Gene-set Scores Research Articles

Schwann Cell-Mediated M2-Like Macrophage Polarization in Rhabdomyosarcoma.

To investigate the cellular components and immunological characteristics of the head and neck rhabdomyosarcoma (RMS) microenvironment. We conducted single-cell transcriptomics to analyze the cellular components of the RMS microenvironment. CellChat was utilized for analyzing intercellular interactions. The cancer genome atlas database was used for validation. CIBERSORT was applied for immune infiltration profiling. Functional enrichment analyses were performed using the Kyoto Encyclopedia of Genes and Genomes. Gene set scores were calculated using single-sample gene set enrichment analysis. Subcutaneous allograft models and bulk RNA sequencing were used for validation. Flow cytometry and immunohistochemistry were used to identify M2-like macrophages. Our findings revealed an extremely low presence of neutrophils in RMS samples compared with normal sample. RMS sample with high Schwann cell infiltration exhibited an increase in M2-like macrophage infiltration. Receptor-ligand pairs, specifically MIF-CD74 and PTN-SDC3, were identified between Schwann cells and M2-like macrophages. In the RMS sample characterized by significant Schwann cell infiltration, M2-like macrophages demonstrate robust expression of axon guidance factors and are enriched in the axon guidance pathway. Our study provides valuable insights into the microenvironment and immunological characteristics of RMS, offering crucial information for further research and potential therapeutic strategies.

Machine Learning-based Macrophage Signature for Predicting Prognosis and Immunotherapy Benefits in Cholangiocarcinoma.

We aimed to develop a macrophage signature for predicting clinical outcomes and immunotherapy benefits in cholangiocarcinoma. Macrophages are potent immune effector cells that can change phenotype in different environments to exert anti-tumor and anti-tumor functions. The role of macrophages in the prognosis and therapy benefits of cholangiocarcinoma was not fully clarified. The objective of this study is to develop a prognostic model for cholangiocarcinoma. The macrophage-related signature (MRS) was developed using 10 machine learning methods with TCGA, GSE89748 and GSE107943 datasets. Several indicators (TIDE score, TMB score and MATH score) and two immunotherapy datasets (IMvigor210 and GSE91061) were used to investigate the performance of MRS in predicting the benefits of immunotherapy. The Lasso + CoxBoost method's MRS was considered a robust and stable model that demonstrated good accuracy in predicting the clinical outcome of patients with cholangiocarcinoma; the AUC of the 2-, 3-, and 4-year ROC curves in the TCGA dataset were 0.965, 0.957, and 1.000. Moreover, MRS acted as an independent risk factor for the clinical outcome of cholangiocarcinoma cases. Cholangiocarcinoma cases with higher MRS scores are correlated with a higher TIDE score, higher tumor escape score, higher MATH score, and lower TMB score. Further analysis suggested high MRS score indicated a higher gene set score correlated with cancer-related hallmarks. With regard to cholangiocarcinoma, the current study created a machine learning-based MRS that served as an indication for forecasting the prognosis and therapeutic advantages of individual cases.

Multiomic single cell sequencing identifies stemlike nature of mixed phenotype acute leukemia

Despite recent work linking mixed phenotype acute leukemia (MPAL) to certain genetic lesions, specific driver mutations remain undefined for a significant proportion of patients and no genetic subtype is predictive of clinical outcomes. Moreover, therapeutic strategy for MPAL remains unclear, and prognosis is overall poor. We performed multiomic single cell profiling of 14 newly diagnosed adult MPAL patients to characterize the inter- and intra-tumoral transcriptional, immunophenotypic, and genetic landscapes of MPAL. We show that neither genetic profile nor transcriptome reliably correlate with specific MPAL immunophenotypes. Despite this, we find that MPAL blasts express a shared stem cell-like transcriptional profile indicative of high differentiation potential. Patients with the highest differentiation potential demonstrate inferior survival in our dataset. A gene set score, MPAL95, derived from genes highly enriched in the most stem-like MPAL cells, is applicable to bulk RNA sequencing data and is predictive of survival in an independent patient cohort, suggesting a potential strategy for clinical risk stratification.

Cellular heterogeneity and key subsets of tissue-resident memory T cells in cervical cancer

Tissue-resident memory T cells (TRMs) play a critical role in cancer immunity by offering quick and effective immune responses. However, the cellular heterogeneity of TRMs and their significance in cervical cancer (CC) remain unknown. In this study, we generated and analyzed single-cell RNA sequencing data from 12,945 TRMs (ITGAE+CD3D+) and 25,627 non-TRMs (ITGAE−CD3D+), derived from 11 CC tissues and 5 normal cervical tissues. We found that TRMs were more immunoreactive than non-TRMs, and TRMs in CC tissues were more activated than those in normal cervical tissues. Six CD8+ TRM subclusters and one CD4+ TRM subcluster were identified. Among them, CXCL13+ CD8+ TRMs were more abundant in CC tissues than in normal cervical tissues, had both cytotoxic and inhibitory features, and were enriched in pathways related to defense responses to the virus. Meanwhile, PLAC8+ CD8+ TRMs were less abundant in CC tissues than in normal cervical tissues but had highly cytotoxic features. The signature gene set scores of both cell subclusters were positively correlated with the overall survival and progression-free survival of patients with CC following radiotherapy. Of note, the association between HLA-E and NKG2A, either alone or in a complex with CD94, was enriched in CXCL13+ CD8+ TRMs interacting with epithelial cells at CC tissues. The in-depth characterization of TRMs heterogeneity in the microenvironment of CC could have important implications for advancing treatment and improving the prognosis of patients with CC.

Application of Transcriptome-Based Gene Set Featurization for Machine Learning Model to Predict the Origin of Metastatic Cancer.

Identifying the primary site of origin of metastatic cancer is vital for guiding treatment decisions, especially for patients with cancer of unknown primary (CUP). Despite advanced diagnostic techniques, CUP remains difficult to pinpoint and is responsible for a considerable number of cancer-related fatalities. Understanding its origin is crucial for effective management and potentially improving patient outcomes. This study introduces a machine learning framework, ONCOfind-AI, that leverages transcriptome-based gene set features to enhance the accuracy of predicting the origin of metastatic cancers. We demonstrate its potential to facilitate the integration of RNA sequencing and microarray data by using gene set scores for characterization of transcriptome profiles generated from different platforms. Integrating data from different platforms resulted in improved accuracy of machine learning models for predicting cancer origins. We validated our method using external data from clinical samples collected through the Kangbuk Samsung Medical Center and Gene Expression Omnibus. The external validation results demonstrate a top-1 accuracy ranging from 0.80 to 0.86, with a top-2 accuracy of 0.90. This study highlights that incorporating biological knowledge through curated gene sets can help to merge gene expression data from different platforms, thereby enhancing the compatibility needed to develop more effective machine learning prediction models.

T-cell Mediated Rejection Associated Microvascular Inflammation in the Allograft Kidney: RNAseq Analysis Using the Banff Human Organ Transplant Gene Panel.

Microvascular inflammation (MVI) can occur in biopsies showing T-cell mediated rejection (TCMR), but it is not well established that T-cells can directly mediate microvascular injury (TCMR-MVI). This was a cross sectional RNAseq based Banff Human Organ Transplant (BHOT) gene expression (GE) analysis. The objective of this study was to probe the molecular signature of TCMR-MVI in comparison with C4d+, DSA+ antibody mediated rejection (ABMR), stable renal function (STA), and TCMR without MVI. Transcriptome analysis utilized CLC genomic workbench and R-studio software. No gene set was specific for any diagnostic category, and all were expressed at low levels in STA biopsies. BHOT gene set scores could differentiate ABMR from TCMR and TCMR-MVI, but not TCMR from TCMR-MVI. TCMR-MVI underexpressed several genes associated with ABMR including DSATs, ENDAT, immunoglobulin genes, ADAMDEC1, PECAM1 and NK cell transcripts (MYBL1, GNLY), but overexpressed C3, NKBBIZ, and LTF. On the other hand, there was no significant difference in the expression of these genes in TCMR-MVI versus TCMR. This indicates that the GE profile of TCMR MVI aligns more closely with TCMR than ABMR. The limitations of classifying biopsies using the binary ABMR-TCMR algorithm, and the occurrence of common pathogenesis mechanisms amongst different rejection phenotype was highlighted by the frequent presence of molecular mixed rejection. T-cell mediated mechanisms play a significant role in the pathogenesis of MVI. GE was broadly different between rejection phenotypes, but molecular scores varied substantially between biopsies with the same Banff grade. It was not always possible to achieve precise molecular score-based diagnostic categorization of individual patients.

Elucidating sleep disorders: a comprehensive bioinformatics analysis of functional gene sets and hub genes.

Sleep disorders (SD) are known to have a profound impact on human health and quality of life although their exact pathogenic mechanisms remain poorly understood. The study first accessed SD datasets from the GEO and identified DEGs. These DEGs were then subjected to gene set enrichment analysis. Several advanced techniques, including the RF, SVM-RFE, PPI networks, and LASSO methodologies, were utilized to identify hub genes closely associated with SD. Additionally, the ssGSEA approach was employed to analyze immune cell infiltration and functional gene set scores in SD. DEGs were also scrutinized in relation to miRNA, and the DGIdb database was used to explore potential pharmacological treatments for SD. Furthermore, in an SD murine model, the expression levels of these hub genes were confirmed through RT-qPCR and Western Blot analyses. The findings of the study indicate that DEGs are significantly enriched in functions and pathways related to immune cell activity, stress response, and neural system regulation. The analysis of immunoinfiltration demonstrated a marked elevation in the levels of Activated CD4+ T cells and CD8+ T cells in the SD cohort, accompanied by a notable rise in Central memory CD4 T cells, Central memory CD8 T cells, and Natural killer T cells. Using machine learning algorithms, the study also identified hub genes closely associated with SD, including IPO9, RAP2A, DDX17, MBNL2, PIK3AP1, and ZNF385A. Based on these genes, an SD diagnostic model was constructed and its efficacy validated across multiple datasets. In the SD murine model, the mRNA and protein expressions of these 6 hub genes were found to be consistent with the results of the bioinformatics analysis. In conclusion, this study identified 6 genes closely linked to SD, which may play pivotal roles in neural system development, the immune microenvironment, and inflammatory responses. Additionally, the key gene-based SD diagnostic model constructed in this study, validated on multiple datasets showed a high degree of reliability and accuracy, predicting its wide potential for clinical applications. However, limited by the range of data sources and sample size, this may affect the generalizability of the results.

Combining bulk and scRNA-seq to explore the molecular mechanisms governing the distinct efferocytosis activities of a macrophage subpopulation in PDAC.

Pancreatic ductal adenocarcinoma (PDAC), a very aggressive tumour, is currently the third leading cause of cancer-related deaths. Unfortunately, many patients face the issue of inoperability at the diagnostic phase leading to a quite dismal prognosis. The onset of metastatic processes has a crucial role in the elevated mortality rates linked to PDAC. Individuals with metastatic advances receive only palliative therapy and have a grim prognosis. It is essential to carefully analyse the intricacies of the metastatic process to enhance the prognosis for individuals with PDAC. Malignancy development is greatly impacted by the process of macrophage efferocytosis. Our current knowledge about the complete range of macrophage efferocytosis activities in PDAC and their intricate interactions with tumour cells is still restricted. This work aims to resolve communication gaps and pinpoint the essential transcription factor that is vital in the immunological response of macrophage populations. We analysed eight PDAC tissue samples sourced from the gene expression omnibus. We utilized several software packages such as Seurat, DoubletFinder, Harmony, Pi, GSVA, CellChat and Monocle from R software together with pySCENIC from Python, to analyse the single-cell RNA sequencing (scRNA-seq) data collected from the PDAC samples. This study involved the analysis of a comprehensive sample of 22,124 cells, which were classified into distinct cell types. These cell types encompassed endothelial and epithelial cells, PDAC cells, as well as various immune cells, including CD4+ T cells, CD8+ T cells, NK cells, B cells, plasma cells, mast cells, monocytes, DC cells and different subtypes of macrophages, namely C0 macrophage TGM2+, C1 macrophage PFN1+, C2 macrophage GAS6+ and C3 macrophage APOC3+. The differentiation between tumour cells and epithelial cells was achieved by the implementation of CopyKat analysis, resulting in the detection and categorization of 1941 PDAC cells. The amplification/deletion patterns observed in PDAC cells on many chromosomes differ significantly from those observed in epithelial cells. The study of Pseudotime Trajectories demonstrated that the C0 macrophage subtype expressing TGM2+ had the lowest level of differentiation. Additionally, the examination of gene set scores related to efferocytosis suggested that this subtype displayed higher activity during the efferocytosis process compared to other subtypes. The most active transcription factors for each macrophage subtype were identified as BACH1, NFE2, TEAD4 and ARID3A. In conclusion, the examination of human PDAC tissue samples using immunofluorescence analysis demonstrated the co-localization of CD68 and CD11b within regions exhibiting the presence of keratin (KRT) and alpha-smooth muscle actin (α-SMA). This observation implies a spatial association between macrophages, fibroblasts, and epithelial cells. There is variation in the expression of efferocytosis-associated genes between C0 macrophage TGM2+ and other macrophage cell types. This observation implies that the diversity of macrophage cells might potentially influence the metastatic advancement of PDAC. Moreover, the central transcription factor of different macrophage subtypes offers a promising opportunity for targeted immunotherapy in the treatment of PDAC.

Machine learning-based cell death signature for predicting the prognosis and immunotherapy benefit in stomach adenocarcinoma.

Stomach adenocarcinoma (STAD) is a one of most common malignancies with high mortality-to-incidence ratio. Programmed cell death (PCD) exerts vital functions in the progression of cancer. The role of PCD-related genes (PRGs) in STAD are not fully clarified. Using TCGA, GSE15459, GSE26253, GSE62254 and GSE84437 datasets, PCD-related signature (PRS) was constructed with an integrative procedure including 10 machine learning methods. The role of PRS in predicting the immunotherapy benefits was evaluated by several predicting score and 3 immunotherapy datasets (GSE91061, GSE78220, and IMvigor210). The model developed by Lasso + CoxBoost algorithm having a highest average C-index of 0.66 was considered as the optimal PRS. As an independent risk factor for STAD patients, PRS had a good performance in predicting the overall survival rate of patients, with an AUC of 1-, 3-, and 5-year ROC curve being 0.771, 0.751 and 0.827 in TCGA cohort. High PRS score demonstrated a lower gene set score of some immune-activated cells and immune-activated activities. Patient with high PRS score had a higher TIDE score, higher immune escape score, lower PD1&CTLA4 immunophenoscore, lower TMB score, lower response rate and poor prognosis, indicating a less immunotherapy response. The IC50 value of some drugs correlated with chemotherapy and targeted therapy was higher in high PRS score group. Our investigation developed an optimal PRS in STAD and it acted as an indicator for predicting the prognosis, stratifying risk and guiding treatment for STAD patients.

Tissue-adjusted pathway analysis of cancer (TPAC): A novel approach for quantifying tumor-specific gene set dysregulation relative to normal tissue.

We describe a novel single sample gene set testing method for cancer transcriptomics data named tissue-adjusted pathway analysis of cancer (TPAC). The TPAC method leverages information about the normal tissue-specificity of human genes to compute a robust multivariate distance score that quantifies gene set dysregulation in each profiled tumor. Because the null distribution of the TPAC scores has an accurate gamma approximation, both population and sample-level inference is supported. As we demonstrate through an analysis of gene expression data for 21 solid human cancers from The Cancer Genome Atlas (TCGA) and associated normal tissue expression data from the Human Protein Atlas (HPA), TPAC gene set scores are more strongly associated with patient prognosis than the scores generated by existing single sample gene set testing methods.

Machine Learning Developed a Programmed Cell Death Signature for Predicting Prognosis, Ecosystem, and Drug Sensitivity in Ovarian Cancer.

Ovarian cancer (OC) is the leading cause of gynecological cancer death and the fifth most common cause of cancer-related death in women in America. Programmed cell death played a vital role in tumor progression and immunotherapy response in cancer. The prognostic cell death signature (CDS) was constructed with an integrative machine learning procedure, including 10 methods, using TCGA, GSE14764, GSE26193, GSE26712, GSE63885, and GSE140082 datasets. Several methods and single-cell analysis were used to explore the correlation between CDS and the ecosystem and therapy response of OC patients. The prognostic CDS constructed by the combination of StepCox (n = both) + Enet (alpha = 0.2) acted as an independent risk factor for the overall survival (OS) of OC patients and showed stable and powerful performance in predicting the OS rate of OC patients. Compared with tumor grade, clinical stage, and many developed signatures, the CDS had a higher C-index. OC patients with low CDS score had a higher level of CD8+ cytotoxic T, B cell, and M1-like macrophage, representing a related immunoactivated ecosystem. A low CDS score indicated a higher PD1 and CTLA4 immunophenoscore, higher tumor mutation burden score, lower tumor immune dysfunction and exclusion score, and lower tumor escape score in OC, demonstrating a better immunotherapy response. OC patients with high CDS score had a higher gene set score of cancer-related hallmarks, including angiogenesis, epithelial-mesenchymal transition, hypoxia, glycolysis, and notch signaling. The current study constructed a novel CDS for OC, which could serve as an indicator for predicting the prognosis, ecosystem, and immunotherapy benefits of OC patients.

Dissecting Systemic T Cell Responses after Stereotactic Ablative Radiotherapy in NSCLC by Single-Cell RNA and T Cell Receptor Sequencing

There is accumulating evidence that stereotactic ablative radiotherapy (SABR) modulates immune responses to cancer; combining SABR and immunotherapy could promote the abscopal effect, but the precise effects of SABR on patients' systemic T cells is unclear. Here, we investigated SABR-induced systemic T cell response in early-stage non-small cell lung cancer (NSCLC) by single-cell RNA and T cell receptor sequencing. We performed single-cell RNA and T cell receptor sequencing on 29,439 T cells from four pairs of peripheral blood before and after SABR in early-stage NSCLC patients. Cell clustering and dimensionality reduction, SingleR, feature genes score, and TCR profiling analyses were used to investigate the heterogeneity of T cells and their changes following SABR. We identified fourteen T cell subtypes using unsupervised graph-based clustering of uniform manifold approximation and projection. By comparing the gene set scores of CD8_TE and CD8_EM pre- and post-SABR, we found both cytotoxic and inhibitory scores were significantly elevated in CD8_TE (both P < 0.001), while cytotoxic score was significantly increased in CD8_EM (P < 0.001) after SABR. We also found that CD4_TE showed increased cytotoxic scores and decreased Treg scores (P < 0.001 and < 0.05, respectively), while Treg cells showed decreased inhibitory and Treg scores (P < 0.001 and <0.01, respectively) after SABR. The proportion of large TCR clones was higher after SABR, which was accompanied by a decrease in proportion of single clones. When we compared the transcriptomes of CD8_TE cells between the single, small and large clones post-SABR, we found high expression of GZMB and KLRC3 in cells with large clones, and GZMK, IL7R, and SELL in small and single clones. This suggested that T cells after SABR with large clones may have higher cytotoxicity than those with small and single clones. Our study identified systemic T cell activation after SABR at single-cell resolution, providing unprecedented insight into the immune-modulatory role of SABR in early-stage NSCLC.

Immune landscape in rejection of renal transplantation revealed by high-throughput single-cell RNA sequencing.

Background: The role of the cellular level in kidney transplant rejection is unclear, and single-cell RNA sequencing (scRNA-seq) can reveal the single-cell landscape behind rejection of human kidney allografts at the single-cell level. Methods: High-quality transcriptomes were generated from scRNA-seq data from five human kidney transplantation biopsy cores. Cluster analysis was performed on the scRNA-seq data by known cell marker genes in order to identify different cell types. In addition, pathways, pseudotime developmental trajectories and transcriptional regulatory networks involved in different cell subpopulations were explored. Next, we systematically analyzed the scoring of gene sets regarding single-cell expression profiles based on biological processes associated with oxidative stress. Results: We obtained 81,139 single cells by scRNA-seq from kidney transplant tissue biopsies of three antibody-mediated rejection (ABMR) patients and two acute kidney injury (AKI) patients with non-rejection causes and identified 11 cell types, including immune cells, renal cells and several stromal cells. Immune cells such as macrophages showed inflammatory activation and antigen presentation and complement signaling, especially in rejection where some subpopulations of cells specifically expressed in rejection showed specific pro-inflammatory responses. In addition, patients with rejection are characterized by an increased number of fibroblasts, and further analysis of subpopulations of fibroblasts revealed their involvement in inflammatory and fibrosis-related pathways leading to increased renal rejection and fibrosis. Notably, the gene set score for response to oxidative stress was higher in patients with rejection. Conclusion: Insight into histological differences in kidney transplant patients with or without rejection was gained by assessing differences in cellular levels at single-cell resolution. In conclusion, we applied scRNA-seq to rejection after renal transplantation to deconstruct its heterogeneity and identify new targets for personalized therapeutic approaches.

Transcriptomic Heterogeneity of Expansile Cribriform and Other Gleason Pattern 4 Prostate Cancer Subtypes

BackgroundProstate cancers featuring an expansile cribriform (EC) pattern are associated with worse clinical outcomes following radical prostatectomy (RP). However, studies of the genomic characteristics of Gleason pattern 4 subtypes are limited. ObjectiveTo explore transcriptomic characteristics and heterogeneity within Gleason pattern 4 subtypes (fused/poorly formed, glomeruloid, small cribriform, EC/intraductal carcinoma [IDC]) and the association with biochemical recurrence (BCR)-free survival. Design, setting, and participantsThis was a retrospective cohort study including 165 men with grade group 2–4 prostate cancer who underwent RP at a single academic institution (2016–2020) and Decipher testing of the RP specimen. Patients with Gleason pattern 5 were excluded. IDC and EC patterns were grouped. Median follow-up was 2.5 yr after RP for patients without BCR. Outcomes measurements and statistical analysisPrompted by heterogeneity within pattern 4 subtypes identified via exploratory analyses, we investigated transcriptomic consensus clusters using partitioning around medoids and hallmark gene set scores. The primary clinical outcome was BCR, defined as two consecutive prostate-specific antigen measurements >0.2 ng/ml at least 8 wk after RP, or any additional treatment. Multivariable Cox proportional-hazards models were used to determine factors associated with BCR-free survival. Results and limitationsIn this cohort, 99/165 patients (60%) had EC and 67 experienced BCR. Exploratory analyses and clustering demonstrated transcriptomic heterogeneity within each Gleason pattern 4 subtype. In the multivariable model controlled for pattern 4 subtype, margin status, Cancer of the Prostate Risk Assessment Post-Surgical score, and Decipher score, a newly identified steroid hormone–driven cluster (hazard ratio 2.35 95% confidence interval 1.01–5.47) was associated with worse BCR-free survival. The study is limited by intermediate follow-up, no validation cohort, and lack of accounting for intratumoral and intraprostatic heterogeneity. ConclusionsTranscriptomic heterogeneity was present within and across each Gleason pattern 4 subtype, demonstrating there is additional biologic diversity not captured by histologic subtypes. This heterogeneity can be used to develop novel signatures and to classify transcriptomic subtypes, which may help in refining risk stratification following RP to further guide decision-making on adjuvant and salvage treatments. Patient summaryWe studied prostatectomy specimens and found that tumors with similar microscopic appearance can have genetic differences that may help to predict outcomes after prostatectomy for prostate cancer. Our results demonstrate that further gene expression analysis of prostate cancer subtypes may improve risk stratification after prostatectomy. Future studies are needed to develop novel gene expression signatures and validate these findings in independent sets of patients.

EXOSOME MIRNAS PROFILING IN SERUM AND PROGNOSTIC EVALUATION IN PATIENTS WITH MULTIPLE MYELOMA

Introduction: Multiple Myeloma (MM) is an incurable hematological malignancy. Tightly crosstalk through microRNAs (miRNAs) carried by exosomes between the bone marrow microenvironment and MM cells plays pivotal roles in MM. Our study aimed to identify the expression profile of exosomal miRNAs (exo-miRNAs) in the serum of MM patients and investigate the regulation network and their potential functions. Methods: Exosomes in serum from 19 newly diagnosed multiple myeloma patients and 9 healthy donors were isolated and the miRNA profile was investigated by small RNA sequencing. Differential expression of exo-miRNAs was calculated and target genes of miRNAs were predicted. CytoHubba was applied to identify the hub miRNAs and core target genes. The LASSO Cox regression model was utilized to develop the prognostic model, and the ESTIMATE immune score was calculated to investigate the correlation between the model and immune status in MM patients. Results: Our study clarified 313 differentially expressed serum exo-miRNAs between MM patients and HD. GO analysis of target genes of differential miRNAs showed that these target genes are mainly involved in the critical biological processes related to MM pathogenesis such as proteasome-mediated ubiquitin-dependent protein catabolic process, indicating the indispensable roles of these miRNAs in MM pathogenesis. Top six hub differentially expressed serum exo-miRNAs (hsa-miR-4728-5p, hsa-miR-455-3p, hsa-miR-6779-5p, hsa-miR-124-3p, hsa-miR-615-3p, and hsa-miR-7106-5p) were identified. 513 target genes of six hub exo-miRNAs were confirmed to be differentially expressed in MM cells in Zhan Myeloma microarray dataset. Functional enrichment analysis indicated that these target genes mainly involved mRNA splicing, cellular response to stress, and deubiquitination. Thirteen core exo-miRNA target genes were applied to create a novel prognostic signature to facilitate the risk stratification of MM patients, and two groups of MM patients with diverse outcomes were recognized (Figure 1A). The high-score patients identified by thirteen core genes signature displayed a higher 70 high-risk gene set (UAMS-70) score and the 56 drug-resistance genes set score, which might partly explain their inferior outcomes (Figure 1B–C). Additionally, our results suggest that the thirteen core genes signature model was highly correlated with the status of the immune microenvironment, and patients in the low-score group had higher immune cell infiltration levels (Figure 1D). Besides, patients in the high-risk group displayed lower HLA family gene expression (Figure 1E). Encore Abstract - previously submitted to EHA 2023 Keywords: Bioinformatics, Computational and Systems Biology, Diagnostic and Prognostic Biomarkers, Risk Models No conflicts of interests pertinent to the abstract.

Abstract 144: Spatial transcriptomic landscape of the primary and recurrent glioblastoma tumor microenvironment

Abstract Differences in clonality, genomic and epigenetic alterations are known to occur between primary Glioblastoma (pGBM) and recurrent (rGBM), but this has been insufficient to yield a promising therapeutic approach. Recent studies show that the GBM tumor microenvironment (TME), composed of reactive glial cells, microglial subtypes, neuronal activity, myeloid cells, lymphocytes, etc., plays a crucial role in the invasiveness and evolution of the glioma cells. Nevertheless, how the spatial architecture and interplay among these cells facilitates GBM resurgence is largely unknown. To this point, we developed a pipeline for characterizing the TME changes using single nucleus and spatial transcriptomics (ST). We retrospectively collected matched, fresh-frozen, IDH-wildtype pGBM and rGBM samples (n=14, 7 patients) from the Mayo Clinic Neuroscience Biobank. Single nucleus RNA sequencing was performed using the 10x Chromium platform. Matched FFPE blocks from 8 samples were collected from the Mayo Clinic pathology department for 4 of the 7 patients. A region of interest was identified from the corresponding histopathology slide by a pathologist, avoiding areas of necrosis and hemorrhage. The region was collected from the block using a 5mm biopsy punch. The tissue was re-paraffinized, and a 5µm thick section was used to perform ST with the 10x Visium for FFPE platform. We performed transcriptomic and image texture-based clustering of capture dots along with differential gene expression analysis. Based on gene expression, we identified and characterized conserved regions across samples. We used single nucleus data to infer cell composition. Spatial correlation and autocorrelation statistics were calculated on features, such as gene expression, gene set scores, cell type prediction, and antibody capture. The features’ relative spatial changes were compared between pGBM and rGBM. Our analyses showed high heterogeneity within and across samples, but we found higher molecular and textural heterogeneity in rGBM samples compared to pGBM. We found higher androgen response activity (p &amp;lt; 0.001) presents diffusely (I: 0.09) throughout rGBM tissue but localized to mesenchymal-like regions in pGBM (I: 0.15). KRAS signaling signature was decreased in pGBM (p &amp;lt; 0.01) and increased in rGBM (p &amp;lt; 0.01). This increase was significantly overexpressed in cluster 0 for rGBM (p=0.02), which is an area characterized by neural progenitor signature. No significant difference in KRAS or EGFR magnitude or distribution of expression was present for either GBM type, indicating an independent mechanism for pGBM. We established a pipeline for assessing the spatio-temporal changes of the GBM TME. We found evidence indicating differences in the type of growth signaling, the cells bing targeted, and their distribution across the TME between pGBM and rGBM. Our work suggests how ST can help us better understand the intricate dynamics of GBM. Citation Format: Jean R. Clemenceau, Paola Suarez Meade, Mark E. Jentoft, Alfredo Quiñones-Hinojosa, Tae Hyun Hwang. Spatial transcriptomic landscape of the primary and recurrent glioblastoma tumor microenvironment [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 144.

Abstract PD2-10: MiR-150 expression in breast cancer cells attract infiltration of lymphocytes to tumor microenvironment and is associated with better survival of the patients

Abstract MicroRNA (miRNA) are known as a key player in tumor biology and is shown to epigenetically regulate a large number of protein-coding genes, including tumor-related genes. MiR-150, a hematopoietic cell-specific miRNA, has been suggested to have various effects on cell proliferation, differentiation, apoptosis, migration, and invasion. However, there has been no study that investigated the role of miR-150 in the tumor microenvironment (TME) of breast cancer patients. We hypothesized that miR-150 expressed in breast cancer cells attract infiltration of lymphocytes to tumor microenvironment and is associated with better survival of the patients. In silico analyses of 1961 breast cancer patients were performed using multiple independent large cohorts, and in vitro studies by overexpressing miR-150 using its mimic in triple negative breast cancer (TNBC) cell lines were conducted. We found that miR-150 expression in patient breast tumors were strongly correlated with immune-related gene set scores, Allograft rejection, IL6/JAK/STAT3 signaling, Interferon-γ response, Inflammatory response, IL2/STAT5 signaling, and complement, in Hallmark collection of gene set variation analyses consistently in both METABRIC and TCGA cohorts (all spearman’s rank correlation coefficient (r) &amp;gt; 0.50, all p &amp;lt; 0.01). MiR-150 expression was also strongly correlated with cytolytic activity (CYT) score in both cohorts (r = 0.824 and 0.786, respectively, both p &amp;lt; 0.01). Furthermore, miR-150 expression was significantly correlated with infiltrating fraction of CD8+ T cells (r = 0.799 and 0.525, respectively), CD4+ memory T cells (r = 0.759 and 0.656, respectively), dendritic cells (r = 0.735 and 0.696, respectively), and B cells (r = 0.759 and 0.576, respectively), as well as mRNA expression of major immune checkpoint molecules, including PD-1, CTLA4, IDO1, TIGIT, BTLA, and LAG3, in both cohorts (all r &amp;gt; 0.50, and all p &amp;lt; 0.01). MiR-150 expression in triple negative breast cancer (TNBC) was the highest among the subtypes (both p &amp;lt; 0.001). MiR-150 expression level was significantly correlated with Nottingham histologic grade (both p &amp;lt; 0.001). MiR-150 high tumor enriched not only immune-related gene sets but also apoptosis, KRAS signaling up, MTORC1, and p53 pathway by gene set enrichment analysis in both cohorts. High miR-150 expression patients were significantly associated with better overall survival (OS) in both cohorts (p &amp;lt; 0.001 and p = 0.030, respectively). Subgroup analysis revealed that a high miR-150 was associated with better OS in ER-positive/HER2-negative breast cancer in both cohorts (p = 0.002 and 0.044, respectively), and in TNBC in the METABRIC cohort (p = 0.006). Unexpectedly, overexpression of miR-150 by its mimic shown no significant effect on cell proliferation, migration, nor invasion consistently in two TNBC cell lines. On the other hand, overexpression of miR-150 by its mimic significantly attracted Jurkart immortalized lymphocytes consistently in two cell lines, which effect was abolished by addition of miR-150 inhibitor. Finally, we found that mimic overexpression of miR-150 significantly expressed multiple cytokines and multiple inflammatory signaling pathways which at least partially explained the increased attraction of T cells in patient samples and in vitro condition. In conclusion, miR-150 expression in breast cancer cells may be associated with better patient survival outcomes by attracting and activating immune cells in breast cancer TME. Citation Format: Masanori Oshi, Aparna Maiti, Raj.G Vaghjiani, Rongrong Wu, Li Yan, Akimitsu Yamada, Nitai Hait, Takashi Ishikawa, Itaru Endo, Kazuaki Takabe, Yoshihisa Tokumaru. MiR-150 expression in breast cancer cells attract infiltration of lymphocytes to tumor microenvironment and is associated with better survival of the patients [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr PD2-10.

Single-cell gene set scoring with nearest neighbor graph smoothed data (gssnng).

Gene set scoring (or enrichment) is a common dimension reduction task in bioinformatics that can be focused on the differences between groups or at the single sample level. Gene sets can represent biological functions, molecular pathways, cell identities, and more. Gene set scores are context dependent values that are useful for interpreting biological changes following experiments or perturbations. Single sample scoring produces a set of scores, one for each member of a group, which can be analyzed with statistical models that can include additional clinically important factors such as gender or age. However, the sparsity and technical noise of single-cell expression measures create difficulties for these methods, which were originally designed for bulk expression profiling (microarrays, RNAseq). This can be greatly remedied by first applying a smoothing transformation that shares gene measure information within transcriptomic neighborhoods. In this work, we use the nearest neighbor graph of cells for matrix smoothing to produce high quality gene set scores on a per-cell, per-group, level which is useful for visualization and statistical analysis. The gssnng software is available using the python package index (PyPI) and works with Scanpy AnnData objects. It can be installed using "pip install gssnng." More information and demo notebooks: see https://github.com/IlyaLab/gssnng.

HPV status represents dominant trait driving delineation of survival-associated gene co-expression networks in head and neck cancer

Integration of high-dimensional tumor gene expression data with clinicopathological data can increase our understanding of disease diversity, enable retrospective patient stratification, and identify new potential biomarkers and therapeutic targets. Using a systems biology approach, we provide a holistic overview of gene co-expression networks in head and neck squamous cell carcinomas (HNSCC). Weighted gene co-expression network analysis of HNSCC RNA sequencing data from 519 patients from The Cancer Genome Atlas (TCGA) was used to determine correlates of 5-year survival, using regression tree-based optimal threshold calculations. Survival-associated gene sets were transformed to gene set scores that were assessed for correlation with clinicopathological data. We identified 8 gene co-expression modules for HNSCC tumors, each of which contained co-expressed genes associated significantly with 5-year survival. Survival-associated co-expression gene signatures correlated dominantly with tumor HPV and p16 status. Network analysis identified that survival was associated with signaling networks of infection, immunity, epithelial-mesenchymal transition (EMT), hypoxia, glycolysis, focal adhesion, extracellular matrix, MYC signaling, autophagy and transcriptional regulation. EMT-associated gene signatures were expressed dominantly in fibroblasts, and cancer-associated fibroblasts were inversely correlated with immune activity. Interestingly, a high Immune Suppression Score based on expression of 21 genes associated with immune inhibition and including immune checkpoints, cytokines and regulatory T cell factors, was also associated with increased survival probability, and was significantly higher in HPV+ HNSCC. Networks associated with HNSCC survival were further associated with survival in cervical cancer, melanoma and lung cancer. This study defines 5129 genes associated with HNSCC survival, organized into co-expressed networks, their correlation with clinicopathological data, and with gene expression data from other malignant diseases, and provides a source for the discovery of biomarkers and novel therapies for HNSCC.

Integrated analysis of bulk and single-cell RNA-seq reveals the role of MYC signaling in lung adenocarcinoma.

MYC is one of the well-known oncogenes, and its important role in cancer still remains largely unknown. We obtained lung adenocarcinoma (LUAD) multi-omics data including genome, transcriptome, and single-cell sequencing data from multiple cohorts. We calculated the GSVA score of the MYC target v1 using the ssGSEA method, and obtained the genes highly correlated with this score by Spearman correlation analysis. Subsequent hierarchical clustering divided these genes into two gene sets highly associated with MYC signaling (S1 and S2). Unsupervised clustering based on these genes divided the LUAD samples into two distinct subgroups, namely, the MYC signaling inhibition group (C1) and activation group (C2). The MCP counter package in R was used to assess tumor immune cell infiltration abundance and ssGSEA was used to calculate gene set scores. The scRNA-seq was used to verify the association of MYC signaling to cell differentiation. We observed significant differences in prognosis, clinical characteristics, immune microenvironment, and genomic alterations between MYC signaling inhibition and MYC signaling activation groups. MYC-signaling is associated with genomic instability and can mediate the immunosuppressive microenvironment and promote cell proliferation, tumor stemness. Moreover, MYC-signaling activation is also subject to complex post-transcriptional regulation and is highly associated with cell differentiation. In conclusion, MYC signaling is closely related to the genomic instability, genetic alteration and regulation, the immune microenvironment landscape, cell differentiation, and disease survival in LUAD. The findings of this study provide a valuable reference to revealing the mechanism of cancer-promoting action of MYC in LUAD.