Association of H3K9me3 with breast cancer prognosis by estrogen receptor status

H4K20me3, H3K4me2 and H3K9me2 mediate the effect of ER on prognosis in breast cancer

Previous studies have indicated that histone methylations act as mediators in the relationship between oestrogen receptor (ER) and breast cancer prognosis, yet the mediating role has never been assessed. Therefore, we investigated seven histone methylations (H3K4me2, H3K4me3, H3K9me1, H3K9me2, H3K9me3, H3K27me3 and H4K20me3) to determine whether they mediate the prognostic impact of ER on breast cancer. Tissue microarrays were constructed from 1045 primary invasive breast tumours, and the expressions of histone methylations were examined by immunohistochemistry. Multifactorial logistic regression was used to analyse the associations between ER and histone methylations. Cox proportional hazard model was performed to assess the relationship between histone methylations and breast cancer prognosis. The mediation effects of histone methylations were evaluated by model-based causal mediation analysis. High expressions of H3K9me1, H3K9me2, H3K4me2, H3K27me3, H4K20me3 were associated with ER positivity, while high expression of H3K9me3 was associated ER negativity. Higher H3K9me2, H3K4me2 and H4K20me3 levels were associated with better prognosis. The association between ER and breast cancer prognosis was most strongly mediated by H4K20me3 (29.07% for OS; 22.42% for PFS), followed by H3K4me2 (11.5% for OS; 10.82% for PFS) and least by H3K9me2 (9.35% for OS; 7.34% for PFS). H4K20me3, H3K4me2 and H3K9me2 mediated the relationship between ER and breast cancer prognosis, which would help to further elucidate the impact of ER on breast cancer prognosis from an epigenetic perspective and provide new ideas for breast cancer treatment.

Machine learning in onco-pharmacogenomics: a path to precision medicine with many challenges

Over the past two decades, Next-Generation Sequencing (NGS) has revolutionized the approach to cancer research. Applications of NGS include the identification of tumor specific alterations that can influence tumor pathobiology and also impact diagnosis, prognosis and therapeutic options. Pharmacogenomics (PGx) studies the role of inheritance of individual genetic patterns in drug response and has taken advantage of NGS technology as it provides access to high-throughput data that can, however, be difficult to manage. Machine learning (ML) has recently been used in the life sciences to discover hidden patterns from complex NGS data and to solve various PGx problems. In this review, we provide a comprehensive overview of the NGS approaches that can be employed and the different PGx studies implicating the use of NGS data. We also provide an excursus of the ML algorithms that can exert a role as fundamental strategies in the PGx field to improve personalized medicine in cancer.

Estrogen-Receptor-Low-Positive Breast Cancer: Pathological and Clinical Perspectives

The expression of estrogen receptors (ERs) in breast cancer (BC) represents a strong prognostic and predictive biomarker and directs therapeutic decisions in early and advanced stages. ER-low-positive BC, defined by the immunohistochemical (IHC) expression of ERs from 1% to 9%, constitutes a distinct subset of total BC cases. Guidelines recommend that a low expression of ERs be reported in pathology reports since the benefit of endocrine therapy in patients with ER-low-positive BC is uncertain. Recently, several cohorts, mostly of a retrospective nature, have been published, reporting the clinicopathological characteristics and outcomes of ER-low-positive BC. However, the majority of the data focus on early-stage BC and the use of (neo)adjuvant therapy, and there is a significant lack of data regarding metastatic ER-low-positive BC. Further factors, including tumor heterogeneity as well as the potential loss of ER expression due to endocrine resistance, should be considered. Including patients with ER-low-positive BC in clinical trials for triple-negative breast cancer (TNBC) might improve the understanding of this entity and allow novel therapeutic approaches. The design and conduction of randomized clinical trials regarding this subgroup of patients are greatly anticipated.