Cyclin E2 overexpression is associated with endocrine resistance but not insensitivity to CDK2 inhibition in human breast cancer cells

Cyclin E2, but not cyclin E1, is included in several gene signatures that predict disease progression in either tamoxifen-resistant or metastatic breast cancer. We therefore examined the role of cyclin E2 in antiestrogen resistance in vitro and its potential for therapeutic targeting through cyclin-dependent kinase (CDK) inhibition. High expression of CCNE2, but not CCNE1, was characteristic of the luminal B and HER2 subtypes of breast cancer and was strongly predictive of shorter distant metastasis-free survival following endocrine therapy. After antiestrogen treatment of MCF-7 breast cancer cells, cyclin E2 mRNA and protein were downregulated and cyclin E2–CDK2 activity decreased. However, this regulation was lost in tamoxifen-resistant (MCF-7 TAMR) cells, which overexpressed cyclin E2. Expression of either cyclin E1 or E2 in T-47D breast cancer cells conferred acute antiestrogen resistance, suggesting that cyclin E overexpression contributes to the antiestrogen resistance of tamoxifen-resistant cells. Ectopic expression of cyclin E1 or E2 also reduced sensitivity to CDK4, but not CDK2, inhibition. Proliferation of tamoxifen-resistant cells was inhibited by RNAi-mediated knockdown of cyclin E1, cyclin E2, or CDK2. Furthermore, CDK2 inhibition of E-cyclin overexpressing cells and tamoxifen-resistant cells restored sensitivity to tamoxifen or CDK4 inhibition. Cyclin E2 overexpression is therefore a potential mechanism of resistance to both endocrine therapy and CDK4 inhibition. CDK2 inhibitors hold promise as a component of combination therapies in endocrine-resistant disease as they effectively inhibit cyclin E1 and E2 overexpressing cells and enhance the efficacy of other therapeutics

The twin hypotheses

The Brain Code (BC) relies on several essential concepts that are found across a range of physiological and behavioral functions. The Fundamental Code Unit (FCU) assumes an abstract code unit to allow for a higher order of abstractions that informs information exchanges at the cellular and genetic levels, together the two hypotheses provide a foundation for a system level understanding and potentially cyphering of the Brain Code [1–3]. This paper discusses an organizing principle for an abstract framework tested in a limited scope experimental approach as a means to show an empirical example of cognitive measurement as well as a framework for a Cortical Computation methodology. Four important concepts of the BC and FCU are discussed. First, the principle of activation based on Guyton thresholds. This is seen in the well-known and widely documented action potential threshold in neurons, where once a certain threshold is reached, the neuron will fire, reflecting the transmission of information. The concept of thresholds is also valid in Weber minimum detectable difference in our sensing, which applies to our hearing, seeing and touching. Not only the intensity, but also the temporal pattern is affected by this [4]. This brings insight to the second important component, which is duration. The combination of both threshold crossing and duration may define the selection mechanisms, depending on both external and intrinsic factors. However, ranges exist within which tuning can take place. Within reason it can be stated that no functional implication will occur beyond this range. Transfer of information and processing itself relies on energy and can be described in waveforms, which is the third concept. The human sensing system acts as transducer between the different forms of energy, the fourth principle. The aim of the brain code approach is to incorporate these four principles in an explanatory, descriptive and predictive model. The model will take into account fundamental physiological knowledge and aims to reject assumptions that are not yet fully established. In order to fill in the gaps with regards to the missing information, modules consisting of the previous described four principles are explored. This abstraction should provide a reasonable placeholder, as it is based on governing principles in nature. The model is testable and allows for updating as more data becomes available. It aims to replace methods that rely on structural levels to abstraction of functions, or approaches that are evidence-based, but across many noisy-elements and assumptions that outcomes might not reflect behavior at the organism level. </p