Identification of novel driver tumor suppressors through functional interrogation of putative passenger mutations in colorectal cancer

Lu Zhang, Kakajan Komurov, Woodring E. Wright, Jerry W. Shay

Research output: Contribution to journalArticlepeer-review

20 Scopus citations


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. What's new? Although the field of cancer genomics has produced long lists of mutated genes from many types of cancer, researchers have assumed that most of these are "passenger" mutations that aren't directly involved in tumorigenesis. However, by using RNA interference to inactivate particular genes and mapping their interactions with known "driver" mutations (i.e. those that lead to malignant growth), the authors were able to uncover a number of potential tumor-suppressors among genes that had previously been dismissed. Combining functional assays with biological filters may lead to better predictive models and more potential therapeutic targets than bioinformatics alone.

Original languageEnglish (US)
Pages (from-to)732-737
Number of pages6
JournalInternational Journal of Cancer
Issue number3
StatePublished - Feb 1 2013


  • anchorage-independent growth
  • colon cancer
  • driver mutations
  • interaction map

ASJC Scopus subject areas

  • Oncology
  • Cancer Research


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