Department of Pathology, Yale School of Medicine, New Haven, CT, USA.
Melanoma Institute Australia, The University of Sydney, Sydney, NSW, Australia; Faculty of Medicine and Health, The University of Sydney, Sydney, NSW, Australia; Charles Perkins Centre, The University of Sydney, Sydney, NSW, Australia.
EBioMedicine. 2022 Aug;82:104143. doi: 10.1016/j.ebiom.2022.104143. Epub 2022 Jul 7.
The prognostic value of tumor-infiltrating lymphocytes (TILs) assessed by machine learning algorithms in melanoma patients has been previously demonstrated but has not been widely adopted in the clinic. We evaluated the prognostic value of objective automated electronic TILs (eTILs) quantification to define a subset of melanoma patients with a low risk of relapse after surgical treatment.
We analyzed data for 785 patients from 5 independent cohorts from multiple institutions to validate our previous finding that automated TIL score is prognostic in clinically-localized primary melanoma patients. Using serial tissue sections of the Yale TMA-76 melanoma cohort, both immunofluorescence and Hematoxylin-and-Eosin (H&E) staining were performed to understand the molecular characteristics of each TIL phenotype and their associations with survival outcomes.
Five previously-described TIL variables were each significantly associated with overall survival (p<0.0001). Assessing the receiver operating characteristic (ROC) curves by comparing the clinical impact of two models suggests that etTILs (electronic total TILs) (AUC: 0.793, specificity: 0.627, sensitivity: 0.938) outperformed eTILs (AUC: 0.77, specificity: 0.51, sensitivity: 0.938). We also found that the specific molecular subtype of cells representing TILs includes predominantly cells that are CD3+ and CD8+ or CD4+ T cells.
eTIL% and etTILs scores are robust prognostic markers in patients with primary melanoma and may identify a subgroup of stage II patients at high risk of recurrence who may benefit from adjuvant therapy. We also show the molecular correlates behind these scores. Our data support the need for prospective testing of this algorithm in a clinical trial.
This work was also supported by a sponsored research agreements from Navigate Biopharma and NextCure and by grants from the NIH including the Yale SPORE in in Skin Cancer, P50 CA121974, the Yale SPORE in Lung Cancer, P50 CA196530, NYU SPORE in Skin Cancer P50CA225450 and the Yale Cancer Center Support Grant, P30CA016359.
机器学习算法评估的肿瘤浸润淋巴细胞（TILs）在黑色素瘤患者中的预后价值已得到先前证实，但尚未在临床中广泛采用。我们评估了客观自动电子 TILs（eTILs）定量的预后价值，以定义一组手术后复发风险较低的黑色素瘤患者。
我们分析了来自 5 个独立机构的 785 名患者的数据，以验证我们之前的发现，即自动 TIL 评分在临床局限性原发性黑色素瘤患者中具有预后价值。使用耶鲁 TMA-76 黑色素瘤队列的连续组织切片，进行免疫荧光和苏木精和伊红（H&E）染色，以了解每个 TIL 表型的分子特征及其与生存结果的关联。
五个先前描述的 TIL 变量均与总生存（p<0.0001）显著相关。通过比较两种模型的接收者操作特征（ROC）曲线评估，表明 etTILs（电子总 TILs）（AUC：0.793，特异性：0.627，敏感性：0.938）优于 eTILs（AUC：0.77，特异性：0.51，敏感性：0.938）。我们还发现，代表 TIL 的细胞的特定分子亚型主要包括 CD3+和 CD8+或 CD4+T 细胞。
eTIL%和 etTILs 评分是原发性黑色素瘤患者的稳健预后标志物，可能确定出具有高复发风险的 II 期患者亚组，这些患者可能受益于辅助治疗。我们还展示了这些评分背后的分子相关性。我们的数据支持在临床试验中对该算法进行前瞻性测试的需要。
这项工作还得到了 Navigate Biopharma 和 NextCure 的赞助研究协议以及 NIH 资助的支持，包括耶鲁皮肤癌 SPORE，P50 CA121974，耶鲁肺癌 SPORE，P50 CA196530，NYU 皮肤癌 SPORE，P50 CA225450 和耶鲁癌症中心支持拨款，P30CA016359。
