Evolution and heterogeneity of non-hereditary colorectal cancer revealed by single-cell exome sequencing

PhenoDriver: interpretable framework for studying personalized phenotype-associated driver genes in breast cancer

Identifying personalized cancer driver genes and further revealing their oncogenic mechanisms is critical for understanding the mechanisms of cell transformation and aiding clinical diagnosis. Almost all existing methods primarily focus on identifying driver genes at the cohort or individual level but fail to further uncover their underlying oncogenic mechanisms. To fill this gap, we present an interpretable framework, PhenoDriver, to identify personalized cancer driver genes, elucidate their roles in cancer development and uncover the association between driver genes and clinical phenotypic alterations. By analyzing 988 breast cancer patients, we demonstrate the outstanding performance of PhenoDriver in identifying breast cancer driver genes at the cohort level compared to other state-of-the-art methods. Otherwise, our PhenoDriver can also effectively identify driver genes with both recurrent and rare mutations in individual patients. We further explore and reveal the oncogenic mechanisms of some known and unknown breast cancer driver genes (e.g. TP53, MAP3K1, HTT, etc.) identified by PhenoDriver, and construct their subnetworks for regulating clinical abnormal phenotypes. Notably, most of our findings are consistent with existing biological knowledge. Based on the personalized driver profiles, we discover two existing and one unreported breast cancer subtypes and uncover their molecular mechanisms. These results intensify our understanding for breast cancer mechanisms, guide therapeutic decisions and assist in the development of targeted anticancer therapies.

Single-cell phylogenies reveal changes in the evolutionary rate within cancer and healthy tissues

Cell lineages accumulate somatic mutations during organismal development, potentially leading to pathological states. The rate of somatic evolution within a cell population can vary due to multiple factors, including selection, a change in the mutation rate, or differences in the microenvironment. Here, we developed a statistical test called the Poisson Tree (PT) test to detect varying evolutionary rates among cell lineages, leveraging the phylogenetic signal of single-cell DNA sequencing (scDNA-seq) data. We applied the PT test to 24 healthy and cancer samples, rejecting a constant evolutionary rate in 11 out of 15 cancer and five out of nine healthy scDNA-seq datasets. In six cancer datasets, we identified subclonal mutations in known driver genes that could explain the rate accelerations of particular cancer lineages. Our findings demonstrate the efficacy of scDNA-seq for studying somatic evolution and suggest that cell lineages often evolve at different rates within cancer and healthy tissues.

Association of LAMA1 Single-Nucleotide Polymorphisms with Risk of Esophageal Squamous Cell Carcinoma among the Eastern Chinese Population

Introduction

LAMA1, also known as laminin subunit α1, is a member of the laminin family, which is widely reported to be a key basement membrane molecule that affects various biological activities and is associated with many kinds of diseases. We aimed to investigate the association between LAMA1single-nucleotide polymorphisms and the occurrence and progression of esophageal squamous cell carcinoma in the Chinese population.

Method

2,186 participants were collected retrospectively between October 2008 and January 2017, including 1,043 ESCC patients and 1,143 noncancer patients. A 2 mL blood sample was obtained intravenously for the LDR for SNP analysis. The 6 SNP loci of LAMA1 were selected and examined. We analyzed the association of several genetic models of 6 LAMA1 SNP loci, sex, age, smoking and drinking status, and the occurrence of esophageal squamous cell carcinoma.

Results

In the rs62081531 G > A locus, genotype GA was a protective factor for ESCC compared with GG (OR: 0.830, P=0.046), especially among the younger and nondrinkers. At rs607230 T > C, genotype TC was linked with a lower risk of ESCC compared with TT. (OR: 0.613, P=0.034). Haplotype Frequencies revealed that A_rs62081531G_rs621993A_rs539713T_rs566655A_rs73938538C_rs607230 (OR: 0.803, P=0.028) and G_rs62081531G_rs621993A_rs539713T_rs566655C_rs73938538C_rs607230 (OR: 0.679, P=0.010) were strongly associated with lower susceptibility of ESCC.

Conclusion

The LAMA1 rs62081531, rs539713, rs566655, and rs607230 polymorphisms were demonstrated to be related to susceptibility to ESCC in the Chinese population. LAMA1 SNPs may have a significant impact on the occurrence of esophageal cancer and may serve as potential diagnostic biomarkers.

Advances in SEMA3F regulation of clinically high-incidence cancers

Cancer has become a leading cause of morbidity and mortality in recent years. Its high prevalence has had a severe impact on society. Researchers have achieved fruitful results in the causative factors, pathogenesis, treatment strategies, and cancer prevention. Semaphorin 3F (SEMA3F), a member of the signaling family, was initially reported in the literature to inhibit the growth, invasion, and metastasis of cancer cells in lung cancer. Later studies showed it has cancer-inhibiting effects in malignant tumors such as breast, colorectal, ovarian, oral squamous cell carcinoma, melanoma, and head and neck squamous carcinoma. In contrast, recent studies have reported that SEMA3F is expressed more in hepatocellular carcinoma than in normal tissue and promotes metastasis of hepatocellular carcinoma. We chose lung, breast, colorectal, and hepatocellular carcinomas with high clinical prevalence to review the roles and molecular mechanisms of SEMA3F in these four carcinomas. We concluded with an outlook on clinical interventions for patients targeting SEMA3F.

SIEVE: joint inference of single-nucleotide variants and cell phylogeny from single-cell DNA sequencing data

We present SIEVE, a statistical method for the joint inference of somatic variants and cell phylogeny under the finite-sites assumption from single-cell DNA sequencing. SIEVE leverages raw read counts for all nucleotides and corrects the acquisition bias of branch lengths. In our simulations, SIEVE outperforms other methods in phylogenetic reconstruction and variant calling accuracy, especially in the inference of homozygous variants. Applying SIEVE to three datasets, one for triple-negative breast (TNBC), and two for colorectal cancer (CRC), we find that double mutant genotypes are rare in CRC but unexpectedly frequent in the TNBC samples.

Intestinal cellular heterogeneity and disease development revealed by single-cell technology

The intestinal epithelium is responsible for food digestion and nutrient absorption and plays a critical role in hormone secretion, microorganism defense, and immune response. These functions depend on the integral single-layered intestinal epithelium, which shows diversified cell constitution and rapid self-renewal and presents powerful regeneration plasticity after injury. Derailment of homeostasis of the intestine epithelium leads to the development of diseases, most commonly including enteritis and colorectal cancer. Therefore, it is important to understand the cellular characterization of the intestinal epithelium at the molecular level and the mechanisms underlying its homeostatic maintenance. Single-cell technologies allow us to gain molecular insights at the single-cell level. In this review, we summarize the single-cell RNA sequencing applications to understand intestinal cell characteristics, spatiotemporal evolution, and intestinal disease development.

Single-cell sequencing reveals CD133+CD44−-originating evolution and novel stemness related variants in human colorectal cancer

Background

Tumor heterogeneity of human colorectal cancer (CRC)-initiating cells (CRCICs) in cancer tissues often represents aggressive features of cancer progression. For high-resolution examination of CRCICs, we performed single-cell whole-exome sequencing (scWES) and bulk cell targeted exome sequencing (TES) of CRCICs to investigate stemness-specific somatic alterations or clonal evolution.

Methods

Single cells of three subpopulations of CRCICs (CD133⁺CD44⁺, CD133⁻CD44⁺, and CD133⁺CD44⁻ cells), CRC cells (CRCCs), and control cells from one CRC tissue were sorted for scWES. Then, we set up a mutation panel from scWES data and TES was used to validate mutation distribution and clonal evolution in additional 96 samples (20 patients) those were also sorted into the same three groups of CRCICs and CRCCs. The knock-down experiments were used to analyze stemness-related mutant genes. Neoantigens of these mutant genes and their MHC binding affinity were also analyzed.

Findings

Clonal evolution analysis of scWES and TES showed that the CD133⁺CD44⁻ CRCICs were the likely origin of CRC before evolving into other groups of CRCICs/CRCCs. We revealed that AHNAK2, PLIN4, HLA-B, ALK, CCDC92 and ALMS1 genes were specifically mutated in CRCICs followed by the validation of their functions. Furthermore, four predicted neoantigens of AHNAK2 were identified and validated, which might have applications in immunotherapy for CRC patients.

Interpretation

All the integrative analyses above revealed clonal evolution of CRC and new markers for CRCICs and demonstrate the important roles of CRCICs in tumorigenesis and progression of CRCs.

Funding

A full list of funding bodies that contributed to this study can be found in the Acknowledgements section.

Automated histological classification for digital pathology images of colonoscopy specimen via deep learning

Colonoscopy is an effective tool to detect colorectal lesions and needs the support of pathological diagnosis. This study aimed to develop and validate deep learning models that automatically classify digital pathology images of colon lesions obtained from colonoscopy-related specimen. Histopathological slides of colonoscopic biopsy or resection specimens were collected and grouped into six classes by disease category: adenocarcinoma, tubular adenoma (TA), traditional serrated adenoma (TSA), sessile serrated adenoma (SSA), hyperplastic polyp (HP), and non-specific lesions. Digital photographs were taken of each pathological slide to fine-tune two pre-trained convolutional neural networks, and the model performances were evaluated. A total of 1865 images were included from 703 patients, of which 10% were used as a test dataset. For six-class classification, the mean diagnostic accuracy was 97.3% (95% confidence interval [CI], 96.0–98.6%) by DenseNet-161 and 95.9% (95% CI 94.1–97.7%) by EfficientNet-B7. The per-class area under the receiver operating characteristic curve (AUC) was highest for adenocarcinoma (1.000; 95% CI 0.999–1.000) by DenseNet-161 and TSA (1.000; 95% CI 1.000–1.000) by EfficientNet-B7. The lowest per-class AUCs were still excellent: 0.991 (95% CI 0.983–0.999) for HP by DenseNet-161 and 0.995 for SSA (95% CI 0.992–0.998) by EfficientNet-B7. Deep learning models achieved excellent performances for discriminating adenocarcinoma from non-adenocarcinoma lesions with an AUC of 0.995 or 0.998. The pathognomonic area for each class was appropriately highlighted in digital images by saliency map, particularly focusing epithelial lesions. Deep learning models might be a useful tool to help the diagnosis for pathologic slides of colonoscopy-related specimens.

A Detailed Overview About the Single-Cell Analyses of Solid Tumors Focusing on Colorectal Cancer

In recent years, the evolution of the molecular biological technical background led to the widespread application of single-cell sequencing, a versatile tool particularly useful in the investigation of tumor heterogeneity. Even 10 years ago the comprehensive characterization of colorectal cancers by The Cancer Genome Atlas was based on measurements of bulk samples. Nowadays, with single-cell approaches, tumor heterogeneity, the tumor microenvironment, and the interplay between tumor cells and their surroundings can be described in unprecedented detail. In this review article we aimed to emphasize the importance of single-cell analyses by presenting tumor heterogeneity and the limitations of conventional investigational approaches, followed by an overview of the whole single-cell analytic workflow from sample isolation to amplification, sequencing and bioinformatic analysis and a review of recent literature regarding the single-cell analysis of colorectal cancers.

Applications and Achievements of Single-Cell Sequencing in Gastrointestinal Cancer

Gastrointestinal cancer represents a public health concern that seriously endangers human health. The emerging single-cell sequencing (SCS) technologies are different from the large-scale sequencing technologies which provide inaccurate data. SCS is a powerful tool for deciphering the single-cell resolutions of cellular and molecular landscapes, revealing the features of single-cell genomes, transcriptomes, and epigenomes. Recently, SCS has been applied in the field of gastrointestinal cancer research for clarifying the origin and heterogeneity of gastrointestinal cancer, acquiring micro-environmental information, and improving diagnostic and treatment methods. This review outlines the applications of SCS in gastrointestinal cancer research and summarizes the most recent advances in the field.

High-throughput single-сell sequencing in cancer research

With advances in sequencing and instrument technology, bioinformatics analysis is being applied to batches of massive cells at single-cell resolution. High-throughput single-cell sequencing can be utilized for multi-omics characterization of tumor cells, stromal cells or infiltrated immune cells to evaluate tumor progression, responses to environmental perturbations, heterogeneous composition of the tumor microenvironment, and complex intercellular interactions between these factors. Particularly, single-cell sequencing of T cell receptors, alone or in combination with single-cell RNA sequencing, is useful in the fields of tumor immunology and immunotherapy. Clinical insights obtained from single-cell analysis are critically important for exploring the biomarkers of disease progression or antitumor treatment, as well as for guiding precise clinical decision-making for patients with malignant tumors. In this review, we summarize the clinical applications of single-cell sequencing in the fields of tumor cell evolution, tumor immunology, and tumor immunotherapy. Additionally, we analyze the tumor cell response to antitumor treatment, heterogeneity of the tumor microenvironment, and response or resistance to immune checkpoint immunotherapy. The limitations of single-cell analysis in cancer research are also discussed.

Single-cell transcriptomic profiling unravels the adenoma-initiation role of protein tyrosine kinases during colorectal tumorigenesis

The adenoma-carcinoma sequence is a well-accepted roadmap for the development of sporadic colorectal cancer. However, cellular heterogeneity in aberrant epithelial cells limits our understanding of carcinogenesis in colorectal tissues. Here, we performed a single-cell RNA sequencing survey of 54,788 cells from patient-matched tissue samples, including blood, normal tissue, para-cancer, polyp, and colorectal cancer. At each stage of carcinogenesis, we characterized cell types, transcriptional signatures, and differentially expressed genes of distinct cell populations. The molecular signatures of epithelial cells at normal, benign, and malignant stages were defined at the single-cell scale. Adenoma and carcinoma precursor cell populations were identified and characterized followed by validation with large cohort biopsies. Protein tyrosine kinases (PTKs) BMX and HCK were identified as potential drivers of adenoma initiation. Specific BMX and HCK upregulations were observed in adenoma precursor cell populations from normal and adenoma biopsies. Overexpression of BMX and HCK significantly promoted colorectal epithelial cell proliferation. Importantly, in the organoid culture system, BMX and HCK upregulations resulted in the formation of multilayered polyp-like buds protruding towards the organoid lumen, mimicking the pathological polyp morphology often observed in colorectal cancer. Molecular mechanism analysis revealed that upregulation of BMX or HCK activated the JAK-STAT pathway. In conclusion, our work improved the current knowledge regarding colorectal epithelial evolution during carcinogenesis at the single-cell resolution. These findings may lead to improvements in colorectal cancer diagnosis and treatment.

Intratumor genetic heterogeneity and clonal evolution to decode endometrial cancer progression

Analyzing different tumor regions by next generation sequencing allows the assessment of intratumor genetic heterogeneity (ITGH), a phenomenon that has been studied widely in some tumor types but has been less well explored in endometrial carcinoma (EC). In this study, we sought to characterize the spatial and temporal heterogeneity of 9 different ECs using whole-exome sequencing, and by performing targeted sequencing validation of the 42 primary tumor regions and 30 metastatic samples analyzed. In addition, copy number alterations of serous carcinomas were assessed by comparative genomic hybridization arrays. From the somatic mutations, identified by whole-exome sequencing, 532 were validated by targeted sequencing. Based on these data, the phylogenetic tree reconstructed for each case allowed us to establish the tumors’ evolution and correlate this to tumor progression, prognosis, and the presence of recurrent disease. Moreover, we studied the genetic landscape of an ambiguous EC and the molecular profile obtained was used to guide the selection of a potential personalized therapy for this patient, which was subsequently validated by preclinical testing in patient-derived xenograft models. Overall, our study reveals the impact of analyzing different tumor regions to decipher the ITGH in ECs, which could help make the best treatment decision.

Multifocal Organoid Capturing of Colon Cancer Reveals Pervasive Intratumoral Heterogenous Drug Responses

Intratumor heterogeneity (ITH) stands as one of the main difficulties in the treatment of colorectal cancer (CRC) as it causes the development of resistant clones and leads to heterogeneous drug responses. Here, 12 sets of patient-derived organoids (PDOs) and cell lines (PDCs) isolated from multiple regions of single tumors from 12 patients, capturing ITH by multiregion sampling of individual tumors, are presented. Whole-exome sequencing and RNA sequencing of the 12 sets are performed. The PDOs and PDCs of the 12 sets are also analyzed with a clinically relevant 24-compound library to assess their drug responses. The results reveal unexpectedly widespread subregional heterogeneity among PDOs and PDCs isolated from a single tumor, which is manifested by genetic and transcriptional heterogeneity and strong variance in drug responses, while each PDO still recapitulates the major histologic, genomic, and transcriptomic characteristics of the primary tumor. The data suggest an imminent drawback of single biopsy-originated PDO-based clinical diagnosis in evaluating CRC patient responses. Instead, the results indicate the importance of targeting common somatic driver mutations positioned in the trunk of all tumor subregional clones in parallel with a comprehensive understanding of the molecular ITH of each tumor.

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.

Expression of adipokine ghrelin and ghrelin receptor in human colorectal adenoma and correlation with the grade of dysplasia

BACKGROUND

Ghrelin is an adipokine that plays an important role in energy balance. Expression of ghrelin and ghrelin receptor has been investigated in different tissues and tumors. Studies regarding expression of ghrelin and ghrelin receptor in colorectal tumors are scarce and no data on expression of ghrelin and its receptor in colorectal adenomas has been published. Ghrelin and ghrelin receptor were highly expressed in colon carcinoma cells while expression was decreased in less differentiated tumors, presuming that ghrelin might be important in early phases of tumorigenesis.

AIM

To investigate the expression of ghrelin and ghrelin receptor in human colorectal adenomas and adjacent colorectal tissue.

METHODS

In this prospective study (conducted from June 2015 until May 2019) we included 92 patients (64 male and 28 female) who underwent polypectomy for colorectal adenomas in the Department of Gastroenterology and Hepatology, “Sestre milosrdnice” Clinical Hospital Center in Zagreb, Croatia. After endoscopic removal of colorectal adenoma, an additional sample of colon mucosa in the proximity of the adenoma was collected for pathohistological analysis. Adenomas were graded according to the stage of dysplasia, and ghrelin and ghrelin receptor expression were determined immunohistochemically in both adenoma and adjacent colon tissue using the polyclonal antibody for ghrelin (ab150514, ABCAM Inc, Cambridge, United States) and ghrelin receptor (ab48285, ABCAM Inc, Cambridge, United States). Categorical and nominal variables were described through frequencies and proportions and the difference between specific groups were analyzed with Fisher’s and Fisher-Freeman-Halton’s method respectively. Spearman's rank correlation coefficient was determined for correlation of expression of ghrelin and ghrelin receptor in adenoma and adjacent colon tissue with the grade of adenoma dysplasia.

RESULTS

Among 92 patients with colorectal adenoma 43 had adenomas with high-grade dysplasia (46.7%). High expression of ghrelin was 7 times more common in high-grade adenoma compared to low-grade adenomas (13.95% to 2.04%, P = 0.048), while the expression of ghrelin in adjacent colon tissue was low. We found no correlation between ghrelin receptor expression in adenoma and adjacent colon tissue and the grade of colorectal adenoma dysplasia. The most significant correlation was found between ghrelin and ghrelin receptor expression in adenomas with high-grade dysplasia (rho = 0.519, P < 0.001).

CONCLUSION

Ghrelin and ghrelin receptor are expressed in colorectal adenoma and adjacent tissue with ghrelin expression being more pronounced in high grade dysplasia as a possible consequence of increased local synthesis.

Intra-Tumor Heterogeneity of Colorectal Cancer Necessitates the Multi-Regional Sequencing for Comprehensive Mutational Profiling

Background

The panorama and details of quantitative intratumor heterogeneity have not been fully investigated in colorectal cancer (CRC) patients with solitary lesion without distal metastasis, and its influences on sequencing interpretation and therapeutic strategies have not been explored.

Methods

Cancer tissues and matched blood from 70 sporadic CRC patients were collected and were divided into two cohorts. Four individual tissue biopsies were obtained from each of the 47 patients (multi-sample cohort). One random cancer tissue biopsy was obtained from each of the rest 23 patients (single-sample cohort). A 10 mL of blood was collected from all patients and the circulating cell-free DNA (cfDNA) was extracted. A 605-gene panel was used for targeted sequencing with tissue and paired blood.

Results

Mutational landscape revealed significantly higher mutational frequency in APC, CARD11 and CSMD3 in multi-sample cohort than single-sample cohort (P<0.05). The number of mutations and the ratio of trunk, shared and branch mutations showed extensive heterogeneity in multi-sample cohort, and the percentage of trunk mutations in major driver genes, including APC, TP53 and KRAS, was higher than 70%. A total of 929 mutations were detected in tissue/blood in multi-sample group, with 921(99.1%) from tissue and 472(50.8%) from blood (464 common mutations,49.9%). In contrast, 394 mutations were detected in tissue/blood in single-sample group, with 231 (58.6%) from tissue and 219 (55.6%) from blood (56 common mutations, 11.9%). The number of mutations of major driver genes detected in tissue was higher than that in blood in the multi-sample cohort, while it was similar in the single-sample group. Quantification analysis revealed differential correlation between tissue and blood VAF in trunk, shared and branch mutations. Meanwhile, VAF of trunk mutations was significantly higher than shared mutations and branch mutations. VAF exhibited significant differences among distinct stages, locations, differentiation and sex status.

Conclusion

Characteristic extensive heterogeneity was revealed for solitary CRC without distal metastasis. Multi-regional biopsy was necessary for comprehensive mutation detection in CRC.

A Portrait of Intratumoral Genomic and Transcriptomic Heterogeneity at Single-Cell Level in Colorectal Cancer

In the study of cancer, omics technologies are supporting the transition from traditional clinical approaches to precision medicine. Intra-tumoral heterogeneity (ITH) is detectable within a single tumor in which cancer cell subpopulations with different genome features coexist in a patient in different tumor areas or may evolve/differ over time. Colorectal carcinoma (CRC) is characterized by heterogeneous features involving genomic, epigenomic, and transcriptomic alterations. The study of ITH is a promising new frontier to lay the foundation towards successful CRC diagnosis and treatment. Genome and transcriptome sequencing together with editing technologies are revolutionizing biomedical research, representing the most promising tools for overcoming unmet clinical and research challenges. Rapid advances in both bulk and single-cell next-generation sequencing (NGS) are identifying primary and metastatic intratumoral genomic and transcriptional heterogeneity. They provide critical insight in the origin and spatiotemporal evolution of genomic clones responsible for early and late therapeutic resistance and relapse. Single-cell technologies can be used to define subpopulations within a known cell type by searching for differential gene expression within the cell population of interest and/or effectively isolating signal from rare cell populations that would not be detectable by other methods. Each single-cell sequencing analysis is driven by clustering of cells based on their differentially expressed genes. Genes that drive clustering can be used as unique markers for a specific cell population. In this review we analyzed, starting from published data, the possible achievement of a transition from clinical CRC research to precision medicine with an emphasis on new single-cell based techniques; at the same time, we focused on all approaches and issues related to this promising technology. This transition might enable noninvasive screening for early diagnosis, individualized prediction of therapeutic response, and discovery of additional novel drug targets.

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

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

Evolution and progression of Barrett's oesophagus to oesophageal cancer

Cancer cells are shaped through an evolutionary process of DNA mutation, cell selection and population expansion. Early steps in this process are driven by a set of mutated driver genes and structural alterations to the genome through copy number gains or losses. Oesophageal adenocarcinoma (EAC) and the pre-invasive tissue, Barrett's oesophagus (BE), provide an ideal example in which to observe and study this evolution. BE displays early genomic instability, specifically in copy number changes that may later be observed in EAC. Furthermore, these early changes result in patterns of progression (that is, 'born bad', gradual or catastrophic) that may help to describe the evolution of EAC. As only a small proportion of patients with BE will go on to develop cancer, a better understanding of these patterns and the resulting genomic changes should improve early detection in EAC and may provide clues for the evolution of cancer more broadly.

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.

Clinical Implications of Colorectal Cancer Stem Cells in the Age of Single-Cell Omics and Targeted Therapies

The cancer stem cell (CSC) concept emerged from the recognition of inherent tumor heterogeneity and suggests that within a given tumor, in analogy to normal tissues, there exists a cellular hierarchy composed of a minority of more primitive cells with enhanced longevity (ie, CSCs) that give rise to shorter-lived, more differentiated cells (ie, cancer bulk populations), which on their own are not capable of tumor perpetuation. CSCs can be responsible for cancer therapeutic resistance to conventional, targeted, and immunotherapeutic treatment modalities, and for cancer progression through CSC-intrinsic molecular mechanisms. The existence of CSCs in colorectal cancer (CRC) was first established through demonstration of enhanced clonogenicity and tumor-forming capacity of this cell subset in human-to-mouse tumor xenotransplantation experiments and subsequently confirmed through lineage-tracing studies in mice. Surface markers for CRC CSC identification and their prospective isolation are now established. Therefore, the application of single-cell omics technologies to CSC characterization, including whole-genome sequencing, RNA sequencing, and epigenetic analyses, opens unprecedented opportunities to discover novel targetable molecular pathways and hence to develop novel strategies for CRC eradication. We review recent advances in this field and discuss the potential implications of next-generation CSC analyses for currently approved and experimental targeted CRC therapies.

Discovering the Mutational Profile of Early Colorectal Lesions: A Translational Impact

Simple Summary

Colorectal cancer (CRC) is one of the most common malignancies worldwide. Next-generation sequencing technologies have identified new candidate genes and deepened the knowledge of the molecular mechanisms underlying the progression of colonic adenomas towards CRC. The main genetic, epigenetic, and molecular alterations driving the onset and progression of CRC in both hereditary and sporadic settings have also been investigated. The evaluation of the CRC risk based on the molecular characterization of early pre-cancerous lesions may contribute to the development of targeted preventive strategies development, help define specific risk profiles, and identify patients who will benefit from targeted endoscopic surveillance.

Abstract

Colorectal cancer (CRC) develops through a multi-step process characterized by the acquisition of multiple somatic mutations in oncogenes and tumor-suppressor genes, epigenetic alterations and genomic instability. These events lead to the progression from precancerous lesions to advanced carcinomas. This process requires several years in a sporadic setting, while occurring at an early age and or faster in patients affected by hereditary CRC-predisposing syndromes. Since advanced CRC is largely untreatable or unresponsive to standard or targeted therapies, the endoscopic treatment of colonic lesions remains the most efficient CRC-preventive strategy. In this review, we discuss recent studies that have assessed the genetic alterations in early colorectal lesions in both hereditary and sporadic settings. Establishing the genetic profile of early colorectal lesions is a critical goal in the development of risk-based preventive strategies.

Colorectal Cancer Stem Cell States Uncovered by Simultaneous Single-Cell Analysis of Transcriptome and Telomeres

Cancer stem cells (CSCs) presumably contribute to tumor progression and drug resistance, yet their definitive features have remained elusive. Here, simultaneous measurement of telomere length and transcriptome in the same cells enables systematic assessment of CSCs in primary colorectal cancer (CRC). The in-depth transcriptome profiled by SMART-seq2 is independently validated by high-throughput scRNA-seq using 10 × Genomics. It is found that rare CSCs exist in dormant state and display plasticity toward cancer epithelial cells (EPCs) that essentially are presumptive tumor-initiating cells (TICs), while both retaining the prominent signaling pathways including WNT, TGF-β, and HIPPO/YAP. Moreover, CSCs exhibit chromosome copy number variation (CNV) pattern resembling cancer EPCs but distinct from normal stem cells, suggesting the phylogenetic relationship between CSCs and cancer EPCs. Notably, CSCs maintain shorter telomeres and possess minimal telomerase activity consistent with their nonproliferative nature, unlike cancer EPCs. Additionally, the specific signature of CSCs particularly NOTUM, SMOC2, BAMBI, PHLDA1, and TNFRSF19 correlates with the prognosis of CRC. These findings characterize the heterogeneity of CSCs and their linkage to cancer EPCs/TICs, some of which are conventionally regarded as CSCs.

Single-cell sequencing technology in tumor research

Tumor heterogeneity is a key characteristic of malignant tumors and a significant obstacle in cancer treatment and research. Although bulk tissue sequencing has wide coverage and high accuracy, it can only represent the dominant cell signal information of each sample, while masking the unique gene expression of rare cells; therefore it cannot represent genes that are unstable within a subgroup, but unchanged in a majority of cells. With the progress of genomic technology, the emergence of single-cell sequencing (SCS) has effectively solved the above problem. Genetic, transcriptomic and epigenetic sequencing at the single-cell level provides an important basis for us to correctly classify the cell subsets of heterogeneous tumor populations and to reveal the process of complex changes in tumor cells at the molecular level. Single-cell sequencing technology has been applied to the field of cancer, revealing exciting discoveries in the potential mechanisms of tumor driver gene mutation, clonal evolution, invasion and metastasis. It also provides favorable conditions for developing new tumor biomarkers and providing more accurate and individualized targeted tumor therapy. Herein, we review the steps and methods of single-cell sequencing and highlight the application of SCS in tumor diagnosis and clinical treatment.

BnpC: Bayesian non-parametric clustering of single-cell mutation profiles

Motivation

The high resolution of single-cell DNA sequencing (scDNA-seq) offers great potential to resolve intratumor heterogeneity (ITH) by distinguishing clonal populations based on their mutation profiles. However, the increasing size of scDNA-seq datasets and technical limitations, such as high error rates and a large proportion of missing values, complicate this task and limit the applicability of existing methods.

Results

Here, we introduce BnpC, a novel non-parametric method to cluster individual cells into clones and infer their genotypes based on their noisy mutation profiles. We benchmarked our method comprehensively against state-of-the-art methods on simulated data using various data sizes, and applied it to three cancer scDNA-seq datasets. On simulated data, BnpC compared favorably against current methods in terms of accuracy, runtime and scalability. Its inferred genotypes were the most accurate, especially on highly heterogeneous data, and it was the only method able to run and produce results on datasets with 5000 cells. On tumor scDNA-seq data, BnpC was able to identify clonal populations missed by the original cluster analysis but supported by Supplementary Experimental Data. With ever growing scDNA-seq datasets, scalable and accurate methods such as BnpC will become increasingly relevant, not only to resolve ITH but also as a preprocessing step to reduce data size.

Availability and implementation

BnpC is freely available under MIT license at https://github.com/cbg-ethz/BnpC.

Supplementary information

Supplementary data are available at Bioinformatics online.

Identification and Functional Validation of Radioresistance-Related Genes AHNAK2 and EVPL in Esophageal Squamous Cell Carcinoma by Exome and Transcriptome Sequencing Analyses

Introduction

Esophageal squamous cell carcinoma (ESCC) is often resistant to radiotherapy, likely due to sub-clones that survive and repopulate in the tumor. The analysis of genomic sequencing data related to radiotherapy will provide a better understanding of the intratumoral heterogeneity and genetic evolution of ESCC during radiotherapy.

Methods

We analyzed whole-exome sequencing data from pre- and post-irradiation ESCC patients at single-cell and bulk levels in public datasets. We investigated the gene functions involving radioresistance in ESCC cell lines. Furthermore, we established gene knockdown cell lines and explored the transcriptional alterations induced by RNA interference (RNAi) of these genes in KYSE-150 ESCC cell line.

Results

We identified three candidate genes related to radioresistance: AHNAK2, EVPL and LAMA5. Knockdown of AHNAK2 and EVPL genes led to increased radioresistance in ESCC cell lines, but not LAMA5. The transcriptome analysis indicated that these genes may regulate the expression of interleukins, interleukin receptors and chemokines by inhibiting the NF-κB and TNF signaling pathways in radioresistant ESCC cells, thereby suppressing their immune response.

Conclusion

These data may provide new therapeutic strategies by targeting general ESCC radioresistance-related genes, which may eventually help the development of targeted therapies.

Transcriptome profiling unveils GAP43 regulates ABC transporters and EIF2 signaling in colorectal cancer cells

Background

The growth- and plasticity-associated protein-43 (GAP43) is biasedly expressed in indigestive system and nervous system. Recent study has shown that GAP43 is responsible for the development of neuronal growth and axonal regeneration in normal nervous tissue, while serves as a specific biomarker of relapsed or refractory neuroblastoma. However, its expression pattern and function in digestive system cancer remains to be clarified.

Methods

In this study, we examined the GAP43 status with qRT-PCR and bisulfite genomic sequencing in colorectal cancer (CRC). We investigated the effect of overexpressed GAP43 in CRC cells with RNA-seq. The RNA-seq data was analyzed with DAVID and IPA.

Results

GAP43 was downregulated in CRC compared to the adjacent tissues. DNA methylase inhibitor 5-Aza-CdR treatment could significantly induce GAP43, indicated that the silencing of GAP43 gene in CRC is closely related to DNA methylation. Bisulfite genomic sequencing confirmed the promoter methylation of GAP43 in CRC. To explore the transcriptional alterations by overexpressed GAP43 in CRC, we performed RNA-seq and found that upregulated genes were significantly enriched in the signaling pathways of ABC transporters and ECM-receptor interaction, while downregulated genes were significantly enriched in Ribosome signaling pathway. Further Ingenuity Pathway Analysis (IPA) showed that EIF2 signaling pathway was significantly repressed by overexpression of GAP43.

Conclusion

Our findings provide a novel mechanistic insight of GAP43 in CRC. Transcriptome profiling of overexpressed GAP43 in CRC uncovered the functional roles of GAP43 in the development of human CRC.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-020-07728-x.

Single-Cell Sequencing of Mouse Thymocytes Reveals Mutational Landscape Shaped by Replication Errors, Mismatch Repair, and H3K36me3

DNA mismatch repair (MMR) corrects replication errors and is recruited by the histone mark H3K36me3, enriched in exons of transcriptionally active genes. To dissect in vivo the mutational landscape shaped by these processes, we employed single-cell exome sequencing on T cells of wild-type and MMR-deficient (Mlh1-/-) mice. Within active genes, we uncovered a spatial bias in MMR efficiency: 3' exons, often H3K36me3-enriched, acquire significantly fewer MMR-dependent mutations compared with 5' exons. Huwe1 and Mcm7 genes, both active during lymphocyte development, stood out as mutational hotspots in MMR-deficient cells, demonstrating their intrinsic vulnerability to replication error in this cell type. Both genes are H3K36me3-enriched, which can explain MMR-mediated elimination of replication errors in wild-type cells. Thus, H3K36me3 can boost MMR in transcriptionally active regions, both locally and globally. This offers an attractive concept of thrifty MMR targeting, where critical genes in each cell type enjoy preferential shielding against de novo mutations.

Accurate and efficient cell lineage tree inference from noisy single cell data: the maximum likelihood perfect phylogeny approach

MOTIVATION

Cells in an organism share a common evolutionary history, called cell lineage tree. Cell lineage tree can be inferred from single cell genotypes at genomic variation sites. Cell lineage tree inference from noisy single cell data is a challenging computational problem. Most existing methods for cell lineage tree inference assume uniform uncertainty in genotypes. A key missing aspect is that real single cell data usually has non-uniform uncertainty in individual genotypes. Moreover, existing methods are often sampling based and can be very slow for large data.

RESULTS

In this article, we propose a new method called ScisTree, which infers cell lineage tree and calls genotypes from noisy single cell genotype data. Different from most existing approaches, ScisTree works with genotype probabilities of individual genotypes (which can be computed by existing single cell genotype callers). ScisTree assumes the infinite sites model. Given uncertain genotypes with individualized probabilities, ScisTree implements a fast heuristic for inferring cell lineage tree and calling the genotypes that allow the so-called perfect phylogeny and maximize the likelihood of the genotypes. Through simulation, we show that ScisTree performs well on the accuracy of inferred trees, and is much more efficient than existing methods. The efficiency of ScisTree enables new applications including imputation of the so-called doublets.

AVAILABILITY AND IMPLEMENTATION

The program ScisTree is available for download at: https://github.com/yufengwudcs/ScisTree.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Identifying tumor clones in sparse single-cell mutation data

MOTIVATION

Recent single-cell DNA sequencing technologies enable whole-genome sequencing of hundreds to thousands of individual cells. However, these technologies have ultra-low sequencing coverage (<0.5× per cell) which has limited their use to the analysis of large copy-number aberrations (CNAs) in individual cells. While CNAs are useful markers in cancer studies, single-nucleotide mutations are equally important, both in cancer studies and in other applications. However, ultra-low coverage sequencing yields single-nucleotide mutation data that are too sparse for current single-cell analysis methods.

RESULTS

We introduce SBMClone, a method to infer clusters of cells, or clones, that share groups of somatic single-nucleotide mutations. SBMClone uses a stochastic block model to overcome sparsity in ultra-low coverage single-cell sequencing data, and we show that SBMClone accurately infers the true clonal composition on simulated datasets with coverage at low as 0.2×. We applied SBMClone to single-cell whole-genome sequencing data from two breast cancer patients obtained using two different sequencing technologies. On the first patient, sequenced using the 10X Genomics CNV solution with sequencing coverage ≈0.03×, SBMClone recovers the major clonal composition when incorporating a small amount of additional information. On the second patient, where pre- and post-treatment tumor samples were sequenced using DOP-PCR with sequencing coverage ≈0.5×, SBMClone shows that tumor cells are present in the post-treatment sample, contrary to published analysis of this dataset.

AVAILABILITY AND IMPLEMENTATION

SBMClone is available on the GitHub repository https://github.com/raphael-group/SBMClone.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

A general mathematical framework for understanding the behavior of heterogeneous stem cell regeneration

Stem cell heterogeneity is essential for homeostasis in tissue development. This paper establishes a general mathematical framework to model the dynamics of stem cell regeneration with cell heterogeneity and random transitions of epigenetic states. The framework generalizes the classical G0 cell cycle model and incorporates the epigenetic states of individual cells represented by a continuous multidimensional variable. In the model, the kinetic rates of cell behaviors, including proliferation, differentiation, and apoptosis, are dependent on their epigenetic states, and the random transitions of epigenetic states between cell cycles are represented by an inheritance probability function that describes the conditional probability of cell state changes. Moreover, the model can be extended to include genotypic changes and describe the process of gene mutation-induced tumor development. The proposed mathematical framework provides a generalized formula that helps us to understand various dynamic processes of stem cell regeneration, including tissue development, degeneration, and abnormal growth.

Digital Microfluidics for Single Bacteria Capture and Selective Retrieval Using Optical Tweezers

When screening microbial populations or consortia for interesting cells, their selective retrieval for further study can be of great interest. To this end, traditional fluorescence activated cell sorting (FACS) and optical tweezers (OT) enabled methods have typically been used. However, the former, although allowing cell sorting, fails to track dynamic cell behavior, while the latter has been limited to complex channel-based microfluidic platforms. In this study, digital microfluidics (DMF) was integrated with OT for selective trapping, relocation, and further proliferation of single bacterial cells, while offering continuous imaging of cells to evaluate dynamic cell behavior. To enable this, magnetic beads coated with Salmonella Typhimurium-targeting antibodies were seeded in the microwell array of the DMF platform, and used to capture single cells of a fluorescent S. Typhimurium population. Next, OT were used to select a bead with a bacterium of interest, based on its fluorescent expression, and to relocate this bead to a different microwell on the same or different array. Using an agar patch affixed on top, the relocated bacterium was subsequently allowed to proliferate. Our OT-integrated DMF platform thus successfully enabled selective trapping, retrieval, relocation, and proliferation of bacteria of interest at single-cell level, thereby enabling their downstream analysis.

Bulk and Single-Cell Next-Generation Sequencing: Individualizing Treatment for Colorectal Cancer

The increasing incidence combined with constant rates of early diagnosis and mortality of colorectal cancer (CRC) over the past decade worldwide, as well as minor overall survival improvements in the industrialized world, suggest the need to shift from conventional research and clinical practice to the innovative development of screening, predictive and therapeutic tools. Explosive integration of next-generation sequencing (NGS) systems into basic, translational and, more recently, basket trials is transforming biomedical and cancer research, aiming for substantial clinical implementation as well. Shifting from inter-patient tumor variability to the precise characterization of intra-tumor genetic, genomic and transcriptional heterogeneity (ITH) via multi-regional bulk tissue NGS and emerging single-cell transcriptomics, coupled with NGS of circulating cell-free DNA (cfDNA), unravels novel strategies for therapeutic response prediction and drug development. Remarkably, underway and future genomic/transcriptomic studies and trials exploring spatiotemporal clonal evolution represent most rational expectations to discover novel prognostic, predictive and therapeutic tools. This review describes latest advancements and future perspectives of integrated sequencing systems for genome and transcriptome exploration to overcome unmet research and clinical challenges towards Precision Oncology.

Population and single‑cell transcriptome analyses reveal diverse transcriptional changes associated with radioresistance in esophageal squamous cell carcinoma

Esophageal squamous cell carcinoma (ESCC) is a tumor composed of heterogeneous cells that easily become radioresistant, which leads to tumor recurrence. The most commonly used treatment for ESCC is fractionated irradiation (FIR) therapy that utilizes ionizing radiation to directly induce cytotoxic cell death. However, this treatment may not be able to eliminate all cancer cells due to high adaptive evolution. To determine whether the transcriptome dynamics during ESCC recurrence formation are associated with FIR response, an in vitro cell culture model for ESCC radioresistance that mimics the common radiotherapy process in patients with ESCC was established in the present study. High‑throughput sequencing analysis of in vitro cultured ESCC cells was performed using different cumulative irradiation doses, as well as tumor samples from FIR‑treated patients with ESCC before and after the development of radioresistance. Radioresistance‑associated genes and signaling pathways that were aberrantly expressed in radioresistant ESCC cells were identified, including autophagy‑related 9B (regulation of autophagy), DNA damage‑inducible transcript 4, myoglobin and plasminogen activator tissue type, which are associated with response to hypoxia, Bcl2‑binding component 3, tumor protein P63 and interferon γ‑inducible protein 16, which are associated with DNA damage response. The heterogeneity and dynamic gene expression of ESCC cells during acquired radioresistance were further studied in primary (41 single cells), 12 Gy FIR‑treated (87 single cells) and 30 Gy FIR‑treated (89 single cells) cancer cells using a single‑cell RNA sequencing approach. The results of the present study comprehensively characterized the transcriptome dynamics during acquired radioresistance in an in vitro model of ESCC and patient tumor samples at the population and single cell level. Single‑cell RNA sequencing revealed the heterogeneity of irradiated ESCC cells and an increase in the radioresistant ESCC cell subpopulation during acquired radioresistance. Overall, these results are of potential clinical relevance as they identify a number of signaling molecules associated with radioresistance, as well as opportunities for the development of novel therapeutic options for the treatment of ESCC.

Nonsynonymous, synonymous and nonsense mutations in human cancer-related genes undergo stronger purifying selections than expectation

Background

Nonsynonymous mutations change the protein sequences and are frequently subjected to natural selection. The same goes for nonsense mutations that introduce pre-mature stop codons into CDSs (coding sequences). Synonymous mutations, however, are intuitively thought to be functionally silent and evolutionarily neutral. Now researchers know that the optimized synonymous codon usage is advantageous in the speedy mRNA translation process. With the advent of NGS technique, the explosion of NGS data generated from the tumor tissues help researchers identify driver mutations in cancer-related genes, but relatively less attention is paid to the SNP data in healthy human populations when studying cancer.

Methods

Here, we analyzed the publically available human SNPs. We classified these SNPs according to their functional and evolutionary categories. By simply dividing the human genes into cancer-related genes and other genes, we compared the features of nonsynonymous, synonymous and nonsense mutations in these two gene sets from multiple aspects.

Results

We provided lines of evidence that the nonsynonymous, synonymous and nonsense mutations in cancer-related genes undergo stronger purifying selection when compared to the expected pattern in other genes. The lower nonsynonymous to synonymous ratio observed in cancer-related genes suggests the suppression of amino acid substitutions in these genes. The synonymous SNPs, after excluding those in splicing regions, exhibit preferred changes in codon usage and higher codon frequencies in cancer-related genes compared to other genes, indicating the constraint exerted on these mutations. Nonsense mutations are less frequent and located closer to stop codons in cancer-related genes than in other genes, which putatively minimize their deleterious effects.

Conclusion

Our study demonstrated the evolutionary constraint on mutations in CDS of cancer-related genes without the requirement of data from cancer tissues or patients. Our work provides novel perspectives on interpreting the constraint on mutations in cancer-related genes. We reveal extra constraint on synonymous mutations in cancer-related genes which is related to codon usage bias and is in addition to the splicing effect.

Electronic supplementary material

The online version of this article (10.1186/s12885-019-5572-x) contains supplementary material, which is available to authorized users.

Detecting Chromosome Instability in Cancer: Approaches to Resolve Cell-to-Cell Heterogeneity

Chromosome instability (CIN) is defined as an increased rate of chromosome gains and losses that manifests as cell-to-cell karyotypic heterogeneity and drives cancer initiation and evolution. Current research efforts are aimed at identifying the etiological origins of CIN, establishing its roles in cancer pathogenesis, understanding its implications for patient prognosis, and developing novel therapeutics that are capable of exploiting CIN. Thus, the ability to accurately identify and evaluate CIN is critical within both research and clinical settings. Here, we provide an overview of quantitative single cell approaches that evaluate and resolve cell-to-cell heterogeneity and CIN, and discuss considerations when selecting the most appropriate approach to suit both research and clinical contexts.

Advances in Identification of Susceptibility Gene Defects of Hereditary Colorectal Cancer

Colorectal cancer (CRC) is a common malignant tumor of the digestive system worldwide, associated with hereditary genetic features. CRC with a Mendelian genetic predisposition accounts for approximately 5-10% of total CRC cases, mainly caused by a single germline mutation of a CRC susceptibility gene. The main subtypes of hereditary CRC are hereditary non-polyposis colorectal cancer (HNPCC) and familial adenomatous polyposis (FAP). With the rapid development of genetic testing methods, especially next-generation sequencing technology, multiple genes have now been confirmed to be pathogenic, including DNA repair or DNA mismatch repair genes such as APC, MLH1, and MSH2. Since familial CRC patients have poor clinical outcomes, timely clinical diagnosis and mutation screening of susceptibility genes will aid clinicians in establishing appropriate risk assessment and treatment interventions at a personal level. Here, we systematically summarize the susceptibility genes identified to date and the potential pathogenic mechanism of HNPCC and FAP development. Moreover, clinical recommendations for susceptibility gene screening, diagnosis, and treatment of HNPCC and FAP are discussed.

Understanding tumor ecosystems by single-cell sequencing: promises and limitations

Cellular heterogeneity within and across tumors has been a major obstacle in understanding and treating cancer, and the complex heterogeneity is masked if bulk tumor tissues are used for analysis. The advent of rapidly developing single-cell sequencing technologies, which include methods related to single-cell genome, epigenome, transcriptome, and multi-omics sequencing, have been applied to cancer research and led to exciting new findings in the fields of cancer evolution, metastasis, resistance to therapy, and tumor microenvironment. In this review, we discuss recent advances and limitations of these new technologies and their potential applications in cancer studies.

Single-cell RNA sequencing reveals diverse intratumoral heterogeneities and gene signatures of two types of esophageal cancers

Single-cell RNA sequencing and transcriptome analysis enable novel discovery and precise characterization of new cell types and states, which improves the understanding of the cellular context of tumorigenesis. Herein, we applied this powerful approach to analyze 368 single cells from three esophageal squamous cell carcinoma (ESCC) and two esophageal adenocarcinoma (EAC) tumors. Using inferred copy number variation analysis, we successfully distinguished carcinoma cells from heterogeneous cellular populations, identifying gene signatures and crucial cancer-related signaling pathways related to ESCC and EAC. In particular, we found that NOTCH signaling was exclusively activated in ESCC, but not in EAC. ESCC tumors with higher NOTCH activity were associated with significantly worse survival rates than those with lower NOTCH activity. Collectively, this study revealed that ESCC and EAC are distinct in terms of cellular transcriptome profiles, which leads to a wide range of intratumoral cellular heterogeneity. The findings suggest that different therapeutic strategies that target the differences between two types of esophageal cancers are required, guiding cancer-specific future drug development.

The landscape of somatic mutation in sporadic Chinese colorectal cancer

Colorectal cancer is the fifth prevalent cancer in China. Nevertheless, a large-scale characterization of Chinese colorectal cancer mutation spectrum has not been carried out. In this study, we have performed whole exome-sequencing analysis of 98 patients’ tumor samples with matched pairs of normal colon tissues using Illumina and Complete Genomics high-throughput sequencing platforms. Canonical CRC somatic gene mutations with high prevalence (>10%) have been verified, including TP53, APC, KRAS, SMAD4, FBXW7 and PIK3CA. PEG3 is identified as a novel frequently mutated gene (10.6%). APC and Wnt signaling exhibit significantly lower mutation frequencies than those in TCGA data. Analysis with clinical characteristics indicates that APC gene and Wnt signaling display lower mutation rate in lymph node positive cancer than negative ones, which are not observed in TCGA data. APC gene and Wnt signaling are considered as the key molecule and pathway for colorectal cancer initiation, and these findings greatly undermine their importance in tumor progression for Chinese patients. Taken together, the application of next-generation sequencing has led to the determination of novel somatic mutations and alternative disease mechanisms in colorectal cancer progression, which may be useful for understanding disease mechanism and personalizing treatment for Chinese patients.

Spatiotemporal switching signals for cancer stem cell activation in pediatric origins of adulthood cancer: Towards a watch-and-wait lifetime strategy for cancer treatment

Pediatric origin of cancer stem cell hypothesis holds great promise and potential in adult cancer treatment, however; the road to innovation is full of obstacles as there are plenty of questions left unanswered. First, the key question is to characterize the nature of such stem cells (concept). Second, the quantitative imaging of pediatric stem cells should be implemented (technology). Conceptually, pediatric stem cell origins of adult cancer are based on the notion that plasticity in early life developmental programming evolves local environments to cancer. Technologically, such imaging in children is lacking as all imaging is designed for adult patients. We postulate that the need for quantitative imaging to measure space-time changes of plasticity in early life developmental programming in children may trigger research and development of the imaging technology. Such quantitative imaging of pediatric origin of adulthood cancer will help develop a spatiotemporal monitoring system to determine cancer initiation and progression. Clinical validation of such speculative hypothesis-that cancer originates in a pediatric environment-will help implement a wait-and-watch strategy for cancer treatment.

Transcriptome profiling identified differentially expressed genes and pathways associated with tamoxifen resistance in human breast cancer

Tamoxifen (TAM) resistance is an important clinical problem in the treatment of breast cancer. In order to identify the mechanism of TAM resistance for estrogen receptor (ER)-positive breast cancer, we screened the transcriptome using RNA-seq and compared the gene expression profiles between the MCF-7 mamma carcinoma cell line and the TAM-resistant cell line TAMR/MCF-7, 52 significant differential expression genes (DEGs) were identified including SLIT2, ROBO, LHX, KLF, VEGFC, BAMBI, LAMA1, FLT4, PNMT, DHRS2, MAOA and ALDH. The DEGs were annotated in the GO, COG and KEGG databases. Annotation of the function of the DEGs in the KEGG database revealed the top three pathways enriched with the most DEGs, including pathways in cancer, the PI3K-AKT pathway, and focal adhesion. Then we compared the gene expression profiles between the Clinical progressive disease (PD) and the complete response (CR) from the cancer genome altas (TCGA). 10 common DEGs were identified through combining the clinical and cellular analysis results. Protein-protein interaction network was applied to analyze the association of ER signal pathway with the 10 DEGs. 3 significant genes (GFRA3, NPY1R and PTPRN2) were closely related to ER related pathway. These significant DEGs regulated many biological activities such as cell proliferation and survival, motility and migration, and tumor cell invasion. The interactions between these DEGs and drug resistance phenomenon need to be further elucidated at a functional level in further studies. Based on our findings, we believed that these DEGs could be therapeutic targets, which can be explored to develop new treatment options.

SiFit: inferring tumor trees from single-cell sequencing data under finite-sites models

Single-cell sequencing enables the inference of tumor phylogenies that provide insights on intra-tumor heterogeneity and evolutionary trajectories. Recently introduced methods perform this task under the infinite-sites assumption, violations of which, due to chromosomal deletions and loss of heterozygosity, necessitate the development of inference methods that utilize finite-sites models. We propose a statistical inference method for tumor phylogenies from noisy single-cell sequencing data under a finite-sites model. The performance of our method on synthetic and experimental data sets from two colorectal cancer patients to trace evolutionary lineages in primary and metastatic tumors suggests that employing a finite-sites model leads to improved inference of tumor phylogenies.