Robust unmixing of tumor states in array comparative genomic hybridization data.
Robust unmixing of tumor states in array comparative genomic hybridization data.
复制标题
DOI：
10.1093/bioinformatics/btq213
复制
发表时间：
2010-06-15
期刊：
影响因子：
--
通讯作者：
Schwartz R
中科院分区：
文献类型：
--
作者：
Tolliver D;Tsourakakis C;Subramanian A;Shackney S;Schwartz R
动机：肿瘤发生是一个进化过程，通过该过程，肿瘤细胞获得导致生长增加、侵袭性和最终转移的突变序列。希望通过确定主要癌症亚型的常见突变模式，我们可以更好地了解肿瘤发展的分子基础，并确定新的诊断和治疗靶点。这一目标促使了几次尝试将进化树重建方法应用于肿瘤状态的测定。肿瘤进化的推断原则上是由肿瘤是异质性的事实辅助的，在其发展过程中保留了不同阶段的残余群体沿着污染的健康细胞群体。然而，在实践中，这种异质性使肿瘤数据的解释复杂化，因为不同的细胞类型被用于测定肿瘤状态的常用方法混淆。我们以前提出了一种方法，通过混合物类型分离的几何解释，从肿瘤范围内的基因表达的测量中计算推断细胞群体，但这种方法处理噪声和离群数据的效果很差。结果：在目前的工作中，我们提出了一种新的方法来进行肿瘤混合物的分离有效和强大的实验误差。该方法建立在先前的几何方法，但使用一种新的目标函数，允许强大的拟合，大大降低了对噪声和离群值的敏感性。我们进一步开发了一种有效的梯度优化方法，以优化这种“软几何解混”的目标，通过阵列比较基因组杂交（aCGH）数据评估肿瘤DNA拷贝数的测量。我们表明，半合成和真实的数据的组合，该方法产生快速，准确的肿瘤状态的分离。结论：我们已经展示了一种新颖的目标函数和优化方法，用于从aCGH数据中稳健地分离肿瘤亚型，并表明该方法可以快速、准确地从混合样本中重建肿瘤状态。这个问题的更好解决方案可以提高我们准确识别原发性肿瘤样本中遗传异常和推断肿瘤演变模式的能力。联系方式：tolliver@cs.cmu.edu补充信息：补充数据可从生物信息学在线网站获得。
Motivation: Tumorigenesis is an evolutionary process by which tumor cells acquire sequences of mutations leading to increased growth, invasiveness and eventually metastasis. It is hoped that by identifying the common patterns of mutations underlying major cancer sub-types, we can better understand the molecular basis of tumor development and identify new diagnostics and therapeutic targets. This goal has motivated several attempts to apply evolutionary tree reconstruction methods to assays of tumor state. Inference of tumor evolution is in principle aided by the fact that tumors are heterogeneous, retaining remnant populations of different stages along their development along with contaminating healthy cell populations. In practice, though, this heterogeneity complicates interpretation of tumor data because distinct cell types are conflated by common methods for assaying the tumor state. We previously proposed a method to computationally infer cell populations from measures of tumor-wide gene expression through a geometric interpretation of mixture type separation, but this approach deals poorly with noisy and outlier data. Results: In the present work, we propose a new method to perform tumor mixture separation efficiently and robustly to an experimental error. The method builds on the prior geometric approach but uses a novel objective function allowing for robust fits that greatly reduces the sensitivity to noise and outliers. We further develop an efficient gradient optimization method to optimize this ‘soft geometric unmixing’ objective for measurements of tumor DNA copy numbers assessed by array comparative genomic hybridization (aCGH) data. We show, on a combination of semi-synthetic and real data, that the method yields fast and accurate separation of tumor states. Conclusions: We have shown a novel objective function and optimization method for the robust separation of tumor sub-types from aCGH data and have shown that the method provides fast, accurate reconstruction of tumor states from mixed samples. Better solutions to this problem can be expected to improve our ability to accurately identify genetic abnormalities in primary tumor samples and to infer patterns of tumor evolution. Contact: tolliver@cs.cmu.edu Supplementary information:Supplementary data are available at Bioinformatics online.
登录
查看更多内容
影响因子：
56.9
作者：
Golub, TR;Slonim, DK;Lander, ES
通讯作者：
Lander, ES
影响因子：
7
作者：
Navin, Nicholas;Krasnitz, Alexander;Wigler, Michael
通讯作者：
Wigler, Michael
影响因子：
2.1
作者：
Olshen, AB;Venkatraman, ES;Wigler, M
通讯作者：
Wigler, M
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：
1.7
作者：
Desper, R;Jiang, F;Schäffer, AA
通讯作者：
Schäffer, AA