Yanagisawa Kiyoshi, Shyr Yu, Xu Baogang J, Massion Pierre P, Larsen Paul H, White Bill C, Roberts John R, Edgerton Mary, Gonzalez Adriana, Nadaf Sorena, Moore Jason H, Caprioli Richard M, Carbone David P
Vanderbilt-Ingram Cancer Center, Nashville, Tennessee 37232-6838, USA.
Lancet. 2003 Aug 9;362(9382):433-9. doi: 10.1016/S0140-6736(03)14068-8.
Proteomics-based approaches complement the genome initiatives and may be the next step in attempts to understand the biology of cancer. We used matrix-assisted laser desorption/ionisation mass spectrometry directly from 1-mm regions of single frozen tissue sections for profiling of protein expression from surgically resected tissues to classify lung tumours.
Proteomic spectra were obtained and aligned from 79 lung tumours and 14 normal lung tissues. We built a class-prediction model with the proteomic patterns in a training cohort of 42 lung tumours and eight normal lung samples, and assessed their statistical significance. We then applied this model to a blinded test cohort, including 37 lung tumours and six normal lung samples, to estimate the misclassification rate.
We obtained more than 1600 protein peaks from histologically selected 1 mm diameter regions of single frozen sections from each tissue. Class-prediction models based on differentially expressed peaks enabled us to perfectly classify lung cancer histologies, distinguish primary tumours from metastases to the lung from other sites, and classify nodal involvement with 85% accuracy in the training cohort. This model nearly perfectly classified samples in the independent blinded test cohort. We also obtained a proteomic pattern comprised of 15 distinct mass spectrometry peaks that distinguished between patients with resected non-small-cell lung cancer who had poor prognosis (median survival 6 months, n=25) and those who had good prognosis (median survival 33 months, n=41, p<0.0001).
Proteomic patterns obtained directly from small amounts of fresh frozen lung-tumour tissue could be used to accurately classify and predict histological groups as well as nodal involvement and survival in resected non-small-cell lung cancer.
基于蛋白质组学的方法是对基因组计划的补充，可能是深入了解癌症生物学特性的下一步研究方向。我们直接采用基质辅助激光解吸/电离质谱法，对手术切除组织的单个冰冻组织切片的1毫米区域进行分析，以描绘蛋白质表达图谱，从而对肺肿瘤进行分类。
获取并比对了79例肺肿瘤和14例正常肺组织的蛋白质组谱图。我们利用42例肺肿瘤和8例正常肺样本组成的训练队列中的蛋白质组模式构建了一个类别预测模型，并评估其统计学意义。然后，我们将该模型应用于一个包括37例肺肿瘤和6例正常肺样本的盲法测试队列，以估计错误分类率。
我们从每个组织的单个冰冻切片中经组织学选择的直径1毫米区域获得了1600多个蛋白质峰。基于差异表达峰的类别预测模型使我们能够完美地对肺癌组织学类型进行分类，区分原发性肿瘤与其他部位转移至肺部的肿瘤，并在训练队列中以85%的准确率对淋巴结受累情况进行分类。该模型在独立的盲法测试队列中对样本进行了近乎完美的分类。我们还获得了一个由15个不同质谱峰组成的蛋白质组模式，该模式区分了切除的非小细胞肺癌预后较差（中位生存期6个月，n = 25）和预后良好（中位生存期33个月，n = 41，p < 0.0001）的患者。
直接从少量新鲜冰冻肺肿瘤组织中获得的蛋白质组模式可用于准确分类和预测组织学类型，以及切除的非小细胞肺癌中的淋巴结受累情况和生存期。
