Cancer research is not correlated with driver gene mutation burdens.

BACKGROUND: Cancer research is pursued with the goal of positively impacting patients with cancer. Decisions regarding how to allocate research funds reflect a complex balancing of priorities and factors. Even though these are subjective decisions, they should be made with consideration of all available objective facts. An accurate estimate of the affected cancer patient population by mutation is one variable that has only recently become available to inform funding decisions. METHODS: We compared the overall incident burden of mutations within each cancer-associated gene with two measures of cancer research efforts: research grant funding amounts and numbers of academic manuscripts. We ask to what degree the aggregate set of cancer research efforts reflects the relative burdens of the different cancer genetic drivers. We thoroughly investigate the design of our queries to ensure that the presented results are robust and conclusions are well justified. FINDINGS: We find cancer research is generally not correlated with the relative burden of mutation within the different genetic drivers of cancer. CONCLUSIONS: We suggest that cancer research would benefit from incorporating, among other factors, an epidemiologically informed mutation-estimate baseline into a larger framework for funding and research allocation decisions. FUNDING: This work was supported in part by the National Institutes of Health (NIH) P30CA014195 and NIH DP2AT011327.

Theoretical analysis reveals a role for RAF conformational autoinhibition in paradoxical activation.

RAF kinase inhibitors can, under certain conditions, increase RAF kinase signaling. This process, which is commonly referred to as 'paradoxical activation' (PA), is incompletely understood. We use mathematical and computational modeling to investigate PA and derive rigorous analytical expressions that illuminate the underlying mechanism of this complex phenomenon. We find that conformational autoinhibition modulation by a RAF inhibitor could be sufficient to create PA. We find that experimental RAF inhibitor drug dose-response data that characterize PA across different types of RAF inhibitors are best explained by a model that includes RAF inhibitor modulation of three properties: conformational autoinhibition, dimer affinity, and drug binding within the dimer (i.e., negative cooperativity). Overall, this work establishes conformational autoinhibition as a robust mechanism for RAF inhibitor-driven PA based solely on equilibrium dynamics of canonical interactions that comprise RAF signaling and inhibition.

Manipulating mitochondrial electron flow enhances tumor immunogenicity.

Although tumor growth requires the mitochondrial electron transport chain (ETC), the relative contribution of complex I (CI) and complex II (CII), the gatekeepers for initiating electron flow, remains unclear. In this work, we report that the loss of CII, but not that of CI, reduces melanoma tumor growth by increasing antigen presentation and T cell-mediated killing. This is driven by succinate-mediated transcriptional and epigenetic activation of major histocompatibility complex-antigen processing and presentation (MHC-APP) genes independent of interferon signaling. Furthermore, knockout of methylation-controlled J protein (MCJ), to promote electron entry preferentially through CI, provides proof of concept of ETC rewiring to achieve antitumor responses without side effects associated with an overall reduction in mitochondrial respiration in noncancer cells. Our results may hold therapeutic potential for tumors that have reduced MHC-APP expression, a common mechanism of cancer immunoevasion.

How often is each gene mutated within the cancer patient population?

Genome sequenced samples from cancer patients helped identify roles of different mutation types and enabled targeted therapy development. However, critical questions like what are the gene mutation rates among the patients? or what genes are most commonly mutated, pan-cancer? have only been recently answered. Here, we highlight this recent advance.

Cancer gene mutation frequencies for the U.S. population.

Mutations play a fundamental role in the development of cancer, and many create targetable vulnerabilities. There are both public health and basic science benefits from the determination of the proportion of all cancer cases within a population that include a mutant form of a gene. Here, we provide the first such estimates by combining genomic and epidemiological data. We estimate KRAS is mutated in only 11% of all cancers, which is less than PIK3CA (13%) and marginally higher than BRAF (8%). TP53 is the most commonly mutated gene (35%), and KMT2C, KMT2D, and ARID1A are among the ten most commonly mutated driver genes, highlighting the role of epigenetic dysregulation in cancer. Analysis of major cancer subclassifications highlighted varying dependencies upon individual cancer drivers. Overall, we find that cancer genetics is less dominated by high-frequency, high-profile cancer driver genes than studies limited to a subset of cancer types have suggested.