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Additional file 1. Figure 1: DFI Benefit From 10 vs 5 y Tamoxifen is associated with increasing BCI (H/I) Levels. Figure 2: DFS benefit from 10 vs 5 y Tamoxifen is associated with increasing BCI (H/I) levels. Figure 3: Extended endocrine benefit from 10 vs 5 y Tamoxifen is not associated with Allred scores of ER, PR, or AR , or expression of ER or PR mRNA. Figure 4. Extended endocrine benefit from 10 vs 5 y Tamoxifen in the overall HR+ cohort (n=2,445). Table 1: BCI is prognostic for late distant recurrence

Novel HER2-directed antibody-drug conjugates (ADC) have demonstrated efficacy in HER2-low expressing breast cancers, which are currently defined as those with IHC scores of 1+ or 2+ with a negative ISH assay. However, current HER2 testing methods are designed to identify HER2 amplified tumors with “high” expression levels. The true definition of “HER2-low” expressing breast cancers remains controversial. Using quantitative molecular analysis of breast cancers based on RNA expression, the dynamic range of HER2 expression exceeds that detected by in situ IHC approaches. ERBB2 mRNA expression levels across IHC groups using patient samples derived from the Tamoxifen Exemestane Adjuvant Multicenter (TEAM) Trial were investigated. The standardized mean differences in ERBB2 mRNA scores in log base 2 are 0.47 (95% CI: 0.36,0.57), 0.58 (95% CI: 0.26,0.70), and 0.32 (95% CI: -0.12,0.75) when comparing IHC 0+ without staining vs IHC 0+ with some staining, IHC 0+ with some staining vs IHC 1+, and IHC 1+ vs IHC 2+/FISH-ve, respectively. The results showed that immunohistochemical methods have a comparatively limited dynamic range for measuring HER2 protein expression. The range of expression based on RNA abundance suggest that a molecular method defining HER2-low cancers may better serve the treatment decision needs of this group. Indeed, the validity of RNA abundance to identify HER2-low cancers and predict treatment response need to be further evaluated by prospective clinical trials.

Multiparametric assays for risk stratification are widely used in the management of both node negative and node positive hormone receptor positive invasive breast cancer. Recent data from multiple sources suggests that different tests may provide different risk estimates at the individual patient level. The TEAM pathology study consists of 3284 postmenopausal ER+ve breast cancers treated with endocrine therapy Using genes comprising the following multi-parametric tests OncotypeDx(R), Prosigna (TM) and MammaPrint(R) signatures were trained to recapitulate true assay results. Patients were then classified into risk groups and survival assessed. Whilst likelihood chi(2) ratios suggested limited value for combining tests, Kaplan-Meier and LogRank tests within risk groups suggested combinations of tests provided statistically significant stratification of potential clinical value. Paradoxically whilst Prosigna-trained results stratified Oncotype-trained subgroups across low and intermediate risk categories, only intermediate risk Prosigna-trained cases were further stratified by Oncotype-trained results. Both Oncotype-trained and Prosigna-trained results further stratified MammaPrint-trained low risk cases, and MammaPrint-trained results also stratified Oncotype-trained low and intermediate risk groups but not Prosigna-trained results. Comparisons between existing multiparametric tests are challenging, and evidence on discordance between tests in risk stratification presents further dilemmas. Detailed analysis of the TEAM pathology study suggests a complex inter-relationship between test results in the same patient cohorts which requires careful evaluation regarding test utility. Further prognostic improvement appears both desirable and achievable.

The Nottingham Prognostic Index Plus (NPI+) is a clinical decision making tool in breast cancer (BC) that aims to provide improved patient outcome stratification superior to the traditional NPI. This study aimed to validate the NPI+ in an independent series of BC. Eight hundred and eighty five primary early stage BC cases from Edinburgh were semi‐quantitatively assessed for 10 biomarkers [Estrogen Receptor (ER), Progesterone Receptor (PgR), cytokeratin (CK) 5/6, CK7/8, epidermal growth factor receptor (EGFR), HER2, HER3, HER4, p53, and Mucin 1] using immunohistochemistry and classified into biological classes by fuzzy logic‐derived algorithms previously developed in the Nottingham series. Subsequently, NPI+ Prognostic Groups (PGs) were assigned for each class using bespoke NPI‐like formulae, previously developed in each NPI+ biological class of the Nottingham series, utilising clinicopathological parameters: number of positive nodes, pathological tumour size, stage, tubule formation, nuclear pleomorphism and mitotic counts. Biological classes and PGs were compared between the Edinburgh and Nottingham series using Cramer's V and their role in patient outcome prediction using Kaplan–Meier curves and tested using Log Rank. The NPI+ biomarker panel classified the Edinburgh series into seven biological classes similar to the Nottingham series (p > 0.01). The biological classes were significantly associated with patient outcome (p 0.01). The good PGs were similarly validated in Luminal B, Basal p53 normal, HER2+/ER− tumours and the poor PG in the Luminal N class (p > 0.01). Due to small patient numbers assigned to the remaining PGs, Luminal N, Luminal B, Basal p53 normal and HER2+/ER− classes could not be validated. This study demonstrates the reproducibility of NPI+ and confirmed its prognostic value in an independent cohort of primary BC. Further validation in large randomised controlled trial material is warranted.

Many trials from yesteryear looked at the benefit of treatment A v treatment B in terms of recurrence and survival data only. With the advent of new research methods available such as whole genome sequencing, digital image analysis, Nanostring technologies, there is a burgeoning trend towards revisiting these older cohorts to collect tissue samples and update the follow-up data. The idea of applying modern research techniques to historic tissue cohorts with 30 years of follow-up data is attractive to commercial investors who are trying to validate new personalised medicine approaches to breast cancer treatments. Applying new methods to patient samples from long ago combined with a wealth of long term follow up is a dream research project. However it is not without several hurdles to overcome. Patient consent was gathered in a more lax way back in the early 1990’s so although patients consented to enter a trial, the process would not have been as rigorously documented as it is today. This means accessing these patients data records causes issue as we do not have ‘explicit informed consent’ to do so. We could go back to patients and re-consent them but this brings its own issues. How do we locate these patients to get permission to access their patient records without accessing the patient records to find out if they are still alive or are mentally competent to be re-consented? What data do you require from the patient records? The date of death and /or relapse? Or more specific data such as tumour grade, patient age at diagnosis, surgery or death? Where will the data be stored and who will have access to such data? This is the biggest hurdle we have faced as with overseas funders unable to perform their own analysis on their own sequencing data as the patient derived data must not leave the UK/EU. With the introduction of the Patient Benefit and Privacy Panel in 2016 and the introduction of the new GDPR regulations in May 2018 the access and use of data seems to be getting tighter yet the opportunities to use it keep expanding. Finding the correct balance to satisfy the regulators is key. A video of this presentation can be viewed at https://media.ed.ac.uk/media/0_z6dtx4zx

Context.-Hormone receptors HER2/neu and Ki-67 are markers of residual risk in early breast cancer. An algorithm (IHC4) combining these markers may provide additional information on residual risk of recurrence in patients treated with hormone therapy. Objective.-To independently validate the IHC4 algorithm in the multinational Tamoxifen Versus Exemestane Adjuvant Multicenter Trial (TEAM) cohort, originally developed on the trans-ATAC (Arimidex, Tamoxifen, Alone or in Combination Trial) cohort, by comparing 2 methodologies. Design.-The IHC4 biomarker expression was quantified on TEAM cohort samples (n = 2919) by using 2 independent methodologies (conventional 3,3’-diaminobezidine [DAB] immunohistochemistry with image analysis and standardized quantitative immunofluorescence [QIF] by AQUA technology). The IHC4 scores were calculated by using the same previously established coefficients and then compared with recurrence-free and distant recurrence-free survival, using multivariate Cox proportional hazards modeling. Results.-The QIF model was highly significant for prediction of residual risk (P < .001), with continuous model scores showing a hazard ratio (HR) of 1.012 (95% confidence interval [95% CI]: 1.010-1.014), which was significantly higher than that for the DAB model (HR: 1.008, 95% CI: 1.006-1.009); P < .001). Each model added significant prognostic value in addition to recognized clinical prognostic factors, including nodal status, in multivariate analyses. Quantitative immunofluorescence, however, showed more accuracy with respect to overall residual risk assessment than the DAB model. Conclusions.-The use of the IHC4 algorithm was validated on the TEAM trial for predicting residual risk in patients with breast cancer. These data support the use of the IHC4 algorithm clinically, but quantitative and standardized approaches need to be used.

Purpose Deregulation of key PI3K/AKT pathway genes may contribute to endocrine resistance in breast cancer (BC). PIK3CA is the most frequently mutated gene in luminal BC (similar to 35%); however, the effect of mutations in helical versus kinase domains remains controversial. We hypothesize that improved outcomes occur in patients with estrogen receptor-positive (ER positive) BC receiving endocrine therapy and possessing PIK3CA mutations. Materials and Methods DNA was extracted from 4,540 formalin-fixed paraffin-embedded BC samples from the Exemestane Versus Tamoxifen-Exemestane pathology study. Mutational analyses were performed for 25 mutations (PIK3CAx10, AKT1x1, KRASx5, HRASx3, NRASx2 and BRAFx4). Results PIK3CA mutations were frequent (39.8%), whereas RAS/RAF mutations were rare (

Purpose Some postmenopausal patients with hormone-sensitive early breast cancer remain at high risk of relapse despite endocrine therapy and, in addition, might benefit from adjuvant chemotherapy. The challenge is to prospectively identify such patients. The Mammostrat test uses five immunohistochemical markers to stratify patients regarding recurrence risk and may inform treatment decisions. We tested the efficacy of this panel in the Tamoxifen versus Exemestane Adjuvant Multicenter (TEAM) trial. Patients and Methods Pathology blocks from 4,598 TEAM patients were collected, and tissue microarrays (TMAs) were constructed. The cohort was 47% node-positive, and 36% of patients in the cohort were treated with adjuvant chemotherapy. Triplicate 0.6-mm(2) TMA cores were stained, and positivity for p53, HTF9C, CEACAM5, NDRG1, and SLC7A5 was assessed. Cases were assigned a Mammostrat risk score, and distant relapse-free survival (DRFS) and disease-free survival (DFS) were analyzed. Results In multivariate regression analyses, which were corrected for conventional clinicopathologic markers, Mammostrat provided significant additional information on DRFS after endocrine therapy in estrogen receptor (ER) -positive node-negative patients (n = 1,226) who did not receive chemotherapy (P=.004). Additional analyses in all patients not exposed to chemotherapy, irrespective of nodal status (n = 2,559) and in the entire cohort (n = 3,837) showed Mammostrat scores provided additional information on DRFS in these groups (P=.001 and P