Massively Parallel Tumor Multigene Sequencing to Evaluate Response to Panitumumab in a Randomized Phase 3 Study of Metastatic Colorectal Cancer
Menée sur 320 échantillons tumoraux prélevés sur des patients atteints d'un cancer colorectal métastatique et inclus dans un essai de phase III du panitumumab, cette étude évalue la faisabilité et l'utilité clinique d'une technique de séquençage de prochaine génération pour identifier des biomarqueurs prédictifs de la réponse thérapeutique
Purpose: To investigate whether epidermal growth factor receptor (EGFR) pathway mutations predicted response to monotherapy with panitumumab, an anti-EGFR monoclonal antibody, in a randomized phase 3 study of metastatic colorectal cancer.
Experimental Design: Using massively parallel multigene sequencing, we analyzed 320 samples for 9 genes, with multigene sequence data from 288 (90%) samples.
Results: Mutation rates were: KRAS (45%), NRAS (5%), BRAF (7%), PIK3CA (9%), PTEN (6%), TP53 (60%), EGFR (1%), AKT1 (<1%), and CTNNB1 (2%). In the randomized study and open-label extension, 22/138 (16%) wild-type KRAS (codons 12/13/61) patients versus 0/103 mutant KRAS (codons 12/13) patients had objective responses. Of 6 mutant KRAS (codon 61) patients, 1 with a Q61H mutation achieved partial response during the extension. Among wild-type KRAS (codons 12/13/61) patients, 0/9 patients with NRAS mutations, 0/13 with BRAF mutations, 2/10 with PIK3CA mutations, 1/9 with PTEN mutations, and 1/2 with CTNNB1 mutations responded to panitumumab. No patients responded to best supportive care alone. Panitumumab treatment was associated with longer progression-free survival (PFS) among wild-type KRAS (codons 12/13/61) patients (HR=0.39; 95%-CI, 0.28─0.56). Among wild-type KRAS patients, a treatment effect for PFS favoring panitumumab occurred in patients with wild-type NRAS (0.39; 0.27─0.56) and wild-type BRAF (0.37; 0.24─0.55) but not mutant NRAS (1.94; 0.44─8.44).
Conclusions: These results demonstrate the feasibility and potential clinical utility of next-generation sequencing for evaluating predictive biomarkers.
Clinical Cancer Research , résumé, 2013