hiv 1 resistance testing from proviral dna alexander
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HIV-1 resistance testing from proviral DNA Alexander Thielen AREVIR 2018 Resistance testing from proviral DNA? amount of samples with low viral loads increasing desire to switch under successful therapy Zusammensetzung der Viruslasten


  1. HIV-1 resistance testing from proviral DNA Alexander Thielen AREVIR 2018

  2. Resistance testing from proviral DNA? – amount of samples with low viral loads increasing – desire to switch under successful therapy

  3. Zusammensetzung der Viruslasten 2005-2017 (jeweils Q2, Kaiserslautern) 100% 90% – amount of samples with low viral loads increasing 80% 70% – desire to switch under successful therapy 60% >1000 Kop/ml 401-1000 Kop/ml 50% 201-400 Kop/ml – studies, e.g. the LOWER study 40% 50-200 Kop/ml <50 Kop/ml 30% 20% 10% 0% Däumer, M., 2018, unpublished

  4. Resistenzteste 2017, KL 1000 900 800 700 600 500 Plasma RNA: 913 400 Provirale DNA: 138 300 200 100 0 Plasma-RNA provirale DNA provirale DNA 2017 2010 Däumer, M., 2018, unpublished

  5. Resistance testing from proviral DNA? – amount of samples with low viral loads increasing – desire to switch under successful therapy – studies, e.g. the LOWER study

  6. The LOWER study “Limited Options with Extended Resistance to antiretroviral therapy: A National Survey of Triple Class Resistance” (LOWER) – headed by PD Dr. med. Christian Hoffmann – analysis of HIV patients with triple class resistance – resistance testing from proviral DNA with NGS – comparison of current resistance status with historical data

  7. Resistance testing from proviral DNA

  8. Resistance testing from proviral DNA – problem: not only old populations archived but also hypermutated reads – Apobec 3F/3G mutations (G to A) in DNA: • Apobec3F: GA  AA • Apobec3G: GG  AG, further preference for TGG, TGGG motifs! – so, what is really there?

  9. How to detect Apobec mutations – typical mutations: • ATG  ATA M  I • GGY  AGY G  S • GGR  AGR G  R • TGG  TAG/TGA W  * – potential (resistance-associated) amino acid substitutions WT AA MZ AA NRT NNRTI PI INI AI EI Tropism G (Gly) S (Ser) G190S G73S G140S, G163S G357S G36S G11S G (Gly) R (Arg) G163R, G193R G11R, G25R G (Gly) E (Glu) G190E G16E G163E, G193E D (Asp) N (Asn) D67N D30N E (Glu) K (Lys) E138K E138K E25K R (Arg) Q (Gln) R (Arg) K (Lys) M (met) I (Ile) M184I M184I, M230I M36I, M46I M154I – attention: differences in codon usage between subtypes

  10. How to deal with Apobec? 1.The political solution: sit it out – just use the data as it is – problem: M184I and M230I

  11. How to deal with Apobec? 2.The non-believer solution: ignore it – remove all mutations that could occur from Apobec – problem: M184I and M230I sometimes occur in the viral population – really! – M184I usually emerges before M184V which then outcompetes the M184I within several weeks of viral replication

  12. How to deal with Apobec? 2.The non-believer solution: ignore it

  13. How to deal with Apobec? 3.The linkage solution: link & delete – idea: if several mutations possibly induced by Apobec have similar frequencies, then delete them – otherwise stay with them

  14. How to deal with Apobec? 3.The linkage solution: link & delete – idea: if several mutation possibly induced by Apobec have similar frequencies, then delete them – otherwise stay with it mutation frequency M184I 12.4% M230I 14.6% W24* 15.7% W88* 13.3% W153* 13.3% G51R 16.4% ... ...

  15. How to deal with Apobec? 3.The linkage solution: link & delete – idea: if several mutations possibly induced by Apobec have similar frequencies, then delete them – otherwise stay with them – often works very well, but...

  16. How to deal with Apobec? 3.The linkage solution: link & delete

  17. How to deal with Apobec? 3.The linkage solution: link & delete – idea: if several mutations possibly induced by Apobec have similar frequencies, then delete them – otherwise stay with them – often works very well, but... • unfortunately not always

  18. How to deal with Apobec? 3.The linkage solution: link & delete mutation frequency M184I 15.8% V82I 20.0% – idea: if several mutation possibly induced by Apobec have V108I 12.3% similar frequencies, then delete them – otherwise stay with it G190R 14.3% G84R (PR) 19.9% W153* 19.1% – often works very well, but... W71* 14.8% W88* 14.3%

  19. How to deal with Apobec? 3.The linkage solution: link & delete mutation frequency M184I 15.8% V82I 20.0% – idea: if several mutation possibly induced by Apobec have V108I 12.3% similar frequencies, then delete them – otherwise stay with it G190R 14.3% G84R (PR) 19.9% W153* 19.1% – often works very well, but... W71* 14.8% W88* 14.3% how is the K103N affected? will it go below 10% L90M 21.1% L210W 14.3% T215Y 12.4% do we remove them, too? M230L 14.8% ... ...

  20. How to deal with Apobec? 3.The linkage solution: link & delete – idea: if several mutations possibly induced by Apobec have similar frequencies, then delete them – otherwise stay with them – often works very well, but... • unfortunately not always • we do not know how other mutations are affected – how do the frequencies change?

  21. How to deal with Apobec? 4.The lazy solution: filter it automatically – create a filtering method – work on the reads themselves, not on the final result – approach: naïve Bayes classifiers (related to Reuman et al., 2010)

  22. How to deal with Apobec? 4.The lazy solution: filter it automatically – stops: the number of stop codons – atypical aa: the number of atypical amino acid substitutions – burden: #G-to-A / #G – preference: #G-to-A / #substitutions

  23. How to deal with Apobec? 4.The lazy solution: filter it automatically – trained on >100mio reads – tested on 523 samples (111 DNA, 412 RNA) hypermutated 5% 10% 15% 20% 30% reads DNA 19.82% 12.61% 11.71% 9.01% 4.50% RNA 15.05% 5.58% 1.46% 0.73% 0.00% – problems: • too much weight on stop-codons • less power on PR and IN

  24. How to deal with Apobec? 4.The lazy solution: filter it automatically

  25. Acknowledgments Kirsten Becker Elisa Danner Nina Engel Anja Förster Anna Memmer Bettina Welter Martin Däumer Bernhard Thiele

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