Abstract 4284: Evaluation of cancer-related mutations in tumorgraft models (2014)

Abstract Personalized TumorGraft models are generated from tumor fragments resected from patients with refractory advanced cancers that are implanted and propagated in immunodeficient mice. They maintain the genetic characteristics of the original tumor and can potentially be used for individualized therapy selection. We used samples from 120 TumorGrafts to analyze genetic changes (353 mutations and 31 copy number assays) with a customized Qiagen qBiomarker Somatic mutation PCR array. We chose the alterations to be analyzed based on the recent and classic literature data on significant known somatic mutations in cancer, either with biological or therapy response relevance in different cancer types (prioritizing lung and colorectal tumors). Our models consisted of: 39 colorectal cancer samples, 30 pancreatic cancers, 25 non-small cell lung cancers (NSCLC), 11 melanomas, 7 ovarian cancers, 4 breast cancers, and 4 small cell lung cancers. Among our 120 samples, 22% (26) did not show any of the analyzed mutations or deletions, 78% (94) showed at least one mutation, and of those: 56 had 2 or more mutations (44%). In colorectal tumors, the most frequently observed mutation was Kras, followed by p53, and PIK3CA. Among NSCLC: p53, PIK3CA, deletion of p16 and mutation of Kras. In melanoma: Braf and deletion of p16. All 30 cases of pancreatic cancer showed mutations, being Kras the most frequent, followed by p16 deletion and p53 mutation. On the other tumor types, not more than three samples displayed the same mutation. A subgroup of the samples (95) was validated for mutations with other two techniques: conventional Sanger sequencing and/or IonAmpliseq Cancer panel (a panel of key cancer related genes, Life Technologies). 56/69 (81%) cases had the same mutation(s) detected by two or three techniques simultaneously. Three samples were concordantly wild type for the evaluated genes by three or two techniques. Comparison of the genetic alterations with the demographic and clinicopathological data available for these samples (including tumor stage, therapy response, patient's gender and age) is underway to evaluate possible correlations among these parameters and the molecular changes. With the recent massive data produced by next generation sequencing, and with our growing knowledge on the applicability of genetic alterations in the clinic, it is important to have cost-effective, easy straight- forward to perform and to analyze techniques in order to profile the tumors for relevant mutations on the era of personalized medicine. In the present work, we assess a quick and simple, real time PCR based method with selected targets for mutation detection and compare with two other widely used methods. Moreover we aim to profile tumors and uncover their possible genetic targets to best utilize our powerful investigational method of TumorGrafts. Further studies with larger cohorts and alternative mutation detection techniques are needed to validate and expand the present results. Citation Format: Mariana Brait, Luciane T. Kagohara, Evgeny Izumchenko, Samuel Long, Tin Khor, Elizabeth Bruckheimer, David Sidransky. Evaluation of cancer-related mutations in tumorgraft models. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 4284. doi:10.1158/1538-7445.AM2014-4284