Hi-C detects genomic structural variants in peripheral blood of pediatric leukemia patients

ETV6::ACSL6 translocation-driven super-enhancer activation leads to eosinophilia in acute lymphoblastic leukemia through IL-3 overexpression

ETV6::ACSL6 represents a rare genetic aberration in hematopoietic neoplasms and is often associated with severe eosinophilia, which confers an unfavorable prognosis requiring additional anti-inflammatory treatment. However, since the translocation is unlikely to produce a fusion protein, the mechanism of ETV6::ACSL6 action remains unclear. Here, we performed multi-omics analyses of primary leukemia cells and patient-derived xenografts from an acute lymphoblastic leukemia (ALL) patient with ETV6::ACSL6 translocation. We identified a super-enhancer located within the ETV6 gene locus, and revealed translocation and activation of the super-enhancer associated with the ETV6::ACSL6 fusion. The translocated super-enhancer exhibited intense interactions with genomic regions adjacent to and distal from the breakpoint at chromosomes 5 and 12, including genes coding inflammatory factors such as IL-3. This led to modulations in DNA methylation, histone modifications, and chromatin structures, triggering transcription of inflammatory factors leading to eosinophilia. Furthermore, the bromodomain and extraterminal domain (BET) inhibitor synergized with standard-of-care drugs for ALL, effectively reducing IL-3 expression and inhibiting ETV6::ACSL6 ALL growth in vitro and in vivo. Overall, our study revealed for the first time a cis-regulatory mechanism of super-enhancer translocation in ETV6::ACSL6ALL, leading to an ALL-accompanying clinical syndrome. These findings may stimulate novel treatment approaches for this challenging ALL subtype.

3C methods in cancer research: recent advances and future prospects

In recent years, Hi-C technology has revolutionized cancer research by elucidating the mystery of three-dimensional chromatin organization and its role in gene regulation. This paper explored the impact of Hi-C advancements on cancer research by delving into high-resolution techniques, such as chromatin loops, structural variants, haplotype phasing, and extrachromosomal DNA (ecDNA). Distant regulatory elements interact with their target genes through chromatin loops. Structural variants contribute to the development and progression of cancer. Haplotype phasing is crucial for understanding allele-specific genomic rearrangements and somatic clonal evolution in cancer. The role of ecDNA in driving oncogene amplification and drug resistance in cancer cells has also been revealed. These innovations offer a deeper understanding of cancer biology and the potential for personalized therapies. Despite these advancements, challenges, such as the accurate mapping of repetitive sequences and precise identification of structural variants, persist. Integrating Hi-C with multiomics data is key to overcoming these challenges and comprehensively understanding complex cancer genomes. Thus, Hi-C is a powerful tool for guiding precision medicine in cancer research and treatment.

Cytogenetics in the management of hematological malignancies: An overview of alternative technologies for cytogenetic characterization

Genomic characterization is an essential part of the clinical management of hematological malignancies for diagnostic, prognostic and therapeutic purposes. Although CBA and FISH are still the gold standard in hematology for the detection of CNA and SV, some alternative technologies are intended to complement their deficiencies or even replace them in the more or less near future. In this article, we provide a technological overview of these alternatives. CMA is the historical and well established technique for the high-resolution detection of CNA. For SV detection, there are emerging techniques based on the study of chromatin conformation and more established ones such as RTMLPA for the detection of fusion transcripts and RNA-seq to reveal the molecular consequences of SV. Comprehensive techniques that detect both CNA and SV are the most interesting because they provide all the information in a single examination. Among these, OGM is a promising emerging higher-solution technique that offers a complete solution at a contained cost, at the expense of a relatively low throughput per machine. WGS remains the most adaptable solution, with long-read approaches enabling very high-resolution detection of CAs, but requiring a heavy bioinformatics installation and at a still high cost. However, the development of high-resolution genome-wide detection techniques for CAs allows for a much better description of chromoanagenesis. Therefore, we have included in this review an update on the various existing mechanisms and their consequences and implications, especially prognostic, in hematological malignancies.

Chromosome conformation capture technologies as tools to detect structural variations and their repercussion in chromatin 3D configuration

3D genome organization regulates gene expression in different physiological and pathological contexts. Characterization of chromatin structure at different scales has provided information about how the genome organizes in the nuclear space, from chromosome territories, compartments of euchromatin and heterochromatin, topologically associated domains to punctual chromatin loops between genomic regulatory elements and gene promoters. In recent years, chromosome conformation capture technologies have also been used to characterize structural variations (SVs) de novo in pathological conditions. The study of SVs in cancer, has brought information about transcriptional misregulation that relates directly to the incidence and prognosis of the disease. For example, gene fusions have been discovered arising from chromosomal translocations that upregulate oncogenes expression, and other types of SVs have been described that alter large genomic regions encompassing many genes. However, studying SVs in 2D cannot capture all their regulatory implications in the genome. Recently, several bioinformatic tools have been developed to identify and classify SVs from chromosome conformation capture data and clarify how they impact chromatin structure in 3D, resulting in transcriptional misregulation. Here, we review recent literature concerning bioinformatic tools to characterize SVs from chromosome conformation capture technologies and exemplify their vast potential to rebuild the 3D landscape of genomes in cancer. The study of SVs from the 3D perspective can produce essential information about drivers, molecular targets, and disease evolution.

Detection and genomic analysis of BRAF fusions in Juvenile Pilocytic Astrocytoma through the combination and integration of multi-omic data

BACKGROUND

Juvenile Pilocytic Astrocytomas (JPAs) are one of the most common pediatric brain tumors, and they are driven by aberrant activation of the mitogen-activated protein kinase (MAPK) signaling pathway. RAF-fusions are the most common genetic alterations identified in JPAs, with the prototypical KIAA1549-BRAF fusion leading to loss of BRAF's auto-inhibitory domain and subsequent constitutive kinase activation. JPAs are highly vascular and show pervasive immune infiltration, which can lead to low tumor cell purity in clinical samples. This can result in gene fusions that are difficult to detect with conventional omics approaches including RNA-Seq.

METHODS

To this effect, we applied RNA-Seq as well as linked-read whole-genome sequencing and in situ Hi-C as new approaches to detect and characterize low-frequency gene fusions at the genomic, transcriptomic and spatial level.

RESULTS

Integration of these datasets allowed the identification and detailed characterization of two novel BRAF fusion partners, PTPRZ1 and TOP2B, in addition to the canonical fusion with partner KIAA1549. Additionally, our Hi-C datasets enabled investigations of 3D genome architecture in JPAs which showed a high level of correlation in 3D compartment annotations between JPAs compared to other pediatric tumors, and high similarity to normal adult astrocytes. We detected interactions between BRAF and its fusion partners exclusively in tumor samples containing BRAF fusions.

CONCLUSIONS

We demonstrate the power of integrating multi-omic datasets to identify low frequency fusions and characterize the JPA genome at high resolution. We suggest that linked-reads and Hi-C could be used in clinic for the detection and characterization of JPAs.

Chromatin structure in cancer

In the past decade, we have seen the emergence of sequence-based methods to understand chromosome organization. With the confluence of in situ approaches to capture information on looping, topological domains, and larger chromatin compartments, understanding chromatin-driven disease is becoming feasible. Excitingly, recent advances in single molecule imaging with capacity to reconstruct “bulk-cell” features of chromosome conformation have revealed cell-to-cell chromatin structural variation. The fundamental question motivating our analysis of the literature is, can altered chromatin structure drive tumorigenesis? As our community learns more about rare disease, including low mutational frequency cancers, understanding “chromatin-driven” pathology will illuminate the regulatory structures of the genome. We describe recent insights into altered genome architecture in human cancer, highlighting multiple pathways toward disruptions of chromatin structure, including structural variation, noncoding mutations, metabolism, and de novo mutations to architectural regulators themselves. Our analysis of the literature reveals that deregulation of genome structure is characteristic in distinct classes of chromatin-driven tumors. As we begin to integrate the findings from single cell imaging studies and chromatin structural sequencing, we will be able to understand the diversity of cells within a common diagnosis, and begin to define structure–function relationships of the misfolded genome.

CRISPRthripsis: The Risk of CRISPR/Cas9-induced Chromothripsis in Gene Therapy

The Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)/Cas9 nuclease system has allowed the generation of disease models and the development of therapeutic approaches for many genetic and non-genetic disorders. However, the generation of large genomic rearrangements has raised safety concerns for the clinical application of CRISPR/Cas9 nuclease approaches. Among these events, the formation of micronuclei and chromosome bridges due to chromosomal truncations can lead to massive genomic rearrangements localized to one or few chromosomes. This phenomenon, known as chromothripsis, was originally described in cancer cells, where it is believed to be caused by defective chromosome segregation during mitosis or DNA double-strand breaks. Here, we will discuss the factors influencing CRISPR/Cas9-induced chromothripsis, hereafter termed CRISPRthripsis, and its outcomes, the tools to characterize these events and strategies to minimize them.

Bioinformatics: From NGS Data to Biological Complexity in Variant Detection and Oncological Clinical Practice

The use of next-generation sequencing (NGS) techniques for variant detection has become increasingly important in clinical research and in clinical practice in oncology. Many cancer patients are currently being treated in clinical practice or in clinical trials with drugs directed against specific genomic alterations. In this scenario, the development of reliable and reproducible bioinformatics tools is essential to derive information on the molecular characteristics of each patient’s tumor from the NGS data. The development of bioinformatics pipelines based on the use of machine learning and statistical methods is even more relevant for the determination of complex biomarkers. In this review, we describe some important technologies, computational algorithms and models that can be applied to NGS data from Whole Genome to Targeted Sequencing, to address the problem of finding complex cancer-associated biomarkers. In addition, we explore the future perspectives and challenges faced by bioinformatics for precision medicine both at a molecular and clinical level, with a focus on an emerging complex biomarker such as homologous recombination deficiency (HRD).

Structural Variation in Cancer: Role, Prevalence, and Mechanisms

Somatic rearrangements resulting in genomic structural variation drive malignant phenotypes by altering the expression or function of cancer genes. Pan-cancer studies have revealed that structural variants (SVs) are the predominant class of driver mutation in most cancer types, but because they are difficult to discover, they remain understudied when compared with point mutations. This review provides an overview of the current knowledge of somatic SVs, discussing their primary roles, prevalence in different contexts, and mutational mechanisms. SVs arise throughout the life history of cancer, and 55% of driver mutations uncovered by the Pan-Cancer Analysis of Whole Genomes project represent SVs. Leveraging the convergence of cell biology and genomics, we propose a mechanistic classification of somatic SVs, from simple to highly complex DNA rearrangement classes. The actions of DNA repair and DNA replication processes together with mitotic errors result in a rich spectrum of SV formation processes, with cascading effects mediating extensive structural diversity after an initiating DNA lesion has formed. Thanks to new sequencing technologies, including the sequencing of single-cell genomes, open questions about the molecular triggers and the biomolecules involved in SV formation as well as their mutational rates can now be addressed.

Expected final online publication date for the Annual Review of Genomics and Human Genetics, Volume 23 is October 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.