Academic literature on the topic 'Functional precision medicine'
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Journal articles on the topic "Functional precision medicine":
Caskey, Thomas. "Precision Medicine: Functional Advancements." Annual Review of Medicine 69, no. 1 (January 29, 2018): 1–18. http://dx.doi.org/10.1146/annurev-med-041316-090905.
Letai, Anthony. "Functional precision cancer medicine—moving beyond pure genomics." Nature Medicine 23, no. 9 (September 2017): 1028–35. http://dx.doi.org/10.1038/nm.4389.
Mattson, David L., and Mingyu Liang. "From GWAS to functional genomics-based precision medicine." Nature Reviews Nephrology 13, no. 4 (March 6, 2017): 195–96. http://dx.doi.org/10.1038/nrneph.2017.21.
Friedman, Adam A., Anthony Letai, David E. Fisher, and Keith T. Flaherty. "Precision medicine for cancer with next-generation functional diagnostics." Nature Reviews Cancer 15, no. 12 (November 5, 2015): 747–56. http://dx.doi.org/10.1038/nrc4015.
van den Berg, Albert, Christine L. Mummery, Robert Passier, and Andries D. van der Meer. "Personalised organs-on-chips: functional testing for precision medicine." Lab on a Chip 19, no. 2 (2019): 198–205. http://dx.doi.org/10.1039/c8lc00827b.
Rusert, Jessica M., Edwin F. Juarez, Sebastian Brabetz, James Jensen, Alexandra Garancher, Lianne Q. Chau, Silvia K. Tacheva-Grigorova, et al. "Functional Precision Medicine Identifies New Therapeutic Candidates for Medulloblastoma." Cancer Research 80, no. 23 (October 12, 2020): 5393–407. http://dx.doi.org/10.1158/0008-5472.can-20-1655.
Papaccio, Federica, Manuel Cabeza-Segura, Blanca Garcia-Micò, Noelia Tarazona, Desamparados Roda, Josefa Castillo, and Andres Cervantes. "Will Organoids Fill the Gap towards Functional Precision Medicine?" Journal of Personalized Medicine 12, no. 11 (November 21, 2022): 1939. http://dx.doi.org/10.3390/jpm12111939.
Shneider, Olga V., Tatyana A. Kamilova, Alexander S. Golota, Andrey M. Sarana, and Sergey G. Sсherbak. "Biomarkers and Target Therapy for Lung Cancer." Physical and rehabilitation medicine, medical rehabilitation 3, no. 1 (April 28, 2021): 74–94. http://dx.doi.org/10.36425/rehab63268.
Her, Nam-Gu, Gi Ju Lee, Seung Yoon Hyun, San Ha Park, Jae Woo Ahn, Ji Soo Kang, Hong Boon Toh, and Do-Hyun Nam. "Abstract 3410: AVATASCAN®, a pioneer of functional precision medicine in guiding clinical decision-making." Cancer Research 83, no. 7_Supplement (April 4, 2023): 3410. http://dx.doi.org/10.1158/1538-7445.am2023-3410.
Kropivsek, K., P. Kachel, S. Goetze, R. Wegmann, Y. Severin, B. D. Hale, Y. Festl, et al. "P856: A SINGLE-CELL FUNCTIONAL PRECISION MEDICINE LANDSCAPE OF MULTIPLE MYELOMA." HemaSphere 6 (June 2022): 749–50. http://dx.doi.org/10.1097/01.hs9.0000846304.52658.85.
Dissertations / Theses on the topic "Functional precision medicine":
Boilève, Alice. "La médecine de précision dans le cancer du pancréas." Electronic Thesis or Diss., université Paris-Saclay, 2024. http://www.theses.fr/2024UPASL013.
Pancreatic ductal adenocarcinoma (PDAC) is an increasingly common cancer with limited therapeutic options and a poor prognosis. Conventional chemotherapies have limited efficacy, emphasizing the need for new therapeutic approaches. Genomic precision medicine, made possible by advances in high-throughput sequencing technologies, has seen significant development in oncology over the past decade. However, the utility of molecular profiling in PDAC has not yet been established, despite improved overall survival when patients receive molecularly matched treatment. Functional precision medicine (FPM) is another promising strategy that relies on testing a panel of drugs on live tumor cells to identify the sensitivity and resistance profile of each tumor. Organoids are robust and promising tools for assessing a specific tumor's sensitivity to various drugs and identifying the best therapeutic option for each patient.Three axes were developed in this thesis. First, a translational axis evaluated the impact of organoids as tools for functional precision medicine in pancreatic cancer. The primary objective of this project was to establish a framework for integrating organoid-based drug sensitivity testing into the clinical management of PDAC patients. Organoid responses to a panel of anticancer drugs were correlated with clinical responses in patients, suggesting potential clinical benefits. Additionally, the contribution of organoids to preclinical studies was tested by assessing the efficacy of a KRASG12D inhibitor, MRTX1133, in monotherapy and in combination with other inhibitors. The combination of MRTX1133 and anti-EGFR proved to be the most promising.Secondly, a clinical axis assessed the impact of KRAS mutation in PDAC in terms of clinical and molecular characteristics, treatment response, and prognosis, particularly in cases where targeted treatment was received. Comparing non-mutated KRAS tumors to mutated KRAS tumors revealed clinical differences and better prognosis for non-mutated tumors. Furthermore, the impact of different KRAS mutated codons was studied by comparing KRASG12 mutated tumors versus other KRAS mutations. KRASG12 mutations were associated with a worse prognosis, although there was no difference in treatment sensitivity.Finally, a fundamental axis investigated the invasive phenotype of PDAC. A new "onco-morphogenetic" program was identified as a mediator of metastatic dissemination in colorectal cancers (CRC) through TSIPs (tumor spheres with reversed polarity, malignant tumor intermediates). The presence of TSIPs in pancreatic cancers was confirmed, and their transcriptional program and chemosensitivity were characterized using organoids. However, the clinical and prognostic impact of TSIP presence in pancreatic cancer appears to be minor.In conclusion, this doctoral project aimed to develop a comprehensive framework for the use of PDOs as a tool for modeling PDAC (fundamental axis), selecting personalized treatments, and conducting preclinical drug tests (translational and clinical axes). By bridging the gap between preclinical trials and clinical practice, this project aims to bring us closer to precision medicine in managing PDAC
PETRINI, ALESSANDRO. "HIGH PERFORMANCE COMPUTING MACHINE LEARNING METHODS FOR PRECISION MEDICINE." Doctoral thesis, Università degli Studi di Milano, 2021. http://hdl.handle.net/2434/817104.
Precision Medicine is a new paradigm which is reshaping several aspects of clinical practice, representing a major departure from the "one size fits all" approach in diagnosis and prevention featured in classical medicine. Its main goal is to find personalized prevention measures and treatments, on the basis of the personal history, lifestyle and specific genetic factors of each individual. Three factors contributed to the rapid rise of Precision Medicine approaches: the ability to quickly and cheaply generate a vast amount of biological and omics data, mainly thanks to Next-Generation Sequencing; the ability to efficiently access this vast amount of data, under the Big Data paradigm; the ability to automatically extract relevant information from data, thanks to innovative and highly sophisticated data processing analytical techniques. Machine Learning in recent years revolutionized data analysis and predictive inference, influencing almost every field of research. Moreover, high-throughput bio-technologies posed additional challenges to effectively manage and process Big Data in Medicine, requiring novel specialized Machine Learning methods and High Performance Computing techniques well-tailored to process and extract knowledge from big bio-medical data. In this thesis we present three High Performance Computing Machine Learning techniques that have been designed and developed for tackling three fundamental and still open questions in the context of Precision and Genomic Medicine: i) identification of pathogenic and deleterious genomic variants among the "sea" of neutral variants in the non-coding regions of the DNA; ii) detection of the activity of regulatory regions across different cell lines and tissues; iii) automatic protein function prediction and drug repurposing in the context of biomolecular networks. For the first problem we developed parSMURF, a novel hyper-ensemble method able to deal with the huge data imbalance that characterizes the detection of pathogenic variants in the non-coding regulatory regions of the human genome. We implemented this approach with highly parallel computational techniques using supercomputing resources at CINECA (Marconi – KNL) and HPC Center Stuttgart (HLRS Apollo HAWK), obtaining state-of-the-art results. For the second problem we developed Deep Feed Forward and Deep Convolutional Neural Networks to respectively process epigenetic and DNA sequence data to detect active promoters and enhancers in specific tissues at genome-wide level using GPU devices to parallelize the computation. Finally we developed scalable semi-supervised graph-based Machine Learning algorithms based on parametrized Hopfield Networks to process in parallel using GPU devices large biological graphs, using a parallel coloring method that improves the classical Luby greedy algorithm. We also present ongoing extensions of parSMURF, very recently awarded by the Partnership for Advance in Computing in Europe (PRACE) consortium to further develop the algorithm, apply them to huge genomic data and embed its results into Genomiser, a state-of-the-art computational tool for the detection of pathogenic variants associated with Mendelian genetic diseases, in the context of an international collaboration with the Jackson Lab for Genomic Medicine.
Morice, Pierre-Marie. "Evaluation de la déficience de la recombinaison homologue et de la réponse des tumeurs ovariennes aux inhibiteurs de PARP grâce à l'utilisation de modèles de culture 3D en vue du développement d'un test prédictif Identifying eligible patients to PARP inhibitors: from NGS-based tests to promising 3D functional assays Automated scoring for assessment of RAD51-mediated homologous recombination in patient-derived tumor organoids of ovarian cancers Risk of myelodysplastic syndrome and acute myeloid leukemia related to PARP inhibitors: a combined approach using a safety meta-analysis of placebo randomized controlled trials and the World Health Organization's pharmacovigilance database The long non-coding RNA ‘UCA1’ modulates the response to chemotherapy of ovarian cancer through direct binding to miR-27a-5p and control of UBE2N levels." Thesis, Normandie, 2020. http://www.theses.fr/2020NORMC414.
Worldwide each year, more than 150 000 women die from epithelial ovarian cancer largely due to emergence of resistance to chemotherapy. Approximately half of these cancers display molecular alterations that cause deficiency of DNA repair via homologous recombination (HRD), which confer sensitivity to PARP protein inhibitors (PARPi). To date, there is no test capable of fully identifying the HRD phenotype, thus limiting access to these treatments. In this context, we are developing functional assays based on the use of tumor explant slices and then, on the use of tumor organoids derived from ovarian tumors of chemotherapy-naive or previously treated patients. The culture of explants was unsuitable for this application and we then focused our work on tumor organoids. Tumor organoids were exposed to carboplatin (first-line treatment) and two PARP inhibitors (olaparib and niraparib) used for maintenance therapy. In parallel, we collected clinical data from patients (survival, platinum-free interval, RECIST, treatments) to evaluate the predictive potential of these models. The established tumor organoids responded heterogeneously to different drugs, and our results show that the organoid-based assay is capable of identifying patients highly resistant to carboplatin, suggesting that this functional assay could have a predictive value for patients treated with carboplatin. Regarding the potential of organoids in predicting PARPi response, multiple sensitivity profiles have been identified, but the correlation with clinical response has yet to be determined by studies conducted on tumor samples from patients treated with these drugs
Liu, Mu-N., and 劉慕恩. "The application of precision medicine for cognitive function and dementia." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/5m2p9w.
國立陽明大學
腦科學研究所
107
Background: Prior studies reported that genes contribute to individual differences in brain function and behavior. It is likely that genes expressed in the brain have variations in their sequence that impact upon their function (functional polymorphisms) leading to changes in expression, activity or binding of proteins. These genetic polymorphisms are thought to cause individual differences in the brain structure and function. The association between genes and brain structure and function using brain magnetic resonance imaging (MRI) is an emerging and promising area of research that help to better characterize the influence of genes on cognitive ageing as well as the association between genetic susceptibility and neurodegenerative and neuropsychiatric disorders. Aims: For exploring the genetic effects on brain structure, we imaged the influence of genes that affect catecholaminergic signaling and transcription of brain derived neurotrophic factor (BDNF) in the human brain. We tested the hypothesis that: whether the catechol-O-methyltransferase (COMT) Val158Met polymorphism may affect the white matter hyperintensity (WMH); whether the BDNF Val66Met polymorphism may influence the regional gray matter (GM) volumes and cognitive function among the healthy Han population. To understand the impact of precision medicine on cognitive deficit, we also focus on the application of precision medicine to frontotemporal dementia. Methods: We recruited ethnic Chinese adults aged 20 and over; cognitive tests, structural MRI scans, and genotyping of COMT Val158Met and BDNF Val66Met were conducted for each participant. We also studied the biomarkers for frontotemporal dementia, including cognitive and behavior markers, imaging, genetic, and neuropathological biomarkers. Result: For the genetic effect of COMT on WMH volume, Met homozygotes and Met/Val heterozygotes exhibited larger WMH volumes than the Val homozygotes over the subcortical region, whole brain, and the frontal region. We also found that BDNF Met homozygotes had greater GM volumes than Val homozygotes and Val/Met heterozygotes in several brain areas. Conclusions: Based on advances in neuroimaging and genomic research that has explored underlying genetic risk variants and cerebral structural change in order to determine specific molecular pathways and pathophysiological processes, precision medicine is useful applied in the treatment of neurocognitive disease.
Book chapters on the topic "Functional precision medicine":
Wu, Rongling, Mengmeng Sang, and Li Feng. "Pharmacogenetic Dissection by Functional Mapping." In Quantitative Methods for Precision Medicine, 19–42. Boca Raton: Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9780429171512-3.
Downs, Jaclyn. "Optimize Your Liver Detoxification Pathways and Detoxify with Precision." In Enhancing Fertility through Functional Medicine, 115–24. Boca Raton: CRC Press, 2023. http://dx.doi.org/10.1201/b23201-15.
Manzano-Muñoz, Albert, Jose Yeste, María A. Ortega, Josep Samitier, Javier Ramón-Azcón, and Joan Montero. "A New Microfluidic Device to Facilitate Functional Precision Medicine Assays." In Methods in Molecular Biology, 99–108. New York, NY: Springer US, 2023. http://dx.doi.org/10.1007/978-1-0716-3593-3_8.
Rukhiya, S., X. Joseph, K. B. Megha, and P. V. Mohanan. "Lab-on-a-Chip for Functional Testing for Precision Medicine." In Microfluidics and Multi Organs on Chip, 663–80. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-1379-2_27.
Watson, Geoffrey Alan, Kirsty Taylor, and Lillian L. Siu. "Innovation and Advances in Precision Medicine in Head and Neck Cancer." In Critical Issues in Head and Neck Oncology, 355–73. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-63234-2_24.
Schnorbusch, Kathy, Robrecht Lembrechts, Inge Brouns, Isabel Pintelon, Jean-Pierre Timmermans, and Dirk Adriaensen. "Precision-Cut Vibratome Slices Allow Functional Live Cell Imaging of the Pulmonary Neuroepithelial Body Microenvironment in Fetal Mice." In Advances in Experimental Medicine and Biology, 157–66. Dordrecht: Springer Netherlands, 2012. http://dx.doi.org/10.1007/978-94-007-4584-1_22.
He, Wenliang, Peng Li, and Guoyao Wu. "Amino Acid Nutrition and Metabolism in Chickens." In Advances in Experimental Medicine and Biology, 109–31. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-54462-1_7.
Ooi, Brandon N. S., Ashley J. W. Lim, Samuel S. Chong, and Caroline G. L. Lee. "Using Genome Wide Studies to Generate and Test Hypotheses that Provide Mechanistic Details of How Synonymous Codons Affect Protein Structure and Function: Functional SNPs in the Age of Precision Medicine." In Single Nucleotide Polymorphisms, 171–83. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-05616-1_8.
Flye-Blakemore, Leanne, Christèle Gonneau, Nithianandan Selliah, Ajay Grover, Sriram Ramanan, Alan Lackey, and Yoav Peretz. "Precision Medicine: The Function of Receptor Occupancy in Drug Development." In Methods in Pharmacology and Toxicology, 167–97. New York, NY: Springer US, 2020. http://dx.doi.org/10.1007/978-1-0716-0171-6_11.
Li, Mark M., Sharad Awasthi, Sumanta Ghosh, Deepa Bisht, Zeynep H. Coban Akdemir, Gloria M. Sheynkman, Nidhi Sahni, and S. Stephen Yi. "Gain-of-Function Variomics and Multi-omics Network Biology for Precision Medicine." In Cancer Systems and Integrative Biology, 357–72. New York, NY: Springer US, 2023. http://dx.doi.org/10.1007/978-1-0716-3163-8_24.
Conference papers on the topic "Functional precision medicine":
Salpeter, Seth, Vered Bar, Sara Aharon, Luba Torovsky, Adi Zundelevich, Hamutal Shachar, Hagit Shapira, et al. "Abstract CT209: A clinical trial of cResponse, a functional assay for cancer precision medicine." In Proceedings: AACR Annual Meeting 2021; April 10-15, 2021 and May 17-21, 2021; Philadelphia, PA. American Association for Cancer Research, 2021. http://dx.doi.org/10.1158/1538-7445.am2021-ct209.
Liu, Xuefeng, Ewa Krawczyk, Ogla Timofeeva, Nancy Palechor-Ceron, Aleksandra Dakic, Vera Simic, Bhaskar Kallakury, Anatoly Dritschilo, and Richard Schlegel. "Abstract 4256: Functional analysis for cancer precision medicine using patient-derived 2D and 3D cell models." In Proceedings: AACR 107th Annual Meeting 2016; April 16-20, 2016; New Orleans, LA. American Association for Cancer Research, 2016. http://dx.doi.org/10.1158/1538-7445.am2016-4256.
Hahn, William C. "Abstract IA5: Functional genomics and synthetic lethality." In Abstracts: AACR Precision Medicine Series: Synthetic Lethal Approaches to Cancer Vulnerabilities - May 17-20, 2013; Bellevue, WA. American Association for Cancer Research, 2013. http://dx.doi.org/10.1158/1535-7163.pms-ia5.
Zein-Sabatto, Ahbid, Adrian Bico, Madison Woo, Ramisa Fariha, Blanche Ip, Diane Hoffman-Kim, Jeffrey Morgan, and Jonghwan Lee. "OCT Viability Imaging of 3D Microtissues." In Optical Coherence Tomography. Washington, D.C.: Optica Publishing Group, 2024. http://dx.doi.org/10.1364/oct.2024.cs1e.4.
Maxfield, Kimberly, Aleix Prat, Kathleen Corcoran, and Angelique Whitehurst. "Abstract A27: Dissecting the functional landscape of triple-negative breast cancer." In Abstracts: AACR Precision Medicine Series: Synthetic Lethal Approaches to Cancer Vulnerabilities - May 17-20, 2013; Bellevue, WA. American Association for Cancer Research, 2013. http://dx.doi.org/10.1158/1535-7163.pms-a27.
Marcotte, Richard, Azin Sayad, Maliha Haider, Kevin Brown, Troy Ketela, Jason Moffat, and Benjamin G. Neel. "Abstract PR01: Functional characterization of breast cancer using pooled lentivirus shRNA screens." In Abstracts: AACR Precision Medicine Series: Synthetic Lethal Approaches to Cancer Vulnerabilities - May 17-20, 2013; Bellevue, WA. American Association for Cancer Research, 2013. http://dx.doi.org/10.1158/1535-7163.pms-pr01.
Toyoshima, Masafumi, In Sock Jang, Silvia Cermelli, Brady Bernard, and Carla Grandori. "Abstract IA11: Identification of therapeutic targets for MYC-driven cancers by functional genomics." In Abstracts: AACR Precision Medicine Series: Synthetic Lethal Approaches to Cancer Vulnerabilities - May 17-20, 2013; Bellevue, WA. American Association for Cancer Research, 2013. http://dx.doi.org/10.1158/1535-7163.pms-ia11.
Aranha, Valentin, Diogo Tomaz, Irene Gutierrez Perez, Florian Rohrer, Joost Van Ham, Lukas Hefler, Laudia Hadjari, et al. "Abstract 1303: AI driven single cell analysis of drug action in solid tumor material: An entry point to functional precision medicine." In Proceedings: AACR Annual Meeting 2021; April 10-15, 2021 and May 17-21, 2021; Philadelphia, PA. American Association for Cancer Research, 2021. http://dx.doi.org/10.1158/1538-7445.am2021-1303.
Johnsen, Hannah, Aphrothiti Hanrahan, Alexis Jones, and David Solit. "Abstract 36: Functional characterization of ERBB2 mutations and response to targeted therapies." In Abstracts: AACR Precision Medicine Series: Integrating Clinical Genomics and Cancer Therapy; June 13-16, 2015; Salt Lake City, UT. American Association for Cancer Research, 2016. http://dx.doi.org/10.1158/1557-3265.pmsclingen15-36.
Sahni, Nidhi, and Song Yi. "Abstract B03: Functional Stratification of Cancer Variants via Network Perturbations." In Abstracts: AACR Precision Medicine Series: Opportunities and Challenges of Exploiting Synthetic Lethality in Cancer; January 4-7, 2017; San Diego, CA. American Association for Cancer Research, 2017. http://dx.doi.org/10.1158/1538-8514.synthleth-b03.
Reports on the topic "Functional precision medicine":
Skelly, Andrea C., Roger Chou, Joseph R. Dettori, Erika D. Brodt, Andrea Diulio-Nakamura, Kim Mauer, Rongwei Fu, et al. Integrated and Comprehensive Pain Management Programs: Effectiveness and Harms. Agency for Healthcare Research and Quality (AHRQ), October 2021. http://dx.doi.org/10.23970/ahrqepccer251.