Skeletal Muscle Loss During Neoadjuvant Chemotherapy for Breast Cancer: Diabetes as an Independent Predictor

Simple SummaryThe loss of skeletal muscle mass is known to be associated with poor treatment outcome, treatment-related toxicity, and high mortality. The association between loss of skeletal muscle mass and the response to treatment is not well-defined yet. In this study, we evaluated the impact of loss of skeletal muscle mass on responsiveness to neoadjuvant chemotherapy in breast cancer. The prediction of response to neoadjuvant chemotherapy could be helpful to guide the treatment direction.There are no means to predict patient response to neoadjuvant chemotherapy (NAC); the impact of skeletal muscle loss on the response to NAC remains undefined. We investigated the association between response to chemotherapy and skeletal muscle loss in breast cancer patients. Patients diagnosed with invasive breast cancer who were treated with NAC, surgery, and radiotherapy were analyzed. We quantified skeletal muscle loss using pre-NAC and post-NAC computed tomography scans. The response to treatment was determined using the Response Evaluation Criteria in Solid Tumors. We included 246 patients in this study (median follow-up, 28.85 months). The median age was 48 years old (interquartile range 42–54) and 115 patients were less than 48 years old (46.7%). Patients showing a complete or partial response were categorized into the responder group (208 patients); the rest were categorized into the non-responder group (38 patients). The skeletal muscle mass cut-off value was determined using a receiver operating characteristic curve; it showed areas under the curve of 0.732 and 0.885 for the pre-NAC and post-NAC skeletal muscle index (p < 0.001 for both), respectively. Skeletal muscle loss and cancer stage were significantly associated with poor response to NAC in locally advanced breast cancer patients. Accurately measuring muscle loss to guide treatment and delaying muscle loss through various interventions would help enhance the response to NAC and improve clinical outcomes.

Background: Human epidermal growth factor receptor 2 (HER2) status can undergo alteration following neoadjuvant chemotherapy (NAC) in breast cancer. This study aimed to investigate the alteration of HER2 status after NAC in breast cancer and its impact on clinical outcomes of patients, focusing on HER2-low status. Methods: We retrospectively reviewed 1,063 breast cancer patients who received NAC followed by surgery between 2013 and 2020. Using paired samples of 670 patients with residual disease, HER2 discordance rate between pre- and post-NAC samples, the relationships between HER2 discordance and clinicopathological characteristics, and clinical outcomes of the patients were analyzed. Results: As a whole, HER2-low status before NAC was associated with a lower pathological complete response rate and higher Residual Cancer Burden (RCB) class, compared with HER2-zero and HER2-positive status. However, in subgroup analysis by hormone receptor (HR) status, no statistical differences were found in chemo-responsiveness between HER2-low and HER2-zero breast cancers. Following NAC, the overall HER2 discordance rate was 21.2% (κ = 0.676). The most common type of alteration was zero-to-low (10.8%) conversion, followed by low-to-positive (3.4%) conversion. HER2 discordance was significantly associated with lower HER2 levels and HR positivity before NAC, as well as lymphovascular invasion, higher ypT stage, lymph node metastasis, and higher RCB class in residual disease after NAC. In further analyses, HER2-zero-to-low conversion showed an association with HR positivity and low histologic grade. In multivariate logistic regression analyses, HR positivity and higher RCB class were identified as independent predictive factors for HER2 discordance. In survival analyses, HER2 discordance revealed a worse prognostic impact on disease-free survival of the patients, particularly within HR-positive subgroup, which remained statistically significant on multivariate Cox regression analysis. However, no survival differences were found between patients with HER2-zero-concordant and those with zero-to-low conversion. Conclusion: Given the prognostic implications of HER2 discordance, which primarily involves zero-to-low conversion, and the therapeutic benefits of newly developed antibody-drug conjugates in HER2-low breast cancer, HER2 status should be re-evaluated in surgical resection specimens following NAC, especially in cases showing HR positivity and high RCB class. Citation Format: Hyun-Jung Sung, Hyun Jung Kwon, Hee-Chul Shin, Eun-Kyu Kim, Koung Jin Suh, Se Hyun Kim, Jee Hyun Kim, So Yeon Park. Alteration of HER2 status following neoadjuvant chemotherapy in breast cancer: a clinicopathological analysis focusing on HER2-low status [abstract]. In: Proceedings of the San Antonio Breast Cancer Symposium 2024; 2024 Dec 10-13; San Antonio, TX. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(12 Suppl):Abstract nr P1-07-02.

Background: NIRST, a noninvasive imaging with no ionizing radiation, has been found to be prognostic as a tool to monitor early pathologic response to NAC in BC using biophysical properties of the tumor compared with normal breast tissue. We aim to establish NIRST indicators as early surrogates of treatment response and to evaluate its potential as a predictive tool in treatment decisions. Methods: 27 women with locally advanced BC undergoing NAC were enrolled in this pilot study. NIRST imaging was performed pre-treatment, after cycle 1 and 2, at the mid-point of NAC, and at the conclusion of NAC prior to surgery. Biophysical data including oxy- and deoxy-hemoglobin, water, lipid, and scatter components were obtained at these time points. To minimize inter-subject variability due to breast density and its effects on the NIRST data, statistical analysis was conducted using ratios of obtained biophysical data to pretreatment average of the contralateral normal breast tissue. Residual Cancer Burden (RCB) index was used to evaluate residual disease after treatment with NAC. RCB scores and classes were determined in 24 of the 27 surgical tissue specimens and these were compared to the NIRST data. RCB data for 3 patients were excluded: 2 patients had undergone positive excisional lymph node biopsy prior to NAC and 1 patient had surgery at an outside hospital. Results: Of the 27 patients, 7 had triple negative BC and 13 had HER-2 positive BC. The change in total hemoglobin (ΔHb-T %) after the first cycle of NAC when compared to the pre-treatment total hemoglobin was determined to be the best predicting factor for RCB (p-value &amp;lt;0.001). The Pearson correlation coefficient was calculated for both RBC class and RBC score (0.7 and 0.6). The significance of the correlation coefficient was evaluated using two-sided t-test and the resulting P-values of 0.006 and 0.001 respectively demonstrate that these correlations are statistically significant. Summary of the NIRST biophysical data and the correlating RCBPatientAgeERPRHer2RCB ScoreHbT -ΔHbT-pre136+-+0-139.933.30251---0-43.532.53341+++0-42.281.89430---0-43.061.59552---0-42.541.71663+++0-151.883.12760--+0-46.471.59852---0-42.721.96966--+0-110.022.471039--+0-9.091.101171+-+0-13.331.501252++-4.12153.201.581362++-3.7475.741.531470--+1.931100.521.631553++-3.4447.831.181641+++4.18929.841.511756+++4.44459.001.471850++-4.008-41.112.181954---3.05011.111.802063++-2.90020.001.502149---0.78026.321.902257++-1.8505.881.702347++-3.600-7.692.602470---3.10047.061.70 Conclusions: We have demonstrated a statistically significant correlation between ΔHb-T % after the first cycle of NAC and the RCB. These findings suggest the potential of using NIRST as an early assessment tool to evaluate response to NAC in BC patients and warrant further evaluation in a larger study. Citation Format: Batukbhai B, Jiang S, Bernhardt EB, Muller K, Cao X, Gui J, DiFlorio-Alexander RM, Chamberlin MD, Schwartz GN, Paulsen KD, Pogue BW, Kaufman PA. Near-infrared spectral tomography (NIRST): A prognostic assessment tool for predicting residual cancer burden (RCB) during neoadjuvant chemotherapy (NAC) in breast cancer (BC) [abstract]. In: Proceedings of the 2018 San Antonio Breast Cancer Symposium; 2018 Dec 4-8; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2019;79(4 Suppl):Abstract nr P6-02-02.

BackgroundSarcopenia is receiving attention in oncology as a predictor of increased chemotherapy toxicities. Research into body composition change during neoadjuvant chemotherapy for breast cancer is both urgently needed and generally lacking. This study assessed sarcopenia prevalence before and after neoadjuvant chemotherapy using CT imaging, evaluated body composition changes during neoadjuvant chemotherapy, and determined predictors of sarcopenia status after neoadjuvant chemotherapy for breast cancer.Materials and MethodsIn this retrospective, descriptive study, we used data collected from 2017 to 2020 to measure body composition parameters on cross-sectional CT slices for 317 Korean women with breast cancer patients before and at completion of neoadjuvant chemotherapy. Changes in skeletal muscle index, visceral fat index, subcutaneous fat index, and sarcopenia were assessed and correlated, and multivariate logistic regression was conducted to identify predictive factors associated with sarcopenia status at completion of neoadjuvant chemotherapy.ResultsOf the 80 breast cancer patients (25.2%) who had sarcopenia before beginning neoadjuvant chemotherapy, 64 (80.0%) retained their sarcopenia status after chemotherapy. Weight, body mass index, body surface area, and visceral fat index showed significant increases after neoadjuvant chemotherapy; notably, only skeletal muscle index significantly decreased, showing a reduction of 0.44 cm2/m2 (t (316) = 2.15, p <.5). Lower skeletal muscle index at baseline was associated with greater loss of muscle mass during neoadjuvant chemotherapy (r = −.24, p <.001). Multivariate logistic regression showed that baseline sarcopenia status was the only significant predictor of sarcopenia status after neoadjuvant chemotherapy (p <.001). Specifically, the log odds of sarcopenia after neoadjuvant chemotherapy were 3.357 higher in the baseline sarcopenia group than in the group without baseline sarcopenia (β = 3.357, p <.001).ConclusionSarcopenia during neoadjuvant chemotherapy can be obscured by an increasing proportion of fat in body composition if clinical assessment focuses on only body mass index or body surface area rather than muscle mass. For breast cancer patients who have sarcopenia when they begin neoadjuvant chemotherapy, the risk of muscle mass loss during treatment is alarmingly high. To reduce masking of muscle mass loss during treatment, comprehensive evaluation of body composition, beyond body surface area assessment, is clearly needed.

Purpose: To evaluate the value of early FDG-PET (18F-Fluorodeoxyglucose-Positron Emission Tomography) metabolic criteria for prediction of pathologic complete response in axilla (pCRAx) after neoadjuvant chemotherapy (NAC) in breast cancer. Methods: Inclusion criteria were all T-stage breast cancers, non-metastatic, with initial lymph node involvement estimated by PET +/- lymph node biopsy, treated with NAC followed by surgery with axillary lymph node dissection (ALND), managed at the George-François Leclerc Cancer Center in Dijon, France, between 2009 and 2019. A PET was performed before and after the first course of chemotherapy (PET1 and PET2). pCRAx was defined as the absence of invasive cells in the nodes at the time of ALND (i.e. ypN0). The Sataloff classification was used as reference on each pathological report. Patients with a Sataloff NA classification (i.e. evidence of therapeutic effect, and no residual disease) and, if axillary involvement was proven at diagnosis, NB (i.e. no metastasis, no therapeutic effect) were considered as pCRAx. The PET metabolic criteria studied in the axilla were: - SUVmax (Standard Uptake Value) on PET1 and PET2 = fixation in the axillary voxel with the highest activity (kBq/mL)/(injected dose (kBq)/weight (g)) - ΔSUVmax (%) = metabolic response after the first course of NAC = 100 x (SUVmax1 - SUVmax2)/(SUVmax1). Univariate and multivariate analysis were performed to identify factors (clinical, pathologic, metabolic) that may be associated with pCRAx. Relationships between baseline TEP uptake and prognostic parameters were assessed using Receiver Operating Characteristic (ROC) curves. Results: Among 188 patients included, the rate of pathologically proven node involvement was 63.3% (n=119). The pCRAx rate was 45.7% (n=86/188) but varied according to tumor subtypes: 14.5% (n=9/62) of HR(Hormone Receptor)+/HER2-negative, 47.7% (n=21/44) of HR+/HER2-positive, 61.4% (n=27/44) of triple-negative (TN) and 76.3% (n=29/38) of HR-/HER2-positive. Factors significantly associated with pCRAx were by univariate analysis: HER2-positive (HR+ and HR-) and TN subtypes (p&amp;lt; 0.001), SBR (Scarff-Bloom-Richardson) grade (p=0.01), breast pCR (ypT0/is) (p&amp;lt; 0.001), SUVmax2 (p=0.01) and ΔSUVmax (p&amp;lt; 0.001). By multivariate analysis, it persisted the HR-/HER2-positive (p=0.02) and TN (p=0.02) subtypes and breast pCR (p&amp;lt; 0.001). In global population, a decrease in ΔSUVmax of 63% was the optimal threshold to predict pCRAx (Area Under the Curve AUC = 0.73) with a sensitivity (Se) of 51% and specificity (Sp) of 83%. ΔSUVmax remains the best performing parameter in TN (AUC = 0.72; Se at 52%; Sp at 88%). In HR-/HER2-positive patients, SUVmax2 appeared to be a better predictor of pCRAx than ΔSUVmax. A SUVmax2 value of 1.99 was the optimal threshold for predicting pCRAx (AUC = 0.72), yielding a Se of 66% and a Sp of 78%. None of the PET criteria predicted axillary response with sufficient accuracy for HR+ subtypes. Conclusion: PET alone does not appear to be sufficient to predict pCRAx. It seems necessary to use other parameters, whether clinical, biological or imaging, to discriminate responders from non-responders to NAC in order to adapt the subsequent surgical management. Citation Format: ELOISE MICHEL, FRANCOISE BELTJENS, ALEXANDRE COCHET, JEAN LOUIS ALBERINI, CHARLES COUTANT, CLEMENTINE JANKOWSKI. Utility of 18F-FDG PET/CT for the prediction of pathologic complete response in axilla to neoadjuvant chemotherapy in breast cancer [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P5-01-11.

Background: Early clinical response to neoadjuvant chemotherapy (NACT) in breast cancer correlates with pathological response at surgery. A tailored approach using biomarkers to predict response to NACT has become a research priority. Predictors of response can be divided into pathological and radiological biomarkers. Advances in gene expression profiling and diffusion-weighted MRI techniques are used to predict tumour response, and combinations thereof are the future of predicting response to NACT in early-stage breast cancer. Methods: We searched Medline, CINAHL and Embase databases for studies on NACT. Key words used were NACT, breast cancer, pathological* complete response, primary chemotherapy, radiological*, predictor*, gene expression and biomarkers limited to the English language. Pathological markers such as tumour subtypes, topoisomerase IIα expression, Ki67, apoptosis-related markers and gene expression profiling were included. Results: From 119 articles, 42 studies were reviewed; the majority of studies identified used pathological clinical response as an end point to NACT, whilst others used complete clinical response. Despite extensive studies, results regarding long-term survival following NACT and potential predictors are inconclusive. Conclusion: Future development of a predictive model combining key pathological and radiological biomarkers could provide personalised treatment regimens that improve pathological complete response rates and longer-term outcomes.

Previous studies have pointed out that magnetic resonance- and fluorodeoxyglucose positron emission tomography-based radiomics had a high predictive value for the response of the neoadjuvant chemotherapy (NAC) in breast cancer by respectively characterizing tumor heterogeneity of the relaxation time and the glucose metabolism. However, it is unclear whether computed tomography (CT)-based radiomics based on density heterogeneity can predict the response of NAC. This study aimed to develop and validate a CT-based radiomics nomogram to predict the response of NAC in breast cancer. A total of 162 breast cancer patients (110 in the training cohort and 52 in the validation cohort) who underwent CT scans before receiving NAC and had pathological response results were retrospectively enrolled. Grades 4 to 5 cases were classified as response to NAC. According to the Miller-Payne grading system, grades 1 to 3 cases were classified as nonresponse to NAC. Radiomics features were extracted, and the optimal radiomics features were obtained to construct a radiomics signature. Multivariate logistic regression was used to develop the clinical prediction model and the radiomics nomogram that incorporated clinical characteristics and radiomics score. We assessed the performance of different models, including calibration and clinical usefulness. Eight optimal radiomics features were obtained. Human epidermal growth factor receptor 2 status and molecular subtype showed statistical differences between the response group and the nonresponse group. The radiomics nomogram had more favorable predictive efficacy than the clinical prediction model (areas under the curve, 0.82 vs 0.70 in the training cohort; 0.79 vs 0.71 in the validation cohort). The Delong test showed that there are statistical differences between the clinical prediction model and the radiomics nomogram ( z = 2.811, P = 0.005 in the training cohort). The decision curve analysis showed that the radiomics nomogram had higher overall net benefit than the clinical prediction model. The radiomics nomogram based on CT radiomics signature and clinical characteristics has favorable predictive efficacy for the response of NAC in breast cancer.

Low skeletal muscle mass and high adipose tissue coexist across the body weight spectrum and independently predict the survival ratio of colorectal cancer (CRC) patients. This combination may lead to a mutually exacerbating vicious cycle. Tumor-associated metabolic conditions primarily affect subcutaneous adipose tissue, but the nature and direction of its relationship with skeletal muscle are unclear. This study aims to examine the bidirectional causal relationship between skeletal muscle index (SMI) and subcutaneous fat index (SFI) during the perioperative period in CRC patients; as well as to validate the association between perioperative SMI, SFI, and CRC prognosis. This population-based retrospective cohort study included patients with stage I-III colorectal cancer who underwent radical resection at the Third Affiliated Hospital of Kunming Medical University between September 2012 and February 2019. Based on inclusion and exclusion criteria, 1,448 patients were analyzed. Preoperative (P1), 2 months postoperative (P2), and 5 months postoperative (P3) CT scans were collected to evaluate the skeletal muscle index (SMI; muscle area at the third lumbar vertebra divided by height squared) and subcutaneous fat index (SFI; subcutaneous fat area at the third lumbar vertebra divided by height squared). A random intercept cross-lagged panel model (RI-CLPM) was used to examine the intra-individual relationship between SMI and SFI, and Cox regression was employed to assess the association between SMI, SFI, recurrence-free survival (RFS), and overall survival (OS). The median age at diagnosis was 59.00 years (IQR: 51.00-66.00), and 587 patients (40.54%) were female. RI-CLPM analysis revealed a negative correlation between SFI and subsequent SMI at the individual level: P1-P2 (β = -0.372, p = 0.038) and P2-P3 (β = -0.363, p = 0.001). SMI and SFI showed a negative correlation during P1-P2 (β = -0.363, p = 0.001) but a positive correlation during P2-P3 (β = 0.357, p = 0.006). No significant correlation was found between the random intercepts of SFI and SMI at the between-person level (r = 0.157, p = 0.603). The Cox proportional hazards multivariate regression model identified that patients with elevated SFI had poorer recurrence-free survival (HR, 1.24; 95% CI: 1.00-1.55). Compared to patients with normal preoperative SMI and SFI, those with low SMI or high SFI had poorer recurrence-free survival (HR, 1.26; 95% CI: 1.03-1.55) and overall survival (HR, 1.39; 95% CI: 1.04-1.87). However, no significant association between SMI and SFI and the prognosis of colorectal cancer patients was observed postoperatively. In CRC patients, preoperative muscle loss leads to postoperative fat accumulation, exacerbating muscle loss in a feedback loop. Elevated preoperative SFI predicts poorer survival outcomes. Monitoring SMI and SFI is crucial as prognostic indicators, despite non-significant postoperative associations. Further research is needed to improve patient outcomes.

Breast cancer (BC) is the most common cancer in women worldwide, and neoadjuvant chemotherapy (NAC) is considered the standard of treatment for most patients with BC. However, response rates to NAC vary among patients, which leads to delays in appropriate treatment and affects the prognosis for patients who ineffectively respond to NAC. This study aimed to investigate the feasibility of deep learning radiomics (DLR) in the prediction of NAC response at an early stage. In total, 168 patients with clinicopathologically confirmed BC were enrolled in this prospective study, from March 2016 to December 2020. All patients completed NAC treatment and underwent ultrasonography (US) at three time points (before NAC, after the second course, and after the fourth course). We developed two DLR models, DLR-2 and DLR-4, for predicting responses after the second and fourth courses of NAC. Furthermore, a novel deep learning radiomics pipeline (DLRP) was proposed for stepwise prediction of response at different time points of NAC administration. In the validation cohort, DLR-2 achieved an AUC of 0.812 (95% CI: 0.770-0.851) with an NPV of 83.3% (95% CI: 76.5-89.6). DLR-4 achieved an AUC of 0.937 (95% CI: 0.913-0.955) with a specificity of 90.5% (95% CI: 86.3-94.2). Moreover, 19 of 21 non-response patients were successfully identified by DLRP, suggesting that they could benefit from treatment strategy adjustment at an early stage of NAC. The proposed DLRP strategy holds promise for effectively predicting NAC response at its early stage for BC patients. • We proposed two novel deep learning radiomics (DLR) models to predict response to neoadjuvant chemotherapy (NAC) in breast cancer (BC) patients based on US images at different NAC time points. • Combining two DLR models, a deep learning radiomics pipeline (DLRP) was proposed for stepwise prediction of response to NAC. • The DLRP may provide BC patients and physicians with an effective and feasible tool to predict response to NAC at an early stage and to determine further personalized treatment options.

e12084 Background: Activation of the immune response including T lymphocytes, natural killer (NK) cells, and tumor microenvironment factors (TMEFs) is important in inducing a therapeutic response after neoadjuvant chemotherapy (NAC) in breast cancer. We examined the significance of systemic and local immune responses to the pathological therapeutic effect of NAC in breast cancer. Methods: From 2012 to 2018, 38 patients with stage II–III breast cancer received NAC with anthracyclines and taxanes followed by surgery. Therapeutic effects were evaluated according to the histopathology criteria for the assessment of therapeutic effects in breast cancer indicated by the Japanese Breast Cancer Society. Peripheral NK (pNK) cell activity was measured by chromium release assay. Levels of TMEFs were assessed by next-generation sequencing for CD4, CD8, NK, FOXP3, CTLA-4, PD-1, PD-L1, IL-2, IL-6, IL-12, IFN-γ, IL-10, TGF-β, and VEGF in FFPE sections collected from preoperative VAB samples and surgical specimens. Results: The stages, tumor subtypes, and therapeutic outcomes were as follows: II (N = 21), III (N = 17); luminal (N = 22), HER-2 positive (N = 12), TN (N = 4); G1a (N = 8), G1b (N = 13), G2a (N = 7), G2b (N = 4), G3 (i.e. complete) (N = 6). A G2 or better therapeutic effect were significantly associated with high post-NAC levels of NK, and potentially associated with higher CD4, CD8, and lower CTLA-4 after NAC. Multivariate logistic regression analysis showed that a G2 or better therapeutic effect was significantly associated with higher NK after NAC (OR = 1.07; 95% CI, 1.00–1.14; P = .0255). Disappearance of axillary lymph node metastasis was significantly associated with increased NK and pNK cell activity, as well as decreased VEGF level, and potentially associated with lower CTLA-4 after NAC. Conclusions: Increased NK cells after NAC are critical in producing a better therapeutic effect in collaboration with increased CD4 and CD8, and decreased CTLA-4 and VEGF. Systemic activation of pNK cells may improve the elimination of metastatic tumor cells in axillary lymph nodes by coordinating with release of immunosuppression derived from VEGF and activation of immune cells in the tumor microenvironment in breast cancer patients after NAC. An immune checkpoint inhibitor targeting CTLA-4 may improve NAC efficacy for breast cancer.

Background and Objective: The aim of this study was to investigate the value of programmed death ligand 1 (PD-L1) expression as a predictive biomarker for Miller/Payne grading before neoadjuvant chemotherapy (NACT) in breast cancer. Patients and Methods: The expression of PD-L1 in pretreatment biopsies of breast cancer was assessed by immunohistochemistry in tissue microarrays. The results were analyzed using SPSS 22.0 statistical software. Results: Of 53 female patients, 10 (18.9%) patients had a grade 5 (G5) response, and 12 (22.6%) patients showed PD-L1 expression, including 7 (13.2%) patients with staining in tumor cells (TCs) and 8 (15.1%) patients with staining in peritumoral lymphocytes (PTLCs). Logistic regression analysis revealed that G5 response to NACT was significantly associated with TCs or PTLCs PD-L1 positivity, whether with univariate analysis (TCs PD-L1: p = 0.00, OR 20.50, 95% CI 3.11–134.94; PTLCs PD-L1: p = 0.02, OR 6.50, 95% CI 1.27–33.20) or with multivariate analysis (TCs PD-L1: p = 0.00, OR 42.23, 95% CI 3.36–530.90; PTLCs PD-L1: p = 0.02, OR 9.07, 95% CI 1.37–60.02). The same trend was found in the luminal subgroup analysis (TCs PD-L1: p = 0.02, OR 23.43, 95% CI 1.66–331.58; PTLCs PD-L1: p = 0.01, OR 47.89, 95% CI 2.47–927.41). Conclusion: G5 response to NACT in breast cancer was significantly associated with TCs or PTLCs PD-L1-positive expression in pretreatment biopsies; it can be expected that PD-L1 will become a new independent biomarker of response to NACT in breast cancer.

The potential role of shear wave elastography (SWE) and superb microvascular imaging (SMI) for early assessment of treatment response to neoadjuvant chemotherapy (NAC) in breast cancer remains unexplored. This study aimed to identify potential factors associated with the pathological response to NAC using these advanced ultrasound techniques. Between August 2021 and October 2022, 68 patients with breast cancer undergoing NAC were recruited. Patients underwent conventional ultrasonography, SMI, and SWE examinations at baseline and post-2nd cycle of NAC. Maximum tumor diameter (Dmax), maximum elastic value (Emax), peak systolic velocity (PSV), and resistance index (RI) at baseline and the rate of change of these parameters post-2nd cycle were recorded. After chemotherapy, all patients underwent surgery. Using the Miller-Payne's grade, patients were categorized into response (grades 3, 4, or 5) and non-response (grades 1 or 2) group. Parameters were compared using t-tests at baseline and post-2nd cycle. Binary logistic regression analysis was used to identify variables and their odds ratios (ORs) related to responses and a prediction model was established. ROC curves were drawn to analyze the efficacy of each parameter and their combined model for early NAC response prediction. Among the 68 patients, 15(22.06%) were categorized into the non-response group, whereas 53(77.94%) were categorized into the response group. At baseline, no significant differences were observed between the two groups (p>0.05). Post-2nd cycle of NAC, rates of change of Emax, PSV and RI (ΔEmax, ΔPSV and ΔRI) were higher in responders than non-responders (p<0.05). Binary logistic regression analysis revealed that ΔEmax (OR 0.797 95% CI, 0.683-0.929), ΔPSV (OR 0.926, 95%CI, 0.860-0.998), and ΔRI (OR 0.841, 95%CI, 0.736-0.960) were independently associated with the pathological response of breast cancer after NAC. The combined prediction model exhibited higher accuracy in the early evaluation of the response to NAC (AUC 0.945, 95%CI, 0.873-1.000). SWE and SMI techniques enable early identification of tumor characteristics associated with the pathological response to NAC and may be potentially indicative of an effective response. These factors may eventually be used for the early assessment of NAC treatment for clinical management.

The role of TP53 mutations in predicting response to neoadjuvant chemotherapy in breast cancer remains controversial. The aims of this study were to investigate whether TP53 mutations were associated with response and survival in breast cancer patients who received neoadjuvant chemotherapy. Therefore, we identified TP53 mutations in the core-needle biopsy tumor samples obtained before the neoadjuvant chemotherapy from 351 operable primary breast cancer patients who either received anthracycline/cyclophosphamide-based (n = 252) or paclitaxel (n = 99) neoadjuvant chemotherapy. We found that 41.0% (144 of 351) of patients harbored TP53 mutations, and 14.8% of patients achieved a pCR (pathologic complete response) after neoadjuvant chemotherapy. Among patients treated with anthracycline/cyclophosphamide (n = 252), patients with TP53 mutations had a significantly higher pCR rate than those with wild-type (28.6 vs.7.1%; p < 0.001), and TP53 mutation was an independent favorable predictor of pCR [odds ratio (OR) = 3.41; 95% confidence interval (CI) 1.50-7.77; p = 0.003] in this group; moreover, patients with TP53 mutation had a better distant recurrence-free survival (DRFS) than those with wild-type [unadjusted hazard ratio (HR) = 0.43; 95% CI 0.20-0.94; p = 0.030] in this group. Among patients treated with paclitaxel (n = 99), no significant difference in pCR rates was observed between patients with or without TP53 mutations (15.2 vs. 11.3%; p = 0.57). Our results suggested that patients with TP53 mutations are more likely to respond to anthracycline/ cyclophosphamide-based neoadjuvant chemotherapy and have a favorable survival.

This study aimed to investigate alteration of HER2 status after neoadjuvant chemotherapy (NAC) in breast cancer and its impact on clinical outcomes of patients, focusing on HER2-low status. We retrospectively reviewed clinicopathological data of 1,063 breast cancer patients who underwent NAC between 2013 and 2020. Using paired samples of 670 patients with residual disease after NAC, we analyzed HER2 discordance rates between pre- and post-NAC samples, relationships between HER2 discordance and clinicopathological characteristics of tumors, and clinical outcomes of the patients. Pre-NAC HER2-low status was associated with a lower pathological complete response rate and higher Residual Cancer Burden class compared with HER2-zero and HER2-positive status. However, in subgroup analysis by hormone receptor (HR) status, no statistical differences were found in chemo-responsiveness between them. Following NAC, the overall HER2 discordance rate was 21.2% (κ = 0.676), and the most common type of alteration was zero-to-low (11.5%) conversion, followed by low-to-positive (3.6%) conversion. HER2 discordance was significantly associated with lower HER2 levels and HR positivity before NAC, as well as lymphovascular invasion, higher ypT stage, and axillary node metastasis in residual disease after NAC. In survival analyses, HER2 discordance was found to be an independent prognostic factor for poor disease-free survival of the patients, particularly within the HR-positive subgroup. Given the prognostic implications of HER2 discordance which primarily involves zero-to-low conversion and the therapeutic benefits of newly developed antibody-drug conjugates in HER2-low breast cancers, HER2 status should be re-evaluated in surgical resection specimens following NAC.

Purpose The accurate assessment of residual tumor tissue after neoadjuvant chemotherapy (NAC) for breast cancer is closely related to the subsequent treatment and prognosis of patients. Currently commonly used assessment methods, including Miller and Payne system (MPS), Residual Cancer Burden (RCB), and Residual Disease in Breast and Nodes (RDBN) assessment system, etc., have certain limitations in terms of accurate evaluation and determining prognosis. The limitation of MPS lies in the need to review the original tumor biopsy specimen and compare the cell contents of the biopsy specimen and the surgical specimen. The limitation of RCB is that it requires a broader sampling, as well as more time and energy in microscopic examination. Furthermore, determining the number of cells is subjective and differences exist among observers. The limitation of RDBN is its poor correlation with prognosis. Ki-67, as a marker to reflect cell proliferation, is widely used in prognostic judgment of invasive breast cancer and is also an important reference in treatment decision-making. This study aimed to combine the Ki-67 expression status after NAC with RDBN to design a new pathological assessment method, which we called residual disease in breast and nodes combined with Ki-67 (RDBN-K), and to study its significance for the prognosis of patients.Methods RDBN-K = 0.2 (residual breast tumor size in centimeters) + index of involved nodes + tumor histological grade+ index of Ki-67. The residual tumor size, index of involved nodes, and histological grade are the same as RDBN. The index of Ki-67 is scored as 0 for less than 14% and 1 for greater than or equal to 14%. The residual diseases of 723 patients with TNM staging of stage II to stage III who had undergone NAC and surgical treatment were evaluated by RDBN-K. RDBN-K includes 4 risk levels (levels 1-4) according to residual disease magnitude after neoadjuvant chemotherapy. The RDBN-K levels were defined as follows: RDBN-K-1 (equivalent to pCR) is an index of 0, RDBN-K-2 is an index between 0.1 and 3, RDBN-K-3 is an index between 3.1 and 5.3, and RDBN-4 is an index 5.4 or more. At the same time, RDBN was used to evaluate the residual disease of all patients after NAC. This study followed up the survival status of 723 patients. After combining the prognoses, the accuracy and clinical significance of the RDBN and RDBN-K were compared.Results During the follow-up, in the entire cohort, 147 (20.3%) recurrences or metastases were observed; local recurrence was 58 (8.0%), distant metastasis was 103 (14.2%), and 69 (9.5%) patients died. Among the RDBN-2 cases, 40 (5.5%) of 122 cases were reclassified to RDBN-K-3. Among the RDBN-3 cases, 17 (2.4%) of 295 cases were reclassified: 2 cases were reclassified to RDBN-K-4, and 15 cases were reclassified to RDBN-K-2. Among the 220 cases in the RDBN-4 category, 40 (5.5%) were reclassified to the RDBN-K-3 category using the RDBN-K calculation. Over the follow-up period, 13.6% of patients in the RDBN-4 category died, and 15.9% of patients in the RDBN-K-4 category died. RDBN and RDBN-K showed statistically significant differences in the disease-free survival (DFS) and overall survival (OS) of all patients (P values are all less than 0.05). Pairwise stratified analysis showed that the differences in DFS and OS between RDBN-K-3 and RDBN-K-4 (DFS: P = 0.019, OS: P = 0.035) were greater than that between RDBN-3 and RDBN-4 (DFS: P = 0.052, OS: P = 0.214), and the differences in OS between RDBN-K-2 and RDBN-K-3 (P = 0.023) were greater than that between RDBN-2 and RDBN-3 (P = 0.157).Conclusion Compared with RDBN, RDBN-K is more accurate in assessing the residual tumor burden after breast cancer NAC and predicting the prognosis for breast cancer patients, and provides more basis for follow-up intensive treatment of patients. Citation Format: Ruoqi Han, Yueping Liu, Yanqi Ma, Zhikun Liu, Chunxiao Li, Cuizhi Geng. A new pathological assessment method to assess residual lesions after neoadjuvant chemotherapy for breast cancer: Residual disease in breast and nodes combined with Ki-67 (RDBN-K) [abstract]. In: Proceedings of the 2020 San Antonio Breast Cancer Virtual Symposium; 2020 Dec 8-11; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2021;81(4 Suppl):Abstract nr PS6-29.