Identification of key genes involved in tamoxifen-resistant breast cancer using bioinformatics analysis

ONECUT2 is a druggable driver of luminal to basal breast cancer plasticity

PURPOSE

Tumor heterogeneity complicates patient treatment and can be due to transitioning of cancer cells across phenotypic cell states. This process is associated with the acquisition of independence from an oncogenic driver, such as the estrogen receptor (ER) in breast cancer (BC), resulting in tumor progression, therapeutic failure and metastatic spread. The transcription factor ONECUT2 (OC2) has been shown to be a master regulator protein of metastatic castration-resistant prostate cancer (mCRPC) tumors that promotes lineage plasticity to a drug-resistant neuroendocrine (NEPC) phenotype. Here, we investigate the role of OC2 in the dynamic conversion between different molecular subtypes in BC.

METHODS

We analyze OC2 expression and clinical significance in BC using public databases and immunohistochemical staining. In vitro, we perform RNA-Seq, RT-qPCR and western-blot after OC2 enforced expression. We also assess cellular effects of OC2 silencing and inhibition with a drug-like small molecule in vitro and in vivo.

RESULTS

OC2 is highly expressed in a substantial subset of hormone receptor negative human BC tumors and tamoxifen-resistant models, and is associated with poor clinical outcome, lymph node metastasis and heightened clinical stage. OC2 inhibits ER expression and activity, suppresses a gene expression program associated with luminal differentiation and activates a basal-like state at the gene expression level. We also show that OC2 is required for cell growth and survival in metastatic BC models and that it can be targeted with a small molecule inhibitor providing a novel therapeutic strategy for patients with OC2 active tumors.

CONCLUSIONS

The transcription factor OC2 is a driver of BC heterogeneity and a potential drug target in distinct cell states within the breast tumors.

Dynamic proximity interaction profiling suggests that YPEL2 is involved in cellular stress surveillance

YPEL2 is a member of the evolutionarily conserved YPEL family involved in cellular proliferation, mobility, differentiation, senescence, and death. However, the mechanism by which YPEL2, or YPEL proteins, mediates its effects is largely unknown. Proteins perform their functions in a network of proteins whose identities, amounts, and compositions change spatiotemporally in a lineage-specific manner in response to internal and external stimuli. Here, we explored interaction partners of YPEL2 by using dynamic TurboID-coupled mass spectrometry analyses to infer a function for the protein. Our results using inducible transgene expressions in COS7 cells indicate that proximity interaction partners of YPEL2 are mainly involved in RNA and mRNA metabolic processes, ribonucleoprotein complex biogenesis, regulation of gene silencing by miRNA, and cellular responses to stress. We showed that YPEL2 interacts with the RNA-binding protein ELAVL1 and the selective autophagy receptor SQSTM1. We also found that YPEL2 localizes stress granules in response to sodium arsenite, an oxidative stress inducer, which suggests that YPEL2 participates in stress granule-related processes. Establishing a point of departure in the delineation of structural/functional features of YPEL2, our results suggest that YPEL2 may be involved in stress surveillance mechanisms.

Favipiravir, an antiviral drug, in combination with tamoxifen exerts synergistic effect in tamoxifen-resistant breast cancer cells via hTERT inhibition

Tamoxifen (TAM) is one of the most successful treatments for breast cancer; however, TAM resistance continues to be a significant barrier. TAM resistance has been reported to be associated with increased expression of human telomerase reverse transcriptase (hTERT). This enzyme shares structural similarity with RNA-dependent RNA polymerase (RdRp) enzyme of RNA viruses, suggesting that RdRp inhibitors may also inhibit hTERT. Favipiravir (FAV) is an antiviral drug that inhibits RdRp of RNA viruses. Thus, we propose that FAV may also elicit an antitumor effect by suppressing hTERT. This study aimed to investigate the effect of FAV and TAM on TAM-resistant breast cancer (TAMR-1). The cell viabilities were determined. The levels of CDK1/ hTERT, in addition to regulators of hTERT-targeted signaling pathways were measured. Apoptosis, migration, and cell cycle distribution were also determined. Our data revealed that the combination of TAM and FAV suppressed cell proliferation synergistically (CI < 1) and resulted in a significant change in cell migration and apoptosis. Indeed, this was associated with reduced levels of hTERT and CDK1 and shift in the cell cycle distribution. Our findings suggest that the TAM/FAV combination exhibits synergistic effects against TAMR-1 human breast cancer cells by targeting hTERT.

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.

The role of mitochondrial/metabolic axis in development of tamoxifen resistance in breast cancer

Only a few investigations, to our knowledge, have examined the bioenergetics of Tamoxifen (TMX) resistant individuals and reported altered mitochondrial activity and metabolic profile. The primary cause of TMX resistance is firmly suggested to be metabolic changes. Metabolic variations and hypoxia have also been linked in a bidirectional manner. Increased hypoxic levels correlate with early recurrence and proliferation and have a negative therapeutic impact on breast cancer (BC) patients. Hypoxia, carcinogenesis, and patient death are all correlated, resulting in more aggressive traits, a higher chance of metastasis, and TMX resistance. Consequently, we sought to investigate the possible role of the metabolic/hypoxial axis Long non-coding RNA (LncRNA) Taurine up-regulated 1 (TUG-1), Micro-RNA 186-5p (miR-186), Sirtuin-3 (SIRT3), Peroxisome Proliferator Activator Receptor alpha (PPAR-α), and Hypoxia-Inducible Factor-1 (HIF-1) in the development of TMX resistance in BC patients and to correlate this axis with tumor progression. Interestingly, this will be the first time to explore epigenetic regulation of this axis in BC.

High expression of the RET receptor tyrosine kinase and its ligand GDNF identifies a high-risk subset of estrogen receptor positive breast cancer

PURPOSE

Resistance to endocrine therapy is the primary cause of treatment failure and death in patients with ER-positive (ER +)/luminal breast cancer. Expression and activation of the RET receptor tyrosine kinase may be driving poor outcomes. We aim to identify high-risk patients and druggable pathways for biomarker-based clinical trials.

METHODS

We obtained batch-normalized mRNA expression data from Breast Invasive Carcinoma-The Cancer Genome Atlas, PanCancer Atlas (BRCA-TCGA). To determine clinically significant cutoffs for RET expression, patients were grouped at different thresholds for Kaplan-Meier plotting. Differential gene expression (DGE) analysis and enrichment for gene sets was performed. transcriptomic dataset of antiestrogen-treated ER + tumors stratified by clinical response was then analyzed.

RESULTS

High RET expression was associated with worse outcomes in patients with ER + tumors, and stratification was enhanced by incorporating GDNF expression. High RET/GDNF patients had significantly lower overall survival (HR = 2.04, p = 0.012), progression-free survival (HR = 2.87, p < 0.001), disease-free survival (HR = 2.67, p < 0.001), and disease-specific survival (HR = 3.53, p < 0.001) than all other ER + patients. High RET/GDNF tumors were enriched for estrogen-independent signaling and targetable pathways including NTRK, PI3K, and KRAS. Tumors with adaptive resistance to endocrine therapy were enriched for gene expression signatures of high RET/GDNF primary tumors.

CONCLUSION

Expression and activation of the RET receptor tyrosine kinase may be driving poor outcomes in some patients with ER + breast cancer. ER + patients above the 75th percentile may benefit from clinical trials with tyrosine kinase inhibitors.

Correlative studies of the Breast Cancer Index (HOXB13/IL17BR) and ER, PR, AR, AR/ER ratio and Ki67 for prediction of extended endocrine therapy benefit: a Trans-aTTom study

BACKGROUND

Multiple clinical trials demonstrate consistent but modest benefit of adjuvant extended endocrine therapy (EET) in HR + breast cancer patients. Predictive biomarkers to identify patients that benefit from EET are critical to balance modest reductions in risk against potential side effects of EET. This study compares the performance of the Breast Cancer Index, BCI (HOXB13/IL17BR, H/I), with expression of estrogen (ER), progesterone (PR), and androgen receptors (AR), and Ki67, for prediction of EET benefit.

METHODS

Node-positive (N+) patients from the Trans-aTTom study with available tissue specimen and BCI results (N = 789) were included. Expression of ER, PR, AR, and Ki67 was assessed by quantitative immunohistochemistry. BCI (H/I) gene expression analysis was conducted by quantitative RT-PCR. Statistical significance of the treatment by biomarker interaction was evaluated by likelihood ratio tests based on multivariate Cox proportional models, adjusting for age, tumor size, grade, and HER2 status. Pearson's correlation coefficients were calculated to evaluate correlations between BCI (H/I) versus ER, PR, AR, Ki67 and AR/ER ratio.

RESULTS

EET benefit, measured by the difference in risk of recurrence between patients treated with tamoxifen for 10 versus 5 years, is significantly associated with increasing values of BCI (H/I) (interaction P = 0.01). In contrast, expression of ER (P = 0.83), PR (P = 0.66), AR (P = 0.78), Ki67 (P = 0.87) and AR/ER ratio (P = 0.84) exhibited no significant relationship with EET benefit. BCI (H/I) showed a very weak negative correlation with ER (r = - 0.18), PR (r = - 0.25), and AR (r = - 0.14) expression, but no correlation with either Ki67 (r = 0.04) or AR/ER ratio (r = 0.02).

CONCLUSION

These findings are consistent with the growing body of evidence that BCI (H/I) is significantly predictive of response to EET and outcome. Results from this direct comparison demonstrate that expression of ER, PR, AR, Ki67 or AR/ER ratio are not predictive of benefit from EET. BCI (H/I) is the only clinically validated biomarker that predicts EET benefit.