Exploring the influencing factors of the pathologic complete response in estrogen receptor-positive, HER2-negative breast cancer after neoadjuvant chemotherapy: a retrospective study

Prediction of non-sentinel lymph node metastases in T1-2 sentinel lymph node-positive breast cancer patients undergoing mastectomy following neoadjuvant therapy

BACKGROUND

Axillary lymph node dissection (ALND) is the standard axillary management for breast cancer patients with positive sentinel lymph node biopsy (SLNB) after neoadjuvant therapy. Nevertheless, when that happens, the frequency of additional positive nodes is not properly evaluated. We aim to develop a prediction model to assess the frequency of additional nodal disease after a positive sentinel lymph node following neoadjuvant therapy.

METHODS

We retrospectively analyzed the ultrasound and clinicopathological characteristics of breast cancer patients with 1-3 positive sentinel lymph nodes (SLN) undergoing mastectomy after neoadjuvant therapy (NAT) at our institution, and performed univariate and multivariate logistic analyses to confirm the factors affecting non-SLN metastasis. These factors were included to establish a nomogram, and the area under receiver operating characteristic curve (AUC) and decision curve analysis (DCA) were utilized to assess the validity of this model.

RESULTS

A total of 126 breast cancer patients were ultimately included in our study, 38 (53.5%) patients were diagnosed with non-SLN metastases of all 71 patients in training set. The results of multifactorial logistic analysis suggested that lymph node metastasis ratio (LNR), short axis of lymph node and progesterone receptor (PR) were strongly associated with non-SLN metastasis. We established a nomogram using the above three variables as predictors, which yielded an area under the curve of 0.795, and validated with a favorable AUC of 0.876.

CONCLUSION

The nomogram we constructed can accurately predict the likelihood of non-SLN metastasis in our patients with 1-3 positive SLN after NAT, which may help guide decision making regarding axillary management.

Develop and Validate a Nomogram Combining Contrast-Enhanced Spectral Mammography Deep Learning with Clinical-Pathological Features to Predict Neoadjuvant Chemotherapy Response in Patients with ER-Positive/HER2-Negative Breast Cancer

RATIONALE AND OBJECTIVES

To develop and validate a nomogram that combines contrast-enhanced spectral mammography (CESM) deep learning with clinical-pathological features to predict neoadjuvant chemotherapy (NAC) response (either low Miller Payne (MP-L) grades 1-2 or high MP (MP-H) grades 3-5) in patients with ER-positive/HER2-negative breast cancer.

MATERIALS AND METHODS

In this retrospective study, 265 breast cancer patients were randomly allocated into training and test sets (used for models training and testing, respectively) at a 4:1 ratio. Deep learning models, based on the pre-trained ResNet34 model and initially fine-tuned for identifying breast cancer, were trained using low-energy and subtracted CESM images. The predicted results served as deep learning features for the deep learning-based model. Clinical-pathological features, including age, progesterone receptor (PR) status, estrogen receptor (ER) status, Ki67 expression levels, and neutrophil-to-lymphocyte ratio, were used for the clinical model. All these features contributed to the nomogram. Feature selection was performed through univariate analysis. Logistic regression models were developed and chosen using a stepwise selection method. The deep learning-based and clinical models, along with the nomogram, were evaluated using precision-recall curves, receiver operating characteristic (ROC) curves, specificity, recall, accuracy, negative predictive value, positive predictive value (PPV), balanced accuracy, F1-score, and decision curve analysis (DCA).

RESULTS

The nomogram demonstrated considerable predictive ability, with higher area under the ROC curve (0.95, P < 0.05), accuracy (0.94), specificity (0.98), PPV (0.89), and precision (0.89) compared to the deep learning-based and clinical models. In DCA, the nomogram showed substantial clinical value in assisting breast cancer treatment decisions, exhibiting a higher net benefit than the other models.

CONCLUSION

The nomogram, integrating CESM deep learning with clinical-pathological features, proved valuable for predicting NAC response in patients with ER-positive/HER2-negative breast cancer. Nomogram outperformed deep learning-based and clinical models.

Factors influencing pathological complete response and tumor regression in neoadjuvant radiotherapy and chemotherapy for high-risk breast cancer

BACKGROUND

Pathological complete response (pCR) is a well-established prognostic factor in breast cancer treated with neoadjuvant systemic therapy (naST). The determining factors of pCR are known to be intrinsic subtype, proliferation index, grading, clinical tumor and nodal stage as well as type of systemic therapy. The addition of neoadjuvant radiotherapy (naRT) to this paradigm might improve response, freedom from disease, toxicity and cosmetic outcome compared to adjuvant radiotherapy. The factors for pCR and primary tumor regression when neoadjuvant radiation therapy is added to chemotherapy have not been thoroughly described.

METHODS

We performed a retrospective analysis of 341 patients (cT1-cT4/cN0-N+) treated with naRT and naST between 1990 and 2003. Patients underwent naRT to the breast and mostly to the supra-/infraclavicular lymph nodes combined with an electron or brachytherapy boost. NaST was given either sequentially or simultaneously to naRT using different regimens. We used the univariate and multivariate regression analysis to estimate the effect of different subgroups and treatment modalities on pCR (ypT0/Tis and ypN0) as well as complete primary tumor response (ypT0/Tis; bpCR) in our cohort. Receiver operating characteristic (ROC) analysis was performed to evaluate the interval between radiotherapy (RT) and resection (Rx) as well as radiotherapy dose.

RESULTS

Out of 341 patients, pCR and pbCR were achieved in 31% and 39%, respectively. pCR rate was influenced by resection type, breast cancer subtype, primary tumor stage and interval from radiation to surgery in the multivariate analysis. Univariate analysis of bpCR showed age, resection type, breast cancer subtype, clinical tumor stage and grading as significant factors. Resection type, subtype and clinical tumor stage remained significant in multivariate analysis. Radiation dose to the tumor and interval from radiation to surgery were not significant factors for pCR. However, when treatment factors were added to the model, a longer interval from radiotherapy to resection was a significant predictor for pCR.

CONCLUSIONS

The factors associated with pCR following naST and naRT are similar to known factors after naST alone. Longer interval to surgery might to be associated with higher pCR rates. Dose escalation beyond 60 Gy did not result in higher response rates.

Predicting pathological complete response after neoadjuvant chemotherapy in breast cancer by clinicopathological indicators and ultrasound parameters using a nomogram

The study explored the impact of pretreatment serum albumin-to-alkaline phosphatase ratio (AAPR) and changes in tumor blood supply on pathological complete response (pCR) in breast cancer (BC) patients following neoadjuvant chemotherapy (NACT). Additionally, a nomogram for predicting pCR was established and validated. The study included BC patients undergoing NACT at Yongchuan Hospital of Chongqing Medical University from January 2019 to October 2023. We analyzed the correlation between pCR and clinicopathological factors, as well as tumor ultrasound features, using chi-square or Fisher's exact test. We developed and validated a nomogram predicting pCR based on regression analysis results. The study included 176 BC patients. Logistic regression analysis identified AAPR [odds ratio (OR) 2.616, 95% confidence interval (CI) 1.140-5.998, P = 0.023], changes in tumor blood supply after two NACT cycles (OR 2.247, 95%CI 1.071-4.716, P = 0.032), tumor histological grade (OR 3.843, 95%CI 1.286-10.659, P = 0.010), and HER2 status (OR 2.776, 95%CI 1.057-7.240, P = 0.038) as independent predictors of pCR after NACT. The nomogram, based on AAPR, changes in tumor blood supply after two NACT cycles, tumor histological grade, and HER2 status, demonstrated a good predictive capability.

Prognosis and influencing factors of ER-positive, HER2-low breast cancer patients with residual disease after neoadjuvant chemotherapy: a retrospective study

Previously, we found that patients with estrogen receptor (ER)-positive, HER2-low breast cancer are resistant to neoadjuvant chemotherapy (NACT) and have worse outcomes than those who achieve pathological complete response (pCR) after NACT. This study aimed to investigate the prognosis and influencing factors in these patients. A total of 618 patients with ER-positive breast cancer who received standard thrice-weekly NACT were enrolled, including 411 patients with ER-positive, HER2-low breast cancer. Data on the clinicopathological features of these patients before and after NACT were collected. Univariate and multivariate Cox regression analyses were used to identify the independent factors affecting 5-year disease-free survival (DFS). Among the ER-positive, HER2-low patients, 49 (11.9%) achieved a pCR after NACT. A significant difference in survival was observed between patients with and without residual disease after NACT. Additionally, changes in immunohistochemical markers and tumor stages before and after NACT were found to be significant. According to univariate and multivariate analyses, cN_stage (P = 0.002), ER (P = 0.002) and Ki67 (P = 0.023) expression before NACT were significantly associated with 5-year DFS, while pT_stage (P = 0.015), pN_stage (P = 0.029), ER (P = 0.020) and Ki67 (P < 0.001) levels after NACT were related to 5-year DFS in ER-positive, HER2-low patients with residual disease. Our study suggested that high proliferation, low ER expression and advanced stage before and after NACT are associated with a poor prognosis, providing useful information for developing long-term treatment strategies for ER-positive, HER2-low breast cancer in patients with residual disease in the future.

Predictive value of pretreatment circulating inflammatory response markers in the neoadjuvant treatment of breast cancer: meta-analysis

BACKGROUND

Systemic inflammatory response markers have been found to have a prognostic role in several cancers, but their value in predicting the response to neoadjuvant chemotherapy in breast cancer is uncertain. A systematic review and meta-analysis of the literature was carried out to investigate this.

METHODS

A systematic search of electronic databases was conducted to identify studies that explored the predictive value of circulating systemic inflammatory response markers in patients with breast cancer before commencing neoadjuvant therapy. A meta-analysis was undertaken for each inflammatory marker where three or more studies reported pCR rates in relation to the inflammatory marker. Outcome data are reported as ORs and 95% confidence intervals.

RESULTS

A total of 49 studies were included, of which 42 were suitable for meta-analysis. A lower pretreatment neutrophil-to-lymphocyte ratio was associated with an increased pCR rate (pooled OR 1.66 (95% c.i. 1.32 to 2.09); P < 0.001). A lower white cell count (OR 1.96 (95% c.i. 1.29 to 2.97); P = 0.002) and a lower monocyte count (OR 3.20 (95% c.i. 1.71 to 5.97); P < 0.001) were also associated with a pCR. A higher lymphocyte count was associated with an increased pCR rate (OR 0.44 (95% c.i. 0.30 to 0.64); P < 0.001).

CONCLUSION

The present study found the pretreatment neutrophil-to-lymphocyte ratio, white cell count, lymphocyte count, and monocyte count of value in the prediction of a pCR in the neoadjuvant treatment of breast cancer. Further research is required to determine their value in specific breast cancer subtypes and to establish optimal cut-off values, before their adoption in clinical practice.

Predictive factors for complete pathological response in hormone receptor-negative breast cancer patients undergoing neoadjuvant chemotherapy

Complete pathological response (pCR) is a pivotal predictor of enhanced disease-free and overall survival rates in breast cancer patients. Accurate prediction of pCR is therefore of paramount clinical significance. This retrospective study aimed to delineate the factors associated with pCR through a comprehensive analysis encompassing clinical, pathological, and immunohistochemical profiling of patients diagnosed with hormone receptor-negative invasive ductal carcinomas. The study cohort was composed of 73 female patients. The cases were reviewed retrospectively using data from University Hospital "Tsaritsa Yoanna" in Sofia, spanning the ten-year period from 2010 to 2020. Univariate analyses demonstrated that patients diagnosed with a higher disease stage, specifically stage IIIb, exhibited a notable association with an unfavorable response to neoadjuvant chemotherapy (NCT) [OR 4.5455 (95%CI 1.6810 - 12.2910); p = 0.0029]. Invasive carcinomas containing a ductal carcinoma in situ (DCIS) component [OR 0.3333 (95%CI 0.1226 - 0.9063); p = 0.0313] or were classified as poorly differentiated [OR 0.3056 (95%CI 0.1159 - 0.8055); p = 0.0165] demonstrated an enhanced likelihood of achieving pCR. Tumors expressing CD10 [OR 0.1452 (95%CI 0.0515 - 0.4093); p = 0.0003] and tumors lacking EGFR [OR 3.9722 (95%CI 1.4691 - 10.7399); p = 0.0066] exhibited a markedly elevated rate of pCR. Multivariate regression analysis supported findings. In conclusion, hormone receptor-negative breast tumors stand to benefit from increased pCR rates if they encompass a DCIS component and exhibit CD10 expression while lacking EGFR expression. These findings underscore the importance of comprehensive profiling in predicting pCR outcomes in hormone receptor-negative breast cancer patients undergoing neoadjuvant chemotherapy.

A multi-dimensional nomogram to predict non-sentinel lymph node metastases in T1-2HR+ breast cancer

Background

Axillary lymph node dissection (ALND) could be omitted for T1-2 breast cancer patients with 1-2 positive sentinel lymph node (SLN) after breast-conserving surgery when radiation is planned. However, whether ALND could be replaced by radiation in patients with 1-3 positive SLNs when no more non-SLN metastasis were observed after mastectomy are still controversial. The aim of our study was to develop and validate a nomogram for predicting the possibility of non-SLN metastasis in T1-2 and hormone receptor (HR) positive breast cancer patients with 1-3 positive SLNs after mastectomy.

Methods

We retrospectively reviewed and analyzed the data including the basic information, preoperative sonographic characteristics, and pathological features in breast cancer patients with 1-3 positive SLNs in our medical center between Jan 2016 and Dec 2021. The Chi-square, Fisher's exact test, and t test were used for comparison of categorical and qualitative variables among patients with or without non-SLN metastasis. Univariate and multivariate logistic regression were used to determine the risk factors for non-SLN metastasis. These predictors were used to build the nomogram. The C-index and area under the receiver operating characteristic curve (AUC) was calculated to assess the accuracy of the model.

Results

A total of 49 in 107 (45.8%) patients were identified with non-SLN metastasis. In multivariate analysis, four variables including younger age, lower estrogen receptor (ER) expression, higher histological score, and cortex thickening of the lymph nodes were determined to be significantly associated with non-SLN metastasis. An individualized nomogram was consequently established with a favorable C-index of 0.822 and verified via two internal validation cohorts.

Conclusions

The current study developed a nomogram predicting non-SLN metastasis for T1-2 and HR+ breast cancer with 1-3 positive SLNs after mastectomy and found that patients in the high-risk group exhibited worse relapse-free survival. The novel nomogram may further help surgeons to determine whether ALND could be omitted when 1-3 positive SLNs were observed in T1-2 and HR+ breast cancer patients.

Overexpressed VDAC1 in breast cancer as a novel prognostic biomarker and correlates with immune infiltrates

BACKGROUND

More and more evidence suggests that cancer is a mitochondrial metabolic disease recently and mitochondria dysfunction is critical to tumorigenesis. As a gatekeeper of mitochondria, the voltage-dependent anion channel 1 (VDAC1) is associated with the development of breast cancer (BC). However, its potential mechanism and clinical significance remain unclear; thus, in this research, we aimed to explore it.

METHODS

VDAC1 expression in BC tissues and normal tissues was obtained from The Cancer Genome Atlas (TCGA) and validated by datasets from the gene expression omnibus (GEO) database. Then, the relationships between VDAC1 expression and clinicopathological features were analyzed. Receiver operating characteristics (ROC) curves were used to identify the diagnostic value of VDAC1. The prognostic value was evaluated by Kaplan-Meier survival curves and Cox regression analysis. VDAC1 with its co-expression genes were subjected to enrichment analysis to explore potential mechanisms in BC and the protein-protein interaction (PPI) network was constructed. At last, the association between VDAC1 expression and infiltration levels of immune cell infiltration by various methods, as well as their corresponding markers, was analyzed. We also analyzed the correction between VDAC1 expression and eight immune checkpoint genes and the tumor immune dysfunction and exclusion (TIDE) scores of each BC sample in TCGA were calculated and the differences between high and low VDAC1 expression groups were analyzed.

RESULTS

VDAC1 expression was remarkably elevated in BC (p < 0.001), and high expression of VDAC1 was associated with the positive expression of ER (p = 0.004), PR (p = 0.033), and HER2 (p = 0.001). ROC analysis suggested that VDAC1 had diagnosed value in BC. The Kaplan-Meier analysis suggested that higher expression of VDAC1 was associated with shorter overall survival (OS), and further Cox regression analysis revealed that VDAC1 was an independent factor of unfavorable prognosis in BC patients. Enrichment analysis of VDAC1 and its co-expression suggested that VDAC1 was related to the regulation of mitochondrial energy metabolism and protein modification, and the HIF-1 singing pathway might be the potential mechanism in BC. Notably, we found that VDAC1 expression was infiltration levels of most types of immune cells, as well as the expression of marker genes of immune cells. The ICGs PDCD1, CTLA4, LAG3, SIGLEC15, and TIGIT were negatively corrected with VDAC1 expression in BC. TIDE scores between the low and high expression groups showed no difference.

CONCLUSION

Overexpressed VDAC1 in BC could be severed as a novel biomarker for diagnosis and VDAC1 was an independent factor for adverse prognosis prediction. Our study revealed that VDAC1 might inhibit tumor immunity and might be a novel therapeutic target in BC.

Differences in tumor-infiltrating lymphocyte density and prognostic factors for breast cancer by patient age

Background

Lymphocytes that surround cancer participate in tumor-related immune responses and are called tumor-infiltrating lymphocytes (TILs). Several recent reports suggest TILs index the tumor microenvironment and predict the therapeutic effect of chemotherapy. However, only few studies have studied the relationship between age and TILs. Aging reduces host immunity, and we predict that it may also affect TILs. Thus, we hypothesized that older breast cancer (BC) patients may have low TIL density than younger BC patients. Here, we retrospectively analyzed the differences in TILs by age and the therapeutic effects of preoperative chemotherapy (POC) in BC patients who were aged either less than 45 years or more than 60 years.

Methods

We retrospectively examined the data of 356 breast cancer patients who underwent POC, including 75 patients aged ≤ 45 years and 116 patients aged > 60 years. Using pre-treatment needle biopsy specimens, TIL density was compared for each age group by Student’s t-test. After analyzing different factors that affect TIL density, prognostic factors were also examined.

Results

Older patients with triple-negative BC had significantly lower TIL density than younger patients, while in human epidermal growth factor receptor 2 (HER2)-enriched BC, TIL density was significantly higher in the younger age group than that in the older age group. In addition, younger patients with HER2-rich breast cancer showed significantly higher complete pathological response rates than older patients with HER2-rich BC. In addition, significant differences in overall survival were observed among these patients with triple-negative BC.

Conclusions

Our study suggests that younger BC patients possess significantly higher TIL density than older patients. These differences may influence the therapeutic efficacy in highly immunogenic subtypes.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12957-022-02513-5.

Individualized model for predicting pathological complete response to neoadjuvant chemotherapy in patients with breast cancer: A multicenter study

BACKGROUND

Pathological complete response (pCR) is considered a surrogate for favorable survival in breast cancer (BC) patients treated with neoadjuvant chemotherapy (NACT), which is the goal of NACT. This study aimed to develop and validate a nomogram for predicting the pCR probability of BC patients after NACT based on the clinicopathological features.

METHODS

A retrospective analysis of 527 BC patients treated with NACT between January 2018 and December 2021 from two institutions was conducted. Univariate and multivariate logistic regression analyses were performed to select the most useful predictors from the training cohort (n = 225), and then a nomogram model was developed. The performance of the nomogram was evaluated with respect to its discrimination, calibration, and clinical usefulness. Internal validation and external validation were performed in an independent validation cohort of 96 and 205 consecutive BC patients, respectively.

RESULTS

Among the 18 clinicopathological features, five variables were selected to develop the prediction model, including age, American Joint Committee on Cancer (AJCC) T stage, Ki67 index before NACT, human epidermal growth factor receptor 2 (HER2), and hormone receptor (HR) status. The model showed good discrimination with an area under the receiver operating characteristic curve (AUC) of 0.825 (95% CI, 0.772 to 0.878) in the training cohort, and 0.755 (95% CI, 0.658 to 0.851) and 0.79 (95% CI, 0.724 to 0.856) in the internal and external validation cohorts, respectively. The calibration curve presented good agreement between prediction by nomogram and actual observation, and decision curve analysis (DCA) indicated that the nomogram had good net benefits in clinical scenarios.

CONCLUSION

This study constructed a validated nomogram based on age, AJCC T stage, Ki67 index before NACT, HER2, and HR status, which could be non-invasively applied to personalize the prediction of pCR in BC patients treated with NACT.