Cytognomix Inc | Date: 2015-01-10
A method is described for the automatic validation of DNA sequencing variants that alter mRNA splicing from nucleic acids isolated from a patient or tissue sample. Evidence the a predicted splicing mutation is demonstrated by performing statistically valid comparisons between sequence read counts of abnormal RNA species in mutant versus non-mutant tissues. The method leverages large numbers of control samples to corroborate the consequences of predicted splicing variants in complete genomes and exomes for individuals carrying such mutations. Because the method examines all transcript evidence in a genome, it is not necessary a priori to know which gene or genes carry a splicing mutation.
Cytognomix Inc | Date: 2014-05-09
Dorman S.N.,University of Western Ontario |
Baranova K.,University of Western Ontario |
Knoll J.H.M.,University of Western Ontario |
Knoll J.H.M.,London Health Sciences Center |
And 5 more authors.
Increasingly, the effectiveness of adjuvant chemotherapy agents for breast cancer has been related to changes in the genomic profile of tumors. We investigated correspondence between growth inhibitory concentrations of paclitaxel and gemcitabine (GI50) and gene copy number, mutation, and expression first in breast cancer cell lines and then in patients. Genes encoding direct targets of these drugs, metabolizing enzymes, transporters, and those previously associated with chemoresistance to paclitaxel (n = 31 genes) or gemcitabine (n = 18) were analyzed. A multi-factorial, principal component analysis (MFA) indicated expression was the strongest indicator of sensitivity for paclitaxel, and copy number and expression were informative for gemcitabine. The factors were combined using support vector machines (SVM). Expression of 15 genes (ABCC10, BCL2, BCL2L1, BIRC5, BMF, FGF2, FN1, MAP4, MAPT, NFKB2, SLCO1B3, TLR6, TMEM243, TWIST1, and CSAG2) predicted cell line sensitivity to paclitaxel with 82% accuracy. Copy number profiles of 3 genes (ABCC10, NT5C, TYMS) together with expression of 7 genes (ABCB1, ABCC10, CMPK1, DCTD, NME1, RRM1, RRM2B), predicted gemcitabine response with 85% accuracy. Expression and copy number studies of two independent sets of patients with known responses were then analyzed with these models. These included tumor blocks from 21 patients that were treated with both paclitaxel and gemcitabine, and 319 patients on paclitaxel and anthracycline therapy. A new paclitaxel SVM was derived from an 11-gene subset since data for 4 of the original genes was unavailable. The accuracy of this SVM was similar in cell lines and tumor blocks (70-71%). The gemcitabine SVM exhibited 62% prediction accuracy for the tumor blocks due to the presence of samples with poor nucleic acid integrity. Nevertheless, the paclitaxel SVM predicted sensitivity in 84% of patients with no or minimal residual disease. © 2015 Federation of European Biochemical Societies. Source
Khan W.A.,University of Western Ontario |
Rogan P.K.,University of Western Ontario |
Rogan P.K.,Cytognomix Inc. |
Knoll J.H.M.,University of Western Ontario |
Knoll J.H.M.,Cytognomix Inc.
Background: Chromatin-modifying reagents that alter histone associating proteins, DNA conformation or its sequence are well established strategies for studying chromatin structure in interphase (G1, S, G2). Little is known about how these compounds act during metaphase. We assessed the effects of these reagents at genomic loci that show reproducible, non-random differences in accessibility to chromatin that distinguish homologous targets by single copy DNA probe fluorescence in situ hybridization (scFISH). By super-resolution 3-D structured illumination microscopy (3D-SIM) and other criteria, the differences correspond to 'differential accessibility' (DA) to these chromosomal regions. At these chromosomal loci, DA of the same homologous chromosome is stable and epigenetic hallmarks of less accessible interphase chromatin are present. Results: To understand the basis for DA, we investigate the impact of epigenetic modifiers on these allelic differences in chromatin accessibility between metaphase homologs in lymphoblastoid cell lines. Allelic differences in metaphase chromosome accessibility represent a stable chromatin mark on mitotic metaphase chromosomes. Inhibition of the topoisomerase IIα-DNA cleavage complex reversed DA. Inter-homolog probe fluorescence intensity ratios between chromosomes treated with ICRF-193 were significantly lower than untreated controls. 3D-SIM demonstrated that differences in hybridized probe volume and depth between allelic targets were equalized by this treatment. By contrast, DA was impervious to chromosome decondensation treatments targeting histone modifying enzymes, cytosine methylation, as well as in cells with regulatory defects in chromatid cohesion. These data altogether suggest that DA is a reflection of allelic differences in metaphase chromosome compaction, dictated by the localized catenation state of the chromosome, rather than by other epigenetic marks. Conclusions: Inhibition of the topoisomerase IIα-DNA cleavage complex mitigated DA by decreasing DNA superhelicity and axial metaphase chromosome condensation. This has potential implications for the mechanism of preservation of cellular phenotypes that enables the same chromatin structure to be correctly reestablished in progeny cells of the same tissue or individual. © 2015 Khan et al. Source
Li Y.,University of Western Ontario |
Knoll J.H.,University of Western Ontario |
Knoll J.H.,Cytognomix Inc. |
Wilkins R.C.,Consumer and Clinical Radiation Protection Bureau |
And 3 more authors.
Microscopy Research and Technique
Dose from radiation exposure can be estimated from dicentric chromosome (DC) frequencies in metaphase cells of peripheral blood lymphocytes. We automated DC detection by extracting features in Giemsa-stained metaphase chromosome images and classifying objects by machine learning (ML). DC detection involves (i) intensity thresholded segmentation of metaphase objects, (ii) chromosome separation by watershed transformation and elimination of inseparable chromosome clusters, fragments and staining debris using a morphological decision tree filter, (iii) determination of chromosome width and centreline, (iv) derivation of centromere candidates, and (v) distinction of DCs from monocentric chromosomes (MC) by ML. Centromere candidates are inferred from 14 image features input to a Support Vector Machine (SVM). Sixteen features derived from these candidates are then supplied to a Boosting classifier and a second SVM which determines whether a chromosome is either a DC or MC. The SVM was trained with 292 DCs and 3135 MCs, and then tested with cells exposed to either low (1 Gy) or high (2-4 Gy) radiation dose. Results were then compared with those of 3 experts. True positive rates (TPR) and positive predictive values (PPV) were determined for the tuning parameter, σ. At larger σ, PPV decreases and TPR increases. At high dose, for σ=1.3, TPR=0.52 and PPV=0.83, while at σ=1.6, the TPR=0.65 and PPV=0.72. At low dose and σ=1.3, TPR=0.67 and PPV=0.26. The algorithm differentiates DCs from MCs, overlapped chromosomes and other objects with acceptable accuracy over a wide range of radiation exposures. Microsc. Res. Tech. 79:393-402, 2016. © 2016 Wiley Periodicals, Inc. Source