scholarly journals Construction and Validation of a Serum Albumin-to-Alkaline Phosphatase Ratio-Based Nomogram for Predicting Pathological Complete Response in Breast Cancer

2021 ◽  
Vol 11 ◽  
Author(s):  
Fanli Qu ◽  
Zongyan Li ◽  
Shengqing Lai ◽  
XiaoFang Zhong ◽  
Xiaoyan Fu ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Alkaline Phosphatase ◽  
Cancer Patients ◽  
Predictive Value ◽  
Validation Cohort ◽  
Pathological Complete Response ◽  
Complete Response ◽  
Breast Cancer Patients ◽  
Training Cohort ◽  
Pretreatment Serum

BackgroundBreast cancer patients who achieve pathological complete response (pCR) after neoadjuvant chemotherapy (NAC) have favorable outcomes. Reliable predictors for pCR help to identify patients who will benefit most from NAC. The pretreatment serum albumin-to-alkaline phosphatase ratio (AAPR) has been shown to be a prognostic predictor in several malignancies, but its predictive value for pCR in breast cancer is still unknown. This study aims to investigate the predictive role of AAPR in breast cancer patients and develop an AAPR-based nomogram for pCR rate prediction.MethodsA total of 780 patients who received anthracycline and taxane-based NAC from January 2012 to March 2018 were retrospectively analyzed. Univariate and multivariate analyses were performed to assess the predictive value of AAPR and other clinicopathological factors. A nomogram was developed and calibrated based on multivariate logistic regression. A validation cohort of 234 patients was utilized to further validate the predictive performance of the model. The C-index, calibration plots and decision curve analysis (DCA) were used to evaluate the discrimination, calibration and clinical value of the model.ResultsPatients with a lower AAPR (<0.583) had a significantly reduced pCR rate (OR 2.228, 95% CI 1.246-3.986, p=0.007). Tumor size, clinical nodal status, histological grade, PR, Ki67 and AAPR were identified as independent predictors and included in the final model. The nomogram was used as a graphical representation of the model. The nomogram had satisfactory calibration and discrimination in both the training cohort and validation cohort (the C-index was 0.792 in the training cohort and 0.790 in the validation cohort). Furthermore, DCA indicated a clinical net benefit from the nomogram.ConclusionsPretreatment serum AAPR is a potentially valuable predictor for pCR in breast cancer patients who receive NAC. The AAPR-based nomogram is a noninvasive tool with favorable predictive accuracy for pCR, which helps to make individualized treatment strategy decisions.

Predicative value of [18F]-FDG PET scan for pathological complete response to neoadjuvant chemotherapy in breast cancer

2007 ◽  
Vol 25 (18_suppl) ◽  
pp. 505-505 ◽  
Author(s):  
L. Favier ◽  
A. Berriolo-Riedinger ◽  
B. Coudert ◽  
C. Touzery ◽  
J. Riedinger ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Logistic Regression ◽  
Neoadjuvant Chemotherapy ◽  
Cancer Patients ◽  
Predictive Value ◽  
Pathological Complete Response ◽  
Complete Response ◽  
Chemotherapy Response ◽  
Breast Cancer Patients ◽  
Fdg Uptake

505 Background: To evaluate, in breast cancer patients treated by neoadjuvant chemotherapy, the early predictive value of the FDG uptake decrease for the assessment of the pathological complete response (pCR). Methods: Forty seven women with non metastatic with conventional imaging, non inflammatory, large or locally advanced breast cancer were included. Pathological tumour regression determined on surgical resection specimens served as the gold standard for the assessment of the neoadjuvant chemotherapy response. According to the Sataloff classification, patients were classified in two groups: patients with a pathological complete response (pCR) and patients with a pathological non complete response (non pCR). FDG uptake of breast lesions was evaluated before and after the first course of neoadjuvant chemotherapy, using Standard Uptake Value maximum (SUV) corrected by body surface area and glycaemia. Relations between baseline [18F]-FDG uptake and clinical, histopathological and biological parameters were assessed by Mann-Whitney test. Predictive value of the FDG decrease for the assessment of the pCR was studied with logistic regression analysis. Results: An elevated baseline SUV was found independently associated with a high mitotic activity (p<0.002), tumour grading (p<0.004), high score of nuclear pleomorphism (p= 0.03) and positive hormonal receptor status (p<0.005). After completion of chemotherapy, 11 (23%) of the 47 breast tumours examined at surgery showed a pCR while 36 (77%) showed a non pCR. The relative decrease (ΔSUV) after the first course of neoadjuvant chemotherapy was significantly greater in the pCR group than in the non pCR group (p< 10-4). A SUV decrease of 85.4% ± 21.9% in pCR patients versus 22.6% ± 36.6% in non pCR patients was found. ΔSUV<-60% predicted pCR with an accuracy of 87%. With multivariate logistic regression analyses, ΔSUV<-60% was the only predictive factor of the pCR Conclusions: In breast cancer patients treated by neoadjuvant chemotherapy, the FDG uptake decrease, after only one course of treatment, is an early and powerful predictor of the pCR. No significant financial relationships to disclose.

scholarly journals Multi-Gene Prognostic Signatures and Prediction of Pathological Complete Response to Neoadjuvant Chemotherapy in ER-Positive, HER2-Negative Breast Cancer Patients

Cancers ◽  
2020 ◽  
Vol 12 (5) ◽  
pp. 1133 ◽  
Author(s):  
Claudia Mazo ◽  
Stephen Barron ◽  
Catherine Mooney ◽  
William M. Gallagher
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Neoadjuvant Chemotherapy ◽  
Cancer Patients ◽  
Predictive Value ◽  
Pathological Complete Response ◽  
Complete Response ◽  
Oncotype Dx ◽  
Breast Cancer Patients ◽  
Er Positive

Determining which patients with early-stage breast cancer should receive chemotherapy is an important clinical issue. Chemotherapy has several adverse side effects, impacting on quality of life, along with significant economic consequences. There are a number of multi-gene prognostic signatures for breast cancer recurrence but there is less evidence that these prognostic signatures are predictive of therapy benefit. Biomarkers that can predict patient response to chemotherapy can help avoid ineffective over-treatment. The aim of this work was to assess if the OncoMasTR prognostic signature can predict pathological complete response (pCR) to neoadjuvant chemotherapy, and to compare its predictive value with other prognostic signatures: EndoPredict, Oncotype DX and Tumor Infiltrating Leukocytes. Gene expression datasets from ER-positive, HER2-negative breast cancer patients that had pre-treatment biopsies, received neoadjuvant chemotherapy and an assessment of pCR were obtained from the Gene Expression Omnibus repository. A total of 813 patients with 66 pCR events were included in the analysis. OncoMasTR, EndoPredict, Oncotype DX and Tumor Infiltrating Leukocytes numeric risk scores were approximated by applying the gene coefficients to the corresponding mean probe expression values. OncoMasTR, EndoPredict and Oncotype DX prognostic scores were moderately well correlated according to the Pearson’s correlation coefficient. Association with pCR was estimated using logistic regression. The odds ratio for a 1 standard deviation increase in risk score, adjusted for cohort, were similar in magnitude for all four signatures. Additionally, the four signatures were significant predictors of pCR. OncoMasTR added significant predictive value to Tumor Infiltrating Leukocytes signatures as determined by bivariable and trivariable analysis. In this in silico analysis, OncoMasTR, EndoPredict, Oncotype DX, and Tumor Infiltrating Leukocytes were significantly predictive of pCR to neoadjuvant chemotherapy in ER-positive and HER2-negative breast cancer patients.

Integrated immune-related gene signature to predict clinical outcome for patients with luminal B breast cancer.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. e12526-e12526
Author(s):  
Xiaying Kuang ◽  
Du Cai ◽  
Ying Lin ◽  
Feng Gao
Keyword(s):  
Breast Cancer ◽  
Cancer Patients ◽  
Validation Cohort ◽  
Risk Groups ◽  
Gene Signature ◽  
Breast Cancer Patients ◽  
Training Cohort ◽  
Luminal B ◽  
Immune Related Gene ◽  
Immune Related Genes

e12526 Background: Luminal B breast cancer is always routinely treated with chemotherapy and endocrine therapy but heterogeneous with respect to sensitivity to treatment, identification of patients who may most benefit remains a matter of controversy. Immune-related genes (IRGs) was found to be associated with the prognosis of breast cancer. The aim of this study is to evaluate the impact of IRGs in predicting the outcome of luminal B breast cancer patients. Methods: According to the Metabric microarray dataset also as a training cohort, 488 luminal B breast cancer patients were selected for generation of immune-related gene signature (IRGS). Another independent dataset (n=250) of patients with complete prognostic information was analyzed as a validation cohort. Prognostic analysis was assessed to test the predictive value of IRGS. Results: A model of prognostic IRGS containing 12 immune-related genes was developed. In both training and validation cohorts, IRGS significantly stratified luminal B breast cancer patients into immune low- and high-risk groups in terms of disease free survival (DFS, HR=4.95, 95% CI=3.22-7.62, P<0.001 in training cohort, HR=2.47, 95% CI=1.29-4.75, P<0.001 in validation cohort). Multivariate analysis revealed IRGS as an independent prognostic factor (HR=4.96, 95% CI=3.00-8.18, P<0.001 in training cohort, HR=2.56, 95% CI=1.28-5.09, P=0.007 in validation cohort). Furthermore, those 12 genes mostly related with response to chemical, and the expression levels of them were completely opposite in patients of immune low- and high-risk groups. Conclusions: The proposed IRGS is a satisfactory prognostic model for estimating DFS of luminal B breast cancer patients. Further studies are needed to assess the clinical effectiveness of this system in predicting prognosis and treatment options for luminal B breast cancer patients. This work was supported by National Natural Science Foundation of China (No. 81602520), Natural Science Foundation of Guangdong Province (No. 2017A030313596).

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