Our history in cancer bioinformatics is long: we have had the privilege to work with biologists seeking for a deeper understanding of cancer, companies developing new treatments to cancer, as well as oncologists wishing to optimize therapies to individual patients.
What causes cancer? Which alterations in DNA, pathways and metabolic processes allow a tumor to grow, spread and evade treatment? How does tumorigenic reprogramming relate to normal cellular differentiation? Our experience in cancer biology covers research into fundamental questions across cancer types and high-throughput molecular data types. Together with our collaborators and customers, we have studied heritable and somatic variants and their downstream molecular effects as well as the evolution and microenvironment of tumors, to name but a few aspects of cancer biology. Whether you are setting out to characterize an understudied malignancy or dive deep into the molecular biology of a more common cancer, we have you covered, bioinformatically speaking.
Developing a new cancer therapy is a long, costly and risky process. High-throughput measurements coupled with cutting-edge bioinformatics has a lot to offer along the way to both speed up the process and to increase the chances of success. We can help in identifying targets for a given disease based on public and proprietary molecular and clinical data. Public data on molecular drug perturbation profiles, on the other hand, enables scanning for new applications for pharmaceuticals that are already on the market. For both preclinical and clinical research on a new treatment, transcriptomic, epigenomic and proteomic measurements can be used to study the molecular mechanism of action. This allows for further optimizing the treatment and ruling out off-target effects.
Being able to predict the onset and development of cancer enables better treatment through early and accurate diagnosis and personalized treatment. We use survival analyses and machine learning approaches with clinical and molecular data to predict patient-specific risks. Such analyses result in biomarkers or multi-marker signatures with clinical potential.
