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1.  Critical research gaps and translational priorities for the successful prevention and treatment of breast cancer 
Eccles, Suzanne A | Aboagye, Eric O | Ali, Simak | Anderson, Annie S | Armes, Jo | Berditchevski, Fedor | Blaydes, Jeremy P | Brennan, Keith | Brown, Nicola J | Bryant, Helen E | Bundred, Nigel J | Burchell, Joy M | Campbell, Anna M | Carroll, Jason S | Clarke, Robert B | Coles, Charlotte E | Cook, Gary JR | Cox, Angela | Curtin, Nicola J | Dekker, Lodewijk V | dos Santos Silva, Isabel | Duffy, Stephen W | Easton, Douglas F | Eccles, Diana M | Edwards, Dylan R | Edwards, Joanne | Evans, D Gareth | Fenlon, Deborah F | Flanagan, James M | Foster, Claire | Gallagher, William M | Garcia-Closas, Montserrat | Gee, Julia M W | Gescher, Andy J | Goh, Vicky | Groves, Ashley M | Harvey, Amanda J | Harvie, Michelle | Hennessy, Bryan T | Hiscox, Stephen | Holen, Ingunn | Howell, Sacha J | Howell, Anthony | Hubbard, Gill | Hulbert-Williams, Nick | Hunter, Myra S | Jasani, Bharat | Jones, Louise J | Key, Timothy J | Kirwan, Cliona C | Kong, Anthony | Kunkler, Ian H | Langdon, Simon P | Leach, Martin O | Mann, David J | Marshall, John F | Martin, Lesley Ann | Martin, Stewart G | Macdougall, Jennifer E | Miles, David W | Miller, William R | Morris, Joanna R | Moss, Sue M | Mullan, Paul | Natrajan, Rachel | O’Connor, James PB | O’Connor, Rosemary | Palmieri, Carlo | Pharoah, Paul D P | Rakha, Emad A | Reed, Elizabeth | Robinson, Simon P | Sahai, Erik | Saxton, John M | Schmid, Peter | Smalley, Matthew J | Speirs, Valerie | Stein, Robert | Stingl, John | Streuli, Charles H | Tutt, Andrew N J | Velikova, Galina | Walker, Rosemary A | Watson, Christine J | Williams, Kaye J | Young, Leonie S | Thompson, Alastair M
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.
Methods
More 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.
Results
The 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.
Conclusions
With 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.
doi:10.1186/bcr3493
PMCID: PMC3907091  PMID: 24286369
2.  Modelling the overdiagnosis of breast cancer due to mammography screening in women aged 40 to 49 in the United Kingdom 
Breast Cancer Research : BCR  2012;14(6):R152.
Introduction
Overdiagnosis of breast cancer due to mammography screening, defined as the diagnosis of screen-detected cancers that would not have presented clinically in a women's lifetime in the absence of screening, has emerged as a highly contentious issue, as harm caused may question the benefit of mammographic screening. Most studies included women over 50 years old and little information is available for younger women.
Methods
We estimated the overdiagnosis of breast cancer due to screening in women aged 40 to 49 years using data from a randomised trial of annual mammographic screening starting at age 40 conducted in the UK. A six-state Markov model was constructed to estimate the sensitivity of mammography for invasive and in situ breast cancer and the screen-detectable mean sojourn time for non-progressive in situ, progressive in situ, and invasive breast cancer. Then, a 10-state simulation model of cancer progression, screening, and death, was developed to estimate overdiagnosis attributable to screening.
Results
The sensitivity of mammography for invasive and in situ breast cancers was 90% (95% CI, 72 to 99) and 82% (43 to 99), respectively. The screen-detectable mean sojourn time of preclinical non-progressive and progressive in situ cancers was 1.3 (0.4 to 3.4) and 0.11 (0.05 to 0.19) years, respectively, and 0.8 years (0.6 to 1.2) for preclinical invasive breast cancer. The proportion of screen-detected in situ cancers that were non-progressive was 55% (25 to 77) for the first and 40% (22 to 60) for subsequent screens. In our main analysis, overdiagnosis was estimated as 0.7% of screen-detected cancers. A sensitivity analysis, covering a wide range of alternative scenarios, yielded a range of 0.5% to 2.9%.
Conclusion
Although a high proportion of screen-detected in situ cancers were non-progressive, a majority of these would have presented clinically in the absence of screening. The extent of overdiagnosis due to screening in women aged 40 to 49 was small. Results also suggest annual screening is most suitable for women aged 40 to 49 in the United Kingdom due to short cancer sojourn times.
doi:10.1186/bcr3365
PMCID: PMC4053139  PMID: 23194032
3.  Localized fibroglandular tissue as a predictor of future tumour location within the breast 
Background
Mammographic density (MD) is a strong marker of breast cancer risk, but it is unclear whether tumours arise specifically within dense tissue.
Methods
In 231 British women diagnosed with breast cancer after at least one negative screen during a mammographic screening trial, we assessed whether tumour location was related to localised MD 5 years prior to diagnosis. Radiologists identified tumour locations on digitized diagnostic films and used a validated algorithm to align serial images from the same woman to locate the corresponding point on the pre-diagnostic film. A virtual 1cm-square grid was overlaid on pre-diagnostic films and MD calculated for each square within a woman’s breast (mean=271 squares/film). Conditional logistic regression, matching on a woman’s breast, was used to estimate the odds of a tumour arising in a square in relation to its pre-diagnostic square-specific MD.
Results
Median (inter-quartile range) pre-diagnostic MD was 98.2% (46.8%-100%) in 1cm-squares that subsequently contained the tumour and 41.0% (31.5%-53.9%) for the whole-breast. The odds of a tumour arising in a 1cm-square were, respectively, 6.1 (95%CI: 1.9, 20.1), 16.6 (5.2, 53.2) and 25.5-fold (8.1, 80.3) higher for squares in the 2nd, 3rd and 4th quartiles of pre-diagnostic MD relative to those in the lowest quartile within that breast (Ptrend<0.001). The corresponding odds were 2.3 (1.3, 4.0), 3.9 (2.3, 6.4) and 4.6 (2.8, 7.6) if a 3cm-square grid was used.
Conclusion
Tumours arise predominantly within the radiodense breast tissue.
Impact
Localised MD may be used as a predictor of subsequent tumour location within the breast.
doi:10.1158/1055-9965.EPI-11-0423
PMCID: PMC3154655  PMID: 21693627
breast density; mammography; breast cancer; early detection; screening
4.  The number of women who would need to be screened regularly by mammography to prevent one death from breast cancer 
Journal of Medical Screening  2011;18(4):210-212.
The number of women who would need to be screened regularly by mammography to prevent one death from breast cancer depends strongly on several factors, including the age at which regular screening starts, the period over which it continues, and the duration of follow-up after screening. Furthermore, more women would need to be INVITED for screening than would need to be SCREENED to prevent one death, since not all women invited attend for screening or are screened regularly. Failure to consider these important factors accounts for many of the major discrepancies between different published estimates. The randomised evidence indicates that, in high income countries, around one breast cancer death would be prevented in the long term for every 400 women aged 50–70 years regularly screened over a ten-year period.
doi:10.1258/jms.2011.011134
PMCID: PMC3266234  PMID: 22184734
5.  Mammographic density and markers of socioeconomic status: a cross-sectional study 
BMC Cancer  2010;10:35.
Background
Socioeconomic status (SES) is known to be positively associated with breast cancer risk but its relationship with mammographic density, a marker of susceptibility to breast cancer, is unclear. This study aims to investigate whether mammographic density varies by SES and to identify the underlying anthropometric, lifestyle and reproductive factors leading to such variation.
Methods
In a cross-sectional study of mammographic density in 487 pre-menopausal women, SES was assessed from questionnaire data using highest achieved level of formal education, quintiles of Census-derived Townsend scores and urban/rural classification of place of residence. Mammographic density was measured on digitised films using a computer-assisted method. Linear regression models were fitted to assess the association between SES variables and mammographic density, adjusting for correlated variables.
Results
In unadjusted models, percent density was positively associated with SES, with an absolute difference in percent density of 6.3% (95% CI 1.6%, 10.5%) between highest and lowest educational categories, and of 6.6% (95% CI -0.7%, 12.9%) between highest and lowest Townsend quintiles. These associations were mainly driven by strong negative associations between these SES variables and lucent area and were attenuated upon adjustment for body mass index (BMI). There was little evidence that reproductive factors explained this association. SES was not associated with the amount of dense tissue in the breast before or after BMI adjustment. The effect of education on percent density persisted after adjustment for Townsend score. Mammographic measures did not vary according to urban/rural place of residence.
Conclusions
The observed SES gradients in percent density paralleled known SES gradients in breast cancer risk. Although consistent with the hypothesis that percent density may be a mediator of the SES differentials in breast cancer risk, the SES gradients in percent density were mainly driven by the negative association between SES and BMI. Nevertheless, as density affects the sensitivity of screen-film mammography, the higher percent density found among high SES women would imply that these women have a higher risk of developing cancer but a lower likelihood of having it detected earlier.
doi:10.1186/1471-2407-10-35
PMCID: PMC2829497  PMID: 20144221
6.  The spatial distribution of radiodense breast tissue: a longitudinal study 
Introduction
Mammographic breast density is one of the strongest known markers of susceptibility to breast cancer. To date research into density has relied on a single measure (for example, percent density (PD)) summarising the average level of density for the whole breast, with no consideration of how the radiodense tissue may be distributed. This study aims to investigate the spatial distribution of density within the breast using 493 mammographic images from a sample of 165 premenopausal women (~3 medio-lateral oblique views per woman).
Methods
Each breast image was divided into 48 regions and the PD for the whole breast (overall PD) and for each one of its regions (regional PD) was estimated. The spatial autocorrelation (Moran's I value) of regional PD for each image was calculated to investigate spatial clustering of density, whether the degree of clustering varied between a woman's two breasts and whether it was affected by age and other known density correlates.
Results
The median Moran's I value for 165 women was 0.31 (interquartile range: 0.26, 0.37), indicating a clustered pattern. High-density areas tended to cluster in the central regions of the breast, regardless of the level of overall PD, but with considerable between-woman variability in regional PD. The degree of clustering was similar between a woman's two breasts (mean within-woman difference in Moran's I values between left and right breasts = 0.00 (95% confidence interval (CI) = -0.01, 0.01); P = 0.76) and did not change with aging (mean within-woman difference in I values between screens taken on average 8 years apart = 0.01 (95% CI = -0.01, 0.02); P = 0.30). Neither parity nor age at first birth affected the level of spatial autocorrelation of density, but increasing body mass index (BMI) was associated with a decrease in the degree of spatial clustering.
Conclusions
This study is the first to demonstrate that the distribution of radiodense tissue within the breast is spatially autocorrelated, generally with the high-density areas clustering in the central regions of the breast. The degree of clustering was similar within a woman's two breasts and between women, and was little affected by age or reproductive factors although it declined with increasing BMI.
doi:10.1186/bcr2318
PMCID: PMC2716501  PMID: 19490643

Results 1-6 (6)