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Virtual-Karyotyping with SNP microarrays in morphologically challenging renal cell neoplasms

ABSTRACT: Genetic lesions characteristic for RCC subtypes can be identified by virtual karyotyping with SNP microarrays. In this study, we examined whether virtual karyotypes could be used to better classify a cohort of morphologically challenging/unclassified RCC.

ORGANISM(S): Homo sapiens

PROVIDER: GSE14670 | GEO | 2009/03/13

SECONDARY ACCESSION(S): PRJNA112365

REPOSITORIES: GEO

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Virtual-Karyotyping with SNP microarrays in morphologically challenging renal cell neoplasms

Project description:Genetic lesions characteristic for RCC subtypes can be identified by virtual karyotyping with SNP microarrays. In this study, we examined whether virtual karyotypes could be used to better classify a cohort of morphologically challenging/unclassified RCC. Tumor resection specimens from 17 patients were profiled by virtual karyotyping with Affymetrix 10K 2.0 or 250K Nsp SNP Mapping arrays and were also evaluated independently by a panel of seven genito-urinary pathologists. Tumors were classified by the established pattern of genomic imbalances based on a reference cohort of 98 cases with classic morphology and compared to the morphologic diagnosis of the pathologist panel. In 3 cases, samples from areas with different morphologic appearance were also tested (n=5).

2009-03-13 | E-GEOD-14670 | biostudies-arrayexpress

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Project description:Although copy number alterations (CNAs) and translocations constitute the backbone of the diagnosis and prognostication of acute myeloid leukemia (AML), techniques used for their assessment in routine diagnostics have not been reconsidered for decades. We used a combination of 2 next-generation sequencing-based techniques to challenge the currently recommended conventional cytogenetic analysis (CCA), comparing the approaches in a series of 281 intensively treated patients with AML. Shallow whole-genome sequencing (sWGS) outperformed CCA in detecting European Leukemia Net (ELN)-defining CNAs and showed that CCA overestimated monosomies and suboptimally reported karyotype complexity. Still, the concordance between CCA and sWGS for all ELN CNA-related criteria was 94%. Moreover, using in silico dilution, we showed that 1 million reads per patient would be enough to accurately assess ELN-defining CNAs. Total genomic loss, defined as a total loss ≥200 Mb by sWGS, was found to be a better marker for genetic complexity and poor prognosis compared with the CCA-based definition of complex karyotype. For fusion detection, the concordance between CCA and whole-transcriptome sequencing (WTS) was 99%. WTS had better sensitivity in identifying inv(16) and KMT2A rearrangements while showing limitations in detecting lowly expressed PML-RARA fusions. Ligation-dependent reverse transcription polymerase chain reaction was used for validation and was shown to be a fast and reliable method for fusion detection. We conclude that a next-generation sequencing-based approach can replace conventional CCA for karyotyping, provided that efforts are made to cover lowly expressed fusion transcripts.

| S-EPMC7903223 | biostudies-literature

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