Therapeutically Applicable Research To Generate Effective Treatments Research Articles

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 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 < 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 < 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 < 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 < 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.

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.

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 > 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.

Free Energy Changes at State-Transition Critical Points As a Patient-Specific Biomarker in Acute Myeloid Leukemia

Acute myeloid leukemia (AML) is an aggressive hematopoietic malignancy with a poor overall survival rate. AML is a highly heterogeneous disease driven by combinations of genomic mutations, epigenetic alterations, and biochemical signaling. The heterogeneity underlying AML needs novel approaches for individualized clinical assessment and treatment selection. However, approaches that rely on differential expression or correlation analysis lack an underlying theoretical model of the disease to inform the interpretation of ongoing processes in each individual which is crucial for personalized diagnostics and treatment selection. We have previously shown that the state-transition (ST) theory, state-space could predict AML disease evolution using the mRNA of a conditional Cbfb-MYH11 (CM) knock-in mouse model. The ST model represents AML evolution from health to disease as a state transition of the transcriptome state described as a particle undergoing Brownian motion in a double-well quasi-potential, where critical points define states of healthy, c1, transition to AML, c2, and overt AML, c3. We integrated two approaches, the ST model and surprisal analysis (SA) from thermodynamics, to create a patient-specific characterization that can be used to identify the individualized state of the disease. The publicly available datasets of human blood including normal samples, AML samples of bone marrow (BM), and peripheral blood (PB) were utilized for the methods. A total of 858 samples from three publicly available RNA-seq datasets were used. The Therapeutically Applicable Research to Generate Effective Treatments (TARGET) dataset is a pediatric study, from which we analyzed 84 normal samples and 126 AML inv(16) samples, consisting of 105 BM and 21 PB samples with ages from 0.3 to 28 years. Two additional datasets from the BEATAML study and the Cancer Genome Atlas (TCGA), were downloaded from the Genomic Data Commons (GDC) portal using GDCRNATools. The BEATAML dataset included 21 normal and 476 AML samples consisting of 226 PB and 243 BM samples with ages from 2 to 87, and TCGA dataset contained 151 AML PB samples, with ages 21 to 88 years. We used a pseudotime approach to infer disease dynamics for ST using KIT and CD33. We found via t-test analysis that KIT expression between normal and AML samples was significantly different for both TARGET (p<0.01) and BEATAML (p<0.01). CD33 expression between normal and AML samples was significant in BEATAML (p=0.012), but not TARGET (p=0.30). We combined the ST with SA to analyze free energy changes (FEC) that occurred at ST critical points, which predicted AML progression as normal hematopoiesis, c1, transition to AML, c2, and overt AML, c3. Significant differences in FEC at the critical points c1, c2, and c3 were observed by t-test comparisons as c1 vs. c2 (p <0.001) and c1 vs. c3 (p <0.001) in TARGET dataset, and c2 vs. c3 (p < 0.01) in the BEATAML dataset. Our results showed that the transition disease state, c2, had higher FEC as compared to normal states, c1, suggesting that samples were less stable from a thermodynamic perspective. Certain samples at the more advanced state, c3, exhibited a trend toward decreasing FEC levels, suggesting a process of state stabilization at more advanced stages of the disease. SA and Gene Ontology analysis suggested that interleukin pathways such as IL1, IL2, IL8 and IL10, MAPK, NFkB, and migration-related pathways, which are known to be involved in AML progression, were found in multiple unbalanced processes in SA characterizing AML across all three datasets. The IL1 pathway was found in 3 unbalanced processes characterizing TARGET dataset and in four processes characterizing TCGA and BEATAML. IL2 related categories were found among induced transcripts in 4 unbalanced processes characterizing TARGET dataset, in 6 characterizing TCGA, and in 4 processes characterizing BEATAML. We observed that AML samples with comparable FEC levels or clinical characteristics could be defined by different sets of unbalanced processes, implying that this information might be used to determine tailored therapies in subgroups of individuals with similar clinical or thermodynamic characteristics. We showed that mapping FEC into an AML state-space given by ST critical points could provide a high resolution, patient-specific disease characterization, and these methods could be used in personalized diagnostics and individualized treatment procedures.

Relapse-Enriched Gene Expression Signature of Prognostic Relevance in Pediatric Acute Myeloid Leukemia

Acute myeloid leukemia (AML) is a hematologic malignancy characterized by the abnormal proliferation and accumulation of immature myeloid cells. While standard induction therapy achieves remission in a substantial proportion of AML patients, relapse rates remain high, particularly in children (approximately 40%) and adults (around 70%). Despite advancements in AML treatment strategies, the management of relapsed and refractory disease remains significant clinical challenge, resulting in dismal outcomes with a three-year survival rate below 10%. Therefore, there is an urgent need to identify predictive markers for relapse and refractory AML to improve treatment outcomes. To address this urgent need, we evaluated differential gene expression in relapse vs diagnosis and its impact on outcomes. RNA sequencing data from bone marrow samples were obtained from pediatric AML patients enrolled in four Children's Oncology Group clinical trials: AAML0531 (NCT00372593), AAML1031 (NCT01371981), AAML03P19 (NCT00070174), and CCG2961 (NCT00003790) through the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) initiative. Gene expression data was batch corrected using Combat. RNA-seq data from 1441 patients available at https://portal.gdc.cancer.gov/projects/TARGET-AML were included in the current analysis of which 31 had RNA-seq data from both diagnosis and relapse and 1420 had RNA-seq data from only from diagnosis. In step 1, we identified genes differentially expressed at relapse using RNA-seq data from 31 matched diagnosis and relapse samples using wilcoxon signed rank test. At p-values <0.005, 201 genes were differentially expressed, with 147 upregulated and 54 downregulated at relapse compared to diagnosis (Figure 1). To further establish clinical relevance of these genes, we evaluated their association with event-free survival (EFS) in the 1420 patients with RNA-seq data at diagnosis (N=1420) using Cox proportional hazard model, identifying 47 genes significantly associated with EFS (p<0.05). Of these, 9 were pseudogenes, 4 had lower expression at relapse and 34 were expressed at higher levels in the relapse samples. We evaluated these 34 genes using a LASSO penalized Cox proportional hazards model, with over 1000 iterations of 10-fold cross-validation. Genes passing more than 95% of the iterations were further selected to build a relapse signature which constituted 23 genes.Notably, this signature was comprised of several important genes including TCF3, a transcription factor with known mutations in some B-cell acute lymphoblastic leukemia (B-ALL) cases; BIRC5 (Survivin), an apoptosis inhibitor whose overexpression has been linked to therapeutic resistance across cancers; PAX5, a key gene for B-cell differentiation that has identified aberrations B-ALL; BCL7A, a member of the BCL gene family associated with lymphoid malignancies, as B-cell lymphomas; and PARP1, known for its role in DNA repair and the recent therapeutic implications of its inhibitors in BRCA-mutated solid tumors. Patients were further stratified into high and low relapse risk groups based on relapse signature scores using recursive partitioning. Patients with high relapse signature score had inferior EFS and OS as compared to those with low signature score (EFS, HR: 2.25, 95% CI 0.25-0.99, p<0.001); OS, (HR 2.35,95% CI 0.40-0.99, p<0.001), Figure 1). Multivariable Cox regression analysis further revealed that the relapse score remained a significant predictor of patient outcomes after adjusting for established risk factors including risk group, race, white blood cell count, and age at diagnosis (EFS: HR 1.64, 95% CI, 1.40-1.92, p<0.001 and OS: HR 1.62, 95% CI 1.33-1.97, p<0.001). To validate the signature, we evaluated it in an independent cohort of 147 pediatric AML patients treated on St Jude AML08 clinical trial with RNA seq data available. Consistent with the discovery cohort, patients with high score had worse EFS (HR: 1.83, p=0.045) and tended to have lower OS (HR: 1.71, p=0.12). These results indicate the relapse-enriched gene expression signature can effectively risk-stratify newly diagnosed pediatric AML patients. Ongoing studies are focused on validating this signature in other independent cohorts, as well as integrating it with cytogenetic features and other previously reported signatures of relevance such as LSC to establish its prognostic utility.

A 46 Long Non-Coding RNAs Expression Signature Accurately Predicts Relapse and Acts As an Independent Prognostic Factor in Pediatric Acute Myeloid Leukemia

Risk stratification using cytogenetics, mutations and minimal residual disease (MRD) has allowed to increase the cure rates of pediatric acute myeloid leukemia (AML), reaching now up to 70% in contemporary protocols. Nevertheless, approximately 30% of patients still experience relapse, indicating that there is a need to optimize stratification strategies. Recently, long non-coding RNA (lncRNA) expression has been shown to hold prognostic power in multiple cancer types. Here, we built a 46 relapse-related lncRNAs prognostic signature, named AML lnc46, using 790 pediatric acute myeloid leukemia transcriptomes obtained from the Therapeutically Applicable Research To Generate Effective Treatments (TARGET) repository. Bootstrap validation (bootstrap resampling times = 1000) was used for internal validation and showed that the signature performed good in predicting the 1-, 2-, and 3-year relapse-free survival (RFS), with an area under the curve of 0.825, 0.824, and 0.822, respectively. Moreover, external validation using the TARGET repository validated the efficiency of the signature. Most importantly, we demonstrate that AML lnc46 not only is an independent predictor of RFS, but also identifies patients who were assigned a different risk-group by currently-used classification methods. In conclusion, the identified AML lnc46 might, after further prospective validation, provide additional information to guide management of pediatric AML patients. Figure 1. (A) Kaplan-Meier survival curve of relapse-free survival of high-and-low risk AML lnc46 pediatric AML patients, (B) Univariate and multivariate independent Cox regression analyses of known clinical and molecular characteristics and the AML lnc46 signature.

Unveiling Cellular Traits of Osteosarcoma Cancer Stem Cells via Bulk and Single-Cell RNA-Sequencing Analysis

Osteosarcoma (OS) is a prototypical sarcoma, predominantly affecting adolescents. The hallmark of cancer stem cell (CSC) behavior pervades OS, invariably signifying an unfavorable prognosis. However, the intricacies underlying OS metastasis remain incompletely comprehended. As the frontiers of the scientific investigation push forward, encompassing both bulk sequencing and single-cell sequencing (scRNA), the domain of bioinformatics finds multifaceted utility. In this study, we combined scRNA and bulk sequencing with the clinical metadata to excavate the latent molecular substrates governing metastatic propensities within OS. Our scRNA analysis indicated that cell-stemness-related pathways might play vital roles in OS metastasis. Subsequently, an autonomous reservoir of bulk sequencing data set was subjected to weighted gene co-expression network analysis (WGCNA) to identify 10 gene clusters. After analyzing the clinical data, we were able to identify two hub genes, HDAC2 and HSPA4, which are strongly linked to the cancer cell stemness. Moreover, we performed transwell assays to validate the regulation of metastatic behaviors by miR-1-3p, HDAC2, and HSPA4 in OS cells. Significantly, our study was fortified by immunohistochemistry (IHC) analyses performed on tumor tissues acquired from OS patients, thereby accentuating the clinical import of our experimental endeavor. Notably, we unveiled the suppressive influence of miR-1-3p on both HDAC2 and HSPA4, with miRTarBase substantiating lower expression in tumor tissue relative to the normative cohort. Insights gleaned from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) program (https://ocg.cancer.gov/programs/target) further augmented the clinical significance of the identified hub genes. In conclusion, our findings highlight the significant role of cell junctions in governing OS stemness and metastasis, underscored by the integration of single-cell sequencing and bulk-sequencing analyses. Moreover, our foundational experiments identified three hub molecules closely associated with the metastatic behaviors. Our findings provide novel insights into the clinical treatment and fundamental understanding of OS.

Pan-cancer analysis of the prognostic significance of ACKR2 expression and the related genetic/epigenetic dysregulations

ABSTRACT Objective ACKR2 is a scavenger for most inflammation-related CC chemokines. This study aimed to assess the pan-cancer prognostic significance of ACKR2 and the genetic and epigenetic mechanisms underlying its dysregulation. Methods Pan-cancer data from The Cancer Genome Atlas (TCGA), Therapeutically Applicable Research to Generate Effective Treatments (TARGET) and The Genotype-Tissue Expression (GTEx) were integrated and analyzed. Results ACKR2 is consistently associated with favorable progression-free interval (PFI) and overall survival (OS) in TCGA-uveal melanoma (UVM) and TCGA-liver hepatocellular carcinoma (LIHC). ACKR2 is negatively correlated with the expression of CCL1, CCL4, CCL5, CXCL8, CCL17, and CCL20 in TCGA-UVM and TCGA-LIHC. The group with gene copy gain had significantly higher ACKR2 expression than those with loss. The lower ACKR2 expression groups were associated with a significantly higher ratio of BAP1 mutations. In addition, ACKR2 was negatively corrected with DNMT1 expression but was positively corrected with ZC3H13, an m6A writer gene and NSUN3, an RNA m5C writer gene. Conclusions ACKR2 expression was associated with favorable prognosis in patients with uveal melanoma and hepatocellular carcinoma. ACKR2 dysregulation might be an accumulated result of gene copy number alterations, transcriptional disruption, and RNA modifications.

Comprehensive bioinformatics analysis reveals the oncogenic role of FoxM1 and its impact on prognosis, immune microenvironment, and drug sensitivity in osteosarcoma.

Osteosarcoma, a highly malignant bone tumor primarily affecting adolescents, presents a significant challenge in cancer therapy due to its resistance to chemotherapy. This study explores the multifaceted impact of the transcription factor FoxM1 on osteosarcoma, shedding light on its pivotal role in tumor progression, immune microenvironment modulation, and drug response. Utilizing publicly available datasets from the Gene Expression Omnibus (GEO) and Therapeutically Applicable Research To Generate Effective Treatments (TARGET) databases, we conducted an in-depth bioinformatics analysis. Our findings illuminate the far-reaching implications of FoxM1 in osteosarcoma, emphasizing its significance as a potential therapeutic target. Differential expression analysis and Gene Set Enrichment Analysis (GSEA) revealed FoxM1's influence on critical pathways related to apoptosis, cell cycle regulation, and DNA repair. Notably, FoxM1 expression correlated with poor clinical outcomes in osteosarcoma patients, highlighting its prognostic relevance. Additionally, FoxM1 was found to modulate the immune microenvironment within tumor tissues, impacting immune cell infiltration, immunomodulators, immune checkpoints, and chemokines. Furthermore, a prognostic model based on FoxM1-coexpressed genes demonstrated its effectiveness in predicting patient survival. Drug sensitivity analysis indicated FoxM1's association with drug response, potentially guiding personalized treatment approaches. Hub gene screening identified RAB23 as a key target regulated by FoxM1, with RAB23 shown to influence osteosarcoma cell growth. This study also confirmed FoxM1's overexpression in osteosarcoma tissues compared to normal tissues, and its association with clinicopathological characteristics, including clinical stage, pathological type, and lung metastasis. In conclusion, FoxM1 emerges as a central player in the pathogenesis of osteosarcoma, impacting gene expression, immune responses, and therapeutic outcomes. This comprehensive analysis deepens our understanding of FoxM1's role in osteosarcoma and offers potential avenues for improved diagnosis and treatment.

A novel clinical tool and risk stratification system for predicting the event-free survival of neuroblastoma patients: A TARGET-based study.

Neuroblastoma (NB), considered the most common non-intracranial solid tumor in children, accounts for nearly 8% of pediatric malignancies. This study aimed to develop a simple and practical nomogram to predict event-free survival (EFS) in NB patients and establish a new risk stratification system. In this study, 763 patients primarily diagnosed with NB in the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database were included and randomly assigned to a training set (70%) and a validation set (30%) in a 7:3 ratio. First, the independent prognostic factors of EFS for NB patients were identified through univariate and multivariate Cox regression analyses. Second, a nomogram was created based on these factors and was validated for calibration capability, discriminative, and clinical significance by C-curves, receiver operating characteristic (ROC) curves, and decision curve analysis. Finally, a new risk stratification system was established for NB patients based on the nomogram. The univariate Cox analysis demonstrated that NB patients with age at diagnosis >318 days, International Neuroblastoma Staging System (INSS) stage 4, DNA diploidy, MYCN amplification status, and children oncology group (COG) high-risk group had a relatively poor prognosis. However, according to the multivariate Cox regression analysis, only age, INSS stage, and DNA ploidy were independent predictive factors in NB patients regarding EFS, and a nomogram was created based on these factors. The area under the curve (AUC) values of the ROC curves for the 3-, 5-, and 10-year EFS of this nomogram were 0.681, 0.706, and 0.720, respectively. Additionally, the AUC values of individual independent prognostic factors of EFS were lower than those of the nomogram, suggesting that the developed nomogram had a higher predictive reliability for prognosis. In addition, a new risk stratification system was developed to better stratify NB patients and provide clinical practitioners with a better reference for clinical decision-making. NB patients' EFS could be predicted more accurately and easily through the constructed nomogram and event-occurrence risk stratification system, allowing clinicians to better differentiate NB patients and establish individualized treatment plans to maximize patient benefits.