Neoadjuvant Chemotherapy In Breast Cancer Research Articles

Abstract PO1-27-06: HER2 Amplification Level and Focal/Broad Region Size as Predictors of Treatment Response in Neoadjuvant Chemotherapy and Anti-HER2 Therapy for HER2-Positive Breast Cancer

Abstract Background: Neoadjuvant chemotherapy combined with anti-HER2 therapy has become the standard treatment approach for HER2-positive breast cancer. However, not all HER2-positive patients can achieve pathological complete response after neoadjuvant therapy. This raises the question of which subset of HER2+ patients derive the greatest benefit from pre-operative HER2-targeted treatment. Therefore, this study aims to determine whether the level and amplification region size of HER2 amplification are associated with the efficacy of neoadjuvant chemotherapy combined with anti-HER2 therapy. Methods: A total of 523 breast cancer samples were collected from 2017 to 2018, among which 202 cases were HER2-positive patients. Among these, 55 HER2-positive patients received neoadjuvant chemotherapy combined with anti-HER2 targeted therapy. The amplification status of HER2 was assessed using immunohistochemistry/fluorescence in situ hybridization (IHC/FISH) and next-generation sequencing (NGS). The HER2/CEP17 ratio, determined by FISH, was used to represent the amplification level of HER2. The size of the amplification region was calculated using NGS and neighboring genes located in chr17q. Amplification regions smaller than 1 Mb were classified as focal amplification, while those larger than 1 Mb were classified as broad amplification.> Results: We compared the HER2/ERBB2 status of 523 breast cancer patients using different detection methods and found a high concordance between ERBB2 amplification detected by NGS and HER2-positivity detected by IHC and FISH, with a sensitivity of 96.0%, specificity of 97.8%, and overall concordance of 97.1%. Based on the HER2/CEP17 ratio determined by FISH, we observed that using a threshold of 6 for classification, the high amplification group had a significantly higher pCR rate compared to the low amplification group (86.7% vs. 41.7%; p=0.037, odds ratio [OR]=0.121, 95% confidence interval [CI]: 0.009-0.919). Moreover, the focal amplification group showed a higher pCR rate compared to the broad amplification group (65.9% vs. 30.8%, p=0.051, OR=0.237, 95% CI: 0.045-1.035). Additionally, within the low amplification group (HER2/CEP17 < 6), the pCR rate was 66.7% for focal amplification and 20% for broad amplification, suggesting that focal amplification of ERBB2 may further guide clinical benefit in the low HER2/CEP17 ratio group. Conclusion: This study demonstrates a high concordance between ERBB2 amplification determined by NGS and HER2-positivity determined by IHC/FISH. High HER2/CEP17 ratio and focal amplification of ERBB2 are associated with a higher pCR rate in neoadjuvant chemotherapy combined with anti-HER2 targeted therapy. Citation Format: Ning Liao, Guochun Zhang, Xinze Lv, Kai Li, Ting Hou, Zhou Zhang. HER2 Amplification Level and Focal/Broad Region Size as Predictors of Treatment Response in Neoadjuvant Chemotherapy and Anti-HER2 Therapy for HER2-Positive 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 PO1-27-06.

Abstract PO3-12-08: Hypersensitivity reactions to paclitaxel and carboplatin in breast cancer: safety and effectiveness of desensitization

Abstract Introduction. Taxanes and platinums are first line treatment of breast cancer for early stages and metastatic disease and constitute the most commonly implicated chemotherapeutics in hypersensitivity reactions.1 Clinical manifestations range from mild skin reactions to anaphylaxis.2 Hypersensitivity reactions (HSR) to paclitaxel and carboplatin occur in a significant percentage of patients, with estimates of 30% and 40% respectively,3,4,5 leading to discontinuation of treatment.6 Using alternative drugs can be poorly tolerated or much less effective, entailing to high morbidity and mortality.7 Desensitization is the procedure performed to achieve a tolerance to the drug to which a HSR previously occurred, in order to maintain first-line treatment.8 The evaluation of the oncological response remains in the importance of assessing the shrinkage of the tumor and the time of progression of the disease.9 Objective: To describe the hypersensitivity reactions to paclitaxel and carboplatin, and the safety and effectiveness of desensitization protocols after anaphylaxis in patients with breast cancer. Materials and Methods: Original, retrospective, descriptive, and analytical study, approved by the Bioethics and Research Committee ON23-00016 code, included patients >18 years with breast cancer who developed a hypersensitivity reaction to first-line chemotherapy and performed a 3-bag-12-step desensitization according to the Brigham and Women's Hospital, Dana Farber Institute, Boston protocol. Results: Forty-five desensitization to paclitaxel or carboplatin were performed in 8 women with breast cancer, mean age 38.6 years. 6 patients developed HSR to paclitaxel, 83% of which were in the first application, and 2 patients developed HSR to carboplatin after several cycles. The most predominant symptom of HSR was cutaneous in 87% of the cases, and all patients presented cardiovascular HSR to paclitaxel. No patients developed HSR during desensitization. Currently, only 1 patient developed disease progression, while the rest presented a complete response to treatment. Discussion. HSR to paclitaxel appear in the 1-2 cycle,10 while to platinums after various exposures ( >6 cycles) similar to our study.3,4 The symptoms associated with paclitaxel are cardiovascular10,11 while urticaria is the most frequent in carboplatin.3,11 The desensitization protocol implemented did not develop HSR in any of the patients. Neoadjuvant chemotherapy in breast cancer has been shown to yield better disease-free interval results alongside improved pathological response.12 Conclusions. The program implemented in our hospital's chemotherapy desensitization clinic, replicating the model of the Dana-Farber Cancer Institute, Brigham, and Women's Hospital in Boston, has enabled cancer patients to continue with first-line treatment, thus improving their prognosis and quality of life, opening new areas of opportunity in the multidisciplinary management of these patients to improve overall survival. Characteristics of patients with hypersensitivity reactions HSR: hypersensitivity reactions ND: No data m: months yrs.: years Desensitization protocol to Paclitaxel IV (300 mg) IV: intravenous mg: milligram mL: milliliter h: hour Citation Format: Rosalaura V. Villarreal-González, Oscar Vidal-Gutiérrez, Javier A. Martínez-Moyano, Marianela Madrazo-Morales, Kathia S. Sáenz-Cantú, Patricia Rodríguez-Niño. Hypersensitivity reactions to paclitaxel and carboplatin in breast cancer: safety and effectiveness of desensitization [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 PO3-12-08.

Abstract PO4-08-01: Methylsterol monooxygenase 1 (MSMO1) modulates endoplasmic reticulum stress through cholesterol metabolic reprogramming to alter chemotherapy sensitivity in breast cancer

Abstract Background Endoplasmic reticulum stress (ER stress) is a process in which cells activate unfolded protein response and other signaling pathways in response to misfolded and unfolded protein aggregation and calcium ion balance disturbance in the endoplasmic reticulum cavity. Depending on the intensity of endoplasmic reticulum stress, it can not only induce chaperone expression to play a protective effect, but also induce apoptosis independently. Metabolic reprogramming is one of the characteristics of tumor, which refers to the metabolic changes that cells make in response to various stimuli. Here, we demonstrate that methylsterol monooxygenase 1 (MSMO1) regulates cellular cholesterol metabolism and influences breast cancer susceptibility to chemotherapy by regulating the relative intracellular T-MAS content. Methods RNA-seq was performed on core needle biopsy samples from 26 patients receiving neoadjuvant chemotherapy for breast cancer to screen for genes that may influence neoadjuvant chemotherapy sensitivity. Cell proliferation, CCK8 assay and apoptosis assay were performed for MSMO1 phenotype study. Transmission electron microscopy (TEM) was conducted to visualize the structure of endoplasmic reticulum. Western Blot was used to detect the activation level of the unfolded protein response pathway. The relative content of sterols in MSMO1 knockdown cells was detected by targeted lipid mass spectrometry. AO/PI staining of breast cancer organoids was performed to verify the effect of MSMO1 metabolic substrate T-MAS on chemotherapy drug sensitivity. Results RNA-seq of core needle biopsy samples from breast cancer patients receiving neoadjuvant chemotherapy suggested that MSMO1 was highly expressed in non-pCR patients, and clinical survival data suggested that MSMO1 was associated with poor prognosis in patients with all types of breast cancer, suggesting that MSMO1 might be related to chemotherapy sensitivity of breast cancer. Phenotypic experiments revealed that MSMO1 regulates chemotherapy sensitivity of breast cancer both in vivo and in vitro. Ectopic overexpression of MSMO1 promoted cancer cell resistance to chemotherapy. To uncover the underlying mechanisms, RNA-seq of MSMO1 knock-down cells were carried out and identified that MSMO1 was associated with ER stress. TEM and western blot assay indicated that downregulation of MSMO1 expression leads to endoplasmic reticulum swelling and activation of the unfolded protein response. Considering that MSMO1 is a cholesterol metabolizing enzyme, targeted lipids mass spectrometry was performed to detect the changes in the contents of various intermediate metabolites in the cholesterol pathway, which indicated that the relative content of T-MAS, a metabolic substrate of MSMO1, was significantly up-regulated in MSMO1 knockdown cells. Then, in order to verify whether this metabolite can independently regulate ER stress, breast cancer cells were given T-MAS externally, and TEM and Western Blot analysis indicated that T-MAS could induce ER stress and activate the unfolded protein response independently. Apoptosis assay and AO/PI staining of breast cancer organoids revealed that T-MAS can improve the sensitivity of breast cancer to chemotherapy drugs. Conclusion In summary, these results shed light on the role of MSMO1 in cholesterol metabolism reprogramming, which adjust the endoplasmic reticulum stress state of cells by changing the relative content of intracellular T-MAS, and thereby influencing the sensitivity of breast cancer chemotherapy. As an intermediate metabolite of cholesterol metabolism, T-MAS has potential clinical application value of chemotherapy sensitization. Citation Format: Hengyu Ren, Liren Wangxu, Yayun Chi, Jiong Wu. Methylsterol monooxygenase 1 (MSMO1) modulates endoplasmic reticulum stress through cholesterol metabolic reprogramming to alter chemotherapy sensitivity in 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 PO4-08-01.

Abstract PO4-03-04: Characterization of Gut Microbiome of patients with early-stage breast cancer treated with neoadjuvant chemotherapy

Abstract Objective: The gut microbiome (GM) has been identified as one of the major environmental factors that can regulate the development and maintenance of the immune system. In oncology, while the importance of GM has been evaluated in more immunogenic tumors such as melanoma, lung carcinoma and clear cell carcinoma, its importance in early-stage breast cancer (eBC) is underexplored. The aim of this study was to characterize the GM of patients with eBC who underwent neoadjuvant chemotherapy (NACT) and to evaluate its association with clinicopathologic prognostic factors and outcomes. Methods: This was a prospective study conducted at two Brazilian institutions. Fecal samples were collected at baseline (time 1) and prior to surgery (time 2) from 55 patients who received NACT with anthracycline/taxane. Gut microbiome was analyzed by 16S rRNA amplicon sequencing to characterize the α (InvSimpson index) and β (weighted UniFrac distance metric) diversity, as well as taxonomic composition. Clinicopathologic prognostic factors were retrieved from medical records. Pathological complete response (pCR) was defined as the absence of invasive carcinoma in breast and axillary nodes (ypT0/Tis ypN0). Stromal tumor-infiltrating lymphocytes (sTIL) were reported according to the TIL working group criteria. Information about prior use (< 60 days before stating on NACT) of antibiotic (ATB) was obtained directly from patients. Statistical significance was set at P ≤ 0.05 and analysis of composition of microbiomes (ANCOM-BC2) was used to identify enriched genera with p-values adjusted using FDR correction ≤ 0.25. Results: Median age was 49 years (range 31-73). Eleven patients (20%) reported prior antibiotic use at baseline. About 34.5% of patients had HR+/HER2- tumors, 45.4% had triple negative breast cancer (TNBC), and 20% had HER2-positive breast cancer. Fourteen (25%) patients achieved a pCR. Out of 55, 47 (85.4%) had sTIL information. There was no significant difference in alpha diversity (p = 0.6) or beta-diversity (p = 0.8) between the pCR and residual disease group nor within clinical pathologic factors (age, staging, histological tumor grade, subtype of breast cancer based on IHC, sTIL). The most abundant taxon in both time 1 and time 2 were the same (Blautia at genus level, Lachnospiraceae at family level, and Lachnospirales at order level), and longitudinal samples collected during NACT showed no significant changes in GM composition. At baseline, we found an enrichment for Clostridia sp. among patients who did not use ATB (p < 0.05, pFDR ≤ 0.25). Additionally, we found enrichment for Bifidobacterium (p < 0.05, pFDR ≤ 0.25) among patients with HER2 subtype who presented residual disease. Conclusions: Gut microbiome analysis in early breast cancer is feasible and to our knowledge, this is the first study to evaluate the intestinal microbiota of Brazilian patients with breast cancer. We did not observe any significant association between GM diversity and taxonomic composition with clinical pathologic factors and pCR in this study, nor did we observe changes in the ecological balance of the GM following NACT in breast cancer. Further studies are needed to explore the potential influence of the gut microbial ecosystem on tumor biology and response to neoadjuvant chemo-immunotherapy. Citation Format: Ludmila Thommen, Vitor Heidrich, Rosângela Vieira de Andrade, Luiza Nardin Weis, Maria Sueli Soares Felipe, Melissa Lole Da Cas Vita, Thiago David Alves Pinto, Anamaria Camargo, Romualdo Barroso-Sousa. Characterization of Gut Microbiome of patients with early-stage breast cancer treated with neoadjuvant chemotherapy [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 PO4-03-04.

Predicting response to neoadjuvant chemotherapy in breast cancer by biomechanics quantified with magnetic resonance elastography

Predicting response to neoadjuvant chemotherapy in breast cancer by biomechanics quantified with magnetic resonance elastography

Evolving practice changes in neoadjuvant chemotherapy for breast cancer, in a district general hospital

Evolving practice changes in neoadjuvant chemotherapy for breast cancer, in a district general hospital

Research progress on ultrasound medicine in diagnosis and evaluation of chemotherapy effect of breast cancer

Breast cancer has the highest incidence rate among cancers in the world and is the main cause of death among female cancer patients around the world. The progress of ultrasound technology has continuously improved the ability to identify the shape and microvessels of breast masses. It plays an important role in the differentiation of benign and malignant breast masses and is the main auxiliary diagnostic method for breast cancer. The widespread implementation of neoadjuvant chemotherapy for breast cancer has improved the survival rate of patients. Evaluating the efficacy after chemotherapy and adjusting chemotherapy strategies as needed can help improve the prognosis of patients. Ultrasound can conduct multi-parameter measurements of breast tumor hardness, blood flow velocity and vascular distribution, which can be applied to evaluate the efficacy of neoadjuvant chemotherapy for breast cancer. This article reviewed the application and research progress of multi-modal ultrasound in the diagnosis and evaluation of chemotherapy efficacy of breast cancer.

One-step abductive multi-target learning with diverse noisy samples and its application to tumour segmentation for breast cancer

Recent studies have demonstrated the effectiveness of the combination of machine learning and logical reasoning, including data-driven logical reasoning, knowledge driven machine learning and abductive learning, in inventing advanced technologies for different artificial intelligence applications. One-step abductive multi-target learning (OSAMTL), an approach inspired by abductive learning, via simply combining machine learning and logical reasoning in a one-step balanced multi-target learning way, has as well shown its effectiveness in handling complex noisy labels of a single noisy sample in medical histopathology whole slide image analysis (MHWSIA). However, OSAMTL is not suitable for the situation where diverse noisy samples (DiNS) are provided for a learning task. In this paper, giving definition of DiNS, we propose one-step abductive multi-target learning with DiNS (OSAMTL-DiNS) to expand the original OSAMTL to handle complex noisy labels of DiNS. Applying OSAMTL-DiNS to tumour segmentation for breast cancer in MHWSIA, we show that OSAMTL-DiNS is able to enable various state-of-the-art approaches for learning from noisy labels to achieve more rational predictions. We released a model pre-trained with OSAMTL-DiNS for tumour segmentation in HE-stained pre-treatment biopsy images in breast cancer, which has been successfully applied as a pre-processing tool to extract tumour-associated stroma compartment for predicting the pathological complete response to neoadjuvant chemotherapy in breast cancer.

Abstract LB242: Prediction of patient response to neoadjuvant chemotherapy in breast cancer from their deconvolved tumor microenvironment transcriptome

Abstract Introduction: The tumor microenvironment (TME) is a complex and dynamic ecosystem that plays critical roles in tumor development and clinical outcome. While the multifarious cellular interactions within the TME have been extensively studied with a significant emphasis on immunotherapy, understanding its role in chemotherapy outcome remains less explored. To this end, we present a novel generic computational framework named DECODEM (DEcoupling Cell-type-specific Outcomes using DEconvolution and Machine learning). DECODEM facilitates the exploration of the association of cell-type-specific gene expression profiles with the effectiveness of neoadjuvant chemotherapy (NAC) in treating breast cancer (BC). This approach holds promise for elucidating the nuanced ways in which the TME can modulate therapeutic efficacy, potentially paving the way for more personalized and effective cancer treatments. Methods: DECODEM employs cellular deconvolution of bulk transcriptomics to extract cell-type-specific gene expression profiles within the TME, and subsequently harnesses machine learning to construct cell-type-specific predictors of clinical outcomes. Utilizing DECODEM, we analyzed three publicly available cohorts of HER2-negative BC patients who received NAC. We assessed the predictive capabilities of the individual cell types as well as their collective influence, benchmarking against a state-of-the-art predictor that employs both clinical and transcriptomic features. We corroborated our findings in a single-cell (SC) transcriptomics cohort of patients with triple negative breast cancer (TNBC) undergoing chemotherapy. Further, we expanded DECODEM to DECODEMi, which accounts for cell-cell interactions (CCIs) among the predominant cell types, offering potential insights into the mechanistic processes governing clinical responses. Results: Our analysis indicated that the heterogeneous composition of the TME is crucial for chemotherapy response prediction in HER2-negative BC, beyond the influence of malignant cells alone. The key insights from our study are as follows:1. Gene expression of immune cells (myeloid, plasmablasts, B-cells) and stromal cells (endothelial, normal epithelial, cancer-associated fibroblasts) displayed comparable-to-superior predictive powers over both bulk expression and the state-of-the-art predictor.2. The predictive accuracy is further augmented by multi-cell-type ensembles, with a triad ensemble comprising endothelial, myeloid, and plasmablast cells achieving the highest predictive accuracy across multiple cohorts.3. DECODEMi can effectively capture the regulatory impact of CCIs (validated in a SC dataset sourced from the SC-TNBC cohort) and has identified novel CCIs likely to influence chemotherapy response in HER2-negative BC. Conclusion: We provide an unprecedented computational methodology to quantitatively assess the cell-type-specific influences of the TME on clinical outcome. Our analysis offers insights into the complex interactions among TME cells and their correlation with chemotherapy efficacy in HER2-negative BC, thereby enriching the knowledge base of BC treatment effectiveness. Citation Format: Saugato Rahman Dhruba, Sahil Sahni, Binbin Wang, Di Wu, Yael Schmidt, Eldad Shulman, Sanju Sinha, Stephen-John Sammut, Carlos Caldas, Kun Wang, Eytan Ruppin. Prediction of patient response to neoadjuvant chemotherapy in breast cancer from their deconvolved tumor microenvironment transcriptome [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr LB242.

Noninvasive Artificial Intelligence System for Early Predicting Residual Cancer Burden during Neoadjuvant Chemotherapy in Breast Cancer.

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 four institutions, who were all receiving NAC. Magnetic resonance images were collected at the pre- and mid-NAC stages, and radiomics and deep learning features were extracted. A multitask AI system was developed to classify patients into three groups (RCB 0-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 three external validation cohorts. (EVCs, n=713). Among the patients, 442 (42.18%) were RCB 0-I, 462 (44.08%) were RCB II and 144 (13.74%) were RCB III. Model-I achieved an area under the curve (AUC) of 0.975 in the PC and 0.923 in the EVCs for differentiating RCB III from RCB 0-II. Model-II distinguished RCB 0-I from RCB II-III, with an AUC 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.

Advancements in the Utilization of Deep Learning Techniques based on Histopathological Imaging for Predicting the Efficacy of Neoadjuvant Chemotherapy in Breast Cancer

Advancements in the Utilization of Deep Learning Techniques based on Histopathological Imaging for Predicting the Efficacy of Neoadjuvant Chemotherapy in Breast Cancer

Predictive model for pancreatic steatosis after neoadjuvant chemotherapy in breast cancer: Construction and ultrasound validation

ObjectiveTo develop and verify a nomogram model for predicting occurrence of pancreatic steatosis (PS) after chemotherapy in breast cancer patients. MethodsA total of 215 breast cancer (BC) patients who underwent neoadjuvant chemotherapy in the hospital from February 2018 to November 2018 were prospectively collected for imaging and clinical data. Subjects were randomly divided into a training set (172 cases) and a validation set (43 cases) according to a random number table in a 4:1 ratio, and independent risk factors associated with PS were screened and modeled in a nomogram model after using single-factor and multifactorial logistic regression analyses on the training set. The model's performance was evaluated through various analyses, including subject operating characteristic curve(ROC), calibration curves, decision curve (DCA), and clinical impact curve (CIC), assessing its predictive performance, precision, and practical value in a clinical setting. ResultsThe results of univariate and multifactorial binary logistic regression analyses showed that age, triglycerides, and whether or not neoadjuvant chemotherapy was an independent risk factor for the development of PS, and these three independent risk factors were used to further construct a nomogram model. The model demonstrated strong predictive efficacy with an area under the ROC curve of 0.817 (95% CI = 0.753–0.880) in the training set and 0.762 (95% CI = 0.615–0.901) in the validation set. Calibration curve analysis confirmed accurate prediction of PS, and both DCA and CIC confirmed the model's clinical utility and validity. ConclusionThe nomogram model developed in this study shows strong prediction effect, discriminatory power, and clinical applicability, serving as an intuitive and visual tool for assessing the risk of PS in breast cancer chemotherapy patients.

MRI and RNA-seq fusion for prediction of pathological response to neoadjuvant chemotherapy in breast cancer

Accurate prediction of the pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) is crucial for precise treatment of breast cancer. However, current studies mainly rely on single-modal data, with limited studies focusing on multimodal data. In this study, we developed and validated a deep learning-based multimodal fusion model that predicts the response of breast tumor to NAC by integrating multi-parametric magnetic resonance imaging (MRI) and RNA sequencing (RNA-seq) information related to breast tumor. For comparison, we separately built four single-modal models with either MR images or RNA-seq data. Moreover, our approach has demonstrated better performance in integrating MR images and RNA-seq data. The average accuracy is 90.20% and area under the receiver operating characteristic curve (AUC) is 0.936 for our model. These findings indicate that our proposed approach has achieved higher accuracy in predicting the pathological response to NAC.

Abstract 6394: Tissue nanomechanical signature predicts response to neoadjuvant chemotherapy in patients with breast cancer

Abstract Background: Neoadjuvant chemotherapy (NACT) is increasingly used in patients with breast cancer. However, predicting response to NACT remains a challenge in everyday clinical practice. Increasing (pre)- and clinical evidence demonstrate the importance of cancer biomechanics in mediating therapy response. We and others have previously demonstrated that complex biomechanical alterations in cancer tissues, can be measured by the Atomic Force Microscopy (AFM) at the nanoscale level. Incorporating AFM measurements of clinical biopsies within standard of care is thus a promising avenue to leverage tissue mechanics as prognostic and predictive biomarker for solid cancers. Methods: Fresh clinical biopsy samples from 545 breast cancer patients were measured using the AFM-based Automated and Reliable Tissue Diagnostics (ARTIDIS) investigational device - ART-1. Measurements were performed on baseline biopsies within routine clinical workflow in the Breast Clinic, University Hospital Basel. This cohort comprises 30 patients who received NACT including: anthracycline and/or taxane based chemo (n=17), and anthracycline and/or taxane with platinum chemo (n=13). Study endpoints were radiological complete response (rCR) prior and pathological complete response (pCR) after surgery. Multiparametric nnanomechanical signature data has been analyzed in an automated manner using the proprietary ARTIDISNET software platform. Results: From the 30 patients who received NACT, 12 patients presented with rCR, 17 patients presented with rPR, and 1 patient presented with rNR. pCR has been achieved only in 9 patients. In this study, he ARTIDIS nanomechanical signature of response to NACT in breast cancer showed 100% negative predictive value (NPV) and 89% positive predictive value (PPV), with 93% of accuracy for patients receiving chemotherapy, independent of molecular subtype or treatment regimen. Sensitivity was 100% and specificity was 85% (AUC=0.91). Conclusion: ARTIDIS nanomechanical signature can be seamlessly integrated within clinical settings to predict response to standard of care NACT in patients with breast cancer in a prospective manner. These results are currently being validated in a global, multicentre prospective study in breast cancer (ANGEL, NCT06085833), enrolling in total 2706 patients, with the aim to support implementation of the ARTIDIS nanomechanical signature into routine clinical practice. Citation Format: Mariam Gachechiladze, Sara Nizzero, Tobias Appenzeller, Leonie Briner, Rosemarie Burian, Sabine Schädelin, Simone Muenst-Soysal, Tatjana Vlaijnic, Ellen Obermann, Sophie Dellas, Serafino Forte, Zlatko Marušić, Ahmed Jizawi, Papa Diogop Ndiaye, Philipp Oertle, Gregory Zaugg, Marko Loparic, Marija Plodinec. Tissue nanomechanical signature predicts response to neoadjuvant chemotherapy in patients with breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6394.

De-escalating axillary management after neoadjuvant chemotherapy in breast cancer: The ratio of positive sentinel lymph nodes matters

BackgroundDe-escalation of axillary surgery in breast cancer (BC) patients diminishes sequelae without compromising cancer outcomes. Surgical management of the axilla is challenging after neoadjuvant treatment. We aimed to identify the factors associated with residual axillary disease amenable to lymphadenectomy in patients with positive sentinel lymph node biopsy (SLNB). MethodsWe conducted a retrospective observational study in Hospital 12 de Octubre (Spain). We included BC patients with positive SLNB who underwent axillary dissection after neoadjuvant chemotherapy. Univariate and multivariate logistic regression models were performed to identify independent predictors of residual axillary disease. We estimated the ratio of positive nodes in SLNB and assessed the diagnostic validity of this ratio in relation to residual axillary disease. ResultsWe included 103 patients in the study. Residual axillary disease was identified in 54 patients (52.4%). Clinically node positive status at diagnosis (OR = 18.3, 95%CI: 4.0–83.6) and a ratio of positive nodes in SLNB ≥0.5 (OR = 6.5, 95%CI 41.7–23.7) were associated with residual axillary disease. The sensitivity and negative predictive value of a ratio of positive nodes in SLNB ≥0.5 were 87% (95%CI 75.1%–94.6%) and 75% (95%CI 55.1%–89.3%), respectively. ConclusionsIn our study, for patients with positive SLNB after neoadjuvant chemotherapy, stage N+ at diagnosis and a ratio of positive nodes in SLNB ≥0.5 were independent risk factors of positive residual axillary disease. This ratio is a feasible measure with a good diagnostic validity for residual axillary disease and could be used as a guiding factor in the surgical management of these patients.

An ultrasound-based nomogram model in the assessment of pathological complete response of neoadjuvant chemotherapy in breast cancer.

We aim to predict the pathological complete response (pCR) of neoadjuvant chemotherapy (NAC) in breast cancer patients by constructing a Nomogram based on radiomics models, clinicopathological features, and ultrasound features. Ultrasound images of 464 breast cancer patients undergoing NAC were retrospectively analyzed. The patients were further divided into the training cohort and the validation cohort. The radiomics signatures (RS) before NAC treatment (RS1), after 2 cycles of NAC (RS2), and the different signatures between RS2 and RS1 (Delta-RS/RS1) were obtained. LASSO regression and random forest analysis were used for feature screening and model development, respectively. The independent predictors of pCR were screened from clinicopathological features, ultrasound features, and radiomics models by using univariate and multivariate analysis. The Nomogram model was constructed based on the optimal radiomics model and clinicopathological and ultrasound features. The predictive performance was evaluated with the receiver operating characteristic (ROC) curve. We found that RS2 had better predictive performance for pCR. In the validation cohort, the area under the ROC curve was 0.817 (95%CI: 0.734-0.900), which was higher than RS1 and Delta-RS/RS1. The Nomogram based on clinicopathological features, ultrasound features, and RS2 could accurately predict the pCR value, and had the area under the ROC curve of 0.897 (95%CI: 0.866-0.929) in the validation cohort. The decision curve analysis showed that the Nomogram model had certain clinical practical value. The Nomogram based on radiomics signatures after two cycles of NAC, and clinicopathological and ultrasound features have good performance in predicting the NAC efficacy of breast cancer.

An ultrasound-based nomogram for predicting axillary node pathologic complete response after neoadjuvant chemotherapy in breast cancer: Modeling and external validation.

The staging and treatment of axillary nodes in breast cancer have become a focus of research. For breast cancer patients with fine-needle aspiration-or core needle biopsy-confirmed positive nodes, axillary lymph node dissection (ALND) after neoadjuvant chemotherapy (NAC) is still a standard treatment. However, some patients achieve an axillary pathologic complete response (pCR) after NAC. In this study, the authors sought to construct a model to predict axillary pCR in patients with positive axillary lymph nodes (cN+) breast cancer. Data from patients with pathologically proven cN+ breast cancer treated with NAC followed by ALND between January 2010 and April 2019 at the Peking University Cancer Hospital were reviewed. Axillary lymph node status was assessed using ultrasonography before and after NAC. The patient cohort was assigned to the construction and internal validation cohorts according to admission time. A nomogram was constructed based on the significant factors associated with axillary pCR. The predictive performance of the model was externally validated using data from Peking University First Hospital. This study included 953 and 267 patients from Peking University Cancer Hospital and Peking University First Hospital, respectively. In the construction cohort, 39.7% (238 of 600) of patients achieved axillary pCR after NAC. The result of multivariate logistic regression analysis showed that tumor grade, clinical nodal response, NAC regimen, tumor pCR, lymphovascular invasion, and tumor biologic subtype were significant independent predictors of ypN0 (p < 0.05). The areas under the receiver operating characteristic curves for the construction, validation, and independent testing cohorts were 0.87 (95% confidence interval [CI], 0.84-0.90), 0.83 (95% CI, 0.79-0.87), and 0.84 (0.79-0.89), respectively. A nomogram was constructed to predict the pCR of axillary lymph nodes after NAC for breast cancer. Validation of both the internal and external cohorts achieved good predictive performance, indicating that the model has preliminary clinical application prospects.

Characterization of BCL-X L , MCL-1, and BAX Protein Expression in Response to Neoadjuvant Chemotherapy in Breast Cancer.

The use of chemotherapy has improved the overall treatment of breast cancer, which is frequently administered in the form of neoadjuvant chemotherapy (NAC). Apoptosis is an established cell stress response to NAC in preclinical models; however, there is limited understanding of its role in clinical cancer, specifically, its contribution to favorable pathologic responses in breast cancer therapy. Here, we aimed to characterize the change in protein expression of 3 apoptosis-associated biomarkers, namely, BCL-X L , MCL-1, and BAX in breast cancer in response to NAC. For this, we utilized a set of 68 matched invasive breast cancer FFPE samples that were collected before (pre) and after (post) the exposure to NAC therapy that were characterized by incomplete pathologic response. Immunohistochemistry (IHC) analysis suggested that most of the samples show a decrease in the protein expression of all 3 markers following exposure to NAC as 90%, 69%, and 76% of the matched samples exhibited a decrease in expression for BCL-X L , MCL-1, and BAX, respectively. The median H-score of BCL-X L post-NAC was 150/300 compared with 225/300 pre-NAC ( P value <0.0001). The median H-score of MCL-1 declined from 200 pre-NAC to 160 post-NAC ( P value <0.0001). The median H-score of BAX protein expression decreased from 260 pre-NAC to 190 post-NAC ( P value <0.0001). There was no statistically significant association between the expression of these markers and stage, grade, and hormone receptor profiling (luminal status). Collectively, our data indicate that the expression of apoptosis regulatory proteins changes following exposure to NAC in breast cancer tissue, developing a partial pathologic response.

Photoacoustic/ultrasound dual-modality imaging for marker clip localization in neoadjuvant chemotherapy of breast cancer.

To ensure precise tumor localization and subsequent pathological examination, a metal marker clip (MC) is placed within the tumor or lymph node prior to neoadjuvant chemotherapy for breast cancer. However, as tumors decrease in size following treatment, detecting the MC using ultrasound imaging becomes challenging in some patients. Consequently, a mammogram is often required to pinpoint the MC, resulting in additional radiation exposure, time expenditure, and increased costs. Dual-modality imaging, combining photoacoustic (PA) and ultrasound (US), offers a promising solution to this issue. Our objective is to localize the MC without radiation exposure using PA/US dual-modality imaging. A PA/US dual-modality imaging system was developed. Utilizing this system, both phantom and clinical experiments were conducted to demonstrate that PA/US dual-modality imaging can effectively localize the MC. The PA/US dual-modality imaging can identify and localize the MC. In clinical trials encompassing four patients and five MCs, the recognition rate was . Three experiments to verify the accuracy of marker position recognition were successful. We effectively localized the MC in real time using PA/US dual-modality imaging. Unlike other techniques, the new method enables surgeons to pinpoint nodules both preoperatively and intraoperatively. In addition, it boasts non-radioactivity and is comparatively cost-effective.

A real-world clinicopathological model for predicting pathological complete response to neoadjuvant chemotherapy in breast cancer.

This study aimed to develop and validate a clinicopathological model to predict pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) in breast cancer patients and identify key prognostic factors. This retrospective study analyzed data from 279 breast cancer patients who received NAC at Zhejiang Provincial People's Hospital from 2011 to 2021. Additionally, an external validation dataset, comprising 50 patients from Lanxi People's Hospital and Second Affiliated Hospital, Zhejiang University School of Medicine from 2022 to 2023 was utilized for model verification. A multivariate logistic regression model was established incorporating clinical, ultrasound features, circulating tumor cells (CTCs), and pathology variables at baseline and post-NAC. Model performance for predicting pCR was evaluated. Prognostic factors were identified using survival analysis. In the 279 patients enrolled, a pathologic complete response (pCR) rate of 27.96% (78 out of 279) was achieved. The predictive model incorporated independent predictors such as stromal tumor-infiltrating lymphocyte (sTIL) levels, Ki-67 expression, molecular subtype, and ultrasound echo features. The model demonstrated strong predictive accuracy for pCR (C-statistics/AUC 0.874), especially in human epidermal growth factor receptor 2 (HER2)-enriched (C-statistics/AUC 0.878) and triple-negative (C-statistics/AUC 0.870) subtypes, and the model performed well in external validation data set (C-statistics/AUC 0.836). Incorporating circulating tumor cell (CTC) changes post-NAC and tumor size changes further improved predictive performance (C-statistics/AUC 0.945) in the CTC detection subgroup. Key prognostic factors included tumor size >5cm, lymph node metastasis, sTIL levels, estrogen receptor (ER) status and pCR. Despite varied pCR rates, overall prognosis after standard systemic therapy was consistent across molecular subtypes. The developed predictive model showcases robust performance in forecasting pCR in NAC-treated breast cancer patients, marking a step toward more personalized therapeutic strategies in breast cancer.