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.

“Automated Cytogenetic Biodosimetry at Population-Scale”

PK Rogan, R Lu, E Mucaki, S Ali, B Shirley, Y Li, R Wilkins, F Norton, O Sevriukova, D Pham, E Ainsbury, J Moquat, R Cooke,

T Peerlaproulx, E Waller, JHM Knoll

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

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

We will present a new geostatistical approach to reduce biodosimetry workload in a large scale nuclear event at the International Congress of Radiation Research in Manchester UK, 25-20 August, 2019:

(link to full abstract in conference program)

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)

This article has now been accepted for publication and indexing in PubMed:

 Lu R and Rogan PK. Transcription factor binding site clusters identify target genes with similar tissue-wide expression and buffer against mutations [version 2; peer review: 2 approved]. F1000Research 2019, 7:1933 (https://doi.org/10.12688/f1000research.17363.2)

New article in bioRxiv:
Expression changes confirm predicted single nucleotide variants affecting mRNA splicing. E. J. Mucaki and P.K. Rogan. (https://www.biorxiv.org/content/10.1101/549089v1)

This paper describes high quality qRT-PCR and microarray expression data of predicted splicing variants. The results confirm results of genome-wide TCGA and ICGC RNASeq findings (https://f1000research.com/articles/7-1908/v1). The genome scale results were obtained using CytoGnomix’s MutationForecaster (http://mutationforecaster.com ) system.

CytoGnomix is licensing its Automated Dicentric Chromosome Identifier and Dose Estimator  product  to a Canadian government-owned contractor-operated laboratory through 2020.  This organization’s primary business is in nuclear science and technology.

We have just updated the CytoVA software product. It now contains all PubMed references through Dec 2018 and the most recent version of the human phenotype ontology (HPO). With CytoVA (see below), you can query Shannon pipeline, Veridical or VEP output, the list of predicted deleterious variants, with this software  for HPO clinical phenotypes.  The software  reports the articles, and PubMed index codes that support the relationship between the gene variants and the queried phenotype. It’s a very effective way of filtering exome or complete genome sequencing results to limit the best candidate variants.

Lu R and Rogan PK. Transcription factor binding site clusters identify target genes with similar tissue-wide expression and buffer against mutations [version 1 ]. F1000Research 2018, 7:1933 (DOI: 10.12688/f1000research.17363.1):     https://f1000research.com/articles/7-1933/v1

Shirley BC, Mucaki EJ and Rogan PK. Pan-cancer repository of validated natural and cryptic mRNA splicing mutations [version 1; referees: awaiting peer review].  F1000Research 2018, 7:1908     (https://doi.org/10.12688/f1000research.17204.1

Website to perform database search:   https://validsplicemut.cytognomix.com/

RADIATION DOSE ESTIMATION BY COMPLETELY AUTOMATED INTERPRETATION OF THE

DICENTRIC CHROMOSOME ASSAY

Li, Yanxin; Shirley, Ben; Wilkins, Ruth; Norton, Farrah; Knoll, Joan; Rogan,Peter

Radiation Protection Dosimetry, in press

Figure 2 (from the article): Representative heat maps of chromosome object count distributions for Health Canada calibration (HC ##Gy.csv) and exercise (S##.csv) samples. Show: legend indicates significance of pairwise comparisons of object counts in (bottom panel) unselected vs. (top panel) Model B_C filter (5) selected images from each sample. Values close to 10^0 are not significant, whereas values such as 10^-20 are significant differences between samples. The legend displays probability thresholds of Wilcoxon signed rank tests of chromosome object counts between each pair of samples from the HC laboratory. Similar results were obtained for other chromosomal features.

Mucaki EJ, Zhao J, Lizotte D, Rogan PK. Predicting response to platin chemotherapy agents with biochemically-inspired machine learning. Signal Transduction and Targeted Therapy, in press.

A preprint of this article has been released on the  BioRxiv server:   https://doi.org/10.1101/231712