Neoadjuvant chemotherapy (NAC) for breast cancer is widely used in the clinical setting to improve the chance of surgery, breast conservation and quality of life for patients with advanced breast cancer. A more accurate efficacy evaluation system is important for the decision of surgery timing and chemotherapy regimen implementation. However, current methods, encompassing imaging techniques such as ultrasound and MRI, along with non-imaging approaches like pathological evaluations, often fall short in accurately depicting the therapeutic effects of NAC. Imaging techniques are subjective and only reflect macroscopic morphological changes, while pathological evaluation is the gold standard for efficacy assessment but has the disadvantage of delayed results. In an effort to identify assessment methods that align more closely with real-world clinical demands, this paper provides an in-depth exploration of the principles and clinical applications of various assessment approaches in the neoadjuvant chemotherapy process.

The pathology is decisive for disease diagnosis but relies heavily on experienced pathologists. In recent years, there has been growing interest in the use of artificial intelligence in pathology (AIP) to enhance diagnostic accuracy and efficiency. However, the impressive performance of deep learning-based AIP in laboratory settings often proves challenging to replicate in clinical practice. As the data preparation is important for AIP, the paper has reviewed AIP-related studies in the PubMed database published from January 2017 to February 2022, and 118 studies were included. An in-depth analysis of data preparation methods is conducted, encompassing the acquisition of pathological tissue slides, data cleaning, screening, and subsequent digitization. Expert review, image annotation, dataset division for model training and validation are also discussed. Furthermore, we delve into the reasons behind the challenges in reproducing the high performance of AIP in clinical settings and present effective strategies to enhance AIP’s clinical performance. The robustness of AIP depends on a randomized collection of representative disease slides, incorporating rigorous quality control and screening, correction of digital discrepancies, reasonable annotation, and sufficient data volume. Digital pathology is fundamental in clinical-grade AIP, and the techniques of data standardization and weakly supervised learning methods based on whole slide image (WSI) are effective ways to overcome obstacles of performance reproduction. The key to performance reproducibility lies in having representative data, an adequate amount of labeling, and ensuring consistency across multiple centers. Digital pathology for clinical diagnosis, data standardization and the technique of WSI-based weakly supervised learning will hopefully build clinical-grade AIP.

Molecular subtypes of breast cancer are important references to personalized clinical treatment. For cost and labor savings, only one of the patient’s paraffin blocks is usually selected for subsequent immunohistochemistry (IHC) to obtain molecular subtypes. Inevitable block sampling error is risky due to the tumor heterogeneity and could result in a delay in treatment. Molecular subtype prediction from conventional H&E pathological whole slide images (WSI) using the AI method is useful and critical to assist pathologists to pre-screen proper paraffin block for IHC. It is a challenging task since only WSI-level labels of molecular subtypes from IHC can be obtained without detailed local region information. Gigapixel WSIs are divided into a huge amount of patches to be computationally feasible for deep learning, while with coarse slide-level labels, patch-based methods may suffer from abundant noise patches, such as folds, overstained regions, or non-tumor tissues. A weakly supervised learning framework based on discriminative patch selection and multi-instance learning was proposed for breast cancer molecular subtype prediction from H&E WSIs. Firstly, co-teaching strategy using two networks was adopted to learn molecular subtype representations and filter out some noise patches. Then, a balanced sampling strategy was used to handle the imbalance in subtypes in the dataset. In addition, a noise patch filtering algorithm that used local outlier factor based on cluster centers was proposed to further select discriminative patches. Finally, a loss function integrating local patch with global slide constraint information was used to fine-tune MIL framework on obtained discriminative patches and further improve the prediction performance of molecular subtyping. The experimental results confirmed the effectiveness of the proposed AI method and our models outperformed even senior pathologists, which has the potential to assist pathologists to pre-screen paraffin blocks for IHC in clinic.

Background: The molecular features of fibroblasts and the role of fibrosis in neoadjuvant chemotherapy (NAC) response and breast cancer (BRCA) prognosis remain unclear. Therefore, this study aimed to investigate the impact of interstitial fibrosis on the response and prognosis of patients with BRCA undergoing NAC treatment. Materials and Methods: The molecular characteristics of pathologic complete response (pCR) and non-pCR (npCR) in patients with BRCA were analyzed using multi-omics analysis. A clinical cohort was collected to investigate the predictive value of fibrosis in patients with BRCA. Results: Fibrosis-related signaling pathways were significantly upregulated in patients with npCR. npCR may be associated with distinct and highly active fibroblast subtypes. Patients with high fibrosis had lower pCR rates. The fibrosis-dependent nomogram for pCR showed efficient predictive ability (training set: area under the curve [AUC]=0.871, validation set: AUC=0.792). Patients with low fibrosis had a significantly better prognosis than those with high fibrosis, and those with a high fibrotic focus index had significantly shorter overall and recurrence-free survival. Therefore, fibrosis can be used to predict pCR. Our findings provide a basis for decision-making in the treatment of BRCA. Conclusions: npCR is associated with a distinct and highly active fibroblast subtype. Furthermore, patients with high fibrosis have lower pCR rates and shorter long-term survival. Therefore, fibrosis can predict pCR. A nomogram that includes fibrosis can provide a basis for decision-making in the treatment of BRCA. Export

The amplification of epidermal growth factor receptor 2 (HER2) is associated with a poor prognosis and HER2 gene is overexpressed in approximately 15–30% of breast cancers. In HER2-positive breast cancer patients, HER2-targeted therapies improved clinical outcomes and survival rates. However, drug resistance to anti-HER2 drugs is almost unavoidable, leaving some patients with an unmet need for better prognoses. Therefore, exploring strategies to delay or revert drug resistance is urgent. In recent years, new targets and regimens have emerged continuously. This review discusses the fundamental mechanisms of drug resistance in the targeted therapies of HER2-positive breast cancer and summarizes recent research progress in this field, including preclinical and basic research studies.

Breast cancer is a multifactorial disease driven by acquired genetic and epigenetic changes that lead to aberrant regulation of cellular signaling pathways. Receptor tyrosine kinases (RTKs), a class of critical receptors, are involved in the initiation and progression of breast cancer. RTKs are cell surface receptors with unique structures and biological characteristics, which respond to environmental signals by initiating signaling cascades such as the mitogen-activated protein kinase (MAPK) pathway, Janus kinase (JAK)/signal transducer, activator of transcription (STAT) pathway, and phosphoinositide 3-kinase (PI3K)/AKT pathway. The critical role of RTKs makes them suitable targets for breast cancer treatment. Targeted therapies against RTKs have been developed in recent years, evaluated in clinical trials, and approved for several cancer types, including breast cancer. However, breast cancer displays molecular heterogeneity and exhibits different therapeutic responses to various drug types, leading to limited effectiveness of targeted therapy against RTKs. In this review, we summarize the structural and functional characteristics of selected RTKs and discuss the mechanisms and current status of drug therapy involving different protein tyrosine kinases in breast cancer progression.