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Deep learning-based scoring of tumour-infiltrating lymphocytes is prognostic in primary melanoma and predictive to PD-1 checkpoint inhibition in melanoma metastases

BACKGROUND: Recent advances in digital pathology have enabled accurate and standardised enumeration of tumour-infiltrating lymphocytes (TILs). Here, we aim to evaluate TILs as a percentage electronic TIL score (eTILs) and investigate its prognostic and predictive relevance in cutaneous melanoma. MET...

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Autores principales: Chatziioannou, Eftychia, Roßner, Jana, Aung, Thazin New, Rimm, David L., Niessner, Heike, Keim, Ulrike, Serna-Higuita, Lina Maria, Bonzheim, Irina, Kuhn Cuellar, Luis, Westphal, Dana, Steininger, Julian, Meier, Friedegund, Pop, Oltin Tiberiu, Forchhammer, Stephan, Flatz, Lukas, Eigentler, Thomas, Garbe, Claus, Röcken, Martin, Amaral, Teresa, Sinnberg, Tobias
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10363450/
https://www.ncbi.nlm.nih.gov/pubmed/37295047
http://dx.doi.org/10.1016/j.ebiom.2023.104644
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author Chatziioannou, Eftychia
Roßner, Jana
Aung, Thazin New
Rimm, David L.
Niessner, Heike
Keim, Ulrike
Serna-Higuita, Lina Maria
Bonzheim, Irina
Kuhn Cuellar, Luis
Westphal, Dana
Steininger, Julian
Meier, Friedegund
Pop, Oltin Tiberiu
Forchhammer, Stephan
Flatz, Lukas
Eigentler, Thomas
Garbe, Claus
Röcken, Martin
Amaral, Teresa
Sinnberg, Tobias
author_facet Chatziioannou, Eftychia
Roßner, Jana
Aung, Thazin New
Rimm, David L.
Niessner, Heike
Keim, Ulrike
Serna-Higuita, Lina Maria
Bonzheim, Irina
Kuhn Cuellar, Luis
Westphal, Dana
Steininger, Julian
Meier, Friedegund
Pop, Oltin Tiberiu
Forchhammer, Stephan
Flatz, Lukas
Eigentler, Thomas
Garbe, Claus
Röcken, Martin
Amaral, Teresa
Sinnberg, Tobias
author_sort Chatziioannou, Eftychia
collection PubMed
description BACKGROUND: Recent advances in digital pathology have enabled accurate and standardised enumeration of tumour-infiltrating lymphocytes (TILs). Here, we aim to evaluate TILs as a percentage electronic TIL score (eTILs) and investigate its prognostic and predictive relevance in cutaneous melanoma. METHODS: We included stage I to IV cutaneous melanoma patients and used hematoxylin-eosin-stained slides for TIL analysis. We assessed eTILs as a continuous and categorical variable using the published cut-off of 16.6% and applied Cox regression models to evaluate associations of eTILs with relapse-free, distant metastasis-free, and overall survival. We compared eTILs of the primaries with matched metastasis. Moreover, we assessed the predictive relevance of eTILs in therapy-naïve metastases according to the first-line therapy. FINDINGS: We analysed 321 primary cutaneous melanomas and 191 metastatic samples. In simple Cox regression, tumour thickness (p < 0.0001), presence of ulceration (p = 0.0001) and eTILs ≤16.6% (p = 0.0012) were found to be significant unfavourable prognostic factors for RFS. In multiple Cox regression, eTILs ≤16.6% (p = 0.0161) remained significant and downgraded the current staging. Lower eTILs in the primary tissue was associated with unfavourable relapse-free (p = 0.0014) and distant metastasis-free survival (p = 0.0056). In multiple Cox regression adjusted for tumour thickness and ulceration, eTILs as continuous remained significant (p = 0.019). When comparing TILs in primary tissue and corresponding metastasis of the same patient, eTILs in metastases was lower than in primary melanomas (p < 0.0001). In therapy-naïve metastases, an eTILs >12.2% was associated with longer progression-free survival (p = 0.037) and melanoma-specific survival (p = 0.0038) in patients treated with anti-PD-1-based immunotherapy. In multiple Cox regression, lactate dehydrogenase (p < 0.0001) and eTILs ≤12.2% (p = 0.0130) were significantly associated with unfavourable melanoma-specific survival. INTERPRETATION: Assessment of TILs is prognostic in primary melanoma samples, and the eTILs complements staging. In therapy-naïve metastases, eTILs ≤12.2% is predictive of unfavourable survival outcomes in patients receiving anti-PD-1-based therapy. FUNDING: See a detailed list of funding bodies in the Acknowledgements section at the end of the manuscript.
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spelling pubmed-103634502023-07-25 Deep learning-based scoring of tumour-infiltrating lymphocytes is prognostic in primary melanoma and predictive to PD-1 checkpoint inhibition in melanoma metastases Chatziioannou, Eftychia Roßner, Jana Aung, Thazin New Rimm, David L. Niessner, Heike Keim, Ulrike Serna-Higuita, Lina Maria Bonzheim, Irina Kuhn Cuellar, Luis Westphal, Dana Steininger, Julian Meier, Friedegund Pop, Oltin Tiberiu Forchhammer, Stephan Flatz, Lukas Eigentler, Thomas Garbe, Claus Röcken, Martin Amaral, Teresa Sinnberg, Tobias eBioMedicine Articles BACKGROUND: Recent advances in digital pathology have enabled accurate and standardised enumeration of tumour-infiltrating lymphocytes (TILs). Here, we aim to evaluate TILs as a percentage electronic TIL score (eTILs) and investigate its prognostic and predictive relevance in cutaneous melanoma. METHODS: We included stage I to IV cutaneous melanoma patients and used hematoxylin-eosin-stained slides for TIL analysis. We assessed eTILs as a continuous and categorical variable using the published cut-off of 16.6% and applied Cox regression models to evaluate associations of eTILs with relapse-free, distant metastasis-free, and overall survival. We compared eTILs of the primaries with matched metastasis. Moreover, we assessed the predictive relevance of eTILs in therapy-naïve metastases according to the first-line therapy. FINDINGS: We analysed 321 primary cutaneous melanomas and 191 metastatic samples. In simple Cox regression, tumour thickness (p < 0.0001), presence of ulceration (p = 0.0001) and eTILs ≤16.6% (p = 0.0012) were found to be significant unfavourable prognostic factors for RFS. In multiple Cox regression, eTILs ≤16.6% (p = 0.0161) remained significant and downgraded the current staging. Lower eTILs in the primary tissue was associated with unfavourable relapse-free (p = 0.0014) and distant metastasis-free survival (p = 0.0056). In multiple Cox regression adjusted for tumour thickness and ulceration, eTILs as continuous remained significant (p = 0.019). When comparing TILs in primary tissue and corresponding metastasis of the same patient, eTILs in metastases was lower than in primary melanomas (p < 0.0001). In therapy-naïve metastases, an eTILs >12.2% was associated with longer progression-free survival (p = 0.037) and melanoma-specific survival (p = 0.0038) in patients treated with anti-PD-1-based immunotherapy. In multiple Cox regression, lactate dehydrogenase (p < 0.0001) and eTILs ≤12.2% (p = 0.0130) were significantly associated with unfavourable melanoma-specific survival. INTERPRETATION: Assessment of TILs is prognostic in primary melanoma samples, and the eTILs complements staging. In therapy-naïve metastases, eTILs ≤12.2% is predictive of unfavourable survival outcomes in patients receiving anti-PD-1-based therapy. FUNDING: See a detailed list of funding bodies in the Acknowledgements section at the end of the manuscript. Elsevier 2023-06-07 /pmc/articles/PMC10363450/ /pubmed/37295047 http://dx.doi.org/10.1016/j.ebiom.2023.104644 Text en © 2023 The Authors https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Articles
Chatziioannou, Eftychia
Roßner, Jana
Aung, Thazin New
Rimm, David L.
Niessner, Heike
Keim, Ulrike
Serna-Higuita, Lina Maria
Bonzheim, Irina
Kuhn Cuellar, Luis
Westphal, Dana
Steininger, Julian
Meier, Friedegund
Pop, Oltin Tiberiu
Forchhammer, Stephan
Flatz, Lukas
Eigentler, Thomas
Garbe, Claus
Röcken, Martin
Amaral, Teresa
Sinnberg, Tobias
Deep learning-based scoring of tumour-infiltrating lymphocytes is prognostic in primary melanoma and predictive to PD-1 checkpoint inhibition in melanoma metastases
title Deep learning-based scoring of tumour-infiltrating lymphocytes is prognostic in primary melanoma and predictive to PD-1 checkpoint inhibition in melanoma metastases
title_full Deep learning-based scoring of tumour-infiltrating lymphocytes is prognostic in primary melanoma and predictive to PD-1 checkpoint inhibition in melanoma metastases
title_fullStr Deep learning-based scoring of tumour-infiltrating lymphocytes is prognostic in primary melanoma and predictive to PD-1 checkpoint inhibition in melanoma metastases
title_full_unstemmed Deep learning-based scoring of tumour-infiltrating lymphocytes is prognostic in primary melanoma and predictive to PD-1 checkpoint inhibition in melanoma metastases
title_short Deep learning-based scoring of tumour-infiltrating lymphocytes is prognostic in primary melanoma and predictive to PD-1 checkpoint inhibition in melanoma metastases
title_sort deep learning-based scoring of tumour-infiltrating lymphocytes is prognostic in primary melanoma and predictive to pd-1 checkpoint inhibition in melanoma metastases
topic Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10363450/
https://www.ncbi.nlm.nih.gov/pubmed/37295047
http://dx.doi.org/10.1016/j.ebiom.2023.104644
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