Strategies for subtypes--dealing with the diversity of breast cancer: highlights of the St. Gallen International Expert Consensus on the Primary Therapy of Early Breast Cancer 2011

Study on the Differentiation of Infiltrating Breast Cancer Molecular Subtypes Based on Ultrasound Radiomics

OBJECTIVE

To establish and validate a 2-dimensional ultrasound (US) radiomics model for the noninvasive preoperative differentiation of various molecular subtypes of infiltrating breast cancer (IBC).

METHODS

A retrospective analysis of 210 female patients diagnosed with IBC through surgical operation or needle biopsy pathology at our hospital between May 2019 and February 2024 was conducted. Relevant data were collected to establish predictive models for different molecular subtypes of IBC.

RESULTS

Based on 5936 US radiomics features, 39, 25 and 19 optimal features were identified for the differentiation of luminal versus nonluminal types, luminal A versus luminal B types and human epidermal growth factor receptor 2 (HER2) overexpression versus triple-negative (TN) IBC subgroups, respectively. The corresponding areas under the curve for the training and validation sets were 0.901 and 0.752 (luminal vs. nonluminal), 0.931 and 0.773 (luminal A vs. luminal B) and 0.962 and 0.842 (HER2 overexpression vs. TN), respectively, indicating robust discriminatory performance of these models for different pathological molecular subtypes of IBC.

CONCLUSION

A radiomics model based on US images is capable of effectively differentiating between various molecular subtypes of IBC prior to surgery, holding promise in assisting medical professionals in crafting tailored diagnostic and therapeutic strategies.

Real-world utilization of aromatase inhibitors, tamoxifen, and ovarian function suppression in premenopausal patients with early hormone receptor-positive, HER2-negative breast cancer with increased recurrence risk

BACKGROUND

The optimal adjuvant endocrine treatment in premenopausal patients with hormone receptor-positive, HER2-negative (HRpos/HER2neg) early breast cancer (eBC) remains debated, particularly the choice between aromatase inhibitors plus ovarian function suppression (AI + OFS) or tamoxifen (TAM) with or without additional OFS. This study assessed the use of adjuvant endocrine therapies for premenopausal patients with intermediate/high-risk HRpos/HER2neg eBC.

METHODS

CLEAR-B (AGO-B-059; NCT05870813) was a retrospective study analyzing data, collected from January 2016 to June 2019 and from January 2022 to December 2023 during the certification process of breast centers in Germany. Premenopausal patients with HRpos/HER2neg intermediate/high-risk eBC were eligible. Patient and disease characteristics, in addition to recommended and received adjuvant treatments, were evaluated.

RESULTS

The number of registered patients was 3137, of whom 2789 had complete information on endocrine treatments (1717 for 2016-2019 and 1072 for 2022-2023). In 2016-2019, 8.4 % of the patients were recommended to be treated with AI + OFS, whereas in 2022-2023, the proportion of patients with a treatment recommendation for AI + OFS rose to 42.1 %. In 2016-2019, TAM monotherapy was most frequently recommended (80.8 %). Conversely, TAM + OFS was not commonly recommended (9.3 % in 2016-2019 and 16.5 % in 2022-2023). While no clear association between tumor stage and chosen endocrine therapy was found in 2016-2019, most patients with ≥stage IIA were recommended to be treated with AI + OFS in 2022-2023.

CONCLUSION

This analysis shows that treatment recommendation for AI + OFS in premenopausal patients with HRpos/HER2neg eBC increased relevantly in the past years, reflecting latest guideline recommendations.

DCE-MRI based deep learning analysis of intratumoral subregion for predicting Ki-67 expression level in breast cancer

OBJECTIVE

To evaluate whether deep learning (DL) analysis of intratumor subregion based on dynamic contrast-enhanced MRI (DCE-MRI) can help predict Ki-67 expression level in breast cancer.

MATERIALS AND METHODS

A total of 290 breast cancer patients from two hospitals were retrospectively collected. A k-means clustering algorithm confirmed subregions of tumor. DL features of whole tumor and subregions were extracted from DCE-MRI images based on 3D ResNet18 pre-trained model. The logistic regression model was constructed after dimension reduction. Model performance was assessed using the area under the curve (AUC), and clinical value was demonstrated through decision curve analysis (DCA).

RESULTS

The k-means clustering method clustered the tumor into two subregions (habitat 1 and habitat 2) based on voxel values. Both the habitat 1 model (validation set: AUC = 0.771, 95 %CI: 0.642-0.900 and external test set: AUC = 0.794, 95 %CI: 0.696-0.891) and the habitat 2 model (AUC = 0.734, 95 %CI: 0.605-0.862 and AUC = 0.756, 95 %CI: 0.646-0.866) showed better predictive capabilities for Ki-67 expression level than the whole tumor model (AUC = 0.686, 95 %CI: 0.550-0.823 and AUC = 0.680, 95 %CI: 0.555-0.804). The combined model based on the two subregions further enhanced the predictive capability (AUC = 0.808, 95 %CI: 0.696-0.921 and AUC = 0.842, 95 %CI: 0.758-0.926), and it demonstrated higher clinical value than other models in DCA.

CONCLUSIONS

The deep learning model derived from subregion of tumor showed better performance for predicting Ki-67 expression level in breast cancer patients. Additionally, the model that integrated two subregions further enhanced the predictive performance.

Role of artificial intelligence -based machine learning model in predicting HER2/neu gene status in breast cancer

Our study investigated the predictive efficacy of AI-based Machine Learning (ML) model for determining HER2 status in a population of 3424 breast cancer patients. Multivariate logistic regression analysis identified several independent variables that were predictive of HER2 positivity, namely age ≤ 40 years, tumor multicentricity, high tumor grade, high-grade DCIS, N3 stage disease, and negative ER status (p < 0.05). These findings suggest that patients presenting with these factors may benefit from more aggressive and targeted therapies. Furthermore, XGBoost ML model was trained using the dataset of 3324 patients, which was divided into an 80 % training set and a 20 % test set. The model achieved an impressive accuracy of 95 % on both training and test sets, as evidenced by the area under the curve (AUC) values of 0.95. The model ranked the presence of DCIS, DCIS component (major versus minor), DCIS grade, multiplicity of the tumor, and ER status as the top four variables for predicting HER2/neu status. To validate the performance of the proposed model, blind HER2 status data from an external validation cohort of 100 cases were utilized. Notably, the model demonstrated a sensitivity of 90.5 %, indicating its ability to accurately identify HER2-positive cases, and a specificity of 84.4 %, suggesting its capability to correctly classify HER2-negative cases. These results highlight the promising predictive efficacy of AI-based ML in determining HER2 status in breast cancer patients. The model's ability to accurately identify HER2-positive cases can assist in guiding treatment decisions, ensuring that patients receive appropriate and targeted therapies. However, further research with larger datasets is necessary to validate and generalize these findings.

Intraoperative detection of axillary metastasis of breast cancer using nucleic acid amplification methods: review of advantages and limitations

INTRODUCTION

In the management of breast cancer, the need for assessment of axillary status has been questionable in recent years. However, it is still applicable for making a decision on adjuvant therapy and evaluating the prognosis. Molecular tests have been widely used for intraoperative detection of axillary lymph node metastases and have prevented a second surgery for dissection of the lymph nodes in at least 20% of the cases. Unlike histopathological examination, molecular tests do not need a specialized technologist to provide the results.

AREAS COVERED

We have reviewed recent advancements in the assessment of axillary nodes by molecular studies such as one-step nucleic acid amplification (OSNA) assay and metasin test. Our work concentrated on reported thresholds for the tests, economical aspects, and newly developed devices throughout the current literature.

EXPERT OPINION

Well-designed clinical trials on molecular assays could lead to individualized management of the axillary, while preventing additional surgical operations in a large proportion of women with breast cancer.

HIV status alters immune cell infiltration and activation profile in women with breast cancer

The breast cancer (BC)-related mortality is higher and the immunity is altered in women living with HIV (WLWH) compared to HIV-negative women. Therefore, tumor samples of 296 black BC patients from South Africa and Namibia with known age, HIV status, tumor stage, hormone receptor and HER2 status and overall survival (OS) are analyzed for components of the tumor microenvironment (TME). WLWH (n = 117), either with suppressed viral activity (HR = 1.25) or with immune suppression (HR = 2.04), have a shorter OS. HIV status is associated with increased numbers of CD8+ T cells in the TME compared to HIV-negative patients; no correlation is found with CD4+ T cell numbers in the blood. Moreover, an increased expression of CD276/B7-H3 and a more pronounced IFN-γ signaling in the tumors are found in WLWH, independent of age, stage, and BC subtypes. In conclusion, altered T cell composition and CD276 expression in WLWH may contribute to inferior survival and can be used for targeted treatment.

Atypical ductal hyperplasia diagnosed by US-guided core needle biopsy: clinical, pathological and US features associated with upgrading to malignancy

BACKGROUND

To develop a predictive model to identify atypical ductal hyperplasia (ADH) that was underestimated by US-guided core needle biopsy (CNB) and to evaluate the risk factors for underestimation for ADH with intraductal papilloma diagnosed by CNB.

METHODS

In this retrospective study, 300 CNB-diagnosed ADH lesions in 291 consecutive women between January 2014 and July 2023 were included and divided into training set (n = 181), internal validation set (n = 54), and external validation set (n = 65). The review included clinical, pathological, and US features, as well as final outcomes. Multivariate logistic regression was employed to establish predictive model and to evaluate risk factors. Model performance was evaluated using area under the receiver operating characteristic curve (AUC), calibration curve, decision curve analysis, and utility (patient stratification into low and high-risk groups). Model was validated both internally and externally by calculating its performance on validation sets.

RESULTS

The upgrade rate to malignancy was 51.0%. Predictors included in the model were age, the pathological pattern of ADH with intraductal papilloma or ADH alone, Ki-67 positivity, and imaging-pathological discordance. The AUC was 0.915 (95% CI: 0.858, 0.955) in the training set, 0.906 (95% CI: 0.785, 0.972) in the internal validation set, and 0.934 (95% CI: 0.836, 0.983) in the external validation set. Using a cutoff value of 0.11, 38.3% of nonmalignant lesions in the training set were stratified into low-risk group with an upgrade rate of 4.1%. Similar results were obtained in the validation sets. For ADH with intraductal papilloma, age and imaging-pathological discordance were the independent risk factors for malignancy upgrading.

CONCLUSIONS

The model established to predict ADH upgrading can help in individualized risk management. If predictors of non-upgraded ADH lesions can be confirmed with larger studies, more than one-third of non-malignant lesions are expected to be candidates for non-excision.

TRIAL REGISTRATION

This is a retrospective study.

Prediction models of breast cancer molecular subtypes based on multimodal ultrasound and clinical features

BACKGROUND AND AIMS

Breast cancer classify into four molecular subtypes: Luminal A, Luminal B, HER2-overexpressing (HER2), and triple-negative (TNBC) based on immunohistochemical assessments. The multimodal ultrasound features correlate with biological biomarkers and molecular subtypes, facilitating personalized, precision-guided treatment strategies for patients. In this study, we aimed to explore the differences of multimodal ultrasound features generated from conventional ultrasound (CUS), shear wave elastography (SWE) and contrast-enhanced ultrasound (CEUS) between molecular subtypes of breast cancer, investigate the value of prediction model of breast cancer molecular subtypes based on multimodal ultrasound and clinical features.

METHODS

Breast cancer patients who visited our hospital from January 2023 to June 2024 and underwent CUS, SWE and CEUS were selected, according to inclusion criteria. Based on the selected effective feature subset, binary prediction models of features of CUS, features of SWE, features of CEUS and full parameters were constructed separately for the four breast cancer subtypes Luminal A, Luminal B, HER2, and TNBC, respectively.

RESULTS

There were ten parameters that showed significant differences between molecular subtypes of breast cancer, including BI-RADS, palpable mass, aspect ratio, maximum diameter, calcification, heterogeneous echogenicity, irregular shape, standard deviation elastic modulus value of lesion, time of appearance, peak intensity. Full parameter models had highest area under the curve (AUC) values in every test set. In aggregate, judging from the values of accuracy, precision, recall, F1 score and AUC, models used features selected from full parameters showed better prediction results than those used features selected from CUS, SWE and CEUS alone (AUC: Luminal A, 0.81; Luminal B, 0.74; HER2, 0.89; TNBC, 0.78).

CONCLUSIONS

In conclusion, multimodal ultrasound features had differences between molecular subtypes of breast cancer and models based on multimodal ultrasound data facilitated the prediction of molecular subtypes.

Development and validation of an LC-MS/MS method for simultaneous determination of XZP-3287(bireociclib) and its metabolites in human plasma, and its clinical pharmacokinetics application

XZP-3287(bireociclib) is a novel and selective inhibitor of the cell cyclin-dependent kinases 4/6 (CDK4/6), which is primarily employed for the treatment of breast cancer in clinical trials. In this study, a novel and sensitive liquid chromatography-tandem mass spectrometry (LC-MS/MS) method was developed and validated for the simultaneous determination of XZP-3287 and its metabolites XZP-5286, XZP-3584 and XZP-5736 in human plasma in accordance with international conference on harmonization of technical requirements for registration of Pharmaceuticals for Human use (ICH) guideline R3 (M10) guideline. The multiple reaction monitoring mode (MRM) of mass spectrometer was used and all compounds were monitored in electrospray ionization (ESI+) mode. The correlation coefficients (R2) of all calibration curves for linear regression were greater than 0.99. The intra- and inter-day precision of XZP-3287 and its metabolites XZP-5286, XZP-3584 and XZP-5736 were determined to be 5.2 %-5.5 %, 14.9 %-10.1 %, 6.9 %-13.8 % and 7.3 %-5.6 %, and their accuracy were determined to be 5.2 %-6.0 % 6.9 %-4.4 %, 11.1 %-5.0 % and 7.4 %-5.6 %, respectively. In conclusion, a method for the simultaneous detection of the pharmacokinetic profiles of XZP-3287 and its metabolites in human plasma had been successfully developed. The results demonstrated the efficacy, sensitivity, and reliability of this method.

The Use of Mutational Signatures to Decipher the Inter-Relationship of Reactive Oxygen Species with Homologous Recombination and Non-Homologous End-Joining Deficiencies as Well as Their Effects on APOBEC Mutagenesis in Breast Cancer

Background: Defective DNA repair systems result in the accumulation of mutations, loss of genomic integrity, and eventually cancer. Following initial malignant transformation due to specific DNA damage and defective DNA repair, cancer cells become reliant upon other DNA repair pathways for their survival. The co-occurrence of specific repair deficiencies brings catastrophic outcomes such as cell death for cancer cells and thus holds promise as a potential therapeutic strategy. Exploring the co-occurrence and mutual exclusivity of mutational signatures provides valuable knowledge regarding combinations of defective repair pathways that are cooperative and confer selective advantage to cancer cells and those that are detrimental and cannot be tolerated by them. Methods: Taking advantage of mutational signature profiling, we analyzed whole-genome sequences of 1014 breast cancers to reveal the underlying mutational processes and their interrelationships. Results: We found an inverse relationship between deficiencies of homologous recombination (HRd) and non-homologous end joining (NHEJd) with reactive oxygen species (ROS). Moreover, HRd and NHEJd co-occurred with APOBEC but were mutually exclusive with mismatch repair deficiency (MMRd) and ROS. Our analysis revealed that SBS8 and SBS39 signatures of currently unknown etiology correlate with NHEJd. ID1 and ID2 signatures co-occur with ROS and have mutual exclusivity with HRd, SBS8, SBS39 and NHEJd. The ID4 signature, with currently unknown etiology, has mutual exclusivity with HRd and NHEJd and co-occurred with ROS. On the other hand, the ID15 signature, with currently unknown etiology, co-occurred with SBS8, SBS39, HRd, NHEJd and DBS2, while having an inverse relationship with MMRd and ROS. Comparing the mutational signatures of HRd and non-HRd TNBC genomes reveals the unique presence of ROS signatures in non-HRd tumors and the lack of ROS signature in HRd tumors. Conclusion: Taken together, these analyses indicate the possible application of mutation signatures and their interactions in advancing patient stratification and suggest appropriate therapies targeting the make-up of individual tumors' mutational processes. Ultimately, this information provides the opportunity to discover promising synthetic lethal candidates targeting DNA repair systems.

Study on predicting breast cancer Ki-67 expression using a combination of radiomics and deep learning based on multiparametric MRI

PURPOSE

To develop a multiparametric breast MRI radiomics and deep learning-based multimodal model for predicting preoperative Ki-67 expression status in breast cancer, with the potential to advance individualized treatment and precision medicine for breast cancer patients.

METHODS

We included 176 invasive breast cancer patients who underwent breast MRI and had Ki-67 results. The dataset was randomly split into training (70 %) and test (30 %) sets. Features from T1-weighted imaging (T1WI), diffusion-weighted imaging (DWI), T2-weighted imaging (T2WI), and dynamic contrast-enhanced MRI (DCE-MRI) were fused. Separate models were created for each sequence: T1, DWI, T2, and DCE. A multiparametric MRI (mp-MRI) model was then developed by combining features from all sequences. Models were trained using five-fold cross-validation and evaluated on the test set with receiver operating characteristic (ROC) curve area under the curve (AUC), accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and F1 score. Delong's test compared the mp-MRI model with the other models, with P < 0.05 indicating statistical significance.

RESULTS

All five models demonstrated good performance, with AUCs of 0.83 for the T1 model, 0.85 for the DWI model, 0.90 for the T2 model, 0.92 for the DCE model, and 0.96 for the mp-MRI model. Delong's test indicated statistically significant differences between the mp-MRI model and the other four models, with P values < 0.05.

CONCLUSIONS

The multiparametric breast MRI radiomics and deep learning-based multimodal model performs well in predicting preoperative Ki-67 expression status in breast cancer.

Optimization of guidelines for Risk Of Recurrence/Prosigna testing using a machine learning model: a Swedish multicenter study

PURPOSE

Gene expression profiles are used for decision making in the adjuvant setting in hormone receptor-positive, HER2-negative (HR+/HER2-) breast cancer. While algorithms to optimize testing exist for RS/Oncotype Dx, no such efforts have focused on ROR/Prosigna. This study aims to enhance pre-selection of patients for testing using machine learning.

METHODS

We included 348 postmenopausal women with resected HR+/HER2-node-negative breast cancer tested with ROR/Prosigna across four Swedish regions. We developed a machine learning model using simple prognostic factors (size, progesterone receptor expression, grade, and Ki67) to predict ROR/Prosigna output and compared the performance regarding over- and undertreatment with commonly employed risk stratification schemes.

RESULTS

Previous classifications resulted in significant undertreatment or large intermediate groups needing gene expression profiling. The machine learning model achieved AUC under ROC of 0.77 in training and 0.83 in validation cohorts for prediction of indication for adjuvant chemotherapy according to ROR/Prosigna. By setting and validating upper and lower cut-offs corresponding to low, intermediate and high-risk disease, we improved risk stratification accuracy and reduced the proportion of patients needing ROR/Prosigna testing compared to current risk stratification.

CONCLUSION

Machine learning algorithms can enhance patient selection for gene expression profiling, though further external validation is needed.

Challenges and barriers for the adoption of personalized medicine in Europe: the case of Oncotype DX Breast Recurrence Score® test

Personalized medicine, aiming to tailor treatments based on individual patient characteristics, holds immense potential in oncology. However, its widespread adoption in Europe faces numerous challenges, as illustrated by the case study of the Oncotype DX Breast Recurrence Score® assay, a genomic test for breast cancer. This manuscript delineates the multifaceted obstacles encountered during the introduction of the Oncotype DX®test (Oncotype DX Breast Recurrence Score test) in Europe from 2004 to 2018. In June 2018, the TAILORx results were published in the New England Journal of Medicine (Sparano JA, Gray RJ, Makower DF, Pritchard KI, Albain KS, Hayes DF, et al. Adjuvant chemotherapy guided by a 21-gene expression assay in breast cancer. N Engl J Med 2018;379:111-21, Sparano JA, Gray RJ, Ravdin PM, Makower DF, Pritchard KI, Albain KS, et al. Clinical and genomic risk to guide the use of adjuvant therapy for breast cancer. N Engl J Med 2019;380:2395-405), and reported that among 6,711 women with hormone-receptor-positive, HER2-negative, node-negative breast cancer and a midrange recurrence score of 11-25 on the Oncotype DX assay, endocrine therapy was not inferior to chemoendocrine therapy, which provides evidence that adjuvant chemotherapy was not beneficial in these patients. Through a comprehensive analysis of clinical evidence, commercial presence, reimbursement mechanisms, guideline recommendations, regulatory pathways, and local experiences, this study sheds light on the intricate dynamics influencing the adoption of personalized medicine technologies. This article examines the various obstacles encountered during the introduction of the Oncotype DX Breast Cancer Assay in Europe from 2004 to 2018. By analyzing clinical evidence, commercial presence, reimbursement mechanisms, guideline recommendations, regulatory pathways, and local experiences, this study reveals the complex factors that influence the adoption of personalized medicine technologies. By highlighting these challenges, this article offers valuable insights into strategies to facilitate the integration of innovative diagnostic tools into clinical practice across Europe, ultimately leading to improved treatment decision-making for cancer patients.

Photoacoustic Imaging with Attention-Guided Deep Learning for Predicting Axillary Lymph Node Status in Breast Cancer

RATIONALE AND OBJECTIVES

Preoperative assessment of axillary lymph node (ALN) status is essential for breast cancer management. This study explores the use of photoacoustic (PA) imaging combined with attention-guided deep learning (DL) for precise prediction of ALN status.

MATERIALS AND METHODS

This retrospective study included patients with histologically confirmed early-stage breast cancer from 2022 to 2024, randomly divided (8:2) into training and test cohorts. All patients underwent preoperative dual modal photoacoustic-ultrasound (PA-US) examination, were treated with surgery and sentinel lymph node biopsy or ALN dissection, and were pathologically examined to determine the ALN status. Attention-guided DL model was developed using PA-US images to predict ALN status. A clinical model, constructed via multivariate logistic regression, served as the baseline for comparison. Subsequently, a nomogram incorporating the DL model and independent clinical parameters was developed. The performance of the models was evaluated through discrimination, calibration, and clinical applicability.

RESULTS

A total of 324 patients (mean age ± standard deviation, 51.0 ± 10.9 years) were included in the study and were divided into a development cohort (n = 259 [79.9%]) and a test cohort (n = 65 [20.1%]). The clinical model incorporating three independent clinical parameters yielded an area under the curve (AUC) of 0.775 (95% confidence interval [CI], 0.711-0.829) in the training cohort and 0.783 (95% CI, 0.654-0.897) in the test cohort for predicting ALN status. In comparison, the nomogram showed superior predictive performance, with an AUC of 0.906 (95% CI, 0.867-0.940) in the training cohort and 0.868 (95% CI, 0.769-0.954) in the test cohort. Decision curve analysis further confirmed the nomogram's clinical applicability, demonstrating a better net benefit across relevant threshold probabilities.

CONCLUSION

This study highlights the effectiveness of attention-guided PA imaging in breast cancer patients, providing novel nomograms for individualized clinical decision-making in predicting ALN node status.

The Value of Whole-Volume Radiomics Machine Learning Model Based on Multiparametric MRI in Predicting Triple-Negative Breast Cancer

OBJECTIVE

This study aimed to investigate the value of radiomics analysis in the precise diagnosis of triple-negative breast cancer (TNBC) based on breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and apparent diffusion coefficient (ADC) maps.

METHODS

This retrospective study included 326 patients with pathologically proven breast cancer (TNBC: 129, non-TNBC: 197). The lesions were segmented using the ITK-SNAP software, and whole-volume radiomics features were extracted using a radiomics platform. Radiomics features were obtained from DCE-MRI and ADC maps. The least absolute shrinkage and selection operator regression method was employed for feature selection. Three prediction models were constructed using a support vector machine classifier: Model A (based on the selected features of the ADC maps), Model B (based on the selected features of DCE-MRI), and Model C (based on the selected features of both combined). Receiver operating characteristic curves were used to evaluate the diagnostic performance of the conventional MR image model and the 3 radiomics models in predicting TNBC.

RESULTS

In the training dataset, the AUCs for the conventional MR image model and the 3 radiomics models were 0.749, 0.801, 0.847, and 0.896. The AUCs for the conventional MR image model and 3 radiomics models in the validation dataset were 0.693, 0.742, 0.793, and 0.876, respectively.

CONCLUSIONS

Radiomics based on the combination of whole volume DCE-MRI and ADC maps is a promising tool for distinguishing between TNBC and non-TNBC.

Drug Interactions With Tamoxifen and Treatment Effectiveness in Premenopausal Breast Cancer Patients: A Bayesian Joint Modeling Approach

PURPOSE

Tamoxifen is guideline treatment for premenopausal women with estrogen receptor-positive (ER+) breast cancer. Therapeutic efficacy relies partly on tamoxifen biotransformation by CYP2D6, CYP2C19, and CYP3A4 enzymes. We conducted a cohort study to evaluate whether concomitant prescription of drugs that inhibit these enzymes impacted breast cancer recurrence.

METHODS

We enrolled 4493 premenopausal women with stage I-III ER+ breast cancer (2002-2011) treated with tamoxifen. We defined time-varying CYP-inhibiting drug exposures as the proportion of overlapping days during the tamoxifen treatment period. We estimated associations of concomitant medication use with recurrence using: (1) Bayesian joint modeling (hazard ratio [HR] and 95% credible intervals [95% CrI]), (2) traditional Cox regression (HR and 95% confidence intervals [95% CI]).

RESULTS

During tamoxifen therapy, 13% of the cohort used strong CYP2D6 inhibitors, 31% weak CYP2D6 inhibitors, 37% CYP2C19 inhibitors, and 12% CYP3A4/5 inhibitors. Bayesian joint models showed that women with ≥ 50% overlap between tamoxifen and CYP2D6 inhibitors had increased recurrence risk compared with 0% overlap (HR: 1.24, 95% CrI: 0.96, 1.58). No recurrence association was seen for CYP2C19 inhibitors (≥ 50% vs. 0%, HR = 1.0, 95% CrI: 0.69, 1.40), but traditional Cox models yielded positive associations for CYP2C19 overlap (≥ 50% vs. 0%, HR = 1.45, 95% CI: 1.07, 1.96). With Bayesian joint models, we observed no association between ≥ 50% versus 0% overlap with CYP3A4/5 inhibitors (HR: 0.84, 95% CrI: 0.32, 1.93).

CONCLUSIONS

With Bayesian joint modeling, we saw a slight increase in recurrence among CYP2D6-inhibitor users, but no increase among CYP2C19- or CYP3A4-inhibitor users. Results from Cox regression models were less plausible.

Therapeutic Ultrasound for Multimodal Cancer Treatment: A Spotlight on Breast Cancer

Cancer remains a leading cause of mortality worldwide, and the demand for improved efficacy, precision, and safety of management options has never been greater. Focused ultrasound (FUS) is a rapidly emerging strategy for nonionizing, noninvasive intervention that holds promise for the multimodal treatment of solid cancers. Owing to its versatile array of bioeffects, this technology is now being evaluated across preclinical and clinical oncology trials for tumor ablation, therapeutic delivery, radiosensitization, sonodynamic therapy, and enhancement of tumor-specific immune responses. Given the breadth of this burgeoning domain, this review places a spotlight on recent advancements in breast cancer care to exemplify the multifaceted role of FUS technology for oncology indications-outlining physical principles of FUS-mediated thermal and mechanical bioeffects, giving an overview of results from recent preclinical and clinical studies investigating FUS with and without adjunct therapeutics in primary or disseminated breast cancer settings, and offering perspectives on the future of the field.

Correlation study of tumor-infiltrating lymphocytes (TILs) and subtypes analysis before and after neoadjuvant chemotherapy in triple-negative breast cancer

Background

In recent years, neoadjuvant chemotherapy (NACT) has become an increasingly important treatment for triple-negative breast cancer (TNBC). As the most immunogenic subtype of breast cancer, it is imperative to comprehensively study the immune microenvironment of TNBC and the effects of NACT on it. Our study aims to address this need and provide valuable insights for the development of effective postoperative adjuvant treatment strategies.

Methods

Samples were taken prior to and following NACT with docetaxel, epirubicin, and cyclophosphamide (TEC) from 71 TNBC patients who were included in this trial. We examined the clinicopathological alterations in patients before and after NACT treatment, assessed the impact of stromal tumor-infiltrating lymphocytes (sTILs) and immune biomarkers [CD8, CD4, CD3, FOXP3, CD20, CD163, programmed cell death ligand 1 (PD-L1)] on the efficacy of neoadjuvant therapy, and identified changes in NACT-induced immune subsets and specific immune biomarkers.

Results

Our study revealed that tumor size, histological grade, Ki-67 status, and sTILs content in baseline clinical features, as well as CD3, CD4, and CD8 content before NACT, showed significant differences between pathological complete response (pCR) and non-pCR patients (P<0.05). The expression of sTILs and PD-L1 in residual lesions after NACT was found to be higher than before NACT. Univariate analysis indicated that the levels of sTILs, CD3, CD4, and CD8 immune subsets before NACT were correlated with pCR. Importantly, multivariate regression analysis demonstrated that sTILs and CD8 immune subsets before NACT served as independent predictors of TNBC neoadjuvant therapy (P<0.05), providing crucial insights into the individualized management of TNBC.

Conclusions

The findings of this study suggest that increasing sTILs and CD8 can significantly enhance the neoadjuvant efficacy of TNBC. Furthermore, the addition of CD3 and CD8 immune subsets can substantially improve the efficacy of TNBC neoadjuvant prediction. The detection of relevant immune markers after neoadjuvant therapy holds great promise in providing more comprehensive and accurate prognostic and therapeutic information for TNBC patients.

Intratumoral and peritumoral radiomics signature based on DCE-MRI can distinguish between luminal and non-luminal breast cancer molecular subtypes

Distinguishing the luminal subtypes of breast cancer (BC) remaining challenging. Thus, the aim of this study was to investigate the feasibility of radiomic signature using intratumoral and peritumoral features obtained from dynamic contrast-enhanced MRI (DCE-MRI) in preoperatively discriminating the luminal from non-luminal type in patients with BC. A total of 305 patients with pathologically confirmed BC from three hospitals were retrospectively enrolled. The LASSO method was then used for selecting features, and the radiomic score (radscore) for each patient was calculated. Based on the radscore, Radiomic signature of intratumoral, peritumoral, and combined intratumoral and peritumoral were established, respectively. The performances of the radiomic signatures were validated with receiver operator characteristic (ROC) curve and decision curve analysis. For predicting molecular subtypes, the AUC for intratumoral radiomic signature was 0.817, 0.838, and 0.883 in the training set, internal validation set, and external validation set, respectively. AUC for the peritumoral radiomic signature was 0.863, 0.895, and 0.889 in the training set, internal validation set, and external validation set, respectively. The AUC for combined intratumoral and peritumoral radiomic signature was 0.956, 0.945, and 0.896 in the training set, internal validation set, and external validation set, respectively. Additional contributing value of combined intratumoral and peritumoral radiomic signature to the intratumoral radiomic signature was statistically significant [NRI, 0.300 (95% CI: 0.117-0.482), P = 0.001 in internal validation set; NRI, 0.224 (95% CI: 0.038-0.410), P = 0.018 in external validation set]. These results indicated that the radiomic signature combining intratumoral and peritumoral features showed good performance in predicting the luminal type of breast cancer.

Predicting sentinel lymph node metastatic burden with intravoxel incoherent motion diffusion-weighted imaging and dynamic contrast-enhanced magnetic resonance imaging in clinical early-stage breast cancer patients

PURPOSE

The goal of this study was to investigate the value of IVIM-MRI and DCE-MRI in predicting SLN metastatic burden in clinical practice for early-stage breast cancer patients.

METHODS

The clinicopathologic and MRI data from 132 early-stage breast cancer patients were retrospectively reviewed and analyzed using logistic regression to identify risk factors for a high SLN metastatic burden. The diagnostic performance of those factors was then assessed via receiver operating characteristic (ROC) curve analysis.

RESULTS

Lymphovascular invasion (OR, 0.220; 95 % CI, 0.076-0.642; p = 0.006), Ktrans (OR, 0.971; 95 % CI, 0.944-0.998; p = 0.034) and D (OR, 1.010; 95 % CI, 1.003-1.017; p = 0.004) were independently associated with high metastatic burden. The area under the curve (AUC) for combined MRI & pathologic features (0.893; 95 % CI, 0.830-0.956; p < 0.001) and combined MRI (0.870; 95 % CI, 0.802-0.937; p < 0.001) was significantly higher than for each single MRI parameter alone (p = 0.002, 0.004), while the difference in AUCs between the combined MRI & pathologic features and combined MRI was not significant ((p = 0.154).

CONCLUSION

IVIM-MRI and DCE-MRI can be used to predict SLN metastatic burden in early-stage breast cancer patients in clinical practice.

The treatment of breast cancer in the era of precision medicine

The management of breast cancer, one of the most common and heterogeneous malignancies, has transformed with the advent of precision medicine. This review explores current developments in genetic profiling, molecular diagnostics, and targeted therapies that have revolutionized breast cancer treatment. Key innovations, such as cyclin-dependent kinases 4/6 (CDK4/6) inhibitors, antibody-drug conjugates (ADCs), and immune checkpoint inhibitors (ICIs), have improved outcomes for hormone receptor-positive (HR+), HER2-positive (HER2+), and triple-negative breast cancer (TNBC) subtypes remarkably. Additionally, emerging treatments, such as PI3K inhibitors, poly (ADP-ribose) polymerase (PARP) inhibitors, and mRNA-based therapies, offer new avenues for targeting specific genetic mutations and improving treatment response, particularly in difficult-to-treat breast cancer subtypes. The integration of liquid biopsy technologies provides a non-invasive approach for real-time monitoring of tumor evolution and treatment response, thus enabling dynamic adjustments to therapy. Molecular imaging and artificial intelligence (AI) are increasingly crucial in enhancing diagnostic precision, personalizing treatment plans, and predicting therapeutic outcomes. As precision medicine continues to evolve, it has the potential to significantly improve survival rates, decrease recurrence, and enhance quality of life for patients with breast cancer. By combining cutting-edge diagnostics, personalized therapies, and emerging treatments, precision medicine can transform breast cancer care by offering more effective, individualized, and less invasive treatment options.

Framework nucleic acid-programmed aptamer-paclitaxel conjugates as targeted therapeutics for triple-negative breast cancer

Triple-negative breast cancer (TNBC) is highly invasive with a poor prognosis, and chemotherapy remains the clinical treatment of choice. Paclitaxel is a commonly used first-line chemotherapy drug, but its untargeted distribution poses clinical challenges. Inspired by antibody-drug conjugates, we develop a precisely structured framework nucleic acid-programmed aptamer-paclitaxel conjugate (FAPC) with chemically well-defined paclitaxel loading dosing, enabling the regulation of receptor-aptamer affinity to facilitate tumor-targeted chemotherapy. Utilizing framework nucleic acids as a precise addressing scaffold, we organize the AS1411 aptamer with accurate intermolecular spacing and find that an inter-aptamer spacing of 19.04 nm could enhance the affinity of the FAPC for tumor cells. Then, the multifunctional FAPC can disrupt actin reorganization to achieve cytotoxicity in tumor cells. Furthermore, the AS1411-specifically modified FAPC further enhances the structure-dependent selective accumulation of drugs at tumor sites in a human xenograft model of triple-negative breast cancer, subsequently leading to significantly improved antitumor efficacy and reduced toxicity. The FAPC provides a precisely programmable platform for efficient targeted delivery of chemotherapeutic agents to malignancies.

Breast cancer-specific survival by molecular subtype in different age groups of women in Scotland

BACKGROUND

Age and molecular subtypes are important prognostic factors in breast cancer (BC). Here, we explore how age and molecular subtypes influence BC survival in Scotland.

METHODS

We analysed data from 71,784 women diagnosed with invasive BC in Scotland between 1997 and 2016, with follow-up until 31st December 2018 (median follow-up time = 5.5 years). Cox models estimated Hazard Ratios (HR) for BC-specific death by age group (with women of screening age, 50-69 years old, as the reference) within each molecular subtype, adjusting for prognostic factors. The cumulative incidence function was plotted to account for competing risks.

RESULTS

During the study period, 37% of women died, with 53% of deaths attributed to BC. Women aged 70 + years had increased BC-specific death compared to women aged 50 to 69 years with the same subtype. HRs (95% CI) were 1.49 (1.23-1.80) for luminal A, 1.39 (1.14 to 1.69) for luminal B tumours and 1.49 (1.15 to 1.94) for triple negative breast cancer (TNBC). Women aged < 50 years had lower risk of BC death in luminal A subtype only, with HR of 0.66 (0.51-0.86) compared to women aged 50 to 69 years. Competing risks analysis showed higher cumulative incidence of death from non-BC causes, particularly for women aged 70 + years with hormone positive subtypes. Stage, treatment, and molecular subtype were the strongest prognostic factors for BC-specific mortality across all ages.

CONCLUSIONS

Age influences BC-specific mortality particularly within luminal subtypes. In contrast, other tumour characteristics and treatment are key prognostic factors for non-luminal subtypes. Future studies should investigate other markers of BC mortality particularly among over 70-year-olds, who account for 60% of BC deaths in the UK.

Interpretable model based on MRI radiomics to predict the expression of Ki-67 in breast cancer

This study aimed to develop an interpretable machine learning model that accurately predicts Ki-67 expression in breast cancer (BC) patients using a combination of dynamic-contrast enhanced magnetic resonance imaging (DCE-MRI) radiomics and clinical-imaging features. A total of 195 BC patients, including 201 lesions, were enrolled retrospectively. These lesions were randomized into training and testing set (7:3). The correlation between clinical-imaging features and Ki-67 expression was analyzed via univariate analysis and binary logistic regression, leading to the development of a Clinical-imaging model. Radiomics features were extracted based on the early and delayed phases of DCE-MRI. These features were screened by Pearson correlation coefficient and recursive feature elimination (RFE). The logistic regression classifier was used to develop the Radiomics model. The clinical imaging and radiomics features were combined to form a Combined model. The Shapley Additive Explanation (SHAP) algorithm was employed to explain the optimal model, and the AUC was used to assess the model's performance. Ki-67 expression was markedly different from the internal enhancement pattern and necrosis among the imaging features. Compared to the Clinical-imaging model (AUC = 0.682), the AUCs of the Radiomics and the Combined models in the training set were 0.797 and 0.821, respectively. Clinical-imaging, Radiomics, and Combined models had AUCs of 0.666, 0.796, and 0.802 in the test set. Based on the IDI results, the combined model outperformed the Clinical-imaging and Radiomics models in the training set by 11.8% and 2.1%, respectively. They increased by 11% and 1.74% in the test set. SHAP analysis showed that ph2-original-shape-surface volume ratio was the most important feature of the model. Based on clinical-imaging features and DCE-MRI radiomics, the interpretable machine learning model can accurately predict the expression of Ki-67 in BC. Combining the SHAP algorithm with the model improves its interpretability, which may assist clinicians in formulating more accurate treatment strategies.

Application of DCE-MRI radiomics and heterogeneity analysis in predicting luminal and non-luminal subtypes of breast cancer

Purpose

The aim of this study was to explore the application value of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) radiomics and heterogeneity analysis in the differentiation of molecular subtypes of luminal and non-luminal breast cancer.

Methods

In this retrospective study, 388 female breast cancer patients (48.37 ± 9.41 years) with luminal (n = 190) and non-luminal (n = 198) molecular subtypes who received surgical treatment at Jilin Cancer Hospital were recruited from January 2019 to June 2023. All patients underwent breast MRI scan and DCE scan before surgery. The patients were then divided into a training set (n = 272) and a validation set (n = 116) in a 7:3 ratio. The three-dimensional texture feature parameters of the breast lesion areas were extracted. Four tumor heterogeneity parameters, including type I curve proportion, type II curve proportion, type III curve proportion and tumor heterogeneity values were calculated and normalized. Five machine learning (ML) models, including the logistic regression, naive Bayes algorithm (NB), k-nearest neighbor (KNN), decision tree algorithm (DT) and extreme gradient boosting (XGBoost) model were used to process the training data and were further validated. The best ML model was selected according to the performance in the validation set.

Results

In luminal subtype breast lesions, type III curve proportion and heterogeneity index were significantly lower than the corresponding parameters of the non-luminal subtype lesions both in the training set and validation set. Eight features together with four heterogeneity-related parameters with significant differences between luminal and non-luminal groups were retained as radiomics signatures for constructing the prediction model. The logistic regression ML model achieved the best performance in the validation set with the highest area under the curve value (0.93), highest accuracy (86.94%), sensitivity (87.55%) and specificity (86.25%).

Conclusion

The radiomics and heterogeneity analysis based on the DCE-MRI exhibit good application value in discriminating luminal and non-luminal subtype breast cancer. The logistic regression model demonstrates the best predictive performance among various machine learning models.

Oncologic Outcomes of Young Breast Cancer Patients According to Tumor Biology

BACKGROUND/OBJECTIVES

Young women frequently present with more aggressive breast cancer tumors. This retrospective study analyzed the oncological outcomes of patients under the age of 40 according to the tumor biology.

METHODS

Group comparisons were performed via the log-rank test. Recurrence and survival rates are presented according to the Kaplan-Meier method.

RESULTS

In total, 88 women (mean age 36) were included, but two presented with bilateral cancer, resulting in 90 tumors. Triple-negative carcinoma was most common, with 26.7% (n = 24); 11.1% (n = 10) were luminal A; 23.3% (n = 21) were luminal B HER2-negative; 15.6% (n = 14) were luminal B HER2-positive; and 6.7% (n = 6) were HER2-positive (non-luminal). Moreover, 26.1% (n = 23) of patients experienced recurrence (mean 40 months), with the highest recurrence rate in the HER2-positive (50%) and triple-negative (30.4%) groups. The 3- and 5-year recurrence-free survival rates were 84.9% and 77.3%, and the overall survival rates were 93.1% and 90.3%, respectively. No statistically significant differences in oncological outcomes were observed (p = 0.164).

CONCLUSIONS

The results show that young women tend to have triple-negative and fast-growing breast carcinomas, with worse overall survival in the triple-negative group. More research is needed on the pathomechanisms of breast cancer development in young women, especially those leading to disease progression and resistance to therapy.

Comparative Analysis of Clinical Efficacy and Safety of Pyrotinib Plus Capecitabine versus Trastuzumab Emtansine (T-DM1) as Second-Line Treatment for HER2-Positive Advanced Breast Cancer: A Retrospective Study

Background

HER2-positive advanced breast cancer poses significant treatment challenges. In China, T-DM1 and pyrotinib are key second-line therapies. A comprehensive evaluation of the comparative efficacy and safety profiles of these therapies is imperative for optimizing therapeutic strategies and enhancing patient outcomes. This study aims to compare the clinical efficacy and safety of T-DM1 against pyrotinib plus capecitabine.

Methods

Patients are females with HER2-positive, locally advanced, or metastatic breast cancer who at least 18 years old and have received anti-HER2 therapy in the past. This study included 148 patients who satisfied the inclusion criteria. Of these, 74 patients received intravenous T-DM1 (3.6 mg/kg) every 21 days, while the other 74 patients got oral pyrotinib (400 mg, once daily) plus capecitabine (1000 mg/m2, twice daily on days 1-14 of each 21-day cycle). Progression-free survival (PFS) was the main outcome, while overall survival (OS), objective response rate (ORR), disease control rate (DCR), and adverse events (AEs) were the secondary outcomes.

Results

The median PFS was 12.2 months for the pyrotinib group vs 9.1 months for the T-DM1 group. The median follow-up was 12.7 months for pyrotinib and 9.3 months for T-DM1. The pyrotinib group had better DCR (56.8% vs 54.1%) and ORR (40.5% vs 29.7%). While adverse events were manageable, the most common severe AE in the pyrotinib group was diarrhea (24.3%), and in the T-DM1 group, it was thrombocytopenia (16.2%). However, by reducing the drug dosage or providing symptomatic treatment, most adverse events could be controlled at grades 1 to 2, indicating that the adverse events were manageable. Neither group recorded any adverse event-related deaths.

Conclusion

Pyrotinib plus capecitabine significantly improves median PFS compared to T-DM1 in patients with HER2-positive advanced breast cancer, demonstrating a favorable efficacy profile alongside manageable safety concerns.

Role of Progesterone Receptor Level in Predicting Axillary Lymph Node Metastasis in Clinical T1-T2N0 Luminal Type Breast Cancer

Background and Objectives: Axillary lymph node metastasis and the number of metastatic lymph nodes are important prognostic factors which are directly related to overall survival in women with breast cancer. Several factors have been identified to predict the likelihood of axillary lymph node metastasis in early-stage breast cancer. High PR expression is often more prevalent in the luminal A subgroup, which is associated with a better prognosis. The aim of this study was to determine the relationship between the percentage of PR expression and the likelihood of axillary metastasis in Her-2-negative, clinical T1-T2N0 luminal type breast cancer. Materials and Methods: A hundred and ninety-nine cases with luminal type, Her-2-negative, clinically and radiologically axilla-negative T1-T2 breast cancer who received radiotherapy were evaluated retrospectively. The pathological specimens were assessed by an experienced pathologist. Results: The statistical evaluation showed that tumor diameter greater than 2 cm, (p = 0.003), presence of lymphovascular invasion (p = 0.001), and PR expression level below 80% (p = 0.037) were identified as significant predictors of lymph node positivity in breast cancer patients. Conclusions: Percentage of progesterone receptor expression along with other molecular biological markers and clinicopathological parameters should be evaluated altogether when predicting axillary metastasis risk before surgery.

Distinct proteomic profiles of plasma-derived extracellular vesicles in healthy, benign, and triple-negative breast cancer: candidate biomarkers for liquid biopsy

Extracellular vesicles (EVs) derived from plasma, measuring up to 150 nm, act as molecular messengers transmitting critical information to recipient cells, making them valuable candidates for liquid biopsy applications in cancer diagnostics. Triple-negative breast cancer (TNBC) is particularly challenging due to its aggressive nature, metastasis potential, and limited treatment options. This study aimed to identify EV-associated proteins in blood samples that could serve as potential TNBC biomarkers. Using mass spectrometry-based proteomic analysis, we detected unique and differentially expressed proteins across healthy individuals, patients with benign breast conditions, and those with TNBC. While EVs size and concentration showed no differences, the proteomic profile varied significantly among these groups. Several immune-related proteins were found exclusively in healthy individuals but were diminished in both benign and malignant cases. We also assessed the impact of surgery on EVs protein content and identified Histone H2A as a TNBC-specific marker present before surgery. Its expression was further validated through immunohistochemistry and Western blotting in TNBC biopsies and cell lines. Notably, surgical intervention enhanced immune response pathways in TNBC patients. In conclusion, liquid biopsy has the potential to serve as a non-invasive tool for TNBC diagnosis and monitoring, revealing a post-surgery molecular landscape that supports combining immunotherapy with mastectomy.

High Ki-67 expression is associated with increased risk of distant recurrence in Oncotype Dx low risk breast cancer

PURPOSE

To assess whether high Ki-67 protein expression level could independently predict the distant recurrence in early-stage breast cancer with low Oncotype Dx scores (≤ 25).

METHODS

This single-center retrospective cohort study included 278 patients with hormone receptor positive (HR+) human epidermal growth factor receptor 2 negative (HER2-), T1-2N0M0, low Oncotype Dx recurrence score (RS) (≤ 25) breast cancer. We identified 2 groups: "high Ki-67″ ≥ 15% (n = 130, 47%) and "low Ki-67″ < 15% (n = 148, 53%). Clinical characteristics, treatment and survival were abstracted from chart review. Fisher's exact test was used to assess differences between Ki-67 groups. Cox-regression models were used to assess differences in survival.

RESULTS

After a median follow up of 7 years, 13 (4.7%) patients experienced distant metastasis. Recurrence rate was significantly higher in the "high Ki-67″ group 9.2% (12/130) versus the "low Ki-67″ group 0.7% (1/148) (P = .001). Distant metastasis-free survival (dMFS) was significantly shorter in the "high Ki-67″ group (HR 12.90, 95% CI, 12.53-13.27, P = .008). Tumor size ≥ 2 cm was associated with shorter dMFS (HR, 12.90; 95% CI, 12.53-13.27; P < .001). In a multivariable analysis, tumor size ≥ 2 cm and "High Ki-67″ were independent prognosis factors for dMFS.

CONCLUSION

Ki-67 expression level may help to identify a subset of low risk Oncotype Dx patients who could benefit from adjuvant chemotherapy.

Comprehensive germline and somatic profiling of high-risk Thai breast cancer via next-generation sequencing

Breast cancer genomic landscapes differ across ethnic groups, yet the somatic profile of Thai breast tumours has remained uncharacterised. This study analysed 1676 high-hereditary-risk Thai breast cancer patients, identified according to National Comprehensive Cancer Network (NCCN) guideline. Germline alterations were assessed in 1370 cases using a custom 36-core cancer panel. Somatic mutations were characterised in formalin-fixed, paraffin-embedded tumour tissues from 180 of the 1676 patients using the 501-gene Oncomine Comprehensive Assay Plus panel. Pathogenic or likely pathogenic (P/LP) variants were detected in 13% of the 1370 germline analyses, with BRCA1 and BRCA2 being the most frequently altered genes. The prevalence of P/LP variants in BRCA1, BRCA2, and PALB2 differed from that observed in other ethnic cohorts. In somatic profiling, TP53 emerged as the most frequently mutated gene, especially in HER2 and TNBC tumours, whereas MAP3K1 and GATA3 were the most frequently mutated genes in the HR+/HER2- tumours. Moreover, hormone-receptor-positive (HR+) tumours showed distinct mutation patterns compared with other ethnicities. Notably, germline carriers exhibited lower PIK3CA mutation rates than non-carriers. These findings advance our understanding of Thai breast cancer genomics and underscore the importance of ethnic diversity in cancer research, offering insights into tailored screening and therapeutic approaches.

Characteristics of premenopausal breast cancer patients with a midrange 21-gene recurrence score

Purpose

The results of the TAILORx trial have shown that premenopausal patients with intermediate Oncotype Dx (ODx) recurrence score of 16-25 may benefit from adjuvant chemotherapy. In addition, the clinicopathological features showed the information complementary to ODx results. However, the characteristics may vary depending on menopausal status even in the same score. This study aimed to analyze the differences in the clinical characteristics by menopausal status.

Methods

This study conducted a retrospective analysis of 756 patients with estrogen receptor-positive, human epidermal growth factor receptor 2-negative, and node-negative breast cancer who underwent the ODx test from July 2013 to December 2020 at the Severance Hospital.

Results

Of the 756 patients, 261 patients were postmenopausal, and 495 were premenopausal. The premenopausal patients with a midrange ODx had similar clinicopathological features as compared to those with a high ODx. Conversely, the postmenopausal patients with a midrange ODx did not show significantly different clinicopathological features from those with a low ODx, whereas a difference was seen as compared to those with a high ODx.

Conclusion

In this study, unlike the postmenopausal patients, some of the clinicopathological characteristics of the premenopausal patients with a midrange ODx were closer to those with a high ODx than those with a low ODx. In the premenopausal patients with a midrange ODx, considering the baseline characteristic itself, there was a significant difference between those with a low ODx when compared with postmenopausal patients. Therefore, more aggressive treatment decisions may be helpful in premenopausal patients with a midrange ODx.

Breast cancer staging with contrast-enhanced imaging. The benefits and drawbacks of MRI, CEM, and dedicated breast CT

Pretherapeutic breast cancer staging is pivotal for patient-centered disease management, guiding treatment stratification and assessing prognostic outcomes. Breast imaging plays a key role in both anatomic and prognostic staging by providing comprehensive insights into disease extent, localization, and tumor aggressiveness characteristics. To date, clinical image-based staging relies on conventional modalities such as digital mammography (DM), digital breast tomosynthesis (DBT), and ultrasound. Considering the phenotypic disparities of breast cancer and their relation to treatment response, other imaging techniques based on contrast-enhanced mechanisms, which highlight areas of increased neovascularity, appear indispensable in breast cancer staging. Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) offers a comprehensive 3-dimensional assessment of extent of disease and characterization of lesions, capturing both morphological and functional aspects which are crucial for treatment and monitoring of the disease. Based on this established approach, contrast-enhanced x-ray-based techniques, with high spatial resolution, such as contrast-enhanced mammography (CEM) and dedicated contrast-enhanced breast computed tomography (dCEBCT), have emerged. This review outlines the current status, limitations, and ongoing challenges associated with each one contrast-enhanced imaging modality, while emphasis is given to key breast cancer manifestations and the optimal interpretation of their imaging phenotypes, in the current era of image-based (anatomic and prognostic) breast cancer staging.

Molecular Subtypes and Ki-67 index in Breast Carcinoma with Special Emphasis on Triple Negative Breast Cancer. A 3-year Study in a Tertiary Care Center

Purpose

Molecular subtyping of breast carcinoma and Ki-67 index has gained prominence in the recent past, as conventional factors such as surgical margins, tumor size, grade and lymph node involvement, are not sufficient to assess prognosis and make better therapeutic decisions. These subtypes include Luminal A, Luminal B, Triple Negative breast cancer (TNBC), and HER2-enriched subtypes. This study aimed to analyze the molecular subtypes and Ki-67 index in prognosis of breast carcinoma.

Method

This retrospective study was conducted in the department of Pathology in a tertiary care center over a period of 3 years. All invasive breast carcinomas (IDC) which were molecularly subtyped and Ki-67 indexed were included in the study. Statistical analysis was done using SPSS software.

Results and Discussion

Out of 253 cases, 231 cases (91.3%) were IDC-NST and 22 cases (8.7%) were special types. Metaplastic and papillary tumors were associated with higher grade and high Ki-67 value. TNBC (35.2%) showing a majority of high-grade tumors, was the most prevalent subtype followed by Luminal A (32%) showing low grade, unlike other studies which showed luminal A to be most common subtype. The rare PR positive subtype was also observed in our study.

Conclusion

TNBC and HER 2-positive subtypes exhibited bad prognosis with higher histological grade, high Ki-67 index and higher age at presentation whereas Luminal A subtype, with lower grade and low Ki-67 index showed better prognosis. Thus, this vast array of predictive and prognostic information obtained by molecular subtyping will help clinicians in not only distinguishing between low-risk and high-risk subtypes but also in customization of the treatment and follow-up of the patients.

Retrospective Case-Cohort Study on Risk Factors for Developing Distant Metastases in Women With Breast Cancer

OBJECTIVE

This study aimed to identify risk factors associated with the development of metastases in breast cancer patients, to investigate survival rates, and the relationship between local recurrences and distant metastases.

METHODS

This retrospective case-cohort study included women with breast cancer who were treated at a certified Breast Unit between 2001 and 2015. Cases who developed distant metastases were compared to controls based on diagnosis year, stage, and age at diagnosis. Comprehensive information on patient characteristics, tumor biology, and treatment options was gathered.

RESULTS

The study included 412 patients who developed distant metastases and 433 controls who remained metastasis-free over a median follow-up of 150 months (interquartile range 87-202). The 20-year overall survival was 99.23% for the control group and 23.62% for those with metastasis (p < 0.01). Significant risk factors for metastasis included lobular invasive carcinoma (odds ratio (OR) 2.26, p < 0.001), triple-negative subtype (OR 4.06, p = 0.002), high tumor grade (OR 2.62, p = 0.004), larger tumor size (OR 1.02, p < 0.001), lymph node involvement (p < 0.001), and loco-regional recurrence (OR 4.32, p < 0.001). Progesterone receptor (PR) expression was protective (OR 0.52, 95% confidence interval 0.34-0.81, p = 0.003). Machine learning models supported these findings, though their clinical significance was limited.

CONCLUSIONS

Lobular invasive carcinoma, specific tumor subtypes, high grade, large tumor size, lymph node involvement, and loco-regional recurrence are all significant risk factors for distant metastasis, whereas PR expression is protective. The potential of machine learning in predicting metastasis was explored, showing promise for future personalized risk assessment.

Prediction of Response to Anti-HER2 Therapy Using A Multigene Assay

HER2-positive breast cancer (BC), which constitutes 13% to 15% of cases, shows variable response to anti-HER2 therapies. HER2 positivity, defined as protein overexpression (immunohistochemistry [IHC] score 3+) or equivocal expression (IHC 2+) with evidence of HER2 gene amplification, determines the eligibility for anti-HER2 therapy. The MammaTyper assay (Cerca Biotech GmbH) is an RT-qPCR BC subtyping platform based on the micro RNA expression of ERBB2, ESR1, PGR, and MKI67. This study aimed to evaluate the accuracy of the MammaTyper assay in predicting the response of HER2-positive patients to therapy. A well-characterized HER2-positive BC cohort of 287 cases diagnosed at Nottingham University hospitals between 2006 and 2018 was included. The cohort was divided into 2 groups: a trastuzumab-treated group (n = 159) and a chemotherapy-only treated group (n = 128). Tumor clinicopathologic characteristics were matched between the 2 groups. Cases with discordant HER2 status were validated through staining of surgical excision specimens. ERBB2 micro RNA identified 251/287 (87.5%) cases as HER2-positive, 10.8% (31/287) as HER2 low and 1.7% (5/287) as HER2 negative. According to the MammaTyper assay, ERBB2-positive patients treated with anti-HER2 therapy had significantly prolonged 5-year disease-free survival and distant metastasis-free survival (hazard ratio = 0.56, P = .003 and hazard ratio = 0.62, P = .023, respectively). MammaTyper-defined HER2-enriched subtype showed a better response to anti-HER2 therapy compared with IHC-defined subtypes, with significant differences in both 5-year disease-free survival and BC-specific survival (P = .01 and < .001, respectively). Patients who were ERBB2 negative did not show a survival difference between the group of patients who were treated with trastuzumab and those who were treated with chemotherapy only (P > .05). Validation analysis revealed that 11/36 ERBB2-negative cases were IHC 2+/ISH positive with very low level of gene amplification and 25 cases were false classified as HER2-positive using current protocols. Combining the MammaTyper assay with IHC to assess HER2 status improves the identification of HER2-positive patients with BC who would benefit from anti-HER2 therapy.

Real World Evidence Study to Assess Incidence, Treatment Patterns, Clinical Outcomes, and Health Care Resource Utilization in Early-Stage, High-Risk HER2-Negative Breast Cancer in Alberta, Canada

BACKGROUND

Data are needed to improve the current understanding of the epidemiology of patients with high-risk, HER2-negative, early breast cancer (eBC) (hormone receptor positive [HR+]/HER2-negative BC and triple-negative BC [TNBC]).

PATIENTS AND METHODS

This retrospective longitudinal cohort study used real-world, population-level data that included all individuals newly diagnosed with high-risk, HER2-negative eBC in Alberta, Canada, between 2010 and 2019. Data on treatment, laboratory results and pathology findings were collected through electronic health records and administrative databases.

RESULTS

The annual cumulative incidence of high-risk, HER2-negative eBC ranged from 6% to 9% of all incident BC cases. Individuals with TNBC were more likely to be younger, had stage II disease, grade 3 histology and received systemic therapy at a community centre (P < .05) compared to individuals with HR+/HER2-negative eBC. Only 14% of individuals diagnosed in 2010-2017 underwent germline BRCA testing postdiagnosis. Neoadjuvant systemic therapy was given to 37% of individuals. Adjuvant systemic therapy use increased from 77% (2012-2015) to 84% (2019). The 5-year overall survival (OS) from initiation of adjuvant systemic therapy or date of surgery (for individuals who did not receive adjuvant systemic therapy) was 77% (95% CI: 75-79). OS was significantly worse among individuals who were older, had grade 3 histology, had stage III disease, or had nodal involvement (P < .05). OS among individuals with TNBC between 2016 and 2019 who initiated adjuvant capecitabine was markedly worse compared to the overall cohort (2-year OS: 70% vs. 89%).

CONCLUSION

Outcomes analyses in this high-risk, HER2-negative eBC population suggest a continued unmet clinical need.

Radiomics-clinical nomogram for preoperative tumor-node-metastasis staging prediction in breast cancer patients using dynamic enhanced magnetic resonance imaging

Background

Breast cancer is one of the most commonly diagnosed malignancies in women worldwide, and the disease burden continues to aggravate. The tumor-node-metastasis (TNM) staging information is crucial for oncology physicians to develop appropriate clinical strategies. This study aimed to investigate the value of a radiomics-clinical model for predicting TNM stage in breast cancer patients using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI).

Methods

DCE-MRI images from 166 patients with pathologically confirmed breast cancer were retrospectively collected, including early stage (TNM0-TNM2) and locally advanced or advanced stage (TNM3-TNM4). Included patients were divided into a training cohort (n=116) and a test cohort (n=50). The radiomics, clinical and integrated models were constructed and a nomogram was established to distinguish the TNM0-TNM2 stage from the TNM3-TNM4 stage. Receiver operating characteristic (ROC) curves, calibration curves and decision curve analysis (DCA) were employed to assess the predictability of the models.

Results

Eighty-five patients were at the early stages, while 81 patients were at the other stages. In the training and test cohorts, the area under the curve (AUC) values for distinguishing early and advanced breast cancer were 0.870 and 0.818 for the nomogram, respectively. The nomogram calibration curves showed good agreement between the predicted and observed TNM stages in the training and test cohorts. The Hosmer-Lemeshow test showed that the nomogram fit perfectly in the two cohorts. DCA indicated that the nomogram displayed clear superiority in forecasting TNM staging over clinical and radiomic signatures.

Conclusions

Compared to traditional imaging methods, the clinical-radiomics nomogram acquired by DCE-MRI could potentially be utilized to preoperatively evaluate the TNM stage of breast cancer with relatively high accuracy. It can be an effective method to guide clinical decisions.

Digital mammography with AI-based computer-aided diagnosis to predict neoadjuvant chemotherapy response in HER2-positive and triple-negative breast cancer patients: comparison with MRI

OBJECTIVE

To investigate whether digital mammography (DM) with artificial intelligence-based computer-aided diagnosis (AI-CAD) predicts pathologic complete response (pCR) after neoadjuvant chemotherapy (NAC) in human epidermal growth factor receptor 2 (HER2)-positive and triple-negative (TN) breast cancers and compare performance with dynamic contrast-enhanced (DCE)-MRI.

MATERIALS AND METHODS

In this single-center study, patients who underwent NAC and surgery for HER2-positive or TN cancers between September 2020 and August 2021 were retrospectively selected to develop prediction models for pCR after NAC. From a prospective ASLAN (Avoid axillary Sentinel Lymph node biopsy After Neoadjuvant chemotherapy) trial, HER2-positive and TN cancer patients who underwent NAC and surgery between December 2021 and July 2022 were prospectively selected for model validation. Clinical-pathologic data and DM and MRI scans were obtained before and after NAC. Logistic regression analyses identified factors associated with pCR for model development and four models (clinical-pathologic, MRI, DM-AI-CAD, and combined) were evaluated.

RESULTS

A total of 259 women (mean age, 53 years ± 10.5 [SD]) constituted the development cohort and 119 (50.8 years ± 11.1) the validation cohort. Age, clinical N stage, estrogen receptor, progesterone receptor, and Ki-67 were incorporated into the clinical-pathologic model. In the validation cohort, the DM-AI-CAD model, applying AI-CAD score ≤ 16 on post-NAC DM as the radiologic CR criterion, showed a higher area under the receiver operating characteristic curve (AUC) compared to the clinical-pathologic model (0.72 vs. 0.62; p = 0.01) for pCR. However, the MRI model showed the highest AUC (0.83), then the combined model (0.78).

CONCLUSION

The model utilizing post-NAC DM with AI-CAD score ≤ 16 predicted pCR more accurately than the clinical-pathologic model in HER2-positive and TN cancers but was inferior to the MRI model.

KEY POINTS

Question The performance of digital mammography (DM) with AI-based computer-aided diagnosis (AI-CAD) for predicting pathologic complete response (pCR) after neoadjuvant chemotherapy (NAC) is unclear. Findings The DM-AI-CAD model incorporating AI-CAD score ≤ 16 on post-NAC DM predicted pCR more accurately than the clinical-pathologic model but not the MRI model. Clinical relevance The DM-AI-CAD model has potential to predict pCR after NAC in breast cancer patients for whom MRI is unavailable or contraindicated.

Imaging Microstructural Parameters of Breast Tumor in Patient Using Time-Dependent Diffusion: A Feasibility Study

Objectives: To explore the feasibility of time-dependent diffusion in clinical applications of breast MRI, as well as the capacity of quantitative microstructural mapping for characterizing the cellular properties in malignant and benign breast tumors. Methods: 38 patients with 45 lesions were enrolled. Diffusion MRI acquisition was conducted with a combination of pulsed gradient spin-echo sequences (PGSE) and oscillating gradient spin-echo (OGSE) on a 3T MRI scanner. The microstructural parameters including cellularity extracellular diffusivity (Dex), mean cell size, intracellular volume fraction (νin), and the apparent diffusion coefficient (ADC) values were calculated. Each parameter was compared using the unpaired t-test between malignant and benign tumors. The area under the receiver operating characteristic curve (AUC) values was used to evaluate the diagnostic performance of different indices. Results: The mean diameter, Dex, ADC0Hz, ADC25Hz, and ADC50Hz were significantly lower in the malignant group than in the benign group (p < 0.001), while νin and cellularity were significantly higher in the malignant group (p < 0.001). All the microstructural parameters and time-dependent ADC values achieved high accuracy in differentiating between malignant and benign tumors of the breast. For microstructural parameters, the AUC of the cellularity was greater than others (AUC = 0.936). In an immunohistochemical subgroup comparison, the PR-positive group had significantly lower νin and cellularity, and significantly elevated Dex and ADC0Hz compared to the negative groups (p < 0.05). When combining diffusion parameters (cellularity, diameter, and ADC25Hz), the highest diagnostic performance was obtained with an AUC of 0.969. Conclusions: DWI with a short diffusion time is capable of providing additional microstructural parameters in differentiating between benign and malignant breast tumors. The time-dependent diffusion MRI parameters have the potential to serve as a non-invasive tool to probe the differences in the internal structures of breast lesions.

Incidence and Prognostic Significance of Hormonal Receptors and HER2 Status Conversion in Recurrent Breast Cancer: A Retrospective Study in a Single Institute

Background and Objectives: Changes in biomarker status are not rare and usually occur in an unfavorable direction. Evaluating changes in biomarker status is advantageous for assessing treatment options and prognosticating patients. Currently, only a few studies have explored the association between biomarker conversion and breast cancer relapse. In this study, we sought to determine the incidence of receptor conversions in patients diagnosed with recurrent breast cancer in comparison to their corresponding primary tumors and to evaluate possible influencing factors. Moreover, we aimed to assess the prognostic significance of biomarker conversion, if any was detected, in breast cancer patients. Materials and Methods: A retrospective cohort study was conducted among breast cancer patients treated at King Khalid University Hospital, Riyadh, Saudi Arabia. Data were collected from recurrent breast cancer patients about different parameters to assess the incidence of hormonal receptors and human epidermal growth factor 2 (HER2) status conversion between primary and recurrent tumors. The calculation of progression-free survival (PFS)/ relapse-free survival (RFS) and the overall survival (OS) was conducted to assess the prognostic value of the assessed biomarker conversion. Results: Progesterone receptor (PR) conversion had the highest incidence (29.9%), followed by HER2 (23%) and estrogen receptor (ER) (12.6%). Menopausal status and concurrent receptor conversion were significant factors influencing receptor status changes. However, no significant associations were found between receptor conversion and other clinical factors, including tumor stage and histological subtype. The survival analysis revealed no statistically significant differences in OS or RFS between patients with and without receptor conversion. Conclusions: Receptor conversion, particularly for PR and HER2, is common in recurrent breast cancer, emphasizing the importance of re-biopsy at recurrence to ensure accurate treatment decisions. While receptor conversion does not significantly impact survival outcomes in this cohort, further large-scale prospective studies are warranted to validate these findings and explore their clinical implications in breast cancer management.

Vitamin D Supplementation Improves Pathological Complete Response in Breast Cancer Patients Undergoing Neoadjuvant Chemotherapy: A Randomized Clinical Trial

This study aimed to evaluate the effect of vitamin D (VD) supplementation on the pathological complete response (pCR) rate in women with breast cancer (BC) undergoing neoadjuvant chemotherapy (NCT). A randomized clinical trial was conducted with 80 women aged ≥45years with BC who were eligible for NCT. Women were randomized into two groups: VD group, daily supplementation with 2,000IU of cholecalciferol (n = 40) or placebo (n = 40), for 6 months. The primary outcome measure was the pCR rate. Serum 25-hydroxyvitamin-D [25(OH)D] levels were measured after BC diagnosis and the end of NCT. Of the 80 randomized women, 75 completed the NCT and underwent surgery. Baseline 25(OH)D values indicated hypovitaminosis D in both groups (VD: 19.6 ± 5.8 ng/mL and placebo: 21 ± 7.9 ng/mL, p = 0.33). After 6 months, 25(OH)D levels increased in the VD group compared to the placebo group (28 ± 8.7 vs. 20.2 ± 6.1 ng/mL, p = 0.03). The pCR rate was higher in women supplemented with VD when compared than the placebo (43% vs. 24%, p = 0.04). Adjusted logistic regression showed that women with 25(OH)D levels ≥20ng/mL were more likely to achieve pCR (OR3.65, 95%CI 1.09-12.8, p = 0.04). Women with BC undergoing NCT who received supplementation with 2,000IU of VD were more likely to achieve a pathological complete response than women in the placebo group.

TRIAL REGISTRATION

Ensaiosclinicos.gov.br, identifier RBR-10k4gqdg.

Differences in risk factors for mortality between T2N1M0 and T3N0M0 lobular breast cancer patients: a comparative study

Objective

This study aimed to explore the differences in risk factors for mortality between T2N1M0 and T3N0M0 lobular breast cancer, and investigate the factors associated with non-lobular breast cancer mortality.

Methods

Data from 2,693 T2N1M0 and 1,384 T3N0M0 lobular breast cancer patients from the SEER database (2008-2018) were analyzed. The lobular breast cancer-specific and non-lobular breast cancer mortality were compared using the Kaplan-Meier curve and Log-rank test. The Cox proportional hazards regression analysis was used to determine the risk factors associated with non-lobular breast cancer mortality.

Results

The total survival time showed a significant difference between the T2N1M0 and T3N0M0 groups (p = 0.0011). Statistically significant difference were found in lung-related disease mortality (p = 0.0023), with the survival rate of T2N1M0 higher than that of T3N0M0. Age, surgery, radiotherapy, and chemotherapy were independent factors associated with mortality in lung-related disease patients with both subtypes, and compared with T2N1M0, radiotherapy in T3N0M0 increased the risk of lung-related disease mortality (HR = 2.076, 95% CI: 1.4318-3.011).

Conclusion

The T3N0M0 group had a higher mortality rate from lung-related diseases compared to the T2N1M0 group, and radiotherapy may increase the risk of lung-related disease death in T3N0M0 patients. These findings provide valuable information for treatment strategies for T2N1M0 and T3N0M0 subtypes of patients and assist physicians and patients make better treatment choices.

Molecular Subtypes of Breast Cancer in Arab Women: Distribution and Prognostic Insights

BACKGROUND

Understanding the ethnic molecular subtype characteristics of breast cancer (BC) in Saudi women is crucial for providing comprehensive prognostic information and optimizing patient outcomes, making it essential to study their distribution and impact on survival.

METHODS

This hospital-based cohort study analyzed clinic-pathological data from 1,035 Saudi women diagnosed with invasive BC and followed for 12 years, at King Faisal Specialist Hospital & Research Center. Cancers were classified into four molecular subtypes: luminal A, luminal B, human epidermal growth factor receptor 2 (HER2)-enriched, and triple-negative. Survival outcomes were assessed using Kaplan-Meier survival curves and Cox proportional hazard models.

RESULTS

Luminal A was the most common molecular subtype (41.7%), followed by luminal B (23.4%), triple-negative (19.5%), and HER2-enriched (15.4%). Age at diagnosis, menopause, and tumor grade were significantly associated with subtypes (p < 0.05). Survival outcomes varied significantly (p = 0.0202), with luminal A and B showing the highest 5-year survival rates (~ 83%), triple-negative at 76.4% (hazard ratio: 1.55), and HER2-enriched tumors had the lowest at 69.1%, with a 1.75-fold higher risk of death. Advanced-stage cancers (III and IV) were strongly associated with increased mortality, with hazard ratios of 2.5 and 7.6, respectively, compared to early-stage disease.

CONCLUSIONS

Molecular subtypes and stage at diagnosis are key predictors of mortality in Saudi women with BC. The poor outcomes for HER2-enriched and TNBC subtypes highlight the need for timely diagnosis and targeted treatments, emphasizing the importance of personalized care and addressing ethnic variations in BC diagnosis.

Rare Histological Types of Breast Cancer: A Single-Center Experience

Background: Breast carcinoma is divided into at least 21 separate histologies, according to the 2019 World Health Organization (WHO) classification. The present study is dedicated to a 5% or rarer group of all breast cancer cases. Method: In this study, we retrospectively considered the data of 4550 patients operated on for breast carcinoma at the Ankara Oncology Training and Research Hospital of the University of Health Sciences between January 2018 and February 2024. Of those cases, 401 were discovered to have rare breast cancer types. We also explored the cases by clinicopathological features, overall survival (OS), and disease-free survival (DFS). Results: Our findings revealed a total of 10 rare breast cancer types in patients explored: mucinous carcinoma, micropapillary carcinoma, papillary group carcinomas, metaplastic carcinoma, neuroendocrine carcinoma, tubular carcinoma, cribriform carcinoma, apocrine carcinoma, acinic cell carcinoma, and secretory carcinoma. While mucinous, tubular, cribriform, papillary group carcinomas, micropapillary, and secretory carcinomas are described as types associated with good prognosis, metaplastic, neuroendocrine, apocrine, and carcinomas are described as types associated with relatively poor prognosis. Conclusion: Scrutinizing the clinicopathological features of rare breast cancer types altogether may be the distinct contribution of this paper to the relevant literature and future research.

Predictors of Recurrence and Overall Survival in Breast Cancer Patients Undergoing Neoadjuvant Chemotherapy and Surgery: A Comprehensive Statistical Analysis

Background/Objectives: This study evaluates the impact of clinical, pathological, and treatment-related factors on breast cancer recurrence and overall survival following neoadjuvant chemotherapy and surgery. Patients and Method: A total of 298 patients treated at Diakoneo Diak Klinikum, Schwäbisch Hall, Germany (2010-2021) were analyzed. Key variables included hormone receptor status, molecular subtypes, tumor grade, treatment protocols, and metastatic disease at diagnosis. Results: Recurrence was strongly associated with metastatic disease (p < 0.001) but not with hormone receptor status or molecular subtypes. Platinum/taxane-based chemotherapy was linked to a lower recurrence risk (p = 0.05) compared to anthracycline-based regimens. Patients with recurrence had significantly lower overall survival (27.91% vs. 8.24%, p < 0.001). Logistic regression suggested a trend toward increased recurrence in ER-positive and PR-negative patients, though not statistically significant. These findings emphasize the importance of personalized treatment strategies and highlight the need for future studies incorporating genomic data and residual disease analysis to refine recurrence risk prediction and therapy selection.

Microstructural diffusion MRI for differentiation of breast tumors and prediction of prognostic factors in breast cancer

Purpose

This study aims to investigate the feasibility of cellular microstructural mapping by the diffusion MRI (IMPULSED, imaging microstructural parameters using limited spectrally edited diffusion) of breast tumors, and further to evaluate whether the MRI-derived microstructural features is associated with the prognostic factors in breast cancer.

Materials and methods

This prospective study collected 232 patients with suspected breast tumors from March to August 2023. The IMPULSED MRI scan included acquisitions of diffusion MRI using both pulsed (PGSE) and oscillating (OGSE) gradient spin echo with the oscillating frequencies up to 33 Hz. The OGSE and PGSE data were fitted by the IMPUSLED method using a two-compartment model to estimate mean cell diameter (d mean), intracellular fraction (fin ), extracellular diffusivity (D ex), and cellularity index (f in/d) within breast tumor lesions. The apparent diffusion coefficients (ADCs) were calculated from the conventional diffusion weighted imaging, PGSE, and OGSE (17 Hz and 33 Hz) sequences (ADCDWI, ADCPGSE, ADC17Hz, and ADC33Hz). The independent samples test was used to compare the d mean, fin , Dex , cellularity index, and ADC values between benign and malignant breast tumors, and between breast cancer subgroups with different risk factors. The receiver operating characteristic (ROC) curve was used to access the diagnostic performance.

Results

213 patients were finally included and divided into malignant (n=130) and benign (n=83) groups according to the histopathological results. The d mean (15.74 ± 2.68 vs. 14.28 ± 4.65 μm, p<0.001), f in (0.346 ± 0.125 vs. 0.279 ± 0.212, p<0.001) and cellularity index (21.19 ± 39.54 vs. 19.38 ± 14.87 ×10-3 um-1, p<0.005) values of malignant lesions were significantly higher than those of benign lesions, and the D ex (2.119 ± 0.395 vs. 2.378 ± 0.332 um2/ms, p<0.001) and ADCDWI (0.877 ± 0.148 vs. 1.453 ± 0.356 um2/ms, p<0.001) of malignant lesions were significantly lower than those of benign lesions. For differentiation between benign and malignant breast lesions, ADCDWI showed the highest AUC of 0.951 with the sensitivity of 80.49% and specificity of 98.28%. The combination of d mean, f in, D ex, and cellularity for differentiation between benign and malignant breast lesions showed AUC of 0.787 (sensitivity = 70.73%, and specificity = 77.86%), and the combination of IMPULSED-derived parameters with ADCs by PGSE and OGSE further improve the AUC to 0.897 (sensitivity = 81.93%, and specificity = 81.54%). The f in values of HER-2(+) tumors were significantly lower than those of HER-2(-) tumors (0.313 ± 0.100 vs. 0.371 ± 0.137, p=0.015), and the ADCDWI, ADC17Hz and ADC33Hz values of HER-2(+) tumors were significantly higher than those of HER-2(-) tumors (ADCDWI: 0.929 ± 0.115 vs. 0.855 ± 0.197 um2/ms, p=0.023; ADC17Hz: 1.373 ± 0.306 vs. 1.242 ± 0.301 um2/s, p =0.025; ADC33Hz: 2.042 ± 0.545 vs. 1.811 ± 0.392 um2/s, p = 0.008). The f in (0.377 ± 0.136 vs. 0.300 ± 0.917, p=0.001) and cellularity index (27.22 ± 12.02 vs. 21.66 ± 7.76 ×10-3 um-1, p=0.007) values of PR(+) tumors were significantly higher than those of PR(-) tumor. The ADC17Hz values of PR(+) tumors were significantly lower than those of PR(-) tumors(1.227 ± 0.299 vs. 1.404 ± 0.294 um2/s, p =0.002).The ADC17Hz and D ex values of ER(+) tumors were significantly lower than those of ER(-) tumors (ADC17Hz: 1.258 ± 0.313 vs. 1.400 ± 0.273 um2/s, p = 0.029; D ex: 2.070 ± 0.405 vs. 2.281 ± 0.331 um2/ms, p=0.011). For differentiation between ER(+) and ER(-), the ADC17Hz and D ex showed AUCs of 0.643 (sensitivity = 76.67%, and specificity = 47.06%) and 0.646 (sensitivity = 80.0%, and specificity = 45.98%), and the combination of D ex and ADC17Hz showed AUCs of 0.663 (sensitivity =93.33%, specificity = 36.78%). For differentiation of PR(+) and PR(-), the ADC17Hz, f in, and cellularity index showed AUCs of 0.666 (sensitivity = 68.18%, and specificity = 61.97%), 0.697 (sensitivity = 77.27%, and specificity = 60.27%) and 0.661 (sensitivity = 68.18%, and specificity = 61.64%), respectively, and their combination showed AUCs of 0.729 (sensitivity =72.73%, specificity = 65.75%). For differentiation of HER-2(+) and HER-2(-), the ADCDWI, ADC17Hz, and ADC33Hz, and f in showed AUCs of 0.625 (sensitivity = 59.42%, specificity = 63.04%), 0.632 (sensitivity = 43.66%, and specificity = 84.78%), 0.664 (sensitivity = 47.95%, and specificity = 82.67%) and 0.650 (sensitivity = 77.46%, and specificity = 56.52%), respectively, and their combination showed AUCs of 0.693 (sensitivity = 69.57%, specificity = 64.79%) of HER-2(+) and HER-2(-).

Conclusion

The IMPULSED method demonstrates promise for characterizing cellular microstructural features in breast tumors, which may be helpful for prognostic risk evaluation in breast cancer.

Molecular Underpinnings of Brain Metastases

Brain metastases are the most commonly diagnosed type of central nervous system tumor, yet the mechanisms of their occurrence are still widely unknown. Lung cancer, breast cancer, and melanoma are the most common etiologies, but renal and colorectal cancers have also been described as metastasizing to the brain. Regardless of their origin, there are common mechanisms for progression to all types of brain metastases, such as the creation of a suitable tumor microenvironment in the brain, priming of tumor cells, adaptations to survive spreading in lymphatic and blood vessels, and development of mechanisms to penetrate the blood-brain barrier. However, there are complex genetic and molecular interactions that are specific to every type of primary tumor, making the understanding of the metastatic progression of tumors to the brain a challenging field of study. In this review, we aim to summarize current knowledge on the pathophysiology of brain metastases, from specific genetic characteristics of commonly metastatic tumors to the molecular and cellular mechanisms involved in progression to the central nervous system. We also briefly discuss current challenges in targeted therapies for brain metastases and how there is still a gap in knowledge that needs to be overcome to improve patient outcomes.

Primary Stereotactic Body Radiation Therapy for Breast Cancer Spinal Metastases

BACKGROUND

To present insights gained from a decade of employing stereotactic body radiation therapy (SBRT) as a primary intervention for spinal bone metastasis (SBM) originating from breast cancer (BC).

METHODS

We retrospectively examined the application of primary SBRT (the CyberKnife System) for BC SBMs between March 2012 and January 2023.

RESULTS

We recruited 47 female patients with 82 SBMs affecting 104 vertebrae. The mean age was 53.2 ± 12.7 years. The overall local control (LC) rate of primary SBRT for BC SBMs was 84.1%. The median local progression (LP) occurred at 12 (3-66) months. The LP rates were 9.7%, 13.3%, and 18.3% at 1, 3, and 5 years following SBRT. We observed a lower LC rate in White patients than that in Asian patients. Factors associated with an increased risk of LP included SBMs from invasive lobular carcinoma, and patients with lower revised Tokuhashi scores. Additionally, the 1-, 3-, and 5-year LP rates of different SFED (≥20 Gy vs. <20 Gy) were 4.3% versus 19.1%, 7.2% versus 24.0%, and 11.5% versus 28.9%. The incidence of acute local adverse events (AEs) was 24.4% and was significantly associated with advanced age and prescribed target coverage of less than 95%.

CONCLUSIONS

We have demonstrated SBRT using the CyberKnife System as an effective primary intervention for BC SBMs. Our findings underscore the importance of treatment planning to optimize outcomes and minimize AEs in patients undergoing SBRT for SBMs.

Leveraging Multi-omics to Disentangle the Complexity of Ovarian Cancer

To better understand ovarian cancer lethality and treatment resistance, sophisticated computational approaches are required that address the complexity of the tumor microenvironment, genomic heterogeneity, and tumor evolution. The ovarian cancer tumor ecosystem consists of multiple tumors and cell types that support disease growth and progression. Over the last two decades, there has been a revolution in -omic methodologies to broadly define components and essential processes within the tumor microenvironment, including transcriptomics, metabolomics, proteomics, genome sequencing, and single-cell analyses. While most of these technologies comprehensively characterize a single biological process, there is a need to understand the biological and clinical impact of integrating multiple -omics platforms. Overall, multi-omics is an intriguing analytic framework that can better approximate biological complexity; however, data aggregation and integration pipelines are not yet sufficient to reliably glean insights that affect clinical outcomes.