Improving breast cancer histology image classification by incorporating Spatial Transcriptomics and clinical data into the model

Pathologists routinely perform cancer identification and risk assessments on breast tissue section images and medical patient data.

Spatial Transcriptomics (ST) is a novel technology developed in Sweden that allows measuring where in a tissue section a certain RNA was expressed.

Our overall aims are to evaluate (a) how the additional consideration of ST gene expression data in addition to tissue section images improves the identification and classification of cancer sub-types in breast tissue sections, (b) if the additional consideration of normal breast tissue adjacent to tumor tissue allows finding cancer onset signatures in the images or in the ST expression data and (c) if considering genome sequence and/or clinical patient data further improves cancer classification.We will computationally analyze our own novel large ST dataset consisting of 48 breast tissue sections from eleven individuals with different cancer types together with non-cancer sections from the same individuals.

Whole genome sequencing data is available from the tumor and matched peripheral blood as control from five individuals.

Detailed clinical characteristics are available for all individuals.We might be able to give advice on how the novel ST technology can improve traditional cancer identification and classification as well as how the inclusion of additional non-cancer samples, genome sequence information and clinical patient data can improve the classification outcome.