统计学技术或可帮助理解肿瘤的组成及遗传学特性 助力个体化抗癌疗法的开发

  近日，在美国西雅图举办的联合统计会议(Joint Statistical Meetings)上，来自美国北岸大学等处的研究者通过研究开发了一种用于分析新一代测序数据的统计学方法，该方法可以帮助研究人员更好地研究多种有机体的基因组，比如人类肿瘤等，其或可以帮助开发新型的个体化抗癌疗法。
  研究者Yuan Ji教授在一项名为“用于人类癌症异质性研究的贝叶斯特征模型（Bayesian Models for Heterogeneity in Human Cancers）”的研究报告中阐述了这种新技术—贝叶斯特征分配模型（Bayesian feature allocation models）。对癌症患者机体肿瘤的遗传组成进行精确地描述或可帮助驱动新型个体化癌症疗法的成功开发；近来癌症基因组学的重大突破发现，每一种恶性瘤中的癌细胞都具有遗传异质性的特征，而且还包含着多种新型进化的DNA突变。

  治疗癌症传统的一刀切方法已经不能够消除癌细胞亚克隆了，这些亚克隆是肿瘤中突变细胞产生的新一代癌细胞类型；在治疗后残留的癌细胞就会变得具有耐受性，而且会重新产生新的肿瘤使得传统疗法更加难以应付；因此对于癌症研究者来讲完全理解肿瘤中亚克隆的遗传特性对于有效利用药物组合来靶向杀灭肿瘤细胞亚克隆非常关键。
  首先研究者确定了哪种亚克隆是存在的，而且亚克隆细胞中是否存在多种类型的突变；利用新一代测序数据，研究人员就开发出了贝叶斯特征分配模型用于推断存在于肿瘤样本中的亚克隆的数量及群体的频率，随后研究者建立了亚克隆的测序及结构性突变来作为推断研究结果的一部分，这些数据都可以帮助临床医生们基于模型的推断结果来选择靶向药物。
  Ji说道，通过应用强大的贝叶斯特征分配模型来对测序数据进行分析，就可以帮助我们理解每一种肿瘤组织内部的细胞异质性及遗传特性，同时还可以通过病人的主治医生提供的数据来促进决策性癌症疗法的开发。
  联合统计会议于2015年8月8日至13日之间在美国西雅图召开，有超过6000名来自各国的统计学专家会在会上讨论学术、商业及工业化的研究议题；该会议也是北美最大的统计科学会议。（转化医学网360zhyx.com）
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Statistical technique helps cancer researchers understand tumor makeup, personalize care
A new statistical method for analyzing next-generation sequencing (NGS) data that helps researchers study the genome of various organisms such as human tumors and could help bring about personalized cancer treatments was presented today at a session of the 2015 Joint Statistical Meetings (JSM 2015) in Seattle.

Yuan Ji, director of the Program for Computational Genomics and Medicine Research Institute at NorthShore University HealthSystem and associate professor of biostatistics at The University of Chicago, described the new technique--called Bayesian feature allocation models--during a presentation titled "Bayesian Models for Heterogeneity in Human Cancers."

Ji collaborated on development of the models with Peter Mueller, professor of mathematics at The University of Texas at Austin; Juhee Lee, assistant professor of applied mathematics and statistics at the University of California, Santa Cruz; Yanxun Xu, assistant professor of statistics at The Johns Hopkins University; Subhajit Sengupta, postdoctoral fellow at NorthShore University HealthSystem; and Kamalakar Gulukota, director of the Center for Molecular Medicine at NorthShore University HealthSystem.

The successful development of personalized cancer treatments will be driven by the accurate description of the genetic composition of a patient's cancerous tumor. Recent breakthroughs in cancer genomics research reveals cells within each malignant tumor are genetically heterogeneous, possessing new and evolving DNA mutations.

Traditional one-size-fit-all approaches for treating cancer cannot eliminate all the subclones--the next generation of a mutant cell arising in a clone--within a tumor. After treatment, residual cells become resistant and repopulate a new tumor that becomes more challenging to treat. Consequently, it is important for cancer researchers to fully understand the subclonal genetics in a tumor so the disease can be attacked more effectively using combinations of drugs that target all the subclones.

To do so, the first step is to determine which subclones are present and the various types of mutations the subclones possess. Using NGS data, Ji and his collaborators developed Bayesian feature allocation models to extrapolate the number and population frequencies of subclones in a tumor sample. They also established subclonal sequence and structural mutations as part of the inference results, both of which help physicians select targeted drugs based on the models' findings.

"By applying the powerful Bayesian feature allocation models to analyze NGS data, we believe we can understand the genetic and cellular heterogeneity within each tumor and thus facilitate precision cancer treatment decisions by a patient's attending physician," said Ji, concluding his presentation.

JSM 2015 is being held August 8-13 at the Washington State Convention Center in Seattle. More than 6,000 statisticians--representing academia, business and industry, as well as national, state and local governments--from numerous countries are attending North America's largest statistical science gathering.