The value of serial [⁶⁸Ga]Ga-FAPI-04 PET/CT in predicting pathological response and evaluating therapeutic efficacy to neoadjuvant chemotherapy in breast cancer.

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 <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.

PurposeTo investigate the relationship between hypoxia-inducible factor 1-alpha (HIF-1α), Twist family BHLH transcription factor 1 (TWIST-1), and β1 integrin (ITGB-1) expression and tumor stiffness, and evaluate performance of HIF-1α, TWIST-1, and ITGB-1 alone and in combination with Ki-67 for predicting pathological responses to neoadjuvant chemotherapy (NACT) in breast cancer (BC).Patients and MethodsThis was a prospective cohort study of 104 BC patients receiving NACT. Tumor stiffness and oxygen score (OS) were evaluated before NACT by shear-wave elastography and optical imaging; HIF-1α, TWIST-1, ITGB-1, and Ki-67 expression were quantitatively assessed by immunohistochemistry of paraffin-embedded tumor samples obtained by core needle biopsy. Indexes were compared among different residual cancer burden (RCB) groups, and associations of HIF-1α, TWIST-1, ITGB-1, and Ki-67 with tumor stiffness and OS were examined. The value of HIF-1α, TWIST-1, ITGB-1, and Ki-67, and a possible new combined index (predRCB) for predicting NACT responses was assessed by receiver operating characteristic (ROC) curves.ResultsHIF-1α, TWIST-1, and ITGB-1 expression were positively correlated with tumor stiffness and negatively with OS. Area under the ROC curves (AUCs) measuring the performance of HIF-1α, TWIST-1, ITGB-1, and Ki-67 for predicting responses to NACT were 0.81, 0.85, 0.79, and 0.80 for favorable responses, and 0.83, 0.86, 0.84, and 0.85 for resistant responses, respectively. PredRCB showed better prediction than the other individual indexes for favorable responses (AUC = 0.88) and resistant responses (AUC = 0.92).ConclusionHIF-1α, TWIST-1, ITGB-1, and Ki-67 performed well in predicting favorable responses and resistance to NACT, and predRCB improved the predictive power of the individual indexes. These results support individualized treatment of BC patients receiving NACT.

Can FDG-PET/CT predict early response to neoadjuvant chemotherapy in breast cancer?

Background Previous studies have suggested that texture analysis is a promising tool in the diagnosis, characterization, and assessment of treatment response in various cancer types. Therefore, application of texture analysis may be helpful for early prediction of pathologic response in breast cancer. Purpose To investigate whether texture analysis of features from MRI is associated with pathologic complete response (pCR) to neoadjuvant chemotherapy (NAC) in breast cancer. Materials and Methods This retrospective study included 136 women (mean age, 47.9 years; range, 31-70 years) who underwent NAC and subsequent surgery for breast cancer between January 2012 and August 2017. Patients were monitored with 3.0-T MRI before (pretreatment) and after (midtreatment) three or four cycles of NAC. Texture analysis was performed at pre- and midtreatment T2-weighted MRI, contrast material-enhanced T1-weighted MRI, diffusion-weighted MRI, and apparent diffusion coefficient (ADC) mapping by using commercial software. A random forest method was applied to build a predictive model for classifying those with pCR with use of texture parameters. Diagnostic performance for predicting pCR was assessed and compared with that of six other machine learning classifiers (adaptive boosting, decision tree, k-nearest neighbor, linear support vector machine, naive Bayes, and linear discriminant analysis) by using the Wald test and DeLong method. Results Forty of the 136 patients (29%) achieved pCR after NAC. In the prediction of pCR, the random forest classifier showed the lowest diagnostic performance with pretreatment ADC (area under the receiver operating characteristic curve [AUC], 0.53; 95% confidence interval: 0.44, 0.61) and the highest diagnostic performance with midtreatment contrast-enhanced T1-weighted MRI (AUC, 0.82; 95% confidence interval: 0.74, 0.88) among pre- and midtreatment T2-weighted MRI, contrast-enhanced T1-weighted MRI, diffusion-weighted MRI, and ADC mapping. Conclusion Texture parameters using a random forest method of contrast-enhanced T1-weighted MRI at midtreatment of neoadjuvant chemotherapy were valuable and associated with pathologic complete response in breast cancer. © RSNA, 2019 Online supplemental material is available for this article.

Synthetic MRI (syMRI) has enabled quantification of multiple relaxation parameters (T1/T2 relaxation time [T1/T2], proton density [PD]), and their longitudinal change during neoadjuvant chemotherapy (NAC) promises to be valuable parameters for treatment response evaluation in breast cancer. To investigate the time course changes of syMRI parameters during NAC and evaluate their value as predictors for pathological complete response (pCR) in breast cancer. Retrospective, longitudinal. A total of 129 women (median age, 50 years; range, 28-69 years) with locally advanced breast cancer who underwent NAC; all performed multiple conventional breast MRI examinations with added syMRI during NAC. A 3.0 T, T1-weighted dynamic contrast enhanced and syMRI acquired by a multiple-dynamic, multiple-echo sequence. Breast MRI was set at four time-points: baseline, after one cycle, after three or four cycles of NAC and preoperation. SyMRI parameters and tumor diameters were measured and their changes from baseline were calculated. All parameters were compared between pCR and non-pCR. Interaction between syMRI parameters and clinicopathological features was analyzed. Mann-Whitney U tests, random effects model of repeated measurement, receiver operating characteristic (ROC) analysis, interaction analysis. Median synthetic T1/T2/PD and tumor diameter generally decreased throughout NAC. Absolute T1 at early-NAC, T1, and PD at mid-NAC were significantly lower in the pCR group. After early-NAC, the T1 change was significantly higher in the pCR (median ± IQR, 18.17 ± 11.33) than the non-pCR group (median ± IQR, 10.90 ± 10.03), with the highest area under the ROC curves (AUC) of 0.769 (95% CI, 0.684-0.838). Interaction analysis showed that histological grade III patients had higher odds ratio (OR) (OR=1.206) compared to grade II patients (OR=1.067). Synthetic T1 changes after one cycle of NAC maybe useful for early evaluating NAC response in breast cancer during whole treatment cycles. However, its discriminative ability is significantly affected by histological grade. 4 TECHNICAL EFFICACY: Stage 2.

To assess early changes in synthetic relaxometry after neoadjuvant chemotherapy (NAC) for breast cancer and establish a model with contrast-free quantitative parameters for early prediction of pathological response. From March 2019 to January 2021, breast MRI were performed for a primary cohort of women with breast cancer before (n = 102) and after the first (n = 93) and second (n = 90) cycle of NAC. Tumor size, synthetic relaxometry (T1/T2 relaxation time [T1/T2], proton density), and ADC were obtained, and the changes after treatment were calculated. Prediction models were established by multivariate logistic regression; evaluated with discrimination, calibration, and clinical application; and compared with Delong tests, net reclassification (NRI), and integrated discrimination index (IDI). External validation was performed from February to June 2021 with an independent cohort of 35 patients. In the primary cohort, all parameters changed after early treatment. Synthetic relaxometry decreased to a greater degree in major histologic responders (MHR, Miller-Payne G4-5) compared with non-MHR (Miller-Payne G1-3). A model combining ADC after treatment, changes in T1 and tumor size, and cancer subtype achieved the highest AUC after the first (primary/validation cohort, 0.83/0.82) and second cycles (primary/validation cohort, 0.85/0.84). No difference of AUC (p ≥ 0.27), NRI (p ≥ 0.31), and IDI (p ≥ 0.32) was found between models with different cycles and size-measured sequences. Model calibration and decision curves demonstrated a good fitness and clinical benefit, respectively. Early reduction in synthetic relaxometry indicated pathological response to NAC. Contrast-free T1 and ADC combined with size and cancer subtype predicted effectively pathological response after one NAC cycle. • Synthetic MRI relaxometry changed after early neoadjuvant chemotherapy, which demonstrated pathological response for mass-like breast cancers. • Contrast-free quantitative parameters including T1 relaxation time and apparent diffusion coefficient, combined with tumor size and cancer subtype, stratified major histologic responders. • A contrast-free model predicted an early pathological response after the first treatment cycle of neoadjuvant chemotherapy.

To develop an artificial intelligence (AI) system for the early prediction of residual cancer burden (RCB) scores during neoadjuvant chemotherapy (NAC) in breast cancer. RCB III indicates drug resistance in breast cancer, and early detection methods are lacking. This study enrolled 1048 patients with breast cancer from 4 institutions, who were all receiving NAC. Magnetic resonance images were collected at the pre-NAC and mid-NAC stages, and radiomics and deep learning features were extracted. A multitask AI system was developed to classify patients into 3 groups (RCB 0 to I, II, and III ) in the primary cohort (PC, n=335). Feature selection was conducted using the Mann-Whitney U test, Spearman analysis, least absolute shrinkage and selection operator regression, and the Boruta algorithm. Single-modality models were developed, followed by model integration. The AI system was validated in 3 external validation cohorts (EVCs, n=713). Among the patients, 442 (42.18%) were RCB 0 to I, 462 (44.08%) were RCB II, and 144 (13.74%) were RCB III. Model I achieved an area under the curve of 0.975 in the PC and 0.923 in the EVCs for differentiating RCB III from RCB 0 to II. Model II distinguished RCB 0 to I from RCB II-III, with an area under the curve of 0.976 in the PC and 0.910 in the EVCs. Subgroup analysis confirmed that the AI system was consistent across different clinical T stages and molecular subtypes. The multitask AI system offers a noninvasive tool for the early prediction of RCB scores in breast cancer, supporting clinical decision-making during NAC.

Early tumor response prediction can help avoid overtreatment with unnecessary chemotherapy sessions. It is important to determine whether multiple apparent diffusion coefficient indices (S index, ADC-diff) are effective in the early prediction of pathological response to neoadjuvant chemotherapy (NAC) in breast cancer (BC). Patients with stage II and III BCs who underwent T1WI, diffusion-weighted imaging (DWI), and dynamic contrast-enhanced MRI using a 3 T system were included. They were divided into two groups: major histological responders (MHRs, Miller-Payne G4/5) and nonmajor histological responders (nMHRs, Miller-Payne G1-3). Three b values were used for DWI to derive the S index; ADC-diff values were obtained using b = 0 and 1000 s/mm2. The different interquartile ranges of percentile S-index and ADC-diff values after treatment were calculated and compared. The assessment was performed at baseline and after two and four NAC cycles. A total of 59 patients were evaluated. There are some correlations of interquartile ranges of S-index parameters and ADC-diff values with histopathological prognostic factors (such as estrogen receptor and human epidermal growth factor receptor 2 expression, all p < 0.05), but no significant differences were found in some other interquartile ranges of S-index parameters or ADC-diff values between progesterone receptor positive and negative or for Ki-67 tumors (all P > 0.05). No differences were found in the dynamic contrast-enhanced MRI characteristics between the two groups. HER-2 expression and kurtosis of the S-index distribution were screened out as independent risk factors for predicting MHR group (p < 0.05, area under the curve (AUC) = 0.811) before NAC. After early NAC (two cycles), only the 10th percentile S index was statistically significant between the two groups (p < 0.05, AUC = 0.714). No significant differences were found in ADC-diff value at any time point of NAC between the two groups (P > 0.1). These findings demonstrate that the S-index value may be used as an early predictor of pathological response to NAC in BC; the value of ADC-diff as an imaging biomarker of NAC needs to be further confirmed by ongoing multicenter prospective trials.

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.

To explore whether conventional elastography and contrast-enhanced ultrasound (CEUS) combined with histopathology can monitor the efficacy of neoadjuvant chemotherapy (NAC) for breast cancer (BC), and develop a Nomogram prediction model monitoring response to NAC. From February 2010 to November 2015, 91 BC patients who received NAC were recruited. The maximum diameter, stiffness, and CEUS features were assessed. Core biopsy, surgical pathology immunophenotype, and Miller-Payne (MP) evaluation were documented. Univariate and multivariate analysis was performed using receiver operating characteristic (ROC) analysis and logistic regression analysis. There were 37 cases showing pathological complete response (pCR) and 54 of non-pCR. The changes of maximal diameter were correlated with MP (P < .05). The sensitivity (SEN), specificity (SPE), and area under the ROC curve (AUC) of baseline size predicting pCR were 57.40%, 70.30%, and 0.64 (P=.024). Baseline Ki-67 index of pCR group is significantly higher than that of non-pCR group (P=.029), and the ROC analysis of baseline Ki-67 indicates the SEN, SPE, and AUC of 51.70%, 78.00%, and 0.638 (P=.050). When combined with size, CEUS features, stiffness, and Ki-67 of baseline, the ROC curve shows good performance with SEN, SPE, and AUC of 70.00%, 76.19%, 0.821 (P=.004). Incorporating the change of characteristics into multivariate regression analysis, the results demonstrate excellent performance (SEN 100.00%, SPE 95.24%, AUC 0.986, P=.000). The change of the maximum size was correlated with MP score, which can provide reference to predict efficacy of NAC and evaluate residual lesions. When combining with elastography, CEUS, and Ki-67, better performance in predicting pathological response was shown.

A quantitative analysis of contrast-enhanced spectral mammography (CESM) enhancement was conducted for the early prediction of the pathological response after neoadjuvant chemotherapy (NAC). Retrospective analysis of the data of 111 patients was conducted, and all of them underwent NAC in our hospital and surgical resection after the end of all cycles from January 2018 to May 2019. They were divided into pathological complete response (PCR) and non-PCR groups. We determined whether a statistical difference in the percentage of CESM grey value reduction (ΔCGV) was present in the PCR and non-PCR groups and whether a statistical difference was observed in the diagnostic efficiency of craniocaudal (CC) and mediolateral oblique (MLO) view subtraction images. Independent sample t-test was used to compare different groups, the receiver operating characteristic (ROC) curve was used to compare the diagnostic efficacy of CC and MLO for pathological response after NAC, and the Delong test was used to compare the area under the ROC curve (AUC). Statistical significance was considered at P < 0.05. A statistical difference was observed in the ΔCGV in the PCR and non-PCR groups. No statistical difference was observed in the AUCs of CC and MLO view subtraction images. The ΔCGV can be used as a quantitative index to predict PCR early, and no statistical difference was observed in the diagnostic efficacy of CC and MLO view subtraction images.

To compare changes in the concentration of choline-containing compounds (tCho) and in tumor size at follow-up after neoadjuvant chemotherapy (NAC) between patients who achieved pathologic complete response (pCR) and those who did not (non-pCR). This study was approved by the institutional review board and was compliant with HIPAA; each patient gave informed consent. Thirty-five patients (mean age, 48 years +/- 11 [standard deviation]; range, 29-75 years) with breast cancer were included. Treatment included doxorubicin and cyclophosphamide followed by a taxane-based regimen. Changes in tCho and tumor size in pCR versus non-pCR groups were compared by using the two-way Mann-Whitney nonparametric test. Receiver operating characteristic (ROC) analysis was performed to differentiate between them and the area under the ROC curve (AUC) was compared. In the pCR group, the tCho level change was greater compared with change in tumor size (P = .003 at first follow-up, P = .01 at second follow-up), but they were not significantly different in the non-pCR group. Changes in tumor size and tCho level at the first follow-up study were not significantly different between the pCR and non-pCR groups but reached significance at the second follow-up. In ROC analysis, the magnetic resonance (MR) imaging and MR spectroscopic parameters had AUCs of 0.65-0.68 at first follow-up; at second follow-up, AUC for change in tumor size was 0.9, AUC for change in tCho was 0.73. Patients who show greater reduction in tCho compared with changes in tumor size are more likely to achieve pCR. The change in tumor size halfway through therapy was the most accurate predictor of pCR.

Accurate assessment of pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) in breast cancer (BC) is crucial for mitigating chemotherapy-related toxicity in patients who do not respond to the treatment. Conventional ultrasound (US) has become a pivotal method for evaluating treatment response due to its cost-effectiveness, convenience, and absence of ionizing radiation. The objective of this study was to develop a model combining US and clinicopathological characteristics at baseline, as well as US features after one cycle of NAC, to predict the pCR to NAC in BC. This retrospective study included 74 patients with invasive BC who underwent NAC from January 2022 to December 2023. Data from US and clinicopathological characteristics before NAC (pre-NAC) and US features after one cycle of NAC were collected from all patients. Univariate and multivariate analyses were used to screen the factors independently associated with pCR and to develop the prediction model. Receiver operating characteristic (ROC) curve analysis was performed, and the area under the curve (AUC), sensitivity, and specificity were calculated to assess the predictive efficiency. Four characteristics, including human epidermal growth factor receptor 2 (HER2)-positive [odds ratio (OR) 9.265; 95% confidence interval (CI): 1.617-53.095, P=0.012] and absence of posterior feature or posterior acoustic enhancement of the breast mass on the US pre-NAC (OR 9.435; 95% CI: 1.585-56.180, P=0.014), the maximum diameter reduction measured with the US (OR 1.081; 95% CI: 1.009-1.157, P=0.026), and the angular or spiculated margin of the breast lesion with the US after one cycle of NAC (OR 9.475; 95% CI: 1.247-71.969, P=0.030), were screened as independent predictors. The AUC, sensitivity, and specificity of the prediction model were 0.912, 90.0%, and 79.6%, respectively. US and clinicopathological characteristics at baseline and the US features after one cycle of NAC helped predict pCR for BC. The prediction model may enable early evaluation of the efficacy of treatment strategies and guide less invasive surgical options or personalized post-treatment plans.

Introduction: The rediscovery of the microbiota has revealed its influence on carcinogenesis and response to treatment. Antibiotic use is common in cancer patients, potentially causing intestinal dysbiosis, disturbing the tumor microenvironment, intratumoral microbiota and affecting pharmacokinetic interactions with chemotherapy. These factors seem to influence cancer treatment benefits, but this is not well understood. Small studies suggest antibiotic use may reduce neoadjuvant treatment benefits in breast cancer. Objectives: This real-life, retrospective study includes a broad series of women diagnosed with breast cancer between 2009 and 2023, candidates for neoadjuvant chemotherapy based on anthracyclines, cyclophosphamide, taxanes +/- antiHER2. We analyze the influence of antibiotic use during neoadjuvant treatment or within 30 days prior to its initiation on pathological complete response (pCR/RCB-0) and achieving a good response defined as RCB-0 and RCB-1, according to residual cancer burden (RCB) index. Methods: The analysis included 1314 patients, divided into two groups: those who did not receive antibiotics during neoadjuvant treatment (60.6%) and those who did while or within 30 days prior to it (39.4%). Clinical and pathological variables were compared using the chi-square test, with a significance level set at &amp;lt;0.05. The average relative dose intensity (RDI) was calculated using the Hryniuk method for each type of chemotherapy administered. To identify the most significant predictors for our logistic regression model, we employed a stepwise selection procedure. Variables were selected based on the Akaike Information Criterion (AIC), which balances model fit and complexity by penalizing less significant predictors. Results: The clinico-pathological characteristics were balanced between groups, with no significant differences. 66% were in stage II and 29% in stage III (p 0.92), 54% and 56% were premenopausal (p 0.66), and 54% and 57% were grade 3 tumors (p 0.22) in both groups respectively. Regarding phenotypic subtypes, there were no differences between the two groups (p 0.85). Among patients without antibiotics, 35% were luminal tumors, 39% HER2+, and 26% triple-negative. Among those with antibiotics, 34% were luminal, 39% HER2+, and 27% triple-negative. Significant differences were observed in achieving an optimal RDI &amp;gt;85%. 95% of patients without antibiotics had an RDI &amp;gt;85% compared to 89% with antibiotics. However, the frequency of patients with an average RDI ≤85% was low in both groups: 5% without antibiotics and 11% with antibiotics (p 0.00026). The effect of antibiotics on neoadjuvant therapy efficacy was explored based on the pathological evaluation of residual disease using the RCB index. Significant associations were observed, indicating patients who received antibiotics were less likely to achieve optimal pathological responses. Complete pathological responses (RCB-0) were higher without antibiotics: 33% vs 28% (p 0.04). For RCB 0/I vs RCB II/III, 49% of patients without antibiotics achieved RCB 0/I vs 37% of those with antibiotics (p &amp;lt;0.0001). This trend was significant too in luminal tumors: 21% without antibiotics achieved RCB 0/I vs 13% with antibiotics (p 0.03) and in HER2+ and triple-negative tumors (63% vs 50%, p&amp;lt;0.0001). The multivariate analysis revealed antibiotic use had a negative effect on achieving pCR (p 0.03) and RCB 0/1 (p&amp;lt;0.0001), along with other predictive variables. Our model demonstrated the RDI did not influence the pathological response. Conclusion: This study suggests that antibiotic use during neoadjuvant treatment may negatively impact the pathological response in breast cancer. These results highlight the need to consider antibiotic use during chemotherapy to optimize outcomes and the need for prospective studies to explain the mechanism of this influence. Citation Format: Manuel Zalabardo Aguilar, Inés Fernández Sánchez, Alberto Girona Torres, Álvaro González Ortiz, José Manuel Jerez Aragonés, Alfonso Sánchez Muñoz, Javier Pascual López, María José Bermejo Pérez, Antonia Márquez Aragonés, Bella Pajares Hachero, Francisco Carabantes Ocón, Begoña Jiménez Rodríguez, María Emilia Domínguez Recio, Ana Godoy Ortiz, Tamara Díaz Redondo, Ester Villar Chamorro, Marcos Iglesias Campos, Irene Zarcos Pedrinaci, Nuria Ribelles Entrena, Emilio Alba Conejo. Influence of antibiotic use on the efficacy of neoadjuvant chemotherapy in breast cancer [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 P5-12-03.

Background: Neoadjuvant chemotherapy for breast cancer has been increased. Many studies have reported on clinicopathologic factors to predict neoadjuvant chemotherapy response. Elastography, which is usually used to differentiate benign and malignant tumors, can be performed to evaluate tissue elasticity during conventional ultrasonography. The purpose of this study was to determine the clinicopathologic factors, including tumor elasticity, that affect neoadjuvant chemotherapy response in stage II or III breast cancer patients. Methods: From April 2014 to March 2017, 95 patients received neoadjuvant chemotherapy for clinical stage IIa-IIIc primary breast cancer. To evaluate tumor elasticity, strain elastography was performed in 74 patients before neoadjuvant chemotherapy. Patients were divided into two groups by the Tsukuba elasticity scoring system (soft group ≤3 vs. hard group ≥4). Histologic type, nuclear grade, tumor infiltrating lymphocytes (TILs), tumor cellularity, characteristics of stroma, and hormone receptor (HR) and human epidermal growth factor receptor 2 (HER2) status were evaluated using core needle biopsy specimens obtained before neoadjuvant chemotherapy. Pathologic complete response (pCR) was defined as the absence of invasive carcinoma in breast (ypT0 and ypTis) and axillary lymph node (ypN0). Residual cancer burden (RCB) was also calculated in 79 cases and the cases were categorized into 2 groups; favorable RCB group (RCB-0 and I) and unfavorable RCB group (RCB-II and III). Results: The mean age of patients was 46.43±8.62 years (range, 27-71 years) and the mean initial tumor size was 3.63±1.95cm (range, 2.1-12.8cm). Twenty-four patients (32.4%) were categorized into the soft group and 50 patients (67.6%) into the hard group. The mean tumor cellularity on core needle biopsy specimens and characteristics of stroma were not significantly different between the two groups (p=0.35 and p=0.79, respectively). Twenty-two patients achieved pCR (23.2%). The patients with pCR were more likely to have estrogen receptor (ER) or progesterone receptor (PR) negative breast cancer (p=0.04 and p=0.03). The rate of nuclear grade 3 was higher in patients with pCR than those without (p=0.03). Tumor elasticity was not correlated with pCR (p=0.28). Thirty patients (38.0%) achieved favorable RCB and forty-nine patients (62.0%) had unfavorable RCB. Not only the rates of ER negativity (p=0.05), PR negativity (p=0.03), nuclear grade 3 (p=0.01), and high TILs level (≥ 10%) (p=0.04) but also the mean TILs level (p=0.05) were significantly higher in the favorable RCB group compared withthe unfavorable RCB group. No significant difference in tumor elasticity was observed between the two groups (p=0.30). In univariate analyses, nuclear grade 3 (p=0.03), and high TILs level (≥10%) (p=0.04) were significantly correlated with favorable RCB. HR negativity was an independent predictor of favorable RCB in multivariate analysis (odds ratio, 2.93; 95% confidence interval, 1.04-8.28; p=0.04). Conclusion: Tumor elasticity was not associated with pCR or RCB. HR negativity was an independent predictor for favorable RCB.Nuclear grade and TILs were also potential predictive factors for neoadjuvant chemotherapy response. Citation Format: Park JY, Choi JE, Bae YK, Lee SJ. Tumor elasticity and clinicopathologic factors affecting neoadjuvant chemotherapy response in breast cancer patients [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 P2-08-53.