Single-cell Transcriptomic Data Research Articles

Machine-learning developed an iron, copper, and sulfur-metabolism associated signature predicts lung adenocarcinoma prognosis and therapy response

BackgroundPrevious studies have largely neglected the role of sulfur metabolism in LUAD, and no study has combine iron, copper, and sulfur-metabolism associated genes together to create prognostic signatures.MethodsThis study encompasses 1564 LUAD patients, 1249 NSCLC patients, and over 10,000 patients with various cancer types from diverse cohorts. We employed the R package ConsensusClusterPlus to separate patients into different ICSM (Iron, Copper, and Sulfur-Metabolism) subtypes. Various machine-learning methods were utilized to develop the ICSMI. Enrichment analyses were conducted using ClusterProfiler and GSVA, while IOBR quantified immune cell infiltration. GISTIC2.0 and maftools were utilized for CNV and SNV data analysis. The Oncopredict package predicted drug information based on GDSC1. TIDE algorithm and cohorts GSE91061 and IMvigor210 evaluated patient response to immunotherapy. Single-cell data was processed using the Seurat package, AUCell package calculated cells geneset activity scores, and the Scissor algorithm identified ICSMI-associated cells. In vitro experiments was conducted to explore the role of ICSMRGs in LUAD.ResultsUnsupervised clustering identified two distinct ICSM subtypes of LUAD, each with unique clinical characteristics. The ICSMI, comprising 10 genes, was constructed using integrated machine-learning methods. Its prognostic power was validated in 10 independent datasets, revealing that LUAD patients with higher ICSMI levels had poorer prognoses. Furthermore, ICSMI demonstrated superior predictive abilities compared to 102 previously published signatures. A nomogram incorporating ICSMI and clinical features exhibited high predictive performance. ICSMI positively correlated with patients gene mutations, and integrated analysis of bulk and single-cell transcriptome data revealed its association with TME modulators. Cells representing the high-ICSMI phenotype exhibited more malignant features. LUAD patients with high ICSMI levels exhibited sensitivity to chemotherapy and targeted therapy but displayed resistance to immunotherapy. In a comprehensive analysis across various cancers, ICSMI retained significant prognostic value and emerged as a risk factor for the majority of cancer patients.ConclusionsICSMI provides critical prognostic insights for LUAD patients, offering valuable insights into the tumor microenvironment and predicting treatment responsiveness.

TauFisher predicts circadian time from a single sample of bulk and single-cell pseudobulk transcriptomic data

As the circadian clock regulates fundamental biological processes, disrupted clocks are often observed in patients and diseased tissues. Determining the circadian time of the patient or the tissue of focus is essential in circadian medicine and research. Here we present tauFisher, a computational pipeline that accurately predicts circadian time from a single transcriptomic sample by finding correlations between rhythmic genes within the sample. We demonstrate tauFisher’s performance in adding timestamps to both bulk and single-cell transcriptomic samples collected from multiple tissue types and experimental settings. Application of tauFisher at a cell-type level in a single-cell RNAseq dataset collected from mouse dermal skin implies that greater circadian phase heterogeneity may explain the dampened rhythm of collective core clock gene expression in dermal immune cells compared to dermal fibroblasts. Given its robustness and generalizability across assay platforms, experimental setups, and tissue types, as well as its potential application in single-cell RNAseq data analysis, tauFisher is a promising tool that facilitates circadian medicine and research.

Sphingolipids in prostate cancer prognosis: integrating single-cell and bulk sequencing.

Stratifying patient risk and exploring the tumor microenvironment are critical endeavors in prostate cancer research, essential for advancing our understanding and management of this disease. Single-cell sequencing data for prostate cancer were sourced from the pradcellatlas website, while bulk transcriptome data were obtained from the TCGA database. Dimensionality reduction cluster analysis was employed to investigate heterogeneity in single-cell sequencing data. Gene set enrichment analysis, utilizing GO and KEGG pathways, was conducted to explore functional aspects. Weighted gene coexpression network analysis (WGCNA) identified key gene modules. Prognostic models were developed using Cox regression and LASSO regression techniques, implemented in R software. Validation of key gene expression levels was performed via PCR assays. Through integrative analysis of single-cell and bulk transcriptome data, key genes implicated in prostate cancer pathogenesis were identified. A prognostic model focused on sphingolipid metabolism (SRSR) was constructed, comprising five genes: "FUS," "MARK3," "CHTOP," "ILF3," and "ARIH2." This model effectively stratified patients into high-risk and low-risk groups, with the high-risk cohort exhibiting significantly poorer prognoses. Furthermore, distinct differences in the immune microenvironment were observed between these groups. Validation of key gene expression, exemplified by ILF3, was confirmed through PCR analysis. This study contributes to our understanding of the role of sphingolipid metabolism in prostate cancer diagnosis and treatment. The identified prognostic model holds promise for improving risk stratification and patient outcomes in clinical settings.

Abstract PO1-25-04: PTN-positive luminal progenitors induce angiogenesis and metastasis in inflammatory breast cancer

Abstract Background Inflammatory breast cancer (IBC) is a lethal subtype of breast cancer characterized by rapidly arising diffuse erythema and edema of the overlying skin that was thought to contribute to rapid metastasis. Dissecting the intricate cellular ecosystems within tumors and affected skin and their complex interplay may be pivotal in unraveling the aggressive nature of IBC. Methods Integrative analysis containing single-cell RNA sequencing, bulk RNA sequencing, and whole exome sequencing, was performed on paired tumor and skin samples from 17 treatment-naive IBC patients and 5 non-IBC patients from multiple centers. Meanwhile, paraffin sections and serum specimens from two centers were conducted to validate the omics results. Additionally, public single-cell transcriptomic data of non-IBC tumors and normal mammary glands, RNA microarray data from IBC cases, and the Cancer Genome Atlas (TCGA) database were utilized to validate the characteristics of IBC microenvironment. The regulatory mechanism was verified by in vitro experiments. Results We delineated the landscapes of IBC tumor and affected skin microenvironment at the single-cell level and demonstrated that the luminal progenitors (LP) cells highly expressing pleiotrophin (PTN) were most significantly enriched in IBC tumors compared to non-IBC tumors and normal mammary glands, in two independent cohorts. Moreover, the serum concentrations of PTN were significantly higher in IBC patients compared with non-IBC patients. Notably, PTN secreted by PTN+ LP cells interacted with Neuropilin 1(NRP1) receptor on endothelial tip cells in both tumor and affected skin of IBC. Moreover, in vitro assay demonstrated knocking down the expression of NRP1 on human umbilical vein endothelial cells significantly blocked PTN-induced migration, tube formation and sprouting, which might be independent of VEGFA. In addition, two immature phenotypes of PVL_MMP9 and PVL_CCL19 cells recruited by endothelial tip cells via PDGFB/PDGFRB axis were accumulated in affected skin of IBC, which might be associated with promotion of malignant cell metastasis. Finally, we revealed that malignant cells in affected skin of IBC had a higher ability of epithelial-mesenchymal transition than IBC intratumoral malignant cells and identified enhanced crosstalk between immature PVL cells and malignant cells in the skin affected with IBC , through TNFSF12-TNFRSF12A, TGFB-TGFBR1/TGFBR2, MDK-SDC1/4/NCL/(ITGA6+ITGB1). Conclusion We identified a unique, non-malignant epithelial cell subpopulation PTN+ LP cells, which induce massive angiogenesis via the PTN-NRP1 axis, recruit the immature phenotype PVL cells accumulated in affected skin of IBC thus promoting tumor progression. Our findings offer comprehensive insights into the pathogenesis of IBC and uncover a previously unknown, promising therapeutic target. Patient of discovery cohort details Clinical and pathology details for inflammatory breast cancer(IBC) and non-IBC patients analysed by single-cell RNA sequencing(scRNA-seq), bulk RNA sequencing(bulk RNA-seq) and whole exome sequencing(WES) in this study. Clinicalpathological feature of validation cohort Citation Format: Mengmeng Zhang, Kaiwen Zhou, Ting Liu, Filipa Lynce, Filipa Lynce, Wendy Chen, Qianjun Chen, Xiaoping Li, Zhongyu Yuan, Juan Xu, Zhenhai Cai, Jianping Guo, Nan Shao, Ying Lin. PTN-positive luminal progenitors induce angiogenesis and metastasis in inflammatory breast cancer [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO1-25-04.

Branching topology of the human embryo transcriptome revealed by entropy sort feature weighting.

Analysis of single cell transcriptomics (scRNA-seq) data is typically performed after sub-setting to highly variable genes (HVGs). Here we show that Entropy Sorting provides an alternative mathematical framework for feature selection. On synthetic datasets, continuous entropy sort feature weighting (cESFW) outperforms HVG selection in distinguishing cell state specific genes. We apply cESFW to six merged scRNA-seq datasets spanning human early embryo development. Without smoothing or augmenting the raw counts matrices, cESFW generates a high-resolution embedding displaying coherent developmental progression from 8-cell to post-implantation stages and delineating 15 distinct cell states. The embedding highlights sequential lineage decisions during blastocyst development while unsupervised clustering identifies branch point populations obscured in previous analyses. The first branching region, where morula cells become specified for inner cell mass or trophectoderm, includes cells previously asserted to lack a developmental trajectory. We quantify the relatedness of different pluripotent stem cell cultures to distinct embryo cell types and identify marker genes of naïve and primed pluripotency. Finally, by revealing genes with dynamic lineage-specific expression we provide markers for staging progression from morula to blastocyst.

Comprehensive proteogenomic characterization of rare kidney tumors.

Non-clear cell renal cell carcinomas (non-ccRCCs) encompass diverse malignant and benign tumors. Refinement of differential diagnosis biomarkers, markers for early prognosis of aggressive disease, and therapeutic targets to complement immunotherapy are current clinical needs. Multi-omics analyses of 48 non-ccRCCs compared with 103 ccRCCs reveal proteogenomic, phosphorylation, glycosylation, and metabolic aberrations in RCC subtypes. RCCs with high genome instability display overexpression of IGF2BP3 and PYCR1. Integration of single-cell and bulk transcriptome data predicts diverse cell-of-origin and clarifies RCC subtype-specific proteogenomic signatures. Expression of biomarkers MAPRE3, ADGRF5, and GPNMB differentiates renal oncocytoma from chromophobe RCC, and PIGR and SOSTDC1 distinguish papillary RCC from MTSCC. This study expands our knowledge of proteogenomic signatures, biomarkers, and potential therapeutic targets in non-ccRCC.

Multiomics Analysis of Disulfidptosis Patterns and Integrated Machine Learning to Predict Immunotherapy Response in Lung Adenocarcinoma.

Recent studies have unveiled disulfidptosis as a phenomenon intimately associated with cellular damage, heralding new avenues for exploring tumor cell dynamics. We aimed to explore the impact of disulfide cell death on the tumor immune microenvironment and immunotherapy in lung adenocarcinoma (LUAD). We initially utilized pan-cancer transcriptomics to explore the expression, prognosis, and mutation status of genes related to disulfidptosis. Using the LUAD multi- -omics cohorts in the TCGA database, we explore the molecular characteristics of subtypes related to disulfidptosis. Employing various machine learning algorithms, we construct a robust prognostic model to predict immune therapy responses and explore the model's impact on the tumor microenvironment through single-cell transcriptome data. Finally, the biological functions of genes related to the prognostic model are verified through laboratory experiments. Genes related to disulfidptosis exhibit high expression and significant prognostic value in various cancers, including LUAD. Two disulfidptosis subtypes with distinct prognoses and molecular characteristics have been identified, leading to the development of a robust DSRS prognostic model, where a lower risk score correlates with a higher response rate to immunotherapy and a better patient prognosis. NAPSA, a critical gene in the risk model, was found to inhibit the proliferation and migration of LUAD cells. Our research introduces an innovative prognostic risk model predicated upon disulfidptosis genes for patients afflicted with Lung Adenocarcinoma (LUAD). This model proficiently forecasts the survival rates and therapeutic outcomes for LUAD patients, thereby delineating the high-risk population with distinctive immune cell infiltration and a state of immunosuppression. Furthermore, NAPSA can inhibit the proliferation and invasion capabilities of LUAD cells, thereby identifying new molecules for clinical targeted therapy.

Comprehensive analysis of bulk and single-cell transcriptomic data reveals a novel signature associated with endoplasmic reticulum stress, lipid metabolism, and liver metastasis in pancreatic cancer

BackgroundPancreatic ductal adenocarcinoma (PDAC) is a lethal malignancy with high probability of recurrence and distant metastasis. Liver metastasis is the predominant metastatic mode developed in most pancreatic cancer cases, which seriously affects the overall survival rate of patients. Abnormally activated endoplasmic reticulum stress and lipid metabolism reprogramming are closely related to tumor growth and metastasis. This study aims to construct a prognostic model based on endoplasmic reticulum stress and lipid metabolism for pancreatic cancer, and further explore its correlation with tumor immunity and the possibility of immunotherapy.MethodsTranscriptomic and clinical data are acquired from TCGA, ICGC, and GEO databases. Potential prognostic genes were screened by consistent clustering and WGCNA methods, and the whole cohort was randomly divided into training and testing groups. The prognostic model was constructed by machine learning method in the training cohort and verified in the test, TCGA and ICGC cohorts. The clinical application of this model and its relationship with tumor immunity were analyzed, and the relationship between endoplasmic reticulum stress and intercellular communication was further explored.ResultsA total of 92 characteristic genes related to endoplasmic reticulum stress, lipid metabolism and liver metastasis were identified in pancreatic cancer. We established and validated a prognostic model for pancreatic cancer with 7 signatures, including ADH1C, APOE, RAP1GAP, NPC1L1, P4HB, SOD2, and TNFSF10. This model is considered to be an independent prognosticator and is a more accurate predictor of overall survival than age, gender, and stage. TIDE score was increased in high-risk group, while the infiltration levels of CD8+ T cells and M1 macrophages were decreased. The number and intensity of intercellular communication were increased in the high ER stress group.ConclusionsWe constructed and validated a novel prognostic model for pancreatic cancer, which can also be used as an instrumental variable to predict the prognosis and immune microenvironment. In addition, this study revealed the effect of ER stress on cell–cell communication in the tumor microenvironment.

Identification of BGN positive fibroblasts as a driving factor for colorectal cancer and development of its related prognostic model combined with machine learning

BackgroundNumerous studies have indicated that cancer-associated fibroblasts (CAFs) play a crucial role in the progression of colorectal cancer (CRC). However, there are still many unknowns regarding the exact role of CAF subtypes in CRC.MethodsThe data for this study were obtained from bulk, single-cell, and spatial transcriptomic sequencing data. Bioinformatics analysis, in vitro experiments, and machine learning methods were employed to investigate the functional characteristics of CAF subtypes and construct prognostic models.ResultsOur study demonstrates that Biglycan (BGN) positive cancer-associated fibroblasts (BGN + Fib) serve as a driver in colorectal cancer (CRC). The proportion of BGN + Fib increases gradually with the progression of CRC, and high infiltration of BGN + Fib is associated with poor prognosis in terms of overall survival (OS) and recurrence-free survival (RFS) in CRC. Downregulation of BGN expression in cancer-associated fibroblasts (CAFs) significantly reduces migration and proliferation of CRC cells. Among 101 combinations of 10 machine learning algorithms, the StepCox[both] + plsRcox combination was utilized to develop a BGN + Fib derived risk signature (BGNFRS). BGNFRS was identified as an independent adverse prognostic factor for CRC OS and RFS, outperforming 92 previously published risk signatures. A Nomogram model constructed based on BGNFRS and clinical-pathological features proved to be a valuable tool for predicting CRC prognosis.ConclusionIn summary, our study identified BGN + Fib as drivers of CRC, and the derived BGNFRS was effective in predicting the OS and RFS of CRC patients.

Single-cell transcriptomics and Mendelian randomization reveal LUCAT1's role in right-sided colorectal cancer risk.

Background: Colorectal cancer (CRC) is a malignancy with high incidence and mortality rates globally, categorized into left-sided and right-sided CRC, each exhibiting significant differences in molecular characteristics, clinical manifestations, and prognosis. Methods: This study employed single-cell transcriptomic data and various bioinformatics approaches, such as two-sample Mendelian randomization, reverse Mendelian randomization, colocalization analysis, directed filtering, pseudotime analysis, and intercellular communication analysis. It analyzed cellular-level disparities between left-sided and right-sided CRC, identifying distinct subpopulations with characteristic variations. For these cells, two-sample Mendelian randomization was utilized to explore gene-to-one-sided CRC causality. Results: LUCAT1 was enriched in high-abundance monocyte subpopulations in right-sided CRC and demonstrated potential risk factor status through Mendelian randomization analysis. The specific single-nucleotide polymorphism (SNP) rs10774624 was associated with an increased risk of CRC. Moreover, metabolic pathway analysis revealed that LUCAT1+ monocytes exhibit lower communication activity in the tumor microenvironment and heightened activity in metabolic functions like glycosaminoglycan degradation. Its biological functions are related to the positive regulation of interleukin-6 production and NF-kappa B signaling, among others. Conclusion: This study confirmed a potential causal relationship between LUCAT1 and right-sided CRC risk through Mendelian randomization analysis. These findings provide novel insights into the pathogenesis of right-sided CRC and may aid in developing early detection and treatment strategies for right-sided CRC.

Single-cell transcriptome atlas in C57BL/6 mice encodes morphological phenotypes in the aging kidneys

C57BL/6 mice are frequently utilized as murine models with the desired genetic background for altertion in multiple research contexts. So far, there is still a lack of comprehensive kidney morphology and single-cell transcriptome atlas at all stages of growth of C57BL/6 mice. To provide an interactive set of reference standards for the scientific community, we performed the current study to investigate the kidney’s development throughout the capillary-loop stage until senescence. Eight groups, with five to six mice each, represented embryonic stage (embryos 18.5 days), suckling period (1 day after birth), juvenile stage (1 month old), adulthood (containing 3 months old, 6 months old and 10 months old), reproductive senescence stage (20 months old), and post-senescence stage (30 months old), respectively. With age, the thickness of the glomerular basement membrane (GBM) was increased. Notably, GBM knobs appeared at three months and became frequent with age. Using single-cell transcriptome data, we evaluated how various biological process appear in particular cell types and investigated the potential mechanism of formation of GBM konbs. In conclusion, having access to detailed kidney morphology and single-cell transcriptome maps from C57BL/6 mice at various developmental stages of C57BL/6 mice would be a novel and major resource for biological research and testing of prospective therapeutic approaches.

Revealing Prdx4 as a potential diagnostic and therapeutic target for acute pancreatitis based on machine learning analysis

Acute pancreatitis (AP) is a common systemic inflammatory disease resulting from the activation of trypsinogen by various incentives in ICU. The annual incidence rate is approximately 30 out of 100,000. Some patients may progress to severe acute pancreatitis, with a mortality rate of up to 40%. Therefore, the goal of this article is to explore the key genes for effective diagnosis and treatment of AP. The analysis data for this study were merged from two GEO datasets. 1357 DEGs were used for functional enrichment and cMAP analysis, aiming to reveal the pathogenic genes and potential mechanisms of AP, as well as potential drugs for treating AP. Importantly, the study used LASSO and SVM-RFE machine learning to screen the most likely AP occurrence biomarker for Prdx4 among numerous candidate genes. A receiver operating characteristic of Prdx4 was used to estimate the incidence of AP. The ssGSEA algorithm was employed to investigate immune cell infiltration in AP. The biomarker Prdx4 gene exhibited significant associations with a majority of immune cells and was identified as being expressed in NKT cells, macrophages, granulocytes, and B cells based on single-cell transcriptome data. Finally, we found an increase in Prdx4 expression in the pancreatic tissue of AP mice through immunohistochemistry. After treatment with recombinant Prdx4, the pathological damage to the pancreatic tissue of AP mice was relieved. In conclusion, our study identified Prdx4 as a potential AP hub gene, providing a new target for treatment.

Defining predictors of responsiveness to advanced therapies in Crohn’s disease and ulcerative colitis: protocol for the IBD-RESPONSE and nested CD-metaRESPONSE prospective, multicentre, observational cohort study in precision medicine

IntroductionCharacterised by chronic inflammation of the gastrointestinal tract, inflammatory bowel disease (IBD) symptoms including diarrhoea, abdominal pain and fatigue can significantly impact patient’s quality of life. Therapeutic developments in the...

Topological benchmarking of algorithms to infer Gene Regulatory Networks from Single-Cell RNA-seq Data

Abstract Motivation In recent years, many algorithms for inferring gene regulatory networks from single-cell transcriptomic data have been published. Several studies have evaluated their accuracy in estimating the presence of an interaction between pairs of genes. However, these benchmarking analyses do not quantify the algorithms’ ability to capture structural properties of networks, which are fundamental, for example, for studying the robustness of a gene network to external perturbations. Here, we devise a three-step benchmarking pipeline called STREAMLINE that quantifies the ability of algorithms to capture topological properties of networks and identify hubs. Results To this aim, we use data simulated from different types of networks as well as experimental data from three different organisms. We apply our benchmarking pipeline to four inference algorithms and provide guidance on which algorithm should be used depending on the global network property of interest. Availability and implementation STREAMLINE is available at https://github.com/ScialdoneLab/STREAMLINE. The data generated in this study are available at https://doi.org/10.5281/zenodo.10710444. Contact Direct inquiries should be addressed to the corresponding authors. Supplementary information Supplementary Information is available online.

Powerful and accurate detection of temporal gene expression patterns from multi-sample multi-stage single-cell transcriptomics data with TDEseq

We present a non-parametric statistical method called TDEseq that takes full advantage of smoothing splines basis functions to account for the dependence of multiple time points in scRNA-seq studies, and uses hierarchical structure linear additive mixed models to model the correlated cells within an individual. As a result, TDEseq demonstrates powerful performance in identifying four potential temporal expression patterns within a specific cell type. Extensive simulation studies and the analysis of four published scRNA-seq datasets show that TDEseq can produce well-calibrated p-values and up to 20% power gain over the existing methods for detecting temporal gene expression patterns.

Integrative analysis of single-cell and bulk transcriptome data reveal the significant role of macrophages in lupus nephritis

ObjectiveWe attempted to identify abnormal immune cell components and signaling pathways in lupus nephritis (LN) and to identify potential therapeutic targets.MethodsDifferentially expressed genes (DEGs) between LN and normal kidney tissues were identified from bulk transcriptome data, and functional annotation was performed. The phenotypic changes in macrophages and aberrant intercellular signaling communications within immune cells were imputed from LN scRNA-seq data using trajectory analysis and verified using immunofluorescence staining. Finally, lentivirus-mediated overexpression of LGALS9, the gene encoding Galectin 9, in THP-1 cells was used to study the functional effect of this gene on monocytic cells.ResultsFrom bulk transcriptome data, a significant activation of interferon (IFN) signaling was observed, and its intensity showed a significantly positive correlation with the abundance of infiltrating macrophages in LN. Analysis of scRNA-seq data revealed 17 immune cell clusters, with macrophages showing the highest enrichment of intercellular signal communication in LN. Trajectory analysis revealed macrophages in LN undergo a phenotypic change from inflammatory patrolling macrophages to phagocytic and then to antigen-presenting macrophages, and secrete various pro-inflammatory factors and complement components. LGALS9 was found significantly upregulated in macrophages in LN, which was confirmed by the immunofluorescence assay. Gene functional study showed that LGALS9 overexpression in THP-1 cells significantly elicited pro-inflammatory activation, releasing multiple immune cell chemoattractants.ConclusionOur results present an important pathophysiological role for macrophages in LN, and our preliminary results demonstrate significant pro-inflammatory effects of LGALS9 gene in LN macrophages.

Abstract CT208: High tumor monocyte content predicts long-term survival for patients with operable oesophageal cancer treated with neoadjuvant immunochemotherapy in the LUD2015-005 trial

Abstract Background: Combined immune checkpoint inhibitors (ICI) with chemotherapy in advanced oesophageal cancer (OC) has shown survival benefit in phase III studies, resulting in ICI’s recent approval to treat advanced OC. The role of ICI in peri-operative OC is debated, and large clinical studies are ongoing. Patients’ response to immunochemotherapy is highly variable and it is unclear what underlies this heterogeneity. To address this challenge, 38 advanced and 35 operable OC patients were enrolled to receive ICI in the LUD2015-005 trial (NCT02735239). Methods: We investigated the clinical and biological effects of adding the anti-PD-L1 ICI durvalumab to peri-operative chemotherapy or chemoradiotherapy (CRT) in a phase II multicentre study (LUD2015-005 trial). Operable OC patients received ICI for four weeks, followed by combination treatment with ICI + chemotherapy (oxaliplatin and capecitabine or fluorouracil, leucovorin, oxaliplatin, and docetaxel (FLOT)), or ICI + CRT (CROSS regimen). Patients could also receive ICI for up to 6 months after surgery. Tissue samples were collected at baseline, after four weeks ICI-only, and on the day of surgery. Further to assessing synergy of ICI with standard peri-operative treatment for OC, our trial design enables an in-depth analysis of molecular features of response, and to dissect the impact of ICI alone on the tumor microenvironment. Single cell and bulk transcriptomic data were generated and used to perform RNA deconvolution to estimate tumor cell composition. Results: The treatment was well-tolerated and two-year survival was 80% and 73.3% with ICI + chemotherapy and ICI + CRT, respectively. Survival was superior to that of propensity score matched patients receiving standard of care (p = 0.047). Tumor cell composition analysis revealed that neither total T-cell numbers nor a specific T-cell functional phenotype was associated with outcome, but high tumor monocyte content (TMC) at baseline was highly predictive of overall survival (p = 0.003). Moreover, a 13-gene core monocyte gene signature was sufficient to predict overall survival (p = 0.022) and major pathological response (mPR, Mandard 1-2, p = 0.009). Patients with mPR at resection showed enrichment of interferon and IL15 signalling at the four-week ICI-only timepoint (p < 0.001). Single cell trajectory interference combined with immune response enrichment analysis also show that high interferon and IL15 signalling drive monocytes to differentiate into M1 macrophages and dendritic cells. Conclusion: Similar to our previous finding in immunochemotherapy treated advanced OC patients (Carroll et al. Cancer Cell 2023), TMC is a highly predictive biomarker of long-term survival for operable OC patients treated with neoadjuvant immunochemotherapy, highlighting the importance of tumor monocytes and their differentiation potential in OC patients’ response to ICI. Citation Format: Hannah S. Fuchs, Sabrina A. James, Thomas M. Carroll, Phil F. Xie, Joseph A. Chadwick, Duncan Parkes, Simon R. Lord, Lucinda Griffiths, Tim Underwood, Ioannis Karydis, Russell T. Petty, Benjamin Schuster-Böckler, Richard P. Owen, Mark R. Middleton, Xin Lu. High tumor monocyte content predicts long-term survival for patients with operable oesophageal cancer treated with neoadjuvant immunochemotherapy in the LUD2015-005 trial [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr CT208.

Abstract LB245: Single cell guided identification of logic-gated cell surface combinations for selective and safe CAR therapy design

Abstract The advancement of chimeric antigen receptor (CAR) T-cell therapy has been groundbreaking in the treatment of hematological malignancies. However, its application in solid tumors is constrained by antigen variability and unintended off-tumor, on-target toxicities. Addressing these issues, we introduce a genetic algorithm to analyze single-cell transcriptomics data from patient tumors. This method pinpoints sets of surface antigens exclusive to cancer cells, utilizing the logical operators "AND," "OR," and "NOT." Our technique scales to gene combinations of any size and exhibits robust performance across diverse parameter settings. When applied to five breast cancer datasets, this algorithm successfully uncovered triplet antigen combinations superior to current clinical single antigen targets in distinguishing between cancerous and healthy cells. This strategy offers a promising pathway toward the design of more selective and safe CAR therapies. Citation Format: Sanna Madan, Tiangen Chang, Binbin Wang, Saugato Rahman Dhruba, Alejandro A. Schäffer, Eytan Ruppin. Single cell guided identification of logic-gated cell surface combinations for selective and safe CAR therapy design [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr LB245.

Aortic Cellular Heterogeneity in Health and Disease: Novel Insights Into Aortic Diseases From Single-Cell RNA Transcriptomic Data Sets.

Aortic diseases such as atherosclerosis, aortic aneurysms, and aortic stiffening are significant complications that can have significant impact on end-stage cardiovascular disease. With limited pharmacological therapeutic strategies that target the structural changes in the aorta, surgical intervention remains the only option for some patients with these diseases. Although there have been significant contributions to our understanding of the cellular architecture of the diseased aorta, particularly in the context of atherosclerosis, furthering our insight into the cellular drivers of disease is required. The major cell types of the aorta are well defined; however, the advent of single-cell RNA sequencing provides unrivaled insights into the cellular heterogeneity of each aortic cell type and the inferred biological processes associated with each cell in health and disease. This review discusses previous concepts that have now been enhanced with recent advances made by single-cell RNA sequencing with a focus on aortic cellular heterogeneity.

Integration analysis of single-cell transcriptome reveals specific monocyte subsets associated with melanoma brain and leptomeningeal metastasis.

Melanoma central nervous system (CNS) metastasis remains a leading cause of patient mortality, and the underlying pathological mechanism has not been fully elucidated, leading to a lack of effective therapeutic strategies. In this study, we conducted an integrated analysis of single-cell transcriptomic data related to melanoma brain metastasis (MBM) and leptomeningeal metastasis (LMM). We focused on differences of subset composition and molecular expression of monocytes in blood, primary tumor, brain metastases, and leptomeningeal metastases. Significant differences were observed among monocytes in blood, primary tumor, and different CNS metastatic tissues, particularly in terms of subset differentiation and gene expression patterns. Subsequent analysis revealed the upregulation of cell proportions of six monocyte subsets in brain metastasis and leptomeningeal metastasis. Based on differential gene analysis, four of these subsets exhibited increased expression of factors promoting tumor migration and survival, including AREG+ monocytes (AREG, EREG, THBS1), FABP5+ monocytes (SPP1, CCL2, CTSL), and CXCL3+ monocytes (CXCL3, IL8, IL1B). The proportions of TPSB2+ monocytes (IL32, CCL5) were notably elevated in melanoma leptomeningeal metastasis tissues. Pathway analysis indicated the activation of signaling pathways such as NOD-like receptors, NFκB, and Toll-like receptors in these metastasis-related subsets. Our findings elucidate that AREG+, FABP5+ and CXCL3+ monocytes are associated with brain metastasis and TPSB2+ monocytes are associated with leptomeningeal metastasis in melanoma, which may be contribute to the development of therapeutic strategies focusing on monocytes or cytokines for melanoma CNS metastasis.