Epithelial tissues have sparse stroma, in contrast to their corresponding tumours. The effect of cancer cells on stromal cells is well recognized. Increasingly, stromal components, such as endothelial and immune cells, are considered indispensable for cancer progression. The role of desmoplastic stroma, in contrast, is poorly understood. Targeting such cellular components within the tumour is attractive. Recent evidence strongly points towards a dynamic stromal cell participation in cancer progression that impacts patient prognosis. The role of specific desmoplastic stromal cells, such as stellate cells and myofibroblasts in pancreatic, oesophageal and skin cancers, was studied in bio-engineered, physiomimetic organotypic cultures and by regression analysis. For pancreatic cancer, the maximal effect on increasing cancer cell proliferation and invasion, as well as decreasing cancer cell apoptosis, occurs when stromal (pancreatic stellate cells) cells constitute the majority of the cellular population (maximal effect at a stromal cell proportion of 0.66–0.83), accompanied by change in expression of key molecules such as E-cadherin and β-catenin. Gene-expression microarrays, across three tumour types, indicate that stromal cells consistently and significantly alter global cancer cell functions such as cell cycle, cell–cell signalling, cell movement, cell death and inflammatory response. However, these changes are mediated through cancer type-specific alteration of expression, with very few common targets across tumour types. As highlighted by these in vitro data, the reciprocal relationship of E-cadherin and polymeric immunoglobulin receptor (PIGR) expression in cancer cells could be shown, in vivo, to be dependent on the stromal content of human pancreatic cancer. These studies demonstrate that context-specific cancer–stroma crosstalk requires to be precisely defined for effective therapeutic targeting. These data may be relevant to non-malignant processes where epithelial cells interact with stromal cells, such as chronic inflammatory and fibrotic conditions.
IntroductionMammographic density is well-established as a risk factor for breast cancer, however, adjustment for age and body mass index (BMI) is vital to its clinical interpretation when assessing individual risk. In this paper we develop a model to adjust mammographic density for age and BMI and show how this adjusted mammographic density measure might be used with existing risk prediction models to identify high-risk women more precisely.MethodsWe explored the association between age, BMI, visually assessed percent dense area and breast cancer risk in a nested case-control study of women from the placebo arm of the International Breast Cancer Intervention Study I (72 cases, 486 controls). Linear regression was used to adjust mammographic density for age and BMI. This adjusted measure was evaluated in a multivariable logistic regression model that included the Tyrer-Cuzick (TC) risk score, which is based on classical breast cancer risk factors.ResultsPercent dense area adjusted for age and BMI (the density residual) was a stronger measure of breast cancer risk than unadjusted percent dense area (odds ratio per standard deviation 1.55 versus 1.38; area under the curve (AUC) 0.62 versus 0.59). Furthermore, in this population at increased risk of breast cancer, the density residual added information beyond that obtained from the TC model alone, with the AUC for the model containing both TC risk and density residual being 0.62 compared to 0.51 for the model containing TC risk alone (P =0.002).Approximately 16% of controls and 19% of cases moved into the highest risk group (8% or more absolute risk of developing breast cancer within 10 years) when the density residual was taken into account. The net reclassification index was +15.7%.ConclusionsIn women at high risk of breast cancer, adjusting percent mammographic density for age and BMI provides additional predictive information to the TC risk score, which already incorporates BMI, age, family history and other classic breast cancer risk factors. Furthermore, simple selection criteria can be developed using mammographic density, age and BMI to identify women at increased risk in a clinical setting.Clinical trial registration numberhttp://www.controlled-trials.com/ISRCTN91879928 (Registered: 1 June 2006).Electronic supplementary materialThe online version of this article (doi:10.1186/s13058-014-0451-5) contains supplementary material, which is available to authorized users.
Background:To quantify the benefits (cancer prevention and down-staging) and harms (recall and excess treatment) of cervical screening starting from age 20 years rather than from age 25 years.Methods:We use routine screening and cancer incidence statistics from Wales (for screening from age 20 years) and England (screening from 25 years), and unpublished data from the National Audit of Invasive Cervical Cancer to estimate the number of: screening tests, women with abnormal results, referrals to colposcopy, women treated, and diagnoses of micro-invasive (stage 1A) and frank-invasive (stage IB+) cervical cancers (under three different scenarios) in women invited for screening from age 20 years and from 25 years.Results:Inviting 100 000 women from age 20 years yields an additional: 119 000 screens, 20 000 non-negative results, 8000 colposcopy referrals, and an extra 3000 women treated when compared with inviting from age 25 years. Screening from age 20 years prevents between three and nine frank invasive cancers and between 0 and 23 cancers in total (depending on the scenario). A cumulative increase of nine stage IB+ cancers corresponds to an annual rate increase of 0.9 per 100 000 women aged 20–29 years.Conclusions:To prevent one frank invasive cancer, one would need to do between 12 500 and 40 000 additional screening tests in the age group 20–24 years and treat between 300 and 900 women.
Background:There is a need to research interventions that improve access to and convenience of breast cancer screening services.Methods:We conducted a randomised trial comparing invitations to out-of-hours appointments with standard office hour appointments. Women who were to be invited for routine breast screening were randomised (3 : 1 : 1 : 1) to one of these screening invitations: standard office hour appointment, office hour appointment with the option to change to an out-of-hours appointment, weekday evening appointment, or weekend appointment.Results:A total of 9410 women were invited to an office hour, 3519 to an office hour with the option to change, 3271 to a weekday evening, and 3162 to a weekend appointment. The offer of an initial out-of-hours appointment was associated with a non-significant decrease in attendance rates (73.7% vs 74.1%). The highest attendance was observed in the group offered an initial office hour appointment with the option to change to out-of-hours (76.1% vs 73.3% for standard office hour, P=0.001), with 7% of invitees exercising the option to change.Conclusion:The optimum strategy for improving attendance at breast screening is to offer a traditional office hour appointment and including in the letter of invitation an option to change to an evening or weekend appointment if wished.
Weil sich Käufer in Buchmärkten wechselseitig beeinflussen, gilt die Entstehung von Bestsellern als schwer vorherzusagen. Dies resultiert in einer erheblichen Planungsunsicherheit im Verlagswesen. Vorgestellt wird ein Verfahren zur Prognose von Bucherfolg, welches die starke Pfadabhängigkeit von Verkaufszahlen in Kulturmärkten für belastbare Erfolgsvorhersagen nutzt. Die Methode ermöglicht eine frühzeitige Einschätzung des Marktpotenzials von Ersterscheinungen und erlaubt Verlagen eine datenbasierte Planung von Auflagenzahlen und Werbemaßnahmen.
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