Abstract
Objectives
Small cell lung cancer (SCLC) accounts for approximately 15% of lung tumors and is marked by aggressive growth and early metastatic spread. In this study, we used two SCLC mouse models with differing tumor mutation burdens (TMB). To investigate tumor composition, spatial architecture, and interactions with the surrounding microenvironment, we acquired multiplexed images of mouse lung tumors using imaging mass cytometry (IMC). These data build upon a previously published characterization of the mouse model.
Data description
After tumor detection, mice were assigned to one of five treatment groups. Lung tumor tissues were imaged with a 37-marker IMC panel designed to identify major cell types—tumor, immune, and structural—as well as their functional states. When possible, each tumor was sampled both at its center and border regions. Tumor masks in the form of binary images are provided to delineate tumor areas. Additional metadata include tumor onset and endpoint dates to support downstream correlation or predictive analyses based on the image data. This dataset offers a valuable resource for studying the histological and cellular complexity of SCLC in a genetically controlled mouse model across multiple therapeutic conditions.
Keywords: SCLC, IMC, MIBI-TOF, Hyperion, Tumor microenvironment, Mouse models
Objective
Small cell lung cancer (SCLC) remains one of the most lethal forms of lung cancer. Although initial responses to chemotherapy are often strong, most patients ultimately relapse, and overall survival gains brought by immune checkpoint inhibitors (ICI) have been limited [1, 2]. Progress in developing more effective treatments has been hampered in part by preclinical models not fully capturing the disease’s complexity. The widely used RP mouse model, characterized by the loss of Rb1 and Trp53, fails to capture the full molecular heterogeneity and mismatch repair (MMR) deficiencies now identified in subsets of SCLC patients [3, 4]. Recently a novel SCLC mouse model capturing MMR deficiency, called the RPM model, was introduced [5]. Here, we report the first tissue-level imaging of SCLC tumors in RPM model, alongside with the classical RP model.
The characterization of this new mouse line was conducted at the macro scale under five different lines of treatments (Platinum-based chemotherapy; Immune checkpoint inhibition; Treg suppressor; Combination of chemotherapy and immune checkpoint inhibition; Combination of Treg suppressor with chemotherapy and immune checkpoint inhibition; Vehicle control) [5]. The following key differences were observed: 1/ RPM tumors exhibited MMR deficiency, a high tumor mutational burden (TMB), and an elevated load of candidate neoantigens compared to RP lesions, suggesting enhanced immunogenicity; 2/ The time from tumor manifestation to death was substantially shortened for animals with MMR-defective tumors, indicating increased aggressiveness; and 3/ The overall survival of RPM animals was significantly improved when exposed to ICI.
As a follow-up study, tissues from this cohort were collected and imaged with a high-multiplexed panel to uncover tumor heterogeneity and spatial characteristics. The objective was to further characterize this new RPM model and its responses to treatments. Unfortunately, the project ran out of funding before an in-depth analysis of the multiplexed image data could be carried out.
Data description
The dataset comprises 221 regions of interest (ROIs) imaged from two frozen tissue microarrays (TMAs) sections stained with metal-coupled antibodies and imaged via Imaging Mass Cytometry (IMC) [6]. One TMA contains all tissues from the RP mice, the other from the RPM mice. Each TMA block contains cores (1.5 mm in diameter) from mouse lung tumor samples and additional tissues for control (spleen, lymph node). For each mouse tumor sample, two areas were selected for imaging with the help of the IMC software viewer: one located in the central tumor region and one at the tumor-normal lung border (see metadata file 1).
A 37-marker panel was employed (see metadata file 2). The panel includes cellular markers for tumor cells, stromal compartments (endothelial cells, fibroblasts), and various immune cell types (T cells, B cells, macrophages, etc.). The panel also includes functional markers that assess cellular states like activation, exhaustion, and proliferation. This panel was optimized to minimize overlap and enhance the specificity of antibody binding based on prior samples. Control murine spleen samples were utilized to validate antibody specificity and sensitivity. The high number of simultaneous markers enables a detailed description of cellular content as well as spatial relationships between cells.
Each ROI is paired with a binary tumor mask (manually delineated tumor regions using normalized RGB composites DNA [blue], NCAM [red], and PD-L1 [green] via the Fiji software), allowing for comparisons of immune and cellular profiles between tumor interiors and tumor-normal boundaries. Metadata link each ROI to the mouse line, tissue type, treatment groups, dates of tumor onset, and mouse termination dates (see metadata file 1). From the original .mcd file format obtained from the IMC device, zarr ome files were extracted, discarding background channels only (see files in the RP-TMA and RPM-TMA Data sets, cf. Table 1; each zarr ome contains all 37 channels for each ROI and the tumor mask when the tissue was extracted from the lung). Furthermore, some ROIs were excluded if the imaged area was too small (5 excluded ROIs) or the staining quality was too low (7 excluded ROIs; the list of discarded ROIs is in the Study description file).
No further processing (e.g. signal processing, segmentation) was conducted, as the empty metadata file 3 shows, leaving the rest of the analysis pipeline up to the users Table 1.
Table 1.
Overview of data files/data sets
| Label | Name of data file/data set | File types (file extension) | Data repository and identifier (DOI or accession number) |
|---|---|---|---|
| Study description | idr0170-study.txt | Text (.txt) | Image Data Resource DOI: https://doi.org/10.17867/10000208 [6] |
| Metadata File 1 | idr0170-experimentA-assays.tsv | Table (.tsv) | Image Data Resource DOI: https://doi.org/10.17867/10000208 [6] |
| Metadata File 2 | idr0170-experimentA-IMCchannels.tsv | Table (.tsv) | Image Data Resource DOI: https://doi.org/10.17867/10000208 [6] |
| Metadata File 3 | idr0170-experimentA-processed.tsv | Table (.tsv) | Image Data Resource DOI: https://doi.org/10.17867/10000208 [6] |
| Data set 1 | RP-TMA | Ome zarr files (.ome.zarr) | Image Data Resource DOI: https://doi.org/10.17867/10000208 [6] |
| Data set 2 | RPM-TMA | Ome zarr files (.ome.zarr) | Image Data Resource DOI: https://doi.org/10.17867/10000208 [6] |
Limitations
The spatial resolution of IMC is fixed at 1 μm² in the xy-plane, as the technology relies on a laser to ablate successive small tissue regions, with the spatial layout reconstructed from these discrete measurements.
SCLC tumors are known for their high heterogeneity: variations in tumor architecture, immune cell infiltration, and rare cell populations (e.g., specific immune subsets) can be significant across different regions [7, 8]. The high cost and time constraints associated with IMC imaging necessitated selective sampling of TMA cores, resulting in incomplete spatial coverage of the tissue arrays. Given the limited sample size (numbers of mice per treatment vary from 9 – vehicle RPM mice – to 23; this information for each group of mice is retrievable from metadata file 1), such heterogeneity could hinder robust conclusions and reduce the statistical power of analyses.
Additionally, not all markers exhibited optimal staining quality, as noted in the metadata file 2, which may introduce variability or reduce the reliability of specific protein measurements.
These limitations underscore the need for careful experimental design, validation against established studies on SCLC heterogeneity, and cautious interpretation of results, particularly concerning rare or low-signal markers.
Acknowledgements
We are grateful to Bianca Göbel, Yagmur Sahbaz, and Melanie Nelles from the University Hospital Cologne for their exceptional technical support. We also thank the CyTOF Facility at the CMMC, in particular Petra Hofmann, for her valuable assistance with operating the Hyperion system. Additionally, we acknowledge the CMMC Microscopy Facility for providing access to their microscopy equipment.
Abbreviations
- ICI
Immune Checkpoint Inhibitor
- IMC
Imaging Mass Cytometry
- MIBI-TOF
Multiplexed Ion Beam Imaging by Time Of Flight
- MMR
Mismatch Repair
- ROI
Region of Interest
- RP
Rb1fl/fl;Trp53fl/fl
- RPM
Rb1fl/fl;Trp53fl/fl;Msh2fl/fl
- SCLC
Small Cell Lung Cancer
- TMA
Tissue Microarray
Author contributions
G.S.H.-S., H.C.R. designed the study. O.I., F.R., L.L., F.B., J.M.H., H.G., R.B., G.B., J.G., K.B., G.S.H.-S., H.C.R designed the experiments. O.I., L.L., M.K., G.G., J.C., L.N., M.I.C. performed the experiments. F.R. formatted the data, added the metadata, and uploaded them in the IDR archive. F.R., O.I., H.C.R., K.B. wrote the manuscript. All authors read and approved the final manuscript.
Funding
Open Access funding enabled and organized by Projekt DEAL. This work was supported by the German Research Foundation (DFG) (SFB1399-grant no.413326622, to H.C.R., G.S.H.-S., J.G., R.K.T., F.B., H.G.; SFB1430–grant no.424228829, to H.C.R. and SFB1530–grant no. 455784452, to H.C.R., HE 6897/2 − 1 to G.S.H.-S., HE6810/3 − 1 to J.M.H.), the Else Kröner-Fresenius Stiftung (EKFS-2014-A06 to H.C.R., 2016_Kolleg.19 to H.C.R.), the Deutsche Krebshilfe (1117240 to H.C.R., 70113041 to H.C.R. and F.B. and the Mildred Scheel Nachwuchszentrum Grant 70113307 to F.B.), the German Ministry of Education and Research (BMBF e: Med Consortium InCa, grant 01ZX1901 and 01ZX2201A to R.K.T., J.G. and H.C.R.), the Center for Molecular Medicine Cologne (CAP-13 to J.M.H., CAP-16 to G.S.H.-S., B02 to G.S.H.-S. and J.M.H.),and the Faculty of Medicine, University of Cologne: Koeln Fortune Program(272/2019) and Gusyk family support grant (to G.S.H.-S.). Additional funding was received from the program “Netzwerke 2021”, an initiative of the Ministry of Culture and Science of the State of North Rhine-Westphalia for the CANTAR project to H.C.R., J.G. K.B. and F.R. were funded by the BMBF program for Female Junior Researchers in Artificial Intelligence 01IS20054. F.R. received funding from North Rhine-Westphalia Ministry of Culture and Science, KI-Starter 2210kis004.
Data availability
The data described in this Data note will be freely and openly accessed on Image Data Resource (https://idr.openmicroscopy.org) under accession number idr0170, under the DOI: https://doi.org/10.17867/10000208 [6]. Please see Table 1 for details.
Declarations
Ethics approval ans consent to participate
Animal experiments in this study were approved by the local Ethics Committee of Animal Experiments authorities (LANUV, North Rhine-Westphalia, Germany) under license number 81-02.04.2019-A491. All mice were maintained according to FELASA recommendations and in compliance with the European Union and German guidelines. Mice were monitored biweekly for tumor progression using MRI imaging, and animals were euthanized once a sufficient tumor burden was reached, as per predefined criteria (e.g., weight loss > 20%, severe tumor ulceration, or breathing difficulties). Euthanasia was conducted in compliance with the license 8102.04.2019-A491 approved by the LANUV authority (North-Rhine-Westphalia, Germany) and adhered to FELASA, EU, and German guidelines. The euthanasia method involved terminal anesthesia followed by transcardial perfusion with 10 ml sterile PBS to clear blood from the lungs and minimize contamination of tissue samples. The specific anesthesia used for imaging and terminal procedures was isoflurane (2.5% in oxygen) or intraperitoneal injection of ketamine (100 mg/kg) combined with xylazine (20 mg/kg). Anesthesia depth was confirmed by the absence of response to toe pinch before proceeding with perfusion and tissue harvesting. This approach was chosen to ensure minimal suffering, rapid unconsciousness, and high-quality tissue preservation suitable for downstream analyses such as IMC and genomic studies.
Consent for publication
Not applicable.
Competing interests
H.C.R. received consulting and lecture fees from Abbvie, AstraZeneca, Vertex, and Merck. H.C.R. received research funding from AstraZeneca and Gilead Pharmaceuticals. H.C.R. is a co-founder of CDL Therapeutics GmbH. The remaining authors declare no competing financial interest.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
France Rose and Olta Ibruli contributed equally to this work.
Grit S. Herter-Sprie, Hans Christian Reinhardt and Katarzyna Bozek contributed equally to this work.
References
- 1.Nicholson AG, Chansky K, Crowley J, Beyruti R, Kubota K, Turrisi A, Eberhardt WEE, Van Meerbeeck J, Rami-Porta R. The international association for the study of lung cancer lung cancer staging project: proposals for the revision of the clinical and pathologic staging of small cell lung cancer in the forthcoming eighth edition of the Tnm classification for lung cancer. J Thorac Oncol. 2016;11(3):300–11. [DOI] [PubMed] [Google Scholar]
- 2.Rudin CM, Brambilla E, Corinne Faivre-Finn, and Julien Sage. Small-Cell lung cancer. Nat Reviews Disease Primers. 2021;7(1). 10.1038/s41572-020-00235-0. [DOI] [PMC free article] [PubMed]
- 3.Dietlein F, Thelen L, Reinhardt HC. Cancer-specific defects in DNA repair pathways as targets for personalized therapeutic approaches. Trends Genet. 2014 a;30:326–39. (Elsevier Ltd.). [DOI] [PubMed] [Google Scholar]
- 4.Liu Q, Zhang J, Guo C, Wang M, Wang C, Yan Y, Sun L, Wang D, Zhang L, Yu H, Hou L, Wu C, Zhu Y, Jiang G, Zhu H, Zhou Y, Fang S, Zhang T, Hu L, Zhang P. Proteogenomic characterization of small cell lung cancer identifies biological insights and subtype-specific therapeutic strategies. Cell. 2024;187(1):184–e20328. [DOI] [PubMed] [Google Scholar]
- 5.Ibruli O, Rose F, Beleggia F, Schmitt A, Cartolano M, Torres Fernandez L, Saggau J, Bonasera D, Kiljan M, Gozum G, Lichius L, Niu L, Iannicelli Caiaffa M, Herter JM, Walczak H, Liccardi G, Grüll H, Büttner R, Bosco G, George J, Thomas RK, Bozek K, Reinhardt HC, Herter-Sprie GS. A novel mouse model recapitulating the MMR-defective SCLC subtype uncovers an actionable sensitivity to immune checkpoint Blockade. J Cancer Res Clin Oncol. 2024;150:496. 10.1007/s00432-024-05942-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Image data are available in the Image Data Resource. (https://idr.openmicroscopy.org) under accession number idr0170. 10.17867/10000208
- 7.George J, Lim JS, Jang SJ, Cun Y, Ozretia L, Kong G, Leenders F, Lu X, Fernández-Cuesta L, Bosco G, Müller C, Dahmen I, Jahchan NS, Park KS, Yang D, Karnezis AN, Vaka D, Torres A, Wang MS, Thomas RK. Comprehensive genomic profiles of small cell lung cancer. Nature. 2015;524(7563):47–53. 10.1038/nature14664. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.George J, Maas L, Abedpour N, Cartolano M, Kaiser L, Fischer RN, Scheel AH, Weber JP, Hellmich M, Bosco G, Volz C, Mueller C, Dahmen I, John F, Alves CP, Werr L, Panse JP, Kirschner M, Engel-Riedel W, et al. Evolutionary trajectories of small cell lung cancer under therapy. Nature. 2024;627(8005):880–9. 10.1038/s41586-024-07177-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The data described in this Data note will be freely and openly accessed on Image Data Resource (https://idr.openmicroscopy.org) under accession number idr0170, under the DOI: https://doi.org/10.17867/10000208 [6]. Please see Table 1 for details.