The combination of CDK4/6 inhibitors with antiestrogen therapies signifi cantly improves clinical outcomes in ER-positive advanced breast cancer. To identify mechanisms of acquired resistance, we analyzed serial biopsies and rapid autopsies from patients treated with the combination of the CDK4/6 inhibitor ribociclib with letrozole. This study revealed that some resistant tumors acquired RB loss, whereas other tumors lost PTEN expression at the time of progression. In breast cancer cells, ablation of PTEN , through increased AKT activation, was suffi cient to promote resistance to CDK4/6 inhibition in vitro and in vivo. Mechanistically, PTEN loss resulted in exclusion of p27 from the nucleus, leading to increased activation of both CDK4 and CDK2. Because PTEN loss also causes resistance to PI3Kα inhibitors, currently approved in the post-CDK4/6 setting, these fi ndings provide critical insight into how this single genetic event may cause clinical crossresistance to multiple targeted therapies in the same patient, with implications for optimal treatmentsequencing strategies. SIGNIFICANCE: Our analysis of serial biopsies uncovered RB and PTEN loss as mechanisms of acquired resistance to CDK4/6 inhibitors, utilized as fi rst-line treatment for ER-positive advanced breast cancer. Importantly, these fi ndings have near-term clinical relevance because PTEN loss also limits the effi cacy of PI3Kα inhibitors currently approved in the post-CDK4/6 setting.

Predicting the execution time of code segments in real-

The contributions of this paper are twofold. First, an automatic tool-based approach is described to bound worst-case data cache performance. The gaven approach works on fully optimized code, performs the analysis over the entire control flow of a program, de-tects and exploits both spatial and temporal locality within data references, produces results typically within a few seconds, and estimates, on average, 30% tighter WCET bounds than can be predicted without analyzing data cache behavior. Results obtained by running the system on representative programs are presented and indicate that timing analysis of data cache behavior can result in significantly tighter worst-case performance predictions. Second, a framework to bound worst-case instruction cache performance for set-associative caches is formally introduced and operationally described. Results of incorporating instruction cache predictions within pipeline simulation show that timing predictions for set-associative caches remain just as tight as predictions for direct-mapped caches. The cache simulation overhead scales linearly with increasing associativity.

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Embedded systems often have real-time constraints. Traditional timing analysis statically determines the maximum execution time of a task or a program in a real-time system. These systems typically depend on the worst-case execution time of tasks in order to make static scheduling decisions so that tasks can meet their deadlines. Static determination of worst-case execution times imposes numerous restrictions on real-time programs, which include that the maximum number of iterations of each loop must be known statically. These restrictions can significantly limit the class of programs that would be suitable for a real-time embedded system. This paper describes work-in-progress that uses static timing analysis to aid in making dynamic scheduling decisions. For instance, different algorithms with varying levels of accuracy may be selected based on the algorithm's predicted worst-case execution time and the time allotted for the task. We represent the worstcase execution time of a function or a loop as a formula, where the unknown values affecting the execution time are parameterized. This parametric timing analysis produces formulas that can then be quickly evaluated at run-time so dynamic scheduling decisions can be made with little overhead. Benefits of this work include expanding the class of applications that can be used in a real-time system, improving the accuracy of dynamic scheduling decisions, and more effective utilization of system resources.