A 20-gene signature in predicting the chemoresistance of breast cancer to taxane-based chemotherapy

An in vivo genome-wide shRNA screen identifies BCL6 as a targetable biomarker of paclitaxel resistance in breast cancer

Paclitaxel is a common breast cancer drug; however, some tumors are resistant. The identification of biomarkers for paclitaxel resistance or sensitivity would enable the development of strategies to improve treatment efficacy. A genome-wide in vivo shRNA screen was performed on paclitaxel-treated mice with MDA-MB-231 tumors to identify genes associated with paclitaxel sensitivity or resistance. Gene expression of the top screen hits was associated with tumor response (resistance or sensitivity) among patients who received neoadjuvant chemotherapy containing paclitaxel. We focused our validation on screen hit B-cell lymphoma 6 (BCL6), which is a therapeutic target in cancer but for which no effects on drug response have been reported. Knockdown of BCL6 resulted in increased tumor regression in mice treated with paclitaxel. Similarly, inhibiting BCL6 using a small molecule inhibitor enhanced paclitaxel treatment efficacy both in vitro and in vivo in breast cancer models. Mechanism studies revealed that reduced BCL6 enhances the efficacy of paclitaxel by inducing sustained G1/S arrest, concurrent with increased apoptosis and expression of target gene cyclin-dependent kinase inhibitor 1A. In summary, the genome-wide shRNA knockdown screen has identified BCL6 as a potential targetable resistance biomarker of paclitaxel response in breast cancer.

Genome-wide analysis of the three-way interplay among gene expression, estrogen receptor expression and chemotherapeutic sensitivity in breast cancer

The expression of estrogen receptor α (ER) in breast cancers may be indicative of a favorable prognosis and most of these cancers respond to anti-estrogens or aromatase inhibitors. However, ER-positive (ER+) breast cancers receiving anti-hormone and/or chemotherapy sometimes lose their ER expression, which leads to the evolution of the disease to higher aggressiveness and drug resistance. In the present study, an ER-modified signature (EMS) was developed from the expression profile of a chemoresistant MCF-7 breast cancer cell line that lost ER expression during long-term treatment with a chemotherapeutic agent. The EMS could discriminate the ER-negative (ER-) breast cancer cells from the ER+ ones, which included seven pathways essential for the ER- cell development. Furthermore, the EMS indicated a more malignant subgroup of the ER- cells by discriminating the chemoresistant ER- cells from the chemosensitive ones. In addition, the classified chemoresistant ER- patients demonstrated worse prognosis. In conclusion, we developed a new method to discriminate subgroups of ER- breast cancer cells.

Targeting PSG1 to enhance chemotherapeutic efficacy: new application for anti-coagulant the dicumarol

Chemotherapeutic response is critical for the successful treatment and good prognosis in cancer patients. In this study, we analysed the gene expression profiles of preoperative samples from oestrogen receptor (ER)-negative breast cancer patients with different responses to taxane-anthracycline-based (TA-based) chemotherapy, and identified a group of genes that was predictive. Pregnancy specific beta-1-glycoprotein 1 (PSG1) played a central role within signalling pathways of these genes. Inhibiting PSG1 can effectively reduce chemoresistance via a transforming growth factor-β (TGF-β)-related pathway in ER-negative breast cancer cells. Drug screening then identified dicumarol (DCM) to target the PSG1 and inhibit chemoresistance to TA-based chemotherapy in vitro, in vivo, and in clinical samples. Taken together, this study highlights PSG1 as an important mediator of chemoresistance, whose effect could be diminished by DCM.

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

Increasingly, 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 = 31 genes) or gemcitabine (n = 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 unavailable. The accuracy of this SVM was similar in cell lines and tumor blocks (70-71%). The gemcitabine SVM exhibited 62% prediction accuracy for the tumor blocks due to the presence of samples with poor nucleic acid integrity. Nevertheless, the paclitaxel SVM predicted sensitivity in 84% of patients with no or minimal residual disease.