A biobank of small cell lung cancer CDX models elucidates inter- and intratumoral phenotypic heterogeneity

Abstract

Although small cell lung cancer (SCLC) is treated as a homogeneous disease, biopsies and preclinical models reveal heterogeneity in transcriptomes and morphology. SCLC subtypes were recently defined by neuroendocrine transcription factor (NETF) expression. Circulating-tumor-cell-derived explant models (CDX) recapitulate donor patients’ tumor morphology, diagnostic NE marker expression and chemotherapy responses. We describe a biobank of 38 CDX models, including six CDX pairs generated pretreatment and at disease progression revealing complex intra- and intertumoral heterogeneity. Transcriptomic analysis confirmed three of four previously described subtypes based on ASCL1, NEUROD1 and POU2F3 expression and identified a previously unreported subtype based on another NETF, ATOH1. We document evolution during disease progression exemplified by altered MYC and NOTCH gene expression, increased ‘variant’ cell morphology, and metastasis without strong evidence of epithelial to mesenchymal transition. This CDX biobank provides a research resource to facilitate SCLC personalized medicine.

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Fig. 1: CDX generation and overview.
Fig. 2: Global transcriptomic analysis of CDX models.
Fig. 3: Inter- and intratumoral heterogeneity of NE transcriptional regulators.
Fig. 4: Expression of MYC family members.
Fig. 5: EMT gene expression.
Fig. 6: Analysis of paired CDX models.
Fig. 7: Metastasis of s.c. CDX cells to brain, liver and lung.

Data availability

RNA-seq data that support the findings of this study (Figs. 2, 3e, 4a,b, 5c and 6c,d and Extended Data Figs. 2, 4 and 57a) have been deposited in the EMBL-EBI ArrayExpress database under accession code E-MTAB-8465, with the title ‘RNA of Small Cell Lung Cancer Circulating Tumor Cells Derived Explants’, and can be accessed at http://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-8465. Source data for Fig. 4c and Extended Data Fig. 7b can be accessed from ref. 2 and source data for Extended Data Fig. 7c are publicly available from The Broad Institute CCLE at https://portals.broadinstitute.org/ccle.

Source data for Figs. 35 and Extended Data Figs. 2 and 3 are presented with the paper. All other data supporting the findings of this study are available from the corresponding author on reasonable request.

Code availability

No algorithms or software were developed in this study. Software that was used is free and open source and details on acquiring them can be found in the associated references. All code that was used to generate the figures can be found at https://doi.org/10.5281/zenodo.3574846.

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Acknowledgements

We dedicate this manuscript to our collaborator A. Gazdar, a ground-breaking pathologist and pioneer in cell line and SCLC research. We thank J. Sage for his constructive review of the manuscript and the patients who donated their samples. This work was supported by The Christie Charitable Fund, and by Core Funding to CRUK Manchester Institute (grant no. A27412), Manchester CRUK Centre Award (grant no. A25254), the CRUK Manchester Experimental Cancer Medicines Centre (grant no. A25146) and the CRUK Lung Cancer Centre of Excellence (grant no. A20465). Patient recruitment was supported by the NIHR Manchester Biomedical Research Centre and NIHR Manchester Clinical research Facility at The Christie Hospital. Sample collection was undertaken via the ChemoRes Trial (molecular mechanisms underlying chemotherapy resistance, therapeutic escape, efficacy and toxicity—improving knowledge of treatment resistance in patients with lung cancer) and The TARGET Study. A.F.G. was supported by a grant from the National Cancer Institute, Bethesda, MD, USA: ‘Small Cell Lung Cancer Consortium Coordinating Center’ no. U24CA21327.

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Contributions

K.L.S. and C.D. supervised and devised the study, interpreted data and cowrote the manuscript. N.S., L.B., F.T., D.M., M.R. and T.H. carried out IHC and immunofluorescence including data analysis and interpretation. S.P.P., S.H., R.S., W.R. and A.K. carried out bioinformatics analyses and interpretation. M.D. carried out bioinformatics analysis. A.C. developed multiplex immunofluorescence protocols, analyzed data and generated resultant figures. K.K.F. has oversight of all CDX model generation and helped plan the study, analyze/interpret all data and edited the manuscript. M.G. is responsible for all in vivo work described. A.F.G. and L.G. generated the NE score and A.F.G. carried out an extensive pathology review of the models described as well as data interpretation and manuscript revision. D.N. carried out a pathology review of CDX models. L.C., M.G.K. and N.C. oversaw the acquisition of ethical permission and patient consent for patients on the TARGET study. M.C., L.F. and F.B. oversaw the acquisition of ethical permission and patient consent and the collection of blood samples for patients on the CHEMORES study. F.B. assisted with study design, data interpretation and manuscript revision and is the chief investigator of the CHEMORES study. All authors read and approved the final manuscript.

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Correspondence to Caroline Dive.

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Extended data

Extended Data Fig. 1 Engraftment Rate For CDX Generation.

For each CDX attempt, the CTC number was quantified by CellSearch in a parallel 7.5ml blood sample. Successful CDX generation was based on whether a measurable tumor grew within one year of implantation. Total samples implanted with matched CTC count <49 = 146, resulting in 2 successful CDX models, total samples implanted with matched CTC count >50 = 71, resulting in 35 successful CDX models.

Extended Data Fig. 2 CDX Cell Nuclear Size.

Average Nuclear area (µm2) was calculated for the CDX panel and used as a surrogate for total cell size and shows that the majority of CDX contain SCLC cells of a comparable size to human SCLC (approximately 40 µm2, ~size of 3–4 resting lymphocytes). Some CDX with relatively large cell nuclei were present (CDX3, CDX13, CDX17P, CDX30P and CDX38 and CDX41P ≥ 40 µm, horizontal line). Mean value was calculated from is shown with error bars representing ± SEM. Source data

Extended Data Fig. 3 Expression of SCLC Diagnostic Biomarkers.

a Representative Immunohistochemistry (IHC, brown stain) for selected CDX models for NKX2-1 and the neuroendocrine markers CHGA, NCAM, and SYP. White scale bar, 50 µm. b Quantification of IHC data using Definiens software followed by hierarchical clustering (white, low, pink, intermediate, red, high expression, see methods). For a and b 3 whole tumour sections (biological replicates, different animals) were scanned and scored and the average value was used to generate the heatmap. Quantification was carried out according to the methods. Source data

Extended Data Fig. 4 Correlation of NE and non-NE genes in CDX.

A previously derived 50 gene panel comprising NE and non-NE genes 4 was mapped to CDX RNA sequencing data. Pearson correlation across the CDX dataset was calculated between all pairs of genes in the NE gene panel. Cells are coloured according to Pearson correlation between each pair of genes.

Extended Data Fig. 5 Putative SCLC tuft cell markers in CDX Models.

RNA-seq data of putative tuft cell marker expression in the CDX panel with cells coloured according to normalised z-scores. Data were pre-processed to remove reads of mouse origin, as described in 70. Many of the key tuft markers are restricted to CDX13, which shows low expression of key transcription factors ASCL1 and NEUROD1.

Extended Data Fig. 6 Comparison of MYC family expression with ASCL1, NEUROD1, POU2F3 and ATOH1 expressing CDX.

RNA Expression analysis of ASCL1, NEUROD1, ATOH1, POU2F3 and REST with MYC family members. The bar at the top of the heatmap shows NE score. CDX13 has the only negative NE score and is unique in expressing POU2F3 and REST.

Extended Data Fig. 7 ATOH1 and YAP1 Expression is Distinct in CDX, Patient Samples and Cell Lines.

a Scatterplot RNA Expression (CPM) of ATOH1 and YAP1 in CDX. Samples are colour coded according to sub-groups defined by previous transcriptomics analysis: Purple, ASCL1, Blue, POU2F3, Pink, NEUROD1, Green, ATOH1; note that POU2F3 and NEUROD1 subgroups are obscured as the majority of samples represent the double negative population. b Scatterplot RNA Expression (RPKM) of ATOH1 and YAP1 in Surgically Resected SCLC Patient Samples. c Scatterplot RNA Expression (RPKM) of ATOH1 and YAP1 in SCLC Cell lines in the CCLE. In both (b) and (c), samples are colour coded according to 4 sub-groups defined by Rudin et al23; ASCL1, light blue, NEUROD1, green, POU2F3, dark blue, YAP1, orange.

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Supplementary Information

Supplementary Fig. 1 and Tables 1–3.

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Source data

Source Data Fig. 3

Image analysis raw data used for histopathology.

Source Data Fig. 4

Image analysis raw data used for histopathology.

Source Data Fig. 4

Unprocessed western blots.

Source Data Fig. 5

Image analysis raw data used for histopathology.

Source Data Extended Data Fig. 2

Image analysis raw data used for histopathology.

Source Data Extended Data Fig. 3

Image analysis raw data used for histopathology.

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Simpson, K.L., Stoney, R., Frese, K.K. et al. A biobank of small cell lung cancer CDX models elucidates inter- and intratumoral phenotypic heterogeneity. Nat Cancer 1, 437–451 (2020). https://doi.org/10.1038/s43018-020-0046-2

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