4-gene Signature Research Articles

Bioinformatics analysis of the role of lysosome-related genes in breast cancer

This study aimed to investigate the roles of lysosome-related genes in BC prognosis and immunity. Transcriptome data from TCGA and MSigDB, along with lysosome-related gene sets, underwent NMF cluster analysis, resulting in two subtypes. Using lasso regression and univariate/multivariate Cox regression analysis, an 11-gene signature was successfully identified and verified. High- and low-risk populations were dominated by HR+ sample types. There were differences in pathway enrichment, immune cell infiltration, and immune scores. Sensitive drugs targeting model genes were screened using GDSC and CCLE. This study constructed a reliable prognostic model with lysosome-related genes, providing valuable insights for BC clinical immunotherapy.

The subcutaneous adipose transcriptome identifies a molecular signature of insulin resistance shared with visceral adipose.

The objective of this study was to identify the transcriptional landscape of insulin resistance (IR) in subcutaneous adipose tissue (SAT) in humans across the spectrum of obesity. We used SAT RNA sequencing in 220 individuals with metabolic phenotyping. We identified a 35-gene signature with high predictive accuracy for homeostatic model of IR that was expressed across a variety of non-immune cell populations. We observed primarily "protective" IR associations for adipocyte transcripts and "deleterious" associations for macrophage transcripts, as well as a high concordance between SAT and visceral adipose tissue (VAT). Multiple SAT genes exhibited dynamic expression 5 years after weight loss surgery and with insulin stimulation. Using available expression quantitative trait loci in SAT and/or VAT, we demonstrated similar genetic effect sizes of SAT and VAT on type 2 diabetes and BMI. SAT is conventionally viewed as a metabolic buffer for lipid deposition during positive energy balance, whereas VAT is viewed as a dominant contributor to and prime mediator of IR and cardiometabolic disease risk. Our results implicate a dynamic transcriptional architecture of IR that resides in both immune and non-immune populations in SAT and is shared with VAT, nuancing the current VAT-centric concept of IR in humans.

Prognostic signature based on S100 calcium-binding protein family members for lung adenocarcinoma and its clinical significance

The S100 family proteins (S100s) participate in multiple stages of tumorigenesis and are considered to have potential value as biomarkers for detecting and predicting various cancers. But the role of S100s in lung adenocarcinoma (LUAD) prognosis is elusive. Transcriptional data of LUAD patients were retrieved from TCGA, and relevant literature was extensively reviewed to collect S100 genes. Differential gene expression analysis was performed on the LUAD data, followed by intersection analysis between the differentially expressed genes (DEGs) and S100 genes. Unsupervised consensus clustering analysis identified two clusters. Significant variations in overall survival between the two clusters were shown by Kaplan-Meier analysis. DEGs between the two clusters were analyzed using Lasso regression and univariate/multivariate Cox regression analysis, leading to construction of an 11-gene prognostic signature. The signature exhibited stable and accurate predictive capability in TCGA and GEO datasets. Subsequently, we observed distinct immune cell infiltration, immunotherapy response, and tumor mutation characteristics in high and low-risk groups. Finally, small molecular compounds targeting prognostic genes were screened using CellMiner database, and molecular docking confirmed the binding of AMG-176, Estramustine, and TAK-632 with prognostic genes. In conclusion, we generated a prognostic signature with robust and reliable predictive ability, which may provide guidance for prognosis and treatment of LUAD.

Stratifying osteosarcoma patients using an epigenetic modification-related prognostic signature: implications for immunotherapy and chemotherapy selection.

Osteosarcoma (OS) poses significant challenges in treatment and lacks reliable prognostic markers. Epigenetic alterations play a crucial role in disease progression. This study aimed to develop an accurate prognostic signature for OS using epigenetic modification genes (EMGs). The Therapeutically Applicable Research to Generate Effective Treatments (TARGET)-OS cohort was analyzed. Univariate Cox analysis identified survival-associated EMGs. Based on least absolute shrinkage and selection operator (LASSO) regression and multivariate analysis, a 6-gene prognostic signature termed the epigenetic modification-related prognostic signature (EMRPS) was derived in the testing cohort. Kaplan-Meier and receiver operating characteristic (ROC) curve analysis confirmed predictive accuracy through internal and external validation (GEO accession GSE21257). A prognostic nomogram incorporating EMRPS and clinical features was constructed. Transcriptomic analysis including differential gene expression, Gene Ontology (GO), gene set enrichment analysis (GSEA), and immune infiltration analysis was conducted to explore mechanisms linking EMRPS to OS prognosis. Additionally, EMRPS impact on drug sensitivity was predicted. A 6-gene EMRPS comprising DDX24, DNAJC1, HDAC4, SIRT7, SP140 and UHRF2 was successfully developed. The high-risk group showed significantly shorter survival, consistently observed in both internal and external validation. EMRPS demonstrated high predictive efficacy for 1-, 3-, and 5-year overall survival, with area under curve (AUC) >0.85 in training and ~0.7 in testing. The nomogram integrating age, gender, metastasis status, and EMRPS exhibited high predictive performance based on concordance index analysis. Mechanistic analysis indicated the low-risk group had increased immune infiltration and activity with higher immune checkpoint expression, reflecting an immune-activated tumor microenvironment (TME) suitable for immunotherapy. Drug sensitivity analysis revealed the low-risk group had increased sensitivity to cisplatin, a first-line OS chemotherapy. Our study successfully established an efficient EMRPS and nomogram, highlighting their potential as novel prognostic markers and indicators for selecting appropriate immunotherapy and chemotherapy candidates in OS treatment.

Eleven inflammation-related genes risk signature model predicts prognosis of patients with breast cancer.

Changes in gene expression are associated with malignancy. Analysis of gene expression data could be used to reveal cancer subtypes, key molecular drivers, and prognostic characteristics and to predict cancer susceptibility, treatment response, and mortality. It has been reported that inflammation plays an important role in the occurrence and development of tumors. Our aim was to establish a risk signature model of breast cancer with inflammation-related genes (IRGs) to evaluate their survival prognosis. We downloaded 200 IRGs from the Molecular Signatures Database (MSigDB). The data of breast cancer were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). Differential gene expression analysis, the least absolute shrinkage and selection operator (LASSO), Cox regression analysis, and overall survival (OS) analysis were used to construct a multiple-IRG risk signature. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were carried out to annotate functions of the differentially expressed IRGs (DEIRGs) The predictive accuracy of the prognostic model was evaluated by time-dependent receiver operating characteristic (ROC) curves. Subsequently, nomograms were constructed to guide clinical application according to the univariate and multivariate Cox proportional hazards regression analyses. Eventually, we applied gene set variation analysis (GSVA), mutation analysis, immune infiltration analysis, and drug response analysis to compare the differences between high- and low-risk patients. Totally, 65 DEIRGs were obtained after comparing 1,092 breast cancer tissues with 113 paracancerous tissues in TCGA. Among them, 11 IRGs (IL18, IL12B, RASGRP1, HPN, CLEC5A, SCARF1, TACR3, VIP, CCL2, CALCRL, ABCA1) were screened with nonzero coefficient by LASSO regression analysis to construct the prognostic model, which was validated in GSE96058.The 11-gene IRGs risk signature model stratified patients into high- or low-risk groups, with those in the low-risk group having longer survival time and less deaths. Multivariate Cox analysis manifested that risk score, age, and stage were the three independent prognostic factors for breast cancer patients. There were 12 pathways with higher activities and 24 pathways with lower activities in the high-risk group compared with the low-risk group, yet no difference of gene mutation load was observed between the two groups. In immune infiltration analysis, we noted that the proportion of T cells showed a decreased trend according to the increase of risk score and most of the immune cells were enriched in the low-risk group. Inversely, macrophages M2 were more highly distributed in the high-risk group. We identified 67 approved drugs that showed a different effect between the high- and low-risk patients and the top 2 gene-drug pairs were IL12B-sunitinib and SCARF1-ruxolitinib. The 11-IRG risk signature model is a promising tool to predict the survival of breast cancer patients and the expressions of IL12B and SCARF1 may serve as potential targets for therapy of breast cancer.

A novel ARHGAP family gene signature for survival prediction in glioma patients.

ARHGAP family genes are often used as glioma oncogenic factors, and their mechanism of action remains unexplained. Our research entailed a thorough examination of the immune microenvironment and enrichment pathways across various glioma subtypes. A distinctive 6-gene signature was developed employing the CGGA cohort, leading to insights into the disparities in clinical characteristics, mutation patterns, and immune cell infiltration among distinct risk categories. Additionally, a unique nomogram was established, grounded on ARHGAPs, with DCA curves illustrating the model's prospective clinical utility in guiding therapeutic strategies. Emphasizing the role of ARHGAP30, integral to our model, its impact on glioma severity and the credibility of our risk assessment model were substantiated through RT-qPCR, Western blot analysis, and cellular functional assays. We identified 6 ARHGAP family genes associated with glioma prognosis. Analysis using the Kaplan-Meier method indicated a correlation between elevated risk levels and adverse outcomes in glioma patients. The risk score, linked with tumour staging and IDH mutation status, emerged as an independent factor predicting prognosis. Patients in the high-risk category exhibited increased immune cell infiltration, enhanced tumour mutational burden, more pronounced expression of immune checkpoint genes, and a better response to ICB therapy. A nomogram, integrating the risk score with the pathological features of glioma patients, was developed. DCA analysis and cellular studies confirmed the model's potential to improve clinical treatment outcomes for patients. A novel ARHGAP family gene signature reveals the prognosis of glioma.

Detection and assessment of immune and stromal related risk genes to predict preeclampsia: A bioinformatics analysis with dataset.

This study aimed to investigate immune score and stromal score-related signatures associated with preeclampsia (PE) and identify key genes for diagnosing PE using bioinformatics analysis. Four microarray datasets, GSE75010, GSE25906, GSE44711, and GSE10588 were obtained from the Gene Expression Omnibus database. GSE75010 was utilized for differential expressed gene (DEGs) analysis. Subsequently, bioinformatic tools such as gene ontology, Kyoto Encyclopedia of Genes and Genomes, weighted gene correlation network analysis, and gene set enrichment analysis were employed to functionally characterize candidate target genes involved in the pathogenesis of PE. The least absolute shrinkage and selection operator regression approach was employed to identify crucial genes and develop a predictive model. This method also facilitated the creation of receiver operating characteristic (ROC) curves, enabling the evaluation of the model's precision. Furthermore, the model underwent external validation through the other three datasets. A total of 3286 DEGs were identified between normal and PE tissues. Gene ontology and Kyoto Encyclopedia of Genes and Genomes analyses revealed enrichments in functions related to cell chemotaxis, cytokine binding, and cytokine-cytokine receptor interaction. weighted gene correlation network analysis identified 2 color modules strongly correlated with immune and stromal scores. After intersecting DEGs with immune and stromal-related genes, 13 genes were selected and added to the least absolute shrinkage and selection operator regression. Ultimately, 7 genes were screened out to establish the risk model for discriminating preeclampsia from controls, with each gene having an area under the ROC curve >0.70. The constructed risk model demonstrated that the area under the ROC curves in internal and the other three external datasets were all greater than 0.80. A 7-gene risk signature was identified to build a potential diagnostic model and performed well in the external validation group for PE patients. These findings illustrated that immune and stromal cells played essential roles in PE during its progression.

A T-Cell-Derived 3-Gene Signature Distinguishes SARS-CoV-2 from Common Respiratory Viruses.

Research on the host responses to respiratory viruses could help develop effective interventions and therapies against the current and future pandemics from the host perspective. To explore the pathogenesis that distinguishes SARS-CoV-2 infections from other respiratory viruses, we performed a multi-cohort analysis with integrated bioinformatics and machine learning. We collected 3730 blood samples from both asymptomatic and symptomatic individuals infected with SARS-CoV-2, seasonal human coronavirus (sHCoVs), influenza virus (IFV), respiratory syncytial virus (RSV), or human rhinovirus (HRV) across 15 cohorts. First, we identified an enhanced cellular immune response but limited interferon activities in SARS-CoV-2 infection, especially in asymptomatic cases. Second, we identified a SARS-CoV-2-specific 3-gene signature (CLSPN, RBBP6, CCDC91) that was predominantly expressed by T cells, could distinguish SARS-CoV-2 infection, including Omicron, from other common respiratory viruses regardless of symptoms, and was predictive of SARS-CoV-2 infection before detectable viral RNA on RT-PCR testing in a longitude follow-up study. Thereafter, a user-friendly online tool, based on datasets collected here, was developed for querying a gene of interest across multiple viral infections. Our results not only identify a unique host response to the viral pathogenesis in SARS-CoV-2 but also provide insights into developing effective tools against viral pandemics from the host perspective.

Dissecting gastric cancer heterogeneity and exploring therapeutic strategies using bulk and single-cell transcriptomic analysis and experimental validation of tumor microenvironment and metabolic interplay.

Gastric cancer, the fifth most prevalent cancer worldwide, is often diagnosed in advanced stages with limited treatment options. Examining the tumor microenvironment (TME) and its metabolic reprogramming can provide insights for better diagnosis and treatment. This study investigates the link between TME factors and metabolic activity in gastric cancer using bulk and single-cell RNA-sequencing data. We identified two molecular subtypes in gastric cancer by analyzing the distinct expression patterns of 81 prognostic genes related to the TME and metabolism, which exhibited significant protein-level interactions. The high-risk subtype had increased stromal content, fibroblast and M2 macrophage infiltration, elevated glycosaminoglycans/glycosphingolipids biosynthesis, and fat metabolism, along with advanced clinicopathological features. It also exhibited low mutation rates and microsatellite instability, associating it with the mesenchymal phenotype. In contrast, the low-risk group showed higher tumor content and upregulated protein and sugar metabolism. We identified a 15-gene prognostic signature representing these characteristics, including CPVL, KYNU, CD36, and GPX3, strongly correlated with M2 macrophages, validated through single-cell analysis and an internal cohort. Despite resistance to immunotherapy, the high-risk group showed sensitivity to molecular targeted agents directed at IGF-1R (BMS-754807) and the PI3K-mTOR pathways (AZD8186, AZD8055). We experimentally validated these promising drugs for their inhibitory effects on MKN45 and MKN28 gastric cells. This study unveils the intricate interplay between TME and metabolic pathways in gastric cancer, offering potential for enhanced diagnosis, patient stratification, and personalized treatment. Understanding molecular features in each subtype enriches our comprehension of gastric cancer heterogeneity and potential therapeutic targets.

BIOM-03. IMMUNO-ONCOLOGIC PROFILING OF PEDIATRIC BRAIN TUMORS REVEALS MAJOR CLINICAL SIGNIFICANCE OF THE TUMOR IMMUNE MICROENVIRONMENT

Abstract With the success of immunotherapy in cancer, understanding the tumor immune microenvironment (TIME) has become increasingly important; however in pediatric brain tumors this remains poorly characterized. Accordingly, we developed a clinical immune-oncology gene expression assay, including the 18-gene tumor inflammation signature (TIS) as a marker of overall immune activation, and validated this with immunohistochemistry for key cell-type markers. We used this assay, in conjunction with public data from the Pediatric Brain Tumor Atlas (PBTA), to profile a diverse range of 1382 samples in total with detailed clinical and molecular annotation. Overall among the main types of pediatric brain tumors, low-grade gliomas (LGG) had the highest inflammation levels (regardless of genetic driver alteration) and medulloblastomas the lowest. In LGG we identified three distinct patterns of immune activation with prognostic significance in BRAF V600E-mutant tumors, specifically that higher inflammation predicted worse outcomes. In high-grade gliomas (HGG), we observed immune activation and T-cell infiltrates in tumors that have historically been considered exclusively immune cold. There were genomic correlates of inflammation levels, with BRAF-V600E-mutant HGG having the highest inflammation levels and suggesting these may be candidates for combination MEK inhibition and immunotherapy. In mismatch repair deficient HGG, we found that high TIS was a significant predictor of response to immune checkpoint inhibition, and demonstrated the potential for multimodal biomarkers (TIS plus tumor mutation burden) to improve treatment stratification. Importantly, while overall patterns of immune activation were observed for histologically and genetically defined tumor types, there was significant variability within each entity, indicating that the TIME must be evaluated as an independent feature from diagnosis. In sum, in addition to the histology and molecular profile, this work underscores the importance of reporting on the TIME as an essential axis of cancer diagnosis in the era of personalized medicine.

Circulating cell-free RNA in blood as a host response biomarker for detection of tuberculosis

Tuberculosis (TB) remains a leading cause of death from an infectious disease worldwide, partly due to a lack of effective strategies to screen and triage individuals with potential TB. Whole blood RNA signatures have been tested as biomarkers for TB, but have failed to meet the World Health Organization’s (WHO) optimal target product profiles (TPP). Here, we use RNA sequencing and machine-learning to investigate the utility of plasma cell-free RNA (cfRNA) as a host-response biomarker for TB in cohorts from Uganda, Vietnam and Philippines. We report a 6-gene cfRNA signature, which differentiates TB-positive and TB-negative individuals with AUC = 0.95, 0.92, and 0.95 in test, training and validation, respectively. This signature meets WHO TPPs (sensitivity: 97.1% [95% CI: 80.9-100%], specificity: 85.2% [95% CI: 72.4-100%]) regardless of geographic location, sample collection method and HIV status. Overall, our results identify plasma cfRNA as a promising host response biomarker to diagnose TB.

Deep learning-based pathway-centric approach to characterize recurrent hepatocellular carcinoma after liver transplantation

BackgroundLiver transplantation (LT) is offered as a cure for Hepatocellular carcinoma (HCC), however 15–20% develop recurrence post-transplant which tends to be aggressive. In this study, we examined the transcriptome profiles of patients with recurrent HCC to identify differentially expressed genes (DEGs), the involved pathways, biological functions, and potential gene signatures of recurrent HCC post-transplant using deep machine learning (ML) methodology.Materials and methodsWe analyzed the transcriptomic profiles of primary and recurrent tumor samples from 7 pairs of patients who underwent LT. Following differential gene expression analysis, we performed pathway enrichment, gene ontology (GO) analyses and protein-protein interactions (PPIs) with top 10 hub gene networks. We also predicted the landscape of infiltrating immune cells using Cibersortx. We next develop pathway and GO term-based deep learning models leveraging primary tissue gene expression data from The Cancer Genome Atlas (TCGA) to identify gene signatures in recurrent HCC.ResultsThe PI3K/Akt signaling pathway and cytokine-mediated signaling pathway were particularly activated in HCC recurrence. The recurrent tumors exhibited upregulation of an immune-escape related gene, CD274, in the top 10 hub gene analysis. Significantly higher infiltration of monocytes and lower M1 macrophages were found in recurrent HCC tumors. Our deep learning approach identified a 20-gene signature in recurrent HCC. Amongst the 20 genes, through multiple analysis, IL6 was found to be significantly associated with HCC recurrence.ConclusionOur deep learning approach identified PI3K/Akt signaling as potentially regulating cytokine-mediated functions and the expression of immune escape genes, leading to alterations in the pattern of immune cell infiltration. In conclusion, IL6 was identified to play an important role in HCC recurrence.

Identification of Stem Cell-related Gene Markers by Comprehensive Transcriptome Analysis to Predict the Prognosis and Immunotherapy of Lung Adenocarcinoma.

Cancer stem cells (CSCs) contribute to metastasis and drug resistance to immunotherapy in lung adenocarcinoma (LUAD), so the stemness evaluation of cancer cells is of great significance. The single-cell RNA sequencing (scRNA-seq) data of the GSE149655 dataset were collected and analyzed. Malignant cells were distinguished by CopyKAT. CytoTRACE score of marker genes in malignant cells was counted by CytoTRACE to construct the stemness score formula. Sample stemness score in TCGA was determined by the formula and divided into high-, medium- and low-stemness score groups. LASSO and COX regression analyses were carried out to screen the key genes related to the prognosis of LUAD from the differentially expressed genes (DEGs) in high- and low-stemness score groups and a risk score model was constructed. Seven types of cells were identified from a total of 4 samples, and 193 marker genes of 3455 malignant cells were identified. There were 1098 DEGs between low- and high-stemness score groups of TCGA, of which CPS1, CENPK, GJB3, and TPSB2 constituted gene signatures. The 4-gene signature could independently evaluate LUAD survival in the training and validation sets and showed an acceptable area under the receiver operator characteristic (ROC) curves (AUCs). This study provides insights into the cellular heterogeneity of LUAD and develops a new cancer stemness evaluation indicator and a 4-gene signature as a potential tool for evaluating the response of LUAD to immune checkpoint blockade (ICB) therapy or antineoplastic therapy.

Deliberate inclusion of multiple co-correlated gene target sets during discovery for improved biomarker translation to a clinical assay in early-stage breast cancer.

e12560 Background: Co-correlation between gene targets provides a more robust, and often nonlinear, understanding of human biology, resulting in more accurate and reliable diagnostics. When translating from discovery to a clinical assay, detectability of promising targets often hinders diagnostic development. Translating a single set of co-correlated targets becomes more challenging, as the success of the program is dependent on the interrelationship between all targets and the ability of the entire set to translate clinically. Given target translation uncertainty, we hypothesized that the deliberate inclusion of multiple independent sets of co-correlated targets during discovery could bridge the gap between promising biology and a clinically viable assay. We set out to discover and clinically validate multiple sets of co-correlated targets required for noninvasive breast cancer detection. Methods: Nonlinear statistical methods were repeatedly applied to microarray gene expression profiles from peripheral blood (PBMCs) obtained from 140 patients with and without breast cancer. Four independent cohorts were used for subsequent studies to assess efficacy and reproducibility of discovered targets. In total, five independent sets of highly efficacious co-correlated targets, totaling 26 unique genes, were discovered and analytically validated across the five patient cohorts (n=357). A sixth independent cohort of 197 plasma samples (breast cancer n=116, controls n=81) was obtained and used to clinically translate and validate the 26 targets using real-time qPCR (qPCR). Results: qPCR revealed strong and reproducible clinical signal from 8/26 previously discovered targets (Table). To evaluate the efficacy of the targets, an 8-gene signature was trained on the PBMC discovery cohort, achieving >99% sensitivity, 83% specificity, indicating a promising classifier could be trained from the 8 qPCR targets. During CLIA validation of the assay, in normalized qPCR data from 87 patients, the 8-gene assay achieved a statistically significant >99% sensitivity, 89% specificity in stage I breast cancer. Conclusions: Given the performance observed from the 8-gene qPCR assay, we conclude that the deliberate inclusion of multiple sets of co-correlated targets, including those considered mathematically redundant during discovery, contributed to the success of a clinically viable assay for the detection of early-stage breast cancer in plasma. [Table: see text]

5-hydroxymethylcytosine in cell-free DNA as liquid biomarker for immune checkpoint inhibitor therapy in non–small cell lung cancer.

e20563 Background: Immune checkpoint inhibitors (ICIs) targeting programmed cell death 1 (PD-1), and its ligand (PD-L1) are the mainstay of treatment for metastatic non-small cell lung cancer (NSCLC) in patients lacking targetable driver mutations. However, optimal predictive biomarkers are lacking. We used a plasma cell-free DNA (cfDNA) 5-hydroxymethylcytosine (5hmC) signature-based predictive model to assess treatment response and potentially select patients eligible for ICI monotherapy. Methods: 40 plasma samples were collected from adult patients with stage III or IV lung cancer at Houston Methodist Hospital between 2019 and 2022. These were divided into a training (n = 24) and validation (n = 16) set. By identifying genes associated with progression free survival (PFS), a 16-gene signature was used to develop a 5hmC predictive model. A weighted predictive score (wp-score) based on the model was calculated for each patient and grouped into low and high wp-scores. Results: Low wp-scores were associated with higher objective response rate(ORR) in both training and validation set and better correlated with response rates compared to PD-L1. In the training set, PFS was longer in low wp-scores compared to high wp-scores (median 12.3 versus 3.0 months; p = 3.5x10-6; hazard ratio (HR) 4.6x10-10). This was also seen in the validation set (median 7.6 versus 1.8 months p = .0012; HR 0.12). Contrarily, high ( > 1%) or low ( < 1%) PD-L1 was not associated with PFS (median 6.8 versus 6.0 months; p = .40; HR 0.70; 95% CI, 0.30–1.60) or overall survival (OS) (median 10.1 versus 12.0 months; p = .38; HR 0.56; 95% CI, 0.15–2.10). 15 out of the 40 total patients received ICI monotherapy. Among these, low wp-scores (n = 8) were significantly associated with longer PFS compared to high wp-scores (n = 7), (median 19.6 versus 2.8 months; p = .00073; HR: 0.059). OS although longer, was not significant among the two groups (median 38.1 versus 10.5 months; p = .12). Conclusions: Our study provides a proof of concept that cfDNA 5hmC signature can be used to predict response to ICI therapy and select patients appropriate for monotherapy. It is a more sensitive and specific biomarker to PD-L1. Larger studies are underway to further validate cfDNA 5hmC based-signature predictive model. [Table: see text]

A blind retrospective analysis of a novel predictive marker to ICB response in NSCLC, calculated directly from histopathological slides.

2665 Background: Immune checkpoint blockers (ICB), and primarily PD-1/PD-L1 inhibitors, are in the forefront of contemporary clinical oncology and have become an integral part of treatment of many malignancies, including non-small cell lung cancer (NSCLC). Nevertheless, tumor response to ICB varies widely. Predictive markers commonly used to distinguish patients likely to respond to ICB, such as PD-L1 expression and tumor mutational burden (TMB) have limited predictive value, which calls for the development of practical and more accurate tests. We present results of a blind retrospective analysis of a novel predictive marker of ICB response in NSCLC, relying solely on histopathological slides. Methods: We obtained high resolution Hematoxylin and Eosin (H&E) slides from tumor-tissue samples of 50 cases of metastatic NSCLC patients treated with first-line PD-1 inhibitors. We retrospectively applied our ENLIGHT-DeepPT (ENLIGHT-DP for short) pipeline to generate, in a blinded manner, an individual response score to PD-1 inhibition for each slide. ENLIGHT-DP is composed of two main steps: (i) predict mRNA expression directly from an H&E slide using DeepPT, our digital-pathology based algorithm; and (ii) use these values as input to ENLIGHT, our transcriptome-based precision oncology platform, which generates a score that predicts response to targeted therapies and ICB (based on a 10-gene signature in this case). We then unblinded the clinical outcome (RECIST1.1), and evaluated ENLIGHT-DP’s performance vs. standard markers. Results: ENLIGHT-DP’s score is predictive of response in this cohort, which had an overall response rate of 68% (34 of 50), with ROC AUC = 0.69 (p = 0.01, one-sided permutation test). Using a predefined threshold for binary classification of response derived from independent data, all 15 patients that were predicted to respond by ENLIGHT-DP indeed responded (100% PPV, 44% sensitivity). In comparison, predicting response according to PD-L1 > 1% achieves 68% PPV and 62% sensitivity, while PD-L1 > 50% achieves 65% PPV and 38% sensitivity, i.e, both thresholds exhibit no predictive power (PPV <= baseline response rate). Patients with high TMB (>10) had 82% PPV and 26% sensitivity, showing lower predictive benefit than ENLIGHT-DP. ENLIGHT-DP was particularly good at stratifying patients with PD-L1 < 1% (18 patients, ROC AUC = 0.8, p = 0.03). Conclusions: ENLIGHT-DP demonstrates high predictive power for response to ICB in NSCLC relying solely on accessible H&E slides, outperforming the commonly used PD-L1 and TMB markers. ENLIGHT-DP is also able to identify responders within patients with PD-L1 < 1%, for whom ICB is usually considered ineffective. Importantly, our approach does not require training on prior treatment outcomes, and can therefore be generalized to drugs for which such data is unavailable or scarce.

Transcriptome-based response predictor to identify potential responders among patients with negative standard markers for response to immune checkpoint blockers.

2666 Background: Immune checkpoint blockers (ICB) are revolutionizing cancer treatment, and are being approved for an increasingly wide range of cancer types. The most common biomarkers currently in use to select patients for ICB are PD-L1 expression on tumor cells, as measured by IHC, and tumor mutational burden (TMB) and microsatellite instability (MSI), both measured by NGS. However, some patients that are negative for these markers still respond to ICB. This calls for complementary biomarkers to better identify responders to ICB, especially in patients that are negative to current biomarkers. Here, we focus on predicting response to anti-PD1 in patients with negative PD-L1, TMB or MSI. Methods: We employ ENLIGHT, our transcriptome-based precision oncology platform, which identifies and utilizes clinically relevant genetic interactions to predict a patient’s response to a wide range of targeted drugs, including ICB. ENLIGHT generates an individual ICB response score calculated from a 10-gene expression signature - the ENLIGHT Matching Score (EMS). Patients with EMS above a predetermined threshold are considered matched by ENLIGHT to anti-PD-1 treatments. We have previously shown, based on more than 1000 cases analyzed retrospectively, that this signature can identify responders to anti-PD-1 with high accuracy. Here, we use ENLIGHT to perform a retrospective analysis of 125 cases from three different datasets, who had low PD-L1 presentation (<1%), low TMB (< 10) or microsatellite stable tumors (MSS), and were treated with anti-PD-1, to specifically assess ENLIGHT’s performance in this biomarker-negative sub-population. Results: Patients who responded to anti-PD-1 treatments had significantly higher EMS in all three datasets. Correspondingly, ENLIGHT is highly predictive of response to anti-PD-1 in patients with negative ICB markers in the three datasets (ROC AUCs of 0.80, 0.84, 0.77, respectively, for low PDL1, low TMB and MSS). It is important to note that ENLIGHT was not trained on any of these datasets, and is applied as-is using previously published parameters. Overall, we find that patients who are ENLIGHT-Matched to anti-PD1 in this biomarker-negative cohort are almost 3 times more likely to respond than patients who are not (31% vs. 11%, p=5e-13). Conclusions: ENLIGHT is a powerful tool for predicting response to anti-PD-1 treatment in patients with negative standard biomarkers for ICB, a currently unmet need with considerable clinical importance.

Unraveling pancreatic ductal adenocarcinoma immune prognostic signature through a naive B cell gene set

BackgroundPancreatic ductal adenocarcinoma (PDAC), a leading cause of cancer mortality, has a complex pathogenesis involving various immune cells, including B cells and their subpopulations. Despite emerging research on the role of these cells within the tumor microenvironment (TME), the detailed molecular interactions with tumor-infiltrating immune cells (TIICs) are not fully understood. MethodsWe applied CIBERSORT to quantify TIICs and naive B cells, which are prognostic for PDAC. Marker genes from scRNA-seq and modular genes from weighted gene co-expression network analysis (WGCNA) were integrated to identify naive B cell-related genes. A prognostic signature was constructed utilizing ten machine-learning algorithms, with validation in external cohorts. We further assessed the immune cell diversity, ESTIMATE scores, and immune checkpoint genes (ICGs) between patient groups stratified by risk to clarify the immune landscape in PDAC. ResultsOur analysis identified 994 naive B cell-related genes across single-cell and bulk transcriptomes, with 247 linked to overall survival. We developed a 12-gene prognostic signature using Lasso and plsRcox algorithms, which was confirmed by 10-fold cross-validation and showed robust predictive power in training and real-world cohorts. Notably, we observed substantial differences in immune infiltration between patients with high and low risk. ConclusionOur study presents a robust prognostic signature that effectively maps the complex immune interactions in PDAC, emphasizing the critical function of naive B cells and suggesting new avenues for immunotherapeutic interventions. This signature has potential clinical applications in personalizing PDAC treatment, enhancing the understanding of immune dynamics, and guiding immunotherapy strategies.

#1121 Identification of kidney transcripts associated with prognosis in ANCA-associated glomerulonephritis

Abstract Background and Aims Kidney involvement in ANCA-associated vasculitis (AAV-GN) predicts poor patient and kidney survival. Deciphering the transcriptomic landscape in AAV-GN may provide insights into pathogenic mechanisms or identify biomarkers for refining diagnosis and/or prognosis, which would help stratify risk and tailor therapeutic management. We aimed to investigate the potential prognostic value of kidney transcripts associated with kidney survival. Method This study included adult patients with AAV-GN from the French Maine-Anjou Registry. Immune gene transcript analysis was performed on RNA extracted from 97 AAV-GN kidney biopsies using NanoString technology. Transcripts of interest were selected, and their prognostic performance was assessed with respect to current histological-based classifications. Following the identification of a possible role for clusterin (CLU), the relationship between serum CLU and prognosis was assessed. Results Among the 750 evaluated transcripts, we identified a 4-gene signature (XRCC6, PRKCD, TEK, and CLU) that was strongly associated with kidney survival (Fig. 1A). This signature predicted kidney survival better than histological-based classifications (global C-Index 0.87 vs. 0.65 for Berden classification or 0.81 for Renal Risk Score, with better time-dependent AUC and Brier scores, especially beyond 1 year after diagnosis) (Fig. 1B). Among these 4 transcripts, the expression level of the CLU transcript had the highest correlation with glomerular involvement, kidney function at diagnosis, and kidney survival. Serum CLU levels were associated with kidney survival, especially when assessed at 6 months from diagnosis (Fig. 1C, P = .023). Conclusion Transcriptomic analysis of kidney biopsies of AAV-GN identified potential transcripts that may improve prediction of kidney survival. This transcriptomic signature may help us gain a deeper understanding of the AAV-GN pathogenesis and provide insights for developing new therapeutic options.

A blood-based 3-gene signature score for therapeutic monitoring in patients with pulmonary tuberculosis

ObjectiveTo assess the validity of Xpert Tuberculosis Fingerstick score for monitoring treatment response and analyze factors influencing its performance. Methods122 adults with pulmonary tuberculosis were recruited and stratified into three cohorts: Diabetic-drug-susceptible-TB (DM-TB), Non-diabetic-drug-susceptible-TB (NDM-TB) and Non-diabetic Multidrug-resistant TB (MDR-TB). Fingerstick blood specimens were tested at treatment initiation (M0) and the end of the first (M1), second (M2), and sixth month (M6) to generate a TB-score. ResultsThe TB-score in all participants yielded an AUC of 0.707 (95% CI: 0.579–0.834) at M2 when its performance was evaluated against sputum culture conversion. In all non-diabetes patients, the AUC reached 0.88 (95% CI: 0.756–1.000) with an optimal cut-off value of 1.95 at which sensitivity was 90.0% (95% CI: 59.6–98.2%) and specificity was 81.3% (95% CI: 70.0–88.9%). The mean TB score was higher in patients with low bacterial loads (n = 31) than those with high bacterial loads (n = 91) at M0, M1, M2, and M6, and was higher in non-cavitary patients (n = 71) than those with cavitary lesions (n = 51) at M0, M1, and M2. ConclusionXpert TB-score shows promising predictive value for culture conversion in non-diabetic TB patients. Sputum bacterial load and lung cavitation status have an influence on the value of TB score.