Clinicopathological analysis and prognostic treatment study of angiosarcoma of the breast: a SEER population-based analysis.

INTRODUCTION: Breast angiosarcoma is a rare malignancy of endovascular origin, accounting for less than 1% of all mammary cancers. Our aim was to explore clinicopathological features and the factors associated with prognosis. METHODS: We extracted information from the Surveillance, Epidemiology, and End Results Program (SEER) for all patients with breast angiosarcoma between 2004 and 2015. Chi-square analysis was used to compare the clinicopathological features in all patients. Overall survival (OS) was assessed using the Kaplan and Meier method. Univariate and multivariate analyses were performed to evaluate the factors associated with prognosis. RESULTS: A total of 247 patients were included in the analyses. The median OS of patients with primary breast angiosarcoma (PBSA) and secondary breast angiosarcoma (SBAB) was 38 months and 42 months, respectively. The 1-, 3- and 5-year OS with PBSA was 80%, 39%, and 25%, respectively, and the 1-, 3- and 5-year OS with SBAB was 80%, 42%, and 34%, respectively. Multivariate analysis revealed that tumor size (p = 0.001), tumor grade (p < 0.001), tumor extension (p = 0.015), and tumor spread (p < 0.001) were statistically significant factors for OS. Partial mastectomy with radiation (HR = 0.160, 95% CI, 0.036-0.719, p = 0.016), partial mastectomy with chemotherapy (HR = 0.105, 95% CI, 0.011-1.015, p = 0.052), and partial mastectomy (HR = 0.125, 95% CI, 0.028-0.583, p = 0.007) were related to significantly better OS outcomes in primary angiosarcoma. CONCLUSION: Primary breast angiosarcoma has a better clinical phenotype than secondary breast angiosarcoma. Although overall survival was not statistically significant, primary breast angiosarcoma was better than secondary breast angiosarcoma with systemic therapy. Depending on the outcome of survival, partial mastectomy is effective in treating primary breast angiosarcoma.

CXCL4 suppresses tolerogenic immune signature of monocyte-derived dendritic cells.

RNA sequencing and DNA methylomic profiling were performed after differentiating monocytes for 6 days into moDCs with/without CXCL4 presence. We show that CXCL4 downregulates genes associated with tolerogenicity in DCs including C1Q. Expression profiles of C1Q genes were negatively correlated with their DNA methylation profiles and with immunogenic genes.

Broad proteomic screen reveals shared serum proteomic signature in patients with psoriatic arthritis and psoriasis without arthritis.

OBJECTIVE: To identify novel serum proteins involved in the pathogenesis of PsA as compared with healthy controls, psoriasis (Pso) and AS, and to explore which proteins best correlated to major clinical features of the disease. METHODS: A high-throughput serum biomarker platform (Olink) was used to assess the level of 951 unique proteins in serum of patients with PsA (n = 20), Pso (n = 18) and AS (n = 19), as well as healthy controls (HC, n = 20). Pso and PsA were matched for Psoriasis Area and Severity Index (PASI) and other clinical parameters. RESULTS: We found 68 differentially expressed proteins (DEPs) in PsA as compared with HC. Of those DEPs, 48 proteins (71%) were also dysregulated in Pso and/or AS. Strikingly, there were no DEPs when comparing PsA with Pso directly. On the contrary, hierarchical cluster analysis and multidimensional scaling revealed that HC clustered distinctly from all patients, and that PsA and Pso grouped together. The number of swollen joints had the strongest positive correlation to ICAM-1 (r = 0.81, P < 0.001) and CCL18 (0.76, P < 0.001). PASI score was best correlated to PI3 (r = 0.54, P < 0.001) and IL-17 receptor A (r = -0.51, P < 0.01). There were more proteins correlated to PASI score when analysing Pso and PsA patients separately, as compared with analysing Pso and PsA patients pooled together. CONCLUSION: PsA and Pso patients share a serum proteomic signature, which supports the concept of a single psoriatic spectrum of disease. Future studies should target skin and synovial tissues to uncover differences in local factors driving arthritis development in Pso.

Proteomic Profiling of Two Distinct Populations of Extracellular Vesicles Isolated from Human Seminal Plasma.

Body fluids contain many populations of extracellular vesicles (EV) that differ in size, cellular origin, molecular composition, and biological activities. EV in seminal plasma are in majority originating from prostate epithelial cells, and hence are also referred to as prostasomes. Nevertheless, EV are also contributed by other accessory sex glands, as well as by the testis and epididymis. In a previous study, we isolated EV from seminal plasma of vasectomized men, thereby excluding contributions from the testis and epididymis, and identified two distinct EV populations with diameters of 50 and 100 nm, respectively. In the current study, we comprehensively analyzed the protein composition of these two EV populations using quantitative Liquid Chromatography-Mass Spectrometry (LC-MS/MS). In total 1558 proteins were identified. Of these, ≈45% was found only in the isolated 100 nm EV, 1% only in the isolated 50 nm EV, and 54% in both 100 nm and 50 nm EV. Gene ontology (GO) enrichment analysis suggest that both originate from the prostate, but with distinct biogenesis pathways. Finally, nine proteins, including KLK3, KLK2, MSMB, NEFH, PSCA, PABPC1, TGM4, ALOX15B, and ANO7, with known prostate specific expression and alternate expression levels in prostate cancer tissue were identified. These data have potential for the discovery of EV associated prostate cancer biomarkers in blood.

Upregulation of lncRNA snoRNA host gene 6 regulates NUAK family SnF1-like kinase-1 expression by competitively binding microRNA-125b and interacting with Snail1/2 in bladder cancer.

BACKGROUNDS/AIMS: Numerous studies have reported that long noncoding RNAs (lncRNAs) play critical roles in the development and progression of bladder cancer (BC). LncRNA snoRNA host gene 6 (SNHG6) is ectopically expressed in tumor tissues of patients with BC and BC cell lines. However, little is known about the molecular mechanism of SNHG6-mediated bladder urothelial carcinoma cell migration and invasion. METHODS: We detected the SNHG6 levels in human BC specimens and cell lines by quantitative real-time polymerase chain reaction and Western blot, and investigated its role in BC using in vitro assays. RESULTS: We showed that overexpression of SNHG6 induced epithelial-mesenchymal transition (EMT) and promoted the migration and invasion capabilities of BC cells. Mechanistically, SNHG6 induced EMT of BC cells by upregulating the expression levels of Snail1/2 and regulated BC cell migration and invasion by tumor suppressive hsa-miR-125b and its target gene NUAK Family Kinase 1 (NUAK1). Furthermore, we found that SNHG6 was positively correlated with Snail1/2 expression, and negatively correlated with hsa-miR-125b expression in BC specimens. Further study showed that SNHG6 repressed hsa-miR-125b expression to upregulate Snail1/2. Conversely, hsa-miR-125b knockdown augmented SNHG6 expression in BC cells. CONCLUSION: Overall, our study demonstrated that SNHG6 promotes BC cell migration and invasion partly via the hsa-miR-125b/Snail1/2/NUAK1 pathway. Therefore, SNHG6 may be a potential prognostic biomarker in BC, and targeting hsa-miR-125b/Snail1/2/NUAK1 axis may be a promising therapeutic approach for BC patients.

Pred-binding: large-scale protein-ligand binding affinity prediction.

Drug target interactions (DTIs) are crucial in pharmacology and drug discovery. Presently, experimental determination of compound-protein interactions remains challenging because of funding investment and difficulties of purifying proteins. In this study, we proposed two in silico models based on support vector machine (SVM) and random forest (RF), using 1589 molecular descriptors and 1080 protein descriptors in 9948 ligand-protein pairs to predict DTIs that were quantified by Ki values. The cross-validation coefficient of determination of 0.6079 for SVM and 0.6267 for RF were obtained, respectively. In addition, the two-dimensional (2D) autocorrelation, topological charge indices and three-dimensional (3D)-MoRSE descriptors of compounds, the autocorrelation descriptors and the amphiphilic pseudo-amino acid composition of protein are found most important for Ki predictions. These models provide a new opportunity for the prediction of ligand-receptor interactions that will facilitate the target discovery and toxicity evaluation in drug development.

Expression of ribosome-binding protein 1 correlates with shorter survival in Her-2 positive breast cancer.

The aim of this study is to investigate the expression of ribosome-binding protein 1 (RRBP1) in invasive breast cancer and to analyze its relationship to clinical features and prognosis. RRBP1 expression was studied using real-time quantitative PCR and western blotting using pair-matched breast samples and immunohistochemical staining using a tissue microarray. Then the correlation between RRBP1 expression and clinicopathologic features was analyzed. RRBP1 mRNA and protein expression were significantly increased in breast cancer tissues compared with normal tissues. The protein level of RRBP1 is proved to be positively related to histological grade (P = 0.02), molecular subtype (P = 0.048) and status of Her-2 (P = 0.026) and P53 (P = 0.015). We performed a grade-stratified analysis of all patients according to the level of RRBP1 expression and found that RRBP1 overexpression highly affected overall survival in patients with early-stage (I and II) tumors (P = 0.042). Furthermore, Her-2 positive patients with negative RRBP1 expression had longer overall survival rates than those with positive RRBP1 expression (P = 0.031). Using multivariate analysis, it was determined that lymph node metastasis (LNM, P = 0.002) and RRBP1 expression (P = 0.005) were independent prognosis factors for overall survival. RRBP1 is a valuable prognostic factor in Her-2-positive breast cancer patients, indicating that RRBP1 is a potentially important target for the prediction of prognosis.

Tumor-suppressive microRNA-145 induces growth arrest by targeting SENP1 in human prostate cancer cells.

Prostate cancer (PCa) prevails as the most commonly diagnosed malignancy in men and the third leading cause of cancer-related deaths in developed countries. One of the distinct characteristics of prostate cancer is overexpression of the small ubiquitin-like modifier (SUMO)-specific protease 1 (SENP1), and the upregulation of SENP1 contributes to the malignant progression and cell proliferation of PCa. Previous studies have shown that the expression of microRNA-145 (miRNA-145) was extensively deregulated in PCa cell lines and primary clinical prostate cancer samples. Independent target prediction methods have indicated that the 3'-untranslated region of SENP1 mRNA is a potential target of miR-145. Here we found that low expression of miR-145 was correlated with high expression of SENP1 in PCa cell line PC-3. The transient introduction of miR-145 caused cell cycle arrest in PC-3 cells, and the opposite effect was observed when miR-145 inhibitor was transfected. Further studies revealed that the SENP1 3'-untranslated region was a regulative target of miR-145 in vitro. MicroRNA-145 also suppressed tumor formation in vivo in nude mice. Taken together, miR-145 plays an important role in tumorigenesis of PCa through interfering SENP1.

Long non­coding RNA DANCR aggravates breast cancer through the miR­34c/E2F1 feedback loop.

Emerging scientific evidence has suggested that the long non­coding (lnc)RNA differentiation antagonizing non­protein coding RNA (DANCR) serves a significant role in human tumorigenesis and cancer progression; however, the precise mechanism of its function in breast cancer remains to be fully understood. Therefore, the objective of the present study was to manipulate DANCR expression in MCF7 and MDA­MB­231 cells using lentiviral vectors to knock down or overexpress DANCR. This manipulation, alongside the analysis of bioinformatics data, was performed to investigate the potential mechanism underlying the role of DANCR in cancer. The mRNA and/or protein expression levels of DANCR, miR­34c­5p and E2F transcription factor 1 (E2F1) were assessed using reverse transcription­quantitative PCR and western blotting, respectively. The interactions between these molecules were validated using chromatin immunoprecipitation and dual­luciferase reporter assays. Additionally, fluorescence in situ hybridization was used to confirm the subcellular localization of DANCR. Cell proliferation, migration and invasion were determined using 5­ethynyl­2'­deoxyuridine, wound healing and Transwell assays, respectively. The results of the present study demonstrated that DANCR had a regulatory role as a competing endogenous RNA and upregulated the expression of E2F1 by sequestering miR­34c­5p in breast cancer cells. Furthermore, E2F1 promoted DANCR transcription by binding to its promoter in breast cancer cells. Notably, the DANCR/miR­34c­5p/E2F1 feedback loop enhanced cell proliferation, migration and invasion in breast cancer cells. Thus, these findings suggested that targeting DANCR may potentially provide a promising future therapeutic strategy for breast cancer treatment.

Mechanism and treatment of diarrhea associated with tyrosine kinase inhibitors.

Tyrosine kinase inhibitors (TKIs) have become first-line drugs for cancer treatment. However, their clinical use is seriously hindered since many patients experience diarrhea after receiving TKIs. The mechanisms of TKI-associated diarrhea remain unclear. Most existing therapies are symptomatic treatments based on experience and their effects are unsatisfactory. Therefore, clarification of the mechanisms underlying diarrhea is critical to develop effective anti-diarrhea drugs. This article summarizes several potential mechanisms of TKI-associated diarrhea and reviews current treatment progress.

A novel nomogram and survival analysis for different lymph node status in breast cancer based on the SEER database.

INTRODUCTION: The axillary lymph node status (ALNS) and internal mammary lymph nodes (IMLN) expression associated with breast cancer are closely linked to prognosis. This study aimed to establish a nomogram to predict survival at 3, 5, and 10 years in patients with various lymph node statuses. METHODS: We obtained data from patients with breast cancer between 2004 and 2015 from the Surveillance, Epidemiology, and End Results (SEER database). Chi-square analysis was performed to test for differences in the pathological characteristics of the groups, and Kaplan-Meier analysis and the log-rank test were used to plot and compare the correlation between overall survival (OS) and breast cancer specific survival (BCSS). The log-rank test was used for the univariate analysis, and statistically significant characteristics were included in the multivariate and Cox regression analyses. Finally, Independent factor identification was included in constructing the nomogram using R studio 4.2.0; area under curve (AUC) values were calculated, and receiver operating characteristic (ROC) curve, calibration, and decision curve analysis (DCA) curves were plotted for evaluation. RESULTS: A total of 279,078 patients were enrolled and analysed, demonstrating that the isolated tumour cells (ITC) group had clinicopathological characteristics similar to those of micrometastases (Mic). Multivariate analysis was performed to identify each subgroup's independent risk factors and construct a nomogram. The AUC values were 74.7 (95% CI 73.6-75.8), 72.8 (95% CI 71.9-73.8), and 71.2 (95% CI 70.2-72.2) for 3-, 5-, and 10-year OS, respectively, and 82.2 (95% CI 80.9-83.6), 80.1 (95% CI 79.0-81.2), and 75.5 (95% CI 74.3-76.8) for BCSS in overall breast cancer cases, respectively. AUC values for 3-, 5-, and 10-year OS in the ITC group were 64.8 (95% CI 56.5-73.2), 67.7 (95% CI 62.0-73.4), and 65.4 (95% CI 60.0-70.7), respectively. For those in the Mic group, AUC values for 3-, 5-, and 10-year OS were 72.9 (95% CI 70.7-75.1), 72.4 (95% CI 70.6-74.1), and 71.3 (95% CI 69.6-73.1), respectively, and AUC values for BCSS were 77.8 (95% CI 74.9-80.7), 75.7 (95% CI 73.5-77.9), and 70.3 (95% CI 68.0-72.6), respectively. In the IMLN group, AUC values for 3-, 5-, and 10-year OS were 75.2 (95% CI 71.7-78.7), 73.4 (95% CI 70.0-76.8), and 74.0 (95% CI 69.6-78.5), respectively, and AUC values for BCSS were 76.6 (95% CI 73.0-80.3), 74.1 (95% CI 70.5-77.7), and 74.7 (95% CI 69.8-79.5), respectively. The ROC, calibration, and DCA curves verified that the nomogram had better predictability and benefits. CONCLUSION: This study is the first to investigate the predictive value of different axillary lymph node statuses and internal mammary lymph node metastases in breast cancer, providing clinicians with additional aid in treatment decisions.

Multi-omics approach identifies PI3 as a biomarker for disease severity and hyper-keratinization in psoriasis.

BACKGROUND: Psoriasis is an immune-mediated inflammatory skin disease. Psoriasis severity evaluation is important for clinicians in the assessment of disease severity and subsequent clinical decision making. However, no objective biomarker is available for accurately evaluating disease severity in psoriasis. OBJECTIVE: To define and compare biomarkers of disease severity and progression in psoriatic skin. METHODS: We performed proteome profiling to study the proteins circulating in the serum from patients with psoriasis, psoriatic arthritis and ankylosing spondylitis, and transcriptome sequencing to investigate the gene expression in skin from the same cohort. We then used machine learning approaches to evaluate different biomarker candidates across several independent cohorts. In order to reveal the cell-type specificity of different biomarkers, we also analyzed a single-cell dataset of skin samples. In-situ staining was applied for the validation of biomarker expression. RESULTS: We identified that the peptidase inhibitor 3 (PI3) was significantly correlated with the corresponding local skin gene expression, and was associated with disease severity. We applied machine learning methods to confirm that PI3 was an effective psoriasis classifier, Finally, we validated PI3 as psoriasis biomarker using in-situ staining and public datasets. Single-cell data and in-situ staining indicated that PI3 was specifically highly expressed in keratinocytes from psoriatic lesions. CONCLUSION: Our results suggest that PI3 may be a psoriasis-specific biomarker for disease severity and hyper-keratinization.

Tamoxifen adherence and its relationship to mortality in 116 men with breast cancer.

Recent studies have revealed that many, perhaps most women with hormone-responsive breast cancer have low adherence to tamoxifen adjuvant hormonal therapy. However, limited data are available on tamoxifen adherence in male breast cancer (MBC) patients. The goal of this study was to assess tamoxifen adherence and its relationship to mortality in MBC patients. A cohort of 116 men who were diagnosed with receptor-positive breast cancer between June 1987 and July 2012 was recruited for the study using the cancer prevention and treatment system database of Heilongjiang Province. From the 116 patients who received a five-year tamoxifen prescription, only 64.6 % were still taking their medication 1 year later, and this percentage decreased to 46.4 and 28.7 % after 2 and 3 years, respectively, to 25.8 % after 4 years, and to 17.7 % in the last year. After multivariate adjustment, factors that significantly decreased tamoxifen adherence were low social support [Hazard ratio (HR) = 2.45, 95 % CI 1.32-4.55], age (HR = 1.10, 95 % CI 1.01-1.21), and adverse effects (HR = 2.19, 95 % CI 1.57-3.04). The primary endpoints in the adherence or low-adherence groups from this study were overall survival (OS) and disease-free survival (DFS) of the MBC patients. The five- and ten-year OS of the patients was 97.9 and 79.6 %, respectively, in the adherence group, and 84.7 and 50.4 %, respectively, in the low-adherence group (p = 0.008). The five- and ten-year DFS of the patients was 95.4 and 72.8 %, respectively, in the adherence group, and 72.6 and 42.3 %, respectively, in the low-adherence group (p = 0.007). The consequences of low treatment adherence in men, who have a potentially long life expectancy, may be significant. In light of these findings, there is an urgent need to acknowledge and tackle the issue of tamoxifen adherence in this patient group.

A systems biology approach to uncovering pharmacological synergy in herbal medicines with applications to cardiovascular disease.

Background. Clinical trials reveal that multiherb prescriptions of herbal medicine often exhibit pharmacological and therapeutic superiority in comparison to isolated single constituents. However, the synergistic mechanisms underlying this remain elusive. To address this question, a novel systems biology model integrating oral bioavailability and drug-likeness screening, target identification, and network pharmacology method has been constructed and applied to four clinically widely used herbs Radix Astragali Mongolici, Radix Puerariae Lobatae, Radix Ophiopogonis Japonici, and Radix Salviae Miltiorrhiza which exert synergistic effects of combined treatment of cardiovascular disease (CVD). Results. The results show that the structural properties of molecules in four herbs have substantial differences, and each herb can interact with significant target proteins related to CVD. Moreover, the bioactive ingredients from different herbs potentially act on the same molecular target (multiple-drug-one-target) and/or the functionally diverse targets but with potentially clinically relevant associations (multiple-drug-multiple-target-one-disease). From a molecular/systematic level, this explains why the herbs within a concoction could mutually enhance pharmacological synergy on a disease. Conclusions. The present work provides a new strategy not only for the understanding of pharmacological synergy in herbal medicine, but also for the rational discovery of potent drug/herb combinations that are individually subtherapeutic.

A systems biology approach to understanding the mechanisms of action of chinese herbs for treatment of cardiovascular disease.

Traditional Chinese Medicine (TCM) involves a broad range of empirical testing and refinement and plays an important role in the health maintenance for people all over the world. However, due to the complexity of Chinese herbs, a full understanding of TCM's action mechanisms is still unavailable despite plenty of successful applications of TCM in the treatment of various diseases, including especially cardiovascular diseases (CVD), one of the leading causes of death. Thus in the present work, by incorporating the chemical predictors, target predictors and network construction approaches, an integrated system of TCM has been constructed to systematically uncover the underlying action mechanisms of TCM. From three representative Chinese herbs, i.e., Ligusticum chuanxiong Hort., Dalbergia odorifera T. Chen and Corydalis yanhusuo WT Wang which have been widely used in CVD treatment, by combinational use of drug absorption, distribution, metabolism and excretion (ADME) screening and network pharmacology techniques, we have generated 64 bioactive ingredients and identified 54 protein targets closely associated with CVD, of which 29 are common targets (52.7%) of the three herbs. The result provides new information on the efficiency of the Chinese herbs for the treatment of CVD and also explains one of the basic theories of TCM, i.e., "multiple herbal drugs can treat one disease". The predicted potential targets were then mapped to target-disease and target-signal pathway connections, which revealed the relationships of the active ingredients with their potential targets, diseases and signal systems. This means that for the first time, the action mechanism of these three important Chinese herbs for the treatment of CVD is uncovered, by generating and identifying both their active ingredients and novel targets specifically related to CVD, which clarifies some of the common conceptions in TCM, and thus provides clues to modernize such specific herbal medicines.

RegEnrich gene regulator enrichment analysis reveals a key role of the ETS transcription factor family in interferon signaling.

Changes in a few key transcriptional regulators can lead to different biological states. Extracting the key gene regulators governing a biological state allows us to gain mechanistic insights. Most current tools perform pathway/GO enrichment analysis to identify key genes and regulators but tend to overlook the gene/protein regulatory interactions. Here we present RegEnrich, an open-source Bioconductor R package, which combines differential expression analysis, data-driven gene regulatory network inference, enrichment analysis, and gene regulator ranking to identify key regulators using gene/protein expression profiling data. By benchmarking using multiple gene expression datasets of gene silencing studies, we found that RegEnrich using the GSEA method to rank the regulators performed the best. Further, RegEnrich was applied to 21 publicly available datasets on in vitro interferon-stimulation of different cell types. Collectively, RegEnrich can accurately identify key gene regulators from the cells under different biological states, which can be valuable in mechanistically studying cell differentiation, cell response to drug stimulation, disease development, and ultimately drug development.

A Blood Protein Signature Stratifies Clinical Response to csDMARD Therapy in Pediatric Uveitis.

PURPOSE: To identify a serum biomarker signature that can help predict response to conventional synthetic disease-modifying antirheumatic drug (csDMARD) therapy in pediatric noninfectious uveitis. METHODS: In this case-control cohort study, we performed a 368-plex proteomic analysis of serum samples of 72 treatment-free patients with active uveitis (new onset or relapse) and 15 healthy controls. Among these, 37 patients were sampled at diagnosis before commencing csDMARD therapy. After 6 months, csDMARD response was evaluated and cases were categorized as "responder" or "nonresponder." Patients were considered "nonresponders" if remission was not achieved under csDMARD therapy. Serum protein profiles were used to train random forest models to predict csDMARD failure and compared to a model based on eight clinical parameters at diagnosis (e.g., maximum cell grade). RESULTS: In total, 19 of 37 (51%) cases were categorized as csDMARD nonresponders. We identified a 10-protein signature that could predict csDMARD failure with an overall accuracy of 84%, which was higher compared to a model based on eight clinical parameters (73% accuracy). Adjusting for age, sex, anatomic location of uveitis, and cell grade, cases stratified by the 10-protein signature at diagnosis showed a large difference in risk for csDMARD failure (hazard ratio, 12.8; 95% confidence interval, 2.5-64.6; P = 0.002). CONCLUSIONS: Machine learning models based on the serum proteome can stratify pediatric patients with uveitis at high risk for csDMARD failure. TRANSLATIONAL RELEVANCE: The identified protein signature has implications for the development of clinical decision tools that integrate clinical parameters with biological data to better predict the best treatment option.

Multi-omics integration reveals a core network involved in host defence and hyperkeratinization in psoriasis.

OBJECTIVES: The precise pathogenesis of psoriasis remains incompletely explored. We aimed to better understand the underlying mechanisms of psoriasis, using a systems biology approach based on transcriptomics and microbiome profiling. METHODS: We collected the skin tissue biopsies and swabs in both lesional and non-lesional skin of 13 patients with psoriasis, 15 patients with psoriatic arthritis and healthy skin from 12 patients with ankylosing spondylitis. To study the similarities and differences in the molecular profiles between these three conditions, and the associations between the host defence and microbiota composition, we performed high-throughput RNA-sequencing to quantify the gene expression profile in tissues. The metagenomic composition of 16S on local skin sites was quantified by clustering amplicon sequences and counted into operational taxonomic units. We further analysed associations between the transcriptome and microbiome profiling. RESULTS: We found that lesional and non-lesional samples were remarkably different in terms of their transcriptome profiles. The functional annotation of differentially expressed genes showed a major enrichment in neutrophil activation. By using co-expression gene networks, we identified a gene module that was associated with local psoriasis severity at the site of biopsy. From this module, we found a 'core' set of genes that was functionally involved in neutrophil activation, epidermal cell differentiation and response to bacteria. Skin microbiome analysis revealed that the abundances of Enhydrobacter, Micrococcus and Leptotrichia were significantly correlated with the genes in core network. CONCLUSIONS: We identified a core gene network that associated with local disease severity and microbiome composition, involved in the inflammation and hyperkeratinization in psoriatic skin.

Multiomics and Machine Learning Accurately Predict Clinical Response to Adalimumab and Etanercept Therapy in Patients With Rheumatoid Arthritis.

OBJECTIVE: To predict response to anti-tumor necrosis factor (anti-TNF) prior to treatment in patients with rheumatoid arthritis (RA), and to comprehensively understand the mechanism of how different RA patients respond differently to anti-TNF treatment. METHODS: Gene expression and/or DNA methylation profiling on peripheral blood mononuclear cells (PBMCs), monocytes, and CD4+ T cells obtained from 80 RA patients before they began either adalimumab (ADA) or etanercept (ETN) therapy was studied. After 6 months, treatment response was evaluated according to the European League Against Rheumatism criteria for disease response. Differential expression and methylation analyses were performed to identify the response-associated transcription and epigenetic signatures. Using these signatures, machine learning models were built by random forest algorithm to predict response prior to anti-TNF treatment, and were further validated by a follow-up study. RESULTS: Transcription signatures in ADA and ETN responders were divergent in PBMCs, and this phenomenon was reproduced in monocytes and CD4+ T cells. The genes up-regulated in CD4+ T cells from ADA responders were enriched in the TNF signaling pathway, while very few pathways were differential in monocytes. Differentially methylated positions (DMPs) were strongly hypermethylated in responders to ETN but not to ADA. The machine learning models for the prediction of response to ADA and ETN using differential genes reached an overall accuracy of 85.9% and 79%, respectively. The models using DMPs reached an overall accuracy of 84.7% and 88% for ADA and ETN, respectively. A follow-up study validated the high performance of these models. CONCLUSION: Our findings indicate that machine learning models based on molecular signatures accurately predict response before ADA and ETN treatment, paving the path toward personalized anti-TNF treatment.

Lymphoid and myeloid immune cell reconstitution after nicotinamide-expanded cord blood transplantation.

Omidubicel (nicotinamide-expanded cord blood) is a potential alternative source for allogeneic hematopoietic cell transplantation (HCT) when an HLA-identical donor is lacking. A phase I/II trial with standalone omidubicel HCT showed rapid and robust neutrophil and platelet engraftment. In this study, we evaluated the immune reconstitution (IR) of patients receiving omidubicel grafts during the first 6 months post-transplant, as IR is critical for favorable outcomes of the procedure. Data was collected from the omidubicel phase I-II international, multicenter trial. The primary endpoint was the probability of achieving adequate CD4+ T-cell IR (CD4IR: > 50 × 106/L within 100 days). Secondary endpoints were the recovery of T-cells, natural killer (NK)-cells, B-cells, dendritic cells (DC), and monocytes as determined with multicolor flow cytometry. LOESS-regression curves and cumulative incidence plots were used for data description. Thirty-six omidubicel recipients (median 44; 13-63 years) were included, and IR data was available from 28 recipients. Of these patients, 90% achieved adequate CD4IR. Overall, IR was complete and consisted of T-cell, monocyte, DC, and notably fast NK- and B-cell reconstitution, compared to conventional grafts. Our data show that transplantation of adolescent and adult patients with omidubicel results in full and broad IR, which is comparable with IR after HCT with conventional graft sources.