Department of Physiology and Medical Physics, RCSI Centre for Systems Medicine, Royal College of Surgeons in Ireland University of Medicine and Health Sciences, Dublin, Ireland.
GE HealthCare Technology and Innovation Center (formerly GE Research Center), Niskayuna, NY, USA.
J Pathol. 2024 Oct;264(2):148-159. doi: 10.1002/path.6327. Epub 2024 Aug 2.
Colorectal cancer (CRC) is one of the most frequently occurring cancers, but prognostic biomarkers identifying patients at risk of recurrence are still lacking. In this study, we aimed to investigate in more detail the spatial relationship between intratumoural T cells, cancer cells, and cancer cell hallmarks as prognostic biomarkers in stage III colorectal cancer patients. We conducted multiplexed imaging of 56 protein markers at single-cell resolution on resected fixed tissue from stage III CRC patients who received adjuvant 5-fluorouracil (5FU)-based chemotherapy. Images underwent segmentation for tumour, stroma, and immune cells, and cancer cell 'state' protein marker expression was quantified at a cellular level. We developed a Python package for estimation of spatial proximity, nearest neighbour analysis focusing on cancer cell-T-cell interactions at single-cell level. In our discovery cohort (Memorial Sloan Kettering samples), we processed 462 core samples (total number of cells: 1,669,228) from 221 adjuvant 5FU-treated stage III patients. The validation cohort (Huntsville Clearview Cancer Center samples) consisted of 272 samples (total number of cells: 853,398) from 98 stage III CRC patients. While there were trends for an association between the percentage of cytotoxic T cells (across the whole cancer core), it did not reach significance (discovery cohort: p = 0.07; validation cohort: p = 0.19). We next utilised our region-based nearest neighbour approach to determine the spatial relationships between cytotoxic T cells, helper T cells, and cancer cell clusters. In both cohorts, we found that shorter distance between cytotoxic T cells, T helper cells, and cancer cells was significantly associated with increased disease-free survival. An unsupervised trained model that clustered patients based on the median distance between immune cells and cancer cells, as well as protein expression profiles, successfully classified patients into low-risk and high-risk groups (discovery cohort: p = 0.01; validation cohort: p = 0.003). © 2024 The Author(s). The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.
结直肠癌（CRC）是最常见的癌症之一，但仍缺乏识别复发风险患者的预后生物标志物。在这项研究中，我们旨在更详细地研究肿瘤内 T 细胞、癌细胞和癌细胞特征作为 III 期结直肠癌患者的预后生物标志物之间的空间关系。我们对接受辅助 5-氟尿嘧啶（5FU）为基础的化疗的 III 期 CRC 患者切除固定组织进行了 56 种蛋白质标记物的多重成像，分辨率为单细胞水平。对肿瘤、基质和免疫细胞进行了分割，并在细胞水平上对癌细胞“状态”蛋白标记物的表达进行了量化。我们开发了一个用于估计空间接近度的 Python 包，该包用于关注单细胞水平上癌细胞-T 细胞相互作用的最近邻分析。在我们的发现队列（纪念斯隆凯特琳样本）中，我们处理了 221 名接受辅助 5FU 治疗的 III 期患者的 462 个核心样本（总细胞数：1,669,228）。验证队列（亨茨维尔 Clearview 癌症中心样本）由 98 名 III 期 CRC 患者的 272 个样本组成（总细胞数：853,398）。虽然整个癌症核心的细胞毒性 T 细胞（across the whole cancer core）百分比之间存在关联的趋势，但未达到显著水平（发现队列：p=0.07；验证队列：p=0.19）。接下来，我们利用基于区域的最近邻方法来确定细胞毒性 T 细胞、辅助 T 细胞和癌细胞簇之间的空间关系。在两个队列中，我们发现细胞毒性 T 细胞、辅助 T 细胞和癌细胞之间的距离较短与无病生存期的增加显著相关。一个基于免疫细胞和癌细胞中位数距离以及蛋白质表达谱对患者进行聚类的无监督训练模型，成功地将患者分为低风险和高风险组（发现队列：p=0.01；验证队列：p=0.003）。 © 2024 作者。John Wiley & Sons Ltd 代表英国和爱尔兰的病理学会出版《病理学杂志》。
