Dataset Information

Determining breast cancer histological grade from RNA-sequencing data.

ABSTRACT: 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.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.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.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.

SUBMITTER: Wang M

PROVIDER: S-EPMC4862203 | biostudies-literature | 2016 May

REPOSITORIES: biostudies-literature

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Determining breast cancer histological grade from RNA-sequencing data.

Wang Mei M Klevebring Daniel D Lindberg Johan J Czene Kamila K Grönberg Henrik H Rantalainen Mattias M

Breast cancer research : BCR 20160510 1

<h4>Background</h4>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.<h4>Methods</h4>RNA-sequencing analysis was conducted in a cohort of 275 women di ...[more]

PMID: 27165105

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Project description:Astrocytomas are the most common and aggressive type of primary brain tumors in adults. The World Health Organization (WHO) assorts them into grades, from I to IV, based on histopathological features that reflect their malignancy [1]. Alongside with tumor progression, comes an increased proliferation, genomic instability, infiltration in normal brain tissue and resistance to treatments. The high genomic instability forges tumor cells enhancing key proteins that avoid cells from collapsing and favor therapy resistance [2]. To explore genes and pathways associated with tumor progression phenotypes we analyzed gene expression in a panel of non-tumor and glioma cell lines, namely: ACBRI371, non-tumor human astrocytes; HDPC, fibroblasts derived from dental pulp; Res186, Res259, Res286 and UW467 that include grade I, II and III astrocytoma cell lines derived from pediatric tumors; and T98G, U343MG, U87MG, U138MG and U251MG, all derived from GBM (grade IV). We also profiled gene expression changes caused by exogenously induced replicative stress, performing RNA sequencing with camptothecin (CPT)-treated cells. Here we describe the RNA-sequencing data set acquired, including quality of reads and sequencing consistency, as well as the bioinformatics strategy used to analyze it. We also compared gene expression patterns and pathway enrichment between non-tumor versus lower-grade (LGG), non-tumor versus GBM, LGG versus GBM, and CPT-treated versus non-treated cells. In brief, a total of 6467 genes showed differential expression and 5 pathways were enriched in tumor progression, while 2279 genes and 7 pathways were altered under the replication stress condition. The raw data was deposited in the NCBI BioProject database under the accession number PRJNA631805. Our dataset is valuable for researchers interested in differential gene expression among different astrocytoma grades and in expression changes caused by replicative stress, facilitating studies that seek novel biomarkers of glioma progression and treatment resistance.

Project description:BackgroundHistological grade (HG) is commonly adopted as a prognostic factor for ER-positive breast cancer patients. However, HG evaluation methods, such as the pathological Nottingham grading system, are highly subjective with only 50-85% inter-observer agreements. Specifically, the subjectivity in the pathological assignment of the intermediate grade (HG2) breast cancers, comprising of about half of breast cancer cases, results in uncertain disease outcomes prediction. Here, we developed a qualitative transcriptional signature, based on within-sample relative expression orderings (REOs) of gene pairs, to define HG1 and HG3 and reclassify pathologically-determined HG2 (denoted as pHG2) breast cancer patients.ResultsFrom the gene pairs with significantly stable REOs in pathologically-determined HG1 (denoted as pHG1) samples and reversely stable REOs in pathologically-determined HG3 (denoted as pHG3) samples, concordantly identified from seven datasets, we extracted a signature which could determine the HG state of samples through evaluating whether the within-sample REOs match with the patterns of the pHG1 REOs or pHG3 REOs. A sample was classified into the HG3 group if at least a half of the REOs of the 10 gene pairs signature within this sample voted for HG3; otherwise, HG1. Using four datasets including samples of early stage (I-II) ER-positive breast cancer patients who accepted surgery only, we validated that this signature was able to reclassify pHG2 patients into HG1 and HG3 groups with significantly different survival time. For the original pHG1 and pHG3 patients, the signature could also more accurately and objectively stratify them into distinct prognostic groups. And the up-regulated and down down-regulated genes in HG1 compared with HG3 involved in cell proliferation and extracellular signal transduction pathways respectively. By comparing with existing signatures, 10-GPS was with prognostic significance and was more aligned with survival of patients especially for pHG2 samples.ConclusionsThe transcriptional qualitative signature can provide an objective assessment of HG states of ER-positive breast cancer patients, especially for reclassifying patients with pHG2, to assist decision making on clinical therapy.

Dataset Information

Determining breast cancer histological grade from RNA-sequencing data.

Publications

Determining breast cancer histological grade from RNA-sequencing data.

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