Approaching Neoadjuvant Therapy in the Management of Early-Stage Breast Cancer

Fatigue and quality of life during neoadjuvant chemotherapy of early breast cancer: a prospective multicenter cohort study

BACKGROUND

Few measurements of fatigue and quality of life have been performed during neoadjuvant chemotherapy of early breast cancer. This study evaluates fatigue and quality of life experienced by early breast cancer patients during neoadjuvant chemotherapy and their association with different clinical parameters.

METHODS

Fifty-four stage I-III patients' responses to the Multidimensional Fatigue Inventory (MFI) and to the Functional Assessment of Cancer Therapy-Breast (FACT-B) were analyzed by a linear covariance pattern model. Chemotherapy regimen, age, baseline fatigue level, body-mass-index and cancer stage were added to the model to estimate their impact on both outcomes.

RESULTS

All fatigue dimensions worsened in clinically relevant levels. Physical fatigue worsened the most, mental fatigue the least. For quality of life, physical and functional well-being worsened the most. Only emotional well-being improved during chemotherapy. Physical well-being worsened more during standard than during dose-dense chemotherapy, and more during anthracycline than during taxane cycles. Age, body-mass-index and cancer stage had no impact. The higher the fatigue levels at baseline, the less they worsened during chemotherapy.

CONCLUSIONS

Further actions to reduce fatigue and improve quality of life during neoadjuvant chemotherapy of early breast cancer are needed. Focus should be laid on the physical dimension. Future research should also investigate the impact of different chemotherapy sequences and densities on fatigue and quality of life.

STUDY REGISTRATION

The study was registered in the German Clinical Trials Register in May 2019 (DRKS00016761).

Tumor Microenvironment Can Predict Chemotherapy Response of Patients with Triple-Negative Breast Cancer Receiving Neoadjuvant Chemotherapy

PURPOSE

Triple-negative breast cancer (TNBC) is a breast cancer subtype that has poor prognosis and exhibits a unique tumor microenvironment. Analysis of the tumor microbiome has indicated a relationship between the tumor microenvironment and treatment response. Therefore, we attempted to reveal the role of the tumor microbiome in patients with TNBC receiving neoadjuvant chemotherapy.

MATERIALS AND METHODS

We collected TNBC patient RNA-sequencing samples from the Gene Expression Omnibus and extracted microbiome count data. Differential and relative abundance were estimated with linear discriminant analysis effect size. We calculated the immune cell fraction with CIBERSORTx and conducted survival analysis using the Cancer Genome Atlas patient data. Correlations between the microbiome and immune cell compositions were analyzed and a prediction model was constructed to estimate drug response.

RESULTS

Among the pathological complete response group (pCR), the beta diversity varied considerably; consequently, 20 genera and 24 species were observed to express a significant differential and relative abundance. Pandoraea pulmonicola and Brucella melitensis were found to be important features in determining drug response. In correlation analysis, Geosporobacter ferrireducens, Streptococcus sanguinis, and resting natural killer cells were the most correlated factors in the pCR, whereas Nitrosospira briensis, Plantactinospora sp. BC1, and regulatory T cells were key features in the residual disease group.

CONCLUSION

Our study demonstrated that the microbiome analysis of tumor tissue can predict chemotherapy response of patients with TNBC. Further, the immunological tumor microenvironment may be impacted by the tumor microbiome, thereby affecting the corresponding survival and treatment response.

Advances in the application of computational pathology in diagnosis, immunomicroenvironment recognition, and immunotherapy evaluation of breast cancer: a narrative review

BACKGROUND

Breast cancer (BC) is a prevalent and highly lethal malignancy affecting women worldwide. Immunotherapy has emerged as a promising therapeutic strategy for BC, offering potential improvements in patient survival. Neoadjuvant therapy (NAT) has also gained significant clinical traction. With the advancement of computer technology, Artificial Intelligence (AI) has been increasingly applied in pathology research, expanding and redefining the scope of the field. This narrative review aims to provide a comprehensive overview of the current literature on the application of computational pathology in BC, specifically focusing on diagnosis, immune microenvironment recognition, and the evaluation of immunotherapy and NAT response.

METHODS

A thorough examination of relevant literature was conducted, focusing on studies investigating the role of computational pathology in BC diagnosis, immune microenvironment recognition, and immunotherapy and NAT assessment.

RESULTS

The application of computational pathology has shown significant potential in BC management. AI-based techniques enable improved diagnosis and classification of BC subtypes, enhance the identification and characterization of the immune microenvironment, and facilitate the evaluation of immunotherapy and NAT response. However, challenges related to data quality, standardization, and algorithm development still need to be addressed.

CONCLUSION

The integration of computational pathology and AI has transformative implications for BC patient care. By leveraging AI-based technologies, clinicians can make more informed decisions in diagnosis, treatment planning, and therapeutic response assessment. Future research should focus on refining AI algorithms, addressing technical challenges, and conducting large-scale clinical validation studies to facilitate the translation of computational pathology into routine clinical practice for BC patients.

Systemic therapy for hormone receptor-positive/human epidermal growth factor receptor 2-negative early stage and metastatic breast cancer

Hormone receptor (HR)-positive and human epidermal growth factor receptor 2 (HER2)-negative breast cancer is defined by the presence of the estrogen receptor and/or the progesterone receptor and the absence of HER2 gene amplification. HR-positive/HER2-negative breast cancer accounts for 65%-70% of all breast cancers, and incidence increases with increasing age. Treatment varies by stage, and endocrine therapy is the mainstay of treatment in both early stage and late-stage disease. Combinations with cyclin-dependent kinase 4/6 inhibitors have reduced distant recurrence in the early stage setting and improved overall survival in the metastatic setting. Chemotherapy is used based on stage and tumor biology in the early stage setting and after endocrine resistance for advanced disease. New therapies, including novel endocrine agents and antibody-drug conjugates, are now changing the treatment landscape. With the availability of new treatment options, it is important to define the optimal sequence of treatment to maximize clinical benefit while minimizing toxicity. In this review, the authors first discuss the pathologic and molecular features of HR-positive/HER2-negative breast cancer and mechanisms of endocrine resistance. Then, they discuss current and emerging therapies for both early stage and metastatic HR-positive/HER2-negative breast cancer, including treatment algorithms based on current data.

Resistance of breast cancer cells to paclitaxel is associated with low expressions of miRNA-186 and miRNA-7

Aim: Neo-adjuvant chemotherapy is a common approach for the complex treatment of breast cancer (BC) and paclitaxel (PTX) is frequently included in the therapeutic regimen. However, the effect of PTX-based treatment is hard to predict precisely based on routinely used markers. As microRNAs are considered a new promising class of biomarkers, the link between miRNA expression and PTX resistance of BC cells needs to be well investigated. This study aimed at the identification of miRNAs associated with responses of BC cells to PTX. Methods: Intrinsic PTX sensitivity and miRNA profiling were assayed in five BC cell lines to identify candidate miRNAs. Selected miRNA (n. 15) expressions were analyzed by real-time-quantitative polymerase chain reaction (RT-qPCR) in BC tissue samples (n. 31) obtained from a diagnostic biopsy. Results were analyzed in the context of the effect of two cycles of PTX and the effect of the completed scheme of neoadjuvant therapy. The study's design facilitated the evaluation of the effect of PTX on cells and the identification of features of the microRNA expression profiles associated exclusively with sensitivity to this drug. Results: miR-186 and miR-7 expression in BC tissues was higher in patients with better outcomes of PTX-based neoadjuvant therapy. Conclusion: High expressions of miR-186 and miR-7 are associated with good response to PTX, whereas their low expressions may be associated with resistance to PTX in BC, indicating the possibility of developing innovative test systems for the prediction of the PTX response, which can be used before the start of neo-adjuvant chemotherapy for BC.

Fortified anti-proliferative activity of niclosamide for breast cancer treatment: In-vitro and in-vivo assessment

Breast cancer represents one of the top lethal cancer types among the females worldwide. Several factors manipulate the clinical outcome of the treatment as the stage of the cancer upon detection, genetic and hormonal factors, drug resistance and metastasis. Accordingly, drug's repositioning, enhancing the bioavailability and encapsulation into nanoparticles (NPs) are among the predilected pathways for enhanced therapeutic outcome. Niclosamide (NIC) is an anthelmintic drug and has been repositioned as anticancer agent after revealing its anti-neoplastic activity. Piperine (PIP) was used as food spice until its anticancer activity was discovered. However, their hydrophobicity constrains their therapeutic efficiency. The cytotoxicity of both drugs in the free form was tested on MCF-7 cells, and the results indicated a NIC cytotoxicity enhancement by PIP. Then, NIC and PIP were encapsulated successfully into F127-NPs with entrapment efficiency of 97 % and 82 %, respectively. Particle size, zeta potential, TEM and FTIR confirmed the micellization process and drug encapsulation. The developed NIC-NPs and PIP-NPs exerted potent anticancer effect as compared to the free forms. Accordingly, the mixture; NIC-NPs/PIP-NPs was tested and its cytotoxicity exceeded the individually encapsulated drugs. Flowcytometry assessment was performed and demonstrated an induced cell death through the apoptotic stage. Additionally, in-vivo therapeutic efficiency of NIC-NPs/PIP-NPs was assessed through Ehrlich ascites tumor and the nanocombination therapy exerted superior additive anticancer effect when compared to NIC-NPs which is attributed to the PIP-NPs induced bioavailability. The study can be considered the first one investigating the PIP role in bioenhancing the anti-proliferative activity of NIC to combat breast cancer.

Correlation analysis of HIF-1α and Ca15-3 in response to neoadjuvant chemotherapy in locally advanced breast cancer: A cohort study in Indonesia

BACKGROUND

Breast cancer (BC) is the most common cancer among women worldwide and a leading cause of death in Indonesia. The primary treatment of locally advanced BC is neoadjuvant chemotherapy (NAC). The rapid proliferation of tumor cells in a neoplastic microenvironment is largely due to hypoxia, which also encourages the development of chemoresistant BC. The master regulator of the hypoxia response is hypoxia-inducible factor-1α (HIF-1α). The response evaluation criteria in solid tumors (RECIST) is an objective response metric that demonstrates the efficacy of a NAC based mostly on the size of the tumor. Ca15-3 is the protein product of the MUC1 gene and is the most widely used serum marker in BC. The purpose of this study is to investigate the relationship between HIF-1α and RECIST and between Ca15-3 and RECIST and to assess the relationship among all of them in BC.

METHODS

This observational study used the prospective cohort method included 11 patients with histopathologically confirmed BC, specifically invasive ductal carcinoma. We evaluated the changes in HIF-1α and Ca15-3 serum levels using ELISA and measured tumor lesions with RECIST. The procedure was carried out twice. Serum levels were measured at baseline, and after receiving two cycles of NAC (5 weeks).

RESULTS

Among the 11 patients included in this study, HIF-1α, Ca15-3, and RECIST decreased significantly after NAC. The changes in RECIST correlated with Ca15-3: each unit decrease in RECIST score was associated with a 0.3-unit decrease in Ca15-3 levels (p = 0.019).

CONCLUSIONS

There was a decrease in HIF-1α, followed by a decrease in Ca15-3 and RECIST in response to chemotherapy. There was a statistically significant correlation between Ca15-3 and response to chemotherapy. This study evidences the relationship between factors that shape the local tumor microenvironment.

SCUBE2 as a Marker of Resistance to Taxane-based Neoadjuvant Chemotherapy and a Potential Therapeutic Target in Breast Cancer

Objective

Taxane-based neoadjuvant chemotherapy is the most common neoadjuvant approach in breast cancer, especially in human epidermal growth factor receptor 2 (HER2)-positive and triple-negative subtypes. However, chemoresistance is a problem in many patients, and success rates are low in estrogen receptor (ER)-positive breast cancer. The aim of this study was to identify predictive markers for resistance to taxane-based therapy, which may have a potential as therapeutic targets in breast cancer.

Materials and Methods

Three comprehensive breast cancer Gene Expression Omnibus datasets were analyzed to identify differentially expressed genes (DEGs) in breast cancer patients resistant to taxane-based neoadjuvant chemotherapy. Functional annotation clustering and enrichment analysis were performed on the DEGs list. A protein-protein interaction network was established with the upregulated genes. The predictive value and the differential expression of the central genes were validated in the extensive ROC Plotter database.

Results

Seventeen upregulated genes were found which were associated with resistance to taxane-based neoadjuvant therapy and high connectivity in the network analysis. ESR1, CCND1, and SCUBE2 emerged as the top three key genes associated with resistance. SCUBE2 displayed a high predictive power comparable to ESR1, and better than CCND1, the two commonly accepted markers. The predictive ability of SCUBE2 was higher in ER-positive and HER2-positive breast cancers.

Conclusion

These results suggest that SCUBE2 may be used as a predictive marker to guide decisions on neoadjuvant therapy. Emerging evidence about the role of SCUBE2 as a coreceptor involved in tumor progression and angiogenesis also suggests SCUBE2 as a potential therapeutic target. These points should be investigated in further studies.

Machine Learning Models and Multiparametric Magnetic Resonance Imaging for the Prediction of Pathologic Response to Neoadjuvant Chemotherapy in Breast Cancer

Simple Summary

Achieving pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) in breast cancer (BC) is crucial, as pCR is a surrogate marker for survival. However, only 10–30% of patients achieve it. It is therefore essential to develop tools that enable to accurately predict response. Recently, different studies have demonstrated the feasibility of applying machine learning approaches to non-invasively predict pCR before NAC administration from magnetic resonance imaging (MRI) data. Some of those models are based on radiomics, an emerging field that allows the automated extraction of clinically relevant information from radiologic images. However, the research is still at an early stage and further data are needed. Here, we determine whether the combination of imaging data (radiomic features and perfusion/diffusion imaging biomarkers) extracted from multiparametric MRIs and clinical variables can improve pCR prediction to NAC compared to models only including imaging or clinical data, potentially avoiding unnecessary treatment and delays to surgery.

Abstract

Background: Most breast cancer (BC) patients fail to achieve pathological complete response (pCR) after neoadjuvant chemotherapy (NAC). The aim of this study was to evaluate whether imaging features (perfusion/diffusion imaging biomarkers + radiomic features) extracted from pre-treatment multiparametric (mp)MRIs were able to predict, alone or in combination with clinical data, pCR to NAC. Methods: Patients with stage II-III BC receiving NAC and undergoing breast mpMRI were retrospectively evaluated. Imaging features were extracted from mpMRIs performed before NAC. Three different machine learning models based on imaging features, clinical data or imaging features + clinical data were trained to predict pCR. Confusion matrices and performance metrics were obtained to assess model performance. Statistical analyses were conducted to evaluate differences between responders and non-responders. Results: Fifty-eight patients (median [range] age, 52 [45–58] years) were included, of whom 12 showed pCR. The combined model improved pCR prediction compared to clinical and imaging models, yielding 91.5% of accuracy with no false positive cases and only 17% false negative results. Changes in different parameters between responders and non-responders suggested a possible increase in vascularity and reduced tumour heterogeneity in patients with pCR, with the percentile 25th of time-to-peak (TTP), a classical perfusion parameter, being able to discriminate both groups in a 75% of the cases. Conclusions: A combination of mpMRI-derived imaging features and clinical variables was able to successfully predict pCR to NAC. Specific patient profiles according to tumour vascularity and heterogeneity might explain pCR differences, where TTP could emerge as a putative surrogate marker for pCR.

Using the apparent diffusion coefficient histogram analysis to predict response to neoadjuvant chemotherapy in patients with breast cancer: comparison among three region of interest selection methods

Background

The apparent diffusion coefficient (ADC) value using histogram analysis is helpful to predict responses to neoadjuvant chemotherapy (NAC) in breast cancer. However, the measurement method has not reached a consensus. This study was to assess the diagnostic performance of the ADC histogram analysis at predicting patient response prior to NAC in breast cancer patients using different region of interest (ROI) selection methods.

Methods

A total of 75 patients who underwent diffusion weighted imaging (DWI) prior to NAC were retrospectively enrolled from February 2017 to December 2019. Images were measured using small 2-dimensional (2D) ROI, large 2D ROI, and volume ROI methods. The measurement time and ROI size were recorded. Histopathologic responses were acquired using the Miller-Payne grading system after surgery. The inter- and intra-observer repeatability was analyzed and the ADC histogram values from the three ROI methods were compared. The efficacy of each method at predicting patient response prior to NAC was assessed using the area under the receiver operating characteristic curve (AUC) for the whole study population and subgroups according to molecular subtype.

Results

Among the 75 enrolled patients, 26 (34.67%) were responsive to NAC therapy. The ADC histogram values were significantly different among the three ROI methods (P≤0.038). Inter- and intra-observer repeatability of the large 2D ROI method and the volume ROI method was generally greater than that observed with the 2D ROI method. The 10% ADC value of the large 2D ROI method showed the greatest AUC (0.701) in the whole study population and in the luminal subgroup (AUC 0.804). The volume ROI method required significantly more time than the other two ROI methods (P<0.001).

Conclusions

The small 2D ROI method is not appropriate for predicting response prior to NAC in breast cancer patients due to the poor repeatability. When choosing the ROI method and the histogram parameters for predicting response prior to NAC in breast cancer patients using ADC-derived histogram analysis, 10% of the large 2D ROI method is recommended, especially in luminal A subtype patients.

Psychological Distress, Coping Strategies, and Quality of Life in Breast Cancer Patients Under Neoadjuvant Therapy: Protocol of a Systematic Review

Introduction

Women with breast cancer eligible for neoadjuvant therapy (NAT) present a disorder of biopsychosocial variables and alteration of their quality of life. They cope with this changing by adopting psychological adjustment strategies, either passive or active. This systematic review aims to investigate the psychological distress, coping strategies, and quality of life in breast cancer patients under neoadjuvant therapy.

Methods

Cochrane Library, PubMed, ScienceDirect, Scopus, Web of Science, and Wiley Online library represent the databases that were searched to identify relevant published articles until September 27, 2021. Full-text published articles, written in English and assessing the main outcomes (namely: psychological distress, coping strategies, and quality of life) in women with breast cancer under NAT will be included. Also, we will integrate papers dealing with the related bio psychosocial variables to the main variables. The paper selection, data extraction, and quality assessment of selected studies will be performed independently by two researchers, and disagreements will be resolved through discussions. We will bring together the results of all of the included studies to draw conclusions based on the body of evidence. The narrative approach will be adopted to analyze the results and conclusions extracted and we would perform quantitative groupings if we have similar data.

Ethics and dissemination

Ethical approval is not required as the proposed systematic review will not use primary data. The results of this review will be disseminated through publication in a peer-reviewed journal and conference presentation(s). PROSPERO registration number: CRD42021230300.

Impact of body mass index on pathological complete response and survival of breast cancer patients receiving neoadjuvant chemotherapy

BACKGROUND

High body mass index (BMI) is regarded as a poor prognostic factor in breast cancer (BC). However, its association with pathological complete response (pCR) and survival after neoadjuvant chemotherapy (NAC) remains controversial.

OBJECTIVE

We wanted to assess the prognostic impact of BMI in this setting.

METHODS

Single-center, retrospective review of 314 BC patients undergoing NAC from 2010 to 2018. Patients were categorized as underweight/normal weight (UW/NW) (BMI < 25) or overweight/obese (OW/OB) (BMI ≥ 25). The relationship between BMI and other traditional clinical-pathological prognostic variables with the pCR rate was investigated using logistic regression analysis. The effect on event-free survival (EFS) and overall survival (OS) was estimated by the Cox proportional hazards regression analysis.

RESULTS

One hundred and twenty-two patients were UW/NW while 192 were OW/OB. Multivariate analysis revealed that hormonal receptors negative, HER2 positive, and clinical tumor stage (cT) 1-2 were independent predictor factors for pCR. Multivariate analysis confirmed tumor grade G3 and lack of pCR as independent adverse prognostic factors for EFS, while factors associated with worse OS were cT3-4, hormone receptors negative, and lack of pCR. Non-significant differences in pCR, EFS, or OS were observed between the two baseline BMI categories.

CONCLUSIONS

In our experience, BMI is not associated with pCR, EFS, or OS in BC patients receiving NAC. Achieving pCR is the most consistent factor associated with EFS and OS. Prospective and well-designed studies taking into account other important biological and anthropometric factors are needed to determine the exact role of BMI in this setting.

TGF-β-Induced TMEPAI Promotes Epithelial-Mesenchymal Transition in Doxorubicin-Treated Triple-Negative Breast Cancer Cells via SMAD3 and PI3K/AKT Pathway Alteration

Introduction

Epithelial–mesenchymal transition (EMT) and overexpression of drug efflux transporters have been reported to cause doxorubicin resistance. Our previous study indicated that TMEPAI (transmembrane prostate androgen-induced protein) attenuated doxorubicin sensitivity in triple-negative breast cancer cells. However, how TMEPAI contributes to doxorubicin resistance in TNBC remains unclear. Thus, the present study aimed to elucidate the mechanism of TMEPAI in doxorubicin resistance in triple-negative breast cancer cells.

Methods

We used BT549, triple-negative cells wild type (WT), and BT549 TMEPAI knock-out. Both cells were treated with TGF-β 2 ng/mL for 24 hours, followed by TGF-β 2 ng/mL and doxorubicin 12.9 nM for another 24 hours. Afterward, the cells were harvested and counted. Cells were further lysed and used for RT-PCR and Western blot analysis. We determined the expression levels of proliferation, apoptosis, EMT markers, and drug efflux transporters. Additionally, we investigated the expressions of PI3K as well as SMAD3 and AKT phosphorylation.

Results

TNBC cells were shown to be less sensitive to doxorubicin in the presence of TMEPAI. TMEPAI was shown to alleviate the mRNA expressions of apoptosis markers: Bax, Bcl2, Caspase-3, and Caspase-9. Our results indicated that the presence of TMEPAI greatly amplifies EMT and increases drug efflux transporter expressions after doxorubicin treatment. Furthermore, our findings demonstrated that TMEPAI reduced the action of doxorubicin in inhibiting SMAD3 phosphorylation. TMEPAI was also shown to modify the effect of doxorubicin by reducing PI3K expressions and Akt phosphorylation in triple-negative breast cancer cells.

Conclusion

Our findings indicate that TMEPAI promotes EMT and drug efflux transporters at least in part by shifting doxorubicin action from SMAD3 phosphorylation reduction to PI3K/AKT inhibition in triple-negative breast cancer cells.