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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...

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Autores principales: 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.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2019
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.
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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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