Breast cancer tumor growth estimated through mammography screening data

Is being diagnosed at a dedicated breast assessment unit associated with a reduction in the time to diagnosis for symptomatic breast cancer patients?

The length of the cancer diagnostic interval can affect a patient's survival and psychosocial well-being. Ontario Diagnostic Assessment Units (DAUs) were designed to expedite the diagnostic process through coordinated care. We examined the effect of DAUs on the diagnostic interval among female patients with symptomatic breast cancer in Ontario using the Ontario Cancer Registry linked to administrative healthcare data. The diagnostic interval was defined as the time from patients' first referral or test to the cancer diagnosis. DAU use was determined based on the hospital where the breast biopsy/surgery was performed. Multivariable quantile regression and logistic regression analyses adjusted for possible confounders. Forty-seven per cent of patients were diagnosed in a DAU and 53% in usual care (UC). DAUs achieved the Canadian timeliness targets more often than UC (71.7% vs. 58.1%, respectively). DAU use was associated with a 10-day (95% CI: 7.8-11.9) reduction in the median diagnostic interval. This effect increased to 19 days for patients at the 75th percentile and 22 days for those at the 90th percentile of the diagnostic interval distribution. Use of an Ontario DAU is associated with a shorter time to diagnosis in patients with symptomatic breast cancer, especially for those who would otherwise wait the longest.

Evaluating different breast tumor progression models using screening data

Background

Mammography screening is used to detect breast cancer at an early treatable stage, reducing breast cancer mortality. Traditionally, breast cancer has been seen as a disease with only progressive lesions, and here we examine the validity of this assumption by testing if incidence levels after introducing mammography screening can be reproduced assuming only progressive tumors.

Methods

Breast cancer incidence data 1990–2009 obtained from the initially screened Norwegian counties (Akershus, Oslo, Rogaland and Hordaland) was included, covering the time-period before, during and after the introduction of mammography screening. From 1996 women aged 50–69 were invited for biennial public screening. Using estimates of tumor growth and screening sensitivity based on pre-screening and prevalence screening data (1990–1998), we simulated incidence levels during the following period (1999–2009).

Results

The simulated incidence levels during the period with repeated screenings were markedly below the observed levels. The results were robust to changes in model parameters. Adjusting for hormone replacement therapy use, we obtained levels closer to the observed levels. However, there was still a marked gap, and only by assuming some tumors that undergo regressive changes or enter a markedly less detectable state, was our model able to reproduce the observed incidence levels.

Conclusions

Models with strictly progressive tumors are only able to partly explain the changes in incidence levels observed after screening introduction in the initially screened Norwegian counties. More complex explanations than a time shift in detection of future clinical cancers seem to be needed to reproduce the incidence trends, questioning the basis for many over-diagnosis calculations. As data are not randomized, similar studies in other populations are wanted to exclude effect of unknown confounders.

Electronic supplementary material

The online version of this article (10.1186/s12885-018-4130-2) contains supplementary material, which is available to authorized users.

Can circulating tumor DNA be used for direct and early stage cancer detection?

In the August 16th issue of Science Translational Medicine, Phallen et al propose a method for early cancer diagnosis by using circulating tumor DNA (1). One major advance of this paper includes optimized sequencing of cell-free/circulating tumor DNA (ctDNA) without knowledge of tumor mutations. Evaluation of 200 patients with colorectal, breast, lung and ovarian cancer revealed mutations in ctDNA in approx. 60-70% of all patients, including stage 1 and stage 2 disease. If this data can be reproduced in asymptomatic individuals, they will likely have a major impact on early cancer detection and patient outcomes. In this commentary, we examine the feasibility of this approach for detecting small, asymptomatic tumors, based on previously published empirical data.

Modelling breast cancer tumour growth for a stable disease population

Statistical models of breast cancer tumour progression have been used to further our knowledge of the natural history of breast cancer, to evaluate mammography screening in terms of mortality, to estimate overdiagnosis, and to estimate the impact of lead-time bias when comparing survival times between screen detected cancers and cancers found outside of screening programs. Multi-state Markov models have been widely used, but several research groups have proposed other modelling frameworks based on specifying an underlying biological continuous tumour growth process. These continuous models offer some advantages over multi-state models and have been used, for example, to quantify screening sensitivity in terms of mammographic density, and to quantify the effect of body size covariates on tumour growth and time to symptomatic detection. As of yet, however, the continuous tumour growth models are not sufficiently developed and require extensive computing to obtain parameter estimates. In this article, we provide a detailed description of the underlying assumptions of the continuous tumour growth model, derive new theoretical results for the model, and show how these results may help the development of this modelling framework. In illustrating the approach, we develop a model for mammography screening sensitivity, using a sample of 1901 post-menopausal women diagnosed with invasive breast cancer.

The effect of population-based mammography screening in Dutch municipalities on breast cancer mortality: 20 years of follow-up

Long-term follow-up data on the effects of screening are scarce, and debate exists on the relative contribution of screening versus treatment to breast cancer mortality reduction. Our aim was therefore to assess the long-term effect of screening by age and time of implementation. We obtained data on 69,630 breast cancer deaths between 1980 and 2010 by municipality (N = 431) and age of death (40-79) in the Netherlands. Breast cancer mortality trends were analyzed by defining the municipality-specific calendar year of introduction of screening as Year 0. Additionally, log-linear Poisson regression was used to estimate the turning point in the trend after Year 0, per municipality, and the annual percentage change (APC) before and after this point. Twenty years after introduction of screening breast cancer mortality was reduced by 30% in women aged 55-74 and by 34% in women aged 75-79, compared to Year 0. A similar and significant decrease was present in municipalities that started early (1987-1992) and late (1995-1997) with screening, despite the difference in availability of effective adjuvant treatment. In the age groups 55-74 and 75-79, the turning point in the trend in breast cancer mortality was estimated in Years 2 and 6 after the introduction of screening, respectively, after which mortality decreased significantly by 1.9% and 2.6% annually. These findings show that the implementation of mammography screening in Dutch municipalities is associated with a significant decline in breast cancer mortality in women aged 55-79, irrespective of time of implementation.

Evaluation of a Stratified National Breast Screening Program in the United Kingdom: An Early Model-Based Cost-Effectiveness Analysis

OBJECTIVES

To identify the incremental costs and consequences of stratified national breast screening programs (stratified NBSPs) and drivers of relative cost-effectiveness.

METHODS

A decision-analytic model (discrete event simulation) was conceptualized to represent four stratified NBSPs (risk 1, risk 2, masking [supplemental screening for women with higher breast density], and masking and risk 1) compared with the current UK NBSP and no screening. The model assumed a lifetime horizon, the health service perspective to identify costs (£, 2015), and measured consequences in quality-adjusted life-years (QALYs). Multiple data sources were used: systematic reviews of effectiveness and utility, published studies reporting costs, and cohort studies embedded in existing NBSPs. Model parameter uncertainty was assessed using probabilistic sensitivity analysis and one-way sensitivity analysis.

RESULTS

The base-case analysis, supported by probabilistic sensitivity analysis, suggested that the risk stratified NBSPs (risk 1 and risk-2) were relatively cost-effective when compared with the current UK NBSP, with incremental cost-effectiveness ratios of £16,689 per QALY and £23,924 per QALY, respectively. Stratified NBSP including masking approaches (supplemental screening for women with higher breast density) was not a cost-effective alternative, with incremental cost-effectiveness ratios of £212,947 per QALY (masking) and £75,254 per QALY (risk 1 and masking). When compared with no screening, all stratified NBSPs could be considered cost-effective. Key drivers of cost-effectiveness were discount rate, natural history model parameters, mammographic sensitivity, and biopsy rates for recalled cases. A key assumption was that the risk model used in the stratification process was perfectly calibrated to the population.

CONCLUSIONS

This early model-based cost-effectiveness analysis provides indicative evidence for decision makers to understand the key drivers of costs and QALYs for exemplar stratified NBSP.

Improved systemic treatment for early breast cancer improves cure rates, modifies metastatic pattern and shortens post-metastatic survival: 35-year results from the Munich Cancer Registry

PURPOSE

Systemic therapies (ATHs) in early breast cancer have improved the survival of breast cancer (BC) patients in recent decades. The magnitude of the changes in overall, metastasis-free (MFS) and post-metastatic (PMS) survival and in the metastasis (MET) pattern will be described.

PATIENT AND METHODS

We analysed 60,227 patients with a diagnosis of T-N-M0 BC between 1978 and 2013 and 11,983 patients with metastases (MET) in the Munich Cancer Registry. Patients will be divided into four time periods to identify relationships between BC and METs. Survival was estimated using Kaplan-Meier curves, and Cox proportional hazards models were used to explore the impact of the BC subtype and MET status on survival with the time periods as surrogate markers for ATH evolution.

RESULTS

During the observation period, 5-year relative survival has improved from 80.3 to 93.6% with an adjusted hazard ratio of 0.54 (P < 0.0001). Successful implementation of ATH has changed the MET pattern. The percentage of liver and CNS METs has more than doubled, the rate of lung METs remains stable, and the rate of bone METs has been reduced by approximately 50%. MFS has been prolonged with a hazard ratio 0.75 (P < 0.0001), but PMS has declined (hazard ratio 1.36; P < 0.0001); however, effects of adjuvant and palliative treatments cannot be separated. These results do not contradict improvements in advanced BC and do not suggest alterations of MET tumour biology by ATH.

CONCLUSIONS

Over the past three decades, ATHs have dramatically improved patient survival after BC diagnosis-most likely, by eradicating prevalent micro-METs; as a result, the MET pattern has changed. Eradicating only a portion of the first METs results in delaying the onset of subsequent MET, which leads to an apparently paradoxical effect: an extension of the MET-free interval and a reduction in PMS.

Application of a PK-PD Modeling and Simulation-Based Strategy for Clinical Translation of Antibody-Drug Conjugates: a Case Study with Trastuzumab Emtansine (T-DM1)

Successful clinical translation of antibody-drug conjugates (ADCs) can be challenging due to complex pharmacokinetics and differences between preclinical and clinical tumors. To facilitate this translation, we have developed a general pharmacokinetic-pharmacodynamic (PK-PD) modeling and simulation (M&S)-based strategy for ADCs. Here we present the validation of this strategy using T-DM1 as a case study. A previously developed preclinical tumor disposition model for T-DM1 (Singh and Shah, AAPSJ. 2015; 18(4):861-875) was used to develop a PK-PD model that can characterize in vivo efficacy of T-DM1 in preclinical tumor models. The preclinical data was used to estimate the efficacy parameters for T-DM1. Human PK of T-DM1 was a priori predicted using allometric scaling of monkey PK parameters. The predicted human PK, preclinically estimated efficacy parameters, and clinically observed volume and growth parameters for breast cancer were combined to develop a translated clinical PK-PD model for T-DM1. Clinical trial simulations were performed using the translated PK-PD model to predict progression-free survival (PFS) and objective response rates (ORRs) for T-DM1. The model simulated PFS rates for HER2 1+ and 3+ populations were comparable to the rates observed in three different clinical trials. The model predicted only a modest improvement in ORR with an increase in clinically approved dose of T-DM1. However, the model suggested that a fractionated dosing regimen (e.g., front loading) may provide an improvement in the efficacy. In general, the PK-PD M&S-based strategy presented here is capable of a priori predicting the clinical efficacy of ADCs, and this strategy has been now retrospectively validated for all clinically approved ADCs.

Survival of de novo stage IV breast cancer patients over three decades

BACKGROUND

De novo stage IV breast cancer patients (BC_IV) depict a clinical picture not influenced by adjuvant therapy. Therefore, the time-dependent impact of changes in diagnostics and treatments on progression and survival can best be evaluated in this subgroup.

METHODS

BC_IV patients from 1978 to 2013 registered in the Munich Cancer Registry were divided into four periods, and the trends were analysed. Survival was estimated by Kaplan-Meier methods, and prognostic factors were fitted with Cox proportional hazard models.

RESULTS

Between 1978 and 2013, 88,759 patients were diagnosed with 92,807 cases of invasive and non-invasive BC. Of these patients, 4756 patients had distant metastases (MET) at diagnosis. The 5-year survival rate improved from 17.4 to 24.7%, while the pattern of metastases did not change. Improved staging diagnostics, a screening programme and primary systemic therapy changed the composition of prognostic strata. Patients with a similar composition as the 1978-1987 cohort exhibited a median survival difference of 13 months; however, neither univariate nor multivariate analysis showed a survival effect for the four periods as a surrogate indicator for changing treatments. HER2+ patients have with 27.6 months a slightly longer survival than all other BC_IV patients.

CONCLUSIONS

Survival of de novo BC_IV has only modestly improved since the late 1970s, partially masked by changing distributions of prognostic factors due to changes in diagnostics.

The influence of personality and perceived stress on the development of breast cancer: 20-year follow-up of 29,098 Japanese women

Breast cancer is the most common cancer in women. However, it remains unproven whether psychological factors have an influence on breast cancer incidence. In our earlier study, subjects possessing two personality traits, decisiveness and "ikigai" (a Japanese word meaning something that makes one's life worth living), showed a significantly lower risk of developing breast cancer, although no psychological factors have been convincingly demonstrated to have an influence on breast cancer development in other studies. Therefore, we conducted this follow-up analysis to confirm the association between breast cancer incidence and psychological traits, using the final dataset of a large-scale prospective cohort study in Japan. We identified 209 cases of incident breast cancer out of a maximum 21-year follow-up of 29,098 Japanese women. Cox proportional hazard regression analysis, adjusted for the same potential confounders used in our previous study, did not reveal any significant relationships between breast cancer incidence and four psychological traits: having "ikigai", decisiveness, ease of anger arousal, and perceived stress. Our finding is consistent with previous studies, and suggests that the psychological traits are unlikely to be an important risk factor for breast cancer.

Tumor growth rate of invasive breast cancers during wait times for surgery assessed by ultrasonography

Several studies suggest that delay in the surgical treatment of breast cancer is significantly associated with lower survival. This study evaluated the tumor growth rate (TGR) of invasive breast cancers during wait times for surgery quantitatively using ultrasonography (US) and identified clinicopathologic factors associated with TGR.This retrospective study was approved by our institutional review board and the requirement for written informed consent was waived. Between August 2013 and September 2014, a total of 323 unifocal invasive breast cancers in 323 women with serial US images at the time of diagnosis and surgery were included. Tumor diameters and volumes were measured using 2-orthogonal US images. TGR during wait times for surgery was quantified as specific growth rates (SGR; %/day) and was compared with clinicopathologic variables using univariate and multivariate analyses.Median time from diagnosis to surgery was 31 days (range, 8-78 days). Maximum tumor diameters and volumes at the time of surgery (mean, 15.6 mm and 1.6 cm) were significantly larger than at diagnosis (14.7 mm and 1.3 cm) (P < 0.001). On multivariate analysis, surrogate molecular subtype was a significant independent factor of SGR (P = 0.001); triple negative cancers showed the highest SGR (1.003%/day) followed by HER2-positive (0.859 %/day) and luminal cancers (luminal B, 0.208 %/day; luminal A, 0.175%/day) (P < 0.001). Clinical T stage was more frequently upgraded in nonluminal (triple negative, 18% [12/67]; HER2-positive, 14% [3/22]) than luminal cancers (luminal B, 3% [1/30]; luminal A, 2% [4/204]) (P < 0.001).Invasive breast cancers with aggressive molecular subtypes showed faster TGR and more frequent upgrading of clinical T stage during wait times for surgery.

ESTIMATES OF BREAST CANCER GROWTH RATE FROM MAMMOGRAMS AND ITS RELATION TO TUMOUR CHARACTERISTICS

This study aimed to investigate the growth rate of 31 consecutive invasive breast cancers based on volume measures on at least two serial mammograms and its relation to histopathological findings. The average tumour volume-doubling time in all invasive breast cancer subtypes was 282 d (range 46-749 d). Grade III breast cancers had a significantly shorter average tumour volume-doubling time of 105 d (range 46-157 d) compared with Grade I and II tumours (average of 296 d, range 147-531 d and average of 353 d, range 139-749 d, respectively) (p = 0.002). Multiple linear regression identified that tumour volume-doubling time was positively associated with patient age, histological grade and progesterone receptor expression and inversely associated with axillary lymph node involvement, human epidermal growth factor receptor 2 and Ki-67 expression (p < 0.001). In conclusion, tumour volume-doubling time as estimated on serial mammography may provide important prognostic information relevant for clinical decision-making.

In Vivo Tumor Growth Rate Measured by US in Preoperative Period and Long Term Disease Outcome in Breast Cancer Patients

OBJECTIVE

The aim of our study was to evaluate the effect of tumor growth rate, calculated from tumor size measurements by US, on breast cancer patients' outcome.

PATIENTS AND METHODS

Breast cancer patients who received at least two serial breast ultrasonographies (US) in our institution during preoperative period and were surgically treated between 2002 and 2010 were reviewed. Tumor growth rate was determined by specific growth rate (SGR) using the two time point tumor sizes by US.

RESULTS

A total of 957 patients were analyzed. The median duration between initial and second US was 28 days (range, 8-140). The median initial tumor size was 1.7 cm (range, 0.4-7.0) and median second size was 1.9 cm (range, 0.3-7.2). 523 (54.6%) cases had increase in size. The median SGR(x10-2) was 0.59 (range, -11.90~31.49) and mean tumor doubling time was 14.51 days. Tumor growth rate was higher when initial tumor size was smaller. Lymphovascular invasion, axillary lymph node metastasis, and higher histologic grade were significantly associated with higher SGR. SGR was significantly associated with disease-free survival (DFS) in a univariate analysis (p = 0.04), but not in a multivariate Cox analysis (p>0.05). High SGR was significantly associated with worse DFS in a subgroup of initial tumor size >2 cm (p = 0.018), but not in those with tumor size <2 cm (p>0.05).

CONCLUSION

Our results showed that tumor growth rate measured by US in a relatively short time interval was associated with other worse prognostic factors and DFS, but it was not an independent prognostic factor in breast cancer patients.

Analysis of ESR1 mutation in circulating tumor DNA demonstrates evolution during therapy for metastatic breast cancer

Acquired ESR1 mutations are a major mechanism of resistance to aromatase inhibitors (AIs). We developed ultra high-sensitivity multiplex digital polymerase chain reaction assays for ESR1 mutations in circulating tumor DNA (ctDNA) and investigated the clinical relevance and origin of ESR1 mutations in 171 women with advanced breast cancer. ESR1 mutation status in ctDNA showed high concordance with contemporaneous tumor biopsies and was accurately assessed in samples shipped at room temperature in preservative tubes. ESR1 mutations were found exclusively in estrogen receptor-positive breast cancer patients previously exposed to AI. Patients with ESR1 mutations had a substantially shorter progression-free survival on subsequent AI-based therapy [hazard ratio, 3.1; 95% confidence interval (CI), 1.9 to 23.1; P = 0.0041]. ESR1 mutation prevalence differed markedly between patients who were first exposed to AI during the adjuvant and metastatic settings [5.8% (3 of 52) versus 36.4% (16 of 44), respectively; P = 0.0002]. In an independent cohort, ESR1 mutations were identified in 0% (0 of 32; 95% CI, 0 to 10.9) tumor biopsies taken after progression on adjuvant AI. In a patient with serial sampling, ESR1 mutation was selected during metastatic AI therapy to become the dominant clone in the cancer. ESR1 mutations can be robustly identified with ctDNA analysis and predict for resistance to subsequent AI therapy. ESR1 mutations are rarely acquired during adjuvant AI but are commonly selected by therapy for metastatic disease, providing evidence that mechanisms of resistance to targeted therapy may be substantially different between the treatment of micrometastatic and overt metastatic cancer.

Estimating Tumor Growth Rates In Vivo

In this paper, we develop methods for inferring tumor growth rates from the observation of tumor volumes at two time points. We fit power law, exponential, Gompertz, and Spratt’s generalized logistic model to five data sets. Though the data sets are small and there are biases due to the way the samples were ascertained, there is a clear sign of exponential growth for the breast and liver cancers, and a 2/3’s power law (surface growth) for the two neurological cancers.

Growth rate of early-stage hepatocellular carcinoma in patients with chronic liver disease

Background/Aims

The goal of this study was to estimate the growth rate of hepatocellular carcinoma (HCC) and identify the host factors that significantly affect this rate.

Methods

Patients with early-stage HCC (n=175) who underwent two or more serial dynamic imaging studies without any anticancer treatment at two tertiary care hospitals in Korea were identified. For each patient, the tumor volume doubling time (TVDT) of HCC was calculated by comparing tumor volumes between serial imaging studies. Clinical and laboratory data were obtained from the medical records of the patients.

Results

The median TVDT was 85.7 days, with a range of 11 to 851.2 days. Multiple linear regression revealed that the initial tumor diameter (a tumor factor) and the etiology of chronic liver disease (a host factor) were significantly associated with the TVDT. The TVDT was shorter when the initial tumor diameter was smaller, and was shorter in HCC related to hepatitis B virus (HBV) infection than in HCC related to hepatitis C virus (HCV) infection (median, 76.8 days vs. 137.2 days; P=0.0234).

Conclusions

The etiology of chronic liver disease is a host factor that may significantly affect the growth rate of early-stage HCC, since HBV-associated HCC grows faster than HCV-associated HCC.

Estimates of over-diagnosis of breast cancer due to population-based mammography screening in South Australia after adjustment for lead time effects

OBJECTIVE

To estimate over-diagnosis due to population-based mammography screening using a lead time adjustment approach, with lead time measures based on symptomatic cancers only.

SUBJECTS

Women aged 40-84 in 1989-2009 in South Australia eligible for mammography screening.

METHODS

Numbers of observed and expected breast cancer cases were compared, after adjustment for lead time. Lead time effects were modelled using age-specific estimates of lead time (derived from interval cancer rates and predicted background incidence, using maximum likelihood methods) and screening sensitivity, projected background breast cancer incidence rates (in the absence of screening), and proportions screened, by age and calendar year.

RESULTS

Lead time estimates were 12, 26, 43 and 53 months, for women aged 40-49, 50-59, 60-69 and 70-79 respectively. Background incidence rates were estimated to have increased by 0.9% and 1.2% per year for invasive and all breast cancer. Over-diagnosis among women aged 40-84 was estimated at 7.9% (0.1-12.0%) for invasive cases and 12.0% (5.7-15.4%) when including ductal carcinoma in-situ (DCIS).

CONCLUSIONS

We estimated 8% over-diagnosis for invasive breast cancer and 12% inclusive of DCIS cancers due to mammography screening among women aged 40-84. These estimates may overstate the extent of over-diagnosis if the increasing prevalence of breast cancer risk factors has led to higher background incidence than projected.

Breast cancer tumour growth modelling for studying the association of body size with tumour growth rate and symptomatic detection using case-control data

Introduction

A large body size is associated with larger breast cancer tumours at diagnosis. Standard regression models for tumour size at diagnosis are not sufficient for unravelling the mechanisms behind the association.

Methods

Using Swedish case-control data, we identified 1352 postmenopausal women with incident invasive breast cancer diagnosed between 1993 and 1995. We used a novel continuous tumour growth model, which models tumour sizes at diagnosis through three submodels: for tumour growth, time to symptomatic detection, and screening sensitivity. Tumour size at other time points is thought of as a latent variable.

Results

We quantified the relationship between body size with tumour growth and time to symptomatic detection. High body mass index and large breast size are, respectively, significantly associated with fast tumour growth rate and delayed time to symptomatic detection (combined P value = 5.0 × 10⁻⁵ and individual P values = 0.089 and 0.022). We also quantified the role of mammographic density in screening sensitivity.

Conclusions

The times at which tumours will be symptomatically detected may vary substantially between women with different breast sizes. The proposed tumour growth model represents a novel and useful approach for quantifying the effects of breast cancer risk factors on tumour growth and detection.

Modelling circulating tumour cells for personalised survival prediction in metastatic breast cancer

Ductal carcinoma is one of the most common cancers among women, and the main cause of death is the formation of metastases. The development of metastases is caused by cancer cells that migrate from the primary tumour site (the mammary duct) through the blood vessels and extravasating they initiate metastasis. Here, we propose a multi-compartment model which mimics the dynamics of tumoural cells in the mammary duct, in the circulatory system and in the bone. Through a branching process model, we describe the relation between the survival times and the four markers mainly involved in metastatic breast cancer (EPCAM, CD47, CD44 and MET). In particular, the model takes into account the gene expression profile of circulating tumour cells to predict personalised survival probability. We also include the administration of drugs as bisphosphonates, which reduce the formation of circulating tumour cells and their survival in the blood vessels, in order to analyse the dynamic changes induced by the therapy. We analyse the effects of circulating tumour cells on the progression of the disease providing a quantitative measure of the cell driver mutations needed for invading the bone tissue. Our model allows to design intervention scenarios that alter the patient-specific survival probability by modifying the populations of circulating tumour cells and it could be extended to other cancer metastasis dynamics.

Serial analysis of 38 proteins during the progression of human breast tumor in mice using an antibody colocalization microarray

Proteins in serum or plasma hold great potential for use in disease diagnosis and monitoring. However, the correlation between tumor burden and protein biomarker concentration has not been established. Here, using an antibody colocalization microarray, the protein concentration in serum was measured and compared with the size of mammary xenograft tumors in 11 individual mice from the time of injection; seven blood samples were collected from each tumor-bearing mouse as well as control mice on a weekly basis. The profiles of 38 proteins detected in sera from these animals were analyzed by clustering, and we identified 10 proteins with the greatest relative increase in serum concentration that correlated with growth of the primary mammary tumor. To evaluate the diagnosis of cancer based on these proteins using either an absolute threshold (i.e. a concentration cutoff) or self-referenced differential threshold based on the increase in concentration before cell injection, receiver operating characteristic curves were produced for 10 proteins with increased concentration, and the area under curve was calculated for each time point based on a single protein or on a panel of proteins, in each case showing a rapid increase of the area under curve. Next, the sensitivity and specificity of individual and optimal protein panels were calculated, showing high accuracy as early as week 2. These results provide a foundation for studies of tumor growth through measuring serial changes of protein concentration in animal models.

Predicting the stage shift as a result of breast cancer screening in low- and middle-income countries: a proof of concept

OBJECTIVE

To provide proof of concept for a simple model to estimate the stage shift as a result of breast cancer screening in low- and middle-income countries (LMICs). Stage shift is an essential early detection indicator and an important proxy for the performance and possible further impact of screening programmes. Our model could help LIMCs to choose appropriate control strategies.

METHODS

We assessed our model concept in three steps. First, we calculated the proportional performance rates (i.e. index number Z) based on 16 screening rounds of the Nijmegen Screening Program (384,884 screened women). Second, we used linear regression to assess the association between Z and the amount of stage shift observed in the programme. Third, we hypothesized how Z could be used to estimate the stage shift as a result of breast cancer screening in LMICs.

RESULTS

Stage shifts can be estimated by the proportional performance rates (Zs) using linear regression. Zs calculated for each screening round are highly associated with the observed stage shifts in the Nijmegen Screening Program (Pearson's R: 0.798, R square: 0.637).

CONCLUSIONS

Our model can predict the stage shifts in the Nijmegen Screening Program, and could be applied to settings with different characteristics, although it should not be straightforwardly used to estimate the impact on mortality. Further research should investigate the extrapolation of our model to other settings. As stage shift is an essential screening performance indicator, our model could provide important information on the performance of breast cancer screening programmes that LMICs consider implementing.

Racial comparison of receptor-defined breast cancer in Southern African women: subtype prevalence and age-incidence analysis of nationwide cancer registry data

BACKGROUND

Receptor-defined breast cancer proportions vary across Africa. They have important implications for survival prospects and research priorities.

METHODS

We studied estrogen receptor (ER), progesterone receptor (PR), and HER2 receptor statuses in two multiracial Southern African countries with routine diagnostic immunohistochemistry. A total of 12,361 women with histologically confirmed breast cancer diagnosed at age ≥20 years during (i) 2009-2011 from South Africa's national cancer registry (public sector) and (ii) 2011-2013 from Namibia's only cancer hospital were included. Crude, age, and age + laboratory-adjusted ORs of receptor status were analyzed using logistic regression, and age-incidence curves were analyzed using Poisson regression.

RESULTS

A total of 10,047 (81%) women had known ER status. Ranking of subtypes was consistent across races: ER(+)/PR(+)HER2(-) was most common (race-specific percentage range, 54.6%-64.8%), followed by triple-negative (17.4%-21.9%), ER(+)/PR(+)HER2(+) (9.6%-13.9%), and ER(-)PR(-)HER2(+) (7.8%-10.9%). Percentages in black versus white women were 33.8% [95% confidence (CI), 32.5-35.0] versus 26.0% (24.0-27.9) ER(-); 20.9% (19.7-22.1) versus 17.5% (15.4-19.6) triple-negative; and 10.7% (9.8-11.6) versus 7.8% (6.3-9.3) ER(-)PR(-)HER2(+). Indian/Asian and mixed-ancestry women had intermediate values. Age-incidence curves had similar shapes across races: rates increased by 12.7% per year (12.2-13.1) across ER subtypes under the age of 50 years, and thereafter slowed for ER(+) (1.95%) and plateaued for ER(-) disease (-0.1%).

CONCLUSIONS

ER(+) breast cancer dominates in all Southern African races, but black women have a modest excess of aggressive subtypes.

IMPACT

On the basis of the predominant receptor-defined breast tumors in Southern Africa, improving survival for the growing breast cancer burden should be achievable through earlier diagnosis and appropriate treatment.

Stage at breast cancer diagnosis and distance from diagnostic hospital in a periurban setting: a South African public hospital case series of over 1,000 women

Advanced stage at diagnosis contributes to low breast cancer survival rates in sub-Saharan Africa. Living far from health services is known to delay presentation, but the effect of residential distance to hospital, the radius at which this effect sets in and the women most affected have not been quantified. In a periurban South African setting, we examined the effect of a geographic information system (GIS)-measured straight-line distance, from a patient's residence to diagnostic hospital, on stage at diagnosis in 1,071 public-sector breast cancer patients diagnosed during 2006-2012. Generalized linear models were used to estimate risk ratios for late stage (stage III/IV vs. stage I/II) associated with distance, adjusting for year of diagnosis, age, race and socioeconomic indicators. Mean age of patients was 55 years, 90% were black African and diagnoses were at stages I (5%), II (41%), III (46%) and IV (8%). Sixty-two percent of patients with distances >20 km (n = 338) had a late stage at diagnosis compared to 50% with distances <20 km (n = 713, p = 0.02). Risk of late stage at diagnosis was 1.25-fold higher (95% CI: 1.09, 1.42) per 30 km. Effects were pronounced in an underrepresented group of patients over age 70. This positive stage-distance association held to 40 km, and plateaued or slightly reversed in patients (9%) living beyond this distance. Studies of woman and the societal and healthcare-level influences on these delays and on the late stage at diagnosis distribution are needed to inform interventions to improve diagnostic stage and breast cancer survival in this and similar settings.

A stochastic model of cancer growth subject to an intermittent treatment with combined effects: reduction in tumor size and rise in growth rate

A model of cancer growth based on the Gompertz stochastic process with jumps is proposed to analyze the effect of a therapeutic program that provides intermittent suppression of cancer cells. In this context, a jump represents an application of the therapy that shifts the cancer mass to a return state and it produces an increase in the growth rate of the cancer cells. For the resulting process, consisting in a combination of different Gompertz processes characterized by different growth parameters, the first passage time problem is considered. A strategy to select the inter-jump intervals is given so that the first passage time of the process through a constant boundary is as large as possible and the cancer size remains under this control threshold during the treatment. A computational analysis is performed for different choices of involved parameters. Finally, an estimation of parameters based on the maximum likelihood method is provided and some simulations are performed to illustrate the validity of the proposed procedure.

Cost-effectiveness of alternative strategies for integrating MRI into breast cancer screening for women at high risk

BACKGROUND

Magnetic resonance imaging (MRI) is recommended for women at high risk for breast cancer. We evaluated the cost-effectiveness of alternative screening strategies involving MRI.

METHODS

Using a microsimulation model, we generated life histories under different risk profiles, and assessed the impact of screening on quality-adjusted life-years, and lifetime costs, both discounted at 3%. We compared 12 screening strategies combining annual or biennial MRI with mammography and clinical breast examination (CBE) in intervals of 0.5, 1, or 2 years vs without, and reported incremental cost-effectiveness ratios (ICERs).

RESULTS

Based on an ICER threshold of $100,000/QALY, the most cost-effective strategy for women at 25% lifetime risk was to stagger MRI and mammography plus CBE every year from age 30 to 74, yielding ICER $58,400 (compared to biennial MRI alone). At 50% lifetime risk and with 70% reduction in MRI cost, the recommended strategy was to stagger MRI and mammography plus CBE every 6 months (ICER=$84,400). At 75% lifetime risk, the recommended strategy is biennial MRI combined with mammography plus CBE every 6 months (ICER=$62,800).

CONCLUSIONS

The high costs of MRI and its lower specificity are limiting factors for annual screening schedule of MRI, except for women at sufficiently high risk.

Enhanced breast cancer therapy with nsPEFs and low concentrations of gemcitabine

Background

Chemotherapy either before or after surgery is a common breast cancer treatment. Long-term, high dose treatments with chemotherapeutic drugs often result in undesirable side effects, frequent recurrences and resistances to therapy.

Methods

The anti-cancer drug, gemcitabine (GEM) was used in combination with pulse power technology with nanosecond pulsed electric fields (nsPEFs) for treatment of human breast cancer cells in vitro. Two strategies include sensitizing mammary tumor cells with GEM before nsPEF treatment or sensitizing cells with nsPEFs before GEM treatment. Breast cancer cell lines MCF-7 and MDA-MB-231 were treated with 250 65 ns-duration pulses and electric fields of 15, 20 or 25 kV/cm before or after treatment with 0.38 μM GEM.

Results

Both cell lines exhibited robust synergism for loss of cell viability 24 h and 48 h after treatment; treatment with GEM before nsPEFs was the preferred order. In clonogenic assays, only MDA-MB-231 cells showed synergism; again GEM before nsPEFs was the preferred order. In apoptosis/necrosis assays with Annexin-V-FITC/propidium iodide 2 h after treatment, both cell lines exhibited apoptosis as a major cell death mechanism, but only MDA-MB-231 cells exhibited modest synergism. However, unlike viability assays, nsPEF treatment before GEM was preferred. MDA-MB-231 cells exhibited much greater levels of necrosis then in MCF-7 cells, which were very low. Synergy was robust and greater when nsPEF treatment was before GEM.

Conclusions

Combination treatments with low GEM concentrations and modest nsPEFs provide enhanced cytotoxicity in two breast cancer cell lines. The treatment order is flexible, although long-term survival and short-term cell death analyses indicated different treatment order preferences. Based on synergism, apoptosis mechanisms for both agents were more similar in MCF-7 than in MDA-MB-231 cells. In contrast, necrosis mechanisms for the two agents were distinctly different in MDA-MB-231, but too low to reliably evaluate in MCF-7 cells. While disease mechanisms in the two cell lines are different based on the differential synergistic response to treatments, combination treatment with GEM and nsPEFs should provide an advantageous therapy for breast cancer ablation in vivo.

Accumulation of 4-1BBL+ B cells in the elderly induces the generation of granzyme-B+ CD8+ T cells with potential antitumor activity

Although the accumulation of highly-differentiated and granzyme B (GrB)-expressing CD8(+)CD28(-) T cells has been associated with aging, the mechanism for their enrichment and contribution to immune function remains poorly understood. Here we report a novel B-cell subset expressing 4-1BBL, which increases with age in humans, rhesus macaques, and mice, and with immune reconstitution after chemotherapy and autologous progenitor cell transplantation. These cells (termed 4BL cells) induce GrB(+)CD8(+) T cells by presenting endogenous antigens and using the 4-1BBL/4-1BB axis. We found that the 4BL cells increase antitumor responses in old mice, which may explain in part the paradox of retarded tumor growth in the elderly. 4BL cell accumulation and its capacity to evoke the generation of GrB(+)CD8(+) T cells can be eliminated by inducing reconstitution of B cells in old mice, suggesting that the age-associated skewed cellular immune responses are reversible. We propose that 4BL cells and the 4-1BBL signaling pathway are useful targets for improved effectiveness of natural antitumor defenses and therapeutic immune manipulations in the elderly.

Patient Delay in Accessing Breast Cancer Care in a Sub Saharan African Country: Uganda

Aims

To assess patient delay differences between early and late stage breast cancer among women in Uganda.

Study Design

A retrospective analytical study.

Place and Duration of the Study

A study conducted at a tertiary teaching hospital. Selected patients’ data available for the period between 2008 and 2011 were included in this study.

Methodology

We included 201 women with histologically confirmed breast cancer. The variables analysed included age, residence, histological subtype, stage at presentation and time delays. Ethical approval was obtained.

Results

The mean age for the early and late presenters was 49 and 46 years respectively (p=0.065). Rural women were more likely to present late. Triple negative breast cancer (TNBC) and HER2+ were the majority cancer subtypes for the late presenters. On average women waited for 29 months before they presented for specialized cancer treatment (median 12 months; range 1-120 months). The duration of symptoms didn’t differ between the two groups (p=0.295) and 75% of early stage presenters, reported at least 6 months after noticing symptoms. Only 9% of the TNBC patients presented under 3 months in comparison to 14 % for HER2+, 33% for Luminal B and 36% for luminal A. Overall 23% (39/168) presented with early stage disease.

Conclusion

Delay in seeking appropriate breast cancer care in Uganda was excessive, a sign of a neglected disease. Tumor biology factors seem to play a role in late stage presentation. Research in factors that lead to prolonged delay in accessing care in a resource poor context are needed urgently.

Bridging population and tissue scale tumor dynamics: a new paradigm for understanding differences in tumor growth and metastatic disease

To provide a better understanding of the relationship between primary tumor growth rates and metastatic burden, we present a method that bridges tumor growth dynamics at the population level, extracted from the SEER database, to those at the tissue level. Specifically, with this method, we are able to relate estimates of tumor growth rates and metastatic burden derived from a population-level model to estimates of the primary tumor vascular response and the circulating tumor cell (CTC) fraction derived from a tissue-level model. Variation in the population-level model parameters produces differences in cancer-specific survival and cure fraction. Variation in the tissue-level model parameters produces different primary tumor dynamics that subsequently lead to different growth dynamics of the CTCs. Our method to bridge the population and tissue scales was applied to lung and breast cancer separately, and the results were compared. The population model suggests that lung tumors grow faster and shed a significant number of lethal metastatic cells at small sizes, whereas breast tumors grow slower and do not significantly shed lethal metastatic cells until becoming larger. Although the tissue-level model does not explicitly model the metastatic population, we are able to disengage the direct dependency of the metastatic burden on primary tumor growth by introducing the CTC population as an intermediary and assuming dependency. We calibrate the tissue-level model to produce results consistent with the population model while also revealing a more dynamic relationship between the primary tumor and the CTCs. This leads to exponential tumor growth in lung and power law tumor growth in breast. We conclude that the vascular response of the primary tumor is a major player in the dynamics of both the primary tumor and the CTCs, and is significantly different in breast and lung cancer.

Cancer dormancy: time to explore its clinical relevance

Dormant disseminated cancer cells, arrested and nonproliferating, are “good” cancer cells because there is no need to worry unless they resume growth. The mechanisms by which dormant disseminated cancer cells are put to sleep at distant sites and re-awakened are poorly understood. Moreover, it is not clear whether re-awakened cancer cells have a role in disease courses. Cyrus Ghajar and colleagues identified a mechanism of dormancy and growth resumption that might become important when more closely linked to clinical reality.

Selection and adaptation during metastatic cancer progression

Cancer is often regarded as a process of asexual evolution driven by genomic and genetic instability. Mutation, selection and adaptation are by convention thought to occur primarily within, and to a lesser degree outside, the primary tumour. However, disseminated cancer cells that remain after 'curative' surgery exhibit extreme genomic heterogeneity before the manifestation of metastasis. This heterogeneity is later reduced by selected clonal expansion, suggesting that the disseminated cells had yet to acquire key traits of fully malignant cells. Abrogation of the cells' progression outside the primary tumour implies new challenges and opportunities for diagnosis and adjuvant therapies.

A possible explanation for the variable frequencies of cancer stem cells in tumors

A controversy surrounds the frequency of cancer stem cells (CSCs) in solid tumors. Initial studies indicated that these cells had a frequency ranging from 0.0001 to 0.1% of the total cells. Recent studies have shown that this does not always seem to be the case. Some of these studies have indicated a frequency of [Formula: see text]. In this paper we propose a stochastic model that is able to capture this potential variability in the frequency of CSCs among the various type of tumors. Considerations regarding the heterogeneity of the tumor cells and its consequences are included. Possible effects on conventional treatments in clinical practice are also described. The model results suggest that traditional attempts to combat cancer cells with rapid cycling can be very stimulating for the cancer stem cell populations.

A statistical model of breast cancer tumour growth with estimation of screening sensitivity as a function of mammographic density

Understanding screening sensitivity and tumour progression is important for designing and evaluating screening programmes for breast cancer. Several approaches for estimating tumour growth rates have been described, some of which simultaneously estimate (mammography) screening sensitivity. None of the continuous tumour growth modelling approaches has incorporated mammographic density, although it is known to have a profound influence on mammographic screening sensitivity. We describe a new approach for estimating breast cancer tumour growth which builds on recently described continuous tumour growth models and estimates mammographic screening sensitivity as a function of tumour size and mammographic density.

Tumor growth dynamics: insights into evolutionary processes

Identifying the types of event that drive tumor evolution and progression is crucial for understanding cancer. We suggest that the analysis of tumor growth dynamics can provide a window into tumor biology and evolution by connecting them with the types of genetic change that have occurred. Although fundamentally important, the documentation of tumor growth kinetics is more sparse in the literature than is the molecular analysis of cells. Here, we provide a historical summary of tumor growth patterns and argue that they can be classified into five basic categories. We then illustrate how those categories can provide insights into events that drive tumor progression, by discussing a particular evolutionary model as an example and encouraging such analysis in a more general setting.

Detection of cancer before distant metastasis

Background

To establish a distant metastasis (DM) cells must disseminate from the primary tumor and overcome a series of obstacles, the metastatic cascade. In this study we develop a mathematical model for this cascade to estimate the tumor size and the circulating tumor cell (CTC) load before the first metastasis has formed from a primary breast cancer tumor.

Methods

The metastatic cascade is described in discrete steps: 1. local tumor growth; 2. dissemination into circulation; 3. survival in circulation; 4. extravasation into tissue; and 5. growth into a metastasis. The model was built using data and relationships described in the literature to predict the relationship between tumor size and probability of distant metastasis for 38715 patients with surgically removed T_XN_XM₀ primary breast cancer from the Netherlands Cancer Registry. The model was calibrated using primary tumor size, probability of distant metastasis and time to distant metastasis for 1489 patients with stage T_1BN_XM₀ (25% of total patients with T_1BN_XM₀). Validation of the model was done with data for all patients.

Results

From the time to distant metastasis of these 38715 breast cancer patients, we determined a tumor doubling time of 1.7 ± 0.9 months. Fitting the data for 25% of T_1B patients estimates a metastatic efficiency of 1 metastasis formed per 60 million disseminated tumor cells. Validation of the model to data of patients in all T-stages shows good agreement between model and epidemiological data. To reduce the 5-year risk of distant metastasis for T_XN_XM₀ from 9.2% to 1.0%, the primary tumor needs to be detected and removed before it reaches a diameter of 2.7 ± 1.6 mm. At this size, the model predicts that there will be 9 ± 6 CTC/L blood.

Conclusions

To reduce the rate of distant metastasis in surgically treated T_XN_XM₀ breast cancer to 1%, imaging technology will need to be able to detect lesions of 2.7 mm in diameter or smaller. Before CTC detection can be applied in the early disease setting, sensitivity will need to be improved by at least 15-fold and combined with technology that minimizes false positives.

Modeling protective anti-tumor immunity via preventative cancer vaccines using a hybrid agent-based and delay differential equation approach

A next generation approach to cancer envisions developing preventative vaccinations to stimulate a person's immune cells, particularly cytotoxic T lymphocytes (CTLs), to eliminate incipient tumors before clinical detection. The purpose of our study is to quantitatively assess whether such an approach would be feasible, and if so, how many anti-cancer CTLs would have to be primed against tumor antigen to provide significant protection. To understand the relevant dynamics, we develop a two-compartment model of tumor-immune interactions at the tumor site and the draining lymph node. We model interactions at the tumor site using an agent-based model (ABM) and dynamics in the lymph node using a system of delay differential equations (DDEs). We combine the models into a hybrid ABM-DDE system and investigate dynamics over a wide range of parameters, including cell proliferation rates, tumor antigenicity, CTL recruitment times, and initial memory CTL populations. Our results indicate that an anti-cancer memory CTL pool of 3% or less can successfully eradicate a tumor population over a wide range of model parameters, implying that a vaccination approach is feasible. In addition, sensitivity analysis of our model reveals conditions that will result in rapid tumor destruction, oscillation, and polynomial rather than exponential decline in the tumor population due to tumor geometry.

An innovative approach to treating cancer envisions developing preventative anti-cancer vaccines to train a person's immune cells to eliminate early-stage tumors close to genesis. The design of such a treatment strategy requires an understanding of the tumor and immune interactions leading to a successful anti-cancer immune response. To engage this problem, we formulate a mathematical model of the immune response against incipient tumours consisting of as low as hundreds to thousands of cancer cells, which is far below the clinical detection threshold of over 100,000 cells. The model considers the initial stimulation of the immune response and the resulting immune attack on the tumor mass and is formulated as a hybrid agent-based and delay differential equation model. We apply the model to test dynamics over a wide range of dynamic parameters, including immune and tumor cell growth rates and the size of the initial anti-cancer immune population. Our results show that an anti-cancer memory immune cell population of 3% or less can successfully eradicate an incipient tumor population over a wide range of dynamic parameters, indicating that a vaccination approach is feasible.

Recurrence of invasive micropapillary carcinoma of the breast with different ultrasound features according to lesion site: case report

Invasive micropapillary carcinoma (IMPC) of the breast is a distinct variant of breast cancer. Extensive lymphatic penetration, lymph node metastasis, and local recurrence are seen at a relatively high frequency. On ultrasound (US) findings, IMPC has been reported to be an irregular or lobulated mass with hypoechoic internal areas, but as yet there is no consensus regarding typical findings. A 52-year-old female noticed a mass less than 3 cm in diameter in her left upper breast. US findings indicated an irregularly shaped, hypoechoic tumor with indistinct margins. The diagnosis according to fine-needle aspiration cytology was invasive ductal carcinoma. She underwent lymph node dissection with mastectomy of the left breast. Histological examination revealed mixed-type IMPC. Three years and three months after surgery, IMPC recurred under the skin of the surgical scar. US findings indicated a hyperechoic tumor in this region. Eight months after further surgery, a tumor in the anterior chest wall was observed. US findings indicated an oval hypoechoic tumor with posterior acoustic enhancement. US findings differed between primary and recurrent IMPC because of differences in the occupancy and distribution of IMPC. We describe here a comparison between US and histological findings, as well as differences in IMPC between primary, secondary and tertiary sites.

Safety and cost-effectiveness of shortening hospital follow-up after breast cancer treatment

BACKGROUND

In the Netherlands, the first 5 years of follow-up after treatment for breast cancer are carried out in hospital with yearly mammography. After this, for patients aged over 60 years who have undergone mastectomy, there is a shift of care to the National Screening Programme (NSP) for mammography every 2 years. After breast-conserving therapy follow-up is perfomed by the general practitioner (GP), with mammography every second year and physical examination annually. The aim of this study was to evaluate the clinical effects and costs of four different strategies for follow-up after breast cancer treatment.

METHODS

An extended and validated simulation model for breast cancer follow-up was used. The current guidelines for follow-up (baseline strategy) and three less intensive follow-up strategies were evaluated. The main outcome measure was the detection rate of small tumours (2 cm or smaller) and associated costs for each strategy.

RESULTS

Shortening the follow-up time in hospital by shifting care to the NSP or GP after 2 years instead of 5 years of hospital follow-up, lowering the age of referral to the NSP or GP from 60 to 50 years, and termination of annual physical examination by the GP after hospital follow-up did not decrease the detection of small tumours. In addition, a substantial decrease in costs was observed with simplified follow-up.

CONCLUSION

Decreasing hospital follow-up time, lowering the age of referral to the NSP or GP, and termination of annual physical examinations would lead to a substantial reduction in costs while maintaining the possibility of detecting small breast cancers.

Overdiagnosis of breast cancer after 14 years of mammography screening

BACKGROUND

In 2004 we wrote in Tidsskriftet that mammography screening resulted in massive over-diagnosis and over-treatment of breast cancer. Our study was criticised because we had only five years of follow-up time and did not take account of the fact that increased use of hormone replacement therapy could lead to more breast cancer. We have now been screening women for 14 years, and during a period when the use of hormones has fallen by 70 %.

MATERIAL AND METHOD

Age-specific incidence rates, detection rates and interval rates for breast cancer in the period 1991-2009 have been computed for 40-79 year-old women. Incidence trends have been calculated using Poisson regression.

RESULTS

The incidence of breast cancer in the age group 40-49 was stable throughout the period, but rose by 50 % in the age group 50-69 years immediately after the start of screening. There was no significant reduction in the incidence of breast cancer in the age group 70-74. The number of new cases of breast cancer in the period increased from around 2000 to 2750. About 300 cases of ductal carcinoma in situ (DCIS) were also diagnosed. Today a total of some 1050 more women have been diagnosed than before screening started. Our calculations indicate that in the absence of screening, around 800 of these women would never have become breast cancer patients.

INTERPRETATION

The figures from 14 years of mammography screening indicate that all increase in the incidence of breast cancer is due to over-diagnosis: findings of tumours that in the absence of screening would never have given rise to clinical illness.

Maintaining Breast Cancer Specimen Integrity and Individual or Simultaneous Extraction of Quality DNA, RNA, and Proteins from Allprotect-Stabilized and Nonstabilized Tissue Samples

The Saint James's Hospital Biobank was established in 2008, to develop a high-quality breast tissue BioResource, as a part of the breast cancer clinical care pathway. The aims of this work were: (1) to ascertain the quality of RNA, DNA, and protein in biobanked carcinomas and normal breast tissues, (2) to assess the efficacy of AllPrep(®) (Qiagen) in isolating RNA, DNA, and protein simultaneously, (3) to compare AllPrep with RNEasy(®) and QIAamp(®) (both Qiagen), and (4) to examine the effectiveness of Allprotect(®) (Qiagen), a new tissue stabilization medium in preserving DNA, RNA, and proteins. One hundred eleven frozen samples of carcinoma and normal breast tissue were analyzed. Tumor and normal tissue morphology were confirmed by frozen sections. Tissue type, tissue treatment (Allprotect vs. no Allprotect), extraction kit, and nucleic acid quantification were analyzed by utilizing a 4 factorial design (SPSS PASW 18 Statistics Software(®)). QIAamp (DNA isolation), AllPrep (DNA, RNA, and Protein isolation), and RNeasy (RNA isolation) kits were assessed and compared. Mean DNA yield and A(260/280) values using QIAamp were 33.2 ng/μL and 1.86, respectively, and using AllPrep were 23.2 ng/μL and 1.94. Mean RNA yield and RNA Integrity Number (RIN) values with RNeasy were 73.4 ng/μL and 8.16, respectively, and with AllPrep were 74.8 ng/μL and 7.92. Allprotect-treated tissues produced higher RIN values of borderline significance (P=0.055). No discernible loss of RNA stability was detected after 6 h incubation of stabilized or nonstabilized tissues at room temperature or 4°C or in 9 freeze-thaw cycles. Allprotect requires further detailed evaluation, but we consider AllPrep to be an excellent option for the simultaneous extraction of RNA, DNA, and protein from tumor and normal breast tissues. The essential presampling procedures that maintain the diagnostic integrity of pathology specimens do not appear to compromise the quality of molecular isolates.

Comparing the benefits of screening for breast cancer and lung cancer using a novel natural history model

To estimate the impact of early detection of cancer, knowledge of how quickly primary tumors grow and at what size they shed lethal metastases is critical. We developed a natural history model of cancer to estimate the probability of disease-specific cure as a function of tumor size, the tumor volume doubling time (TVDT), and disease-specific mortality reduction achievable by screening. The model was applied to non-small-cell lung carcinoma (NSCLC) and invasive ductal carcinoma (IDC), separately. Model parameter estimates were based on Surveillance Epidemiology and End Results (SEER) cancer registry datasets and validated on screening trials. Compared to IDC, NSCLC is estimated to have a lower probability of disease-specific cure at the same detected tumor size, shed lethal metastases at smaller sizes (median: 19 mm for IDC versus 8 mm for NSCLC), have a TVDT that is almost half as long (median: 252 days for IDC versus 134 days for NSCLC). Consequently, NSCLC is associated with a lower mortality reduction from screening at the same screen detection threshold and screening interval. In summary, using a similar natural history model of cancer, we quantify the disease-specific curability attributable to screening for breast cancer, and separately lung cancer, in terms of the TVDT and onset of lethal metastases.

DW4TR: A Data Warehouse for Translational Research

The linkage between the clinical and laboratory research domains is a key issue in translational research. Integration of clinicopathologic data alone is a major task given the number of data elements involved. For a translational research environment, it is critical to make these data usable at the point-of-need. Individual systems have been developed to meet the needs of particular projects though the need for a generalizable system has been recognized. Increased use of Electronic Medical Record data in translational research will demand generalizing the system for integrating clinical data to support the study of a broad range of human diseases. To ultimately satisfy these needs, we have developed a system to support multiple translational research projects. This system, the Data Warehouse for Translational Research (DW4TR), is based on a light-weight, patient-centric modularly-structured clinical data model and a specimen-centric molecular data model. The temporal relationships of the data are also part of the model. The data are accessed through an interface composed of an Aggregated Biomedical-Information Browser (ABB) and an Individual Subject Information Viewer (ISIV) which target general users. The system was developed to support a breast cancer translational research program and has been extended to support a gynecological disease program. Further extensions of the DW4TR are underway. We believe that the DW4TR will play an important role in translational research across multiple disease types.