Faculty of Medicine and Life Sciences, Hasselt University, Hasselt.
Biomolecule Design Group, Institute for Materials Research, Hasselt University, Hasselt Applied and Analytical Chemistry, Institute for Materials Research, Hasselt University, Hasselt.
Ann Oncol. 2016 Jan;27(1):178-84. doi: 10.1093/annonc/mdv499. Epub 2015 Oct 20.
Accumulating evidence has shown that cancer cell metabolism differs from that of normal cells. However, up to now it is not clear whether different cancer types are characterized by a specific metabolite profile. Therefore, this study aims to evaluate whether the plasma metabolic phenotype allows to discriminate between lung and breast cancer.
The proton nuclear magnetic resonance spectrum of plasma is divided into 110 integration regions, representing the metabolic phenotype. These integration regions reflect the relative metabolite concentrations and were used to train a classification model in discriminating between 80 female breast cancer patients and 54 female lung cancer patients, all with an adenocarcinoma. The validity of the model was examined by permutation testing and by classifying an independent validation cohort of 60 female breast cancer patients and 81 male lung cancer patients, all with an adenocarcinoma.
The model allows to classify 99% of the breast cancer patients and 93% of the lung cancer patients correctly with an area under the curve (AUC) of 0.96 and can be validated in the independent cohort with a sensitivity of 89%, a specificity of 82% and an AUC of 0.94. Decreased levels of sphingomyelin and phosphatidylcholine (phospholipids with choline head group) and phospholipids with short, unsaturated fatty acid chains next to increased levels of phospholipids with long, saturated fatty acid chains seem to indicate that cell membranes of lung tumors are more rigid and less sensitive to lipid peroxidation. The other discriminating metabolites are pointing to a more pronounced response of the body to the Warburg effect for lung cancer.
Metabolic phenotyping of plasma allows to discriminate between lung and breast cancer, indicating that the metabolite profile reflects more than a general cancer marker.
NCT02362776.
越来越多的证据表明，癌细胞代谢与正常细胞不同。然而，到目前为止，还不清楚不同类型的癌症是否具有特定的代谢物特征。因此，本研究旨在评估血浆代谢表型是否可以区分肺癌和乳腺癌。
将血浆的质子核磁共振波谱分为 110 个积分区，代表代谢表型。这些积分区反映了相对代谢物浓度，并用于训练分类模型，以区分 80 名女性乳腺癌患者和 54 名女性肺癌患者，所有患者均为腺癌。通过置换检验和对 60 名女性乳腺癌患者和 81 名男性肺癌患者（均为腺癌）的独立验证队列进行分类来检验模型的有效性。
该模型可正确分类 99%的乳腺癌患者和 93%的肺癌患者，曲线下面积（AUC）为 0.96，可在独立队列中验证，灵敏度为 89%，特异性为 82%，AUC 为 0.94。鞘磷脂和磷脂酰胆碱（带有胆碱头基团的磷脂）水平降低，靠近饱和脂肪酸链的短不饱和脂肪酸链的磷脂水平升高，似乎表明肺癌肿瘤的细胞膜更硬，对脂质过氧化的敏感性更低。其他有区别的代谢物表明，肺癌患者的机体对沃伯格效应的反应更为明显。
血浆代谢表型可区分肺癌和乳腺癌，表明代谢物谱反映的不仅仅是一般的癌症标志物。
NCT02362776。
