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.
经典霍奇金淋巴瘤（cHL）是一种可治愈的癌症，其10年以上无病生存率较高。超过80%的确诊患者对一线化疗反应良好，但目前仍缺乏能够预测15-20%患者出现难治性或早期复发疾病的生物标志物。迄今为止，基于疾病分期和传统临床风险因素分析，仍无法准确识别对一线治疗无应答的患者。采用TeloView®软件平台进行的三维端粒分析已被证实是量化基因组不稳定性、评估多种癌症疾病进展及治疗反应的有效工具。该技术还揭示了cHL中端粒功能障碍与疾病进展相关的生物学机制。本研究对156例多中心cHL患者队列进行了三维端粒分析。我们将队列数据作为训练数据集，鉴定出可用于诊断时预测个体预后的关键三维端粒参数。通过逻辑回归多变量分析，我们构建了以四个三维端粒参数为预测因子的评分模型，其中包括t-stump（极短端粒）比例——该指标在先前发表的TeloView®分析研究中已被证实是cHL预后的重要预测因子。在开始阿霉素、博来霉素、长春碱和达卡巴嗪（ABVD）方案治疗前，t-stump百分比是识别难治/复发（RR）型cHL最显著的预测指标。该模型特征包括：ROC曲线下面积（AUC）为0.83，敏感性与特异性分别为0.82和0.78。
肿瘤(癌症)患者之家