The Cancer Cell Line Encyclopedia (CCLE) is a comprehensive dataset containing genomic and pharmacologic information from 947 human cancer cell lines. This resource enables the identification of genetic, lineage, and gene expression-based predictors of drug sensitivity. By integrating gene expression, chromosomal copy number, and sequencing data with pharmacologic profiles of 24 anticancer drugs, the CCLE allows for the prediction of drug responses and the development of personalized therapeutic strategies. The dataset includes 36 tumor types and provides detailed genomic and expression data for each cell line, enabling the comparison of genomic similarities between cell lines and primary tumors. The CCLE also includes pharmacologic data for 479 cell lines, allowing for the identification of molecular correlates of drug sensitivity. Predictive models using machine learning techniques have identified key genetic and expression features that predict drug response, including mutations in BRAF and NRAS, expression of AHR, and SLFN11. These findings suggest that large, annotated cell line collections can help in the preclinical stratification of anticancer agents and improve the design of clinical trials. The CCLE provides a valuable resource for understanding the molecular mechanisms of drug response and for developing more effective cancer therapies.The Cancer Cell Line Encyclopedia (CCLE) is a comprehensive dataset containing genomic and pharmacologic information from 947 human cancer cell lines. This resource enables the identification of genetic, lineage, and gene expression-based predictors of drug sensitivity. By integrating gene expression, chromosomal copy number, and sequencing data with pharmacologic profiles of 24 anticancer drugs, the CCLE allows for the prediction of drug responses and the development of personalized therapeutic strategies. The dataset includes 36 tumor types and provides detailed genomic and expression data for each cell line, enabling the comparison of genomic similarities between cell lines and primary tumors. The CCLE also includes pharmacologic data for 479 cell lines, allowing for the identification of molecular correlates of drug sensitivity. Predictive models using machine learning techniques have identified key genetic and expression features that predict drug response, including mutations in BRAF and NRAS, expression of AHR, and SLFN11. These findings suggest that large, annotated cell line collections can help in the preclinical stratification of anticancer agents and improve the design of clinical trials. The CCLE provides a valuable resource for understanding the molecular mechanisms of drug response and for developing more effective cancer therapies.