Non-small cell lung cancers (NSCLCs) from patients exhibit large differences in sensitivity or resistance tochemotherapy and targeted drugs. We hypothesize that these differences will be reflected in tumor mRNAand protein signatures prior to treatment, and that these signatures can be used to improve the effectivenessof therapy. The eventual goal to develop and use such signatures to determine the best available treatmentfor that individual. To move towards this goal, however, there is a critical need for preclinical models todevelop such signatures and test new therapies. This project proposes to use a large panel of NSCLC celllines and xenografts to systematically measure preclinical therapy response phenotypes, define associatedmRNA and protein biomarker signatures of these responses, and then validate these in other cell lines,xenografts, and patient tumor specimens. We will also identify mRNA and protein biomarkers in patientspecimens and test them in the preclinical models, eventually resulting in validated biomarkers for responseprediction in patients and validated preclinical models.
Specific Aims are:
Aim 1) To measure quantitativedrug sensitivity/resistance phenotypes in a large panel (-100) of human NSCLC cell lines and xenografts(~50), including xenografts made directly from patient tumors without intervening culture, and compare invitro drug response phenotypes with those of orthotopic (lung) xenografts;
Aim 2) To identify microarraymRNA and reverse phase protein array (RPPA)-based expression signatures of response to therapeuticagents in NSCLC lines; using these signatures we will predict drug responses in a new 'test' set of NSCLCcell lines and xenografts, and conduct a 'preclinical trial' comparing the approach of standard non-selectedtherapy (NST) versus signature-selected therapy (SST);
Aim 3) To validate these signatures on availableNSCLC specimens clinically annotated as to response to standard and targeted agents (~100 frozen and200 formalin-fixed paraffin embedded specimens). We will also independently develop response signaturesdirectly from patient samples and test these for predictive ability using preclinical models and other patientspecimens. Finally, we will test signatures reported by other investigators and integrate the most informativewith our own. From all of this we will develop new methods to select the best available therapies forindividual patients, establish a preclinical human tumor model system for systematically testing new drugs,and develop signatures to guide their most efficient use. This project has assembled a multidisciplinaryteam of basic and clinical investigators, has considerable preliminary data, and clinically annotated tumorspecimens for study. It makes use of the Pathology, Biostatistics, and Bioinformatics Cores and interactswith multiple other projects in this SPORE as well as nucleating multiple inter-SPORE collaborations.
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