• CBL gene
• Noonan syndrome
• RAS/MAPK signaling pathway
• mastocytosis

• AKT/mTOR signaling pathway
• Autophagy
• CBL expression
• Gastric cancer
• Tumor cell proliferation
• miR-188-3p

Differential expressions of certain genes during tumorigenesis may serve to identify novel manageable targets in the clinic. In this work with an integrated bioinformatics approach, we analyzed public microarray datasets from Gene Expression Omnibus (GEO) to explore the key differentially expressed genes (DEGs) in non-small cell lung cancer (NSCLC). We identified a total of 984 common DEGs in 252 healthy and 254 NSCLC gene expression samples. The top 10 DEGs as a result of pathway enrichment and protein–protein interaction analysis were further investigated for their prognostic performances. Among these, we identified high expressions of CDC20, AURKA, CDK1, EZH2, and CDKN2A genes that were associated with significantly poorer overall survival in NSCLC patients. On the contrary, high mRNA expressions of CBL, FYN, LRKK2, and SOCS2 were associated with a significantly better prognosis. Furthermore, our drug target analysis for these hub genes suggests a potential use of Trichostatin A, Pracinostat, TGX-221, PHA-793887, AG-879, and IMD0354 antineoplastic agents to reverse the expression of these DEGs in NSCLC patients.

Dendritic cells (DCs) are composed of multiple lineages of hematopoietic cells and orchestrate immune responses upon detecting the danger and inflammatory signals associated with pathogen and damaged tissues. Under steady-state, DCs are maintained at limited numbers and the functionally quiescent status. While it is known that a fine balance in the DC homeostasis and activation status is also important to prevent autoimmune diseases and hyperinflammation, mechanisms that control DC development and activation under stead-state remain not fully understood. Here we show that DC-specific ablation of CBL and CBL-B (CBL^-/-CBL-B^-/-) leads to spontaneous liver inflammation and fibrosis and early death of the mice. The mutant mice have a marked expansion of classic CD8α⁺/CD103⁺ DCs (cDC1s) in peripheral lymphoid organs and the liver. These DCs exhibit atypical activation phenotypes characterized by an increased production of inflammatory cytokines and chemokines but not the cell surface MHC-II and costimulatory ligands. While the mutant mice also have massive T cell activation, lymphocytes are not required for the disease development. The CBL^-/-CBL-B^-/- mutation enhances FLT3-mTOR signaling, due to defective FLT3 ubiquitination and degradation. Blockade of FLT3-mTOR signaling normalizes the homeostasis of cDC1s and attenuates liver inflammation. Our result thus reveals a critical role of CBLs in the maintenance of DC homeostasis and immune quiescence. This regulation could be relevant to liver inflammatory diseases and fibrosis in humans.

• E3 ubiquitin ligase
• FLT3
• dendritic cell (DC)
• homeostasis
• liver inflammation

• CMML
• chronic myelomonocytic leukemia
• clinical trials
• novel therapies
• therapies

Nef is an accessory protein encoded by human immunodeficiency virus type-1 (HIV-1) and plays important roles in regulating HIV-1 infection and viral replication. Interestingly, HIV-1 Nef can promote degradation of numerous host proteins to disrupt cellular antiviral immune response. However, how HIV-1 Nef is degraded by host factors remains largely unexplored. Here, we identified c-Cbl as a host ubiquitin E3 ligase of HIV-1 Nef. We found that c-Cbl interacts with Nef and reduces protein levels of HIV-1 Nef. Further studies demonstrated that c-Cbl promoted Lys48-linked polyubiquitination of HIV-1 Nef, thus attenuating protein stability of HIV-1 Nef. Importantly, cellular c-Cbl ubiquitinated and degraded Nef proteins produced by HIV-1 NL4-3 virions, and ultimately attenuated HIV-1 virulence for infection of THP1 cells. This study reveals a ubiquitination and proteasome-dependent degradation mechanism of HIV-1 Nef protein, and could provide potential strategies for fighting against HIV-1.

• E3 ubiquitin ligase
• HIV-1
• Nef
• c-Cbl
• ubiquitination

This is a cohort study, conducted at a university-based reproductive medicine center and private reproductive medicine center that aimed to evaluate granulosa cumulus cell gene expression in the insulin signaling pathway in Polycystic Ovary Syndrome (PCOS) patients undergoing in vitro fertilization (IVF) treatment and to compare the cumulus gene expression between normal weight and obese women without clinical insulin resistance. Fifteen PCOS patients, nine normal weight patients and six obese patients presenting normal HOMA IR (Homeostasis Model Assessment–Insulin Resistance), participated. Patients underwent oocyte retrieval for IVF and after the procedure, granulosa cumulus cells were removed from the oocytes for RNA extraction. Quantitative polymerase chain reaction (PCR) array analysis of 84 genes from insulin signaling pathway was conducted. The results were expressed as fold up- or fold down-expression in obese patients compared with normal weight patients. Any fold change ⩾3 or ⩽3 and any p ⩽ 0.05 were considered statistically significant.

There were 10 genes that were overexpressed in obese compared with normal weight women, BCL2L1, BRAF, CBL, DOK1, FBP1, FRS2, MTOR, PCK2, RPS6KA1, and SORBS1, that had a fold change ⩾3 and p ⩽ 0.05.

In the obese group, the overexpressed genes are mainly responsible for the proliferation and differentiation of cumulus cells during oocyte maturation, insulin resistance, apoptosis regulation, and glucose metabolism during early embryogenesis, suggesting that in the follicular environment, insulin resistance is present even in the absence of clinical signs.

Together, our findings and the related literature suggest that those alterations may be associated with the worse prognosis of follicular development and oocyte maturation observed in PCOS obese women.

• Polycystic Ovary Syndrome
• cumulus cells
• gene expression
• in vitro fertilization
• insulin
• obesity

• AML
• FLT3
• leukaemia
• mutant CBL
• tyrosine kinase

Cancer is a complex disease that is driven by genetic alterations. There has been a rapid development of genome-wide techniques during the last decade along with a significant lowering of the cost of gene sequencing, which has generated widely available cancer genomic data. However, the interpretation of genomic data and the prediction of the association of genetic variations with cancer and disease phenotypes still requires significant improvement. Missense mutations, which can render proteins non-functional and provide a selective growth advantage to cancer cells, are frequently detected in cancer. Effects caused by missense mutations can be pinpointed by in silico modeling, which makes it more feasible to find a treatment and reverse the effect. Specific human phenotypes are largely determined by stability, activity, and interactions between proteins and other biomolecules that work together to execute specific cellular functions. Therefore, analysis of missense mutations’ effects on proteins and their complexes would provide important clues for identifying functionally important missense mutations, understanding the molecular mechanisms of cancer progression and facilitating treatment and prevention. Herein, we summarize the major computational approaches and tools that provide not only the classification of missense mutations as cancer drivers or passengers but also the molecular mechanisms induced by driver mutations. This review focuses on the discussion of annotation and prediction methods based on structural and biophysical data, analysis of somatic cancer missense mutations in 3D structures of proteins and their complexes, predictions of the effects of missense mutations on protein stability, protein-protein and protein-nucleic acid interactions, and assessment of conformational changes in protein conformations induced by mutations.

• cancer driver missense mutations
• conformational dynamics
• macromolecular interactions
• macromolecular stability

CBL and CBL-B ubiquitin ligases play key roles in hematopoietic stem cell homeostasis and their aberrations are linked to leukemogenesis. Mutations of CBL, often genetically-inherited, are particularly common in Juvenile Myelomonocytic Leukemia (JMML), a disease that manifests early in children. JMML is fatal unless corrected by bone marrow transplant, which is effective in only half of the recipients, stressing the need for animal models that recapitulate the key clinical features of this disease. However, mouse models established so far only develop hematological malignancy in adult animals. Here, using VAV1-Cre-induced conditional CBL/CBL-B double knockout (DKO) in mice, we established an animal model that exhibits a neonatal myeloproliferative disease (MPD). VAV1-Cre induced DKO mice developed a strong hematological phenotype at postnatal day 10, including severe leukocytosis and hepatomegaly, bone marrow cell hypersensitivity to cytokines including GM-CSF, and rapidly-progressive disease and invariable lethality. Interestingly, leukemic stem cells were most highly enriched in neonatal liver rather than bone marrow, which, along with the spleen and thymus, were hypo-cellular. Nonetheless, transplantation assays showed that both DKO bone marrow and liver cells can initiate leukemic disease in the recipient mice with seeding of both spleen and bone marrow. Together, our results support the usefulness of the new hematopoietic-specific CBL/CBL-B double KO animal model to study JMML-related pathogenesis and to further understand the function of CBL family proteins in regulating fetal and neonatal hematopoiesis. To our knowledge, this is the first mouse model that exhibits neonatal MPD in infancy, by day 10 of postnatal life.

• CBL
• HSC
• JMML
• neonatal hematopoiesis
• ubiquitin ligase

• CBL
• protein degradation
• stress response
• tyrosine kinase
• ubiquitin ligase

Human transposable element (TE) activity in somatic tissues causes mutations that can contribute to tumorigenesis. Indeed, TE insertion mutations have been implicated in the etiology of a number of different cancer types. Nevertheless, the full extent of somatic TE activity, along with its relationship to tumorigenesis, have yet to be fully explored. Recent developments in bioinformatics software make it possible to analyze TE expression levels and TE insertional activity directly from transcriptome (RNA-seq) and whole genome (DNA-seq) next-generation sequence data. We applied these new sequence analysis techniques to matched normal and primary tumor patient samples from the Cancer Genome Atlas (TCGA) in order to analyze the patterns of TE expression and insertion for three cancer types: breast invasive carcinoma, head and neck squamous cell carcinoma, and lung adenocarcinoma. Our analysis focused on the three most abundant families of active human TEs: Alu, SVA, and L1. We found evidence for high levels of somatic TE activity for these three families in normal and cancer samples across diverse tissue types. Abundant transcripts for all three TE families were detected in both normal and cancer tissues along with an average of ~80 unique TE insertions per individual patient/tissue. We observed an increase in L1 transcript expression and L1 insertional activity in primary tumor samples for all three cancer types. Tumor-specific TE insertions are enriched for private mutations, consistent with a potentially causal role in tumorigenesis. We used genome feature analysis to investigate two specific cases of putative cancer-causing TE mutations in further detail. An Alu insertion in an upstream enhancer of the CBL tumor suppressor gene is associated with down-regulation of the gene in a single breast cancer patient, and an L1 insertion in the first exon of the BAALC gene also disrupts its expression in head and neck squamous cell carcinoma. Our results are consistent with widespread somatic activity of human TEs leading to numerous insertion mutations that can contribute to tumorigenesis in a variety of tissues.

• Alu
• L1
• LINE-1
• SVA
• bioinformatics
• mutation
• retrotransposons
• tumorigenesis

Next generation sequencing has shown the frequent occurrence of point mutations in the ubiquitin E3 ligase c-Cbl in myeloid malignancies. Mouse models revealed a causal contribution of c-Cbl for the onset of such neoplasms. The point mutations typically cluster in the linker region and RING finger domain and affect both alleles by acquired uniparental disomy. The fast progress in the detection of c-Cbl mutations is contrasted by our scarce knowledge on their functional consequences. The c-Cbl protein displays several enzymatic functions by promoting the attachment of differentially composed ubiquitin chains and of the ubiquitin-like protein NEDD8 to its target proteins. In addition, c-Cbl functions as an adapter protein and undergoes phosphorylation-dependent inducible conformation changes. Studies on the impact of c-Cbl mutations on its functions as a dynamic and versatile adapter protein, its interactomes and on its various enzymatic activities are now important to allow the identification of druggable targets within the c-Cbl signaling network.

• cbl
• myeloid malignancies

Development of high-throughput monitoring technologies enables interrogation of cancer samples at various levels of cellular activity. Capitalizing on these developments, various public efforts such as The Cancer Genome Atlas (TCGA) generate disparate omic data for large patient cohorts. As demonstrated by recent studies, these heterogeneous data sources provide the opportunity to gain insights into the molecular changes that drive cancer pathogenesis and progression. However, these insights are limited by the vast search space and as a result low statistical power to make new discoveries. In this paper, we propose methods for integrating disparate omic data using molecular interaction networks, with a view to gaining mechanistic insights into the relationship between molecular changes at different levels of cellular activity. Namely, we hypothesize that genes that play a role in cancer development and progression may be implicated by neither frequent mutation nor differential expression, and that network-based integration of mutation and differential expression data can reveal these “silent players”. For this purpose, we utilize network-propagation algorithms to simulate the information flow in the cell at a sample-specific resolution. We then use the propagated mutation and expression signals to identify genes that are not necessarily mutated or differentially expressed genes, but have an essential role in tumor development and patient outcome. We test the proposed method on breast cancer and glioblastoma multiforme data obtained from TCGA. Our results show that the proposed method can identify important proteins that are not readily revealed by molecular data, providing insights beyond what can be gleaned by analyzing different types of molecular data in isolation.

Identification of cancer-related genes is an important task, made more difficult by heterogeneity between samples and even within individual patients. Methods for identifying disease-related genes typically focus on individual data sets such as mutational and differential expression data, and therefore are limited to genes that are implicated by each data set in isolation. In this work we propose a method that uses protein interaction network information to integrate mutational and differential expression data on a sample-specific level, and combine this information across samples in ways that respect the commonalities and differences between distinct mutation and differential expression profiles. We use this information to identify genes that are associated with cancer but not readily identifiable by mutations or differential expression alone. Our method highlights the features that significantly predict a gene’s association with cancer, shows improved predictive power in recovering cancer-related genes in known pathways, and identifies genes that are neither frequently mutated nor differentially expressed but show significant association with survival.

• Algorithms
• Chromosome Mapping/methods
• Data Mining/methods
• Databases, Genetic
• Gene Expression Profiling/methods
• Genes, Neoplasm/genetics
• Genetic Association Studies/methods
• Genetic Markers/genetics
• Genomics/methods
• Humans
• Neoplasm Proteins/genetics
• Neoplasms/genetics
• Signal Transduction/genetics
• Silent Mutation/genetics

• R01 LM011247 NLM NIH HHS
• U01 CA198941 NCI NIH HHS
• R01-LM011247 NLM NIH HHS

Myeloproliferative neoplasms are caused by a clonal proliferation of a hematopoietic progenitor. First described in 1951 as ‘Myeloproliferative Diseases’ and reevaluated by the World Health Organization classification system in 2011, myeloproliferative neoplasms include polycythemia vera, essential thrombocythemia and primary myelofibrosis in a subgroup called breakpoint cluster region-Abelson fusion oncogene-negative neoplasms. According to World Health Organization regarding diagnosis criteria for myeloproliferative neoplasms, the presence of the JAK2 V617F mutation is considered the most important criterion in the diagnosis of breakpoint cluster region-Abelson fusion oncogene-negative neoplasms and is thus used as a clonal marker. The V617F mutation in the Janus kinase 2 (JAK2) gene produces an altered protein that constitutively activates the Janus kinase/signal transducers and activators of transcription pathway and other pathways downstream as a result of signal transducers and activators of transcription which are subsequently phosphorylated. This affects the expression of genes involved in the regulation of apoptosis and regulatory proteins and modifies the proliferation rate of hematopoietic stem cells.

• Hematologic neoplasms
• JAK2V617F mutation
• Janus kinase 2
• STAT transcription factors

• CBLC
• DNA damage response
• PARP inhibitors
• RNA interference screens
• ubiquitin-proteasome system

• BRCA2 Protein/genetics
• BRCA2 Protein/metabolism
• Breast Neoplasms/drug therapy
• Breast Neoplasms/enzymology
• Breast Neoplasms/genetics
• Breast Neoplasms/pathology
• Cell Line, Tumor
• Cell Survival/drug effects
• DNA Repair
• Dose-Response Relationship, Drug
• Female
• Gene Expression Regulation, Neoplastic
• Humans
• Phthalazines/pharmacology
• Piperazines/pharmacology
• Poly (ADP-Ribose) Polymerase-1
• Poly(ADP-ribose) Polymerase Inhibitors/pharmacology
• Poly(ADP-ribose) Polymerases/genetics
• Poly(ADP-ribose) Polymerases/metabolism
• Protein Processing, Post-Translational
• Proto-Oncogene Proteins c-cbl/genetics
• Proto-Oncogene Proteins c-cbl/metabolism
• RNA Interference
• Recombination, Genetic
• Signal Transduction/drug effects
• Time Factors
• Transfection
• Ubiquitination

• 14276 Cancer Research UK

• Domain knowledge
• Machine learning
• Omics data
• Random forest
• Regularization
• microRNA

• CBL
• Growing Teratoma Syndrome (GTS)
• germinoma
• lymphedema
• noonan
• teratoma

• Fluorescence Polarization/methods
• Lymphoma/enzymology
• Protein Binding
• Proto-Oncogene Proteins c-cbl/metabolism
• Ubiquitin-Conjugating Enzymes/metabolism

• PIM kinases
• cancer
• oncogenes

• Cbl
• Cellular signaling
• E3 ubiquitin ligase
• Hematopoietic
• Leukemia
• MDS/MPN
• Tyrosine kinase