Chromosome structural variation in tumorigenesis: mechanisms of formation and carcinogenesis

Role of homeobox genes in cancer: immune system interactions, long non-coding RNAs, and tumor progression

The intricate interplay between Homeobox genes, long non-coding RNAs (lncRNAs), and the development of malignancies represents a rapidly expanding area of research. Specific discernible lncRNAs have been discovered to adeptly regulate HOX gene expression in the context of cancer, providing fresh insights into the molecular mechanisms that govern cancer development and progression. An in-depth comprehension of these intricate associations may pave the way for innovative therapeutic strategies in cancer treatment. The HOX gene family is garnering increasing attention due to its involvement in immune system regulation, interaction with long non-coding RNAs, and tumor progression. Although initially recognized for its crucial role in embryonic development, this comprehensive exploration of the world of HOX genes contributes to our understanding of their diverse functions, potentially leading to immunology, developmental biology, and cancer research discoveries. Thus, the primary objective of this review is to delve into these aspects of HOX gene biology in greater detail, shedding light on their complex functions and potential therapeutic applications.

Patterns of Aneuploidy and Signaling Consequences in Cancer

Aneuploidy, or a change in the number of whole chromosomes or chromosome arms, is a near-universal feature of cancer. Chromosomes affected by aneuploidy are not random, with observed cancer-specific and tissue-specific patterns. Recent advances in genome engineering methods have allowed the creation of models with targeted aneuploidy events. These models can be used to uncover the downstream effects of individual aneuploidies on cancer phenotypes including proliferation, apoptosis, metabolism, and immune signaling. Here, we review the current state of research into the patterns of aneuploidy in cancer and their impact on signaling pathways and biological processes.

Full-spectral genome analysis of natural killer/T cell lymphoma highlights impacts of genome instability in driving its progression

BACKGROUND

Natural killer/T cell lymphoma (NKTCL) is a clinically and genetically heterogeneous disease with poor prognosis. Genome sequencing and mutation characterization provides a powerful approach for patient stratification, treatment target discovery, and etiology identification. However, previous studies mostly concentrated on base-level mutations in primary NKTCL, whereas the large-scale genomic alterations in NKTCL and the mutational landscapes in relapsed/refractory NKTCL remain largely unexplored.

METHODS

Here, we assembled whole-genome sequencing and whole-exome sequencing data from 163 patients with primary or relapsed/refractory NKTCL and compared their somatic mutational landscapes at both nucleotide and structure levels.

RESULTS

Our study not only confirmed previously reported common NKTCL mutational targets like STAT3, TP53, and DDX3X but also unveiled several novel high-frequency mutational targets such as PRDM9, DST, and RBMX. In terms of the overall mutational landscape, we observed striking differences between primary and relapsed/refractory NKTCL patient groups, with the latter exhibits higher levels of tumor mutation burden, copy number variants (CNVs), and structural variants (SVs), indicating a strong signal of genomic instability. Complex structural rearrangements such as chromothripsis and focal amplification are also significantly enriched in relapsed/refractory NKTCL patients, exerting a substantial impact on prognosis. Accordingly, we devised a novel molecular subtyping system (i.e., C0-C4) with distinct prognosis by integrating potential driver mutations at both nucleotide and structural levels, which further provides an informative guidance for novel treatments that target these specific driver mutations and genome instability as a whole.

CONCLUSIONS

The striking differences underlying the mutational landscapes between the primary and relapsed/refractory NKTCL patients highlight the importance of genomic instability in driving the progression of NKTCL. Our newly proposed molecular subtyping system is valuable in assisting patient stratification and novel treatment design towards a better prognosis in the age of precision medicine.

Functional Materials for Subcellular Targeting Strategies in Cancer Therapy: Progress and Prospects

Neoadjuvant and adjuvant therapies have made significant progress in cancer treatment. However, tumor adjuvant therapy still faces challenges due to the intrinsic heterogeneity of cancer, genomic instability, and the formation of an immunosuppressive tumor microenvironment. Functional materials possess unique biological properties such as long circulation times, tumor-specific targeting, and immunomodulation. The combination of functional materials with natural substances and nanotechnology has led to the development of smart biomaterials with multiple functions, high biocompatibilities, and negligible immunogenicities, which can be used for precise cancer treatment. Recently, subcellular structure-targeting functional materials have received particular attention in various biomedical applications including the diagnosis, sensing, and imaging of tumors and drug delivery. Subcellular organelle-targeting materials can precisely accumulate therapeutic agents in organelles, considerably reduce the threshold dosages of therapeutic agents, and minimize drug-related side effects. This review provides a systematic and comprehensive overview of the research progress in subcellular organelle-targeted cancer therapy based on functional nanomaterials. Moreover, it explains the challenges and prospects of subcellular organelle-targeting functional materials in precision oncology. The review will serve as an excellent cutting-edge guide for researchers in the field of subcellular organelle-targeted cancer therapy.

LPAD: using network construction and label propagation to detect topologically associating domains from Hi-C data

With the development of chromosome conformation capture technique, the study of spatial conformation of a genome based on Hi-C technique has made a quantum leap. Previous studies reveal that genomes are folded into hierarchy of three-dimensional (3D) structures associated with topologically associating domains (TADs), and detecting TAD boundaries is of great significance in the chromosome-level analysis of 3D genome architecture. In this paper, we propose a novel TAD identification method, LPAD, which first extracts node correlations from global interactions of chromosomes based on the random walk with restart and then builds an undirected graph from Hi-C contact matrix. Next, LPAD designs a label propagation-based approach to discover communities and generates TADs. Experimental results verify the effectiveness and quality of TAD detections compared with existing methods. Furthermore, experimental evaluation of chromatin immunoprecipitation sequencing data shows that LPAD performs high enrichment of histone modifications remarkably nearby the TAD boundaries, and these results demonstrate LPAD's advantages on TAD identification accuracy.

Molecular hallmarks of long non-coding RNAs in aging and its significant effect on aging-associated diseases

Aging is linked to the deterioration of many physical and cognitive abilities and is the leading risk factor for Alzheimer's disease. The growing aging population is a significant healthcare problem globally that researchers must investigate to better understand the underlying aging processes. Advances in microarrays and sequencing techniques have resulted in deeper analyses of diverse essential genomes (e.g., mouse, human, and rat) and their corresponding cell types, their organ-specific transcriptomes, and the tissue involved in aging. Traditional gene controllers such as DNA- and RNA-binding proteins significantly influence such programs, causing the need to sort out long non-coding RNAs, a new class of powerful gene regulatory elements. However, their functional significance in the aging process and senescence has yet to be investigated and identified. Several recent researchers have associated the initiation and development of senescence and aging in mammals with several well-reported and novel long non-coding RNAs. In this review article, we identified and analyzed the evolving functions of long non-coding RNAs in cellular processes, including cellular senescence, aging, and age-related pathogenesis, which are the major hallmarks of long non-coding RNAs in aging.

Towards routine chromosome-scale haplotype-resolved reconstruction in cancer genomics

Cancer genomes are highly complex and heterogeneous. The standard short-read sequencing and analytical methods are unable to provide the complete and precise base-level structural variant landscape of cancer genomes. In this work, we apply high-resolution long accurate HiFi and long-range Hi-C sequencing to the melanoma COLO829 cancer line. Also, we develop an efficient graph-based approach that processes these data types for chromosome-scale haplotype-resolved reconstruction to characterise the cancer precise structural variant landscape. Our method produces high-quality phased scaffolds on the chromosome level on three healthy samples and the COLO829 cancer line in less than half a day even in the absence of trio information, outperforming existing state-of-the-art methods. In the COLO829 cancer cell line, here we show that our method identifies and characterises precise somatic structural variant calls in important repeat elements that were missed in short-read-based call sets. Our method also finds the precise chromosome-level structural variant (germline and somatic) landscape with 19,956 insertions, 14,846 deletions, 421 duplications, 52 inversions and 498 translocations at the base resolution. Our simple pstools approach should facilitate better personalised diagnosis and disease management, including predicting therapeutic responses.

Characterizing chromatin interactions of regulatory elements and nucleosome positions, using Hi-C, Micro-C, and promoter capture Micro-C

BACKGROUND

Regulatory elements such as promoters, enhancers, and insulators interact each other to mediate molecular processes. To capture chromatin interactions of regulatory elements, 3C-derived methods such as Hi-C and Micro-C are developed. Here, we generated and analyzed Hi-C, Micro-C, and promoter capture Micro-C datasets with different sequencing depths to study chromatin interactions of regulatory elements and nucleosome positions in human prostate cancer cells.

RESULTS

Compared to Hi-C, Micro-C identifies more high-resolution loops, including ones around structural variants. By evaluating the effect of sequencing depth, we revealed that more than 2 billion reads of Micro-C are needed to detect chromatin interactions at 1 kb resolution. Moreover, we found that deep-sequencing identifies additional long-range loops that are longer than 1 Mb in distance. Furthermore, we found that more than 50% of the loops are involved in insulators while less than 10% of the loops are promoter-enhancer loops. To comprehensively capture chromatin interactions that promoters are involved in, we performed promoter capture Micro-C. Promoter capture Micro-C identifies loops near promoters with a lower amount of sequencing reads. Sequencing of 160 million reads of promoter capture Micro-C resulted in reaching a plateau of identifying loops. However, there was still a subset of promoters that are not involved in loops even after deep-sequencing. By integrating Micro-C with NOMe-seq and ChIP-seq, we found that active promoters involved in loops have a more accessible region with lower levels of DNA methylation and more highly phased nucleosomes, compared to active promoters that are not involved in loops.

CONCLUSION

We determined the required sequencing depth for Micro-C and promoter capture Micro-C to generate high-resolution chromatin interaction maps and loops. We also investigated the effect of sequencing coverage of Hi-C, Micro-C, and promoter capture Micro-C on detecting chromatin loops. Our analyses suggest the presence of distinct regulatory element groups, which are differently involved in nucleosome positions and chromatin interactions. This study does not only provide valuable insights on understanding chromatin interactions of regulatory elements, but also present guidelines for designing research projects on chromatin interactions among regulatory elements.

Effects of Human Papilloma Virus E6/E7 Oncoproteins on Genomic Structure in Head and Neck Squamous Cell Carcinoma

Human Papilloma Virus (HPV) is highly prevalent within the U.S., with studies estimating that over 80% of individuals will contract the virus in their lifetime. HPV is considered a primary risk factor for the development and progression of oropharyngeal cancers. The impact of the HPV virus's E6 and E7 oncoproteins on cellular signaling pathways and genomic integration has been extensively characterized. Indirect genomic effects; however, remain relatively unidentified. In this study, we analyzed 83 HPV+ Head and Neck Squamous Cell Carcinoma (HNSCC) patients of varying HPV types. Expression counts of the HPV E6 and E7 oncogenes were estimated across samples and correlated with genomic mutational classes. High expression of E6 and E7 oncoproteins was associated with a greater number of total point mutations, especially on chromosomes 1, 11, and 17, which have been implicated in HPV-mediated cancers in previous studies. Samples with high E6 and E7 expression also exhibited more frequent non-clustered structural variation and a lack of clustered variation altogether. Copy number segments were present with fewer number of repeats in high E6 and E7 expression samples, which is known to correlate with decreased expression of affected genes. E6 and E7 expression was associated with increased activity of several cellular pathways associated in oncogenesis and telomere maintenance. In comprehensively characterizing the effects of the HPV oncoproteins on the human genome, potential mechanisms of HNSCC pathogenesis may be further elucidated.

KAT5 histone acetyltransferase mutations in cancer cells

Cancer cells are characterized by accumulation of mutations due to improperly repaired DNA damage. The DNA double strand break is one of the most severe form of damage and several redundant mechanisms have evolved to facilitate accurate repair. During DNA replication and in mitosis, breaks are primarily repaired by homologous recombination which is facilitated by several genes. Key to this process is the breast cancer susceptibility genes BRCA1 and BRCA2 as well as the accessory RAD52 gene. Proper chromatin remodeling is also essential for repair and the KAT5 histone acetyltransferase facilitates histone removal at the break. Here we undertook a pan cancer analysis to investigate mutations within the KAT5 gene in cancer cells. We employed two standard artificial algorithms to classify mutations as either driver (CHASMPlus algorithm) or pathogenic (VEST4 algorithm). We find that most predicted driver and disease-causing mutations occur in the catalytic site or within key regulatory domains. In silico analysis of protein structure using AlphaFold shows that these mutations are likely to destabilize the function of KAT5 or interactions with DNA or its other partners. The data presented here, although preliminary, could be used to inform clinical strategies.

Modeling tissue-specific breakpoint proximity of structural variations from whole-genomes to identify cancer drivers

Structural variations (SVs) in cancer cells often impact large genomic regions with functional consequences. However, identification of SVs under positive selection is a challenging task because little is known about the genomic features related to the background breakpoint distribution in different cancers. We report a method that uses a generalized additive model to investigate the breakpoint proximity curves from 2,382 whole-genomes of 32 cancer types. We find that a multivariate model, which includes linear and nonlinear partial contributions of various tissue-specific features and their interaction terms, can explain up to 57% of the observed deviance of breakpoint proximity. In particular, three-dimensional genomic features such as topologically associating domains (TADs), TAD-boundaries and their interaction with other features show significant contributions. The model is validated by identification of known cancer genes and revealed putative drivers in cancers different than those with previous evidence of positive selection.

PerSVade: personalized structural variant detection in any species of interest

Structural variants (SVs) underlie genomic variation but are often overlooked due to difficult detection from short reads. Most algorithms have been tested on humans, and it remains unclear how applicable they are in other organisms. To solve this, we develop perSVade (personalized structural variation detection), a sample-tailored pipeline that provides optimally called SVs and their inferred accuracy, as well as small and copy number variants. PerSVade increases SV calling accuracy on a benchmark of six eukaryotes. We find no universal set of optimal parameters, underscoring the need for sample-specific parameter optimization. PerSVade will facilitate SV detection and study across diverse organisms.

Unravelling the tumour genome: The evolutionary and clinical impacts of structural variants in tumourigenesis

Structural variants (SVs) represent a major source of aberration in tumour genomes. Given the diversity in the size and type of SVs present in tumours, the accurate detection and interpretation of SVs in tumours is challenging. New classes of complex structural events in tumours are discovered frequently, and the definitions of the genomic consequences of complex events are constantly being refined. Detailed analyses of short-read whole-genome sequencing (WGS) data from large tumour cohorts facilitate the interrogation of SVs at orders of magnitude greater scale and depth. However, the inherent technical limitations of short-read WGS prevent us from accurately detecting and investigating the impact of all the SVs present in tumours. The expanded use of long-read WGS will be critical for improving the accuracy of SV detection, and in fully resolving complex SV events, both of which are crucial for determining the impact of SVs on tumour progression and clinical outcome. Despite the present limitations, we demonstrate that SVs play an important role in tumourigenesis. In particular, SVs contribute significantly to late-stage tumour development and to intratumoural heterogeneity. The evolutionary trajectories of SVs represent a window into the clonal dynamics in tumours, a comprehensive understanding of which will be vital for influencing patient outcomes in the future. Recent findings have highlighted many clinical applications of SVs in cancer, from early detection to biomarkers for treatment response and prognosis. As the methods to detect and interpret SVs improve, elucidating the full breadth of the complex SV landscape and determining how these events modulate tumour evolution will improve our understanding of cancer biology and our ability to capitalise on the utility of SVs in the clinical management of cancer patients. © 2022 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Geotemporospatial and causal inferential epidemiological overview and survey of USA cannabis, cannabidiol and cannabinoid genotoxicity expressed in cancer incidence 2003-2017: part 3 - spatiotemporal, multivariable and causal inferential pathfinding and exploratory analyses of prostate and ovarian cancers

BACKGROUND

The epidemiology of cannabinoid-related cancerogenesis has not been studied with cutting edge epidemiological techniques. Building on earlier bivariate papers in this series we aimed to conduct pathfinding studies to address this gap in two tumours of the reproductive tract, prostate and ovarian cancer.

METHODS

Age-standardized cancer incidence data for 28 tumour types (including "All (non-skin) Cancer") was sourced from Centres for Disease Control and National Cancer Institute using SEER*Stat software across US states 2001-2017. Drug exposure was sourced from the nationally representative household survey National Survey of Drug Use and Health conducted annually by the Substance Abuse and Mental Health Services Administration 2003-2017 with response rate 74.1%. Federal seizure data provided cannabinoid concentration data. US Census Bureau provided income and ethnicity data. Inverse probability weighted mixed effects, robust and panel regression together with geospatiotemporal regression analyses were conducted in R. E-Values were also calculated.

RESULTS

19,877 age-standardized cancer rates were returned. Based on these rates and state populations this equated to 51,623,922 cancer cases over an aggregated population 2003-2017 of 124,896,418,350. Inverse probability weighted regressions for prostate and ovarian cancers confirmed causal associations robust to adjustment. Cannabidiol alone was significantly associated with prostate cancer (β-estimate = 1.61, (95%C.I. 0.99, 2.23), P = 3.75 × 10- 7). In a fully adjusted geospatiotemporal model at one spatial and two temporal years lags cannabidiol was significantly independently associated with prostate cancer (β-estimate = 2.08, (1.19, 2.98), P = 5.20 × 10- 6). Cannabidiol alone was positively associated with ovarian cancer incidence in a geospatiotemporal model (β-estimate = 0.36, (0.30, 0.42), P <  2.20 × 10- 16). The cigarette: THC: cannabidiol interaction was significant in a fully adjusted geospatiotemporal model at six years of temporal lag (β-estimate = 1.93, (1.07, 2.78), P = 9.96 × 10- 6). Minimal modelled polynomial E-Values for prostate and ovarian cancer ranged up to 5.59 × 1059 and 1.92 × 10125. Geotemporospatial modelling of these tumours showed that the cannabidiol-carcinogenesis relationship was supra-linear and highly sigmoidal (P = 1.25 × 10- 45 and 12.82 × 10- 52 for linear v. polynomial models).

CONCLUSION

Cannabinoids including THC and cannabidiol are therefore important community carcinogens additive to the effects of tobacco and greatly exceeding those of alcohol. Reproductive tract carcinogenesis necessarily implies genotoxicity and epigenotoxicity of the germ line with transgenerational potential. Pseudoexponential and causal dose-response power functions are demonstrated.

How to train your cell - Towards controlling phenotypes by harnessing the epigenome of Chinese hamster ovary production cell lines

Recent advances in omics technologies and the broad availability of big datasets have revolutionized our understanding of Chinese hamster ovary cells in their role as the most prevalent host for production of complex biopharmaceuticals. In consequence, our perception of this "workhorse of the biopharmaceutical industry" has successively shifted from that of a nicely working, but unknown recombinant protein producing black box to a biological system governed by multiple complex regulatory layers that might possibly be harnessed and manipulated at will. Despite the tremendous progress that has been made to characterize CHO cells on various omics levels, our understanding is still far from complete. The well-known inherent genetic plasticity of any immortalized and rapidly dividing cell line also characterizes CHO cells and can lead to problematic instability of recombinant protein production. While the high mutational frequency has been a focus of CHO cell research for decades, the impact of epigenetics and its role in differential gene expression has only recently been addressed. In this review we provide an overview about the current understanding of epigenetic regulation in CHO cells and discuss its significance for shaping the cell's phenotype. We also look into current state-of-the-art technology that can be applied to harness and manipulate the epigenetic network so as to nudge CHO cells towards a specific phenotype. Here, we revise current strategies on site-directed integration and random as well as targeted epigenome modifications. Finally, we address open questions that need to be investigated to exploit the full repertoire of fine-tuned control of multiplexed gene expression using epigenetic and systems biology tools.

DriverFuse: An R package for analysis of next-generation sequencing datasets to identify cancer driver fusion genes

We developed the DriverFuse package to integrate orthogonal data types such as Structural Variants (SV) and Copy Number Variations (CNV) to characterize fusion genes in cancer datasets. A fusion gene is reported as a driver or passenger fusion gene, based on mapping SV and CNV profiles. DriverFuse generates a fusion plot of fusion genes with their mapping SV, CNV profile, domain architecture and classification of its role in cancer. The analysis facilitates discrimination of driver fusions from passenger fusions. To demonstrate the utility of DriverFuse, we analyzed two datasets, one each for CCLE (Cancer Cell Line Encyclopedia) for lung cancer and HCC1395BL for breast cancer. The analysis validates the driver fusion genes that are already reported for the datasets. Thus, DriverFuse is a valuable tool for studying the driver fusion genes in cancers, enabling the identification of recurrent complex rearrangements that provide intuitive insights into disease driver events.

Distribution of copy number variations and rearrangement endpoints in human cancers with a review of literature

Copy number variations (CNVs) which include deletions, duplications, inversions, translocations, and other forms of chromosomal re-arrangements are common to human cancers. In this report we investigated the pattern of these variations with the goal of understanding whether there exist specific cancer signatures. We used re-arrangement endpoint data deposited on the Catalogue of Somatic Mutations in Cancers (COSMIC) for our analysis. Indeed, we find that human cancers are characterized by specific patterns of chromosome rearrangements endpoints which in turn result in cancer specific CNVs. A review of the literature reveals tissue specific mutations which either drive these CNVs or appear as a consequence of CNVs because they confer an advantage to the cancer cell. We also identify several rearrangement endpoints hotspots that were not previously reported. Our analysis suggests that in addition to local chromosomal architecture, CNVs are driven by the internal cellular or nuclear physiology of each cancer tissue.

3D disorganization and rearrangement of genome provide insights into pathogenesis of NAFLD by integrated Hi-C, Nanopore, and RNA sequencing

The three-dimensional (3D) conformation of chromatin is integral to the precise regulation of gene expression. The 3D genome and genomic variations in non-alcoholic fatty liver disease (NAFLD) are largely unknown, despite their key roles in cellular function and physiological processes. High-throughput chromosome conformation capture (Hi-C), Nanopore sequencing, and RNA-sequencing (RNA-seq) assays were performed on the liver of normal and NAFLD mice. A high-resolution 3D chromatin interaction map was generated to examine different 3D genome hierarchies including A/B compartments, topologically associated domains (TADs), and chromatin loops by Hi-C, and whole genome sequencing identifying structural variations (SVs) and copy number variations (CNVs) by Nanopore sequencing. We identified variations in thousands of regions across the genome with respect to 3D chromatin organization and genomic rearrangements, between normal and NAFLD mice, and revealed gene dysregulation frequently accompanied by these variations. Candidate target genes were identified in NAFLD, impacted by genetic rearrangements and spatial organization disruption. Our data provide a high-resolution 3D genome interaction resource for NAFLD investigations, revealed the relationship among genetic rearrangements, spatial organization disruption, and gene regulation, and identified candidate genes associated with these variations implicated in the pathogenesis of NAFLD. The newly findings offer insights into novel mechanisms of NAFLD pathogenesis and can provide a new conceptual framework for NAFLD therapy.

Effects of low dose and low dose rate low linear energy transfer radiation on animals - review of recent studies relevant for carcinogenesis

Purpose: Carcinogenic effects of radiation are often assumed to be universally understood, more often than, for example, carcinogenic effects of many different chemicals. This in turn leads to an assumption that any dose of radiation, delivered at any dose rate, poses a serious health challenge. This remains an issue of dispute and low dose radiation research is focused on understanding whether these exposures contribute to cancer incidence. This review is focused on the low linear energy transfer (low LET) radiation exposures for which the data is the most abundant in recent years. Materials and methods: Review of the literature between 2008 and today, highlighting some of the most diverse studies in low dose research. Results: Low dose and low dose rate, low LET ionizing radiation animal studies suggest that the effects of exposure very much depend on animal genotype and health status.Conclusions: Only the integration of all of the data from different models and studies will lead to a fuller understanding of low dose radiation effects. Therefore, we hope to see an increase in international archival efforts and exchange of raw data information opening the possibilities for new types of meta analyses.

Cancer cells employ an evolutionarily conserved polyploidization program to resist therapy

Unusually large cancer cells with abnormal nuclei have been documented in the cancer literature since 1858. For more than 100 years, they have been generally disregarded as irreversibly senescent or dying cells, too morphologically misshapen and chromatin too disorganized to be functional. Cell enlargement, accompanied by whole genome doubling or more, is observed across organisms, often associated with mitigation strategies against environmental change, severe stress, or the lack of nutrients. Our comparison of the mechanisms for polyploidization in other organisms and non-transformed tissues suggest that cancer cells draw from a conserved program for their survival, utilizing whole genome doubling and pausing proliferation to survive stress. These polyaneuploid cancer cells (PACCs) are the source of therapeutic resistance, responsible for cancer recurrence and, ultimately, cancer lethality.