Lineage-dependent gene expression programs influence the immune landscape of colorectal cancer

MDFI promotes the proliferation and tolerance to chemotherapy of colorectal cancer cells by binding ITGB4/LAMB3 to activate the AKT signaling pathway

Colorectal cancer (CRC) is one of the most lethal cancers. Single-cell RNA sequencing (scRNA-seq) and protein-protein interactions (PPIs) have enabled the systematic study of CRC. In our research, the activation of the AKT pathway in CRC was analyzed by KEGG using single-cell sequencing data from the GSE144735 dataset. The correlation and PPIs of MDFI and ITGB4/LAMB3 were examined. The results were verified in the TCGA and CCLE and further tested by coimmunoprecipitation experiments. The effect of MDFI on the AKT pathway via ITGB4/LAMB3 was validated by knockdown and lentiviral overexpression experiments. The effect of MDFI on oxaliplatin/fluorouracil sensitivity was probed by colony formation assay and CCK8 assay. We discovered that MDFI was positively associated with ITGB4/LAMB3. In addition, MDFI was negatively associated with oxaliplatin/fluorouracil sensitivity. MDFI upregulated the AKT pathway by directly interacting with LAMB3 and ITGB4 in CRC cells, and enhanced the proliferation of CRC cells via the AKT pathway. Finally, MDFI reduced the sensitivity of CRC cells to oxaliplatin and fluorouracil. In conclusion, MDFI promotes the proliferation and tolerance to chemotherapy of colorectal cancer cells, partially through the activation of the AKT signaling pathway by the binding to ITGB4/LAMB3. Our findings provide a possible molecular target for CRC therapy.

TIGIT Blockade Reshapes the Tumor Microenvironment Based on the Single-cell RNA-Sequencing Analysis

SUMMARY

Immune checkpoint blockade therapy is a pivotal approach in treating malignant tumors. TIGIT has emerged as a focal point of interest among the diverse targets for tumor immunotherapy. Nonetheless, there is still a lack of comprehensive understanding regarding the immune microenvironment alterations following TIGIT blockade treatment. To bridge this knowledge gap, we performed single-cell sequencing on mice both before and after the administration of anti-TIGIT therapy. Our analysis revealed that TIGIT was predominantly expressed on T cells and natural killer (NK) cells. The blockade of TIGIT exhibited inhibitory effects on Treg cells by downregulating the expression of Foxp3 and reducing the secretion of immunosuppressive cytokines. In addition, TIGIT blockade facilitated the activation of NK cells, leading to an increase in cell numbers, and promoted cDC1 maturation through the secretion of XCL1 and Flt3L. This activation, in turn, stimulated the TCR signaling of CD8 + T cells, thereby enhancing their antitumor effect. Consequently, anti-TIGIT therapy demonstrated substantial potential for cancer immunotherapy. Our research provided novel insights into future therapeutic strategies targeting TIGIT for patients with cancer.

Navigating colorectal cancer prognosis: A Treg-related signature discovered through single-cell and bulk transcriptomic approaches

BACKGROUND

The significance of regulatory T cells (Tregs) in colorectal cancer is unclear.

METHODS

The single-cell sequencing data for colorectal cancer, specifically GSE132465 and GSE188711, were retrieved from the GEO database. Simultaneously, bulk transcriptome data were obtained from the UCSC Xena website. To delve into the heterogeneity of Treg cells and identify key genes at the single-cell sequencing level, we employed dimensionality reduction techniques alongside clustering and conducted differential expression gene analysis. For the bulk transcriptome data, we utilized weighted co-expression network analysis to investigate critical gene modules. Additionally, we employed COX regression and Lasso regression methodologies to construct prognostic models, thereby assessing patient outcomes. To facilitate outcome evaluation, nomograms were constructed. The integration of these diverse approaches aims to comprehensively study colorectal cancer, encompassing single-cell heterogeneity, key gene identification, and prognosis modeling using both single-cell and bulk transcriptome data. Polymerase chain reaction (PCR) experiments are used to verify mRNA expression levels of key genes. The analysis software was R software (version 4.3.2).

RESULTS

Through single-cell sequencing analysis and bulk transcriptome analysis, we constructed a prognostic model composed with Treg-associated signatures. The high-risk group demonstrated significantly worse prognosis compared with the low-risk group, highlighting the clinical relevance of our models. PCR confirmed that the key gene DEAH-box helicase 15 (DHX15) was significantly overexpressed in colorectal cancer.

CONCLUSIONS

The prognostic models developed in this study offer a potential tool for risk assessment, guiding treatment decisions for colorectal cancer patients.

Multiregional transcriptomics identifies congruent consensus subtypes with prognostic value beyond tumor heterogeneity of colorectal cancer

Intra-tumor heterogeneity compromises the clinical value of transcriptomic classifications of colorectal cancer. We investigated the prognostic effect of transcriptomic heterogeneity and the potential for classifications less vulnerable to heterogeneity in a single-hospital series of 1093 tumor samples from 692 patients, including multiregional samples from 98 primary tumors and 35 primary-metastasis sets. We show that intra-tumor heterogeneity of the consensus molecular subtypes (CMS) is frequent and has poor-prognostic associations independently of tumor microenvironment markers. Multiregional transcriptomics uncover cancer cell-intrinsic and low-heterogeneity signals that recapitulate the intrinsic CMSs proposed by single-cell sequencing. Further subclassification identifies congruent CMSs that explain a larger proportion of variation in patient survival than intra-tumor heterogeneity. Plasticity is indicated by discordant intrinsic phenotypes of matched primary and metastatic tumors. We conclude that multiregional sampling reconciles the prognostic power of tumor classifications from single-cell and bulk transcriptomics in the context of intra-tumor heterogeneity, and phenotypic plasticity challenges the reconciliation of primary and metastatic subtypes.

Cell type specific long non-coding RNA targets identified by integrative analysis of single-cell and bulk colorectal cancer transcriptomes

Long non-coding RNAs (lncRNAs) represent an emerging class of genes which play significant and diverse roles in human cancers. Nevertheless, the functional repertoires of lncRNAs in cancer cell subtypes remains unknown since most studies are focused on protein coding genes. Here, we explored the contribution of lncRNAs in Colorectal Cancer (CRC) heterogeneity. We analyzed 49'436 single-cells from 29 CRC patients and showed that lncRNAs are significantly more cell type specific compared to protein-coding genes. We identified 996 lncRNAs strongly enriched in epithelial cells. Among these, 98 were found to be differentially expressed in tumor samples compared to normal controls, when integrating 270 bulk CRC profiles. We validated the upregulation of two of them (CASC19 and LINC00460) in CRC cell lines and showed their involvement in CRC proliferation by CRISPR-Cas9 knock down experiments. This study highlights a list of novel RNA targets for potential CRC therapeutics, substantiated through experimental validation.

Identification of hypoxic macrophages in glioblastoma with therapeutic potential for vasculature normalization

Monocyte-derived tumor-associated macrophages (Mo-TAMs) intensively infiltrate diffuse gliomas with remarkable heterogeneity. Using single-cell transcriptomics, we chart a spatially resolved transcriptional landscape of Mo-TAMs across 51 patients with isocitrate dehydrogenase (IDH)-wild-type glioblastomas or IDH-mutant gliomas. We characterize a Mo-TAM subset that is localized to the peri-necrotic niche and skewed by hypoxic niche cues to acquire a hypoxia response signature. Hypoxia-TAM destabilizes endothelial adherens junctions by activating adrenomedullin paracrine signaling, thereby stimulating a hyperpermeable neovasculature that hampers drug delivery in glioblastoma xenografts. Accordingly, genetic ablation or pharmacological blockade of adrenomedullin produced by Hypoxia-TAM restores vascular integrity, improves intratumoral concentration of the anti-tumor agent dabrafenib, and achieves combinatorial therapeutic benefits. Increased proportion of Hypoxia-TAM or adrenomedullin expression is predictive of tumor vessel hyperpermeability and a worse prognosis of glioblastoma. Our findings highlight Mo-TAM diversity and spatial niche-steered Mo-TAM reprogramming in diffuse gliomas and indicate potential therapeutics targeting Hypoxia-TAM to normalize tumor vasculature.

Transcript and protein signatures derived from shared molecular interactions across cancers are associated with mortality

BACKGROUND

Characterization of shared cancer mechanisms have been proposed to improve therapy strategies and prognosis. Here, we aimed to identify shared cell-cell interactions (CCIs) within the tumor microenvironment across multiple solid cancers and assess their association with cancer mortality.

METHODS

CCIs of each cancer were identified by NicheNet analysis of single-cell RNA sequencing data from breast, colon, liver, lung, and ovarian cancers. These CCIs were used to construct a shared multi-cellular tumor model (shared-MCTM) representing common CCIs across cancers. A gene signature was identified from the shared-MCTM and tested on the mRNA and protein level in two large independent cohorts: The Cancer Genome Atlas (TCGA, 9185 tumor samples and 727 controls across 22 cancers) and UK biobank (UKBB, 10,384 cancer patients and 5063 controls with proteomics data across 17 cancers). Cox proportional hazards models were used to evaluate the association of the signature with 10-year all-cause mortality, including sex-specific analysis.

RESULTS

A shared-MCTM was derived from five individual cancers. A shared gene signature was extracted from this shared-MCTM and the most prominent regulatory cell type, matrix cancer-associated fibroblast (mCAF). The signature exhibited significant expression changes in multiple cancers compared to controls at both mRNA and protein levels in two independent cohorts. Importantly, it was significantly associated with mortality in cancer patients in both cohorts. The highest hazard ratios were observed for brain cancer in TCGA (HR [95%CI] = 6.90[4.64-10.25]) and ovarian cancer in UKBB (5.53[2.08-8.80]). Sex-specific analysis revealed distinct risks, with a higher mortality risk associated with the protein signature score in males (2.41[1.97-2.96]) compared to females (1.84[1.44-2.37]).

CONCLUSION

We identified a gene signature from a comprehensive shared-MCTM representing common CCIs across different cancers and revealed the regulatory role of mCAF in the tumor microenvironment. The pathogenic relevance of the gene signature was supported by differential expression and association with mortality on both mRNA and protein levels in two independent cohorts.

scRank infers drug-responsive cell types from untreated scRNA-seq data using a target-perturbed gene regulatory network

Cells respond divergently to drugs due to the heterogeneity among cell populations. Thus, it is crucial to identify drug-responsive cell populations in order to accurately elucidate the mechanism of drug action, which is still a great challenge. Here, we address this problem with scRank, which employs a target-perturbed gene regulatory network to rank drug-responsive cell populations via in silico drug perturbations using untreated single-cell transcriptomic data. We benchmark scRank on simulated and real datasets, which shows the superior performance of scRank over existing methods. When applied to medulloblastoma and major depressive disorder datasets, scRank identifies drug-responsive cell types that are consistent with the literature. Moreover, scRank accurately uncovers the macrophage subpopulation responsive to tanshinone IIA and its potential targets in myocardial infarction, with experimental validation. In conclusion, scRank enables the inference of drug-responsive cell types using untreated single-cell data, thus providing insights into the cellular-level impacts of therapeutic interventions.

Integrating spatial and single-cell transcriptomics reveals tumor heterogeneity and intercellular networks in colorectal cancer

Single cell RNA sequencing (scRNA-seq), a powerful tool for studying the tumor microenvironment (TME), does not preserve/provide spatial information on tissue morphology and cellular interactions. To understand the crosstalk between diverse cellular components in proximity in the TME, we performed scRNA-seq coupled with spatial transcriptomic (ST) assay to profile 41,700 cells from three colorectal cancer (CRC) tumor-normal-blood pairs. Standalone scRNA-seq analyses revealed eight major cell populations, including B cells, T cells, Monocytes, NK cells, Epithelial cells, Fibroblasts, Mast cells, Endothelial cells. After the identification of malignant cells from epithelial cells, we observed seven subtypes of malignant cells that reflect heterogeneous status in tumor, including tumor_CAV1, tumor_ATF3_JUN | FOS, tumor_ZEB2, tumor_VIM, tumor_WSB1, tumor_LXN, and tumor_PGM1. By transferring the cellular annotations obtained by scRNA-seq to ST spots, we annotated four regions in a cryosection from CRC patients, including tumor, stroma, immune infiltration, and colon epithelium regions. Furthermore, we observed intensive intercellular interactions between stroma and tumor regions which were extremely proximal in the cryosection. In particular, one pair of ligands and receptors (C5AR1 and RPS19) was inferred to play key roles in the crosstalk of stroma and tumor regions. For the tumor region, a typical feature of TMSB4X-high expression was identified, which could be a potential marker of CRC. The stroma region was found to be characterized by VIM-high expression, suggesting it fostered a stromal niche in the TME. Collectively, single cell and spatial analysis in our study reveal the tumor heterogeneity and molecular interactions in CRC TME, which provides insights into the mechanisms underlying CRC progression and may contribute to the development of anticancer therapies targeting on non-tumor components, such as the extracellular matrix (ECM) in CRC. The typical genes we identified may facilitate to new molecular subtypes of CRC.

Microenvironmental changes in familial adenomatous polyposis during colorectal cancer carcinogenesis

Familial adenomatous polyposis (FAP) is a heritable disease that increases the risk of colorectal cancer (CRC) development because of heterozygous mutations in APC. Little is known about the microenvironment of FAP. Here, single-cell RNA sequencing was performed on matched normal tissues, adenomas, and carcinomas from four patients with FAP. We analyzed the transcriptomes of 56,225 unsorted single cells, revealing the heterogeneity of each cell type, and compared gene expression among tissues. Then we compared the gene expression with that of sporadic CRC. Furthermore, we analyzed specimens of 26 FAP patients and 40 sporadic CRC patients by immunohistochemistry. Immunosuppressiveness of myeloid cells, fibroblasts, and regulatory T cells was upregulated even in the early stages of carcinogenesis. CD8+ T cells became exhausted only in carcinoma, although the cytotoxicity of CD8+ T cells was gradually increased according to the carcinogenic step. When compared with those in the sporadic CRC microenvironment, the composition and function of each cell type in the FAP-derived CRC microenvironment had differences. Our findings indicate that an immunosuppressive microenvironment is constructed from a precancerous stage in FAP.

A deep neural network predictor to predict the sensitivity of neoadjuvant chemoradiotherapy in locally advanced rectal cancer

Neoadjuvant chemoradiotherapy (NCRT) is widely used for locally advanced rectal cancer (LARC). This study aimed to conduct an effective model to predict NCRT sensitivity and provide guidance for clinical treatment. Biomarkers for NCRT sensitivity were identified by applying transcriptome profiles using logistic regression and subsequently screened out by Spearman correlation analysis and four machine learning algorithms. A deep neural network (DNN) predictor was constructed by using in-house dataset and validated in two independent datasets. Additionally, a web-based program was developed. Wnt/β-catenin signaling and linoleic acid metabolism (LA) pathways were associated with NCRT sensitivity and prognosis in LARC, antagonistically. A DNN predictor with an 18-gene signature was conducted within in-house datasets. In two validation cohorts, area under ROC curve (AUC) achieved 0.706 and 0.897. The DNN subtypes were significantly associated with NCRT sensitivity, survival status et al. Moreover, NK and cytotoxic T cells were observed contribution to NCRT sensitivity while regulatory T, myeloid-derived suppressor cells and dysfunction of CD4 T effector memory cells could impede NCRT response. A DNN predictor could predict NCRT sensitivity in LARC and stratify LARC patients with different clinical and immunity characteristic.

De novo detection of somatic mutations in high-throughput single-cell profiling data sets

Characterization of somatic mutations at single-cell resolution is essential to study cancer evolution, clonal mosaicism and cell plasticity. Here, we describe SComatic, an algorithm designed for the detection of somatic mutations in single-cell transcriptomic and ATAC-seq (assay for transposase-accessible chromatin sequence) data sets directly without requiring matched bulk or single-cell DNA sequencing data. SComatic distinguishes somatic mutations from polymorphisms, RNA-editing events and artefacts using filters and statistical tests parameterized on non-neoplastic samples. Using >2.6 million single cells from 688 single-cell RNA-seq (scRNA-seq) and single-cell ATAC-seq (scATAC-seq) data sets spanning cancer and non-neoplastic samples, we show that SComatic detects mutations in single cells accurately, even in differentiated cells from polyclonal tissues that are not amenable to mutation detection using existing methods. Validated against matched genome sequencing and scRNA-seq data, SComatic achieves F1 scores between 0.6 and 0.7 across diverse data sets, in comparison to 0.2-0.4 for the second-best performing method. In summary, SComatic permits de novo mutational signature analysis, and the study of clonal heterogeneity and mutational burdens at single-cell resolution.

Single-cell RNA sequencing reveals myeloid and T cell co-stimulation mediated by IL-7 anti-cancer immunotherapy

BACKGROUND

Immune checkpoint inhibitors unleash inhibitory signals on T cells conferred by tumors and surrounding stromal cells. Despite the clinical efficacy of checkpoint inhibitors, the lack of target expression and persistence of immunosuppressive cells limit the pervasive effectiveness of the therapy. These limitations may be overcome by alternative approaches that co-stimulate T cells and the immune microenvironment.

METHODS

We analyzed single-cell RNA sequencing data from multiple human cancers and a mouse tumor transplant model to discover the pleiotropic expression of the Interleukin 7 (IL-7) receptor on T cells, macrophages, and dendritic cells.

RESULTS

Our experiment on the mouse model demonstrated that recombinant IL-7 therapy induces tumor regression, expansion of effector CD8 T cells, and pro-inflammatory activation of macrophages. Moreover, spatial transcriptomic data support immunostimulatory interactions between macrophages and T cells.

CONCLUSION

These results indicate that IL-7 therapy induces anti-tumor immunity by activating T cells and pro-inflammatory myeloid cells, which may have diverse therapeutic applicability.

Spatial and single-cell colocalisation analysis reveals MDK-mediated immunosuppressive environment with regulatory T cells in colorectal carcinogenesis

BACKGROUND

Cell-cell interaction factors that facilitate the progression of adenoma to sporadic colorectal cancer (CRC) remain unclear, thereby hindering patient survival.

METHODS

We performed spatial transcriptomics on five early CRC cases, which included adenoma and carcinoma, and one advanced CRC. To elucidate cell-cell interactions within the tumour microenvironment (TME), we investigated the colocalisation network at single-cell resolution using a deep generative model for colocalisation analysis, combined with a single-cell transcriptome, and assessed the clinical significance in CRC patients.

FINDINGS

CRC cells colocalised with regulatory T cells (Tregs) at the adenoma-carcinoma interface. At early-stage carcinogenesis, cell-cell interaction inference between colocalised adenoma and cancer epithelial cells and Tregs based on the spatial distribution of single cells highlighted midkine (MDK) as a prominent signalling molecule sent from tumour epithelial cells to Tregs. Interaction between MDK-high CRC cells and SPP1+ macrophages and stromal cells proved to be the mechanism underlying immunosuppression in the TME. Additionally, we identified syndecan4 (SDC4) as a receptor for MDK associated with Treg colocalisation. Finally, clinical analysis using CRC datasets indicated that increased MDK/SDC4 levels correlated with poor overall survival in CRC patients.

INTERPRETATION

MDK is involved in the immune tolerance shown by Tregs to tumour growth. MDK-mediated formation of the TME could be a potential target for early diagnosis and treatment of CRC.

FUNDING

Japan Society for the Promotion of Science (JSPS) Grant-in-Aid for Science Research; OITA Cancer Research Foundation; AMED under Grant Number; Japan Science and Technology Agency (JST); Takeda Science Foundation; The Princess Takamatsu Cancer Research Fund.

TGFβ-Responsive Stromal Activation Occurs Early in Serrated Colorectal Carcinogenesis

Activated TGFβ signaling in the tumor microenvironment, which occurs independently of epithelial cancer cells, has emerged as a key driver of tumor progression in late-stage colorectal cancer (CRC). This study aimed to elucidate the contribution of TGFβ-activated stroma to serrated carcinogenesis, representing approximately 25% of CRCs and often characterized by oncogenic BRAF mutations. We used a transcriptional signature developed based on TGFβ-responsive, stroma-specific genes to infer TGFβ-dependent stromal activation and conducted in silico analyses in 3 single-cell RNA-seq datasets from a total of 39 CRC samples and 12 bulk transcriptomic datasets consisting of 2014 CRC and 416 precursor samples, of which 33 were serrated lesions. Single-cell analyses validated that the signature was expressed specifically by stromal cells, effectively excluding transcriptional signals derived from epithelial cells. We found that the signature was upregulated during malignant transformation and cancer progression, and it was particularly enriched in CRCs with mutant BRAF compared to wild-type counterparts. Furthermore, across four independent precursor datasets, serrated lesions exhibited significantly higher levels of TGFβ-responsive stromal activation compared to conventional adenomas. This large-scale analysis suggests that TGFβ-dependent stromal activation occurs early in serrated carcinogenesis. Our study provides novel insights into the molecular mechanisms underlying CRC development via the serrated pathway.

Predicting prognosis and immunotherapy response in colorectal cancer by pericytes insights from single-cell RNA sequencing

Immunotherapy has revolutionized the treatment of tumors, but there are still a large number of patients who do not benefit from immunotherapy. Pericytes play an important role in remodeling the immune microenvironment. However, how pericytes affect the prognosis and treatment resistance of tumors is still unknown. This study jointly analyzed single-cell RNA sequencing (scRNA-seq) data and bulk RNA sequencing data of multiple cancers to reveal pericyte function in the colorectal cancer microenvironment. Analyzing over 800 000 cells, it was found that colorectal cancer had more pericyte enrichment in tumor tissues than other cancers. We then combined the TCGA database with multiple public datasets and enrolled more than 1000 samples, finding that pericyte may be closely related to poor prognosis due to the higher epithelial-mesenchymal transition (EMT) and hypoxic characteristics. At the same time, patients with more pericytes have higher immune checkpoint molecule expressions and lower immune cell infiltration. Finally, the contributions of pericyte in poor treatment response have been demonstrated in multiple immunotherapy datasets (n = 453). All of these observations suggest that pericyte can be used as a potential biomarker to predict patient disease progression and immunotherapy response.

Tumor-agnostic transcriptome-based classifier identifies spatial infiltration patterns of CD8+T cells in the tumor microenvironment and predicts clinical outcome in early-phase and late-phase clinical trials

BACKGROUND

The immune status of a patient's tumor microenvironment (TME) may guide therapeutic interventions with cancer immunotherapy and help identify potential resistance mechanisms. Currently, patients' immune status is mostly classified based on CD8+tumor-infiltrating lymphocytes. An unmet need exists for comparable and reliable precision immunophenotyping tools that would facilitate clinical treatment-relevant decision-making and the understanding of how to overcome resistance mechanisms.

METHODS

We systematically analyzed the CD8 immunophenotype of 2023 patients from 14 phase I-III clinical trials using immunohistochemistry (IHC) and additionally profiled gene expression by RNA-sequencing (RNA-seq). CD8 immunophenotypes were classified by pathologists into CD8-desert, CD8-excluded or CD8-inflamed tumors using CD8 IHC staining in epithelial and stromal areas of the tumor. Using regularized logistic regression, we developed an RNA-seq-based classifier as a surrogate to the IHC-based spatial classification of CD8+tumor-infiltrating lymphocytes in the TME.

RESULTS

The CD8 immunophenotype and associated gene expression patterns varied across indications as well as across primary and metastatic lesions. Melanoma and kidney cancers were among the strongest inflamed indications, while CD8-desert phenotypes were most abundant in liver metastases across all tumor types. A good correspondence between the transcriptome and the IHC-based evaluation enabled us to develop a 92-gene classifier that accurately predicted the IHC-based CD8 immunophenotype in primary and metastatic samples (area under the curve inflamed=0.846; excluded=0.712; desert=0.855). The newly developed classifier was prognostic in The Cancer Genome Atlas (TCGA) data and predictive in lung cancer: patients with predicted CD8-inflamed tumors showed prolonged overall survival (OS) versus patients with CD8-desert tumors (HR 0.88; 95% CI 0.80 to 0.97) across TCGA, and longer OS on immune checkpoint inhibitor administration (phase III OAK study) in non-small-cell lung cancer (HR 0.75; 95% CI 0.58 to 0.97).

CONCLUSIONS

We provide a new precision immunophenotyping tool based on gene expression that reflects the spatial infiltration patterns of CD8+ lymphocytes in tumors. The classifier enables multiplex analyses and is easy to apply for retrospective, reverse translation approaches as well as for prospective patient enrichment to optimize the response to cancer immunotherapy.

Enteric glia regulate Paneth cell secretion and intestinal microbial ecology

Glial cells of the enteric nervous system (ENS) interact closely with the intestinal epithelium and secrete signals that influence epithelial cell proliferation and barrier formation in vitro. Whether these interactions are important in vivo, however, is unclear because previous studies reached conflicting conclusions [1]. To better define the roles of enteric glia in steady state regulation of the intestinal epithelium, we characterized the glia in closest proximity to epithelial cells and found that the majority express PLP1 in both mice and humans. To test their functions using an unbiased approach, we genetically depleted PLP1+ cells in mice and transcriptionally profiled the small and large intestines. Surprisingly, glial loss had minimal effects on transcriptional programs and the few identified changes varied along the gastrointestinal tract. In the ileum, where enteric glia had been considered most essential for epithelial integrity, glial depletion did not drastically alter epithelial gene expression but caused a modest enrichment in signatures of Paneth cells, a secretory cell type important for innate immunity. In the absence of PLP1+ glia, Paneth cell number was intact, but a subset appeared abnormal with irregular and heterogenous cytoplasmic granules, suggesting a secretory deficit. Consistent with this possibility, ileal explants from glial-depleted mice secreted less functional lysozyme than controls with corresponding effects on fecal microbial composition. Collectively, these data suggest that enteric glia do not exert broad effects on the intestinal epithelium but have an essential role in regulating Paneth cell function and gut microbial ecology.

Membrane to cortex attachment determines different mechanical phenotypes in LGR5+ and LGR5- colorectal cancer cells

Colorectal cancer (CRC) tumors are composed of heterogeneous and plastic cell populations, including a pool of cancer stem cells that express LGR5. Whether these distinct cell populations display different mechanical properties, and how these properties might contribute to metastasis is poorly understood. Using CRC patient derived organoids (PDOs), we find that compared to LGR5- cells, LGR5+ cancer stem cells are stiffer, adhere better to the extracellular matrix (ECM), move slower both as single cells and clusters, display higher nuclear YAP, show a higher survival rate in response to mechanical confinement, and form larger transendothelial gaps. These differences are largely explained by the downregulation of the membrane to cortex attachment proteins Ezrin/Radixin/Moesin (ERMs) in the LGR5+ cells. By analyzing single cell RNA-sequencing (scRNA-seq) expression patterns from a patient cohort, we show that this downregulation is a robust signature of colorectal tumors. Our results show that LGR5- cells display a mechanically dynamic phenotype suitable for dissemination from the primary tumor whereas LGR5+ cells display a mechanically stable and resilient phenotype suitable for extravasation and metastatic growth.

The heterogeneity of tumour-associated macrophages contributes to the clinical outcomes and indications for immune checkpoint blockade in colorectal cancer patients

Tumor-associated macrophages (TAMs), one of the major immune cell types in colorectal cancer (CRC) tumor microenvironment (TME), play indispensable roles in immune responses against tumor progression. In this study, we aimed to know whether the extensive inter and intra heterogeneity of TAMs contributes to the clinical outcomes and indications for immune checkpoint blockade (ICB) in CRC. We used single-cell RNA sequencing (scRNA-Seq) data from 60 CRC patients and charactrized TAMs based on anatomic locations, tumor regions, stages, grades, metastatic status, MSS/MSI classification and pseudotemporal differentiation status. We then defined a catalog of 21 gene modules that determine macrophage status, and identified 7 of them as relevant to clinical outcomes and 11 as indications for ICB therapy. On this basis, we constructed a unique TAM subgroup profile, aiming to find features that may be highly responsive to immunotherapy for the CRC with poor prognosis under conventional treatment. This TAM subpopulation is enriched in tumors and is associated with poor prognosis, but exhibits a high immunotherapy response signature (HIM TAM). Further spatial transcriptome analysis and ligand-receptor interaction analysis confirmed that HIM TAM is involved in shaping TIME, especially the regulation of T cells. Our study provides insights into different TAM subtypes, highlights the importance of TAM heterogeneity in relation to patient prognosis and immunotherapy response, and reveals potential immunotherapy strategies based on TAM characteristics for CRC that does not respond well to conventional therapy.

Single-cell transcriptome analysis profiling lymphatic invasion-related TME in colorectal cancer

Lymphatic invasion (LI) is extremely aggressive and induces worse prognosis among patients with colorectal cancer (CRC). Thus, it is critical to characterize the cellular and molecular mechanisms underlying LI in order to establish novel and efficacious therapeutic targets that enhance the prognosis of CRC patients. RNA-seq data, clinical and survival information of colon adenocarcinoma (COAD) patients were obtained from the TCGA database. In addition, three scRNA-seq datasets of CRC patients were acquired from the GEO database. Data analyses were conducted with the R packages. We assessed the tumor microenvironment (TME) differences between LI+ and LI- based scRNA-seq data, LI+ cells exhibited augmented abundance of immunosuppression and invasive subset. Marked extracellular matrix network activation was also observed in LI+ cells within SPP1+ macrophages. We revealed that an immunosuppressive and pro-angiogenic TME strongly enhanced LI, as was evidenced by the CD4+ Tregs, CD8+ GZMK+, SPP1+ macrophages, e-myCAFs, and w-myCAFs subcluster infiltrations. Furthermore, we identified potential LI targets that influenced tumor development, metastasis, and immunotherapeutic response. Finally, a novel LIRS model was established based on the expression of 14 LI-related signatures, and in the two testing cohorts, LIRS was also proved to have accurate prognostic predictive ability. In this report, we provided a valuable resource and extensive insights into the LI of CRC. Our conclusions can potentially benefit the establishment of highly efficacious therapeutic targets as well as diagnostic biomarkers that improve patient outcomes.

GTADC: A Graph-Based Method for Inferring Cell Spatial Distribution in Cancer Tissues

The heterogeneity of tumors poses a challenge for understanding cell interactions and constructing complex ecosystems within cancer tissues. Current research strategies integrate spatial transcriptomics (ST) and single-cell sequencing (scRNA-seq) data to thoroughly analyze this intricate system. However, traditional deep learning methods using scRNA-seq data tend to filter differentially expressed genes through statistical methods. In the context of cancer tissues, where cancer cells exhibit significant differences in gene expression compared to normal cells, this heterogeneity renders traditional analysis methods incapable of accurately capturing differences between cell types. Therefore, we propose a graph-based deep learning method, GTADC, which utilizes Silhouette scores to precisely capture genes with significant expression differences within each cell type, enhancing the accuracy of gene selection. Compared to traditional methods, GTADC not only considers the expression similarity of genes within their respective clusters but also comprehensively leverages information from the overall clustering structure. The introduction of graph structure effectively captures spatial relationships and topological structures between the two types of data, enabling GTADC to more accurately and comprehensively resolve the spatial composition of different cell types within tissues. This refinement allows GTADC to intricately reconstruct the cellular spatial composition, offering a precise solution for inferring cell spatial composition. This method allows for early detection of potential cancer cell regions within tissues, assessing their quantity and spatial information in cell populations. We aim to achieve a preliminary estimation of cancer occurrence and development, contributing to a deeper understanding of early-stage cancer and providing potential support for early cancer diagnosis.

Identification of Colorectal Cancer Cell Stemness from Single-Cell RNA Sequencing

Cancer stem cells (CSC) play a critical role in metastasis, relapse, and therapy resistance in colorectal cancer. While characterization of the normal lineage of cell development in the intestine has led to the identification of many genes involved in the induction and maintenance of pluripotency, recent studies suggest significant heterogeneity in CSC populations. Moreover, while many canonical colorectal cancer CSC marker genes have been identified, the ability to use these classical markers to annotate stemness at the single-cell level is limited. In this study, we performed single-cell RNA sequencing on a cohort of 6 primary colon, 9 liver metastatic tumors, and 11 normal (nontumor) controls to identify colorectal CSCs at the single-cell level. Finding poor alignment of the 11 genes most used to identify colorectal CSC, we instead extracted a single-cell stemness signature (SCS_sig) that robustly identified "gold-standard" colorectal CSCs that expressed all marker genes. Using this SCS_sig to quantify stemness, we found that while normal epithelial cells show a bimodal distribution, indicating distinct stem and differentiated states, in tumor epithelial cells stemness is a continuum, suggesting greater plasticity in these cells. The SCS_sig score was quite variable between different tumors, reflective of the known transcriptomic heterogeneity of CRC. Notably, patients with higher SCS_sig scores had significantly shorter disease-free survival time after curative intent surgical resection, suggesting stemness is associated with relapse.

IMPLICATIONS

This study reveals significant heterogeneity of expression of genes commonly used to identify colorectal CSCs, and identifies a novel stemness signature to identify these cells from scRNA-seq data.

Conserved methylation signatures associate with the tumor immune microenvironment and immunotherapy response

BACKGROUND

Aberrant DNA methylation is a major characteristic of cancer genomes. It remains unclear which biological processes determine epigenetic reprogramming and how these processes influence the variants in the cancer methylome, which can further impact cancer phenotypes.

METHODS

We performed pairwise permutations of 381,900 loci in 569 paired DNA methylation profiles of cancer tissue and matched normal tissue from The Cancer Genome Atlas (TCGA) and defined conserved differentially methylated positions (DMPs) based on the resulting null distribution. Then, we derived independent methylation signatures from 2,465 cancer-only methylation profiles from the TCGA and 241 cell line-based methylation profiles from the Genomics of Drug Sensitivity in Cancer (GDSC) cohort using nonnegative matrix factorization (NMF). We correlated DNA methylation signatures with various clinical and biological features, including age, survival, cancer stage, tumor immune microenvironment factors, and immunotherapy response. We inferred the determinant genes of these methylation signatures by integrating genomic and transcriptomic data and evaluated the impact of these signatures on cancer phenotypes in independent bulk and single-cell RNA/methylome cohorts.

RESULTS

We identified 7,364 differentially methylated positions (2,969 Hyper-DMPs and 4,395 Hypo-DMPs) in nine cancer types from the TCGA. We subsequently retrieved three highly conserved, independent methylation signatures (Hyper-MS1, Hypo-MS1, and Hypo-MS4) from cancer tissues and cell lines based on these Hyper and Hypo-DMPs. Our data suggested that Hypo-MS4 activity predicts poor survival and is associated with immunotherapy response and distant tumor metastasis, and Hypo-MS4 activity is related to TP53 mutation and FOXA1 binding specificity. In addition, we demonstrated a correlation between the activities of Hypo-MS4 in cancer cells and the fractions of regulatory CD4 + T cells with the expression levels of immunological genes in the tumor immune microenvironment.

CONCLUSIONS

Our findings demonstrated that the methylation signatures of distinct biological processes are associated with immune activity in the cancer microenvironment and predict immunotherapy response.

Drug repurposing screening and mechanism analysis based on human colorectal cancer organoids

Colorectal cancer (CRC) is a highly heterogeneous cancer and exploring novel therapeutic options is a pressing issue that needs to be addressed. Here, we established human CRC tumor-derived organoids that well represent both morphological and molecular heterogeneities of original tumors. To efficiently identify repurposed drugs for CRC, we developed a robust organoid-based drug screening system. By combining the repurposed drug library and computation-based drug prediction, 335 drugs were tested and 34 drugs with anti-CRC effects were identified. More importantly, we conducted a detailed transcriptome analysis of drug responses and divided the drug response signatures into five representative patterns: differentiation induction, growth inhibition, metabolism inhibition, immune response promotion, and cell cycle inhibition. The anticancer activities of drug candidates were further validated in the established patient-derived organoids-based xenograft (PDOX) system in vivo. We found that fedratinib, trametinib, and bortezomib exhibited effective anticancer effects. Furthermore, the concordance and discordance of drug response signatures between organoids in vitro and pairwise PDOX in vivo were evaluated. Our study offers an innovative approach for drug discovery, and the representative transcriptome features of drug responses provide valuable resources for developing novel clinical treatments for CRC.

Colorectal Cancer-Associated Myofibroblasts Exhibit Enhanced Angiogenin Expression and Signaling via the PLXNB2 Receptor

INTRODUCTION

Dynamic cell-cell interactions shape the tumor microenvironment to regulate tumor growth and invasiveness. Myofibroblasts are gastrointestinal stromal cells that are upregulated in the setting of colorectal cancer (CRC) and may play an important role in tumor-stromal cell communication. Angiogenin is a 14-kDa ribonuclease that regulates myofibroblast function and has been implicated in myofibroblast-CRC cell communication in mouse models. However, its role in human patients has not been well established.

METHODS

Open access, annotated single-cell RNA sequencing data of paired normal human colon and CRC tissue were available in the National Center for Biotechnology Information Gene Expression Omnibus Database. We supplemented and verified these data by analyzing scRNA-seq data from an independent set of paired normal human colon and CRC tissue. CellChat was used to quantitatively infer biologically meaningful cell-cell communication networks from scRNA-seq data. PLXNB2 and α-2 actin (ACTA2) are cell surface angiogenin receptors that regulate angiogenin signaling. Ligand-receptor interactions involving angiogenin, PLXNB2, and ACTA2 were analyzed between cell populations in each sample.

RESULTS

We found no difference in overall angiogenin expression comparing normal colon and CRC tissue. In normal colon tissue, myofibroblasts do not express angiogenin or the PLXNB2 receptor. In the presence of CRC, there was a striking increase in the number of myofibroblast cells within the surrounding stroma. CRC-associated myofibroblasts were characterized by a significant upregulation of both angiogenin and PLXNB2 receptor expression (P < 0.05), while no difference was seen in ACTA2. CRC cells not only use angiogenin for autocrine signaling but also communicate with myofibroblasts via the PLXNB2 receptor.

CONCLUSIONS

Compared to normal human colon tissue, CRC tissue is associated with an enrichment of myofibroblasts that exhibit upregulated expression of angiogenin and the angiogenin receptor PLXNB2. CRC cells engage in autocrine signaling via angiogenin and paracrine signaling with myofibroblasts via PLXNB2. Angiogenin appears to be directly involved in tumor-stromal cell communication in human CRC tissue and may play an important role in disease progression.

The FOXP3+ Pro-Inflammatory T Cell: A Potential Therapeutic Target in Crohn's Disease

BACKGROUND & AIMS

The incidence of Crohn's disease (CD) continues to increase worldwide. The contribution of CD4+ cell populations remains to be elucidated. We aimed to provide an in-depth transcriptional assessment of CD4+ T cells driving chronic inflammation in CD.

METHODS

We performed single-cell RNA-sequencing in CD4+ T cells isolated from ileal biopsies of patients with CD compared with healthy individuals. Cells underwent clustering analysis, followed by analysis of gene signaling networks. We overlapped our differentially expressed genes with publicly available microarray data sets and performed functional in vitro studies, including an in vitro suppression assay and organoid systems, to model gene expression changes observed in CD regulatory T (Treg) cells and to test predicted therapeutics.

RESULTS

We identified 5 distinct FOXP3+ regulatory Treg subpopulations. Tregs isolated from healthy controls represent the origin of pseudotemporal development into inflammation-associated subtypes. These proinflammatory Tregs displayed a unique responsiveness to tumor necrosis factor-α signaling with impaired suppressive activity in vitro and an elevated cytokine response in an organoid coculture system. As predicted in silico, the histone deacetylase inhibitor vorinostat normalized gene expression patterns, rescuing the suppressive function of FOXP3+ cells in vitro.

CONCLUSIONS

We identified a novel, proinflammatory FOXP3+ T cell subpopulation in patients with CD and developed a pipeline to specifically target these cells using the US Food and Drug Administration-approved drug vorinostat.

Single cell analyses of cancer cells identified two regulatorily and functionally distinct categories in differentially expressed genes among tumor subclones

To explore the feature of cancer cells and tumor subclones, we analyzed 101,065 single-cell transcriptomes from 12 colorectal cancer (CRC) patients and 92 single cell genomes from one of these patients. We found cancer cells, endothelial cells and stromal cells in tumor tissue expressed much more genes and had stronger cell-cell interactions than their counterparts in normal tissue. We identified copy number variations (CNVs) in each cancer cell and found correlation between gene copy number and expression level in cancer cells at single cell resolution. Analysis of tumor subclones inferred by CNVs showed accumulation of mutations in each tumor subclone along lineage trajectories. We found differentially expressed genes (DEGs) between tumor subclones had two populations: DEGCNV and DEGreg. DEGCNV, showing high CNV-expression correlation and whose expression differences depend on the differences of CNV level, enriched in housekeeping genes and cell adhesion associated genes. DEGreg, showing low CNV-expression correlation and mainly in low CNV variation regions and regions without CNVs, enriched in cytokine signaling genes. Furthermore, cell-cell communication analyses showed that DEGCNV tends to involve in cell-cell contact while DEGreg tends to involve in secreted signaling, which further support that DEGCNV and DEGreg are two regulatorily and functionally distinct categories.

CRIF1 deficiency induces FOXP3LOW inflammatory non-suppressive regulatory T cells, thereby promoting antitumor immunity

Recently identified human FOXP3lowCD45RA- inflammatory non-suppressive (INS) cells produce proinflammatory cytokines, exhibit reduced suppressiveness, and promote antitumor immunity unlike conventional regulatory T cells (Tregs). In spite of their implication in tumors, the mechanism for generation of FOXP3lowCD45RA- INS cells in vivo is unclear. We showed that the FOXP3lowCD45RA- cells in human tumors demonstrate attenuated expression of CRIF1, a vital mitochondrial regulator. Mice with CRIF1 deficiency in Tregs bore Foxp3lowINS-Tregs with mitochondrial dysfunction and metabolic reprograming. The enhanced glutaminolysis activated α-ketoglutarate-mTORC1 axis, which promoted proinflammatory cytokine expression by inducing EOMES and SATB1 expression. Moreover, chromatin openness of the regulatory regions of the Ifng and Il4 genes was increased, which facilitated EOMES/SATB1 binding. The increased α-ketoglutarate-derived 2-hydroxyglutarate down-regulated Foxp3 expression by methylating the Foxp3 gene regulatory regions. Furthermore, CRIF1 deficiency-induced Foxp3lowINS-Tregs suppressed tumor growth in an IFN-γ-dependent manner. Thus, CRIF1 deficiency-mediated mitochondrial dysfunction results in the induction of Foxp3lowINS-Tregs including FOXP3lowCD45RA- cells that promote antitumor immunity.

Implementable assay for monitoring minimum residual disease after radical treatment for colorectal cancer

Considering the cost and invasiveness of monitoring postoperative minimal residual disease (MRD) of colorectal cancer (CRC) after adjuvant chemoradiotherapy (ACT), we developed a favorable approach based on methylated circulating tumor DNA to detect MRD after radical resection. Analyzing the public database, we identified the methylated promoter regions of the genes FGD5, GPC6, and MSC. Using digital polymerase chain reaction (dPCR), we termed the "amplicon of methylated sites using a specific enzyme" assay as "AMUSE." We examined 180 and 114 pre- and postoperative serial plasma samples from 28 recurrent and 19 recurrence-free pathological stage III CRC patients, respectively. The results showed 22 AMUSE-positive of 28 recurrent patients (sensitivity, 78.6%) and 17 AMUSE-negative of 19 recurrence-free patients (specificity, 89.5%). AMUSE predicted recurrence 208 days before conventional diagnosis using radiological imaging. Regarding ACT evaluation by the reactive response, 19 AMUSE-positive patients during their second or third blood samples showed a significantly poorer prognosis than the other patients (p = 9E-04). The AMUSE assay stratified four groups by the altered patterns of tumor burden postoperatively. Interestingly, only 34.8% of cases tested AMUSE-negative during ACT treatment, indicating eligibility for ACT. The AMUSE assay addresses the clinical need for accurate MRD monitoring with universal applicability, minimal invasiveness, and cost-effectiveness, thereby enabling the timely detection of recurrences. This assay can effectively evaluate the efficacy of ACT in patients with stage III CRC following curative resection. Our study strongly recommends reevaluating the clinical application of ACT using the AMUSE assay.

Assessing GPT-4 for cell type annotation in single-cell RNA-seq analysis

Here we demonstrate that the large language model GPT-4 can accurately annotate cell types using marker gene information in single-cell RNA sequencing analysis. When evaluated across hundreds of tissue and cell types, GPT-4 generates cell type annotations exhibiting strong concordance with manual annotations. This capability can considerably reduce the effort and expertise required for cell type annotation. Additionally, we have developed an R software package GPTCelltype for GPT-4's automated cell type annotation.

Single-cell transcriptional signature-based drug repurposing and in vitro evaluation in colorectal cancer

BACKGROUND

The need for intelligent and effective treatment of diseases and the increase in drug design costs have raised drug repurposing as one of the effective strategies in biomedicine. There are various computational methods for drug repurposing, one of which is using transcription signatures, especially single-cell RNA sequencing (scRNA-seq) data, which show us a clear and comprehensive view of the inside of the cell to compare the state of disease and health.

METHODS

In this study, we used 91,103 scRNA-seq samples from 29 patients with colorectal cancer (GSE144735 and GSE132465). First, differential gene expression (DGE) analysis was done using the ASAP website. Then we reached a list of drugs that can reverse the gene signature pattern from cancer to normal using the iLINCS website. Further, by searching various databases and articles, we found 12 drugs that have FDA approval, and so far, no one has reported them as a drug in the treatment of any cancer. Then, to evaluate the cytotoxicity and performance of these drugs, the MTT assay and real-time PCR were performed on two colorectal cancer cell lines (HT29 and HCT116).

RESULTS

According to our approach, 12 drugs were suggested for the treatment of colorectal cancer. Four drugs were selected for biological evaluation. The results of the cytotoxicity analysis of these drugs are as follows: tezacaftor (IC10 = 19 µM for HCT-116 and IC10 = 2 µM for HT-29), fenticonazole (IC10 = 17 µM for HCT-116 and IC10 = 7 µM for HT-29), bempedoic acid (IC10 = 78 µM for HCT-116 and IC10 = 65 µM for HT-29), and famciclovir (IC10 = 422 µM for HCT-116 and IC10 = 959 µM for HT-29).

CONCLUSIONS

Cost, time, and effectiveness are the main challenges in finding new drugs for diseases. Computational approaches such as transcriptional signature-based drug repurposing methods open new horizons to solve these challenges. In this study, tezacaftor, fenticonazole, and bempedoic acid can be introduced as promising drug candidates for the treatment of colorectal cancer. These drugs were evaluated in silico and in vitro, but it is necessary to evaluate them in vivo.

Single cell deciphering of progression trajectories of the tumor ecosystem in head and neck cancer

Head and neck squamous cell carcinoma is the sixth most common cancer worldwide and has high heterogeneity and unsatisfactory outcomes. To better characterize the tumor progression trajectory, we perform single-cell RNA sequencing of normal tissue, precancerous tissue, early-stage, advanced-stage cancer tissue, lymph node, and recurrent tumors tissue samples. We identify the transcriptional development trajectory of malignant epithelial cells and a tumorigenic epithelial subcluster regulated by TFDP1. Furthermore, we find that the infiltration of POSTN+ fibroblasts and SPP1+ macrophages gradually increases with tumor progression; their interaction or interaction with malignant cells also gradually increase to shape the desmoplastic microenvironment and reprogram malignant cells to promote tumor progression. Additionally, we demonstrate that during lymph node metastasis, exhausted CD8+ T cells with high CXCL13 expression strongly interact with tumor cells to acquire more aggressive phenotypes of extranodal expansion. Finally, we delineate the distinct features of malignant epithelial cells in primary and recurrent tumors, providing a theoretical foundation for the precise selection of targeted therapy for tumors at different stages. In summary, the current study offers a comprehensive landscape and deep insight into epithelial and microenvironmental reprogramming throughout initiation, progression, lymph node metastasis and recurrence of head and neck squamous cell carcinoma.

UFL1 ablation in T cells suppresses PD-1 UFMylation to enhance anti-tumor immunity

UFMylation is an emerging ubiquitin-like post-translational modification that regulates various biological processes. Dysregulation of the UFMylation pathway leads to human diseases, including cancers. However, the physiological role of UFMylation in T cells remains unclear. Here, we report that mice with conditional knockout (cKO) Ufl1, a UFMylation E3 ligase, in T cells exhibit effective tumor control. Single-cell RNA sequencing analysis shows that tumor-infiltrating cytotoxic CD8+ T cells are increased in Ufl1 cKO mice. Mechanistically, UFL1 promotes PD-1 UFMylation to antagonize PD-1 ubiquitination and degradation. Furthermore, AMPK phosphorylates UFL1 at Thr536, disrupting PD-1 UFMylation to trigger its degradation. Of note, UFL1 ablation in T cells reduces PD-1 UFMylation, subsequently destabilizing PD-1 and enhancing CD8+ T cell activation. Thus, Ufl1 cKO mice bearing tumors have a better response to anti-CTLA-4 immunotherapy. Collectively, our findings uncover a crucial role of UFMylation in T cells and highlight UFL1 as a potential target for cancer treatment.

An improved hierarchical variational autoencoder for cell-cell communication estimation using single-cell RNA-seq data

Analysis of cell-cell communication (CCC) in the tumor micro-environment helps decipher the underlying mechanism of cancer progression and drug tolerance. Currently, single-cell RNA-Seq data are available on a large scale, providing an unprecedented opportunity to predict cellular communications. There have been many achievements and applications in inferring cell-cell communication based on the known interactions between molecules, such as ligands, receptors and extracellular matrix. However, the prior information is not quite adequate and only involves a fraction of cellular communications, producing many false-positive or false-negative results. To this end, we propose an improved hierarchical variational autoencoder (HiVAE) based model to fully use single-cell RNA-seq data for automatically estimating CCC. Specifically, the HiVAE model is used to learn the potential representation of cells on known ligand-receptor genes and all genes in single-cell RNA-seq data, respectively, which are then utilized for cascade integration. Subsequently, transfer entropy is employed to measure the transmission of information flow between two cells based on the learned representations, which are regarded as directed communication relationships. Experiments are conducted on single-cell RNA-seq data of the human skin disease dataset and the melanoma dataset, respectively. Results show that the HiVAE model is effective in learning cell representations, and transfer entropy could be used to estimate the communication scores between cell types.

The transcriptional landscape of glycosylation-related genes in cancer

Changes in glycosylation patterns have been associated with malignant transformation and clinical outcomes in several cancer types, prompting ongoing research into the mechanisms involved and potential clinical applications. In this study, we performed an extensive transcriptomic analysis of glycosylation-related genes and pathways, using publicly available bulk and single cell transcriptomic datasets from tumor samples and cancer cell lines. We identified genes and pathways strongly associated with different tumor types, which may represent novel diagnostic biomarkers. By using single cell RNA-seq data, we characterized the contribution of different cell types to the overall tumor glycosylation. Transcriptomic analysis of cancer cell lines revealed that they present a simplified landscape of genes compared to tissue. Lastly, we describe the association of different genes and pathways with the clinical outcome of patients. These results can serve as a resource for future research aimed to unravel the role of the glyco-code in cancer.

Cancer-associated fibroblast phenotypes are associated with patient outcome in non-small cell lung cancer

Despite advances in treatment, lung cancer survival rates remain low. A better understanding of the cellular heterogeneity and interplay of cancer-associated fibroblasts (CAFs) within the tumor microenvironment will support the development of personalized therapies. We report a spatially resolved single-cell imaging mass cytometry (IMC) analysis of CAFs in a non-small cell lung cancer cohort of 1,070 patients. We identify four prognostic patient groups based on 11 CAF phenotypes with distinct spatial distributions and show that CAFs are independent prognostic factors for patient survival. The presence of tumor-like CAFs is strongly correlated with poor prognosis. In contrast, inflammatory CAFs and interferon-response CAFs are associated with inflamed tumor microenvironments and higher patient survival. High density of matrix CAFs is correlated with low immune infiltration and is negatively correlated with patient survival. In summary, our data identify phenotypic and spatial features of CAFs that are associated with patient outcome in NSCLC.

Precursor exhausted CD8+T cells in colorectal cancer tissues associated with patient's survival and immunotherapy responsiveness

Exhausted CD8+T cells represent a distinct cellular lineage that emerges during both chronic infections and cancers. Recent studies have shown that persistent antigen exposure can drive the differentiation of precursor exhausted CD8+T cells, termed Tpex cells, which are characterized as TCF-1+PD-1+CD8+T cells. Elevated Tpex cell frequencies in the tumor microenvironment (TME) are associated with improved overall survival (OS) in cancer patients and heightened responsiveness to anti-PD-1 therapy. In our present study, we utilized multi-color immunohistochemistry (mIHC) to determine the localization and clinical implications of tumor-infiltrating Tpex cells within the TME of human colorectal cancer (CRC) tissues. We also conducted a multi-omics integrative analysis using single-cell RNA sequencing (scRNA-seq) data derived from both the murine MC38 tumor model and human CRC tissues. This analysis helped delineate the transcriptional and functional attributes of Tpex cells within the CRC TME. Furthermore, we employed spatial transcriptome sequencing data from CRC patients to investigate the interactions between Tpex cells and other immune cell subsets within the TME. In conclusion, our study not only established a method for Tpex cell detection using mIHC technology but also confirmed that assessing Tpex cells within the CRC TME could be indicative of patients' survival. We further uncovered the transcriptional and functional characteristics of Tpex cells in the TME and ascertained their pivotal role in the efficacy of immunotherapy against CRC.

Tumor-secreted FGF21 acts as an immune suppressor by rewiring cholesterol metabolism of CD8+T cells

Tumors employ diverse strategies for immune evasion. Unraveling the mechanisms by which tumors suppress anti-tumor immunity facilitates the development of immunotherapies. Here, we have identified tumor-secreted fibroblast growth factor 21 (FGF21) as a pivotal immune suppressor. FGF21 is upregulated in multiple types of tumors and promotes tumor progression. Tumor-secreted FGF21 significantly disrupts anti-tumor immunity by rewiring cholesterol metabolism of CD8+T cells. Mechanistically, FGF21 sustains the hyperactivation of AKT-mTORC1-sterol regulatory-element-binding protein 1 (SREBP1) signal axis in the activated CD8+T cells, resulting in the augment of cholesterol biosynthesis and T cell exhaustion. FGF21 knockdown or blockade using a neutralizing antibody normalizes AKT-mTORC1 signaling and reduces excessive cholesterol accumulation in CD8+T cells, thus restoring CD8+T cytotoxic function and robustly suppressing tumor growth. Our findings reveal FGF21 as a "secreted immune checkpoint" that hampers anti-tumor immunity, suggesting that inhibiting FGF21 could be a valuable strategy to enhance the cancer immunotherapy efficacy.

Tumor-infiltrating monocytic myeloid-derived suppressor cells contribute to the development of an immunosuppressive tumor microenvironment in gastric cancer

BACKGROUND

Gastric cancer (GC) is characterized by an immunosuppressive and treatment-resistant tumor immune microenvironment (TIME). Here, we investigated the roles of different immunosuppressive cell types in the development of the GC TIME.

METHODS

Single-cell RNA sequencing (scRNA-seq) and multiplex immunostaining of samples from untreated or immune checkpoint inhibitor (ICI)-resistant GC patients were used to examine the correlation between certain immunosuppressive cells and the prognosis of GC patients.

RESULTS

The results of the scRNA-seq analysis revealed that tumor-infiltrating monocytic myeloid-derived suppressor cells (TI-M-MDSCs) expressed higher levels of genes with immunosuppressive functions than other immunosuppressive cell types. Additionally, M-MDSCs in GC tissues expressed significantly higher levels of these markers than adjacent normal tissues. The M-MDSCs were most enriched in GC tissues relative to adjacent normal tissues. Among the immunosuppressive cell types assessed, the M-MDSCs were most enriched in GC tissues relative to adjacent normal tissues; moreover, their presence was most strongly associated with a poor prognosis. Immediate early response 3 (IER3), which we identified as a differentially expressed gene between M-MDSCs of GC and adjacent normal tissues, was an independent poor prognostic factor in GC patients (P = 0.0003). IER3+ M-MDSCs expressed higher levels of genes with immunosuppressive functions than IER3- M-MDSCs and were abundant in treatment-resistant GC patients.

CONCLUSIONS

The present study suggests that TI-M-MDSCs, especially IER3+ ones, may play a predominant role in the development of the immunosuppressive and ICI-resistant GC TIME.

scRNA-seq of colorectal cancer shows regional immune atlas with the function of CD20+ B cells

Colorectal cancer (CRC) from different regions exhibits different histological, genetic characteristics, and molecular subtypes, even in response to conventional chemotherapies and immunotherapies. To characterize the immune landscape in different regions of CRC and search for potential therapeutic targets, we analyzed 39,484 single-cell transcription data from 19 samples of CRC and paired normal tissues from four regions to identify the immune characteristics of CRC among anatomic locations, especially in B cells. We discovered that immune cell infiltration in tumors significantly varied among different regions of CRC. B cells from right- and left-sided CRC had different development trajectories, but both had extensive interactions with myeloid cells and T cells. Survival analysis suggested that CD20+ B cells correlated with good prognosis in CRC patients, especially on the right side. Furthermore, the depletion of CD20+ B cells demonstrated that anti-CD20 promoted tumor growth progression and reversed the tumor-killing activity of anti-PD-1 treatment in vivo and in vitro. Our results highlight the characterization of the immune landscape of CRC in different regions. CD20+ B-cell infiltration has been associated with CRC patient prognosis and may promote the tumor-killing role of PD-1 antibodies.

Population-level comparisons of gene regulatory networks modeled on high-throughput single-cell transcriptomics data

Single-cell technologies enable high-resolution studies of phenotype-defining molecular mechanisms. However, data sparsity and cellular heterogeneity make modeling biological variability across single-cell samples difficult. Here we present SCORPION, a tool that uses a message-passing algorithm to reconstruct comparable gene regulatory networks from single-cell/nuclei RNA-sequencing data that are suitable for population-level comparisons by leveraging the same baseline priors. Using synthetic data, we found that SCORPION outperformed 12 existing gene regulatory network reconstruction techniques. Using supervised experiments, we show that SCORPION can accurately identify differences in regulatory networks between wild-type and transcription factor-perturbed cells. We demonstrate SCORPION's scalability to population-level analyses using a single-cell RNA-sequencing atlas containing 200,436 cells from colorectal cancer and adjacent healthy tissues. The differences between tumor regions detected by SCORPION are consistent across multiple cohorts as well as with our understanding of disease progression, and elucidate phenotypic regulators that may impact patient survival.

Rebuilding the microenvironment of primary tumors in humans: a focus on stroma

Conventional tumor models have critical shortcomings in that they lack the complexity of the human stroma. The heterogeneous stroma is a central compartment of the tumor microenvironment (TME) that must be addressed in cancer research and precision medicine. To fully model the human tumor stroma, the deconstruction and reconstruction of tumor tissues have been suggested as new approaches for in vitro tumor modeling. In this review, we summarize the heterogeneity of tumor-associated stromal cells and general deconstruction approaches used to isolate patient-specific stromal cells from tumor tissue; we also address the effect of the deconstruction procedure on the characteristics of primary cells. Finally, perspectives on the future of reconstructed tumor models are discussed, with an emphasis on the essential prerequisites for developing authentic humanized tumor models.

The dopamine transporter antagonist vanoxerine inhibits G9a and suppresses cancer stem cell functions in colon tumors

Cancer stem cells (CSCs), functionally characterized by self-renewal and tumor-initiating activity, contribute to decreased tumor immunogenicity, while fostering tumor growth and metastasis. Targeting G9a histone methyltransferase (HMTase) effectively blocks CSC functions in colorectal tumors by altering pluripotent-like molecular networks; however, existing molecules directly targeting G9a HMTase activity failed to reach clinical stages due to safety concerns. Using a stem cell-based phenotypic drug-screening pipeline, we identified the dopamine transporter (DAT) antagonist vanoxerine, a compound with previously demonstrated clinical safety, as a cancer-specific downregulator of G9a expression. Here we show that gene silencing and chemical antagonism of DAT impede colorectal CSC functions by repressing G9a expression. Antagonizing DAT also enhanced tumor lymphocytic infiltration by activating endogenous transposable elements and type-I interferon response. Our study unveils the direct implication of the DAT-G9a axis in the maintenance of CSC populations and an approach to improve antitumor immune response in colon tumors.

Single-cell transcriptomic analysis deciphers heterogenous cancer stem-like cells in colorectal cancer and their organ-specific metastasis

OBJECTIVE

Metastasis is the major cause of cancer death. However, what types of heterogenous cancer cells in primary tumour and how they metastasise to the target organs remain largely undiscovered.

DESIGN

We performed single-cell RNA sequencing and spatial transcriptomic analysis in primary colorectal cancer (CRC) and metastases in the liver (lCRC) or ovary (oCRC). We also conducted immunofluorescence staining and functional experiments to examine the mechanism.

RESULTS

Integrative analyses of epithelial cells reveal a stem-like cell cluster with high protein tyrosine phosphatase receptor type O (PTPRO) and achaete scute-like 2 (ASCL2) expression as the metastatic culprit. This cell cluster comprising distinct subpopulations shows distinct liver or ovary metastatic preference. Population 1 (P1) cells with high delta-like ligand 4 (DLL4) and MAF bZIP transcription factor A (MAFA) expression are enriched in primary CRC and oCRC, thus may be associated with ovarian metastasis. P3 cells having a similar expression pattern as cholangiocytes are found mainly in primary CRC and lCRC, presuming to be likely the culprits that specifically metastasise to the liver. Stem-like cells interacted with cancer-associated fibroblasts and endothelial cells via the DLL4-NOTCH signalling pathway to metastasise from primary CRC to the ovary. In the oCRC microenvironment, myofibroblasts provide cancer cells with glutamine and perform a metabolic reprogramming, which may be essential for cancer cells to localise and develop in the ovary.

CONCLUSION

We uncover a mechanism for organ-specific CRC metastasis.

Stromal Signals Dominate Gene Expression Signature Scores That Aim to Describe Cancer Cell-intrinsic Stemness or Mesenchymality Characteristics

Epithelial-to-mesenchymal transition (EMT) in cancer cells confers migratory abilities, a crucial aspect in the metastasis of tumors that frequently leads to death. In multiple studies, authors proposed gene expression signatures for EMT, stemness, or mesenchymality of tumors based on bulk tumor expression profiling. However, recent studies suggested that noncancerous cells from the microenvironment or macroenvironment heavily influence such signature profiles. Here, we strengthen these findings by investigating 11 published and frequently referenced gene expression signatures that were proposed to describe EMT-related (EMT, mesenchymal, or stemness) characteristics in various cancer types. By analyses of bulk, single-cell, and pseudobulk expression data, we show that the cell type composition of a tumor sample frequently dominates scores of these EMT-related signatures. A comprehensive, integrated analysis of bulk RNA sequencing (RNA-seq) and single-cell RNA-seq data shows that stromal cells, most often fibroblasts, are the main drivers of EMT-related signature scores. We call attention to the risk of false conclusions about tumor properties when interpreting EMT-related signatures, especially in a clinical setting: high patient scores of EMT-related signatures or calls of "stemness subtypes" often result from low cancer cell content in tumor biopsies rather than cancer cell-specific stemness or mesenchymal/EMT characteristics.

SIGNIFICANCE

Cancer self-renewal and migratory abilities are often characterized via gene module expression profiles, also called EMT or stemness gene expression signatures. Using published clinical tumor samples, cancer cell lines, and single cancer cells, we highlight the dominating influence of noncancer cells in low cancer cell content biopsies on their scores. We caution on their application for low cancer cell content clinical cancer samples with the intent to assign such characteristics or subtypes.

A multiomics analysis-assisted deep learning model identifies a macrophage-oriented module as a potential therapeutic target in colorectal cancer

Colorectal cancer (CRC) is a common malignancy involving multiple cellular components. The CRC tumor microenvironment (TME) has been characterized well at single-cell resolution. However, a spatial interaction map of the CRC TME is still elusive. Here, we integrate multiomics analyses and establish a spatial interaction map to improve the prognosis, prediction, and therapeutic development for CRC. We construct a CRC immune module (CCIM) that comprises FOLR2+ macrophages, exhausted CD8+ T cells, tolerant CD8+ T cells, exhausted CD4+ T cells, and regulatory T cells. Multiplex immunohistochemistry is performed to depict the CCIM. Based on this, we utilize advanced deep learning technology to establish a spatial interaction map and predict chemotherapy response. CCIM-Net is constructed, which demonstrates good predictive performance for chemotherapy response in both the training and testing cohorts. Lastly, targeting FOLR2+ macrophage therapeutics is used to disrupt the immunosuppressive CCIM and enhance the chemotherapy response in vivo.

Measuring the composition of the tumor microenvironment with transcriptome analysis: past, present and future

Interactions between tumor cells and immune cells in the tumor microenvironment (TME) play a vital role the mechanisms of immune evasion, by which cancer cells escape immune elimination. Thus, the characterization and quantification of different components in the TME is a hot topic in molecular biology and drug discovery. Since the development of transcriptome sequencing in bulk tissue, single cells and spatial dimensions, there are increasing methods emerging to deconvolute and subtype the TME. This review discusses and compares such computational strategies and downstream subtyping analyses. Integrative analyses of the transcriptome with other data, such as epigenetics and T-cell receptor sequencing, are needed to obtain comprehensive knowledge of the dynamic TME.

scCancer2: data-driven in-depth annotations of the tumor microenvironment at single-level resolution

SUMMARY

Single-cell RNA-seq (scRNA-seq) is a powerful technique for decoding the complex cellular compositions in the tumor microenvironment (TME). As previous studies have defined many meaningful cell subtypes in several tumor types, there is a great need to computationally transfer these labels to new datasets. Also, different studies used different approaches or criteria to define the cell subtypes for the same major cell lineages. The relationships between the cell subtypes defined in different studies should be carefully evaluated. In this updated package scCancer2, designed for integrative tumor scRNA-seq data analysis, we developed a supervised machine learning framework to annotate TME cells with annotated cell subtypes from 15 scRNA-seq datasets with 594 samples in total. Based on the trained classifiers, we quantitatively constructed the similarity maps between the cell subtypes defined in different references by testing on all the 15 datasets. Secondly, to improve the identification of malignant cells, we designed a classifier by integrating large-scale pan-cancer TCGA bulk gene expression datasets and scRNA-seq datasets (10 cancer types, 175 samples, 663 857 cells). This classifier shows robust performances when no internal confidential reference cells are available. Thirdly, scCancer2 integrated a module to process the spatial transcriptomic data and analyze the spatial features of TME.

AVAILABILITY AND IMPLEMENTATION

The package and user documentation are available at http://lifeome.net/software/sccancer2/ and https://doi.org/10.5281/zenodo.10477296.