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: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.

Project description:BackgroundHistological grade has been demonstrated to be an important factor of breast cancer outcome and is associated with cell differentiation and is currently being evaluated via H&E-stained sections. Molecular biomarkers are essential to improve the accuracy of histological grading. ATBF1, a large transcription factor, has been considered a tumor suppressor gene with frequent mutations or deletions in multiple cancers. In breast cancer, ATBF1 was reported to function in cell differentiation and mammary development. However, its role in the clinic has rarely been reported.MethodsBreast cancer tissues (BCTs) and adjacent noncancerous tissues (ANCTs) were collected to analyze the expression of ATBF1 at the mRNA and protein levels. Three anti-ATBF1 antibodies recognizing independent peptides of ATBF1 (N-terminal end, middle region and C-terminal end) were applied for IHC staining. Small interfering RNA (siRNA) was used to silence ATBF1 expression and to investigate the roles of ATBF1 in MCF7 cells. Microarrays were introduced to analyze the differentially expressed genes, enriched GO terms and KEGG terms regulated by ATBF1 and its potential downstream genes, which were further confirmed in vitro and in clinical samples.ResultsThe expression of ATBF1 was reduced in BCTs at both the mRNA and protein levels compared with that in ANCTs. ATBF1 protein was predominantly localized in the nucleus of ANCTs but in the cytoplasm of BCTs. Both the mRNA and protein levels of ATBF1 were significantly correlated with histological grade. Consistently, knockdown of ATBF1 increased stemness marker expression and reduced differentiation markers in vitro. Further analysis identified WNT5A as an essential downstream gene of ATBF1 in breast cancer cells. Treatment of WNT5A disrupted cell proliferation induced by ATBF1 silencing. In BCTs, a significant correlation was observed between the expression of WNT5A and ATBF1.ConclusionThe results indicated that ATBF1 expression might be a useful diagnostic marker associated with histological grade and breast cancer malignancy. WNT5A and its signaling pathway are novel mechanisms by which ATBF1 contributes to breast cancer tumorigenesis.

Dataset Information

Determining breast cancer histological grade from RNA-sequencing data.

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Determining breast cancer histological grade from RNA-sequencing data.

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