Background: Immune-related adverse events (irAEs) are a common phenomenon in cancer patients treated with immune checkpoint inhibitors (ICIs). Surprisingly, the toxicity burdens of these irAEs have not been illustrated clearly. In this study, we analyzed irAEs for seven FDA-approved ICIs in cancer treatment to show the pattern of toxicity burden among cancer patients. Methods: irAEs associated with seven FDA-approved ICIs, including three PD-1 inhibitors (cemiplimab, nivolumab and pembrolizumab), three PD-L1 inhibitors (atezolizumab, avelumab and durvalumab), and one CTLA-4 inhibitor (ipilimumab), were analyzed based on data from 149,303 reported cases (from January 1, 2015 to June 30, 2022) collected from the FDA Adverse Events Reporting System (FAERS) public dashboard. Proportions of serious irAEs and correlations with tumor type, age and sex were assessed via R package and GraphPad software. Results: irAEs related to anti-PD-1 ICIs required less hospital care resources compared with anti-PD-L1 and anti-CTLA-4 ICIs. Patients treated with pembrolizumab had relatively fewer serious cases. Treatment with ICIs led to the highest probability of serious irAEs in patients with lung cancer. ‘Respiratory, thoracic and mediastinal disorders’ and ‘gastrointestinal disorders’ were the two most common groups of disorders caused by the seven ICIs studied. ‘Cardiac disorders’ was the main type of disorders caused by these ICIs in cancer patients aged 65–85, while ‘reproductive system and breast disease’ was the main type of disorder in cancer patients aged 18–64. ‘Respiratory, thoracic, mediastinal diseases’ and ‘reproductive system and breast diseases’ were the main types of disorders associated with treatment with these ICIs in male and female patients, respectively. Conclusion: Tissue and organ toxicities of ICIs are age and sex specific. There are risks of respiratory and urinary system toxicity in male patients and reproductive system toxicity in female patients treated with the ICIs studied. Future studies on the toxicity burden of ICIs should incorporate age and sex differences to better understand the relevance of ICI toxicity burden to human immune function to develop appropriate tumor immune and therapeutic intervention strategies.
A major question in systems biology is how to identify the core gene regulatory circuit that governs the decision-making of a biological process. Here, we develop a computational platform, named NetAct, for constructing core transcription factor regulatory networks using both transcriptomics data and literature-based transcription factor-target databases. NetAct robustly infers regulators’ activity using target expression, constructs networks based on transcriptional activity, and integrates mathematical modeling for validation. Our in silico benchmark test shows that NetAct outperforms existing algorithms in inferring transcriptional activity and gene networks. We illustrate the application of NetAct to model networks driving TGF-β-induced epithelial-mesenchymal transition and macrophage polarization.
This manuscript presents a comprehensive collection of diverse epigenomic profiling data for the human genome in 100-bp resolution with full genome-wide coverage. The datasets are processed from raw read count data collected from five types of sequencing-based assays collected by the Encyclopedia of DNA Elements consortium (ENCODE, http://www.encodeproject.org). Data from high-throughput sequencing assays were processed and crystallized into a total of 6,305 genome-wide profiles. To ensure the quality of the features, we filtered out assays with low read depth, inconsistent read counts, and poor data quality. The types of sequencing-based experiment assays include DNase-seq, histone and TF ChIP-seq, ATAC-seq, and Poly(A) RNA-seq. Merging of processed data was done by averaging read counts across technical replicates to obtain signals in about 30 million predefined 100-bp bins that tile the entire genome. We provide an example of fetching read counts using disease-related risk variants from the GWAS Catalog. Additionally, we have created a tabix index enabling fast user retrieval of read counts given coordinates in the human genome. The data processing pipeline is replicable for users’ own purposes and for other experimental assays. The processed data can be found on Zenodo at https://zenodo.org/record/7015783. These data can be used as features for statistical and machine learning models to predict or infer a wide range of variables of biological interest. They can also be applied to generate novel insights into gene expression, chromatin accessibility, and epigenetic modifications across the human genome. Finally, the processing pipeline can be easily applied to data from any other genome-wide profiling assays, expanding the amount of available data.
Fragile X–associated tremor/ataxia syndrome (FXTAS) is a debilitating late-onset neurodegenerative disease in premutation carriers of the expanded CGG repeat in FMR1 that presents with a spectrum of neurological manifestations, such as gait ataxia, intention tremor, and parkinsonism [P. J. Hagerman, R. J. Hagerman, Ann. N. Y. Acad. Sci. 1338, 58–70 (2015); S. Jacquemont et al., JAMA 291, 460–469 (2004)]. Here, we performed whole-genome sequencing (WGS) on male premutation carriers (CGG55–200) and prioritized candidate variants to screen for candidate genetic modifiers using a Drosophila model of FXTAS. We found 18 genes that genetically modulate CGG-associated neurotoxicity in Drosophila, such as Prosbeta5 (PSMB5), pAbp (PABPC1L), e(y)1 (TAF9), and CG14231 (OSGEPL1). Among them, knockdown of Prosbeta5 (PSMB5) suppressed CGG-associated neurodegeneration in the fly as well as in N2A cells. Interestingly, an expression quantitative trait locus variant in PSMB5, PSMB5rs11543947-A, was found to be associated with decreased expression of PSMB5 and delayed onset of FXTAS in human FMR1 premutation carriers. Finally, we demonstrate evidence that PSMB5 knockdown results in suppression of CGG neurotoxicity via both the RAN translation and RNA-mediated toxicity mechanisms, thereby presenting a therapeutic strategy for FXTAS.
There is growing evidence that the metabolism is deeply intertwined with head and neck squamous cell carcinoma (HNSCC) progression and survival but little is known about circulating metabolite patterns and their clinical potential. We performed unsupervised hierarchical clustering of 209 HNSCC patients via pre-treatment plasma metabolomics to identify metabolic subtypes. We annotated the subtypes via pathway enrichment analysis and investigated their association with overall and progression-free survival. We stratified the survival analyses by smoking history. High-resolution metabolomics extracted 186 laboratory-confirmed metabolites. The optimal model created two patient clusters, of subtypes A and B, corresponding to 41% and 59% of the study population, respectively. Fatty acid biosynthesis, acetyl-CoA transport, arginine and proline, as well as the galactose metabolism pathways differentiated the subtypes. Relative to subtype B, subtype A patients experienced significantly worse overall and progression-free survival but only among ever-smokers. The estimated three-year overall survival was 61% for subtype A and 86% for subtype B; log-rank p = 0.001. The association with survival was independent of HPV status and other HNSCC risk factors (adjusted hazard ratio = 3.58, 95% CI: 1.46, 8.78). Our findings suggest that a non-invasive metabolomic biomarker would add crucial information to clinical risk stratification and raise translational research questions about testing such a biomarker in clinical trials.
by
Anumita Sahoo;
Andrew T Jones;
Narayanaiah Cheedarla;
Sailaja Gangadhara;
Vicky Roy;
Tiffany M Styles;
Ayalnesh Shiferaw;
Korey L Walter;
LaTonya D Williams;
Xiaoying Shen;
Gabriel Ozorowski;
Wen-Hsin Lee;
Samantha Burton;
Lasnajak Yi;
Xuezheng Song;
Zhaohui Qin;
Cynthia Derdeyn;
Andrew B Ward;
John D Clements;
Raghavan Varadarajan;
Georgia D Tomaras;
Pamela A Kozlowski;
Galit Alter;
Rama Amara
The rising global HIV-1 burden urgently requires vaccines capable of providing heterologous protection. Here, we developed a clade C HIV-1 vaccine consisting of priming with modified vaccinia Ankara (MVA) and boosting with cyclically permuted trimeric gp120 (CycP-gp120) protein, delivered either orally using a needle-free injector or through parenteral injection. We tested protective efficacy of the vaccine against intrarectal challenges with a pathogenic heterologous clade C SHIV infection in rhesus macaques. Both routes of vaccination induced a strong envelope-specific IgG in serum and rectal secretions directed against V1V2 scaffolds from a global panel of viruses with polyfunctional activities. Envelope-specific IgG showed lower fucosylation compared with total IgG at baseline, and most of the vaccine-induced proliferating blood CD4+ T cells did not express CCR5 and α4β7, markers associated with HIV target cells. After SHIV challenge, both routes of vaccination conferred significant and equivalent protection, with 40% of animals remaining uninfected at the end of six weekly repeated challenges with an estimated efficacy of 68% per exposure. Induction of envelope-specific IgG correlated positively with G1FB glycosylation, and G2S2F glycosylation correlated negatively with protection. Vaccine-induced TNF-α+ IFN-γ+ CD8+ T cells and TNF-α+ CD4+ T cells expressing low levels of CCR5 in the rectum at prechallenge were associated with decreased risk of SHIV acquisition. These results demonstrate that the clade C MVA/CycP-gp120 vaccine provides heterologous protection against a tier2 SHIV rectal challenge by inducing a polyfunctional antibody response with distinct Fc glycosylation profile, as well as cytotoxic CD8 T cell response and CCR5-negative T helper response in the rectum.
Animal models of adversity have yielded few molecular mechanisms that translate to human stress-related diseases like major depressive disorder (MDD). We congruently analyze publicly available bulk-tissue transcriptomic data from prefrontal cortex (PFC) in multiple mouse models of adversity and in MDD. We apply strategies, to quantify cell-type specific enrichment from bulk-tissue transcriptomics, utilizing reference single cell RNA sequencing datasets. These analyses reveal conserved patterns of oligodendrocyte (OL) dysregulation across animal experiments, including susceptibility to social defeat, acute cocaine withdrawal, chronic unpredictable stress, early life stress, and adolescent social isolation. Using unbiased methodologies, we further identify a dysregulation of layer 6 neurons that associate with deficits in goal-directed behavior after social isolation. Human post-mortem brains with MDD show similar OL transcriptome changes in Brodmann Areas 8/9 in both male and female patients. This work assesses cell type involvement in an unbiased manner from differential expression analyses across animal models of adversity and human MDD and finds a common signature of OL dysfunction in the frontal cortex.
The collection of expression quantitative trait loci (eQTLs) is an important resource to study complex traits through understanding where and how transcriptional regulations are controlled by genetic variations in the non-coding regions of the genome. Previous studies have focused on associating eQTLs with traits to identify the roles of trait-related eQTLs and their corresponding target genes involved in trait determination. Since most genes function as a part of pathways in a systematic manner, it is crucial to explore the pathways’ involvements in complex traits to test potentially novel hypotheses and to reveal underlying mechanisms of disease pathogenesis. In this study, we expanded and applied loci2path software to perform large-scale eQTLs enrichment [i.e., eQTLs’ target genes (eGenes) enrichment] analysis at pathway level to identify the tissue-specific enriched pathways within trait-related genomic intervals. By utilizing 13,791,909 eQTLs cataloged in the Genotype-Tissue Expression (GTEx) V8 data for 49 tissue types, 2,893 pathway sets reported from MSigDB, and query regions derived from the Phenotype-Genotype Integrator (PheGenI) catalog, we identified intriguing biological pathways that are likely to be involved in ten traits [Alzheimer’s disease (AD), body mass index, Parkinson’s disease (PD), schizophrenia, amyotrophic lateral sclerosis, non-small cell lung cancer (NSCLC), stroke, blood pressure, autism spectrum disorder, and myocardial infarction]. Furthermore, we extracted the most significant pathways for AD, such as BioCarta D4-GDI pathway and WikiPathways sulfation biotransformation reaction and viral acute myocarditis pathways, to study specific genes within pathways. Our data presented new hypotheses in AD pathogenesis supported by previous studies, like the increased level of caspase-3 in the amygdala that cleaves GDP dissociation inhibitor and binds to beta-amyloid, leading to increased apoptosis and neuronal loss. Our findings also revealed potential pathogenesis mechanisms for PD, schizophrenia, NSCLC, blood pressure, autism spectrum disorder, and myocardial infarction, which were consistent with past studies. Our results indicated that loci2path′s eQTLs enrichment test was valuable in unveiling novel biological mechanisms of complex traits. The discovered mechanisms of disease pathogenesis and traits require further in-depth analysis and experimental validation.
Identifying biomarkers to predict the clinical outcomes of individual patients is a fundamental problem in clinical oncology. Multiple single-gene biomarkers have already been identified and used in clinics. However, multiple oncogenes or tumor-suppressor genes are involved during the process of tumorigenesis. Additionally, the efficacy of single-gene biomarkers is limited by the extensively variable expression levels measured by high-throughput assays. In this study, we hypothesize that in individual tumor samples, the disruption of transcription homeostasis in key pathways or gene sets plays an important role in tumorigenesis and has profound implications for the patient's clinical outcome. We devised a computational method named iPath to identify, at the individual-sample level, which pathways or gene sets significantly deviate from their norms. We conducted a pan-cancer analysis and demonstrated that iPath is capable of identifying highly predictive biomarkers for clinical outcomes, including overall survival, tumor subtypes, and tumor-stage classifications.