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基于深度学习的肿瘤浸润淋巴细胞评分在原发性黑色素瘤中具有预后价值,并可预测黑色素瘤转移中的 PD-1 检查点抑制作用。

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

机构信息

Department 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, University of Heidelberg, Im Neuenheimer Feld 440, 69120 Heidelberg, Germany.

出版信息

EBioMedicine. 2023 Jul;93:104644. doi: 10.1016/j.ebiom.2023.104644. Epub 2023 Jun 7.

DOI:10.1016/j.ebiom.2023.104644
PMID:37295047
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10363450/
Abstract

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.

摘要

背景

数字病理学的最新进展使得肿瘤浸润淋巴细胞(TILs)的准确和标准化计数成为可能。在这里,我们旨在评估 TILs 作为百分比电子 TIL 评分(eTILs),并研究其在皮肤黑色素瘤中的预后和预测相关性。

方法

我们纳入了 I 期至 IV 期皮肤黑色素瘤患者,并使用苏木精-伊红染色切片进行 TIL 分析。我们使用已发表的 16.6%的截断值评估 eTILs 作为连续和分类变量,并应用 Cox 回归模型来评估 eTILs 与无复发生存、无远处转移生存和总生存之间的关联。我们将原发肿瘤的 eTILs 与匹配的转移瘤进行比较。此外,我们根据一线治疗评估了 eTILs 在未经治疗的转移瘤中的预测相关性。

结果

我们分析了 321 例原发性皮肤黑色素瘤和 191 例转移性样本。在简单的 Cox 回归中,肿瘤厚度(p<0.0001)、溃疡存在(p=0.0001)和 eTILs≤16.6%(p=0.0012)被发现是 RFS 的显著不利预后因素。在多变量 Cox 回归中,eTILs≤16.6%(p=0.0161)仍然具有显著意义,并降低了当前的分期。原发组织中较低的 eTILs 与无复发生存(p=0.0014)和无远处转移生存(p=0.0056)不良相关。在调整肿瘤厚度和溃疡的多变量 Cox 回归中,eTILs 作为连续变量仍然具有显著意义(p=0.019)。当比较同一患者的原发组织和相应转移组织中的 TILs 时,转移组织中的 eTILs 低于原发黑色素瘤(p<0.0001)。在未经治疗的转移瘤中,eTILs>12.2%与接受抗 PD-1 为基础的免疫治疗的患者的无进展生存期(p=0.037)和黑色素瘤特异性生存期(p=0.0038)较长相关。在多变量 Cox 回归中,乳酸脱氢酶(p<0.0001)和 eTILs≤12.2%(p=0.0130)与黑色素瘤特异性生存不良显著相关。

结论

TILs 在原发性黑色素瘤样本中的评估具有预后意义,eTILs 补充了分期。在未经治疗的转移瘤中,eTILs≤12.2%是接受抗 PD-1 为基础的治疗的患者不良生存结局的预测指标。

资助

请在文章末尾的致谢部分查看详细的资助机构列表。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3360/10363450/8295330b5934/gr5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3360/10363450/69a8a640ebad/gr1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3360/10363450/c989d68de302/gr2.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3360/10363450/6a5cff78147f/gr4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3360/10363450/8295330b5934/gr5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3360/10363450/69a8a640ebad/gr1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3360/10363450/c989d68de302/gr2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3360/10363450/6bd8d450e46c/gr3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3360/10363450/6a5cff78147f/gr4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3360/10363450/8295330b5934/gr5.jpg

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