Pathway-extended gene expression signatures integrate novel biomarkers that improve predictions of patient responses to kinase inhibitors.
Ashis J Bagchee-ClarkEliseos J MucakiTyson WhiteheadEliseos J MucakiPublished in: MedComm (2020)
Cancer chemotherapy responses have been related to multiple pharmacogenetic biomarkers, often for the same drug. This study utilizes machine learning to derive multi-gene expression signatures that predict individual patient responses to specific tyrosine kinase inhibitors, including erlotinib, gefitinib, sorafenib, sunitinib, lapatinib and imatinib. Support vector machine (SVM) learning was used to train mathematical models that distinguished sensitivity from resistance to these drugs using a novel systems biology-based approach. This began with expression of genes previously implicated in specific drug responses, then expanded to evaluate genes whose products were related through biochemical pathways and interactions. Optimal pathway-extended SVMs predicted responses in patients at accuracies of 70% (imatinib), 71% (lapatinib), 83% (sunitinib), 83% (erlotinib), 88% (sorafenib) and 91% (gefitinib). These best performing pathway-extended models demonstrated improved balance predicting both sensitive and resistant patient categories, with many of these genes having a known role in cancer aetiology. Ensemble machine learning-based averaging of multiple pathway-extended models derived for an individual drug increased accuracy to >70% for erlotinib, gefitinib, lapatinib and sorafenib. Through incorporation of novel cancer biomarkers, machine learning-based pathway-extended signatures display strong efficacy predicting both sensitive and resistant patient responses to chemotherapy.
Keyphrases
- machine learning
- gene expression
- epidermal growth factor receptor
- genome wide
- small cell lung cancer
- papillary thyroid
- case report
- dna methylation
- squamous cell
- advanced non small cell lung cancer
- artificial intelligence
- poor prognosis
- big data
- drug induced
- renal cell carcinoma
- emergency department
- radiation therapy
- squamous cell carcinoma
- locally advanced
- lymph node metastasis
- young adults
- positive breast cancer
- childhood cancer
- chemotherapy induced
- rectal cancer