Abstract 64: Targeting immune checkpoints: using a big data approach for their identification, prioritization and application

Abstract

Tumor cells have long been known to successfully evade immune surveillance mechanisms.In order to curtail the tumor progression as well as to develop effective therapeutic anti-tumor strategies, key immune regulators,or so-called immune checkpoints, havebeen recognizedas playing an important role. To prevent excess tissue damage,immune checkpoints that refer to a number of inhibitory players involved in immune responses,are crucial for maintaining self-tolerance and modulating the duration and amplitude of physiological immune responses.Therapies targeting such immune checkpoints such as CTLA-4 and PD-1 have shown great clinical successas they boost the already existing immune response working against the tumorprogression. To identify novel immune checkpoints it becomes crucial to comprehensively analyze the wealth of existing knowledge regarding key signaling pathways, theircross-talk with immune regulators, protein expression profiles in tumor tissues, and patient outcomes data. These data silos require a big data analytics approach to successfully hypothesize, query and derive meaningful conclusions from this data. With a proprietary big data analytics platform PharmGPS™, we have identified a number of immune checkpoints that have clear therapeutic potential for solid tumor regenesis. First, second and third degree associations between tumorigenic pathways and immune checkpoints were identified and preliminary findings will be presented. Citation Format: Sheetal Kaw, Sanatan Upmanyu, Himani Sharma, Krishnan Nandabalan. Targeting immune checkpoints: using a big data approach for their identification, prioritization and application. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 64. doi:10.1158/1538-7445.AM2015-64