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Chung YS, Kang S, Kim J, Lee S, Kim S. CLEMENT: genomic decomposition and reconstruction of non-tumor subclones. Nucleic Acids Res 2024; 52:e62. [PMID: 38922688 DOI: 10.1093/nar/gkae527] [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: 06/07/2023] [Revised: 05/27/2024] [Accepted: 06/12/2024] [Indexed: 06/28/2024] Open
Abstract
Genome-level clonal decomposition of a single specimen has been widely studied; however, it is mostly limited to cancer research. In this study, we developed a new algorithm CLEMENT, which conducts accurate decomposition and reconstruction of multiple subclones in genome sequencing of non-tumor (normal) samples. CLEMENT employs the Expectation-Maximization (EM) algorithm with optimization strategies specific to non-tumor subclones, including false variant call identification, non-disparate clone fuzzy clustering, and clonal allele fraction confinement. In the simulation and in vitro cell line mixture data, CLEMENT outperformed current cancer decomposition algorithms in estimating the number of clones (root-mean-square-error = 0.58-0.78 versus 1.43-3.34) and in the variant-clone membership agreement (∼85.5% versus 70.1-76.7%). Additional testing on human multi-clonal normal tissue sequencing confirmed the accurate identification of subclones that originated from different cell types. Clone-level analysis, including mutational burden and signatures, provided a new understanding of normal-tissue composition. We expect that CLEMENT will serve as a crucial tool in the currently emerging field of non-tumor genome analysis.
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Affiliation(s)
- Young-Soo Chung
- Department of Biomedical Systems Informatics, Brain Korea 21 PLUS Project for Medical Science, Yonsei University College of Medicine, Seoul 03722, Republic of Korea
| | - Seungseok Kang
- Department of Biomedical Systems Informatics, Brain Korea 21 PLUS Project for Medical Science, Yonsei University College of Medicine, Seoul 03722, Republic of Korea
| | - Jisu Kim
- DataShape team, Inria Saclay Île-De-France, Palaiseau 91120, France
- Department of Statistics, Seoul National University, Seoul 08826, Republic of Korea
| | - Sangbo Lee
- Department of Biomedical Systems Informatics, Brain Korea 21 PLUS Project for Medical Science, Yonsei University College of Medicine, Seoul 03722, Republic of Korea
| | - Sangwoo Kim
- Department of Biomedical Systems Informatics, Brain Korea 21 PLUS Project for Medical Science, Yonsei University College of Medicine, Seoul 03722, Republic of Korea
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2
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Lai J, Yang Y, Liu Y, Scharpf RB, Karchin R. Assessing the merits: an opinion on the effectiveness of simulation techniques in tumor subclonal reconstruction. BIOINFORMATICS ADVANCES 2024; 4:vbae094. [PMID: 38948008 PMCID: PMC11213631 DOI: 10.1093/bioadv/vbae094] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/12/2024] [Revised: 05/28/2024] [Accepted: 06/15/2024] [Indexed: 07/02/2024]
Abstract
Summary Neoplastic tumors originate from a single cell, and their evolution can be traced through lineages characterized by mutations, copy number alterations, and structural variants. These lineages are reconstructed and mapped onto evolutionary trees with algorithmic approaches. However, without ground truth benchmark sets, the validity of an algorithm remains uncertain, limiting potential clinical applicability. With a growing number of algorithms available, there is urgent need for standardized benchmark sets to evaluate their merits. Benchmark sets rely on in silico simulations of tumor sequence, but there are no accepted standards for simulation tools, presenting a major obstacle to progress in this field. Availability and implementation All analysis done in the paper was based on publicly available data from the publication of each accessed tool.
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Affiliation(s)
- Jiaying Lai
- Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD 21218, United States
| | - Yi Yang
- Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD 21218, United States
| | - Yunzhou Liu
- Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD 21218, United States
| | - Robert B Scharpf
- Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD 21231, United States
- Department of Oncology, Johns Hopkins Medical Institutions, Baltimore, MD 21231, United States
| | - Rachel Karchin
- Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD 21218, United States
- Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD 21231, United States
- Department of Oncology, Johns Hopkins Medical Institutions, Baltimore, MD 21231, United States
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, United States
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3
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Lai J, Liu Y, Scharpf RB, Karchin R. Evaluation of simulation methods for tumor subclonal reconstruction. ARXIV 2024:arXiv:2402.09599v1. [PMID: 38410652 PMCID: PMC10896360] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 02/28/2024]
Abstract
Most neoplastic tumors originate from a single cell, and their evolution can be genetically traced through lineages characterized by common alterations such as small somatic mutations (SSMs), copy number alterations (CNAs), structural variants (SVs), and aneuploidies. Due to the complexity of these alterations in most tumors and the errors introduced by sequencing protocols and calling algorithms, tumor subclonal reconstruction algorithms are necessary to recapitulate the DNA sequence composition and tumor evolution in silico. With a growing number of these algorithms available, there is a pressing need for consistent and comprehensive benchmarking, which relies on realistic tumor sequencing generated by simulation tools. Here, we examine the current simulation methods, identifying their strengths and weaknesses, and provide recommendations for their improvement. Our review also explores potential new directions for research in this area. This work aims to serve as a resource for understanding and enhancing tumor genomic simulations, contributing to the advancement of the field.
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Affiliation(s)
- Jiaying Lai
- Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD
| | - Yunzhou Liu
- Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD
| | - Robert B. Scharpf
- Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD
- Department of Oncology, Johns Hopkins Medical Institutions, Baltimore, MD
| | - Rachel Karchin
- Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD
- Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD
- Department of Oncology, Johns Hopkins Medical Institutions, Baltimore, MD
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD
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4
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Samur MK, Roncador M, Aktas Samur A, Fulciniti M, Bazarbachi AH, Szalat R, Shammas MA, Sperling AS, Richardson PG, Magrangeas F, Minvielle S, Perrot A, Corre J, Moreau P, Thakurta A, Parmigiani G, Anderson KC, Avet-Loiseau H, Munshi NC. High-dose melphalan treatment significantly increases mutational burden at relapse in multiple myeloma. Blood 2023; 141:1724-1736. [PMID: 36603186 PMCID: PMC10273091 DOI: 10.1182/blood.2022017094] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Revised: 12/02/2022] [Accepted: 12/22/2022] [Indexed: 01/07/2023] Open
Abstract
High-dose melphalan (HDM) improves progression-free survival in multiple myeloma (MM), yet melphalan is a DNA-damaging alkylating agent; therefore, we assessed its mutational effect on surviving myeloma cells by analyzing paired MM samples collected at diagnosis and relapse in the IFM 2009 study. We performed deep whole-genome sequencing on samples from 68 patients, 43 of whom were treated with RVD (lenalidomide, bortezomib, and dexamethasone) and 25 with RVD + HDM. Although the number of mutations was similar at diagnosis in both groups (7137 vs 7230; P = .67), the HDM group had significantly more mutations at relapse (9242 vs 13 383, P = .005). No change in the frequency of copy number alterations or structural variants was observed. The newly acquired mutations were typically associated with DNA damage and double-stranded breaks and were predominantly on the transcribed strand. A machine learning model, using this unique pattern, predicted patients who would receive HDM with high sensitivity, specificity, and positive prediction value. Clonal evolution analysis showed that all patients treated with HDM had clonal selection, whereas a static progression was observed with RVD. A significantly higher percentage of mutations were subclonal in the HDM cohort. Intriguingly, patients treated with HDM who achieved complete remission (CR) had significantly more mutations at relapse yet had similar survival rates as those treated with RVD who achieved CR. This similarity could have been due to HDM relapse samples having significantly more neoantigens. Overall, our study identifies increased genomic changes associated with HDM and provides rationale to further understand clonal complexity.
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Affiliation(s)
- Mehmet Kemal Samur
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA
| | | | - Anil Aktas Samur
- Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Harvard University, Boston, MA
| | - Mariateresa Fulciniti
- Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Harvard University, Boston, MA
| | - Abdul Hamid Bazarbachi
- Department of Internal Medicine, Jacobi Medical Center, Albert Einstein College of Medicine, New York, NY
| | - Raphael Szalat
- Department of Hematology and Medical Oncology, Boston University Medical Center, Boston, MA
| | - Masood A. Shammas
- Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Harvard University, Boston, MA
| | - Adam S. Sperling
- Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Harvard University, Boston, MA
| | - Paul G. Richardson
- Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Harvard University, Boston, MA
| | - Florence Magrangeas
- Center for Research in Cancerology and Immunology Nantes-Angers (CRCINA), INSERM, French National Centre for Scientific Research (CNRS), Angers University, and Nantes University, Nantes, France
| | - Stephane Minvielle
- Center for Research in Cancerology and Immunology Nantes-Angers (CRCINA), INSERM, French National Centre for Scientific Research (CNRS), Angers University, and Nantes University, Nantes, France
| | - Aurore Perrot
- University Cancer Center of Toulouse Institut National de la Santé, Toulouse, France
| | - Jill Corre
- University Cancer Center of Toulouse Institut National de la Santé, Toulouse, France
| | - Philippe Moreau
- Center for Research in Cancerology and Immunology Nantes-Angers (CRCINA), INSERM, French National Centre for Scientific Research (CNRS), Angers University, and Nantes University, Nantes, France
| | | | - Giovanni Parmigiani
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA
| | - Kenneth C. Anderson
- Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Harvard University, Boston, MA
| | - Hervé Avet-Loiseau
- University Cancer Center of Toulouse Institut National de la Santé, Toulouse, France
| | - Nikhil C. Munshi
- Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Harvard University, Boston, MA
- VA Boston Healthcare System, Boston, MA
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5
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Sandmann S, Richter S, Jiang X, Varghese J. Reconstructing Clonal Evolution-A Systematic Evaluation of Current Bioinformatics Approaches. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:5128. [PMID: 36982036 PMCID: PMC10049679 DOI: 10.3390/ijerph20065128] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/08/2023] [Revised: 03/04/2023] [Accepted: 03/13/2023] [Indexed: 06/18/2023]
Abstract
The accurate reconstruction of clonal evolution, including the identification of newly developing, highly aggressive subclones, is essential for the application of precision medicine in cancer treatment. Reconstruction, aiming for correct variant clustering and clonal evolution tree reconstruction, is commonly performed by tedious manual work. While there is a plethora of tools to automatically generate reconstruction, their reliability, especially reasons for unreliability, are not systematically assessed. We developed clevRsim-an approach to simulate clonal evolution data, including single-nucleotide variants as well as (overlapping) copy number variants. From this, we generated 88 data sets and performed a systematic evaluation of the tools for the reconstruction of clonal evolution. The results indicate a major negative influence of a high number of clones on both clustering and tree reconstruction. Low coverage as well as an extreme number of time points usually leads to poor clustering results. An underlying branched independent evolution hampers correct tree reconstruction. A further major decline in performance could be observed for large deletions and duplications overlapping single-nucleotide variants. In summary, to explore the full potential of reconstructing clonal evolution, improved algorithms that can properly handle the identified limitations are greatly needed.
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Affiliation(s)
- Sarah Sandmann
- Institute of Medical Informatics, University of Münster, 48149 Münster, Germany
| | - Silja Richter
- Institute of Medical Informatics, University of Münster, 48149 Münster, Germany
| | - Xiaoyi Jiang
- Department of Computer Science, University of Münster, 48149 Münster, Germany
| | - Julian Varghese
- Institute of Medical Informatics, University of Münster, 48149 Münster, Germany
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6
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Kaur G, Jena L, Gupta R, Farswan A, Gupta A, Sriram K. Correlation of changes in subclonal architecture with progression in the MMRF CoMMpass study. Transl Oncol 2022; 23:101472. [PMID: 35777247 PMCID: PMC9253848 DOI: 10.1016/j.tranon.2022.101472] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2021] [Revised: 06/03/2022] [Accepted: 06/20/2022] [Indexed: 11/28/2022] Open
Abstract
Multiple myeloma (MM) is a heterogeneous plasma cell proliferative disorder that arises from its premalignant precursor stages through a complex cascade of interactions between clonal mutations and co-evolving microenvironment. The temporo-spatial evolutionary trajectories of MM are established early during myelomatogenesis in precursor stages and retained in MM. Such molecular events impact subsequent disease progression and clinical outcomes. Identification of clonal sweeps of actionable gene targets in MM could reveal potential vulnerabilities that may exist in early stages and thus potentiate prognostication and customization of early therapeutic interventions. We have evaluated clonal evolution at multiple time points in 76 MM patients enrolled in the MMRF CoMMpass study. The major findings of this study are (a) MM progresses predominantly through branching evolution, (b) there is a heterogeneous spectrum of mutational landscapes that include unique actionable gene targets at diagnosis compared to progression, (c) unique clonal gains/ losses of mutant driver genes can be identified in patients with different cytogenetic aberrations, (d) there is a significant correlation between co-occurring oncogenic mutations/ co-occurring subclones e.g., with mutated TP53+SYNE1, NRAS+MAGI3, and anticorrelative dependencies between FAT3+FCGBP gene pairs. Such co-trajectories may synchronize molecular events of drug response, myelomatogenesis and warrant future studies to explore their potential for early prognostication and development of risk stratified personalized therapies in MM.
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Affiliation(s)
- Gurvinder Kaur
- Laboratory Oncology Unit, Dr. B. R.A. IRCH, AIIMS, New Delhi
| | - Lingaraja Jena
- Laboratory Oncology Unit, Dr. B. R.A. IRCH, AIIMS, New Delhi
| | - Ritu Gupta
- Laboratory Oncology Unit, Dr. B. R.A. IRCH, AIIMS, New Delhi.
| | - Akanksha Farswan
- SBILab, Department of Electronics and Communication Engineering, IIIT, Delhi
| | - Anubha Gupta
- SBILab, Department of Electronics and Communication Engineering, IIIT, Delhi.
| | - K Sriram
- Department of Computational Biology & Centre for Computational Biology, IIIT, Delhi
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7
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Ahmadinejad N, Troftgruben S, Wang J, Chandrashekar PB, Dinu V, Maley C, Liu L. Accurate Identification of Subclones in Tumor Genomes. Mol Biol Evol 2022; 39:6617617. [PMID: 35749590 PMCID: PMC9260306 DOI: 10.1093/molbev/msac136] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Understanding intra-tumor heterogeneity is critical for studying tumorigenesis and designing personalized treatments. To decompose the mixed cell population in a tumor, subclones are inferred computationally based on variant allele frequency (VAF) from bulk sequencing data. In this study, we showed that sequencing depth, mean VAF, and variance of VAF of a subclone are confounded. Without considering this effect, current methods require deep-sequencing data (>300x depth) to reliably infer subclones. Here we present a novel algorithm that incorporates depth-variance and mean-variance dependencies in a clustering error model and successfully identifies subclones in tumors sequenced at depths of as low as 30x. We implemented the algorithm as a model-based adaptive grouping of subclones (MAGOS) method. Analyses of computer simulated data and empirical sequencing data showed that MAGOS outperformed existing methods on minimum sequencing depth, decomposition accuracy, and computation efficiency. The most prominent improvements were observed in analyzing tumors sequenced at depths between 30x and 200x, while the performance was comparable between MAGOS and existing methods on deeply sequenced tumors. MAGOS supports analysis of single nucleotide variants and copy number variants from a single sample or multiple samples of a tumor. We applied MAGOS to whole-exome data of late-stage liver cancers and discovered that high subclone count in a tumor was a significant risk factor of poor prognosis. Lastly, our analysis suggested that sequencing multiple samples of the same tumor at standard depth is more cost-effective and robust for subclone characterization than deep sequencing a single sample. MAGOS is available at github (https://github.com/liliulab/magos).
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Affiliation(s)
- Navid Ahmadinejad
- College of Health Solutions, Arizona State University, Phoenix, AZ, 85054, USA.,Biodesign Institute, Arizona State University, Tempe, AZ, 85281, USA
| | - Shayna Troftgruben
- College of Health Solutions, Arizona State University, Phoenix, AZ, 85054, USA
| | - Junwen Wang
- College of Health Solutions, Arizona State University, Phoenix, AZ, 85054, USA.,Department of Health Sciences Research & Center for Individualized Medicine, Mayo Clinic Arizona, Scottsdale, AZ, 85259, USA
| | - Pramod B Chandrashekar
- College of Health Solutions, Arizona State University, Phoenix, AZ, 85054, USA.,Biodesign Institute, Arizona State University, Tempe, AZ, 85281, USA
| | - Valentin Dinu
- College of Health Solutions, Arizona State University, Phoenix, AZ, 85054, USA.,Biodesign Institute, Arizona State University, Tempe, AZ, 85281, USA
| | - Carlo Maley
- Biodesign Institute, Arizona State University, Tempe, AZ, 85281, USA
| | - Li Liu
- College of Health Solutions, Arizona State University, Phoenix, AZ, 85054, USA.,Biodesign Institute, Arizona State University, Tempe, AZ, 85281, USA
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8
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Abnormal Expression of N6-Methyladenosine RNA Methylation Regulator IGF2BP3 in Colon Cancer Predicts a Poor Prognosis. DISEASE MARKERS 2022; 2022:5883101. [PMID: 35677634 PMCID: PMC9170420 DOI: 10.1155/2022/5883101] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Accepted: 05/14/2022] [Indexed: 11/17/2022]
Abstract
The value of insulin-like growth factor 2 mRNA-binding protein 3 (IGF2BP3), an N6-methyladenosine (m6A) RNA methylation regulatory factor, in the prognosis of colon cancer was still unclear. High levels of IGF2BP3 were expressed in colon adenocarcinoma (COAD) samples and in human colon cancer tissues, which was associated with poorer overall survival (OS). We validated IGF2BP3 as an independent prognostic risk biomarker in COAD patients. Moreover, functional enrichment analysis suggested that differentially expressed genes (DEGs) of groups with high versus low IGF2BP3 expression were related to immune- and cancer-related pathways. Furthermore, the tumor microenvironments of high- versus low-IGF2BP3 expression groups showed significant differences and IGF2BP3 predicted the efficiency of immunotherapy. Finally, protein-protein interaction network analysis suggested that there was a direct or indirect interaction among IGF2BP3, WNT7B, VANGL2, NKD1, AXIN2, RNF43, and CDKN2A. In brief, IGF2BP3 was confirmed as an independent prognostic signature in COAD patients and might be a therapeutic target in this study. Moreover, IGF2BP3 could be used in personalized immunotherapy for COAD.
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9
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Cimmino F, Montella A, Tirelli M, Avitabile M, Lasorsa VA, Visconte F, Cantalupo S, Maiorino T, De Angelis B, Morini M, Castellano A, Locatelli F, Capasso M, Iolascon A. FGFR1 is a potential therapeutic target in neuroblastoma. Cancer Cell Int 2022; 22:174. [PMID: 35488346 PMCID: PMC9052553 DOI: 10.1186/s12935-022-02587-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2022] [Accepted: 04/13/2022] [Indexed: 12/16/2022] Open
Abstract
BACKGROUND FGFR1 regulates cell-cell adhesion and extracellular matrix architecture and acts as oncogene in several cancers. Potential cancer driver mutations of FGFR1 occur in neuroblastoma (NB), a neural crest-derived pediatric tumor arising in sympathetic nervous system, but so far they have not been studied experimentally. We investigated the driver-oncogene role of FGFR1 and the implication of N546K mutation in therapy-resistance in NB cells. METHODS Public datasets were used to predict the correlation of FGFR1 expression with NB clinical outcomes. Whole genome sequencing data of 19 paired diagnostic and relapse NB samples were used to find somatic mutations. In NB cell lines, silencing by short hairpin RNA and transient overexpression of FGFR1 were performed to evaluate the effect of the identified mutation by cell growth, invasion and cologenicity assays. HEK293, SHSY5Y and SKNBE2 were selected to investigate subcellular wild-type and mutated protein localization. FGFR1 inhibitor (AZD4547), alone or in combination with PI3K inhibitor (GDC0941), was used to rescue malignant phenotypes induced by overexpression of FGFR1 wild-type and mutated protein. RESULTS High FGFR1 expression correlated with low relapse-free survival in two independent NB gene expression datasets. In addition, we found the somatic mutation N546K, the most recurrent point mutation of FGFR1 in all cancers and already reported in NB, in one out of 19 matched primary and recurrent tumors. Loss of FGFR1 function attenuated invasion and cologenicity in NB cells, whereas FGFR1 overexpression enhanced oncogenicity. The overexpression of FGFR1N546K protein showed a higher nuclear localization compared to wild-type protein and increased cellular invasion and cologenicity. Moreover, N546K mutation caused the failure in response to treatment with FGFR1 inhibitor by activation of ERK, STAT3 and AKT pathways. The combination of FGFR1 and PI3K pathway inhibitors was effective in reducing the invasive and colonigenic ability of cells overexpressing FGFR1 mutated protein. CONCLUSIONS FGFR1 is an actionable driver oncogene in NB and a promising therapy may consist in targeting FGFR1 mutations in patients with therapy-resistant NB.
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Affiliation(s)
- Flora Cimmino
- CEINGE Biotecnologie Avanzate, Via Gaetano Salvatore, 486, 80145, Naples, Italy
| | - Annalaura Montella
- CEINGE Biotecnologie Avanzate, Via Gaetano Salvatore, 486, 80145, Naples, Italy.,Dipartimento di Medicina Molecolare e Biotecnologie Mediche, Università degli Studi di Napoli Federico II, 80145, Naples, Italy
| | - Matilde Tirelli
- CEINGE Biotecnologie Avanzate, Via Gaetano Salvatore, 486, 80145, Naples, Italy.,European School of Molecular Medicine, Università Degli Studi di Milano, 20122, Milan, Italy
| | - Marianna Avitabile
- CEINGE Biotecnologie Avanzate, Via Gaetano Salvatore, 486, 80145, Naples, Italy
| | | | - Feliciano Visconte
- CEINGE Biotecnologie Avanzate, Via Gaetano Salvatore, 486, 80145, Naples, Italy
| | - Sueva Cantalupo
- CEINGE Biotecnologie Avanzate, Via Gaetano Salvatore, 486, 80145, Naples, Italy.,Dipartimento di Medicina Molecolare e Biotecnologie Mediche, Università degli Studi di Napoli Federico II, 80145, Naples, Italy
| | - Teresa Maiorino
- CEINGE Biotecnologie Avanzate, Via Gaetano Salvatore, 486, 80145, Naples, Italy.,Dipartimento di Medicina Molecolare e Biotecnologie Mediche, Università degli Studi di Napoli Federico II, 80145, Naples, Italy
| | - Biagio De Angelis
- Hematology/Oncology and Cell and Gene Therapy Department, IRCCS Bambino Gesù Children's Hospital, 00165, Rome, Italy
| | - Martina Morini
- Laboratory of Molecular Biology, IRCCS Istituto Giannina Gaslini, 16147, Genoa, Italy
| | - Aurora Castellano
- Paediatric Haematology/Oncology Department, IRCCS Bambino Gesù Children's Hospital, 00165, Rome, Italy
| | - Franco Locatelli
- IRCCS Bambino Gesù Children's Hospital, Sapienza, University of Rome, 00165, Rome, Italy
| | - Mario Capasso
- CEINGE Biotecnologie Avanzate, Via Gaetano Salvatore, 486, 80145, Naples, Italy. .,Dipartimento di Medicina Molecolare e Biotecnologie Mediche, Università degli Studi di Napoli Federico II, 80145, Naples, Italy.
| | - Achille Iolascon
- CEINGE Biotecnologie Avanzate, Via Gaetano Salvatore, 486, 80145, Naples, Italy. .,Dipartimento di Medicina Molecolare e Biotecnologie Mediche, Università degli Studi di Napoli Federico II, 80145, Naples, Italy.
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10
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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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11
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Sandmann S, Richter S, Jiang X, Varghese J. Exploring Current Challenges and Perspectives for Automatic Reconstruction of Clonal Evolution. Cancer Genomics Proteomics 2022; 19:194-204. [PMID: 35181588 DOI: 10.21873/cgp.20314] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Revised: 12/11/2021] [Accepted: 12/17/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND/AIM In the field of cancer research, reconstructing clonal evolution is of major interest. The technique provides new insights for analysis and prediction of tumor development. However, reconstruction based on mutational data is characterized by several challenges. MATERIALS AND METHODS By performing extensive literature research, we identified 51 currently available tools for reconstructing clonal evolution. By analyzing two cancer data sets (n=21), we investigated the applicability and performance of each tool. RESULTS Seventeen out of 51 tools could be applied to our data. Correct clustering of variants can be observed for 4 patients in the presence of ≤3 clusters and ≥5 time points. Correct phylogenetic trees are determined for 10 patients. Accurate visualization is possible, by applying adjustments to the original algorithms. CONCLUSION Despite bearing considerable potential, automatic reconstruction of clonal evolution remains challenging. To replace tedious manual reconstruction, further research including systematic error analyses using simulation tools needs to be conducted.
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Affiliation(s)
- Sarah Sandmann
- Institute of Medical Informatics, University of Münster, Münster, Germany;
| | - Silja Richter
- Institute of Medical Informatics, University of Münster, Münster, Germany
| | - Xiaoyi Jiang
- Department of Computer Science, University of Münster, Münster, Germany
| | - Julian Varghese
- Institute of Medical Informatics, University of Münster, Münster, Germany
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12
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Laganà A. Computational Approaches for the Investigation of Intra-tumor Heterogeneity and Clonal Evolution from Bulk Sequencing Data in Precision Oncology Applications. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2022; 1361:101-118. [DOI: 10.1007/978-3-030-91836-1_6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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13
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Laganà A. The Architecture of a Precision Oncology Platform. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2022; 1361:1-22. [DOI: 10.1007/978-3-030-91836-1_1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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14
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Farswan A, Jena L, Kaur G, Gupta A, Gupta R, Rani L, Sharma A, Kumar L. Branching clonal evolution patterns predominate mutational landscape in multiple myeloma. Am J Cancer Res 2021; 11:5659-5679. [PMID: 34873486 PMCID: PMC8640818] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2021] [Accepted: 09/27/2021] [Indexed: 06/13/2023] Open
Abstract
Multiple Myeloma (MM) arises from malignant transformation and deregulated proliferation of clonal plasma cells (PCs) harbouring heterogeneous molecular anomalies. The effect of evolving mutations on clone fitness and their cellular prevalence shapes the progressing myeloma genome and impacts clinical outcomes. Although clonal heterogeneity in MM is well established, which subclonal mutations emerge/persist/perish with progression in MM and which of these can be targeted therapeutically remains an open question. In line with this, we have sequenced pairwise whole exomes of 62 MM patients collected at two time points, i.e., at diagnosis and on progression. Somatic variants were called using a novel ensemble approach where a consensus was deduced from four variant callers (Illumina's Dragen, Strelka2, SomaticSniper and SpeedSeq) and actionable/druggable gene targets were identified. A marked intraclonal heterogeneity was observed. Branching evolution was observed among 72.58% patients, of whom 64.51% had low TMBs (<10) and 61.29% had 2 or more founder clones. The hypermutator patients (with high TMB levels ≥10 to ≤100) showed a significant decrease in their TMBs from diagnosis (median TMB 77.11) to progression (median TMB 31.22). A distinct temporal fall in subclonal driver mutations was identified recurrently across diagnosis to progression e.g., in PABPC1, BRAF, KRAS, CR1, DIS3 and ATM genes in 3 or more patients suggesting such patients could be treated early with target specific drugs like Vemurafenib/Cobimetinib. An analogous rise in driver mutations was observed in KMT2C, FOXD4L1, SP140, NRAS and other genes. A few drivers such as FAT4, IGLL5 and CDKN1A retained consistent distribution patterns at two time points. These findings are clinically relevant and point at consideration of evaluating multi time point subclonal mutational landscapes for designing better risk stratification strategies and tailoring time to time risk adapted combination therapies in future.
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Affiliation(s)
- Akanksha Farswan
- SBILab, Department of Electronics and Communication Engineering, Indraprastha Institute of Information Technology-Delhi (IIIT-D)Delhi 110020, India
| | - Lingaraja Jena
- Laboratory Oncology Unit, Dr. B.R.A. IRCH, All India Institute of Medical Sciences (AIIMS)New Delhi 110029, India
| | - Gurvinder Kaur
- Laboratory Oncology Unit, Dr. B.R.A. IRCH, All India Institute of Medical Sciences (AIIMS)New Delhi 110029, India
| | - Anubha Gupta
- SBILab, Department of Electronics and Communication Engineering, Indraprastha Institute of Information Technology-Delhi (IIIT-D)Delhi 110020, India
| | - Ritu Gupta
- Laboratory Oncology Unit, Dr. B.R.A. IRCH, All India Institute of Medical Sciences (AIIMS)New Delhi 110029, India
| | - Lata Rani
- Laboratory Oncology Unit, Dr. B.R.A. IRCH, All India Institute of Medical Sciences (AIIMS)New Delhi 110029, India
| | - Atul Sharma
- Department of Medical Oncology, Dr. B.R.A. IRCH, All India Institute of Medical Sciences (AIIMS)New Delhi 110029, India
| | - Lalit Kumar
- Department of Medical Oncology, Dr. B.R.A. IRCH, All India Institute of Medical Sciences (AIIMS)New Delhi 110029, India
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15
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Qian D, Zheng Q, Wu D, Ye B, Qian Y, Zhou T, Qiu J, Meng X. Integrated Analysis of ceRNA Network Reveals Prognostic and Metastasis Associated Biomarkers in Breast Cancer. Front Oncol 2021; 11:670138. [PMID: 34055638 PMCID: PMC8158160 DOI: 10.3389/fonc.2021.670138] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2021] [Accepted: 04/07/2021] [Indexed: 01/17/2023] Open
Abstract
Background Breast cancer is a malignancy and lethal tumor in women. Metastasis of breast cancer is one of the causes of poor prognosis. Increasing evidences have suggested that the competing endogenous RNAs (ceRNAs) were associated with the metastasis of breast cancer. Nonetheless, potential roles of ceRNAs in regulating the metastasis of breast cancer remain unclear. Methods The RNA expression (3 levels) and follow-up data of breast cancer and noncancerous tissue samples were downloaded from the Cancer Genome Atlas (TCGA). Differentially expressed and metastasis associated RNAs were identified for functional analysis and constructing the metastasis associated ceRNA network by comprehensively bioinformatic analysis. The Kaplan-Meier (K-M) survival curve was utilized to screen the prognostic RNAs in metastasis associated ceRNA network. Moreover, we further identified the metastasis associated biomarkers with operating characteristic (ROC) curve. Ultimately, the data of Cancer Cell Line Encyclopedia (CCLE, https://portals.broadinstitute.org/ccle) website were selected to obtained the reliable metastasis associated biomarkers. Results 1005 mRNAs, 22 miRNAs and 164 lncRNAs were screened as differentially expressed and metastasis associated RNAs. The results of GO function and KEGG pathway enrichment analysis showed that these RNAs are mainly associated with the metabolic processes and stress responses. Next, a metastasis associated ceRNA (including 104 mRNAs, 19 miRNAs, and 16 lncRNAs) network was established, and 12 RNAs were found to be related to the overall survival (OS) of patients. In addition, 3 RNAs (hsa-miR-105-5p, BCAR1, and PANX2) were identified to serve as reliable metastasis associated biomarkers. Eventually, the results of mechanism analysis suggested that BCAR1 might promote the metastasis of breast cancer by facilitating Rap 1 signaling pathway. Conclusion In the present research, we identified 3 RNAs (hsa-miR-105-5p, BCAR1 and PANX2) might associated with prognosis and metastasis of breast cancer, which might be provide a new perspective for metastasis of breast cancer and contributed to the treatment of breast cancer.
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Affiliation(s)
- Da Qian
- College of Medicine, Soochow University, Soochow, China.,Department of Breast Surgery, Zhejiang Provincial People's Hospital, Hangzhou, China.,Department of Burn and Plastic Surgery-Hand Surgery, First People's Hospital of Changshu City, Changshu Hospital Affiliated to Soochow University, Soochow, China
| | - Qinghui Zheng
- Department of Breast Surgery, Zhejiang Provincial People's Hospital, Hangzhou, China
| | - Danping Wu
- Department of Breast Surgery, First People's Hospital of Changshu City, Changshu Hospital Affiliated to Soochow University, Soochow, China
| | - Buyun Ye
- Second Clinical College, Zhejiang Chinese Medical University, Hangzhou, China
| | - Yangyang Qian
- Department of Breast Surgery, Zhejiang Provincial People's Hospital, Hangzhou, China
| | - Tao Zhou
- Faculty of Basic Medicine, Hangzhou Medical College, Hangzhou, China
| | - Jie Qiu
- Second Clinical College, Zhejiang Chinese Medical University, Hangzhou, China
| | - Xuli Meng
- Department of Breast Surgery, Zhejiang Provincial People's Hospital, Hangzhou, China
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16
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PyClone-VI: scalable inference of clonal population structures using whole genome data. BMC Bioinformatics 2020; 21:571. [PMID: 33302872 PMCID: PMC7730797 DOI: 10.1186/s12859-020-03919-2] [Citation(s) in RCA: 55] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2020] [Accepted: 12/02/2020] [Indexed: 01/20/2023] Open
Abstract
Background At diagnosis tumours are typically composed of a mixture of genomically distinct malignant cell populations. Bulk sequencing of tumour samples coupled with computational deconvolution can be used to identify these populations and study cancer evolution. Existing computational methods for populations deconvolution are slow and/or potentially inaccurate when applied to large datasets generated by whole genome sequencing data. Results We describe PyClone-VI, a computationally efficient Bayesian statistical method for inferring the clonal population structure of cancers. We demonstrate the utility of the method by analyzing data from 1717 patients from PCAWG study and 100 patients from the TRACERx study. Conclusions Our proposed method is 10–100× times faster than existing methods, while providing results which are as accurate. Software implementing our method is freely available https://github.com/Roth-Lab/pyclone-vi.
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17
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Abstract
BACKGROUND Bacterial cells during many replication cycles accumulate spontaneous mutations, which result in the birth of novel clones. As a result of this clonal expansion, an evolving bacterial population has different clonal composition over time, as revealed in the long-term evolution experiments (LTEEs). Accurately inferring the haplotypes of novel clones as well as the clonal frequencies and the clonal evolutionary history in a bacterial population is useful for the characterization of the evolutionary pressure on multiple correlated mutations instead of that on individual mutations. RESULTS In this paper, we study the computational problem of reconstructing the haplotypes of bacterial clones from the variant allele frequencies observed from an evolving bacterial population at multiple time points. We formalize the problem using a maximum likelihood function, which is defined under the assumption that mutations occur spontaneously, and thus the likelihood of a mutation occurring in a specific clone is proportional to the frequency of the clone in the population when the mutation occurs. We develop a series of heuristic algorithms to address the maximum likelihood inference, and show through simulation experiments that the algorithms are fast and achieve near optimal accuracy that is practically plausible under the maximum likelihood framework. We also validate our method using experimental data obtained from a recent study on long-term evolution of Escherichia coli. CONCLUSION We developed efficient algorithms to reconstruct the clonal evolution history from time course genomic sequencing data. Our algorithm can also incorporate clonal sequencing data to improve the reconstruction results when they are available. Based on the evaluation on both simulated and experimental sequencing data, our algorithms can achieve satisfactory results on the genome sequencing data from long-term evolution experiments. AVAILABILITY The program (ClonalTREE) is available as open-source software on GitHub at https://github.com/COL-IU/ClonalTREE.
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Affiliation(s)
- Wazim Mohammed Ismail
- School of Informatics, Computing and Engineering, Indiana University, Bloomington, IN, USA
| | - Haixu Tang
- School of Informatics, Computing and Engineering, Indiana University, Bloomington, IN, USA
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18
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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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19
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Thole TM, Toedling J, Sprüssel A, Pfeil S, Savelyeva L, Capper D, Messerschmidt C, Beule D, Groeneveld-Krentz S, Eckert C, Gambara G, Henssen AG, Finkler S, Schulte JH, Sieber A, Bluethgen N, Regenbrecht CRA, Künkele A, Lodrini M, Eggert A, Deubzer HE. Reflection of neuroblastoma intratumor heterogeneity in the new OHC-NB1 disease model. Int J Cancer 2019; 146:1031-1041. [PMID: 31304977 DOI: 10.1002/ijc.32572] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2019] [Accepted: 07/05/2019] [Indexed: 01/21/2023]
Abstract
Accurate modeling of intratumor heterogeneity presents a bottleneck against drug testing. Flexibility in a preclinical platform is also desirable to support assessment of different endpoints. We established the model system, OHC-NB1, from a bone marrow metastasis from a patient diagnosed with MYCN-amplified neuroblastoma and performed whole-exome sequencing on the source metastasis and the different models and passages during model development (monolayer cell line, 3D spheroid culture and subcutaneous xenograft tumors propagated in mice). OHC-NB1 harbors a MYCN amplification in double minutes, 1p deletion, 17q gain and diploid karyotype, which persisted in all models. A total of 80-540 single-nucleotide variants (SNVs) was detected in each sample, and comparisons between the source metastasis and models identified 34 of 80 somatic SNVs to be propagated in the models. Clonal reconstruction using the combined copy number and SNV data revealed marked clonal heterogeneity in the originating metastasis, with four clones being reflected in the model systems. The set of OHC-NB1 models represents 43% of somatic SNVs and 23% of the cellularity in the originating metastasis with varying clonal compositions, indicating that heterogeneity is partially preserved in our model system.
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Affiliation(s)
- Theresa M Thole
- Department of Pediatric Hematology and Oncology, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Joern Toedling
- Department of Pediatric Hematology and Oncology, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Annika Sprüssel
- Department of Pediatric Hematology and Oncology, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Sebastian Pfeil
- Department of Pediatric Hematology and Oncology, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Larissa Savelyeva
- Research Group Neuroblastoma Genomics, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - David Capper
- Department of Neuropathology, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Clemens Messerschmidt
- Core Unit Bioinformatics, Berliner Institut für Gesundheitsforschung (BIH), Berlin, Germany
| | - Dieter Beule
- Core Unit Bioinformatics, Berliner Institut für Gesundheitsforschung (BIH), Berlin, Germany
| | | | - Cornelia Eckert
- Department of Pediatric Hematology and Oncology, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Guido Gambara
- CELLPhenomics GmbH, Berlin, Germany.,Charité Comprehensive Cancer Center (CCCC), Charité - Universitätsmedizin Berlin, Berlin, Germany.,German Cancer Consortium (DKTK), Berlin, Germany.,German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Anton G Henssen
- Department of Pediatric Hematology and Oncology, Charité - Universitätsmedizin Berlin, Berlin, Germany.,Berliner Institut für Gesundheitsforschung (BIH), Berlin, Germany
| | - Sabine Finkler
- Department of Pediatric Hematology and Oncology, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Johannes H Schulte
- Department of Pediatric Hematology and Oncology, Charité - Universitätsmedizin Berlin, Berlin, Germany.,German Cancer Consortium (DKTK), Berlin, Germany.,German Cancer Research Center (DKFZ), Heidelberg, Germany.,Berliner Institut für Gesundheitsforschung (BIH), Berlin, Germany
| | - Anja Sieber
- Computational Modelling in Medicine, Charité - Universitätsmedizin Berlin, Institute for Pathology, Berlin, Germany.,IRI Life Sciences, Humboldt University Berlin, Berlin, Germany
| | - Nils Bluethgen
- Berliner Institut für Gesundheitsforschung (BIH), Berlin, Germany.,Computational Modelling in Medicine, Charité - Universitätsmedizin Berlin, Institute for Pathology, Berlin, Germany.,IRI Life Sciences, Humboldt University Berlin, Berlin, Germany
| | - Christian R A Regenbrecht
- CELLPhenomics GmbH, Berlin, Germany.,Department for Pathology, Medical Faculty, Otto-von-Guericke University of Magdeburg, Magdeburg, Germany
| | - Annette Künkele
- Department of Pediatric Hematology and Oncology, Charité - Universitätsmedizin Berlin, Berlin, Germany.,Berliner Institut für Gesundheitsforschung (BIH), Berlin, Germany
| | - Marco Lodrini
- Department of Pediatric Hematology and Oncology, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Angelika Eggert
- Department of Pediatric Hematology and Oncology, Charité - Universitätsmedizin Berlin, Berlin, Germany.,German Cancer Consortium (DKTK), Berlin, Germany.,German Cancer Research Center (DKFZ), Heidelberg, Germany.,Berliner Institut für Gesundheitsforschung (BIH), Berlin, Germany
| | - Hedwig E Deubzer
- Department of Pediatric Hematology and Oncology, Charité - Universitätsmedizin Berlin, Berlin, Germany.,German Cancer Consortium (DKTK), Berlin, Germany.,German Cancer Research Center (DKFZ), Heidelberg, Germany.,Berliner Institut für Gesundheitsforschung (BIH), Berlin, Germany.,Neuroblastoma Research Group, Experimental and Clinical Research Center (ECRC) of the Charité and the Max-Delbrück-Center for Molecular Medicine (MDC) in the Helmholtz Association, Berlin, Germany
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20
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Hammerl D, Rieder D, Martens JWM, Trajanoski Z, Debets R. Adoptive T Cell Therapy: New Avenues Leading to Safe Targets and Powerful Allies. Trends Immunol 2018; 39:921-936. [PMID: 30309702 DOI: 10.1016/j.it.2018.09.004] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2018] [Revised: 09/12/2018] [Accepted: 09/12/2018] [Indexed: 12/30/2022]
Abstract
Adoptive transfer of TCR-engineered T cells is a potent therapy, able to induce clinical responses in different human malignancies. Nevertheless, treatment toxicities may occur and, in particular for solid tumors, responses may be variable and often not durable. To address these challenges, it is imperative to carefully select target antigens and to immunologically interrogate the corresponding tumors when designing optimal T cell therapies. Here, we review recent advances, covering both omics- and laboratory tools that can enable the selection of optimal T cell epitopes and TCRs as well as the identification of dominant immune evasive mechanisms within tumor tissues. Furthermore, we discuss how these techniques may aid in a rational design of effective combinatorial adoptive T cell therapies.
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Affiliation(s)
- Dora Hammerl
- Laboratory of Tumor Immunology, Erasmus MC-Cancer Institute, Rotterdam, The Netherlands
| | - Dietmar Rieder
- Division of Bioinformatics, Biocenter, Innsbruck Medical University, Innsbruck, Austria
| | - John W M Martens
- Department of Medical Oncology, Erasmus MC-Cancer Institute, Rotterdam, The Netherlands
| | - Zlatko Trajanoski
- Division of Bioinformatics, Biocenter, Innsbruck Medical University, Innsbruck, Austria
| | - Reno Debets
- Laboratory of Tumor Immunology, Erasmus MC-Cancer Institute, Rotterdam, The Netherlands.
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Abstract
The rapid development of immunomodulatory cancer therapies has led to a concurrent increase in the application of informatics techniques to the analysis of tumors, the tumor microenvironment, and measures of systemic immunity. In this review, the use of tumors to gather genetic and expression data will first be explored. Next, techniques to assess tumor immunity are reviewed, including HLA status, predicted neoantigens, immune microenvironment deconvolution, and T-cell receptor sequencing. Attempts to integrate these data are in early stages of development and are discussed in this review. Finally, we review the application of these informatics strategies to therapy development, with a focus on vaccines, adoptive cell transfer, and checkpoint blockade therapies.
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Affiliation(s)
- J Hammerbacher
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York
- Department of Microbiology and Immunology, Medical University of South Carolina, Charleston
| | - A Snyder
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York
- Adaptive Biotechnologies, Seattle, USA
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