Genomic signatures for paclitaxel and gemcitabine resistance in breast cancer derived by machine learning

Abstract

GemcitabineResistance Drug sensitivity Genomic profiles Breast cancer A B S T R A C TIncreasingly, the effectiveness of adjuvant chemotherapy agents for breast cancer has been related to changes in the genomic profile of tumors. We investigated correspondence between growth inhibitory concentrations of paclitaxel and gemcitabine (GI50) and gene copy number, mutation, and expression first in breast cancer cell lines and then in patients.Genes encoding direct targets of these drugs, metabolizing enzymes, transporters, and those previously associated with chemoresistance to paclitaxel (n ¼ 31 genes) or gemcitabine (n ¼ 18) were analyzed. A multi-factorial, principal component analysis (MFA) indicated expression was the strongest indicator of sensitivity for paclitaxel, and copy number and expression were informative for gemcitabine. The factors were combined using support vector machines (SVM). Expression of 15 genes (ABCC10, BCL2, BCL2L1, BIRC5, BMF, FGF2, FN1, MAP4, MAPT, NFKB2, SLCO1B3, TLR6, TMEM243, TWIST1, and CSAG2) predicted cell line sensitivity to paclitaxel with 82% accuracy. Copy number profiles of 3 genes (ABCC10, NT5C, TYMS ) together with expression of 7 genes (ABCB1, ABCC10, CMPK1, DCTD, NME1, RRM1, RRM2B), predicted gemcitabine response with 85% accuracy. Expression and copy number studies of two independent sets of patients with known responses were then analyzed with these models.These included tumor blocks from 21 patients that were treated with both paclitaxel and gemcitabine, and 319 patients on paclitaxel and anthracycline therapy. A new paclitaxel SVM was derived from an 11-gene subset since data for 4 of the original genes was * Corresponding author.E-mail address: progan@uwo.ca (P.K. Rogan).

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

“…The SVM was trained using the function fitcsvm in MATLAB R2014a 7 and tested with either leave-oneout or 9 fold cross-validation (indicated in Table 1). The Gaussian kernel was used for this study, unlike Dorman et al 1 which used the linear kernel. The SVM requires selection of two different parameters, C (misclassification cost) and sigma (which controls the flexibility and smoothness of Gaussians) 8 ; these parameters determine how strictly the SVM learns the training set, and hence if not selected properly, can lead to overfitting.…”

“…These genes were selected (in Dorman et al 1 ) based either on their known involvement in paclitaxel metabolism, or evidence that their expression levels and/or copy numbers correlate with paclitaxel GI 50 values. mRMR and SVM were combined to obtain a subset of genes that can accurately predict patient survival outcomes; here, we considered 3, 4 and 5 years as survival thresholds for breast cancer patients.…”

“…While not a replication study sensu stricto, the initial paclitaxel gene set used for feature selection was the same as in our previous study 1 . Predictions for the METABRIC patient cohort, which was independent of the previous validation set 5 used in Dorman et al 1 , of the either same (SVM) or different ML methods (RF and SVM with mRMR) exhibited comparable or better accuracies than our previous gene signature 1 .…”

“…We have applied a supervised machine learning (ML) approach to derive accurate gene signatures, based on the biochemically-guided response to chemotherapies with breast cancer cell lines 1 , which show variable responses to growth inhibition by paclitaxel and gemcitabine therapies 2,3 . We analyzed stable 4 and linked unstable genes in pathways that determine their disposition.…”

“…We analyzed stable 4 and linked unstable genes in pathways that determine their disposition. This involved investigating the correspondence between 50% growth inhibitory concentrations (GI 50 ) of paclitaxel and gemcitabine and gene copy number, mutation, and expression first in breast cancer cell lines and then in patients 1 . Genes encoding direct targets of these drugs, metabolizing enzymes, transporters, and those previously associated with chemo-resistance to paclitaxel (n=31 genes) were then pruned by multiple factor analysis (MFA), which indicated that expression levels of genes ABCC10, BCL2, BCL2L1, BIRC5, BMF, FGF2, FN1, MAP4, MAPT, NKFB2, SLCO1B3, TLR6, TMEM243, TWIST1, and CSAG2 could predict sensitivity in breast cancer cell lines with 84% accuracy.…”