Natural History Of Breast Cancer Research Articles

Hematological and biochemical markers influencing breast cancer risk and mortality: Prospective cohort study in the UK Biobank by multi-state models

BackgroundBreast cancer is the most common cancer and the leading cause of cancer-related death among women. However, evidence concerning hematological and biochemical markers influencing the natural history of breast cancer from in situ breast cancer to mortality is limited. MethodsIn the UK Biobank cohort, 260,079 women were enrolled during 2006–2010 and were followed up until 2019 to test the 59 hematological and biochemical markers associated with breast cancer risk and mortality. The strengths of these associations were evaluated using the multivariable Cox regression models. To understand the natural history of breast cancer, multi-state survival models were further applied to examine the effects of biomarkers on transitions between different states of breast cancer. ResultsEleven biomarkers were found to be significantly associated with the risk of invasive breast cancer, including mainly inflammatory-related biomarkers and endogenous hormones, while serum testosterone was also associated with the risk of in-situ breast cancer. Among them, C-reactive protein (CRP) was more likely to be associated with invasive breast cancer and its transition to death from breast cancer (HR for the highest quartile = 1.46, 95 % CI = 1.07–1.97), while testosterone and insulin-like growth factor-1 (IGF-1) were more likely to impact the early state of breast cancer development (Testosterone: HR for the highest quartile = 1.31, 95 % CI = 1.12–1.53; IGF-1: HR for the highest quartile = 1.17, 95 % CI = 1.00–1.38). ConclusionSerum CRP, testosterone, and IGF-1 have different impacts on the transitions of different breast cancer states, confirming the role of chronic inflammation and endogenous hormones in breast cancer progression. This study further highlights the need of closer surveillance for these biomarkers during the breast cancer development course.

Approximate Bayesian computation for the natural history of breast cancer, with application to data from a Milan cohort study.

We explore models for the natural history of breast cancer, where the main events of interest are the start of asymptomatic detectability of the disease (through screening) and the time of symptomatic detection (through symptoms). We develop several parametric specifications based on a cure rate structure, and present the results of the analysis of data collected as part of a motivating study from Milan. Participants in the study were part of a regional breast cancer screening program, and their ten-year trajectories were obtained from administrative data available from the Italian national health care system. We first present a tractable model for which we develop the likelihood contributions of the observed trajectories and perform maximum likelihood inference on the latent process. Likelihood based inference is not feasible for more flexible models, and we implement approximate Bayesian computation (ABC) for inference. Issues that arise from the use of ABC for model choice and parameter estimation are discussed, including the problem of choosing appropriate summary statistics. The estimated parameters of the underlying disease process allow for the study of the effect of different examination schedules (age range and frequency of screening examinations) on a population of asymptomatic subjects.

Tumour Growth Models of Breast Cancer for Evaluating Early Detection-A Summary and a Simulation Study.

With the advent of nationwide mammography screening programmes, a number of natural history models of breast cancers have been developed and used to assess the effects of screening. The first half of this article provides an overview of a class of these models and describes how they can be used to study latent processes of tumour progression from observational data. The second half of the article describes a simulation study which applies a continuous growth model to illustrate how effects of extending the maximum age of the current Swedish screening programme from 74 to 80 can be evaluated. Compared to no screening, the current and extended programmes reduced breast cancer mortality by 18.5% and 21.7%, respectively. The proportion of screen-detected invasive cancers which were overdiagnosed was estimated to be 1.9% in the current programme and 2.9% in the extended programme. With the help of these breast cancer natural history models, we can better understand the latent processes, and better study the effects of breast cancer screening.

Histone deacetylase inhibitors promote breast cancer metastasis by elevating NEDD9 expression

Histone deacetylase (HDAC) is a kind of protease that modifies histone to regulate gene expression, and is usually abnormally activated in tumors. The approved pan-HDAC inhibitors have demonstrated clinical benefits for patients in some hematologic malignancies. Only limited therapeutic success in breast cancer has been observed in clinical trials. In this study, we declare that pan-HDAC inhibitors targeting NEDD9-FAK pathway exacerbate breast cancer metastasis in preclinical models, which may severely impede their clinical success. NEDD9 is not an oncogene, however, it has been demonstrated recently that there are high level or activity changes of NEDD9 in a variety of cancer, including leukemia, colon cancer, and breast cancer. Mechanistically, pan-HDAC inhibitors enhance H3K9 acetylation at the nedd9 gene promoter via inhibition of HDAC4 activity, thus increase NEDD9 expression, and then activate FAK phosphorylation. The realization that pan-HDAC inhibitors can alter the natural history of breast cancer by increasing invasion warrants clinical attention. In addition, although NEDD9 has been reported to have a hand in breast cancer metastasis, it has not received much attention, and no therapeutic strategies have been developed. Notably, we demonstrate that FAK inhibitors can reverse breast cancer metastasis induced by upregulation of NEDD9 via pan-HDAC inhibitors, which may offer a potential combination therapy for breast cancer.

An optimization approach for parameter selection in natural history of breast cancer in China

Breast cancer is one of the most common cancers for females. In order to propose efficient screening strategies and evaluate the effect, one fundamental and important step is to choose proper parameters, i.e., transition rates, in the natural history model of breast cancer in China. There are two challenges to choosing reasonable transition rates. Firstly, the transition rates used in other countries may be not applicable to China, due to the cancer epidemiology. Secondly, little screening sample data are available, making the traditional statistical based approaches such as maximum likelihood methods fail. In this paper, we propose an optimization approach for parameter selection in the natural history of breast cancer in China. A mathematical optimization model is established based on the published statistical data. A coordinate descent algorithm is proposed to solve the resulting box-constrained and highly nonlinear problem, whose objective function does not have an explicit form. Finally, the best matching method is proposed to estimate the distributions of each transition rate. Our approach provides solid foundations to further propose reasonable screening strategies for breast cancer in China and analyze the economic burden of breast cancer.

No Impact of Seasonality of Diagnoses on Baseline Tumor Immune Infiltration, Response to Treatment, and Prognosis in BC Patients Treated with NAC.

Simple SummaryHigh tumor-infiltrating lymphocyte (TIL) levels are associated with an increased response to neoadjuvant chemotherapy (NAC) in breast cancer (BC). The seasonal fluctuation of TILs in breast cancer is poorly documented. In this study, we compared pre- and post-treatment immune infiltration, the treatment response as assessed by means of pathological complete response (pCR) rates, and survival according to the seasonality of BC diagnoses in a clinical cohort of patients treated with NAC. We found no association between seasonality and baseline TIL levels or pCR rates. We found that post-NAC stromal lymphocyte infiltration was lower when cancer was diagnosed in the summer, especially in the subgroup of patients with TNBC. Our data do not support the hypothesis that the seasonality of diagnoses has a major impact on the natural history of BC treated with NAC.Breast cancer (BC) is the most common cancer in women worldwide. Neoadjuvant chemotherapy (NAC) makes it possible to monitor in vivo response to treatment. Several studies have investigated the impact of the seasons on the incidence and detection of BC, on tumor composition, and on the prognosis of BC. However, no evidence is available on their association with immune infiltration and the response to treatment. The objective of this study was to analyze pre- and post-NAC immune infiltration as assessed by TIL levels, the response to treatment as assessed by pathological complete response (pCR) rates, and oncological outcomes as assessed by relapse-free survival (RFS) or overall survival (OS) according to the seasonality of BC diagnoses in a clinical cohort of patients treated with neoadjuvant chemotherapy. Out of 1199 patients, the repartition of the season at BC diagnosis showed that 27.2% were diagnosed in fall, 25.4% in winter, 24% in spring, and 23.4% in summer. Baseline patient and tumor characteristics, including notable pre-NAC TIL levels, were not significantly different in terms of the season of BC diagnosis. Similarly, the pCR rates were not different. No association for oncological outcome was identified. Our data do not support the idea that the seasonality of diagnoses has a major impact on the natural history of BC treated with NAC.

Abstract 5941: A simulation modeling study to support personalized breast cancer prevention and early detection in high-risk women

Abstract Purpose: To evaluate the impact of early detection with screening and primary prevention with risk-reducing medication to provide personalized data that will help identify women who are more likely to benefit from various interventions or combinations of interventions with the least harms. Methods: We adapted the CISNET microsimulation model G-E of breast cancer natural history to evaluate the harms and benefits of annual mammography and risk reducing medication among high-risk women (i.e., 5-year risk greater than or equal to 3%). Model G-E is a discrete event microsimulation model that follows millions of women from birth to death and captures the variability in distributions of each event. Each simulated woman is assigned a cohort-specific life expectancy which is used to select a date of breast cancer death. For this study, we dynamically updated the risk of developing breast cancer for each simulated woman based on her family history, breast density, age and history of biopsy. We used large observational and clinical trial data to derive input parameters for cohort-specific birth rates, incidence and stage without screening, other cause mortality by age, screening performance (sensitivity/specificity), survival by age, stage, and subtype without treatment, treatment efficacy, and other cause mortality. We compared model outcomes for screening alone vs. screening with a 5-year course of risk reducing medication. We modeled various screening strategies including annual or biennial screening starting at ages 35, 40, 45 and stopping at ages 65 and 74 years. Model outcomes for each strategy included, the benefits of risk-reducing drugs (avoiding breast cancer) and screening (breast cancer stage, breast cancer-specific survival), and harms of screening (false positives, overdiagnosis). We also conducted sensitivity analysis to estimate the effects of uncertainty in model inputs or assumptions on results. Results: We found that risk reducing medication could result in an additional 28% decrease in invasive breast cancer incidence, 20% decrease in stage IV diagnosis, and a 30% decrease in breast cancer death if screening started at age 35 in a high-risk woman. However, potential breast density changes due to risk reducing medication among high-risk women could result in a 19% increase in false positive screening results compared to screening alone. The results varied by the starting age of screening. Conclusions: Simulation modeling is useful in assessing the relative benefits and harms of screening and risk reducing medication in high-risk women. Citation Format: Jinani Jayasekera, Amy Zhao. A simulation modeling study to support personalized breast cancer prevention and early detection in high-risk women [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5941.

Mechanistic Effects of Estrogens on Breast Cancer.

Current concepts regarding estrogen and its mechanistic effects on breast cancer in women are evolving. This article reviews studies that address estrogen-mediated breast cancer development, the prevalence of occult tumors at autopsy, and the natural history of breast cancer as predicted by a newly developed tumor kinetic model. This article reviews previously published studies from the authors and articles pertinent to the data presented. We discuss the concepts of adaptive hypersensitivity that develops in response to long-term deprivation of estrogen and results in both increased cell proliferation and apoptosis. The effects of menopausal hormonal therapy on breast cancer in postmenopausal women are interpreted based on the tumor kinetic model. Studies of the administration of a tissue selective estrogen complex in vitro, in vivo, and in patients are described. We review the various clinical studies of breast cancer prevention with selective estrogen receptor modulators and aromatase inhibitors. Finally, the effects of the underlying risk of breast cancer on the effects of menopausal hormone therapy are outlined. The overall intent of this review is to present data supporting recent concepts, discuss pertinent literature, and critically examine areas of controversy.

Estimating Age-Specific Mean Sojourn Time of Breast Cancer and Sensitivity of Mammographic Screening by Breast Density among Korean Women.

High breast cancer incidence and dense breast prevalence among women in forties are specific to Asian. This study examined the natural history of breast cancer among Korean women. We applied a three-state Markov model (i.e., healthy, preclinical, and clinical state) to fit the natural history of breast cancer to data in the Korean National Cancer Screening Program. Breast cancer was ascertained by linkage to the Korean Central Cancer Registry. Disease-progression rates (i.e., transition rates between three states), mean sojourn time (MST) and mammographic sensitivity were estimated across 10-year age groups and levels of breast density determined by the Breast Imaging, Reporting and Data System. Overall prevalence of dense breast was 53.9%. Transition rate from healthy to preclinical state, indicating the preclinical incidence of breast cancer, was higher among women in forties (0.0019; 95% confidence interval [CI], 0.0017 to 0.0021) and fifties (0.0020; 95% CI, 0.0017 to 0.0022), than women in sixties (0.0014; 95% CI, 0.0012 to 0.0017). The MSTs, in which the tumor is asymptomatic but detectable by screening, were also fastest among younger age groups, estimated as 1.98 years (95% CI, 1.67 to 2.33), 2.49 years (95% CI, 1.92 to 3.22), and 3.07 years (95% CI, 2.11 to 4.46) for women in forties, fifties, and sixties, respectively. Having dense breasts increased the likelihood of the preclinical cancer risk (1.96 to 2.35 times) and decreased the duration of MST (1.53 to 2.02 times). This study estimated Korean-specific natural history parameters of breast cancer that would be utilized for establishing optimal screening strategies in countries with higher dense breast prevalence.

The natural history of breast cancer: a chronological analysis of breast cancer progression using data from the SEER database

BackgroundNumerous mathematical models and retrospective analyses have been conducted to explore the natural progression of breast cancer, but there is no precise timeline as to how long it takes breast cancer to progress from stages 0 to IV. Studying the natural history of breast cancer requires the follow-up of a large cohort of patients who received no treatment following diagnosis for a long period, but this has become unpractical in the modern era. In this study, we quantified the natural progression of breast cancer using the Surveillance, Epidemiology, and End Results (SEER) database which was collected the clinical-pathological characteristics and survival data of untreated breast cancer patients.MethodsWe included 12,687 female patients who did not receive surgical treatment, radiotherapy, or chemotherapy, had complete information on tumor staging, and had only 1 primary tumor in their lifetime in our study as the untreated group. Overall survival (OS) was calculated from the date of the latest diagnosis of breast cancer to the date of death. Medium survival time (MST) was defined as the time at which 50% of the patients reached the endpoint. The medium survival time (MST) was calculated for each stage. The progression time was calculated by subtracting the MST of a higher stage from that of a lower stage, which also indicated “life loss”. Estrogen receptor (ER) negative and positive patients were analyzed as two separate groups.ResultsFor the ER positive patients, the median progression times from stages 0 to I, I to II, II to III, and III to IV were 5.2±1.2, 4.2±0.5, 2±0.3, and 0.9±0.2 years, respectively, which were similar to those of the ER negative patients of 5.4±2.9, 4.7±1.3, 2.5±0.7, and 1.1±0.4 years (P=0.49). The survival of stage IV patients was longer in ER positive patients than ER negative patients (2.7±0.1 vs. 0.9±0.2 year, P<0.001).ConclusionsER negative patients appear to have the same progression time as that of ER positive patients in the primary site, but had a much worse process in the metastasis site.

Estimation of Breast Cancer Overdiagnosis in a U.S. Breast Screening Cohort.

Mammography screening can lead to overdiagnosis-that is, screen-detected breast cancer that would not have caused symptoms or signs in the remaining lifetime. There is no consensus about the frequency of breast cancer overdiagnosis. To estimate the rate of breast cancer overdiagnosis in contemporary mammography practice accounting for the detection of nonprogressive cancer. Bayesian inference of the natural history of breast cancer using individual screening and diagnosis records, allowing for nonprogressive preclinical cancer. Combination of fitted natural history model with life-table data to predict the rate of overdiagnosis among screen-detected cancer under biennial screening. Breast Cancer Surveillance Consortium (BCSC) facilities. Women aged 50 to 74 years at first mammography screen between 2000 and 2018. Screening mammograms and screen-detected or interval breast cancer. The cohort included 35986 women, 82677 mammograms, and 718 breast cancer diagnoses. Among all preclinical cancer cases, 4.5% (95% uncertainty interval [UI], 0.1% to 14.8%) were estimated to be nonprogressive. In a program of biennial screening from age 50 to 74 years, 15.4% (UI, 9.4% to 26.5%) of screen-detected cancer cases were estimated to be overdiagnosed, with 6.1% (UI, 0.2% to 20.1%) due to detecting indolent preclinical cancer and 9.3% (UI, 5.5% to 13.5%) due to detecting progressive preclinical cancer in women who would have died of an unrelated cause before clinical diagnosis. Exclusion of women with first mammography screen outside BCSC. On the basis of an authoritative U.S. population data set, the analysis projected that among biennially screened women aged 50 to 74 years, about 1 in 7 cases of screen-detected cancer is overdiagnosed. This information clarifies the risk for breast cancer overdiagnosis in contemporary screening practice and should facilitate shared and informed decision making about mammography screening. National Cancer Institute.

Estimating Distributions of Breast Cancer Onset and Growth in a Swedish Mammography Screening Cohort.

In recent years, biologically motivated continuous tumor growth models have been introduced for breast cancer screening data. These provide a novel framework from which mammography screening effectiveness can be studied. We use a newly developed natural history model, which is unique in that it includes a carcinogenesis model for tumor onset, to analyze data from a large Swedish mammography cohort consisting of 65,536 participants, followed for periods of up to 6.5 years. Using patient data on age at diagnosis, tumor size, and mode of detection, as well as screening histories, we estimate distributions of patient's age at onset, (inverse) tumor growth rates, symptomatic detection rates, and screening sensitivities. We also allow the growth rate distribution to depend on the age at onset. We estimate that by the age of 75, 13.4% of women have experienced onset. On the basis of a model that accounts for the role of mammographic density in screening sensitivity, we estimated median tumor doubling times of 167 days for tumors with onset occurring at age 40, and 207 days for tumors with onset occurring at age 60. With breast cancer natural history models and population screening data, we can estimate latent processes of tumor onset, tumor growth, and mammography screening sensitivity. We can also study the relationship between the age at onset and tumor growth rates. Quantifying the underlying processes of breast cancer progression is important in the era of individualized screening.

Reflecting on 20 years of breast cancer modeling in CISNET: Recommendations for future cancer systems modeling efforts.

Since 2000, the National Cancer Institute’s Cancer Intervention and Surveillance Modeling Network (CISNET) modeling teams have developed and applied microsimulation and statistical models of breast cancer. Here, we illustrate the use of collaborative breast cancer multilevel systems modeling in CISNET to demonstrate the flexibility of systems modeling to address important clinical and policy-relevant questions. Challenges and opportunities of future systems modeling are also summarized. The 6 CISNET breast cancer models embody the key features of systems modeling by incorporating numerous data sources and reflecting tumor, person, and health system factors that change over time and interact to affect the burden of breast cancer. Multidisciplinary modeling teams have explored alternative representations of breast cancer to reveal insights into breast cancer natural history, including the role of overdiagnosis and race differences in tumor characteristics. The models have been used to compare strategies for improving the balance of benefits and harms of breast cancer screening based on personal risk factors, including age, breast density, polygenic risk, and history of Down syndrome or a history of childhood cancer. The models have also provided evidence to support the delivery of care by simulating outcomes following clinical decisions about breast cancer treatment and estimating the relative impact of screening and treatment on the United States population. The insights provided by the CISNET breast cancer multilevel modeling efforts have informed policy and clinical guidelines. The 20 years of CISNET modeling experience has highlighted opportunities and challenges to expanding the impact of systems modeling. Moving forward, CISNET research will continue to use systems modeling to address cancer control issues, including modeling structural inequities affecting racial disparities in the burden of breast cancer. Future work will also leverage the lessons from team science, expand resource sharing, and foster the careers of early stage modeling scientists to ensure the sustainability of these efforts.

Application of proteome profiling in the clinical diagnosis of breast cancer

The early diagnosis, prognosis, and anticipation of breast cancer are crucial for proper treatment and patient survival. This disease imposes quite a severe health care encumbrance on women globally. Breast cancer classification has emphasized several global efforts, and analysis of the subtypes of the molecular basis of breast cancer has aimed to associate them with clinical outcomes and improve the current diagnostic routine. Since the last two decades, proteomics-based methods for studying breast cancer's natural history and treatment are gaining traction. In this review, some of the proteome profiling studies of tissues, plasma, serum and saliva conducted mainly by mass spectrometry-based approaches – including MALDI-TOF and SELDI-TOF are discussed. This review also emphasized tissue microarray studies and their role in identifying clinical tissues and markers in breast cancer.

Circulating Tumor Cell Clusters Are Frequently Detected in Women with Early-Stage Breast Cancer.

Simple SummaryMetastases cause the majority of breast cancer-related deaths. Circulating tumor cells (CTCs), and in particular CTC-clusters, are considered the seeds of metastasis, but their analysis in the early-stages of the disease has so far been limited by the fact that, by using conventional and epithelial-based technologies (as the FDA-approved CellSearch platform), they are more often detected in the metastatic setting. It is known, however, that cancer cells are heterogeneous and can downregulate the expression of epithelial markers, thus limiting the detection capability of epithelial-based technologies. Here, we show that it is possible to increase CTC-cluster detection by using an epithope-independent technology based on blood filtration, and in particular that this strategy allows to detect a high number of CTC-clusters in stage II-III breast cancer patients, before and during neoadjuvant treatment. Our results therefore offer a new opportunity to deepen our understanding of the cancer dissemination process in its early steps.The clinical relevance of circulating tumor cell clusters (CTC-clusters) in breast cancer (BC) has been mostly studied using the CellSearch®, a marker-dependent method detecting only epithelial-enriched clusters. However, due to epithelial-to-mesenchymal transition, resorting to marker-independent approaches can improve CTC-cluster detection. Blood samples collected from healthy donors and spiked-in with tumor mammospheres, or from BC patients, were processed for CTC-cluster detection with 3 technologies: CellSearch®, CellSieve™ filters, and ScreenCell® filters. In spiked-in samples, the 3 technologies showed similar recovery capability, whereas, in 19 clinical samples processed in parallel with CellSearch® and CellSieve™ filters, filtration allowed us to detect more CTC-clusters than CellSearch® (median number = 7 versus 1, p = 0.0038). Next, samples from 37 early BC (EBC) and 23 metastatic BC (MBC) patients were processed using ScreenCell® filters for attaining both unbiased enrichment and marker-independent identification (based on cytomorphological criteria). At baseline, CTC-clusters were detected in 70% of EBC cases and in 20% of MBC patients (median number = 2, range 0–20, versus 0, range 0–15, p = 0.0015). Marker-independent approaches for CTC-cluster assessment improve detection and show that CTC-clusters are more frequent in EBC than in MBC patients, a novel finding suggesting that dissemination of CTC-clusters is an early event in BC natural history.

Prognostic factors and survival of patients undergoing surgical intervention for breast cancer bone metastases

IntroductionBone is the most common distant site of breast cancer metastasis. Skeletal lesions can cause significant morbidity due to pain, pathologic fracture, and electrolyte abnormalities. Current treatment for patients with bone metastases (BoM) from breast cancer is highly personalized and often involves a multidisciplinary approach with chemotherapy, hormone therapy, bone-targeted antiresorptive agents, radiation therapy, and surgery. We have retrospectively collected clinical data from a series of patients with bone metastases to evaluate the clinical characteristics, prognostic factors, and survival patterns of patients with breast cancer BoM receiving standard multimodal therapy. MethodsA consecutive series of 167 patients with breast cancer BoM treated at a single institution between August 2013 and March 2020 were identified. Clinical information was obtained from the medical record and survival analyses were performed to evaluate patient outcomes and identify prognostic factors. ResultsThirty-seven patients (22%) presented with de novo BoM – bone metastases at the time of breast cancer diagnosis – and were 2.6 times more likely to die within the study period than those with asynchronous BoM (HR = 2.62, p = <0.0001). Patients who received bone-targeted medical therapy were 61% less likely to die after BoM diagnosis than those who did not (HR = 0.39, p = 0.001). Operative stabilization of BoM was more frequently employed in patients with lytic (p = 0.02) or mixed (p = 0.02) tumors than it was for those with blastic lesions. Patients treated with surgery had a lower overall bone metastasis survival than those treated without (p < 0.03). DiscussionThese findings reflect the current patterns in metastatic breast cancer treatment and associated outcomes. In a series of 167 consecutive patients, we demonstrate the natural history of breast cancer with BoM being treated with modern multimodal therapy. Understanding these treatment patterns and prognostic factors enhances the provider’s ability to counsel patients and direct appropriate treatments.

ASSESSING THE FEASIBILITY OF USING THE RETROGLANDULAR APPROACH IN THE MANAGEMENT OF DEEPLY SEATED MALIGNANT BREAST TUMOR

Breast cancer is the most common site-specific cancer among women and it is the leading cause of cancer related deaths among women. It accounts for 29% of all newly diagnosed cancers in females and is responsible for 14% of the cancer-related deaths in women. Approximately 1 in 8 women (13%) will be diagnosed with invasive breast cancer in their lifetime. Understanding the biological behavior and natural history of breast cancer has made breast-conserving therapy (BCT) the choice of treatment in early breast cancer. Oncoplastic breast surgery (OPS) has emerged as a new approach to allow wide excision for BCS without compromising the natural shape of the breast And since one of the main factors for good oncoplastic techniques is the cosmetic outcome, inframammary crease incision is a good choice as hidden scar in the breast envelope. Here we introduce a new approach for deep breast tumors through an inframammary incision and retro glandular exploration of breast parenchyma which is a simple and easily performed techniqu

Incorporating Baseline Breast Density When Screening Women at Average Risk for Breast Cancer : A Cost-Effectiveness Analysis.

Breast density classification is largely determined by mammography, making the timing of the first screening mammogram clinically important. To evaluate the cost-effectiveness of breast cancer screening strategies that are stratified by breast density. Microsimulation model to generate the natural history of breast cancer for women with and those without dense breasts and assessment of the cost-effectiveness of strategies tailored to breast density and nontailored strategies. Model parameters from the literature; statistical modeling; and analysis of Surveillance, Epidemiology, and End Results-Medicare data. Women aged 40 years or older. Lifetime. Societal. No screening; biennial or triennial mammography from age 50 to 75 years; annual mammography from age 50 to 75 years for women with dense breasts at age 50 years and biennial or triennial mammography from age 50 to 75 years for those without dense breasts at age 50 years; and annual mammography at age 40 to 75 years for women with dense breasts at age 40 years and biennial or triennial mammography at age 50 to 75 years for those without dense breasts at age 40 years. Lifetime costs and quality-adjusted life-years (QALYs), discounted at 3% annually. Baseline screening at age 40 years followed by annual screening at age 40 to 75 years for women with dense breasts and biennial screening at age 50 to 75 years for women without dense breasts was effective and cost-effective, yielding an incremental cost-effectiveness ratio of $36 200 per QALY versus the biennial strategy at age 50 to 75 years. At a societal willingness-to-pay threshold of $100 000 per QALY, the probability that the density-stratified strategy at age 40 years was optimal was 56% compared with 6 other strategies. Findings may not be generalizable outside the United States. The study findings advocate for breast density-stratified screening with baseline mammography at age 40 years. National Cancer Institute.

Immunotherapeutic strategies in breast cancer: A clinical update.

Immunotherapy has been incorporated into the standard of care for a wide range of malignancies. The study of tumor-infiltrating lymphocytes has emphasized the importance of the host antitumor immune response in the natural history of breast cancer. Recent clinical trials have used immunotherapeutic approaches to augment this response and improve outcomes for patients with breast cancer. Here, we review several current clinical trial data that indicate checkpoint blockade may mediate clinically significant responses.

Breast cancer survival in Nordic BRCA2 mutation carriers\u2014unconventional association with oestrogen receptor status

BackgroundThe natural history of breast cancer among BRCA2 carriers has not been clearly established. In a previous study from Iceland, positive ER status was a negative prognostic factor. We sought to identify factors that predicted survival after invasive breast cancer in an expanded cohort of BRCA2 carriers.MethodsWe studied 608 women with invasive breast cancer and a pathogenic BRCA2 mutation (variant) from four Nordic countries. Information on prognostic factors and treatment was retrieved from health records and by analysis of archived tissue specimens. Hazard ratios (HR) were estimated for breast cancer-specific survival using Cox regression.ResultsAbout 77% of cancers were ER-positive, with the highest proportion (83%) in patients under 40 years. ER-positive breast cancers were more likely to be node-positive (59%) than ER-negative cancers (34%) (P < 0.001). The survival analysis included 584 patients. Positive ER status was protective in the first 5 years from diagnosis (multivariate HR = 0.49; 95% CI 0.26–0.93, P = 0.03); thereafter, the effect was adverse (HR = 1.91; 95% CI 1.07–3.39, P = 0.03). The adverse effect of positive ER status was limited to women who did not undergo endocrine treatment (HR = 2.36; 95% CI 1.26–4.44, P = 0.01) and patients with intact ovaries (HR = 1.99; 95% CI 1.11–3.59, P = 0.02).ConclusionsThe adverse effect of a positive ER status in BRCA2 carriers with breast cancer may be contingent on exposure to ovarian hormones.