Here we provide a spatiotemporal dataset of triple negative breast cance (TNBC), featuring matched primary tumors and longitudinal biopsies of metastatic lesions collected before and during ICI treatment (specifically anti-PD1) in the context of a prospective clinical trial. We generated multiplexed imaging data of pathology sections for each patient at different timepoints to conduct in-depth analysis of how spatial proteomic characteristics evolve through disease progression and immunotherapy in TNBC and their association with patient response. All analysis scripts for this data can be found here. Top Level Folders analysis_files: This directory should initially contain a cell table (generated with Mesmer and annotated by Pixie). The scripts expect a column named "cell_meta_cluster" containing the cell clusters, as well "fov" with the specific image name. This folder will also contain the final data tables generated by SpaceCat and the TNBC scripts. intermediate_files: This directory should contain subfolders storing any fov and cell level feature analysis done on the data. In addition, there should be a subdirectory containing the metadata about each fov, each timepoint, and each patient, as appropriate for your study. Directory Tree TONIC_Cohort (base directory) image_data segmentation_data deepcell_output analysis_files intermediate_files metadata post_processing - contains cell table outputted from Pixie mask_dir - contains the compartment masks generated in 3_create_image_masks.py, and the cell to compartment labels formatted_files - various files used as input for SpaceCat processing cell_distances_filtered.csv: data detailing linear distance between cell populations in an image (code) formatted_mixing_scores.csv: image level mixing score of various cell population combinations (code) fiber_stats_table.csv: image level fiber analysis (code) fiber_stats_per_tile.csv: tile (512x512 pixel) level fiber analysis neighborhood_image_proportions.csv: K-means clusters aggregated per image (code) neighborhood_compartment_proportions.csv: K-means clusters aggregated per compartment pixie_ecm_stats.csv: pixel cluster stats generated in ECM_Pixie_Cluster_Pixels.ipynb and ECM_pixel_clustering_stats.ipynb ecm_cluster_stats.csv: kmeans stats generated in 4_ecm_preprocessing.py ecm_fraction_stats.csv: ECM proportion stats generated in 4_ecm_preprocessing.py