单个肿瘤病人的外显子数据分析策略
肿瘤外显子测序相信大家都不陌生了，通常都是大队列研究，首先WES相比较于WGS已经是大幅度降低成本了，其次太多超级肿瘤队列已经发表了。如果你的100人以下的肿瘤外显子队列研究仍然是想发表，一般来说靠的是疾病的特殊性了。
但是实际上肿瘤外显子队列是很烧钱的，通常来说，一个肿瘤病人需要测50X的血液加上200X的肿瘤，基本上3000块钱是跑不了的，100人的队列就是三十好几万了。而且收集100个肿瘤病人也只能说是背靠大医院平台了。
那，有没有可能单个肿瘤病人，测一下外显子数据，也是一个独立的课题呢？
有的，我看到了一个免疫原性突变研究，发表在影响因子10分左右的CCR，2017年8月，标题是：《Characterization of an Immunogenic Mutation in a Patient with Metastatic Triple-Negative Breast Cancer》，链接是：https://clincancerres.aacrjournals.org/content/23/15/4347
病人基本信息：Tumor 4062 (lung) was resected from a 61-year-old woman diagnosed with metastatic TNBC
选择的测序是：
Whole-exome sequencing (WES) was performed at Personal Genome Diagnostics (PGDx) mRNA sequencing library
但是并没有上传共享测序数据，也仅仅是描述WES数据分析流程。所以虽然你可以follow我的教程来处理这些数据，但是巧妇难为无米之炊哦：
文章描述的生物信息学数据分析流程是；
GATK best practices workflow (https://www.broadinstitute.org/gatk/). After cleanup of data, samtools mpileup (http://samtools.sourceforge.net) was used to create pileup files, Varscan2 (http://varscan.sourceforge.net) was used to call somatic variants. These variants were annotated using Annovar (http://annovar.openbioinformatics.org). Variant frequency cutoff of ≥10% and ≥2 variant reads in tumor. The segmented copy number, cellularity, and ploidy were determined using Sequenza v2.1.2 with normal samples as references and hg19 coordinates.
数据分析结果：
72 nonsynonymous point mutations were identified after WES of the lesion, 43 of which were expressed on the basis of RNA-seq data
有意思的是病人的结局并不美好：however, the patient's disease progressed rapidly, and she expired prior to treatment. At autopsy, the RBPJ mutation was detected in all 16 sites of metastases。
如果是6年前，
于December 2014发表在Human Genomics 杂志的标题是：《Whole exome sequencing of a single osteosarcoma case—integrative analysis with whole transcriptome RNA-seq data》就无需那么多花样了，测序出来了简单描述你的数据分析就足够了。
链接是：https://humgenomics.biomedcentral.com/articles/10.1186/s40246-014-0020-0
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