4-gene Signature Research Articles

Prognostic insights, immune infiltration, and therapeutic response: Cytoplasmic poly(A) tail regulators in hepatocellular carcinoma

The presence of a poly(A) tail is indispensable for the post-transcriptional regulation of gene expression in cancer. This dynamic and modifiable feature of transcripts is under the control of various nuclear and cytoplasmic proteins. This study aimed to develop a novel cytoplasmic poly(A)-related signature for predicting prognosis, clinical attributes, tumor immune microenvironment (TIME), and treatment response in hepatocellular carcinoma (HCC). Utilizing RNA-seq data from The Cancer Genome Atlas (TCGA), non-negative matrix factorization (NMF) and principal component analysis (PCA) were employed to categorize HCC patients into three clusters, thus demonstrating the pivotal prognostic role of cytoplasmic poly(A) tail regulators. Furthermore, machine learning algorithms such as least absolute shrinkage and selection operator (LASSO), survival analysis, and Cox proportional hazards modeling were able to distinguish distinct cytoplasmic poly(A) subtypes. As a result, a 5-gene signature derived from TCGA was developed and validated using International Cancer Genome Consortium (ICGC) HCC datasets. This novel classification based on cytoplasmic poly(A) regulators has the potential to improve prognostic predictions and provide guidance for chemotherapy, immunotherapy, and transarterial chemoembolization (TACE) in HCC.

A novel prognostic signature based on smoking-associated genes for predicting prognosis and immune microenvironment in NSCLC smokers

BackgroundAs a highly heterogeneous tumor, non-small cell lung cancer (NSCLC) is famous for its high incidence and mortality worldwide. Smoking can cause genetic changes, which leading to the occurrence and progress of NSCLC. Nevertheless, the function of smoking-related genes in NSCLC needs more research.MethodsWe downloaded transcriptome data and clinicopathological parameters from Gene Expression Omnibus (GEO) databases, and screened smoking-related genes. Lasso regression were applied to establish the 7-gene signature. The associations between the 7-gene signature and immune microenvironment analysis, survival analysis, drug sensitivity analysis and enriched molecular pathways were studied. Ultimately, cell function experiments were conducted to research the function of FCGBP in NSCLC.ResultsThrough 7-gene signature, NSCLC samples were classified into high-risk group (HRG) and low-risk group (LRG). Significant difference in overall survival (OS) between HRG and LRG was found. Nomograms and ROC curves indicated that the 7-gene signature has a stable ability in predicting prognosis. Through the analysis of immune microenvironment, we found that LRG patients had better tumor immune activation. FCGBP showed the highest mutation frequency among the seven prognostic smoking related genes (LRRC31, HPGD, FCGBP, SPINK5, CYP24A1, S100P and FGG), and was notable down-regulated in NSCLC smokers compared with non-smoking NSCLC patients. The cell experiments confirmed that FCGBP knockdown promoting proliferation, migration, and invasion in NSCLC cells.ConclusionThis smoking-related prognostic signature represents a promising tool for assessing prognosis and tumor microenvironment in smokers with NSCLC. The role of FCGBP in NSCLC was found by cell experiments, which can be served as diagnostic biomarker and immunotherapy target for NSCLC.

An integrated machine learning framework for developing and validating a diagnostic model of major depressive disorder based on interstitial cystitis-related genes

BackgroundMajor depressive disorder (MDD) and interstitial cystitis (IC) are two highly debilitating conditions that often coexist with reciprocal effect, significantly exacerbating patients' suffering. However, the molecular underpinnings linking these disorders remain poorly understood. MethodsTranscriptomic data from GEO datasets including those of MDD and IC patients was systematically analyzed to develop and validate our model. Following removal of batch effect, differentially expressed genes (DEGs) between respective disease and control groups were identified. Shared DEGs of the conditions then underwent functional enrichment analyses. Additionally, immune infiltration analysis was quantified through ssGSEA. A diagnostic model for MDD was constructed by exploring 113 combinations of 12 machine learning algorithms with 10-fold cross-validation on the training sets following by external validation on test sets. Finally, the “Enrichr” platform was utilized to identify potential drugs for MDD. ResultsTotally, 21 key genes closely associated with both MDD and IC were identified, predominantly involved in immune processes based on enrichment analyses. Immune infiltration analysis revealed distinct profiles of immune cell infiltration in MDD and IC compared to healthy controls. From these genes, a robust 11-gene (ABCD2, ATP8B4, TNNT1, AKR1C3, SLC26A8, S100A12, PTX3, FAM3B, ITGA2B, OLFM4, BCL7A) diagnostic signature was constructed, which exhibited superior performance over existing MDD diagnostic models both in training and testing cohorts. Additionally, epigallocatechin gallate and 10 other drugs emerged as potential targets for MDD. ConclusionOur work developed a diagnostic model for MDD employing a combination of bioinformatic techniques and machine learning methods, focusing on shared genes between MDD and IC.

A 10-Gene Signature to Predict the Prognosis of Early-Stage Triple-Negative Breast Cancer.

Triple-negative breast cancer (TNBC) is a particularly challenging subtype of breast cancer, with a poorer prognosis compared to other subtypes. Unfortunately, unlike luminal-type cancers, there is no validated biomarker to predict the prognosis of patients with early-stage TNBC. Accurate biomarkers are needed to establish effective therapeutic strategies. In this study, we analyzed gene expression profiles of tumor samples from 184 TNBC patients (training cohort, n=76; validation cohort, n=108) using RNA sequencing. By combining weighted gene expression, we identified a 10-gene signature (DGKH, GADD45B, KLF7, LYST, NR6A1, PYCARD, ROBO1, SLC22A20P, SLC24A3, and SLC45A4) that stratified patients by risk score with high sensitivity (92.31%), specificity (92.06%), and accuracy (92.11%) for invasive disease-free survival. The 10-gene signature was validated in a separate institution cohort and supported by meta-analysis for biological relevance to well-known driving pathways in TNBC. Furthermore, the 10-gene signature was the only independent factor for invasive disease-free survival in multivariate analysis when compared to other potential biomarkers of TNBC molecular subtypes and T-cell receptor β diversity. 10-gene signature also further categorized patients classified as molecular subtypes according to risk scores. Our novel findings may help address the prognostic challenges in TNBC and the 10-gene signature could serve as a novel biomarker for risk-based patient care.

SARS-CoV-2-Induced Type I Interferon Signaling Dysregulation in Olfactory Networks Implications for Alzheimer's Disease.

Type I interferon signaling (IFN-I) perturbations are major drivers of COVID-19. Dysregulated IFN-I in the brain, however, has been linked to both reduced cognitive resilience and neurodegenerative diseases such as Alzheimer's. Previous works from our group have proposed a model where peripheral induction of IFN-I may be relayed to the CNS, even in the absence of fulminant infection. The aim of our study was to identify significantly enriched IFN-I signatures and genes along the transolfactory route, utilizing published datasets of the nasal mucosa and olfactory bulb amygdala transcriptomes of COVID-19 patients. We furthermore sought to identify these IFN-I signature gene networks associated with Alzheimer's disease pathology and risk. Gene expression data involving the nasal epithelium, olfactory bulb, and amygdala of COVID-19 patients and transcriptomic data from Alzheimer's disease patients were scrutinized for enriched Type I interferon pathways. Gene set enrichment analyses and gene-Venn approaches were used to determine genes in IFN-I enriched signatures. The Agora web resource was used to identify genes in IFN-I signatures associated with Alzheimer's disease risk based on its aggregated multi-omic data. For all analyses, false discovery rates (FDR) <0.05 were considered statistically significant. Pathways associated with type I interferon signaling were found in all samples tested. Each type I interferon signature was enriched by IFITM and OAS family genes. A 14-gene signature was associated with COVID-19 CNS and the response to Alzheimer's disease pathology, whereas nine genes were associated with increased risk for Alzheimer's disease based on Agora. Our study provides further support to a type I interferon signaling dysregulation along the extended olfactory network as reconstructed herein, ranging from the nasal epithelium and extending to the amygdala. We furthermore identify the 14 genes implicated in this dysregulated pathway with Alzheimer's disease pathology, among which HLA-C, HLA-B, HLA-A, PSMB8, IFITM3, HLA-E, IFITM1, OAS2, and MX1 as genes with associated conferring increased risk for the latter. Further research into its druggability by IFNb therapeutics may be warranted.

EGFR-mutant NSCLC may remodel TME from non-inflamed to inflamed through acquiring resistance to EGFR-TKI treatment

BackgroundEGFR-TKI represent the standard first-line therapy for advanced NSCLC harboring EGFR mutations. However, resistance to EGFR-TKI inevitably develops in nearly all patients. Previous clinical study have demonstrated that, some patients that failed EGFR-TKI therapy show a benefit outcome from immunotherapy. Our objective is to explore the immune microenviroment remodeling induced by EGFR-TKI treatment in EGFR mutant lung cancer patients and to investigate the immune cell types and potential molecular signatures involved. MethodsA cohort of 37 EGFR mutant advanced-stage NSCLC patients, who are resistant to at least one type of TKI treatment, was retrospectively established. Both pre-treatment and TKI resistance tumor FFPE samples of each pairs were collected. Transcriptional profiling and bioinformatics analysis were employed to evaluate the change of immune associated hallmarks before and after EGFR-TKI therapy. ResultsTumor samples after EGFR-TKI treatment displayed enrichment of proinflammatory signaling like interferon-γ, allograft rejection and inflammatory response. Of note, cytotoxic factor granzyme A as well as PD-L1 were found to be more expressed in EGFR-TKI resistance samples. Approximately 33.3 % (11/33) of EGFR-TKI treated samples were classified as “hot” tumor, especially for EGFR L858R mutated NSCLC patients (46.7 %,7/15). Effector cells were significantly overexpressed in ‘hot’ tumors feature following TKI resistance. In addition, we found that four effector genes (CD8A, CDB8, GZMB, GZMK) showed higher expression in ‘hot’ tumors post-TKI resistance, and its 4-gene effector cell signature was found to have a good correlation with survival benefit in external immunotherapy database. ConclusionsTKI treatment may initiate immune activation in EGFR mutant NSCLC, leading to changes in immune cell infiltration following TKI resistance. We mechanistically explored that this might be due to an increased immune response caused by the rise in effector cells post-TKI resistance.

Construction of exosome-related genes risk model in kidney cell carcinoma predicts prognosis and immune therapy response.

Renal cell carcinoma (RCC) is one of the most prevalent types of urological cancer. Exosomes are vesicles derived from cells and have been found to promote the development of RCC, but the potential biomarker and molecular mechanism of exosomes on RCC remain ambiguous. Here, we first screened differentially expressed exosome-related genes (ERGs) by analyzing The Cancer Genome Atlas (TCGA) database and exoRBase 2.0 database. We then determined prognosis-related ERGs (PRERGs) by univariate Cox regression analysis. Gene Dependency Score (gDS), target development level, and pathway correlation analysis were utilized to examine the importance of PRERGs. Machine learning and lasso-cox regression were utilized to screen and construct a 5-gene risk model. The risk model showed high predictive accuracy for the prognosis of patients and proved to be an independent prognostic factor in three RCC datasets, including TCGA-KIRC, E-MTAB-1980, and TCGA-KIRP datasets. Patients with high-risk scores showed worse outcomes in different clinical subgroups, revealing that the risk score is robust. In addition, we found that immune-related pathways are highly enriched in the high-risk group. Activities of immune cells were distinct in high-/low-risk groups. In independent immune therapeutic cohorts, high-risk patients show worse immune therapy responses. In summary, we identified several exosome-derived genes that might play essential roles in RCC and constructed a 5-gene risk signature to predict the prognosis of RCC and immune therapy response.

Abstract PO4-14-08: A gene expression-based classifier for HER2-low breast cancer

Abstract Background: The definition of HER2-low breast cancer from clinical trials of antibody-conjugated drugs (ADCs) relies on immunohistochemistry scoring (IHC). However, in daily-practice the accuracy of IHC is hindered by inter-observer variability in assessing HER2 non-overexpressing status. Here, we aimed to identify breast cancer primary tumors with low HER2 expression by leveraging gene expression profiling. Materials and methods: A discovery approach was applied to gene expression profile of internal INT1 (n= 125) and INT2 (n= 84) datasets. We identified specific differently expressed genes (DEGs) according to HER2 IHC categories 0, 1+, 2+ and 3+. Principal Component Analysis (PCA) was used to generate a HER2-low signature whose performance was confirmed in the independent INT3 (n= 95), and TCGA and GSE20194 publicly available datasets. The association between the HER2-low signature and HER2 IHC categories was evaluated by non-parametric Kruskal-Wallis (KW) test with post hoc pair wise comparisons (i.e. contrasts); the HER2-low signature discriminatory capability was assessed by estimating the area under the receiver operating characteristic (ROC) curve (AUC) with its corresponding 95% Confidence Interval (CI). Gene Ontology and KEGG analyses were performed to enrich the DEGs for functional information. Results: A 20-gene HER2-low signature, consisting of both up-regulated (n=11) and down-regulated (n=9) genes, was computed based on the DEGs according to HER2 IHC categories as follows: 11 genes specific for the 1+ category, 8 genes for the 2+ category, and 1 gene for the HER2-low category. HER2-low signature genes were significantly enriched with lipid and steroid metabolism pathways, peptidase regulation, and humoral immune response. The HER2-low signature levels demonstrated a significant association with HER2 IHC categories (KW p-value &amp;lt; 0.001) and were distributed in a bell-shaped pattern across IHC categories (low values 0 and 3+; high values 1+ and 2+), effectively distinguishing HER2-low from 0 (contrast p-value &amp;lt; 0.001) and 3+ (contrast p-value &amp;lt; 0.001). Notably, the signature levels were significantly higher in tumors scored with 1+ as compared to 0 (contrast p =0.002). The HER2-low signature association with IHC categories and discriminatory capability was confirmed in the independent INT3 and TCGA datasets with higher values in HER2-low compared to both 0 and 3+. The HER2-low signature achieved an AUC value of 0.72 (95%CI 0.62-0.83) in differentiating HER2 0/3+ from HER2 1+/2+ categories, which is worth noting in light of the individual ERBB2 mRNA AUC value of 0.48 (95%CI 0.34-0.62). Conclusions: The 20-gene HER2-low signature was generated by maximizing the differences in gene expression between tumors with different HER2 status according to IHC. In contrast to the previously published single ERBB2 gene expression assessment, our data presents compelling evidence for effectively distinguishing HER2-low tumors, including those scored as 1+ from HER2-0 tumors. Our signature holds potential in selecting novel candidates for ADC therapy. Citation Format: Serena Di Cosimo, Sara Pizzamiglio, Chiara Maura Ciniselli, Valeria Duroni, Vera Cappelletti, Loris de Cecco, Maria Carmen De Santis, Rosaria Orlandi, Marilena Iorio, Marco Silvestri, Giancarlo Pruneri, Paolo Verderio. A gene expression-based classifier for HER2-low breast cancer [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO4-14-08.

Abstract PO5-24-10: Clinical significance and immune landscape analyses of the immune related genes based prognostic signature for HER2 positive breast cancer

Abstract Objective: To date, significant breakthrough has yet to be achieved in immunotherapy for HER2-positive breast cancer (HPBC). In this study, we aimed to construct a risk model for predicting prognosis and therapy response and evaluating the tumor immune landscape in HPBC patients. Methods: Differentially expressed immune-related genes (IRGs) were used as input to weighted correlation network analysis (WGCNA) to identify modules of highly correlated gene. An optimal prognostic TPBC-IRGs signature were constructed by least absolute shrinkage and selection operator (LASSO) cox regression. Survival analysis and ROC curves were analyzed to identify the predictive value in a training cohort and external validation cohorts. With three GEO datasets and samples from our center, the anti-HER2 therapeutic efficacy in HPBC patients was predicted through this prognostic model. Additionally, the immunotherapy outcome was forecasted using IMvigor210 dataset. We also investigated the connection between high and low-risk populations and immune checkpoints and analyzed immune infiltration levels of these populations with several algorithms. Ultimately, we develop a combined model and nomogram by integrating the risk score with clinical factors. Results: 11 key genes associated with overall survival (OS) in the blue module were selected for further analysis. A 3-gene signature (CX3CL1, RARB, TANK) based prognostic risk model was developed. The Kaplan-Meier survival curve revealed that patients with high-risk score had shorter OS compared with the low-risk group. The AUC of the training cohort at 1-, 3- and 5-year were 0.718, 0.784 and 0.767. The performance of the risk model was validated with GSE25066 and GSE55348. It’s showed that the risk scores of HPBC who were resistant to trastuzumab were significantly higher than those of patients who were sensitive to trastuzumab. The results indicated that the levels of immune checkpoints expression (such as PD-1, CTLA4 and TIGIT) and immune infiltration were higher in low-risk group than in high-risk group. The combined model showed better prognostic prediction accuracy compared to the clinical model or gene signature alone (1-year AUC=0.910;3-year AUC=0.832;5-year AUC=0.882). Conclusion: The immune gene-based risk profile and combined prognostic model in this study may hold promise for clinical use in predicting the prognosis and treatment efficacy of HPBC patients. figure3. Construction of HPBC-IRGs signature based prognostic model. Citation Format: Xiaofen Li, Wenfen Fu, Yushuai Yu, Jie Zhang, Chuangui Song. Clinical significance and immune landscape analyses of the immune related genes based prognostic signature for HER2 positive breast cancer [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO5-24-10.

Abstract PO3-07-04: Prediction of PAM50 molecular subtypes from H&amp;E-stained breast cancer specimens using tumor microenvironment features and additive multiple instance learning models

Abstract Background: PAM50, a 50-gene signature, classifies breast cancers into one of five subtypes (basal, luminal A, luminal B, HER2-enriched, and normal-like), revealing information about underlying tumor biology, and has emerged as a key prognostic indicator influencing treatment decisions. There is growing interest in bridging the gap between expression-based metrics and histopathology, where immunohistochemistry (IHC) and sequencing-based approaches have been proposed for this purpose. However, hematoxylin and eosin (H&amp;E)-stained slides are ubiquitously utilized by pathologists for cancer diagnosis, while IHC and sequencing-based approaches require additional tissue and specialized processing and/or analysis. Here, we describe a computer vision-based approach to predict PAM50 classification using H&amp;E-stained whole slide images (WSIs). Methods: We obtained expression-based PAM50 subtype labels and corresponding H&amp;E-stained WSIs for 961 breast carcinomas from the TCGA BRCA cohort. We used two separate machine learning (ML) approaches to predict PAM50 subtypes from WSIs. In the first approach, we deployed previously trained PathExplore models to extract quantitative human-interpretable features (HIFs) that summarize the TME. We subsequently trained random forest classification models on these HIFs to predict PAM50 subtypes. For the second approach, we developed additive multiple instance learning (aMIL) models. Additionally, we explored the effects of PAM50 subtype labeling and aggregation strategies beyond the 5-class approach. Our 3-class approach combines Luminal A and B, as seen in IHC efforts to increase agreement with PAM50 assays, while excluding Normal, a category containing few and heterogeneous samples. We also performed binary classification for each subtype in the 3-class model (e.g. luminal vs. other). Slides were split into training (60%), validation (20%), and test (20%) sets, stratified by PAM50 labels, and model performance was assessed using the area under the receiver operator curve (AUROC) metric on the held-out test set, using a one vs. rest approach for multi-class models. To establish a baseline for PAM50 prediction, we developed random forest classification models using only clinical covariates (tumor stage, histologic grade, histological subtype, and BRCA1/2 status). Results: We compared the performance of our two ML models (HIF and aMIL) to that of the baseline model, and we report the AUROC values in Table 1. These models both performed well in predicting Basal, Luminal A, Luminal B, and Luminal (A+B), while the model performance was less strong for predictions of the HER2 and Normal classifications. The three-class model showed improved performance of predicting Luminal classifications relative to the five-class model that separates Luminal A and B. Although simplifying classification problems to a binary use case typically provides improved performance, this phenomenon was not observed for any of the PAM50 subtypes. Conclusions: These results demonstrate that AI-powered digital pathology can accurately and reproducibly perform molecular-based classification tasks, such as predicting PAM50 classifications, using WSIs, suggesting a more efficient path toward clinically relevant breast cancer characterization. Table 1. Performance of all models in predicting PAM50 molecular subtypes. AUROC values are shown. Shaded cells represent the best test-set performance for each class (row). Citation Format: Maria Guramare, Syed Ashar Javed, Christian Kirkup, Dinkar Juyal, Jacqueline Brosnan-Cashman, Victoria Mountain, Ryan Leung, Bahar Rahsepar, John Abel, Amaro Taylor-Weiner, Jake Conway. Prediction of PAM50 molecular subtypes from H&amp;E-stained breast cancer specimens using tumor microenvironment features and additive multiple instance learning models [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO3-07-04.

Abstract PO3-05-06: Targeting PAM50 HER2-Enriched intrinsic subtype with enzalutamide in hormone receptor-positive/HER2-negative (HR+/HER2-) advanced breast cancer: results of the SOLTI-1502 ARIANNA trial

Abstract Background Pre-clinical evidence and retrospective studies suggest that PAM50 HER2-Enriched (HER2-E), HR+/HER2- tumors have estrogen receptor (ER) independence and poor prognosis but seem to have androgen receptor (AR)-addiction. Enzalutamide (ENZ) is a potent inhibitor of AR signaling. We hypothesized that ENZ may induce a significant proliferative arrest in PAM50 HER2-E, HR+/HER2-negative advanced breast cancer (ABC), leading to clinical benefit in this poor prognosis population. Methods Eligible patients (pts), males or pre/post-menopausal women with advanced HR+/HER2- ABC resistant to endocrine therapy, received ENZ 160 mg/day until disease progression. Pts were assigned to different cohorts according to PAM50 intrinsic subtypes identified in the pre-treatment tumor biopsy: Cohort A included pts with HER2-E and Cohort B pts with Luminal A/B tumors. Centralized molecular assessment of pre-treatment and C1D15 tumor biopsies were required for the primary endpoint evaluation. After C1D15 biopsy, exemestane 50mg QD could be added to ENZ at physician’s discretion. Tumor assessments were performed every 8 weeks. Primary efficacy endpoint was the relative change in the PAM50 11-gene proliferation-related signature in Cohort A. Secondary objectives included anti-proliferative effect of ENZ in Cohort B, overall response rate (ORR), Clinical benefit rate (CBR; partial response [PR] + stable disease ≥ 24 weeks) progression-free survival (PFS), and safety. Results From June 2020 to October 2022, 47 tumors were screened, and 6 PAM50 HER2-E tumors were identified (13%). A total of 34 pts were enrolled: 6 pts in Cohort A and 28 pts in Cohort B. Among them, 4 pts from Cohort A received ENZ plus exemestane and 23 pts in Cohort B. Mean age was 59 (range 39-76), 89% of pts were postmenopausal, 74% had liver disease, and 59% received (neo)adjuvant treatment. The median number of lines for ABC was 4 (1-8). Mean Ki67 (central assessment) in Cohort A was 37% (5%-70%), while in Cohort B it was 30% (3%-90%). Mean central AR in Cohort A was 66% (30-100), while in Cohort B it was 55% (1-100). The mean suppression of PAM50 11-gene proliferation-signature after 2 weeks of treatment with ENZ was 3.7% (p=0.848) in Cohort A and 1.2% (p=0.909) in Cohort B. The median change in Ki67 levels in Cohort A was -0.43% (p=0.812), while in Cohort B it was +3.86% (p=0.081). No statistically significant differentially expressed genes or changes in intrinsic subtypes were observed. Overall, the median PFS was 1.8 months (95% CI 1.6-1.8). In Cohort A, median PFS was 1.6 months (95% CI 1.0-1.9), and in Cohort B, it was 1.8 months (95% CI 1.6-1.8). CBR was 0% and 11% (n=3, 95% CI 2.8-25.9%) in Cohorts A and B, respectively. A PR with 16 months duration in cohort B was observed in a patient with an AR-mutated tumor (AR c. 689C &amp;gt;T). Regarding safety, grade 1-2 and 3-4 toxicities occurred in 94.1% and 35.3% of patients, respectively. ENZ dose reduction and discontinuation occurred in 2 (5.9%) and 3 (8.8%) pts, respectively. The most common toxicities related to ENZ were nausea (n=8, 24%), decreased appetite (n=8, 24%), AST increase (n=6, 18%) and vomiting (n=6, 18%). Conclusions The hypothesis that ENZ could induce proliferation arrest in HR+/HER2-, PAM50 HER2-E tumors was not confirmed, and ARIANNA was prematurely closed due to the lack of efficacy. Additional research is needed to explore if ENZ may benefit specific populations of pts with breast cancer (e.g. AR-mut tumors) and whether alternative AR modulators are more effective in blocking AR signaling in HR+/HER2-, PAM50 HER2-E tumors Citation Format: Mafalda Oliveira, Tomás Pascual, Sonia Pernas, Mireia Margelí, Salvador Blanch, Barbara Adamo, Francisco Javier Salvador Bofill, Xavier Gonzalez-Farré, Samyukta Chillara, Patricia Galván, Esther Sanfeliu, Paula Blasco, Valeria Sirenko, Alejandra Espinosa, Charles M Perou, Aleix Prat, Luis Manso. Targeting PAM50 HER2-Enriched intrinsic subtype with enzalutamide in hormone receptor-positive/HER2-negative (HR+/HER2-) advanced breast cancer: results of the SOLTI-1502 ARIANNA trial [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO3-05-06.

Comprehensive scRNA-seq Analysis and Identification of CD8_+T Cell Related Gene Markers for Predicting Prognosis and Drug Resistance of Hepatocellular Carcinoma.

Tumor heterogeneity of immune infiltration of cells plays a decisive role in hepatocellular carcinoma (HCC) therapy response and prognosis. This study investigated the effect of different subtypes of CD8+T cells on the HCC tumor microenvironment about its prognosis. Single-cell RNA sequencing, transcriptome, and single-nucleotide variant data from LUAD patients were obtained based on the GEO, TCGA, and HCCD18 databases. CD8+ T cells-associated subtypes were identified by consensus clustering analysis, and genes with the highest correlation with prognostic CD8+ T cell subtypes were identified using WGCNA. The ssGSEA and ESTIMATE algorithms were used to calculate pathway enrichment scores and immune cell infiltration levels between different subtypes. Finally, the TIDE algorithm, CYT score, and tumor responsiveness score were utilized to predict patient response to immunotherapy. We defined 3 CD8+T cell clusters (CD8_0, CD8_1, CD8_2) based on the scRNA- seq dataset (GSE149614). Among, CD8_2 was prognosis-related risk factor with HCC. We screened 30 prognosis genes from CD8_2, and identified 3 molecular subtypes (clust1, clust2, clust3). Clust1 had better survival outcomes, higher gene mutation, and enhanced immune infiltration. Furthermore, we identified a 12 genes signature (including CYP7A1, SPP1, MSC, CXCL8, CXCL1, GCNT3, TMEM45A, SPP2, ME1, TSPAN13, S100A9, and NQO1) with excellent prediction performance for HCC prognosis. In addition, High-score patients with higher immune infiltration benefited less from immunotherapy. The sensitivity of low-score patients to multiple drugs including Parthenolide and Shikonin was significantly higher than that of high-score patients. Moreover, high-score patients had increased oxidative stress pathways scores, and the RiskScore was closely associated with oxidative stress pathways scores. And the nomogram had good clinical utility. To predict the survival outcome and immunotherapy response for HCC, we developed a 12-gene signature based on the heterogeneity of the CD8+ T cells.

Discovery and Validation of a 15-Gene Prognostic Signature for Clear Cell Renal Cell Carcinoma.

Develop and validate gene expression-based biomarker associated with recurrent disease to facilitate risk stratification of clear cell renal cell carcinoma (ccRCC). We retrospectively identified 110 patients who underwent radical nephrectomy for ccRCC (discovery cohort). Patients who recurred were matched on the basis of grade/stage to patients without recurrence. Capture whole-transcriptome sequencing was performed on RNA isolated from archival tissue using the Illumina platform. We developed a gene-expression signature to predict recurrence-free survival/disease-free survival (DFS) using a 15-fold lasso and elastic-net regularized linear Cox model. We derived the 31-gene cell cycle progression (mxCCP) score using RNA-seq data for each patient. Kaplan-Meier (KM) curves and multivariable Cox proportional hazard testing were used to validate the independent prognostic impact of the gene-expression signature on DFS, disease-specific survival (DSS), and overall survival (OS) in two validation data sets (combined n = 761). After quality control, the discovery cohort comprised 50 patients with recurrence and 41 patients without, with a median follow-up of 26 and 36 months, respectively. We developed a 15-gene (15G) signature, which was independently associated with worse DFS and DSS (DFS: hazard ratio [HR], 11.08 [95% CI, 4.9 to 25.1]; DSS: HR, 9.67 [95% CI, 3.4 to 27.7]) in a multivariable model adjusting for clinicopathologic parameters (including stage, size, grade, and necrosis [SSIGN] score and Memorial Sloan Kettering Cancer Center nomogram) and mxCCP score. The 15G signature was also independently associated with worse DFS and DSS in both validation data sets (Validation A [n = 382], DFS: HR, 2.6 [95% CI, 1.6 to 4.3]; DSS: HR, 3 [95% CI, 1.4 to 6.1] and Validation B (n = 379), DFS: HR, 2.1 [95% CI, 1.2 to 3.6]; OS: HR, 3 [95% CI, 1.6 to 5.7]) adjusting for clinicopathologic variables and mxCCP score. We developed and validated a novel 15G prognostic signature to improve risk stratification of patients with ccRCC. Pending further validation, this signature has the potential to facilitate optimal treatment allocation.

An inflammation-related subtype classification for analyzing tumor microenvironment and clinical prognosis in colorectal cancer.

The inflammatory response plays an essential role in the tumor microenvironment (TME) of colorectal cancer (CRC) by modulating tumor growth, progression, and response to therapy through the recruitment of immune cells, production of cytokines, and activation of signaling pathways. However, the molecular subtypes and risk score prognostic model based on inflammatory response remain to be further explored. Inflammation-related genes were collected from the molecular signature database and molecular subtypes were identified using nonnegative matrix factorization based on the TCGA cohort. We compared the clinicopathological features, immune infiltration, somatic mutation profile, survival prognosis, and drug sensitivity between the subtypes. The risk score model was developed using LASSO and multivariate Cox regression in the TCGA cohort. The above results were independently validated in the GEO cohort. Moreover, we explored the biological functions of the hub gene, receptor interacting protein kinase 2 (RIPK2), leveraging proteomics data, in vivo, and in vitro experiments. We identified two inflammation-related subtypes (inflammation-low and inflammation-high) and have excellent internal consistency and stability. Inflammation-high subtype showed higher immune cell infiltration and increased sensitivity to common chemotherapeutic drugs, while inflammation-low subtype may be more suitable for immunotherapy. Besides, the two subtypes differ significantly in pathway enrichment and biological functions. In addition, the 11-gene signature prognostic model constructed from inflammation-related genes showed strong prognostic assessment power and could serve as a novel prognostic marker to predict the survival of CRC patients. Finally, RIPK2 plays a crucial role in promoting malignant proliferation of CRC cell validated by experiment. This study provides new insights into the heterogeneity of CRC and provides novel opportunities for treatment development and clinical decision making.

A novel pyroptosis-related gene signature exhibits distinct immune cells infiltration landscape in Wilms’ tumor

BackgroundWilms’ tumor (WT) is the most common renal tumor in childhood. Pyroptosis, a type of inflammation-characterized and immune-related programmed cell death, has been extensively studied in multiple tumors. In the current study, we aim to construct a pyroptosis-related gene signature for predicting the prognosis of Wilms’ tumor.MethodsWe acquired RNA-seq data from TARGET kidney tumor projects for constructing a gene signature, and snRNA-seq data from GEO database for validating signature-constructing genes. Pyroptosis-related genes (PRGs) were collected from three online databases. We constructed the gene signature by Lasso Cox regression and then established a nomogram. Underlying mechanisms by which gene signature is related to overall survival states of patients were explored by immune cell infiltration analysis, differential expression analysis, and functional enrichment analysis.ResultsA pyroptosis-related gene signature was constructed with 14 PRGs, which has a moderate to high predicting capacity with 1-, 3-, and 5-year area under the curve (AUC) values of 0.78, 0.80, and 0.83, respectively. A prognosis-predicting nomogram was established by gender, stage, and risk score. Tumor-infiltrating immune cells were quantified by seven algorithms, and the expression of CD8( +) T cells, B cells, Th2 cells, dendritic cells, and type 2 macrophages are positively or negatively correlated with risk score. Two single nuclear RNA-seq samples of different histology were harnessed for validation. The distribution of signature genes was identified in various cell types.ConclusionsWe have established a pyroptosis-related 14-gene signature in WT. Moreover, the inherent roles of immune cells (CD8( +) T cells, B cells, Th2 cells, dendritic cells, and type 2 macrophages), functions of differentially expressed genes (tissue/organ development and intercellular communication), and status of signaling pathways (proteoglycans in cancer, signaling pathways regulating pluripotent of stem cells, and Wnt signaling pathway) have been elucidated, which might be employed as therapeutic targets in the future.

Comprehensive analysis of macrophage-related genes in prostate cancer by integrated analysis of single-cell and bulk RNA sequencing.

Macrophages, as essential components of the tumor immune microenvironment (TIME), could promote growth and invasion in many cancers. However, the role of macrophages in tumor microenvironment (TME) and immunotherapy in PCa is largely unexplored at present. Here, we investigated the roles of macrophage-related genes in molecular stratification, prognosis, TME, and immunotherapeutic response in PCa. Public databases provided single-cell RNA sequencing (scRNA-seq) and bulk RNAseq data. Using the Seurat R package, scRNA-seq data was processed and macrophage clusters were identified automatically and manually. Using the CellChat R package, intercellular communication analysis revealed that tumor-associated macrophages (TAMs) interact with other cells in the PCa TME primarily through MIF - (CD74+CXCR4) and MIF - (CD74+CD44) ligand-receptor pairs. We constructed coexpression networks of macrophages using the WGCNA to identify macrophage-related genes. Using the R package ConsensusClusterPlus, unsupervised hierarchical clustering analysis identified two distinct macrophage-associated subtypes, which have significantly different pathway activation status, TIME, and immunotherapeutic efficacy. Next, an 8-gene macrophage-related risk signature (MRS) was established through the LASSO Cox regression analysis with 10-fold cross-validation, and the performance of the MRS was validated in eight external PCa cohorts. The high-risk group had more active immune-related functions, more infiltrating immune cells, higher HLA and immune checkpoint gene expression, higher immune scores, and lower TIDE scores. Finally, the NCF4 gene has been identified as the hub gene in MRS using the "mgeneSim" function.

A novel prognostic signature based on cancer stemness and metabolism-related genes for cervical squamous cell carcinoma and endocervical adenocarcinoma.

CESC is the second most commonly diagnosed gynecological malignancy. Given the pivotal involvement of metabolism-related genes (MRGs) in the etiology of multiple tumors, our investigation aims to devise a prognostic risk signature rooted in cancer stemness and metabolism. The stemness index based on mRNA expression (mRNAsi) of samples from the TCGA dataset was computed using the One-class logistic regression (OCLR) algorithm. Furthermore, potential metabolism-related genes related to mRNAsi were identified through weighted gene co-expression network analysis (WGCNA). We construct a stemness-related metabolic gene signature through shrinkage estimation and univariate analysis, thereby calculating the corresponding risk scores. Moreover, we selected corresponding DEGs between groups with high- and low-risk score and conducted routine bioinformatic analyses. Furthermore, we validated the expression of four hub genes at the protein level through immunohistochemistry (IHC) in samples obtained from our patient cohort. According to the findings, it was found that six genes-AKR1B10, GNA15, ALDH1B1, PLOD2, LPCAT1, and GPX8- were differentially expressed in both TCGA-CSEC and GEO datasets among 23 differentially expressed metabolism-related genes (DEMRGs). mRNAsi exhibited a notable association with the extent of key oncogene mutation. The results showed that the AUC values for forecasting survival at 1, 3, and 5 years are 0.715, 0.689, and 0.748, individually. We observed a notable association between the risk score and different immune cell populations, along with enrichment in crucial signaling pathways in CESC. Four genes differentially expressed between different risk score groups were validated by IHC to be highly expressed in the CESC samples at the protein level. The current investigation indicated that a 3-gene signature based on stemness-related metabolic and 4 hub genes with differential expression between high and low-risk score subgroups may serve as valuable prognostic markers and potential therapeutic targets in CESC.

A novel network-based method identifies a cuproplasia-related pan-cancer gene signature to predict patient outcome.

Copper is a vital micronutrient involved in many biological processes and is an essential component of tumour cell growth and migration. Copper influences tumour 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. Utilising RNA-sequencing data from The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) datasets, we generated gene regulatory networks to identify the critical cuproplasia-related genes across 23 different cancer types. From this, we identified a novel 8-gene cuproplasia-related gene signature associated with pan-cancer survival, and a 6-gene prognostic risk score model in low grade glioma. These findings highlight the use of gene regulatory networks to identify cuproplasia-related gene signatures that could be used to generate risk score models. This can potentially identify patients who could benefit from copper-chelation therapy and identifies novel targeted therapeutic strategies.

Identification of key genes in Wilms tumor based on high-throughput RNA sequencing and their impacts on prognosis and immune responses

To identify the key genes differentially expressed in Wilms tumor and analyze their potential impacts on prognosis and immune responses of the patients. High-throughput RNA sequencing was used to identify the differentially expressed mRNAs in clinical samples of Wilms tumor and paired normal tissues, and their biological functions were analyzed using GO, KEGG and GSEA enrichment analyses. The hub genes were identified using STRING database, based on which a prognostic model was constructed using LASSO regression. The mutations of the key hub genes were analyzed and their impacts on immunotherapy efficacy was predicted using the cBioPortal platform. RT-qPCR was used to verify the differential expressions of the key hub genes in Wilms tumor. Of the 1612 differentially expressed genes identified in Wilms tumor, 1030 were up-regulated and 582 were down-regulated, involving mainly cell cycle processes and immune responses. Ten hub genes were identified, among which 4 genes (TP53, MED1, CCNB1 and EGF) were closely related to the survival of children with Wilms tumor. A 3-gene prognostic signature was constructed through LASSO regression analysis, and the patients stratified into with high- and low-risk groups based on this signature had significantly different survival outcomes (HR=1.814, log-rank P=0.002). The AUCs of the 3-, 5- and 7-year survival ROC curves of this model were all greater than 0.7. The overall mutations in the key hub genes or the individual mutations in TP53/CCNB1 were strongly correlated with a lower survival rates, and a high TP53 expression was correlated with a poor immunotherapy efficacy. RT-qPCR confirmed that the key hub genes had significant differential expressions in Wilms tumor tissues and cells. TP53 gene plays an important role in the Wilms tumor and may potentially serve as a new immunotherapeutic biomarker as well as a therapeutic target.

An exosome mRNA-related gene risk model to evaluate the tumor microenvironment and predict prognosis in hepatocellular carcinoma

BackgroundThe interplay between exosomes and the tumor microenvironment (TME) remains unclear. We investigated the influence of exosomes on the TME in hepatocellular carcinoma (HCC), focusing on their mRNA expression profile.MethodsmRNA expression profiles of exosomes were obtained from exoRBase. RNA sequencing data from HCC patients’ tumors were acquired from The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC). An exosome mRNA-related risk score model of prognostic value was established. The patients in the two databases were divided into high- and low-risk groups based on the median risk score value, and used to validate one another. Functional enrichment analysis was performed based on a differential gene prognosis model (DGPM). CIBERSORT was used to assess the abundance of immune cells in the TME. The correlation between the expression levels of immune checkpoint-related genes and DGPM was analyzed alongside the prediction value to drug sensitivity.ResultsA prognostic exosome mRNA-related 4-gene signature (DYNC1H1, PRKDC, CCDC88A, and ADAMTS5) was constructed and validated. A prognostic nomogram had prognostic ability for HCC. The genes for this model are involved in extracellular matrix, extracellular matrix (ECM)-receptor interaction, and the PI3K-Akt signaling pathway. Expression of genes here had a positive correlation with immune cell infiltration in the TME.ConclusionsOur study results demonstrate that an exosome mRNA-related risk model can be established in HCC, highlighting the functional significance of the molecules in prognosis and risk stratification.