Single cell analysis of cancer genomes

From complexity to clarity: unravelling tumor heterogeneity through the lens of tumor microenvironment for innovative cancer therapy

Despite the tremendous clinical successes recorded in the landscape of cancer therapy, tumor heterogeneity remains a formidable challenge to successful cancer treatment. In recent years, the emergence of high-throughput technologies has advanced our understanding of the variables influencing tumor heterogeneity beyond intrinsic tumor characteristics. Emerging knowledge shows that drivers of tumor heterogeneity are not only intrinsic to cancer cells but can also emanate from their microenvironment, which significantly favors tumor progression and impairs therapeutic response. Although much has been explored to understand the fundamentals of the influence of innate tumor factors on cancer diversity, the roles of the tumor microenvironment (TME) are often undervalued. It is therefore imperative that a clear understanding of the interactions between the TME and other tumor intrinsic factors underlying the plastic molecular behaviors of cancers be identified to develop patient-specific treatment strategies. This review highlights the roles of the TME as an emerging factor in tumor heterogeneity. More particularly, we discuss the role of the TME in the context of tumor heterogeneity and explore the cutting-edge diagnostic and therapeutic approaches that could be used to resolve this recurring clinical conundrum. We conclude by speculating on exciting research questions that can advance our understanding of tumor heterogeneity with the goal of developing customized therapeutic solutions.

Over-expression of mir-181a-3p in serum of breast cancer patients as diagnostic biomarker

BACKGROUND

Recently, the significance of epigenetics, particularly in breast cancer (BC), has become increasingly recognized. MicroRNAs (miRNAs), as an epigenetic factor, are offering valuable insights into various aspects of BC, such as its origins and clinical features. Therefore, studying the disruption of specific miRNAs through microarray and RNA-seq techniques is considered useful.

METHODS AND MATERIALS

We analyzed two microarray datasets (GSE106817 and GSE113486) in order to discover dysregulated miRNAs in the serum of BC patients. Subsequently, RNA-seq analysis was employed on the TCGA data. Two miRNAs, mir-450a-5p and mir-181a-3p, as novel miRNAs in BC studies, were selected for assessment in the serum of 100 BC patients versus 100 healthy female participants. The quantities of the miRNAs described above were determined through the utilization of RT-qPCR. Furthermore, ROC curve assessments were conducted for each individual miRNA. Next, an assessment was conducted to determine the prognostic significance of these miRNAs.

CONCLUSIONS

In summary, the utilization of microarray and RNA-seq analysis techniques has demonstrated that mir-450a-5p and mir-181a-3p exhibit elevated expression levels in the serum of BC patients. However, it is noteworthy that only mir-181a-3p displayed clinical dysregulation, as confirmed by RT-PCR findings. These miRNAs have been found to play a crucial role in modulating essential cellular processes and biological functions that contribute to cancer development. Furthermore, noteworthy outcomes have been observed for mir-181a-3p in relation to diagnostic and prognostic clinical characteristics.

Multi-enzymatic systems synergize new RCA technique amplified super-long dsDNA from DNA circle

BACKGROUND

In the field of DNA amplification, there are great challenges in the effectively amplify of long-chain amplification, especially amplification up to several hundred kb level.

RESULTS

A novel technique for the unbiased whole genome amplification from a thimbleful of DNA circles, such as low as 10 ng/ 10 μL of the circular cpDNA or low as 5 ng/ 10 μL of the plasmid, is developed, which can amplify an abundance of the whole genome sequences. Specifically, the new technique that combines rolling-amplification and triple-enzyme system presents a tightly controlled process of a series of buffers/reactions and optimized procedures, that applies from the primer-template duplexes to the Elution step. The result of this technique provides a new approach for extending RCA capacity, where it can reach 200 kb from the circular cpDNA amplification and 150 kb from the plasmid DNA amplification, that demonstrates superior breadth and evenness of genome coverage, high reproducibility, small amplification bias with the amplification efficiency.

SIGNIFICANCE AND NOVELTY

This new technique will develop into one of the powerful tools for isothermal DNA amplification in vitro, genome sequencing/analysis, phylogenetic analysis, physical mapping, and other molecular biology applications.

A Micropillar Array Based Microfluidic Device for Rare Cell Detection and Single-Cell Proteomics

Advancements in single-cell-related technologies have opened new possibilities for analyzing rare cells, such as circulating tumor cells (CTCs) and rare immune cells. Among these techniques, single-cell proteomics, particularly single-cell mass spectrometric analysis (scMS), has gained significant attention due to its ability to directly measure transcripts without the need for specific reagents. However, the success of single-cell proteomics relies heavily on efficient sample preparation, as protein loss in low-concentration samples can profoundly impact the analysis. To address this challenge, an effective handling system for rare cells is essential for single-cell proteomic analysis. Herein, we propose a microfluidics-based method that offers highly efficient isolation, detection, and collection of rare cells (e.g., CTCs). The detailed fabrication process of the micropillar array-based microfluidic device is presented, along with its application for CTC isolation, identification, and collection for subsequent proteomic analysis.

Chimera: The spoiler in multiple displacement amplification

Multiple displacement amplification (MDA) based on isothermal random priming and high fidelity phi29 DNA polymerase-mediated processive extension has revolutionized the field of whole genome amplification by enabling the amplification of minute amounts of DNA, such as from a single cell, generating vast amounts of DNA with high genome coverage. Despite its advantages, MDA has its own challenges, one of the grandest being the formation of chimeric sequences (chimeras), which presents in all MDA products and seriously disturbs the downstream analysis. In this review, we provide a comprehensive overview of current research on MDA chimeras. We first reviewed the mechanisms of chimera formation and chimera detection methods. We then systematically summarized the characteristics of chimeras, including overlap, chimeric distance, chimeric density, and chimeric rate, as found in independently published sequencing data. Finally, we reviewed the methods used to process chimeric sequences and their impacts on the improvement of data utilization efficiency. The information presented in this review will be useful for those interested in understanding the challenges with MDA and in improving its performance.

Single-Circulating Tumor Cell Whole Genome Amplification to Unravel Cancer Heterogeneity and Actionable Biomarkers

The field of single-cell analysis has advanced rapidly in the last decade and is providing new insights into the characterization of intercellular genetic heterogeneity and complexity, especially in human cancer. In this regard, analyzing single circulating tumor cells (CTCs) is becoming particularly attractive due to the easy access to CTCs from simple blood samples called “liquid biopsies”. Analysis of multiple single CTCs has the potential to allow the identification and characterization of cancer heterogeneity to guide best therapy and predict therapeutic response. However, single-CTC analysis is restricted by the low amounts of DNA in a single cell genome. Whole genome amplification (WGA) techniques have emerged as a key step, enabling single-cell downstream molecular analysis. Here, we provide an overview of recent advances in WGA and their applications in the genetic analysis of single CTCs, along with prospective views towards clinical applications. First, we focus on the technical challenges of isolating and recovering single CTCs and then explore different WGA methodologies and recent developments which have been utilized to amplify single cell genomes for further downstream analysis. Lastly, we list a portfolio of CTC studies which employ WGA and single-cell analysis for genetic heterogeneity and biomarker detection.

Receptor–ligand pair typing and prognostic risk model for papillary thyroid carcinoma based on single-cell sequencing

The papillary thyroid carcinoma (PTC) microenvironment consists of various cancer and surrounding cells, and the communication between them is mainly performed through ligand–receptor (LR) interactions. Single-cell RNA sequencing (scRNA-seq) has been performed to investigate the role of intercellular communication networks in tumor progression. In addition, scRNA-seq can accurately identify the characteristics of immune cell subsets, which is of great significance for predicting the efficacy of immunotherapy. In this study, the cell–cell communication network was analyzed through LR pairs, and a new PTC molecular phenotype was developed based on LR pairs. Furthermore, a risk model was established to predict patient response to PD-1 blockade immunotherapy. The scRNA-seq dataset was obtained from GSE184362, and the bulk tumor RNA-seq dataset was obtained from The Cancer Genome Atlas. CellPhoneDB was used for cellular communication analysis. LR pair correlations were calculated and used to identify molecular subtypes, and the least absolute shrinkage and selection operator (Lasso) Cox regression was used to develop a risk model based on LR pairs. The IMvigor210 and GSE78220 cohorts were used as external validations for the LR.score to predict responses to PD-L1 blockade therapy. A total of 149 LR pairs with significant expression and prognostic correlation were included, and three PTC molecular subtypes were obtained from those with significant prognostic differences. Then, five LR pairs were selected to construct the risk scoring model, a reliable and independent prognostic factor in the training set, test set, and whole dataset. Furthermore, two external validation sets confirmed the predictive efficacy of the LR.score for response to PD-1 blockade therapy.

Leveraging single cell sequencing to unravel intra-tumour heterogeneity and tumour evolution in human cancers

Intra-tumour heterogeneity and tumour evolution are well-documented phenomena in human cancers. While the advent of next-generation sequencing technologies has facilitated the large-scale capture of genomic data, the field of single cell genomics is nascent but rapidly advancing and generating many new insights into the complex molecular mechanisms of tumour biology. In this review, we provide an overview of current single cell DNA sequencing technologies, exploring how recent methodological advancements have enumerated new insights into intra-tumour heterogeneity and tumour evolution. Areas highlighted include the potential power of single cell genome sequencing studies to explore evolutionary dynamics contributing to tumourigenesis through to progression, metastasis and therapy resistance. We also explore the use of in-situ sequencing technologies to study intra-tumour heterogeneity in a spatial context, as well as examining the use of single cell genomics to perform lineage tracing in both normal and malignant tissues. Finally, we consider the use of multi-modal single cell sequencing technologies. Taken together, it is hoped that these many facets of single cell genome sequencing will improve our understanding of tumourigenesis, progression and lethality in cancer leading to the development of novel therapies. This article is protected by copyright. All rights reserved.

Analysis of single-cell RNA-sequencing data identifies a hypoxic tumor subpopulation associated with poor prognosis in triple-negative breast cancer

Triple-negative breast cancer (TNBC) is an aggressive subtype of mammary carcinoma characterized by low expression levels of estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2). Along with the rapid development of the single-cell RNA-sequencing (scRNA-seq) technology, the heterogeneity within the tumor microenvironment (TME) could be studied at a higher resolution level, facilitating an exploration of the mechanisms leading to poor prognosis during tumor progression. In previous studies, hypoxia was considered as an intrinsic characteristic of TME in solid tumors, which would activate downstream signaling pathways associated with angiogenesis and metastasis. Moreover, hypoxia-related genes (HRGs) based risk score models demonstrated nice performance in predicting the prognosis of TNBC patients. However, it is essential to further investigate the heterogeneity within hypoxic TME, such as intercellular communications. In the present study, utilizing single-sample Gene Set Enrichment Analysis (ssGSEA) and cell-cell communication analysis on the scRNA-seq data retrieved from Gene Expression Omnibus (GEO) database with accession number GSM4476488, we identified four tumor subpopulations with diverse functions, particularly a hypoxia-related one. Furthermore, results of cell-cell communication analysis revealed the dominant role of the hypoxic tumor subpopulation in angiogenesis- and metastasis-related signaling pathways as a signal sender. Consequently, regard the TNBC cohorts acquired from The Cancer Genome Atlas (TCGA) and GEO as train set and test set respectively, we constructed a risk score model with reliable capacity for the prediction of overall survival (OS), where ARTN and L1CAM were identified as risk factors promoting angiogenesis and metastasis of tumors. The expression of ARTN and L1CAM were further analyzed through tumor immune estimation resource (TIMER) platform. In conclusion, these two marker genes of the hypoxic tumor subpopulation played vital roles in tumor development, indicating poor prognosis in TNBC patients.

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.

Microfluidics applications for high-throughput single cell sequencing

The inherent heterogeneity of individual cells in cell populations plays significant roles in disease development and progression, which is critical for disease diagnosis and treatment. Substantial evidences show that the majority of traditional gene profiling methods mask the difference of individual cells. Single cell sequencing can provide data to characterize the inherent heterogeneity of individual cells, and reveal complex and rare cell populations. Different microfluidic technologies have emerged for single cell researches and become the frontiers and hot topics over the past decade. In this review article, we introduce the processes of single cell sequencing, and review the principles of microfluidics for single cell analysis. Also, we discuss the common high-throughput single cell sequencing technologies along with their advantages and disadvantages. Lastly, microfluidics applications in single cell sequencing technology for the diagnosis of cancers and immune system diseases are briefly illustrated.

Cell-specific gene association network construction from single-cell RNA sequence

The recent development of a high throughput single-cell RNA sequence devises the opportunity to study entire transcriptomes in the smallest detail. It also leads to the characterization of molecules and subtypes of a cell. Cancer epigenetics induced not only from individual molecules but also from the dysfunction of the system and the coupling effect of genes. While rapid advances are being made in the development of tools for single-cell RNA-seq data analysis, few slants are noticed in the potential advantages of single-cell network construction.Here, we used network perturbation theory with significant analysis to develop a cell-specific network that provides an insight into gene-gene association based on molecular expressions in a single-cell resolution. Besides, using this method, we can characterize each cell by inspecting how genes are connected and can identify the hub genes using network degree theory. Pathway & Gene enrichment analysis of the identified cell-specific high network degree genes supported the effectiveness of this method. This method could be beneficial for personalized drug design and even therapeutics.

Cancer evolution: Darwin and beyond

Clinical and laboratory studies over recent decades have established branched evolution as a feature of cancer. However, while grounded in somatic selection, several lines of evidence suggest a Darwinian model alone is insufficient to fully explain cancer evolution. First, the role of macroevolutionary events in tumour initiation and progression contradicts Darwin's central thesis of gradualism. Whole-genome doubling, chromosomal chromoplexy and chromothripsis represent examples of single catastrophic events which can drive tumour evolution. Second, neutral evolution can play a role in some tumours, indicating that selection is not always driving evolution. Third, increasing appreciation of the role of the ageing soma has led to recent generalised theories of age-dependent carcinogenesis. Here, we review these concepts and others, which collectively argue for a model of cancer evolution which extends beyond Darwin. We also highlight clinical opportunities which can be grasped through targeting cancer vulnerabilities arising from non-Darwinian patterns of evolution.

Using single-cell sequencing technology to detect circulating tumor cells in solid tumors

Circulating tumor cells are tumor cells with high vitality and high metastatic potential that invade and shed into the peripheral blood from primary solid tumors or metastatic foci. Due to the heterogeneity of tumors, it is difficult for high-throughput sequencing analysis of tumor tissues to find the genomic characteristics of low-abundance tumor stem cells. Single-cell sequencing of circulating tumor cells avoids interference from tumor heterogeneity by comparing the differences between single-cell genomes, transcriptomes, and epigenetic groups among circulating tumor cells, primary and metastatic tumors, and metastatic lymph nodes in patients' peripheral blood, providing a new perspective for understanding the biological process of tumors. This article describes the identification, biological characteristics, and single-cell genome-wide variation in circulating tumor cells and summarizes the application of single-cell sequencing technology to tumor typing, metastasis analysis, progression detection, and adjuvant therapy.

Studying the History of Tumor Evolution from Single-Cell Sequencing Data by Exploring the Space of Binary Matrices

Single-cell sequencing (SCS) data have great potential in reconstructing the evolutionary history of tumors. Rapid advances in SCS technology in the past decade were followed by the design of various computational methods for inferring trees of tumor evolution. Some of the earliest methods were based on the direct search in the space of trees with the goal of finding the maximum likelihood tree. However, it can be shown that instead of searching directly in the tree space, we can perform a search in the space of binary matrices and obtain maximum likelihood tree directly from the maximum likelihood matrix. The potential of the latter tree search strategy has recently been recognized by different research groups and several related methods were published in the past 2 years. Here we provide a review of the theoretical background of these methods and a detailed discussion, which are largely missing in the available publications, of the correlation between the two tree search strategies. We also discuss each of the existing methods based on the search in the space of binary matrices and summarize the best-known single-cell DNA sequencing data sets, which can be used in the future for assessing performance on real data of newly developed methods.

Application of single cell genomics to focal epilepsies: A call to action

Focal epilepsies are the largest epilepsy subtype and associated with significant morbidity. Somatic variation is a newly recognized genetic mechanism underlying a subset of focal epilepsies, but little is known about the processes through which somatic mosaicism causes seizures, the cell types carrying the pathogenic variants, or their developmental origin. Meanwhile, the inception of single cell biology has completely revolutionized the study of neurological diseases and has the potential to answer some of these key questions. Focusing on single cell genomics, transcriptomics, and epigenomics in focal epilepsy research, circumvents the averaging artifact associated with studying bulk brain tissue and offers the kind of granularity that is needed for investigating the consequences of somatic mosaicism. Here we have provided a brief overview of some of the most developed single cell techniques and the major considerations around applying them to focal epilepsy research.

Analysis of Intratumoral Heterogeneity in Myelodysplastic Syndromes with Isolated del(5q) Using a Single Cell Approach

Simple Summary

Myelodysplastic syndromes (MDS) are a heterogeneous group of clonal hematopoietic stem cell malignancies characterized by ineffective differentiation of one or more bone marrow cell lineages. Only 50% of patients with de novo MDS will be found to have cytogenetic abnormalities, of which del(5q) is the most common. In 10% of MDS cases, del(5q) is found as a sole abnormality. In this work, a single cell approach was used to analyze intratumoral heterogeneity in four patients with MDS with isolated del(5q). We were able to observe that an ancestral event in one patient can appear as a secondary hit in another one, thus reflecting the high intratumoral heterogeneity in MDS with isolated del(5q) and the importance of patient-specific molecular characterization.

Abstract

Myelodysplastic syndromes (MDS) are a heterogeneous group of hematological diseases. Among them, the most well characterized subtype is MDS with isolated chromosome 5q deletion (MDS del(5q)), which is the only one defined by a cytogenetic abnormality that makes these patients candidates to be treated with lenalidomide. During the last decade, single cell (SC) analysis has emerged as a powerful tool to decipher clonal architecture and to further understand cancer and other diseases at higher resolution level compared to bulk sequencing techniques. In this study, a SC approach was used to analyze intratumoral heterogeneity in four patients with MDS del(5q). Single CD34+CD117+CD45+CD19- bone marrow hematopoietic stem progenitor cells were isolated using the C1 system (Fluidigm) from diagnosis or before receiving any treatment and from available follow-up samples. Selected somatic alterations were further analyzed in SC by high-throughput qPCR (Biomark HD, Fluidigm) using specific TaqMan assays. A median of 175 cells per sample were analyzed. Inferred clonal architectures were relatively simple and either linear or branching. Similar to previous studies based on bulk sequencing to infer clonal architecture, we were able to observe that an ancestral event in one patient can appear as a secondary hit in another one, thus reflecting the high intratumoral heterogeneity in MDS del(5q) and the importance of patient-specific molecular characterization.

scGMAI: a Gaussian mixture model for clustering single-cell RNA-Seq data based on deep autoencoder

The rapid development of single-cell RNA sequencing (scRNA-Seq) technology provides strong technical support for accurate and efficient analyzing single-cell gene expression data. However, the analysis of scRNA-Seq is accompanied by many obstacles, including dropout events and the curse of dimensionality. Here, we propose the scGMAI, which is a new single-cell Gaussian mixture clustering method based on autoencoder networks and the fast independent component analysis (FastICA). Specifically, scGMAI utilizes autoencoder networks to reconstruct gene expression values from scRNA-Seq data and FastICA is used to reduce the dimensions of reconstructed data. The integration of these computational techniques in scGMAI leads to outperforming results compared to existing tools, including Seurat, in clustering cells from 17 public scRNA-Seq datasets. In summary, scGMAI is an effective tool for accurately clustering and identifying cell types from scRNA-Seq data and shows the great potential of its applicative power in scRNA-Seq data analysis. The source code is available at https://github.com/QUST-AIBBDRC/scGMAI/.

Cell-level somatic mutation detection from single-cell RNA sequencing

MOTIVATION

Both single-cell RNA sequencing (scRNA-seq) and DNA sequencing (scDNA-seq) have been applied for cell-level genomic profiling. For mutation profiling, the latter seems more natural. However, the task is highly challenging due to the limited input materials from only two copies of DNA molecules, while whole-genome amplification generates biases and other technical noises. ScRNA-seq starts with a higher input amount, so generally has better data quality. There exists various methods for mutation detection from DNA sequencing, it is not clear whether these methods work for scRNA-seq data.

RESULTS

Mutation detection methods developed for either bulk-cell sequencing data or scDNA-seq data do not work well for the scRNA-seq data, as they produce substantial numbers of false positives. We develop a novel and robust statistical method-called SCmut-to identify specific cells that harbor mutations discovered in bulk-cell data. Statistically SCmut controls the false positives using the 2D local false discovery rate method. We apply SCmut to several scRNA-seq datasets. In scRNA-seq breast cancer datasets SCmut identifies a number of highly confident cell-level mutations that are recurrent in many cells and consistent in different samples. In a scRNA-seq glioblastoma dataset, we discover a recurrent cell-level mutation in the PDGFRA gene that is highly correlated with a well-known in-frame deletion in the gene. To conclude, this study contributes a novel method to discover cell-level mutation information from scRNA-seq that can facilitate investigation of cell-to-cell heterogeneity.

AVAILABILITY AND IMPLEMENTATION

The source codes and bioinformatics pipeline of SCmut are available at https://github.com/nghiavtr/SCmut.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

The promise of toxicogenomics for genetic toxicology: past, present and future

Toxicogenomics, the application of genomics to toxicology, was described as 'a new era' for toxicology. Standard toxicity tests typically involve a number of short-term bioassays that are costly, time consuming, require large numbers of animals and generally focus on a single end point. Toxicogenomics was heralded as a way to improve the efficiency of toxicity testing by assessing gene regulation across the genome, allowing rapid classification of compounds based on characteristic expression profiles. Gene expression microarrays could measure and characterise genome-wide gene expression changes in a single study and while transcriptomic profiles that can discriminate between genotoxic and non-genotoxic carcinogens have been identified, challenges with the approach limited its application. As such, toxicogenomics did not transform the field of genetic toxicology in the way it was predicted. More recently, next generation sequencing (NGS) technologies have revolutionised genomics owing to the fact that hundreds of billions of base pairs can be sequenced simultaneously cheaper and quicker than traditional Sanger methods. In relation to genetic toxicology, and thousands of cancer genomes have been sequenced with single-base substitution mutational signatures identified, and mutation signatures have been identified following treatment of cells with known or suspected environmental carcinogens. RNAseq has been applied to detect transcriptional changes following treatment with genotoxins; modified RNAseq protocols have been developed to identify adducts in the genome and Duplex sequencing is an example of a technique that has recently been developed to accurately detect mutation. Machine learning, including MutationSeq and SomaticSeq, has also been applied to somatic mutation detection and improvements in automation and/or the application of machine learning algorithms may allow high-throughput mutation sequencing in the future. This review will discuss the initial promise of transcriptomics for genetic toxicology, and how the development of NGS technologies and new machine learning algorithms may finally realise that promise.

The Use of Molecular Subtypes for Precision Therapy of Recurrent and Metastatic Gastrointestinal Stromal Tumor

Gastrointestinal stromal tumors (GISTs) are the most common mesenchymal tumor in the digestive tract. Tyrosine kinase inhibitors (TKIs), represented by imatinib, sunitinib, and regorafenib, have become the main treatment for recurrent and metastatic GISTs. With the wide application of mutation analysis and the precision medicine, molecular characteristics have been determined that not only predict the prognosis of patients with recurrent and metastatic GISTs, but also are closely related to the efficacy of first-, second- and third-line TKIs for GISTs, as well as other TKIs. Despite the significant effects of TKIs, the emergence of primary and secondary resistance ultimately leads to treatment failure and tumor progression. Currently, due to the signal transmission of KIT/PDGFRA during onset and tumor progression, strategies to counteract drug resistance include the replacement of TKIs and the development of new drugs that are directed towards carcinogenic mutations. In addition, it is also the embodiment of precision medicine for GISTs to explore new carcinogenic mechanisms and develop new drugs relying on new biotechnology. Surgery can benefit specific patients but its major purpose is to diminish the resistant clones. However, the prognosis of recurrent and metastatic patients is still unsatisfactory. Therefore, it is worth paying attention to how to maximize the benefits for patients.

Single-Cell Clustering Based on Shared Nearest Neighbor and Graph Partitioning

Clustering of single-cell RNA sequencing (scRNA-seq) data enables discovering cell subtypes, which is helpful for understanding and analyzing the processes of diseases. Determining the weight of edges is an essential component in graph-based clustering methods. While several graph-based clustering algorithms for scRNA-seq data have been proposed, they are generally based on k-nearest neighbor (KNN) and shared nearest neighbor (SNN) without considering the structure information of graph. Here, to improve the clustering accuracy, we present a novel method for single-cell clustering, called structural shared nearest neighbor-Louvain (SSNN-Louvain), which integrates the structure information of graph and module detection. In SSNN-Louvain, based on the distance between a node and its shared nearest neighbors, the weight of edge is defined by introducing the ratio of the number of the shared nearest neighbors to that of nearest neighbors, thus integrating structure information of the graph. Then, a modified Louvain community detection algorithm is proposed and applied to identify modules in the graph. Essentially, each community represents a subtype of cells. It is worth mentioning that our proposed method integrates the advantages of both SNN graph and community detection without the need for tuning any additional parameter other than the number of neighbors. To test the performance of SSNN-Louvain, we compare it to five existing methods on 16 real datasets, including nonnegative matrix factorization, single-cell interpretation via multi-kernel learning, SNN-Cliq, Seurat and PhenoGraph. The experimental results show that our approach achieves the best average performance in these datasets.

A community effort to create standards for evaluating tumor subclonal reconstruction

Tumor DNA sequencing data can be interpreted by computational methods that analyze genomic heterogeneity to infer evolutionary dynamics. A growing number of studies have used these approaches to link cancer evolution with clinical progression and response to therapy. Although the inference of tumor phylogenies is rapidly becoming standard practice in cancer genome analyses, standards for evaluating them are lacking. To address this need, we systematically assess methods for reconstructing tumor subclonality. First, we elucidate the main algorithmic problems in subclonal reconstruction and develop quantitative metrics for evaluating them. Then we simulate realistic tumor genomes that harbor all known clonal and subclonal mutation types and processes. Finally, we benchmark 580 tumor reconstructions, varying tumor read depth, tumor type and somatic variant detection. Our analysis provides a baseline for the establishment of gold-standard methods to analyze tumor heterogeneity.

Joint analysis of single-cell and bulk tissue sequencing data to infer intratumor heterogeneity

Many computational methods have been developed to discern intratumor heterogeneity (ITH) using DNA sequence data from bulk tumor samples. These methods share an assumption that two mutations arise from the same subclone if they have similar mutant allele-frequencies (MAFs), and thus it is difficult or impossible to distinguish two subclones with similar MAFs. Single-cell DNA sequencing (scDNA-seq) data can be very informative for ITH inference. However, due to the difficulty of DNA amplification, scDNA-seq data are often very noisy. A promising new study design is to collect both bulk and single-cell DNA-seq data and jointly analyze them to mitigate the limitations of each data type. To address the analytic challenges of this new study design, we propose a computational method named BaSiC (Bulk tumor and Single Cell), to discern ITH by jointly analyzing DNA-seq data from bulk tumor and single cells. We demonstrate that BaSiC has comparable or better performance than the methods using either data type. We further evaluate BaSiC using bulk tumor and single-cell DNA-seq data from a breast cancer patient and several leukemia patients.

Liquid biopsy: one cell at a time

As an alternative target to surgically resected tissue specimens, liquid biopsy has gained much attention over the past decade. Of the various circulating biomarkers, circulating tumor cells (CTCs) have particularly opened new windows into the metastatic cascade, with their functional, biochemical, and biophysical properties. Given the extreme rarity of intact CTCs and the associated technical challenges, however, analyses have been limited to bulk-cell strategies, missing out on clinically significant sources of information from cellular heterogeneity. With recent technological developments, it is now possible to probe genetic material of CTCs at the single-cell resolution to study spatial and temporal dynamics in circulation. Here, we discuss recent transcriptomic profiling efforts that enabled single-cell characterization of patient-derived CTCs spanning diverse cancer types. We further highlight how expression data of these putative biomarkers have advanced our understanding of metastatic spectrum and provided a basis for the development of CTC-based liquid biopsies to track, monitor, and predict the efficacy of therapy and any emergent resistance.

The pre-analytical phase of the liquid biopsy

The term 'liquid biopsy', introduced in 2013 in reference to the analysis of circulating tumour cells (CTCs) in cancer patients, was extended to cell-free nucleic acids (cfNAs) circulating in blood and other body fluids. CTCs and cfNAs are now considered diagnostic and prognostic markers, used as surrogate materials for the molecular characterisation of solid tumours, in particular for research on tumour-specific or actionable somatic mutations. Molecular characterisation of cfNAs and CTCs (especially at the single cell level) is technically challenging, requiring highly sensitive and specific methods and/or multi-step processes. The analysis of the liquid biopsy relies on a plethora of methods whose standardisation cannot be accomplished without disclosing criticisms related to the pre-analytical phase. Thus, pre-analytical factors potentially influencing downstream cellular and molecular analyses must be considered in order to translate the liquid biopsy approach into clinical practice. The present review summarises the most recent reports in this field, discussing the main pre-analytical aspects related to CTCs, cfNAs and exosomes in blood samples for liquid biopsy analysis. A short discussion on non-blood liquid biopsy samples is also included.

Computational enhancement of single-cell sequences for inferring tumor evolution

Motivation

Tumor sequencing has entered an exciting phase with the advent of single-cell techniques that are revolutionizing the assessment of single nucleotide variation (SNV) at the highest cellular resolution. However, state-of-the-art single-cell sequencing technologies produce data with many missing bases (MBs) and incorrect base designations that lead to false-positive (FP) and false-negative (FN) detection of somatic mutations. While computational methods are available to make biological inferences in the presence of these errors, the accuracy of the imputed MBs and corrected FPs and FNs remains unknown.

Results

Using computer simulated datasets, we assessed the robustness performance of four existing methods (OncoNEM, SCG, SCITE and SiFit) and one new method (BEAM). BEAM is a Bayesian evolution-aware method that improves the quality of single-cell sequences by using the intrinsic evolutionary information in the single-cell data in a molecular phylogenetic framework. Overall, BEAM and SCITE performed the best. Most of the methods imputed MBs with high accuracy, but effective detection and correction of FPs and FNs is a challenge, especially for small datasets. Analysis of an empirical dataset shows that computational methods can improve both the quality of tumor single-cell sequences and their utility for biological inference. In conclusion, tumor cells descend from pre-existing cells, which creates evolutionary continuity in single-cell sequencing datasets. This information enables BEAM and other methods to correctly impute missing data and incorrect base assignments, but correction of FPs and FNs remains challenging when the number of SNVs sampled is small relative to the number of cells sequenced.

Availability and implementation

BEAM is available on the web at https://github.com/SayakaMiura/BEAM.

Application of single-cell sequencing in human cancer

Precision medicine is emerging as a cornerstone of future cancer care with the objective of providing targeted therapies based on the molecular phenotype of each individual patient. Traditional bulk-level molecular phenotyping of tumours leads to significant information loss, as the molecular profile represents an average phenotype over large numbers of cells, while cancer is a disease with inherent intra-tumour heterogeneity at the cellular level caused by several factors, including clonal evolution, tissue hierarchies, rare cells and dynamic cell states. Single-cell sequencing provides means to characterize heterogeneity in a large population of cells and opens up opportunity to determine key molecular properties that influence clinical outcomes, including prognosis and probability of treatment response. Single-cell sequencing methods are now reliable enough to be used in many research laboratories, and we are starting to see applications of these technologies for characterization of human primary cancer cells. In this review, we provide an overview of studies that have applied single-cell sequencing to characterize human cancers at the single-cell level, and we discuss some of the current challenges in the field.

ChimeraMiner: An Improved Chimeric Read Detection Pipeline and Its Application in Single Cell Sequencing

As the most widely-used single cell whole genome amplification (WGA) approach, multiple displacement amplification (MDA) has a superior performance, due to the high-fidelity and processivity of phi29 DNA polymerase. However, chimeric reads, generated in MDA, cause severe disruption in many single-cell studies. Herein, we constructed ChimeraMiner, an improved chimeric read detection pipeline for analyzing the sequencing data of MDA and classified the chimeric sequences. Two datasets (MDA1 and MDA2) were used for evaluating and comparing the efficiency of ChimeraMiner and previous pipeline. Under the same hardware condition, ChimeraMiner spent only 43.4% (43.8% for MDA1 and 43.0% for MDA2) processing time. Respectively, 24.4 million (6.31%) read pairs out of 773 million reads, and 17.5 million (6.62%) read pairs out of 528 million reads were accurately classified as chimeras by ChimeraMiner. In addition to finding 83.60% (17,639,371) chimeras, which were detected by previous pipelines, ChimeraMiner screened 6,736,168 novel chimeras, most of which were missed by the previous pipeline. Applying in single-cell datasets, all three types of chimera were discovered in each dataset, which introduced plenty of false positives in structural variation (SV) detection. The identification and filtration of chimeras by ChimeraMiner removed most of the false positive SVs (83.8%). ChimeraMiner revealed improved efficiency in discovering chimeric reads, and is promising to be widely used in single-cell sequencing.

Analysis of Single-Cell RNA-seq Data by Clustering Approaches

Background:

The recently developed single-cell RNA sequencing (scRNA-seq) has attracted a great amount of attention due to its capability to interrogate expression of individual cells, which is superior to traditional bulk cell sequencing that can only measure mean gene expression of a population of cells. scRNA-seq has been successfully applied in finding new cell subtypes. New computational challenges exist in the analysis of scRNA-seq data.

Objective:

We provide an overview of the features of different similarity calculation and clustering methods, in order to facilitate users to select methods that are suitable for their scRNA-seq. We would also like to show that feature selection methods are important to improve clustering performance.

Results:

We first described similarity measurement methods, followed by reviewing some new clustering methods, as well as their algorithmic details. This analysis revealed several new questions, including how to automatically estimate the number of clustering categories, how to discover novel subpopulation, and how to search for new marker genes by using feature selection methods.

Conclusion:

Without prior knowledge about the number of cell types, clustering or semisupervised learning methods are important tools for exploratory analysis of scRNA-seq data.</P>

Translating insights into tumor evolution to clinical practice: promises and challenges

Accelerating technological advances have allowed the widespread genomic profiling of tumors. As yet, however, the vast catalogues of mutations that have been identified have made only a modest impact on clinical medicine. Massively parallel sequencing has informed our understanding of the genetic evolution and heterogeneity of cancers, allowing us to place these mutational catalogues into a meaningful context. Here, we review the methods used to measure tumor evolution and heterogeneity, and the potential and challenges for translating the insights gained to achieve clinical impact for cancer therapy, monitoring, early detection, risk stratification, and prevention. We discuss how tumor evolution can guide cancer therapy by targeting clonal and subclonal mutations both individually and in combination. Circulating tumor DNA and circulating tumor cells can be leveraged for monitoring the efficacy of therapy and for tracking the emergence of resistant subclones. The evolutionary history of tumors can be deduced for late-stage cancers, either directly by sampling precursor lesions or by leveraging computational approaches to infer the timing of driver events. This approach can identify recurrent early driver mutations that represent promising avenues for future early detection strategies. Emerging evidence suggests that mutational processes and complex clonal dynamics are active even in normal development and aging. This will make discriminating developing malignant neoplasms from normal aging cell lineages a challenge. Furthermore, insight into signatures of mutational processes that are active early in tumor evolution may allow the development of cancer-prevention approaches. Research and clinical studies that incorporate an appreciation of the complex evolutionary patterns in tumors will not only produce more meaningful genomic data, but also better exploit the vulnerabilities of cancer, resulting in improved treatment outcomes.

Highly Multiplexed Fluorescence in Situ Hybridization for in Situ Genomics

The quantification of changes in gene copy number is critical to our understanding of tumor biology and for the clinical management of cancer patients. DNA fluorescence in situ hybridization is the gold standard method to detect copy number alterations, but it is limited by the number of genes one can quantify simultaneously. To increase the throughput of this informative technique, a fluorescent bar-code system for the unique labeling of dozens of genes and an automated image analysis algorithm that enabled their simultaneous hybridization for the quantification of gene copy numbers were devised. We demonstrate the reliability of this multiplex approach on normal human lymphocytes, metaphase spreads of transformed cell lines, and cultured circulating tumor cells. It also opens the door to the development of gene panels for more comprehensive analysis of copy number changes in tissue, including the study of heterogeneity and of high-throughput clinical assays that could provide rapid quantification of gene copy numbers in samples with limited cellularity, such as circulating tumor cells.

Gel-based cell manipulation method for isolation and genotyping of single-adherent cells

The simple and rapid method for isolation of single-adherent cells from a culture dish was developed and applied to genetic analysis of single-cells.

Single-Cell Sequencing in Precision Medicine

The application of next-generation sequencing in cancer genomics allowed for a better understanding of the genetics and pathogenesis of cancer. Single-cell genomics is a relatively new field that has enhanced our current knowledge of the genetic diversity of cells involved in the complex biological systems of cancer. Single-cell genomics is a rapidly developing field, and current technologies can assay a single cell's gene expression, DNA variation, epigenetic state, and nuclear structure. Statistical and computational methods are central to single-cell genomics and allows for extraction of meaningful information. The translational application of single-cell sequencing in precision cancer therapy has the potential to improve cancer diagnostics, prognostics, targeted therapy, early detection, and noninvasive monitoring. Furthermore, single-cell genomics will transform cancer research as even initial experiments have revolutionized our current understanding of gene regulation and disease.

Single Cell Omics of Breast Cancer: An Update on Characterization and Diagnosis

Breast cancer is recognized for its different clinical behaviors and patient outcomes, regardless of common histopathological features at diagnosis. The heterogeneity and dynamics of breast cancer undergoing clonal evolution produces cells with distinct degrees of drug resistance and metastatic potential. Presently, single cell analysis have made outstanding advancements, overshadowing the hurdles of heterogeneity linked with vast populations. The speedy progression in sequencing analysis now allow unbiased, high-output and high-resolution elucidation of the heterogeneity from individual cell within a population. Classical therapeutics strategies for individual patients are governed by the presence and absence of expression pattern of the estrogen and progesterone receptors and human epidermal growth factor receptor 2. However, such tactics for clinical classification have fruitfulness in selection of targeted therapies, short-term patient responses but unable to predict the long-term survival. In any phenotypic alterations, like breast cancer disease, molecular signature have proven its implication, as we aware that individual cell's state is regulated at diverse levels, such as DNA, RNA and protein, by multifaceted interplay of intrinsic biomolecules pathways existing in the organism and extrinsic stimuli such as ambient environment. Thus for complete understanding, complete profiling of single cell requires a synchronous investigations from different levels (multi-omics) to avoid incomplete information produced from single cell. In this article, initially we briefed on novel updates of various methods available to explore omics and then we finally pinpointed on various omics (i.e. genomics, transcriptomics, epigenomics, proteomics and metabolomics) data and few special aspects of circulating tumor cells, disseminated tumor cells and cancer stem cells, so far available from various studies that can be used for better management of breast cancer patients.

Tumour heterogeneity and metastasis at single-cell resolution

Tumours comprise a heterogeneous collection of cells with distinct genetic and phenotypic properties that can differentially promote progression, metastasis and drug resistance. Emerging single-cell technologies provide a new opportunity to profile individual cells within tumours and investigate what roles they play in these processes. This Review discusses key technological considerations for single-cell studies in cancer, new findings using single-cell technologies and critical open questions for future applications.

Single-Cell High-Resolution Detection and Quantification of Protein Isoforms Differing by a Single Charge Unit

Isoelectric focusing (IEF) is an electrophoretic technique that enables the separation of proteins based on their isoelectric points. Until recently, this valuable method was not feasible for single-cell applications, which are necessary to interrogate heterogeneous cell populations. Herein we highlight a recently published method enabling the analysis of single-cell proteomics, which utilizes microfluidics coupled with IEF, photocapture, and immunoprobing of the protein in the same micro-gel, which can be stripped and reprobed multiple times.

Somatic Mutagenesis in Mammals and Its Implications for Human Disease and Aging

DNA mutations as a consequence of errors during DNA damage repair, replication, or mitosis are the substrate for evolution. In multicellular organisms, mutations can occur in the germline and also in somatic tissues, where they are associated with cancer and other chronic diseases and possibly with aging. Recent advances in high-throughput sequencing have made it relatively easy to study germline de novo mutations, but in somatic cells, the vast majority of mutations are low-abundant and can be detected only in clonal lineages, such as tumors, or single cells. Here we review recent results on somatic mutations in normal human and animal tissues with a focus on their possible functional consequences.

Timing somatic events in the evolution of cancer

Cancer arises through the accumulation of somatic mutations over time. An understanding of the sequence of events during this process should allow both earlier diagnosis and better prediction of cancer progression. However, the pathways of tumor evolution have not yet been comprehensively characterized. With the advent of whole genome sequencing, it is now possible to infer the evolutionary history of single tumors from the snapshot of their genome taken at diagnosis, giving new insights into the biology of tumorigenesis.

Characterization of cancer genomic heterogeneity by next-generation sequencing advances precision medicine in cancer treatment

Cancer is a heterogeneous disease with unique genomic and phenotypic features that differ between individual patients and even among individual tumor regions. In recent years, large-scale genomic studies and new next-generation sequencing technologies have uncovered more scientific details about tumor heterogeneity, with significant implications for the choice of specific molecular biomarkers and clinical decision making. Genomic heterogeneity significantly contributes to the generation of a diverse cell population during tumor development and progression, representing a determining factor for variation in tumor treatment response. It has been considered a prominent contributor to therapeutic failure, and increases the likelihood of resistance to future therapies in most common cancers. The understanding of molecular heterogeneity in cancer is a fundamental component of precision oncology, enabling the identification of genomic alteration of key genes and pathways that can be targeted therapeutically. Here, we review the emerging knowledge of tumor genomics and heterogeneity, as well as potential implications for precision medicine in cancer treatment and new therapeutic discoveries. An analysis and interpretation of the TCGA database was included.

Sensitivity to sequencing depth in single-cell cancer genomics

BACKGROUND

Querying cancer genomes at single-cell resolution is expected to provide a powerful framework to understand in detail the dynamics of cancer evolution. However, given the high costs currently associated with single-cell sequencing, together with the inevitable technical noise arising from single-cell genome amplification, cost-effective strategies that maximize the quality of single-cell data are critically needed. Taking advantage of previously published single-cell whole-genome and whole-exome cancer datasets, we studied the impact of sequencing depth and sampling effort towards single-cell variant detection.

METHODS

Five single-cell whole-genome and whole-exome cancer datasets were independently downscaled to 25, 10, 5, and 1× sequencing depth. For each depth level, ten technical replicates were generated, resulting in a total of 6280 single-cell BAM files. The sensitivity of variant detection, including structural and driver mutations, genotyping, clonal inference, and phylogenetic reconstruction to sequencing depth was evaluated using recent tools specifically designed for single-cell data.

RESULTS

Altogether, our results suggest that for relatively large sample sizes (25 or more cells) sequencing single tumor cells at depths > 5× does not drastically improve somatic variant discovery, characterization of clonal genotypes, or estimation of single-cell phylogenies.

CONCLUSIONS

We suggest that sequencing multiple individual tumor cells at a modest depth represents an effective alternative to explore the mutational landscape and clonal evolutionary patterns of cancer genomes.

Lab-on-a-Chip Platforms for Biophysical Studies of Cancer with Single-Cell Resolution

Recent cancer research has more strongly emphasized the biophysical aspects of tumor development, progression, and microenvironment. In addition to genetic modifications and mutations in cancer cells, it is now well accepted that the physical properties of cancer cells such as stiffness, electrical impedance, and refractive index vary with tumor progression and can identify a malignant phenotype. Moreover, cancer heterogeneity renders population-based characterization techniques inadequate, as individual cellular features are lost in the average. Hence, platforms for fast and accurate characterization of biophysical properties of cancer cells at the single-cell level are required. Here, we highlight some of the recent advances in the field of cancer biophysics and the development of lab-on-a-chip platforms for single-cell biophysical analyses of cancer cells.

Perfusion double-channel micropipette probes for oxygen flux mapping with single-cell resolution

Measuring cellular respiration with single-cell spatial resolution is a significant challenge, even with modern tools and techniques. Here, a double-channel micropipette is proposed and investigated as a probe to achieve this goal by sampling fluid near the point of interest. A finite element model (FEM) of this perfusion probe is validated by comparing simulation results with experimental results of hydrodynamically confined fluorescent molecule diffusion. The FEM is then used to investigate the dependence of the oxygen concentration variation and the measurement signal on system parameters, including the pipette's shape, perfusion velocity, position of the oxygen sensors within the pipette, and proximity of the pipette to the substrate. The work demonstrates that the use of perfusion double-barrel micropipette probes enables the detection of oxygen consumption signals with micrometer spatial resolution, while amplifying the signal, as compared to sensors without the perfusion system. In certain flow velocity ranges (depending on pipette geometry and configuration), the perfusion flow increases oxygen concentration gradients formed due to cellular oxygen consumption. An optimal perfusion velocity for respiratory measurements on single cells can be determined for different system parameters (e.g., proximity of the pipette to the substrate). The optimum perfusion velocities calculated in this paper range from 1.9 to 12.5 μm/s. Finally, the FEM model is used to show that the spatial resolution of the probe may be varied by adjusting the pipette tip diameter, which may allow oxygen consumption mapping of cells within tissue, as well as individual cells at subcellular resolution.

Molecular Profiling and Significance of Circulating Tumor Cell Based Genetic Signatures

Cancer kills by metastasizing beyond the primary site. Early detection, surgical intervention and other treatments have improved the survival rates of patients with cancer, however, once metastasis occurs, responses to conventional therapies become significantly less effective, and this remains the leading cause of death. Circulating tumor cells (CTCs) are tumor cells that have preferentially disseminated from the primary tumor mass into the hematological system, and are en route to favorable distant sites where if they survive, can develop into metastases. They may be the earliest detectable cells with metastatic ability, and are gaining increasing attention because of their prognostic value in many types of cancers including breast, prostate, colon and lung. Recent technological advances have removed barriers that previously hindered the detection and isolation of these rare cells from blood, and have exponentially improved the genetic resolution at which we can characterize signatures that define CTCs. Some of the most significant observations from such examinations are described here. Firstly, aberrations that were thought to be unique to CTCs are detected at subclonal frequencies within primary tumors with measurable heterogeneity, indicating pre-existing genetic signatures for metastasis. Secondly, these subclonal events are enriched in CTCs and metastases, pointing towards the selection of a more 'fit' component of tumor cells with survival advantages. Lastly, this component of cancer cells may also be the chemoresistant portion that escapes systemic treatment, or acquires resistance during progression of the disease. The future of cancer management may include a standardized method of measuring intratumor heterogeneity of the primary as well as matched CTCs. This will help identify and target rare aberrations within primary tumors that make them more adept to disseminate, and also to monitor the development of treatment resistant subclones as cancer progresses.

Single-cell sequencing data reveal widespread recurrence and loss of mutational hits in the life histories of tumors

Intra-tumor heterogeneity poses substantial challenges for cancer treatment. A tumor's composition can be deduced by reconstructing its mutational history. Central to current approaches is the infinite sites assumption that every genomic position can only mutate once over the lifetime of a tumor. The validity of this assumption has never been quantitatively assessed. We developed a rigorous statistical framework to test the infinite sites assumption with single-cell sequencing data. Our framework accounts for the high noise and contamination present in such data. We found strong evidence for the same genomic position being mutationally affected multiple times in individual tumors for 11 of 12 single-cell sequencing data sets from a variety of human cancers. Seven cases involved the loss of earlier mutations, five of which occurred at sites unaffected by large-scale genomic deletions. Four cases exhibited a parallel mutation, potentially indicating convergent evolution at the base pair level. Our results refute the general validity of the infinite sites assumption and indicate that more complex models are needed to adequately quantify intra-tumor heterogeneity for more effective cancer treatment.