近日，美国基因泰克公司Romain Banchereau及其研究小组揭示尿路上皮癌的分子异质性及其对PD-L1阻断临床获益的决定因素。2024年11月21日，《癌细胞》杂志在线发表了这项成果。

研究人员表示，针对程序性细胞死亡蛋白1（PD-1）/程序性死亡配体1（PD-L1）的检查点抑制剂已经在包括尿路上皮癌（UC）在内的多个癌症指征中彻底改变了癌症治疗。然而，由于许多患者未能从中获益，因此需要更好地理解响应和耐药的分子机制，以改善治疗效果。

研究人员通过RNA测序（RNA-seq）、靶向DNA组合、免疫组织化学和数字病理学对来自四项晚期随机临床试验中的2803名UC患者的肿瘤样本进行了分析。这些试验评估了PD-L1抑制剂阿替利珠单抗的效果。

机器学习方法确定了四种转录亚型，分别代表腔道沙漠型、基质型、免疫型和基底型肿瘤。免疫型和基底型肿瘤在接受阿替利珠单抗治疗后，相较于标准治疗，整体生存获益显著，但其响应机制不同。自监督数字病理学方法能够高精度地从HE切片中分类分子亚型，可能加速肿瘤分子分型。

这项研究代表了在随机临床试验中对UC分子和临床数据的大规模整合，为临床研究定制针对UC及其他适应症特定分子亚型的治疗方案铺平了道路。

附：英文原文

Title: Molecular heterogeneity in urothelial carcinoma and determinants of clinical benefit to PD-L1 blockade

Author: Habib Hamidi, Yasin Senbabaoglu, Niha Beig, Juliette Roels, Cyrus Manuel, Xiangnan Guan, Hartmut Koeppen, Zoe June Assaf, Barzin Y. Nabet, Adrian Waddell, Kobe Yuen, Sophia Maund, Ethan Sokol, Jennifer M. Giltnane, Amber Schedlbauer, Eloisa Fuentes, James D. Cowan, Edward E. Kadel, Viraj Degaonkar, Alexander Andreev-Drakhlin, Patrick Williams, Corey Carter, Suyasha Gupta, Elizabeth Steinberg, Yohann Loriot, Joaquim Bellmunt, Petros Grivas, Jonathan Rosenberg, Michiel S. van der Heijden, Matthew D. Galsky, Thomas Powles, Sanjeev Mariathasan, Romain Banchereau

Issue&Volume: 2024-11-21

Abstract: Checkpoint inhibitors targeting programmed cell death protein 1 (PD-1)/programmed death-ligand 1 (PD-L1) have revolutionized cancer therapy across many indications including urothelial carcinoma (UC). Because many patients do not benefit, a better understanding of the molecular mechanisms underlying response and resistance is needed to improve outcomes. We profiled tumors from 2,803 UC patients from four late-stage randomized clinical trials evaluating the PD-L1 inhibitor atezolizumab by RNA sequencing (RNA-seq), a targeted DNA panel, immunohistochemistry, and digital pathology. Machine learning identifies four transcriptional subtypes, representing luminal desert, stromal, immune, and basal tumors. Overall survival benefit from atezolizumab over standard-of-care is observed in immune and basal tumors, through different response mechanisms. A self-supervised digital pathology approach can classify molecular subtypes from H&E slides with high accuracy, which could accelerate tumor molecular profiling. This study represents a large integration of UC molecular and clinical data in randomized trials, paving the way for clinical studies tailoring treatment to specific molecular subtypes in UC and other indications.

DOI: 10.1016/j.ccell.2024.10.016

Source: https://www.cell.com/cancer-cell/abstract/S1535-6108(24)00404-5