Jonathan Dry is Director and global strategy lead for AstraZeneca’s world-recognised Oncology Bioinformatics team.
Jonathan completed a BSc (Hons) in Biomedical Science at the University of Manchester where his research focused on the genetics of diabetes and exposed him to the richness of information in DNA that can be uncovered with computational tools. To pursue this interest, he was awarded a scholarship at the University of Exeter to study for an MSc in Bioinformatics and he graduated with distinction. Jonathan collaborated with GlaxoSmithKline to develop computational models determining the risk of recombinant protein breakdown by proteases in host cells.
At AstraZeneca, Jonathan specialised in gene expression microarray data analysis and has supported and influenced a number of discovery programmes, including drugs targeting MEK, PARP, mTOR, PI3K and ERBB. He introduced pioneering approaches for harnessing genomic data from cancer cell lines, uncovering numerous disease associations, and discovering mechanisms of drug target dependency leading to biomarkers of drug response.
Perhaps his most notable contribution to date is the identification of transcriptional readouts which demonstrate MEK activity. This was the first transcriptomic personalised healthcare (PHC) hypothesis tested in AstraZeneca clinical trials.
There is an expanding universe of biological data available which can revolutionise our understanding of disease and therapy if we find innovative ways to share and explore it.
INFLUENTIAL RESEARCH RECOGNIZED BY THE FIELD
Regularly invited to speak at prominent conferences including BioData West, AI World Pharma, Festival of Genomics, Systems Biology of Human Disease, and RECOMB/ISCB Conference on Regulatory & Systems Genomics
GOLD-STANDARD INTERPRETATION OF NGS DATA
Oversaw development and publication of a novel variant caller for cancer NGS data, now considered amongst the gold standards in the field, as well as a high profile review on best practices for clinical actionability in 2016
ASTRAZENECA-SANGER DREAM CHALLENGE
2016 saw Jonathan champion the DREAM crowd-sourcing challenge with its highest ever participation rate and a focus on drug combinations
Targets outside the cancer cell present new drug combination opportunities
Looking beyond the cancer cell for effective drug combinations. Jonathan R Dry, Mi Yang, Julio Saez-Rodriguez. Genome Medicine [in press]
Novel variant caller for NGS in cancer research
VarDict: a novel and versatile variant caller for next-generation sequencing in cancer research. Lai Z, Markovets A, Ahdesmaki M, Chapman B, Hofmann O, McEwen R, Johnson J, Dougherty B, Barrett JC, Dry JR. Nucleic Acids Res. 2016 Jun 20;44(11)
NGS characterisation of tumour properties
BubbleTree: an intuitive visualization to elucidate tumoral aneuploidy and clonality using next generation sequencing data. Zhu W, Kuziora M, Creasy T, Lai Z, Morehouse C, Guo X, Sebastian Y, Shen D, Huang J, Dry JR, Xue F, Jiang L, Yao Y, Higgs BW. Nucleic Acids Res. 2016 Feb 29;44(4)
Defining actionable mutations for cancer drug development
Defining actionable mutations for oncology therapeutic development. Carr TH, McEwen R, Dougherty B, Johnson JH, Dry JR, Lai Z, Ghazoui Z, Laing NM, Hodgson DR, Cruzalegui F, Hollingsworth SJ, Barrett JC. Nat Rev Cancer. 2016 Apr 26;16(5):319-29
Multi-omic screening identifies novel cancer drug targets
Multi-omic measurement of mutually exclusive loss-of-function enriches for candidate synthetic lethal gene pairs. Wappett M, Dulak A, Yang ZR, Al-Watban A, Bradford JR, Dry JR. BMC Genomics. 2016 Jan 19;17(65)
SIMS technology predicts best drug combinations for NSCLC
A simplified interventional mapping system (SIMS) for the selection of combinations of targeted treatments in non-small cell lung cancer. Lazar V, Rubin E, Depil S, Pawitan Y, Martini JF, Gomez-Navarro J, Yver A, Kan Z, Dry JR, Kehren J, Validire P, Rodon J, Vielh P, Ducreux M, Galbraith S, Lehnert M, Onn A, Berger R, Pierotti MA, Porgador A, Pramesh CS, Ye DW, Carvalho AL, Batist G, Le Chevalier T, Morice P, Besse B, Vassal G, Mortlock A, Hansson J, Berindan-Neagoe I, Dann R, Haspel J, Irimie A, Laderman S, Nechushtan H, Al Omari AS, Haywood T, Bresson C, Soo KC, Osman I, Mata H, Lee JJ, Jhaveri K, Meurice G, Palmer G, Lacroix L, Koscielny S, Eterovic KA, Blay JY, Buller R, Eggermont A, Schilsky RL, Mendelsohn J, Soria JC, Rothenberg M, Scoazec JY, Hong WK, Kurzrock R. Oncotarget. 2015 Jun 10;6(16):14139-52.
RAS signalling is associated with acquired EGFR inhibitor resistance
Acquired Resistance to the Mutant-Selective EGFR Inhibitor AZD9291 Is Associated with Increased Dependence on RAS Signaling in Preclinical Models. Eberlein CA, Stetson D, Markovets AA, Al-Kadhimi KJ, Lai Z, Fisher PR, Meador CB, Spitzler P, Ichihara E, Ross SJ, Ahdesmaki MJ, Ahmed A, Ratcliffe LE, O'Brien EL, Barnes CH, Brown H, Smith PD, Dry JR, Beran G, Thress KS, Dougherty B, Pao W, Cross DA. Cancer Res. 2015 Jun 15;75(12):2489-500
RAS activity predicts drug resistance in colorectal cancer
Modeling RAS phenotype in colorectal cancer uncovers novel molecular traits of RAS dependency and improves prediction of response to targeted agents in patients. Guinney J, Ferté C, Dry J, McEwen R, Manceau G, Kao KJ, Chang KM, Bendtsen C, Hudson K, Huang E, Dougherty B, Ducreux M, Soria JC, Friend S, Derry J, Laurent-Puig P. Clin Cancer Res. 2014 Jan 1;20(1):265-72
RNA-Seq compares host and tumour gene expression post-treatment
RNA-Seq Differentiates Tumour and Host mRNA Expression Changes Induced by Treatment of Human Tumour Xenografts with the VEGFR Tyrosine Kinase Inhibitor Cediranib. Bradford JR, Farren M, Powell SJ, Runswick S, Weston SL, Brown H, Delpuech O, Wappett M, Smith NR, Carr TH, Dry JR, Gibson NJ, Barry ST. PLoS One. 2013 Jun 19;8(6)
BRAF activity underpins resistance to MEK1/2 inhibitors
Amplification of the driving oncogene, KRAS or BRAF, underpins acquired resistance to MEK1/2 inhibitors in colorectal cancer cells. Little AS, Balmanno K, Sale MJ, Newman S, Dry JR, Hampson M, Edwards PA, Smith PD, Cook SJ. Sci Signal. 2011 Mar 29;4(166)
Selumetinib sensitivity correlates with MEK activation in numerous cancer types
Transcriptional pathway signatures predict MEK addiction and response to selumetinib (AZD6244). Dry JR, Pavey S, Pratilas CA, Harbron C, Runswick S, Hodgson D, Chresta C, McCormack R, Byrne N, Cockerill M, Graham A, Beran G, Cassidy A, Haggerty C, Brown H, Ellison G, Dering J, Taylor BS, Stark M, Bonazzi V, Ravishankar S, Packer L, Xing F, Solit DB, Finn RS, Rosen N, Hayward NK, French T, Smith PD. Cancer Res. 2010 Mar 15;70(6):2264-73
Learning algorithm analyses protein activity
A bio-basis function neural network for protein peptide cleavage activity characterisation. Yang ZR, Dry J, Thomson R, Charles Hodgman T. Neural Netw. 2006 May;19(4):401-7
Rule-reading algorithm predicts protein proteolytic activity
Searching for discrimination rules in protease proteolytic cleavage activity using genetic programming with a min-max scoring function. Yang ZR, Thomson R, Hodgman TC, Dry J, Doyle AK, Narayanan A, Wu X. Biosystems. 2003 Nov;72(1-2):159-76
Comparative analysis of primary versus relapse/refractory DLBCL identifies shifts in mutation spectrum.
Oncotarget. 2017 8(59).
Cell-specific computational modeling of the PIM pathway in acute myeloid leukemia.
Cancer Res. 2017 15(77).
PDX-MI: Minimal Information for Patient-Derived Tumor Xenograft Models.
Cancer Res. 2017 77(21).
Awards and honors
2008 AstraZeneca Innovation in Science
2010 AstraZeneca Innovation in Science
2012 AstraZeneca Innovation in Science
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