Breast Cancer

Breast Cancer (BC) is the most common cancer in women and the second most common cause of cancer mortality among females. The complexity of cancer forms a major obstacle to a comprehensive understanding of the molecular mechanisms of oncogenesis. Combined analyses of molecular data at multiple levels, such as DNA copy-number alteration, mRNA and miRNA expression, can clarify biological functions and pathways deregulated in breast cancer. The integrative methods that are used to investigate these data involve different fields, including computational biology, bioinformatics, and statistics.

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 breast cancer.

Computational Methods and Tools

• Computational models of breast cancer.
• Bioinformatics methods in identifying disease mechanisms.
• Methods integrating medical images and sequencing data.
• Drug repositioning and drug target prediction.
• Validation of results from computational studies by experiments.

Applying computational modeling approaches can elucidate the pathways most critically involved in breast cancer formation and progression, the impact of particular mutations on pathway operation, consequences of altered cell behavior in tissue environments, and effects of molecular therapeutics. Methodologies for computational analysis can vary widely depending on the question being posed and the quantitative experimental data, ranging from highly abstracted models using correlative regression to highly specified models using differential equations, with network component interaction and logic modeling techniques intermediate to these.

The goal of our services is to combine molecular information of models to understand diseases and their complexity, and finally attempt to predict biological function at the cellular, tissue, organ, and whole-organism levels. We have multiple resources including academic research and preclinical works in the identification of a suitable disease target and its corresponding hit. We are also interested in how the machine learning-based integration of multi-omic datasets can aid in the discovery of new cancer subgroups and biomarkers. Contact us for more service details.