Prognosis Of Breast Cancer Patients Research Articles

CLDN11 deficiency upregulates FOXM1 to facilitate breast tumor progression through hedgehog signaling pathway.

Claudins (CLDNs) play a crucial role in regulating the permeability of epithelial barriers and can impact tumor behavior through alterations in their expression. However, the precise mechanisms underlying the involvement of CLDNs in breast cancer progression remain unclear. This study aimed to investigate the role of CLDN11 in breast cancer progression. Utilizing the TCGA database and clinical specimens from breast cancer patients, we observed reduced expression of CLDN11 in tumor tissues, which correlated with poor prognosis in breast cancer patients. In vitro, silencing of CLDN11 enhanced the proliferative and migratory characteristics of breast cancer cell lines MCF-7 and MDA-MB-231. Mechanistically, CLDN11 deficiency promoted the upregulation of Forkhead Box M1 (FOXM1) by activating the hedgehog signaling pathway, thereby sustaining tumor progression in breast cancer. In vivo, blockade of hedgehog signaling suppressed the tumor progression induced by CLDN11 silencing. Our study highlights the significance of the CLDN11/FOXM1 axis in breast cancer progression, suggesting CLDN11 as a potential diagnostic indicator and therapeutic target for clinical therapy.

Predictive value of methylene blue combined with indocyanine green in sentinel lymph node metastasis in breast cancer: a prospective pilot cohort study.

The status of sentinel lymph nodes is crucial for prognosis and treatment decisions in breast cancer patients. This study aimed to evaluate the predictive value of combined methylene blue and indocyanine green for sentinel lymph node metastasis in breast cancer. This prospective cohort study enrolled 90 clinically node-negative breast cancer patients. Methylene blue and indocyanine green were injected locally before surgery. Sentinel lymph nodes were grouped based on fluorescence intensity and methylene blue staining. A binary logistic regression model was established using 285 lymph node groups to predict metastatic risk. A total of 475 lymph nodes were identified, with 33 being metastatic. The metastatic risk reached 70% for partially blue-stained and weakly fluorescent lymph nodes between 1-2 cm. The model revealed associations between lymph node size, dye staining patterns, and metastatic risks (P<0.05). The AUC of the ROC curve was 0.855. The staining pattern of combined methylene blue and indocyanine green could predict risks of sentinel lymph node metastasis and facilitate rapid intraoperative identification of high-risk lymph nodes.

The prognostic value of skeletal muscle mass and density in breast cancer: a systematic review and meta-analysis.

Aim: The effect of skeletal muscle mass and density on the long-term survival outcome of breast cancer patients is unclear.Materials & methods: Systematically searched all articles in PubMed, Web of science, Springerlink, EMBASEand Wiley databases that studied the association between skeletal muscle and survival outcomes of breast cancer by 25September 2023. The hazard ratios and confidence intervals of the multiple factor analysis results controlling for confounding variables in the study were collected and analyzed using STATA 14.0 software.Results: This meta-analysis included a total of 13 studies, with a median age of 48.2years. Meta results showed that the survival (hazard ratio [HR]: 0.98, 95% CI: 0.89-1.08) and recurrence (HR: 0.96, 95% CI: 0.92-1.00) outcomes of breast cancer patients with sarcopenia were not significantly affected compared with those without sarcopenia. No significant heterogeneity or publication bias was observed in the study.Conclusion: The conclusion that skeletal muscle is regarded as a useful factor that can guide and optimize the prognosis of breast cancer patients is uncertain, or the result is very weak. Considering the impact of research quality and confounding factors, prospective studies are needed in the future to further demonstrate.PROSPERO identifier: CRD42023463480 (www.crd.york.ac.uk/prospero).

7615 Genomic Consequences of GRHL2 Overexpression in ER+ Breast Cancer Cells

Abstract Disclosure: K.O. Allen: None. C. Zheng: None. N. Solodin: None. M.S. Ozers: Owner/Co-Owner; Self; Proteovista, LLC. C.L. Warren: None. P.E. Messa: None. D.T. Vogt: None. E.T. Alarid: None. Breast cancer progression is characterized by the presence and prevalence of metastatic disease. At a genomic level, metastasis can be facilitated via rearrangement of the chromatin landscape to expose oncogenic genes for transcription. Grainyhead like protein 2 (GRHL2) is a nuclear transcription factor implicated in epithelial cell differentiation. The GRHL2 motif has been found near activated estrogen receptor (ER) binding sites, and elevated levels of GRHL2 have been linked to worse prognosis in hormone receptor positive breast cancer patients. In its capacity to contribute to a more severe disease state when overexpressed, GRHL2 may function as either a transcription or pioneer factor to aid in the expression of metastasis-associated genes. Our lab previously showed that GRHL2 genomic binding precedes ER binding at co-regulated binding sites. Also, overexpression of GRHL2 in MCF7 cells for 24 hours did not appreciably alter gene expression by RNA-Seq. These data taken together indicate that GRHL2 may be exhibiting pioneer factor activity. However, the specific role of GRHL2 overexpression in hormone positive breast cancer progression remains unknown. In order to examine the mechanism of GRHL2 action, GRHL2 genomic binding was investigated using a tetracycline-inducible MCF7 model that allows controlled overexpression of GRHL2. ChIP studies were performed to determine GRHL2 binding intensity at specific and genome wide GRHL2 binding sites. Specific focus was given to established GRHL2 binding sites near oncogenic genes responsible for epithelial to mesenchymal transition (EMT), dormancy, and proto-oncogenic behavior. A similar analysis examined H3K27 acetylation to validate active gene transcription. An orthogonal technology, Specificity and Affinity for Protein (SNAP) microarrays, which characterizes direct GRHL2 binding to tiled genomic regions identified in ChIP-Seq analyses from multiple labs, was utilized to discriminate direct and indirect GRHL2 binding sites. The requirement for GRHL2 transactivation function in regulation of EMT related genes was further interrogated using transactivation GRHL2 dominant negative mutants. This work aims to distinguish the transcriptional and non-transcriptional activities of GRHL2 in breast cancer cells on a genome wide level and thereby, provide a deeper understanding of how overexpressed GRHL2 contributes to hormone positive breast cancer. Presentation: 6/3/2024

The prognostic role of survivin expression in breast cancer: A systematic review and meta-analysis.

Breast cancer is a heterogeneous condition with variations in histopathological, genomic, and biological characteristics. Although clinicopathological prognostic factors and gene expression profiles are commonly used to guide treatment decisions in patients with breast cancer, there is still a need for new prognostic markers. One potential marker is survivin, a protein belonging to the apoptosis inhibitor family. However, studies examining the relationship between survivin and prognosis in breast cancer have yielded inconsistent results. This study aimed to evaluate the impact of survivin expression on the prognosis of breast cancer patients through a meta-analysis. Studies evaluating survivin expression were sourced from the PubMed, Embase, and Cochrane databases. We conducted a meta-analysis based on full-text articles that evaluated the relationship between survivin expression and survival by immunochemistry or polymerase chain reaction. The studies were initially divided into 2 groups based on the evaluation of overall survival (OS) and disease-free survival (DFS). Subsequently, each group was further categorized according to the method used to detect survivin expression. Statistical analyses for this study were conducted using Stata and JAMOVI. After screening with keywords, we identified 24 retrospective studies evaluating OS and 15 retrospective studies evaluating DFS, which were included in the analysis. We found that the studies in the meta-analysis were not heterogeneous, and this remained consistent when categorizing the groups by survivin expression detection. Survivin expression was associated with OS (HR 1.23, 95% CI 0.81-1.65) and DFS (HR 0.89, CI 0.42-1.36), indicating poor prognosis. This significant relationship between survivin expression and survival persisted when the studies were categorized by the detection method, either immunohistochemistry or polymerase chain reaction. In this study, we evaluated the prognostic significance of survivin expression in patients with breast cancer through a meta-analysis. These results support the use of survivin expression as a prognostic marker in breast cancer, potentially guiding treatment decisions.

Withaferin A decreases glycolytic reprogramming in breast cancer

Reprogrammed glucose metabolism is considered as the hallmark of cancer with therapeutic implications. Phytocompounds have potential to inhibit cancer metabolism. Here, we tested the ability of Withaferin A (WA), a withanolide derived from Withania somnifera, in modulating cancer metabolism. The assessed effect of WA on aerobic glycolysis in breast cancer cell lines showed that WA decreases the glucose uptake, lactate production and ATP generation by inhibiting the expression of key glycolytic enzymes i.e., GLUT1, HK2 and PKM2. We also identified that WA induced inhibition of cancer glycolysis by targeting c-myc as validated by silencing experiments followed by metabolic readouts. Decreased glycolysis resulted in reduced cell viability, biomass and colony forming ability of breast cancer cells. To further validate our in vitro findings in breast cancer patients, we analyzed 90 metabolic pathways in ~ 2000 breast tumors and observed that glycolysis is the most deregulated pathway in breast tumors. Deregulated glycolysis also predicted poor prognosis in breast cancer patients. In addition, patient data showed correlation between c-myc expression and glycolytic deregulation in breast cancer. Taken together, our results highlight the role of WA in inhibiting breast cancer metabolism via c-myc/glycolysis axis.

FMRP protects breast cancer cells from ferroptosis by promoting SLC7A11 alternative splicing through interacting with hnRNPM

Ferroptosis is a unique modality of regulated cell death that is driven by iron-dependent phospholipid peroxidation. N6-methyladenosine (m6A) RNA modification participates in varieties of cellular processes. However, it remains elusive whether m6A reader Fragile X Mental Retardation Protein (FMRP) are involved in the modulation of ferroptosis in breast cancer (BC). In this study, we found that FMRP expression was elevated and associated with poor prognosis and pathological stage in BC patients. Overexpression of FMRP induced ferroptosis resistance and exerted oncogenic roles by positively regulating a critical ferroptosis defense gene SLC7A11. Mechanistically, upregulated FMRP catalyzes m6A modification of SLC7A11 mRNA and further influences the SLC7A11 translation through METTL3-dependent manner. Further studies revealed that FMRP interacts with splicing factor hnRNPM to recognize the splice site and then modulated the exon skip splicing event of SLC7A11 transcript. Interestingly, SLC7A11-S splicing variant can effectively promote FMRP overexpression-induced ferroptosis resistance in BC cells. Moreover, our clinical data suggested that FMRP/hnRNPM/SLC7A11 expression were significantly increased in the tumor tissues, and this signal axis was important evaluation factors closely related to the worse survival and prognosis of BC patients. Overall, our results uncovered a novel regulatory mechanism by which high FMRP expression protects BC cells from undergoing ferroptosis. Targeting the FMRP–SLC7A11 axis has a dual effect of inhibiting ferroptosis resistance and tumor growth, which could be a promising therapeutic target for treating BC.

A novel arginine methylation-associated lncRNA signature effectively predicts prognosis in breast cancer patients.

Breast cancer (BC) is a disease highly associated with epigenetic modification, and arginine methylation is particularly important in its genetic regulation. However, the role of arginine methylation related lncRNAs in breast cancer has not been studied. First, we identified the related lncRNAs (from TCGA database) according to the differentially expressed genes related to arginine methylation in breast cancer. Then the lncRNAs related to protein arginine methylation were obtained by regression analysis, and the risk score model was constructed. Finally, the cell experiment and subcutaneous tumor model verified that the arginine methylation related lncRNA z68871.1 in the model had a significant effect on the proliferation and invasion of breast cancer cells. In conclusion, we successfully constructed an arginine methylation related lncRNA model, which has strong predictive ability. At the same time, this study provides an experimental basis for exploring the mechanism of arginine methylation in BC and helps to find new biomarkers of BC.

TMCO1 is upregulated in breast cancer and regulates the response to pro-apoptotic agents in breast cancer cells

AbstractThe release of Ca2+ ions from endoplasmic reticulum calcium stores is a key event in a variety of cellular processes, including gene transcription, migration and proliferation. This release of Ca2+ often occurs through inositol 1,4,5-triphosphate receptors and the activity of these channels and the levels of stored Ca2+ in the endoplasmic reticulum are important regulators of cell death in cancer cells. A recently identified Ca2+ channel of the endoplasmic reticulum is transmembrane and coiled-coil domains 1 (TMCO1). In this study, we link the overexpression of TMCO1 with prognosis in node-positive basal breast cancer patients. We also identify interacting proteins of TMCO1, which include endoplasmic reticulum-resident proteins involved in Ca2+ regulation and proteins directly involved in nucleocytoplasmic transport. Interacting proteins included nuclear transport proteins and TMCO1 was shown to have both nuclear and endoplasmic reticulum localisation in MDA-MB-231 basal breast cancer cells. These studies also define a role for TMCO1 in the regulation of breast cancer cells in their sensitivity to BCL-2/MCL-1 inhibitors, analogous to the role of inositol 1,4,5-triphosphate receptors in the regulation of cell death pathways activated by these agents.

Honokiol Exhibits Anti-Tumor Effects in Breast Cancer by Modulating the miR-148a-5p-CYP1B1 Axis.

Breast cancer (BC) is the most frequently diagnosed malignancy in female patients. There is a significant lack of therapeutic strategies for BC, particularly triple-negative breast cancer (TNBC). Honokiol (HNK), a lignin extracted from the Magnolia genus plant, has demonstrated numerous pharmacological effects. Therefore, this study aims to investigate the antitumor effect of HNK on BC cells and employ high-throughput sequencing to elucidate its potential mechanism. We found that HNK significantly inhibited proliferation and induced apoptosis on BC cell lines in a dose-dependent manner. Moreover, HNK treatment suppressed migration and colony formation and initiated the intrinsic apoptotic pathway specifically in MDA-MB-231 cells. High-throughput sequencing and bioinformatics analysis revealed that miR-148a-5p expression was significantly up-regulated, whereas CYP1B1 expression was down-regulated following HNK treatment. Importantly, survival analysis based on TCGA database showed high miR-148a-5p expression was correlated with a better prognosis for BC patients. Inhibition of miR-148a-5p by inhibitor not only increased cell viability but also attenuated apoptosis induced by HNK.Finally, a strong synergistic effect between HNK and paclitaxel was observed in vitro. In conclusion, our study validated the antitumor efficacy of HNK against human BC cells and elucidated its underlying mechanism through high-throughput sequencing, thereby providing compelling evidence for further exploration of the potential clinical application of HNK towards the treatment of BC.

Prognostic prediction of breast cancer patients using machine learning models: a retrospective analysis.

Breast cancer is a common and complex disease, with various clinical features affecting prognosis. Accurate prediction of prognosis is essential for guiding personalized treatment strategies. This study aimed to develop machine learning models for predicting prognosis in breast cancer patients using retrospective data. A total of 6,477 patients from Affiliated Sir Run Run Shaw Hospital were included, and their electronic medical records (EMRs) were thoroughly examined to identify 15 clinical features significantly associated with breast cancer survival. We employed eight different machine learning algorithms, including Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), and Extreme Gradient Boosting (XGBoost), to develop and evaluate the predictive performance of the models. In addition, to investigate the sensitivity of different training/testing set radio to model performance, we examined five sets of ratios: 50:50, 60:40, 70:30, 80:20, 90:10. Among these models, XGBoost demonstrated the highest performance with receiver operating characteristic (ROC) area under the curve (AUC) of 0.813, accuracy of 0.739, sensitivity of 0.815, and specificity of 0.735. Further statistical analysis identified several significant predictors of prognosis, including age, tumor size, lymph node status, and hormone receptor status. The XGBoost model was found to exhibit superior predictive power compared to established prognostic models such as the Nottingham Prognostic Index (NPI) and Predict Breast. Based on the successful performance of the XGBoost model, we developed a prognosis prediction tool specifically designed for breast cancer, providing valuable insights to clinicians, and aiding them in making informed treatment decisions tailored to individual patients. Our study highlights the potential of machine learning models in accurately predicting prognosis for breast cancer patients, ultimately facilitating personalized treatment strategies. Further research and validation are warranted to fully integrate these models into clinical practice.

Peripheral NK cell count predicts response and prognosis in breast cancer patients underwent neoadjuvant chemotherapy.

The count of lymphocyte subsets in blood can reflect the immune status of the body which is closely related to the tumor immune microenvironment and the efficacy of NAT. This study aims to explore the relationship between peripheral blood lymphocyte subsets and the efficacy and prognosis of NAT in breast cancer. We retrospectively analyzed clinicopathological information and peripheral blood lymphocyte subpopulation counts of patients receiving NAT from January 2015 to November 2021 at Sun Yat-sen University Cancer Center. Kaplan-Meier curves were used to estimate the survival probability. The independent predictors of NAT response and survival prognosis were respectively analyzed by multivariate logistic regression and Cox regression, and nomograms were constructed accordingly. The prediction efficiency of three nomograms was validated separately in the training cohort and the testing cohort. 230 patients were included in the study, consisting of 161 in the training cohort and 69 in the testing cohort. After a median follow-up of 1238 days, patients with higher NK cell value showed higher pCR rates and higher OS and RFS after NAT (all P < 0.001). Multivariate analyses suggested NK cell count was an independent predictor of NAT response, OS and RFS. We then built nomograms accordingly and validated the prediction performance in the testing cohort (C index for NAT response: 0.786; for OS: 0.877, for RFS: 0.794). Peripheral blood NK cell count is a potential predictive marker for BC patients receiving NAT. Nomograms based on it might help predict NAT response and prognosis in BC.

Examining the influence of tumor-infiltrating macrophages on breast cancer outcomes and identifying relevant genes for diagnostic purposes

ObjectiveThe purpose of this research was to investigate how different types of immune cells impact the outlook of individuals with breast cancer, as well as identify the essential genes associated with immune cell subtype enrichment.MethodsThe Cancer Genome Atlas (TCGA) database and Gene Expression Omnibus (GEO) database were used to obtain global transcriptome sequencing data sets of breast tissue. The study utilized the CIBERSORT algorithm to determine the presence of 22 different types of immune cells in both breast cancer tissue and normal breast tissue.Immune cell infiltration content was utilized to conduct univariate COX analysis in order to identify risk factors linked to breast cancer prognosis.ResultsUnivariate COX analysis indicates that Macrophages M1 and B cells naive are beneficial factors for the outlook of individuals with breast cancer (P < 0.05), while Macrophages M2 and Monocytes are detrimental factors for the prognosis of breast cancer patients (P < 0.05). The high infiltration group of macrophage M2 had a poorer prognosis compared to the low infiltration group (P < 0.001); Conversely, the high infiltration group of macrophage M1 had a better prognosis than the low infiltration group (P = 0.002).ConclusionThe study provided an overview of immune cell infiltration in breast cancer tissues, identifying macrophage M1 and macrophage M2 as potential factors in breast cancer development and progression. Additionally, genes associated with macrophage phenotype were analyzed, offering insights into macrophage polarization mechanisms.

Decorin, an exercise-induced secretory protein, is associated with improved prognosis in breast cancer patients but does not mediate anti-tumorigenic tissue crosstalk in mice

BackgroundRegular exercise can reduce incidence and progression of breast cancer, but the mechanisms for such effects are not fully understood. MethodsWe used a variety of rodent and human experimental model systems to determine whether exercise training can reduce tumor burden in breast cancer and to identify mechanism associated with any exercise training effects on tumor burden. ResultsWe show that voluntary wheel running slows tumor development in the mammary specific polyomavirus middle T antigen overexpression (MMTV-PyMT) mouse model of breast cancer but only when mice are not housed alone. We identify the proteoglycan decorin as a contraction-induced secretory factor that systemically increases in patients with breast cancer immediately following exercise. Moreover, high expression of decorin in tumors is associated with improved prognosis in patients, while treatment of breast cancer cells in vitro with decorin reduces cell proliferation. Notwithstanding, when we overexpressed decorin in murine muscle or injected recombinant decorin systemically into mouse models of breast cancer, elevated plasma decorin concentrations did not result in higher tumor decorin levels and tumor burden was not improved. ConclusionExercise training is anti-tumorigenic in a mouse model of luminal breast cancer, but the effect is abrogated by social isolation. The proteoglycan decorin is an exercise-induced secretory protein, and tumor decorin levels are positively associated with improved prognosis in patients. The hypothesis that elevated plasma decorin is a mechanism by which exercise training improves breast cancer progression in humans is not, however, supported by our pre-clinical data since elevated circulating decorin did not increase tumor decorin levels in these models.

Efficacy and prognosis of neoadjuvant chemotherapy in HER2 low-expressing breast cancer: a retrospective single-center study.

Human epidermal growth factor receptor 2 (HER2) is vital for breast cancer prognosis. The aim of this study was to analyze the clinicopathological data of HER2-negative breast cancer patients receiving neoadjuvant chemotherapy and the associated factors affecting the pathological complete response rate (pCR) and prognosis. Clinical data of 173 patients with primary HER2-negative breast cancer, who initially received neoadjuvant chemotherapy followed by surgical treatment at the Breast Surgery Department of Bethune Hospital in Shanxi Province from January 2012 to December 2022, were collected. Compared to HER2-0 patients, HER2-low patients had higher T staging (p = 0.008), higher Ki67 proliferation index (p < 0.001), lower N staging (p = 0.001), and lower pCR rate (p < 0.001). Univariate analysis revealed that T stage, TNM stage, HR status, HER2 status, and Ki67 are risk factors that affect the pCR rate in HER-2 negative. Multivariate analysis identified HR status as an independent predictor of pCR rate. Kaplan-Meier survival curves showed that menstrual status, N staging, T staging, TNM staging, and pCR status affected the prognosis of HER2-low breast cancer patients (p < 0.05). HER2-low breast cancer exhibits distinct biological behaviors, suggesting personalized treatment approaches.

Abstract C105: Novel cancer-specific hypoxia signature predicts poor prognosis and highlights racial disparities in breast cancer

Abstract Triple-negative breast cancer (TNBC) is the most aggressive subtype of breast cancer. TNBC displays exceptional transcriptional heterogeneity, both between patients and intratumorally, and disproportionately affects underserved women. Hypoxia, a state of low oxygen levels, is often present in the solid tumor microenvironment. In TNBC, hypoxia is a key driver of aggressiveness and therapeutic resistance. Past bulk RNA sequencing (RNAseq) studies in cancer research have lacked spatial resolution, failing to uncover precise transcriptional differences within tumor regions. This spatial limitation has been especially problematic for studying transcriptional heterogeneity of the cellular hypoxia response within tumors. By integrating single-cell RNA sequencing and spatial transcriptomic analyses in a TNBC xenograft tumor model, our research has uncovered a novel hypoxia gene signature, specific to cancer cells. Elevated expression of this hypoxia signature predicts poorer prognosis in breast cancer patients, especially within TNBC patients and those of African ancestry. This suggests that hypoxia may play a role in the racial disparities observed in TNBC prognosis, potentially informing treatment strategies. This workflow introduces a novel framework for translating general spatial transcriptomics data into clinically relevant and accurate gene signatures. Citation Format: Kent Schechter, Long C. Nguyen, Geetha P. Yerradoddi, Rania Bassiouni, John D. Carpten, Marsha R. Rosner. Novel cancer-specific hypoxia signature predicts poor prognosis and highlights racial disparities in breast cancer [abstract]. In: Proceedings of the 17th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2024 Sep 21-24; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2024;33(9 Suppl):Abstract nr C105.

Abstract C022: Pathomics-based classifiers for inferring breast cancer genomic assays and prognosis in sub-Saharan Africa: Results from the Ghana Breast Health Study

Abstract Background: Although several gene expression-based assays are validated for informing prognosis and treatment decision-making for breast cancer (BC) patients, their uptake has been hampered by technical complexities and cost, particularly in underrepresented and low-resource settings. Here, we explored whether machine learning-based features on standard hematoxylin and eosin (H&amp;E)-stained images can be used in conjunction with routinely available pathology data (pathomics) to infer clinically relevant tumor genomic assays among BC patients from a sub-Saharan African population. Methods: This study comprised 563 BC patients with diagnostic H&amp;E-stained images, clinicopathological data, and Nanostring gene expression data from the Ghana Breast Health Study (GBHS), a population-based case-control study that recruited BC patients between 2013 and 2015. H&amp;E images were analyzed using high-accuracy machine learning algorithms to extract data on several, prospectively selected, human interpretable imaging features. The assessed features encompassed characteristics of the tumor (e.g., average nuclear size, nuclear optical density, nuclear roundness, etc.) and stroma (e.g., degree of stromal cellularity, stromal cell phenotype, extent of stromal desmoplasia, remodeling, necrosis, etc.). Nanostring technology was used to generate data on PAM50 subtype [luminal (A and B), non-luminal (HER2-enriched and Basal), normal-like], 21-gene recurrence score (RS), and TP53 pathway function. Multivariable logistic regression models were fitted to luminal (vs other subtypes), non-luminal (vs other subtypes), TP53 mutant-like (vs wildtype-like), and RS (4th vs 1st quartile) data to develop predictive classifiers in a discovery set (60% of the data). The performance of the classifiers was then tested in a held-out internal validation set (40% of the data). Results: In the discovery set, the pathomics-based classifiers achieved varying but excellent discriminatory accuracy [AUROC = 0.90 (0.86-0.94), 0.94 (0.91-0.97), 0.95 (0.91-0.98), and 0.96 (0.94-0.99) for TP53, non-luminal, RS, and luminal classification, respectively]. In the held-out (validation) set, the corresponding AUROC values were 0.82 (0.75-0.88), 0.87 (0.81-0.92), 0.85 (0.77-0.92), and 0.88 (0.83-0.93) for TP53, non-luminal, RS, and luminal classification. The TP53 classifier was correlated with the luminal (R=-0.73), non-luminal (R=0.80), and RS (R=0.77) classifiers. The distribution of the pathomics-based TP53 probability score varied considerably between PAM50 subtypes (P&amp;lt;0.0001), and according to RS categories within both ER+ (P=0.009) and ER- (P=0.006) BC subtypes in the validation set. Conclusion: H&amp;Es are cost-effective and routinely performed as part of the diagnostic workup for BC patients. Accordingly, the results open promising avenues for the use of interpretable, machine learning-based, H&amp;E imaging and pathology data to infer breast tumor genomic signatures and prognosis in low-resource settings. Further work is required to validate findings in independent populations.: Citation Format: Mustapha Abubakar, Amber N. Hurson, Thomas U. Ahearn, Ebonee N. Butler, Alina M. Hamilton, Maire A. Duggan, Scott M. Lawrence, Ernest Adjei, Joe-Nat Clegg-Lamptey, Joel Yarney, Beatrice Wiafe-Addai, Baffour Awuah, Seth Wiafe, Kofi Nyarko, Francis Aitpillah, Daniel Ansong, Stephen Hewitt, Louise A. Brinton, Melissa A. Troester, Lawrence Edusei, Nicolas Titiloye, Jonine D. Figueroa, Montserrat Garcia-Closas. Pathomics-based classifiers for inferring breast cancer genomic assays and prognosis in sub-Saharan Africa: Results from the Ghana Breast Health Study [abstract]. In: Proceedings of the 17th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2024 Sep 21-24; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2024;33(9 Suppl):Abstract nr C022.

EZH2 mutation is associated with the development of visceral metastasis by enhancing proliferation and invasion and inhibiting apoptosis in breast cancer cells

BackgroundThe prognosis of breast cancer patients with visceral metastasis (VM) is significantly worse than that of patients without VM. We aimed to evaluate EZH2 (enhancer of zeste homolog 2) mutation as a biomarker associated with VM.MethodsData from forty-nine patients with metastatic breast cancer (MBC) pathologically confirmed at our hospital between March 2016 and September 2018 were collected. Metastatic tissue samples were obtained via ultrasound-guided needle biopsy, and paired peripheral blood samples were also collected. Tissue and blood samples were subjected to targeted next-generation sequencing via a 247-gene panel. Stably transfected MDA-MB-231 cells expressing wild-type EZH2 (EZH2WT) or a mutant form of EZH2 (EZH2K515R) were generated. Cell proliferation, colony formation ability, migration and invasion abilities and apoptosis were assessed using CCK-8 assays, plate colony formation assays, Transwell chamber assays and flow cytometry.ResultsThe incidence of EZH2 mutations in the VM subgroup was greater than that in the non-VM subgroup in the entire cohort (n = 49, 42.3% vs. 13.0%, p = 0.024) and in the triple-negative breast cancer (TNBC) subgroup (n = 20, 50.0% vs. 10.0%, p = 0.05). Patients carrying EZH2 mutations had a significantly greater risk of developing VM than did those in the non-EZH2 mutation group in the entire cohort (HR 2.9) and in the TNBC subgroup (HR 6.45). Multivariate analysis revealed that EZH2 mutation was an independent prognostic factor for VM (HR 2.99, p = 0.009) in the entire cohort and in the TNBC subgroup (HR 10.1, p = 0.006). Data from cBioPortal also showed that patients with EZH2 mutations had a significantly greater risk of developing VM (HR 3.1), and the time to develop VM was significantly earlier in the EZH2 mutation group (31.5 months vs. 109.7 months, p = 0.008). Multivariate analysis revealed that EZH2 mutation (HR 2.73, p = 0.026) was an independent factor for VM after breast cancer surgery. There was no correlation between EZH2 mutations and BRCA1/2 mutations. Most of the patients (81.8%) in our cohort who developed VM carried the “c.1544A > G (p.K515R)” mutation. Compared with EZH2WT MDA-MB-231 cells, EZH2K515R MDA-MB-231 cells had greater colony formation rates (p < 0.01), greater migration and invasion rates (p < 0.001), and lower apoptosis rates (p < 0.01). The proportion of S + G2/M phase cells in the EZH2K515R group was significantly greater than that in the EZH2WT group.ConclusionsEZH2 mutation is associated with VM development in breast cancer patients. The EZH2K515R mutation leads to VM and a poor prognosis by enhancing proliferation and invasion and inhibiting apoptosis in breast cancer cells.

The downregulation of SASH1 expression promotes breast cancer occurrence and invasion accompanied by the activation of PI3K-Akt-mTOR signaling pathway

SASH1 (SAM and SH3 domain containing 1) has been increasingly reported as a tumor suppressor gene. However, there is limited research on the role of SASH1 in breast cancer. This manuscript aims to investigate the mechanism of SASH1 in the occurrence, development, and prognosis of breast cancer. Firstly, we obtained RNA-sequencing data of the tumors from the Genomic Data Commons data portal website, along with the corresponding clinical information of patients. Pan-cancer analysis was performed to analyze the expression of SASH1 across all tumors. Univariate Cox regression analysis was used to assess the correlation between SASH1 expression and the prognosis of breast cancer patients. Then, immunohistochemistry was utilized to evaluate the expression levels of SASH1, p-Akt, p-PI3K, and p-mTOR in breast cancer tissue. Finally, a cell assay was employed to analyze the impact of SASH1 on the proliferation and invasion of breast cancer cells (MDA-MB-231). The results revealed that SASH1 expression is decreased in BRCA, LUSC, LUAD, CESC, ESCA, and COAD. Meta-analysis also found that SASH1 is downregulated in most tumor tissues, and the expression level of SASH1 in breast cancer was significantly lower than that in the control group (OR = 0.14, 95% CI = 0.08–0.25; P < 0.001). Further experimental validation showed that SASH1 expression is significantly downregulated in breast cancer tissue (38.33%, 23/60), and the overexpression of SASH1 can inhibit the proliferation and invasion of breast cancer cells accompanied by the suppression of PI3K-Akt-mTOR signaling pathway. Additionally, SASH1 overexpression can improve OS and RFS of breast cancer patients.

In-situ nanoplatform with synergistic neutrophil intervention and chemotherapy to prevent postoperative tumor recurrence and metastasis

In addition to residual tumor cells, surgery-induced inflammation significantly contributes to tumor recurrence and metastasis by recruiting polymorphonuclear neutrophils (PMNs) and promoting their involvement in tumor cell proliferation, invasion and immune evasion. Efficiently eliminating residual tumor cells while concurrently intervening in PMN function represents a promising approach for enhanced postoperative cancer treatment. Here, a chitosan/polyethylene oxide electrospun fibrous scaffold co-delivering celecoxib (CEL) and doxorubicin-loaded tumor cell-derived microparticles (DOX-MPs) is developed for postoperative in-situ treatment in breast cancer. This implant (CEL/DOX-MPs@CP) ensures prolonged drug retention and sustained release within the surgical tumor cavity. The released DOX-MPs effectively eliminate residual tumor cells, while the released CEL inhibits the function of inflammatory PMNs, suppressing their promotion of residual tumor cell proliferation, migration and invasion, as well as remodeling the tumor immune microenvironment. Importantly, the strategy is closely associated with interference in neutrophil extracellular trap (NET) released from inflammatory PMNs, leading to a substantial reduction in postoperative tumor recurrence and metastasis. Our results demonstrate that CEL/DOX-MPs@CP holds great promise as an implant to enhance the prognosis of breast cancer patients following surgery.