Chemotherapy For Breast Cancer Research Articles

Abstract C013: Racial disparities in Interpersonal Processes of Care (IPC) during early stage breast cancer chemotherapy: Associations with symptoms and the ability to receive dose-intensive chemotherapy

Abstract C013: Racial disparities in Interpersonal Processes of Care (IPC) during early stage breast cancer chemotherapy: Associations with symptoms and the ability to receive dose-intensive chemotherapy

Surgical outcomes following neoadjuvant chemotherapy with and without immunotherapy in patients with triple-negative breast cancer

PurposeAdding pembrolizumab to neoadjuvant chemotherapy (NAC) for triple-negative breast cancer (TNBC) improves pathologic complete response (pCR) rates and event-free survival. The impact of adding immunotherapy to NAC on surgical outcomes is unknown. This study compares 90-day post-surgical complications (PSCs) and time to adjuvant treatment among patients undergoing NAC for TNBC with and without immunotherapy.MethodsPatients treated with NAC alone or with immunotherapy (NAC-I) for stage I–III TNBC between 2018 and 2022 were retrospectively identified at a single academic institution. Kruskal–Wallis rank sum and Fisher's exact tests compared patient sociodemographic and clinical characteristics. Multivariable logistic regression determined odds ratios (OR) predicting PSCs.ResultsOf 54 patients, 29 received NAC alone and 25 received NAC-I. Compared to NAC patients, NAC-I patients had more advanced stage tumors (p = 0.038), and had slightly higher rates of mastectomy with reconstruction (p = 0.193). 72.0% of NAC-I patients experienced a pCR, compared with 44.8% of NAC patients (p = 0.193). There were 10 PSCs (34.5%) in NAC patients compared to 9 PSCs (36.0%) in NAC-I patients (p > 0.99). Regression analysis demonstrated no association of PSCs with NAC-I (OR 0.83, 95% CI 0.19–3.60). Time to adjuvant therapy was shorter for NAC-I patients (28 days vs 36 days, p = 0.013).ConclusionsPatients with TNBC receiving NAC-I have higher pCR rates and do not appear to have added 90-day PSCs or delays to adjuvant therapy despite trending toward more extensive surgical procedures compared to NAC alone. Larger studies are needed to further evaluate the surgical safety of immunotherapy.

AI-assisted models to predict chemotherapy drugs modified with C60 fullerene derivatives

Employing quantitative structure–activity relationship (QSAR)/ quantitative structure–property relationship (QSPR) models, this study explores the application of fullerene derivatives as nanocarriers for breast cancer chemotherapy drugs. Isolated drugs and two drug–fullerene complexes (i.e., drug–pristine C60 fullerene and drug–carboxyfullerene C60–COOH) were investigated with the protein CXCR7 as the molecular docking target. The research involved over 30 drugs and employed Pearson’s hard–soft acid–base theory and common QSAR/QSPR descriptors to build predictive models for the docking scores. Energetic descriptors were computed using quantum chemistry at the density functional-based tight binding DFTB3 level. The results indicate that drug–fullerene complexes interact more with CXCR7 than isolated drugs. Specific binding sites were identified, with varying locations for each drug complex. Predictive models, developed using multiple linear regression and IBM Watson artificial intelligence (AI), achieved mean absolute percentage errors below 12%, driven by AI-identified key variables. The predictive models included mainly quantitative descriptors collected from datasets as well as computed ones. In addition, a water-soluble fullerene was used to compare results obtained by DFTB3 with a conventional density functional theory approach. These findings promise to enhance breast cancer chemotherapy by leveraging fullerene-based drug nanocarriers.

Use of chemotherapy and loco-regional therapy in stage IA triple-negative breast cancer and their association with oncologic outcomes: A cancer registry study.

We aimed to evaluate the role of adjuvant chemotherapy and loco-regional therapy for stage IA (pT1, pN0) triple-negative breast cancer (TNBC) in a real-world setting. We identified patients with pT1, pN0 TNBC diagnosed between 2009 and 2021 within the Baden-Württemberg cancer registry (BWCR), Germany. Overall survival (OS) was assessed using Kaplan-Meier statistics and multivariate Cox regression models (adjusted for age, use of chemotherapy, local therapy (breast conserving therapy [breast conserving surgery + radiotherapy] vs. mastectomy), and tumor histologic subtype). A total of 1231 patients with a median follow-up of 45.9 months were identified: 1.0% (12 of 1231) with pT1mi stage, 9.5% (117 of 1231) with pT1a, 23.7% (292 of 1231) with pT1b, and 65.8% (810 of 1231) with pT1c. Multivariate Cox regression analysis revealed no significant influence for the use of chemotherapy on OS in pT1b patients (HR 0.90, 95% CI 0.43-1.90). For pT1c patients with Grade 1-2 tumors, the use of chemotherapy was not significantly associated OS (HR 1.01, 95% CI 0.48-2.11) but breast conserving therapy was associated with improved OS (HR 0.41, 95% CI 0.18-0.93). For pT1c patients with Grade 3 tumors, the use of chemotherapy (HR 0.51, 95% CI 0.33-0.78) as well as breast conserving therapy (HR 0.42, 95% CI 0.23-0.76) was associated with OS. This data suggests that OS in stage IA TNBC is strongly influenced by local therapy rather than the use of chemotherapy, except for pT1c patients with Grade 3 tumors. Larger studies with longer-term follow-up are welcomed to fully inform this discussion.

Prediction of early clinical response to neoadjuvant chemotherapy in Triple-negative breast cancer: Incorporating Radiomics through breast MRI

This study assessed pretreatment breast MRI coupled with machine learning for predicting early clinical responses to neoadjuvant chemotherapy (NAC) in triple-negative breast cancer (TNBC), focusing on identifying non-responders. A retrospective analysis of 135 TNBC patients (107 responders, 28 non-responders) treated with NAC from January 2015 to October 2022 was conducted. Non-responders were defined according to RECIST guidelines. Data included clinicopathologic factors and clinical MRI findings, with radiomics features from contrast-enhanced T1-weighted images, to train a stacking ensemble of 13 machine learning models. For subgroup analysis, propensity score matching was conducted to adjust for clinical disparities in NAC response. The efficacy of the models was evaluated using the area under the receiver-operating-characteristic curve (AUROC) before and after matching. The model combining clinicopathologic factors and clinical MRI findings achieved an AUROC of 0.752 (95% CI 0.644–0.860) for predicting non-responders, while radiomics-based models showed 0.749 (95% CI 0.614–0.884). An integrated model of radiomics, clinicopathologic factors, and clinical MRI findings reached an AUROC of 0.802 (95% CI 0.699–0.905). After propensity score matching, the hierarchical order of key radiomics features remained consistent. Our study demonstrated the potential of using machine learning models based on pretreatment MRI to non-invasively predict TNBC non-responders to NAC.

Tumor infiltrating lymphocytes and change in tumor load on MRI to assess response and prognosis after neoadjuvant chemotherapy in breast cancer.

In this study, we aimed to explore if the combination of tumor infiltrating lymphocytes (TILs) and change in tumor load on dynamic contrast-enhanced magnetic resonance imaging leads to better assessment of response to neoadjuvant chemotherapy (NAC) in patients with breast cancer, compared to either alone. In 190 NAC treated patients, MRI scans were performed before and at the end of treatment. The percentage of stromal TILs (%TILs) was assessed in pre-NAC biopsies according to established criteria. Prediction models were developed with linear regression by least absolute shrinkage and selection operator and cross validation (CV), with residual cancer burden as the dependent variable. Discrimination for pathological complete response (pCR) was evaluated using area under the receiver operating characteristic curves (AUC). We used Cox regression analysis for exploring the association between %TILs and recurrence-free survival (RFS). Fifty-one patients reached pCR. In all patients, the %TILs model and change in MRI tumor load model had an estimated CV AUC of 0.69 (95% confidence interval (CI) 0.53-0.78) and 0.69 (95% CI 0.61-0.79), respectively, whereas a model combining the variables resulted in an estimated CV AUC of 0.75 (95% CI 0.66-0.83). In the group with tumors that were ER positive and HER2 negative (ER+/HER2-) and in the group with tumors that were either triple negative or HER2 positive (TN&HER2+) separately, the combined model reached an estimated CV AUC of 0.72 (95% CI 0.60-0.88) and 0.70(95% CI 0.59-0.82), respectively. A significant association was observed between pre-treatment %TILS and RFS (hazard ratio (HR) 0.72 (95% CI 0.53-0.98), for every standard deviation increase in %TILS, p = 0.038). The combination of TILs and MRI is informative of response to NAC in patients with both ER+/HER2- and TN&HER2+ tumors.

TRF-Leu reverse breast cancer cells chemoresistance by regulation of BIRC5

ObjectiveAccumulating studies reported the crucial roles of tRFs in tumorigenesis. However, their further mechanisms and clinical values remains unclear. This study aimed at the further investigation of tRF-Leu in breast cancer chemotherapy resistance.MethodsThe high-throughput sequencing was performed and identified the downregulation of tRF-Leu in MCF7/ADR cells. The function of tRF-Leu in breast cancer cells and breast cancer chemotherapy resistance was investigated in vitro and in vivo, including colony formation assay, CCK-8 assay, transwell assay and apoptosis assay. The binding site of tRF-Leu on BIRC5 was verified by dual-luciferase assay.ResultstRF-Leu was downregulated in MCF7/ADR cells. Overexpression of tRF-Leu inhibited the migration of breast cancer cells. Furthermore, tRF-Leu could reverse the resistance of MCF7/ADR cells to Adriamycin both in vitro and in vivo. BIRC5 was a target of tRF-Leu, which might be involved in the chemotherapy resistance regulation.ConclusionWe demonstrated that tRF-Leu could inhibit the chemotherapy resistance of breast cancer by targeting BIRC5. These findings might identify new biomarkers of breast cancer therapy and bring new strategies to reverse chemotherapy resistance.

Trastuzumab Deruxtecan after Endocrine Therapy in Metastatic Breast Cancer.

Outcomes in patients with hormone receptor-positive metastatic breast cancer worsen after one or more lines of endocrine-based therapy. Trastuzumab deruxtecan has shown efficacy in patients with metastatic breast cancer with low expression of human epidermal growth factor receptor 2 (HER2) after previous chemotherapy. We conducted a phase 3, multicenter, open-label trial involving patients with hormone receptor-positive metastatic breast cancer with low HER2 expression (a score of 1+ or 2+ on immunohistochemical [IHC] analysis and negative results on in situ hybridization) or ultralow HER2 expression (IHC 0 with membrane staining) who had received one or more lines of endocrine-based therapy and no previous chemotherapy for metastatic breast cancer. Patients were randomly assigned in a 1:1 ratio to receive trastuzumab deruxtecan or the physician's choice of chemotherapy. The primary end point was progression-free survival (according to blinded independent central review) among the patients with HER2-low disease. Secondary end points included progression-free survival among all the patients who had undergone randomization, overall survival, and safety. Of the 866 patients who underwent randomization, 713 had HER2-low disease, and 153 had HER2-ultralow disease. Among the patients with HER2-low disease, the median progression-free survival was 13.2 months (95% confidence interval [CI], 11.4 to 15.2) in the trastuzumab deruxtecan group and 8.1 months (95% CI, 7.0 to 9.0) in the chemotherapy group (hazard ratio for disease progression or death, 0.62; 95% CI, 0.51 to 0.74; P<0.001); the results were consistent in the exploratory HER2-ultralow population. Data for overall survival were immature. Adverse events of grade 3 or higher occurred in 52.8% of the patients in the trastuzumab deruxtecan group and in 44.4% of those in the chemotherapy group. Adjudicated interstitial lung disease or pneumonitis occurred in 49 patients (11.3%; three events were grade 5 in severity) and in 1 patient (0.2%; grade 2), respectively. Among patients with hormone receptor-positive, HER2-low or HER2-ultralow metastatic breast cancer who had received one or more lines of endocrine-based therapy, treatment with trastuzumab deruxtecan resulted in longer progression-free survival than chemotherapy. No new safety signals were identified. (Funded by AstraZeneca and Daiichi Sankyo; DESTINY-Breast06 ClinicalTrials.gov number, NCT04494425.).

Monte Carlo simulation of spatial frequency domain imaging for breast tumors during compression.

Near-infrared optical imaging methods have shown promise for monitoring response to neoadjuvant chemotherapy (NAC) for breast cancer, with endogenous contrast coming from oxy- and deoxyhemoglobin. Spatial frequency domain imaging (SFDI) could be used to detect this contrast in a low-cost and portable format, but it has limited imaging depth. It is possible that local tissue compression could be used to reduce the effective tumor depth. To evaluate the potential of SFDI for therapy response prediction, we aim to predict how changes to tumor size, stiffness, and hemoglobin concentration would be reflected in contrast measured by SFDI under tissue compression. Finite element analysis of compression on an inclusion-containing soft material is combined with Monte Carlo simulation to predict the measured optical contrast. When the effect of compression on blood volume is not considered, contrast gain from compression increases with the size and stiffness of the inclusion and decreases with the inclusion depth. With a model of reduction of blood volume from compression, compression reduces imaging contrast, an effect that is greater for larger inclusions and stiffer inclusions at shallower depths. This computational modeling study represents a first step toward tracking tumor changes induced by NAC using SFDI and local compression.

CRGD-Peptide Modified Covalent Organic Frameworks for Precision Chemotherapy in Triple-Negative Breast Cancer.

This study presents the use of nanoscale covalent organic frameworks (nCOFs) conjugated with tumor-targeting peptides for the targeted therapy of triple-negative breast cancer (TNBC). While peptides have previously been used for targeted delivery, their conjugation with COFs represents an innovative approach in this field. In particular, we have developed alkyne-functionalized nCOFs chemically modified with cyclic RGD peptides (Alkyn-nCOF-cRGD). This configuration is designed to specifically target αvβ3 integrins that are overexpressed in TNBC cells. These nCOFs exhibit excellent biocompatibility and are engineered to selectively disintegrate under acidic conditions, allowing for precise and localized drug release in tumor environment. Doxorubicin, a chemotherapeutic agent, has been encapsulated in these nCOFs with high loading efficiency. The therapeutic potential of Alkyn-nCOF-cRGD has been demonstrated in vitro and in vivo models. It shows significantly improved drug uptake and targeted cell death in TNBC, highlighting the efficacy of receptor-mediated endocytosis and pH-controlled drug release. This strategy leverages the unique properties of nCOFs with targeted drug delivery to achieve significant advances in personalized cancer therapy and set a new standard for precision chemotherapeutic delivery.

Deep learning Radiomics Based on Two-Dimensional Ultrasound for Predicting the Efficacy of Neoadjuvant Chemotherapy in Breast Cancer.

We investigate the predictive value of a comprehensive model based on preoperative ultrasound radiomics, deep learning, and clinical features for pathological complete response (pCR) after neoadjuvant chemotherapy (NAC) for the breast cancer. We enrolled 155 patients with pathologically confirmed breast cancer who underwent NAC. The patients were randomly divided into the training set and the validation set in the ratio of 7:3. The deep learning and radiomics features of pre-treatment ultrasound images were extracted, and the random forest recursive elimination algorithm and the least absolute shrinkage and selection operator were used for feature screening and DL-Score and Rad-Score construction. According to multifactorial logistic regression, independent clinical predictors, DL-Score, and Rad-Score were selected to construct the comprehensive prediction model DLRC. The performance of the model was evaluated in terms of its predictive effect, and clinical practicability. Compared to the clinical, radiomics (Rad-Score), and deep learning (DL-Score) models, the DLRC accurately predicted the pCR status, with an area under the curve (AUC) of 0.937 (95%CI: 0.895-0.970) in the training set and 0.914 (95%CI: 0.838-0.973) in the validation set. Moreover, decision curve analysis confirmed that the DLRC had the highest clinical value among all models. The comprehensive model DLRC based on ultrasound radiomics, deep learning, and clinical features can effectively and accurately predict the pCR status of breast cancer after NAC, which is conducive to assisting clinical personalized diagnosis and treatment plan.

Cytotoxic and cytostatic activity of five new imidazotetrazine derivatives on breast cancer cell cultures MDAMB231, BT474, and MCF-7

Introduction: The work presents the results of the study of new imidazotetrazine derivatives to establish the possibility of using them as anticancer agents, including for chemotherapy of metastatic breast cancer. The relevance of the work is due to the wide spread of oncological diseases and high cancer mortality, which dictates the need to constantly obtain cell lines and improve cultivation protocols for testing new antitumor drugs. The goal of this study is to check the potential of five new imidazotetrazine derivatives to become new antitumor drugs, in the scope of studying their cytotoxic and cytostatic activities on breast cancer cell cultures. Materials and Methods: The culturing MCF-7, MDAMB231, BT474, and MCF-10a cells with determining cytotoxic and cytostatic activities of five new azolotetrazine derivatives are base methods used in this study. Results: For the MCF-7 culture, MCS of comparison drug temozolomide was equal to 2.44 and IC50 was 6.81 mM/L; for other cultures CTA indicators were worse. Imidazotetrazine 2 and imidazotetrazine 3 demonstrated CTA indicators lower than those of temozolomide. IC50 was not achieved, and the MCS value varied between 1.34 and 1.74. These two derivatives were classified as the compounds with an extremely low CTA. Imidazotetrazine 1 and iminothiotriazine 5 showed cytotoxic activity higher than that of the comparison drug and we classified these compounds as the ones with a moderate CTA. Finally, we found imidazotetrazine 4 with IC50 of 0.85 mM/L and CTA of 7.34 as a compound with a potentially strong anticancer effect for further investigation. The cytostatic activity of four of the five azoloazine derivatives studied was in a narrow range corresponding to the survival rate from 0.21 to 0.32, depending on the compound and cell culture. Against this background, imidazotetrazine 4 demonstrated a higher CSA, determined by the survival rate from 0.17 to 0.20. Conclusion: As a result of an in vitro study, we found that five new azolotriazine derivatives can be evaluated in the ascending order of these properties, as a combination of CTA+CSA in order imidazotetrazine 2, imidazotetrazine 3 &lt; temozolomide &lt; imidazotetrazine 1, iminothiotriazine 5 &lt; imidazotetrazine 4, although the CSA of all the studied compounds turned out to be high. Thus, 3-Cyclohexyl-4-oxoimidazo[5,1-d]-[1,2,3,5]tetrazine-8-N-piperidinyl-carboxamide (imidazotetrazine 4) is an unconditional leader in the tested series of new azoloazine derivatives and we recommend it for further preclinical trials.

Cross-modal deep learning model for predicting pathologic complete response to neoadjuvant chemotherapy in breast cancer

Pathological complete response (pCR) serves as a critical measure of the success of neoadjuvant chemotherapy (NAC) in breast cancer, directly influencing subsequent therapeutic decisions. With the continuous advancement of artificial intelligence, methods for early and accurate prediction of pCR are being extensively explored. In this study, we propose a cross-modal multi-pathway automated prediction model that integrates temporal and spatial information. This model fuses digital pathology images from biopsy specimens and multi-temporal ultrasound (US) images to predict pCR status early in NAC. The model demonstrates exceptional predictive efficacy. Our findings lay the foundation for developing personalized treatment paradigms based on individual responses. This approach has the potential to become a critical auxiliary tool for the early prediction of NAC response in breast cancer patients.

389P Efficacy and safety of sintilimab in combination with anlotinib plus metronomic chemotherapy in advanced triple negative breast cancer (SPACE): Preliminary results of a single-arm, multicenter phase II trial

389P Efficacy and safety of sintilimab in combination with anlotinib plus metronomic chemotherapy in advanced triple negative breast cancer (SPACE): Preliminary results of a single-arm, multicenter phase II trial

261P Subtyping of residual disease (RD) following neoadjuvant chemotherapy (NACT) for triple negative breast cancer (TNBC): Evolution and prognostic impact

261P Subtyping of residual disease (RD) following neoadjuvant chemotherapy (NACT) for triple negative breast cancer (TNBC): Evolution and prognostic impact

249P Neoadjuvant chemotherapy in inflammatory breast cancer: A meta-analysis of 10 trials of the German Breast Group (GBG)

249P Neoadjuvant chemotherapy in inflammatory breast cancer: A meta-analysis of 10 trials of the German Breast Group (GBG)

333P Efficacy and cell-free DNA whole methylome based biomarker analysis of carboplatin plus taxanes neoadjuvant chemotherapy in triple negative breast cancer: A prospective multi-center cohort study

333P Efficacy and cell-free DNA whole methylome based biomarker analysis of carboplatin plus taxanes neoadjuvant chemotherapy in triple negative breast cancer: A prospective multi-center cohort study

256P Protein signature of tertiary lymphoid structure predicts efficacy of neoadjuvant chemotherapy in triple-negative breast cancer

256P Protein signature of tertiary lymphoid structure predicts efficacy of neoadjuvant chemotherapy in triple-negative breast cancer

388P Anlotinib or bevacizumab combined with chemotherapy for the second-line HER-2 negative metastatic breast cancer: A retrospective cohort study

388P Anlotinib or bevacizumab combined with chemotherapy for the second-line HER-2 negative metastatic breast cancer: A retrospective cohort study

CN74 Exploring the correlation between spiritual health, quality of survival, and meaning of life among breast cancer chemotherapy patients

CN74 Exploring the correlation between spiritual health, quality of survival, and meaning of life among breast cancer chemotherapy patients