Dataset Information

ATHENA: Analysis of Tumor Heterogeneity from Spatial Omics Measurements.

ABSTRACT: Tumor heterogeneity has emerged as a fundamental property of most human cancers, with broad implications for diagnosis and treatment. Recently, spatial omics have enabled spatial tumor profiling, however computational resources that exploit the measurements to quantify tumor heterogeneity in a spatially-aware manner are largely missing. We present ATHENA, a computational framework that facilitates the visualization, processing and analysis of tumor heterogeneity from spatial omics measurements. ATHENA employs graph representations of tumors and bundles together a large collection of established and novel heterogeneity scores that quantify different aspects of the complexity of tumor ecosystems. ATHENA is available as a Python package under an open-source licence at: https://github.com/AI4SCR/ATHENA. Detailed documentation and step-by-step tutorials with example datasets are also available at: https://ai4scr.github.io/ATHENA/. Supplementary data are available at Bioinformatics online.

SUBMITTER: Martinelli AL

PROVIDER: S-EPMC9154280 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature

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ATHENA: analysis of tumor heterogeneity from spatial omics measurements.

Martinelli Adriano Luca AL Rapsomaniki Maria Anna MA

Bioinformatics (Oxford, England) 20220501 11

<h4>Summary</h4>Tumor heterogeneity has emerged as a fundamental property of most human cancers, with broad implications for diagnosis and treatment. Recently, spatial omics have enabled spatial tumor profiling, however computational resources that exploit the measurements to quantify tumor heterogeneity in a spatially aware manner are largely missing. We present ATHENA (Analysis of Tumor HEterogeNeity from spAtial omics measurements), a computational framework that facilitates the visualization ...[more]

PMID: 35485743

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Project description:Metabolic preferences of tumor cells vary within a single tumor, contributing to tumor heterogeneity, drug resistance, and patient relapse. However, the relationship between tumor treatment response and metabolically distinct tumor cell populations is not well-understood. Here, a quantitative approach was developed to characterize spatial patterns of metabolic heterogeneity in tumor cell populations within in vivo xenografts and 3D in vitro cultures (i.e., organoids) of head and neck cancer. Label-free images of cell metabolism were acquired using two-photon fluorescence lifetime microscopy of the metabolic co-enzymes NAD(P)H and FAD. Previous studies have shown that NAD(P)H mean fluorescence lifetimes can identify metabolically distinct cells with varying drug response. Thus, density-based clustering of the NAD(P)H mean fluorescence lifetime was used to identify metabolic sub-populations of cells, then assessed in control, cetuximab-, cisplatin-, and combination-treated xenografts 13 days post-treatment and organoids 24 h post-treatment. Proximity analysis of these metabolically distinct cells was designed to quantify differences in spatial patterns between treatment groups and between xenografts and organoids. Multivariate spatial autocorrelation and principal components analyses of all autofluorescence intensity and lifetime variables were developed to further improve separation between cell sub-populations. Spatial principal components analysis and Z-score calculations of autofluorescence and spatial distribution variables also visualized differences between models. This analysis captures spatial distributions of tumor cell sub-populations influenced by treatment conditions and model-specific environments. Overall, this novel spatial analysis could provide new insights into tumor growth, treatment resistance, and more effective drug treatments across a range of microscopic imaging modalities (e.g., immunofluorescence, imaging mass spectrometry).

Dataset Information

ATHENA: Analysis of Tumor Heterogeneity from Spatial Omics Measurements.

Publications

ATHENA: analysis of tumor heterogeneity from spatial omics measurements.

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