Clinical and molecular characteristics of estrogen receptor-positive ultralow risk breast cancer tumors identified by the 70-gene signature.

The metastatic potential of estrogen receptor (ER)-positive breast cancers is heterogeneous and distant recurrences occur months to decades after primary diagnosis. We have previously shown that patients with tumors classified as ultralow risk by the 70-gene signature have a minimal long-term risk of fatal breast cancer. Here, we evaluate the previously unexplored underlying clinical and molecular characteristics of ultralow risk tumors in 538 ER-positive patients from the Stockholm tamoxifen randomized trial (STO-3). Out of the 98 ultralow risk tumors, 89% were luminal A molecular subtype, whereas 26% of luminal A tumors were of ultralow risk. Compared to other ER-positive tumors, ultralow risk tumors were significantly (Fisher's test, P < .05) more likely to be of smaller tumor size, lower grade, progesterone receptor (PR)-positive, human epidermal growth factor 2 (HER2)-negative and have low Ki-67 levels (proliferation-marker). Moreover, ultralow risk tumors showed significantly lower expression scores of multi-gene modules associated with the AKT/mTOR-pathway, proliferation (AURKA), HER2/ERBB2-signaling, IGF1-pathway, PTEN-loss and immune response (IMMUNE1 and IMMUNE2) and higher expression scores of the PIK3CA-mutation-associated module. Furthermore, 706 genes were significantly (FDR < 0.001) differentially expressed in ultralow risk tumors, including lower expression of genes involved in immune response, PI3K/Akt/mTOR-pathway, histones, cell cycle, DNA repair, apoptosis and higher expression of genes coding for epithelial-to-mesenchymal transition and homeobox proteins, among others. In conclusion, ultralow risk tumors, associated with minimal long-term risk of fatal disease, differ from other ER-positive tumors, including luminal A molecular subtype tumors. Identification of these characteristics is important to improve our prediction of nonfatal vs fatal breast cancer.

Clinical characteristics of COVID-19 hospitalized patients associated with mortality: A cohort study in Spain.

Background: The heterogeneity of patients with COVID-19 may explain the wide variation of mortality rate due to the population characteristics, presence of comorbidities and clinical manifestations. Methods: In this study, we analyzed 5342 patients' recordings and selected a cohort of 177 hospitalized patients with a poor prognosis at an early stage. We assessed during 6 months their symptomatology, coexisting health conditions, clinical measures and health assistance related to mortality. Multiple Cox proportional hazards models were built to identify the associated factors with mortality risk. Results: We observed that cough and kidney failure triplicate the mortality risk and both bilirubin levels and oncologic condition are shown as the most associated with the demise, increasing in four and ten times the risk, respectively. Other clinical characteristics such as fever, diabetes mellitus, breathing frequency, neutrophil-lymphocyte ratio, oxygen saturation, and troponin levels, were also related to mortality risk of in-hospital death. Conclusions: The present study shows that some symptomatology, comorbidities and clinical measures could be the target of prevention tools to improve survival rates.

Association of Vitamin K and Non-Vitamin K Oral Anticoagulant Use and Cancer Incidence in Atrial Fibrillation Patients.

The association between the use of vitamin K antagonists (VKAs) and cancer risk reduction remains unclear. We aimed to assess the association between the use of VKAs or direct oral anticoagulants (DOACs) and the incidence of cancer in a large cohort of patients with atrial fibrillation (AF) by means of a population-based, propensity-weighted cohort study using population-wide databases including patients diagnosed with nonvalvular AF (NVAF) followed for up of 5 years (median 2.94 years). We created two cohorts based on the initiation therapy (VKA or DOAC). Initiation with VKA or DOAC was defined as filling a prescription with no previous exposure in the preceding 12 months. Cancer diagnoses of any type and for specific tumors (lung, colon, prostate, bladder, and breast). We included 39,989 patients, 31,200 (78.0%) in the VKA cohort. Incidence rate for any cancer was 12.45 per 1,000 person-year in the DOAC cohort vs. 14.55 in the VKA cohort (adjusted hazard ratio (HR): 1.16, 95% confidence interval (CI): 1.02-1.32). In secondary outcomes, no differences were found for specific types of cancer, such as lung (HR: 1.28, CI: 0.89-1.83), colon (HR: 0.84, CI: 0.62-1.13), prostate (HR: 1.40, CI: 0.94-2.10), bladder (HR: 1.07, CI: 0.76-1.52), and breast (HR: 1.05, CI: 0.66-1.69). Sensitivity analyses yielded similar results. Subgroup analyses also produced consistent findings, except for men, for whom VKA was associated with a lower risk of colon cancer (HR: 0.68, 95% CI: 0.48-0.96). Our results do not confirm a chemoprotective effect of VKA when compared with DOAC in a large, real-world cohort of patients with NVAF followed for up to 5 years.

Characterization of Benign Breast Diseases and Association With Age, Hormonal Factors, and Family History of Breast Cancer Among Women in Sweden.

Importance: Benign breast diseases (BBDs) are common and associated with breast cancer risk, yet the etiology and risk of BBDs have not been extensively studied. Objective: To investigate the risk of BBDs by age, hormonal factors, and family history of breast cancer. Design, Setting, and Participants: This retrospective cohort study assessed 70 877 women from the population-based Karolinska Mammography Project for Risk Prediction of Breast Cancer (KARMA) who attended mammographic screening or underwent clinical mammography from January 1, 2011, to March 31, 2013, at 4 Swedish hospitals. Participants took part in a comprehensive questionnaire on recruitment. All participants had complete follow-up through high-quality Swedish national registers until December 31, 2015. Pathology medical records on breast biopsies were obtained for the participants, and BBD subtypes were classified according to the latest European guidelines. Analyses were conducted from January 1 to July 31, 2020. Exposures: Hormonal risk factors and family history of breast cancer. Main Outcomes and Measures: For each BBD subtype, incidence rates (events per 100 000 person-years) and multivariable Cox proportional hazards ratios (HRs) with time-varying covariates were estimated between the ages of 25 and 69 years. Results: A total of 61 617 women within the mammographic screening age of 40 to 69 years (median age, 53 years) at recruitment with available questionnaire data were included in the study. Incidence rates and risk estimates varied by age and BBD subtype. At premenopausal ages, nulliparity (compared with parity ≥3) was associated with reduced risk of epithelial proliferation without atypia (EP; HR, 0.62; 95% CI, 0.46-0.85) but increased risk of cysts (HR, 1.38; 95% CI, 1.03-1.85). Current and long (≥8 years) oral contraceptive use was associated with reduced premenopausal risk of fibroadenoma (HR, 0.65; 95% CI, 0.47-0.90), whereas hormone replacement therapy was associated with increased postmenopausal risks of epithelial proliferation with atypia (EPA; HR, 1.81; 95% CI, 1.07-3.07), fibrocystic changes (HR, 1.60; 95% CI, 1.03-2.48), and cysts (HR, 1.98; 95% CI, 1.40-2.81). Furthermore, predominantly at premenopausal ages, obesity was associated with reduced risk of several BBDs (eg, EPA: HR, 0.31; 95% CI, 0.17-0.56), whereas family history of breast cancer was associated with increased risk (eg, EPA: HR, 2.11; 95% CI, 1.48-3.00). Conclusions and Relevance: These results suggest that the risk of BBDs varies by subtype, hormonal factors, and family history of breast cancer and is influenced by age. Better understanding of BBDs is important to improve the understanding of benign and malignant breast diseases.

Assessment of 25-Year Survival of Women With Estrogen Receptor-Positive/ERBB2-Negative Breast Cancer Treated With and Without Tamoxifen Therapy: A Secondary Analysis of Data From the Stockholm Tamoxifen Randomized Clinical Trial.

Importance: Clinically used breast cancer markers, such as tumor size, tumor grade, progesterone receptor (PR) status, and Ki-67 status, are known to be associated with short-term survival, but the association of these markers with long-term (25-year) survival is unclear. Objective: To assess the association of clinically used breast cancer markers with long-term survival and treatment benefit among postmenopausal women with lymph node-negative, estrogen receptor [ER]-positive and ERBB2-negative breast cancer who received tamoxifen therapy. Design, Setting, and Participants: This study was a secondary analysis of data from a subset of 565 women with ER-positive/ERBB2-negative breast cancer who participated in the Stockholm tamoxifen (STO-3) randomized clinical trial. The STO-3 clinical trial was conducted from 1976 to 1990 and comprised 1780 postmenopausal women with lymph node-negative breast cancer who were randomized to receive adjuvant tamoxifen therapy or no endocrine therapy. Complete 25-year follow-up data through December 31, 2016, were obtained from Swedish national registers. Immunohistochemical markers were reannotated in 2014. Data were analyzed from April to December 2020. Interventions: Patients in the original STO-3 clinical trial were randomized to receive 2 years of tamoxifen therapy vs no endocrine therapy. In 1983, patients who received tamoxifen therapy without cancer recurrence during the 2-year treatment and who consented to continued participation in the STO-3 study were further randomized to receive 3 additional years of tamoxifen therapy or no endocrine therapy. Main Outcomes and Measures: Distant recurrence-free interval (DRFI) by clinically used breast cancer markers was assessed using Kaplan-Meier and multivariable Cox proportional hazards analyses adjusted for age, period of primary diagnosis, tumor size (T1a and T1b [T1a/b], T1c, and T2), tumor grade (1-3), PR status (positive vs negative), Ki-67 status (low vs medium to high), and STO-3 clinical trial arm (tamoxifen treatment vs no adjuvant treatment). A recursive partitioning analysis was performed to evaluate which markers were able to best estimate long-term DRFI. Results: The study population comprised 565 postmenopausal women (mean [SD] age, 62.0 [5.3] years) with lymph node-negative, ER-positive/ERBB2-negative breast cancer. A statistically significant difference in long-term DRFI was observed by tumor size (88% for T1a/b vs 76% for T1c vs 63% for T2 tumors; log-rank P < .001) and tumor grade (81% for grade 1 vs 77% for grade 2 vs 65% for grade 3 tumors; log-rank P = .02) but not by PR status or Ki-67 status. Patients with smaller tumors (hazard ratio [HR], 0.31 [95% CI, 0.17-0.55] for T1a/b tumors and 0.58 [95% CI, 0.38-0.88] for T1c tumors) and grade 1 tumors (HR, 0.48; 95% CI, 0.24-0.95) experienced a significant reduction in the long-term risk of distant recurrence compared with patients with larger (T2) tumors and grade 3 tumors, respectively. A significant tamoxifen treatment benefit was observed among patients with larger tumors (HR, 0.53 [95% CI, 0.32-0.89] for T1c tumors and 0.34 [95% CI, 0.16-0.73] for T2 tumors), lower tumor grades (HR, 0.24 [95% CI, 0.07-0.82] for grade 1 tumors and 0.50 [95% CI, 0.31-0.80] for grade 2 tumors), and PR-positive status (HR, 0.38; 95% CI, 0.24-0.62). The recursive partitioning analysis revealed that tumor size was the most important characteristic associated with long-term survival, followed by clinical trial arm among patients with larger tumors. Conclusions and Relevance: This secondary analysis of data from the STO-3 clinical trial indicated that, among the selected subgroup of patients, tumor size followed by tumor grade were the markers most significantly associated with long-term survival. Furthermore, a significant long-term tamoxifen treatment benefit was observed among patients with larger tumors, lower tumor grades, and PR-positive tumors.

Assessment of Long-term Distant Recurrence-Free Survival Associated With Tamoxifen Therapy in Postmenopausal Patients With Luminal A or Luminal B Breast Cancer.

IMPORTANCE: Patients with estrogen receptor (ER)-positive breast cancer have a long-term risk for fatal disease. However, the tumor biological factors that influence the long-term risk and the benefit associated with endocrine therapy are not well understood. OBJECTIVE: To compare the long-term survival from tamoxifen therapy for patients with luminal A or luminal B tumor subtype. DESIGN, SETTING, AND PARTICIPANTS: Secondary analysis of patients from the Stockholm Tamoxifen (STO-3) trial conducted from 1976 to 1990, which randomized postmenopausal patients with lymph node-negative breast cancer to receive adjuvant tamoxifen or no endocrine therapy. Tumor tissue sections were assessed in 2014 using immunohistochemistry and Agilent microarrays. Only patients with luminal A or B subtype tumors were evaluated. Complete long-term follow-up data up to the end of the STO-3 trial on December 31, 2012, were obtained from the Swedish National registers. Data analysis for the secondary analysis was conducted in 2017 and 2018. INTERVENTIONS: Patients were randomized to receive at least 2 years of tamoxifen therapy or no endocrine therapy; patients without recurrence who reconsented were further randomized to 3 additional years of tamoxifen therapy or no endocrine therapy. MAIN OUTCOMES AND MEASURES: Distant recurrence-free interval (DRFI) by luminal A and luminal B subtype and trial arm was assessed by Kaplan-Meier analyses and time-dependent flexible parametric models to estimate time-varying hazard ratios (HRs) that were adjusted for patient and tumor characteristics. RESULTS: In the STO-3 treated trial arm, 183 patients had luminal A tumors and 64 patients had luminal B tumors. In the untreated arm, 153 patients had luminal A tumors and 62 had luminal B tumors. Age at diagnosis ranged from 45 to 73 years. A statistically significant difference in DRFI by trial arm was observed (log rank, P < .001 [luminal A subtype, n = 336], P = .04 [luminal B subtype, n = 126]): the 25-year DRFI for luminal A vs luminal B subtypes was 87% (95% CI, 82%-93%) vs 67% (95% CI, 56%-82%) for treated patients, and 70% (95% CI, 62%-79%) vs 54% (95% CI, 42%-70%) for untreated patients, respectively. Patients with luminal A tumors significantly benefited from tamoxifen therapy for 15 years after diagnosis (HR, 0.57; 95% CI, 0.35-0.94), and those with luminal B tumors benefited from tamoxifen therapy for 5 years (HR, 0.38; 95% CI, 0.24-0.59). CONCLUSIONS AND RELEVANCE: Patients with luminal A subtype tumors had a long-term risk of distant metastatic disease, which was reduced by tamoxifen treatment, whereas patients with luminal B tumors had an early risk of distant metastatic disease, and tamoxifen benefit attenuated over time.

Strategic procedure in three stages for the selection of variables to obtain balanced results in public health research / Procedimiento estratégico en tres fases para la selección de variables, con el fin de obtener resultados equilibrados en investigación sobre salud pública / Procedimento estratégico em três estágios de seleção de variáveis para a obtenção de resultados equilibrados na pesquisa em saúde pública

Multidisciplinary research in public health is approached using methods from many scientific disciplines. One of the main characteristics of this type of research is dealing with large data sets. Classic statistical variable selection methods, known as "screen and clean", and used in a single-step, select the variables with greater explanatory weight in the model. These methods, commonly used in public health research, may induce masking and multicollinearity, excluding relevant variables for the experts in each discipline and skewing the result. Some specific techniques are used to solve this problem, such as penalized regressions and Bayesian statistics, they offer more balanced results among subsets of variables, but with less restrictive selection thresholds. Using a combination of classical methods, a three-step procedure is proposed in this manuscript, capturing the relevant variables of each scientific discipline, minimizing the selection of variables in each of them and obtaining a balanced distribution that explains most of the variability. This procedure was applied on a dataset from a public health research. Comparing the results with the single-step methods, the proposed method shows a greater reduction in the number of variables, as well as a balanced distribution among the scientific disciplines associated with the response variable. We propose an innovative procedure for variable selection and apply it to our dataset. Furthermore, we compare the new method with the classic single-step procedures.

La investigación multidisciplinaria en salud pública se enfoca usando métodos de muchas disciplinas científicas. Una de las principales características de este tipo de investigación es lidiar con conjuntos voluminosos de datos. Los métodos clásicos estadísticos de selección de variables, conocidos como "screen and clean", y utilizados en un solo paso, seleccionan las variables con mayor peso explicativo en su modelo. Estos métodos, comúnmente usados en investigación pública en salud, pueden inducir a enmascarar la multicolinealidad, excluyendo variables relevantes para los expertos en cada disciplina y sesgando el resultado. Se usan algunas técnicas específicas para resolver este problema, como las regresiones penalizadas y estadísticas bayesianas, que ofrecen resultados más equilibrados entre subconjuntos de variables, pero con umbrales menos restrictivos de selección. Usando la combinación de métodos clásicos, se propone en este trabajo un tercer paso en el procedimiento, recogiendo variables relevantes de cada disciplina científica, minimizando la selección de variables en cada una de ellas y obteniendo una distribución equilibrada que explica la mayor parte de la variabilidad. Este procedimiento fue aplicado en un conjunto de datos de una investigación en salud pública. Comparando los resultados con los métodos de un solo paso, el método propuesto expone una gran reducción en el número de variables, así como la distribución equilibrada entre las disciplinas científicas asociadas con la variable de respuesta. Proponemos un procedimiento innovador para la selección de variables y aplicarlo a nuestro conjunto de datos. Asimismo, comparamos el nuevo método con los procedimientos clásicos de un solo paso.

A pesquisa multidisciplinar em saúde pública emprega métodos provenientes de diversas disciplinas científicas. Uma das principais características desse tipo de pesquisa é o fato de lidar com conjuntos de dados grandes. Os métodos clássicos de seleção de variáveis estatísticas, conhecidos como "screen and clean" (filtrar e limpar), e aplicados a partir de um passo único, selecionam as variáveis com o maior peso explanatório no modelo. Esses métodos, amplamente disseminados na pesquisa em saúde pública, podem induzir ao mascaramento e à multi-colinearidade, excluindo variáveis que seriam relevantes para os especialistas em cada disciplina e enviesando os resultados. Algumas técnicas específicas usadas para resolver esse problema, como regressões penalizadas e estatísticas Bayesianas, oferecem resultados mais equilibrados entre subconjuntos de variáveis, porém com limiares de seleção menos restritivos. O artigo propõe um procedimento com três passos, usando uma combinação de métodos clássicos, captando as variáveis relevantes de cada disciplina científica, minimizando a seleção de variáveis em cada disciplina e obtendo uma distribuição equilibrada que explica a maior parte da variabilidade. O procedimento foi aplicado a um conjunto de dados de uma pesquisa em saúde pública. Ao comparar os resultados com os métodos que utilizam um único passo, o método proposto demonstra maior redução no número de variáveis, assim como, uma distribuição equilibrada entre as disciplinas científicas relacionadas à variável dependente. Propomos um procedimento inovador para a seleção de variáveis, que aplicamos depois ao nosso conjunto de dados. Além disso, comparamos o método novo com os procedimentos clássicos de apenas um estágio.