HIV-1 resistance testing from proviral DNA

Transcription

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50 Kop/ml 20% 10% 0% Däumer, M., 2018, unpublished

4 Resistenzteste 2017, KL Plasma RNA: 913 Provirale DNA: Plasma-RNA provirale DNA 2017 provirale DNA 2010 Däumer, M., 2018, unpublished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 1.The political solution: sit it out just use the data as it is problem: M184I and M230I

11 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 2.The non-believer solution: ignore it

13 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 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 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 3.The linkage solution: link & delete

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

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

20 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 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)

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23 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

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

25 4.The lazy solution: filter it automatically

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