Texas Medical Center

About: Texas Medical Center is a healthcare organization based out in Houston, Texas, United States. It is known for research contribution in the topics: Population & Cancer. The organization has 2845 authors who have published 2394 publications receiving 79426 citations.

4. use of genetically modified stem cells in experimental gene therapies

Abstract: Gene therapy is a novel therapeutic branch of modern medicine. Its emergence is a direct consequence of the revolution heralded by the introduction of recombinant DNA methodology in the 1970s. Gene therapy is still highly experimental, but has the potential to become an important treatment regimen. In principle, it allows the transfer of genetic information into patient tissues and organs. Consequently, diseased genes can be eliminated or their normal functions rescued. Furthermore, the procedure allows the addition of new functions to cells, such as the production of immune system mediator proteins that help to combat cancer and other diseases.

UTH-CCB: The Participation of the SemEval 2015 Challenge -- Task 14

Abstract: This paper describes the system developed by the University of Texas Health Science Center at Houston (UTHealth), for the 2015 SemEval shared task on “Analysis of Clinical Text” (Task 14). We participated in both sub-tasks: Task 1 for “Disorder Identification”, which aims to detect disorder entities and encode them to UMLS (Unified Medial Language System) CUI (Concept Unique Identifier) and Task 2 for Disorder Slot Filling, where the task is to identify normalized value for modifiers of disorders. For Task 1, we developed an ensemble approach that combined machine learning based named entity recognition classifiers with MetaMap, an existing symbolic biomedical NLP system, to recognize disorder entities, and we used a general Vector Space Model-based approach for disorder encoding to UMLS CUIs. To identify modifiers of disorders (Task 2), we developed Support Vector Machines-based classifiers for each type of modifier, by exploring various types of features. Our system was ranked 3 for Task 1 and 1 for the Task 2 (both 2A and 2B), demonstrating the effectiveness of machine learning-based approaches for extracting clinical entities and their modifiers from clinical narratives.

5mC regulator-mediated molecular subtypes depict the hallmarks of the tumor microenvironment and guide precision medicine in bladder cancer.

Abstract: Background Depicting the heterogeneity and functional characteristics of the tumor microenvironment (TME) is necessary to achieve precision medicine for bladder cancer (BLCA). Although classical molecular subtypes effectively reflect TME heterogeneity and characteristics, their clinical application is limited by several issues. Methods In this study, we integrated the Xiangya cohort and multiple external BLCA cohorts to develop a novel 5-methylcytosine (5mC) regulator-mediated molecular subtype system and a corresponding quantitative indicator, the 5mC score. Unsupervised clustering was performed to identify novel 5mC regulator-mediated molecular subtypes. The principal component analysis was applied to calculate the 5mC score. Then, we correlated the 5mC clusters (5mC score) with classical molecular subtypes, immunophenotypes, clinical outcomes, and therapeutic opportunities in BLCA. Finally, we performed pancancer analyses on the 5mC score. Results Two 5mC clusters, including 5mC cluster 1 and cluster 2, were identified. These novel 5mC clusters (5mC score) could accurately predict classical molecular subtypes, immunophenotypes, prognosis, and therapeutic opportunities of BLCA. 5mC cluster 1 (high 5mC score) indicated a luminal subtype and noninflamed phenotype, characterized by lower anticancer immunity but better prognosis. Moreover, 5mC cluster 1 (high 5mC score) predicted low sensitivity to cancer immunotherapy, neoadjuvant chemotherapy, and radiotherapy, but high sensitivity to antiangiogenic therapy and targeted therapies, such as blocking the β-catenin, FGFR3, and PPAR-γ pathways. Conclusions The novel 5mC regulator-based subtype system reflects many aspects of BLCA biology and provides new insights into precision medicine in BLCA. Furthermore, the 5mC score may be a generalizable predictor of immunotherapy response and prognosis in pancancers.