Detection of Structural Variations and Fusion Genes in Breast Cancer Samples Using Third-Generation Sequencing

IFDlong: an isoform and fusion detector for accurate annotation and quantification of long-read RNA-seq data

Advancements in long-read transcriptome sequencing (long-RNA-seq) technology have revolutionized the study of isoform diversity. These full-length transcripts enhance the detection of various transcriptome structural variations, including novel isoforms, alternative splicing events, and fusion transcripts. By shifting the open reading frame or altering gene expressions, studies have proved that these transcript alterations can serve as crucial biomarkers for disease diagnosis and therapeutic targets. In this project, we proposed IFDlong, a bioinformatics and biostatistics tool to detect isoform and fusion transcripts using bulk or single-cell long-RNA-seq data. Specifically, the software performed gene and isoform annotation for each long-read, defined novel isoforms, quantified isoform expression by a novel expectation-maximization algorithm, and profiled the fusion transcripts. For evaluation, IFDlong pipeline achieved overall the best performance when compared with several existing tools in large-scale simulation studies. In both isoform and fusion transcript quantification, IFDlong is able to reach more than 0.8 Spearman's correlation with the truth, and more than 0.9 cosine similarity when distinguishing multiple alternative splicing events. In novel isoform simulation, IFDlong can successfully balance the sensitivity (higher than 90%) and specificity (higher than 90%). Furthermore, IFDlong has proved its accuracy and robustness in diverse in-house and public datasets on healthy tissues, cell lines and multiple types of diseases. Besides bulk long-RNA-seq, IFDlong pipeline has proved its compatibility to single-cell long-RNA-seq data. This new software may hold promise for significant impact on long-read transcriptome analysis. The IFDlong software is available at https://github.com/wenjiaking/IFDlong .

The Application of Long-Read Sequencing to Cancer

Cancer is a multifaceted disease arising from numerous genomic aberrations that have been identified as a result of advancements in sequencing technologies. While next-generation sequencing (NGS), which uses short reads, has transformed cancer research and diagnostics, it is limited by read length. Third-generation sequencing (TGS), led by the Pacific Biosciences and Oxford Nanopore Technologies platforms, employs long-read sequences, which have marked a paradigm shift in cancer research. Cancer genomes often harbour complex events, and TGS, with its ability to span large genomic regions, has facilitated their characterisation, providing a better understanding of how complex rearrangements affect cancer initiation and progression. TGS has also characterised the entire transcriptome of various cancers, revealing cancer-associated isoforms that could serve as biomarkers or therapeutic targets. Furthermore, TGS has advanced cancer research by improving genome assemblies, detecting complex variants, and providing a more complete picture of transcriptomes and epigenomes. This review focuses on TGS and its growing role in cancer research. We investigate its advantages and limitations, providing a rigorous scientific analysis of its use in detecting previously hidden aberrations missed by NGS. This promising technology holds immense potential for both research and clinical applications, with far-reaching implications for cancer diagnosis and treatment.

Determining the degree of chromosomal instability in breast cancer cells by atomic force microscopy

Chromosomal instability (CIN) is a source of genetic variation and is highly linked to the malignance of cancer. Determining the degree of CIN is necessary for understanding the role that it plays in tumor development. There is currently a lack of research on high-resolution characterization of CIN and the relationship between CIN and cell mechanics. Here, a method to determine CIN of breast cancer cells by high resolution imaging with atomic force microscopy (AFM) is explored. The numerical and structural changes of chromosomes in human breast cells (MCF-10A), moderately malignant breast cells (MCF-7) and highly malignant breast cells (MDA-MB-231) were observed and analyzed by AFM. Meanwhile, the nuclei, cytoskeleton and cell mechanics of the three kinds of cells were also investigated. The results showed the differences in CIN between the benign and cancer cells. Also, the degree of structural CIN increased with enhanced malignancy of cancer cells. This was also demonstrated by calculating the probability of micronucleus formation in these three kinds of cells. Meanwhile, we found that the area of the nucleus was related to the number of chromosomes in the nucleus. In addition, reduced or even aggregated actin fibers led to decreased elasticities in MCF-7 and MDA-MB-231 cells. It was found that the rearrangement of actin fibers would affect the nucleus, and then lead to wrong mitosis and CIN. Using AFM to detect chromosomal changes in cells with different malignancy degrees provides a new detection method for the study of cell carcinogenesis with a perspective for targeted therapy of cancer.

Benchmarking long-read aligners and SV callers for structural variation detection in Oxford nanopore sequencing data

Structural variants (SVs) are one of the significant types of DNA mutations and are typically defined as larger-than-50-bp genomic alterations that include insertions, deletions, duplications, inversions, and translocations. These modifications can profoundly impact the phenotypic characteristics and contribute to disorders like cancer, response to treatment, and infections. Four long-read aligners and five SV callers have been evaluated using three Oxford Nanopore NGS human genome datasets in terms of precision, recall, and F1-score statistical metrics, depth of coverage, and speed of analysis. The best SV caller regarding recall, precision, and F1-score when matched with different aligners at different coverage levels tend to vary depending on the dataset and the specific SV types being analyzed. However, based on our findings, Sniffles and CuteSV tend to perform well across different aligners and coverage levels, followed by SVIM, PBSV, and SVDSS in the last place. The CuteSV caller has the highest average F1-score (82.51%) and recall (78.50%), and Sniffles has the highest average precision value (94.33%). Minimap2 as an aligner and Sniffles as an SV caller act as a strong base for the pipeline of SV calling because of their high speed and reasonable accomplishment. PBSV has a lower average F1-score, precision, and recall and may generate more false positives and overlook some actual SVs. Our results are valuable in the comprehensive evaluation of popular SV callers and aligners as they provide insight into the performance of several long-read aligners and SV callers and serve as a reference for researchers in selecting the most suitable tools for SV detection.

When Livestock Genomes Meet Third-Generation Sequencing Technology: From Opportunities to Applications

Third-generation sequencing technology has found widespread application in the genomic, transcriptomic, and epigenetic research of both human and livestock genetics. This technology offers significant advantages in the sequencing of complex genomic regions, the identification of intricate structural variations, and the production of high-quality genomes. Its attributes, including long sequencing reads, obviation of PCR amplification, and direct determination of DNA/RNA, contribute to its efficacy. This review presents a comprehensive overview of third-generation sequencing technologies, exemplified by single-molecule real-time sequencing (SMRT) and Oxford Nanopore Technology (ONT). Emphasizing the research advancements in livestock genomics, the review delves into genome assembly, structural variation detection, transcriptome sequencing, and epigenetic investigations enabled by third-generation sequencing. A comprehensive analysis is conducted on the application and potential challenges of third-generation sequencing technology for genome detection in livestock. Beyond providing valuable insights into genome structure analysis and the identification of rare genes in livestock, the review ventures into an exploration of the genetic mechanisms underpinning exemplary traits. This review not only contributes to our understanding of the genomic landscape in livestock but also provides fresh perspectives for the advancement of research in this domain.

Long-Read DNA and RNA Sequencing to Streamline Clinical Genetic Testing and Reduce Barriers to Comprehensive Genetic Testing

BACKGROUND

Obtaining a precise molecular diagnosis through clinical genetic testing provides information about disease prognosis or progression, allows accurate counseling about recurrence risk, and empowers individuals to benefit from precision therapies or take part in N-of-1 trials. Unfortunately, more than half of individuals with a suspected Mendelian condition remain undiagnosed after a comprehensive clinical evaluation, and the results of any individual clinical genetic test ordered during a typical evaluation may take weeks or months to return. Furthermore, commonly used technologies, such as short-read sequencing, are limited in the types of disease-causing variation they can identify. New technologies, such as long-read sequencing (LRS), are poised to solve these problems.

CONTENT

Recent technical advances have improved accuracy, increased throughput, and decreased the costs of commercially available LRS technologies. This has resolved many historical concerns about the use of LRS in the clinical environment and opened the door to widespread clinical adoption of LRS. Here, we review LRS technology, how it has been used in the research setting to clarify complex variants or identify disease-causing variation missed by prior clinical testing, and how it may be used clinically in the near future.

SUMMARY

LRS is unique in that, as a single data source, it has the potential to replace nearly every other clinical genetic test offered today. When analyzed in a stepwise fashion, LRS will simplify laboratory processes, reduce barriers to comprehensive genetic testing, increase the rate of genetic diagnoses, and shorten the amount of time required to make a molecular diagnosis.

Genomics in Clinical trials for Breast Cancer

Breast cancer (B.C.) still has increasing incidences and mortality rates globally. It is known that B.C. and other cancers have a very high rate of genetic heterogeneity and genomic mutations. Traditional oncology approaches have not been able to provide a lasting solution. Targeted therapeutics have been instrumental in handling the complexity and resistance associated with B.C. However, the progress of genomic technology has transformed our understanding of the genetic landscape of breast cancer, opening new avenues for improved anti-cancer therapeutics. Genomics is critical in developing tailored therapeutics and identifying patients most benefit from these treatments. The next generation of breast cancer clinical trials has incorporated next-generation sequencing technologies into the process, and we have seen benefits. These innovations have led to the approval of better-targeted therapies for patients with breast cancer. Genomics has a role to play in clinical trials, including genomic tests that have been approved, patient selection and prediction of therapeutic response. Multiple clinical trials in breast cancer have been done and are still ongoing, which have applied genomics technology. Precision medicine can be achieved in breast cancer therapy with increased efforts and advanced genomic studies in this domain. Genomics studies assist with patient outcomes improvement and oncology advancement by providing a deeper understanding of the biology behind breast cancer. This article will examine the present state of genomics in breast cancer clinical trials.

Third-generation sequencing for genetic disease

Third-generation sequencing (TGS) has led to a brave new revolution in detecting genetic diseases over the last few years. TGS has been rapidly developed for genetic disease applications owing to its significant advantages such as long read length, rapid detection, and precise detection of complex and rare structural variants. This approach greatly improves the efficiency of disease diagnosis and complements the shortcomings of short-read sequencing. In this paper, we first briefly introduce the working mechanism of one of the most important representatives of TGS, single-molecule real-time (SMRT) sequencing by Pacific Bioscience (PacBio), followed by a review and comparison of the advantages and disadvantages of different sequencing technologies. Finally, we focused on the progress of SMRT sequencing applications in genetic disease detection. Future perspectives on the applications of TGS in other fields were also presented. With the continuous innovation of the SMRT technologies and the expansion of their fields of application, SMRT sequencing has broad clinical application prospects in genetic diseases detection, and is expected to become an important tool for the molecular diagnosis of other diseases.

Third generation sequencing transforms the way of the screening and diagnosis of thalassemia: a mini-review

Thalassemia is an inherited blood disorder imposing a significant social and economic burden. Comprehensive screening strategies are essential for the prevention and management of this disease. Third-generation sequencing (TGS), a breakthrough technology, has shown great potential for screening and diagnostic applications in various diseases, while its application in thalassemia detection is still in its infancy. This review aims to understand the latest and most widespread uses, advantages of TGS technologies, as well as the challenges and solutions associated with their incorporation into routine screening and diagnosis of thalassemia. Overall, TGS has exhibited higher rates of positive detection and diagnostic accuracy compared to conventional methods and next-generation sequencing technologies, indicating that TGS will be a feasible option for clinical laboratories conducting in-house thalassemia testing. The implementation of TGS technology in thalassemia diagnosis will facilitate the development of effective prevention and management strategies, thereby reducing the burden of this disease on individuals and society.

Neoantigens: promising targets for cancer therapy

Recent advances in neoantigen research have accelerated the development and regulatory approval of tumor immunotherapies, including cancer vaccines, adoptive cell therapy and antibody-based therapies, especially for solid tumors. Neoantigens are newly formed antigens generated by tumor cells as a result of various tumor-specific alterations, such as genomic mutation, dysregulated RNA splicing, disordered post-translational modification, and integrated viral open reading frames. Neoantigens are recognized as non-self and trigger an immune response that is not subject to central and peripheral tolerance. The quick identification and prediction of tumor-specific neoantigens have been made possible by the advanced development of next-generation sequencing and bioinformatic technologies. Compared to tumor-associated antigens, the highly immunogenic and tumor-specific neoantigens provide emerging targets for personalized cancer immunotherapies, and serve as prospective predictors for tumor survival prognosis and immune checkpoint blockade responses. The development of cancer therapies will be aided by understanding the mechanism underlying neoantigen-induced anti-tumor immune response and by streamlining the process of neoantigen-based immunotherapies. This review provides an overview on the identification and characterization of neoantigens and outlines the clinical applications of prospective immunotherapeutic strategies based on neoantigens. We also explore their current status, inherent challenges, and clinical translation potential.

Recent advances in cancer fusion transcript detection

Extensive investigation of gene fusions in cancer has led to the discovery of novel biomarkers and therapeutic targets. To date, most studies have neglected chromosomal rearrangement-independent fusion transcripts and complex fusion structures such as double or triple-hop fusions, and fusion-circRNAs. In this review, we untangle fusion-related terminology and propose a classification system involving both gene and transcript fusions. We highlight the importance of RNA-level fusions and how long-read sequencing approaches can improve detection and characterization. Moreover, we discuss novel bioinformatic tools to identify fusions in long-read sequencing data and strategies to experimentally validate and functionally characterize fusion transcripts.