Signature Panel Research Articles

Identification of a 5-gene signature panel for the prediction of prostate cancer progression.

Despite nearly 100% 5-year survival for localised prostate cancer, the survival rate for metastatic prostate cancer significantly declines to 32%. Thus, it is crucial to identify molecular indicators that reflect the progression from localised disease to metastatic prostate cancer. To search for molecular indicators associated with prostate cancer metastasis, we performed proteomic analysis of rapid autopsy tissue samples from metastatic prostate cancer (N = 8) and localised prostate cancer (N = 2). Then, we utilised multiple independent, publicly available prostate cancer patient datasets to select candidates that also correlate with worse prostate cancer clinical prognosis. We identified 154 proteins with increased expressions in metastases relative to localised prostate cancer through proteomic analysis. From the subset of these candidates that correlate with prostate cancer recurrence (N = 28) and shorter disease-free survival (N = 37), we identified a 5-gene signature panel with improved performance in predicting worse clinical prognosis relative to individual candidates. Our study presents a new 5-gene signature panel that is associated with worse clinical prognosis and is elevated in prostate cancer metastasis on both protein and mRNA levels. Our 5-gene signature panel represents a potential modality for the prediction of prostate cancer progression towards the onset of metastasis.

The JNK-associated leucine zipper protein exerts a protective effect on renal parenchymal injury by limiting the inflammatory secretome in tubular cells

JNK-associated leucine zipper protein (JLP) is a newly identified renal endogenous anti-fibrotic factor that is selectively enriched in renal tubular epithelial cells (TECs). The loss of JLP by TECs is a landmark event that heralds the progression of kidney fibrosis. JLP deficiency ensues a series of pathogenetic cellular processes that are conducive to fibrotic injury. Inflammatory injury is functionally relevant in fibrotic kidneys, and TECs play an essential role in fueling inflammation through aberrant secretions. It is speculated that the loss of JLP in TECs is associated with the relentless inflammation during the development of kidney fibrosis. This study examined the alteration of a panel of inflammatory signatures in TECs under kidney fibrotic circumstances using a Jlp gene-modified unilateral ureteral obstruction (UUO) mouse model or cultured HK-2 cells. It was found that a deficiency of JLP in TECs led to a significant increase in the secretion of inflammatory cytokines including interleukin-1β (IL-1β), tumor necrosis factor (TNF-α), and monocyte chemotactic protein-1 (MCP-1), overactivation of the nuclear factor (NF)-κB/c-Jun N-terminal kinase (JNK) pathway, as well as nucleotide-binding oligomerization domain-like receptor protein 3 (NLRP3) inflammasome-mediated pyroptosis in response to pro-fibrotic damage. Additionally, the absence of JLP resulted in enhanced macrophage migration and fibroblast activation as paracrine effects elicited by injured TECs. In conclusion, the loss of JLP in TECs catalyses inflammatory injuries in the development of kidney fibrosis.

Pilot study of plasma miRNA signature panel for differentiating single vs multiglandular parathyroid disease.

The ability to differentiate sporadic primary hyperparathyroidism (sPHPT) caused by a single parathyroid adenoma (PTA) from multiglandular disease (MGD) pre-operatively, as well as definitely diagnose sPHPT in difficult patients, would enhance surgical decision making. Identify miRNA (miR) signatures for MGD, single- and double-PTA, as well as cell-free miRNA (cfmiR) in plasma samples from patients with single-PTAs to use as biomarkers. 47 patients with sPHPT (single-PTA n=32, double-PTA n=12, MGD n=9). Pre-operative plasma samples from 16 single-PTA and 29 normal healthy donors (NHD). All specimens were processed and analyzed for 2,083 miRs using HTG EdgeSeq miR whole transcriptome assay and normalized using DESeq2 to identify differentially expressed (DE) miRs. MiR classifiers were identified using Random Forest. ROC curves and AUC. MiR signatures distinguished normal parathyroid from MGD and PTA as well as MGD from PTA in tissue samples. Common miRs were found in the single-PTA and double-PTAs. Data integration identified a 27-miR signature in single-PTA tissue samples compared to the rest of the tissue samples. In plasma samples analysis, significant cfmiRs were DE in single-PTA patients compared to NHD. Of those, only 9 miRNAs/cfmiRs were found DE in both tissue and plasma samples from patients diagnosed with a single-PTA (AUC=76%). Twenty-seven miRs were consistently found DE in single-PTA tissue and plasma samples. Data integration showed a 9-cfmiR signature with potential clinical utility to pre-operatively diagnose sPHPT caused by a single-PTA, which could decrease more invasive parathyroid explorations.

Circulating extracellular vesicular microRNA signatures in early gestation show an association with subsequent clinical features of pre-eclampsia

In a prospective cohort of subjects who subsequently developed preeclampsia (PE, n = 14) versus remaining healthy (NORM, n = 12), early gestation circulating extracellular vesicles (EVs) containing a panel of microRNA signatures were characterized and their biological networks of targets deciphered. Multiple microRNAs of which some arose from the placenta (19MC and 14MC) demonstrated changes in association with advancing gestation, while others expressed were pathognomonic of the subsequent development of characteristic clinical features of PE which set in as a late-onset subtype. This panel of miRNAs demonstrated a predictability with an area under the curve of 0.96 using leave-one-out cross-validation training in a logistic regression model with elastic-net regularization and precautions against overfitting. In addition, this panel of miRNAs, some of which were previously detected in either placental tissue or as maternal cell-free non-coding transcripts, lent further validation to our EV studies and the observed association with PE. Further, the identified biological networks of targets of these detected miRNAs revealed biological functions related to vascular remodeling, cellular proliferation, growth, VEGF, EGF and the PIP3/Akt signaling pathways, all mediating key cellular functions. We conclude that we have demonstrated a proof-of-principle by detecting a panel of EV packaged miRNAs in the maternal circulation early in gestation with possibilities of biological function in the placenta and other maternal tissues, along with the probability of predicting the subsequent clinical appearance of PE, particularly the late-onset subtype.

Identification of a circulating long non-coding RNA signature panel in plasma as a novel biomarker for the detection of acute/early-stage HIV-1 infection

BackgroundIndividuals with acute / early HIV-1 infection are often unaware that they are infected with HIV-1 and may be involved in high-risk behavior leading to transmission of HIV-1. Identifying individuals with acute / early HIV-1 infection is critical to prevent further HIV-1 transmission, as diagnosis can lead to several effective HIV-1 prevention strategies. Identification of disease-stage specific non-viral host biomarkers would be useful as surrogate markers to accurately identify new HIV-1 infections. The goal of this study was to identify a panel of host derived plasma long non-coding RNAs (lncRNAs) that could serve as prognostic and predictive biomarkers to detect early/acute HIV-1 infection.MethodsA total of 84 lncRNAs were analyzed in sixteen plasma samples from HIV-1 infected individuals and four healthy controls using the lncRNA PCR-array. Twenty-one lncRNAs were selected and validated in 80 plasma samples from HIV-1 infected individuals [HIV-1 infected patients in the eclipse stage (n = 20), acute stage (n = 20), post-seroconversion p31 negative stage (n = 20), and post-seroconversion p31 positive stage (n = 20) of infection] and 20 healthy controls. The validation study results were used to develop a plasma lncRNA panel that was evaluated in the panel test phase to detect early/acute HIV-1 infection in 52 independent samples.ResultsWe identified a lncRNA panel (Pmodel−I) containing eight lncRNAs (DISC2, H19, IPW, KRASP1, NEAT1, PRINS, WT1-AS and ZFAS1) that could distinguish HIV-1 infection from healthy controls with high AUC 0·990 (95% CI 0.972-1.000), sensitivity (98.75%), and specificity (95%). We also found that Pmodel−II and Pmodel−III demonstrates 100% sensitivity and specificity (AUC 1·00; 95%CI:1·00–1·00) and could distinguish eclipse stage and acute stage of HIV-1 infection from healthy controls respectively. Antiretroviral treatment (ART) cumulatively restored the levels of lncRNAs to healthy controls levels.ConclusionlncRNA expression changes significantly in response to HIV-1 infection. Our findings also highlight the potential of using circulating lncRNAs to detect both the eclipse and acute stages of HIV-1 infection, which may help to shorten the window period and facilitate early detection and treatment initiation. Initiating ART treatment at this stage would significantly reduce HIV-1 transmission. The differentially expressed lncRNAs identified in this study could serve as potential prognostic and diagnostic biomarkers of HIV-1 infection, as well as new therapeutic targets.

Circulating bacterial peptides and linked metabolomic signatures are indicative of early mortality in pediatric cirrhosis.

Patients with pediatric cirrhosis-sepsis (PC-S) attain early mortality. Plasma bacterial composition, the cognate metabolites, and their contribution to the deterioration of patients with PC-S to early mortality are unknown. We aimed to delineate the plasma metaproteome-metabolome landscape and identify molecular indicators capable of segregating patients with PC-S predisposed to early mortality in plasma, and we further validated the selected metabolite panel in paired 1-drop blood samples using untargeted metaproteomics-metabolomics by UHPLC-HRMS followed by validation using machine-learning algorithms. We enrolled 160 patients with liver diseases (cirrhosis-sepsis/nonsepsis [n=110] and noncirrhosis [n=50]) and performed untargeted metaproteomics-metabolomics on a training cohort of 110 patients (Cirrhosis-Sepsis/Nonsepsis, n=70 and noncirrhosis, n=40). The candidate predictors were validated on 2 test cohorts-T1 (plasma test cohort) and T2 (1-drop blood test cohort). Both T1 and T2 had 120 patients each, of which 70 were from the training cohort. Increased levels of tryptophan metabolites and Salmonella enterica and Escherichia coli-associated peptides segregated patients with cirrhosis. Increased levels of deoxyribose-1-phosphate, N5-citryl-d-ornithine, and Herbinix hemicellulolytic and Leifsonia xyli segregated patients with PC-S. MMCN-based integration analysis of WMCNA-WMpCNA identified key microbial-metabolic modules linked to PC-S nonsurvivors. Increased Indican, Staphylobillin, glucose-6-phosphate, 2-octenoylcarnitine, palmitic acid, and guanidoacetic acid along with L. xyli, Mycoplasma genitalium, and Hungateiclostridium thermocellum segregated PC-S nonsurvivors and superseded the liver disease severity indices with high accuracy, sensitivity, and specificity for mortality prediction using random forest machine-learning algorithm. Our study reveals a novel metabolite signature panel capable of segregating patients with PC-S predisposed to early mortality using as low as 1-drop blood.

Novel non-invasive molecular signatures for oral cavity cancer, by whole transcriptome and small non-coding RNA sequencing analyses: Predicted association with PI3K/AKT/mTOR pathway.

Identification of molecular biomarkers in the saliva and serum of oral cavity cancer patients represents a first step in the development of essential and efficient clinical tools for early detection and post-treatment monitoring. We hypothesized that molecular analyses of paired saliva and serum samples from an individual would likely yield better results than analyses of either serum or saliva alone. We performed whole-transcriptome and small non-coding RNA sequencing analyses on 32 samples of saliva and serum collected from the same patients with oral squamous cell carcinoma (OSCC) and healthy controls (HC). We identified 12 novel saliva and serum miRNAs and a panel of unique miRNA and mRNA signatures, significantly differentially expressed in OSCC patients relative to HC (log2 fold change: 2.6-26.8; DE: 0.02-0.000001). We utilized a combined panel of the 10 top-deregulated miRNAs and mRNAs and evaluated their putative diagnostic potential (>87% sensitivity; 100% specificity), recommending seven of them for further validation. We also identified unique saliva and serum miRNAs associated with OSCC and smoking history (OSCC smokers vs. never-smokers or HC: log2 fold change: 22-23; DE: 0.00003-0.000000001). Functional and pathway analyses indicated interactions between the discovered OSCC-related non-invasive miRNAs and mRNAs and their targets, through PI3K/AKT/mTOR signaling. Our data support our hypothesis that using paired saliva and serum from the same individuals and deep sequencing analyses can provide unique combined mRNA and miRNA signatures associated with canonical pathways that may have a diagnostic advantage relative to saliva or serum alone and may be useful for clinical testing. We believe this data will contribute to effective preventive care by post-treatment monitoring of patients, as well as suggesting potential targets for therapeutic approaches.

Deciphering fatty acid biosynthesis-driven molecular subtypes in pancreatic ductal adenocarcinoma with prognostic insights.

Pancreatic ductal adenocarcinoma (PDAC) poses a significant challenge due to its high heterogeneity and aggressiveness. Recognizing the urgency to delineate molecular subtypes, our study focused on the emerging field of lipid metabolism remodeling in PDAC, particularly exploring the prognostic potential and molecular classification associated with fatty acid biosynthesis. Gene set variation analysis (GSVA) and single-sample gene set enrichment analysis (ssGSEA) were performed to evaluate the dysregulation of lipid metabolism in PDAC. Univariate cox analysis and the LASSO module were used to build a prognostic risk score signature. The distinction of gene expression in different risk groups was explored by the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis and Weighted Gene Co-expression Network Analysis (WGCNA). The biological function of Acyl-CoA Synthetase Long Chain Family Member 5 (ACSL5), a pivotal gene within 7-hub gene signature panel, was validated through in vitro assays. Our study identified a 7-hub gene signature associated with fatty acid biosynthesis-related genes (FRGs), providing a robust tool for prognosis prediction. The high-FRGs score group displayed a poorer prognosis, decreased immune cell infiltration, and a higher tumor mutation burden. Interestingly, this group exhibited enhanced responsiveness to various compounds according to the Genomics of Drug Sensitivity in Cancer (GDSC) database. Notably, ACSL5 was upregulated in PDAC and essential for tumor progression. In conclusion, our research defined two novel fatty acid biosynthesis-based subtypes in PDAC, characterized by distinct transcriptional profiles. These subtypes not only served as prognostic indicator, but also offered valuable insights into their metastatic propensity and therapeutic potential.

Single-nucleus transcriptomics uncovers a geroprotective role of YAP in primate gingival aging.

Aging has a profound impact on the gingiva and significantly increases its susceptibility to periodontitis, a worldwide prevalent inflammatory disease. However, a systematic characterization and comprehensive understanding of the regulatory mechanism underlying gingival aging is still lacking. Here, we systematically dissected the phenotypic characteristics of gingiva during aging in primates and constructed the first single-nucleus transcriptomic landscape of gingival aging, by which a panel of cell type-specific signatures were elucidated. Epithelial cells were identified as the most affected cell types by aging in the gingiva. Further analyses pinpointed the crucial role of YAP in epithelial self-renew and homeostasis, which declined during aging in epithelial cells, especially in basal cells. The decline of YAP activity during aging was confirmed in the human gingival tissues, and downregulation of YAP in human primary gingival keratinocytes recapitulated the major phenotypic defects observed in the aged primate gingiva while overexpression of YAP showed rejuvenation effects. Our work provides an in-depth understanding of gingival aging and serves as a rich resource for developing novel strategies to combat aging-associated gingival diseases, with the ultimate goal of advancing periodontal health and promoting healthy aging.

Abstract 1525: Integration of high-plex tumor-Immune phenotyping and checkpoint interactions for deeper spatial characterization of human cancer tissues

Abstract Background: Immune evasion is a key cancer hallmark, and one of the primary mechanisms is the PD-1/PD-L1 checkpoint axis. While immunotherapies that target these interactions have reshaped cancer treatment, the variable efficacy in patients is not fully understood. Due to the weak correlation between PD-1/PD-L1 expression measured by single-plex assays and observed clinical outcomes, there is a need to understand protein interactions at the cellular and subcellular level. Proximity Ligation Assays, detecting spatial interactions between ligand-receptor targets, provide insights into the activation of signaling pathways. Coupled with high-plex spatial phenotyping with tumor and immune profiling biomarkers, the integration of cellular and functional information will be beneficial for a deeper characterization of the tumor microenvironment (TME), understanding immune responses, and identifying spatial signatures for patient stratification and targeted treatment strategies. Methods: In this study, we phenotyped biopsies from head and neck cancer patients enrolled in immune checkpoint inhibitor therapies. We integrated Naveni® PD-1/PD-L1 proximity ligation assays and a customizable high-plex PhenoCode™ Signature Panel (CD3ε/CD8/CD20/CD68/PanCK) for high-throughput profiling of the TME utilizing the PhenoImager® HT 2.0 platform. Comprehensive bioinformatic analyses were conducted for whole-slide segmentation, identifying cellular phenotypes, spatial neighborhoods, functional interactions, and distinct spatial signatures via the open-source image analysis software QuPath. Results: Our analyses revealed key differences in the localization of the PD-1/PD-L1 interactions within the TME of head and neck cancers. Areas of PD-1/PD-L1 interactions are associated with immune cell types on the periphery of the tumor and some within the tumor-infiltrating lymphocytes. The PhenoCode Signature panels helped resolve the localization of PD-1/PD-L1 interactions to immune cell types within the TME and provided a dual workflow for spatial co-localization of interacting receptor/ligand interactions and cell phenotypes within the TME. Conclusions: By combining spatial immune profiling with Akoya’s PhenoCode Signature panels and protein-protein interaction data from Navinci’s Naveni® PD-1/PD-L1 assay, deep correlative insights of the TME can be applied to improve predictions of clinical outcomes. This innovative approach enriches our comprehension of underlying mechanisms and stands as a promising tool for refining patient selection and optimizing treatment outcomes in immunotherapy. Citation Format: Sara Bodbin, Ning Ma, Aditya Pratapa, Nadya Nikulina, James Monkman, Niyati Jhaveri, Hampus Elofsson, Subham Basu, Agata Zieba-Wicher, Arutha Kulasinghe. Integration of high-plex tumor-Immune phenotyping and checkpoint interactions for deeper spatial characterization of human cancer tissues [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 1525.

Abstract 5504: Integrating ultrahigh-plex spatial phenotyping: From discovery to clinical applications

Abstract Background The journey from discovering predictive spatial biomarkers (known as spatial signatures) for immunotherapy responses to their clinical application necessitates a cohesive approach bridging ultrahigh-plex discovery experiments with high-throughput translational studies. This study focuses on integrating Akoya Biosciences spatial multiplexed imaging technologies with advanced data analysis techniques for comprehensive spatial phenotyping across the discovery-to-clinical continuum. Methods Human formalin-fixed, paraffin-embedded (FFPE) cancer tissues were profiled using ultrahigh-plex PhenoCode™ Discovery panels to examine cell lineage, immune activation, and checkpoint markers via the PhenoCycler®-Fusion spatial biology platform. The subsequent use of PhenoCode™ Signature panels targeted specific biomarkers of immune profile, immune contexture, tumor-infiltrating lymphocytes (TIL), macrophage polarization, and T cell status via the PhenoImager® HT platform. Open-source whole slide image analysis software QuPath was employed for precise image analysis, including ROI segmentation, cell detection, classification, exploration of spatial interactions, and identification of distinct spatial signatures. Results Our analysis revealed distinct spatial relationships within various tumor types, quantifying immune cell distributions and their interactions. The ultrahigh-plex data correlated with high-throughput signature panel analyses, thus paving a new simplified way for targeted identification and development of predictive spatial signatures for immunotherapy outcomes. Conclusions The combined use of ultrahigh-plex discovery panels, high-throughput signature panels, and deep-learning image analysis provides a comprehensive understanding of cellular interactions in the tumor microenvironment. This integrated approach, utilizing Akoya's end-to-end workflows, accelerates the identification of predictive spatial biomarker signatures across various human tissue samples. Citation Format: Aditya Pratapa, Ning Ma, Niyati Jhaveri. Integrating ultrahigh-plex spatial phenotyping: From discovery to clinical applications [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 5504.

ChaC1 upregulation reflects poor prognosis in a variety of cancers: analysis of the major missense SNPs of ChaC1 as an aid to refining prognosis

The ChaC1 enzyme that catalyzes cytosolic glutathione degradation is highly upregulated in several cancers. In a systematic review of gene signature panels for cancer prognosis based on oxidative stress and ferroptosis genes, we observed that ChaC1 was found in panels in a wide variety of different cancers, with the upregulation correlating with poor prognosis. Since SNPs can have an impact on functionality and prognosis, ChaC1 SNPs from various databases were also investigated. Six frequently observed missense SNPs were chosen for reconstruction, and their functionality was evaluated. Three out of six SNPs resulted in either a partial or complete loss of ChaC1 function, and these SNPs had the changes R72Q, A156V, and G173S in their proteins. This study highlights the importance of ChaC1 in cancer prognosis across a wide variety of cancers. Additionally, the information on the SNPs of ChaC1 with altered enzymatic activities would improve the prognostic ability of these panels and facilitate treatment regimens.

MicroRNAs Signature Panel Identifies Heavy Drinkers with Alcohol-Associated Cirrhosis from Heavy Drinkers without Liver Injury.

Background: Alcohol-associated liver disease (ALD) is the most common disorder of prolonged drinking. Mechanisms underlying cirrhosis in such patients remain unclear. MicroRNAs play regulatory role in several diseases, are affected by alcohol and may be important players in alcohol use disorders, such as cirrhosis. Methods: We investigated serum samples from heavy chronic alcohol users (80 g/day (male) and 50 g/day (female) for ≥10 years) that were available from our previously reported GenomALC study. A subset of GenomALC drinkers with liver cirrhosis (cases, n = 24) and those without significant liver disease (drinking controls, n = 23) were included. Global microRNA profiling was performed using high-throughput real-time quantitative PCR to identify the microRNA signatures associated with cirrhosis. Ingenuity Pathway Analysis (IPA) software was utilized to identify target mRNAs of significantly altered microRNAs, and molecular pathways were analysed. Identified microRNAs were analysed for correlation with traditional liver disease biomarkers and risk gene variants previously reported from GenomALC genome-wide association study. Results: The expression of 21 microRNAs was significantly downregulated in cases compared to drinking controls (p < 0.05, ∆∆Ct > 1.5-fold). Seven microRNAs (miR-16, miR-19a, miR-27a, miR-29b, miR-101, miR-130a, and miR-191) had a highly significant correlation (p < 0.001) with INR, bilirubin and MELD score. Three microRNAs (miR-27a, miR-130a and miR-191) significantly predicted cases with AUC-ROC 0.8, 0.78 and 0.85, respectively (p < 0.020); however, INR performed best (0.97, p < 0.001). A different set of six microRNAs (miR-19a, miR-26a, miR-101, miR-151-3p, miR-221, and miR-301) showed positive correlation (ranging from 0.32 to 0.51, p < 0.05) with rs10433937:HSD17B13 gene variant, associated with the risk of cirrhosis. IPA analysis revealed mRNA targets of the significantly altered microRNAs associated with cell death/necrosis, fibrosis and increased steatosis, particularly triglyceride metabolism. Conclusions: MicroRNA signatures in drinkers distinguished those with liver cirrhosis from drinkers without liver disease. We identified mRNA targets in liver functions that were enriched for disease pathogenesis pathways.

RNA-Seq-Based Molecular Classification Analyses in Colorectal Cancer and Synchronous Adenoma.

Colorectal cancers (CRC) are classified into consensus molecular subtypes (CMS) based on gene expression profiles. The revised classification system iCMS was proposed by considering intrinsic epithelial status, microsatellite instability (MSI), and fibrosis. This study aimed to provide molecular evidence for the adenoma-carcinoma sequence concept by examining CRC and synchronous adenomas using iCMS. Epithelial CMS cell proportion was estimated using CiberSortx, an in silico cell fractionation method that included CMS cell types among the reference cell types. A random forest (RF) model estimated the posterior probabilities of CMS classes, which were compared with the CiberSortx results. Gene expression profiles of the published iCMS signature panel were retrieved from our dataset and subjected to heatmap clustering for classification. Bulk RNA sequencing data were collected from 29 adenocarcinomas and 11 adenoma samples. CiberSortx showed all CRC contained either CMS2 or CMS3 as the major epithelial cancer cell type. The RF model classified approximately half of the CRC as CMS4, whereas CMS4 was hardly detected by CiberSortx. Because they were enriched with myofibroblasts as per the CiberSortx classification, we tentatively designated them as iCMS2-F/iCMS3-F. iCMS coupled with the application of an in silico cell fractionation method can provide the molecular dissection of CRC and adenoma.

Blood-based Migration Signature Biomarker Panel Discriminates Early Stage New Onset Diabetes related Pancreatic Ductal Adenocarcinoma from Type 2 Diabetes

Background and aimsWhile type 2 diabetes is a well-known risk factor for pancreatic ductal adenocarcinoma (PDAC), PDAC-induced new-onset diabetes (PDAC-NOD) is a manifestation of underlying PDAC. In this study, we sought to identify potential blood-based biomarkers for distinguishing PDAC-NOD from type 2 diabetes (T2DM) without PDAC. Materials and methodsBy ELISA analysis, a migration signature biomarker panel comprising tissue factor pathway inhibitor (TFPI), tenascin C (TNC-FNIII-C) and CA 19–9 was analyzed in plasma samples from 50 PDAC-NOD and 50 T2DM controls. ResultsBoth TFPI (area under the curve (AUC) 0.71) and TNC-FNIII-C (AUC 0.69) outperformed CA 19–9 (AUC 0.60) in distinguishing all stages of PDAC-NOD from T2DM controls. The combined panel showed an AUC of 0.82 (95% CI = 0.73–0.90) (p = 0.002). In the PDAC-NOD early stage II samples, the three biomarkers had an AUC of 0.84 (95% CI = 0.73–0.93) vs CA 19-9, AUC = 0.60, (95% CI = 0.45–0.73), which also improved significance (p = 0.0123). ConclusionThe migration signature panel adds significantly to CA 19–9 to discriminate PDAC-NOD from T2DM controls and warrants further validation for high-risk group stratification.

Precise Identification and Profiling of Surface Proteins of Ultra Rare Tumor Specific Extracellular Vesicle with Dynamic Quantitative Plasmonic Imaging.

Specific detection of tumor-derived EVs (tEVs) in plasma is complicated by nontumor EVs and non-EV particles. To accurately identify tEVs and profile their surface protein expression at single tEV resolution directly with clinical plasma is still an unmet need. Here, we present a Dynamic Immunoassay for Single tEV surface protein Profiling (DISEP), a kinetic assay based on surface plasmon resonance microscopy (SPRM) for specific single tEV profiling. DISEP adopts a pair of low-affinity oligonucleotide probes to respectively label EV surface proteins and functionalize an SPRM biosensor interface. tEVs labeled with the oligonucleotide probes possess distinctive binding kinetics from nonspecific particles in plasma, which permits accurate digital plasmonic counting of single EVs. We demonstrate DISEP for recognizing target EVs among 350-fold background plasma particles with high sensitivity (4677 EVs per μL). Clinical plasma samples were analyzed to discriminate between pancreatic cancer patients (n = 40) and healthy donors (n = 45). With a panel of biomarker signatures (EpCAM, HER2, and GPC1), DISEP only requires 10 μL primary sample from each donor to classify tumor patients with an area under the curve of 0.98. DISEP provides a highly specific EV detection and surface protein profiling strategy for early cancer diagnosis.

Identification of cytokine-predominant immunosuppressive class and prognostic risk signatures in glioma.

The advent of immune checkpoint blockade (ICB) therapies this year has changed the way glioblastoma (GBM) is treated. Meanwhile, some patients with strong PD-L1 expression remain immune checkpoint resistant. To better understand the molecular processes that influence the immune environment, there is an urgent need to characterize the immunosuppressive tumor microenvironment and identify biomarkers to predict patient survival outcomes. Our study analyzed RNA-sequencing data from 178 GBM samples. Their unique gene expression patterns in the tumor microenvironment were analyzed by an unsupervised clustering algorithm. Through these expression patterns, a panel of T-cell exhaustion signatures, immunosuppressive cells, and clinical features correlates with immunotherapy response. The presence or absence of immune status and prognostic signatures was then validated with the test dataset. 38.2% of GBM patients showed increased expression of anti-inflammatory cytokines, significant enrichment of T cell exhaustion signals, higher proportion of immunosuppressive cells (macrophages and CD4 regulatory T cells) and nine inhibitory checkpoints (CTLA4, PDCD1, LAG3, BTLA, TIGIT, HAVCR2, IDO1, SIGLEC7, and VISTA). The immunodepleted class (IDC) was used to classify these immunocompromised individuals. Despite the high density of tumor-infiltrating lymphocytes shown by IDC, such patients have a poor prognosis. Although PD-L1 was highly expressed in IDC, it suggested that there might be ICB resistance. There are many IDC predictive signatures to discover. PD-1 is strongly expressed in a novel immunosuppressive class of GBM, but this cluster may be resistant to ICB therapy. A comprehensive description of this drug-resistant tumor microenvironment could provide new insights into drug resistance mechanisms and improved immunotherapy techniques.

Regulatory T Cell Biomarkers Identify Patients at Risk of Developing Acute Cellular Rejection in the First Year Following Heart Transplantation.

Acute cellular rejection (ACR), an alloimmune response involving CD4+ and CD8+ T cells, occurs in up to 20% of patients within the first year following heart transplantation. The balance between a conventional versus regulatory CD4+ T cell alloimmune response is believed to contribute to developing ACR. Therefore, tracking these cells may elucidate whether changes in these cell populations could signal ACR risk. We used a CD4+ T cell gene signature (TGS) panel that tracks CD4+ conventional T cells (Tconv) and regulatory T cells (Treg) on longitudinal samples from 94 adult heart transplant recipients. We evaluated combined diagnostic performance of the TGS panel with a previously developed biomarker panel for ACR diagnosis, HEARTBiT, while also investigating TGS' prognostic utility. Compared with nonrejection samples, rejection samples showed decreased Treg- and increased Tconv-gene expression. The TGS panel was able to discriminate between ACR and nonrejection samples and, when combined with HEARTBiT, showed improved specificity compared with either model alone. Furthermore, the increased risk of ACR in the TGS model was associated with lower expression of Treg genes in patients who later developed ACR. Reduced Treg gene expression was positively associated with younger recipient age and higher intrapatient tacrolimus variability. We demonstrated that expression of genes associated with CD4+ Tconv and Treg could identify patients at risk of ACR. In our post hoc analysis, complementing HEARTBiT with TGS resulted in an improved classification of ACR. Our study suggests that HEARTBiT and TGS may serve as useful tools for further research and test development.

Artificial intelligence-driven pan-cancer analysis reveals miRNA signatures for cancer stage prediction.

The ability to detect cancer at an early stage in patients who would benefit from effective therapy is a key factor in increasing survivability. This work proposes an evolutionary supervised learning method called CancerSig to identify cancer stage-specific microRNA (miRNA) signatures for early cancer predictions. CancerSig established a compact panel of miRNA signatures as potential markers from 4,667 patients with 15 different types of cancers for the cancer stage prediction, and achieved a mean performance: 10-fold cross-validation accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve of 84.27%± 6.31%, 0.81± 0.12, 0.80± 0.10, and 0.80± 0.06, respectively. The pan-cancer analysis of miRNA signatures suggested that three miRNAs, hsa-let-7i-3p, hsa-miR-362-3p, and hsa-miR-3651, contributed significantly toward stage prediction across 8 cancers, and each of the 67 miRNAs of the panel was a biomarker of stage prediction in more than one cancer. CancerSig may serve as the basis for cancer screening and therapeutic selection..

Use of immune checkpoint gene (ICG) signatures to identify selective enrichment of FGL1 and CEACAM1 in histologic subtypes of lung cancer.

e20527 Background: Immune checkpoint blockade has changed the management paradigm for lung cancer. Identification of vulnerable subsets using reliable predictive biomarkers is an area of unmet need. We systematically compared the pattern of expression of validated mediators of adaptive and innate immune response in clinical and histologic subsets of lung cancer using publicly available datasets. Methods: Gene expression data for lung adenocarcinoma (LUAD, N = 501) and and lung squamous cell carcinoma (LUSC, N = 491) was obtained from The Cancer Genome Atlas. We compared the expression of ICGs between LUSC and LUAD using DESeq2 and estimated the proportion of different immune cells in the tumor microenvironment by CIBERSORT. An expanded panel of ICG signatures from MSigDB was compared between LUSC and LUAD using gene set variation analysis (GSVA). Weighted gene co-expression network analysis (WGCNA) identified modules of immune checkpoint inhibitor genes with significant correlation with histology, which were further investigated for biologic pathway involvement and survival analysis using Ingenuity Pathway Analysis and Gene Expression Profiling Interactive Analysis (GEPIA2), respectively. Results: Majority of the ICGs had a higher expression in LUAD than LUSC with the strongest differential expression recorded for FGL1 (LogFC = 6.2, FDR = 1.2E-239) and CEACAM1 (LogFC = 1.4, FDR = 2E-71). Treg cells and monocytes were higher in LUAD while NK cells and macrophages were higher in LUSC. There was no difference in CD8(+) T cell population by histology. Genesets related to PD1 signaling from MSigDB had significantly higher GSVA scores in LUAD than LUSC. WGCNA identified 2 modules containing immune checkpoint genes – one module containing 15 of the 23 ICGs involved in allograft rejection, interferon gamma response, interferon alpha response, IL6-signaling, IL2-signaling and TNF-alpha signaling showed moderate correlation with LUAD (module-trait correlation = 0.3, P = 8e-26). A second module with 4 ICGs ( FGL1, LGALS3, CEACAM1, PVRL2) enriched in pathways related to carbohydrate and lipid metabolism (galactose degradation, gamma-linolenate biosynthesis) showed very strong correlation with LUAD (module-trait correlation = 0.85, P = 5e-278). Additional comparison by gender and smoking status along with external validation using independent dataset is ongoing. Conclusions: ICG expression profile differs between LUAD and LUSC with a stronger expression profile in LUAD than LUSC.