Late Recurrence Following Early Breast Cancer

Advances in Early Breast Cancer Risk Profiling: From Histopathology to Molecular Technologies

Early breast cancer (BC) is the definition applied to breast-confined tumors with or without limited involvement of locoregional lymph nodes. While risk stratification is essential for guiding clinical decisions, it can be a complex endeavor in these patients due to the absence of comprehensive guidelines. Histopathological analysis and biomarker assessment play a pivotal role in defining patient outcomes. Traditional histological criteria such as tumor size, lymph node involvement, histological type and grade, lymphovascular invasion, and immune cell infiltration are significant prognostic indicators. In addition to the hormone receptor, HER2, and-in specific scenarios-BRCA1/2 testing, molecular subtyping through gene expression profiling provides valuable insights to tailor clinical decision-making. The emergence of "omics" technologies, applicable to both tissue and liquid biopsy samples, has broadened our arsenal for evaluating the risk of early BC. However, a pressing need remains for standardized methodologies and integrated pathological models that encompass multiple analytical dimensions. In this study, we provide a detailed examination of the existing strategies for early BC risk stratification, intending to serve as a practical guide for histopathologists and molecular pathologists.

Using Machine Learning Methods to Assess Lymphovascular Invasion and Survival in Breast Cancer: Performance of Combining Preoperative Clinical and MRI Characteristics

BACKGROUND

Preoperative assessment of lymphovascular invasion (LVI) in invasive breast cancer (IBC) is of high clinical relevance for treatment decision-making and prognosis.

PURPOSE

To investigate the associations of preoperative clinical and magnetic resonance imaging (MRI) characteristics with LVI and disease-free survival (DFS) by using machine learning methods in patients with IBC.

STUDY TYPE

Retrospective.

POPULATION

Five hundred and seventy-five women (range: 24-79 years) with IBC who underwent preoperative MRI examinations at two hospitals, divided into the training (N = 386) and validation datasets (N = 189).

FIELD STRENGTH/SEQUENCE

Axial fat-suppressed T2-weighted turbo spin-echo sequence and dynamic contrast-enhanced with fat-suppressed T1-weighted three-dimensional gradient echo imaging.

ASSESSMENT

MRI characteristics (clinical T stage, breast edema score, MRI axillary lymph node status, multicentricity or multifocality, enhancement pattern, adjacent vessel sign, and increased ipsilateral vascularity) were reviewed independently by three radiologists. Logistic regression (LR), eXtreme Gradient Boosting (XGBoost), k-Nearest Neighbor (KNN), and Support Vector Machine (SVM) algorithms were used to establish the models by combing preoperative clinical and MRI characteristics for assessing LVI status in the training dataset, and the methods were further applied in the validation dataset. The LVI score was calculated using the best-performing of the four models to analyze the association with DFS.

STATISTICAL TESTS

Chi-squared tests, variance inflation factors, receiver operating characteristics (ROC), Kaplan-Meier curve, log-rank, Cox regression, and intraclass correlation coefficient were performed. The area under the ROC curve (AUC) and hazard ratios (HR) were calculated. A P-value <0.05 was considered statistically significant.

RESULTS

The model established by the XGBoost algorithm had better performance than LR, SVM, and KNN models, achieving an AUC of 0.832 (95% confidence interval [CI]: 0.789, 0.876) in the training dataset and 0.838 (95% CI: 0.775, 0.901) in the validation dataset. The LVI score established by the XGBoost model was an independent indicator of DFS (adjusted HR: 2.66, 95% CI: 1.22-5.80).

DATA CONCLUSION

The XGBoost model based on preoperative clinical and MRI characteristics may help to investigate the LVI status and survival in patients with IBC.

LEVEL OF EVIDENCE

3 TECHNICAL EFFICACY: Stage 2.

Formation of a traditional Chinese medicine self-assembly nanostrategy and its application in cancer: a promising treatment

Traditional Chinese medicine (TCM) has been used for centuries to prevent and treat a variety of illnesses, and its popularity is increasing worldwide. However, the clinical applications of natural active components in TCM are hindered by the poor solubility and low bioavailability of these compounds. To address these issues, Chinese medicine self-assembly nanostrategy (CSAN) is being developed. Many active components of TCM possess self-assembly properties, allowing them to form nanoparticles (NPs) through various noncovalent forces. Self-assembled NPs (SANs) are also present in TCM decoctions, and they are closely linked to the therapeutic effects of these remedies. SAN is gaining popularity in the nano research field due to its simplicity, eco-friendliness, and enhanced biodegradability and biocompatibility compared to traditional nano preparation methods. The self-assembly of active ingredients from TCM that exhibit antitumour effects or are combined with other antitumour drugs has generated considerable interest in the field of cancer therapeutics. This paper provides a review of the principles and forms of CSAN, as well as an overview of recent reports on TCM that can be used for self-assembly. Additionally, the application of CSAN in various cancer diseases is summarized, and finally, a concluding summary and thoughts are proposed. We strongly believe that CSAN has the potential to offer fresh strategies and perspectives for the modernization of TCM.

Managing a Long and Winding Road: Estrogen Receptor-Positive Breast Cancer

We review key topics in the management of estrogen receptor (ER)-positive human epidermal growth factor receptor 2-negative breast cancer. The single biggest challenge in management of this disease is late relapse, and we review new methods for identifying which patients are at risk of late relapse and potential therapeutic approaches in clinical trials. CDK4/6 inhibitors have become a standard treatment option for high-risk patients in both the adjuvant setting and the first-line metastatic setting, and we review data on optimal treatment after progression on CDK4/6 inhibitors. Targeting the estrogen receptor remains the single most effective way of targeting the cancer, and we review the developments in new oral selective ER degraders that are becoming a standard of care in cancers with ESR1 mutations and potential future directions.

Postneoadjuvant treatment for triple-negative breast cancer

PURPOSE OF REVIEW

Triple-negative breast cancer (TNBC) has been conventionally associated with poor prognosis, as a result of limited therapeutic options. In the early setting, prognosis is informed by clinical-pathological factors; for patients receiving neoadjuvant treatments, pathological complete response (pCR) is the strongest factor. In this review, we mapped the landscape of clinical trials in the postneoadjuvant space, and identified three patterns of clinical trial design.

RECENT FINDINGS

For patients at higher risk, effective postneoadjuvant treatments are of paramount importance to address a high clinical need. Postneoadjuvant risk-adapted treatments have demonstrated to improve survival in patients at high of recurrence.

SUMMARY

Patients at high risk have indication for adjuvant treatment intensification, informed by baseline clinical, pathological or molecular factors (type 1 approach), on the presence, extent and molecular characteristics of the residual disease at the time of surgery (type 2) or on risk factors assessed in the postsurgical setting (type 3), for example, circulating tumour DNA. Most of the past trials were based on type 2 approaches, for example, with capecitabine and Olaparib. Few trials were based on a type 1 approach, notably pembrolizumab for early TNBC. The clinical validity of type 3 approaches is under investigation in several ongoing trials.