BMC research notes · 2025
Imaging mass cytometry dataset of small-cell lung cancer tumors and tumor microenvironments.
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Rose France, Ibruli Olta, Lichius Luca, Kiljan Martha, Gozum Gokcen, Caiaffa Manoela Iannicelli, Cai Jiali, Niu Li-Na, Herter Jan M, Grüll Holger, Büttner Reinhard, Beleggia Filippo, Bosco Graziella, George Julie, Herter-Sprie Grit S, Reinhardt Hans Christian, Bozek Katarzyna
Аннотация
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.
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.