CytoGnomix has finalized our contract with Public Works Government Services Canada under the Build in Canada Innovation Program. This agreement licenses the Automated Dicentric Chromosome Identifier (ADCI) to the Consumer and Clinical Radiation Protection Bureau at Health Canada and Canadian Nuclear Laboratories and provides on-site training to these labs. These biodosimetry reference labs will test the software and provide feedback. Test results will support CytoGnomix’s submitted application to the Medical Device Bureau at Health Canada.
We have published a new version of:
Predicting Outcomes of Hormone and Chemotherapy in the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) Study by Biochemically-inspired Machine Learning. F1000Research 2017, 5:2124 (doi:10.12688/f1000research.9417.2)
The revision addresses the comments of the reviewers and adds several new analyses and results. Among our findings was the discovery of significant batch effects that, respectively, differentiate gene expression of signature genes in the Discovery and Validation patient datasets. This is an important cautionary message that should be considered when analyzing the performance of any machine learning based method.
Jan. 23, 2017. Automated interpretation of digital pathology images is currently at an embryonic stage of development
Counting pixel area and pixel intensities (stained antibodies, DNA or RNA) does not determine the identities of the cellular objects that are labeled. The challenge is that every microscope field exhibits different morphology, so traditional image segmentation algorithms aimed at identifying specific subcellular components may not be reliable. We need to be clever to ferret out generalizable image properties of specific cellular components, invariant to morphological variability, that will uniquely discriminate normal from abnormal subcellular distributions of the biomarker of interest. We have done this to identify dicentric chromosomes – see red objects in the attached figure (green are normal, monocentric chromosomes). It should be possible to do this for other subcellular objects. Contact CytoGnomix (mailto://firstname.lastname@example.org) to discuss further.
December 13, 2016. Postdoctoral Position available for high performance computing application in radiation biodosimetry
A postdoctoral position is available to work on a newly funded high-performance computing project:
“Automated Cytogenetic Dosimetry as a Public Health Emergency Medical Countermeasure.”
This 2 year project is supported by the SOSCIP-TalentEdge program. Candidates should be qualified in C++ development, preferably with experience in parallel computing. The position is at Western University in combination with the project partner Cytognomix.
Please contact either Drs. Knoll or Rogan if interested:
Department of Pathology and Laboratory Medicine
London, ON N6A 2C1, Canada
t. 519-661-2111 ext. 86407
Once you see the discoveries that only MutationForecaster® can make, we are confident that you will sign up for a subscription to analyze your own data.
Contact us if you have questions.
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.
- February 27, 2017. CytoGnomix finalizes contract with Government of Canada
- Jan. 28, 2017. New version of F1000Research paper on chemotherapy response in breast cancer
- January 25, 2017. Comment from the Transforming Genetic Medicine Initiative Blog
- Jan. 23, 2017. Automated interpretation of digital pathology images is currently at an embryonic stage of development