Prognostic and predictive blood-based biomarkers in patients with advanced pancreatic cancer: results from CALGB80303 (Alliance).
Prognostic and predictive blood-based biomarkers in patients with advanced pancreatic cancer: results from CALGB80303 (Alliance).
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DOI：
10.1158/1078-0432.ccr-13-0926
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发表时间：
2013-12-15
期刊：
影响因子：
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通讯作者：
Alliance for Clinical Trials In Oncology
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文献类型：
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作者：
Nixon AB;Pang H;Starr MD;Friedman PN;Bertagnolli MM;Kindler HL;Goldberg RM;Venook AP;Hurwitz HI;Alliance for Clinical Trials In Oncology
CALGB80303是一项针对602例局部晚期或转移性胰腺癌患者的III期试验，比较了吉西他滨/贝伐单抗与吉西他滨/安慰剂。该研究发现，在吉西他滨中添加贝伐单抗在任何结果方面均无益处。采集了血液样本，对多种血管生成因子进行了评估，然后将其与总体临床结果（预后标志物）以及与贝伐单抗治疗的特定获益（预测标志物）相关联。
通过一种新型多重酶联免疫吸附测定平台对血浆样本中与肿瘤生长、血管生成和炎症相关的31种因子进行了分析。使用单变量考克斯比例风险回归模型以及留一法交叉验证的多变量考克斯回归模型，将这些因子的基线值与总生存期（OS）相关联。使用考克斯模型中的标志物与治疗相互作用项确定预测标志物。
有328例患者可获取基线血浆。确定了总生存期的单变量预后标志物，包括：血管生成素2（Ang2）、C反应蛋白（CRP）、细胞间黏附分子 - 1（ICAM - 1）、胰岛素样生长因子结合蛋白 - 1（IGFBP - 1）、血小板反应蛋白 - 2（TSP - 2）（所有P < 0.001）。即使在对已知临床因素进行调整后，这些预后因素仍具有高度显著性。其他建模方法从多变量考克斯回归中得出了预后特征。吉西他滨/贝伐单抗特征包括胰岛素样生长因子结合蛋白 - 1、白细胞介素 - 6、血小板衍生生长因子 - AA、血小板衍生生长因子 - BB、血小板反应蛋白 - 2；而吉西他滨/安慰剂特征包括C反应蛋白、胰岛素样生长因子结合蛋白 - 1、纤溶酶原激活物抑制剂 - 1、血小板衍生生长因子 - AA、P - 选择素（两者P < 0.0001）。最后，确定了三个贝伐单抗疗效的潜在预测标志物：血管内皮生长因子 - D（VEGF - D）（P < 0.01）、基质细胞衍生因子1（SDF1）（P < 0.05）和血管生成素2（Ang2）（P < 0.05）。
这项研究确定了胰腺癌患者的强预后标志物。预测标志物分析表明，血浆中血管内皮生长因子 - D、血管生成素2和基质细胞衍生因子1的水平可显著预测该人群中从贝伐单抗治疗中获益或未获益的情况。
CALGB80303 was a phase III trial of 602 patients with locally advanced or metastatic pancreatic cancer comparing gemcitabine/bevacizumab versus gemcitabine/placebo. The study found no benefit in any outcome from the addition of bevacizumab to gemcitabine. Blood samples were collected and multiple angiogenic factors were evaluated and then correlated with clinical outcome in general (prognostic markers) and with benefit specifically from bevacizumab treatment (predictive markers). Plasma samples were analyzed via a novel multiplex ELISA platform for 31 factors related to tumor growth, angiogenesis, and inflammation. Baseline values for these factors were correlated with overall survival (OS) using univariate Cox proportional hazard regression models and multivariable Cox regression models with leave-one-out cross validation. Predictive markers were identified using a treatment by marker interaction term in the Cox model. Baseline plasma was available from 328 patients. Univariate prognostic markers for OS were identified including: Ang2, CRP, ICAM-1, IGFBP-1, TSP-2 (all P < 0.001). These prognostic factors were found to be highly significant, even after adjustment for known clinical factors. Additional modeling approaches yielded prognostic signatures from multivariable Cox regression. The gemcitabine/bevacizumab signature consisted of IGFBP-1, interleukin-6, PDGF-AA, PDGF-BB, TSP-2; whereas the gemcitabine/ placebo signature consisted of CRP, IGFBP-1, PAI-1, PDGF-AA, P-selectin (both P < 0.0001). Finally, three potential predictive markers of bevacizumab efficacy were identified: VEGF-D (P <0.01), SDF1 (P <0.05), and Ang2 (P < 0.05). This study identified strong prognostic markers for pancreatic cancer patients. Predictive marker analysis indicated that plasma levels of VEGF-D, Ang2, and SDF1 significantly predicted for benefit or lack of benefit from bevacizumab in this population.