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Full-Text Articles in Life Sciences
Understanding The Kinomic Contributions To Tyrosine Kinase Inhibitor Resistance In Triple Negative Breast Cancer, Cory Lefebvre
Understanding The Kinomic Contributions To Tyrosine Kinase Inhibitor Resistance In Triple Negative Breast Cancer, Cory Lefebvre
Electronic Thesis and Dissertation Repository
Resistance to tyrosine kinase inhibitors (TKIs) presents a growing challenge in the development of therapeutic targets for cancers such as triple negative breast cancer (TNBC), where conventional therapies are ineffective at combatting systemic disease. Potential targets in TNBC include the receptor tyrosine kinases EGFR (epidermal growth factor receptor) and c-Met, however, targeted anti-EGFR and anti-c-Met therapies have faced challenges in clinical trials due to acquired resistance. We hypothesize that response versus resistance of triple negative breast cancer to the tyrosine kinase inhibitors erlotinib and cabozantinib is mediated by compensatory changes in the kinome and phosphoproteome. To test this, we (1) …
Pathway‐Extended Gene Expression Signatures Integrate Novel Biomarkers That Improve Predictions Of Patient Responses To Kinase Inhibitors, Ashis Bagchee‐Clark, Eliseos J. Mucaki, Tyson Whitehead, Peter Rogan
Pathway‐Extended Gene Expression Signatures Integrate Novel Biomarkers That Improve Predictions Of Patient Responses To Kinase Inhibitors, Ashis Bagchee‐Clark, Eliseos J. Mucaki, Tyson Whitehead, Peter Rogan
Biochemistry Publications
Cancer chemotherapy responses have been related to multiple pharmacogenetic biomarkers, often for the same drug. This study utilizes machine learning to derive multi‐gene expression signatures that predict individual patient responses to specific tyrosine kinase inhibitors, including erlotinib, gefitinib, sorafenib, sunitinib, lapatinib and imatinib. Support vector machine (SVM) learning was used to train mathematical models that distinguished sensitivity from resistance to these drugs using a novel systems biology‐based approach. This began with expression of genes previously implicated in specific drug responses, then expanded to evaluate genes whose products were related through biochemical pathways and interactions. Optimal pathway‐extended SVMs predicted responses in …
Pathway-Extended Gene Expression Signatures Integrate Novel Biomarkers That Improve Predictions Of Patient Responses To Kinase Inhibitors, Ashis Jem Bagchee-Clark, Eliseos J. Mucaki, Tyson Whitehead, Peter Rogan
Pathway-Extended Gene Expression Signatures Integrate Novel Biomarkers That Improve Predictions Of Patient Responses To Kinase Inhibitors, Ashis Jem Bagchee-Clark, Eliseos J. Mucaki, Tyson Whitehead, Peter Rogan
Biochemistry Publications
No abstract provided.
Pathway-Extended Gene Expression Signatures Integrate Novel Biomarkers That Improve Predictions Of Patient Responses To Kinase Inhibitors, Jem Bagchee-Clark, Eliseos J. Mucaki, Tyson Whitehead, Peter Rogan
Pathway-Extended Gene Expression Signatures Integrate Novel Biomarkers That Improve Predictions Of Patient Responses To Kinase Inhibitors, Jem Bagchee-Clark, Eliseos J. Mucaki, Tyson Whitehead, Peter Rogan
Biochemistry Publications
Cancer chemotherapy responses have been related to multiple pharmacogenetic biomarkers, often for the same drug. This study utilizes machine learning to derive multi-gene expression signatures that predict individual patient responses to specific tyrosine kinase inhibitors, including erlotinib, gefitinib, sorafenib, sunitinib, lapatinib and imatinib. Support Vector Machine learning was used to train mathematical models that distinguished sensitivity from resistance to these drugs using a novel systems biology-based approach. This began with expression of genes previously implicated in specific drug responses, then expanded to evaluate genes whose products were related through biochemical pathways and interactions. Optimal pathway-extended support vector machines predicted responses …