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Polyclonal-to-monoclonal transition in colorectal precancerous evolution

Nature volume 636, pages 233–240 (2024) Cite this article

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Abstract

Unravelling the origin and evolution of precancerous lesions is crucial for effectively preventing malignant transformation, yet our current knowledge remains limited1,2,3. 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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Fig. 1: SMALT lineage tracing of mouse intestinal tumorigenesis.
The alternative text for this image may have been generated using AI.
Fig. 2: Single-cell phylogenies reveal the origin of inflammation-driven neoplasms.
The alternative text for this image may have been generated using AI.
Fig. 3: Polyclonal-to-monoclonal transition in human sporadic polyps.
The alternative text for this image may have been generated using AI.
Fig. 4: Intercellular interactions and polyclonal-to-monoclonal evolution model.
The alternative text for this image may have been generated using AI.

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Data availability

All processed data generated in this study have been deposited and are available at Zenodo (https://zenodo.org/records/11647317 (ref. 82)). The single-cell phylogenies generated from this study are displayed at https://smalt-phylogeny.org/. Raw data are publicly available from the National Genomics Data Center (NGDC) under the accession numbers PRJCA024217 and PRJCA023981. Public scRNA-seq data for two wild-type mouse colon samples can be accessed with GSE134255 (https://www.ncbi.nlm.nih.gov/).

Code availability

All computer code used in this study is available from the GitHub repositories https://github.com/zhaolianlu/SMALT-mouse and https://github.com/zhaolianlu/Homo-preCRC.

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Acknowledgements

The authors thank C. Curtis, Q. Nie, H. Ji, W. Zhai and Hu laboratory members for constructive discussions, and B. Chen for the guidance of gland isolations. This work was supported by National Key R&D Program of China (2021YFA1302500 to Z. Hu and X.H.), National Natural Sciences Foundation of China (82241236 and 32270693 to Z. Hu, 32293190 and 32293191 to X.H., 82273346 to Z. He and 32100486 to Z.L.), Guangdong Basic and Applied Basic Research Foundation (2021B1515020042 to Z. Hu) and Shenzhen Science and Technology Program (RCBS20210706092346032 to Z.L.).

Author information

Author notes

  1. These authors contributed equally: Zhaolian Lu, Shanlan Mo, Duo Xie, Xiangwei Zhai, Shanjun Deng

Authors and Affiliations

  1. Key Laboratory of Quantitative Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China

    Zhaolian Lu, Shanlan Mo, Duo Xie, Kantian Zhou, Kun Wang, Xueling Kang, Hao Zhang, Juanzhen Tong, Liangzhen Hou, Huijuan Hu, Xuefei Li & Zheng Hu

  2. SIAT-HKUST Joint Laboratory of Cell Evolution and Digital Health, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China

    Zhaolian Lu, Shanlan Mo, Kantian Zhou, Xueling Kang, Hao Zhang, Juanzhen Tong, Jiguang Wang & Zheng Hu

  3. Cancer Centre, Faculty of Health Sciences, University of Macau, Taipa, Macau, China

    Duo Xie, Liangzhen Hou & Leo Tsz On Lee

  4. 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

    Xiangwei Zhai, Shanjun Deng, Li Liu & Xionglei He

  5. School of Mathematical Sciences, Xiamen University, Xiamen, China

    Kun Wang & Da Zhou

  6. National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, China

    Kun Wang & Da Zhou

  7. Ministry of Education Frontiers Science Center for Precision Oncology, University of Macau, Macau, China

    Leo Tsz On Lee

  8. Department of Pathology, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China

    Yaxi Zhu

  9. Department of General Surgery, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China

    Jing Yu, Ping Lan & Zhen He

  10. Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China

    Jing Yu & Ping Lan

  11. 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

    Jiguang Wang

  12. Key Laboratory of Human Microbiome and Chronic Diseases (Sun Yat-sen University), Ministry of Education, Guangzhou, China

    Zhen He

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  1. Zhaolian Lu
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Contributions

Z. Hu, X.H. and Z.L. conceived and designed the study. Z.L., S.D., K.Z. and J.T. performed mouse experiments. Z.L. analysed the targeted long-read sequencing data. S.M. and H.Z. conducted single gland-related experiments. S.M. and D.X. analysed the WGS and WES data. X.Z. analysed the scRNA-seq data. Z. Hu and D.X. performed computational modelling and inferences. X.K. performed organoid cultures. J.W. provided guidance on data analysis. L.T.O.L. provided guidance on mouse experiments. Z. He, P.L. and J.Y. identified patients with synchronous polyps and CRC, performed colonoscopic polypectomy and collected clinical specimens. Y.Z. conducted pathological diagnosis and analysis. Z. Hu, Z.L., S.M., D.X. and X.Z. wrote the manuscript with contributions from all co-authors. Z. Hu, X.H. and Z. He supervised the project.

Corresponding authors

Correspondence to Zhen He, Xionglei He or Zheng Hu.

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Nature thanks James DeGregori, Richard Halberg and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

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Extended data figures and tables

Extended Data Fig. 1 Establishment of AOM/DSS and ApcMin/+ mouse models carrying the SMALT lineage tracing system.

a, The 3 kb DNA barcode consists of 16 different tandem targets, each with an 18 bp iSceI binding site and a 156 bp editing region. The HsAID protein mainly induces C-to-T (or G-to-A) mutations in the editing region, which can be used to reconstruct single-cell phylogenies. b, The SMALT system was knocked into the H11 locus of C57BL/6 J mouse. The insert contains hsAID, iSceI and a 3 kb DNA barcode. c, Schematic of our mouse experiments following standard dose and course of drug treatment. d, Mouse absolute weight (top) and relative weight to initial timepoint (bottom) were recorded over time in days. Error bars presented as mean ± SEM. e, At the endpoint of experiment, normal colons, AOM/DSS colons or whole small intestines in ApcMin/+ mice were dissected.

Extended Data Fig. 2 Barcode mutation burden among sorted immune (CD45+) and epithelial (EpCAM+) cells.

a, FACS sorting of immune and epithelial neoplastic cells using the cell-surface markers, CD45 and EpCAM, respectively. Vertical and horizontal lines indicate the threshold for positive cells. b, The mutation burden for sorted immune cells and neoplastic cells, where a filter step for the tumor cells was applied using the cutoff of 75th percentile of total mutation counts among adjacent normal cells. P values, two-sided Wilcoxon rank-sum test. c, Representative single-cell phylogenies including real normal cells, plausible normal cells within neoplastic samples and post-filtering neoplastic cells. Red, post-filtering neoplastic cells; blue, normal colon cells from adjacent colon tissues; purple, plausible normal cells within neoplastic tissues (discarded in downstream analysis). Each phylogenetic tree are reconstructed using approximately 500 cells sampled from each cell subpopulation.

Extended Data Fig. 3 Phylogenetic trees of all 30 AOM/DSS neoplasms.

Each dot at the external node of phylogenetic trees represents an individual cell. Neoplastic cells are colored in red while normal colon cells from corresponding adjacent colons are colored in blue. Total number of cells from paired normal/neoplastic tissues and the number of founding progeniors are shown.

Extended Data Fig. 4 Polyclonal origins of ApcMin/+ mouse polyps.

a, Single-cell phylogenies including neoplastic cells from an individual polyp and normal intestinal cells from the same mouse. b, Image of the polyp Apc68_P5 which was dissected into five regions and sequenced separately. c, Single-cell phylogeny of Apc68_P5 showing strong clonal expansions in regions R1 and R5. d, Mutational cellular frequencies (MCF) on barcodes support that all polyps from the two mice are polyclonal, whereas certain regions of Apc68_P5 (R1 and R5) are under strong clonal expansions. e, Cells from clonally expanding regions of R1 (n = 924 cells) and R5 (n = 1,139 cells) have higher fitness scores as compared to regions R2 (n = 196 cells), R3 (n = 197 cells) and R4 (n = 454 cells). Box plots: bar, median; box, 25th to 75th interquartile range (IQR); vertical/horizontal line across box, data within 1.5 times the IQR.

Extended Data Fig. 5 The number and timing of founding progenitors in ApcMin/+ mouse polyps.

a, Schematic of the timing inference for the founding of ApcMin/+ polyclonal polyps. The average barcode mutation burden in normal cells can be expressed as m0 = µ0T, where T is the time from fertilized egg to time of polyp sampling. The average mutation burden for neoplastic cells within each monophyletic clade in the phylogenetic tree is m1. The ratio of the neoplastic cell mutation rate to normal cell mutation rate (denoted by r) is estimated from mutation accumulation data by in vitro organoid cultures. b-c, Organoids were generated from normal small intestine tissues and neoplastic polyps from a SMALT -carrying ApcMin/+ mouse. Organoids were cultured for 30 days with Dox (2 µg/ml)-containing medium. The SMALT barcode in organoid samples were sequenced by PacBio long-read sequencer at day 0, day 15 and day 30, respectively. d-e, Linear regression for the number of barcode mutations over the time of culture (days) for ApcMin/+ normal organoids (d) and neoplastic organoids (e), respectively. f, The estimated number of founding progenitors (Np) for each polyp from Apc68 (left) or Apc72 (right). Cells from each polyp were randomly downsampled 20 times for tree reconstruction and Np calculation (n = 20 downsamplings) (Methods). g, The estimated timing (in postnatal days) of progenitors founding each polyp in Apc68 (left) or Apc72 (right) (n = 20 downsamplings). Dot and error bars represent the median and interquartile range (IQR), respectively. Red and grey dashed lines denote the time of mouse birth and tissue sampling, respectively. Box plots: bar, median; box, 25th to 75th interquartile range (IQR); vertical/horizontal line across box, data within 1.5 times the IQR.

Extended Data Fig. 6 The landscape of somatic copy number alterations (SCNAs) in polyps and CRCs.

Each column in the figure represents a sample and each row defines a regional window in the chromosome. P_poly, polyclonal polyp; P_mono, monoclonal polyp; T_poly, polyclonal tumor; T_mono, monoclonal tumor. AMP, amplification; DEL, deletion. wGII, weighted genome instability index. CIN, chromosomal instability.

Extended Data Fig. 7 Whole-genome sequencing (WGS) of 32 single glands from normal and neoplastic tissues of a sporadic patient B139.

a, H&E images of the normal and five different neoplastic tissues. Scale bar: 200 um. b, Heatmap showing the occurrence of all SSNVs among individual glands. c, Heatmap showing the occurrence of putative driver mutations among individual glands. fs_Del, frameshift deletion; fs_Ins, frameshift insertion. SSNVs, somatic single nucleotide variants; SCNA, somatic copy number alterations; LOH, loss of heterozygosity. AMP, amplification.

Extended Data Fig. 8 Single-cell RNA-seq profiles of AOM/DSS-induced colon neoplasms.

a, Diagram of uniform manifold approximation and projection (UMAP) showing the integration of single-cell RNA-seq data from wild-type (WT) normal samples33 (n  =  2) and AOM/DSS neoplasms in this study (n  =  9). b, UMAP plot showing the single cells colored by individual samples. c, UMAP plot showing the single cells colored by annotated cell types. d, Proportion of cell types in each sample ordered by increase of lesion clonality (1/Np), where Np represents the number of founding progenitors. e, Spearman’s correlation between the proportion of macrophages, neutrophils, endothelial or epithelial cells and the lesion clonality (1/Np). The shaded region represents 95% CI of regression. Spearman’s ρ and P value are shown. d, Proportion of cell subtypes in each sample ordered by increase of lesion clonality (1/Np). g, Hallmark pathway analysis of differentially expressed genes between high clonality versus low clonality AOM/DSS neoplasms. Late and early lesions are defined as Np ≤ 3 and >3, respectively. NES, normalized enrichment score.

Extended Data Fig. 9 Circular network plots showing the intensity of 14 L/R interactions between epithelial cell types in early versus late AOM/DSS neoplasms.

The nodes represent major epithelial cell subtypes. The thickness of edges represents the average number of ligand/receptor interactions (LRIs) between two cell subtypes after 50 times of downsamplings.

Extended Data Fig. 10 Inter-cellular interactions and models of polyclonal orgins.

a-b, The number of LRIs among the 14 L/R pairs that are contributed by each of the 10 epithelial cell types. Each epithelial cell type can act as a sender or receiver. c, Pathways enriched and activated in Krt20+ neoplastic cells (Epi-Krt20) compared to other epithelial cell subclusters. Early and late represents the lesions with Np > 3 and Np \(\le \) 3, respectively. d, Two models of polyclonal origins in precancer. In the recruitment model, a neoplastic clone facilitates the growth of its neighboring normal cells. Subsequently, multiple lineages arise and contribute to a polyclonal neoplastic lesion. In the collision model, multiple neoplastic lineages, such as those independently acquiring a driver mutation, form a single lesion through random collisions in the same spatial proximity.

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Lu, Z., Mo, S., Xie, D. et al. Polyclonal-to-monoclonal transition in colorectal precancerous evolution. Nature 636, 233–240 (2024). https://doi.org/10.1038/s41586-024-08133-1

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