Predicting Outcomes of Hormone and Chemotherapy in the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) Study by Biochemically-inspired Machine Learning

Eliseos J Mucaki; Katherina Baranova; Huy Q Pham; Iman Rezaeian; Dimo Angelov; Alioune Ngom; Luis Rueda; Peter K Rogan

doi:10.12688/f1000research.9417.3

Predicting Outcomes of Hormone and Chemotherapy in the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) Study by Biochemically-inspired Machine Learning

F1000Res. 2016 Aug 31:5:2124. doi: 10.12688/f1000research.9417.3. eCollection 2016.

Authors

Eliseos J Mucaki¹, Katherina Baranova¹, Huy Q Pham², Iman Rezaeian², Dimo Angelov³, Alioune Ngom², Luis Rueda², Peter K Rogan^{1

3

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Affiliations

¹ Deparment of Biochemistry , University of Western Ontario, London, Canada.
² School of Computer Science, University of Windsor, Windsor, Canada.
³ Department of Computer Science, University of Western Ontario, London, Canada.
⁴ CytoGnomix Inc, London, Canada.

Abstract

Genomic aberrations and gene expression-defined subtypes in the large METABRIC patient cohort have been used to stratify and predict survival. The present study used normalized gene expression signatures of paclitaxel drug response to predict outcome for different survival times in METABRIC patients receiving hormone (HT) and, in some cases, chemotherapy (CT) agents. This machine learning method, which distinguishes sensitivity vs. resistance in breast cancer cell lines and validates predictions in patients; was also used to derive gene signatures of other HT (tamoxifen) and CT agents (methotrexate, epirubicin, doxorubicin, and 5-fluorouracil) used in METABRIC. Paclitaxel gene signatures exhibited the best performance, however the other agents also predicted survival with acceptable accuracies. A support vector machine (SVM) model of paclitaxel response containing genes ABCB1, ABCB11, ABCC1, ABCC10, BAD, BBC3, BCL2, BCL2L1, BMF, CYP2C8, CYP3A4, MAP2, MAP4, MAPT, NR1I2, SLCO1B3, TUBB1, TUBB4A, and TUBB4B was 78.6% accurate in predicting survival of 84 patients treated with both HT and CT (median survival ≥ 4.4 yr). Accuracy was lower (73.4%) in 304 untreated patients. The performance of other machine learning approaches was also evaluated at different survival thresholds. Minimum redundancy maximum relevance feature selection of a paclitaxel-based SVM classifier based on expression of genes BCL2L1, BBC3, FGF2, FN1, and TWIST1 was 81.1% accurate in 53 CT patients. In addition, a random forest (RF) classifier using a gene signature ( ABCB1, ABCB11, ABCC1, ABCC10, BAD, BBC3, BCL2, BCL2L1, BMF, CYP2C8, CYP3A4, MAP2, MAP4, MAPT, NR1I2,SLCO1B3, TUBB1, TUBB4A, and TUBB4B) predicted >3-year survival with 85.5% accuracy in 420 HT patients. A similar RF gene signature showed 82.7% accuracy in 504 patients treated with CT and/or HT. These results suggest that tumor gene expression signatures refined by machine learning techniques can be useful for predicting survival after drug therapies.

Keywords: Gene expression signatures; breast cancer; chemotherapy resistance; hormone therapy; machine learning; random forest; support vector machine.

Grants and funding

AN and LR are funded by NSERC grants RGPIN-2016-05017 and RGPIN-2014-05084 and by the Windsor Essex County Cancer Centre Foundation under a Seeds4Hope grant. PKR has been supported by NSERC [Discovery Grant RGPIN-2015-06290], Canadian Foundation for Innovation, Canada Research Chairs and CytoGnomix Inc.