spatialTIME and iTIME: R package and Shiny application for visualization and analysis of immunofluorescence data

Creed, Jordan H.; Wilson, Christopher M.; Soupir, Alex C.; Colin, Christelle; Kimmel, Gregory J.; Ospina, Oscar E.; Chakiryan, Nicholas H.; Markowitz, Joseph; Peres, Lauren C.; Coghill, Anna E.; Fridley, Brooke L.

doi:10.1093/bioinformatics/btab757

Cited by 16 publications

(19 citation statements)

References 14 publications

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“…In particular, the authors highlighted the plausibility of the tissue slides being folded or torn during the slicing process, leading to some sections of the image with no cells present(e.g., panel (C) of S4 Fig ). To overcome such challenge, Wilson et al [ 48 ] introduced a framework (with the accompanying package [ 49 ]) that utilized the K-function in a permutation-based setting to account for such drawbacks. Inspired by the study, it would be interesting to extend our proposed framework to include the permutation-based summary function.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

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

Wrobel

Bitler

et al. 2022

PLoS Comput Biol

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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.

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Section: Conclusion and Discussionmentioning

confidence: 99%

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

Wrobel

Bitler

et al. 2022

PLoS Comput Biol

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“…Likelihood ratio tests were used to compare survival models with and without the spatial information. Permutations were performed with the package spatialTIME [ 32 ], and all statistical analyses were completed using RStudio with R v4.1.1.…”

Section: Methodsmentioning

confidence: 99%

Tumor immune cell clustering and its association with survival in African American women with ovarian cancer

et al. 2022

Self Cite

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New technologies, such as multiplex immunofluorescence microscopy (mIF), are being developed and used for the assessment and visualization of the tumor immune microenvironment (TIME). These assays produce not only an estimate of the abundance of immune cells in the TIME, but also their spatial locations. However, there are currently few approaches to analyze the spatial context of the TIME. Therefore, we have developed a framework for the spatial analysis of the TIME using Ripley’s K, coupled with a permutation-based framework to estimate and measure the departure from complete spatial randomness (CSR) as a measure of the interactions between immune cells. This approach was then applied to epithelial ovarian cancer (EOC) using mIF collected on intra-tumoral regions of interest (ROIs) and tissue microarrays (TMAs) from 160 high-grade serous ovarian carcinoma patients in the African American Cancer Epidemiology Study (AACES) (94 subjects on TMAs resulting in 263 tissue cores; 93 subjects with 260 ROIs; 27 subjects with both TMA and ROI data). Cox proportional hazard models were constructed to determine the association of abundance and spatial clustering of tumor-infiltrating lymphocytes (CD3+), cytotoxic T-cells (CD8+CD3+), and regulatory T-cells (CD3+FoxP3+) with overall survival. Analysis was done on TMA and ROIs, treating the TMA data as validation of the finding from the ROIs. We found that EOC patients with high abundance and low spatial clustering of tumor-infiltrating lymphocytes and T-cell subsets in their tumors had the best overall survival. Additionally, patients with EOC tumors displaying high co-occurrence of cytotoxic T-cells and regulatory T-cells had the best overall survival. Grouping women with ovarian cancer based on both cell abundance and spatial contexture showed better discrimination for survival than grouping ovarian cancer cases only by cell abundance. These findings underscore the prognostic importance of evaluating not only immune cell abundance but also the spatial contexture of the immune cells in the TIME. In conclusion, the application of this spatial analysis framework to the study of the TIME could lead to the identification of immune content and spatial architecture that could aid in the determination of patients that are likely to respond to immunotherapies.

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“…Ripley's K of proliferating tumor cells: Cytokeratin-positive tumor cells were classified as proliferative based on positivity for the cell-cycle markers Ki67, PCNA, and pHH3. The R package spatialTIME (version 1.3.3.3) (Creed et al 2021) was used to calculate univariate Ripley's K metrics for proliferating tumor cells for each whole slide image within a radius of 50µm. Radii between 0-100µm were tested for this analysis, and 50µm was selected to maximize inter-sample variation in the degree of clustering.…”

Section: Spatial Analysismentioning

confidence: 99%

“Longitudinal and multimodal auditing of tumor adaptation to CDK4/6 inhibitors in HR+ metastatic breast cancers”

Creason,

Egger,

Watson

et al. 2023

Preprint

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CDK4/6 inhibitors (CDK4/6i) have transformed the treatment of hormone receptor-positive (HR+), HER2-negative (HR+) breast cancers as they are effective across all clinicopathological, age, and ethnicity subgroups for metastatic HR+ breast cancer. In metastatic ER+ breast cancer, CDK4/6i lead to strong and consistent improvement in survival across different lines of therapy. To understand how metastatic HR+ breast cancers become refractory to CDK4/6i, we have created a multimodal and longitudinal tumor atlas to investigate therapeutic adaptations in malignant cells and in the tumor immune microenvironment. This atlas is part of the NCI Cancer Moonshot Human Tumor Atlas Network and includes seven pairs of pre- and on-progression biopsies from five metastatic HR+ breast cancer patients treated with CDK4/6i. Biopsies were profiled with bulk genomics, transcriptomics, and proteomics as well as single-cell ATAC-seq and multiplex tissue imaging for spatial, single-cell resolution. These molecular datasets were then linked with detailed clinical metadata to create an atlas for understanding tumor adaptations during therapy. Analysis of our atlas datasets revealed a diverse but tractable set of tumor adaptations to CDK4/6i therapy. Malignant cells adapted to therapy via mTORC1 activation, cell cycle bypass, and increased replication stress. The tumor immune microenvironment displayed evidence of both immune activation and immune suppression, including increased PD-1 expression, features of T cell dysfunction, and CD163+macrophage infiltration. Together, our metastatic ER+ breast cancer atlas represents a rich multimodal resource to understand tumor therapeutic adaptations to CDK4/6i therapy.

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spatialTIME and iTIME: R package and Shiny application for visualization and analysis of immunofluorescence data

Cited by 16 publications

References 14 publications

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

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

Tumor immune cell clustering and its association with survival in African American women with ovarian cancer

“Longitudinal and multimodal auditing of tumor adaptation to CDK4/6 inhibitors in HR+ metastatic breast cancers”

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