Breast Cancer Research Articles

The difficulties of differential diagnosis of Crohn’s disease and metastatic breast cancer (case report)

The difficulties of differential diagnosis of Crohn’s disease and metastatic breast cancer (case report)

Mechanisms of action and resistance to anti-HER2 antibody-drug conjugates in breast cancer

Human epidermal growth factor 2 (HER2)-positive breast cancer (BC) represents nearly 20% of all breast tumors. Historically, these patients had a high rate of relapse and dismal prognosis. The advent of HER2-targeting monoclonal antibodies such as trastuzumab followed by pertuzumab had improved the prognosis of HER2-positive metastatic BC. More recently, antibody-drug conjugates (ADCs) are now reshaping the treatment paradigm of solid tumors, especially breast cancer. Tratsuzumab emtansine (T-DM1) was one of the first ADC developed in oncology and was approved for the management of HER2-positive metastatic BC. In a head-to-head comparison, trastuzumab deruxtecan (T-DXd) defeated T-DM1 as a second-line treatment. The efficacy of ADCs is counterbalanced by the appearance of acquired resistance to these agents. In this paper, we summarize the mechanisms of action and resistance of T-DM1 and T-DXd, as well as their clinical efficacy. Additionally, we also discuss potential strategies for addressing resistance to ADC.

Comparative analysis of machine learning models for breast cancer prediction and diagnosis: a dual-dataset approach

<p>Breast cancer is ranked as a significant cause of mortality among females globally. Its complex nature poses principal challenges for physicians and researchers for rapid diagnosis and prognosis. Hence, machine learning algorithms are employed to forecast and identify diseases. This study discusses the comparative analysis of seven machine learning models, e.g., logistic regression (LR), support vector machine (SVM), k-nearest neighbor classifier (KNN), decision tree classifier (DT), random forest classifier (RF), Naïve Bayes (NB), and artificial neural network (ANN) to predict breast cancer using Wisconsin breast cancer and breast cancer datasets. In the Wisconsin breast cancer dataset, KNN depicted 99% accuracy, followed by RF (98%), SVM (96%), NB (96%), LR (96%), ANN (93%), and DT (92%). On the contrary, in the breast cancer (BC) dataset, the highest accuracy was achieved by LR at 83%, and the lowest was achieved by DT (65%), which depicted that the numeric dataset WBC has better accuracy than the breast cancer dataset.</p>

Hyperoxic-hypoxic Paradox: Breast Cancer Microenvironment and an Innovative Treatment Strategy.

A small therapeutic range of oxygen is required for effective metabolism. As a result, hypoxia (low oxygen concentration) is one of the most potent inducers of gene expression, metabolic alterations, and regenerative processes, such as angiogenesis, stem cell proliferation, migration, and differentiation. The cellular response is controlled by sensing the increased oxygen levels (hyperoxia) or hypoxia via specific chemoreceptor cells. Surprisingly, changes in free oxygen concentration instead of absolute oxygen levels may be regarded as a deficiency of oxygen at the cellular level. Recurrent intermittent hyperoxia may trigger many mediators of cellular pathways typically generated during hypoxia. The dilemma of hyperoxic-hypoxic conditions is known as the hyperoxic-hypoxic paradox. According to the latest data, the hypoxic microenvironment, crucial during cancer formation, has been demonstrated to play a key role in regulating breast cancer growth and metastasis. Hypoxic circumstances cause breast cancer cells to respond in a variety of ways. Transcription factors are identified as hypoxia-inducible factors (HIFs) that have been suggested to be a factor in the pathobiology of breast cancer and a possible therapeutic target, driving the cellular response to hypoxia. Breast cancer has a dismal prognosis due to a high level of resistance to practically all well-known cancer management that has been related to hypoxia-based interactions between tumor cells and the stromal milieu. We attempt to review the enigma by exploring the starring roles of HIFs in breast cancer, the HIF paradox, and the hyperoxic-hypoxic enigma.

Artemisia argyi extracts overcome lapatinib resistance via enhancing TMPRSS2 activation in HER2-positive breast cancer.

Breast cancer stands as the predominant malignancy and primary cause of cancer-related mortality among females globally. Approximately 25% of breast cancers exhibit HER2 overexpression, imparting a more aggressive tumor phenotype and correlating with poor prognoses. Patients with metastatic breast cancer receiving HER2 tyrosine kinase inhibitors (HER2 TKIs), such as Lapatinib, develop acquired resistance within a year, posing a critical challenge in managing this disease. Here, we explore the potential of Artemisia argyi, a Chinese herbal medicine known for its anti-cancer properties, in mitigating HER2 TKI resistance in breast cancer. Analysis of the Cancer Genome Atlas (TCGA) revealed diminished expression of transmembrane serine protease 2 (TMPRSS2), a subfamily of membrane proteolytic enzymes, in breast cancer patients, correlating with unfavorable outcomes. Intriguingly, lapatinib-responsive patients exhibited higher TMPRSS2 expression. Our study unveiled that the compounds from Artemisia argyi, eriodictyol, and umbelliferone could inhibit the growth of lapatinib-resistant HER2-positive breast cancer cells. Mechanistically, they suppressed HER2 kinase activation by enhancing TMPRSS2 activity. Our findings propose TMPRSS2 as a critical determinant in lapatinib sensitivity, and Artemisia argyi emerges as a potential agent to overcome lapatinib via activating TMPRSS2 in HER2-positive breast cancer. This study not only unravels the molecular mechanisms driving cell death in HER2-positive breast cancer cells induced by Artemisia argyi but also lays the groundwork for developing novel inhibitors to enhance therapy outcomes.

Synthesis and In Silico Studies of Quinazolinones as PARP-1 Inhibitors.

Cancer is a leading threat to humankind, accounting for nearly one million deaths in 2018, and the expected number of cancer-related deaths in 2040 is more than 16 million. The most common causes of cancer deaths are lung, colorectal, stomach, liver and breast cancer, while the highest number of new cancer cases belong to lung, breast, colorectal, prostate, stomach and liver cancer. PARP-1 is an enzyme that plays an important role in DNA repair, cell propagation/survival and death due to its influence on numerous biological processes. Quinazolinones represent an important scaffold in medicinal chemistry and have a broad spectrum of biological activities. In this study, we have synthesized quinazolinones by reaction of 2-aminobenzamide and substituted aldehydes. Molecular docking studies of synthesized compounds were performed for their PARP-1 binding affinities using Schrodinger 2016 software. In silico ADME studies were also performed for the synthesized compounds using the QikProp tool of Schrodinger software. Results of molecular docking studies indicated that synthesized quinazolinones had a good affinity towards active site of PARP-1 and compound 4 had the best docking score (-10.343). Results of ADME studies indicated the drug-like properties of synthesized compounds, which make them suitable drug candidates. All the synthesized compounds have a better docking score than niraparib (-9.05). Further, the synthesized compounds have a favorable ADME profile. Therefore, they may serve as important leads in discovering PARP-1 inhibitors.

Combination of Etoposide and Quercetin-Loaded Solid Lipid Nanoparticles Potentiates Apoptotic Effects on MDA-MB-231 Breast Cancer Cells

BackgroundBreast cancer is a major global cancer, for which radiation and chemotherapy are the main treatments. Natural remedies are being studied to reduce the side effects. Etoposide (ETO), a chemo-drug, and quercetin (QC), a phytochemical, are considered potential factors for adaptation to conventional treatments. ObjectivesThe anticancer effect of the synergy between ETO and Quercetin-loaded solid lipid nanoparticles (QC-SLNs), was investigated in MDA-MB-231 cells. MethodsWe developed QC-SLNs for efficient cellular delivery, characterizing their morphology, particle size, and zeta potential. We assessed the cytotoxicity of QC-SLNs and ETO on breast cancer cells via the MTT assay. Effects on apoptosis intensity in MDA-MB-231 cells have been detected utilizing annexin V-FITC, PI, and caspase activities. Real-time PCR assessed Bax gene and Bcl-2 gene fold change expression, while Western blot analysis determined p53 and p21 protein levels. ResultsSpherical, negatively charged QC-SLNs, when combined with ETO, significantly enhanced inhibition of MDA-MB-231 cell proliferation compared to ETO or QC-SLNs alone. The combined treatment also notably increased the apoptosis pathway. QC-SLNs + ETO increased the Bax/Bcl-2 gene ratio, elevated p53 and p21 proteins, and activated caspase 3 and 9 enzymes. These results indicate the potential for QC-SLNs + ETO as a strategy for breast cancer treatment, potentially overcoming ETO-resistant breast cancer chemoresistance. ConclusionThese results suggest that QC-SLN has the potential to have a substantial impact on the breast cancer cure by improving the efficacy of ETO. This enhancement could potentially help overcome chemoresistance observed in ETO-resistant breast cancer.

Breast cancer detection through attention based feature integration model

<span lang="EN-US">Breast cancer is detected by screening mammography wherein X-rays are used to produce images of the breast. Mammograms for screening can detect breast cancer early. This research focuses on the challenges of using multi-view mammography to diagnose breast cancer. By examining numerous perspectives of an image, an attention-based feature-integration mechanism (AFIM) model that concentrates on local abnormal areas associated with cancer and displays the essential features considered for evaluation, analyzing cross-view data. This is segmented into two views the bi-lateral attention module (BAM) module integrates the left and right activation maps for a similar projection is used to create a spatial attention map that highlights the impact of asymmetries. Here the module's focus is on data gathering through medio-lateral oblique (MLO) and bilateral craniocaudal (CC) for each breast to develop an attention module. The proposed AFIM model generates using spatial attention maps obtained from the identical image through other breasts to identify bilaterally uneven areas and</span><span lang="EN-US">class activation map (CAM) generated from two similar breast images to emphasize the feature channels connected to a single lesion in a breast. AFIM model may easily be included in ResNet-style architectures to develop multi-view classification models.</span>

Imaging the Male Breast: Gynecomastia, Male Breast Cancer, and Beyond.

The number of men undergoing breast imaging has increased in recent years, according to some reports. Most male breast concerns are related to benign causes, most commonly gynecomastia. The range of abnormalities typically encountered in the male breast is less broad than that encountered in women, given that lobule formation rarely occurs in men. Other benign causes of male breast palpable abnormalities with characteristic imaging findings include lipomas, sebaceous or epidermal inclusion cysts, and intramammary lymph nodes. Male breast cancer (MBC) is rare, representing up to 1% of breast cancer cases, but some data indicate that its incidence is increasing. MBC demonstrates some clinical features that overlap with those of gynecomastia, including a propensity for the subareolar breast. Men with breast cancer tend to present at a later stage than do women. MBC typically has similar imaging features to those of female breast cancer, often characterized by an irregular mass that may have associated calcifications. Occasionally, however, MBC has a benign-appearing imaging phenotype, with an oval shape and circumscribed margins, and therefore most solid breast masses in men require tissue diagnosis. Histopathologic evaluation may alternatively reveal other benign breast masses found in men, including papillomas, myofibroblastomas, and hemangiomas. Radiologists must be familiar with the breadth of male breast abnormalities to meet the rising challenge of caring for these patients. ©RSNA, 2024 Supplemental material is available for this article.

A comparative analysis of convolutional neural networks for breast cancer prediction

Breast cancer continues to be a substantial worldwide health concern, affecting millions of individuals each year; this emphasizes the critical nature of early detection in order to enhance patient prognoses. The present study aims to assess the classification performance of three convolutional neural network (CNN) architectures-visual geometry group 19 (VGG19), AlexNet, and residual network 50 (ResNet50)-with respect to breast cancer detection in medical images. Thorough assessments, encompassing metrics such as accuracy, precision, recall, and F-score, were undertaken to evaluate the diagnostic performance of the models. ResNet50 consistently outperforms other models, as evidenced by its highest accuracy and F-score. The research highlights the significant importance of carefully choosing suitable architectures for medical image analysis, with a specific focus on the detection of breast cancer. In addition, it demonstrates the capacity of deep learning models, such as ResNet50, to improve the diagnosis of breast cancer with exceptional precision and sensitivity, which is critical for reducing the occurrence of false positives and negatives in clinical environments. In addition, computational efficiency is taken into account; AlexNet is recognized as the most efficient model, which is advantageous in environments with limited resources. This study advances medical image processing by demonstrating the potential of CNNs in the detection of breast cancer. The results of this study establish a fundamental basis for sub- sequent inquiries and suggest approaches to improve timely detection and treatment, which will ultimately be advantageous for both patients and healthcare professionals.

Design, 3D-QSAR, molecular docking, ADMET, molecular dynamics and MM-PBSA simulations for new anti-breast cancer agents

Breast cancer is the most frequent form of malignant tumor in women, and represents a major public health problem due to its high mortality rate. Although a multitude of therapeutic options exist for control of this disease, the emergence of resistance to current pharmaceutical treatments underscores the urgency of developing new anti- breast cancer drugs, with a focus on reducing the adverse effects associated with current therapeutic agents. The present study concerns a new series of (23) compounds based on 1,4-quinone and quinoline derivatives to design candidate drugs against breast cancer. For this purpose, integrated computational techniques were applied, including 3D-QSAR, molecular docking and molecular dynamics simulations (MD). CoMFA and CoMSIA were used to build a robust and highly reliable 3D-QSAR models. To validate the model's predictive capabilities, an external validation was carried out. The results of the best model (CoMSIA/SEA) revealed that electrostatic, steric and hydrogen bond acceptor fields had a significant effect on the anti-breast cancer activity of molecules studied. In addition, evaluation of ADMET properties determined whether these newly designed ligands were likely to be selected as drug-candidates. To confirm the binding stability of the selected ligands to aromatase (3S7S) and validate the molecular docking results, molecular dynamics simulations lasting 100 nanoseconds were performed by calculating RMSD, RMSF, RoG, H-bond, SASA and MM-PBSA parameters. As a result, only one designed compound (ligand 5) emerged as the most promising drug candidate for experimental in vitro and in vivo testing, due to its potential inhibition of breast cancer.

A Short Appraisal of Magnetic Nanoparticles for Breast Cancer: In vitro and In vivo Research.

The increasing incidence of breast cancer and the associated morbidity due to higher metastasis created the urge to develop a nanocarrier that can be used as a potent therapeutic carrier with targeting efficacy. The use of superparamagnetic nanoparticles in breast cancer research and treatment has gained considerable attention in recent years. Magnetic nanoparticles (MNPs) can be used to construct nanocarriers since they possess superior properties such as superparamagnetism, easy surface functionalization to attach ligands, and non-immunogenic. MNPs are superior carriers that are used to target cancer cells without harming the normal cells in the body, which leads to therapeutic efficacy in the body. Along with their established anticancer potential and enhanced drug concentration at endosomal pH, the superparamagnetic property of MNPs was further exploited for their applications in reticuloendothelial uptake, drug delivery, medical imaging, and theranostics applications in breast cancer. Moreover, the clinical translational of MNPs, along with future prospects and key challenges in vivo, have been duly presented in the final review. The scientists preferred the ongoing research in MNPs due to their high biocompatibility and ease of targeting at molecular and cellular levels. The review highlighted the in vitro and in vivo research and patent supported data for potential use of MNPs for the treatment of breast cancer.

Mechanistic evaluation of myristicin on apoptosis and cell cycle regulation in breast cancer cells.

The current study was focused on the anticancer activity of myristicin against MCF-7 human breast cancer (BC) cells. BC is the most common and leading malignant disease in women worldwide. Now-a-days, various conventional therapies are used against BC and still represent a chief challenge because those treatments fail to differentiate normal cells from malignant cells, and they have severe side effects also. So, there is a need develop new therapies to decrease BC-related morbidity and mortality. Myristicin, a 1‑allyl‑5‑methoxy‑3, 4‑methylenedioxybenzene, is a main active aromatic compound present in various spices, such as nutmeg, mace, carrot, cinnamon, parsely and some essential oils. Myristicin has a wide range of effects, including antitumor, antioxidative and antimicrobial activity. Nevertheless, the effects of myristicin on human BC cells remain largely unrevealed. The cytotoxicity effect of myristicin on MCF‑7 cells was increased dose dependently detected by (4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide and Lactate Dehydrogenase assays. Myristicin was found to be significantly inducing the cell apoptosis, as compared to control, using acridine orange/ethidium bromide, Hoechst stain and annexin V. Moreover, it activates cell antimigration, intracellular reactive oxygen species generation and cell cycle arrest in the G1/S phase. In addition, myristicin induces the expression of apoptosis and cell cycle genes (Caspases8, Bax, Bid, Bcl2, PARP, p53, and Cdk1) was demonstrated by quantitative polymerase chain reaction and apoptosis proteins (c-PARP, Caspase 9, Cytochrome C, PDI) expression was also analyzed with western blot. Overall, we illustrated that myristicin could regulate apoptosis signaling pathways in MCF-7 BC cells.

A Comprehensive Review on the State of the Art of Breast Cancers in Italy.

Breast cancer (BC) currently represents one of the most prevalent cancers among women worldwide and the leading cause of cancer death among women, also negatively affecting the quality of life (QoL) in patients. Over the past two decades, BC research has led to extraordinary advances in our understanding of the disease, resulting in more effective treatments. However, its occurrence is still increasing. Several new treatments are now under development worldwide, but they are not devoid of well-- known side effects, and a great number of patients develop endocrine resistance. Nevertheless, the design and synthesis of more suitable strategies and new drugs to treat breast cancers, overcome resistance and side effects, and obtain better therapeutic outcomes are needed. In this review, we summarize the therapies and the clinical studies currently ongoing in Italy for the treatment of BCs, mainly HER2+ MBC, HER2-low MBC, and TNBC, focusing on the most recent ones, also in consideration of diverse facets, including some aspects related to QoL. Finally, some studies related to the usefulness of physical activity in BC will be cited.

Association between endocrine adjuvant therapy intake timing and disease-free survival in patients with high-risk early breast cancer: results of a sub-study of the UCBG- UNIRAD trial

Circadian rhythms regulate cellular physiology and could influence the efficacy of endocrine therapy (ET) in breast cancer (BC). We prospectively tested this hypothesis within the UNIRAD adjuvant phase III trial (NCT01805271). 1278 patients with high-risk hormonal receptor positive (HR+)/HER2 negative (HER2-) primary BC were randomly assigned to adjuvant ET with placebo or everolimus. Patients prospectively reported in a diary the daily timing of ET intake among four 6-h slots (06:00-11:59 (morning), 12:00-17:59 (afternoon), 18:00-23:59 (evening), or 24:00-05:59 (nighttime). The association between ET timing and disease-free survival (DFS) was a prespecified secondary endpoint of the trial and the results of this observational study are reported here. ET timing was recorded by 855 patients (67.2%). Patients declaring morning (n=465, 54.4%) or afternoon (n=45, 5.4%) ET intake were older than those declaring evening (n=339, 39.6%) or nighttime (n=5, 0.6%) intake. With a median follow-up of 46.7 months, 118 patients had a local (n=30) or metastasis relapse (n=84), and 41 patients died. ET intake timing was not associated with DFS in the whole population (HR=0.77, 95% CI [0.53-1.12]). The association between ET intake timing and DFS according to the stratification factors revealed interactions with ET agent (tamoxifen versus Aromatase inhibitors (AI) with an increased DFS in the group of evening/nighttime versus morning/afternoon tamoxifen intake (HR=0.43, 95% CI [0.22-0.85]), while no association was found for AI intake (HR=1.07, 95% CI [0.68-1.69]). The interaction between ET intake timing and ET agent remained in multivariable analysis (HR=0.38 [0.16-0.91]). Tamoxifen intake in the evening/nighttime could be recommended in patients with high-risk HR+/HER2- BC while awaiting for results from further ET timing studies. UNIRAD was Supported by a grant from the French Ministry of Health PHRC 2012 and received funding from La Ligue contre le Cancer, Cancer Research-UK, Myriad Genetics, and Novartis.

Blood and Tissue Levels of Persistent Organic Pollutants and Genetic Susceptibility in Patients with Breast Cancer

We investigated possible associations between the internal concentrations of POPs and correlations between blood and tumor tissue concentrations in patients who underwent surgery for breast cancer and breast reduction as controls. Genetic variations in CYP1A1, GSTP1, GSTM1, and GSTT1 and hOGG1 were evaluated to determine whether they represent risk factors for breast cancer. Certain POPs have been found to be associated with breast cancer development. GST-P1 polymorphism represented a significant risk for breast cancer with unadjusted OR. However, the GSTT1 null polymorphism represented a significant risk for breast cancer when OR adjusted for age and smoking status. CYP1A1 polymorphism was a significant risk factor for breast cancer, regardless of whether the OR was adjusted. These results suggest that exposure to certain POPs, GSTT1 and CYP1A1 polymorphisms, age, and smoking status are risk factors for breast cancer. In addition, the blood concentrations of some POPs represent surrogates for breast tissue concentrations.

A method to comprehensively identify germline SNVs, INDELs and CNVs from whole exome sequencing data of BRCA1/2 negative breast cancer patients.

In the rapidly evolving field of genomics, understanding the genetic basis of complex diseases like breast cancer, particularly its familial/hereditary forms, is crucial. Current methods often examine genomic variants-such as Single Nucleotide Variants (SNVs), insertions/deletions (Indels), and Copy Number Variations (CNVs)-separately, lacking an integrated approach. Here, we introduced a robust, flexible methodology for a comprehensive variants' analysis using Whole Exome Sequencing (WES) data. Our approach uniquely combines meticulous validation with an effective variant filtering strategy. By reanalyzing two germline WES datasets from BRCA1/2 negative breast cancer patients, we demonstrated our tool's efficiency and adaptability, uncovering both known and novel variants. This contributed new insights for potential diagnostic, preventive, and therapeutic strategies. Our method stands out for its comprehensive inclusion of key genomic variants in a unified analysis, and its practical resolution of technical challenges, offering a pioneering solution in genomic research. This tool presents a breakthrough in providing detailed insights into the genetic alterations in genomes, with significant implications for understanding and managing hereditary breast cancer.

Validation on selected breast cancer drugs of physicochemical features by using machine learning models

Breast cancer is one of the leading causes of death among females today. The elbow approach determines the ideal number of clusters after determining that the Dataset is highly cluster able with the Hopkins statistic. Three distinct groups with distinct differences were produced using the dataset's proposed expectation maximization fuzzy k-means clustering algorithm (PEMFKM). Different fuzzy clustering techniques, such as fuzzy k-means (FKM), fuzzy k-means with entropy (FKM.ENT), fuzzy k-means with entropy and noise (FKM.ENT.NOISE), Gustafson and Kessel - like fuzzy k-means (FKM.GK), Gustafson and Kessel - like fuzzy k-means with entropy regularization (FKM.GK.ENT), Gustafson and Kessel - like fuzzy kmeans with entropy regularization and noise (FKM.GK.ENT.NOISE), and PEMFKM, are evaluated. The partition coefficient (PC), partition entropy (PE), and Modified partition coefficient index (MPC) index values are better for FKM.GK than the suggested PEMFKM method. When compared to the FKM.GK method, the index values for the proposed PEMFKM algorithm have superior results for the parameters Silhouette (SIL), Xie and Beni index (XB), and fuzzy silhouette index (SIL.F). The results shows that the PEMFKM algorithm will provide better clusters and that the drugs in a given cluster may be combined for use in combination therapy for breast cancer treatment.

Hyperthermia/glutathione-triggered ferritin nanoparticles amplify the ferroptosis for synergistic tumor therapy

Breast cancer is the most diagnosed malignancy in women globally, and drug resistance is among the major obstacles to effective breast cancer treatment. Emerging evidence indicates that photothermal therapy and ferroptosis are both promising therapeutic techniques for the treatment of drug-resistant breast tumors. In this study, we proposed a thermal/ferroptosis/magnetic resonance imaging (MRI) triple functional nanoparticle (I@P-ss-FRT) in which ferritin, an iron storage material with excellent cellular uptake capacity, was attached via disulfide bonds onto polydopamine coated iron oxide nanoparticle (I@P) as photothermal transduction agent and MRI probe. I@P-ss-FRT converted the near-infrared light (NIR) into localized heat which accelerated the release of ferrous ions from ferritin accomplished by glutathione reduction and subsequently induced ferroptosis. The drug-resistant cancer cell lines exhibited a more significant uptake of I@P-ss-FRT and sensitivity to PTT/ferroptosis compared with normal cancer cell lines. In vivo, I@P-ss-FRT plus NIR displayed the best tumor-killing potential with inhibitory rate of 83.46%, along with a decline in GSH/GPX-4 content and an increase in lipid peroxides generation at tumor sites. Therefore, I@P-ss-FRT can be applied to combat drug-resistant breast cancer.

Breast cancer awareness messages: investigating response among Lebanese women.

While national Lebanese studies done on breast cancer attest to the importance of awareness campaigns none, however, examine the type of content that most effectively reaches women. The purpose of this study, therefore, was to examine, through an experiment, the effectiveness of direct versus indirect messaging, in altering knowledge, risk perception, attitudes, behaviors and self-efficacy among two groups of women, those aged 18-40 and 41-65 years. Findings suggest that the designed breast cancer communication messages were perceived as informative, trustworthy and believable regardless of message type. An interaction effect indicated that the older age group (41+) who were exposed to the indirect message felt more confident in taking proactive measures than the 18- to 40-year-old participants. In addition, those exposed to the indirect message were more likely to behave as communicated than those in the direct message condition. At the very least, the messages, both direct and indirect, helped minimize misconception or correct people's knowledge about the subject. This shows that participants and by extension people, in general, pay attention to breast cancer awareness messaging. Making these messages available to all age groups and motivating them to take proactive/preventative measures becomes even more significant, especially with the higher incidence of cancer among women aged 18-40 years in Lebanon.