Characterisation of tumour immune microenvironment remodelling following oncogene inhibition in preclinical studies using an optimised imaging mass cytometry workflow

Abstract

Mouse models are critical in pre-clinical studies of cancer therapy, allowing dissection of mechanisms through chemical and genetic manipulations that are not feasible in the clinical setting. In studies of the tumour microenvironment (TME), novel highly multiplexed imaging methods can provide a rich source of information. However, the application of such technologies in mouse tissues is still in its infancy. Here we present a workflow for studying the TME using imaging mass cytometry with a panel of 27 antibodies on frozen mouse tissues. We optimised and validated image segmentation strategies and automated the process in a Nextflow-based pipeline (imcyto) that is scalable and portable, allowing for parallelised segmentation of large multi-image datasets. Incorporating user-specific plugins, imcyto can be flexibly tailored to a wide range of segmentation needs. With these methods we interrogated the dramatic remodelling of the TME induced by a KRAS G12C inhibitor in an immune competent mouse orthotopic lung cancer model, showcasing their potential as key discovery tools to enhance understanding of the interplay between tumour, stroma, and immune cells in the spatial context of the tissue.

Therapeutic KRAS G12C inhibition drives effective interferon-mediated antitumor immunity in immunogenic lung cancers

“…4A). Using IMC, we recently showed that there was an inclusion of macrophages and neutrophils in the core of 3LL ΔNRAS lung tumors, while effector cells remained at the tumor periphery ( 21 ). Consistent with this apparent immunosuppressive TME, growth of these tumors was not affected by a lack of B and T cells in Rag1 −/− mice (fig.…”

“…However, similarly to other targeted therapies, early clinical results indicate that drug resistance frequently arises, resulting in clinical relapses ( 11 , 12 ). KRAS G12C inhibitors not only affect the survival of cancer cells but also can mediate immunomodulatory effects by reversing KRAS-driven immunosuppressive mechanisms and generate a TME that is more favorable for an antitumor immune response ( 8 , 19 , 21 ). This knowledge has served as a rationale to investigate clinical combinations of KRAS G12C inhibitors with anti-PD1 or PD-L1 antibodies ( 29 ).…”

“…Tissue processing and antibody staining was performed as described in detail in ( 21 ). In short, 5-μm cryosections of fresh frozen lungs were fixed (Image-iT Fixative Solution, Thermo Fisher Scientific) and stained with the antibody panel listed in Table 3 and Cell-ID Intercalator-Ir (Fluidigm).…”

“…Fedele et al ( 20 ) showed that a combination of SHP2 and KRAS G12C inhibition led to good tumor control and increased T cell infiltration in an orthotopic model of lung cancer. Using the strongly immune evasive 3LL ΔNRAS lung cancer cell line ( 14 ), we recently developed an imaging mass cytometry (IMC) analysis pipeline that showed that KRAS G12C inhibition was able to induce remodeling of the lung TME ( 21 ). Here, we use this 3LL ΔNRAS alongside other preclinical lung cancer models varying in degree of immunogenicity to perform an in-depth investigation of the impact of KRAS G12C inhibition on the TME and antitumor immunity and explore the mechanisms that underlie the changes observed.…”

A SIMPLI (Single-cell Identification from MultiPLexed Images) approach for spatially resolved tissue phenotyping at single-cell resolution

“…Currently available tools are technology-specific and cover only some steps of the whole analytical workflow (Table 1). For example, several computational approaches have been developed to process raw images and extract single-cell data either interactively (Ilastik 10 , CellProfiler4 11 , CODEX Toolkit 4 ) or via command line (imcyto 12 , ImcSegmentationPipeline 13 ). Distinct sets of tools can then perform cell phenotyping (CellProfiler Analyst 14 , Cytomapper 15 , Immunocluster 16 ) or analyse cell-cell spatial interactions (CytoMap 17 , ImaCytE 18 , SPIAT 19 , neighbouRhood 20 ).…”