Background: Observing the spatial pattern of tumour infiltrating lymphocytes in follicular lymphoma can lead to the development of promising novel biomarkers for survival prognosis. We have developed the “Hypothesised Interactions Distribution” (HID) analysis, to quantify the spatial heterogeneity of cell type interactions between lymphocytes in the tumour microenvironment. HID features were extracted to train a machine learning model for survival prediction and their performance was compared to other architectural biomarkers. Scalability of the method was examined by observing interactions between cell types that were identified using 6-plexed immunofluorescent staining. Methods: Two follicular lymphoma datasets were used in this study; a microarray with tissue cores from patients, stained with CD69, CD3 and FOXP3 using multiplexed brightfield immunohistochemistry and a second tissue microarray, stained with PD1, PDL1, CD4, FOXP3, CD68 and CD8 using immunofluorescence. Spectral deconvolution, nuclei segmentation and cell type classification was carried out, followed by extraction of features based on cell type interaction probabilities. Random Forest classifiers were built to assign patients into groups of different overall survival and the performance of HID features was assessed. Results: HID features constructed over a range of interaction distances were found to significantly predict overall survival in both datasets (p = 0.0363, p = 0.0077). Interactions of specific phenotype pairs, correlated with unfavourable prognosis, could be identified, such as the interactions between CD3+FOXP3+ cells and CD3+CD69+ cells. Conclusion: Further validation of HID demonstrates its potential for development of clinical biomarkers in follicular lymphoma.
Anna Maria Tsakiroglou, Sophie Fitzpatrick, Lilli Nelson, Catharine West, Kim Linton, Kang Zeng, Garry Ashton, Sue Astley, Richard Byers, and Martin Fergie, "Quantifying cell-type interactions and their spatial patterns as prognostic biomarkers in follicular lymphoma," Proc. SPIE 10581, Medical Imaging 2018: Digital Pathology, 105810G (Presented at SPIE Medical Imaging: February 11, 2018; Published: 6 March 2018); https://doi.org/10.1117/12.2293572.
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