Neoadjuvant Therapy For Breast Cancer Research Articles

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 < 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 < 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 & 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.

Secreted Protein Acidic and Rich in Cysteine (SPARC) Polymorphisms in Response to Neoadjuvant Chemotherapy in HER2-Negative Breast Cancer Patients.

Secreted protein acidic and rich in cysteine (SPARC) expression has been proposed as a prognostic and predictive biomarker for some cancer types, but knowledge about the predictive value of SPARC polymorphisms in the context of neoadjuvant therapy for breast cancer (BC) is lacking. In 132 HER2-negative BC patients treated with neoadjuvant chemotherapy, we determined polymorphisms in the SPARC gene and analyzed their association with outcome. We also determined SPARC protein expression in tumor tissue. SPARC rs19789707 was significantly associated with response to treatment according to the Miller and Payne system in the breast (multivariate: odds ratio (OR), 3.81; p = 0.028). This association was significant in the subgroup of patients with luminal tumors (univariate: p = 0.047). Regarding survival, two SPARC variants showed significant associations with event-free survival: the rs19789707 variant in the subgroup of luminal A tumors (univariate: p = 0.006), and the rs4958487 variant in the subgroup of luminal B tumors (univariate: p = 0.022). In addition, SPARC rs4958487, rs10065756, and rs12153644 were significantly correlated with SPARC protein expression. Our findings suggest that SPARC polymorphisms could be good predictors of treatment response and survival in BC patients treated with neoadjuvant chemotherapy, especially those with luminal tumors.

Invited Commentary: Path to Omission of Axillary Surgery after Neoadjuvant Therapy for Breast Cancer.

Invited Commentary: Path to Omission of Axillary Surgery after Neoadjuvant Therapy for Breast Cancer.

Treatment of residual disease following neoadjuvant therapy in breast cancer.

Substantial advances have been made in the systemic treatment of breast cancer with residual disease following neoadjuvant therapy. We reviewed recent and ongoing studies informing the standard clinical management of residual disease by subtype: HER2+, TNBC, and HR+/HER2-,as well as strategies for BRCA+ disease. We conclude with a discussion of ongoing clinical trials and current controversies regarding the treatment of residual disease in breast cancer.

Prognostic significance of platelet-to-lymphocyte ratio (PLR) in patients with breast cancer treated with neoadjuvant chemotherapy: a meta-analysis

ObjectivePlatelet-to-lymphocyte ratio (PLR), known as a key systemic inflammatory parameter, has been proved to be associated with response to neoadjuvant therapy in breast cancer (BC); however, the results remain controversial....

Combined conventional ultrasonography with clinicopathological features to predict axillary status after neoadjuvant therapy for breast cancer: A case-control study.

This study aimed to evaluate the value of a model combining conventional ultrasonography and clinicopathologic features for predicting axillary status after neoadjuvant therapy in breast cancer. This retrospective study included 329 patients with lymph node-positive who underwent neoadjuvant systemic treatment (NST) from June 2019 to March 2022. Ultrasound and clinicopathological characteristics of breast lesions and axillary lymph nodes were analyzed before and after NST. The diagnostic efficacy of ultrasound, clinicopathological characteristics, and combined model were evaluated using multivariate logistic regression and receiver operator characteristic curve (ROC) analyses. The area under ROC (AUC) for the ability of the combined model to predict the axillary pathological complete response (pCR) after NST was 0.882, that diagnostic effectiveness was significantly better than that of the clinicopathological model (AUC of 0.807) and the ultrasound feature model (AUC of 0.795). In addition, eight features were screened as independent predictors of axillary pCR, including clinical N stage, ERBB2 status, Ki-67, and after NST the maximum diameter reduction rate and margins of breast lesions, the short diameter, cortical thickness, and fatty hilum of lymph nodes. The combined model constructed from ultrasound and clinicopathological features for predicting axillary pCR has favorable diagnostic results, which allowed more accurate identification of BC patients who had received axillary pCR after NST. A combined model incorporated ultrasound and clinicopathological characteristics of breast lesions and axillary lymph nodes demonstrated favorable performance in evaluating axillary pCR preoperatively and non-invasively.

ASO Author Reflections: Exploring Discordant Response to Neoadjuvant Therapy in Breast Cancer: Insights into the Clinical and Biological Implications of Persistent Lymph Node Metastasis.

ASO Author Reflections: Exploring Discordant Response to Neoadjuvant Therapy in Breast Cancer: Insights into the Clinical and Biological Implications of Persistent Lymph Node Metastasis.

2274P Deep learning-based prediction of pathologic complete response to neoadjuvant therapy in breast cancer using H&E images and RNA-Seq in the IMMUcan study

2274P Deep learning-based prediction of pathologic complete response to neoadjuvant therapy in breast cancer using H&E images and RNA-Seq in the IMMUcan study

Restriction spectrum imaging with elastic image registration for automated evaluation of response to neoadjuvant therapy in breast cancer.

Dynamic contrast-enhanced MRI (DCE) and apparent diffusion coefficient (ADC) are currently used to evaluate treatment response of breast cancer. The purpose of the current study was to evaluate the three-component Restriction Spectrum Imaging model (RSI3C), a recent diffusion-weighted MRI (DWI)-based tumor classification method, combined with elastic image registration, to automatically monitor breast tumor size throughout neoadjuvant therapy. Breast cancer patients (n=27) underwent multi-parametric 3T MRI at four time points during treatment. Elastically-registered DWI images were used to generate an automatic RSI3C response classifier, assessed against manual DCE tumor size measurements and mean ADC values. Predictions of therapy response during treatment and residual tumor post-treatment were assessed using non-pathological complete response (non-pCR) as an endpoint. Ten patients experienced pCR. Prediction of non-pCR using ROC AUC (95% CI) for change in measured tumor size from pre-treatment time point to early-treatment time point was 0.65 (0.38-0.92) for the RSI3C classifier, 0.64 (0.36-0.91) for DCE, and 0.45 (0.16-0.75) for change in mean ADC. Sensitivity for detection of residual disease post-treatment was 0.71 (0.44-0.90) for the RSI3C classifier, compared to 0.88 (0.64-0.99) for DCE and 0.76 (0.50-0.93) for ADC. Specificity was 0.90 (0.56-1.00) for the RSI3C classifier, 0.70 (0.35-0.93) for DCE, and 0.50 (0.19-0.81) for ADC. The automatic RSI3C classifier with elastic image registration suggested prediction of response to treatment after only three weeks, and showed performance comparable to DCE for assessment of residual tumor post-therapy. RSI3C may guide clinical decision-making and enable tailored treatment regimens and cost-efficient evaluation of neoadjuvant therapy of breast cancer.