Project description:Breast cancer exhibits significant molecular, pathological, and clinical heterogeneity. Current clinicopathological evaluation is imperfect for predicting outcome, which results in overtreatment for many patients, and for others, leads to death from recurrent disease. Therefore, additional criteria are needed to better personalize care and maximize treatment effectiveness and survival. To address these challenges, the Sweden Cancerome Analysis Network - Breast (SCAN-B) consortium was initiated in 2010 as a multicenter prospective study with longsighted aims to 1) analyze breast cancers with next-generation genomic technologies for translational research in a population-based manner and integrated with healthcare; 2) decipher fundamental tumor biology from these analyses; 3) utilize genomic data to develop and validate new clinically-actionable biomarker assays; and 4) build the infrastructure for real-time clinical implementation of molecular diagnostic, prognostic, and predictive tests. In the first phase, we focus on molecular profiling by next-generation RNA-sequencing on the Illumina platform. In the three years from August 30, 2010 through August 31, 2013, we have consented and enrolled 3,979 patients with primary breast cancer at the seven hospital sites in South Sweden, representing approximately 85% of eligible patients in the catchment area. Pre-operative blood samples have been collected for 3,942 (99%) patients and primary tumor specimens collected for 2,929 (74%) patients. Herein we describe the study infrastructure and present initial proof of concept results from prospective RNA-sequencing including tumor molecular subtyping and detection of driver gene mutations. We demonstrate that large-scale population-based collection and RNA-sequencing analysis of breast cancer is feasible. The SCAN-B Initiative should significantly reduce the time to discovery, validation, and clinical implementation of novel molecular diagnostic and predictive tests. We welcome the participation of additional comprehensive cancer treatment centers.

Project description:Breast cancer exhibits significant molecular, pathological, and clinical heterogeneity. Current clinicopathological evaluation is imperfect for predicting outcome, which results in overtreatment for many patients, and for others, leads to death from recurrent disease. Therefore, additional criteria are needed to better personalize care and maximize treatment effectiveness and survival. To address these challenges, the Sweden Cancerome Analysis Network - Breast (SCAN-B) consortium was initiated in 2010 as a multicenter prospective study with longsighted aims to 1) analyze breast cancers with next-generation genomic technologies for translational research in a population-based manner and integrated with healthcare; 2) decipher fundamental tumor biology from these analyses; 3) utilize genomic data to develop and validate new clinically-actionable biomarker assays; and 4) build the infrastructure for real-time clinical implementation of molecular diagnostic, prognostic, and predictive tests. In the first phase, we focus on molecular profiling by next-generation RNA-sequencing on the Illumina platform. In the three years from August 30, 2010 through August 31, 2013, we have consented and enrolled 3,979 patients with primary breast cancer at the seven hospital sites in South Sweden, representing approximately 85% of eligible patients in the catchment area. Pre-operative blood samples have been collected for 3,942 (99%) patients and primary tumor specimens collected for 2,929 (74%) patients. Herein we describe the study infrastructure and present initial proof of concept results from prospective RNA-sequencing including tumor molecular subtyping and detection of driver gene mutations. We demonstrate that large-scale population-based collection and RNA-sequencing analysis of breast cancer is feasible. The SCAN-B Initiative should significantly reduce the time to discovery, validation, and clinical implementation of novel molecular diagnostic and predictive tests. We welcome the participation of additional comprehensive cancer treatment centers. Due to privacy concerns, submitters were not allowed to release the actual sequencing reads

Project description:BackgroundBreast cancer exhibits significant molecular, pathological, and clinical heterogeneity. Current clinicopathological evaluation is imperfect for predicting outcome, which results in overtreatment for many patients, and for others, leads to death from recurrent disease. Therefore, additional criteria are needed to better personalize care and maximize treatment effectiveness and survival.MethodsTo address these challenges, the Sweden Cancerome Analysis Network - Breast (SCAN-B) consortium was initiated in 2010 as a multicenter prospective study with longsighted aims to analyze breast cancers with next-generation genomic technologies for translational research in a population-based manner and integrated with healthcare; decipher fundamental tumor biology from these analyses; utilize genomic data to develop and validate new clinically-actionable biomarker assays; and establish real-time clinical implementation of molecular diagnostic, prognostic, and predictive tests. In the first phase, we focus on molecular profiling by next-generation RNA-sequencing on the Illumina platform.ResultsIn the first 3 years from 30 August 2010 through 31 August 2013, we have consented and enrolled 3,979 patients with primary breast cancer at the seven hospital sites in South Sweden, representing approximately 85% of eligible patients in the catchment area. Preoperative blood samples have been collected for 3,942 (99%) patients and primary tumor specimens collected for 2,929 (74%) patients. Herein we describe the study infrastructure and protocols and present initial proof of concept results from prospective RNA sequencing including tumor molecular subtyping and detection of driver gene mutations. Prospective patient enrollment is ongoing.ConclusionsWe demonstrate that large-scale population-based collection and RNA-sequencing analysis of breast cancer is feasible. The SCAN-B Initiative should significantly reduce the time to discovery, validation, and clinical implementation of novel molecular diagnostic and predictive tests. We welcome the participation of additional comprehensive cancer treatment centers.Trial registrationClinicalTrials.gov identifier NCT02306096.

Project description:This SuperSeries is composed of the SubSeries GSE81538 [cohort 405] and GSE96058 [cohort 3273] linked to below. PURPOSE In early breast cancer (BC), five conventional biomarkers—estrogen receptor (ER), progesterone receptor (PgR), human epidermal growth factor receptor 2 (HER2), Ki67, and Nottingham histologic grade (NHG)—are used to determine prognosis and treatment. We aimed to develop classifiers for these biomarkers that were based on tumor mRNA sequencing (RNA-seq), compare classification performance, and test whether such predictors could add value for risk stratification. METHODS In total, 3,678 patients with BC were studied. For 405 tumors, a comprehensive multi-rater histopathologic evaluation was performed. Using RNA-seq data, single-gene classifiers and multigene classifiers (MGCs) were trained on consensus histopathology labels. Trained classifiers were tested on a prospective population-based series of 3,273 BCs that included a median follow-up of 52 months (Sweden Cancerome Analysis Network—Breast [SCAN-B], ClinicalTrials.gov identifier: NCT02306096), and results were evaluated by agreement statistics and Kaplan-Meier and Cox survival analyses. RESULTS Pathologist concordance was high for ER, PgR, and HER2 (average κ, 0.920, 0.891, and 0.899, respectively) but moderate for Ki67 and NHG (average κ, 0.734 and 0.581). Concordance between RNA-seq classifiers and histopathology for the independent cohort of 3,273 was similar to interpathologist concordance. Patients with discordant classifications, predicted as hormone responsive by histopathology but non–hormone responsive by MGC, had significantly inferior overall survival compared with patients who had concordant results. This extended to patients who received no adjuvant therapy (hazard ratio [HR], 3.19; 95% CI, 1.19 to 8.57), or endocrine therapy alone (HR, 2.64; 95% CI, 1.55 to 4.51). For cases identified as hormone responsive by histopathology and who received endocrine therapy alone, the MGC hormone-responsive classifier remained significant after multivariable adjustment (HR, 2.45; 95% CI, 1.39 to 4.34). CONCLUSION Classification error rates for RNA-seq–based classifiers for the five key BC biomarkers generally were equivalent to conventional histopathology. However, RNA-seq classifiers provided added clinical value in particular for tumors determined by histopathology to be hormone responsive but by RNA-seq to be hormone insensitive.

Project description:BackgroundBeginning in 2012, Lean was introduced to improve health care quality and promote patient-centredness throughout the province of Saskatchewan, Canada with the aim of producing coordinated, system-wide change. Significant investments have been made in training and implementation, although limited evaluation of the outcomes have been reported. In order to better understand the complex influences that make innovations such as Lean "workable" in practice, Normalization Process Theory guided this study. The objectives of the study were to: a) evaluate the implementation processes associated with Lean implementation in the Saskatchewan health care system from the perspectives of health care professionals; and b) identify demographic, training and role variables associated with normalization of Lean.MethodsLicensed health care professionals were invited through their professional associations to complete a cross-sectional, modified, online version of the NoMAD questionnaire in March, 2016. Analysis was based on 1032 completed surveys. Descriptive and univariate analyses were conducted. Multivariate multinomial regressions were used to quantify the associations between five NoMAD items representing the four Normalization Process Theory constructs (coherence, cognitive participation, collective action and reflexive monitoring).ResultsMore than 75% of respondents indicated that neither sufficient training nor resources (collective action) had been made available to them for the implementation of Lean. Compared to other providers, nurses were more likely to report that Lean increased their workload. Significant differences in responses were evident between: leaders vs. direct care providers; nurses vs. other health professionals; and providers who reported increased workload as a result of Lean vs. those who did not. There were no associations between responses to normalization construct proxy items and: completion of introductory Lean training; participation in Lean activities; age group; years of professional experience; or employment status (full-time or part-time). Lean leader training was positively associated with proxy items reflecting coherence, cognitive participation and reflexive monitoring.ConclusionsFrom the perspectives of the cross-section of health care professionals responding to this survey, major gaps remain in embedding Lean into healthcare. Strategies that address the challenges faced by nurses and direct care providers, in particular, are needed if intended goals are to be achieved.

Project description:PURPOSE In early breast cancer (BC), five conventional biomarkers—estrogen receptor (ER), progesterone receptor (PgR), human epidermal growth factor receptor 2 (HER2), Ki67, and Nottingham histologic grade (NHG)—are used to determine prognosis and treatment. We aimed to develop classifiers for these biomarkers that were based on tumor mRNA sequencing (RNA-seq), compare classification performance, and test whether such predictors could add value for risk stratification. METHODS In total, 3,678 patients with BC were studied. For 405 tumors, a comprehensive multi-rater histopathologic evaluation was performed. Using RNA-seq data, single-gene classifiers and multigene classifiers (MGCs) were trained on consensus histopathology labels. Trained classifiers were tested on a prospective population-based series of 3,273 BCs that included a median follow-up of 52 months (Sweden Cancerome Analysis Network—Breast [SCAN-B], ClinicalTrials.gov identifier: NCT02306096), and results were evaluated by agreement statistics and Kaplan-Meier and Cox survival analyses. RESULTS Pathologist concordance was high for ER, PgR, and HER2 (average κ, 0.920, 0.891, and 0.899, respectively) but moderate for Ki67 and NHG (average κ, 0.734 and 0.581). Concordance between RNA-seq classifiers and histopathology for the independent cohort of 3,273 was similar to interpathologist concordance. Patients with discordant classifications, predicted as hormone responsive by histopathology but non–hormone responsive by MGC, had significantly inferior overall survival compared with patients who had concordant results. This extended to patients who received no adjuvant therapy (hazard ratio [HR], 3.19; 95% CI, 1.19 to 8.57), or endocrine therapy alone (HR, 2.64; 95% CI, 1.55 to 4.51). For cases identified as hormone responsive by histopathology and who received endocrine therapy alone, the MGC hormone-responsive classifier remained significant after multivariable adjustment (HR, 2.45; 95% CI, 1.39 to 4.34). CONCLUSION Classification error rates for RNA-seq–based classifiers for the five key BC biomarkers generally were equivalent to conventional histopathology. However, RNA-seq classifiers provided added clinical value in particular for tumors determined by histopathology to be hormone responsive but by RNA-seq to be hormone insensitive.

Project description:ImportanceUrgent Care (UC) encounters result in more inappropriate antibiotic prescriptions than other outpatient setting. Few stewardship interventions have focused on UC.ObjectiveTo evaluate the effectiveness of an antibiotic stewardship initiative to reduce antibiotic prescribing for respiratory conditions in a UC network.Design, setting, and participantsThis quality improvement study conducted in a UC network with 38 UC clinics and 1 telemedicine clinic included 493 724 total UC encounters. The study compared the antibiotic prescribing rates of all UC clinicians who encountered respiratory conditions for a 12-month baseline period (July 1, 2018, through June 30, 2019) with an intervention period (July 1, 2019, through June 30, 2020). A sustainability period (July 1, 2020, through June 30, 2021) was added post hoc.InterventionsStewardship interventions included (1) education for clinicians and patients, (2) electronic health record (EHR) tools, (3) a transparent clinician benchmarking dashboard, and (4) media. Occurring independently but concurrent with the interventions, a stewardship measure was introduced by UC leadership into the quality measures, including a financial incentive.Main outcomes and measuresThe primary outcome was the percentage of UC encounters with an antibiotic prescription for a respiratory condition. Secondary outcomes included antibiotic prescribing when antibiotics were not indicated (tier 3 encounters) and first-line antibiotics for acute otitis media, sinusitis, and pharyngitis. Interrupted time series with binomial generalized estimating equations were used to compare periods.ResultsThe baseline period included 207 047 UC encounters for respiratory conditions (56.8% female; mean [SD] age, 30.0 [21.4] years; 92.0% White race); the intervention period included 183 893 UC encounters (56.4% female; mean [SD] age, 30.7 [20.8] years; 91.2% White race). Antibiotic prescribing for respiratory conditions decreased from 47.8% (baseline) to 33.3% (intervention). During the initial intervention month, a 22% reduction in antibiotic prescribing occurred (odds ratio [OR], 0.78; 95% CI, 0.71-0.86). Antibiotic prescriptions decreased by 5% monthly during the intervention (OR, 0.95; 95% CI, 0.94-0.96). Antibiotic prescribing for tier 3 encounters decreased by 47% (OR, 0.53; 95% CI, 0.44-63), and first-line antibiotic prescriptions increased by 18% (OR, 1.18; 95% CI, 1.09-1.29) during the initial intervention month. Antibiotic prescriptions for tier 3 encounters decreased by an additional 4% each month (OR, 0.96; 95% CI, 0.94-0.98), whereas first-line antibiotic prescriptions did not change (OR, 1.00; 95% CI, 0.99-1.01). Antibiotic prescribing for respiratory conditions remained stable in the sustainability period.Conclusions and relevanceThe findings of this quality improvement study indicated that a UC antibiotic stewardship initiative was associated with decreased antibiotic prescribing for respiratory conditions. This study provides a model for UC antibiotic stewardship.

Dataset Information

The Sweden Canceromics Analysis Network - Breast (SCAN-B) Initiative: a large-scale multicenter infrastructure towards implementation of breast cancer genomic analyses in the clinical routine

Dataset's files

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets