Immune Cell Subtypes Research Articles

Abstract B037: Single-cell analysis reveals immune characteristics linked to poor prognosis in Fusobacterium nucleatum-infected colorectal cancer

Abstract Fusobacterium nucleatum (Fn) is commonly detected in colorectal cancer (CRC) and worsens patient survival. Fn appears to play a role in colorectal cancer carcinogenesis through suppression of the antitumor immune response. Aiming to uncover the underlying mechanisms, we collected 42 samples of surgically removed colon tissues from patients newly diagnosed with colon cancer. To analyze the bacterial community composition within these tissues, we utilized amplicon sequencing, targeting the V4 variable region of the 16S rRNA. We performed single-cell RNA sequencing (scRNA-seq) analysis of tumor-infiltrating immune cells from Fn-infected [Fn (+)] and Fn-uninfected [Fn (−)] patients. By utilizing gene expression signature derived from scRNA-seq data and bulk transcriptome profiles of the TCGA cohort, we identified immune cell types associated with Fn infection in CRC. Using RNA velocity and cell-cell interaction analysis of single-cell transcriptome data, we unraveled immune cell subtypes modulated by Fn infection. Furthermore, trajectory-based differential expression analysis and single-cell gene network analysis shed light on how the intratumor immune system is disturbed by Fn infection. A novel gene signature had a poor prognostic impact in patients with Fn-infected tumors, compared with Fn-uninfected patients in the TCGA cohort. Overall, this study identified a novel potential Fn-related immune evasion mechanism, beyond suppression of T cell-mediated immune response. Novel gene signatures with a poor prognostic impact can be used for patient stratification and developing targeted strategies in patients with Fn infection. Citation Format: Ilseok Choi, Kyung-A Kim, Yoon Dae Han, Sang Cheol Kim, Han Sang Kim, Insuk Lee. Single-cell analysis reveals immune characteristics linked to poor prognosis in Fusobacterium nucleatum-infected colorectal cancer [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Tumor Immunology and Immunotherapy; 2023 Oct 1-4; Toronto, Ontario, Canada. Philadelphia (PA): AACR; Cancer Immunol Res 2023;11(12 Suppl):Abstract nr B037.

Abstract C007: Improved T-cell and B-cell receptor repertoire profiling and immunophenotyping for biomarker discovery

Abstract T-cell receptor (TCR) and B-cell receptor (BCR) repertoire profiling, also referred to as adaptive immune receptor repertoire (AIRR) profiling, holds great potential for the understanding of disease mechanisms and for the development of new treatments in infectious disease, autoimmunity, and immuno-oncology. This potential could be greatly improved by combining information about receptor clonotypes with immunophenotypes of T- and B- cells. A new technology we developed that combines profiling of all human TCR and BCR variable regions with phenotypic characterization of immune cells using the same workflow could be particularly useful. The TCR and BCR immunophenotyping method proposed involves RT-PCR amplification and sequencing of the CDR3 regions of the TCR and BCR genes, and subsequently determining the expression levels of the most informative T- and B-cell phenotyping genes. Preliminary results show that this method allows for comprehensive profiling of all seven TCR and BCR chains from a single sample, in a highly reproducible manner, directly from micro-samples including cancer tissue, whole blood, sorted cells and more. Bioinformatic analysis of the next-generation sequencing (NGS) data from the TCR and BCR clonotypes profile combined with RNA expression profiling of the same samples results in a richer data set that includes the identification of major immune cell subtypes and their activation status. Data from human cancer tissues and whole blood samples will be presented. Citation Format: Alex Chenchik, Mikhail Makhanov, Tianbing Liu, Dongfang Hu, Khadija Ghias, Lester Kobzik. Improved T-cell and B-cell receptor repertoire profiling and immunophenotyping for biomarker discovery [abstract]. In: Proceedings of the AACR-NCI-EORTC Virtual International Conference on Molecular Targets and Cancer Therapeutics; 2023 Oct 11-15; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2023;22(12 Suppl):Abstract nr C007.

Upregulation of TIM3 and reduced expression of PD-1 on immune cell subsets in advanced prostate cancers

Abstract Introduction/Objective Although most prostate cancers behave in an indolent manner, a small proportion is highly aggressive. Both primary and advanced prostate cancer is widely known as a non-inflamed cancer that is characterized by a paucity of immune infiltration. Methods/Case Report To assess the spatial interplay of more than 30 TIM3, CTLA-4, PD-1/-L1 expressing leukocyte subpopulations in 453 prostate cancers, tissue microarrays were stained with 21 antibodies using our BLEACH&STAIN multiplex fluorescence immunohistochemistry approach and analyzed using a deep learning-based image analysis framework. Results (if a Case Study enter NA) The immune cell density of CD8+ cytotoxic T-cells, CD4+ T-helper cells, FOXP3+ regulatory T-cells, M1/ M2 macrophages, as well as CD11c+ dendritic cells increased consistently along with the Gleason grade in primary prostate cancer (p≤ 0.034 each). In recurrent prostate cancers under therapy, the density of FOXP3+regulatory T-cells and M1 macrophages further increased, while the density of CD8+ cytotoxic T-cells, CD4+ T-helper cells, as well as CD11c+ dendritic cells decreased (p≤0.017 each). Although the immune checkpoint expression of TIM3 on T-cell subsets, macrophages and dendritic cells was upregulated in advanced/ recurrent tumors, the expression level of PD-1 was downregulated in all analyzed T-cell subsets. Conclusion Although prostate cancer is a generally considered a low inflamed tumor, the degree of tumor infiltration by various immune cell subtypes increases markedly along with tumor progression and in recurrent tumors under therapy. Taken together, these data suggest that the evaluation of the spatial distribution of immune cell types along with their immune checkpoint expression can provide relevant clinical information in prostate cancer.

A Novel, Probe-Based and Instrument-Free Method for Improved Ultrahigh-Throughput Single-Cell Gene Expression Analysis of Peripheral Blood Mononuclear Cells

Single-cell gene expression assays are critical tools for identifying functional subtypes of immune cells, as well as changes within cells resulting from pathology or treatment. However, most current methods are expensive, low in sample throughput, rely on reverse transcription and lack the sensitivity to consistently measure low expressed genes from single cells - preventing measurements of many key immune biomarkers and molecular pathways. Another problem with existing single-cell sequencing methods is that many of the cell-associated reads either fail to map to transcripts or map to uninformative ribosomal and mitochondrial genes. Some methods also require the use of live cells, which means the single-cell assay has to be carried out at the specific time of treatment or when culture is terminated. The aforementioned limitations have contributed to most single-cell sequencing studies being restricted to small numbers of samples and have prevented their wider scale adoption in biopharma, clinical, translational and applied applications. Here we report on the development and performance of a novel combinatorial split-pool-based workflow, TempO-LINC™, that adds cell-identifying molecular barcodes onto high-sensitivity gene expression probes within fixed cells. Cells can be fixed at the end of a treatment, or when obtained from a patient and stably stored for months until they are ready to be processed with other samples through the TempO-LINC assay. All probes within the same fixed cell receive an identical barcode, enabling the reconstruction of single-cell gene expression profiles across as few as several hundred cells and up to 100,000 cells per run. TempO-LINC simultaneously processes up to 96 fixed cell samples per run, has a simple workflow that is amenable to automation and as we will show, has a higher gene detection rate than existing single-cell platforms. We will also show that TempO-LINC has an observed doublet rate of less than 0.5% for 12,500 cells analyzed. Critically, TempO-LINC reduces sample sequencing costs and although we show the assay accurately profiles the whole transcriptome (23,000 transcripts), it can also be targeted to measure only genes/pathways of interest - dramatically reducing sequencing costs yet further. We applied TempO-LINC to assay PBMCs and a human hepatocyte model for toxicology and demonstrated the ability to correctly identify unique cell states and molecular pathways from these populations. We will also present time course and dose response data from compound treatment of cells. All of these features position TempO-LINC as a potential disruptive technology that hematology researchers, clinicians and drug developers can use for new large-scale applications/studies using single-cell sequencing across thousands of samples while obtaining improved data quality.

Towards a Decision Engine for Therapeutic Optimization of Relapsed Refractory Multiple Myeloma

Introduction Substantial progress in the treatment of Multiple Myeloma (MM) extends survival for many patients (Pts), even after multiple relapses, owing to a broad and rapidly extending therapeutic arsenal, with major advances in the arena of bispecific antibodies and CART therapy. However, this also creates an increasingly complex challenge in selecting optimal therapeutic modalities for each patient, as current guidelines rely on empirical considerations. To address this, our study employs a population-scale single cell genomics database of over 250 patient samples covering all disease stages and cycles of treatment with complete patient medical records and machine learning approaches to analyze molecular signatures of myeloma cells and bone marrow immune microenvironment. Methods This study constitutes a meta-trial of RRMM patients who underwent single-cell RNA sequencing (scRNA-seq) between 6.2017 and 6.2023 in our lab. We gathered comprehensive demographic and myeloma clinical data, with a particular focus on therapeutic history and drug refractoriness, defined based on IMWG criteria (progression on or within 60 days from treatment). Our clinical scRNA-seq pipeline, includes a tailored protocol involving index sorting of CD38+ CD138+ positive cells for myeloma plasma cells (PCs) (Ledergor et al, Nat Med 2018; Cohen et al Nat Med 2020), together with CD45+ sorting and scRNA-seq cells from the tumor microenvironment for to identify tumor -immune molecular signatures associated with drug resistance. Results A total of 243 patient samples were included in the study, comprising RRMM (N=128), NDMM (N=21), SMM (N=9), and MGUS (N=4) patients with clinical annotation. Among RRMM patients, the median age was 72 (range 40-89), and 59% exhibited high-risk FISH cytogenetics (t(4:14), del17p, t(14:16), t(14:20), +1q21), while 17% had double-hit myeloma. Refractoriness to specific drugs was observed, such as bortezomib (49%), carfilzomib (27%), lenalidomide (49%), pomalidomide (33%), daratumumab (49). In total, we sequenced 158,444 myeloma/plasma cells, with an average of 880 cells per patient, along with 800 CD45+ cells. By using plasma cells from 11 healthy donors as a reference, we calculated transcriptional changes for each patient, determining the upregulated and downregulated genes profile. Clustering the patient's tumor profiles revealed 7 main tumor archetypes [TA] defined by the expression of 8 gene modules. Refractoriness score, defined as the average number of drugs to which myeloma is refractory, exhibited variations across different tumor archetypes. Notably, the expression levels of several tumor drivers and crucial targets of novel immunotherapies also showed diversity among the tumor archetypes. For instance, TA#6, primarily comprising newly diagnosed patients, displayed lower expression of GPRC5D. Conversely, in TA#1, which consisted of ultra-refractory patients with high MYC and CD47 expression, both GPRC5D and TNFRSF17 (BCMA) were downregulated. Examination of bone marrow CD45+ immune cells revealed a striking bimodal immune composition, characterized by one subtype with increased T-cell abundance and another enriched with myeloid cells. Each immune cell subtype contributed to the drug responsiveness score and provided additional insights complementary to the tumor cells' profile. Further clinical and transcriptional correlations involving both MM cells and immune cells will be presented at the meeting. Conclusions We constructed a clinically-annotated transcriptional matrix revealing the blueprint of relapsed refractory myeloma. This novel decision engine provides a basis for optimizing treatment selection by considering both tumor and tumor microenvironment characteristics. By incorporating critical information on drug resistance patterns and expression of key targets like GPRC5D, TNFRSF17 (BCMA) and FcRH5, we enable precision medicine approaches for improved management of relapsed refractory myeloma. These findings open avenues for tailored and more effective therapeutic strategies, promising better outcomes for patients with relapsed refractory myeloma.

CXCR2-Blocking Has Context-Sensitive Effects on Rat Glioblastoma Cell Line Outgrowth (S635) in an Organotypic Rat Brain Slice Culture Depending on Microglia-Depletion (PLX5622) and Dexamethasone Treatment

In glioblastoma (GBM), the interplay of different immune cell subtypes, cytokines, and/or drugs shows high context-dependencies. Interrelations between the routinely applied dexamethasone (Dex) and microglia remain elusive. Here, we exploited rat organotypic brain slice co-cultures (OBSC) to examine the effects on a rat GBM cell line (S635) outgrowth resulting from the presence of Dex and pretreatment with the colony-stimulating factor receptor 1 (CSF1-R) inhibitor PLX5622: in native OBSC (without PLX5622-pretreatment), a diminished S635 spheroid outgrowth was observable, whereas Dex-treatment enhanced outgrowth in this condition compared to PLX5622-pretreated OBSC. Screening the supernatants of our model with a proteome profiler, we found that CXCL2 was differentially secreted in a Dex- and PLX5622-dependent fashion. To analyze causal interrelations, we interrupted the CXCL2/CXCR2-axis: in the native OBSC condition, CXCR2-blocking resulted in increased outgrowth, in combination with Dex, we found potentiated outgrowth. No effect was found in the PLX5622-pretreated. Our method allowed us to study the influence of three different factors—dexamethasone, PLX5622, and CXCL2—in a well-controlled, simplified, and straight-forward mechanistic manner, and at the same time in a more realistic ex vivo scenario compared to in vitro studies. In our model, we showed a GBM outgrowth enhancing synergism between CXCR2-blocking and Dex-treatment in the native condition, which was levelled by PLX5622-pretreatment.

Multistain deep learning as a prognostic and predictive biomarker in colorectal cancer

The tumor immune microenvironment (TIME) plays acrucial prognostic and predictive role in solid malignancies such as colorectal cancer (CRC). Nevertheless, scoring systems based on TIME such as the Immunoscore (IS) are rarely used in clinical practice. Among other reasons, this might be due to the additional time required for manual quantification of tumor-associated immune cells or costs associated with proprietary/commercial solutions. To address these issues, we developed amultistain deep learning model (MSDLM) and trained, validated, and tested it on immunohistochemical image data of different immune cell subtypes from over 1000 patients with CRC. Our model showed high prognostic accuracy and outperformed other clinical, molecular, and immune cell-based parameters. It might also be used for therapy response prediction in rectal cancer patients undergoing neoadjuvant therapy. Leveraging artificial intelligence interpretability/explainability methods, we ascertained that the MSDLM's predictions align with recognized antitumor immune response patterns. Consequently, the AImmunoscore (AIS) could emerge as apotential TIME-based decision-making tool for clinicians.

Simultaneous selection of nanobodies for accessible epitopes on immune cells in the tumor microenvironment

In the rapidly advancing field of synthetic biology, there exists a critical need for technology to discover targeting moieties for therapeutic biologics. Here we present INSPIRE-seq, an approach that utilizes a nanobody library and next-generation sequencing to identify nanobodies selected for complex environments. INSPIRE-seq enables the parallel enrichment of immune cell-binding nanobodies that penetrate the tumor microenvironment. Clone enrichment and specificity vary across immune cell subtypes in the tumor, lymph node, and spleen. INSPIRE-seq identifies a dendritic cell binding clone that binds PHB2. Single-cell RNA sequencing reveals a connection with cDC1s, and immunofluorescence confirms nanobody-PHB2 colocalization along cell membranes. Structural modeling and docking studies assist binding predictions and will guide nanobody selection. In this work, we demonstrate that INSPIRE-seq offers an unbiased approach to examine complex microenvironments and assist in the development of nanobodies, which could serve as active drugs, modified to become drugs, or used as targeting moieties.

Characterization of innate immune cell subtypes in kidney transplant recipients with BK virus infection through single-cell transcriptomic profiling of peripheral blood

Characterization of innate immune cell subtypes in kidney transplant recipients with BK virus infection through single-cell transcriptomic profiling of peripheral blood

Bioinformatics analysis of immune cell infiltration patterns and potential diagnostic markers in atherosclerosis

This study aimed to investigate efficient diagnostic markers and molecular mechanisms of atherosclerosis and to analyze the role of immune infiltration through bioinformatics analysis. Expression profile datasets (GSE28829 and GSE43292) of patients with atherosclerosis and healthy controls were downloaded from the GEO database. Glutamine (GLN) metabolism-associated genes were obtained from the Molecular Signatures Database (MSigDB). The limma package in R was used to identify differentially expressed genes (DEGs). Significant modules were filtered using Weighted Gene Co-expression Network Analysis (WGCNA). MSigDB sets were subjected to Gene Set Enrichment Analysis and Gene Set Variation Analysis. The biological functions of DEGs were examined using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. STRING and Cytoscape software were used to identify hub genes and functional modules through protein–protein interaction (PPI) network analysis. The xCell software was adopted to assess the composition patterns of immune and stromal cells. Correlation analyses were performed for key genes and immune cell subtypes. We identified 308 DEGs and GLN-associated genes. Functional enrichment analysis showed that these genes were strongly enriched in muscle contract, muscle tissue development, cutile fiber, mycobacterial, and actin binding. Enriched KEGG pathways comprised dilated cardiomyopathy, hypertrophic cardiomyopathy, and the cAMP signaling pathway. In the PPI network analysis, 27 genes were identified as hub genes. The area under the curve (AUC) values of 27 biomarkers were relatively high, indicating high diagnostic values. The atherosclerosis group exhibited a markedly higher degree of infiltration than the control group. This study identified 27 GLN-associated genes as potential biomarkers for the diagnosis of atherosclerosis. It provides a new perspective on immune responses that facilitates exploration of the molecular mechanisms of atherosclerosis.

TMIC-60. CROSS-SPECIES ANALYSIS OF HUMAN AND NEW MOUSE MODELS OF GBM SUBTYPES IDENTIFIES VISTA AS A PROMISING IMMUNOTHERAPY TARGET

Abstract Immunotherapy is a promising treatment modality for GBM; however, clinical trials thus far have failed to provide significant clinical benefit to most patients. GBM is one of the “immune cold” tumors characterized by poor infiltration of T cells and the abundance of immune-suppressive myeloid cells that block T cell infiltration and/or activation. A major bottleneck to developing more effective immunotherapies is the paucity of faithful preclinical models to discover and validate new treatments. Here, we report multi-dimensional analyses of six different transplantable mouse glioma models in the C57BL6/J background that represent the three human GBM molecular subtypes. We performed whole exome sequencing, STR fingerprinting, immune phenotyping, and single-cell RNA-sequencing of each model and confirmed that the new models exhibit highly heterogeneous and immune suppressive TME, similar to human GBM. In addition, we performed cross-species comparisons of glioma and immune cell subtypes between human and mouse GBMs at the single-cell level and demonstrate strong concordances in molecular phenotypes and cellular heterogeneity. These result in qualitative and quantitative differences in the cell: cell communication among different stromal cells and glioma cells in each model, and we propose that these interactions shape the local niche and immune suppressive neighborhoods. Notably, in both human tissues and mouse models, PD1/PDL1 expression is low or sporadic, and, unlike the GL261 model, the new mouse models are resistant to anti-PD1 and CTLA4 treatment in vivo. Finally, we discovered that VISTA is a widely expressed immune checkpoint in both human and mouse GBM and demonstrate the efficacy of anti-VISTA and anti-PD1 combination therapy in the most immune-suppressive mouse model. In summary, we demonstrate the utility of these immune-competent mouse models and nominate anti-VISTA+PD1 treatment as a promising novel combination therapy for GBM.

IMMU-17. INDUCING IMMUNE RESPONSE WITH FLASH AND CONVENTIONAL DOSE-RATE RADIATION IN DIFFUSE MIDLINE GLIOMA (DMG)

Abstract Diffuse Midline Glioma is a universally fatal inoperable pediatric brain tumor associated with H3K27M mutation and median survival of 9-15 months. Radiation therapy (RT) is the primary treatment, extending life by only three months. Conventional dose-rate RT (2Gy/min, CONV) can induce an immune response with type 1 interferon (IFN1) in multiple cancers including adult brain tumors. Ultra-fast dose-rate RT (90Gy/second, FLASH) has comparable tumor control to CONV with decreased toxicity, however, evaluation of immune response is limited. Using an orthotopic syngeneic model of brainstem DMG, we performed single-cell RNA sequencing (scRNA-seq) on CD45+ immune cells isolated from tumors four days post 15Gy FLASH or CONV RT, and compared to unirradiated tumor. We performed flow cytometry at day 4 and day 10 post-RT. ScRNA-seq reveals 33,308 immune cells in 17 unique clusters. Microglia (MG) make up 73.8% and non-resident myeloid cells make up 7.9% of cells and include macrophages (MAC), dendritic cells (DC) and Monocytes (MONO). Flow at day 4 and day 10 show no significant difference in proportion of immune cell subtypes comparing FLASH, CONV and tumor. With differential gene expression, FLASH and CONV provoke a similar inflammatory and IFN1 response in MG while in MACs and DCs IFN1 pathway expression is increased in CONV compared to FLASH (p< 0.001 for both), confirmed by flow at day 4 using IFNAR1 expression (MACs p< 0.05, DCs p< 0.01). Flow at day 10 post-RT shows increased IFNAR1 expression in MACs in both CONV and FLASH compared to tumor (p< 0.0001) while day 10 IFNAR1 in DCs is similar in all three groups. In summary, while immune proportions are similar comparing different dose rates of RT, IFN1 is differentially expressed in CONV compared to FLASH at day 4 in MACs and DCs, while this response at day 10 is similar comparing dose rates.

Biallelic CRELD1 variants cause a multisystem syndrome, including neurodevelopmental phenotypes, cardiac dysrhythmias, and frequent infections

PurposeWe sought to delineate a multisystem disorder caused by recessive cysteine-rich with epidermal growth factor–like domains 1 (CRELD1) gene variants. MethodsThe impact of CRELD1 variants was characterized through an international collaboration utilizing next-generation DNA sequencing, gene knockdown, and protein overexpression in Xenopus tropicalis, and in vitro analysis of patient immune cells. ResultsBiallelic variants in CRELD1 were found in 18 participants from 14 families. Affected individuals displayed an array of phenotypes involving developmental delay, early-onset epilepsy, and hypotonia, with about half demonstrating cardiac arrhythmias and some experiencing recurrent infections. Most harbored a frameshift in trans with a missense allele, with 1 recurrent variant, p.(Cys192Tyr), identified in 10 families. X tropicalis tadpoles with creld1 knockdown displayed developmental defects along with increased susceptibility to induced seizures compared with controls. Additionally, human CRELD1 harboring missense variants from affected individuals had reduced protein function, indicated by a diminished ability to induce craniofacial defects when overexpressed in X tropicalis. Finally, baseline analyses of peripheral blood mononuclear cells showed similar proportions of immune cell subtypes in patients compared with healthy donors. ConclusionThis patient cohort, combined with experimental data, provide evidence of a multisystem clinical syndrome mediated by recessive variants in CRELD1.

ZEBRA: a hierarchically integrated gene expression atlas of the murine and human brain at single-cell resolution.

The molecular causes and mechanisms of neurodegenerative diseases remain poorly understood. A growing number of single-cell studies have implicated various neural, glial, and immune cell subtypes to affect the mammalian central nervous system in many age-related disorders. Integrating this body of transcriptomic evidence into a comprehensive and reproducible framework poses several computational challenges. Here, we introduce ZEBRA, a large single-cell and single-nucleus RNA-seq database. ZEBRA integrates and normalizes gene expression and metadata from 33 studies, encompassing 4.2 million human and mouse brain cells sampled from 39 brain regions. It incorporates samples from patients with neurodegenerative diseases like Alzheimer's disease, Parkinson's disease, and Multiple sclerosis, as well as samples from relevant mouse models. We employed scVI, a deep probabilistic auto-encoder model, to integrate the samples and curated both cell and sample metadata for downstream analysis. ZEBRA allows for cell-type and disease-specific markers to be explored and compared between sample conditions and brain regions, a cell composition analysis, and gene-wise feature mappings. Our comprehensive molecular database facilitates the generation of data-driven hypotheses, enhancing our understanding of mammalian brain function during aging and disease. The data sets, along with an interactive database are freely available at https://www.ccb.uni-saarland.de/zebra.

An Activated Dendritic-Cell-Related Gene Signature Indicative of Disease Prognosis and Chemotherapy and Immunotherapy Response in Colon Cancer Patients

Accumulating evidence has underscored the prognostic value of tumor-infiltrating immune cells in the tumor microenvironment of colon cancer (CC). In this retrospective study, based on publicly available transcriptome profiles and clinical data from the Gene Expression Omnibus and The Cancer Genome Atlas databases, we derived and verified an activated dendritic cell (aDC)-related gene signature (aDCRS) for predicting the survival outcomes and chemotherapy and immunotherapy response of CC patients. We quantified the infiltration abundance of 22 immune cell subtypes via the “CIBERSORT” R script. Univariate Cox proportional hazards (PHs) regression was used to identify aDC as the most robust protective cell type for CC prognosis. After selecting differentially expressed genes (DEGs) significantly correlated with aDC infiltration, we performed univariate Cox-PH regression, LASSO regression, and stepwise multivariate Cox-PH regression successively to screen out prognosis-related genes from selected DEGs for constructing the aDCRS. Receiver operating characteristic (ROC) curves and Kaplan–Meier (KM) analysis were employed to assess the discriminatory ability and risk-stratification capacity. The “oncoPredict” package, Cancer Treatment Response gene signature DataBase, and Tumor Immune Dysfunction and Exclusion algorithm were utilized to estimate the practicability of the aDCRS in predicting response to chemotherapy and immune checkpoint blockade. Gene set enrichment analysis and single-cell RNA sequencing analysis were also implemented. Furthermore, an aDCRS-based nomogram was constructed and validated via ROC curves, calibration plots and decision curve analysis. In conclusion, aDCRS and an aDCRS-based nomogram will facilitate precise prognosis prediction and individualized therapeutic interventions, thus improving the survival outcomes of CC patients in the future.

Machine learning-based cluster analysis of immune cell subtypes and breast cancer survival

Host immunity involves various immune cells working in concert to achieve balanced immune response. Host immunity interacts with tumorigenic process impacting disease outcome. Clusters of different immune cells may reveal unique host immunity in relation to breast cancer progression. CIBERSORT algorithm was used to estimate relative abundances of 22 immune cell types in 3 datasets, METABRIC, TCGA, and our study. The cell type data in METABRIC were analyzed for cluster using unsupervised hierarchical clustering (UHC). The UHC results were employed to train machine learning models. Kaplan–Meier and Cox regression survival analyses were performed to assess cell clusters in association with relapse-free and overall survival. Differentially expressed genes by clusters were interrogated with IPA for molecular signatures. UHC analysis identified two distinct immune cell clusters, clusters A (83.2%) and B (16.8%). Memory B cells, plasma cells, CD8 positive T cells, resting memory CD4 T cells, activated NK cells, monocytes, M1 macrophages, and resting mast cells were more abundant in clusters A than B, whereas regulatory T cells and M0 and M2 macrophages were more in clusters B than A. Patients in cluster A had favorable survival. Similar survival associations were also observed in other independent studies. IPA analysis showed that pathogen-induced cytokine storm signaling pathway, phagosome formation, and T cell receptor signaling were related to the cell type clusters. Our finding suggests that different immune cell clusters may indicate distinct immune responses to tumor growth, suggesting their potential for disease management.

Integrative Multi-Omics Analysis of Tumor-Immune Microenvironment in Multiple Myeloma: Novel Insights and Therapeutic Implications

Despite notable therapeutic advances that have improved the survival of multiple myeloma (MM) patients, development of drug resistance remains a major problem. Transcriptomic analysis provides an opportunity to dissect the complexity of tumors, including the surrounding microenvironment, which has a significant impact on MM tumor progression and patients' response to treatment, as demonstrated by the effectiveness of immunomodulatory therapies. To improve the tailoring of targeted and immune based therapeutic strategies, it is crucial to decipher the tumor-immune microenvironment profile in MM patients. We used a multi-omics data integration approach, including RNAseq-based gene expression for MM tumor cells (MMCs) (n=196) and for the corresponding purified tumor microenvironment (TME) (n=124), single nucleotide variant data from whole exome sequencing of MMCs (n=100), deconvolution of TME immune subtypes (n=124), and relevant clinical metadata. This allowed us to thoroughly characterize both the tumor and its TME. Applying Multiomics Factor Analysis (MOFA) identified 13 principal factors capturing the diversity of the disease, encompassing both MM malignant and microenvironment components. Factors 1 and 2 specifically represented the heterogeneity in the TME, incorporating gene expression profiles and deconvolution-based immune cell subtypes. On the other hand, Factors 6, 9, 11, and 12 were specific to variations in the gene expression profile of the malignant component. Importantly, the analysis of the variables linked to these main factors uncovers not only genes with established functions in myeloma but also novel candidates with potential importance. Based on the top-weight in the gene expression and deconvolution data, Factor 1 showed a positive association with MMP8 expression by TME cells and a negative correlation with the presence of CD8 positive T cells in the TME. Factor 2, on the other hand, exhibited positive alignments with the expression of BHLHA15, TNFRSF17 (BCMA), KLF15, and IGF1 by TME cells. Regarding Factor 6 and Factor 11, which capture tumor fraction heterogeneity, Factor 6 showed alignment with the expression of cancer testis antigens ( MAGEA6, MAGEA3), CXCL12, and KLF4. Meanwhile, Factor 11 was associated with the expression of genes such as SULF2, as well as CRP levels, percentage of CD138+ cells, and the percentage of cells in cell cycle phase S. Next, we investigated the predictive potential of the latent factors inferred by MOFA in models of clinical outcomes. Interestingly, 6 out of the 13 factors identified by MOFA showed significant associations with patients' overall survival (cox regression, p <= 0.05). These factors include Factor 1 and 2, which are related to the composition of the TME, and Factor 6 and 9, which are associated with myeloma progression status, osteolysis, t(11;14), t(4;14), and MM molecular classification. Finally, performing unsupervised clustering using the latent MOFA factors, we identified two main groups with distinct TME subtypes. These groups can be further divided into nine subgroups, each exhibiting unique TME characteristics. Notably, the TME subtypes showed correlations with essential gene expression signatures related to immune response regulation, T cell activation, and natural killer mediated immunity. Additionally, the significant presence of deconvolution-based immune cell types, including T cell CD8+, T cell regulatory, T cell CD4+ (non-regulatory), NK cells and Monocytes was observed. Moreover, the TME subtypes were associated with patients' overall survival and response to daratumumab. Patients presenting TME enriched in immune cells were associated with a significant better outcome ( p = 0.0026). These findings highlight the importance of TME analysis in predicting treatment response and MM patients' outcome in the context of immune-based therapies. Our integrative multi-omics analysis revealed comprehensive MM heterogeneity and distinct immune subtypes that could be of therapeutic interest for personalized therapeutic approaches.

Real-Time Two-Photon Microscopy Reveals Spatially Heterogeneous T Lymphocyte Trafficking in the GI Microenvironment Influenced By GI Tissue Damage Level

Introduction: The trafficking of immune cells in the GI is crucial for tissue immunity and can be targeted for the treatment of immune-related GI damage. Flow cytometry, as the primary technique to identify immune cells in organs such as the GI, has made substantial progress, offering valuable insights into a variety of immune cell subtypes and their microenvironments. However, one major limitation is the destruction of tissue samples during processing, lacking spatial information on immune cells in their microenvironment. Furthermore, there is a lack of information about how stress-induced tissue damage affects T-cell trafficking. In particular, the pre-HCT conditioning regime is essential for identifying the extent of organ damage and donor immune cell trafficking to the GI microenvironment and the subsequent occurrence of Graft vs Host Disease (GVHD). To address this need, it is necessary to develop a real-time imaging method for characterizing immune cells in the GI in an allogeneic bone marrow transplant (BMT) system. Here, we report developing a temperature-controlled Two-photon microscopy (TPM) setup investigating the pattern of T cell trafficking in the GI in real time and how modulating radiation dose affects immune cell trafficking in the preclinical mouse GVHD model of allogeneic BMT. Method: BALB/c Host mice were treated with 8 Gy TBI in 2 fractions, 6 h apart. For the GI dose reduction study (GI-4 Gy), Total Marrow Irradiation (TMI 8:4; 8 Gy to Bone marrow, 4 Gy to rest of the body including GI) was used as described (Darren et al. 2021). Donor B6 T cell Depleted bone marrow cells along with 1 million enriched tdtomato+ T cells were transplanted, and 7 days later, live T cell trafficking in GI was monitored by TPM (Prairie Ultima microscope). Vessels were labeled with QTracker 655, and TPM parameters were set as described (J Brooks et al. 2021). For imaging, a temperature-controlled externalized intestine's intravital imaging (EIII) window was developed (Fig 1.A) and was first evaluated to visualize the three-dimensional basis of Jejunum. It allows continuous blood flow while effectively controlling peristalsis, cardiac and respiratory motion artifacts with minimal tissue damage, delay in tissue degradation. A section of the intestine (Jejunum) was externalized from anesthetized mice and fixed on the EIII window's surface using n-butyl cyanoacrylate veterinary adhesive. The luminal surface was exposed by making an incision along the intestine, carefully avoiding the vascular plexus. The spatial distribution of vessels and tdtomato+ T-cells (green) at different z-stacks was evaluated by imaging through both serosa and luminal sides. GI samples were fixed in 10% NBF, OCT embedded, cryosection (5-10 m), and Anti-MAdCAM1 immunofluorescence (IF) performed using a standard protocol. Results: EIII window usage significantly reduced peristaltic movement, enabling T cells imaging at different depths from serosa to villi crypt (Fig 1.A-B). T cells reside preferentially in the lower crypt region and are affected by the radiation dose given to GI (Fig 1.D-G). T-cell localization at the crypt base region (~ 100µ from serosa) was significantly higher in TBI (8 Gy GI dose) mice than in TMI (4 Gy GI dose). Fig. 1 (F-G) shows T cell distribution at the top, middle, and base of the villi, and in the crypt, during luminal side imaging. Further, microvascular structures were affected at higher radiation dose, as seen by reduced vessel density in 8 Gy over 4 Gy treatments or control (Fig 1.C). Thus, ~50% dose reduction improved vasculature and reduced T cell trafficking. Histological analysis of intestinal tissue with lysozyme and anti-MAdCAM1 staining for confocal imaging further shows reduced crypt damage and T cell trafficking in lower radiation (Fig 1.H). Summary: TPM enabled us to visualize T cells in real-time within the GVHD mouse intestine. Radiation-induced tissue damage-dependent T-cell trafficking was evident in the mouse GI with GVHD and validated through the target receptor of MAdCAM1 endothelial cell expression. The spatial heterogeneity of T cells emphasizes the preferential niche of immune trafficking and associated GI environment and thus, the need for cautious interpretation of GVHD biopsies and warrants further studies on their spatial localization in immune cell-mediated GI diseases.

Genetic alterations in the neuronal development genes are associated with changes of the tumor immune microenvironment in pancreatic cancer.

Pancreatic ductal adenocarcinoma (PDAC) has a poor prognosis and is highly metastatic. Our prior studies have demonstrated the critical role of axon guidance pathway genes in PDAC and the connection between neuronal development and the tumor microenvironment. A recent study newly identified 20 neuronal development genes [disks large homolog 2 (DLG2), neuron-glial-related cell adhesion molecule (NRCAM), neurexin3 (NRXN3), mitogen-activated protein kinase 10 (MAPK10), platelet-derived growth factor D (PDGFD), protein kinase C epsilon (PRKCE), potassium calcium-activated channel subfamily M alpha 1 (KCNMA1), polycystic kidney and hepatic disease 1 (PKHD1), neural cell adhesion molecule 1 (NCAM1), neuregulin-1 (NRG1), zinc finger protein 667 (ZNF667), cystic fibrosis transmembrane conductance regulator (CFTR), acyl-CoA medium-chain synthetase-3 (ACSM3), complement 6 (C6), protein tyrosine phosphatase receptor type M (PTPRM), hypoxia-inducible factor 1 alpha (HIF1A), adenylyl cyclase 5 (ADCY5), adherens junctions-associated protein 1 (AJAP1), neurobeachin (NBEA), sodium voltage-gated channel alpha subunit 9 (SCN9A)] that are associated with perineural invasion and poor prognosis of PDAC. The relationship between genetic alterations in these 20 genes and tumor immune microenvironment (TME) has not previously been investigated. We hence applied the sequential multiplex immunohistochemistry results of biopsy specimens from 63 PDAC patients to investigate this relationship. We found that, except for PTPRM and NBEA, genetic alterations involving these 20 genes are associated with significant changes in the densities of major immune cell subtypes. Except for AJAP1, the copy number loss involving this panel of neuronal development genes is significantly associated with changes in immune cell infiltrates. In contrast, the copy number gain in fewer genes, including NRXN3, ZNF667, ACSM3, C6, ADCY5, SCN9A, and PRKCE, is significantly associated with changes in immune cell infiltrates. Our study suggested that neuronal development genes play a role in modulating TME in a pancreatic cancer setting.

Single-cell RNA-sequencing atlas reveals an FABP1-dependent immunosuppressive environment in hepatocellular carcinoma

BackgroundSingle-cell RNA sequencing, also known as scRNA-seq, is a method profiling cell populations on an individual cell basis. It is particularly useful for more deeply understanding cell behavior in a...