创新性研究发现数千种罕见的癌症相关基因突变

这周PLOS Computational Biology杂志上发表了一项创新性的研究，解释了数千个“先前被忽视的基因突变”可能对恶性肿瘤的生长有促进作用。研究人员使用一种新的统计方法发现了蛋白质的新模式。

当基因突变导致异常的细胞增殖时癌症就开始了。这些体细胞变体——即出生后发生的DNA变化——终可能引发肿瘤的生长。

现代药物干预措施利用我们已知的某些癌症相关基因突变知识，靶向定位由于这些基因突变编码的而被改变的蛋白质。

到目前为止，这些重要的致癌突变只有很小一部分被确定。

尽管大样本研究已经发现了有统计学意义的突变，但即使一些被认为是癌症重要驱动因素的体细胞变体，出现的频率也很低。更多被研究的突变没有达到足够可靠的统计学意义水平。

来自马里兰州大学的一组研究人员，由Thomas Peterson领导，使用一种新的统计分析方法来解决我们知识中的这一差距，他们检测了跨基因组的类似突变。

研究人员使用遗传数据，定位到相关蛋白家族共享的类似突变，专门研究被称为蛋白质结构域的蛋白质亚组分中的突变。

蛋白质的结构域是蛋白质中具有特异结构和立功能的区域，构成同一个蛋白质的每个结构域都有其特殊的功能。但即使是被不同基因编码且生理作用完全不同的蛋白质，也可以共享共同的结构域。

研究团队利用关于蛋白质结构域功能和结构的现有知识，来确定结构域中致癌突变很可能发生的具体区域。他们的研究工作利用了以前基因研究产生的大量数据，正如研究的作者所写道的：“我们利用了结构基因组学几十年的重要发现。”

换句话说，该团队不是专注于特定基因的单个区域的突变，而是集中在跨越蛋白质家族的类似区域发生的突变。

总之，他们从The Cancer Genome Atlas收集了5848例患者的体细胞变异数据，其中包括20种不同类型的癌症。

使用这种创新的方法，团队发现了数千种罕见的肿瘤突变，这些突变发生在与其他肿瘤其他蛋白质中发现的突变相同的结构域位置，这意味着它们参与癌症的发生。

文章的作者Maricel Kann 说：“可能只有两名患者在特定蛋白质中发生突变，但当你发现患者其他蛋白质突变与该突变位置完全相同时，你就会意识到调查这两种突变的重要性。”

作者将这些可能包含致癌突变的蛋白质结构域称为“癌巢”，了解更多关于癌巢的信息可能终会改善癌症的治疗，正如Kann解释的那样：“因为这么多蛋白质的结构域是相同的，所以相同的一个治疗方式治疗各种各样突变蛋白质引起的癌症就成为了可能。”

虽然这项研究只是新研究方向的步，但从长远来看，这是改善癌症治疗非常重要的一步。从不同的角度研究癌症的遗传基础给其他研究人员提供了解决问题的新方向。

作者总结到：“确定哪些变体对于肿瘤发生重要将有助于阐明驱动肿瘤进展的机制，并且可以终为在结构和功能水平上显示相似变异的基因家族提供一系列新的药物靶点。（文章来源：今日医学新闻    作者：Tim Newman   麻省医疗国际何静翻译）

Thousands of rare cancer-related gene mutations found

Written by Tim Newman     Published: Saturday 22 April 2017

Innovative research, published in PLOS Computational Biology this week, explains how thousands of "previously ignored genetic mutations" may contribute to the growth of malignant tumors. Using a new statistical approach, scientists find new patterns in proteins.

Cancer begins when a genetic mutation produces abnormal cell growth. These somatic variants - which are changes to DNA that occur after birth - can eventually spark the growth of a tumor.

Modern pharmaceutical interventions have been designed to exploit our knowledge of certain cancer-related mutations; they target proteins that are altered due to the mutations in the genes that code them.

To date, only a tiny number of these important cancer-causing mutations have been pinpointed.

Although studies using large numbers of participants have identified statistically significant mutations, even somatic variants that are considered to be important drivers of cancer appear in relatively low frequencies.

Similarly, many more mutations have been noted that do not quite reach a reliable enough level of statistical significance.

A group of researchers from the University of Maryland in College Park, led by Thomas Peterson, used a new method of statistical analysis to tackle this gap in our knowledge and examine similar mutations that are spread across the genome.

Using genetic data, they targeted similar mutations "shared by families of related proteins," specifically examining mutations in subcomponents of proteins known as protein domains.

Perusing protein domains

Protein domains are distinct units within proteins - each domain carries out specific functions independently from the other domains that make up the same protein. Proteins, even if they are coded by different genes and carry out completely different physiological roles, can share common protein domains.

The research team utilized existing knowledge about protein domain function and structure to identify regions within the domains where mutations might be more likely to occur in tumors. Their work makes use of the great swathes of data produced by previous gene research. As the authors of the study write, "we leverage decades of important findings from structural genomics."

In other words, rather than focus on mutations in single regions of specific genes, the team focused on mutations that occurred in similar regions across families of proteins.

In all, they collected data about somatic variants from 5,848 patients in The Cancer Genome Atlas, which included patients with 20 different cancer types.

Using this fresh approach, the team unearthed thousands of rare tumor mutations that occur in the "same domain location as mutations found in other proteins in other tumors." This implies that they are involved in cancer.

"Maybe only two patients have a mutation in a particular protein, but when you realize it is in exactly the same position within the domain as mutations in other proteins in cancer patients, you realize it's important to investigate those two mutations."——Maricel Kann, senior author

The authors refer to these protein domains that are likely to comprise cancer-causing mutations as "oncodomains." Understanding more about oncodomains could eventually lead to improved cancer treatments. As Kann explains: "Because the domains are the same across so many proteins, it is possible that a single treatment could tackle cancers caused by a broad spectrum of mutated proteins."

Although this work represents the first step in a new direction, it is a significant first step that promises to improve cancer treatment in the long run. Investigating the genetic basis of cancer from a different standpoint gives other researchers a new angle from which to approach the problem.

The authors conclude: "Determining which variants are most important for tumorigenesis will help elucidate the mechanisms driving tumor progression and could ultimately provide a new set of drug targets for families of genes that display similar variation at the structural and functional level."

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