Mutant p53 drives pancreatic cancer metastasis through cell-autonomous PDGF receptor ß signaling.

Missense mutations in the p53 tumor suppressor inactivate its antiproliferative properties but can also promote metastasis through a gain-of-function activity. We show that sustained expression of mutant p53 is required to maintain the prometastatic phenotype of a murine model of pancreatic cancer, a highly metastatic disease that frequently displays p53 mutations. Transcriptional profiling and functional screening identified the platelet-derived growth factor receptor b (PDGFRb) as both necessary and sufficient to mediate these effects. Mutant p53 induced PDGFRb through a cell-autonomous mechanism involving inhibition of a p73/NF-Y complex that represses PDGFRb expression in p53-deficient, noninvasive cells. Blocking PDGFRb signaling by RNA interference or by small molecule inhibitors prevented pancreatic cancer cell invasion in vitro and metastasis formation in vivo. Finally, high PDGFRb expression correlates with poor disease-free survival in pancreatic, colon, and ovarian cancer patients, implicating PDGFRb as a prognostic marker and possible target for attenuating metastasis in p53 mutant tumors.

ReProMSig: an integrative platform for development and application of reproducible multivariable models for cancer prognosis supporting guideline-based transparent reporting.

Adequate reporting is essential for evaluating the performance and clinical utility of a prognostic prediction model. Previous studies indicated a prevalence of incomplete or suboptimal reporting in translational and clinical studies involving development of multivariable prediction models for prognosis, which limited the potential applications of these models. While reporting templates introduced by the established guidelines provide an invaluable framework for reporting prognostic studies uniformly, there is a widespread lack of qualified adherence, which may be due to miscellaneous challenges in manual reporting of extensive model details, especially in the era of precision medicine. Here, we present ReProMSig (Reproducible Prognosis Molecular Signature), a web-based integrative platform providing the analysis framework for development, validation and application of a multivariable prediction model for cancer prognosis, using clinicopathological features and/or molecular profiles. ReProMSig platform supports transparent reporting by presenting both methodology details and analysis results in a strictly structured reporting file, following the guideline checklist with minimal manual input needed. The generated reporting file can be published together with a developed prediction model, to allow thorough interrogation and external validation, as well as online application for prospective cases. We demonstrated the utilities of ReProMSig by development of prognostic molecular signatures for stage II and III colorectal cancer respectively, in comparison with a published signature reproduced by ReProMSig. Together, ReProMSig provides an integrated framework for development, evaluation and application of prognostic/predictive biomarkers for cancer in a more transparent and reproducible way, which would be a useful resource for health care professionals and biomedical researchers.

Dual roles of ß-arrestin 1 in mediating cell metabolism and proliferation in gastric cancer.

ß-Arrestin 1 (ARRB1) has been recognized as a multifunctional adaptor protein in the last decade, beyond its original role in desensitizing G protein-coupled receptor signaling. Here, we identify that ARRB1 plays essential roles in mediating gastric cancer (GC) cell metabolism and proliferation, by combining cohort analysis and functional investigation using patient-derived preclinical models. Overexpression of ARRB1 was associated with poor outcome of GC patients and knockdown of ARRB1 impaired cell proliferation both ex vivo and in vivo. Intriguingly, ARRB1 depicted diverse subcellular localizations during a passage of organoid cultures (7 d) to exert dual functions. Further analysis revealed that nuclear ARRB1 binds with transcription factor E2F1 triggering up-regulation of proliferative genes, while cytoplasmic ARRB1 modulates metabolic flux by binding with the pyruvate kinase M2 isoform (PKM2) and hindering PKM2 tetramerization, which reduces pyruvate kinase activity and leads to cellular metabolism shifts from oxidative phosphorylation to aerobic glycolysis. As ARRB1 localization was shown mostly in the cytoplasm in human GC samples, therapeutic potential of the ARRB1-PKM2 axis was tested, and we found tumor proliferation could be attenuated by the PKM2 activator DASA-58, especially in ARRB1high organoids. Together, the data in our study highlight a spatiotemporally dependent role of ARRB1 in mediating GC cell metabolism and proliferation and implies reactivating PKM2 may be a promising therapeutic strategy in a subset of GC patients.

Integrated Multi-Omics Landscape of Liver Metastases.

BACKGROUND & AIMS: Lack of thorough knowledge about the complicated immune microenvironment (IM) within a variety of liver metastases (LMs) leads to inappropriate treatment and unsatisfactory prognosis. We aimed to characterize IM subtypes and investigate potential mechanisms in LMs. METHODS: Mass cytometry was applied to characterize immune landscape of a primary liver cancers and liver metastases cohort. Transcriptomic and whole-exome sequencing were used to explore potential mechanisms across distinct IM subtypes. Single-cell transcriptomic sequencing, multiplex fluorescent immunohistochemistry, cell culture, mouse model, Western blot, quantitative polymerase chain reaction, and immunohistochemistry were used for validation. RESULTS: Five IM subtypes were revealed in 100 LMs and 50 primary liver cancers. Patients featured terminally exhausted (IM1) or rare T-cell-inflamed (IM2 and IM3) immune characteristics showed worse outcome. Increased intratumor heterogeneity, enriched somatic TP53, KRAS, APC, and PIK3CA mutations and hyperactivated hypoxia signaling accounted for the formation of vicious subtypes. SLC2A1 promoted immune suppression and desert via increasing proportion of Spp1+ macrophages and their inhibitory interactions with T cells in liver metastatic lesions. Furthermore, SLC2A1 promoted immune escape and LM through inducing regulatory T cells, including regulatory T cells and LAG3+CD4+ T cells in primary colorectal cancer. CONCLUSIONS: The study provided integrated multi-omics landscape of LM, uncovering potential mechanisms for vicious IM subtypes and confirming the roles of SLC2A1 in regulating tumor microenvironment remodeling in both primary tumor and LM lesions.

High-precision Helicobacter pylori infection diagnosis using a dual-element multimodal gas sensor array.

Helicobacter pylori (H. pylori) is a globally widespread bacterial infection. Early diagnosis of this infection is vital for public and individual health. Prevalent diagnosis methods like the isotope 13C or 14C labelled urea breath test (UBT) are not convenient and may do harm to the human body. The use of cross-response gas sensor arrays (GSAs) is an alternative way for label-free detection of metabolite changes in exhaled breath (EB). However, conventional GSAs are complex to prepare, lack reliability, and fail to discriminate subtle changes in EB due to the use of numerous sensing elements and single dimensional signal. This work presents a dual-element multimodal GSA empowered with multimodal sensing signals including conductance (G), capacitance (C), and dissipation factor (DF) to improve the ability for gas recognition and H. pylori-infection diagnosis. Sensitized by poly(diallyldimethylammonium chloride) (PDDA) and the metal-organic framework material NH2-UiO66, the dual-element graphene oxide (GO)-composite GSAs exhibited a high specific surface area and abundant adsorption sites, resulting in high sensitivity, repeatability, and fast response/recovery speed in all three signals. The multimodal sensing signals with rich sensing features allowed the GSA to detect various physicochemical properties of gas analytes, such as charge transfer and polarization ability, enhancing the sensing capabilities for gas discrimination. The dual-element GSA could differentiate different typical standard gases and non-dehumidified EB samples, demonstrating the advantages in EB analysis. In a case-control clinical study on 52 clinical EB samples, the diagnosis model based on the multimodal GSA achieved an accuracy of 94.1%, a sensitivity of 100%, and a specificity of 90.9% for diagnosing H. pylori infection, offering a promising strategy for developing an accurate, non-invasive and label-free method for disease diagnosis.

Design, synthesis and biological evaluation of indoline-maleimide conjugates as potential antitumor agents for the treatment of colorectal cancer.

An efficient protocol for direct coupling of maleimides and indolines at the C7-position was achieved under Rh(III) catalysis. Thirty four novel indoline-maleimide conjugates were prepared in good to excellent yields using this method. All compounds were evaluated for their anti-proliferative effect against colorectal cell lines. Among them, compound 3ab showed the most potent anti-proliferative activity against the CRC cells, and displayed low toxicity in the normal cell. Further investigation indicated that 3ab could effectively suppress the proliferation and migration of CRC cells, along with inducing cell cycle arrest and apoptosis. Mechanistic studies revealed that compound 3ab inhibited the proliferation of CRC cells via suppressing the AKT/GSK-3ß pathway. In vivo evaluation demonstrated remarkable antitumor effect of 3ab (10 mg/kg) in the HCT116 xenograft model with no obvious toxicity, which is superior to that of 5-Fluorouracil (20 mg/kg). Therefore, conjugate 3ab could be considered as a potential CRC therapy agent for further development.

In-Depth Comparison of Matrigel Dissolving Methods on Proteomic Profiling of Organoids.

Patient-derived organoids recently emerged as promising ex vivo 3D culture models recapitulating histological and molecular characteristics of original tissues, thus proteomic profiling of organoids could be valuable for function investigation and clinical translation. However, organoids are usually cultured in murine Matrigel (served as scaffolds and matrix), which brings an issue to separate organoids from Matrigel. Because of the complex compositions of Matrigel and thousands of identical peptides shared between Matrigel and organoids, insufficiently dissolved Matrigel could influence proteomic analysis of organoids in multiple ways. Thus, how to dissolve Matrigel matrix and recovery organoid cells efficiently is vital for sample preparation. Here, we comprehensively compared three popular Matrigel dissolving methods (cell recovery solution, dispase, and PBS-EDTA buffer) and investigated the effect of undissolved Matrigel proteins on proteomic profiles of organoids. By integrative analysis of label-free proteomes of Matrigel and stable isotope labeling by amino acids in cell culture proteomes of organoids collected by three methods, respectively, we found that dispase showed an optimal efficiency, with the highest peptide yield and the highest incorporation ratio of stable isotope labeling by amino acids in cell culture labels (97.1%), as well as with the least potential Matrigel contaminants. To help analysis of proteomic profiles of organoids collected by the other two methods, we identified 312 high-confidence Matrigel contaminants, which could be filtered out to attenuate Matrigel interference with minimal loss of biological information. Together, our study identifies bioinformatics and experimental approaches to eliminate interference of Matrigel contaminants efficiently, which will be valuable for basic and translational proteomic research using organoid models.

Accurate Discrimination of Benign Biliary Diseases and Cholangiocarcinoma with Serum Multiomics Revealed by High-Throughput Nanoassisted Laser Desorption Ionization Mass Spectrometry.

Cholangiocarcinoma (CCA) is an aggressive malignant tumor with a poor prognosis. Carbohydrate antigen 19-9 is an essential biomarker for CCA diagnosis, but its low sensitivity (72%) makes the diagnosis unreliable. To explore potential biomarkers for the diagnosis of CCA, a high-throughput nanoassisted laser desorption ionization mass spectrometry technique was constructed. We performed serum lipidomics and peptidomics analyses from 112 patients with CCA and 123 patients with benign biliary diseases. Lipidomics analysis showed that various lipids, such as glycerophospholipids, glycerides, and sphingolipids, were perturbed. Peptidomics analysis revealed perturbations of multiple proteins involved in the coagulation cascade, lipid transport, and so on. After data mining, 25 characteristic molecules including 20 lipids and 5 peptides were identified as potential diagnostic biomarkers. After screening various machine learning algorithms, artificial neural network was selected to construct a multiomics model for CCA diagnosis with 96.5% sensitivity and 96.4% specificity. The sensitivity and specificity of the model in the independent test cohort were 93.8 and 87.5%, respectively. Furthermore, integrated analysis with transcriptomic data in the cancer genome atlas confirmed that genes altered in CCA significantly affected multiple lipid- and protein-related pathways. Data are available via MetaboLights with the identifier MTBLS6712.

Real-world treatment patterns of immunosuppressants in Adults with systemic lupus erythematosus: A claims database analysis in the United States.

OBJECTIVE: To explore initiation, persistence, and adherence to second-line prescribed treatments for SLE, specifically regarding the immunosuppressants azathioprine, methotrexate, and mycophenolate (conventional DMARDs), and belimumab (a biologic). METHODS: Clinical and insurance records were obtained for 801 patients with SLE who initiated treatment with azathioprine, belimumab, methotrexate, or mycophenolate between July 2015 and June 2019. The date of initiation defined the index date, with a 6-month pre-index and 12-month post-index period. Patient characteristics (age, gender, race, sex, ethnicity, geographic region of the US, diagnosing specialty, and type of insurance) and treatment patterns were tabulated overall and by each index medication. Logistic regression was used to model predictors of persistence for the entire sample and for each treatment cohort. FINDINGS: Approximately one-third of patients initiated methotrexate (n = 282, 35.2%) or mycophenolate (n = 258, 32.2%), with the remaining receiving azathioprine (n = 173, 21.6%) or belimumab (n = 88, 11.0%). 30% of patients were persistent with their index immunosuppressant therapy over the 12-month follow-up. The most common non-persistent treatment pattern was discontinuation which occurred in 55% of patients and was highest in the mycophenolate (58%) and lowest in the azathioprine (47%) groups. In total, 17% of patients switched to a different immunosuppressant, which was highest for the belimumab (25%) group. The average time to discontinuation was over 3 months and average time to switch was about 5 months, with patients receiving azathioprine tending to have shorter and belimumab having longer times to discontinuation or switch.Predictors of persistence were limited. Patients under the care of rheumatologists versus primary care and having higher co-morbidity assessed by CCI were associated with non-persistence for the overall sample. Race, number of SLE-related medications, census region, sex, and age were not found to be significantly related to non-persistence of immunosuppressants in this study.

PINA 3.0: mining cancer interactome.

Protein-protein interactions (PPIs) are crucial to mediate biological functions, and understanding PPIs in cancer type-specific context could help decipher the underlying molecular mechanisms of tumorigenesis and identify potential therapeutic options. Therefore, we update the Protein Interaction Network Analysis (PINA) platform to version 3.0, to integrate the unified human interactome with RNA-seq transcriptomes and mass spectrometry-based proteomes across tens of cancer types. A number of new analytical utilities were developed to help characterize the cancer context for a PPI network, which includes inferring proteins with expression specificity and identifying candidate prognosis biomarkers, putative cancer drivers, and therapeutic targets for a specific cancer type; as well as identifying pairs of co-expressing interacting proteins across cancer types. Furthermore, a brand-new web interface has been designed to integrate these new utilities within an interactive network visualization environment, which allows users to quickly and comprehensively investigate the roles of human interacting proteins in a cancer type-specific context. PINA is freely available at https://omics.bjcancer.org/pina/.

Postbiotics Derived from L. paracasei ET-22 Inhibit the Formation of S. mutans Biofilms and Bioactive Substances: An Analysis.

Globally, dental caries is one of the most common non-communicable diseases for patients of all ages; Streptococcus mutans (S. mutans) is its principal pathogen. Lactobacillus paracasei (L. paracasei) shows excellent anti-pathogens and immune-regulation functions in the host. The aim of this study is to evaluate the effects of L. paracasei ET-22 on the formation of S. mutans biofilms. The living bacteria, heat-killed bacteria, and secretions of L. paracasei ET-22 were prepared using the same number of bacteria. In vitro, they were added into artificial-saliva medium, and used to coculture with the S. mutans. Results showed that the living bacteria and secretions of L. paracasei ET-22 inhibited biofilm-growth, the synthesis of water-soluble polysaccharide and water-insoluble polysaccharide, and virulence-gene-expression levels related to the formation of S. mutans biofilms. Surprisingly, the heat-killed L. paracasei ET-22, which is a postbiotic, also showed a similar regulation function. Non-targeted metabonomics technology was used to identify multiple potential active-substances in the postbiotics of L. paracasei ET-22 that inhibit the formation of S. mutans biofilms, including phenyllactic acid, zidovudine monophosphate, and citrulline. In conclusion, live bacteria and its postbiotics of L. paracasei ET-22 all have inhibitory effects on the formation of S. mutans biofilm. The postbiotics of L. paracasei ET-22 may be a promising biological anticariogenic-agent.

Relative Impact of Pain and Disease Activity on Improvements in Fatigue: Results From 2 Baricitinib Phase 3 Clinical Trials.

BACKGROUND/OBJECTIVE: Fatigue is common in patients with rheumatoid arthritis (RA). We assessed the relative impact of pain and disease activity on improvements in fatigue in 2 phase 3 baricitinib clinical trials. METHODS: RA-BEAM (NCT01710358) and RA-BEACON (NCT01721044) were randomized, double-blind, placebo-controlled studies in adults with moderate to severe RA. RA-BEAM assessed baricitinib + methotrexate (MTX) and adalimumab + MTX in patients with prior inadequate response/intolerance (IR) to MTX (MTX-IR). RA-BEACON assessed patients with IR to ≥1 biologic disease-modifying antirheumatic drug (bDMARD-IR). Measures included the Functional Assessment of Chronic Illness Therapy-Fatigue scale, Clinical Disease Activity Index (CDAI) for RA, and pain visual analog scale (VAS). Analyses were implemented separately for each study. RESULTS: Significant improvements were seen in disease activity and pain, which were greater with baricitinib versus adalimumab. A statistically significant improvement was seen in fatigue with both active treatments versus placebo. Moderate correlations were observed between improvements in disease activity and fatigue and between improvements in pain and fatigue in both MTX-IR and bDMARD-IR patients. Reductions in pain (≥50%) and remission or low disease activity (CDAI ≤10) had significant associations with fatigue improvement at week 24. In mediation analysis, improvements in fatigue attributable to CDAI and pain VAS in MTX-IR patients were 31% and 52%, respectively, for baricitinib, and 30% and 47%, respectively, for adalimumab. In bDMARD-IR patients, improvement in fatigue was attributed 48% to CDAI and 48% to pain VAS. CONCLUSIONS: In both MTX-IR and bDMARD-IR patients, a large proportion of improvements in fatigue across treatment arms were accounted for by improvements in pain and disease activity.

Dual-Mechanism-Driven Strategy for High-Coverage Detection of Serum Lipids on a Novel SALDI-MS Target.

Serum lipid metabolites have been emerging as ideal biomarkers for disease diagnosis and prediction. In the current stage, nontargeted or targeted lipidomic research mainly relies on a liquid chromatography-mass spectrometry (LC-MS) platform, but future clinical applications need more robust and high-speed platforms. Surface-assisted laser desorption ionization mass spectrometry (SALDI-MS) has shown excellent advantages in the high-speed analysis of lipid metabolites. However, the platform in the positive ion mode is more inclined to target a certain class of lipids, leading to the low coverage of lipid detection and limiting its practical translation to clinical applications. Herein, we proposed a dual-mechanism-driven strategy for high-coverage detection of serum lipids on a novel SALDI-MS target, which is a composite nanostructure comprising vertical silicon nanowires (VSiNWs) decorated with AuNPs and polydopamine (VSiNW-Au-PDA). The performance of laser desorption and ionization on the target can be enhanced by charge-driven desorption coupled with thermal-driven desorption. Simultaneous detection of 236 serum lipids (S/N ≥ 5) including neutral and polar lipids can be achieved in the positive ion mode. Among these, 107 lipid peaks were successfully identified. When combined with VSiNW-Au-PDA and VSiNW chips, 479 lipid peaks can be detected in serum samples in positive and negative ion modes, respectively. Based on the platform, serum samples from 57 hepatocellular carcinoma (HCC) patients and 76 healthy controls were analyzed. After data mining, 14 lipids containing different lipid types (TAG, CE, PC) were selected as potential lipidomic biomarkers. With the assistance of an artificial neural network, a diagnostic model with a sensitivity of 92.7% and a specificity of 96% was constructed for HCC diagnosis.

Discovery of a Carbamoyl Phosphate Synthetase 1-Deficient HCC Subtype With Therapeutic Potential Through Integrative Genomic and Experimental Analysis.

BACKGROUND AND AIMS: Metabolic reprogramming plays an important role in tumorigenesis. However, the metabolic types of different tumors are diverse and lack in-depth study. Here, through analysis of big databases and clinical samples, we identified a carbamoyl phosphate synthetase 1 (CPS1)-deficient hepatocellular carcinoma (HCC) subtype, explored tumorigenesis mechanism of this HCC subtype, and aimed to investigate metabolic reprogramming as a target for HCC prevention. APPROACH AND RESULTS: A pan-cancer study involving differentially expressed metabolic genes of 7,764 tumor samples in 16 cancer types provided by The Cancer Genome Atlas (TCGA) demonstrated that urea cycle (UC) was liver-specific and was down-regulated in HCC. A large-scale gene expression data analysis including 2,596 HCC cases in 7 HCC cohorts from Database of HCC Expression Atlas and 17,444 HCC cases from in-house hepatectomy cohort identified a specific CPS1-deficent HCC subtype with poor clinical prognosis. In vitro and in vivo validation confirmed the crucial role of CPS1 in HCC. Liquid chromatography-mass spectrometry assay and Seahorse analysis revealed that UC disorder (UCD) led to the deceleration of the tricarboxylic acid cycle, whereas excess ammonia caused by CPS1 deficiency activated fatty acid oxidation (FAO) through phosphorylated adenosine monophosphate-activated protein kinase. Mechanistically, FAO provided sufficient ATP for cell proliferation and enhanced chemoresistance of HCC cells by activating forkhead box protein M1. Subcutaneous xenograft tumor models and patient-derived organoids were employed to identify that blocking FAO by etomoxir may provide therapeutic benefit to HCC patients with CPS1 deficiency. CONCLUSIONS: In conclusion, our results prove a direct link between UCD and cancer stemness in HCC, define a CPS1-deficient HCC subtype through big-data mining, and provide insights for therapeutics for this type of HCC through targeting FAO.

The effect of biliary obstruction, biliary drainage and bile reinfusion on bile acid metabolism and gut microbiota in mice.

BACKGROUND & AIMS: Preoperative obstructive jaundice is usually associated with higher post-operative mortality. Although external biliary drainage (EBD) has been widely used to relieve obstructive jaundice, the role of bile reinfusion after EBD is still controversial. The aim of our study was to study the effects of biliary obstruction, biliary drainage and bile reinfusion on bile acid metabolism and gut microbiota. METHODS: Firstly, we created a mice bile drainage collection (BDC) model to simulate the process of biliary obstruction, drainage and bile reinfusion. Then, we analysed the faecal, serum, liver and bile samples to investigate the effects of the process on bile acid profiles and gut microbiota. Finally, we evaluated the clinical effects of bile reinfusion. RESULTS: We evaluated the bile acid profiles of faeces, serum, liver and bile of normal mice. During biliary obstruction, secondary bile acids can still be produced, and increased in the liver and serum of mice. Compared with no bile reinfusion, bile reinfusion was beneficial to the recovery of T-ωMCA in the liver and bile, and can restore the colon crypt length shortened by biliary obstruction. Only Ruminococcus_1 proliferated when the biliary obstruction lasted for 12 days. In the clinic, bile reinfusion cannot accelerate the patient's perioperative recovery or prolong long-term survival. CONCLUSION: We have successfully created a mice bile drainage collection model. Short-term bile reinfusion can partially benefit the recovery of the secondary bile acids in the liver and bile, but hardly benefit the patient's perioperative recovery or long-term survival. (247 words).

Dietary nutrients shape gut microbes and intestinal mucosa via epigenetic modifications.

The imbalance of intestinal microecology firstly impairs intestinal mucosa barrier and function, then further damages the functions and homeostasis of distal organs, leading to systemic diseases. Nutrients, transplantation of bacteria flora and modes of life can shape gut microbiota and intestinal mucosa barrier and mitigate stress. Current researches demonstrate that dynamic epigenetic modifications of intestinal tissue strongly mediate the crosstalk between gut microbes and gut mucosa barrier. Lactobacillus and Bifidobacterium species can synthesize folate to increase DNA methylation and mRNA N6-methyladenosine (m6A) of gut, which ensures intestinal normal development. Clostridial cluster, Anaerostipes and Eubacterium can induce histone acylation modifications by butyrate to enhance the development and immune balance of gut. Herein, we summarizes the present scientific understanding of how dietary nutrients shape gut microbiota and further regulate intestinal mucosa functions via epigenetic modifications, which will shed light on manipulation of gut microbiota by dietary nutrients, for prevention or clinical treatment of intestinal diseases.

Genomic analyses identify molecular subtypes of pancreatic cancer.

Integrated genomic analysis of 456 pancreatic ductal adenocarcinomas identified 32 recurrently mutated genes that aggregate into 10 pathways: KRAS, TGF-ß, WNT, NOTCH, ROBO/SLIT signalling, G1/S transition, SWI-SNF, chromatin modification, DNA repair and RNA processing. Expression analysis defined 4 subtypes: (1) squamous; (2) pancreatic progenitor; (3) immunogenic; and (4) aberrantly differentiated endocrine exocrine (ADEX) that correlate with histopathological characteristics. Squamous tumours are enriched for TP53 and KDM6A mutations, upregulation of the TP63∆N transcriptional network, hypermethylation of pancreatic endodermal cell-fate determining genes and have a poor prognosis. Pancreatic progenitor tumours preferentially express genes involved in early pancreatic development (FOXA2/3, PDX1 and MNX1). ADEX tumours displayed upregulation of genes that regulate networks involved in KRAS activation, exocrine (NR5A2 and RBPJL), and endocrine differentiation (NEUROD1 and NKX2-2). Immunogenic tumours contained upregulated immune networks including pathways involved in acquired immune suppression. These data infer differences in the molecular evolution of pancreatic cancer subtypes and identify opportunities for therapeutic development.

CVCDAP: an integrated platform for molecular and clinical analysis of cancer virtual cohorts.

Recent large-scale multi-omics studies resulted in quick accumulation of an overwhelming amount of cancer-related data, which provides an unprecedented resource to interrogate diverse questions. While certain existing web servers are valuable and widely used, analysis and visualization functions with regard to re-investigation of these data at cohort level are not adequately addressed. Here, we present CVCDAP, a web-based platform to deliver an interactive and customizable toolbox off the shelf for cohort-level analysis of TCGA and CPTAC public datasets, as well as user uploaded datasets. CVCDAP allows flexible selection of patients sharing common molecular and/or clinical characteristics across multiple studies as a virtual cohort, and provides dozens of built-in customizable tools for seamless genomic, transcriptomic, proteomic and clinical analysis of a single virtual cohort, as well as, to compare two virtual cohorts with relevance. The flexibility and analytic competence of CVCDAP empower experimental and clinical researchers to identify new molecular mechanisms and develop potential therapeutic approaches, by building and analyzing virtual cohorts for their subject of interests. We demonstrate that CVCDAP can conveniently reproduce published findings and reveal novel insights by two applications. The CVCDAP web server is freely available at https://omics.bjcancer.org/cvcdap/.

Ultrasensitive and High Reproducible Detection of Urinary Metabolites Using the Tip-Contact Extraction Method Coupled with Negative LDI-MS.

More and more evidence has proved that urinary metabolites can instantly reflect disease state. Therefore, ultra-sensitive and reproducible detection of urinary metabolites in a high-throughput way is urgently desirable for clinical diagnosis. Matrix-free laser desorption/ionization mass spectrometry (LDI-MS) is a high-throughput platform for metabolites detection, but it is encountered by severe interference from numerous salts in urine samples, because the crystallized urine salt on dried samples could result in poor reproducibility in LDI-MS detection. The present work proposed a tip-contact extraction (TCE) technique to eliminate interference from the urine salt. Vertical silicon nanowire arrays decorated with the fluorinated ethylene propylene film (FEP@VSiNWs) could effectively extract metabolites from the urine sample dropping on its surface. High salt tolerance was observed in the subsequent LDI-MS detection of the metabolites extracted on the tip of FEP@VSiNWs even in the presence of 1 M urea. Stable and reproducible mass spectra for non-target metabolic analysis were obtained in real urine samples with different dilution folds. Urinary metabolites collected from bladder cancer (BC) patients were reliably profiled by the TCE method coupled with negative LDI-MS. Based on this platform, potential metabolic biomarkers that can distinguish BC patients and normal controls were uncovered.

High-Throughput Salivary Metabolite Profiling on an Ultralow Noise Tip-Enhanced Laser Desorption Ionization Mass Spectrometry Platform for Noninvasive Diagnosis of Early Lung Cancer.

Lung cancer (LC) is a widespread cancer that is the cause of the highest mortality rate accounting for 25% of all cancer deaths. To date, most LC patients are diagnosed at the advanced stage owing to the lack of obvious symptoms in the early stage and the limitations of current clinical diagnostic techniques. Therefore, developing a high throughput technique for early screening is of great importance. In this work, we established an effective and rapid salivary metabolic analysis platform for early LC diagnosis and combined metabolomics and transcriptomics to reveal the metabolic fluctuations correlated to LC. Saliva samples were collected from a total of 150 volunteers including 89 patients with early LC, 11 patients with advanced LC, and 50 healthy controls. The metabolic profiling of noninvasive samples was investigated on an ultralow noise TELDI-MS platform. In addition, data normalization methods were screened and assessed to overcome the MS signal variation caused by individual difference for biomarker mining. For untargeted metabolic profiling of saliva samples, around 264 peaks could be reliably detected in each sample. After multivariate analysis, 23 metabolites were sorted out and verified to be related to the dysfunction of the amino acid and nucleotide metabolism in early LC. Notably, transcriptomic data from online TCGA repository were utilized to support findings from the salivary metabolomics experiment, including the disorder of amino acid biosynthesis and amino acid metabolism. Based on the verified differential metabolites, early LC patients could be clearly distinguished from healthy controls with a sensitivity of 97.2% and a specificity of 92%. The ultralow noise TELDI-MS platform displayed satisfactory ability to explore salivary metabolite information and discover potential biomarkers that may help develop a noninvasive screening tool for early LC.