Selected Breast Cancer Patients Research Articles

MitoBKCa is functionally expressed in murine and human breast cancer cells and potentially contributes to metabolic reprogramming.

Alterations in the function of K+ channels such as the voltage- and Ca2+-activated K+ channel of large conductance (BKCa) reportedly promote breast cancer (BC) development and progression. Underlying molecular mechanisms remain, however, elusive. Here, we provide electrophysiological evidence for a BKCa splice variant localized to the inner mitochondrial membrane of murine and human BC cells (mitoBKCa). Through a combination of genetic knockdown and knockout along with a cell permeable BKCa channel blocker, we show that mitoBKCa modulates overall cellular and mitochondrial energy production, and mediates the metabolic rewiring referred to as the 'Warburg effect', thereby promoting BC cell proliferation in the presence and absence of oxygen. Additionally, we detect mitoBKCa and BKCa transcripts in low or high abundance, respectively, in clinical BC specimens. Together, our results emphasize, that targeting mitoBKCa could represent a treatment strategy for selected BC patients in future.

MitoBKCa is functionally expressed in murine and human breast cancer cells and potentially contributes to metabolic reprogramming

Alterations in the function of K+ channels such as the voltage- and Ca2+-activated K+ channel of large conductance (BKCa) reportedly promote breast cancer (BC) development and progression. Underlying molecular mechanisms remain, however, elusive. Here, we provide electrophysiological evidence for a BKCa splice variant localized to the inner mitochondrial membrane of murine and human BC cells (mitoBKCa). Through a combination of genetic knockdown and knockout along with a cell permeable BKCa channel blocker, we show that mitoBKCa modulates overall cellular and mitochondrial energy production, and mediates the metabolic rewiring referred to as the ‘Warburg effect’, thereby promoting BC cell proliferation in the presence and absence of oxygen. Additionally, we detect mitoBKCa and BKCa transcripts in low or high abundance, respectively, in clinical BC specimens. Together, our results emphasize, that targeting mitoBKCa could represent a treatment strategy for selected BC patients in future.

Abstract PO4-07-01: Deep learning guided radiation therapy machine selection via a multi-headed dose prediction model

Abstract Background: Accelerated Partial Breast Irradiation (APBI) is gaining popularity for the treatment of appropriately selected breast cancer patients in lieu of whole breast irradiation regimens due to its comparable clinical outcomes and improved patient convenience and quality-of-life. Currently, several machines for delivery of APBI are available such as CyberKnife, Elekta Unity, Varian Ethos and Varian TrueBeam. Each treatment machine yields a unique distribution of radiation dose, so a given modality may afford particular advantages or liabilities based on a patient’s anatomy. Existing methods for identification of an ideal modality that best suits a patient’s unique anatomical features are generally lacking, manual, and resource intensive. To overcome this limitation and personalize modality selection, we propose a deep learning (DL) based multi-modality dose prediction model. The model takes a patient’s computed tomography (CT) scan, segmented planning target volumes (PTVs) and segmented organs at risk (OARs) as input, and outputs predicted dose distributions for all trained modalities. Differences may then be evaluated between each radiation treatment modality’s corresponding dose distribution and dose volumetric histograms (DVH) to select the optimal modality for each patient. Methods: Our dataset contains 16 partial breast patients, each with 4 unique treatment plans generated for 4 distinct modalities – CyberKnife, Elekta Unity, Varian Ethos and Varian Truebeam. 10, 2, and 4 patients were dedicated to the train, validation and test datasets respectively. Our model’s architecture is a U-net with a shared encoder and 4 independent dose decoders, in which each decoder predicts dose for one modality. Each training patient’s CT scan, PTVs, OARs, and a PTV distance map are input to the model during training, and the shared encoder shares the input data’s latent representations with each of the 4 decoders. The 4 distinct dose distributions from each training patient are used as labels for the 4 corresponding decoders to supervise model training. The mean squared error, mean squared log error, and DVH losses are minimized during model training. The model trained until validation loss converged. The 4 dose distributions were predicted in ~ 1 minute per test patient, yielding 16 dose distributions in the test dataset. Model performance across the test dataset was evaluated by calculation of the mean absolute percent error (MAPE) of dose differences within the patient’s body between the model’s predicted doses and label doses, relative to patient prescription dose. Results: The performance of the multi-modal dose prediction model was evaluated in terms of dose differences throughout each patient’s body across the 16 test dose distributions as compared to the actual planned doses. The average MAPE of the multi-modal dose predictions was 1.27 ± 0.38% of a patient’s prescription dose as compared to the actual planned dose across the test dataset. For a prescription dose of 3000 cGy, that corresponds to an average MAPE of 38.1 ± 11.4 cGy. Conclusion: Our multi-modality dose prediction model’s novelty lies in its rapid provision and comparison of accurate and detailed dosimetric data for each treatment modality. This model can empower treating radiation oncologists to make informed decisions in radiation therapy machine selection on a patient-by-patient basis, potentially optimizing radiation treatment plans by reduction of delivered dose to organs at risk. Table. Citation Format: Austen Maniscalco, Ezek Mathew, David Parsons, Asal Rahimi, Mona Arbab, Prasanna Alluri, Xingzhe Li, Mu-Han Lin, Steve Jiang, Dan Nguyen. Deep learning guided radiation therapy machine selection via a multi-headed dose prediction model [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 PO4-07-01.

Omission of breast surgery in selected breast cancer patients with excellent response to neoadjuvant systemic therapy

Modern neoadjuvant systemic therapy (NST) can result in high pathologic complete response rates (pCR) in triple negative (TN) and human epidermal growth factor receptor 2 positive (HER2+) breast cancer. The role of surgery is, therefore, being reconsidered in this rapidly evolving field. This report presents oncological outcomes of seven patients with TN or HER2+ breast cancer, with exceptional response to NST, and a post-NST image-guided vacuum assisted biopsy showing no residual disease (ypT0), who opted not to have breast surgery. The median age was 49 (IQR 36–61) years and the median tumour size at diagnosis was 50 (IQR 16–65) mm. All patients received breast radiotherapy and continued adjuvant systemic therapies as appropriate. At a median follow-up of 67 (IQR 61–77) months, all patients were alive and free of disease. This small case series supports the need for further research in ‘exceptional responders’ to provide safe, individualized patient-centred care.

Tumor Contact With Internal Mammary Perforator Vessels as Risk Factor for Gross Internal Mammary Lymph Node Involvement in Patients With Breast Cancer

ImportanceThe identification of internal mammary lymph node metastases and the assessment of associated risk factors are crucial for adjuvant regional lymph node irradiation in breast cancer patients. The current study aims to investigate if tumor contact with internal mammary perforator vessels is associated with gross internal mammary lymph node involvement. MethodsWe included 297 patients with primary breast cancer and gross internal mammary (IMN+) and/or axillary metastases (AXN+) as well as 230 patients without lymph node metastasis. Based on pre-treatment dynamic contrast-enhanced magnetic resonance imaging (DE-MRI), we assessed contact of the tumor to the internal mammary perforating vessels (IMPV). Results59 patients had ipsilateral IMN+ (iIMN+), 10 patients contralateral IMN+ (cIMN+) and 228 patients had ipsilateral axillary metastases without IMN. 230 patients had node negative breast cancer. In patients with ipsilateral IMN+, 100% of tumors had contact with ipsilateral IMPV with 94.9% (n=56) classified as major contact. In iIMN- patients on the other hand, major IMPV contact was observed in only 25.3% (n=116) and 36.2% (n=166) had no IMPV contact at all. Receiver operating characteristic (ROC) analysis revealed that “major IMPV contact” was more accurate in predicting ipsilateral IMN+ (AUC 0.85) compared to a multivariate model combining grade of differentiation, tumor site, size and molecular subtype (AUC 0.65). Strikingly, among patients with contralateral IMN+ (cIMN+), 100% of tumors had contact to a crossing contralateral IMPV, whereas in contralateral IMN- (cIMN-) patients, IMPVs to the contralateral side were observed in only 53.4% (iIMN+) and 24.8% (iIMN-), respectively. ConclusionTumor contact with the IMPV is highly associated with risk of gross IMN involvement. Further studies are warranted to investigate whether this identified risk factor is also associated with microscopic IMN involvement and whether it can assist in the selection of breast cancer patients for IMNI.

Benefit of respiratory gating in the Danish Breast Cancer Group partial breast irradiation trial

Background and purposePartial breast irradiation (PBI)has beenthe Danish Breast Cancer Group(DBCG) standard for selected breast cancer patients since 2016 based onearlyresults from the DBCG PBI trial.During trial accrual, respiratory-gated radiotherapy was introduced in Denmark. This study aims to investigate the effect of respiratory-gating on mean heart dose (MHD). Patients and methodsFrom 2009 to 2016 the DBCG PBI trial included 230 patientswith left-sided breast cancer receiving external beam PBI, 40 Gy/15 fractions/3 weeks.Localization of the tumor bed on the planning CT scan, the use of respiratory-gating, coverage of the clinical target volume (CTV), and doses to organs at risk were collected. ResultsRespiratory-gating was used in 123 patients (53 %). In 176 patients (77 %) the tumor bed was in the upper and in 54 patients (23 %) in the lower breast quadrants. The median MHD was 0.37 Gy (interquartile range 0.26–0.57 Gy), 0.33 Gy (0.23–0.49 Gy) for respiratory-gating, and 0.49 Gy (0.31–0.70 Gy) for free breathing, p < 0.0001. MHD was < 1 Gy in 206 patients (90 %) and < 2 Gy in 221 patients (96 %).Respiratory-gating led to significantly lower MHD for upper-located, but not for lower-located tumor beds, however, all MHD were low irrespective of respiratory-gating. Respiratory-gating did not improve CTV coverage or lower lung doses. ConclusionsPBI ensured a low MHD for most patients. Adding respiratory-gating further reduced MHD for upper-located but not for lower-located tumor beds but did not influence target coverage or lung doses. Respiratory-gating is no longer DBCG standard for left-sided PBI.

An XGBoost Machine Learning Based Model for Predicting Ki-67 Value ≥ 15% in T2NXM0 Stage Primary Breast Cancer Receiving Neoadjuvant Chemotherapy Using Clinical Data and Delta-Radiomic Features on Ultrasound Images and Overall Survival Analysis: A 5-Year Postoperative Follow-Up Study.

Objective: To establish a model based on clinical and delta-radiomic features within ultrasound images using XGBoost machine learning to predict proliferation-associated nuclear antigen Ki-67 value ≥ 15% in T2NXM0 stage primary breast cancer (BC). Method: Data were collected from 228 randomly selected BC patients who received ultrasound screening and postoperative pathologic assessment from April 2015 to September 2018. The patients were classified into the study group (n = 80) and control group (n = 148), and the data were apportioned into the training set and test set at a 7:3 ratio based on time intervals. In the training set, crucial factors were identified from clinical features and grayscale and delta-radiomic features within ultrasound images, by using the chi-square test, t-test, and rank-sum test. The clinical model, imaging model, and combined model were built using multivariate logistic regression, respectively. The model's predictive performance and clinical net benefit were assessed using DeLong's method and decision curve analysis. Meanwhile, an XGBoost algorithm is used to establish a prediction model to verify the above results. Results: The crucial factors affecting Ki-67 value ≥ 15% included BMI, lymph node metastases, BC volume, CA153, pathology type, tumor boundaries, tumor morphology, elastography score, and delta-radscore. The predictive performance of the combined model [AUC 0.857, OR 0.0290, 95% CI 0.793-0.908] was considerably improved on the training set than the clinical model [AUC 0.724, OR 0.0422, 95% CI 0.648-0.792] and the imaging model [AUC 0.798, OR 0.0355, 95% CI 0.727-0.857]. The decision curve analysis also confirmed that the combined model delivered a higher clinical net benefit, and the verification on the test set yielded similar results. The nomogram and the calibration curve plotted based on the combined model achieved satisfactory clinical effects. The SHAP value of the XGBoost algorithm also confirmed that lymph node metastasis, BC volume, elastography score, and delta-radscore are the best independent factors for predicting BC Ki-67 value ≥ 15%. Conclusion: The XGBoost machine learning-based combined model integrating clinical features and delta-radiomic features on ultrasound images was able to predict the Ki-67 value ≥ 15% in an efficient and noninvasive manner, providing important clues for clinical decision-making and follow-up in BC.

Machine-Learning Based Risk Assessment for Cancer Therapy-Related Cardiac Adverse Events Among Breast Cancer Patients.

The study aims to develop machine-learning models to predict cardiac adverse events in female breast cancer patients who receive adjuvant therapy. We selected breast cancer patients from a retrospective dataset of the Taipei Medical University Clinical Research Database and Taiwan Cancer Registry between January 2004 and December 2020. Patients were monitored at the date of prescribed chemo- and/or -target therapies until cardiac adverse events occurred during a year. Variables were used, including demographics, comorbidities, medications, and lab values. Logistics regression (LR) and artificial neural network (ANN) were used. The performance of the algorithms was measured by the area under the receiver operating characteristic curve (AUC). In total, 1321 patients (an equal 15039 visits) were included. The best performance of the artificial neural network (ANN) model was achieved with the AUC, precision, recall, and F1-score of 0.89, 0.14, 0.82, and 0.2, respectively. The most important features were a pre-existing cardiac disease, tumor size, estrogen receptor (ER), human epidermal growth factor receptor 2 (HER2), cancer stage, and age at index date. Further research is necessary to determine the feasibility of applying the algorithm in the clinical setting and explore whether this tool could improve care and outcomes.

Intraoperative Radiation Therapy Delivered by Brachytherapy in Breast Cancer: An Interim Analysis of a Phase 2 Trial

PurposeIntraoperative breast cancer radiotherapy (IORT) offers an alternative to external beam radiotherapy (EBRT) after breast-conserving surgery (BCS). The Intraoperative brachytherapy (IOBT) trial applies high dose rate (HDR) brachytherapy with a new applicator prototype as IORT after BCS. In this interim analysis of the IOBT trial, we present the oncological safety and toxicity of the method MethodsEligible patients were women, ≥ 50 years old with an unifocal nonlobular, estrogen-receptor-positive, HER2-negative breast cancer, cN0, ≤ 3 cm, treated with BCS and sentinel node biopsy (SNB). Toxicity was registered according to the LENT-SOMA scale. Cumulative incidence of local (LR) and regional recurrence (RR) were calculated through cumulative incidence function whereas overall survival (OS) was illustrated through Kaplan–Meier curve. ResultsUntil February 2023, 155 women (median age 68 years) were included in the trial. Twenty-nine women (18.7%) received supplemental EBRT, mostly due to positive SNB. Three-year cumulative incidence of LR and RR were 1.0% (CI 95 % 0.1%-2.3%) and 2.1% (CI 95% 0.8%-4.2%) respectively. Five- year cumulative incidence of LR and RR were 3.9% (CI 95% 1.8%-6.4%) and 2.1% (CI 95% 0.8%-4.2%) respectively. Five-year OS was 96.3% (CI 95% 93.6%-98.4%). Side effects were limited, low grade, and transient. ConclusionAcknowledging the short median follow-up time at interim analysis, our initial results indicate that delivering IORT through HDR brachytherapy in carefully selected breast cancer patients is feasible and oncological safe so far. A long-term follow-up is essential to confirm the initial results.

Development of an interpretable machine learning model for Ki-67 prediction in breast cancer using intratumoral and peritumoral ultrasound radiomics features.

Traditional immunohistochemistry assessment of Ki-67 in breast cancer (BC) via core needle biopsy is invasive, inaccurate, and nonrepeatable. While machine learning (ML) provides a promising alternative, its effectiveness depends on extensive data. Although the current mainstream MRI-centered radiomics offers sufficient data, its unsuitability for repeated examinations, along with limited accessibility and an intratumoral focus, constrain the application of predictive models in evaluating Ki-67 levels. This study aims to explore ultrasound (US) image-based radiomics, incorporating both intra- and peritumoral features, to develop an interpretable ML model for predicting Ki-67 expression in BC patients. A retrospective analysis was conducted on 263 BC patients, divided into training and external validation cohorts. From intratumoral and peritumoral regions of interest (ROIs) in US images, 849 distinctive radiomics features per ROI were derived. These features underwent systematic selection to analyze Ki-67 expression relationships. Four ML models-logistic regression, random forests, support vector machine (SVM), and extreme gradient boosting-were formulated and internally validated to identify the optimal predictive model. External validation was executed to ascertain the robustness of the optimal model, followed by employing Shapley Additive Explanations (SHAP) to reveal the significant features of the model. Among 231 selected BC patients, 67.5% exhibited high Ki-67 expression, with consistency observed across both training and validation cohorts as well as other clinical characteristics. Of the 1698 radiomics features identified, 15 were significantly correlated with Ki-67 expression. The SVM model, utilizing combined ROI, demonstrated the highest accuracy [area under the receiver operating characteristic curve (AUROC): 0.88], making it the most suitable for predicting Ki-67 expression. External validation sustained an AUROC of 0.82, affirming the model's robustness above a 40% threshold. SHAP analysis identified five influential features from intra- and peritumoral ROIs, offering insight into individual prediction. This study emphasized the potential of SVM model using radiomics features from both intra- and peritumoral US images, for predicting elevated Ki-67 levels in BC patients. The model exhibited strong performance in validations, indicating its promise as a noninvasive tool to enable personalized decision-making in BC care.

ASO Visual Abstract: Cryoablation Allows the Ultimate De-escalation of Surgical Therapy in Select Breast Cancer Patients.

ASO Visual Abstract: Cryoablation Allows the Ultimate De-escalation of Surgical Therapy in Select Breast Cancer Patients.

Cryoablation Allows the Ultimate De-escalation of Surgical Therapy for Select Breast Cancer Patients

BackgroundWidespread use of screening mammography has allowed breast cancer to be detected at earlier stages. This allows for increased customization of treatment and less aggressive management. De-escalation of therapy plays an important role in decreasing treatment burden and improving patient quality of life. This report examines cryoablation as the next step in the surgical de-escalation of breast cancer.MethodsWomen with a diagnosis of clinically node-negative, estrogen receptor-positive (ER +), progesterone receptor-positive (PR +), human epidermal growth factor receptor 2-negative (HER2 −) infiltrating ductal carcinomas 1.5 cm or smaller underwent ultrasound-guided cryoablation. Either the Visica 2 treatment system (before 2020) or the ProSense treatment system (since 2020) was used to perform the cryoablation. Patients received mammograms and ultrasounds at a 6 months follow-up visit, and magnetic resonance images at baseline, then at 1 year follow-up intervals. Adjuvant therapy decisions and disease status were recorded.ResultsThis study enrolled 32 patients who underwent 33 cryoablation procedures (1 patient had bilateral cancer). One patient had a sentinel node biopsy in addition to clinical staging of the axilla. For all the patients, adjuvant endocrine therapy was recommended, and six patients (18.75%) received adjuvant radiation. Of the 32 patients, 20 (60.6%) have been followed up for 2 years or longer, with no residual or recurrent disease at the site of ablation.ConclusionCryoablation of the primary tumor foregoing sentinel node biopsy offers an oncologically safe and feasible minimally invasive office-based procedure option in lieu of surgery for patients with early-stage, low-risk breast cancer.

Breast-conserving surgery versus mastectomy for treatment of breast cancer after neoadjuvant chemotherapy.

To compare recurrence and survival outcomes between breast-conserving surgery (BCS) and mastectomy after neoadjuvant chemotherapy (NACT). The data of 730 patients who underwent NACT between 2000 and 2014 were retrospectively reviewed. A total of 104 (14.2%) patients received BCS and 626 (85.8%) received mastectomy. Locoregional recurrence (LRR), distant metastases (DM), disease-free survival (DFS), breast cancer-specific survival (BCSS), and overall survival (OS) were analyzed using the Kaplan-Meier method. The impact of BCS versus mastectomy on outcomes was assessed by multivariate Cox models. Inverse probability of treatment weighting (IPTW) was used to balance covariates between the two groups. The median follow-up of BCS and mastectomy groups were 86.5 and 87.4 months, respectively. There were significant differences in distribution of most baseline characteristics between two groups. Compared with those who underwent mastectomy, the patients with BCS had similar 5-year LRR, DM, and DFS rates, but had significantly higher 5-year BCSS (98.9% vs. 90.4%, P = 0.005) and OS (98.9% vs. 90.1%, P = 0.003) rates. Multivariate analysis also showed that BCS significantly improved BCSS (HR = 0.27, 95% CI: 0.08-0.85, P = 0.025) and OS (HR = 0.25, 95% CI: 0.08-0.79, P = 0.018). After IPTW adjustment, the LRR, DM, DFS, BCSS and OS between two groups had no significant differences. The recurrence and survival outcomes are comparable with BCS and mastectomy. Thus, BCS is a safe treatment option for selected breast cancer patients after NACT.

68Ga-FAPI-04 PET/CT in Selected Breast Cancer Patients With Low FDG Affinity: A Head-to-Head Comparative Study.

In this prospective study, PET findings of 18F-FDG PET/CT in breast cancer staging were compared with 68Ga-FAP inhibitor (FAPI) 04 PET/CT findings in selected cases with relatively low 18F-FDG uptake in the primary tumor or with suspected nodal or distant metastases. Twenty-four women with a median age of 45 years (range, 36-67 years) who underwent initial staging 18F-FDG PET/CT for breast cancer underwent 68Ga-FAPI-04 PET/CT imaging within the same week. SUVmax and quantities of primary tumors and locoregional and distant metastatic lesions were recorded from both images, and these data were compared statistically. The histological subtypes of primary tumors were 11 invasive lobular, 8 invasive ductal, and 5 mucinous subtypes. More intense 68Ga-FAPI-04 uptake (mean, 17.1 ± 7.9; 7.4-38.5) was detected in all primary tumoral lesions compared with 18F-FDG (mean, 6.3 ± 3.9; 1.5-20.5) (P = <0.001, Z = 4.107). Additional lesions showing 68Ga-FAPI-04 uptake in the breast were detected in 29.2% (n = 7) of patients. In 8.3% (n = 2) of the patients, the primary lesion showed only 68Ga-FAPI-04 uptake. In 68Ga-FAPI-04 PET/CT, 25% (n = 6) of the patients showed more lymph node involvement and more intense uptake in the ipsilateral axilla. In addition, infraclavicular (level 3) lymph nodes in 4 patients (16.7%), supraclavicular lymph nodes in 1 patient, and internal mammary lymph nodes in 1 patient were detected only on 68Ga-FAPI-04 PET/CT. In a patient with lung and bone metastases, 68Ga-FAPI-04 uptake was higher, and the lung nodule showed only 68Ga-FAPI-04 uptake. In selected low-FDG-affinity breast cancer patients, 68Ga-FAPI-04 PET/CT showed the primary tumor with higher sensitivity and higher SUVmax values compared with 18F-FDG PET/CT. In addition, it is believed that this method may contribute to the management of patients' treatment by increasing the nodal stage.

Abstract 5726: Pan-cancer proteogenomics expands the landscape of therapeutic targets

Abstract Background: Molecularly targeted therapies are critical for improving cancer treatment. Since proteins are the targets of these therapies and functional effectors of genomic aberrations, proteogenomics data from the Clinical Proteomics Tumor Analysis Consortium (CPTAC) provides an unprecedented opportunity to characterize existing and future therapeutic targets for cancer treatment. Approach: CPTAC proteogenomics data from &amp;gt;1000 cancer patients spanning 10 cancer types was used to evaluate current and potential therapeutic targets curated from four databases. Cell line data from DepMap was further integrated to distinguish causations from associations. Computational pipelines were deployed to identify synthetic lethality for targeting tumor suppressor loss and to prioritize tumor associated antigens as immunotherapy targets. Results: We systematically collected 3050 druggable proteins and classified them into 5 tiers to facilitate different applications such as companion diagnostics, drug repurposing, and new therapy development. Many druggable proteins showed poor mRNA-protein correlation, including secreted proteins and proteins whose abundance was correlated with their interaction partners instead of cognate mRNA, highlighting the necessity of direct proteomic quantification of drug targets. 618 druggable proteins showed both overexpression in tumors compared to normal and significant dependency in CRISPR-Cas9 screens of cell lines of the same lineage. Notably, PAK1, a kinase targeted by investigational drugs, demonstrated both overexpression and dependency in all cancer types. A similar analysis of phosphoproteomics data focusing on known activating sites of druggable proteins further revealed targetable dependencies driven by protein hyperactivation. The phosphosite pS50 on PTPN1, a phosphatase targeted by experimental drugs, was increased in 7 cancer types and PTPN1 demonstrated dependency in related cancer cell lines. Based on tumor proteogenomic data and cell line CRISPR-Cas9 screen data, we identified synthetic lethality for difficult to target tumor suppressor losses, revealing TP53 mutations as a candidate biomarker to select breast cancer patients for CHEK1 inhibition, and endometrial cancer patients for treatment with doxorubicin. We identified 140 proteins whose expression was restricted in normal tissues but abnormal in tumors. Experimental analysis of peptides predicted to have high binding affinity to the most common allotype HLA-A02 for 7 prioritized proteins identified 21 peptides from 5 proteins with both strong binding affinity and immunogenicity which could be further investigated as immunotherapy targets. Conclusion: We generate a comprehensive resource of protein and peptide targets that covers multiple therapeutic modalities. This unique resource will pave the way for repurposing of currently available drugs and developing new drugs for cancer treatment. Citation Format: Jonathan T. Lei, Sara R. Savage, Xinpei Yi, Bo Wen, Hongwei Zhao, Lauren K. Somes, Paul W. Shafer, Yongchao Dou, Qiang Gao, Valentina Hoyos, Bing Zhang. Pan-cancer proteogenomics expands the landscape of therapeutic targets. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5726.

Abstract PD16-04: To monitor or not to monitor? MRI monitoring of neoadjuvant chemotherapy effect and its benefit on breast cancer patients’ pathological complete response rate

Abstract Introduction: Neoadjuvant chemotherapy (NACT) represents an essential tool for the treatment of selected breast cancer patients aimed to reduce the tumor size for a more conservative resection and to gain early information about the sensitivity to applied treatment. An effective early response assessment helps both to change the therapeutic strategy for patients nonresponding to the treatment used and to avoid the toxicity related to ineffective treatment. We assumed that changing the chemotherapy regimen might avoid the residual cancer burden (RCB) III outcome and lead to the highest possible rate of pathological complete response (pCR) achieved. We examined the benefit of MRI as a preferred method for interim response assessment to NACT with a primary focus on the relation between the pattern of responses to NACT within the MRI- monitored and non-monitored patients and the pattern of final outcomes on the RCB scale. Methods We present a real-life data-based retrospective analysis of 124 female patients with locally advanced breast carcinoma. All patients received NACT (anthracycline-based regimen followed by taxane-based regimen/early switch to taxanes after the 2nd cycle in nonresponders, and antiHER2 therapy if indicated) prior to surgery (mastectomy/breast-conserving surgery, sentinel lymph node biopsy/axillary dissection). Postoperative histopathological analysis of tumor specimens categorised 120 patients according to the RCB scale 0 – III. Four patients had surgery ex muros and were lost to follow-up. Patients were divided into two cohorts. Group A covered monitored patients with pre-treatment MRI and follow-up MRI after the 2nd cycle of NACT. If no response was detected, another follow-up MRI was indicated after the 2nd cycle of a new treatment. Group B comprised patients with no/incomplete MRI monitoring and patients without the change of therapy even though no response was detected by MRI after the 2nd cycle of NACT. Association between categorical variables was tested using chi-square tests. Statistical analysis was performed using StatsDirect® 3.3.5 (StatsDirect Ltd., Cheshire, UK). The data analysis was supported by a grant from the Cultural and Educational Grant Agency of the Ministry of Education, Science, Research, and Sport of the Slovak Republic (KEGA 041UK-4/2020). Results MRI-monitored patients had two times the odds of being RBC 0 (OR = 2.02, P = 0.122) and almost three times the odds of being RCB 0 or RCB I (OR = 2.83, P = 0.0206) than patients with no monitoring. Changing the NACT after the 2nd cycle in the cohort of monitored patients with no response to initial therapy was significantly associated with better outcomes on the RCB scale (P = 0.0042). This result was confirmed by comparing the pattern of results in patients with no response within the group of incompletely monitored patients with known MRI results after the 2nd cycle (P = 0.0257). Changing the ineffective NACT after the 2nd cycle significantly increased the proportion of RCB 0-I by 23.4%, confirming the benefit of response monitoring by MRI. Conclusion Breast cancer constitutes a heterogeneity of tumor subtypes and it is acknowledged that their behaviour during NACT is highly variable and often unpredictable. MRI represents an effective tool for the assessment of tumor response to applied NACT. To achieve the most clinically meaningful impact of the selected treatment it is critical to monitor the tumor response within the first 3 cycles. Our data clearly confirmed that clinical decisions related to the detection of early response or no response (resulting in the change of NACT) lead to better treatment outcomes and less toxicity. The rate of pCR defines the applied treatment efficacy. Every single patient with results different from RCB III has a better prognosis, reduced need for further expensive treatment and also a more favourable quality of life. These key facts need to be carefully considered when discussing the cost and benefits of MRI monitoring. Citation Format: Lucia Vanovcanova, Iveta Waszulikova, Bibiana Vertakova Krakovska. To monitor or not to monitor? MRI monitoring of neoadjuvant chemotherapy effect and its benefit on breast cancer patients’ pathological complete response rate [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 PD16-04.

Interactive effects of molecular subtypes with tumor size and extracranial metastatic pattern on risk of brain metastasis in breast cancer patients: A population-based study.

Early detection of brain metastasis (BM) is essential for prognostic improvement in breast cancer (BC) patients. The aim was to identify predictors of BCBM in different molecular subtypes on a population-based level. The Surveillance, Epidemiology, and End Results database was used to select BC patients diagnosed from 2010 to 2018. We evaluated the incidence and risk factors of BCBM and tested the interaction effects between molecular subtypes and other risk factors. Among the 527,525 selected patients, molecular subtypes significantly interacted with T stage and extracranial metastasis (ECM) patterns on the risk of BM in the whole BC population (interaction p=0.002, <0.001, respectively) and after excluding patients with unknown states of key factors. BM development was independent of the T stage only in HR-/HER2- patients (trend p=0.126). We selected BC patients with single-organ ECM and found a significant interaction between molecular subtypes and ECM patterns (interaction p=0.013). The impact of ECM patterns on the risk of BM was limited to HR-/HER2- patients (trend p < 0.001), for whom using bone metastasis as a reference, lung metastasis increased the risk of BM (OR=1.936, 95% CI: 1.300-2.882, p=0.001). T stage and ECM patterns had different associations with BM in different molecular subtypes. HR-/HER2- BC had distinct features on BM development, manifested as a lack of tumor size effect and is associated with lung metastasis. Close surveillance for BM should be considered for HR-/HER2- BC patients.

234 (PB-058) Poster - Neratinib could be effective as monotherapy or in combination with Trastuzumab in HER2 low expressing breast cancer cells and organoid model

The selection of breast cancer patients for anti-HER2 treatment is based on HER2 positivity defined by IHC (Immunohistochemistry) and/or in situ hybridization (ISH). However, there have been some discrepancies and ambiguity on HER2 classification, leading to several cut-off changes over the years and resulting in some of the patients with lower HER2 expressing tumours not offered anti-HER2 treatments. Several studies also suggested that a subset of the HER2 low breast tumours may respond to anti-HER2 treatments. The aim of this study is to understand the mechanisms of action and resistance of HER2 targeting treatments in HER2-low expressing breast cancer cells (BC cells). The HER2 expression of a panel of 8 breast cell lines was assessed by IHC, FISH, western blot and qRT-PCR and their responses to trasztuzumab was assessed based on HER2 expression. We also established a panel of patient-derived organoids (PDOs) from the cancer tissues obtained from breast cancer patients to assess the effects of anti-HER2 agents.

151 (PB-064) Poster - First results on acute skin toxicity in model-based selected breast cancer patients treated with adjuvant intensity modulated proton therapy

Background: Several studies have reported on acute skin toxicity outcomes in breast cancer (BC) patients treated with proton therapy (PT). However, limited data is available on (1) acute skin toxicity outcomes of mildly hypofractionated intensity modulated PT, and (2) on PT outcomes of model-based selected BC patients. Therefore, the primary objective of this study was to assess the incidence and severity of acute skin toxicity in these BC patients, and the secondary objectives were to assess predictive factors and dose-effect relationships.

Comparison of Breast-Conserving Surgery vs. Mastectomy for Patients with Breast Cancer after Neoadjuvant Chemotherapy

<h3>Purpose/Objective(s)</h3> To compare recurrence and survival outcomes between breast-conserving surgery (BCS) and mastectomy after neoadjuvant chemotherapy (NACT). <h3>Materials/Methods</h3> A total of 730 eligible patients who underwent NACT between 2000 and 2014 were retrospectively reviewed. Of those, 104 (14.2%) patients received BCS and 626 (85.8%) patients received mastectomy. Locoregional recurrence (LRR), distant metastases (DM), disease-free survival (DFS), Breast cancer - specific survival (BCSS) and overall survival (OS) were estimated using the Kaplan-Meier method. The impact of BCS versus (vs) mastectomy on outcomes was assessed by multivariate Cox models and inverse probability of treatment weighting (IPTW) method by adjusting for age, clinical T stage, clinical N stage, NACT cycles, histological grade, lymphovascular invasion (LVI), molecular subtype/trastuzumab, ypT (pathologic tumor stage after NACT), ypN (pathologic lymph node stage after NACT), endocrine therapy, and adjuvant chemotherapy. Recurrence and survival curves before or after weighting were compared with the log rank test. <h3>Results</h3> The median follow-up was 87.4 months. The 5-year LRR, DM, DFS, BCSS and OS rates after BCS and mastectomy were 6.9% vs 7.6% (P = 0.805), 10.8% vs 19.9% (P = 0.145), 83.4% vs 78.2% (P = 0.514), 98.9% vs 90.4% (P = 0.005) and 98.9% vs 90.1% (P = 0.003), respectively. There were significant differences in distribution of most baseline characteristics between the BCS and mastectomy group. After IPTW adjustment, the clinical characteristics between two groups were comparable. Multivariate analysis showed that BCS had no significant impact on LRR (HR 1.08, 95% CI 0.48-2.43, P = 0.849), DM (HR 0.82, 95% CI 0.49-1.40, P = 0.473) and DFS (HR 1.12, 95% CI 0.70-1.80, P = 0.626) compared with mastectomy, but BCS significantly improved BCSS (HR 0.29, 95% CI 0.09-0.92, P = 0.035) and OS (HR 0.27, 95% CI 0.08-0.86, P = 0.027) compared with mastectomy. After IPTW adjustment, the results of 5-year LRR, DM, DFS remained the same, which were 10.0% vs 7.3% (P = 0.598), 11.7% vs 19.0% (P = 0.341), 78.6% vs 79.1% (P = 0.908) for BCS and mastectomy groups, respectively. The 5-year BCSS and OS were better in the BCS group than in the mastectomy group, but the difference was no longer statistically significant (BCSS: 99.7% vs. 90.8%, <i>P</i> = 0.095 and OS: 99.7% vs. 90.6%, <i>P</i> = 0.083). <h3>Conclusion</h3> Locoregional and distant control and DFS are comparable with BCS and mastectomy, but BCSS and OS may be better with BCS. Thus, BCS is a safe treatment option for selected breast cancer patients after NACT.