Statistical measures of transcriptional diversity capture genomic heterogeneity of cancer
Statistical measures of transcriptional diversity capture genomic heterogeneity of cancer
复制标题
DOI：
10.1186/1471-2164-15-876
复制
发表时间：
2014-10-08
期刊：
影响因子：
4.4
通讯作者：
Hatzis, Christos
中科院分区：
文献类型：
--
作者：
Jiang, Tingting;Shi, Weiwei;Hatzis, Christos
背景：肿瘤的分子异质性表明存在多个不同的亚克隆，这可能限制对靶向治疗的反应，并有助于获得耐药性，但其量化仍然具有挑战性。结果：我们进行了模拟，以评估最能捕获一组肿瘤内连续（基因表达）或离散（突变，拷贝数改变）分子数据的分子多样性的统计方法。主成分空间中基于色散的度量最能捕捉到潜在的异质性。为了证明这些方法的实用性，我们对不同乳腺癌亚型的转录和基因组谱的多样性进行了表征，并表明基底样或三阴性乳腺癌（TNBC）在分子上的异质性明显高于其他亚型。我们的分析还表明，转录多样性是在大多数分子途径中观察到的肿瘤的一个全局特征。在基底样肿瘤中，与化疗敏感肿瘤相比，对多药化疗耐药的肿瘤表现出更大的转录多样性，这表明可能有多种机制导致化疗耐药。结论：我们提出并验证了转录和基因组多样性的测量方法，可以量化肿瘤的分子多样性。我们将新的测量方法应用于乳腺肿瘤的基因组数据，并证明基底样乳腺癌明显比其他乳腺癌更具多样性。化疗耐药肿瘤的分子多样性明显高于化疗敏感肿瘤，这意味着多种耐药机制可能是活跃的，从而限制了化疗反应预测标志物的敏感性和准确性。
Background: Molecular heterogeneity of tumors suggests the presence of multiple different subclones that may limit response to targeted therapies and contribute to acquisition of drug resistance, but its quantification has remained challenging.Results: We performed simulations to evaluate statistical measures that best capture the molecular diversity within a group of tumors for either continuous (gene expression) or discrete (mutations, copy number alterations) molecular data. Dispersion based metrics in the principal component space best captured the underlying heterogeneity. To demonstrate utility of these measures, we characterized the diversity in transcriptional and genomic profiles of different breast tumor subtypes, and showed that basal-like or triple-negative breast cancers (TNBC) are significantly more heterogeneous molecularly than other subtypes. Our analysis also suggests that transcriptional diversity is a global characteristic of the tumors observed across the majority of molecular pathways. Among basal-like tumors, those that were resistant to multi-agent chemotherapy showed greater transcriptional diversity compared to chemotherapy-sensitive tumors, suggesting that potentially multiple mechanisms may be contributing to chemotherapy resistance.Conclusions: We proposed and validated measures of transcriptional and genomic diversity that can quantify the molecular diversity of tumors. We applied the new measures to genomic data from breast tumors and demonstrated that basal-like breast cancers are significantly more diverse than other breast cancers. The observation that chemo-resistant tumors are significantly more diverse molecularly than chemosensitive tumors implies that multiple resistance mechanisms may be active, thus limiting the sensitivity and accuracy of predictive markers of chemotherapy response.