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1231 Exploring the landscape of pan-cancer immune-related adverse events through clinical trials big data mining
  1. Muhammad Z Fadlullah Wilmot and
  2. Aik Choon Tan
  1. Huntsman Cancer Institute, University of Utah, Salt Lake City, UT, USA


Background Immune checkpoint inhibitors (ICIs) have revolutionized the treatment for multiple advanced solid cancers by reinvigorate patient’s own immune system to eliminate tumour cells for durable responses in selected patients. However, the benefit of ICIs is usually hampered by immune-related adverse events (irAEs), which are autoimmune reactions that can affect diverse organs and potentially life-threatening. Therefore, it is imperative to understand the incidence of irAEs for ICIs in either treatment- or cancer-specific to improve clinical management and identify intervention opportunities. To address this knowledge gap, we report the development of irAE portal - a novel and interactive web-portal to explore relationships between treatment- or cancer-specific irAEs across pan-cancer cohorts.

Methods We performed data mining of over 434,000 clinical trial records from to extract reported adverse events in participants administered with ten FDA-approved ICIs. In total, the records of over 74,000 pan-cancer participants across 343 clinical trials records were included (figure 1). We consolidated the records to harmonize the irAE categories and differentiated trials involving single agent ICI or combination therapies. We applied reporting odds ratio (ROR) statistics to summarize the incidence of irAE in a systematic manner across pan-cancer cohort.

Results Exploratory analyses revealed treatment- or cancer-specific irAEs. For example, melanoma patients had the highest incident of colitis when Ipilimumab were administered (ROR = 5.6) compared to either Nivolumab (ROR = 0.2) or Pembrolizumab (ROR = 0.5). In contrast, administration of Pembrolizumab in lung cancer patients increased the incident of hypophysitis (ROR = 1.9) and pneumonotitis (ROR = 2.5=4). These findings were supported by published literature.

Conclusions In conclusion, we developed an interactive data visualization and analytics web-portal to assist researchers and clinicians understand and compare treatment- or cancer-specific irAEs. Future works include incorporation of gene expression studies in our web-portal to allow user to identify gene signatures associated with treatment- and cancer-specific irAEs.

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