Development of a Drug-Response Modeling Framework to Identify Cell Line Derived Translational Biomarkers That Can Predict Treatment Outcome to Erlotinib or Sorafenib

<div><p>Development of drug responsive biomarkers from pre-clinical data is a critical step in drug discovery, as it enables patient stratification in clinical trial design. Such translational biomarkers can be validated in early clinical trial phases and utilized as a patient inclusion parameter in later stage trials. Here we present a study on building accurate and selective drug sensitivity models for Erlotinib or Sorafenib from pre-clinical in vitro data, followed by validation of individual models on corresponding treatment arms from patient data generated in the BATTLE clinical trial. A Partial Least Squares Regression (PLSR) based modeling framework was designed and implemented, using a special splitting strategy and canonical pathways to capture robust information for model building. Erlotinib and Sorafenib predictive models could be used to identify a sub-group of patients that respond better to the corresponding treatment, and these models are specific to the corresponding drugs. The model derived signature genes reflect each drug’s known mechanism of action. Also, the models predict each drug’s potential cancer indications consistent with clinical trial results from a selection of globally normalized GEO expression datasets.</p></div

PLSR modeling workflow applied on 183 cancer cell lines on OncoPanel.

<p>(A). Flow chart on the model building and testing steps. (B). A specially designed splitting strategy divides the training dataset into random training, random validation and balance validation subsets. (C). Representative example of random validation and balance validation. Red points were top performing models on 1000 random splits on this balanced split, based on both AUC and correlation measures. (D). AUC and correlation cutoff selection for the core PLSR model.</p

Survival analysis on biomarker identified treatment sensitive/resistant sub-groups.

<p>A. Using the Erlotinib model to stratify Erlotinib treated patients; B. Using Sorafenib model to stratify Sorafenib treated patients; C. Using Erlotinib model to stratify Sorafenib treated patients; and D. Using Sorafenib model to stratify Erlotinib treated patients.</p

PLSR models performance in predicting Erlotinib-treated patient survival in the BATTLE trial.

<p>A. Erlotinib model predicting Erlotinib treated patients; B. Sorafenib model predicting Sorafenib treated patients; C. Erlotinib model predicting Sorafenib treated patients; and D. Sorafenib model predicting Erlotinib treated patients. TP: true positive; FP: false positive; TN: true negative; FN: false negative; PPV: positive predictive value.</p

Causal network to depict functional relations between sensitivity-specific and resistance-specific signature genes.

<p>The network was reconstructed from canonical signaling pathways regulated by signature genes and a signature specific direct interaction network. Sensitivity-specific signature genes are highlighted with blue thermometers, resistance-specific genes with red thermometers.</p

Predicted percentage of Erlotinib and Sorafenib sensitive samples for some cancer indications from Gene Expression Omnibus datasets.

<p>The predictive models were derived from cell line Oncopanel expression data. Patient data normalization is described in the result section.</p><p>Predicted percentage of Erlotinib and Sorafenib sensitive samples for some cancer indications from Gene Expression Omnibus datasets.</p