Neoadjuvant Therapy For Breast Cancer Research Articles

Hypoxia-inducible factor-1α can reverse the Adriamycin resistance of breast cancer adjuvant chemotherapy by upregulating transferrin receptor and activating ferroptosis.

Breast cancer is a common malignant tumor in women. Ferroptosis, a programmed cell death pathway, is closely associated with breast cancer and its resistance. The transferrin receptor (TFRC) is a key factor in ferroptosis, playing a crucial role in intracellular iron accumulation and the occurrence of ferroptosis. This study investigates the influence and significance of TFRC and its upstream transcription factor hypoxia-inducible factor-1α (HIF1α) on the efficacy of neoadjuvant therapy in breast cancer. The differential gene obtained from clinical samples through genetic sequencing is TFRC. Bioinformatics analysis revealed that TFRC expression in breast cancer was significantly greater in breast cancer tissues than in normal tissues, but significantly downregulated in Adriamycin (ADR)-resistant tissues. Iron-responsive element-binding protein 2 (IREB2) interacts with TFRC and participates in ferroptosis. HIF1α, an upstream transcription factor, positively regulates TFRC. Experimental results indicated higher levels of ferroptosis markers in breast cancer tissue than in normal tissue. In the TAC neoadjuvant regimen-sensitive group, iron ion (Fe2+) and malondialdehyde (MDA) levels were greater than those in the resistant group (all p < .05). Expression levels of TFRC, IREB2, FTH1, and HIF1α were higher in breast cancer tissue compared to normal tissue. Additionally, the expression of the TFRC protein in the TAC neoadjuvant regimen-sensitive group was significantly higher than that in the resistant group (all p < .05), while the difference in the level of expression of IREB2 and FTH1 between the sensitive and resistant groups was not significant (p > .05). The dual-luciferase assay revealed that HIF1α acts as an upstream transcription factor of TFRC (p < .05). Overexpression of HIF1α in ADR-resistant breast cancer cells increased TFRC, Fe2+, and MDA content. After ADR treatment, the cell survival rate decreased significantly, and ferroptosis could be reversed by the combined application of Fer-1 (all p < .05). In conclusion, ferroptosis and chemotherapy resistance are correlated in breast cancer. TFRC is a key regulatory factor influenced by HIF1α and is associated with chemotherapy resistance. Upregulating HIF1α in resistant cells may reverse resistance by activating ferroptosis through TFRC overexpression.

18F-FDG PET/CT for early prediction of pathological complete response in breast cancer neoadjuvant therapy: a retrospective analysis.

Neoadjuvant treatment has been developed as a systematic approach for patients with early breast cancer and has resulted in improved breast-conserving rate and survival. However, identifying treatment-sensitive patients at the early phase of therapy remains a problem, hampering disease management and raising the possibility of disease progression during treatment. In this retrospective analysis, we collected 2-deoxy-2-[F-18] fluoro-d-glucose (18F-FDG) positron emission tomography (PET)/computed tomography (CT) images of primary tumor sites and axillary areas and reciprocal clinical pathological data from 121 patients who underwent neoadjuvant treatment and surgery in our center. The univariate and multivariate logistic regression analyses were performed to investigate features associated with pathological complete response (pCR). An 18F-FDG PET/CT-based prediction model was trained, and the performance was evaluated by receiver operating characteristic curves (ROC). The maximum standard uptake values (SUVmax) of 18F-FDG PET/CT were a powerful indicator of tumor status. The SUVmax values of axillary areas were closely related to metastatic lymph node counts (R = 0.62). Moreover, the early SUVmax reduction rates (between baseline and second cycle of neoadjuvant treatment) were statistically different between pCR and non-pCR patients. The early SUVmax reduction rates-based model showed great ability to predict pCR (AUC = 0.89), with all molecular subtypes (HR+HER2-, HR+HER2+, HR-HER2+, and HR-HER2-) considered. Our research proved that the SUVmax reduction rate of 18F-FDG PET/CT contributed to the early prediction of pCR, providing rationales for utilizing PET/CT in NAT in the future.

Comparison of radiomics-based machine-learning classifiers for the pretreatment prediction of pathologic complete response to neoadjuvant therapy in breast cancer.

Machine learning classifiers are increasingly used to create predictive models for pathological complete response (pCR) in breast cancer after neoadjuvant therapy (NAT). Few studies have compared the effectiveness of different ML classifiers. This study evaluated radiomics models based on pre- and post-contrast first-phase T1 weighted images (T1WI) in predicting breast cancer pCR after NAT and compared the performance of ML classifiers. This retrospective study enrolled 281 patients undergoing NAT from the Duke-Breast-Cancer-MRI dataset. Radiomic features were extracted from pre- and post-contrast first-phase T1WI images. The Synthetic Minority Oversampling Technique (SMOTE) was applied, then the dataset was randomly divided into training and validation groups (7:3). The radiomics model was built using selected optimal features. Support vector machine (SVM), random forest (RF), decision tree (DT), k-nearest neighbor (KNN), extreme gradient boosting (XGBoost), and light gradient boosting machine (LightGBM) were classifiers. Receiver operating characteristic curves were used to assess predictive performance. LightGBM performed best in predicting pCR [area under the curve (AUC): 0.823, 95% confidence interval (CI) [0.743-0.902], accuracy 74.0%, sensitivity 85.0%, specificity 67.2%]. During subgroup analysis, RF was most effective in pCR prediction in luminal breast cancers (AUC: 0.914, 95% CI [0.847-0.981], accuracy 87.0%, sensitivity 85.2%, specificity 88.1%). In triple-negative breast cancers, LightGBM performed best (AUC: 0.836, 95% CI [0.708-0.965], accuracy 78.6%, sensitivity 68.2%, specificity 90.0%). The LightGBM-based radiomics model performed best in predicting pCR in patients with breast cancer. RF and LightGBM showed promising results for luminal and triple-negative breast cancers, respectively.

Adjuvant and Neoadjuvant Therapy for Breast Cancer: A Systematic Review.

Breast cancer (BC) is the most frequent type of cancer among women. The neoadjuvant therapy was administered before surgery, and the adjuvant therapy was administered post-surgery. The goal of this systematic review is to study the effects of adjuvant and neoadjuvant BC therapy on patient outcomes and mortality. In July 2023, systematic searches were conducted through the Cochrane Library, Web of Sciences, Google Scholar, EMBASE, and PubMed databases. The search method focused on studies that included all patients with BC stages 1, 2, and 3 and excluded studies that included patients with metastatic and recurrent BC. The risk of bias in the included studies was evaluated using the Cochrane risk of bias technique. Throughout our search, 27 relevant studies with 161,552 patients were discovered. Anti-human epidermal growth factor 2 therapy (trastuzumab, pertuzumab), chemotherapy (anthracycline), endocrine therapy (tamoxifen, aromatase inhibitor), and bisphosphonates were recommended treatments for BC patients. Choices for radiotherapy included whole breast, partial breast, tumor bed boost, regional nodes, and chest wall choices after breast-conserving surgery. We discover that while the majority of treatments reduced the mortality or recurrence rates of BC, anthracycline, chemotherapy, and radiation led to an overall rise in non-BC deaths. The systemic assessment discovered several variables that impact a patient's quality of life. Based on these advantages and disadvantages, various treatment options for patients and recommendations for groups of women are made.

Predictive value of lymphocyte subsets and lymphocyte-to-monocyte ratio in assessing the efficacy of neoadjuvant therapy in breast cancer

Lymphocyte subsets are the most intuitive expression of the body’s immune ability, and the lymphocyte-to-monocyte ratio (LMR) also clearly reflect the degree of chronic inflammation activity. The purpose of this study is to investigate their predictive value of lymphocyte subsets and LMR to neoadjuvant therapy (NAT) efficacy in breast cancer patients. In this study, lymphocyte subsets and LMR were compared between breast cancer patients (n = 70) and benign breast tumor female populations (n = 48). Breast cancer patients were treated with NAT, and the chemotherapy response of the breast was evaluated using established criteria. The differences in lymphocyte subsets and LMR were also compared between pathological complete response (pCR) and non-pCR patients before and after NAT. Finally, data were analyzed using SPSS. The analytical results demonstrated that breast cancer patients showed significantly lower levels of CD3 + T cells, CD4 + T cells, CD4 + /CD8 + ratio, NK cells, and LMR compared to benign breast tumor women (P < 0.05). Among breast cancer patients, those who achieved pCR had higher levels of CD4 + T cells, NK cells, and LMR before NAT (P < 0.05). NAT increased CD4 + /CD8 + ratio and decreased CD8 + T cells in pCR patients (P < 0.05). Additionally, both pCR and non-pCR patients exhibited an increase in CD3 + T cells and CD4 + T cells after treatment, but the increase was significantly higher in pCR patients (P < 0.05). Conversely, both pCR and non-pCR patients experienced a decrease in LMR after treatment. However, this decrease was significantly lower in pCR patients (P < 0.05). These indicators demonstrated their predictive value for therapeutic efficacy. In conclusion, breast cancer patients experience tumor-related immunosuppression and high chronic inflammation response. But this phenomenon can be reversed to varying degrees by NAT. It has been found that lymphocyte subsets and LMR have good predictive value for pCR. Therefore, these markers can be utilized to identify individuals who are insensitive to NAT early on, enabling the adjustment of treatment plans and achieving precise breast cancer treatment.

Utility of [18F]FDG PET/CT in predicting pathological complete response to neoadjuvant therapy in breast cancer: A prospective study.

Utility of [18F]FDG PET/CT in predicting pathological complete response to neoadjuvant therapy in breast cancer: A prospective study.

An MRI-based multi-parameter clinical decision support system (NeoMDSS) for early prediction of pathological complete response after the first cycle of neoadjuvant therapy in breast cancer: A multi-center prospective observational cohort study.

592 Background: Currently, there is no reliable and convenient way to predict response rates in early stages of neoadjuvant therapy (NAT) for breast cancer, which can affect the prognosis of non-responsive patients. Methods: In this prospective multi-center observational study, eligible participants who underwent NAT followed by radical surgery were recruited. All patients underwent magnetic resonance imaging (MRI) examination before and after the first cycle of NAT (1st-NAT). We analyzed the effects of clinicopathological features, such as histological grade, hormone receptor (HR) and HER2 status, Ki-67 expression, blood cell analysis, and MRI parameters including the reduction ratio of tumor diameter (ΔD%), apparent diffusion coefficient (ADC), and early enhancement ratio (EER) on NAT. By incorporating independent influencing factors, we developed a multiparametric clinical decision support system (NeoMDSS) model based on a retrospective cohort and validated its accuracy in both a prospective single-center internal cohort and a prospective multi-center external cohort. Relevant clinical trial details can be found under the identifier NCT04909554. Results: A total of 301 breast cancer patients were enrolled between January 2019 and December 2023, including 140 patients in the training cohort, 120 patients in the internal validation cohort, and 41 patients in the external validation cohort. The NeoMDSS model demonstrated excellent performance in predicting pathological complete response (pCR) at 1st-NAT, with an AUC of 0.874 (95% CI 0.813-0.935) in the training cohort, 0.845 (95% CI 0.771-0.919) in the internal validation cohort, and 0.867 (95% CI 0.742-0.992) in the external validation cohort. The NeoMDSS model exhibited a sensitivity, specificity, and accuracy of 80.0%, 81.3%, and 80.5%, respectively, in the internal validation cohort, and 87.0%, 83.3%, and 85.4%, respectively, in the external validation cohort. Calibration curves and decision curve analysis (DCA) further supported the clinical value of the NeoMDSS model. To facilitate the clinical application of the NeoMDSS model, a nomogram and an online website ( www.gdphneomdss.com ) were developed to calculate the probability of pCR, which the sensitivity, specificity, and accuracy of the NeoMDSS model were all 80.0% in the internal validation cohort and 80.0%, 81.3%, and 80.5% in the external validation cohort. Conclusions: The NeoMDSS model serves as a convenient tool in clinical practice at 1st-NAT to calculate and accurately predict the probability of pCR after NAT. This assists clinicians in determining whether to modify the treatment plan and improving personalized treatment for patients with poor response to NAT. Clinical trial information: NCT04909554 .

Harnessing cell-free orphan non-coding RNAs as a predictive measure of long-term survival in neoadjuvant breast cancer therapy.

585 Background: Pathologic complete response (pCR) is a valuable metric for predicting survival following neoadjuvant therapy (NAT) in breast cancer; however, blood-based biomarkers may refine the prediction of treatment outcomes and inform follow-up treatment decisions. Orphan non-coding RNAs (oncRNAs) are a novel category of small RNAs that are present in tumors and largely absent in healthy tissue. Here, we explore the feasibility of a tumor-naive oncRNA-based liquid biopsy assay for predicting distant recurrence-free survival (DRFS) in breast cancer patients using a standard blood draw. Methods: Our cohort included 538 high-risk breast cancer patients from the multicenter I-SPY2 trial having serum available after NAT and before surgery (timepoint T3). Of the 538 patients (mean age: 48.8 ± 10.3), 191 (36%) achieved pCR. We isolated small RNA from 0.8 mL of patient serum and sequenced it at an average depth of 50 million 100-bp single-end reads. We used our catalog of tumor-derived oncRNAs, discovered in The Cancer Genome Atlas, to develop an AI model for predicting tumor burden based on tumor size. Our AI model was trained on oncRNA profiles from an independent cohort of 719 serum samples collected at diagnosis from individuals with cancer. We assessed tumor burden by oncRNA in the I-SPY2 cohort at T3 and associated this measure with DRFS using robust cutpoints established for each of the two pCR groups: high- ( n=70) and low-burden ( n=468) cutpoints were verified through cross-validation and almost all folds converged on a single value, which we used in our analysis. Results: The continuous oncRNA-derived tumor burden at T3 had a hazards ratio (HR) of 2.4 (95% CI: 1.1–5.1) for predicting DRFS. This measure remained significant in multivariate Cox models that included pCR (HR = 2.7 [1.3–5.6]) or residual cancer burden (RCB II/III versus 0/I; HR = 3.4 [1.6–7.1]). Patients with pCR had a 3-year DRFS of 94%; patients with pCR having low ( n=180) versus high ( n=11) oncRNA burden resulted in 95% and 73% 3-year DRFS, respectively (HR = 7.4 [2.3–24]). Those without pCR had a 3-year DRFS of 78% and stratification by oncRNA burden resulted in 85% and 71% 3-year DRFS for the low ( n=288) and high ( n=59) burden groups, respectively (HR=2.4 [1.5–3.9]). Importantly, we saw no restricted mean survival time difference (RMST) at 3 years between those with and without pCR among the high oncRNA burden group (0.1-month difference, p=0.97). Conclusions: We demonstrate the utility of a tumor-naive, oncRNA-derived tumor burden AI model as a predictive measure of treatment outcome and as a prognostic biomarker that can be used in addition to pCR to further stratify patients as low or high risk of recurrence. Integration of our oncRNA assay with existing biomarkers and clinicopathologic variables, has the potential to add value for predicting outcomes and prioritizing patients that would benefit from additional care.

Abstract PO1-02-09: Efficacy of breast conserving treatment of minimal residual tumors after neoadjuvant therapy for breast cancer

Abstract Background. Determining the most effective drug therapy regimens is one of the top priorities in the individualization of the treatment of patients with breast cancer (BC). Achievement of pathological complete response (pCR) is important prognostic factor, and used as the surrogate primary end point in many clinical trials. According to the literature, the achievement of pCR after neoadjuvant systemic treatment (NST) in breast cancer varies from 30 to 70%. Patients who do not achieve pCR require post-neoadjuvant treatment, even when reaching minimal size of residual tumors. Initially, primary minimal tumors are characterized by high survival rates, which are proved by international and domestic studies. It is required to clarify the survival rates in the presence of a minimal residual tumor (ypT &amp;lt; 10mm) after neoadjuvant systemic therapy and to compare with primary minimal forms of breast cancer (pT1a-b N0M0), characterized by the most favorable prognosis. Objective. Improvement of the effectiveness of treatment of residual minimal forms (ypT1a-b) of BC after NST. Determination of the correlation between the schemes of NST and the rates of tumor response based on the results of postoperative pathological reports including RCB scale. Methods. The study included 602 patients aged 18–65 years with early and locally advanced I–IIIC stage T1-3N1-3M0 breast cancer,who underwent complex or combined treatment. This study included patients treated at the N.N. Petrov National Medical Research Center of Oncology from 2015 to 2021 Conclusion. After reaching a complete pathomorphological response of the tumor to NST (RCB 0), the 5-year disease-free (DFS) and overall (OS) survival rates (DFS – 78.5%; OS 90.4%) were inferior to the survival rates of patients with primary minimal breast cancer (pT1ab N0M0) [DFS – 86.4%; OS – 98.6%]. Neoadjuvant chemotherapy regimens that allow achieving a pathological complete response in triple-negative and HER2- positive breast cancer have been defined. An assessment of adverse factors that affect the effectiveness of breast conserving treatment and correlate with a deterioration in overall survival was performed. Results. Our data analysis revealed that the survival rates (DFS and OS) of patients with minimal residual tumor after neojuvant systemic therapy were not comparable with those in the group of patients with primary minimal breast cancer. Data analysis revealed the most effective combinations of drugs expressed in the rates of pathomorphological response according to the RCB scale. An assessment of adverse factors that affect the effectiveness of organ-preserving treatment and correlate with a deterioration in overall survival was performed. Citation Format: Roman Pesotsky, Vladimir Semiglazov, Petr Krivorotko, Tengiz Tabagua, Konstantin Zernov, Alexander Emelyanov, Viktoria Mortada, Nikolay Amirov, Daria Ulrikh, Valerii Levchenko, Yana Bondarchuk, Diana Enaldieva, Nestan Bekkeldieva, Konsantin Zirov, Sergey Yerechshenko, Vladislav Evseev, Kirill Rychagov, Sergey Novikov, Maria Kazantseva, Larisa Gigolaeva, Elena Zhiltsova, Denis Nesterov, Olga Ivanova, Tatiana Semiglazova. Efficacy of breast conserving treatment of minimal residual tumors after neoadjuvant therapy for breast cancer [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO1-02-09.

Abstract PO2-26-03: Patient decision making and preferences regarding neoadjuvant chemotherapy in Mexican women with early breast cancer

Abstract Background: Neoadjuvant therapy in breast cancer is considered standard treatment for the majority of patients with locally advanced breast cancer. Its use has been extended to early breast cancer aiming to downstage the extent of disease and surgery in the breast and/or axillary lymph nodes, to provide information regarding effectiveness of systemic therapy (which might guide adjuvant treatment recommendations), to provide prognostic information, as well as to extend time for appropriate genetic testing and planning breast reconstruction. However, little is known about patient decision making and preferences in this scenario. We aimed to describe patients’ preferences regarding neoadjuvant chemotherapy (NAC) for early breast cancer, patient decision-making patterns, and satisfaction with their decisions. Methods: Single arm prospective study at the Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán in Mexico City. We included patients aged ≥18 years with recent localized and operable breast cancer diagnosis. Electronic preferences and decision-making questionnaires were completed by each patient before NAC was started. A decision regret scale was completed within the first month after surgery was performed. Demographic and clinico-pathologic characteristics were extracted from the electronic medical records. Results: From March 2020 to April 2023, 29 women were enrolled. Mean age was 49.2 (SD 16.2), 55.5% (n=16) were married or had a partner, 79.3% (n= 23) had at least one child, 58.6% (n=17) had high school or higher education, and 48.2% (n=14) were housewives. 75.8% of women answered that they were proposed to be treated with NAC because of the tumor size, 55.1% due to positive lymph nodes, 72.4% to obtain a conserving surgery and 72.4% to obtain prognostic information. In 75.8% of the cases the oncologist made the decision of NAC and in 96.5% the chemotherapy schedule was explained to patients and their families. 82.7% of women answered that it was important for them to have a breast conserving surgery, 93.1% were worried about adverse events, 79.3% thought that there were possibilities to disappear the tumor with the chemotherapy and 31% thought that if the tumor disappear, they would not require surgery. After surgery was performed 96.1% answered that the decision of NAC was the right one, 7.6% would not make the same choice, and 23% felt that the decision to receive NAC caused them harm. Conclusion: Our study suggests that the reason to make a treatment decision related to NAC for early breast cancer was discussed and explained to the majority of the patients. However, physicians’ opinion was the most powerful to influence decision. A significant proportion of patients desire to have a conserving surgery and were worried about adverse events. Perspectives of patients’ preferences regarding NAC should be considered in treatment decision making to improve their decision satisfaction. Citation Format: Yanin Chavarri-Guerra, Haydee Cristina Verduzco-Aguirre, Roberto Gonzalez-Salazar, Ilana de la Puente Tawil, Paulina Quiroz-Friedman, Alejandro Alfaro-Goldaracena, Sofia Sanchez-Roman, Zuleyma Nieto-Garcia. Patient decision making and preferences regarding neoadjuvant chemotherapy in Mexican women with early breast cancer [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO2-26-03.

Abstract PS05-08: Restriction Spectrum Imaging MRI for automated evaluation of response to neoadjuvant therapy in breast cancer

Abstract Introduction: Dynamic contrast-enhanced MRI (DCE) is currently used to evaluate neoadjuvant therapy response of breast cancer (1). However, DCE requires expert radiologist readers to assess the change in longest tumor dimension during therapy, as well as administration of Gadolinium contrast agents. One MRI modality that does not require contrast agents is diffusion-weighted MRI (DWI), a method that detects the microscopic diffusion of water molecules. However, the commonly used DWI method apparent diffusion coefficient (ADC) is not fully optimised in the breast (2). The purpose of the current study was to evaluate the recent DWI method Restriction Spectrum Imaging (RSI) (2) to automatically monitor breast tumor size during neoadjuvant therapy. Methods: Twenty-seven women underwent 3T MRI at four time points during therapy at University of California San Diego; 17 received all four scans (see Table 1 for patient details). Inclusion criteria included biopsy-proven unilateral invasive breast cancer ≥2.5 cm (defined on clinical examination/imaging) with indication for neoadjuvant therapy. The therapy used was primarily paclitaxel (+/-experimental agent) followed by anthracycline. The MRI protocol included Gadolinium DCE and DWI (b-values 0, 500, 1500, 4000 s/mm2); TE/TR = 82/9000 ms. ADC was calculated using b-values &amp;lt; 1000 s/mm2 while signal from all available b-values were fitted to the previously-developed three-component RSI model (2). The tumor size by RSI was assessed against manual DCE tumor size and mean ADC values. Prediction of therapy response during therapy and residual tumor post-therapy were assessed using non-pathological complete response (non-pCR) as endpoint. pCR was defined as ypT0/is or ypN0. Results: Ten patients experienced pCR. Prediction of non-pCR by ROC AUC at the early-therapy time point was 0.65 for RSI, 0.64 for DCE and 0.45 for ADC (Table 2). Prediction of post-therapy residual tumor is given in Table 3. Discussion: The novel RSI cancer tissue classifier predicted response to neoadjuvant therapy after only 19 days. RSI could also identify 71% of cases with residual tumor at surgery with 90% specificity post-therapy. RSI performance was similar to performance by standard MRI by manual tumor measurement on DCE. In contrast to standard-of-care MRI by using DCE and ADC that requires manual user input, the RSI classifier is automatic. This suggests that RSI may aid to cost-efficiently evaluate neoadjuvant therapy of breast cancer, with the aim to help guide clinical decision-making and enable tailored therapy regimens.

PREHABILITATION AND SUPPORTIVE CARE IN ONCOLOGY TREATMENT OF BREAST CANCER: PROACTIVE-B

INTRODUCTION &amp; AIMS Neoadjuvant therapy has become standard treatment for patients with Stage II/III HER2 positive and triple negative breast cancer and in selected patients with locally advanced and borderline resectable high risk, luminal B breast cancer (1). Side effects such as fatigue, cardiotoxicity, neurotoxicity, anxiety, insomnia, vasomotor symptoms, gastrointestinal disturbance as well as a raft of immune-related adverse events, impact treatment tolerance, long term outcomes and quality of life. Post-treatment, many women increase body fat and decrease lean mass and develop metabolic syndrome (2) and accelerated cardiac aging (3). All of these are modifiable targets of exercise. The aim of this study was to determine if an early multi-modal supportive care program, designed through a qualitative study from consumers and healthcare professionals, can mitigate these side effects and improve chemotherapy completion, cardiometabolic, residual cancer burden (pCR) and surgical outcomes. METHODS This was a prospective, mixed-method, feasibility study that recruited 23 women receiving neoadjuvant therapy for breast cancer, combining qualitative and quantitative data collection and analysis. The supervised exercise intervention was designed to include aerobic interval, resistance and balance training twice/week, adapted for symptom burden in each session with relative training dose intensity calculated. An optional home program as also provided with therabands. Body composition, upper and lower body strength and cardiometabolic outcome measures were collected at baseline, end of first and second line of treatment and then 6 months post-surgery. RESULTS Full data will be completed in March 2024 to be presented. There was a high rate of complete pathological response (18/22). Currently, 13 participants have completed 6 month post-surgery assessments and preliminary analyses indicate increases in strength and maintenance of muscle mass during and after treatment. The program was found to be acceptable and feasible with high attendance and satisfaction ratings. The additional home program had poor uptake. Of interest to exercise physiologists was participant responses to cluster set training, how poorly rate of perceived exertion reflected training and strength testing loads, heart rate and blood pressure responses and treatment toxicities including cases of immunotherapy induced hepatitis. Many participants were stronger at the end of chemotherapy than before and reported that exercise gave them a sense of control. CONCLUSION Adapting exercise during neoadjuvant chemotherapy with additional supportive therapies for symptom management, improved physical and psychosocial functioning. The program was feasible and acceptable, with high satisfaction reported. Muscle mass can be maintained with supervised exercise training and maintained and 6 months after surgery.

Development and validation of a pathological model predicting the efficacy of neoadjuvant therapy for breast cancer based on RCB scoring

IntroductionBreast cancer has become the most prevalent malignant tumor among women globally, posing a serious threat to women's life and health. Neoadjuvant therapy (NAT) has emerged as one of the standard treatment approaches for breast cancer patients. However, due to varying responses to NAT among different patients, significant differences in treatment effectiveness occur, impacting the timely alteration of treatment strategies for patients.Material and methodsBased on clinical and pathological characteristics along with the RCB scoring, we utilized a support vector machine (SVM) algorithm to construct a Breast Cancer Pathological Characteristics(PBCS) prediction model. We thoroughly validated the PBCS and compared it to a Pathomics Signatures (PS) prediction model.Resultswe developed a pathological prediction label, named PS. Subsequently, through univariate and multivariate analysis, we discovered a significant correlation between HER2 and the patients' RCB scores. Integrating HER2 into PS, we constructed a breast cancer pathological prediction model, named PBCS.PBCS exhibits good performance in predicting the effectiveness of postoperative therapy（RCB 0~Ⅰ） in both the training sets(AUC0.86[95%CI0.7988-0.9173])and validation sets（AUC0.83[95%CI0.7219-0.9382 ]). In the validation set, PBCS significantly outperforms the PS（AUC0.65[95%CI0.5121-0.7886 ]）.Calibration curves and clinical decision curves also strongly support PBCS's ability to effectively predict the efficacy of therapy（RCB 0~Ⅰ）. ConclusionsConclusionsPBCS can assist clinical and pathological physicians in accurately predicting patients' post-treatment RCB grading before initiating NAT. This offers a new approach to forecast breast cancer patients' responsiveness to NAT, aiding in devising personalized treatment strategies for patients.

Evolution of neoadjuvant therapy for breast cancer regimens over 12 years and pathologic response rates according to tumor subtypes and clinical stage: A single-center retrospective study.

Given the evolution of neoadjuvant therapy (NAT) for breast cancer, this study aimed to analyze trends in NAT regimens over time and patients' pathological responses, tumor stages, and subtypes. Data were analyzed for 548 patients with cT1-4N0-3M0 breast cancer who received NAT at Shandong Cancer Hospital between 2011 and 2022. The 12-year study period was divided into six 2-year periods termed P1 to P6. From P1 to P6, the proportion of stage II patients treated with NAT increased from 6.4% to 33.8% compared with same-stage operable breast cancer (r = 0.228, P < 0.001), while the proportion of the full-course group increased from 50.0% to 99.0% (r = 0.354, P < 0.001). The pathologic complete remission (pCR) rate in the full-course group increased from 30.8% to 54.6% (r = 0.248, P < 0.001). In the full-course human epidermal growth factor receptor-2 positive (HER2+) group, the proportion of chemotherapy combined with inhibition therapy increased from 33.3% to 100% (r = 0.530, P < 0.001). Furthermore, dual inhibition therapy increased from 0 to 98.9%. The proportion of the nonanthracycline group (dual inhibition) increased from 56.0% at P5 to 76.6% at P6 (r = 0.190, P = 0.042). In the full-course Triple-Negative Breast Cancer (TNBC) group, the proportion of platinum therapy increased from 0 to 41.9% (r = 0.324, P < 0.001) and immune drugs increased from 0 to 53.2% (r = 0.500, P < 0.001). Overall, the results indicate an increasing proportion of patients receiving NAT therapy over time. Furthermore, there were increases in HER2 + patients receiving inhibition therapy (especially dual inhibition) and TNBC patients receiving platinum and immune therapy as part of NAT. Notably, these changes were associated with improved outcomes.

Feasibility study of a multimodal prehabilitation programme in women receiving neoadjuvant therapy for breast cancer in a major cancer hospital: a protocol

IntroductionNeoadjuvant therapy has become a standard treatment for patients with stage II/III HER2 positive and triple negative breast cancer, and in well-selected patients with locally advanced and borderline resectable high...

Internal mammary regional management after neoadjuvant therapy in breast cancer.

Internal mammary regional management after neoadjuvant therapy in breast cancer.

MRI-based tumor shrinkage patterns after early neoadjuvant therapy in breast cancer: correlation with molecular subtypes and pathological response after therapy

BackgroundMRI-based tumor shrinkage patterns (TSP) after neoadjuvant therapy (NAT) have been associated with pathological response. However, the understanding of TSP after early NAT remains limited. We aimed to analyze the relationship between TSP after early NAT and pathological response after therapy in different molecular subtypes.MethodsWe prospectively enrolled participants with invasive ductal breast cancers who received NAT and performed pretreatment DCE-MRI from September 2020 to August 2022. Early-stage MRIs were performed after the first (1st-MRI) and/or second (2nd-MRI) cycle of NAT. Tumor shrinkage patterns were categorized into four groups: concentric shrinkage, diffuse decrease (DD), decrease of intensity only (DIO), and stable disease (SD). Logistic regression analysis was performed to identify independent variables associated with pathologic complete response (pCR), and stratified analysis according to tumor hormone receptor (HR)/human epidermal growth factor receptor 2 (HER2) disease subtype.Results344 participants (mean age: 50 years, 113/345 [33%] pCR) with 345 tumors (1 bilateral) had evaluable 1st-MRI or 2nd-MRI to comprise the primary analysis cohort, of which 244 participants with 245 tumors had evaluable 1st-MRI (82/245 [33%] pCR) and 206 participants with 207 tumors had evaluable 2nd-MRI (69/207 [33%] pCR) to comprise the 1st- and 2nd-timepoint subgroup analysis cohorts, respectively. In the primary analysis, multivariate analysis showed that early DD pattern (OR = 12.08; 95% CI 3.34–43.75; p < 0.001) predicted pCR independently of the change in tumor size (OR = 1.37; 95% CI 0.94–2.01; p = 0.106) in HR+/HER2− subtype, and the change in tumor size was a strong pCR predictor in HER2+ (OR = 1.61; 95% CI 1.22–2.13; p = 0.001) and triple-negative breast cancer (TNBC, OR = 1.61; 95% CI 1.22–2.11; p = 0.001). Compared with the change in tumor size, the SD pattern achieved a higher negative predictive value in HER2+ and TNBC. The statistical significance of complete 1st-timepoint subgroup analysis was consistent with the primary analysis.ConclusionThe diffuse decrease pattern in HR+/HER2− subtype and stable disease in HER2+ and TNBC after early NAT could serve as additional straightforward and comprehensible indicators of treatment response.Trial registration: Trial registration at https://www.chictr.org.cn/. Registration number: ChiCTR2000038578, registered September 24, 2020.

Evolution of HER2 expression between pre-treatment biopsy and residual disease after neoadjuvant therapy for breast cancer

IntroductionWe have previously found that HER2 expression is dynamic, and can change from the primary breast tumor to matched recurrences. With this work, we aimed to assess the dynamics of HER2 during neoadjuvant treatment.(NAT). MethodsWe reviewed HER2 expression in pre- and post-treatment samples from consecutive patients with early-stage breast cancer that received NAT and underwent surgery at Dana-Farber Brigham Cancer Center between 01/2016–08/2022. The primary outcome was evolution of HER2 expression from pre- to post-NAT specimens in patients with residual disease. ResultsAmong 1613 patients receiving NAT, 1080 had residual disease at surgery. A total of 319 patients (29.5%) experienced a change in HER2 expression (HER2 0 vs. HER2-low vs. HER2-positive) from the pre-treatment sample to residual disease, with roughly equal distribution between decreased (50.5%) and increased HER2 expression (49.5%). Similar rates of change in HER2 expression were observed with anthracycline-based (31.8%) or taxane/platinum-based regimens (32.4%). Patients with HER2–0 or HER2-low tumors at diagnosis were likelier to experience a change in HER2 expression post-NAT compared to HER2-positive (32.3% vs. 21.3%, p < 0.001). Changes in HER2 expression post-NAT were prognostic among patients with HER2-positive tumors at diagnosis (3-year recurrence-free survival for change vs. no change: 71.6% vs. 89.6%, p = 0.006) but not among those with HER2-negative tumors at diagnosis (3-year recurrence-free survival for change vs. no change: 79.3% vs. 81.1%, p = 0.31). ConclusionsNearly 30% of patients with early-stage breast cancer showed a change in HER2 expression after NAT. Changes in HER2 expression post-NAT were only prognostic in the setting of HER2-positive tumors becoming HER2-negative at surgery.

Monitoring response to neoadjuvant therapy for breast cancer in all treatment phases using an ultrasound deep learning model.

The aim of this study was to investigate the value of a deep learning model (DLM) based on breast tumor ultrasound image segmentation in predicting pathological response to neoadjuvant chemotherapy (NAC) in breast cancer. The dataset contains a total of 1393 ultrasound images of 913 patients from Renmin Hospital of Wuhan University, of which 956 ultrasound images of 856 patients were used as the training set, and 437 ultrasound images of 57 patients underwent NAC were used as the test set. A U-Net-based end-to-end DLM was developed for automatically tumor segmentation and area calculation. The predictive abilities of the DLM, manual segmentation model (MSM), and two traditional ultrasound measurement methods (longest axis model [LAM] and dual-axis model [DAM]) for pathological complete response (pCR) were compared using changes in tumor size ratios to develop receiver operating characteristic curves. The average intersection over union value of the DLM was 0.856. The early-stage ultrasound-predicted area under curve (AUC) values of pCR were not significantly different from those of the intermediate and late stages (p< 0.05). The AUCs for MSM, DLM, LAM and DAM were 0.840, 0.756, 0.778 and 0.796, respectively. There was no significant difference in AUC values of the predictive ability of the four models. Ultrasonography was predictive of pCR in the early stages of NAC. DLM have a similar predictive value to conventional ultrasound for pCR, with an add benefit in effectively improving workflow.

The predictive value of systemic immune-inflammatory markers before and after treatment for pathological complete response in patients undergoing neoadjuvant therapy for breast cancer: a retrospective study of 1994 patients.

Systemic immune-inflammatory markers have a certain predictive role in pathological complete response (pCR) after neoadjuvant treatment (NAT) in breast cancer. However, there is a lack of research exploring the predictive value of markers after treatment. This retrospective study collected data from 1994 breast cancer patients who underwent NAT. Relevant clinical and pathological characteristics were included, and pre- and post-treatment complete blood cell counts were evaluated to calculate four systemic immune-inflammatory markers: neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), lymphocyte-to-monocyte ratio (LMR), and systemic immune-inflammation index (SII). The optimal cutoff values for these markers were determined using ROC curves, and patients were classified into high-value and low-value groups based on these cutoff values. Univariate and multivariate logistic regression analyses were conducted to analyze factors influencing pCR. The factors with independent predictive value were used to construct a nomogram. After NAT, 383 (19.2%) patients achieved pCR. The area under the ROC curve is generally larger for post-treatment markers compared to pre-treatment markers. Pre-treatment NLR and PLR, as well as post-treatment LMR and SII, were identified as independent predictive factors for pCR, along with Ki-67, clinical tumor stage, clinical lymph node stage, molecular subtype, and clinical response. Higher pre-NLR (OR = 1.320; 95% CI 1.016-1.716; P = 0.038), pre-PLR (OR = 1.474; 95% CI 1.058-2.052; P = 0.022), post-LMR (OR = 1.532; 95% CI 1.175-1.996; P = 0.002), and lower post-SII (OR = 0.596; 95% CI 0.429-0.827; P = 0.002) are associated with a higher likelihood of achieving pCR. The established nomogram had a good predictive performance with an area under the ROC curve of 0.754 (95% CI 0.674-0.835). Both pre- and post-treatment systemic immune-inflammatory markers have a significant predictive role in achieving pCR after NAT in breast cancer patients. Indeed, it is possible that post-treatment markers have stronger predictive ability compared to pre-treatment markers.