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Despite the clinical utility of endocrine therapies for estrogen receptor-positive (ER) breast cancer, up to 40% of patients eventually develop resistance, leading to disease progression. The molecular determinants that drive this adaptation to treatment remain poorly understood. Methylome aberrations drive cancer growth yet the functional role and mechanism of these epimutations in drug resistance are poorly elucidated. Genome-wide multi-omics sequencing approach identified a differentially methylated hub of prodifferentiation genes in endocrine resistant breast cancer patients and cell models. Clinical relevance of the functionally validated methyl-targets was assessed in a cohort of endocrine-treated human breast cancers and patient-derived metastatic tumors. Enhanced global hypermethylation was observed in endocrine treatment resistant cells and patient metastasis relative to sensitive parent cells and matched primary breast tumor, respectively. Using paired methylation and transcriptional profiles, we found that SRC-1-dependent alterations in endocrine resistance lead to aberrant hypermethylation that resulted in reduced expression of a set of differentiation genes. Analysis of ER-positive endocrine-treated human breast tumors ( = 669) demonstrated that low expression of this prodifferentiation gene set significantly associated with poor clinical outcome ( = 0.00009). We demonstrate that the reactivation of these genes and reverses the aggressive phenotype. Our work demonstrates that SRC-1-dependent epigenetic remodeling is a 'high level' regulator of the poorly differentiated state in ER-positive breast cancer. Collectively these data revealed an epigenetic reprograming pathway, whereby concerted differential DNA methylation is potentiated by SRC-1 in the endocrine resistant setting. .

Biomarkers such as calcium channel binding protein S100 subunit beta (S100B), glial fibrillary acidic protein (GFAP), ubiquitin c-terminal hydrolase L1 (UCH-L1) and neuron-specific enolase (NSE) have been proposed to aid in screening patients presenting with mild traumatic brain injury (mTBI). As such, we aimed to characterise their accuracy at various thresholds. MEDLINE, SCOPUS and EMBASE were searched, and articles reporting the diagnostic performance of included biomarkers were eligible for inclusion. Risk of bias was assessed using the QUADAS-II criteria. A meta-analysis was performed to assess the predictive value of biomarkers for imaging abnormalities on CT. A total of 2939 citations were identified, and 38 studies were included. Thirty-two studies reported data for S100B. At its conventional threshold of 0.1 μg/L, S100B had a pooled sensitivity of 91% (95%CI 87-94) and a specificity of 30% (95%CI 26-34). The optimal threshold for S100B was 0.72 μg/L, with a sensitivity of 61% (95% CI 50-72) and a specificity of 69% (95% CI 64-74). Nine studies reported data for GFAP. The optimal threshold for GFAP was 626 pg/mL, at which the sensitivity was 71% (95%CI 41-91) and specificity was 71% (95%CI 43-90). Sensitivity of GFAP was maximised at a threshold of 22 pg/mL, which had a sensitivity of 93% (95%CI 73-99) and a specificity of 36% (95%CI 12-68%). Three studies reported data for NSE and two studies for UCH-L1, which precluded meta-analysis. There is evidence to support the use of S100B as a screening tool in mild TBI, and potential advantages to the use of GFAP, which requires further investigation.

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