December 12, 2017. New preprint predicting response to platin drugs

Mucaki et al. Predicting Response to Platin Chemotherapy Agents with Biochemically-inspired Machine Learning. bioRxiv.


Selection of effective drugs that accurately predict chemotherapy response could improve cancer outcomes. We derive optimized gene signatures for response to common platinum-based drugs, cisplatin, carboplatin, and oxaliplatin, and respectively validate each with bladder, ovarian, and colon cancer patient data. Initially, using breast cancer cell line gene expression and growth inhibition (GI50) data, we performed backwards feature selection with cross-validation to derive predictive gene sets in a supervised support vector machine (SVM) learning approach. These signatures were also verified in bladder cancer cell lines. Aside from published associations between drugs and genes, we also expanded these gene signatures using a systems biology approach. Signatures at different GI50 thresholds distinguishing sensitivity from resistance to each drug contrast the contributions of different genes at extreme vs. median thresholds. An ensemble machine learning technique combining different GI50 thresholds was used to create threshold independent gene signatures. The most accurate models for each platinum drug in cell lines consisted of cisplatin: BARD1, BCL2, BCL2L1, CDKN2C, FAAP24, FEN1, MAP3K1, MAPK13, MAPK3, NFKB1, NFKB2, SLC22A5, SLC31A2, TLR4, TWIST1; carboplatin: AKT1, EIF3K, ERCC1, GNGT1, GSR, MTHFR, NEDD4L, NLRP1, NRAS, RAF1, SGK1, TIGD1, TP53, VEGFB, VEGFC; and oxaliplatin: BRAF, FCGR2A, IGF1, MSH2, NAGK, NFE2L2, NQO1, PANK3, SLC47A1, SLCO1B1, UGT1A1. Recurrence in bladder urothelial carcinoma patients from the Cancer Genome Atlas (TCGA) treated with cisplatin after 18 months was 71% accurate (59% in disease-free patients). In carboplatin-treated ovarian cancer patients, predicted recurrence was 60.2% (61% disease-free) accurate after 4 years, while the oxaliplatin signature predicted disease-free colorectal cancer patients with 72% accuracy (54.5% for recurrence) after 1 year. The best performing cisplatin model best predicted outcome for non-smoking TCGA bladder cancer patients (100% accuracy for recurrent, 57% for disease-free; N=19), the median GI50 model (GI50 = 5.12) predicted outcome in smokers with 79% with recurrence, and 62% who were disease free; N=35). Cisplatin and carboplatin signatures were comprised of overlapping gene sets and GI50 values, which contrasted with models for oxaliplatin response.

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