Genomic Approach Towards Personalized Anticancer Drug Therapy

Abstract

Stratification of patients for multidrug response is a promising strategy for cancer treatment. Genome-based prediction models have great potential for this purpose because the extent of drug sensitivity may be attributed to the heterogeneity of the underlying genetic characteristics of cancer. However, microarray data is difficult to analyze and is not reproducible. Several machine-learning algorithms have therefore been developed in a repeatable manner. Random forests algorithm, which uses an ensemble approach based on classification and regression trees, appears to be superior for predicting multidrug sensitivity. This is because ensemble methods are more effective when there are much more predictors than samples. Here, we review recent advances in the development of classification algorithms using microarray technology for prediction of anticancer sensitivity, discuss the availability of ensemble methods for prediction models, and present data regarding the identification of potential responders to FOLFOX therapy using random forests algorithm.

KEYWORDS::

• anticancer therapy
• ensemble method
• expression array
• machine-learning algorithm
• personalized therapy
• predictor genes

Acknowledgements

The authors would like to thank H Meguro for valuable technical assistance.

Financial & competing interests disclosure

This work was mainly supported by Grants-in-Aid for Scientific Research ([S] 20221009 [H Aburatani] and [C] 23591972 [Y Midorikawa]) from the Ministry of Education, Culture, Sports, Science and Technology (MEXT), Japan, and the Program of Fundamental Studies in Health Sciences of the National Institute of Biomedical Innovation (NIBIO), Japan. The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.

The authors utilized writing assistance from Forte, Inc. in the production of this manuscript.

Additional information

Funding

This work was mainly supported by Grants-in-Aid for Scientific Research ([S] 20221009 [H Aburatani] and [C] 23591972 [Y Midorikawa]) from the Ministry of Education, Culture, Sports, Science and Technology (MEXT), Japan, and the Program of Fundamental Studies in Health Sciences of the National Institute of Biomedical Innovation (NIBIO), Japan. The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed. The authors utilized writing assistance from Forte, Inc. in the production of this manuscript.