Circulating miR-221/222 expression as microRNA biomarker predicting tamoxifen treatment outcome: a case-control study

Investigation of circulating miR-182-3p, miR -382-3p and miR -93, miR -142-3p involved in tamoxifen resistance and sensitivity in luminal-subtype breast cancer patients: a case-control study

Breast cancer (BC) commonly expresses estrogen receptors (ERs); hence, endocrine therapy targeting ERs is considered an effective treatment. Tamoxifen (TAM) resistance is an essential clinical complication leading to cancer progression and metastasis. This study investigated MicroRNAs (miRNAs) potentially implicated in drug resistance (miR-182-3p, miR-382-3p) or sensitivity (miR-93, miR- 142- 3p). This study aimed to provide new insights into serum microRNA expression profiles in BC. This case-control study included patients with luminal-A BC who received TAM for approximately one year. The case and control groups included 40 patients with or without local recurrence or metastasis. The expression levels of miR-182-3p, miR-382-3p, miR-93, and miR-142-3p in plasma samples were measured using real-time PCR with target-specific primers. The multivariate model of miR-93 (p = 0.0002), miR-182-3p (p = 0.0002), and miR-382-3p (p = 0.0028) demonstrated higher predictive power for TAM resistance. The only significant association was observed between miR-382-3p expression and lymphovascular invasion (LVI) (p = 0.0314). Moreover, lower expression levels of miR-93 and miR-382-3p were observed in the TAM-sensitive group compared to the TAM-resistant counterparts (p = 0.0002 and p = 0.0028, respectively). In contrast, the expression level of miR-182-3p was significantly higher in the TAM-sensitive group compared to the TAM-resistant group (p = 0.0002). receiver operating characteristic (ROC) curve analysis also indicated the expression of miR-182-3p (p < 0.001; area under curve (AUC): 0.753), miR-382-3p (p = 0.0028; AUC: 0.697), and miR-93 (p < 0.001; AUC: 0.762) as predictive markers for TAM resistance. Multivariate models based on miR-182-3p, miR-382-3p, and miR-93 can predict the response to hormone therapy. Measuring these miRNAs is also recommended for patients with luminal-subtype BC undergoing TAM therapy.

Circulating Micro-RNAs Predict the Risk of Recurrence in Triple-Negative Breast Cancer

Triple-negative breast cancer (TNBC) is an aggressive subtype of breast cancer with a high tendency for developing a recurrent disease. Circulating micro-RNAs (cmiRNAs) obtained through liquid biopsy are potential prognostic biomarkers for the assessment of TNBC recurrence risk. In this study, we sequenced cmiRNAs from the serum samples of 14 recurrent and 19 non-recurrent TNBC cases and compared expression profiles in relation to recurrence status, survival data and miRNA expression in matched tumor samples. Differential expression analysis between recurrent and non-recurrent cases identified ten differentially expressed (DE) cmiRNAs, of which cmiRNAs miR-21-5p (p = 0.030, HR = 1.87, 95% CI 1.06-3.30), miR-16-5p (p = 0.032, HR = 0.53, 95% CI 0.30-0.95), and miR-26b-5p (p = 0.023, HR = 0.52, 95% CI 0.29-0.91) were associated with recurrence-free survival in multivariable survival analysis. Expression profiles of matched tumor and serum samples were shown to correlate with each other. DE cmiRNAs were associated with common cancer-related signaling pathways and improved the overall predictive performance of the logistic regression model assessing the recurrence risk. Our results indicate that recurrent and non-recurrent TNBC differ in their cmiRNA expression profiles, and that three specific cmiRNAs can be used to assess the risk of recurrence in TNBC.

Machine learning in onco-pharmacogenomics: a path to precision medicine with many challenges

Over the past two decades, Next-Generation Sequencing (NGS) has revolutionized the approach to cancer research. Applications of NGS include the identification of tumor specific alterations that can influence tumor pathobiology and also impact diagnosis, prognosis and therapeutic options. Pharmacogenomics (PGx) studies the role of inheritance of individual genetic patterns in drug response and has taken advantage of NGS technology as it provides access to high-throughput data that can, however, be difficult to manage. Machine learning (ML) has recently been used in the life sciences to discover hidden patterns from complex NGS data and to solve various PGx problems. In this review, we provide a comprehensive overview of the NGS approaches that can be employed and the different PGx studies implicating the use of NGS data. We also provide an excursus of the ML algorithms that can exert a role as fundamental strategies in the PGx field to improve personalized medicine in cancer.