Abstract 651: Analysis of the immune microenvironment to advance breast cancer risk prediction and prevention

Existing risk models underperform among women who have undergone a benign breast disease (BBD) biopsy (> one million performed annually in the US) and with respect to estimating the risk of aggressive BCs. We've shown that molecular pathologic analysis of BBD biopsies can improve individual risk prediction, compared with standard risk models relying on self-reported factors, and provide insights into mechanisms mediating BC risk. BC risk factors such as obesity and ethanol use are proposed to increase production of cytokines and chemokines, resulting in chronic inflammation, and leading to production of DNA damaging free radicals and growth factors (e.g., VEGF, IGFs) that activate pro-carcinogenic pathways (e.g. NF-KB and JAK/STAT). The effects of most BC risk factors on immunity are poorly understood. Further, whereas experimental models implicate immunity throughout carcinogenesis, knowledge of immune markers and mechanisms related to BC development among women is limited. Therefore, we aim to define tissue immune cell content throughout BC development, and particularly at the earliest stages, to improve risk assessment and discover immune-based prevention strategies. Our study combines novel resources and technologies to define the immune landscape in: 1) normal breast tissues in relation to BC risk factors, 2) BBD with respect to BC risk, and 3) invasive BC in relation to neoantigen expression and molecular subtype. We will perform NanoString GeoMxTM Digital Spatial Profiling (DSP) to quantitatively map expression of key immune proteins in healthy breast tissues donated to the Komen Tissue Bank (KTB) in relation to BC risk factors and in BBD biopsies from two cohorts to predict BC risk. We will re-analyze top prognostic markers in BBD biopsies using Vectra multiplex IF staining with machine learning algorithms to refine how immune cell content and organization impacts prognosis, and assess critical immune pathways related to BBD progression with the NanoString PanCancer IO 360 panel, which provides RNA expression of 770 immune genes (13 signatures). Finally, we will evaluate immune responses in BCs categorized for predicted neoantigen load and underlying mutation type with our novel bioinformatics pipeline that enables accurate prediction of MHC class I and class II missense, fusion and frameshift mutation neoantigens generated through faulty DNA repair, aberrant DNA and RNA splicing, insertions, and deletions. Our project demonstrates a novel approach