Tissue Phenomics for predictive spatial immuno-profiling of NSCLC patients treated with durvalumab
S Althammer1 and G Schmidt1*
1Definiens AG, Germany
Current immuno-therapies for advanced non-small cell lung cancer (NSCLC) show remarkable response rates and long survival. The lives of many patients can be saved by modulating their immune system with blockades of immune checkpoints such as programmed death ligand-1 (PD-L1). We applied the Tissue Phenomics methodology to identify that subpopulation of durvalumab-treated NSCLC patients enrolled in clinical trial NCT01693562  which benefits most from therapy. Durvalumab is a human IgG1 mAb that inhibits PD-L1.
The phene discovery comprised staining of consecutive tissue sections with PD-L1 (Ventana SP263) and CD8 (Ventana SP239), automated image coregistration , cell detection , and data mining. Data mining correlated the overall and disease free (as defined by RESIST 1.1) survival time of the patients with characteristics from virtually multiplexed CD8(+) and PDL1(+) cell density heatmaps. We optimized the phene parameters to maximize the positive predictive value within prevalence >20% and Kaplan-Meier p-value for survival times <0.05. All phenes were prevalidated using Monte-Carlo methods. We discovered that a predictive score (phene) based on spatially co-occurring CD8(+) and PD-L1(+) cell densities is superior to the manual PD-L1(+) scoring of positive tumor cells .
The success of any immune-therapy is determined by the understanding of the interactions between various immune and tumor cell populations. We showed on the example of durvalumab for NSCLC patients that the Tissue Phenomics approach opens the door for a new generation of predictive diagnostic tests for the benefit of patients.
Rizvi NA, et al. J Clin Oncol 2015;33(Suppl.):[Abstract 8032].
Yigitsoy, et al. Proc. SPIE 10140 Medical Imaging 2017: in print
Brieu, et al. Proc. SPIE 9784 Medical Imaging 2016: doi:10.1117/12.2208620.
Althammer, et al. 31st Annual Meeting and Associated Programs of the Society for Immunotherapy of Cancer (SITC 2016): late breaking abstracts O1.
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