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 ClonArch: visualizing the spatial clonal architecture of tumors

2020 ◽  
Vol 36 (Supplement_1) ◽  
pp. i161-i168
Author(s):  
Jiaqi Wu ◽  
Mohammed El-Kebir
Keyword(s):  
Visual Analytics ◽  
Phylogenetic Trees ◽  
Cancer Genomics ◽  
Clonal Evolution ◽  
Response To Treatment ◽  
Tumor Evolution ◽  
Web Based ◽  
Clonal Architecture ◽  
Spatial Coordinates ◽  
Carcinoma Tumor

Abstract Motivation Cancer 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. Results This article 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 intra-tumor heterogeneity. Availability and implementation https://github.com/elkebir-group/ClonArch.

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.

scholarly journals TMOD-27. A NEURAL CREST CELL SUBPOPULATION UNDERLIES INTRATUMOR HETEROGENEITY IN MENINGIOMA

2019 ◽  
Vol 21 (Supplement_6) ◽  
pp. vi268-vi268 ◽  
Author(s):  
Stephen Magill ◽  
Harish Vasudevan ◽  
Kyounghee Seo ◽  
S John Liu ◽  
Stephanie Hilz ◽  
...  
Keyword(s):  
Neural Crest ◽  
Rna Sequencing ◽  
Clonal Evolution ◽  
Neural Crest Cell ◽  
Cell Subpopulation ◽  
Intratumor Heterogeneity ◽  
Tumor Evolution ◽  
Molecular Heterogeneity ◽  
High Grade ◽  
Who Grade

Abstract BACKGROUND Meningiomas are the most common primary intracranial tumor, and high grade meningiomas are resistant to most cancer therapies. Intratumor heterogeneity is a recognized source of resistance to treatment in numerous malignancies. Thus, we hypothesized that investigating molecular heterogeneity in meningiomas would elucidate biologic drivers and shed light on tumor evolution and mechanisms of resistance. METHODS We collected 86 spatially distinct samples at the time of resection from 13 meningiomas. Seven meningiomas were WHO grade I (46 samples), three were grade II (22 samples), and three were grade III (18 samples). Seven meningiomas were sampled at the time of salvage surgery (48 samples), and 6 were sampled at the time of initial diagnosis (38 samples). We performed multiplatform molecular profiling of these samples to identify drivers of intratumor heterogeneity, and validated our results using meningioma cells co-cultured with human cerebral organoids and RNA sequencing of paired primary and recurrent meningiomas. RESULTS Using bulk RNA sequencing, DNA methylation profiling and phylogenetic analysis of spatially distinct samples, we discovered significant transcriptomic, epigenomic and genomic heterogeneity in meningioma. In particular, we identified chromosomal structural alterations and differences in immune and neuronal signaling that underlie clonal evolution in high grade tumors. Using MRI-stratified bulk RNA sequencing, single nuclear RNA sequencing, RNA sequencing of paired primary and recurrent meningiomas, and live cell microscopy and single cell RNA sequencing of meningioma cells in co-culture with human cerebral organoids, we revealed a rare meningioma cell subpopulation with strong transcriptional concordance to the neural crest, a multipotent embryonic tissue that forms the meninges in development. CONCLUSIONS These data suggest that misactivation of a developmental cell population underlies intratumor heterogeneity in meningioma and that expression of neural crest and immediate early genes are an important step in meningeal oncogenesis.

scholarly journals The genomic and epigenomic evolutionary history of papillary renal cell carcinomas

2018 ◽  
Author(s):  
Bin Zhu ◽  
Maria Luana Poeta ◽  
Manuela Costantini ◽  
Tongwu Zhang ◽  
Jianxin Shi ◽  
...  
Keyword(s):  
Kidney Cancer ◽  
Renal Cell ◽  
Large Scale ◽  
Clonal Evolution ◽  
Prognostic Significance ◽  
Intratumor Heterogeneity ◽  
Tumor Evolution ◽  
Driver Gene ◽  
Renal Cell Carcinomas ◽  
Cancer Subtypes

ABSTRACTIntratumor heterogeneity (ITH) and tumor evolution have been well described for clear cell renal cell carcinomas (ccRCC), but they are less studied for other kidney cancer subtypes. Here we investigate ITH and clonal evolution of papillary renal cell carcinoma (pRCC) and rarer kidney cancer subtypes, integrating whole-genome sequencing and DNA methylation data. In 29 tumors, up to 10 samples from the center to the periphery of each tumor, and metastatic samples in 2 cases, enable phylogenetic analysis of spatial features of clonal expansion, which shows congruent patterns of genomic and epigenomic evolution. In contrast to previous studies of ccRCC, in pRCC driver gene mutations and most arm-level somatic copy number alterations (SCNAs) are clonal. These findings suggest that a single biopsy would be sufficient to identify the important genetic drivers and targeting large-scale SCNAs may improve pRCC treatment, which is currently poor. While type 1 pRCC displays near absence of structural variants (SVs), the more aggressive type 2 pRCC and the rarer subtypes have numerous SVs, which should be pursued for prognostic significance.

PSiTE: a Phylogeny guided Simulator for Tumor Evolution

2019 ◽  
Vol 35 (17) ◽  
pp. 3148-3150 ◽  
Author(s):  
Hechuan Yang ◽  
Bingxin Lu ◽  
Lan Huong Lai ◽  
Abner Herbert Lim ◽  
Jacob Josiah Santiago Alvarez ◽  
...  
Keyword(s):  
Cancer Genomics ◽  
Clonal Evolution ◽  
Cell Tumor ◽  
Supplementary Information ◽  
Tumor Evolution ◽  
Supplementary Data ◽  
Efficient Tool ◽  
Wide Range ◽  
Different Types ◽  
Evolutionary Trajectories

Abstract Summary Simulating realistic clonal dynamics of tumors is an important topic in cancer genomics. Here, we present Phylogeny guided Simulator for Tumor Evolution, a tool that can simulate different types of tumor samples including single sector, multi-sector bulk tumor as well as single-cell tumor data under a wide range of evolutionary trajectories. Phylogeny guided Simulator for Tumor Evolution provides an efficient tool for understanding clonal evolution of cancer. Availability and implementation PSiTE is implemented in Python and is available at https://github.com/hchyang/PSiTE. Supplementary information Supplementary data are available at Bioinformatics online.

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 Inferring tumor evolution from longitudinal samples

2019 ◽  
Author(s):  
Matthew A. Myers ◽  
Gryte Satas ◽  
Benjamin J. Raphael
Keyword(s):  
Dna Sequencing ◽  
Phylogenetic Trees ◽  
Simulated Data ◽  
Lymphocytic Leukemia ◽  
Response To Treatment ◽  
Tumor Evolution ◽  
Sequencing Data ◽  
Considerable Uncertainty ◽  
Longitudinal Sampling ◽  
Clonal Composition

Background: Determining the clonal composition and somatic evolution of a tumor greatly aids in accurate prognosis and effective treatment for cancer. In order to understand how a tumor evolves over time and/or in response to treatment, multiple recent studies have performed longitudinal DNA sequencing of tumor samples from the same patient at several different time points. However, none of the existing algorithms that infer clonal composition and phylogeny using several bulk tumor samples from the same patient integrate the information that these samples were obtained from longitudinal observations. Results: We introduce a model for a longitudinally-observed phylogeny and derive constraints that longitudinal samples impose on the reconstruction of a phylogeny from bulk samples. These constraints form the basis for a new algorithm, Cancer Analysis of Longitudinal Data through Evolutionary Reconstruction (CALDER), which infers phylogenetic trees from longitudinal bulk DNA sequencing data. We show on simulated data that constraints from longitudinal sampling can substantially reduce ambiguity when deriving a phylogeny from multiple bulk tumor samples, each a mixture of tumor clones. On real data, where there is often considerable uncertainty in the clonal composition of a sample, longitudinal constraints yield more parsimonious phylogenies with fewer tumor clones per sample. We demonstrate that CALDER reconstructs more plausible phylogenies than existing methods on two longitudinal DNA sequencing datasets from chronic lymphocytic leukemia patients. These findings show the advantages of directly incorporating temporal information from longitudinal sampling into tumor evolution studies. Availability: CALDER is available at https://github.com/raphael-group.

scholarly journals Trasposon Mediated Horizontal Gene Transfer (T-HGT) of Circulating Tumor DNA Reshapes MM Clonal Architecture

Blood ◽  
2019 ◽  
Vol 134 (Supplement_1) ◽  
pp. 3065-3065
Author(s):  
Munevver Cinar ◽  
Steven Flygare ◽  
Marina Mosunjac ◽  
Ganji Nagaraju ◽  
Dongkyoo Park ◽  
...  
Keyword(s):  
Gene Transfer ◽  
Horizontal Gene Transfer ◽  
Drug Sensitivity ◽  
Genetic Material ◽  
Host Cells ◽  
Response To Treatment ◽  
Hierarchical Architecture ◽  
Tumor Evolution ◽  
Sequencing Analysis

Spatial genetic heterogeneity is a characteristic phenomenon that influences multiple myeloma's (MM) phenotype and drug sensitivity (Rasche L. et al and Bolli N et al.). Hence, the branch model of tumor evolution is not sufficient to explain the disorganized architecture observed in MM. In this study, we investigated whether MM ctDNA horizontal gene transfer (HGT) affect tumor genetic architecture and drug sensitivity, resembling what is seen in prokaryotes, and elucidated the mechanisms involved in the mobilization of genetic material from one cell to another. We identified that plasma from patients with MM transmits drug sensitivity or resistance to cells in culture. This transmission of drug sensitivity is mediated by ctDNA transfer of oncogenes to a host cell. Importantly, in vitro and in vivo demonstrated that ctDNA mainly targets cells resembling the cell of origin (tropism). Karyotype spreads and whole genome sequencing demonstrated that once patients ctDNA encounters host cells, it migrates into the nucleus where it ultimately integrates into the cell's genome. Integration to the genome was confirmed to be targeted to myeloma cells. Further sequencing analysis of multiple MM samples identified ctDNA tropism and integration is dependent on the 5' and 3' end presence of transposable elements (TE), particularly of the MIR and ALUsq family. These results were further validated by TE mediated delivery of GFP into MM cells in vitro and HSVTK in tumors of mouse xenografts. In conclusion, this data indicates for the first time that TE mediates MM ctDNA HGT into homologous tumor cells shaping the hierarchical architecture of tumor clones and affecting tumor response to treatment. Therapeutically, this unique quality of ctDNA can be exploited for targeted gene therapeutic approaches in MM and potentially other cancers. Disclosures Bernal-Mizrachi: Kodikas Therapeutic Solutions, Inc: Equity Ownership; TAKEDA: Research Funding; Winship Cancer Institute: Employment, Patents & Royalties.

Sign in / Sign up

Export Citation Format

Share Document