Pathway-Based Drug-Repurposing Schemes in Cancer: The Role of Translational Bioinformatics

Abstract

Cancer is a set of complex pathologies that has been recognized as a major public health problem worldwide for decades. A myriad of therapeutic strategies is indeed available. However, the wide variability in tumor physiology, response to therapy, added to multi-drug resistance poses enormous challenges in clinical oncology. The last years have witnessed a fast-paced development of novel experimental and translational approaches to therapeutics, that supplemented with computational and theoretical advances are opening promising avenues to cope with cancer defiances. At the core of these advances, there is a strong conceptual shift from gene-centric emphasis on driver mutations in specific oncogenes and tumor suppressors—let us call that the silver bullet approach to cancer therapeutics—to a systemic, semi-mechanistic approach based on pathway perturbations and global molecular and physiological regulatory patterns—we will call this the shrapnel approach. The silver bullet approach is still the best one to follow when clonal mutations in driver genes are present in the patient, and when there are targeted therapies to tackle those. Unfortunately, due to the heterogeneous nature of tumors this is not the common case. The wide molecular variability in the mutational level often is reduced to a much smaller set of pathway-based dysfunctions as evidenced by the well-known hallmarks of cancer. In such cases “shrapnel gunshots” may become more effective than “silver bullets”. Here, we will briefly present both approaches and will abound on the discussion on the state of the art of pathway-based therapeutic designs from a translational bioinformatics and computational oncology perspective. Further development of these approaches depends on building collaborative, multidisciplinary teams to resort to the expertise of clinical oncologists, oncological surgeons, and molecular oncologists, but also of cancer cell biologists and pharmacologists, as well as bioinformaticians, computational biologists and data scientists. These teams will be capable of engaging on a cycle of analyzing high-throughput experiments, mining databases, researching on clinical data, validating the findings, and improving clinical outcomes for the benefits of the oncological patients.

Overcoming Therapy Resistance in Colon Cancer by Drug Repurposing

“…It is important to highlight that the heterogeneity of cancer physiology and response to therapy and the multiple resistance mechanisms these cells develop represent an enormous challenge in oncology. The combination of computational frameworks (translational bioinformatics, computational intelligence, and methodological and systems biology) in multidisciplinary teams has successfully sped up clinical trials for repurposing drugs [143]. The emergence of new technologies in this field, such as large-scale multi-omics sequencing, genome-wide positioning systems network (GPSnet) algorithms, and other artificial intelligence algorithms, has helped identify new targets for older drugs [144].…”

Geldanamycin treatment does not result in anti-cancer activity in a preclinical model of orthotopic mesothelioma

“…Alternatively, the myogenesis gene expression profile may be a bystander effect of exposure to the orthotopic environment, rather than a program driving invasive mesothelioma growth. Lastly, drug repurposing approaches have their limitations, in particular when based on an overlap in gene expression signatures between disease-associated tissues and cell lines treated with compounds, such as LINCS/CMap, resulting in false-positive hits (53) (54) (55). Other treatment avenues will need to be explored to specifically target mesothelioma invasion.…”

Gene Signature-Based Drug Repositioning

“…Secondly, it could be a feature summarizing the gene-signature at a higher level, which is useful in machine learning-based modeling. It is different from the gene level as it captures different information about drugs or diseases [28][29][30]. Thirdly, the pathway analysis could enhance the confidence of the prediction of the candidate drugs [31].…”