Search

Rare tumor has a huge unmet medical need without standard regimens, calling for novel therapeutic interventions. The National Cancer Center of China identified a threshold of incidence for rare tumor as 2.5/100,000, based on the characteristics of Chinese population. Molecular profiles for rare tumor patients in China further provided prospects for precise and individualized targeted treatment. An ongoing phase II clinical trial, the PLATFORM study, is the first trial tailored for rare solid tumors in China, featured by molecule-guided therapeutics. With the promulgation of supportive policies to encourage the development of innovative drugs for rare tumors in China, opportunities will be provided for these patients and the gap will be filled in the treatment of rare tumors.

Natural killer (NK) cell therapy is emerging as a cancer treatment. NK cells are innate cytotoxic lymphocytes that act as first-line responders to kill target cells without prior encounters. NK cells recognize cancer cells, virus-infected cells, and other types of stressed cell through a reservoir of germline-encoded receptors. NK cells are safe for allogeneic applications. Therefore, they are the ideal off-the-shelf cell, which overcome the low efficiency issue caused by the patient-by-patient nature of autologous cell therapy. Unlike T cells, NK cells cannot form a strong immune memory; therefore, they suffer from short in vivo persistence. However, different from T cells, NK cells have a reservoir of innate immune receptors targeting a variety of malignant cells. In addition, they can utilize antibody guidance in target recognition. With suitable engineering, NK cells can function as universal anticancer drugs that are not restricted to HLA and cancer types, which will benefit the large cohort of patients with rare cancer types and patients with no convenient drug targets for precision and personalized medicine. Here, we summarize and discuss the designs of current anticancer NK cell therapies.

This study examines phase 1 anticancer drug clinical trials performed in China from 2017 to 2021.

The prevalence of allergic diseases such as asthma is globally increasing, posing threat to the life quality of the affected population. Genome-wide association studies (GWAS) suggest that genetic variations only account for a small proportion of immunoglobulin E (IgE)-mediated type I hypersensitivity. Recently, epigenetics has gained attention as an approach to further understand the missing heritability and underpinning mechanisms of allergic diseases. Furthermore, epigenetic regulation allows the evaluation of the interaction between an individual's genetic predisposition and their environmental exposures. This chapter summarizes several large-scale epigenome-wide association studies (EWAS) on asthma and other allergic diseases and draws a blueprint for future analysis and research direction.

Methylation data, similar to other omics data, is susceptible to various technical issues that are potentially associated with unexplained or unrelated factors. Any difference in the measurement of DNA methylation, such as laboratory operation and sequencing platform, may lead to batch effects. With the accumulation of large-scale omics data, scientists are making joint efforts to generate and analyze omics data to answer various scientific questions. However, batch effects are inevitable in practice, and careful adjustment is needed. Multiple statistical methods for controlling bias and inflation between batches have been developed either by correcting based on known batch factors or by estimating directly from the output data. In this chapter, we will review and demonstrate several popular methods for batch effect correction and make practical recommendations in epigenome-wide association studies (EWAS).

International audience; Background: Allergic diseases often occur in combination (multimorbidity). Human blood transcriptome studies have not addressed multimorbidity. Large-scale gene expression data were combined to retrieve biomarkers and signaling pathways to disentangle allergic multimorbidity phenotypes. Methods: Integrated transcriptomic analysis was conducted in 1233 participants with a discovery phase using gene expression data (Human Transcriptome Array 2.0) from whole blood of 786 children from three European birth cohorts (MeDALL), and a replication phase using RNA Sequencing data from an independent cohort (EVA-PR, n = 447). Allergic diseases (asthma, atopic dermatitis, rhinitis) were considered as single disease or multimorbidity (at least two diseases), and compared with no disease. Results: Fifty genes were differentially expressed in allergic diseases. Thirty-two were not previously described in allergy. Eight genes were consistently overexpressed in all types of multimorbidity for asthma, dermatitis, and rhinitis (CLC, EMR4P, IL5RA, FRRS1, HRH4, SLC29A1, SIGLEC8, IL1RL1). All genes were replicated the in EVA-PR cohort. RT-qPCR validated the overexpression of selected genes. In MeDALL, 27 genes were differentially expressed in rhinitis alone, but none was significant for asthma or dermatitis alone. The multimorbidity signature was enriched in eosinophil-associated immune response and signal transduction. Protein-protein interaction network analysis identified IL5/JAK/STAT and IL33/ST2/IRAK/TRAF as key signaling pathways in multimorbid diseases. Synergistic effect of multimorbidity on gene expression levels was found. Conclusion: A signature of eight genes identifies multimorbidity for asthma, rhinitis, and dermatitis. Our results have clinical and mechanistic implications, and suggest that multimorbidity should be considered differently than allergic diseases occurring alone.

In a transcription-wide association study of nasal epithelium, we identify novel and previously reported susceptibility genes for atopic asthma in children and show that gene co-expression networks differ markedly between children with and without atopic asthma.

Latinos are heavily affected with childhood asthma. Little is known about epigenetic mechanisms of asthma in Latino youth. We conducted a meta-analysis of two epigenome-wide association studies (EWAS) of asthma, using DNA from white blood cells (WBCs) from 1,136 Latino children and youth aged 6 to 20 years. Genes near the top CpG sites in this EWAS were examined in a pathway enrichment analysis, and we then assessed whether our results replicated those from publicly available data from three independent EWAS conducted in non-Latino populations. We found that DNA methylation profiles differed between subjects with and without asthma. After adjustment for covariates and multiple testing, two CpGs were differentially methylated at a false discovery rate (FDR)-adjusted P

Drug conjugates are conjugates comprising a tumor-homing carrier tethered to a cytotoxic agent via a linker that are designed to deliver an ultra-toxic payload directly to the target cancer cells. This strategy has been successfully used to increase the therapeutic efficacy of cytotoxic agents and reduce their toxic side effects. Drug conjugates are being developed worldwide, with the potential to revolutionize current cancer treatment strategies. Antibody-drug conjugates (ADCs) have developed rapidly, and 14 of them have received market approval since the first approval event by the Food and Drug Administration in 2000. However, there are some limitations in the use of antibodies as carriers. Other classes of drug conjugates are emerging, such as targeted drugs conjugated with peptides (peptide-drug conjugates, PDCs) and polymers (polymer-drug conjugates, PolyDCs) with the remaining constructs similar to those of ADCs. These novel drug conjugates are gaining attention because they overcome the limitations of ADCs. This review summarizes the current state and advancements in knowledge regarding the design, constructs, and clinical efficacy of different drug conjugates.

Ivacaftor is a drug that was recently approved by the U.S. Food and Drug Administration for the treatment of patients with cystic fibrosis (CF) and at least one copy of the G511D mutation in the CFTR (CF transmembrane conductance regulator) gene. The transcriptomic effect of ivacaftor in patients with CF remains unclear. Here, we sought to examine whether and how the transcriptome of patients is influenced by ivacaftor treatment, and to determine whether these data allow prediction of ivacaftor responsiveness. Our data originated from the G551D Observational Study (GOAL). We performed RNA sequencing (RNA-seq) on peripheral blood mononuclear cells (PBMCs) from 56 patients and compared the transcriptomic changes that occurred before and after ivacaftor treatment. We used consensus clustering to stratify patients into subgroups based on their clinical responses after treatment, and we determined differences between subgroups in baseline gene expression. A random forest model was built to predict ivacaftor responsiveness. We identified 239 genes (false discovery rate < 0.1) that were significantly influenced by ivacaftor in PBMCs. The functions of these genes relate to cell differentiation, microbial infection, inflammation, Toll-like receptor signaling, and metabolism. We classified patients into "good" and "moderate" responder groups based on their clinical response to ivacaftor. We identified a panel of signature genes and built a statistical model for predicting CFTR modulator responsiveness. Despite a limited sample size, adequate prediction performance was achieved with an accuracy of 0.92. In conclusion, for the first time, the present study demonstrates profound transcriptomic impacts of ivacaftor in PBMCs from patients with CF, and provides a pilot statistical model for predicting clinical responsiveness to ivacaftor before treatment.

Summary Background Epigenetic mechanisms could alter the airway epithelial barrier and ultimately lead to atopic diseases such as asthma. We aimed to identify DNA methylation profiles that are associated with—and could accurately classify—atopy and atopic asthma in school-aged children. Methods We did a genome-wide study of DNA methylation in nasal epithelium and atopy or atopic asthma in 483 Puerto Rican children aged 9–20 years, recruited using multistage probability sampling. Atopy was defined as at least one positive IgE (≥0·35 IU/mL) to common aeroallergens, and asthma was defined as a physician's diagnosis plus wheeze in the previous year. Significant (false discovery rate p Findings DNA methylation profiles were markedly different between children with (n=312) and without (n=171) atopy in the Puerto Rico discovery cohort, recruited from Feb 12, 2014, until May 8, 2017. After adjustment for covariates and multiple testing, we found 8664 differentially methylated CpGs by atopy, with false discovery rate-adjusted p values ranging from 9·58 × 10 −17 to 2·18 × 10 −22 for the top 30 CpGs. These CpGs were in or near genes relevant to epithelial barrier function, including CDHR3 and CDH26 , and in other genes related to airway epithelial integrity and immune regulation, such as FBXL7, NTRK1 , and SLC9A3 . Moreover, 28 of the top 30 CpGs replicated in the same direction in both independent cohorts. Classification models of atopy based on nasal methylation performed well in the Puerto Rico cohort (area under the curve [AUC] 0·93–0·94 and accuracy 85–88%) and in both replication cohorts (AUC 0·74–0·92, accuracy 68–82%). The models also performed well for atopic asthma in the Puerto Rico cohort (AUC 0·95–1·00, accuracy 88%) and the replication cohorts (AUC 0·82–0·88, accuracy 86%). Interpretation We identified specific methylation profiles in airway epithelium that are associated with atopy and atopic asthma in children, and a nasal methylation panel that could classify children by atopy or atopic asthma. Our findings support the feasibility of using the nasal methylome for future clinical applications, such as predicting the development of asthma among wheezing infants. Funding US National Institutes of Health.

Improvement of longevity is an eternal dream of human beings. Here we report that a single protein recombinant mouse serum albumin (rMSA) improved the lifespan and healthspan of C57BL/6N mice. The median lifespan extensions were 17.6% for female and 20.3% for male, respectively. The grip strength of rMSA-treated female and male mice increased by 29.6% and 17.4%, respectively. Meanwhile, the percentage of successful escape increased 23.0% in rMSA-treated male mice using the Barnes Maze test. The rMSA used in this study is young and almost undamaged. We define the concept “young and undamaged” to any protein without any unnecessary modifications by four parameters: intact free thiol (if any), no advanced glycation end-product, no carbonylation, and no homocysteinylation. Here “young and undamaged” rMSA is much younger and less damaged than the endogenous serum albumin from young mice at 1.5 months of age. We predict that young and undamaged proteins altogether can further improve the longevity.

Identifying and removing multiplets are essential to improving the scalability and the reliability of single cell RNA sequencing (scRNA-seq). Multiplets create artificial cell types in the dataset. We propose a Gaussian mixture model-based multiplet identification method, GMM-Demux. GMM-Demux accurately identifies and removes multiplets through sample barcoding, including cell hashing and MULTI-seq. GMM-Demux uses a droplet formation model to authenticate putative cell types discovered from a scRNA-seq dataset. We generate two in-house cell-hashing datasets and compared GMM-Demux against three state-of-the-art sample barcoding classifiers. We show that GMM-Demux is stable and highly accurate and recognizes 9 multiplet-induced fake cell types in a PBMC dataset.

Additional file 1 Supplementary figures

Latinos are heavily affected with childhood asthma. Little is known about epigenetic mechanisms of asthma in Latino youth. We conducted a meta-analysis of two epigenome-wide association studies (EWAS) of asthma, using DNA from white blood cells (WBCs) from 1,136 Latino children and youth aged 6 to 20 years. Genes near the top CpG sites in this EWAS were examined in a pathway enrichment analysis, and we then assessed whether our results replicated those from publicly available data from three independent EWAS conducted in non-Latino populations. We found that DNA methylation profiles differed between subjects with and without asthma. After adjustment for covariates and multiple testing, two CpGs were differentially methylated at a false discovery rate (FDR)-adjusted P CAMK1D (Calcium/Calmodulin Dependent Protein Kinase ID) and TIGIT (T Cell Immunoreceptor With Ig And ITIM Domains). Moreover, 25 genomic regions were differentially methylated between subjects with and without asthma, at Šidák-corrected P

Acute respiratory distress syndrome (ARDS) results from overwhelming pulmonary inflammation. Prior bulk RNA sequencing provided limited insights into ARDS pathogenesis. We used single cell RNA sequencing to probe ARDS at a higher resolution. PBMCs of patients with pneumonia and sepsis with early ARDS were compared with those of sepsis patients who did not develop ARDS. Monocyte clusters from ARDS patients revealed multiple distinguishing characteristics in comparison with monocytes from patients without ARDS, including downregulation of SOCS3 expression, accompanied by a proinflammatory signature with upregulation of multiple type I IFN–induced genes, especially in CD16(+) cells. To generate an ARDS risk score, we identified upregulation of 29 genes in the monocytes of these patients, and 17 showed a similar profile in cells of patients in independent cohorts. Monocytes had increased expression of RAB11A, known to inhibit neutrophil efferocytosis; ATP2B1, a calcium pump that exports Ca(2+) implicated in endothelial barrier disruption; and SPARC, associated with processing of procollagen to collagen. These data show that monocytes of ARDS patients upregulate expression of genes not just restricted to those associated with inflammation. Together, our findings identify molecules that are likely involved in ARDS pathogenesis that may inform biomarker and therapeutic development.

Identifying and removing multiplets from downstream analysis is essential to improve the scalability and reliability of single cell RNA sequencing (scRNA-seq). High multiplet rates create artificial cell types in the dataset. Sample barcoding, including the cell hashing technology and the MULTI-seq technology, enables analytical identification of a fraction of multiplets in a scRNA-seq dataset.We propose a Gaussian-mixture-model-based multiplet identification method, GMM-Demux. GMM-Demux accurately identifies and removes the sample-barcoding-detectable multiplets and estimates the percentage of sample-barcoding-undetectable multiplets in the remaining dataset. GMM-Demux describes the droplet formation process with an augmented binomial probabilistic model, and uses the model to authenticate cell types discovered from a scRNA-seq dataset.We conducted two cell-hashing experiments, collected a public cell-hashing dataset, and generated a simulated cellhashing dataset. We compared the classification result of GMM-Demux against a state-of-the-art heuristic-based classifier. We show that GMM-Demux is more accurate, more stable, reduces the error rate by up to 69×, and is capable of reliably recognizing 9 multiplet-induced fake cell types and 8 real cell types in a PBMC scRNA-seq dataset.

Numerous independent susceptibility variants have been identified for Age-related macular degeneration (AMD) by genome-wide association studies (GWAS). Since advanced AMD is currently incurable, an accurate prediction of a person’s AMD risk using genetic information is desirable for early diagnosis and clinical management. In this study, genotype data of 32,215 Caucasian individuals with age above 50 years from the International AMD Genomics Consortium in dbGAP were used to establish and validate prediction models for AMD risk using four different machine learning approaches: neural network, lasso regression, support vector machine, and random forest. A standard logistic regression model was also considered using a genetic risk score. To identify feature SNPs for AMD prediction models, we selected the genome-wide significant SNPs from GWAS. All methods achieved good performance for predicting normal controls versus advanced AMD cases (AUC=0.81∼0.82 in a separate test dataset) and normal controls versus any AMD (AUC=0.78∼0.79). By applying the state-of-art machine learning approaches on the large AMD GWAS data, the predictive models we established can provide an accurate estimation of an individual’s AMD risk profile across the person’s lifespan based on a comprehensive genetic information.

The Trans-National Asthma Genetic Consortium (TAGC) identified 878 SNPs associated with asthma. We hypothesized that those SNPs affect asthma risk by regulating gene expression in airway epithelium, and conducted expression quantitative trait loci (eQTL) and mediation analyses to identify direct associations between the SNPs and expression levels of cis-genes (within 1 Mb) in nasal (airway) epithelium from Puerto Rican children with (n=228) and without (n=241) asthma. We then tested whether genes whose expression is associated with TAGC SNPs are differentially expressed (DE) in atopic asthma. We identified 1,150 direct associations between 418 TAGC SNPs and the expression of 55 cis-genes. Most SNPs regulate distant cis-genes (average distance ~200 kb). Our mediation analysis showed that 4,571 (89.2%) of 5,119 (direct and indirect) SNP-gene expression associations are mediated by methylation. Of 114 genes whose expression is associated with TAGC SNPs, 54 are DE in atopic asthma, including novel and previously reported genes. In an independent cohort of 72 African American children, 50 of the 54 DE genes were available, and 21 (42%) were also DE in atopic asthma. Thus, we show that many TAGC SNPs are associated with expression of distant cis-genes in airway epithelium, and that this is predominantly mediated by DNA methylation. Moreover, nearly half of the genes whose expression in airway epithelium is associated with TAGC SNPs are also DE in atopic asthma. Our findings support a key role of regulation of airway epithelial gene expression on atopic asthma in children.

Nuclear paraspeckles assembly transcript 1 (NEAT1) is a well-known long noncoding RNA (LncRNA) with unclear mechanism in Alzheimer’s disease (AD) progression. Here, we found that NEAT1 down-regulates in the early stage of AD patients and APPswe/PS1dE9 mouse. Moreover, knockdown of NEAT1 induced de-polymerization of microtubule (MT) and axonal retraction of nerve cells by dysregulation of the FZD3/GSK3β/p-tau signaling pathway. Histone acetylation analysis at the Frizzled Class Receptor 3 (FZD3) promoter shows a marked decreased in the levels of the H3K27 acetylation (H3K27Ac) after NEAT1 knockdown. Our data demonstrates that P300/CBP recruited by NEAT1 to the FZD3 promoter and induced its transcription via histone acetylation. In recent years a growing number of evidences have shown an abnormal brain glucose homeostasis in AD. In the present study we also observed an abnormal brain glucose homeostasis and enhanced sirtuin1 (SIRT1) activity after knockdown of NEAT similarly as in AD. Our results provided insight into the role of NEAT1 in the maintenance of MT stability and its effect on glucose metabolism during early stages of AD.

Early allergic sensitisation (atopy) is the first step in the development of allergic diseases such as atopic asthma later in life. Genes and pathways associated with atopy and atopic asthma in children and adolescents have not been well characterised.A transcriptome-wide association study (TWAS) of atopy and atopic asthma in white blood cells (WBCs) or whole blood was conducted in a cohort of 460 Puerto Ricans aged 9–20 years (EVA-PR study) and in a cohort of 250 Swedish adolescents (BAMSE study). Pathway enrichment and network analyses were conducted to further assess top findings, and classification models of atopy and atopic asthma were built using expression levels for the top differentially expressed genes (DEGs).In a meta-analysis of the study cohorts, both previously implicated genes (e.g. IL5RA and IL1RL1) and genes not previously reported in TWASs (novel) were significantly associated with atopy and/or atopic asthma. Top novel genes for atopy included SIGLEC8 (p=8.07×10−13), SLC29A1 (p=7.07×10−12) and SMPD3 (p=1.48×10−11). Expression quantitative trait locus analyses identified multiple asthma-relevant genotype–expression pairs, such as rs2255888/ALOX15. Pathway enrichment analysis uncovered 16 significantly enriched pathways at adjusted pWe have identified genes and pathways for atopy and atopic asthma in children and adolescents, using transcriptome-wide data from WBCs and whole blood samples.

The recently developed droplet-based single-cell transcriptome sequencing (scRNA-seq) technology makes it feasible to perform a population-scale scRNA-seq study, in which the transcriptome is measured for tens of thousands of single cells from multiple individuals. Despite the advances of many clustering methods, there are few tailored methods for population-scale scRNA-seq studies. Here, we develop a Bayesian mixture model for single-cell sequencing (BAMM-SC) method to cluster scRNA-seq data from multiple individuals simultaneously. BAMM-SC takes raw count data as input and accounts for data heterogeneity and batch effect among multiple individuals in a unified Bayesian hierarchical model framework. Results from extensive simulation studies and applications of BAMM-SC to in-house experimental scRNA-seq datasets using blood, lung and skin cells from humans or mice demonstrate that BAMM-SC outperformed existing clustering methods with considerable improved clustering accuracy, particularly in the presence of heterogeneity among individuals., With the development of large scale single cell RNA-seq technology, population-scale scRNA-seq studies are emerging. Here, the authors develop BAMM-SC, a tool for clustering droplet-based scRNA-seq data from multiple individuals simultaneously.

A comprehensive understanding of the changes in gene expression in cell types involved in idiopathic pulmonary fibrosis (IPF) will shed light on the mechanisms underlying the loss of alveolar epithelial cells and development of honeycomb cysts and fibroblastic foci. We sought to understand changes in IPF lung cell transcriptomes and gain insight into innate immune aspects of pathogenesis.We investigated IPF pathogenesis using single-cell RNA-sequencing of fresh lung explants, comparing human IPF fibrotic lower lobes reflecting late disease, upper lobes reflecting early disease and normal lungs.IPF lower lobes showed increased fibroblasts, and basal, ciliated, goblet and club cells, but decreased alveolar epithelial cells, and marked alterations in inflammatory cells. We found three discrete macrophage subpopulations in normal and fibrotic lungs, one expressing monocyte markers, one highly expressing FABP4 and INHBA (FABP4hi), and one highly expressing SPP1 and MERTK (SPP1hi). SPP1hi macrophages in fibrotic lower lobes showed highly upregulated SPP1 and MERTK expression. Low-level local proliferation of SPP1hi macrophages in normal lungs was strikingly increased in IPF lungs.Co-localisation and causal modelling supported the role for these highly proliferative SPP1hi macrophages in activation of IPF myofibroblasts in lung fibrosis. These data suggest that SPP1hi macrophages contribute importantly to lung fibrosis in IPF, and that therapeutic strategies targeting MERTK and macrophage proliferation may show promise for treatment of this disease.