Minimizing inequality in access to precision medicine in breast cancer by real-time population-based molecular analysis in the SCAN-B initiative

Rydén, Lisa; Loman, Niklas; Larsson, Christer; Hegardt, Cecilia; Vallon‐Christersson, Johan; Malmberg, Martin; Lindman, Henrik; Ehinger, Anna; Saal, Lao H.; Borg, Åke

doi:10.1002/bjs.10741

Cited by 44 publications

(75 citation statements)

References 46 publications

(48 reference statements)

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“…The study was conducted in accordance with the Declaration of Helsinki. Tumor material is obtained by SCAN‐B (http://clinicaltrials.gov NCT02306096), an ongoing multicenter prospective study that until now have included more than 13,000 breast cancer patients. The study has been approved by the Regional Ethical Review Board of Lund (registration numbers 2007/155, 2009/658, 2009/659, 2014/8), the county governmental biobank center and the Swedish Data Inspection group (registration number 364‐2010).…”

Section: Methodsmentioning

confidence: 99%

Analysis of fusion transcripts indicates widespread deregulation of snoRNAs and their host genes in breast cancer

Persson

Søkilde

Häkkinen

et al. 2020

Intl Journal of Cancer

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Genomic rearrangements in cancer can join the sequences of two separate genes. Studies of such gene fusion events have mainly focused on identification of fusion proteins from the chimeric transcripts. We have previously investigated how fusions instead can affect the expression of intronic microRNA (miRNA) genes that are encoded within fusion gene partners. Here, we extend our analysis to small nucleolar RNAs (snoRNAs) that also are embedded within protein-coding or noncoding host genes. We found that snoRNA hosts are selectively enriched in fusion transcripts, like miRNA host genes, and that this enrichment is associated with all snoRNA classes. These structural changes may have functional consequences for the cell; proteins involved in the protein translation machinery are overrepresented among snoRNA host genes, a gene architecture assumed to be needed for closely coordinated expression of snoRNAs and host proteins. Our data indicate that this structure is frequently disrupted in cancer. We furthermore observed that snoRNA genes involved in fusions tend to associate with stronger promoters than the natural host, suggesting a mechanism that selects for snoRNA overexpression. In summary, we highlight a previously unexplored frequent structural change in cancer that affects important components of cellular physiology.Additional Supporting Information may be found in the online version of this article.

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Section: Methodsmentioning

confidence: 99%

Analysis of fusion transcripts indicates widespread deregulation of snoRNAs and their host genes in breast cancer

Persson

Søkilde

Häkkinen

et al. 2020

Intl Journal of Cancer

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“…SCAN-B employs a prospective, population-based, observational cohort study design (ClinicalTrials.gov ID: NCT02306096; refs. 32,33) and the option for inclusion was proposed to all patients with primary breast cancer in the catchment area. High-quality tumor RNA-seq profiles were obtained for 3,023 patients ( Fig.…”

Section: Patient Cohortmentioning

confidence: 99%

“…Gene expression profiling was performed using RNA-seq as previously described (32,33) and reported elsewhere (GSE96058; ref. 36) with complementary material in Supplementary Table S1.…”

Section: Gene Expression Analysesmentioning

confidence: 99%

Prediction of Lymph Node Metastasis in Breast Cancer by Gene Expression and Clinicopathological Models: Development and Validation within a Population-Based Cohort

Dihge

Vallon‐Christersson

Hegardt

et al. 2019

Clinical Cancer Research

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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 ¼ 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/Nþ in development (n ¼ 2,278) and independent validation cohorts (n ¼ 745) stratified as ER þ HER2 À , HER2 þ , 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 ¼ 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 þ HER2 À and HER2 þ tumors, while upregulated basal-like features were observed in node-negative TNBC tumors. The SLNB reduction rates in patients with ER þ HER2 À 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. a Values are median. b According to the TNM classification for breast cancer, seventh edition. c Percentages are calculated from the total number of patients who received adjuvant treatment. d Percentages are calculated from the total number of patients within the age range (40-74 years) for mammography within the National Breast Cancer Screening Programme.Dihge et al.Abbreviations: FN, false negative; FP, false positive; Max NPV, maximum negative predictive value; Nþ, axillary lymph node metastasis; No, no regional lymph node metastasis; SLNB, sentinel lymph node biopsy; TN, true negative; TP, true positive. Dihge et al.

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“…For these reasons, among others, in 2010 the Sweden Cancerome Analysis Network-Breast (SCAN-B) initiative (ClinicalTrials.gov ID NCT02306096) selected RNA-seq as the primary tool [13,14]. SCAN-B is a prospective real-world and population-based multicenter study with the aim of developing, validating, and clinically-implementing novel biomarkers.…”

Section: Introductionmentioning

confidence: 99%

The Mutational Landscape of the SCAN-B Real-World Primary Breast Cancer Transcriptome

Brueffer

Gladchuk

Winter

et al. 2020

Preprint

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Breast cancer is a disease of genomic alterations, of which the complete panorama of somatic mutations and how these relate to molecular subtypes and therapy response is incompletely understood. Within the Sweden Cancerome Analysis Network-Breast project (SCAN-B; ClinicalTrials.gov NCT02306096), an ongoing study elucidating the tumor transcriptomic profiles for thousands of breast cancers prospectively, we developed an optimized pipeline for detection of single nucleotide variants and small insertions and deletions from RNA sequencing (RNA-seq) data, and profiled a large real-world population-based cohort of 3,217 breast tumors. We use it to describe the mutational landscape of primary breast cancer viewed through the transcriptome of a large population-based cohort of patients, and relate it to patient overall survival. We demonstrate that RNA-seq can be used to call mutations in important breast cancer genes such as PIK3CA, TP53, and ERBB2, as well as the status of key molecular pathways and tumor mutational burden, and identify potentially druggable genes in 86.8% percent of tumors. To make this rich and growing mutational portraiture of breast cancer available for the wider research community, we developed an open source web-based application, the SCAN-B MutationExplorer, accessible at http://oncogenomics.bmc.lu.se/MutationExplorer. These results add another dimension to the use of RNA-seq as a potential clinical tool, where both gene expression-based and gene mutation-based biomarkers can be interrogated simultaneously and in real-time within one week of tumor sampling. and sampling of tissue for this study. We also thank the Swedish National Breast Cancer Registry and Regional Cancer Center South for clinical data. The SweGen allele frequency data was generated

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Minimizing inequality in access to precision medicine in breast cancer by real-time population-based molecular analysis in the SCAN-B initiative

Cited by 44 publications

References 46 publications

Analysis of fusion transcripts indicates widespread deregulation of snoRNAs and their host genes in breast cancer

Analysis of fusion transcripts indicates widespread deregulation of snoRNAs and their host genes in breast cancer

Prediction of Lymph Node Metastasis in Breast Cancer by Gene Expression and Clinicopathological Models: Development and Validation within a Population-Based Cohort

The Mutational Landscape of the SCAN-B Real-World Primary Breast Cancer Transcriptome

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