trials.

Details

Title
Signaling pathway screening platforms are an efficient approach to identify therapeutic targets in cancers that lack known driver mutations: a case report for a cancer of unknown primary origin
Author
Torres-Ayuso, Pedro 1   VIAFID ORCID Logo  ; Sahoo, Sudhakar 2 ; Ashton, Garry 3 ; An, Elvira 4 ; Simms, Nicole 5 ; Galvin, Melanie 5 ; Hui Sun Leong 2 ; Frese, Kristopher K 5 ; Simpson, Kathryn 5 ; Cook, Natalie 6   VIAFID ORCID Logo  ; Hughes, Andrew 6 ; Miller, Crispin J 2 ; Marais, Richard 7 ; Dive, Caroline 8 ; Krebs, Matthew G 9 ; Brognard, John 1 

 Signalling Networks in Cancer Group, Cancer Research UK, Manchester Institute, University of Manchester, Manchester, UK; Signaling Networks in Cancer Section, Laboratory of Cell and Developmental Signaling, Center for Cancer Research, National Cancer Institute, Frederick, MD, USA 
 Computational Biology Support Team, Cancer Research UK, Manchester Institute, University of Manchester, Manchester, UK 
 Histology, Cancer Research UK Manchester Institute, The University of Manchester, Manchester, UK 
 Signaling Networks in Cancer Section, Laboratory of Cell and Developmental Signaling, Center for Cancer Research, National Cancer Institute, Frederick, MD, USA 
 Clinical and Experimental Pharmacology Group; Cancer Research UK Manchester Institute, The University of Manchester, Manchester, UK 
 Division of Molecular and Clinical Cancer Sciences, Faculty of Biology, Medicine and Health, The University of Manchester and Experimental Cancer Medicine Team, The Christie NHS Foundation Trust, Manchester, UK 
 Molecular Oncology Group, Cancer Research UK Manchester Institute, The University of Manchester, Manchester, UK; Cancer Research UK Manchester Experimental Cancer Medicines Centre, The University of Manchester, Manchester, UK 
 Clinical and Experimental Pharmacology Group; Cancer Research UK Manchester Institute, The University of Manchester, Manchester, UK; Cancer Research UK Manchester Experimental Cancer Medicines Centre, The University of Manchester, Manchester, UK 
 Division of Molecular and Clinical Cancer Sciences, Faculty of Biology, Medicine and Health, The University of Manchester and Experimental Cancer Medicine Team, The Christie NHS Foundation Trust, Manchester, UK; Cancer Research UK Manchester Experimental Cancer Medicines Centre, The University of Manchester, Manchester, UK 
Pages
1-7
Publication year
2018
Publication date
Jun 2018
Publisher
Nature Publishing Group
e-ISSN
20567944
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2057428617
Copyright
© 2018. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.