Based on transcriptomic analyses of thousands of samples from The Cancer Genome Atlas, we report that expression of constitutive proteasome (CP) genes (PSMB5, PSMB6, PSMB7) and immunoproteasome (IP) genes (PSMB8, PSMB9, PSMB10) is increased in most cancer types. In breast cancer, expression of IP genes was determined by the abundance of tumor infiltrating lymphocytes and high expression of IP genes was associated with longer survival. In contrast, IP upregulation in acute myeloid leukemia (AML) was a cell-intrinsic feature that was not associated with longer survival. Expression of IP genes in AML was IFN-independent, correlated with the methylation status of IP genes, and was particularly high in AML with an M5 phenotype and/or MLL rearrangement. Notably, PSMB8 inhibition led to accumulation of polyubiquitinated proteins and cell death in IPhigh but not IPlow AML cells. Co-clustering analysis revealed that genes correlated with IP subunits in non-M5 AMLs were primarily implicated in immune processes. However, in M5 AML, IP genes were primarily co-regulated with genes involved in cell metabolism and proliferation, mitochondrial activity and stress responses. We conclude that M5 AML cells can upregulate IP genes in a cell-intrinsic manner in order to resist cell stress.
Modern artificial intelligence (AI) approaches mainly rely on neural network (NN) or deep NN methodologies. However, these approaches require large amounts of data to train, given, that the number of their trainable parameters has a polynomial relationship to their neuron counts. This property renders deep NN not applicable in fields operating with small, albeit representative datasets such as healthcare. In this paper, we propose a novel neural network architecture which trains spatial positions of neural soma and axon pairs, where weights are calculated by axon-soma distances of connected neurons. We refer to this method as distance-encoding biomorphic-informational (DEBI) neural network. This concept significantly minimizes the number of trainable parameters compared to conventional neural networks. We demonstrate that DEBI models can yield comparable predictive performance in tabular and imaging datasets, where they require a fraction of trainable parameters compared to conventional NNs, resulting in a highly scalable solution.
Graduate students and postdoctoral fellows at the Institute for Research in Immunology and Cancer (IRIC) organized the 9th IRIC International Symposium on 14–15 May, 2015. The symposium was held at the IRIC, an ultra-modern research hub and training center located on the hilltop of the Université de Montréal campus in Montreal, Canada. This year's title was ‘Molecular Targets in Cancer Genomics', reflecting the common interest of the IRIC student community. Through four broadly themed sessions, organizers sought to highlight the new generation of anti-cancer strategies including targeted therapies directed against actionable cancer-specific mutations, and immunotherapies, which enhance immune responses against cancer. Both targeted and immunotherapies are tailored to cancer-specific features, and require precise knowledge of cancer cells, from their genome to their proteome. The focus of this symposium was on translating the molecular basis of cancer into a functional understanding of aberrant pathways, and to uncover novel targets to be exploited for cancer therapeutic strategies.
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