Dataset Information

Identifying Transcripts with Tandem Duplications from RNA-Sequencing Data to Predict BRCA1-Type Primary Breast Cancer.

ABSTRACT: Patients with cancers that are deficient for homologous recombination repair (HRD) may benefit from PARP inhibitor treatment. Therefore, methods that identify such cancers are crucial. Using whole genome sequencing data, specific genomic scars derived from somatic mutations and genomic rearrangements can identify HRD tumors, with only BRCA1-like HRD cancers profoundly displaying small (<10 kb) tandem duplications (TDs). In this manuscript we describe a method of detecting BRCA1-type HRD in breast cancer (BC) solely from RNA sequencing data by identifying TDs surfacing in transcribed genes. We find that the number of identified TDs (TD-score) is significantly higher in BRCA1-type vs. BRCA2-type BCs, or vs. HR-proficient BCs (p = 2.4 × 10-6 and p = 2.7 × 10-12, respectively). A TD-score ≥2 shows an 88.2% sensitivity (30 out of 34) to detect a BRCA1-type BC, with a specificity of 64.7% (143 out of 221). Pathway enrichment analyses showed genes implicated in cancer to be affected by TDs of which PTEN was found significantly more frequently affected by a TD in BRCA1-type BC. In conclusion, we here describe a novel method to identify TDs in transcripts and classify BRCA1-type BCs with high sensitivity.

SUBMITTER: Qu S

PROVIDER: S-EPMC8833645 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature

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Identifying Transcripts with Tandem Duplications from RNA-Sequencing Data to Predict BRCA1-Type Primary Breast Cancer.

Qu Shuoying S Martens John W M JWM Hollestelle Antoinette A Smid Marcel M

Cancers 20220131 3

Patients with cancers that are deficient for homologous recombination repair (HRD) may benefit from PARP inhibitor treatment. Therefore, methods that identify such cancers are crucial. Using whole genome sequencing data, specific genomic scars derived from somatic mutations and genomic rearrangements can identify HRD tumors, with only BRCA1-like HRD cancers profoundly displaying small (<10 kb) tandem duplications (TDs). In this manuscript we describe a method of detecting BRCA1-type HRD in breas ...[more]

PMID: 35159019

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Project description:BACKGROUND:Gene fusions arising from chromosomal translocations have been implicated in cancer. However, the role of gene fusions in BRCA1-related breast cancers is not well understood. Mutations in BRCA1 are associated with an increased risk for breast cancer (up to 80% lifetime risk) and ovarian cancer (up to 50%). We sought to identify putative gene fusions in the transcriptomes of these cancers using high-throughput RNA sequencing (RNA-Seq). METHODS:We used Illumina sequencing technology to sequence the transcriptomes of five BRCA1-mutated breast cancer cell lines, three BRCA1-mutated primary tumors, two secretory breast cancer primary tumors and one non-tumorigenic breast epithelial cell line. Using a bioinformatics approach, our initial attempt at discovering putative gene fusions relied on analyzing single-end reads and identifying reads that aligned across exons of two different genes. Subsequently, latter samples were sequenced with paired-end reads and at longer cycles (producing longer reads). We then refined our approach by identifying misaligned paired reads, which may flank a putative gene fusion junction. RESULTS:As a proof of concept, we were able to identify two previously characterized gene fusions in our samples using both single-end and paired-end approaches. In addition, we identified three novel in-frame fusions, but none were recurrent. Two of the candidates, WWC1-ADRBK2 in HCC3153 cell line and ADNP-C20orf132 in a primary tumor, were confirmed by Sanger sequencing and RT-PCR. RNA-Seq expression profiling of these two fusions showed a distinct overexpression of the 3' partner genes, suggesting that its expression may be under the control of the 5' partner gene's regulatory elements. CONCLUSIONS:In this study, we used both single-end and paired-end sequencing strategies to discover gene fusions in breast cancer transcriptomes with BRCA1 mutations. We found that the use of paired-end reads is an effective tool for transcriptome profiling of gene fusions. Our findings suggest that while gene fusions are present in some BRCA1-mutated breast cancers, they are infrequent and not recurrent. However, private fusions may still be valuable as potential patient-specific biomarkers for diagnosis and treatment.

Project description:MOTIVATION:Several algorithms have been developed that use high-throughput sequencing technology to characterize structural variations (SVs). Most of the existing approaches focus on detecting relatively simple types of SVs such as insertions, deletions and short inversions. In fact, complex SVs are of crucial importance and several have been associated with genomic disorders. To better understand the contribution of complex SVs to human disease, we need new algorithms to accurately discover and genotype such variants. Additionally, due to similar sequencing signatures, inverted duplications or gene conversion events that include inverted segmental duplications are often characterized as simple inversions, likewise, duplications and gene conversions in direct orientation may be called as simple deletions. Therefore, there is still a need for accurate algorithms to fully characterize complex SVs and thus improve calling accuracy of more simple variants. RESULTS:We developed novel algorithms to accurately characterize tandem, direct and inverted interspersed segmental duplications using short read whole genome sequencing datasets. We integrated these methods to our TARDIS tool, which is now capable of detecting various types of SVs using multiple sequence signatures such as read pair, read depth and split read. We evaluated the prediction performance of our algorithms through several experiments using both simulated and real datasets. In the simulation experiments, using a 30× coverage TARDIS achieved 96% sensitivity with only 4% false discovery rate. For experiments that involve real data, we used two haploid genomes (CHM1 and CHM13) and one human genome (NA12878) from the Illumina Platinum Genomes set. Comparison of our results with orthogonal PacBio call sets from the same genomes revealed higher accuracy for TARDIS than state-of-the-art methods. Furthermore, we showed a surprisingly low false discovery rate of our approach for discovery of tandem, direct and inverted interspersed segmental duplications prediction on CHM1 (<5% for the top 50 predictions). AVAILABILITY AND IMPLEMENTATION:TARDIS source code is available at https://github.com/BilkentCompGen/tardis, and a corresponding Docker image is available at https://hub.docker.com/r/alkanlab/tardis/. SUPPLEMENTARY INFORMATION:Supplementary data are available at Bioinformatics online.

Dataset Information

Identifying Transcripts with Tandem Duplications from RNA-Sequencing Data to Predict BRCA1-Type Primary Breast Cancer.

Publications

Identifying Transcripts with Tandem Duplications from RNA-Sequencing Data to Predict BRCA1-Type Primary Breast Cancer.

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