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Biology subjects

Swamy, A.

Publications and source records attributed to Swamy, A..

2 recordsLinked to original sources

On-target toxicity limits the efficacy of CDK11 inhibition against cancers with 1p36 deletions

The cyclin-dependent kinase CDK11 is an understudied kinase that has been the subject of conflicting reports regarding its function in cancer. Here, we combine genetic and pharmacological approaches to demonstrate that CDK11 is a critical regulator of cancer cell survival that is required for RNA splicing and the expression of homologous recombination genes. Inhibition of CDK11 disrupts genome stability, promotes the retention of intronic sequences in mature mRNAs, and induces synthetic lethality with PARP inhibitors. Through integrative analysis of functional genomics datasets, we identify heterozygous deletions of chromosome 1p36 - which encompasses CDK11 and its activating cyclin CCNL2 - as a recurrent and predictive biomarker of sensitivity to CDK11 inhibition. To assess the therapeutic potential of CDK11, we develop MEL-495R, a selective and orally bioavailable CDK11 inhibitor. Additionally, we establish a genetically-engineered mouse model that allows us to differentiate between the on-target and off-target effects of CDK11 inhibitors in vivo. Using this platform, we demonstrate that MEL-495R induces widespread on-target toxicity, revealing a narrow therapeutic index. Together, these findings define CDK11 as a core cancer dependency, uncover a chromosomal deletion that sensitizes tumors to CDK11 inhibition, and provide a generalizable strategy for deconvolving drug efficacy and toxicity in vivo for novel oncology targets.

cancer biology↗

Simple Neurofeedback via Machine Learning: Challenges in real time multivariate assessment of meditation state

Attaining proficiency in meditation is difficult, especially without feedback since the mind may be easily distracted with thoughts and only long term efforts see any impact. Self-regulation would be much more effective if provided real time assessment and this can be achieved through EEG neurofeedback. Therefore, this work proposes a scheme for assessing meditation-like state in real time from short EEG segments, using low computational settings. Signal processing techniques are used to extract features from long term meditation practitioners multichannel EEG data. An autoencoder model is then trained on these features such that the model can be run in real time. Its reconstruction errors or its latent variables are used to provide non typical feedback parameters which are used to establish an objective measure of meditation ability. Our approach is optimised to have lightweight architectures handling small blocks of data and can be conveniently used on low density EEG acquisition systems as it requires only a few channels. However, our experimental results suggest that the meditation state has substantial overlap even in terms of multivariate EEG features and show prominent temporal dynamics, both of which are not captured using simple one class algorithms. Being an extremely flexible one-class model, we have described multiple improvements to the proposed autoencoder model to address the above issues and develop simple yet high precision neurofeedback protocols.

neuroscience↗