Maciej Sykulski

Detection of Clinically Relevant Exonic Copy-Number Changes by Array CGH

Array comparative genomic hybridization (aCGH) is a powerful tool for the molecular elucidation and diagnosis of disorders resulting from genomic copy‐number variation (CNV). However, intragenic deletions or duplications—those including genomic intervals of a size smaller than a gene—have remained beyond the detection limit of most clinical aCGH analyses. Increasing array probe number improves genomic resolution, although higher cost may limit implementation, and enhanced detection of benign CNV can confound clinical interpretation. We designed an array with exonic coverage of selected disease and candidate genes and used it clinically to identify losses or gains throughout the genome involving at least one exon and as small as several hundred base pairs in size. In some patients, the detected copy‐number change occurs within a gene known to be causative of the observed clinical phenotype, demonstrating the ability of this array to detect clinically relevant CNVs with subkilobase resolution. In summary, we demonstrate the utility of a custom‐designed, exon‐targeted oligonucleotide array to detect intragenic copy‐number changes in patients with various clinical phenotypes. Hum Mutat 31:1–17, 2010.

Application of custom-designed oligonucleotide array CGH in 145 patients with autistic spectrum disorders

Autism spectrum disorders (ASDs) are a heterogeneous group of neurodevelopmental disorders, including childhood autism, atypical autism, and Asperger syndrome, with an estimated prevalence of 1.0–2.5% in the general population. ASDs have a complex multifactorial etiology, with genetic causes being recognized in only 10–20% of cases. Recently, copy-number variants (CNVs) have been shown to contribute to over 10% of ASD cases. We have applied a custom-designed oligonucleotide array comparative genomic hybridization with an exonic coverage of over 1700 genes, including 221 genes known to cause autism and autism candidate genes, in a cohort of 145 patients with ASDs. The patients were classified according to ICD-10 standards and the Childhood Autism Rating Scale protocol into three groups consisting of 45 individuals with and 69 individuals without developmental delay/intellectual disability (DD/ID), and 31 patients, in whom DD/ID could not be excluded. In 12 patients, we have identified 16 copy-number changes, eight (5.5%) of which likely contribute to ASDs. In addition to known recurrent CNVs such as deletions 15q11.2 (BP1-BP2) and 3q13.31 (including DRD3 and ZBTB20), and duplications 15q13.3 and 16p13.11, our analysis revealed two novel genes clinically relevant for ASDs: ARHGAP24 (4q21.23q21.3) and SLC16A7 (12q14.1). Our results further confirm the diagnostic importance of array CGH in detection of CNVs in patients with ASDs and demonstrate that CNVs are an important cause of ASDs as a heterogeneous condition with a variety of contributory genes.

Application of array comparative genomic hybridization in 102 patients with epilepsy and additional neurodevelopmental disorders

Copy‐number variants (CNVs) collectively represent an important cause of neurodevelopmental disorders such as developmental delay (DD)/intellectual disability (ID), autism, and epilepsy. In contrast to DD/ID, for which the application of microarray techniques enables detection of pathogenic CNVs in ∼10–20% of patients, there are only few studies of the role of CNVs in epilepsy and genetic etiology in the vast majority of cases remains unknown. We have applied whole‐genome exon‐targeted oligonucleotide array comparative genomic hybridization (array CGH) to a cohort of 102 patients with various types of epilepsy with or without additional neurodevelopmental abnormalities. Chromosomal microarray analysis revealed 24 non‐polymorphic CNVs in 23 patients, among which 10 CNVs are known to be clinically relevant. Two rare deletions in 2q24.1q24.3, including KCNJ3 and 9q21.13 are novel pathogenic genetic loci and 12 CNVs are of unknown clinical significance. Our results further support the notion that rare CNVs can cause different types of epilepsy, emphasize the efficiency of detecting novel candidate genes by whole‐genome array CGH, and suggest that the clinical application of array CGH should be extended to patients with unexplained epilepsies.

Gene Expression Profile of the Clinically Aggressive Micropapillary Variant of Bladder Cancer.

BACKGROUND Progression of conventional urothelial carcinoma of the bladder to a tumor with unique microscopic features referred to as micropapillary carcinoma is coupled with aggressive clinical behavior signified by a high propensity for metastasis to regional lymph nodes and distant organs resulting in shorter survival. OBJECTIVE To analyze the expression profile of micropapillary cancer and define its molecular features relevant to clinical behavior. DESIGN, SETTING, AND PARTICIPANTS We retrospectively identified 43 patients with micropapillary bladder cancers and a reference set of 89 patients with conventional urothelial carcinomas and performed whole-genome expression messenger RNA profiling. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS The tumors were segregated into distinct groups according to hierarchical clustering analyses. They were also classified according to luminal, p53-like, and basal categories using a previously described algorithm. We applied Ingenuity Pathway Analysis software (Qiagen, Redwood City, CA, USA) and gene set enrichment analysis for pathway analyses. Cox proportional hazards models and Kaplan-Meier methods were used to assess the relationship between survival and molecular subtypes. The expression profile of micropapillary cancer was validated for selected markers by immunohistochemistry on parallel tissue microarrays. RESULTS AND LIMITATIONS We show that the striking features of micropapillary cancer are downregulation of miR-296 and activation of chromatin-remodeling complex RUVBL1. In contrast to conventional urothelial carcinomas that based on their expression can be equally divided into luminal and basal subtypes, micropapillary cancer is almost exclusively luminal, displaying enrichment of active peroxisome proliferator-activated receptor γ and suppression of p63 target genes. As with conventional luminal urothelial carcinomas, a subset of micropapillary cancers exhibit activation of wild-type p53 downstream genes and represent the most aggressive molecular subtype of the disease with the shortest survival. The involvement of miR-296 and RUVBL1 in the development of micropapillary bladder cancer was identified by the analyses of correlative associations of genome expression profiles and requires mechanistic validation. CONCLUSIONS Micropapillary cancer evolves through the luminal pathway and is characterized by the activation of miR-296 and RUVBL1 target genes. PATIENT SUMMARY Our observations have important implications for prognosis and for possible future development of more effective therapies for micropapillary bladder cancer.

Application of array comparative genomic hybridization in 256 patients with developmental delay or intellectual disability

We used whole-genome exon-targeted oligonucleotide array comparative genomic hybridization (array CGH) in a cohort of 256 patients with developmental delay (DD)/intellectual disability (ID) with or without dysmorphic features, additional neurodevelopmental abnormalities, and/or congenital malformations. In 69 patients, we identified 84 non-polymorphic copy-number variants, among which 41 are known to be clinically relevant, including two recently described deletions, 4q21.21q21.22 and 17q24.2. Chromosomal microarray analysis revealed also 15 potentially pathogenic changes, including three rare deletions, 5q35.3, 10q21.3, and 13q12.11. Additionally, we found 28 copy-number variants of unknown clinical significance. Our results further support the notion that copy-number variants significantly contribute to the genetic etiology of DD/ID and emphasize the efficacy of the detection of novel candidate genes for neurodevelopmental disorders by whole-genome array CGH.

Subset seed extension to Protein BLAST

The seeding technique became central in the theory of sequence alignment and there are several efficient tools applying seeds to DNA homology search. Recently, a concept of subset seeds has been proposed for similarity search in protein sequences. We experimentally evaluate the applicability of subset seeds to protein homology search. We advocate the use of multiple subset seeds derived from a hierarchical tree of amino acid residues. Our method computes, by an evolutionary algorithm, seeds that are specifically designed for a given protein family. The representation of seeds by deterministic finite automata (DFAs) is developed and built into the NCBI-BLAST software. This extended tool, named SeedBLAST, is compared to the original NCBI-BLAST and PSI-BLAST on several protein families. Our results demonstrate a superiority of SeedBLAST in terms of efficiency, especially in the case of twilight zone hits. SeedBLAST is an open source software freely available http://bioputer.mimuw.edu.pl/papers/sblast . Supplementary material and user manual are also provided.

Multiple samples aCGH analysis for rare CNVs detection

BackgroundDNA copy number variations (CNV) constitute an important source of genetic variability. The standard method used for CNV detection is array comparative genomic hybridization (aCGH).ResultsWe propose a novel multiple sample aCGH analysis methodology aiming in rare CNVs detection. In contrast to the majority of previous approaches, which deal with cancer datasets, we focus on constitutional genomic abnormalities identified in a diverse spectrum of diseases in human. Our method is tested on exon targeted aCGH array of 366 patients affected with developmental delay/intellectual disability, epilepsy, or autism. The proposed algorithms can be applied as a post–processing filtering to any given segmentation method.ConclusionsThanks to the additional information obtained from multiple samples, we could efficiently detect significant segments corresponding to rare CNVs responsible for pathogenic changes. The robust statistical framework applied in our method enables to eliminate the influence of widespread technical artifact termed ‘waves’.

Functional performance of aCGH design for clinical cytogenetics

Array-comparative genomic hybridization (aCGH) technology enables rapid, high-resolution analysis of genomic rearrangements. With the use of it, genome copy number changes and rearrangement breakpoints can be detected and analyzed at resolutions down to a few kilobases. An exon array CGH approach proposed recently accurately measures copy-number changes of individual exons in the human genome. The crucial and highly non-trivial starting task is the design of an array, i.e. the choice of appropriate (multi)set of oligos. The success of the whole high-level analysis depends on the quality of the design. Also, the comparison of several alternative designs of array CGH constitutes an important step in development of new diagnostic chip. In this paper, we deal with these two often neglected issues. We propose a new approach to measure the quality of array CGH designs. Our measures reflect the robustness of rearrangements detection to the noise (mostly experimental measurement error). The method is parametrized by the segmentation algorithm used to identify aberrations. We implemented the efficient Monte Carlo method for testing noise robustness within DNAcopy procedure. Developed framework has been applied to evaluation of functional quality of several optimized array designs.

Efficient Multiple Samples aCGH Analysis for Rare CNVs Detection

We propose a novel multiple sample aCGH analysis methodology aiming in rare Copy-Number Variations (CNVs) detection. Our method is tested on exon targeted aCGH array of 366 patients affected with developmental delay/intellectual disability, epilepsy, or autism. The proposed algorithms can be applied as a post -- processing filtering to any given segmentation method. Thanks to the additional information obtained from multiple samples, we could efficiently detect significant segments corresponding to rare CNVs responsible for pathogenic changes. More detailed description of the method is available in Supplementary Materials at: http://bioputer.mimuw.edu.pl/acgh.

Truncated Robust Principal Component Analysis and Noise Reduction for Single Cell RNA-seq Data.

The development of single cell RNA sequencing (scRNA-seq) has enabled innovative approaches to investigating mRNA abundances. In our study, we are interested in extracting the systematic patterns of scRNA-seq data in an unsupervised manner, thus we have developed two extensions of robust principal component analysis (RPCA). First, we present a truncated version of RPCA (tRPCA), that is much faster and memory efficient. Second, we introduce a noise reduction in tRPCA with \(L_2\) regularization (tRPCAL2). Unlike RPCA that only considers a low-rank L and sparse S matrices, the proposed method can also extract a noise E matrix inherent in modern genomic data. We demonstrate its usefulness by applying our methods on the peripheral blood mononuclear cell (PBMC) scRNA-seq data. Particularly, the clustering of a low-rank L matrix showcases better classification of unlabeled single cells. Overall, the proposed variants are well-suited for high-dimensional and noisy data that are routinely generated in genomics.