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 the ABCB1, ABCB11, ABCC1, ABCC10, BAD, BBC3, BCL2, BCL2L1, BMF, CYP2C8, CYP3A4, MAP2, MAP4, MAPT, NR1I2, SLCO1B3, TUBB1, TUBB4A, TUBB4B genes was 78.6% accurate in 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 were also evaluated at different survival thresholds. Minimum redundancy maximum relevance feature selection of a paclitaxel-based SVM classifier based on expression of ABCB11, ABCC1, BAD, BBC3 and BCL2L1 was 79% accurate in 53 CT patients. A random forest (RF) classifier produced a gene signature (ABCB11, ABCC1, BAD, BCL2, CYP2C8, CYP3A4, MAP4, MAPT, NR1I2, TUBB1, GBP1, OPRK1) that predicted >3 year survival with 82.4% accuracy in 420 HT patients. A similar RF gene signature showed 79.6% 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.
Highlights
Current pharmacogenetic analysis of chemotherapy makes qualitative decisions about drug efficacy in patients based on variants present in genes involved in the transport, biotransformation, or disposition of a drug
Genes encoding direct targets of these drugs, metabolizing enzymes, transporters, and those previously associated with chemo-resistance to paclitaxel (n=31 genes) were pruned by multiple factor analysis (MFA), which indicated expression of 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
We used a 26-gene signature as the base feature set. These genes were selected based either on their known involvement in paclitaxel metabolism, or evidence that their expression levels and/or copy numbers correlate with paclitaxel GI50 values (Table 3). minimum redundancy and maximum relevance (mRMR) and support vector machine (SVM) were combined to obtain a subset of genes that can accurately predict patient survival outcome; here, we considered 3, 4 and 5 years as survival thresholds for breast cancer patients (Table 3)
Summary
Current pharmacogenetic analysis of chemotherapy makes qualitative decisions about drug efficacy in patients (determination of good, intermediate or poor metabolizer phenotypes) based on variants present in genes involved in the transport, biotransformation, or disposition of a drug. We have applied a supervised 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 This involved investigating the correspondence between 50% growth inhibitory concentrations (GI50) of paclitaxel and gemcitabine and gene copy number, mutation, and expression first in breast cancer cell lines and 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 pruned by multiple factor analysis (MFA), which indicated expression of 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. The present study derives related gene signatures with ML approaches that predict outcome of hormone- and chemotherapies in the large METABRIC breast cancer cohort[6]
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