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Breast Cancer Research
Published in: Breast Cancer Research 1/2016

Open Access 01-12-2016 | Research Article

Determining breast cancer histological grade from RNA-sequencing data

Authors: Mei Wang, Daniel Klevebring, Johan Lindberg, Kamila Czene, Henrik Grönberg, Mattias Rantalainen

Published in: Breast Cancer Research | Issue 1/2016

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Abstract

Background

The histologic grade (HG) of breast cancer is an established prognostic factor. The grade is usually reported on a scale ranging from 1 to 3, where grade 3 tumours are the most aggressive. However, grade 2 is associated with an intermediate risk of recurrence, and carries limited information for clinical decision-making. Patients classified as grade 2 are at risk of both under- and over-treatment.

Methods

RNA-sequencing analysis was conducted in a cohort of 275 women diagnosed with invasive breast cancer. Multivariate prediction models were developed to classify tumours into high and low transcriptomic grade (TG) based on gene- and isoform-level expression data from RNA-sequencing. HG2 tumours were reclassified according to the prediction model and a recurrence-free survival analysis was performed by the multivariate Cox proportional hazards regression model to assess to what extent the TG model could be used to stratify patients. The prediction model was validated in N=487 breast cancer cases from the The Cancer Genome Atlas (TCGA) data set. Differentially expressed genes and isoforms associated with HGs were analysed using linear models.

Results

The classification of grade 1 and grade 3 tumours based on RNA-sequencing data achieved high accuracy (area under the receiver operating characteristic curve = 0.97). The association between recurrence-free survival rate and HGs was confirmed in the study population (hazard ratio of grade 3 versus 1 was 2.62 with 95 % confidence interval = 1.04–6.61). The TG model enabled us to reclassify grade 2 tumours as high TG and low TG gene or isoform grade. The risk of recurrence in the high TG group of grade 2 tumours was higher than in low TG group (hazard ratio = 2.43, 95 % confidence interval = 1.13–5.20). We found 8200 genes and 13,809 isoforms that were differentially expressed between HG1 and HG3 breast cancer tumours.

Conclusions

Gene- and isoform-level expression data from RNA-sequencing could be utilised to differentiate HG1 and HG3 tumours with high accuracy. We identified a large number of novel genes and isoforms associated with HG. Grade 2 tumours could be reclassified as high and low TG, which has the potential to reduce over- and under-treatment if implemented clinically.
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Metadata
Title
Determining breast cancer histological grade from RNA-sequencing data
Authors
Mei Wang
Daniel Klevebring
Johan Lindberg
Kamila Czene
Henrik Grönberg
Mattias Rantalainen
Publication date
01-12-2016
Publisher
BioMed Central
Published in
Breast Cancer Research / Issue 1/2016
Electronic ISSN: 1465-542X
DOI
https://doi.org/10.1186/s13058-016-0710-8

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