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HIV-1 resistance testing from proviral DNA
Alexander Thielen
AREVIR 2018
HIV-1 resistance testing from proviral DNA Alexander Thielen AREVIR - - PowerPoint PPT Presentation
HIV-1 resistance testing from proviral DNA Alexander Thielen AREVIR - - PowerPoint PPT Presentation
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
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– amount of samples with low viral loads increasing – desire to switch under successful therapy – studies, e.g. the LOWER study
Zusammensetzung der Viruslasten 2005-2017 (jeweils Q2, Kaiserslautern)
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
>1000 Kop/ml 401-1000 Kop/ml 201-400 Kop/ml 50-200 Kop/ml <50 Kop/ml
Däumer, M., 2018, unpublished
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Plasma RNA: 913 Provirale DNA: 138
Plasma-RNA provirale DNA 2017 provirale DNA 2010 100 200 300 400 500 600 700 800 900 1000
Resistenzteste 2017, KL
Däumer, M., 2018, unpublished
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– amount of samples with low viral loads increasing – desire to switch under successful therapy – studies, e.g. the LOWER study
Resistance testing from proviral DNA?
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“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
The LOWER study
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Resistance testing from proviral DNA
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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?
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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 – attention: differences in codon usage between subtypes
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
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How to deal with Apobec? 1.The political solution: sit it out
– just use the data as it is – problem: M184I and M230I
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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
- utcompetes the M184I within several weeks of viral
replication
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How to deal with Apobec? 2.The non-believer solution: ignore it
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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
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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% ... ...
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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...
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How to deal with Apobec? 3.The linkage solution: link & delete
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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
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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 – 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%
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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 – 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% L90M 21.1% L210W 14.3% T215Y 12.4% M230L 14.8% ... ... do we remove them, too? how is the K103N affected? will it go below 10%
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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?
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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)
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– 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
How to deal with Apobec? 4.The lazy solution: filter it automatically
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– trained on >100mio reads – tested on 523 samples (111 DNA, 412 RNA) – problems:
- too much weight on stop-codons
- less power on PR and IN
How to deal with Apobec? 4.The lazy solution: filter it automatically
hypermutated reads 5% 10% 15% 20% 30% DNA 19.82% 12.61% 11.71% 9.01% 4.50% RNA 15.05% 5.58% 1.46% 0.73% 0.00%
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How to deal with Apobec? 4.The lazy solution: filter it automatically
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