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posted on 2023-05-02, 08:20 authored by Hanyun Zhang, Khalid AbdulJabbar, David A. Moore, Ayse Akarca, Katey S.S. Enfield, Mariam Jamal-Hanjani, Shan E. Ahmed Raza, Selvaraju Veeriah, Roberto Salgado, Nicholas McGranahan, John Le Quesne, Charles Swanton, Teresa Marafioti, Yinyin Yuan The deep learning pipeline to map T-cell and B-cell subsets at intratumoral and peritumoral IH. A, Framework of the deep learning pipeline for spatial analysis. The pipeline comprised 3 steps: serial section alignment, deep learning–based cell classification, and hotspots mapping. IHC staining with a B-cell panel (CD20/CXCR5/CD79b/P40) and a T-cell panel (CD4/FOXP3/CD8) was performed on serial sections, which were then aligned to the corresponding H&E section. Dominant B-cell subsets (CD20+CXCR5−, CD20+CXCR5+, CD79b+) and T-cell subsets (CD4+FOXP3−, CD4+FOXP3+, CD8+) were detected and classified by deep learning models and projected to the H&E slide. The magnified region example illustrates this entire framework, which combines immune cell detection from the B-/T-cell IHC sections together with hotspot identification on the corresponding H&E slide. B–E, Representative TLS, LAG, non-TLS/LAG intratumoral IH, and non-TLS/LAG peritumoral IH. Cell classification results are shown below the original image, with color codes for each cell type shown at the bottom. Scale bar, 100 μm.
Funding
Cancer Research UK (CRUK)
Breast Cancer Now (BCN)
Rosetrees Trust (Rosetrees)
Children's Cancer and Leukaemia Group (CCLG)
National Institutes of Health (NIH)
Congressionally Directed Medical Research Programs (CDMRP)
European Commission (EC)
NIHR Biomedical Research Centre, Royal Marsden NHS Foundation Trust/Institute of Cancer Research (BRC)
Wellcome Trust (WT)
Novo Nordisk Foundation Center for Basic Metabolic Research (NovoNordisk Foundation Center for Basic Metabolic Research)
Royal Society (The Royal Society)
University College London Hospitals Biomedical Research Centre (UCLH BRC)
Breast Cancer Research Foundation (BCRF)
European Research Council (ERC)
Horizon 2020 Framework Programme (H2020)
HORIZON EUROPE Marie Sklodowska-Curie Actions (MSCA)
History
ARTICLE ABSTRACT
Beyond tertiary lymphoid structures, a significant number of immune-rich areas without germinal center-like structures are observed in non–small cell lung cancer. Here, we integrated transcriptomic data and digital pathology images to study the prognostic implications, spatial locations, and constitution of immune rich areas (immune hotspots) in a cohort of 935 patients with lung cancer from The Cancer Genome Atlas. A high intratumoral immune hotspot score, which measures the proportion of immune hotspots interfacing with tumor islands, was correlated with poor overall survival in lung squamous cell carcinoma but not in lung adenocarcinoma. Lung squamous cell carcinomas with high intratumoral immune hotspot scores were characterized by consistent upregulation of B-cell signatures. Spatial statistical analyses conducted on serial multiplex IHC slides further revealed that only 4.87% of peritumoral immune hotspots and 0.26% of intratumoral immune hotspots were tertiary lymphoid structures. Significantly lower densities of CD20+CXCR5+ and CD79b+ B cells and less diverse immune cell interactions were found in intratumoral immune hotspots compared with peritumoral immune hotspots. Furthermore, there was a negative correlation between the percentages of CD8+ T cells and T regulatory cells in intratumoral but not in peritumoral immune hotspots, with tertiary lymphoid structures excluded. These findings suggest that the intratumoral immune hotspots reflect an immunosuppressive niche compared with peritumoral immune hotspots, independent of the distribution of tertiary lymphoid structures. A balance toward increased intratumoral immune hotspots is indicative of a compromised antitumor immune response and poor outcome in lung squamous cell carcinoma.
Intratumoral immune hotspots beyond tertiary lymphoid structures reflect an immunosuppressive microenvironment, different from peritumoral immune hotspots, warranting further study in the context of immunotherapies.
