Wednesday, April 2, 2025
4/2/2025
Proteomics-driven precision molecular mapping of patient tumors to preclinical models in lung cancer - ABSTRACT/SUMMARY The majority of lung cancer patients do not respond to treatment effectively or develop the resistance quickly. To increase the response rate and minimize the drug resistance, it is critical to develop novel treatment strategies based on molecularly matched preclinical models. Currently, mapping preclinical models relies mainly on genomic and transcriptomic data, which, according to our analysis, fails to match >30% patients to representative preclinical models. Given that proteins are key functional units responsible for major cellular activities and therapeutic targets, we hypothesize that proteomic data are more informative to identify suitable preclinical models. Reverse-phase protein arrays (RPPAs) offer a powerful functional proteomic approach to identifying biomarkers, targets, and biological mechanisms, enabling the evaluation of numerous protein markers in hundreds of samples in a cost-effective, sensitive, and high-throughput manner. Our team has been a leader in implementing this platform and disseminating our RPPA data to the biomedical research community. Our current objective is to develop a functional proteomics approach to accurately mapping tumor samples with the preclinical models. To accomplish our goal, we have assembled a highly productive team with a long collaboration history and diverse expertise, and will pursue two specific aims, Aim #1: Expand the proteomics profiling of lung cancer PDX models using the RPPA platform. Our platform has recently been upgraded to cover ~500 cancer-related proteins and has characterized ~9,000 TCGA patient and CCLE cell line samples. This includes ~700 non-small cell lung cancer tumor samples and 175 lung cancer cell lines. Using this platform, we will expand this dataset to include both small cell and non-small cell lung cancer PDX models. Aim #2: Build a comprehensive correspondence map linking lung cancer tumors with preclinical models. Leveraging the RPPA data generated from patient, cell line, and PDX samples, along with other omics data, we will systematically assess their pairwise associations. We will develop deep-learning models based on both RPPA data and functional response data, to identify and validate novel lung cancer vulnerabilities and potent drug combinations. The expected outcome is (i) a well-characterized and harmonized cohort of lung cancer preclinical models as well as patient samples coupled with high-quality RPPA profiling data; (ii) a mechanistically interpretable correspondence map linking individual patient tumors to preclinical models; (iii) a shortlist of novel lung cancer vulnerabilities through our state-of-the-art computational framework that are ready for clinical evaluation. Our proposed research is innovative because it offers a fundamental, mechanistic view on proteome- guided associations between multi-platform preclinical models and patient tumors. This project will have a lasting, positive impact by: i) generating a unique lung cancer proteome resource for preclinical models; ii) establishing a highly accurate, comprehensive multi-omics molecular mapping between lung cancer tumors and preclinical models; and iii) developing novel therapeutic strategies based on RPPA-guided molecular mappings.
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