Precision Oncology Beyond Targeted Therapy: Combining Omics Data with Machine Learning Matches the Majority of Cancer Cells to Effective Therapeutics

Ding MQ, Chen L, Cooper GF, Young JD, Lu X. Precision Oncology Beyond Targeted Therapy: Combining Omics Data with Machine Learning Matches the Majority of Cancer Cells to Effective Therapeutics. Mol Cancer Res. 2017 Nov 13. pii: molcanres.0378.2017. doi: 10.1158/1541-7786.MCR-17-0378. [Epub ahead of print] PMID: 29133589

Precision oncology involves identifying drugs that will effectively treat a tumor and then prescribing an optimal clinical treatment regimen. However, most first-line chemotherapy drugs do not have biomarkers to guide their application. For molecularly targeted drugs, using the genomic status of a drug target as a therapeutic indicator has limitations. In this study, machine learning methods (e.g., deep learning) were used to identify informative features from genome scale omics data and to train classifiers for predicting the effectiveness of drugs in cancer cell lines. The methodology introduced here can accurately predict the efficacy of drugs, regardless of whether they are molecularly targeted or non-specific chemotherapy drugs. This approach, on a per-drug basis, can identify sensitive cancer cells with an average sensitivity of 0.82 and specificity of 0.82; on a per-cell line basis, it can identify effective drugs with an average sensitivity of 0.80 and specificity of 0.82. This report describes a data-driven precision medicine approach that is not only generalizable but also optimizes therapeutic efficacy. The framework detailed herein, when successfully translated to clinical environments, could significantly broaden the scope of precision oncology beyond targeted therapies, benefiting an expanded proportion of cancer patients.

IMPLICATIONS:

Developing the capability of mapping cancer cells to effective therapeutics is the foundation of precision oncology. This paper presents an integrative machine learning framework for predicting cancer cell sensitivity to anti-cancer drugs based on multiple omics data from cancer cells.

Publication Year: 
2017
Publication Credits: 
Ding MQ, Chen L, Cooper GF, Young JD, Lu X
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