Melanoma

Melanoma is a neoplasm of the skin and originates from transformed melanocytes. Since the discovery of the high prevalence of mutations in b-Raf proto-oncogene (BRAF) and NRAS protooncogene, GTPase (NRAS), small-molecule inhibitors have been developed. According to its molecular behavior, melanoma represents an interesting tumor model in which computational modeling can be applied.

Computational Melanoma Research

There is a growing body of evidence highlighting the applications of computational modeling in the field of biomedicine. Computational approaches help researchers understand how biochemical pathways change during melanoma cell proliferation, invasiveness, survival, and drug resistance based on network structure and dynamic behavior. Cancer dynamics analysis may allow the discovery of new molecular targets useful for the design of novel therapies and for overcoming resistance to anticancer drugs. Integrative network-based tools and well-grounded inductive in silico research can reveal disease mechanisms, stratify patients, and support treatment individualization. 

Identify Good Molecular Targets

Because of the heterogeneity of many tumors, it is very challenging work to identify good molecular targets. For instance, resistant subclones of overexpressed and mutated genes may prevent them from being good molecular targets. Therefore, the best target is a core gene whose mutation occurs early in oncogenesis and dysregulates a key pathway that drives tumor growth in all of the subclones. Examples include mutations in the genes ABL, HER-2, KIT, EGFR, and probably BRAF, in melanoma.

Computational Methods

• Well-designed computational models of melanoma.
• Models of melanoma genomics
• Models of melanoma transcriptomics
• Models of melanoma proteomics
• Models of melanoma metabolomics
• Mechanistic network models of melanoma
• Cell population models of the interplay of melanoma cells
• Spatial models of melanoma
• Bioinformatics methods in identifying disease mechanisms.
• Methods integrating medical images and sequencing data.
• Methods for drug repositioning and drug target prediction.
• Validation of results from computational studies by experiments.

The use of advanced computer models allowing the simulation of complex biological processes provides hypotheses and supports experimental design. The research in fighting aggressive cancers, such as melanoma, is making great strides. Due to the combinatorial complexity of new drug discovery, a systematic approach based only on experimentation is not possible. Computational models are necessary for bringing cancer drug discovery into the era of omics, big data, and personalized medicine.

CD ComputaBio has multiple resources including academic research and preclinical works in the identification of a suitable disease target and its corresponding hit. Contact us for more service details.