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Moritz Widmaier



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    FP07 - Pathology (ID 109)

    • Event: WCLC 2020
    • Type: Posters (Featured)
    • Track: Pathology, Molecular Pathology and Diagnostic Biomarkers
    • Presentations: 1
    • Moderators:
    • Coordinates: 1/28/2021, 00:00 - 00:00, ePoster Hall
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      FP07.02 - Deep Learning Based Analysis of Multiplex IHC Accurately Interprets PD-L1 and Provides Prognostic Information in NSCLC (ID 1044)

      00:00 - 00:00  |  Author(s): Moritz Widmaier

      • Abstract
      • Slides

      Introduction

      Most assessment of PD-L1 expression by immunochemistry to guide immuno-modulatory (IM) therapy in non-small cell lung cancer (NSCLC) requires only tumour cells to be scored, the so-called ‘tumour proportion score’ (TPS). This narrow approach ignores what is almost certainly crucial information within the tumour microenvironment (TME), particularly the nature and distribution of tumour infiltrating lymphocytes (TILs). Such features of the TME are underutilised in the clinical setting and are strong candidates for improving the predictive power of PD-L1 assessment alone. We describe a novel approach to analysing the TME in this context.

      Methods

      Consecutive whole slide sections from 92 resected NSCLCs were stained with H&E and immunolabelled for PD-L1 alone using the SP263 clone (monoplex), PD-L1 using the SP142 clone, CD68 and CD3 (triplex), and FoxP3, PD-1 and CD8 (triplex). Monoplex PD-L1 expression was scored by two pathologists to generate a consensus TPS score and categorised as negative, weak or strong expression; <1%, 1-49% and ≥50% TPS respectively. An existing deep-learning based PD-L1 solution was used to automatically score PD-L1 TPS. For the triplex images, densities of ‘positive’ cells were computed automatically using assay-specific deep learning algorithms, with a separate deep learning algorithm used to segment epithelial regions. The density of PD-L1+ve/CD3-ve/CD68-ve cells was used as a surrogate PD-L1 TPS with 33rd and 66th quartiles defining clinical group categorisation. Survival data were used in Kaplan-Meier survival analysis of groups divided by PD-L1 expression and immune cell densities.

      Results

      TPS scores as a continuous variable correlated well between pathologist assessment and both monoplex (Pearson correlation coefficient 0.977) and triplex (Pearson CC 0.849) assessments. Automated interpretation via triplex was similar to monoplex for grouping samples by dichotomous division at a 50% cut-off (91.2% vs. 94.6% of cases) and for placement into clinically relevant categories (79.1%, Cohen’s kappa coefficient Κ = 0.687 vs. 85.9%, K = 0.786). Sub-group analysis of tumours divided by the median for each variable into ‘high’ or ‘low’ revealed no significant difference in overall survival (OS) when stratified by CD3, FoxP3, PD-1 or CD68. However, high CD8+ve TIL densities and strong PD-L1 expression both correlated with improved OS (56 vs. 39 months, p=0.028; 60 vs. 41 months, p=0.035 respectively). In addition, tumours with a PD-L1 high/CD8+ve high profile showed significantly better OS than those assessed as PD-L1 low/CD8+ve (57 vs. 36 months, p=0.019).

      Conclusion

      Automated, deep-learning based, algorithmic scoring of PD-L1 expression is a valid and accurate approach to its assessment, and utilising triplex data provides important prognostic information. Discrepancies between monoplex and triplex assessment might be attributed to the different anti-PD-L1 antibody clones used, but the automated nature of triplex that excludes macrophages and TILs still performs very well. Our study shows the power of using this approach to augment the power of PD-L1 expression alone as a predictor of response to IM therapy and to provide prognostic information.

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