不论你是做TCGA数据挖掘，还是自己的研究结果与TCGA数据库比较，都需要掌握一定的TCGA数据处理方法。当然，很多公众号都有TCGA数据挖掘的讲解，这里就不做赘述了。
什么是TMB?
如果你从事肿瘤相关研究，那么对Tumor mutation burden(TMB)一定不陌生，是指每Mb上产生的非同义突变的数目，产生的突变数目越多，意味着可能产生更多的新抗原，即越有可能被免疫系统识别，对免疫治疗敏感。TMB可以作为免疫治疗疗效的潜在指标，小编在文末整理了几篇经典的文献供大家学习。
如何计算TCGA全外显子突变数据的TMB？
计算TCGA数据(WES)的TMB，目前主要有两种方式：
1. 直接用非同义突变的数目来代表肿瘤的突变负荷。适用于同一批WES数据不同分组的TMB比较。
（Tips: 除了TCGA的全外显子数据外，任何全外显子数据都可以这样来计算TMB）
2. 非同义突变数目除以外显子芯片大小。TCGA全外显子芯片大小约38Mb。适用于不同研究间的TMB比较。
(Tips.非TCGA的WES数据，要根据实际芯片大小的情况作为分母，计算每Mb下产生了多少个非同义突变)
参考文献：
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5.Hellmann, M. D. et al. Nivolumab plus ipilimumab in lung cancer with a high tumor mutational burden. N. Engl. J. Med. 378, 2093–2104 (2018).
6.Carbone, D. P. et al. First-line nivolumab in stage IV or recurrent non-small-cell lung cancer. N. Engl. J. Med. 376, 2415–2426 (2017).
7.Hellmann, M. D. et al. Tumor mutational burden and efcacy of nivolumab monotherapy and in combination with ipilimumab in small-cell lung cancer. Cancer Cell 33, 853–861 e854 (2018).
8.Hellmann, Matthew D., et al. "Genomic features of response to combination immunotherapy in patients with advanced non-small-cell lung cancer." Cancer Cell 33.5 (2018): 843-852.
9.Chalmers, Zachary R., et al. "Analysis of 100,000 human cancer genomes reveals the landscape of tumor mutational burden." Genome medicine 9.1 (2017): 34.
