Predicting genetic interactions, cell line dependencies and drug sensitivities with variational graph auto-encoder.

Large scale cancer genomics data provide crucial information about the disease and reveal points of intervention. However, systematic data have been collected in specific cell lines and their collection is laborious and costly. Hence, there is a need to develop computational models that can predict such data for any genomic context of interest. Here we develop novel models that build on variational graph auto-encoders and can integrate diverse types of data to provide high quality predictions of genetic interactions, cell line dependencies and drug sensitivities, outperforming previous methods. Our models, data and implementation are available at: https://github.com/aijag/drugGraphNet.

Exosomal telomerase transcripts reprogram the microRNA transcriptome profile of fibroblasts and partially contribute to CAF formation.

It is now well accepted that cancer cells change their microenvironment from normal to tumor-supportive state to provide sustained tumor growth, metastasis and drug resistance. These processes are partially carried out by exosomes, nano-sized vesicles secreted from cells, shuttled from donor to recipient cells containing a cargo of nucleic acids, proteins and lipids. By transferring biologically active molecules, cancer-derived exosomes may transform microenvironmental cells to become tumor supportive. Telomerase activity is regarded as a hallmark of cancer. We have recently shown that the transcript of human telomerase reverse transcriptase (hTERT), is packaged in cancer cells derived- exosomes. Following the engulfment of the hTERT transcript into fibroblasts, it is translated into a fully active enzyme [after assembly with its RNA component (hTERC) subunit]. Telomerase activity in the recipient, otherwise telomerase negative cells, provides them with a survival advantage. Here we show that exosomal telomerase might play a role in modifying normal fibroblasts into cancer associated fibroblasts (CAFs) by upregulating [Formula: see text]SMA and Vimentin, two CAF markers. We also show that telomerase activity changes the transcriptome of microRNA in these fibroblasts. By ectopically expressing microRNA 342, one of the top identified microRNAs, we show that it may mediate the proliferative phenotype that these cells acquire upon taking-up exosomal hTERT, providing them with a survival advantage.