Locally advanced breast cancer treated with neoadjuvant chemotherapy and adjuvant radiotherapy: a retrospective cohort analysis

Klein, Jonathan; Tran, William T.; Watkins, Elyse; Vesprini, Danny; Wright, Frances C.; Hong, Nicole J. Look; Ghandi, Khashayar; Kiss, Alex; Czarnota, Gregory J.

doi:10.1186/s12885-019-5499-2

Cited by 37 publications

(26 citation statements)

References 39 publications

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“…In order to find more prognostic factors, we found that the hormone receptor status of the patient before surgery and the size of the patient's tumor were related to the survival prognosis of the patient: the smaller the tumor, the longer the OS/DFS (p <0.001, p = 0.004); compared with HR negative, HR positive can obtain longer OS and DFS (p = 0.003, p = 0,011). Jonathan et al's analysis of 103 neoadjuvant chemotherapy studies for locally advanced breast cancer found that the tumor size of patients before chemotherapy was related to LRR (p = 0.003) [30] , and our results are consistent with this. Chou et al confirmed that the younger patients (<50years) is the only independent factor favorable for LRR-freesurvival in patients with breast cancer after NAC with concurrent epirubicin and docetaxel [18] .…”

Section: Discussionsupporting

confidence: 90%

The relationship between hormone receptor status, tumor size and clinical stage and the efficacy of neoadjuvant chemotherapy for locally advanced breast cancer, predictors of survival, and the establishment of Cox-Based risk prediction model and nomogram

Fengjiao

et al. 2020

Preprint

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Background: Neoadjuvant chemotherapy is currently mainly used for locally advanced breast cancer. However, there is currently no effective way to predict the prognosis of neoadjuvant chemotherapy patients. Methods: All relevant data before and after chemotherapy were collected and evaluate the efficacy of chemotherapy through RECIST1.1 and pathology. Survival curves were generated by the Kaplan-Meier method and compared using the log-rank test. A nomogram was developed using a binary logisti regression model with a cross validation. Results: A total of 158 patients were included in our study. The ORR was 64.6% (cCR, 3.8%; cPR, 60.8%). The pCR rates wer 12.0% for breast tumor and axillary nodes. The pCR rates were 17.1% for breast tumor and 30.4% for axillary nodes. Our study found that only differences in HER-2 status could predict different clinical efficacy, and clinical remission (CR+PR) was more likely to be achieved if HER-2 positive than negative (16.7% vs 7.5%, p=0.05). There was no significant difference in the clinical efficacy of neoadjuvant chemotherapy in age, clinical stage, and molecular classification of patients. In a single factor COX proportional hazard model study, it was found that ER positive relative to HR negative(HR 0.479,CI 95% 0.272-0.844, p=0.011), PR positive relative to PR negative (HR 0.423,CI 95% 0.235-0.763, p=0.004), HER-2 positive relative to HER-2 negative (HR 2.011, CI 95% 1.000-4.042, p=0.050), TNBC (HR 2.229, CI 95% 1.189-4.178, p=0.012), and HER-2 (HR 2.808, CI 95% 1.301-6.062, p=0.009) type are more effective than patients with Luminal type breast cancer 0btain a longer survival time, which is considered a protective factor. A nomogram was developed based on the clinical and statistically significant predictors. ROC curve for 3-year survial with area under the curve = 0.731 (95% confidence interval, 0.663-0.828); ROC curve for 5-year survial with area under the curve = 0.743 (95% confidence interval, 0.652-0.834); ROC curve for 10-year survial with area under the curve = 0.726 (95% confidence interval, 0.639-0.812). Conclusion: In conclusion, the hormone receptor status before chemotherapy can predict the prognosis of patients in advance, and we successfully constructed nomogram, which can help us predict the OS of patients with NAC.

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Section: Discussionsupporting

confidence: 90%

The relationship between hormone receptor status, tumor size and clinical stage and the efficacy of neoadjuvant chemotherapy for locally advanced breast cancer, predictors of survival, and the establishment of Cox-Based risk prediction model and nomogram

Fengjiao

et al. 2020

Preprint

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“…Dynamic contrast-enhanced MRI is the most sensitive of available imaging modalities in characterization of breast malignant tumours as a functional technique that allows the evaluation of residual viable tumour after neoadjuvant therapy by detecting changes in tumour vascularity [7][8][9][10][11][12][13][14]. Multiple studies have clarified that breast MRI is the most accurate imaging modality to determine disease response to neoadjuvant chemotherapy, with sensitivity approaching 90% and specificity of 60% to 100%, and it is particularly helpful in patients with confirmed multifocal and multicentric tumours on the pre-treatment study [1,2,4,7,8,[15][16][17]. Breast MRI has been considered to have a role in guiding breast cancer surgery by differentiating residual tumour from pathological complete response after neoadjuvant therapy, and planning resections to achieve clear margins in BCS [11,18].…”

Section: Introductionmentioning

confidence: 99%

Accuracy of breast MRI in patients receiving neoadjuvant endocrine therapy: comprehensive imaging analysis and correlation with clinical and pathological assessments

Reis

Lindstrøm

Boavida

et al. 2020

Breast Cancer Res Treat

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Purpose To assess the accuracy of magnetic resonance imaging (MRI) measurements in locally advanced oestrogen receptor-positive and human epidermal growth factor receptor 2-negative breast tumours before, during and after neoadjuvant endocrine treatment (NET) for evaluation of tumour response in comparison with clinical and pathological assessments. Methods This prospective study enrolled postmenopausal patients treated neoadjuvant with letrozole and exemestane given sequentially in an intra-patient cross-over regimen. Fifty-four patients were initially recruited, but only 35 fulfilled the inclusion criteria and confirmed to participate with a median age of 77. Tumours were scanned with MRI prior to treatment, during the eighth week of treatment and prior to surgery. Additionally, changes in longest diameter on clinical examination (CE) and tumour size at pathology were determined. Pre- and post-operative measurements of tumour size were compared in order to evaluate tumour response. Results The correlation between post-treatment MRI size and pathology was moderate and higher with a correlation coefficient (r) 0.64 compared to the correlation between CE and pathology r = 0.25. Post-treatment MRI and clinical results had a negligible bias towards underestimation of lesion size. Tumour size on MRI and CE had 0.82 cm and 0.52 cm lower mean size than tumour size measured by pathology, respectively. Conclusions The higher correlation between measurements of residual disease obtained on MRI and those obtained with pathology validates the accuracy of imaging assessment during NET. MRI was found to be more accurate for estimating complete responses than clinical assessments and warrants further investigation in larger cohorts to validate this finding.

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“…In the past few years, the incidence of breast cancer has decreased significantly around the world, but the 5-year survival rate of BRCA patients is still 67.6% (26) , and there are still many key issues that have not been resolved. To a certain extent, the decline in mortality is due to advanced medical examination methods (27) , advanced diagnostic techniques (28) , and advanced treatment methods (29) . Among them, targeted therapy has a very good effect in prolonging the survival time of patients, but the most challenging problem in tumor resistance and the burden on the economy.…”

Section: Discussionmentioning

confidence: 99%

m6A RNA methylation regulators contribute to progression and impact the prognosis of breast cancer

Jiang

Dong

et al. 2020

Preprint

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N6-methyladenosine (m6A) is the most commonly modified form of mRNA. M6A RNA methylation regulators are proved to be expressed clearly in some cancers by plenty of studies. Moreover, they also are proved to be indirectly involved in the growth of cancers. However, it remains unclear that the role of m6A RNA methylation regulator in the prognosis of breast cancer (BRCA). The data that we used in this study is the mRNA expression data obtained from the corresponding clinical information and the Tumor Genome Atlas (TCGA) database. And the goal we used the Wilcoxon rank-sum test was to evaluate the difference in the expression of m6A RNA methylation regulators in the normal group and the tumor group, and analyze the correlation between m6A RNA methylation regulators. We identified two subgroups of BRCA (cluster1 and 2) by using the K-mean algorithm and analyzing the correlation between clinic information and subgroups. The LASSO regression model then was used to figure out three m6A RNA methylation regulators, namely YTHDF3, ZC3H13, and HNRNPC. The riskScore of each patient was calculated according to the regression coefficients of the three m6A RNA methylation regulators. Base on the riskScore, we divided the patients into two groups, the high-risk group, and the low-risk group. After analyzing, we found that the overall survival rate (OS) of the low-risk group was higher than that of the other group. We conducted a univariate and multi-factor independent prognostic analysis of riskScore and three m6A RNA methylation regulators, and found that riskScore has a significant correlation with BRCA. In conclusion, the m6A RNA methylation regulator is closely related to the development of BRCA, and the prognostic factor riskScore obtained from the regression of the expression of the three m6A RNA methylation regulators in the human body are likely to guide the individualization of BRCA patients A useful prognostic biomarker for treatment.

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Locally advanced breast cancer treated with neoadjuvant chemotherapy and adjuvant radiotherapy: a retrospective cohort analysis

Cited by 37 publications

References 39 publications

The relationship between hormone receptor status, tumor size and clinical stage and the efficacy of neoadjuvant chemotherapy for locally advanced breast cancer, predictors of survival, and the establishment of Cox-Based risk prediction model and nomogram

The relationship between hormone receptor status, tumor size and clinical stage and the efficacy of neoadjuvant chemotherapy for locally advanced breast cancer, predictors of survival, and the establishment of Cox-Based risk prediction model and nomogram

Accuracy of breast MRI in patients receiving neoadjuvant endocrine therapy: comprehensive imaging analysis and correlation with clinical and pathological assessments

m6A RNA methylation regulators contribute to progression and impact the prognosis of breast cancer

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