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Готові списки джерел за темами / Functional precision medicine

Добірка наукової літератури з теми "Functional precision medicine"

Автор: Grafiati

Опубліковано: 7 липня 2024

Оновлено: 7 липня 2024

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Статті в журналах з теми "Functional precision medicine":

1

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.

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2

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.

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3

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.

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4

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.

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5

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.

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6

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.

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7

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.

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Precision medicine approaches for solid tumors are mainly based on genomics. Its employment in clinical trials has led to somewhat underwhelming results, except for single responses. Moreover, several factors can influence the response, such as gene and protein expression, the coexistence of different genomic alterations or post-transcriptional/translational modifications, the impact of tumor microenvironment, etc., therefore making it insufficient to employ a genomics-only approach to predict response. Recently, the implementation of patient-derived organoids has shed light on the possibility to use them to predict patient response to drug treatment. This could offer for the first time the possibility to move precision medicine to a functional environment.

8

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.

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Precision (target) medicine is proposed as a new strategy to identify and develop new highly selective drugs against specific targets for the disease and more precise tailoring of medicines to the target populations of patients. Precision medicine can be an important approach to create more novel and safer therapeutics (tyrosine kinase inhibitors, tumour specific monoclonal antibodies) for patients with gene mutation, aberrations, or protein over-expression. Precision medicine requires an understanding mutational processes, and heterogeneity between cancer cells during tumor evolution. The present review briefly define various heterogeneities and potential associations with drug efficacy and resistance, emphasize the importance to develop functional biomarkers to monitor drug efficacy and resistance, and define opportunities and challenges of precision medicine for clinical practice.

9

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.

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Abstract Precision medicine refers to the tailoring of individual therapeutics based on each patient’s genetic, phenotypic, and clinical characteristics, therefore seeking the most effective treatment for the patient. With recent advances in genome sequencing technology, it was anticipated that identifying specific genetic alterations would contribute greatly to the realization of precision medicine. However, most cancer patients do not benefit from genomic precision medicine, as shown in recent Next Generation Sequencing (NGS)-driven clinical trials. Functional precision medicine directly uses patient tumor cells to test their ex vivo responses to diverse drugs to predict the most effective drugs. Functional precision medicine is emerging because it provides immediate translatable information to select drugs among clinically available therapeutics. AVATASCAN®, developed by Samsung Medical Center and AimedBio Inc., is a robust and accurate high throughput functional precision medicine platform with more than 1,500 historical sample data. AVATASCAN® has tested more than 1,500 cancer patient samples across 14 different tumor types, including glioblastoma, lung, colorectal, stomach and breast cancers. We achieved a tissue culture success rate and a drug screening success rate of more than 90% in most tumor types. A retrospective analysis of the clinical outcomes of patients given matched drugs demonstrated actual complete/partial response in 85% of AVATASCAN® screening responders. Recognizing its potential for clinical application, AVATASCAN® was selected as one of the datasets referenced by the pediatric tumor board at Seoul National University Hospital for clinical decision-making. Furthermore, AVATASCAN® is now available in Singapore and Thailand as an early-access, premium precision medicine service. Overall, we present compelling evidence that AVATASCAN® is a valuable platform for personalized cancer therapy. Therefore, we are moving forward to adopt and expand this platform in clinical applications. Citation Format: Nam-Gu Her, Gi Ju Lee, Seung Yoon Hyun, San Ha Park, Jae Woo Ahn, Ji Soo Kang, Hong Boon Toh, Do-Hyun Nam. AVATASCAN®, a pioneer of functional precision medicine in guiding clinical decision-making [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3410.

10

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.

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Дисертації з теми "Functional precision medicine":

1

Boilève, Alice. "La médecine de précision dans le cancer du pancréas." Electronic Thesis or Diss., université Paris-Saclay, 2024. http://www.theses.fr/2024UPASL013.

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L'adénocarcinome canalaire pancréatique (PDAC) est un cancer de plus en plus fréquent, dont les options thérapeutiques sont limitées et le pronostic mauvais. Les chimiothérapies conventionnelles ont une efficacité limitée, ce qui souligne la nécessité de nouvelles approches thérapeutiques. La médecine de précision génomique, rendue permise par l'amélioration des techniques de séquençage haut débit, a connu un essor important en oncologie au cours de la dernière décennie. Néanmoins, l'utilité du profilage moléculaire dans le PDAC n'a pas encore été établie, malgré l'amélioration de la survie globale lorsque les patients reçoivent un traitement adapté sur le plan moléculaire. La médecine de précision fonctionnelle (MPF) est une autre stratégie prometteuse qui repose sur des tests d'un panel de médicaments sur des cellules tumorales vivantes afin d'identifier le profil de sensibilité et de résistance de chaque tumeur. Les organoïdes sont à ce titre des outils robustes et prometteurs pour tester la sensibilité d'une tumeur donnée à divers médicaments et identifier la meilleure option thérapeutique pour chaque patient.Trois axes ont été développés dans cette thèse. Premièrement, un axe translationnel qui a évalué l'impact des organoïdes comme outils pour la médecine de précision fonctionnelle dans le cancer du pancréas. L'objectif principal de ce projet était d'établir un cadre pour intégrer les tests de sensibilité aux médicaments basés sur les organoïdes dans la gestion clinique des patients atteints de PDAC. La réponse des organoïdes à un panel de médicaments anticancéreux a été corrélée avec les réponses observées en clinique chez les patients, suggérant un potentiel bénéfice clinique pour les patients. Par ailleurs, l'apport des organoïdes aux études précliniques a été testé en testant l'efficacité d'un inhibiteur de KRASG12D, le MRTX1133, en monothérapie qu'en combinaison avec d'autres inhibiteurs. La combinaison MRTX1133 et anti-EGFR s'est avérée la plus prometteuse.Deuxièmement, un axe clinique qui a évalué l'impact de la mutation KRAS dans le PDAC, en termes de caractéristiques cliniques, moléculaires, réponse au traitement et de pronostic, notamment si un traitement ciblé a été reçu. En comparant les tumeurs KRAS non-mutées et les tumeurs KRAS mutées, on a montré des différences cliniques et également un pronostic meilleur pour les tumeurs non mutées. Par ailleurs, l'impact des différents codons mutés de KRAS a été étudié en comparant tumeurs mutées KRASG12 vs KRASautre. Les mutations KRASG12 se sont révélées de plus mauvais pronostic, cependant sans différence de sensibilité aux traitements.Enfin, un axe fondamental pour étudier le phénotype invasif des PDAC. En effet, un nouveau programme « onco-morphogénétique » a été identifié comme médiateur de la dissémination métastatique des cancers colorectaux (CRC) via des TSIP (sphères tumorales à polarité inversée, intermédiaires tumoraux malins). Nous avons pu mettre en évidence la présence de TSIP dans les cancers du pancreas, et caractérisé leur programme transcriptionnel ainsi que leur chimiosensibilité à l'aide d'organoïdes. L'impact clinique et pronostic de la présence de TSIP dans le cancer du pancréas ne semble néanmoins pas majeur.Ainsi, ce projet de doctorat a visé à développer un cadre complet pour l'utilisation des PDO comme outil de modélisation des PDAC (axe fondamental), de sélection de traitements personnalisés et de tests de médicaments en pré-cliniques (axe translationnel et clinique). En comblant le fossé entre les essais précliniques et la pratique clinique, ce projet vise à nous rapprocher de la médecine de précision dans la gestion des PDAC
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

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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.

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La Medicina di Precisione (Precision Medicine) è un nuovo paradigma che sta rivoluzionando diversi aspetti delle pratiche cliniche: nella prevenzione e diagnosi, essa è caratterizzata da un approccio diverso dal "one size fits all" proprio della medicina classica. Lo scopo delle Medicina di Precisione è di trovare misure di prevenzione, diagnosi e cura che siano specifiche per ciascun individuo, a partire dalla sua storia personale, stile di vita e fattori genetici. Tre fattori hanno contribuito al rapido sviluppo della Medicina di Precisione: la possibilità di generare rapidamente ed economicamente una vasta quantità di dati omici, in particolare grazie alle nuove tecniche di sequenziamento (Next-Generation Sequencing); la possibilità di diffondere questa enorme quantità di dati grazie al paradigma "Big Data"; la possibilità di estrarre da questi dati tutta una serie di informazioni rilevanti grazie a tecniche di elaborazione innovative ed altamente sofisticate. In particolare, le tecniche di Machine Learning introdotte negli ultimi anni hanno rivoluzionato il modo di analizzare i dati: esse forniscono dei potenti strumenti per l'inferenza statistica e l'estrazione di informazioni rilevanti dai dati in maniera semi-automatica. Al contempo, però, molto spesso richiedono elevate risorse computazionali per poter funzionare efficacemente. Per questo motivo, e per l'elevata mole di dati da elaborare, è necessario sviluppare delle tecniche di Machine Learning orientate al Big Data che utilizzano espressamente tecniche di High Performance Computing, questo per poter sfruttare al meglio le risorse di calcolo disponibili e su diverse scale, dalle singole workstation fino ai super-computer. In questa tesi vengono presentate tre tecniche di Machine Learning sviluppate nel contesto del High Performance Computing e create per affrontare tre questioni fondamentali e ancora irrisolte nel campo della Medicina di Precisione, in particolare la Medicina Genomica: i) l'identificazione di varianti deleterie o patogeniche tra quelle neutrali nelle aree non codificanti del DNA; ii) l'individuazione della attività delle regioni regolatorie in diverse linee cellulari e tessuti; iii) la predizione automatica della funzione delle proteine nel contesto di reti biomolecolari. Per il primo problema è stato sviluppato parSMURF, un innovativo metodo basato su hyper-ensemble in grado di gestire l'elevato grado di sbilanciamento che caratterizza l'identificazione di varianti patogeniche e deleterie in mezzo al "mare" di varianti neutrali nelle aree non-coding del DNA. L'algoritmo è stato implementato per sfruttare appositamente le risorse di supercalcolo del CINECA (Marconi - KNL) e HPC Center Stuttgart (HLRS Apollo HAWK), ottenendo risultati allo stato dell'arte, sia per capacità predittiva, sia per scalabilità. Il secondo problema è stato affrontato tramite lo sviluppo di reti neurali "deep", in particolare Deep Feed Forward e Deep Convolutional Neural Networks per analizzare - rispettivamente - dati di natura epigenetica e sequenze di DNA, con lo scopo di individuare promoter ed enhancer attivi in linee cellulari e tessuti specifici. L'analisi è compiuta "genome-wide" e sono state usate tecniche di parallelizzazione su GPU. Infine, per il terzo problema è stato sviluppato un algoritmo di Machine Learning semi-supervisionato su grafo basato su reti di Hopfield per elaborare efficacemente grandi network biologici, utilizzando ancora tecniche di parallelizzazione su GPU; in particolare, una parte rilevante dell'algoritmo è data dall'introduzione di una tecnica parallela di colorazione del grafo che migliora il classico approccio greedy introdotto da Luby. Tra i futuri lavori e le attività in corso, viene presentato il progetto inerente all'estensione di parSMURF che è stato recentemente premiato dal consorzio Partnership for Advance in Computing in Europe (PRACE) allo scopo di sviluppare ulteriormente l'algoritmo e la sua implementazione, applicarlo a dataset di diversi ordini di grandezza più grandi e inserire i risultati in Genomiser, lo strumento attualmente allo stato dell'arte per l'individuazione di varianti genetiche Mendeliane. Questo progetto è inserito nel contesto di una collaborazione internazionale con i Jackson Lab for Genomic Medicine.
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.

3

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.

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Chaque année, plus de 150 000 décès sont associés aux cancers épithéliaux de l’ovaire dans le monde, notamment en raison du développement d’une résistance à la chimiothérapie. Environ la moitié de ces cancers présentent des altérations moléculaires provoquant une déficience de la réparation de l’ADN par recombinaison homologue (HRD) qui les sensibilise à l’action des inhibiteurs de la protéine PARP (PARPi). A ce jour, il n’existe pas de test capable d’appréhender le phénotype HRD dans sa globalité, limitant ainsi l’accès à ces traitements. Dans ce contexte, nous avons entrepris de mettre au point des tests fonctionnels basés sur l’utilisation d’explants tumoraux tranchés puis sur l’utilisation d’organoïdes tumoraux dérivés de tumeurs ovariennes de patientes chimio-naïves ou antérieurement traitées. La culture d’explants s’est révélée inappropriée pour la réalisation de ces tests et nous avons alors focalisé nos travaux sur les organoïdes tumoraux. Ces derniers ont été exposés au carboplatine (traitement de 1e ligne) et à deux inhibiteurs de PARP (l’olaparib et le niraparib) utilisés en traitement d’entretien. En parallèle, nous avons collecté les données cliniques des patientes (survie, intervalle sans platine, RECIST, traitements) afin d’évaluer le potentiel prédictif de ces modèles. Les organoïdes tumoraux établis ont répondu de façon hétérogène aux différents médicaments testés, et nos résultats montrent que les tests réalisés sur les organoïdes sont capables d’identifier des patientes présentant un niveau de résistance élevé au carboplatine, suggérant que ce test fonctionnel pourrait présenter un intérêt prédictif vis-à-vis de ce médicament. Concernant le potentiel prédictif des organoïdes vis-à-vis des PARPi, des profils de sensibilité variés ont été identifiés, mais la corrélation avec la réponse clinique reste à établir par des études menées sur des échantillons de tumeurs issus de patientes traitées par ces médicaments
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

4

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.

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博士
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.

Частини книг з теми "Functional precision medicine":

1

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.

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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.

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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.

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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.

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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.

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AbstractThe clinical utility of precision medicine through molecular characterization of tumors has been demonstrated in some malignancies, especially in cases where oncogenic driver alterations are identified. Next generation sequencing data from thousands of patients with head and neck cancers have provided vast amounts of information about the genomic landscape of this disease. Thus far, only a limited number of genomic alterations have been druggable, such as NTRK gene rearrangements in salivary gland cancers (mainly mammary analogue secretory carcinoma), NOTCH mutations in adenoid cystic cancers, HRAS mutations in head and neck squamous cell cancers, and even a smaller number of these have reached regulatory approval status. In order to expand the scope of precision medicine in head and neck cancer, additional evaluation beyond genomics is necessary. For instance, there is increasing interest to perform transcriptomic profiling for target identification. Another advance is in the area of functional testing such as small interfering RNA and drug libraries on patient derived cell cultures. Liquid biopsies to detect specific tumor clones or subclones, or viral sequences such as HPV, are of great interest to enable non-invasive tracking of response or resistance to treatment. In addition, precision immuno-oncology is a tangible goal, with a growing body of knowledge on the interactions between the host immunity, the tumor and its microenvironment. Immuno-oncology combinations that are tailored to immunophenotypes of the host-tumor-microenvironment triad, personalized cancer vaccines, and adoptive cell therapies, among others, are in active development. Many therapeutic possibilities and opportunities lie ahead that ultimately will increase the reality of precision medicine in head and neck cancer.

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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.

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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.

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AbstractBoth poultry meat and eggs provide high-quality animal protein [containing sufficient amounts and proper ratios of amino acids (AAs)] for human consumption and, therefore, play an important role in the growth, development, and health of all individuals. Because there are growing concerns about the suboptimal efficiencies of poultry production and its impact on environmental sustainability, much attention has been paid to the formulation of low-protein diets and precision nutrition through the addition of low-cost crystalline AAs or alternative sources of animal-protein feedstuffs. This necessitates a better understanding of AA nutrition and metabolism in chickens. Although historic nutrition research has focused on nutritionally essential amino acids (EAAs) that are not synthesized or are inadequately synthesized in the body, increasing evidence shows that the traditionally classified nutritionally nonessential amino acids (NEAAs), such as glutamine and glutamate, have physiological and regulatory roles other than protein synthesis in chicken growth and egg production. In addition, like other avian species, chickens do not synthesize adequately glycine or proline (the most abundant AAs in the body but present in plant-source feedstuffs at low content) relative to their nutritional and physiological needs. Therefore, these two AAs must be sufficient in poultry diets. Animal proteins (including ruminant meat & bone meal and hydrolyzed feather meal) are abundant sources of both glycine and proline in chicken nutrition. Clearly, chickens (including broilers and laying hens) have dietary requirements for all proteinogenic AAs to achieve their maximum productivity and maintain optimum health particularly under adverse conditions such as heat stress and disease. This is a paradigm shift in poultry nutrition from the 70-year-old “ideal protein” concept that concerned only about EAAs to the focus of functional AAs that include both EAAs and NEAAs.

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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.

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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.

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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.

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Тези доповідей конференцій з теми "Functional precision medicine":

1

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.

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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.

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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.

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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.

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Functional precision medicine directly screens chemotherapeutics in patient tissue; however, technical challenges limit its clinical feasibility. OCT viability imaging in 3D microtissues overcomes these limitations and has shown tissue and treatment specific changes in viability.

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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.

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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.

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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.

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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.

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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.

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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.

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Звіти організацій з теми "Functional precision medicine":

1

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.

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Objectives. To evaluate the effectiveness and harms of pain management programs that are based on the biopsychosocial model of care, particularly in the Medicare population. Data sources. Electronic databases (Ovid® MEDLINE®, PsycINFO®, CINAHL®, Cochrane Central Register of Controlled Trials, and Cochrane Database of Systematic Reviews) from 1989 to May 24, 2021; reference lists; and a Federal Register notice. Review methods. Given lack of consensus on terminology and program definition for pain management, we defined programs as integrated (based in and integrated with primary care) and comprehensive (referral based and separate from primary care) pain management programs (IPMPs and CPMPs). Using predefined criteria and dual review, we selected randomized controlled trials (RCTs) comparing IPMPs and CPMPs with usual care or waitlist, physical activity, pharmacologic therapy, and psychological therapy in patients with complex acute/subacute pain or chronic nonactive cancer pain. Patients needed to have access to medication support/review, psychological support, and physical function support in programs. Meta-analyses were conducted to improve estimate precision. We classified the magnitude of effects as small, moderate, or large based on predefined criteria. Strength of evidence (SOE) was assessed for the primary outcomes of pain, function, and change in opioid use. Results. We included 57 RCTs; 8 evaluated IPMPs and 49 evaluated CPMPs. Compared with usual care or waitlist, IPMPs were associated with small improvements in pain in the short and intermediate term (SOE: low) and in function in the short term (SOE: moderate), but there were no clear differences at other time points. CPMPs were associated with small improvements in pain immediately postintervention (SOE: moderate) but no differences in the short, intermediate, and long term (SOE: low); for function, improvements were moderate immediately postintervention and in the short term; there were no differences in the intermediate or long term (SOE: low at all time points). CPMPs were associated with small to moderate improvements in function and pain versus pharmacologic treatment alone at multiple time frames (SOE: moderate for function intermediate term; low for pain and function at all other times), and with small improvements in function but no improvements in pain in the short term when compared with physical activity alone (SOE: moderate). There were no differences between CPMPs and psychological therapy alone at any time (SOE: low). Serious harms were not reported, although evidence on harms was insufficient. The mean age was 57 years across IPMP RCTs and 45 years across CPMP RCTs. None of the trials specifically enrolled Medicare beneficiaries. Evidence on factors related to program structure, delivery, coordination, and components that may impact outcomes is sparse and there was substantial variability across studies on these factors. Conclusions. IPMPs and CPMPs may provide small to moderate improvements in function and small improvements in pain in patients with chronic pain compared with usual care. Formal pain management programs have not been widely implemented in the United States for general populations or the Medicare population. To the extent that programs are tailored to patients’ needs, our findings are potentially applicable to the Medicare population. Programs that address a range of biopsychosocial aspects of pain, tailor components to patient need, and coordinate care may be of particular importance in this population.