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DUSP28 is a novel biomarker responsible for aggravating malignancy via the autocrine signaling pathways in metastatic pancreatic cancer

Pancreatic cancer remains one of the most dangerous cancers with a grave prognosis. We previously reported that pancreatic cancer cells can secrete dual specificity phosphatise 28 (DUSP28) to the cultured medium. However, its biological function is poorly understood. Here, we have identified the function of DUSP28 in human metastatic pancreatic cancer. Treatment with recombinant DUSP28 (rDUSP28) significantly increased the migration, invasion, and viability of metastatic pancreatic cancer cells through the activation of CREB, AKT, and ERK1/2 signaling pathways. Furthermore, rDUSP28 acted as an oncogenic reagent through the interaction with integrin 1 in metastatic pancreatic cancer cells. In addition, rDUSP28 induced pro-angiogenic effects in human umbilical vein endothelial cells (HUVECs). Administration of rDUSP28 also produced tumor growth in vivo. Notably, sDUSP28 can easily be detected by immunoassay. The results establish the rationale for sDUSP28 as a promising therapeutic target and biomarker for metastatic pancreatic cancer patients.

cancer biology

Long Range Force Transmission in Fibrous Matrices Enabled by Tension-Driven Alignment of Fibers

Cells can sense and respond to mechanical signals over relatively long distances across fibrous extracellular matrices. Recently proposed models suggest that long-range force transmission can be attributed to the nonlinear elasticity or fibrous nature of collagen matrices, yet the mechanism whereby fibers align remains unknown. Moreover, cell shape and anisotropy of cellular contraction are not considered in existing models, although recent experiments have shown that they play crucial roles. Here, we explore all of the key factors that influence long-range force transmission in cell-populated collagen matrices: alignment of collagen fibers, responses to applied force, strain stiffening properties of the aligned fibers, aspect ratios of the cells, and the polarization of cellular contraction. A constitutive law accounting for mechanically-driven collagen fiber reorientation is proposed. We systematically investigate the range of collagen fiber alignment using both finite element simulations and analytical calculations. Our results show that tension-driven collagen fiber alignment plays a crucial role in force transmission. Small critical stretch for fiber alignment, large fiber stiffness and fiber strain-hardening behavior enable long-range interaction. Furthermore, the range of collagen fiber alignment for elliptical cells with polarized contraction is much larger than that for spherical cells with diagonal contraction. A phase diagram showing the range of force transmission as a function of cell shape and polarization and matrix properties is presented. Our results are in good agreement with recent experiments, and highlight the factors that influence long-range force transmission, in particular tension-driven alignment of fibers. Our work has important relevance to biological processes including development, cancer metastasis and wound healing, suggesting conditions whereby cells communicate over long distances.

Bioengineering

Acidic pH is a Metabolic Switch for 2‐Hydroxyglutarate Generation and Signaling

2-hydroxyglutarate (2-HG) is an important epigenetic regulator, with potential roles in cancer and stem cell biology. The D (R) enantiomer (D-2-HG) is an oncometabolite generated from ketoglutarate (-KG) by mutant isocitrate dehydrogenase (ICDH), while L (S) 2-HG is generated by lactate dehydrogenase (LDH) and malate dehydrogenase (MDH) in response to hypoxia. Since acidic pH is a common feature of hypoxia, as well as tumor and stem cell microenvironments, we hypothesized that pH may regulate cellular 2-HG levels. Herein we report that cytosolic acidification under normoxia moderately elevated 2-HG in cells, and boosting endogenous substrate -KG levels further stimulated this elevation. Studies with isolated LDH-1 and MDH-2 revealed that generation of 2-HG by both enzymes was stimulated several-fold at acidic pH, relative to normal physiologic pH. In addition, acidic pH was found to inhibit the activity of the mitochondrial L-2-HG removal enzyme L-2-HG dehydrogenase, and to stimulate the reverse reaction of ICDH (carboxylation of KG to isocitrate). Furthermore, since acidic pH is known to stabilize hypoxia-inducible factor (HIF), and 2-HG is a known inhibitor of HIF prolyl hydroxylases, we hypothesized that 2-HG may be required for acid-induced HIF stabilization. Accordingly, cells stably over-expressing L-2HGDH exhibited a blunted HIF response to acid. Together these results suggest that acidosis is an important and previously overlooked regulator of 2-HG accumulation and other oncometabolic events, with implications for HIF signaling.

Biochemistry

Network Curvature as a Hallmark of Brain Structural Connectivity

Studies show that while brain networks are remarkably robust to a variety of adverse events, such as injuries and lesions due to accidents or disease, they may be fragile when the disturbance takes place in specific locations. This seems to be the case for diseases in which accumulated changes in network topology dramatically affect certain sensitive areas. To this end, previous attempts have been made to quantify robustness and fragility of brain functionality in two broadly defined ways: (i) utilizing model-based techniques to predict lesion effects, and (ii) studying empirical effects from brain lesions due to injury or disease. Both directions aim at assessing functional connectivity changes resulting from structural network variations. In the present work, we follow a more geometric viewpoint that is based on a notion of curvature of networks, the so-called Ollivier-Ricci curvature. A similar approach has been used in recent studies to quantify financial market robustness as well as to differentiate biological networks corresponding to cancer cells from normal cells. The same notion of curvature, defined at the node level for brain networks obtained from MRI data, may help identify and characterize the effects of diseases on specific brain regions. In the present paper, we apply the Ollivier-Ricci curvature to brain structural networks to: i) Demonstrate its unique ability to identify robust (or fragile) brain regions in healthy subjects. We compare our results to previously published work which identified a unique set of regions (called structural core) of the human cerebral cortex. This novel characterization of brain networks, complementary to measures such as degree, strength, clustering or efficiency, may be particularly useful to detect and monitor candidate areas for targeting by surgery (e.g. deep brain stimulation) or pharmaco-therapeutic agents; ii) Illustrate the power our curvature-derived measures to track changes in brain connectivity with healthy development/aging and; iii) Detect changes in brain structural connectivity in people with Autism Spectrum Disorders (ASD) which are in agreement with previous morphometric MRI studies.

neuroscience

ModulOmics: Integrating Multi-Omics Data to Identify Cancer Driver Modules

The identification of molecular pathways driving cancer progression is a fundamental unsolved problem in tumorigenesis, which can substantially further our understanding of cancer mechanisms and inform the development of targeted therapies. Most current approaches to address this problem use primarily somatic mutations, not fully exploiting additional layers of biological information. Here, we describe ModulOmics, a method to de novo identify cancer driver pathways, or modules, by integrating multiple data types (protein-protein interactions, mutual exclusivity of mutations or copy number alterations, transcriptional co-regulation, and RNA co-expression) into a single probabilistic model. To efficiently search the exponential space of candidate modules, ModulOmics employs a two-step optimization procedure that combines integer linear programming with stochastic search. Across several cancer types, ModulOmics identifies highly functionally connected modules enriched with cancer driver genes, outperforming state-of-the-art methods. For breast cancer subtypes, the inferred modules recapitulate known molecular mechanisms and suggest novel subtype-specific functionalities. These findings are supported by an independent patient cohort, as well as independent proteomic and phosphoproteomic datasets.

cancer biology

Multi-omic analysis of urothelial cancer patients treated with PD-L1 blockade demonstrates the contribution of both systemic and somatic factors to the biology of response and resistance

BackgroundInhibition of programmed death-ligand one (PD-L1) with atezolizumab can induce durable clinical benefit (DCB) in patients with metastatic urothelial cancers, including complete remissions in patients with chemotherapy refractory disease. Although mutation load and PD-L1 immune cell (IC) staining have been associated with response, they lack sufficient sensitivity and specificity for clinical use. Thus, there is a need to evaluate the peripheral blood immune environment and to conduct detailed analyses of mutation load, predicted neoantigens and immune cellular infiltration in tumors to enhance our understanding of the biologic underpinnings of response and resistance.\n\nMethods and FindingsThe goals of this study were to (1) evaluate the association of mutation load and predicted neoantigen load with therapeutic benefit, and (2) determine whether intratumoral and peripheral blood T cell receptor (TCR) clonality inform clinical outcomes in urothelial carcinoma treated with atezolizumab. We hypothesized that an elevated mutation load in combination with T cell clonal dominance among intratumoral lymphocytes prior to treatment or among peripheral T cells after treatment would be associated with effective tumor control upon treatment with anti-PD-L1 therapy. We performed whole exome sequencing (WES), RNA sequencing (RNA-seq), and T cell receptor sequencing (TCR-seq) of pre-treatment tumor samples as well as TCR sequencing of matched, serially collected peripheral blood collected before and after treatment with atezolizumab. These parameters were assessed for correlation with DCB (defined as progression free survival (PFS) > 6 months), PFS, and overall survival (OS), both alone and in the context of clinical and intratumoral parameters known to be predictive of survival in this disease state.\n\nPatients with DCB displayed a higher proportion of tumor infiltrating T lymphocytes (TIL) (n=24, Mann-Whitney p=0.047). Pre-treatment peripheral blood TCR clonality below the median was associated with improved PFS (n=29, log-rank p=0.048) and OS (n=29, log-rank p=0.011). Patients with DCB also demonstrated more substantial expansion of tumor-associated TCR clones in the peripheral blood 3 weeks after starting treatment (n=22, Mann-Whitney p=0.022). The combination of high pre-treatment peripheral blood TCR clonality with elevated PD-L1 IC staining in tumor tissue was strongly associated with poor clinical outcomes (n=10, HR (mean)=89.88, HR (median)=23.41, 95% CI (2.43, 506.94), p(HR>1)=0.0014). Marked variations in mutation loads were seen with different somatic variant calling methodologies, which in turn impacted associations with clinical outcomes. Missense mutation load, predicted neoantigen load and expressed neoantigen load did not demonstrate significant association with DCB (n=25, Mann-Whitney p=0.22, n=25, Mann-Whitney p=0.55, and n=25, Mann-Whitney p=0.29 respectively). Instead, we found evidence of time-varying effects of somatic mutation load on progression-free survival in this cohort (n=25, p=0.044). A limitation of our study is its small sample size (n=29), a subset of the patients treated on IMvigor 210 (NCT02108652). Given the number of exploratory analyses performed, we intend for these results to be hypothesis-generating.\n\nConclusionsThese results demonstrate the complex nature of immune response to checkpoint blockade and the compelling need for greater interrogation and data integration of both host and tumor factors. Incorporating these variables in prospective studies will facilitate identification and treatment of resistant patients.

cancer biology

Enrichment of short mutant cell-free DNA fragments enhanced detection of pancreatic cancer

Analysis of cell-free DNA (cfDNA) is promising for broad applications in clinical settings, but with significant bias towards late-stage cancers. Although recent studies have discussed the diverse and degraded nature of cfDNA molecules, little is known about its impact on the practice of cfDNA analysis. Here we reported a new targeted sequencing by combining single-strand library preparation and target capture (SLHC-seq). By applying the new technology in plasma cfDNA from pancreatic cancer patients, we achieved higher efficiency in analysis of mutations than previously reported using other detection assays. SLHC-seq rescued short or damaged cfDNA fragments along to increase the sensitivity and accuracy of circulating-tumor DNA detection. Most importantly, we found that the small mutant fragments are prevalent in early-stage patients, which provides strong evidence for fragment size-based early detection of pancreatic cancer. Collectively, the new pipeline enhanced our understanding of cfDNA biology and provide new insights for liquid biopsy.

cancer biology

GDSCTools for Mining Pharmacogenomic Interactions in Cancer

MotivationLarge pharmacogenomic screenings integrate heterogeneous cancer genomic data sets as well as anti-cancer drug responses on thousand human cancer cell lines. Mining this data to identify new therapies for cancer sub-populations would benefit from common data structures, modular computational biology tools and user-friendly interfaces.\n\nResultsWe have developed GDSCTools: a software aimed at the identification of clinically relevant genomic markers of drug response. The Genomics of Drug Sensitivity in Cancer (GDSC) database (www.cancerRxgene.org) integrates heterogeneous cancer genomic data sets as well as anti-cancer drug responses on a thousand cancer cell lines. Including statistical tools (ANOVA) and predictive methods (Elastic Net), as well as common data structures, GDSCTools allows users to reproduce published results from GDSC, to analyse their own drug responses or genomic datasets, and to implement new analytical methods.\n\nContactthomas.cokelaer@pasteur.fr

cancer biology

Integrated Genomic Analysis of Hypoxia Genes across Cancer Types Identifies Significant Associations with Cancer Hallmarks

Hypoxia is a generic micro-environmental factor in most solid tumours. While most published literature focused on in vitro or single tumour type investigations, we carried out the first multi-omics pan cancer analysis of hypoxia with the aim of gaining a comprehensive understanding of its implication in tumour biology. A core set of 52 mRNAs were curated based on experimentally validated hypoxia gene sets from multiple cancer types. The 52 mRNAs collectively stratified high- and low-hypoxia tumours from The Cancer Genome Atlas (TCGA) database (9698 primary tumours) in each of the 32 cancer types available. High- hypoxia tumours had high expression of not only mRNA but also protein and microRNA markers of hypoxia. In a pan cancer transcriptomic analysis, [≥]70% of the known cancer hallmark pathways were enriched in high-hypoxia tumours, most notably epithelial mesenchymal transition potential, proliferation (G2M checkpoint, E2F targets, MYC targets) and immunology response. In a multi-omics analysis, gene expression-determined high- hypoxia tumours had a higher non-silent mutation rate, DNA damage repair deficiency and leukocyte infiltration. The associations largely remained significant after correcting for confounding factors, showing a profound impact of hypoxia in tumour evolution across cancer types. High-hypoxia tumours determined using the core gene set had a poor prognosis in 16/32 cancer types, with statistical significances remaining in five after adjusting for tumour stage and omics biomarkers. In summary, this first comprehensive in vivo map of hypoxia in cancers highlights the importance of this micro-environmental factor in driving tumour progression.

cancer biology

Identification of microRNA clusters cooperatively acting on Epithelial to Mesenchymal Transition in Triple Negative Breast Cancer

MicroRNAs play important roles in many biological processes. Their aberrant expression can have oncogenic or tumor suppressor function directly participating to carcinogenesis, malignant transformation, invasiveness and metastasis. Indeed, miRNA profiles can distinguish not only between normal and cancerous tissue but they can also successfully classify different subtypes of a particular cancer. Here, we focus on a particular class of transcripts encoding polycistronic miRNA genes that yields multiple miRNA components. We describe clustered MiRNA Master Regulator Analysis (ClustMMRA), a fully redesigned release of the MMRA computational pipeline (MiRNA Master Regulator Analysis), developed to search for clustered miRNAs potentially driving cancer molecular subtyping. Genomically clustered miRNAs are frequently co-expressed to target different components of pro-tumorigenic signalling pathways. By applying ClustMMRA to breast cancer patient data, we identified key miRNA clusters driving the phenotype of different tumor subgroups. The pipeline was applied to two independent breast cancer datasets, providing statistically concordant results between the two analysis. We validated in cell lines the miR-199/miR-214 as a novel cluster of miRNAs promoting the triple negative subtype phenotype through its control of proliferation and EMT.

systems biology

Reconstructing phylogenies of metastatic cancers

Reconstructing the evolutionary history of metastases is critical for understanding their basic biological principles and has profound clinical implications1-3. Genome-wide sequencing data has enabled modern phylogenomic methods to accurately dissect subclones and their phylogenies from noisy and impure bulk tumor samples at unprecedented depth4-7. However, existing methods are not designed to infer metastatic seeding patterns. We have developed a tool, called Treeomics, that utilizes Bayesian inference and Integer Linear Programming to reconstruct the phylogeny of metastases. Treeomics allowed us to infer comprehensive seeding patterns for pancreatic8, ovarian9, and prostate cancers10,11. Moreover, Treeomics correctly disambiguated true seeding patterns from sequencing artifacts; 7% of variants were misclassified by conventional statistical methods. These artifacts can skew phylogenies by creating illusory tumor heterogeneity among distinct samples. Last, we performed in silico benchmarking on simulated tumor phylogenies across a wide range of sample purities (30-90%) and sequencing depths (50-800x) to demonstrate the high accuracy of Treeomics compared to existing methods.

Cancer Biology

Epigenetic analysis identifies factors driving racial disparity in prostate cancer

Prostate cancer (PCa) is the second most leading cause of death in men worldwide. African American men (AA) represent more aggressive form of PCa as compared to Caucasian (CA) counterparts. Evidence suggests that genetic and other biological factors could account for the observed racial disparity. We analyzed the cancer genome atlas (TCGA) dataset (2015) for existing epigenetic variation in AA and CA prostate cancer patients, and carried out Reduced Representation Bisulphite Sequencing (RRBS) analysis to identify global methylation changes in AA and CA prostate cancer patients. The TCGA dataset analysis revealed that the epigenetic heterogeneity could be categorized into 4 classes, where AA associated primarily to methylation cluster 1 (p value 0.048), and CA associated to methylation cluster 3 (p value 0.000146). We identified enrichment of Wnt signaling genes in both AA and CA, however they were differentially activated in terms of canonical and non-canonical Wnt signaling pathway activation. This was further validated using the GenomeDx expression data. Our RRBS data also suggested distinct methylation patterns in AA compared to CA, and in part validated our TCGA findings. Survival analysis using the RRBS data suggested hypomethylated genes to be significantly associated with recurrence of prostate cancer in CA (p=6.07x10-6) as well as in AA (p=0.0077). Overall, the observed racial disparity in the molecular mechanism involved in the pathogenesis of prostate cancer suggests diverse heterogeneity that potentially could affect survival and should be considered during prognosis and treatment.

cancer biology

Infer related genes from large gene expression dataset with embedding

Artificial neural networks (ANNs) have been utilized for classification and prediction task with remarkable accuracy. However, its implications for unsupervised data mining using molecular data is under-explored. We adopted a method of unsupervised ANN, namely word embedding, to extract biologically relevant information from TCGA gene expression dataset. Ground truth relationship, such as cancer types of the input sample and semantic meaning of genes, were showed to retain in the resulting entity matrices. We also demonstrated the interpretability and usage of these matrices in shortlisting candidates from a long gene list. This method is feasible to mine big volume of biological data, and would be a valuable tool to discover novel knowledge from omics data. The resulting embedding matrices mined from TCGA gene expression data are interactively explorable online (http://bit.ly/tcga-embedding-cancer) and could serve as an informative reference.

cancer biology

A multi-center study on factors influencing the reproducibility of in vitro drug-response studies

Evidence that some influential biomedical results cannot be repeated has increased interest in practices that generate data meeting findable, accessible, interoperable and reproducible (FAIR) standards. Multiple papers have identified examples of irreproducibility, but practical steps for increasing reproducibility have not been widely studied. Here, seven research centers in the NIH LINCS Program Consortium investigate the reproducibility of a prototypical perturbational assay: quantifying the responsiveness of cultured cells to anti-cancer drugs. Such assays are important for drug development, studying cell biology, and patient stratification. While many experimental and computational factors have an impact on intra- and inter-center reproducibility, the factors most difficult to identify and correct are those with a strong dependency on biological context. These factors often vary in magnitude with the drug being analyzed and with growth conditions. We provide ways of identifying such context-sensitive factors, thereby advancing the conceptual and practical basis for greater experimental reproducibility.

cancer biology

O-GlcNAc modification of oncogenic transcription factor Sox2 promotes protein stability and regulates self-renewal in pancreatic cancer

Pancreatic cancer is among the 3rd leading cause of cancer related deaths in the United States along with a 5-year survival rate of 7%. The aggressive biology of the disease is responsible for such dismal outcome and is manifested by an increase in self-renewal capacity of the cancer cells, which leads to an increased rate of tumor-recurrence, contributing to poor prognosis. Transcription factor SOX2 maintains a critical balance between differentiation and \"stemness\" and is thus tightly regulated within a cell. In cancer, SOX2 is aberrantly \"turned-on\" leading to activation of self-renewal pathways in cancer. Regulation of Sox2 in cancer is poorly understood. In the current study, we show for the first time that in pancreatic cancer, Sox2 is modified by addition of O-GlcNAc moiety, catalyzed by OGT (O-GlcNAc Transferase) at S246. This activates Sox2 transcriptional activity by stabilizing the protein in the nucleus. A CRISPR-OGT knockout in pancreatic cancer cell line S2VP10 resulted in a delayed tumor initiation. We further showed that mutation of this site (S246A) prevents the modification of Sox2 and its downstream activity. Our study also demonstrated that targeting OGT in vivo with a small molecule inhibitor OSMI, results in decreased tumor burden, delayed tumor progression and a decreased expression of SOX2 in pancreatic cancer cells. Our study highlights for the first time that that the O-GlcNAc transferase dependent SOX2 glycosylation has a profound effect on the transcriptional activity of SOX2 and is instrumental in determining self-renewal in pancreatic cancer.\n\nSignificanceOur study highlights for the first time that that the O-GlcNAc transferase dependent SOX2 glycosylation determines self-renewal in pancreatic cancer which is responsible for tumor initiation.

cancer biology

Homeostasis Back and Forth: An Eco-Evolutionary Perspective of Cancer

The role of genetic mutations in cancer is indisputable: they are a key source of tumor heterogeneity and drive its evolution to malignancy. But the success of these new mutant cells relies on their ability to disrupt the homeostasis that characterizes healthy tissues. Mutated clones unable to break free from intrinsic and extrinsic homeostatic controls will fail to establish a tumor. Here we will discuss, through the lens of mathematical and computational modeling, why an evolutionary view of cancer needs to be complemented by an ecological perspective in order to understand why cancer cells invade and subsequently transform their environment during progression. Importantly, this ecological perspective needs to account for tissue homeostasis in the organs that tumors invade, since they perturb the normal regulatory dynamics of these tissues, often co-opting them for its own gain. Furthermore, given our current lack of success in treating advanced metastatic cancers through tumor centric therapeutic strategies, we propose that treatments that aim to restore homeostasis could become a promising venue of clinical research. This eco-evolutionary view of cancer requires mechanistic mathematical models in order to both integrate clinical with biological data from different scales but also to detangle the dynamic feedback between the tumor and its environment. Importantly, for these models to be useful, they need to embrace a higher degree of complexity than many mathematical modelers are traditionally comfortable with.

cancer biology

Transposable Element Expression Predicts Prognosis in Acute Myeloid Leukemia

Over half of the human genome is comprised of transposable elements (TE). TE have been implicated in cancer pathogenesis. Despite large-scale studies of the transcriptome in cancer, a comprehensive look at TE expression investigating its relationship to various mutations and its role in predicting prognosis has not been performed. We characterized TE expression in 178 adult acute myeloid leukemia (AML) patients using transcriptome data from The Cancer Genome Atlas (TCGA). We identified significant dysregulation of TE, with distinct patterns of TE expression correlated to specific mutations and distinct coding gene networks. TP53 mutated AML had a unique TE expression signature and was associated with significantly suppressed expression of TE and various classes of non-coding RNA. We identified 17 candidate prognostic TE transcripts that can classify AML subtypes as either high or low risk. These 17 TE were able to further sub-stratify low risk AML (based on mutational profile and coding gene expression) into favorable and unfavorable prognostic categories. The expression signature of the 17 TE was able to predict prognosis in an independent cohort of 284 pediatric AML patients, and was also able to predict time to relapse in an independent dataset of relapsed adult cases. This first comprehensive study of TE expression in AML demonstrates that TE expression can be used as a biomarker for predicting prognosis in AML and also provides novel insights into the biology of TP53 mutated AML. Studies characterizing its role in other cancers are warranted.

cancer biology

Application of Atlas of Cancer Signalling Network in pre-clinical studies

Initiation and progression of cancer involve multiple molecular mechanisms. The knowledge on these mechanisms is expanding and should be converted into guidelines for tackling the disease. We discuss here formalization of biological knowledge into a comprehensive resource Atlas of Cancer Signalling Network (ACSN) and Google Maps-based tool NaviCell that supports map navigation. The application of maps for omics data visualisation in the context of signalling maps is possible using NaviCell Web Service module and NaviCom tool for generation of network-based molecular portraits of cancer using multi-level omics data. We review how these resources and tools are applied for cancer pre-clinical studies among others for rationalizing synergistic effect of drugs and designing complex disease stage-specific druggable interventions following structural analysis of the maps together with omics data. Modules and maps of ACSN as signatures of biological functions, can help in cancer data analysis and interpretation. In addition, they can also be used to find association between perturbations in particular molecular mechanisms to the risk of a specific cancer type development. These approaches and beyond help to study interplay between molecular mechanisms of cancer, deciphering how gene interactions govern hallmarks of cancer in specific context. We discuss a perspective to develop a flexible methodology and a pipeline to enable systematic omics data analysis in the context of signalling network maps, for stratifying patients and suggesting interventions points and drug repositioning in cancer and other human diseases.

systems biology