4-gene Signature Research Articles

Comprehensive single-cell transcriptomic profiling reveals molecular subtypes and prognostic biomarkers with implications for targeted therapy in esophageal squamous cell carcinoma

BackgroundEsophageal squamous cell carcinoma (ESCC) is a genetically heterogeneous disease with poor clinical outcomes. Identification of biomarkers linked to DNA replication stress may enable improved prognostic risk stratification and guide therapeutic decision making. We performed integrated single-cell RNA sequencing and computational analyses to define the molecular determinants and subtypes underlying ESCC heterogeneity. MethodsSingle-cell RNA sequencing was performed on ESCC samples and analyzed using Seurat. Differential gene expression analysis was used to identify esophageal cell phenotypes. DNA replication stress-related genes were intersected with single-cell differential expression data to identify potential prognostic genes, which were used to generate a DNA replication stress (DRS) score. This score and associated genes were evaluated in survival analysis. Putative prognostic biomarkers were evaluated by Cox regression and consensus clustering. Mendelian randomization analyses assessed the causal role of PRKCB. ResultsHigh DRS score associated with poor survival. Four genes (CDKN2A, NUP155, PPP2R2A, PRKCB) displayed prognostic utility. Three molecular subtypes were identified with discrete survival and immune properties. A 12-gene signature displayed robust prognostic performance. PRKCB was overexpressed in ESCC, while PRKCB knockdown reduced ESCC cell migration. ConclusionsThis integrated single-cell sequencing analysis provides new insights into the molecular heterogeneity and prognostic determinants underlying ESCC. The findings identify potential prognostic biomarkers and a gene expression signature that may enable improved patient risk stratification in ESCC. Experimental validation of the role of PRKCB substantiates the potential clinical utility of our results.

Development and validation of a pyroptosis-related prognostic signature associated with osteosarcoma metastasis and immune infiltration.

Pyroptosis is a programmed cell death, which has garnered increasing attention because it relates to the immune and therapy response. However, few studies focus on the application of pyroptosis-related genes (PRGs) in predicting osteosarcoma (OS) patients' prognoses. In this study, the gene expression and clinical information of OS patients were downloaded from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database. Based on these PRGs and unsupervised clustering analysis, all OS samples can be classified into 2 clusters. The 8 key differential expressions for PRGs (LAG3, ITGAM, CCL2, TLR4, IL2RA, PTPRC, FCGR2B, and CD5) were established through the univariate Cox regression and utilized to calculate the risk score of all samples. According to the 8-gene signature, OS samples can be divided into high and low-risk groups and correlation analysis can be performed using immune cell infiltration and immune checkpoints. Finally, we developed a nomogram to improve the PRG-predictive model in clinical application. We verified the predictive performance using receiver operating characteristic (ROC) and calibration curves. There were significant differences in survival, immune cell infiltration and immune checkpoints between the low and high-risk groups. A nomogram was developed with clinical indicators and the risk scores were effective in predicting the prognosis of patients with OS. In this study, a prognostic model was constructed based on 8 PRGs were proved to be independent prognostic factors of OS and associated with tumor immune microenvironment. These 8 prognostic genes were involved in OS development and may serve as new targets for developing therapeutic drugs.

Prognostic model based on B cell marker genes for NSCLC patients under neoadjuvant immunotherapy by integrated analysis of single-cell and bulk RNA-sequencing data.

Neoadjuvant immunotherapy has evolved as an effective option to treat non-small cell lung cancer (NSCLC). B cells play essential roles in the immune system as well as cancer progression. However, the repertoire of B cells and its association with clinical outcomes remains unclear in NSCLC patients receiving neoadjuvant immunotherapy. Single-cell RNA sequencing (scRNA-seq) and bulk RNA sequencing data for LUAD samples were accessed from the TCGA and GEO databases. LUAD-related B cell marker genes were confirmed based on comprehensive analysis of scRNA-seq data. We then constructed the B cell marker gene signature (BCMGS) and validated it. In addition, we evaluated the association of BCGMS with tumor immune microenvironment (TIME) characteristics. Furthermore, we validated the efficacy of BCGMS in a cohort of NSCLC patients receiving neoadjuvant immunotherapy. A BCMGS was constructed based on the TCGA cohort and further validated in three independent GSE cohorts. In addition, the BCMGS was proven to be significantly associated with TIME characteristics. Moreover, a relatively higher risk score indicated poor clinical outcomes and a worse immune response among NSCLC patients receiving neoadjuvant immunotherapy. We constructed an 18-gene prognostic signature derived from B cell marker genes based on scRNA-seq data, which had the potential to predict the prognosis and immune response of NSCLC patients receiving neoadjuvant immunotherapy.

Circulating Biomarkers for Tumour Monitoring, Prognosis Predictions and Detection of Temozolomide Resistance Mutations in Glioma

INTRODUCTION: The potential benefits of circulating biomarkers have been well demonstrated in other cancers. Despite this, no circulating biomarker exists for glioma due to poor sensitivity and hetereogeneity of results. METHODS: Serum miRNA and plasma ctDNA was isolated in a cohort of 212 gliomas using next-generation sequencing (NGS) and digital droplet PCR (ddPCR). Differentially expressed miRNAs were discovered using a machine learning algorithm and compared to tumor volume on MRI. A Lasso-regression model identified miRNAs that were associated with both progression-free and overall survival. Tumor mutations and ctDNA was compared in 49 plasma samples from 10 patients with tumor tissue available from at least two operations using NGS. Identified mutations were validated using ddPCR in the whole cohort. RESULTS: A 9-gene miRNA signature was found that could distinguish between glioma and healthy controls with 99.8% accuracy, including two that demonstrated dynamic changes that correlated with tumor volume on MRI. We found a separate 4-gene miRNA signature that was predictive of survival outcomes and could be incorporated into a clinical nomogram. Common glioma mutations were identified within 93% of plasma samples, including 25% that were not found in the tumor tissue. Mutations in the mismatch repair genes (MMR) were frequently detected in the plasma following temozolomide treatment and observed prior to their appearance in tissue at progression. CONCLUSIONS: We have identified serum miRNA biomarker signatures that can monitor tumour burden and improve prognostic predictions. We also show that contrary to previous reports, ctDNA can be reliably detected in the plasma of gliomas. We found that ctDNA offers complementary information to tissue biopsy alone and provides early evidence of temozolomide resistance through mutations in MMR genes.

A single-cell characterised signature integrating heterogeneity and microenvironment of lung adenocarcinoma for prognostic stratification

The high heterogeneity of tumour and the complexity of tumour microenvironment (TME) greatly impacted the tumour development and the prognosis of cancer in the era of immunotherapy. In this study, we aimed to portray the single cell-characterised landscape of lung adenocarcinoma (LUAD), and develop an integrated signature incorporating both tumour heterogeneity and TME for prognosis stratification. Single-cell tagged reverse transcription sequencing (STRT-seq) was performed on tumour tissues and matched normal tissues from 14 patients with LUAD for immune landscape depiction and candidate key genes selection for signature construction. Kaplan-Meier survival analyses and in-vitro cell experiments were conducted to confirm the gene functions. The transcriptomic profile of 1949 patients from 11 independent cohorts including nine public datasets and two in-house cohorts were obtained for validation. We selected 11 key genes closely related to cell-to-cell interaction, tumour development, T cell phenotype transformation, and Ma/Mo cell distribution, including HLA-DPB1, FAM83A, ITGB4, OAS1, FHL2, S100P, FSCN1, SFTPD, SPP1, DBH-AS1, CST3, and established an integrated 11-gene signature, stratifying patients to High-Score or Low-Score group for better or worse prognosis. Moreover, the prognostically-predictive potency of the signature was validated by 11 independent cohorts, and the immunotherapeutic predictive potency was also validated by our in-house cohort treated by immunotherapy. Additionally, the in-vitro cell experiments and drug sensitivity prediction further confirmed the gene function and generalizability of this signature across the entire RNA profile spectrum. This single cell-characterised 11-gene signature might offer insights for prognosis stratification and potential guidance for treatment selection. Support for the study was provided by National key research and development project (2022YFC2505004, 2022YFC2505000 to Z.W. and J.W.), Beijing Natural Science Foundation (7242114 to J.X.), National Natural Science Foundation of China of China (82102886 to J.X., 81871889 and 82072586 to Z.W.), Beijing Nova Program (20220484119 to J.X.), NSFC general program (82272796 to J.W.), NSFC special program (82241229 to J.W.), CAMS Innovation Fund for Medical Sciences (2021-1-I2M-012, 2022-I2M-1-009 to Z.W. and J.W.), Beijing Natural Science Foundation (7212084 to Z.W.), CAMS Key lab of translational research on lung cancer (2018PT31035 to J.W.), Aiyou Foundation (KY201701 to J.W.). Medical Oncology Key Foundation of Cancer Hospital Chinese Academy of Medical Sciences (CICAMS-MOCP2022003 to J.X.).

Developing a novel immune infiltration-associated mitophagy prediction model for amyotrophic lateral sclerosis using bioinformatics strategies.

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease, which leads to muscle weakness and eventual paralysis. Numerous studies have indicated that mitophagy and immune inflammation have a significant impact on the onset and advancement of ALS. Nevertheless, the possible diagnostic and prognostic significance of mitophagy-related genes associated with immune infiltration in ALS is uncertain. The purpose of this study is to create a predictive model for ALS using genes linked with mitophagy-associated immune infiltration. ALS gene expression profiles were downloaded from the Gene Expression Omnibus (GEO) database. Univariate Cox analysis and machine learning methods were applied to analyze mitophagy-associated genes and develop a prognostic risk score model. Subsequently, functional and immune infiltration analyses were conducted to study the biological attributes and immune cell enrichment in individuals with ALS. Additionally, validation of identified feature genes in the prediction model was performed using ALS mouse models and ALS patients. In this study, a comprehensive analysis revealed the identification of 22 mitophagy-related differential expression genes and 40 prognostic genes. Additionally, an 18-gene prognostic signature was identified with machine learning, which was utilized to construct a prognostic risk score model. Functional enrichment analysis demonstrated the enrichment of various pathways, including oxidative phosphorylation, unfolded proteins, KRAS, and mTOR signaling pathways, as well as other immune-related pathways. The analysis of immune infiltration revealed notable distinctions in certain congenital immune cells and adaptive immune cells between the low-risk and high-risk groups, particularly concerning the T lymphocyte subgroup. ALS mouse models and ALS clinical samples demonstrated consistent expression levels of four mitophagy-related immune infiltration genes (BCKDHA, JTB, KYNU, and GTF2H5) with the results of bioinformatics analysis. This study has successfully devised and verified a pioneering prognostic predictive risk score for ALS, utilizing eighteen mitophagy-related genes. Furthermore, the findings indicate that four of these genes exhibit promising roles in the context of ALS prognostic.

Abstract 1761: Comprehensive profiling of the FAT3-associated gene signature in deciphering immunophenotypes of gastrointestinal cancers: Analysis of institutional cohort and TCGA dataset

Abstract Gastrointestinal (GI) cancers are among the most prevalent cancers affecting the US population. Anatomically, GI cancer includes cancers of the organs in the digestive tract, from the esophagus to the rectum. Advances in molecular oncology have started to transform the therapeutic landscape and offer tremendous promise for patients across diverse lineages. However, the equitable benefit of this approach has been hampered due to tumor heterogeneity and the lack of accurate, tumor-agnostic biomarkers with prognosis and predictive utility. Thus, there is a need to identify novel biomarkers that affect gastrointestinal malignancies to improve the management of cancer patients. This study analyzed a single institution's whole genome sequencing dataset to explore the genomic variation in GI cancers. Between 2020 and 2022, 302 AU patients underwent molecular profiling, including whole-exome sequencing (WES) profiling at Caris Life Sciences. WES is an NGS assay that analyzes the DNA sequences of all protein-coding exons in the genome, representing approximately 1-2% of the human genome with coverage of over 22,000 genes. We analyzed the TCGA dataset for comparative analysis, which included cancer data from >1800 GI cancer patients. In addition, survival and network analyses were performed to identify a 6-gene FAT3-related signature for GI cancers. In our cohort, we identified FAT3 (Fat atypical cadherin 3) as the most frequently mutated gene after TP53, APC, and KRAS. FAT3 encodes a cadherin protein involved in cell-cell interactions and adhesion. In the institutional cohort, FAT3 was found to be mutated in 16% of all GI cases. Further analysis of TCGA datasets comprising 1808 GI cancer patients revealed FAT3 mutations in 12% of the cases. To identify additional prognostic biomarkers associated with FAT3, we performed network analysis and identified a 6-gene FAT3-related signature (FAT3, RYK, FAT2, EGFLAM, NTRK3, IGSF9, and HMGA2) that significantly stratified GI cancer patients based on overall survival, progression-free survival, and disease-specific survival. The perturbation profile of the 6-gene signature was associated with 509 patients or 28% of total GI patients. Further, immune deconvolution analysis of stratified patients revealed increased infiltration of immune cells with immunosuppressive phenotypic properties in GI cancer patients with higher expression of the FAT3-related gene signature. In summary, this analysis reveals the distribution of FAT3 mutational profiles in GI patients and identifies a gene signature that stratifies patients based on survival outcomes. This analysis can provide new tools for patient stratification and therapy implementation, leading to better outcomes for cancer patients. Citation Format: Pankaj Kumar Ahluwalia, Tiffanie Leeman, Ashis Mondal, Ashutosh Vashisht, Harmanpreet Singh, Ravindra Kolhe. Comprehensive profiling of the FAT3-associated gene signature in deciphering immunophenotypes of gastrointestinal cancers: Analysis of institutional cohort and TCGA dataset [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 1761.

Abstract 6430: Gene expression signature quantifies the cellular contribution of the tumor vs. the microenvironment in determining colorectal cancer prognosis and therapeutic outcome

Abstract Accumulating evidence has suggested that cancer progression and therapeutic response may depend on not only tumor cells but also their tumor microenvironment (TME). However, their contributory roles are complex and not well understood. Here we report an analysis of 2373 human colorectal cancer (CRC) tumors representing all stages, leading to identification of a 20-gene signature capable of quantifying the cellular contribution of the tumor vs. the TME in determining colorectal cancer prognosis and therapeutic outcome. We classified these tumors into the consensus molecular subtypes (CMS1-4) with distinct, variable TME cellular features defined by CIBERSORT deconvolution analysis of bulk gene expression data. Surprisingly, all the 10 positive signature genes (portending worse survival) were strongly correlated with the TME-rich CMS1 and CMS4 tumors. By contrast, all the 10 negative signature genes (portending better survival) were highly related to the TME-poor CMS2 and CMS3 tumors. A single cell expression analysis from an independent database further revealed that strikingly, all the 10 positive genes were principally expressed by the immune/stromal TME whereas all the 10 negative genes were predominantly expressed in epithelial tumor cells. These data suggest that the TME has an equally important role as do tumor cells in determining CRC prognosis and support targeting the TME to improve survival. Moreover, distinct gene expression from tumor cells versus the TME also showed a differential impact on therapeutic outcome. Retrospective analyses on two independent clinical trial datasets show that the 20-gene signature score significantly predicted progression free survival in metastatic CRC patients treated with cetuximab, an FDA-approved EGFR inhibitor therapy. These data suggest a potential for the 20-gene signature as a predictive biomarker to identify “sensitive” versus “resistant” subpopulations of CRC patients to improve outcome of EGFR-targeted therapy. Citation Format: Mingli Yang, Timothy J. Yeatman, Michael V. Nebozhyn, Michael J. Schell, Lance Pflieger, Andrey Loboda, Warren J. Pledger, Ramani Soundararajan, Michelle Maurin, Heiman Wang, Jetsen Rodriguez Silva, Ashley Alden. Gene expression signature quantifies the cellular contribution of the tumor vs. the microenvironment in determining colorectal cancer prognosis and therapeutic outcome [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 6430.

Abstract 3652: Spatial transcriptomic study of the tumor microenvironment in HNSCC

Abstract Background: The complex and dynamic nature of the tumor-immune microenvironment (TME) presents challenges for identification of robust and predictive biomarkers in immuno-oncology (IO). Standard multiplex immunohistochemistry (mIHC) or multiplex immuno-fluorescent assays (mIF) allow for the phenotyping on the cellular level but are limited by the number of markers available and the detection systems. Gene expression or next generation sequencing (NGS) platforms provide high-plex bulk data sets about the TME, but the spatial context of the tumor and immune cell interactions is lost. The NanoString GeoMx® Digital Spatial Profiler (DSP) combines spatial context, utilizing fluorescent morphological markers, with molecular profiling capabilities. This combination allows for the quantitative analysis of high-plex analyte abundance which can be traced back to a region of interest within a sample, providing biological inference in the region of interest. Methods: Formalin-fixed paraffin-embedded (FFPE) specimens from HNSCC patients were cut in 5µm sections for all analyses. Whole section tissue analysis was previously performed utilizing the NanoString nCounter® PanCancer IO 360™ Panel. Tumor inflammation signatures (TIS), an 18-gene signature measuring the adaptive immune response within tumors, was generated for each specimen and categorized each as immune-hot or immune-cold based on predefined parameters. Four specimens were selected based on their TIS score and two sets of cut sections were processed by either Labcorp lab in Morrisville, NC or Nanostring lab in Seattle, WA for precision evaluation, using the GeoMx Human Whole Transcriptome Atlas (WTA) panel (18,000 genes) with the NanoString GeoMx digital spatial profiling technology. The TME regions of interest (ROI) were identified using a set of morphology markers consisting of panCK, CD45 and Syto13; 12 ROIs were selected from each sectioned tumor material. The same ROIs were selected at the two testing sites with matching tissue location and morphology characters. Results: GeoMx WTA panel provided an unbiased view of 18,000 protein-coding genes at specific TME regions. These counts were successfully mapped to the 12 ROIs on the tissue. A pseudo-TIS score was generated using the same 18 gene signature from the WTA panel for each ROI. The pseudo-TIS scores from ROIs from the four specimens showed consistency with the specimen-wise TIS score, yet variations were observed across different TME/ROI within each specimen. Whole transcriptome expression results showed high correlation between the two testing sites, providing confidence in the reproducibility of the technology. High resolution fluorescent staining images were able to be fed to downstream quantitative digital analysis to generate complementary spatial data. Citation Format: Caitlin Schroyer, Shanshan Zhou. Spatial transcriptomic study of the tumor microenvironment in HNSCC [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 3652.

Abstract 1141: Spatial-specific gene signatures to predict immunotherapy outcomes in lung cancer

Abstract The widespread use of immunotherapy in lung cancer, and its more recent approval for early stages, underscores the need for biomarkers that can identify the most responsive patients. Spatial transcriptomics, which maps gene expression in its spatial tissue context, offers a unique approach over bulk transcriptomics. By incorporating spatial information, the predictive power of the signature can be enhanced. Here, we aim to develop spatially informed gene signatures that could be translated into clinical RNA in situ assays, distinguishing patients unlikely to benefit from immunotherapy and thereby sparing them unnecessary side effects. We utilized the NanoString GeoMX Whole Transcriptome Atlas for spatially resolved transcriptomic profiling of retrospectively collected lung cancer tissue samples from patients treated with immunotherapy in an advanced-stage setting (N=60). By targeting 18,190 genes within distinct areas of interest (AOIs)—including stromal (macrophages/CD68+ and leukocytes/CD45+) and tumor (cytokeratin, CK+) cells—we developed AOI-specific gene signatures to predict objective responses. These were derived from a robust computational framework employing LASSO logistic regression on a split-sample approach, yielding predictive models for treatment outcome.We achieved high predictive accuracy on the training set, with the area under the curve (AUC) exceeding 0.86 for all AOI-specific spatial signatures, indicating strong potential for clinical application. Validation against an independent cohort (N=42) corroborated the efficacy of these signatures. Our 6-gene tumor signature was validated with an AUC of 0.73 (95% CI: 0.67-0.89, p = 0.009**), while the CD45 5-gene signature showed an AUC of 0.75 (95% CI: 0.53-0.97, p = 0.022*). Our 18-gene CD68 signature trended towards validation but lacked statistical significance. A combined CD68 and CD45 signature predicted outcomes with greater accuracy, achieving an AUC of 0.79 (95% CI: 0.62-0.98, p = 0.0088*). Following gene set enrichment analysis on our differentially expressed and signature genes, we identified genes with positive coefficients in both tumor and stroma signatures that were associated with glucocorticoid response and glycolytic processes linked to T-cell homeostasis, while genes with negative coefficients were associated with epithelial cell differentiation and cytokine production. These associations are concordant with the observation that genes with positive coefficients are predictors of treatment response, while those with negative coefficients indicate resistance.Our findings indicate that AOI-specific signatures predict the immunotherapy outcome in lung cancer with high accuracy, suggesting that spatial assessment can provide substantial predictive information. The high performance of these signatures indicates their potential for prospective clinical applications. Citation Format: Thazin N. Aung, Myrto Moutafi, Ioannis Trontzas, Arutha Kulasinghe, James Monkman, Niki Gavrielatou, Ioannis Vathiotis, Jonathan H. Warrell, David L. Rimm. Spatial-specific gene signatures to predict immunotherapy outcomes in lung cancer [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 1141.

Abstract 7612: Genetic predictors of anti-PD(L)-1 therapy response in hepatocellular carcinoma

Abstract Introduction: Immunotherapy targeting PD-1/PD-L1 shows promise in treating hepatocellular carcinoma (HCC), but standard predictors like PD-L1 expression are unreliable for gauging treatment response. RNA sequencing has identified a BMS 4-gene inflammatory signature that could more accurately forecast clinical outcomes. However, genomic alterations and tumor mutational burden (TMB) haven't consistently predicted anti-PD(L)-1 therapy response in HCC. Therefore, the aim of this research is to identify potential genetic alternation that could serve as predictive marker for anti-PD(L)-1 therapy responses in advanced HCC. Methods: Formalin-fixed paraffin-embedded (FFPE) tumor tissues from 38 HCC patients were analyzed using targeted next-generation sequencing with the ACTOncoTM panel before anti-PD(L)-1 therapy. This analysis identified somatic variants across 440 genes and calculated TMB. In silico tools like NetMHC and IEDB predicted neo-peptide-HLA class I binding affinity. Additionally, peripheral blood mononuclear cells provided germline data for paired TMB calculations, with tumor responses assessed by RECIST criteria. Results: Within a cohort of 167 patients with HCC undergoing ICI therapy, 35 (20.9%) responded to treatment, while 132 (79.1%) did not. Available formalin-fixed paraffin-embedded (FFPE) samples from this population included those from 14 responders and 30 non-responders. After conducting quality control measures, which assessed tumor purity, DNA quantity and integrity, as well as sequencing quality, 13 samples from responders and 25 from non-responders qualified for further analysis. Gene alteration analysis identified mutations in TP53 (47%), MUC16 (34%), CTNNB1 (32%), USH2A (16%), ARID1A (13%), NOTCH3 (13%), and ADAMTS16 (11%) of the cases. A TP53 mutation was linked to poor overall survival, whereas mutations in CTNNB1 and the RTK/RAS/RAF pathway did not correlate with clinical outcomes. The median TMB, predicted at 3.2 ± 4.3 mutations per megabase (muts/Mb), was nominally higher in patients who achieved disease control compared to non-responders (4.6 vs. 3.7 muts/Mb, p=0.53). Utilizing paired TMB analysis, those with disease control exhibited a higher TMB trend than non-responders (6.0 vs. 2.2 muts/Mb, p=0.06). Notably, the 13 cases with MUC16 mutations were associated with a significantly higher predicted TMB (10.0 vs. 3.3 muts/Mb, p=0.0015). Conclusions: This study suggests that certain genetic alterations, especially TP53 mutations, may have predictive value for the response to anti-PD(L)-1 therapy in HCC. Moreover, an elevated TMB, particularly when associated with MUC16 mutations, seems to be correlated with improved disease control. Citation Format: San-Chi Chen, Nai-Jung Chiang, MingHuang Chen, Muh-Hwa Yang. Genetic predictors of anti-PD(L)-1 therapy response in hepatocellular carcinoma [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 7612.

Abstract 3641: Pharmacodynamic and predictive biomarker results from the phase I dose escalation study of NGM707, an ILT2/ILT4 dual antagonist antibody, in patients with advanced solid tumors

Abstract Background: NGM707 is a dual antagonist antibody that binds a shared epitope of the immune inhibitory receptors ILT2 (LILRB1) and ILT4 (LILRB2). In preclinical studies, NGM707 reprogrammed suppressive myeloid cells via blockade of ILT2/ILT4 and enhanced NK and CD8+ T cell activity via blockade of ILT2; furthermore, NGM707 and pembrolizumab acted additively in vitro to enhance CD4+ T cell activation in mixed lymphocyte reactions. NGM707-IO-101 (NCT04913337) is a first-in-human Phase 1/2 clinical trial of NGM707 monotherapy and combination with pembrolizumab. Here we report pharmacodynamic and predictive biomarker data from NGM707-IO-101 monotherapy and combination dose escalation cohorts. Methods: Eligible patients with advanced or metastatic solid tumors were enrolled into escalating dose cohorts from 6 to 1800 mg NGM707, alone or in combination with 200 mg pembrolizumab, administered Q3W iv. Biomarker assessments of tumors comprised RNA-sequencing and immunohistochemistry (including ILT2, ILT4, CD163) of archival/baseline (N= 41 IHC, 47 RNA) and paired on-treatment tumor biopsies (N=13 IHC, 21 RNA). Peripheral blood biomarker samples were collected on days 1, 4, 8, and 15 of cycle 1 and prior to dosing at each subsequent cycle. Blood biomarker assessments included circulating cytokines/chemokines, PBMC RNA-sequencing, and flow cytometry immunophenotyping. Baseline biomarkers were associated with anti-tumor activity per RECIST v1.1. Results: Pre-treatment biopsies from patients with disease control (DC) showed higher expression of genes in the 18-gene tumor inflammation signature (TIS) than patients with progressive disease (PD). A composite gene signature incorporating gene sets related to TIS, NK cell activity, and tumor cell proliferation had a statistically stronger association with DC than TIS genes alone. Baseline ILT2/ILT4 target expression (≥1% of tumor area) was observed in 31 (75.6%) patients, with similar levels observed in patients with DC and PD. Pharmacodynamic changes were observed in tumor samples following treatment, including increased expression of gene signatures that reflect myeloid cell activation and a proinflammatory tumor microenvironment. Decreased protein expression of the immunosuppressive myeloid marker CD163 was observed following treatment in both blood and tumor for a subset of patients. Proinflammatory chemokines including CXCL9 increased in serum following treatment. Conclusion: Baseline gene expression signatures have shown an encouraging association with clinical benefit from NGM707 and merit further investigation as promising predictive biomarkers. Protein and gene expression changes reflecting the expected modes of action of NGM707 and pembrolizumab were observed in both peripheral blood and tumor biopsies following treatment. Citation Format: Lisa K. Blum, Hung-I H. Chen, Anushka De Costa, Anjushree Iyer, Geoffrey W. Stone, Nicole D. Galicia, Jerry Chan, Brenda Dampier, Katherine Wu, Ursula Jeffry, Kefei Zhou, Joanne Sloan-Lancaster, Dhiraj Abhayankar, Vladimir Hanes, Hsiao D. Lieu, Daniel D. Kaplan, Julie M. Roda. Pharmacodynamic and predictive biomarker results from the phase I dose escalation study of NGM707, an ILT2/ILT4 dual antagonist antibody, in patients with advanced solid tumors [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 3641.

Abstract 4942: Unraveling a prognostic and predictive 8-gene signature related to cuproplasia in pan-cancer

Abstract This study aims at identifying a cuproplasia-related gene signature to predict outcome for cancer patients based on gene regulatory networks. Copper is a vital micronutrient involved in many biological processes. Copper influences tumor growth through a process called cuproplasia, defined as abnormal copper-dependent cell-growth and proliferation. Copper-chelation therapy targeting this process has demonstrated efficacy in several clinical trials against cancer. While the molecular pathways associated with cuproplasia are partially known, genetic heterogeneity across different cancer types has limited the understanding of how cuproplasia impacts patient survival. We proposed a novel framework based on gene regulatory networks to identify critical cuproplasia-related genes (CCGs) across different cancer types. Utilizing RNA-sequencing data from The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) datasets, we generated gene regulatory networks for 23 different cancer types. We applied a network control method to discover critical genes in these networks and combined with copper metabolism related genes to identify CCGs across 23 cancer types. We then performed a comprehensive analysis on the identified CCGs to better understand their potential roles and associations with cancer patient survival. We discovered 30 CCGs which were enriched in pathways related to a wide range of biological processes supporting cancer progression and immune evasion processes. The specific processes related to the identified CCGs include autophagy, cell cycle regulation, cell proliferation, kinase signaling pathways, immune infiltration, and immune checkpoint regulation. From this, we identified a novel 8-CCG signature significantly associated with survival pan-cancer, including CDK1, AP1S1, CASP3, MAP1LC3A, SNCA, TMPRSS6, MAPT, and GSK3B. Although some of these genes are known for their important roles in various malignancies and are associated with poor survival, this is the first study defining their connection with high copper levels in cancer cells. Furthermore, in keeping with previous studies that have used metal regulatory genes to predict outcomes in a specific cancer type, we specifically used low grade glioma (LGG), a cancer type highly impacted by intracellular copper level, as an example to explore the role of CCGs in a specific cancer type. Through our proposed gene regulatory network methodology, we identified a 3-gene signature that overlapped with a 6-gene prognostic risk score model for LGG patients. These 6 genes included ALB, CASP3, CDK1, CP, CYP1A1, and MT-CO1. To our knowledge, this is the first pan-cancer analysis for genes related to cuproplasia. The findings from our study highlight the use of gene regulatory networks to identify CCGs which could be used to develop novel targeted therapeutic strategies. Citation Format: Vu Viet Hoang Pham, Toni Jue, Jessica Bell, Fabio Luciani, Filip Michniewicz, Giuseppe Cirillo, Linda Vahdat, Chelsea Mayoh, Orazio Vittorio. Unraveling a prognostic and predictive 8-gene signature related to cuproplasia in pan-cancer [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 4942.

Retracted: Development of a 5-Gene Signature to Evaluate Lung Adenocarcinoma Prognosis Based on the Features of Cancer Stem Cells.

[This retracts the article DOI: 10.1155/2022/4404406.].

Comprehensive investigation into the influence of glycosylation on head and neck squamous cell carcinoma and development of a prognostic model for risk assessment and anticipating immunotherapy.

It has been well established that glycosylation plays a pivotal role in initiation, progression, and therapy resistance of several cancers. However, the correlations between glycosylation and head and neck squamous cell carcinoma (HNSCC) have not been elucidated in detail. The paramount genes governing glycosylation were discerned via the utilization of the Protein-Protein Interaction (PPI) network and correlation analysis, coupled with single-cell RNA sequencing (scRNA-seq) analysis. To construct risk models exhibiting heightened predictive efficacy, cox- and lasso-regression methodologies were employed, and the veracity of these models was substantiated across both internal and external datasets. Subsequently, an exploration into the distinctions within the tumor microenvironment (TME), immunotherapy responses, and enriched pathways among disparate risk cohorts ensued. Ultimately, cell experiments were conducted to validate the consequential impact of SMS in Head and Neck Squamous Cell Carcinoma (HNSCC). A total of 184 genes orchestrating glycosylation were delineated for subsequent scrutiny. Employing cox- and lasso-regression methodologies, we fashioned a 3-gene signature, proficient in prognosticating the outcomes for patients afflicted with HNSCC. Noteworthy observations encompassed distinctions in the Tumor Microenvironment (TME), levels of immune cell infiltration, and the presence of immune checkpoint markers among divergent risk cohorts, holding potentially consequential implications for the clinical management of HNSCC patients. The prognosis of HNSCC can be proficiently anticipated through risk signatures based on Glycosylation-related genes (GRGs). A thorough delineation of the GRGs signature in HNSCC holds the potential to facilitate the interpretation of HNSCC's responsiveness to immunotherapy and provide innovative strategies for cancer treatment.

Identification and Verification of Metabolism-related Immunotherapy Features and Prognosis in Lung Adenocarcinoma.

Lung cancer is a frequent malignancy with a poor prognosis. Extensive metabolic alterations are involved in carcinogenesis and could, therefore, serve as a reliable prognostic phenotype. Our study aimed to develop a prognosis signature and explore the relationship between metabolic characteristic-related signature and immune infiltration in lung adenocarcinoma (LUAD). TCGA-LUAD and GSE31210 datasets were used as a training set and a validation set, respectively. A total of 513 LUAD samples collected from The Cancer Genome Atlas database (TCGA-LUAD) were used as a training dataset. Molecular subtypes were classified by consensus clustering, and prognostic genes related to metabolism were analyzed based on Differentially Expressed Genes (DEGs), Protein-Protein Interaction (PPI) network, the univariate/multivariate- and Lasso- Cox regression analysis. Two molecular subtypes with significant survival differences were divided by the metabolism gene sets. The DEGs between the two subtypes were identified by integrated analysis and then used to develop an 8-gene signature (TTK, TOP2A, KIF15, DLGAP5, PLK1, PTTG1, ECT2, and ANLN) for predicting LUAD prognosis. Overexpression of the 8 genes was significantly correlated with worse prognostic outcomes. RiskScore was an independent factor that could divide LUAD patients into low- and high-risk groups. Specifically, high-risk patients had poorer prognoses and higher immune escape. The Receiver Operating Characteristic (ROC) curve showed strong performance of the RiskScore model in estimating 1-, 3- and 5-year survival in both training and validation sets. Finally, an optimized nomogram model was developed and contributed the most to the prognostic prediction in LUAD. The current model could help effectively identify high-risk patients and suggest the most effective drug and treatment candidates for patients with LUAD.

Integrating tertiary lymphoid structure-associated genes into computational models to evaluate prognostication and immune infiltration in pancreatic cancer.

Pancreatic ductal adenocarcinoma (PDAC) is characterized by poor response to all therapeutic modalities and dismal prognosis. The presence of tertiary lymphoid structures (TLSs) in various solid cancers is of crucial prognostic significance, highlighting the intricate interplay between the tumor microenvironment and immune cells aggregation. However, the extent to which TLSs and immune status affect PDAC prognosis remains incompletely understood. Here, we sought to unveil the unique properties of TLSs in PDAC by leveraging both single-cell and bulk transcriptomics, culminating in a risk model that predicts clinical outcomes. We used TLS scores based on a 12-gene (CCL2, CCL3, CCL4, CCL5, CCL8, CCL18, CCL19, CCL21, CXCL9, CXCL10, CXCL11, and CXCL13) and 9-gene (PTGDS, RBP5, EIF1AY, CETP, SKAP1, LAT, CCR6, CD1D, and CD79B) signature, respectively, and examined their distribution in cell clusters of single-cell data from PDAC samples. The markers involved in these clusters were selected to develop a prognostic model using The Cancer Genome Atlas Program database as the training cohort and Gene Expression Omnibus database as the validation cohort. Further, we compared the immune infiltration, drug sensitivity, and enriched and differentially expressed genes between the high- and low-risk groups in our model. Therefore, we established a risk model that has significant implications for the prognostic assessment of PADC patients with remarkable differences in immune infiltration and chemosensitivity between the low- and high-risk groups. This paradigm established by TLS-related cell marker genes provides a prognostic prediction and a panel of novel therapeutic targets for exploring potential immunotherapy.

Integrated clinical and prognostic analyses of mTOR/Hippo pathway core genes in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is one of the most aggressive and dismal cancers globally. Emerging evidence has established that mTOR and Hippo pathways are oncogenic drivers of HCC. However, the prognostic value of these pathways in HCC remains unclear. In this study, we aimed to develop a gene signature utilizing the mTOR/Hippo genes for HCC prognostication. A multiple stage strategy was employed to screen, and a 12-gene signature based on mTOR/Hippo pathways was constructed to predict the prognosis of HCC patients. The risk scores calculated by the signature were inversely correlated with patient prognosis. Validation of the signature in independent cohort confirmed its predictive power. Further analysis revealed molecular differences between high and low-risk groups at genomic, transcriptomic, and protein-interactive levels. Moreover, immune infiltration analysis revealed an immunosuppressive state in the high-risk group. Finally, the gene signature could predict the sensitivity to current chemotherapeutic drugs. This study demonstrated that combinatorial mTOR/Hippo gene signature was a robust and independent prognostic tool for survival prediction of HCC. Our findings not only provide novel insights for the molecular understandings of mTOR/Hippo pathways in HCC, but also have important clinical implications for guiding therapeutic strategies.

Immunogenic Cell Death-related Signature Evaluates the Tumor Microenvironment and Predicts the Prognosis in Diffuse Large B-Cell Lymphoma.

Current literatures suggest a growing body of evidence highlighting the pivotal role of Immunogenic Cell Death (ICD) in multiple tumor types. Nevertheless, the potential and mechanisms of ICD in diffuse large B-cell lymphoma (DLBCL) remain inadequately studied. To address this gap, our current study aims to examine the impact of ICD on DLBCL and identify a corresponding gene signature in DLBC. Using the expression profiles of ICD-associated genes, the gene expression omnibus (GEO) samples were segregated into ICD-high and ICD-lowsubtypes utilizing non-negative matrix factorization clustering. Next, univariate and LASSO Cox regression analyses were employed to establish the ICD-related gene signature. Subsequently, the CIBERSORT tool, ssGSEA, and ESTIMATE algorithm were utilized to examine the association between the signature and tumor immune microenvironment of DLBC. Finally, the oncoPredict algorithm was implemented to evaluate the drug sensitivity prediction of DLBCL patients. These findings suggest that the immune microenvironment of the ICD-high group with a poor prognosis was significantly suppressed. An 8-gene ICD-related signature was identified and validated to prognosticate and evaluate the tumor immune microenvironment in DLBCL. Similarly, the high-risk group exhibited a worse prognosis compared to the low-risk group, and the immune function was considerably suppressed. Moreover, the results of oncoPredict algorithm indicated that patients in the high-risk group exhibited higher sensitivity to Cisplatin, Cytarabine, Epirubicin, Oxaliplatin, and Vincristine with low IC50. In conclusion, the present study provides novel insights into the role of ICD in DLBCL by identifying a new biomarker for the disease and may have implications for the development of immune-targeted therapies for the tumor.

Identification of prognostic models for glycosylation-related subtypes and tumor microenvironment infiltration characteristics in clear cell renal cell cancer

One of the most fatal forms of cancer of the urinary system, renal cell carcinoma (RCC), significantly negatively impacts human health. Recent research reveals that abnormal glycosylation contributes to the growth and spread of tumors. However, there is no information on the function of genes related to glycosylation in RCC. In this study, we created a technique that can be used to guide the choice of immunotherapy and chemotherapy regimens for RCC patients while predicting their survival prognosis. The Cancer Genome Atlas (TCGA) provided us with patient information, while the GeneCards database allowed us to collect genes involved in glycosylation. GSE29609 was used as external validation to assess the accuracy of prognostic models. The "ConsensusClusterPlus" program created molecular subtypes based on genes relevant to glycosylation discovered using differential expression analysis and univariate Cox analysis. We examined immune cell infiltration as measured by estimate, CIBERSORT, TIMER, and ssGSEA algorithms, Tumor Immune Dysfunction and Exclusion (TIDE) and exclusion of tumour stemness indices (TSIs) based on glycosylation-related molecular subtypes and risk profiles. Stratification, somatic mutation, nomogram creation, and chemotherapy response prediction were carried out based on risk factors. We built and verified 16 gene signatures associated with the prognosis of ccRCC patients, which are independent prognostic variables, and identified glycosylation-related genes by bioinformatics research. Cluster 2 is associated with lower human leukocyte antigen expression, worse overall survival, higher immunological checkpoints, and higher immune escape scores. In addition, cluster 2 had significantly better angiogenic activity, mesenchymal EMT, and stem ability scores. Higher immune checkpoint genes and human leukocyte antigens are associated with lower overall survival and a higher risk score. Higher estimated and immune scores, lesser tumor purity, lower mesenchymal EMT, and higher stem scores were all characteristics of the high-risk group. High amounts of tumor-infiltrating lymphocytes, a high mutation load, and a high copy number alteration frequency were present in the high-risk group.Discussion.According to our research, the 16-gene prognostic signature may be helpful in predicting prognosis and developing individualized treatments for patients with renal clear cell carcinoma, which may result in new personalized management options for these patients.