December 29, 2020. Coming soon… ADCI_Online

Greetings to you for a safe and healthy New Year.

The Automated Dicentric Chromosome Identifier and Dose Estimator (ADCI) has become the biodosimetry industry’s leading software system for accurate and rapid quantification of absorbed ionizing radiation. This year we upgraded our Windows-based system to also determine partial body exposures, both fraction of cells exposed and whole body equivalent dose levels (Shirley et al. 2020).

In the coming year, CytoGnomix will introduce ADCI in the Cloud. This version of our software will make ADCI available as a highly secure web-application.  All of the same functionality found in the Windows software will be available in ADCI_Online , except users will upload metaphase images to our AWS application. We have already validated the Demonstration Version of ADCI in this virtual environment. It is no longer necessary to download and install this software on your own computer in order to test drive it.

Contact us  to access a Demo of  ADCI_Online.

Screenshot:

 

 

Dec. 10, 2020. New article on chemotherapy response prediction

We have published:
Pathway‐extended gene expression signatures integrate novel biomarkers that improve predictions of patient responses to kinase inhibitors.

Ashis J. Bagchee‐Clark , Eliseos J. Mucaki, Tyson Whitehead, and Peter K. Rogan

MedComm (Wiley) 1(3): 311-327, 2020.  (https://doi.org/10.1002/mco2.46)

Abstract:
Cancer chemotherapy responses have been related to multiple pharmacogenetic biomarkers, often for the same drug. This study utilizes machine learning to derive multi‐gene expression signatures that predict individual patient responses to specific tyrosine kinase inhibitors, including erlotinib, gefitinib, sorafenib, sunitinib, lapatinib and imatinib. Support vector machine (SVM) learning was used to train mathematical models that distinguished sensitivity from resistance to these drugs using a novel systems biology‐based approach. This began with expression of genes previously implicated in specific drug responses, then expanded to evaluate genes whose products were related through biochemical pathways and interactions. Optimal pathway‐extended SVMs predicted responses in patients at accuracies of 70% (imatinib), 71% (lapatinib), 83% (sunitinib), 83% (erlotinib), 88% (sorafenib) and 91% (gefitinib). These best performing pathway‐extended models demonstrated improved balance predicting both sensitive and resistant patient categories, with many of these genes having a known role in cancer aetiology. Ensemble machine learning‐based averaging of multiple pathway‐extended models derived for an individual drug increased accuracy to >70% for erlotinib, gefitinib, lapatinib and sorafenib. Through incorporation of novel cancer biomarkers, machine learning‐based pathway‐extended signatures display strong efficacy predicting both sensitive and resistant patient responses to chemotherapy.

Nov. 9, 2020. Notice of Allowance for US Pat App. Ser. No. 16/057,710

CytoGnomix’s Automated Dicentric Chromosome Identifier and Dose Estimator (ADCI) system will be awarded a US Patent for all claims covering “Smart Microscope System for Radiation Biodosimetry.”  The patent application is available at:

https://patents.google.com/patent/US20200050831A1

The abstract reads:

An automated microscope system is described that detects dicentric chromosomes (DCs) in metaphase cells arising from exposure to ionizing radiation. The radiation dose depends on the accuracy of DC detection. Accuracy is increased using image segmentation methods are used to rank high quality cytogenetic images and eliminate suboptimal metaphase cell data in a sample based on novel quality measures. When a sufficient number of high quality images are detected, the microscope system is directed to terminate metaphase image collection for a sample. The microscope system integrates image selection procedures that control an automated digitally controlled microscope with the analysis of acquired metaphase cell images to accurately determine radiation dose. Early termination of image acquisition reduces sample processing time without compromising accuracy. This approach constitutes a reliable and scalable solution that will be essential for analysis of large numbers of potentially exposed individuals.

New support for geostatistical analysis of COVID19 hotspots in Ontario

In response to the Call for Proposals under its Innovation for Defence Excellence and Security (IDEaS) program to address COVID-19 challenges, the Department of National Defence has recommended CytoGnomix’s project:

Locating emerging COVID19 hotspots in Ontario after community transmission by time-correlated, geostatistical analysis

for funding following evaluation against mandatory, point rated criteria, and strategic considerations.

The project will “provide financial support through a non-repayable Contribution Agreement up to $200,000 for the development of your proposed solution for a maximum performance period of six months.”

September 4, 2020. New article on automated partial body radiation exposure determination

We have added the capability to determine whether samples exposed to ionizing radiation are wholly or partially irradiated. If partially, the approach determines the fraction of metaphase cells exposed and the whole body-equivalent dose completely automatically. CytoGnomix’s Automated Dicentric Chromosome Identifier and Dose Estimation software has been upgraded to generate these results as part of the the dose estimation report. The article has been accepted for publication by the International Journal of Radiation Biology  V. 96 (https://doi.org/10.1080/09553002.2020.1820611).  It is also currently available on BioRxiv:

Estimating partial body ionizing radiation exposure by automated cytogenetic biodosimetry
Ben C. ShirleyJoan H.M. KnollJayne MoquetElizabeth AinsburyPham Ngoc DuyFarrah NortonRuth C. WilkinsPeter K. Rogan

Full text:   ShirleyetalBioRxiv2020

Aug. 31, 2020. Presentation at the 2020 American Society of Human Genetics meeting

We are giving a platform presentation at the upcoming American Society of Human Genetics virtual meeting #ASHG2020

Session: Personalized Medicine Approaches in Healthcare.

Paper: Pathway-extended gene expression signatures integrate novel biomarkers that improve predictions of responses to kinase inhibitors

P. K. Rogan(1,2), A. J. Bagchee-Clark(1), E. J. Mucaki(1). 1. Department of Biochemistry, University of Western Ontario and 2. CytoGnomix Inc., London ON Canada

Abstract: Individualized chemotherapy selection in cancer potentially maximizes drug efficacy while minimizing drug toxicity. Despite the knowledge of many pharmacogenetic biomarkers, inter-individual variability in response to chemotherapeutic response has limited the success of the approach. We derive multi-gene expression signatures that predict individual patient responses to tyrosine kinase inhibitors (TKIs): erlotinib, gefitinib, sorafenib, sunitinib, lapatinib and imatinib. Gene models for TKIs implicated from the published literature tend to predict either sensitivity or resistance to TKIs well (but not both). This
issue was addressed with a systems biology-based strategy that expanded with candidate gene products related to these genes in these models through biochemical pathways and interactions. Using patient transcriptome data, these Pathway-Extended (PE) models predicted responses for individual patients that matched observed outcomes at accuracies of 65% (imatinib), 71% (lapatinib and gefitinib), 78% (sunitinib), 83% (erlotinib) and 89% (sorafenib). After training and evaluating many extended signatures, those with the strongest predictive performance were composed primarily of pathway-related genes that according to post-hoc analysis were clearly implicated in cancer phenotypes. Machine learning-based PE expression signatures display strong efficacy in predicting both sensitivity and specificity in patients through incorporation
of novel cancer biomarkers.

#genomics #medicine #chemotherapy #cancertreatment #kinaseinhibitors

August 14, 2020. Interview on Scientific Sense podcast

Peter Rogan was interviewed by Gill Eapin for his daily podcast, Scientific Sense, focused on Science & Economics about our research projects about COVID19. Listen at the link below.

https://anchor.fm/scientificsense/episodes/Prof–Peter-Rogan–Professor-of-Biochemistry-and-Biostatistics-at-Western-University-ei5i40

#medicine #sarscov2 #covidー19 #medicalsciences #epidemiology #health #geostatistics #hotspots #publichealth #genomics #molecularmechanism

August 7, 2020. Publication of novel molecular mechanism of severe RNA-viral lung infections

We have described and provide evidence for an explanation for how rapid onset, severe RNA viral infections, such as SARS-CoV-2 or Influenza, may develop:

Rogan PK, Mucaki EJ and Shirley BC. A proposed molecular mechanism for pathogenesis of severe RNA-viral pulmonary infections. F1000Research 2020, 9:943 (https://doi.org/10.12688/f1000research.25390.1)

There is an accompanying infographic describing the mechanism, which is cited in the article:
Finally, the paper cites this data archive that contains our genomic analyses and related software programs:
Zenodo: Characteristics of human and viral RNA binding sites and site clusters recognized by SRSF1 and RNPS1. http://doi.org/10.5281/zenodo.3737089

August 6, 2020. Updated: Geostatistical Analysis of SARS-CoV-2 Positive Cases in the United States

We have just published a new version (#4) of our archive containing geostatistical analyses of SARS-CoV-2 positive cases in the United States (https://doi.org/10.5281/zenodo.3890284). This version adds state-by-state results after Federal and State relaxation of distancing constraints on Memorial Day weekend using space-time, countywide geostatistical analysis.

July 13, 2020. New article on Virtual Molecular Tumor Boards from the Variants in Cancer Consortium

CytoGnomix has coauthored a new article on collaborative gene variant interpretation applied to selection of cancer therapy:

Shruti RaoBeth PitelAlex H WagnerSimina M BocaMatthew McCoyIan KingSamir GuptaBen Ho ParkJeremy L WarnerJames ChenPeter K RoganDebyani ChakravartyMalachi GriffithObi L GriffithSubha Madhava. Collaborative, Multidisciplinary Evaluation of Cancer Variants Through Virtual Molecular Tumor Boards Informs Local Clinical Practices.  DOI: 10.1200/CCI.19.00169  Journal of Clinical Oncology. Clinical Cancer Informatics no. 4 (2020) 602-613. July 9, 2020.

PDF: Rao et al. JCO_cci.19.00169 2020

Geostatical Biodosimetry

We have consolidated our geostatistical biodosimetry contributions at Grow Kudos. We have been developing a new application of this technology for tracking hotspots of infections during the current COVID-19 pandemic. This animation shows geostatistically significant hotspot counties in the US relative to 3 nearest neighbors from March through June 2020.  Please stay tuned for more:

April 24, 2020. Assessing radiation exposure across a population by geolocation

We present a new approach for estimating exposures in radiation incidents or accidents using geolocated dosimetry data:
Rogan P, Mucaki E, Lu R, Shirley B, Waller E, and Knoll J. Meeting radiation dosimetry capacity requirements of population-scale exposures with geostatistical sampling, PLoS ONE 15(4): e0232008. [https://dx.doi.org/10.1371/journal.pone.0232008].
The MedRxiv version contains a Note Added in Proof describing how this method could be used for finding hotspots during the monitoring phase of the COVID19 pandemic. (https://www.medrxiv.org/content/10.1101/2020.04.08.20058446v1)
The article is Open Access and contains links to an online protocol and a data repository.

Sept. 7, 2019. Pan cancer article and database update

We have published another revision to our pan-cancer splicing mutation database  paper: Shirley BC, Mucaki EJ and Rogan PK. Pan-cancer repository of validated natural and cryptic mRNA splicing mutations F1000Research 2019, 7:1908 (https://f1000research.com/articles/7-1908/v3). The paper is already indexed in PubMed.
In this version, we derive a simplified variant classification scheme with  ClinVar designations calibrated to the molecular phenotypes of mRNA splicing mutations. These are now indicated in the ValidspliceMut (https://validsplicemut.cytognomix.com) query results. The majority of variants cause aberrant splicing or are likely aberrant. Interestingly, a significant fraction of variants that have the same information, expression, population frequency properties as ClinVar variants designated as “benign” cause allele-specific alternative splicing.

Figure 1 of Pan-cancer repository of validated natural and cryptic mRNA splicing mutations. F1000Research 2019, 7:1908