The development of technologies to better characterize patient disease states, including methods such as sequencing, microarrays, metabolomics, and proteomics from multiple tissues, has produced datasets with potential utility in discovering new targets and identification of patients who might benefit from a given therapy. However, establishing a context for the high dimension content of omics datasets is necessary in order to maximize their predictive potential for the impact of single or combination therapies. Progress towards frameworks for omics data interpretation has arguably been uneven across functional cellular systems. For example, signaling systems currently present challenges even at the level of identifying network structure, and scientists have suggested using approaches such as fuzzy logic and decision trees due to difficulties in identifying a unique functional network for a patient. Genome-scale approaches have been developed for metabolic subsystems, using tissue and patient-specific data to define the metabolic conversions present in a cell and hence allowed functional network states, but there are challenges in extending these approaches to other cellular subsystems. Alternatively, methods based on graph theory have facilitated the integration and interpretation of omics datasets from multiple cellular subsystems, but do little to predict the magnitude of the impact of therapeutic interventions. On the other hand, multiscale quantitative systems pharmacology models of disease biology and therapeutic interventions simulate a breadth of aspects of pathophysiology, the dynamics of a number of subsystems and biomarkers, and yield quantitative predictions for clinical outcomes, but have been slow to take advantage of the breadth of omics datasets. There remain fundamental issues on how to best utilize commonly collected samples, such as isolated tissue homogenate or plasma measurements far removed from the site of diseased tissue, for a patient transcriptome, proteome, or metabolome state to mechanistically inform a multiscale simulation. We are largely left with data integration and simulation frameworks that are suited for horizontal integration across a cellular network or vertical integration of select markers toward clinical phenotypes, but rarely both. Furthermore, with the sizeable evidence of the polyfactorial nature of tumor immune escape mechanisms and alternate states of the immune system dysfunction in autoimmune disease, there is clear utility both for horizontal data integration at the network level and vertical integration from the molecular to the cellular, tissue, and clinical outcome scale.
We encourage researchers to submit articles that emphasize approaches that have been most successful as a framework for integrating omics data sets, both across cellular sub-systems critical for disease pathophysiology and through additional scales for tissue interactions and clinical phenotypic outcome, with demonstrated or promising potential in immuno-oncology, oncology, and immunoscience biomedical research.
The development of technologies to better characterize patient disease states, including methods such as sequencing, microarrays, metabolomics, and proteomics from multiple tissues, has produced datasets with potential utility in discovering new targets and identification of patients who might benefit from a given therapy. However, establishing a context for the high dimension content of omics datasets is necessary in order to maximize their predictive potential for the impact of single or combination therapies. Progress towards frameworks for omics data interpretation has arguably been uneven across functional cellular systems. For example, signaling systems currently present challenges even at the level of identifying network structure, and scientists have suggested using approaches such as fuzzy logic and decision trees due to difficulties in identifying a unique functional network for a patient. Genome-scale approaches have been developed for metabolic subsystems, using tissue and patient-specific data to define the metabolic conversions present in a cell and hence allowed functional network states, but there are challenges in extending these approaches to other cellular subsystems. Alternatively, methods based on graph theory have facilitated the integration and interpretation of omics datasets from multiple cellular subsystems, but do little to predict the magnitude of the impact of therapeutic interventions. On the other hand, multiscale quantitative systems pharmacology models of disease biology and therapeutic interventions simulate a breadth of aspects of pathophysiology, the dynamics of a number of subsystems and biomarkers, and yield quantitative predictions for clinical outcomes, but have been slow to take advantage of the breadth of omics datasets. There remain fundamental issues on how to best utilize commonly collected samples, such as isolated tissue homogenate or plasma measurements far removed from the site of diseased tissue, for a patient transcriptome, proteome, or metabolome state to mechanistically inform a multiscale simulation. We are largely left with data integration and simulation frameworks that are suited for horizontal integration across a cellular network or vertical integration of select markers toward clinical phenotypes, but rarely both. Furthermore, with the sizeable evidence of the polyfactorial nature of tumor immune escape mechanisms and alternate states of the immune system dysfunction in autoimmune disease, there is clear utility both for horizontal data integration at the network level and vertical integration from the molecular to the cellular, tissue, and clinical outcome scale.
We encourage researchers to submit articles that emphasize approaches that have been most successful as a framework for integrating omics data sets, both across cellular sub-systems critical for disease pathophysiology and through additional scales for tissue interactions and clinical phenotypic outcome, with demonstrated or promising potential in immuno-oncology, oncology, and immunoscience biomedical research.
