Chronic lymphocytic leukemia (CLL) is a clinically heterogeneous disease. The methods currently used for monitoring CLL and determining conditions for treatment are limited in their ability to predict disease progression, patient survival, and response to therapy. Although clonal diversity and the acquisition of new chromosomal abnormalities during the disease course (clonal evolution) have been associated with disease progression, their prognostic potential has been underappreciated because cytogenetic and fluorescence in situ hybridization (FISH) studies have a restricted ability to detect genomic abnormalities and clonal evolution. We hypothesized that whole genome analysis using high resolution single nucleotide polymorphism (SNP) microarrays would be useful to detect diversity and infer clonal evolution to offer prognostic information. In this study, we used the Infinium Omni1 BeadChip (Illumina, San Diego, CA) array for the analysis of genetic variation and percent mosaicism in 25 non-selected CLL patients to explore the prognostic value of the assessment of clonal diversity in patients with CLL. We calculated the percentage of mosaicism for each abnormality by applying a mathematical algorithm to the genotype frequency data and by manual determination using the Simulated DNA Copy Number (SiDCoN) tool, which was developed from a computer model of mosaicism. At least one genetic abnormality was identified in each case, and the SNP data was 98% concordant with FISH results. Clonal diversity, defined as the presence of two or more genetic abnormalities with differing percentages of mosaicism, was observed in 12 patients (48%), and the diversity correlated with the disease stage. Clonal diversity was present in most cases of advanced disease (Rai stages III and IV) or those with previous treatment, whereas 9 of 13 patients without detected clonal diversity were asymptomatic or clinically stable. In conclusion, SNP microarray studies with simultaneous evaluation of genomic alterations and mosaic distribution of clones can be used to assess apparent clonal evolution via analysis of clonal diversity. Since clonal evolution in CLL is strongly correlated with disease progression, whole genome SNP microarray analysis provides a new comprehensive and reliable prognostic tool for CLL patients.
[Show abstract][Hide abstract] ABSTRACT: With a worldwide prevalence of about 1 in 3500-5000 individuals, Retinitis Pigmentosa (RP) is the most common form of hereditary retinal degeneration. It is an extremely heterogeneous group of genetically determined retinal diseases leading to progressive loss of vision due to impairment of rod and cone photoreceptors. RP can be inherited as an autosomal-recessive, autosomal-dominant, or X-linked trait. Non-Mendelian inheritance patterns such as digenic, maternal (mitochondrial) or compound heterozygosity have also been reported. To date, more than 65 genes have been implicated in syndromic and non-syndromic forms of RP, which account for only about 60% of all RP cases. Due to this high heterogeneity and diversity of inheritance patterns, the molecular diagnosis of syndromic and non-syndromic RP is very challenging, and the heritability of 40% of total RP cases worldwide remains unknown. However new sequencing methodologies, boosted by the human genome project, have contributed to exponential plummeting in sequencing costs, thereby making it feasible to include molecular testing for RP patients in routine clinical practice within the coming years. Here, we summarize the most widely used state-of-the-art technologies currently applied for the molecular diagnosis of RP, and address their strengths and weaknesses for the molecular diagnosis of such a complex genetic disease.
Vision research 09/2012; 75. DOI:10.1016/j.visres.2012.09.012 · 1.82 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Several prognostic markers based on genetic, phenotypic, and molecular characteristics of chronic lymphocytic leukemia (CLL) B cells have emerged in the past decade. The clinical utility of these newer prognostic indicators, alone or in combination with each other and other clinical predictive systems, is still being determined. This chapter attempts to define biologic and molecular underpinnings of 3 sets of prognostic indicators in CLL: genetic abnormalities quantified by FISH and/or defined by exploratory sensitive molecular techniques, expression of specific proteins in or on CLL cells (ie, CD38, CD49d, and ZAP-70), and the IGHV mutation status of a CLL clone. Although not demonstrated conclusively, each probably reflects the biologic properties of the leukemic cells of individual CLL patients. This reflection may be direct, indicating a specific property of the CLL cell itself, or indirect, representing how the CLL cell interacts with the host's microenvironment. The new tyrosine kinase inhibitors that are currently in clinical trials support this interpretation. These and other biology-based indicators of patient clinical course and outcome can be used as starting points from which to understand and treat CLL.
[Show abstract][Hide abstract] ABSTRACT: Recent technological advances in the detection of genomic structural variation have revolutionized the field of medical genetics. Genome-wide screening for copy-number variants in routine molecular diagnostics unveiled the presence of an unforeseen amount of structural variation in the genome. Owing to the massive amount of patients analyzed, the analysis of the resulting data became exponentially more complex. Simultaneously, novel insights in the impact of structural variation on the phenotype forced the re-evaluation of the pathogenicity of copy-number variations in more complex inheritance models. As a consequence, the challenge of today's genetics shifted from the mere detection of structural variation to the correct annotation and interpretation of the data. Various databases and data mining tools are available to help in the interpretation of the data, but making decisions on the pathogeniticy of the variation is still challenging. This review provides an overview of current laboratory techniques to detect structural variation, options to analyze and annotate data from genome-wide methods and caveats to take into account in interpretation of results.
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