Virtual Library

Start Your Search

Ronald Van Marion



Author of

  • +

    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
    • +

      P2.04-47 - Tumor Mutational Load, CD8+ T Cells, Expression of PD-L1 and HLA Class I to Guide Immunotherapy Decisions (ID 1259)

      10:15 - 18:15  |  Author(s): Ronald Van Marion

      • Abstract

      Background

      A minority of NSCLC patients benefit from anti-PD1 immune checkpoint inhibitor therapy. A rational combination of biomarkers is needed. The value of using a series of mechanism-of-action based parameters was studied for response prediction of immunotherapy: tumor mutational load (TML), CD8+ T cell infiltration, HLA class I expression and the currently used PD-L1 tumor proportion score.

      Method

      Patients were prospectively included between April 2016 and August 2017, and retrospectively analyzed. Metastatic NSCLC patients (n=30) with sufficient archival tissue, obtained prior to the first nivolumab administration, were selected. Response was assessed by RECIST v1.1. Progression-free survival (PFS) and overall survival (OS) were analyzed by Kaplan-Meier methodology. TML was determined using a next-genome sequencing panel (409 cancer-related genes). Immunohistochemistry was performed to score PD-L1, total CD8+ T cell infiltration and HLA class I.

      Result

      In 30 patients with adenocarcinoma (67%) or squamous cell carcinoma (33%), high TML was significantly associated with better PFS (p=0.004) and OS (p=0.025). Interaction analyses revealed that patients with both high TML and high total CD8+ T cell infiltrate (p=0.023) or no loss of HLA class I (p=0.026), patients with high total CD8+ T cell infiltrate and no loss of HLA class I (p=0.041) or patients with both high PD-L1 and high TML (p=0.003) or no loss of HLA class I (p=0.032) were significantly associated with better PFS. Unsupervised cluster analysis based on the four markers revealed three sub-clusters, of which cluster 1A was overrepresented by patients with progressive disease (15 out of 16), with significant effect on PFS (p=0.007).

      Conclusion

      This proof-of-concept study suggests that a combination of PD-L1 expression, TML, CD8+ T cell infiltration and HLA class I expression function as a better predictive biomarker for the response to anti-PD-1 immunotherapy and PFS. Consequently, refinement of this proposed set of biomarkers and validation in a larger set of patients is warranted.