Search

Predict (www.predict.nhs.uk) is an online, breast cancer prognostication and treatment benefit tool. The aim of this study was to incorporate the prognostic effect of HER2 status in a new version (Predict+), and to compare its performance with the original Predict and Adjuvant!. The prognostic effect of HER2 status was based on an analysis of data from 10 179 breast cancer patients from 14 studies in the Breast Cancer Association Consortium. The hazard ratio estimates were incorporated into Predict. The validation study was based on 1653 patients with early-stage invasive breast cancer identified from the British Columbia Breast Cancer Outcomes Unit. Predicted overall survival (OS) and breast cancer-specific survival (BCSS) for Predict+, Predict and Adjuvant! were compared with observed outcomes. All three models performed well for both OS and BCSS. Both Predict models provided better BCSS estimates than Adjuvant!. In the subset of patients with HER2-positive tumours, Predict+ performed substantially better than the other two models for both OS and BCSS. Predict+ is the first clinical breast cancer prognostication tool that includes tumour HER2 status. Use of the model might lead to more accurate absolute treatment benefit predictions for individual patients.

Introduction: The cancer stem cell (CSC) hypothesis states that tumours consist of a cellular hierarchy with CSCs at the apex driving tumour recurrence and metastasis. Hence, CSCs are potentially of profound clinical importance. We set out to establish the clinical relevance of breast CSC markers by profiling a large cohort of breast tumours in tissue microarrays (TMAs) using immunohistochemistry (IHC).Methods: We included 4, 125 patients enrolled in the SEARCH population-based study with tumours represented in TMAs and classified into molecular subtype according to a validated IHC-based five-marker scheme. IHC was used to detect CD44/CD24, ALDH1A1, aldehyde dehydrogenase family 1 member A3 (ALDH1A3) and integrin alpha-6 (ITGA6). A 'Total CSC' score representing expression of all four CSC markers was also investigated. Association with breast cancer specific survival (BCSS) at 10 years was assessed using a Cox proportional-hazards model. This study was complied with REMARK criteria.Results: In ER negative cases, multivariate analysis showed that ITGA6 was an independent prognostic factor with a time-dependent effect restricted to the first two years of follow-up (hazard ratio (HR) for 0 to 2 years follow-up, 2.4; 95% confidence interval (95% CI), 1.2 to 4.8; P = 0.009). The composite 'Total CSC' score carried independent prognostic significance in ER negative cases for the first four years of follow-up (HR for 0 to 4 years follow-up, 1.3; 95% CI, 1.1 to 1.6; P = 0.006).Conclusions: Breast CSC markers do not identify identical subpopulations in primary tumours. Both ITGA6 and a composite Total CSC score show independent prognostic significance in ER negative disease. The use of multiple markers to identify tumours enriched for CSCs has the greatest prognostic value. In the absence of more specific markers, we propose that the effective translation of the CSC hypothesis into patient benefit will necessitate the use of a panel of markers to robustly identify tumours enriched for CSCs. [ABSTRACT FROM AUTHOR]

Background: Transglutaminase 2 (TGM2) is a protein expressed in several isoforms in both intra- and extra-cellular tissue compartments. It has multiple functions that are important in cancer biology and several small studies have suggested expression of TGM2 in breast cancers is associated with a poorer prognosis. The aim of this study was to evaluate the role of intra-cellular and extra-cellular TGM2 expression in breast cancer and to determine whether there were any differences by hormone receptor status., Methods: We carried out TGM2 immunostaining of tissue micro-arrays comprising 2169 tumour cores and scored these for both intra- and extra-cellular and expression., Results: Intra-cellular (tumour cell) TGM2 positivity was associated with a better prognosis (HR = 0.74, 95% CI 0.59-0.92) with a larger effect stronger in hormone-receptor-negative cases (HR = 0.56, 95% CI 0.37-0.85). Extra-cellular (stromal) TGM2 expression was associated with a poorer prognosis (HR = 1.47, 95% CI 1.06-2.03) with a stronger association in hormone-receptor-positive cases (HR = 1.60, 95% CI 1.09-2.34)., Conclusion: Tissue compartment and hormone receptor status differences in the effect of TGM2 expression on clinical outcomes of breast cancer may reflect the different functions of TGM2., (© 2024. The Author(s).)

Background: Predict Breast (www.predict.nhs.uk) is an online prognostication and treatment benefit tool for early invasive breast cancer. The aim of this study was to incorporate the prognostic effect of progesterone receptor (PR) status into a new version of PREDICT and to compare its performance to the current version (2.2)., Method: The prognostic effect of PR status was based on the analysis of data from 45,088 European patients with breast cancer from 49 studies in the Breast Cancer Association Consortium. Cox proportional hazard models were used to estimate the hazard ratio for PR status. Data from a New Zealand study of 11,365 patients with early invasive breast cancer were used for external validation. Model calibration and discrimination were used to test the model performance., Results: Having a PR-positive tumour was associated with a 23% and 28% lower risk of dying from breast cancer for women with oestrogen receptor (ER)-negative and ER-positive breast cancer, respectively. The area under the ROC curve increased with the addition of PR status from 0.807 to 0.809 for patients with ER-negative tumours (p = 0.023) and from 0.898 to 0.902 for patients with ER-positive tumours (p = 2.3 × 10 -6 ) in the New Zealand cohort. Model calibration was modest with 940 observed deaths compared to 1151 predicted., Conclusion: The inclusion of the prognostic effect of PR status to PREDICT Breast has led to an improvement of model performance and more accurate absolute treatment benefit predictions for individual patients. Further studies should determine whether the baseline hazard function requires recalibration., (Copyright © 2022 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

TP53 overexpression is indicative of somatic TP53 mutations and associates with aggressive tumors and poor prognosis in breast cancer. We utilized a two-stage SNP association study to detect variants associated with breast cancer survival in a TP53-dependent manner. Initially, a genome-wide study (n = 575 cases) was conducted to discover candidate SNPs for genotyping and validation in the Breast Cancer Association Consortium (BCAC). The SNPs were then tested for interaction with tumor TP53 status (n = 4,610) and anthracycline treatment (n = 17,828). For SNPs interacting with anthracycline treatment, siRNA knockdown experiments were carried out to validate candidate genes.In the test for interaction between SNP genotype and TP53 status, we identified one locus, represented by rs10916264 (p(interaction) = 3.44 × 10-5; FDR-adjusted p = 0.0011) in estrogen receptor (ER) positive cases. The rs10916264 AA genotype associated with worse survival among cases with ER-positive, TP53-positive tumors (hazard ratio [HR] 2.36, 95% confidence interval [C.I] 1.45 - 3.82). This is a cis-eQTL locus for FBXO28 and TP53BP2; expression levels of these genes were associated with patient survival specifically in ER-positive, TP53-mutated tumors. Additionally, the SNP rs798755 was associated with survival in interaction with anthracycline treatment (p(interaction) = 9.57 × 10-5, FDR-adjusted p = 0.0130). RNAi-based depletion of a predicted regulatory target gene, FAM53A, indicated that this gene can modulate doxorubicin sensitivity in breast cancer cell lines.If confirmed in independent data sets, these results may be of clinical relevance in the development of prognostic and predictive marker panels for breast cancer.

As data-rich medical datasets are becoming routinely collected, there is a growing demand for regression methodology that facilitates variable selection over a large number of predictors. Bayesian variable selection algorithms offer an attractive solution, whereby a sparsity inducing prior allows inclusion of sets of predictors simultaneously, leading to adjusted effect estimates and inference of which covariates are most important. We present a new implementation of Bayesian variable selection, based on a Reversible Jump MCMC algorithm, for survival analysis under the Weibull regression model. A realistic simulation study is presented comparing against an alternative LASSO-based variable selection strategy in datasets of up to 20,000 covariates. Across half the scenarios, our new method achieved identical sensitivity and specificity to the LASSO strategy, and a marginal improvement otherwise. Runtimes were comparable for both approaches, taking approximately a day for 20,000 covariates. Subsequently, we present a real data application in which 119 protein-based markers are explored for association with breast cancer survival in a case cohort of 2287 patients with oestrogen receptor-positive disease. Evidence was found for three independent prognostic tumour markers of survival, one of which is novel. Our new approach demonstrated the best specificity.

Sectioning a whole tissue microarrray (TMA block) and storing the sections maximizes the number of sections obtained, but may impair the antigenicity of the stored sections. We have investigated the impact of TMA section storage on antigenicity. First, we reexamined existing TMA data to determine whether antigenicity in stored sections changes over time. Component scores for each marker, based on cellular compartment of staining and score-type, were evaluated separately. Residual components scores adjusted for grade, tumor size, and node positivity, were regressed on the number of days storage to evaluate the effect of storage time. Storage time ranged from 2 to 1897 days, and the mean change in antigenicity per year ranged from -0.88 (95% confidence interval, -1.11 to -0.65) to 0.035 (95% confidence interval, 0.016-0.054). Further analysis showed no significant improvement in the fit of survival models if storage time adjusted scores were included in the models rather than unadjusted scores. We then compared 3 ways of processing TMA sections after cutting-immediate staining, staining after 1 year, and staining after 1 year coated in wax-on the immunohistochemistry results for: progesterone receptor, a routinely used, robust antibody, and MKI67, which is generally considered less robust. The progesterone receptor scores for stored sections were similar to those for unstored sections, whereas the MKI67 scores for stored sections were substantially different to those for unstored sections. Wax coating made little difference to the results. Biomarker antigenicity shows a small decline over time that is unlikely to have an important effect on studies of prognostic biomarkers.

Background: Luminal A breast cancer defined as hormone receptor positive and human epidermal growth factor receptor 2 (HER2) negative is known to be heterogeneous. Previous study showed that luminal A tumours with the expression of basal markers ((cytokeratin (CK) 5 or CK5/6) or epidermal growth factor receptor (EGFR)) were associated with poorer prognosis compared with those that stained negative for basal markers. Prompted by this study, we assessed whether tumour characteristics and risk factors differed by basal marker status within luminal A tumours., Methods: We pooled 5040 luminal A cases defined by immunohistochemistry (4490 basal-negative ((CK5 (or CK5/6))- and EGFR-) and 550 basal-positive ((CK5 (or CK5/6+)) or EGFR+)) from eight studies participating in the Breast Cancer Association Consortium. Case-case comparison was performed using unconditional logistic regression., Results: Tumour characteristics and risk factors did not vary significantly by the expression of basal markers, although results suggested that basal-positive luminal tumours tended to be smaller and node negative, and were more common in women with a positive family history and lower body mass index., Conclusions: Most established breast cancer risk factors were similar in basal-positive and basal-negative luminal A tumours. The non-significant but suggestive differences in tumour features and family history warrant further investigations.

Background: Annexin A1 (ANXA1) is a protein related with the carcinogenesis process and metastasis formation in many tumors. However, little is known about the prognostic value of ANXA1 in breast cancer. The purpose of this study is to evaluate the association between ANXA1 expression, BRCA1/2 germline carriership, specific tumor subtypes and survival in breast cancer patients., Methods: Clinical-pathological information and follow-up data were collected from nine breast cancer studies from the Breast Cancer Association Consortium (BCAC) (n = 5,752) and from one study of familial breast cancer patients with BRCA1/2 mutations (n = 107). ANXA1 expression was scored based on the percentage of immunohistochemical staining in tumor cells. Survival analyses were performed using a multivariable Cox model., Results: The frequency of ANXA1 positive tumors was higher in familial breast cancer patients with BRCA1/2 mutations than in BCAC patients, with 48.6 % versus 12.4 %, respectively; P <0.0001. ANXA1 was also highly expressed in BCAC tumors that were poorly differentiated, triple negative, EGFR-CK5/6 positive or had developed in patients at a young age. In the first 5 years of follow-up, patients with ANXA1 positive tumors had a worse breast cancer-specific survival (BCSS) than ANXA1 negative (HRadj = 1.35; 95 % CI = 1.05-1.73), but the association weakened after 10 years (HRadj = 1.13; 95 % CI = 0.91-1.40). ANXA1 was a significant independent predictor of survival in HER2+ patients (10-years BCSS: HRadj = 1.70; 95 % CI = 1.17-2.45)., Conclusions: ANXA1 is overexpressed in familial breast cancer patients with BRCA1/2 mutations and correlated with poor prognosis features: triple negative and poorly differentiated tumors. ANXA1 might be a biomarker candidate for breast cancer survival prediction in high risk groups such as HER2+ cases.

Background: Expression of programmed death ligand 1 (PD-L1) in solid tumours has been shown to predict whether patients are likely to respond to anti-PD-L1 therapies. To estimate the therapeutic potential of PD-L1 inhibition in breast cancer, we evaluated the prevalence and significance of PD-L1 protein expression in a large collection of breast tumours., Patients and Methods: Correlations between CD274 (PD-L1) copy number, transcript and protein levels were evaluated in tumours from 418 patients recruited to the METABRIC genomic study. Immunohistochemistry was used to detect PD-L1 protein in breast tumours in tissue microarrays from 5763 patients recruited to the SEARCH population-based study (N = 4079) and the NEAT randomised, controlled trial (N = 1684)., Results: PD-L1 protein data was available for 3916 of the possible 5763 tumours from the SEARCH and NEAT studies. PD-L1 expression by immune cells was observed in 6% (235/3916) of tumours and expression by tumour cells was observed in just 1.7% (66/3916). PD-L1 was most frequently expressed in basal-like tumours. This was observed both where tumours were subtyped by combined copy number and expression profiling [39% (17/44) of IntClust 10 i.e. basal-like tumours were PD-L1 immune cell positive; P < 0.001] and where a surrogate IHC-based classifier was used [19% (56/302) of basal-like tumours were PD-L1 immune cell positive; P < 0.001]. Moreover, CD274 (PD-L1) amplification was observed in five tumours of which four were IntClust 10. Expression of PD-L1 by either tumour cells or infiltrating immune cells was positively correlated with infiltration by both cytotoxic and regulatory T cells (P < 0.001). There was a nominally significant association between PD-L1 and improved disease-specific survival (hazard ratio 0.53, 95% confidence interval 0.26-1.07; P = 0.08) in ER-negative disease., Conclusions: Expression of PD-L1 is rare in breast cancer, markedly enriched in basal-like tumours and is correlated with infiltrating lymphocytes. PD-L1 inhibition may benefit the 19% of patients with basal-like tumours in which the protein is expressed., Neat Clinicaltrialsgov: NCT00003577., (© The Author 2015. Published by Oxford University Press on behalf of the European Society for Medical Oncology. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Background: Citizen science, scientific research conducted by non-specialists, has the potential to facilitate biomedical research using available large-scale data, however validating the results is challenging. The Cell Slider is a citizen science project that intends to share images from tumors with the general public, enabling them to score tumor markers independently through an internet-based interface., Methods: From October 2012 to June 2014, 98,293 Citizen Scientists accessed the Cell Slider web page and scored 180,172 sub-images derived from images of 12,326 tissue microarray cores labeled for estrogen receptor (ER). We evaluated the accuracy of Citizen Scientist's ER classification, and the association between ER status and prognosis by comparing their test performance against trained pathologists., Findings: The area under ROC curve was 0.95 (95% CI 0.94 to 0.96) for cancer cell identification and 0.97 (95% CI 0.96 to 0.97) for ER status. ER positive tumors scored by Citizen Scientists were associated with survival in a similar way to that scored by trained pathologists. Survival probability at 15 years were 0.78 (95% CI 0.76 to 0.80) for ER-positive and 0.72 (95% CI 0.68 to 0.77) for ER-negative tumors based on Citizen Scientists classification. Based on pathologist classification, survival probability was 0.79 (95% CI 0.77 to 0.81) for ER-positive and 0.71 (95% CI 0.67 to 0.74) for ER-negative tumors. The hazard ratio for death was 0.26 (95% CI 0.18 to 0.37) at diagnosis and became greater than one after 6.5 years of follow-up for ER scored by Citizen Scientists, and 0.24 (95% CI 0.18 to 0.33) at diagnosis increasing thereafter to one after 6.7 (95% CI 4.1 to 10.9) years of follow-up for ER scored by pathologists., Interpretation: Crowdsourcing of the general public to classify cancer pathology data for research is viable, engages the public and provides accurate ER data. Crowdsourced classification of research data may offer a valid solution to problems of throughput requiring human input.

Previous studies have suggested that polymorphisms in CASP8 on chromosome 2 are associated with breast cancer risk. To clarify the role of CASP8 in breast cancer susceptibility, we carried out dense genotyping of this region in the Breast Cancer Association Consortium (BCAC). Single-nucleotide polymorphisms (SNPs) spanning a 1 Mb region around CASP8 were genotyped in 46 450 breast cancer cases and 42 600 controls of European origin from 41 studies participating in the BCAC as part of a custom genotyping array experiment (iCOGS). Missing genotypes and SNPs were imputed and, after quality exclusions, 501 typed and 1232 imputed SNPs were included in logistic regression models adjusting for study and ancestry principal components. The SNPs retained in the final model were investigated further in data from nine genome-wide association studies (GWAS) comprising in total 10 052 case and 12 575 control subjects. The most significant association signal observed in European subjects was for the imputed intronic SNP rs1830298 in ALS2CR12 (telomeric to CASP8), with per allele odds ratio and 95% confidence interval [OR (95% confidence interval, CI)] for the minor allele of 1.05 (1.03-1.07), P = 1 × 10(-5). Three additional independent signals from intronic SNPs were identified, in CASP8 (rs36043647), ALS2CR11 (rs59278883) and CFLAR (rs7558475). The association with rs1830298 was replicated in the imputed results from the combined GWAS (P = 3 × 10(-6)), yielding a combined OR (95% CI) of 1.06 (1.04-1.08), P = 1 × 10(-9). Analyses of gene expression associations in peripheral blood and normal breast tissue indicate that CASP8 might be the target gene, suggesting a mechanism involving apoptosis., (© The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

Introduction: The distribution of histopathological features of invasive breast tumors in BRCA1 or BRCA2 germline mutation carriers differs from that of individuals with no known mutation. Histopathological features thus have utility for mutation prediction, including statistical modeling to assess pathogenicity of BRCA1 or BRCA2 variants of uncertain clinical significance. We analyzed large pathology datasets accrued by the Consortium of Investigators of Modifiers of BRCA1/2 (CIMBA) and the Breast Cancer Association Consortium (BCAC) to reassess histopathological predictors of BRCA1 and BRCA2 mutation status, and provide robust likelihood ratio (LR) estimates for statistical modeling., Methods: Selection criteria for study/center inclusion were estrogen receptor (ER) status or grade data available for invasive breast cancer diagnosed younger than 70 years. The dataset included 4,477 BRCA1 mutation carriers, 2,565 BRCA2 mutation carriers, and 47,565 BCAC breast cancer cases. Country-stratified estimates of the likelihood of mutation status by histopathological markers were derived using a Mantel-Haenszel approach., Results: ER-positive phenotype negatively predicted BRCA1 mutation status, irrespective of grade (LRs from 0.08 to 0.90). ER-negative grade 3 histopathology was more predictive of positive BRCA1 mutation status in women 50 years or older (LR = 4.13 (3.70 to 4.62)) versus younger than 50 years (LR = 3.16 (2.96 to 3.37)). For BRCA2, ER-positive grade 3 phenotype modestly predicted positive mutation status irrespective of age (LR = 1.7-fold), whereas ER-negative grade 3 features modestly predicted positive mutation status at 50 years or older (LR = 1.54 (1.27 to 1.88)). Triple-negative tumor status was highly predictive of BRCA1 mutation status for women younger than 50 years (LR = 3.73 (3.43 to 4.05)) and 50 years or older (LR = 4.41 (3.86 to 5.04)), and modestly predictive of positive BRCA2 mutation status in women 50 years or older (LR = 1.79 (1.42 to 2.24))., Conclusions: These results refine likelihood-ratio estimates for predicting BRCA1 and BRCA2 mutation status by using commonly measured histopathological features. Age at diagnosis is an important variable for most analyses, and grade is more informative than ER status for BRCA2 mutation carrier prediction. The estimates will improve BRCA1 and BRCA2 variant classification and inform patient mutation testing and clinical management.

Breast cancer risk factors and clinical outcomes vary by tumour marker expression. However, individual studies often lack the power required to assess these relationships, and large-scale analyses are limited by the need for high throughput, standardized scoring methods. To address these limitations, we assessed whether automated image analysis of immunohistochemically stained tissue microarrays can permit rapid, standardized scoring of tumour markers from multiple studies. Tissue microarray sections prepared in nine studies containing 20 263 cores from 8267 breast cancers stained for two nuclear (oestrogen receptor, progesterone receptor), two membranous (human epidermal growth factor receptor 2 and epidermal growth factor receptor) and one cytoplasmic (cytokeratin 5/6) marker were scanned as digital images. Automated algorithms were used to score markers in tumour cells using the Ariol system. We compared automated scores against visual reads, and their associations with breast cancer survival. Approximately 65-70% of tissue microarray cores were satisfactory for scoring. Among satisfactory cores, agreement between dichotomous automated and visual scores was highest for oestrogen receptor (Kappa = 0.76), followed by human epidermal growth factor receptor 2 (Kappa = 0.69) and progesterone receptor (Kappa = 0.67). Automated quantitative scores for these markers were associated with hazard ratios for breast cancer mortality in a dose-response manner. Considering visual scores of epidermal growth factor receptor or cytokeratin 5/6 as the reference, automated scoring achieved excellent negative predictive value (96-98%), but yielded many false positives (positive predictive value = 30-32%). For all markers, we observed substantial heterogeneity in automated scoring performance across tissue microarrays. Automated analysis is a potentially useful tool for large-scale, quantitative scoring of immunohistochemically stained tissue microarrays available in consortia. However, continued optimization, rigorous marker-specific quality control measures and standardization of tissue microarray designs, staining and scoring protocols is needed to enhance results.

Background: PREDICT (http://www.predict.nhs.uk) is a prognostication and treatment benefit tool for early breast cancer (EBC). The aim of this study was to incorporate the prognostic effect of KI67 status in a new version (v3), and compare performance with the Predict model that includes HER2 status (v2)., Methods: The validation study was based on 1,726 patients with EBC treated in Nottingham between 1989 and 1998. KI67 positivity for PREDICT is defined as >10% of tumour cells staining positive. ROC curves were constructed for Predict models with (v3) and without (v2) KI67 input. Comparison was made using the method of DeLong., Results: In 1274 ER+ patients the predicted number of events at 10 years increased from 196 for v2 to 204 for v3 compared to 221 observed. The area under the ROC curve (AUC) improved from 0.7611 to 0.7676 (p=0.005) in ER+ patients and from 0.7546 to 0.7595 (p=0.0008) in all 1726 patients (ER+ and ER-)., Conclusion: Addition of KI67 to PREDICT has led to a statistically significant improvement in the model performance for ER+ patients and will aid clinical decision making in these patients. Further studies should determine whether other markers including gene expression profiling provide additional prognostic information to that provided by PREDICT.

Background: T-cell infiltration in estrogen receptor (ER)-negative breast tumours has been associated with longer survival. To investigate this association and the potential of tumour T-cell infiltration as a prognostic and predictive marker, we have conducted the largest study of T cells in breast cancer to date., Patients and Methods: Four studies totalling 12 439 patients were used for this work. Cytotoxic (CD8+) and regulatory (forkhead box protein 3, FOXP3+) T cells were quantified using immunohistochemistry (IHC). IHC for CD8 was conducted using available material from all four studies (8978 samples) and for FOXP3 from three studies (5239 samples)-multiple imputation was used to resolve missing data from the remaining patients. Cox regression was used to test for associations with breast cancer-specific survival., Results: In ER-negative tumours [triple-negative breast cancer and human epidermal growth factor receptor 2 (human epidermal growth factor receptor 2 (HER2) positive)], presence of CD8+ T cells within the tumour was associated with a 28% [95% confidence interval (CI) 16% to 38%] reduction in the hazard of breast cancer-specific mortality, and CD8+ T cells within the stroma with a 21% (95% CI 7% to 33%) reduction in hazard. In ER-positive HER2-positive tumours, CD8+ T cells within the tumour were associated with a 27% (95% CI 4% to 44%) reduction in hazard. In ER-negative disease, there was evidence for greater benefit from anthracyclines in the National Epirubicin Adjuvant Trial in patients with CD8+ tumours [hazard ratio (HR) = 0.54; 95% CI 0.37-0.79] versus CD8-negative tumours (HR = 0.87; 95% CI 0.55-1.38). The difference in effect between these subgroups was significant when limited to cases with complete data (P heterogeneity = 0.04) and approached significance in imputed data (P heterogeneity = 0.1)., Conclusions: The presence of CD8+ T cells in breast cancer is associated with a significant reduction in the relative risk of death from disease in both the ER-negative [supplementary Figure S1, available at Annals of Oncology online] and the ER-positive HER2-positive subtypes. Tumour lymphocytic infiltration may improve risk stratification in breast cancer patients classified into these subtypes. NEAT ClinicalTrials.gov: NCT00003577., (© The Author 2014. Published by Oxford University Press on behalf of the European Society for Medical Oncology. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

E-cadherin is involved in cell-cell adhesion and epithelial-to-mesenchymal transitions. In cancers, loss or inactivation of E-cadherin is associated with epithelial cell proliferation and invasion. Here, we sought to determine, if risk associations for 18 breast cancer susceptibility single nucleotide polymorphisms (SNPs) differed by E-cadherin tumor tissue expression in the Polish Breast Cancer Study (PBCS), using data on 1,347 invasive breast cancer cases and 2,366 controls. E-cadherin expression (low/high) was assessed using immunohistochemical staining of tumor tissue microarrays. Replication data on 2,006 cases and 6,714 controls from the Study of Epidemiology and Risk Factors in Cancer Heredity was used to follow-up promising findings from PBCS. In PBCS, we found the rs11249433 SNP at the 1p11.2 locus to be more strongly associated with risk of E-cadherin low tumors (OR = 1.30, 95 % CI = 1.08-1.56) than with E-cadherin high tumors [OR = 1.06, 95 % CI = 0.95-1.18; case-only p-heterogeneity (p-het) = 0.05]. Findings in PBCS for rs11249433 were replicated in SEARCH. Combined analyses of the two datasets for SNP rs11249433 revealed significant heterogeneity by E-cadherin expression (combined case-only p-het = 0.004). Further, among carriers of rs11249433, the highest risk was seen for E-cadherin low tumors that were ER-positive and of lobular histology. Our results in two independent data sets suggest that rs11249433, which is located between the NOTCH2 and FCGR1B genes within the 1p11.2 locus, is more strongly associated with risk of breast tumors with low or absent E-cadherin expression, and suggest that evaluation of E-cadherin tumor tissue expression may be useful in clarifying breast cancer risk factor associations.

Background: High-throughput evaluation of tissue biomarkers in oncology has been greatly accelerated by the widespread use of tissue microarrays (TMAs) and immunohistochemistry. Although TMAs have the potential to facilitate protein expression profiling on a scale to rival experiments of tumour transcriptomes, the bottleneck and imprecision of manually scoring TMAs has impeded progress., Methods: We report image analysis algorithms adapted from astronomy for the precise automated analysis of IHC in all subcellular compartments. The power of this technique is demonstrated using over 2000 breast tumours and comparing quantitative automated scores against manual assessment by pathologists., Results: All continuous automated scores showed good correlation with their corresponding ordinal manual scores. For oestrogen receptor (ER), the correlation was 0.82, P<0.0001, for BCL2 0.72, P<0.0001 and for HER2 0.62, P<0.0001. Automated scores showed excellent concordance with manual scores for the unsupervised assignment of cases to 'positive' or 'negative' categories with agreement rates of up to 96%., Conclusion: The adaptation of astronomical algorithms coupled with their application to large annotated study cohorts, constitutes a powerful tool for the realisation of the enormous potential of digital pathology.

Background: Our recent genome-wide association study identified a novel breast cancer susceptibility locus at 9q31.2 (rs865686)., Methods: To further investigate the rs865686-breast cancer association, we conducted a replication study within the Breast Cancer Association Consortium, which comprises 37 case-control studies (48,394 cases, 50,836 controls)., Results: This replication study provides additional strong evidence of an inverse association between rs865686 and breast cancer risk [study-adjusted per G-allele OR, 0.90; 95% confidence interval (CI), 0.88; 0.91, P = 2.01 × 10(-29)] among women of European ancestry. There were ethnic differences in the estimated minor (G)-allele frequency among controls [0.09, 0.30, and 0.38 among, respectively, Asians, Eastern Europeans, and other Europeans; P for heterogeneity (P(het)) = 1.3 × 10(-143)], but no evidence of ethnic differences in per allele OR (P(het) = 0.43). rs865686 was associated with estrogen receptor-positive (ER(+)) disease (per G-allele OR, 0.89; 95% CI, 0.86-0.91; P = 3.13 × 10(-22)) but less strongly, if at all, with ER-negative (ER(-)) disease (OR, 0.98; 95% CI, 0.94-1.02; P = 0.26; P(het) = 1.16 × 10(-6)), with no evidence of independent heterogeneity by progesterone receptor or HER2 status. The strength of the breast cancer association decreased with increasing age at diagnosis, with case-only analysis showing a trend in the number of copies of the G allele with increasing age at diagnosis (P for linear trend = 0.0095), but only among women with ER(+) tumors., Conclusions: This study is the first to show that rs865686 is a susceptibility marker for ER(+) breast cancer., Impact: The findings further support the view that genetic susceptibility varies according to tumor subtype., (2012 AACR)

Background: Proliferation has emerged as a major prognostic factor in luminal breast cancer. The immunohistochemical (IHC) proliferation marker Ki67 has been most extensively investigated but has not gained widespread clinical acceptance., Methods: We have conducted a head-to-head comparison of a panel of proliferation markers, including Ki67. Our aim was to establish the marker of the greatest prognostic utility. Tumour samples from 3093 women with breast cancer were constructed as tissue microarrays. We used IHC to detect expression of mini-chromosome maintenance protein 2, Ki67, aurora kinase A (AURKA), polo-like kinase 1, geminin and phospho-histone H3. We used a Cox proportional-hazards model to investigate the association with 10-year breast cancer-specific survival (BCSS). Missing values were resolved using multiple imputation., Results: The prognostic significance of proliferation was limited to oestrogen receptor (ER)-positive breast cancer. Aurora kinase A emerged as the marker of the greatest prognostic significance in a multivariate model adjusted for the standard clinical and molecular covariates (hazard ratio 1.3; 95% confidence interval 1.1-1.5; P=0.005), outperforming all other markers including Ki67., Conclusion: Aurora kinase A outperforms other proliferation markers as an independent predictor of BCSS in ER-positive breast cancer. It has the potential for use in routine clinical practice., (© 2012 Cancer Research UK)

There is an urgent need to improve prognostic classifiers in breast cancer. Ki67 and B-cell lymphoma 2 protein (BCL2) are established prognostic markers which have traditionally been assessed separately, in a dichotomous manner. This study was conducted to test the hypothesis that combinatorial assessment of these markers would provide superior prognostic information and improve their clinical utility. Tissue microarrays were used to assess the expression of Ki67 and BCL2 in 2749 cases of invasive breast cancer. We devised a Ki67/BCL2 index representing the relative expression of each protein and assessed its association with breast cancer-specific survival (BCSS) using a Cox proportional-hazards model. Based on our findings, an independent cohort of 3992 cases was used to validate the prognostic significance of the Ki67/BCL2 index. All survival analyses were conducted on complete data as well as data where missing values were resolved using multiple imputation. This study complied with reporting recommendations for tumour marker prognostic studies (REMARK) criteria. The Ki67/BCL2 index showed a significant association with BCSS at 10 years in estrogen receptor (ER)-positive disease. In multivariate analysis, adjusting for major clinical and molecular markers, the Ki67/BCL2 index retained prognostic significance, robustly classifying cases into three risk groups [intermediate- versus low-risk hazard ratio (HR), 1.5; 95% confidence interval (95% CI), 1.0-2.0; p = 0.031; high- versus low-risk HR, 2.6; 95% CI, 1.3-5.0; p = 0.005]. This finding was validated in an independent cohort of 3992 tumours containing 2761 ER-positive tumours (intermediate- versus low-risk HR, 1.7; 95% CI, 1.3-2.1; p < 0.001; high- versus low-risk HR, 2.0; 95% CI, 1.4-2.9; p < 0.001). Ki67 and BCL2 can be effectively combined to produce an index which is an independent predictor of BCSS in ER-positive breast cancer, enhancing their potential prognostic utility., (Copyright © 2011 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.)

Triple-negative breast cancers are an aggressive subtype of breast cancer with poor survival, but there remains little known about the etiologic factors that promote its initiation and development. Commonly inherited breast cancer risk factors identified through genome-wide association studies display heterogeneity of effect among breast cancer subtypes as defined by the status of estrogen and progesterone receptors. In the Triple Negative Breast Cancer Consortium (TNBCC), 22 common breast cancer susceptibility variants were investigated in 2,980 Caucasian women with triple-negative breast cancer and 4,978 healthy controls. We identified six single-nucleotide polymorphisms, including rs2046210 (ESR1), rs12662670 (ESR1), rs3803662 (TOX3), rs999737 (RAD51L1), rs8170 (19p13.1), and rs8100241 (19p13.1), significantly associated with the risk of triple-negative breast cancer. Together, our results provide convincing evidence of genetic susceptibility for triple-negative breast cancer.

Background: Tissue micro-arrays (TMAs) are increasingly used to generate data of the molecular phenotype of tumours in clinical epidemiology studies, such as studies of disease prognosis. However, TMA data are particularly prone to missingness. A variety of methods to deal with missing data are available. However, the validity of the various approaches is dependent on the structure of the missing data and there are few empirical studies dealing with missing data from molecular pathology. The purpose of this study was to investigate the results of four commonly used approaches to handling missing data from a large, multi-centre study of the molecular pathological determinants of prognosis in breast cancer., Patients and Methods: We pooled data from over 11,000 cases of invasive breast cancer from five studies that collected information on seven prognostic indicators together with survival time data. We compared the results of a multi-variate Cox regression using four approaches to handling missing data - complete case analysis (CCA), mean substitution (MS) and multiple imputation without inclusion of the outcome (MI-) and multiple imputation with inclusion of the outcome (MI+). We also performed an analysis in which missing data were simulated under different assumptions and the results of the four methods were compared., Results: Over half the cases had missing data on at least one of the seven variables and 11 percent had missing data on 4 or more. The multi-variate hazard ratio estimates based on multiple imputation models were very similar to those derived after using MS, with similar standard errors. Hazard ratio estimates based on the CCA were only slightly different, but the estimates were less precise as the standard errors were large. However, in data simulated to be missing completely at random (MCAR) or missing at random (MAR), estimates for MI+ were least biased and most accurate, whereas estimates for CCA were most biased and least accurate., Conclusion: In this study, empirical results from analyses using CCA, MS, MI- and MI+ were similar, although results from CCA were less precise. The results from simulations suggest that in general MI+ is likely to be the best. Given the ease of implementing MI in standard statistical software, the results of MI+ and CCA should be compared in any multi-variate analysis where missing data are a problem.

Background: Breast cancer is heterogeneous and the existing prognostic classifiers are limited in accuracy, leading to unnecessary treatment of numerous women. B-cell lymphoma 2 (BCL2), an antiapoptotic protein, has been proposed as a prognostic marker, but this effect is considered to relate to oestrogen receptor (ER) status. This study aimed to test the clinical validity of BCL2 as an independent prognostic marker., Methods: Five studies of 11 212 women with early-stage breast cancer were analysed. Individual patient data included tumour size, grade, lymph node status, endocrine therapy, chemotherapy and mortality. BCL2, ER, progesterone receptor (PR) and human epidermal growth factor receptor 2 (HER2) levels were determined in all tumours. A Cox model incorporating the time-dependent effects of each variable was used to explore the prognostic significance of BCL2., Results: In univariate analysis, ER, PR and BCL2 positivity was associated with improved survival and HER2 positivity with inferior survival. For ER and PR this effect was time dependent, whereas for BCL2 and HER2 the effect persisted over time. In multivariate analysis, BCL2 positivity retained independent prognostic significance (hazard ratio (HR) 0.76, 95% confidence interval (CI) 0.66-0.88, P<0.001). BCL2 was a powerful prognostic marker in ER- (HR 0.63, 95% CI 0.54-0.74, P<0.001) and ER+ disease (HR 0.56, 95% CI 0.48-0.65, P<0.001), and in HER2- (HR 0.55, 95% CI 0.49-0.61, P<0.001) and HER2+ disease (HR 0.70, 95% CI 0.57-0.85, P<0.001), irrespective of the type of adjuvant therapy received. Addition of BCL2 to the Adjuvant! Online prognostic model, for a subset of cases with a 10-year follow-up, improved the survival prediction (P=0.0039)., Conclusions: BCL2 is an independent indicator of favourable prognosis for all types of early-stage breast cancer. This study establishes the rationale for introduction of BCL2 immunohistochemistry to improve prognostic stratification. Further work is now needed to ascertain the exact way to apply BCL2 testing for risk stratification and to standardise BCL2 immunohistochemistry for this application.

Background: Immunohistochemical markers are often used to classify breast cancer into subtypes that are biologically distinct and behave differently. The aim of this study was to estimate mortality for patients with the major subtypes of breast cancer as classified using five immunohistochemical markers, to investigate patterns of mortality over time, and to test for heterogeneity by subtype., Methods and Findings: We pooled data from more than 10,000 cases of invasive breast cancer from 12 studies that had collected information on hormone receptor status, human epidermal growth factor receptor-2 (HER2) status, and at least one basal marker (cytokeratin [CK]5/6 or epidermal growth factor receptor [EGFR]) together with survival time data. Tumours were classified as luminal and nonluminal tumours according to hormone receptor expression. These two groups were further subdivided according to expression of HER2, and finally, the luminal and nonluminal HER2-negative tumours were categorised according to expression of basal markers. Changes in mortality rates over time differed by subtype. In women with luminal HER2-negative subtypes, mortality rates were constant over time, whereas mortality rates associated with the luminal HER2-positive and nonluminal subtypes tended to peak within 5 y of diagnosis and then decline over time. In the first 5 y after diagnosis the nonluminal tumours were associated with a poorer prognosis, but over longer follow-up times the prognosis was poorer in the luminal subtypes, with the worst prognosis at 15 y being in the luminal HER2-positive tumours. Basal marker expression distinguished the HER2-negative luminal and nonluminal tumours into different subtypes. These patterns were independent of any systemic adjuvant therapy., Conclusions: The six subtypes of breast cancer defined by expression of five markers show distinct behaviours with important differences in short term and long term prognosis. Application of these markers in the clinical setting could have the potential to improve the targeting of adjuvant chemotherapy to those most likely to benefit. The different patterns of mortality over time also suggest important biological differences between the subtypes that may result in differences in response to specific therapies, and that stratification of breast cancers by clinically relevant subtypes in clinical trials is urgently required.

The development of molecular pathologic components in epidemiologic studies offers opportunities to relate etiologic factors to specific tumor types, which in turn may allow the development of better overall risk prediction and provide clues about mechanisms that mediate risk factors. In addition, this research may help identify or validate tissue biomarkers related to prognosis and prediction of treatment responses. In this mini review, we highlight specific considerations related to the incorporation of pathology in epidemiologic studies, using breast cancer research as a model. Issues related to ensuring the representativeness of cases for which research tissue is available and understanding limitations resulting from variable procedures for tissue collection, fixation, and processing are discussed. The growing importance of molecular pathology in clinical medicine has led to increased emphasis on optimized tissue preparation, which should enhance this type of research. In addition, the availability of new technologies including tissue microarrays, image scanning, and automated analysis to achieve high-throughput standardized assessment of immunohistochemical markers, and potentially other assays, is enabling consistent scoring of a growing list of markers in large studies. Concurrently, methodologic research to extend the range of assays that can be done on fixed tissues is expanding possibilities for molecular pathologic studies in epidemiologic research.

Background: Several recent studies have shown that screen detection remains an independent prognostic factor after adjusting for disease stage at presentation. This study compares the molecular characteristics of screen-detected with symptomatic breast cancers to identify if differences in tumour biology may explain some of the survival benefit conferred by screen detection., Methods: A total of 1379 women (aged 50-70 years) with invasive breast cancer from a large population-based case-control study were included in the analysis. Individual patient data included tumour size, grade, lymph node status, adjuvant therapy, mammographic screening status and mortality. Immunohistochemistry was performed on tumour samples using 11 primary antibodies to define five molecular subtypes. The effect of screen detection compared with symptomatic diagnosis on survival was estimated after adjustment for grade, nodal status, Nottingham Prognostic Index (NPI) and the molecular markers., Results: Fifty-six per cent of the survival benefit associated with screen-detected breast cancer was accounted for by a shift in the NPI, a further 3-10% was explained by the biological variables and more than 30% of the effect remained unexplained., Conclusion: Currently known biomarkers remain limited in their ability to explain the heterogeneity of breast cancer fully. A more complete understanding of the biological profile of breast tumours will be necessary to assess the true impact of tumour biology on the improvement in survival seen with screen detection.