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

Abstract

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.

Figures

Figure 1

Overview of the SCAN-B infrastructure. Shown are the SCAN-B clinical (green boxes), laboratory (blue), and computational and analytical (orange) components. Solid black arrows indicate flow of material, and dashed black lines indicate flow of information. Enrollment and sampling of patients at time of preoperative (neoadjuvant) biopsy is not shown. ds, double-stranded; INCA, Swedish national breast cancer registry; TMA, tissue microarray.

Figure 2

Study demographics and clinical variables. (A) For the period 30 August 2010 to 31 August 2013, yearly (non-calendar) summary of the number of enrolled patients, the number of patients with preoperative blood sample collected, and number of patients with tumor specimen collected. (B-H) For the two complete calendar years 2011 and 2012 that could be matched to the INCA Swedish national breast cancer registry, (B) chart of all cases with a preoperative diagnosis of primary breast cancer within the catchment region divided into those that were accrued or not accrued. Comparison of baseline clinical variables between all eligible breast cancer patients, patients accrued, and patients accrued with tumor sample, for (C) estrogen receptor (ER) status, (D) progesterone receptor (PgR) status, (E) HER2 status, (F) age at diagnosis, (G) Nottingham histological grade (NHG), and (H) tumor size. †,‡ Significant differences were identified between all diagnoses and accrued with tumor specimen for NHG (P = 0.005) and tumor size (P <0.001), and between patients accrued and accrued with biopsy for NHG (P = 0.025) and tumor size (P <0.001).

Figure 3

RNA sequencing and microarray analysis for population-based breast tumors. (A) Hierarchical clustering of 49 primary breast tumors (clustered columns) using the RNA-seq gene expression measurements and the PAM50 intrinsic gene signature (clustered rows). Clinical annotations for estrogen receptor (ER), progesterone receptor (PgR), and HER2 are indicated below the sample dendrogram, and PAM50 intrinsic subtyping is shown for classification using RNA-seq data as well as using microarray data generated from the same input RNA (90% concordant; results for Sørlie (92%) and Hu (96%) signatures are presented in Additional file 2: Figure S2). Genes of interest are highlighted in red, and relative expression level is indicated by the box color (see color key below the heatmap). For six tumor samples, technical replicates from the same RNA sources were performed for both RNA-seq and microarrays; plotted in (B) and (C) are representative examples comparing the fold-change for all RefSeq genes between two tumors (Y axis), and the fold-change between the replicated experiments for the same two tumors (X axis). Consistently, RNA-seq demonstrated values closer to the ideal line of identity and for a broader dynamic range. The +/- 2 fold-change (|log2| = 1) thresholds are indicated by blue dashed lines. (D) RNA-seq-derived expression level of ESR1, which encodes the ER alpha protein, is shown compared to the clinical ER IHC score for each of the 49 tumors. See Additional file 2: Figure S3 for corresponding plots for progesterone receptor and ERBB2 (HER2).

Figure 4

Detection of mutations by RNA-seq. (A) Eighteen genes with at least one mutation (out of 90 genes screened) across the 49 population primary breast tumors are shown, in order of frequency (see totals and percentages to the right of each gene row). Mutant allele frequency is indicated by the box color (see key below matrix). All mutations are non-synonymous missense mutations except those indicated by F (frameshift) and X (nonsense). Tumor sample dendrogram is as in Figure 3A. Predicted mutant amino acids are shown for (B)PIK3CA which encodes the p110-alpha catalytic subunit of the phosphatidylinositol-4,5-bisphosphate 3-kinase oncogene, and (C)TP53 which encodes the tumor suppressor TP53.