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Lazaro, C.

Publications and source records attributed to Lazaro, C..

2 recordsLinked to original sources

A detailed landscape of genomic alterations in malignant peripheral nerve sheath tumor cell lines challenges the current MPNST diagnosis

BackgroundMalignant peripheral nerve sheath tumors (MPNSTs) are soft tissue sarcomas that arise from the peripheral nervous system. Half of the tumors develop in the context of the genetic disease Neurofibromatosis type 1 (NF1) and the rest are sporadic sarcomas. MPNSTs have a dismal prognosis due to their aggressiveness and tendency to metastasize, and new treatment options are needed. The diagnosis of MPNSTs can be challenging, especially outside of the NF1 context since specific histological criteria have not been completely established. Genomic analysis may both facilitate differential diagnoses and suggest precision medicine strategies. MethodsWe generated a complete genomic resource of a set of widely used human NF1-related and sporadic MPNST cell lines by applying ploidy analysis, whole genome and whole exome sequencing and SNP-array analysis, complemented by methylome-based classification and immunofluorescence of cell identity markers (SOX9, SOX10, S100B). ResultsNF1 MPNST cell lines faithfully recapitulated the genomic copy number profile of primary MPNSTs. Structural variants were key players in the complete inactivation of most recurrently altered tumor suppressor genes (TSGs) (NF1, CDKN2A, SUZ12/EED), while small variants played a minor role in the NF1 context, both concerning TSG inactivation and due to the absence of gain-of-function mutations. In clear contrast, the sporadic cell lines (STS-26T, HS-Sch-2, HS-PSS) did not recapitulate the copy number profile of primary MPNSTs. They carried different TSG inactivation and exhibited gain-of-function mutations by predicted kinase activation or generation of fusion genes. Mutational frequencies and signatures emerged as promising informative tools for aiding in MPNST differential diagnosis. Due to the multiple genomic differences exhibited, we complemented their characterization using a methylome-based classifier. All NF1-related cell lines were assigned within the MPNST group, while sporadic cell lines clustered either with melanomas or with an uncertain MPNST-like sarcoma group. The staining of cell identity markers reinforced the idea of a potential misdiagnose of the MPNSTs used to derive the sporadic cell lines analyzed. ConclusionsDeep genomic analysis, together with methylome-based sarcoma classification and cell identity marker analysis, challenged the MPNST identity of sporadic cell lines. Results presented here open an opportunity to revise MPNST differential diagnosis and classification.

genomics↗

Benchmark of tools for CNV detection from NGS panel data in a genetic diagnostics context

MotivationAlthough germline copy number variants (CNVs) are the genetic cause of multiple hereditary diseases, detecting them from targeted next-generation sequencing data (NGS) remains a challenge. Existing tools perform well for large CNVs but struggle with single and multi-exon alterations. The aim of this work is to evaluate CNV calling tools working on gene panel NGS data with CNVs up to single-exon resolution and their suitability as a screening step before orthogonal confirmation in genetic diagnostics strategies. ResultsFive tools (DECoN, CoNVaDING, panelcn.MOPS, ExomeDepth and CODEX2) were tested against four genetic diagnostics datasets (495 samples, 231 CNVs), using the default and sensitivity-optimized parameters. Most tools were highly sensitive and specific, but the performance was dataset-dependant. In our in-house datasets, DECoN and panelcn.MOPS with optimized parameters showed enough sensitivity to be used as screening methods in genetic diagnostics. AvailabilityBenchmarking-optimization code is freely available at https://github.com/TranslationalBioinformaticsIGTP/CNVbenchmarkeR.

bioinformatics↗