A novel SMARCA2-CREM fusion expending the molecular spectrum of salivary gland hyalinazing clear cell carcinoma beyond the FET genes.

Hyalinizing clear cell carcinoma (HCCC) is a rare salivary gland carcinoma with a generally indolent behavior, characterized by recurrent chromosomal translocation involving EWSR1 (22q12.2) leading to two fusion genes EWSR1::ATF1 or EWSR1::CREM. We report one case of HCCC with a novel SMARCA2::CREM fusion, identified by targeted RNA next generation sequencing by LD-RT-PCR, which has until now never been described in salivary glands. The exon 4 of SMARCA2 is fused to exon 5 of CREM. This fusion has been described previously in only one tumor, a central nervous system tumor (intracranial mesenchymal tumor) but not in other FET::CREB fused tumors. This fusion was confirmed by CREM break-apart FISH and reverse transcriptase polymerase chain reaction (RT-PCR). The tumor cells showed retained expression of INI1, SMARCA2, and SMARCA4 by immunohistochemistry. We compare its clinical, histopathological, immunophenotypic, genetic features with those previously described in HCCC, FET::CREB fusion-positive. Our results added data suggesting that different histomolecular tumor subtypes seem to be included within the terminology "HCCC, FET::CREB fusion-positive," and that further series of cases are needed to better characterize them.

Detection of salivary gland and sinonasal fusions by a next-generation sequencing based, ligation-dependent, multiplex RT-PCR assay.

AIMS: The discovery of tumour type-specific gene fusion oncogenes in benign and malignant salivary gland and sinonasal (SGSN) tumours has significantly increased our knowledge about their molecular pathology and classification. METHODS AND RESULTS: We developed a new targeted multiplexed next-generation sequencing (NGS)-based method that utilizes ligation dependent reverse-transcriptase polymerase chain reaction (LD-RT-PCR) to detect oncogenic fusion transcripts involving 116 genes, leading to 96 gene fusions known to be recurrently rearranged in these tumours. In all, 180 SGSN tumours (formalin-fixed, paraffin-embedded samples, 141 specimens and 39 core needle biopsies) from the REFCORpath (French network for rare head and neck cancers) with previously identified fusion genes by fluorescent in situ hybridisation (FISH), RT-PCR, or molecular immunohistochemistry were selected to test its specificity and sensitivity and validate its diagnostic use. Tested tumours encompassed 14 major tumours types, including secretory carcinoma, mucoepidermoid carcinoma, adenoid cystic carcinoma, salivary gland intraductal carcinoma, clear cell carcinoma, pleomorphic adenoma, adamantinoma-like Ewing Sarcoma, EWSR1::COLCA2 sinonasal sarcoma, DEK::AFF2 sinonasal carcinoma, and biphenotypic sinonasal sarcoma. In-frame fusion transcripts were detected in 97.8% of cases (176/180). Gene fusion assay results correlated with conventional techniques (immunohistochemistry [IHC], FISH, and RT-PCR) in 176/180 tumours (97.8%). CONCLUSION: This targeted multiplexed NGS-based LD-RT-PCR method is a robust, highly sensitive method for the detection of recurrent gene fusions from routine clinical SGSN tumours. It can be easily customized to cover new fusions. These results are promising for implementing an integrated NGS system to rapidly detect genetic aberrations, facilitating accurate, genomics-based diagnoses, and accelerate time to precision therapies in SGSN tumours.

Detection of sarcoma fusions by a next-generation sequencing based-ligation-dependent multiplex RT-PCR assay.

Morphological, immunohistochemical, and molecular methods often need to be combined for accurate diagnosis and optimal clinical management of sarcomas. Here, we have developed, a new molecular diagnostic assay, for the detection of gene fusions in sarcomas. This targeted multiplexed next-generation sequencing (NGS)-based method utilizes ligation dependent reverse-transcriptase polymerase chain reaction (LD-RT-PCR-NGS) to detect oncogenic fusion transcripts involving 137 genes, leading to 139 gene fusions known to be recurrently rearranged in soft-tissue and bone tumors. 158 bone and soft-tissue tumors with previously identified fusion genes by fluorescent in situ hybridization (FISH) or RT-PCR were selected to test the specificity and the sensitivity of this assay. RNA were extracted from formalin-fixed paraffin-embedded (n = 143) or frozen (n = 15) material (specimen; n = 42 or core needle biopsies; n = 116). Tested tumors encompassed 23 major translocation-related sarcomas types, including Ewing and Ewing-like sarcomas, rhabdomyosarcomas, desmoplastic small round-cell tumors, clear-cell sarcomas, infantile fibrosarcomas, endometrial stromal sarcomas, epithelioid hemangioendotheliomas, alveolar soft-part sarcomas, biphenotypic sinonasal sarcomas, extraskeletal myxoid chondrosarcomas, myxoid/round-cell liposarcomas, dermatofibrosarcomas protuberans and solitary fibrous tumors. In-frame fusion transcripts were detected in 98.1% of cases (155/158). Gene fusion assay results correlated with conventional techniques (FISH and RT-PCR) in 155/158 tumors (98.1%). These data demonstrate that this assay is a rapid, robust, highly sensitive, and multiplexed targeted RNA sequencing assay for the detection of recurrent gene fusions on RNA extracted from routine clinical specimens of sarcomas (formalin-fixed paraffin-embedded or frozen). It facilitates the precise diagnosis and identification of tumors with potential targetable fusions. In addition, this assay can be easily customized to cover new fusions.

Improving high-resolution copy number variation analysis from next generation sequencing using unique molecular identifiers.

BACKGROUND: Recently, copy number variations (CNV) impacting genes involved in oncogenic pathways have attracted an increasing attention to manage disease susceptibility. CNV is one of the most important somatic aberrations in the genome of tumor cells. Oncogene activation and tumor suppressor gene inactivation are often attributed to copy number gain/amplification or deletion, respectively, in many cancer types and stages. Recent advances in next generation sequencing protocols allow for the addition of unique molecular identifiers (UMI) to each read. Each targeted DNA fragment is labeled with a unique random nucleotide sequence added to sequencing primers. UMI are especially useful for CNV detection by making each DNA molecule in a population of reads distinct. RESULTS: Here, we present molecular Copy Number Alteration (mCNA), a new methodology allowing the detection of copy number changes using UMI. The algorithm is composed of four main steps: the construction of UMI count matrices, the use of control samples to construct a pseudo-reference, the computation of log-ratios, the segmentation and finally the statistical inference of abnormal segmented breaks. We demonstrate the success of mCNA on a dataset of patients suffering from Diffuse Large B-cell Lymphoma and we highlight that mCNA results have a strong correlation with comparative genomic hybridization. CONCLUSION: We provide mCNA, a new approach for CNV detection, freely available at https://gitlab.com/pierrejulien.viailly/mcna/ under MIT license. mCNA can significantly improve detection accuracy of CNV changes by using UMI.

UMI-VarCal: a new UMI-based variant caller that efficiently improves low-frequency variant detection in paired-end sequencing NGS libraries.

MOTIVATION: Next-generation sequencing has become the go-to standard method for the detection of single-nucleotide variants in tumor cells. The use of such technologies requires a PCR amplification step and a sequencing step, steps in which artifacts are introduced at very low frequencies. These artifacts are often confused with true low-frequency variants that can be found in tumor cells and cell-free DNA. The recent use of unique molecular identifiers (UMI) in targeted sequencing protocols has offered a trustworthy approach to filter out artefactual variants and accurately call low-frequency variants. However, the integration of UMI analysis in the variant calling process led to developing tools that are significantly slower and more memory consuming than raw-reads-based variant callers. RESULTS: We present UMI-VarCal, a UMI-based variant caller for targeted sequencing data with better sensitivity compared to other variant callers. Being developed with performance in mind, UMI-VarCal stands out from the crowd by being one of the few variant callers that do not rely on SAMtools to do their pileup. Instead, at its core runs an innovative homemade pileup algorithm specifically designed to treat the UMI tags in the reads. After the pileup, a Poisson statistical test is applied at every position to determine if the frequency of the variant is significantly higher than the background error noise. Finally, an analysis of UMI tags is performed, a strand bias and a homopolymer length filter are applied to achieve better accuracy. We illustrate the results obtained using UMI-VarCal through the sequencing of tumor samples and we show how UMI-VarCal is both faster and more sensitive than other publicly available solutions. AVAILABILITY AND IMPLEMENTATION: The entire pipeline is available at https://gitlab.com/vincent-sater/umi-varcal-master under MIT license. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Analysis of immunoglobulin/T-cell receptor repertoires by high-throughput RNA sequencing reveals a continuous dynamic of positive clonal selection in follicular lymphoma.

Follicular lymphoma (FL) course is highly variable, making its clinical management challenging. In this incurable and recurring pathology, the interval between relapses tends to decrease while aggressiveness increases, sometimes resulting in the transformation to higher-grade lymphoma. These evolutions are particularly difficult to anticipate, resulting from complex clonal evolutions where multiple subclones compete and thrive due to their capacity to proliferate and resist therapies. Here, to apprehend further these processes, we used a high-throughput RNA sequencing approach to address simultaneously the B-cell immunoglobulin repertoires and T-cell immunoglobulin repertoires repertoires of lymphoma cells and their lymphoid microenvironment in a large cohort of 131 FL1/2-3A patients. Our data confirm the existence of a high degree of intra-clonal heterogeneity in this pathology, resulting from ongoing somatic hyper-mutation and class switch recombination. Through the evaluation of the Simpson ecological-diversity index, we show that the contribution of the cancerous cells increases during the course of the disease to the detriment of the reactive compartment, a phenomenon accompanied by a concomitant decrease in the diversity of the tumoral population. Clonal evolution in FL thus contrasts with many tumors, where clonal heterogeneity steadily increases over time and participates in treatment evasion. In this pathology, the selection of lymphoma subclones with proliferative advantages progressively outweighs clonal diversification, ultimately leading in extreme cases to transformation to high-grade lymphoma resulting from the rapid emergence of homogeneous subpopulations.

UMI-Varcal: A Low-Frequency Variant Caller for UMI-Tagged Paired-End Sequencing Data.

The rapid transition from traditional sequencing methods to Next-Generation Sequencing (NGS) has allowed for a faster and more accurate detection of somatic variants (Single-Nucleotide Variant (SNV) and Copy Number Variation (CNV)) in tumor cells. NGS technologies require a succession of steps during which false variants can be silently added at low frequencies. Filtering these artifacts can be a rather difficult task especially when the experiments are designed to look for very low frequency variants. Recently, adding unique molecular barcodes called UMI (Unique Molecular Identifier) to the DNA fragments appears to be a very effective strategy to specifically filter out false variants from the variant calling results (Kukita et al. DNA Res 22(4):269-277, 2015; Newman et al. Nat Biotechnol 34(5):547-555, 2016; Schmitt et al. Proc Natl Acad Sci U S A 109(36):14508-14513). Here, we describe UMI-VarCal (Sater et al. Bioinformatics 36:2718-2724, 2020), which can use the UMI information from UMI-tagged reads to offer a faster and more accurate variant calling analysis.

Detection of Gene Fusion Transcripts in Peripheral T-Cell Lymphoma Using a Multiplexed Targeted Sequencing Assay.

The genetic basis of peripheral T-cell lymphoma (PTCL) is complex and encompasses several recurrent fusion transcripts discovered over the past years by means of massive parallel sequencing. However, there is currently no affordable and rapid technology for their simultaneous detection in clinical samples. Herein, we developed a multiplex ligation-dependent RT-PCR-based assay, followed by high-throughput sequencing, to detect 33 known PTCL-associated fusion transcripts. Anaplastic lymphoma kinase (ALK) fusion transcripts were detected in 15 of 16 ALK-positive anaplastic large-cell lymphomas. The latter case was further characterized by a novel SATB1_ALK fusion transcript. Among 239 other PTCLs, representative of nine entities, non-ALK fusion transcripts were detected in 24 samples, mostly of follicular helper T-cell (TFH) derivation. The most frequent non-ALK fusion transcript was ICOS_CD28 in nine TFH-PTCLs, one PTCL not otherwise specified, and one adult T-cell leukemia/lymphoma, followed by VAV1 rearrangements with multiple partners (STAP2, THAP4, MYO1F, and CD28) in five samples (three PTCL not otherwise specified and two TFH-PTCLs). The other rearrangements were CTLA4_CD28 (one TFH-PTCL), ITK_SYK (two TFH-PTCLs), ITK_FER (one TFH-PTCL), IKZF2_ERBB4 (one TFH-PTCL and one adult T-cell leukemia/lymphoma), and TP63_TBL1XR1 (one ALK-negative anaplastic large-cell lymphoma). All fusions detected by our assay were validated by conventional RT-PCR and Sanger sequencing in 30 samples with adequate material. The simplicity and robustness of this targeted multiplex assay make it an attractive tool for the characterization of these heterogeneous diseases.

UMI-Gen: A UMI-based read simulator for variant calling evaluation in paired-end sequencing NGS libraries.

MOTIVATION: With Next Generation Sequencing becoming more affordable every year, NGS technologies asserted themselves as the fastest and most reliable way to detect Single Nucleotide Variants (SNV) and Copy Number Variations (CNV) in cancer patients. These technologies can be used to sequence DNA at very high depths thus allowing to detect abnormalities in tumor cells with very low frequencies. Multiple variant callers are publicly available and are usually efficient at calling out variants. However, when frequencies begin to drop under 1%, the specificity of these tools suffers greatly as true variants at very low frequencies can be easily confused with sequencing or PCR artifacts. The recent use of Unique Molecular Identifiers (UMI) in NGS experiments has offered a way to accurately separate true variants from artifacts. UMI-based variant callers are slowly replacing raw-read based variant callers as the standard method for an accurate detection of variants at very low frequencies. However, benchmarking done in the tools publication are usually realized on real biological data in which real variants are not known, making it difficult to assess their accuracy. RESULTS: We present UMI-Gen, a UMI-based read simulator for targeted sequencing paired-end data. UMI-Gen generates reference reads covering the targeted regions at a user customizable depth. After that, using a number of control files, it estimates the background error rate at each position and then modifies the generated reads to mimic real biological data. Finally, it will insert real variants in the reads from a list provided by the user. AVAILABILITY: The entire pipeline is available at https://gitlab.com/vincent-sater/umigen under MIT license.

Combining gene expression profiling and machine learning to diagnose B-cell non-Hodgkin lymphoma.

Non-Hodgkin B-cell lymphomas (B-NHLs) are a highly heterogeneous group of mature B-cell malignancies. Their classification thus requires skillful evaluation by expert hematopathologists, but the risk of error remains higher in these tumors than in many other areas of pathology. To facilitate diagnosis, we have thus developed a gene expression assay able to discriminate the seven most frequent B-cell NHL categories. This assay relies on the combination of ligation-dependent RT-PCR and next-generation sequencing, and addresses the expression of more than 130 genetic markers. It was designed to retrieve the main gene expression signatures of B-NHL cells and their microenvironment. The classification is handled by a random forest algorithm which we trained and validated on a large cohort of more than 400 annotated cases of different histology. Its clinical relevance was verified through its capacity to prevent important misclassification in low grade lymphomas and to retrieve clinically important characteristics in high grade lymphomas including the cell-of-origin signatures and the MYC and BCL2 expression levels. This accurate pan-B-NHL predictor, which allows a systematic evaluation of numerous diagnostic and prognostic markers, could thus be proposed as a complement to conventional histology to guide the management of patients and facilitate their stratification into clinical trials.