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Background: 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. Methods: 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 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. Results: In 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. Conclusions: 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. Trial registration: ClinicalTrials.gov identifier NCT02306096. [ABSTRACT FROM AUTHOR]

Purpose: More than 70% of patients with breast cancer present with node-negative disease, yet all undergo surgical axillary staging. We aimed to define predictors of nodal metastasis using clinicopathological characteristics (CLINICAL), gene expression data (GEX), and mixed features (MIXED) and to identify patients at low risk of metastasis who might be spared sentinel lymph node biopsy (SLNB). Experimental Design: Breast tumors ( n = 3,023) from the population-based Sweden Cancerome Analysis Network-Breast initiative were profiled by RNA sequencing and linked to clinicopathologic characteristics. Seven machine-learning models present the discriminative ability of N0/N+ in development ( n = 2,278) and independent validation cohorts ( n = 745) stratified as ER + HER2 - , HER2 + , and TNBC. Possible SLNB reduction rates are proposed by applying CLINICAL and MIXED predictors., Results: In the validation cohort, the MIXED predictor showed the highest area under ROC curves to assess nodal metastasis; AUC = 0.72. For the subgroups, the AUCs for MIXED, CLINICAL, and GEX predictors ranged from 0.66 to 0.72, 0.65 to 0.73, and 0.58 to 0.67, respectively. Enriched proliferation metagene and luminal B features were noticed in node-positive ER + HER2 - and HER2 + tumors, while upregulated basal-like features were observed in node-negative TNBC tumors. The SLNB reduction rates in patients with ER + HER2 - tumors were 6% to 7% higher for the MIXED predictor compared with the CLINICAL predictor accepting false negative rates of 5% to 10%., Conclusions: Although CLINICAL and MIXED predictors of nodal metastasis had comparable accuracy, the MIXED predictor identified more node-negative patients. This translational approach holds promise for development of classifiers to reduce the rates of SLNB for patients at low risk of nodal involvement., (©2019 American Association for Cancer Research.)

Objective: International differences in colorectal cancer (CRC) survival and stage at diagnosis have been reported previously. They may be linked to differences in time intervals and routes to diagnosis. The International Cancer Benchmarking Partnership Module 4 (ICBP M4) reports the first international comparison of routes to diagnosis for patients with CRC and the time intervals from symptom onset until the start of treatment. Data came from patients in 10 jurisdictions across six countries (Canada, the UK, Norway, Sweden, Denmark and Australia)., Design: Patients with CRC were identified via cancer registries. Data on symptomatic and screened patients were collected; questionnaire data from patients' primary care physicians and specialists, as well as information from treatment records or databases, supplemented patient data from the questionnaires. Routes to diagnosis and the key time intervals were described, as were between-jurisdiction differences in time intervals, using quantile regression., Participants: A total of 14 664 eligible patients with CRC diagnosed between 2013 and 2015 were identified, of which 2866 were included in the analyses., Primary and Secondary Outcome Measures: Interval lengths in days (primary), reported patient symptoms (secondary)., Results: The main route to diagnosis for patients was symptomatic presentation and the most commonly reported symptom was 'bleeding/blood in stool'. The median intervals between jurisdictions ranged from: 21 to 49 days (patient); 0 to 12 days (primary care); 27 to 76 days (diagnostic); and 77 to 168 days (total, from first symptom to treatment start). Including screen-detected cases did not significantly alter the overall results., Conclusion: ICBP M4 demonstrates important differences in time intervals between 10 jurisdictions internationally. The differences may justify efforts to reduce intervals in some jurisdictions., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2018. Re-use permitted under CC BY. Published by BMJ.)