| 1. |
- Menden, MP, et al.
(författare)
-
Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen
- 2019
-
Ingår i: Nature communications. - : Springer Science and Business Media LLC. - 2041-1723. ; 10:1, s. 2674-
-
Tidskriftsartikel (refereegranskat)abstract
- The effectiveness of most cancer targeted therapies is short-lived. Tumors often develop resistance that might be overcome with drug combinations. However, the number of possible combinations is vast, necessitating data-driven approaches to find optimal patient-specific treatments. Here we report AstraZeneca’s large drug combination dataset, consisting of 11,576 experiments from 910 combinations across 85 molecularly characterized cancer cell lines, and results of a DREAM Challenge to evaluate computational strategies for predicting synergistic drug pairs and biomarkers. 160 teams participated to provide a comprehensive methodological development and benchmarking. Winning methods incorporate prior knowledge of drug-target interactions. Synergy is predicted with an accuracy matching biological replicates for >60% of combinations. However, 20% of drug combinations are poorly predicted by all methods. Genomic rationale for synergy predictions are identified, including ADAM17 inhibitor antagonism when combined with PIK3CB/D inhibition contrasting to synergy when combined with other PI3K-pathway inhibitors in PIK3CA mutant cells.
|
|
| 2. |
- Andrews, B. J., et al.
(författare)
-
Quantitative human cell encyclopedia
- 2016
-
Ingår i: Science Signaling. - : American Association for the Advancement of Science (AAAS). - 1945-0877 .- 1937-9145. ; 9:443
-
Tidskriftsartikel (refereegranskat)abstract
- Scientists gathered to discuss the necessity, feasibility, and challenges of generating a quantitative catalog of the components in human cells that is essential for our understanding of human physiology in health and disease and to support future breakthroughs in treating diseases. This report summarizes the discussion that emerged at the Human Quantitative Dynamics Workshop held in Bethesda, MD, USA, in December 2015.
|
|
| 3. |
|
|
| 4. |
- Sieberts, SK, et al.
(författare)
-
Crowdsourced assessment of common genetic contribution to predicting anti-TNF treatment response in rheumatoid arthritis
- 2016
-
Ingår i: Nature communications. - : Springer Science and Business Media LLC. - 2041-1723. ; 7, s. 12460-
-
Tidskriftsartikel (refereegranskat)abstract
- Rheumatoid arthritis (RA) affects millions world-wide. While anti-TNF treatment is widely used to reduce disease progression, treatment fails in ∼one-third of patients. No biomarker currently exists that identifies non-responders before treatment. A rigorous community-based assessment of the utility of SNP data for predicting anti-TNF treatment efficacy in RA patients was performed in the context of a DREAM Challenge (http://www.synapse.org/RA_Challenge). An open challenge framework enabled the comparative evaluation of predictions developed by 73 research groups using the most comprehensive available data and covering a wide range of state-of-the-art modelling methodologies. Despite a significant genetic heritability estimate of treatment non-response trait (h2=0.18, P value=0.02), no significant genetic contribution to prediction accuracy is observed. Results formally confirm the expectations of the rheumatology community that SNP information does not significantly improve predictive performance relative to standard clinical traits, thereby justifying a refocusing of future efforts on collection of other data.
|
|
| 5. |
- Gustafsson, Mika, et al.
(författare)
-
Reverse Engineering of Gene Networks with LASSO and Nonlinear Basis Functions
- 2009
-
Ingår i: CHALLENGES OF SYSTEMS BIOLOGY: COMMUNITY EFFORTS TO HARNESS BIOLOGICAL COMPLEXITY. - : Wiley. - 0077-8923 .- 1749-6632. ; 1158, s. 265-275
-
Tidskriftsartikel (refereegranskat)abstract
- The quest to determine cause from effect is often referred to as reverse engineering in the context of cellular networks. Here we propose and evaluate an algorithm for reverse engineering a gene regulatory network from time-series kind steady-state data. Our algorithmic pipeline, which is rather standard in its parts but not in its integrative composition, combines ordinary differential equations, parameter estimations by least angle regression, and cross-validation procedures for determining the in-degrees and selection of nonlinear transfer functions. The result of the algorithm is a complete directed net-work, in which each edge has been assigned a score front it bootstrap procedure. To evaluate the performance, we submitted the outcome of the algorithm to the reverse engineering assessment competition DREAM2, where we used the data corresponding to the InSillico1 and InSilico2 networks as input. Our algorithm outperformed all other algorithms when inferring one of the directed gene-to-gene networks.
|
|
| 6. |
|
|
| 7. |
|
|
| 8. |
|
|
