Abstract 4279: Rapid biomarker discovery using large-scale, patient-derived cancer genomic cohorts

• Published in: Cancer research (Chicago, Ill.) Ill.), 2014-10, Vol.74 (19_Supplement), p.4279-4279
• Main Authors: Sanga, Sandeep, Nair, Praveen, Mirsaidi, Cyrus, Broudy, Thomas
• Format: Article
• Language: English
• ISSN: 0008-5472; 1538-7445
• DOI: 10.1158/1538-7445.AM2014-4279

Target discovery and validation in oncology has largely relied on molecular and functional studies performed in cell lines. Recent advances in genomics have now created large databases based on well-characterized tumor tissue, which has enabled direct investigation of patient tumors for novel targets and predictive markers. Following these discoveries, it is routine to perform functional studies in cell line-based systems; however, it is often challenging to find a relevant cell line model and if found, there are often numerous factors which confound biology when using historical cell lines for functional studies. The result can be a process, which takes considerable time and does not readily translate to clinical relevance. We report here our efforts to identify novel targets and markers through data mining large cohorts of cancer genomics data consisting of samples deriving from both drug-naïve and -treated patient-derived cancer samples. To serve as the drug-naïve reference, we look to the thousands of patient-derived tumor specimens, covering 30+ tissue types, genomically characterized by The Cancer Genome Atlas. For both drug-naïve and drug-treated primary data, we leverage Molecular Response's proprietary bank of viable cryopreserved tumor cells. The bank contains more than 144,000 tumor specimens, covering 25 tissue types and 76 clinical diagnoses. Each of the specimens has been profiled against a panel of drugs ex vivo, and levels of resistance recorded. Nearly 400 tumor specimens from Molecular Response's proprietary bank have been genomically characterized. Station X has designed and developed a software environment called GenePool for the management, analysis and communication of genomic information for cohort-scale biomarker studies. The genomics data along with sample-associated clinical metadata deriving from Molecular Response's bank and The Cancer Genome Atlas were imported into GenePool. We evaluated consistency between drug-naïve samples deriving from the Molecular Response bank and TCGA, and subsequently mined for expression-based markers differentiating varying grades of drug sensitivities from drug-naïve specimens. We are currently evaluating these markers for further functional study in patient-derived models and plan to report to these findings. Citation Format: Sandeep Sanga, Praveen Nair, Cyrus Mirsaidi, Thomas Broudy. Rapid biomarker discovery using large-scale, patient-derived cancer genomic cohorts. [abstract]. In: Proceedings o