人工智能支持的肿瘤浸润性淋巴细胞空间分析作为非小细胞肺癌免疫检查点抑制的补充生物标记物

SCI

10 April 2022

Artificial Intelligence-Powered Spatial Analysis of Tumor-Infiltrating Lymphocytes as Complementary Biomarker for Immune Checkpoint Inhibition in Non-Small-Cell Lung Cancer 

（J Clin Oncol IF：44.544）

• Park Sehhoon,Ock Chan-Young,Kim Hyojin et al. Artificial Intelligence-Powered Spatial Analysis of Tumor-Infiltrating Lymphocytes as Complementary Biomarker for Immune Checkpoint Inhibition in Non-Small-Cell Lung Cancer.[J] .J Clin Oncol, 2022, undefined: JCO2102010.

Purpose 目的

Biomarkers on the basis of tumor-infiltrating lymphocytes (TIL) are potentially valuable in predicting the effectiveness of immune checkpoint inhibitors (ICI). However, clinical application remains challenging because of methodologic limitations and laborious process involved in spatial analysis of TIL distribution in whole-slide images (WSI).

基于肿瘤浸润性淋巴细胞(TIL)的生物标志物在预测免疫检查点抑制剂(ICI)的疗效方面具有潜在的价值。然而，由于全视野数字切片中TIL分布的空间分析方法的局限性和繁琐的过程，临床应用仍然具有挑战性。

Methods 方法

We have developed an artificial intelligence (AI)-powered WSI analyzer of TIL in the tumor microenvironment that can define three immune phenotypes (IPs): inflamed, immune-excluded, and immune-desert. These IPs were correlated with tumor response to ICI and survival in two independent cohorts of patients with advanced non-small-cell lung cancer (NSCLC).

我们研制了一种基于人工智能(AI)的肿瘤微环境中TIL的WSI分析仪，它可以区分三种免疫表型：炎症、免疫排斥和免疫沙漠型。在两组独立的晚期非小细胞肺癌(NSCLC)患者中，这些免疫表型与肿瘤对ICI的反应和生存有关。

Results 结果

 Inflamed IP correlated with enrichment in local immune cytolytic activity, higher response rate, and prolonged progression-free survival compared with patients with immune-excluded or immune-desert phenotypes. At the WSI level, there was significant positive correlation between tumor proportion score (TPS) as determined by the AI model and control TPS analyzed by pathologists (P < .001). Overall, 44.0% of tumors were inflamed, 37.1% were immune-excluded, and 18.9% were immune-desert. Incidence of inflamed IP in patients with programmed death ligand-1 TPS at < 1%, 1%-49%, and ≥ 50% was 31.7%, 42.5%, and 56.8%, respectively. Median progression-free survival and overall survival were, respectively, 4.1 months and 24.8 months with inflamed IP, 2.2 months and 14.0 months with immune-excluded IP, and 2.4 months and 10.6 months with immune-desert IP.

与免疫排斥或免疫沙漠表型相比，炎性IP与局部免疫细胞溶解活性丰富、有效率高、无进展生存期延长相关。在WSI水平上，AI模型所确定的肿瘤比例评分(TPS)与病理学家分析的对照TPS之间存在显著正相关(P<.001)。总体而言，44.0%的肿瘤是炎症的，37.1%的肿瘤是免疫排斥的，18.9%的肿瘤是免疫沙漠的。TPS<1%、1%~49%和≥50%的患者IP炎症发生率分别为31.7%、42.5%和56.8%。炎症IP中位无进展生存期和总生存期分别为4.1个月和24.8个月，免疫排除IP分别为2.2个月和14.0个月，免疫沙漠IP分别为2.4个月和10.6个月。

Conclusion 结论

The AI-powered spatial analysis of TIL correlated with tumor response and progression-free survival of ICI in advanced NSCLC. This is potentially a supplementary biomarker to TPS as determined by a pathologist.

AI支持的TIL空间分析与晚期非小细胞肺癌ICI的肿瘤疗效和无进展生存期相关。病理学家认为，这可能是TPS的一个补充生物标志物。

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