Toward the functional interpretation of somatic structural variations: bulk- and single-cell approaches

Benchmarking long-read structural variant calling tools and combinations for detecting somatic variants in cancer genomes

Cancer genomes have a complicated landscape of mutations, including large-scale rearrangements known as structural variants (SVs). These SVs can disrupt genes or regulatory elements, playing a critical role in cancer development and progression. Despite their importance, accurate identification of somatic structural variants (SVs) remains a significant bottleneck in cancer genomics. Long-read sequencing technologies hold great promise in SV discovery, and there is an increasing number of efforts to develop new tools to detect them. In this study, we employ eight widely used SV callers on paired tumor and matched normal samples from both the NCI-H2009 lung cancer cell line and the COLO829 melanoma cell line, the latter of which has a well-established somatic SV truth set. Following separate variation detection in both tumor and normal DNA, the VCF merging procedure and a subtraction method were used to identify candidate somatic SVs. Additionally, we explored different combinations of the tools to enhance the accuracy of true somatic SV detection. Our analysis adopts a comprehensive approach, evaluating the performance of each SV caller across a spectrum of variant types and numbers in finding cancer-related somatic SVs. This study, by comparing eight different tools and their combinations, not only reveals the benefits and limitations of various techniques but also establishes a framework for developing more robust SV calling pipelines. Our findings highlight the strengths and weaknesses of current SV calling tools and suggest that combining multiple tools and testing different combinations can significantly enhance the validation of somatic alterations.

SVCFit: Inferring structural variant cellular fraction in tumors

Many tumors evolve through cellular mutation and selection, where subpopulations of cells (subclones) with shared ancestry compete for dominance. Introduction of next generation sequencing enables subclone identification using small somatic. However, there are several advantages to marking subclones with structural variants: they have greater functional impact, play a crucial role in late-stage tumors, and provide a more complete view of genomic instability driving tumor evolution. Here, we present SVCFit, a scalable method to estimate the cellular prevalence of somatic deletions, duplications and inversions. We demonstrate that cellular prevalence estimation can be improved by incorporating distinct read patterns for each structural variant type. Additionally, this improvement is achieved without prior knowledge of tumor purity, which is often inaccurate. Using a combination of simulated data and patient-derived metastatic samples with known mixture proportions, we show that our algorithm achieves significantly greater accuracy than state-of-the-art in estimating the structural variants cellular prevalence (p<0.05). The speed of SVCFit estimation from cost-effective bulk whole-genome sequencing (WGS) makes it well-suited for analyzing large cohorts of sequenced tumor samples, enhancing the accessibility of SV-based clonal reconstruction.

Replication stress increases de novo CNVs across the malaria parasite genome

Changes in the copy number of large genomic regions, termed copy number variations (CNVs), contribute to important phenotypes in many organisms. CNVs are readily identified using conventional approaches when present in a large fraction of the cell population. However, CNVs that are present in only a few genomes across a population are often overlooked but important; if beneficial under specific conditions, a de novo CNV that arises in a single genome can expand during selection to create a larger population of cells with novel characteristics. While the reach of single cell methods to study de novo CNVs is increasing, we continue to lack information about CNV dynamics in rapidly evolving microbial populations. Here, we investigated de novo CNVs in the genome of the Plasmodium parasite that causes human malaria. The highly AT-rich P. falciparum genome readily accumulates CNVs that facilitate rapid adaptation to new drugs and host environments. We employed a low-input genomics approach optimized for this unique genome as well as specialized computational tools to evaluate the de novo CNV rate both before and after the application of stress. We observed a significant increase in genomewide de novo CNVs following treatment with a replication inhibitor. These stress-induced de novo CNVs encompassed genes that contribute to various cellular pathways and tended to be altered in clinical parasite genomes. This snapshot of CNV dynamics emphasizes the connection between replication stress, DNA repair, and CNV generation in this important microbial pathogen.

Best practices for germline variant and DNA methylation analysis of second- and third-generation sequencing data

This comprehensive review provides insights and suggested strategies for the analysis of germline variants using second- and third-generation sequencing technologies (SGS and TGS). It addresses the critical stages of data processing, starting from alignment and preprocessing to quality control, variant calling, and the removal of artifacts. The document emphasized the importance of meticulous data handling, highlighting advanced methodologies for annotating variants and identifying structural variations and methylated DNA sites. Special attention is given to the inspection of problematic variants, a step that is crucial for ensuring the accuracy of the analysis, particularly in clinical settings where genetic diagnostics can inform patient care. Additionally, the document covers the use of various bioinformatics tools and software that enhance the precision and reliability of these analyses. It outlines best practices for the annotation of variants, including considerations for problematic genetic alterations such as those in the human leukocyte antigen region, runs of homozygosity, and mitochondrial DNA alterations. The document also explores the complexities associated with identifying structural variants and copy number variations, underscoring the challenges posed by these large-scale genomic alterations. The objective is to offer a comprehensive framework for researchers and clinicians, ensuring that genetic analyses conducted with SGS and TGS are both accurate and reproducible. By following these best practices, the document aims to increase the diagnostic accuracy for hereditary diseases, facilitating early diagnosis, prevention, and personalized treatment strategies. This review serves as a valuable resource for both novices and experts in the field, providing insights into the latest advancements and methodologies in genetic analysis. It also aims to encourage the adoption of these practices in diverse research and clinical contexts, promoting consistency and reliability across studies.

The axis of tumor-associated macrophages, extracellular matrix proteins, and cancer-associated fibroblasts in oncogenesis

The extracellular matrix (ECM) is a complex, dynamic network of multiple macromolecules that serve as a crucial structural and physical scaffold for neighboring cells. In the tumor microenvironment (TME), ECM proteins play a significant role in mediating cellular communication between cancer-associated fibroblasts (CAFs) and tumor-associated macrophages (TAMs). Revealing the ECM modification of the TME necessitates the intricate signaling cascades that transpire among diverse cell populations and ECM proteins. The advent of single-cell sequencing has enabled the identification and refinement of specific cellular subpopulations, which has substantially enhanced our comprehension of the intricate milieu and given us a high-resolution perspective on the diversity of ECM proteins. However, it is essential to integrate single-cell data and establish a coherent framework. In this regard, we present a comprehensive review of the relationships among ECM, TAMs, and CAFs. This encompasses insights into the ECM proteins released by TAMs and CAFs, signaling integration in the TAM-ECM-CAF axis, and the potential applications and limitations of targeted therapies for CAFs. This review serves as a reliable resource for focused therapeutic strategies while highlighting the crucial role of ECM proteins as intermediates in the TME.

Optical Genome Mapping as a Tool to Unveil New Molecular Findings in Hematological Patients with Complex Chromosomal Rearrangements

Standard cytogenetic techniques (chromosomal banding analysis-CBA, and fluorescence in situ hybridization-FISH) show limits in characterizing complex chromosomal rearrangements and structural variants arising from two or more chromosomal breaks. In this study, we applied optical genome mapping (OGM) to fully characterize two cases of complex chromosomal rearrangements at high resolution. In case 1, an acute myeloid leukemia (AML) patient showing chromothripsis, OGM analysis was fully concordant with classic cytogenetic techniques and helped to better refine chromosomal breakpoints. The OGM results of case 2, a patient with non-Hodgkin lymphoma, were only partially in agreement with previous cytogenetic analyses and helped to better define clonal heterogeneity, overcoming the bias related to clonal selection due to cell culture of cytogenetic techniques. In both cases, OGM analysis led to the identification of molecular markers, helping to define the pathogenesis, classification, and prognosis of the analyzed patients. Despite extensive efforts to study hematologic diseases, standard cytogenetic methods display unsurmountable limits, while OGM is a tool that has the power to overcome these limitations and provide a cytogenetic analysis at higher resolution. As OGM also shows limits in defining regions of a repetitive nature, combining OGM with CBA to obtain a complete cytogenetic characterization would be desirable.