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Invest New Drugs. 2020 Jan 6. doi: 10.1007/s10637-020-00892-8. [Epub ahead of print]

Patient selection for a developmental therapeutics program using whole genome and Transcriptome analysis.

Author information

1
Department of Medical Oncology, BC Cancer, 600 West 10th Avenue, Vancouver, BC, V5Z 4E6, Canada. jeanmichel.lavoie@bccancer.bc.ca.
2
Department of Medical Oncology, BC Cancer, 600 West 10th Avenue, Vancouver, BC, V5Z 4E6, Canada.
3
Canada's Michael Smith Genome Sciences Centre, BC Cancer, Vancouver, Canada.
4
Department of Medical Genetics, University of British Columbia, Vancouver, Canada.

Abstract

Introduction Given the high level of uncertainty surrounding the outcomes of early phase clinical trials, whole genome and transcriptome analysis (WGTA) can be used to optimize patient selection and study assignment. In this retrospective analysis, we reviewed the impact of this approach on one such program. Methods Patients with advanced malignancies underwent fresh tumor biopsies as part of our personalized medicine program (NCT02155621). Tumour molecular data were reviewed for potentially clinically actionable findings and patients were referred to the developmental therapeutics program. Outcomes were reviewed in all patients, including those where trial selection was driven by molecular data (matched) and those where there was no clear molecular rationale (unmatched). Results From January 2014 to January 2018, 28 patients underwent WGTA and enrolled in clinical trials, including 2 patients enrolled in two trials. Fifteen patients were matched to a treatment based on a molecular target. Five patients were matched to a trial based upon single-gene DNA changes, all supported by RNA data. Ten cases were matched on the basis of genome-wide data (n = 4) or RNA gene expression only (n = 6). With a median follow-up of 6.7 months, the median time on treatment was 8.2 weeks. Discussion When compared to single-gene DNA-based data alone, WGTA led to a 3-fold increase in treatment matching. In a setting where there is a high level of uncertainty around both the investigational agents and the biomarkers, more data are needed to fully evaluate the impact of routine use of WGTA.

KEYWORDS:

Personalized medicine; Phase 1 clinical trials; Treatment allocation; Whole genome and Transcriptome analysis

PMID:
31907737
DOI:
10.1007/s10637-020-00892-8

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