Chromatin accessibility contributes to simultaneous mutations of cancer genes

3D genome contributes to MHC-II neoantigen prediction

Reliable and ultra-fast DNA and RNA sequencing have been achieved with the emergence of high-throughput sequencing technology. When combining the results of DNA and RNA sequencing for tumor cells of cancer patients, neoantigens that potentially stimulate the immune response of either CD4+ or CD8+ T cells can be identified. However, due to the abundance of somatic mutations and the high polymorphic nature of human leukocyte antigen (HLA) it is challenging to accurately predict the neoantigens. Moreover, comparing to HLA-I presented peptides, the HLA-II presented peptides are more variable in length, making the prediction of HLA-II loaded neoantigens even harder. A number of computational approaches have been proposed to address this issue but none of them considers the DNA origin of the neoantigens from the perspective of 3D genome. Here we investigate the DNA origins of the immune-positive and non-negative HLA-II neoantigens in the context of 3D genome and discovered that the chromatin 3D architecture plays an important role in more effective HLA-II neoantigen prediction. We believe that the 3D genome information will help to increase the precision of HLA-II neoantigen discovery and eventually benefit precision and personalized medicine in cancer immunotherapy.

Potential Role of DNA Methylation as a Driver of Plastic Responses to the Environment Across Cells, Organisms, and Populations

There is great interest in exploring epigenetic modifications as drivers of adaptive organismal responses to environmental change. Extending this hypothesis to populations, epigenetically driven plasticity could influence phenotypic changes across environments. The canonical model posits that epigenetic modifications alter gene regulation and subsequently impact phenotypes. We first discuss origins of epigenetic variation in nature, which may arise from genetic variation, spontaneous epimutations, epigenetic drift, or variation in epigenetic capacitors. We then review and synthesize literature addressing three facets of the aforementioned model: (i) causal effects of epigenetic modifications on phenotypic plasticity at the organismal level, (ii) divergence of epigenetic patterns in natural populations distributed across environmental gradients, and (iii) the relationship between environmentally induced epigenetic changes and gene expression at the molecular level. We focus on DNA methylation, the most extensively studied epigenetic modification. We find support for environmentally associated epigenetic structure in populations and selection on stable epigenetic variants, and that inhibition of epigenetic enzymes frequently bears causal effects on plasticity. However, there are pervasive confounding issues in the literature. Effects of chromatin-modifying enzymes on phenotype may be independent of epigenetic marks, alternatively resulting from functions and protein interactions extrinsic of epigenetics. Associations between environmentally induced changes in DNA methylation and expression are strong in plants and mammals but notably absent in invertebrates and nonmammalian vertebrates. Given these challenges, we describe emerging approaches to better investigate how epigenetic modifications affect gene regulation, phenotypic plasticity, and divergence among populations.

Associations between DNA methylation and gene regulation depend on chromatin accessibility during transgenerational plasticity

BACKGROUND

Epigenetic processes are proposed to be a mechanism regulating gene expression during phenotypic plasticity. However, environmentally induced changes in DNA methylation exhibit little-to-no association with differential gene expression in metazoans at a transcriptome-wide level. It remains unexplored whether associations between environmentally induced differential methylation and expression are contingent upon other epigenomic processes such as chromatin accessibility. We quantified methylation and gene expression in larvae of the purple sea urchin Strongylocentrotus purpuratus exposed to different ecologically relevant conditions during gametogenesis (maternal conditioning) and modeled changes in gene expression and splicing resulting from maternal conditioning as functions of differential methylation, incorporating covariates for genomic features and chromatin accessibility. We detected significant interactions between differential methylation, chromatin accessibility, and genic feature type associated with differential expression and splicing.

RESULTS

Differential gene body methylation had significantly stronger effects on expression among genes with poorly accessible transcriptional start sites while baseline transcript abundance influenced the direction of this effect. Transcriptional responses to maternal conditioning were 4-13 × more likely when accounting for interactions between methylation and chromatin accessibility, demonstrating that the relationship between differential methylation and gene regulation is partially explained by chromatin state.

CONCLUSIONS

DNA methylation likely possesses multiple associations with gene regulation during transgenerational plasticity in S. purpuratus and potentially other metazoans, but its effects are dependent on chromatin accessibility and underlying genic features.

Three-dimensional genome landscape comprehensively reveals patterns of spatial gene regulation in papillary and anaplastic thyroid cancers: a study using representative cell lines for each cancer type

BACKGROUND

Spatial chromatin structure is intricately linked with somatic aberrations, and somatic mutations of various cancer-related genes, termed co-mutations (CoMuts), occur in certain patterns during cancer initiation and progression. The functional mechanisms underlying these genetic events remain largely unclear in thyroid cancer (TC). With discrepant differentiation, papillary thyroid cancer (PTC) and anaplastic thyroid cancer (ATC) differ greatly in characteristics and prognosis. We aimed to reveal the spatial gene alterations and regulations between the two TC subtypes.

METHODS

We systematically investigated and compared the spatial co-mutations between ATC (8305C), PTC (BCPAP and TPC-1), and normal thyroid cells (Nthy-ori-3-1). We constructed a framework integrating whole-genome sequencing (WGS), high-throughput chromosome conformation capture (Hi-C), and transcriptome sequencing, to systematically detect the associations between the somatic co-mutations of cancer-related genes, structural variations (SVs), copy number variations (CNVs), and high-order chromatin conformation.

RESULTS

Spatial co-mutation hotspots were enriched around topologically associating domains (TADs) in TC. A common set of 227 boundaries were identified in both ATC and PTC, with significant overlaps between them. The spatial proximities of the co-mutated gene pairs in the two TC types were significantly greater than in the gene-level and overall backgrounds, and ATC cells had higher TAD contact frequency with CoMuts > 10 compared with PTC cells. Compared with normal thyroid cells, in ATC the number of the created novel three-dimensional chromatin structural domains increased by 10%, and the number of shifted TADs decreased by 7%. We found five TAD blocks with CoMut genes/events specific to ATC with certain mutations in genes including MAST-NSUN4, AM129B/TRUB2, COL5A1/PPP1R26, PPP1R26/GPSM1/CCDC183, and PRAC2/DLX4. For the majority of ATC and PTC cells, the HOXA10 and HIF2α signals close to the transcription start sites of CoMut genes within TADs were significantly stronger than those at the background. CNV breakpoints significantly overlapped with TAD boundaries in both TC subtypes. ATCs had more CNV losses overlapping with TAD boundaries, and noncoding SVs involved in intrachromosomal SVs, amplified inversions, and tandem duplication differed between ATC and PTC. TADs with short range were more abundant in ATC than PTC. More switches of A/B compartment types existed in ATC cells compared with PTC. Gene expression was significantly synchronized, and orchestrated by complex epigenetics and regulatory elements.

CONCLUSION

Chromatin interactions and gene alterations and regulations are largely heterogeneous in TC. CNVs and complex SVs may function in the TC genome by interplaying with TADs, and are largely different between ATC and PTC. Complexity of TC genomes, which are highly organized by 3D genome-wide interactions mediating mutational and structural variations and gene activation, may have been largely underappreciated. Our comprehensive analysis may provide key evidence and targets for more customized diagnosis and treatment of TC.

Pan-Cancer Genome-Wide DNA Methylation Analyses Revealed That Hypermethylation Influences 3D Architecture and Gene Expression Dysregulation in HOXA Locus During Carcinogenesis of Cancers

DNA methylation dysregulation during carcinogenesis has been widely discussed in recent years. However, the pan-cancer DNA methylation biomarkers and corresponding biological mechanisms were seldom investigated. We identified differentially methylated sites and regions from 5,056 The Cancer Genome Atlas (TCGA) samples across 10 cancer types and then validated the findings using 48 manually annotated datasets consisting of 3,394 samples across nine cancer types from Gene Expression Omnibus (GEO). All samples’ DNA methylation profile was evaluated with Illumina 450K microarray to narrow down the batch effect. Nine regions were identified as commonly differentially methylated regions across cancers in TCGA and GEO cohorts. Among these regions, a DNA fragment consisting of ∼1,400 bp detected inside the HOXA locus instead of the boundary may relate to the co-expression attenuation of genes inside the locus during carcinogenesis. We further analyzed the 3D DNA interaction profile by the publicly accessible Hi-C database. Consistently, the HOXA locus in normal cell lines compromised isolated topological domains while merging to the domain nearby in cancer cell lines. In conclusion, the dysregulation of the HOXA locus provides a novel insight into pan-cancer carcinogenesis.

DeepAntigen: a novel method for neoantigen prioritization via 3D genome and deep sparse learning

MOTIVATION

The mutations of cancers can encode the seeds of their own destruction, in the form of T-cell recognizable immunogenic peptides, also known as neoantigens. It is computationally challenging, however, to accurately prioritize the potential neoantigen candidates according to their ability of activating the T-cell immunoresponse, especially when the somatic mutations are abundant. Although a few neoantigen prioritization methods have been proposed to address this issue, advanced machine learning model that is specifically designed to tackle this problem is still lacking. Moreover, none of the existing methods considers the original DNA loci of the neoantigens in the perspective of 3D genome which may provide key information for inferring neoantigens' immunogenicity.

RESULTS

In this study, we discovered that DNA loci of the immunopositive and immunonegative MHC-I neoantigens have distinct spatial distribution patterns across the genome. We therefore used the 3D genome information along with an ensemble pMHC-I coding strategy, and developed a group feature selection-based deep sparse neural network model (DNN-GFS) that is optimized for neoantigen prioritization. DNN-GFS demonstrated increased neoantigen prioritization power comparing to existing sequence-based approaches. We also developed a webserver named deepAntigen (http://yishi.sjtu.edu.cn/deepAntigen) that implements the DNN-GFS as well as other machine learning methods. We believe that this work provides a new perspective toward more accurate neoantigen prediction which eventually contribute to personalized cancer immunotherapy.

AVAILABILITY AND IMPLEMENTATION

Data and implementation are available on webserver: http://yishi.sjtu.edu.cn/deepAntigen.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

The Origin of B-cells: Human Fetal B Cell Development and Implications for the Pathogenesis of Childhood Acute Lymphoblastic Leukemia

Human B-lymphopoiesis is a dynamic life-long process that starts in utero by around six post-conception weeks. A detailed understanding of human fetal B-lymphopoiesis and how it changes in postnatal life is vital for building a complete picture of normal B-lymphoid development through ontogeny, and its relevance in disease. B-cell acute lymphoblastic leukemia (B-ALL) is one of the most common cancers in children, with many of the leukemia-initiating events originating in utero. It is likely that the biology of B-ALL, including leukemia initiation, maintenance and progression depends on the developmental stage and type of B-lymphoid cell in which it originates. This is particularly important for early life leukemias, where specific characteristics of fetal B-cells might be key to determining how the disease behaves, including response to treatment. These cellular, molecular and/or epigenetic features are likely to change with age in a cell intrinsic and/or microenvironment directed manner. Most of our understanding of fetal B-lymphopoiesis has been based on murine data, but many recent studies have focussed on characterizing human fetal B-cell development, including functional and molecular assays at a single cell level. In this mini-review we will give a short overview of the recent advances in the understanding of human fetal B-lymphopoiesis, including its relevance to infant/childhood leukemia, and highlight future questions in the field.

A novel neoantigen discovery approach based on chromatin high order conformation

Background

High-throughput sequencing technology has yielded reliable and ultra-fast sequencing for DNA and RNA. For tumor cells of cancer patients, when combining the results of DNA and RNA sequencing, one can identify potential neoantigens that stimulate the immune response of the T cell. However, when the somatic mutations are abundant, it is computationally challenging to efficiently prioritize the identified neoantigen candidates according to their ability of activating the T cell immuno-response.

Methods

Numerous prioritization or prediction approaches have been proposed to address this issue but none of them considers the original DNA loci of the neoantigens from the perspective of 3D genome. Based on our previous discoveries, we propose to investigate the distribution of neoantigens with different immunogenicity abilities in 3D genome and propose to adopt this important information into neoantigen prediction.

Results

We retrospect the DNA origins of the immuno-positive and immuno-negative neoantigens in the context of 3D genome and discovered that DNA loci of the immuno-positive neoantigens and immuno-negative neoantigens have very different distribution pattern. Specifically, comparing to the background 3D genome, DNA loci of the immuno-positive neoantigens tend to locate at specific regions in the 3D genome. We thus used this information into neoantigen prediction and demonstrated the effectiveness of this approach.

Conclusion

We believe that the 3D genome information will help to increase the precision of neoantigen prioritization and discovery and eventually benefit precision and personalized medicine in cancer immunotherapy.

Benchmarking of computational error-correction methods for next-generation sequencing data

BACKGROUND

Recent advancements in next-generation sequencing have rapidly improved our ability to study genomic material at an unprecedented scale. Despite substantial improvements in sequencing technologies, errors present in the data still risk confounding downstream analysis and limiting the applicability of sequencing technologies in clinical tools. Computational error correction promises to eliminate sequencing errors, but the relative accuracy of error correction algorithms remains unknown.

RESULTS

In this paper, we evaluate the ability of error correction algorithms to fix errors across different types of datasets that contain various levels of heterogeneity. We highlight the advantages and limitations of computational error correction techniques across different domains of biology, including immunogenomics and virology. To demonstrate the efficacy of our technique, we apply the UMI-based high-fidelity sequencing protocol to eliminate sequencing errors from both simulated data and the raw reads. We then perform a realistic evaluation of error-correction methods.

CONCLUSIONS

In terms of accuracy, we find that method performance varies substantially across different types of datasets with no single method performing best on all types of examined data. Finally, we also identify the techniques that offer a good balance between precision and sensitivity.

Impact of mouse contamination in genomic profiling of patient-derived models and best practice for robust analysis

BACKGROUND

Patient-derived xenograft and cell line models are popular models for clinical cancer research. However, the inevitable inclusion of a mouse genome in a patient-derived model is a remaining concern in the analysis. Although multiple tools and filtering strategies have been developed to account for this, research has yet to demonstrate the exact impact of the mouse genome and the optimal use of these tools and filtering strategies in an analysis pipeline.

RESULTS

We construct a benchmark dataset of 5 liver tissues from 3 mouse strains using human whole-exome sequencing kit. Next-generation sequencing reads from mouse tissues are mappable to 49% of the human genome and 409 cancer genes. In total, 1,207,556 mouse-specific alleles are aligned to the human genome reference, including 467,232 (38.7%) alleles with high sensitivity to contamination, which are pervasive causes of false cancer mutations in public databases and are signatures for predicting global contamination. Next, we assess the performance of 8 filtering methods in terms of mouse read filtration and reduction of mouse-specific alleles. All filtering tools generally perform well, although differences in algorithm strictness and efficiency of mouse allele removal are observed. Therefore, we develop a best practice pipeline that contains the estimation of contamination level, mouse read filtration, and variant filtration.

CONCLUSIONS

The inclusion of mouse cells in patient-derived models hinders genomic analysis and should be addressed carefully. Our suggested guidelines improve the robustness and maximize the utility of genomic analysis of these models.

One protein to rule them all: The role of CCCTC-binding factor in shaping human genome in health and disease

The eukaryotic genome, constituting several billion base pairs, must be contracted to fit within the volume of a nucleus where the diameter is on the scale of μm. The 3D structure and packing of such a long sequence cannot be left to pure chance, as DNA must be efficiently used for its primary roles as a matrix for transcription and replication. In recent years, methods like chromatin conformation capture (including 3C, 4C, Hi-C, ChIA-PET and Multi-ChIA) and optical microscopy have advanced substantially and have shed new light on how eukaryotic genomes are hierarchically organized; first into 10-nm fiber, next into DNA loops, topologically associated domains and finally into interphase or mitotic chromosomes. This knowledge has allowed us to revise our understanding regarding the mechanisms governing the process of DNA organization. Mounting experimental evidence suggests that the key element in the formation of loops is the binding of the CCCTC-binding factor (CTCF) to DNA; a protein that can be referred to as the chief organizer of the genome. However, CTCF does not work alone but in cooperation with other proteins, such as cohesin or Yin Yang 1 (YY1). In this short review, we briefly describe our current understanding of the structure of eukaryotic genomes, how they are established and how the formation of DNA loops can influence gene expression. We discuss the recent discoveries describing the 3D structure of the CTCF-DNA complex and the role of CTCF in establishing genome structure. Finally, we briefly explain how various genetic disorders might arise as a consequence of mutations in the CTCF target sequence or alteration of genomic imprinting.

Identification of mutations in patients with acquired pure red cell aplasia

Idiopathic acquired pure red cell aplasia (PRCA) is a rare, autoimmune-related disease. This study aimed to describe the previously unidentified DNA alterations associated with PRCA. Here, next generation sequencing using a panel containing 295 critical genes was applied to detect potentially pathogenic mutations in four patients with PRCA. A total of 529 mutations were identified and further classified into three categories, namely, uncertain (n = 25), likely benign (n = 20) and benign (n = 484) mutations, based on the American College of Medical Genetics and Genomics (ACMG) 2015 guidelines and ClinVar database. The spatial proximity between two loci of the uncertain or benign mutations was evaluated using Hi-C datasets of KBM7 and K562 cell lines, respectively. Significant spatial proximity was observed in uncertain mutation pairs compared with benign mutation pairs. In addition, 17 variants were eventually identified after excluding those with mutant frequencies >0.001, including 7 newly identified variants. FANCF and LRP1B mutations existed twice in patients. FANCF and LRP1B mutations were likely to affect protein stability based on prediction analysis. Taken together, our data may provide valuable information about PRCA. FANCF and LRP1B mutations may be associated with acquired PRCA.

Modeling the process of human tumorigenesis

Modelling the genesis of human cancers is at a scientific turning point. Starting from primary sources of normal human cells, it is now possible to reproducibly generate several types of malignant cell populations. Powerful methods for clonally tracking and manipulating their appearance and progression in serially transplanted immunodeficient mice are also in place. These developments circumvent historic drawbacks inherent in analyses of cancers produced in model organisms, established human malignant cell lines, or highly heterogeneous patient samples. In this review, we survey the advantages, contributions and limitations of current de novo human tumorigenesis strategies and note several exciting prospects on the horizon.

A better understanding of the earliest stages of human cancer formation can enable future improvements in early detection, diagnosis and treatment. In this review, the authors summarize the methods enabling de novo tumorigenesis protocols to be applied to human cells and the insights derived from them to date, as well as the exciting and relevant technical developments anticipated to extend even further the utility of these strategies.