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Lu Z, Mo S, Xie D, Zhai X, Deng S, Zhou K, Wang K, Kang X, Zhang H, Tong J, Hou L, Hu H, Li X, Zhou D, Lee LTO, Liu L, Zhu Y, Yu J, Lan P, Wang J, He Z, He X, Hu Z. Polyclonal-to-monoclonal transition in colorectal precancerous evolution. Nature 2024; 636:233-240. [PMID: 39478225 DOI: 10.1038/s41586-024-08133-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Accepted: 09/27/2024] [Indexed: 12/06/2024]
Abstract
Unravelling the origin and evolution of precancerous lesions is crucial for effectively preventing malignant transformation, yet our current knowledge remains limited1-3. Here we used a base editor-enabled DNA barcoding system4 to comprehensively map single-cell phylogenies in mouse models of intestinal tumorigenesis induced by inflammation or loss of the Apc gene. Through quantitative analysis of high-resolution phylogenies including 260,922 single cells from normal, inflamed and neoplastic intestinal tissues, we identified tens of independent cell lineages undergoing parallel clonal expansions within each lesion. We also found polyclonal origins of human sporadic colorectal polyps through bulk whole-exome sequencing and single-gland whole-genome sequencing. Genomic and clinical data support a model of polyclonal-to-monoclonal transition, with monoclonal lesions representing a more advanced stage. Single-cell RNA sequencing revealed extensive intercellular interactions in early polyclonal lesions, but there was significant loss of interactions during monoclonal transition. Therefore, our data suggest that colorectal precancer is often founded by many different lineages and highlight their cooperative interactions in the earliest stages of cancer formation. These findings provide insights into opportunities for earlier intervention in colorectal cancer.
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Affiliation(s)
- Zhaolian Lu
- Key Laboratory of Quantitative Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- SIAT-HKUST Joint Laboratory of Cell Evolution and Digital Health, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Shanlan Mo
- Key Laboratory of Quantitative Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- SIAT-HKUST Joint Laboratory of Cell Evolution and Digital Health, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Duo Xie
- Key Laboratory of Quantitative Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- Cancer Centre, Faculty of Health Sciences, University of Macau, Taipa, Macau, China
| | - Xiangwei Zhai
- MOE Key Laboratory of Gene Function and Regulation, State Key Laboratory of Biocontrol, Innovation Center for Evolutionary Synthetic Biology, School of Life Sciences, Sun Yat-Sen University, Guangzhou, China
| | - Shanjun Deng
- MOE Key Laboratory of Gene Function and Regulation, State Key Laboratory of Biocontrol, Innovation Center for Evolutionary Synthetic Biology, School of Life Sciences, Sun Yat-Sen University, Guangzhou, China
| | - Kantian Zhou
- Key Laboratory of Quantitative Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- SIAT-HKUST Joint Laboratory of Cell Evolution and Digital Health, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Kun Wang
- Key Laboratory of Quantitative Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- School of Mathematical Sciences, Xiamen University, Xiamen, China
- National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, China
| | - Xueling Kang
- Key Laboratory of Quantitative Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- SIAT-HKUST Joint Laboratory of Cell Evolution and Digital Health, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Hao Zhang
- Key Laboratory of Quantitative Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- SIAT-HKUST Joint Laboratory of Cell Evolution and Digital Health, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Juanzhen Tong
- Key Laboratory of Quantitative Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- SIAT-HKUST Joint Laboratory of Cell Evolution and Digital Health, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Liangzhen Hou
- Key Laboratory of Quantitative Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- Cancer Centre, Faculty of Health Sciences, University of Macau, Taipa, Macau, China
| | - Huijuan Hu
- Key Laboratory of Quantitative Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Xuefei Li
- Key Laboratory of Quantitative Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Da Zhou
- School of Mathematical Sciences, Xiamen University, Xiamen, China
- National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, China
| | - Leo Tsz On Lee
- Cancer Centre, Faculty of Health Sciences, University of Macau, Taipa, Macau, China
- Ministry of Education Frontiers Science Center for Precision Oncology, University of Macau, Macau, China
| | - Li Liu
- MOE Key Laboratory of Gene Function and Regulation, State Key Laboratory of Biocontrol, Innovation Center for Evolutionary Synthetic Biology, School of Life Sciences, Sun Yat-Sen University, Guangzhou, China
| | - Yaxi Zhu
- Department of Pathology, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Jing Yu
- Department of General Surgery, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Ping Lan
- Department of General Surgery, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Jiguang Wang
- SIAT-HKUST Joint Laboratory of Cell Evolution and Digital Health, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- Division of Life Science, Department of Chemical and Biological Engineering, State Key Laboratory of Molecular Neuroscience, The Hong Kong University of Science and Technology, Hong Kong SAR, China
| | - Zhen He
- Department of General Surgery, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
- Key Laboratory of Human Microbiome and Chronic Diseases (Sun Yat-sen University), Ministry of Education, Guangzhou, China.
| | - Xionglei He
- MOE Key Laboratory of Gene Function and Regulation, State Key Laboratory of Biocontrol, Innovation Center for Evolutionary Synthetic Biology, School of Life Sciences, Sun Yat-Sen University, Guangzhou, China.
| | - Zheng Hu
- Key Laboratory of Quantitative Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
- SIAT-HKUST Joint Laboratory of Cell Evolution and Digital Health, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
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Sadien ID, Adler S, Mehmed S, Bailey S, Sawle A, Couturier DL, Eldridge M, Adams DJ, Kemp R, Lourenço FC, Winton DJ. Polyclonality overcomes fitness barriers in Apc-driven tumorigenesis. Nature 2024; 634:1196-1203. [PMID: 39478206 PMCID: PMC11525183 DOI: 10.1038/s41586-024-08053-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Accepted: 09/16/2024] [Indexed: 11/02/2024]
Abstract
Loss-of-function mutations in the tumour suppressor APC are an initial step in intestinal tumorigenesis1,2. APC-mutant intestinal stem cells outcompete their wild-type neighbours through the secretion of Wnt antagonists, which accelerates the fixation and subsequent rapid clonal expansion of mutants3-5. Reports of polyclonal intestinal tumours in human patients and mouse models appear at odds with this process6,7. Here we combine multicolour lineage tracing with chemical mutagenesis in mice to show that a large proportion of intestinal tumours have a multiancestral origin. Polyclonal tumours retain a structure comprising subclones with distinct Apc mutations and transcriptional states, driven predominantly by differences in KRAS and MYC signalling. These pathway-level changes are accompanied by profound differences in cancer stem cell phenotypes. Of note, these findings are confirmed by introducing an oncogenic Kras mutation that results in predominantly monoclonal tumour formation. Further, polyclonal tumours have accelerated growth dynamics, suggesting a link between polyclonality and tumour progression. Together, these findings demonstrate the role of interclonal interactions in promoting tumorigenesis through non-cell autonomous pathways that are dependent on the differential activation of oncogenic pathways between clones.
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Affiliation(s)
- Iannish D Sadien
- Cancer Research UK Cambridge Institute, Li Ka Shing Centre, Cambridge, UK
| | - Sam Adler
- Cancer Research UK Cambridge Institute, Li Ka Shing Centre, Cambridge, UK
| | - Shenay Mehmed
- Cancer Research UK Cambridge Institute, Li Ka Shing Centre, Cambridge, UK
| | - Sasha Bailey
- Tumour Cell Biology Laboratory, The Francis Crick Institute, London, UK
| | - Ashley Sawle
- Cancer Research UK Cambridge Institute, Li Ka Shing Centre, Cambridge, UK
| | | | - Matthew Eldridge
- Cancer Research UK Cambridge Institute, Li Ka Shing Centre, Cambridge, UK
| | - David J Adams
- Wellcome Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK
| | - Richard Kemp
- Cancer Research UK Cambridge Institute, Li Ka Shing Centre, Cambridge, UK
| | - Filipe C Lourenço
- Cancer Research UK Cambridge Institute, Li Ka Shing Centre, Cambridge, UK
| | - Douglas J Winton
- Cancer Research UK Cambridge Institute, Li Ka Shing Centre, Cambridge, UK.
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3
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Islam M, Yang Y, Simmons AJ, Shah VM, Pavan MK, Xu Y, Tasneem N, Chen Z, Trinh LT, Molina P, Ramirez-Solano MA, Sadien I, Dou J, Chen K, Magnuson MA, Rathmell JC, Macara IG, Winton D, Liu Q, Zafar H, Kalhor R, Church GM, Shrubsole MJ, Coffey RJ, Lau KS. Temporal recording of mammalian development and precancer. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.18.572260. [PMID: 38187699 PMCID: PMC10769302 DOI: 10.1101/2023.12.18.572260] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/09/2024]
Abstract
Key to understanding many biological phenomena is knowing the temporal ordering of cellular events, which often require continuous direct observations [1, 2]. An alternative solution involves the utilization of irreversible genetic changes, such as naturally occurring mutations, to create indelible markers that enables retrospective temporal ordering [3-8]. Using NSC-seq, a newly designed and validated multi-purpose single-cell CRISPR platform, we developed a molecular clock approach to record the timing of cellular events and clonality in vivo , while incorporating assigned cell state and lineage information. Using this approach, we uncovered precise timing of tissue-specific cell expansion during murine embryonic development and identified new intestinal epithelial progenitor states by their unique genetic histories. NSC-seq analysis of murine adenomas and single-cell multi-omic profiling of human precancers as part of the Human Tumor Atlas Network (HTAN), including 116 scRNA-seq datasets and clonal analysis of 418 human polyps, demonstrated the occurrence of polyancestral initiation in 15-30% of colonic precancers, revealing their origins from multiple normal founders. Thus, our multimodal framework augments existing single-cell analyses and lays the foundation for in vivo multimodal recording, enabling the tracking of lineage and temporal events during development and tumorigenesis.
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