Browsing by Subject "Precision medicine"
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Item A Participant-Centered Approach to Understanding Risks and Benefits of Participation in Research Informed by the Kidney Precision Medicine Project(Elsevier, 2022) Butler, Catherine R.; Appelbaum, Paul S.; Ascani, Heather; Aulisio, Mark; Campbell, Catherine E.; de Boer, Ian H.; Dighe, Ashveena L.; Hall, Daniel E.; Himmelfarb, Jonathan; Knight, Richard; Mehl, Karla; Murugan, Raghavan; Rosas, Sylvia E.; Sedor, John R.; O'Toole, John F.; Tuttle, Katherine R.; Waikar, Sushrut S.; Freeman, Michael; Kidney Precision Medicine Project; Medicine, School of MedicineAn understanding of the ethical underpinnings of human subjects research that involves some risk to participants without anticipated direct clinical benefit-such as the kidney biopsy procedure as part of the Kidney Precision Medicine Project (KPMP)-requires a critical examination of the risks as well as the diverse set of countervailing potential benefits to participants. This kind of deliberation has been foundational to the development and conduct of the KPMP. Herein, we use illustrative features of this research paradigm to develop a more comprehensive conceptualization of the types of benefits that may be important to research participants, including respecting pluralistic values, supporting the opportunity to act altruistically, and enhancing benefits to a participant's community. This approach may serve as a model to help researchers, ethicists, and regulators to identify opportunities to better respect and support participants in future research that entails some risk to these participants as well as to improve the quality of research for people with kidney disease.Item Association of Maternal Age and Blood Markers for Metabolic Disease in Newborns(MDPI, 2023-12-20) Xie, Yuhan; Peng, Gang; Zhao, Hongyu; Scharfe, Curt; Medical and Molecular Genetics, School of MedicinePregnancy at an advanced maternal age is considered a risk factor for adverse maternal, fetal, and neonatal outcomes. Here we investigated whether maternal age could be associated with differences in the blood levels of newborn screening (NBS) markers for inborn metabolic disorders on the Recommended Universal Screening Panel (RUSP). Population-level NBS data from screen-negative singleton infants were examined, which included blood metabolic markers and covariates such as age at blood collection, birth weight, gestational age, infant sex, parent-reported ethnicity, and maternal age at delivery. Marker levels were compared between maternal age groups (age range: 1544 years) using effect size analyses, which controlled for differences in group sizes and potential confounding from other covariates. We found that 13% of the markers had maternal age-related differences, including newborn metabolites with either increased (Tetradecanoylcarnitine [C14], Palmitoylcarnitine [C16], Stearoylcarnitine [C18], Oleoylcarnitine [C18:1], Malonylcarnitine [C3DC]) or decreased (3-Hydroxyisovalerylcarnitine [C5OH]) levels at an advanced maternal age (≥35 years, absolute Cohen’s d > 0.2). The increased C3DC levels in this group correlated with a higher false-positive rate in newborn screening for malonic acidemia (p-value < 0.001), while no significant difference in screening performance was seen for the other markers. Maternal age is associated with inborn metabolic differences and should be considered together with other clinical variables in genetic disease screening.Item Big Data Edge on Consumer Devices for Precision Medicine(IEEE, 2022) Stauffer, Jake; Zhang, Qingxue; Biomedical Engineering and Informatics, Luddy School of Informatics, Computing, and EngineeringConsumer electronics like smartphones and wearable computers are furthering precision medicine significantly, through capturing/leveraging big data on the edge towards real-time, interactive healthcare applications. Here we propose a big data edge platform that can, not only capture/manage different biomedical dynamics, but also enable real-time visualization of big data. The big data can also be uploaded to cloud for long-term management. The system has been evaluated on the real-world biomechanical data-based application, and demonstrated its effectiveness on big data management and interactive visualization. This study is expected to greatly advance big data-driven precision medicine applications.Item A bioinformatics approach for precision medicine off-label drug drug selection among triple negative breast cancer patients(Oxford Academic, 2016-07) Cheng, Lijun; Schneider, Bryan P.; Li, Lang; Medical and Molecular Genetics, School of MedicineCancer has been extensively characterized on the basis of genomics. The integration of genetic information about cancers with data on how the cancers respond to target based therapy to help to optimum cancer treatment. OBJECTIVE: The increasing usage of sequencing technology in cancer research and clinical practice has enormously advanced our understanding of cancer mechanisms. The cancer precision medicine is becoming a reality. Although off-label drug usage is a common practice in treating cancer, it suffers from the lack of knowledge base for proper cancer drug selections. This eminent need has become even more apparent considering the upcoming genomics data. METHODS: In this paper, a personalized medicine knowledge base is constructed by integrating various cancer drugs, drug-target database, and knowledge sources for the proper cancer drugs and their target selections. Based on the knowledge base, a bioinformatics approach for cancer drugs selection in precision medicine is developed. It integrates personal molecular profile data, including copy number variation, mutation, and gene expression. RESULTS: By analyzing the 85 triple negative breast cancer (TNBC) patient data in the Cancer Genome Altar, we have shown that 71.7% of the TNBC patients have FDA approved drug targets, and 51.7% of the patients have more than one drug target. Sixty-five drug targets are identified as TNBC treatment targets and 85 candidate drugs are recommended. Many existing TNBC candidate targets, such as Poly (ADP-Ribose) Polymerase 1 (PARP1), Cell division protein kinase 6 (CDK6), epidermal growth factor receptor, etc., were identified. On the other hand, we found some additional targets that are not yet fully investigated in the TNBC, such as Gamma-Glutamyl Hydrolase (GGH), Thymidylate Synthetase (TYMS), Protein Tyrosine Kinase 6 (PTK6), Topoisomerase (DNA) I, Mitochondrial (TOP1MT), Smoothened, Frizzled Class Receptor (SMO), etc. Our additional analysis of target and drug selection strategy is also fully supported by the drug screening data on TNBC cell lines in the Cancer Cell Line Encyclopedia. CONCLUSIONS: The proposed bioinformatics approach lays a foundation for cancer precision medicine. It supplies much needed knowledge base for the off-label cancer drug usage in clinics.Item Biomarker-And Pathway-Informed Polygenic Risk Scores for Alzheimer's Disease and Related Disorders(2022-05) Chasioti, Danai; Yan, Jingwen; Saykin, Andrew J.; Nho, Kwangsik; Risacher, Shannon L.; Wu, HuanmeiDetermining an individual’s genetic susceptibility in complex diseases like Alzheimer’s disease (AD) is challenging as multiple variants each contribute a small portion of the overall risk. Polygenic Risk Scores (PRS) are a mathematical construct or composite that aggregates the small effects of multiple variants into a single score. Potential applications of PRS include risk stratification, biomarker discovery and increased prognostic accuracy. A systematic review demonstrated that methodological refinement of PRS is an active research area, mostly focused on large case-control genome-wide association studies (GWAS). In AD, where there is considerable phenotypic and genetic heterogeneity, we hypothesized that PRS based on endophenotypes, and pathway-relevant genetic information would be particularly informative. In the first study, data from the NIA Alzheimer’s Disease Neuroimaging Initiative (ADNI) was used to develop endophenotype-based PRS based on amyloid (A), tau (T), neurodegeneration (N) and cerebrovascular (V) biomarkers, as well as an overall/combined endophenotype-PRS. Results indicated that combined phenotype-PRS predicted neurodegeneration biomarkers and overall AD risk. By contrast, amyloid and tau-PRSs were strongly linked to the corresponding biomarkers. Finally, extrinsic significance of the PRS approach was demonstrated by application of AD biological pathway-informed PRS to prediction of cognitive changes among older women with breast cancer (BC). Results from PRS analysis of the multicenter Thinking and Living with Cancer (TLC) study indicated that older BC patients with high AD genetic susceptibility within the immune-response and endocytosis pathways have worse cognition following chemotherapy±hormonal therapy rather than hormonal-only therapy. In conclusion, PRSs based on biomarker- or pathway- specific genetic information may provide mechanistic insights beyond disease susceptibility, supporting development of precision medicine with potential application to AD and other age-associated cognitive disorders.Item Building the case for actionable ethics in digital health research supported by artificial intelligence(Springer Nature, 2019-07-17) Nebeker, Camille; Torous, John; Bartlett Ellis, Rebecca J.; School of NursingThe digital revolution is disrupting the ways in which health research is conducted, and subsequently, changing healthcare. Direct-to-consumer wellness products and mobile apps, pervasive sensor technologies and access to social network data offer exciting opportunities for researchers to passively observe and/or track patients ‘in the wild’ and 24/7. The volume of granular personal health data gathered using these technologies is unprecedented, and is increasingly leveraged to inform personalized health promotion and disease treatment interventions. The use of artificial intelligence in the health sector is also increasing. Although rich with potential, the digital health ecosystem presents new ethical challenges for those making decisions about the selection, testing, implementation and evaluation of technologies for use in healthcare. As the ‘Wild West’ of digital health research unfolds, it is important to recognize who is involved, and identify how each party can and should take responsibility to advance the ethical practices of this work. While not a comprehensive review, we describe the landscape, identify gaps to be addressed, and offer recommendations as to how stakeholders can and should take responsibility to advance socially responsible digital health research.Item Cardioinformatics Advancements in Healthcare and Biotechnology(American Heart Association, 2023) Khomtchouk, Bohdan B.; Biomedical Engineering and Informatics, Luddy School of Informatics, Computing, and EngineeringItem ChatGPT-4 and the Global Burden of Disease Study: Advancing Personalized Healthcare Through Artificial Intelligence in Clinical and Translational Medicine(Springer Nature, 2023-05-23) Temsah, Mohamad-Hani; Jamal, Amr; Aljamaan, Fadi; Al-Tawfiq, Jaffar A.; Al-Eyadhy, Ayman; Medicine, School of MedicineThe fusion of insights from the comprehensive global burden of disease (GBD) study and the advanced artificial intelligence of open artificial intelligence (AI) chat generative pre-trained transformer version 4 (ChatGPT-4) brings the potential to transform personalized healthcare planning. By integrating the data-driven findings of the GBD study with the powerful conversational capabilities of ChatGPT-4, healthcare professionals can devise customized healthcare plans that are adapted to patients' lifestyles and preferences. We propose that this innovative partnership can lead to the creation of a novel AI-assisted personalized disease burden (AI-PDB) assessment and planning tool. For the successful implementation of this unconventional technology, it is crucial to ensure continuous and accurate updates, expert supervision, and address potential biases and limitations. Healthcare professionals and stakeholders should have a balanced and dynamic approach, emphasizing interdisciplinary collaborations, data accuracy, transparency, ethical compliance, and ongoing training. By investing in the unique strengths of both ChatGPT-4, especially its newly introduced features such as live internet browsing or plugins, and the GBD study, we may enhance personalized healthcare planning. This innovative approach has the potential to improve patient outcomes and optimize resource utilization, as well as pave the way for the worldwide implementation of precision medicine, thereby revolutionizing the existing healthcare landscape. However, to fully harness these benefits at both the global and individual levels, further research and development are warranted. This will ensure that we effectively tap into the potential of this synergy, bringing societies closer to a future where personalized healthcare is the norm rather than the exception.Item Clinical and educational impact of pharmacogenomics testing: a case series from the INGENIOUS trial(Future Medicine, 2017-06) Pierson, Rebecca C.; Gufford, Brandon T.; Desta, Zeruesenay; Eadon, Michael T.; Medicine, School of MedicinePharmacogenomic testing has become increasingly widespread. However, there remains a need to bridge the gap between test results and providers lacking the expertise required to interpret these results. The Indiana Genomics Implementation trial is underway at our institution to examine total healthcare cost and patient outcomes after genotyping in a safety-net healthcare system. As part of the study, trial investigators and clinical pharmacology fellows interpret genotype results, review patient histories and medication lists and evaluate potential drug-drug interactions. We present a case series of patients in whom pharmacogenomic consultations aided providers in appropriately applying pharmacogenomic results within the clinical context. Formal consultations not only provide valuable patient care information but educational opportunities for the fellows to cement pharmacogenomic concepts.Item Clinical benefit of a precision medicine based approach for guiding treatment of refractory cancers(Impact Journals, 2016-08-30) Radovich, Milan; Kiel, Patrick J.; Nance, Stacy M.; Niland, Erin E.; Parsley, Megan E.; Ferguson, Meagan E.; Jiang, Guanglong; Ammakkanavar, Natraj R.; Einhorn, Lawrence H.; Cheng, Liang; Nassiri, Mehdi; Davidson, Darrell D.; Rushing, Daniel A.; Loehrer, Patrick J.; Pili, Roberto; Hanna, Nasser; Callaghan, J. Thomas; Skaar, Todd C.; Helft, Paul R.; Shahda, Safi; O’Neil, Bert H.; Schneider, Bryan P.; Medicine, School of MedicinePatients and methods: Patients with metastatic solid tumors who had progressed on at least one line of standard of care therapy were referred to the Indiana University Health Precision Genomics Program. Tumor samples were submitted for DNA & RNA next-generation sequencing, fluorescence in situ hybridization, and immunohistochemistry for actionable targets. A multi-disciplinary tumor board reviewed all results. For each patient, the ratio of progression-free survival (PFS) of the genomically guided line of therapy divided by the PFS of their prior line was calculated. Patients whose PFS ratio was ≥ 1.3 were deemed to have a meaningful improvement in PFS. Results: From April 2014-October 2015, 168 patients were evaluated and 101 patients achieved adequate clinical follow-up for analysis. 19 of 44 (43.2%) patients treated with genomically guided therapy attained a PFS ratio ≥ 1.3 vs. 3 of 57 (5.3%) treated with non-genomically guided therapy (p < 0.0001). Similarly, overall PFS ratios (irrespective of cutoff) were higher for patients with genomically guided therapy vs non-genomically guided therapy (p = 0.05). Further, patients treated with genomically guided therapy had a superior median PFS compared to those treated with non-genomically guided therapy (86 days vs. 49 days, p = 0.005, H.R. = 0.55, 95% C.I.:0.37-0.84). Conclusion: Patients with refractory metastatic cancer who receive genomically guided therapy have improved PFS ratios and longer median PFS compared to patients who do not receive genomically guided therapy.