Introduction 
Few studies have performed expression profiling of both miRNA and mRNA from the same primary breast carcinomas. In this study we present and analyze data derived from expression profiling of 799 miRNAs in 101 primary human breast tumors, along with genome-wide mRNA profiles and extensive clinical information.


Methods 
We investigate the relationship between these molecular components, in terms of their correlation with each other and with clinical characteristics. We use a systems biology approach to examine the correlative relationship between miRNA and mRNAs using statistical enrichment methods.


Results 
We identify statistical significant differential expression of miRNAs between molecular intrinsic subtypes, and between samples with different levels of proliferation. Specifically, we point to miRNAs significantly associated with TP53 and ER status. We also show that several cellular processes, such as proliferation, cell adhesion and immune response, are strongly associated with certain miRNAs. We validate the role of miRNAs in regulating proliferation using high-throughput lysate-microarrays on cell lines and point to potential drivers of this process.


Conclusion 
This study provides a comprehensive dataset as well as methods and system-level results that jointly form a basis for further work on understanding the role of miRNA in primary breast cancer.

/pdf/mirna-mrna-integrated-analysis-reveals-roles-for-mirnas-in-3rb48jqps8.pdf

miRNA-mRNA integrated analysis reveals roles for miRNAs in primary breast tumors

Advances in genome sequencing have progressed at a rapid pace, with increased throughput accompanied by plunging costs. But these advances go far beyond faster and cheaper. High-throughput sequencing technologies are now routinely being applied to a wide range of important topics in biology and medicine, often allowing researchers to address important biological questions that were not possible before. In this review, we discuss these innovative new approaches—including ever finer analyses of transcriptome dynamics, genome structure and genomic variation—and provide an overview of the new insights into complex biological systems catalyzed by these technologies. We also assess the impact of genotyping, genome sequencing and personal omics profiling on medical applications, including diagnosis and disease monitoring. Finally, we review recent developments in single-cell sequencing, and conclude with a discussion of possible future advances and obstacles for sequencing in biology and health.

https://www.embopress.org/doi/pdf/10.1038/msb.2012.61

High‐throughput sequencing for biology and medicine

Structural variants (SVs) can contribute to oncogenesis through a variety of mechanisms. Despite their importance, the identification of SVs in cancer genomes remains challenging. Here, we present a framework that integrates optical mapping, high-throughput chromosome conformation capture (Hi-C), and whole-genome sequencing to systematically detect SVs in a variety of normal or cancer samples and cell lines. We identify the unique strengths of each method and demonstrate that only integrative approaches can comprehensively identify SVs in the genome. By combining Hi-C and optical mapping, we resolve complex SVs and phase multiple SV events to a single haplotype. Furthermore, we observe widespread structural variation events affecting the functions of noncoding sequences, including the deletion of distal regulatory sequences, alteration of DNA replication timing, and the creation of novel three-dimensional chromatin structural domains. Our results indicate that noncoding SVs may be underappreciated mutational drivers in cancer genomes.

/pdf/integrative-detection-and-analysis-of-structural-variation-122yjghvxl.pdf

Integrative detection and analysis of structural variation in cancer genomes

Next-generation sequencing (NGS) has transformed genomic research by decreasing the cost of sequencing and increasing the throughput. Now, the focus is on using NGS technology for diagnostics and therapeutics. In this review, we discuss the possible clinical applications of NGS and the potential of some of the current systems to transition to the clinic. Clinical use of NGS technologies will enable the identification of causative mutations for rare genetic disorders through whole-genome or targeted genome resequencing, rapid pathogen screening and cancer diagnosis along with the identification of appropriate therapy. Routine clinical use of NGS technologies is appealing, but mandates high accuracy, simple assays, small inexpensive instruments, flexible throughput, short run times and most importantly, easy data analysis as well as interpretation. A number of NGS systems launched recently have least some of these characteristics, namely, small instruments, flexible throughput and short run time, but still face a few challenges. Moreover, simplified data analysis tools will need to be developed to minimize the requirement of sophisticated bioinformatics support in clinics. In summary, for successful transition of NGS to clinic, a sustained collaboration between research labs, clinical practitioners and vendors offering sequencing based genetic tests is required.

Next-generation sequencing: ready for the clinics?

Genomic instability is a hallmark of cancer and, as such, structural alterations and fusion genes are common events in the cancer landscape. RNA sequencing (RNA-Seq) is a powerful method for profiling cancers, but current methods for identifying fusion genes are optimised for short reads. JAFFA (https://github.com/Oshlack/JAFFA/wiki) is a sensitive fusion detection method that outperforms other methods with reads of 100 bp or greater. JAFFA compares a cancer transcriptome to the reference transcriptome, rather than the genome, where the cancer transcriptome is inferred using long reads directly or by de novo assembling short reads.

/pdf/jaffa-high-sensitivity-transcriptome-focused-fusion-gene-25e8emzx5d.pdf

JAFFA: High sensitivity transcriptome-focused fusion gene detection

Using a long-span, paired-end deep sequencing strategy, we have comprehensively identified cancer genome rearrangements in eight breast cancer genomes. Herein, we show that 40%–54% of these structural genomic rearrangements result in different forms of fusion transcripts and that 44% are potentially translated. We find that single segmental tandem duplication spanning several genes is a major source of the fusion gene transcripts in both cell lines and primary tumors involving adjacent genes placed in the reverse-order position by the duplication event. Certain other structural mutations, however, tend to attenuate gene expression. From these candidate gene fusions, we have found a fusion transcript (RPS6KB1–VMP1) recurrently expressed in ∼30% of breast cancers associated with potential clinical consequences. This gene fusion is caused by tandem duplication on 17q23 and appears to be an indicator of local genomic instability altering the expression of oncogenic components such as MIR21 and RPS6KB1.

/pdf/transcriptional-consequences-of-genomic-structural-1nvy267ph9.pdf

Transcriptional consequences of genomic structural aberrations in breast cancer.

Abstract We aim to identify predictors of tumor dynamics during radiotherapy for glioblastoma. METHODS: This is a prospective serial MRI study in patients with glioblastoma. MRIs were obtained at radiotherapy planning (F0), fraction-10 (F10), and fraction-20 (F20). The tumor dynamics metrics (relative to F0) assessed included: relative gross tumor volume (GTV) changes (Vrel) and migration distance (dM). RESULTS: 129 patients were included in this study. Median GTV was 20.9cc at F0, 17.6cc at F10 (Vrel 0.85), and 16.1cc at F20 (Vrel 0.78). Patients with no corpus-callosum involvement (CCI) (vs with CCI) had more marked GTV volume reduction: Vrel 0.82 vs 1.02 at F10 (P=0.05), and Vrel 0.77 vs 0.88 at F20 (P=0.03). Patients with gross total resection (GTR) had more marked GTV volume reduction (vs subtotal resection (STR) vs biopsy (Bx)): Vrel 0.78, 0.85 and 1.07 at F10 (P=0.001), and Vrel 0.69, 0.80, 1.04 at F20 (P=0.001). The median dM were 4.7mm at both F10 and F20. Patients with CCI had larger dM (vs no CCI): 41% vs 12% had dM>10mm at F10 (P=0.01), and 45% vs 9% had dM>10mm at F20 (P<0.001). At F20, 0%, 25% and 19% of patients with GTR, STR and Bx had dM>10mm (P=0.002). Age, sex, MGMT methylation and IDH-mutation status were not associated with tumor dynamics during treatment. CONCLUSION: CCI and extent of surgery predict for tumor dynamics, which may allow us to better select patients who benefit the most from re-planning/ treatment adaptation during radiotherapy.

https://academic.oup.com/noa/article-pdf/5/Supplement_2/i9/50860418/vdad071.039.pdf

Wai Loon Ong

Papers

Transcriptional consequences of genomic structural aberrations in breast cancer.

Predictors of tumor dynamics during a course of 30-fraction radiotherapy for glioblastoma