Novel 18-gene signature for predicting relapse in ER-positive, HER2-negative breast cancer

Selecting postoperative adjuvant systemic therapy for early-stage breast cancer: An updated assessment and systematic review of leading commercially available gene expression assays

Gene expression assays (GEAs) can guide treatment for early-stage breast cancer. Several large prospective randomized clinical trials, and numerous additional studies, now provide new information for selecting an appropriate GEA. This systematic review builds upon prior reviews, with a focus on five widely commercialized GEAs (Breast Cancer Index®, EndoPredict®, MammaPrint®, Oncotype DX®, and Prosigna®). The comprehensive dataset available provides a contemporary opportunity to assess each GEA's utility as a prognosticator and/or predictor of adjuvant therapy benefit.

The crucial role of epigenetic regulation in breast cancer anti-estrogen resistance: Current findings and future perspectives

Breast cancer (BC) cell de-sensitization to Tamoxifen (TAM) or other selective estrogen receptor (ER) modulators (SERM) is a complex process associated with BC heterogeneity and the transformation of ER signalling. The most influential resistance-related mechanisms include modifications in ER expression and gene regulation patterns. During TAM/SERM treatment, epigenetic mechanisms can effectively silence ER expression and facilitate the development of endocrine resistance. ER status is efficiently regulated by specific epigenetic tools including hypermethylation of CpG islands within ER promoters, increased histone deacetylase activity in the ER promoter, and/or translational repression by miRNAs. Over-methylation of the ER α gene (ESR1) promoter by DNA methyltransferases was associated with poor prognosis and indicated the development of resistance. Moreover, BC progression and spreading were marked by transformed chromatin remodelling, post-translational histone modifications, and expression of specific miRNAs and/or long non-coding RNAs. Therefore, targeted inhibition of histone acetyltransferases (e.g. MYST3), deacetylases (e.g. HDAC1), and/or demethylases (e.g. lysine-specific demethylase LSD1) was shown to recover and increase BC sensitivity to anti-estrogens. Indicated as a powerful molecular instrument, the administration of epigenetic drugs can regain ER expression along with the activation of tumour suppressor genes, which can in turn prevent selection of resistant cells and cancer stem cell survival. This review examines recent advances in the epigenetic regulation of endocrine drug resistance and evaluates novel anti-resistance strategies. Underlying molecular mechanisms of epigenetic regulation will be discussed, emphasising the utilization of epigenetic enzymes and their inhibitors to re-program irresponsive BCs.

XGBoost-based and tumor-immune characterized gene signature for the prediction of metastatic status in breast cancer

Background

For a long time, breast cancer has been a leading cancer diagnosed in women worldwide, and approximately 90% of cancer-related deaths are caused by metastasis. For this reason, finding new biomarkers related to metastasis is an urgent task to predict the metastatic status of breast cancer and provide new therapeutic targets.

Methods

In this research, an efficient model of eXtreme Gradient Boosting (XGBoost) optimized by a grid search algorithm is established to realize auxiliary identification of metastatic breast tumors based on gene expression. Estimated by ten-fold cross-validation, the optimized XGBoost classifier can achieve an overall higher mean AUC of 0.82 compared to other classifiers such as DT, SVM, KNN, LR, and RF.

Results

A novel 6-gene signature (SQSTM1, GDF9, LINC01125, PTGS2, GVINP1, and TMEM64) was selected by feature importance ranking and a series of in vitro experiments were conducted to verify the potential role of each biomarker. In general, the effects of SQSTM in tumor cells are assigned as a risk factor, while the effects of the other 5 genes (GDF9, LINC01125, PTGS2, GVINP1, and TMEM64) in immune cells are assigned as protective factors.

Conclusions

Our findings will allow for a more accurate prediction of the metastatic status of breast cancer and will benefit the mining of breast cancer metastasis-related biomarkers.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12967-022-03369-9.

A nine-lncRNA signature predicts distant relapse-free survival of HER2-negative breast cancer patients receiving taxane and anthracycline-based neoadjuvant chemotherapy

Multi-gene prognostic signatures of long non-coding RNAs (lncRNAs) provide new insights into mechanisms of HER2-negative breast cancer development and progression, and predict distant relapse-free survival (DRFS) of patients receiving taxane and anthracycline-based neoadjuvant chemotherapy. The aim of this study was to develop such a multi-lncRNAs signature. Optimal multiple candidate signature lncRNAs associated with DRFS were firstly identified by a univariate Cox proportional hazard regression survival analysis and a robust likelihood-based survival analysis of the GEO dataset GSE25055. A nine-lncRNA prognostic risk score model Risk Score = 0.0289 × EXP_LOC100507388 - 0.0814 × EXP_LINC00094 - 0.2422 × EXP_SMG7-AS1 - 0.2433 × EXP_PP14571 + 0.4690 × EXP_ASAP1-IT1 - 0.2483 × EXP_LOC103344931 - 0.2464 × EXP_FAM182A + 0.3349 × EXP_HCG26 - 0.0216 × EXP_LINC00963 was built according to the coefficients of multivariate survival analysis of the association between the candidate lncRNAs and survival. EXP_lncRNA was the standardized log2-transformed expression level of the gene. According to this model, higher scores predicted lower survival probability. The area under Receiver operating characteristic (ROC) curve (AUC) was 0.777 to 0.823 from 1- to 7- year survival rate. The model and its individual lncRNAs differentiated survival probability between the higher scores (expression) and the lower scores (expression). The nine-lncRNA signature had the robust prognostic power compared with ER, PR, tumor size (T), lymph node invasion (N), TNM stage, pathologic response, chemosensitivity prediction and PAM50 signature. These results were consistent with those based on the GEO dataset GSE25065. The predictive nomograms integrating both the nine-lncRNA signature classifier and clinical-pathological risk factors were robust in predicting 1-, 3- and 5- year survival probabilities. These results supported that the nine-lncRNA signature was a robust and effective model in predicting DRFS of patients with HER2-negative breast cancer following taxane and anthracycline-based neoadjuvant chemotherapy.

Mammographic density change in a cohort of premenopausal women receiving tamoxifen for breast cancer prevention over 5 years

Background

A decrease in breast density due to tamoxifen preventive therapy might indicate greater benefit from the drug. It is not known whether mammographic density continues to decline after 1 year of therapy, or whether measures of breast density change are sufficiently stable for personalised recommendations.

Methods

Mammographic density was measured annually over up to 5 years in premenopausal women with no previous diagnosis of breast cancer but at increased risk of breast cancer attending a family-history clinic in Manchester, UK (baseline 2010-2013). Tamoxifen (20 mg/day) for prevention was prescribed for up to 5 years in one group; the other group did not receive tamoxifen and were matched by age. Fully automatic methods were used on mammograms over the 5-year follow-up: three area-based measures (NN-VAS, Stratus, Densitas) and one volumetric (Volpara). Additionally, percentage breast density at baseline and first follow-up mammograms was measured visually. The size of density declines at the first follow-up mammogram and thereafter was estimated using a linear mixed model adjusted for age and body mass index. The stability of density change at 1 year was assessed by evaluating mean squared error loss from predictions based on individual or mean density change at 1 year.

Results

Analysis used mammograms from 126 healthy premenopausal women before and as they received tamoxifen for prevention (median age 42 years) and 172 matched controls (median age 41 years), with median 3 years follow-up. There was a strong correlation between percentage density measures used on the same mammogram in both the tamoxifen and no tamoxifen groups (all correlation coeficients > 0.8). Tamoxifen reduced mean breast density in year 1 by approximately 17–25% of the inter-quartile range of four automated percentage density measures at baseline, and from year 2, it decreased further by approximately 2–7% per year. Predicting change at 2 years using individual change at 1 year was approximately 60–300% worse than using mean change at 1year.

Conclusions

All measures showed a consistent and large average tamoxifen-induced change in density over the first year, and a continued decline thereafter. However, these measures of density change at 1 year were not stable on an individual basis.

High FOXA1 protein expression might predict late recurrence in patients with estrogen-positive and HER2-negative breast cancer

BACKGROUND

Multi-gene expression assays have been developed with the aim of predicting late recurrence in patients with estrogen receptor (ER)-positive breast cancer. However, establishment of alternative markers based on immunohistochemistry is also important for achieving practical use. Based on our previous study, forkhead box A1 (FOXA1) protein was tested as a potentially useful predictive marker for late recurrence.

METHODS

117 patients with ER-positive HER2-negative invasive breast cancer who developed distant metastasis following curative surgery were retrospectively investigated. We also evaluated responsiveness to endocrine therapy according to FOXA1 expression. Furthermore, publicly available mRNA microarray data were analyzed to examine patterns of metastasis according to FOXA1 mRNA expression, employing the Kaplan-Meier plotter.

RESULTS

High expression of FOXA1 was an independent factor predicting long disease-free survival (DFS), along with small tumor size (p = 0.010 and 0.016, respectively). Discrimination of DFS was improved by combining these two factors, i.e., patients with FOXA1-high small tumors had the longest DFS while those with FOXA1-low large tumors had the shortest DFS. Moreover, we revealed that risk of distant metastasis started to increase after the completion of adjuvant endocrine therapy in patients with FOXA1-high tumors.

CONCLUSION

Among patients who developed distant metastasis, those with FOXA1-high tumors had significantly longer DFS. We believe our data to raise the possibility of FOXA1 being a useful predictive marker for late recurrence and to provide new insights into the biology of FOXA1-high breast cancers.

Dual TGFβ/BMP Pathway Inhibition Enables Expansion and Characterization of Multiple Epithelial Cell Types of the Normal and Cancerous Breast

Functional modeling of normal breast epithelial hierarchy and stromal-epithelial cell interactions have been difficult due to inability to obtain sufficient stem-progenitor-mature epithelial and stromal cells. Recently reported epithelial reprogramming assay has partially overcome this limitation, but cross-contamination of cells from the feeder layer is a concern. The purpose of this study was to develop a feeder-layer-independent and inexpensive method to propagate multiple cell types from limited tissue resources. Cells obtained after enzymatic digestion of tissues collected at surgery or by core-needle biopsies were plated on tissue culture dishes precoated with laminin-5-rich-conditioned media from the rat bladder tumor cell line 804G and a defined growth media with inhibitors of ROCK, TGFβ, and BMP signaling. Cells were characterized by flow cytometry, mammosphere assay, 3D cultures, and xenograft studies. Cells from the healthy breasts included CD10⁺/EpCAM^- basal/myoepithelial, CD49f⁺/EpCAM⁺ luminal progenitor, CD49f^-/EpCAM⁺ mature luminal, CD73⁺/EpCAM⁺/CD90^- rare endogenous pluripotent somatic stem, CD73⁺/CD90⁺/EpCAM^-, estrogen receptor alpha-expressing ALCAM (CD166)⁺/EpCAM⁺, and ALDFLUOR⁺ stem/luminal progenitor subpopulations. Epithelial cells were luminal (KRT19⁺), basal (KRT14⁺), or dual-positive luminal/basal hybrid cells. While breast cells derived from BRCA1, BRCA2, and PALB2 mutation carriers did not display unique characteristics, cells from women with breast cancer-protective alleles showed enhanced differentiation. Cells could also be propagated from primary tumors and metastasis of breast, ovarian, and pancreatic cancer-neuroendocrine subtype. Xenograft studies confirmed tumorigenic properties of tumor-derived cells. IMPLICATIONS: Our method expands the scope of individualized studies of patient-derived cells and provides resources to model epithelial-stromal interactions under normal and pathologic conditions.

High-Dimensional LASSO-Based Computational Regression Models: Regularization, Shrinkage, and Selection

Regression models are a form of supervised learning methods that are important for machine learning, statistics, and general data science. Despite the fact that classical ordinary least squares (OLS) regression models have been known for a long time, in recent years there are many new developments that extend this model significantly. Above all, the least absolute shrinkage and selection operator (LASSO) model gained considerable interest. In this paper, we review general regression models with a focus on the LASSO and extensions thereof, including the adaptive LASSO, elastic net, and group LASSO. We discuss the regularization terms responsible for inducing coefficient shrinkage and variable selection leading to improved performance metrics of these regression models. This makes these modern, computational regression models valuable tools for analyzing high-dimensional problems.