Limits...
Systems Medicine: from molecular features and models to the clinic in COPD.

Gomez-Cabrero D, Menche J, Cano I, Abugessaisa I, Huertas-Migueláñez M, Tenyi A, Marin de Mas I, Kiani NA, Marabita F, Falciani F, Burrowes K, Maier D, Wagner P, Selivanov V, Cascante M, Roca J, Barabási AL, Tegnér J - J Transl Med (2014)

Bottom Line: In both use cases we were able to achieve predictive modeling but we also identified several key challenges, the most pressing being the validation and implementation into actual clinical practice.The results confirm the potential of the Systems Medicine approach to study complex diseases and generate clinically relevant predictive models.Our study also highlights important obstacles and bottlenecks for such approaches (e.g. data availability and normalization of frameworks among others) and suggests specific proposals to overcome them.

View Article: PubMed Central - HTML - PubMed

ABSTRACT

Background and hypothesis: Chronic Obstructive Pulmonary Disease (COPD) patients are characterized by heterogeneous clinical manifestations and patterns of disease progression. Two major factors that can be used to identify COPD subtypes are muscle dysfunction/wasting and co-morbidity patterns. We hypothesized that COPD heterogeneity is in part the result of complex interactions between several genes and pathways. We explored the possibility of using a Systems Medicine approach to identify such pathways, as well as to generate predictive computational models that may be used in clinic practice.

Objective and method: Our overarching goal is to generate clinically applicable predictive models that characterize COPD heterogeneity through a Systems Medicine approach. To this end we have developed a general framework, consisting of three steps/objectives: (1) feature identification, (2) model generation and statistical validation, and (3) application and validation of the predictive models in the clinical scenario. We used muscle dysfunction and co-morbidity as test cases for this framework.

Results: In the study of muscle wasting we identified relevant features (genes) by a network analysis and generated predictive models that integrate mechanistic and probabilistic models. This allowed us to characterize muscle wasting as a general de-regulation of pathway interactions. In the co-morbidity analysis we identified relevant features (genes/pathways) by the integration of gene-disease and disease-disease associations. We further present a detailed characterization of co-morbidities in COPD patients that was implemented into a predictive model. In both use cases we were able to achieve predictive modeling but we also identified several key challenges, the most pressing being the validation and implementation into actual clinical practice.

Conclusions: The results confirm the potential of the Systems Medicine approach to study complex diseases and generate clinically relevant predictive models. Our study also highlights important obstacles and bottlenecks for such approaches (e.g. data availability and normalization of frameworks among others) and suggests specific proposals to overcome them.

Show MeSH

Related in: MedlinePlus

Mechanistic Models and extensions. (a) Oxygen transport and utilization model (M1). (b) Mitochondrial respiration and reactive-oxygen species generation model (M2). (3) Personalized model of M2: M2 model is personalized by a Bayesian network that predicts the values of UQCR2 (oxidative phosphorylation chain) by inflammation-associated measurements (IL11RA and TNFRSF25). All models can be simulated in the Simulation Environment [58] and the patient specific values can be obtained through a COPD Knowledge Based [7].
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
getmorefigures.php?uid=PMC4255907&req=5

Figure 2: Mechanistic Models and extensions. (a) Oxygen transport and utilization model (M1). (b) Mitochondrial respiration and reactive-oxygen species generation model (M2). (3) Personalized model of M2: M2 model is personalized by a Bayesian network that predicts the values of UQCR2 (oxidative phosphorylation chain) by inflammation-associated measurements (IL11RA and TNFRSF25). All models can be simulated in the Simulation Environment [58] and the patient specific values can be obtained through a COPD Knowledge Based [7].

Mentions: The etiology of COPD involves a multitude of intertwined molecular processes, many of which still remain unknown. These processes are embedded in the larger context of the Interactome, referring to a single comprehensive network integrating all molecular interactions, such as protein-protein interactions, regulatory protein-DNA interactions or metabolic interactions (see Figure 2). While ongoing efforts to systematically map the complete Interactome stand only at the beginning, currently available databases (Table 1) already include several hundreds of thousands of interactions. In order to explore such large networks, Systems Medicine has extensively adopted tools from network science [10-12]. Network approaches to human disease are based on the observation that the cellular components associated with a specific disease are not scattered randomly within the Interactome, but segregate in certain neighborhoods or disease modules. The identification of the specific disease modules is therefore an important step towards a holistic understanding of how molecular variations with small isolated effect sizes collectively give rise to a certain disease phenotype. The local agglomeration of disease-associated proteins within the Interactome can be used in this process, by extrapolating from the connectivity patterns of known disease-associated proteins to infer novel disease proteins [13,14]. Other applications of this principle include the identification of pathway members [15] or prioritization of weak GWAS loci [16]. In [17] a COPD specific protein interaction network was constructed around genes differentially expressed between healthy and COPD subjects. This network was then queried for potential drug targets that could reverse the expression changes. COPD specific gene expression is also the basis of a Systems Medicine method proposed in [18] that aims at identifying subgroups of COPD patients with different molecular signatures.


Systems Medicine: from molecular features and models to the clinic in COPD.

Gomez-Cabrero D, Menche J, Cano I, Abugessaisa I, Huertas-Migueláñez M, Tenyi A, Marin de Mas I, Kiani NA, Marabita F, Falciani F, Burrowes K, Maier D, Wagner P, Selivanov V, Cascante M, Roca J, Barabási AL, Tegnér J - J Transl Med (2014)

Mechanistic Models and extensions. (a) Oxygen transport and utilization model (M1). (b) Mitochondrial respiration and reactive-oxygen species generation model (M2). (3) Personalized model of M2: M2 model is personalized by a Bayesian network that predicts the values of UQCR2 (oxidative phosphorylation chain) by inflammation-associated measurements (IL11RA and TNFRSF25). All models can be simulated in the Simulation Environment [58] and the patient specific values can be obtained through a COPD Knowledge Based [7].
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4255907&req=5

Figure 2: Mechanistic Models and extensions. (a) Oxygen transport and utilization model (M1). (b) Mitochondrial respiration and reactive-oxygen species generation model (M2). (3) Personalized model of M2: M2 model is personalized by a Bayesian network that predicts the values of UQCR2 (oxidative phosphorylation chain) by inflammation-associated measurements (IL11RA and TNFRSF25). All models can be simulated in the Simulation Environment [58] and the patient specific values can be obtained through a COPD Knowledge Based [7].
Mentions: The etiology of COPD involves a multitude of intertwined molecular processes, many of which still remain unknown. These processes are embedded in the larger context of the Interactome, referring to a single comprehensive network integrating all molecular interactions, such as protein-protein interactions, regulatory protein-DNA interactions or metabolic interactions (see Figure 2). While ongoing efforts to systematically map the complete Interactome stand only at the beginning, currently available databases (Table 1) already include several hundreds of thousands of interactions. In order to explore such large networks, Systems Medicine has extensively adopted tools from network science [10-12]. Network approaches to human disease are based on the observation that the cellular components associated with a specific disease are not scattered randomly within the Interactome, but segregate in certain neighborhoods or disease modules. The identification of the specific disease modules is therefore an important step towards a holistic understanding of how molecular variations with small isolated effect sizes collectively give rise to a certain disease phenotype. The local agglomeration of disease-associated proteins within the Interactome can be used in this process, by extrapolating from the connectivity patterns of known disease-associated proteins to infer novel disease proteins [13,14]. Other applications of this principle include the identification of pathway members [15] or prioritization of weak GWAS loci [16]. In [17] a COPD specific protein interaction network was constructed around genes differentially expressed between healthy and COPD subjects. This network was then queried for potential drug targets that could reverse the expression changes. COPD specific gene expression is also the basis of a Systems Medicine method proposed in [18] that aims at identifying subgroups of COPD patients with different molecular signatures.

Bottom Line: In both use cases we were able to achieve predictive modeling but we also identified several key challenges, the most pressing being the validation and implementation into actual clinical practice.The results confirm the potential of the Systems Medicine approach to study complex diseases and generate clinically relevant predictive models.Our study also highlights important obstacles and bottlenecks for such approaches (e.g. data availability and normalization of frameworks among others) and suggests specific proposals to overcome them.

View Article: PubMed Central - HTML - PubMed

ABSTRACT

Background and hypothesis: Chronic Obstructive Pulmonary Disease (COPD) patients are characterized by heterogeneous clinical manifestations and patterns of disease progression. Two major factors that can be used to identify COPD subtypes are muscle dysfunction/wasting and co-morbidity patterns. We hypothesized that COPD heterogeneity is in part the result of complex interactions between several genes and pathways. We explored the possibility of using a Systems Medicine approach to identify such pathways, as well as to generate predictive computational models that may be used in clinic practice.

Objective and method: Our overarching goal is to generate clinically applicable predictive models that characterize COPD heterogeneity through a Systems Medicine approach. To this end we have developed a general framework, consisting of three steps/objectives: (1) feature identification, (2) model generation and statistical validation, and (3) application and validation of the predictive models in the clinical scenario. We used muscle dysfunction and co-morbidity as test cases for this framework.

Results: In the study of muscle wasting we identified relevant features (genes) by a network analysis and generated predictive models that integrate mechanistic and probabilistic models. This allowed us to characterize muscle wasting as a general de-regulation of pathway interactions. In the co-morbidity analysis we identified relevant features (genes/pathways) by the integration of gene-disease and disease-disease associations. We further present a detailed characterization of co-morbidities in COPD patients that was implemented into a predictive model. In both use cases we were able to achieve predictive modeling but we also identified several key challenges, the most pressing being the validation and implementation into actual clinical practice.

Conclusions: The results confirm the potential of the Systems Medicine approach to study complex diseases and generate clinically relevant predictive models. Our study also highlights important obstacles and bottlenecks for such approaches (e.g. data availability and normalization of frameworks among others) and suggests specific proposals to overcome them.

Show MeSH
Related in: MedlinePlus