College
College of Arts and Sciences
Mentor Information
Dr. Olukemi Akintewe
Description
Our research focused on the effectiveness of using computational methods such as sequencing models to find biomarkers that confirm the existence of Triple-Negative Breast Cancer (TNBC) in patients. The research focused on analyzing the importance of being able to detect patterns in genomic sequences that were consistently seen in patients who were TNBC positive. Additionally, our aim was to understand resistance mechanisms that make TNBC harder to treat through sequencing analytics. Understanding these patterns is especially important for early intervention so patients who have TNBC can get started on treatment before the disease progresses. Triple-negative breast cancer lacks hormone receptors and is difficult to diagnose early, creating an urgent need for computational methods that can uncover hidden molecular signatures. Our research asks how computational measures of gene-expression biomarkers can enhance early detection and improve patient outcomes in TNBC. To address this question, studies were analyzed that used RNA-Seq, microarray datasets, protein–protein interaction networks, and machine-learning models to detect gene-expression patterns that differentiate TNBC from normal tissue and classify TNBC into molecular subtypes. This is seen in measures taken to understand the effectiveness of neoadjuvant chemotherapy, the most popular treatment measure for triple-negative breast cancer, on a patient by patient basis seen through the presence of certain gene expression signatures. This topic is worth examining because triple-negative breast cancer is one of the most aggressive and treatment-resistant forms of breast cancer, with limited targeted therapy options and a high risk of relapse and mortality. Current treatments often rely on chemotherapy and immunotherapy, which show highly variable effectiveness across patients due to underlying genetic resistance mechanisms. By exploring computational analysis of gene expression and sequencing-based biomarkers, researchers can uncover the molecular drivers of TNBC progression, resistance, and recurrence. Understanding these mechanisms is essential for improving early detection, minimizing ineffective treatments, and developing personalized, precision-based therapies that can significantly improve patient outcomes and survival rates. Computational tools reveal early TNBC biomarkers, classify molecular subtypes, and predict outcomes with over 90% accuracy. Machine learning, network analysis, and sequencing uncover signatures linked to treatment response, including clonal extinction versus persistence after chemotherapy. Understanding gene expression and genetic drivers enables personalized, time-sensitive therapy and improves neoadjuvant treatment selection, advancing precision care for TNBC patients. Triple Negative Breast Cancer is a cancer prone to poor patient outcomes, relapse, and metastasis so the ability to effectively intercept this allows for better survival rates of a very common breast cancer type. Earlier detection and specified patient treatments will allow for better outcomes and less cancer metastasis. With the commonality and improvement of these practices it can begin to be used on other cancers with higher mortality rates and shorter distant-metastasis-free survival windows.
Computational Analysis of Gene Expression Profiles to Identify Novel Biomarkers for Triple-Negative Breast Cancer
Our research focused on the effectiveness of using computational methods such as sequencing models to find biomarkers that confirm the existence of Triple-Negative Breast Cancer (TNBC) in patients. The research focused on analyzing the importance of being able to detect patterns in genomic sequences that were consistently seen in patients who were TNBC positive. Additionally, our aim was to understand resistance mechanisms that make TNBC harder to treat through sequencing analytics. Understanding these patterns is especially important for early intervention so patients who have TNBC can get started on treatment before the disease progresses. Triple-negative breast cancer lacks hormone receptors and is difficult to diagnose early, creating an urgent need for computational methods that can uncover hidden molecular signatures. Our research asks how computational measures of gene-expression biomarkers can enhance early detection and improve patient outcomes in TNBC. To address this question, studies were analyzed that used RNA-Seq, microarray datasets, protein–protein interaction networks, and machine-learning models to detect gene-expression patterns that differentiate TNBC from normal tissue and classify TNBC into molecular subtypes. This is seen in measures taken to understand the effectiveness of neoadjuvant chemotherapy, the most popular treatment measure for triple-negative breast cancer, on a patient by patient basis seen through the presence of certain gene expression signatures. This topic is worth examining because triple-negative breast cancer is one of the most aggressive and treatment-resistant forms of breast cancer, with limited targeted therapy options and a high risk of relapse and mortality. Current treatments often rely on chemotherapy and immunotherapy, which show highly variable effectiveness across patients due to underlying genetic resistance mechanisms. By exploring computational analysis of gene expression and sequencing-based biomarkers, researchers can uncover the molecular drivers of TNBC progression, resistance, and recurrence. Understanding these mechanisms is essential for improving early detection, minimizing ineffective treatments, and developing personalized, precision-based therapies that can significantly improve patient outcomes and survival rates. Computational tools reveal early TNBC biomarkers, classify molecular subtypes, and predict outcomes with over 90% accuracy. Machine learning, network analysis, and sequencing uncover signatures linked to treatment response, including clonal extinction versus persistence after chemotherapy. Understanding gene expression and genetic drivers enables personalized, time-sensitive therapy and improves neoadjuvant treatment selection, advancing precision care for TNBC patients. Triple Negative Breast Cancer is a cancer prone to poor patient outcomes, relapse, and metastasis so the ability to effectively intercept this allows for better survival rates of a very common breast cancer type. Earlier detection and specified patient treatments will allow for better outcomes and less cancer metastasis. With the commonality and improvement of these practices it can begin to be used on other cancers with higher mortality rates and shorter distant-metastasis-free survival windows.
