SPF: A spatial and functional data analytic approach to cell imaging data

• Published in: PLoS computational biology 2022-06, Vol.18 (6), p.e1009486-e1009486
• Main Authors: Vu, Thao, Wrobel, Julia, Bitler, Benjamin G, Schenk, Erin L, Jordan, Kimberly R, Ghosh, Debashis
• Format: Article
• Language: English
• Subjects: Biology and Life Sciences; Carcinoma, Non-Small-Cell Lung; Data Science; Humans; Immunohistochemistry; Lung Neoplasms; Medicine and Health Sciences; Tumor Microenvironment
• ISSN: 1553-7358; 1553-734X; 1553-7358
• DOI: 10.1371/journal.pcbi.1009486

The tumor microenvironment (TME), which characterizes the tumor and its surroundings, plays a critical role in understanding cancer development and progression. Recent advances in imaging techniques enable researchers to study spatial structure of the TME at a single-cell level. Investigating spatial patterns and interactions of cell subtypes within the TME provides useful insights into how cells with different biological purposes behave, which may consequentially impact a subject's clinical outcomes. We utilize a class of well-known spatial summary statistics, the K-function and its variants, to explore inter-cell dependence as a function of distances between cells. Using techniques from functional data analysis, we introduce an approach to model the association between these summary spatial functions and subject-level outcomes, while controlling for other clinical scalar predictors such as age and disease stage. In particular, we leverage the additive functional Cox regression model (AFCM) to study the nonlinear impact of spatial interaction between tumor and stromal cells on overall survival in patients with non-small cell lung cancer, using multiplex immunohistochemistry (mIHC) data. The applicability of our approach is further validated using a publicly available multiplexed ion beam imaging (MIBI) triple-negative breast cancer dataset.