Abstract 881: Leveraging the molecular heterogeneity of patient-derived tumor samples to simulate clinical trial settings and model combination treatments

• Published in: Cancer research (Chicago, Ill.) Ill.), 2012-04, Vol.72 (8_Supplement), p.881-881
• Main Authors: Streiner, Nicole, Praveen, Kesavan Nair, Ruiz-Larroya, Tatiana, Vo, Martin, Murali, Bhavya, Hoffmaster, Steve, Broudy, Thomas, Mirsaidi, Cyrus, Anderes, Kenna
• Format: Article
• Language: English
• ISSN: 0008-5472; 1538-7445
• DOI: 10.1158/1538-7445.AM2012-881

Combination therapy, with rare exception, is a requirement for successfully treating cancer patients. Advances in genetic characterization coupled with a growing understanding of tumor heterogeneity are poised to revolutionize future strategies for combination treatments. Historically, working out the best combinations has been challenging and largely through iterative cycles of clinical trial and error due to gross limitations of preclinical models to accurately predict activity. Current strategies for developing new anticancer drugs rely heavily on preclinical testing in cancer cell lines and their derived in vivo xenograft models. These models are fraught with significant limitations including highly passaged cancer cell lines grown on plastic that ignores the microenvironment and tempered representation of the complex heterogeneity of cancer. Tumor cell microenvironment has significant impact on growth kinetics, cell signaling and response to drug treatments. 3D models attempt to recapitulate elements of the microenvironment, are more biologically relevant models compared to 2D models and have gained preference among cancer researchers and drug developers. The purpose of this study is to investigate the utility of molecularly characterized patient derived lung tumor cells (PTCs) for steering decisions for effective combination treatments in the clinical setting. We have established preclinical lung cancer models using PTCs grown in a 3D culture system. Here we present a retrospective study in lung PTCs evaluating single agents and combinations of molecularly targeted as well as cytotoxic agents including erlotinib, crizotinib, etoposide, cisplatin, carboplatin, gemcitabine, paclitaxel, vinorelbine, topotecan and irinotecan. We utilized a range of methods (PCR, FISH, IF, WB) to determine the genetic and molecular features of the PTCs prior to performing drug treatments. We used high content imaging to evaluate subpopulations within PTCs, colony morphology and proliferative endpoints. Our results identified responder and non-responder populations and emphasize the need to base combination treatment strategies on subpopulation analysis of the tumor. Patient-derived tumor cell models grown in 3D culture conditions coupled with molecular characterization are essential for conducting hypothesis driven studies. This approach offers an informed starting point for subsequent in vivo studies from which data can guide personalized medicine decisions toward true t