Distinct molecular mechanisms underlying clinically relevant subtypes of breast cancer: gene expression analyses across three different platforms.
Distinct molecular mechanisms underlying clinically relevant subtypes of breast cancer: gene expression analyses across three different platforms.
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DOI：
10.1186/1471-2164-7-127
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发表时间：
2006-05-26
期刊：
影响因子：
4.4
通讯作者：
Børresen-Dale AL
中科院分区：
文献类型：
--
作者：
Sørlie T;Wang Y;Xiao C;Johnsen H;Naume B;Samaha RR;Børresen-Dale AL
基因表达谱已被用于确定复杂疾病如乳腺癌的分子表型。在乳腺癌的五种分子亚型中，腔A和基底样亚型被反复鉴定和验证为两种主要亚型。这两者与明显不同的基因表达模式有关，更重要的是，临床结果有显著差异。为了进一步验证和更彻底地表征肿瘤早期分子水平上的这两种亚型，我们报告了一项使用三种不同DNA微阵列平台的基因表达谱研究。在三种不同的DNA微阵列平台上生成了20例早期乳腺癌肿瘤活检的表达数据；应用生物系统公司的人类基因组调查微阵列、斯坦福大学的cDNA微阵列和安捷伦公司的全人类基因组寡核苷酸微阵列，并分析结果的基因表达模式。无监督和有监督的分析都确定了不同的临床相关乳腺癌亚型，并且在所有三个平台上的结果都是一致的。两种主要亚型差异表达基因的基因分类和生物学通路分析揭示了两种基于表达的亚型的不同分子机制：luminal A亚型的特征基因被脂肪酸代谢和类固醇激素介导的信号通路，特别是雌激素受体信号通路的基因过多代表，而basal样亚型的特征基因被细胞增殖和分化、p21介导的信号通路和细胞周期信号通路G1-S检查点的基因过多代表。使用从三个不同的阵列平台生成的组合数据集，鉴定出最能区分两种亚型的最小54个基因集。这些预测基因通过TaqMan®基因表达试验进一步验证。我们已经在一小部分早期乳腺癌中确定并验证了两种主要的先前定义的临床相关亚型，腔A和基底样亚型。表征这两种亚型的特征基因表明，不同的分子机制可能在疾病不同亚型的早期阶段被预先编程。我们的研究结果进一步证明，这些乳腺肿瘤亚型代表了生物学上不同的疾病实体，可能需要不同的治疗策略。最后，通过包括定量PCR在内的多个基因表达平台的验证，本研究中鉴定的54个预测基因可能定义乳腺癌潜在的预后分子标记。
Gene expression profiling has been used to define molecular phenotypes of complex diseases such as breast cancer. The luminal A and basal-like subtypes have been repeatedly identified and validated as the two main subtypes out of a total of five molecular subtypes of breast cancer. These two are associated with distinctly different gene expression patterns and more importantly, a significant difference in clinical outcome. To further validate and more thoroughly characterize these two subtypes at the molecular level in tumors at an early stage, we report a gene expression profiling study using three different DNA microarray platforms. Expression data from 20 tumor biopsies of early stage breast carcinomas were generated on three different DNA microarray platforms; Applied Biosystems Human Genome Survey Microarrays, Stanford cDNA Microarrays and Agilent's Whole Human Genome Oligo Microarrays, and the resulting gene expression patterns were analyzed. Both unsupervised and supervised analyses identified the different clinically relevant subtypes of breast tumours, and the results were consistent across all three platforms. Gene classification and biological pathway analyses of the genes differentially expressed between the two main subtypes revealed different molecular mechanisms descriptive of the two expression-based subtypes: Signature genes of the luminal A subtype were over-represented by genes involved in fatty acid metabolism and steroid hormone-mediated signaling pathways, in particular estrogen receptor signaling, while signature genes of the basal-like subtype were over-represented by genes involved in cell proliferation and differentiation, p21-mediated pathway, and G1-S checkpoint of cell cycle-signaling pathways. A minimal set of 54 genes that best discriminated the two subtypes was identified using the combined data sets generated from the three different array platforms. These predictor genes were further verified by TaqMan® Gene Expression assays. We have identified and validated the two main previously defined clinically relevant subtypes, luminal A and basal-like, in a small set of early stage breast carcinomas. Signature genes characterizing these two subtypes revealed that distinct molecular mechanisms might have been pre-programmed at an early stage in different subtypes of the disease. Our results provide further evidence that these breast tumor subtypes represent biologically distinct disease entities and may require different therapeutic strategies. Finally, validated by multiple gene expression platforms, including quantitative PCR, the set of 54 predictor genes identified in this study may define potential prognostic molecular markers for breast cancer.
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影响因子：
4.8
作者：
Prescott, JE;Osthus, RC;Dang, CV
通讯作者：
Dang, CV
影响因子：
158.5
作者：
van de Vijver, MJ;He, YD;Bernards, R
通讯作者：
Bernards, R
DOI：
10.1073/pnas.0932692100
发表时间：
2003-07-08
影响因子：
11.1
作者：
Sorlie, T;Tibshirani, R;Botstein, D
通讯作者：
Botstein, D
影响因子：
30.8
作者：
Ross, DT;Scherf, U;Brown, PO
通讯作者：
Brown, PO
影响因子：
48
作者：
Irizarry, RA;Warren, D;Yu, W
通讯作者：
Yu, W