15/10/2014 18 t h Annual Resistance and Antiviral Therapy Meeting v Dr Emma Thomson University of Glasgow Thursday 18 September 2014, Royal College of Physicians, London HCV genomes in disease and treatment (the STOP HCV study) 18th Annual Resistance and Antiviral Therapy Meeting Emma Thomson 18th September 2014 1 15/10/2014 PIs Approved Phase 3 Phase 2 Phase 1 NS5A inhibitors Cyclophilin inhibitors Boceprevir Teleprevir Simeprevir NS5B NRTI NNRTI Sofosbuvir Faldaprevir Asunaprevir ABT-450r MK-5172 Danoprevi GS-9451 r GS-9256 Vaniprevir ACH1625 Daclatasvir Ledipasvir Ombitasvir MK-8742 GSK-2336805 MK-6325 MK-2748 ABT-493 GS-5816 ABT-530 IDX-719 MK-8326 Alisporivir SCY-635 Dasabuvir Mericitabin Tegobuvir ABT-072 e BIVX-222 IDX-184 207127 PFVX-135 GS-9669 868554 BMS791325 GSK-625433 IDX-375 Adapted from Weiser, Drug Discov Today: Tech 2012 New guidelines for HCV treatment 2014 2 15/10/2014 Sofosbuvir and ribavirin for 24 Weeks for HCV genotype 3 100 94 92 87 85 SVR12 (%) 80 60 60 40 20 0 Overall 86/92 12/13 Naïve, Noncirrhotic Naïve, Cirrhotic 87/100 27/45 Experienced, Experienced, Noncirrhotic Cirrhotic Valence study: Gilead-Zeuzem S, et al. AASLD 2013. Washington, DC. #1085 • STOP-HCV: “stratified medicine to optimise treatment for patients with HCV” • Research grant from MRC (UK); £5 million • 22 Co-investigators (UK and US partners)-led by Ellie Barnes, Oxford University • Working in collaboration with HCV research UK (Glasgow University)-biobanking 10,000 samples from 42 NHS sites 3 15/10/2014 Stratified Medicine to Optimise Treatment for Hepatitis C Virus Infection 1. Viral genomic sequencing methodological evaluation 2. Viral genetic determinants and treatment outcome in HCV genotype 3 infection 3. Viral genomic determinants in patients receiving novel therapies (PEG/RV/DAA or combination DAAs) 4. Viral genetic determinants in genotype 1 infection 5. Assessment of genomic sequence in cirrhotic patients who do/do not develop HCC 6. Assessment of viral genetic determinants in HIV-infected patients 30 plasma samples from HCV Research UK cohort x 5 Public Health England Sanger Centre for Virus Research, Glasgow Sent to 5 sites Varying genotypes Varying viral load Oxford University / Wellcome Trust Centre for Human Genetics UCL 4 15/10/2014 Whole genome sequencing of HCV methods PCR-based enrichment Metagenomic/RNASeq Target 1. Synthesise Viral cDNA 2. Fragment 3. Ligate adaptors + indexes 4. Sequence Preliminary results Genotype 1a Genotype 1b Genotype 4 Genotype 3a Genotype 2 Maximum likelihood tree constructed using MEGA 6.0, GTR model with 1000 bootstrap replicates. Branch thickness is proportional to • Consensus sequences similar across 3 centres • PCR-based method resulted in more divergent consensus sequences • 100% success with metagenomic and target enrichment approaches • 87% success with PCR • Low level contamination occurred frequently with target enrichment • Resistance profile of selected majority variants identical 5 15/10/2014 Probe library includes 1a, 2b, 3a and 4a Sample is genotype 1a Depth of coverage (with enrichment) Depth of coverage (no enrichment) Viral RNAseq (no enrichment) Viral RNAseq + enrichment HCV genome position When probe subtype matches the sample subtype, the enrichment is effectively unbiased. Probe library includes 1a, 2b, 3a and 4a Sample is genotype 1b Depth of coverage (with enrichment) Depth of coverage (no enrichment) Viral RNAseq (no enrichment) Viral RNAseq + enrichment When probe genotype matches the sample genotype but not the subtype, the enrichment is almost unbiased. 6 15/10/2014 Probe is genotype 1a Sample is genotype 4a Depth of coverage (with enrichment) Depth of coverage (no enrichment) Viral RNAseq (no enrichment) Viral RNAseq + enrichment When probe genotype differs from the sample genotype, the enrichment is biased. Metagenomic sequencing Virus-specific probe enrichment Pros Pros Minimal selection-bias Minimal human contamination Diverse genotype sequencing (a priori) Greater depth of coverage Other pathogens and co-infections Fewer failures DNA and RNA Cheaper (pounds, plasma, processing time and terabytes of data) Cons Requires higher viral load or more sequencing Contamination with human DNA/rRNA Cons No metagenomic data More opportunities for contamination between samples Expensive (pounds, plasma, processing time and terabytes) 7 15/10/2014 Bioinformatic analysis Tanoti – a novel small genome mapper • • • Developed in C programming language Memory efficient algorithm Requires BLAST, AWK, SED and SHELL libraries Pros: • Fast and Sensitive • Better handling of reads with InDels • Superior coverage and depth • Handles single and paired end reads Green circles - number of reads mapped only by Tanoti Blue circles - number of reads mapped only by alternative mapper Intersection values - reads mapped by both programs Read depth and coverage by Tanoti and BWA (a) Percentage of simulated reads without indels mapped erroneously with indels. (b) Percentage of simulated reads with insertions mapped erroneously with deletions. 8 15/10/2014 HCV Database Overview The example shown below is just one scenario under which sequence data and clinical data are linked. Protocols for linking data in the ‘patient’, ‘sample’ and ‘sequence’ domains of the HCV database should probably be explicitly defined. 9 15/10/2014 Acknowledgements 10
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