Arevir University of Cologne Institute of Virology Analysis of - - PowerPoint PPT Presentation
Arevir University of Cologne Institute of Virology Analysis of resistance mutations of HI-Virus Bioinformatics analysis of relations between mutations of the HIV genome and phenotypical drug resistance for the optimization of anti-retroviral
University of Cologne Institute of Virology ES / Bonn Apr 2007 1
Institut für Virologie der Universität zu Köln - Cologne center of advanced european studies and research - Bonn
Bioinformatics analysis of relations between mutations of the HIV genome and phenotypical drug resistance for the
University of Cologne Institute of Virology ES / Bonn Apr 2007 2
Background Database scheme Requirements of data protection Problems resulting from 'to much protection' Data cleansing Comparison between 'old' and 'new' Arevir DB Basic statistics / samples of derived information Collaborations / What next
University of Cologne Institute of Virology ES / Bonn Apr 2007 3
1999 The Arevir project1) is founded by Daniel Hoffmann, Rolf Kaiser and Joachim Selbig. Aim: develop computer based methods to enhance the inter- pretation of genotipic resistance tests. 2000 Niko Beerenwinkel created the basis for Arevir in his dissertation: Computational Analysis of HIV Drug Resistance Data 2001 Barbara Schmidt, Hauke Walter and Klaus Korn provided ~650 genotype phenotype pairs 2001 First version of geno2pheno available online, predicting drug resistance from genotype
1) The project was funded by the German Research Foundation (Grants HO 1582/1-1 to -3 and KA 1569/1-1 to -3)
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Genotype: 41L, 67N, 68G, 70R, 86DE, 88S, 90I, 102Q, 103S, 118IV, 135T, 162H, 190A, 203DE,
210W, 211K, 214F, 215Y, 219E, 228H, 248D, 277K, 283I, 326V, 329IV, 334L Resistance Test VL t
Question: How will the current virus population change, faced with a new therapy? Which new therapy is the optimal choice? (~1500 drug combinations seen!) Why develop computer based methods?
Therapy must be changed!
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To give assistance with both questions, we tried to correlate: genotype, VL, CD4, therapy we needed a database The Arevir DB is a MySQL database and consists mainly of 50 tables organized in 5 groups:
Patient related data (demographic data) Therapy data Isolate related data (serology, clinical chemistry) Genotipic data (mutations) Administrative data (access rights etc.)
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patients
patID year_of_birth gender transmission
pseudonyms
patID pseudonym
diagnoses
diagID patID ICD_code
therapies
therapyID patID therapy_start therapy_stop
therapycom- ponents
therapyID compound dosage
isolates
isoID patID sampling_date
sequences
seqID isoID nt_seq nt_crc
isolate_values
isoID propID methodID value
mutations
seqID mutations
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patients
2342 1963 'M' 'IVDA'
pseudonyms
2342 'FA3E359D1...'
diagnoses
7431 2342 'R75'
therapies
4288 2342 2002-06-23 2003-11-05
therapycom- ponents
4288 '3TC' 150 mg
isolates
12630 2342 2002-06-02
sequences
2966 12630 'CCTCAGATC...' 3247583203
isolate_values
12630 'HIVRNA' 'bDNA' 165000
mutations
2966 K65R, L74V...
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Requirements from data protection officials:
Trace back to the identity of a patient must be impossible
Restricted access to the data (physician can see 'her/his' data,
bioinformatics have read access but not to pseudonyms)
Names are not stored in Arevir at all
When a new patient record is added a pseudonym using the SHA-1 algorithm
is generated on the fly from the fields first name, last name and birthday.
The only way to connect the server is:
SSH (secure shell) Using public/private key authorization From a computer whose IP-Address is known to Arevir
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0101
Arevir Arevir
Login with private key protected by pass phrase VNC login with password VNC tunneled via SSH
0101 0101 0101 0101 0101 0101
Internet TCP/IP
0101 0101
Arevir web application login with password
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Arevir's web interface showing the input form for personal data
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For a meaningful correlation a large amount of data is needed!
P a t i e n t s P a t i e n t s Therapies Therapies C D 4 C D 4 V L V L Sequence
Steady small result despite of an increasing amount of data
s Sequences
2001 Data from ~500 patients ~150 records suitable for evaluation 2005 Data from ~4500 patients ~350 records suitable for evaluation
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t
Clinical data Genotypes
Δ n / time 2000 2005
Bad patient identification was spotted as main cause for the funnel effect
Arevir pseudonym algorithm was dependent on the exact spelling
Maintenance of patient ↔ genotype list at the laboratory was done
with an Excel sheet
Solution:
'New' pseudonym algorithm using some kind of fuzzy pre processing Standalone application (Rosie) for the Institute of Virology Migration to new MySQL version with some alterations in the scheme
While the lab was producing more genotypes per month, the number of follow up data was decreasing steadily
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André Ramirez 23.03.1969 Andre Ramires 23.03.1969 Hans-Peter Schmidt 05.11.1982 Hans Schmidt 05.11.1982 Georgios Koehler 15.04.1958 Georgious Koeler 15.04.1958 Anna Meier 29.12.1978 Anna da Silveira 29.12.1978 John Miller 16.03.1970 John Miller 10.03.1970 Mgabe Osamba 12.04.1974 Ossamba Mgabe 11.04.1974
Cleansing of patient names and assignment of an unique patient ID was done with new fuzzy indices (name aliases allowed)
Examples are fictive!
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Several checks applied to uncover suspicious data:
Genotypes without sampling date Duplicate genotypes Overlapping therapies Date checks (e.g. infection < first positive test < first treatment etc.) Therapies with 'forbidden' drug combinations More than one isolate value (of a kind) in a period of 7 days Lab values out of specified range (e.g. HIVRNA > 10.000.000 copies/ml) . . .
Data cleansing is hard work, time consuming, tedious but absolutely necessary!
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Small errors can have quite big effects:
TCE 15.03.2002 20.06.200
1,6
Therapy B Therapy A 12W (15.06.2002)
4,0
2
VL t
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Two entry forms of Rosie
Data quality and consistency was
improved by early checks
Pseudonym is generated in Rosie
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Patients ~ 4.500 Diagnoses ~ 2.900 Therapies ~ 12.000 CD4 values ~ 52.000 VL values ~ 41.000 Sequences ~ 3.000
Patients 2.444 Diagnoses 4.412 Therapies 6.154 CD4 values 53.031 VL values 25.972 Sequences 2.180 ~ 350 complete TCEs ~ 750 complete TCEs
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blood transfusion haemophiliac heterosexual homosexual IVDA pattern II unknown
Male Female 0,0 5,0 10,0 15,0 20,0 25,0 30,0 35,0 40,0 45,0
Male Female
Transmission quotas
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Percentage of silent mutations per genotype
5,0 6,0 7,0 8,0 9,0 10,0 11,0 12,0 13,0 14,0 1999 2000 2001 2002 2003 2004 2005 2006 PRO (treated) RT (treated) PRO (naïve) RT (naïve)
n naïve = 1013 n pretreated = 2327
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At the moment Arevir receives data mainly from the University
Clinics from Bonn, Köln (through Medeora, www.medeora.com) and Düsseldorf.
The system is open and any contribution is welcome! There is a close collaboration with EuResist (www.euresist.org)
Coming soon:
Integrase Genotypes Hepatitis B and Hepatitis C data
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By supporting therapy optimization algorithms By enhancing our understanding of HIV
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