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John R Gosney
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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): John R Gosney
- Abstract
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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IS12 - Industry Symposium Sponsored by: Boehringer Ingelheim: Know Your Patients With NSCLC (ID 289)
- Event: WCLC 2020
- Type: Industry Symposium
- Track:
- Presentations: 1
- Moderators:
- Coordinates: 1/30/2021, 13:00 - 14:00, Industry Symposia Auditorium (via Industry Hub)
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IS12.02 - Know How to Test Your Patients (ID 4360)
13:00 - 14:00 | Presenting Author(s): John R Gosney
- Abstract
Abstract not provided
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MA08 - Advances in Biomarkers for Immune Checkpoint Blockade and Targeted Therapy in Non Small Cell Lung Carcinoma (ID 166)
- Event: WCLC 2020
- Type: Mini Oral
- Track: Pathology, Molecular Pathology and Diagnostic Biomarkers
- Presentations: 1
- Moderators:
- Coordinates: 1/30/2021, 16:45 - 17:45, Scientific Program Auditorium
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MA08.05 - High Concordance in Scoring PD-L1 in NSCLC Using the Roche Digital Pathology uPath system and PD-L1 Assessment Algorithm (ID 1569)
16:45 - 17:45 | Author(s): John R Gosney
- Abstract
- Presentation
Introduction
Accurate and reproducible assessment of PD-L1 expression is crucial in guiding the use of anti PD-1/PD-L1 immunomodulatory therapy. Interpretation can be challenging and both intra- and inter-pathologist discordance has been shown to be potentially significant in affecting clinical outcomes. Digital pathology and algorithmic assessment tools provide an opportunity to increase accuracy and concordance when making this important assessment in non-small cell lung cancer (NSCLC).
Methods
Sections of 198 NSCLCs, (84 EBUS aspirates or small tissue biopsies and 114 resections) immunolabelled for PD-L1 using the Roche-Ventana SP263 antibody were scanned with a Roche DP200 digital image scanner and analysed using uPath slide viewer and the Roche PD-L1 interpretative algorithm. Sections were assessed by three ‘blinded’ pathologists for expression of PD-L1 which was given as an absolute percentage, the tumour proportion score (TPS) in four ways; (1) unassisted (by pathologist using conventional microscopy), (2) automated whole slide (unsupervised uPath assessment of whole slide), (3) automated annotated (uPath assessment of slide annotated by pathologist) and (4) assisted (by pathologist using digital image assisted by uPath assessment of annotated slide). The last method of assessment was repeated after a six-week ‘washout’ period. Intraclass correlation co-efficient (ICC) and Cohen’s Kappa (for clinical categories of <1%, 1-49% or ≥50% TPS) were used to compare concordance of scoring assessment methods.
Results
Table 1. Comparison of TPS scoring methods
TPS assessment methods - Full Cohort
ICC
Kappa
p-value
Assisted vs unassisted
0.973
0.856
<0.0001
Assisted vs automated whole slide
0.911
0.475
<0.0001
Assisted vs automated annotated
0.953
0.598
<0.0001
TPS assessment methods - small biopsies/EBUS only
Assisted vs automated annotated
0.952
0.836
<0.0001
Intra-pathologist concordance between repeated assisted reads was very high (ICC 0.991 K 0.989 p<0.0001) as was inter-pathologist concordance for assisted interpretations (ICC 0.986 K 0.910 p<0.0001 respectively)
Conclusion
Automated algorithmic assessment guided by pathologists is an accurate approach that helps produce consistent and reliable interpretation of PD-L1 expression in NSCLC by reducing intra- and inter- observer discordance. Digital pathology and automated algorithms are powerful tools that can help optimise the predictive power of PD-L1 expression as a predictor of response to immunomodulatory therapy.
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