Breast cancer is a complex and heterogeneous disease. Gene expression profiling has contributed significantly to our understanding of this heterogeneity at a molecular level, refining taxonomy based on simple measures such as histological type, tumour grade, lymph node status and the presence of predictive markers like oestrogen receptor and human epidermal growth factor receptor 2 (HER2) to a more sophisticated classification comprising luminal A, luminal B, basal-like, HER2-positive and normal subgroups. In the laboratory, breast cancer is often modelled using established cell lines. In the present review we discuss some of the issues surrounding the use of breast cancer cell lines as experimental models, in light of these revised clinical classifications, and put forward suggestions for improving their use in translational breast cancer research.

This is a repository copy of Pan-cancer image-based detection of clinically actionable genetic alterations.

One of the benefits of Digital PCR (dPCR) is the potential for unparalleled precision enabling smaller fold change measurements. An example of an assessment that could benefit from such improved precision is the measurement of tumour-associated copy number variation (CNV) in the cell free DNA (cfDNA) fraction of patient blood plasma. To investigate the potential precision of dPCR and compare it with the established technique of quantitative PCR (qPCR), we used breast cancer cell lines to investigate HER2 gene amplification and modelled a range of different CNVs. We showed that, with equal experimental replication, dPCR could measure a smaller CNV than qPCR. As dPCR precision is directly dependent upon both the number of replicate measurements and the template concentration, we also developed a method to assist the design of dPCR experiments for measuring CNV. Using an existing model (based on Poisson and binomial distributions) to derive an expression for the variance inherent in dPCR, we produced a power calculation to define the experimental size required to reliably detect a given fold change at a given template concentration. This work will facilitate any future translation of dPCR to key diagnostic applications, such as cancer diagnostics and analysis of cfDNA.

IntroductionBreast cancer remains a significant scientific, clinical and societal challenge. This gap analysis has reviewed and critically assessed enduring issues and new challenges emerging from recent research, and proposes strategies for translating solutions into practice.MethodsMore than 100 internationally recognised specialist breast cancer scientists, clinicians and healthcare professionals collaborated to address nine thematic areas: genetics, epigenetics and epidemiology; molecular pathology and cell biology; hormonal influences and endocrine therapy; imaging, detection and screening; current/novel therapies and biomarkers; drug resistance; metastasis, angiogenesis, circulating tumour cells, cancer ‘stem’ cells; risk and prevention; living with and managing breast cancer and its treatment. The groups developed summary papers through an iterative process which, following further appraisal from experts and patients, were melded into this summary account.ResultsThe 10 major gaps identified were: (1) understanding the functions and contextual interactions of genetic and epigenetic changes in normal breast development and during malignant transformation; (2) how to implement sustainable lifestyle changes (diet, exercise and weight) and chemopreventive strategies; (3) the need for tailored screening approaches including clinically actionable tests; (4) enhancing knowledge of molecular drivers behind breast cancer subtypes, progression and metastasis; (5) understanding the molecular mechanisms of tumour heterogeneity, dormancy, de novo or acquired resistance and how to target key nodes in these dynamic processes; (6) developing validated markers for chemosensitivity and radiosensitivity; (7) understanding the optimal duration, sequencing and rational combinations of treatment for improved personalised therapy; (8) validating multimodality imaging biomarkers for minimally invasive diagnosis and monitoring of responses in primary and metastatic disease; (9) developing interventions and support to improve the survivorship experience; (10) a continuing need for clinical material for translational research derived from normal breast, blood, primary, relapsed, metastatic and drug-resistant cancers with expert bioinformatics support to maximise its utility. The proposed infrastructural enablers include enhanced resources to support clinically relevant in vitro and in vivo tumour models; improved access to appropriate, fully annotated clinical samples; extended biomarker discovery, validation and standardisation; and facilitated cross-discipline working.ConclusionsWith resources to conduct further high-quality targeted research focusing on the gaps identified, increased knowledge translating into improved clinical care should be achievable within five years.

Quantitative expression of ER and PgR and HER-2 status did not identify patients with differential relative benefit from anastrozole over tamoxifen: TTR was longer for anastrozole than for tamoxifen in all molecular subgroups. Low ER or PgR or high HER-2 expression are associated with a high risk of recurrence with either anastrozole or tamoxifen.

The majority of breast cancer research is conducted using established breast cancer cell lines as in vitro models. An alternative is to use cultures established from primary breast tumours. Here, we discuss the pros and cons of using both of these models in translational breast cancer research.

To gain insights into the possible role of oestrogen receptor (ER) beta in breast carcinogenesis, immunohistochemical analysis of ER beta was performed on 512 breast specimens encompassing normal (n = 138), pure ductal carcinoma in situ (n = 16), invasive cancers (n = 319), lymph node metastases (n = 31), and recurrences (n = 8). Real-time polymerase chain reaction (PCR) was used to investigate the methylation status of the ER beta gene in the ER beta negative breast cancer cell lines SkBr3 and MDA-MB-435. A gradual reduction in, but not a complete loss of, ER beta expression was observed during the transition from normal and pre-invasive lesions to invasive cancers, where ER beta was lost in 21% of cases. This was more pronounced in invasive ductal than in lobular carcinomas, a significantly higher proportion of which were ER beta-positive (74% compared with 91%, respectively, p = 0.0004). Examination of paired primary cancers with their axillary lymph node metastases showed that if ER beta was present in the primary tumour, it persisted in the metastasis. Treatment of ER beta-negative cell lines with DNA methyl transferase inhibitors restored ER beta expression, providing experimental evidence that silencing of ER beta in breast carcinomas could be due to promoter hypermethylation. These results suggest that loss of ER beta expression is one of the hallmarks of breast carcinogenesis and that it may be a reversible process involving methylation.

Background Chromosomal instability (CIN) is thought to be associated with poor prognosis in solid tumours, however, evidence from pre-clinical and mouse tumour models suggest that CIN may paradoxically enhance or impair cancer cell fitness. Breast cancer prognostic expression signature sets, which reflect tumour CIN status, efficiently delineate outcome in ER-positive breast cancer in contrast to ER-negative breast cancer, suggesting that the relationship of CIN with prognosis differs in these two breast cancer subtypes. Methods Direct assessment of CIN requires single cell analysis methods such as centromeric fluorescence in situ hybridisation (FISH) aimed at determining the variation around the modal number of two or more chromosomes within individual tumour nuclei. Here we document the frequency of tumour CIN by dual centromeric FISH analysis in a retrospective primary breast cancer cohort of 246 patients with survival outcome. Results There was increased CIN and clonal heterogeneity in ER-negative compared to ER-positive breast cancer. Consistent with a negative impact of CIN on cellular fitness, extreme CIN in ER-negative breast cancer was an independent variable associated with improved long-term survival in multivariate analysis. In contrast, a linear relationship of increasing CIN with poorer prognosis in ER-positive breast cancer was observed, using three independent measures of CIN. Conclusions The paradoxical relationship between extreme CIN and cancer outcome in the ER-negative cohorts may explain why prognostic expression signatures, reflecting tumour CIN status, fail to predict outcome in this subgroup. Impact Assessment of tumour CIN status may support risk stratification in ER negative breast cancer and requires prospective validation.