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

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.

Overexpression of cytoplasmic poly(A)-binding protein 1 as a biomarker for the prognosis and selection of postoperative regimen in breast cancer

PURPOSE

The dysregulation of the cytoplasmic poly(A)-binding protein 1 (PABPC1) is involved in a variety of tumors but little is known about its role in human breast cancer. Therefore, the effect of PABPC1 in the prognosis and regimen selection in breast cancer patients was evaluated.

METHODS

A total of 791 cases of invasive breast cancer were included in this study, although only 416 were involved in subsequent analyses after the propensity score matching (PSM) test. PABPC1 expression was detected by immunohistochemistry. The relationship between PABPC1 expression and clinicopathological factors, postoperative regimens, and outcomes was determined.

RESULTS

In the total 791 cases, 583 cases were positive for PABPC1, but only 212 (26.8%) showed high PABPC1 expression (PABPC1-HE). The overall survival (OS) and disease-free survival (DFS) of PABPC1-HE patients after PSM were significantly worse than those in patients with PABPC1 low expression (PABPC1-LE), regardless of age, molecular type, tumor size, nodal status, or pStage. Postoperative chemotherapy (CT) increased the OS of PABPC1-HE patients but not that of PABPC1-LE patients. Among patients receiving endocrine therapy, those in the PABPC-LE group had an extended OS, while CT or chemoradiotherapy (CT/CRT) only significantly extended the OS time of PABPC-HE patients. CT/CRT did not significantly extend the survival of PABPC1-LE HER2-positive patients but extended the OS of PABPC1-HE HER2-positive patients. However, the OS of patients treated with CT/CRT + trastuzumab therapy was significantly longer than that of other patients under other therapies in the PABPC1-HE group, suggesting that PABPC1-HE might be sensitive to trastuzumab-based therapy. The multivariate analysis revealed that PABPC1-HE was an independent prognostic factor for both poor OS and DFS in breast cancer except luminal A type.

CONCLUSIONS

Our results revealed that PABPC1 might be considered as a biomarker to help in subtyping, as well as in the prognosis and regimen selection of breast cancer patients.

ATP ion channel-type P2X purinergic receptors as a key regulatory molecule in breast cancer progression

Studies have confirmed that ATP ion channel P2X purinergic receptors play a key role in tumor growth and metastasis. Similarly, P2X purinergic receptors can be used as a favorable regulatory molecule of breast cancer cells to participate in the progression of breast cancer. There are abundant ATP and its cleavage products in breast cancer microenvironment, which can be used as natural activators of P2X purinergic receptors. P2X purinergic receptors play a role in regulating the growth and metastasis of breast cancer cells by mediating signal transduction, growth regulation and immune cell activity in microenvironment. However, the application of P2X purinergic receptors antagonist has the pharmacological characteristics of inhibiting the progression of breast cancer cells. Among P2X purinergic receptors, there is a close relationship between P2X7 receptor and breast cancer patients. P2X purinergic receptors can be used as a biological marker for breast cancer patients. In this paper, we discuss the functional role and regulatory molecular mechanism of P2X purinergic receptors in the progression of breast cancer. The pharmacological effects of P2X purinergic receptors selective antagonist on the growth, metastasis and invasion of breast cancer cells were further discussed. Therefore, P2X purinergic receptors can be used as a key regulatory molecule of breast cancer and a pharmacological target for potential therapy.

Ultrasound-based radiogenomics: status, applications, and future direction

Radiogenomics, an extension of radiomics, explores the relationship between imaging features and underlying gene expression patterns. This field is instrumental in providing reliable imaging surrogates, thus potentially representing an alternative to genetic testing. The rapidly growing area of radiogenomics that utilizes ultrasound (US) imaging seeks to elucidate the connections between US image characteristics and genomic data. In this review, the authors outline the radiogenomics workflow and summarize the applications of US-based radiogenomics. These include the prediction of gene variations, molecular subtypes, and other biological characteristics, as well as the exploration of the relationships between US phenotypes and cancer gene profiles. Although the field faces various challenges, US-based radiogenomics offers promising prospects and avenues for future research.

[Importance of parametric and molecular imaging for therapeutic management of breast cancer]

BACKGROUND

In recent years, various magnetic resonance (MRI) and positron emission tomography (PET) parameters have been investigated in breast cancer. Parametric imaging focuses on the visualization and quantification of biological, physiological, and pathological processes at the cellular and molecular level. It therefore provides important insights into the key processes in carcinogenesis and tumor progression. This article aims to illustrate the importance for the management of breast cancer.

MATERIALS AND METHODS

Based on the current literature, an overview of the current state of parametric breast imaging and its importance in therapy management is given. Moreover, future opportunities and challenges are highlighted.

RESULTS

Parametric imaging in breast cancer includes MRI, nuclear medicine procedures such as PET, the combination of different techniques (PET-CT, PET-MRI) and the use of specific radiotracers. Parametric MRI of the breast mainly uses T2 and diffusion-weighted imaging (DWI) as well as dynamic contrast-enhanced MRI (CM-MRI). Quantitative and qualitative imaging biomarkers provide insights into tumor biology and allow conclusions to be drawn about the molecular subtype or prognosis.

CONCLUSIONS

Recently, parametric imaging has become established in breast diagnostics. It is constantly evolving and will continue to gain in importance in the forthcoming years. It offers the opportunity to improve the diagnosis and treatment management of breast cancer.

Cognitive function and breast cancer molecular subtype before and after chemotherapy

Chemotherapy-related cognitive impairment has been reported in patients with breast cancer and received growing attention due to increased survival rate. However, cognitive outcome according to pathological tumor features, especially human epidermal growth factor receptor (HER2) status, has not been clearly elucidated. Despite its potential link with cognitive status through neuroinflammatory response, existing research is sparse and limited to cross-sectional studies. In this observational cohort study, 52 breast cancer patients received a series of neuropsychological examinations before and after chemotherapy. Patients' performances were compared with normative data, and analyzed with Reliable Change Indices and mixed-model analysis of covariance. Results showed that there was a higher percentage of HER2+ patients than HER2- patients who showed defective attention and processing speed before chemotherapy, and that there were more patients with HER2+ status showing cognitive decline on tests of attention and executive functions following chemotherapy. Group-wise analyses confirmed the foregoing pattern and further revealed that patients with HER2+ status also tended to deteriorate more in verbal memory after chemotherapy. These findings indicate that HER2 overexpression may serve as prognostic factors that help explain the heterogeneous cognitive outcome in breast cancer survivors. Further studies are needed to replicate this finding and delineate the underlying mechanisms.

Superior performance in classification of breast cancer molecular subtype and histological factors by radiomics based on ultrafast MRI over standard MRI: evidence from a prospective study

PURPOSE

To compare the performance of ultrafast MRI with standard MRI in classifying histological factors and subtypes of invasive breast cancer among radiologists with varying experience.

METHODS

From October 2021 to November 2022, this prospective study enrolled 225 participants with 233 breast cancers before treatment (NCT06104189 at clinicaltrials.gov). Tumor segmentation on MRI was performed independently by two readers (R1, dedicated breast radiologist; R2, radiology resident). We extracted 1618 radiomic features and four kinetic features from ultrafast and standard images, respectively. Logistic regression algorithms were adopted for prediction modeling, following feature selection by the least absolute shrinkage and selection operator. The performance of predicting histological factors and subtypes was evaluated using the area under the receiver-operating characteristic curve (AUC). Performance differences between MRI methods and radiologists were assessed using the DeLong test.

RESULTS

Ultrafast MRI outperformed standard MRI in predicting HER2 status (AUCs [95% CI] of ultrafast MRI vs standard MRI; 0.87 [0.83-0.91] vs 0.77 [0.64-0.90] for R1 and 0.88 [0.83-0.91] vs 0.77 [0.69-0.84] for R2) (all P < 0.05). Both ultrafast MRI and standard MRI showed comparable performance in predicting hormone receptors. Ultrafast MRI exhibited superior performance to standard MRI in classifying subtypes. The classification of the luminal subtype for both readers, the HER2-overexpressed subtype for R2, and the triple-negative subtype for R1 was significantly better with ultrafast MRI (P < 0.05).

CONCLUSION

Ultrafast MRI-based radiomics holds promise as a noninvasive imaging biomarker for classifying hormone receptors, HER2 status, and molecular subtypes compared to standard MRI, regardless of radiologist experience.

Multi-Omics Profiling Identifies a High-Risk Subgroup of Breast Cancer Stem Cells for Prognostic Stratification and Personalized Treatment

Background: Breast cancer is the most prevalent malignancy among females worldwide. Extensive research has highlighted cancer stem cells (CSCs) as critical drivers of tumor initiation, progression, recurrence, and therapy resistance. However, the heterogeneity of breast cancer stem cells (BCSCs) and their dynamic roles within the tumor microenvironment remain inadequately understood. Methods: This study utilized the single-cell RNA sequencing dataset to categorize BCSCs into two subgroups within the breast cancer microenvironment and investigate their pseudo-time developmental dynamics. Bulk transcriptomic data from TCGA-BRCA were integrated to assess the prognostic significance and infiltration abundance of the BCSCs-2 subgroup. Functional enrichment, co-expression network analysis, and somatic mutation profiling were performed to elucidate key biological pathways and genetic features. Additionally, drug sensitivity analyses were conducted using the Connectivity Map database to identify potential therapeutic strategies. Results: A total of 459 BCSCs were identified and further classified into two distinct subpopulations: BCSCs-1 and BCSCs-2. High infiltration of BCSCs-2 was associated with poor prognosis and an immunosuppressive tumor microenvironment. Co-expression network analysis identified 16 key genes linked to BCSCs-2, while somatic mutation analysis revealed distinct mutation patterns associated with its infiltration. Drug sensitivity analysis suggested that patients with high BCSCs-2 infiltration could benefit from classical chemotherapy agents, such as Cisplatin, and other novel therapeutic compounds. Conclusions: This study offers novel insights into the heterogeneity and functional roles of BCSCs in breast cancer. The findings highlight the prognostic and therapeutic importance of the BCSCs-2 subgroup, providing potential biomarkers and therapeutic targets for precision medicine in breast cancer management.

The association between Ki-67 expression and survival in breast cancer subtypes: a cross-sectional study of Ki-67 cut-point in northern Thailand

BACKGROUND

Breast cancer is a major health concern worldwide, and Ki-67 level index is a prognostic factor that indicates tumor proliferation and predicts survival outcomes. However, the standard Ki 67 cut-off level varies between local laboratories, and in Thailand, there is no established optimal cut-off level.

OBJECTIVE

This study aimed to determine the optimal cut-off point for Ki-67 expression and investigate the association between Ki-67 levels and other prognostic factors with 8-year overall survival.

METHOD

A retrospective review of Ki-67 levels was conducted in non-metastatic breast cancer patients treated at Maharaj Nakorn Chiangmai hospital from January 2013-December 2015, including 507 breast cancer patients.

RESULTS

The ROC curve analysis identified the optimal Ki-67 cut-point as ≥ 30%, with 75% sensitivity and 48.85% specificity. Age over 60 was associated with higher mortality regardless of cancer stage. Locally advanced staging, nodal involvement, Ki-67 ≥ 30%, and triple-negative subtype correlated with poorer survival. Even after adjustments, these factors remained significant in prognostic evaluation. Chemotherapy notably improved survival, especially in high Ki-67 (≥ 30) patients. However, this effect was not seen in low Ki-67 patients. High Ki-67 patients receiving chemotherapy showed improved survival in early-stage, node-negative cases compared to those who did not receive chemotherapy. HER2-positive patients with high Ki-67 benefited from chemotherapy, but statistical significance was not reached in hormone-positive patients.

CONCLUSION

This study identified the optimal cut point for Ki-67 in Northern Thailand as 30%. Patients with KI-67 above 30% show significantly lower 8-year survival rates. This is especially relevant for low-risk patients, like those with hormonal subtypes or early-stage nodal negativity. In these cases, KI-67 becomes crucial for treatment decisions. Our study not only aids Northern Thailand's understanding but also aligns with broader research, emphasizing KI-67's vital role in planning treatment for low-risk breast cancer patients.

Correlation of Radiological and Pathological Tumor Sizes in Breast Cancer Based on Molecular Subtypes and Accompanying DCIS: A Retrospective Multicenter Study. TR-BRC 2023-01

PURPOSE

This study aims to compare radiological tumor sizes obtained by mammography (MMG), ultrasonography (US), and magnetic resonance imaging (MRI) with pathological sizes to determine if molecular subtypes and the presence of accompanying ductal carcinoma in-situ (DCIS) affect accuracy.

METHODS

A total of 559 cases diagnosed with breast cancer in 11 different centers between 2010-2023 were included in the study. The patients' MMG, US, and MRI images were re-evaluated, and radiological findings and tumor sizes were recorded. Histological diagnosis (invasive/in-situ/mixed), receptor status, Ki-67 index, and tumor size were recorded from the pathology reports. Pathologic tumor size (pT) was accepted as the gold standard.

RESULTS

The mean pT was 21.1±14.9 (2.7-100) mm in Luminal A tumors, 20.6±12.6 (2-70) mm in Luminal B tumors, 26.3±14.7 (6-80) mm in HER-2(+) tumors, 26.3±14.7 (8-125) mm in triple (-) (TN) tumors. The highest agreement in invasive tumors was obtained with MRI (MRI r:0.831, US r:0.769, MMG r:0.650). In DCIS cases, the agreement was strong with MRI (r:0.770) and intermediate with MMG and US (r:0.517 and r:0.593, respectively). In mixed tumors, agreement was strong with MRI (r:0.817), intermediate with US (r:0.656), and low with MMG (r:0.499). Based on molecular subtypes, MRI had a strong correlation (r>0.7) in both invasive and mixed tumors of all subtypes. US had a strong correlation in all invasive tumors (r>0.7). The correlation was intermediate in Luminal mixed tumors. Mammography had a strong correlation only in invasive Luminal A tumors (r>0.7), and an intermediate correlation in the other invasive tumor subtypes. Regarding mixed tumors, its correlation level was intermediate in Luminal B and TN tumors, and low in Luminal A and HER-2(+) tumors.

CONCLUSION

This multicenter study shows that MRI is the most reliable method for determining preoperative tumor size of invasive and in-situ tumors and all molecular subtypes. The correlation levels of all modalities decreased in pure and mixed DCIS cases, however the difference was minimal with MRI.

Breast cancer: pathogenesis and treatments

Breast cancer, characterized by unique epidemiological patterns and significant heterogeneity, remains one of the leading causes of malignancy-related deaths in women. The increasingly nuanced molecular subtypes of breast cancer have enhanced the comprehension and precision treatment of this disease. The mechanisms of tumorigenesis and progression of breast cancer have been central to scientific research, with investigations spanning various perspectives such as tumor stemness, intra-tumoral microbiota, and circadian rhythms. Technological advancements, particularly those integrated with artificial intelligence, have significantly improved the accuracy of breast cancer detection and diagnosis. The emergence of novel therapeutic concepts and drugs represents a paradigm shift towards personalized medicine. Evidence suggests that optimal diagnosis and treatment models tailored to individual patient risk and expected subtypes are crucial, supporting the era of precision oncology for breast cancer. Despite the rapid advancements in oncology and the increasing emphasis on the clinical precision treatment of breast cancer, a comprehensive update and summary of the panoramic knowledge related to this disease are needed. In this review, we provide a thorough overview of the global status of breast cancer, including its epidemiology, risk factors, pathophysiology, and molecular subtyping. Additionally, we elaborate on the latest research into mechanisms contributing to breast cancer progression, emerging treatment strategies, and long-term patient management. This review offers valuable insights into the latest advancements in Breast Cancer Research, thereby facilitating future progress in both basic research and clinical application.

Radiomics Integration of Mammography and DCE-MRI for Predicting Molecular Subtypes in Breast Cancer Patients

Background

Accurate identification of the molecular subtypes of breast cancer is essential for effective treatment selection and prognosis prediction.

Aim

This study aimed to evaluate the diagnostic performance of a radiomics model, which integrates breast mammography and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in predicting the molecular subtypes of breast cancer.

Methods

We retrospectively included 462 female patients with pathologically confirmed breast cancer, including 53 cases of triple-negative, 94 cases of HER2 overexpression, 95 cases of luminal A, and 215 cases of luminal B breast cancer. Radiomics analysis was performed using FAE software, wherein the radiomic features were examined about the hormone receptor status. The performance of the model was evaluated using the area under the receiver operating characteristic curve (AUC) and accuracy.

Results

In multivariate analysis, radiomic features were the only independent predictive factors for molecular subtypes. The model that incorporates multimodal fusion features from breast mammography and DCE-MRI images exhibited superior overall performance compared to using either modality independently. The AUC values (or accuracies) for six pairings were as follows: 0.648 (0.627) for luminal A vs luminal B, 0.819 (0.793) for luminal A vs HER2 overexpression, 0.725 (0.696) for luminal A vs triple-negative subtype, 0.644 (0.560) for luminal B vs HER2 overexpression, 0.625 (0.636) for luminal B vs triple-negative subtype, and 0.598 (0.500) for triple-negative subtype vs HER2 overexpression.

Conclusion

The radionics model utilizing multimodal fusion features from breast mammography combined with DCE-MRI images showed high performance in distinguishing molecular subtypes of breast cancer. It is of significance to accurately predict prognosis and determine treatment strategy of breast cancer by molecular classification.

Combining KPNA2 and FOXM1 Expression as Prognostic Markers and Therapeutic Targets in Hormone Receptor-Positive, HER2-Negative Breast Cancer

Background/Objectives: Breast cancer remains the leading malignancy affecting women worldwide, with significant mortality rates. This study aimed to evaluate the prognostic significance of FOXM1 expression specifically in hormone receptor-positive, HER2-negative (HR+HER2-) breast cancer patients with high KPNA2 expression, and to identify potential FOXM1-targeted therapeutic strategies for this patient subgroup. Methods: We analyzed RNA sequencing and microarray data from three independent cohorts: Mackay Memorial Hospital patient samples, The Cancer Genome Atlas, and Gene Expression Omnibus databases. The expression levels of KPNA2, FOXM1, CCNB1, and CCNB2 were evaluated, with particular emphasis on stratifying patients based on KPNA2 expression levels. Their associations with clinical outcomes were assessed using Gene Set Enrichment Analysis and survival analyses. Results: While KPNA2 expression showed strong positive correlations with FOXM1, CCNB1, and CCNB2 across all datasets, our analysis revealed a distinct prognostic pattern in HR+HER2- breast cancer patients with high KPNA2 expressions. In this specific subgroup, low FOXM1 expression emerged as a favorable prognostic indicator, despite the generally poor prognosis associated with high KPNA2 levels. Gene Set Enrichment Analysis demonstrated significant enrichment of the G2/M checkpoint pathway in high KPNA2-expressing patients, suggesting potential therapeutic vulnerability to FOXM1 inhibition in this subgroup. Conclusions: This study establishes FOXM1 expression as a critical prognostic marker, specifically in KPNA2-high HR+HER2- breast cancer patients, where low FOXM1 levels correlate with improved survival outcomes. These findings suggest that FOXM1 inhibition could be particularly effective in patients with high KPNA2 expression, offering a novel therapeutic strategy for this specific molecular subtype. Several FOXM1 inhibitors, including thiostrepton and FDI-6, warrant investigation as potential targeted treatments for KPNA2-high HR+HER2- breast cancer patients.

Is the Neutrophil-to-Lymphocyte Ratio a Predictive Factor of Pathological Complete Response in Egyptian Breast Cancer Patients Treated with Neoadjuvant Chemotherapy?

Background and Objectives: The role of the neutrophil-to-lymphocyte ratio (NLR) as a predictor of response in breast cancers after neoadjuvant chemotherapy is controversial. This study aims to explore the relationship of NLR with pathological complete response (pCR) in a cohort of Egyptian breast cancer patients who received neoadjuvant chemotherapy. Materials and Methods: Forty-six breast cancer females received preoperative neoadjuvant chemotherapy and then underwent surgery. All resected tumors were evaluated to determine the pathologic effect of the neoadjuvant chemotherapy. A complete blood count was carried out at baseline before beginning the neoadjuvant chemotherapy. The absolute count of neutrophils was divided by the absolute count of lymphocytes to calculate the NLR. Results: Of the study patients, 18 (39.1%) were considered to have a low NLR (NLR < 1.76), and 28 (60.9%) were considered to have a high NLR (NLR ≥ 1.76). Patients with a low NLR had 18-fold higher rates of pCR when compared to patients with a high NLR (OR 18.1; 95% CI (1.058-310.757); p = 0.046). Conclusions: Our findings indicate that the pretreatment NLR is a pivotal predictor factor of the pathological complete response in Egyptian breast cancer patients treated with neoadjuvant chemotherapy.

Dynamics of molecular heterogeneity in high-risk luminal breast cancer-From intrinsic to adaptive subtyping

We evaluate therapy-induced molecular heterogeneity in longitudinal samples from high-risk, hormone-receptor positive/HER2-negative breast cancer patients with residual tumor after neoadjuvant chemotherapy from the Penelope-B trial (NCT01864746; EudraCT 2013-001040-62). Intrinsic subtypes are prognostic in pre-therapeutic (Tx) samples (n = 629, p < 0.0001) and post-Tx residual tumors (n = 782, p < 0.0001). After neoadjuvant chemotherapy, a shift of intrinsic subtypes is observed from pre-Tx luminal (Lum) B to post-Tx LumA, with reverse transition back to LumB in metastases. In a combined analysis of 540 paired pre-Tx and post-Tx samples, we identify five adaptive clusters (AC-1-5) based on transcriptomic changes before and after neoadjuvant chemotherapy. These AC-subtypes are prognostic beyond classical intrinsic subtyping, categorizing patients into groups with excellent prognosis (AC-1 and AC-2), poor prognosis (AC-3 and AC-4), and very poor prognosis (AC-5, enriched for basal-like subtype). Our analysis provides a basis for an extended molecular classification of breast cancer patients and improved identification of high-risk patient populations.

Comparison of Invasive Ductolobular Carcinoma and Lobular Carcinoma: An Observational Study

Background and Objectives: Mixed ductolobular carcinomas (mDLCs) are tumors that contain both ductal and lobular components. The clinicopathological characteristics and impacts on survival of the two components, which have distinct biological behaviors, are still not clearly understood. This study aimed to compare the clinicopathological characteristics, recurrence/metastasis patterns, and survival outcomes of mDLC and invasive lobular carcinoma (ILC), as well as to investigate the prognostic significance of both histopathologies. Materials and Methods: The outcomes of 132 patients who were followed and treated between 2010 and 2021 were analyzed. Patients were examined in two groups, ILC and mDLC. Chi-square tests were performed to compare the baseline clinicopathological characteristics and treatments. Survival rates were subsequently analyzed using the Kaplan-Meier method and compared using the Cox proportional hazards model. Results: In this study, 80 (61%) patients had ILC histopathology, while 52 (39%) had mDLC histopathology. Differences between the groups were observed in median age (p = 0.038), N stage (p = 0.046), estrogen receptor (ER) status (p = 0.005), lymphovascular invasion (p = 0.007), median tumor diameter (p = 0.050), and frequency of distant metastasis (p = 0.029). The treatments, relapse patterns, and metastasis patterns were similar (p > 0.05). No differences in overall survival (OS) and disease-free survival (DFS) were observed. In the multivariate analysis, mDLC histopathology was identified as a poor prognostic factor (HR: 2.95, CI 95%: 1.10-7.88, p = 0.030). Histopathology (ILC vs. mDCL) was not identified as a prognostic factor in the Cox regression analysis for DFS. Conclusion: Although mDLC has poor clinicopathological features (younger age, more advanced N stage, more ER negativity, more lymphovascular invasion, and more frequency of metastases) and appears more aggressive than ILC, these changes do not affect survival in this study. However, mDLC histopathology seems to be associated with poor prognosis for OS.

Plasma Iron Levels at Early Breast Cancer Diagnosis Are Associated With Development of Secondary Metastases: A Single-Center Retrospective Cohort Study

Background

Breast cancer is the most common malignancy in women and the leading cause of cancer-related death. Although most early-stage patients are cured, 20% to 30% develop metastases, significantly reducing survival rates. Recent research highlights the role of iron in cancer progression, although its full impact on breast cancer metastasis is not yet fully understood.

Objectives

The aim of this study is to investigate the association between plasma iron levels at diagnosis of early-stage breast cancer and the risk of developing metastatic disease.

Design

Retrospective single-center study.

Methods

Patients with stage I to III breast cancer, diagnosed between 2007 and 2017, and with serum iron, transferrin saturation, and ferritin values available within 1.5 months before or after diagnosis were included. Cox proportional hazard models were applied to determine the association between iron levels and risk of metastasis.

Results

In total, 1113 patients were included, 10% of them developed distant metastasis over a median follow-up period of 7 years. In multivariable analysis adjusting for age, stage, and subtype, transferrin saturation and serum iron were significantly associated with an increased risk of breast cancer metastasis. For each 10% increment of transferrin saturation at baseline, there was a 19% increase in metastatic risk (hazard ratio [HR] = 1.19; 95% confidence interval [CI] = [1.02-1.38]). Similarly, a serum iron increment of 10 µg/dL led to a 6% increase in risk (HR = 1.06; 95% CI = [1.01-1.12]). Ferritin was found not to be associated with metastatic risk (HR = 0.99; 95% CI = [0.98, 1.01]). There was no significant association with metastatic site or breast cancer subtype when adjusting for age and stage.

Conclusion

Elevated transferrin saturation and serum iron at early breast cancer diagnosis are associated with increased risk for metastatic disease but not with location of metastases or breast cancer subtype. Further research is needed to understand the underlying mechanisms and to explore the potential of iron-targeted therapies.

The Current State of Breast Cancer Genetics in Populations of African Ancestry

Breast cancer (BC) constitutes a significant global health burden, particularly among women, with disparities observed across populations. Notably, women of African ancestry often experience BC at earlier ages and in more aggressive forms, with a higher prevalence of metastasis. Genetic studies, including those focused on BRCA1 and BRCA2 genes, have revealed population-specific variations in BC susceptibility. Despite efforts to investigate BC genetics in African and African-descendant populations, research remains limited compared to studies conducted in populations of European descent. Socioeconomic factors further compound the challenges faced by marginalized populations, influencing disease outcomes and treatment efficacy. This review explores the BC literature in African and African-descendant populations, highlighting population-specific genetic variants associated with the disease's subtypes, treatment response, and disease evolution. Limited sample sizes and lack of data on genetic ancestry hinder the development of precise risk stratification and treatment strategies. Efforts to expand research, improve data collection, and enhance genetic analyses in diverse populations are crucial steps toward addressing racial disparities and advancing BC care on a global scale.

Intratumoral heterogeneity of Ki67 proliferation index outperforms conventional immunohistochemistry prognostic factors in estrogen receptor-positive HER2-negative breast cancer

In breast cancer (BC), pathologists visually score ER, PR, HER2, and Ki67 biomarkers to assess tumor properties and predict patient outcomes. This does not systematically account for intratumoral heterogeneity (ITH) which has been reported to provide prognostic value. This study utilized digital image analysis (DIA) and computational pathology methods to investigate the prognostic value of ITH indicators in ER-positive (ER+) HER2-negative (HER2-) BC patients. Whole slide images (WSIs) of surgically excised specimens stained for ER, PR, Ki67, and HER2 from 254 patients were used. DIA with tumor tissue segmentation and detection of biomarker-positive cells was performed. The DIA-generated data were subsampled by a hexagonal grid to compute Haralick's texture indicators for ER, PR, and Ki67. Cox regression analyses were performed to assess the prognostic significance of the immunohistochemistry (IHC) and ITH indicators in the context of clinicopathologic variables. In multivariable analysis, the ITH of Ki67-positive cells, measured by Haralick's texture entropy, emerged as an independent predictor of worse BC-specific survival (BCSS) (hazard ratio (HR) = 2.64, p-value = 0.0049), along with lymph node involvement (HR = 2.26, p-value = 0.0195). Remarkably, the entropy representing the spatial disarrangement of tumor proliferation outperformed the proliferation rate per se established either by pathology reports or DIA. We conclude that the Ki67 entropy indicator enables a more comprehensive risk assessment with regard to BCSS, especially in cases with borderline Ki67 proliferation rates. The study further demonstrates the benefits of high-capacity DIA-generated data for quantifying the essentially subvisual ITH properties.

Personalized treatment strategies for breast adenoid cystic carcinoma: A machine learning approach

BACKGROUND

Breast adenoid cystic carcinoma (BACC) is a rare subtype of breast cancer that accounts for less than 0.1 % of all cases. This study was designed to assess the efficacy of various treatment approaches for BACC and to create the first web-based tool to facilitate personalized treatment decisions.

METHODS

The Surveillance, Epidemiology, and End Results (SEER) database was used for this study's analysis. To identify the prognostic variables, we conducted Cox regression analysis and constructed prognostic models using five Machine Learning (ML) algorithms to predict the 5-year survival. A validation method incorporating the area under the curve (AUC) of the receiver operating characteristic (ROC) curve was used to validate the accuracy and reliability of ML models. We also performed a Kaplan-Meier (K-M) survival analysis.

RESULTS

This study included 1212 patients. The median age was 60 years, with most tumors being localized and less than 2 cm in size. The 5-year overall survival (OS) rates were highest for surgery + radiotherapy (RT) (94.9 %) and lowest for surgery + chemotherapy (CTX) + RT (80.1 %). Positive estrogen receptor (ER) status and younger age were associated with better survival outcomes. ML models identified key predictive features for survival, including age, nodal status, and ER status.

CONCLUSION

Age, lymph node metastasis, and ER status are crucial prognostic indicators for BACC. Although postoperative RT enhances survival, the advantages of adjuvant CTX are uncertain, implying that it may be eschewed to avert adverse effects. Our online tool offers essential resources for prognostication and treatment optimization.

Development of a Nomogram-Integrated Model Incorporating Intra-tumoral and Peri-tumoral Ultrasound Radiomics Alongside Clinical Parameters for the Prediction of Histological Grading in Invasive Breast Cancer

OBJECTIVE

To develop a comprehensive nomogram to predict the histological grading of breast cancer and further examine its clinical significance by integrating both intra-tumoral and peri-tumoral ultrasound radiomics features.

METHODS

In a retrospective study 468 female breast cancer patients were analyzed from 2017 to 2020 at the Second Affiliated Hospital of Harbin Medical University. Patients were grouped into high-grade (n = 215) and low-grade (n = 253) categories based on pathological evaluation. Tumor regions of interest were defined and expanded automatically to peri-tumor regions of interest. Ultrasound radiomics features were extracted independently. To ensure rigor, cases were randomly divided into 80% training and 20% test sets. Optimal features were selected using statistical and machine learning methods. Intra-tumor, peri-tumor, and combined radiomics models were constructed. To determine the best predictors of breast cancer histological grading, we screened the features using single- and multi-factor logistic regression analyses. Finally, a nomogram was developed and evaluated for its predictive value in this context.

RESULTS

By applying logistic regression, we integrated ultrasound, clinicopathologic, and radiomics features to generate a nomogram. The combined model outperformed others, achieving areas under the curve of 0.934 and 0.812 in training and test sets. Calibration curves also showed high accuracy and reliability.

CONCLUSION

A nomogram constructed through the integration of combined intra-tumor-peri-tumor ultrasound radiomics features along with clinicopathologic characteristics exhibited remarkable performance in distinguishing the histologic grades of invasive breast cancer.

Taxane/anthracycline combinations reduced incidence of breast cancer recurrence in young women across molecular subtypes: a real-world evidence of Taiwan from 2011 to 2019

PURPOSE

Adolescent and young adult (AYA) patients with breast cancer generally have poor prognoses and a higher risk of secondary cancers compared to those at the same cancer stage. Notably, AYA patients in Asia exhibit a higher incidence rate of breast cancer, with Luminal A as the predominant molecular subtype, which contrasts with the trends observed in Western countries. This study aims to compare the efficacy of Taxane/Anthracycline combination-based regimens (TACB) versus Anthracycline-based regimens (AB) in AYA patients with stage I-II breast cancer, focusing on different molecular subtypes.

METHODS

This study utilized data from the Taiwan National Health Insurance Research Database (NHIRD) and the Taiwan Cancer Registry (TCR) from 2011 to 2019. The study cohort included patients aged 15 to 39 years who were diagnosed with stage I-II breast cancer and received either TACB or AB regimens. Propensity score matching and Cox proportional hazards regression models were used to calculate the hazard ratios (HR) for recurrence.

RESULTS

The results showed that TACB regimens significantly reduced the risk of recurrence compared to AB regimens across all patients (aHR 0.73, 95% CI 0.55-0.97). Specifically, for low/middle-recurrence risk groups, the aHR was 0.68 (95% CI 0.49-0.96), and for high-recurrence risk groups, it was 0.43 (95% CI 0.21-0.87). The analysis further indicated no significant differences in recurrence risk between AYA and non-AYA patients using TACB regimens.

CONCLUSION

The TACB regimens showed a more favorable prognosis than AB regimens across all molecular subtypes. Furthermore, TACB regimens not only outperformed AB treatments but also closed the gap in prognostic outcomes between AYA and non-AYA patients. We believe the findings of this study are highly reliable and can provide valuable guidance for physicians in choosing the most appropriate treatment strategies for AYA patients with stage I-II breast cancer.

Prognostic implications of response to neoadjuvant chemotherapy in breast cancer subtypes

The current study was designed to assess the response to treatment, as well as clinical and survival outcomes, across different breast cancer subtypes in patients who underwent neoadjuvant chemotherapy (NAC). From 2014 to 2019, a total of 139 patients who were histologically confirmed to have breast cancer, underwent NAC, and subsequently received breast and axillary surgery, were retrospectively included in this study. The rates of pathological complete response (pCR) to NAC were significantly higher for HER2-positive and triple-negative subtypes than for luminal A and HER2-negative subtypes (p = 0.013). Multivariate analysis for disease-free survival (DFS) revealed that tumour grade and the presence of pCR were independent prognostic factors. The presence or absence of a pCR with NAC was an independent prognostic indicator in the multivariate analysis for overall survival (OS). Lastly, achieving a pCR was independently predicted by 18F-FDG PET/CT findings, the HER2-positive subtype, and the triple-negative subtype. Despite the inherent methodological limitations, our findings underscore the significance of identifying predictive markers to tailor NAC plans, with the aim of improving survival outcomes.

Borderline Phyllodes Breast Tumors: A Comprehensive Review of Recurrence, Histopathological Characteristics, and Treatment Modalities

Phyllodes tumors account for 2-3% of all fibroepithelial breast tumors and less than 1% of all breast cancers. These tumors are categorized into benign, borderline, or malignant based on cellular atypia, mitotic activity, and stromal overgrowth. Surgical excision with clear margins, ideally greater than 1 cm, is the primary treatment for phyllodes tumors to ensure effective local control. Preoperative diagnosis is challenging due to the clinical and radiological similarities between phyllodes tumors and fibroadenomas. The efficacy and role of adjuvant treatments remain subjects of ongoing debate and investigation. Borderline phyllodes tumors exhibit biological characteristics that straddle the line between benign and malignant, presenting significant clinical and surgical management challenges. Given the rarity of this specific subgroup and the ambiguity of the risk of recurrence or progression to malignant phyllodes, this narrative review aims to provide a comprehensive overview of the recurrence risk associated with these tumors.

Evaluation of Grading Estrogen Receptors in Breast Cancer Using Fully Automated Rapid Immunohistochemistry Based on Alternating-Current Electric Field Technology

BACKGROUND

Immunohistochemistry (IHC) is crucial for determining cancer treatments. We previously developed a rapid IHC method and have now developed a fully automated rapid IHC stainer (R-Auto). This study aimed to evaluate the clinical reliability of the R-Auto protocol for staining estrogen receptors (ERs) in breast cancer specimens and evaluate the staining performance.

METHODS

Between January 2015 and June 2020, 188 surgical specimens collected from breast cancer patients treated at our hospital were evaluated via ER staining using R-Auto, conventional manual IHC, and a commercial autostainer. The specimens were scored using Allred scores, after which the staining results were compared between R-Auto and conventional IHC or the commercial autostainer. Weighted kappa coefficients and AC1 statistics were used to assess the agreement between the methods.

RESULTS

The AC1 statistic for comparison between R-Auto and conventional IHC was 0.9490 (0.9139-0.9841), with a 95.7% agreement rate, and that for comparison between R-Auto and the commercial autostainer was 0.9095 (0.8620-0.9570), with a 92.6% agreement. There was, thus, substantial agreement between R-Auto and both conventional IHC and the commercial autostainer. However, R-Auto shortened the time required for IHC from 209 min with conventional IHC to 121 min.

CONCLUSIONS

R-Auto enables a good staining performance in a shorter time with less effort.

Classification of Molecular Subtypes of Breast Cancer Using Radiomic Features of Preoperative Ultrasound Images

Radiomics has been used as a non-invasive medical image analysis technique for diagnosis and prognosis prediction of breast cancer. This study intended to use radiomics based on preoperative Doppler ultrasound images to classify four molecular subtypes of breast cancer. A total of 565 female breast cancer patients diagnosed by postoperative pathology in a hospital between 2014 and 2022 were included in this study. Radiomic features extracted from preoperative ultrasound images and clinical features were used to construct models for the classification of molecular subtypes of breast cancer. The least absolute shrinkage and selection operator (LASSO) regression was applied for the final screening of radiomic features and clinical features. Three classifiers including Logistic regression, support vector machine (SVM), and XGBoost were utilized to construct model. Model performance was assessed primarily by the area under the receiver operating characteristic curve (AUC) and 95% confidence interval (CI). The mean age of these patients was 54.58 (± 11.27) years. Of these 565 patients, 130 (23.01%) were Luminal A subtype, 329 (58.23%) were Luminal B subtype, 65 (11.50%) were human epidermal growth factor receptor-2 (HER-2) subtype, and 41 (7.26%) were triple negative (TN) subtype. A total of 12 clinical features and 8 radiomic features were selected for model construction. The AUC of the SVM model [0.826 (95%CI 0.808-0.845)] was higher than that of the Logistic regression model [0.776 (95%CI 0.756-0.796)] and the XGB model [0.800 (95%CI 0.779-0.821)] in the multiple classification of breast cancer. For the single classification of breast cancer, the AUC of the SVM model was 0.710 (95%CI 0.660-0.760) for Luminal A subtype, 0.639 (95%CI 0.592-0.685) for Luminal B subtype, 0.754 (95%CI 0.695-0.813) for HER-2 subtype, and 0.832 (95%CI 0.771-0.892) for TN subtype. The SVM model with radiomic features combined with clinical features shows good performance in classifying four molecular subtypes of breast cancer.

Development of metastasis and survival prediction model of luminal and non-luminal breast cancer with weakly supervised learning based on pathomics

Objective

Breast cancer stands as the most prevalent form of cancer among women globally. This heterogeneous disease exhibits varying clinical behaviors. The stratification of breast cancer patients into risk groups, determined by their metastasis and survival outcomes, is pivotal for tailoring personalized treatments and therapeutic interventions. The pathological sections of radical specimens encompass a diverse range of histological information pertinent to the metastasis and survival of patients. In this study, our objective is to develop a deep learning model utilizing pathological images to predict the metastasis and survival outcomes for breast cancer patients.

Methods

This study utilized pathological sections from 204 radical mastectomy specimens obtained between January 2013 and December 2014 at the Second Affiliated Hospital of the Medical College of Zhejiang University. The 204 pathological slices were scanned and transformed into whole slide imaging (WSI), with manual labeling of all tumor areas. The WSI was then partitioned into smaller tiles measuring 512 × 512 pixels. Three networks, namely Densely Connected Convolutional Network 121 (DenseNet121), Residual Network (ResNet50), and Inception_v3, were assessed. Subsequently, we combined patch-level predictions, probability histograms, and Term Frequency-Inverse Document Frequency (TF-IDF) features to create comprehensive participants representations. These features served as the foundational input for developing a machine learning algorithm for metastasis analysis and a Cox regression model for survival analysis.

Result

Our results show that the Inception_v3 model shows a particularly robust patch recognition ability for estrogen receptor (ER) recognition. Our pathological model shows high accuracy in predicting tumor regions. The train area under the curve (AUC) of the Inception_v3 model based on supervised learning is 0.975, which is higher than the model established by weakly supervised learning. But the AUC of the metastasis prediction in training and testing sets is higher than value based on supervised learning. Furthermore, the C-index of the survival prediction model is 0.710 in the testing sets, which is also better than the value by supervised learning.

Conclusion

Our study demonstrates the significant potential of deep learning models in predicting breast cancer metastasis and prognosis, with the pathomic model showing high accuracy in identifying tumor areas and ER status. The integration of clinical features and pathomics signature into a nomogram further provides a valuable tool for clinicians to make individualized treatment decisions.

Evaluating the correlation between pretreatment 18F-FDG PET/CT metabolic parameters and tumor-infiltrating lymphocyte levels in nonluminal breast cancer and impact on survival

Background and Objectives

This study aims to evaluate the correlation between Tumor-Infiltrating Lymphocyte (TIL) levels and Fluorine-18 fluorodeoxyglucose (18F-FDG) metabolic parameters, including spleen and bone marrow FDG uptake and tumor heterogeneity in non-luminal breast cancers (NLBC), and to elucidate their association with survival outcomes.

Methods

We retrospectively analyzed data from 100 females with stage 2-4 NLBC who underwent pretreatment 18F-FDG Positron emission tomography-computed tomography (PET/CT). TIL was scored based on Hematoxylin-Eosin-stained specimens and 18F-FDG PET metabolic parameters, including maximum standardized uptake value (SUVmax), mean standardized uptake value (SUVmean), metabolic tumor volume (MTV), total lesion glycolysis (TLG), liver, spleen, and bone marrow FDG uptake were calculated. Heterogeneity Index (HI)1, HI2, and HI3 indices were analyzed with FDG metabolic parameters. The association between these factors and overall survival was analyzed using multivariate Cox regression models.

Results

TIL showed weak negative correlations with tumor size, tumor (T), and metastasis (M) stages. No significant correlation was found between TIL levels and overall SUV values. However, in stage 4, TIL correlated positively with liver, spleen, and bone marrow SUV values and negatively with heterogeneity indices (HI2, HI3). Higher tumor size, HI values, and Bone marrow-to-liver ratio (BLR) SUVmean were associated with increased mortality. A TIL cut-off value of <5 was linked to significantly worse survival.

Conclusion

Our study demonstrates a strong connection between TIL, FDG metabolic parameters, and tumor heterogeneity, particularly in advanced NLBC. Although TIL is not generally associated with SUV values, its association with certain metabolic and heterogeneity indices suggests that it is important in influencing survival. Further research involving larger cohorts and diverse breast cancer subtypes is needed to validate these results.

Endocrine treatment mechanisms in triple-positive breast cancer: from targeted therapies to advances in precision medicine

Triple-positive breast cancer (TPBC), defined by the co-expression of estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2), poses unique therapeutic challenges due to complex signaling interactions and resulting treatment resistance. This review summarizes key findings on the molecular mechanisms and cross-talk among ER, PR, and HER2 pathways, which drive tumor proliferation and resistance to conventional therapies. Current strategies in TPBC treatment, including endocrine and HER2-targeted therapies, are explored alongside emerging approaches such as immunotherapy and CRISPR/Cas9 gene editing. Additionally, we discuss the tumor microenvironment (TME) and its role in treatment resistance, highlighting promising avenues for intervention through combination therapies and predictive biomarkers. By addressing these interdependent pathways and optimizing therapeutic strategies, precision medicine holds significant potential for improving TPBC patient outcomes and advancing individualized cancer care.

Diffusion-weighted imaging based on intravoxel incoherent motion: correlation with molecular prognostic factors and subtypes in breast cancer

BACKGROUND

Intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI), which indicates biological tissue attributes, may be applied to accurately assess breast tumors.

PURPOSE

To analyze the IVIM parameters of different molecular prognostic factors and subtypes to find out whether there are any connections.

MATERIAL AND METHODS

A total of 181 patients enrolled in this retrospective study had preoperative magnetic resonance imaging (MRI) examinations, and pathologies were verified as breast cancers. Regions of interest were placed at all slices of the parameter maps (D, tissue diffusivity; ADC, apparent diffusion coefficient; f, perfusion fraction; and D*, pseudo-diffusivity maps) of IVIM and generated parameter values to be used for comparative analysis among molecular prognostic factors and subtypes.

RESULTS

D and ADC were greater in estrogen receptor (ER)-negative, human epidermal growth factor receptor 2 (HER2)-positive, and Ki67-low expression groups (all P values < 0.05). The progesterone receptor (PR)-negative group had a higher D value (P < 0.05). f was larger in the lymph node metastasis-negative group and the PR-positive group (P = 0.012 and 0.046, respectively). Among breast cancer subtypes, D and ADC were different between the HER2-overexpression and the Luminal B (HER2-negative) subtypes (P = 0.019 and 0.028, respectively). The difference in D between the luminal and non-luminal subtypes was statistically significant (P = 0.008). The triple-negative subtype significantly differs from the other subtypes in D* and f (P = 0.012 and 0.016, respectively).

CONCLUSION

IVIM-related metrics exhibited relationships with breast cancer molecular prognosis factors and subtypes.

Circulating CD3+CD8+ T Lymphocytes as Indicators of Disease Status in Patients With Early Breast Cancer

Circulating CD3+CD8+ cell levels were lower in breast cancer patients, elevated posttreatment, and subsequently declining upon recurrence. Elevated plasma chemokine (C-C motif) ligand 2 (CCL2) levels distinguished patients with breast cancer from healthy controls. In summary, circulating CD3+CD8+ CTL and plasma CCL2 levels emerged as promising dual-purpose biomarkers and therapeutic targets in breast cancer management.

Clinical Implications of Breast Cancer Intrinsic Subtypes

Estrogen receptor-positive (ER+) and estrogen receptor-negative (ER-) breast cancers have different genomic architecture and show large-scale gene expression differences consistent with different cellular origins, which is reflected in the luminal (i.e., ER+) versus basal-like (i.e., ER-) molecular class nomenclature. These two major molecular subtypes have distinct epidemiological risk factors and different clinical behaviors. Luminal cancers can be subdivided further based on proliferative activity and ER signaling. Those with a high expression of proliferation-related genes and a low expression of ER-associated genes, called luminal B, have a high risk of early recurrence (i.e., within 5 years), derive significant benefit from adjuvant chemotherapy, and may benefit from adding immunotherapy to chemotherapy. This subset of luminal cancers is identified as the genomic high-risk ER+ cancers by the MammaPrint, Oncotype DX Recurrence Score, EndoPredict, Prosigna, and several other molecular prognostic assays. Luminal A cancers are characterized by low proliferation and high ER-related gene expression. They tend to have excellent prognosis with adjuvant endocrine therapy. Adjuvant chemotherapy may not improve their outcome further. These cancers correspond to the genomic low-risk categories. However, these cancers remain at risk for distant recurrence for extended periods of time, and over 50% of distant recurrences occur after 5 years. Basal-like cancers are uniformly highly proliferative and tend to recur within 3-5 years of diagnosis. In the absence of therapy, basal-like breast cancers have the worst survival, but these also include many highly chemotherapy-sensitive cancers. Basal-like cancers are often treated with preoperative chemotherapy combined with an immune checkpoint inhibitor which results in 60-65% pathologic complete response rates that herald excellent long-term survival. Patients with residual cancer after neoadjuvant therapy can receive additional postoperative chemotherapy that improves their survival. Currently, there is no clinically actionable molecular subclassification for basal-like cancers, although cancers with high androgen receptor (AR)-related gene expression and those with high levels of immune infiltration have better prognosis, but currently their treatment is not different from basal-like cancers in general. A clinically important, minor subset of breast cancers are characterized by frequent HER2 gene amplification and high expression of a few dozen genes, many residing on the HER2 amplicon. This is an important subset because of the highly effective HER2 targeted therapies which are synergistic with endocrine therapy and chemotherapy. The clinical behavior of HER2-enriched cancers is dominated by the underlying ER subtype. ER+/HER2-enriched cancers tend to have more indolent course and lesser chemotherapy sensitivity than their ER counterparts.

An Ultrasound-based Machine Learning Model for Predicting Tumor-Infiltrating Lymphocytes in Breast Cancer

IntroductionTumor-infiltrating lymphocytes (TILs) are key indicators of immune response and prognosis in breast cancer (BC). Accurate prediction of TIL levels is essential for guiding personalized treatment strategies. This study aimed to develop and evaluate machine learning models using ultrasound-derived radiomics and clinical features to predict TIL levels in BC.MethodsThis retrospective study included 256 BC patients between January 2019 and August 2023, who were randomly divided into training (n = 179) and test (n = 77) cohorts. Radiomics features were extracted from the intratumor and peritumor regions in ultrasound images. Feature selection was performed using the "Boruta" package in R to iteratively remove non-significant features. Extra Trees Classifier was used to construct radiomics and clinical models. A combined radiomics-clinical (R-C) model was also developed. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, and decision curve analysis (DCA) to assess clinical utility. A nomogram was created based on the best-performing model.ResultsA total of 1712 radiomics features were extracted from the intratumor and peritumor regions. The Boruta method selected five key features (four from the peritumor and one from the intratumor) for model construction. Clinical features, including immunohistochemistry, tumor size, shape, and echo characteristics, showed significant differences between high (≥10%) and low (<10%) TIL groups. Both the R-C and radiomics models outperformed the clinical model in the test cohort (area under the curve values of 0.869/0.838 vs 0.627, P < .05). Calibration curves and Brier scores demonstrated superior accuracy and calibration for the R-C and radiomics models. DCA revealed the highest net benefit of the R-C model at intermediate threshold probabilities.ConclusionUltrasound-derived radiomics effectively predicts TIL levels in BC, providing valuable insights for personalized treatment and surveillance strategies.

Analysis of BRCA1 germline variants (exons 5, 11 and 20) in breast cancer families from Libya

Breast cancer (BC) is a leading cause of cancer deaths in Libyan women. BRCA1 variants differ globally due to the diversity of genetic makeup and populations history. Their distribution, prevalence, and significance in Libyans remain largely unexplored. This study investigated the characteristics and distribution of BRCA1 variants in exons 5, 11, and 20 in Libyan families with BC. Thirty-six BC patients at ≤ 45 years, between 46-50 years and with a family history of breast, ovarian, pancreatic or prostate cancer in close relatives, or with triple-negative BC, were selected from 33 unrelated families during 2018-2020 at the National Cancer Institute, Sabratha, Libya. From these 33 families, 20 women (18 BC patients and two unaffected) were screened for BRCA1 exons 5, 11 and 20 using Sanger sequencing. All families completed an epidemiology and family history questionnaire. Twenty-seven variants (26 in exon 11 and 1 in exon 20, minor allele frequency of < 0.01) were detected in 10 of 18 unrelated families (55.6%.) Among the 27 variants, 26 (96%) were heterozygous. A frameshift pathogenic variant, c.2643del, and one novel variant c.1366A>G were identified. Furthermore, seven variants with unknown clinical significance were detected: c.1158T>A, c.1346C>G, c.1174C>G, c.3630 G>T, c.3599A>T, and c.3400 G>C in exon 11, and c.5244T>A in exon 20. Six variants with conflicting pathogenicity interpretations, c. 3460T>A, c. 3572 G>A, c. 3700 G>C, c. 1246C>G, c. 1344C>G, and c. 1054 G>A, were also identified. Twelve benign/likely benign variants were identified. Rare BRCA1 variants that have not been reported in North Africa were found in Libyan patients. These findings provide preliminary insights into the BRCA1 variants that could contribute to hereditary BC risk in Libyans. Further functional, computational, and population analyses are essential to determine their significance and potential impact on BC risk, which could ultimately lead to more personalized management strategies.

Correlation between SWE parameters and histopathological features and immunohistochemical biomarkers in invasive breast cancer

OBJECTIVES

Shear wave elastography (SWE) is a novel quantitative elastography technique that can assess the hardness of different tissues. This study introduces a novel shear wave parameter-frequency of mass characteristic (fmass)-and investigates its correlation, along with other shear wave parameters, with the histopathological features and immunohistochemical (IHC) biomarkers of invasive breast cancer (IBC). The study aims to explore whether SWE can provide useful information for IBC treatment and prognosis.

METHODS

With the pathological results as the gold standard, 258 malignant breast lesions were collected, and all patients underwent conventional ultrasound and SWE examinations. The SWE parameters [maximum elastic value (Emax), minimum elastic value (Emin), mean elastic value (Emean), standard deviation of elastic value of the whole lesion (Esd)] and fmass] in the transverse and longitudinal orthogonal sections were measured, and their correlations with the prognostic factors of IBC [including tumor diameters, axillary lymph node (ALN) metastasis, lymphatic vessel invasion (LVI), calcification, histological type, histological grade, and IHC biomarkers (ER, PR, HER-2, Ki-67), and molecular subtypes] were analyzed. The correlations between the SWE parameters of the transverse and longitudinal sections of the tumors with different prognostic factors and the above indicators were analyzed. At the same time, the receiver operating characteristic (ROC) curve was used to analyze the efficacy of fmass in predicting ER and PR expression.

RESULTS

Emean, Emax, Esd, and fmass were correlated with tumor diameters; Emean, Emax and Esd were correlated with histological types and histological grades. Emax and Esd were correlated with ALN metastasis, LVI and pathological types. In the IHC biomarker-labeled masses, fmass was correlated with ER and PR (both P<0.05), and Emean, Emax, and Esd were correlated with HER-2 and Ki-67 (all P<0.05). Emean, Emax, and fmass were all correlated with breast cancer subtypes (all P<0.05), and Emean and Emax were higher in Luminal B [HER-2(+)] breast cancer, while fmass was lower in HER-2(+) and triple-negative breast cancer. Among the statistically significant prognostic factors, the P values of the transverse sections of the masses were all less than or equal to those of the longitudinal sections. The AUC of fmass in the transverse sections of the masses for predicting ER and PR expression were 0.73 (95% CI 0.65 to 0.80) and 0.67 (95% CI 0.60 to 0.74), respectively, with the optimal cut-off values being 76.50 and 60.66, the sensitivities being 72.45% and 81.98%, the specificities being 66.13% and 45.35%, and the accuracies being 70.93% and 69.77%, respectively. The AUC of fmass in the longitudinal sections of the masses for predicting ER and PR expression were 0.74 (95% CI 0.67 to 0.81) and 0.65 (95% CI 0.58 to 0.72), respectively, with the optimal cut-off values being 131.8 and 137.5, the sensitivities being 69.90% and 66.28%, the specificities being 72.58% and 60.47%, and the accuracies being 70.54% and 64.34%, respectively. The fmass in the transverse sections of the masses was more statistically significant.

CONCLUSIONS

The poor prognosis factors of IBC are related to high Emean, Emin, Emax, Esd, and low fmass. The fmass can predict the expression of ER and PR, and the transverse cut data are more meaningful. SWE is helpful for predicting the invasiveness of IBC.

Novel Molecular Classification of Breast Cancer with PET Imaging

Breast cancer is a heterogeneous disease characterized by a wide range of biomarker expressions, resulting in varied progression, behavior, and prognosis. While traditional biopsy-based molecular classification is the gold standard, it is invasive and limited in capturing tumor heterogeneity, especially in deep or metastatic lesions. Molecular imaging, particularly positron emission tomography (PET) imaging, offering a non-invasive alternative, potentially plays a crucial role in the classification and management of breast cancer by providing detailed information about tumor location, heterogeneity, and progression. This narrative review, which focuses on both clinical patients and preclinical studies, explores the latest advancements in PET imaging for breast cancer, emphasizing the development of new tracers targeting hormone receptors such as the estrogen alpha receptor, progesterone receptor, androgen receptor, estrogen beta receptor, as well as the ErbB family of receptors, VEGF/VEGFR, PARP1, PD-L1, and markers for indirectly assessing Ki-67. These innovative radiopharmaceuticals have the potential to guide personalized treatment approaches based on the unique tumor profiles of individual patients. Additionally, they may improve the assessment of treatment efficacy, ultimately leading to better outcomes for those diagnosed with breast cancer.

Evaluation of functional magnetic resonance APT and DKI imaging for breast cancer

OBJECTIVE

This study aimed to compare the performance of amide proton transfer-weighted imaging (APTWI) and diffusion kurtosis imaging (DKI) in differentiating benign from malignant breast lesions, evaluate molecular subtypes of breast cancer, and determine the diagnostic efficacy of the quantitative magnetic resonance imaging (qMRI) parameters in differentiating benign from malignant breast diseases.

METHODS

The study included 168 women who underwent breast APTWI and DKI at Yunnan Cancer Hospital between December 2022 and July 2023. The APT signal intensity (SI), apparent kurtosis coefficient (Kapp), non-Gaussian diffusion coefficient (Dapp), and apparent diffusion coefficient (ADC) values were measured before surgery. The differences in the aforementioned qMRI parameters in molecular subtypes of breast cancer were analyzed using one-way analysis of variance. The efficacy of each quantitative parameter in differentiating benign from malignant breast diseases was evaluated using the receiver-operating characteristic curve.

RESULTS

Significant differences in qMRI parameters were noted between benign and malignant breast lesions. The Kapp (P < .0001) and APT (P < .05) values were higher for malignant tumors than for benign lesions. Conversely, the ADC (P < .0001) and Dapp (P < .0001) values were lower for malignant tumors than for benign lesions. The diagnostic performance was assessed using the area under the curve (AUC) for various parameter combinations. The AUC of Kapp was 0.871, Dapp was 0.872, APT SI was 0.643, DKI + APT was 0.893, DKI + ADC was 0.936, APT + ADC was 0.925, and DKI + APT + ADC was 0.933. Additionally, ADC values (P = .01) demonstrated superior diagnostic performance compared to Kapp (P = .03), Dapp (P = .03), and APT values (P = .06) in distinguishing between different molecular subtypes of breast cancer.

CONCLUSIONS

APTWI distinguished benign from malignant breast disease and enhanced the utility of diffusion-weighted MRI. However, it was not superior to DKI and DWI in identifying the molecular subtypes of breast cancer.

A Prospective Monocentric Study of Invasive Breast Carcinoma Diagnosed at 80 Years and Older: Survival Outcomes and Peculiar Challenges

Background: The lengthening of the lifespan led to an increase in breast cancer (BC) diagnosed in very old age, but the treatment recommendations in this patient group usually lack evidence-based practice. We conducted a prospective observational monocentric study specifically targeting patients diagnosed with invasive BC at 80 years of age or older. Methods: We enrolled 88 patients consecutively observed for a new BC diagnosis at 80 years or older. The aim was to investigate progression-free (PFS) and overall survival (OS), with a long follow-up period, along with clinico-pathological characteristics of the population. Results: At the end of the 5-year follow-up, the estimated OS and PFS probabilities were 82.9% (95% CI: 71.3-95.3%) and 64.0% (95% CI: 51.7-79.2%), respectively. After 8.5 years from the BC diagnosis, 48.9% died. The cause of death was BC in 32.6% of patients, different from BC in 13.9%, and unknown in the remaining. Surgery was performed in 69.3% of the cases and was associated with improved 12-month PFS (p < 0.001). Adjuvant systemic therapy and radiotherapy were omitted in 32% and 93% of eligible patients, respectively. A higher rate of metastatic disease at the diagnosis was observed in comparison with data described in younger people, as well as a significantly high rate of drop-out (27.3%). Conclusions: Ultra-old patients have a not negligible life expectancy; therefore, the oncologic treatment should be optimal and should adequately fight BC, always considering the quality of life of these frail patients. Future research should focus on developing personalized treatment protocols that incorporate comprehensive geriatric assessments and quality-of-life metrics. Additionally, larger, multicentric studies are needed to validate our findings and explore the role of emerging therapies in this age group.

Comparing core needle biopsy and surgical excision in breast cancer diagnosis: implications for clinical practice from a retrospective cohort study

BACKGROUND

Preoperative ultrasound-guided core needle biopsy (CNB) is currently the standard procedure for managing breast illnesses. However, the differences in outcomes between CNB and surgical excision (SE) have not been thoroughly assessed. This study aimed to explore the disparities in pathological outcomes between these two procedures, using a large sample dataset.

METHODS

This retrospective study consecutively included patients who underwent CNB and SE at Shenzhen People's Hospital from May 2016 to June 2023. Immunohistochemistry (IHC) was utilized to determine the status of estrogen receptor (ER), progesterone receptor (PgR), human epidermal growth factor receptor-2 (HER2), and Ki-67. Patients presenting with HER2 IHC 2+ underwent additional fluorescence in situ hybridization (FISH) examination. The cutoff value for high Ki-67 expression was established at 14%. Molecular subtypes were classified into four groups (Luminal A, Luminal B, Triple-negative, and HER2-positive) and five groups [Luminal A, Luminal B+ (HER2-positive), Luminal B- (HER2-negative), Triple-negative, and HER2-positive], based on different criteria.

RESULTS

A total of 4,209 patients were included in this study. Post-surgical confirmation revealed 2,410 cases as benign and 1,799 as malignant. Among the malignant cases, 334 were excluded due to either not having undergone direct surgery or having incomplete IHC results. The remaining 1,465 cases underwent IHC testing. CNB demonstrated a 97% concordance rate (CR) in diagnosing benign cases. The CRs for diagnosing invasive breast cancer (IBC) and carcinoma in situ (CIS) were 92% and 54%, respectively. ER, PgR, HER2, and Ki-67 exhibited CRs of 94%, 91%, 98%, and 84%, respectively. In the four-group classification, the overall diagnostic CR was 82%, with CRs for Luminal A, Luminal B, HER2-positive, and triple-negative breast cancer (TNBC) being 84%, 82%, 78%, and 85%, respectively. Under the five-group classification, the overall diagnostic CR was also 82%, with CRs for Luminal A, Luminal B+, Luminal B-, HER2-positive, and TNBC being 86%, 85%, 94%, 88%, and 92%, respectively.

CONCLUSIONS

This study demonstrates that CNB is highly accurate in differentiating benign from malignant breast lesions, particularly showing significant consistency in the diagnosis of molecular subtypes, providing a reliable reference for clinical diagnosis.

Image analysis-based identification of high risk ER-positive, HER2-negative breast cancers

BACKGROUND

Breast cancer subtypes Luminal A and Luminal B are classified by the expression of PAM50 genes and may benefit from different treatment strategies. Machine learning models based on H&E images may contain features associated with subtype, allowing early identification of tumors with higher risk of recurrence.

METHODS

H&E images (n = 630 ER+/HER2-breast cancers) were pixel-level segmented into epithelium and stroma. Convolutional neural network and multiple instance learning were used to extract image features from original and segmented images. Patient-level classification models were trained to discriminate Luminal A versus B image features in tenfold cross-validation, with or without grade adjustment. The best-performing visual classifier was incorporated into envisioned diagnostic protocols as an alternative to genomic testing (PAM50). The protocols were then compared in time-to-recurrence models.

RESULTS

Among ER+/HER2-tumors, the image-based protocol differentiated recurrence times with a hazard ratio (HR) of 2.81 (95% CI: 1.73-4.56), which was similar to the HR for PAM50 (2.66, 95% CI: 1.65-4.28). Grade adjustment did not improve subtype prediction accuracy, but did help balance sensitivity and specificity. Among high grade participants, sensitivity and specificity (0.734 and 0.474, respectively) became more similar (0.732 and 0.624, respectively) in grade-adjusted models. The original and epithelium-specific images had similar performance and highest accuracy, followed by stroma or binarized images showing only the epithelial-stromal interface.

CONCLUSIONS

Given low rates of genomic testing uptake nationally, image-based methods may help identify ER+/HER2-patients who could benefit from testing.

FBLN2 is associated with basal cell markers Krt14 and ITGB1 in mouse mammary epithelial cells and has a preferential expression in molecular subtypes of human breast cancer

BACKGROUND

Fibulin-2 (FBLN2) is a secreted extracellular matrix (ECM) glycoprotein and has been identified in the mouse mammary gland, in cap cells of terminal end buds (TEBs) during puberty, and around myoepithelial cells during early pregnancy. It is required for basement membrane (BM) integrity in mammary epithelium, and its loss has been associated with human breast cancer invasion. Herein, we attempted to confirm the relevance of FBLN2 to myoepithelial phenotype in mammary epithelium and to assess its expression in molecular subtypes of human breast cancer.

METHODS

The relationship between FBLN2 expression and epithelial markers was investigated in pubertal mouse mammary glands and the EpH4 mouse mammary epithelial cell line using immunohistochemistry, immunocytochemistry, and immunoblotting. Human breast cancer mRNA data from the METABRIC and TCGA datasets from Bioportal were analyzed to assess the association of Fbln2 expression with epithelial markers, and with molecular subtypes. Survival curves were generated using data from the METABRIC dataset and the KM databases.

RESULTS

FBLN2 knockdown in mouse mammary epithelial cells was associated with a reduction in KRT14 and an increase in KRT18. Further, TGFβ3 treatment resulted in the upregulation of FBLN2 in vitro. Meta-analyses of human breast cancer datasets from Bioportal showed a higher expression of Fbln2 mRNA in claudin-low, LumA, and normal-like breast cancers compared to LumB, Her2 +, and Basal-like subgroups. Fbln2 mRNA levels were positively associated with mesenchymal markers, myoepithelial markers, and markers of epithelial-mesenchymal transition. Higher expression of Fbln2 mRNA was associated with better prognosis in less advanced breast cancer and this pattern was reversed in more advanced lesions.

CONCLUSION

With further validation, these observations may offer a molecular prognostic tool for human breast cancer for more personalized therapeutic approaches.

Utilizing multiclassifier radiomics analysis of ultrasound to predict high axillary lymph node tumour burden in node-positive breast cancer patients: a multicentre study

BACKGROUND

The tumor burden within the axillary lymph nodes (ALNs) constitutes a pivotal factor in breast cancer, serving as the primary determinant for treatment decisions and exhibiting a close correlation with prognosis.

OBJECTIVE

This study aimed to investigate the potential of ultrasound-based radiomics and clinical characteristics in non-invasively distinguishing between low tumor burden (1-2 positive nodes) and high tumor burden (more than 2 positive nodes) in patients with node-positive breast cancer.

METHODS

A total of 215 patients with node-positive breast cancer, who underwent preoperative ultrasound examinations, were enrolled in this study. Among these patients, 144 cases were allocated to the training set, 37 cases to the validation set, and 34 cases to the testing set. Postoperative histopathology was used to determine the status of ALN tumor burden. The region of interest for breast cancer was delineated on the ultrasound image. Nine models were developed to predict high ALN tumor burden, employing a combination of three feature screening methods and three machine learning classifiers. Ultimately, the optimal model was selected and tested on both the validation and testing sets. In addition, clinical characteristics were screened to develop a clinical model. Furthermore, Shapley additive explanations (SHAP) values were utilized to provide explanations for the machine learning model.

RESULTS

During the validation and testing sets, the models demonstrated area under the curve (AUC) values ranging from 0.577 to 0.733 and 0.583 to 0.719, and accuracies ranging from 64.9% to 75.7% and 64.7% to 70.6%, respectively. Ultimately, the Boruta_XGB model, comprising five radiomics features, was selected as the final model. The AUC values of this model for distinguishing low from high tumor burden were 0.828, 0.715, and 0.719 in the training, validation, and testing sets, respectively, demonstrating its superiority over the clinical model.

CONCLUSIONS

The developed radiomics models exhibited a significant level of predictive performance. The Boruta_XGB radiomics model outperformed other radiomics models in this study.

Bilobed lateral artery perforator-based flap for partial breast reconstruction - Technique description and results from a ten-year cohort

INTRODUCTION

We present a new technique, the bilobed lateral artery perforator-based flap, for breast-conserving surgery of large central tumors or nearby, combining Zymany's bilobed flap and a Lateral Intercostal Perforator (LICAP) flap, and its 10-year outcomes.

MATERIALS AND METHODS

We studied 37 patients with malignant breast tumors near or involving the central skin, without ptosis or desire to correct it, who avoided mastectomy with this modified bilobed flap from 2013 to 2022. The same surgeon operated on them in different institutions. This research project was approved by our ethical committee (n. 2.322.212).

RESULTS

The mean age was 57.17 (±12.60) years. The mean specimen weight was 74.32 (±25.84)g, and the mean tumor size was 40.35 (±15.81) mm. Fourteen (37.84 %) tumors were larger than 5 cm and one was multicentric. Thirty-two (86.49 %) patients had invasive ductal carcinomas. Nipple areola complex was removed in 19 (51.35 %) cases due to clinical involvement, and immediately reconstructed in two cases with contralateral free grafting. Twenty-one (56.76 %) patients received neoadjuvant chemotherapy. Three (8.11 %) patients had immediate contralateral mastopexy. Radiotherapy was indicated in all cases. There were 3 (8.11 %) minor complications, one positive margin, and no surgical revisions. In a mean follow-up of 39.97 (±29.43) months, there were no local recurrences, 2 metastasis, and one death. Satisfaction and aesthetic results were good or excellent in most cases.

CONCLUSIONS

The new technique enabled breast conservation, with high rates of free margins, high levels of satisfaction, few complications in women with large central tumors on small breasts with limited ptosis.

Targeting STAT3 signaling pathway by curcumin and its analogues for breast cancer: A narrative review

BACKGROUND

Breast cancer (BC) continues to be a significant global health issue, with a rising number of cases requiring ongoing research and innovation in treatment strategies. Curcumin (CUR), a natural compound derived from Curcuma longa, and similar compounds have shown potential in targeting the STAT3 signaling pathway, which plays a crucial role in BC progression.

AIMS

The aim of this study was to investigate the effects of curcumin and its analogues on BC based on cellular and molecular mechanisms.

MATERIALS & METHODS

The literature search conducted for this study involved utilizing the Scopus, ScienceDirect, PubMed, and Google Scholar databases in order to identify pertinent articles.

RESULTS

This narrative review explores the potential of CUR and similar compounds in inhibiting STAT3 activation, thereby suppressing the proliferation of cancer cells, inducing apoptosis, and inhibiting metastasis. The review demonstrates that CUR directly inhibits the phosphorylation of STAT3, preventing its movement into the nucleus and its ability to bind to DNA, thereby hindering the survival and proliferation of cancer cells. CUR also enhances the effectiveness of other therapeutic agents and modulates the tumor microenvironment by affecting tumor-associated macrophages (TAMs). CUR analogues, such as hydrazinocurcumin (HC), FLLL11, FLLL12, and GO-Y030, show improved bioavailability and potency in inhibiting STAT3, resulting in reduced cell proliferation and increased apoptosis.

CONCLUSION

CUR and its analogues hold promise as effective adjuvant treatments for BC by targeting the STAT3 signaling pathway. These compounds provide new insights into the mechanisms of action of CUR and its potential to enhance the effectiveness of BC therapies.

Machine learning radiomics based on intra and peri tumor PA/US images distinguish between luminal and non-luminal tumors in breast cancers

PURPOSE

This study aimed to evaluate a radiomics model using Photoacoustic/ultrasound (PA/US) imaging at intra and peri-tumoral area to differentiate Luminal and non-Luminal breast cancer (BC) and to determine the optimal peritumoral area for accurate classification.

MATERIALS AND METHODS

From February 2022 to April 2024, this study continuously collected 322 patients at Shenzhen People's Hospital, using standardized conditions for PA/US imaging of BC. Regions of interest were delineated using ITK-SNAP, with peritumoral regions of 2 mm, 4 mm, and 6 mm automatically expanded using code from the Pyradiomic package. Feature extraction was subsequently performed using Pyradiomics. The study employed Z-score normalization, Spearman correlation for feature correlation, and LASSO regression for feature selection, validated through 10-fold cross-validation. The radiomics model integrated intra and peri-tumoral area, evaluated by receiver operating characteristic curve(ROC), Calibration and Decision Curve Analysis(DCA).

RESULTS

We extracted and selected features from intratumoral and peritumoral PA/US images regions at 2 mm, 4 mm, and 6 mm. The comprehensive radiomics model, integrating these regions, demonstrated enhanced diagnostic performance, especially the 4 mm model which showed the highest area under the curve(AUC):0.898(0.78-1.00) and comparably high accuracy (0.900) and sensitivity (0.937). This model outperformed the standalone clinical model and combined clinical-radiomics model in distinguishing between Luminal and non-Luminal BC, as evidenced in the test set results.

CONCLUSION

This study developed a radiomics model integrating intratumoral and peritumoral at 4 mm region PA/US model, enhancing the differentiation of Luminal from non-Luminal BC. It demonstrated the diagnostic utility of peritumoral characteristics, reducing the need for invasive biopsies and aiding chemotherapy planning, while emphasizing the importance of optimizing tumor surrounding size for improved model accuracy.

Immune marker expression and prognosis of early breast cancer expressing HER3

INTRODUCTION

There is a strong rationale for targeting HER3, as HER3 contributes to tumorigenesis and treatment resistance. However, the prognostic role of HER3 and their association with immunoregulatory protein expression has not been established.

METHODS

The main objective of this study was to investigate the prognostic role of HER3 expression and identify immunoregulatory marker expression according to HER3 status. HER3 expression and 10 immunoregulatory protein (PD-1/PD-L1/PD-L2/IDO/TIM-3/OX40/OX40L/B7-H2/B7-H3/B7-H4) expression was identified in 320 stage I-III breast cancer patients who received curative surgery at Seoul National University Hospital in 2008. The median follow-up duration was 88.8 months. Criteria for HER3 IHC was adopted from HER2 IHC score and only those with 3 + was considered positive.

RESULTS

Among 320 patients, 213 (67.2 %) had luminal A disease, 30 (9.5 %) had luminal B disease, 28 (8.8 %) had HER2-positive disease, and 46 (14.5 %) had triple negative disease. HER3 expression was shown in 153 patients (47.8 %). Tumors with HER3-expression had more immunogenic tumor microenvironment compared to HER3-negative tumor. In addition, patients with HER3 expression had favorable 5-year relapse free survival compared to HER3-negative patients (5-year RFS 92.5 % vs. 85.2 %, p = 0.038). However, in the multivariate analysis, HER3 expression was not a prognostic factor, but expression of immunoregulatory protein was a prognostic factor.

CONCLUSIONS

This study identified immunoregulatory protein expression according to HER3 status in breast cancer patients. As tumor with HER3 expression have more immunogenic microenvironment, investigating combination treatment of HER3 targeting agent and immunotherapy in HER3 expressing breast cancer may be promising.