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Regenerative lineages and immune-mediated pruning in lung cancer metastasis

Nature Medicine volume 26, pages 259–269 (2020)Cite this article

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Abstract

Developmental processes underlying normal tissue regeneration have been implicated in cancer, but the degree of their enactment during tumor progression and under the selective pressures of immune surveillance, remain unknown. Here we show that human primary lung adenocarcinomas are characterized by the emergence of regenerative cell types, typically seen in response to lung injury, and by striking infidelity among transcription factors specifying most alveolar and bronchial epithelial lineages. In contrast, metastases are enriched for key endoderm and lung-specifying transcription factors, SOX2 and SOX9, and recapitulate more primitive transcriptional programs spanning stem-like to regenerative pulmonary epithelial progenitor states. This developmental continuum mirrors the progressive stages of spontaneous outbreak from metastatic dormancy in a mouse model and exhibits SOX9-dependent resistance to natural killer cells. Loss of developmental stage-specific constraint in macrometastases triggered by natural killer cell depletion suggests a dynamic interplay between developmental plasticity and immune-mediated pruning during metastasis.

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Fig. 1: The single-cell transcriptional landscape of human LUAD.
Fig. 2: Regenerative and mixed lineages in primary tumors.
Fig. 3: Metastases exhibit a continuum of stem to lung epithelial progenitor states.
Fig. 4: Developmental continuum in a mouse model of metastatic escape.
Fig. 5: Developmental stage-specific differential immune sensitivity.
Fig. 6: NK cell-dependent pruning limits the phenotypic expansion of metastasis-initiating cells.

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Data availability

Raw data from western blots is included in Source Data. A ranked list of DEGs and complete GSEA results for all PhenoGraph clusters analyzed in this manuscript and provided in Supplementary Tables 4–15. A custom GSEA annotation file, assembled to query cell types and pathways related lung epithelial development and regeneration is provided in the Supplementary Dataset. All raw and processed scRNA-seq data with cell type annotations was deposited in NCBI’s Gene Expression Omnibus and are accessible through accession number GSE123904. Fully annotated count matrices are available for download at (https://s3.amazonaws.com/dp-lab-data-public/lung-development-cancer-progression/PATIENT_LUNG_ADENOCARCINOMA_ANNOTATED.h5 and https://s3.amazonaws.com/dp-lab-data-public/lung-development-cancer-progression/MOUSE_LUNG_ADENOCARCINOMA_METASTASIS_ANNOTATED.h5). All other datasets generated and analyzed in the current study are available from the corresponding authors upon request.

Code availability

All custom code, statistical analysis and visualizations were performed in Python and are demonstrated in a Jupyter notebook available for download at https://github.com/dpeerlab/lung-development-cancer-progression. The following open-source algorithms were additionally used as described in the methods: SEQC (https://github.com/ambrosejcarr/seqc), t-SNE (https://lvdmaaten.github.io/software/), MAGIC (https://github.com/dpeerlab/magic) and PhenoGraph (https://github.com/jacoblevine/PhenoGraph).

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Acknowledgements

We thank the Thoracic Oncology Service, the Flow Cytometry Core, the Molecular Cytology Core, the Genomic Editing and Screening Core and the Integrated Genomics Operation at MSKCC for their assistance. We especially thank the tissue donors at MSKCC for participating in this study. We thank J. Nainys and V. Kiseliovas for providing hands-on training of single-cell library preparations. We thank E. Azizi and T. Nawy for their critical feedback on this manuscript and F. Bakhoum and V. Bakhoum for their support. This work was supported by National Institutes of Health grants U54-CA209975 (J.M. and D.P.), P01-CA129243 (J.M.), DP1- HD084071 (D.P.), R01CA164729 (D.P.), P30-CA008748 (J.M.); DoD Innovator Award W81XWH-12-0074 (J.M.); and awards from the Burroughs Wellcome Fund Career Award at the Scientific Interface (A.M.L.), the Lung Cancer Research Foundation (A.M.L.), the Alan and Sandra Gerry Metastasis and Tumor Ecosystems Center at MSKCC (A.M.L. and J.H.), the Kirschstein-NRSA F30-CA220954 Predoctoral Fellowship (N.R.C.) and by a T32-GM007729 Medical Scientist Training Program Grant (N.R.C.).

Author information

Author notes

  1. These authors jointly supervised this work: Dana Pe’er, Joan Massagué.

Authors and Affiliations

  1. Cancer Biology and Genetics Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Ashley M. Laughney, Jing Hu, Nathaniel R. Campbell & Joan Massagué

  2. Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Ashley M. Laughney, Nathaniel R. Campbell, Manu Setty, Vincent-Philippe Lavallée, Yubin Xie, Ignas Masilionis, Ambrose J. Carr, Sanjay Kottapalli, Joao B. Xavier, Linas Mazutis & Dana Pe’er

  3. Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY, USA

    Ashley M. Laughney

  4. Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA

    Ashley M. Laughney

  5. Sandra and Edward Meyer Cancer Center, Weill Cornell Medicine, New York, NY, USA

    Ashley M. Laughney

  6. Tri-Institutional MD-PhD Program, Weill Cornell/Rockefeller University/Sloan Kettering Institute, New York, NY, USA

    Nathaniel R. Campbell

  7. Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Samuel F. Bakhoum

  8. Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Samuel F. Bakhoum

  9. Tri-Institutional Training Program in Computational Biology and Medicine, Weill Cornell/Rockefeller University/Sloan Kettering Institute, New York, NY, USA

    Yubin Xie

  10. The Alan and Sandra Gerry Metastasis and Tumor Ecosystems Center, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Ignas Masilionis, Sanjay Kottapalli & Linas Mazutis

  11. Molecular Pharmacology Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Viola Allaj, Marissa Mattar & Charles M. Rudin

  12. Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Viola Allaj, Marissa Mattar & Charles M. Rudin

  13. Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Natasha Rekhtman

  14. Perlmutter Cancer Center, New York University Langone Health, New York, NY, USA

    John T. Poirier

  15. Parker Institute for Cancer Immunotherapy, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Dana Pe’er

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Contributions

A.M.L., J.M. and D.P. conceived and oversaw the project, performed data interpretation and wrote the manuscript. A.M.L. generated and analyzed all scRNA-seq data and performed mouse experiments for single cell analyses; J.H. generated additional NK depleted macrometastases for immunofluorescence, which were processed by A.M.L. and J.H. Matched primary-metastasis pairs were identified, analyzed and interpreted by J.H. and S.F.B. NK cell cytotoxicity assays in vitro were designed and performed by J.H. N.R.C. and J.B.X. developed and applied image cytometry methods for NK cell cytotoxicity assays in vitro. M.S., V.-P.L., A.J.C. and S.K. assisted with scRNA-seq analysis. Y.X. developed and applied algorithms to segment nuclei in fixed tissues. N.R., V.A., M.M., J.T.P. and C.M.R. oversaw procurement of tissue samples, clinical specimen processing and histopathologic data interpretation. I.M. and L.M. assisted with scRNA-seq library preparation.

Corresponding authors

Correspondence to Dana Pe’er or Joan Massagué.

Ethics declarations

Competing interests

J.M. is a scientific advisor and owns company stock in Scholar Rock. C.M.R. has consulted with AbbVie, Amgen, Ascentage, Astra Zeneca, BMS, Celgene, Daiichi Sankyo, Genentech/Roche, Ipsen, Loxo and Pharmar, and is on the scientific advisory boards of Elucida and Harpoon. S.F.B. owns equity in, receives compensation from, and serves as a consultant, board member and a scientific advisory board member for Volastra Therapeutics. He also has consulted for Sanofi. All other authors declare no competing conflicts.

Additional information

Peer review information Javier Carmona was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Patient attributes.

Patient resection site, smoking history, primary lesion size, disease stage, diagnostic pathology, oncogenic mutations and treatment history.

Extended Data Fig. 2 Single cell parameters and pre-processing.

Cells were filtered based on (a) cumulative number of transcript counts, (b) cell complexity and (c) fraction of mitochondrial mRNA content detected per cell as described in the Methods; shown here for one representative library. Excluded cells are labeled in red. Histograms showing the distribution of (d) total number of transcripts detected per cell and (e) number of unique genes detected per cell in retained cells colored by sample. f-h, t-SNE projection of the complete atlas of normal lung, primary tumor and metastatic LUAD (same projection as Fig. 1c, n = 40,505 cells), cells colored by (f) sample and (g) meta-cell class as determined by (h) unsupervised clustering of canonical gene signatures within each meta-cell class across all cells. Clustering of canonical cell type expression signatures (annotated in Supplementary Table 2), z-score normalized per gene across cells. Assignment to meta-cell classes (detailed in Methods) are colored on the dendrogram.

Extended Data Fig. 3 Phenotyping myeloid, epithelial and stromal cell types.

a, t-SNE projection of myeloid, epithelial and stromal cells (purple and tan populations from Extended Data Fig. 2g, n = 9,195 cells) colored and labeled by their PhenoGraph cluster assignment (Phenograph run on subset as detailed in Methods). b, Heatmap of gene signatures differentially expressed by PhenoGraph clusters with abs(NES) >2 and padj < 0.05, clustered (rows and columns) according to the cosine distance metric for visualization; hits not meeting these criteria are whited out. Numbered by Phenograph clusters (top) and colored by inferred cell type assignments (bottom, see Methods). NES, normalized enrichment score; padj, Bonferroni corrected, two-sided p-value. c, Pearson correlations between PhenoGraph cluster centroids and bulk mRNA profiles from purified immune subpopulations47,48 (n = 5,987 genes, Methods). Correlation coefficients are whited out if p > 0.01 for the Pearson test for non-correlation. d, t-SNE projection of all myeloid/epithelial/stromal cells (same as a) colored and labeled by inferred cell types. PhenoGraph clusters were mapped to cell types using (b-c) and are directly mapped back to the complete patient dataset in (Fig. 1c) using the same color scheme. e, Clustered heatmap of the average imputed expression per cell type of distinguishing markers, standardized by z-score. Rows are colored by annotated cell type.

Extended Data Fig. 4 Phenotyping NK, T and B cells in the lymphoid compartment.

a, t-SNE projection of all lymphoid cells (blue cells from Extended Data Fig. 2g, n = 25,726 cells) colored by PhenoGraph cluster. b, Pearson correlations between PhenoGraph cluster expression centroids and bulk mRNA data published from purified immune subpopulations47,48 computed based on intersecting, variably expressed genes (n = 5,613, Methods). Rows are colored and labeled by PhenoGraph clusters. Correlation coefficients are whited out if p > 0.01 for the Pearson test for non-correlation. c, Clustered heatmap of the average imputed expression per PhenoGraph cluster of canonical lymphoid markers, standardized by z-scores. Rows are colored by PhenoGraph clusters (left) and annotated cell types (right, see Methods). d, t-SNE projection of all lymphoid cells (same as a) colored and labeled by inferred cell type. PhenoGraph clusters were mapped to cell types using (b, c) and are directly mapped back to the complete patient dataset in (Fig. 1c) using the same color scheme. e, The cell distribution of NKG7 imputed expression, a canonical NK cell marker, across all annotated lymphoid cell types. f, Dot plots showing relative frequency of expressing cells and mean normalized expression (un-imputed data) of canonical markers per lymphoid cell type.

Extended Data Fig. 5 Reproducibility of cell types across patients.

a, Clustered heatmap of the imputed average expression of key cell type markers, for all cell types annotated in the complete patient dataset (Fig. 1c) and standardized by z-scores. Rows are colored by cell type annotation, number of patients in which the cell type was detected, and the total number of cells assigned to this cell type (left to right on left side of clustered heatmap). b, Kernel density plot depicting patient entropy as a measure of sample mixing across all patients within each cell type; computed with bootstrapping to correct for number of cells in each cluster (n = 100 random subsamples) as described in Methods. High entropy indicates most similar cells come from a well-mixed set of patient samples, whereas low entropy indicates most similar cells come from the same patient sample. Distributions are colored by annotated cell type. c, Estimates of tumor purity measured by scRNA-seq and targeted panel DNA sequencing of matched bulk tumor using FACETS, an allele-specific copy number analysis tool19. We test effectiveness of pairing between tumor purity estimates by reporting the Pearson correlation coefficient for n = 7 samples for which matched scRNA-seq and bulk, targeted panel DNA sequencing was available with one-sided P value testing for non-correlation. d, t-SNE projection of individual patient tumors from n = 5 representative patient samples; annotated number of cells per patient. scRNA-seq data for each tumor was processed independently as described in the Methods; each dot represents a cell colored by PhenoGraph cluster, labeled by inferred cell type. e, Number of patients in which each cell type was detected during individual patient analyses. This is concordant with patient frequencies observed in the pooled analysis, summarized in a. Power to detect minority cell types like neutrophils and plasma cells is reduced when analyzing patient samples one-by-one.

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Extended Data Fig. 6 Epithelial cell types detected in the normal, adult human lung.

a, Force-directed layout of epithelial cells detected in the normal lung colored by PhenoGraph cluster and labeled with annotated cell type (n = 658 cells; Methods and below). b, Bar graphs representing the fraction of each cell type detected per patient. c, Clustered heatmap of the top 60 DEGs per cell type; imputed values standardized by z-scores are shown for visualization. DEGs were identified in un-imputed data using MAST43 as described in the Methods. Rows are colored by PhenoGraph cluster and labeled by annotated cell type. d, A clustered heatmap of differentially expressed gene signatures within each cell type. NES is shown for pathways in which abs(NES) > 2.5 and padj < 0.05; signatures not meeting this criterium are whited out. Columns are colored by PhenoGraph cluster. NES, normalized enrichment score; padj, Bonferroni corrected, two-sided p-value. Complete GSEA results per PhenoGraph Cluster, including nominal p-values, are provided in Supplementary Table 7. e, Histograms showing the fraction of cells per PhenoGraph cluster (that is cell loadings) expressing at or above the 75th percentile a fraction of each cell type-specific gene signature (AEC1, AEC2, Club and Ciliated) computed on imputed data; each distribution represents cells from one PhenoGraph cluster. Colors associated with each annotated epithelial cell lineage are maintained as in Fig. 2.

Extended Data Fig. 7 The relationship between primary tumor and normal lung epithelial cell types.

a, Top DEGs per PhenoGraph Clusters annotated in Fig. 2b, compared to all other cells, computed using MAST43. Each lineage-specific gene is colored by associated cell type, with diameter proportional to −log10(padj). See Supplementary Table 1 for lineage-specific genes. b, Clustered heatmap of gene signatures differentially enriched (abs(NES) >2 and padj < 0.05) in one or more PhenoGraph Clusters. NES, Normalized Enrichment Score. Column colors (top) correspond to annotated epithelial lineages. Signatures not meeting these criteria are whited out. See Supplementary Table 9 for complete GSEA results per PhenoGraph Cluster. c, Violin plots showing imputed expression of canonical lineage-specific transcription factors (columns) for each annotated epithelial cell lineage (color), scaled such that each plot has the same width; lines distinguish data quartiles. d, Force-directed layout of all epithelia (n = 2,140 cells) colored by extrema of the three most informative diffusion components (DCs, above) and by DC2 (below); GSEA of cells ranked along DC2 are positively enriched for embryonic stem cell gene signatures and pathways associated with proximal cell types, and negatively associated distal cell types. Complete GSEA results for DCs are provided in Supplementary Table 9. e, Fraction of normal- and primary tumor-derived cells comprising the union of all three DC extrema (center values, mean; error bars, 95% confidence interval; points, fraction of cells measured at n = 3 diffusion extrema). f, Fraction of each annotated cell type detected per diffusion component extrema and non-extrema. g, Cumulative imputed expression of a bulk-derived gene signature up-regulated in LUAD and not expressed in non-cancerous epithelium30 evaluated per cell in normal- vs. tumor-derived epithelium (center line, median; box limits, upper and lower quartiles; whiskers, 1.5X interquartile range; points, n = 2,140 individual cells); two-sided p < 0.001, Mann Whitney U test.

Extended Data Fig. 8 Identification of transcriptionally distinct metastatic subpopulations.

All data in this figure relates to the combined normal, primary tumor and metastatic epithelium. a, Clustered heatmap showing gene signatures differentially expressed across PhenoGraph clusters. Normalized enrichment score (NES) is colored for gene signatures in which abs(NES) > 1.5 and two-sided padj < 0.05. padj, Bonferroni corrected, two-sided p-value. Rows correspond to gene signatures, column corresponds to PhenoGraph clusters. See Supplementary Table 11 for complete GSEA results per cluster. Fraction of each PhenoGraph cluster derived per tissue source is visualized above each column. White stars (bottom) denote patient metastatic clusters based on fraction of metastatic cells (>10%). (b) Tissue source, PhenoGraph clusters (matching those characterized in a), and cell types (annotated as in Fig. 2), are visualized on a force directed layout (n = 3,786 cells). c, SOX2, SOX9 and DAPI immunofluorescence in three additional matched primary tumor-metastasis pairs. Scale bars, 100 µm. d, Fraction of each PhenoGraph cluster detected per metastasis sample (center line, median; box limits, upper and lower quartiles; whiskers, 1.5X interquartile range; points, fraction detected per n = 5 metastatic samples). e, Entropy of patient distribution in each PhenoGraph cluster, computed with bootstrapping to correct for number of cells per cluster (center line, median; box limits, upper and lower quartiles; whiskers, 1.5X interquartile range, n = 100 random subsamples of data). Metastatic clusters are shaded by PhenoGraph cluster ID and clusters are ordered by average entropy. Metastatic clusters associated with alveolar epithelial progenitor signature (type III), and two clusters comprised predominantly of primary tumor cells treated with neoadjuvant therapy are patient-specific. f, Left, Patient metastatic clusters ranked according to average expression of the lung epithelial development signature (n = 34 genes), iteratively leaving out one gene. Each row shows metastatic cluster ranking for each left-out gene. Right, kernel density plot of imputed and normalized expression of each left-out gene.

Extended Data Fig. 9 Developmental stage-specific differential immune sensitivity extended.

a, Boxplots showing the average expression of NK cell-specific genes in TCGA lung adenocarcinoma patients stratified by SOX2 or SOX9 expression. NK cells are more abundant in SOX9high tumors and conversely, less abundant in SOX2high tumors (two-sided p < 0.001 based on Mann Whitney U test). Center line represents median; box limits, upper and lower quartiles; whiskers, 1.5X interquartile range; points, outliers. b, Cropped Western blots showing SOX2 and SOX9 protein levels upon DOX induction in the H2087-LCC model; no independent repeats were performed. Un-cropped Western blots are provided in Source Data. c, mRNA expression of Sox2 and Sox9 in bulk KP mouse LUAD derivatives38, from primary tumors (circles) or metastases (x); red indicates the derivative is metatstatic. d, Endogenous expression of nuclear SOX9 enumerated by quantitative immunofluorescence in KP482T1 metastatic cells before and after co-culture with IL2-activated mouse NK cells. Fraction of SOX9 positive cells before and after NK cell co-culture are reported. Average of n = 3 technical replicates for each of 3 biological replicates. Between 5 and 9 locations were imaged and quantified per biological and technical replicate; resulting in quantitation of 4,534 cells before NK cell co-culture and 2,556 individual cells after NK cell co-culture. P-values from paired two-tailed t-tests (n = 3 biological replicates, degrees of freedom = 2, t = 8.33, p = 0.01 for SOX9 single positive comparison). e, Relative mRNA expression of NK activating ligands in H2087-LCC cells with and without induction of SOX2 or SOX9 (n = 3 technical replicates; center values, mean; error bars, 95% confidence interval).

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Extended Data Fig. 10 Clonality and phenotypic landscape of NK cell-depleted macrometastases.

a, Integrated radiance of H2087-LCC cells intracardially injected in mice,+/− anti-GM1 antibody treatment to deplete NK cells measured over time. b, Schematic illustrating trichromatic marking system implemented to assay the clonality of NK cell-depleted metastases. Fluorescence of trichromic reporter visualized in metastatic outbreaks generated in NSG mice lacking NK cells. c, FACS plots shows distribution of cells expressing Cerulean, Venus and mCherry per NK cell-depleted metastasis (lower) as compared to single and multi-color controls (upper); repeated independently for n = 6 NK cell-depleted macrometastases. d, Force-directed layout of all single cells (n = 6,073 cells) transcriptionally profiled from 8 NK cell-depleted macrometastases colored by PhenoGraph cluster. e, Kernel density plot of the imputed expression of SOX2 and SOX9 in each NK cell-depleted PhenoGraph cluster; clusters are ranked by median SOX2 expression. f, A bipartite graph representing genome-wide correlations across all common, variably expressed genes (n = 2,895; Methods) between each NK cell-depleted PhenoGraph cluster (n = 18, circular nodes) and each developmental state observed in human tumors (n = 4, square nodes, annotated in Fig. 3c). The Pearson correlation is computed across all categorical assignments between the two independent sets and edges link NK cell-depleted PhenoGraph clusters to human developmental for Pearson R > 0.20 and two-sided p < 0.05; edge width is scaled by the magnitude of the correlation (observed range: 0.20–0.62). Pearson correlation coefficients are also reported in Supplementary Table 2. Shading is used to highlight nodes assigned the three metastatic states detailed in Fig. 4.

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Laughney, A.M., Hu, J., Campbell, N.R. et al. Regenerative lineages and immune-mediated pruning in lung cancer metastasis. Nat Med 26, 259–269 (2020). https://doi.org/10.1038/s41591-019-0750-6

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