| 1. |
- de Graaf, Albert A, et al.
(författare)
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Nutritional systems biology modeling: from molecular mechanisms to physiology.
- 2009
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Ingår i: PLoS computational biology. - : Public Library of Science (PLoS). - 1553-7358 .- 1553-734X. ; 5:11
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Forskningsöversikt (refereegranskat)abstract
- The use of computational modeling and simulation has increased in many biological fields, but despite their potential these techniques are only marginally applied in nutritional sciences. Nevertheless, recent applications of modeling have been instrumental in answering important nutritional questions from the cellular up to the physiological levels. Capturing the complexity of today's important nutritional research questions poses a challenge for modeling to become truly integrative in the consideration and interpretation of experimental data at widely differing scales of space and time. In this review, we discuss a selection of available modeling approaches and applications relevant for nutrition. We then put these models into perspective by categorizing them according to their space and time domain. Through this categorization process, we identified a dearth of models that consider processes occurring between the microscopic and macroscopic scale. We propose a "middle-out" strategy to develop the required full-scale, multilevel computational models. Exhaustive and accurate phenotyping, the use of the virtual patient concept, and the development of biomarkers from "-omics" signatures are identified as key elements of a successful systems biology modeling approach in nutrition research--one that integrates physiological mechanisms and data at multiple space and time scales.
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| 2. |
- Jamshidi, Neema, et al.
(författare)
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The Radiogenomic Risk Score : construction of a Prognostic Quantitative, Noninvasive Image-based Molecular Assay for Renal Cell Carcinoma
- 2015
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Ingår i: Radiology. - : Radiological Society of North America (RSNA). - 0033-8419 .- 1527-1315. ; 277:1, s. 114-123
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Tidskriftsartikel (refereegranskat)abstract
- Purpose: To evaluate the feasibility of constructing radiogenomic-based surrogates of molecular assays (SOMAs) in patients with clear-cell renal cell carcinoma (CCRCC) by using data extracted from a single computed tomographic (CT) image.Materials and Methods: In this institutional review board approved study, gene expression profile data and contrast material–enhanced CT images from 70 patients with CCRCC in a training set were independently assessed by two radiologists for a set of predefined imaging features. A SOMA for a previously validated CCRCC-specific supervised principal component (SPC) risk score prognostic gene signature was constructed and termed the radiogenomic risk score (RRS). It uses the microarray data and a 28-trait image array to evaluate each CT image with multiple regression of gene expression analysis. The predictive power of the RRS SOMA was then prospectively validated in an independent dataset to confirm its relationship to the SPC gene signature (n = 70) and determination of patient outcome (n = 77). Data were analyzed by using multivariate linear regression–based methods and Cox regression modeling, and significance was assessed with receiver operator characteristic curves and Kaplan-Meier survival analysis.Results: Our SOMA faithfully represents the tissue-based molecular assay it models. The RRS scaled with the SPC gene signature (R = 0.57,P < .001, classification accuracy 70.1%, P < .001) and predicted disease-specific survival (log rank P < .001). Independent validation confirmed the relationship between the RRS and the SPC gene signature (R = 0.45, P < .001, classification accuracy 68.6%, P < .001) and disease-specific survival (log-rank P < .001) and that it was independent of stage, grade, and performance status (multivariate Cox model P < .05, log-rank P < .001).Conclusion: A SOMA for the CCRCC-specific SPC prognostic gene signature that is predictive of disease-specific survival and independent of stage was constructed and validated, confirming that SOMA construction is feasible.
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| 3. |
- Jamshidi, Neema, et al.
(författare)
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The radiogenomic risk score stratifies outcomes in a renal cell cancer phase 2 clinical trial
- 2016
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Ingår i: European Radiology. - : Springer Science and Business Media LLC. - 0938-7994 .- 1432-1084. ; 26:8, s. 2798-2807
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Tidskriftsartikel (refereegranskat)abstract
- OBJECTIVES: To characterize a radiogenomic risk score (RRS), a previously defined biomarker, and to evaluate its potential for stratifying radiological progression-free survival (rPFS) in patients with metastatic renal cell carcinoma (mRCC) undergoing pre-surgical treatment with bevacizumab.METHODOLOGY: In this IRB-approved study, prospective imaging analysis of the RRS was performed on phase II clinical trial data of mRCC patients (n = 41) evaluating whether patient stratification according to the RRS resulted in groups more or less likely to have a rPFS to pre-surgical bevacizumab prior to cytoreductive nephrectomy. Survival times of RRS subgroups were analyzed using Kaplan-Meier survival analysis.RESULTS: The RRS is enriched in diverse molecular processes including drug response, stress response, protein kinase regulation, and signal transduction pathways (P < 0.05). The RRS successfully stratified rPFS to bevacizumab based on pre-treatment computed tomography imaging with a median progression-free survival of 6 versus >25 months (P = 0.005) and overall survival of 25 versus >37 months in the high and low RRS groups (P = 0.03), respectively. Conventional prognostic predictors including the Motzer and Heng criteria were not predictive in this cohort (P > 0.05).CONCLUSIONS: The RRS stratifies rPFS to bevacizumab in patients from a phase II clinical trial with mRCC undergoing cytoreductive nephrectomy and pre-surgical bevacizumab.KEY POINTS: • The RRS SOMA stratifies patient outcomes in a phase II clinical trial. • RRS stratifies subjects into prognostic groups in a discrete or continuous fashion. • RRS is biologically enriched in diverse processes including drug response programs.
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