A36 patient-specific humanized PDX model for overcoming tumor resistance to immune checkpoint inhibitors in NSCLC patients

A Sobarzo, L Roisman, O Pikovsky, L Atlas, P Christopoulos, H Sültmann, N Peled

Research output: Contribution to conferencePosterpeer-review

Abstract

Background
Lung cancer is the most common cause of cancer-related mortality worldwide. Over the past few years, immune checkpoint inhibitors (ICI) have been shown to provide unprecedented clinical success in non-small cell lung cancer (NSCLC). However, ICI have some drawbacks, including initial and acquired resistance, which was observed after a complete response during and after previous ICI treatment. This relapse phenomenon was suggested to be associated with the state of the immune system and the tumor-immune response microenvironment interaction. The critical observation of cancer resistance or progression under ICI treatment suggests that a better and deeper understanding of the dynamic responses between the antitumor immune system and the tumor interaction, as it accrues in the patient setting, is therefore of utmost importance.
Methods
Using a patient-specific humanized patient-derived xerograph (PDX) (huMicX) model, we will study the coevolution between tumor and the immune system with and without ICI intervention. Comprehensive OMICS analysis on the proteomic, transcriptomic, and genomic levels will be performed on samples collected from human patients and the huMicX model.
Results
Sample biobank of whole blood and tumor tissues, and consensus protocols for peripheral HSC CD34+ isolation, are being established from NSCLC patients. Tumor tissue samples have been used to generate a PDX in mice model. Data from PDX models have demonstrated the feasibility of testing the activity of autologous transplanted lymphocytes against the patient’s tumor in vivo with a clinical benefit in the same patient overcoming ICI resistance.
Conclusion
The huMicX model is designed to provide vital knowledge of the patient-specific tumor and immune system microenvironment, and the dynamic assessment of the mechanisms of ICI tumor resistance. This preclinical model is expected to present both treatment intervention and prognostic or predictable biomarkers, which will be exploited subsequently in actual clinical settings.
Original languageEnglish
PagesS24
DOIs
StatePublished - Feb 2020

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