A novel group of genes that cause endocrine resistance in breast cancer identified by dynamic gene expression analysis

Self-consistent signal transduction analysis for modeling context-specific signaling cascades and perturbations

Biological signal transduction networks are central to information processing and regulation of gene expression across all domains of life. Dysregulation is known to cause a wide array of diseases, including cancers. Here I introduce self-consistent signal transduction analysis, which utilizes genome-scale -omics data (specifically transcriptomics and/or proteomics) in order to predict the flow of information through these networks in an individualized manner. I apply the method to the study of endocrine therapy in breast cancer patients, and show that drugs that inhibit estrogen receptor α elicit a wide array of antitumoral effects, and that their most clinically-impactful ones are through the modulation of proliferative signals that control the genes GREB1, HK1, AKT1, MAPK1, AKT2, and NQO1. This method offers researchers a valuable tool in understanding how and why dysregulation occurs, and how perturbations to the network (such as targeted therapies) effect the network itself, and ultimately patient outcomes.

Pretreatment Circulating Albumin, Platelet, and RDW-SD Associated with Worse Disease-Free Survival in Patients with Breast Cancer

Objective

Breast cancer is the second most common malignancy globally and a leading cause of cancer death in women. Analysis of factors related to disease-free survival (DFS) has improved understanding of the disease and characteristics related to recurrence. The aim of this study was to investigate the predictors of DFS in patients with breast cancer to enable the identification of patients at high risk who may benefit from prevention interventions.

Methods

We retrospectively analyzed 559 women with breast cancer who underwent treatment between 2004 and 2022. The study endpoint was DFS. Recurrence was defined as local recurrence, regional recurrence, distant metastases, contralateral breast cancer, other second primary cancer, and death. Baseline tumor-related characteristics, treatment-related characteristics, sociodemographic and biochemical data were analyzed using Cox proportional hazards analysis.

Results

The median DFS was 45 months (range, 2 to 225 months). Breast cancer recurred in 86 patients (15.4%), of whom 10 had local recurrence, 10 had regional recurrence, 17 had contralateral breast cancer, 29 had distant metastases, 10 had second primary cancer, and 10 patients died. Multivariate forward stepwise Cox regression analysis showed that AJCC stage III, Ki67 ≥14%, albumin, platelet, and red cell distribution width-standard deviation (RDW-SD) were predictors of worse DFS. In addition, the effects of albumin, platelet, and RDW-SD on disease recurrence were confirmed by structural equation model (SEM) analysis.

Conclusion

In addition to the traditional predictors of worse DFS such as AJCC stage III and Ki67 ≥14%, lower pretreatment circulating albumin, higher pretreatment circulating platelet count and RDW-SD could significantly predict worse DFS in this study, and SEM delineated possible causal pathways and inter-relationships of albumin, platelet, and RDW-SD contributing to the disease recurrence among Chinese women with breast cancer.

A Novel Mathematical Approach for Analysis of Integrated Cell-Patient Data Uncovers a 6-Gene Signature Linked to Endocrine Therapy Resistance

A significant number of breast cancers develop resistance to hormone therapy. This progression, while posing a major clinical challenge, is difficult to predict. Despite important contributions made by cell models and clinical studies to tackle this problem, both present limitations when taken individually. Experiments with cell models are highly reproducible but do not reflect the indubitable heterogenous landscape of breast cancer. On the other hand, clinical studies account for this complexity but introduce uncontrolled noise due to external factors. Here, we propose a new approach for biomarker discovery that is based on a combined analysis of sequencing data from controlled MCF7 cell experiments and heterogenous clinical samples that include clinical and sequencing information from The Cancer Genome Atlas. Using data from differential gene expression analysis and a Bayesian logistic regression model coupled with an original simulated annealing-type algorithm, we discovered a novel 6-gene signature for stratifying patient response to hormone therapy. The experimental observations and computational analysis built on independent cohorts indicated the superior predictive performance of this gene set over previously known signatures of similar scope. Together, these findings revealed a new gene signature to identify patients with breast cancer with an increased risk of developing resistance to endocrine therapy.

JMJD6 orchestrates a transcriptional program in favor of endocrine resistance in ER+ breast cancer cells

High expression of Jumonji domain containing protein 6 (JMJD6) is strongly associated with poor prognosis in estrogen receptor positive (ER+) breast cancer. We overexpressed JMJD6 in MCF7 cells (JOE cells) and performed RNA-seq analysis. 76% of differentially expressed genes (DEGs) overlapped with ER target genes. Pathway analysis revealed that JMJD6 upregulated a larger subset of genes related to cell proliferation as compared to ER. Interestingly, JOE cells showed a decrease in ER target gene expression prompting us to check ER levels. Indeed, JOE cells showed a significant decrease in both ESR1 and ER levels and JMJD6 siRNA transfection increased the expression of both. Additionally, JOE cells showed increased RET and ERK1 expression, events associated with resistance to endocrine therapy. Accordingly, JOE cells displayed lower sensitivity and survived better at higher doses of 4-hydroxy tamoxifen (Tam) as compared to parental MCF-7 cells. Conversely, LTED-I and TAM R that resist Tam induced death, showed high expression of JMJD6. Further, JMJD6 siRNA treatment decreased growth and improved Tam sensitivity in TAM R. Comparison of JOE DEGs with known Tam signature genes showed a substantial overlap. Overall, these data suggest that blocking ER alone in patients may not eradicate proliferation of JMJD6 expressing ER+ cells and JMJD6 may predispose and sustain endocrine therapy resistance. We propose that immunostaining for JMJD6 could be developed as a potential marker for predicting endocrine therapy resistance. Further, antagonizing JMJD6 action in women expressing higher amounts of this protein, may offer a greater clinical benefit than endocrine therapy.