Abstract P3-11-15: Development of pan-cancer transcriptional signatures that predict chemosensitivity

Background: Despite the increasing understanding of the molecular characteristics of cancer, chemotherapy success rates remain low for many cancer types. Studies have attempted to identify patient and tumor characteristics that predict sensitivity or resistance to different types of conventional chemotherapies, yet a concise model that predicts chemosensitivity based on gene expression signatures across cancer types remains to be formulated. We attempted to generate a pan-cancer chemosensitivity predictive model using publicly available data from multiple sources. Such a model may increase the likelihood of identifying the type of chemotherapy most likely to succeed for a given patient based on the gene expression signature of their tumor. Methods: Data used to build the predictive model were obtained from the Genomics of Drug Sensitivity in Cancer (GDSC) database, consisting of gene expression profiles from 962 cancer cell lines via RNA sequencing (RNA-seq) and drug sensitivity profiles reported as ln(IC50). Predictive gene signatures were generated using a cross-validated generalized linear model (leave one out cross-validation) using elasticnet penalization parameters. Models were generated for each individual drug tested by the GDSC cohort, as well as different classes of chemotherapeutics (platinum agents, topoisomerase inhibitors, mustard agents, antibiotics and anti-fungals, anti-metabolites, and taxanes). Accuracy of the models was determined using normalized mean square error (nRMSE). Models were then validated using publicly available data from Cancer Cell Line Encyclopedia (CCLE), NCI-60, and the Patient-Derived Xenograft (PDX) Clinical Trial (PCT) database. Models were further validated using human tumor datasets available via the Gene Expression Omnibus (GEO). As the training data used to generate the models were from RNA-seq, and some of the testing and validation data were generated using microarray technology, feature-specific quantile normalization was used to enable cross-platform analyses. Results: For most single-drug gene signatures, accuracy measured by nRMSE ranged from 0.10-0.20, which suggests that for any given model the root mean squared error is 10-20% of the range of actual ln(IC50) in the tested data. Chemotherapy class-level models yielded slightly less accuracy, with nRMSE ranging from 0.15-0.25 for most classes. When considering how well the models predicted chemosensitivity within cancer types, accuracy was improved in some cancer types (e.g., lung cancer and head and neck cancer), with more heterogeneous cancer types (e.g., breast cancer) giving less accuracy. Conclusions: Our results show that the models generated can predict chemosensitivity across cancer types with clinical useful levels of accuracy, with some cancer types resulting in a high rate of accuracy across several classes of chemotherapy. The inclusion of future datasets, particularly from those cancer types in which chemosensitivity has been difficult to predict, may provide opportunities to strengthen model accuracy as well as decrease the numbers of genes needed to assess chemosensitivity. Citation Format: Wells JD, Miller TW. Development of pan-cancer transcriptional signatures that predict chemosensitivity [abstract]. In: Proceedings of the 2018 San Antonio Breast Cancer Symposium; 2018 Dec 4-8; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2019;79(4 Suppl):Abstract nr P3-11-15.