scholarly journals Single-cell atlas of tumor clonal evolution in liver cancer

2020 ◽  
Author(s):  
Lichun Ma ◽  
Limin Wang ◽  
Ching-Wen Chang ◽  
Sophia Heinrich ◽  
Dana Dominguez ◽  
...  
Keyword(s):  
Liver Cancer ◽  
Tumor Cell ◽  
Single Cell ◽  
Immune Cell ◽  
Clonal Evolution ◽  
Response To Therapy ◽  
Intratumor Heterogeneity ◽  
Tumor Evolution ◽  
Liver Cancers ◽  
Cell Clonality

SUMMARYTumor evolution is a key feature of tumorigenesis and plays a pivotal role in driving intratumor heterogeneity, treatment failure and patients’ prognosis. Here we performed single-cell transcriptome profiling of 46 primary liver cancers from 37 patients enrolled for interventional studies. We surveyed the landscape of ~57,000 malignant and non-malignant cells and determined tumor cell clonality by developing a machine learning-based consensus clustering method. We found evidence of tumor cell branching evolution using hierarchical clustering, RNA velocity as well as reverse graph embedding methods. Interestingly, an increasing tumor cell clonality was tightly linked to patients’ prognosis, accompanied by a polarized immune cell landscape. We identified osteopontin as a key player for tumor cell evolution and microenvironmental reprogramming. Our study offers insight into the collective behavior of tumor cell communities in liver cancer as well as potential drivers for tumor evolution in response to therapy.

scholarly journals Single-cell and single-variant resolution analysis of clonal evolution in human liver cancer

2021 ◽  
Author(s):  
Xianbin Su ◽  
Linan Zhao ◽  
Yi Shi ◽  
Rui Zhang ◽  
Qi Long ◽  
...  
Keyword(s):  
Liver Cancer ◽  
Single Cell ◽  
Genetic Heterogeneity ◽  
Evolutionary History ◽  
Phenotypic Diversity ◽  
Single Cells ◽  
Clonal Evolution ◽  
Patient Specific ◽  
Liver Cancers ◽  
Resolution Analysis

AbstractGenetic heterogeneity of tumor is closely related to clonal evolution, phenotypic diversity and treatment resistance. Such heterogeneity has been characterized in liver cancer at single-cell sub-chromosomal scale, and a more precise single-variant resolution analysis is lacking. Here we employed a strategy to analyze both the single-cell genomic mutations and transcriptomic changes in 5 patients with liver cancer. Target sequencing was done for a total of 480 single cells in a patient-specific manner. DNA copy number status of point mutations was obtained from single-cell mutational profiling. The clonal structures of liver cancers were then uncovered at single-variant resolution, and mutation combinations in single cells enabled reconstruction of their evolutionary history. A common origin but independent evolutionary fate was revealed for primary liver tumor and intrahepatic metastatic portal vein tumor thrombus. The mutational signature suggested early evolutionary process may be related to specific etiology like aristolochic acids. By parallel single-cell RNA-Seq, the transcriptomic phenotype was found to be related with genetic heterogeneity in liver cancer. We reconstructed the single-cell and single-variant resolution clonal evolutionary history of liver cancer, and dissection of both genetic and phenotypic heterogeneity provides knowledge for mechanistic understanding of liver cancer initiation and progression.

scholarly journals Remarkably stable copy-number profiles in osteosarcoma revealed using single-cell DNA sequencing

2021 ◽  
Author(s):  
Sanjana Rajan ◽  
Simone Zaccaria ◽  
Matthew V. Cannon ◽  
Maren Cam ◽  
Amy C. Gross ◽  
...  
Keyword(s):  
Dna Sequencing ◽  
Single Cell ◽  
Genomic Instability ◽  
Copy Number ◽  
Fitness Landscape ◽  
Clonal Evolution ◽  
Response To Therapy ◽  
Tumor Evolution ◽  
Sequencing Data ◽  
Recurrent Mutations

AbstractOsteosarcoma is an aggressive malignancy characterized by high genomic complexity. Identification of few recurrent mutations in protein coding genes suggests that somatic copy-number aberrations (SCNAs) are the genetic drivers of disease. Models around genomic instability conflict-it is unclear if osteosarcomas result from pervasive ongoing clonal evolution with continuous optimization of the fitness landscape or an early catastrophic event followed by stable maintenance of an abnormal genome. We address this question by investigating SCNAs in 12,019 tumor cells obtained from expanded patient tissues using single-cell DNA sequencing, in ways that were previously impossible with bulk sequencing. Using the CHISEL algorithm, we inferred allele- and haplotype-specific SCNAs from whole-genome single-cell DNA sequencing data. Surprisingly, we found that, despite extensive genomic aberrations, cells within each tumor exhibit remarkably homogeneous SCNA profiles with little sub-clonal diversification. Longitudinal analysis between two pairs of patient samples obtained at distant time points (early detection, relapse) demonstrated remarkable conservation of SCNA profiles over tumor evolution. Phylogenetic analysis suggests that the bulk of SCNAs was acquired early in the oncogenic process, with few new events arising in response to therapy or during adaptation to growth in distant tissues. These data suggest that early catastrophic events, rather than sustained genomic instability, drive formation of these extensively aberrant genomes. Overall, we demonstrate the power of combining single-cell DNA sequencing with an allele- and haplotype-specific SCNA inference algorithm to resolve longstanding questions regarding genetics of tumor initiation and progression, questioning the underlying assumptions of genomic instability inferred from bulk tumor data.

IMMU-04. UNVEILING THE TUMOR-METABOLOME-IMMUNITY AXIS OF GLIOMA

2021 ◽  
Vol 23 (Supplement_6) ◽  
pp. vi92-vi92
Author(s):  
Mirco Friedrich ◽  
Lukas Bunse ◽  
Roman Sankowski ◽  
Wolfgang Wick ◽  
Marco Prinz ◽  
...  
Keyword(s):  
T Cell ◽  
Single Cell ◽  
T Cell Activation ◽  
Cell Activation ◽  
Immune Cell ◽  
Myeloid Cell ◽  
Tumor Evolution ◽  
Large Neutral Amino Acid ◽  
Immune Microenvironment ◽  
Idh Mutations

Abstract The glioma microenvironment orchestrates tumor evolution, progression, and resistance to therapy. In high-grade gliomas, microglia and monocyte-derived macrophages constitute up to 70% of the tumor mass. However, the dynamics and phenotypes of intratumoral myeloid cells during tumor progression are poorly understood. Here we define myeloid cellular states in gliomas by longitudinal single-cell profiling and demonstrate their strict control by the tumor genotype. We report the unexpected and clinically highly relevant finding that human as well as murine gliomas with Isocitrate Dehydrogenase (IDH)1-R132H, a key oncogenic driver mutation of glioma, subdue their innate immune microenvironment by prompting a multifaceted reprogramming of myeloid and T cell metabolism. We employed integrated single-cell transcriptomic, time-of-flight mass cytometry and proteomic analyses of human healthy cortex control and glioma samples to identify myeloid cell subsets with distinct fates in IDH-mutated glioma that diverge from canonical trajectories of antigen-presenting cells as a result of a monocyte-to-macrophage differentiation block. Moving beyond single time point assessments, we now longitudinally describe differential immune cell infiltration and phenotype dynamics during glioma progression that are orchestrated by a fluctuating network of resident microglial cells and educated recruited immune cells. IDH mutations in glioma induce a tolerogenic alignment of their immune microenvironment through increased tryptophan uptake via large neutral amino acid transporter (LAT1)-CD98 and subsequent activation of the aryl hydrocarbon receptor (AHR) in educated blood-borne macrophages. In experimental tumor models, this immunosuppressive phenotype was reverted by LAT1-CD98 and AHR inhibitors. Taken together with direct effects on T cell activation, our findings not only link this oncogenic metabolic pathway to distinct immunosuppressive pathways but also provide the rationale and novel molecular targets for the development of immunotherapeutic concepts addressing the disease-defining microenvironmental effects of IDH mutations.

scholarly journals ClonArch: Visualizing the Spatial Clonal Architecture of Tumors

2020 ◽  
Author(s):  
Jiaqi Wu ◽  
Mohammed El-Kebir
Keyword(s):  
Visual Analytics ◽  
Phylogenetic Trees ◽  
Cancer Genomics ◽  
Clonal Evolution ◽  
Response To Treatment ◽  
Intratumor Heterogeneity ◽  
Tumor Evolution ◽  
Web Based ◽  
Clonal Architecture ◽  
Spatial Coordinates

AbstractMotivationCancer is caused by the accumulation of somatic mutations that lead to the formation of distinct populations of cells, called clones. The resulting clonal architecture is the main cause of relapse and resistance to treatment. With decreasing costs in DNA sequencing technology, rich cancer genomics datasets with many spatial sequencing samples are becoming increasingly available, enabling the inference of high-resolution tumor clones and prevalences across different spatial coordinates. While temporal and phylogenetic aspects of tumor evolution, such as clonal evolution over time and clonal response to treatment, are commonly visualized in various clonal evolution diagrams, visual analytics methods that reveal the spatial clonal architecture are missing.ResultsThis paper introduces ClonArch, a web-based tool to interactively visualize the phylogenetic tree and spatial distribution of clones in a single tumor mass. ClonArch uses the marching squares algorithm to draw closed boundaries representing the presence of clones in a real or simulated tumor. ClonArch enables researchers to examine the spatial clonal architecture of a subset of relevant mutations at different prevalence thresholds and across multiple phylogenetic trees. In addition to simulated tumors with varying number of biopsies, we demonstrate the use of ClonArch on a hepatocellular carcinoma tumor with ~280 sequencing biopsies. ClonArch provides an automated way to interactively examine the spatial clonal architecture of a tumor, facilitating clinical and biological interpretations of the spatial aspects of intratumor heterogeneity.Availabilityhttps://github.com/elkebir-group/ClonArch

scholarly journals Abstract 1500: Understanding tumor cell community and its evolution by single cell analysis in liver cancer

2020 ◽  
Author(s):  
Lichun Ma ◽  
Maria O. Hernandez ◽  
Yongmei Zhao ◽  
Monika Mehta ◽  
Bao Tran ◽  
...  
Keyword(s):  
Liver Cancer ◽  
Tumor Cell ◽  
Single Cell ◽  
Single Cell Analysis ◽  
Cell Analysis ◽  
Cell Community

scholarly journals Intratumor Heterogeneity and Circulating Tumor Cell Clusters

2017 ◽  
Author(s):  
Zafarali Ahmed ◽  
Simon Gravel
Keyword(s):  
Tumor Cell ◽  
Growth Parameters ◽  
Numerical Models ◽  
Circulating Tumor Cell ◽  
Intratumor Heterogeneity ◽  
Tumor Evolution ◽  
List Type ◽  
Cell Turnover ◽  
Cell Clusters ◽  
Spatial Trends

SummaryGenetic diversity plays a central role in tumor progression, metastasis, and resistance to treatment. Experiments are shedding light on this diversity at ever finer scales, but interpretation is challenging. Using recent progress in numerical models, we simulate macroscopic tumors to investigate the interplay between growth dynamics, microscopic composition, and circulating tumor cell cluster diversity. We find that modest differences in growth parameters can profoundly change microscopic diversity. Simple outwards expansion leads to spatially segregated clones and low diversity, as expected. However, a modest cell turnover can result in an increased number of divisions and mixing among clones resulting in increased microscopic diversity in the tumor core. Using simulations to estimate power to detect such spatial trends, we find that multiregion sequencing data from contemporary studies is marginally powered to detect the predicted effects. Slightly larger samples, improved detection of rare variants, or sequencing of smaller biopsies or circulating tumor cell clusters would allow one to distinguish between leading models of tumor evolution. The genetic composition of circulating tumor cell clusters, which can be obtained from non-invasive blood draws, is therefore informative about tumor evolution and its metastatic potential.HighlightsNumerical and theoretical models show interaction of front expansion, mutation, and clonal mixing in shaping tumor heterogeneity.Cell turnover increases intratumor heterogeneity.Simulated circulating tumor cell clusters and microbiopsies exhibit substantial diversity with strong spatial trends.Simulations suggest attainable sampling schemes able to distinguish between prevalent tumor growth models.

scholarly journals Identification of Breast Cancer Immune Subtypes by Analyzing Bulk Tumor and Single Cell Transcriptomes

2022 ◽  
Vol 9 ◽  
Author(s):  
Jia Yao ◽  
Shengwei Li ◽  
Xiaosheng Wang
Keyword(s):  
Breast Cancer ◽  
Immune Response ◽  
Single Cell ◽  
Mutation Rate ◽  
Immune Cell ◽  
Growth Factor Receptor ◽  
Intratumor Heterogeneity ◽  
Strong Immune Response ◽  
Survival Prognosis ◽  
Immune Signature

Background: The histological and molecular classification of breast cancer (BC) is being used in the clinical management of this disease. However, subtyping of BC based on the tumor immune microenvironment (TIME) remains insufficiently explored, although such investigation may provide new insights into intratumor heterogeneity in BC and potential clinical implications for BC immunotherapy.Methods: Based on the enrichment scores of 28 immune cell types, we performed clustering analysis of transcriptomic data to identify immune-specific subtypes of BC using six different datasets, including five bulk tumor datasets and one single-cell dataset. We further analyzed the molecular and clinical features of these subtypes.Results: Consistently in the six datasets, we identified three BC subtypes: BC-ImH, BC-ImM, and BC-ImL, which had high, medium, and low immune signature scores, respectively. BC-ImH displayed a significantly better survival prognosis than BC-ImL. Triple-negative BC (TNBC) and human epidermal growth factor receptor-2-positive (HER2+) BC were likely to have the highest proportion in BC-ImH and the lowest proportion in BC-ImL. In contrast, hormone receptor-positive (HR+) BC had the highest proportion in BC-ImL and the lowest proportion in BC-ImH. Furthermore, BC-ImH had the highest tumor mutation burden (TMB) and predicted neoantigens, while BC-ImL had the highest somatic copy number alteration (SCNA) scores. It is consistent with that TMB and SCNA correlate positively and negatively with anti-tumor immune response, respectively. TP53 had the highest mutation rate in BC-ImH and the lowest mutation rate in BC-ImL, supporting that TP53 mutations promote anti-tumor immune response in BC. In contrast, PIK3CA displayed the highest mutation rate in BC-ImM, while GATA3 had the highest mutation rate in BC-ImL. Besides immune pathways, many oncogenic pathways were upregulated in BC-ImH, including ErbB, MAPK, VEGF, and Wnt signaling pathways; the activities of these pathways correlated positively with immune signature scores in BC.Conclusions: The tumors with the strong immune response (“hot” tumors) have better clinical outcomes than the tumors with the weak immune response (“cold” tumors) in BC. TNBC and HER2+ BC are more immunogenic, while HR + BC is less immunogenic. Certain HER2+ or HR + BC patients could be propitious to immunotherapy in addition to TNBC.

Measuring Clonal Evolution in Cancer with Genomics

2019 ◽  
Vol 20 (1) ◽  
pp. 309-329 ◽  
Author(s):  
Marc J. Williams ◽  
Andrea Sottoriva ◽  
Trevor A. Graham
Keyword(s):  
Evolutionary Biology ◽  
Quantitative Measurement ◽  
Clonal Evolution ◽  
Evolutionary Process ◽  
Treatment Resistance ◽  
Genetic Alterations ◽  
Cell Number ◽  
Intratumor Heterogeneity ◽  
Tumor Evolution ◽  
Powerful Means

Cancers originate from somatic cells in the human body that have accumulated genetic alterations. These mutations modify the phenotype of the cells, allowing them to escape the homeostatic regulation that maintains normal cell number. Viewed through the lens of evolutionary biology, the transformation of normal cells into malignant cells is evolution in action. Evolution continues throughout cancer growth, progression, treatment resistance, and disease relapse, driven by adaptation to changes in the cancer's environment, and intratumor heterogeneity is an inevitable consequence of this evolutionary process. Genomics provides a powerful means to characterize tumor evolution, enabling quantitative measurement of evolving clones across space and time. In this review, we discuss concepts and approaches to quantify and measure this evolutionary process in cancer using genomics.

Sign in / Sign up

Export Citation Format

Share Document