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基于内在亚型,蛋白质和脂质 MALDI 图谱可对乳腺癌进行分类。

Protein and lipid MALDI profiles classify breast cancers according to the intrinsic subtype.

机构信息

National Cancer Center, Goyang, 410-769, Korea.

出版信息

BMC Cancer. 2011 Oct 27;11:465. doi: 10.1186/1471-2407-11-465.

Abstract

BACKGROUND

Matrix-assisted laser desorption/ionization (MALDI) mass spectrometry (MS) has been demonstrated to be useful for molecular profiling of common solid tumors. Using recently developed MALDI matrices for lipid profiling, we evaluated whether direct tissue MALDI MS analysis on proteins and lipids may classify human breast cancer samples according to the intrinsic subtype.

METHODS

Thirty-four pairs of frozen, resected breast cancer and adjacent normal tissue samples were analyzed using histology-directed, MALDI MS analysis. Sinapinic acid and 2,5-dihydroxybenzoic acid/α-cyano-4-hydroxycinnamic acid were manually deposited on areas of each tissue section enriched in epithelial cells to identify lipid profiles, and mass spectra were acquired using a MALDI-time of flight instrument.

RESULTS

Protein and lipid profiles distinguish cancer from adjacent normal tissue samples with the median prediction accuracy of 94.1%. Luminal, HER2+, and triple-negative tumors demonstrated different protein and lipid profiles, as evidenced by permutation P values less than 0.01 for 0.632+ bootstrap cross-validated misclassification rates with all classifiers tested. Discriminatory proteins and lipids were useful for classifying tumors according to the intrinsic subtype with median prediction accuracies of 80.0-81.3% in random test sets.

CONCLUSIONS

Protein and lipid profiles accurately distinguish tumor from adjacent normal tissue and classify breast cancers according to the intrinsic subtype.

摘要

背景

基质辅助激光解吸/电离(MALDI)质谱(MS)已被证明对常见实体瘤的分子谱分析很有用。使用最近开发的用于脂质分析的 MALDI 基质,我们评估了直接对蛋白质和脂质进行组织 MALDI MS 分析是否可以根据固有亚型对人乳腺癌样本进行分类。

方法

使用组织学定向 MALDI MS 分析对 34 对冷冻的、切除的乳腺癌和相邻正常组织样本进行了分析。在每个组织切片中上皮细胞丰富的区域手动沉积了 sinapinic 酸和 2,5-二羟基苯甲酸/α-氰基-4-羟基肉桂酸,以识别脂质谱,并使用 MALDI-飞行时间仪器获取质谱。

结果

蛋白质和脂质谱可区分癌症与相邻正常组织样本,中位数预测准确率为 94.1%。腔型、HER2+和三阴性肿瘤表现出不同的蛋白质和脂质谱,这一点通过所有测试分类器的 0.632+bootstrap 交叉验证错误分类率的置换 P 值小于 0.01 得到证实。具有鉴别力的蛋白质和脂质可用于根据固有亚型对肿瘤进行分类,在随机测试集中的中位数预测准确率为 80.0-81.3%。

结论

蛋白质和脂质谱可准确区分肿瘤与相邻正常组织,并根据固有亚型对乳腺癌进行分类。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cce4/3218066/d88bd8cb56d4/1471-2407-11-465-1.jpg

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