Updated recommendations for HER2 testing in the UK

This paper serves to update previously published guidance on rationale and methodology for HER2 laboratory testing following the recommendation for the use of HER2 targeted treatment in the management of advanced breast cancer in the UK. Emphasis is placed on the standardisation of methodology and assessment and strategies to achieve high quality performance. A two phase testing algorithm based on first line immunocytochemistry evaluation and second line fluorescence in situ hybridisation assessment of borderline cases is recommended. To ensure maintenance of expertise, an annual caseload volume of at least 250 cases is recommended for laboratories providing a testing service

Recommendations for HER2 testing in the UK

Determining the HER2 status of breast carcinomas is a prerequisite for the use of the monoclonal antibody trastuzumab (Herceptin(R)), which has recently been licensed for the treatment of metastatic disease. This necessitates a test based on archival material. The preferred analyses are immunohistochemistry with fluorescent in situ hybridisation (FISH) as a follow up test for ambiguous results. Guidelines have been developed for standardised, well controlled procedures for the provision of reliable results. A group of three reference laboratories has been established to provide advice, quality assurance, and materials, where needed

Expression of Lamin A/C in early-stage breast cancer and its prognostic value

Purpose: Lamins A/C, a major component of the nuclear lamina, plays key roles in maintaining nuclear integrity, regulation of gene expression, cell proliferation and apoptosis. Reduced lamin A/C expression in cancer has been reported to be a sign of poor prognosis. However, its clinical significance in breast cancer remains to be defined. This study aimed to evaluate expression and prognostic significance of lamin A/C in early-stage breast cancer.Methods: Using immunohistochemical staining of tissue microarrays, expression of lamin A/C was evaluated in a large well-characterised series of early-stage operable breast cancer (n=938) obtained from Nottingham Primary Breast Carcinoma Series. Association of lamin A/C expression with clinicopathological parameters and outcome was evaluated.Results: Positive expression rate of lamin A/C in breast cancer was 42.2% (n=398). Reduced/loss of expression of lamin A/C was significantly associated with high histological grade (p [less than] 0.001), larger tumour size (p=0.004), poor Nottingham Prognostic Index (NPI) score (p [less than] 0.001), lymphovascular invasion (p=0.014) and development of distant metastasis (p=0.027). Survival analysis showed that reduced/loss of expression of lamin A/C was significantly associated with shorter breast cancer specific survival (p=0.008).Conclusion: This study suggests lamin A/C plays a role in breast cancer and loss of its expression is associated with variables of poor prognosis and shorter outcome

Human epidermal growth receptor-2 overexpressing early operable primary breast cancers in older (>=70 years) women: biology and clinical outcome in comparison with younger (less than 70 years) patients

Introduction: There is dearth of literature reporting the prevalence and biological characteristics as well as the long-termclinical outcome of human epidermal growth factor receptor-2 (HER2) overexpressing tumours in older women. Currently,research involving trastuzumab at large focuses on the younger population. This study aimed to analyse their biological characteristicsand to compare them with their younger counterparts from a single centre with a long-term clinical follow-up.Methods: Over 37 years (1973–2010), 1758 older (≥70 years) women with early operable (less than 5 cm) primary breast cancer were managed in a dedicated clinic and have complete clinical information available. Of these, 813 patients underwent primary surgery and 575 had good quality tumour samples available for tissue microarray analysis using indirect immunohistochemistry. Comparison was made with data from a well-characterised younger (70 years = 70%, P = 0.51).Conclusion:HER2 overexpressing tumours in older women showed relatively a less aggressive phenotype and did not show any inferior long-term clinical outcome despite not having received chemotherapy when compared with the younger patients. The precise role of different adjuvant systemic therapies in this population needs to be delineated

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

Current management of breast cancer (BC) relies on risk stratification based on well-defined clinicopathologic factors. Global gene expression profiling studies have demonstrated that BC comprises distinct molecular classes with clinical relevance. In this study, we hypothesized that molecular features of BC are a key driver of tumour behaviour and when coupled with a novel and bespoke application of established clinicopathologic prognostic variables, can predict both clinical outcome and relevant therapeutic options more accurately than existing methods. In the current study, a comprehensive panel of biomarkers with relevance to BC was applied to a large and well-characterised series of BC, using immunohistochemistry and different multivariate clustering techniques, to identify the key molecular classes. Subsequently, each class was further stratified using a set of well-defined prognostic clinicopathologic variables. These variables were combined in formulae to prognostically stratify different molecular classes, collectively known as the Nottingham Prognostic Index Plus (NPI+). NPI+ was then used to predict outcome in the different molecular classes with.Seven core molecular classes were identified using a selective panel of 10 biomarkers. Incorporation of clinicopathologic variables in a second stage analysis resulted in identification of distinct prognostic groups within each molecular class (NPI+). Outcome analysis showed that using the bespoke NPI formulae for each biological breast cancer class provides improved patient outcome stratification superior to the traditional NPI. This study provides proof-of-principle evidence for the use of NPI+ in supporting improved individualised clinical decision making

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

The combined expression of solute carriers is associated with a poor prognosis in highly proliferative ER+ breast cancer

Purpose: Breast cancer (BC) is a heterogeneous disease characterised by variant biology, metabolic activity, and patient outcome. Glutamine availability for growth and progression of BC is important in several BC subtypes. This study aimed to evaluate the biological and prognostic role of the combined expression of key glutamine transporters, SLC1A5, SLC7A5 and SLC3A2 in BC with emphasis on the intrinsic molecular subtypes. Methods: SLC1A5, SLC7A5 and SLC3A2 were assessed at the protein level, using immunohistochemistry on tissue microarrays constructed from a large well characterised BC cohort (n=2,248). Patients were stratified into accredited clusters based on protein expression and correlated with clinicopathological parameters, molecular subtypes, and patient outcome. Results: Clustering analysis of SLC1A5, SLC7A5 and SLC3A2 identified three clusters Low SLCs (SLC1A5-/SLC7A5-/SLC3A2-), High SLC1A5 (SLC1A5+/SLC7A5-/SLC3A2-) and High SLCs (SLC1A5+/SLC7A5+/SLC3A2+) which had distinct correlations to known prognostic factors and patient outcome (p<0.001). The key regulator of tumour cell metabolism, c-MYC, was significantly expressed in tumours in the High SLCs cluster (p<0.001). When different BC subtypes were considered, the association with the poor outcome was observed in the ER+ high proliferation/luminal B class only (p= 0.003). In multivariate analysis, SLC clusters were independent risk factor for shorter breast cancer specific survival (p= 0.001). Conclusion: The co-operative expression of SLC1A5, SLC7A5 and SLC3A2 appears to play a role in the aggressive subclass of ER+ high proliferation/ luminal BC, driven by c-MYC, and therefore have the potential to act as therapeutic targets, particularly in synergism

Current trials to reduce surgical intervention in ductal carcinoma in situ of the breast: critical review

The high proportion of ductal carcinoma in situ (DCIS) presented in mammographic screening and the relatively low risk of progression to invasive disease have raised questions related to overtreatment. Following a review of current DCIS management protocols a more conservative approach has been suggested. Clinical trials have been introduced to evaluate the option of avoiding surgical intervention in a proportion of patients with DCIS defined as “low-risk” using certain clinicopathological criteria. These trials can potentially provide evidence-based models of active surveillance (with or without endocrine therapy) as a future management approach. Despite the undisputable fact of our need to address the obvious overtreatment of screen-detected DCIS, some important questions need to be considered regarding these trials including the eligibility criteria and definition of risk, the proportion of patient eligible for inclusion, and the length of time required for proper analysis of the trials' outcome in view of the long-term natural history of DCIS progression particularly the low-risk group. These factors can potentially affect the practicality and future impact of such trials. This review provides critical analysis of current DCIS management trials and highlights critical issues related to their practicality and the expected outcome

An end-to-end deep learning histochemical scoring system for breast cancer TMA

One of the methods for stratifying different molecular classes of breast cancer is the Nottingham Prognostic Index Plus (NPI+) which uses breast cancer relevant biomarkers to stain tumour tissues prepared on tissue microarray (TMA). To determine the molecular class of the tumour, pathologists will have to manually mark the nuclei activity biomarkers through a microscope and use a semi-quantitative assessment method to assign a histochemical score (H-Score) to each TMA core. Manually marking positively stained nuclei is a time consuming, imprecise and subjective process which will lead to inter-observer and intra-observer discrepancies. In this paper, we present an end-to-end deep learning system which directly predicts the H-Score automatically. Our system imitates the pathologists’ decision process and uses one fully convolutional network (FCN) to extract all nuclei region (tumour and non-tumour), a second FCN to extract tumour nuclei region, and a multi-column convolutional neural network which takes the outputs of the first two FCNs and the stain intensity description image as input and acts as the high-level decision making mechanism to directly output the H-Score of the input TMA image. To the best of our knowledge, this is the first end-to-end system that takes a TMA image as input and directly outputs a clinical score. We will present experimental results which demonstrate that the H-Scores predicted by our model have very high and statistically significant correlation with experienced pathologists’ scores and that the H-Score discrepancy between our algorithm and the pathologists is on par with the inter-subject discrepancy between the pathologists

Cell division cycle 25C (CDC25C) expression confers poor prognosis in invasive breast cancer

Background: CDC25C, belonging to the Cdc25 phosphatase family, plays a major role in cell cycle control, impacting on DNA repair and apoptosis. It has been shown that poor prognosis/copy number high Luminal A breast cancers (BCs) are enriched for the Aurora kinase pathway including CDC25C leading to CDK1 activation (Ciriello et al, Breast Cancer Research Treatment, 2013:409). This study examined the associations of CDC25C with clinicopathological and molecular features in BCs including the low grade ER positive cohort. Methodology: CDC25C mRNA expression was studied in the METABRIC BC cohort (n=1980) and externally validated using online expression datasets [bc-GenExMiner v4.0]. CDC25C protein expression level was assessed immunohistochemically on a large annotated series of BC (n= 1330) and correlations made with clinicopathological parameters and patient outcome. Results: High CDC25C expression was significantly associated with poor prognostic factors including high grade, large tumour size, medullary like tumours, poorer NPI, ER-/PR- Her2+ status (p<0.001) and was differentially expressed in poor prognosis integrative clusters 5 and 10 (p<0.001). Cytoplasmic CDC25C (c-CDC25C) protein showed positive association with non-NST and non-medullary tumour subtypes while nuclear CDC25C (n-CDC25C) negatively associated with tumour stage (p<0.05). There was no association with ER, PR status, NPI and lymph nodes. However, high c-CDC25C resulted in poor survival at 20 years in the Grade 1 ER+ cohort (p=0.007), while high n-CDC25C showed better long term survival (p<0.001). Pooled CDC25C expression data in the external validation cohort showed an association with poor outcome (p<0.0001, HR = 1.45, 95 % CI 1.28—1.64). Conclusion: CDC25C appears to be associated with poor prognosis in BC including the Grade 1 ER+ cohort, indicating the importance of further functional analyses