Single-cell profiling identifies IL1Bhi macrophages associated with inflammation in PD-1 inhibitor-induced inflammatory arthritis.

Inflammatory arthritis (IA) is a common rheumatic adverse event following immune checkpoint inhibitors treatment. The clinical disparities between IA and rheumatoid arthritis (RA) imply disease heterogeneity and distinct mechanisms, which remain elusive. Here, we profile CD45+ cells from the peripheral blood or synovial fluid (SF) of patients with PD-1-induced IA (PD-1-IA) or RA using single-cell RNA sequencing. We report the predominant expansion of IL1Bhi myeloid cells with enhanced NLRP3 inflammasome activity, in both the SF and peripheral blood of PD-1-IA, but not RA. IL1Bhi macrophages in the SF of PD-1-IA shared similar inflammatory signatures and might originate from peripheral IL1Bhi monocytes. Exhausted CD8+ T cells (Texs) significantly accumulated in the SF of patients with PD-1-IA. IL1Bhi myeloid cells communicated with CD8+ Texs possibly via the CCR1-CCL5/CCL3 and CXCL10-CXCR3 axes. Collectively, these results demonstrate different cellular and molecular pathways in PD-1-IA and RA and highlight IL1Bhi macrophages as a possible therapeutic target in PD-1-IA.

Identification of Anti-SNRPA as a Novel Serological Biomarker for Systemic Sclerosis Diagnosis.

Systemic sclerosis (SSc) is a systemic autoimmune disorder that leads to vasculopathy and tissue fibrosis. A lack of reliable biomarkers has been a challenge for clinical diagnosis of the disease. We employed a protein array-based approach to identify and validate SSc-specific autoantibodies. Phase I involved profiled autoimmunity using human proteome microarray (HuProt arrays) with 90 serum samples: 40 patients with SSc, 30 patients diagnosed with autoimmune diseases, and 20 healthy subjects. In Phase II, we constructed a focused array with candidates identified antigens and used this to profile a much larger cohort comprised of serum samples. Finally, we used a western blot analysis to validate the serum of validated proteins with high signal values. Bioinformatics analysis allowed us to identify 113 candidate autoantigens that were significantly associated with SSc. This two-phase strategy allowed us to identify and validate anti-small nuclear ribonucleoprotein polypeptide A (SNRPA) as a novel SSc-specific serological biomarker. The observed positive rate of anti-SNRPA antibody in patients with SSc was 11.25%, which was significantly higher than that of any disease control group (3.33%) or healthy controls (1%). In conclusion, anti-SNRPA autoantibody serves as a novel biomarker for SSc diagnosis and may be promising for clinical applications.

Oncogene or tumor suppressor gene: An integrated pan-cancer analysis of NBPF1.

Neuroblastoma breakpoint family, member 1 (NBPF1), appears to be a double-edged sword with regard to its role in carcinogenesis. On the one hand, the tumor-suppressing functions of NBPF1 have been definitively observed in neuroblastoma, prostate cancer, cutaneous squamous cell carcinoma, and cervical cancer. On the other hand, there is evidence that NBPF1 regulates the colony formation, invasion, and maintenance of liver cancer cells and hence functions as an oncogene. The roles of NBPF1 are strictly dependent on the biological context and type of organization. However, a systematic pan-cancer analysis has thus far not been undertaken, and the significance of NBPF1 in the occurrence and progression of many malignancies is uncertain. In this paper, bioinformatics techniques were employed to analyze NBPF1 expression across different cancers and investigate the relationship between NBPF1 and clinical features, prognosis, genetic alteration, and tumor immune microenvironment, respectively. Our results show that NBPF1 is variably expressed in distinct tumor tissues and is also closely linked to clinical outcomes. In particular, compared to other tumor types, there was a strong negative correlation between NBPF1 expression and various components of the tumor microenvironment in adrenocortical carcinoma (ACC). We thus developed an NBPF1-derived immune risk model based on NBPF1-related immune genes; ACC patients with a high-risk score tended to have a poorer prognosis, accompanied by immune hyporesponsiveness. NBPF1 can be used as a prognostic biomarker for multiple cancers. Moreover, anti-NBPF1 immunotherapy may be suitable for treating ACC patients.

Exploration of Target Spaces in the Human Genome for Protein and Peptide Drugs.

After decades of development, protein and peptide drugs have now grown into a major drug class in the marketplace. Target identification and validation are crucial for the discovery of protein and peptide drugs, and bioinformatics prediction of targets based on the characteristics of known target proteins will help improve the efficiency and success rate of target selection. However, owing to the developmental history in the pharmaceutical industry, previous systematic exploration of the target spaces has mainly focused on traditional small-molecule drugs, while studies related to protein and peptide drugs are lacking. Here, we systematically explore the target spaces in the human genome specifically for protein and peptide drugs. Compared with other proteins, both successful protein and peptide drug targets have many special characteristics, and are also significantly different from those of small-molecule drugs in many aspects. Based on these features, we develop separate effective genome-wide target prediction models for protein and peptide drugs. Finally, a user-friendly web server, Predictor Of Protein and PeptIde drugs' therapeutic Targets (POPPIT) (http://poppit.ncpsb.org.cn/), is established, which provides not only target prediction specifically for protein and peptide drugs but also abundant annotations for predicted targets.

Identification and Validation of Immune Molecular Subtypes in Pancreatic Ductal Adenocarcinoma: Implications for Prognosis and Immunotherapy.

Background: Pancreatic ductal adenocarcinoma (PDAC) remains treatment refractory. Immunotherapy has achieved success in the treatment of multiple malignancies. However, the efficacy of immunotherapy in PDAC is limited by a lack of promising biomarkers. In this research, we aimed to identify robust immune molecular subtypes of PDAC to facilitate prognosis prediction and patient selection for immunotherapy. Methods: A training cohort of 149 PDAC samples from The Cancer Genome Atlas (TCGA) with mRNA expression data was analyzed. By means of non-negative matrix factorization (NMF), we virtually dissected the immune-related signals from bulk gene expression data. Detailed immunogenomic and survival analyses of the immune molecular subtypes were conducted to determine their biological and clinical relevance. Validation was performed in five independent datasets on a total of 615 samples. Results: Approximately 31% of PDAC samples (46/149) had higher immune cell infiltration, more active immune cytolytic activity, higher activation of the interferon pathway, a higher tumor mutational burden (TMB), and fewer copy number alterations (CNAs) than the other samples (all P < 0.001). This new molecular subtype was named Immune Class, which served as an independent favorable prognostic factor for overall survival (hazard ratio, 0.56; 95% confidence interval, 0.33-0.97). Immune Class in cooperation with previously reported tumor and stroma classifications had a cumulative effect on PDAC prognostic stratification. Moreover, programmed cell death-1 (PD-1) inhibitors showed potential efficacy for Immune Class (P = 0.04). The robustness of our immune molecular subtypes was further verified in the validation cohort. Conclusions: By capturing immune-related signals in the PDAC tumor microenvironment, we reveal a novel molecular subtype, Immune Class. Immune Class serves as an independent favorable prognostic factor for overall survival in PDAC patients.

Next-generation sequencing reveals heterogeneous genetic alterations in key signaling pathways of mismatch repair deficient colorectal carcinomas.

Colorectal carcinoma (CRC) with deficient mismatch repair (dMMR) is an etiologically heterogeneous molecular entity. We investigated the genetic profile, focusing on key signaling pathways and molecular diversity of dMMR CRCs. In this study, next-generation sequencing was applied to 156 consecutive dMMR CRCs and 225 randomly selected proficient MMR (pMMR) CRCs diagnosed between July 2015 and December 2019 at Peking Union Medical College Hospital. Genetic alterations and MLH1 promoter hypermethylation (MLH1me+) were analyzed. Among the most frequently mutated genes, RNF43, ARID1A, PIK3CA, ATM, and BRCA2 mutants were enriched in dMMR CRCs, whereas APC and TP53 mutations were enriched in pMMR CRCs. In dMMR group, RNF43, APC, ARID1A, and BRCA2 mutations were largely microsatellite instability events. WNT pathway was commonly altered regardless of MMR status. Compared to pMMR CRCs, dMMR CRCs had remarkably more prevalent PI3K, RTK-RAS, TGFß, and DNA damage repair pathway alterations and more multiple mutations in WNT and PI3K pathways. Within dMMR tumors, mutual exclusivity occurred between CTNNB1 mutation and APC or RNF43 mutation, while coexistence existed between BRAF and RNF43 mutation, as well as RAS and APC mutation. MLH1me+ dMMR CRCs had significantly more frequent RNF43 mutations but less frequent KRAS, APC, and CTNNB1 mutations comparing to MLH1-unmethylated dMMR CRCs. RNF43/BRAF comutations were detected in MLH1me+ dMMR CRCs, whereas RAS/APC comutations were largely detected in Lynch syndrome-associated cases. RNF43 mutation was independently associated with MLH1me+ rather than BRAF mutations. dMMR CRCs bearing receptor tyrosine kinase fusion demonstrated no additional RTK-RAS mutations, significantly fewer PI3K alterations and more TGFBR2 mutations than other dMMR tumors. Our study revealed that dMMR CRCs had distinctive gene mutation spectra and signaling pathway interaction patterns compared to proficient mismatch repair (pMMR) CRCs, and molecular heterogeneity was evident for these divergent oncogenic pathways. These findings justify the use of individualized therapy targeted to dMMR CRC subgroups.

Mutational profiling of poorly differentiated and anaplastic thyroid carcinoma by the use of targeted next-generation sequencing.

AIMS: To characterise the mutational profiles of poorly differentiated thyroid carcinoma (PDTC) and anaplastic thyroid carcinoma (ATC) and to identify markers with potential diagnostic, prognostic and therapeutic significance. METHODS AND RESULTS: Targeted next-generation sequencing with a panel of 18 thyroid carcinoma-related genes was performed on tissue samples from 41 PDTC and 25 ATC patients. Genetic alterations and their correlations with clinicopathological factors, including survival outcomes, were also analysed. Our results showed that ATC had significantly higher mutation rates of BRAF, TP53, TERT and PIK3CA than PDTC (P = 0.005, P = 0.007, P = 0.005, and P = 0.033, respectively). Nine (69%) ATC cases with papillary thyroid carcinoma (PTC) components harboured BRAF mutations, all of which coexisted with a late mutation event (TP53, TERT, or PIK3CA). Nine cases with oncogenic fusion (six RET cases, one NTRK1 case, one ALK case, and one PPARG case) were identified in 41 PDTCs, whereas only one case with oncogenic fusion (NTRK1) was found among 25 ATCs. Moreover, all six cases of RET fusion were found in PDTC with PTC components, accounting for 33%. In PDTC/ATC patients, concurrent TERT and PIK3CA mutations were associated with poor overall survival after adjustment for TNM stage (P = 0.001). CONCLUSIONS: ATC with PTC components is typically characterised by a BRAF mutation with a late mutation event, whereas PDTC with PTC components is more closely correlated with RET fusion. TERT and concurrent PIK3CA mutations predict worse overall survival in PDTC/ATC patients.

Revealing the metabolic characteristics of human embryonic stem cells by genome-scale metabolic modeling.

Embryonic stem cells (ESCs) are characterized by a dual capacity, self-renewal and pluripotency, which can be regulated by metabolism. A better understanding of ESC metabolism and regulatory mechanisms is pivotal for research into development, ageing, and cancer treatment. However, a systematic and comprehensive delineation of human ESC metabolism is still lacking. Here, we reconstructed the first genome-scale metabolic model (GEM) of human ESCs (hESCs). By GEM simulation and analyses, hESC global metabolic characteristics including essential metabolites and network motifs were identified. Potential metabolic subsystems responsible for self-renewal and pluripotency were also identified by analyses and experiments. This first GEM of hESCs provides a novel view and resource for stem cell metabolism research and will contribute to the elucidation of their metabolic characteristics.