1. Laboratory and clinical data for therapy outcome prediction an overview of presently available tools
Francesca Incardona Russian Congress of Laboratory Medicine, Moscow October 2016

2. A foreword: The importance of sharing data
Evidence based medicine
Research results: higher number of studies
Funding from companies
Research results: different kind of studies when large data numbers are needed bioinformatics
Integrating laboratory and clinical data

3. Problems with sharing data (at least in European perspective)
Will: data is power, data is money
Organisation: plethora of local IT systems, codes, permission rules, burocracy
Technical issues: how to integrate different data-sets
Privacy, ethical issues
PAPER! From a research in Tuscany region (Italy) 60% of infectious diseases centres still collects data on paper and 60% of the labs is not connected with clinics
Similar situation in the UK

4. Solutions for sharing data
Will: Transparency of the governance, data ownership and authorship agreements
Technical issues:
physical versus virtual integration
Ex. EuResist Integrated DataBase: patients
Ex. EuroCoord, COHERE: federative DBs: patients (now closed)
Ontology-based data integration, use of metadata
STANDARDS: HL7, ICD10, HICDEP

5. Solution for sharing data
Adoption of the same system at regional or national level with clinical health records integrated with laboratory and administrative IT platforms
Possibly a validated Good Practice
Ex. InfCare HIV: born at Karolinska Institute, now Swedish HIV registry + adopted in whole Baltic region + in centres in India and Africa

6. InfCare HIV and Hepatitis

7. InfCare HIV

8. The strength of integrated data
“Data need to be made more widely and transparently available to people in a form that they are able to interpret and use. Information drives change, and data drive decisions and accountability. The missing piece is often the data.”
ACTIONS FOR THE FUTURE - UNAIDS. How AIDS changed everything — August 2015
Retrospective - observational - epidemiological studies
Bioinformatics studies
Ex. Geno2Pheno
Ex. EuResist Engine
Ex. Phylogeotool

9. Background: HIV resistance and computer assisted therapy
Lifelong treatment + extraordinary HIV variability → Resistance + toxicity, adherence →
Choosing the best treatment can be problematic
With expanded access to therapy, resistance has recently become more important in LMIC too [Hamers 2011, Hamers 2012, Gupta 2012]
High dimensional problem (mutations in HIV genome, number of drugs in different combined regimens, patients’ characteristics),
Today, HIV genotype analysis with computer-rules- based algorithms is state-of-the-art in clinical practice:
ex. HIVDB, ANRS, Rega

10. HIVDB https://hivdb.stanford.edu

11. www.hivfrenchresistance.org https://rega.kuleuven.be
Known as the “ANRS” system for predicting drug resistance associated to a sequence: only for Protease and Reverse Trascriptase
Rega algorithm

12. The bioinformatics approach
Machine learning techniques to provide objective, clinically relevant resistance data interpretation
Need of large repository of data to train the models
Historically, bioinformatics systems trained on genotypes which cultured in vitro showed certain phenotypes
Since 2008, EuResist Engine: Integration of viral genomics with clinical data to predict response to anti HIV treatment

13. S M A LL Large data set Web interface Combined predictive system
Integrating DBs from different sources S M A LL
Large data set
Combined predictive system
Web interface
Individual engines
End users
Connections used during project life and then for system updates
Connections used by the final users

14. Geno2Pheno - Max Planck Institut Informatik
The importance of sharing data
The situation in Europe
The two main solutions: physical integration and federation with examples
The bioinformatics use of data in HIV:
Geno2Pheno
EuResist old
EuResist new

15. Geno2pheno [resistance] 3.4

16. HIV tropism
In 2008 Pfizer released a new antiviral, maraviroc, acting on a different region of the virus: the protein (gp120) binding to the CD4 receptor
This binding needs also a coreceptor which can be of two types: ccr5 or cxcr4
Preference for one type of coreceptor or the other by the virus is called tropism
Maraviroc is active only on ccr5 virus 
Very expensive tropism testing

17. HIV tropism

18. Geno2pheno [coreceptor] 2.5

19. Geno2Pheno [coreceptor]
Uses a statistical method called Support Vector Machines (SVM)
Has been trained on 1100 V3 sequences from 332 patients
Has become a zero-cost substitution for genotypic tropism test
Geno2Pheno [integrase], [HCV], [HBV]
are rules based

20. Rega HIV-1 subtyping tool
Uses phylogenetic methods to identify the subtype of a given sequence, for HIV 1 and HIV 2
The golden standard for subtyping, adopted by Stanford and several others
regatools.med.kuleuven.be/typing

21. EuResist Network GEIE A European no-profit grouping
Founded by Informa, Karolinska Institute, Max Planck Institute, University of Cologne, University of Siena, now has 16 partners worldwide
It manages the EuResist Integrated Database (EIDB) which contains more than 66,000 patients and is open for research
All partners are members of the Scientific Board which evaluates the research proposals that require access to the EIDB

23. The bioinformatics approach

24. EuResist Integrated DataBase
More than 66,000 patients

25. The EuResist prediction Engine
It is not rules based
It is formed by three independent statistical models
Each model uses SVM combined with specific features extraction and/or a resistance tree model
Each model is trained on the EIDB where sequences are linked to clinical follow-up of the patient (initially 8 weeks)
The three models are combined to form one robust prediction engine 25 25

26. The Prediction Engine (in English and Russian)26 26

27. EuResist Engine 2015

28. EuResist versus expert: EVE study
25 HAART cases randomly selected form the EuResist db:
Obsolete therapies excluded
Wild type genotype excluded
All clinical and virological information available
12 experts enrolled, response obtained from 10:
On-line anonymous rating
Only European vs. non-European setting traceable
Use of any interpretation system allowed (and declared)
Zazzi et al. HIVmed 2010 28 28

29. Performance
Both the single engines and the combined engine outperform significantly Stanford HIVDB
The AVERAGE is the best option for engine combination
Rosen-Zvi Bioinformatics 2008
Altmann PlosOne 2008
Prosperi Antivir Ther 2009

30. Engine update 2015 Updated on the larger data set
Time of clinical follow-up extended to 24 weeks
Performance remains similar

31. New Engine 2016
New definition of therapy success: number of aviremic semesters
Presented at ACM Conference on Bioinformatics and Computational Biology Seattle, 2-7 October

32. Phylogeotool Visualisation tools can help interpret large datasets
Eg. Geographic visualization of viral strains can be applied to surveillance for epidemics and outbreak of viral pathogens
Tracking of individual variants with specific characteristics e.g. risk group, drug resistance, … can elaborate their relation to geographic or phylogenetic spread

33. Phylogeotool

34. Contact: f.incardona@informacro.info
“A computer makes as many mistakes in two seconds as 20 men working 20 years make”
Arthur Bloch
Murphy's Laws for Technology
CΠACИБO
Contact: 34 34