A novel twelve-gene signature to predict neoadjuvant chemotherapy response and prognosis in breast cancer

Abstract

BackgroundAccurate evaluation of the response to neoadjuvant chemotherapy (NAC) provides important information about systemic therapies for breast cancer, which implies pharmacological response, prognosis, and guide further therapy. Gene profiles overcome the shortcomings of the relatively limited detection indicators of the classical pathological evaluation criteria and the subjectivity of observation, but are complicated and expensive. Therefore, it is essential to develop a more accurate, repeatable, and economical evaluation approach for neoadjuvant chemotherapy responses.MethodsWe analyzed the transcriptional profiles of chemo-resistant breast cancer cell lines and tumors of chemo-resistant breast cancer patients in the GSE25066 dataset. We preliminarily screened out common significantly differentially expressed genes and constructed a NAC response risk model using LASSO regression and univariate and multivariate analyses. The differences in bioinformatic features of tumor cells, immune characteristics, and prognosis were compared between high and low-risk group. The potential drugs that could reverse chemotherapy resistance in breast cancer were screened by the CMap database.ResultsThirty-six genes were commonly up/down-regulated in both NAC chemo-resistant tumors and cells compared to the sensitive tumors and wild-type cells. Through LASSO regression, we obtained a risk model composed of 12 genes. The risk model divided patients into high and low-risk groups. Univariate and multivariate Cox regression analyses suggested that the risk score is an independent prognostic factor for evaluating NAC response in breast cancer. Tumors in risk groups exhibited significant differences in molecular biological characteristics, tumor-infiltrating lymphocytes, and immunosuppressive molecule expression. Our results suggested that the risk score was also a good prognostic factor for breast cancer. Finally, we screened potential drugs that could reverse chemotherapy resistance in breast cancer.ConclusionA novel 12 gene-signature could be used to predict NAC response and predict prognosis in breast cancer.

maGENEgerZ: An Efficient AI-Based Framework Can Extract More Expressed Genes and Biological Insights Underlying Breast Cancer Drug Response Mechanism

“…Future work includes (1) utilizing our framework to unveil various biological knowledge behind drug response mechanisms, related to different cancer types such as pancreatic cancer, liver cancer, and multiple myeloma; (2) collaborating with clinical research physicians to apply our tool to analyze drug response mechanism in the neoadjuvant setting; and (3) integrating different profiling data related to cancer drug response and performing an assessment from biological perspective.…”

“…Compared to existing baseline methods including deep learning, results demonstrate superiority and efficiency of esvm achieving high performance results and having more expressed genes in well-established breast cancer cell lines including MD-MB231, MCF7, and HS578T. Moreover, esvm is able to identify (1) various drugs including clinically approved ones (e.g., tamoxifen and erlotinib); (2) seventy-four unique genes (including tumor suppression genes such as TP53 and BRCA1); and (3) thirty-six unique TFs (including SP1 and RELA). These results have been reported to be linked to breast cancer drug response mechanism, progression, and metastasizing.…”

“…Wu et al [3] utilized a bioinformatics approach to identify a gene signature that aid in predicting neoadjuvant chemotherapy response for breast cancer patients. They downloaded a gene expression dataset from the GEO database according to the GEO accession number GSE25066.…”

“…The ability to elucidate various mechanisms underlying breast cancer drug response and resistance is a critical part in the clinical decision-making process, not only aiding in finding out the potential effectiveness of a drug compound but also spanning to (1) reducing the search space for candidate compounds; (2) having a greater awareness and management of probable adverse reactions before conducting clinical trials [1]; and (3) identifying potential drug targets associated with drug compounds. Studies have been conducted to analyze gene expression data obtained from biological experiments pertaining to breast cancer drug response to unveil various molecular mechanisms.…”

maGENEgerZ: An Efficient Artificial Intelligence-Based Framework Can Extract More Expressed Genes and Biological Insights Underlying Breast Cancer Drug Response Mechanism

“…Wu et al [3] utilized a bioinformatics approach to identify a gene signature that aids in predicting neoadjuvant chemotherapy response for breast cancer patients. They downloaded a gene expression dataset from the GEO database according to the GEO accession number GSE25066.…”