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Drug Response Prediction as a Link Prediction Problem.

October 24, 2018 dna 0
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Drug Response Prediction as a Link Prediction Problem.

Sci Rep. 2017 01 09;7:40321

Authors: Stanfield Z, Coşkun M, Koyutürk M

Abstract
Drug response prediction is a well-studied problem in which the molecular profile of a given sample is used to predict the effect of a given drug on that sample. Effective solutions to this problem hold the key for precision medicine. In cancer research, genomic data from cell lines are often utilized as features to develop machine learning models predictive of drug response. Molecular networks provide a functional context for the integration of genomic features, thereby resulting in robust and reproducible predictive models. However, inclusion of network data increases dimensionality and poses additional challenges for common machine learning tasks. To overcome these challenges, we here formulate drug response prediction as a link prediction problem. For this purpose, we represent drug response data for a large cohort of cell lines as a heterogeneous network. Using this network, we compute “network profiles” for cell lines and drugs. We then use the associations between these profiles to predict links between drugs and cell lines. Through leave-one-out cross validation and cross-classification on independent datasets, we show that this approach leads to accurate and reproducible classification of sensitive and resistant cell line-drug pairs, with 85% accuracy. We also examine the biological relevance of the network profiles.

PMID: 28067293 [PubMed – indexed for MEDLINE]

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Novel Bioinformatics-Based Approach for Proteomic Biomarkers Prediction of Calpain-2 &Caspase-3 Protease Fragmentation: Application to βII-Spectrin Protein.

October 24, 2018 dna 0
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Novel Bioinformatics-Based Approach for Proteomic Biomarkers Prediction of Calpain-2 &Caspase-3 Protease Fragmentation: Application to βII-Spectrin Protein.

Sci Rep. 2017 01 23;7:41039

Authors: El-Assaad A, Dawy Z, Nemer G, Kobeissy F

Abstract
The crucial biological role of proteases has been visible with the development of degradomics discipline involved in the determination of the proteases/substrates resulting in breakdown-products (BDPs) that can be utilized as putative biomarkers associated with different biological-clinical significance. In the field of cancer biology, matrix metalloproteinases (MMPs) have shown to result in MMPs-generated protein BDPs that are indicative of malignant growth in cancer, while in the field of neural injury, calpain-2 and caspase-3 proteases generate BDPs fragments that are indicative of different neural cell death mechanisms in different injury scenarios. Advanced proteomic techniques have shown a remarkable progress in identifying these BDPs experimentally. In this work, we present a bioinformatics-based prediction method that identifies protease-associated BDPs with high precision and efficiency. The method utilizes state-of-the-art sequence matching and alignment algorithms. It starts by locating consensus sequence occurrences and their variants in any set of protein substrates, generating all fragments resulting from cleavage. The complexity exists in space O(mn) as well as in O(Nmn) time, where N, m, and n are the number of protein sequences, length of the consensus sequence, and length per protein sequence, respectively. Finally, the proposed methodology is validated against βII-spectrin protein, a brain injury validated biomarker.

PMID: 28112201 [PubMed – indexed for MEDLINE]

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Using information of relatives in genomic prediction to apply effective stratified medicine.

October 24, 2018 dna 0
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Using information of relatives in genomic prediction to apply effective stratified medicine.

Sci Rep. 2017 02 09;7:42091

Authors: Lee SH, Weerasinghe WM, Wray NR, Goddard ME, van der Werf JH

Abstract
Genomic prediction shows promise for personalised medicine in which diagnosis and treatment are tailored to individuals based on their genetic profiles for complex diseases. We present a theoretical framework to demonstrate that prediction accuracy can be improved by targeting more informative individuals in the data set used to generate the predictors (“discovery sample”) to include those with genetically close relationships with the subjects put forward for risk prediction. Increase of prediction accuracy from closer relationships is achieved under an additive model and does not rely on any family or interaction effects. Using theory, simulations and real data analyses, we show that the predictive accuracy or the area under the receiver operating characteristic curve (AUC) increased exponentially with decreasing effective size (Ne), i.e. when individuals are closely related. For example, with the sample size of discovery set N = 3000, heritability h2 = 0.5 and population prevalence K = 0.1, AUC value approached to 0.9 and the top percentile of the estimated genetic profile scores had 23 times higher proportion of cases than the general population. This suggests that there is considerable room to increase prediction accuracy by using a design that does not exclude closer relationships.

PMID: 28181587 [PubMed – indexed for MEDLINE]

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The Precision Medicine Nation.

October 24, 2018 dna 0
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The Precision Medicine Nation.

Hastings Cent Rep. 2017 Jul;47(4):19-29

Authors: Sabatello M, Appelbaum PS

Abstract
The United States’ ambitious Precision Medicine Initiative proposes to accelerate exponentially the adoption of precision medicine, an approach to health care that tailors disease diagnosis, treatment, and prevention to individual variability in genes, environment, and lifestyle. It aims to achieve this by creating a cohort of volunteers for precision medicine research, accelerating biomedical research innovation, and adopting policies geared toward patients’ empowerment. As strategies to implement the PMI are formulated, critical consideration of the initiative’s ethical and sociopolitical dimensions is needed. Drawing on scholarship of nationalism and democracy, we discuss the PMI’s construction of what we term “genomic citizenship”; the possible normative obligations arising therefrom; and the ethical, legal, and social challenges that will ensue. Although the PMI is a work in progress, discussion of the existing and emerging issues can facilitate the development of policies, structures, and procedures that can maximize the initiative’s ability to produce equitable and socially sensitive outcomes. Our analysis can also be applied to other population-based, precision medicine research programs.

PMID: 28749054 [PubMed – indexed for MEDLINE]

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Genetic Competence Drives Genome Diversity in Bacillus subtilis.

October 24, 2018 dna 0
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Genetic Competence Drives Genome Diversity in Bacillus subtilis.

Genome Biol Evol. 2018 01 01;10(1):108-124

Authors: Brito PH, Chevreux B, Serra CR, Schyns G, Henriques AO, Pereira-Leal JB

Abstract
Prokaryote genomes are the result of a dynamic flux of genes, with increases achieved via horizontal gene transfer and reductions occurring through gene loss. The ecological and selective forces that drive this genomic flexibility vary across species. Bacillus subtilis is a naturally competent bacterium that occupies various environments, including plant-associated, soil, and marine niches, and the gut of both invertebrates and vertebrates. Here, we quantify the genomic diversity of B. subtilis and infer the genome dynamics that explain the high genetic and phenotypic diversity observed. Phylogenomic and comparative genomic analyses of 42 B. subtilis genomes uncover a remarkable genome diversity that translates into a core genome of 1,659 genes and an asymptotic pangenome growth rate of 57 new genes per new genome added. This diversity is due to a large proportion of low-frequency genes that are acquired from closely related species. We find no gene-loss bias among wild isolates, which explains why the cloud genome, 43% of the species pangenome, represents only a small proportion of each genome. We show that B. subtilis can acquire xenologous copies of core genes that propagate laterally among strains within a niche. While not excluding the contributions of other mechanisms, our results strongly suggest a process of gene acquisition that is largely driven by competence, where the long-term maintenance of acquired genes depends on local and global fitness effects. This competence-driven genomic diversity provides B. subtilis with its generalist character, enabling it to occupy a wide range of ecological niches and cycle through them.

PMID: 29272410 [PubMed – indexed for MEDLINE]

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High-Throughput Screening in Colorectal Cancer Tissue-Originated Spheroids.

October 23, 2018 dna 0
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High-Throughput Screening in Colorectal Cancer Tissue-Originated Spheroids.

Cancer Sci. 2018 Oct 21;:

Authors: Kondo J, Ekawa T, Endo H, Yamazaki K, Tanaka N, Kukita Y, Okuyama H, Okami J, Imamura F, Ohue M, Kato K, Nomura T, Kohara A, Mori S, Dan S, Inoue M

Abstract
Patient-derived cancer organoid culture is an important live material that reflects clinical heterogeneity. However, the limited amount of organoids available for each case as well as the considerable amount of time and cost to expand in vitro makes it impractical to perform high-throughput drug screening using organoid cultures from multiple patients. Here, we report an advanced system for the high-throughput screening of 2427 drugs using the cancer tissue-originated spheroid (CTOS) method. In this system, we apply the CTOS method in an ex vivo platform from xenograft tumors, using machines to handle CTOSs and reagents, and testing a CTOS reference panel of multiple CTOS lines for the hit drugs. CTOS passages in xenograft tumors resulted in minimal changes of morphological and genomic status, and xenograft tumor generation efficiently expanded the number of CTOSs to evaluate multiple drugs. Our panel of colorectal cancer CTOS lines exhibited diverse sensitivities to the hit compounds, demonstrating the usefulness of this system for investigating highly heterogeneous disease. This article is protected by copyright. All rights reserved.

PMID: 30343529 [PubMed – as supplied by publisher]

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Developing a common framework for evaluating the implementation of genomic medicine interventions in clinical care: the IGNITE Network’s Common Measures Working Group.

October 18, 2018 dna 0
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Developing a common framework for evaluating the implementation of genomic medicine interventions in clinical care: the IGNITE Network’s Common Measures Working Group.

Genet Med. 2018 06;20(6):655-663

Authors: Orlando LA, Sperber NR, Voils C, Nichols M, Myers RA, Wu RR, Rakhra-Burris T, Levy KD, Levy M, Pollin TI, Guan Y, Horowitz CR, Ramos M, Kimmel SE, McDonough CW, Madden EB, Damschroder LJ

Abstract
PurposeImplementation research provides a structure for evaluating the clinical integration of genomic medicine interventions. This paper describes the Implementing Genomics in Practice (IGNITE) Network’s efforts to promote (i) a broader understanding of genomic medicine implementation research and (ii) the sharing of knowledge generated in the network.MethodsTo facilitate this goal, the IGNITE Network Common Measures Working Group (CMG) members adopted the Consolidated Framework for Implementation Research (CFIR) to guide its approach to identifying constructs and measures relevant to evaluating genomic medicine as a whole, standardizing data collection across projects, and combining data in a centralized resource for cross-network analyses.ResultsCMG identified 10 high-priority CFIR constructs as important for genomic medicine. Of those, eight did not have standardized measurement instruments. Therefore, we developed four survey tools to address this gap. In addition, we identified seven high-priority constructs related to patients, families, and communities that did not map to CFIR constructs. Both sets of constructs were combined to create a draft genomic medicine implementation model.ConclusionWe developed processes to identify constructs deemed valuable for genomic medicine implementation and codified them in a model. These resources are freely available to facilitate knowledge generation and sharing across the field.

PMID: 28914267 [PubMed – indexed for MEDLINE]