Research Article

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

Sabrina Claßen-von Spee¹, Mar Mallo², Manfred Beier¹, Simone de Leve¹, Leonor Arenillas³, Carmen Pedro⁴, Francesc Solé² and Brigitte Royer-Pokora^1*
1Institute of Human Genetics, Heinrich-Heine University, Duesseldorf, Germany
²MDS Research Group, Institut de Recerca Contra la Leucèmia Josep Carreras, Barcelona, Spain
³Laboratori de Citologia Hematològica Servei de Patologia, Hospital del Mar, Barcelona, Spain
⁴Servei d'Hematologia Clínica, Hospital del Mar, Barcelona, Spain

^*Corresponding author: Brigitte Royer-Pokora, Institute of Human Genetics, Heinrich-Heine University of Duesseldorf, Postfach 101007, D40001 Duesseldorf, Germany

Published: 19 Oct, 2016
Cite this article as: 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.

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

Introduction

The Myelodysplastic Syndromes (MDS) are a heterogeneous group of clonal disorders in the haematopoietic system. Characteristic for MDS is an ineffective haematopoiesis, dysplasia, various degrees of cytopenia and a risk to evolve in Acute Myeloid Leukaemia (AML). For risk calculation the international prognostic scoring system (IPSS) [1] is used and, recently, the IPSS-R has been introduced [2]. In this new IPSS-R, the patients are divided into five risk groups for AML transformation and survival (very low, low, intermediate, high and very high), depending on clinical parameters and the karyotype, being the parameter with the strongest impact [2,3,4]. In both systems, the IPSS and IPSS-R, a cytogenetic normal karyotype often leads to the allocation into a better (good or int-1) risk group and 40-50% of the patients show a normal bone marrow karyotype.
Classical cytogenetic analyses are limited by their resolution and the need of mitotic cells, which is not always successful in MDS. Therefore, array CGH and SNP microarrays were used to analyze MDS bone marrow samples.
However, not in all microarray studies exclusively cytogenetically normal cases were included and in some cases it was stated that cytogenetic analyses were not successful. Therefore, it is not surprising that in some studies a simple addition of all genomic imbalances correlates with a shorter survival as in some cases cytogenetic abnormalities most likely were present. Nevertheless, cryptic imbalances were useful for better prognosis prediction [5,6,7]. In our previous aCGH study of exclusively MDS patients with a normal karyotype we found that 42 of 107 (45%) showed hidden genomic aberrations and many of these were verified by other methods [8]. The patients with hidden additional imbalances had a shorter survival than patients without sub microscopic alterations [8].
The study of bone marrow samples of MDS patients is specifically challenging as per definition only maximally 20% of the bone marrow cells are blast cells. Therefore, some authors used CD34⁺ enriched cell populations but this is a more time consuming method and only small amounts of DNA can be obtained. We therefore compared aCGH and aSNP for their efficiency to detect cryptic aberrations in MDS with normal karyotype using the same DNA from bone marrow samples. The aCGH results were evaluated using four algorithms and the results were compared.

Materials and Methods

Patients and samples
A total of 21 patients with primary MDS and a normal karyotype were included. MDS diagnoses were made according to the 2008 World Health Organization (WHO) [9] classification. The cytogenetic analyses were performed by one of us (FS) and normally 20 metaphases were analyzed. Diagnoses and IPSS scores for these patients are shown in Additional file 1: Table S1. The study design was approved by the Institutional review board (CEIC-Comité Ético de Investigación Clínica Parc de Salut MAR (no. 2008/3268/I)) before its initiation. Informed consent was obtained from all patients enrolled in the study in accordance with the Declaration of Helsinki.
Tumour DNA was isolated from whole bone marrow. All samples were obtained from Parc de Salut Mar Biobank, Mar Biobanc. Germline DNA from eight patients was extracted from isolated peripheral blood CD3+ T-cells (MACS, Miltenyi Biotec GmbH, Germany). DNA was extracted with the Gentra Puregene kit (Qiagen Inc, Valencia, CA, USA). The purity and concentration of genomic DNA was evaluated using the ND-1000 Spectrophotometer (Thermo Fisher Scientific, Wilmington, DE, USA). The integrity was checked by a 2% agarose gel.
Array platforms and evaluations
For the aSNP analyses we used the Genome-Wide Human SNP Array 6.0 (Affymetrix, Santa Clara, CA, USA). Analyses were performed as described in the protocol by the manufacturer (P/N 702607 rev. 2, Affymetrix). Only DNA that fulfilled quality controls required by Affymetrix was submitted for array procedure. Briefly, 500 ng of genomic DNA was digested with restriction enzymes. Then, fragments were ligated to the adapter and amplified by polymerase chain reaction, purified by magnetic beads, fragmented and end-labelled using terminal deoxynucleotidyl transferase. Labelled fragmented amplicons were hybridized to the SNParrays and after washing and staining in a Fluidics Station, arrays were scanned, data analyzed using Genotyping Console Software Version 4.0, Chromosome Analysis Suite Version 1.0.1 (Affymetrix), using annotations of genome version NCBIv30 (hg18). Only those achieving manufacturers’ quality cut-off parameters were included in the analysis. In addition to software-reported CNAs of 100 Kb that carried a minimum of 10 aberrant probes, a visual analysis was performed. Paired sample analysis with T-cell-derived DNA was used to identify germ-line lesions. For CN-LOH, we applied the following threshold: ≥ 50 altered probe sets (SNPs) at least 2 Mb in size [10] for paired sample analysis. Size and location based exclusion criteria (interstitial ≥25 Mb and telomeric ≥50 probes in ≥2 Mb) was applied for non-paired analysis [11].
For the aCGH analyses, we used 4x180K oligonucleotide microarrays (Human Genome CGH Microarray, Agilent Technologies, Palo Alto, CA, USA). Analyses were performed as described in the protocol by the manufacturer (Protocol Version 6.2.1, February 2010; Agilent). Between 500-900 ng DNA was used for labelling and hybridization. In all cases same-gender reference DNA, pooled from 200 individuals was used as control (Kreatech). In cases, where CD3⁺ T-cell DNA was available this was hybridized separately with the same control reference DNA, to be able to detect germ-line alterations. The DNA of the patients was labelled with cyanine 5, the reference DNA was labelled with cyanine 3 and both were hybridized simultaneously on the same slide. After scanning, the data were analyzed using the Agilent Feature Extraction Software (Version 10.7.1.1) and visualized with Agilent Genomic Workbench (Version 7.0), algorithms ADM-2 and threshold 6.2. In addition, three other analysis were used to detect aberrations. Alterations were filtered for affecting at least three oligonucleotides and a minimal absolute log2-ratio of 0.2.
Both aSNP and aCGH data were submitted and are available at the Gene Expression Omnibus (GEO) database under accession number GSE49004 (aSNP) and GSE50897 (aCGH).

Verification Methods

Imbalances >200kb identified with aCGH were confirmed with FISH, smaller aberrations were verified by Q-PCR. One aberration was analyzed by FISH. Therefore two FISH probes on 16p13.3 were chosen from UCSC Genome Bioinformatics site, one probe located inside the aberrations (RP11-20I23) and the other probe mapping outside of the alteration (RP11-346B16). The DNA was isolated with a plasmid DNA purification kit (Macherey-Nagel, Düren, Germany). The probes were labelled with different dyes; DNA of RP11-346B16 was indirectly labelled with digoxigenin and the DNA of RP11-20I23 with biotin by nick-translation and both probes were hybridized simultaneously. Digoxigenin labelled DNA was detected with anti- DIG-fluorescein, biotin labelled DNA was detected with streptavidincyanine 3. At least 200 cells were evaluated and a control sample was hybridized to determine the cut off for the respective BAC probes [12].
For the smaller aberrations, specific Q-PCR primers were designed (Additional file 2: Table S2). The Q-PCR was done with the Fast Start Universal SYBR Green Master Mix from Roche (Roche Applied Science, Mannheim, Germany). The analyses were done in triplicates and every run was repeated at least once. From these six values the mean and standard deviation was calculated. As controls, pooled female and male DNAs were used to calculate the copy number. As reference we used the single copy gene PRNP. The 2^-ddCt-values from the controls and the patients were calculated and compared statistically (2-sided t-test assuming equal variance of the triplicates).
In one case a gain was verified with a custom array, designed to cover the aberration densely including the areas where the two presumed breakpoints were localized. This was achieved by using the eArray platform of Agilent (http://earray.chem.-agilent.com/earray/). The average distance of oligonucleotides within the aberration and the putative breakpoints was 1 Kb.

Results

To make a comparison between the different array platforms the same DNA isolated from 21 bone marrow samples was analyzed in Barcelona with aSNP (Affymetrix) and in Duesseldorf with aCGH (Agilent). To compare the results we used the entire set of alterations including all known CNVs. The focus of this work was to study how many aberrations were detected with either method alone or with both methods and to validate the various algorithms used for aCGH aberration calling. The difficulty in studying tumor genomes is the mixture of cells. In the aCGH analysis we considered a germ-line aberration if it was present in all cells, i.e. log₂-ratio -1 (heterozygous deletion) or +0.6 (heterozygous duplication). Furthermore, a normal control sample from the same patient is desirable for UPD calling and to rule-out germ-line alterations accurately. Therefore in this study eight samples from isolated T-cells were used from the same patients, as T-cells are not regarded as being part of the malignant cells in MDS [13].
Determination of aberrant regions and testing of various algorithms
The aCGH arrays were first analyzed with Agilent's Genomic Workbench software version 7.0, using its ADM-2 algorithm with default settings for breakpoint detection. The threshold for calling aberrations was lowered from the standard setting of 0.25 to a logratio of 0.2, which was also our cut-off for all further analyses. To support the predictions made with the aCGH platform we applied three more methods to the raw feature extraction data, namely the "haarseg" algorithm [14], "lawsglad" [15] and "dnacopy" [16]. All three methods are available as additional packages for the R Statistical Environment [17]. Usage of the first two R-packages for breakpoint detection without changing default values was straightforward, but dnacopy required some tuning: to ensure that aberrations with an absolute log-ratio above our 0.2 cut-off would be detectable, the parameter "undo.SD" was calculated as 0.2 / DLRS (Derivative Log Ratio Spread according to Agilent), preventing two adjacent segments with a difference in log-ratios of 0.2 or more from being merged in the "undo"-step of the dnacopy algorithm.
For Affymetrix aSNP data, the aberrations reported by the Genotyping Console Software version 4.0 (Affymetrix) were converted to Agilent calls by mapping Agilent oligonucleotide positions to the aberrant Affymetrix regions, subsequently treating these aberrations as if called by the mapped oligonucleotides, with a log-ratio corresponding to the stated copy number. With 1.8 million probes and an average distance between markers of 700 bases, the targets of the Affymetrix platform are spotted much more densely than those of Agilent arrays covering the human genome with only 170.000 probes, but at the same time a larger number of aberrant spots (>10) is recommended to warrant a call. Still, even requiring at least 10 oligonucleotides the Affymetrix array would be able to detect much smaller regions not detectable with the Agilent platform. By converting Affymetrix calls to Agilent calls it was possible to apply the same lower threshold of at least three Agilent oligonucleotides, i.e. the same average minimal length of aberrant regions to both data sets. Therefore, in this work we considered only aberrations that are large enough to be seen with both platforms. An R-script was written to combine the results of all five methods, merging overlapping predictions into contiguous regions. Figure 1 shows how a “contig” was created from the dataset. In the following, each such contig is regarded as exactly one aberration, predicted by the union of the methods involved, but usually consisting of a number of segments called by different combinations of these methods. Contigs with less than three oligonucleotides were removed.
330 alterations were only detected with aSNP and 163 only with aCGH. In addition, 74 aberrations were found with both platforms, resulting in a total of 237 aCGH and 404 aSNP aberrations (Figure 2a). The inner circle in the Venn diagram depicts the number of aCGH aberrations seen with all four algorithms. Of the 74 alterations seen with both platforms 31 were seen with all four aCGH algorithms; in addition 41 of the 163 aCGH only alterations were detected with all four different methods, resulting in total of 72 imbalances. In summary, 82% of the alterations detected with the Affymetrix program were not seen with aCGH (330/404), and 68% of the alterations detected with aCGH were not seen with aSNP (163/237) (Figure 2a). Vice versa, 18% of the aSNP alterations were in common with aCGH and 32% of the aCGH imbalances were also seen with aSNP, indicating that both platforms have a relatively high discordance.
Figure 2b shows a more detailed analysis of the aCGH data using the three additional programs. It can be seen that 37 aberrations were called with ADM-2 only. Further 36 alterations were detected with ADM-2 plus one and 92 aberrant regions were found with ADM-2 and two further algorithms (Figure 2b). Of the 72 alterations called with ADM-2 and all three additional programs 31 were also seen with aSNP. These are the most reliable alterations as they were called with all four programs and both platforms.
Table 1 shows the core region of these 31 aberrations and 27 of these correspond 100% to known CNVs. Of note one of these, the TET2 deletion is also listed as 100% CNV in the Table, although it is a tumor specific deletion. CNVs were identified by using the database of genomic variants (http://dgv.tcag.ca; NCBI36_hg18_ variants_2014-10-16.txt). The imbalances that are seen with both platforms and all four aCGH algorithms need not be verified as these are the most reliable, although in one case a custom array verified the small gain. The core regions of the 41 alterations detected with all four aCGH algorithms but not with aSNP are listed in Table 2. As all Q-PCRs performed verified the imbalances we suggest that in these cases another verification is not necessary.
To further explore the basis for the inconsistencies between the platforms, we performed Q-PCR analyses. For this purpose we selected aberrations either seen with all four programs in aCGH and but not with aSNP, or apparently homozygous aberrations called as four or zero copies with aSNP but not with aCGH. A total of 11 aberrations were thus analyzed in 15 aSNP only altered cases and in 10 aCGH only altered cases. The positions in the genome and the primer sequences for Q-PCR are listed in additional file 2, Table S2 and all correspond to known CNVs. In some cases where enough DNA from bone marrow cells was unavailable we used DNA from T-cells as CNVs should be seen in bone marrow and T-cells. (Figure 3) shows the results for these analyses: the top panel shows the relative intensity of aCGH, where 1.5 corresponds to three copies (heterozygous gain), 0.5 to a heterozygous and 0.0 to a homozygous deletion in all cells; the middle panel shows the 2^-ddCt values for the Q-PCRs relative to a probe that is present in 2 copies in the genome (2^-ddCt=1), the bottom panel shows the copy number called by aSNP. The left side of the panel represents alterations called by aSNP only, 14 were gain-4 and one a homozygous loss. The analyzed alterations are listed in additional file 3: Table S3. In seven cases with gain the Q-PCR revealed an increase in the copy number. However, due to the large confidence interval the exact copy number increase cannot be determined. These correspond to known CNVs (e.g. 4q13.2, UGT; 3q26.1 no gene; 16q22 DPR) and at these positions the aCGH arrays have only few oligonucleotides. Therefore, it is not surprising that four breakpoint detection algorithms performed poorly at these positions. In another seven cases with aSNP gain, Q-PCR could not confirm any gain. It is interesting, that the same alteration in 4q13.2 with a gain in case MD144 was not confirmed, whereas in five other cases a gain was confirmed. The gain of 2p11.2 seen by aSNP in cases MD160, MD69, MD121, MD140T was not verified by Q-PCR. It was not observed in aCGH, although the segment was covered by 19 oligonucleotides supporting the notion that it is a false positive result. In addition, the homozygous deletion in MD93H was not confirmed with Q-PCR although the 2-ddCt level was less than 1 (Figure 3).
The right side of the panel shows 10 alterations only seen with aCGH and all were verified with Q-PCR. For example, in case MD140, bone marrow showed a homozygous loss of 11q11 and the loss was verified by Q-PCR. Aberrations affecting a known CNV on 8p11.23 were seen by aCGH in cases PD160 and MD144 as a homozygous loss and in case NM21384 as a gain, verified by Q-PCR. Figure 4a shows the homozygous deletion in PD160 as seen in the Genomic Workbench and Figure 4b the heterozygous gain in NM21384. Both alterations could be verified by Q-PCR as shown in Figure 4c. In contrast, a homozygous loss in the same region in 8p11.23 seen in case MD93H only by aSNP could not be verified by Q-PCR, although a slight reduction can be observed (Figure 4c and d). In case MD69, a gain to three copies in 12p13.31 was observed by aCGH and was observed as a four copy gain by Q-PCR (Figure 3a and b). It is interesting that none of these 10 verified imbalances were called by aSNP, although the smallest of these regions was covered by more than 20 oligonucleotides.
In summary the tested homozygous losses, a heterozygous loss and two gains observed only by aCGH were verified by Q-PCR, and gains as observed by aSNP were verified in 50% of the cases. In some cases (e.g. 4q13.2, 16q22) these were not seen with aCGH because these regions were not covered by enough oligonucleotides.
Larger tumor specific alterations not present in all cells
In two cases, known MDS tumor specific aberrations were found with both platforms and all aCGH algorithms (Table 1). In case MD44, a heterozygous deletion of 424 kb including the TET2 gene was identified as the sole imbalance with a log-ratio of -0.85, present in 89% of the cells. This deletion was identified with all four aCGH evaluation programs and with aSNP and it is listed in the database of genomic variants as CNV. Sequencing revealed a mutation in exon 5 (p.Q1191X) without a wild type allele, confirming the heterozygous deletion. Loss of the normal allele supports its function as a tumour suppressor gene.
In case MD72, a 901 kb deletion in 2p23.3 containing CENPO (disrupted by the deletion), ADCY3, DNAJC27, EFR3B, DNMT3A and DTNBL (disrupted) was revealed with all programs and both methods. It had a log ratio of -0.76, corresponding to a heterozygous deletion in 82% of the cells. We have observed a similar but larger deletion in our previous series of karyotypically normal MDS patients [8]. DNMT3A gene, often mutated in MDS/AML, resides in this deletion. As this deletion spans a large segment, was present in a high percentage of cells and is a known MDS associated gene, verification was not necessary.
In one case (MD69) a tumour specific 3 Mb gain of 16p13.3 with a log-ratio of 0.19 was detected only with aCGH by two programs (ADM-2, lawsglad) (Figure 5). The log-ratio of 0.19 was just below our cut-off used; however, the large size with 248 affected oligonucleotides suggests that this gain is tumor specific and present in only a few cells. This gain was confirmed with FISH in 7.5% of the cells (data not shown).
Furthermore we re-evaluated our previously published aCGH cases using the three additional algorithms. For this purpose, we analyzed tumor specific alterations that were confirmed with either FISH or Q-PCR [8]. The two 7q22, two 5q31, two RUNX1 and two TET2 deletions were seen with all four algorithms. However, it should be noted that these were present in 45-95% of the cells. Therefore, it is currently not resolved whether aberrations present in a lower percentage of cells are always detectable with all four analysis methods.
One small germ-line alteration identified by aCGH and not entirely described as CNV
One gain of 695 kb in 18q22.3 with a log-ratio of 0.54 was detected with all four aCGH programs and aSNP in case MD51 and was verified by a custom array. This alteration is present in bone marrow and T-cells confirming that it is a germ-line gain. Several small CNVs map to this segment; however, the entire region was not described as variant (64%, Table 1). This segment contains several genes FBOX5 (disrupted), CYB5A, FAM69C, CNDP2, CNDP1, ZNF107 (disrupted), that might be risk factors for MDS development. For example the disrupted FBOXO15 gene codes for a protein with the 40-amino acid F-box motif and may act as protein-ubiquitin ligase; an intact gene with increased copy number, CYB5a, encodes cytochrome b5 reductase, reducing met haemoglobin (ferric haemoglobin) to normal haemoglobin (ferrous Hb). One patient with the autosomal recessive disease type IV hereditary methaemoglobinemia was described with a homozygous splice mutation in this gene [18]. Another gene with increased copy number, CNDP2 encodes tissue carnosinase/with a putative function in glutathione metabolism and in xenobiotic metabolic processes. This gene could play a role after exposure to toxic substances, known to be risk factors for MDS development. The same alteration was not observed in any of 515 cases that we have analyzed with the same array type (unpublished observation). Therefore it is possible that the germ-line gain affecting one or several of the genes in this novel 695 kb gain could contribute to MDS development.
UPDs seen with SNP arrays
The detection of UPDs can be helpful to identify new putative genes involved in MDS or other malignancies [19]. UPDs containing relevant genes implicated in cancer were detected in two cases (2/22, 9%) (Figure 6). In case MD116 one UPD was found in 3q and in case MD117, two UPD regions were identified in 4q and 5p (Figure 6). It is interesting that these two cases did not have any other tumour specific gains or deletions. Several genes related to tumour growth map to these segments, for example the 3q25qter region spans 38 Mb and contains several important genes involved in tumour development such as PIK3CA, EVI1/MDS, BCL6 and ETV5 and in apoptosis for example TNFS10. Several tumor related genes map to the UPD on 4q, e.g. CDKN2AIP, a novel regulator of the p53 pathway, ING2 (inhibitor of growth), modulating histone acetyltransferase and histone deacetylase complexes and with a function in DNA repair and apoptosis, CASP3, encoding a protein involved in the apoptotic cell both by extrinsic (death ligand) and intrinsic (mitochondrial) pathways, MLF1, a factor required for centromere assembly and the tumour suppressor gene FAT1. The UPD region in 5p15 contains TERT, encoding a catalytic subunit of the enzyme telomerase with an important function in maintaining the chromosome ends, NKD2 a negative regulator of Wnt receptor signalling and two homeobox genes, one with a possible tumour suppressor function in gastric cancer (IRX1).

Figure 1

Figure 2

Table 1

Table 2

Figure 3

Figure 4

Figure 5

Discussion

In this study of MDS with normal karyotype, we compared two different array platforms (Agilent vs. Affymetrix) using the same DNA samples in two different labs. In addition to the standard Agilent Workbench program, used in most laboratories for the evaluation of aCGH data, three further algorithms were applied and their usefulness was determined. With the default setting of the Agilent Workbench program ADM-2 and a lower log-ratio cut off of 0.2, 237 aberrations were detected. Adding more programs for the evaluation of the data reduced the number significantly and only 72 were called with all four algorithms used. It is not surprising that most of these correspond to CNVs present in all cells. However, two tumor specific deletions present in a high percentage of cells were among these and one was labelled 100% and the other as 80% CNV by the DGV database. Clustering of the aCGH methods showed that ADM- 2 and lawsglad had the highest concordance (Figure 7). Therefore, it is advisable adding at least lawsglad to the standard Agilent program to improve the reliability of the analysis for identifying true aberrations.
It is much more challenging to identify alterations present only in a low percentage of cells as would be expected in a mix of cells in MDS bone marrow. Only three tumor specific imbalances were detected and it is interesting that one was found in 86% and the other in 93%, while the third was only present in 7.5% of the total bone marrow cells. In our previous series of karyotypically normal MDS cases we also found tumor specific aberrations in a high percentage of cells [8]. The current reanalysis of eight tumor specific alterations from this previous work [8] revealed that all were seen with the three additional algorithms. The terminal gain in 16p in 7.5% of the cells and with a log-ratio of 0.19 was seen with ADM-2 and lawsglad, but not with the other two programs. This shows that tumor specific imbalances when present in a low percentage of cells, may not be called by all algorithms but could nevertheless be true imbalances. However, we have analyzed two small aberrations called with ADM-2 and one additional algorithm that could not be verified by Q-PCR. In both cases the log-ratio of 0.4 suggested that about 50% of the cells have a heterozygous deletion and this should have been detectable. Of note, if an alteration is present in a very low percentage of cells it is difficult to confirm with Q-PCR as the sensitivity is not high enough. In such cases we have successfully used custom arrays, covering the region of interest densely with oligonucleotides to verify the aberration [8]. Thus an aberration present in a low percentage of cells can be verified with another method.
It is interesting to point out, that in our previous studies of MDS, we did find that all hidden additional imbalances were present in a high percentage of bone marrow cells either by FISH (between 17 and 96%) or Q-PCR [8]. In addition, we also showed that the 5q deletions were present in 42-91% of total bone marrow cells, although the number of bone marrow blasts ranges between 1 and 30% [20]. This is due to the presence of mature cells in the total bone marrow that also harbour the genetic abnormalities. These studies showed that tumor specific imbalances in MDS are usually found in a higher percentage of cells.
The aSNP data were evaluated with the Affymetrix’ software and of the 404 aberrations (without UPDs) only 74 aberrations were also seen with aCGH (18%), demonstrating that a large percentage of the detected abnormalities were not observed with the other platform. Of the 15 selected aSNP alterations with the highest level of gain or loss not seen with aCGH only half could be confirmed by Q-PCR. The higher number of additional imbalances observed with aSNP only, can partially be explained by the fact that aCGH arrays have few oligonucleotides in many known CNV regions and these are therefore not detected. This was observed in the Q-PCR analyses as several of the confirmed imbalances resided in those CNV regions not covered by aCGH oligonucleotides. This would also suggest that a number of aSNP only imbalances not detected by aCGH map to known CNVs. In contrast, seven tested homozygous losses, one heterozygous loss and two heterozygous gains only seen with aCGH were confirmed. It is therefore sensible to verify somatic imbalances detected with aSNP.
Another important, so far not completely resolved issue, is whether these hidden alterations have the same prognostic significance as karyotypically detected abnormalities [21]. In light of our results it is important to remember that in many array studies imbalances detected by aSNP were not verified by other methods. Furthermore, as cases were included with unsuccessful cytogenetics, the "hidden" imbalances might have been detected if karyotype analysis would have been successful. This could explain why often high frequencies of "hidden" aberrations are described in these studies, whereas in this report we have used only karyotypically normal cases. We found two tumor specific deletions and one gain and two cases with UPD, i.e. 5/21 (23.8%) cases had additional hidden imbalances. Therefore, in order to determine the correct prognostic significance of these hidden aberrations large series of cases with a successful normal karyotype should be studied in the future with verification of at least some of the imbalances. In addition, another question that needs to be addressed is the identity of the gene/s affected by the aberration. For example if either TET2 or TP53 are deleted this will have a different impact for prognosis, while some reports merely count the size of the total genomic imbalances [22,23]. Furthermore, another question is whether a single hidden aberration might have a different impact than several hidden imbalances as seen here in the case with two UPD segments. On top of all these questions we have shown here that not all alterations detected with different platforms or algorithms can be confirmed with other methods.

Figure 6

Figure 7

Conclusion

In summary, we show here that of 567 combined imbalances from two different platforms with the same DNA samples 74 (13%) are in common. The aCGH studies revealed less aberrations than aSNP, suggesting that the latter platform could result in more false positives or could detect more aberrations. The data from the aSNP platform with a higher number of alterations should be interpreted with caution and verification with other methods are also advisable, when these are not present in all cells. Maybe studies with different aSNP algorithms are required. Using different analysis aCGH algorithms improves the reliability for the aCGH results and reduces the number of highly likely true alterations. Therefore one or two other analysis methods should be included in addition to ADM-2. Verification of alterations found with four aCGH analysis methods is not necessary. In contrast, for aCGH alterations found in a lower percentage of cells and with less that three methods another verification is advisable.

Acknowledgement

We thank Dr. Barbara Hildebrandt, Institute of Human Genetics, Duesseldorf for help with the FISH analysis. This collaborative work was initiated through the activities of COST Action BM0801: European Genetic and Epigenetic Study on AML and MDS.

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