Flotillin-2 regulates epidermal growth factor receptor activation, degradation by Cbl-mediated ubiquitination, and cancer growth.

Epidermal growth factor receptor (EGFR) signaling is frequently dysregulated in various cancers. The ubiquitin ligase Casitas B-lineage lymphoma proto-oncogene (Cbl) regulates degradation of activated EGFR through ubiquitination and acts as an adaptor to recruit proteins required for trafficking. Here, we used stable isotope labeling with amino acids in cell culture mass spectrometry to compare Cbl complexes with or without epidermal growth factor (EGF) stimulation. We identified over a hundred novel Cbl interactors, and a secondary siRNA screen found that knockdown of Flotillin-2 (FLOT2) led to increased phosphorylation and degradation of EGFR upon EGF stimulation in HeLa cells. In PC9 and H441 cells, FLOT2 knockdown increased EGF-stimulated EGFR phosphorylation, ubiquitination, and downstream signaling, reversible by EGFR inhibitor erlotinib. CRISPR knockout (KO) of FLOT2 in HeLa cells confirmed EGFR downregulation, increased signaling, and increased dimerization and endosomal trafficking. Furthermore, we determined that FLOT2 interacted with both Cbl and EGFR. EGFR downregulation upon FLOT2 loss was Cbl dependent, as coknockdown of Cbl and Cbl-b restored EGFR levels. In addition, FLOT2 overexpression decreased EGFR signaling and growth. Overexpression of wildtype (WT) FLOT2, but not the soluble G2A FLOT2 mutant, inhibited EGFR phosphorylation upon EGF stimulation in HEK293T cells. FLOT2 loss induced EGFR-dependent proliferation and anchorage-independent growth. Lastly, FLOT2 KO increased tumor formation and tumor volume in nude mice and NSG mice, respectively. Together, these data demonstrated that FLOT2 negatively regulated EGFR activation and dimerization, as well as its subsequent ubiquitination, endosomal trafficking, and degradation, leading to reduced proliferation in vitro and in vivo.

TNF plays a crucial role in inflammation by signaling via T cell TNFR2.

TNF, produced largely by T and innate immune cells, is potently proinflammatory, as are cytokines such as IFN-γ and IL-17 produced by Th1 and Th17 cells, respectively. Here, we asked if TNF is upstream of Th skewing toward inflammatory phenotypes. Exposure of mouse CD4+ T cells to TNF and TGF-ß generated Th17 cells that express low levels of IL-17 (ROR-γt+IL-17lo) and high levels of inflammatory markers independently of IL-6 and STAT3. This was mediated by the nondeath TNF receptor TNFR2, which also contributed to the generation of inflammatory Th1 cells. Single-cell RNA sequencing of central nervous system-infiltrating CD4+ T cells in mouse experimental autoimmune encephalomyelitis (EAE) found an inflammatory gene expression profile similar to cerebrospinal fluid-infiltrating CD4+ T cells from patients with multiple sclerosis. Notably, TNFR2-deficient CD4+ T cells produced fewer inflammatory mediators and were less pathogenic in EAE and colitis. IL-1ß, a Th17-skewing cytokine, induced TNF and proinflammatory granulocyte-macrophage colony-stimulating factor (GM-CSF) in T cells, which was inhibited by disruption of TNFR2 signaling, demonstrating IL-1ß can function indirectly via the production of TNF. Thus, TNF is not just an effector but also an initiator of inflammatory Th differentiation.

3DVizSNP: a tool for rapidly visualizing missense mutations identified in high throughput experiments in iCn3D.

BACKGROUND: High throughput experiments in cancer and other areas of genomic research identify large numbers of sequence variants that need to be evaluated for phenotypic impact. While many tools exist to score the likely impact of single nucleotide polymorphisms (SNPs) based on sequence alone, the three-dimensional structural environment is essential for understanding the biological impact of a nonsynonymous mutation. RESULTS: We present a program, 3DVizSNP, that enables the rapid visualization of nonsynonymous missense mutations extracted from a variant caller format file using the web-based iCn3D visualization platform. The program, written in Python, leverages REST APIs and can be run locally without installing any other software or databases, or from a webserver hosted by the National Cancer Institute. It automatically selects the appropriate experimental structure from the Protein Data Bank, if available, or the predicted structure from the AlphaFold database, enabling users to rapidly screen SNPs based on their local structural environment. 3DVizSNP leverages iCn3D annotations and its structural analysis functions to assess changes in structural contacts associated with mutations. CONCLUSIONS: This tool enables researchers to efficiently make use of 3D structural information to prioritize mutations for further computational and experimental impact assessment. The program is available as a webserver at https://analysistools.cancer.gov/3dvizsnp or as a standalone python program at https://github.com/CBIIT-CGBB/3DVizSNP .

gcMECM: graph clustering of mutual exclusivity of cancer mutations.

BACKGROUND: Next-generation sequencing platforms allow us to sequence millions of small fragments of DNA simultaneously, revolutionizing cancer research. Sequence analysis has revealed that cancer driver genes operate across multiple intricate pathways and networks with mutations often occurring in a mutually exclusive pattern. Currently, low-frequency mutations are understudied as cancer-relevant genes, especially in the context of networks. RESULTS: Here we describe a tool, gcMECM, that enables us to visualize the functionality of mutually exclusive genes in the subnetworks derived from mutation associations, gene-gene interactions, and graph clustering. These subnetworks have revealed crucial biological components in the canonical pathway, especially those mutated at low frequency. Examining the subnetwork, and not just the impact of a single gene, significantly increases the statistical power of clinical analysis and enables us to build models to better predict how and why cancer develops. CONCLUSIONS: gcMECM uses a computationally efficient and scalable algorithm to identify subnetworks in a canonical pathway with mutually exclusive mutation patterns and distinct biological functions.

Molecular Profiling of Exceptional Responders to Cancer Therapy.

BACKGROUND: The vast majority of metastatic cancers cannot be cured. Palliative treatment may relieve disease symptoms by stopping or slowing cancer growth and may prolong patients' lives, but almost all patients will inevitably develop disease progression after initial response. However, for reasons that are not fully understood, a very few patients will have extraordinary durable responses to standard anticancer treatments. MATERIALS AND METHODS: We analyzed exceptional responders treated at Fox Chase Cancer Center between September 2009 and November 2017. An exceptional response was defined as a complete response lasting more than 1 year or a partial response or stable disease for more than 2 years. Tumor samples were analyzed using an Ambry Genetics test kit with a 142-gene panel. Messenger RNA expression was evaluated using NanoString's nCounter PanCancer Pathways Panel and Immune Profiling Panel and compared with matched controls for gender, age, and cancer type. RESULTS: Twenty-six exceptional responders with metastatic bladder, kidney, breast, lung, ovarian, uterine, and colon cancers were enrolled. Mutations were identified in 45 genes. The most common mutation was an EPHA5 nonsynonymous mutation detected in 87.5% of patients. Mutations in DNA damage repair pathway genes were also frequent, suggesting increased genome instability. We also found varying expression of 73 genes in the Pathways panel and 85 genes in the Immune Profiling panel, many of them responsible for improvement in tumor recognition and antitumor immune response. CONCLUSIONS: The genomic instability detected in our exceptional responders, plus treatment with DNA damage compounds combined with favorable anticancer immunity, may have contributed to exceptional responses to standard anticancer therapies in the patients studied. IMPLICATIONS FOR PRACTICE: With recent advances in the treatment of cancer, there is increased emphasis on the importance of identifying molecular markers to predict treatment outcomes, thereby allowing precision oncology. In this study, it was hypothesized that there is a "specific biologic signature" in the biology of the cancer in long-term survivors that allows sensitivity to systemic therapy and durability of response. Results showed that DNA damage repair pathway alterations, combined with favorable anticancer immunity, may have contributed to exceptional responses. It is very likely that an in-depth examination of outlier responses will become a standard component of drug development in the future.

Landscape of Germline Genetic Variants in AGT, MGMT, and TP53 in Mexican Adult Patients with Astrocytoma.

Astrocytoma is the most common type of primary brain tumor. The risk factors for astrocytoma are poorly understood; however, germline genetic variants account for 25% of the risk of developing gliomas. In this study, we assessed the risk of astrocytoma associated with variants in AGT, known by its role in angiogenesis, TP53, a well-known tumor suppressor and the DNA repair gene MGMT in a Mexican population. A case-control study was performed in 49 adult Mexican patients with grade II-IV astrocytoma. Sequencing of exons and untranslated regions of AGT, MGMT, and TP53 from was carried in an Ion Torrent platform. Individuals with Mexican Ancestry from the 1000 Genomes Project were used as controls. Variants found in our cohort were then assessed in a The Cancer Genome Atlas astrocytoma pan-ethnic validation cohort. Variants rs1926723 located in AGT (OR 2.74, 1.40-5.36 95% CI), rs7896488 in MGMT (OR 3.43, 1.17-10.10 95% CI), and rs4968187 in TP53 (OR 2.48, 1.26-4.88 95% CI) were significantly associated with the risk of astrocytoma after multiple-testing correction. This is the first study where the AGT rs1926723 variant, TP53 rs4968187, and MGMT rs7896488 were found to be associated with the risk of developing an astrocytoma.

Comparing the performance of selected variant callers using synthetic data and genome segmentation.

BACKGROUND: High-throughput sequencing has rapidly become an essential part of precision cancer medicine. But validating results obtained from analyzing and interpreting genomic data remains a rate-limiting factor. The gold standard, of course, remains manual validation by expert panels, which is not without its weaknesses, namely high costs in both funding and time as well as the necessarily selective nature of manual validation. But it may be possible to develop more economical, complementary means of validation. In this study we employed four synthetic data sets (variants with known mutations spiked into specific genomic locations) of increasing complexity to assess the sensitivity, specificity, and balanced accuracy of five open-source variant callers: FreeBayes v1.0, VarDict v11.5.1, MuTect v1.1.7, MuTect2, and MuSE v1.0rc. FreeBayes, VarDict, and MuTect were run in bcbio-next gen, and the results were integrated into a single Ensemble call set. The known mutations provided a level of "ground truth" against which we evaluated variant-caller performance. We further facilitated the comparison and evaluation by segmenting the whole genome into 10,000,000 base-pair fragments which yielded 316 segments. RESULTS: Differences among the numbers of true positives were small among the callers, but the numbers of false positives varied much more when the tools were used to analyze sets one through three. Both FreeBayes and VarDict produced strikingly more false positives than did the others, although VarDict, somewhat paradoxically also produced the highest number of true positives. The Ensemble approach yielded results characterized by higher specificity and balanced accuracy and fewer false positives than did any of the five tools used alone. Sensitivity and specificity, however, declined for all five callers as the complexity of the data sets increased, but we did not uncover anything more than limited, weak correlations between caller performance and certain DNA structural features: gene density and guanine-cytosine content. Altogether, MuTect2 performed the best among the callers tested, followed by MuSE and MuTect. CONCLUSIONS: Spiking data sets with specific mutations -single-nucleotide variations (SNVs), single-nucleotide polymorphisms (SNPs), or structural variations (SVs) in this study-at known locations in the genome provides an effective and economical way to compare data analyzed by variant callers with ground truth. The method constitutes a viable alternative to the prolonged, expensive, and noncomprehensive assessment by expert panels. It should be further developed and refined, as should other comparatively "lightweight" methods of assessing accuracy. Given that the scientific community has not yet established gold standards for validating NGS-related technologies such as variant callers, developing multiple alternative means for verifying variant-caller accuracy will eventually lead to the establishment of higher-quality standards than could be achieved by prematurely limiting the range of innovative methods explored by members of the community.

Somatic Genomics and Clinical Features of Lung Adenocarcinoma: A Retrospective Study.

BACKGROUND: Lung adenocarcinoma (LUAD) is the most common histologic subtype of lung cancer and has a high risk of distant metastasis at every disease stage. We aimed to characterize the genomic landscape of LUAD and identify mutation signatures associated with tumor progression. METHODS AND FINDINGS: We performed an integrative genomic analysis, incorporating whole exome sequencing (WES), determination of DNA copy number and DNA methylation, and transcriptome sequencing for 101 LUAD samples from the Environment And Genetics in Lung cancer Etiology (EAGLE) study. We detected driver genes by testing whether the nonsynonymous mutation rate was significantly higher than the background mutation rate and replicated our findings in public datasets with 724 samples. We performed subclonality analysis for mutations based on mutant allele data and copy number alteration data. We also tested the association between mutation signatures and clinical outcomes, including distant metastasis, survival, and tumor grade. We identified and replicated two novel candidate driver genes, POU class 4 homeobox 2 (POU4F2) (mutated in 9 [8.9%] samples) and ZKSCAN1 (mutated in 6 [5.9%] samples), and characterized their major deleterious mutations. ZKSCAN1 was part of a mutually exclusive gene set that included the RTK/RAS/RAF pathway genes BRAF, EGFR, KRAS, MET, and NF1, indicating an important driver role for this gene. Moreover, we observed strong associations between methylation in specific genomic regions and somatic mutation patterns. In the tumor evolution analysis, four driver genes had a significantly lower fraction of subclonal mutations (FSM), including TP53 (p = 0.007), KEAP1 (p = 0.012), STK11 (p = 0.0076), and EGFR (p = 0.0078), suggesting a tumor initiation role for these genes. Subclonal mutations were significantly enriched in APOBEC-related signatures (p < 2.5×10-50). The total number of somatic mutations (p = 0.0039) and the fraction of transitions (p = 5.5×10-4) were associated with increased risk of distant metastasis. Our study's limitations include a small number of LUAD patients for subgroup analyses and a single-sample design for investigation of subclonality. CONCLUSIONS: These data provide a genomic characterization of LUAD pathogenesis and progression. The distinct clonal and subclonal mutation signatures suggest possible diverse carcinogenesis pathways for endogenous and exogenous exposures, and may serve as a foundation for more effective treatments for this lethal disease. LUAD's high heterogeneity emphasizes the need to further study this tumor type and to associate genomic findings with clinical outcomes.

Addition of MR imaging features and genetic biomarkers strengthens glioblastoma survival prediction in TCGA patients.

PURPOSE: The purpose of our study was to assess whether a model combining clinical factors, MR imaging features, and genomics would better predict overall survival of patients with glioblastoma (GBM) than either individual data type. METHODS: The study was conducted leveraging The Cancer Genome Atlas (TCGA) effort supported by the National Institutes of Health. Six neuroradiologists reviewed MRI images from The Cancer Imaging Archive (http://cancerimagingarchive.net) of 102 GBM patients using the VASARI scoring system. The patients' clinical and genetic data were obtained from the TCGA website (http://www.cancergenome.nih.gov/). Patient outcome was measured in terms of overall survival time. The association between different categories of biomarkers and survival was evaluated using Cox analysis. RESULTS: The features that were significantly associated with survival were: (1) clinical factors: chemotherapy; (2) imaging: proportion of tumor contrast enhancement on MRI; and (3) genomics: HRAS copy number variation. The combination of these three biomarkers resulted in an incremental increase in the strength of prediction of survival, with the model that included clinical, imaging, and genetic variables having the highest predictive accuracy (area under the curve 0.679±0.068, Akaike's information criterion 566.7, P<0.001). CONCLUSION: A combination of clinical factors, imaging features, and HRAS copy number variation best predicts survival of patients with GBM.

The ESCRT protein CHMP5 promotes T cell leukemia by controlling BRD4-p300-dependent transcription.

Oncogene activity rewires cellular transcription, creating new transcription networks to which cancer cells become addicted, by mechanisms that are still poorly understood. Using human and mouse models of T cell acute lymphoblastic leukemia (T-ALL), we identify an essential nuclear role for CHMP5, a cytoplasmic endosomal sorting complex required for transport (ESCRT) protein, in establishing and maintaining the T-ALL transcriptional program. Nuclear CHMP5 promoted the T-ALL gene program by augmenting recruitment of the co-activator BRD4 by the histone acetyl transferase p300 selectively at enhancers and super-enhancers, an interaction that potentiated H3K27 acetylation at these regulatory enhancers. Consequently, loss of CHMP5 diminished BRD4 occupancy at enhancers and super-enhancers and impaired RNA polymerase II pause release, which resulted in downregulation of key T-ALL genes, notably MYC. Reinforcing its importance in T-ALL pathogenesis, CHMP5 deficiency mitigated chemoresistance in human T-ALL cells and abrogated T-ALL induction by oncogenic NOTCH1 in vivo. Thus, the ESCRT protein CHMP5 is an essential positive regulator of the transcriptional machinery promoting T-ALL disease.

NCI Cancer Research Data Commons: Lessons Learned and Future State.

More than ever, scientific progress in cancer research hinges on our ability to combine datasets and extract meaningful interpretations to better understand diseases and ultimately inform the development of better treatments and diagnostic tools. To enable the successful sharing and use of big data, the NCI developed the Cancer Research Data Commons (CRDC), providing access to a large, comprehensive, and expanding collection of cancer data. The CRDC is a cloud-based data science infrastructure that eliminates the need for researchers to download and store large-scale datasets by allowing them to perform analysis where data reside. Over the past 10 years, the CRDC has made significant progress in providing access to data and tools along with training and outreach to support the cancer research community. In this review, we provide an overview of the history and the impact of the CRDC to date, lessons learned, and future plans to further promote data sharing, accessibility, interoperability, and reuse. See related articles by Brady et al., p. 1384, Wang et al., p. 1388, and Pot et al., p. 1396.

Corrigendum: Glioblastoma survival is associated with distinct proteomic alteration signatures post chemoirradiation in a large-scale proteomic panel.

[This corrects the article DOI: 10.3389/fonc.2023.1127645.].

Nitric oxide inhibits ten-eleven translocation DNA demethylases to regulate 5mC and 5hmC across the genome.

DNA methylation at cytosine bases of eukaryotic DNA (5-methylcytosine, 5mC) is a heritable epigenetic mark that can regulate gene expression in health and disease. Enzymes that metabolize 5mC have been well-characterized, yet the discovery of endogenously produced signaling molecules that regulate DNA methyl-modifying machinery have not been described. Herein, we report that the free radical signaling molecule nitric oxide (NO) can directly inhibit the Fe(II)/2-OG-dependent DNA demethylases ten-eleven translocation (TET) and human AlkB homolog 2 (ALKBH2). Physiologic NO concentrations reversibly inhibited TET and ALKBH2 demethylase activity by binding to the mononuclear non-heme iron atom which formed a dinitrosyliron complex (DNIC) preventing cosubstrates (2-OG and O2) from binding. In cancer cells treated with exogenous NO, or cells endogenously synthesizing NO, there was a global increase in 5mC and 5-hydroxymethylcytosine (5hmC) in DNA, the substrates for TET, that could not be attributed to increased DNA methyltransferase activity. 5mC was also elevated in NO-producing cell-line-derived mouse xenograft and patient-derived xenograft tumors. Genome-wide DNA methylome analysis of cells chronically treated with NO (10 days) demonstrated enrichment of 5mC and 5hmC at gene-regulatory loci which correlated to changes in the expression of NO-regulated tumor-associated genes. Regulation of DNA methylation is distinctly different from canonical NO signaling and represents a novel epigenetic role for NO.

Evaluation of somatic copy number variation detection by NGS technologies and bioinformatics tools on a hyper-diploid cancer genome.

BACKGROUND: Copy number variation (CNV) is a key genetic characteristic for cancer diagnostics and can be used as a biomarker for the selection of therapeutic treatments. Using data sets established in our previous study, we benchmark the performance of cancer CNV calling by six most recent and commonly used software tools on their detection accuracy, sensitivity, and reproducibility. In comparison to other orthogonal methods, such as microarray and Bionano, we also explore the consistency of CNV calling across different technologies on a challenging genome. RESULTS: While consistent results are observed for copy gain, loss, and loss of heterozygosity (LOH) calls across sequencing centers, CNV callers, and different technologies, variation of CNV calls are mostly affected by the determination of genome ploidy. Using consensus results from six CNV callers and confirmation from three orthogonal methods, we establish a high confident CNV call set for the reference cancer cell line (HCC1395). CONCLUSIONS: NGS technologies and current bioinformatics tools can offer reliable results for detection of copy gain, loss, and LOH. However, when working with a hyper-diploid genome, some software tools can call excessive copy gain or loss due to inaccurate assessment of genome ploidy. With performance matrices on various experimental conditions, this study raises awareness within the cancer research community for the selection of sequencing platforms, sample preparation, sequencing coverage, and the choice of CNV detection tools.

Proteogenomic analysis of enriched HGSOC tumor epithelium identifies prognostic signatures and therapeutic vulnerabilities.

We performed a deep proteogenomic analysis of bulk tumor and laser microdissection enriched tumor cell populations from high-grade serous ovarian cancer (HGSOC) tissue specimens spanning a broad spectrum of purity. We identified patients with longer progression-free survival had increased immune-related signatures and validated proteins correlating with tumor-infiltrating lymphocytes in 65 tumors from an independent cohort of HGSOC patients, as well as with overall survival in an additional 126 HGSOC patient cohort. We identified that homologous recombination deficient (HRD) tumors are enriched in pathways associated with metabolism and oxidative phosphorylation that we validated in independent patient cohorts. We further identified that polycomb complex protein BMI-1 is elevated in HR proficient (HRP) tumors, that elevated BMI-1 correlates with poor overall survival in HRP but not HRD HGSOC patients, and that HRP HGSOC cells are uniquely sensitive to BMI-1 inhibition.

Cohesin regulates alternative splicing.

Cohesin, a trimeric complex that establishes sister chromatid cohesion, has additional roles in chromatin organization and transcription. We report that among those roles is the regulation of alternative splicing through direct interactions and in situ colocalization with splicing factors. Degradation of cohesin results in marked changes in splicing, independent of its effects on transcription. Introduction of a single cohesin point mutation in embryonic stem cells alters splicing patterns, demonstrating causality. In primary human acute myeloid leukemia, mutations in cohesin are highly correlated with distinct patterns of alternative splicing. Cohesin also directly interacts with BRD4, another splicing regulator, to generate a pattern of splicing that is distinct from either factor alone, documenting their functional interaction. These findings identify a role for cohesin in regulating alternative splicing in both normal and leukemic cells and provide insights into the role of cohesin mutations in human disease.

Multi-omics Pathways Workflow (MOPAW): An Automated Multi-omics Workflow on the Cancer Genomics Cloud.

Introduction: In the era of big data, gene-set pathway analyses derived from multi-omics are exceptionally powerful. When preparing and analyzing high-dimensional multi-omics data, the installation process and programing skills required to use existing tools can be challenging. This is especially the case for those who are not familiar with coding. In addition, implementation with high performance computing solutions is required to run these tools efficiently. Methods: We introduce an automatic multi-omics pathway workflow, a point and click graphical user interface to Multivariate Single Sample Gene Set Analysis (MOGSA), hosted on the Cancer Genomics Cloud by Seven Bridges Genomics. This workflow leverages the combination of different tools to perform data preparation for each given data types, dimensionality reduction, and MOGSA pathway analysis. The Omics data includes copy number alteration, transcriptomics data, proteomics and phosphoproteomics data. We have also provided an additional workflow to help with downloading data from The Cancer Genome Atlas and Clinical Proteomic Tumor Analysis Consortium and preprocessing these data to be used for this multi-omics pathway workflow. Results: The main outputs of this workflow are the distinct pathways for subgroups of interest provided by users, which are displayed in heatmaps if identified. In addition to this, graphs and tables are provided to users for reviewing. Conclusion: Multi-omics Pathway Workflow requires no coding experience. Users can bring their own data or download and preprocess public datasets from The Cancer Genome Atlas and Clinical Proteomic Tumor Analysis Consortium using our additional workflow based on the samples of interest. Distinct overactivated or deactivated pathways for groups of interest can be found. This useful information is important in effective therapeutic targeting.

Whole organism profiling of the Timp gene family.

Tissue inhibitor of metalloproteinases (TIMPs/Timps) are an endogenous family of widely expressed matrisome-associated proteins that were initially identified as inhibitors of matrix metalloproteinase activity (Metzincin family proteases). Consequently, TIMPs are often considered simply as protease inhibitors by many investigators. However, an evolving list of new metalloproteinase-independent functions for TIMP family members suggests that this concept is outdated. These novel TIMP functions include direct agonism/antagonism of multiple transmembrane receptors, as well as functional interactions with matrisome targets. While the family was fully identified over two decades ago, there has yet to be an in-depth study describing the expression of TIMPs in normal tissues of adult mammals. An understanding of the tissues and cell-types that express TIMPs 1 through 4, in both normal and disease states are important to contextualize the growing functional capabilities of TIMP proteins, which are often dismissed as non-canonical. Using publicly available single cell RNA sequencing data from the Tabula Muris Consortium, we analyzed approximately 100,000 murine cells across eighteen tissues from non-diseased organs, representing seventy-three annotated cell types, to define the diversity in Timp gene expression across healthy tissues. We describe the unique expression profiles across tissues and organ-specific cell types that all four Timp genes display. Within annotated cell-types, we identify clear and discrete cluster-specific patterns of Timp expression, particularly in cells of stromal and endothelial origins. RNA in-situ hybridization across four organs expands on the scRNA sequencing analysis, revealing novel compartments associated with individual Timp expression. These analyses emphasize a need for specific studies investigating the functional significance of Timp expression in the identified tissues and cell sub-types. This understanding of the tissues, specific cell types and microenvironment conditions in which Timp genes are expressed adds important physiological context to the growing array of novel functions for TIMP proteins.

Glioblastoma survival is associated with distinct proteomic alteration signatures post chemoirradiation in a large-scale proteomic panel.

Background: Glioblastomas (GBM) are rapidly progressive, nearly uniformly fatal brain tumors. Proteomic analysis represents an opportunity for noninvasive GBM classification and biological understanding of treatment response. Purpose: We analyzed differential proteomic expression pre vs. post completion of concurrent chemoirradiation (CRT) in patient serum samples to explore proteomic alterations and classify GBM by integrating clinical and proteomic parameters. Materials and methods: 82 patients with GBM were clinically annotated and serum samples obtained pre- and post-CRT. Serum samples were then screened using the aptamer-based SOMAScan® proteomic assay. Significant traits from uni- and multivariate Cox models for overall survival (OS) were designated independent prognostic factors and principal component analysis (PCA) was carried out. Differential expression of protein signals was calculated using paired t-tests, with KOBAS used to identify associated KEGG pathways. GSEA pre-ranked analysis was employed on the overall list of differentially expressed proteins (DEPs) against the MSigDB Hallmark, GO Biological Process, and Reactome databases with weighted gene correlation network analysis (WGCNA) and Enrichr used to validate pathway hits internally. Results: 3 clinical clusters of patients with differential survival were identified. 389 significantly DEPs pre vs. post-treatment were identified, including 284 upregulated and 105 downregulated, representing several pathways relevant to cancer metabolism and progression. The lowest survival group (median OS 13.2 months) was associated with DEPs affiliated with proliferative pathways and exhibiting distinct oppositional response including with respect to radiation therapy related pathways, as compared to better-performing groups (intermediate, median OS 22.4 months; highest, median OS 28.7 months). Opposite signaling patterns across multiple analyses in several pathways (notably fatty acid metabolism, NOTCH, TNFα via NF-κB, Myc target V1 signaling, UV response, unfolded protein response, peroxisome, and interferon response) were distinct between clinical survival groups and supported by WGCNA. 23 proteins were statistically signficant for OS with 5 (NETO2, CST7, SEMA6D, CBLN4, NPS) supported by KM. Conclusion: Distinct proteomic alterations with hallmarks of cancer, including progression, resistance, stemness, and invasion, were identified in serum samples obtained from GBM patients pre vs. post CRT and corresponded with clinical survival. The proteome can potentially be employed for glioma classification and biological interrogation of cancer pathways.

Abundant binary promoter switches in lineage-determining transcription factors indicate a digital component of cell fate determination.

Previous studies of the murine Ly49 and human KIR gene clusters implicated competing sense and antisense promoters in the control of variegated gene expression. In the current study, an examination of transcription factor genes defines an abundance of convergent and divergent sense/antisense promoter pairs, suggesting that competing promoters may control cell fate determination. Differentiation of CD34+ hematopoietic progenitors in vitro shows that cells with GATA1 antisense transcription have enhanced GATA2 transcription and a mast cell phenotype, whereas cells with GATA2 antisense transcription have increased GATA1 transcripts and an erythroblast phenotype. Detailed analyses of the AHR and RORC genes demonstrate the ability of competing promoters to act as binary switches and the association of antisense transcription with an immature/progenitor cell phenotype. These data indicate that alternative cell fates generated by promoter competition in lineage-determining transcription factors contribute to the programming of cell differentiation.