mTOR, a serine/threonine kinase first discovered in 1994, is a key signaling node that integrates a number of inputs to control processes such as cell growth and metabolism, among others. Due to its role in controlling a broad spectrum of cellular processes, mTOR has clinical significance in neurodegenerative diseases, diabetes and cancer. Currently, the FDA has approved treatment for metastatic clear cell Renal Cell Carcinoma (ccRCC) that includes mTOR inhibitors such as Everolimus andTemsirolimus. A study to understand the patient-to-patient variation in response to these drugs by studying extraordinary responders lead to the characterization of mTOR activating missense mutations in these patients. Most of these mutants cluster in two domains of mTOR: the kinase and FAT domains. While these mutations occur most frequently in ccRCC, they are also observed in 2% of all cancer patients (millions of people!). Understanding which of these mutations indicate sensitivity to mTOR inhibition will provide for the scientific basis for setting up a basket trial, where patients with these mutations can be enrolled in a study and get treated with mTOR inhibitors regardless of their cancer type.
In a paper recently published in the Journal of Clinical Investigation, researchers in the Hsieh and Chodera Labs at MSKCC characterized these clinically observed mutants. Combining experiment and computation, the paper details how these mutants cluster into distinct groups that can cooperate with each other to increase the kinase activity levels above that of a single mutant, suggesting multiple mechanisms for activating mTOR. In Folding@Home Project 10495, these mutants were simulated and analyzed to identify structural rearrangements that might play a mechanistic role in activating the protein. Thanks to the donors at Folding@home we’ve now completed over 225,000 work units, or the equivalent of 2.25 milliseconds of aggregate simulation time. In mutant S2215F, a number of contacts were predicted to be disrupted, suggestive of regulatory alpha helices moving away from each other. The results of this work suggest a framework to identify patients carrying mTOR mutations that make them candidates for treatment with mTOR inhibitors, potentially benefiting millions of patients who before would not have been candidates for this type of therapy.
In the figure below, you can see some how Folding@home data can start to tell us about the microscopic mechanism underlying how mutations observed in patients could activate the kinase and cause downstream oncogenic changes.
We’ve only begun to scratch the surface of the enormously valuable mTOR dataset, so stay tuned for many more insights to follow!