Understanding why driver mutations that promote cancer are sometimes rare is important for precision medicine since it would help in their identification. Driver mutations are largely discovered through their frequencies. Thus, rare mutations often escape detection. Unlike high-frequency drivers, low-frequency drivers can be tissue specific; rare drivers have extremely low frequencies. Here, we discuss rare drivers and strategies to discover them. We suggest that allosteric driver mutations shift the protein ensemble from the inactive to the active state. Rare allosteric drivers are statistically rare since, to switch the protein functional state, they cooperate with additional mutations, and these are not considered in the patient cancer-specific protein sequence analysis. A complete landscape of mutations that drive cancer will reveal tumorspecific therapeutic vulnerabilities.
Mutations That Drive Cancer Can Be Frequent or RareHere we ask: Why are some driver mutations rare? Both frequent and rare drivers bestow a selective advantage to a clone in its microenvironment by promoting cell survival and proliferation; thus why are driver mutations sometimes rare? This question is important. If understood, it would help not only in uncovering the mechanisms of driver mutations, but also their identification and drug discovery. Yet, to date it has not been asked.Precision medicine efforts to uncover actionable mutations that drive cancer in patients have galvanized big-data initiatives. They have spurred development of experimental approaches to accumulate data [e.g., The Cancer Genome Atlas (TCGA) [1], Cancer Genome Project (CGP) [2], and Catalogue of Somatic Mutations in Cancer (COSMIC) [3]], and prompted the formation of the International Cancer Genome Consortium [4,5], and advances in computational algorithms to search and analyze it [6][7][8][9][10][11][12][13]. The outcome witnessed an increase in experimental and efficient computational methods, analyses tools and applications, servers, and reviews [5][6][7][8][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29]. At the same time, they have also shown that the state-of-the-art computational strategies that integrate and prioritize the data [30,31] have been unsuccessful in predicting all clinically diagnosed cancer drivers. This implies some deficiencies, not necessarily of the software itself, but its underlying conceptual biological basis. Among the emerging flaws have been the unidentified, yet clinically diagnosed, rare driver mutations. This raised the challenging question of how to identify rare driver mutations [8,[32][33][34]. Driver mutations contribute to cancer development. They can be statistically frequent or rare, at the tail of the distribution, as are passenger mutations that do not promote cancer [35]. Rare drivers are likely to be in oncogenes or tumor suppressors and are present in <1% of cancers. If direct functional or signaling data are unavailable, which is often the case, how then can we distinguish between rare driver and pa...