Network and Systems Medicine: Position Paper of the European Collaboration on Science and Technology Action on Open Multiscale Systems Medicine

Abstract

Introduction: Network and systems medicine has rapidly evolved over the past decade, thanks to computational and integrative tools, which stem in part from systems biology. However, major challenges and hurdles are still present regarding validation and translation into clinical application and decision making for precision medicine. Methods: In this context, the Collaboration on Science and Technology Action on Open Multiscale Systems Medicine (OpenMultiMed) reviewed the available advanced technologies for multidimensional data generation and integration in an open-science approach as well as key clinical applications of network and systems medicine and the main issues and opportunities for the future. Results: The development of multi-omic approaches as well as new digital tools provides a unique opportunity to explore complex biological systems and networks at different scales. Moreover, the application of findable, applicable, interoperable, and reusable principles and the adoption of standards increases data availability and sharing for multiscale integration and interpretation. These innovations have led to the first clinical applications of network and systems medicine, particularly in the field of personalized therapy and drug dosing. Enlarging network and systems medicine application would now imply to increase patient engagement and health care providers as well as to educate the novel generations of medical doctors and biomedical researchers to shift the current organ- and symptom-based medical concepts toward network- and systems-based ones for more precise diagnoses, interventions, and ideally prevention. Conclusion: In this dynamic setting, the health care system will also have to evolve, if not revolutionize, in terms of organization and management.

Keywords: big data; data integration; integrated health care; omics; systems medicine.

FIG. 1.

Medical knowledge gaps and the ground-breaking nature of Network and Systems Medicine. (A) Time course of most chronic diseases without knowing the causal mechanism. Diagnosis relies on signs and symptoms pointing to (B) specific organs. Therapy focuses on achieving patient-relevant outcomes only in (C) a small fraction (green) of patients. (D) Network and Systems Medicine aims at defining and diagnosing a disease mechanistically, and at treating it with higher precision, based on (E) mechanism-based diagnostics and therapeutics (i.e., theranostics).

FIG. 2.

Traditional choices to handle different omics data sources before deriving an integrated solution. Different colors and symbols represent different data origins. (A) Data fusion, which allows accounting for structure between omics data. Evidence for such structural relationships may be derived from biological knowledge or analytically (full lines), or it may be deduced from the latter (dashed lines). (B) Changing the representation of each data source. This may be based on principles of dimensionality reduction or the identification of communities (cf. corresponding data corresponding symbols with gradient fill). (C) Obtaining a data-specific solution, hereby ignoring detailed inter-relationships between data sources as part of obtaining an integrative solution. Once data are represented as in (B), cross-data source relationships may be accounted for (A) or specific within-data source solutions may be targeted first (C), before obtaining an integrative solution. This is indicated by the arrows connecting panel B with, respectively (A, C).

FIG. 3.

Fully acknowledging inter-relationships between omics data on reduced genomic sets when deriving integrative solutions. (A) Per meaningful genomic concept, such as a gene, create a network of inter-relationships between omics elements “mapped” to that concept. (B) Represents the concept-based integrated data by using kernel-based principal components, where the kernel is chosen in such a way that the structure of the data is optimally captured. This leads to a new integrated concept-related signature for each individual in the sample. Each concept, therefore, gives rise to a new variable. The combined set of concepts (new variables) is submitted to subsequent analyses to obtain an integrated solution to the problem of interest.

FIG. 4.

The proposed implementation of logical model predictions and patient-derived spheroid testing of drug therapies. An individual patient's tumor material (top row) is used to produce spheroid cultures for small-scale drug combination screening. In parallel, biomarkers are produced from these spheroids (bottom row) and used to configure a generic logical model so that it optimally represents the tumor of the patient. This model is used for a large-scale in silico screening of the complete available drug combination space, resulting in a limited set of potential synergistic drugs that are tested in the spheroids. This whole procedure can be completed in a couple of weeks. Validated drug combinations can be considered by a clinician for therapy decisions.

FIG. 5.

Strengths, weaknesses, opportunities, and threats analysis for network and systems medicine.