SOAPfuse: an algorithm for identifying fusion transcripts from paired-end RNA-Seq data

Evaluation of the gene fusion landscape in early onset sporadic rectal cancer reveals association with chromatin architecture and genome stability

Gene fusions represent a distinct class of structural variants identified frequently in cancer genomes across cancer types. Several gene fusions exhibit gain of oncogenic function and thus have been the focus of development of efficient targeted therapies. However, investigation of fusion landscape in early-onset sporadic rectal cancer, a poorly studied colorectal cancer subtype prevalent in developing countries, has not been performed. Here, we present a comprehensive landscape of gene fusions in EOSRC and CRC using patient derived tumor samples and data from The Cancer Genome Atlas, respectively. Gene Ontology analysis revealed enrichment of unique biological process terms associated with 5'- and 3'- fusion partner genes. Extensive network analysis highlighted genes exhibiting significant promiscuity in fusion formation and their association with chromosome fragile sites. Investigation of fusion formation in the context of global chromatin architecture unraveled a novel mode of gene activation that arose from fusion between genes located in orthogonal chromatin compartments. The study provides novel evidence linking fusions to genome stability and architecture and unearthed a hitherto unidentified mode of gene activation in cancer.

RTCpredictor: identification of read-through chimeric RNAs from RNA sequencing data

Read-through chimeric RNAs are being recognized as a means to expand the functional transcriptome and contribute to cancer tumorigenesis when mis-regulated. However, current software tools often fail to predict them. We have developed RTCpredictor, utilizing a fast ripgrep tool to search for all possible exon-exon combinations of parental gene pairs. We also added exonic variants allowing searches containing common SNPs. To our knowledge, it is the first read-through chimeric RNA specific prediction method that also provides breakpoint coordinates. Compared with 10 other popular tools, RTCpredictor achieved high sensitivity on a simulated and three real datasets. In addition, RTCpredictor has less memory requirements and faster execution time, making it ideal for applying on large datasets.

CTAT-LR-fusion: accurate fusion transcript identification from long and short read isoform sequencing at bulk or single cell resolution

Gene fusions are found as cancer drivers in diverse adult and pediatric cancers. Accurate detection of fusion transcripts is essential in cancer clinical diagnostics, prognostics, and for guiding therapeutic development. Most currently available methods for fusion transcript detection are compatible with Illumina RNA-seq involving highly accurate short read sequences. Recent advances in long read isoform sequencing enable the detection of fusion transcripts at unprecedented resolution in bulk and single cell samples. Here we developed a new computational tool CTAT-LR-fusion to detect fusion transcripts from long read RNA-seq with or without companion short reads, with applications to bulk or single cell transcriptomes. We demonstrate that CTAT-LR-fusion exceeds fusion detection accuracy of alternative methods as benchmarked with simulated and real long read RNA-seq. Using short and long read RNA-seq, we further apply CTAT-LR-fusion to bulk transcriptomes of nine tumor cell lines, and to tumor single cells derived from a melanoma sample and three metastatic high grade serous ovarian carcinoma samples. In both bulk and in single cell RNA-seq, long isoform reads yielded higher sensitivity for fusion detection than short reads with notable exceptions. By combining short and long reads in CTAT-LR-fusion, we are able to further maximize detection of fusion splicing isoforms and fusion-expressing tumor cells. CTAT-LR-fusion is available at https://github.com/TrinityCTAT/CTAT-LR-fusion/wiki.

Paediatric B lymphoblastic leukaemia with hyperdiploidy and a false-positive KMT2A fluorescence in situ hybridization result

The dramatic improvement in the event-free survival of paediatric B-lymphoblastic leukaemia (B-ALL) has led to risk-stratified treatment. Through a combination of clinical features, cytogenetic abnormalities and assessment of treatment response, patients are stratified to receive different intensities of therapy. The presence of high hyperdiploidy (>50 chromosomes) is considered a favourable genetic feature. Conversely, KMT2A fusion genes in B-ALL are associated with a poor prognosis, resulting in intensification of treatment. We present a seven-year-old female with B-ALL, a high hyperdiploid karyotype (56 chromosomes) and KMT2A rearrangement detected on FISH, but with no productive fusion identified. Single nucleotide polymorphism (SNP) array suggested the KMT2A rearrangement was due to chromosome 11 chromothripsis. Subsequent targeted RNA fusion panel and whole transcriptomic sequencing (mRNA-seq) did not detect an expressed KMT2A fusion. Differential expression analyses of the mRNA-seq data led to clustering of this case with other hyperdiploid cases, consistent with the hyperdiploid cytogenetic results. Given the additional intensity and potential toxicity of high-risk treatment, unusual findings by chromosome analysis, FISH and/or chromosomal microarray should prompt consideration of testing for a KMT2A fusion by another method to avoid misclassification.

SFyNCS detects oncogenic fusions involving non-coding sequences in cancer

Fusion genes are well-known cancer drivers. However, most known oncogenic fusions are protein-coding, and very few involve non-coding sequences due to lack of suitable detection tools. We develop SFyNCS to detect fusions of both protein-coding genes and non-coding sequences from transcriptomic sequencing data. The main advantage of this study is that we use somatic structural variations detected from genomic data to validate fusions detected from transcriptomic data. This allows us to comprehensively evaluate various fusion detection and filtering strategies and parameters. We show that SFyNCS has superior sensitivity and specificity over existing algorithms through extensive benchmarking in cancer cell lines and patient samples. We then apply SFyNCS to 9565 tumor samples across 33 tumor types in The Cancer Genome Atlas cohort and detect a total of 165,139 fusions. Among them, 72% of the fusions involve non-coding sequences. We find a long non-coding RNA to recurrently fuse with various oncogenes in 3% of prostate cancers. In addition, we discover fusions involving two non-coding RNAs in 32% of dedifferentiated liposarcomas and experimentally validated the oncogenic functions in mouse model.

FOXP2 confers oncogenic effects in prostate cancer

Identification oncogenes is fundamental to revealing the molecular basis of cancer. Here, we found that FOXP2 is overexpressed in human prostate cancer cells and prostate tumors, but its expression is absent in normal prostate epithelial cells and low in benign prostatic hyperplasia. FOXP2 is a FOX transcription factor family member and tightly associated with vocal development. To date, little is known regarding the link of FOXP2 to prostate cancer. We observed that high FOXP2 expression and frequent amplification are significantly associated with high Gleason score. Ectopic expression of FOXP2 induces malignant transformation of mouse NIH3T3 fibroblasts and human prostate epithelial cell RWPE-1. Conversely, FOXP2 knockdown suppresses the proliferation of prostate cancer cells. Transgenic overexpression of FOXP2 in the mouse prostate causes prostatic intraepithelial neoplasia. Overexpression of FOXP2 aberrantly activates oncogenic MET signaling and inhibition of MET signaling effectively reverts the FOXP2-induced oncogenic phenotype. CUT&Tag assay identified FOXP2-binding sites located in MET and its associated gene HGF. Additionally, the novel recurrent FOXP2-CPED1 fusion identified in prostate tumors results in high expression of truncated FOXP2, which exhibit a similar capacity for malignant transformation. Together, our data indicate that FOXP2 is involved in tumorigenicity of prostate.

BORG family proteins in physiology and human disease

The binder of rho GTPases (BORG)/Cdc42 effector proteins (Cdc42EP) family is composed of five Rho GTPase binding proteins whose functions and mechanism of actions are of emerging interest. Here, we review recent findings pertaining to the family as a whole and consider how these change our understanding of cellular organization. Recent studies have implicated BORGs in both fundamental physiology and in human diseases, mainly cancers. An emerging pattern suggests that BORG family members cancer-promoting properties are related to their ability to regulate the cytoskeleton, with many impacting the organization of acto-myosin stress fibers. This is consistent with the broader literature indicating that BORG family members are regulators of both the septin and actin cytoskeleton networks. The exact mechanism through which BORGs modify the cytoskeleton is not clear, but we consider here a few data-supported and speculative possibilities. Finally, we delve into how the Rho GTPase Cdc42 modifies BORG function in cells. This remains open-ended as Cdc42's effects on BORGs appear cell type- and cell state-dependent. Collectively, these data point to the importance of the BORG family and suggest broader themes in their function and regulation.

ADAR1-mediated RNA editing promotes B cell lymphomagenesis

Diffuse large B cell lymphoma (DLBCL) is one of the most common types of aggressive lymphoid malignancies. Here, we explore the contribution of RNA editing to DLBCL pathogenesis. We observed that DNA mutations and RNA editing events are often mutually exclusive, suggesting that tumors can modulate pathway outcomes by altering sequences at either the genomic or the transcriptomic level. RNA editing targets transcripts within known disease-driving pathways such as apoptosis, p53 and NF-κB signaling, as well as the RIG-I-like pathway. In this context, we show that ADAR1-mediated editing within MAVS transcript positively correlates with MAVS protein expression levels, associating with increased interferon/NF-κB signaling and T cell exhaustion. Finally, using targeted RNA base editing tools to restore editing within MAVS 3'UTR in ADAR1-deficient cells, we demonstrate that editing is likely to be causal to an increase in downstream signaling in the absence of activation by canonical nucleic acid receptor sensing.

SFyNCS detects oncogenic fusions involving non-coding sequences in cancer

Fusion genes are well-known cancer drivers. However, very few known oncogenic fusions involve non-coding sequences. We develop SFyNCS with superior performance to detect fusions of both protein-coding genes and non-coding sequences from transcriptomic sequencing data. We validate fusions using somatic structural variations detected from the genomes. This allows us to comprehensively evaluate various fusion detection and filtering strategies and parameters. We detect 165,139 fusions in 9,565 tumor samples across 33 tumor types in the Cancer Genome Atlas cohort. Among them, 72% of the fusions involve non-coding sequences and many are recurrent. We discover two long non-coding RNAs recurrently fused with various partner genes in 32% of dedifferentiated liposarcomas and experimentally validated the oncogenic functions in mouse model.

Novornabreak: Local Assembly for Novel Splice Junction and Fusion Transcript Detection from RNA-Seq Data

We present novoRNABreak, a unified framework for cancer specific novel splice junction and fusion transcript detection in RNA-seq data obtained from human cancer samples. novoRNABreak is based on a local assembly model, which offers a tradeoff between the alignment-based and de novo whole transcriptome assembly (WTA) methods. This approach is accurate and sensitive in assembling novel junctions that are difficult to directly align or have multiple alignments. Additionally, it is more efficient due to the strategy that focuses on junctions rather than full length transcripts. The performance of novoRNABreak is demonstrated by a comprehensive set of experiments using synthetic data generated based on genome reference, as well as real RNA-seq data from breast cancer and prostate cancer samples. The results show that our tool has a better performance by fully utilizing unmapped reads and precisely identifying the junctions where short reads or small exons have multiple alignments. novoRNABreak is a fully-fledged program available on GitHub (https://github.com/KChen-lab/novoRNABreak).

RTCpredictor: Identification of Read-Through Chimeric RNAs from RNA Sequencing Data

Read-through chimeric RNAs are gaining attention in cancer and other research fields, yet current tools often fail in predicting them. We have thus developed the first read-through chimeric RNA specific prediction method, RTCpredictor, utilizing a fast ripgrep algorithm to search for all possible exon-exon combinations of parental gene pairs. Compared with other ten popular tools, RTCpredictor achieved top performance on both simulated and real datasets. We randomly selected up to 30 candidate read-through chimeras predicted from each software method and experimentally validated a total of 109 read-throughs and on this set, RTCpredictor outperformed all the other methods. In addition, RTCpredictor ( https://github.com/sandybioteck/RTCpredictor ) has less memory requirements and faster execution time.

Chimeric RNA RRM2-C2orf48 plays an oncogenic role in the development of NNK-induced lung cancer

Chimeric RNAs have been used as biomarkers and therapeutic targets for multiple types of cancers. However, less attention has been paid to their mechanism of action in neoplasia. Here, we reported that high-expressed chimeric RNA RRM2-C2orf48 was found in malignantly transformed BEAS-2B cells induced by 4-(methyl nitrosamine)-1-(3-pyridinyl)-1-butanone (NNK) in 74 lung cancer patients and several lung cancer cell lines. The expression level of RRM2-C2orf48 was significantly correlated with lymph node metastasis, distant metastasis, tumor-lymph node-metastasis (TNM) stage, and smoking. Overexpressing RRM2-C2orf48 promoted cell growth and accelerated the process of NNK-induced lung cancer. RRM2-C2orf48 knockdown inhibited the growth of RRM2-C2orf48-overexpressing BEAS-2B cells. Finally, we identified miR-219a-2-3p as a potential target of RRM2-C2orf48 in lung cancer. In summary, chimeric RNA RRM2-C2orf48 accelerated the process of NNK-induced lung cancer, and miR-219a-2-3p may be involved in this process.

Myeloid neoplasm with ETV6::ACSl6 fusion: landscape of molecular and clinical features

OBJECTIVES

Since the publication of the third edition, the WHO classification of tumors of hematopoietic and lymphoid disorders has introduced the disease entity of 'myeloid/lymphoid neoplasms with eosinophilia and PDGFRB rearrangement', in which the most common chromosomal abnormality is t(5;12) (q32;p13.2), and this abnormality generates the ETV6::PDGFRB fusion gene. However, there have been patients with hematologic features and chromosomal abnormalities that are extremely similar to those carrying ETV6::PDGFRB fusion. These rare disorders harbor ETV6::ACSL6 fusion, and only sporadic cases have been reported at present.

METHODS

We report a patient with chronic eosinophilic leukemia (CEL) carrying chromosome translocation t(5;12)(q32;p13.2), and we present the clinical features. In addition, we conducted a literature review to collect all reported cases and summarized the genetic and clinical profiling as well as the treatments and outcomes.

RESULT

In addition to our patient, a total of 19 cases have been previously reported, including 6 variants of ETV6::ACSL6 and 3 reciprocals. We identified a novel variant of the ETV6::ACSL6 transcript in our patient, and the breakpoint was flanked by exon 2 of ETV6 and exon 2 of ACSL6. The cellular morphology features consisted of myeloproliferative neoplasm (MPN); myelodysplastic/myeloproliferative neoplasm (MDS/MPN), specifically CEL; and acute myelocytic leukemia (AML). The treatments and outcomes varied greatly depending on the type of disease, although tyrosine kinase inhibitors (TKIs) were not effective.

CONCLUSION

In contrast to neoplasms with ETV6::PDGFRB fusion, myeloid neoplasms with ETV6::ACSL6 fusion have unique characteristics.

RaScALL: Rapid (Ra) screening (Sc) of RNA-seq data for prognostically significant genomic alterations in acute lymphoblastic leukaemia (ALL)

RNA-sequencing (RNA-seq) efforts in acute lymphoblastic leukaemia (ALL) have identified numerous prognostically significant genomic alterations which can guide diagnostic risk stratification and treatment choices when detected early. However, integrating RNA-seq in a clinical setting requires rapid detection and accurate reporting of clinically relevant alterations. Here we present RaScALL, an implementation of the k-mer based variant detection tool km, capable of identifying more than 100 prognostically significant lesions observed in ALL, including gene fusions, single nucleotide variants and focal gene deletions. We compared genomic alterations detected by RaScALL and those reported by alignment-based de novo variant detection tools in a study cohort of 180 Australian patient samples. Results were validated using 100 patient samples from a published North American cohort. RaScALL demonstrated a high degree of accuracy for reporting subtype defining genomic alterations. Gene fusions, including difficult to detect fusions involving EPOR and DUX4, were accurately identified in 98% of reported cases in the study cohort (n = 164) and 95% of samples (n = 63) in the validation cohort. Pathogenic sequence variants were correctly identified in 75% of tested samples, including all cases involving subtype defining variants PAX5 p.P80R (n = 12) and IKZF1 p.N159Y (n = 4). Intragenic IKZF1 deletions resulting in aberrant transcript isoforms were also detectable with 98% accuracy. Importantly, the median analysis time for detection of all targeted alterations averaged 22 minutes per sample, significantly shorter than standard alignment-based approaches. The application of RaScALL enables rapid identification and reporting of previously identified genomic alterations of known clinical relevance.

Human papillomavirus integration perspective in small cell cervical carcinoma

Small cell cervical carcinoma (SCCC) is a rare but aggressive malignancy. Here, we report human papillomavirus features and genomic landscape in SCCC via high-throughput HPV captured sequencing, whole-genome sequencing, whole-transcriptome sequencing, and OncoScan microarrays. HPV18 infections and integrations are commonly detected. Besides MYC family genes (37.9%), we identify SOX (8.4%), NR4A (6.3%), ANKRD (7.4%), and CEA (3.2%) family genes as HPV-integrated hotspots. We construct the genomic local haplotype around HPV-integrated sites, and find tandem duplications and amplified HPV long control regions (LCR). We propose three prominent HPV integration patterns: duplicating oncogenes (MYCN, MYC, and NR4A2), forming fusions (FGFR3-TACC3 and ANKRD12-NDUFV2), and activating genes (MYC) via the cis-regulations of viral LCRs. Moreover, focal CNA amplification peaks harbor canonical cancer genes including the HPV-integrated hotspots within MYC family, SOX2, and others. Our findings may provide potential molecular criteria for the accurate diagnosis and efficacious therapies for this lethal disease.

Accurate detection of tumor-specific gene fusions reveals strongly immunogenic personal neo-antigens

Cancer-associated gene fusions are a potential source for highly immunogenic neoantigens, but the lack of computational tools for accurate, sensitive identification of personal gene fusions has limited their targeting in personalized cancer immunotherapy. Here we present EasyFuse, a machine learning computational pipeline for detecting cancer-specific gene fusions in transcriptome data obtained from human cancer samples. EasyFuse predicts personal gene fusions with high precision and sensitivity, outperforming previously described tools. By testing immunogenicity with autologous blood lymphocytes from patients with cancer, we detected pre-established CD4⁺ and CD8⁺ T cell responses for 10 of 21 (48%) and for 1 of 30 (3%) identified gene fusions, respectively. The high frequency of T cell responses detected in patients with cancer supports the relevance of individual gene fusions as neoantigens that might be targeted in personalized immunotherapies, especially for tumors with low mutation burden.

Gasdermins in Innate Host Defense Against Entamoeba histolytica and Other Protozoan Parasites

Gasdermins (GSDMs) are a group of proteins that are cleaved by inflammatory caspases to induce pore formation in the plasma membrane to cause membrane permeabilization and lytic cell death or pyroptosis. All GSDMs share a conserved structure, containing a cytotoxic N-terminal (NT) pore-forming domain and a C-terminal (CT) repressor domain. Entamoeba histolytica (Eh) in contact with macrophages, triggers outside-in signaling to activate inflammatory caspase-4/1 via the noncanonical and canonical pathway to promote cleavage of gasdermin D (GSDMD). Cleavage of GSDMD removes the auto-inhibition that masks the active pore-forming NT domain in the full-length protein by interactions with GSDM-CT. The cleaved NT-GSDMD monomers then oligomerize to form pores in the plasma membrane to facilitate the release of IL-1β and IL-18 with a measured amount of pyroptosis. Pyroptosis is an effective way to counteract intracellular parasites, which exploit replicative niche to avoid killing. To date, most GSDMs have been verified to perform pore-forming activity and GSDMD-induced pyroptosis is rapidly emerging as a mechanism of anti-microbial host defence. Here, we review our comprehensive and current knowledge on the expression, activation, biological functions, and regulation of GSDMD cleavage with emphases on physiological scenario and related dysfunctions of each GSDM member as executioner of cell death, cytokine secretion and inflammation against Eh and other protozoan parasitic infections.

Epigenetic Silencing of PTEN and Epi-Transcriptional Silencing of MDM2 Underlied Progression to Secondary Acute Myeloid Leukemia in Myelodysplastic Syndrome Treated with Hypomethylating Agents

In myelodysplastic syndrome (MDS), resistance to hypomethylating agents (HMA) portends a poor prognosis, underscoring the importance of understanding the molecular mechanisms leading to HMA-resistance. In this study, P39 and Kasumi-1 cells and their azacitidine-resistant and decitabine-resistant sublines were evaluated comparatively with transcriptomic and methylomic analyses. Expression profiling and genome-wide methylation microarray showed downregulation of PTEN associated with DNA hypermethylation in P39 cell lines resistant to azacitidine and decitabine. This pattern of PTEN dysregulation was also confirmed in a cohort of patients failing treatment with HMA. DNA hypomethylation of MDM2 was detected with downregulation of MDM2 in HMA resistant cell lines. Long-read sequencing revealed significant RNA hypomethylation of MDM2 resulting in alternative splicing and production of a truncated MDM2 transcript in azacitidine-resistant P39 cells. The expression of this MDM2 truncated transcript was also significantly increased in HMA-resistant patients compared with HMA-responsive patients. In conclusion, epigenetic and epi-transcriptomic dysregulation of PTEN and MDM2 were associated with resistance to hypomethylating agents.

Multi-Cohort Transcriptomic Subtyping of B-Cell Acute Lymphoblastic Leukemia

RNA sequencing provides a snapshot of the functional consequences of genomic lesions that drive acute lymphoblastic leukemia (ALL). The aims of this study were to elucidate diagnostic associations (via machine learning) between mRNA-seq profiles, independently verify ALL lesions and develop easy-to-interpret transcriptome-wide biomarkers for ALL subtyping in the clinical setting. A training dataset of 1279 ALL patients from six North American cohorts was used for developing machine learning models. Results were validated in 767 patients from Australia with a quality control dataset across 31 tissues from 1160 non-ALL donors. A novel batch correction method was introduced and applied to adjust for cohort differences. Out of 18,503 genes with usable expression, 11,830 (64%) were confounded by cohort effects and excluded. Six ALL subtypes (ETV6::RUNX1, KMT2A, DUX4, PAX5 P80R, TCF3::PBX1, ZNF384) that covered 32% of patients were robustly detected by mRNA-seq (positive predictive value ≥ 87%). Five other frequent subtypes (CRLF2, hypodiploid, hyperdiploid, PAX5 alterations and Ph-positive) were distinguishable in 40% of patients at lower accuracy (52% ≤ positive predictive value ≤ 73%). Based on these findings, we introduce the Allspice R package to predict ALL subtypes and driver genes from unadjusted mRNA-seq read counts as encountered in real-world settings. Two examples of Allspice applied to previously unseen ALL patient samples with atypical lesions are included.

FusionAI, a DNA-sequence-based deep learning protocol reduces the false positives of human fusion gene prediction

Even though there were many tool developments of fusion gene prediction from NGS data, too many false positives are still an issue. Wise use of the genomic features around the fusion gene breakpoints will be helpful to identify reliable fusion genes efficiently. For this aim, we developed FusionAI, a deep learning pipeline predicting human fusion gene breakpoints from DNA sequence. FusionAI is freely available via https://compbio.uth.edu/FusionGDB2/FusionAI.

For complete details on the use and execution of this protocol, please refer to Kim et al. (2021b).

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FusionAI can predict the fusion breakpoints from the given DNA sequence

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FusionAI can reduce the false positives of the predicted fusion genes by other tools

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FusionAI can identify the genomic features related to the genomic breakage

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FusionAI creates a landscape image of 44 human genomic features around the breakpoints

JAFFAL: detecting fusion genes with long-read transcriptome sequencing

In cancer, fusions are important diagnostic markers and targets for therapy. Long-read transcriptome sequencing allows the discovery of fusions with their full-length isoform structure. However, due to higher sequencing error rates, fusion finding algorithms designed for short reads do not work. Here we present JAFFAL, to identify fusions from long-read transcriptome sequencing. We validate JAFFAL using simulations, cell lines, and patient data from Nanopore and PacBio. We apply JAFFAL to single-cell data and find fusions spanning three genes demonstrating transcripts detected from complex rearrangements. JAFFAL is available at https://github.com/Oshlack/JAFFA/wiki .

ChiTaH: a fast and accurate tool for identifying known human chimeric sequences from high-throughput sequencing data

Fusion genes or chimeras typically comprise sequences from two different genes. The chimeric RNAs of such joined sequences often serve as cancer drivers. Identifying such driver fusions in a given cancer or complex disease is important for diagnosis and treatment. The advent of next-generation sequencing technologies, such as DNA-Seq or RNA-Seq, together with the development of suitable computational tools, has made the global identification of chimeras in tumors possible. However, the testing of over 20 computational methods showed these to be limited in terms of chimera prediction sensitivity, specificity, and accurate quantification of junction reads. These shortcomings motivated us to develop the first ‘reference-based’ approach termed ChiTaH (Chimeric Transcripts from High–throughput sequencing data). ChiTaH uses 43,466 non–redundant known human chimeras as a reference database to map sequencing reads and to accurately identify chimeric reads. We benchmarked ChiTaH and four other methods to identify human chimeras, leveraging both simulated and real sequencing datasets. ChiTaH was found to be the most accurate and fastest method for identifying known human chimeras from simulated and sequencing datasets. Moreover, especially ChiTaH uncovered heterogeneity of the BCR-ABL1 chimera in both bulk and single-cells of the K-562 cell line, which was confirmed experimentally.

Apigenin Inhibits the Histamine-Induced Proliferation of Ovarian Cancer Cells by Downregulating ERα/ERβ Expression

Background

Apigenin (APG), a natural flavonoid, can affect the development of a variety of tumors, but its role in ovarian cancer remains unclear. There has been an increasing amount of evidence supporting the vital role played by mast cells and the bioactive mediators they release, as components of the tumor microenvironment, in the progression of ovarian cancer (OC); however, the mechanism warrants further exploration.

Methods and Results

In this study, a combination of transcriptomics analysis and application of TCGA database was performed, and we found that the expression of genes related to mast cell degranulation in ovarian cancer tissues changed remarkably. We then explored whether histamine, a major constituent of mast cell degranulation, could affect the development of ovarian cancer through immunohistochemistry analysis and cell proliferation assays. The results showed that a certain concentration of histamine promoted the proliferation of ovarian cancer cells by upregulating the expression of estrogen receptor α (ERα)/estrogen receptor β (ERβ). Additionally, we found that the inhibition of ERα or the activation of ERβ could inhibit the proliferation of ovarian cancer cells induced by histamine through real-time PCR and western blot assays. Finally, we demonstrated the attenuation effect imparted by apigenin in histamine-mediated ovarian cancer via the PI3K/AKT/mTOR signaling pathway.

Conclusion

Our research revealed that apigenin decelerated ovarian cancer development by downregulating ER-mediated PI3K/AKT/mTOR expression, thus providing evidence of its applicability as a potentially effective therapeutic agent for ovarian cancer treatment.

Gene Duplication and Gene Fusion Are Important Drivers of Tumourigenesis during Cancer Evolution

Chromosomal rearrangement and genome instability are common features of cancer cells in human. Consequently, gene duplication and gene fusion events are frequently observed in human malignancies and many of the products of these events are pathogenic, representing significant drivers of tumourigenesis and cancer evolution. In certain subsets of cancers duplicated and fused genes appear to be essential for initiation of tumour formation, and some even have the capability of transforming normal cells, highlighting the importance of understanding the events that result in their formation. The mechanisms that drive gene duplication and fusion are unregulated in cancer and they facilitate rapid evolution by selective forces akin to Darwinian survival of the fittest on a cellular level. In this review, we examine current knowledge of the landscape and prevalence of gene duplication and gene fusion in human cancers.

Acquired JAK2 mutations confer resistance to JAK inhibitors in cell models of acute lymphoblastic leukemia

Ruxolitinib (rux) Phase II clinical trials are underway for the treatment of high-risk JAK2-rearranged (JAK2r) B-cell acute lymphoblastic leukemia (B-ALL). Treatment resistance to targeted inhibitors in other settings is common; elucidating potential mechanisms of rux resistance in JAK2r B-ALL will enable development of therapeutic strategies to overcome or avert resistance. We generated a murine pro-B cell model of ATF7IP-JAK2 with acquired resistance to multiple type-I JAK inhibitors. Resistance was associated with mutations within the JAK2 ATP/rux binding site, including a JAK2 p.G993A mutation. Using in vitro models of JAK2r B-ALL, JAK2 p.G993A conferred resistance to six type-I JAK inhibitors and the type-II JAK inhibitor, CHZ-868. Using computational modeling, we postulate that JAK2 p.G993A enabled JAK2 activation in the presence of drug binding through a unique resistance mechanism that modulates the mobility of the conserved JAK2 activation loop. This study highlights the importance of monitoring mutation emergence and may inform future drug design and the development of therapeutic strategies for this high-risk patient cohort.

Identification of the cross-strand chimeric RNAs generated by fusions of bi-directional transcripts

A major part of the transcriptome complexity is attributed to multiple types of DNA or RNA fusion events, which take place within a gene such as alternative splicing or between different genes such as DNA rearrangement and trans-splicing. In the present study, using the RNA deep sequencing data, we systematically survey a type of non-canonical fusions between the RNA transcripts from the two opposite DNA strands. We name the products of such fusion events cross-strand chimeric RNA (cscRNA). Hundreds to thousands of cscRNAs can be found in human normal tissues, primary cells, and cancerous cells, and in other species as well. Although cscRNAs exhibit strong tissue-specificity, our analysis identifies thousands of recurrent cscRNAs found in multiple different samples. cscRNAs are mostly originated from convergent transcriptions of the annotated genes and their anti-sense DNA. The machinery of cscRNA biogenesis is unclear, but the cross-strand junction events show some features related to RNA splicing. The present study is a comprehensive survey of the non-canonical cross-strand RNA junction events, a resource for further characterization of the originations and functions of the cscRNAs.

Comparative study of bioinformatic tools for the identification of chimeric RNAs from RNA Sequencing

Chimeric RNAs are gaining more and more attention as they have broad implications in both cancer and normal physiology. To date, over 40 chimeric RNA prediction methods have been developed to facilitate their identification from RNA sequencing data. However, a limited number of studies have been conducted to compare the performance of these tools; additionally, previous studies have become outdated as more software tools have been developed within the last three years. In this study, we benchmarked 16 chimeric RNA prediction software, including seven top performers in previous benchmarking studies, and nine that were recently developed. We used two simulated and two real RNA-Seq datasets, compared the 16 tools for their sensitivity, positive prediction value (PPV), F-measure, and also documented the computational requirements (time and memory). We noticed that none of the tools are inclusive, and their performance varies depending on the dataset and objects. To increase the detection of true positive events, we also evaluated the pair-wise combination of these methods to suggest the best combination for sensitivity and F-measure. In addition, we compared the performance of the tools for the identification of three classes (read-through, inter-chromosomal and intra-others) of chimeric RNAs. Finally, we performed TOPSIS analyses and ranked the weighted performance of the 16 tools.

Transcriptome Analysis Reveals MFGE8-HAPLN3 Fusion as a Novel Biomarker in Triple-Negative Breast Cancer

Background

Triple-negative breast cancer (TNBC) is a highly aggressive cancer with poor prognosis. The lack of effective targeted therapies for TNBC remains a profound clinical challenge. Fusion transcripts play critical roles in carcinogenesis and serve as valuable diagnostic and therapeutic targets in cancer. The present study aimed to identify novel fusion transcripts in TNBC.

Methods

We analyzed the RNA sequencing data of 360 TNBC samples to identify and filter fusion candidates through SOAPfuse and ChimeraScan analysis. The characteristics, including recurrence, fusion type, chromosomal localization, TNBC subgroup distribution, and clinicopathological correlations, were analyzed in all candidates. Furthermore, we selected the promising fusion transcript and predicted its fusion type and protein coding capacity.

Results

Using the RNA sequencing data, we identified 189 fusion transcripts in TNBC, among which 22 were recurrent fusions. Compared to para-tumor tissues, TNBC tumor tissues accumulated more fusion events, especially in high-grade tumors. Interestingly, these events were enriched at specific chromosomal loci, and the distribution pattern varied in different TNBC subtypes. The vast majority of fusion partners were discovered on chromosomes 1p, 11q, 19p, and 19q. Besides, fusion events mainly clustered on chromosome 11 in the immunomodulatory subtype and chromosome 19 in the luminal androgen receptor subtype of TNBC. Considering the tumor specificity and frameshift mutation, we selected MFGE8-HAPLN3 as a novel biomarker and further validated it in TNBC samples using PCR and Sanger sequencing. Further, we successfully identified three types of MFGE8-HAPLN3 (E6-E2, E5-E3, and E6-E3) and predicted the ORF of E6-E2, which could encode a protein of 712 amino acids, suggesting its critical role in TNBC.

Conclusions

Improved bioinformatic stratification and comprehensive analysis identified the fusion transcript MFGE8-HAPLN3 as a novel biomarker with promising clinical application in the future.

The CTNNBIP1-CLSTN1 fusion transcript regulates human neocortical development

Fusion transcripts or RNAs have been found in both disordered and healthy human tissues and cells; however, their physiological functions in the brain development remain unknown. In the analysis of deposited RNA-sequence libraries covering early to middle embryonic stages, we identify 1,055 fusion transcripts present in the developing neocortex. Interestingly, 98 fusion transcripts exhibit distinct expression patterns in various neural progenitors (NPs) or neurons. We focus on CTNNBIP1-CLSTN1 (CTCL), which is enriched in outer radial glial cells that contribute to cortex expansion during human evolution. Intriguingly, downregulation of CTCL in cultured human cerebral organoids causes marked reduction in NPs and precocious neuronal differentiation, leading to impairment of organoid growth. Furthermore, the expression of CTCL fine-tunes Wnt/β-catenin signaling that controls cortex patterning. Together, this work provides evidence indicating important roles of fusion transcript in human brain development and evolution.

MetaFusion: A high-confidence metacaller for filtering and prioritizing RNA-seq gene fusion candidates

MOTIVATION

Current fusion detection tools use diverse calling approaches and provide varying results, making selection of the appropriate tool challenging. Ensemble fusion calling techniques appear promising; however, current options have limited accessibility and function.

RESULTS

MetaFusion is a flexible meta-calling tool that amalgamates outputs from any number of fusion callers. Individual caller results are standardized by conversion into the new file type Common Fusion Format (CFF). Calls are annotated, merged using graph clustering, filtered, and ranked to provide a final output of high confidence candidates. MetaFusion consistently achieves higher precision and recall than individual callers on real and simulated datasets, and reaches up to 100% precision, indicating that ensemble calling is imperative for high confidence results. MetaFusion uses FusionAnnotator to annotate calls with information from cancer fusion databases, and is provided with a benchmarking toolkit to calibrate new callers.

AVAILABILITY

MetaFusion is freely available at https://github.com/ccmbioinfo/MetaFusion.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Unraveling Gene Fusions for Drug Repositioning in High-Risk Neuroblastoma

High-risk neuroblastoma (NB) remains a significant therapeutic challenge facing current pediatric oncology patients. Structural variants such as gene fusions have shown an initial promise in enhancing mechanistic understanding of NB and improving survival rates. In this study, we performed a comprehensive in silico investigation on the translational ability of gene fusions for patient stratification and treatment development for high-risk NB patients. Specifically, three state-of-the-art gene fusion detection algorithms, including ChimeraScan, SOAPfuse, and TopHat-Fusion, were employed to identify the fusion transcripts in a RNA-seq data set of 498 neuroblastoma patients. Then, the 176 high-risk patients were further stratified into four different subgroups based on gene fusion profiles. Furthermore, Kaplan-Meier survival analysis was performed, and differentially expressed genes (DEGs) for the redefined high-risk group were extracted and functionally analyzed. Finally, repositioning candidates were enriched in each patient subgroup with drug transcriptomic profiles from the LINCS L1000 Connectivity Map. We found the number of identified gene fusions was increased from clinical the low-risk stage to the high-risk stage. Although the technical concordance of fusion detection algorithms was suboptimal, they have a similar biological relevance concerning perturbed pathways and regulated DEGs. The gene fusion profiles could be utilized to redefine high-risk patient subgroups with significant onset age of NB, which yielded the improved survival curves (Log-rank p value ≤ 0.05). Out of 48 enriched repositioning candidates, 45 (93.8%) have antitumor potency, and 24 (50%) were confirmed with either on-going clinical trials or literature reports. The gene fusion profiles have a discrimination power for redefining patient subgroups in high-risk NB and facilitate precision medicine-based drug repositioning implementation.

YAP1-FAM118B Fusion Defines a Rare Subset of Childhood and Young Adulthood Meningiomas

Meningiomas are a central nervous system tumor primarily afflicting adults, with <1% of cases diagnosed during childhood or adolescence. Somatic variation in NF2 may be found in ∼50% of meningiomas, with other genetic drivers (eg, SMO, AKT1, TRAF7) contributing to NF2 wild-type tumors. NF2 is an upstream negative regulator of YAP signaling and loss of the NF2 protein product, Merlin, results in YAP overexpression and target gene transcription. This mechanism of dysregulation is described in NF2-driven meningiomas, but further work is necessary to understand the NF2-independent mechanism of tumorigenesis. Amid our institutional patient-centric comprehensive molecular profiling study, we identified an individual with meningioma harboring a YAP1-FAM118B fusion, previously reported only in supratentorial ependymoma. The tumor histopathology was remarkable, characterized by prominent islands of calcifying fibrous nodules within an overall collagen-rich matrix. To gain insight into this finding, we subsequently evaluated the genetic landscape of 11 additional pediatric and adolescent/young adulthood meningioma patients within the Children's Brain Tumor Tissue Consortium. A second individual harboring a YAP1-FAM118B gene fusion was identified within this database. Transcriptomic profiling suggested that YAP1-fusion meningiomas are biologically distinct from NF2-driven meningiomas. Similar to other meningiomas, however, YAP1-fusion meningiomas demonstrated overexpression of EGFR and MET. DNA methylation profiling further distinguished YAP1-fusion meningiomas from those observed in ependymomas. In summary, we expand the genetic spectrum of somatic alteration associated with NF2 wild-type meningioma to include the YAP1-FAM118B fusion and provide support for aberrant signaling pathways potentially targetable by therapeutic intervention.

FUNGI: Fusion Gene Integration Toolset

Motivation

Fusion genes are both useful cancer biomarkers and important drug targets. Finding relevant fusion genes is challenging due to genomic instability resulting in a high number of passenger events. To reveal and prioritize relevant gene fusion events we have developed FUsionN Gene Identification toolset (FUNGI) that uses an ensemble of fusion detection algorithms with prioritization and visualization modules.

Results

We applied FUNGI to an ovarian cancer dataset of 107 tumor samples from 36 patients. Ten out of 11 detected and prioritized fusion genes were validated. Many of detected fusion genes affect the PI3K-AKT pathway with potential role in treatment resistance.

Availabilityand implementation

FUNGI and its documentation are available at https://bitbucket.org/alejandra_cervera/fungi as standalone or from Anduril at https://www.anduril.org.

Supplementary information

Supplementary data are available at Bioinformatics online.

Integrated analysis of virus and host transcriptomes in cervical cancer in Asian and Western populations

Race may influence vulnerability to HPV variants in viral infection and perisistence. Integrated analysis of the virus and host transcriptomes from different populations provides an unprecedented opportunity to understand these racial disparities in the prevalence of HPV and cervical cancers. We performed RNA-Seq analysis of 90 tumors and 39 adjacent normal tissues from cervical cancer patients at Zhejiang University (ZJU) in China, and conducted a comparative analysis with RNA-Seq data of 286 cervical cancers from TCGA. We found a modestly higher rate of HPV positives and HPV integrations in TCGA than in ZJU. In addition to LINC00393 and HSPB3 as new common integration hotspots in both cohorts, we found new hotspots such as SH2D3C and CASC8 in TCGA, and SCGB1A1 and ABCA1 in ZJU. We described the first, to our knowledge, virus-transcriptome-based classification of cervical cancer associated with clinical outcome. Particularly, patients with expressed E5 performed better than those without E5 expression. However, the constituents of these virus-transcriptome-based tumor subtypes differ dramatically between the two cohorts. We further characterized the immune infiltration landscapes between different HPV statuses and revealed significantly elevated levels of regulatory T cells and M0 macrophages in HPV positive tumors, which were associated with poor prognosis. These findings increase our understanding of the racial disparities in the prevalence of HPV and its associated cervical cancers between the two cohorts, and also have important implications in the classification of tumor subtypes, prognosis, and anti-cancer immunotherapy in cervical cancer.

Landscape of Chimeric RNAs in Non-Cancerous Cells

Gene fusions and their products (RNA and protein) have been traditionally recognized as unique features of cancer cells and are used as ideal biomarkers and drug targets for multiple cancer types. However, recent studies have demonstrated that chimeric RNAs generated by intergenic alternative splicing can also be found in normal cells and tissues. In this study, we aim to identify chimeric RNAs in different non-neoplastic cell lines and investigate the landscape and expression of these novel candidate chimeric RNAs. To do so, we used HEK-293T, HUVEC, and LO2 cell lines as models, performed paired-end RNA sequencing, and conducted analyses for chimeric RNA profiles. Several filtering criteria were applied, and the landscape of chimeric RNAs was characterized at multiple levels and from various angles. Further, we experimentally validated 17 chimeric RNAs from different classifications. Finally, we examined a number of validated chimeric RNAs in different cancer and non-cancer cells, including blood from healthy donors, and demonstrated their ubiquitous expression pattern.

Accurate and efficient detection of gene fusions from RNA sequencing data

The identification of gene fusions from RNA sequencing data is a routine task in cancer research and precision oncology. However, despite the availability of many computational tools, fusion detection remains challenging. Existing methods suffer from poor prediction accuracy and are computationally demanding. We developed Arriba, a novel fusion detection algorithm with high sensitivity and short runtime. When applied to a large collection of published pancreatic cancer samples (n = 803), Arriba identified a variety of driver fusions, many of which affected druggable proteins, including ALK, BRAF, FGFR2, NRG1, NTRK1, NTRK3, RET, and ROS1. The fusions were significantly associated with KRAS wild-type tumors and involved proteins stimulating the MAPK signaling pathway, suggesting that they substitute for activating mutations in KRAS In addition, we confirmed the transforming potential of two novel fusions, RRBP1-RAF1 and RASGRP1-ATP1A1, in cellular assays. These results show Arriba's utility in both basic cancer research and clinical translation.

Kaiso (ZBTB33) subcellular partitioning functionally links LC3A/B, the tumor microenvironment, and breast cancer survival

The use of digital pathology for the histomorphologic profiling of pathological specimens is expanding the precision and specificity of quantitative tissue analysis at an unprecedented scale; thus, enabling the discovery of new and functionally relevant histological features of both predictive and prognostic significance. In this study, we apply quantitative automated image processing and computational methods to profile the subcellular distribution of the multi-functional transcriptional regulator, Kaiso (ZBTB33), in the tumors of a large racially diverse breast cancer cohort from a designated health disparities region in the United States. Multiplex multivariate analysis of the association of Kaiso’s subcellular distribution with other breast cancer biomarkers reveals novel functional and predictive linkages between Kaiso and the autophagy-related proteins, LC3A/B, that are associated with features of the tumor immune microenvironment, survival, and race. These findings identify effective modalities of Kaiso biomarker assessment and uncover unanticipated insights into Kaiso’s role in breast cancer progression.

Through automated image analysis, Singhal et al quantify nuclear versus cytoplasmic distribution of the Kaiso transcription factor in breast cancer patient tissue. They find that Kaiso distribution correlates with breast cancer subtype and overall survival, and discover a link between cytoplasmic Kaiso and autophagy marker LC3.

LongGF: computational algorithm and software tool for fast and accurate detection of gene fusions by long-read transcriptome sequencing

BACKGROUND

Long-read RNA-Seq techniques can generate reads that encompass a large proportion or the entire mRNA/cDNA molecules, so they are expected to address inherited limitations of short-read RNA-Seq techniques that typically generate < 150 bp reads. However, there is a general lack of software tools for gene fusion detection from long-read RNA-seq data, which takes into account the high basecalling error rates and the presence of alignment errors.

RESULTS

In this study, we developed a fast computational tool, LongGF, to efficiently detect candidate gene fusions from long-read RNA-seq data, including cDNA sequencing data and direct mRNA sequencing data. We evaluated LongGF on tens of simulated long-read RNA-seq datasets, and demonstrated its superior performance in gene fusion detection. We also tested LongGF on a Nanopore direct mRNA sequencing dataset and a PacBio sequencing dataset generated on a mixture of 10 cancer cell lines, and found that LongGF achieved better performance to detect known gene fusions over existing computational tools. Furthermore, we tested LongGF on a Nanopore cDNA sequencing dataset on acute myeloid leukemia, and pinpointed the exact location of a translocation (previously known in cytogenetic resolution) in base resolution, which was further validated by Sanger sequencing.

CONCLUSIONS

In summary, LongGF will greatly facilitate the discovery of candidate gene fusion events from long-read RNA-Seq data, especially in cancer samples. LongGF is implemented in C++ and is available at https://github.com/WGLab/LongGF .

Oviz-Bio: a web-based platform for interactive cancer genomics data visualization

Genetics data visualization plays an important role in the sharing of knowledge from cancer genome research. Many types of visualization are widely used, most of which are static and require sufficient coding experience to create. Here, we present Oviz-Bio, a web-based platform that provides interactive and real-time visualizations of cancer genomics data. Researchers can interactively explore visual outputs and export high-quality diagrams. Oviz-Bio supports a diverse range of visualizations on common cancer mutation types, including annotation and signatures of small scale mutations, haplotype view and focal clusters of copy number variations, split-reads alignment and heatmap view of structural variations, transcript junction of fusion genes and genomic hotspot of oncovirus integrations. Furthermore, Oviz-Bio allows landscape view to investigate multi-layered data in samples cohort. All Oviz-Bio visual applications are freely available at https://bio.oviz.org/.

Best practices for variant calling in clinical sequencing

Next-generation sequencing technologies have enabled a dramatic expansion of clinical genetic testing both for inherited conditions and diseases such as cancer. Accurate variant calling in NGS data is a critical step upon which virtually all downstream analysis and interpretation processes rely. Just as NGS technologies have evolved considerably over the past 10 years, so too have the software tools and approaches for detecting sequence variants in clinical samples. In this review, I discuss the current best practices for variant calling in clinical sequencing studies, with a particular emphasis on trio sequencing for inherited disorders and somatic mutation detection in cancer patients. I describe the relative strengths and weaknesses of panel, exome, and whole-genome sequencing for variant detection. Recommended tools and strategies for calling variants of different classes are also provided, along with guidance on variant review, validation, and benchmarking to ensure optimal performance. Although NGS technologies are continually evolving, and new capabilities (such as long-read single-molecule sequencing) are emerging, the “best practice” principles in this review should be relevant to clinical variant calling in the long term.

Infantile fibrosarcoma-like tumor driven by novel RBPMS-MET fusion consolidated with cabozantinib

Infantile fibrosarcoma (IFS) is nearly universally driven by gene fusions involving the NTRK family. ETV6-NTRK3 fusions account for ∼85% of alterations; the remainder are attributed to NTRK-variant fusions. Rarely, other genomic aberrations have been described in association with tumors identified as IFS or IFS-like. We describe the utility of genomic characterization of an IFS-like tumor. We also describe the successful treatment combination of VAC (vincristine, actinomycin, cyclophosphamide) with tyrosine kinase inhibitor (TKI) maintenance in this entity. This patient presented at birth with a right facial mass, enlarging at 1 mo to 4.9 × 4.5 × 6.3 cm. Biopsy demonstrated hypercellular fascicles of spindle cells with patchy positivity for smooth muscle actin (SMA) and negativity for S100, desmin, myogenin, and MyoD1. Targeted RNA sequencing identified a novel RBPMS-MET fusion with confirmed absence of ETV6-NTRK3, and the patient was diagnosed with an IFS-like tumor. A positron emission tomography (PET) scan was negative for metastatic disease. VAC was given for a duration of 10 mo. Resection at 13 mo of age demonstrated positive margins. Cabozantinib, a MET-targeting TKI, was initiated. The patient tolerated cabozantinib well and has no evidence of disease at 24 mo of age. We describe a novel RBPMS-MET driver fusion in association with a locally aggressive IFS-like tumor. MET functions as an oncogene and, when associated with the RNA binding protein RBPMS, forms an in-frame fusion product that retains the MET kinase domain. This fusion is associated with aberrant cell signaling pathway expression and subsequent malignancy. We describe treatment with cabozantinib in a patient with an IFS-like neoplasm.

EWSR1-CREM fusion in pulmonary mesenchymal neoplasm showing distinctive clear cell morphology

EWSR1-CREM gene fusions were recently discovered in several mesenchymal and epithelial tumors, including myxoid mesenchymal tumors of the central nervous system, rare cases of soft tissue clear cell sarcoma and angiomatoid fibrous histiocytoma, and hyalinizing clear cell carcinoma, which implicates the potential phenotypic diversities of tumors harboring an EWSR1-CREM fusion. We herein present an exceedingly indolent pulmonary mesenchymal tumor showing distinctive clinicopathological features. This tumor histologically displayed a small nest and alveolar pattern consisting of monomorphic clear cells intermingled with dilated anastomosing vasculature. Immunophenotypically, tumor cells were positive for vimentin and focally positive for synaptophysin, but negative for many immunohistochemical panels including keratins, EMA, desmin, mesothelial markers, melanotic markers, smooth muscle actin, inhibin and S-100 protein. Interestingly, RNA sequencing identified an in-frame EWSR1-CREM fusion, which was confirmed by subsequent real-time/reverse transcription polymerase chain reaction and fluorescence in situ hybridization assay. Clinical follow-up showed no evidence of recurrence and metastasis. Our pathological findings further expand the phenotypic spectrum of tumors associated with EWSR1-CREM fusions, implying the emergence of a possible novel tumor entity.

Transcriptomic characterization and innovative molecular classification of clear cell renal cell carcinoma in the Chinese population

Background

Large-scale initiatives like The Cancer Genome Atlas (TCGA) performed genomics studies on predominantly Caucasian kidney cancer. In this study, we aimed to investigate genomics of Chinese clear cell renal cell carcinoma (ccRCC).

Methods

We performed whole-transcriptomic sequencing on 55 tumor tissues and 11 matched normal tissues from Chinese ccRCC patients. We systematically analyzed the data from our cohort and comprehensively compared with the TCGA ccRCC cohort.

Results

It found that PBRM1 mutates with a frequency of 11% in our cohort, much lower than that in TCGA Caucasians (33%). Besides, 31 gene fusions including 5 recurrent ones, that associated with apoptosis, tumor suppression and metastasis were identified. We classified our cohort into three classes by gene expression. Class 1 shows significantly elevated gene expression in the VEGF pathway, while Class 3 has comparably suppressed expression of this pathway. Class 2 is characterized by increased expression of extracellular matrix organization genes and is associated with high-grade tumors. Applying the classification to TCGA ccRCC patients revealed better distinction of tumor prognosis than reported classifications. Class 2 shows worst survival and Class 3 is a rare subtype ccRCC in the TCGA cohort. Furthermore, computational analysis on the immune microenvironment of ccRCC identified immune-active and tolerant tumors with significant increased macrophages and depleted CD4 positive T-cells, thus some patients may benefit from immunotherapies.

Conclusion

In summary, results presented in this study shed light into distinct genomic expression profiles in Chinese population, modified the stratification patterns by new molecular classification, and gave practical guidelines on clinical treatment of ccRCC patients.

Systematic Identification of Hub Genes in Placenta Accreta Spectrum Based on Integrated Transcriptomic and Proteomic Analysis

Placenta accreta spectrum (PAS) is a pathological condition of the placenta with abnormal adhesion or invasion of the placental villi to the uterine wall, which is associated with a variety of adverse maternal and fetal outcomes. Although some PAS-related molecules have been reported, the underlying regulatory mechanism is still unclear. Compared with the study of single gene or pathway, omics study, using advanced sequencing technology and bioinformatics methods, can increase our systematic understanding of diseases. In this study, placenta tissues from 5 patients with PAS and 5 healthy pregnant women were collected for transcriptomic and proteomic sequencing and integrated analysis. A total of 728 messenger RNAs and 439 proteins were found to be significantly different between PAS group and non-PAS group, in which 23 hub genes were differentially expressed in both transcriptome and proteome. Functional enrichment analysis showed that the differentially expressed genes were mainly related to cell proliferation, migration and vascular development. Totally 18 long non-coding RNA were found that might regulate the expression of hub genes. Many kinds of single nucleotide polymorphism, alternative splicing and gene fusion of hub genes were detected. This is the first time to systematically explore the hub genes and gene structure variations of PAS through integrated omics analysis, which provided a genetic basis for further in-depth study on the underlying regulatory mechanism of PAS.

Multi-Omics Characterization of the 4T1 Murine Mammary Gland Tumor Model

Background: Tumor models are critical for our understanding of cancer and the development of cancer therapeutics. The 4T1 murine mammary cancer cell line is one of the most widely used breast cancer models. Here, we present an integrated map of the genome, transcriptome, and immunome of 4T1.

Results: We found Trp53 (Tp53) and Pik3g to be mutated. Other frequently mutated genes in breast cancer, including Brca1 and Brca2, are not mutated. For cancer related genes, Nav3, Cenpf, Muc5Ac, Mpp7, Gas1, MageD2, Dusp1, Ros, Polr2a, Rragd, Ros1, and Hoxa9 are mutated. Markers for cell proliferation like Top2a, Birc5, and Mki67 are highly expressed, so are markers for metastasis like Msln, Ect2, and Plk1, which are known to be overexpressed in triple-negative breast cancer (TNBC). TNBC markers are, compared to a mammary gland control sample, lower (Esr1), comparably low (Erbb2), or not expressed at all (Pgr). We also found testis cancer antigen Pbk as well as colon/gastrointestinal cancer antigens Gpa33 and Epcam to be highly expressed. Major histocompatibility complex (MHC) class I is expressed, while MHC class II is not. We identified 505 single nucleotide variations (SNVs) and 20 insertions and deletions (indels). Neoantigens derived from 22 SNVs and one deletion elicited CD8⁺ or CD4⁺ T cell responses in IFNγ-ELISpot assays. Twelve high-confidence fusion genes were observed. We did not observe significant downregulation of mismatch repair (MMR) genes or SNVs/indels impairing their function, providing evidence for 6-thioguanine resistance. Effects of the integration of the murine mammary tumor virus were observed at the genome and transcriptome level.

Conclusions: 4T1 cells share substantial molecular features with human TNBC. As 4T1 is a common model for metastatic tumors, our data supports the rational design of mode-of-action studies for pre-clinical evaluation of targeted immunotherapies.

Alignment-free filtering for cfNA fusion fragments

Motivation

Cell-free nucleic acid (cfNA) sequencing data require improvements to existing fusion detection methods along multiple axes: high depth of sequencing, low allele fractions, short fragment lengths and specialized barcodes, such as unique molecular identifiers.

Results

AF4 was developed to address these challenges. It uses a novel alignment-free kmer-based method to detect candidate fusion fragments with high sensitivity and orders of magnitude faster than existing tools. Candidate fragments are then filtered using a max-cover criterion that significantly reduces spurious matches while retaining authentic fusion fragments. This efficient first stage reduces the data sufficiently that commonly used criteria can process the remaining information, or sophisticated filtering policies that may not scale to the raw reads can be used. AF4 provides both targeted and de novo fusion detection modes. We demonstrate both modes in benchmark simulated and real RNA-seq data as well as clinical and cell-line cfNA data.

Availability and implementation

AF4 is open sourced, licensed under Apache License 2.0, and is available at: https://github.com/grailbio/bio/tree/master/fusion.

Chimeric RNAs in cancer

Chimeric RNAs are hybrid transcripts containing exons from two separate genes. Chimeric RNAs are traditionally considered to be transcribed from fusion genes caused by chromosomal rearrangement. These canonical chimeric RNAs are well characterized to be expressed in a cancer-unique pattern and/or act as oncogene products. However, benefited by the development of advanced deep sequencing technologies, novel types of non-canonical chimeric RNAs have been discovered to be generated from intergenic splicing without genomic aberrations. They can be formed through trans-splicing or cis-splicing between adjacent genes (cis-SAGe) mechanisms. Non-canonical chimeric RNAs are widely detected in normal physiology, although several have been shown to have a cancer-specific expression pattern. Further studies have indicated that some of them play fundamental roles in controlling cell growth and motility, and may have functions independent of the parental genes. These discoveries are unveiling a new layer of the functional transcriptome and are also raising the possibility of utilizing non-canonical chimeric RNAs as cancer diagnostic markers and therapeutic targets. In this chapter, we will overview different categories of chimeric RNAs and their expression in various types of cancerous and normal samples. Acknowledging that chimeric RNAs are not unique to cancer, we will discuss both bioinformatic and biological methods to identify credible cancer-specific chimeric RNAs. Furthermore, we will describe downstream methods to explore their molecular processing mechanisms and potential functions. A better understanding of the biogenesis mechanisms and functional products of cancer-specific chimeric RNAs will pave ways for the development of novel cancer biomarkers and therapeutic targets.

Identification of gene fusion events in Mycobacterium tuberculosis that encode chimeric proteins

Mycobacterium tuberculosis is a facultative intracellular pathogen responsible for causing tuberculosis. The harsh environment in which M. tuberculosis survives requires this pathogen to continuously adapt in order to maintain an evolutionary advantage. However, the apparent absence of horizontal gene transfer in M. tuberculosis imposes restrictions in the ways by which evolution can occur. Large-scale changes in the genome can be introduced through genome reduction, recombination events and structural variation. Here, we identify a functional chimeric protein in the ppe38-71 locus, the absence of which is known to have an impact on protein secretion and virulence. To examine whether this approach was used more often by this pathogen, we further develop software that detects potential gene fusion events from multigene deletions using whole genome sequencing data. With this software we could identify a number of other putative gene fusion events within the genomes of M. tuberculosis isolates. We were able to demonstrate the expression of one of these gene fusions at the protein level using mass spectrometry. Therefore, gene fusions may provide an additional means of evolution for M. tuberculosis in its natural environment whereby novel chimeric proteins and functions can arise.