Precision Medicine In Breast Cancer Research Articles

Precision medicine in breast cancer (Review).

Precision medicine in breast cancer is a revolutionary approach that customizes diagnosis and treatment based on individual and tumor characteristics, departing from the traditional one-size-fits-all approach. Breast cancer is diverse, with various subtypes driven by distinct genetic mutations. Understanding this diversity is crucial for tailored treatment strategies that target specific vulnerabilities in each tumor. Genetic testing, particularly for mutations in breast cancer gene (BRCA) DNA repair-associated genes, helps assess hereditary risks and influences treatment decisions. Molecular subtyping guides personalized treatments, such as hormonal therapies for receptor-positive tumors and human epidermal growth factor receptor 2 (HER2)-targeted treatments. Targeted therapies, including those for HER2-positive and hormone receptor-positive breast cancers, offer more effective and precise interventions. Immunotherapy, especially checkpoint inhibitors, shows promise, particularly in certain subtypes such as triple-negative breast cancer, with ongoing research aiming to broaden its effectiveness. Integration of big data and artificial intelligence enhances personalized treatment strategies, while liquid biopsies provide real-time insights into tumor dynamics, aiding in treatment monitoring and modification. Challenges persist, including accessibility and tumor complexity, but emerging technologies and precision prevention offer hope for improved outcomes. Ultimately, precision medicine aims to optimize treatment efficacy, minimize adverse effects and enhance the quality of life for patients with breast cancer.

Precision medicine in breast cancer: Targeting molecular subtypes with gold nanoparticle-loaded liposomes

PurposeBreast cancer is a complex disease with several molecular subtypes that respond differently to therapy. This paper describes liposomes loaded with gold nanoparticles as a targeted drug delivery method in the rapidly developing precision breast cancer treatment area. The aim was to investigate the cytotoxicity level and cellular uptake using several breast cancer cell lines and a normal breast cell line. Materials and methodsWe synthesized gold nanoparticles incorporated in liposomes. Nanostructures were incubated with breast cancer cell lines of different subtypes. The analysis included MTT assay, flow cytometry and immunofluorescence. ResultsCell viability varied among different cancer cells. Moreover, the time- and concentration-dependent manner of viability change was observed. The internalization of liposomes with gold nanoparticles and nanoparticles alone determined different results depending on molecular breast cancer subtypes. The luminal B and triple-negative breast cancer cells demonstrated the highest resistance and sensitivity, respectively. The intensity of cells’ interaction with the proposed nanostructures was observed in both cell lines. In this study, we compare the molecular subtypes of breast cancer and discuss how this novel method might improve the therapy success. ConclusionsOur research sheds light on the possibility of new individualized treatments for breast cancer patients, opening the path for better results and a more detailed cancer therapy strategy.

Abstract ED09-01: Emerging approaches to targeting pathways using precision medicine in breast cancer

Abstract The past few decades have yielded significant strides forward in the pursuit to extend survival for patients with breast cancer. Much of this success is directly attributed to breakthroughs in the development of targeted therapies intersecting advances in genomic medicine. Despite these achievements, challenges remain, and targeted therapeutic development is still evolving rapidly to offset them. The focus of this educational session is centered around reviewing the latest in targeted therapeutic advancements and precision medicine. Recent advances have centered on novel selective estrogen receptor degraders (SERDs) for advanced ESR1-mutated or endocrine-resistant disease. Antibody-drug conjugates (ADCs) continued to make gains, targeting HER2-positive/low advanced cancers as well as an expanded indications for TROP2-targeted ADCs. New ADCs are also on the horizon leveraging new cell surface targets. Emerging are forward-looking cell-surface methodologies that harness bispecific antibodies and T-cell engagers. Various strategies combining established targeted immunotherapies such as PD-1 and PD-L1 with additional therapies continue. Attempts to target the frequently mutated PI3K pathway have yielded some success, but clinical exploration continues using new approaches that utilize mutation specific inhibitors. Second generation PARP inhibitors have emerged recently and are being explored in a variety of indications with promising early results. Finally, understanding treatment resistance in particular with the use of longitudinal liquid biopsies is fueling further target discovery. Altogether, development in precision medicine is advancing rapidly with advanced genomics, novel targets, and cutting-edge drug modalities. Citation Format: M. Radovich. Emerging approaches to targeting pathways using precision medicine in breast cancer [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr ED09-01.

Addressing Social Determinants in the Era of Precision Medicine in Breast Cancer: Is It Sufficient to Reduce Disparities?

The Oncotype DX (ODX) assay predicts recurrence risk and demonstrates the benefits of adjuvant therapy in patients with early-stage, hormone receptor (HR)-positive/HER2-negative breast cancer. ODX uptake varies by patients' racial/ethnic backgrounds and socioeconomic status (SES). However, community-level variability remains unknown, and research regarding the association between testing status and receipt of adjuvant chemotherapy is limited. To fill these knowledge gaps, Van Alsten and colleagues found a 6% lower prevalence of ODX uptake among patients residing in high SES-deprived areas than among those residing in low SES-deprived areas. Among patients with low and median ODX recurrence scores, those who underwent testing were 28% and 21% less likely to receive adjuvant chemotherapy than those who did not, respectively. The findings emphasize the role of social determinants of health. However, to further reduce or eliminate racial/ethnic disparities and SES inequities, we would need sufficient and effective multi-level approaches. These involve lower ODX testing costs, health insurance coverage expansion, re-classification and validation of ODX recurrence scores in patients of minority ancestry, and the development of a faster, more accurate, and affordable test. See related article by Van Alsten et al., p. 654.

Mitochondrial calcium uniporter as biomarker and therapeutic target for breast cancer: Prognostication, immune microenvironment, epigenetic regulation and precision medicine

IntroductionMitochondrial calcium uniporter (MCU) is a central subunit of MCU complex that regulate the levels of calcium ions within mitochondria. A comprehensive understanding the implications of MCU in clinical prognostication, biological understandings and therapeutic opportunity of breast cancer (BC) is yet to be determined. ObjectivesThis study aims to investigate the role of MCU in predictive performance, tumor progression, epigenetic regulation, shaping of tumor immune microenvironment, and pharmacogenetics and the development of anti-tumor therapy for BC. MethodsThe downloaded TCGA datasets were used to identify predictive ability of MCU expressions via supervised learning principle. Functional enrichment, mutation landscape, immunological profile, drug sensitivity were examined using bioinformatics analysis and confirmed by experiments exploiting human specimens, in vitro and in vivo models. ResultsMCU copy numbers increase with MCU gene expression. MCU expression, but not MCU genetic alterations, had a positive correlation with known BC prognostic markers. Higher MCU levels in BC showed modest efficacy in predicting overall survival. In addition, high MCU expression was associated with known BC prognostic markers and with malignancy. In BC tumor and sgRNA-treated cell lines, enrichment pathways identified the involvement of cell cycle and immunity. miR-29a was recognized as a negative epigenetic regulator of MCU. High MCU levels were associated with increased mutation levels in oncogene TP53 and tumor suppression gene CDH1, as well as with an immunosuppressive microenvironment. Sigle-cell sequencing indicated that MCU mostly mapped on to tumor cell and CD8 T-cells. Inter-databases verification further confirmed the aforementioned observation. miR-29a-mediated knockdown of MCU resulted in tumor suppression and mitochondrial dysfunction, as well as diminished metastasis. Furthermore, MCU present pharmacogenetic significance in cellular docetaxel sensitivity and in prediction of patients’ response to chemotherapeutic regimen. ConclusionMCU shows significant implication in prognosis, outcome prediction, microenvironmental shaping and precision medicine for BC. miR-29a-mediated MCU inhibition exerts therapeutic effect in tumor growth and metastasis.

Next-Generation Sequencing in Breast Cancer Patients: Real-World Data for Precision Medicine.

Breast cancer is one of the most common causes of cancer-related death in females. Numerous drug-targetable biomarkers and predictive biomarkers have been developed. Some researchers have expressed doubts about the need for next-generation sequencing (NGS) studies in daily practice. This study analyzed the results of NGS studies on breast cancer at a single institute and evaluated the real-world applications of NGS data to precision medicine for breast cancer. We retrospectively collected the results of NGS studies and analyzed the histopathologic features and genetic profiles of patients treated for breast cancer from 2010 to 2021. Seventy cases had data from CancerSCAN, a customized panel of 375 cancer-associated genes, and 110 cases had data from TruSight Oncology 500. The most frequently detected single nucleotide variant was the TP53 mutation (123/180, 68.3%), followed by PIK3CA mutations (51/180, 28.3%). Estrogen receptor 1 (ESR1) mutation was detected in 11 patients (6.1%), of whom 10 had hormone receptor-positive, human epidermal growth factor receptor 2-negative breast cancer, and two had no history of prior endocrine therapy. Based on their NGS study results, 13 patients (7.2%) received target therapy. Among them, four patients had a BRCA1 or BRCA2 germline mutation, and nine patients had a PIK3CA mutation. NGS can provide information about predictive biomarkers and drug-targetable biomarkers that can enable treatment and participation in clinical trials based on precision medicine. Further studies should be conducted to excavate novel drug-targetable biomarkers and develop additional target therapies.

Breast Cancer, a Global Health Crisis

Breast cancer is still a public health problem worldwide and is currently the most common tumor in the world. Breast cancer is a life-threatening disease in women and the leading cause of death in the female population. Breast cancer is a heterogeneous disorder with different molecular subtypes and biological characteristics, with different therapeutic functions and clinical outcomes for each molecular subtype. Furthermore, the treatment of BC is complicated by intratumoral complexity. Consequently, breast cancer risk assessment is an important part of public health. Precision medicine for breast cancer is a practice for diagnosis, treatment, screening and prevention of the disease that takes into account the patient's genetic makeup. Therefore, by identifying women who are at high risk of breast cancer, it is possible to make personalized recommendations regarding screening methods, the age at which breast screening should begin, the frequency of completing screenings, and individual counselling.

Computational pathology to improve biomarker testing in breast cancer: how close are we?

The recent advancements in breast cancer precision medicine have highlighted the urgency for the precise and reproducible characterization of clinically actionable biomarkers. Despite numerous standardization efforts, biomarker testing by conventional methodologies is challenged by several issues such as high inter-observer variabilities, the spatial heterogeneity of biomarkers expression, and technological heterogeneity. In this respect, artificial intelligence-based digital pathology approaches are being increasingly recognized as promising methods for biomarker testing and subsequently improved clinical management. Here, we provide an overview on the most recent advances for artificial intelligence-assisted biomarkers testing in breast cancer, with a particular focus on tumor-infiltrating lymphocytes, programmed death-ligand 1, phosphatidylinositol-3 kinase catalytic alpha, and estrogen receptor 1. Challenges and solutions for this integrative analysis in pathology laboratories are also provided.

Precision Medicine in Breast Cancer: Do MRI Biomarkers Identify Patients Who Truly Benefit from the Oncotype DX Recurrence Score® Test?

The aim of this study was to combine breast MRI-derived biomarkers with clinical-pathological parameters to identify patients who truly need an Oncotype DX Breast Recurrence Score® (ODXRS) genomic assay, currently used to predict the benefit of adjuvant chemotherapy in ER-positive/HER2-negative early breast cancer, with the ultimate goal of customizing therapeutic decisions while reducing healthcare costs. Patients who underwent a preoperative multiparametric MRI of the breast and ODXRS tumor profiling were retrospectively included in this study. Imaging sets were evaluated independently by two breast radiologists and classified according to the 2013 American College of Radiology Breast Imaging Reporting and Data System (ACR BI-RADS) lexicon. In a second step of the study, a combined oncologic and radiologic assessment based on clinical-pathological and radiological data was performed, in order to identify patients who may need adjuvant chemotherapy. Results were correlated with risk levels expressed by ODXRS, using the decision made on the basis of the ODXRS test as a gold standard. The χ2 test was used to evaluate associations between categorical variables, and significant ones were further investigated using logistic regression analyses. A total of 58 luminal-like, early-stage breast cancers were included. A positive correlation was found between ODXRS and tumor size (p = 0.003), staging (p = 0.001) and grading (p = 0.005), and between BI-RADS categories and ODXRS (p < 0.05 for both readers), the latter being confirmed at multivariate regression analysis. Moreover, BI-RADS categories proved to be positive predictors of the therapeutic decision taken after performing an ODXRS assay. A statistically significant association was also found between the therapeutic decision based on the ODXRS and the results of combined onco-radiologic assessment (p < 0.001). Our study suggests that there is a correlation between BI-RADS categories at MRI and ODXRS and that a combined onco-radiological assessment may predict the decision made on the basis of the results of ODXRS genomic test.

Breast cancer in the era of precision medicine.

Breast cancer is a heterogeneous disorder with different molecular subtypes and biological characteristics for which there are diverse therapeutic approaches and clinical outcomes specific to any molecular subtype. It is a global health concern due to a lack of efficient therapy regimens that might be used for all disease subtypes. Therefore, treatment customization for each patient depending on molecular characteristics should be considered. Precision medicine for breast cancer is an approach to diagnosis, treatment, and prevention of the disease that takes into consideration the patient's genetic makeup. Precision medicine provides the promise of highly individualized treatment, in which each individual breast cancer patient receives the most appropriate diagnostics and targeted therapies based on the genetic profile of cancer. The knowledge about the molecular features and development of breast cancer treatment approaches has increased, which led to the development of new targeted therapeutics. Tumor genomic profiling is the standard of care for breast cancer that could contribute to taking steps to better management of malignancies. It holds great promise for accurate prognostication, prediction of response to common systemic therapies, and individualized monitoring of the disease. The emergence of targeted treatment has significantly enhanced the survival of patients with breast cancer and contributed to reducing the economic costs of the health system. In this review, we summarized the therapeutic approaches associated with the molecular classification of breast cancer to help the best treatment selection specific to the target patient.

Tumor cell intrinsic and extrinsic features predictprognosis in estrogen receptor positive breast cancer.

Although estrogen-receptor-positive (ER+) breast cancer is generally associated with favorable prognosis, clinical outcome varies substantially among patients. Genomic assays have been developed and applied to predict patient prognosis for personalized treatment. We hypothesize that the recurrence risk of ER+ breast cancer patients is determined by both genomic mutations intrinsic to tumor cells and extrinsic immunological features in the tumor microenvironment. Based on the Cancer Genome Atlas (TCGA) breast cancer data, we identified the 72 most common genomic aberrations (including gene mutations and indels) in ER+ breast cancer and defined sample-specific scores that systematically characterized the deregulated pathways intrinsic to tumor cells. To further consider tumor cell extrinsic features, we calculated immune infiltration scores for six major immune cell types. Many individual intrinsic features are predictive of patient prognosis in ER+ breast cancer, and some of them achieved comparable accuracy with the Oncotype DX assay. In addition, statistical learning models that integrated these features predicts the recurrence risk of patients with significantly better performance than the Oncotype DX assay (our optimized random forest model AUC = 0.841, Oncotype DX model AUC = 0.792, p = 0.04). As a proof-of-concept, our study indicates the great potential of genomic and immunological features in prognostic prediction for improving breast cancer precision medicine. The framework introduced in this work can be readily applied to other cancers.

Development and pilot of an online, personalized risk assessment tool for a breast cancer precision medicine trial

Breast cancer risk reduction has been validated by large-scale clinical trials, but uptake remains low. A risk communication tool could provide personalized risk-reduction information for high-risk women. A low-literacy-friendly, visual, and personalized tool was designed as part of the Women Informed to Screen Depending On Measures of risk (WISDOM) study. The tool integrates genetic, polygenic, and lifestyle factors, and quantifies the risk-reduction from undertaking medication and lifestyle interventions. The development and design process utilized feedback from clinicians, decision-making scientists, software engineers, and patient advocates. We piloted the tool with 17 study participants, collecting quantitative and qualitative feedback. Overall, participants felt they better understood their personalized breast cancer risk, were motivated to reduce their risk, and considered lifestyle interventions. The tool will be used to evaluate whether risk-based screening leads to more informed decisions and higher uptake of risk-reduction interventions among those most likely to benefit.

Predicting breast cancer drug response using a multiple-layer cell line drug response network model

BackgroundPredicting patient drug response based on a patient’s molecular profile is one of the key goals of precision medicine in breast cancer (BC). Multiple drug response prediction models have been developed to address this problem. However, most of them were developed to make sensitivity predictions for multiple single drugs within cell lines from various cancer types instead of a single cancer type, do not take into account drug properties, and have not been validated in cancer patient-derived data. Among the multi-omics data, gene expression profiles have been shown to be the most informative data for drug response prediction. However, these models were often developed with individual genes. Therefore, this study aimed to develop a drug response prediction model for BC using multiple data types from both cell lines and drugs.MethodsWe first collected the baseline gene expression profiles of 49 BC cell lines along with IC50 values for 220 drugs tested in these cell lines from Genomics of Drug Sensitivity in Cancer (GDSC). Using these data, we developed a multiple-layer cell line-drug response network (ML-CDN2) by integrating a one-layer cell line similarity network based on the pathway activity profiles and a three-layer drug similarity network based on the drug structures, targets, and pan-cancer IC50 profiles. We further used ML-CDN2 to predict the drug response for new BC cell lines or patient-derived samples.ResultsML-CDN2 demonstrated a good predictive performance, with the Pearson correlation coefficient between the observed and predicted IC50 values for all GDSC cell line-drug pairs of 0.873. Also, ML-CDN2 showed a good performance when used to predict drug response in new BC cell lines from the Cancer Cell Line Encyclopedia (CCLE), with a Pearson correlation coefficient of 0.718. Moreover, we found that the cell line-derived ML-CDN2 model could be applied to predict drug response in the BC patient-derived samples from The Cancer Genome Atlas (TCGA).ConclusionsThe ML-CDN2 model was built to predict BC drug response using comprehensive information from both cell lines and drugs. Compared with existing methods, it has the potential to predict the drug response for BC patient-derived samples.

Answers Are in the Blood: cfDNA to Enhance Precision Medicine for Breast Cancer.

Identification of biomarkers to drive treatment using cell-free DNA allows dynamic and safe assessment of tumor biology representing an important advance adding to tissue-based genotyping. Caution must be exercised interpreting commercial matching information as validation is often lacking, and true matching is feasible in a minority of patients.See related article by Vidula et al., p. 3404.

Current Biomarkers for Precision Medicine in Breast Cancer.

Breast cancer has become the prototypical solid tumor where targets have been identified within the tumor allowing for personalized approach for systemic therapy. Biomarkers are beginning to play an important role in preparing the way for precision treatment. Mandatory biomarkers for every newly diagnosed case of breast cancer are estrogen receptors and progesterone receptors in selecting patients for endocrine treatment and HER2 for identifying patients likely to benefit from antiHER2 therapy. Although methodological problems exist in the determination of Ki67, because of its clearly established clinical value, wide availability, and low costs relative to the available multianalyte signatures, Ki67 may be used for determining prognosis, especially if values are low or high. Also, the androgen receptor (AR) pathway is emerging as a potential therapeutic target in breast cancer. AR-targeted treatments for breast cancer are in development and have shown promising preliminary results. While, most established biomarkers in breast cancer require tissue samples, serum tumor markers are easily accessible and require a less invasive procedure. Among them, tissue polypeptide-specific antigen (TPS), a specific epitope structure of a peptide in serum associated with human cytokeratin 18, is linked to the proliferative activity of tumors. TPS may be a valuable and independent prognostic biomarker for breast cancer.In order to accelerate progress towards precision treatment for women with breast cancer, we need additional predictive biomarker, especially for enhancing the positive predictive value for endocrine and antiHER2 therapies, as well as biomarkers for predicting response to specific forms of chemotherapy.

Imaging features of breast cancer molecular subtypes: state of the art.

Characterization of breast cancer molecular subtypes has been the standard of care for breast cancer management. We aimed to provide a review of imaging features of breast cancer molecular subtypes for the field of precision medicine. We also provide an update on the recent progress in precision medicine for breast cancer, implications for imaging, and recent observations in longitudinal functional imaging with radiomics.

Precision Medicine for Breast Cancer Utilizing Circulating Tumor DNA: It Is in the Blood.

Liquid biopsy using blood components to assess circulating tumor DNA (ctDNA) is rapidly becoming a new standard-of-care technology in many tumor types, including breast cancer, due to the potential to provide predictive and prognostic information. The minimally invasive and repeatable nature of plasma based mutational testing is appealing for patients and facilitates enhanced disease monitoring. It is important for the clinician to understand the benefits and limitations of this emerging assay and the potential applications in breast cancer. Multiple technologies have been employed to assess breast cancer ctDNA with high sensitivity and specificity leading to assays that have been useful in research trials and are entering widespread clinical application. ctDNA analysis of breast cancer is of clinical utility today in selecting targeted therapy for advanced breast cancer, most notably by assessing PIK3CA mutations in hormone receptor-positive, HER2-negative disease. It will be employed in the near future in a variety of clinical settings including early detection of primary breast cancer, minimal residual disease after initial therapy, and use in advanced breast cancer for prognosis, early identification of non-response, and monitoring genomic markers of resistance.

Abstract 198: Molecular genetic characterization of primary breast cancer via clinical next-generation sequencing

Abstract Targeting appropriate driver genes is crucial in cancer precision medicine. To find oncogenic drivers, targeted next-generation sequencing is becoming widely used in the clinical field. Using targeted next-generation sequencing, we aimed to detect targetable genetic alterations and characterize mutational profiles of breast cancer. We analyzed breast cancer biopsy specimens using customized 50-gene panel. In total, 196 patients of primary operable breast cancer patients were selected from single institution in South Korea. Among the 196 patients, 22 kinds of genes were significantly mutated or amplified. They comprised 161 nonsynonymous missense mutations, 54 frameshift mutations, 29 missense mutations and 5 small indels; 64 gene amplifications and 2 deletions. The most frequently mutated gene was PIK3CA (n=81/196; 41.3%), followed by TP53 (n=62; 31.6%), AKT1 (n=21; 10.7%), GATA3 (n=17; 8.7%) and PTEN (n=14; 7.1%). The most frequently amplified gene was ERBB2 (n=25/196; 12.8%), followed by FGFR1 (n=11; 5.6%), MYC (n=9; 4.6%), FGFR2 (n=3; 1.5%) and FGFR3 (n=3; 1.5%). Targeted next-generation sequencing can be readily performed in daily clinical practice. The most commonly found targetable genetic alterations were mutations of PI3K/AKT/mTOR pathway genes and FGFR family gene amplifications. Despite the limited number of available targeted agents, our study shows the feasible application of precision medicine in breast cancer. Citation Format: Jung Ho Park, Seung Yeon Ko, Sang Hwa Kim, Ho Young Kim, Sung Hoon Ko, Lee Su Kim. Molecular genetic characterization of primary breast cancer via clinical next-generation sequencing [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 198.

Microarray Normalization Revisited for Reproducible Breast Cancer Biomarkers

Precision medicine for breast cancer relies on biomarkers to select therapies. However, the reliability of biomarkers drawn from gene expression arrays has been questioned and calls for reassessment, in particular for large datasets. We revisit widely used data-normalization procedures and evaluate differences in outcome in order to pinpoint the most reliable reprocessing methods biomarkers can be based upon. We generated a database of 3753 breast cancer patients out of 38 studies by downloading and curating patient samples from NCBI-GEO. As gene-expression biomarkers, we select the assessment of receptor status and breast cancer subtype classification. Each normalization procedure is applied separately, and biomarkers are then evaluated for each patient. Differences between normalization pipelines are quantified as percentages of patients having outcomes different for each pipeline. Some normalization procedures lead to quite consistent biomarkers, differing only in 1-2% of patients. Other normalization procedures—some of them have been used in many clinical studies—end up with distrusting discrepancies (10% and more). A good deal of doubt regarding the reliability of microarrays may root in the haphazard application of inadequate preprocessing pipelines. Several modes of batch corrections are evaluated regarding a possible improvement of receptor prediction from gene expression versus the golden standard of immunohistochemistry. Finally, we nominate those normalization methods yielding consistent and trustable results. Adequate bioinformatics data preprocessing is key and crucial for any subsequent statistics to arrive at trustable results. We conclude with a suggestion for future bioinformatics development to further increase the reliability of cancer biomarkers.

Hippo/MST blocks breast cancer by downregulating WBP2 oncogene expression via miRNA processor Dicer

WBP2 transcription coactivator is an emerging oncoprotein and a key node of convergence between EGF and Wnt signaling pathways. Understanding how WBP2 is regulated has important implications for cancer therapy. WBP2 is tightly controlled by post-translational modifications, including phosphorylation and ubiquitination, leading to changes in subcellular localization, protein–protein interactions, and protein turnover. As the function of WBP2 is intricately linked to YAP and TAZ, we hypothesize that WBP2 is negatively regulated by the Hippo tumor suppressor pathway. Indeed, MST is demonstrated to negatively regulate WBP2 expression in a kinase-dependent but LATS-independent manner. This was observed in the majority of the breast cancer cell lines tested. The effect of MST was enhanced by SAV and concomitant with the inhibition of the transcription co-activation, in vitro and in vivo tumorigenesis activities of WBP2, resulting in good prognosis in xenografts. Downregulation of WBP2 by MST involved miRNA but not proteasomal or lysosomal degradation. Our data support the existence of a novel MST-Dicer signaling axis, which in turn regulates both WBP2 CDS- and UTR-targeting miRNAs expression, including miR-23a. MiR-23a targets the 3′UTR of WBP2 mRNA directly. Significant inverse relationships between WBP2 and MST or miR23a expression levels in clinical specimens were observed. In conclusion, WBP2 is a target of the Hippo/MST kinase; MST is identified as yet another rheostat in the regulation of WBP2 and its oncogenic function. The findings have implications in targeted therapeutics and precision medicine for breast cancer.