Project description:While taxane-platin standard chemotherapy provides benefit in advanced and localized non-small cell lung cancer (NSCLC), the majority of patients relapse with drug resistant tumors. Mechanisms underlying NSCLC resistance to this standard doublet chemotherapy are still not fully understood, and treatment options for chemoresistant lung tumors are limited. The goals of this work were to establish new preclinical NSCLC models of resistance to taxane-platin doublet chemotherapy, identify mechanisms of resistance, and develop new rational pharmacologic approaches to target drug resistant NSCLCs.
Project description:Characterization of histone 3 lysine 4 and lysine 27 trimethylation changes upon development of taxane-platin drug resistance in NSCLC cells and evaluation of these histone modifications after treatment with Jumonji KDM inhibitors, JIB-04 and GSK-J4.
Project description:Characterization of gene expression changes upon development of taxane-platin drug resistance in NSCLC cells and further, upon treatment of these resistant cells with the Jumonji KDM inhibitor, GSK-J4.
Project description:Purpose: Breast cancer is a genetically heterogenous disease with subtypes differing in prognosis and chemosensitivity. The basal-like breast cancer (BLBC) molecular subtype is associated with poorer outcomes, but is more responsive to taxane-based chemotherapy. We evaluated the role of kinesins, motor proteins interacting with microtubules, in influencing taxane resistance. Experimental Design: Kinesin (KIF) expression was studied in one local dataset comprising all taxane resistant breast cancers in relation to taxane resistance. Data in the NCI-60 cell line dataset (GSE5846) nd the MDACC dataset (GSE20194) is separately detailed. Results: In the local dataset, the kinesin KIF26B is overexpressed in taxane-resistant residual breast cancers post-chemotherapy. Conclusions: We show that kinesin overexpression correlates with taxane resistance in BLBC cell lines and tissue. Our results suggest a potential approach to overcoming taxane resistance through concurrent or sequential use of kinesin inhibitors, highlighting the ATP-binding domain as a drug development target. Kinesin (KIF) expression was studied in one local dataset comprising all taxane resistant breast cancers in relation to taxane resistance. Data in the NCI-60 cell line dataset (GSE5846) and the MDACC dataset (GSE20194) is separately detailed.
Project description:Purpose: Breast cancer is a genetically heterogenous disease with subtypes differing in prognosis and chemosensitivity. The basal-like breast cancer (BLBC) molecular subtype is associated with poorer outcomes, but is more responsive to taxane-based chemotherapy. We evaluated the role of kinesins, motor proteins interacting with microtubules, in influencing taxane resistance. Experimental Design: Kinesin (KIF) expression was studied in one local dataset comprising all taxane resistant breast cancers in relation to taxane resistance. Data in the NCI-60 cell line dataset (GSE5846) nd the MDACC dataset (GSE20194) is separately detailed. Results: In the local dataset, the kinesin KIF26B is overexpressed in taxane-resistant residual breast cancers post-chemotherapy. Conclusions: We show that kinesin overexpression correlates with taxane resistance in BLBC cell lines and tissue. Our results suggest a potential approach to overcoming taxane resistance through concurrent or sequential use of kinesin inhibitors, highlighting the ATP-binding domain as a drug development target.
Project description:We investigated whether prognostic information is reflected in the expression patterns of ovarian carcinoma samples. RNA obtained from seven FIGO stage I without recurrence, seven platin-sensitive advanced-stage (III or IV), and six platin-resistant advanced-stage ovarian tumors was hybridized on a complementary DNA microarray with 21,372 spotted clones. The results revealed that a considerable number of genes exhibit nonaccidental differential expression between the different tumor classes. Principal component analysis reflected the differences between the three tumor classes and their order of transition. Using a leave-one-out approach together with least squares support vector machines, we obtained an estimated classification test accuracy of 100% for the distinction between stage I and advanced-stage disease and 76.92% for the distinction between platin-resistant versus platin-sensitive disease in FIGO stage III/IV. These results indicate that gene expression patterns could be useful in clinical management of ovarian cancer.<br> KEYWORDS: clinical, FIGO stage, microarrays, ovarian cancer, platin resistance.
