Abstract
Multigene expression signatures provide a molecular subdivision of early breast cancer associated with patient outcome. A gap remains in the validation of such signatures in clinical treatment groups of patients within population-based cohorts of unselected primary breast cancer representing contemporary disease stages and current treatments. A cohort of 3520 resectable breast cancers with RNA sequencing data included in the population-based SCAN-B initiative (ClinicalTrials.gov ID NCT02306096) were selected from a healthcare background population of 8587 patients diagnosed within the years 2010–2015. RNA profiles were classified according to 19 reported gene signatures including both gene expression subtypes (e.g. PAM50, IC10, CIT) and risk predictors (e.g. Oncotype DX, 70-gene, ROR). Classifications were analyzed in nine adjuvant clinical assessment groups: TNBC-ACT (adjuvant chemotherapy, n = 239), TNBC-untreated (n = 82), HER2+/ER− with anti-HER2+ ACT treatment (n = 110), HER2+/ER+ with anti-HER2 + ACT + endocrine treatment (n = 239), ER+/HER2−/LN− with endocrine treatment (n = 1113), ER+/HER2−/LN− with endocrine + ACT treatment (n = 243), ER+/HER2−/LN+ with endocrine treatment (n = 423), ER+/HER2−/LN+ with endocrine + ACT treatment (n = 433), and ER+/HER2−/LN− untreated (n = 200). Gene signature classification (e.g., proportion low-, high-risk) was generally well aligned with stratification based on current immunohistochemistry-based clinical practice. Most signatures did not provide any further risk stratification in TNBC and HER2+/ER– disease. Risk classifier agreement (low-, medium/intermediate-, high-risk groups) in ER+ assessment groups was on average 50–60% with occasional pair-wise comparisons having <30% agreement. Disregarding the intermediate-risk groups, the exact agreement between low- and high-risk groups was on average ~80–95%, for risk prediction signatures across all assessment groups. Outcome analyses were restricted to assessment groups of TNBC-ACT and endocrine treated ER+/HER2−/LN− and ER+/HER2−/LN+ cases. For ER+/HER2− disease, gene signatures appear to contribute additional prognostic value even at a relatively short follow-up time. Less apparent prognostic value was observed in the other groups for the tested signatures. The current study supports the usage of gene expression signatures in specific clinical treatment groups within population-based breast cancer. It also stresses the need of further development to reach higher consensus in individual patient classifications, especially for intermediate-risk patients, and the targeting of patients where current gene signatures and prognostic variables provide little support in clinical decision-making.
Highlights
We aimed to address this gap by analyzing classification proportions and patient outcome associations of 19 different gene expression phenotypes (GEPs) and risk predictors (RPs) type gene signatures within a 3520-sample consecutive observational cohort of resectable primary breast cancers from south Sweden
To more thoroughly study consensus we focused on the risk prediction (RP)
The novelty and impact of this study lies in the RNAseq analysis of ~3500 consecutive breast cancer patients collected over a period of five years (2010–2015) in a defined geographic region and healthcare region following contemporary treatment guidelines
Summary
All analyses were performed in accordance with patient consent and ethical regulations and decisions. Population-based representativeness for each step in the patient selection process was assessed through comparison between: i) the general comparable breast cancer population in the catchment region (n = 8587), ii) the subset of enrolled SCAN-B patients (n = 5417), and iii) the RNAseq cohort subset (n = 3520). Gene expression profiling of the 3520 patients were performed using RNA sequencing as described[16,18]. A complete list of classifications for each sample and signature is available as Supplementary Table S1 together with patient characteristics and survival data. Gene set activation status, activated (1), repressed (−1), or latent (NA) for molecular processes was determined using absolute inference of patient signatures (AIPS) models[21]. The full available follow-up time was used in calculations
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