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Kutz O, Drukewitz S, Krüger A, Aust D, William D, Oster S, Schröck E, Baretton G, Link T, Wimberger P, Kuhlmann JD. Exploring evolutionary trajectories in ovarian cancer patients by longitudinal analysis of ctDNA. Clin Chem Lab Med 2024; 62:2070-2081. [PMID: 38577791 DOI: 10.1515/cclm-2023-1266] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Accepted: 03/12/2024] [Indexed: 04/06/2024]
Abstract
OBJECTIVES We analysed whether temporal heterogeneity of ctDNA encodes evolutionary patterns in ovarian cancer. METHODS Targeted sequencing of 275 cancer-associated genes was performed in a primary tumor biopsy and in ctDNA of six longitudinal plasma samples from 15 patients, using the Illumina platform. RESULTS While there was low overall concordance between the mutational spectrum of the primary tumor biopsies vs. ctDNA, TP53 variants were the most commonly shared somatic alterations. Up to three variant clusters were detected in each tumor biopsy, likely representing predominant clones of the primary tumor, most of them harbouring a TP53 variant. By tracing these clusters in ctDNA, we propose that liquid biopsy may allow to assess the contribution of ancestral clones of the tumor to relapsed abdominal masses, revealing two evolutionary patterns. In pattern#1, clusters detected in the primary tumor biopsy were likely relapse seeding clones, as they contributed a major share to ctDNA at relapse. In pattern#2, similar clusters were present in tumors and ctDNA; however, they were entirely cleared from liquid biopsy after chemotherapy and were undetectable at relapse. ctDNA private variants were present among both patterns, with some of them mirroring subclonal expansions after chemotherapy. CONCLUSIONS We demonstrate that tracing the temporal heterogeneity of ctDNA, even below exome scale resolution, deciphers evolutionary trajectories in ovarian cancer. Furthermore, we describe two evolutionary patterns that may help to identify relapse seeding clones for targeted therapy.
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Affiliation(s)
- Oliver Kutz
- Department of Gynecology and Obstetrics, Medical Faculty and University Hospital Carl Gustav Carus, 9169 Technische Universität Dresden , Dresden, Germany
- 9169 National Center for Tumour Diseases (NCT) , Dresden, Germany
- 9169 German Cancer Research Center (DKFZ) , Heidelberg, Germany
- Faculty of Medicine and University Hospital Carl Gustav Carus, 9169 Technische Universität Dresden , Dresden, Germany
- 9169 Helmholtz-Zentrum Dresden-Rossendorf (HZDR) , Dresden, Germany
- 9169 German Cancer Consortium (DKTK) , Dresden, Germany
- Institute for Clinical Genetics, 9169 University Hospital Carl Gustav Carus at TU Dresden , Dresden, Germany
- ERN GENTURIS, 9169 Hereditary Cancer Syndrome Center , Dresden, Germany
- 9169 Faculty of Medicine and University Hospital Carl Gustav Carus at TU Dresden , Dresden, Germany
- 9169 Max Planck Institute of Molecular Cell Biology and Genetics , Dresden, Germany
| | - Stephan Drukewitz
- 9169 National Center for Tumour Diseases (NCT) , Dresden, Germany
- 9169 German Cancer Research Center (DKFZ) , Heidelberg, Germany
- Faculty of Medicine and University Hospital Carl Gustav Carus, 9169 Technische Universität Dresden , Dresden, Germany
- 9169 German Cancer Consortium (DKTK) , Dresden, Germany
- Institute of Human Genetics, University of Leipzig Medical Center, Leipzig, Germany
- Core Unit for Molecular Tumor Diagnostics (CMTD), 9169 Technische Universitat Dresden , Dresden, Sachsen, Germany
| | - Alexander Krüger
- 9169 National Center for Tumour Diseases (NCT) , Dresden, Germany
- 9169 German Cancer Research Center (DKFZ) , Heidelberg, Germany
- Faculty of Medicine and University Hospital Carl Gustav Carus, 9169 Technische Universität Dresden , Dresden, Germany
- 9169 German Cancer Consortium (DKTK) , Dresden, Germany
- Core Unit for Molecular Tumor Diagnostics (CMTD), 9169 Technische Universitat Dresden , Dresden, Sachsen, Germany
| | - Daniela Aust
- 9169 National Center for Tumour Diseases (NCT) , Dresden, Germany
- 9169 German Cancer Research Center (DKFZ) , Heidelberg, Germany
- Faculty of Medicine and University Hospital Carl Gustav Carus, 9169 Technische Universität Dresden , Dresden, Germany
- 9169 Helmholtz-Zentrum Dresden-Rossendorf (HZDR) , Dresden, Germany
- 9169 German Cancer Consortium (DKTK) , Dresden, Germany
- 9169 Faculty of Medicine and University Hospital Carl Gustav Carus at TU Dresden , Dresden, Germany
- Institute for Pathology, 9169 University Hospital Carl Gustav Carus at the TU Dresden , Dresden, Germany
- 9169 Tumor- and Normal Tissue Bank of the NCT/UCC Dresden , Dresden, Germany
| | - Doreen William
- 9169 National Center for Tumour Diseases (NCT) , Dresden, Germany
- 9169 German Cancer Research Center (DKFZ) , Heidelberg, Germany
- Faculty of Medicine and University Hospital Carl Gustav Carus, 9169 Technische Universität Dresden , Dresden, Germany
- 9169 Helmholtz-Zentrum Dresden-Rossendorf (HZDR) , Dresden, Germany
- 9169 German Cancer Consortium (DKTK) , Dresden, Germany
- Institute for Clinical Genetics, 9169 University Hospital Carl Gustav Carus at TU Dresden , Dresden, Germany
- ERN GENTURIS, 9169 Hereditary Cancer Syndrome Center , Dresden, Germany
- 9169 National Center for Tumor Diseases Dresden (NCT/UCC) , Dresden, Germany
- 9169 Faculty of Medicine and University Hospital Carl Gustav Carus at TU Dresden , Dresden, Germany
- 9169 Max Planck Institute of Molecular Cell Biology and Genetics , Dresden, Germany
- Core Unit for Molecular Tumor Diagnostics (CMTD), 9169 National Center for Tumor Diseases Dresden (NCT/UCC) , Dresden, Germany
- 9169 Faculty of Medicine and University Hospital Carl Gustav Carus, TU Dresden , Dresden, Germany
| | - Sandra Oster
- 9169 National Center for Tumour Diseases (NCT) , Dresden, Germany
- 9169 German Cancer Research Center (DKFZ) , Heidelberg, Germany
- Faculty of Medicine and University Hospital Carl Gustav Carus, 9169 Technische Universität Dresden , Dresden, Germany
- 9169 German Cancer Consortium (DKTK) , Dresden, Germany
- Core Unit for Molecular Tumor Diagnostics (CMTD), 9169 National Center for Tumor Diseases Dresden (NCT/UCC) , Dresden, Germany
- 9169 Faculty of Medicine and University Hospital Carl Gustav Carus, TU Dresden , Dresden, Germany
| | - Evelin Schröck
- 9169 National Center for Tumour Diseases (NCT) , Dresden, Germany
- 9169 German Cancer Research Center (DKFZ) , Heidelberg, Germany
- Faculty of Medicine and University Hospital Carl Gustav Carus, 9169 Technische Universität Dresden , Dresden, Germany
- 9169 Helmholtz-Zentrum Dresden-Rossendorf (HZDR) , Dresden, Germany
- 9169 German Cancer Consortium (DKTK) , Dresden, Germany
- Institute for Clinical Genetics, 9169 University Hospital Carl Gustav Carus at TU Dresden , Dresden, Germany
- ERN GENTURIS, 9169 Hereditary Cancer Syndrome Center , Dresden, Germany
- 9169 Faculty of Medicine and University Hospital Carl Gustav Carus at TU Dresden , Dresden, Germany
- 9169 Max Planck Institute of Molecular Cell Biology and Genetics , Dresden, Germany
- Core Unit for Molecular Tumor Diagnostics (CMTD), 9169 Technische Universitat Dresden , Dresden, Sachsen, Germany
- 9169 Faculty of Medicine and University Hospital Carl Gustav Carus, TU Dresden , Dresden, Germany
| | - Gustavo Baretton
- 9169 National Center for Tumour Diseases (NCT) , Dresden, Germany
- 9169 German Cancer Research Center (DKFZ) , Heidelberg, Germany
- Faculty of Medicine and University Hospital Carl Gustav Carus, 9169 Technische Universität Dresden , Dresden, Germany
- 9169 Helmholtz-Zentrum Dresden-Rossendorf (HZDR) , Dresden, Germany
- 9169 German Cancer Consortium (DKTK) , Dresden, Germany
- 9169 Faculty of Medicine and University Hospital Carl Gustav Carus at TU Dresden , Dresden, Germany
- Institute for Pathology, 9169 University Hospital Carl Gustav Carus at the TU Dresden , Dresden, Germany
- 9169 Tumor- and Normal Tissue Bank of the NCT/UCC Dresden , Dresden, Germany
| | - Theresa Link
- Department of Gynecology and Obstetrics, Medical Faculty and University Hospital Carl Gustav Carus, 9169 Technische Universität Dresden , Dresden, Germany
- 9169 National Center for Tumour Diseases (NCT) , Dresden, Germany
- 9169 German Cancer Research Center (DKFZ) , Heidelberg, Germany
- Faculty of Medicine and University Hospital Carl Gustav Carus, 9169 Technische Universität Dresden , Dresden, Germany
- 9169 Helmholtz-Zentrum Dresden-Rossendorf (HZDR) , Dresden, Germany
- 9169 German Cancer Consortium (DKTK) , Dresden, Germany
- 9169 Faculty of Medicine and University Hospital Carl Gustav Carus at TU Dresden , Dresden, Germany
| | - Pauline Wimberger
- Department of Gynecology and Obstetrics, Medical Faculty and University Hospital Carl Gustav Carus, 9169 Technische Universität Dresden , Dresden, Germany
- 9169 National Center for Tumour Diseases (NCT) , Dresden, Germany
- 9169 German Cancer Research Center (DKFZ) , Heidelberg, Germany
- Faculty of Medicine and University Hospital Carl Gustav Carus, 9169 Technische Universität Dresden , Dresden, Germany
- 9169 Helmholtz-Zentrum Dresden-Rossendorf (HZDR) , Dresden, Germany
- 9169 German Cancer Consortium (DKTK) , Dresden, Germany
- 9169 Faculty of Medicine and University Hospital Carl Gustav Carus at TU Dresden , Dresden, Germany
| | - Jan Dominik Kuhlmann
- Department of Gynecology and Obstetrics, Medical Faculty and University Hospital Carl Gustav Carus, 9169 Technische Universität Dresden , Dresden, Germany
- 9169 National Center for Tumour Diseases (NCT) , Dresden, Germany
- 9169 German Cancer Research Center (DKFZ) , Heidelberg, Germany
- Faculty of Medicine and University Hospital Carl Gustav Carus, 9169 Technische Universität Dresden , Dresden, Germany
- 9169 Helmholtz-Zentrum Dresden-Rossendorf (HZDR) , Dresden, Germany
- 9169 German Cancer Consortium (DKTK) , Dresden, Germany
- 9169 Faculty of Medicine and University Hospital Carl Gustav Carus at TU Dresden , Dresden, Germany
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Yao G, Zhu Y, Liu C, Man Y, Liu K, Zhang Q, Tan Y, Duan Q, Chen D, Du Z, Fan Y. Comparative analysis of the mutational landscape and evolutionary patterns of pancreatic ductal adenocarcinoma metastases in the liver or peritoneum. Heliyon 2024; 10:e35428. [PMID: 39170579 PMCID: PMC11336646 DOI: 10.1016/j.heliyon.2024.e35428] [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: 02/19/2024] [Revised: 07/02/2024] [Accepted: 07/29/2024] [Indexed: 08/23/2024] Open
Abstract
Background Pancreatic ductal adenocarcinoma (PDAC) often presents with liver or peritoneal metastases at diagnosis. Despite similar treatment approaches, patient outcomes vary between these metastatic sites. To improve targeted therapies for metastatic PDAC, a comprehensive analysis of the genetic profiles and evolutionary patterns at these distinct metastatic locations is essential. Methods We performed whole exome sequencing on 44 tissue samples from 27 PDAC patients, including primary tumours and matched liver or peritoneal metastases. We analysed somatic mutation profiles, signatures, and affected pathways for each group, and examined clonal evolution using subclonal architectures and phylogenetic trees. Results KRAS mutations remained the predominant driver alteration, with a prevalence of 89 % across all tumours. Notably, we observed site-specific differences in mutation frequencies, with KRAS alterations detected in 77.8 % (7/9) of peritoneal metastases and 87.5 % (7/8) of liver metastases. TP53 mutations exhibited a similar pattern, occurring in 55.6 % (5/9) of peritoneal and 37.5 % (3/8) of liver metastases. Intriguingly, we identified site-specific alterations in DNA repair pathway genes, including ATM and BRCA1, with distinct mutational profiles in liver versus peritoneal metastases. Furthermore, liver metastases demonstrated a significantly higher tumor mutational burden (TMB) compared to peritoneal metastases (median [IQR]: 2.14 [1.77-2.71] vs. 1.29 [1.21-1.69] mutations/Mb; P = 0.048). Conclusions In conclusion, metastasis of pancreatic cancer may be influenced by variables other than KRAS mutations, such as TP53. PDAC peritoneal and liver metastases may differ in potential therapeutic biomarkers. Further inquiry is needed on the biological mechanisms underlying metastasis and the treatment of diverse metastases.
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Affiliation(s)
- Guoliang Yao
- Department of General Surgery, The First Affiliated Hospital of Henan University of Science and Technology, 636 Guanlin Road, Luoyang, China
| | - Yanfeng Zhu
- Department of Nursing, Huashan Hospital, Fudan University, No.12 Middle Urumqi Road, Shangha, China
| | - Chunhui Liu
- Department of General Surgery, The First Affiliated Hospital of Henan University of Science and Technology, 636 Guanlin Road, Luoyang, China
| | - Yanwen Man
- Department of General Surgery, The First Affiliated Hospital of Henan University of Science and Technology, 636 Guanlin Road, Luoyang, China
| | - Kefeng Liu
- Department of General Surgery, The First Affiliated Hospital of Henan University of Science and Technology, 636 Guanlin Road, Luoyang, China
| | - Qin Zhang
- Jiangsu Simcere Diagnostics Co., Ltd., Nanjing Simcere Medical Laboratory Science Co., Ltd., The State Key Laboratory of Neurology and Oncology Drug Development, China
| | - Yuan Tan
- Jiangsu Simcere Diagnostics Co., Ltd., Nanjing Simcere Medical Laboratory Science Co., Ltd., The State Key Laboratory of Neurology and Oncology Drug Development, China
| | - Qianqian Duan
- Jiangsu Simcere Diagnostics Co., Ltd., Nanjing Simcere Medical Laboratory Science Co., Ltd., The State Key Laboratory of Neurology and Oncology Drug Development, China
| | - Dongsheng Chen
- Jiangsu Simcere Diagnostics Co., Ltd., Nanjing Simcere Medical Laboratory Science Co., Ltd., The State Key Laboratory of Neurology and Oncology Drug Development, China
| | - Zunguo Du
- Department of Pathology, Huashan Hospital, Fudan University, No.12 Middle Urumqi Road, Shanghai, China
| | - Yonggang Fan
- Department of General Surgery, The First Affiliated Hospital of Henan University of Science and Technology, 636 Guanlin Road, Luoyang, China
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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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Foltz SM, Li Y, Yao L, Terekhanova NV, Weerasinghe A, Gao Q, Dong G, Schindler M, Cao S, Sun H, Jayasinghe RG, Fulton RS, Fronick CC, King J, Kohnen DR, Fiala MA, Chen K, DiPersio JF, Vij R, Ding L. Somatic mutation phasing and haplotype extension using linked-reads in multiple myeloma. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.08.09.607342. [PMID: 39149342 PMCID: PMC11326269 DOI: 10.1101/2024.08.09.607342] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/17/2024]
Abstract
Somatic mutation phasing informs our understanding of cancer-related events, like driver mutations. We generated linked-read whole genome sequencing data for 23 samples across disease stages from 14 multiple myeloma (MM) patients and systematically assigned somatic mutations to haplotypes using linked-reads. Here, we report the reconstructed cancer haplotypes and phase blocks from several MM samples and show how phase block length can be extended by integrating samples from the same individual. We also uncover phasing information in genes frequently mutated in MM, including DIS3, HIST1H1E, KRAS, NRAS, and TP53, phasing 79.4% of 20,705 high-confidence somatic mutations. In some cases, this enabled us to interpret clonal evolution models at higher resolution using pairs of phased somatic mutations. For example, our analysis of one patient suggested that two NRAS hotspot mutations occurred on the same haplotype but were independent events in different subclones. Given sufficient tumor purity and data quality, our framework illustrates how haplotype-aware analysis of somatic mutations in cancer can be beneficial for some cancer cases.
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Affiliation(s)
- Steven M. Foltz
- Department of Medicine, Washington University in St. Louis, St. Louis, MO, 63110, USA
- McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO, 63108, USA
| | - Yize Li
- Department of Medicine, Washington University in St. Louis, St. Louis, MO, 63110, USA
- McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO, 63108, USA
| | - Lijun Yao
- Department of Medicine, Washington University in St. Louis, St. Louis, MO, 63110, USA
- McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO, 63108, USA
| | - Nadezhda V. Terekhanova
- Department of Medicine, Washington University in St. Louis, St. Louis, MO, 63110, USA
- McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO, 63108, USA
| | - Amila Weerasinghe
- Department of Medicine, Washington University in St. Louis, St. Louis, MO, 63110, USA
- McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO, 63108, USA
| | - Qingsong Gao
- Department of Medicine, Washington University in St. Louis, St. Louis, MO, 63110, USA
- McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO, 63108, USA
| | - Guanlan Dong
- Department of Medicine, Washington University in St. Louis, St. Louis, MO, 63110, USA
- McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO, 63108, USA
| | - Moses Schindler
- Department of Medicine, Washington University in St. Louis, St. Louis, MO, 63110, USA
- McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO, 63108, USA
| | - Song Cao
- Department of Medicine, Washington University in St. Louis, St. Louis, MO, 63110, USA
- McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO, 63108, USA
| | - Hua Sun
- Department of Medicine, Washington University in St. Louis, St. Louis, MO, 63110, USA
- McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO, 63108, USA
| | - Reyka G. Jayasinghe
- Department of Medicine, Washington University in St. Louis, St. Louis, MO, 63110, USA
- McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO, 63108, USA
| | - Robert S. Fulton
- McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO, 63108, USA
| | - Catrina C. Fronick
- McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO, 63108, USA
| | - Justin King
- Department of Medicine, Washington University in St. Louis, St. Louis, MO, 63110, USA
| | - Daniel R. Kohnen
- Department of Medicine, Washington University in St. Louis, St. Louis, MO, 63110, USA
| | - Mark A. Fiala
- Department of Medicine, Washington University in St. Louis, St. Louis, MO, 63110, USA
| | - Ken Chen
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - John F. DiPersio
- Department of Medicine, Washington University in St. Louis, St. Louis, MO, 63110, USA
- Siteman Cancer Center, Washington University in St. Louis, St. Louis, MO, 63110, USA
| | - Ravi Vij
- Department of Medicine, Washington University in St. Louis, St. Louis, MO, 63110, USA
- Siteman Cancer Center, Washington University in St. Louis, St. Louis, MO, 63110, USA
| | - Li Ding
- Department of Medicine, Washington University in St. Louis, St. Louis, MO, 63110, USA
- McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO, 63108, USA
- Siteman Cancer Center, Washington University in St. Louis, St. Louis, MO, 63110, USA
- Department of Genetics, Washington University in St. Louis, St. Louis, MO, 63110, USA
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Fridland S, Kim HS, Chae YK. Differential impact of intratumor heterogeneity (ITH) on survival outcomes in early-stage lung squamous and adenocarcinoma based on tumor mutational burden (TMB). Transl Lung Cancer Res 2024; 13:1481-1494. [PMID: 39118891 PMCID: PMC11304137 DOI: 10.21037/tlcr-24-226] [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: 03/20/2024] [Accepted: 06/06/2024] [Indexed: 08/10/2024]
Abstract
Background Molecular biomarkers are reshaping patient stratification and treatment decisions, yet their precise use and best implementation remain uncertain. Intratumor heterogeneity (ITH), an area of increasing research interest with prognostic value across various conditions, lacks defined clinical relevance in certain non-small cell lung cancer (NSCLC) subtypes. Exploring the relationship between ITH and tumor mutational burden (TMB) is crucial, as their interplay might reveal distinct patient subgroups. This study evaluates how the ITH-TMB dynamic affects prognosis across the two main histological subtypes of NSCLC, squamous cell and adenocarcinoma, with a specific focus on early-stage cases to address their highly unmet clinical needs. Methods We stratify a cohort of 741 early-stage NSCLC patients from The Cancer Genome Atlas (TCGA) based on ITH and TMB and evaluate differences in clinical outcomes. Additionally, we compare driver mutations and the tumor microenvironment (TME) between high and low ITH groups. Results In lung squamous cell carcinoma (LUSC), high ITH predicts an extended progression-free survival (PFS) (median: 21 vs. 14 months, P=0.01), while in lung adenocarcinoma (LUAD), high ITH predicts a reduced PFS (median: 15 vs. 20 months, P=0.04). This relationship is driven by the low TMB subset of patients. Additionally, we found that CD8 T cells were enriched in better-performing subgroups, regardless of histologic subtype or ITH status. Conclusions There are significant differences in clinical outcomes, driver mutations, and the TME between high and low ITH groups among early-stage NSCLC patients. These differences may have treatment implications, necessitating further validation in other NSCLC datasets.
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Affiliation(s)
- Stanislav Fridland
- Department of Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Hye Sung Kim
- Department of Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Young Kwang Chae
- Department of Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
- Department of Medicine, Robert H. Lurie Comprehensive Cancer Center of Northwestern University, Chicago, IL, USA
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Atzeni R, Massidda M, Pieroni E, Rallo V, Pisu M, Angius A. A Novel Affordable and Reliable Framework for Accurate Detection and Comprehensive Analysis of Somatic Mutations in Cancer. Int J Mol Sci 2024; 25:8044. [PMID: 39125613 PMCID: PMC11311285 DOI: 10.3390/ijms25158044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2024] [Revised: 07/11/2024] [Accepted: 07/22/2024] [Indexed: 08/12/2024] Open
Abstract
Accurate detection and analysis of somatic variants in cancer involve multiple third-party tools with complex dependencies and configurations, leading to laborious, error-prone, and time-consuming data conversions. This approach lacks accuracy, reproducibility, and portability, limiting clinical application. Musta was developed to address these issues as an end-to-end pipeline for detecting, classifying, and interpreting cancer mutations. Musta is based on a Python command-line tool designed to manage tumor-normal samples for precise somatic mutation analysis. The core is a Snakemake-based workflow that covers all key cancer genomics steps, including variant calling, mutational signature deconvolution, variant annotation, driver gene detection, pathway analysis, and tumor heterogeneity estimation. Musta is easy to install on any system via Docker, with a Makefile handling installation, configuration, and execution, allowing for full or partial pipeline runs. Musta has been validated at the CRS4-NGS Core facility and tested on large datasets from The Cancer Genome Atlas and the Beijing Institute of Genomics. Musta has proven robust and flexible for somatic variant analysis in cancer. It is user-friendly, requiring no specialized programming skills, and enables data processing with a single command line. Its reproducibility ensures consistent results across users following the same protocol.
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Affiliation(s)
- Rossano Atzeni
- Center for Advanced Studies, Research and Development in Sardinia (CRS4), 09050 Pula, Italy; (R.A.); (E.P.); (M.P.)
| | - Matteo Massidda
- Department of Medical, Surgical and Experimental Sciences, University of Sassari, 07100 Sassari, Italy;
| | - Enrico Pieroni
- Center for Advanced Studies, Research and Development in Sardinia (CRS4), 09050 Pula, Italy; (R.A.); (E.P.); (M.P.)
| | - Vincenzo Rallo
- Istituto di Ricerca Genetica e Biomedica (IRGB), Consiglio Nazionale delle Ricerche (CNR), Cittadella Universitaria di Cagliari, 09042 Monserrato, Italy;
| | - Massimo Pisu
- Center for Advanced Studies, Research and Development in Sardinia (CRS4), 09050 Pula, Italy; (R.A.); (E.P.); (M.P.)
| | - Andrea Angius
- Istituto di Ricerca Genetica e Biomedica (IRGB), Consiglio Nazionale delle Ricerche (CNR), Cittadella Universitaria di Cagliari, 09042 Monserrato, Italy;
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Yang C, Xiang E, Chen P, Fang X. Evolutionary history of adenomas to colorectal cancer in FAP families. Front Genet 2024; 15:1391851. [PMID: 39021676 PMCID: PMC11252899 DOI: 10.3389/fgene.2024.1391851] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2024] [Accepted: 06/04/2024] [Indexed: 07/20/2024] Open
Abstract
Objective Familial adenomatous polyposis (FAP) is a genetic syndrome characterized by multiple polyps at various evolutionary stages, which, if left untreated, inevitably progress to colorectal cancer (CRC). In this study, we present a comprehensive analysis of the evolutionary history of FAP-CRC from precancerous adenoma to carcinoma. Design Tissues were collected from gastrointestinal endoscopy or surgical resection. Exome sequencing was performed on multiple regions of adenocarcinoma (n = 8), villous adenoma (n = 10), tubular adenoma (n = 9) and blood samples were obtained from 9 patients belonging to 7 Chinese FAP families. Phylogenetic trees were reconstructed, and evolutionary analysis was conducted to reveal the temporal sequence of events leading to CRC. Results Inherited germline mutation sites in APC gene were identified in FAP01 (p.S1281*, COSM19212), FAP03 (p.S384Tfs*19), FAP04 (p.E1538*, COSM6041693), FAP05 (p.Q1062*, COSM3696862), and FAP07-FAP09 (p.V677Sfs*3). Notably, p.V677Sfs*3 mutation was recognized as a novel germline mutation in APC, supported by evidence of genotype-phenotype correlation in pedigree analysis. Adenomas exhibited lower mutational rates than FAP-CRC and displayed recurrent alterations in well-known chromosomal instability (CIN) genes (APC, RAS, SMAD4 and TP53) and DNA damage repair genes (SUZ12, KMT2C, BCLAF1, RUNX1, and ARID1B), suggesting the presence of genomic instability. Furthermore, a progressive increase in the HRD score (a measure of "genomic scars") was observed from tubular adenomas to villous adenomas and ultimately to carcinomas. TP53 emerged as the primary driver gene for adenoma-carcinoma transition, with driver mutations consistently appearing simultaneously rather than sequentially acquired from adenomas to carcinomas. Clonal evolution demonstrated that liver metastases can originate from the same cancer-primed cell present in a primary cancerous lesion. Conclusion We identified a novel pathogenic variant in APC, namely, p.V677Sfs*3. The process of carcinogenesis in FAP-CRC supports the classical cancerization model, where an initial APC mutation leads to the activation of the WNT signaling pathway and CIN. Subsequently, additional mutations occur in other putative CIN genes (e.g., DNA repair, chromatin remodeling), ultimately leading to the development of microsatellite stable (MSS) tumors. Our study provides a comprehensive understanding of the genomic landscapes that underlie the transition from adenoma to carcinoma.
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Affiliation(s)
- Cuiping Yang
- Department of Gastroenterology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Enfei Xiang
- Shanghai Institute of Digestive Surgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Ping Chen
- Department of Gastroenterology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xuqian Fang
- Department of Pathology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
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8
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Johanns TM, Garfinkle EA, Miller KE, Livingstone AJ, Roberts KF, Rao Venkata LP, Dowling JL, Chicoine MR, Dacey RG, Zipfel GJ, Kim AH, Mardis ER, Dunn GP. Integrating Multisector Molecular Characterization into Personalized Peptide Vaccine Design for Patients with Newly Diagnosed Glioblastoma. Clin Cancer Res 2024; 30:2729-2742. [PMID: 38639919 PMCID: PMC11215407 DOI: 10.1158/1078-0432.ccr-23-3077] [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: 10/16/2023] [Revised: 01/18/2024] [Accepted: 04/15/2024] [Indexed: 04/20/2024]
Abstract
PURPOSE Outcomes for patients with glioblastoma (GBM) remain poor despite multimodality treatment with surgery, radiation, and chemotherapy. There are few immunotherapy options due to the lack of tumor immunogenicity. Several clinical trials have reported promising results with cancer vaccines. To date, studies have used data from a single tumor site to identify targetable antigens, but this approach limits the antigen pool and is antithetical to the heterogeneity of GBM. We have implemented multisector sequencing to increase the pool of neoantigens across the GBM genomic landscape that can be incorporated into personalized peptide vaccines called NeoVax. PATIENTS AND METHODS In this study, we report the findings of four patients enrolled onto the NeoVax clinical trial (NCT0342209). RESULTS Immune reactivity to NeoVax neoantigens was assessed in peripheral blood mononuclear cells pre- and post-NeoVax for patients 1 to 3 using IFNγ-ELISPOT assay. A statistically significant increase in IFNγ producing T cells at the post-NeoVax time point for several neoantigens was observed. Furthermore, a post-NeoVax tumor biopsy was obtained from patient 3 and, upon evaluation, revealed evidence of infiltrating, clonally expanded T cells. CONCLUSIONS Collectively, our findings suggest that NeoVax stimulated the expansion of neoantigen-specific effector T cells and provide encouraging results to aid in the development of future neoantigen vaccine-based clinical trials in patients with GBM. Herein, we demonstrate the feasibility of incorporating multisector sampling in cancer vaccine design and provide information on the clinical applicability of clonality, distribution, and immunogenicity of the neoantigen landscape in patients with GBM.
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Affiliation(s)
- Tanner M. Johanns
- Division of Medical Oncology, Washington University School of Medicine, St. Louis, Missouri.
- Andrew M. and Jane M. Bursky Center for Human Immunology and Immunotherapy Programs, Washington University School of Medicine, St. Louis, Missouri.
- The Brain Tumor Center at Siteman Cancer Center, Washington University School of Medicine, St. Louis, Missouri.
| | - Elizabeth A.R. Garfinkle
- The Steve and Cindy Rasmussen Institute for Genomic Medicine, Nationwide Children’s Hospital, Columbus, Ohio.
| | - Katherine E. Miller
- The Steve and Cindy Rasmussen Institute for Genomic Medicine, Nationwide Children’s Hospital, Columbus, Ohio.
| | | | - Kaleigh F. Roberts
- Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, Missouri.
| | - Lakshmi P. Rao Venkata
- The Steve and Cindy Rasmussen Institute for Genomic Medicine, Nationwide Children’s Hospital, Columbus, Ohio.
| | - Joshua L. Dowling
- The Brain Tumor Center at Siteman Cancer Center, Washington University School of Medicine, St. Louis, Missouri.
- Department of Neurological Surgery, Washington University School of Medicine, St. Louis, Missouri.
| | - Michael R. Chicoine
- Department of Neurosurgery, University of Missouri in Columbia, Columbia, Missouri.
| | - Ralph G. Dacey
- Department of Neurological Surgery, Washington University School of Medicine, St. Louis, Missouri.
| | - Gregory J. Zipfel
- The Brain Tumor Center at Siteman Cancer Center, Washington University School of Medicine, St. Louis, Missouri.
- Department of Neurological Surgery, Washington University School of Medicine, St. Louis, Missouri.
| | - Albert H. Kim
- The Brain Tumor Center at Siteman Cancer Center, Washington University School of Medicine, St. Louis, Missouri.
- Department of Neurological Surgery, Washington University School of Medicine, St. Louis, Missouri.
| | - Elaine R. Mardis
- The Steve and Cindy Rasmussen Institute for Genomic Medicine, Nationwide Children’s Hospital, Columbus, Ohio.
- Department of Pediatrics, Ohio State University College of Medicine, Columbus, Ohio.
| | - Gavin P. Dunn
- Department of Neurosurgery, Harvard Medical School, Massachusetts General Hospital, Boston, Massachusetts.
- Brain Tumor Immunology and Immunotherapy Program, Department of Neurosurgery, Harvard Medical School, Massachusetts General Hospital, Boston, Massachusetts.
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9
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Li L, Xie W, Zhan L, Wen S, Luo X, Xu S, Cai Y, Tang W, Wang Q, Li M, Xie Z, Deng L, Zhu H, Yu G. Resolving tumor evolution: a phylogenetic approach. JOURNAL OF THE NATIONAL CANCER CENTER 2024; 4:97-106. [PMID: 39282584 PMCID: PMC11390690 DOI: 10.1016/j.jncc.2024.03.001] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2023] [Revised: 02/28/2024] [Accepted: 03/20/2024] [Indexed: 09/19/2024] Open
Abstract
The evolutionary dynamics of cancer, characterized by its profound heterogeneity, demand sophisticated tools for a holistic understanding. This review delves into tumor phylogenetics, an essential approach bridging evolutionary biology with oncology, offering unparalleled insights into cancer's evolutionary trajectory. We provide an overview of the workflow, encompassing study design, data acquisition, and phylogeny reconstruction. Notably, the integration of diverse data sets emerges as a transformative step, enhancing the depth and breadth of evolutionary insights. With this integrated perspective, tumor phylogenetics stands poised to redefine our understanding of cancer evolution and influence therapeutic strategies.
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Affiliation(s)
- Lin Li
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Wenqin Xie
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Li Zhan
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Shaodi Wen
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
- Department of Oncology, The Affiliated Cancer Hospital of Nanjing Medical University & Jiangsu Cancer Hospital, Nanjing, China
| | - Xiao Luo
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Shuangbin Xu
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
- Division of Laboratory Medicine, Microbiome Center, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Yantong Cai
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
- Dermatology Hospital, Southern Medical University, Guangzhou, China
| | - Wenli Tang
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Qianwen Wang
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Ming Li
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Zijing Xie
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Lin Deng
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Hongyuan Zhu
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Guangchuang Yu
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
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10
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Roddur MS, Snir S, El-Kebir M. Enforcing Temporal Consistency in Migration History Inference. J Comput Biol 2024; 31:396-415. [PMID: 38754138 DOI: 10.1089/cmb.2023.0352] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/18/2024] Open
Abstract
In addition to undergoing evolution, members of biological populations may also migrate between locations. Examples include the spread of tumor cells from the primary tumor to distant metastases or the spread of pathogens from one host to another. One may represent migration histories by assigning a location label to each vertex of a given phylogenetic tree such that an edge connecting vertices with distinct locations represents a migration. Some biological populations undergo comigration, a phenomenon where multiple taxa from distinct lineages simultaneously comigrate from one location to another. In this work, we show that a previous problem statement for inferring migration histories that are parsimonious in terms of migrations and comigrations may lead to temporally inconsistent solutions. To remedy this deficiency, we introduce precise definitions of temporal consistency of comigrations in a phylogenetic tree, leading to three successive problems. First, we formulate the temporally consistent comigration problem to check if a set of comigrations is temporally consistent and provide a linear time algorithm for solving this problem. Second, we formulate the parsimonious consistent comigrations (PCC) problem, which aims to find comigrations given a location labeling of a phylogenetic tree. We show that PCC is NP-hard. Third, we formulate the parsimonious consistent comigration history (PCCH) problem, which infers the migration history given a phylogenetic tree and locations of its extant vertices only. We show that PCCH is NP-hard as well. On the positive side, we propose integer linear programming models to solve the PCC and PCCH problems. We demonstrate our algorithms on simulated and real data.
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Affiliation(s)
- Mrinmoy Saha Roddur
- Department of Computer Science, University of Illinois Urbana-Champaign, Urbana, Illinois, USA
| | - Sagi Snir
- Department of Evolutionary Biology, University of Haifa, Haifa, Israel
| | - Mohammed El-Kebir
- Department of Computer Science, University of Illinois Urbana-Champaign, Urbana, Illinois, USA
- Cancer Center at Illinois, University of Illinois Urbana-Champaign, Urbana, Illinois, USA
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11
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Cui R, Duan H, Hu W, Li C, Zhong S, Liang L, Chen S, Hu H, He Z, Wang Z, Guo X, Chen Z, Xu C, Zhu Y, Chen Y, Sai K, Yang Q, Guo C, Mou Y, Jiang X. Establishment of Human Pituitary Neuroendocrine Tumor Derived Organoid and Its Pilot Application for Drug Screening. J Clin Endocrinol Metab 2024:dgae228. [PMID: 38656317 DOI: 10.1210/clinem/dgae228] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/29/2023] [Indexed: 04/26/2024]
Abstract
CONTEXT Precision medicine for pituitary neuroendocrine tumors (PitNETs) is limited by the lack of reliable research models. OBJECTIVE To generate patient-derived organoids (PDOs), which could serve as a platform for personalized drug screening for PitNET patients. DESIGN From July 2019 to May 2022, a total of 32 human PitNET specimens were collected for the establishment of organoids with an optimized culture protocol. SETTING This study was conducted at Sun Yat-Sen University Cancer Center. PATIENTS PitNET patients who were pathologically confirmed were enrolled in this study. INTERVENTIONS Histological staining and whole-exome sequencing were utilized to confirm the pathologic and genomic features of PDOs. A drug response assay on PDOs was also performed. MAIN OUTCOME MEASURES PDOs retained key genetic and morphological features of their parental tumors. RESULTS PDOs were successfully established from various types of PitNET samples with an overall success rate of 87.5%. Clinical nonfunctioning PitNETs-derived organoids (22/23, 95.7%) showed a higher likelihood of successful generation compared to those from functioning PitNETs (6/9, 66.7%). Preservation of cellular structure, subtype-specific neuroendocrine profiles, mutational features, and tumor microenvironment heterogeneity from parental tumors was observed. A distinctive response profile in drug tests was observed among the organoids from patients with different subtypes of PitNETs. With the validation of key characteristics from parental tumors in histological, genomic, and microenvironment heterogeneity consistency assays, we demonstrated the predictive value of the PDOs in testing individual drugs. CONCLUSION The established PDOs, retaining typical features of parental tumors, indicate a translational significance in innovating personalized treatment for refractory PitNETs.
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Affiliation(s)
- Run Cui
- Department of Neurosurgery/Neuro-Oncology, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, S Yat-Sen University Cancer Center, Guangzhou, 510060 Guangdong, China
- Department of Neurosurgery, Guangdong 2nd Provincial Peoples Hospital, Guangzhou, 523058 Guangdong, China
| | - Hao Duan
- Department of Neurosurgery/Neuro-Oncology, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, S Yat-Sen University Cancer Center, Guangzhou, 510060 Guangdong, China
| | - Wanming Hu
- Department of Pathology, Sun Yat-Sen University Cancer Center, Guangzhou, 510000 Guangdong, China
| | - Chang Li
- Department of Neurosurgery/Neuro-Oncology, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, S Yat-Sen University Cancer Center, Guangzhou, 510060 Guangdong, China
| | - Sheng Zhong
- Department of Neurosurgery/Neuro-Oncology, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, S Yat-Sen University Cancer Center, Guangzhou, 510060 Guangdong, China
| | - Lun Liang
- Department of Neurosurgery/Neuro-Oncology, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, S Yat-Sen University Cancer Center, Guangzhou, 510060 Guangdong, China
| | - Siyu Chen
- Department of Neurosurgery/Neuro-Oncology, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, S Yat-Sen University Cancer Center, Guangzhou, 510060 Guangdong, China
| | - Hongrong Hu
- Department of Neurosurgery/Neuro-Oncology, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, S Yat-Sen University Cancer Center, Guangzhou, 510060 Guangdong, China
| | - Zhenqiang He
- Department of Neurosurgery/Neuro-Oncology, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, S Yat-Sen University Cancer Center, Guangzhou, 510060 Guangdong, China
| | - Zhenning Wang
- Department of Neurosurgery, Dongguan People's Hospital, Dongguan, 523058 Guangdong, China
| | - Xiaoyu Guo
- Department of Neurosurgery, First Affiliated Hospital of Ji'nan University, Guangzhou, 510630 Guangdong, China
| | - Zexin Chen
- Guangdong Research Center of Organoid Engineering and Technology, Guangzhou, 510320 Guangdong, China
| | - Cong Xu
- Guangdong Research Center of Organoid Engineering and Technology, Guangzhou, 510320 Guangdong, China
| | - Yu Zhu
- Guangdong Research Center of Organoid Engineering and Technology, Guangzhou, 510320 Guangdong, China
| | - Yinsheng Chen
- Department of Neurosurgery/Neuro-Oncology, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, S Yat-Sen University Cancer Center, Guangzhou, 510060 Guangdong, China
| | - Ke Sai
- Department of Neurosurgery/Neuro-Oncology, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, S Yat-Sen University Cancer Center, Guangzhou, 510060 Guangdong, China
| | - Qunying Yang
- Department of Neurosurgery/Neuro-Oncology, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, S Yat-Sen University Cancer Center, Guangzhou, 510060 Guangdong, China
| | - Chengcheng Guo
- Department of Neurosurgery/Neuro-Oncology, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, S Yat-Sen University Cancer Center, Guangzhou, 510060 Guangdong, China
| | - Yonggao Mou
- Department of Neurosurgery/Neuro-Oncology, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, S Yat-Sen University Cancer Center, Guangzhou, 510060 Guangdong, China
| | - Xiaobing Jiang
- Department of Neurosurgery/Neuro-Oncology, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, S Yat-Sen University Cancer Center, Guangzhou, 510060 Guangdong, China
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12
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Bertucci F, Lerebours F, Ceccarelli M, Guille A, Syed N, Finetti P, Adélaïde J, Van Laere S, Goncalves A, Viens P, Birnbaum D, Mamessier E, Callens C, Bedognetti D. Mutational landscape of inflammatory breast cancer. J Transl Med 2024; 22:374. [PMID: 38637846 PMCID: PMC11025259 DOI: 10.1186/s12967-024-05198-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2024] [Accepted: 04/12/2024] [Indexed: 04/20/2024] Open
Abstract
BACKGROUND Inflammatory breast cancer (IBC) is the most pro-metastatic form of BC. Better understanding of its enigmatic pathophysiology is crucial. We report here the largest whole-exome sequencing (WES) study of clinical IBC samples. METHODS We retrospectively applied WES to 54 untreated IBC primary tumor samples and matched normal DNA. The comparator samples were 102 stage-matched non-IBC samples from TCGA. We compared the somatic mutational profiles, spectra and signatures, copy number alterations (CNAs), HRD and heterogeneity scores, and frequencies of actionable genomic alterations (AGAs) between IBCs and non-IBCs. The comparisons were adjusted for the molecular subtypes. RESULTS The number of somatic mutations, TMB, and mutational spectra were not different between IBCs and non-IBCs, and no gene was differentially mutated or showed differential frequency of CNAs. Among the COSMIC signatures, only the age-related signature was more frequent in non-IBCs than in IBCs. We also identified in IBCs two new mutational signatures not associated with any environmental exposure, one of them having been previously related to HIF pathway activation. Overall, the HRD score was not different between both groups, but was higher in TN IBCs than TN non-IBCs. IBCs were less frequently classified as heterogeneous according to heterogeneity H-index than non-IBCs (21% vs 33%), and clonal mutations were more frequent and subclonal mutations less frequent in IBCs. More than 50% of patients with IBC harbored at least one high-level of evidence (LOE) AGA (OncoKB LOE 1-2, ESCAT LOE I-II), similarly to patients with non-IBC. CONCLUSIONS We provide the largest mutational landscape of IBC. Only a few subtle differences were identified with non-IBCs. The most clinically relevant one was the higher HRD score in TN IBCs than in TN non-IBCs, whereas the most intriguing one was the smaller intratumor heterogeneity of IBCs.
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Affiliation(s)
- François Bertucci
- Département d'Oncologie Médicale, Predictive Oncology Laboratory, Centre de Recherche en Cancérologie de Marseille (CRCM), Inserm, U1068, CNRS UMR7258, Institut Paoli-Calmettes, Aix-Marseille Université, 232, Boulevard de Sainte-Marguerite, 13009, Marseille, France.
- Department of Medical Oncology, Institut Paoli-Calmettes, Aix-Marseille Université, Marseille, France.
| | - Florence Lerebours
- Department of Medical Oncology, Institut Curie Saint-Cloud, Paris, France
| | - Michele Ceccarelli
- Sylvester Comprehensive Cancer Center, University of Miami, Miami, USA
- Department of Public Health Sciences, University of Miami, Miami, USA
| | - Arnaud Guille
- Département d'Oncologie Médicale, Predictive Oncology Laboratory, Centre de Recherche en Cancérologie de Marseille (CRCM), Inserm, U1068, CNRS UMR7258, Institut Paoli-Calmettes, Aix-Marseille Université, 232, Boulevard de Sainte-Marguerite, 13009, Marseille, France
| | - Najeeb Syed
- Center for Oncological Research (CORE), Integrated Personalized and Precision Oncology Network (IPPON), University of Antwerp, Universiteitsplein 1, Wilrijk, Belgium
| | - Pascal Finetti
- Département d'Oncologie Médicale, Predictive Oncology Laboratory, Centre de Recherche en Cancérologie de Marseille (CRCM), Inserm, U1068, CNRS UMR7258, Institut Paoli-Calmettes, Aix-Marseille Université, 232, Boulevard de Sainte-Marguerite, 13009, Marseille, France
| | - José Adélaïde
- Département d'Oncologie Médicale, Predictive Oncology Laboratory, Centre de Recherche en Cancérologie de Marseille (CRCM), Inserm, U1068, CNRS UMR7258, Institut Paoli-Calmettes, Aix-Marseille Université, 232, Boulevard de Sainte-Marguerite, 13009, Marseille, France
| | - Steven Van Laere
- Center for Oncological Research (CORE), Integrated Personalized and Precision Oncology Network (IPPON), University of Antwerp, Universiteitsplein 1, Wilrijk, Belgium
| | - Anthony Goncalves
- Department of Medical Oncology, Institut Paoli-Calmettes, Aix-Marseille Université, Marseille, France
| | - Patrice Viens
- Department of Medical Oncology, Institut Paoli-Calmettes, Aix-Marseille Université, Marseille, France
| | - Daniel Birnbaum
- Département d'Oncologie Médicale, Predictive Oncology Laboratory, Centre de Recherche en Cancérologie de Marseille (CRCM), Inserm, U1068, CNRS UMR7258, Institut Paoli-Calmettes, Aix-Marseille Université, 232, Boulevard de Sainte-Marguerite, 13009, Marseille, France
| | - Emilie Mamessier
- Département d'Oncologie Médicale, Predictive Oncology Laboratory, Centre de Recherche en Cancérologie de Marseille (CRCM), Inserm, U1068, CNRS UMR7258, Institut Paoli-Calmettes, Aix-Marseille Université, 232, Boulevard de Sainte-Marguerite, 13009, Marseille, France
| | - Céline Callens
- Department of Medical Oncology, Institut Curie Saint-Cloud, Paris, France
| | - Davide Bedognetti
- Tumor Biology and Immunology Laboratory, Research Branch, Sidra Medicine, Doha, Qatar
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13
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Streuer A, Jann JC, Boch T, Mossner M, Riabov V, Schmitt N, Altrock E, Xu Q, Demmerle M, Nowak V, Oblaender J, Palme I, Weimer N, Rapp F, Metzgeroth G, Hecht A, Höger T, Merz C, Hofmann WK, Nolte F, Nowak D. Treatment with the apoptosis inhibitor Asunercept reduces clone sizes in patients with lower risk Myelodysplastic Neoplasms. Ann Hematol 2024; 103:1221-1233. [PMID: 38413410 PMCID: PMC10940491 DOI: 10.1007/s00277-024-05664-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2023] [Accepted: 02/14/2024] [Indexed: 02/29/2024]
Abstract
In low-risk Myelodysplastic Neoplasms (MDS), increased activity of apoptosis-promoting factors such as tumor necrosis factor (TNFα) and pro-apoptotic Fas ligand (CD95L) have been described as possible pathomechanisms leading to impaired erythropoiesis. Asunercept (APG101) is a novel therapeutic fusion protein blocking CD95, which has previously shown partial efficacy in reducing transfusion requirement in a clinical phase I trial for low-risk MDS patients (NCT01736436; 2012-11-26). In the current study we aimed to evaluate the effect of Asunercept therapy on the clonal bone marrow composition to identify potential biomarkers to predict response. Bone marrow samples of n = 12 low-risk MDS patients from the above referenced clinical trial were analyzed by serial deep whole exome sequencing in a total of n = 58 time points. We could distinguish a mean of 3.5 molecularly defined subclones per patient (range 2-6). We observed a molecular response defined as reductions of dominant clone sizes by a variant allele frequency (VAF) decrease of at least 10% (mean 20%, range: 10.5-39.2%) in dependency of Asunercept treatment in 9 of 12 (75%) patients. Most of this decline in clonal populations was observed after completion of 12 weeks treatment. Particularly early and pronounced reductions of clone sizes were found in subclones driven by mutations in genes involved in regulation of methylation (n = 1 DNMT3A, n = 1 IDH2, n = 1 TET2). Our results suggest that APG101 could be efficacious in reducing clone sizes of mutated hematopoietic cells in the bone marrow of Myelodysplastic Neoplasms, which warrants further investigation.
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Affiliation(s)
- Alexander Streuer
- Department of Hematology and Oncology, Medical Faculty Mannheim of the Heidelberg University, Pettenkoferstr. 22, 68169, Mannheim, Germany.
| | - Johann-Christoph Jann
- Department of Hematology and Oncology, Medical Faculty Mannheim of the Heidelberg University, Pettenkoferstr. 22, 68169, Mannheim, Germany
| | - Tobias Boch
- Department of Hematology and Oncology, Medical Faculty Mannheim of the Heidelberg University, Pettenkoferstr. 22, 68169, Mannheim, Germany
| | - Maximilian Mossner
- Centre for Genomics and Computational Biology, Barts Cancer Institute, London, UK
| | - Vladimir Riabov
- Department of Hematology and Oncology, Medical Faculty Mannheim of the Heidelberg University, Pettenkoferstr. 22, 68169, Mannheim, Germany
| | - Nanni Schmitt
- Department of Hematology and Oncology, Medical Faculty Mannheim of the Heidelberg University, Pettenkoferstr. 22, 68169, Mannheim, Germany
| | - Eva Altrock
- Department of Hematology and Oncology, Medical Faculty Mannheim of the Heidelberg University, Pettenkoferstr. 22, 68169, Mannheim, Germany
| | - Qingyu Xu
- Department of Hematology and Oncology, Medical Faculty Mannheim of the Heidelberg University, Pettenkoferstr. 22, 68169, Mannheim, Germany
| | - Marie Demmerle
- Department of Hematology and Oncology, Medical Faculty Mannheim of the Heidelberg University, Pettenkoferstr. 22, 68169, Mannheim, Germany
| | - Verena Nowak
- Department of Hematology and Oncology, Medical Faculty Mannheim of the Heidelberg University, Pettenkoferstr. 22, 68169, Mannheim, Germany
| | - Julia Oblaender
- Department of Hematology and Oncology, Medical Faculty Mannheim of the Heidelberg University, Pettenkoferstr. 22, 68169, Mannheim, Germany
| | - Iris Palme
- Department of Hematology and Oncology, Medical Faculty Mannheim of the Heidelberg University, Pettenkoferstr. 22, 68169, Mannheim, Germany
| | - Nadine Weimer
- Department of Hematology and Oncology, Medical Faculty Mannheim of the Heidelberg University, Pettenkoferstr. 22, 68169, Mannheim, Germany
| | - Felicitas Rapp
- Department of Hematology and Oncology, Medical Faculty Mannheim of the Heidelberg University, Pettenkoferstr. 22, 68169, Mannheim, Germany
| | - Georgia Metzgeroth
- Department of Hematology and Oncology, Medical Faculty Mannheim of the Heidelberg University, Pettenkoferstr. 22, 68169, Mannheim, Germany
| | - Anna Hecht
- Department of Hematology and Oncology, Medical Faculty Mannheim of the Heidelberg University, Pettenkoferstr. 22, 68169, Mannheim, Germany
| | | | | | - Wolf-Karsten Hofmann
- Department of Hematology and Oncology, Medical Faculty Mannheim of the Heidelberg University, Pettenkoferstr. 22, 68169, Mannheim, Germany
| | - Florian Nolte
- Department of Hematology and Oncology, Medical Faculty Mannheim of the Heidelberg University, Pettenkoferstr. 22, 68169, Mannheim, Germany
| | - Daniel Nowak
- Department of Hematology and Oncology, Medical Faculty Mannheim of the Heidelberg University, Pettenkoferstr. 22, 68169, Mannheim, Germany
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14
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Kacar Z, Slud E, Levy D, Candia J, Budhu A, Forgues M, Wu X, Raziuddin A, Tran B, Shetty J, Pomyen Y, Chaisaingmongkol J, Rabibhadana S, Pupacdi B, Bhudhisawasdi V, Lertprasertsuke N, Auewarakul C, Sangrajrang S, Mahidol C, Ruchirawat M, Wang XW. Characterization of tumor evolution by functional clonality and phylogenetics in hepatocellular carcinoma. Commun Biol 2024; 7:383. [PMID: 38553628 PMCID: PMC11245610 DOI: 10.1038/s42003-024-06040-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2023] [Accepted: 03/11/2024] [Indexed: 04/02/2024] Open
Abstract
Hepatocellular carcinoma (HCC) is a molecularly heterogeneous solid malignancy, and its fitness may be shaped by how its tumor cells evolve. However, ability to monitor tumor cell evolution is hampered by the presence of numerous passenger mutations that do not provide any biological consequences. Here we develop a strategy to determine the tumor clonality of three independent HCC cohorts of 524 patients with diverse etiologies and race/ethnicity by utilizing somatic mutations in cancer driver genes. We identify two main types of tumor evolution, i.e., linear, and non-linear models where non-linear type could be further divided into classes, which we call shallow branching and deep branching. We find that linear evolving HCC is less aggressive than other types. GTF2IRD2B mutations are enriched in HCC with linear evolution, while TP53 mutations are the most frequent genetic alterations in HCC with non-linear models. Furthermore, we observe significant B cell enrichment in linear trees compared to non-linear trees suggesting the need for further research to uncover potential variations in immune cell types within genomically determined phylogeny types. These results hint at the possibility that tumor cells and their microenvironment may collectively influence the tumor evolution process.
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Affiliation(s)
- Zeynep Kacar
- Laboratory of Human Carcinogenesis, Center for Cancer Research, National Cancer Institute, Bethesda, MD, 20892, USA
- Department of Mathematics, University of Maryland, College Park, MD, 20742, USA
| | - Eric Slud
- Department of Mathematics, University of Maryland, College Park, MD, 20742, USA
| | - Doron Levy
- Department of Mathematics, University of Maryland, College Park, MD, 20742, USA
| | - Julián Candia
- Longitudinal Studies Section, Translational Gerontology Branch, National Institute on Aging, Baltimore, MD, 21224, USA
| | - Anuradha Budhu
- Laboratory of Human Carcinogenesis, Center for Cancer Research, National Cancer Institute, Bethesda, MD, 20892, USA
- Liver Cancer Program, Center for Cancer Research, National Cancer Institute, Bethesda, MD, 20892, USA
| | - Marshonna Forgues
- Laboratory of Human Carcinogenesis, Center for Cancer Research, National Cancer Institute, Bethesda, MD, 20892, USA
| | - Xiaolin Wu
- Cancer Research Technology Program, Frederick, MD, 21702, USA
| | - Arati Raziuddin
- Cancer Research Technology Program, Frederick, MD, 21702, USA
| | - Bao Tran
- Cancer Research Technology Program, Frederick, MD, 21702, USA
| | - Jyoti Shetty
- Cancer Research Technology Program, Frederick, MD, 21702, USA
| | - Yotsawat Pomyen
- Laboratory of Chemical Carcinogenesis, Chulabhorn Research Institute, Bangkok, 10210, Thailand
| | | | - Siritida Rabibhadana
- Laboratory of Chemical Carcinogenesis, Chulabhorn Research Institute, Bangkok, 10210, Thailand
| | - Benjarath Pupacdi
- Laboratory of Chemical Carcinogenesis, Chulabhorn Research Institute, Bangkok, 10210, Thailand
| | | | | | - Chirayu Auewarakul
- Princess Srisavangavadhana College of Medicine, Chulabhorn Royal Academy, Bangkok, 10210, Thailand
| | | | - Chulabhorn Mahidol
- Laboratory of Chemical Carcinogenesis, Chulabhorn Research Institute, Bangkok, 10210, Thailand
| | - Mathuros Ruchirawat
- Laboratory of Chemical Carcinogenesis, Chulabhorn Research Institute, Bangkok, 10210, Thailand
- Center of Excellence on Environmental Health and Toxicology (EHT), OPS, MHESI, Bangkok, Thailand
| | - Xin Wei Wang
- Laboratory of Human Carcinogenesis, Center for Cancer Research, National Cancer Institute, Bethesda, MD, 20892, USA.
- Liver Cancer Program, Center for Cancer Research, National Cancer Institute, Bethesda, MD, 20892, USA.
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15
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Wu Z, Gu T, Xiong C, Shi J, Wang J, Guo T, Xing X, Pang F, He N, Miao R, Shan F, Zhou Y, Li Z, Ji J. Genomic characterization of peritoneal lavage cytology-positive gastric cancer. Chin J Cancer Res 2024; 36:66-77. [PMID: 38455368 PMCID: PMC10915641 DOI: 10.21147/j.issn.1000-9604.2024.01.07] [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: 07/25/2023] [Accepted: 02/04/2024] [Indexed: 03/09/2024] Open
Abstract
Objective Positive peritoneal lavege cytology (CY1) gastric cancer is featured by dismal prognosis, with high risks of peritoneal metastasis. However, there is a lack of evidence on pathogenic mechanism and signature of CY1 and there is a continuous debate on CY1 therapy. Therefore, exploring the mechanism of CY1 is crucial for treatment strategies and targets for CY1 gastric cancer. Methods In order to figure out specific driver genes and marker genes of CY1 gastric cancer, and ultimately offer clues for potential marker and risk assessment of CY1, 17 cytology-positive gastric cancer patients and 31 matched cytology-negative gastric cancer patients were enrolled in this study. The enrollment criteria were based on the results of diagnostic laparoscopy staging and cytology inspection of exfoliated cells. Whole exome sequencing was then performed on tumor samples to evaluate genomic characterization of cytology-positive gastric cancer. Results Least absolute shrinkage and selection operator (LASSO) algorithm identified 43 cytology-positive marker genes, while MutSigCV identified 42 cytology-positive specific driver genes. CD3G and CDKL2 were both driver and marker genes of CY1. Regarding mutational signatures, driver gene mutation and tumor subclone architecture, no significant differences were observed between CY1 and negative peritoneal lavege cytology (CY0). Conclusions There might not be distinct differences between CY1 and CY0, and CY1 might represent the progression of CY0 gastric cancer rather than constituting an independent subtype. This genomic analysis will thus provide key molecular insights into CY1, which may have a direct effect on treatment recommendations for CY1 and CY0 patients, and provides opportunities for genome-guided clinical trials and drug development.
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Affiliation(s)
- Zhouqiao Wu
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Gastrointestinal Cancer Center, Peking University Cancer Hospital & Institute, Beijing 100142, China
| | - Tingfei Gu
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Gastrointestinal Cancer Center, Peking University Cancer Hospital & Institute, Beijing 100142, China
| | - Changxian Xiong
- Department of Biomedical Informatics, Department of Physiology and Pathophysiology, Center for Noncoding RNA Medicine, MOE Key Lab of Cardiovascular Sciences, School of Basic Medical Sciences, Peking University, Beijing 100191, China
| | - Jinyao Shi
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Gastrointestinal Cancer Center, Peking University Cancer Hospital & Institute, Beijing 100142, China
| | - Jingpu Wang
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Gastrointestinal Cancer Center, Peking University Cancer Hospital & Institute, Beijing 100142, China
| | - Ting Guo
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Gastrointestinal Cancer Center, Peking University Cancer Hospital & Institute, Beijing 100142, China
| | - Xiaofang Xing
- State Key Laboratory of Holistic Integrative Management of Gastrointestinal Cancers, Beijing Key Laboratory of Carcinogenesis and Translational Research, Gastrointestinal Cancer Center, Peking University Cancer Hospital & Institute, Beijing 100142, China
| | - Fei Pang
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Gastrointestinal Cancer Center, Peking University Cancer Hospital & Institute, Beijing 100142, China
| | - Ning He
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Gastrointestinal Cancer Center, Peking University Cancer Hospital & Institute, Beijing 100142, China
| | - Rulin Miao
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Gastrointestinal Cancer Center, Peking University Cancer Hospital & Institute, Beijing 100142, China
| | - Fei Shan
- State Key Laboratory of Holistic Integrative Management of Gastrointestinal Cancers, Beijing Key Laboratory of Carcinogenesis and Translational Research, Gastrointestinal Cancer Center, Peking University Cancer Hospital & Institute, Beijing 100142, China
| | - Yuan Zhou
- Department of Biomedical Informatics, Department of Physiology and Pathophysiology, Center for Noncoding RNA Medicine, MOE Key Lab of Cardiovascular Sciences, School of Basic Medical Sciences, Peking University, Beijing 100191, China
| | - Ziyu Li
- State Key Laboratory of Holistic Integrative Management of Gastrointestinal Cancers, Beijing Key Laboratory of Carcinogenesis and Translational Research, Gastrointestinal Cancer Center, Peking University Cancer Hospital & Institute, Beijing 100142, China
| | - Jiafu Ji
- State Key Laboratory of Holistic Integrative Management of Gastrointestinal Cancers, Beijing Key Laboratory of Carcinogenesis and Translational Research, Gastrointestinal Cancer Center, Peking University Cancer Hospital & Institute, Beijing 100142, China
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16
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Duell J, Leipold AM, Appenzeller S, Fuhr V, Rauert-Wunderlich H, Da Via M, Dietrich O, Toussaint C, Imdahl F, Eisele F, Afrin N, Grundheber L, Einsele H, Weinhold N, Rosenwald A, Topp MS, Saliba AE, Rasche L. Sequential antigen loss and branching evolution in lymphoma after CD19- and CD20-targeted T-cell-redirecting therapy. Blood 2024; 143:685-696. [PMID: 37976456 PMCID: PMC10900140 DOI: 10.1182/blood.2023021672] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Revised: 11/03/2023] [Accepted: 11/03/2023] [Indexed: 11/19/2023] Open
Abstract
ABSTRACT CD19 chimeric antigen receptor (CAR) T cells and CD20 targeting T-cell-engaging bispecific antibodies (bispecs) have been approved in B-cell non-Hodgkin lymphoma lately, heralding a new clinical setting in which patients are treated with both approaches, sequentially. The aim of our study was to investigate the selective pressure of CD19- and CD20-directed therapy on the clonal architecture in lymphoma. Using a broad analytical pipeline on 28 longitudinally collected specimen from 7 patients, we identified truncating mutations in the gene encoding CD20 conferring antigen loss in 80% of patients relapsing from CD20 bispecs. Pronounced T-cell exhaustion was identified in cases with progressive disease and retained CD20 expression. We also confirmed CD19 loss after CAR T-cell therapy and reported the case of sequential CD19 and CD20 loss. We observed branching evolution with re-emergence of CD20+ subclones at later time points and spatial heterogeneity for CD20 expression in response to targeted therapy. Our results highlight immunotherapy as not only an evolutionary bottleneck selecting for antigen loss variants but also complex evolutionary pathways underlying disease progression from these novel therapies.
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Affiliation(s)
- Johannes Duell
- Department of Internal Medicine 2, University Hospital of Würzburg, Würzburg, Germany
| | - Alexander M. Leipold
- Helmholtz Institute for RNA-based Infection Research, Helmholtz Center for Infection Research, Würzburg, Germany
| | - Silke Appenzeller
- Core Unit Bioinformatics, Comprehensive Cancer Center Mainfranken, University Hospital of Würzburg, Würzburg, Germany
| | - Viktoria Fuhr
- Institute of Pathology, University of Würzburg, Würzburg, Germany
| | | | - Matteo Da Via
- Department of Internal Medicine 2, University Hospital of Würzburg, Würzburg, Germany
- Hematology Unit, Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Oliver Dietrich
- Helmholtz Institute for RNA-based Infection Research, Helmholtz Center for Infection Research, Würzburg, Germany
| | - Christophe Toussaint
- Helmholtz Institute for RNA-based Infection Research, Helmholtz Center for Infection Research, Würzburg, Germany
| | - Fabian Imdahl
- Helmholtz Institute for RNA-based Infection Research, Helmholtz Center for Infection Research, Würzburg, Germany
| | - Florian Eisele
- Department of Internal Medicine 2, University Hospital of Würzburg, Würzburg, Germany
| | - Nazia Afrin
- Mildred Scheel Early Career Center, University Hospital of Würzburg, Würzburg, Germany
| | - Lars Grundheber
- Mildred Scheel Early Career Center, University Hospital of Würzburg, Würzburg, Germany
| | - Hermann Einsele
- Department of Internal Medicine 2, University Hospital of Würzburg, Würzburg, Germany
| | - Niels Weinhold
- Department of Internal Medicine V, Heidelberg University Hospital, Heidelberg, Germany
| | | | - Max S. Topp
- Department of Internal Medicine 2, University Hospital of Würzburg, Würzburg, Germany
| | - Antoine-Emmanuel Saliba
- Helmholtz Institute for RNA-based Infection Research, Helmholtz Center for Infection Research, Würzburg, Germany
- University of Würzburg, Faculty of Medicine, Institute of Molecular Infection Biology, Würzburg, Germany
| | - Leo Rasche
- Department of Internal Medicine 2, University Hospital of Würzburg, Würzburg, Germany
- Mildred Scheel Early Career Center, University Hospital of Würzburg, Würzburg, Germany
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17
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Gardi N, Chaubal R, Parab P, Pachakar S, Kulkarni S, Shet T, Joshi S, Kembhavi Y, Chandrani P, Quist J, Kowtal P, Grigoriadis A, Sarin R, Govindarajan R, Gupta S. Natural History of Germline BRCA1 Mutated and BRCA Wild-type Triple-negative Breast Cancer. CANCER RESEARCH COMMUNICATIONS 2024; 4:404-417. [PMID: 38315150 PMCID: PMC10865976 DOI: 10.1158/2767-9764.crc-23-0277] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Revised: 09/09/2023] [Accepted: 01/26/2024] [Indexed: 02/07/2024]
Abstract
We report a deep next-generation sequencing analysis of 13 sequentially obtained tumor samples, eight sequentially obtained circulating tumor DNA (ctDNA) samples and three germline DNA samples over the life history of 3 patients with triple-negative breast cancer (TNBC), 2 of whom had germline pathogenic BRCA1 mutation, to unravel tumor evolution. Tumor tissue from all timepoints and germline DNA was subjected to whole-exome sequencing (WES), custom amplicon deep sequencing (30,000X) of a WES-derived somatic mutation panel, and SNP arrays for copy-number variation (CNV), while whole transcriptome sequencing (RNA-seq) was performed only on somatic tumor.There was enrichment of homologous recombination deficiency signature in all tumors and widespread CNV, which remained largely stable over time. Somatic tumor mutation numbers varied between patients and within each patient (range: 70-216, one outlier). There was minimal mutational overlap between patients with TP53 being the sole commonly mutated gene, but there was substantial overlap in sequential samples in each patient. Each patient's tumor contained a founding ("stem") clone at diagnosis, which persisted over time, from which all other clones ("subclone") were derived ("branching evolution"), which contained mutations in well-characterized cancer-related genes like PDGFRB, ARID2, TP53 (Patient_02), TP53, BRAF, BRIP1, CSF3R (Patient_04), and TP53, APC, EZH2 (Patient_07). Including stem and subclones, tumors from all patients were polyclonal at diagnosis and during disease progression. ctDNA recapitulated most tissue-derived stem clonal and subclonal mutations while detecting some additional subclonal mutations. RNA-seq revealed a stable basal-like pattern, with most highly expressed variants belonging to stem clone. SIGNIFICANCE In germline BRCA1 mutated and BRCA wild-type patients, TNBC shows a branching evolutionary pattern of mutations with a single founding clone, are polyclonal throughout their disease course, and have widespread copy-number aberrations. This evolutionary pattern may be associated with treatment resistance or sensitivity and could be therapeutically exploited.
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Affiliation(s)
- Nilesh Gardi
- Department of Medical Oncology, Tata Memorial Centre, Mumbai
- Clinician Scientist Laboratory, Advanced Centre for Treatment, Research and Education in Cancer (ACTREC), Tata Memorial Centre, Navi Mumbai
- Homi Bhabha National Institute, Mumbai
| | - Rohan Chaubal
- Clinician Scientist Laboratory, Advanced Centre for Treatment, Research and Education in Cancer (ACTREC), Tata Memorial Centre, Navi Mumbai
- Homi Bhabha National Institute, Mumbai
- Department of Surgical Oncology, Tata Memorial Centre, Mumbai
| | - Pallavi Parab
- Department of Medical Oncology, Tata Memorial Centre, Mumbai
- Homi Bhabha National Institute, Mumbai
| | - Sunil Pachakar
- Department of Medical Oncology, Tata Memorial Centre, Mumbai
- Clinician Scientist Laboratory, Advanced Centre for Treatment, Research and Education in Cancer (ACTREC), Tata Memorial Centre, Navi Mumbai
- Homi Bhabha National Institute, Mumbai
| | - Suyash Kulkarni
- Homi Bhabha National Institute, Mumbai
- Department of Radiodiagnosis, Tata Memorial Centre, Mumbai
| | - Tanuja Shet
- Homi Bhabha National Institute, Mumbai
- Department of Pathology, Tata Memorial Centre, Mumbai
| | - Shalaka Joshi
- Homi Bhabha National Institute, Mumbai
- Department of Surgical Oncology, Tata Memorial Centre, Mumbai
| | - Yogesh Kembhavi
- Department of Medical Oncology, Tata Memorial Centre, Mumbai
- Homi Bhabha National Institute, Mumbai
| | - Pratik Chandrani
- Department of Medical Oncology, Tata Memorial Centre, Mumbai
- Homi Bhabha National Institute, Mumbai
| | - Jelmar Quist
- Cancer Bioinformatics, School of Cancer & Pharmaceutical Sciences, Faculty of Life Sciences and Medicine, King's College London, London, United Kingdom
- School of Cancer & Pharmaceutical Sciences, Faculty of Life Sciences and Medicine, King's College London, London, United Kingdom
- Breast Cancer Now Unit, School of Cancer and Pharmaceutical Sciences, Faculty of Life Sciences and Medicine, King's College London, London, United Kingdom
| | - Pradnya Kowtal
- Homi Bhabha National Institute, Mumbai
- DNA sequencing Facility, Advanced Centre for Treatment, Research and Education in Cancer (ACTREC), Tata Memorial Centre, Navi Mumbai
| | - Anita Grigoriadis
- Cancer Bioinformatics, School of Cancer & Pharmaceutical Sciences, Faculty of Life Sciences and Medicine, King's College London, London, United Kingdom
- School of Cancer & Pharmaceutical Sciences, Faculty of Life Sciences and Medicine, King's College London, London, United Kingdom
- Breast Cancer Now Unit, School of Cancer and Pharmaceutical Sciences, Faculty of Life Sciences and Medicine, King's College London, London, United Kingdom
| | - Rajiv Sarin
- Homi Bhabha National Institute, Mumbai
- Department of Radiation Oncology, Tata Memorial Centre, Mumbai
| | | | - Sudeep Gupta
- Department of Medical Oncology, Tata Memorial Centre, Mumbai
- Clinician Scientist Laboratory, Advanced Centre for Treatment, Research and Education in Cancer (ACTREC), Tata Memorial Centre, Navi Mumbai
- Homi Bhabha National Institute, Mumbai
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18
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Qiao Y, Huang X, Moos PJ, Ahmann JM, Pomicter AD, Deininger MW, Byrd JC, Woyach JA, Stephens DM, Marth GT. A Bayesian framework to study tumor subclone-specific expression by combining bulk DNA and single-cell RNA sequencing data. Genome Res 2024; 34:94-105. [PMID: 38195207 PMCID: PMC10903947 DOI: 10.1101/gr.278234.123] [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: 06/29/2023] [Accepted: 11/22/2023] [Indexed: 01/11/2024]
Abstract
Genetic and gene expression heterogeneity is an essential hallmark of many tumors, allowing the cancer to evolve and to develop resistance to treatment. Currently, the most commonly used data types for studying such heterogeneity are bulk tumor/normal whole-genome or whole-exome sequencing (WGS, WES); and single-cell RNA sequencing (scRNA-seq), respectively. However, tools are currently lacking to link genomic tumor subclonality with transcriptomic heterogeneity by integrating genomic and single-cell transcriptomic data collected from the same tumor. To address this gap, we developed scBayes, a Bayesian probabilistic framework that uses tumor subclonal structure inferred from bulk DNA sequencing data to determine the subclonal identity of cells from single-cell gene expression (scRNA-seq) measurements. Grouping together cells representing the same genetically defined tumor subclones allows comparison of gene expression across different subclones, or investigation of gene expression changes within the same subclone across time (i.e., progression, treatment response, or relapse) or space (i.e., at multiple metastatic sites and organs). We used simulated data sets, in silico synthetic data sets, as well as biological data sets generated from cancer samples to extensively characterize and validate the performance of our method, as well as to show improvements over existing methods. We show the validity and utility of our approach by applying it to published data sets and recapitulating the findings, as well as arriving at novel insights into cancer subclonal expression behavior in our own data sets. We further show that our method is applicable to a wide range of single-cell sequencing technologies including single-cell DNA sequencing as well as Smart-seq and 10x Genomics scRNA-seq protocols.
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Affiliation(s)
- Yi Qiao
- Eccles Institute of Human Genetics, University of Utah, Salt Lake City, Utah 84112, USA
| | - Xiaomeng Huang
- Eccles Institute of Human Genetics, University of Utah, Salt Lake City, Utah 84112, USA
| | - Philip J Moos
- Department of Pharmacology and Toxicology, University of Utah, Salt Lake City, Utah 84112, USA
| | - Jonathan M Ahmann
- Huntsman Cancer Institute, University of Utah, Salt Lake City, Utah 84112, USA
| | - Anthony D Pomicter
- Huntsman Cancer Institute, University of Utah, Salt Lake City, Utah 84112, USA
| | - Michael W Deininger
- Huntsman Cancer Institute, University of Utah, Salt Lake City, Utah 84112, USA
- Division of Hematology and Hematologic Malignancies, University of Utah, Salt Lake City, Utah 84112, USA
| | - John C Byrd
- The James Comprehensive Cancer Center, The Ohio State University, Columbus, Ohio 43210, USA
| | - Jennifer A Woyach
- The James Comprehensive Cancer Center, The Ohio State University, Columbus, Ohio 43210, USA
| | - Deborah M Stephens
- Huntsman Cancer Institute, University of Utah, Salt Lake City, Utah 84112, USA
| | - Gabor T Marth
- Eccles Institute of Human Genetics, University of Utah, Salt Lake City, Utah 84112, USA;
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19
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Antonello A, Bergamin R, Calonaci N, Househam J, Milite S, Williams MJ, Anselmi F, d'Onofrio A, Sundaram V, Sosinsky A, Cross WCH, Caravagna G. Computational validation of clonal and subclonal copy number alterations from bulk tumor sequencing using CNAqc. Genome Biol 2024; 25:38. [PMID: 38297376 PMCID: PMC10832148 DOI: 10.1186/s13059-024-03170-5] [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: 05/02/2023] [Accepted: 01/10/2024] [Indexed: 02/02/2024] Open
Abstract
Copy number alterations (CNAs) are among the most important genetic events in cancer, but their detection from sequencing data is challenging because of unknown sample purity, tumor ploidy, and general intra-tumor heterogeneity. Here, we present CNAqc, an evolution-inspired method to perform the computational validation of clonal and subclonal CNAs detected from bulk DNA sequencing. CNAqc is validated using single-cell data and simulations, is applied to over 4000 TCGA and PCAWG samples, and is incorporated into the validation process for the clinically accredited bioinformatics pipeline at Genomics England. CNAqc is designed to support automated quality control procedures for tumor somatic data validation.
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Affiliation(s)
- Alice Antonello
- Department of Mathematics, Informatics and Geosciences (MIGe), University of Trieste, Trieste, Italy
| | - Riccardo Bergamin
- Department of Mathematics, Informatics and Geosciences (MIGe), University of Trieste, Trieste, Italy
| | - Nicola Calonaci
- Department of Mathematics, Informatics and Geosciences (MIGe), University of Trieste, Trieste, Italy
| | - Jacob Househam
- Evolution and Cancer Lab, Centre for Genomics and Computational Biology, Barts Cancer Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Salvatore Milite
- Department of Mathematics, Informatics and Geosciences (MIGe), University of Trieste, Trieste, Italy
- Centre for Computational Biology, Human Technopole, Milan, Italy
| | - Marc J Williams
- Department of Computational Oncology, Memorial Sloan Kettering, New York, USA
| | - Fabio Anselmi
- Department of Mathematics, Informatics and Geosciences (MIGe), University of Trieste, Trieste, Italy
| | - Alberto d'Onofrio
- Department of Mathematics, Informatics and Geosciences (MIGe), University of Trieste, Trieste, Italy
| | | | | | - William C H Cross
- Department of Research Pathology, UCL Cancer Institute, University College London, London, UK
| | - Giulio Caravagna
- Department of Mathematics, Informatics and Geosciences (MIGe), University of Trieste, Trieste, Italy.
- Evolutionary Genomics and Modelling Team, Centre for Evolution and Cancer, Institute of Cancer Research, London, UK.
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20
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Addala V, Newell F, Pearson JV, Redwood A, Robinson BW, Creaney J, Waddell N. Computational immunogenomic approaches to predict response to cancer immunotherapies. Nat Rev Clin Oncol 2024; 21:28-46. [PMID: 37907723 DOI: 10.1038/s41571-023-00830-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/03/2023] [Indexed: 11/02/2023]
Abstract
Cancer immunogenomics is an emerging field that bridges genomics and immunology. The establishment of large-scale genomic collaborative efforts along with the development of new single-cell transcriptomic techniques and multi-omics approaches have enabled characterization of the mutational and transcriptional profiles of many cancer types and helped to identify clinically actionable alterations as well as predictive and prognostic biomarkers. Researchers have developed computational approaches and machine learning algorithms to accurately obtain clinically useful information from genomic and transcriptomic sequencing data from bulk tissue or single cells and explore tumours and their microenvironment. The rapid growth in sequencing and computational approaches has resulted in the unmet need to understand their true potential and limitations in enabling improvements in the management of patients with cancer who are receiving immunotherapies. In this Review, we describe the computational approaches currently available to analyse bulk tissue and single-cell sequencing data from cancer, stromal and immune cells, as well as how best to select the most appropriate tool to address various clinical questions and, ultimately, improve patient outcomes.
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Affiliation(s)
- Venkateswar Addala
- Cancer Program, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia.
- Faculty of Medicine, The University of Queensland, Brisbane, Queensland, Australia.
| | - Felicity Newell
- Cancer Program, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - John V Pearson
- Cancer Program, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Alec Redwood
- National Centre for Asbestos Related Diseases, University of Western Australia, Perth, Western Australia, Australia
- Institute of Respiratory Health, Perth, Western Australia, Australia
- School of Biomedical Science, University of Western Australia, Perth, Western Australia, Australia
| | - Bruce W Robinson
- National Centre for Asbestos Related Diseases, University of Western Australia, Perth, Western Australia, Australia
- Institute of Respiratory Health, Perth, Western Australia, Australia
- Department of Respiratory Medicine, Sir Charles Gairdner Hospital, Perth, Western Australia, Australia
- Medical School, University of Western Australia, Perth, Western Australia, Australia
| | - Jenette Creaney
- National Centre for Asbestos Related Diseases, University of Western Australia, Perth, Western Australia, Australia
- Institute of Respiratory Health, Perth, Western Australia, Australia
- School of Biomedical Science, University of Western Australia, Perth, Western Australia, Australia
- Department of Respiratory Medicine, Sir Charles Gairdner Hospital, Perth, Western Australia, Australia
| | - Nicola Waddell
- Cancer Program, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia.
- Faculty of Medicine, The University of Queensland, Brisbane, Queensland, Australia.
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21
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Cohen Shvefel S, Pai JA, Cao Y, Pal LR, Levy R, Yao W, Cheng K, Zemanek M, Bartok O, Weller C, Yin Y, Du PP, Yakubovich E, Orr I, Ben-Dor S, Oren R, Fellus-Alyagor L, Golani O, Goliand I, Ranmar D, Savchenko I, Ketrarou N, Schäffer AA, Ruppin E, Satpathy AT, Samuels Y. Temporal genomic analysis of melanoma rejection identifies regulators of tumor immune evasion. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.29.569032. [PMID: 38077050 PMCID: PMC10705560 DOI: 10.1101/2023.11.29.569032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/22/2023]
Abstract
Decreased intra-tumor heterogeneity (ITH) correlates with increased patient survival and immunotherapy response. However, even highly homogenous tumors may display variability in their aggressiveness, and how immunologic-factors impinge on their aggressiveness remains understudied. Here we studied the mechanisms responsible for the immune-escape of murine tumors with low ITH. We compared the temporal growth of homogeneous, genetically-similar single-cell clones that are rejected vs. those that are not-rejected after transplantation in-vivo using single-cell RNA sequencing and immunophenotyping. Non-rejected clones showed high infiltration of tumor-associated-macrophages (TAMs), lower T-cell infiltration, and increased T-cell exhaustion compared to rejected clones. Comparative analysis of rejection-associated gene expression programs, combined with in-vivo CRISPR knockout screens of candidate mediators, identified Mif (macrophage migration inhibitory factor) as a regulator of immune rejection. Mif knockout led to smaller tumors and reversed non-rejection-associated immune composition, particularly, leading to the reduction of immunosuppressive macrophage infiltration. Finally, we validated these results in melanoma patient data.
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Affiliation(s)
- Sapir Cohen Shvefel
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Joy A Pai
- Department of Pathology, Stanford University, Stanford, CA, USA
| | - Yingying Cao
- Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA
| | - Lipika R Pal
- Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA
| | - Ronen Levy
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Winnie Yao
- Department of Pathology, Stanford University, Stanford, CA, USA
| | - Kuoyuan Cheng
- Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA
- MSD R&D (China) Co., Ltd
| | - Marie Zemanek
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Osnat Bartok
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Chen Weller
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Yajie Yin
- Department of Pathology, Stanford University, Stanford, CA, USA
| | - Peter P Du
- Department of Pathology, Stanford University, Stanford, CA, USA
| | - Elizabeta Yakubovich
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Irit Orr
- Bioinformatics Unit, Department of Life Sciences Core Facilities, Weizmann Institute of Science, Rehovot, Israel
| | - Shifra Ben-Dor
- Bioinformatics Unit, Department of Life Sciences Core Facilities, Weizmann Institute of Science, Rehovot, Israel
| | - Roni Oren
- Department of Veterinary Resources, Weizmann Institute of Science, Rehovot, Israel
| | - Liat Fellus-Alyagor
- Department of Veterinary Resources, Weizmann Institute of Science, Rehovot, Israel
| | - Ofra Golani
- Department of Life Sciences Core Facilities, Weizmann Institute of Science, Rehovot, Israel
| | - Inna Goliand
- Department of Life Sciences Core Facilities, Weizmann Institute of Science, Rehovot, Israel
| | - Dean Ranmar
- Department of Life Sciences Core Facilities, Weizmann Institute of Science, Rehovot, Israel
| | - Ilya Savchenko
- Department of Veterinary Resources, Weizmann Institute of Science, Rehovot, Israel
| | - Nadav Ketrarou
- Department of Veterinary Resources, Weizmann Institute of Science, Rehovot, Israel
| | - Alejandro A Schäffer
- Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA
| | - Eytan Ruppin
- Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA
| | - Ansuman T Satpathy
- Department of Pathology, Stanford University, Stanford, CA, USA
- Gladstone-UCSF Institute of Genomic Immunology, San Francisco, CA, USA
- Parker Institute for Cancer Immunotherapy, San Francisco, CA, USA
| | - Yardena Samuels
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
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22
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Wang Z, Liu T, Liu W, Gao X, Wan L, Qiu S, Song Y, Gu R, Tian Z, Wang M, Wang J, Mi Y, Wei S. A novel subclonal rearrangement of the STRN3::PDGFRB gene in de novo acute myeloid leukemia with NPM1 mutation and its leukemogenic effects. Cancer Gene Ther 2023; 30:1471-1484. [PMID: 37550570 DOI: 10.1038/s41417-023-00651-w] [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: 05/10/2023] [Revised: 07/06/2023] [Accepted: 07/24/2023] [Indexed: 08/09/2023]
Abstract
Chromosome translocations in the 5q31-33 region are associated with a range of hematologic malignancies, some of which involve the platelet-derived growth factor receptor beta (PDGFRB) gene. We report a case of acute myeloid leukemia (AML) with a mutation in the NPM1 gene (NPM1-mut AML) and a subclonal gene rearrangement involving the PDGFRB gene. We identified a novel fusion gene, STRN3::PDGFRB, resulting from t(5;14) (q32;q12) chromosomal rearrangement. Sequential FISH confirmed that ~15% of leukemic cells carried the PDGFRB gene rearrangement, which suggests that STRN3::PDGFRB is a previously unreported fusion gene in a subclone. Reverse transcription PCR (RT-PCR) and Sanger sequencing confirmed that the fusion gene consisted of STRN3 exon 7 fused to PDGFRB exon 11, resulting in a chimeric protein containing the coiled-coil domain of striatin-3 and the transmembrane and intracellular tyrosine kinase domains of the PDGFRB. The new protein exhibited distinct cytoplasmic localization and had leukemogenic effects, as demonstrated by its ability to transform Ba/F3 cells to growth factor independence and cause a fatal myelodysplastic/myeloproliferative neoplasm (MDS/MPN)-like disease in mice, which then transformant to T-cell lymphoblastic lymphoma in secondary recipients. Ba/F3 cells expressing STRN3::PDGFRB or ETV6::PDGFRB were sensitive to tyrosine kinase inhibitors (TKIs) and selinexor, but in vitro experiments showed that the combination of imatinib and selinexor had a marked synergistic effect, although only the imatinib alone group could prolong the survival of T-cell blast transformation recipient mice. Our findings demonstrate the leukemogenic effects of the novel fusion gene and provide insights into the clone evolution of AML, which can be influenced by therapy selection. Furthermore, our results provide insight into the potential therapeutic options for patients with this type of mutation, as well as the need for careful consideration of treatment selection to prevent undesirable side effects.
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Affiliation(s)
- Zhe Wang
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, 288 Nanjing Road, Tianjin, 300020, China
| | - Ting Liu
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, 288 Nanjing Road, Tianjin, 300020, China
| | - Wenbing Liu
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, 288 Nanjing Road, Tianjin, 300020, China
| | - Xin Gao
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, 288 Nanjing Road, Tianjin, 300020, China
| | - Li Wan
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, 288 Nanjing Road, Tianjin, 300020, China
| | - Shaowei Qiu
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, 288 Nanjing Road, Tianjin, 300020, China
| | - Yang Song
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, 288 Nanjing Road, Tianjin, 300020, China
| | - Runxia Gu
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, 288 Nanjing Road, Tianjin, 300020, China
| | - Zheng Tian
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, 288 Nanjing Road, Tianjin, 300020, China
| | - Min Wang
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, 288 Nanjing Road, Tianjin, 300020, China
| | - Jianxiang Wang
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, 288 Nanjing Road, Tianjin, 300020, China
| | - Yingchang Mi
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, 288 Nanjing Road, Tianjin, 300020, China.
| | - Shuning Wei
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, 288 Nanjing Road, Tianjin, 300020, China.
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23
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Valero C, Golkaram M, Vos JL, Xu B, Fitzgerald C, Lee M, Kaplan S, Han CY, Pei X, Sarkar R, Boe LA, Pandey A, Koh ES, Zuur CL, Solit DB, Pawlowski T, Liu L, Ho AL, Chowell D, Riaz N, Chan TA, Morris LG. Clinical-genomic determinants of immune checkpoint blockade response in head and neck squamous cell carcinoma. J Clin Invest 2023; 133:e169823. [PMID: 37561583 PMCID: PMC10541199 DOI: 10.1172/jci169823] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Accepted: 08/03/2023] [Indexed: 08/12/2023] Open
Abstract
BACKGROUNDRecurrent and/or metastatic (R/M) head and neck squamous cell carcinoma (HNSCC) is generally an incurable disease, with patients experiencing median survival of under 10 months and significant morbidity. While immune checkpoint blockade (ICB) drugs are effective in approximately 20% of patients, the remaining experience limited clinical benefit and are exposed to potential adverse effects and financial costs. Clinically approved biomarkers, such as tumor mutational burden (TMB), have a modest predictive value in HNSCC.METHODSWe analyzed clinical and genomic features, generated using whole-exome sequencing, in 133 ICB-treated patients with R/M HNSCC, of whom 69 had virus-associated and 64 had non-virus-associated tumors.RESULTSHierarchical clustering of genomic data revealed 6 molecular subtypes characterized by a wide range of objective response rates and survival after ICB therapy. The prognostic importance of these 6 subtypes was validated in an external cohort. A random forest-based predictive model, using several clinical and genomic features, predicted progression-free survival (PFS), overall survival (OS), and response with greater accuracy than did a model based on TMB alone. Recursive partitioning analysis identified 3 features (systemic inflammatory response index, TMB, and smoking signature) that classified patients into risk groups with accurate discrimination of PFS and OS.CONCLUSIONThese findings shed light on the immunogenomic characteristics of HNSCC tumors that drive differential responses to ICB and identify a clinical-genomic classifier that outperformed the current clinically approved biomarker of TMB. This validated predictive tool may help with clinical risk stratification in patients with R/M HNSCC for whom ICB is being considered.FUNDINGFundación Alfonso Martín Escudero, NIH R01 DE027738, US Department of Defense CA210784, The Geoffrey Beene Cancer Research Center, The MSKCC Population Science Research Program, the Jayme Flowers Fund, the Sebastian Nativo Fund, and the NIH/NCI Cancer Center Support Grant P30 CA008748.
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Affiliation(s)
- Cristina Valero
- Head and Neck Service, Immunogenomic Oncology Platform, Department of Surgery, Memorial Sloan Kettering Cancer Center (MSKCC), New York, New York, USA
| | | | - Joris L. Vos
- Head and Neck Service, Immunogenomic Oncology Platform, Department of Surgery, Memorial Sloan Kettering Cancer Center (MSKCC), New York, New York, USA
| | - Bin Xu
- Department of Pathology and Laboratory Medicine
| | - Conall Fitzgerald
- Head and Neck Service, Immunogenomic Oncology Platform, Department of Surgery, Memorial Sloan Kettering Cancer Center (MSKCC), New York, New York, USA
| | - Mark Lee
- Head and Neck Service, Immunogenomic Oncology Platform, Department of Surgery, Memorial Sloan Kettering Cancer Center (MSKCC), New York, New York, USA
| | | | - Catherine Y. Han
- Head and Neck Service, Immunogenomic Oncology Platform, Department of Surgery, Memorial Sloan Kettering Cancer Center (MSKCC), New York, New York, USA
| | - Xin Pei
- Department of Radiation Oncology, and
| | | | - Lillian A. Boe
- Department of Biostatistics and Epidemiology, MSKCC, New York, New York, USA
| | - Abhinav Pandey
- Head and Neck Service, Immunogenomic Oncology Platform, Department of Surgery, Memorial Sloan Kettering Cancer Center (MSKCC), New York, New York, USA
| | - Elizabeth S. Koh
- Head and Neck Service, Immunogenomic Oncology Platform, Department of Surgery, Memorial Sloan Kettering Cancer Center (MSKCC), New York, New York, USA
| | - Charlotte L. Zuur
- Department of Head and Neck Oncology and Surgery, Antoni van Leeuwenhoek Hospital–Netherlands Cancer Institute, Amsterdam, Netherlands
- Department of Otorhinolaryngology and Head and Neck Surgery, Leiden University Medical Center, Leiden, Netherlands
| | | | | | - Li Liu
- Illumina Inc., San Diego, California, USA
| | - Alan L. Ho
- Department of Medicine, MSKCC, New York, New York, USA
| | - Diego Chowell
- Department of Oncological Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | | | - Timothy A. Chan
- Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio, USA
| | - Luc G.T. Morris
- Head and Neck Service, Immunogenomic Oncology Platform, Department of Surgery, Memorial Sloan Kettering Cancer Center (MSKCC), New York, New York, USA
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24
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Yaghoubi Naei V, Bordhan P, Mirakhorli F, Khorrami M, Shrestha J, Nazari H, Kulasinghe A, Ebrahimi Warkiani M. Advances in novel strategies for isolation, characterization, and analysis of CTCs and ctDNA. Ther Adv Med Oncol 2023; 15:17588359231192401. [PMID: 37692363 PMCID: PMC10486235 DOI: 10.1177/17588359231192401] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2022] [Accepted: 07/19/2023] [Indexed: 09/12/2023] Open
Abstract
Over the past decade, the detection and analysis of liquid biopsy biomarkers such as circulating tumor cells (CTCs) and circulating tumor DNA (ctDNA) have advanced significantly. They have received recognition for their clinical usefulness in detecting cancer at an early stage, monitoring disease, and evaluating treatment response. The emergence of liquid biopsy has been a helpful development, as it offers a minimally invasive, rapid, real-time monitoring, and possible alternative to traditional tissue biopsies. In resource-limited settings, the ideal platform for liquid biopsy should not only extract more CTCs or ctDNA from a minimal sample volume but also accurately represent the molecular heterogeneity of the patient's disease. This review covers novel strategies and advancements in CTC and ctDNA-based liquid biopsy platforms, including microfluidic applications and comprehensive analysis of molecular complexity. We discuss these systems' operational principles and performance efficiencies, as well as future opportunities and challenges for their implementation in clinical settings. In addition, we emphasize the importance of integrated platforms that incorporate machine learning and artificial intelligence in accurate liquid biopsy detection systems, which can greatly improve cancer management and enable precision diagnostics.
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Affiliation(s)
- Vahid Yaghoubi Naei
- School of Biomedical Engineering, University of Technology Sydney, Sydney, Australia
- Faculty of Medicine, Frazer Institute, The University of Queensland, Brisbane, QLD, Australia
| | - Pritam Bordhan
- School of Biomedical Engineering, University of Technology Sydney, Sydney, Australia
- Faculty of Science, Institute for Biomedical Materials & Devices, University of Technology Sydney, Australia
| | - Fatemeh Mirakhorli
- School of Biomedical Engineering, University of Technology Sydney, Sydney, Australia
| | - Motahare Khorrami
- Immunology Research Center, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Jesus Shrestha
- School of Biomedical Engineering, University of Technology Sydney, Sydney, Australia
| | - Hojjatollah Nazari
- School of Biomedical Engineering, University of Technology Sydney, Sydney, Australia
| | - Arutha Kulasinghe
- Faculty of Medicine, Frazer Institute, The University of Queensland, Brisbane, QLD, Australia
| | - Majid Ebrahimi Warkiani
- School of Biomedical Engineering, University of Technology Sydney, 1, Broadway, Ultimo New South Wales 2007, Australia
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25
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Chen Y, Cai G, Jiang J, He C, Chen Y, Ding Y, Lu J, Zhao W, Yang Y, Zhang Y, Wu G, Wang H, Zhou Z, Teng L. Proteomic profiling of gastric cancer with peritoneal metastasis identifies a protein signature associated with immune microenvironment and patient outcome. Gastric Cancer 2023; 26:504-516. [PMID: 36930369 PMCID: PMC10284991 DOI: 10.1007/s10120-023-01379-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/15/2023] [Accepted: 02/24/2023] [Indexed: 03/18/2023]
Abstract
BACKGROUND Peritoneal metastasis (PM) frequently occurs in patients with gastric cancer (GC) and is a major cause of mortality. Risk stratification for PM can optimize decision making in GC treatment. METHODS A total of 25 GC patients (13 with synchronous, 6 with metachronous PM and 6 PM-free) were included in this study. Quantitative proteomics by high-depth tandem mass tags labeling and whole-exome sequencing were conducted in primary GC and PM samples. Proteomic signature and prognostic model were established by machine learning algorithms in PM and PM-free GC, then validated in two external cohorts. Tumor-infiltrating immune cells in GC were analyzed by CIBERSORT. RESULTS Heterogeneity between paired primary and PM samples was observed at both genomic and proteomic levels. Compared to primary GC, proteome of PM samples was enriched in RNA binding and extracellular exosomes. 641 differently expressed proteins (DEPs) between primary GC of PM group and PM-free group were screened, which were enriched in extracellular exosome and cell adhesion pathways. Subsequently, a ten-protein signature was derived based on DEPs by machine learning. This signature was significantly associated with patient prognosis in internal cohort and two external proteomic datasets of diffuse and mixed type GC. Tumor-infiltrating immune cell analysis showed that the signature was associated with immune microenvironment of GC. CONCLUSIONS We characterized proteomic features that were informative for PM progression of GC. A protein signature associated with immune microenvironment and patient outcome was derived, and it could guide risk stratification and individualized treatment.
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Affiliation(s)
- Yanyan Chen
- Department of Surgical Oncology, The First Affiliated Hospital, Zhejiang University School of Medicine, 79# Qingchun Road, Hangzhou, 310003, China
| | - Guoxin Cai
- Institute of Drug Metabolism and Pharmaceutical Analysis and Zhejiang Provincial Key Laboratory of Anti-Cancer Drug Research, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
| | - Junjie Jiang
- Department of Gastroenterology, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Chao He
- Department of Surgical Oncology, The First Affiliated Hospital, Zhejiang University School of Medicine, 79# Qingchun Road, Hangzhou, 310003, China
| | - Yiran Chen
- Department of Surgical Oncology, The First Affiliated Hospital, Zhejiang University School of Medicine, 79# Qingchun Road, Hangzhou, 310003, China
| | - Yongfeng Ding
- Department of Medical Oncology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Jun Lu
- Department of Surgical Oncology, The First Affiliated Hospital, Zhejiang University School of Medicine, 79# Qingchun Road, Hangzhou, 310003, China
| | - Wenyi Zhao
- Institute of Drug Metabolism and Pharmaceutical Analysis and Zhejiang Provincial Key Laboratory of Anti-Cancer Drug Research, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
| | - Yan Yang
- Department of Surgical Oncology, The First Affiliated Hospital, Zhejiang University School of Medicine, 79# Qingchun Road, Hangzhou, 310003, China
| | - Yiqin Zhang
- Department of Informatics, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Guanghao Wu
- School of Clinical Medicine, Hangzhou Normal University Medical College, Hangzhou, China
| | - Haiyong Wang
- Department of Surgical Oncology, The First Affiliated Hospital, Zhejiang University School of Medicine, 79# Qingchun Road, Hangzhou, 310003, China
| | - Zhan Zhou
- Institute of Drug Metabolism and Pharmaceutical Analysis and Zhejiang Provincial Key Laboratory of Anti-Cancer Drug Research, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
| | - Lisong Teng
- Department of Surgical Oncology, The First Affiliated Hospital, Zhejiang University School of Medicine, 79# Qingchun Road, Hangzhou, 310003, China.
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26
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Liu Y, Li XC, Rashidi Mehrabadi F, Schäffer AA, Pratt D, Crawford DR, Malikić S, Molloy EK, Gopalan V, Mount SM, Ruppin E, Aldape KD, Sahinalp SC. Single-cell methylation sequencing data reveal succinct metastatic migration histories and tumor progression models. Genome Res 2023; 33:1089-1100. [PMID: 37316351 PMCID: PMC10538489 DOI: 10.1101/gr.277608.122] [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: 01/12/2023] [Accepted: 06/06/2023] [Indexed: 06/16/2023]
Abstract
Recent studies exploring the impact of methylation in tumor evolution suggest that although the methylation status of many of the CpG sites are preserved across distinct lineages, others are altered as the cancer progresses. Because changes in methylation status of a CpG site may be retained in mitosis, they could be used to infer the progression history of a tumor via single-cell lineage tree reconstruction. In this work, we introduce the first principled distance-based computational method, Sgootr, for inferring a tumor's single-cell methylation lineage tree and for jointly identifying lineage-informative CpG sites that harbor changes in methylation status that are retained along the lineage. We apply Sgootr on single-cell bisulfite-treated whole-genome sequencing data of multiregionally sampled tumor cells from nine metastatic colorectal cancer patients, as well as multiregionally sampled single-cell reduced-representation bisulfite sequencing data from a glioblastoma patient. We show that the tumor lineages constructed reveal a simple model underlying tumor progression and metastatic seeding. A comparison of Sgootr against alternative approaches shows that Sgootr can construct lineage trees with fewer migration events and with more in concordance with the sequential-progression model of tumor evolution, with a running time a fraction of that used in prior studies. Lineage-informative CpG sites identified by Sgootr are in inter-CpG island (CGI) regions, as opposed to intra-CGIs, which have been the main regions of interest in genomic methylation-related analyses.
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Affiliation(s)
- Yuelin Liu
- Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland 20892, USA
- Department of Computer Science, University of Maryland, College Park, Maryland 20742, USA
- Center for Bioinformatics and Computational Biology, University of Maryland, College Park, Maryland 20742, USA
| | - Xuan Cindy Li
- Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland 20892, USA
- Program in Computational Biology, Bioinformatics, and Genomics, University of Maryland, College Park, Maryland 20742, USA
| | - Farid Rashidi Mehrabadi
- Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland 20892, USA
- Department of Computer Science, Indiana University, Bloomington, Indiana 47408, USA
- Laboratory of Human Carcinogenesis, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland 20892, USA
| | - Alejandro A Schäffer
- Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland 20892, USA
| | - Drew Pratt
- Laboratory of Pathology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland 20892, USA
| | - David R Crawford
- Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland 20892, USA
- Program in Computational Biology, Bioinformatics, and Genomics, University of Maryland, College Park, Maryland 20742, USA
- Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, Maryland 20742, USA
| | - Salem Malikić
- Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland 20892, USA
| | - Erin K Molloy
- Department of Computer Science, University of Maryland, College Park, Maryland 20742, USA
- Center for Bioinformatics and Computational Biology, University of Maryland, College Park, Maryland 20742, USA
| | - Vishaka Gopalan
- Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland 20892, USA
| | - Stephen M Mount
- Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, Maryland 20742, USA
| | - Eytan Ruppin
- Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland 20892, USA
| | - Kenneth D Aldape
- Laboratory of Pathology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland 20892, USA
| | - S Cenk Sahinalp
- Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland 20892, USA;
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27
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Qiu MZ, Chen Q, Zheng DY, Zhao Q, Wu QN, Zhou ZW, Yang LQ, Luo QY, Sun YT, Lai MY, Yuan SS, Wang FH, Luo HY, Wang F, Li YH, Zhang HZ, Xu RH. Precise microdissection of gastric mixed adeno-neuroendocrine carcinoma dissects its genomic landscape and evolutionary clonal origins. Cell Rep 2023; 42:112576. [PMID: 37285266 DOI: 10.1016/j.celrep.2023.112576] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Revised: 03/02/2023] [Accepted: 05/15/2023] [Indexed: 06/09/2023] Open
Abstract
Gastric mixed adenoneuroendocrine carcinoma (MANEC) is a clinically aggressive and heterogeneous tumor composed of adenocarcinoma (ACA) and neuroendocrine carcinoma (NEC). The genomic properties and evolutionary clonal origins of MANEC remain unclear. We conduct whole-exome and multiregional sequencing on 101 samples from 33 patients to elucidate their evolutionary paths. We identify four significantly mutated genes, TP53, RB1, APC, and CTNNB1. MANEC resembles chromosomal instability stomach adenocarcinoma in that whole-genome doubling in MANEC is predominant and occurs earlier than most copy-number losses. All tumors are of monoclonal origin, and NEC components show more aggressive genomic properties than their ACA counterparts. The phylogenetic trees show two tumor divergence patterns, including sequential and parallel divergence. Furthermore, ACA-to-NEC rather than NEC-to-ACA transition is confirmed by immunohistochemistry on 6 biomarkers in ACA- and NEC-dominant regions. These results provide insights into the clonal origin and tumor differentiation of MANEC.
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Affiliation(s)
- Miao-Zhen Qiu
- Department of Medical Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University, Guangzhou 510060, P.R. China
| | - Qingjian Chen
- Department of Basic Research, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University, Guangzhou 510060, P.R. China; State Key Laboratory of Systems Medicine for Cancer, Department of Oncology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200080, P.R. China
| | - Dan-Yang Zheng
- Department of Pathology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University, Guangzhou 510060, P.R. China; Department of Clinical Oncology, LKS Faculty of Medicine, The University of Hong Kong, Pok Fu Lam, Hong Kong
| | - Qi Zhao
- Department of Basic Research, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University, Guangzhou 510060, P.R. China
| | - Qi-Nian Wu
- Department of Pathology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University, Guangzhou 510060, P.R. China
| | - Zhi-Wei Zhou
- Department of Gastric Surgery, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University, Guangzhou 510060, P.R. China
| | - Li-Qiong Yang
- Department of Basic Research, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University, Guangzhou 510060, P.R. China
| | - Qiu-Yun Luo
- Department of Basic Research, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University, Guangzhou 510060, P.R. China
| | - Yu-Ting Sun
- Department of Medical Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University, Guangzhou 510060, P.R. China
| | - Ming-Yu Lai
- Department of Medical Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University, Guangzhou 510060, P.R. China
| | - Sha-Sha Yuan
- Department of Pathology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University, Guangzhou 510060, P.R. China
| | - Feng-Hua Wang
- Department of Medical Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University, Guangzhou 510060, P.R. China
| | - Hui-Yan Luo
- Department of Medical Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University, Guangzhou 510060, P.R. China
| | - Feng Wang
- Department of Medical Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University, Guangzhou 510060, P.R. China
| | - Yu-Hong Li
- Department of Medical Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University, Guangzhou 510060, P.R. China
| | - Hui-Zhong Zhang
- Department of Pathology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University, Guangzhou 510060, P.R. China
| | - Rui-Hua Xu
- Department of Medical Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University, Guangzhou 510060, P.R. China; Research Unit of Precision Diagnosis and Treatment for Gastrointestinal Cancer, Chinese Academy of Medical Sciences, Guangzhou 510060, P.R. China.
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28
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Sarfaty M, Golkaram M, Funt SA, Al-Ahmadie H, Kaplan S, Song F, Regazzi A, Makarov V, Kuo F, Ostrovnaya I, Seshan V, Zhao C, Greenbaum B, Liu L, Rosenberg JE, Chan TA. Novel Genetic Subtypes of Urothelial Carcinoma With Differential Outcomes on Immune Checkpoint Blockade. J Clin Oncol 2023; 41:3225-3235. [PMID: 36927002 PMCID: PMC10256354 DOI: 10.1200/jco.22.02144] [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: 09/25/2022] [Accepted: 02/09/2023] [Indexed: 03/18/2023] Open
Abstract
PURPOSE Immune checkpoint blockade (ICB) therapy has significantly improved clinical outcomes in bladder cancer. Identification of correlates of benefit is critical to select appropriate therapy for individual patients. METHODS To reveal genetic variables associated with benefit from ICB, we performed whole-exome sequencing on tumor specimens from 88 patients with advanced bladder cancer treated with ICB. RESULTS We identified several genetic factors that correlated with progression-free and overall survival after ICB therapy including ARID1A mutation, tumor mutational burden, intratumoral heterogeneity, the ratio of nonsynonymous to synonymous mutations in the immunopeptidome (immune dN/dS), and tumor cell purity. In addition, we noted that neutrophil-to-lymphocyte ratio and smoking history were negatively associated with overall survival. These genetic characteristics define four molecular subtypes demonstrating differential sensitivity to ICB. We validated the association of these four subtypes with clinical benefit from ICB in an independent cohort (IMvigor210). Finally, we showed that these molecular subtypes also correlate with outcome, although with distinct relationships, among patients not treated with ICB using The Cancer Genome Atlas (TCGA) bladder cancer cohort. Using parallel RNA sequencing data, the subtypes were also shown to correlate with immune infiltration and inflammation, respectively, in the IMvigor210 and TCGA cohorts. CONCLUSION Together, our study defines molecular subgroups of bladder cancer that influence benefit from ICB.
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Affiliation(s)
- Michal Sarfaty
- Genitourinary Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY
- Institute of Oncology, Sheba Medical Center, Ramat Gan, Israel
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | | | - Samuel A. Funt
- Genitourinary Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Hikmat Al-Ahmadie
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY
| | | | | | - Ashley Regazzi
- Genitourinary Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Vladimir Makarov
- Center for Immunotherapy and Precision-Immuno-Oncology, Cleveland Clinic, Cleveland, OH
| | - Fengshen Kuo
- Department of Urology, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Irina Ostrovnaya
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Venkatraman Seshan
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY
| | | | - Benjamin Greenbaum
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Li Liu
- Illumina, Inc, San Diego, CA
| | - Jonathan E. Rosenberg
- Genitourinary Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Timothy A. Chan
- Center for Immunotherapy and Precision-Immuno-Oncology, Cleveland Clinic, Cleveland, OH
- National Center for Regenerative Medicine, Cleveland Clinic, Cleveland, OH
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29
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Huang K, Zhang Y, Gong H, Qiao Z, Wang T, Zhao W, Huang L, Zhou X. Inferring evolutionary trajectories from cross-sectional transcriptomic data to mirror lung adenocarcinoma progression. PLoS Comput Biol 2023; 19:e1011122. [PMID: 37228122 DOI: 10.1371/journal.pcbi.1011122] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Accepted: 04/24/2023] [Indexed: 05/27/2023] Open
Abstract
Lung adenocarcinoma (LUAD) is a deadly tumor with dynamic evolutionary process. Although much endeavors have been made in identifying the temporal patterns of cancer progression, it remains challenging to infer and interpret the molecular alterations associated with cancer development and progression. To this end, we developed a computational approach to infer the progression trajectory based on cross-sectional transcriptomic data. Analysis of the LUAD data using our approach revealed a linear trajectory with three different branches for malignant progression, and the results showed consistency in three independent cohorts. We used the progression model to elucidate the potential molecular events in LUAD progression. Further analysis showed that overexpression of BUB1B, BUB1 and BUB3 promoted tumor cell proliferation and metastases by disturbing the spindle assembly checkpoint (SAC) in the mitosis. Aberrant mitotic spindle checkpoint signaling appeared to be one of the key factors promoting LUAD progression. We found the inferred cancer trajectory allows to identify LUAD susceptibility genetic variations using genome-wide association analysis. This result shows the opportunity for combining analysis of candidate genetic factors with disease progression. Furthermore, the trajectory showed clear evident mutation accumulation and clonal expansion along with the LUAD progression. Understanding how tumors evolve and identifying mutated genes will help guide cancer management. We investigated the clonal architectures and identified distinct clones and subclones in different LUAD branches. Validation of the model in multiple independent data sets and correlation analysis with clinical results demonstrate that our method is effective and unbiased.
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Affiliation(s)
- Kexin Huang
- School of Life Science and Technology, Xidian University, Xi'an, China
- West China Biomedical Big Data Centre, West China Hospital of Sichuan University, Chengdu, China
| | - Yun Zhang
- School of Life Science and Technology, Xidian University, Xi'an, China
| | - Haoran Gong
- West China Biomedical Big Data Centre, West China Hospital of Sichuan University, Chengdu, China
| | - Zhengzheng Qiao
- School of Life Science and Technology, Xidian University, Xi'an, China
| | - Tiangang Wang
- School of Life Science and Technology, Xidian University, Xi'an, China
| | - Weiling Zhao
- Center for Computational Systems Medicine, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, United States of America
| | - Liyu Huang
- School of Life Science and Technology, Xidian University, Xi'an, China
| | - Xiaobo Zhou
- Center for Computational Systems Medicine, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, United States of America
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30
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Zhang S, Wu L, Li Z, Li Q, Zong Y, Zhu K, Chen L, Qin H, Meng R. An unusual ectopic thymoma clonal evolution analysis: A case report. Open Life Sci 2023; 18:20220600. [PMID: 37215501 PMCID: PMC10199323 DOI: 10.1515/biol-2022-0600] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2022] [Revised: 03/13/2023] [Accepted: 03/22/2023] [Indexed: 05/24/2023] Open
Abstract
Thymomas and thymic carcinomas are rare and primary tumors of the mediastinum which is derived from the thymic epithelium. Thymomas are the most common primary anterior mediastinal tumor, while ectopic thymomas are rarer. Mutational profiles of ectopic thymomas may help expand our understanding of the occurrence and treatment options of these tumors. In this report, we sought to elucidate the mutational profiles of two ectopic thymoma nodules to gain deeper understanding of the molecular genetic information of this rare tumor and to provide guidance treatment options. We presented a case of 62-year-old male patient with a postoperative pathological diagnosis of type A mediastinal thymoma and ectopic pulmonary thymoma. After mediastinal lesion resection and thoracoscopic lung wedge resection, the mediastinal thymoma was completely removed, and the patient recovered from the surgery and no recurrence was found by examination until now. Whole exome sequencing was performed on both mediastinal thymoma and ectopic pulmonary thymoma tissue samples of the patient and clonal evolution analysis were further conducted to analyze the genetic characteristics. We identified eight gene mutations that were co-mutated in both lesions. Consistent with a previous exome sequencing analysis of thymic epithelial tumor, HRAS was also observed in both mediastinal lesion and lung lesion tissues. We also evaluated the intratumor heterogeneity of non-silent mutations. The results showed that the mediastinal lesion tissue has higher degree of heterogeneity and the lung lesion tissue has relatively low amount of variant heterogeneity in the detected variants. Through pathology and genomics sequencing detection, we initially revealed the genetic differences between mediastinal thymoma and ectopic thymoma, and clonal evolution analysis showed that these two lesions originated from multi-ancestral regions.
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Affiliation(s)
- Sijia Zhang
- Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, No. 156 Wujiadun, Jianghan District, Wuhan, Hubei Province, 430022, China
| | - Lu Wu
- Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, No. 156 Wujiadun, Jianghan District, Wuhan, Hubei Province, 430022, China
| | - Zhenyu Li
- Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, No. 156 Wujiadun, Jianghan District, Wuhan, Hubei Province, 430022, China
| | - Qianwen Li
- Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, No. 156 Wujiadun, Jianghan District, Wuhan, Hubei Province, 430022, China
| | - Yan Zong
- Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, No. 156 Wujiadun, Jianghan District, Wuhan, Hubei Province, 430022, China
| | - Kuikui Zhu
- Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, No. 156 Wujiadun, Jianghan District, Wuhan, Hubei Province, 430022, China
| | - Leichong Chen
- Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, No. 156 Wujiadun, Jianghan District, Wuhan, Hubei Province, 430022, China
| | - Haifeng Qin
- Department of Pulmonary Neoplasm Internal Medicine, Fifth Medical Center of Chinese PLA General Hospital, Beijing, 100071, China
| | - Rui Meng
- Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, No. 156 Wujiadun, Jianghan District, Wuhan, Hubei Province, 430022, China
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31
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Xu Q, Streuer A, Jann JC, Altrock E, Schmitt N, Flach J, Sens-Albert C, Rapp F, Wolf J, Nowak V, Weimer N, Obländer J, Palme I, Kuzina M, Jawhar A, Darwich A, Weis CA, Marx A, Wuchter P, Costina V, Jäger E, Sperk E, Neumaier M, Fabarius A, Metzgeroth G, Nolte F, Steiner L, Levkin PA, Jawhar M, Hofmann WK, Riabov V, Nowak D. Inhibition of lysyl oxidases synergizes with 5-azacytidine to restore erythropoiesis in myelodysplastic and myeloid malignancies. Nat Commun 2023; 14:1497. [PMID: 36932114 PMCID: PMC10023686 DOI: 10.1038/s41467-023-37175-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2022] [Accepted: 03/01/2023] [Indexed: 03/19/2023] Open
Abstract
Limited response rates and frequent relapses during standard of care with hypomethylating agents in myelodysplastic neoplasms (MN) require urgent improvement of this treatment indication. Here, by combining 5-azacytidine (5-AZA) with the pan-lysyl oxidase inhibitor PXS-5505, we demonstrate superior restoration of erythroid differentiation in hematopoietic stem and progenitor cells (HSPCs) of MN patients in 20/31 cases (65%) versus 9/31 cases (29%) treated with 5-AZA alone. This effect requires direct contact of HSPCs with bone marrow stroma components and is dependent on integrin signaling. We further confirm these results in vivo using a bone marrow niche-dependent MN xenograft model in female NSG mice, in which we additionally demonstrate an enforced reduction of dominant clones as well as significant attenuation of disease expansion and normalization of spleen sizes. Overall, these results lay out a strong pre-clinical rationale for efficacy of combination treatment of 5-AZA with PXS-5505 especially for anemic MN.
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Affiliation(s)
- Qingyu Xu
- Department of Hematology and Oncology, Medical Faculty Mannheim, Heidelberg University, Mannheim, 68167, Germany
| | - Alexander Streuer
- Department of Hematology and Oncology, Medical Faculty Mannheim, Heidelberg University, Mannheim, 68167, Germany
| | - Johann-Christoph Jann
- Department of Hematology and Oncology, Medical Faculty Mannheim, Heidelberg University, Mannheim, 68167, Germany
| | - Eva Altrock
- Department of Hematology and Oncology, Medical Faculty Mannheim, Heidelberg University, Mannheim, 68167, Germany
| | - Nanni Schmitt
- Department of Hematology and Oncology, Medical Faculty Mannheim, Heidelberg University, Mannheim, 68167, Germany
| | - Johanna Flach
- Department of Hematology and Oncology, Medical Faculty Mannheim, Heidelberg University, Mannheim, 68167, Germany
| | - Carla Sens-Albert
- Department of Hematology and Oncology, Medical Faculty Mannheim, Heidelberg University, Mannheim, 68167, Germany
| | - Felicitas Rapp
- Department of Hematology and Oncology, Medical Faculty Mannheim, Heidelberg University, Mannheim, 68167, Germany
| | - Julia Wolf
- Department of Hematology and Oncology, Medical Faculty Mannheim, Heidelberg University, Mannheim, 68167, Germany
| | - Verena Nowak
- Department of Hematology and Oncology, Medical Faculty Mannheim, Heidelberg University, Mannheim, 68167, Germany
| | - Nadine Weimer
- Department of Hematology and Oncology, Medical Faculty Mannheim, Heidelberg University, Mannheim, 68167, Germany
| | - Julia Obländer
- Department of Hematology and Oncology, Medical Faculty Mannheim, Heidelberg University, Mannheim, 68167, Germany
| | - Iris Palme
- Department of Hematology and Oncology, Medical Faculty Mannheim, Heidelberg University, Mannheim, 68167, Germany
| | - Mariia Kuzina
- Institute of Biological and Chemical Systems - Functional Molecular Systems, Karlsruhe Institute of Technology, Eggenstein-Leopoldshafen, 76344, Germany
| | - Ahmed Jawhar
- Department of Orthopedic Surgery, Medical Faculty Mannheim, Heidelberg University, Mannheim, 68167, Germany
| | - Ali Darwich
- Department of Orthopedic Surgery, Medical Faculty Mannheim, Heidelberg University, Mannheim, 68167, Germany
| | - Cleo-Aron Weis
- Institute of Pathology, Medical Faculty Mannheim, Heidelberg University, Mannheim, 68167, Germany
| | - Alexander Marx
- Institute of Pathology, Medical Faculty Mannheim, Heidelberg University, Mannheim, 68167, Germany
| | - Patrick Wuchter
- Institute of Transfusion Medicine and Immunology, German Red Cross Blood Service Baden-Württemberg-Hessen, Medical Faculty Mannheim, Heidelberg University, Mannheim, 68167, Germany
| | - Victor Costina
- Institute of Clinical Chemistry, Medical Faculty Mannheim, Heidelberg University, Mannheim, 68167, Germany
| | - Evelyn Jäger
- Institute of Clinical Chemistry, Medical Faculty Mannheim, Heidelberg University, Mannheim, 68167, Germany
| | - Elena Sperk
- Department of Hematology and Oncology, Medical Faculty Mannheim, Heidelberg University, Mannheim, 68167, Germany
| | - Michael Neumaier
- Institute of Clinical Chemistry, Medical Faculty Mannheim, Heidelberg University, Mannheim, 68167, Germany
| | - Alice Fabarius
- Department of Hematology and Oncology, Medical Faculty Mannheim, Heidelberg University, Mannheim, 68167, Germany
| | - Georgia Metzgeroth
- Department of Hematology and Oncology, Medical Faculty Mannheim, Heidelberg University, Mannheim, 68167, Germany
| | - Florian Nolte
- Department of Hematology and Oncology, Medical Faculty Mannheim, Heidelberg University, Mannheim, 68167, Germany
| | - Laurenz Steiner
- Department of Hematology and Oncology, Medical Faculty Mannheim, Heidelberg University, Mannheim, 68167, Germany
| | - Pavel A Levkin
- Institute of Biological and Chemical Systems - Functional Molecular Systems, Karlsruhe Institute of Technology, Eggenstein-Leopoldshafen, 76344, Germany
- Institute of Organic Chemistry, Karlsruhe Institute of Technology, Karlsruhe, 76131, Germany
| | - Mohamad Jawhar
- Department of Hematology and Oncology, Medical Faculty Mannheim, Heidelberg University, Mannheim, 68167, Germany
| | - Wolf-Karsten Hofmann
- Department of Hematology and Oncology, Medical Faculty Mannheim, Heidelberg University, Mannheim, 68167, Germany
| | - Vladimir Riabov
- Department of Hematology and Oncology, Medical Faculty Mannheim, Heidelberg University, Mannheim, 68167, Germany.
| | - Daniel Nowak
- Department of Hematology and Oncology, Medical Faculty Mannheim, Heidelberg University, Mannheim, 68167, Germany.
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32
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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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33
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Gao S, Wang J, Zhu Z, Fang J, Zhao Y, Liu Z, Qin H, Wei Y, Xu H, Dan X, Yang L, Xu Q. Effective personalized neoantigen vaccine plus anti-PD-1 in a PD-1 blockade-resistant lung cancer patient. Immunotherapy 2023; 15:57-69. [PMID: 36651232 DOI: 10.2217/imt-2021-0339] [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: 01/19/2023] Open
Abstract
Background: Although significant progress has been made in immune checkpoint inhibitor (ICI) treatment of advanced squamous cell carcinoma (SqCC), most patients still experience acquired drug resistance. Methods: We used a dendritic cell-based neoantigen vaccine combined with ICIs to treat advanced SqCC in a PD-1 blockade-resistant patient. Results: The follow-up of this patient after 12 months revealed significant tumor regression. We also identified a new JAK1 ICI-resistant mutation that could become a potential universal neoantigen target for tumor vaccines. Conclusion: Individualized management of advanced SqCC through a combined neoantigen vaccine and ICI administration could yield beneficial clinical outcomes. Vaccines targeting anti-PD-1-resistant JAK1 mutations might be of particular benefit to a specific group of solid tumor patients.
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Affiliation(s)
- Song Gao
- Department of Oncology, Shanghai Tenth People's Hospital, Tongji University, Shanghai, 200072, China.,Tongji University Cancer Center, Shanghai, 200072, China
| | - Jiaqian Wang
- YuceBio, Shenzhen, 518000, China.,YuceNeo, Shenzhen, 518121, China
| | - Zhongzheng Zhu
- Department of Oncology, Shanghai Tenth People's Hospital, Tongji University, Shanghai, 200072, China.,Tongji University Cancer Center, Shanghai, 200072, China
| | - Juemin Fang
- Department of Oncology, Shanghai Tenth People's Hospital, Tongji University, Shanghai, 200072, China.,Tongji University Cancer Center, Shanghai, 200072, China
| | - Yu Zhao
- Department of Oncology, Shanghai Tenth People's Hospital, Tongji University, Shanghai, 200072, China.,Tongji University Cancer Center, Shanghai, 200072, China
| | - Zhuqing Liu
- Department of Oncology, Shanghai Tenth People's Hospital, Tongji University, Shanghai, 200072, China.,Tongji University Cancer Center, Shanghai, 200072, China
| | - Huanlong Qin
- Department of Oncology, Shanghai Tenth People's Hospital, Tongji University, Shanghai, 200072, China.,Tongji University Cancer Center, Shanghai, 200072, China
| | - Yuquan Wei
- Department of Biotherapy, State Key Laboratory of Biotherapy & Cancer Center, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Heng Xu
- Department of Biotherapy, State Key Laboratory of Biotherapy & Cancer Center, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Xu Dan
- YuceBio, Shenzhen, 518000, China.,YuceNeo, Shenzhen, 518121, China
| | - Li Yang
- Department of Biotherapy, State Key Laboratory of Biotherapy & Cancer Center, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Qing Xu
- Department of Oncology, Shanghai Tenth People's Hospital, Tongji University, Shanghai, 200072, China.,Tongji University Cancer Center, Shanghai, 200072, China
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34
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Álvarez-Prado ÁF, Maas RR, Soukup K, Klemm F, Kornete M, Krebs FS, Zoete V, Berezowska S, Brouland JP, Hottinger AF, Daniel RT, Hegi ME, Joyce JA. Immunogenomic analysis of human brain metastases reveals diverse immune landscapes across genetically distinct tumors. Cell Rep Med 2023; 4:100900. [PMID: 36652909 PMCID: PMC9873981 DOI: 10.1016/j.xcrm.2022.100900] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Revised: 09/20/2022] [Accepted: 12/19/2022] [Indexed: 01/19/2023]
Abstract
Brain metastases (BrMs) are the most common form of brain tumors in adults and frequently originate from lung and breast primary cancers. BrMs are associated with high mortality, emphasizing the need for more effective therapies. Genetic profiling of primary tumors is increasingly used as part of the effort to guide targeted therapies against BrMs, and immune-based strategies for the treatment of metastatic cancer are gaining momentum. However, the tumor immune microenvironment (TIME) of BrM is extremely heterogeneous, and whether specific genetic profiles are associated with distinct immune states remains unknown. Here, we perform an extensive characterization of the immunogenomic landscape of human BrMs by combining whole-exome/whole-genome sequencing, RNA sequencing of immune cell populations, flow cytometry, immunofluorescence staining, and tissue imaging analyses. This revealed unique TIME phenotypes in genetically distinct lung- and breast-BrMs, thereby enabling the development of personalized immunotherapies tailored by the genetic makeup of the tumors.
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Affiliation(s)
- Ángel F Álvarez-Prado
- Department of Oncology, University of Lausanne, 1011 Lausanne, Switzerland; Ludwig Institute for Cancer Research, University of Lausanne, 1011 Lausanne, Switzerland; Agora Cancer Research Center, 1011 Lausanne, Switzerland; L. Lundin and Family Brain Tumor Research Center, Departments of Oncology and Clinical Neurosciences, Centre Hospitalier Universitaire Vaudois, 1011 Lausanne, Switzerland
| | - Roeltje R Maas
- Department of Oncology, University of Lausanne, 1011 Lausanne, Switzerland; Ludwig Institute for Cancer Research, University of Lausanne, 1011 Lausanne, Switzerland; Agora Cancer Research Center, 1011 Lausanne, Switzerland; L. Lundin and Family Brain Tumor Research Center, Departments of Oncology and Clinical Neurosciences, Centre Hospitalier Universitaire Vaudois, 1011 Lausanne, Switzerland; Neuroscience Research Center, Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland; Department of Neurosurgery, Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland
| | - Klara Soukup
- Department of Oncology, University of Lausanne, 1011 Lausanne, Switzerland; Ludwig Institute for Cancer Research, University of Lausanne, 1011 Lausanne, Switzerland; Agora Cancer Research Center, 1011 Lausanne, Switzerland
| | - Florian Klemm
- Department of Oncology, University of Lausanne, 1011 Lausanne, Switzerland; Ludwig Institute for Cancer Research, University of Lausanne, 1011 Lausanne, Switzerland; Agora Cancer Research Center, 1011 Lausanne, Switzerland
| | - Mara Kornete
- Department of Oncology, University of Lausanne, 1011 Lausanne, Switzerland; Ludwig Institute for Cancer Research, University of Lausanne, 1011 Lausanne, Switzerland; Agora Cancer Research Center, 1011 Lausanne, Switzerland
| | - Fanny S Krebs
- Department of Oncology, University of Lausanne, 1011 Lausanne, Switzerland; Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Vincent Zoete
- Department of Oncology, University of Lausanne, 1011 Lausanne, Switzerland; Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Sabina Berezowska
- Department of Pathology, Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland
| | - Jean-Philippe Brouland
- Department of Pathology, Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland
| | - Andreas F Hottinger
- Department of Oncology, University of Lausanne, 1011 Lausanne, Switzerland; L. Lundin and Family Brain Tumor Research Center, Departments of Oncology and Clinical Neurosciences, Centre Hospitalier Universitaire Vaudois, 1011 Lausanne, Switzerland; Brain and Spine Tumor Center, Departments of Clinical Neurosciences and Oncology, Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland
| | - Roy T Daniel
- L. Lundin and Family Brain Tumor Research Center, Departments of Oncology and Clinical Neurosciences, Centre Hospitalier Universitaire Vaudois, 1011 Lausanne, Switzerland; Department of Neurosurgery, Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland
| | - Monika E Hegi
- L. Lundin and Family Brain Tumor Research Center, Departments of Oncology and Clinical Neurosciences, Centre Hospitalier Universitaire Vaudois, 1011 Lausanne, Switzerland; Neuroscience Research Center, Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland; Department of Neurosurgery, Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland
| | - Johanna A Joyce
- Department of Oncology, University of Lausanne, 1011 Lausanne, Switzerland; Ludwig Institute for Cancer Research, University of Lausanne, 1011 Lausanne, Switzerland; Agora Cancer Research Center, 1011 Lausanne, Switzerland; L. Lundin and Family Brain Tumor Research Center, Departments of Oncology and Clinical Neurosciences, Centre Hospitalier Universitaire Vaudois, 1011 Lausanne, Switzerland.
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Lybaert L, Lefever S, Fant B, Smits E, De Geest B, Breckpot K, Dirix L, Feldman SA, van Criekinge W, Thielemans K, van der Burg SH, Ott PA, Bogaert C. Challenges in neoantigen-directed therapeutics. Cancer Cell 2023; 41:15-40. [PMID: 36368320 DOI: 10.1016/j.ccell.2022.10.013] [Citation(s) in RCA: 31] [Impact Index Per Article: 31.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Revised: 08/19/2022] [Accepted: 10/11/2022] [Indexed: 11/11/2022]
Abstract
A fundamental prerequisite for the efficacy of cancer immunotherapy is the presence of functional, antigen-specific T cells within the tumor. Neoantigen-directed therapy is a promising strategy that aims at targeting the host's immune response against tumor-specific antigens, thereby eradicating cancer cells. Initial forays have been made in clinical environments utilizing vaccines and adoptive cell therapy; however, many challenges lie ahead. We provide an in-depth overview of the current state of the field with an emphasis on in silico neoantigen discovery and the clinical aspects that need to be addressed to unlock the full potential of this therapy.
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Affiliation(s)
| | | | | | - Evelien Smits
- Center for Oncological Research, University of Antwerp, 2610 Wilrijk, Belgium
| | - Bruno De Geest
- Department of Pharmaceutics, Ghent University, 9000 Ghent, Belgium
| | - Karine Breckpot
- Laboratory of Molecular and Cellular Therapy, Department of Biomedical Sciences, Vrije Universiteit Brussel, Brussels, Belgium
| | - Luc Dirix
- Translational Cancer Research Unit, Center for Oncological Research, Faculty of Medicine and Health Sciences, University of Antwerp, Antwerp, Belgium
| | - Steven A Feldman
- Center for Cancer Cell Therapy, Stanford University School of Medicine, Stanford, CA, USA
| | - Wim van Criekinge
- Department of Data Analysis and Mathematical Modelling, Ghent University, Ghent, Belgium
| | - Kris Thielemans
- Laboratory of Molecular and Cellular Therapy, Department of Biomedical Sciences, Vrije Universiteit Brussel, Brussels, Belgium
| | - Sjoerd H van der Burg
- Medical Oncology, Oncode Institute, Leiden University Medical Center, Leiden, the Netherlands
| | - Patrick A Ott
- Department of Medical Oncology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA, USA
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36
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Bigelow E, Saria S, Piening B, Curti B, Dowdell A, Weerasinghe R, Bifulco C, Urba W, Finkelstein N, Fertig EJ, Baras A, Zaidi N, Jaffee E, Yarchoan M. A Random Forest Genomic Classifier for Tumor Agnostic Prediction of Response to Anti-PD1 Immunotherapy. Cancer Inform 2022; 21:11769351221136081. [PMID: 36439024 PMCID: PMC9685115 DOI: 10.1177/11769351221136081] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Accepted: 10/14/2022] [Indexed: 11/23/2022] Open
Abstract
Tumor mutational burden (TMB), a surrogate for tumor neoepitope burden, is used as a pan-tumor biomarker to identify patients who may benefit from anti-program cell death 1 (PD1) immunotherapy, but it is an imperfect biomarker. Multiple additional genomic characteristics are associated with anti-PD1 responses, but the combined predictive value of these features and the added informativeness of each respective feature remains unknown. We evaluated whether machine learning (ML) approaches using proposed determinants of anti-PD1 response derived from whole exome sequencing (WES) could improve prediction of anti-PD1 responders over TMB alone. Random forest classifiers were trained on publicly available anti-PD1 data (n = 104), and subsequently tested on an independent anti-PD1 cohort (n = 69). Both the training and test datasets included a range of cancer types such as non-small cell lung cancer (NSCLC), head and neck squamous cell carcinoma (HNSCC), melanoma, and smaller numbers of patients from other tumor types. Features used include summaries such as TMB and number of frameshift mutations, as well as more gene-level features such as counts of mutations associated with immune checkpoint response and resistance. Both ML algorithms demonstrated area under the receiver-operator curves (AUC) that exceeded TMB alone (AUC 0.63 "human-guided," 0.64 "cluster," and 0.58 TMB alone). Mutations within oncogenes disproportionately modulate anti-PD1 responses relative to their overall contribution to tumor neoepitope burden. The use of a ML algorithm evaluating multiple proposed genomic determinants of anti-PD1 responses modestly improves performance over TMB alone, highlighting the need to integrate other biomarkers to further improve model performance.
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Affiliation(s)
- Emma Bigelow
- Sidney Kimmel Comprehensive Cancer
Center, Johns Hopkins, Baltimore, MD, USA
| | - Suchi Saria
- Departments of Computer Science and
Statistics, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD,
USA
- Department of Health Policy and
Management, Bloomberg School of Public Health, Johns Hopkins University, Baltimore,
MD, USA
- Bayesian Health, New York, NY,
USA
| | - Brian Piening
- Earle A. Chiles Research Institute,
Providence Portland Medical Center, Portland, OR, USA
| | - Brendan Curti
- Earle A. Chiles Research Institute,
Providence Portland Medical Center, Portland, OR, USA
| | - Alexa Dowdell
- Earle A. Chiles Research Institute,
Providence Portland Medical Center, Portland, OR, USA
| | | | - Carlo Bifulco
- Earle A. Chiles Research Institute,
Providence Portland Medical Center, Portland, OR, USA
| | - Walter Urba
- Earle A. Chiles Research Institute,
Providence Portland Medical Center, Portland, OR, USA
| | - Noam Finkelstein
- Departments of Computer Science and
Statistics, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD,
USA
| | - Elana J Fertig
- Sidney Kimmel Comprehensive Cancer
Center, Johns Hopkins, Baltimore, MD, USA
| | - Alex Baras
- Sidney Kimmel Comprehensive Cancer
Center, Johns Hopkins, Baltimore, MD, USA
| | - Neeha Zaidi
- Sidney Kimmel Comprehensive Cancer
Center, Johns Hopkins, Baltimore, MD, USA
| | - Elizabeth Jaffee
- Sidney Kimmel Comprehensive Cancer
Center, Johns Hopkins, Baltimore, MD, USA
| | - Mark Yarchoan
- Sidney Kimmel Comprehensive Cancer
Center, Johns Hopkins, Baltimore, MD, USA
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Lao Y, Wang Y, Yang J, Liu T, Ma Y, Luo Y, Sun Y, Li K, Zhao X, Niu X, Xi Y, Zhong C. Characterization of genomic alterations and neoantigens and analysis of immune infiltration identified therapeutic and prognostic biomarkers in adenocarcinoma at the gastroesophageal junction. Front Oncol 2022; 12:941868. [DOI: 10.3389/fonc.2022.941868] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Accepted: 10/24/2022] [Indexed: 11/13/2022] Open
Abstract
ObjectivesAdenocarcinoma at the gastroesophageal junction (ACGEJ) refers to a malignant tumor that occurs at the esophagogastric junction. Despite some progress in targeted therapies for HER2, FGFR2, EGFR, MET, Claudin 18.2 and immune checkpoints in ACGEJ tumors, the 5-year survival rate of patients remains poor. Thus, it is urgent to explore genomic alterations and neoantigen characteristics of tumors and identify CD8+ T-cell infiltration-associated genes to find potential therapeutic targets and develop a risk model to predict ACGEJ patients’ overall survival (OS).MethodsWhole-exome sequencing (WES) was performed on 55 paired samples from Chinese ACGEJ patients. Somatic mutations and copy number variations were detected by Strelka2 and FACETS, respectively. SigProfiler and SciClone were employed to decipher the mutation signature and clonal structure of each sample, respectively. Neoantigens were predicted using the MuPeXI pipeline. RNA sequencing (RNA-seq) data of ACGEJ samples from our previous studies and The Cancer Genome Atlas (TCGA) were used to identify genes significantly associated with CD8+ T-cell infiltration by weighted gene coexpression network analysis (WGCNA). To construct a risk model, we conducted LASSO and univariate and multivariate Cox regression analyses.ResultsRecurrent MAP2K7, RNF43 and RHOA mutations were found in ACGEJ tumors. The COSMIC signature SBS17 was associated with ACGEJ progression. CCNE1 and VEGFA were identified as putative CNV driver genes. PI3KCA and TP53 mutations conferred selective advantages to cancer cells. The Chinese ACGEJ patient neoantigen landscape was revealed for the first time, and 58 potential neoantigens common to TSNAdb and IEDB were identified. Compared with Siewert type II samples, Siewert type III samples had significant enrichment of the SBS17 signature, a lower TNFRSF14 copy number, a higher proportion of samples with complex clonal architecture and a higher neoantigen load. We identified 10 important CD8+ T-cell infiltration-related Hub genes (CCL5, CD2, CST7, GVINP1, GZMK, IL2RB, IKZF3, PLA2G2D, P2RY10 and ZAP70) as potential therapeutic targets from the RNA-seq data. Seven CD8+ T-cell infiltration-related genes (ADAM28, ASPH, CAMK2N1, F2R, STAP1, TP53INP2, ZC3H3) were selected to construct a prognostic model. Patients classified as high risk based on this model had significantly worse OS than low-risk patients, which was replicated in the TCGA-ACGEJ cohort.ConclusionsThis study provides new neoantigen-based immunotherapeutic targets for ACGEJ treatment and effective disease prognosis biomarkers.
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Wang H, Chan KYY, Cheng CK, Ng MH, Lee PY, Cheng FWT, Lam GKS, Chow TW, Ha SY, Chiang AK, Leung WH, Leung AY, Wang CC, Zhang T, Zhang XB, So CC, Yuen YP, Sun Q, Zhang C, Xu Y, Cheung JTK, Ng WH, Tang PMK, Kang W, To KF, Lee WYW, Wong RS, Poon ENY, Zhao Q, Huang J, Chen C, Yuen PMP, Li CK, Leung AWK, Leung KT. Pharmacogenomic Profiling of Pediatric Acute Myeloid Leukemia to Identify Therapeutic Vulnerabilities and Inform Functional Precision Medicine. Blood Cancer Discov 2022; 3:516-535. [PMID: 35960210 PMCID: PMC9894568 DOI: 10.1158/2643-3230.bcd-22-0011] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Revised: 05/31/2022] [Accepted: 08/09/2022] [Indexed: 11/16/2022] Open
Abstract
Despite the expanding portfolio of targeted therapies for adults with acute myeloid leukemia (AML), direct implementation in children is challenging due to inherent differences in underlying genetics. Here we established the pharmacologic profile of pediatric AML by screening myeloblast sensitivity to approved and investigational agents, revealing candidates of immediate clinical relevance. Drug responses ex vivo correlated with patient characteristics, exhibited age-specific alterations, and concorded with activities in xenograft models. Integration with genomic data uncovered new gene-drug associations, suggesting actionable therapeutic vulnerabilities. Transcriptome profiling further identified gene-expression signatures associated with on- and off-target drug responses. We also demonstrated the feasibility of drug screening-guided treatment for children with high-risk AML, with two evaluable cases achieving remission. Collectively, this study offers a high-dimensional gene-drug clinical data set that could be leveraged to research the unique biology of pediatric AML and sets the stage for realizing functional precision medicine for the clinical management of the disease. SIGNIFICANCE We conducted integrated drug and genomic profiling of patient biopsies to build the functional genomic landscape of pediatric AML. Age-specific differences in drug response and new gene-drug interactions were identified. The feasibility of functional precision medicine-guided management of children with high-risk AML was successfully demonstrated in two evaluable clinical cases. This article is highlighted in the In This Issue feature, p. 476.
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Affiliation(s)
- Han Wang
- Department of Paediatrics, The Chinese University of Hong Kong, Shatin, Hong Kong
| | - Kathy Yuen Yee Chan
- Department of Paediatrics, The Chinese University of Hong Kong, Shatin, Hong Kong
| | - Chi Keung Cheng
- Department of Anatomical and Cellular Pathology, The Chinese University of Hong Kong, Shatin, Hong Kong
| | - Margaret H.L. Ng
- Department of Anatomical and Cellular Pathology, The Chinese University of Hong Kong, Shatin, Hong Kong
| | - Po Yi Lee
- Department of Paediatrics, The Chinese University of Hong Kong, Shatin, Hong Kong
| | - Frankie Wai Tsoi Cheng
- Department of Paediatrics and Adolescent Medicine, Hong Kong Children's Hospital, Kowloon, Hong Kong
| | - Grace Kee See Lam
- Department of Paediatrics and Adolescent Medicine, Hong Kong Children's Hospital, Kowloon, Hong Kong
| | - Tin Wai Chow
- Department of Paediatrics and Adolescent Medicine, Hong Kong Children's Hospital, Kowloon, Hong Kong
| | - Shau Yin Ha
- Department of Paediatrics and Adolescent Medicine, The University of Hong Kong, Pokfulam, Hong Kong
| | - Alan K.S. Chiang
- Department of Paediatrics and Adolescent Medicine, The University of Hong Kong, Pokfulam, Hong Kong
| | - Wing Hang Leung
- Department of Paediatrics and Adolescent Medicine, The University of Hong Kong, Pokfulam, Hong Kong
| | - Anskar Y.H. Leung
- Department of Medicine, The University of Hong Kong, Pokfulam, Hong Kong
| | - Chi Chiu Wang
- Department of Obstetrics and Gynaecology, The Chinese University of Hong Kong, Shatin, Hong Kong
| | - Tao Zhang
- Department of Obstetrics and Gynaecology, The Chinese University of Hong Kong, Shatin, Hong Kong
| | - Xiao-Bing Zhang
- Department of Medicine, Loma Linda University, Loma Linda, California
| | - Chi Chiu So
- Department of Pathology, Hong Kong Children's Hospital, Kowloon, Hong Kong
| | - Yuet Ping Yuen
- Department of Pathology, Hong Kong Children's Hospital, Kowloon, Hong Kong
| | - Qiwei Sun
- Department of Paediatrics, The Chinese University of Hong Kong, Shatin, Hong Kong
| | - Chi Zhang
- Department of Paediatrics, The Chinese University of Hong Kong, Shatin, Hong Kong
| | - Yaqun Xu
- Department of Paediatrics, The Chinese University of Hong Kong, Shatin, Hong Kong
| | - John Tak Kit Cheung
- Department of Paediatrics, The Chinese University of Hong Kong, Shatin, Hong Kong
| | - Wing Hei Ng
- Department of Paediatrics, The Chinese University of Hong Kong, Shatin, Hong Kong
| | - Patrick Ming-Kuen Tang
- Department of Anatomical and Cellular Pathology, The Chinese University of Hong Kong, Shatin, Hong Kong
| | - Wei Kang
- Department of Anatomical and Cellular Pathology, The Chinese University of Hong Kong, Shatin, Hong Kong
| | - Ka-Fai To
- Department of Anatomical and Cellular Pathology, The Chinese University of Hong Kong, Shatin, Hong Kong
| | - Wayne Yuk Wai Lee
- Department of Orthopaedics and Traumatology, The Chinese University of Hong Kong, Shatin, Hong Kong
| | - Raymond S.M. Wong
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Shatin, Hong Kong
| | - Ellen Ngar Yun Poon
- School of Biomedical Sciences, The Chinese University of Hong Kong, Shatin, Hong Kong
| | - Qi Zhao
- Institute of Translational Medicine, Faculty of Health Sciences, University of Macau, Taipa, Macau
| | - Junbin Huang
- Division of Hematology/Oncology, Department of Pediatrics, The Seventh Affiliated Hospital, Sun Yat-Sen University, Shenzhen, China
| | - Chun Chen
- Division of Hematology/Oncology, Department of Pediatrics, The Seventh Affiliated Hospital, Sun Yat-Sen University, Shenzhen, China
| | - Patrick Man Pan Yuen
- Department of Paediatrics, The Chinese University of Hong Kong, Shatin, Hong Kong
| | - Chi-kong Li
- Department of Paediatrics, The Chinese University of Hong Kong, Shatin, Hong Kong.,Hong Kong Hub of Paediatric Excellence, The Chinese University of Hong Kong, Shatin, Hong Kong.,Corresponding Authors: Kam Tong Leung, E-mail: ; Chi-kong Li, Hong Kong Children's Hospital, 1 Shing Cheong Road, Kowloon Bay, Kowloon, Hong Kong. Phone: 852-3513-3176; Fax: 852-2636-0020; E-mail: ; and Alex Wing Kwan Leung, E-mail:
| | - Alex Wing Kwan Leung
- Department of Paediatrics, The Chinese University of Hong Kong, Shatin, Hong Kong.,Hong Kong Hub of Paediatric Excellence, The Chinese University of Hong Kong, Shatin, Hong Kong.,Corresponding Authors: Kam Tong Leung, E-mail: ; Chi-kong Li, Hong Kong Children's Hospital, 1 Shing Cheong Road, Kowloon Bay, Kowloon, Hong Kong. Phone: 852-3513-3176; Fax: 852-2636-0020; E-mail: ; and Alex Wing Kwan Leung, E-mail:
| | - Kam Tong Leung
- Department of Paediatrics, The Chinese University of Hong Kong, Shatin, Hong Kong.,Hong Kong Hub of Paediatric Excellence, The Chinese University of Hong Kong, Shatin, Hong Kong.,Corresponding Authors: Kam Tong Leung, E-mail: ; Chi-kong Li, Hong Kong Children's Hospital, 1 Shing Cheong Road, Kowloon Bay, Kowloon, Hong Kong. Phone: 852-3513-3176; Fax: 852-2636-0020; E-mail: ; and Alex Wing Kwan Leung, E-mail:
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Lu T, Park S, Han Y, Wang Y, Hubert SM, Futreal PA, Wistuba I, Heymach JV, Reuben A, Zhang J, Wang T. Netie: inferring the evolution of neoantigen-T cell interactions in tumors. Nat Methods 2022; 19:1480-1489. [PMID: 36303017 PMCID: PMC10083098 DOI: 10.1038/s41592-022-01644-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Accepted: 09/09/2022] [Indexed: 11/08/2022]
Abstract
Neoantigens are the key targets of antitumor immune responses from cytotoxic T cells and play a critical role in affecting tumor progressions and immunotherapy treatment responses. However, little is known about how the interaction between neoantigens and T cells ultimately affects the evolution of cancerous masses. Here, we develop a hierarchical Bayesian model, named neoantigen-T cell interaction estimation (netie) to infer the history of neoantigen-CD8+ T cell interactions in tumors. Netie was systematically validated and applied to examine the molecular patterns of 3,219 tumors, compiled from a panel of 18 cancer types. We showed that tumors with an increase in immune selection pressure over time are associated with T cells that have an activation-related expression signature. We also identified a subset of exhausted cytotoxic T cells postimmunotherapy associated with tumor clones that newly arise after treatment. These analyses demonstrate how netie enables the interrogation of the relationship between individual neoantigen repertoires and the tumor molecular profiles. We found that a T cell inflammation gene expression profile (TIGEP) is more predictive of patient outcomes in the tumors with an increase in immune pressure over time, which reveals a curious synergy between T cells and neoantigen distributions. Overall, we provide a new tool that is capable of revealing the imprints left by neoantigens during each tumor's developmental process and of predicting how tumors will progress under further pressure of the host's immune system.
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Affiliation(s)
- Tianshi Lu
- Quantitative Biomedical Research Center, Peter O'Donnell Jr. School of Public Health, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Seongoh Park
- School of Mathematics, Statistics and Data Science, Sungshin Women's University, Seoul, Republic of Korea
| | - Yi Han
- Quantitative Biomedical Research Center, Peter O'Donnell Jr. School of Public Health, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Yunguan Wang
- Quantitative Biomedical Research Center, Peter O'Donnell Jr. School of Public Health, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Shawna Marie Hubert
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - P Andy Futreal
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Ignacio Wistuba
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - John V Heymach
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Alexandre Reuben
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Jianjun Zhang
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Tao Wang
- Quantitative Biomedical Research Center, Peter O'Donnell Jr. School of Public Health, University of Texas Southwestern Medical Center, Dallas, TX, USA.
- Center for the Genetics of Host Defense, University of Texas Southwestern Medical Center, Dallas, TX, USA.
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40
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Jiménez‐Santos MJ, García‐Martín S, Fustero‐Torre C, Di Domenico T, Gómez‐López G, Al‐Shahrour F. Bioinformatics roadmap for therapy selection in cancer genomics. Mol Oncol 2022; 16:3881-3908. [PMID: 35811332 PMCID: PMC9627786 DOI: 10.1002/1878-0261.13286] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Revised: 06/22/2022] [Accepted: 07/08/2022] [Indexed: 12/24/2022] Open
Abstract
Tumour heterogeneity is one of the main characteristics of cancer and can be categorised into inter- or intratumour heterogeneity. This heterogeneity has been revealed as one of the key causes of treatment failure and relapse. Precision oncology is an emerging field that seeks to design tailored treatments for each cancer patient according to epidemiological, clinical and omics data. This discipline relies on bioinformatics tools designed to compute scores to prioritise available drugs, with the aim of helping clinicians in treatment selection. In this review, we describe the current approaches for therapy selection depending on which type of tumour heterogeneity is being targeted and the available next-generation sequencing data. We cover intertumour heterogeneity studies and individual treatment selection using genomics variants, expression data or multi-omics strategies. We also describe intratumour dissection through clonal inference and single-cell transcriptomics, in each case providing bioinformatics tools for tailored treatment selection. Finally, we discuss how these therapy selection workflows could be integrated into the clinical practice.
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Affiliation(s)
| | | | - Coral Fustero‐Torre
- Bioinformatics UnitSpanish National Cancer Research Centre (CNIO)MadridSpain
| | - Tomás Di Domenico
- Bioinformatics UnitSpanish National Cancer Research Centre (CNIO)MadridSpain
| | - Gonzalo Gómez‐López
- Bioinformatics UnitSpanish National Cancer Research Centre (CNIO)MadridSpain
| | - Fátima Al‐Shahrour
- Bioinformatics UnitSpanish National Cancer Research Centre (CNIO)MadridSpain
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41
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Jiang P, Sinha S, Aldape K, Hannenhalli S, Sahinalp C, Ruppin E. Big data in basic and translational cancer research. Nat Rev Cancer 2022; 22:625-639. [PMID: 36064595 PMCID: PMC9443637 DOI: 10.1038/s41568-022-00502-0] [Citation(s) in RCA: 60] [Impact Index Per Article: 30.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 07/26/2022] [Indexed: 02/07/2023]
Abstract
Historically, the primary focus of cancer research has been molecular and clinical studies of a few essential pathways and genes. Recent years have seen the rapid accumulation of large-scale cancer omics data catalysed by breakthroughs in high-throughput technologies. This fast data growth has given rise to an evolving concept of 'big data' in cancer, whose analysis demands large computational resources and can potentially bring novel insights into essential questions. Indeed, the combination of big data, bioinformatics and artificial intelligence has led to notable advances in our basic understanding of cancer biology and to translational advancements. Further advances will require a concerted effort among data scientists, clinicians, biologists and policymakers. Here, we review the current state of the art and future challenges for harnessing big data to advance cancer research and treatment.
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Affiliation(s)
- Peng Jiang
- Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
| | - Sanju Sinha
- Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Kenneth Aldape
- Laboratory of Pathology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Sridhar Hannenhalli
- Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Cenk Sahinalp
- Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Eytan Ruppin
- Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
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42
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Kulman E, Wintersinger J, Morris Q. Reconstructing cancer phylogenies using Pairtree, a clone tree reconstruction algorithm. STAR Protoc 2022; 3:101706. [PMID: 36129821 PMCID: PMC9494285 DOI: 10.1016/j.xpro.2022.101706] [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: 06/01/2022] [Revised: 07/21/2022] [Accepted: 08/22/2022] [Indexed: 01/25/2023] Open
Abstract
Pairtree is a clone tree reconstruction algorithm that uses somatic point mutations to build clone trees describing the evolutionary history of individual cancers. Using the Pairtree software package, we describe steps to preprocess somatic mutation data, cluster mutations into subclones, search for clone trees, and visualize clone trees. Pairtree builds clone trees using up to 100 samples from a single cancer with at least 30 subclonal populations. For complete details on the use and execution of this protocol, please refer to Wintersinger et al. (2022).
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Affiliation(s)
- Ethan Kulman
- Memorial Sloan Kettering Cancer Center, New York, NY, USA,Corresponding author
| | | | - Quaid Morris
- Memorial Sloan Kettering Cancer Center, New York, NY, USA,Corresponding author
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43
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Su X, Bai S, Xie G, Shi Y, Zhao L, Yang G, Tian F, He KY, Wang L, Li X, Long Q, Han ZG. Accurate tumor clonal structures require single-cell analysis. Ann N Y Acad Sci 2022; 1517:213-224. [PMID: 36081327 DOI: 10.1111/nyas.14897] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Tumor clonal structure is closely related to future progression, which has been mainly investigated as mutation abundance clustering in bulk samples. With relatively limited studies at single-cell resolution, a systematic comparison of the two approaches is still lacking. Here, using bulk and single-cell mutational data from the liver and colorectal cancers, we checked whether co-mutations determined by single-cell analysis had corresponding bulk variant allele frequency (VAF) peaks. While bulk analysis suggested the absence of subclonal peaks and, possibly, neutral evolution in some cases, the single-cell analysis identified coexisting subclones. The overlaps of bulk VAF ranges for co-mutations from different subclones made it difficult to separate them. Complex subclonal structures and dynamic evolution could be hidden under the seemingly clonal neutral pattern at the bulk level, suggesting single-cell analysis is necessary to avoid underestimation of tumor heterogeneity.
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Affiliation(s)
- Xianbin Su
- Key Laboratory of Systems Biomedicine (Ministry of Education), Shanghai Center for Systems Biomedicine, Shanghai Jiao Tong University, Shanghai, China
| | - Shihao Bai
- Key Laboratory of Systems Biomedicine (Ministry of Education), Shanghai Center for Systems Biomedicine, Shanghai Jiao Tong University, Shanghai, China
| | - Gangcai Xie
- Institute of Reproductive Medicine, Medical School of Nantong University, Nantong, China
| | - Yi Shi
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders, Shanghai Jiao Tong University, Shanghai, China
| | - Linan Zhao
- Key Laboratory of Systems Biomedicine (Ministry of Education), Shanghai Center for Systems Biomedicine, Shanghai Jiao Tong University, Shanghai, China
| | - Guoliang Yang
- Department of Urology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Futong Tian
- Department of Design, Politecnico di Milano, Milan, Italy
| | - Kun-Yan He
- Key Laboratory of Systems Biomedicine (Ministry of Education), Shanghai Center for Systems Biomedicine, Shanghai Jiao Tong University, Shanghai, China
| | - Lan Wang
- Key Laboratory of Systems Biomedicine (Ministry of Education), Shanghai Center for Systems Biomedicine, Shanghai Jiao Tong University, Shanghai, China
| | - Xiaolin Li
- Key Laboratory of Systems Biomedicine (Ministry of Education), Shanghai Center for Systems Biomedicine, Shanghai Jiao Tong University, Shanghai, China
| | - Qi Long
- Joint School of Life Sciences, Guangzhou Medical University & Guangzhou Institutes of Biomedicine and Health-Chinese Academy of Sciences, Guangzhou, China
| | - Ze-Guang Han
- Key Laboratory of Systems Biomedicine (Ministry of Education), Shanghai Center for Systems Biomedicine, Shanghai Jiao Tong University, Shanghai, China
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Jiang J, Ding Y, Lu J, Chen Y, Chen Y, Zhao W, Chen W, Kong M, Li C, Teng X, Zhou Q, Xu N, Zhou D, Zhou Z, Wang H, Teng L. Integrative analysis reveals a clinicogenomic landscape associated with liver metastasis and poor prognosis in hepatoid adenocarcinoma of the stomach. Int J Biol Sci 2022; 18:5554-5574. [PMID: 36147475 PMCID: PMC9461653 DOI: 10.7150/ijbs.71449] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2022] [Accepted: 08/20/2022] [Indexed: 12/02/2022] Open
Abstract
Hepatoid adenocarcinoma of the stomach (HAS) is a rare subtype of gastric cancer (GC) that histologically resembles hepatocellular carcinoma (HCC). Despite its low incidence, HAS had a poor 5-year survival rate. Currently, the linkages between clinicopathological and genomic features of HAS and its therapeutic targets remain largely unknown. Herein, we enrolled 90 HAS patients and 270 stage-matched non-HAS patients from our institution for comparing clinicopathological features. We found that HAS had worse overall survival and were more prone to develop liver metastasis than non-HAS in our cohort, which was validated via meta-analysis. By comparing whole-exome sequencing data of HAS (n=30), non-HAS (n=63), and HCC (n=355, The Cancer Genome Atlas), we identified a genomic landscape associated with unfavorable clinical features in HAS, which contained frequent somatic mutations and widespread copy number variations. Notably, signaling pathways regulating pluripotency of stem cells affected by frequent genomic alterations might contribute to liver metastasis and poor prognosis in HAS patients. Furthermore, HAS developed abundant multiclonal architecture associated with liver metastasis. Encouragingly, target analysis suggested that HAS patients might potentially benefit from anti-ERBB2 or anti-PD-1 therapy. Taken together, this study systematically demonstrated a high risk of liver metastasis and poor prognosis in HAS, provided a clinicogenomic landscape underlying these unfavorable clinical features, and identified potential therapeutic targets, laying the foundations for developing precise diagnosis and therapy in this rare but lethal disease.
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Affiliation(s)
- Junjie Jiang
- Department of Surgical Oncology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Yongfeng Ding
- Department of Medical Oncology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Jun Lu
- Department of Surgical Oncology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Yanyan Chen
- Department of Surgical Oncology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Yiran Chen
- Department of Surgical Oncology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Wenyi Zhao
- Institute of Drug Metabolism & Pharmaceutical Analysis & Zhejiang Provincial Key Laboratory of Anti-Cancer Drug Research, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
| | - Wenfan Chen
- Institute of Drug Metabolism & Pharmaceutical Analysis & Zhejiang Provincial Key Laboratory of Anti-Cancer Drug Research, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
| | - Mei Kong
- Department of Pathology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Chengzhi Li
- Department of Pathology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Xiaodong Teng
- Department of Pathology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Quan Zhou
- Institute of Immunology, Zhejiang University School of Medicine, Hangzhou, China
| | - Nong Xu
- Department of Medical Oncology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Donghui Zhou
- Department of Surgical Oncology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Zhan Zhou
- Institute of Drug Metabolism & Pharmaceutical Analysis & Zhejiang Provincial Key Laboratory of Anti-Cancer Drug Research, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
| | - Haiyong Wang
- Department of Surgical Oncology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Lisong Teng
- Department of Surgical Oncology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
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45
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Lin PC, Tsai YS, Yeh YM, Shen MR. Cutting-Edge AI Technologies Meet Precision Medicine to Improve Cancer Care. Biomolecules 2022; 12:1133. [PMID: 36009026 PMCID: PMC9405970 DOI: 10.3390/biom12081133] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Revised: 08/11/2022] [Accepted: 08/15/2022] [Indexed: 11/18/2022] Open
Abstract
To provide precision medicine for better cancer care, researchers must work on clinical patient data, such as electronic medical records, physiological measurements, biochemistry, computerized tomography scans, digital pathology, and the genetic landscape of cancer tissue. To interpret big biodata in cancer genomics, an operational flow based on artificial intelligence (AI) models and medical management platforms with high-performance computing must be set up for precision cancer genomics in clinical practice. To work in the fast-evolving fields of patient care, clinical diagnostics, and therapeutic services, clinicians must understand the fundamentals of the AI tool approach. Therefore, the present article covers the following four themes: (i) computational prediction of pathogenic variants of cancer susceptibility genes; (ii) AI model for mutational analysis; (iii) single-cell genomics and computational biology; (iv) text mining for identifying gene targets in cancer; and (v) the NVIDIA graphics processing units, DRAGEN field programmable gate arrays systems and AI medical cloud platforms in clinical next-generation sequencing laboratories. Based on AI medical platforms and visualization, large amounts of clinical biodata can be rapidly copied and understood using an AI pipeline. The use of innovative AI technologies can deliver more accurate and rapid cancer therapy targets.
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Affiliation(s)
- Peng-Chan Lin
- Department of Oncology, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan 704, Taiwan
- Department of Genomic Medicine, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan 704, Taiwan
| | - Yi-Shan Tsai
- Department of Medical Imaging, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan 704, Taiwan
| | - Yu-Min Yeh
- Department of Oncology, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan 704, Taiwan
| | - Meng-Ru Shen
- Institute of Clinical Medicine, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan 704, Taiwan
- Department of Obstetrics and Gynecology, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan 704, Taiwan
- Department of Pharmacology, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan 704, Taiwan
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46
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Rasche L, Schinke C, Maura F, Bauer MA, Ashby C, Deshpande S, Poos AM, Zangari M, Thanendrarajan S, Davies FE, Walker BA, Barlogie B, Landgren O, Morgan GJ, van Rhee F, Weinhold N. The spatio-temporal evolution of multiple myeloma from baseline to relapse-refractory states. Nat Commun 2022; 13:4517. [PMID: 35922426 PMCID: PMC9349320 DOI: 10.1038/s41467-022-32145-y] [Citation(s) in RCA: 29] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Accepted: 07/19/2022] [Indexed: 11/18/2022] Open
Abstract
Deciphering Multiple Myeloma evolution in the whole bone marrow is key to inform curative strategies. Here, we perform spatial-longitudinal whole-exome sequencing, including 140 samples collected from 24 Multiple Myeloma patients during up to 14 years. Applying imaging-guided sampling we observe three evolutionary patterns, including relapse driven by a single-cell expansion, competing/co-existing sub-clones, and unique sub-clones at distinct locations. While we do not find the unique relapse sub-clone in the baseline focal lesion(s), we show a close phylogenetic relationship between baseline focal lesions and relapse disease, highlighting focal lesions as hotspots of tumor evolution. In patients with ≥3 focal lesions on positron-emission-tomography at diagnosis, relapse is driven by multiple distinct sub-clones, whereas in other patients, a single-cell expansion is typically seen (p < 0.01). Notably, we observe resistant sub-clones that can be hidden over years, suggesting that a prerequisite for curative therapies would be to overcome not only tumor heterogeneity but also dormancy.
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Affiliation(s)
- Leo Rasche
- Myeloma Center, University of Arkansas for Medical Sciences, Little Rock, AR, USA
- Department of Internal Medicine 2, University Hospital of Würzburg, Würzburg, Germany
- Mildred Scheel Early Career Center (MSNZ), University Hospital of Würzburg, Würzburg, Germany
| | - Carolina Schinke
- Myeloma Center, University of Arkansas for Medical Sciences, Little Rock, AR, USA
| | - Francesco Maura
- Myeloma Program, Sylvester Comprehensive Cancer Center, University of Miami, Miami, FL, USA
| | - Michael A Bauer
- Myeloma Center, University of Arkansas for Medical Sciences, Little Rock, AR, USA
| | - Cody Ashby
- Myeloma Center, University of Arkansas for Medical Sciences, Little Rock, AR, USA
| | - Shayu Deshpande
- Myeloma Center, University of Arkansas for Medical Sciences, Little Rock, AR, USA
| | - Alexandra M Poos
- Department of Internal Medicine V, University Hospital of Heidelberg, Heidelberg, Germany
| | - Maurizio Zangari
- Myeloma Center, University of Arkansas for Medical Sciences, Little Rock, AR, USA
| | | | - Faith E Davies
- Perlmutter Cancer Center, New York University Langone Health, New York, NY, USA
| | - Brian A Walker
- Division of Hematology Oncology, Indiana University, Indianapolis, IN, USA
| | - Bart Barlogie
- Myeloma Center, University of Arkansas for Medical Sciences, Little Rock, AR, USA
| | - Ola Landgren
- Myeloma Program, Sylvester Comprehensive Cancer Center, University of Miami, Miami, FL, USA
| | - Gareth J Morgan
- Perlmutter Cancer Center, New York University Langone Health, New York, NY, USA
| | - Frits van Rhee
- Myeloma Center, University of Arkansas for Medical Sciences, Little Rock, AR, USA
| | - Niels Weinhold
- Myeloma Center, University of Arkansas for Medical Sciences, Little Rock, AR, USA.
- Department of Internal Medicine V, University Hospital of Heidelberg, Heidelberg, Germany.
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47
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Wolf Y, Samuels Y. Intratumor Heterogeneity and Antitumor Immunity Shape One Another Bidirectionally. Clin Cancer Res 2022; 28:2994-3001. [PMID: 35380639 PMCID: PMC9306293 DOI: 10.1158/1078-0432.ccr-21-1355] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Revised: 02/10/2022] [Accepted: 03/28/2022] [Indexed: 01/07/2023]
Abstract
Over the last decade, it has become clear that the genomic landscapes of tumors profoundly impact their immunogenicity and how tumor cells interact with immune cells. Whereas past discoveries mainly focused on the interplay between tumor immunogenicity and tumor mutational burden (TMB), under the assumption that a higher mutation load would give rise to a better patient response to immune checkpoint blockade therapies, we and others have underlined intratumor heterogeneity (ITH) as an important determinant of the magnitude of the antitumor response and the nature of the tumor microenvironment. In this review, we define TMB versus ITH and how the two factors are being inferred from data, examine key findings in the cancer immunogenomics literature deciphering the complex cross-talk between TMB, ITH, and antitumor immunity in human cancers and in vivo models, and discuss the mutual influence of ITH and immunity-how the antitumor response can give rise to tumors with higher ITH, and how higher ITH can put shackles on the antitumor response.
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Affiliation(s)
- Yochai Wolf
- Ella Lemelbaum Institute for Immuno-Oncology and Skin Cancer, Sheba Medical Center, Tel Hashomer, Ramat Gan, Israel.,Corresponding Authors: Yochai Wolf, Ella Lemelbaum Institute for Immuno-Oncology and Skin Cancer, Sheba Medical Center, Tel Hashomer, Ramat Gan 5265601, Israel. E-mail: ; and Yardena Samuels, Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot 761000, Israel. E-mail:
| | - Yardena Samuels
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel.,Corresponding Authors: Yochai Wolf, Ella Lemelbaum Institute for Immuno-Oncology and Skin Cancer, Sheba Medical Center, Tel Hashomer, Ramat Gan 5265601, Israel. E-mail: ; and Yardena Samuels, Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot 761000, Israel. E-mail:
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48
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Angaroni F, Guidi A, Ascolani G, d'Onofrio A, Antoniotti M, Graudenzi A. J-SPACE: a Julia package for the simulation of spatial models of cancer evolution and of sequencing experiments. BMC Bioinformatics 2022; 23:269. [PMID: 35804300 PMCID: PMC9270769 DOI: 10.1186/s12859-022-04779-8] [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: 02/23/2022] [Accepted: 06/09/2022] [Indexed: 11/15/2022] Open
Abstract
Background The combined effects of biological variability and measurement-related errors on cancer sequencing data remain largely unexplored. However, the spatio-temporal simulation of multi-cellular systems provides a powerful instrument to address this issue. In particular, efficient algorithmic frameworks are needed to overcome the harsh trade-off between scalability and expressivity, so to allow one to simulate both realistic cancer evolution scenarios and the related sequencing experiments, which can then be used to benchmark downstream bioinformatics methods. Result We introduce a Julia package for SPAtial Cancer Evolution (J-SPACE), which allows one to model and simulate a broad set of experimental scenarios, phenomenological rules and sequencing settings.Specifically, J-SPACE simulates the spatial dynamics of cells as a continuous-time multi-type birth-death stochastic process on a arbitrary graph, employing different rules of interaction and an optimised Gillespie algorithm. The evolutionary dynamics of genomic alterations (single-nucleotide variants and indels) is simulated either under the Infinite Sites Assumption or several different substitution models, including one based on mutational signatures. After mimicking the spatial sampling of tumour cells, J-SPACE returns the related phylogenetic model, and allows one to generate synthetic reads from several Next-Generation Sequencing (NGS) platforms, via the ART read simulator. The results are finally returned in standard FASTA, FASTQ, SAM, ALN and Newick file formats. Conclusion J-SPACE is designed to efficiently simulate the heterogeneous behaviour of a large number of cancer cells and produces a rich set of outputs. Our framework is useful to investigate the emergent spatial dynamics of cancer subpopulations, as well as to assess the impact of incomplete sampling and of experiment-specific errors. Importantly, the output of J-SPACE is designed to allow the performance assessment of downstream bioinformatics pipelines processing NGS data. J-SPACE is freely available at: https://github.com/BIMIB-DISCo/J-Space.jl.
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Affiliation(s)
- Fabrizio Angaroni
- Dept. of Informatics, Systems and Communication, Univ. of Milan-Bicocca, Milan, Italy.
| | - Alessandro Guidi
- Dept. of Informatics, Systems and Communication, Univ. of Milan-Bicocca, Milan, Italy
| | - Gianluca Ascolani
- Dept. of Informatics, Systems and Communication, Univ. of Milan-Bicocca, Milan, Italy
| | - Alberto d'Onofrio
- Department of Mathematics and Geosciences, Univ. of Trieste, Trieste, Italy
| | - Marco Antoniotti
- Dept. of Informatics, Systems and Communication, Univ. of Milan-Bicocca, Milan, Italy.,Bicocca Bioinformatics, Biostatistics and Bioimaging Centre (B4), Milan, Italy
| | - Alex Graudenzi
- Dept. of Informatics, Systems and Communication, Univ. of Milan-Bicocca, Milan, Italy.,Bicocca Bioinformatics, Biostatistics and Bioimaging Centre (B4), Milan, Italy.,Inst. of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR), Segrate, Italy
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49
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Menssen AJ, Khanna A, Miller CA, Nonavinkere Srivatsan S, Chang GS, Shao J, Robinson J, O'Laughlin M, Fronick CC, Fulton RS, Brendel K, Heath SE, Saba R, Welch JS, Spencer DH, Payton JE, Westervelt P, DiPersio JF, Link DC, Schuelke MJ, Jacoby MA, Duncavage EJ, Ley TJ, Walter MJ. Convergent Clonal Evolution of Signaling Gene Mutations Is a Hallmark of Myelodysplastic Syndrome Progression. Blood Cancer Discov 2022; 3:330-345. [PMID: 35709710 PMCID: PMC9338759 DOI: 10.1158/2643-3230.bcd-21-0155] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2021] [Revised: 02/21/2022] [Accepted: 05/06/2022] [Indexed: 12/17/2022] Open
Abstract
Progression from myelodysplastic syndromes (MDS) to secondary acute myeloid leukemia (AML) is associated with the acquisition and expansion of subclones. Our understanding of subclone evolution during progression, including the frequency and preferred order of gene mutation acquisition, remains incomplete. Sequencing of 43 paired MDS and secondary AML samples identified at least one signaling gene mutation in 44% of MDS and 60% of secondary AML samples, often below the level of standard sequencing detection. In addition, 19% of MDS and 47% of secondary AML patients harbored more than one signaling gene mutation, almost always in separate, coexisting subclones. Signaling gene mutations demonstrated diverse patterns of clonal evolution during disease progression, including acquisition, expansion, persistence, and loss of mutations, with multiple patterns often coexisting in the same patient. Multivariate analysis revealed that MDS patients who had a signaling gene mutation had a higher risk of AML progression, potentially providing a biomarker for progression. SIGNIFICANCE Subclone expansion is a hallmark of progression from MDS to secondary AML. Subclonal signaling gene mutations are common at MDS (often at low levels), show complex and convergent patterns of clonal evolution, and are associated with future progression to secondary AML. See related article by Guess et al., p. 316 (33). See related commentary by Romine and van Galen, p. 270. This article is highlighted in the In This Issue feature, p. 265.
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Affiliation(s)
- Andrew J. Menssen
- Division of Oncology, Department of Internal Medicine, Washington University School of Medicine, St. Louis, Missouri
| | - Ajay Khanna
- Division of Oncology, Department of Internal Medicine, Washington University School of Medicine, St. Louis, Missouri
| | - Christopher A. Miller
- Division of Oncology, Department of Internal Medicine, Washington University School of Medicine, St. Louis, Missouri
| | - Sridhar Nonavinkere Srivatsan
- Division of Oncology, Department of Internal Medicine, Washington University School of Medicine, St. Louis, Missouri
| | - Gue Su Chang
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, Missouri
| | - Jin Shao
- Division of Oncology, Department of Internal Medicine, Washington University School of Medicine, St. Louis, Missouri
| | - Joshua Robinson
- Division of Oncology, Department of Internal Medicine, Washington University School of Medicine, St. Louis, Missouri
| | - Michele O'Laughlin
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, Missouri
| | - Catrina C. Fronick
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, Missouri
| | - Robert S. Fulton
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, Missouri
| | - Kimberly Brendel
- Division of Oncology, Department of Internal Medicine, Washington University School of Medicine, St. Louis, Missouri
| | - Sharon E. Heath
- Division of Oncology, Department of Internal Medicine, Washington University School of Medicine, St. Louis, Missouri
| | - Raya Saba
- Division of Oncology, Department of Internal Medicine, Washington University School of Medicine, St. Louis, Missouri
| | - John S. Welch
- Division of Oncology, Department of Internal Medicine, Washington University School of Medicine, St. Louis, Missouri
- Siteman Cancer Center, Washington University School of Medicine, St. Louis, Missouri
| | - David H. Spencer
- Division of Oncology, Department of Internal Medicine, Washington University School of Medicine, St. Louis, Missouri
- Siteman Cancer Center, Washington University School of Medicine, St. Louis, Missouri
| | - Jacqueline E. Payton
- Siteman Cancer Center, Washington University School of Medicine, St. Louis, Missouri
- Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, Missouri
| | - Peter Westervelt
- Division of Oncology, Department of Internal Medicine, Washington University School of Medicine, St. Louis, Missouri
- Siteman Cancer Center, Washington University School of Medicine, St. Louis, Missouri
| | - John F. DiPersio
- Division of Oncology, Department of Internal Medicine, Washington University School of Medicine, St. Louis, Missouri
- Siteman Cancer Center, Washington University School of Medicine, St. Louis, Missouri
| | - Daniel C. Link
- Division of Oncology, Department of Internal Medicine, Washington University School of Medicine, St. Louis, Missouri
- Siteman Cancer Center, Washington University School of Medicine, St. Louis, Missouri
| | - Matthew J. Schuelke
- Division of Biostatistics, Washington University School of Medicine in St. Louis, St. Louis, Missouri
| | - Meagan A. Jacoby
- Division of Oncology, Department of Internal Medicine, Washington University School of Medicine, St. Louis, Missouri
- Siteman Cancer Center, Washington University School of Medicine, St. Louis, Missouri
| | - Eric J. Duncavage
- Siteman Cancer Center, Washington University School of Medicine, St. Louis, Missouri
- Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, Missouri
| | - Timothy J. Ley
- Division of Oncology, Department of Internal Medicine, Washington University School of Medicine, St. Louis, Missouri
- Siteman Cancer Center, Washington University School of Medicine, St. Louis, Missouri
| | - Matthew J. Walter
- Division of Oncology, Department of Internal Medicine, Washington University School of Medicine, St. Louis, Missouri
- Siteman Cancer Center, Washington University School of Medicine, St. Louis, Missouri
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50
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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:msac136. [PMID: 35749590 PMCID: PMC9260306 DOI: 10.1093/molbev/msac136] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Understanding intratumor 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 (>300× 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 30×. 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 30× and 200×, whereas 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 and 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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