Nottingham Prognostic Index Plus (NPI+): a modern clinical decision making tool in breast cancer

Nottingham prognostic x (NPx): a risk stratification tool in ER-positive HER2-negative breast cancer: a validation study

AIMS

In this study, we validate the use of Nottingham Prognostic x (NPx), consisting of tumour size, tumour grade, progesterone receptor (PR) and Ki67 in luminal BC.

MATERIALS AND METHODS

Two large cohorts of luminal early-stage BC (n = 2864) were included. PR and Ki67 expression were assessed using full-face resection samples using immunohistochemistry. NPx was calculated and correlated with clinical variables and outcome, together with Oncotype DX recurrence score (RS), that is frequently used as a risk stratifier in luminal BC.

RESULTS

In the whole cohort, 38% of patients were classified as high risk using NPx which showed significant association with parameters characteristics of aggressive tumour behaviour and shorter survival (P < 0.0001). NPx classified the moderate Nottingham Prognostic Index (NPI) risk group (n = 1812) into two distinct prognostic subgroups. Of the 82% low-risk group, only 3.8% developed events. Contrasting this, 14% of the high-risk patients developed events during follow-up. A strong association was observed between NPx and Oncotype Dx RS (P < 0.0001), where 66% of patients with intermediate risk RS who had subsequent distant metastases also had a high-risk NPx.

CONCLUSION

NPx is a reliable prognostic index in patients with luminal early-stage BC, and in selected patients may be used to guide adjuvant chemotherapy recommendations.

Immunohistochemical Profile of Triple-Negative Breast Cancers: SOX10 and AR Dual Negative Tumors Have Worse Outcomes

Triple-negative breast cancer (TNBC) refers to an estrogen receptor-negative, progesterone receptor-negative, and HER2-negative breast cancer. Although accepted as a clinically valid category, TNBCs are heterogeneous at the histologic, immunohistochemical, and molecular levels. Gene expression profiling studies have molecularly classified TNBCs into multiple groups, but the prognostic significance is unclear except for a relatively good prognosis for the luminal androgen receptor subtype. Immunohistochemistry (IHC) has been used as a surrogate for basal and luminal subtypes within TNBC, but prognostication of TNBC using IHC is not routinely performed. We aimed to study immunophenotypic correlations in a well-annotated cohort of consecutive TNBCs, excluding postneoadjuvant chemotherapy cases. Tissue microarrays were constructed from a total of 245 TNBC cases. IHC stains were performed and consisted of luminal (AR and INPP4B), basal (SOX10, nestin, CK5, and EGFR), and diagnostic (GCDFP15, mammaglobin, GATA3, and TRPS1) markers. Survival analysis was performed to assess the significance of clinical-pathologic variables including age, histology, grade, lymphovascular invasion, Nottingham prognostic index category, American Joint Committee on Cancer (AJCC) stage, stromal tumor-infiltrating lymphocytes at 10% increment, CD8+ T-cell count, Ki-67 index, PD-L1 status, and chemotherapy along with the results of IHC markers. Apocrine tumors show prominent reactivity for luminal markers and GCDFP15, whereas no special-type carcinomas are often positive for basal markers. TRPS1 is a sensitive marker of breast carcinoma but shows low or no expression in apocrine tumors. High AJCC stage, lack of chemotherapy, and dual SOX10/AR negativity are associated with worse outcomes on both univariable and multivariable analyses. Lymphovascular invasion and higher Nottingham prognostic index category were associated with worse outcomes on univariable but not multivariable analysis. The staining for IHC markers varies based on tumor histology, which may be considered in determining breast origin. Notably, we report that SOX10/AR dual negative status in TNBC is associated with a worse prognosis along with AJCC stage and chemotherapy status.

Differential response of placental cells to high D-glucose and its impact on extracellular vesicle biogenesis and trafficking via small GTPase Ras-related protein RAB-7A

Placental extracellular vesicles (EVs) can be found in the maternal circulation throughout gestation, and their concentration, content and bioactivity are associated with pregnancy outcomes, including gestational diabetes mellitus (GDM). However, the effect of changes in the maternal microenvironment on the mechanisms associated with the secretion of EVs from placental cells remains to be fully established. Here, we evaluated the effect of high glucose on proteins associated with the trafficking and release of different populations of EVs from placental cells. BeWo and HTR8/SVneo cells were used as placental models and cultured under 5-mM D-glucose (i.e. control) or 25-mM D-glucose (high glucose). Cell-conditioned media (CCM) and cell lysate were collected after 48 h. Different populations of EVs were isolated from CCM by ultracentrifugation (i.e. pellet 2K-g, pellet 10K-g, and pellet 100K-g) and characterised by Nanoparticle Tracking Analysis. Quantitative proteomic analysis (IDA/SWATH) and multiple reaction monitoring protocols at high resolution (MRMHR) were developed to quantify 37 proteins related to biogenesis, trafficking/release and recognition/uptake of EVs. High glucose increased the secretion of total EVs across the pellets from BeWo cells, an effect driven mainly by changes in the small EVs concentration in the CCM. Interestingly, no effect of high glucose on HTR8/SVneo cells EVs secretion was observed. High glucose induces changes in proteins associated with vesicle trafficking in BeWo cells, including Heat Shock Protein Family A (Hsp70) Member 9 (HSPA9) and Member 8 (HSPA8). For HTR8/SVneo, altered proteins including prostaglandin F2α receptor regulatory protein (FPRP), RAB5A, RAB35, RAB5B, and RB11B, STAM1 and TSG101. These proteins are associated with the secretion and trafficking of EVs, which could explain in part, changes in the levels of circulating EVs in diabetic pregnancies. Further, we identified that proteins RAB11B, PDCD6IP, STAM, HSPA9, HSPA8, SDCBP, RAB5B, RAB5A, RAB7A and ERAP1 regulate EV release in response to high and low glucose when overexpressed in cells. Interestingly, immunohistochemistry analysis of RAB7A revealed distinct changes in placental tissues obtained from women with normal glucose tolerance (NGT, n = 6) and those with GDM (n = 6), influenced by diet or insulin treatment. High glucose regulation of proteins involved in intercellular dynamics and the trafficking of multivesicular bodies to the plasma membrane in placental cells is relevant in the context of GDM pregnancies.

Subgroup analysis for longitudinal data based on a partial linear varying coefficient model with a change plane

Subgroup analysis has become an important tool to characterize the treatment effect heterogeneity, and finally towards precision medicine. On the other hand, longitudinal study is widespread in many fields, but subgroup analysis for this data type is still limited. In this article, we study a partial linear varying coefficient model with a change plane, in which the subgroups are defined based on linear combination of grouping variables, and the time-varying effects in different subgroups are estimated to capture the dynamic association between predictors and response. The varying coefficients are approximated by basis functions and the group indicator function is smoothed by kernel function, which are included in the generalized estimating equation for estimation. Asymptotic properties of the estimators for the varying coefficients, the constant coefficients and the change plane coefficients are established. Simulations are conducted to demonstrate the flexibility, efficiency and robustness of the proposed method. Based on the Standard and New Antiepileptic Drugs study, we successfully identify a subgroup in which patients are sensitive to the newer drug in a specific period of time.

DDX43 mRNA expression and protein levels in relation to clinicopathological profile of breast cancer

BACKGROUND

Breast cancer (BC) is the most often diagnosed cancer in women globally. Cancer cells appear to rely heavily on RNA helicases. DDX43 is one of DEAD- box RNA helicase family members. But, the relationship between clinicopathological, prognostic significance in different BC subtypes and DDX43 expression remains unclear. Therefore, the purpose of this study was to assess the clinicopathological significance of DDX43 protein and mRNA expression in different BC subtypes.

MATERIALS AND METHODS

A total of 80 females newly diagnosed with BC and 20 control females that were age-matched were recruited for this study. DDX43 protein levels were measured by ELISA technique. We used a real-time polymerase chain reaction quantification (real-time PCR) to measure the levels of DDX43 mRNA expression. Levels of DDX43 protein and mRNA expression within BC patients had been compared to those of control subjects and correlated with clinicopathological data.

RESULTS

The mean normalized serum levels of DDX43 protein were slightly higher in control than in both benign and malignant groups, but this result was non-significant. The mean normalized level of DDX43 mRNA expression was higher in the control than in both benign and malignant cases, although the results were not statistically significant and marginally significant, respectively. Moreover, the mean normalized level of DDX43 mRNA expression was significantly higher in benign than in malignant cases. In malignant cases, low DDX43 protein expression was linked to higher nuclear grade and invasive duct carcinoma (IDC), whereas high mRNA expression was linked to the aggressive types of breast cancer such as TNBC, higher tumor and nuclear grades.

CONCLUSION

This study explored the potential of using blood DDX43 mRNA expression or protein levels, or both in clinical settings as a marker of disease progression in human breast cancer. DDX43 mRNA expression proposes a less invasive method for discriminating benign from malignant BC.

Thyroglobulin expression, Ki-67 index, and lymph node ratio in the prognostic assessment of papillary thyroid cancer

The clinical significance of thyroglobulin (Tg) expression in papillary thyroid cancer (PTC) has not been systematically explored in relation to the Ki-67 index, lymph node ratio (LNR), or other conventional prognostic predictors. In this retrospective study of 327 patients with PTC, we investigated the immunohistochemical expression of Tg in both primary tumors and their matching lymph node metastases in relation to the Ki-67 index, LNR, and clinical data. Tumoral Tg immunoreactivity was inversely correlated to the Ki-67 index and tumor recurrence. The Ki-67 index was higher in lymph node metastases (mean 4%) than in the primary tumors (mean 3%). Reduced Tg expression, estimated as 0-25% Tg positive tumor cells, was more common in lymph node metastases compared to primary tumors. In addition to advanced metastatic burden (defined as N1b stage and LNR ≥ 21%), low Tg expression (0-25% positive tumor cells) in lymph node metastases had a significant prognostic impact with shorter recurrence-free survival. These findings support the potential value of histopathological assessment of Tg expression and Ki-67 index in lymph node metastases as complementary predictors to anticipate the prognosis of PTC patients better.

Comparison of the Nottingham Prognostic Index and OncotypeDX© recurrence score in predicting outcome in estrogen receptor positive breast cancer

INTRODUCTION

Traditionally, Nottingham prognostic index (NPI) informed prognosis in patients with estrogen receptor positive, human epidermal growth factor receptor-2 negative, node negative (ER+/HER2-/LN-) breast cancer. At present, OncotypeDX© Recurrence Score (RS) predicts prognosis and response to adjuvant chemotherapy (AC).

AIMS

To compare NPI and RS for estimating prognosis in ER + breast cancer.

METHODS

Consecutive patients with ER+/HER2-/LN- disease were included. Disease-free (DFS) and overall survival (OS) were determined using Kaplan-Meier and Cox regression analyses.

RESULTS

1471 patients met inclusion criteria. The mean follow-up was 110.7months. NPI was calculable for 1382 patients: 19.8% had NPI≤2.4 (291/1471), 33.0% had NPI 2.41-3.4 (486/1471), 30.0% had NPI 3.41-4.4 (441/1471), 10.9% had NPI 4.41-5.4 (160/1471), and 0.3% had NPI>5.4 (4/1471). In total, 329 patients underwent RS (mean RS: 18.7) and 82.1% had RS < 25 (270/329) and 17.9% had RS ≥ 25 (59/329). Using multivariable Cox regression analyses (n = 1382), NPI independently predicted DFS (Hazard ratio (HR): 1.357, 95% confidence interval (CI): 1.140-1.616, P < 0.001) and OS (HR: 1.003, 95% CI: 1.001-1.006, P = 0.024). When performing a focused analysis of those who underwent both NPI and RS (n = 329), neither biomarker predicted DFS or OS. Using Kaplan Meier analyses, NPI category predicted DFS (P = 0.008) and (P = 0.026) OS. Conversely, 21-gene RS group failed to predict DFS (P = 0.187) and OS (P = 0.296).

CONCLUSION

In our focused analysis, neither NPI nor RS predicted survival outcomes. However, in the entire series, NPI independently predicted both DFS and OS. On the 40th anniversary since its derivation, NPI continues to provide accurate prognostication in breast cancer, outperforming RS in the current study.

The role of CPT1A as a biomarker of breast cancer progression: a bioinformatic approach

Breast cancer is the commonest malignancy of women and with its incidence on the rise, the need to identify new targets for treatment is imperative. There is a growing interest in the role of lipid metabolism in cancer. Carnitine palmitoyl-transferase-1 (CPT-1); the rate limiting step in fatty acid oxidation, has been shown to be overexpressed in a range of tumours. There are three isoforms of CPT-1; A, B and C. It is CPT-1A that has been shown to be the predominant isoform which is overexpressed in breast cancer. We performed a bioinformatic analysis using readily available online platforms to establish the prognostic and predictive effects related to CPT-1A expression. These include the KM plotter, the Human Protein Atlas, the cBioPortal, the G2O, the MethSurvand the ROC plotter. A Network analysis was performed using the Oncomine platform and signalling pathways constituting the cancer hallmarks, including immune regulation as utilised by NanoString. The epigenetic pathways were obtained from the EpiFactor website. Spearman correlations (r) to determine the relationship between CPT-1A and the immune response were obtained using the TISIDB portal. Overexpression of CPT-1A largely confers a worse prognosis and CPT-1A progressively recruits a range of pathways as breast cancer progresses. CPT-1A's interactions with cancer pathways is far wider than previously realised and includes associations with epigenetic regulation and immune evasion pathways, as well as wild-type moderate to high penetrant genes involved in hereditary breast cancer. Although CPT-1A genomic alterations are detected in 9% of breast carcinomas, both the alteration and the metagene associated with it, confers a poor prognosis. CPT-1A expression can be utilised as a biomarker of disease progression and as a potential therapeutic target.

The current staging and classification systems of breast cancer and their pitfalls: Is it possible to integrate the complexity of this neoplasm into a unified staging system?

Breast cancer is one of the leading causes of cancer death in women worldwide due to its variable aggressiveness and high propensity to develop distant metastases. The staging can be performed clinically or pathologically, generating the stage stratification by the TNM (T - tumor size; N- lymph node metastasis; M - distant organ metastasis) system. However, cancers with virtually identical TNM characteristics can present highly contrasting behaviors due to the divergence of molecular profiles. This review focuses on the histopathological nuances and molecular understanding of breast cancer through the profiling of gene and protein expression, culminating in improvements promoted by the integration of this information into the traditional staging system. As a culminating point, it will highlight predictive statistical tools for genomic risks and decision algorithms as a possible solution to integrate the various systems because they have the potential to reduce the indications for such tests, serving as a funnel in association with staging and previous classification.

Classification of Breast Cancer Nottingham Prognostic Index Using High-Dimensional Embedding and Residual Neural Network

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.

Classification of Breast Cancer Nottingham Prognostic Index Using High-Dimensional Embedding and Residual Neural Network

Simple Summary

A deep learning model based on multi-omics data to classify Nottingham prognostic Index score levels. The model represents each omic dataset using 2-dimensional map before integrating all omics maps into the prediction model. The literature confirms the relationship between the extracted omics features with the progression and survival of breast cancer.

Abstract

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.

Advancement of prognostic models in breast cancer: a narrative review

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.

Prediction of Late Recurrence and Distant Metastasis in Early-stage Breast Cancer: Overview of Current and Emerging Biomarkers

Recently, a significant number of breast cancer (BC) patients have been diagnosed at an early stage. It is therefore critical to accurately predict the risk of recurrence and distant metastasis for better management of BC in this setting. Clinicopathologic patterns, particularly lymph node status, tumor size, and hormonal receptor status are routinely used to identify women at increased risk of recurrence. However, these factors have limitations regarding their predictive ability for late metastasis risk in patients with early BC. Emerging molecular signatures using gene expression-based approaches have improved the prognostic and predictive accuracy for this indication. However, the use of their based-scores for risk assessment has provided contradictory findings. Therefore, developing and using newly emerged alternative predictive and prognostic biomarkers for identifying patients at high- and low-risk is of great importance. The present review discusses some serum biomarkers and multigene profiling scores for predicting late recurrence and distant metastasis in early-stage BC based on recently published studies and clinical trials.

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AIMS

The mechanisms that drive breast cancer (BC) progression and poor outcome are not fully understood. The human heat shock protein 90 alpha family class A member 1 (HSP90α) encoded by the HSP90ΑA1 gene has a vital role in cellular responses to stress and is implicated in the development and progression of many cancers. The current study aims to explore the clinical and prognostic importance of HSP90α in BC.

METHODS

The Molecular Taxonomy of Breast Cancer International Consortium (n=1980); The Cancer Genome Atlas (n=1097) and the Breast Cancer Gene-Expression Miner (Bc-GenExMiner) BC datasets (n=5056) were used to evaluate HSP90ΑA1 mRNA expression. HSP90α protein expression was further assessed using immunohistochemistry in a large (n=911) well-characterised BC series. The association between mRNA and protein expressions with other clinicopathological parameters and outcome was analysed.

RESULTS

High expression of HSP90ΑA1 both at the mRNA and protein levels was significantly associated with characteristics of BC poor prognosis, including high grade, lymphovascular invasion, poor Nottingham Prognostic Index and positive expression of p53 and PIK3CA. Outcome analysis revealed that high HSP90α protein expression is an independent predictor of shorter BC-specific survival.

CONCLUSION

HSP90α can be used as a potential prognostic marker in BC. Further mechanistic studies are warranted to determine the underlying molecular mechanisms mediated by HSP90α in BC.

Quantitative parameters of MRI and 18F-FDG PET/CT in the prediction of breast cancer prognosis and molecular type: an original study

The purpose of this work is to evaluate the quantitative parameters of magnetic resonance imaging (MRI), particularly diffusion-weighted imaging (DWI) and dynamic contrast enhancement (DCE) as well as positron-emission tomography, combined with computer tomography (PET/CT), with 18F-fluorodesoxyglucose, in the prediction of breast cancer molecular type. We studied the correlation between a set of parameters in the invasive ductal carcinoma of the breast, not otherwise specified (IDC-NOS) as it is the most common invasive breast tumor. The parameters were as follows: apparent diffusion coefficient (ADC) in DWI, positive enhancement integral (PEI) in DCE, maximum standardized uptake value (SUVmax) in 18F-FDG PET/CT, tumor size, grade, and Ki-67 index, level of lymph node metastatic lesions. We also evaluated the probability of a statistically significant difference in mean ADC, PEI, and SUVmax values for patient groups with different Nottingham prognostic index (NPI) and molecular tumor type. Statistically significant correlations between SUVmax, tumor size, and NPI, mean and minimal ADC values with Ki-67 and molecular tumor type were found. The PEI showed a correlation with the NPI risk level and was characterized by a relationship with the magnitude of the predicted NPI risk and regional lymph node involvement. The prognostic model created in our work allows for NPI risk group prediction. The SUVmax, ADC and PEI are non-invasive prognostic markers in the invasive breast cancer of no specific type. The correlation between ADC values and the expression of some tumor receptors can be used for in vivo molecular tumor type monitoring and treatment adjustment.

A prognostic model based on cell-cycle control predicts outcome of breast cancer patients

Background

A prognostic model combining biomarkers of metaphase-anaphase transition of the cell cycle was developed for invasive breast cancer. The prognostic value and clinical applicability of the model was evaluated in comparison with the routine prognosticators of invasive breast carcinoma.

Methods

The study comprised 1135 breast cancer patients with complete clinical data and up to 22-year follow-up. Regulators of metaphase-anaphase transition were detected immunohistochemically and the biomarkers with the strongest prognostic impacts were combined into a prognostic model. The prognostic value of the model was tested and evaluated in separate patient materials originating from two Finnish breast cancer centers.

Results

The designed model comprising immunoexpressions of Securin, Separase and Cdk1 identified 8.4-fold increased risk of breast cancer mortality (p < 0.0001). A survival difference exceeding 15 years was observed between the majority (> 75%) of patients resulting with favorable as opposed to unfavorable outcome of the model. Along with nodal status, the model showed independent prognostic impact for all breast carcinomas and for subgroups of luminal, N+ and N- disease.

Conclusions

The impact of the proposed prognostic model in predicting breast cancer survival was comparable to nodal status. However, the model provided additional information in N- breast carcinoma in identifying patients with aggressive course of disease, potentially in need of adjuvant treatments. Concerning N+, in turn, the model could provide evidence for withholding chemotherapy from patients with favorable outcome.

New Advances in Molecular Breast Cancer Pathology

Breast cancer (BC) comprises a diverse spectrum of diseases featuring distinct presentation, morphological, biological, and clinical phenotypes. BC behaviour and response to therapy also vary widely. Current evidence indicates that traditional prognostic and predictive classification systems are insufficient to reflect the biological and clinical heterogeneity of BC. Advancements in high-throughput molecular techniques and bioinformatics have contributed to the improved understanding of BC biology, refinement of molecular taxonomies and the development of novel prognostic and predictive molecular assays. Molecular testing has also become increasingly important in the diagnosis and treatment of BC in the era of precision medicine. Despite the enormous amount of research work to develop and refine BC molecular prognostic and predictive assays, it is still in evolution and proper incorporation of these molecular tests into clinical practice to guide patient's management remains a challenge. With the increasing use of more sophisticated high throughput molecular techniques, large amounts of data will continue to emerge, which could potentially lead to identification of novel therapeutic targets and allow more precise classification systems that can accurately predict outcome and response to therapy. In this review, we provide an update on the molecular classification of BC and molecular prognostic assays. Companion diagnostics, contribution of massive parallel sequencing and the use of liquid biopsy are also highlighted.

Correlation between HER2 Expression and Clinicopathological Features of Breast Cancer: A Cross- Sectional Study in Vietnam

BACKGROUND

HER2 is the target of the therapeutic agents which are used to treat HER2-positive breast cancer. Reports have shown that the HER2 oncogene expression and its association with clinicopathological factors remain unclear in breast cancer (BC) patients.  This study aimed to determine the correlation between HER2 expression and clinicalpathological characteristics of breast cancer in Vietnamese women.

METHODS

Between June 2016 and August 2018, paraffin-embedded specimens from 237 patients with primary invasive breast carcinoma in Hue University Hospital and Hue Center Hospital, Hue city, Vietnam were examined for pathological features. The gene expression of HER2, ER, PR and Ki-67 were determined by immunohistochemistry (IHC). The gene amplification of Her2 was assessed by using Dual color in situ hybridization (DISH).

RESULTS

The most frequent histological type was invasive carcinoma of no special type (NST) with 77.35%, the highest percentage of patients with Grade II was detected (59.36%), tumor size > 2 cm accounted for 71.31% of cases, Lymph node metastases were available in 57.86% cases. Most patients were diagnosed at stage II (59.18%). The majority of patients were classified as moderate Nottingham prognostic index (54.9%). Estrogen receptor and Progesterone receptor were positive in 53.16% and 50.63%, respectively. 76.37% of cases were in high expression group of Ki-67 (≥14%). HER2 IHC 2+, 3+ were accounted for 28.69% and HER2 gene amplification was detected in 31% cases. HER2 gene amplification and/or overexpression was significantly associated with cell proliferation index Ki67. Furthermore, HER2 gene expression tended to be more frequently found in tumors with large tumor size, high grade, high stage and high Nottingham prognostic index and confirmed their prognostic independent role.

CONCLUSIONS

Our data indicated that HER2 gene expression was significantly correlated with cell proliferation index Ki67, but not significantly associated with another clinicopathological factors in breast cancer of Vietnamese women.
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Gene Expression Profiling Tests for Early-Stage Invasive Breast Cancer: A Health Technology Assessment

BACKGROUND

Breast cancer is a disease in which cells in the breast grow out of control. They often form a tumour that may be seen on an x-ray or felt as a lump.Gene expression profiling (GEP) tests are intended to help predict the risk of metastasis (spread of the cancer to other parts of the body) and to identify people who will most likely benefit from chemotherapy. We conducted a health technology assessment of four GEP tests (EndoPredict, MammaPrint, Oncotype DX, and Prosigna) for people with early-stage invasive breast cancer, which included an evaluation of effectiveness, safety, cost effectiveness, the budget impact of publicly funding GEP tests, and patient preferences and values.

METHODS

We performed a systematic literature search of the clinical evidence. We assessed the risk of bias of each included study using either the Cochrane Risk of Bias tool, Prediction model Risk Of Bias ASsessment Tool (PROBAST), or Risk of Bias Assessment tool for Non-randomized Studies (RoBANS), depending on the type of study and outcome of interest, and the quality of the body of evidence according to the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) Working Group criteria. We also performed a literature survey of the quantitative evidence of preferences and values of patients and providers for GEP tests.We performed an economic evidence review to identify published studies assessing the cost-effectiveness of each of the four GEP tests compared with usual care or with one another for people with early-stage invasive breast cancer. We adapted a decision-analytic model to compare the costs and outcomes of care that includes a GEP test with usual care without a GEP test over a lifetime horizon. We also estimated the budget impact of publicly funding GEP tests to be conducted in Ontario, compared with funding tests conducted through the out-of-country program and compared with no funding of tests in any location.To contextualize the potential value of GEP tests, we spoke with people who have been diagnosed with early-stage invasive breast cancer.

RESULTS

We included 68 studies in the clinical evidence review. Within the lymph-node-negative (LN-) population, GEP tests can prognosticate the risk of distant recurrence (GRADE: Moderate) and may predict chemotherapy benefit (GRADE: Low). The evidence for prognostic and predictive ability (ability to indicate the risk of an outcome and ability to predict who will benefit from chemotherapy, respectively) was lower for the lymph-node-positive (LN+) population (GRADE: Very Low to Low). GEP tests may also lead to changes in treatment (GRADE: Low) and generally may increase physician confidence in treatment recommendations (GRADE: Low).Our economic evidence review showed that GEP tests are generally cost-effective compared with usual care.Our primary economic evaluation showed that all GEP test strategies were more effective (led to more quality-adjusted life-years [QALYs]) than usual care and can be considered cost-effective below a willingness-to-pay of $20,000 per QALY gained. There was some uncertainty in our results. At a willingness-to-pay of $50,000 per QALY gained, the probability of each test being cost-effective compared to usual care was 63.0%, 89.2%, 89.2%, and 100% for EndoPredict, MammaPrint, Oncotype DX, and Prosigna, respectively.Sensitivity analyses showed our results were robust to variation in subgroups considered (i.e., LN+ and premenopausal), discount rates, age, and utilities. However, cost parameter assumptions did influence our results. Our scenario analysis comparing tests showed Oncotype DX was likely cost-effective compared with MammaPrint, and Prosigna was likely cost-effective compared with EndoPredict. When the GEP tests were compared with a clinical tool, the cost-effectiveness of the tests varied. Assuming a higher uptake of GEP tests, we estimated the budget impact to publicly fund GEP tests in Ontario would be between $1.29 million (Year 1) and $2.22 million (Year 5) compared to the current scenario of publicly funded GEP tests through the out-of-country program.Gene expression profiling tests are valued by patients and physicians for the additional information they provide for treatment decision-making. Patients are satisfied with what they learn from GEP tests and feel GEP tests can help reduce decisional uncertainty and anxiety.

CONCLUSIONS

Gene expression profiling tests can likely prognosticate the risk of distant recurrence and some tests may also predict chemotherapy benefit. In people with breast cancer that is ER+, LN-, and human epidermal growth factor receptor 2 (HER2)-negative, GEP tests are likely cost-effective compared with no testing. The GEP tests are also likely cost-effective in LN+ and premenopausal people. Compared with funding GEP tests through the out-of-country program, publicly funding GEP tests in Ontario would cost an additional $1 million to $2 million annually, assuming a higher uptake of tests. GEP tests are valued by both patients and physicians for chemotherapy treatment decision-making.

Clinicopathological significance of lipocalin 2 nuclear expression in invasive breast cancer

PURPOSE

The epithelial-mesenchymal transition (EMT) plays a key role in breast cancer progression and metastasis. Lipocalin 2 (LCN2) is involved in the regulation of EMT. The aim of this study was to investigate the clinicopathological significance of LCN2 expression in breast cancer.

METHODS

The expression of LCN2 protein was immunohistochemically assessed in two well-characterised annotated cohorts of breast cancer (discovery cohort, n = 612; validation cohort, n = 1363). The relationship of LCN2 expression and subcellular location with the clinicopathological factors and outcomes of patients was analysed.

RESULTS

Absent or reduced nuclear LCN2 expression was associated with features of aggressive behaviour, including high histological grade, high Nottingham Prognostic Index, high Ki67 labelling index, hormone receptor negativity and human epidermal growth factor receptor 2 positivity. The high cytoplasmic expression of LCN2 was correlated with lymph node positivity. The nuclear downregulation of LCN2 was correlated with the overexpression of EMT associated proteins (N-cadherin and Twist-related protein 2) and basal biomarkers (cytokeratin 5/6 and epidermal growth factor receptor). Unlike the cytoplasmic expression of LCN2, the loss of nuclear expression was a significant predictor of poor outcome. The combinatorial expression tumours with high cytoplasmic and low nuclear expression were associated with the worst prognosis.

CONCLUSIONS

Tumour cell expression of LCN2 plays a role in breast cancer progression with loss of its nuclear expression which is associated with aggressive features and poor outcome. Further functional analysis is warranted to confirm the relationship between the subcellular localisation LCN2 and behaviour of breast cancer.

The influence of size, depth and histologic characteristics of invasive ductal breast carcinoma on thermographic properties of the breast

Invasive breast carcinoma is the most common oncologic disease worldwide. The existing diagnostic methods use morphologic changes in the breast to diagnose a carcinoma when it has reached a certain size. Therefore, it is important to augment the morphologic diagnostic examinations with a new method that focuses on characteristics other than morphology such as electromagnetic changes produced by cancer. 50 adult female patients with confirmed ductal carcinoma following a core biopsy due to a suspicious breast mass were included in the study. They underwent breast thermography using a specially designed infrared camera. The data collected was statistically analyzed to determine how the presence of a tumor and its histologic characteristics influence breast thermographic properties. Twenty eight [56 %] patients in the study had an abnormal thermogram. Following statistical analysis, it was found that temperature of the diseased breast was directly correlated to tumor volume [p=0.009] and negatively correlated to depth of tumor [p=0.042]. Tumors that were ER+ and PR+ tumors produced warmer temperatures [p=0.017 and p=0.038 respectively] than tumors without these receptors. HER2 status and Ki-67 index had no statistical correlation with breast temperature. Tumor size, distance from the skin surface and receptor status cause changes in breast thermographic properties. Despite technical advances in the field of thermography, there are still contradictory results associated with thermography. Its diagnostic abilities are generally poorer than conventional methods and its use in breast cancer screening or as an adjunctive tool for diagnostic purposes is not recommended.

A 23 gene-based molecular prognostic score precisely predicts overall survival of breast cancer patients

BACKGROUND

Although many prognosis-predicting molecular scores for breast cancer have been developed, they are applicable to only limited disease subtypes. We aimed to develop a novel prognostic score that is applicable to a wider range of breast cancer patients.

METHODS

We initially examined The Cancer Genome Atlas breast cancer cohort to identify potential prognosis-related genes. We then performed a meta-analysis of 36 international breast cancer cohorts to validate such genes. We trained artificial intelligence models (random forest and neural network) to predict prognosis precisely, and we finally validated our prediction with the log-rank test.

FINDINGS

We identified a comprehensive list of 184 prognosis-related genes, most of which have been not extensively studied to date. We then established a universal molecular prognostic score (mPS) that relies on the expression status of only 23 of these genes. The mPS system is almost universally applicable to breast cancer patients (log-rank P < 0.05) in a manner independent of platform (microarray or RNA sequencing).

INTERPRETATION

The mPS system is simple and cost-effective to apply and yet is able to reveal previously unrecognized heterogeneity among patient subpopulations in a platform-independent manner. The combination of mPS and clinical stage stratifies prognosis even more precisely and should prove of value for avoidance of overtreatment. In addition, the prognosis-related genes uncovered in this study are potential drug targets. FUND: This work was supported by KAKENHI grants from the Ministry of Education, Culture, Sports, Science, and Technology of Japan to H.S. (19K20403) and to K.I·N (18H05215).

Genomics applied to the treatment of breast cancer

Breast cancer remains a major health issue in the world with 1.7 million new cases in 2012 worldwide. It is the second cause of death from cancer in western countries. Genomics have started to modify the treatment of breast cancer, and the developments should become more and more significant, especially in the present era of treatment personalization and with the implementation of new technologies. With molecular signatures, genomics enabled a de-escalation of chemotherapy and personalized treatments of localized forms of estrogen-dependent breast cancers. Genomics can also make a real contribution to constitutional genetics, so as to identify mutations in a panel of candidate genes. In this review, we will discuss the contributions of genomics applied to the treatment of breast cancer, whether already validated contributions or possible future applications linked to research data.

Combining clustering and classification ensembles: A novel pipeline to identify breast cancer profiles

Breast Cancer is one of the most common causes of cancer death in women, representing a very complex disease with varied molecular alterations. To assist breast cancer prognosis, the classification of patients into biological groups is of great significance for treatment strategies. Recent studies have used an ensemble of multiple clustering algorithms to elucidate the most characteristic biological groups of breast cancer. However, the combination of various clustering methods resulted in a number of patients remaining unclustered. Therefore, a framework still needs to be developed which can assign as many unclustered (i.e. biologically diverse) patients to one of the identified groups in order to improve classification. Therefore, in this paper we develop a novel classification framework which introduces a new ensemble classification stage after the ensemble clustering stage to target the unclustered patients. Thus, a step-by-step pipeline is introduced which couples ensemble clustering with ensemble classification for the identification of core groups, data distribution in them and improvement in final classification results by targeting the unclustered data. The proposed pipeline is employed on a novel real world breast cancer dataset and subsequently its robustness and stability are examined by testing it on standard datasets. The results show that by using the presented framework, an improved classification is obtained. Finally, the results have been verified using statistical tests, visualisation techniques, cluster quality assessment and interpretation from clinical experts.

Prognostic models for breast cancer: a systematic review

BACKGROUND

Breast cancer is the most common cancer in women worldwide, with a great diversity in outcomes among individual patients. The ability to accurately predict a breast cancer outcome is important to patients, physicians, researchers, and policy makers. Many models have been developed and tested in different settings. We systematically reviewed the prognostic models developed and/or validated for patients with breast cancer.

METHODS

We conducted a systematic search in four electronic databases and some oncology websites, and a manual search in the bibliographies of the included studies. We identified original studies that were published prior to 1st January 2017, and presented the development and/or validation of models based mainly on clinico-pathological factors to predict mortality and/or recurrence in female breast cancer patients.

RESULTS

From the 96 articles selected from 4095 citations found, we identified 58 models, which predicted mortality (n = 28), recurrence (n = 23), or both (n = 7). The most frequently used predictors were nodal status (n = 49), tumour size (n = 42), tumour grade (n = 29), age at diagnosis (n = 24), and oestrogen receptor status (n = 21). Models were developed in Europe (n = 25), Asia (n = 13), North America (n = 12), and Australia (n = 1) between 1982 and 2016. Models were validated in the development cohorts (n = 43) and/or independent populations (n = 17), by comparing the predicted outcomes with the observed outcomes (n = 55) and/or with the outcomes estimated by other models (n = 32), or the outcomes estimated by individual prognostic factors (n = 8). The most commonly used methods were: Cox proportional hazards regression for model development (n = 32); the absolute differences between the predicted and observed outcomes (n = 30) for calibration; and C-index/AUC (n = 44) for discrimination. Overall, the models performed well in the development cohorts but less accurately in some independent populations, particularly in patients with high risk and young and elderly patients. An exception is the Nottingham Prognostic Index, which retains its predicting ability in most independent populations.

CONCLUSIONS

Many prognostic models have been developed for breast cancer, but only a few have been validated widely in different settings. Importantly, their performance was suboptimal in independent populations, particularly in patients with high risk and in young and elderly patients.

Nottingham Prognostic Index is an Applicable Prognostic Tool in Non-Metastatic Triple-Negative Breast Cancer

Introduction:

Triple-negative breast cancer (TNBC) is characterized by a poor prognosis due to high mortality and early relapse, requiring the study of its prognostic factors. Tumor size, histological grade and lymph node status represent important parameters that are widely studied in breast cancer, and are retained as prognostic factors by several international guidelines. The Nottingham team combined these parameters into a prognostic score called the Nottingham prognostic index (NPI). In this study, we investigated the influence of NPI on outcomes in non metastatic TNBC.

Methodology:

This retrospective cohort study included all female patients with non metastatic TNBC who received treatment at the Regional Oncology Center Hassan II Oujda - Morocco, between January 2009 and December 2011. The prognostic impact of the NPI on the survival curves at 5 years was studied using multivariate Cox proportional hazards models.

Results:

The analysis of the data involved 98 patients, 39 patients (39.8%) were classed in the poor prognosis group with a NPI > 5.4. The Overall survival (OS) and Disease free survival (DFS) rates at 5 years, in this group, were 70 and 55.6 % respectively. After adjusting for clinic-pathological variables, a NPI > 5.4 was associated with mortality (HR: 2.598, 95% CI: 1.423 – 4.744, p = 0.002) and disease progression (HR: 2.512, CI to 95%: 1.496 – 4.219, p <0.001) in patients with non-metastatic TNBC.

Conclusion:

This retrospective cohort analysis showed that NPI was an independent prognostic factor for OS and DFS at 5 years in women with non metastatic TNBC. Once validated, the impact of this score on survival outcomes could be considered in the clinical management of TNBC.

[Survival difference due to types of health coverage in breast cancer patients treated at a specialized cancer center in Medellín, Colombia]

The study aimed to estimate the effect of health insurance on overall survival and disease-free survival in breast cancer patients undergoing surgery at the Las Américas Oncology Institute in Medellín, Colombia, with data from the institutional registry. The variables were compared between subsidized coverage and contributive coverage with chi-squared test (χ2) or Student t test, Kaplan-Meier, and log-rank test. The target variable was adjusted with Cox regression. There were 2,732 patients with a median follow-up of 36 months. Ten percent of the women with contributive coverage died, compared to 23% of the subsidized coverage group. There were differences in time-to-treatment (contributive group with 52 days versus subsidized group with 112 days, p < 0.05). Disease-free survival and overall survival were better in women with contributive coverage compared to those with subsidized coverage (p < 0.05), and overall survival varied according to tumor and treatment variables. Overall survival and disease-free survival and early time-to-diagnosis and treatment were better in patients with contributive coverage compared to those with subsidized coverage.

KLP-PI: a new prognostic index for luminal B HER-2-negative breast cancer

Luminal B HER-2-negative (LBHN) subtype is one of the major subtypes of breast cancer according to different features, clinical behaviors, and treatment response. The LBHN subtype shows a poor prognosis and is insensitive to endocrine therapy. Our work aim is to investigate the prognostic factor in the LBHN subgroup and, meanwhile, try to obtain an optimal prognostic index (PI) contrapose LBHN subgroup which helps to guide chemotherapy. A total of 515 female LBNH patients who underwent diagnosis and surgery at our hospitals from August 2008 to August 2018 were enrolled. Clinical-pathological information was obtained and immunohistochemistry result was available. From these cases, a 30% Ki-67 LI was employed to divide LBHN into two groups with low and high levels; high Ki-67 LI was associated with GIII tumor grade (P < 0.001), positive axillary lymph nodes (ALN) status (P = 0.018) and negative PR status (P = 0.016), and also seemed to be related to T2-T3 tumor size (P = 0.058). High Ki-67 level (HR = 3.30; P < 0.011), positive ALN (HR = 7.29; P < 0.001) and PR negative (HR = 2.63; P = 0.034) significantly associated with poor 5-year DFS in multivariate Cox's proportional hazard regression model. A novel prognosis prediction model (KLP-PI), based on Ki-67 LI, ALN and PR status, showed a better discriminatory ability compared with traditional Nottingham prognostic index targeted to LBHN breast cancer. Our study highlights that high Ki-67 LI, positive ALN and negative PR status were associated with poor outcome in LBHN patients, and composed by these prognostic factors, KLP-PI improves the prognostic assessment using the Nottingham Prognostic Index when aiming at LBHN subtype.

Prognostic indices predictive of short-term disease-free survival of breast carcinoma patients receiving primary surgical treatment in Sri Lanka

Background

Breast carcinoma (BCa) is the commonest malignancy among women worldwide and in Sri Lanka. Several prognostic indices are described for BCa.

Aims

To assess clinicopathological features and prognostic indices derived from routine clinical, histopathological and immunohistochemical (IHC) data, in a cohort of patients undergoing primary surgery for BCa and to determine their prognostic impact on short-term disease free survival.

Setting and Design

: This is a bidirectional cohort study of 208 women undergoing primary surgery for BCa at the National Hospital of Sri Lanka, from 2012-2014, excluding post-neoadjuvant chemotherapy cases.

Material and Methods

Clinical details, tumor size and nodal status were obtained from histopathology reports. Histopathology and estrogen/progesterone receptor and HER2 status were reviewed. Molecular subtype based on IHC was determined. Nodal ratio (number of positive nodes/total number retrieved) and Nottingham prognostic index were calculated. Follow up information was obtained by patient interviews and record review.

Statistical Analysis

Data was analyzed by univariate and multivariate Cox regression using SPSS19.0.

Results

Mean follow-up duration was 27.16 months (0.5-52 months, s = 9.35 months). 174 (82.9%) remained disease free with 19 (9%) deaths. Thirteen (6.2%) survived with metastasis and 4 (1.9%) with recurrences. On univariate Cox regression, tumor, nodal and TNM stages, nodal ratio and lymphovascular invasion (LVI) were predictive of disease free survival (DFS) (P = 0.001, P = 0.021, P = 0.022, P = 0.002, P = 0.018). On multivariate analysis TNM stage and LVI were predictive of DFS.

Conclusion

TNM stage and LVI were the most important predictors of short-term disease free survival in this study population, confirming that early detection of BCa at a lower stage has a significant impact on short-term outcomes.

Survival estimates stratified by the Nottingham Prognostic Index for early breast cancer: a systematic review and meta-analysis of observational studies

BACKGROUND

Estimates of survival for women diagnosed with early staged breast cancer are available based on stratification into prognostic categories defined using the Nottingham Prognostic Index (NPI). This review aimed to identify and summarize the estimated survival statistics from separate sources in the literature and to explore the extent of between-study heterogeneity in survival estimates.

METHODS

Observational studies in women diagnosed with early and locally advanced breast cancer reporting overall survival by NPI category were identified using a systematic literature search. An exploratory meta-analysis was conducted to describe survival estimates and assess between-study heterogeneity.

RESULTS

Twenty-eight studies were identified. Nineteen studies with sufficient data on overall survival were included in meta-analysis. A high level of heterogeneity in survival estimates was evident with I² values in the range of 90 to 98%.

CONCLUSIONS

The substantial differences between studies in the relationship between NPI categories and survival at 5 and 10 years poses challenges for use of this prognostic score in both clinical settings and in decision-analytic model-based economic evaluations.

Clinicopathological and prognostic significance of Ras association and pleckstrin homology domains 1 (RAPH1) in breast cancer

BACKGROUND

Ras association and pleckstrin homology domains 1 (RAPH1) is involved in cytoskeleton regulation and re-epithelialisation in invasive carcinoma and, therefore, may play a key role in carcinogenesis and metastasis. We, herein, investigated the biological and clinical significance of RAPH1 in breast cancer using large annotated cohorts.

METHODS

The clinicopathological and prognostic significance of RAPH1 was assessed at the genomic and transcriptomic levels using The Cancer Genome Atlas (TCGA) dataset (n = 1039) and the results were validated using the Molecular taxonomy of breast cancer international consortium (METABRIC) cohort (n = 1980). RAPH1 protein expression was evaluated by immunohistochemistry in a large, well-characterised cohort of early-stage breast cancer (n = 1040).

RESULTS

In both the TCGA and METABRIC cohorts, RAPH1 mRNA expression and RAPH1 copy number alteration were strongly correlated. RAPH1 mRNA overexpression was significantly correlated with high expression of adhesion and EMT markers including CDH1, TGFβ1 and CD44. RAPH1 mRNA overexpression was a significant predictor of a poor prognosis (Hazard ratio 3.88; p = 0.049). High RAPH1 protein expression was associated with higher grade tumours with high proliferation index, triple negative phenotype and high E-cadherin expression. High RAPH1 protein expression was an independent predictor of shorter survival (Hazard ratio 4.37; p = 0.037).

CONCLUSIONS

High RAPH1 expression is correlated with aggressive breast cancer phenotypes and provides independent prognostic value in invasive breast cancer.

Current issues with luminal subtype classification in terms of prediction of benefit from endocrine therapy in early breast cancer

Endocrine therapy for oestrogen receptor-positive (ER+) breast cancer (BC) is arguably the most successful targeted cancer therapy to date. Nevertheless, resistance to endocrine therapy still occurs in a significant proportion of patients, limiting its clinical utility. ER+ or luminal BC, which represents approximately three-quarters of all breast malignancies, are biologically heterogeneous, with no distinct, clinically defined subclasses able to predict the benefit of endocrine therapy in early settings. To improve patient outcomes there is a clear need for improved understanding of the biology of the luminal BC, with subsequent translation into more effective methods of diagnosis to identify potential predictive biomarkers for endocrine therapy. This review summarises current knowledge of factors predictive of benefit of endocrine therapy and discusses why molecular classification systems of BC have yet to be translated into the clinic.

Expression of Fibroblast Growth Factor 5 (FGF5) and Its Influence on Survival of Breast Cancer Patients

BACKGROUND The clinical outcome of patients with breast cancer (BC) remains poor. MATERIAL AND METHODS We analyzed BC microarray studies GSE37751, GSE7390, and GSE21653 to investigate the expression of FGF5 gene between BC patients and their normal counterparts and the relationship between FGF5 expression and age, tumor size, histopathological grading, estrogen receptors, clinical risk group according to St Gallen criteria, clinical risk group according to NPI criteria, clinical risk group according to Veridex signature, distant metastasis-free survival (DMFS), time to distant metastasis (TDM), disease-free survival (DFS), and overall survival (OS) of BC patients. Gene set enrichment analysis (GSEA) was used to investigate the exact mechanisms. RESULTS FGF5 expression was significantly upregulated in BC patients relative to that in normal controls (P<0.0001). BC patients in the FGF5 low-expression group were correlated with better clinical characteristics, including tumor size, histopathological grading, estrogen receptors, clinical risk group according to St Gallen criteria, NPI criteria and Veridex signature, DMFS, TDM, and DFS compared with those in the FGF5 high-expression cohort. The result of GSEA indicated that FGF5 inhibits the proliferation of BC cells via ultraviolet response and TGF-b signaling. Quantitative PCR verified that FGF5 was overexpressed in patients with BC. CONCLUSIONS Our results suggest that FGF5 is an independent protective factor for BC patients.

Molecular Genomic Testing for Breast Cancer: Utility for Surgeons

Molecular genomic testing provides clinicians with both prognostic and (sometimes) predictive information that can help individualize treatment and decrease the risk of over- or under-treatment. We review the genomic tests that are currently available for clinical use in management of breast cancer, discuss ongoing research related to validating and expanding their utility in different patient populations, and explain why it is important for surgeons to know how to incorporate these tools into their clinical practice in order to individualize patient treatment, reduce unnecessary morbidity, and, accordingly, improve outcomes.

Effectiveness of a clinical knowledge support system for reducing diagnostic errors in outpatient care in Japan: A retrospective study

Clinical evidence has indicated the effectiveness of computer-based systems for preventing and reducing diagnostic errors. Our study aimed to evaluate the effectiveness of UpToDate, a computer-based clinical knowledge management system, for reducing diagnostic errors. We retrospectively identified 100 patients who visited an outpatient department in a community-based hospital from July 2014 to June 2015. Fifty patients (exposure group) were seen by UpToDate-equipped physicians and another 50 (control group) were seen by UpToDate-unequipped physicians. We extracted data on patient sex, age, primary diagnosis, and case difficulty that could potentially affect diagnostic outcomes. We compared the two groups regarding diagnostic error rate and performed logistic regression analysis to analyze the concurrent effects of various factors affecting diagnostic error. The diagnostic error rate was 2% in the exposure group, while the error rate was 24% in the control group. Multivariate logistic regression analysis showed that error rate reduction was significantly associated with exposure to UpToDate with an odds ratio of 15.21 (95% CI 1.86-124.36). Our results demonstrated the effectiveness of UpToDate for the prevention and reduction of diagnostic error.

Intelligent Techniques Using Molecular Data Analysis in Leukaemia: An Opportunity for Personalized Medicine Support System

The use of intelligent techniques in medicine has brought a ray of hope in terms of treating leukaemia patients. Personalized treatment uses patient's genetic profile to select a mode of treatment. This process makes use of molecular technology and machine learning, to determine the most suitable approach to treating a leukaemia patient. Until now, no reviews have been published from a computational perspective concerning the development of personalized medicine intelligent techniques for leukaemia patients using molecular data analysis. This review studies the published empirical research on personalized medicine in leukaemia and synthesizes findings across studies related to intelligence techniques in leukaemia, with specific attention to particular categories of these studies to help identify opportunities for further research into personalized medicine support systems in chronic myeloid leukaemia. A systematic search was carried out to identify studies using intelligence techniques in leukaemia and to categorize these studies based on leukaemia type and also the task, data source, and purpose of the studies. Most studies used molecular data analysis for personalized medicine, but future advancement for leukaemia patients requires molecular models that use advanced machine-learning methods to automate decision-making in treatment management to deliver supportive medical information to the patient in clinical practice.

Molecular signatures in breast cancer

The use of molecular signatures to add value to standard clinical and pathological parameters has impacted clinical practice in many cancer types, but perhaps most notably in the breast cancer field. This is, in part, due to the considerable complexity of the disease at the clinical, morphological and molecular levels. The adoption of molecular profiling of DNA, RNA and protein continues to reveal important differences in the intrinsic biology between molecular subtypes and has begun to impact the way patients are managed. Several bioinformatic tools have been developed using DNA or RNA-based signatures to stratify the disease into biologically and/or clinically meaningful subgroups. Here, we review the approaches that have been used to develop gene expression signatures into currently available diagnostic assays (e.g., OncotypeDX® and Mammaprint®), plus we describe the latest work on genome sequencing, the methodologies used in the discovery process of mutational signatures, and the potential of these signatures to impact the clinic.

Do online prognostication tools represent a valid alternative to genomic profiling in the context of adjuvant treatment of early breast cancer? A systematic review of the literature

INTRODUCTION

Decision-making regarding adjuvant chemotherapy has been based on clinical and pathological features. However, such decisions are seldom consistent. Web-based predictive models have been developed using data from cancer registries to help determine the need for adjuvant therapy. More recently, with the recognition of the heterogenous nature of breast cancer, genomic assays have been developed to aid in the therapeutic decision-making.

METHODS

We have carried out a comprehensive literature review regarding online prognostication tools and genomic assays to assess whether online tools could be used as valid alternatives to genomic profiling in decision-making regarding adjuvant therapy in early breast cancer.

RESULTS AND CONCLUSIONS

Breast cancer has been recently recognized as a heterogenous disease based on variations in molecular characteristics. Online tools are valuable in guiding adjuvant treatment, especially in resource constrained countries. However, in the era of personalized therapy, molecular profiling appears to be superior in predicting clinical outcome and guiding therapy.

Correlation analysis between molecular subtypes and Nottingham Prognostic Index in breast cancer

Molecular subtypes and Nottingham Prognostic Index (NPI) are both prognostic models for breast cancer patients. We evaluated the association between molecular subtypes and NPI in 1042 breast cancer patients. The molecular subtypes indicating poorer prognosis were positively correlated to higher NPI (r = 0.138, P = 0.001). ER positive expression and PR high expression were positively correlated with NPI (r = 0.142, P = 0.001; r = 0.139, P = 0.001; respectively) and negatively correlated with histological grade (r = −0.233, P < 0.001; r = −0.176, P < 0.001; respectively). Ki67 status was negatively correlated with NPI and positively correlated with histological grade (r = −0.120, P =0.004; r = 0.197, P < 0.001; respectively). The percentages of cases with NPI score 2.00–3.40 were higher in the luminar A, ER+, PR high expression and Ki67 low expression group, and the percentages of cases with NPI > 5.40 were higher in the HER2 overexpression subtype, basal-like subtype, ER-, PR low/negative expression, and Ki67 high expression groups. The excellent consistence was observed between histological grade and molecular subtypes, ER, PR, Ki67. The difference of histological grade between the HER2 positive and negative group was statistically significant. In conclusion, there was closely association between molecular subtypes and NPI in breast cancer. For further comparing the prognostic significance of molecular subtypes and NPI, survival analyses should be performed on the same population in a large-scale prospective study.

A Prognostic Model for Patients with Triple-Negative Breast Cancer: Importance of the Modified Nottingham Prognostic Index and Age

Purpose

Considering the distinctive biology of triple-negative breast cancer (TNBC), this study aimed to identify TNBC-specific prognostic factors and determine the prognostic value of the Nottingham Prognostic Index (NPI) and its variant indices.

Methods

A total of 233 patients with newly diagnosed stage I to III TNBC from 2003 to 2012 were reviewed. We retrospectively analyzed the patients' demographics, clinicopathologic parameters, treatment, and survival outcomes. The NPI was calculated as follows: tumor size (cm)×0.2+node status+Scarff-Bloom-Richardson (SBR) grade. The modified NPI (MNPI) was obtained by adding the modified SBR grade rather than the SBR grade.

Results

The median follow-up was 67.8 months. Five-year disease-free survival (DFS) and overall survival (OS) were 81.4% and 89.9%, respectively. Multivariate analyses showed that the MNPI was the most significant and common prognostic factor of DFS (p=0.001) and OS (p=0.019). Young age (≤35 years) was also correlated with poor DFS (p=0.006). A recursive partitioning for establishing the prognostic model for DFS was performed based on the results of multivariate analysis. Patients with a low MNPI (≤6.5) were stratified into the low-risk group (p<0.001), and patients with a high MNPI (>6.5) were subdivided into the intermediate (>35 years) and high-risk (≤35 years) groups. Age was not a prognostic factor in patients with a low MNPI, whereas in patients with a high MNPI, it was the second key factor in subdividing patients according to prognosis (p=0.023).

Conclusion

The MNPI could be used to stratify patients with stage I to III TNBC according to prognosis. It was the most important prognosticator for both DFS and OS. The prognostic significance of young age for DFS differed by MNPI.

Expression of CD74 in invasive breast carcinoma: its relation to Nottingham Prognostic Index, hormone receptors, and HER2 immunoprofile

PURPOSE

To study the immunohistochemical expression of CD74 in series of invasive breast carcinomas classified according to their estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) immunoprofile and explore its correlation to Nottingham Prognostic Index (NPI) and tumor pathologic stage to determine if it has a prognostic value.

METHODS

A total of 160 cases of mammary carcinoma were classified broadly according to their ER, PR, and HER2 expression into luminal, HER2-positive, and triple-negative groups. The NPI was calculated and pathologic stage was recorded for each individual case and cases were classified into different prognostic groups. The CD74 expression was evaluated immunohistochemically and correlated to different prognostic variables.

RESULTS

The CD74 immunohistochemical expression in invasive breast carcinoma was significantly higher in triple-negative tumors, higher tumor grades, presence of lymph nodal metastasis, higher tumor stages, and higher NPI scores.

CONCLUSIONS

The CD74 might be a useful prognostic indicator predicting poor outcome of patients with breast carcinoma. Its consistent expression in triple-negative breast carcinomas points to the need of further studies to test the possibility if it can be targeted in treatment of breast carcinoma, especially in such groups.

More than meets the eye: the changing face of histopathology

This personal reflection on trends in histopathology over the past 50 years draws upon experience of professional training and practice in the specialty in the UK. Developments during this period often resulted from new therapies (and their adverse effects) necessitating greater precision in the histopathological classification of disease, for which morphology alone can be insufficient. Conversely, histopathology has contributed to advances in our understanding of disease, leading directly to novel and more effective treatments. New infections, some involving histopathology in their discovery, have also led to fresh diagnostic challenges. Increasingly, patients have benefited from fundamental changes in professionalism in pathology. Through audit, external quality assurance, continuing professional development, standardized reporting, and increasing specialization, the consistency and reliability of histopathological diagnoses have steadily improved. Regarding the specialty's future, some now see rivalry between the morphological and molecular approaches to diagnosis and classification, particularly for neoplastic disease. An integrated strategy led by the specialty is more likely to strengthen histopathology and ultimately to have the greatest benefit for patients.

Immunohistochemical Surrogates for Molecular Classification of Breast Carcinoma: A 2015 Update

CONTEXT

-The pioneering works on molecular classification (MC) by Perou and Sorlie et al in the early 2000s using global gene expression profiling identified 5 intrinsic subtypes of invasive breast cancers (IBCs): luminal A, luminal B, normal breast-like, HER2-enriched, and basal-like subtypes, each unique in incidence, survival, and response to therapy. Because the application of gene expression profiling in daily practice is not economical or practical at the present time, many investigators have studied the use of immunohistochemical (IHC) surrogates as a substitute for determining the MC of IBC.

OBJECTIVE

-To discuss the continuing efforts that have been made to develop clinically significant and readily available IHC surrogates for the MC of IBC.

DATA SOURCES

-Data were obtained from pertinent peer-reviewed English-language literature.

CONCLUSIONS

-The most commonly used IHC surrogates are estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor-2 (HER2), dividing IBC into luminal, HER2, and triple-negative subtypes. The addition of Ki-67, cytokeratin 5, and epidermal growth factor receptor (EGFR) separates luminal B from luminal A subtypes, and basal-like subtype from triple-negative breast cancer. More recently, biomarkers such as androgen receptor and p53 have been shown to further stratify these molecular subtypes. Although many studies of IHC-based MC have shown clinical significance similar to gene expression profiling-defined MC, its critical limitations are: (1) a lack of standardization in terminology, (2) a lack of standardization in biomarkers used for each subtype, and (3) the lack of a uniform cutoff for each biomarker. A panel of IHC surrogates for each subtype of IBC is proposed.

Prognostic contribution of mammographic breast density and HER2 overexpression to the Nottingham Prognostic Index in patients with invasive breast cancer

Background

To investigate whether very low mammographic breast density (VLD), HER2, and hormone receptor status holds any prognostic significance within the different prognostic categories of the widely used Nottingham Prognostic Index (NPI). We also aimed to see whether these factors could be incorporated into the NPI in an effort to enhance its performance.

Methods

This study included 270 patients with newly diagnosed invasive breast cancer. Patients with mammographic breast density of <10 % were considered as VLD. In this study, we compared the performance of NPI with and without VLD, HER2, ER and PR. Cox multivariate analysis, time-dependent receiver operating characteristic curve (tdROC), concordance index (c-index) and prediction error (0.632+ bootstrap estimator) were used to derive an updated version of NPI.

Results

Both mammographic breast density (VLD) (p < 0.001) and HER2 status (p = 0.049) had a clinically significant effect on the disease free survival of patients in the intermediate and high risk groups of the original NPI classification. The incorporation of both factors (VLD and HER2 status) into the NPI provided improved patient outcome stratification by decreasing the percentage of patients in the intermediate prognostic groups, moving a substantial percentage towards the low and high risk prognostic groups.

Conclusions

Very low density (VLD) and HER2 positivity were prognostically significant factors independent of the NPI. Furthermore, the incorporation of VLD and HER2 to the NPI served to enhance its accuracy, thus offering a readily available and more accurate method for the evaluation of patient prognosis.