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Recently, we published 2 papers describing our unifying framework for non-coding mutation analysis (Mucaki et al. BMC Medical Genomic, 2016; http://bmcmedgenomics.biomedc
(Lu et al. 2016;http://nar.oxfordjournals.org/
I am not claiming that the variants we prioritize with our framework are definitively pathogenic, but do believe that strategies that are narrowly focused on the genetic code itself won’t advance the field or help patients much. Clinical molecular geneticists seriously consider sequencing beyond coding regions and trying to interpret the variants detected in the regions. The incremental costs to do this aren’t exorbitant, and the excuse of ignorance about the meaning of such variants is simply not valid any longer.
Many non-coding mutations have been proven ‘anecdotally’; studies have not been designed to determine the incidence of these types of mutations, in part due to the higher densities of variants in non-coding regions, identifying the clinically relevant ones is more daunting. This has been compounded by the lack of bioinformatic and genomic methods to generate a reliable and comprehensive and high throughput validation of variants outside of coding regions with adverse functional consequences . Suffice it to say, there are many individual reports in the published literature, but they are not generally being systemically uncovered because of the narrow focus on changes in coding regions that affect amino acid sequences.
The problem is not only where the variants reside, but an overly conservative philosophy that fails to consider other interpretations for the effects of variants, even within coding regions. It’s not just non-coding regions that contain missing pathogenic variants, but also coding variants where the change in the amino acid code may not be the source of the disease pathology. There are actually numerous examples of this phenomenon (and a number of good reviews eg. Cartegni et al (https://www.ncbi.nlm.nih.gov/
There is inevitably some bias against the reporting of intronic cryptic splicing mutations, because these sequences are not routinely determined in either research or clinical studies. Besides these classes, our studies also identify variants that alter transcription factor binding site strength and mRNA stability (in untranslated regions of mRNAs).
The exchange of mutation information about inherited breast cancer among various testing companies (except Myriad) has increased confidence in mutation interpretation. Those with rare mutations that are not shared among multiple patients do not benefit from this exchange. But these are generally based almost entirely on variants that cause amino acid substitutions or nonsense codons. I contend that such exercises, while very useful, are simply not scalable to the true volumes of all variants found in genes, and they ignore other mechanisms of pathogenicity such as those described above.
To reiterate, my argument is that current clinical molecular diagnostic practices will continue to leave many patients without known pathogenic mutations. Until this point of view changes and we seriously focus on functional and bioinformatic methods to analyze and prioritize VUSs thoughout genes, there will be a lot of frustration about the lack of results among the companies, academics and the patients they are purporting to help. We should also question whether the cost of testing can be justified, with the knowledge that a significant amount of genetic real estate is not being sequenced nor interpreted.
Peter K. Rogan
Cytognomix receives contract from the Build in Canada Innovation Program from the Government of Canada to test our novel ADCI software to estimate effects of exposure to ionizing radation. The project will be a collaboration with Health Canada and Canadian Nuclear Laboratories. ADCI determines the biological dose received without manual review and is suitable for evaluation of exposures in a mass casualty event.
US Patent No. 8,605,981 on CytoGnomix’s centromere finding algorithm, which is a key component of the Automated Dicentric Chromosome Identifier and Dose Estimator (ADCI) software, was awarded in 2013. On November 8th 2016, German patent application No. 11 2011 103 687.6 on the same invention was granted as Patent No. 11 2011103687. We note that both of the major manufacturers of automated cytogenetic image capture systems are German and we look forward to working with them.
November 28, 2016. Article on transcription factor binding sites published in Nucleic Acids Research
Lu R, Mucaki E and Rogan PK. Discovery and Validation of Information Theory-Based Transcription Factor and Cofactor Binding Site Motifs, Nucleic Acids Research. DOI: 10.1093/nar/gkw1036 (pdf)
Copyright licence (CC-BY)
Manuscript with Figures - Lu, Mucaki and Rogan, Nucl. Acids Res. 2016
The Atlas of Science has published a simplified description for the lay public of our 2016 study of gene variants in hereditary breast and ovarian cancer in BMC Medical Genomics (citation below).
Please see: Focusing on the most relevant gene variants in inherited breast and ovarian cancer by Eliseos Mucaki and Peter Rogan.
Original technical paper: A unified analytic framework for prioritization of non-coding variants of uncertain significance in heritable breast and ovarian cancer. Mucaki EJ, Caminsky NG, Perri AM, Lu R, Laederach A, Halvorsen M, Knoll JH, Rogan PK BMC Med Genomics. 2016 Apr 11
Cytognomix has received a notice of allowance of all claims for US Pat. App. Ser. No. 13/744,459:
Stable gene targets in breast cancer and use thereof for optimizing therapy
Inventors: Peter K. Rogan and Joan H.M. Knoll
The patent is based on our previous publication:
August 31, 2016. New publication on predicting outcomes of hormone and chemotherapy in breast cancer
Dr. Peter K. Rogan presented “Reversing chromatin accessibility differences that distinguish homologous mitotic meetaphase chromosomes,”at the Gordon Research Conference on DNA Topoisomerases in Biology and Medicine at Sunday River in Newry, ME, United States.
MutationForecaster is catching on. Researchers, clinicians and commercial laboratories are realizing the value of being able to detect and interpret mutations that other platforms miss. Cytognomix has picked up multiple new subscribers from Germany, Switzerland, Australia, China, and Canada this year, and subscription renewals from last year. Cytognomix continues to push the envelope, for the first time publishing papers describing a Unified framework for analyzing gene variants in non-coding and coding gene regions and applying this framework in a large clinical study of inherited breast and ovarian cancer. These reports have led to invitations to contribute our unique expertise to interpretation of results of large inherited cancer genetic studies in the United States and in France. These ongoing projects are showing that the effects of mutations we predict by information theory-based approaches can be confirmed with corresponding gene expression studies in collaborators’ laboratories. What are we working on next for the MutationForecaster suite?
- Adding to our Interactive Report generator to summarize key findings (currently available at MutationForecaster).
- Incorporating our Unified Analytical Framework for complete gene and genome sequence analysis.
- Bespoke Consulting Services to assist you with variant analysis using our software products
This will give our customers will have access to our latest for analysis, filter and interpret their own data. Wouldn’t you like access to these capabilities? Subscribe! NGS sequencing itself may be more accessible and economical today than it has ever been. What we’ve learned from our complete gene sequencing projects is that this success comes with rapidly expanding collections of gene variants, many of which have never been reported before or have been found only rarely. Comprehensive sequencing significantly magnifies the challenges of accurate genome interpretation. Our approach allows you to focus these large collections on only the most functionally relevant variants for review, experimental validation, and prioritization. See what others think of MutationForecaster to gain access to our patented technologies. They are only available from Cytognomix.
Please contact us if you would like a copy for non-commercial use.
Our previous preprint in bioRxiv on centromere detection has been published in F1000Research:
Subasinghe A, Samarabandu J, Li Y et al. Centromere detection of human metaphase chromosome images using a candidate based method. F1000Research 2016, 5
Our paper, “Radiation Dose Estimation by Automated Cytogenetic Biodosimetry” by Peter K. Rogan, Yanxin Li, Ruth Wilkins, Farrah N. Flegal, and Joan H. M. Knoll, has been accepted for publication in the journal Radiation Protection Dosimetry.
Figure 1. Representative processed metaphase image in ADCI:
- Peter K. Rogan, Yanxin Li, Ruth Wilkins, Farrah Flegal, Joan HM Knoll. Radiation Dose Estimation by Automated Cytogenetic Biodosimetry, Great Lakes/Canadian Bioinformatics Conference (CCBC/GLBIO). May 16, 2016. University of Toronto (Platform Presentation).
- Peter K. Rogan. Radiation Dose Estimation by Automated Cytogenetic Biodosimetry. Platform presentation. Great Lakes Chromosome Conference. May 20, 2016. University of Toronto.
- Peter K. Rogan. Cisplatin Response Prediction in Recurrent Bladder Cancer using Biochemically-inspired Machine Learning. Oral and Poster presentations. 3rd International Molecular Pathological Epidemiology Meeting. May 13, 2016. Dana-Farber Cancer Institute, Boston.
- Rezaeian I, Mucaki E, Baranova K, Quang HP, Angelov D, Ilie L, Ngom A, Rueda L, Rogan PK. Predicting outcome of hormone and chemotherapy in the METABRIC breast cancer study. Great Lakes/Canadian Bioinformatics Conference (GLBIO/CCBC). May 16, 2016. University of Toronto.
- Baranova K, Mucaki EJ, Angelov D, Lizotte D, and Rogan PK. Cisplatin Response Prediction in Recurrent Bladder Cancer using Biochemically-inspired Machine Learning. Great Lakes/Canadian Bioinformatics Conference (GLBIO/CCBC). May 16, 2016. University of Toronto.
- Lu R and Rogan PK. Predicting cis-regulation in human promoters by information density-based clustering of heterotypic transcription factor binding sites. Great Lakes/Canadian Bioinformatics Conference (GLBIO/CCBC). May 16, 2016. University of Toronto.