Buijsen Jeroen, van Stiphout Ruud G, Menheere Paul P C A, Lammering Guido, Lambin Philippe
Dept. of Radiation Oncology (MAASTRO Clinic), GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre, The Netherlands.
Dept. of Radiation Oncology (MAASTRO Clinic), GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre, The Netherlands.
Radiother Oncol. 2014 May;111(2):237-42. doi: 10.1016/j.radonc.2014.03.006. Epub 2014 Apr 16.
PURPOSE/OBJECTIVE: Chemoradiation (CRT) has been shown to lead to downsizing of an important portion of rectal cancers. In order to tailor treatment at an earlier stage during treatment, predictive models are being developed. Adding blood biomarkers may be attractive for prediction, as they can be collected very easily and determined with excellent reproducibility in clinical practice. The hypothesis of this study was that blood biomarkers related to tumor load, hypoxia and inflammation can help to predict response to CRT in rectal cancer.
MATERIAL/METHODS: 295 patients with locally advanced rectal cancer who were planned to undergo CRT were prospectively entered into a biobank protocol (NCT01067872). Blood samples were drawn before start of CRT. Nine biomarkers were selected, based on a previously defined hypothesis, and measured in a standardized way by a certified lab: CEA, CA19-9, LDH, CRP, IL-6, IL-8, CA IX, osteopontin and 25-OH-vitamin D. Outcome was analyzed in two ways: pCR vs. non-pCR and responders (defined as ypT0-2N0) vs. non-responders (all other ypTN stages).
276 patients could be analyzed. 20.7% developed a pCR and 47.1% were classified as responders. In univariate analysis CEA (p=0.001) and osteopontin (p=0.012) were significant predictors for pCR. Taking response as outcome CEA (p<0.001), IL-8 (p<0.001) and osteopontin (p=0.004) were significant predictors. In multivariate analysis CEA was the strongest predictor for pCR (OR 0.92, p=0.019) and CEA and IL-8 predicted for response (OR 0.97, p=0.029 and OR 0.94, p=0.036). The model based on biomarkers only had an AUC of 0.65 for pCR and 0.68 for response; the strongest model included clinical data, PET-data and biomarkers and had an AUC of 0.81 for pCR and 0.78 for response.
CEA and IL-8 were identified as predictive biomarkers for tumor response and PCR after CRT in rectal cancer. Incorporation of these blood biomarkers leads to an additional accuracy of earlier developed prediction models using clinical variables and PET-information. The new model could help to an early adaptation of treatment in rectal cancer patients.
已证明放化疗（CRT）可使相当一部分直肠癌缩小。为了在治疗早期调整治疗方案，正在开发预测模型。添加血液生物标志物用于预测可能很有吸引力，因为它们很容易采集，并且在临床实践中检测具有出色的可重复性。本研究的假设是，与肿瘤负荷、缺氧和炎症相关的血液生物标志物有助于预测直肠癌对CRT的反应。
材料/方法：295例计划接受CRT的局部晚期直肠癌患者前瞻性地纳入生物样本库方案（NCT01067872）。在CRT开始前采集血样。基于先前定义的假设选择了9种生物标志物，并由认证实验室以标准化方式进行检测：癌胚抗原（CEA）、糖类抗原19-9（CA19-9）、乳酸脱氢酶（LDH）、C反应蛋白（CRP）、白细胞介素-6（IL-6）、白细胞介素-8（IL-8）、碳酸酐酶IX（CA IX）、骨桥蛋白和25-羟基维生素D。结果以两种方式分析：病理完全缓解（pCR）与非pCR，以及反应者（定义为ypT0-2N0）与无反应者（所有其他ypTN分期）。
276例患者可进行分析。20.7%发生pCR，47.1%被分类为反应者。单因素分析中，CEA（p=0.001）和骨桥蛋白（p=0.012）是pCR的显著预测因子。以反应作为结果，CEA（p<0.001）、IL-8（p<0.001）和骨桥蛋白（p=0.004）是显著预测因子。多因素分析中，CEA是pCR最强的预测因子（比值比[OR]0.92，p=0.019），CEA和IL-8预测反应（OR 0.97，p=0.029和OR 0.94，p=0.036）。仅基于生物标志物的模型对于pCR的曲线下面积（AUC）为0.65，对于反应的AUC为0.68；最强的模型包括临床数据、PET数据和生物标志物，对于pCR的AUC为0.81，对于反应的AUC为0.78。
CEA和IL-8被确定为直肠癌CRT后肿瘤反应和pCR的预测生物标志物。纳入这些血液生物标志物可提高早期使用临床变量和PET信息开发的预测模型的准确性。新模型有助于早期调整直肠癌患者的治疗方案。
