Identification of novel driver tumor suppressors through functional interrogation of putative passenger mutations in colorectal cancer.
Identification of novel driver tumor suppressors through functional interrogation of putative passenger mutations in colorectal cancer.
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DOI：
10.1002/ijc.27705
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发表时间：
2013-02-01
影响因子：
6.4
通讯作者：
Shay JW
中科院分区：
文献类型：
--
作者：
Zhang L;Komurov K;Wright WE;Shay JW
癌症基因组测序的努力导致在许多类型的恶性肿瘤的基因突变的鉴定。然而，大多数这些遗传改变被认为是随机乘客，不直接导致肿瘤发生。我们之前已经使用核型二倍体hTERT和CDK 4永生化的人结肠上皮细胞（HCEC）模型在结肠直肠癌（CRC）候选驱动基因（CAN基因）内进行了基于软琼脂的短发夹RNA（shRNA）筛选，并发现151个CAN基因中的65个的缺失增强了具有K-RasV 12和/或TP 53敲低的异位表达的HCEC的锚定非依赖性生长。我们现在构建了一个确认的CAN基因与CRC非CAN基因的相互作用图谱，并筛选功能性肿瘤抑制因子。值得注意的是，与确认的CAN基因相互作用的25个假定乘客基因中的15个（60%）的耗尽促进了TP 53敲低的HCEC中的软琼脂生长，而与此相比，55个假定乘客基因中只有7个（12.5%）不相互作用。因此，我们已经证明了推定的CRC乘客/偶然突变中的驱动突变池，建立了采用生物过滤器的重要性，除了生物信息学，以确定驱动突变。
Cancer genome sequencing efforts are leading to the identification of genetic mutations in many types of malignancy. However, the majority of these genetic alterations have been considered random passengers that do not directly contribute to tumorigenesis. We have previously conducted a soft agar-based short hairpin RNA (shRNA) screen within colorectal cancer (CRC) candidate driver genes (CAN-genes) using a karyotypically diploid hTERT- and CDK4-immortalized human colonic epithelial cell (HCEC) model and discovered that depletion of 65 of the 151 CAN-genes enhanced anchorage-independent growth in HCECs with ectopic expression of K-RasV12 and/or TP53 knockdown. We now constructed an interaction map of the confirmed CAN-genes with CRC non-CAN-genes and screened for functional tumor suppressors. Remarkably, depletion of 15 out of 25 presumed passenger genes that interact with confirmed CAN-genes (60%) promoted soft agar growth in HCECs with TP53 knockdown compared to only 7 out of 55 (12.5%) of presumed passenger genes that do not interact. We have thus demonstrated a pool of driver mutations among the putative CRC passenger/incidental mutations, establishing the importance of employing biological filters, in addition to bioinformatics, to identify driver mutations.