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Biology subjects

Bianchi, N.

Publications and source records attributed to Bianchi, N..

2 recordsLinked to original sources

CDK12 controls transcription at damaged genes and prevents MYC-induced transcription-replication conflicts

Oncogene-induced replicative stress is a potent tumor-suppressive mechanism that must be kept in check for cancer cells to thrive. Thus, the identification of genes and pathways involved in replicative stress is key to understand cancer evolution and to identify prospective therapeutic targets. Here, we investigated factors that modulate replicative stress upon deregulation of the MYC oncogene. We identified the cyclin-dependent kinase CDK12 as selectively required to prevent transcription-replication conflicts and the activation of a cytotoxic DNA-damage response (DDR). At the mechanistic level, CDK12 was recruited to damaged genes by PARP-dependent DDR-signaling and elongation-competent RNAPII. Once recruited, CDK12 repressed transcription by preventing the association of CDK9 with RNAPII. Either loss or chemical inhibition of CDK12 led to DDR-resistant transcription at damaged genes. Genome-wide profiling revealed that loss of CDK12 exacerbated transcription-replication conflicts in MYC-overexpressing cells and led to the accumulation of double-strand DNA breaks (DSBs), occurring preferentially between early- replicating regions and transcribed genes, organized in a co-directional head-to-tail orientation. Overall, our data demonstrate that CDK12 protects genome integrity by repressing transcription of damaged genes, which is required for proper resolution of DSBs at oncogene-induced transcription-replication conflicts. This provides a rationale that explains both how CDK12 deficiency can promote tandem duplications of early-replicated regions during tumor evolution, and how CDK12 targeting can exacerbate replicative-stress in tumors.

cancer biology↗

TumFlow: An AI Model for Predicting New Anticancer Molecules

MotivationMelanoma is a severe form of skin cancer increasing globally with about 324.000 cases in 2020, making it the fifth most common cancer in the United States. Conventional drug discovery methods face limitations due to the inherently time consuming and costly. However, the emergence of artificial intelligence (AI) has opened up new possibilities. AI models can effectively simulate and evaluate the properties of a vast number of potential drug candidates, substantially reducing the time and resources required by traditional drug discovery processes. In this context, the development of AI normalizing flow models, employing machine learning techniques to create new molecular structures, holds great promise for accelerating the discovery of effective anticancer therapies. ResultsThis manuscript introduces a novel AI model, named TumFlow, aimed at generating new molecular entities with potential therapeutic value in cancer treatment. It has been trained on the comprehensive NCI-60 dataset, encompassing thousands of molecules tested across 60 tumour cell lines, with a specific emphasis on the melanoma SK-MEL-28 cell line. The model successfully generated new molecules with predicted improved efficacy in inhibiting tumour growth while being synthetically feasible. This represents a significant advancement over conventional generative models, which often produce molecules that are challenging or impossible to synthesize. Furthermore, TumFlow has also been utilized to optimize molecules known for their efficacy in clinical melanoma treatments. This led to the creation of novel molecules with a predicted enhanced likelihood of effectiveness against melanoma, currently undocumented on PubChem. Availability and Implementationhttps://github.com/drigoni/TumFlow. Supplementary informationUploaded.

bioinformatics↗