ESR1 gene amplification and MAP3K mutations are selected during adjuvant endocrine therapies in relapsing Hormone Receptor-positive, HER2-negative breast cancer (HR+ HER2- BC)

Expression profiling and single nucleotide polymorphism of mitogen-activated protein kinase kinase kinase 8 MAP3K8 in white muscovy ducks (Cairina moschata)

A previous study on ovarian and hypothalami transcriptome analysis in white Muscovy duck revealed that MAP3K8 gene participated in MAPK signaling pathway that influence egg production. Additionally, MAP3K8 was predicted as a target gene of miRNA-509-3p that promotes the secretion of oestradiol which is an important hormone in egg ovulation. This suggested that MAP3K8 might have a functional role in the reproductive performance "egg production" of white Muscovy ducks. Herein, we focused on expression level of MAP3K8 in reproductive and non-reproductive tissues of highest (HP) and lowest (LP) egg producing white Muscovy ducks and identified the polymorphism in MAP3K8 and its association with three egg production traits; Age at first egg (AFE), number of eggs at 300 days (N300D) and 59 weeks (N59W). The results of expression level indicated that mRNA of MAP3K8 was significantly (p < 0.01) expressed in the oviduct than in the ovary and hypothalamus. Seven synonymous SNPs were detected, and association analysis showed that g.148303340 G>A and g.148290065 A>G were significantly (p < 0.05) associated with N300D and N59W. The results of this study might serve as molecular marker for marker-assisted selection of white Muscovy ducks for egg production.

Molecular mechanisms and therapeutic strategies in overcoming chemotherapy resistance in cancer

Cancer remains a leading cause of mortality globally and a major health burden, with chemotherapy often serving as the primary therapeutic option for patients with advanced-stage disease, partially compensating for the limitations of non-curative treatments. However, the emergence of chemotherapy resistance significantly limits its efficacy, posing a major clinical challenge. Moreover, heterogeneity of resistance mechanisms across cancer types complicates the development of universally effective diagnostic and therapeutic approaches. Understanding the molecular mechanisms of chemoresistance and identifying strategies to overcome it are current research focal points. This review provides a comprehensive analysis of the key molecular mechanisms underlying chemotherapy resistance, including drug efflux, enhanced DNA damage repair (DDR), apoptosis evasion, epigenetic modifications, altered intracellular drug metabolism, and the role of cancer stem cells (CSCs). We also examine specific causes of resistance in major cancer types and highlight various molecular targets involved in resistance. Finally, we discuss current strategies aiming at overcoming chemotherapy resistance, such as combination therapies, targeted treatments, and novel drug delivery systems, while proposing future directions for research in this evolving field. By addressing these molecular barriers, this review lays a foundation for the development of more effective cancer therapies aimed at mitigating chemotherapy resistance.

CWGCNA: an R package to perform causal inference from the WGCNA framework

WGCNA (weighted gene co-expression network analysis) is a very useful tool for identifying co-expressed gene modules and detecting their correlations to phenotypic traits. Here, we explored more possibilities about it and developed the R package CWGCNA (causal WGCNA), which works from the traditional WGCNA pipeline but mines more information. It couples a mediation model with WGCNA, so the causal relationships among WGCNA modules, module features, and phenotypes can be found, demonstrating whether the module change causes the phenotype change or vice versa. After that, when annotating the module gene set functions, it uses a novel network-based method, considering the modules' topological structures and capturing their influence on the gene set functions. In addition to conducting these biological explorations, CWGCNA also contains a machine learning section to perform clustering and classification on multi-omics data, given the increasing popularity of this data type. Some basic functions, such as differential feature identification, are also available in our package. Its effectiveness is proved by the performance on three single or multi-omics datasets, showing better performance than existing methods. CWGCNA is available at: https://github.com/yuabrahamliu/CWGCNA.

Long-term Multimodal Recording Reveals Epigenetic Adaptation Routes in Dormant Breast Cancer Cells

Patients with estrogen receptor-positive breast cancer receive adjuvant endocrine therapies (ET) that delay relapse by targeting clinically undetectable micrometastatic deposits. Yet, up to 50% of patients relapse even decades after surgery through unknown mechanisms likely involving dormancy. To investigate genetic and transcriptional changes underlying tumor awakening, we analyzed late relapse patients and longitudinally profiled a rare cohort treated with long-term neoadjuvant ETs until progression. Next, we developed an in vitro evolutionary study to record the adaptive strategies of individual lineages in unperturbed parallel experiments. Our data demonstrate that ETs induce nongenetic cell state transitions into dormancy in a stochastic subset of cells via epigenetic reprogramming. Single lineages with divergent phenotypes awaken unpredictably in the absence of recurrent genetic alterations. Targeting the dormant epigenome shows promising activity against adapting cancer cells. Overall, this study uncovers the contribution of epigenetic adaptation to the evolution of resistance to ETs.

SIGNIFICANCE

This study advances the understanding of therapy-induced dormancy with potential clinical implications for breast cancer. Estrogen receptor-positive breast cancer cells adapt to endocrine treatment by entering a dormant state characterized by strong heterochromatinization with no recurrent genetic changes. Targeting the epigenetic rewiring impairs the adaptation of cancer cells to ETs. See related commentary by Llinas-Bertran et al., p. 704. This article is featured in Selected Articles from This Issue, p. 695.

Prognostic impact of tumor-associated neutrophils in breast cancer

OBJECTIVES

Neutrophils are the most common type of leukocyte in mammals and play an essential role in the innate immune system and anti-cancer responses. However, recent studies identified the presence of tumor-associated neutrophils (TANs) as a poor prognostic factor. The present study investigated whether relationships exist between TANs and the clinicopathological factors and genetic status of breast cancer.

METHODS

A total of 196 breast cancer patients with sufficient biopsy, breast-conserving surgery, or mastectomy specimens between 2014 and 2021 in Hokuto Hospital were included.

RESULTS

TANs were individually counted in the tumor stroma (TS) and tumor nest (TN). A higher density of TANs in both TS and TN correlated with tumor size (TS P = 0.010; TN P = 0.001), a high histological grade (TS P < 0.001; TN P < 0.001), the histological type (TS P = 0.009; TN P = 0.034), a high ratio of lymph node metastasis (TS P < 0.001; TN P < 0.001), an advanced stage of cancer (TS P < 0.001; TN P = 0.002), intrinsic subtypes (TS P < 0.001; TN P < 0.001), ERBB2 (TS P < 0.001; TN P < 0.001), MAP3K1 (TS P = 0.002; TN P = 0.023), and TP53 (TS P < 0.001; TN P < 0.001). A higher density of TANs in TS and TN also correlated with shorter disease-free survival and overall survival (P < 0.001).

CONCLUSION

The present results suggest that a higher density of TANs correlates with unfavorable prognostic factors in breast cancer. Further research on clinicopathological and genetic factors associated with TANs in breast cancer is needed.

Revisiting Estrogen for the Treatment of Endocrine-Resistant Breast Cancer: Novel Therapeutic Approaches

Estrogen receptor (ER)-positive breast cancer is the most common subtype, representing 70-75% of all breast cancers. Several ER-targeted drugs commonly used include the selective estrogen receptor modulator (SERM), tamoxifen (TAM), aromatase inhibitors (AIs) and selective estrogen receptor degraders (SERDs). Through different mechanisms of action, all three drug classes reduce estrogen receptor signaling. Inevitably, resistance occurs, resulting in disease progression. The counterintuitive action of estrogen to inhibit ER-positive breast cancer was first observed over 80 years ago. High-dose estrogen and diethylstilbestrol (DES) were used to treat metastatic breast cancer accompanied by harsh side effects until the approval of TAM in the 1970s. After the development of TAM, randomized trials comparing TAM to estrogen found similar or slightly inferior efficacy but much better tolerability. After decades of research, it was learned that estrogen induces tumor regression only after a period of long-term estrogen deprivation, and the mechanisms of tumor regression were described. Despite the long history of breast cancer treatment with estrogen, this therapeutic modality is now revitalized due to the development of novel estrogenic compounds with improved side effect profiles, newly discovered predictive biomarkers, the development of non-estrogen small molecules and new combination therapeutic approaches.

Tamoxifen Response at Single Cell Resolution in Estrogen Receptor-Positive Primary Human Breast Tumors

In ER+/HER2- breast cancer, multiple measures of intra-tumor heterogeneity are associated with worse response to endocrine therapy. To investigate heterogeneity in response to treatment, we developed an operating room-to-laboratory pipeline for the collection of live human tumors and normal breast specimens immediately after surgical resection for processing into single-cell workflows for experimentation and genomic analyses. We demonstrate differences in tamoxifen response by cell type and identify distinctly responsive and resistant subpopulations within the malignant cell compartment of human tumors. Tamoxifen resistance signatures from 3 distinct resistant subpopulations are prognostic in large cohorts of ER+ breast cancer patients and enriched in endocrine therapy resistant tumors. This novel ex vivo model system now provides a foundation to define responsive and resistant sub-populations within heterogeneous tumors, to develop precise single cell-based predictors of response to therapy, and to identify genes and pathways driving resistance to therapy.

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.

St. Gallen/Vienna 2023: Optimization of Treatment for Patients with Primary Breast Cancer - A Brief Summary of the Consensus Discussion

The St. Gallen Consensus Conference on early breast cancer treatment 2023 was again a live event and took place in Vienna, Austria. After 4 years and one virtual event due to the pandemic, more than 2,800 participants from over 100 countries came together in Vienna, and the 2023 St. Gallen/Vienna conference was a great success. Over 3 days, the global faculty reviewed the most important evidence published during the last 2 years and debated over controversial topics, and finally, the consensus votes aimed to define the impact of the new data on everyday routine practice. Focuses of this year's conference were radiotherapy and local management of the axilla, genetics, and their impact on treatment, as well as the role of the immune system and tumor-infiltrating lymphocytes in pathological reports and treatment decision-making. The traditional panel votes were moderated for the first time by Harold Burstein from Boston, and with questions previously voted on and live voting, the panel managed for the most part to clarify the critical questions. This report by editors of BREAST CARE summarizes the results of the 2023 international panel votes with respect to locoregional and systemic treatment as a brief news update but does not intend to replace the official St. Gallen Consensus publication that not just reports but also interprets the panel votes and will follow shortly in a major oncological journal. The next (19th) St. Gallen International Breast Cancer Conference will again take place in Vienna (save the date: March 12-15, 2025).