Independent Clinical Cohorts Research Articles

Enhancing Preoperative Diagnosis Accuracy of Stage III Gastric Cancer with Circulating circRNAs.

The prognosis remains poor for stage III gastric cancer, and neoadjuvant chemotherapy is increasingly used to improve outcomes. Accurate diagnosis prior to treatment is essential to develop appropriate treatment strategies for poor prognosis subgroups. This study aims to enhance the accuracy of pre-treatment gastric cancer diagnosis using a biological approach centered on circulating circular RNA (circRNA). We conducted a comprehensive analysis of circRNA expression profiles using two Gene Expression Omnibus datasets to identify circRNA candidates associated with stage III gastric cancer. Subsequently, we validated these circRNA biomarkers in two independent clinical cohorts comprising a total of 174 patients with gastric cancer and non-disease controls through real-time polymerase chain reaction (PCR). Genome-wide circRNA analysis identified a panel of four biomarkers capable of diagnosing pathologically confirmed stage III (pStage III) gastric cancer. In a training cohort (n = 83), a clinically applicable panel of four circRNAs was developed (AUC 0.81), which was successfully validated in an independent clinical cohort (n = 82; AUC 0.76). To assess clinical utility, we combined clinical imaging (cStage) with the circRNA panel. Among those initially diagnosed as cStage III but later confirmed as pStage I/II, 86% were accurately diagnosed using the molecular biological approach with circRNAs. We have developed a circRNA-based non-invasive liquid biopsy that can improve the diagnostic performance of pStage III gastric cancer before treatment. Our circRNA model could provide a sophisticated and personalized approach to assist in treatment planning for patients with advanced gastric cancer.

Large-scale proteomics reveals precise biomarkers for detection of ovarian cancer in symptomatic women

Ovarian cancer is the 8th most common cancer among women and has a 5-year survival of only 30–50%. While the survival is close to 90% for stage I tumours it is only 20% for stage IV. Current biomarkers are not sensitive nor specific enough, and novel biomarkers are urgently needed. We used the Explore PEA technology for large-scale analysis of 2943 plasma proteins to search for new biomarkers using two independent clinical cohorts. The discovery analysis using the first cohort identified 296 proteins that had significantly different levels in malign tumours as compared to benign and for 269 (91%) of these, the association was replicated in the second cohort. Multivariate modelling, including all proteins independent of their association in the univariate analysis, identified a model for separating benign conditions from malign tumours (stage I–IV) consisting of three proteins; WFDC2, KRT19 and RBFOX3. This model achieved an AUC of 0.92 in the replication cohort and a sensitivity and specificity of 0.93 and 0.77 at a cut-off developed in the discovery cohort. There was no statistical difference of the performance in the replication cohort compared to the discovery cohort. WFDC2 and KRT19 have previously been associated with ovarian cancer but RBFOX3 has not previously been identified as a potential biomarker. Our results demonstrate the ability of using high-throughput precision proteomics for identification of novel plasma protein biomarker for ovarian cancer detection.

Decoding polyphenol metabolism in patients with Crohn’s disease: Insights from diet, gut microbiota, and metabolites

Crohn’s disease (CD) is a chronic and progressive inflammatory disease that can involve any part of the gastrointestinal tract. The protective role of dietary polyphenols has been documented in preclinical models of CD. Gut microbiota mediates the metabolism of polyphenols and affects their bioactivity and physiological functions. However, it remains elusive the capacity of microbial polyphenol metabolism in CD patients and healthy controls (HCs) along with its correlation with polyphenols intake and polyphenol-derived metabolites. Thus, we aimed to decode polyphenol metabolism in CD patients through aspects of diet, gut microbiota, and metabolites. Dietary intake analysis revealed that CD patients exhibited decreased intake of polyphenols. Using metagenomic data from two independent clinical cohorts (FAH-SYSU and PRISM), we quantified abundance of polyphenol degradation associated bacteria and functional genes in CD and HCs and observed a lower capacity of flavonoids degradation in gut microbiota residing in CD patients. Furthermore, through analysis of serum metabolites and enterotypes in participants of FAH-SYSU cohort, we observed that CD patients exhibited reduced levels of serum hippuric acid (HA), one of polyphenol-derived metabolites. HA level was higher in healthier enterotypes (characterized by dominance of Ruminococcaceae and Prevotellaceae, dominant by HCs) and positively correlated with multiple polyphenols intake and abundance of bacteria engaged in flavonoids degradation as well as short-chain fatty acid production, which could serve as a biomarker for effective polyphenol metabolism by the gut microbiota and a healthier gut microbial community structure. Overall, our findings provide a foundation for future work exploring the polyphenol-based or microbiota-targeted therapeutic strategies in CD.

A comparison of anti-cyclic citrullinated peptides (CCP3 and CCP3.1) autoantibody tests in rheumatoid arthritis

BackgroundAnti-citrullinated protein antibodies (ACPA) are a specific serological biomarker used in the diagnosis of rheumatoid arthritis (RA). In clinical practice ACPA can be identified using immunoassays targeting synthetic cyclic citrullinated peptides (CCP). The 3rd generation anti-CCP IgG antibody (CCP3) offers improved sensitivity compared to the earlier versions. Recently, CCP3.1, capable of detecting both IgG and IgA antibodies, was introduced to enhance sensitivity, especially in patients with early RA. MethodsWe assessed serum CCP3.1 against CCP3 in 331 subjects undergoing RA panel serology, comprising 136 patients with RA and 195 patients without RA. Sera were tested for anti-CCP IgG (CCP3) and anti-CCP IgG/IgA (CCP3.1) antibodies. Clinical performance of these tests was compared at manufacturer-suggested cutoffs. A separate set of 81 patients with a diagnosis of RA by 2010 criteria and whose samples were obtained from within 1-year of RA diagnosis was similarly assessed to evaluate assay performance in an independent clinical RA cohort. ResultsOverall diagnostic accuracy was similar; CCP3 had an area under the curve (AUC) of 0.88, CCP3.1 had an AUC of 0.89. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) for CCP3 were 79 %, 91 %, 86 %, and 86 %, respectively. For CCP3.1, sensitivity was 78 %, specificity 93 %, PPV 89 %, NPV 86 %. Both assays demonstrated excellent agreement; positive percent agreement of 94 % and negative percent agreement of 99 %. ConclusionOur findings indicate comparable diagnostic accuracy between CCP3 and CCP3.1 assays in these clinical cohorts.

Patient-ventilator asynchrony classification in mechanically ventilated patients: Model-based or machine learning method?

Background and objectivePatient-ventilator asynchrony (PVA) is associated with poor clinical outcomes and remains under-monitored. Automated PVA detection would enable complete monitoring standard observational methods do not allow. While model-based and machine learning PVA approaches exist, they have variable performance and can miss specific PVA events. This study compares a model and rule-based algorithm with a machine learning PVA method by retrospectively validating both methods using an independent patient cohort. MethodsHysteresis loop analysis (HLA) which is a rule-based method (RBM) and a tri-input convolutional neural network (TCNN) machine learning model are used to classify 7 different types of PVA, including: 1) flow asynchrony; 2) reverse triggering; 3) premature cycling; 4) double triggering; 5) delayed cycling; 6) ineffective efforts; and 7) auto triggering. Class activation mapping (CAM) heatmaps visualise sections of respiratory waveforms the TCNN model uses for decision making, improving result interpretability. Both PVA classification methods were used to classify incidence in an independent retrospective clinical cohort of 11 mechanically ventilated patients for validation and performance comparison. ResultsSelf-validation with the training dataset shows overall better HLA performance (accuracy, sensitivity, specificity: 97.5 %, 96.6 %, 98.1 %) compared to the TCNN model (accuracy, sensitivity, specificity: 89.5 %, 98.3 %, 83.9 %). In this study, the TCNN model demonstrates higher sensitivity in detecting PVA, but HLA was better at identifying non-PVA breathing cycles due to its rule-based nature. While the overall AI identified by both classification methods are very similar, the intra-patient distribution of each PVA type varies between HLA and TCNN. ConclusionThe collective findings underscore the efficacy of both HLA and TCNN in PVA detection, indicating the potential for real-time continuous monitoring of PVA. While ML methods such as TCNN demonstrate good PVA identification performance, it is essential to ensure optimal model architecture and diversity in training data before widespread uptake as standard care. Moving forward, further validation and adoption of RBM methods, such as HLA, offers an effective approach to PVA detection while providing clear distinction into the underlying patterns of PVA, better aligning with clinical needs for transparency, explicability, adaptability and reliability of these emerging tools for clinical care.

Localization of stuttering based on causal brain lesions.

Stuttering affects approximately 1 in 100 adults and can result in significant communication problems and social anxiety. It most often occurs as a developmental disorder but can also be caused by focal brain damage. These latter cases may lend unique insight into the brain regions causing stuttering. Here, we investigated the neuroanatomical substrate of stuttering using three independent datasets: (i) case reports from the published literature of acquired neurogenic stuttering following stroke (n = 20, 14 males/six females, 16-77 years); (ii) a clinical single study cohort with acquired neurogenic stuttering following stroke (n = 20, 13 males/seven females, 45-87 years); and (iii) adults with persistent developmental stuttering (n = 20, 14 males/six females, 18-43 years). We used the first two datasets and lesion network mapping to test whether lesions causing acquired stuttering map to a common brain network. We then used the third dataset to test whether this lesion-based network was relevant to developmental stuttering. In our literature dataset, we found that lesions causing stuttering occurred in multiple heterogeneous brain regions, but these lesion locations were all functionally connected to a common network centred around the left putamen, including the claustrum, amygdalostriatal transition area and other adjacent areas. This finding was shown to be specific for stuttering (PFWE < 0.05) and reproducible in our independent clinical cohort of patients with stroke-induced stuttering (PFWE < 0.05), resulting in a common acquired stuttering network across both stroke datasets. Within the common acquired stuttering network, we found a significant association between grey matter volume and stuttering impact for adults with persistent developmental stuttering in the left posteroventral putamen, extending into the adjacent claustrum and amygdalostriatal transition area (PFWE < 0.05). We conclude that lesions causing acquired neurogenic stuttering map to a common brain network, centred to the left putamen, claustrum and amygdalostriatal transition area. The association of this lesion-based network with symptom severity in developmental stuttering suggests a shared neuroanatomy across aetiologies.

Metabolic pathway-based subtypes associate glycan biosynthesis and treatment response in head and neck cancer

Head and Neck Squamous Cell Carcinoma (HNSCC) is a heterogeneous malignancy that remains a significant challenge in clinical management due to frequent treatment failures and pronounced therapy resistance. While metabolic dysregulation appears to be a critical factor in this scenario, comprehensive analyses of the metabolic HNSCC landscape and its impact on clinical outcomes are lacking. This study utilized transcriptomic data from four independent clinical cohorts to investigate metabolic heterogeneity in HNSCC and define metabolic pathway-based subtypes (MPS). In HPV-negative HNSCCs, MPS1 and MPS2 were identified, while MPS3 was enriched in HPV-positive cases. MPS classification was associated with clinical outcome post adjuvant radio(chemo)therapy, with MPS1 consistently exhibiting the highest risk of therapeutic failure. MPS1 was uniquely characterized by upregulation of glycan (particularly chondroitin/dermatan sulfate) metabolism genes. Immunohistochemistry and pilot mass spectrometry imaging analyses confirmed this at metabolite level. The histological context and single-cell RNA sequencing data identified the malignant cells as key contributors. Globally, MPS1 was distinguished by a unique transcriptomic landscape associated with increased disease aggressiveness, featuring motifs related to epithelial-mesenchymal transition, immune signaling, cancer stemness, tumor microenvironment assembly, and oncogenic signaling. This translated into a distinct histological appearance marked by extensive extracellular matrix remodeling, abundant spindle-shaped cancer-associated fibroblasts, and intimately intertwined populations of malignant and stromal cells. Proof-of-concept data from orthotopic xenotransplants replicated the MPS phenotypes on the histological and transcriptome levels. In summary, this study introduces a metabolic pathway-based classification of HNSCC, pinpointing glycan metabolism-enriched MPS1 as the most challenging subgroup that necessitates alternative therapeutic strategies.

A spatial architecture-embedding HLA signature to predict clinical response to immunotherapy in renal cell carcinoma.

An important challenge in the real-world management of patients with advanced clear-cell renal cell carcinoma (aRCC) is determining who might benefit from immune checkpoint blockade (ICB). Here we performed a comprehensive multiomics mapping of aRCC in the context of ICB treatment, involving discovery analyses in a real-world data cohort followed by validation in independent cohorts. We cross-connected bulk-tumor transcriptomes across >1,000 patients with validations at single-cell and spatial resolutions, revealing a patient-specific crosstalk between proinflammatory tumor-associated macrophages and (pre-)exhausted CD8+ T cells that was distinguished by a human leukocyte antigen repertoire with higher preference for tumoral neoantigens. A cross-omics machine learning pipeline helped derive a new tumor transcriptomic footprint of neoantigen-favoring human leukocyte antigen alleles. This machine learning signature correlated with positive outcome following ICB treatment in both real-world data and independent clinical cohorts. In experiments using the RENCA-tumor mouse model, CD40 agonism combined with PD1 blockade potentiated both proinflammatory tumor-associated macrophages and CD8+ T cells, thereby achieving maximal antitumor efficacy relative to other tested regimens. Thus, we present a new multiomics and spatial map of the immune-community architecture that drives ICB response in patients with aRCC.

Abstract LB098: Deep plasma proteome characterization in two independent clinical cohorts identifies clusters of biomarkers separating benign and malign tumors in women with suspicion of ovarian cancer

Abstract Early detection is essential for ovarian cancer survival but today’s biomarkers are not specific enough to clear benign from malignant tumors nor sensitive enough to be used in screening. Here we have used the proximity extension assay (PEA) Olink Explore HT to characterize 5416 plasma proteins. For a subset of the women, we have also characterized the transcriptional landscape in corresponding tumor tissue using total RNA-sequencing on the Illumina NovaSeq 6000 instrument. The plasma samples were from two independent Swedish clinical cohorts (N1 = 171, N2 = 233) and tumor RNA was extracted from 112 of the samples from the second cohort. Each cohort consisted of women who after suspicion of ovarian cancer were surgically diagnosed with either benign or malign tumors. All plasma samples were collected at time of diagnosis, before commencement of treatment. In the plasma protein analyses, the first cohort was used as a discovery and the second as replication. In the discovery cohort, we found a total of 328 plasma proteins differing significantly between women with benign tumors and; early stage (I-II), late stage (III-IV) or any stage(I-IV) malignant tumors. These associations were examined in the replication cohort, and were all found to have similar foldchange between women with benign and malignant tumors. 99.7% (327/328) differed significantly after adjustment for multiple hypothesis testing. The 328 associations corresponded to a total of 192 proteins out of which 26 (13.5%) were found to have a significant correlation with RNA-expression in the corresponding tumor. A correlation network analysis based on the 192 proteins identified 5 major clusters, consisting of 18 to 59 protein-protein couplings. 80% of these clusters contained proteins that are annotated with up to five major KEGG cancer pathways and several of these contained proteins correlating significantly with the tumor RNA-SEQ data. All except one cluster contained one or two proteins that are either currently approved FDA drug targets or listed as druggable in DrugBank by specific compounds. Ovarian cancer has a low 5-year survival and eludes the current treatment regimens if discovered late. By applying high-throughput proteomics in two independent cohorts, we identified a large number of affected proteins out of which only a few have corresponding changes in tumor RNA-expression. We also identified large highly co-regulated networks of proteins, suggesting large downstream systemic effects which could be a contributing factor to the difficulties in finding efficient treatments. Among these proteins, there are several druggable targets, however, not all the associated compounds are currently primarily used as anti-cancer drugs, highlighting the possibility of drug re-purposing for future use against ovarian cancer. Citation Format: Stefan Enroth, Mikaela Moskov, Julia Lindberg Hedlund, Svetlana Popova, Simn K. Forsberg, Klev Diamanti, Maria Lycke, Emma Ivansson, Anna Tolf, Karin Sundfeldt, Karin Stålberg, Ulf Gyllensten. Deep plasma proteome characterization in two independent clinical cohorts identifies clusters of biomarkers separating benign and malign tumors in women with suspicion of ovarian cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr LB098.

Abstract 2750: Leveraging machine-learning approaches to dissect drivers of clinical metastatic dynamics in lung adenocarcinoma

Abstract Background: Non-small cell lung cancer (NSCLC) survival is closely linked to metastatic progression. 5-year survival rates drop from &amp;gt;65% when localized to ~9% when distant metastases occur. Despite this impact on mortality, drivers of overall metastasis and site-specific organotropism are not well understood in NSCLC. To investigate these questions, we focused on Lung adenocarcinoma (LUAD), the most common subtype of NSCLC. Although previous efforts have been made to examine LUAD organotropism, we sought to expand upon longitudinal modeling approaches and explore associations in an independent large clinical and genomic annotated cohort. Methods: Using the artificial intelligence model from (Kehl et al. 2021), we annotated a clinico-genomic cohort of 2777 patients with lung adenocarcinoma, resulting in 59,177 annotated imaging reports. This resulted in time to metastatic site annotations for each patient, with median of 2 years follow up. Metastatic sites included brain, bone, adrenal, liver, lymph node, and mesentery. All patients were evaluated at the Dana-Farber Cancer Institute, and each patient had at least one tumor biopsy sequenced with targeted panel sequencing of 400+ cancer related genes. To infer genomic and clinical correlates of site-specific metastasis, we performed cause-specific and Fine-Gray hazard modeling in this cohort. We used the recurrent event Andersen-Gill model to infer predictors of longitudinal metastasis rates. Results: Site-specific analyses yielded genes whose mutant status were significantly associated with a change in risk and/or rate of certain metastatic sites. We found that NOTCH1 is significantly associated with an increased rate and risk of brain metastases (HR=2.46, P&amp;lt;0.001). SETD2 is associated with decreased incidence of brain (HR=0.42, P&amp;lt;0.001). SMARCA4 is significantly associated with increased incidence and rate of adrenal metastasis (HR=1.64, P&amp;lt;0.01), while NF1 is significantly associated with decreased rate of adrenal metastasis (HR=0.72, P&amp;lt;0.01). In a multivariable model, Female patients and younger patients had a lower rate of new metastatic sites. As previously seen, TP53 and SMARCA4 mutations associated with higher rates of new metastatic sites (HR=1.17, P&amp;lt;0.001; HR=1.36, P&amp;lt;0.001 respectively), and SETD2 is associated with a lower rate of new metastatic sites (HR=0.71, P&amp;lt;0.001). Conclusions: By using time-to-event analysis with longitudinal metastatic annotations, we identify potential drivers of overall metastasis and site-specific organotropism in LUAD patients. Identification of new associations and concordance of our results with previous studies also supports the utility of artificial-intelligence-annotated datasets, allowing for larger cohorts without the need for manual annotation. Citation Format: Tyler Aprati, Michael Manos, Giuseppe Tarantino, Marc Glettig, Maryclare Griffin, Alexander Gusev, Kenneth Kehl, David Liu. Leveraging machine-learning approaches to dissect drivers of clinical metastatic dynamics in lung adenocarcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2750.

Lifespan development of thalamic nuclei and characterizing thalamic nuclei abnormalities in schizophrenia using normative modeling.

Thalamic abnormalities have been repeatedly implicated in the pathophysiology of schizophrenia and other neurodevelopmental disorders. Uncovering the etiology of thalamic abnormalities and how they may contribute to illness phenotypes faces at least two obstacles. First, the typical developmental trajectories of thalamic nuclei and their association with cognition across the lifespan are largely unknown. Second, modest effect sizes indicate marked individual differences and pose a significant challenge to personalized medicine. To address these knowledge gaps, we characterized the development of thalamic nuclei volumes using normative models generated from the Human Connectome Project Lifespan datasets (5-100+ years), then applied them to an independent clinical cohort to determine the frequency of thalamic volume deviations in people with schizophrenia (17-61 years). Normative models revealed diverse non-linear age effects across the lifespan. Association nuclei exhibited negative age effects during youth but stabilized in adulthood until turning negative again with older age. Sensorimotor nuclei volumes remained relatively stable through youth and adulthood until also turning negative with older age. Up to 18% of individuals with schizophrenia exhibited abnormally small (i.e., below the 5th centile) mediodorsal and pulvinar volumes, and the degree of deviation, but not raw volumes, correlated with the severity of cognitive impairment. While case-control differences are robust, only a minority of patients demonstrate unusually small thalamic nuclei volumes. Normative modeling enables the identification of these individuals, which is a necessary step toward precision medicine.

Mendelian Randomization Study With Clinical Follow-Up Links Metabolites to Risk and Severity of Pulmonary Arterial Hypertension.

Pulmonary arterial hypertension (PAH) exhibits phenotypic heterogeneity and variable response to therapy. The metabolome has been implicated in the pathogenesis of PAH, but previous works have lacked power to implicate specific metabolites. Mendelian randomization (MR) is a method for causal inference between exposures and outcomes. Using genome-wide association study summary statistics, we implemented MR analysis to test for potential causal relationships between serum concentration of 575 metabolites and PAH. Five metabolites were causally associated with the risk of PAH after multiple testing correction. Next, we measured serum concentration of candidate metabolites in an independent clinical cohort of 449 patients with PAH to check whether metabolite concentrations are correlated with markers of disease severity. Of the 5 candidates nominated by our MR work, serine was negatively associated and homostachydrine was positively associated with clinical severity of PAH via direct measurement in this independent clinical cohort. Finally we used conditional and orthogonal approaches to explore the biology underlying our lead metabolites. Rare variant burden testing was carried out using whole exome sequencing data from 578 PAH cases and 361 675 controls. Multivariable MR is an extension of MR that uses a single set of instrumental single-nucleotide polymorphisms to measure multiple exposures; multivariable MR is used to determine interdependence between the effects of different exposures on a single outcome. Rare variant analysis demonstrated that loss-of-function mutations within activating transcription factor 4, a transcription factor responsible for upregulation of serine synthesis under conditions of serine starvation, are associated with higher risk for PAH. Homostachydrine is a xenobiotic metabolite that is structurally related to l-proline betaine, which has previously been linked to modulation of inflammation and tissue remodeling in PAH. Our multivariable MR analysis suggests that the effect of l-proline betaine is actually mediated indirectly via homostachydrine. Our data present a method for study of the metabolome in the context of PAH, and suggests several candidates for further evaluation and translational research.

Multi-omics subgroups associated with glycaemic deterioration in type 2 diabetes: an IMI-RHAPSODY Study.

Type 2 diabetes (T2D) onset, progression and outcomes differ substantially between individuals. Multi-omics analyses may allow a deeper understanding of these differences and ultimately facilitate personalised treatments. Here, in an unsupervised "bottom-up" approach, we attempt to group T2D patients based solely on -omics data generated from plasma. Circulating plasma lipidomic and proteomic data from two independent clinical cohorts, Hoorn Diabetes Care System (DCS) and Genetics of Diabetes Audit and Research in Tayside Scotland (GoDARTS), were analysed using Similarity Network Fusion. The resulting patient network was analysed with Logistic and Cox regression modelling to explore relationships between plasma -omic profiles and clinical characteristics. From a total of 1,134 subjects in the two cohorts, levels of 180 circulating plasma lipids and 1195 proteins were used to separate patients into two subgroups. These differed in terms of glycaemic deterioration (Hazard Ratio=0.56;0.73), insulin sensitivity and secretion (C-peptide, p=3.7e-11;2.5e-06, DCS and GoDARTS, respectively; Homeostatic model assessment 2 (HOMA2)-B; -IR; -S, p=0.0008;4.2e-11;1.1e-09, only in DCS). The main molecular signatures separating the two groups included triacylglycerols, sphingomyelin, testican-1 and interleukin 18 receptor. Using an unsupervised network-based fusion method on plasma lipidomics and proteomics data from two independent cohorts, we were able to identify two subgroups of T2D patients differing in terms of disease severity. The molecular signatures identified within these subgroups provide insights into disease mechanisms and possibly new prognostic markers for T2D.

Identification of an immune-related signature as a prognostic classifier for patients with early-stage head and neck squamous cell carcinoma.

Head and neck squamous cell carcinoma (HNSCC) is the most common type and accounts for 90% of all head and neck cancer cases. Despite advances in early diagnosis and treatment strategies-chemotherapy, surgical resection, and radiotherapy-5-year survival remains grim. For patients with early-stage HNSCC, accurately predicting clinical outcomes is challenging. Considering the pivotal role of the immune system in HNSCC, we developed a reliable immune-related gene signature (IRGS) and explored its predictive accuracy in patients with early-stage HNSCC. We examined immune gene expression profiles and clinical information from 230 early-stage HNSCC specimens, including 100 cases from The Cancer Genome Atlas (TCGA), 49 cases from the Gene Expression Omnibus (GEO; GSE65858), and 81 cases from an independent clinical cohort. The prognostic signature was constructed using Kaplan-Meier analysis and the least absolute shrinkage and selection operator (LASSO) Cox algorithm. We also explored the IRGS-related biological pathways and immune landscape using bioinformatics analysis. A nine-immune-gene signature was generated to significantly stratify patients into high and low-risk groups. High risk patients exhibited shorter survival time [hazard ratio (HR) =13.795, 95% confidence interval (CI): 3.275-58.109, P<0.001]. The signature demonstrated robust prognostic ability in the training and validation sets and could independently predict overall survival (OS) and relapse-free survival (RFS). Subsequently, the receiver operating characteristic (ROC) curve and C-index confirmed the signature's predictive accuracy compared to clinical parameters. Additionally, cases classified as low risk showed more immune cell infiltration than high-risk cases. Our novel IRGS is a reliable and robust classifier for accurate patient stratification and prognostic evaluation. Future studies will attempt to affirm the signature's clinical application to early-stage HNSCC.

Identifying G6PC3 as a Potential Key Molecule in Hypoxic Glucose Metabolism of Glioblastoma Derived from the Depiction of 18F-Fluoromisonidazole and 18F-Fluorodeoxyglucose Positron Emission Tomography.

Glioblastoma is the most aggressive primary brain tumor, characterized by its distinctive intratumoral hypoxia. Sequential preoperative examinations using fluorine-18-fluoromisonidazole (18F-FMISO) and fluorine-18-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET) could depict the degree of glucose metabolism with hypoxic condition. However, molecular mechanism of glucose metabolism under hypoxia in glioblastoma has been unclear. The aim of this study was to identify the key molecules of hypoxic glucose metabolism. Using surgically obtained specimens, gene expressions associated with glucose metabolism were analyzed in patients with glioblastoma (n = 33) who underwent preoperative 18F-FMISO and 18F-FDG PET to identify affected molecules according to hypoxic condition. Tumor in vivo metabolic activities were semiquantitatively evaluated by lesion-normal tissue ratio (LNR). Protein expression was confirmed by immunofluorescence staining. To evaluate prognostic value, relationship between gene expression and overall survival was explored in another independent nonoverlapping clinical cohort (n = 17) and validated by The Cancer Genome Atlas (TCGA) database (n = 167). Among the genes involving glucose metabolic pathway, mRNA expression of glucose-6-phosphatase 3 (G6PC3) correlated with 18F-FDG LNR (P = 0.03). In addition, G6PC3 mRNA expression in 18F-FMISO high-accumulated glioblastomas was significantly higher than that in 18F-FMISO low-accumulated glioblastomas (P < 0.01). Protein expression of G6PC3 was consistent with mRNA expression, which was confirmed by immunofluorescence analysis. These findings indicated that the G6PC3 expression might be facilitated by hypoxic condition in glioblastomas. Next, we investigated the clinical relevance of G6PC3 in terms of prognosis. Among the glioblastoma patients who received gross total resection, mRNA expressions of G6PC3 in the patients with poor prognosis (less than 1-year survival) were significantly higher than that in the patients who survive more than 3 years. Moreover, high mRNA expression of G6PC3 was associated with poor overall survival in glioblastoma, as validated by TCGA database. G6PC3 was affluently expressed in glioblastoma tissues with coincidentally high 18F-FDG and 18F-FMISO accumulation. Further, it might work as a prognostic biomarker of glioblastoma. Therefore, G6PC3 is a potential key molecule of glucose metabolism under hypoxia in glioblastoma.

Development of a Novel Biomarker for the Progression of Idiopathic Pulmonary Fibrosis.

The progression of idiopathic pulmonary fibrosis (IPF) is diverse and unpredictable. We identified and validated a new biomarker for IPF progression. To identify a candidate gene to predict progression, we assessed differentially expressed genes in patients with advanced IPF compared with early IPF and controls in three lung sample cohorts. Candidate gene expression was confirmed using immunohistochemistry and Western blotting of lung tissue samples from an independent IPF clinical cohort. Biomarker potential was assessed using an enzyme-linked immunosorbent assay of serum samples from the retrospective validation cohort. We verified that the final candidate gene reflected the progression of IPF in a prospective validation cohort. In the RNA-seq comparative analysis of lung tissues, CD276, COL7A1, CTSB, GLI2, PIK3R2, PRAF2, IGF2BP3, and NUPR1 were up-regulated, and ADAMTS8 was down-regulated in the samples of advanced IPF. Only CTSB showed significant differences in expression based on Western blotting (n = 12; p < 0.001) and immunohistochemistry between the three groups of the independent IPF cohort. In the retrospective validation cohort (n = 78), serum CTSB levels were higher in the progressive group (n = 25) than in the control (n = 29, mean 7.37 ng/mL vs. 2.70 ng/mL, p < 0.001) and nonprogressive groups (n = 24, mean 7.37 ng/mL vs. 2.56 ng/mL, p < 0.001). In the prospective validation cohort (n = 129), serum CTSB levels were higher in the progressive group than in the nonprogressive group (mean 8.30 ng/mL vs. 3.00 ng/mL, p < 0.001). After adjusting for baseline FVC, we found that CTSB was independently associated with IPF progression (adjusted OR = 2.61, p < 0.001). Serum CTSB levels significantly predicted IPF progression (AUC = 0.944, p < 0.001). Serum CTSB level significantly distinguished the progression of IPF from the non-progression of IPF or healthy control.

Targeting of RRM2 suppresses DNA damage response and activates apoptosis in atypical teratoid rhabdoid tumor

BackgroundAtypical teratoid rhabdoid tumors (ATRT) is a rare but aggressive malignancy in the central nervous system, predominantly occurring in early childhood. Despite aggressive treatment, the prognosis of ATRT patients remains poor. RRM2, a subunit of ribonucleotide reductase, has been reported as a biomarker for aggressiveness and poor prognostic conditions in several cancers. However, little is known about the role of RRM2 in ATRT. Uncovering the role of RRM2 in ATRT will further promote the development of feasible strategies and effective drugs to treat ATRT.MethodsExpression of RRM2 was evaluated by molecular profiling analysis and was confirmed by IHC in both ATRT patients and PDX tissues. Follow-up in vitro studies used shRNA knockdown RRM2 in three different ATRT cells to elucidate the oncogenic role of RRM2. The efficacy of COH29, an RRM2 inhibitor, was assessed in vitro and in vivo. Western blot and RNA-sequencing were used to determine the mechanisms of RRM2 transcriptional activation in ATRT.ResultsRRM2 was found to be significantly overexpressed in multiple independent ATRT clinical cohorts through comprehensive bioinformatics and clinical data analysis in this study. The expression level of RRM2 was strongly correlated with poor survival rates in patients. In addition, we employed shRNAs to silence RRM2, which led to significantly decrease in ATRT colony formation, cell proliferation, and migration. In vitro experiments showed that treatment with COH29 resulted in similar but more pronounced inhibitory effect. Therefore, ATRT orthotopic mouse model was utilized to validate this finding, and COH29 treatment showed significant tumor growth suppression and prolong overall survival. Moreover, we provide evidence that COH29 treatment led to genomic instability, suppressed homologous recombinant DNA damage repair, and subsequently induced ATRT cell death through apoptosis in ATRT cells.ConclusionsCollectively, our study uncovers the oncogenic functions of RRM2 in ATRT cell lines, and highlights the therapeutic potential of targeting RRM2 in ATRT. The promising effect of COH29 on ATRT suggests its potential suitability for clinical trials as a novel therapeutic approach for ATRT.

ALZpath Dx: A novel accessible, validated, and scalable phosphorylated‐tau 217 (pTau217) assay in blood

AbstractBackgroundBlood‐based biomarkers for phosphorylated tau demonstrate strong diagnostic accuracy and compare favorably with current corresponding CSF and neuroimaging biomarkers. Of the four major pTau assays in use (pTau181, pTau217, and pTau231, Nfl), pTau217 appears to be the most robust marker, consistently outperforming plasma pTau 181, neurofilament light, and imaging markers. pTau217 has been shown to correlate with post‐mortem AD pathology, with the ability to differentiate AD from other types of dementia and predict future progression from both normal cognition and mild cognitive impairment to AD.(1,2) Additionally, pTau217 can be used to monitor AD progression because longitudinal increases in pTau217 is indicative of worsening of brain atrophy and cognition in AD.(3) pTau217, therefore, represents a cost‐effective and accessible blood‐based biomarker for tau pathophysiology, that can transform the AD diagnostic field.MethodALZpath has developed reagents for pTau217 and establish a robust and scalable fit for purpose plasma‐based ultra‐sensitive assay utilizing a proprietary monoclonal pTau217 antibody.ResultThe ultra‐sensitive blood‐based ELISA assay, using a peptide calibrator, has been developed on the semi‐automated single‐molecule array Simoa® platform. The assay has been launched for clinical use as laboratory‐developed test (LDT). Evaluation in independent clinical cohorts with multiple co‐morbidities is currently being established to advance to an in‐vitro diagnostic (IVD).ConclusionThe ALZpath pTau217 Simoa® assay is now a fit for purpose validated assay now available for commercial use. Clinical use of this assay is described in Snyder et al. Gouda et al. and Ashton et al. AAIC 2023.

CSF proteome profiling identifies novel biomarkers for Frontotemporal Dementia and its pathological subtypes

AbstractBackgroundFrontotemporal dementia (FTD) is caused by frontotemporal lobar degeneration (FTLD) and the most common forms are characterized by either tau (FTLD‐Tau) or TDP43 (FTLD‐TDP) brain aggregates. However, FTD‐specific fluid biomarkers are lacking. Furthermore, the pathological subtypes are not distinct in their presentation, hampering accurate subtyping at clinical diagnosis. Therefore, there is a strong need to identify fluid biomarkers that could aid in FTD diagnosis and to discriminate the pathological subtypes.MethodWe employed an antibody‐based proteomic technology to analyze &gt;600 proteins in a large multicenter cohort including cerebrospinal fluid (CSF) samples from FTD (n = 189), AD (n = 235) and cognitively unimpaired individuals (n = 196). For a subset of cases the underlying neuropathology was known or could be predicted (FTLD‐Tau = 85 and FTLD‐TDP = 57). Differences in protein expression profiles were analyzed by nested linear models. Penalized generalized linear modeling was used to identify classification protein panels. Protein panels were then validated in independent clinical cohorts (cohort 1: n = 157; cohort 2: n = 165) and a neuropathology cohort (n = 100) using customized assays.ResultWe observed 65 differentially regulated proteins in FTD versus controls and AD patients, associated with axonogenesis, synapse assembly, or locomotory behavior pathways. We identified panels of 14 and 13 proteins that could discriminate FTD from controls (AUC = 0.96, 95%CI:0.91‐0.99) and AD patients (AUC = 0.91, 95%CI:0.85‐0.96), respectively. Most of these proteins (21 out of 27) were translated into customized panels, which discriminated between groups with high accuracy for all three cohorts (FTDvsCon: AUCs &gt; 0.96, FTDvsAD: AUCs &gt; 0.88). When comparing the FTLD‐Tau and FTLD‐TDP subtypes, we observed that 86 proteins were increased in FTLD‐Tau, and associated with developmental and cellular processes and locomotion pathways. A panel of 8 proteins could discriminate between the pathological subtypes (AUC = 0.82, 95%CI:0.66‐0.95), which was however not stable during cross‐validation.ConclusionWe identified and validated CSF panels to discriminate FTD from controls and AD with high accuracy. The identification of a biomarker panel to discriminate between the FTLD pathological subtypes likely requires larger and more homogeneous groups. The panels developed within this study might be useful for diagnosis and trial inclusion of FTD patients.

Identification and validation of novel biomarkers for cold-dampness syndrome of rheumatoid arthritis based on integration of multiple bioinformatics methods

This study aims to identify the novel biomarkers of cold-dampness syndrome(RA-Cold) of rheumatoid arthritis(RA) by gene set enrichment analysis(GSEA), weighted gene correlation network analysis(WGCNA), and clinical validation. Firstly, transcriptome sequencing was carried out for the whole blood samples from RA-Cold patients, RA patients with other traditional Chinese medicine(TCM) syndromes, and healthy volunteers. The differentially expressed gene(DEG) sets of RA-Cold were screened by comparison with the RA patients with other TCM syndromes and healthy volunteers. Then, GSEA and WGCNA were carried out to screen the key DEGs as candidate biomarkers for RA-Cold. Experimentally, the expression levels of the candidate biomarkers were determined by RT-qPCR for an independent clinical cohort(not less than 10 cases/group), and the clinical efficacy of the candidates was assessed using the receiver operating characteristic(ROC) curve. The results showed that 3 601 DEGs associated with RA-Cold were obtained, including 106 up-regulated genes and 3 495 down-regulated genes. The DEGs of RA-Cold were mainly enriched in the pathways associated with inflammation-immunity regulation, hormone regulation, substance and energy metabolism, cell function regulation, and synovial pannus formation. GSEA and WGCNA showed that recombinant proteasome 26S subunit, ATPase 2(PSMC2), which ranked in the top 50% in terms of coefficient of variation, representativeness of pathway, and biological modules, was a candidate biomarker of RA-Cold. Furthermore, the validation results based on the clinical independent sample set showed that the F1 value, specificity, accuracy, and precision of PSMC2 for RA-Cold were 70.3%, 61.9%, 64.5%, and 81.3%, respectively, and the area under the curve(AUC) value was 0.96. In summary, this study employed the &quot;GSEA-WGCNA-validation&quot; integrated strategy to identify novel biomarkers of RA-Cold, which helped to improve the TCM clinical diagnosis and treatment of core syndromes in RA and provided an experimental basis for TCM syndrome differentiation.