Zebrafish as a model to assess cancer heterogeneity,progression and relapse

Clonal evolution is the process by which genetic and epigenetic diversity is created within malignant tumor cells. This process culminates in a heterogeneous tumor, consisting of multiple subpopulations of cancer cells that often do not contain the same underlying mutations. Continuous selective pressure permits outgrowth of clones that harbor lesions that are capable of enhancing disease progression, including those that contribute to therapy resistance, metastasis and relapse. Clonal evolution and the resulting intratumoral heterogeneity pose a substantial challenge to biomarker identification, personalized cancer therapies and the discovery of underlying driver mutations in cancer. The purpose of this Review is to highlight the unique strengths of zebrafish cancer models in assessing the roles that intratumoral heterogeneity and clonal evolution play in cancer, including transgenesis, imaging technologies, high-throughput cell transplantation approaches and in vivo single-cell functional assays.KEY WORDS: Cancer stem cell, Fluorescence, Intratumoral, Single cell, Targeted therapy, Tumor     

Evolution and Phenotypic Selection of Cancer Stem Cells

Cells of different organs at different ages have an intrinsic set of kinetics that dictates their behavior. Transformation into cancer cells will inherit these kinetics that determine initial cell and tumor population progression dynamics. Subject to genetic mutation and epigenetic alterations, cancer cell kinetics can change, and favorable alterations that increase cellular fitness will manifest themselves and accelerate tumor progression. We set out to investigate the emerging intratumoral heterogeneity and to determine the evolutionary trajectories of the combination of cell-intrinsic kinetics that yield aggressive tumor growth. We develop a cellular automaton model that tracks the temporal evolution of the malignant subpopulation of so-called cancer stem cells(CSC), as these cells are exclusively able to initiate and sustain tumors. We explore orthogonal cell traits, including cell migration to facilitate invasion, spontaneous cell death due to genetic drift after accumulation of irreversible deleterious mutations, symmetric cancer stem cell division that increases the cancer stem cell pool, and telomere length and erosion as a mitotic counter for inherited non-stem cancer cell proliferation potential. Our study suggests that cell proliferation potential is the strongest modulator of tumor growth. Early increase in proliferation potential yields larger populations of non-stem cancer cells(CC) that compete with CSC and thus inhibit CSC division while a reduction in proliferation potential loosens such inhibition and facilitates frequent CSC division. The sub-population of cancer stem cells in itself becomes highly heterogeneous dictating population level dynamics that vary from long-term dormancy to aggressive progression. Our study suggests that the clonal diversity that is captured in single tumor biopsy samples represents only a small proportion of the total number of phenotypes.     

Prognostic value of infiltrating immune cells in clear cell renal cell carcinoma (ccRCC)

In this study, we attempted to evaluate the prognostic value of infiltrating immune/stromal cells in clear cell renal cell carcinoma (ccRCC), by using the immune scores and stromal scores based on the “Estimation of STromal and Immune cells in MAlignant Tumours using Expression data” algorithm to represent the levels of infiltrating immune cells and stromal cells. We found that the infiltrating immune cells were associated with poor prognosis of ccRCC. To assess the role of infiltrating immune cells in ccRCC cells, first, we performed differentially expressed genes analysis and functional analysis for validation. The results showed that the underlying mechanism by which infiltrating immune cells promoted cancer progression involved in regulating the nuclear division, angiogenesis, and immune response. Next, we investigated the relationship between infiltrating immune cells and mutations in ccRCC cells. We found that the infiltrating immune cells have certain effects on genetic mutations. In conclusion, infiltrating immune cells within the tumor microenvironment can be used to predict prognosis in ccRCC.     

Cell population-based framework of genetic epidemiology in the single-cell omics era

Genetic epidemiology is a rapidly advancing field due to the recent availability of large amounts of omics data. In recent years, it has become possible to obtain omics information at the single-cell level, so genetic epidemiological models need to be updated to integrate with single-cell expression data. In this perspective paper, we propose a cell population-based framework for genetic epidemiology in the single-cell era. In this framework, genetic diversity influences phenotypic diversity through the diversity of cell population profiles, which are defined as high-dimensional probability distributions of the state spaces of biomolecules of each omics layer. We discuss how biomolecular experimental measurement data can capture the different properties of this distribution. In particular, single-cell data constitute a sample from this population distribution where only some coordinate values are observable. From a data analysis standpoint, we introduce methodology for feature extraction from cell population profiles. Finally, we discuss how this framework can be applied not only to genetic epidemiology but also to systems biology.     

Phosphoinositide-dependent kinase 1–associated glycolysis is regulated by miR-409-3p in clear cell renal cell carcinoma

Clear cell renal cell carcinoma (ccRCC) is the most popular kidney cancer in adults. Metabolic shift toward aerobic glycolysis is a fundamental factor for ccRCC therapy. MicroRNAs (miRNAs) are thought to be important regulators in ccRCC development and progression. Phosphoinositide-dependent kinase 1 (PDK1) is required for metabolic activation; however, the role of PDK1-induced glycolytic metabolism regulated by miRNAs is unclear in ccRCC. So, the purpose of the current study is to elucidate the underlying mechanism in ccRCC cell metabolism mediated by PDK1. Our results revealed that miR-409-3p inhibited glycolysis by regulating PDK1 expression in ccRCC cells. We also found that miR-409-3p was regulated by hypoxia. Our results indicated that PDK1 facilitated ccRCC cell glycolysis, regulated by miR-409-3p in hypoxia.     

Identification of microenvironment-related genes with prognostic value in clear cell renal cell carcinoma

Clear cell renal cell carcinoma (ccRCC) is the most common type of kidney tumor. Previous studies have shown that the interaction between tumor cells and microenvironment has an important impact on prognosis. Immune and stromal cells are two vital components of the tumor microenvironment. Our study aimed to better understand and explore the genes involved in immune/stromal cells on prognosis. We used the Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data algorithm to calculate immune/stromal scores. According to the scores, we divided ccRCC patients from The Cancer Genome Atlas database into low and high groups and identified the genes which were differentially expressed and significantly associated with prognosis. The result of functional enrichment analysis and protein-protein interaction networks indicated that these genes mainly were involved in extracellular matrix and regulation of cellular activities. Then another independent cohort from the International Cancer Genome Consortium database was used to validate these genes. Finally, we acquired a list of microenvironment-related genes that can predict prognosis for ccRCC patients.     

Quantitative Phosphoproteomics Analysis Uncovers PAK2- and CDK1-Mediated Malignant Signaling Pathways in Clear Cell Renal Cell Carcinoma

Clear cell Renal Cell Carcinoma (ccRCC) is among the 10 most common cancers in both men and women and causes more than 140,000 deaths worldwide every year. In order to elucidate the underlying molecular mechanisms orchestrated by phosphorylation modifications, we performed a comprehensive quantitative phosphoproteomics characterization of ccRCC tumor and normal adjacent tissues. Here, we identified 16,253 phosphopeptides, of which more than 9000 were singly quantified. Our in-depth analysis revealed 600 phosphopeptides to be significantly differentially regulated between tumor and normal tissues. Moreover, our data revealed that significantly up-regulated phosphoproteins are associated with protein synthesis and cytoskeletal re-organization which suggests proliferative and migratory behavior of renal tumors. This is supported by a mesenchymal profile of ccRCC phosphorylation events. Our rigorous characterization of the renal phosphoproteome also suggests that both epidermal growth factor receptor and vascular endothelial growth factor receptor are important mediators of phospho signaling in RCC pathogenesis. Furthermore, we determined the kinases p21-activated kinase 2, cyclin-dependent kinase 1 and c-Jun N-terminal kinase 1 to be master kinases that are responsible for phosphorylation of many substrates associated with cell proliferation, inflammation and migration. Moreover, high expression of p21-activated kinase 2 is associated with worse survival outcome of ccRCC patients. These master kinases are targetable by inhibitory drugs such as fostamatinib, minocycline, tamoxifen and bosutinib which can serve as novel therapeutic agents for ccRCC treatment.     

Traditional Chinese medicine Bu-Shen-Jian-Pi-Fang attenuates glycolysis and immune escape in clear cell renal cell carcinoma: results based on network pharmacology

Clear cell renal cell carcinoma (ccRCC) is the most common malignant type of kidney cancer. The present study aims to explore the underlying mechanism and potential targets of the traditional Chinese medicine Bu-Shen-Jian-Pi-Fang (BSJPF) in the treatment of ccRCC based on network pharmacology. After obtaining the complete composition information for BSJPF from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform, we analyzed its chemical composition and molecular targets and then established a pharmacological interaction network. Twenty-four significantly differentially expressed genes and nine pathways mainly related to tumor proliferation were identified and screened. Functional enrichment analysis indicated that the potential targets might be significantly involved in glycolysis and the HIF-1 signaling pathway. To further confirm the effect of BSJPF on ccRCC cell proliferation, a BALB/c xenograft mouse model was constructed. Potential targets involved in regulating glycolysis and the tumor immune microenvironment were evaluated using RT-qPCR. VEGF-A expression levels were markedly decreased, and heparin binding-EGF expression was increased in the BSJPF group. BSJPF also inhibited tumor proliferation by enhancing GLUT1- and LDHA-related glycolysis and the expression of the immune checkpoint molecules PD-L1 and CTLA-4, thereby altering the immune-rejection status of the tumor microenvironment. In summary, the present study demonstrated that the mechanism of BSJPF involves multiple targets and signaling pathways related to tumorigenesis and glycolysis metabolism in ccRCC. Our research provides a novel theoretical basis for the treatment of tumors with traditional Chinese medicine and new strategies for immunotherapy in ccRCC patients.     

Discovery of biclonal origin and a novel oncogene SLC12A5 in colon cancer by single-cell sequencing

Single-cell sequencing is a powerful tool for delineating clonal relationship and identifying key driver genes for personalized cancer management. Here we performed single-cell sequencing analysis of a case of colon cancer. Population genetics analyses identified two independent clones in tumor cell population. The major tumor clone harbored APC and TP53 mutations as early oncogenic events, whereas the minor clone contained preponderant CDC27 and PABPC1 mutations. The absence of APC and TP53 mutations in the minor clone supports that these two clones were derived from two cellular origins. Examination of somatic mutation allele frequency spectra of additional 21 whole-tissue exome-sequenced cases revealed the heterogeneity of clonal origins in colon cancer. Next, we identified a mutated gene SLC12A5 that showed a high frequency of mutation at the single-cell level but exhibited low prevalence at the population level. Functional characterization of mutant SLC12A5 revealed its potential oncogenic effect in colon cancer. Our study provides the first exome-wide evidence at single-cell level supporting that colon cancer could be of a biclonal origin, and suggests that low-prevalence mutations in a cohort may also play important protumorigenic roles at the individual level.     

pVHL and PTEN tumour suppressor proteins cooperatively suppress kidney cyst formation

In patients with von Hippel-Lindau (VHL) disease, renal cysts and clear cell renal cell carcinoma (ccRCC) arise from renal tubular epithelial cells containing biallelic inactivation of the VHL tumour suppressor gene. However, it is presumed that formation of renal cysts and their conversion to ccRCC involve additional genetic changes at other loci. Here, we show that cystic lesions in the kidneys of patients with VHL disease also demonstrate activation of the phosphatidylinositol-3-kinase (PI3K) pathway. Strikingly, combined conditional inactivation of Vhlh and the Pten tumour suppressor gene, which normally antagonises PI3K signalling, in the mouse kidney, elicits cyst formation after short latency, whereas inactivation of either tumour suppressor gene alone failed to produce such a phenotype. Interestingly, cells lining these cysts frequently lack a primary cilium, a microtubule-based cellular antenna important for suppression of uncontrolled kidney epithelial cell proliferation and cyst formation. Our results support a model in which the PTEN tumour suppressor protein cooperates with pVHL to suppress cyst development in the kidney.     

Calcium-activated nucleotidase 1 silencing inhibits proliferation,migration, and invasion in human clear cell renal cell carcinoma

Calcium-activated nucleotidase 1 (CANT1, belongs to the apyrase family, is widely expressed in various organs. However, the biological function of CANT1 remains poorly explored. In this study, we aimed to investigate the expression profile and functions of CANT1 in clear cell renal cell carcinoma (ccRCC). Our data show that the protein level of CANT1 was significantly higher in tumor tissues than in adjacent normal tissues. CANT1 silencing suppressed cell proliferation, migration, and invasion obviously in 769-P and 786-O cells, arrested cell cycle in S phase and promoted apoptosis in 769-P cells. In conclusion, the present study shows the different expression mode of CANT1 in human ccRCC tumor tissue and adjacent normal tissue, denotes the function of CANT1 in ccRCC cells and provides potential molecular mechanisms and pathways of CANT1 antitumor function in ccRCC.     

Prognostic and Predictive Value of Tumor-infiltrating Leukocytes and of Immune Checkpoint Molecules PD1 and PDL1 in Clear Cell Renal Cell Carcinoma

INTRODUCTION: Immune checkpoint inhibitors (ICI) have been approved for patients with clear cell renal cell carcinoma (ccRCC), but not all patients benefit from ICI. One reason is the tumor microenvironment (TME) that has substantial influence on patient's prognosis and therapy response. Thus, we comprehensively analyzed the TME of ccRCC regarding prognostic and predictive properties. METHODS: Tumor-infiltrating CD3-positive T-cells, CD8-positive cytotoxic T-lymphocytes (CTLs), regulatory T-cells, B-cells, plasma cells, macrophages, granulocytes, programmed cell death receptor 1 (PD-1), and its ligand PD-L1 were examined in a large hospital-based series of ccRCC with long-term follow-up information (n = 756) and in another patient collective with information on response to nivolumab therapy (n = 8). Tissue microarray technique and digital image analysis were used. Relationship between immune cell infiltration and tumor characteristics, cancer-specific survival (CSS), or response to ICI was examined. RESULTS: Univariate survival analysis revealed that increased tumor-infiltrating B-cells, T-cells, and PD-1-positive cells were significantly associated with favorable CSS and high levels of intratumoral granulocytes, macrophages, cytotoxic T-cells, and PD-L1 significantly with poor CSS. High CTL or B-cell infiltration and high PD-L1 expression of ccRCC tumor cells qualified as independent prognostic biomarkers for patients' CSS. Significantly higher densities of intratumoral T-cells, CTLs, and PD-1-positive immune cells were observed in ccRCC with response to ICI compared with patients with mixed or no response (CD3: p = 0.003; CD8: p = 0.006; PD-1: p = 0.01). DISCUSSION: This study shows that subsets of tumor-infiltrating leukocytes in the TME and also PD-1/PD-L1 provide prognostic and predictive information for patients with ccRCC.     

Hormone resistance and neuroendocrine differentiation due to accumulation of genetic lesions during clonal evolution of prostate cancer

Progression of malignant tumors is largely due to clonal evolution of the primary tumor, clones acquiring different sets of molecular genetic lesions. Lesions can confer a selective advantage in proliferation rate or metastasis on the tumor cell population, especially if developing resistance to anticancer therapy. Prostate cancer (PCa) provides an illustrative example of clinically significant clonal evolution. The review considers the genetic alterations that occur in primary PCa and the mechanism whereby hormone-refractory PCa develops on hormone therapy, including mutations and alternative splicing of the androgen receptor gene (AR) and intratumoral androgen synthesis. Certain molecular genetic lesions determine resistance to new generation inhibitors (AR mutations that block the antagonist effect or allow other hormones to activate the receptor) or lead to neuroendocrine differentiation (repression of the AR signaling pathway, TP53 mutations, and amplification of the AURKA or MYCN oncogene). Multistep therapy based on the data about somatic mutations associated with progression and metastasis of the primary tumor can be expected to significantly improve the survival of patients with advanced PCa in the nearest future.     

Accounting for Errors in Data Improves Divergence Time Estimates in Single-cell Cancer Evolution

Single-cell sequencing provides a new way to explore the evolutionary history of cells. Compared to traditional bulk sequencing, where a population of heterogeneous cells is pooled to form a single observation, single-cell sequencing isolates and amplifies genetic material from individual cells, thereby preserving the information about the origin of the sequences. However, single-cell data are more error-prone than bulk sequencing data due to the limited genomic material available per cell. Here, we present error and mutation models for evolutionary inference of single-cell data within a mature and extensible Bayesian framework, BEAST2. Our framework enables integration with biologically informative models such as relaxed molecular clocks and population dynamic models. Our simulations show that modeling errors increase the accuracy of relative divergence times and substitution parameters. We reconstruct the phylogenetic history of a colorectal cancer patient and a healthy patient from single-cell DNA sequencing data. We find that the estimated times of terminal splitting events are shifted forward in time compared to models which ignore errors. We observed that not accounting for errors can overestimate the phylogenetic diversity in single-cell DNA sequencing data. We estimate that 30–50% of the apparent diversity can be attributed to error. Our work enables a full Bayesian approach capable of accounting for errors in the data within the integrative Bayesian software framework BEAST2.     

Single-cell exome sequencing and monoclonal evolution of a JAK2-negative myeloproliferative neoplasm

Tumor heterogeneity presents a challenge for inferring clonal evolution and driver gene identification. Here, we describe a method for analyzing the cancer genome at a single-cell nucleotide level. To perform our analyses, we first devised and validated a high-throughput whole-genome single-cell sequencing method using two lymphoblastoid cell line single cells. We then carried out whole-exome single-cell sequencing of 90 cells from a JAK2-negative myeloproliferative neoplasm patient. The sequencing data from 58 cells passed our quality control criteria, and these data indicated that this neoplasm represented a monoclonal evolution. We further identified essential thrombocythemia (ET)-related candidate mutations such as SESN2 and NTRK1, which may be involved in neoplasm progression. This pilot study allowed the initial characterization of the disease-related genetic architecture at the single-cell nucleotide level. Further, we established a single-cell sequencing method that opens the way for detailed analyses of a variety of tumor types, including those with high genetic complex between patients.     

Single-Cell Genetic Analysis Using Automated Microfluidics to Resolve Somatic Mosaicism

Somatic mosaicism occurs throughout normal development and contributes to numerous disease etiologies, including tumorigenesis and neurological disorders. Intratumor genetic heterogeneity is inherent to many cancers, creating challenges for effective treatments. Unfortunately, analysis of bulk DNA masks subclonal phylogenetic architectures created by the acquisition and distribution of somatic mutations amongst cells. As a result, single-cell genetic analysis is becoming recognized as vital for accurately characterizing cancers. Despite this, methods for single-cell genetics are lacking. Here we present an automated microfluidic workflow enabling efficient cell capture, lysis, and whole genome amplification (WGA). We find that ~90% of the genome is accessible in single cells with improved uniformity relative to current single-cell WGA methods. Allelic dropout (ADO) rates were limited to 13.75% and variant false discovery rates (SNV FDR) were 4.11x10^-6, on average. Application to ER-/PR-/HER2+ breast cancer cells and matched normal controls identified novel mutations that arose in a subpopulation of cells and effectively resolved the segregation of known cancer-related mutations with single-cell resolution. Finally, we demonstrate effective cell classification using mutation profiles with 10X average exome coverage depth per cell. Our data demonstrate an efficient automated microfluidic platform for single-cell WGA that enables the resolution of somatic mutation patterns in single cells.     

From single-cell genetic architecture to cell population dynamics: quantitatively decomposing the effects of different population heterogeneity sources for a genetic network with positive feedback architecture

Phenotypic cell-to-cell variability or cell population heterogeneity originates from two fundamentally different sources: unequal partitioning of cellular material at cell division and stochastic fluctuations associated with intracellular reactions. We developed a mathematical and computational framework that can quantitatively isolate both heterogeneity sources and applied it to a genetic network with positive feedback architecture. The framework consists of three vastly different mathematical formulations: a), a continuum model, which completely neglects population heterogeneity; b), a deterministic cell population balance model, which accounts for population heterogeneity originating only from unequal partitioning at cell division; and c), a fully stochastic model accommodating both sources of population heterogeneity. The framework enables the quantitative decomposition of the effects of the different population heterogeneity sources on system behavior. Our results indicate the importance of cell population heterogeneity in accurately predicting even average population properties. Moreover, we find that unequal partitioning at cell division and sharp division rates shrink the region of the parameter space where the population exhibits bistable behavior, a characteristic feature of networks with positive feedback architecture. In addition, intrinsic noise at the single-cell level due to slow operator fluctuations and small numbers of molecules further contributes toward the shrinkage of the bistability regime at the cell population level. Finally, the effect of intrinsic noise at the cell population level was found to be markedly different than at the single-cell level, emphasizing the importance of simulating entire cell populations and not just individual cells to understand the complex interplay between single-cell genetic architecture and behavior at the cell population level.