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

Aug. 19, 2019. Review article about machine learning for predicting chemotherapy response

New review article in Molecular Genetics and Metabolism about predicting responses to chemotherapy:

Multigene signatures of responses to chemotherapy derived by biochemically inspired machine learning

Published: https://doi.org/10.1016/j.ymgme.2019.08.005

Abstract:   Pharmacogenomic responses to chemotherapy drugs can be modeled by supervised machine learning of expression and copy number of relevant gene combinations. Such biochemical evidence can form the basis of derived gene signatures using cell line data, which can subsequently be examined in patients that have been treated with the same drugs. These gene signatures typically contain elements of multiple biochemical pathways which together comprise multiple origins of drug resistance or sensitivity. The signatures can capture variation in these responses to the same drug among different patients.

July 31, 2019. New preprint on biodosimetry in BioRxiv

“Automated Cytogenetic Biodosimetry at Population-Scale”

PK RoganR LuE MucakiS AliB ShirleyY LiR WilkinsF NortonO SevriukovaD PhamE AinsburyJ MoquatR Cooke,

T PeerlaproulxE WallerJHM Knoll

https://www.biorxiv.org/content/10.1101/718973v1 (doi: https://doi.org/10.1101/718973)

Introduction The dicentric chromosome (DC) assay accurately quantifies exposure to radiation, however manual and semi-automated assignment of DCs has limited its use for a potential large-scale radiation incident. The Automated Dicentric Chromosome Identifier and Dose Estimator Chromosome (ADCI) software automates unattended DC detection and determines radiation exposures, fulfilling IAEA criteria for triage biodosimetry. We present high performance ADCI (ADCI-HT), with the requisite throughput to stratify exposures of populations in large scale radiation events.

Methods ADCI-HT streamlines dose estimation by optimal scheduling of DC detection, given that the numbers of samples and metaphase cell images in each sample vary. A supercomputer analyzes these data in parallel, with each processor handling a single image at a time. Processor resources are managed hierarchically to maximize a constant stream of sample and image analysis. Metaphase data from populations of individuals with clinically relevant radiation exposures after simulated large nuclear incidents were analyzed. Sample counts were derived from US Census data. Analysis times and exposures were quantified for 15 different scenarios.

Results Processing of metaphase images from 1,744 samples (500 images each) used 16,384 CPUs and was completed in 1hr 11min 23sec, with radiation dose of all samples determined in 32 sec with 1,024 CPUs. Processing of 40,000 samples with varying numbers of metaphase cells, 10 different exposures from 5 different biodosimetry labs met IAEA accuracy criteria (dose estimate differences were < 0.5 Gy; median = 0.07) and was completed in ~25 hours. Population-scale metaphase image datasets within radiation contours of nuclear incidents were defined by exposure levels (either >1 Gy or >2 Gy). The time needed to analyze samples of all individuals receiving exposures from a high yield airborne nuclear device ranged from 0.6-7.4 days, depending on the population density.

Conclusion ADCI-HT delivers timely and accurate dose estimates in a simulated population-scale radiation incident.

July 4, 2019. Presentations describing interlaboratory comparison of radiation exposure determination by automated cytogenetic biodosimetry

We will be presenting:

Determination of radiation exposure levels by fully automated
dicentric chromosome analysis: Results from IAEA MEDBIODOSE
(CRP E35010) interlaboratory comparison

at both the 19th International Congress of Radiation Research (Aug. 25-29, 2019) and the 12th International Symposium on Chromosome Aberrations (Aug. 27, 2019)  in Manchester, UK. This study compared the performance of our Automated Dicentric Chromosome Identifier and Dose Estimator (ADCI) using data from 6 different laboratories.  Each of these members of the  IAEA-sponsored Cooperative Research Project E35010, submitted images for calibration curve construction and at least 2 samples of unknown exposure to CytoGnomix for analysis with ADCI. We will report the results of this analysis during this presentation.

Authors:

Rogan P , Shirley B , Li Y , Guogyte K , Sevriukova O , Ngoc Duy P , Moquet J ,
Ainsbury E , Sudprasert W , Wilkins R , Norton F , Knoll J

Department of Biochemistry , University of Western Ontario, London Ontario, Canada
Department of Pathology and Laboratory Medicine, University of Western Ontario, London
Ontario, Canada
Radiation Protection Centre, Ministry of Health (L T -RPC), Vilnius, Lithuania
Dalat Nuclear Research Institute (VN-DNRI), Dalat, Vietnam
Public Health England (PHE), Oxford, Great Britain
Thai Biodosimetry Network, Kasetsart University (THA), Bangkok, Thailand
Health Canada, Ottawa Ontario, Canada
Canadian Nuclear Laboratories, Chalk River Ontario, Canada
Cytognomix, London Ontario, Canada

 

May 30, 2019. F1000Research splicing mutation database article now indexed in PubMed

Large repository of human mRNA splicing mutations in TCGA and ICGC tumor exomes and genomes, with every mutation validated by transcriptome analysis of tumor vs controls:

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/v2)