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

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

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)

Version 3 of Geostatistical Analysis of SARS-CoV-2 in the United States dataset published (http://doi.org/10.5281/zenodo.3950057). Contains hotspot and cluster analysis of daily case counts across all US counties from Marcy 25-July 13, 2020.

#covid-19 #SARSCov2 #geostatistics #hotspot

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

Shruti Rao, Beth Pitel, Alex H Wagner, Simina M Boca, Matthew McCoy, Ian King, Samir Gupta, Ben Ho Park, Jeremy L Warner, James Chen, Peter K Rogan, Debyani Chakravarty, Malachi Griffith, Obi L Griffith, Subha 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

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:

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 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)