Detailed modeling of positive selection improves detection of cancer driver genes

Abstract

Identifying driver genes from somatic mutations is a central problem in cancer biology. Existing methods, however, either lack explicit statistical models, or use models based on simplistic assumptions. Here, we present driverMAPS (Model-based Analysis of Positive Selection), a model-based approach to driver gene identification. This method explicitly models positive selection at the single-base level, as well as highly heterogeneous background mutational processes. In particular, the selection model captures elevated mutation rates in functionally important sites using multiple external annotations, and spatial clustering of mutations. Simulations under realistic evolutionary models demonstrate the increased power of driverMAPS over current approaches. Applying driverMAPS to TCGA data of 20 tumor types, we identified 159 new potential driver genes, including the mRNA methyltransferase METTL3-METTL14. We experimentally validated METTL3 as a tumor suppressor gene in bladder cancer, providing support to the important role mRNA modification plays in tumorigenesis.

Multiple Functions and Mechanisms Underlying the Role of METTL3 in Human Cancers

“…Patients with high expression of METTL3 had poor prognosis and reduced survival time (28)(29)(30). Knockdown of METTL3 significantly reduced bladder cancer cell invasion, proliferation, and survival in vitro and tumorigenicity in vivo (31). The above studies proved that METTL3 acts as an oncogene in bladder cancer.…”

“…Wild-type METTL3 successfully restored the normal growth rate and somatic mutations in METTL3 may disrupt the m 6 A methylation process and promote cancer cell growth. METTL3 acts as a tumor suppressor gene in bladder cancer (31). Similarly, Li et al showed that METTL3 expression was lower in renal cell carcinoma samples compared with adjacent nontumor samples.…”

A molecular taxonomy of tumors independent of tissue-of-origin

“…One of the biggest hurdles in cancer research is the sparsity of data; ~20,000 proteincoding genes is comparable with the number of tumor samples, even with multiple mutations per sample. This also does not account for the fact that ~30,000 non-coding genes and hundreds of thousands of non-coding regulatory elements exist with oncogenic or tumor suppressor activity (Dees et al 2012;Tamborero, Gonzalez-Perez, and Lopez-Bigas 2013;Lawrence et al 2014;Kumar et al 2015;Jiang et al 2019;Zhao et al 2019;Zhang et al 2020). Hence, we sought to simplify the problem by employing a "knowledge-base driven analysis" (Khatri, Sirota, and Butte 2012), investigating cancer as a disease of basic cellular and biochemical pathways, which we accomplished by translating gene-level mutations into pathway level disruptions.…”

“…Considerable progress has been made analyzing these data. Statistical analyses identify hundreds of global and tissue-specific cancer driver genes (Dees et al 2012;Tamborero, Gonzalez-Perez, and Lopez-Bigas 2013;Lawrence et al 2014;Kumar et al 2015;Tokheim et al 2016;Jiang et al 2019;Zhao et al 2019) using approaches aimed at detecting when genes are mutated at a greater rate than expected due to chance. It has been estimated that fewer than five mutations in key oncogenes and/or tumor suppressors would be sufficient to transform a normal cell to a cancerous state (Vogelstein and Kinzler 2015;Iranzo, Martincorena, and Koonin 2018).…”

Peer Review #1 of "Role of methyltransferase-like enzyme 3 and methyltransferase-like enzyme 14 in urological cancers (v0.1)"

“…Manuscript to be reviewed interrupting RNA methylation. Therefore, they believe that METTL3 acts as a tumor suppressor for bladder cancer (Zhao et al 2019).…”