Tumor-immune partitioning and clustering algorithm for identifying tumor-immune cell spatial interaction signatures within the tumor microenvironment.

Background: Growing evidence supports the importance of characterizing the organizational patterns of various cellular constituents in the tumor microenvironment in precision oncology. Most existing data on immune cell infiltrates in tumors, which are based on immune cell counts or nearest neighbor-type analyses, have failed to fully capture the cellular organization and heterogeneity. Methods: We introduce a computational algorithm, termed Tumor-Immune Partitioning and Clustering (TIPC), that jointly measures immune cell partitioning between tumor epithelial and stromal areas and immune cell clustering versus dispersion. As proof-of-principle, we applied TIPC to a prospective cohort incident tumor biobank containing 931 colorectal carcinoma cases. TIPC identified tumor subtypes with unique spatial patterns between tumor cells and T lymphocytes linked to certain molecular pathologic and prognostic features. T lymphocyte identification and phenotyping were achieved using multiplexed (multispectral) immunofluorescence. In a separate hepatocellular carcinoma cohort, we replaced the stromal component with specific immune cell types—CXCR3+CD68+ or CD8+—to profile their spatial relationships with CXCL9+CD68+ cells. Results: Six unsupervised TIPC subtypes based on T lymphocyte distribution patterns were identified, comprising two cold and four hot subtypes. Three of the four hot subtypes were associated with significantly longer colorectal cancer (CRC)-specific survival compared to a reference cold subtype. Our analysis showed that variations in T-cell densities among the TIPC subtypes did not strictly correlate with prognostic benefits, underscoring the prognostic significance of immune cell spatial patterns. Additionally, TIPC revealed two spatially distinct and cell density-specific subtypes among microsatellite instability-high colorectal cancers, indicating its potential to upgrade tumor subtyping. TIPC was also applied to additional immune cell types, eosinophils and neutrophils, identified using morphology and supervised machine learning; here two tumor subtypes with similarly low densities, namely 'cold, tumor-rich' and 'cold, stroma-rich', exhibited differential prognostic associations. Lastly, we validated our methods and results using The Cancer Genome Atlas colon and rectal adenocarcinoma data (n = 570). Moreover, applying TIPC to hepatocellular carcinoma cases (n = 27) highlighted critical cell interactions like CXCL9-CXCR3 and CXCL9-CD8. Conclusions: Unsupervised discoveries of microgeometric tissue organizational patterns and novel tumor subtypes using the TIPC algorithm can deepen our understanding of the tumor immune microenvironment and likely inform precision cancer immunotherapy. Author summary: We have developed a computational tool called the Tumor-Immune Partitioning and Clustering (TIPC) algorithm, designed to reveal the intricate organization of immune cells within the tumor microenvironment. Traditionally, studies have mainly focused on counting these cells or examining their proximity to one another. Those approaches often underestimate the complex roles of immune cells in tumors. With TIPC, we have uncovered distinct patterns in the arrangement of immune cells across different tumor regions. This advancement has enabled us to identify tumor subtypes that were previously undetectable with existing methods. Our new method can determine which tumors are likely to have longer survival rates or respond better to immunotherapy, based on the layout of immune cells rather than merely their numbers. This breakthrough has significant implications for cancer research, highlighting the importance of understanding the spatial patterns of immune cells. Such knowledge is crucial for selecting appropriate patients for specific treatments and for assessing the potential effectiveness of immunotherapy. By tailoring treatment plans to the unique cellular landscapes of each tumor, we can potentially improve outcomes and provide more personalized and effective cancer care. [ABSTRACT FROM AUTHOR]