Abstract 1522: Predicting drug sensitivity based on gene array data for cytotoxic chemotherapeutic agents
Authors: Mannheimer Joshua , Fowles Jared S. , Shaumberg Katherine , Duval Dawn L. , Prasad Ashok , Gustafson Daniel L. 
Published: July 2016

Abstract
The increasing availability of genetic data has led to a demand for robust methods to extract information and an increased need for complex computational analysis. The DREAM project consisted of 44 teams assessing various computational methods to predict drug sensitivity in 53 breast cancer cell lines. (Nat Biotechnol 32:1202). Results from this and other studies suggest that gene expression data from microarrays are one of the more valuable and robust data sets for predicting drug sensitivity. Whole exome sequencing is a valuable tool for predicting sensitivity to targeted agents, however the mutational spectrum is not as predictive for cytotoxic agents (Cancer Res 73:4372). Thus, a systematic analysis of the predictive ability of gene expression on cytotoxic chemotherapy drug sensitivity (IC50 values) using multiple cell line panels is an important component to understanding the versatility and applicability of gene expression for predicting cytotoxic drug IC50 values in tumor cells. Using GDSC and NCI60 cell panels, linear regression models were constructed to predict IC50 values from array data for 13 cytotoxic agents. To address the limited amount of data compared to a possible feature space &gt; 22,000 genes, the methods of Principle Least Squares Regression (PLSR) and Principle Component Regression (PCR) were employed. Models were generated using four different training and validating scenarios to evaluate performance. Mean Absolute Differences (MAD) achieved between expected and predicted IC50 values as low as 11% and correlations as large as 0.7 (P&lt;0.05) were achieved for some drugs. However, several obstacles have presented themselves. Mainly, among 35 cell lines shared by the GDSC and NCI60 direct correlation across common cell lines averaged only 0.4 highlighting the challenge to obtain, evaluate, and address consistent data suitable for robust models. Model Performance Metrics showing high, average, and low values across all thirteen drugs.ModelMeasurementPCR Low : Avg. : HighPLSR Low : Avg. : HighGDSC: TrainCorrelation0.16 : 0.36 : 0.70.12 : 0.36 : 0.66GDSC: ValidP-Value0a : 0.008 : 0.0090 : 0.02 : 0.16MAD% Range11 : 13 : 1611 : 13 : 16Classification Accuracy0.39 : 0.66 : 0.94NCI60: TrainCorrelation-0.11 : 0.36 : 0.67-0.21 : 0.4 : 0.79NCI60: ValidP-Value0.007 : 0.31 : 0.7550.0007 : 0.24 : 0.87MAD% Range11 : 17 : 2711 : 17 : 29Classification Accuracy0.33 : 0.62 : 0.75N/AGDSC: TrainCorrelation0.2 : 0.36 : 0.550.16 : 0.34 : 0.56NCI60: ValidP-Value0 : 0.03 : 0.130 : 0.06 : 0.23MAD% Range11 : 23 : 3711 : 23 : 39Classification Accuracy0.32 : 0.59 : 0.82N/ANCI60: TrainCorrelation0.023 : 0.19 : 0.48-0.03 : 0.18 : 0.43GDSC: ValidP-Value0 : 0.08 : 0.750 : 0.09 : 0.72MAD% Range12 : 19 : 3612 : 19 : 35Classification Accuracy0.37 : 0.53 : 0.69NAa Low p values of 0 correspond to p values &lt; 10−5
In summary, PCR and PLSR based gene expression models predict tumor cell sensitivity to some cytotoxic drugs with varying degrees of accuracy.
Citation Format: Joshua Mannheimer, Jared S. Fowles, Katherine Shaumberg, Dawn L. Duval, Ashok Prasad, Daniel L. Gustafson. Predicting drug sensitivity based on gene array data for cytotoxic chemotherapeutic agents. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 1522.