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9030 Background: EGFR exon 20 insertions (Ex20Ins), the 3rd most common EGFR activating mutation, are generally unresponsive to 1st and 2nd generation EGFR-TKIs. Development of EGFR-TKIs that effectively target NSCLC with Ex20Ins mutations represents a major unmet need. Osimertinib is an EGFR TKI approved for the treatment of advanced NSCLC harboring EGFR T790M, but the potential of osimertinib remains to be fully assessed in patients (pts) with Ex20Ins NSCLC. Methods: CRISPR engineered Ex20Ins cell line xenografts representing the two most common Ex20Ins (D770_N771InsSVD and V769_D770InsASV) and pt derived xenograft (PDX) of 3 EGFR Ex20Ins (V769_D770InsASV, M766_A767insASV, H773_V774insNPH) were used for in vivo experiments. Xenografts were treated by oral gavage with vehicle, erlotinib (50 mg/kg/day) or afatinib (20 mg/kg/day), osimertinib metabolite AZ5104 (50 mg/kg/day) and osimertinib (25 mg/kg/day) and assessed for tumor growth inhibition (TGI). Immunoblotting was performed for EGFR and relevant signaling pathways. A pt from whom the V769_D770InsASV Ex20ins PDX was derived was treated on a UC Davis IRB approved protocol with osimertinib at 160 mg PO once-daily (QD). Results: At completion of treatment, QD administration of osimertinib or AZ5104 induced significant TGI in xenografts across the 4 EGFR Ex20ins tested (range 60-95% TGI, p < 0.001 compared to control for all models) that was superior to either afatinib or erlotinib. Robust decrease in p-EGFR, p-ERK, p-Akt, p-Stat3 was observed with osimertinib treatment. The patient corresponding to the V769_D770InsASV Ex20ins PDX treated with osimertinib exhibited clinical improvement and tumor shrinkage; unfortunately he was found to have interstitial pneumonitis that necessitated drug discontinuation. Conclusions: Osimertinib at clinically representative doses has in vivo activity across multiple EGFR Ex20ins that comprising the most common Ex20ins detected in patients (~50% prevalence); metabolite AZ5104 may contribute to efficacy. Tumor shrinkage was observed in a patient with lung cancer harboring an Ex20ins treated for a limited time with osimertinib. Based on this in vivo xenograft and pt data, osimertinib warrants further study in pts with EGFR Ex20ins NSCLC.

Multiplex methodologies, especially those with high-throughput capabilities generate large volumes of data. Accumulation of such data (e.g., genomics, proteomics, metabolomics etc.) is fast becoming more common and thus requires the development and implementation of effective data mining strategies designed for biological and clinical applications. Multiplex microbead immunoassay (MMIA), on xMAP or MagPix platform (Luminex), which is amenable to automation, offers a major advantage over conventional methods such as Western blot or ELISA, for increasing the efficiencies in serodiagnosis of infectious diseases. MMIA allows detection of antibodies and/or antigens efficiently for a wide range of infectious agents simultaneously in host blood samples, in one reaction vessel. In the process, MMIA generates large volumes of data. In this report we demonstrate the application of data mining tools on how the inherent large volume data can improve the assay tolerance (measured in terms of sensitivity and specificity) by analysis of experimental data accumulated over a span of two years. The combination of prior knowledge with machine learning tools provides an efficient approach to improve the diagnostic power of the assay in a continuous basis. Furthermore, this study provides an in-depth knowledge base to study pathological trends of infectious agents in mouse colonies on a multivariate scale. Data mining techniques using serodetection of infections in mice, developed in this study, can be used as a general model for more complex applications in epidemiology and clinical translational research.