Journal Basic Info

  • Impact Factor: 2.709**
  • H-Index: 11 
  • ISSN: 2474-1663
  • DOI: 10.25107/2474-1663
**Impact Factor calculated based on Google Scholar Citations. Please contact us for any more details.

Major Scope

  •  Head and Neck Oncology
  •  Hematology
  •  Hormone Therapy
  •  Adjuvant Therapy
  •  Blood Cancer
  •  General Oncology
  •  Lung Cancers
  •  Immunology

Abstract

Citation: Clin Oncol. 2016;1(1):1118.DOI: 10.25107/2474-1663.1118

Bioinformatic Evaluation and Comparison of Parallel aSNP and aCGH Analyses of Myelodysplastic Syndromes Patients with Normal Karyotype

Sabrina Claßen-von Spee, Mar Mallo, Manfred Beier, Simone de Leve, Leonor Arenillas, Carmen Pedro, Francesc Solé and Brigitte Royer-Pokora


Institute of Human Genetics, Heinrich-Heine University, Duesseldorf, Germany
MDS Research Group, Institut de Recerca Contra la Leucèmia Josep Carreras, Barcelona, Spain
Laboratori de Citologia Hematològica Servei de Patologia, Hospital del Mar, Barcelona, Spain
Servei d'Hematologia Clínica, Hospital del Mar, Barcelona, Spain

*Correspondance to: Brigitte Royer-Pokora 

 PDF  Full Text Research Article | Open Access

Abstract:

To study MDS bone marrow samples for tumor specific alterations two different microarray platforms, aSNP and aCGH, have been widely used. The purpose of this study was 1) to compare the two array methods and 2) evaluate the usefulness of different aCGH algorithms for the identification of authentic alterations in tumoral samples. Parallel aSNP and aCGH analyses were performed on the same 21 bone marrow DNA samples from karyotypically normal MDS patients. FISH and Q-PCR methods were used to verify several alterations. The aSNP data were evaluated using Genotyping Console Software; aCGH data were analyzed with the ADM-2 setting of the Agilent Genomic Workbench program, followed by three additional algorithms, haarseq, lawsglad and dnacopy. 404 alterations were seen with aSNP of these 74 were also seen with aCGH with at least the ADM-2 algorithm. With the ADM-2 setting, 237 imbalances were detected, of these 72 were seen with all four aCGH algorithms. Among the latter aberrations were two tumour specific deletions, a TET2 deletion and a larger deletion containing DNMT3A, present in a high percentage of cells. One tumour specific telomeric 16p gain only seen with aCGH was confirmed with FISH in 7.5% of the cells. As expected, uniparental disomies (UPDs) were only detected with aSNP; in one case at 3q and in the other case two UPDs at 4q and 5p. The discrepancies between both methods and the algorithms are discussed in detail. Our results show that 72/237 (30%) aCGH alterations were predicted with all four algorithms. Of the 74 alterations seen with both platforms 31 were seen with all algorithms. 18% of the aSNP alterations and 31% of the aCGH alterations were also seen with the other platform. Of 15 selected aberrations detected with aSNP only and with the highest deviation from normal 50% could be confirmed by Q-PCR, whereas all 10 selected imbalances detected with aCGH only were confirmed by Q-PCR. Therefore, using several algorithms for aCGH analysis, increases the number of true alterations. aSNP data should be interpreted with caution and another verification method is advisable.

Keywords:

aSNP; aCGH; Karyotypically normal MDS; Bioinformatic evaluation

Cite the Article:

Claßen-von Spee S, Mallo M, Beier M, de Leve S, Arenillas L, Pedro C, et al. Bioinformatic Evaluation and Comparison of Parallel aSNP and aCGH Analyses of Myelodysplastic Syndromes Patients with Normal Karyotype. Clin Oncol. 2016; 1: 1118.

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