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Rossi N, Gigante N, Vitacolonna N, Piazza C. Inferring Markov Chains to Describe Convergent Tumor Evolution With CIMICE. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2024; 21:106-119. [PMID: 38015671 DOI: 10.1109/tcbb.2023.3337258] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/30/2023]
Abstract
The field of tumor phylogenetics focuses on studying the differences within cancer cell populations. Many efforts are done within the scientific community to build cancer progression models trying to understand the heterogeneity of such diseases. These models are highly dependent on the kind of data used for their construction, therefore, as the experimental technologies evolve, it is of major importance to exploit their peculiarities. In this work we describe a cancer progression model based on Single Cell DNA Sequencing data. When constructing the model, we focus on tailoring the formalism on the specificity of the data. We operate by defining a minimal set of assumptions needed to reconstruct a flexible DAG structured model, capable of identifying progression beyond the limitation of the infinite site assumption. Our proposal is conservative in the sense that we aim to neither discard nor infer knowledge which is not represented in the data. We provide simulations and analytical results to show the features of our model, test it on real data, show how it can be integrated with other approaches to cope with input noise. Moreover, our framework can be exploited to produce simulated data that follows our theoretical assumptions. Finally, we provide an open source R implementation of our approach, called CIMICE, that is publicly available on BioConductor.
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2
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Peng YC, Xu JX, You XM, Huang YY, Ma L, Li LQ, Qi LN. Specific gut microbiome signature predicts hepatitis B virus-related hepatocellular carcinoma patients with microvascular invasion. Ann Med 2023; 55:2283160. [PMID: 38112540 PMCID: PMC10986448 DOI: 10.1080/07853890.2023.2283160] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/30/2023] [Accepted: 11/08/2023] [Indexed: 12/21/2023] Open
Abstract
BACKGROUND We aimed to assess differences in intestinal microflora between patients with operable hepatitis B virus-related hepatocellular carcinoma (HBV-HCC) with microvascular invasion (MVI) and those without MVI. Additionally, we investigated the potential of the microbiome as a non-invasive biomarker for patients with MVI. METHODS We analyzed the preoperative gut microbiomes (GMs) of two groups, the MVI (n = 46) and non-MVI (n = 56) groups, using 16S ribosomal RNA gene sequencing data. At the operational taxonomic unit level, we employed random forest models to predict MVI risk and validated the results in independent validation cohorts [MVI group (n = 17) and non-MVI group (n = 15)]. RESULTS β diversity analysis, utilizing weighted UniFrac distances, revealed a significant difference between the MVI and non-MVI groups, as indicated by non-metric multidimensional scaling and principal coordinate analysis. We also observed a significant correlation between the characteristic intestinal microbial communities at the genus level and their main functions. Nine optimal microbial markers were identified, with an area under the curve of 79.76% between 46 MVI and 56 non-MVI samples and 79.80% in the independent verification group. CONCLUSION This pioneering analysis of the GM in patients with operable HBV-HCC with and without MVI opens new avenues for treating HBV-HCC with MVI. We successfully established a diagnostic model and independently verified microbial markers for patients with MVI. As preoperative targeted biomarkers, GM holds potential as a non-invasive tool for patients with HBV-HCC with MVI.
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Affiliation(s)
- Yu-Chong Peng
- Department of General Surgery, Chongqing Hospital of Traditional Chinese Medicine, Chongqing, China
| | - Jing-Xuan Xu
- Department of Hepatobiliary Surgery, Guangxi Medical University Cancer Hospital, Nanning, China
- Ministry of Education, Key Laboratory of Early Prevention and Treatment for Regional High Frequency Tumor, Nanning, China
| | - Xue-Mei You
- Department of Hepatobiliary Surgery, Guangxi Medical University Cancer Hospital, Nanning, China
- Ministry of Education, Key Laboratory of Early Prevention and Treatment for Regional High Frequency Tumor, Nanning, China
| | - Yi-Yue Huang
- Department of Hepatobiliary Surgery, Guangxi Medical University Cancer Hospital, Nanning, China
- Ministry of Education, Key Laboratory of Early Prevention and Treatment for Regional High Frequency Tumor, Nanning, China
| | - Liang Ma
- Department of Hepatobiliary Surgery, Guangxi Medical University Cancer Hospital, Nanning, China
- Ministry of Education, Key Laboratory of Early Prevention and Treatment for Regional High Frequency Tumor, Nanning, China
| | - Le-Qun Li
- Department of Hepatobiliary Surgery, Guangxi Medical University Cancer Hospital, Nanning, China
- Ministry of Education, Key Laboratory of Early Prevention and Treatment for Regional High Frequency Tumor, Nanning, China
- Guangxi Liver Cancer Diagnosis and Treatment Engineering and Technology Research Center, Nanning, China
| | - Lu-Nan Qi
- Department of Hepatobiliary Surgery, Guangxi Medical University Cancer Hospital, Nanning, China
- Ministry of Education, Key Laboratory of Early Prevention and Treatment for Regional High Frequency Tumor, Nanning, China
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3
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Khan R, Mallory X. Assessing the performance of methods for cell clustering from single-cell DNA sequencing data. PLoS Comput Biol 2023; 19:e1010480. [PMID: 37824596 PMCID: PMC10597505 DOI: 10.1371/journal.pcbi.1010480] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Revised: 10/24/2023] [Accepted: 09/20/2023] [Indexed: 10/14/2023] Open
Abstract
BACKGROUND Many cancer genomes have been known to contain more than one subclone inside one tumor, the phenomenon of which is called intra-tumor heterogeneity (ITH). Characterizing ITH is essential in designing treatment plans, prognosis as well as the study of cancer progression. Single-cell DNA sequencing (scDNAseq) has been proven effective in deciphering ITH. Cells corresponding to each subclone are supposed to carry a unique set of mutations such as single nucleotide variations (SNV). While there have been many studies on the cancer evolutionary tree reconstruction, not many have been proposed that simply characterize the subclonality without tree reconstruction. While tree reconstruction is important in the study of cancer evolutionary history, typically they are computationally expensive in terms of running time and memory consumption due to the huge search space of the tree structure. On the other hand, subclonality characterization of single cells can be converted into a cell clustering problem, the dimension of which is much smaller, and the turnaround time is much shorter. Despite the existence of a few state-of-the-art cell clustering computational tools for scDNAseq, there lacks a comprehensive and objective comparison under different settings. RESULTS In this paper, we evaluated six state-of-the-art cell clustering tools-SCG, BnpC, SCClone, RobustClone, SCITE and SBMClone-on simulated data sets given a variety of parameter settings and a real data set. We designed a simulator specifically for cell clustering, and compared these methods' performances in terms of their clustering accuracy, specificity and sensitivity and running time. For SBMClone, we specifically designed an ultra-low coverage large data set to evaluate its performance in the face of an extremely high missing rate. CONCLUSION From the benchmark study, we conclude that BnpC and SCG's clustering accuracy are the highest and comparable to each other. However, BnpC is more advantageous in terms of running time when cell number is high (> 1500). It also has a higher clustering accuracy than SCG when cluster number is high (> 16). SCClone's accuracy in estimating the number of clusters is the highest. RobustClone and SCITE's clustering accuracy are the lowest for all experiments. SCITE tends to over-estimate the cluster number and has a low specificity, whereas RobustClone tends to under-estimate the cluster number and has a much lower sensitivity than other methods. SBMClone produced reasonably good clustering (V-measure > 0.9) when coverage is > = 0.03 and thus is highly recommended for ultra-low coverage large scDNAseq data sets.
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Affiliation(s)
- Rituparna Khan
- Department of Computer Science, Florida State University, Tallahassee, Florida, United States of America
| | - Xian Mallory
- Department of Computer Science, Florida State University, Tallahassee, Florida, United States of America
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4
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Zheng Y, Yang X. Application and prospect of single-cell sequencing in cancer metastasis. Future Oncol 2022; 18:2723-2736. [PMID: 35686493 DOI: 10.2217/fon-2022-0156] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
Cancer metastasis is a complicated process driven by internal genetic variations and developed through interactions with the external environment. This process usually causes therapeutic resistance and results in a low survival rate. In recent years, single-cell sequencing has become a popular method for revealing the tumor evolutionary genetic lineage, intra-tumoral heterogeneity and tumor microenvironment of the metastasis process. So as to find more therapeutic targets for clinical application, the spatial transcriptomics method has become a new rising field of cancer studies, which promotes the combination between clinical medicine and molecular biology. In future prospects, more accurate and personalized treatment models will come into reality.
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Affiliation(s)
- Yue Zheng
- Department of Biochemistry & Molecular Biology, Basic Medical College, Shanxi Medical University, Taiyuan City, Shanxi Province, 030000, China
| | - Xiaofeng Yang
- Department of Urology, First Hospital of Shanxi Medical University,Taiyuan City, Shanxi Province, 030000, China
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5
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van den Bosch T, Vermeulen L, Miedema DM. Quantitative models for the inference of intratumor heterogeneity. COMPUTATIONAL AND SYSTEMS ONCOLOGY 2022. [DOI: 10.1002/cso2.1034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022] Open
Affiliation(s)
- Tom van den Bosch
- Laboratory for Experimental Oncology and Radiobiology Center for Experimental and Molecular Medicine Cancer Center Amsterdam and Amsterdam Gastroenterology and Metabolism Amsterdam University Medical Centers Amsterdam The Netherlands
- Oncode Institute Amsterdam The Netherlands
| | - Louis Vermeulen
- Laboratory for Experimental Oncology and Radiobiology Center for Experimental and Molecular Medicine Cancer Center Amsterdam and Amsterdam Gastroenterology and Metabolism Amsterdam University Medical Centers Amsterdam The Netherlands
- Oncode Institute Amsterdam The Netherlands
| | - Daniël M. Miedema
- Laboratory for Experimental Oncology and Radiobiology Center for Experimental and Molecular Medicine Cancer Center Amsterdam and Amsterdam Gastroenterology and Metabolism Amsterdam University Medical Centers Amsterdam The Netherlands
- Oncode Institute Amsterdam The Netherlands
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6
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Giles Doran C, Pennington SR. Copy number alteration signatures as biomarkers in cancer: a review. Biomark Med 2022; 16:371-386. [PMID: 35195030 DOI: 10.2217/bmm-2021-0476] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023] Open
Abstract
Within certain cancers, extensive copy number alterations (CNAs) contribute to a complex and heterogenic genomic profile. This makes it difficult to understand and unravel the distinct molecular dynamics shaping the disease while preventing clinically effective patient stratification. CNA signature analysis represents a novel genomic stratification tool for probing this complexity, offering an intricate framework for deriving CNA patterns at the molecular level. This allows the underlying genomic mechanisms of specific cancers to be revealed, leading to the potential identification of therapeutic targets and prognostic associations. This review outlines the molecular and methodological basis of CNA signatures and focuses on recent advances highlighting their clinical utility, limitations and prospective future as novel diagnostic and prognostic cancer biomarkers.
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Affiliation(s)
- Conor Giles Doran
- UCD Conway Institute, School of Medicine, University College Dublin, Belfield, Dublin 4, Ireland
| | - Stephen R Pennington
- UCD Conway Institute, School of Medicine, University College Dublin, Belfield, Dublin 4, Ireland
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7
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Farswan A, Gupta R, Gupta A. ARCANE-ROG: Algorithm for Reconstruction of Cancer Evolution from single-cell data using Robust Graph Learning. J Biomed Inform 2022; 129:104055. [PMID: 35337943 DOI: 10.1016/j.jbi.2022.104055] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Revised: 02/17/2022] [Accepted: 03/12/2022] [Indexed: 11/27/2022]
Abstract
Tumor heterogeneity, marked by the presence of divergent clonal subpopulations of tumor cells, impedes the treatment response in cancer patients. Single-cell sequencing technology provides substantial prospects to gain an in-depth understanding of the cellular phenotypic variability driving tumor progression. A comprehensive insight into the intra-tumor heterogeneity may further assist in dealing with the treatment-resistant clones in cancer patients, thereby improving their overall survival. However, this task is hampered due to the challenges associated with the single-cell data, such as false positives, false negatives and missing bases, and the increase in their size. As a result, the computational cost of the existing methods increases, thereby limiting their usage. In this work, we propose a robust graph learning-based method, ARCANE-ROG (Algorithm for Reconstruction of CANcer Evolution via RObust Graph learning), for inferring clonal evolution from single-cell datasets. The first step of the proposed method is a joint framework of denoising with data imputation for the noisy and incomplete matrix while simultaneously learning an adjacency graph. Both the operations in the joint framework boost each other such that the overall performance of the denoising algorithm is improved. In the second step, an optimal number of clusters are identified via the Leiden method. In the last step, clonal evolution trees are inferred via a minimum spanning tree algorithm. The method has been benchmarked against a state-of-the-art method, RobustClone, using simulated datasets of varying sizes and five real datasets. The performance of our proposed method is found to be significantly superior (p-value < 0.05) in terms of reconstruction error, False Positive to False Negative (FPFN) ratio, tree distance error and V-measure compared to the other method. Overall, the proposed method is an improvement over the existing methods as it enhances cluster assignment and inference on clonal hierarchies.
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Affiliation(s)
- Akanksha Farswan
- SBILab, Department of ECE, Indraprastha Institute of Information Technology, New Delhi, India
| | - Ritu Gupta
- Laboratory Oncology Unit, Dr. B.R.A. IRCH, AIIMS, New Delhi, India.
| | - Anubha Gupta
- SBILab, Department of ECE, Indraprastha Institute of Information Technology, New Delhi, India.
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8
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Chen Z, Gong F, Wan L, Ma L. RobustClone: a robust PCA method for tumor clone and evolution inference from single-cell sequencing data. Bioinformatics 2020; 36:3299-3306. [PMID: 32159762 DOI: 10.1093/bioinformatics/btaa172] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2019] [Revised: 02/10/2020] [Accepted: 03/06/2020] [Indexed: 12/17/2022] Open
Abstract
MOTIVATION Single-cell sequencing (SCS) data provide unprecedented insights into intratumoral heterogeneity. With SCS, we can better characterize clonal genotypes and reconstruct phylogenetic relationships of tumor cells/clones. However, SCS data are often error-prone, making their computational analysis challenging. RESULTS To infer the clonal evolution in tumor from the error-prone SCS data, we developed an efficient computational framework, termed RobustClone. It recovers the true genotypes of subclones based on the extended robust principal component analysis, a low-rank matrix decomposition method, and reconstructs the subclonal evolutionary tree. RobustClone is a model-free method, which can be applied to both single-cell single nucleotide variation (scSNV) and single-cell copy-number variation (scCNV) data. It is efficient and scalable to large-scale datasets. We conducted a set of systematic evaluations on simulated datasets and demonstrated that RobustClone outperforms state-of-the-art methods in large-scale data both in accuracy and efficiency. We further validated RobustClone on two scSNV and two scCNV datasets and demonstrated that RobustClone could recover genotype matrix and infer the subclonal evolution tree accurately under various scenarios. In particular, RobustClone revealed the spatial progression patterns of subclonal evolution on the large-scale 10X Genomics scCNV breast cancer dataset. AVAILABILITY AND IMPLEMENTATION RobustClone software is available at https://github.com/ucasdp/RobustClone. CONTACT lwan@amss.ac.cn or maliang@ioz.ac.cn. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Ziwei Chen
- NCMIS, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China.,School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Fuzhou Gong
- NCMIS, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China.,School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Lin Wan
- NCMIS, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China.,School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Liang Ma
- Key Laboratory of Zoological Systematics and Evolution, Institute of Zoology, Chinese Academy of Sciences, Beijing 100101, China
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9
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Nam AS, Chaligne R, Landau DA. Integrating genetic and non-genetic determinants of cancer evolution by single-cell multi-omics. Nat Rev Genet 2020; 22:3-18. [PMID: 32807900 DOI: 10.1038/s41576-020-0265-5] [Citation(s) in RCA: 191] [Impact Index Per Article: 47.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/01/2020] [Indexed: 12/17/2022]
Abstract
Cancer represents an evolutionary process through which growing malignant populations genetically diversify, leading to tumour progression, relapse and resistance to therapy. In addition to genetic diversity, the cell-to-cell variation that fuels evolutionary selection also manifests in cellular states, epigenetic profiles, spatial distributions and interactions with the microenvironment. Therefore, the study of cancer requires the integration of multiple heritable dimensions at the resolution of the single cell - the atomic unit of somatic evolution. In this Review, we discuss emerging analytic and experimental technologies for single-cell multi-omics that enable the capture and integration of multiple data modalities to inform the study of cancer evolution. These data show that cancer results from a complex interplay between genetic and non-genetic determinants of somatic evolution.
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Affiliation(s)
- Anna S Nam
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY, USA.,New York Genome Center, New York, NY, USA.,Sandra and Edward Meyer Cancer Center, Weill Cornell Medicine, New York, NY, USA
| | - Ronan Chaligne
- New York Genome Center, New York, NY, USA.,Sandra and Edward Meyer Cancer Center, Weill Cornell Medicine, New York, NY, USA.,Division of Hematology and Medical Oncology, Department of Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Dan A Landau
- New York Genome Center, New York, NY, USA. .,Sandra and Edward Meyer Cancer Center, Weill Cornell Medicine, New York, NY, USA. .,Division of Hematology and Medical Oncology, Department of Medicine, Weill Cornell Medicine, New York, NY, USA. .,Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY, USA.
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10
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Miura S, Vu T, Deng J, Buturla T, Oladeinde O, Choi J, Kumar S. Power and pitfalls of computational methods for inferring clone phylogenies and mutation orders from bulk sequencing data. Sci Rep 2020; 10:3498. [PMID: 32103044 PMCID: PMC7044161 DOI: 10.1038/s41598-020-59006-2] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2020] [Accepted: 01/23/2020] [Indexed: 12/13/2022] Open
Abstract
Tumors harbor extensive genetic heterogeneity in the form of distinct clone genotypes that arise over time and across different tissues and regions in cancer. Many computational methods produce clone phylogenies from population bulk sequencing data collected from multiple tumor samples from a patient. These clone phylogenies are used to infer mutation order and clone origins during tumor progression, rendering the selection of the appropriate clonal deconvolution method critical. Surprisingly, absolute and relative accuracies of these methods in correctly inferring clone phylogenies are yet to consistently assessed. Therefore, we evaluated the performance of seven computational methods. The accuracy of the reconstructed mutation order and inferred clone groupings varied extensively among methods. All the tested methods showed limited ability to identify ancestral clone sequences present in tumor samples correctly. The presence of copy number alterations, the occurrence of multiple seeding events among tumor sites during metastatic tumor evolution, and extensive intermixture of cancer cells among tumors hindered the detection of clones and the inference of clone phylogenies for all methods tested. Overall, CloneFinder, MACHINA, and LICHeE showed the highest overall accuracy, but none of the methods performed well for all simulated datasets. So, we present guidelines for selecting methods for data analysis.
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Affiliation(s)
- Sayaka Miura
- Institute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, PA, 19122, USA.,Department of Biology, Temple University, Philadelphia, PA, 19122, USA
| | - Tracy Vu
- Institute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, PA, 19122, USA.,Department of Biology, Temple University, Philadelphia, PA, 19122, USA
| | - Jiamin Deng
- Institute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, PA, 19122, USA.,Department of Biology, Temple University, Philadelphia, PA, 19122, USA
| | - Tiffany Buturla
- Institute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, PA, 19122, USA.,Department of Biology, Temple University, Philadelphia, PA, 19122, USA
| | - Olumide Oladeinde
- Institute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, PA, 19122, USA.,Department of Biology, Temple University, Philadelphia, PA, 19122, USA
| | - Jiyeong Choi
- Institute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, PA, 19122, USA.,Department of Biology, Temple University, Philadelphia, PA, 19122, USA
| | - Sudhir Kumar
- Institute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, PA, 19122, USA. .,Department of Biology, Temple University, Philadelphia, PA, 19122, USA. .,Center for Excellence in Genome Medicine and Research, King Abdulaziz University, Jeddah, Saudi Arabia.
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11
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Werner B, Case J, Williams MJ, Chkhaidze K, Temko D, Fernández-Mateos J, Cresswell GD, Nichol D, Cross W, Spiteri I, Huang W, Tomlinson IPM, Barnes CP, Graham TA, Sottoriva A. Measuring single cell divisions in human tissues from multi-region sequencing data. Nat Commun 2020; 11:1035. [PMID: 32098957 PMCID: PMC7042311 DOI: 10.1038/s41467-020-14844-6] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2019] [Accepted: 01/29/2020] [Indexed: 01/06/2023] Open
Abstract
Both normal tissue development and cancer growth are driven by a branching process of cell division and mutation accumulation that leads to intra-tissue genetic heterogeneity. However, quantifying somatic evolution in humans remains challenging. Here, we show that multi-sample genomic data from a single time point of normal and cancer tissues contains information on single-cell divisions. We present a new theoretical framework that, applied to whole-genome sequencing data of healthy tissue and cancer, allows inferring the mutation rate and the cell survival/death rate per division. On average, we found that cells accumulate 1.14 mutations per cell division in healthy haematopoiesis and 1.37 mutations per division in brain development. In both tissues, cell survival was maximal during early development. Analysis of 131 biopsies from 16 tumours showed 4 to 100 times increased mutation rates compared to healthy development and substantial inter-patient variation of cell survival/death rates.
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Affiliation(s)
- Benjamin Werner
- Evolutionary Genomics and Modelling Lab, Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK.
- Evolutionary Dynamics Group, Centre for Cancer Genomics & Computational Biology, Barts Cancer Institute, Queen Mary University of London, Charterhouse Square, London, EC1M 6BQ, UK.
| | - Jack Case
- Evolutionary Genomics and Modelling Lab, Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK
- University of Cambridge, Cambridge, UK
| | - Marc J Williams
- Evolution and Cancer Laboratory, Centre for Cancer Genomics & Computational Biology, Barts Cancer Institute, Queen Mary University London, London, Charterhouse Square, London, EC1M 6BQ, UK
- Department of Cell and Developmental Biology, University College London, London, UK
- Centre for Mathematics and Physics in the Life Sciences and Experimental Biology (CoMPLEX), University College London, London, UK
| | - Ketevan Chkhaidze
- Evolutionary Genomics and Modelling Lab, Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK
| | - Daniel Temko
- Evolution and Cancer Laboratory, Centre for Cancer Genomics & Computational Biology, Barts Cancer Institute, Queen Mary University London, London, Charterhouse Square, London, EC1M 6BQ, UK
| | - Javier Fernández-Mateos
- Evolutionary Genomics and Modelling Lab, Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK
| | - George D Cresswell
- Evolutionary Genomics and Modelling Lab, Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK
| | - Daniel Nichol
- Evolutionary Genomics and Modelling Lab, Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK
| | - William Cross
- Evolution and Cancer Laboratory, Centre for Cancer Genomics & Computational Biology, Barts Cancer Institute, Queen Mary University London, London, Charterhouse Square, London, EC1M 6BQ, UK
| | - Inmaculada Spiteri
- Evolutionary Genomics and Modelling Lab, Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK
| | - Weini Huang
- Group of Theoretical Biology, The State Key Laboratory of Biocontrol, School of Life Science, Sun Yat-sen University, 510060, Guangzhou, China
- School of Mathematical Sciences, Queen Mary University London, London, UK
| | - Ian P M Tomlinson
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK
| | - Chris P Barnes
- Department of Cell and Developmental Biology, University College London, London, UK
- UCL Genetics Institute, University College London, London, UK
| | - Trevor A Graham
- Evolution and Cancer Laboratory, Centre for Cancer Genomics & Computational Biology, Barts Cancer Institute, Queen Mary University London, London, Charterhouse Square, London, EC1M 6BQ, UK.
| | - Andrea Sottoriva
- Evolutionary Genomics and Modelling Lab, Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK.
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12
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Andersson N, Bakker B, Karlsson J, Valind A, Holmquist Mengelbier L, Spierings DCJ, Foijer F, Gisselsson D. Extensive Clonal Branching Shapes the Evolutionary History of High-Risk Pediatric Cancers. Cancer Res 2020; 80:1512-1523. [PMID: 32041836 DOI: 10.1158/0008-5472.can-19-3468] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2019] [Revised: 12/18/2019] [Accepted: 02/04/2020] [Indexed: 11/16/2022]
Abstract
Darwinian evolution of tumor cells remains underexplored in childhood cancer. We here reconstruct the evolutionary histories of 56 pediatric primary tumors, including 24 neuroblastomas, 24 Wilms tumors, and 8 rhabdomyosarcomas. Whole-genome copy-number and whole-exome mutational profiling of multiple regions per tumor were performed, followed by clonal deconvolution to reconstruct a phylogenetic tree for each tumor. Overall, 88% of the tumors exhibited genetic variation among primary tumor regions. This variability typically emerged through collateral phylogenetic branching, leading to spatial variability in the distribution of more than 50% (96/173) of detected diagnostically informative genetic aberrations. Single-cell sequencing of 547 individual cancer cells from eight solid pediatric tumors confirmed branching evolution to be a fundamental underlying principle of genetic variation in all cases. Strikingly, cell-to-cell genetic diversity was almost twice as high in aggressive compared with clinically favorable tumors (median Simpson index of diversity 0.45 vs. 0.88; P = 0.029). Similarly, a comparison of multiregional sampling data from a total of 274 tumor regions showed that new phylogenetic branches emerge at a higher frequency per sample and carry a higher mutational load in high-risk than in low-risk tumors. Timelines based on spatial genetic variation showed that the mutations most influencing relapse risk occur at initiation of clonal expansion in neuroblastoma and rhabdomyosarcoma, whereas in Wilms tumor, they are late events. Thus, from an evolutionary standpoint, some high-risk childhood cancers are born bad, whereas others grow worse over time. SIGNIFICANCE: Different pediatric cancers with a high risk of relapse share a common generic pattern of extensively branching evolution of somatic mutations. GRAPHICAL ABSTRACT: http://cancerres.aacrjournals.org/content/canres/80/7/1512/F1.large.jpg.
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Affiliation(s)
- Natalie Andersson
- Division of Clinical Genetics, Department of Laboratory Medicine, Lund University, Lund, Sweden
| | - Bjorn Bakker
- European Research Institute for the Biology of Ageing (ERIBA), University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Jenny Karlsson
- Division of Clinical Genetics, Department of Laboratory Medicine, Lund University, Lund, Sweden
| | - Anders Valind
- Division of Clinical Genetics, Department of Laboratory Medicine, Lund University, Lund, Sweden.,Department of Pediatrics, Skåne University Hospital, Lund, Sweden
| | | | - Diana C J Spierings
- European Research Institute for the Biology of Ageing (ERIBA), University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Floris Foijer
- European Research Institute for the Biology of Ageing (ERIBA), University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - David Gisselsson
- Division of Clinical Genetics, Department of Laboratory Medicine, Lund University, Lund, Sweden. .,Division of Oncology-Pathology, Department of Clinical Sciences, Lund University, Lund, Sweden.,Clinical Genetics and Pathology, Laboratory Medicine, Lund University Hospital, Skåne Healthcare Region, Lund, Sweden
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13
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Ismail WM, Nzabarushimana E, Tang H. Algorithmic approaches to clonal reconstruction in heterogeneous cell populations. QUANTITATIVE BIOLOGY 2019; 7:255-265. [PMID: 32431959 PMCID: PMC7236794 DOI: 10.1007/s40484-019-0188-3] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2019] [Revised: 08/09/2019] [Accepted: 08/25/2019] [Indexed: 12/15/2022]
Abstract
BACKGROUND The reconstruction of clonal haplotypes and their evolutionary history in evolving populations is a common problem in both microbial evolutionary biology and cancer biology. The clonal theory of evolution provides a theoretical framework for modeling the evolution of clones. RESULTS In this paper, we review the theoretical framework and assumptions over which the clonal reconstruction problem is formulated. We formally define the problem and then discuss the complexity and solution space of the problem. Various methods have been proposed to find the phylogeny that best explains the observed data. We categorize these methods based on the type of input data that they use (space-resolved or time-resolved), and also based on their computational formulation as either combinatorial or probabilistic. It is crucial to understand the different types of input data because each provides essential but distinct information for drastically reducing the solution space of the clonal reconstruction problem. Complementary information provided by single cell sequencing or from whole genome sequencing of randomly isolated clones can also improve the accuracy of clonal reconstruction. We briefly review the existing algorithms and their relationships. Finally we summarize the tools that are developed for either directly solving the clonal reconstruction problem or a related computational problem. CONCLUSIONS In this review, we discuss the various formulations of the problem of inferring the clonal evolutionary history from allele frequeny data, review existing algorithms and catergorize them according to their problem formulation and solution approaches. We note that most of the available clonal inference algorithms were developed for elucidating tumor evolution whereas clonal reconstruction for unicellular genomes are less addressed. We conclude the review by discussing more open problems such as the lack of benchmark datasets and comparison of performance between available tools.
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Affiliation(s)
- Wazim Mohammed Ismail
- School of Informatics, Computing and Engineering, Indiana University, Bloomington, IN 47405-7000, USA
| | - Etienne Nzabarushimana
- School of Informatics, Computing and Engineering, Indiana University, Bloomington, IN 47405-7000, USA
| | - Haixu Tang
- School of Informatics, Computing and Engineering, Indiana University, Bloomington, IN 47405-7000, USA
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14
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Abécassis J, Hamy AS, Laurent C, Sadacca B, Bonsang-Kitzis H, Reyal F, Vert JP. Assessing reliability of intra-tumor heterogeneity estimates from single sample whole exome sequencing data. PLoS One 2019; 14:e0224143. [PMID: 31697689 PMCID: PMC6837753 DOI: 10.1371/journal.pone.0224143] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2019] [Accepted: 10/07/2019] [Indexed: 12/14/2022] Open
Abstract
Tumors are made of evolving and heterogeneous populations of cells which arise from successive appearance and expansion of subclonal populations, following acquisition of mutations conferring them a selective advantage. Those subclonal populations can be sensitive or resistant to different treatments, and provide information about tumor aetiology and future evolution. Hence, it is important to be able to assess the level of heterogeneity of tumors with high reliability for clinical applications. In the past few years, a large number of methods have been proposed to estimate intra-tumor heterogeneity from whole exome sequencing (WES) data, but the accuracy and robustness of these methods on real data remains elusive. Here we systematically apply and compare 6 computational methods to estimate tumor heterogeneity on 1,697 WES samples from the cancer genome atlas (TCGA) covering 3 cancer types (breast invasive carcinoma, bladder urothelial carcinoma, and head and neck squamous cell carcinoma), and two distinct input mutation sets. We observe significant differences between the estimates produced by different methods, and identify several likely confounding factors in heterogeneity assessment for the different methods. We further show that the prognostic value of tumor heterogeneity for survival prediction is limited in those datasets, and find no evidence that it improves over prognosis based on other clinical variables. In conclusion, heterogeneity inference from WES data on a single sample, and its use in cancer prognosis, should be considered with caution. Other approaches to assess intra-tumoral heterogeneity such as those based on multiple samples may be preferable for clinical applications.
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Affiliation(s)
- Judith Abécassis
- Institut Curie, PSL Research University, Translational Research Department, INSERM, U932 Immunity and Cancer, Residual Tumor & Response to Treatment Laboratory (RT2Lab), Paris, France
- MINES ParisTech, PSL Research University, CBIO-Centre for Computational Biology, Paris, France
- Institut Curie, PSL Research University, INSERM, U900, Paris, France
| | - Anne-Sophie Hamy
- Institut Curie, PSL Research University, Translational Research Department, INSERM, U932 Immunity and Cancer, Residual Tumor & Response to Treatment Laboratory (RT2Lab), Paris, France
| | - Cécile Laurent
- Institut Curie, PSL Research University, Translational Research Department, INSERM, U932 Immunity and Cancer, Residual Tumor & Response to Treatment Laboratory (RT2Lab), Paris, France
| | - Benjamin Sadacca
- Institut Curie, PSL Research University, Translational Research Department, INSERM, U932 Immunity and Cancer, Residual Tumor & Response to Treatment Laboratory (RT2Lab), Paris, France
- Institut de Mathématiques de Toulouse, UMR5219 Université de Toulouse, CNRS UPS IMT, Toulouse, France
| | - Hélène Bonsang-Kitzis
- Institut Curie, PSL Research University, Translational Research Department, INSERM, U932 Immunity and Cancer, Residual Tumor & Response to Treatment Laboratory (RT2Lab), Paris, France
- Department of Surgery, Institut Curie, Paris, France
| | - Fabien Reyal
- Institut Curie, PSL Research University, Translational Research Department, INSERM, U932 Immunity and Cancer, Residual Tumor & Response to Treatment Laboratory (RT2Lab), Paris, France
- Department of Surgery, Institut Curie, Paris, France
| | - Jean-Philippe Vert
- MINES ParisTech, PSL Research University, CBIO-Centre for Computational Biology, Paris, France
- Google Brain, Paris, France
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15
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Pierga JY. Circulating Tumor Cells for Cancer Management. Clin Chem 2019; 66:clinchem.2019.304766. [PMID: 31672852 DOI: 10.1373/clinchem.2019.304766] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2019] [Accepted: 09/23/2019] [Indexed: 11/06/2022]
Affiliation(s)
- Jean-Yves Pierga
- Department of Medical Oncology, Institut Curie, Paris and Saint Cloud, France;
- Université de Paris, Paris France
- Laboratory of Circulating Tumor Biomarkers, Institut Curie, SIRIC, Paris, France
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16
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Zafar H, Navin N, Chen K, Nakhleh L. SiCloneFit: Bayesian inference of population structure, genotype, and phylogeny of tumor clones from single-cell genome sequencing data. Genome Res 2019; 29:1847-1859. [PMID: 31628257 PMCID: PMC6836738 DOI: 10.1101/gr.243121.118] [Citation(s) in RCA: 67] [Impact Index Per Article: 13.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2018] [Accepted: 07/10/2019] [Indexed: 12/12/2022]
Abstract
Accumulation and selection of somatic mutations in a Darwinian framework result in intra-tumor heterogeneity (ITH) that poses significant challenges to the diagnosis and clinical therapy of cancer. Identification of the tumor cell populations (clones) and reconstruction of their evolutionary relationship can elucidate this heterogeneity. Recently developed single-cell DNA sequencing (SCS) technologies promise to resolve ITH to a single-cell level. However, technical errors in SCS data sets, including false-positives (FP) and false-negatives (FN) due to allelic dropout, and cell doublets, significantly complicate these tasks. Here, we propose a nonparametric Bayesian method that reconstructs the clonal populations as clusters of single cells, genotypes of each clone, and the evolutionary relationship between the clones. It employs a tree-structured Chinese restaurant process as the prior on the number and composition of clonal populations. The evolution of the clonal populations is modeled by a clonal phylogeny and a finite-site model of evolution to account for potential mutation recurrence and losses. We probabilistically account for FP and FN errors, and cell doublets are modeled by employing a Beta-binomial distribution. We develop a Gibbs sampling algorithm comprising partial reversible-jump and partial Metropolis-Hastings updates to explore the joint posterior space of all parameters. The performance of our method on synthetic and experimental data sets suggests that joint reconstruction of tumor clones and clonal phylogeny under a finite-site model of evolution leads to more accurate inferences. Our method is the first to enable this joint reconstruction in a fully Bayesian framework, thus providing measures of support of the inferences it makes.
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Affiliation(s)
- Hamim Zafar
- Department of Computer Science, Rice University, Houston, Texas 77005, USA.,Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030, USA
| | - Nicholas Navin
- Department of Genetics, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030, USA
| | - Ken Chen
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030, USA
| | - Luay Nakhleh
- Department of Computer Science, Rice University, Houston, Texas 77005, USA
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17
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Malikic S, Jahn K, Kuipers J, Sahinalp SC, Beerenwinkel N. Integrative inference of subclonal tumour evolution from single-cell and bulk sequencing data. Nat Commun 2019; 10:2750. [PMID: 31227714 PMCID: PMC6588593 DOI: 10.1038/s41467-019-10737-5] [Citation(s) in RCA: 75] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2018] [Accepted: 05/30/2019] [Indexed: 02/07/2023] Open
Abstract
Understanding the clonal architecture and evolutionary history of a tumour poses one of the key challenges to overcome treatment failure due to resistant cell populations. Previously, studies on subclonal tumour evolution have been primarily based on bulk sequencing and in some recent cases on single-cell sequencing data. Either data type alone has shortcomings with regard to this task, but methods integrating both data types have been lacking. Here, we present B-SCITE, the first computational approach that infers tumour phylogenies from combined single-cell and bulk sequencing data. Using a comprehensive set of simulated data, we show that B-SCITE systematically outperforms existing methods with respect to tree reconstruction accuracy and subclone identification. B-SCITE provides high-fidelity reconstructions even with a modest number of single cells and in cases where bulk allele frequencies are affected by copy number changes. On real tumour data, B-SCITE generated mutation histories show high concordance with expert generated trees. Intra-tumour heterogeneity provides important information about subclonal tumour evolution. Here, the authors develop B-SCITE, a computational method for inferring tumour phylogenies from combined single-cell and bulk sequencing data.
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Affiliation(s)
- Salem Malikic
- School of Computing Science, Simon Fraser University, Burnaby, V5A 1S6, BC, Canada.,Vancouver Prostate Centre, Vancouver, V6H 3Z6, BC, Canada
| | - Katharina Jahn
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, 4058, Switzerland.,Swiss Institute of Bioinformatics, Lausanne, 1015, Switzerland
| | - Jack Kuipers
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, 4058, Switzerland.,Swiss Institute of Bioinformatics, Lausanne, 1015, Switzerland
| | - S Cenk Sahinalp
- Department of Computer Science, Indiana University, Bloomington, 47405, IN, USA.
| | - Niko Beerenwinkel
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, 4058, Switzerland. .,Swiss Institute of Bioinformatics, Lausanne, 1015, Switzerland.
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18
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Williams MJ, Sottoriva A, Graham TA. Measuring Clonal Evolution in Cancer with Genomics. Annu Rev Genomics Hum Genet 2019; 20:309-329. [PMID: 31059289 DOI: 10.1146/annurev-genom-083117-021712] [Citation(s) in RCA: 39] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
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.
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Affiliation(s)
- Marc J Williams
- Evolution and Cancer Laboratory, Barts Cancer Institute, Queen Mary University of London, London EC1M 6BQ, United Kingdom; ,
| | - Andrea Sottoriva
- Evolutionary Genomics and Modelling Lab, Centre for Evolution and Cancer, The Institute of Cancer Research, London SM2 5NG, United Kingdom
| | - Trevor A Graham
- Evolution and Cancer Laboratory, Barts Cancer Institute, Queen Mary University of London, London EC1M 6BQ, United Kingdom; ,
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19
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Ramazzotti D, Graudenzi A, De Sano L, Antoniotti M, Caravagna G. Learning mutational graphs of individual tumour evolution from single-cell and multi-region sequencing data. BMC Bioinformatics 2019; 20:210. [PMID: 31023236 PMCID: PMC6485126 DOI: 10.1186/s12859-019-2795-4] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2018] [Accepted: 04/08/2019] [Indexed: 12/22/2022] Open
Abstract
BACKGROUND A large number of algorithms is being developed to reconstruct evolutionary models of individual tumours from genome sequencing data. Most methods can analyze multiple samples collected either through bulk multi-region sequencing experiments or the sequencing of individual cancer cells. However, rarely the same method can support both data types. RESULTS We introduce TRaIT, a computational framework to infer mutational graphs that model the accumulation of multiple types of somatic alterations driving tumour evolution. Compared to other tools, TRaIT supports multi-region and single-cell sequencing data within the same statistical framework, and delivers expressive models that capture many complex evolutionary phenomena. TRaIT improves accuracy, robustness to data-specific errors and computational complexity compared to competing methods. CONCLUSIONS We show that the application of TRaIT to single-cell and multi-region cancer datasets can produce accurate and reliable models of single-tumour evolution, quantify the extent of intra-tumour heterogeneity and generate new testable experimental hypotheses.
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Affiliation(s)
| | - Alex Graudenzi
- Dipartimento di Informatica, Sistemistica e Comunicazione, Università degli Studi di Milano-Bicocca, Viale Sarca 336, Milan, 20126 Italy
- Institute of Molecular Bioimaging and Physiology of the Italian National Research Council (IBFM-CNR), Viale F.lli Cervi 93, Segrate, Milan, 20090 Italy
| | - Luca De Sano
- Dipartimento di Informatica, Sistemistica e Comunicazione, Università degli Studi di Milano-Bicocca, Viale Sarca 336, Milan, 20126 Italy
| | - Marco Antoniotti
- Dipartimento di Informatica, Sistemistica e Comunicazione, Università degli Studi di Milano-Bicocca, Viale Sarca 336, Milan, 20126 Italy
- Milan Center for Neuroscience, Università degli Studi di Milano-Bicocca, San Gerardo Hospital, Via Pergolesi 33, Monza, 20052 Italy
| | - Giulio Caravagna
- Centre for Evolution and Cancer, The Institute of Cancer Research, 15 Cotswold Road, London, SM2 5NG UK
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20
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Caravagna G, Giarratano Y, Ramazzotti D, Tomlinson I, Graham TA, Sanguinetti G, Sottoriva A. Detecting repeated cancer evolution from multi-region tumor sequencing data. Nat Methods 2018; 15:707-714. [PMID: 30171232 PMCID: PMC6380470 DOI: 10.1038/s41592-018-0108-x] [Citation(s) in RCA: 94] [Impact Index Per Article: 15.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2017] [Accepted: 07/23/2018] [Indexed: 12/13/2022]
Abstract
Recurrent successions of genomic changes, both within and between patients, reflect repeated evolutionary processes that are valuable for the anticipation of cancer progression. Multi-region sequencing allows the temporal order of some genomic changes in a tumor to be inferred, but the robust identification of repeated evolution across patients remains a challenge. We developed a machine-learning method based on transfer learning that allowed us to overcome the stochastic effects of cancer evolution and noise in data and identified hidden evolutionary patterns in cancer cohorts. When applied to multi-region sequencing datasets from lung, breast, renal, and colorectal cancer (768 samples from 178 patients), our method detected repeated evolutionary trajectories in subgroups of patients, which were reproduced in single-sample cohorts (n = 2,935). Our method provides a means of classifying patients on the basis of how their tumor evolved, with implications for the anticipation of disease progression.
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Affiliation(s)
- Giulio Caravagna
- Evolutionary Genomics and Modelling Lab, Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK.
- School of Informatics, University of Edinburgh, Edinburgh, UK.
| | - Ylenia Giarratano
- School of Informatics, University of Edinburgh, Edinburgh, UK
- Centre for Medical Informatics, Usher Institute, University of Edinburgh, Edinburgh, UK
| | | | - Ian Tomlinson
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK
| | - Trevor A Graham
- Centre for Tumour Biology, Barts Cancer Institute, Queen Mary University of London, London, UK
| | | | - Andrea Sottoriva
- Evolutionary Genomics and Modelling Lab, Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK.
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21
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Zaccaria S, El-Kebir M, Klau GW, Raphael BJ. Phylogenetic Copy-Number Factorization of Multiple Tumor Samples. J Comput Biol 2018; 25:689-708. [PMID: 29658782 PMCID: PMC6067108 DOI: 10.1089/cmb.2017.0253] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022] Open
Abstract
Cancer is an evolutionary process driven by somatic mutations. This process can be represented as a phylogenetic tree. Constructing such a phylogenetic tree from genome sequencing data is a challenging task due to the many types of mutations in cancer and the fact that nearly all cancer sequencing is of a bulk tumor, measuring a superposition of somatic mutations present in different cells. We study the problem of reconstructing tumor phylogenies from copy-number aberrations (CNAs) measured in bulk-sequencing data. We introduce the Copy-Number Tree Mixture Deconvolution (CNTMD) problem, which aims to find the phylogenetic tree with the fewest number of CNAs that explain the copy-number data from multiple samples of a tumor. We design an algorithm for solving the CNTMD problem and apply the algorithm to both simulated and real data. On simulated data, we find that our algorithm outperforms existing approaches that either perform deconvolution/factorization of mixed tumor samples or build phylogenetic trees assuming homogeneous tumor samples. On real data, we analyze multiple samples from a prostate cancer patient, identifying clones within these samples and a phylogenetic tree that relates these clones and their differing proportions across samples. This phylogenetic tree provides a higher resolution view of copy-number evolution of this cancer than published analyses.
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Affiliation(s)
- Simone Zaccaria
- Department of Computer Science, Princeton University, Princeton, New Jersey
- Dipartimento di Informatica Sistemistica e Comunicazione (DISCo), Università degli Studi di Milano-Bicocca, Milan, Italy
| | - Mohammed El-Kebir
- Department of Computer Science, Princeton University, Princeton, New Jersey
| | - Gunnar W. Klau
- Algorithmic Bioinformatics, Heinrich Heine University, Düsseldorf, Germany
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22
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Castro-Giner F, Gkountela S, Donato C, Alborelli I, Quagliata L, Ng CKY, Piscuoglio S, Aceto N. Cancer Diagnosis Using a Liquid Biopsy: Challenges and Expectations. Diagnostics (Basel) 2018; 8:diagnostics8020031. [PMID: 29747380 PMCID: PMC6023445 DOI: 10.3390/diagnostics8020031] [Citation(s) in RCA: 79] [Impact Index Per Article: 13.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2018] [Revised: 05/04/2018] [Accepted: 05/07/2018] [Indexed: 01/05/2023] Open
Abstract
The field of cancer diagnostics has recently been impacted by new and exciting developments in the area of liquid biopsy. A liquid biopsy is a minimally invasive alternative to surgical biopsies of solid tissues, typically achieved through the withdrawal of a blood sample or other body fluids, allowing the interrogation of tumor-derived material including circulating tumor cells (CTCs) and circulating tumor DNA (ctDNA) fragments that are present at a given time point. In this short review, we discuss a few studies that summarize the state-of-the-art in the liquid biopsy field from a diagnostic perspective, and speculate on current challenges and expectations of implementing liquid biopsy testing for cancer diagnosis and monitoring in the clinical setting.
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Affiliation(s)
- Francesc Castro-Giner
- Cancer Metastasis Laboratory, Department of Biomedicine, University of Basel and University Hospital Basel, 4058 Basel, Switzerland.
- Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland.
| | - Sofia Gkountela
- Cancer Metastasis Laboratory, Department of Biomedicine, University of Basel and University Hospital Basel, 4058 Basel, Switzerland.
| | - Cinzia Donato
- Cancer Metastasis Laboratory, Department of Biomedicine, University of Basel and University Hospital Basel, 4058 Basel, Switzerland.
| | - Ilaria Alborelli
- Institute of Pathology, University Hospital Basel, 4031 Basel, Switzerland.
| | - Luca Quagliata
- Institute of Pathology, University Hospital Basel, 4031 Basel, Switzerland.
| | - Charlotte K Y Ng
- Institute of Pathology, University Hospital Basel, 4031 Basel, Switzerland.
- Hepatology Laboratory, Department of Biomedicine, University of Basel and University Hospital Basel, 4031 Basel, Switzerland.
| | | | - Nicola Aceto
- Cancer Metastasis Laboratory, Department of Biomedicine, University of Basel and University Hospital Basel, 4058 Basel, Switzerland.
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23
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Parsons BL. Multiclonal tumor origin: Evidence and implications. MUTATION RESEARCH-REVIEWS IN MUTATION RESEARCH 2018; 777:1-18. [PMID: 30115427 DOI: 10.1016/j.mrrev.2018.05.001] [Citation(s) in RCA: 41] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/27/2018] [Revised: 04/11/2018] [Accepted: 05/05/2018] [Indexed: 12/31/2022]
Abstract
An accurate understanding of the clonal origins of tumors is critical for designing effective strategies to treat or prevent cancer and for guiding the field of cancer risk assessment. The intent of this review is to summarize evidence of multiclonal tumor origin and, thereby, contest the commonly held assumption of monoclonal tumor origin. This review describes relevant studies of X chromosome inactivation, analyses of tumor heterogeneity using other markers, single cell sequencing, and lineage tracing studies in aggregation chimeras and engineered rodent models. Methods for investigating tumor clonality have an inherent bias against detecting multiclonality. Despite this, multiclonality has been observed within all tumor stages and within 53 different types of tumors. For myeloid tumors, monoclonal tumor origin may be the predominant path to cancer and a monoclonal tumor origin cannot be ruled out for a fraction of other cancer types. Nevertheless, a large body of evidence supports the conclusion that most cancers are multiclonal in origin. Cooperation between different cell types and between clones of cells carrying different genetic and/or epigenetic lesions is discussed, along with how polyclonal tumor origin can be integrated with current perspectives on the genesis of tumors. In order to develop biologically sound and useful approaches to cancer risk assessment and precision medicine, mathematical models of carcinogenesis are needed, which incorporate multiclonal tumor origin and the contributions of spontaneous mutations in conjunction with the selective advantages conferred by particular mutations and combinations of mutations. Adherence to the idea that a growth must develop from a single progenitor cell to be considered neoplastic has outlived its usefulness. Moving forward, explicit examination of tumor clonality, using advanced tools, like lineage tracing models, will provide a strong foundation for future advances in clinical oncology and better training for the next generation of oncologists and pathologists.
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Affiliation(s)
- Barbara L Parsons
- US Food and Drug Administration, National Center for Toxicological Research, Division of Genetic and Molecular Toxicology, 3900 NCTR Rd., Jefferson, AR 72079, United States.
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24
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25
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Castro-Giner F, Scheidmann MC, Aceto N. Beyond Enumeration: Functional and Computational Analysis of Circulating Tumor Cells to Investigate Cancer Metastasis. Front Med (Lausanne) 2018; 5:34. [PMID: 29520361 PMCID: PMC5827555 DOI: 10.3389/fmed.2018.00034] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Circulating tumor cells (CTCs) are defined as those cells that detach from a cancerous lesion and enter the bloodstream. While generally most CTCs are subjected to high shear stress, anoikis signals, and immune attack in the circulatory system, few are able to survive and reach a distant organ in a viable state, possibly leading to metastasis formation. A large number of studies, both prospective and retrospective, have highlighted the association between CTC abundance and bad prognosis in patients with various cancer types. Yet, beyond CTC enumeration, much less is known about the distinction between metastatic and nonmetastatic CTCs, namely those features that enable only some CTCs to survive and seed a cancerous lesion at a distant site. In addition, critical aspects such as CTC heterogeneity, mechanisms that trigger CTC intravasation and extravasation, as well as vulnerabilities of metastatic CTCs subpopulations are poorly understood. In this short review, we highlight recent studies that successfully adopted functional and computational analysis to gain insights into CTC biology. We also discuss approaches to overcome challenges that are associated with CTC isolation, molecular and computational analysis, and speculate regarding few open questions that currently frame the CTC research field.
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Affiliation(s)
- Francesc Castro-Giner
- Department of Biomedicine, Cancer Metastasis Laboratory, University of Basel, University Hospital Basel, Basel, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Manuel C. Scheidmann
- Department of Biomedicine, Cancer Metastasis Laboratory, University of Basel, University Hospital Basel, Basel, Switzerland
| | - Nicola Aceto
- Department of Biomedicine, Cancer Metastasis Laboratory, University of Basel, University Hospital Basel, Basel, Switzerland
- *Correspondence: Nicola Aceto,
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26
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Maley CC, Aktipis A, Graham TA, Sottoriva A, Boddy AM, Janiszewska M, Silva AS, Gerlinger M, Yuan Y, Pienta KJ, Anderson KS, Gatenby R, Swanton C, Posada D, Wu CI, Schiffman JD, Hwang ES, Polyak K, Anderson ARA, Brown JS, Greaves M, Shibata D. Classifying the evolutionary and ecological features of neoplasms. Nat Rev Cancer 2017; 17:605-619. [PMID: 28912577 PMCID: PMC5811185 DOI: 10.1038/nrc.2017.69] [Citation(s) in RCA: 243] [Impact Index Per Article: 34.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Neoplasms change over time through a process of cell-level evolution, driven by genetic and epigenetic alterations. However, the ecology of the microenvironment of a neoplastic cell determines which changes provide adaptive benefits. There is widespread recognition of the importance of these evolutionary and ecological processes in cancer, but to date, no system has been proposed for drawing clinically relevant distinctions between how different tumours are evolving. On the basis of a consensus conference of experts in the fields of cancer evolution and cancer ecology, we propose a framework for classifying tumours that is based on four relevant components. These are the diversity of neoplastic cells (intratumoural heterogeneity) and changes over time in that diversity, which make up an evolutionary index (Evo-index), as well as the hazards to neoplastic cell survival and the resources available to neoplastic cells, which make up an ecological index (Eco-index). We review evidence demonstrating the importance of each of these factors and describe multiple methods that can be used to measure them. Development of this classification system holds promise for enabling clinicians to personalize optimal interventions based on the evolvability of the patient's tumour. The Evo- and Eco-indices provide a common lexicon for communicating about how neoplasms change in response to interventions, with potential implications for clinical trials, personalized medicine and basic cancer research.
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Affiliation(s)
- Carlo C Maley
- Virginia G. Piper Center for Personalized Diagnostics, Biodesign Institute, Arizona State University, 1001 S. McAllister Ave, Tempe, Arizona 85287, USA
| | - Athena Aktipis
- Department of Psychology, Center for Evolution and Medicine, Arizona State University, 651 E. University Drive, Tempe, Arizona 85287, USA
| | - Trevor A Graham
- Evolution and Cancer Laboratory, Centre for Tumour Biology, Barts Cancer Institute, Queen Mary University of London, Charterhouse Square, London EC1M 6BQ, UK
| | - Andrea Sottoriva
- Centre for Evolution and Cancer, The Institute of Cancer Research, 15 Cotswold Road, Sutton, London SM2 5NG, UK
| | - Amy M Boddy
- Department of Anthropology, University of California Santa Barbara, Santa Barbara, California 93106, USA
| | - Michalina Janiszewska
- Department of Medical Oncology, Dana-Farber Cancer Institute, 450 Brookline Avenue D740C, Boston, Massachusetts 02215, USA
| | - Ariosto S Silva
- Department of Cancer Imaging and Metabolism, Moffitt Cancer Center and Research Institute, 12902 Magnolia Drive, Tampa, Florida 33612, USA
| | - Marco Gerlinger
- Centre for Evolution and Cancer, Division of Molecular Pathology, The Institute of Cancer Research, 237 Fulham Road, London SW3 6JB, UK
| | - Yinyin Yuan
- Centre for Evolution and Cancer, The Institute of Cancer Research, 15 Cotswold Road, Sutton, London SM2 5NG, UK
| | - Kenneth J Pienta
- Brady Urological Institute, The Johns Hopkins School of Medicine, 600 N. Wolfe Street, Baltimore, Maryland 21287, USA
| | - Karen S Anderson
- Virginia G. Piper Center for Personalized Diagnostics, Biodesign Institute, Arizona State University, 1001 S. McAllister Ave, Tempe, Arizona 85287, USA
| | - Robert Gatenby
- Cancer Biology and Evolution Program, Moffitt Cancer Center, 12902 Magnolia Drive, Tampa, Florida 33612, USA
| | - Charles Swanton
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, Paul O'Gorman Building, 72 Huntley Street, London WC1E 6BT, UK
| | - David Posada
- Department of Biochemistry, Genetics and Immunology and Biomedical Research Center (CINBIO), University of Vigo, Spain; Galicia Sur Health Research Institute, Vigo, 36310, Spain
| | - Chung-I Wu
- Department of Ecology and Evolution, University of Chicago, Chicago, Illinois 60637, USA
| | - Joshua D Schiffman
- Departments of Pediatrics and Oncological Sciences, Huntsman Cancer Institute, University of Utah, 2000 Circle of Hope, Salt Lake City, Utah 84108, USA
| | - E Shelley Hwang
- Department of Surgery, Duke University and Duke Cancer Institute, 465 Seeley Mudd Building, Durham, North Carolina 27710, USA
| | - Kornelia Polyak
- Department of Medical Oncology, Dana-Farber Cancer Institute, 450 Brookline Avenue D740C, Boston, Massachusetts 02215, USA
| | - Alexander R A Anderson
- Integrated Mathematical Oncology Department, Moffitt Cancer Center, 12902 Magnolia Drive, Tampa, Florida 33612, USA
| | - Joel S Brown
- Integrated Mathematical Oncology Department, Moffitt Cancer Center, 12902 Magnolia Drive, Tampa, Florida 33612, USA
| | - Mel Greaves
- Centre for Evolution and Cancer, The Institute of Cancer Research, 15 Cotswold Road, Sutton, London SM2 5NG, UK
| | - Darryl Shibata
- Department of Pathology, Norris Comprehensive Cancer Center, University of Southern California, 1441 Eastlake Avenue, NOR2424, Los Angeles, California 90033, USA
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27
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Zafar H, Tzen A, Navin N, Chen K, Nakhleh L. SiFit: inferring tumor trees from single-cell sequencing data under finite-sites models. Genome Biol 2017; 18:178. [PMID: 28927434 PMCID: PMC5606061 DOI: 10.1186/s13059-017-1311-2] [Citation(s) in RCA: 89] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2017] [Accepted: 08/28/2017] [Indexed: 02/06/2023] Open
Abstract
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.
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Affiliation(s)
- Hamim Zafar
- Department of Computer Science, Rice University, Houston, Texas, USA.,Department of Bioinformatics and Computational Biology, University of Texas M.D. Anderson Cancer Center, Houston, Texas, USA
| | - Anthony Tzen
- Department of Computer Science, Rice University, Houston, Texas, USA
| | - Nicholas Navin
- Department of Bioinformatics and Computational Biology, University of Texas M.D. Anderson Cancer Center, Houston, Texas, USA.,Department of Genetics, University of Texas M.D. Anderson Cancer Center, Houston, Texas, USA
| | - Ken Chen
- Department of Bioinformatics and Computational Biology, University of Texas M.D. Anderson Cancer Center, Houston, Texas, USA.
| | - Luay Nakhleh
- Department of Computer Science, Rice University, Houston, Texas, USA.
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28
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Mohan S, Chemi F, Brady G. Challenges and unanswered questions for the next decade of circulating tumour cell research in lung cancer. Transl Lung Cancer Res 2017; 6:454-472. [PMID: 28904889 DOI: 10.21037/tlcr.2017.06.04] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Abstract
Since blood borne circulating tumour cells (CTCs) initially shed from the primary tumour can seed and initiate metastasis at distant sites a better understanding of the biology of CTCs and their dissemination could provide valuable information that could guide therapeutic intervention and real time monitoring of disease progression. Although CTC enumeration has provided a reliable prognostic readout for a number of cancers, including lung cancer, the precise clinical utility of CTCs remains to be established. The rarity of CTCs together with the vanishingly small amounts of nucleic acids present in a single cell as well as cell to cell heterogeneity has stimulated the development of a wide range of powerful cellular and molecular methodologies applied to CTCs. These technical developments are now enabling researchers to focus on understanding the biology of CTCs and their clinical utility as a predictive and pharmacodynamics markers. This review summarises recent advances in the field of CTC research with focus on technical and biological challenges as well the progress made towards clinical utility of characterisation of CTCs with emphasis on studies in lung cancer.
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Affiliation(s)
- Sumitra Mohan
- Clinical and Experimental Pharmacology Group, CRUK Manchester Institute, University of Manchester, Manchester, UK
| | - Francesca Chemi
- Clinical and Experimental Pharmacology Group, CRUK Manchester Institute, University of Manchester, Manchester, UK
| | - Ged Brady
- Clinical and Experimental Pharmacology Group, CRUK Manchester Institute, University of Manchester, Manchester, UK
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29
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Leung ML, Davis A, Gao R, Casasent A, Wang Y, Sei E, Vilar E, Maru D, Kopetz S, Navin NE. Single-cell DNA sequencing reveals a late-dissemination model in metastatic colorectal cancer. Genome Res 2017; 27:1287-1299. [PMID: 28546418 PMCID: PMC5538546 DOI: 10.1101/gr.209973.116] [Citation(s) in RCA: 149] [Impact Index Per Article: 21.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2016] [Accepted: 05/23/2017] [Indexed: 12/31/2022]
Abstract
Metastasis is a complex biological process that has been difficult to delineate in human colorectal cancer (CRC) patients. A major obstacle in understanding metastatic lineages is the extensive intra-tumor heterogeneity at the primary and metastatic tumor sites. To address this problem, we developed a highly multiplexed single-cell DNA sequencing approach to trace the metastatic lineages of two CRC patients with matched liver metastases. Single-cell copy number or mutational profiling was performed, in addition to bulk exome and targeted deep-sequencing. In the first patient, we observed monoclonal seeding, in which a single clone evolved a large number of mutations prior to migrating to the liver to establish the metastatic tumor. In the second patient, we observed polyclonal seeding, in which two independent clones seeded the metastatic liver tumor after having diverged at different time points from the primary tumor lineage. The single-cell data also revealed an unexpected independent tumor lineage that did not metastasize, and early progenitor clones with the "first hit" mutation in APC that subsequently gave rise to both the primary and metastatic tumors. Collectively, these data reveal a late-dissemination model of metastasis in two CRC patients and provide an unprecedented view of metastasis at single-cell genomic resolution.
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Affiliation(s)
- Marco L Leung
- Department of Genetics, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030, USA
- The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, Texas 77030, USA
| | - Alexander Davis
- Department of Genetics, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030, USA
- The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, Texas 77030, USA
| | - Ruli Gao
- Department of Genetics, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030, USA
| | - Anna Casasent
- Department of Genetics, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030, USA
- The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, Texas 77030, USA
| | - Yong Wang
- Department of Genetics, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030, USA
| | - Emi Sei
- Department of Genetics, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030, USA
| | | | | | | | - Nicholas E Navin
- Department of Genetics, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030, USA
- The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, Texas 77030, USA
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030, USA
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