Driver Fusions and Their Implications in the Development and Treatment of Human Cancers.
Driver Fusions and Their Implications in the Development and Treatment of Human Cancers.
复制标题
DOI：
10.1016/j.celrep.2018.03.050
复制
发表时间：
2018-04-03
期刊：
影响因子：
8.8
通讯作者：
Ding L
中科院分区：
文献类型：
--
作者：
Gao Q;Liang WW;Foltz SM;Mutharasu G;Jayasinghe RG;Cao S;Liao WW;Reynolds SM;Wyczalkowski MA;Yao L;Yu L;Sun SQ;Fusion Analysis Working Group;Cancer Genome Atlas Research Network;Chen K;Lazar AJ;Fields RC;Wendl MC;Van Tine BA;Vij R;Chen F;Nykter M;Shmulevich I;Ding L
基因融合代表了癌症中一类重要的体细胞改变。我们使用多种融合调用工具系统地研究了33种癌症类型的9，624个肿瘤中的融合。我们总共识别了25，664个融合，验证率为63%。基因表达，拷贝数和融合注释数据的整合显示，涉及癌基因的融合往往表现出增加的表达，而涉及肿瘤抑制因子的融合具有相反的效果。对于涉及激酶的融合，我们发现1，275个具有完整的激酶结构域，其比例在癌症类型中差异显着。我们的研究表明，融合驱动了16.5%的癌症病例的发展，并在其中超过1%的病例中起着唯一的驱动作用。最后，我们在6.0%的病例中鉴定了涉及TMPRSS2、RET、FGFR3、ALK和ESR1等基因的可药用融合物，并且我们预测了免疫原性肽，这表明融合物可能为靶向药物和免疫治疗提供线索。Gao等人分析了具有33种癌症类型的9，624个样本TCGA队列，以检测基因融合事件。他们提供了检测到的融合事件的景观，将融合与基因表达联系起来，关注激酶融合结构，检查互斥突变和融合模式，并突出融合的可药用性。
Gene fusions represent an important class of somatic alterations in cancer. We systematically investigated fusions in 9,624 tumors across 33 cancer types using multiple fusion calling tools. We identified a total of 25,664 fusions, with a 63% validation rate. Integration of gene expression, copy number, and fusion annotation data revealed that fusions involving oncogenes tend to exhibit increased expression, whereas fusions involving tumor suppressors have the opposite effect. For fusions involving kinases, we found 1,275 with an intact kinase domain, the proportion of which varied significantly across cancer types. Our study suggests that fusions drive the development of 16.5% of cancer cases and function as the sole driver in more than 1% of them. Finally, we identified druggable fusions involving genes such as TMPRSS2, RET, FGFR3, ALK, and ESR1 in 6.0% of cases, and we predicted immunogenic peptides, suggesting that fusions may provide leads for targeted drug and immune therapy. Gao et al. analyze a 9,624 sample TCGA cohort with 33 cancer types to detect gene fusion events. They provide a landscape of fusion events detected, relate fusions to gene expression, focus on kinase fusion structures, examine mutually exclusive mutation and fusion patterns, and highlight fusion druggability.