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Automated image analysis of NSCLC biopsies to predict response to anti-PD-L1 therapy
BACKGROUND: Immune checkpoint therapies (ICTs) targeting the programmed cell death-1 (PD1)/programmed cell death ligand-1 (PD-L1) pathway have improved outcomes for patients with non-small cell lung cancer (NSCLC), particularly those with high PD-L1 expression. However, the predictive value of manua...
Autores principales: | , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6501300/ https://www.ncbi.nlm.nih.gov/pubmed/31060602 http://dx.doi.org/10.1186/s40425-019-0589-x |
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author | Althammer, Sonja Tan, Tze Heng Spitzmüller, Andreas Rognoni, Lorenz Wiestler, Tobias Herz, Thomas Widmaier, Moritz Rebelatto, Marlon C. Kaplon, Helene Damotte, Diane Alifano, Marco Hammond, Scott A. Dieu-Nosjean, Marie-Caroline Ranade, Koustubh Schmidt, Guenter Higgs, Brandon W. Steele, Keith E. |
author_facet | Althammer, Sonja Tan, Tze Heng Spitzmüller, Andreas Rognoni, Lorenz Wiestler, Tobias Herz, Thomas Widmaier, Moritz Rebelatto, Marlon C. Kaplon, Helene Damotte, Diane Alifano, Marco Hammond, Scott A. Dieu-Nosjean, Marie-Caroline Ranade, Koustubh Schmidt, Guenter Higgs, Brandon W. Steele, Keith E. |
author_sort | Althammer, Sonja |
collection | PubMed |
description | BACKGROUND: Immune checkpoint therapies (ICTs) targeting the programmed cell death-1 (PD1)/programmed cell death ligand-1 (PD-L1) pathway have improved outcomes for patients with non-small cell lung cancer (NSCLC), particularly those with high PD-L1 expression. However, the predictive value of manual PD-L1 scoring is imperfect and alternative measures are needed. We report an automated image analysis solution to determine the predictive and prognostic values of the product of PD-L1+ cell and CD8+ tumor infiltrating lymphocyte (TIL) densities (CD8xPD-L1 signature) in baseline tumor biopsies. METHODS: Archival or fresh tumor biopsies were analyzed for PD-L1 and CD8 expression by immunohistochemistry. Samples were collected from 163 patients in Study 1108/NCT01693562, a Phase 1/2 trial to evaluate durvalumab across multiple tumor types, including NSCLC, and a separate cohort of 199 non-ICT- patients. Digital images were automatically scored for PD-L1+ and CD8+ cell densities using customized algorithms applied with Developer XD™ 2.7 software. RESULTS: For patients who received durvalumab, median overall survival (OS) was 21.0 months for CD8xPD-L1 signature-positive patients and 7.8 months for signature-negative patients (p = 0.00002). The CD8xPD-L1 signature provided greater stratification of OS than high densities of CD8+ cells, high densities of PD-L1+ cells, or manually assessed tumor cell PD-L1 expression ≥25%. The CD8xPD-L1 signature did not stratify OS in non-ICT patients, although a high density of CD8+ cells was associated with higher median OS (high: 67 months; low: 39.5 months, p = 0.0009) in this group. CONCLUSIONS: An automated CD8xPD-L1 signature may help to identify NSCLC patients with improved response to durvalumab therapy. Our data also support the prognostic value of CD8+ TILS in NSCLC patients who do not receive ICT. TRIAL REGISTRATION: ClinicalTrials.gov identifier: NCT01693562. Study code: CD-ON-MEDI4736-1108. Interventional study (ongoing but not currently recruiting). Actual study start date: August 29, 2012. Primary completion date: June 23, 2017 (final data collection date for primary outcome measure). ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s40425-019-0589-x) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-6501300 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-65013002019-05-10 Automated image analysis of NSCLC biopsies to predict response to anti-PD-L1 therapy Althammer, Sonja Tan, Tze Heng Spitzmüller, Andreas Rognoni, Lorenz Wiestler, Tobias Herz, Thomas Widmaier, Moritz Rebelatto, Marlon C. Kaplon, Helene Damotte, Diane Alifano, Marco Hammond, Scott A. Dieu-Nosjean, Marie-Caroline Ranade, Koustubh Schmidt, Guenter Higgs, Brandon W. Steele, Keith E. J Immunother Cancer Research Article BACKGROUND: Immune checkpoint therapies (ICTs) targeting the programmed cell death-1 (PD1)/programmed cell death ligand-1 (PD-L1) pathway have improved outcomes for patients with non-small cell lung cancer (NSCLC), particularly those with high PD-L1 expression. However, the predictive value of manual PD-L1 scoring is imperfect and alternative measures are needed. We report an automated image analysis solution to determine the predictive and prognostic values of the product of PD-L1+ cell and CD8+ tumor infiltrating lymphocyte (TIL) densities (CD8xPD-L1 signature) in baseline tumor biopsies. METHODS: Archival or fresh tumor biopsies were analyzed for PD-L1 and CD8 expression by immunohistochemistry. Samples were collected from 163 patients in Study 1108/NCT01693562, a Phase 1/2 trial to evaluate durvalumab across multiple tumor types, including NSCLC, and a separate cohort of 199 non-ICT- patients. Digital images were automatically scored for PD-L1+ and CD8+ cell densities using customized algorithms applied with Developer XD™ 2.7 software. RESULTS: For patients who received durvalumab, median overall survival (OS) was 21.0 months for CD8xPD-L1 signature-positive patients and 7.8 months for signature-negative patients (p = 0.00002). The CD8xPD-L1 signature provided greater stratification of OS than high densities of CD8+ cells, high densities of PD-L1+ cells, or manually assessed tumor cell PD-L1 expression ≥25%. The CD8xPD-L1 signature did not stratify OS in non-ICT patients, although a high density of CD8+ cells was associated with higher median OS (high: 67 months; low: 39.5 months, p = 0.0009) in this group. CONCLUSIONS: An automated CD8xPD-L1 signature may help to identify NSCLC patients with improved response to durvalumab therapy. Our data also support the prognostic value of CD8+ TILS in NSCLC patients who do not receive ICT. TRIAL REGISTRATION: ClinicalTrials.gov identifier: NCT01693562. Study code: CD-ON-MEDI4736-1108. Interventional study (ongoing but not currently recruiting). Actual study start date: August 29, 2012. Primary completion date: June 23, 2017 (final data collection date for primary outcome measure). ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s40425-019-0589-x) contains supplementary material, which is available to authorized users. BioMed Central 2019-05-06 /pmc/articles/PMC6501300/ /pubmed/31060602 http://dx.doi.org/10.1186/s40425-019-0589-x Text en © The Author(s). 2019 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Article Althammer, Sonja Tan, Tze Heng Spitzmüller, Andreas Rognoni, Lorenz Wiestler, Tobias Herz, Thomas Widmaier, Moritz Rebelatto, Marlon C. Kaplon, Helene Damotte, Diane Alifano, Marco Hammond, Scott A. Dieu-Nosjean, Marie-Caroline Ranade, Koustubh Schmidt, Guenter Higgs, Brandon W. Steele, Keith E. Automated image analysis of NSCLC biopsies to predict response to anti-PD-L1 therapy |
title | Automated image analysis of NSCLC biopsies to predict response to anti-PD-L1 therapy |
title_full | Automated image analysis of NSCLC biopsies to predict response to anti-PD-L1 therapy |
title_fullStr | Automated image analysis of NSCLC biopsies to predict response to anti-PD-L1 therapy |
title_full_unstemmed | Automated image analysis of NSCLC biopsies to predict response to anti-PD-L1 therapy |
title_short | Automated image analysis of NSCLC biopsies to predict response to anti-PD-L1 therapy |
title_sort | automated image analysis of nsclc biopsies to predict response to anti-pd-l1 therapy |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6501300/ https://www.ncbi.nlm.nih.gov/pubmed/31060602 http://dx.doi.org/10.1186/s40425-019-0589-x |
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