Spatial Predictors of Immunotherapy Response in Triple Negative Breast Cancer, 2023

IMC data from Wang et al. includes raw multiplexed images, cell masks, and clinical information for triple negative breast cancer patients. The dataset is associated with a 2023 study from the University of Cambridge investigating spatial predictors of immunotherapy response. Processed single-cell data and analysis code for reproducing published figures are also provided.

Use Cases

• Training cell segmentation models based on provided image masks for nuclei, epithelium, and vessels.
• Analyzing spatial relationships between immune cells and tumor cells based on multiplexed image data.
• Developing predictive models for immunotherapy response based on integrated clinical and single-cell data.
• Reproducing published spatial analysis workflows using the accompanying code.

Strengths

• Includes both raw multiplexed image data and processed single-cell data, facilitating analysis at multiple levels.
• Provides a spillover matrix for signal compensation, which is critical for accurate IMC data interpretation.
• Contains accompanying code for analysis and figure reproduction, supporting methodological transparency.

Limitations

• Row count and dataset size are unknown, which may limit suitability assessment for large-scale model training.
• Column-level documentation is absent; field semantics must be inferred from the provided DataGuide.pdf after download.

Provenance

Source

University of Cambridge

Collection Method

Imaging Mass Cytometry (IMC) data collected as part of a research study.