Suppressor of cytokine signaling (SOCS) proteins SOCS1 and SOCS3 are considered tumor suppressors in liver hepatocellular carcinoma (LIHC). To gain insight into the underlying molecular mechanisms, the expression of SOCS1/ SOCS3 was evaluated in The Cancer Genome Atlas LIHC dataset along with key oncogenic signaling pathway genes. SOCS1 expression was not significantly reduced in HCC yet higher expression predicted favorable prognosis, whereas SOCS3 lacked predictive potential despite lower expression. Only a small proportion of the cell cycle, receptor tyrosine kinase, growth factor and RAS-RAF-MEK-MAPK signaling genes negatively correlated with SOCS1 or SOCS3, of which even fewer showed elevated expression in HCC and predicted survival. However, many PI3K-AKT-MTOR pathway genes showed mutual exclusivity with SOCS1/SOCS3 and displayed independent predictive ability. Among genes that negatively correlated with SOCS1/SOCS3, CDK2, MLST8, AURKA, MAP3K4 and RPTOR showed corresponding modulations in the livers of mice lacking Socs1 or Socs3 during liver regeneration and in experimental HCC, and in Hepa1-6 murine HCC cells overexpressing SOCS1/SOCS3. However, Cox proportional hazards model identified CXCL8, DAB2 and PIK3R1 as highly predictive in combination with SOCS1 or SOCS3. These data suggest that developing prognostic biomarkers and precision treatment strategies based on SOCS1/SOCS3 expression need careful testing in different patient cohorts.

/pdf/prognostic-significance-of-socs1-and-socs3-tumor-suppressors-nj1n6097bf.pdf

Prognostic significance of SOCS1 and SOCS3 tumor suppressors in hepatocellular carcinoma and its correlation to key oncogenic signaling pathways

Missense variants are present amongst the healthy population, but some of them are causative of human diseases. Therefore, a classification of variants associated with "healthy" or "diseased" states is not always straightforward. A deeper understanding of the nature of missense variants in health and disease, the cellular processes they may affect, and the general molecular principles which underlie these differences, is essential to better distinguish pathogenic from population variants. Here we quantify variant enrichment across full-length proteins, their domains and 3D-structure defined regions. We integrate this with available transcriptomic and proteomic (protein half-life, thermal stability, abundance) data. Using this approach we have mined a rich set of molecular features which enable us to understand the differences underlying pathogenic and population variants: pathogenic variants mainly affect proteins involved in cell proliferation and nucleotide processing, localise to protein cores and interaction interfaces, and are enriched in more abundant proteins. In terms of their molecular properties, we find that common population variants and pathogenic variants show the greatest contrast. Additionally, in contrary to other studies, we find that rare population variants display features closer to common than pathogenic variants. This study provides molecular details into how different proteins exhibit resilience and/or sensitivity towards missense variants. Such details could be harnessed to predict variant deleteriousness, and prioritise variant-enriched proteins and protein domains for therapeutic targeting and development. The ZoomVar database, which we created for this study, is available at http://fraternalilab.kcl.ac.uk/ZoomVar. It allows users to programmatically annotate a large number of missense variants with protein structural information, and to calculate variant enrichment in different protein structural regions.

/pdf/missense-variants-in-health-and-disease-affect-distinct-4ebhp1x05m.pdf

Missense variants in health and disease affect distinct functional pathways and proteomics features

Missense variants are present amongst the healthy population, but some of them are causative of human diseases. A classification of variants associated with "healthy" or "diseased" states is therefore not always straightforward. A deeper understanding of the nature of missense variants in health and disease, the cellular processes they may affect, and the general molecular principles which underlie these differences is essential to offer mechanistic explanations of the true impact of pathogenic variants. Here, we have formalised a statistical framework which enables robust probabilistic quantification of variant enrichment across full-length proteins, their domains, and 3D structure-defined regions. Using this framework, we validate and extend previously reported trends of variant enrichment in different protein structural regions (surface/core/interface). By examining the association of variant enrichment with available functional pathways and transcriptomic and proteomic (protein half-life, thermal stability, abundance) data, we have mined a rich set of molecular features which distinguish between pathogenic and population variants: Pathogenic variants mainly affect proteins involved in cell proliferation and nucleotide processing and are enriched in more abundant proteins. Additionally, rare population variants display features closer to common than pathogenic variants. We validate the association between these molecular features and variant pathogenicity by comparing against existing in silico variant impact annotations. This study provides molecular details into how different proteins exhibit resilience and/or sensitivity towards missense variants and provides the rationale to prioritise variant-enriched proteins and protein domains for therapeutic targeting and development. The ZoomVar database, which we created for this study, is available at fraternalilab.kcl.ac.uk/ZoomVar. It allows users to programmatically annotate missense variants with protein structural information and to calculate variant enrichment in different protein structural regions.

/pdf/pathogenic-missense-protein-variants-affect-different-320xj7rcac.pdf

Pathogenic missense protein variants affect different functional pathways and proteomic features than healthy population variants.

Recent advances in biotechnologies for genomics and proteomics have expanded our understanding of biological components which play crucial roles in complex mechanisms related to cancer. However, it is still challenging to extract from the available knowledge reliable targets to use in a translational setting. The reasons for this are manifold, but essentially distilling real biological signal from heterogeneous ″big data″ collections is the major hurdle. Here, we aim to establish an in-silico pipeline to explore mutations and their effects on protein-protein interactions, with a focus on acute myeloid leukaemia (AML), one of the most common blood cancers with the highest mortality rate. Our method, based on cyclic interactions of a small number of proteins topologically linked in the network (short loop network motifs), highlights specific protein-protein interactions (PPIs) and their functions in AML when compared with other leukaemias. We also developed a new property named ′short loop commonality′ to measure indirect PPIs occurring via common short loop interactions. This new method detects ″modules″ of PPI networks (PPINs) enriched with common biological functions which have proteins that contain mutation hotspots. We further perform 3D structural modelling to extract atomistic details, which shows that such hotspots map to PPI interfaces as well as active sites. Thus, our study proposes a framework for the macroscopic and microscopic investigation of PPINs, their relation to cancers, and highlights important functional modules in the network to be exploited in targeted drug screening.

/pdf/short-loop-motif-profiling-of-protein-interaction-networks-tba47iwn3k.pdf

Short loop motif profiling of protein interaction networks in acute myeloid leukaemia

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Short loop motif profiling of protein interaction networks in acute myeloid leukaemia