3D modelling identifies novel genetic dependencies associated with breast cancer progression in the isogenic MCF10 model.

The initiation and progression of breast cancer from the transformation of the normal epithelium to ductal carcinoma in situ (DCIS) and invasive disease is a complex process involving the acquisition of genetic alterations, changes in gene expression, alongside microenvironmental and recognised histological alterations. Here we sought to comprehensively characterise the genomic and transcriptomic features of the MCF10 isogenic model of breast cancer progression and to functionally validate potential driver alterations in 3-dimensional (3D) spheroids that may give insight into breast cancer progression and identify targetable alterations in conditions more similar to those encountered in vivo. We performed whole genome, exome and RNA sequencing of the MCF10 progression series to catalogue the copy number, mutational and transcriptomic landscapes associated with progression. We identified a number of predicted driver mutations (including PIK3CA and TP53) that were acquired from non-malignant MCF10A cells to their malignant counterparts that are also present in primary breast cancers re-analysed from The Cancer Genome Atlas (TCGA). Acquisition of genomic alterations identified MYC amplification and previously un-described RAB3GAP1-HRAS and UBA2-PDCD2L expressed in-frame fusion genes in malignant cells. Comparison of pathway aberrations associated with progression identified that when cells are grown as 3D spheroids, they show perturbations of cancer-relevant pathways. Functional interrogation of the dependency on predicted driver events, identified alterations in HRAS, PIK3CA, and TP53 that selectively decreased cell growth and were associated with progression from pre-invasive to invasive disease, only when cells were grown as spheroids. Our results have identified changes in the genomic repertoire in cell lines representative of the stages of breast cancer progression and demonstrate that genetic dependencies can be uncovered when cells are grown in conditions more like in vivo. The MCF10 progression series, therefore, represents a good model to dissect potential biomarkers and evaluation of therapeutic targets involved in the progression of breast cancer.