[4] Cancer systems biology encompasses concrete applications of systems biology approaches to cancer research, notably (a) the need for better methods to distill insights from large-scale networks, (b) the importance of integrating multiple data types in constructing more realistic models, (c) challenges in translating insights about tumorigenic mechanisms into therapeutic interventions, and (d) the role of the tumor microenvironment, at the physical, cellular, and molecular levels.
and molecular imaging, to generate computational algorithms and quantitative models[10] that shed light on mechanisms underlying the cancer process and predict response to intervention.
[11] Such mismatch between clinical results and the massive amounts of data acquired by omics technology highlights the existence of basic gaps in our knowledge of cancer fundamentals.
[48] A cancer systems biology approach can be applied at different levels, from an individual cell to a tissue, a patient with a primary tumour and possible metastases, or to any combination of these situations.
This approach can integrate the molecular characteristics of tumours at different levels (DNA, RNA, protein, epigenetic, imaging)[49] and different intervals (seconds versus days) with multidisciplinary analysis.
[50] One of the major challenges to its success, besides the challenge posed by the heterogeneity of cancer per se, resides in acquiring high-quality data that describe clinical characteristics, pathology, treatment, and outcomes and integrating the data into robust predictive models [51][19][20][21][22][52][53] Mathematical modeling can provide useful context for the rational design, validation and prioritization of novel cancer drug targets and their combinations.
Earlier efforts relating to multimodality imaging of cancer have focused on the integration of anatomical and functional characteristics, such as PET-CT and single-photon emission CT (SPECT-CT), whereas more-recent advances and applications have involved the integration of multiple quantitative, functional measurements (for example, multiple PET tracers, varied MRI contrast mechanisms, and PET-MRI), thereby providing a more-comprehensive characterization of the tumour phenotype.
The enormous amount of complementary quantitative data generated by such studies is beginning to offer unique insights into opportunities to optimize care for individual patients.
Although important technical optimization and improved biological interpretation of multimodality imaging findings are needed, this approach can already be applied informatively in clinical trials of cancer therapeutics using existing tools.