RNA Sequencing-Based Single Sample Predictors of Molecular Subtype and Risk of Recurrence for Clinical Assessment of Early-Stage Breast Cancer

Abstract

BackgroundMultigene expression assays for molecular subtypes and biomarkers can aid clinical management of early breast cancer. Based on RNA-sequencing we aimed to develop robust single-sample predictor (SSP) models for conventional clinical markers as well as molecular intrinsic subtype and risk of recurrence (ROR) that provide clinically relevant prognostic stratification.MethodsA uniformly accrued breast cancer cohort of 7743 patients with RNA-sequencing data from fresh tissue was divided into a training set (n=5250) and a reserved test set (n=2412). We trained SSPs for PAM50 molecular subtypes and ROR assigned by nearest-centroid (NC) methods and SSPs for conventional clinical markers from histopathology data. Additionally, SSP classifications were compared with Prosigna in two external clinical series (ABiM, n=100 and OSLO2-EMIT0, n=103 cases).ResultsIn the test set, agreement between SSP and NC classifications for PAM50 (five subtypes) and Subtype (four subtypes) was high (85%, Kappa=0.78) and very high (90%, Kappa=0.84) respectively. Accuracy for ROR risk category was high (84%, Kappa=0.75, weighted Kappa=0.90). The prognostic value for SSP and NC classification was assessed as equivalent and added clinically relevant prognostic information. Agreement for SSP and histopathology was very high or high for receptor status, while moderate and poor for Ki67 status and Nottingham histological grade (NHG), respectively. SSP concordance with Prosigna was high for subtype (OSLO=83% and ABiM=80%, Kappa=0.73 and 0.72, respectively) and moderate and high for ROR risk category (68% and 84%, Kappa=0.50 and 0.70, weighted Kappa=0.70 and 0.78). In pooled analysis, concordance between SSP and Prosigna for emulated treatment recommendation dichotomized for chemotherapy (yes vs. no) was high (85%, Kappa=0.66). In postmenopausal ER+/ HER2-/N0 patients SSP application suggested changed treatment recommendations for up to 17% of patients, with nearly balanced escalation and de-escalation of therapy.ConclusionsRobust SSP models, mimicking histopathological variables, PAM50, and ROR classifications can be derived from RNA-sequencing that closely matches clinical tests. Agreement and outcome analyses suggest that NC and SSP models are interchangeable on a group-level and nearly so on a patient level. Retrospective evaluation in ER+/HER2-/ N0 early breast cancer suggested that molecular testing could lead to a changed therapy recommendation for about one-fifth of patients.

Molecular phenotyping of malignant canine mammary tumours: Detection of high‐risk group and its relationship with clinicomolecular characteristics

“…If we have more samples, it is possible to confirm this hypothesis. Furthermore, in many cases, molecular classifications have been confirmed to be more robust in predicting survival rates than conventional histopathology methods 24–28 . With the development of more advanced molecular methods, it is possible to develop clinically relevant and practical molecular signatures 29,30 .…”

“…Furthermore, in many cases, molecular classifications have been confirmed to be more robust in predicting survival rates than conventional histopathology methods. [24][25][26][27][28] With the development of more advanced molecular methods, it is possible to develop clinically relevant and practical molecular signatures. 29,30 Proliferation and cell cycle-based gene signatures have been vastly assessed to determine molecular subtypes and to predict the clinical outcome in different human cancers.…”

FGF/FGFR1 system in paired breast tumor-adjacent and tumor tissues, associations with mammographic breast density and tumor characteristics

“…Briefly, gene expression profiles of individual samples were analyzed via whole transcriptome mRNA–sequencing (>19,000 genes) with the use of Illumina sequencers, and subsequent pre–processing and log 2 transformation of the RNA-seq data. Robust single sample predictor (SSP) models were trained for four RNA-sequencing-based molecular subtypes of breast cancer: Luminal A, Luminal B, HER2-enriched, and Basal-like, as described elsewhere ( 26 ).…”

Defining the Regulatory Logic of Breast Cancer Using Single-Cell Epigenetic and Transcriptome Profiling

“…SCAN-B clinical data and gene-level FPKM RNA-seq expression data were downloaded from Mendeley Data (https://data.mendeley.com/datasets/yzxtxn4nmd/3) and came from Staaf et al 2022 (136). The FPKM expression values were further upper quartile normalized and log2(x+1) transformed.…”