Abstract
Rapid advancements in high-throughput omics technologies and experimental protocols have led to the generation of vast amounts of scale-specific biomolecular data on cancer that now populates several online databases and resources. Cancer systems biology models built using this data have the potential to provide specific insights into complex multifactorial aberrations underpinning tumor initiation, development, and metastasis. Furthermore, the annotation of these single- and multi-scale models with patient data can additionally assist in designing personalized therapeutic interventions as well as aid in clinical decision-making. Here, we have systematically reviewed the emergence and evolution of (i) repositories with scale-specific and multi-scale biomolecular cancer data, (ii) systems biology models developed using this data, (iii) associated simulation software for the development of personalized cancer therapeutics, and (iv) translational attempts to pipeline multi-scale panomics data for data-driven in silico clinical oncology. The review concludes that the absence of a generic, zero-code, panomics-based multi-scale modeling pipeline and associated software framework, impedes the development and seamless deployment of personalized in silico multi-scale models in clinical settings.
Highlights
In 1971, President Richard Nixon declared his euphemistic “war on cancer” through the promulgation of the National Cancer Act [1]
These cell lines are defined based on gene expression profiles and morphological features which have been cataloged in various databases such as the Cancer Cell Line Encyclopedia (CCLE) [42] (Figure 1F and Table S5)
The results generated from the Trial of Principle (TOP) and Society of Paediatric Oncology (SIOP) trials enabled the Advancing Clinico-Genomic Trials on Cancer (ACGT) oncosimulators to adapt in light of real clinical conditions and the software to be validated against multi-scale patient data
Summary
In 1971, President Richard Nixon declared his euphemistic “war on cancer” through the promulgation of the National Cancer Act [1]. Hanahan and Weinberg [17, 18] summarized this heterogeneity and plasticity into “Hallmarks of Cancer” – a set of progressively acquired traits during the development of cancer. Experimental techniques such as high-throughput nextgeneration sequencing, and mass spectrometry-based proteomics are providing specific spatiotemporal cues on patient-specific biomolecular aberrations involved in cancer development and growth. The voluminous high-throughput patient data coupled with the remarkable complexity of the disease has given impetus to data integrative in silico cancer modeling and therapeutic evaluation approaches [19]. The review concludes by highlighting the need of integrating and modeling multi-omics data and associated software pipelines for employment in developing personalized therapeutics
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