| Literature DB >> 24286369 |
Suzanne A Eccles, Eric O Aboagye, Simak Ali, Annie S Anderson, Jo Armes, Fedor Berditchevski, Jeremy P Blaydes, Keith Brennan, Nicola J Brown, Helen E Bryant, Nigel J Bundred, Joy M Burchell, Anna M Campbell, Jason S Carroll, Robert B Clarke, Charlotte E Coles, Gary J R Cook, Angela Cox, Nicola J Curtin, Lodewijk V Dekker, Isabel dos Santos Silva, Stephen W Duffy, Douglas F Easton, Diana M Eccles, Dylan R Edwards, Joanne Edwards, D Evans, Deborah F Fenlon, James M Flanagan, Claire Foster, William M Gallagher, Montserrat Garcia-Closas, Julia M W Gee, Andy J Gescher, Vicky Goh, Ashley M Groves, Amanda J Harvey, Michelle Harvie, Bryan T Hennessy, Stephen Hiscox, Ingunn Holen, Sacha J Howell, Anthony Howell, Gill Hubbard, Nick Hulbert-Williams, Myra S Hunter, Bharat Jasani, Louise J Jones, Timothy J Key, Cliona C Kirwan, Anthony Kong, Ian H Kunkler, Simon P Langdon, Martin O Leach, David J Mann, John F Marshall, Lesley Martin, Stewart G Martin, Jennifer E Macdougall, David W Miles, William R Miller, Joanna R Morris, Sue M Moss, Paul Mullan, Rachel Natrajan, James P B O'Connor, Rosemary O'Connor, Carlo Palmieri, Paul D P Pharoah, Emad A Rakha, Elizabeth Reed, Simon P Robinson, Erik Sahai, John M Saxton, Peter Schmid, Matthew J Smalley, Valerie Speirs, Robert Stein, John Stingl, Charles H Streuli, Andrew N J Tutt, Galina Velikova, Rosemary A Walker, Christine J Watson, Kaye J Williams, Leonie S Young, Alastair M Thompson.
Abstract
INTRODUCTION: Breast 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.Entities:
Mesh:
Year: 2013 PMID: 24286369 PMCID: PMC3907091 DOI: 10.1186/bcr3493
Source DB: PubMed Journal: Breast Cancer Res ISSN: 1465-5411 Impact factor: 6.466
Figure 1Gap analysis methodology. The flow chart illustrates the concept, processes and procedures devised to generate the gap analysis review.
Figure 2Familial cancer genetics. The proportion of the familial component of breast cancers that can be ascribed to specific genetic defects. The difference between June 2007 and 2013 shows the impact of genome-wide association studies (GWAS) that have now identified 77 common low-risk SNPs. Courtesy of Professor Douglas Easton (University of Cambridge). Reprinted by permission from Macmillan Publishers Ltd: Nature Genetics (45,345-348), copyright 2013.
Figure 3Tumour heterogeneity. (A) Recent molecular and genetic profiling has demonstrated significant intratumoural heterogeneity that can arise through genomic instability (leading to mutations), epigenetic events and/or microenvironmental influences. The stem cell hypothesis proposes that tumour-initiating cells are pluripotent and can thus give rise to progeny of multiple phenotypes; alternatively heterogeneity could be due to stochastic events. Temporal heterogeneity can be exacerbated by therapy (theoretically due to clonal evolution as some clones are eliminated whilst others expand). The significant molecular/genetic differences between cells in different areas within individual cancers, between primary and metastatic tumours (and potentially between cancer cells that successfully colonise different organs) have implications for the reliability of primary tumour biopsies for diagnosis, seeking biomarkers for treatment planning and responses to therapy. In addition, there is substantial inter-tumour heterogeneity. (B) shows images of two patients who presented with breast cancers of identical histological type and biochemical parameters. Four years later, one patient is clear of disease, while the other has evidence of multiple distant metastases, illustrative of between-patient heterogeneity in terms of response to therapy (clinical images kindly provided by Professor William Gallagher, with thanks to Dr Rut Klinger and Dr Donal Brennan (UCD Conway Institute).
Figure 4Microenvironmental influences on breast cancer. Breast cancer biology, progression and response to therapy is influenced at many levels from epigenetic effects on gene expression (for example methylation) through soluble and cell-mediated stromal interactions, intratumoural inflammatory and angiogenic components, hypoxia, host endocrinological and immunological status through to exposure to multiple agents in the environment in which we live.
Figure 5Molecular heterogeneity of endocrine resistance. Unsupervised hierarchical clustering of mRNA from 60 endocrine-resistant breast cancers shows heterogeneity in gene expression suggesting a multiplicity of underlying mechanisms including changes in oestrogen and interferon signalling and stromal genes. Courtesy of Professor William Miller and Dr Alexey Larionov, based on a poster presentation at the thirty-second annual CTRC-AACR San Antonio Breast Cancer Symposium, Dec 10–13, 2009 [272].
Figure 6Comparative properties of experimental tumour models.In vitro assays of tumour growth and response to therapy can be conducted in two dimensions or three dimensions - the latter more closely approximating the biology of solid tumours than a simple monolayer. Cultures can be enhanced by the addition of matrix proteins and/or host cells and can be adapted to measure not only tumour cell proliferation, but also additional cancer hallmarks such as invasion. Standard in vivo assays depend upon the transplantation of established human tumour cell lines into athymic (immune-incompetent) hosts. These models are relatively simple and easy to use, but are increasingly complemented by genetically engineered mice harbouring targeted genetic mutations which render them susceptible to developing mammary cancers. The figure summarises key advantages and disadvantages of each model and means by which their clinical relevance and utility might be enhanced. Based on a figure provided courtesy of Claire Nash in Dr Valerie Speirs’ group (University of Leeds).
Figure 7Longitudinal sampling and enhanced biobanks. The longitudinal collection of blood and samples from normal breasts, primary cancers and relapsed/metastatic/treatment-resistant disease is essential in order to address the origins, heterogeneity and evolution of breast cancers. Samples are required from as broad a patient population as possible to understand ethnic, age-related and gender differences in incidence, molecular subtypes, prognosis and response to treatment. Sequential samples (ideally patient-matched) from primary tumours and metastases will enable detailed studies of tumour evolution/progression and provide material for generating new cell lines and patient-derived xenografts for translational research. Multimodality imaging and metabolomic analyses will add further dimensions of valuable information. Based on a figure provided courtesy of Professor William Gallagher, with thanks to Dr Rut Klinger (UCD Conway Institute).
Figure 8Integrated vision of multidisciplinary research. Enhanced integration and utilisation of the vast amount of clinical and experimental observations relating to breast cancer is urgently required. Clinical observations generate hypotheses relating to the origins of cancer, its underlying molecular pathology and potential vulnerabilities that could be exploited for therapeutic benefit. Such insights provide opportunities for testing and validation in in vitro, in vivo and in silico models. Drug discovery aims to provide inhibitors of major oncogenic ‘drivers’ for use singly or in combination with conventional therapies; such personalised medicine requires the co-development of predictive and pharmacodynamic biomarkers of response. Results from preclinical therapy studies and clinical trials should be fed back into searchable databases to reveal reasons for treatment failure and allow new strategies to be tested and deployed. Based on a figure provided courtesy of Professor William Gallagher, with thanks to Professor Walter Kolch (UCD Conway Institute).