IO Frontiers World 2019

Computational Medicine in the Immuno-Oncology Age


The human body has established immunological processes to keep cancerous cell growth in check. In the late 1990’s, researchers began to identify mechanisms by which tumor cells could evade a patient’s immune system through selective gain-of-function gene mutations(1). While this was an important observation for how cancerous cells adapt and evolve in order to survive; it also provided a powerful avenue to inhibit tumor growth. To interrogate the full onslaught of changes that were occurring in target cancer cells; techniques such as DNA/RNA profiling were utilized to catalogue the wide array of changes between normal tissue, immune repertoire, and tumor cells(2).

The volumes of these data generated soon outstripped the ability of non-computational approaches to review and document such information for utility. This led to two major needs; one) high-throughput computational methods that would drive the understanding of patient data(3) and two) the continued requirement for sound experimental design in the early age of Precision Medicine. By interrogating the individual molecular changes that occur in cancer patients; approaches such as CAR-T (T-cells modified with Chimeric Antigen Receptors) and customized tumor vaccines have been made possible.  Understanding the patient cellular changes at the molecular level - before treatment, during administration of medicinal care, and post response - allows for researchers to obtain “discreet biological statements” needed in molecular medicine. It is mindful to note the computational methods that assist clinical and physician researchers in the identification of antigen targets; while critically powerful when used in the treatment of human disease, are also another tool in the arsenal of mindful researchers who rely on sound experimental design in order to generate treatments that provide hope to cancer patients. 


1 – Loging and Reisman (1999) Inhibition of the putative tumor suppressor gene TIMP-3 by tumor-derived p53 mutants and wild type p53. Oncogene, 18,7608- 7612.

2- Loging et al. (2000) Identifying potential tumor markers and antigens by database mining and rapid expression screening. Genome Research, 10, 1393-1402.

3- Loging, Harland, and Williams-Jones (2007) High-throughput electronic biology: mining information for drug discovery. Nat Rev Drug Discov. 6(3):220-230.


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