Differential Variation Analysis Enables Detection of Tumor Heterogeneity Using Single-Cell RNA-Sequencing Data

Oncotree2vec - a method for embedding and clustering of tumor mutation trees

MOTIVATION

Understanding the genomic heterogeneity of tumors is an important task in computational oncology, especially in the context of finding personalized treatments based on the genetic profile of each patient's tumor. Tumor clustering that takes into account the temporal order of genetic events, as represented by tumor mutation trees, is a powerful approach for grouping together patients with genetically and evolutionarily similar tumors and can provide insights into discovering tumor subtypes, for more accurate clinical diagnosis and prognosis.

RESULTS

Here, we propose oncotree2vec, a method for clustering tumor mutation trees by learning vector representations of mutation trees that capture the different relationships between subclones in an unsupervised manner. Learning low-dimensional tree embeddings facilitates the visualization of relations between trees in large cohorts and can be used for downstream analyses, such as deep learning approaches for single-cell multi-omics data integration. We assessed the performance and the usefulness of our method in three simulation studies and on two real datasets: a cohort of 43 trees from six cancer types with different branching patterns corresponding to different modes of spatial tumor evolution and a cohort of 123 AML mutation trees.

AVAILABILITY AND IMPLEMENTATION

https://github.com/cbg-ethz/oncotree2vec.

A novel defined programmed cell death related gene signature for predicting the prognosis of serous ovarian cancer

PURPOSE

This study aims to explore the contribution of differentially expressed programmed cell death genes (DEPCDGs) to the heterogeneity of serous ovarian cancer (SOC) through single-cell RNA sequencing (scRNA-seq) and assess their potential as predictors for clinical prognosis.

METHODS

SOC scRNA-seq data were extracted from the Gene Expression Omnibus database, and the principal component analysis was used for cell clustering. Bulk RNA-seq data were employed to analyze SOC-associated immune cell subsets key genes. CIBERSORT and single-sample gene set enrichment analysis (ssGSEA) were utilized to calculate immune cell scores. Prognostic models and nomograms were developed through univariate and multivariate Cox analyses.

RESULTS

Our analysis revealed that 48 DEPCDGs are significantly correlated with apoptotic signaling and oxidative stress pathways and identified seven key DEPCDGs (CASP3, GADD45B, GNA15, GZMB, IL1B, ISG20, and RHOB) through survival analysis. Furthermore, eight distinct cell subtypes were characterized using scRNA-seq. It was found that G protein subunit alpha 15 (GNA15) exhibited low expression across these subtypes and a strong association with immune cells. Based on the DEGs identified by the GNA15 high- and low-expression groups, a prognostic model comprising eight genes with significant prognostic value was constructed, effectively predicting patient overall survival. Additionally, a nomogram incorporating the RS signature, age, grade, and stage was developed and validated using two large SOC datasets.

CONCLUSION

GNA15 emerged as an independent and excellent prognostic marker for SOC patients. This study provides valuable insights into the prognostic potential of DEPCDGs in SOC, presenting new avenues for personalized treatment strategies.

Oral tumor heterogeneity, its implications for patient monitoring and designing anti-cancer strategies

Oral cancer tumors occur in the mouth and are mainly derived from oral mucosa linings. It is one of the most common and fatal malignant diseases worldwide. The intratumor heterogeneity (ITH) of oral cancerous tumor is vast, so it is challenging to study and interpret. Due to environmental selection pressures, ITH arises through diverse genetic, epigenetic, and metabolic alterations. The ITH also talks about peri-tumoral vascular/ lymphatic growth, perineural permeation, tumor necrosis, invasion, and clonal expansion/ the coexistence of multiple subclones in a single tumor. The heterogeneity offers tumors the adaptability to survive, induce growth/ metastasis, and, most importantly, escape antitumor therapy. Unfortunately, the ITH is prioritized less in determining disease pathology than the traditional TNM classifications or tumor grade. Understanding ITH is challenging, but with the advancement of technology, this ITH can be decoded. Tumor genomics, proteomics, metabolomics, and other modern analyses can provide vast information. This information in clinics can assist in understanding a tumor's severity and be used for diagnostic, prognostic, and therapeutic decision-making. Lastly, the oral tumor ITH can lead to individualized, targeted therapy strategies fighting against OC.

Unravelling cancer subtype-specific driver genes in single-cell transcriptomics data with CSDGI

Cancer is known as a heterogeneous disease. Cancer driver genes (CDGs) need to be inferred for understanding tumor heterogeneity in cancer. However, the existing computational methods have identified many common CDGs. A key challenge exploring cancer progression is to infer cancer subtype-specific driver genes (CSDGs), which provides guidane for the diagnosis, treatment and prognosis of cancer. The significant advancements in single-cell RNA-sequencing (scRNA-seq) technologies have opened up new possibilities for studying human cancers at the individual cell level. In this study, we develop a novel unsupervised method, CSDGI (Cancer Subtype-specific Driver Gene Inference), which applies Encoder-Decoder-Framework consisting of low-rank residual neural networks to inferring driver genes corresponding to potential cancer subtypes at the single-cell level. To infer CSDGs, we apply CSDGI to the tumor single-cell transcriptomics data. To filter the redundant genes before driver gene inference, we perform the differential expression genes (DEGs). The experimental results demonstrate CSDGI is effective to infer driver genes that are cancer subtype-specific. Functional and disease enrichment analysis shows these inferred CSDGs indicate the key biological processes and disease pathways. CSDGI is the first method to explore cancer driver genes at the cancer subtype level. We believe that it can be a useful method to understand the mechanisms of cell transformation driving tumours.

AAFL: automatic association feature learning for gene signature identification of cancer subtypes in single-cell RNA-seq data

Single-cell RNA-sequencing (scRNA-seq) technologies have enabled the study of human cancers in individual cells, which explores the cellular heterogeneity and the genotypic status of tumors. Gene signature identification plays an important role in the precise classification of cancer subtypes. However, most existing gene selection methods only select the same informative genes for each subtype. In this study, we propose a novel gene selection method, automatic association feature learning (AAFL), which automatically identifies different gene signatures for different cell subpopulations (cancer subtypes) at the same time. The proposed AAFL method combines the residual network with the low-rank network, which selects genes that are most associated with the corresponding cell subpopulations. Moreover, the differential expression genes are acquired before gene selection to filter the redundant genes. We apply the proposed feature learning method to the real cancer scRNA-seq data sets (melanoma) to identify cancer subtypes and detect gene signatures of identified cancer subtypes. The experimental results demonstrate that the proposed method can automatically identify different gene signatures for identified cancer subtypes. Gene ontology enrichment analysis shows that the identified gene signatures of different subtypes reveal the key biological processes and pathways. These gene signatures are expected to bring important implications for understanding cellular heterogeneity and the complex ecosystem of tumors.

Measuring cell-to-cell expression variability in single-cell RNA-sequencing data: a comparative analysis and applications to B cell aging

BACKGROUND

Single-cell RNA-sequencing (scRNA-seq) technologies enable the capture of gene expression heterogeneity and consequently facilitate the study of cell-to-cell variability at the cell type level. Although different methods have been proposed to quantify cell-to-cell variability, it is unclear what the optimal statistical approach is, especially in light of challenging data structures that are unique to scRNA-seq data like zero inflation.

RESULTS

We systematically evaluate the performance of 14 different variability metrics that are commonly applied to transcriptomic data for measuring cell-to-cell variability. Leveraging simulations and real datasets, we benchmark the metric performance based on data-specific features, sparsity and sequencing platform, biological properties, and the ability to recapitulate true levels of biological variability based on known gene sets. Next, we use scran, the metric with the strongest all-round performance, to investigate changes in cell-to-cell variability that occur during B cell differentiation and the aging processes. The analysis of primary cell types from hematopoietic stem cells (HSCs) and B lymphopoiesis reveals unique gene signatures with consistent patterns of variable and stable expression profiles during B cell differentiation which highlights the significance of these methods. Identifying differentially variable genes between young and old cells elucidates the regulatory changes that may be overlooked by solely focusing on mean expression changes and we investigate this in the context of regulatory networks.

CONCLUSIONS

We highlight the importance of capturing cell-to-cell gene expression variability in a complex biological process like differentiation and aging and emphasize the value of these findings at the level of individual cell types.

Application of single-cell RNA sequencing in head and neck squamous cell carcinoma

Single-cell RNA sequencing has been broadly applied to head and neck squamous cell carcinoma (HNSCC) for characterizing the heterogeneity and genomic mutations of HNSCC benefiting from the advantage of single-cell resolution. We summarized most of the current studies and aimed to explore their research methods and ideas, as well as how to transform them into clinical applications. Through single-cell RNA sequencing, we found the differences in tumor cells' expression programs and differentiation tracks. The studies of immune microenvironment allowed us to distinguish immune cell subpopulations, the extensive expression of immune checkpoints, and the complex crosstalk network between immune cells and non-immune cells. For cancer-associated fibroblasts (CAFs), single-cell RNA sequencing had made an irreplaceable contribution to the exploration of their differentiation status, specific CAFs markers, and the interaction with tumor cells and immune cells. In addition, we demonstrated in detail how single-cell RNA sequencing explored the HNSCC epithelial-to-mesenchymal transition (EMT) model and the mechanism of drug resistance, as well as its clinical value.

sccomp: Robust differential composition and variability analysis for single-cell data

Cellular omics such as single-cell genomics, proteomics, and microbiomics allow the characterization of tissue and microbial community composition, which can be compared between conditions to identify biological drivers. This strategy has been critical to revealing markers of disease progression, such as cancer and pathogen infection. A dedicated statistical method for differential variability analysis is lacking for cellular omics data, and existing methods for differential composition analysis do not model some compositional data properties, suggesting there is room to improve model performance. Here, we introduce sccomp, a method for differential composition and variability analyses that jointly models data count distribution, compositionality, group-specific variability, and proportion mean-variability association, being aware of outliers. sccomp provides a comprehensive analysis framework that offers realistic data simulation and cross-study knowledge transfer. Here, we demonstrate that mean-variability association is ubiquitous across technologies, highlighting the inadequacy of the very popular Dirichlet-multinomial distribution. We show that sccomp accurately fits experimental data, significantly improving performance over state-of-the-art algorithms. Using sccomp, we identified differential constraints and composition in the microenvironment of primary breast cancer.

CD8+ T cell trajectory subtypes decode tumor heterogeneity and provide treatment recommendations for hepatocellular carcinoma

Introduction

Mounting evidence has revealed that the interactions and dynamic alterations among immune cells are critical in shaping the tumor microenvironment and ultimately map onto heterogeneous clinical outcomes. Currently, the underlying clinical significance of immune cell evolutions remains largely unexplored in hepatocellular carcinoma (HCC).

Methods

A total of 3,817 immune cells and 1,750 HCC patients of 15 independent public datasets were retrieved. The Seurat and Monocle algorithms were used to depict T cell evolution, and nonnegative matrix factorization (NMF) was further applied to identify the molecular classification. Subsequently, the prognosis, biological characteristics, genomic variations, and immune landscape among distinct clusters were decoded. The clinical efficacy of multiple treatment approaches was further investigated.

Results

According to trajectory gene expression, three heterogeneous clusters with different clinical outcomes were identified. C2, with a more advanced pathological stage, presented the most dismal prognosis relative to C1 and C3. Eight independent external cohorts validated the robustness and reproducibility of the three clusters. Further explorations elucidated C1 to be characterized as lipid metabolic HCC, and C2 was referred to as cell-proliferative HCC, whereas C3 was defined as immune inflammatory HCC. Moreover, C2 also displayed the most conspicuous genomic instability, and C3 was deemed as “immune-hot”, having abundant immune cells and an elevated expression of immune checkpoints. The assessments of therapeutic intervention suggested that patients in C1 were suitable for transcatheter arterial chemoembolization treatment, and patients in C2 were sensitive to tyrosine kinase inhibitors, while patients in C3 were more responsive to immunotherapy. We also identified numerous underlying therapeutic agents, which might be conducive to clinical transformation in the future.

Conclusions

Our study developed three clusters with distinct characteristics based on immune cell evolutions. For specifically stratified patients, we proposed individualized treatment strategies to improve the clinical outcomes and facilitate the clinical management.

Ovarian cancer-specific dysregulated genes with prognostic significance: scRNA-Seq with bulk RNA-Seq data and experimental validation

A major cause of gynecological cancer -related deaths worldwide, ovarian cancer is characterized by heterogeneity in both tumor cells and the tumor microenvironment (TME). Our study aimed to characterize tumor cell heterogeneity and the infiltration of M2 tumor-associated macrophages (TAMs) in the ovarian cancer TME by single-cell RNA-Seq (scRNA-Seq) analysis combined with bulk RNA sequencing (bulk RNA-Seq). Several highly variable genes were identified in ovarian cancer tissues, and tumor cell heterogeneity and infiltrating immune tumor cell heterogeneity were characterized in ovarian cancer cells. M2 TAMs in the TME were the predominant phenotype of TAM. Further, M2 TAM infiltration in the TME was negatively correlated with poor prognosis of ovarian cancer patients. Four M2 TAM-associated genes (SLAMF7, GNAS, TBX2-AS1, and LYPD6) correlated with the prognostic survival of ovarian cancer patients. Knockdown of SLAMF7 or GNAS mRNA repressed malignancy and cisplatin resistance of ovarian cancer cells. ScRNA-Seq combined with bulk RNA-Seq identified the same four genes associated with M2 TAMs. The prognostic risk score model based on these four genes may hold favorable predictive value for the prognosis of ovarian cancer patients.

SCSit: A high-efficiency preprocessing tool for single-cell sequencing data from SPLiT-seq

SPLiT-seq provides a low-cost platform to generate single-cell data by labeling the cellular origin of RNA through four rounds of combinatorial barcoding. However, an automatic and rapid method for preprocessing and classifying single-cell sequencing (SCS) data from SPLiT-seq, which directly identified and labeled combinatorial barcoding reads and distinguished special cell sequencing data, is currently lacking. Here, we develop a high-efficiency preprocessing tool for single-cell sequencing data from SPLiT-seq (SCSit), which can directly identify combinatorial barcodes and UMI of cell types and obtain more labeled reads, and remarkably enhance the retained data from SCS due to the exact alignment of insertion and deletion. Compared with the original method used in SPLiT-seq, the consistency of identified reads from SCSit increases to 97%, and mapped reads are twice than the original. Furthermore, the runtime of SCSit is less than 10% of the original. It can accurately and rapidly analyze SPLiT-seq raw data and obtain labeled reads, as well as effectively improve the single-cell data from SPLiT-seq platform. The data and source of SCSit are available on the GitHub website https://github.com/shang-qian/SCSit.

From bench to bedside: Single-cell analysis for cancer immunotherapy

Single-cell technologies are emerging as powerful tools for cancer research. These technologies characterize the molecular state of each cell within a tumor, enabling new exploration of tumor heterogeneity, microenvironment cell-type composition, and cell state transitions that affect therapeutic response, particularly in the context of immunotherapy. Analyzing clinical samples has great promise for precision medicine but is technically challenging. Successfully identifying predictors of response requires well-coordinated, multi-disciplinary teams to ensure adequate sample processing for high-quality data generation and computational analysis for data interpretation. Here, we review current approaches to sample processing and computational analysis regarding their application to translational cancer immunotherapy research.

Single cell RNA sequencing for breast cancer: present and future

Breast cancer is one of the most common malignant tumors in women. It is a heterogeneous disease related to genetic and environmental factors. Presently, the treatment of breast cancer still faces challenges due to recurrence and metastasis. The emergence of single-cell RNA sequencing (scRNA-seq) technology has brought new strategies to deeply understand the biological behaviors of breast cancer. By analyzing cell phenotypes and transcriptome differences at the single-cell level, scRNA-seq reveals the heterogeneity, dynamic growth and differentiation process of cells. This review summarizes the application of scRNA-seq technology in breast cancer research, such as in studies on cell heterogeneity, cancer cell metastasis, drug resistance, and prognosis. scRNA-seq technology is of great significance to deeply analyze the mechanism of breast cancer occurrence and development, identify new therapeutic targets and develop new therapeutic approaches for breast cancer.

Adopting an alternative structure for clinical trials in immunotherapy

Background: This evaluation emphasizes the main points of the original article 'Position paper: new insights into the immunobiology and dynamics of tumor-host interactions require adaptations of clinical studies' by Sprenger et al. and provides further justification for the use of an alternative approach in the design of human clinical trials for new investigational drugs in the field of immuno-oncology.Objective: Standard trial design utilizing the double blind placebo trial approach, while effective for drugs that directly treat tumors, is too costly, slow, and not effective for drugs and protocols that depend on activation of the immune system for killing of tumors.Methods/results: This paper has proposed through the use of detailed diagnostic profiling, small groups of patients with similar tumor microenvironment characteristics be grouped to determine the clinical benefit of immunological combinations that enter clinical trials. In addition, mega data from larger trials in which patients are subcategorized as above can provide the necessary data as a substitute for current double blind placebo trials which do not take into account the immune status of the host and tumor.Conclusion: There needs to be evolution of the clinical trial landscape so that it matches the exponential growth of the field of immunotherapy.

Detecting Interactive Gene Groups for Single-Cell RNA-Seq Data Based on Co-Expression Network Analysis and Subgraph Learning

High-throughput sequencing technologies have enabled the generation of single-cell RNA-seq (scRNA-seq) data, which explore both genetic heterogeneity and phenotypic variation between cells. Some methods have been proposed to detect the related genes causing cell-to-cell variability for understanding tumor heterogeneity. However, most existing methods detect the related genes separately, without considering gene interactions. In this paper, we proposed a novel learning framework to detect the interactive gene groups for scRNA-seq data based on co-expression network analysis and subgraph learning. We first utilized spectral clustering to identify the subpopulations of cells. For each cell subpopulation, the differentially expressed genes were then selected to construct a gene co-expression network. Finally, the interactive gene groups were detected by learning the dense subgraphs embedded in the gene co-expression networks. We applied the proposed learning framework on a real cancer scRNA-seq dataset to detect interactive gene groups of different cancer subtypes. Systematic gene ontology enrichment analysis was performed to examine the detected genes groups by summarizing the key biological processes and pathways. Our analysis shows that different subtypes exhibit distinct gene co-expression networks and interactive gene groups with different functional enrichment. The interactive genes are expected to yield important references for understanding tumor heterogeneity.

Resolving Metabolic Heterogeneity in Experimental Models of the Tumor Microenvironment from a Stable Isotope Resolved Metabolomics Perspective

The tumor microenvironment (TME) comprises complex interactions of multiple cell types that determines cell behavior and metabolism such as nutrient competition and immune suppression. We discuss the various types of heterogeneity that exist in solid tumors, and the complications this invokes for studies of TME. As human subjects and in vivo model systems are complex and difficult to manipulate, simpler 3D model systems that are compatible with flexible experimental control are necessary for studying metabolic regulation in TME. Stable Isotope Resolved Metabolomics (SIRM) is a valuable tool for tracing metabolic networks in complex systems, but at present does not directly address heterogeneous metabolism at the individual cell level. We compare the advantages and disadvantages of different model systems for SIRM experiments, with a focus on lung cancer cells, their interactions with macrophages and T cells, and their response to modulators in the immune microenvironment. We describe the experimental set up, illustrate results from 3D cultures and co-cultures of lung cancer cells with human macrophages, and outline strategies to address the heterogeneous TME.

Applications of single-cell sequencing for the field of otolaryngology: A contemporary review

OBJECTIVES

Single-cell RNA sequencing (scRNA-Seq) is a new technique used to interrogate the transcriptome of individual cells within native tissues that have already resulted in key discoveries in auditory basic science research. Rapid advances in scRNA-Seq make it likely that it will soon be translated into clinical medicine. The goal of this review is to inspire the use of scRNA-Seq in otolaryngology by giving examples of how it can be applied to patient samples and how this information can be used clinically.

METHODS

Studies were selected based on the scientific quality and relevance to scRNA-Seq. In addition to mouse auditory system (inner ear including hair cells and supporting cells, spiral ganglion neurons, and inner ear organoids), recent studies using human primary cell samples are discussed. We also perform our own analysis on publicly available, published scRNA-Seq data from oral head and neck squamous cell carcinoma (HNSCC) samples to serve as an example of a clinically relevant application of scRNA-Seq.

RESULTS

Studies focusing on patient tissues show that scRNA-Seq reveals tissue heterogeneity and rare-cell types responsible for disease pathogenesis. The heterogeneity detected by scRNA-Seq can result in both the identification of known or novel disease biomarkers and drug targets. Our analysis of HNSCC data gives an example for how otolaryngologists can use scRNA-Seq for clinical use.

CONCLUSIONS

Although there are limitations to the translation of scRNA-Seq to the clinic, we show that its use in otolaryngology can give physicians insight into the tissue heterogeneity within their patient's diseased tissue giving them information on disease pathogenesis, novel disease biomarkers or druggable targets, and aid in selecting patient-specific drug cocktails.

Integrated single-cell and bulk gene expression and ATAC-seq reveals heterogeneity and early changes in pathways associated with resistance to cetuximab in HNSCC-sensitive cell lines

Background

Identifying potential resistance mechanisms while tumour cells still respond to therapy is critical to delay acquired resistance.

Methods

We generated the first comprehensive multi-omics, bulk and single-cell data in sensitive head and neck squamous cell carcinoma (HNSCC) cells to identify immediate responses to cetuximab. Two pathways potentially associated with resistance were focus of the study: regulation of receptor tyrosine kinases by TFAP2A transcription factor, and epithelial-to-mesenchymal transition (EMT).

Results

Single-cell RNA-seq demonstrates heterogeneity, with cell-specific TFAP2A and VIM expression profiles in response to treatment and also with global changes to various signalling pathways. RNA-seq and ATAC-seq reveal global changes within 5 days of therapy, suggesting early onset of mechanisms of resistance; and corroborates cell line heterogeneity, with different TFAP2A targets or EMT markers affected by therapy. Lack of TFAP2A expression is associated with HNSCC decreased growth, with cetuximab and JQ1 increasing the inhibitory effect. Regarding the EMT process, short-term cetuximab therapy has the strongest effect on inhibiting migration. TFAP2A silencing does not affect cell migration, supporting an independent role for both mechanisms in resistance.

Conclusion

Overall, we show that immediate adaptive transcriptional and epigenetic changes induced by cetuximab are heterogeneous and cell type dependent; and independent mechanisms of resistance arise while tumour cells are still sensitive to therapy.

Assessing Cell Activities rather than Identities to Interpret Intra-Tumor Phenotypic Diversity and Its Dynamics

Despite advances in single-cell and molecular techniques, it is still unclear how to best quantify phenotypic heterogeneity in cancer cells that evolved beyond normal, known classifications. We present an approach to phenotypically characterize cells based on their activities rather than static classifications. We validated the detectability of specific activities (epithelial-mesenchymal transition, glycolysis) in single cells, using targeted RT-qPCR analyses and in vitro inductions. We analyzed 50 established activity signatures as a basis for phenotypic description in public data and computed cell-cell distances in 28,513 cells from 85 patients and 8 public datasets. Despite not relying on any classification, our measure correlated with standard diversity indices in populations of known structure. We identified bottlenecks as phenotypic diversity reduced upon colorectal cancer initiation. This suggests that focusing on what cancer cells do rather than what they are can quantify phenotypic diversity in universal fashion, to better understand and predict intra-tumor heterogeneity dynamics.

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Cells categorized as having the same identity can perform different activities

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Single-cell expression data can be used to infer the activities cells take part in

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Activity profiles provide a basis to measure phenotypic cell-cell divergence

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Cell activity can quantify intra-tumor heterogeneity more fully than identity