Microarray-based genomic analysis identifies germline and somatic copy number variants and loss of heterozygosity in acute myeloid leukaemia

Introduction: Insights into molecular karyotyping using comparative genomic hybridization (CGH) and single nucleotide polymorphism (SNP) arrays enable the identification of copy number variations (CNVs) at a higher resolution and facilitate the detection of copy neutral loss of heterozygosity (CN-LOH) otherwise undetectable by conventional cytogenetics. The applicability of a customised CGH+SNP 180K DNA microarray in the diagnostic evaluation of Acute Myeloid Leukaemia (AML) in comparison with conventional karyotyping was assessed in this study. Methods: Paired tumour and germline post induction (remission sample obtained from the same patient after induction) DNA were used to delineate germline variants in 41 AML samples and compared with the karyotype findings. Results: After comparing the tumour versus germline DNA, a total of 55 imbalances (n 5-10 MB = 21, n 10-20 MB = 8 and n >20 MB = 26) were identified. Gains were most common in chromosome 4 (26.7%) whereas losses were most frequent in chromosome 7 (28.6%) and X (25.0%). CN-LOH was mostly seen in chromosome 4 (75.0%). Comparison between array CGH+SNP and karyotyping revealed 20 cases were in excellent agreement and 13 cases did not concord whereas in 15 cases finding could not be confirmed as no karyotypes available. Conclusion: The use of a combined array CGH+SNP in this study enabled the detection of somatic and germline CNVs and CN-LOHs in AML. Array CGH+SNP accurately determined chromosomal breakpoints compared to conventional cytogenetics in relation to presence of CNVs and CN-LOHs

Haematological Reference Intervals in a Multiethnic Population

Introduction: Similar to other populations, full blood count reference (FBC) intervals in Malaysia are generally derived from non-Malaysian subjects. However, numerous studies have shown significant differences between and within populations supporting the need for population specific intervals. Methods: Two thousand seven hundred twenty five apparently healthy adults comprising all ages, both genders and three principal races were recruited through voluntary participation. FBC was performed on two analysers, Sysmex XE-5000 and Unicel DxH 800, in addition to blood smears and haemoglobin analysis. Serum ferritin, soluble transferrin receptor and Creactive protein assays were performed in selected subjects. All parameters of qualified subjects were tested for normality followed by determination of reference intervals, measures of central tendency and dispersion along with point estimates for each subgroup. Results: Complete data was available in 2440 subjects of whom 56% (907 women and 469 men) were included in reference interval calculation. Compared to other populations there were significant differences for haemoglobin, red blood cell count, platelet count and haematocrit in Malaysians. There were differences between men and women, and between younger and older men; unlike in other populations, haemoglobin was similar in younger and older women. However ethnicity and smoking had little impact. 70% of anemia in premenopausal women, 24% in postmenopausal women and 20% of males is attributable to iron deficiency. There was excellent correlation between Sysmex XE-5000 and Unicel DxH 800. Conclusion: Our data confirms the importance of population specific haematological parameters and supports the need for local guidelines rather than adoption of generalised reference intervals and cut-offs

Incidence of anaemia, iron deficiency and iron deficiency anaemia.

<p>Incidence of anaemia, iron deficiency and iron deficiency anaemia.</p

Comparison of Reference Intervals of various populations.

<p>Gaza Strip (N = 50,127)<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0091968#pone.0091968-Sirdah1" target="_blank">[1]</a>.</p><p>Germany(N = 2967)<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0091968#pone.0091968-Ittermann1" target="_blank">[6]</a>.</p><p>US (N = 7664) <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0091968#pone.0091968-Beutler1" target="_blank">[4]</a>, (N = not available)<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0091968#pone.0091968-NHANES1" target="_blank">[5]</a>, (N = not available)<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0091968#pone.0091968-Hsieh1" target="_blank">[8]</a>.</p><p>UK (N = 700)<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0091968#pone.0091968-OseiBimpong1" target="_blank">[2]</a>.</p><p>South India(N = 500)<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0091968#pone.0091968-Subhashree1" target="_blank">[3]</a>.</p><p>Ghana (N = 691)<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0091968#pone.0091968-Dosoo1" target="_blank">[7]</a>.</p>a<p>19–45 years old.</p>b<p>>45 years old.</p>c<p>20–81 years old.</p>d<p>50–59 years old.</p>e<p>60–69 years old.</p>f<p>19–65 years old.</p>g<p>66 years and above.</p>h<p>16–91 years.</p>i<p>18–70 years.</p>j<p>18–59 years.</p><p>W = White B = Black.</p

Reference intervals from this study compared with previous study.

<p>M =  male, F =  female.</p><p>* Median, range 2.5-97.5 centiles.</p><p>** Ferritin ng/mL<13 excluded.</p>§<p>Reference interval  =  mean±SD.</p