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John P. Groten



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    P2.04 - Immuno-oncology (ID 167)

    • Event: WCLC 2019
    • Type: Poster Viewing in the Exhibit Hall
    • Track: Immuno-oncology
    • Presentations: 1
    • Moderators:
    • Coordinates: 9/09/2019, 10:15 - 18:15, Exhibit Hall
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      P2.04-06 - Blood-Based Multiplex Kinase Activity Profiling as a Predictive Marker for Clinical Response to Checkpoint Blockade in Advanced NSCLC (ID 1322)

      10:15 - 18:15  |  Author(s): John P. Groten

      • Abstract

      Background

      Only a minority of non-small-cell lung cancer (NSCLC) patients benefit from treatment with immune checkpoint inhibitors (ICIs), therefore, there is an urgent need for response prediction. Previously, the potential of using tyrosine kinase activity profiling of baseline peripheral blood mononuclear cells (PBMC) was demonstrated in an analysis with ICI treated advanced melanoma patients [1]. Here, we apply this methodology to evaluate the predictive value for response to ICIs in NSCLC.

      Method

      59 ICI naïve advanced NSCLC patients treated with PD-1 blockade were included in this exploratory analysis of the prospective immuno-monitoring study (MULTOMAB; NTR7015). PBMC were isolated from patient blood samples obtained prior to treatment with ICIs. Kinase phosphorylation signatures of PBMC lysates were measured using a micro-array, comprising of 144 different peptides derived from sites that are substrates for protein tyrosine kinases. Correlation of the profiles with progression free survival (PFS) and overall survival (OS) was analyzed using univariate cox-regression. Predictive multivariate (GLMnet) analysis was performed by binary survival grouping of patients with a cut-off at 140 days for PFS and 365 days for OS. The predictive performance of the models was estimated using cross validation. Multiplex flow cytometry, enumerating 18 immune cell subsets and assessing expression for 28 T cell markers, was performed for a selection of patients to gain additional insight in the immune profile [2].

      Result

      Univariate Cox regression showed significant correlation of phosphorylation signal with PFS for 7 peptides (p < 0.01, False Discovery Rate [FDR] = 10%), and with OS for 34 peptides (p < 0.01, FDR = 2%). Evaluation of the predictive value of GLMnet models resulted in estimates for the Correct Classification rate (CCR) of 67-70% for PFS and 67-73% for OS. When the cross validated predictions of the models were used to categorize the patients in a predicted-high-risk and a predicted-low-risk group, this resulted in a significant difference in survival between these groups. The predicted-low-risk group displayed significant better median PFS and OS than the predicted-high-risk group ([246 vs. 56 days; HR 2.3, 95%CI 1.2-4.7, p=0.02] and [488 vs. 171 days; HR 2.7, 95%CI 1.1-6.6, p=0.03], respectively).

      Conclusion

      Similar to melanoma, kinase activity profiles of baseline PBMC samples of advanced NSCLC patients can predict the response to PD-1 blockade. This assay may serve as a predictive biomarker for response to anti-PD-1 therapy. Involvement of immune receptor kinases is being investigated. An independent validation study is underway.