Delineating copy number and clonal substructure in human tumors from single-cell transcriptomes

Systematic drug screening and target analysis identify digitoxin as a potential therapy for uveal melanoma

BACKGROUND AND PURPOSE

Cardiac glycosides (CGs), traditionally prescribed for heart failure and arrhythmias, show anticancer potential. However, their mechanisms for preferential inhibition of tumour tissue and constituent malignant cells are not fully elucidated. This study aims to elucidate the therapeutic benefits of CGs in targeting specific tumours and dissect their multi-targeting mechanisms that confer their cytotoxicity against malignant cells.

EXPERIMENTAL APPROACH

We designed an integrated workflow to identify therapeutic CGs with high toxicity to certain cancers, investigating their multi-target effects, assessing their toxicity to malignant cells and analysing the prognostic relevance of CGs' target genes. The computational findings were confirmed through gene knockdown, cell viability assays, reactive oxygen species (ROS) measurements and so forth.

KEY RESULTS

CGs modulate multiple genes crucial for ion homeostasis, oxidative stress and apoptosis, with a particularly strong inhibitory effects on uveal melanoma (UVM). Notably, digitoxin suppresses UVM cell proliferation and induces ROS levels by simultaneously targeting STAT3 and KLF5. Single-cell transcriptomic analysis revealed that malignant cells are likely more vulnerable to CGs due to their higher expression of CG target genes compared with surrounding cells in the UVM microenvironment.

CONCLUSIONS AND IMPLICATIONS

Given UVM's limited options, our study highlights the potential of digitoxin as a promising novel therapeutic agent for this aggressive and rare ocular cancer. Our comprehensive approach is effective in identifying the potent, cancer-specific therapeutic agents from herbal plants.

Deciphering programmed cell death mechanisms in osteosarcoma for prognostic modeling

Osteosarcoma (OS), known for its high recurrence and metastasis rates, poses a significant challenge in oncology. Our research investigates the role of programmed cell death (PCD) genes in OS and develops a prognostic model using advanced bioinformatics. We analyzed single-cell sequencing data from the Gene Expression Omnibus (GEO) database to identify subpopulations, distinguish malignant from non-malignant cells, assess cell cycle phases, and map PCD gene distribution. Additionally, we applied consistency clustering to bulk sequencing data from GEO and TARGET (Therapeutically Applicable Research to Generate Effective Treatments) databases, facilitating survival analysis across clusters with the Kaplan-Meier method. We calculated PCD scores for each cluster using the Single-sample Gene Set Enrichment Analysis (ssGSEA), which enabled a detailed examination of PCD-related gene expression and pathway scores. Our study also explored drug sensitivity differences and conducted comprehensive immune cell infiltration analyses using various algorithms. We identified differentially expressed genes, leading to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses that provided insights into relevant biological processes and pathways. The prognostic model, based on five pivotal genes (BAMBI, TMCC2, NOX4, DKK1, and CBS), was developed using the Least Absolute Shrinkage and Selection Operator (LASSO) algorithm and validated in the TARGET-OS and GSE16091 datasets, showing significant predictive accuracy. This research enhances our understanding of PCD in OS and supports the development of effective treatments.

A novel HVEM-Fc recombinant protein for lung cancer immunotherapy

BACKGROUND

The ubiquitously expressed transmembrane protein, Herpesvirus Entry Mediator (HVEM), functions as a molecular switch, capable of both activating and inhibiting the immune response depending on its interacting ligands. HVEM-Fc is a novel recombinant fusion protein with the potential to eradicate tumor cells.

METHODS

The anti-tumor efficacy of HVEM-Fc was evaluated in C57BL/6 mice-bearing lung cancer models: a syngeneic model and an orthotopic model of mouse lung cancer. Additionally, patient-derived organoids were employed in conjunction with T cell co-culture systems. To investigate the underlying mechanisms, a comprehensive array of techniques was utilized, including single-cell RNA sequencing, spatial transcriptomics, bulk RNA sequencing, and flow cytometry. Furthermore, the anti-tumor effects of HVEM-Fc in combination with Programmed Death-1 (PD-1) inhibitors were assessed. Finally, mouse immune cell depletion antibodies were used to elucidate the underlying mechanisms of action.

RESULTS

In vivo, 1 mg/kg HVEM-Fc demonstrated effective inhibition of tumor growth and metastasis in C57BL/6 mice bearing lung cancer model and a KP orthotopic model of mouse lung cancer. Multi-omics analysis showed that HVEM-Fc induced an immune-stimulatory microenvironment. Notably, the combination of HVEM-Fc with a PD-1 inhibitor demonstrated the most potent inhibition of tumor cell growth. In vitro, HVEM-Fc was validated to eradicate tumor cells through the activation of T cells in both non-small cell lung cancer (NSCLC) organoids and T cell co-culture models.

CONCLUSIONS

Our data demonstrate that HVEM-Fc exerts a strong signal that augments and prolongs T-cell activity in both murine models and human NSCLC organoid models. Moreover, the combination of HVEM-Fc with a PD-1 inhibitor yields the most effective anti-tumor outcomes.

Exploring genome-transcriptome correlations in cancer

We examine the complex relationship between genomic copy number variation (CNV) and gene expression, highlighting the relevance to cancer biology and other biological contexts. By tracing the history of genometranscriptome correlations, we emphasize the complexity and challenges in understanding these interactions, particularly within the heterogeneous landscape of human cancers. Recent advances in computational algorithms and high-throughput single-cell multi-omic sequencing technologies are discussed, demonstrating their potential to refine our understanding of cancer biology and their limitations. The integration of genomic and transcriptomic analyses, which offers novel insights into tumor evolution and heterogeneity as well as therapeutic strategies, is presented as a crucial approach for advancing cancer research.

Single-Nucleus and Spatial Transcriptome Profiling Delineates the Multicellular Ecosystem in Hepatocellular Carcinoma After Hepatic Arterial Infusion Chemotherapy

Hepatic arterial infusion chemotherapy (HAIC) has emerged as a promising treatment strategy for hepatocellular carcinoma (HCC), but a detailed understanding of the multicellular ecosystem after HAIC treatment is lacking. Here, we collected tumor samples from treatment-naïve primary and post-HAIC HCC, and integrated single-nucleus RNA sequencing with spatial transcriptomics to characterize the tumor ecosystem in the post-HAIC HCC. Increased fractions and enhanced cellular communication of CD4+ T, CD20+ B, and dendritic cell subtypes were identified in post-HAIC tumors. Moreover, it is substantiated that HAIC promoted tertiary lymphoid structures (TLS) formation, and addressed the roles of TLSs as spatial niches of cellular communication. Specifically, intermediate exhausted CD8+ T cells expressing Granzyme-K and PD-1 (PD-1+CD8+ Tex-int) expanded following HAIC and exhibited a functionally antitumor phenotype. PD-1+CD8+ Tex-int accumulated in the TLS vicinity and disseminated throughout the tumor microenvironment, demonstrating potential as an effective biomarker for HAIC-based treatment in HCC. This study provides valuable resources and biological insights in the cellular underpinnings of HAIC treatment.

Network analyses of brain tumor multiomic data reveal pharmacological opportunities to alter cell state transitions

Glioblastoma Multiforme (GBM) remains a particularly difficult cancer to treat, and survival outcomes remain poor. In addition to the lack of dedicated drug discovery programs for GBM, extensive intratumor heterogeneity and epigenetic plasticity related to cell-state transitions are major roadblocks to successful drug therapy in GBM. To study these phenomenon, publicly available snRNAseq and bulk RNAseq data from patient samples were used to categorize cells from patients into four cell states (i.e., phenotypes), namely: (i) neural progenitor-like (NPC-like), (ii) oligodendrocyte progenitor-like (OPC-like), (iii) astrocyte-like (AC-like), and (iv) mesenchymal-like (MES-like). Patients were subsequently grouped into subpopulations based on which cell-state was the most dominant in their respective tumor. By incorporating phosphoproteomic measurements from the same patients, a protein-protein interaction network (PPIN) was constructed for each cell state. These four-cell state PPINs were pooled to form a single Boolean network that was used for in silico protein knockout simulations to investigate mechanisms that either promote or prevent cell state transitions. Simulation results were input into a boosted tree machine learning model which predicted the cell states or phenotypes of GBM patients from an independent public data source, the Glioma Longitudinal Analysis (GLASS) Consortium. Combining the simulation results and the machine learning predictions, we generated hypotheses for clinically relevant causal mechanisms of cell state transitions. For example, the transcription factor TFAP2A can be seen to promote a transition from the NPC-like to the MES-like state. Such protein nodes and the associated signaling pathways provide potential drug targets that can be further tested in vitro and support cell state-directed (CSD) therapy.

Detecting copy-number alterations from single-cell chromatin sequencing data by AtaCNA

Single-cell assay of transposase-accessible chromatin sequencing (scATAC-seq) unbiasedly profiles genome-wide chromatin accessibility in single cells. In single-cell tumor studies, identification of normal cells or tumor clonal structures often relies on copy-number alterations (CNAs). However, CNA detection from scATAC-seq is difficult due to the high noise, sparsity, and confounding factors. Here, we describe AtaCNA, a computational algorithm that accurately detects high-resolution CNAs from scATAC-seq data. We benchmark AtaCNA using simulation and real data and find AtaCNA's superior performance. Analyses of 10 scATAC-seq datasets show that AtaCNA could effectively distinguish malignant from non-malignant cells. In glioblastoma, endometrial, and ovarian cancer samples, AtaCNA identifies subclones at distinct cellular states, suggesting an important interplay between genetic and epigenetic plasticity. Some tumor subclones only differ in small-scale (10-20 Mb) CNAs, demonstrating the importance of high-resolution CNA detection. These data show that AtaCNA can aid in integrative analysis to understand the complex heterogeneity in cancer.

WWC proteins-mediated compensatory mechanism restricts schwannomatosis driven by NF2 loss of function

NF2-related schwannomatosis, previously known as neurofibromatosis type 2, is a genetic disorder characterized by nerve tumors due to NF2 gene mutations. Mice with Nf2 deletion develop schwannomas slowly with low penetrance, hence inconvenient for preclinical studies. Here, we show that NF2, by recruiting E3 ubiquitin ligases β-TrCP1/2, promotes WWC1-3 ubiquitination and degradation. In NF2 mutated cells, WWC1-3 accumulation is a compensatory mechanism to prevent YAP/TAZ hyperactivation and rapid tumorigenesis. Accordingly, we generate a synthetic mouse model with complete penetrance and short latency by concurrently deleting Nf2 and Wwc1/2 in Schwann cells. This model closely resembles NF2-related schwannomatosis in patients, as confirmed by histological and single-cell transcriptome analysis. Moreover, a cell line from mouse schwannomas and a syngeneic tumor model in immune-competent mice are established. Furthermore, a screen using established models has identified candidate drugs that effectively suppress schwannoma progression. Hence, this work has developed rapid and transplantable models that will facilitate both basic and translational research on NF2-related schwannomatosis.

Immune regulatory genes impact the hot/cold tumor microenvironment, affecting cancer treatment and patient outcomes

Background and aims

Immunologically hot tumors, characterized by an inflamed tumor microenvironment (TME), contrast significantly with immunologically cold tumors. The identification of these tumor immune subtypes holds clinical significance, as hot tumors may exhibit improved prognoses and heightened responsiveness to checkpoint blockade therapy. Nevertheless, as yet there is no consensus regarding the clinically relevant definition of hot/cold tumors, and the influence of immune genes on the formation of hot/cold tumors remains poorly understood.

Methods

Data for 33 different types of cancer were obtained from The Cancer Genome Atlas database, and their immune composition was assessed using the CIBERSORT algorithm. Tumors were categorized as either hot or cold based on their distinct immune composition, ongoing immune response, and overall survival. A customized immunogram was created to identify important immunological characteristics. Kyoto Encyclopedia of Genes and Genomes and Hallmark pathway enrichment were evaluated through gene set variation analysis. Additionally, hub genes that regulate the tumor microenvironment were identified, and their expression patterns were analyzed using single-cell RNA sequencing. Furthermore, drug sensitivity and molecular docking analyses were performed to identify potential drug candidates capable of transforming cold tumors into hot tumors. For validation, a clinical cohort of patients diagnosed with pancreatic adenocarcinoma was examined using multiplex immunohistochemistry.

Results

We were able to differentiate between hot and cold tumors in various types of cancer (bladder urothelial carcinoma, pancreatic adenocarcinoma, and cervical squamous cell carcinoma) by analyzing the presence of CD8+ T cells, activated natural killer cells, and M2-type macrophages, as well as the cytolytic activity and T cell proliferation. Hub genes that regulate the TME, including PDCD1, CD276, and NT5E, were discovered. The increased expression of NT5E and its prognostic significance were confirmed through multiplex immunohistochemistry in pancreatic adenocarcinoma. Finally, dasatinib and tozasertib were identified as drug candidates capable of converting cold pancreatic adenocarcinoma tumors into hot tumors.

Conclusion

In this study, we developed a framework for discerning clinically significant immune subtypes across various cancer types, further identifying several potential targets for converting cold tumors into hot tumors to enhance anticancer treatment efficacy.

Attractor Landscape Analysis Reveals a Reversion Switch in the Transition of Colorectal Tumorigenesis

A cell fate change such as tumorigenesis incurs critical transition. It remains a longstanding challenge whether the underlying mechanism can be unraveled and a molecular switch that can reverse such transition is found. Here a systems framework, REVERT, is presented with which can reconstruct the core molecular regulatory network model and a reversion switch based on single-cell transcriptome data over the transition process is identified. The usefulness of REVERT is demonstrated by applying it to single-cell transcriptome of patient-derived matched organoids of colon cancer and normal colon. REVERT is a generic framework that can be applied to investigate various cell fate transition phenomena.

Integrins identified as potential prognostic markers in osteosarcoma through multi-omics and multi-dataset analysis

Osteosarcoma represents 20% of primary malignant bone tumors globally. Assessing its prognosis is challenging due to the complex roles of integrins in tumor development and metastasis. This study utilized 209,268 osteosarcoma cells from the GEO database to identify integrin-associated genes using advanced analysis methods. A novel machine learning framework combining 10 algorithms was developed to construct an Integrin-related Signature (IRS), which demonstrated robust predictive power across multiple datasets. The IRS's utility in predicting overall survival was confirmed using data from The Cancer Genome Atlas, underscoring its potential in personalized cancer management.

inDrops-2: a flexible, versatile and cost-efficient droplet microfluidic approach for high-throughput scRNA-seq of fresh and preserved clinical samples

The expansion of single-cell analytical techniques has empowered the exploration of diverse biological questions at the individual cells. Droplet-based single-cell RNA sequencing (scRNA-seq) methods have been particularly widely used due to their high-throughput capabilities and small reaction volumes. While commercial systems have contributed to the widespread adoption of droplet-based scRNA-seq, their relatively high cost limits the ability to profile large numbers of cells and samples. Moreover, as the scale of single-cell sequencing continues to expand, accommodating diverse workflows and cost-effective multi-biospecimen profiling becomes more critical. Herein, we present inDrops-2, an open-source scRNA-seq technology designed to profile live or preserved cells with a sensitivity matching that of state-of-the-art commercial systems but at a 6-fold lower cost. We demonstrate the flexibility of inDrops-2, by implementing two prominent scRNA-seq protocols, based on exponential and linear amplification of barcoded-complementary DNA, and provide useful insights into the advantages and disadvantages inherent to each approach. We applied inDrops-2 to simultaneously profile multiple human lung carcinoma samples that had been subjected to cell preservation, long-term storage and multiplexing to obtain a multiregional cellular profile of the tumor microenvironment. The scalability, sensitivity and cost efficiency make inDrops-2 stand out among other droplet-based scRNA-seq methods, ideal for large-scale studies on rare cell molecular signatures.

An integrated single-cell and spatial transcriptomic atlas of thyroid cancer progression identifies prognostic fibroblast subpopulations

Thyroid cancer progression from curable well-differentiated thyroid carcinoma to highly lethal anaplastic thyroid carcinoma is distinguished by tumor cell de-differentiation and recruitment of a robust stromal infiltrate. Combining an integrated thyroid cancer single-cell sequencing atlas with spatial transcriptomics and bulk RNA-sequencing, we define stromal cell subpopulations and tumor-stromal cross-talk occurring across the histologic and mutational spectrum of thyroid cancer. We identify distinct inflammatory and myofibroblastic cancer-associated fibroblast (iCAF and myCAF) populations and perivascular-like populations. The myCAF population is only found in malignant samples and is associated with tumor cell invasion, BRAF V600E mutation, lymph node metastasis, and disease progression. Tumor-adjacent myCAFs abut invasive tumor cells with a partial epithelial-to-mesenchymal phenotype. Tumor-distant iCAFs infiltrate inflammatory autoimmune thyroid lesions and anaplastic tumors. In summary, our study provides an integrated atlas of thyroid cancer fibroblast subtypes and spatial characterization at sites of tumor invasion and de-differentiation, defining the stromal reorganization central to disease progression.

Integration of single-cell transcriptomics and bulk transcriptomics to explore prognostic and immunotherapeutic characteristics of nucleotide metabolism in lung adenocarcinoma

Background

Lung adenocarcinoma (LUAD) is a highly aggressive tumor with one of the highest morbidity and mortality rates in the world. Nucleotide metabolic processes are critical for cancer development, progression, and alteration of the tumor microenvironment. However, the effect of nucleotide metabolism on LUAD remains to be thoroughly investigated.

Methods

Transcriptomic and clinical data of LUAD were downloaded and organized from TCGA and GEO databases. Genes related to nucleotide metabolism were downloaded from the Msigdb database. Genes associated with LUAD prognosis were identified using univariate COX analysis, and a prognostic risk model was constructed using the machine learning combination of Lasso + Stepcox. The model's predictive validity was evaluated using KM survival and timeROC curves. Based on the prognostic model, LUAD patients were classified into different nucleotide metabolism subtypes, and the differences between patients of different subtypes were explored in terms of genomic mutations, functional enrichment, tumor immune characteristics, and immunotherapy responses. Finally, the key gene SNRPA was screened, and a series of in vitro experiments were performed on LUAD cell lines to explore the role of SNRPA in LUAD.

Result

LUAD patients could be accurately categorized into subtypes based on the nucleotide metabolism-related prognostic risk score (NMBRS). There were significant differences in prognosis between patients of different subtypes, and the NMBRS showed high accuracy in predicting the prognosis of LUAD patients. In addition, patients of different subtypes showed significant differences in genomic mutation and functional enrichment and exhibited different anti-tumor immune profiles. Importantly, NMBRS can be used to predict the responsiveness of LUAD patients to immunotherapy. The results of in vitro cellular experiments indicate that SNRPA plays an important role in the development and progression of lung adenocarcinoma.

Conclusion

This study comprehensively reveals the prognostic value and clinical application of nucleotide metabolism in LUAD. A prognostic signature constructed based on genes related to nucleotide metabolism accurately predicted the prognosis of LUAD patients, and this signature can be used as a guide for LUAD immunotherapy.

Single-cell sequencing analysis reveals cancer-associated pericyte subgroup in esophageal squamous cell carcinoma to predict prognosis

Background

The role of cancer-associated pericytes (CAPs) in tumor microenvironment (TME) suggests that they are potential targets for cancer treatment. The mechanism of CAP heterogeneity in esophageal squamous cell carcinoma (ESCC) remains unclear, which has limited the development of treatments for tumors through CAPs. Therefore, a comprehensive understanding of the classification, function, cellular communication and spatial distribution of CAP subpopulations in ESCC is urgently needed.

Methods

This study used large-sample single-cell transcriptome sequencing (scRNA-seq) data to investigate pericytes' subpopulation characteristics, functions, upstream and downstream regulation and interactions with other components of the TME in the ESCC, and analyzed prognostically in conjunction with Bulk RNA-seq data. In addition, pericyte subpopulations were validated and their spatial distribution in the ESCC TME was observed by multiplex immunofluorescence. Drug prediction and molecular docking was further used to validate the medicinal value of drug targets.

Results

CAPs in the ESCC TME were found to be highly heterogeneous, and we identified six pericyte subtypes: c1_ARHGDIB, c2_BCAM, c3_LUM, c4_SOD2, c5_TYMS, and c6_KRT17, which have commonality in a part of their functions, and each of them has a major function to play, by having different strengths of interaction with different components in the TME. In addition, we found that c4_SOD2 was negatively correlated with prognosis, conversely, c5_TYMS was positively correlated with prognosis. The drug with a better effect on c5_TYMS was docetaxel (binding energy = -8.1, -8.7 kcal/mol); raloxifene may be more effective against c4_SOD2, although raloxifene has a slightly lower binding energy to SOD2 (-6.4 kcal/mol), it has a higher binding energy to PDGFRβ (-8.1 kcal/mol).

Conclusion

The present study identified and discovered pericyte subpopulations that were significantly associated with prognosis, which provides new biomarkers for predicting patient prognosis and adds usable targets for immunotherapy, and it is also important for gaining insights into the composition of the TME in ESCC.

Clonal phylogenies inferred from bulk, single cell, and spatial transcriptomic analysis of epithelial cancers

Epithelial cancers are typically heterogeneous with primary prostate cancer being a typical example of histological and genomic variation. Prior studies of primary prostate cancer tumour genetics revealed extensive inter and intra-patient genomic tumour heterogeneity. Recent advances in machine learning have enabled the inference of ground-truth genomic single-nucleotide and copy number variant status from transcript data. While these inferred SNV and CNV states can be used to resolve clonal phylogenies, however, it is still unknown how faithfully transcript-based tumour phylogenies reconstruct ground truth DNA-based tumour phylogenies. We sought to study the accuracy of inferred-transcript to recapitulate DNA-based tumour phylogenies. We first performed in-silico comparisons of inferred and directly resolved SNV and CNV status, from single cancer cells, from three different cell lines. We found that inferred SNV phylogenies accurately recapitulate DNA phylogenies (entanglement = 0.097). We observed similar results in iCNV and CNV based phylogenies (entanglement = 0.11). Analysis of published prostate cancer DNA phylogenies and inferred CNV, SNV and transcript based phylogenies demonstrated phylogenetic concordance. Finally, a comparison of pseudo-bulked spatial transcriptomic data to adjacent sections with WGS data also demonstrated recapitulation of ground truth (entanglement = 0.35). These results suggest that transcript-based inferred phylogenies recapitulate conventional genomic phylogenies. Further work will need to be done to increase accuracy, genomic, and spatial resolution.

SpatialDeX Is a Reference-Free Method for Cell-Type Deconvolution of Spatial Transcriptomics Data in Solid Tumors

The rapid development of spatial transcriptomics (ST) technologies has enabled transcriptome-wide profiling of gene expression in tissue sections. Despite the emergence of single-cell resolution platforms, most ST sequencing studies still operate at a multicell resolution. Consequently, deconvolution of cell identities within the spatial spots has become imperative for characterizing cell-type-specific spatial organization. To this end, we developed Spatial Deconvolution Explorer (SpatialDeX), a regression model-based method for estimating cell-type proportions in tumor ST spots. SpatialDeX exhibited comparable performance to reference-based methods and outperformed other reference-free methods with simulated ST data. Using experimental ST data, SpatialDeX demonstrated superior performance compared with both reference-based and reference-free approaches. Additionally, a pan-cancer clustering analysis on tumor spots identified by SpatialDeX unveiled distinct tumor progression mechanisms both within and across diverse cancer types. Overall, SpatialDeX is a valuable tool for unraveling the spatial cellular organization of tissues from ST data without requiring single-cell RNA-seq references. Significance: The development of a reference-free method for deconvolving the identity of cells in spatial transcriptomics datasets enables exploration of tumor architecture to gain deeper insights into the dynamics of the tumor microenvironment.

scPharm: Identifying Pharmacological Subpopulations of Single Cells for Precision Medicine in Cancers

Intratumour heterogeneity significantly hinders the efficacy of anticancer therapies. Compared with drug perturbation experiments, which yield pharmacological data at the bulk cell level, single-cell RNA sequencing (scRNA-seq) technology provides a means to capture molecular heterogeneity at single-cell resolution. Here, scPharm is introduced, a computational framework that integrates pharmacological profiles with scRNA-seq data to identify pharmacological subpopulations of cells within a tumour and prioritize tailored drugs. scPharm uses the normalized enrichment scores (NESs) determined from gene set enrichment analysis to assess the distribution of cell identity genes in drug response-determined gene lists. Based on the strong correlation between the NES and drug response at single-cell resolution, scPharm successfully identified the sensitive subpopulations in ER-positive and HER2-positive human breast cancer tissues, revealed dynamic changes in the resistant subpopulation of human PC9 cells treated with erlotinib, and expanded its ability to a mouse model. Its superior performance and computational efficiency are confirmed through comparative evaluations with other single-cell prediction tools. Additionally, scPharm predicted combination drug strategies by gauging compensation or booster effects between drugs and evaluated drug toxicity in healthy cells in the tumour microenvironment. Overall, scPharm provides a novel approach for precision medicine in cancers by revealing therapeutic heterogeneity at single-cell resolution.

SpatialData: an open and universal data framework for spatial omics

Spatially resolved omics technologies are transforming our understanding of biological tissues. However, the handling of uni- and multimodal spatial omics datasets remains a challenge owing to large data volumes, heterogeneity of data types and the lack of flexible, spatially aware data structures. Here we introduce SpatialData, a framework that establishes a unified and extensible multiplatform file-format, lazy representation of larger-than-memory data, transformations and alignment to common coordinate systems. SpatialData facilitates spatial annotations and cross-modal aggregation and analysis, the utility of which is illustrated in the context of multiple vignettes, including integrative analysis on a multimodal Xenium and Visium breast cancer study.

Spatial and Single-Cell Transcriptomics Unraveled Spatial Evolution of Papillary Thyroid Cancer

Recurrence and metastasis are the major issues for papillary thyroid cancer (PTC). Current morphological and molecular classification systems are not satisfied for PTC diagnosis due to lacking variant-specific morphological criteria and high signal-to-noise in mutation-based diagnosis, respectively. Importantly, intratumor heterogeneity is largely lost in current molecular classification system, which can be resolved by single cell RNA sequencing (scRNA-seq). However, scRNA-seq loses spatial information and morphological features. Herein, scRNA-seq is integrated and spatially-resolved transcriptomics (SRT) to elaborate the mechanisms underlying the spatial heterogeneity, malignancy and metastasis of PTCs by associating transcriptome and local morphology. This results demonstrated that PTC cells evolved with multiple routes, driven by the enhanced aerobic metabolism and the suppressed mRNA translation and protein synthesis and the involvement of cell-cell interaction. Two curated malignant and metastatic footprints can discriminate PTC cells from normal thyrocytes. Ferroptosis resistance contributed to PTC evolution. This results will advance the knowledge of intratumor spatial heterogeneity and evolution of PTCs at spatial and single-cell levels, and propose better diagnostic strategy.

LKB1 inactivation promotes epigenetic remodeling-induced lineage plasticity and antiandrogen resistance in prostate cancer

Epigenetic regulation profoundly influences the fate of cancer cells and their capacity to switch between lineages by modulating essential gene expression, thereby shaping tumor heterogeneity and therapy response. In castration-resistant prostate cancer (CRPC), the intricacies behind androgen receptor (AR)-independent lineage plasticity remain unclear, leading to a scarcity of effective clinical treatments. Utilizing single-cell RNA sequencing on both human and mouse prostate cancer samples, combined with whole-genome bisulfite sequencing and multiple genetically engineered mouse models, we investigated the molecular mechanism of AR-independent lineage plasticity and uncovered a potential therapeutic strategy. Single-cell transcriptomic profiling of human prostate cancers, both pre- and post-androgen deprivation therapy, revealed an association between liver kinase B1 (LKB1) pathway inactivation and AR independence. LKB1 inactivation led to AR-independent lineage plasticity and global DNA hypomethylation during prostate cancer progression. Importantly, the pharmacological inhibition of TET enzymes and supplementation with S-adenosyl methionine were found to effectively suppress AR-independent prostate cancer growth. These insights shed light on the mechanism driving AR-independent lineage plasticity and propose a potential therapeutic strategy by targeting DNA hypomethylation in AR-independent CRPC.

High-Throughput Single-Nucleus RNA Profiling of Minimal Puncture FFPE Samples Reveals Spatiotemporal Heterogeneity of Cancer

Puncture biopsy, especially those preserved by formalin fixed paraffin embedding (FFPE) samples, play an important role in various research purposes. Diverse single-nucleus RNA sequencing (snRNA-seq) techniques have been developed for FFPE samples, however, how to perform high-throughput snRNA-seq on small FFPE puncture samples is still a challenge. Here, the previously developed snRNA-seq technique (snRandom-seq) is optimized by implementing a pre-indexing procedure for the minimal puncture FFPE samples. In analyzing 20 samples from various solid tumors, optimized snRandom-seq still detected ≈17 000 genes and 12 000 long non-coding RNAs (lncRNAs), achieving precise clustering based on tissue origin. A head-to-head comparison with 10× Genomics on fresh biopsy samples showed a similar gene detection rate, with significantly enhanced lncRNA detection, indicating that the optimized snRandom-seq technique maintains its established gene detection advantages even when applied to small samples. Utilizing 7 puncture FFPE samples of liver metastases from 3 colorectal cancer patients pre- and post-immunotherapy, the cellular developmental trajectories are reconstructed and revealed dynamic spatiotemporal heterogeneity during treatment, including insights into pseudoprogression of immunotherapy. Therefore, the optimized snRandom-seq offers a solution for high-throughput single-cell RNA and non-coding RNA analysis in minimal puncture FFPE sample.

Single-cell RNA-sequencing and spatial transcriptomic analysis reveal a distinct population of APOE- cells yielding pathological lymph node metastasis in papillary thyroid cancer

BACKGROUND

Thyroid cancer is one of the most common endocrine tumors worldwide, especially among women and the metastatic mechanism of papillary thyroid carcinoma remains poorly understood.

METHODS

Thyroid cancer tissue samples were obtained for single-cell RNA-sequencing and spatial transcriptomics, aiming to intratumoral and antimetastatic heterogeneity of advanced PTC. The functions of APOE in PTC cell proliferation and invasion were confirmed through in vivo and in vitro assays. Pseudotime analysis and CellChat were performed to explore the the molecular mechanisms of the APOE in PTC progression.

RESULTS

We identified a subpopulation of tumor cells with lower expression levels of APOE, associated with advanced stages of PTC and cervical metastasis. APOE overexpression significantly reduced tumor cell proliferation and invasion, both in vitro and in vivo, by activating the ABCA1-LXR axis. APOE- tumor cells may promote tumor growth by interacting with dendritic cells and CD4+ T cells via CD99- rather than CD6-regulated signaling. We established a machine learning-based scRNA-seq data, 13-gene signature predictive of lymph node metastasis.

CONCLUSIONS

We identified a distinct APOE- tumor cell population associated with cervical metastasis and poor prognosis. Our results and models have potential clinical, prognostic, and therapeutic implications for advanced PTC.

KEY POINTS

A subpopulation of tumor cells with lower expression levels of APOE was strongly associated with more advanced stages and metastasis of PTC. APOE-negative (APOE-) cellsoverall exhibited weaker interactions with immune cells. A machine-learning bioinformatics model based on scRNA-seq data of in-situ thyroid cancer tissue was established to predict lymph node metastasis.

Bulk integrated single-cell-spatial transcriptomics reveals the impact of preoperative chemotherapy on cancer-associated fibroblasts and tumor cells in colorectal cancer, and construction of related predictive models using machine learning

BACKGROUND

Preoperative chemotherapy (PC) is an important component of Colorectal cancer (CRC) treatment, but its effects on the biological functions of fibroblasts and epithelial cells in CRC are unclear.

METHODS

This study utilized bulk, single-cell, and spatial transcriptomic sequencing data from 22 independent cohorts of CRC. Through bioinformatics analysis and in vitro experiments, the research investigated the impact of PC on fibroblast and epithelial cells in CRC. Subpopulations associated with PC and CRC prognosis were identified, and a predictive model was constructed using machine learning.

RESULTS

PC significantly attenuated the pathways related to tumor progression in fibroblasts and epithelial cells. NOTCH3 + Fibroblast (NOTCH3 + Fib), TNNT1 + Epithelial (TNNT1 + Epi), and HSPA1A + Epithelial (HSPA1A + Epi) subpopulations were identified in the adjacent spatial region and were associated with poor prognosis in CRC. PC effectively diminished the presence of these subpopulations, concurrently inhibiting pathway activity and intercellular crosstalk. A risk signature model, named the Preoperative Chemotherapy Risk Signature Model (PCRSM), was constructed using machine learning. PCRSM emerged as an independent prognostic indicator for CRC, impacting both overall survival (OS) and recurrence-free survival (RFS), surpassing the performance of 89 previously published CRC risk signatures. Additionally, patients with a high PCRSM risk score showed sensitivity to fluorouracil-based adjuvant chemotherapy (FOLFOX) but resistance to single chemotherapy drugs (such as Bevacizumab and Oxaliplatin). Furthermore, this study predicted that patients with high PCRSM were resistant to anti-PD1therapy.

CONCLUSION

In conclusion, this study identified three cell subpopulations (NOTCH3 + Fib, TNNT1 + Epi, and HSPA1A + Epi) associated with PC, which can be targeted to improve the prognosis of CRC patients. The PCRSM model shows promise in enhancing the survival and treatment of CRC patients.

Replication stress increases de novo CNVs across the malaria parasite genome

Changes in the copy number of large genomic regions, termed copy number variations (CNVs), contribute to important phenotypes in many organisms. CNVs are readily identified using conventional approaches when present in a large fraction of the cell population. However, CNVs that are present in only a few genomes across a population are often overlooked but important; if beneficial under specific conditions, a de novo CNV that arises in a single genome can expand during selection to create a larger population of cells with novel characteristics. While the reach of single cell methods to study de novo CNVs is increasing, we continue to lack information about CNV dynamics in rapidly evolving microbial populations. Here, we investigated de novo CNVs in the genome of the Plasmodium parasite that causes human malaria. The highly AT-rich P. falciparum genome readily accumulates CNVs that facilitate rapid adaptation to new drugs and host environments. We employed a low-input genomics approach optimized for this unique genome as well as specialized computational tools to evaluate the de novo CNV rate both before and after the application of stress. We observed a significant increase in genomewide de novo CNVs following treatment with a replication inhibitor. These stress-induced de novo CNVs encompassed genes that contribute to various cellular pathways and tended to be altered in clinical parasite genomes. This snapshot of CNV dynamics emphasizes the connection between replication stress, DNA repair, and CNV generation in this important microbial pathogen.

snRNA-seq of long-preserved FFPE samples from colorectal liver metastasis lesions with diverse prognoses

Differences in prognostic outcomes are prevalent in patients with colorectal cancer liver metastases. Comparative analysis of tissue samples, particularly applying single-cell transcriptome sequencing technology, can provide a deeper understanding of potential impacting factors. However, long-term monitoring for prognosis determination necessitates extended preservation of tissue samples using formalin-fixed and paraffin-embedded (FFPE) treatments, which can cause substantial RNA degradation, presenting challenges to single-cell or single-nucleus sequencing. In this study, employing snRandom-seq, a single-nucleus RNA sequencing (snRNA-seq) technology specifically for FFPE samples, we tested multiple lesion samples from 18 distinctive colorectal cancer liver metastasis cases with diverse prognostic outcomes that have been preserved for at least three years (mostly over five years). The process yielded expression data from 82,285 cells. The high-quality snRNA-seq data demonstrate the feasibility of single-nucleus sequencing in long-term preserved FFPE samples, offering potential insights into the heterogeneity between different prognoses of colorectal cancer liver metastases, and the relationship between the heterogeneity within different lesions of the same patient and prognosis.

Single-cell dissection of multifocal bladder cancer reveals malignant and immune cells variation between primary and recurrent tumor lesions

Bladder carcinoma (BLCA) is characterized by a high rate of post-surgery recurrence and multifocality. Multifocal tumors have a higher risk of recurrence compared to single tumors, significantly impacting bladder cancer-specific mortality. However, the interregional or intraregional heterogeneity within both primary and recurrent tumors remains poorly understood. Here, we employed single-cell RNA sequencing to analyze tumor lesions from five multifocal bladder cancer patients comprising three primary tumors and two recurrent tumors. Our findings revealed that malignant cells derived from recurrent multifocal bladder cancer exhibited higher interregional transcriptional similarity and consistent cellular communication. Furthermore, our analysis uncovered that malignant cells from recurrent tumors may evade immune destruction by suppressing cytokine responses and natural killer cell activity. Notably, we identified a preference for the expression of the tryptophan metabolic enzyme IL4I1 on SPP1+ macrophages in recurrent tumors. Functional analyses have revealed that IL4I1 may promotes tumor progression in recurrent tumors by activating the aryl hydrocarbon receptor (AHR) and recruiting regulatory T cells to suppress adaptive immunity. Taken together, our study provides a comprehensive understanding of primary and recurrent multifocal bladder tumors, offering valuable resources for analyzing the multifocality and recurrence of bladder cancer.

HIPSD&R-seq enables scalable genomic copy number and transcriptome profiling

Single-cell DNA sequencing (scDNA-seq) enables decoding somatic cancer variation. Existing methods are hampered by low throughput or cannot be combined with transcriptome sequencing in the same cell. We propose HIPSD&R-seq (HIgh-throughPut Single-cell Dna and Rna-seq), a scalable yet simple and accessible assay to profile low-coverage DNA and RNA in thousands of cells in parallel. Our approach builds on a modification of the 10X Genomics platform for scATAC and multiome profiling. In applications to human cell models and primary tissue, we demonstrate the feasibility to detect rare clones and we combine the assay with combinatorial indexing to profile over 17,000 cells.

Development of a centrosome amplification-associated signature in kidney renal clear cell carcinoma based on multiple machine learning models

BACKGROUND

Centrosome amplification (CA) has been shown to be capable of initiating tumorigenesis with metastatic potential and enhancing cell invasion. We were interested in discovering how centrosome amplification-associated signature affects the prediction of prognosis and response to therapy in kidney renal clear cell carcinoma (KIRC).

METHODS AND MATERIALS

The TCGA-KIRC dataset was used to construct a centrosome amplification-associated signature using the random survival forest analysis and Cox regression analysis, and the ICGC and GEO datasets were employed for signature validation. Mutation and immune landscapes were outlined and the response to immunotherapy was evaluated. The expression of the screened hub gene was profiled by analyzing single-cell RNA sequencing from GSE159115.

RESULTS

In the TCGA-KIRC cohort, 22 centrosome amplification-associated prognostic genes were discovered. According to the optimal consistency index (0.91), the random survival forest algorithm was selected to determine 7 hub prognostic genes, which were used to construct a centrosome amplification-associated prognostic index (CAAPI). It was discovered that it is connected to high mortality rates, high mutation rates, immunosuppressive cell infiltration, and immune dysfunction. For patients in the high CAAPI group, immunotherapy was not as effective. Single-cell RNA sequencing revealed a high expression of CDK5RAP3 in the tumor cells.

CONCLUSION

Centrosome amplification played a significant role in regulating tumor microenvironment and responding to immunotherapy, emphasizing its crucial importance in the development and treatment of KIRC. Patients with KIRC may benefit from using CAAPI as a biomarker to predict individual prognosis and assess a response to immunotherapy.

CHD6 eviction of promoter nucleosomes maintains housekeeping transcriptional program in prostate cancer

CHD6, a member of the chromodomain helicase DNA-binding protein family, has been implicated in various diseases and tumors. However, its precise binding model of CHD6 on regulatory functional genes remains poorly understood. In this study, we discovered sharp peaks of CHD6, as the first member of CHD family for housekeeping process, binding only to the promoter region of genes in the C4-2 cell line. These genes, with conserved sharp CHD6 peaks across tumor cells, likely represent housekeeping genes ADNP and GOLGA5. Genes with sharp CHD6 peaks exhibit stable and low expression levels, sharing epigenetic features similar to housekeeping genes. Furthermore, this regulatory model also exists in both HEK293 cells and cardiomyocytes. Overall, the results of this study demonstrate that CHD6 binds to the promoter regions of housekeeping genes, regulating their histone modifications, chromatin structure, and gene expression.

The characteristics and functional significance of disulfidptosis-related genes in head and neck squamous cell carcinoma

Disulfidptosis is a newfound programmed cell death (PCD) mode characterized by disulfide stress. Nevertheless, the characteristics and functional significance of disulfidptosis-related genes in head and neck squamous cell carcinoma (HNSCC) are still largely unknown. In this study, several computer-aided bioinformatic analyses were performed. The Nonnegative Matrix Factorization (NMF) method classified The Cancer Genome Atlas (TCGA) patients into two clusters according to the expression of disulfidptosis-related genes. The relative compositions of cells in the tumor microenvironment (TME), mutant landscape, lasso regression analysis, and predicted clinical outcome were performed by analyzing bulk RNA-sequencing data. Besides, single-cell sequencing data (scRNA) was analyzed by Seurat, CopyKAT, and monocle2 to reveal the expression characteristics of disulfidptosis-related genes. Moreover, the spatial distribution characteristics of each cell subgroup in the section and the functional significance of cancer-associated fibroblasts (CAFs) were elucidated by STUtility, SpaCET, and SPATA2. Here, two clusters with different expression characteristics of disulfidptosis-related genes were identified. Cluster 1 (C1) patients had a worse prognosis and a higher proportion of stromal cells but lower effector T cell infiltration than cluster 2 (C2). A novel prognostic model was established and verified in our patient cohort. Additionally, diploid and inflammatory CAFs (iCAFs) showed higher disulfidptosis-related gene expression levels. Furthermore, the CCNC and CHMP1B expressions significantly changed following CAFs differentiation. Disulfidptosis-related genes exhibited extensive and differential spatial expression on tissue sections. Collectively, our study may contribute to revealing the function of disulfidptosis, and improve the expansion of knowledge of crosstalk between cancer cells and CAFs.

Inferring allele-specific copy number aberrations and tumor phylogeography from spatially resolved transcriptomics

Analyzing somatic evolution within a tumor over time and across space is a key challenge in cancer research. Spatially resolved transcriptomics (SRT) measures gene expression at thousands of spatial locations in a tumor, but does not directly reveal genomic aberrations. We introduce CalicoST, an algorithm to simultaneously infer allele-specific copy number aberrations (CNAs) and reconstruct spatial tumor evolution, or phylogeography, from SRT data. CalicoST identifies important classes of CNAs-including copy-neutral loss of heterozygosity and mirrored subclonal CNAs-that are invisible to total copy number analysis. Using nine patients' data from the Human Tumor Atlas Network, CalicoST achieves an average accuracy of 86%, approximately 21% higher than existing methods. CalicoST reconstructs a tumor phylogeography in three-dimensional space for two patients with multiple adjacent slices. CalicoST analysis of multiple SRT slices from a cancerous prostate organ reveals mirrored subclonal CNAs on the two sides of the prostate, forming a bifurcating phylogeography in both genetic and physical space.

Gene choice in cancer cells is exclusive in ion transport but concurrent in DNA replication

Cancers share common cellular and physiological features. Little is known about whether distinctive gene expression patterns can be displayed at the single-cell level by gene families in cancer cells. The expression of gene homologs within a family can exhibit concurrence and exclusivity. Concurrence can promote all-or-none expression patterns of related genes and underlie alternative physiological states. Conversely, exclusive gene families express the same or similar number of homologs in each cell, allowing a broad repertoire of cell identities to be generated. We show that gene families involved in the cell-cycle and antigen presentation are expressed concurrently. Concurrence in the DNA replication complex MCM reflects the replicative status of cells, including cell lines and cancer-derived organoids. Exclusive expression requires precise regulatory mechanism, but cancer cells retain this form of control for ion homeostasis and extend it to gene families involved in cell migration. Thus, the cell adhesion-based identity of healthy cells is transformed to an identity based on migration in the population of cancer cells, reminiscent of epithelial-mesenchymal transition.

Applying single-cell and single-nucleus genomics to studies of cellular heterogeneity and cell fate transitions in the nervous system

Single-cell and single-nucleus genomic approaches can provide unbiased and multimodal insights. Here, we discuss what constitutes a molecular cell atlas and how to leverage single-cell omics data to generate hypotheses and gain insights into cell transitions in development and disease of the nervous system. We share points of reflection on what to consider during study design and implementation as well as limitations and pitfalls.

CopyMix: Mixture model based single-cell clustering and copy number profiling using variational inference

Investigating tumor heterogeneity using single-cell sequencing technologies is imperative to understand how tumors evolve since each cell subpopulation harbors a unique set of genomic features that yields a unique phenotype, which is bound to have clinical relevance. Clustering of cells based on copy number data obtained from single-cell DNA sequencing provides an opportunity to identify different tumor cell subpopulations. Accordingly, computational methods have emerged for single-cell copy number profiling and clustering; however, these two tasks have been handled sequentially by applying various ad-hoc pre- and post-processing steps; hence, a procedure vulnerable to introducing clustering artifacts. We avoid the clustering artifact issues in our method, CopyMix, a Variational Inference for a novel mixture model, by jointly inferring cell clusters and their underlying copy number profile. Our probabilistic graphical model is an improved version of the mixture of hidden Markov models, which is designed uniquely to infer single-cell copy number profiling and clustering. For the evaluation, we used likelihood-ratio test, CH index, Silhouette, V-measure, total variation scores. CopyMix performs well on both biological and simulated data. Our favorable results indicate a considerable potential to obtain clinical impact by using CopyMix in studies of cancer tumor heterogeneity.

Normal breast tissues harbour rare populations of aneuploid epithelial cells

Aneuploid epithelial cells are common in breast cancer1,2; however, their presence in normal breast tissues is not well understood. To address this question, we applied single-cell DNA sequencing to profile copy number alterations in 83,206 epithelial cells from the breast tissues of 49 healthy women, and we applied single-cell DNA and assay for transposase-accessible chromatin sequencing co-assays to the samples of 19 women. Our data show that all women harboured rare aneuploid epithelial cells (median 3.19%) that increased with age. Many aneuploid epithelial cells (median 82.22%) in normal breast tissues underwent clonal expansions and harboured copy number alterations reminiscent of invasive breast cancers (gains of 1q; losses of 10q, 16q and 22q). Co-assay profiling showed that the aneuploid cells were mainly associated with the two luminal epithelial lineages, and spatial mapping showed that they localized in ductal and lobular structures with normal histopathology. Collectively, these data show that even healthy women have clonal expansions of rare aneuploid epithelial cells in their breast tissues.

Integrated analysis of single-cell RNA-seq and bulk RNA-seq revealed key genes for bone metastasis and chemoresistance in prostate cancer

BACKGROUND

Prostate cancer (PCa) is a serious malignancy. The main causes of PCa aggravation and death are unexplained resistance to chemotherapy and bone metastases.

OBJECTIVE

This study aimed to investigate the molecular mechanisms associated with the dynamic processes of progression, bone metastasis, and chemoresistance in PCa.

METHODS

Through comprehensive analysis of single-cell RNA sequencing (scRNA-seq) data, Gene Expression Omnibus (GEO) tumor progression and metastasis-related genes were identified. These genes were subjected to lasso regression modeling using the Cancer Genome Atlas (TCGA) database. Tartrate-resistant acid phosphatase (TRAP) staining and real-time quantitative PCR (RT-qPCR) were used to evaluate osteoclast differentiation. CellMiner was used to confirm the effect of LDHA on chemoresistance. Finally, the relationship between LDHA and chemoresistance was verified using doxorubicin-resistant PCa cell lines.

RESULTS

7928 genes were identified as genes related to tumor progression and metastasis. Of these, 7 genes were found to be associated with PCa prognosis. The scRNA-seq and TCGA data showed that the expression of LDHA was higher in tumors and associated with poor prognosis of PCa. In addition, upregulation of LDHA in PCa cells induces osteoclast differentiation. Additionally, high LDHA expression was associated with resistance to Epirubicin, Elliptinium acetate, and doxorubicin. Cellular experiments demonstrated that LDHA knockdown inhibited doxorubicin resistance in PCa cells.

CONCLUSIONS

LDHA may play a potential contributory role in PCa initiation and development, bone metastasis, and chemoresistance. LDHA is a key target for the treatment of PCa.

EGFR and MUC1 as dual-TAA drug targets for lung cancer and colorectal cancer

Background

Epidermal growth factor receptor (EGFR) is a key protein in cellular signaling that is overexpressed in many human cancers, making it a compelling therapeutic target. On-target severe skin toxicity has limited its clinical application. Dual-targeting therapy represents a novel approach to overcome the challenges of EGFR-targeted therapies.

Methods

A single-cell tumor-normal RNA transcriptomic meta-atlas of lung adenocarcinoma (LUAD) and normal lung tissues was constructed from published data. Tumor associated antigens (TAAs) were screened from the genes which were expressed on cell surface and could distinguish cancer cells from normal cells. Expression of MUC1 and EGFR in tumors and normal tissues was detected by immunohistochemistry (IHC), bulk transcriptomic and single-cell transcriptomic analyses. RNA cut-off values were calculated using paired analysis of RNA sequencing and IHC in patient-derived tumor xenograft samples. They were used to estimate the abundance of EGFR- and MUC-positive subjects in The Cancer Genome Atlas Program (TCGA) database. Survival analysis of EGFR and MUC1 expression was carried out using the transcription and clinical data from TCGA.

Results

A candidate TAA target, transmembrane glycoprotein mucin 1 (MUC1), showed strong expression in cancer cells and low expression in normal cells. Single-cell analysis suggested EGFR and MUC1 together had better tumor specificity than the combination of EGFR with other drug targets. IHC data confirmed that EGFR and MUC1 were highly expressed on LUAD and colorectal cancer (CRC) clinical samples but not on various normal tissues. Notably, co-expression of EGFR and MUC1 was observed in 98.4% (n=64) of patients with LUAD and in 91.6% (n=83) of patients with CRC. It was estimated that EGFR and MUC1 were expressed in 97.5% of LUAD samples in the TCGA dataset. Besides, high expression of EGFR and MUC1 was significantly associated with poor prognosis of LUAD and CRC patients.

Conclusions

Single-cell RNA, bulk RNA and IHC data demonstrated the high expression levels and co-expression patterns of EGFR and MUC1 in tumors but not normal tissues. Therefore, it is a promising TAA combination for therapeutic targeting which could enhance on-tumor efficacy while reducing off-tumor toxicity.

Integrating multi-omics techniques and in vitro experiments reveals that GLRX3 regulates the immune microenvironment and promotes hepatocellular carcinoma cell proliferation and invasion through iron metabolism pathways

Background

Hepatocellular carcinoma (HCC) is a common malignancy worldwide, and its development is closely related to abnormalities in iron metabolism. This study aims to systematically analyze changes in iron metabolism in the tumor microenvironment of HCC using single-cell sequencing technology, and investigate the potential mechanisms by which iron metabolism regulation affects the survival of liver cancer patients.

Materials and methods

Single-cell sequencing data from hepatocellular carcinoma patients were obtained from the GEO database. By iron metabolism genomic scoring, we assessed differences in iron metabolism levels in hepatocellular carcinoma samples. By cell communication analysis as well as GO and KEGG enrichment analysis, we determined the functional role of iron metabolism in different cell types. We used survival analysis and Kaplan-Meier curves to assess the impact of iron metabolism levels on patient prognosis. In addition, we identified and analyzed the expression profile of the GLRX3 gene, investigated its key regulatory role in iron metabolism, and validated its clinical value as a prognostic marker. Finally, we explored the effect of GLRX3 on hepatocellular carcinoma phenotype by in vitro experiments such as PCR, transwell, CCK8, and wound healing assay.

Results

Bioinformatics results and experimental validation confirmed the dysregulation of iron metabolism in the development of hepatocellular carcinoma, revealing iron's regulatory influence across various cell types. Additionally, GLRX3 was identified as a key regulatory factor in iron metabolism, and the mechanism by which GLRX3 regulates tumor cell proliferation and immune evasion was determined. Furthermore, experiments verified GLRX3's role in facilitating tumor cell proliferation and invasion.

Conclusion

This study highlights the critical role of iron metabolism in the progression of hepatocellular carcinoma, particularly the regulatory mechanism of the GLRX3 gene in tumor cell proliferation and immune evasion. Iron metabolism abnormalities are not only drivers of liver cancer development but also key indicators of patient prognosis.

Single cell-spatial transcriptomics and bulk multi-omics analysis of heterogeneity and ecosystems in hepatocellular carcinoma

This study profiled global single cell-spatial-bulk transcriptome landscapes of hepatocellular carcinoma (HCC) ecosystem from six HCC cases and a non-carcinoma liver control donor. We discovered that intratumoral heterogeneity mainly derived from HCC cells diversity and pervaded the genome-transcriptome-proteome-metabolome network. HCC cells are the core driving force of taming tumor-associated macrophages (TAMs) with pro-tumorigenic phenotypes for favor its dominant growth. Remarkably, M1-types TAMs had been characterized by disturbance of metabolism, poor antigen-presentation and immune-killing abilities. Besides, we found simultaneous cirrhotic and HCC lesions in an individual patient shared common origin and displayed parallel clone evolution via driving disparate immune reprograms for better environmental adaptation. Moreover, endothelial cells exhibited phenotypically conserved but executed differential functions in a space-dependent manner. Further, the spatiotemporal traits of rapid recurrence niche genes were identified and validated by immunohistochemistry. Our data unravels the great significance of HCC cells in shaping vibrant tumor ecosystems corresponding to clinical scenarios.

In vivo models of subclonal oncogenesis and dependency in hematopoietic malignancy

Cancer evolution is a multifaceted process leading to dysregulation of cellular expansion and differentiation through somatic mutations and epigenetic dysfunction. Clonal expansion and evolution is driven by cell-intrinsic and -extrinsic selective pressures, which can be captured with increasing resolution by single-cell and bulk DNA sequencing. Despite the extensive genomic alterations revealed in profiling studies, there remain limited experimental systems to model and perturb evolutionary processes. Here, we integrate multi-recombinase tools for reversible, sequential mutagenesis from premalignancy to leukemia. We demonstrate that inducible Flt3 mutations differentially cooperate with Dnmt3a, Idh2, and Npm1 mutant alleles, and that changing the order of mutations influences cellular and transcriptional landscapes. We next use a generalizable, reversible approach to demonstrate that mutation reversion results in rapid leukemic regression with distinct differentiation patterns depending upon co-occurring mutations. These studies provide a path to experimentally model sequential mutagenesis, investigate mechanisms of transformation and probe oncogenic dependency in disease evolution.

Integrating multi-omics data reveals neuroblastoma subtypes in the tumor microenvironment

Neuroblastoma (NB) is a severe pediatric tumor originating from the developing sympathetic nervous system, characterized by diverse clinical outcomes, including spontaneous regression and aggressive metastasis. This variability suggests the existence of different NB subtypes, necessitating accurate classification for effective targeted treatment. In this study, we employed the similarity network fusion (SNF) algorithm and identified three NB subtypes, including mesenchymal-like (MES), MYCN-like (MYCN), and neurogenic-like (Neurogenic). The MES subtype exhibited the highest activation of immune-related pathways. The MYCN subtype demonstrated the worst prognosis, with enrichment in cell growth and proliferation pathways. Conversely, the Neurogenic subtype showed the best prognosis, with enrichment in sympathetic nervous system development processes. Through single-cell RNA sequencing (scRNA-seq) analysis, we examined the tumor microenvironments of these distinct NB subtypes, revealing divergent differentiation trajectories for adrenergic cells within the MYCN and Neurogenic subtypes. We also identified a significant presence of naïve T cells in the MES subtype, as well as mesenchymal cell subtypes associated with the unique plasticity observed in both the MES and MYCN subtypes. Drug sensitivity prediction analysis suggested that the MES subtype may respond favorably to MEK inhibitors, while the MYCN subtype may be susceptible to Bcl-2 inhibitors. Our integrative multi-omics approach enabled precise stratification of NB into biologically distinct subtypes, potentially facilitating the development of subtype-specific therapeutic strategies for improved patient management and survival outcomes.

Unveiling the influence of tumor and immune signatures on immune checkpoint therapy in advanced lung cancer

This study investigates the variability among patients with non-small cell lung cancer (NSCLC) in their responses to immune checkpoint inhibitors (ICIs). Recognizing that patients with advanced-stage NSCLC rarely qualify for surgical interventions, it becomes crucial to identify biomarkers that influence responses to ICI therapy. We conducted an analysis of single-cell transcriptomes from 33 lung cancer biopsy samples, with a particular focus on 14 core samples taken before the initiation of palliative ICI treatment. Our objective was to link tumor and immune cell profiles with patient responses to ICI. We discovered that ICI non-responders exhibited a higher presence of CD4+ regulatory T cells, resident memory T cells, and TH17 cells. This contrasts with the diverse activated CD8+ T cells found in responders. Furthermore, tumor cells in non-responders frequently showed heightened transcriptional activity in the NF-kB and STAT3 pathways, suggesting a potential inherent resistance to ICI therapy. Through the integration of immune cell profiles and tumor molecular signatures, we achieved an discriminative power (area under the curve [AUC]) exceeding 95% in identifying patient responses to ICI treatment. These results underscore the crucial importance of the interplay between tumor and immune microenvironment, including within metastatic sites, in affecting the effectiveness of ICIs in NSCLC.

Integrated analysis of single-cell, spatial and bulk RNA-sequencing identifies a cell-death signature for predicting the outcomes of head and neck cancer

Background

Cell death plays an essential role in carcinogenesis, but its function in the recurrence and postoperative prognosis of head and neck cancer (HNC), which ranks as the 7th most common malignancy globally, remains unclear.

Methods

Data from five main subtypes of HNC related single-cell RNA sequencing (scRNA-seq) were recruited to establish a single-cell atlas, and the distribution of cell death models (CDMs) across different tissues as well as cell subtypes were analyzed. Bulk RNA-seq from the Cancer Genome Atlas Program (TCGA) dataset was subjected to a machine learning-based integrative procedure for constructing a consensus cell death-related signature risk score (CDRscore) model and validated by external data. The biofunctions including different expression analysis, immune cell infiltration, genomic mutations, enrichment analysis as well as cellchat analysis were compared between the high- and low- risk score groups categorized by this CDRscore model. Finally, samples from laryngeal squamous cell cancer (LSCC) were conducted by spatial transcriptomics (ST) to further validate the results of CDRscore model.

Results

T cells from HNC patients manifested the highest levels of cell death while HPV infection attenuates malignant cell death based on single-cell atlas. CDMs are positively correlated with the tumor-cell stemness, immune-related score and T cells are infiltrated. A CDRscore model was established based on the transcription of ten cell death prognostic genes (MRPL10, DDX19A, NDFIP1, PCMT1, HPRT1, SLC2A3, EFNB2, HK1, BTG3 and MAP2K7). It functions as an independent prognostic factor for overall survival in HNC and displays stable and powerful performance validated by GSE41613 and GSE65858 datasets. Patients in high CDRscore manifested worse overall survival, more active of epithelial mesenchymal transition, TGF-β-related pathways and hypoxia, higher transcription of T cell exhausted markers, and stronger TP53 mutation. ST from LSCC showed that spots with high-risk scores were colocalized with TGF-β and the proliferating malignant cells, additionally, the risk scores have a negative correlation with TCR signaling but positive association with LAG3 transcription.

Conclusion

The CDRscore model could be utilized as a powerful prognostic indicator for HNC.

De novo detection of somatic variants in high-quality long-read single-cell RNA sequencing data

In cancer, genetic and transcriptomic variations generate clonal heterogeneity, leading to treatment resistance. Long-read single-cell RNA sequencing (LR scRNA-seq) has the potential to detect genetic and transcriptomic variations simultaneously. Here, we present LongSom, a computational workflow leveraging high-quality LR scRNA-seq data to call de novo somatic single-nucleotide variants (SNVs), including in mitochondria (mtSNVs), copy-number alterations (CNAs), and gene fusions, to reconstruct the tumor clonal heterogeneity. Before somatic variants calling, LongSom re-annotates marker gene based cell types using cell mutational profiles. LongSom distinguishes somatic SNVs from noise and germline polymorphisms by applying an extensive set of hard filters and statistical tests. Applying LongSom to human ovarian cancer samples, we detected clinically relevant somatic SNVs that were validated against matched DNA samples. Leveraging somatic SNVs and fusions, LongSom found subclones with different predicted treatment outcomes. In summary, LongSom enables de novo variant detection without the need for normal samples, facilitating the study of cancer evolution, clonal heterogeneity, and treatment resistance.

Proteome-wide copy-number estimation from transcriptomics

Protein copy numbers constrain systems-level properties of regulatory networks, but proportional proteomic data remain scarce compared to RNA-seq. We related mRNA to protein statistically using best-available data from quantitative proteomics and transcriptomics for 4366 genes in 369 cell lines. The approach starts with a protein's median copy number and hierarchically appends mRNA-protein and mRNA-mRNA dependencies to define an optimal gene-specific model linking mRNAs to protein. For dozens of cell lines and primary samples, these protein inferences from mRNA outmatch stringent null models, a count-based protein-abundance repository, empirical mRNA-to-protein ratios, and a proteogenomic DREAM challenge winner. The optimal mRNA-to-protein relationships capture biological processes along with hundreds of known protein-protein complexes, suggesting mechanistic relationships. We use the method to identify a viral-receptor abundance threshold for coxsackievirus B3 susceptibility from 1489 systems-biology infection models parameterized by protein inference. When applied to 796 RNA-seq profiles of breast cancer, inferred copy-number estimates collectively re-classify 26-29% of luminal tumors. By adopting a gene-centered perspective of mRNA-protein covariation across different biological contexts, we achieve accuracies comparable to the technical reproducibility of contemporary proteomics.

Computational methods for allele-specific expression in single cells

Allele-specific expression (ASE) is a powerful signal that can be used to investigate multiple molecular mechanisms, such as cis-regulatory effects and imprinting. Single-cell RNA-sequencing (scRNA-seq) enables ASE characterization at the resolution of individual cells. In this review, we highlight the computational methods for processing and analyzing single-cell ASE data. We first describe a bioinformatics pipeline to obtain ASE counts from raw reads synthesized from previous literature. We then discuss statistical methods for detecting allelic imbalance and its variability across conditions using scRNA-seq data. In addition, we describe other methods that use single-cell ASE to address specific biological questions. Finally, we discuss future directions and emphasize the need for an integrated, optimized bioinformatics pipeline, and further development of statistical methods for different technologies.

NBAtlas: A harmonized single-cell transcriptomic reference atlas of human neuroblastoma tumors

Neuroblastoma, a rare embryonic tumor arising from neural crest development, is responsible for 15% of pediatric cancer-related deaths. Recently, several single-cell transcriptome studies were performed on neuroblastoma patient samples to investigate the cell of origin and tumor heterogeneity. However, these individual studies involved a small number of tumors and cells, limiting the conclusions that could be drawn. To overcome this limitation, we integrated seven single-cell or single-nucleus datasets into a harmonized cell atlas covering 362,991 cells across 61 patients. We use this atlas to decipher the transcriptional landscape of neuroblastoma at single-cell resolution, revealing associations between transcriptomic profiles and clinical outcomes within the tumor compartment. In addition, we characterize the complex immune-cell landscape and uncover considerable heterogeneity among tumor-associated macrophages. Finally, we showcase the utility of our atlas as a resource by expanding it with additional data and using it as a reference for data-driven cell-type annotation.