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Background: Current management of breast cancer (BC) relies on risk stratification based on well-defined clinicopathologic factors. Global gene expression profiling studies have demonstrated that BC comprises distinct molecular classes with clinical relevance. In this study, we hypothesised that molecular features of BC are a key driver of tumour behaviour and when coupled with a novel and bespoke application of established clinicopathologic prognostic variables can predict both clinical outcome and relevant therapeutic options more accurately than existing methods. Methods: In the current study, a comprehensive panel of biomarkers with relevance to BC was applied to a large and well-characterised series of BC, using immunohistochemistry and different multivariate clustering techniques, to identify the key molecular classes. Subsequently, each class was further stratified using a set of well-defined prognostic clinicopathologic variables. These variables were combined in formulae to prognostically stratify different molecular classes, collectively known as the Nottingham Prognostic Index Plus (NPI+). The NPI+ was then used to predict outcome in the different molecular classes. Results: Seven core molecular classes were identified using a selective panel of 10 biomarkers. Incorporation of clinicopathologic variables in a second-stage analysis resulted in identification of distinct prognostic groups within each molecular class (NPI+). Outcome analysis showed that using the bespoke NPI formulae for each biological BC class provides improved patient outcome stratification superior to the traditional NPI. Conclusion: This study provides proof-of-principle evidence for the use of NPI+ in supporting improved individualised clinical decision making.
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... Considering that molecular features of breast cancer are the critical driver of tumor behavior, Rakha et al. (17) developed a two-tier prognostic scoring system, NPI +, which combined biomarkers and traditional clinicopathologic variables. In NPI +, the initial assessment determined seven core biological classes of the tumor based on ten breast cancer-related biomarkers, and a second-level analysis identified six clinicopathological prognostic factors associated with breast cancer-specific survival (BCSS) subsequently, resulting in tailored NPI-like formulae for each biological class. ...
... In contrast, patients with low-RS (<18) tumors derived minimal benefit from chemotherapy treatment (41). A prospective study of TAILORx (42) confirmed that endocrine therapy was non-inferior to chemotherapy plus tamoxifen treatment for patients with intermediate-RS [11][12][13][14][15][16][17][18][19][20][21][22][23][24][25] tumors in the analysis of invasive DFS. Even in the subgroup at high-risk clinical features, there was no evidence suggesting any chemotherapy benefit in the patients with intermediate-RS. ...
... Bear et al. (53) selected neoadjuvant therapy for ER-positive patients according to RS grouping. Among them, patients with RS <11 received hormone therapy, RS ≥26 group received chemotherapy, and those with intermediate RS [11][12][13][14][15][16][17][18][19][20][21][22][23][24][25] were randomized to be treated with hormone therapy or chemotherapy. The results showed that the clinical response rate was significantly correlated with the RS grouping in each group (P=0.049), ...
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Objective: To provide a reference for clinical work and guide the decision-making of healthcare providers and end-users, we systematically reviewed the development, validation and classification of classical prognostic models for breast cancer. Background: Patients suffering from breast cancer have different prognosis for its high heterogeneity. Accurate prognosis prediction and risk stratification for breast cancer are crucial for individualized treatment. There is a lack of systematic summary of breast cancer prognostic models. Methods: We conducted a PubMed search with keywords "breast neoplasm", "prognostic model", "recurrence" and "metastasis", and screened the retrieved publications at three levels: title, abstract and full text. We identified the articles presented the development and/or validation of models based on clinicopathological factors, genomics, and machine learning (ML) methods to predict survival and/or benefits of adjuvant therapy in female breast cancer patients. Conclusions: Combining prognostic-related variables with long-term clinical outcomes, researchers have developed a series of prognostic models based on clinicopathological parameters, genomic assays, and medical figures. The discrimination, calibration, overall performance, and clinical usefulness were validated by internal and/or external verifications. Clinicopathological models integrated the clinical parameters, including tumor size, histological grade, lymph node status, hormone receptor status to provide prognostic information for patients and doctors. Gene-expression assays deeply revealed the molecular heterogeneity of breast cancer, some of which have been cited by AJCC and National Comprehensive Cancer Network (NCCN) guidelines. In addition, the models based on the ML methods provided more detailed information for prognosis prediction by increasing the data dimension. Combined models incorporating clinical variables and genomics information are still required to be developed as the focus of further researches.
... where: is the size of the index lesion in centimeters; is the node status: 0 nodes = 1, 1-3 nodes = 2, >3 nodes =3; is the grade of tumor: Grade I = 1, Grade II = 2, Grade III = 3 [2] as shown in Table 1. Galea et al. studied a 15-year period of survival for 1,629 breast cancer patients based on their NPI score. ...
... Therefore, we investigate the clinical and the genomic features that play an important role in the advances of the NPI score that reduces the 15-year survival [3]. Table 1: Node status and tumor grade [2]. Nodes Tumor Grade Notation 1 0 1 Grade I 2 1-3 2 Grade II 3 >3 3 Grade III Machine learning models have been proposed to make prognosis classification predict outcomes in individual breast cancer patients [4,5]. ...
Conference Paper
Nottingham Prognostics Index (NPI) is a widely-used prognostics measure used to predict survival of operable primary breast cancer. The NPI value is calculated based on the size of the tumor, the number of lymph nodes and the grade of the tumor. This work builds a prediction model for the NPI < 3.4 versus NPI ≥ 3.4, where this threshold is the cut-off between high survival rate versus the low survival rate. In this study, we present a supervised learning method used to predict the breast cancer NPI. The objectives of this research are (i) build a diagnosis system for breast cancer NPI based on multi-omics data; (ii) find gene biomarkers for each low and high NPI scores; (iii) build a novel prediction model based on t-distributed stochastic neighbor embedding (t-SNE) and residual neural network (ResNet) to integrate multi-omics data in the classification mechanism. The results show that two sets of biomarkers that include two different omics, namely gene expression and copy number alteration, can be integrated in the model to achieve a high prediction accuracy. Findings in the literature confirm the associations between some of these genes and breast cancer.
... S is the size of the index lesion in centimeters; N is the node status-0 nodes = 1, 1-3 nodes = 2, >3 nodes =3; G is the grade of the tumor-grade I = 1, grade II = 2, and grade III = 3 [9]. ...
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The Nottingham Prognostics Index (NPI) is a prognostics measure that predicts operable primary breast cancer survival. The NPI value is calculated based on the size of the tumor, the number of lymph nodes, and the tumor grade. Next-generation sequencing advancements have led to measuring different biological indicators called multi-omics data. The availability of multi-omics data triggered the challenge of integrating and analyzing these various biological measures to understand the progression of the diseases. High-dimensional embedding techniques are incorporated to present the features in the lower dimension, i.e., in a 2-dimensional map. The dataset consists of three -omics: gene expression, copy number alteration (CNA), and mRNA from 1885 female patients. The model creates a gene similarity network (GSN) map for each omic using t-distributed stochastic neighbor embedding (t-SNE) before being merged into the residual neural network (ResNet) classification model. The aim of this work was to (i) extract multi-omics biomarkers that are associated with the prognosis and prediction of breast cancer survival; and (ii) build a prediction model for multi-class breast cancer NPI classes. We evaluated this model and compared it to different high-dimensional embedding techniques and neural network combinations. The proposed model outperformed the other methods with an accuracy of 98.48%, and the area under the curve (AUC) equals 0.9999. The findings in the literature confirm associations between some of the extracted omics and breast cancer prognosis and survival including CDCA5, IL17RB, MUC2, NOD2 and NXPH4 from the gene expression dataset; MED30, RAD21, EIF3H and EIF3E from the CNA dataset; and CENPA, MACF1, UGT2B7 and SEMA3B from the mRNA dataset.
... Combining the status of axillary lymph nodes, tumor size and histological grade, as the most important individual prognostic parameters of breast cancer, the Nottingham Prognostic Index (NPI) was established, which stratifies all patients with breast cancer into three prognostic groups: good, medium and poor (7,8,9) (Table 1). Table 1 Nottingham prognostic index (NPI). ...
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Introduction: breast cancer is a disease that continues to plague women throughout their lives. C-reactive protein (CRP) has been found to be elevated in a variety of inflammatory and malignancies and its level has been found to correlate with prognostic and predicted breast cancer variables. Aim: to determine the preoperative serum levels of CRP in breast carcinoma, and to correlate them with the Nottingham prognostic index. Materials and methods: a total of 71 patients were included in this retrospective, descriptive-analytical study. Serum CRP levels were assessed using an enzyme-linked immunosorbent assay (ELISA), and CRP was measured by immunoturbidimetry. Histological findings included tumor size, lymph node metastases (LN), and histological staging. Statistical analysis was made in the IBM SPSS Statistics v. 21.0 for Windows, and the most important results are presented in the form of tables and graphs. Results: CRP levels are not statistically significantly correlated with the Nottingham prognostic index. Also, CRP values didn't show a statistically significant association with ALN metastases, tumor size and histological stage. Conclusion: serum CRP levels don't correlate statistically significantly with tumor extent, ALN metastases, histological grade in non-invasive breast cancer. The relationship between the Nottingham prognostic index and CRP didn't prove to be statistically significant. Keywords: breast cancer, C-reactive protein, Notthigham prognostic score
... Thus, the NPI cannot reveal the entire clinical and survival outcome of breast cancer heterogeneity, and a new index incorporating molecular-based biomarkers was developed. The Nottingham prognostic index plus (NPI+) was developed using an ANN to combine a large number of molecular expression levels in a non-linear manner (28). This approach has improved the predictive ability of each clinical-pathological feature, considering their complex and non-linear relationship. ...
... In recent years, another version has been developed known as Nottingham prognostic index plus (NPI+). NPI+ is based on the well-established clinicopathological parameters used in the NPI but has been refined to integrate tumor biological factors such as ER, PR, and different cytokeratins [16]. Some studies have also shown that molecular subtypes of breast cancer have different survival outcomes and unique patterns of metastasis [17]. ...
... Positive expression of ER, PR status was inversely related to NPI numerical value. 10,11 A study by Green et al. showed that ER, PR status showed major concordance. 12 Our finding was contradicting most of the studies which stressed on direct correlation with ER, PR status. ...
... We used the obtained data to calculate the NPI using the formula [0.2 × S] + N + G, whereas S is primary tumor size (cm), N is regional lymph node status (no metastatic lymph nodes -1 point, 1-4 nodes -2 points, more than 4 nodes -3 points), G -tumor grade. Patients were assigned to groups with a good (2.0-3.4 points), moderate (3.41-5.4) or poor (> 5.4) survival prognosis [14]. ...
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Background: The breast cancer is the most prevalent cancer among women, on the other hand absence of myoepithelial cells play a pivotal role in pathogenesis of this cancer. Thus we aimed to investigate the possible abilities of the molecular assay technique to find a relationship between mammary serine protease inhibitor (Maspin) gene expression possibly secreted by myoepithelial cells, grade of breast cancer and other prognostics factors (ER, PR, and c-erb-B2). Methods: Paraffin embedded blocks of 31 breast cancer patients together with two normal breast tissues were used for IHC staining and Maspin gene RNA detection uses the real-time PCR method. Applying QIAGEN kit, we were able to measure Maspin RNA and Extract the cDNA of different samples for evaluating the Maspin RNA level. Results: We found that the RNA level was considerably lowerin these cancer samples compared with normal samples. In addition, different grades of breast cancer in the obtained results adopt some distinguishable values. The Maspin expression in samples with grades II and III is much lower than the ones in normal group (P<0.05) which could be considered as a promising way in diagnosing of this disease. The results showed no considerable differences in Maspin gene expression of the c-erb-B2 scores in the tumor group except the samples having score 0. The other observation of this research study confirmed that Maspin gene expression couldn't show any differences between the values of both ER and PR in different scores of the tumor group. On the other hand, the cDNA of these patients showed lower values compared with normal samples. Conclusion: Maspin expression was reduced in samples with grade II& III of invasive ductal carcinoma. Based on expression of Maspin Inc-erb-B2, it seems that more expression happened in normal group comparing with different scores of it. We could suggest that there was a reverse relationship between tumor formation and Maspin gene expression. These results showed possible role of Maspin as prognostic factor Original Article | Iran J Pathol. 2016; 11(2): 104-111
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