Dataset Information

Neoantigen prediction in human breast cancer using RNA sequencing data.

ABSTRACT: Neoantigens have attracted attention as biomarkers or therapeutic targets. However, accurate prediction of neoantigens is still challenging, especially in terms of its accuracy and cost. Variant detection using RNA sequencing (RNA-seq) data has been reported to be a low-accuracy but cost-effective tool, but the feasibility of RNA-seq data for neoantigen prediction has not been fully examined. In the present study, we used whole-exome sequencing (WES) and RNA-seq data of tumor and matched normal samples from six breast cancer patients to evaluate the utility of RNA-seq data instead of WES data in variant calling to detect neoantigen candidates. Somatic variants were called in three protocols using: (i) tumor and normal WES data (DNA method, Dm); (ii) tumor and normal RNA-seq data (RNA method, Rm); and (iii) combination of tumor RNA-seq and normal WES data (Combination method, Cm). We found that the Rm had both high false-positive and high false-negative rates because this method depended greatly on the expression status of normal transcripts. When we compared the results of Dm with those of Cm, only 14% of the neoantigen candidates detected in Dm were identified in Cm, but the majority of the missed candidates lacked coverage or variant allele reads in the tumor RNA. In contrast, about 70% of the neoepitope candidates with higher expression and rich mutant transcripts could be detected in Cm. Our results showed that Cm could be an efficient and a cost-effective approach to predict highly expressed neoantigens in tumor samples.

SUBMITTER: Hashimoto S

PROVIDER: S-EPMC7780012 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature

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Neoantigen prediction in human breast cancer using RNA sequencing data.

Hashimoto Sachie S Noguchi Emiko E Bando Hiroko H Miyadera Hiroko H Morii Wataru W Nakamura Takako T Hara Hisato H

Cancer science 20201129 1

Neoantigens have attracted attention as biomarkers or therapeutic targets. However, accurate prediction of neoantigens is still challenging, especially in terms of its accuracy and cost. Variant detection using RNA sequencing (RNA-seq) data has been reported to be a low-accuracy but cost-effective tool, but the feasibility of RNA-seq data for neoantigen prediction has not been fully examined. In the present study, we used whole-exome sequencing (WES) and RNA-seq data of tumor and matched normal ...[more]

PMID: 33155341

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Project description:BackgroundThe 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.MethodsRNA-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.ResultsThe 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.ConclusionsGene- 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.

Project description:BACKGROUND: This paper introduces and applies a genome wide predictive study to learn a model that predicts whether a new subject will develop breast cancer or not, based on her SNP profile. RESULTS: We first genotyped 696 female subjects (348 breast cancer cases and 348 apparently healthy controls), predominantly of Caucasian origin from Alberta, Canada using Affymetrix Human SNP 6.0 arrays. Then, we applied EIGENSTRAT population stratification correction method to remove 73 subjects not belonging to the Caucasian population. Then, we filtered any SNP that had any missing calls, whose genotype frequency was deviated from Hardy-Weinberg equilibrium, or whose minor allele frequency was less than 5%. Finally, we applied a combination of MeanDiff feature selection method and KNN learning method to this filtered dataset to produce a breast cancer prediction model. LOOCV accuracy of this classifier is 59.55%. Random permutation tests show that this result is significantly better than the baseline accuracy of 51.52%. Sensitivity analysis shows that the classifier is fairly robust to the number of MeanDiff-selected SNPs. External validation on the CGEMS breast cancer dataset, the only other publicly available breast cancer dataset, shows that this combination of MeanDiff and KNN leads to a LOOCV accuracy of 60.25%, which is significantly better than its baseline of 50.06%. We then considered a dozen different combinations of feature selection and learning method, but found that none of these combinations produces a better predictive model than our model. We also considered various biological feature selection methods like selecting SNPs reported in recent genome wide association studies to be associated with breast cancer, selecting SNPs in genes associated with KEGG cancer pathways, or selecting SNPs associated with breast cancer in the F-SNP database to produce predictive models, but again found that none of these models achieved accuracy better than baseline. CONCLUSIONS: We anticipate producing more accurate breast cancer prediction models by recruiting more study subjects, providing more accurate labelling of phenotypes (to accommodate the heterogeneity of breast cancer), measuring other genomic alterations such as point mutations and copy number variations, and incorporating non-genetic information about subjects such as environmental and lifestyle factors.

Project description:Exosomes are nanosized (30-100 nm) membrane vesicles secreted by most cell types. Exosomes have been found to contain various RNA species including miRNA, mRNA and long non-protein coding RNAs. A number of cancer cells produce elevated levels of exosomes. Because exosomes have been isolated from most body fluids they may provide a source for non-invasive cancer diagnostics. Transcriptome profiling that uses deep-sequencing technologies (RNA-Seq) offers enormous amount of data that can be used for biomarkers discovery, however, in case of exosomes this approach was applied only for the analysis of small RNAs. In this study, we utilized RNA-Seq technology to analyze RNAs present in microvesicles secreted by human breast cancer cell lines. Exosomes were isolated from the media conditioned by two human breast cancer cell lines, MDA-MB-231 and MDA-MB-436. Exosomal RNA was profiled using the Ion Torrent semiconductor chip-based technology. Exosomes were found to contain various classes of RNA with the major class represented by fragmented ribosomal RNA (rRNA), in particular 28S and 18S rRNA subunits. Analysis of exosomal RNA content revealed that it reflects RNA content of the donor cells. Although exosomes produced by the two cancer cell lines shared most of the RNA species, there was a number of non-coding transcripts unique to MDA-MB-231 and MDA-MB-436 cells. This suggests that RNA analysis might distinguish exosomes produced by low metastatic breast cancer cell line (MDA-MB-436) from that produced by highly metastatic breast cancer cell line (MDA-MB-231). The analysis of gene ontologies (GOs) associated with the most abundant transcripts present in exosomes revealed significant enrichment in genes encoding proteins involved in translation and rRNA and ncRNA processing. These GO terms indicate most expressed genes for both, cellular and exosomal RNA. For the first time, using RNA-seq, we examined the transcriptomes of exosomes secreted by human breast cancer cells. We found that most abundant exosomal RNA species are the fragments of 28S and 18S rRNA subunits. This limits the number of reads from other RNAs. To increase the number of detectable transcripts and improve the accuracy of their expression level the protocols allowing depletion of fragmented rRNA should be utilized in the future RNA-seq analyses on exosomes. Present data revealed that exosomal transcripts are representative of their cells of origin and thus could form basis for detection of tumor specific markers.

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Neoantigen prediction in human breast cancer using RNA sequencing data.

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Neoantigen prediction in human breast cancer using RNA sequencing data.

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