Therapeutically Applicable Research To Generate Effective Treatments Research Articles

Leveraging homologous recombination deficiency for sarcoma : Unravelling homologous recombination repair deficiency and therapeutic opportunities in soft tissue and bone sarcoma.

Homologous recombination deficiency (HRD) in tumors correlates with poor prognosis and metastases development. Determining HRD is of major clinical relevance as it can be treated with PARP inhibitors (PARPi). HRD remains poorly investigated in sarcoma, arare and heterogeneous cancer of mesenchymal origin. We aimed (i)to investigate predictive biomarkers of HRD in several independent sarcoma cohorts using across-functional strategy by combining genomic, transcriptomic and phenotypic approaches and (ii)to evaluate the therapeutic potential of PARPi and DNA damage response (DDR)-based therapies ex vivo. We performed acomprehensive genomic and transcriptomic characterization of sarcoma using datasets from The Cancer Genome Atlas (TCGA) and Therapeutically Applicable Research to Generate Effective Treatments (TARGET), and our own bone and soft tissue sarcoma cohorts. We evaluated PARP1/2 and WEE1 inhibition ex vivo in patient-derived sarcoma cell models as monotherapy and in combination with chemotherapeutic agents to identify synergistic effects. Firstly, we identified genomic traits of HRD in asubset of sarcomas associated with molecular alterations in homologous recombination repair (HRR) pathway genes and high chromosomal instability. Secondly, we identified and validated distinct SARC-HRD transcriptional signatures that predicted sensitivity to PARPi. Finally, we showed functional defects in HRR in sarcoma cells that were associated with functional dependency towards PARPi and WEE1i and support the clinical use of RAD51 as apredictive biomarker for PARPi sensitivity. We provide apersonalized oncological approach to potentially improve the treatment of sarcoma patients. We encourage the evaluation of gene expression signatures to enhance the identification of patients who might benefit from DDR-based therapies.

Abstract A013: Advanced Gene Signatures for the Diagnosis and Personalized Treatment of Pancreatic Ductal Adenocarcinoma

Abstract Pancreatic ductal adenocarcinoma (PDA) is among the most lethal tumors, with limited treatment options. The absence of reliable screening and diagnostic tools (biomarkers or procedures) significantly contributes to its poor prognosis. Biomarkers that can diagnose PDA, select optimal therapy combinations, and predict the risk of occult metastases are crucial for enhancing treatment outcomes. Previously, we identified methylation signatures capable of predicting outcomes and diagnosing PDA, focusing on genes/proteins with preclinical evidence of influencing key drugs (gemcitabine [Gem], 5-fluorouracil [5FU], oxaliplatin, cisplatin, paclitaxel, and irinotecan) used in treatment. To make these findings more clinically applicable and propose a novel approach for personalized treatment selection between Gem-based (Gem/nab-paclitaxel) and 5FU-based (FOLFIRINOX) combinations, we explored the diagnostic value of genes associated specifically with Gem and 5FU. The objective was to develop a gene signature for diagnosing PDA and aiding in treatment selection. Based on recent literature, we updated our previous gene panels (from 55 to 93 for Gem, and from 20 to 28 for 5FU). We analyzed RNA and gene expression differences between normal (NT) and PDA tissues using TNMplot.com, a web tool that leverages data from the Gene Expression Omnibus (NCBI-GEO), Cancer Genome Atlas (TCGA), Therapeutically Applicable Research to Generate Effective Treatments (TARGET), and The Genotype-Tissue Expression (GTEx) repositories, employing Mann-Whitney or Kruskal-Wallis tests for statistical significance. For the Gem-specific gene panel, the combined RNA expression of 90 out of 93 genes was significantly higher in PDA compared to NT (2.59, p=6.48e-61), while the combined gene expression of 80 out of 93 genes was also significantly higher in PDA tissues (1.62, p=3.48e-29). For the 5FU-specific gene panel, the combined RNA expression of 27 out of 28 genes was significantly elevated in PDA (2.00, p=5.44e-57), and the combined gene expression of 23 out of 28 genes was significantly higher in PDA tissues (1.21, p=1.44e-7). Our preliminary findings suggest that this approach has the potential to serve as an effective tool for diagnosing PDA and guiding treatment selection. However, these results must be validated through larger prospective studies. Additionally, this application could serve as a screening tool for high-risk populations, such as individuals with potentially malignant lesions like intraductal papillary mucinous neoplasms, first-degree relatives of PDA patients, or those with concerning genetic risks (e.g., BRCA1 or BRCA2 mutations). Citation Format: Harshitha Puram, Deepak Sherpally, Sravan Jeepalyam, Ashish Manne. Advanced Gene Signatures for the Diagnosis and Personalized Treatment of Pancreatic Ductal Adenocarcinoma [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pancreatic Cancer Research; 2024 Sep 15-18; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2024;84(17 Suppl_2):Abstract nr A013.

TARGET based m6A methylation-related genes predict prognosis relapsed B-cell acute lymphoblastic leukemia

PurposeThe current study aims to investigate the significance of N6-methyladenosine (m6A) methylationrelated genes in the clinical prognosis of childhood relapsed B-cell acute lymphoblastic leukemia (B-ALLL) patient.MethodsTranscriptome data and corresponding clinical data on m6A methylation-related genes (including 20 genes) were obtained from the Therapeutically Applicable Research To Generate Effective Treatments (TARGET) database.ResultsThe bone marrow (BM) samples of 134 newly diagnosed (naive) and 116 relapsed B-ALL from TARGET were enrolled in the current study. Three genes (FTO, HNRNPC, RBM15B) showed significant up-regulation in relapsed B-ALL compared with that in naive B-ALL.The three genes had a significantly worse survival (P < 0.05). The LASSO Cox regression model was used to select the most predictive genes as prognostic indicators, and YTHDC1 and FTO were identified as prognostic factors for relapsed B-ALL. Finally, the results of multivariate regression analysis showed that the risk score of m6A methylation-related genes was an independent prognostic factor in relapsed B-ALL (P < 0.05).ConclusionWe found that the expression levels of m6A methylation-related genes were different in naive and relapsed patients with B-ALL and correlated with survival and prognosis.This implies that m6A methylation-related genes may be promising prognostic indicators or therapeutic targets for relapsed B-ALL.

Unraveling the role of bisphenol A in osteosarcoma biology: insights into prognosis and immune microenvironment modulation

BackgroundBisphenol A (BPA) is a common environmental pollutant, and its specific mechanisms in cancer development and its impact on the tumor immune microenvironment are not yet fully understood.MethodsTranscriptome data from osteosarcoma (OS) patients were downloaded from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database. BPA-related genes were identified through the Comparative Toxicogenomics Database (CTD), yielding 177 genes. Differentially expressed genes were analyzed using the GSE162454 dataset from the Tumor Immune Single Cell Hub 2 (TISCH2). We constructed the prognostic model using univariate Cox regression and LASSO analysis. The model was validated using the GSE16091 dataset. GO, KEGG, and GSEA analyses were performed to investigate the mechanisms of BPA-related genes.ResultsA total of 15 BPA-related genes were identified as differentially expressed in OS. Univariate Cox regression and LASSO analysis identified four key prognostic genes (FOLR1, MYC, ESRRA, VEGFA). The prognostic model exhibited strong predictive performance with area under the curve (AUC) values of 0.89, 0.6, and 0.79 for predicting 1-, 2-, and 3-year survival, respectively. External validation using the GSE16091 dataset confirmed the model's high accuracy with AUC values exceeding 0.88. Our results indicated that the prognosis of the high-risk population is generally poorer, which may be associated with alterations in the tumor immune microenvironment. In the high-risk group, immune cells showed predominantly low expression levels, while immune checkpoint genes were significantly overexpressed, along with markedly elevated tumor purity. These findings revealed a correlation between upregulation of BPA-related genes and formation of an immunosuppressive microenvironment, leading to unfavorable patient outcomes.ConclusionOur study highlighted the significant association of BPA with OS biology, particularly in its potential role in modulating the tumor immune microenvironment. We offered a fresh insight into the influence of BPA on cancer development, thus providing valuable insights for future clinical interventions and treatment strategies.

Genetic and Epigenetic Biomarkers Associated with Early Relapse in Pediatric Acute Lymphoblastic Leukemia: A Focused Bioinformatics Study on DNA-Repair Genes.

Genomic instability is one of the main drivers of tumorigenesis and the development of hematological malignancies. Cancer cells can remedy chemotherapeutic-induced DNA damage by upregulating DNA-repair genes and ultimately inducing therapy resistance. Nevertheless, the association between the DNA-repair genes, drug resistance, and disease relapse has not been well characterized in acute lymphoblastic leukemia (ALL). This study aimed to explore the role of the DNA-repair machinery and the molecular mechanisms by which it is regulated in early- and late-relapsing pediatric ALL patients. We performed secondary data analysis on the Therapeutically Applicable Research to Generate Effective Treatments (TARGET)-ALL expansion phase II trial of 198 relapsed pediatric precursor B-cell ALL. Comprehensive genetic and epigenetic investigations of 147 DNA-repair genes were conducted in the study. Gene expression was assessed using Microarray and RNA-sequencing platforms. Genomic alternations, methylation status, and miRNA transcriptome were investigated for the candidate DNA-repair genes. We identified three DNA-repair genes, ALKBH3, NHEJ1, and PARP1, that were upregulated in early relapsers compared to late relapsers (p < 0.05). Such upregulation at diagnosis was significantly associated with disease-free survival and overall survival in precursor-B-ALL (p < 0.05). Moreover, PARP1 upregulation accompanied a significant downregulation of its targeting miRNA, miR-1301-3p (p = 0.0152), which was strongly linked with poorer disease-free and overall survivals. Upregulation of DNA-repair genes, PARP1 in particular, increases the likelihood of early relapse of precursor-B-ALL in children. The observation that PARP1 was upregulated in early relapsers relative to late relapsers might serve as a valid rationale for proposing alternative treatment approaches, such as using PARP inhibitors with chemotherapy.

Construction of a Cancer Stem Cell Marker Genes-Related 22-Gene Signature for Overall Survival Prediction in High-Risk Wilms’ Tumor

Background: This study aimed to investigate the comprehensive expression profile of cancer stem cell (CSC)-related genes and construct a prognostic signature for overall survival (OS) prediction in high-risk Wilms’ tumor (WT). Materials and methods: Gene expression and survival data from 120 high-risk WT cases in the Therapeutically Applicable Research to Generate Effective Treatments (TARGET)-WT were used. Results: In total, 229 CSC-related genes were found to be significantly dysregulated in WT compared to tumor-adjacent normal tissues, among which 34 were associated with OS. Using LASSO regression, a 22-gene signature was developed, which exhibited excellent performance in 3-, 5-, and 10-year OS predictions (AUC > 0.86). The high-risk score group showed markedly poorer OS compared to the low-risk score group (median separation, HR = 6.41, 95% CI: 3.18–12.92, p = 3.2e − 9). The 22-gene signature was an independent prognostic factor for OS (HR = 5.086, 95% CI: 3.019–8.568, p < 0.001). Conclusion: This study identified a robust prognostic signature that can effectively support OS prediction.

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

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

Construction of a prognostic model for autophagy in Wilm's tumor.

Wilm's tumor (WT) is one of the most common childhood urological tumors, ranking second in the incidence of pediatric abdominal tumors. The development of WT is associated with various factors, and the correlation with autophagy is currently unclear. To develop a new prognostic model of autophagy-related genes (ATG) for WT. Using the Therapeutically applicable research to generate effective treatments (TARGET) database to screen for differentially expressed ATGs in WT and normal tissues. ATGs were screened for prognostic relevance to WT using one-way and multifactorial Cox regression analyses and prognostic models were constructed. The risk score was calculated according to the model, and the predictive ability of the constructed model was analyzed using the ROC (receiver operating characteristic) curve to verify the significance of the model for the prognosis of WT. Sixty-eight differentially expressed ATGs were identified by univariate Cox regression analysis, and two critical prognostic ATGs (CXCR4 and ERBB2) were identified by multivariate Cox regression analysis. Patients were divided into high-risk and low-risk groups according to the differential expression of these two ATGs. Kaplan-Meier (KM) curves showed a significant difference in survival time between the two groups. The critical prognostic ATGs were combined with race, age, and stage in a multifactorial regression analysis, and the final prognostic model was produced as a line graph. The prognostic model of autophagy-related genes composed of the CXCR4 gene and ERBB2 gene has a specific predictive value for the prognosis of WT, and the present study provides a clear basis for future research on biomarkers and therapeutic targets.

Extramedullary infiltration in pediatric acute myeloid leukemia: Results from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) initiative.

The outcome of extramedullary infiltration (EMI) in pediatric acute myeloid leukemia (AML) is controversial, and little is known about the implications of stem cell transplantation (SCT) and gemtuzumab ozogamicin (GO) treatment on patients with EMI. We retrieved the clinical data of 713 pediatric patients with AML from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) dataset, and analyzed the clinical and prognostic characteristics of patients with EMI at diagnosis and relapse. A total of 123 patients were identified to have EMI at diagnosis and 64 presented with EMI at relapse. The presence of EMI was associated with age ≤2years, M5 morphology, abnormal karyotype, and KMT2A rearrangements. Hyperleukocytosis and complex karyotype were more prevalent in patients with EMI at relapse. Additionally, patients with EMI at diagnosis had a reduced incidence of FLT3 ITD-/NPM1+, whereas those with EMI at relapse displayed a lower frequency of FLT3 ITD+. Patients with EMI at diagnosis exhibited a lower complete remission (CR) rate at the end of Induction Course 1 and higher relapse incidence. Importantly, EMI at diagnosis independently predicted both shorter event-free survival (EFS) and overall survival (OS). Regarding relapse patients, the occurrence of EMI at relapse showed no impact on OS. However, relapse patients with myeloid sarcoma (MS)/no central nervous system (CNS) exhibited poorer OS compared to those with CNS/no MS. Furthermore, regarding patients with EMI at diagnosis, SCT failed to improve the survival, whereas GO treatment potentially enhanced OS. EMI at diagnosis is an independent adverse prognostic risk factor for pediatric AML, and GO treatment potentially improves survival for patients with EMI at diagnosis.

Deciphering programmed cell death mechanisms in osteosarcoma for prognostic modeling.

Osteosarcoma (OS), known for its high recurrence and metastasis rates, poses a significant challenge in oncology. Our research investigates the role of programmed cell death (PCD) genes in OS and develops a prognostic model using advanced bioinformatics. We analyzed single-cell sequencing data from the Gene Expression Omnibus (GEO) database to identify subpopulations, distinguish malignant from non-malignant cells, assess cell cycle phases, and map PCD gene distribution. Additionally, we applied consistency clustering to bulk sequencing data from GEO and TARGET (Therapeutically Applicable Research to Generate Effective Treatments) databases, facilitating survival analysis across clusters with the Kaplan-Meier method. We calculated PCD scores for each cluster using the Single-sample Gene Set Enrichment Analysis (ssGSEA), which enabled a detailed examination of PCD-related gene expression and pathway scores. Our study also explored drug sensitivity differences and conducted comprehensive immune cell infiltration analyses using various algorithms. We identified differentially expressed genes, leading to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses that provided insights into relevant biological processes and pathways. The prognostic model, based on five pivotal genes (BAMBI, TMCC2, NOX4, DKK1, and CBS), was developed using the Least Absolute Shrinkage and Selection Operator (LASSO) algorithm and validated in the TARGET-OS and GSE16091 datasets, showing significant predictive accuracy. This research enhances our understanding of PCD in OS and supports the development of effective treatments.

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.

Nephroblastoma-specific dysregulated gene SNHG15 with prognostic significance: scRNA-Seq with bulk RNA-Seq data and experimental validation

Wilms tumor (WT) is the most common malignancy of the genitourinary system in children. Currently, the Integration of single-cell RNA sequencing (scRNA-Seq) and Bulk RNA sequencing (RNA-Seq) analysis of heterogeneity between different cell types in pediatric WT tissues could more accurately find prognostic markers, but this is lacking. RNA-Seq and clinical data related to WT were downloaded from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database. Small nucleolar RNA host gene 15 (SNHG15) was identified as a risk signature from the TARGET dataset by using weighted gene co-expression network analysis, differentially expressed analysis and univariate Cox analysis. After that, the functional mechanisms, immunological and molecular characterization of SNHG15 were investigated at the scRNA-seq, pan-cancer, and RNA-seq levels using Gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), ESTIMATE, and CIBERSORT. Based on scRNA-seq data, we identified 20 clusters in WT and annotated 10 cell types. Integration of single-cell and spatial data mapped ligand-receptor networks to specific cell types, revealing M2 macrophages as hubs for intercellular communication. In addition, in vitro cellular experiments showed that siRNAs interfering with SNHG15 significantly inhibited the proliferation and migration of G401 cells and promoted the apoptosis of G401 cells compared with the control group. The effect of siRNAs interfering with SNHG15 on EMT-related protein expression was verified by Western blotting assay. Thus, our findings will improve our current understanding of the pathogenesis of WT, and they are potentially valuable in providing novel prognosis markers for the treatment of WT.

Abstract 7420: Optimizing proteomic data access and analysis in the cloud: Leveraging Terra's integration with the Proteomic Data Commons

Abstract The Proteomic Data Commons (PDC) hosts cancer proteomics data with the goal of making this data available to the public to support development of cancer diagnostics, treatment, and progression tracking. As a part of the Cancer Research Data Commons (CRDC), the Terra platform provides a cloud workbench for the PDC data. FireCloud is a Broad Institute project funded by NCI to empower cancer researchers to access data, run analysis tools and collaborate securely in the cloud. It is powered by Terra, a secure, scalable cloud-native platform developed by the Broad Institute, Microsoft, and Verily, an Alphabet company. It provides batch workflow execution, interactive analysis including data visualization, and ~2,900 publicly available tools with the ability to import more tools from Dockstore. The integration of PDC and Terra enables researchers to leverage the data navigation and file-level search capabilities on the PDC web browser (pdc.cancer.gov) and export selected data manifests to a Terra workspace. This integrated hand-off specifically allows metadata and cloud links to PDC data in Portable Format for Bioinformatics (PFB) to be transferred to Terra and used in analysis with cloud resources. A featured workspace for Terra-PDC integration includes tools for (i) downloading data files and relevant additional metadata; (ii) organizing the data and metadata for running the FragPipe workflow, a comprehensive collection of tools for reading and processing raw Mass Spec (MS) data; and (iii) implementing a pre-configured FragPipe workflow to process isobarically labeled MS data from Tandem Mass Tag (TMT) or Isobaric tags for relative and absolute quantitation (iTRAQ) experiments using information automatically imported from PDC via the PFB import. The pipelines can also be customized as needed to process any type of raw MS data the PDC supports. In addition, Terra connects to the NCI Genomic Data Commons (GDC) and hosts The Cancer Genome Atlas (TCGA) and Therapeutically Applicable Research to Generate Effective Treatments (TARGET) datasets allowing users to pull in other data types for co-analysis. As a whole, using the CRDC cloud resources, in conjunction with Terra, allows analysis of data on a massive scale, enabling multi-omic integration spanning the genomic and proteomic commons data, with easy sharing of data and tools. Citation Format: Emily LaPlante, Bingxing Huo, DR Mani, Ratna R. Thangudu. Optimizing proteomic data access and analysis in the cloud: Leveraging Terra's integration with the Proteomic Data Commons [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 7420.

Abstract 5662: PDCD1 and CD48 significantly impact cancer recurrence and survival in pediatric acute lymphoblastic leukemia

Abstract Introduction: Immune checkpoints (ICPs) play a critical role in modulating the immune response, maintaining self-tolerance, and preventing excessive immune system activation. Dysregulation of immune checkpoints has been implicated in cancer progression and treatment resistance. In this study, we aimed to screen for dysregulated immune checkpoint genes that might be associated with cancer recurrence in pediatric acute lymphoblastic leukemia (ALL) following a course of cancer treatment. We hypothesized that certain ICPs would be significantly dysregulated in relapsed ALL patients, potentially providing insights into the underlying mechanisms driving therapy resistance and disease recurrence. Methods: 51 ICP genomic data (gene expression, copy number alterations (CNAs), and mutation) of133 patients with relapse information [relapse-free (n=29) and relapsed (n=104)] extracted from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) were examined in the study. Patient’s t-test (p-value &amp;lt; 0.05) was used to identify significant genetic factors between relapse-free and relapsed patients. Free and overall survival analyses were performed using log-rank test (p-value &amp;lt; 0.05) based on patient gene expression profiles. Results: Among the 51 genes examined, PDCD1, PVR, CD70, and CD48 were significantly upregulated in relapsed patients compared with relapse-free patients (P &amp;lt; 0.05). No significant CNAs nor mutations were detected in these four genes. Patients with high expression profiles of PDCD1 and CD48 at diagnosis were 1.71 and 1.47 more likely to relapse compared to patients with low expression profiles, respectively (P &amp;lt; 0.05). Furthermore, the upregulation of PDCD1 (P= 0.0004, HR= 1.972) and CD48 (P= 0.0344, HR= 1.533) demonstrated significantly increased mortality rates in patients following treatment, as indicated by overall survival analyses. Conclusion: The upregulation of PDCD1 and CD48 was significantly associated with relapse and survival of ALL patients. Promisingly, these two ICPs can potentially be used as prognostic biomarkers for relapse risk and rationale for alternative therapeutic strategies. Our study suggests that targeting PDCD1 and CD48 might be beneficial in enhancing the treatment efficiency and thereby reducing the recurrence risk. The study was funded by the Deputyship for Research and Innovation, Ministry of Education, Saudi Arabia (DRI-KSU-1273). Citation Format: Shatha Fawaz Alotaibi, Lames Abuhadi, Noura Alolyani, Moureq R. Alotaibi, Ali R. Alhoshani, Homood As Sobeai. PDCD1 and CD48 significantly impact cancer recurrence and survival in pediatric acute lymphoblastic leukemia [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 5662.

Immunogenic cell death related mRNAs associated signature to predict immunotherapeutic response in osteosarcoma

BackgroundImmunogenic cell death (ICD) is related to cancer prognosis, which has a synergic effect in combination with chemotherapy or immunotherapy. Yet, the relationship between ICD and osteosarcoma remained unclear. Materials and methodsThree osteosarcoma datasets including therapeutically applicable research to generate effective treatments (TARGET), GSE126209 and GSE21257 datasets were included. A protein-protein interaction network was constructed based on ICD-related genes. We performed unsupervised consensus clustering to classify molecular subtypes (clusters). Survival analysis, Estimation of stromal and immune cells in malignant tumour tissues using expression data (ESTIMATE), Cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT), and differential analysis were employed to characterize the molecular differences between different clusters. Univariate Cox regression analysis was conducted to confirm prognostic genes. Quantitative reverse transcription polymerase chain reaction (qRT-PCR) was used to demonstrate the aberrant expression of ICD-correlated signature genes in osteosarcoma. A series of cellular experiments, including cell counting kit-8 (CCK-8), transwell, and flow cytometry, were used to demonstrate the regulatory role of key genes in the ICD model on the malignant phenotype of osteosarcoma. ResultsThree clusters (cluster1, 2, 3) were constructed and they showed distinct overall survival and immune infiltration. ICD-related genes were highly expressed in cluster1. Moreover, Cluster1 had the best prognosis, high immune score and high expression of human leukocyte antigen (HLA)-related genes. TLR4, LY96, IFNGR1, CD4, and CASP1 were identified as prognostic genes for establishing an ICD-related risk signature. According to the risk signature, two risk groups (high and low risks) showing differential prognosis and response to immunotherapy. The low risks group had a better prognosis but was not sensitive to immunotherapy. Molecular assays verified that prognostic genes were abnormally under-expressed in osteosarcoma. Cellular assays demonstrated that LY96, the most significantly down-regulated gene in osteosarcoma, inhibited the migration, invasion, and proliferation phenotypes of osteosarcoma cells and prolonged the cell cycle. Analysis of oxidative stress related pathway enrichment in tumor microenvironment was conducted by single-sample gene set enrichment analysis (ssGSEA). ConclusionsThis study demonstrated the prognostic significance of ICD-correlated genes in osteosarcoma patients. The five-gene risk signature facilitate prognostic evaluation and prediction of osteosarcoma patients’ response to immunotherapy. The risk signature also offered a possibility for the exploit of novel ICD-related treatment.

A hypoxia-related genes prognostic risk model, and mechanisms of hypoxia contributing to poor prognosis through immune microenvironment and drug resistance in acute myeloid leukemia.

Objective: Acute myeloid leukemia (AML) is a malignant hematologic cancer with poor prognosis. Emerging evidence suggests a close association between AML progression and hypoxia. The purpose of this study was to establish a new risk prognostic model for AML based on hypoxia-related genes, and to explore the mechanisms by which hypoxia-related genes affect the prognosis of AML based on tumor immune microenvironment (TIME) and drug resistance. Methods: The AML patient samples obtained from Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database were classified into C1 and C2 based on hypoxia-related genes, followed by analysis utilizing Gene Ontology (GO), Kyoto Encyclopaedia of Genes and Genomes (KEGG) and Gene Set Enrichment Analysis (GSEA). Through univariate and LASSO Cox regression analysis, the hypoxia-related hub genes 26S proteasome non-ATPase regulatory subunit 11 (PSMD11) and 26S proteasome non-ATPase regulatory subunit 14 (PSMD14) were identified to construct the model. AML patient samples were obtained from the TARGET and The Cancer Genome Atlas (TCGA) databases, serving as the training and the validation sets, and were stratified into high-risk and low-risk group according to the median risk score. The correlations between the model and TIME and anti-tumor drugs were analysed using CIBERSORT and Genomics of Drug Sensitivity in Cancer (GDSC) databases. The expressions of PSMD11/PSMD14 in clinical samples and AML sensitive and drug-resistant cell lines were detected by Western blot and real-time PCR. Results: The C1 group with high expression of hypoxia-related genes had lower overall survival (OS). Immune-related signaling pathways were different between C1/C2, and hypoxia was positively correlated with the activation of mammalian target of rapamycin (mTOR) signaling pathway. The model had good accuracy in both the training and the validation sets. The high-risk group exhibited lower OS and TIME activity, and was more sensitive to several anti-tumor drugs. PSMD11/PSMD14 were highly expressed in relapsed patients and AML drug-resistant cell lines. Conclusion: The established novel risk prognostic model and experiment results offer valuable insights for predicting AML prognosis and guiding drug selection. It also provides a fundamental framework for the mechanisms through which hypoxia impacts AML prognosis by modulating TIME and drug resistance.

Developing a comprehensive cell-free DNA (cfDNA) epigenetic signature (ep-Sig) with diagnostic and prognostic value in hepatocellular carcinoma (HCC).

553 Background: HCC is one of the deadliest tumors with poor outcomes often diagnosed in late stages. Current surveillance strategies (alfa-fetoprotein (AFP) plus ultrasound (USG)) for high-risk population (HRP) have poor sensitivity (SN) and reasonable specificity (SP) of 61% and 92%, respectively. Triple phase computed tomography (CT) imaging has good sensitivity and specificity (≈ 90% for both) but the evidence does not support its use for routine surveillance. All these modalities do not have prognostic (survival) value. We attempted to develop a non-invasive, cfDNA-based tests to improve the diagnostic value of the current standard of care modalities (AFP and USG) and help in predicting the outcomes at diagnosis. We hypothesized that RNA and gene expression in HCC tissues is a surrogate for epigenome (DNA-methylation changes). Methods: We curated a 21-gene cfDNA ep-Sig from the literature with a proven prognostic value. Evidence suggests epigenetic changes in every gene of this panel affect HCC outcomes influencing clinicopathological characteristics (large tumor size, vascular invasion, advanced staging) or early post-operative recurrence, and survival. We compared RNA and gene expression of the genes in the ep-Sig between normal (NT) and HCC tissues using a web-based tool, TNMplot.com that uses the data from the Gene Expression Omnibus of the National Center for Biotechnology Information (NCBI-GEO) or Cancer Genome Atlas (TCGA), Therapeutically Applicable Research to Generate Effective Treatments (TARGET), and The Genotype-Tissue Expression (GTEx) repositories was used create this tool. It uses Mann-Whitney or Kruskal-Wallis tests to compute statistical significance. Results: RNA expression of all the genes from the ep-Sig was significantly higher in HCC than NT (1.133, p=5.84e-15). Gene expression was 19/21 genes was higher in HCC tissues (1.15, p=1.3e-08). The other 2 genes were not detected in the tested samples. Conclusions: Our ep-Sig which has a proven prognostic value and reliable prevalence in HCC patients. This study is the first step in developing clinically useful blood-based test that can revamp the current diagnostic and screening practices when used alongside AFP and USG.

Developing cell-free DNA (cfDNA) epigenetic signature (ep-Sig) for early detection of malignant transformation of intraductal papillary mucinous neoplasms (IPMN).

616 Background: Pancreatic ductal adenocarcinoma (PDA) is one of the deadliest cancers with poor outcomes. Around 20% arise from premalignant lesions (PML) including IPMN. Current guidelines recommend risk stratifying IPMN based on the location, size, and imaging characteristics after first diagnosis, followed by endoscopic ultrasound (EUS) in high-risk populations, and annual or bi-annual imaging. This approach can be improved with cfDNA testing. Traditional mutation testing in cfDNA is not useful secondary to their poor prevalence/detection rates. We propose ep-Sig to improve surveillance and early detection of PDA transformation. Methods: We curated an 11-gene cfDNA ep-Sig from the literature with a proven role in the malignant transformation of IPMN. Evidence suggests that epigenetic changes in every gene of this panel have a role in carcinogenesis in IPMNs. We compared RNA and gene expression of the genes in this ep-Sig between normal (NT) and PDA tissues using a web-based tool, TNMplot.com that uses the data from the Gene Expression Omnibus of the National Center for Biotechnology Information (NCBI-GEO) or Cancer Genome Atlas (TCGA), Therapeutically Applicable Research to Generate Effective Treatments (TARGET), and The Genotype-Tissue Expression (GTEx) repositories was used create this tool. It uses Mann-Whitney or Kruskal-Wallis tests to compute statistical significance. We used cell-free epigenome atlas (CFEA) database to examine the prevalence of these markers in cfDNA of PDA patients. Results: RNA expression of all the genes from the ep-Sig combined was significantly lower in PDA than NT (0.45, p=3.13e-26). Gene expression was 8/11 genes was lower in PDA tissues than NT (0.44, p=3.13e-26). The other 2 genes were not detected in the tested samples. Prevalence of all the markers in ep-Sig was &gt; 90%. Conclusions: Our proposed ep-Sig has a high prevalence in PDA patients and has reliable preliminary evidence in tissues suggesting its diagnostic value. This study is the first step in developing non-invasive cfDNA testing with the potential to improve the current surveillance strategies for IPMN pending larger prospective studies.

Identification of an ADME-related gene for forecasting the prognosis and responding to immunotherapy in sarcomas

There are more than 170 subtypes of sarcomas (SARC), which pose a challenge for diagnosis and patient management. Relatively simple or complex karyotypes play an indispensable role in the early diagnosis and effective treatment of SARC. The genes related to absorption, distribution, metabolism, and excretion (ADME) of a drug can serve as prognostic biomarkers of cancer and potential drug targets. In this study, a risk score signature was created. The SARC cohort was downloaded from The Cancer Genome Atlas (TCGA) database, and divided into high-risk group and low-risk group according to the median value of risk score. Compared with high-risk group, low-risk group has a longer survival time, which is also verified in osteosarcoma cohort from Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database. In addition, the relationship between the signature and immunophenotypes, including status of immune cell infiltration and immune checkpoint expression, was explored. Then, we found that high-risk group is in immunosuppressive status. Finally, we verified that PPARD played a role as a carcinogen in osteosarcoma, which provided a direction for targeted treatment of osteosarcoma in the future. Generally speaking, the signature can not only help clinicians predict the prognosis of patients with SARC, but also provide a theoretical basis for developing more effective targeted drugs in the future.

Pyroptosis-Related Gene Signature Predicts Prognosis and Response to Immunotherapy and Medication in Pediatric and Young Adult Osteosarcoma Patients.

Pyroptosis, a new form of inflammatory programmed cell death, has recently gained attention. However, the impact of the expression levels of pyroptosis-related genes (PRGs) on the overall survival (OS) of osteosarcoma patients remains unclear. This study aims to investigate the impact of the expression levels of PRGs on the OS of pediatric and young adult patients with osteosarcoma. Transcriptome matrix datasets of normal muscle or skeletal tissues from the Genotype-Tissue Expression (GTEx) project and osteosarcoma specimen the National Cancer Institute's (NCI) Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database were used to identify pyroptosis-related genes (PRGs) associated with prognosis. The National Center for Biotechnology Information's (NCBI) GSE21257 dataset was employed to validate the predictive value of the pyroptosis-related signature (PRS). Additionally, reverse transcription polymerase chain reaction (RT-qPCR) experiment was performed in normal and osteosarcoma cell lines. The study identified 18 differentially expressed PRGs (DEPRGs) between normal muscle or skeletal tissues and tumor samples. Multiple machine learning techniques were used to select PRGs, resulting in the identification of four hub PRGs. A PRS-score was calculated for each sample based on the expression of these four hub PRGs, and samples were categorized into low and high PRS-score level groups. It was confirmed that metastatic status and PRS-score level are independent prognostic predictors. A nomogram model for predicting OS of osteosarcoma patients was constructed. Single-cell RNA-sequencing data display the expression patterns of the hub PRGs. RT-qPCR data results were found to be consistent with the differential expression analysis performed on TARGET and GTEx samples. The study developed a novel pyroptosis-related gene signature that can stratify pediatric and young adult osteosarcoma patients into different risk groups, thus predicting their response to immunotherapy and chemotherapy.