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Identification of miRNA Signature in Breast Cancer to Predict Neoadjuvant Chemotherapy Response.

ABSTRACT: Background: Chemotherapy failure causes high breast cancer recurrence and poor patient prognosis. Thus, we studied a cohort of novel biomarkers to predict chemotherapeutic response in breast cancer. In this study, miRNA expression profiling was performed on 10 breast cancer punctured specimens sensitive to chemotherapy (MP grade 4, 5) and 10 chemotherapy resistant (MP grade 1). Differentially expressed miRNAs were verified by qRT-PCR in 60 initial samples, 59 validated samples and 71 independent samples. A miRNA signature was generated using a Logistic regression model. A receiver operating characteristic (ROC) test was used to assess specificity and sensitivity of single miRNA and miRNA signature. Target genes regulated by miRNAs and their involved signaling pathways were analyzed using GO enrichment and KEGG software. MiRNAs expression were separately compared with ER, PR, HER2 immunohistochemical staining and different drugs. qRT-PCR showed that the high expression of miR-23a-3p, miR-200c-3p, miR-214-3p and the low expression of miR-451a and miR-638 were closely related to chemoresistance. According to the formula for calculating the drug resistance risk, patients in the high-risk group were more likely to develop chemotherapy resistance than the low-risk group. Bioinformatics analysis showed that 5 miRNAs and target genes are mainly involved in p53, ubiquitin-mediated proteolysis, mTOR, Wnt, cells skeletal protein regulation, cell adhesion and ErbB signaling pathways. miR-451a expression was associated with ER, HER-2 status and anthracyclines. A miRNA signature of chemotherapeutic response may be clinically valuable for improving current chemotherapy regimens of individual treatment for patients with breast cancer.

SUBMITTER: Xing AY

PROVIDER: S-EPMC8262148 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description: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.

Project description:BackgroundNeoadjuvant chemotherapy (NAC) has become the standard of care for resectable esophageal squamous cell carcinoma (ESCC) which is one of the most lethal cancers, to improve resectability and prognosis. On this basis, to provide individually optimized therapy for ESCC, a minimally-invasive biomarker for response to NAC is strongly desired. This study aimed to identify the miRNA signature in serum specimens taken from ESCC patients undergoing NAC through genome-wide microarray technology.MethodsComprehensive miRNA-expression profiles of serum specimens from ESCC patients before initial treatment were analyzed using microarray. A qPCR assay was performed to test the robustness of identified serum-based miRNA signature for discriminating response to NAC with serum specimens taken from 100 ESCC cases undergoing NAC.ResultsWe prioritized 62 miRNAs differentially expressed between responders and non-responders (absolute log2 fold change?>?1.0, corresponding P?<?0.05) and from the 62 miRNAs, we selected the miR-23a-5p, miR-193b-5p, and miR-873-3p, which were highly expressed in non-responders. Following qPCR analysis indicated the expression of miR-193b-5p and miR-873-3p in serum specimens were significantly higher in non-responders among three selected miRNAs (P?=?0.004 and 0.001, respectively). Subsequently, we developed 2-miR-model (miR-193b-5p and miR-873-3p), 3-miR-model, and 2-miR?+?lymphatic invasion (ly) model based on logistic regression analysis, which achieved the better area under the receiver operating characteristic curves than those of single miRNAs as 2-miR-model, 0.70 (95% CI 0.57 to 0.82); 3-miR-model, 0.70 (95% CI 0.57 to 0.83); and 2-miR?+?ly, 0.73 (95% CI 0.60-0.86), respectively. Furthermore, we compared the detective power of the combined model: 2-miR?+?ly for discriminating non-responders to NAC, to other pretreatment clinical features. Consequently, 2-miR?+?ly model was superior to serum SCC antigen with great significance (P?=?0.01) and to ly, and clinical T stage with marginal significance (P?=?0.18, 0.07, respectively).ConclusionsCollectively, we demonstrated that the potential of a multi-miRNA biomarker for identifying NAC response in ESCC is realistic, and can be used in the clinic with the further validation.

Project description:BackgroundNeoadjuvant chemotherapy is a key component of breast cancer treatment regimens and pathologic complete response to this therapy varies among patients. This is presumably due to differences in the molecular mechanisms that underlie each tumor's disease pathology. Developing genomic clinical assays that accurately categorize responders from non-responders can provide patients with the most effective therapy for their individual disease.MethodsWe applied our previously developed E2F4 genomic signature to predict neoadjuvant chemotherapy response in breast cancer. E2F4 individual regulatory activity scores were calculated for 1129 patient samples across 5 independent breast cancer neoadjuvant chemotherapy datasets. Accuracy of the E2F4 signature in predicting neoadjuvant chemotherapy response was compared to that of the Oncotype DX and MammaPrint predictive signatures.ResultsIn all datasets, E2F4 activity level was an accurate predictor of neoadjuvant chemotherapy response, with high E2F4 scores predictive of achieving pathologic complete response and low scores predictive of residual disease. These results remained significant even after stratifying patients by estrogen receptor (ER) status, tumor stage, and breast cancer molecular subtypes. Compared to the Oncotype DX and MammaPrint signatures, our E2F4 signature achieved similar performance in predicting neoadjuvant chemotherapy response, though all signatures performed better in ER+ tumors compared to ER- ones. The accuracy of our signature was reproducible across datasets and was maintained when refined from a 199-gene signature down to a clinic-friendly 33-gene panel.ConclusionOverall, we show that our E2F4 signature is accurate in predicting patient response to neoadjuvant chemotherapy. As this signature is more refined and comparable in performance to other clinically available gene expression assays in the prediction of neoadjuvant chemotherapy response, it should be considered when evaluating potential treatment options.

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Identification of miRNA Signature in Breast Cancer to Predict Neoadjuvant Chemotherapy Response.

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