The sum of gains and losses of genes encoding the protein tyrosine kinase targets predicts response to multi-kinase inhibitor treatment: Characterization, validation, and prognostic value
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Xiaojun Jiang1, Daniel Pissaloux1, Christelle De La Fouchardiere2, Françoise Desseigne2, Qing Wang1, Valery Attignon1, Marie-Eve Fondrevelle5, Arnaud De La Fouchardiere5, Maurice Perol2, Philippe Cassier2, Christelle Seigne3, David Perol3, Isabelle Ray-Coquard2, Pierre Meeus4, Jerome Fayette2, Aude Flechon2, Axel Le Cesne7, Nicolas Penel6, Olivier Tredan2, Jean-Yves Blay1,2
1Department of Translational Research, Centre Leon Berard, 69008 Lyon France
2Department of Medical Oncology, Centre Leon Berard, 69008 Lyon France
3Department of Clinical Research, Centre Leon Berard, 69008 Lyon France
4Department of Surgery, Centre Leon Berard, 69008 Lyon France
5Department of Pathobiology, Centre Leon Berard, 69008 Lyon France
6Department of Medical Oncology, Centre Oscar Lambret, 59020 Lille France
7Department of Medical Oncology, Gustave Roussy, 94805, Villejuif France
Xiaojun Jiang, e-mail: [email protected]
Jean-Yves Blay, e-mail: [email protected]
Keywords: multi-kinase inhibitor, biomarker, regorafenib, chromosomal instability
Received: May 06, 2015 Accepted: July 09, 2015 Published: July 21, 2015
Validated predictive biomarkers for multi-tyrosine kinase inhibitors (MTKI) efficacy are lacking. We hypothesized that interindividual response variability is partially dependent on somatic DNA copy number alterations (SCNAs), particularly those of genes encoding the protein tyrosines targeted by MTKI (called target genes). Genomic alterations were investigated in MTKI responsive and non responsive patients with different histological subtypes included in the ProfiLER protocol (NCT 01774409). From March 2013 to August 2014, 58 patients with advanced cancer treated with one of 7 MTKIs were included in the ProfiLER trial and split into one discovery cohort (n = 13), and 2 validation cohorts (n = 12 and 33). An analysis of the copy number alterations of kinase-coding genes for each of 7 MTKIs was conducted. A prediction algorithm (SUMSCAN) based on the presence of specific gene gains (Tumor Target Charge, TTC) and losses (Tumor Target Losses, TTL) was conceived and validated in 2 independent validation cohorts. MTKI sensitive tumors present a characteristic SCNA profile including a global gain profile, and specific gains for target genes while MTKI resistant tumors present the opposite. SUMSCAN favorable patients achieved longer progression-free and overall survival. This work shows that the copy number sum of kinase-coding genes enables the prediction of response of cancer patients to MTKI, opening a novel paradigm for the treatment selection of these patients.
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