Breast cancer incidence and overdiagnosis in Catalonia (Spain)

Introduction: Early detection of breast cancer (BC) with mammography may cause overdiagnosis and overtreatment, detecting tumors which would remain undiagnosed during a lifetime. The aims of this study were: first, to model invasive BC incidence trends in Catalonia (Spain) taking into account reproductive and screening data; and second, to quantify the extent of BC overdiagnosis. Methods: We modeled the incidence of invasive BC using a Poisson regression model. Explanatory variables were: age at diagnosis and cohort characteristics (completed fertility rate, percentage of women that use mammography at age 50, and year of birth). This model also was used to estimate the background incidence in the absence of screening. We used a probabilistic model to estimate the expected BC incidence if women in the population used mammography as reported in health surveys. The difference between the observed and expected cumulative incidences provided an estimate of overdiagnosis. Results: Incidence of invasive BC increased, especially in cohorts born from 1940 to 1955. The biggest increase was observed in these cohorts between the ages of 50 to 65 years, where the final BC incidence rates more than doubled the initial ones. Dissemination of mammography was significantly associated with BC incidence and overdiagnosis. Our estimates of overdiagnosis ranged from 0.4% to 46.6%, for women born around 1935 and 1950, respectively. Conclusions: Our results support the existence of overdiagnosis in Catalonia attributed to mammography usage, and the limited malignant potential of some tumors may play an important role. Women should be better informed about this risk. Research should be oriented towards personalized screening and risk assessment tools.

Competing risks to breast cancer mortality in Catalonia

Background: Breast cancer mortality has experienced important changes over the last century. Breast cancer occurs in the presence of other competing risks which can influence breast cancer incidence and mortality trends. The aim of the present work is: 1) to assess the impact of breast cancer deaths among mortality from all causes in Catalonia (Spain), by age and birth cohort and 2) to estimate the risk of death from other causes than breast cancer, one of the inputs needed to model breast cancer mortality reduction due to screening or therapeutic interventions. Methods: The multi-decrement life table methodology was used. First, all-cause mortality probabilities were obtained by age and cohort. Then mortality probability for breast cancer was subtracted from the all-cause mortality probabilities to obtain cohort life tables for causes other than breast cancer. These life tables, on one hand, provide an estimate of the risk of dying from competing risks, and on the other hand, permit to assess the impact of breast cancer deaths on all-cause mortality using the ratio of the probability of death for causes other than breast cancer by the all-cause probability of death. Results: There was an increasing impact of breast cancer on mortality in the first part of the 20th century, with a peak for cohorts born in 1945–54 in the 40–49 age groups (for which approximately 24% of mortality was due to breast cancer). Even though for cohorts born after 1955 there was only information for women under 50, it is also important to note that the impact of breast cancer on all-cause mortality decreased for those cohorts. Conclusion: We have quantified the effect of removing breast cancer mortality in different age groups and birth cohorts. Our results are consistent with US findings. We also have obtained an estimate of the risk of dying from competing-causes mortality, which will be used in the assessment of the effect of mammography screening on breast cancer mortality in Catalonia.

Estimation of age- and stage-specific Catalan breast cancer survival functions using US and Catalan survival data

Background: During the last part of the 1990s the chance of surviving breast cancer increased. Changes in survival functions reflect a mixture of effects. Both, the introduction of adjuvant treatments and early screening with mammography played a role in the decline in mortality. Evaluating the contribution of these interventions using mathematical models requires survival functions before and after their introduction. Furthermore, required survival functions may be different by age groups and are related to disease stage at diagnosis. Sometimes detailed information is not available, as was the case for the region of Catalonia (Spain). Then one may derive the functions using information from other geographical areas. This work presents the methodology used to estimate age- and stage-specific Catalan breast cancer survival functions from scarce Catalan survival data by adapting the age- and stage-specific US functions. Methods: Cubic splines were used to smooth data and obtain continuous hazard rate functions. After, we fitted a Poisson model to derive hazard ratios. The model included time as a covariate. Then the hazard ratios were applied to US survival functions detailed by age and stage to obtain Catalan estimations. Results: We started estimating the hazard ratios for Catalonia versus the USA before and after the introduction of screening. The hazard ratios were then multiplied by the age- and stage-specific breast cancer hazard rates from the USA to obtain the Catalan hazard rates. We also compared breast cancer survival in Catalonia and the USA in two time periods, before cancer control interventions (USA 1975–79, Catalonia 1980–89) and after (USA and Catalonia 1990–2001). Survival in Catalonia in the 1980–89 period was worse than in the USA during 1975–79, but the differences disappeared in 1990–2001. Conclusion: Our results suggest that access to better treatments and quality of care contributed to large improvements in survival in Catalonia. On the other hand, we obtained detailed breast cancer survival functions that will be used for modeling the effect of screening and adjuvant treatments in Catalonia.

Effectiveness of early detection on breast cancer mortality reduction in Catalonia (Spain)

Background: At present, it is complicated to use screening trials to determine the optimal age intervals and periodicities of breast cancer early detection. Mathematical models are an alternative that has been widely used. The aim of this study was to estimate the effect of different breast cancer early detection strategies in Catalonia (Spain), in terms of breast cancer mortality reduction (MR) and years of life gained (YLG), using the stochastic models developed by Lee and Zelen (LZ). Methods: We used the LZ model to estimate the cumulative probability of death for a cohort exposed to different screening strategies after T years of follow-up. We also obtained the cumulative probability of death for a cohort with no screening. These probabilities were used to estimate the possible breast cancer MR and YLG by age, period and cohort of birth. The inputs of the model were: incidence of, mortality from and survival after breast cancer, mortality from other causes, distribution of breast cancer stages at diagnosis and sensitivity of mammography. The outputs were relative breast cancer MR and YLG. Results: Relative breast cancer MR varied from 20% for biennial exams in the 50 to 69 age interval to 30% for annual exams in the 40 to 74 age interval. When strategies differ in periodicity but not in the age interval of exams, biennial screening achieved almost 80% of the annual screening MR. In contrast to MR, the effect on YLG of extending screening from 69 to 74 years of age was smaller than the effect of extending the screening from 50 to 45 or 40 years. Conclusion: In this study we have obtained a measure of the effect of breast cancer screening in terms of mortality and years of life gained. The Lee and Zelen mathematical models have been very useful for assessing the impact of different modalities of early detection on MR and YLG in Catalonia (Spain).

Cost-effectiveness of early detection of breast cancer in Catalonia (Spain)

Background: Breast cancer (BC) causes more deaths than any other cancer among women in Catalonia. Early detection has contributed to the observed decline in BC mortality. However, there is debate on the optimal screening strategy. We performed an economic evaluation of 20 screening strategies taking into account the cost over time of screening and subsequent medical costs, including diagnostic confirmation, initial treatment, follow-up and advanced care. Methods: We used a probabilistic model to estimate the effect and costs over time of each scenario. The effect was measured as years of life (YL), quality-adjusted life years (QALY), and lives extended (LE). Costs of screening and treatment were obtained from the Early Detection Program and hospital databases of the IMAS-Hospital del Mar in Barcelona. The incremental cost-effectiveness ratio (ICER) was used to compare the relative costs and outcomes of different scenarios. Results: Strategies that start at ages 40 or 45 and end at 69 predominate when the effect is measured as YL or QALYs. Biennial strategies 50-69, 45-69 or annual 45-69, 40-69 and 40-74 were selected as cost-effective for both effect measures (YL or QALYs). The ICER increases considerably when moving from biennial to annual scenarios. Moving from no screening to biennial 50-69 years represented an ICER of 4,469€ per QALY. Conclusions: A reduced number of screening strategies have been selected for consideration by researchers, decision makers and policy planners. Mathematical models are useful to assess the impact and costs of BC screening in a specific geographical area.

Contribution of early detection and adjuvant treatments to breast cancer mortality reduction in Catalonia, Spain

Background: Reductions in breast cancer (BC) mortality in Western countries have been attributed to the use of screening mammography and adjuvant treatments. The goal of this work was to analyze the contributions of both interventions to the decrease in BC mortality between 1975 and 2008 in Catalonia. Methodology/Principal Findings: A stochastic model was used to quantify the contribution of each intervention. Age standardized BC mortality rates for calendar years 1975-2008 were estimated in four hypothetical scenarios: 1) Only screening, 2) Only adjuvant treatment, 3) Both interventions, and 4) No intervention. For the 30-69 age group, observed Catalan BC mortality rates per 100,000 women-year rose from 29.4 in 1975 to 38.3 in 1993, and afterwards continuously decreased to 23.2 in 2008. If neither of the two interventions had been used, in 2008 the estimated BC mortality would have been 43.5, which, compared to the observed BC mortality rate, indicates a 46.7% reduction. In 2008 the reduction attributable to screening was 20.4%, to adjuvant treatments was 15.8% and to both interventions 34.1%. Conclusions/Significance: Screening and adjuvant treatments similarly contributed to reducing BC mortality in Catalonia. Mathematical models have been useful to assess the impact of interventions addressed to reduce BC mortality that occurred over nearly the same periods.

Derivation of an observer model adapted to irregular signals based on convolution channels.

Anthropomorphic model observers are mathe- matical algorithms which are applied to images with the ultimate goal of predicting human signal detection and classification accuracy across varieties of backgrounds, image acquisitions and display conditions. A limitation of current channelized model observers is their inability to handle irregularly-shaped signals, which are common in clinical images, without a high number of directional channels. Here, we derive a new linear model observer based on convolution channels which we refer to as the "Filtered Channel observer" (FCO), as an extension of the channelized Hotelling observer (CHO) and the nonprewhitening with an eye filter (NPWE) observer. In analogy to the CHO, this linear model observer can take the form of a single template with an external noise term. To compare with human observers, we tested signals with irregular and asymmetrical shapes spanning the size of lesions down to those of microcalfications in 4-AFC breast tomosynthesis detection tasks, with three different contrasts for each case. Whereas humans uniformly outperformed conventional CHOs, the FCO observer outperformed humans for every signal with only one exception. Additive internal noise in the models allowed us to degrade model performance and match human performance. We could not match all the human performances with a model with a single internal noise component for all signal shape, size and contrast conditions. This suggests that either the internal noise might vary across signals or that the model cannot entirely capture the human detection strategy. However, the FCO model offers an efficient way to apprehend human observer performance for a non-symmetric signal.

The forced adoption of a fast-track appraisal process for a breast cancer treatment in UK

Trastuzumab (Herceptin ®, Roche) is approved in UK for the treatment of the metastatic breast cancer since 2001. As of 2005, concomitantly with the publication of 3 studies that showed it produces a 50% reduction of the recurrence rates of breast cancer, trastuzumab started to be prescribed in the earlt adjuvant treatrnent of this disease. Und June 2006, trastuzumab did not have both: 1) regulatory approval and 2) NICE [National Institute for Health and Clinical Excellence] recommendation for the use in early stages of breast cancer. During the period until June 2006, the trastuzumab use in those patients was not reimbursed and because the cost of trastuzumab is equal with the yearly UK average income, most of patients could not self fund their treatrnent. Before the publication of the final NICE guidance, the new data of trastuzumab in early breast cancer raised enormous patient and professional interest and expectations. A great volume of public and professional pressure was generated to transcend a system by which Primary Care Trusts can reimburse a treatment only after a formal guidance was issued. This paper draw on a case study depicting and analyzing the process by which regulatory approval and NICE recommendations were achieved in a record time and how trastuzumab became a standard treatment on early adjuvant breast cancer. According to the data we gathered in this work we were witnessing one of the fastest processes of adoption of a health care technology since the creation of NICE, in 1999. This study addresses the following research question: How and why does the adoption pattern of trastuzumab differ from the rational decision-making model of the reimbursement process in UK? [Author, p. 4]

Modeling and Classifying Breast Tissue Density in Mammograms

We present a new approach to model and classify breast parenchymal tissue. Given a mammogram, first, we will discover the distribution of the different tissue densities in an unsupervised manner, and second, we will use this tissue distribution to perform the classification. We achieve this using a classifier based on local descriptors and probabilistic Latent Semantic Analysis (pLSA), a generative model from the statistical text literature. We studied the influence of different descriptors like texture and SIFT features at the classification stage showing that textons outperform SIFT in all cases. Moreover we demonstrate that pLSA automatically extracts meaningful latent aspects generating a compact tissue representation based on their densities, useful for discriminating on mammogram classification. We show the results of tissue classification over the MIAS and DDSM datasets. We compare our method with approaches that classified these same datasets showing a better performance of our proposal

Estimation of age- and stage-specific Catalan breast cancer survival functions using US and Catalan survival data

During the last part of the 1990s the chance of surviving breast cancer increased. Changes in survival functions reflect a mixture of effects. Both, the introduction of adjuvant treatments and early screening with mammography played a role in the decline in mortality. Evaluating the contribution of these interventions using mathematical models requires survival functions before and after their introduction. Furthermore, required survival functions may be different by age groups and are related to disease stage at diagnosis. Sometimes detailed information is not available, as was the case for the region of Catalonia (Spain). Then one may derive the functions using information from other geographical areas. This work presents the methodology used to estimate age- and stage-specific Catalan breast cancer survival functions from scarce Catalan survival data by adapting the age- and stage-specific US functions. Methods: Cubic splines were used to smooth data and obtain continuous hazard rate functions. After, we fitted a Poisson model to derive hazard ratios. The model included time as a covariate. Then the hazard ratios were applied to US survival functions detailed by age and stage to obtain Catalan estimations. Results: We started estimating the hazard ratios for Catalonia versus the USA before and after the introduction of screening. The hazard ratios were then multiplied by the age- and stage-specific breast cancer hazard rates from the USA to obtain the Catalan hazard rates. We also compared breast cancer survival in Catalonia and the USA in two time periods, before cancer control interventions (USA 1975–79, Catalonia 1980–89) and after (USA and Catalonia 1990–2001). Survival in Catalonia in the 1980–89 period was worse than in the USA during 1975–79, but the differences disappeared in 1990–2001. Conclusion: Our results suggest that access to better treatments and quality of care contributed to large improvements in survival in Catalonia. On the other hand, we obtained detailed breast cancer survival functions that will be used for modeling the effect of screening and adjuvant treatments in Catalonia

Cardiovascular risk in middle-aged breast cancer survivors: a comparison between two risk models

PURPOSE: It was to assess the risk of cardiovascular disease (CVD) in breast cancer survivors (BCS).METHODS: This cross-sectional study analyzed 67 BCS, aged 45 -65 years, who underwent complete oncological treatment, but had not received hormone therapy, tamoxifen or aromatase inhibitors during the previous 6 months. Lipid profile and CVD risk were evaluated, the latter using the Framingham and Systematic COronary Risk Evaluation (SCORE) models. The agreement between cardiovascular risk models was analyzed by calculating a kappa coefficient and its 95% confidence interval (CI).RESULTS: Mean subject age was 53.2±6.0 years, with rates of obesity, hypertension, and dyslipidemia of 25, 34 and 90%, respectively. The most frequent lipid abnormalities were high total cholesterol (70%), high LDL-C (51%) and high non-HDL-C (48%) concentrations. Based on the Framingham score, 22% of the participants had a high risk for coronary artery disease. According to the SCORE model, 100 and 93% of the participants were at low risk for fatal CVD in populations at low and high risk, respectively, for CVD. The agreement between the Framingham and SCORE risk models was poor (kappa: 0.1; 95%CI 0.01 -0.2) for populations at high risk for CVD.CONCLUSIONS: These findings indicate the need to include lipid profile and CVD risk assessment in the follow-up of BCS, focusing on adequate control of serum lipid concentrations.

Fatigue and quality of life in breast cancer survivors: a comparative study

PURPOSE: To assess fatigue and quality of life in disease-free breast cancer survivors in relation to a sample of age-matched women with no cancer history and to explore the relationship between fatigue and quality of life.METHODS: A cross-sectional study was conducted in a sample of 202 consecutive disease-free Brazilian breast cancer survivors, all of whom had completed treatment, treated at 2 large hospitals. The patients were compared to age-matched women with no cancer history attending a primary health care center. The Piper Fatigue Scale-Revised and the World Health Organization Quality of Life Instrument (WHOQOL-BREF) were used to measure the fatigue and quality of life, respectively. Socio-demographic and clinical variables were also obtained. The χ2 test, generalized linear model, and Spearman correlation coefficient were used for statistical purposes. The adopted level of significance was 5%.RESULTS: Breast cancer survivors experienced significantly greater total and subscale fatigue scores than comparison group (all p-values<0.05). In addition, survivors reported a poorer quality of life in physical (p=0.002), psychological (p=0.03), and social relationships (p=0.03) domains than comparison group. No difference was found for the environmental domain (p=0.08) for both groups. For survivors of breast cancer and for comparison group, the total and subscale fatigue scores were related to lower quality of life (all p-values<0.01).CONCLUSION: The findings of this study highlight the importance of assessing fatigue and quality of life in breast cancer survivors.

The Role of an Oncoprotein CIP2A in Breast Cancer

Cancerous inhibitor of PP2A (CIP2A) is an oncoprotein expressed in several human cancer types. Previously, CIP2A has been shown to promote proliferation of cancer cells. Mechanistically, CIP2A is known to inhibit activity of a tumor suppressor protein phosphatase 2A (PP2A) towards an oncoprotein MYC, further stabilizing MYC in human cancer. However, the molecular mechanisms how CIP2A expression is induced during cellular transformation are not well known. Also, expression, functional role and clinical relevance of CIP2A in breast cancer had not been studied before. The results of this PhD thesis work demonstrate that CIP2A is highly expressed in human breast cancer, and that high expression of CIP2A in tumors is a poor prognostic factor in a subset of breast cancer patients. CIP2A expression correlates with inactivating mutations of tumor suppressor p53 in human cancer. Notably, we demonstrate that p53 inactivation up-regulates CIP2A expression via increased expression of an oncogenic transcription factor E2F1. Moreover, CIP2A promotes expression of E2F1, and this novel positive feedback loop between E2F1 and CIP2A is demonstrated to regulate sensitivity to both p53-dependent and -independent senescence induction in breast cancer cells. Importantly, in a CIP2A deficient breast cancer mouse model, abrogation of CIP2A attenuates mammary tumor formation and progression with features of E2F1 inhibition and induction of senescence. Furthermore, we demonstrate that CIP2A expression defines the cellular response to a senescence-inducing chemotherapy in breast cancer. Taken together, these results demonstrate that CIP2A is an essential promoter of breast cancer tumor growth by inhibiting senescence. Finally, this study implicates inhibition of CIP2A as a promising therapy target for breast cancer.

CIAPIN1 gene silencing enhances chemosensitivity in a drug-resistant animal model in vivo

Overexpression of cytokine-induced apoptosis inhibitor 1 (CIAPIN1) contributes to multidrug resistance (MDR) in breast cancer. This study aimed to evaluate the potential of CIAPIN1 gene silencing by RNA interference (RNAi) as a treatment for drug-resistant breast cancer and to investigate the effect of CIAPIN1 on the drug resistance of breast cancer in vivo. We used lentivirus-vector-based RNAi to knock down CIAPIN1 in nude mice bearing MDR breast cancer tumors and found that lentivirus-vector-mediated silencing of CIAPIN1 could efficiently and significantly inhibit tumor growth when combined with chemotherapy in vivo. Furthermore, Western blot analysis showed that both CIAPIN1 and P-glycoprotein expression were efficiently downregulated, and P53 was upregulated, after RNAi. Therefore, we concluded that lentivirus-vector-mediated RNAi targeting of CIAPIN1 is a potential approach to reverse MDR of breast cancer. In addition, CIAPIN1 may participate in MDR of breast cancer by regulating P-glycoprotein and P53 expression.

The relationship between body mass index and breast cancer recurrence/progression and breast cancer-specific death

The aim of this study was to describe the nonlinear association between body mass index (BMI) and breast cancer outcomes and to determine whether BMI improves prediction of outcomes. A cohort of906 breast cancer patients diagnosed at Henry Ford Health System, Detroit (1985-1990) were studied. The median follow-up was 10 years. Multivariate logistic regression was used to model breast cancer recurrence/progression and breast cancer-specific death. Restricted cubic splines were used to model nonlinear effects. Receiver operator characteristic areas under the curves (ROC AUC) were used to evaluate prediction. BMI was nonlinearly associated with recurrence/progression and death (p= 0.0230 and 0.0101). Probability of outcomes increased with increase or decrease ofBMI away from 25. BMI splines were suggestive of improved prediction of death. The ROC AUCs for nested models with and without BMI were 0.8424 and 0.8331 (p= 0.08). I f causally associated, modifying patients BMI towards 25 may improve outcomes.