Neoadjuvant Chemotherapy In Breast Cancer Research Articles

Curative effect of immediate reconstruction after neoadjuvant chemotherapy for breast cancer: a systematic review and meta-analysis

Curative effect of immediate reconstruction after neoadjuvant chemotherapy for breast cancer: a systematic review and meta-analysis

Integrated single-cell and bulk RNA sequencing analysis identifies a neoadjuvant chemotherapy-related gene signature for predicting survival and therapy in breast cancer

Neoadjuvant chemotherapy (NAC) is a well-established treatment modality for locally advanced breast cancer (BC). However, it can also result in severe toxicities while controlling tumors. Therefore, reliable predictive biomarkers are urgently needed to objectively and accurately predict NAC response. In this study, we integrated single-cell and bulk RNA-seq data to identify nine genes associated with the prognostic response to NAC: NDRG1, CXCL14, HOXB2, NAT1, EVL, FBP1, MAGED2, AR and CIRBP. Furthermore, we constructed a prognostic risk model specifically linked to NAC. The clinical independence and generalizability of this model were effectively demonstrated. Additionally, we explore the underlying cancer hallmarks and microenvironment features of this NAC response-related risk score, and further assess the potential impact of risk score on drug response. In summary, our study constructed and validated a nine-gene signature associated with NAC prognosis, which was accomplished through the integration of single-cell and bulk RNA data. The results of our study are of crucial significance in the prediction of the efficacy of NAC in BC, and may have implications for the clinical management of this disease.

PROACTING: predicting pathological complete response to neoadjuvant chemotherapy in breast cancer from routine diagnostic histopathology biopsies with deep learning.

Invasive breast cancer patients are increasingly being treated with neoadjuvant chemotherapy; however, only a fraction of the patients respond to it completely. To prevent overtreatment, there is an urgent need for biomarkers to predict treatment response before administering the therapy. In this retrospective study, we developed hypothesis-driven interpretable biomarkers based on deep learning, to predict the pathological complete response (pCR, i.e., the absence of tumor cells in the surgical resection specimens) to neoadjuvant chemotherapy solely using digital pathology H&E images of pre-treatment breast biopsies. Our approach consists of two steps: First, we use deep learning to characterize aspects of the tumor micro-environment by detecting mitoses and segmenting tissue into several morphology compartments including tumor, lymphocytes and stroma. Second, we derive computational biomarkers from the segmentation and detection output to encode slide-level relationships of components of the tumor microenvironment, such as tumor and mitoses, stroma, and tumor infiltrating lymphocytes(TILs). We developed and evaluated our method on slides from n=721 patients from three European medical centers with triple-negative and Luminal B breast cancers and performed external independent validation on n=126 patients from a public dataset. We report the predictive value of the investigated biomarkers for predicting pCR with areas under the receiver operating characteristic curve between 0.66 and 0.88 across the tested cohorts. The proposed computational biomarkers predict pCR, but will require more evaluation and finetuning for clinical application. Our results further corroborate the potential role of deep learning to automate TILs quantification, and their predictive value in breast cancer neoadjuvant treatment planning, along with automated mitoses quantification. We made our method publicly available to extract segmentation-based biomarkers for research purposes.

The Value of Tumor Infiltrating Lymphocytes (TIL) for Predicting the Response to Neoadjuvant Chemotherapy (NAC) in Breast Cancer according to the Molecular Subtypes.

The antitumor host immune response is an important factor in breast cancer, but its role is not fully established. The role of tumor infiltrating lymphocytes (TIL) as an immunological biomarker in breast cancer has been significantly explored in recent years. The number of patients treated with neoadjuvant chemotherapy (NAC) has increased and the identification of a biomarker to predict the probability of pCR (pathological complete response) is a high priority. We evaluated 334 cases of BC treated with NAC followed by surgical resection from 2020-2022 at the Ist Clinic of Oncological Surgery, Oncological Institute "Prof Dr I Chiricuta" Cluj Napoca. Of the above, 122 cases were available for histological evaluation both in pre-NAC biopsy and post-NAC resection tissue. Evaluation of biopsy fragments and resection parts were performed using hematoxylin eosin (H&E). The TIL evaluation took place according to the recommendations of the International TIL Working Group (ITILWG). There was a strong association between elevated levels of pre-NAC TIL. At the same time, there is a statistically significant correlation between stromal TIL and tumor grade, the number of lymph node metastases, the molecular subtype and the number of mitoses (p < 0.005). Intratumoral TIL showed a significant correlation with tumor size, distant metastasis, molecular subtype, number of mitosis, stage and lymph node metastasis (p < 0.005). We also demonstrated that high pre-NAC STIL represents a strong predictive marker for pCR. This study reveals the role of TIL as a predictive biomarker in breast cancer not only for the well-established TNBC (triple negative breast cancer) and HER2+ (Her2 overexpressed) subtypes but also in Luminal A and B molecular subtypes. In this scenario, the evaluation of sTIL as a novel predictive and therapy-predicting factor should become a routinely performed analysis that could guide clinicians when choosing the most appropriate therapy.

68Ga-Labeled Fibroblast Activation Protein Inhibitor PET/CT for the Early and Late Prediction of Pathologic Response to Neoadjuvant Chemotherapy in Breast Cancer Patients: A Prospective Study.

68Ga-labeled fibroblast activation protein inhibitor (68Ga-FAPI) PET/CT has demonstrated promising clinical results, with a higher SUVmax and tumor-to-background ratio (TBR) in breast cancer (BC) patients than 18F-FDG PET/CT. Here, we aimed to evaluate the suitability of 68Ga-FAPI PET/CT for the early and late prediction of the pathologic response to neoadjuvant chemotherapy (NAC) in BC. Methods: Twenty-two consecutive patients with newly diagnosed BC and an indication for NAC were prospectively included. All patients underwent standard chemotherapy and 68Ga-FAPI PET/CT at baseline, after 2 cycles of NAC (PET2), and 1 wk before surgery (PET3). SUVmax was measured in the primary tumor region and positive regional lymph nodes. The expression of fibroblast activation protein in the primary lesion was analyzed by immunohistochemistry. Results: Seven patients (31.8%) achieved a pathologic complete response (pCR), and 15 (68.2%) had residual tumors. Thirteen patients (59.1%) showed concentric withdrawal of the primary tumor, and 9 (40.9%) showed diffuse withdrawal. Between PET2 and PET3, the ΔSUVmax of the primary tumor (R 2 = 0.822; P = 0.001) and metastatic lymph nodes (R 2 = 0.645; P = 0.002) were significantly correlated. The absolute values of SUVmax and TBR at PET2 and PET3 were lower in patients with pCR than in those without pCR (P < 0.05). Moreover, a larger ΔSUVmax at any time point was strongly associated with pCR (P < 0.05). Similar downward trends in SUVmax, TBR, and ΔSUVmax were observed in the pattern of primary tumor reduction. For predicting pCR, the optimal cutoff values for ΔSUVmax after 2 chemotherapy cycles, ΔSUVmax before surgery, TBR after 2 chemotherapy cycles, and TBR before surgery of the primary tumor were 3.4 (area under the curve [AUC], 0.890), 1.1 (AUC, 0.978), -63.8% (AUC, 0.879), -90.8% (AUC, 0.978), 7.6 (AUC, 0.848), and 1.4 (AUC, 0.971), respectively. Immunohistochemistry showed that the SUVmax and TBR of 68Ga-FAPI PET/CT were positively correlated with fibroblast activation protein expression (P < 0.001 for both). Conclusion: Assessment of early changes in 68Ga-FAPI uptake during NAC by 68Ga-FAPI PET/CT can predict pCR and primary tumor concentric withdrawal in BC patients. 68Ga-FAPI PET/CT has great potential for the early and late prediction of the pathologic response to NAC in BC.

45P Demographic determinants of pathological complete response after neoadjuvant chemotherapy in breast cancer

45P Demographic determinants of pathological complete response after neoadjuvant chemotherapy in breast cancer

Prognostic significance of platelet-to-lymphocyte ratio (PLR) in patients with breast cancer treated with neoadjuvant chemotherapy: a meta-analysis

ObjectivePlatelet-to-lymphocyte ratio (PLR), known as a key systemic inflammatory parameter, has been proved to be associated with response to neoadjuvant therapy in breast cancer (BC); however, the results remain controversial....

48P The impact of preoperative axillary ultrasound on the false negative rate of sentinel lymph node biopsy in post neoadjuvant chemotherapy breast cancer patients

48P The impact of preoperative axillary ultrasound on the false negative rate of sentinel lymph node biopsy in post neoadjuvant chemotherapy breast cancer patients

Development of MRI-Based Deep Learning Signature for Prediction of Axillary Response After NAC in Breast Cancer

To develop a MRI-based deep learning signature for predicting axillary response after neoadjuvant chemotherapy (NAC) in breast cancer (BC) patients. We enrolled 327 BC patients with axillary lymph node (ALN) metastases receiving axillary operations after NAC. The deep learning features were extracted by ResNet34, which was pretrained by a large, well-annotated dataset from ImageNet. Then we identified deep learning radiomics on magnetic resonance imaging with dynamic contrast enhancement (DCE-MRI) in predicting axillary response after NAC in BC patients. The extraction of 128 deep learning radiomics (DLR) features relied on the DCE-MRI for each patient. After the least absolute shrinkage and selection operator regression analysis, 13, 8, and 21 features remained from the pre-treatment, post-treatment, and combined DCE-MRI, respectively. The DLR signature established based on the combined DCE-MRI achieved good capacity in ALN response after NAC. The support vector machineachieved the best performance with an 0.99area under the curve (AUC) of (95% confidence interval (CI), 0.98-1.00) and 0.83 (95% CI, 0.73-0.92) in the training and test sets, respectively. The LR model established with clinical parameters represented the best performance with 0.73 AUC (95% CI, 0.62-0.84), 0.73 sensitivity, 0.73 specificity, 0.63 PPV, and 0.81 NPV in the test set, respectively. Finally, the integration of radiomic signature and clinical signature resulted in establishing a predictive radiomic nomogram, with an AUC of 0.99 (95%CI, 0.99-1.00). In conclusion, our current study constructed a predictive nomogram through the deep learning method, demonstrating favorable performance in the training and test cohort. The present prognostic model furnishes a precise and objective foundation for directing the surgical strategy toward ALN management in BC patients receiving NAC.

Outcome and response rate in post neoadjuvant chemotherapy for breast cancer in a tertiary hospital in Malaysia

Background: The use of preoperative chemotherapy also increased the rate of breast conserving surgery in breast cancer. This study aims to assess the type of chemotherapy and biological markers that associated with pathological complete response in our population. Methods: This is a data analysis involving breast cancer patients confirmed pathologically that underwent neoadjuvant chemotherapy in Hospital Raja Perempuan Zainab II from January 2018 to December 2021. Retrospective sampling was done using Sistem Pengurusan Pesakit (SPP) which is a hospital-generated database. All patients were given standard chemotherapy regimens. Results: A total of 59 patients enrolled in this study. Half of the patients were luminal A (48%), luminal B and triple negative breast cancer (TNBC) were both 23%. All patients received antracyclin as neoadjuvant regime and 54% had a combination with Docetaxel. Only 1 patient achieved pCR which was TNBC, given Antracyclin + Taxane regime. Tumour reduction &gt;90% highest in TNBC group and using FEC regime. Factors associated with survival were age and miller payne grade. The only pCR was achieved in triple negative breast cancer as being showed by previous study. Tumour loss with more than 30% is equal comparing Antracyline only group and Antracyline + Taxane group. Conclusions: Antracyline + Taxane regime chemotherapy is still the regime of choice for neoadjuvant chemotherapy in breast cancer. Triple negative breast cancer patients should receive preoperative chemotherapy as they will have a higher rate of pCR.

Spatial Distribution of Immune Cells Drives Resistance to Neoadjuvant Chemotherapy in Triple-Negative Breast Cancer.

Neoadjuvant chemotherapy (NAC) alone or combined with target therapies represents the standard of care for localized triple-negative breast cancer (TNBC). However, only a fraction of patients have a response, necessitating better understanding of the complex elements in the TNBC ecosystem that establish continuous and multidimensional interactions. Resolving such complexity requires new spatially-defined approaches. Here, we used spatial transcriptomics to investigate the multidimensional organization of TNBC at diagnosis and explore the contribution of each cell component to response to NAC. Starting from a consecutive retrospective series of TNBC cases, we designed a case-control study including 24 patients with TNBC of which 12 experienced a pathologic complete response (pCR) and 12 no-response or progression (pNR) after NAC. Over 200 regions of interest (ROI) were profiled. Our computational approaches described a model that recapitulates clinical response to therapy. The data were validated in an independent cohort of patients. Differences in the transcriptional program were detected in the tumor, stroma, and immune infiltrate comparing patients with a pCR with those with pNR. In pCR, spatial contamination between the tumor mass and the infiltrating lymphocytes was observed, sustained by a massive activation of IFN-signaling. Conversely, pNR lesions displayed increased pro-angiogenetic signaling and oxygen-based metabolism. Only modest differences were observed in the stroma, revealing a topology-based functional heterogeneity of the immune infiltrate. Thus, spatial transcriptomics provides fundamental information on the multidimensionality of TNBC and allows an effective prediction of tumor behavior. These results open new perspectives for the improvement and personalization of therapeutic approaches to TNBCs.

Analysis of neoadjuvant chemotherapy for breast cancer: a 20-year retrospective analysis of patients of a single institution

BackgroundNeoadjuvant chemotherapy (NAC) has been widely applied in operable breast cancer patients. This study aim to identify the predictive factors of overall survival(OS) and recurrence free survival (RFS) in breast cancer patients who received NAC from a single Chinese institution.Patients and MethodsThere were 646 patients recruited in this study. All the patients were treated at department of Surgical Oncology, Sir Run Run Shaw Hospital between February 25, 1999 and August 22, 2018. The relevant clinicopathological and follow-up data were collected retrospectively. RFS and OS were assessed using the Kaplan-Meier method. Multivariate Cox proportional hazards model was also employed. Multi-variate logistic regression model was simulated to predict pathologic complete response (pCR).ResultsIn total, 118 patients (18.2%) achieved pCR during NAC. The 5-year OS was 94.6% versus 78.1% in patients with and without pCR, respectively (P < 0.001). The 5-year RFS was 95.3% and 72.7%, respectively (P < 0.001). No difference was detected among molecular subtypes of 5-year RFS in patients obtained pCR. Factors independently predicting RFS were HER2-positive subtype (hazard ratio(HR), 1.906; P = 0.004), triple-negative breast cancer (TNBC) (HR,2.079; P = 0.003), lymph node positive after NAC(HR,2.939; P < 0.001), pCR (HR, 0.396;P = 0.010), and clinical stage III (HR,2.950; P = 0.016). Multi-variate logistic regression model was simulated to predict the pCR rate after NAC, according to clinical stage, molecular subtype, ki-67, LVSI, treatment period and histology. In the ROC curve analysis, the AUC of the nomogram was 0.734 (95%CI,0.867–12.867).ConclusionsFollowing NAC, we found that pCR positively correlated with prognosis and the molecular subtype was a prognostic factor.

ASO Visual Abstract: Mutational Status is Associated with Higher Rate of Pathologic Complete Response After Neoadjuvant Chemotherapy in Hormone Receptor-Positive Breast Cancer.

ASO Visual Abstract: Mutational Status is Associated with Higher Rate of Pathologic Complete Response After Neoadjuvant Chemotherapy in Hormone Receptor-Positive Breast Cancer.

Mutational Status is Associated with a Higher Rate of Pathologic Complete Response After Neoadjuvant Chemotherapy in Hormone Receptor-Positive Breast Cancer.

Pathologic complete response (pCR) to neoadjuvant chemotherapy (NAC) occurs in up to 20% of hormone receptor-positive (HR+)/human epidermal growth factor receptor 2-negative (HER2-) breast cancers. Whether this differs among BRCA mutation carriers is uncertain. This study compared pCR between BRCA1/2 mutation carriers and matched sporadic control subjects. From November 2013 to January 2022, this study identified 522 consecutive women with clinical stage I to III HR+/HER2- breast cancer treated with NAC and surgery. The study matched BRCA1/2 mutation carriers 1:2 to non-carriers in terms of age, clinical tumor (cT) and nodal (cN) stage, and differentiation. Two-sample non-parametric tests compared baseline characteristics. Multivariable logistic regression assessed pCR (i.e., ypT0/ispN0) by BRCA1/2 mutational status. Of the 522 women (median age, 50 years), 59 had BRCA1/2 mutations, 78% of which were clinically node positive. Anthracycline-based NAC was administered to 97%. More BRCA1/2 mutation carriers were younger, had cT1 tumors, and had poorly differentiated disease. After matching, 58 BRCA1/2 mutation carriers were similar to 116 non-carriers in terms of age (p = 0.6), cT (p = 0.9), cN stage (p = 0.7), and tumor differentiation (p > 0.9). Among the mutation carriers, the pCR rate was 15.5% for BRCA1/2, 38% (8/21) for BRCA1, and 2.7% (1/37) for BRCA2 versus 7.8% (9/116) for the non-carriers (p < 0.001). After NAC, 5 (41.7%) of the 12 BRCA1 mutation carriers converted to pN0 versus 10 (37%) of the 27 BRCA2 mutation carriers and 19 (20.9%) of the 91 non-carriers (p = 0.3). In the multivariable analysis, BRCA1 mutation status was associated with higher odds of pCR than non-carrier status (odds ratio [OR] 6.31; 95% confidence interval [CI] 1.95-20.5; p = 0.002), whereas BRCA2 mutation status was not (OR 0.45; 95% CI 0.02-2.67; p = 0.5). This study showed that BRCA1 mutation carriers with HR+/HER2- breast cancers have a higher rate of pCR than sporadic cancers and may derive greater benefit from chemotherapy. The use of NAC to downstage these patients should be considered.

Neoadjuvant chemotherapy for breast cancer: an evaluation of its efficacy and research progress.

Neoadjuvant chemotherapy (NAC) for breast cancer is widely used in the clinical setting to improve the chance of surgery, breast conservation and quality of life for patients with advanced breast cancer. A more accurate efficacy evaluation system is important for the decision of surgery timing and chemotherapy regimen implementation. However, current methods, encompassing imaging techniques such as ultrasound and MRI, along with non-imaging approaches like pathological evaluations, often fall short in accurately depicting the therapeutic effects of NAC. Imaging techniques are subjective and only reflect macroscopic morphological changes, while pathological evaluation is the gold standard for efficacy assessment but has the disadvantage of delayed results. In an effort to identify assessment methods that align more closely with real-world clinical demands, this paper provides an in-depth exploration of the principles and clinical applications of various assessment approaches in the neoadjuvant chemotherapy process.

332P Early prediction of residual cancer burden to neoadjuvant chemotherapy in breast cancer by longitudinal MRI-based multitask learning: A multicenter cohort study

332P Early prediction of residual cancer burden to neoadjuvant chemotherapy in breast cancer by longitudinal MRI-based multitask learning: A multicenter cohort study

292P Unlocking the potential of circulating miRNAs in predicting response to neoadjuvant chemotherapy in breast cancer: A systematic review and meta-analysis

292P Unlocking the potential of circulating miRNAs in predicting response to neoadjuvant chemotherapy in breast cancer: A systematic review and meta-analysis

Near-Infrared Spectral Tomography for Predicting Residual Cancer Burden during Early-Stage Neoadjuvant Chemotherapy for Breast Cancer.

The aim of this study is to investigate whether near-infrared spectral tomography (NIRST) might serve as a reliable prognostic tool to predict residual cancer burden (RCB) in patients with breast cancer undergoing neoadjuvant chemotherapy (NAC) based upon early treatment response measurements. A total of thirty-five patients with breast cancer receiving NAC were included in this study. NIRST imaging was performed at multiple time points, including: before treatment, at end of the first cycle, at the mid-point, and post-NAC treatments. From reconstructed NIRST images, average values of total hemoglobin (HbT) were obtained for both the tumor region and contralateral breast at each time point. RCB scores/classes were assessed by a pathologist using histologic slides of the surgical specimen obtained after completing NAC. Logistic regression of the normalized early percentage change of HbT in the tumor region (ΔHbT%) was used to predict RCB and determine its significance as an indicator for differentiating cases within each RCB class. The ΔHbT% at the end of the first cycle, as compared with pretreatment levels, showed excellent prognostic capability in differentiating RCB-0 from RCB-I/II/III or RCB-II from RCB-0/I/III (P < 0.001). Corresponding area under the curve (AUC) values for these comparisons were 0.97 and 0.94, and accuracy values were 0.90 and 0.83, respectively. NIRST holds promise as a potential clinical tool that can be seamlessly integrated into existing clinical workflow within the infusion suite. By providing early assessment of RCB, NIRST has potential to improve breast cancer patient management strategies.

Efficacy and Safety of PD-1/PD-L1 Inhibitors in Neoadjuvant Chemotherapy for Triple-Negative Breast Cancer: A Systematic Review and Meta-Analysis

Efficacy and Safety of PD-1/PD-L1 Inhibitors in Neoadjuvant Chemotherapy for Triple-Negative Breast Cancer: A Systematic Review and Meta-Analysis

The potential role of combined shear wave elastography and superb microvascular imaging for early prediction the pathological response to neoadjuvant chemotherapy in breast cancer.

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