Evidence map›Paper›PMID 37295047›Full record

ArticleEBioMedicine2023

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

Eftychia Chatziioannou, Jana Roßner, Thazin New Aung, David L Rimm, Heike Niessner, Ulrike Keim, Lina Maria Serna-Higuita, Irina Bonzheim, Luis Kuhn Cuellar, Dana Westphal and 10 more

Open access · goldAbstract read
In one paragraph

Article in EBioMedicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.

0numbers the graph read from it
0cells of the map it votes in
21citing papers in PubMed
10.4field-weighted citation impact, top 1% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

21 citing papers in PubMed, 45 citations in OpenAlex.

  1. Article
  2. Interleukin-1β Gene (Current oncology (Toronto, Ont.) · 2026
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

20 authors at 7 institutions in 3 countries.

Eftychia ChatziioannouDepartment of Dermatology, University of Tübingen, Liebermeisterstr. 25, 72076 Tübingen, Germany; Cluster of Excellence iFIT (EXC 2180) "Image-Guided and Functionally Instructed Tumor Therapies", Tübingen, Germany.
Jana RoßnerDepartment of Dermatology, University of Heidelberg, Im Neuenheimer Feld 440, 69120 Heidelberg, Germany.
Thazin New AungDepartment of Pathology, Yale University School of Medicine, New Haven, CT, USA.
David L RimmDepartment of Pathology, Yale University School of Medicine, New Haven, CT, USA.
Heike NiessnerDepartment of Dermatology, University of Tübingen, Liebermeisterstr. 25, 72076 Tübingen, Germany; Cluster of Excellence iFIT (EXC 2180) "Image-Guided and Functionally Instructed Tumor Therapies", Tübingen, Germany.
Ulrike KeimDepartment of Dermatology, University of Tübingen, Liebermeisterstr. 25, 72076 Tübingen, Germany.
Lina Maria Serna-HiguitaDepartment of Clinical Epidemiology and Applied Biostatistics, Eberhard Karls University of Tübingen, 72076 Tübingen, Germany.
Irina BonzheimInstitute of Pathology and Neuropathology, Eberhard Karls University of Tübingen, 72076 Tübingen, Germany.
Luis Kuhn CuellarQuantitative Biology Center (QBiC), University of Tübingen, Tübingen, Germany.
Dana WestphalDepartment of Dermatology, Faculty of Medicine and University Hospital Carl Gustav Carus, Skin Cancer Center at the University Cancer Center and National Center for Tumor Diseases, Technical University Dresden, 01307 Dresden, Germany.
Julian SteiningerDepartment of Dermatology, Faculty of Medicine and University Hospital Carl Gustav Carus, Skin Cancer Center at the University Cancer Center and National Center for Tumor Diseases, Technical University Dresden, 01307 Dresden, Germany.
Friedegund MeierDepartment of Dermatology, Faculty of Medicine and University Hospital Carl Gustav Carus, Skin Cancer Center at the University Cancer Center and National Center for Tumor Diseases, Technical University Dresden, 01307 Dresden, Germany.
Oltin Tiberiu PopInstitute of Immunobiology, Kantonsspital St. Gallen, St. Gallen, Switzerland.
Stephan ForchhammerDepartment of Dermatology, University of Tübingen, Liebermeisterstr. 25, 72076 Tübingen, Germany.
Lukas FlatzDepartment of Dermatology, University of Tübingen, Liebermeisterstr. 25, 72076 Tübingen, Germany; Institute of Immunobiology, Kantonsspital St. Gallen, St. Gallen, Switzerland.
Thomas EigentlerDepartment of Dermatology, Venereology and Allergology, Charité-Universitätsmedizin Berlin, Charitéplatz 1, 10117 Berlin, Germany.
Claus GarbeDepartment of Dermatology, University of Tübingen, Liebermeisterstr. 25, 72076 Tübingen, Germany.
Martin RöckenDepartment of Dermatology, University of Tübingen, Liebermeisterstr. 25, 72076 Tübingen, Germany; Cluster of Excellence iFIT (EXC 2180) "Image-Guided and Functionally Instructed Tumor Therapies", Tübingen, Germany.
Teresa AmaralDepartment of Dermatology, University of Tübingen, Liebermeisterstr. 25, 72076 Tübingen, Germany; Cluster of Excellence iFIT (EXC 2180) "Image-Guided and Functionally Instructed Tumor Therapies", Tübingen, Germany.
Tobias SinnbergDepartment of Dermatology, University of Tübingen, Liebermeisterstr. 25, 72076 Tübingen, Germany; Cluster of Excellence iFIT (EXC 2180) "Image-Guided and Functionally Instructed Tumor Therapies", Tübingen, Germany; Department of Dermatology, Venereology and Allergology, Charité-Universitätsmedizin Berlin, Charitéplatz 1, 10117 Berlin, Germany. Electronic address: tobias.sinnberg@med.uni-tuebingen.de.
University of Tübingen · DEBernstein Center for Computational Neuroscience Tübingen · DENational Center for Tumor Diseases · DECharité - Universitätsmedizin Berlin · DEKantonsspital St. Gallen · CHYale University · USHeidelberg University · DE

Funding

Yale Clinical and Translational Science Award (U Component)UL1TR001863 · NCATS · YALE UNIVERSITY · PI John H. Krystal, LUCILA OHNO-MACHADO · 2016 to 2026
$102.9M
NCATS NIH HHS UL1 TR001863
6 · The paper itself

Abstract

backgroundRecent 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.

methodsWe 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.

findingsWe 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.

interpretationAssessment 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.

fundingSee a detailed list of funding bodies in the Acknowledgements section at the end of the manuscript.

Indexed as

Deep LearningMelanomaSkin NeoplasmsCutaneous Malignant MelanomaHumansLymphocytes, Tumor-InfiltratingNeoplasm Recurrence, LocalPrognosisCutaneous melanomaDigital pathologyPredictive biomarkersPrognostic biomarkersTumour-infiltrating lymphocytes

Identifiers

PMID37295047
PMCPMC10363450
OpenAlexW4379615372

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