Deciphering the tumor microenvironment and accelerating oncology drug target development with machine learning. What’s hot in cancer in 2018.
The field of cancer immunology is rapidly moving towards innovative therapeutic strategies. Beside multiple combination strategies, the influence of the microbiome as well as the tumor microenvironment comes more and more into focus. With regard to the latter, cancer associated fibroblasts (CAF) well-known to influence tumor cell sensitivity towards treatment, seem to significantly influence the tumors immunological landscape. The development towards a hot or cold tumor seems to be at least partly dependent on the characteristics of the CAFs. The next logical step in immune-oncology will be to therapeutically shape this cross-talk for the benefit of the patient. Luckily, the scientific community can use the already established knowledge around CAFs to bring innovative treatments into clinical trials.
— Julia Schueler, Head of Tumor Biology Modeling, Charles River
Machine Learning/Artificial Intelligence
Increasing small virtual companies are turning to machine learning to accelerate the process of identifying novel targets in oncology and in predicting which cancers the novel inhibitors will have best potential efficacy. The analysis of literature, pre-clinical and clinical datasets has facilitated the identification of multiple new indications for existing drugs and helped identify combinations which increase efficacy. As this article points out, companies such as Benevolent, Berg and Deepmind are signing deals with Pharma, including GlaxoSmithKline and Astra Zeneca with the aim of reducing the cost of drug discovery and accelerating the delivery of clinical compounds.
— Martin O’Rourke, Director of In Vitro Oncology Biosciences, Charles River