Quantitative and Digital Pathology, School of Medicine, University of St. Andrews, St. Andrews, UK.
Indica Labs, Inc., Corrales, New Mexico, USA.
Cancer Immunol Res. 2019 Apr;7(4):609-620. doi: 10.1158/2326-6066.CIR-18-0377. Epub 2019 Mar 7.
Both immune profiling and tumor budding significantly correlate with colorectal cancer patient outcome but are traditionally reported independently. This study evaluated the association and interaction between lymphocytic infiltration and tumor budding, coregistered on a single slide, in order to determine a more precise prognostic algorithm for patients with stage II colorectal cancer. Multiplexed immunofluorescence and automated image analysis were used for the quantification of CD3CD8 T cells, and tumor buds (TBs), across whole slide images of three independent cohorts (training cohort: = 114, validation cohort 1: = 56, validation cohort 2: = 62). Machine learning algorithms were used for feature selection and prognostic risk model development. High numbers of TBs [HR = 5.899; 95% confidence interval (CI) 1.875-18.55], low CD3 T-cell density (HR = 9.964; 95% CI, 3.156-31.46), and low mean number of CD3CD8 T cells within 50 μm of TBs (HR = 8.907; 95% CI, 2.834-28.0) were associated with reduced disease-specific survival. A prognostic signature, derived from integrating TBs, lymphocyte infiltration, and their spatial relationship, reported a more significant cohort stratification (HR = 18.75; 95% CI, 6.46-54.43), than TBs, Immunoscore, or pT stage. This was confirmed in two independent validation cohorts (HR = 12.27; 95% CI, 3.524-42.73; HR = 15.61; 95% CI, 4.692-51.91). The investigation of the spatial relationship between lymphocytes and TBs within the tumor microenvironment improves accuracy of prognosis of patients with stage II colorectal cancer through an automated image analysis and machine learning workflow.
淋巴细胞浸润与肿瘤芽显著相关，均与结直肠癌患者预后相关，但传统上两者是分开报告的。本研究通过对单个切片上的免疫组化和肿瘤芽进行共定位分析，评估了淋巴细胞浸润与肿瘤芽之间的关联和相互作用，旨在为 II 期结直肠癌患者建立更精确的预后算法。我们使用多重免疫荧光和自动图像分析技术对三个独立队列的全切片图像中的 CD3CD8 T 细胞和肿瘤芽（TBs）进行了定量分析（训练队列：n = 114，验证队列 1：n = 56，验证队列 2：n = 62）。我们使用机器学习算法进行特征选择和预后风险模型开发。高肿瘤芽数量（HR = 5.899；95%置信区间 [CI] 1.875-18.55）、低 CD3 T 细胞密度（HR = 9.964；95% CI，3.156-31.46）和 TB 周围 50μm 内平均 CD3CD8 T 细胞数量低（HR = 8.907；95% CI，2.834-28.0）与疾病特异性生存率降低相关。一个源自整合肿瘤芽、淋巴细胞浸润及其空间关系的预后特征，与肿瘤芽、免疫评分或 pT 分期相比，能够更好地对队列进行分层（HR = 18.75；95% CI，6.46-54.43）。在两个独立的验证队列中得到了验证（HR = 12.27；95% CI，3.524-42.73；HR = 15.61；95% CI，4.692-51.91）。通过自动图像分析和机器学习工作流程，研究肿瘤微环境中淋巴细胞与肿瘤芽之间的空间关系，可提高 II 期结直肠癌患者预后的准确性。
