Search Results (492)

Background: Breast and hematological cancer treatments, especially with anthracyclines, have been shown to be associated with an increased risk of cardiotoxicity (CTX). An accurate prediction of cardiotoxicity risk and early detection of myocardial injury may allow for effective cardioprotection to be instituted and tailored to reverse cardiac dysfunction and prevent the discontinuation of essential cancer treatments. Objectives: The PRoactive Evaluation of Function to Evade Cardio Toxicity (PREFECT) study sought to evaluate the ability of fast-strain-encoded (F-SENC) cardiac magnetic resonance imaging (CMR) and 2D echocardiography (2D Echo) to stratify patients at risk of CTX prior to initiating cancer treatment, detect early signs of cardiac dysfunction, including subclinical CTX (sub-CTX) and CTX, and monitor for recovery (REC) during cardioprotective therapy. Methods: Fifty-nine patients with breast cancer or lymphoma were prospectively monitored for CTX with F-SENC CMR and 2D Echo over at least 1 year for evidence of cardiac dysfunction during anthracycline based chemotherapy. F-SENC CMR also monitored myocardial deformation in 37 left ventricular (LV) segments to obtain a MyoHealth risk score based on both longitudinal and circumferential strain. Sub-CTX and CTX were classified based on pre-specified cardiotoxicity definitions. Results: CTX was observed in 9/59 (15%) and sub-CTX in 24/59 (41%) patients undergoing chemotherapy. F-SENC CMR parameters at baseline predicted CTX with a lower LVEF (57 ± 5% vs. 61 ± 5% for all, p = 0.05), as well as a lower MyoHealth (70 ± 9 vs. 79 ± 11 for all, p = 0.004) and a worse global circumferential strain (GCS) (−18 ± 1 vs. −20 ± 1 for all, p < 0.001). Pre-chemotherapy MyoHealth had a higher accuracy in predicting the development of CTX compared to CMR LVEF and 2D Echo LVEF (AUC = 0.85, 0.69, and 0.57, respectively). The 2D Echo parameters on baseline imaging did not stratify CTX risk. F-SENC CMR obtained good or excellent images in 320/322 (99.4%) scans. During cancer treatment, MyoHealth had a high accuracy of detecting sub-CTX or CTX (AUC = 0.950), and the highest log likelihood ratio (indicating a higher probability of detecting CTX) followed by F-SENC GLS and F-SENC GCS. CMR LVEF and CMR LV stroke volume index (LVSVI) also significantly worsened in patients developing CTX during cancer treatment. Conclusions: F-SENC CMR provided a reliable and accurate assessment of myocardial function during anthracycline-based chemotherapy, and demonstrated accurate early detection of CTX. In addition, MyoHealth allows for the robust identification of patients at risk for CTX prior to treatment with higher accuracy than LVEF. Full article

Cancer metabolic reprogramming plays a critical role in tumor progression and therapeutic resistance, underscoring the need for advanced analytical strategies. Metabolomics, leveraging mass spectrometry and nuclear magnetic resonance (NMR) spectroscopy, offers a comprehensive and functional readout of tumor biochemistry. By enabling both targeted metabolite quantification and untargeted profiling, metabolomics captures the dynamic metabolic alterations associated with cancer. The integration of metabolomics with machine learning (ML) approaches further enhances the interpretation of these complex, high-dimensional datasets, providing powerful insights into cancer biology from biomarker discovery to therapeutic targeting. This review systematically examines the transformative role of ML in cancer metabolomics. We discuss how various ML methodologies—including supervised algorithms (e.g., Support Vector Machine, Random Forest), unsupervised techniques (e.g., Principal Component Analysis, t-SNE), and deep learning frameworks—are advancing cancer research. Specifically, we highlight three major applications of ML–metabolomics integration: (1) cancer subtyping, exemplified by the use of Similarity Network Fusion (SNF) and LASSO regression to classify triple-negative breast cancer into subtypes with distinct survival outcomes; (2) biomarker discovery, where Random Forest and Partial Least Squares Discriminant Analysis (PLS-DA) models have achieved >90% accuracy in detecting breast and colorectal cancers through biofluid metabolomics; and (3) prognostic modeling, demonstrated by the identification of race-specific metabolic signatures in breast cancer and the prediction of clinical outcomes in lung and ovarian cancers. Beyond these areas, we explore applications across prostate, thyroid, and pancreatic cancers, where ML-driven metabolomics is contributing to earlier detection, improved risk stratification, and personalized treatment planning. We also address critical challenges, including issues of data quality (e.g., batch effects, missing values), model interpretability, and barriers to clinical translation. Emerging solutions, such as explainable artificial intelligence (XAI) approaches and standardized multi-omics integration pipelines, are discussed as pathways to overcome these hurdles. By synthesizing recent advances, this review illustrates how ML-enhanced metabolomics bridges the gap between fundamental cancer metabolism research and clinical application, offering new avenues for precision oncology through improved diagnosis, prognosis, and tailored therapeutic strategies. Full article

Background: Unlike most cancers, breast cancer poses a persistent risk of distant recurrence—often years after initial treatment—making long-term risk stratification uniquely challenging. Current tools fall short in predicting late metastatic events, particularly for early-stage patients. Methods: We present an interpretable machine learning (ML) pipeline to predict distant recurrence-free survival at 5, 10, and 15 years, integrating Bayesian network-based causal feature selection, deep feed-forward neural network models (DNMs), and SHAP-based interpretation. Using electronic health record (EHR)-based clinical data from over 6000 patients, we first applied the Markov blanket and interactive risk factor learner (MBIL) to identify minimally sufficient predictor subsets. These were then used to train optimized DNM classifiers, with hyperparameters tuned via grid search and benchmarked against models from 10 traditional ML methods and models trained using all predictors. Results: Our best models achieved area under the curve (AUC) scores of 0.79, 0.83, and 0.89 for 5-, 10-, and 15-year predictions, respectively—substantially outperforming baselines. MBIL reduced input dimensionality by over 80% without sacrificing accuracy. Importantly, MBIL-selected features (e.g., nodal status, hormone receptor expression, tumor size) overlapped strongly with top SHAP contributors, reinforcing interpretability. Calibration plots further demonstrated close agreement between predicted probabilities and observed recurrence rates. The percentage performance improvement due to grid search ranged from 25.3% to 60%. Conclusions: This study demonstrates that combining causal selection, deep learning, and grid search improves prediction accuracy, transparency, and calibration for long-horizon breast cancer recurrence risk. The proposed framework is well-positioned for clinical use, especially to guide long-term follow-up and therapy decisions in early-stage patients. Full article

Background/Objectives: To develop a DL-based model predicting recurrence risk in HER2-low breast cancer patients and to compare performance of the MRI-alone, clinicopathologic-alone, and combined models. Methods: We analyzed 453 patients with HER2-low breast cancer who underwent surgery and preoperative breast MRI between May 2018 and April 2022. Patients were randomly assigned to either a training cohort (n = 331) or a test cohort (n = 122). Imaging features were extracted from DCE-MRI and ADC maps, with regions of interest manually annotated by radiologists. Clinicopathological features included tumor size, nodal status, histological grade, and hormone receptor status. Three DL prediction models were developed: a CNN-based MRI-alone model, a clinicopathologic-alone model based on a multi-layer perceptron (MLP) and a combined model integrating CNN-extracted MRI features with clinicopathological data via MLP. Model performance was evaluated using AUC, sensitivity, specificity, and F1-score. Results: The MRI-alone model achieved an AUC of 0.69 (95% CI, 0.68–0.69), with a sensitivity of 37.6% (95% CI, 35.7–39.4), specificity of 87.5% (95% CI, 86.9–88.2), and F1-score of 0.34 (95% CI, 0.33–0.35). The clinicopathologic-alone model yielded the highest AUC of 0.92 (95% CI, 0.92–0.92) and sensitivity of 93.6% (95% CI, 93.4–93.8), but showed the lowest specificity (72.3%, 95% CI, 71.8–72.8) and F1-score of 0.50 (95% CI, 0.49–0.50). The combined model demonstrated the most balanced performance, achieving an AUC of 0.90 (95% CI, 0.89–0.91), sensitivity of 80.0% (95% CI, 78.7–81.3), specificity of 83.2% (95% CI: 82.7–83.6), and the highest F1-score of 0.55 (95% CI, 0.54–0.57). Conclusions: The DL-based model combining MRI and clinicopathological features showed superior performance in predicting recurrence in HER2-low breast cancer. This multimodal approach offers a framework for individualized risk assessment and may aid in refining follow-up strategies. Full article

Background/Objectives: The implementation of polygenic scores (PGSs) and multifactorial risk assessments (MFRAs) has the potential to enhance breast cancer risk stratification, particularly in carriers of moderate-penetrance pathogenic variants (PVs), whose risk profiles often remain unclear if testing is limited to monogenic risk factors. Methods: To enhance breast cancer risk stratification, we included the BCAC313 polygenic score, together with MFRA, for carriers of moderate-penetrance pathogenic variants (PVs) during routine diagnostics and assessed its effect on the classification of patients’ risk categories in a real-world cohort at our center in its first year of implementation. Seventeen carriers with PVs in moderate-risk breast cancer genes were included in this study. Thirteen of them qualified for analysis for a full MFRA, including PGS, according to ancestry estimation and clinical criteria. The MFRA was performed using the CanRisk tool, which incorporates clinical, lifestyle, familial, and genetic data, including the BCAC313 score. Results: PGS z-scores were significantly higher in breast cancer patients compared to the unaffected control cohort (p = 0.016). The MFRA, including PGS, increased risk estimates for contralateral breast cancer in seven of eight patients with breast cancer and for primary breast cancer in three of five healthy carriers, compared to the risk conferred by the MFRA and moderate-penetrance pathogenic variant alone. Risk estimates varied widely, demonstrating the value of MFRA in personalized care. In five cases, one with a CHEK2-PV and four with an ATM-PV, the modified risk assessment contributed to the surgical decision for a prophylactic mastectomy. Conclusions: The MFRA, including PGS, provides the clinically meaningful refinement of breast cancer risk estimates in individuals with moderate-risk PVs. Personalized risk predictions can inform clinical management and support decision-making, which highlights the utility of this approach in clinical practice. Full article

Background: Many risk factors for cancer therapy-related cardiovascular toxicity overlap with risk factors for atherosclerosis. According to the ESC 2022 Cardio-Oncology Guidelines, coronary computed tomography angiography and coronary artery calcium score are not recommended as part of routine risk assessment prior to oncological treatment. The aim of this study was to prospectively assess the influence of coronary artery calcium score (CAC score) on cancer therapy-related cardiac dysfunction in patients with moderate and high risk of cardiovascular toxicity, qualified for anthracycline treatment. Methods: In all patients, risk factors were collected, laboratory tests, echocardiography with global longitudinal strain (GLS) assessment and coronary artery tomography with coronary artery calcium score were performed. A total of 80 patients were included in the study, of which 77 (96.25%) were followed for an average of 11.5 months. The mean age at baseline was 60.5 years and 72 (93.51%) were women. Results: During observation, five patients (6.49%) died, including two due to heart failure and three due to cancer progression. The majority of patients (59, 76.6%) had breast cancer, 11 (14.3%) were diagnosed with sarcoma and seven (9.1%) with lymphoma. According to the HFA-ICOS risk score, 40 patients (51.9%) were classified as moderate risk (MR), and 37 patients (48.1%) as high risk (HR) for cancer therapy-related cardiovascular toxicity. A CAC score greater than 100 was calculated in 17 (22.1%) patients and greater than 400 in three (3.9%) patients. The CAC score above zero was more common in older patients and in patients classified as high risk (p < 0.001). There was also a significant association between CAC score and hypertension, hyperlipidemia, chronic kidney disease, and the level of NT-proBNP. During 12-month follow-up, mild CTRCD occurred in 38 (49.4%) patients, moderate CTRCD was diagnosed in seven (9.1%), and severe in three (3.9%) patients. In the univariable analysis, CTRCD was more common in the high-risk group (p = 0.005) and in patients with a CAC score greater than zero (p = 0.036). In multivariable analysis, the incidence of CTRCD remains higher in the CAC score > 0 group, even after adjusting for age, hypertension, and hyperlipidemia. In this study group, the CTRCD rates increased with the HFA-ICOS risk score. Conclusions: In moderate and high-risk patients, a coronary artery calcium score greater than zero was identified as a significant risk factor for the development of cancer therapy-related cardiac dysfunction during anthracycline-based treatment. Furthermore, the HFA-ICOS risk score demonstrated good correlation with the incidence of CTRCD in this study, supporting its validity as a predictive tool in patients receiving anthracycline therapy. Full article

Background: We investigated the role of a high-sensitive cardiac troponin T (hsTnT) increase below the upper limit of normal (ULN) in patients with breast cancer (BC). hsTnT assays accurately quantify very low plasma troponin concentrations and enable the early detection of cardiomyocyte injury before a drop in the left ventricular ejection fraction (LVEF). The increase in hsTnT below the ULN in response to chemotherapy has not previously been studied. Method: This was an open-label pilot study. Female patients with newly diagnosed BC scheduled to receive systemic cancer treatment were recruited. Blood sampling and echocardiography were performed at baseline, at 3 and 6 months of cancer treatment. hsTnT concentrations were measured using the Elecsys TnT hs assay (Roche Diagnostics). The limit of blank and 99th percentile cutoff values for the hsTnT assay were 3 and 14 ng/L. We calculated the rise in hsTnT (ΔhsTnT) by the difference (%) between its baseline level and during follow-up (FU) in each patient. Results: Among eligible subjects, we excluded 4 patients before the start of treatment and 17 patients during the follow-up with values for the hsTnT >14 ng/L. Finally, 60 women with a median age of 48.6 ± 1.3 years were included in the study. The median baseline hsTnT concentration was 5.5 ± 1.4 ng/L. During 6 months of cancer treatment, hsTnT increased in all patients by up to 10–305% from baseline, with an average of 94.2%. LV EF was normal at baseline and decreased significantly compared to the value before cancer treatment (61.9 ± 3.3% vs. 56.3 ± 7.0%; p < 0.045). We correlated the hsTnT rise with a drop in LV EF ≥ 10% from its baseline level. Logistic regression analysis showed that Δ hsTnT has a good predictive value for LV dysfunction, 0.78 (p = 0.05), 95% CI (0.67–0.90). The increase in hsTnT > 81% was determined as the optimal threshold value for detecting early biochemical cardiotoxicity. Conclusion: It was investigated that hsTnT rise within the cutoff < 14 ng/L can be used as a marker of early biochemical cardiotoxicity and is valuable for predicting LV drop in 6 months of FU. We conclude that BC patients with increased hsTnT plasma concentration > 81% from the baseline value should be considered as high-risk patients for cardiotoxicity and need more precise cardiac monitoring and early preventive medical intervention strategies. Full article

Regulatory T cells (Tregs) have multiple roles in the tumor microenvironment (TME), which maintain a balance between autoimmunity and immunosuppression. This research aimed to investigate the interaction between cancer stemness and Regulatory T cells (Tregs) in the breast cancer tumor immune microenvironment. Breast cancer stemness was calculated using one-class logistic regression. Twelve main cell clusters were identified, and the subsequent three subsets of Regulatory T cells with different differentiation states were identified as being closely related to immune regulation and metabolic pathways. A prognostic risk model including MEA1, MTFP1, PASK, PSENEN, PSME2, RCC2, and SH2D2A was generated through the intersection between Regulatory T cell differentiation-related genes and stemness-related genes using LASSO and univariate Cox regression. The patient’s total survival times were predicted and validated with AUC of 0.96 and 0.831 in both training and validation sets, respectively; the immunotherapeutic predication efficacy of prognostic signature was confirmed in four ICI RNA-Seq cohorts. Seven drugs, including Ethinyl Estradiol, Epigallocatechin gallate, Cyclosporine, Gentamicin, Doxorubicin, Ivermectin, and Dronabinol for prognostic signature, were screened through molecular docking and found a synergistic effect among drugs with deep learning. Our prognostic signature potentially paves the way for overcoming immune resistance, and blocking the interaction between cancer stemness and Tregs may be a new approach in the treatment of breast cancer. Full article

Breast cancer accounts for one in four new malignant tumors in women, and misdiagnosis can lead to severe consequences, including delayed treatment. Among patients classified with a BI-RADS 3 rating, the risk of very early-stage malignancy remains over 2%. However, due to the benign imaging characteristics of these lesions, radiologists often recommend follow-up rather than immediate biopsy, potentially missing critical early interventions. This study aims to develop a deep learning (DL) model to accurately identify very early-stage malignancies in BI-RADS 3 lesions using ultrasound (US) images, thereby improving diagnostic precision and clinical decision-making. A total of 852 lesions (256 malignant and 596 benign) from 685 patients who underwent biopsies or 3-year follow-up were collected by Southwest Hospital (SW) and Tangshan People’s Hospital (TS) to develop and validate a deep learning model based on a novel transfer learning method. To further evaluate the performance of the model, six radiologists independently reviewed the external testing set on a web-based rating platform. The proposed model achieved an area under the receiver operating characteristic curve (AUC), sensitivity, and specificity of 0.880, 0.786, and 0.833 in predicting BI-RADS 3 malignant lesions in the internal testing set. The proposed transfer learning method improves the clinical AUC of predicting BI-RADS 3 malignancy from 0.721 to 0.880. In the external testing set, the model achieved AUC, sensitivity, and specificity of 0.910, 0.875, and 0.786 and outperformed the radiologists with an average AUC of 0.653 (p = 0.021). The DL model could detect very early-stage malignancy of BI-RADS 3 lesions in US images and had higher diagnostic capability compared with experienced radiologists. Full article

According to the guidelines of the American Society of Clinical Oncology (ASCO) and the European Society of Medical Oncology (ESMO), the most significant genetic factor in the diagnosis and treatment of breast cancer is the mutation status of the BRCA1 and BRCA2 genes. Additional genes with a significant influence on cancer risk were selected for genetic panel screening. For these genes, the disease risk score was predicted to be greater than 20%. In clinical practice, it is observed that rare genetic variants have a significant impact in young patients, characterized by increased pathogenesis and a poorer overall prognosis. The ability to predict the potential effects of these rare variants may reveal important information regarding possible phenotypes and may also provide new insights leading to more efficacious treatments and overall improved clinical management. This paper presents the case of a 38-year-old woman with bilateral breast cancer who is likely a carrier of a pathogenic point mutation in the LZTR1 gene (LZTR1: c.1260+1del variant). With this clinical case report herein described, we intend to display the usefulness of performing detailed molecular tests in the field of genetic diagnostics for patients with breast cancer. Understanding the pathogenesis of hereditary cancer development, which is more predictable and reliable than that of sporadic tumors, will allow for the discovery of hitherto hidden intrinsic signaling pathways, facilitating replicable experimentation and thereby expediting the discovery of novel therapeutic treatments. Full article

Background: Many patients harbor minimal residual disease (MRD)—small clusters of residual tumor cells that survive therapy and evade conventional detection but drive recurrence. Although advances in molecular and computational methods have improved circulating tumor DNA (ctDNA)-based MRD detection, these approaches face challenges: ctDNA shedding fluctuates widely across tumor types, disease stages, and histological features. Additionally, low levels of driver mutations originating from healthy tissues can create background noise, complicating the accurate identification of bona fide tumor-specific signals. These limitations underscore the need for refined technologies to further enhance MRD detection beyond DNA sequences in solid malignancies. Methods: Profiling circulating cell-free mRNA (cfmRNA), which is hyperactive in tumor and non-tumor microenvironments, could address these limitations to inform postoperative surveillance and treatment strategies. This study reported the development of OncoMRD BREAST, a customized, gene signature-informed cfmRNA assay for residual disease monitoring in breast cancer. OncoMRD BREAST introduces several advanced technologies that distinguish it from the existing ctDNA-MRD tests. It builds on the patient-derived gene signature for capturing tumor activities while introducing significant upgrades to its liquid biopsy transcriptomic profiling, digital scoring systems, and tracking capabilities. Results: The OncoMRD BREAST test processes inputs from multiple cutting-edge biomarkers—tumor and non-tumor microenvironment—to provide enhanced awareness of tumor activities in real time. By fusing data from these diverse intra- and inter-cellular networks, OncoMRD BREAST significantly improves the sensitivity and reliability of MRD detection and prognosis analysis, even under challenging and complex conditions. In a proof-of-concept real-world pilot trial, OncoMRD BREAST’s rapid quantification of potential tumor activity helped reduce the risk of incorrect treatment strategies, while advanced predictive analytics contributed to the overall benefits and improved outcomes of patients. Conclusions: By tailoring the assay to individual tumor profiles, we aimed to enhance early identification of residual disease and optimize therapeutic decision-making. OncoMRD BREAST is the world’s first and only gene signature-powered test for monitoring residual disease in solid tumors. Full article

Background and Objectives: The accuracy of breast cancer diagnosis depends on the concordance between imaging features and pathological findings. While BI-RADS (Breast Imaging Reporting and Data System) provides standardized risk stratification, its correlation with histologic grade and immunohistochemical markers remains underexplored. This study assessed the diagnostic performance of BI-RADS 3, 4, and 5 classifications and their association with tumor grade and markers such as ER, PR, HER2, and Ki-67. Materials and Methods: In this prospective study, 67 women aged 33–82 years (mean 56.4) underwent both mammography and ultrasound. All lesions were biopsied using ultrasound-guided 14G core needles. Imaging characteristics (e.g., margins, echogenicity, calcifications), histopathological subtype, and immunohistochemical data were collected. Statistical methods included logistic regression, Chi-square tests, and Spearman’s correlation to assess associations between BI-RADS, histology, and immunohistochemical markers. Results: BI-RADS 5 lesions showed a 91% malignancy rate. Evaluated features included spiculated margins, pleomorphic microcalcifications, and hypoechoic masses with posterior shadowing, and were correlated with histological and immunohistochemical results. Invasive tumors typically appeared as irregular, hypoechoic masses with posterior shadowing, while mucinous carcinomas mimicked benign features. Higher BI-RADS scores correlated significantly with increased Ki-67 index (ρ = 0.76, p < 0.001). Logistic regression yielded an AUC of 0.877, with 93.8% sensitivity and 80.0% specificity. Conclusions: BI-RADS scoring effectively predicts malignancy and correlates with tumor proliferative markers. Integrating imaging, histopathology, and molecular profiling enhances diagnostic precision and supports risk-adapted clinical management in breast oncology. Full article

Background/Objectives: Accurately correlating mammographic findings with corresponding histopathologic features is considered one of the essential aspects of mammographic evaluation, guiding the next steps in cancer management and preventing overdiagnosis. The objective of this study was to evaluate patterns of mammographic microcalcifications and their association with histopathological findings related to various breast tumors. Methods: 110 out of 3603 women had microcalcification of BIRADS 3 or higher and were subjected to stereotactic/ultrasound (USG) guided biopsies, and hook-wire localization excision procedures. Ultrasound and mammography images were reviewed by experienced radiologists using the standard American College of Radiology Breast-Imaging Reporting and Data System (ACR BI-RADS). Results: Our study showed that features with a high positive predictive value (PPV) of breast malignancy were heterogeneous (75%), fine linear/branching pleomorphic microcalcifications (66.7%), linear (100%), and segmental distributions (57.1%). Features that showed a higher risk of association with ductal carcinoma in situ (DCIS) were fine linear/branching pleomorphic (odds ratio (OR): 3.952), heterogeneous microcalcifications (OR: 3.818), segmental (OR: 5.533), linear (OR: 3.696), and regional (OR: 2.929) distributions. Furthermore, the features with higher risks associated with invasive carcinoma had heterogeneous (OR: 2.022), fine linear/branching pleomorphic (OR: 1.187) microcalcifications, linear (OR: 6.2), and regional (OR: 2.543) distributions. The features of associated masses in mammograms that showed a high PPV of malignancy had high density (75%), microlobulation (100%), and spiculated margins (75%). Conclusions: We concluded that specific patterns and distributions of microcalcifications were indeed associated with a higher risk of malignancy. Those with fine linear or branching pleomorphic and segmental distribution were at a higher risk of DCIS, whereas those with heterogeneous morphology with a linear distribution were at a higher risk of invasive carcinoma. Full article

Background: The national guidelines, informed by evidence from the National Institutes of Health (NIH), define the criteria for genetic testing of BRCA1/2 and other genes associated with Hereditary Breast and Ovarian Cancer (HBOC) and Lynch Syndrome (LS). When a germline pathogenic variant (PV) is identified in an index case, clinical recommendations advise informing at-risk relatives about the availability of predictive genetic testing, as early identification of carriers allows for timely implementation of preventive measures. Methods: This retrospective observational study examined data collected between 2017 and 2024 at the Medical Genetics Unit of the “Renato Dulbecco” University Hospital in Catanzaro, Italy. The analysis focused on trends in the identification of individuals carrying PVs in cancer predisposition genes (CPGs) and the subsequent uptake of cascade genetic testing (CGT) among their family members. Results: Over the study period, from 116 probands were performed 257 CGTs on 251 relatives. A notable reduction of approximately ten years in median age was observed, 39% were found to carry familial mutation and were referred to personalized cancer prevention programs. Among these, 62% accessed Oncological Genetic Counselling (CGO) within one year of the proband’s diagnosis, suggesting effective communication and outreach. Conclusions: The findings highlight the critical role of effective CGO and intrafamilial communication in hereditary cancer prevention. The identification of PVs, followed by timely CGTs and implementation of preventive strategies, significantly contributes to early cancer risk management. Periodic monitoring of CGT uptake and outcome trends, as demonstrated in this study, is essential to refine and optimize genetic services and public health strategies. Full article

Background/Objectives: Breast cancer (BC) is a multifactorial disease, with genetic alterations in cell proliferation and migration pathways being significant risk factors. This study examines the association between three variants in the BIRC5 gene (rs8073069, rs17878467, and rs9904341) and breast cancer (BC) susceptibility. Methods: Peripheral blood DNA samples were collected from 423 women (221 BC patients and 202 healthy controls). Genotyping was performed by polymerase chain reaction restriction fragment length polymorphism (PCR-RFLP) methodology. Associations were calculated using odds ratios (OR), with p-values adjusted by the Bonferroni test (significance at p ≤ 0.016). In silico analyses were conducted to predict the functional impact of the analyzed variants. Results: Patients carrying the C/C genotype for the rs8073069 variant showed increased susceptibility to BC with early TNM (tumor-node-metastasis classification) stage and Luminal A subtype (OR > 2.00; p ≤ 0.004). For the rs17878467 variant, patients with the C/T or T/T genotype exhibited a higher susceptibility to developing breast cancer (BC), particularly at early TNM stages or with a histological lobular type (OR > 2.00; p ≤ 0.012). Regarding the rs9904341 variant, patients with the G/C or C/C genotype had a higher susceptibility to breast cancer. Notably, G/C genotype carriers with Luminal A and B subtypes, and C/C genotype carriers who had TNM stages II and III, and Luminal A, Luminal B, and HER2 subtypes demonstrated increased risk (OR > 2.00; p ≤ 0.009). The C-T-C haplotype (rs8073069–rs17878467–rs9904341) was significantly associated with BC (OR = 4.20; 95% CI = 2.38–7.41; p ≤ 0.001). In silico analysis using CADD indicated a low probability of deleterious effects. Conclusions: The results suggest that the rs8073069, rs17878467, and rs9904341 variants in BIRC5 have a significant influence on breast cancer susceptibility. Full article