This dataset contains all the IMC data associated with Wang et al. Spatial predictors of immunotherapy response in triple negative breast cancer, 2023.

The zip file NTPublic contains:

Raw multiplexed image data, image masks (cell, nuclear, epithelial, vessel), spillover matrix for signal compensation, antibody panel information, clinical data

Processed single-cell data

Code for analysis and creating plots

Please read the accompanying DataGuide.pdf for folder structure guide and information on both the raw and processed data.

Please read the accompanying CodeGuide.pdf to reproduce published figures.

The development of artificial intelligence software to improve the outcomes of breast screening relies on the availability of well-curated image databases. The OPTIMAM Mammography Image Database (OMI-DB) was created to provide a centralized, fully annotated dataset for research. The initial reason for creating the database was for the Cancer Research United Kingdom–funded projects OPTIMAM (2008–2013) and OPTIMAM2 (2013–2018), which evaluated how various factors affect breast cancer detection on mammograms. The images are derived from screening centers in the United Kingdom and combined with systematically collected data on the current screening episode, as well as previous and subsequent episodes. In the United Kingdom, the National Health Service Breast Screening Programme (NHSBSP) invites women to attend breast screening every 3 years between the ages of 50 and 70 years. A screening episode is one attendance at screening by a woman and includes any immediate workup imaging (assessment) if she was recalled for further investigation of a suspicious region on the screening mammograms. Any pathologic finding is also included, and the episode ends with histologic diagnosis or treatment for all lesions. Our objective was to collect mammograms for women with screen-detected cancers as well as representative samples of normal and benign screening cases.

“For processing” and “for presentation” screening mammograms and prior mammograms have been collected for all screen-detected and interval cancers from several screening centres since 2011. All mammography images and data associated with initial screening attendance, further assessment, and surgical outcomes were collected as a screening episode. In addition to continuous collection of cancers, images and clinical data were collected for all women screened during 2014, and for a random selection of 25% of all women screened in 2012, 2013, and 2015 at two of the three sites. Collection into the database is ongoing, and each case is updated with new information and further screening episodes.

The associated data comprise radiologic, clinical, and pathologic information extracted from NBSS. Information on screening history, previous occurrences of cancer, biopsy results, and surgical procedures are collected from NBSS. The exact radiologic locations of lesions are not stored in NBSS. However, such information, important for training and evaluating algorithms, is collected in OMI-DB. Experienced (UK accredited) mammography readers at their own site (radiologists and advanced practice radiographers) annotate the images with reference to records made at the time of initial mammography interpretation and at further (assessment) workup (magnification views, US, and biopsy). This information is used to define rectangular regions of interest indicating the location and area of lesions and other attributes, such as radiologic appearance and conspicuity.

DATA_for_Kreuzaler_etal_Scripts contains input processed data files for the mass spectrometry imaging data used in the manuscript "Vitamin B5 supports Myc oncogenic metabolism and tumour progression in breast cancer". The input files are arranged according to different studies and are provided as HDF5 formatted binary files (.h5). Individual files consist of processed datacubes as a matrix with rows corresponding to pixels and columns corresponding to m/z features. It also includes dimension of the total image as height x width and a list of m/z. For each study, tissue labels are provided as R data file (.rds). It consists of tumour-type labels as a matrix pixel matched with the processed datacube. Additionally following datasets are also available: Immunohistochemistry (IHC) data for PDX and human biopsy samples. Raw GC-MS data for WM Tumours