MacGrogan, G. | bioRxiv Science

bioRxiv2025

The transcription factor AP-2β defines active enhancers conferring molecular apocrine cell identity in breast cancer

The transcription factor Activating Protein 2{beta} (TFAP2B; AP-2{beta}) is a candidate marker for the molecular apocrine subtype of breast cancer, which lacks expression of the estrogen receptor alpha (ESR1; ER) but sustains a luminal breast cancer phenotype due to the presence of key luminal lineage transcription factors (GATA3, FOXA1) and activity of the androgen receptor (AR). Our objective was to better understand the expression patterns and molecular function of AP-2{beta} in breast cancer. Using multiple clinical cohorts, we show that high TFAP2B expression was associated with low proliferation in ER positive (ER+) breast cancers of ductal and lobular histology, but with enrichment for lobular tumours. High TFAP2B was also evident in high proliferation tumours lacking ER, with no specific enrichment for lobular histology. Restoring AP-2{beta} expression to highly proliferative ER+ breast cancer cell lines that lack it strongly induced apoptosis. Conversely, reducing AP-2{beta} expression in a molecular apocrine breast cancer cell line model potently inhibited proliferation and cell viability associated with downregulation of MYC oncogene expression. To identify genomic determinants of AP-2{beta} function in molecular apocrine cells in relation to other known transcriptional regulators, we performed chromatin immunoprecipitation followed by DNA sequencing (ChIP-seq) for AP-2{beta}, AR, GATA3, FOXA1, and acetylated lysine 27 in histone 3 (H3K27ac, a marker of active enhancers and promoters). When present alone, AP-2{beta} was preferentially enriched at active promotors. Enhancers bound by AR, GATA3 and FOXA1 were more active when AP-2{beta} was present and genes that define the molecular apocrine phenotype were significantly more likely to have active enhancers co-occupied by all four transcription factors. We conclude that AP-2{beta} plays a context-dependent role in breast cancer and propose that activation of enhancers defining molecular apocrine identity is a key function of AP-2{beta} in the biology of this subtype of breast cancer.

cancer biology↗

bioRxiv2019

Machine learning versus mechanistic modeling for prediction of metastatic relapse in breast cancer

PurposeFor patients with early-stage breast cancer, prediction of the risk of metastatic relapse is of crucial importance. Existing predictive models rely on agnostic survival analysis statistical tools (e.g. Cox regression). Here we define and evaluate the predictive ability of a mechanistic model for the time to metastatic relapse. MethodsThe data consisted of 642 patients with 21 clinicopathological variables. A mechanistic model was developed on the basis of two intrinsic mechanisms of metastatic progression: growth (parameter ) and dissemination (parameter ). Population statistical distributions of the parameters were inferred using mixed-effects modeling. A random survival forest analysis was used to select a minimal set of 5 covariates with best predictive power. These were further considered to individually predict the model parameters, by using a backward selection approach. Predictive performances were compared to classical Cox regression and machine learning algorithms. ResultsThe mechanistic model was able to accurately fit the data. Covariate analysis revealed statistically significant association of Ki67 expression with (p=0.001) and EGFR with (p=0.009). Achieving a c-index of 0.65 (0.60-0.71), the model had similar predictive performance as the random survival forest (c-index 0.66-0.69) and Cox regression (c-index 0.62 - 0.67), as well as machine learning classification algorithms. ConclusionBy providing informative estimates of the invisible metastatic burden at the time of diagnosis and forward simulations of metastatic growth, the proposed model could be used as a personalized prediction tool of help for routine management of breast cancer patients.

cancer biology↗