Cell line specific method based on functional modules for drug sensitivity prediction

ABSTRACT

It is necessary to predict the efficacy of individual drugs on patients to realize personalized medicine. Testing drugs on patients in clinical trial is the only way to evaluate the efficacy of drugs. The approach is labor intensive and requires overwhelming costs and a number of experiments. Therefore, preclinical model system has been intensively investigated for predicting the efficacy of drugs. Current computational drug sensitivity prediction [1-2] approaches used known genes, known pathways or general biological network module as their prediction features. Therefore, they missed indirect effector or the effects from tissue-specific interactions.

In this study, we developed cell line specific functional modules, which are cell line specific features, as prediction features to predict the efficacy of drugs. Cell line specific functional modules are clusters of genes, which have similar biological functions in cell line specific networks. We used two types of data from the US National Cancer Institute 60 anticancer drug screen (NCI60)[3]: 1) cell line transcription data of nine tissue types and 52 different cell lines, 2)drug response data of 30 drugs across 52 of the cell lines. We used linear regression for drug sensitivity prediction. We assessed the prediction performance in leave-one-out cross-validation (LOOCV). We also selected functions which are associated with drug sensitivity.

Our method performed better than random function module-based model. We also analyzed selected functions, which are associated with drug sensitivity, of five drugs - lapatinib, erotinib, raloxifen, tamoxifen and gefitinib- by our model. Two drug pairs in five drugs have same therapeutic effect. Our model also showed that two drug pairs have same selected functions.

These results suggest that our model can provide drug sensitivity prediction and also provide functions which are associated with drug sensitivity. Therefore, we could utilize drug sensitivity associated functions for drug repositioning or for suggesting secondary drugs for overcoming drug resistance.

References

1. J. C. Costello, L. M. Heiser, E. Georgii, M. Gönen, M. P. Menden, N. J. Wang, M. Bansal, P. Hintsanen, S. A. Khan, and J.-P. Mpindi, "A community effort to assess and improve drug sensitivity prediction algorithms," Nature biotechnology, vol. 32, no. 12, pp. 1202--1212, 2014.
2. S. Papillon-Cavanagh, N. De Jay, N. Hachem, C. Olsen, G. Bontempi, H. J. Aerts, J. Quackenbush, and B. Haibe-Kains, "Comparison and validation of genomic predictors for anticancer drug sensitivity," Journal of the American Medical Informatics Association, vol. 20, no. 4, pp. 597--602, 2013.
3. R. H. Shoemaker, "The NCI60 human tumour cell line anticancer drug screen," Nature Reviews Cancer, vol. 6, no. 10, pp. 813--823, 2006.

Index Terms

• Applied computing

  • Life and medical sciences

    • Health care information systems