Cottrell Tricia R, Roskes Jeffrey S, Fotheringham Michael, Cohen Emily, Boyang Zhang, Engle Logan L, Wang Daphne, Will Elizabeth, Sunshine Joel C, Jimenez-Sanchez Daniel, Zeng Zhen, Caushi Justina X, Zhang Jiajia, D'Amiano Nina M, Deutsch Julie S, Uttam Sonali, Pirie Katie, Vlaminck Darah, Mataj Michelle, Fiorante Alexa, Espinosa Nicole, Popa Teodora, Ogurtsova Aleksandra, Soto-Diaz Sigfredo, Eminizer Margaret, Tabrisky Samuel, Jorquera Andrew, Skidmore Jonathan, Medvedev Dmitry, Chaft Jamie E, Brahmer Julie R, Conroy Michael, Reuss Joshua E, Danilova Ludmila, Ji Hongkai, Forde Patrick M, Pardoll Drew M, Smith Kellie N, Green Benjamin F, Szalay Alexander S, Taube Janis M
Department of Pathology and Molecular Medicine, Queen's University, Kingston, ON, Canada.
Department of Physics & Astronomy; Johns Hopkins University, Baltimore, MD, USA.
bioRxiv. 2025 Aug 15:2025.08.13.665980. doi: 10.1101/2025.08.13.665980.
Probabilistic spatial modelling techniques developed on large-scale tumor-immune Atlases (~35M individually mapped cells; 50,000 high power fields) were used to characterize predictive features of treatment-responsive lung cancer. We identified CD8+FoxP3+ cell density as a robust pre-treatment biomarker for outcomes across disease stages and therapy types. In parallel, single-cell RNAseq studies of CD8+FoxP3+ T-cells revealed an activated, early effector phenotype, substantiating an anti-tumor role, and contrasting with CD4+FoxP3+ T-regulatory cells. A spatial biomarker was developed using an empirical probabilistic model to define the immediate cell neighbors or niche surrounding CD8+FoxP3+ cells and proximity to the tumor-stromal boundary. The resultant 'Diversity of Niches Unlocking Treatment Sensitivity (DONUTS)' are more prevalent than the CD8+FoxP3+ cells themselves, mitigating sampling error in small biopsies. Further, the DONUTS only require four markers, are additive to PD-L1, and associate with tertiary lymphoid structure counts. Taken together, the DONUTS represent a next-generation predictive biomarker poised for clinical implementation.
基于大规模肿瘤免疫图谱（约3500万个单独映射的细胞；50000个高倍视野）开发的概率空间建模技术，被用于表征治疗反应性肺癌的预测特征。我们将CD8+FoxP3+细胞密度确定为一个强大的治疗前生物标志物，可用于预测不同疾病阶段和治疗类型的结果。同时，对CD8+FoxP3+ T细胞的单细胞RNA测序研究揭示了一种活化的早期效应细胞表型，证实了其抗肿瘤作用，并与CD4+FoxP3+调节性T细胞形成对比。利用经验概率模型开发了一种空间生物标志物，以定义CD8+FoxP3+细胞周围的直接细胞邻居或生态位以及与肿瘤基质边界的接近程度。由此产生的“解锁治疗敏感性的生态位多样性（DONUTS）”比CD8+FoxP3+细胞本身更为普遍，减少了小活检中的抽样误差。此外，DONUTS仅需要四种标志物，可作为PD-L1的补充，并与三级淋巴结构计数相关。综上所述，DONUTS代表了一种有望用于临床的新一代预测性生物标志物。
