28

使用可视化工具MutationMapper来看看基因上面突变的分布

how to generate lollipop diagrams ?
这个工具是网页版的,不需要下载,只需上传两列数据即可,就是基因上面的第几个氨基酸是如何突变的这样的信息
网页就可以把这些信息画在基因上面,而且注释到domain和cosmic数据库,挺好用的!
Hugo_Symbol HUGO symbol for the gene TP53
Protein_Change Amino acid change V600E
结果也很容易理解!
我输入了4个基因,网页就会对每个基因画一个图,其实ABCA1这个基因有6个突变,就都画在该基因上面了,基因突变分布在基因的哪个domain也看得一清二楚!
1
我的输入文件是这样的!
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非常好用:
  • Support mutation data with annotated protein effects
  • Mutation diagram/lollipop view
  • Mutation table view
  • 3D structure view if available
而且pfam数据库本身也提供了这个功能:

Pfam provides an online tool to not only generate the domain information in JSON format, but to draw the lollipop diagram using javascript as well. They have more information here: http://pfam.xfam.org/help#tabview=tab9

IMHO, not as pretty as cBioPortal's but it gets you close to a solution.

EDIT / SHAMELESS PLUG: After seeing the data available and how easy it'd be, I made my own quick tool to fetch the data and draw the diagram for me in a style similar to cBioPortal - feel free to fork it and add features: https://github.com/pbnjay/lollipops

Example output (w/ labels per the comments)

3

甚至还有高手用D3.JS写了一个能实现同样需求的模块

We found ourselves in the same need, we wanted such a plot (JavaScript). Thus, I add our solution, Mutations Needle Plot. The library creates an SVG image (with D3), which then may be downloaded.

You will npm in order to be able to install & run the library.

Examples may be found in the snippets folder or also the index.html - The one displayed here below

类似的高手很多:
My colleague @SolenaLS recently asked me to write something like this: (uniprot+SVG+javascript : )http://lindenb.github.io/pages/uniprot/paintsvg.html
而且ensembl数据库也提供类似的功能
Ensemble also gives similar plot.

 

22

用TCGA数据做cox生存分析的风险因子(比例风险模型)

再次强调一下,R里面实现生存分析非常简单!
用my.surv <- surv(OS_MONTHS,OS_STATUS=='DECEASED')构建生存曲线。
用kmfit2 <- survfit(my.surv~TUMOR_STAGE_2009)来做某一个因子的KM生存曲线。用 survdiff(my.surv~type, data=dat)来看看这个因子的不同水平是否有显著差异,其中默认用是的logrank test 方法。
用coxph(Surv(time, status) ~ ph.ecog + tt(age), data=lung) 来检测自己感兴趣的因子是否受其它因子(age,gender等等)的影响。

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14

用RNA-SeQC得到表达矩阵RPKM值

这个软件不仅仅能做QC,而且可以统计各个基因的RPKM值!尤其是TCGA计划里面的都是用它算的
一、程序安装
直接在官网下载java版本软件即可使用:http://www.broadinstitute.org/cancer/cga/tools/rnaseqc/RNA-SeQC_v1.1.8.jar
但是需要下载很多注释数据
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二、输入数据

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箭头所指的文件,一个都不少,只有那个rRNA.tar我没有用, 因为这个软件有两种使用方式,我用的是第一种
三、软件使用
软件的官网给力例子,很容易学习:
RNA-SeQC can be run with or without a BWA-based rRNA level estimation mode. To run without (less accurate, but faster) use the command:
java -jar RNASeQC.jar -n 1000 -s "TestId|ThousandReads.bam|TestDesc" -t gencode.v7.annotation_goodContig.gtf -r Homo_sapiens_assembly19.fasta -o ./testReport/ -strat gc -gc gencode.v7.gc.txt 
我用的就是这个例子,这个例子需要的所有文件里面,染色体都是没有chr的,这个非常重要!!!
代码如下:
 java -jar RNA-SeQC_v1.1.8.jar  \
-n 1000 \
-s "TestId|ThousandReads.bam|TestDesc" \
-t gencode.v7.annotation_goodContig.gtf \
-r ~/ref-database/human_g1k_v37/human_g1k_v37.fasta  \
-o ./testReport/ \
-strat gc \
-gc gencode.v7.gc.txt \
To run the more accurate but slower, BWA-based method :
java -jar RNASeQC.jar -n 1000 -s "TestId|ThousandReads.bam|TestDesc" -t gencode.v7.annotation_goodContig.gtf -r Homo_sapiens_assembly19.fasta -o ./testReport/ -strat gc -gc gencode.v7.gc.txt -BWArRNA human_all_rRNA.fasta
Note: this assumes BWA is in your PATH. If this is not the case, use the -bwa flag to specify the path to BWA
四、结果解读
运行要点时间,就那个一千条reads的测试数据都搞了10分钟!
出来一大堆突变,具体解释,官网上面很详细,不过,比较重要的当然是RPKM值咯,还有QC的信息
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TCGA数据里面都会提供由RNA-SeQC软件处理得到的表达矩阵!
Expression
  • RPKM data are used as produced by RNA-SeQC.
  • Filter on >=10 individuals with >0.1 RPKM and raw read counts greater than 6.
  • Quantile normalization was performed within each tissue to bring the expression profile of each sample onto the same scale.
  • To protect from outliers, inverse quantile normalization was performed for each gene, mapping each set of expression values to a standard normal.
软件的主页是:
 
 
 
 
11

对CCLE数据库可以做的分析

收集了那么多的癌症细胞系的表达数据,拷贝数变异数据,突变数据,总不能放着让它发霉吧!
这些数据可以利用的地方非常多,但是在谷歌里面搜索引用了它的文章却不多,我挑了其中几个,解读了一下别人是如何利用这个数据的,当然,主要是用那个mRNA的表达数据咯!
这篇文献对CCLE的数据进行了八个步骤的处理,一个合格的生物信息学分析着完全可以重写这个过程
step1:Affymetrix U133 Plus2 DNA microarray gene expressions of 27 gastric cancer cell lines (Kato-III, IM95, SNU-620, SNU-16, OCUM-1, NUGC-4, 2313287, HUG1N, MKN45, NCIN87, KE39, AGS, SNU-5, SNU-216, NUGC-3, NUGC-2, MKN74, MKN7, RERFGC1B, GCIY, KE97, Fu97, SH10TC, MKN1, SNU-1, Hs746 T, HGC27) were downloaded from Cancer Cell Line Encyclopedia (CCLE) [16] in March 2013.
step2: Robust Multi-array Average (RMA) normalization was performed. Principal component analysis plot show no obvious batch effect.
step3: The normalized data is then collapsed by taking the probe sets with highest gene expression.
前三步是为了得到27个胃癌相关细胞系的mRNA表达矩阵,方法是下载cel文件,用RMA归一化,对多探针基因去最大表达量探针!

step4:Unsupervised hierarchical clustering (1-Spearman distance, average linkage) was performed on the cell lines using the aCGH data.

Putative driver genes of which copy number aberrations correlated to mRNA gene expression were identified to determine subtypes or clusters that are driven by different mechanisms. This was done using Mann Whitney U-test with p<0.05, and Spearman Correlation Coefficient test with Rho >0.6.

step5:We then performed consensus clustering[17] on the gene expression data of the 27 gastric cancer cell lines from CCLE using these putative driver genes. We selected k = 2 as it gives sufficiently stable similarity matrix.

step6: In order to assign new samples to this integrative cluster, significance analysis of microarray (SAM) [18]with threshold q<2.0 was used to generate subtype signature based on the mRNA expression data of the 1762 genes from the 27 gastric cancer cell lines in CCLE.

先用甲基化数据来聚类,得到putative driver genes,然后再用这些基因的表达数据来再次聚类,分成两类,然后对这两类进行SAM找差异基因

step7:ssGSEA (single sample GSEA)was used to estimate pathway activities of the gastric cancer cell line in the Molecular Signature Database v3.1 (Msigdb v3.1) [19][20]. The pathway activities are represented in enrichment scores which were rank normalized to [0.0, 1.0]. 
step8:SAM analysis was performed with threshold q<0.2, and fold change >2.0 (for up-regulated pathways), or <0.5 (for down-regulated pathways) to obtain subtype-specific pathways from the 27 gastric cell lines in CCLE.
这里既用来gene set的富集分析,又用来超几何分布的富集分析,结果去看看这篇文章就知道了!
 
这篇文章只用了CCLE的一个地方,就是看看不同cancer type里面的某个基因表达boxplot
这个图的数据用GEOquery可以得到,样本的分类信息也用GEOquery可以得到,这样就可以做下面这个图了,非常简单
Further, the Cancer Cell Line Encyclopedia (CCLE) database demonstrated that of 1062 cell lines representing 37 distinct cancer types, glioma cell lines express the highest levels of STK17A
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结论就是:STK17A is highly expressed in glioma cell lines compared to other cancer types. Data was obtained through the Cancer Cell Line Encyclopedia (CCLE).

第三篇文献:http://www.nature.com/ncomms/2013/130709/ncomms3126/fig_tab/ncomms3126_F4.html

这篇文献更简单了,直接对这个表达矩阵进行聚类:
 
The 5,000 most variable genes were used for unsupervised clustering of cell lines by mRNA expression data. Cell lines are colour-coded (vertical bars) according to the reported tissue of origin (a PDF version that can be enlarged at high resolution is in Supplementary InformationSupplementary Fig. S4); horizontal labels at bottom indicate the dominating tissue types within the respective branches of the dendrogram. Most ovarian cancer cell lines (magenta) cluster together, interspersed with endometrial cell lines. However, some ovarian cancer cell lines cluster with other tissue types (*). Top right panels: neighbourhoods (1) of the top cell lines in our analysis, (2) of cell line IGROV1, and (3) of cell line A2780. For the ovarian cancer cell lines in these enlarged areas, the histological subtype as assigned in the original publication is indicated by coloured letters.
就直接拿整个表达矩阵即可,然后挑选变异最大的5000个基因来进行聚类,就可以得到类似的图

 

11

CCLE数据库几个知识点

Here we describe the Cancer Cell Line Encyclopedia (CCLE): a compilation of gene expression, chromosomal copy number, and massively parallel sequencing data from 947 human cancer cell lines. 
收集了三种数据:
The mutational status of >1,600 genes was determined by targeted massively parallel sequencing, followed by removal of variants likely to be germline events . 
Moreover, 392 recurrent mutations affecting 33 known cancer genes were assessed by mass spectrometric genotyping13 . 
DNA copy number was measured using high-density single nucleotide polymorphism arrays (Affymetrix SNP 6.0; Supplementary Methods). 
Finally, mRNA expression levels were obtained for each of the lines using Affymetrix U133 plus 2.0 arrays. 
These data were also used to confirm cell line identities .
一般用得最多的就是表达数据,因为表达数据最简单,大多数生物信息学分析着只会用这个数据!
而它的突变数据又不是通常意义的高通量测序得到的,snp6芯片数据很多人听都没听过
文章的附件有对cell lines的具体描述。
different_kinds_of_cancer_in_CCLE
CCLE的数据在broad institute里面可以下载,也放在GEO数据库里面,我比较喜欢GEO里面的数据
This SuperSeries is composed of the following SubSeries:
GSE36133 Expression data from the Cancer Cell Line Encyclopedia (CCLE)
GSE36138 SNP array data from the Cancer Cell Line Encyclopedia (CCLE)
GSE36133这个study的metadata里面有对每个cellline来源的cancer进行描述!
有人喜欢把这个metadata叫做是clinical data。
library(GEOquery)
ccleFromGEO <- getGEO("GSE36133")
annotBlock1 <- pData(phenoData(ccleFromGEO[[1]]))
>dim(annotBlock1)
[1] 917  38
exprSet=exprs(ccleFromGEO[[1]])
> dim(exprSet)
[1] 18926   917
##它的表达数据矩阵,包含了18926个基因,列名是917个细胞系的名字,行是基因的entrez ID
keyColumns <- c("title", "source_name_ch1", "characteristics_ch1", "characteristics_ch1.1", 
    "characteristics_ch1.2")
options(stringsAsFactors = F)
allAnnot=annotBlock1[,keyColumns]
##这几列信息是比较重要的metadata,里面详细记录了细胞系的收集公司单位,tissue,癌症分类等信息
Cell line (1035个细胞系简介)Gene Sets
1035 sets of genes with high or low expression in each cell line relative to other cell lines from the CCLE Cell Line Gene Expression Profiles dataset.
一些关于CCLE数据库的文章:
http://onlinelibrary.wiley.com/doi/10.1002/cncy.21471/pdf 介绍了几个类似的数据库资源
Anticancer drug sensitivity analysis: An integrated approach applied to Erlotinib sensitivity prediction in the CCLE database
08

TCGA数据里面的生存分析例子

我们知道了生存分析,就是随着时间的流逝,死亡率是如何增加的,一般是用KM法来估计生存函数,然后画个图即可!而根据某些因子把样本分组,可以看到他们死亡率的变化趋势显著的不同,这就说明了我们的这个因子是非常有效的分类方式,这个因子可以是一个biomarker,也可以某些其它指标!
甚至,我们还可以用cox模型来分析这个因子是如何影响生存函数的,那个稍后再讲
这里,我们就简单讲一个例子,是TCGA里面卵巢癌的数据,根据甲基化数据分成了4个组,那么我们就下载这四个组样本的临床数据,
看看这样分组后,他们的死亡率变化趋势是不是有显著区别!

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08

生存分析简介

一般我们谈生存分析,就是说的KM方法估计生存函数,并且画出生存曲线,然后还可以根据分组检验一下它们的生存曲线是否有显著的差异!
在R里面,非常的方便,有个包survival很容易就可以做生存分析了!
只需要记住三个函数即可:
Surv:用于创建生存数据对象
survfit:创建KM生存曲线或是Cox调整生存曲线
survdiff:用于不同组的统计检验

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05

寻找somatic突变的软件大合集

         其实somatic突变很容易理解,你测同一个人的正常组织和癌症组织,然后比较对这两个样本数据call出来的snp位点
         只存在癌症组织数据里面的snp位点就是somatic突变,在两个样本都存在的snp位点就是germline的突变,不过一般大家研究的都是somatic突变。
          当然,理论上是很简单,但是那么多统计学家要吃饭呀,肯定得把这件事搞复杂才行,所以就有了非常多的somatic突变 calling的软件,开个玩笑哈,主要是因为我们的测序并不是对单个细胞测序,我们通常意义取到的正常组织和癌症组织都不是纯的,所以会有很多关于这一点的讨论。
        正好我看到了一篇帖子,收集了大部分比较出名的做somatic mutation calling的软件,当然,我只用过mutect和varscan。

来自于:https://www.biostars.org/p/19104/

Here are a few more, a summary of the other answers, and updated links:

For a much more general discussion of variant calling (not necessarily somatic or limited to SNVs/InDels) check out this thread: What Methods Do You Use For In/Del/Snp Calling?

Some papers describing comparisons of these callers:

The ICGC-TCGA DREAM Mutation Calling challenge has a component on somatic SNV calling.

This paper used validation data to compare popular somatic SNV callers:

Detecting somatic point mutations in cancer genome sequencing data: a comparison of mutation callers

You'll need to update the link to MuTect. Broad Institute has begun to put portable versions of their tools on Github, like thelatest release of MuTectThe Genome Institute at WashU has been using Github for a while, but portable versions of their tools can be found here and here.

其实somatic的calling远比我们想象的要复杂:

To rehash/expand on what Dan said, if you're sequencing normal tissue, you generally expect to see single-nucleotide variant sites fall into one of three bins: 0%, 50%, or 100%, depending on whether they're heterozygous or homozygous.

With tumors, you have to deal with a whole host of other factors:

  1. Normal admixture in the tumor sample: lowers variant allele fraction (VAF)
  2. Tumor admixture in the normal - this occurs when adjacent normals are used, or in hematological cancers, when there is some blood in the skin normal sample
  3. Subclonal variants, which may occur in any fraction of the cells, meaning that your het-site VAF might be anywhere from 50% down to sub-1%, depending on the tumor's clonal architecture and the sensitivity of your method
  4. Copy number variants, cn-neutral loss of heterozygosity, or ploidy changes, all of which again shift the expected distribution of variant fractions

These, and other factors, make calling somatic variants difficult and still an area that is being heavily researched. If someone tells you that somatic variant calling is a solved problem, they probably have never tried to call somatic variants.

Sounds like somatic / tumor variant calling is something that will be solved by improvements at the wet lab side ( single cell selection / amplification / sequencing ) . Rather than at the computational side.

Well, single cell has a role to play (and would have more of one if WGA wasn't so lossy), but realistically, you can't sequence billions of cells from a tumor individually. Bulk sequencing still is going to have a role for quite a while.

Hell germ line calling isn't even a solved problem. Still get lots of false positives (and false negatives). It just tends to work so well that it is hard to improve it much except by making it faster, less memory intensive, etc

Solved was the wrong word. I just meant improved. There is only so much you can do at the computational side. Wet lab also has its part to play.

A germline variant caller generally has a ploidy-based genotyping algorithm built in to part of the algorithm/pipeline. I believe, IIRC, the GATK UnifiedGenotyper for instance does both variant calling and then genotype calling. So to call a genotype for a variant it is expecting a certain number of reads to support the alternative allele. When working with somatic variants all of the assumptions about how many reads you expect with a variant at a position to distinguish between true and false positives are no longer valid. Except for fixed mutations throughout the tumor population only some proportion of cells will hold a somatic variation. You also typically have some contamination from normal non-cancerous cells. Add in complications from significant genomic instability with lots of copy number variations and such and you have a need for a major change in your model for calling variation while minimizing artifactual calls. So you have a host of other programs that have been developed specifically for looking at somatic variation in tumor samples.

一篇文献:

Comparison of somatic mutation calling methods in amplicon and whole exome sequence data

是qiagen公司发的

High-throughput sequencing is rapidly becoming common practice in clinical diagnosis and cancer research. Many algorithms have been developed for somatic single nucleotide variant (SNV) detection in matched tumor-normal DNA sequencing. Although numerous studies have compared the performance of various algorithms on exome data, there has not yet been a systematic evaluation using PCR-enriched amplicon data with a range of variant allele fractions. The recently developed gold standard variant set for the reference individual NA12878 by the NIST-led “Genome in a Bottle” Consortium (NIST-GIAB) provides a good resource to evaluate admixtures with various SNV fractions.

Using the NIST-GIAB gold standard, we compared the performance of five popular somatic SNV calling algorithms (GATK UnifiedGenotyper followed by simple subtraction, MuTect, Strelka, SomaticSniper and VarScan2) for matched tumor-normal amplicon and exome sequencing data.

Nevertheless, detecting somatic mutations is still challenging, especially for low-allelic-fraction variants caused by tumor heterogeneity, copy number alteration, and sample degradation

We used QIAGEN’s GeneRead DNAseq Comprehensive Cancer Gene Panel (CCP, Version 1) for enrichment and library construction in triplicate。

QIAGEN’s GeneRead DNAseq Comprehensive Cancer Gene Panel (Version 1) was used to amplify the target region of interest (124 genes, 800 Kb).

When analyzing different types of data, use of different algorithms may be appropriate.

DNA samples of NA12878 and NA19129 were purchased from Coriell Institute. Sample mixtures were created based on the actual amplifiable DNA in each sample, resulting in 0%, 8%, 16%, 36%, and 100% of NA12878 sample mixed in the NA19129 sample, respectively.We treated the mixed samples at 8%, 16%, 36%, and 100% as the virtual tumor samples and the 0% as the virtual normal sample.

五个软件的算法是:

1. NaiveSubtract — SNVs were called separately from virtual tumor and normal samples using GATK UnifiedGenotyper [22]. For exome sequencing data, reads were already mapped, locally realigned and recalibrated by the 1,000 Genomes Project. So SNVs were directly called on the BAM files using GATK Unified Genotyper. Then, SNVs detected in the virtual normal sample were removed from the list of SNVs detected in the virtual tumor sample, leaving the “somatic” SNVs.

2. MuTect — MuTect is a method developed for detecting the most likely somatic point mutations in NGS data using a Bayesian classifier approach. The method includes pre-processing aligned reads separately in tumor and normal samples and post-processing resulting variants by applying an additional set of filters. We ran MuTect under the High-Confidence mode with its default parameter settings. We disabled the “Clustered position” filter and the “dbSNP filter” for the amplicon sequencing reads, and we disabled the “dbSNP filter” for the exome sequencing.

3. SomaticSniper — SomaticSniper calculates the Bayesian posterior probability of each possible joint genotype across the normal and cancer samples. We tuned the software’s parameters to increase sensitivity and then filtered raw results using a Somatic Score cut-off of 20 to improve specificity.

4. Strelka — Strelka reports the most likely genotype for tumor and normal samples based on a Bayesian probability model. Post-calling filters built into the software are based on factors such as read depth, mismatches, and overlap with indels. We skipped depth filtration for exome and amplicon sequencing data as recommended by the Strelka authors. For the amplicon sequencing reads, we set the minimum MAPQ score at 17 for consistency with the defaults in GATK UnifiedGenotyper. We used variants passing Strelka post-calling filters for analysis.

5. VarScan2 — VarScan2 performs analyses independently on pileup files from the tumor and normal samples to heuristically call a genotype at positions achieving certain thresholds of coverage and quality. Then, sites of the genotypes not matched in tumor and normal samples are classified into somatic, germline, or ambiguous groups using Fisher’s exact test. We generated the pileup files using SAMtools mpileup command.

The compatibility of the output VCF files between different methods as well as the NIST-GIAB gold standard was examined using bcbio.variation tools and manual inspection. The reported SNP call representations between files are comparable to each other.

来自于文献:http://www.biomedcentral.com/1471-2164/15/244

05

使用oncotator做突变注释

功能:vcf格式突变数据进一步注释成maf格式

做过癌症数据分析的童鞋都知道,TCGA里面用maf格式来记录突变!那么maf格式的数据是如何得来的呢,我们都知道,做完snp-calling一般是得到vcf格式的突变记录数据文件,然后再用annovar或者其它蛋白结构功能影响预测软件注释一下,还远达不到maf的近100条记录。

而大名鼎鼎的broad institute就规定了maf格式的突变注释文件,他就是利用了十几个常见的已知数据库来注释我们得到的vcf突变记录,通常是对somatic的突变才注释成maf格式的数据!
大名鼎鼎的broadinstitute出品的突变注释工具:http://www.ncbi.nlm.nih.gov/pubmed/25703262
本身也是一个在线工具:
集成了下面所有的分析资源
而且还提供了API

Genomic Annotations

  • Gene, transcript, and functional consequence annotations using GENCODE for hg19.
  • Reference sequence around a variant.
  • GC content around a variant.
  • Human DNA Repair Gene annotations from Wood et al.

Protein Annotations

  • Site-specific protein annotations from UniProt.
  • Functional impact predictions from dbNSFP.

Cancer Variant Annotations

Non-Cancer Variant Annotations

因为要下载的数据有点多,我这里就不用自己的电脑测试了,安装过程也很简单的!

 

十二 29

用firehose_get 来下载所有TCGA寄存在broad的数据

该软件是broad institute写的一个数据接口,主要是供他人下载TCGA的所有寄存在broad institute的免费数据,主要是level3,level4的数据。(说错了,好像只有level4的数据,就是可以发文章的分析结果及图片)
软件下载地址:https://confluence.broadinstitute.org/display/GDAC/Download

懂它的使用规则,编码规则即可:
就是一个很简单的shell脚本而已,根据几个用户自定义参数来选择性的下载数据。
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我们可以用-t这个参数来指定下载的数据类型,可以是mut/rna/mutsig/gistic等各种数据,至于这些单词代表什么意义,需要自己去看说明书啦
还可以指定时间,截止到什么时间的数据!
它支持的癌症种类:

ACC  BLCA  BRCA  CESC  COAD  COADREAD  DLBC  ESCA  
	GBM  HNSC  KICH  KIRC  KIRP  LAML  LGG  LIHC  
	LUAD  LUSC  OV  PAAD  PANCANCER  PANCAN8  PANCAN12  PRAD  
	READ  SARC  SKCM  STAD  THCA  UCEC  UCS
这些癌症种类的简称,也是可以去官网里面看到的!官网:http://gdac.broadinstitute.org

 

十二 25

做癌症研究一定要把这几十篇TCGA的大文章看完

都是发表在nature,cell还有新英格兰医学杂志上面的超级文章!每个文章附件都有一百多页,比博士论文还长,但是它们的分析套路其实都一样,都是那几种数据,包括WGS,WES,RNA-Seq,芯片表达量,miRNA表达量,甲基化数据,蛋白数据。分析过程也差不多,无法就是对癌症进行进一步的分类,癌症亚型,或者看看driver mutation,进一步解释癌症病变,转移,扩散机理,或者找标记物signature,辅助治疗等等,具体的要等我把这些文献看完了才能再进一步讲解,请做癌症研究方向的一定要把它们看完。

1

我已经下载完了,大家如果没有权限下载,就需要自己想办法啦!

image

非常值得大家阅读!!!

 

十一 05

使用mutsig软件来找驱动基因

从数以万计的突变里面找到driver mutation这个课题很大,里面的软件我接触的就有十几个了,但是我尝试了其中几个,总是无法运行成功,不知道为什么,终于今天成功了一个,就是mutsig软件! 其实关于突变数据找driver mutation ,台湾一个大学做了一个数据库DriverDB http://ngs.ym.edu.tw/driverdb/: 还因此发了一篇文章:http://nar.oxfordjournals.org/content/early/2013/11/07/nar.gkt1025.full.pdf,挺不错的!

关于driver mutation的理论最近也进化了很多,算是比较完善了吧,但是我一直没时间静下心来好好补充理论知识,很多软件,都只是用过,很多数据,也只是处理了一下,不知道为什么要去做,╮(╯▽╰)╭扯远了,开始谈这个软件吧!

mutsig软件是broadinstitute出品的,所以可靠性非常好咯,来源于一篇nature文章:http://www.nature.com/nature/journal/v505/n7484/full/nature12912.html,而该软件的地址是:http://www.broadinstitute.org/cancer/cga/mutsig_run 需要简单注册才能下载的。

该nature文章是这样描述这个软件的优点的:We used the most recent version of the MutSig suite of tools, which looks for three independent signals: highmutational burden relative to background expectation, accounting for heterogeneity; clustering of mutations within the gene; and enrichment of mutations in evolutionarily conserved sites. Wecombined the significance levels (P values) fromeach test to obtain a single significance level per gene (Methods).

这个软件需要安装matlab环境才能使用,所以我前面就写了教程,如何安装!http://www.bio-info-trainee.com/?p=1166

如果已经安装好了matlab环境,那么直接下载这个软件就可以使用了,软件解压就OK拉,而且人家还提供了测试文件!

Capture4

软件下载后,解压可以看到里面的一个脚本,软件说明书写的非常简单,当然,使用这个软件也的确非常简单:

run_MutSigCV.sh <path_to_MCR> mutations.maf coverage.txt covariates.txt output.txt 即可,其中所有的数据都是可以下载的,

运行完了测试数据, 就证明你的软件安装没有问题啦!如果你只有突变数据的maf格式,maf格式可以参考:https://www.biostars.org/p/69222/ ,也可以使用该软件:如下

run_MutSigCV.sh <path_to_MCR> my_mutations.maf exome_full192.coverage.txt gene.covariates.txt my_results mutation_type_dictionary_file.txt chr_files_hg19

Capture5

上面三个zip文件,都是可以在mutsig软件官网找到下载链接的,是必须下载的!使用很简单,就一个命令即可,但是把你的vcf突变数据做成该软件需要的maf格式,是一个难题!

24

broad_institute收集的癌症数据

肾上腺皮质 Adrenocortical carcinoma ACC 92 Browse Browse
膀胱,尿路上皮 Bladder urothelial carcinoma BLCA 412 Browse Browse
乳腺癌 Breast invasive carcinoma BRCA 1098 Browse Browse
子宫颈 Cervical and endocervical cancers CESC 307 Browse Browse
胆管癌 Cholangiocarcinoma CHOL 36 Browse Browse
结肠腺癌 Colon adenocarcinoma COAD 460 Browse Browse
大肠腺癌 Colorectal adenocarcinoma COADREAD 631 Browse Browse
淋巴肿瘤弥漫性大B细胞淋巴瘤 Lymphoid Neoplasm Diffuse Large B-cell Lymphoma DLBC 58 Browse Browse
食管 Esophageal carcinoma ESCA 185 Browse Browse
FFPE试点二期 FFPE Pilot Phase II FPPP 38 None Browse
胶质母细胞瘤 Glioblastoma multiforme GBM 613 Browse Browse
脑胶质瘤 Glioma GBMLGG 1129 Browse Browse
头颈部鳞状细胞癌 Head and Neck squamous cell carcinoma HNSC 528 Browse Browse
肾嫌色 Kidney Chromophobe KICH 113 Browse Browse
泛肾 Pan-kidney cohort (KICH+KIRC+KIRP) KIPAN 973 Browse Browse
肾透明细胞癌 Kidney renal clear cell carcinoma KIRC 537 Browse Browse
肾乳头细胞癌 Kidney renal papillary cell carcinoma KIRP 323 Browse Browse
急性髓系白血病 Acute Myeloid Leukemia LAML 200 Browse Browse
脑低级神经胶质瘤 Brain Lower Grade Glioma LGG 516 Browse Browse
肝癌 Liver hepatocellular carcinoma LIHC 377 Browse Browse
肺腺癌 Lung adenocarcinoma LUAD 585 Browse Browse
肺鳞状细胞癌 Lung squamous cell carcinoma LUSC 504 Browse Browse
间皮瘤 Mesothelioma MESO 87 Browse Browse
卵巢浆液性囊腺癌 Ovarian serous cystadenocarcinoma OV 602 Browse Browse
胰腺癌 Pancreatic adenocarcinoma PAAD 185 Browse Browse
嗜铬细胞瘤和副神经节瘤 Pheochromocytoma and Paraganglioma PCPG 179 Browse Browse
前列腺癌 Prostate adenocarcinoma PRAD 499 Browse Browse
直肠腺癌 Rectum adenocarcinoma READ 171 Browse Browse
肉瘤 Sarcoma SARC 260 Browse Browse
皮肤皮肤黑色素瘤 Skin Cutaneous Melanoma SKCM 470 Browse Browse
胃腺癌 Stomach adenocarcinoma STAD 443 Browse Browse
胃和食管癌 Stomach and Esophageal carcinoma STES 628 Browse Browse
睾丸生殖细胞肿瘤 Testicular Germ Cell Tumors TGCT 150 Browse Browse
甲状腺癌 Thyroid carcinoma THCA 503 Browse Browse
胸腺瘤 Thymoma THYM 124 Browse Browse
子宫内膜癌 Uterine Corpus Endometrial Carcinoma UCEC 560 Browse Browse
子宫癌肉瘤 Uterine Carcinosarcoma UCS 57 Browse Browse
葡萄膜黑色素瘤 Uveal Melanoma UVM 80 Browse Browse

看起来癌症很多呀,任重道远

01

2012-LAD的三个亚型的不同生物学意义

文献名:Differential Pathogenesis of Lung Adenocarcinoma Subtypes Involving Sequence Mutations, Copy Number, Chromosomal Instability, and Methylation
Lung adenocarcinoma (LAD)的遗传变异度很大。
这个癌症可以分成三类:The LAD molecular subtypes (Bronchioid, Magnoid, and Squamoid)
然后我们在三个subtypes里面分析了以下四个特征,发现不同subtypes差异非常显著。
1、Gene mutation rates (EGFR, KRAS, STK11, TP53),
2、chromosomal instability,
3、regional copy number
4、genomewide DNA methylation
另外三个临床特征也是很显著。
1、Patient overall survival,
2、cisplatin plus vinorelbine therapy response
3、predicted gefitinib sensitivity
所以,我们的分类非常好,而且对临床非常有帮助。
对LAD的研究数据包括
1,DNA copy number
2,gene sequence mutation
3,DNA methylation
4,gene expression
即使是TP53这样的基因在LAD的突变率也才35%,所以我们的LAD应该更加细分,因为EGFR mutation and KRAS mutation这样的突变对治疗很有指导意义,细分更加有助于临床针对性治疗方案的选择。
我们选取了116个LAD样本的数据,分析了1,genome-wide gene expression,,2,genomewide DNA copy number, 3,genome-wide DNA methylation, 4,selected gene sequence mutations
得到的结论是:LAD molecular subtypes correlate with grossly distinct genomic alterations and patient therapy response
数据来源如下:
Gene expression --> Agilent 44 K microarrays.
DNA copy number --> Affymetrix 250 K Sty and SNP6 microarrays.
DNA methylation --> MSNP microarray assay.
DNA from EGFR, KRAS, STK11 and TP53 exons --> ABI sequencers
我们用的是R语言包 ConsensusClusterPlus根据gene expression 来对我们的LAD进行分类molecular subtypes
分类的基因有506个(the top 25% most variable genes, 3,045, using ConsensusClusterPlus),A nearest centroid subtype predictor utilizing 506 genes

这三类LAD的过表达基因参与不同的生物功能,
Bronchioid – excretion genes, asthma genes, and surfactants (SFTPB, SFTPC, SFTPD);
Magnoid – DNA repair genes, such as thymine-DNA glycosylase (TDG);
Squamoid – defense response genes, such as chemokine ligand 10 (CXCL10)
而且也对应不同的临床数据
Bronchioid had the most females, nonsmokers, early stage tumors, and low grade tumors, the greatest acinar content, the least necrosis, and the least invasion.
Squamoid had the most high grade tumors, the greatest solid content, and the lowest papillary content.
Magnoid had themost smokers and the heaviest smokers by pack years.
它们的基因突变pattern也有很大区别。
Bronchioid had the greatest EGFR mutation frequency
Magnoid had the greatest mutation frequencies in TP53, KRAS and STK11.
为了研究不同亚型癌症的突变模式的不同(genomewide mutation rates),我们同时又研究了a large set of rarely mutated genes (n = 623) from the Ding et al. cohort

结论:
Bronchioid subtype 更有可能受益于EGFR inhibitory therapy
Magnoid tumors also have severe genomic alterations including the greatest CIN, the most regional CN alterations, DNA hypermethylation, and the greatest genomewide mutation rate.
the Squamoid subtype displayed the fewest distinctive alterations that included only regional CN alterations

31

2013-science-3205tumors-12types-4-ways-find-291HCD

文献名:Comprehensive identification of  mutational cancer driver genes across 12  tumor types
本文比较了四种寻找癌症驱动基因的方法,并且得到了综合性的、可靠的291个HCDs 基因列表。
数据来源于3205个肿瘤样本,共涉及到12种癌症。
 Cancer Gene Census (CGC) 数据库里面已经有了接近500个cancer genes
 癌症基因组研究分析可以得到数以万计的somatic mutations,但是其中很少一部分才是驱动肿瘤发生,发展的突变。
 而且大多数driver genes的突变频率很低,又由于肿瘤的异质性,大量样本的研究是必须的。
 主流的四种找癌症驱动基因的方法如下:
 1、Most common methods identify genes that are mutated more frequently than expected from the background mutation rate (recurrence)
 2、Other methods - a bias towards the accumulation of functional mutations (FM bias)
 3、other methods exploit the tendency to sustain mutations in certain regions of the protein sequence (CLUST bias)
 4、other approaches exploit the overrepresentation of mutations in specific functional residues, such as phosphorylation sites (ACTIVE bias)
 它们的代表软件是MuSiC, OncodriveFM, OncodriveCLUST and ActiveDriver
 本文把这四种方法进行了比较,并且综合了它们的结果。
 In summary, we provide a very reliable list of 291 HCDs and a second one, of 144 CDs, more comprehensive but with an expectedly higher false-positives rate
 One hundred and sixty-five of these candidates are novel findings not included in the CGC.
 然后,作者对这291个HCDs基因进行了功能分析,其中,它们主要集中在以下五个生物功能
Chromatin remodeling,
mRNA processing,
Cell signaling/proliferation,
Cell adhesion,
DNA repair/Cell cycle
然后把四种方法综合得到的291个HCDs基因与Cancer Gene Census (CGC) 数据库里面已经有的接近500个cancer genes进行综合比较
 本文首次展示了综合多种癌症驱动基因寻找方法的可能性,这种综合是基于两个事实:
 1,各种方法找癌症驱动基因本来就没有金标准,所以综合多种方法,更comprehensive。
 2,综合多种方法能更好的比较评估所找到的癌症驱动基因的准确性。
31

2014-4742samples-21tumors-Cancer5000-set-254-genes

文献名: Discovery and saturation analysis of  cancer genes across 21 tumour types.
我们知道对一个癌症的多个样本进行研究,其实很少高达20%样本突变 most intermediate frequencies (2–20%),还有很多低频突变,因为研究样本不够,从而不被发现
我们从 4,742个tumor-normal pairs的外显子测序数据集研究了somatic point mutations,共21种癌症。
癌症基因可能集中于以下七个功能:
proliferation,
apoptosis,
genome stability,
chromatin regulation,
immune evasion,
RNA processing
protein homeostasis
我们用有放回的抽样方法对数据进行统计,得出结论:如果我们对某个癌症的研究样本高达500-6000个的话,可以发现更多的临床低频突变。
这篇文章是为了解决以下三个问题:
1、大规模的研究cancer就能达到鉴别出所有的cancer driver genes的程度吗?(Coverage of known cancer genes)
2,增大样本量是否会揭示很多cancer driver genes?(Analysis of novel candidate cancer genes)
3、我们距离对所有的cancer driver genes的完全认知还有多远?(Saturation analysis)
突变数据的分析流程是Broad’s stringent filtering and annotation pipeline
突变情况如下:
3,078,483 somatic single nucleotide variations(SSNVs),
77,270 small insertions and deletions (SINDELs)
29,837 somatic di-, tri- or oligonucleotide variations (DNVs, TNVs and ONVs, respectively)
an average of 672 per tumour–normal pair
包括:
540,831 missense,
207,144 synonymous,
46,264 nonsense,
33,637 splice-site
2,294,935 non-coding mutations
我们找驱动基因的方法是:
We used the most recent version of the MutSig suite of tools
which looks for three independent signals:
high mutational burden relative to background expectation,
accounting for heterogeneity;
clustering of mutations within the gene;
enrichment of mutations in evolutionarily conserved sites.
我们把以上MutSig的几个独立组件分析得到的p-value组合起来,判断驱动基因,我们即对每种癌症做了单独分析,同时也对这21种癌症做了综合分析。
我们找到的驱动基因的结果:
单独对各个癌症进行分析,可以总共找到334个基因,当然不同癌症找到的基因有交集。
These 334 pairs involve 224 distinct genes.
The number of genes detected per tumour type varied considerably (range of 1–58)
找到的驱动基因的个数差异主要取决于癌症种类的不同,然后,跟该癌症的样本量有关。
只有22种基因能在超过三种癌症里面都是被判定为驱动基因。
如果我们把21种癌症合并起来找驱动基因,可以找到114个,其中有30个是单独对各个癌症进行分析所找不到的,有80个在单独癌症分析可以找到。
所以单独对各个癌症进行分析找到的224个基因里面,有140个是合并癌症分析找不到的。其实画一个韦恩图就很好理解了。
对各个癌症进行分析,共21次分析,加上合并分析,共22次飞行,总共可以得到a Cancer5000 set containing 254 genes.
我们再严格分析一下254个基因在Cancer5000 set,得到219 distinct genes.叫做Cancer5000-S (for ‘stringent’) gene。
 Cancer Gene Census (CGC)组织的 (v65)版本包含着130个cancer genes driven by somatic point mutations,其中82个被我们这次统计分析发现啦。
 Four genes encode anti-proliferative proteins, in which loss-offunction mutations would be expected to contribute to oncogenesis.
 Sixadditionalgenesencode proteins thatare clearlyinvolved incell  proliferation: RHEB, RHOA, SOS1, ELF3, SGK1 and MYOCD.
 Five genes encode pro-apoptotic factors, in which loss-of-function mutations would be expected to promote oncogenesis
Six genes encode proteins related to genome stability.
Fivegenesareassociatedwithchromatinregulation
Three genes encode proteins whose loss is expected to help tumours evade immune attack
Three genes are associated with RNA processing and metabolism.
One gene, TRIM23, is involved in protein homeostasis.
Beyond these 33 genes, the set of 81 novel genes is likely to contain
additional true cancer genes.
有返回抽样方法是:An effective test is to perform ‘down-sampling’; that is, to study how the number of discoveries increases with sample size, by repeating the analysis on random subsets of samples of various smaller sizes.
饱和度分析结果: 还远未到饱和,不同突变频率的基因被发现的个数随着样本量的增大而增多的速度不同。
Genes mutated in 20% of tumours are approaching saturation;
those mutated at frequencies of 10–20% are still rising rapidly, but at a decreasing rate;
those at 5–10%  increasing linearly;
and those at ,5% are increasingly at an accelerating rate.
我们对样本量的要求是:突变背景高的癌症(如,黑色素瘤)需要的样品更多,而那些突变背景低的癌症(如成神经细胞瘤)需要近650个样本就可以很好的分析驱动基因了
Creating a reasonably comprehensive catalogue of candidate cancer genes mutated in 2% of patients will require between approximately 650 samples (for tumours with ,0.5 mutations per Mb, such
as neuroblastoma) to approximately 5,300 samples (for melanoma, with 12.9 mutations per Mb)
31

2015-MADGiC-identify-cancer-driver-gene

最新的一个寻找cancer 的driver gene的软件:
Cancer is thought to result from the accumulation of causal  somatic mutations throughout the lifetime of an individual.
这些cancer-driving mutations 主要影响三类基因: 1、oncogenes 2、tumor-suppressor genes 3, stablity geens
第一个突变是tumorigenesis ,随后的突变就 driver tumor progression
识别这些突变非常有利于了解gene function 和药物靶点设计
区分 driver genes 和 passenger  genes 能更好的利用各种数据库得到的海量突变信息
基于频率的区分方法 rely on an estimate of a background mutation rate which  represents the rate of random passenger mutations.
也就是文献(Ding et al., 2008).提出的方法,但它忽略了以下四点
1、mutation type (transition versus transversion)
2、nucleotide context(which base is at the mutation site
3、dinucleotide context (which bases are located at neighboring sites to the mutation),
4、expression level of the gene
然后有文献提出了以下三种改进
Sjoblom et al.(2006) account for nucleotide and dinucleotide context in searching  for drivers of breast and colorectal cancer.
MuSiC (Dees et al.,2012) accounts for mutation type and allows for sample-specific mutation rates;
Lawrence et al.(2013) (MutSigCV) also allow for the inclusion of gene-specific factors such as expression level and replication timing.
但是他们有个共同延续下来的的缺点,就是默认驱动基因的突变频率要高于背景突变频率。
实际上,除了突变频率,还有一些criteria也很重要, 所以有两个数据库SIFT (first reported by Ng and Henikoff (2001), later updated by Kumar et al. (2009)),  Polyphen (Adzhubei et al., 2010)  和MutationAssessor (Reva et al., 2011)
这两个数据库整合了 sequence context, position, and protein characteristics to assess a mutation’s  functional impact.
总结一下identity cancer driver genes的criteria
1、mutation frequency,
2、mutation type,
3、gene-specific features such as replication timing and expression level that are known to affect background rates of mutation,
4、mutation-specific scores that assess functional impact, and the spatial patterning of mutations that only becomes apparent when thousands of samples are considered.
以前的方法都只是部分涉及到上面的criteria
而我们提出了a unified empirical Bayesian Model-based Approach for identifying Driver Genes in Cancer (MADGiC) that utilizes each of these features.
31

2014-REVIEW-identifying driver mutation in sequenced cancer genome

somatic  mutations 含义很广,包括:SNVs,Indel,CNAs,SVs等
However, the resulting sequencing datasets are large and complex, obscuring the clinically important mutations in a background of errors, noise, and random mutations.
Cancer is driven largely by somatic mutations that accumulate in the genome over an individual’s lifetime, with additional contributions from epigenetic and transcriptomic alterations
低通量时代研究,成功例子: imatinib has been used to target cells expressing the BCR-ABL fusion gene  in chronic myeloid leukemia
gefitinib has been used to inhibit the epidermal growth factor receptor in lung cancer
但远远不够。
NGS的三大挑战:1,indentying somatic mutations,误差/ 肿瘤异质性 2,识别driver genes 3,确定由somatic mutations 改变的pathways和其它生物过程
误差来源:optical PCR duplicates, GC-bias, strand bias (where reads indicating a possible mutation only align to one strand of DNA) and alignment artifacts resulting from low complexity or repetitive regions in the genome.
most methods for somatic mutation detection address only a subset of the possible sources of error,call snp的软件众多
identifying driver mutations的三个要点:
1,identifying recurrent mutations;
2,predicting the functional impact of individual mutations;
3,assessing combinations of mutations using pathways, interaction networks, or statistical correlations.
三个要点分别衍生了大量的软件,它们的问题在于:
1,直接看突变频率的那些软件to determine whether the observed number of mutations in the gene is significantly greater than the number expected according to a background mutation rate (BMR).
BMR 实在是太难确定了,低了会导致很多假阳性,而高了,又错过很多真实的driver mutations,但是突变频率非常高的那些基因肯定是没有问题的,比如说TP53,无论什么样的算法都会认为它是driver gene
2,考虑突变对蛋白功能的影响评分的那些软件,引入了一些先验假设:
evolutionary conservation,
known protein domains,
non-random clustering of mutations,
protein structure,
3,pathways, interaction networks, and de novo approaches的那些软件:
pathway(KEGG,GO,GSEA) 4个limitations,首先,大多数 annotated gene sets 包含的基因数太多,而我们的突变基因占该gene set的比例远达不到统计显著性。
然后,pathway并不是独立的,各个pathway之间的联系更重要
接着,把基因分割成pathway这样的小单元,忽略了单元外的联系
最后,只关注已知的 pathways, or gene set
过去的五年见证了癌症基因组测序研究翻天覆地的变化,但是距离它真正的临床应用还有以下几个挑战:
首先,我们忽略了non-coding somatic mutations
其次,很多我们定义的癌症种类其实是a mixture of these subtypes
然后,哪些癌症是可以合并研究的
最后,不同的NGS数据如何综合研究,包括WGS,WES,RNA sequencing, DNA methylation, and chromatin modifications
对某些患者来说,癌症精准医学已经来临,但是对大部分病人来说,前面的路还很长。
31

2014-review-Next-generation sequencing to guide cancer therapy


 This reductionist thinking led the initial theories on carcinogenesis to be centered on how many “hits” or genetic mutations were necessary for a tumor to develop.
还原论者认为导致癌症发生发展的原因集中在一些必须因子-"hit" or genetic mutations
由于这个假设,早期探索多种癌症的遗传基础的方法主要是低通量的研究具体某些特定的基因或者变异情况。
分析方法的选择:microarray vs WGS vs WES
临床样品的选择:fresh frozen tissue  / FFPE specimens /CTCs / ctDNA
临床NGS数据分析方法:mapping --> SNVs CNVs and SVs --> annotation
挑战:1,低频突变很难从测序错误中区分开
            2,很多临床相关的DNA fushions发生在非编码区,所以WES也会错过不少信息的
临床NGS数据注释 :多种数据库,多种数据分析方法
NGS辅助临床医疗的三个途径: 1, diagnosis,早期诊断,精确分类 2,针对性治疗3,耐药性,及时换药
CTC: Circulating tumor cell;
ctDNA: circulating tumor DNA;
 FDA: Food and Drug Administration;
FFPE: Formalin-fixed, paraffin-embedded;
MATCH: Molecular Analysis for Therapy Choice;
MHC: Majorhistocompatibility complex;
NGS: Next-generation sequencing;
SNV: Singlenucleotide variant;
TCGA: The Cancer Genome Atlas.