Single-cell mRNA sequencing identifies subclonal heterogeneity in anti-cancer drug responses of lung adenocarcinoma cells

Single-cell RNA-seq reveals diverse molecular signatures associated with Methotrexate resistance in primary central nervous system lymphoma cells

PURPOSE

Methotrexate is one of the most essential single agents in patients with primary central nervous system lymphoma (PCNSL). However, 25-50% result in relapse with a poor prognosis. Therefore, studies on methotrexate resistance are warranted to explore salvage chemotherapy for recurrent PCNSL. Single-cell sequence analysis enables the characterization of novel cell types and provides a precise understanding of cancer biology.

METHODS

Single-cell sequence analysis of parental and methotrexate-resistant PCNSL cells was performed. We used a Weighted Gene Co-expression Network Analysis to identify groups of significantly connected genes.

RESULTS

We identified consensus modules in both the HKBML and TK datasets. HLA-DRβ1, HLA-DQβ1,and SNRPG were hub genes those detected in both datasets revealed by network analysis. Cyclosporine A was selected as the candidate drug for treating methotrexate-resistant cells.

CONCLUSION

The results of the present study characterized the methotrexate resistance-related signaling pathways in cultured PCNSL cells. Overall, these results may account for variations in treatment responses and lead potential novel therapeutic strategies for patients with PCNSL.

Patient-derived xenograft model in cancer: establishment and applications

The patient-derived xenograft (PDX) model is a crucial in vivo model extensively employed in cancer research that has been shown to maintain the genomic characteristics and pathological structure of patients across various subtypes, metastatic, and diverse treatment histories. Various treatment strategies utilized in PDX models can offer valuable insights into the mechanisms of tumor progression, drug resistance, and the development of novel therapies. This review provides a comprehensive overview of the establishment and applications of PDX models. We present an overview of the history and current status of PDX models, elucidate the diverse construction methodologies employed for different tumors, and conduct a comparative analysis to highlight the distinct advantages and limitations of this model in relation to other in vivo models. The applications are elucidated in the domain of comprehending the mechanisms underlying tumor development and cancer therapy, which highlights broad applications in the fields of chemotherapy, targeted therapy, delivery systems, combination therapy, antibody-drug conjugates and radiotherapy. Furthermore, the combination of the PDX model with multiomics and single-cell analyses for cancer research has also been emphasized. The application of the PDX model in clinical treatment and personalized medicine is additionally emphasized.

Non-coding RNAs in oral cancer: Emerging biomarkers and therapeutic frontier

Around the world, oral cancer (OC) is a major public health problem, resulting in a significant number of deaths each year. Early detection and treatment are crucial for improving patient outcomes. Recent progress in DNA sequencing and transcriptome profiling has revealed extensive non-coding RNAs (ncRNAs) transcription, underscoring their regulatory importance. NcRNAs influence genomic transcription and translation and molecular signaling pathways, making them valuable for various clinical applications. Combining spatial transcriptomics (ST) and spatial metabolomics (SM) with single-cell RNA sequencing provides deeper insights into tumor microenvironments, enhancing diagnostic and therapeutic precision for OC. Additionally, the exploration of salivary biomarkers offers a non-invasive diagnostic avenue. This article explores the potential of ncRNAs as diagnostic and therapeutic tools for OC.

Applications of single-cell analysis in immunotherapy for lung cancer: Current progress, new challenges and expectations

BACKGROUND

Lung cancer is a prevalent form of cancer worldwide, presenting a substantial risk to human well-being. Lung cancer is classified into two main types: non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC). The advancement of tumor immunotherapy, specifically immune checkpoint inhibitors and adaptive T-cell therapy, has encountered substantial obstacles due to the rapid progression of SCLC and the metastasis, recurrence, and drug resistance of NSCLC. These challenges are believed to stem from the tumor heterogeneity of lung cancer within the tumor microenvironment.

AIM OF REVIEW

This review aims to comprehensively explore recent strides in single-cell analysis, a robust sequencing technology, concerning its application in the realm of tumor immunotherapy for lung cancer. It has been effectively integrated with transcriptomics, epigenomics, genomics, and proteomics for various applications. Specifically, these techniques have proven valuable in mapping the transcriptional activity of tumor-infiltrating lymphocytes in patients with NSCLC, identifying circulating tumor cells, and elucidating the heterogeneity of the tumor microenvironment.

KEY SCIENTIFIC CONCEPTS OF REVIEW

The review emphasizes the paramount significance of single-cell analysis in mapping the immune cells within NSCLC patients, unveiling circulating tumor cells, and elucidating the tumor microenvironment heterogeneity. Notably, these advancements highlight the potential of single-cell analysis to revolutionize lung cancer immunotherapy by characterizing immune cell fates, improving therapeutic strategies, and identifying promising targets or prognostic biomarkers. It is potential to unravel the complexities within the tumor microenvironment and enhance treatment strategies marks a significant step towards more effective therapies and improved patient outcomes.

Graph-Based Spatial Proximity of Super-Resolved Protein-Protein Interactions Predicts Cancer Drug Responses in Single Cells

Purpose

Current bulk molecular assays fail to capture spatial signaling activities in cancers, limiting our understanding of drug resistance mechanisms. We developed a graph-based super-resolution protein-protein interaction (GSR-PPI) technique to spatially resolve single-cell signaling networks and evaluate whether higher resolution microscopy enhances the biological study of PPIs using deep learning classification models.

Methods

Single-cell spatial proximity ligation assays (PLA, ≤ 9 PPI pairs) were conducted on EGFR mutant (EGFRm) PC9 and HCC827 cells (>10,000 cells) treated with 100 nM Osimertinib. Multiplexed PPI images were obtained using wide-field and super-resolution microscopy (Zeiss Airyscan, SRRF). Graph-based deep learning models analyzed subcellular protein interactions to classify drug treatment states and test GSR-PPI on clinical tissue samples. GSR-PPI triangulated PPI nodes into 3D relationships, predicting drug treatment labels. Biological discriminative ability (BDA) was evaluated using accuracy, AUC, and F1 scores. The method was also applied to 3D spatial proteomic molecular pixelation (PixelGen) data from T cells.

Results

GSR-PPI outperformed baseline models in predicting drug responses from multiplexed PPI imaging in EGFRm cells. Super-resolution data significantly improved accuracy over localized wide-field imaging. GSR-PPI classified drug treatment states in cancer cells and human lung tissues, with performance improving as imaging resolution increased. It differentiated single and combination drug therapies in HCC827 cells and human tissues. Additionally, GSR-PPI accurately distinguished T-cell stimulation states, identifying key nodes such as CD44, CD45, and CD54.

Conclusion

The GSR-PPI framework provides valuable insights into spatial protein interactions and drug responses, enhancing the study of signaling biology and drug resistance.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12195-024-00822-1.

Revelation of comprehensive cell profiling of primary and metastatic tumour ecosystems in oral squamous cell carcinoma by single-cell transcriptomic analysis

Background: The analysis of single-cell transcriptome profiling of tumour tissue isolates helps to identify heterogeneous tumour cells, neighbouring stromal cells and immune cells. Local metastasis of lymph nodes is the most dominant and influential biological behaviors of oral squamous cell carcinoma (OSCC) in terms of treatment prognosis. Understanding metastasis initiation and progression is important for the discovery of new treatments for OSCC and prediction of clinical responses to immunotherapy. However, the identity of metastasis-initiating cells in human OSCC remains elusive, and whether metastases are hierarchically organized is unknown. Therefore, this study was conducted to understand the cellular origins and gene expression signature of OSCC at the single-cell level. Methods: Single-cell RNA sequencing (scRNA-seq) was used to analyze cells from tissue of para-carcinoma (PCA: adjacent normal tissue not less than 2 cm from the tumour), carcinoma (CA), lymph node metastasis (LNM) from patients with OSCC and PCA and CA tissue from patients with second primary OSCC (SPOSCC) after radiotherapy of nasopharyngeal carcinoma (NPC). The cell types and their underlying functions were classified. The comparisons were then conducted between the homology and heterogeneity from cell types and both conservative and heterogeneous aspects of evolution were identified. Immunohistochemistry was performed to verify the makers of cell clusters and the expression level of novel genes. Results: A single-cell transcriptomic map of OSCC was created, including 16 clusters of PCA cells, 17 clusters of CA cells, 14 clusters of left LNM cells, and 14 clusters of right LNM cells. We also discovered two novel types of cells including CD1C-CD141-dendritic cells and CD1C+_B dendritic cells. Most of the non-cancer cells are immune cells, with two distinct clusters of T lymphocytes, B lymphocytes, CD1C-CD141-dendritic cells+ and CD1C+_B dendritic cells. We also classified cells into 15 clusters for SPOSCC after radiotherapy of NPC. Determining the upregulated expression levels of IL1RN and C15orf48 as novel markers using immunohistochemistry facilitated the correct classification of OSCC including SPOSCC after radiotherapy of NPC and the prediction of their prognosis. Conclusions: The findings provided an unprecedented and valuable view of the functional states and heterogeneity of cell populations in LNM of OSCC and SPOSCC after radiotherapy of NPC at single-cell genomic resolution. Moreover, this transcriptomic map discovered new cell types in mouth, and novel tumour cell-specific markers/oncogene.

Know your molecule: pharmacological characterization of drug candidates to enhance efficacy and reduce late-stage attrition

Despite advances in chemical, computational and biological sciences, the rate of attrition of drug candidates in clinical development is still high. A key point in the small-molecule discovery process that could provide opportunities to help address this challenge is the pharmacological characterization of hit and lead compounds, culminating in the selection of a drug candidate. Deeper characterization is increasingly important, because the 'quality' of drug efficacy, at least for G protein-coupled receptors (GPCRs), is now understood to be much more than activation of commonly evaluated pathways such as cAMP signalling, with many more 'efficacies' of ligands that could be harnessed therapeutically. Such characterization is being enabled by novel assays to characterize the complex behaviour of GPCRs, such as biased signalling and allosteric modulation, as well as advances in structural biology, such as cryo-electron microscopy. This article discusses key factors in the assessments of the pharmacology of hit and lead compounds in the context of GPCRs as a target class, highlighting opportunities to identify drug candidates with the potential to address limitations of current therapies and to improve the probability of them succeeding in clinical development.

scHD4E: Novel ensemble learning-based differential expression analysis method for single-cell RNA-sequencing data

Differential expression (DE) analysis between cell types for scRNA-seq data by capturing its complicated features is crucial. Recently, different methods have been developed for targeting the scRNA-seq data analysis based on different modeling frameworks, assumptions, strategies and test statistic in considering various data features. The scDEA is an ensemble learning-based DE analysis method developed recently, yielding p-values using Lancaster's combination, generated by 12 individual DE analysis methods, and producing more accurate and stable results than individual methods. The objective of our study is to propose a new ensemble learning-based DE analysis method, scHD4E, using top performers in only 4 separate methods. The top performer 4 methods have been selected through an evaluation process using six real scRNA-seq data sets. We conducted comprehensive experiments for five experimental data sets to evaluate our proposed method based on the sample size effects, batch effects, type I error control, gene ontology enrichment analysis, runtime, identified matched DE genes, and semantic similarity measurement between methods. We also perform similar analyses (except the last 3 terms) and compute performance measures like accuracy, F1 score, Mathew's correlation coefficient etc. for a simulated data set. The results show that scHD4E is performs better than all the individual and scDEA methods in all the above perspectives. We expect that scHD4E will serve the modern data scientists for detecting the DEGs in scRNA-seq data analysis. To implement our proposed method, a Github R package scHD4E and its shiny application has been developed, and available in the following links: https://github.com/bbiswas1989/scHD4E and https://github.com/bbiswas1989/scHD4E-Shiny.

Advancing cancer therapeutics: Integrating scalable 3D cancer models, extracellular vesicles, and omics for enhanced therapy efficacy

Cancer remains as one of the highest challenges to human health. However, anticancer drugs exhibit one of the highest attrition rates compared to other therapeutic interventions. In part, this can be attributed to a prevalent use of in vitro models with limited recapitulative potential of the in vivo settings. Three dimensional (3D) models, such as tumor spheroids and organoids, offer many research opportunities to address the urgent need in developing models capable to more accurately mimic cancer biology and drug resistance profiles. However, their wide adoption in high-throughput pre-clinical studies is dependent on scalable manufacturing to support large-scale therapeutic drug screenings and multi-omic approaches for their comprehensive cellular and molecular characterization. Extracellular vesicles (EVs), which have been emerging as promising drug delivery systems (DDS), stand to significantly benefit from such screenings conducted in realistic cancer models. Furthermore, the integration of these nanomedicines with 3D cancer models and omics profiling holds the potential to deepen our understanding of EV-mediated anticancer effects. In this chapter, we provide an overview of the existing 3D models used in cancer research, namely spheroids and organoids, the innovations in their scalable production and discuss how omics can facilitate the implementation of these models at different stages of drug testing. We also explore how EVs can advance drug delivery in cancer therapies and how the synergy between 3D cancer models and omics approaches can benefit in this process.

Pyroptosis-related long-noncoding RNA signature predicting survival and immunotherapy efficacy in patients with lung squamous cell carcinoma

Pyroptosis-related long-noncoding RNAs (PRlncRNAs) play an important role in cancer progression. However, their role in lung squamous cell carcinoma (LUSC) is unclear. A risk model was constructed using the least absolute shrinkage and selection operator (LASSO) Cox regression analysis based on RNA sequencing data from The Cancer Genome Atlas database. The LUSC cohort was divided into high- and low-risk groups based on the median risk score. For the prognostic value of the model, the Kaplan-Meier analysis, log-rank test, and Cox regression analysis were performed. A nomogram was constructed to predict the prognosis of patients, using a risk score and clinical parameters such as age, sex, clinical stage, and tumor node metastasis classification (TNM) stage. Afterwards, six common algorithms were employed to assess the invasion of immune cells. The Gene Set Enrichment Analysis (GSEA) was conducted to identify differences between patients at high and low risk. Furthermore, the pRRophetic package was employed to forecast the half-maximal inhibitory doses of prevalent chemotherapeutic drugs, while the tumor immune dysfunction and exclusion score was computed to anticipate the response to immunotherapy. The expression levels of the seven PRlncRNAs were examined in both LUSC and normal lung epithelial cell lines using RT-qPCR. Proliferation, migration, and invasion assays were also carried out to investigate the role of MIR193BHG in LUSC cells. Patients in the low-risk group showed prolonged survival in the total cohort or subgroup analysis. The Cox regression analysis showed that the risk model could act as an independent prognostic factor for patients with LUSC. The results of GSEA analysis revealed that the high-risk group showed enrichment of cytokine pathways, Janus tyrosine kinase/signal transducer and activator of the transcription signalling pathway, and Toll-like receptor pathway. Conversely, the low-risk group showed enrichment of several gene repair pathways. Furthermore, the risk score was positively correlated with immune cell infiltration. Moreover, patients in the high-risk category showed reduced responsiveness to conventional chemotherapeutic medications and immunotherapy. The majority of the long noncoding RNAs in the risk model were confirmed to be overexpressed in LUSC cell lines compared to normal lung epithelial cell lines by in vitro tests. Further studies have shown that downregulating the expression of MIR193BHG may inhibit the growth, movement, and infiltration capabilities of LUSC cells, whereas increasing the expression of MIR193BHG could enhance these malignant tendencies. This study found that PRlncRNAs were linked to the prognosis of LUSC patients. The risk model, evaluated across various clinical parameters and treatment modalities, shows potential as a future reference for clinical applications.

Integration of Pan-Cancer Cell Line and Single-Cell Transcriptomic Profiles Enables Inference of Therapeutic Vulnerabilities in Heterogeneous Tumors

Single-cell RNA sequencing (scRNA-seq) greatly advanced the understanding of intratumoral heterogeneity by identifying distinct cancer cell subpopulations. However, translating biological differences into treatment strategies is challenging due to a lack of tools to facilitate efficient drug discovery that tackles heterogeneous tumors. Developing such approaches requires accurate prediction of drug response at the single-cell level to offer therapeutic options to specific cell subpopulations. Here, we developed a transparent computational framework (nicknamed scIDUC) to predict therapeutic efficacies on an individual cell basis by integrating single-cell transcriptomic profiles with large, data-rich pan-cancer cell line screening data sets. This method achieved high accuracy in separating cells into their correct cellular drug response statuses. In three distinct prospective tests covering different diseases (rhabdomyosarcoma, pancreatic ductal adenocarcinoma, and castration-resistant prostate cancer), the predicted results using scIDUC were accurate and mirrored biological expectations. In the first two tests, the framework identified drugs for cell subpopulations that were resistant to standard-of-care (SOC) therapies due to intrinsic resistance or tumor microenvironmental effects, and the results showed high consistency with experimental findings from the original studies. In the third test using newly generated SOC therapy-resistant cell lines, scIDUC identified efficacious drugs for the resistant line, and the predictions were validated with in vitro experiments. Together, this study demonstrates the potential of scIDUC to quickly translate scRNA-seq data into drug responses for individual cells, displaying the potential as a tool to improve the treatment of heterogenous tumors.

SIGNIFICANCE

A versatile method that infers cell-level drug response in scRNA-seq data facilitates the development of therapeutic strategies to target heterogeneous subpopulations within a tumor and address issues such as treatment failure and resistance.

An Extracellular Matrix Overlay Model for Bioluminescence Microscopy to Measure Single-Cell Heterogeneous Responses to Antiandrogens in Prostate Cancer Cells

Prostate cancer (PCa) displays diverse intra-tumoral traits, impacting its progression and treatment outcomes. This study aimed to refine PCa cell culture conditions for dynamic monitoring of androgen receptor (AR) activity at the single-cell level. We introduced an extracellular matrix-Matrigel (ECM-M) culture model, enhancing cellular tracking during bioluminescence single-cell imaging while improving cell viability. ECM-M notably tripled the traceability of poorly adherent PCa cells, facilitating robust single-cell tracking, without impeding substrate permeability or AR response. This model effectively monitored AR modulation by antiandrogens across various PCa cell lines. Single-cell imaging unveiled heterogeneous antiandrogen responses within populations, correlating non-responsive cell proportions with drug IC50 values. Integrating ECM-M culture with the PSEBC-TSTA biosensor enabled precise characterization of ARi responsiveness within diverse cell populations. Our ECM-M model stands as a promising tool to assess heterogeneous single-cell treatment responses in cancer, offering insights to link drug responses to intracellular signaling dynamics. This approach enhances our comprehension of the nuanced and dynamic nature of PCa treatment responses.

The landscape of immune checkpoint-related long non-coding RNAs core regulatory circuitry reveals implications for immunoregulation and immunotherapy responses

Long non-coding RNAs (lncRNAs) could modulate expression of immune checkpoints (ICPs) by cooperating with immunity genes in tumor immunization. However, precise functions in immunity and potential for predicting ICP inhibitors (ICI) response have been described for only a few lncRNAs. Here we present an integrated framework that leverages network-based analyses and Bayesian network inference to identify the regulated relationships including lncRNA, ICP and immunity genes as ICP-related LncRNAs mediated Core Regulatory Circuitry Triplets (ICP-LncCRCTs) that can make robust predictions. Hub ICP-related lncRNAs such as MIR155HG and ADAMTS9-AS2 were highlighted to play central roles in immune regulation. Specific ICP-related lncRNAs could distinguish cancer subtypes. Moreover, the ICP-related lncRNAs are likely to significantly correlated with immune cell infiltration, MHC, CYT. Some ICP-LncCRCTs such as CXCL10-MIR155HG-ICOS could better predict one-, three- and five-year prognosis compared to single molecule in melanoma. We also validated that some ICP-LncCRCTs could effectively predict ICI-response using three kinds of machine learning algorithms follow five independent datasets. Specially, combining ICP-LncCRCTs with the tumor mutation burden (TMB) improves the prediction of ICI-treated melanoma patients. Altogether, this study will improve our grasp of lncRNA functions and accelerating discovery of lncRNA-based biomarkers in ICI treatment.

E2F7 serves as a potential prognostic biomarker for lung adenocarcinoma

E2F transcription factors (E2Fs) are a family of transcription factors critical regulators of the cell cycle, apoptosis, and differentiation, thus influencing tumorigenesis. However, the specific roles of E2Fs in lung adenocarcinoma (LUAD) remain unclear. Data from The Cancer Genome Atlas (TCGA) were used. R version. 4.0.3 and multiple databases (TIMER, cBioportal, gene expression profile interaction analysis [GEPIA], LinkedOmics, and CancerSEA) were utilized to investigate mRNA expression, mutational analysis, prognosis, clinical correlations, co-expressed gene, pathway and network, and single-cell analyses. Immunohistochemistry (IHC) confirmed that E2F transcription factor 7 (E2F7) correlated with LUAD. Among the E2Fs, E2F7 was identified by constructing a prognostic model most significantly associated with overall survival (OS) in LUAD patients. The univariate and multivariate Cox regression analyses showed that E2F7, p-T stage, and p-TNM stage were closely related to OS and progression-free survival (PFS) (P < .05) in LUAD. E2F 7/8 were also identified as significantly associated with tumor stage in the GEPIA database. Compared with paracancerous tissues, E2F7 was up-regulated in LUAD by IHC, and E2F7 might be positively correlated with larger tumors and higher TNM stages. E2F7 may primarily regulate DNA repair, damage, and cell cycle processes and thus affect LUAD tumorigenesis, invasion, and metastasis by LinkedOmics and CancerSEA. E2F7 serves as a potential prognostic biomarker for LUAD.

De novo identification of expressed cancer somatic mutations from single-cell RNA sequencing data

Identifying expressed somatic mutations from single-cell RNA sequencing data de novo is challenging but highly valuable. We propose RESA - Recurrently Expressed SNV Analysis, a computational framework to identify expressed somatic mutations from scRNA-seq data. RESA achieves an average precision of 0.77 on three in silico spike-in datasets. In extensive benchmarking against existing methods using 19 datasets, RESA consistently outperforms them. Furthermore, we applied RESA to analyze intratumor mutational heterogeneity in a melanoma drug resistance dataset. By enabling high precision detection of expressed somatic mutations, RESA substantially enhances the reliability of mutational analysis in scRNA-seq. RESA is available at https://github.com/ShenLab-Genomics/RESA .

Pan-cancer analysis identifies protein arginine methyltransferases PRMT1 and PRMT5 and their related signatures as markers associated with prognosis, immune profile, and therapeutic response in lung adenocarcinoma

Purpose

Protein arginine methyltransferases (PRMTs) regulate several signal transduction pathways involved in cancer progression. Recently, it has been reported that PRMTs are closely related to anti-tumor immunity; however, the underlying mechanisms have yet to be studied in lung adenocarcinoma (LUAD). In this study, we focused on PRMT1 and PRMT5, key members of the PRMT family. And their signatures in lung carcinoma associated with prognosis, immune profile, and therapeutic response including immunotherapy and radiotherapy were explored.

Methods

To understand the function of PRMT1 and PRMT5 in tumor cells, we examined the association between the expression of PRMT1 and PRMT5 and the clinical, genomic, and immune characteristics, as well as the sensitivity to immunotherapy and radiotherapy. Specifically, our investigation focused on the role of PRMT1 and PRMT5 in tumor progression, with particular emphasis on interferon-stimulated genes (ISGs) and the pathway of type I interferon. Furthermore, the influence of proliferation, migration, and invasion ability was investigated based on the expression of PRMT1 and PRMT5 in human lung adenocarcinoma cell lines.

Results

Through the examination of receiver operating characteristic (ROC) and survival studies, PRMT1 and PRMT5 were identified as potential biomarkers for the diagnosis and prognosis. Additionally, heightened expression of PRMT1 or PRMT5 was associated with immunosuppressive microenvironments. Furthermore, a positive correlation was observed between the presence of PRMT1 or PRMT5 with microsatellite instability, tumor mutational burden, and neoantigens in the majority of cancers. Moreover, the predictive potential of PRMT1 or PRMT5 in individuals undergoing immunotherapy has been acknowledged. Our study ultimately revealed that the inhibition of PRMT1 and PRMT5 in lung adenocarcinoma resulted in the activation of the cGAS-STING pathway, especially after radiation. Favorable prognosis was observed in lung adenocarcinoma patients receiving radiotherapy with reduced PRMT1 or PRMT5 expression. It was also found that the expression of PRMT1 and PRMT5 influenced proliferation, migration, and invasion of human lung adenocarcinoma cell lines.

Conclusion

The findings indicate that PRMT1 and PRMT5 exhibit potential as immune-related biomarkers for the diagnosis and prognosis of cancer. Furthermore, these biomarkers could be therapeutically targeted to augment the efficacy of immunotherapy and radiotherapy in lung adenocarcinoma.

Integrated single-cell and bulk RNA sequencing analysis identifies a neoadjuvant chemotherapy-related gene signature for predicting survival and therapy in breast cancer

Neoadjuvant chemotherapy (NAC) is a well-established treatment modality for locally advanced breast cancer (BC). However, it can also result in severe toxicities while controlling tumors. Therefore, reliable predictive biomarkers are urgently needed to objectively and accurately predict NAC response. In this study, we integrated single-cell and bulk RNA-seq data to identify nine genes associated with the prognostic response to NAC: NDRG1, CXCL14, HOXB2, NAT1, EVL, FBP1, MAGED2, AR and CIRBP. Furthermore, we constructed a prognostic risk model specifically linked to NAC. The clinical independence and generalizability of this model were effectively demonstrated. Additionally, we explore the underlying cancer hallmarks and microenvironment features of this NAC response-related risk score, and further assess the potential impact of risk score on drug response. In summary, our study constructed and validated a nine-gene signature associated with NAC prognosis, which was accomplished through the integration of single-cell and bulk RNA data. The results of our study are of crucial significance in the prediction of the efficacy of NAC in BC, and may have implications for the clinical management of this disease.

Cancer-Associated Fibroblasts in Gastrointestinal Cancers: Unveiling Their Dynamic Roles in the Tumor Microenvironment

Gastrointestinal cancers are highly aggressive malignancies with significant mortality rates. Recent research emphasizes the critical role of the tumor microenvironment (TME) in these cancers, which includes cancer-associated fibroblasts (CAFs), a key component of the TME that have diverse origins, including fibroblasts, mesenchymal stem cells, and endothelial cells. Several markers, such as α-SMA and FAP, have been identified to label CAFs, and some specific markers may serve as potential therapeutic targets. In this review article, we summarize the literature on the multifaceted role of CAFs in tumor progression, including their effects on angiogenesis, immune suppression, invasion, and metastasis. In addition, we highlight the use of single-cell transcriptomics to understand CAF heterogeneity and their interactions within the TME. Moreover, we discuss the dynamic interplay between CAFs and the immune system, which contributes to immunosuppression in the TME, and the potential for CAF-targeted therapies and combination approaches with immunotherapy to improve cancer treatment outcomes.

Inferring therapeutic vulnerability within tumors through integration of pan-cancer cell line and single-cell transcriptomic profiles

Single-cell RNA sequencing greatly advanced our understanding of intratumoral heterogeneity through identifying tumor subpopulations with distinct biologies. However, translating biological differences into treatment strategies is challenging, as we still lack tools to facilitate efficient drug discovery that tackles heterogeneous tumors. One key component of such approaches tackles accurate prediction of drug response at the single-cell level to offer therapeutic options to specific cell subpopulations. Here, we present a transparent computational framework (nicknamed scIDUC) to predict therapeutic efficacies on an individual-cell basis by integrating single-cell transcriptomic profiles with large, data-rich pan-cancer cell line screening datasets. Our method achieves high accuracy, with predicted sensitivities easily able to separate cells into their true cellular drug resistance status as measured by effect size (Cohen's d > 1.0). More importantly, we examine our method's utility with three distinct prospective tests covering different diseases (rhabdomyosarcoma, pancreatic ductal adenocarcinoma, and castration-resistant prostate cancer), and in each our predicted results are accurate and mirrored biological expectations. In the first two, we identified drugs for cell subpopulations that are resistant to standard-of-care (SOC) therapies due to intrinsic resistance or effects of tumor microenvironments. Our results showed high consistency with experimental findings from the original studies. In the third test, we generated SOC therapy resistant cell lines, used scIDUC to identify efficacious drugs for the resistant line, and validated the predictions with in-vitro experiments. Together, scIDUC quickly translates scRNA-seq data into drug response for individual cells, displaying the potential as a first-line tool for nuanced and heterogeneity-aware drug discovery.

The relationship between cancer and biomechanics

The onset, development, diagnosis, and treatment of cancer involve intricate interactions among various factors, spanning the realms of mechanics, physics, chemistry, and biology. Within our bodies, cells are subject to a variety of forces such as gravity, magnetism, tension, compression, shear stress, and biological static force/hydrostatic pressure. These forces are perceived by mechanoreceptors as mechanical signals, which are then transmitted to cells through a process known as mechanical transduction. During tumor development, invasion and metastasis, there are significant biomechanical influences on various aspects such as tumor angiogenesis, interactions between tumor cells and the extracellular matrix (ECM), interactions between tumor cells and other cells, and interactions between tumor cells and the circulatory system and vasculature. The tumor microenvironment comprises a complex interplay of cells, ECM and vasculature, with the ECM, comprising collagen, fibronectins, integrins, laminins and matrix metalloproteinases, acting as a critical mediator of mechanical properties and a key component within the mechanical signaling pathway. The vasculature exerts appropriate shear forces on tumor cells, enabling their escape from immune surveillance, facilitating their dissemination in the bloodstream, dictating the trajectory of circulating tumor cells (CTCs) and playing a pivotal role in regulating adhesion to the vessel wall. Tumor biomechanics plays a critical role in tumor progression and metastasis, as alterations in biomechanical properties throughout the malignant transformation process trigger a cascade of changes in cellular behavior and the tumor microenvironment, ultimately culminating in the malignant biological behavior of the tumor.

Ferroptosis and WDFY4 as novel targets for immunotherapy of lung adenocarcinoma

BACKGROUND

Lung cancer exhibits the world's highest mortality rate among malignant cancers worldwide, thereby presenting a significant global challenge in terms of reducing patient mortality. In the field of oncology, targeted immunotherapy has emerged as a novel therapeutic approach for lung cancer. This study aims to explore potential targets for immunotherapy in lung adenocarcinoma (LUAD) through the analysis of Ferroptosis Index (FPI) and Single Cell RNA-Sequencing (scRNA-seq) data. The findings of this research can potentially offer valuable insights for improving LUAD immunotherapy strategies and informing clinical decision-making.

METHODS

Firstly, the relationship between survival and ferroptosis in LUAD patients was analyzed by FPI. Subsequently, the association between ferroptosis and infiltration and regulation of immune cells was explored by immune infiltration analysis and correlation statistics. Lastly, the relationship between major infiltrating immune cell populations and related pathways and prognosis of LUAD patients was analyzed by GSEA and GSVA. To screen out core genes regulating infiltration of immune cell populations, scRNA-seq data of cancer and para-cancerous tissues of LUAD patients were downloaded, followed by cell clustering analysis, cell identification of core subpopulations, pseudotime analysis, single-cell GSVA and pathway enrichment analysis, and identification and functional analysis of core regulatory genes. Moreover, the expression levels of core functional genes in LUAD tissue microarray were detected by immunohistochemistry, and its relationship with the prognosis of LUAD patients was verified. Finally, we used lentivirus with WDFY4 to transfect LUAD A549 cells. CCK-8, flow cytometry apoptosis detection, Scratch wound healing assay, Transwell migration assay, Xenograft nude mice model, immunohistochemical analysis and other experimental methods were used to explore the biological effects of WDFY4 on LUAD in vitro and in vivo.

RESULTS

Survival analysis of FPI values in LUAD patients revealed a positive correlation between smaller FPI values and longer overall survival. Immuno-infiltration analysis and its correlation with FPI values revealed that B cells were most strongly associated with ferroptosis. Ferroptosis of cancer cells could promote infiltration and activation of B cell populations, and LUAD patients with more infiltration of B cell populations had longer long-term survival. scRNA-seq data analysis indicated that the B cell population is one of the major cell populations infiltrated by immune cells in LUAD. During the later phases of B cell differentiation in LUAD, there was a decrease in the expression levels of ACAP1, LINC00926, TLR10, MS4A1, WDFY4, and TRIM22 genes, whereas the expression levels of TMEM59, TP53INP1, and METTL7A genes were elevated. The protein-protein interaction (PPI) network analysis indicated that WDFY4 plays a crucial role in regulating B cell differentiation in LUAD. Immunohistochemical analysis of LUAD tissue microarray revealed a significant downregulation of WDFY4 expression, which was closely related to the occurrence sites of LUAD. Moreover, LUAD patients with a low WDFY4 expression exhibited a poorer prognosis. Additionally, experimental findings demonstrated that the overexpression of WDFY4 could inhibit the proliferation and metastasis of A549 cells while promoting apoptosis. It was also confirmed that WDFY4 could inhibit cancer growth in vivo.

CONCLUSIONS

The results indicate that promoting infiltration and activation of B cell populations could improve the long-term survival of LUAD patients, thereby offering a potential novel immunotherapeutic approach for LUAD. Besides, the promotion of cancer cell ferroptosis and upregulation of WDFY4 expression have been shown to induce the infiltration and activation of B cell populations. Furthermore, the overexpression of WDFY4 can significantly inhibit the growth of lung adenocarcinoma in vitro and in vivo, highlighting its potential as a target for immunotherapy in LUAD.

Loss of SYNCRIP unleashes APOBEC-driven mutagenesis, tumor heterogeneity, and AR-targeted therapy resistance in prostate cancer

Tumor mutational burden and heterogeneity has been suggested to fuel resistance to many targeted therapies. The cytosine deaminase APOBEC proteins have been implicated in the mutational signatures of more than 70% of human cancers. However, the mechanism underlying how cancer cells hijack the APOBEC mediated mutagenesis machinery to promote tumor heterogeneity, and thereby foster therapy resistance remains unclear. We identify SYNCRIP as an endogenous molecular brake which suppresses APOBEC-driven mutagenesis in prostate cancer (PCa). Overactivated APOBEC3B, in SYNCRIP-deficient PCa cells, is a key mutator, representing the molecular source of driver mutations in some frequently mutated genes in PCa, including FOXA1, EP300. Functional screening identifies eight crucial drivers for androgen receptor (AR)-targeted therapy resistance in PCa that are mutated by APOBEC3B: BRD7, CBX8, EP300, FOXA1, HDAC5, HSF4, STAT3, and AR. These results uncover a cell-intrinsic mechanism that unleashes APOBEC-driven mutagenesis, which plays a significant role in conferring AR-targeted therapy resistance in PCa.

MRPL51 is a downstream target of FOXM1 in promoting the malignant behaviors of lung adenocarcinoma

Mitochondrial ribosome protein L51 (MRPL51) is a 39S subunit protein of the mitochondrial ribosome. Its dysregulation may be involved in non-small cell lung cancer. The present study aimed to explore MRPL51 expression in lung adenocarcinoma (LUAD) and normal lung tissues, as well as its regulatory effects on malignant LUAD behaviors. In addition, the role of forkhead box protein M1 (FOXM1) in MRPL51 transcription was studied. Bioinformatics analysis and subsequent in vitro experiments, including western blotting, immunofluorescent staining, Transwell invasion assay, dual-luciferase assay and chromatin immunoprecipitation quantitative PCR were conducted. The results demonstrated that MRPL51 expression was upregulated at both the mRNA and protein levels in LUAD tissues compared with normal lung tissues. Gene Set Enrichment Analysis demonstrated that LUAD tissues with higher MRPL51 expression also had higher expression levels of genes enriched in multiple gene sets, including 'DNA_REPAIR', 'UNFOLDED_PROTEIN_RESPONSE', 'MYC_TARGETS_V1', 'OXIDATIVE_ PHOSPHORYLATION', 'MTORC1_SIGNALING', 'REACTIVE_OXYGEN_SPECIES_PATHWAY', 'MYC_ TARGETS_V2', 'E2F_TARGETS' and 'G2M_ CHECKPOINT'. MRPL51 expression was positively correlated with 'cell cycle', 'DNA damage', 'DNA repair', epithelial-mesenchymal transition ('EMT'), 'invasion' and 'proliferation' of LUAD cells at the single-cell level. Compared to the negative control, MRPL51 knockdown decreased N-cadherin and vimentin expression but increased E-cadherin expression in A549 and Calu-3 cells. MRPL51 knockdown suppressed cell proliferation, induced G1 phase arrest and decreased cell invasion. Patients with LUAD and higher MRPL51 expression had a significantly shorter overall survival (OS). FOXM1 could bind to the MRPL51 gene promoter and activate its transcription. In conclusion, MRPL51 was transcriptionally activated by FOXM1 in LUAD and contributed to the malignant behaviors of tumor cells, including EMT, cell cycle progression and invasion. High MRPL51 expression may be a prognostic biomarker indicating poor OS.

Single-cell RNA sequencing revealed subclonal heterogeneity and gene signatures of gemcitabine sensitivity in pancreatic cancer

Introduction: Resistance to gemcitabine is common and critically limits its therapeutic efficacy in pancreatic ductal adenocarcinoma (PDAC). Methods: We constructed 17 patient-derived xenograft (PDX) models from PDAC patient samples and identified the most notable responder to gemcitabine by screening the PDX sets in vivo. To analyze tumor evolution and microenvironmental changes pre- and post-chemotherapy, single-cell RNA sequencing (scRNA-seq) was performed. Results: ScRNA-seq revealed that gemcitabine promoted the expansion of subclones associated with drug resistance and recruited macrophages related to tumor progression and metastasis. We further investigated the particular drug-resistant subclone and established a gemcitabine sensitivity gene panel (GSGP) (SLC46A1, PCSK1N, KRT7, CAV2, and LDHA), dividing PDAC patients into two groups to predict the overall survival (OS) in The Cancer Genome Atlas (TCGA) training dataset. The signature was successfully validated in three independent datasets. We also found that 5-GSGP predicted the sensitivity to gemcitabine in PDAC patients in the TCGA training dataset who were treated with gemcitabine. Discussion and conclusion: Our study provides new insight into the natural selection of tumor cell subclones and remodeling of tumor microenvironment (TME) cells induced by gemcitabine. We revealed a specific drug resistance subclone, and based on the characteristics of this subclone, we constructed a GSGP that can robustly predict gemcitabine sensitivity and prognosis in pancreatic cancer, which provides a theoretical basis for individualized clinical treatment.

Applications of single-cell RNA sequencing in drug discovery and development

Single-cell technologies, particularly single-cell RNA sequencing (scRNA-seq) methods, together with associated computational tools and the growing availability of public data resources, are transforming drug discovery and development. New opportunities are emerging in target identification owing to improved disease understanding through cell subtyping, and highly multiplexed functional genomics screens incorporating scRNA-seq are enhancing target credentialling and prioritization. ScRNA-seq is also aiding the selection of relevant preclinical disease models and providing new insights into drug mechanisms of action. In clinical development, scRNA-seq can inform decision-making via improved biomarker identification for patient stratification and more precise monitoring of drug response and disease progression. Here, we illustrate how scRNA-seq methods are being applied in key steps in drug discovery and development, and discuss ongoing challenges for their implementation in the pharmaceutical industry.

Cell-free chromatin immunoprecipitation can determine tumor gene expression in lung cancer patients

Cell-free DNA (cfDNA) in blood plasma can be bound to nucleosomes that contain post-translational modifications representing the epigenetic profile of the cell of origin. This includes histone H3 lysine 36 trimethylation (H3K36me3), a marker of active transcription. We hypothesised that cell-free chromatin immunoprecipitation (cfChIP) of H3K36me3-modified nucleosomes present in blood plasma can delineate tumour gene expression levels. H3K36me3 cfChIP followed by targeted NGS (cfChIP-seq) was performed on blood plasma samples from non-small-cell lung cancer (NSCLC) patients (NSCLC, n = 8), small-cell lung cancer (SCLC) patients (SCLC, n = 4) and healthy controls (n = 4). H3K36me3 cfChIP-seq demonstrated increased enrichment of mutated alleles compared with normal alleles in plasma from patients with known somatic cancer mutations. Additionally, genes identified to be differentially expressed in SCLC and NSCLC tumours had concordant H3K36me3 cfChIP enrichment profiles in NSCLC (sensitivity = 0.80) and SCLC blood plasma (sensitivity = 0.86). Findings here expand the utility of cfDNA in liquid biopsies to characterise treatment resistance, cancer subtyping and disease progression.

Trends and Potential of Machine Learning and Deep Learning in Drug Study at Single-Cell Level

Cancer treatments always face challenging problems, particularly drug resistance due to tumor cell heterogeneity. The existing datasets include the relationship between gene expression and drug sensitivities; however, the majority are based on tissue-level studies. Study drugs at the single-cell level are perspective to overcome minimal residual disease caused by subclonal resistant cancer cells retained after initial curative therapy. Fortunately, machine learning techniques can help us understand how different types of cells respond to different cancer drugs from the perspective of single-cell gene expression. Good modeling using single-cell data and drug response information will not only improve machine learning for cell-drug outcome prediction but also facilitate the discovery of drugs for specific cancer subgroups and specific cancer treatments. In this paper, we review machine learning and deep learning approaches in drug research. By analyzing the application of these methods on cancer cell lines and single-cell data and comparing the technical gap between single-cell sequencing data analysis and single-cell drug sensitivity analysis, we hope to explore the trends and potential of drug research at the single-cell data level and provide more inspiration for drug research at the single-cell level. We anticipate that this review will stimulate the innovative use of machine learning methods to address new challenges in precision medicine more broadly.

Application status and future prospects of the PDX model in lung cancer

Lung cancer is one of the most prevalent, fatal, and highly heterogeneous diseases that, seriously threaten human health. Lung cancer is primarily caused by the aberrant expression of multiple genes in the cells. Lung cancer treatment options include surgery, radiation, chemotherapy, targeted therapy, and immunotherapy. In recent decades, significant progress has been made in developing therapeutic agents for lung cancer as well as a biomarker for its early diagnosis. Nonetheless, the alternative applications of traditional pre-clinical models (cell line models) for diagnosis and prognosis prediction are constrained by several factors, including the lack of microenvironment components necessary to affect cancer biology and drug response, and the differences between laboratory and clinical results. The leading reason is that substantial shifts accrued to cell biological behaviors, such as cell proliferative, metastatic, invasive, and gene expression capabilities of different cancer cells after decades of growing indefinitely in vitro. Moreover, the introduction of individualized treatment has prompted the development of appropriate experimental models. In recent years, preclinical research on lung cancer has primarily relied on the patient-derived tumor xenograft (PDX) model. The PDX provides stable models with recapitulate characteristics of the parental tumor such as the histopathology and genetic blueprint. Additionally, PDXs offer valuable models for efficacy screening of new cancer drugs, thus, advancing the understanding of tumor biology. Concurrently, with the heightened interest in the PDX models, potential shortcomings have gradually emerged. This review summarizes the significant advantages of PDXs over the previous models, their benefits, potential future uses and interrogating open issues.

scDrug: From single-cell RNA-seq to drug response prediction

Single-cell RNA sequencing (scRNA-seq) technology allows massively parallel characterization of thousands of cells at the transcriptome level. scRNA-seq is emerging as an important tool to investigate the cellular components and their interactions in the tumor microenvironment. scRNA-seq is also used to reveal the association between tumor microenvironmental patterns and clinical outcomes and to dissect cell-specific effects of drug treatment in complex tissues. Recent advances in scRNA-seq have driven the discovery of biomarkers in diseases and therapeutic targets. Although methods for prediction of drug response using gene expression of scRNA-seq data have been proposed, an integrated tool from scRNA-seq analysis to drug discovery is required. We present scDrug as a bioinformatics workflow that includes a one-step pipeline to generate cell clustering for scRNA-seq data and two methods to predict drug treatments. The scDrug pipeline consists of three main modules: scRNA-seq analysis for identification of tumor cell subpopulations, functional annotation of cellular subclusters, and prediction of drug responses. scDrug enables the exploration of scRNA-seq data readily and facilitates the drug repurposing process. scDrug is freely available on GitHub at https://github.com/ailabstw/scDrug.

SERPINB9 is commonly amplified and high expression in cancer cells correlates with poor immune checkpoint blockade response

Immunotherapies, in particular immune checkpoint blockade (ICB), have improved the clinical outcome of cancer patients, although many fail to mount a durable response. Several resistance mechanisms have been identified, but our understanding of the requirements for a robust ICB response is incomplete. We have engineered an MHC I/antigen: TCR-matched panel of human NSCLC cancer and T cells to identify tumor cell-intrinsic T cell resistance mechanisms. The top differentially expressed gene in resistant tumor cells was SERPINB9. This serine protease inhibitor of the effector T cell-derived molecule granzyme B prevents caspase-mediated tumor apoptosis. Concordantly, we show that genetic ablation of SERPINB9 reverts T cell resistance of NSCLC cell lines, whereas its overexpression reduces T cell sensitivity. SERPINB9 expression in NSCLC strongly correlates with a mesenchymal phenotype. We also find that SERPINB9 is commonly amplified in cancer, particularly melanoma in which it is indicative of poor prognosis. Single-cell RNA sequencing of ICB-treated melanomas revealed that SERPINB9 expression is elevated not only in cells from post- versus pre-treatment cancers, but also in ICB-refractory cancers. In NSCLC we commonly observed rare SERPINB9-positive cancer cells, possibly accounting for reservoirs of ICB-resistant cells. While underscoring SERPINB9 as a potential target to combat immunotherapy resistance, these results suggest its potential to serve as a prognostic and predictive biomarker.

Leveraging data-driven self-consistency for high-fidelity gene expression recovery

Single cell RNA sequencing is a promising technique to determine the states of individual cells and classify novel cell subtypes. In current sequence data analysis, however, genes with low expressions are omitted, which leads to inaccurate gene counts and hinders downstream analysis. Recovering these omitted expression values presents a challenge because of the large size of the data. Here, we introduce a data-driven gene expression recovery framework, referred to as self-consistent expression recovery machine (SERM), to impute the missing expressions. Using a neural network, the technique first learns the underlying data distribution from a subset of the noisy data. It then recovers the overall expression data by imposing a self-consistency on the expression matrix, thus ensuring that the expression levels are similarly distributed in different parts of the matrix. We show that SERM improves the accuracy of gene imputation with orders of magnitude enhancement in computational efficiency in comparison to the state-of-the-art imputation techniques.

Pan-cancer analysis of UBE2T with a focus on prognostic and immunological roles in lung adenocarcinoma

Background

Ubiquitin-conjugating enzyme E2 T (UBE2T) is a potential oncogene. However, Pan-cancer analyses of the functional, prognostic and predictive implications of this gene are lacking.

Methods

We first analyzed UBE2T across 33 tumor types in The Cancer Genome Atlas (TCGA) project. We investigated the expression level of UBE2T and its effect on prognosis using the TCGA database. The correlation between UBE2T and cell cycle in pan-cancer was investigated using the single-cell sequencing data in Cancer Single-cell State Atlas (CancerSEA) database. The Weighted Gene Co-expression Network analysis (WGCNA), Univariate Cox and Least absolute shrinkage and selection operator (LASSO) Cox regression models, and receiver operating characteristic (ROC) were applied to assess the prognostic impact of UBE2T-related cell cycle genes (UrCCGs). Furthermore, the consensus clustering (CC) method was adopted to divide TCGA-lung adenocarcinoma (LUAD) patients into subgroups based on UrCCGs. Prognosis, molecular characteristics, and the immune panorama of subgroups were analyzed using Single-sample Gene Set Enrichment Analysis (ssGSEA). Results derived from TCGA-LUAD patients were validated in International Cancer Genome Consortium (ICGC)-LUAD data.

Results

UBE2T is highly expressed and is a prognostic risk factor in most tumors. CancerSEA database analysis revealed that UBE2T was positively associated with the cell cycle in various cancers(r > 0.60, p < 0.001). The risk signature of UrCCGs can reliably predict the prognosis of LUAD (AUC1 year = 0.720, AUC3 year = 0.700, AUC5 year = 0.630). The CC method classified the TCGA-LUAD cohort into 4 UrCCG subtypes (G1–G4). Kaplan–Meier survival analysis demonstrated that G2 and G4 subtypes had worse survival than G3 (Log-rank test PTCGA training set < 0.001, PICGC validation set < 0.001). A comprehensive analysis of immune infiltrates, immune checkpoints, and immunogenic cell death modulators unveiled different immune landscapes for the four subtypes. High immunophenoscore in G3 and G4 tumors suggested that these two subtypes were immunologically “hot,” tending to respond to immunotherapy compared to G2 subtypes (p < 0.001).

Conclusions

UBE2T is a critical oncogene in many cancers. Moreover, UrCCG classified the LUAD cohort into four subgroups with significantly different survival, molecular features, immune infiltrates, and immunotherapy responses. UBE2T may be a therapeutic target and predictor of prognosis and immunotherapy sensitivity.

TMExplorer: A tumour microenvironment single-cell RNAseq database and search tool

MOTIVATION

The tumour microenvironment (TME) contains various cells including stromal fibroblasts, immune and malignant cells, and its composition can be elucidated using single-cell RNA sequencing (scRNA-seq). scRNA-seq datasets from several cancer types are available, yet we lack a comprehensive database to collect and present related TME data in an easily accessible format.

RESULTS

We therefore built a TME scRNA-seq database, and created the R package TMExplorer to facilitate investigation of the TME. TMExplorer provides an interface to easily access all available datasets and their metadata. The users can search for datasets using a thorough range of characteristics. The TMExplorer allows for examination of the TME using scRNA-seq in a way that is streamlined and allows for easy integration into already existing scRNA-seq analysis pipelines.

The epiphany derived from T-cell–inflamed profiles: Pan-cancer characterization of CD8A as a biomarker spanning clinical relevance, cancer prognosis, immunosuppressive environment, and treatment responses

CD8A encodes the CD8 alpha chain of αβT cells, which has been proposed as a quantifiable indicator for the assessment of CD8⁺ cytotoxic T lymphocytes (CTLs) recruitment or activity and a robust biomarker for anti-PD-1/PD-L1 therapy responses. Nonetheless, the lack of research into the role of CD8A in tumor microenvironment predisposes to limitations in its clinical utilization. In the presented study, multiple computational tools were used to investigate the roles of CD8A in the pan-cancer study, revealing its essential associations with tumor immune infiltration, immunosuppressive environment formation, cancer progression, and therapy responses. Based on the pan-cancer cohorts of the Cancer Genome Atlas (TCGA) database, our results demonstrated the distinctive CD8A expression patterns in cancer tissues and its close associations with the prognosis and disease stage of cancer. We then found that CD8A was correlated with six major immune cell types, and immunosuppressive cells in multiple cancer types. Besides, epigenetic modifications of CD8A were related to CTL levels and T cell dysfunctional states, thereby affecting survival outcomes of specific cancer types. After that, we explored the co-occurrence patterns of CD8A mutation, thus identifying RMND5A, RNF103-CHMP3, CHMP3, CD8B, MRPL35, MAT2A, RGPD1, RGPD2, REEP1, and ANAPC1P1 genes, which co-occurred mutations with CD8A, and are concomitantly implicated in the regulation of cancer-related pathways. Finally, we tested CD8A as a therapeutic biomarker for multiple antitumor agents’ or compounds’ responsiveness on various cancer cell lines and cancer cohorts. Our findings denoted the underlying mechanics of CD8A in reflecting the T-cell-inflamed profiles, which has potential as a biomarker in cancer diagnosis, prognosis, and therapeutic responses.

Missing Value Imputation With Low-Rank Matrix Completion in Single-Cell RNA-Seq Data by Considering Cell Heterogeneity

Single-cell RNA-sequencing (scRNA-seq) technologies enable the measurements of gene expressions in individual cells, which is helpful for exploring cancer heterogeneity and precision medicine. However, various technical noises lead to false zero values (missing gene expression values) in scRNA-seq data, termed as dropout events. These zero values complicate the analysis of cell patterns, which affects the high-precision analysis of intra-tumor heterogeneity. Recovering missing gene expression values is still a major obstacle in the scRNA-seq data analysis. In this study, taking the cell heterogeneity into consideration, we develop a novel method, called single cell Gauss–Newton Gene expression Imputation (scGNGI), to impute the scRNA-seq expression matrices by using a low-rank matrix completion. The obtained experimental results on the simulated datasets and real scRNA-seq datasets show that scGNGI can more effectively impute the missing values for scRNA-seq gene expression and improve the down-stream analysis compared to other state-of-the-art methods. Moreover, we show that the proposed method can better preserve gene expression variability among cells. Overall, this study helps explore the complex biological system and precision medicine in scRNA-seq data.

Characterization of gene cluster heterogeneity in single-cell transcriptomic data within and across cancer types

Despite the remarkable progress in probing tumor transcriptomic heterogeneity by single-cell RNA sequencing (sc-RNAseq) data, several gaps exist in prior studies. Tumor heterogeneity is frequently mentioned but not quantified. Clustering analyses typically target cells rather than genes, and differential levels of transcriptomic heterogeneity of gene clusters are not characterized. Relations between gene clusters inferred from multiple datasets remain less explored. We provided a series of quantitative methods to analyze cancer sc-RNAseq data. First, we proposed two quantitative measures to assess intra-tumoral heterogeneity/homogeneity. Second, we established a hierarchy of gene clusters from sc-RNAseq data, devised an algorithm to reduce the gene cluster hierarchy to a compact structure, and characterized the gene clusters with functional enrichment and heterogeneity. Third, we developed an algorithm to align the gene cluster hierarchies from multiple datasets to a small number of meta gene clusters. By applying these methods to nine cancer sc-RNAseq datasets, we discovered that cancer cell transcriptomes were more homogeneous within tumors than the accompanying normal cells. Furthermore, many gene clusters from the nine datasets were aligned to two large meta gene clusters, which had high and low heterogeneity and were enriched with distinct functions. Finally, we found the homogeneous meta gene cluster retained stronger expression coherence and associations with survival times in bulk level RNAseq data than the heterogeneous meta gene cluster, yet the combinatorial expression patterns of breast cancer subtypes in bulk level data were not preserved in single-cell data. The inference outcomes derived from nine cancer sc-RNAseq datasets provide insights about the contributing factors for transcriptomic heterogeneity of cancer cells and complex relations between bulk level and single-cell RNAseq data. They demonstrate the utility of our methods to enable a comprehensive characterization of co-expressed gene clusters in a wide range of sc-RNAseq data in cancers and beyond.

Summary: We propose quantitative methods to analyze cancer sc-RNAseq data: measures of intra-tumoral heterogeneity, characterization of a hierarchy of gene clusters, and alignment of gene cluster hierarchies from multiple datasets.

Decoding Lung Cancer at Single-Cell Level

Lung cancer is the leading cause of cancer death due to its high degree of malignancy, rapid growth, and early metastasis. Recent studies have found that lung cancer has a high degree of heterogeneity which is characterized by the mixture of different tumor cell types. However, the driving genetic/epigenetic mechanism of lung cancer heterogeneity, how different types of cells interact, and the relationship between heterogeneity and drug resistance have been poorly understood. Single-cell technology can decompose high throughput sequencing information into each cell and provide single-cell information in high resolution. By using single-cell analysis, researchers can not only fully understand the molecular characteristics of different cell types in the same tissue, but also define completely new cell types. Thus, single-cell analysis has been widely utilized in systems biology, drug discovery, disease diagnosis and precision medicine. We review recent exploration of the mechanism of heterogeneity, tumor microenvironment and drug resistance in lung cancer by using single-cell analysis. We propose that the recent findings may pave new ways for the treatment strategies of lung cancer.

Single-Cell Sequencing and Its Applications in Liver Cancer

As one of the most lethal cancers, primary liver cancer (PLC) has high tumor heterogeneity, including the heterogeneity between cancer cells. Traditional methods which have been used to identify tumor heterogeneity for a long time are based on large mixed cell samples, and the research results usually show average level of the cell population, ignoring the heterogeneity between cancer cells. In recent years, single-cell sequencing has been increasingly applied to the studies of PLCs. It can detect the heterogeneity between cancer cells, distinguish each cell subgroup in the tumor microenvironment (TME), and also reveal the clonal characteristics of cancer cells, contributing to understand the evolution of tumor. Here, we introduce the process of single-cell sequencing, review the applications of single-cell sequencing in the heterogeneity of cancer cells, TMEs, oncogenesis, and metastatic mechanisms of liver cancer, and discuss some of the current challenges in the field.

Molecular Pathology of Lung Cancer

This overview of the molecular pathology of lung cancer includes a review of the most salient molecular alterations of the genome, transcriptome, and the epigenome. The insights provided by the growing use of next-generation sequencing (NGS) in lung cancer will be discussed, and interrelated concepts such as intertumor heterogeneity, intratumor heterogeneity, tumor mutational burden, and the advent of liquid biopsy will be explored. Moreover, this work describes how the evolving field of molecular pathology refines the understanding of different histologic phenotypes of non-small-cell lung cancer (NSCLC) and the underlying biology of small-cell lung cancer. This review will provide an appreciation for how ongoing scientific findings and technologic advances in molecular pathology are crucial for development of biomarkers, therapeutic agents, clinical trials, and ultimately improved patient care.

Single-cell sequencing and its applications in bladder cancer

Bladder cancer is the most common malignant tumour of the urinary system that is characterised by significant intra-tumoural heterogeneity. While large-scale sequencing projects have provided a preliminary understanding of tumour heterogeneity, these findings are based on the average signals obtained from the pooled populations of diverse cells. Recent advances in single-cell sequencing (SCS) technologies have been critical in this regard, opening up new ways of understanding the nuanced tumour biology by identifying distinct cellular subpopulations, dissecting the tumour microenvironment, and characterizing cellular genomic mutations. By integrating these novel insights, SCS technologies are expected to make powerful and meaningful changes to the current diagnosis and treatment of bladder cancer through the identification and usage of novel biomarkers as well as targeted therapeutics. SCS can discriminate complex heterogeneity in a large population of tumour cells and determine the key molecular properties that influence clinical outcomes. Here, we review the advances in single-cell technologies and discuss their applications in cancer research and clinical practice, with a specific focus on bladder cancer.

Single-Cell RNA Sequencing in Lung Cancer: Revealing Phenotype Shaping of Stromal Cells in the Microenvironment

The lung tumor microenvironment, which is composed of heterogeneous cell populations, plays an important role in the progression of lung cancer and is closely related to therapeutic efficacy. Increasing evidence has shown that stromal components play a key role in regulating tumor invasion, metastasis and drug resistance. Therefore, a better understanding of stromal components in the tumor microenvironment is helpful for the diagnosis and treatment of lung cancer. Rapid advances in technology have brought our understanding of disease into the genetic era, and single-cell RNA sequencing has enabled us to describe gene expression profiles with unprecedented resolution, enabling quantitative analysis of gene expression at the single-cell level to reveal the correlations among heterogeneity, signaling pathways, drug resistance and microenvironment molding in lung cancer, which is important for the treatment of this disease. In this paper, several common single-cell RNA sequencing methods and their advantages and disadvantages are briefly introduced to provide a reference for selection of suitable methods. Furthermore, we review the latest progress of single-cell RNA sequencing in the study of stromal cells in the lung tumor microenvironment.

Stability of scRNA-Seq Analysis Workflows is Susceptible to Preprocessing and is Mitigated by Regularized or Supervised Approaches

Background:

Statistical methods developed to address various questions in single-cell datasets show increased variability to different parameter regimes. In order to delineate further the robustness of commonly utilized methods for single-cell RNA-Seq, we aimed to comprehensively review scRNA-Seq analysis workflows in the setting of dimension reduction, clustering, and trajectory inference.

Methods:

We utilized datasets with temporal single-cell transcriptomics profiles from public repositories. Combining multiple methods at each level of the workflow, we have performed over 6 k analysis and evaluated the results of clustering and pseudotime estimation using adjusted rand index and rank correlation metrics. We have further integrated neural network methods to assess whether models with increased complexity can show increased bias/variance trade-off.

Results:

Combinatorial workflows showed that utilizing non-linear dimension reduction techniques such as t-SNE and UMAP are sensitive to initial preprocessing steps hence clustering results on dimension reduced space of single-cell datasets should be utilized carefully. Similarly, pseudotime estimation methods that depend on previous non-linear dimension reduction steps can result in highly variable trajectories. In contrast, methods that avoid non-linearity such as WOT can result in repeatable inferences of temporal gene expression dynamics. Furthermore, imputation methods do not improve clustering or trajectory inference results substantially in terms of repeatability. In contrast, the selection of the normalization method shows an increased effect on downstream analysis where ScTransform reduces variability overall.

Exposure to Tobacco Smoking Induces a subset of Activated Tumor-resident Tregs in Non-Small Cell Lung Cancer

Tobacco smoking is the major cause of non-small-cell-lung cancer (NSCLC). However, it is barely known how smoking impact the tumor immune environment (TIME) of lung cancer. We integrated single-cell RNA-seq and bulk RNA-seq data from several studies to systematically study the impact of smoking on T cells in treatment naïve NSCLC patients. We defined a set of smoking-induced differentially expressed genes (SIDEGs) in different cells in TIME.. Specifically, we defined a smoking-related tumor-specific Treg subset, ADAM12+ CTLA4+ Tregs according to the trajectory analysis and highly express genes in cell adhesion pathways and lipid metabolism. Using independent datasets from treatment naïve patients, we found that the fraction of ADAM12+ CTLA4+ Tregs are significantly increased in patients with smoking history. Moreover, the fraction of ADAM12+ CTLA4+ Tregs are positively correlated with the fraction of exhausted T cells. Additionally, we reconstructed the spatial organization of the tumor immune microenvironment and found that ADAM12+ CTLA4+ Tregs more actively communicate with LAYN+CD8+ exhausted T cells compared with ADAM12-CTLA4+ Tregs. Our data demonstrate that smoking induced a unique subset of tumor-specific activated Tregs which interact with exhausted T cells in the TIME. Our findings not only explained how smoking impact the TIME but also provide new targets and biomarkers for precision immunotherapy of lung cancer.

Predicting heterogeneity in clone-specific therapeutic vulnerabilities using single-cell transcriptomic signatures

While understanding molecular heterogeneity across patients underpins precision oncology, there is increasing appreciation for taking intra-tumor heterogeneity into account. Based on large-scale analysis of cancer omics datasets, we highlight the importance of intra-tumor transcriptomic heterogeneity (ITTH) for predicting clinical outcomes. Leveraging single-cell RNA-seq (scRNA-seq) with a recommender system (CaDRReS-Sc), we show that heterogeneous gene-expression signatures can predict drug response with high accuracy (80%). Using patient-proximal cell lines, we established the validity of CaDRReS-Sc's monotherapy (Pearson r>0.6) and combinatorial predictions targeting clone-specific vulnerabilities (>10% improvement). Applying CaDRReS-Sc to rapidly expanding scRNA-seq compendiums can serve as in silico screen to accelerate drug-repurposing studies. Availability: https://github.com/CSB5/CaDRReS-Sc .

Thalidomide Exerts Anti-Inflammatory Effects in Cutaneous Lupus by Inhibiting the IRF4/NF-ҡB and AMPK1/mTOR Pathways

Thalidomide is effective in patients with refractory cutaneous lupus erythematosus (CLE). However, the mechanism of action is not completely understood, and its use is limited by its potential, severe side-effects. Immune cell subset analysis in thalidomide’s CLE responder patients showed a reduction of circulating and tissue cytotoxic T-cells with an increase of iNKT cells and a shift towards a Th2 response. We conducted an RNA-sequencing study using CLE skin biopsies performing a Therapeutic Performance Mapping System (TMPS) analysis in order to generate a predictive model of its mechanism of action and to identify new potential therapeutic targets. Integrating RNA-seq data, public databases, and literature, TMPS analysis generated mathematical models which predicted that thalidomide acts via two CRBN-CRL4A- (CRL4CRBN) dependent pathways: IRF4/NF-ҡB and AMPK1/mTOR. Skin biopsies showed a significant reduction of IRF4 and mTOR in post-treatment samples by immunofluorescence. In vitro experiments confirmed the effect of thalidomide downregulating IRF4 in PBMCs and mTOR in keratinocytes, which converged in an NF-ҡB reduction that led to a resolution of the inflammatory lesion. These results emphasize the anti-inflammatory role of thalidomide in CLE treatment, providing novel molecular targets for the development of new therapies that could avoid thalidomide’s side effects while maintaining its efficacy.

The Single-Cell Sequencing: A Dazzling Light Shining on the Dark Corner of Cancer

Tumorigenesis refers to the process of clonal dysplasia that occurs due to the collapse of normal growth regulation in cells caused by the action of various carcinogenic factors. These “successful” tumor cells pass on the genetic templates to their generations in evolutionary terms, but they also constantly adapt to ever-changing host environments. A unique peculiarity known as intratumor heterogeneity (ITH) is extensively involved in tumor development, metastasis, chemoresistance, and immune escape. An understanding of ITH is urgently required to identify the diversity and complexity of the tumor microenvironment (TME), but achieving this understanding has been a challenge. Single-cell sequencing (SCS) is a powerful tool that can gauge the distribution of genomic sequences in a single cell and the genetic variability among tumor cells, which can improve the understanding of ITH. SCS provides fundamental ideas about existing diversity in specific TMEs, thus improving cancer diagnosis and prognosis prediction, as well as improving the monitoring of therapeutic response. Herein, we will discuss advances in SCS and review SCS application in tumors based on current evidence.

Significance and limitations of the use of next-generation sequencing technologies for detecting mutational signatures

Next generation sequencing technologies (NGS) have been critical in characterizing the genomic landscape and untangling the genetic heterogeneity of human cancer. Since its advent, NGS has played a pivotal role in identifying the patterns of somatic mutations imprinted on cancer genomes and in deciphering the signatures of the mutational processes that have generated these patterns. Mutational signatures serve as phenotypic molecular footprints of exposures to environmental factors as well as deficiency and infidelity of DNA replication and repair pathways. Since the first roadmap of mutational signatures in human cancer was generated from whole-genome and whole-exome sequencing data, there has been a growing interest to extract mutational signatures from other NGS technologies such as targeted panel sequencing, RNA sequencing, single-cell sequencing, duplex sequencing, reduced representation sequencing, and long-read sequencing. Many of these technologies have their inherent sequencing biases and produce technical artifacts that can confound the extraction of reliable and interpretable mutational signatures. In this review, we highlight the relevance, limitations, and prospects of using different NGS technologies for examining mutational patterns and for deciphering mutational signatures.

Cancer cell immune mimicry delineates onco-immunologic modulation

Immune transcripts are essential for depicting onco-immunologic interactions. However, whether cancer cells mimic immune transcripts to reprogram onco-immunologic interaction remains unclear. Here, single-cell transcriptomic analyses of 7,737 normal and 37,476 cancer cells reveal increased immune transcripts in cancer cells. Cells gradually acquire immune transcripts in malignant transformation. Notably, cancer cell-derived immune transcripts contribute to distinct prognoses of immune gene signatures. Optimized immune response signature (oIRS), obtained by excluding cancer-related immune genes from immune gene signatures, and offers a more reliable prognostic value. oIRS reveals that antigen presentation, NK cell killing and T cell signaling are associated with favorable prognosis. Patients with higher oIRS expression are associated with favorable responses to immunotherapy. Indeed, CD83⁺ cell infiltration, which indicates antigen presentation activity, predicts favorable prognosis in breast cancer. These findings unveil that immune mimicry is a distinct cancer hallmark, providing an example of cancer cell plasticity and a refined view of tumor microenvironment.

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Single-cell transcriptome reveals immune mimicry as a distinct cancer hallmark

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Mimicry transcripts contribute to distinct prognoses of immune gene signatures

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oIRS refines prognostic evaluation and points to core favorable immune processes

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oIRS signifies the role of antigen presentation and indicates immunotherapy response

Integrated single-cell and bulk RNA sequencing analysis identifies a cancer associated fibroblast-related signature for predicting prognosis and therapeutic responses in colorectal cancer

Background

Cancer-associated fibroblasts (CAFs) contribute notably to colorectal cancer (CRC) tumorigenesis, stiffness, angiogenesis, immunosuppression and metastasis, and could serve as a promising therapeutic target. Our purpose was to construct CAF-related prognostic signature for CRC.

Methods

We performed bioinformatics analysis on single-cell transcriptome data derived from Gene Expression Omnibus (GEO) and identified 208 differentially expressed cell markers from fibroblasts cluster. Bulk gene expression data of CRC was obtained from The Cancer Genome Atlas (TCGA) and GEO databases. Univariate Cox regression and least absolute shrinkage operator (LASSO) analyses were performed on TCGA training cohort (n = 308) for model construction, and was validated in TCGA validation (n = 133), TCGA total (n = 441), GSE39582 (n = 470) and GSE17536 (n = 177) datasets. Microenvironment Cell Populations-counter (MCP-counter) and Estimate the Proportion of Immune and Cancer cells (EPIC) methods were applied to evaluated CAFs infiltrations from bulk gene expression data. Real-time polymerase chain reaction (qPCR) was performed in tissue microarrays containing 80 colon cancer samples to further validate the prognostic value of the CAF model. pRRophetic and Tumor Immune Dysfunction and Exclusion (TIDE) algorithms were utilized to predict chemosensitivity and immunotherapy response. Human Protein Atlas (HPA) databases and immunohistochemistry were used to evaluate the protein expressions.

Results

A nine-gene prognostic CAF-related signature was established in training cohort. Kaplan–Meier survival analyses revealed patients with higher CAF risk scores were correlated with adverse prognosis in each cohort. MCP-counter and EPIC results consistently revealed CAFs infiltrations were significantly higher in high CAF risk group. Patients with higher CAF risk scores were more prone to not respond to immunotherapy, but were more sensitive to several conventional chemotherapeutics, suggesting a potential strategy of combining chemotherapy with anti-CAF therapy to improve the efficacy of current T-cell based immunotherapies. Univariate and multivariate Cox regression analyses verified the CAF model was as an independent prognostic indicator in predicting overall survival, and a CAF-based nomogram was then built for clinical utility in predicting prognosis of CRC.

Conclusion

To conclude, the CAF-related signature could serve as a robust prognostic indicator in CRC, which provides novel genomics evidence for anti-CAF immunotherapeutic strategies.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-021-02252-9.

Microfluidic single-cell transcriptomics: moving towards multimodal and spatiotemporal omics

Cells are the basic units of life with vast heterogeneity. Single-cell transcriptomics unveils cell-to-cell gene expression variabilities, discovers novel cell types, and uncovers the critical roles of cellular heterogeneity in biological processes. The recent advances in microfluidic technologies have greatly accelerated the development of single-cell transcriptomics with regard to throughput, sensitivity, cost, and automation. In this article, we review state-of-the-art microfluidic single-cell transcriptomics, with a focus on the methodologies. We first summarize six typical microfluidic platforms for isolation and transcriptomic analysis of single cells. Then the on-going trend of microfluidic transcriptomics towards multimodal omics, which integrates transcriptomics with other omics to provide more comprehensive pictures of gene expression networks, is discussed. We also highlight single-cell spatial transcriptomics and single-cell temporal transcriptomics that provide unprecedented spatiotemporal resolution to reveal transcriptomic dynamics in space and time, respectively. The emerging applications of microfluidic single-cell transcriptomics are also discussed. Finally, we discuss the current challenges to be tackled and provide perspectives on the future development of microfluidic single-cell transcriptomics.