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GUESS Study: inconsistencies by experts in interpreting resistance test
results
Reported by Jules Levin
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This report contains results from 2 studies reflecting the difficulties in
using resistance testing. Also in this report is a commentary by me on using
resistance testing. I think resistance testing can be helpful in making
treatment decisions in the hands of the right person but I also think test
results in the wrong hands can be harmful. Education about resistance testing
is crucial.
At the Barcelona Conference, Andew Zolopa, MD, (Stanford University) reported
results from the GUESS Study in an oral session on resistance testing. He
said that although the use of a resistance test may help in outcome (in
picking a regimen that is more effective), he conducted this study to
evaluate how much agreement exists within the expert community in the
interpretation of genotypic testing. He looked at 45 patient specimens and
matching phenotypes from Virco. By looking at the genotype experts were able
to predict the phenotype about 30-40% of the time for nukes and protease
inhibitors, except for 3TC which was correctly predicted about 70% of the
time. For NNRTIs they could predict it about 67% of the time. The experts
tended to over-estimate the phenotypic fold-change (amount of phenotypic
resistance) for most nukes. The experts tended to disagree quite a bit for
nukes and protease inhibitors (50%), but less so for NNRTIs. Although there
was a surprising amount of disagreement for the NNRTIs and 3TC as well-about
20-25%. Zolopa reported the experts disagreed in predicting drug activity
about 50% of the time for nukes and protease inhibitors, and 27% of the time
for 3TC and about 20% of the time for NNRTIs. There was concordance,
agreement by the experts, of about 80% in whether to recommend or not
recommend a specific drug based on the genotype. Experts predicted phenotype
fold-change within 1 category 70% of the time. Zolopa concluded: (1) there
was inconsistent accuracy among experts in predicting phenotypic resistance
from genotype resistance test results; (2) there was variable agreement among
experts in translating genotype to phenotype or drug activity; (3) there were
high levels of agreement in the treatment recommendations by the panel of
experts; (4) outcomes of recommended treatments needs to be assessed in
future studies. I recall that Zolopa stated he would expect similar
discrepancies in interpretation by experts in using phenotypic resistance
testing.
Refining The Use of Resistance Testing (Commentary)
Resistance testing is a relatively new diagnostic development in HIV. It
started to emerge about 4 years ago. Resistance testing was the subject of a
good deal of discussion and attention at the Resistance Workshop. I think
it's apparent that it needs further refinement in the way it is used. This
field is very confusing, in that studies suggest interpreting resistance
testing results and using them to make treatment decisions is more
complicated than we thought. More studies suggesting this are emerging. At
the Resistance Wksp several researchers commented that we need to you learn
how & when to use resistance testing; and I agree. I think the resistance
testing should be used situationally. There are some circumstances in which
testing can be helpful, if the doctor interpreting test results understands
the test results, the limits of the testing & results, and how to mesh the
results with the patient treatment history. These are a lot of ifs. For
certain drugs testing may be more helpful but for certain other drugs testing
may not be so helpful. For certain patient situations testing may be helpful
& for certain other patient situations testing may not be so helpful.
Another question is genotype vs phenotype. Should you use genotype or
phenotype or both. Whichever test you use, patient treatment history is
crucial. There are little data comparing which may be better - the genotype
test or the phenotype test. There is some preliminary research comparing the
various resistance testing technologies: geno vs pheno, geno vs virtual
pheno, but more research will be needed to better evaluate comparisons
between the tests. We also need better studies to identify how & when using a
test can benefit a patient. Following that comes the task of teaching doctors
how to use the tests. The challenges to refining the use of testing are many
and it will take time and efforts to address them. If you were to give the
same set of genotype or phenotype test results to the leading resistance
experts in the world and asked them to interpret them, you might get some
differences in opinion. Andrew Zolopa, MD, Stanford University, reported
results from the GUESS Study at Barcelona in support of this.
Some researchers believe the genotype test is more reliable while others
believe the phenotype is more reliable. If you have the resources it might be
better to do both tests but make sure your treatment history is adequately
considered, and that whoever is making a treatment recommendation to you
knows what they are doing. Bear in mind, improper interpretation of a test
result or printout from the lab conducting the test can result in a bad
treatment decision. Also bear in mind that reliance on the lab test
recommendation can be a mistake, as their interpretation can be wrong.
If you get a genotypic mutation or several reported on a test result this can
rule out the usefulness of certain drugs. But the absence of detectable
resistance does not mean that you don't have resistance; it may just not be
detectable. See how complicated resistance testing can be. Some lab test
results, for example a Quest lab test result, may say you are sensitive to
AZT but you may have very little sensitivity to AZT. Does your doctor
understand all these subtle complications? Or does he/she just read the
sometimes faulty recommendations on the printed lab test result the doctor
receives?
There are a number of algorithms doctors can use to interpret test results.
An algorithm is a system into which you can plug your resistance test results
and it will interpret the results for you. The problem is that there are
numerous algorithms and they may offer different interpretations. There was
discussion at the Wksp on how to bring these different systems into
agreement, but I think this task has tremendous barriers & challenges.
Another suggestion that can help doctors interpret tests are certain websites
where expert resistance researchers are available to help the doctor actually
interpret the test results. An example of such a site is the Stanford
University site. Does your doctor know about this? You can suggest it to him
& see if he/she is open to using it. It takes time to do this. It is less
time consuming for a doctor to just accept the recommendations on the printed
lab report they receive.
DISCREPANCIES IN INTERPRETING RESISTANCE TESTING
At the Intl Conference a research group reported discrepancies in
interpretation between 3 algorithms they studied. Samples were taken from 293
HIV infected patients with treatment failure to evaluate the concordance of
genotypic data between 3 algorithms: Stanford University Database, TruGene
(Visible Genetics), and VirtualPhenotype (Virco). The obtained genotypes were
interpreted by the 3 algorithms to predict susceptibility for 14 ART drugs.
Complete concordance results among the 3 systems for all the drugs studied
were found in 40/293 (14%) of samples. Low concordance (50%) was observed for
most NRTIs, while results more highly agreed (70%) for all NNRTIs and most of
Pis. Discordant interpretations between Stanford & Virco were found in over
50% of samples for ddI, d4T, and abacavir. The level of disagreement between
VGI & Virco exceeded 40% for the same drugs. Major discrepancies (high level
resistance interpretation by 1 algorithm with sensitive interpretation by
another) were observed between VGI & Virco in over 10% of cases for ddI, ddC,
d4T, and abacavir. Most interpretations agree on 3TC 90% of the time. The
authors concluded that this study demonstrates the great level of discordance
in the interpretation of genotyping results among algorithms, clearly showing
the need for clinical validation. They suggest a joint effort is needed to
achievw a consensus on interpretation of genotypic data (MoPeB3125, GH Kijak,
Harrigan, Montaner, Cahn et al). At the Resistance Wksp researchers discussed
this and agreed such an effort is required but I have concerns regarding the
success of such an effort. Nonetheless, until such consensus is found the
interpretation of resistance test results is subject to many vagaries and the
risk for mistakes in treatment decisions.
This is a discussion and commentary by me on the limitations of resistance
testing but it's not meant to be exhaustive. My point is there are
limitations and refinement is needed in how to use testing and advances are
required in educating doctors & patients in using testing properly. Improper
interpretation of test results is common & can result in wrong treatment
decisions & failing a drug and drug class. A high price to pay.
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