经典霍奇金淋巴瘤 (cHL) 是一种可治愈的癌症，无病生存率超过 10 年。超过 80% 的确诊患者对一线化疗有良好反应，但很少有生物标志物可以预测 15-20% 的难治性或早期复发性疾病患者。迄今为止，根据疾病分期和传统的临床危险因素分析来识别对一线治疗无反应的患者仍然不可能。使用 TeloView® 软件平台进行三维 (3D) 端粒分析已被证明是一种可靠的工具，可量化基因组不稳定性，并了解疾病进展和患者对多种癌症治疗的反应。它还证明了 cHL 中的端粒功能障碍，阐明了与疾病进展相关的生物学机制。在这里，我们报告了 156 名 cHL 患者的多中心队列的 3D 端粒分析。我们使用队列数据作为训练数据集，并确定了适合预测诊断时个体患者结果的重要 3D 端粒参数。使用逻辑回归程序的多变量分析允许使用四个 3D 端粒参数作为预测因子来开发预测评分模型，其中包括 t 残端（非常短的端粒）的比例，在之前发表的一项研究中，该模型是 cHL 患者结果的重要预测因子TeloView® 分析。在开始阿霉素、博莱霉素、长春花碱和达卡巴嗪 (ABVD) 治疗之前，T 残端的百分比是迄今为止识别难治性/复发性 (RR) cHL 的最重要的预测因子。该模型特征包括 ROC 分析中的 AUC 为 0.83，敏感性和特异性分别为 0.82 和 0.78。

Classic Hodgkin's lymphoma (cHL) is a curable cancer with a disease-free survival rate of over 10 years. Over 80% of diagnosed patients respond favorably to first-line chemotherapy, but few biomarkers exist that can predict the 15-20% of patients who experience refractory or early relapsed disease. To date, the identification of patients who will not respond to first-line therapy based on disease staging and traditional clinical risk factor analysis is still not possible. Three-dimensional (3D) telomere analysis using the TeloView® software platform has been shown to be a reliable tool to quantify genomic instability and to inform on disease progression and patients' response to therapy in several cancers. It also demonstrated telomere dysfunction in cHL elucidating biological mechanisms related to disease progression. Here, we report 3D telomere analysis on a multicenter cohort of 156 cHL patients. We used the cohort data as a training data set and identified significant 3D telomere parameters suitable to predict individual patient outcomes at the point of diagnosis. Multivariate analysis using logistic regression procedures allowed for developing a predictive scoring model using four 3D telomere parameters as predictors, including the proportion of t-stumps (very short telomeres), which has been a prominent predictor for cHL patient outcome in a previously published study using TeloView® analysis. The percentage of t-stumps was by far the most prominent predictor to identify refractory/relapsing (RR) cHL prior to initiation of adriamycin, bleomycin, vinblastine, and dacarbazine (ABVD) therapy. The model characteristics include an AUC of 0.83 in ROC analysis and a sensitivity and specificity of 0.82 and 0.78 respectively.