Single-cell lineages reveal the rates, routes, and drivers of metastasis in cancer xenografts

doi:10.1126/science.abc1944

. 2021 Feb 26;371(6532):eabc1944.

doi: 10.1126/science.abc1944. Epub 2021 Jan 21.

Single-cell lineages reveal the rates, routes, and drivers of metastasis in cancer xenografts

Jeffrey J Quinn^#^{1

2

3}, Matthew G Jones^#^{1

2

4

5

6}, Ross A Okimoto^{7

8}, Shigeki Nanjo^{7

8}, Michelle M Chan^{1

2

9}, Nir Yosef^{10

11

12

13}, Trever G Bivona^{14

7

8}, Jonathan S Weissman^{14

2

15

16}

Affiliations

¹ Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA, USA.
² Howard Hughes Medical Institute, University of California, San Francisco, San Francisco, CA, USA.
³ Inscripta, Inc., Boulder, CO, USA.
⁴ Biological and Medical Informatics Graduate Program, University of California, San Francisco, San Francisco, CA, USA.
⁵ Integrative Program in Quantitative Biology, University of California, San Francisco, San Francisco, CA, USA.
⁶ Center for Computational Biology, University of California, Berkeley, Berkeley, CA, USA.
⁷ UCSF Department of Medicine, University of California, San Francisco, San Francisco, CA, USA.
⁸ Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA.
⁹ Department of Molecular Biology, Princeton University, Princeton, NJ, USA.
¹⁰ Center for Computational Biology, University of California, Berkeley, Berkeley, CA, USA. weissman@wi.mit.edu trever.bivona@ucsf.edu niryosef@berkeley.edu.
¹¹ Department of Electrical Engineering and Computer Science, University of California, Berkeley, Berkeley, CA, USA.
¹² Chan Zuckerberg Biohub Investigator, San Francisco, CA, USA.
¹³ Ragon Institute of Massachusetts General Hospital, MIT and Harvard University, Cambridge, MA, USA.
¹⁴ Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA, USA. weissman@wi.mit.edu trever.bivona@ucsf.edu niryosef@berkeley.edu.
¹⁵ Whitehead Institute, Cambridge, MA, USA.
¹⁶ Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA.

^# Contributed equally.

PMID: 33479121
PMCID: PMC7983364
DOI: 10.1126/science.abc1944

Single-cell lineages reveal the rates, routes, and drivers of metastasis in cancer xenografts

Jeffrey J Quinn et al. Science. 2021.

. 2021 Feb 26;371(6532):eabc1944.

doi: 10.1126/science.abc1944. Epub 2021 Jan 21.

Authors

Affiliations

¹ Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA, USA.
² Howard Hughes Medical Institute, University of California, San Francisco, San Francisco, CA, USA.
³ Inscripta, Inc., Boulder, CO, USA.
⁴ Biological and Medical Informatics Graduate Program, University of California, San Francisco, San Francisco, CA, USA.
⁵ Integrative Program in Quantitative Biology, University of California, San Francisco, San Francisco, CA, USA.
⁶ Center for Computational Biology, University of California, Berkeley, Berkeley, CA, USA.
⁷ UCSF Department of Medicine, University of California, San Francisco, San Francisco, CA, USA.
⁸ Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA.
⁹ Department of Molecular Biology, Princeton University, Princeton, NJ, USA.
¹⁰ Center for Computational Biology, University of California, Berkeley, Berkeley, CA, USA. weissman@wi.mit.edu trever.bivona@ucsf.edu niryosef@berkeley.edu.
¹¹ Department of Electrical Engineering and Computer Science, University of California, Berkeley, Berkeley, CA, USA.
¹² Chan Zuckerberg Biohub Investigator, San Francisco, CA, USA.
¹³ Ragon Institute of Massachusetts General Hospital, MIT and Harvard University, Cambridge, MA, USA.
¹⁴ Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA, USA. weissman@wi.mit.edu trever.bivona@ucsf.edu niryosef@berkeley.edu.
¹⁵ Whitehead Institute, Cambridge, MA, USA.
¹⁶ Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA.

^# Contributed equally.

PMID: 33479121
PMCID: PMC7983364
DOI: 10.1126/science.abc1944

Abstract

Detailed phylogenies of tumor populations can recount the history and chronology of critical events during cancer progression, such as metastatic dissemination. We applied a Cas9-based, single-cell lineage tracer to study the rates, routes, and drivers of metastasis in a lung cancer xenograft mouse model. We report deeply resolved phylogenies for tens of thousands of cancer cells traced over months of growth and dissemination. This revealed stark heterogeneity in metastatic capacity, arising from preexisting and heritable differences in gene expression. We demonstrate that these identified genes can drive invasiveness and uncovered an unanticipated suppressive role for KRT17 We also show that metastases disseminated via multidirectional tissue routes and complex seeding topologies. Overall, we demonstrate the power of tracing cancer progression at subclonal resolution and vast scale.

PubMed Disclaimer

Figures

**Fig. 1.. Lineage tracing in a lung cancer xenograft model in mice.**
(A) Our Cas9-enabled lineage tracing technology. Cas9 and three sgRNAs bind and cut cognate sequences on genomically integrated Target Sites, resulting in diverse indel outcomes (multicolored rectangles), which act as heritable markers of lineage. (B) Xenograft model of lung cancer metastasis. Approximately 5,000 A549-LT cells were surgically implanted into the left lung of immunodeficient mice. The cells engrafted at the primary site, proliferated, and metastasized within the five lung lobes, mediastinal lymph, and liver. (C) *In vivo* bioluminescence imaging of tumor progression over 54 days of lineage recording, from early engraftment to widespread growth and metastasis. (D) Fluorescent imaging of collected tumorous tissues. (E) Anatomical representation of the six tumorous tissue samples (**left**), and the number of cells collected with paired single-cell transcriptional and lineage datasets (**right**).

**Fig. 2.. High-resolution phylogenetic trees capture the histories of clonal cancer populations.**
Highly detailed phylogenetic reconstructions for each clonal population, represented as radial phylograms. Each cell is represented along the circumference and colored by tissue, as in Fig. 1E and legend. Trees differ in size, tissue distribution, and frequency of tissue transitions. Each tree is scaled by the square-root of the number of cells.

**Fig. 3.. Phylogenetic reconstructions are detailed and accurate.**
(A) Phylogenetic tree and lineage alleles of one clonal population (CP003; N=5,616 cells). The phylogram (**left**) represents cell–cell relationships and the matrix (**right**) represents the lineage alleles for each cell. Alleles are uniquely colored, where saturation indicates allele rarity (legend). (**A, inlays**) Nested zooms of individual clades show the patterns of shared and distinguishing indel alleles, and highlight indel diversity, tree depth, and tree complexity. (B) Correspondence between phylogenetic distance (the normalized pairwise tree distance between two cells) and allelic distance (the normalized pairwise difference in alleles between two cells) for CP003, indicating that the tree accurately models phylogenetic relationships.

**Fig. 4.. Quantifying the diverse metastatic phenotypes of clonal populations directly from cell lineages.**
(A) Theoretical continuum of metastatic phenotypes, spanning non-metastatic (never exiting the primary site) to highly metastatic (frequently transitioning between tumors; arrows). Ancestral metastatic events between tissues leave clear phylogenetic signatures (yellow stars). (B) Example clonal populations that illustrate the wide range of metastatic phenotypes observed: a non-metastatic population that never exits the primary site (CP029); a moderately metastatic population that infrequently transitions between different tissues (CP019); and a frequently metastasizing population with closely related cells residing in different tissues (CP013). Cells colored by tissue as in Fig. 1E; metastatic phenotypes scored by the TreeMetRate. (C) The distribution of TreeMetRates for each clonal population. (D) The distributions of single-cell-resolution metastatic phenotypes (scMetRates) for each clonal population, rank-ordered by TreeMetRate; median scMetRate indicated in black.

**Fig. 5.. Divergent metastatic phenotypes are driven by differences in gene expression.**
(A) Poisson regression analysis of single-cell gene expression and scMetRate for all cells and all tissues; fold-change and coefficient of regression shown. The strongest and most significant positive and negative genes are annotated (red and blue, respectively; Methods). (B) Expression level of several positive and negative metastasis-associated gene candidates (top and bottom rows, respectively) in cells with low or high scMetRate (blue and magenta box-plots, respectively). Boxes: first, second, and third quartiles; whiskers: 9th and 91st percentiles of expression distribution. (C and D) Overlap of identified positive and negative metastasis-associated genes, respectively, from the four mouse experiments; number of genes indicated. Four-way intersections between gene sets are significant by *SuperExactTest* (85) multi-set intersection test. (E and F) *In vitro* transwell invasion assays following CRISPRi or CRISPRa gene perturbation, respectively, in A549 cells. Perturbations were performed using two independent sgRNAs per gene. Differences in invasion phenotype relative to two negative control guides (non-targeting and olfactory receptor) were significant by two-tailed t-test; error bars show standard deviation across triplicates.

**Fig. 6.. Metastatic phenotype is predetermined, heritable, and reproducible.**
(A and B) Projections of transcriptional states of M5k cancer cells and pre-implantation cells (A) or pre-implantation cells alone (B), colored by Metastatic Signature. Association between transcriptional state and Metastatic Signature is measured by inverted Geary’s C’ and significance by false discovery rate (p). (C) Pre-implantation cells exhibit heterogeneity in expression of metastasis-associated genes. (D) Jaccard overlap of intBC sets between clonal populations in M10k and M100k mice. Two pairs of clonal populations (indicated by † and §) were related between the two mouse experiments (Jaccard overlap>50%). (E) Comparison of TreeMetRates from related clones implanted in M10k and M100k, showing minimal difference in metastatic rate (∆) between clone pairs. (F) Cumulative distribution plot of the background distribution of all possible pairwise TreeMetRate differences between M10k and M100k clones (gray), with zoom to show low-∆ regime. Both of the observed differences are statistically smaller than expected (p^†=0.0049 and p^§=0.0198; red dashes). (G) Divergent subclonal metastatic behavior exhibited in the phylogenetic tree of clonal population #7, with annotated subclades; cells colored by tissue as in Fig. 1E. (H) The bimodal distribution of scMetRates for cells in CP007, with cells from the divergent subclades indicated. (I) Comparison of single-cell metastatic phenotype and *Hotspot* transcriptional module scores. (J) Overlay illustrating concordance between CP007 phylogeny, scMetRates, and *Hotspot* Module scores.

**Fig. 7.. Metastases were seeded via complex tissue routes and multidirectional topologies.**
(A and D) Phylogenetic trees and lineage alleles for clonal population #95 and #19 clades, respectively. Notable metastatic events are annotated in the phylogram and represented graphically as arrows (B and E); cells colored by tissue as in Fig. 1E; lineage alleles colored as in Fig. 3A; dashed arrow indicates an assumed transition. (C and F) Tissue transition matrices representing the conditional probability of metastasizing from and to tissues, defining the tissue routes of metastasis for each clonal population. CP095 solely exhibits primary seeding from the left lung, whereas CP019 shows more complex seeding routes. (G) Tissue transition matrices illustrating the diversity of tissue routes, including metastasis from and within the mediastinum (**left**), between the lung lobes (**middle**), or amply to and from all tissues (**right**). (H) Principal component analysis (PCA) of tissue transition probabilities for each clonal population. Displayed clones are annotated in red; percentage of variance explained by components indicated on axes. (I) Component vectors of PCA with descriptive features. (J) Possible phylogenetic topologies of metastatic seeding, represented as in Fig. 4A. (K) Number of clonal populations that exhibit each metastatic seeding topology.

See this image and copyright information in PMC

Comment in

Single-cell recording of metastasis.
Tang L. Tang L. Nat Methods. 2021 Mar;18(3):231. doi: 10.1038/s41592-021-01095-6. Nat Methods. 2021. PMID: 33674798 No abstract available.

Cited by

Recent Advances and Future Prospects in Immune Checkpoint (ICI)-Based Combination Therapy for Advanced HCC.
Dong Y, Wong JSL, Sugimura R, Lam KO, Li B, Kwok GGW, Leung R, Chiu JWY, Cheung TT, Yau T. Dong Y, et al. Cancers (Basel). 2021 Apr 18;13(8):1949. doi: 10.3390/cancers13081949. Cancers (Basel). 2021. PMID: 33919570 Free PMC article. Review.
Signaling levels mold the RAS mutation tropism of urethane.
Li S, Counter CM. Li S, et al. Elife. 2021 May 17;10:e67172. doi: 10.7554/eLife.67172. Elife. 2021. PMID: 33998997 Free PMC article.
Simulation of CRISPR-Cas9 editing on evolving barcode and accuracy of lineage tracing.
Liu F, Zhang X, Yang Y. Liu F, et al. Sci Rep. 2024 Aug 19;14(1):19213. doi: 10.1038/s41598-024-70154-7. Sci Rep. 2024. PMID: 39160220 Free PMC article.
In vivo growth of subclones derived from Lewis lung carcinoma is determined by the tumor microenvironment.
Xu BL, Wang XM, Chen GY, Yuan P, Han L, Qin P, Li TP, You HQ, Zhang CJ, Fu XM, Yuan L, Wang ZB, Gao QL. Xu BL, et al. Am J Cancer Res. 2022 Nov 15;12(11):5255-5270. eCollection 2022. Am J Cancer Res. 2022. PMID: 36504888 Free PMC article.
Theoretical guarantees for phylogeny inference from single-cell lineage tracing.
Wang R, Zhang R, Khodaverdian A, Yosef N. Wang R, et al. Proc Natl Acad Sci U S A. 2023 Mar 21;120(12):e2203352120. doi: 10.1073/pnas.2203352120. Epub 2023 Mar 16. Proc Natl Acad Sci U S A. 2023. PMID: 36927151 Free PMC article.

See all "Cited by" articles

References

1. Turajlic S, Swanton C, Metastasis as an evolutionary process. Science. 352, 169–175 (2016). - PubMed
1. Navin NE, Hicks J, Tracing the tumor lineage. Mol. Oncol 4, 267–283 (2010). - PMC - PubMed
1. Nowell PC, The clonal evolution of tumor cell populations. Science. 194, 23–28 (1976). - PubMed
1. Stratton MR, Campbell PJ, Futreal PA, The cancer genome. Nature. 458, 719–724 (2009). - PMC - PubMed
1. Brannon AR, Vakiani E, Sylvester BE, Scott SN, McDermott G, Shah RH, Kania K, Viale A, Oschwald DM, Vacic V, Emde A-K, Cercek A, Yaeger R, Kemeny NE, Saltz LB, Shia J, D’Angelica MI, Weiser MR, Solit DB, Berger MF, Comparative sequencing analysis reveals high genomic concordance between matched primary and metastatic colorectal cancer lesions. Genome Biol. 15, 454 (2014). - PMC - PubMed

Publication types

Actions
Actions

MeSH terms

Actions
Actions
Actions
Actions
Actions
Actions
Actions
Actions
Actions
Actions
Actions
Actions
Actions
Actions
Actions
Actions
Actions
Actions
Actions
Actions
Actions

Substances

Actions
Actions

Grants and funding

LinkOut - more resources

Full Text Sources
Other Literature Sources
- H1 Connect
- scite Smart Citations
Medical
- MedlinePlus Health Information
Molecular Biology Databases
- NIAID Data Ecosystem - Find datasets on Infectious and Immune-mediated Diseases
Research Materials
- Addgene Non-profit plasmid repository
Miscellaneous
- NCI CPTAC Assay Portal

[1] Turajlic S, Swanton C, Metastasis as an evolutionary process. Science. 352, 169–175 (2016). - PubMed

[2] Turajlic S, Swanton C, Metastasis as an evolutionary process. Science. 352, 169–175 (2016). - PubMed

[3] Navin NE, Hicks J, Tracing the tumor lineage. Mol. Oncol 4, 267–283 (2010). - PMC - PubMed

[4] Navin NE, Hicks J, Tracing the tumor lineage. Mol. Oncol 4, 267–283 (2010). - PMC - PubMed

[5] Nowell PC, The clonal evolution of tumor cell populations. Science. 194, 23–28 (1976). - PubMed

[6] Nowell PC, The clonal evolution of tumor cell populations. Science. 194, 23–28 (1976). - PubMed

[7] Stratton MR, Campbell PJ, Futreal PA, The cancer genome. Nature. 458, 719–724 (2009). - PMC - PubMed

[8] Stratton MR, Campbell PJ, Futreal PA, The cancer genome. Nature. 458, 719–724 (2009). - PMC - PubMed

[9] Brannon AR, Vakiani E, Sylvester BE, Scott SN, McDermott G, Shah RH, Kania K, Viale A, Oschwald DM, Vacic V, Emde A-K, Cercek A, Yaeger R, Kemeny NE, Saltz LB, Shia J, D’Angelica MI, Weiser MR, Solit DB, Berger MF, Comparative sequencing analysis reveals high genomic concordance between matched primary and metastatic colorectal cancer lesions. Genome Biol. 15, 454 (2014). - PMC - PubMed

[10] Brannon AR, Vakiani E, Sylvester BE, Scott SN, McDermott G, Shah RH, Kania K, Viale A, Oschwald DM, Vacic V, Emde A-K, Cercek A, Yaeger R, Kemeny NE, Saltz LB, Shia J, D’Angelica MI, Weiser MR, Solit DB, Berger MF, Comparative sequencing analysis reveals high genomic concordance between matched primary and metastatic colorectal cancer lesions. Genome Biol. 15, 454 (2014). - PMC - PubMed

Save citation to file

Email citation

Add to Collections

Add to My Bibliography

Your saved search

Create a file for external citation management software

Your RSS Feed

Single-cell lineages reveal the rates, routes, and drivers of metastasis in cancer xenografts

Affiliations

Single-cell lineages reveal the rates, routes, and drivers of metastasis in cancer xenografts

Authors

Affiliations

Abstract

Figures

Comment in

Similar articles

Cited by

References

Publication types

MeSH terms

Substances

Grants and funding

LinkOut - more resources

Full Text Sources

Other Literature Sources

Medical

Molecular Biology Databases

Research Materials

Miscellaneous

Abstract

Figures

Comment in

Similar articles

Cited by

References

Publication types

MeSH terms

Substances

Related information

Grants and funding

LinkOut - more resources

Full Text Sources

Other Literature Sources

Medical

Molecular Biology Databases

Research Materials

Miscellaneous