Identification of Biomarker Driven Intervention Opportunities and Advancement of Mechanism of Action Predictions for Anti-Cancer Therapeutics
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Oncogenic lesions arising during cancer progression provide an attractive target for chemical intervention strategies. The extreme molecular heterogeneity of tumors, however, makes it difficult to identify authentic intervention targets and to link patients to the most appropriate treatment. To confront this challenge, we launched a full scale investigation to identify the genetic lesions that arise during cancer progression together with a computational approach to link novel compounds to these lesions. A panel of 103 non-small cell lung cancer cell lines was screened with over 200,000 uncharacterized synthetic chemical compounds and natural products fractions in a tiered HTS approach. Statistical and machine learning procedures were then used to link drug activity to the complexity of cancer genomes by systematically assigning enrollment biomarkers to each compound from measures of gene expression, gene mutation, gene copy number, protein expression, and metabolomics datasets. Using this approach, we have found that genetic vulnerabilities that are not currently actionable can be linked to novel chemicals. Experimental mechanism of action hypotheses can be derived from these chemical/biomarker relationships and were validated for a subset. Notably, we are able to parse KRAS mutant cancers into multiple, distinct molecular subtypes defined by co-occurring mutations. This indicates that KRAS lung cancers are representative of diverse mechanistic subtypes, and we are able to identify putative novel compounds that may target each subtype. Collectively, we are using this approach as a data driven way to parse mechanistic cancer subtypes and identify a diverse cohort of therapies capable of contending with cancer heterogeneity together with enrollment biomarkers that can specify sensitivity.
SubjectCarcinoma, Non-Small-Cell Lung
High-Throughput Screening Assays
NF-E2-Related Factor 2