Other search tools

About this data

The publication data currently available has been vetted by Vanderbilt faculty, staff, administrators and trainees. The data itself is retrieved directly from NCBI's PubMed and is automatically updated on a weekly basis to ensure accuracy and completeness.

If you have any questions or comments, please contact us.

Results: 1 to 10 of 72

Publication Record

Connections

Cardiac Toxicities in the Era of Precision Medicine: Underlying Risk Factors, Targeted Therapies, and Cardiac Biomarkers.
Blaes AH, Thavendiranathan P, Moslehi J
(2018) Am Soc Clin Oncol Educ Book 38: 764-774
MeSH Terms: Biomarkers, Cardiotoxicity, Humans, Precision Medicine, Risk Factors
Show Abstract · Added October 1, 2018
Cancer therapies can cause a variety of cardiac toxicities, including ischemia, cardiomyopathy, heart failure, myocarditis, arrhythmias, vascular disease, hypertension, and hyperlipidemia. Addressing cardiovascular risk at baseline, before initiating therapy, during cancer treatment, and in the survivorship period is imperative. It may be useful to risk stratify individuals with cardiovascular risk factors using biomarkers or imaging before they receive potentially cardiotoxic therapies. Additionally, new guidelines recommend cardiac imaging with echocardiography in the survivorship period 6 to 12 months after completing cancer therapy for these high-risk individuals. Close collaboration between cardiology and oncology in both clinical practice and future research is essential.
0 Communities
1 Members
0 Resources
5 MeSH Terms
Identification of a gene-expression predictor for diagnosis and personalized stratification of lupus patients.
Ding Y, Li H, He X, Liao W, Yi Z, Yi J, Chen Z, Moore DJ, Yi Y, Xiang W
(2018) PLoS One 13: e0198325
MeSH Terms: Biomarkers, Female, Gene Expression Regulation, Humans, Interferons, Male, Monitoring, Physiologic, Precision Medicine, Severity of Illness Index, Signal Transduction, Transcriptome
Show Abstract · Added July 6, 2018
Systemic lupus erythematosus (SLE) is an autoimmune disease characterized by a wide spectrum of clinical manifestations and degrees of severity. Few genomic biomarkers for SLE have been validated and employed to inform clinical classifications and decisions. To discover and assess the gene-expression based SLE predictors in published studies, we performed a meta-analysis using our established signature database and a data similarity-driven strategy. From 13 training data sets on SLE gene-expression studies, we identified a SLE meta-signature (SLEmetaSig100) containing 100 concordant genes that are involved in DNA sensors and the IFN signaling pathway. We rigorously examined SLEmetaSig100 with both retrospective and prospective validation in two independent data sets. Using unsupervised clustering, we retrospectively elucidated that SLEmetaSig100 could classify clinical samples into two groups that correlated with SLE disease status and disease activities. More importantly, SLEmetaSig100 enabled personalized stratification demonstrating its ability to prospectively predict SLE disease at the individual patient level. To evaluate the performance of SLEmetaSig100 in predicting SLE, we predicted 1,171 testing samples to be either non-SLE or SLE with positive predictive value (97-99%), specificity (85%-84%), and sensitivity (60-84%). Our study suggests that SLEmetaSig100 has enhanced predictive value to facilitate current SLE clinical classification and provides personalized disease activity monitoring.
0 Communities
1 Members
0 Resources
11 MeSH Terms
Interdisciplinary Models for Research and Clinical Endeavors in Genomic Medicine: A Scientific Statement From the American Heart Association.
Musunuru K, Arora P, Cooke JP, Ferguson JF, Hershberger RE, Hickey KT, Lee JM, Lima JAC, Loscalzo J, Pereira NL, Russell MW, Shah SH, Sheikh F, Wang TJ, MacRae CA, American Heart Association Council on Genomic and Precision Medicine; Council on Cardiovascular Disease in the Young; Council on Cardiovascular and Stroke Nursing; Council on Cardiovascular Radiology and Intervention; Council on Peripheral Vascular Disease; Council on Quality of Care and Outcomes Research; and Stroke Council
(2018) Circ Genom Precis Med 11: e000046
MeSH Terms: American Heart Association, Biomedical Research, Cardiovascular Diseases, Electronic Health Records, Genomics, Humans, Interdisciplinary Studies, Precision Medicine, United States
Show Abstract · Added April 2, 2019
The completion of the Human Genome Project has unleashed a wealth of human genomics information, but it remains unclear how best to implement this information for the benefit of patients. The standard approach of biomedical research, with researchers pursuing advances in knowledge in the laboratory and, separately, clinicians translating research findings into the clinic as much as decades later, will need to give way to new interdisciplinary models for research in genomic medicine. These models should include scientists and clinicians actively working as teams to study patients and populations recruited in clinical settings and communities to make genomics discoveries-through the combined efforts of data scientists, clinical researchers, epidemiologists, and basic scientists-and to rapidly apply these discoveries in the clinic for the prediction, prevention, diagnosis, prognosis, and treatment of cardiovascular diseases and stroke. The highly publicized US Precision Medicine Initiative, also known as All of Us, is a large-scale program funded by the US National Institutes of Health that will energize these efforts, but several ongoing studies such as the UK Biobank Initiative; the Million Veteran Program; the Electronic Medical Records and Genomics Network; the Kaiser Permanente Research Program on Genes, Environment and Health; and the DiscovEHR collaboration are already providing exemplary models of this kind of interdisciplinary work. In this statement, we outline the opportunities and challenges in broadly implementing new interdisciplinary models in academic medical centers and community settings and bringing the promise of genomics to fruition.
© 2018 American Heart Association, Inc.
0 Communities
1 Members
0 Resources
9 MeSH Terms
Genome Editing and Induced Pluripotent Stem Cell Technologies for Personalized Study of Cardiovascular Diseases.
Chun YW, Durbin MD, Hong CC
(2018) Curr Cardiol Rep 20: 38
MeSH Terms: Cardiovascular Diseases, Cell Differentiation, Gene Editing, Humans, Induced Pluripotent Stem Cells, Models, Biological, Precision Medicine
Show Abstract · Added May 1, 2018
PURPOSE OF REVIEW - The goal of this review is to highlight the potential of induced pluripotent stem cell (iPSC)-based modeling as a tool for studying human cardiovascular diseases. We present some of the current cardiovascular disease models utilizing genome editing and patient-derived iPSCs.
RECENT FINDINGS - The incorporation of genome-editing and iPSC technologies provides an innovative research platform, providing novel insight into human cardiovascular disease at molecular, cellular, and functional level. In addition, genome editing in diseased iPSC lines holds potential for personalized regenerative therapies. The study of human cardiovascular disease has been revolutionized by cellular reprogramming and genome editing discoveries. These exceptional technologies provide an opportunity to generate human cell cardiovascular disease models and enable therapeutic strategy development in a dish. We anticipate these technologies to improve our understanding of cardiovascular disease pathophysiology leading to optimal treatment for heart diseases in the future.
1 Communities
0 Members
0 Resources
7 MeSH Terms
Research Directions in the Clinical Implementation of Pharmacogenomics: An Overview of US Programs and Projects.
Volpi S, Bult CJ, Chisholm RL, Deverka PA, Ginsburg GS, Jacob HJ, Kasapi M, McLeod HL, Roden DM, Williams MS, Green ED, Rodriguez LL, Aronson S, Cavallari LH, Denny JC, Dressler LG, Johnson JA, Klein TE, Leeder JS, Piquette-Miller M, Perera M, Rasmussen-Torvik LJ, Rehm HL, Ritchie MD, Skaar TC, Wagle N, Weinshilboum R, Weitzel KW, Wildin R, Wilson J, Manolio TA, Relling MV
(2018) Clin Pharmacol Ther 103: 778-786
MeSH Terms: Humans, Pharmacogenetics, Precision Medicine, Research, United States
Show Abstract · Added March 14, 2018
Response to a drug often differs widely among individual patients. This variability is frequently observed not only with respect to effective responses but also with adverse drug reactions. Matching patients to the drugs that are most likely to be effective and least likely to cause harm is the goal of effective therapeutics. Pharmacogenomics (PGx) holds the promise of precision medicine through elucidating the genetic determinants responsible for pharmacological outcomes and using them to guide drug selection and dosing. Here we survey the US landscape of research programs in PGx implementation, review current advances and clinical applications of PGx, summarize the obstacles that have hindered PGx implementation, and identify the critical knowledge gaps and possible studies needed to help to address them.
© 2018 American Society for Clinical Pharmacology and Therapeutics.
0 Communities
1 Members
0 Resources
5 MeSH Terms
Translating Knowledge Into Therapy for Acute Kidney Injury.
de Caestecker M, Harris R
(2018) Semin Nephrol 38: 88-97
MeSH Terms: Acute Kidney Injury, Biopsy, Clinical Trials as Topic, Humans, Kidney, Patient Selection, Phenotype, Precision Medicine, Translational Medical Research
Show Abstract · Added October 23, 2018
No therapies have been shown to improve outcomes in patients with acute kidney injury (AKI). Given the high morbidity and mortality associated with AKI this represents an important unmet medical need. A common feature of all of the therapeutic development efforts for AKI is that none were driven by target selection or preclinical modeling that was based primarily on human data. This is important when considering a heterogeneous and dynamic condition such as AKI, in which in the absence of more accurate molecular classifications, clinical cohorts are likely to include patients with different types of injury at different stages in the injury and repair continuum. The National Institutes of Health precision medicine initiative offers an opportunity to address this. By creating a molecular tissue atlas of AKI, defining patient subgroups, and identifying critical cells and pathways involved in human AKI, this initiative has the potential to transform our current approach to therapeutic discovery. In this review, we discuss the opportunities and challenges that this initiative presents, with a specific focus on AKI, what additional efforts will be needed to apply these discoveries to therapeutic development, and how we believe this effort might lead to the development of new therapeutics for subsets of patients with AKI.
Copyright © 2017. Published by Elsevier Inc.
0 Communities
1 Members
0 Resources
9 MeSH Terms
The Influence of Big (Clinical) Data and Genomics on Precision Medicine and Drug Development.
Denny JC, Van Driest SL, Wei WQ, Roden DM
(2018) Clin Pharmacol Ther 103: 409-418
MeSH Terms: Big Data, Drug Repositioning, Electronic Health Records, Humans, Pharmacogenetics, Pharmacology, Clinical, Precision Medicine, Randomized Controlled Trials as Topic
Show Abstract · Added March 14, 2018
Drug development continues to be costly and slow, with medications failing due to lack of efficacy or presence of toxicity. The promise of pharmacogenomic discovery includes tailoring therapeutics based on an individual's genetic makeup, rational drug development, and repurposing medications. Rapid growth of large research cohorts, linked to electronic health record (EHR) data, fuels discovery of new genetic variants predicting drug action, supports Mendelian randomization experiments to show drug efficacy, and suggests new indications for existing medications. New biomedical informatics and machine-learning approaches advance the ability to interpret clinical information, enabling identification of complex phenotypes and subpopulations of patients. We review the recent history of use of "big data" from EHR-based cohorts and biobanks supporting these activities. Future studies using EHR data, other information sources, and new methods will promote a foundation for discovery to more rapidly advance precision medicine.
© 2017 American Society for Clinical Pharmacology and Therapeutics.
0 Communities
1 Members
0 Resources
8 MeSH Terms
Colorectal Cancer Cell Line Proteomes Are Representative of Primary Tumors and Predict Drug Sensitivity.
Wang J, Mouradov D, Wang X, Jorissen RN, Chambers MC, Zimmerman LJ, Vasaikar S, Love CG, Li S, Lowes K, Leuchowius KJ, Jousset H, Weinstock J, Yau C, Mariadason J, Shi Z, Ban Y, Chen X, Coffey RJC, Slebos RJC, Burgess AW, Liebler DC, Zhang B, Sieber OM
(2017) Gastroenterology 153: 1082-1095
MeSH Terms: Antineoplastic Agents, Biomarkers, Tumor, Cell Line, Tumor, Chromatography, Liquid, Colorectal Neoplasms, Databases, Protein, Dose-Response Relationship, Drug, Drug Screening Assays, Antitumor, Gene Expression Profiling, Gene Expression Regulation, Neoplastic, Humans, Mutation, Neoplasm Proteins, Patient Selection, Polymorphism, Single Nucleotide, Precision Medicine, Proteome, Proteomics, Signal Transduction, Stromal Cells, Tandem Mass Spectrometry, Transcriptome, Tumor Microenvironment
Show Abstract · Added July 17, 2017
BACKGROUND AND AIMS - Proteomics holds promise for individualizing cancer treatment. We analyzed to what extent the proteomic landscape of human colorectal cancer (CRC) is maintained in established CRC cell lines and the utility of proteomics for predicting therapeutic responses.
METHODS - Proteomic and transcriptomic analyses were performed on 44 CRC cell lines, compared against primary CRCs (n=95) and normal tissues (n=60), and integrated with genomic and drug sensitivity data.
RESULTS - Cell lines mirrored the proteomic aberrations of primary tumors, in particular for intrinsic programs. Tumor relationships of protein expression with DNA copy number aberrations and signatures of post-transcriptional regulation were recapitulated in cell lines. The 5 proteomic subtypes previously identified in tumors were represented among cell lines. Nonetheless, systematic differences between cell line and tumor proteomes were apparent, attributable to stroma, extrinsic signaling, and growth conditions. Contribution of tumor stroma obscured signatures of DNA mismatch repair identified in cell lines with a hypermutation phenotype. Global proteomic data showed improved utility for predicting both known drug-target relationships and overall drug sensitivity as compared with genomic or transcriptomic measurements. Inhibition of targetable proteins associated with drug responses further identified corresponding synergistic or antagonistic drug combinations. Our data provide evidence for CRC proteomic subtype-specific drug responses.
CONCLUSIONS - Proteomes of established CRC cell line are representative of primary tumors. Proteomic data tend to exhibit improved prediction of drug sensitivity as compared with genomic and transcriptomic profiles. Our integrative proteogenomic analysis highlights the potential of proteome profiling to inform personalized cancer medicine.
Copyright © 2017 AGA Institute. Published by Elsevier Inc. All rights reserved.
0 Communities
1 Members
0 Resources
23 MeSH Terms
Functional Optical Imaging of Primary Human Tumor Organoids: Development of a Personalized Drug Screen.
Walsh AJ, Cook RS, Skala MC
(2017) J Nucl Med 58: 1367-1372
MeSH Terms: Drug Screening Assays, Antitumor, Humans, Neoplasms, Optical Imaging, Organoids, Precision Medicine
Show Abstract · Added April 15, 2019
Primary tumor organoids are a robust model of individual human cancers and present a unique platform for patient-specific drug testing. Optical imaging is uniquely suited to assess organoid function and behavior because of its subcellular resolution, penetration depth through the entire organoid, and functional endpoints. Specifically, optical metabolic imaging (OMI) is highly sensitive to drug response in organoids, and OMI in tumor organoids correlates with primary tumor drug response. Therefore, an OMI organoid drug screen could enable accurate testing of drug response for individualized cancer treatment. The objective of this perspective is to introduce OMI and tumor organoids to a general audience in order to foster the adoption of these techniques in diverse clinical and laboratory settings.
© 2017 by the Society of Nuclear Medicine and Molecular Imaging.
0 Communities
1 Members
0 Resources
MeSH Terms
Challenges and strategies for implementing genomic services in diverse settings: experiences from the Implementing GeNomics In pracTicE (IGNITE) network.
Sperber NR, Carpenter JS, Cavallari LH, J Damschroder L, Cooper-DeHoff RM, Denny JC, Ginsburg GS, Guan Y, Horowitz CR, Levy KD, Levy MA, Madden EB, Matheny ME, Pollin TI, Pratt VM, Rosenman M, Voils CI, W Weitzel K, Wilke RA, Ryanne Wu R, Orlando LA
(2017) BMC Med Genomics 10: 35
MeSH Terms: Electronic Health Records, Genomics, Humans, Patient Participation, Precision Medicine
Show Abstract · Added March 14, 2018
BACKGROUND - To realize potential public health benefits from genetic and genomic innovations, understanding how best to implement the innovations into clinical care is important. The objective of this study was to synthesize data on challenges identified by six diverse projects that are part of a National Human Genome Research Institute (NHGRI)-funded network focused on implementing genomics into practice and strategies to overcome these challenges.
METHODS - We used a multiple-case study approach with each project considered as a case and qualitative methods to elicit and describe themes related to implementation challenges and strategies. We describe challenges and strategies in an implementation framework and typology to enable consistent definitions and cross-case comparisons. Strategies were linked to challenges based on expert review and shared themes.
RESULTS - Three challenges were identified by all six projects, and strategies to address these challenges varied across the projects. One common challenge was to increase the relative priority of integrating genomics within the health system electronic health record (EHR). Four projects used data warehousing techniques to accomplish the integration. The second common challenge was to strengthen clinicians' knowledge and beliefs about genomic medicine. To overcome this challenge, all projects developed educational materials and conducted meetings and outreach focused on genomic education for clinicians. The third challenge was engaging patients in the genomic medicine projects. Strategies to overcome this challenge included use of mass media to spread the word, actively involving patients in implementation (e.g., a patient advisory board), and preparing patients to be active participants in their healthcare decisions.
CONCLUSIONS - This is the first collaborative evaluation focusing on the description of genomic medicine innovations implemented in multiple real-world clinical settings. Findings suggest that strategies to facilitate integration of genomic data within existing EHRs and educate stakeholders about the value of genomic services are considered important for effective implementation. Future work could build on these findings to evaluate which strategies are optimal under what conditions. This information will be useful for guiding translation of discoveries to clinical care, which, in turn, can provide data to inform continual improvement of genomic innovations and their applications.
0 Communities
1 Members
0 Resources
5 MeSH Terms