pitfalls in companion diagnostics

© 2014 SynteractHCR. All rights reserved. SHARED WORK. SHARED VISION.

Pitfalls in Companion Diagnostics

Don't underestimate the power of conditional probabilities


A mystery in numbers and its solution

!   In this presentation, Dr. Stephan de la Motte, Chief Medical Advisor, defines:

! The conditional nature of diagnostics ! Companion diagnostics as an entire therapeutic strategy ! Sensitivity and specificity with regard to diagnoses ! The power of conditional probabilities ! How to determine the best hypothesis in study protocols

when you have a companion diagnostic

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Diagnostics is conditional

!   A biomarker predicts... ! suspicion

–  if used in clinically healthy ! diagnosis

–  if disease symptoms are given ! prognosis

–  if diagnosis is given ! response

–  if treatment is given

!   A treatment produces... ! response (with a probability) in the right patient ! side-effects (with a probability) in any patient

!   A diagnostic assay leads – in the end – to a therapy outcome.

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Biomarker X scenario

!   Biomarker X is target for new drug, but does not influence response to standard of care nor normal course of disease

–  E.g., tumor-specific kinase inhibited specifically by new drug

!   Biomarker X is present in 5% of patients with the disease –  Companion diagnostic prevents many patients being treated with

new drug unnecessarily

!   Assay has 97% sensitivity and 98% specificity –  Good quality assay

!   New drug in patients with marker X ! 80% responders Standard of care ! 10% responders

–  Huge advantage of new drug over standard of care

This is an ideal scenario for Companion Diagnostics, right?

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An ideal scenario, right?

WRONG! Because... !   80% is the true effectiveness of the new drug !   60% is the observed responder rate in a clinical trial

The clinical trial... !   significantly underestimates the true value of the new drug !   leads to more than twice as many patients getting the new

drug unnecessarily

WHY?

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A word of caution...

"Companion Diagnostics" is... !   not only a diagnostic !   not only a drug !   it is an entire therapeutic strategy!

The next slide shows this strategy of the Biomarker X scenario as a tree of consecutive, conditional probabilities.

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Decisional algorithm tree 7

Biomarker X really present?

Assay pos., new drug?


Response to new drug?

Effect of standard?


Effect of standard?

Yes

No 0.03

0.97

Yes

No

0.95

0.05

No

Yes

0.20

0.80

Yes

No

0.02

0.98

Responder ~ 4%

Non-responder ~ 2%

Normal course ~ 84%

Effect < 0.1%

Responder ~ 0.2%

Non-responder ~ 1%

Normal course ~ 0.1%

Effect ~ 9%

Patients actually treated with new drug

No

Yes

0.90

0.10

No

Yes

0.90

0.10

No

Yes

0.90

0.10

Patients misallocated: Not to be treated with new drug, but are, or to be treated with new drug, but should not be.

Biomarker (invisible)

Apparent interpretation Assay outcome Therapy outcome


What we see is not what is real!

!   We never see the real target !   We see only

... the result of an assay

... the outcome of a treatment !   𝐴𝑝𝑝𝑎𝑟𝑒𝑛𝑡 𝑟𝑒𝑠𝑝𝑜𝑛𝑑𝑒𝑟𝑠= 𝑃𝑎𝑡𝑖𝑒𝑛𝑡𝑠 𝑟𝑒𝑠𝑝𝑜𝑛𝑑𝑖𝑛𝑔 𝑡𝑜 𝑡ℎ𝑒 𝑛𝑒𝑤 𝑑𝑟𝑢𝑔/𝐴𝑙𝑙 𝑝𝑎𝑡𝑖𝑒𝑛𝑡𝑠 𝑤ℎ𝑜 𝑔𝑒𝑡 𝑡ℎ𝑒 𝑛𝑒𝑤 𝑑𝑟𝑢𝑔 

= 𝟒%+𝟎.𝟐%/𝟒%+𝟏%+𝟎.𝟐%+𝟐% =𝟎.𝟔𝟎 (Percent numbers taken from previous slide #7, rightmost column)

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Sensitivity and Specificity

!   Don't overlook a diagnosis! ! 𝑆𝑒𝑛𝑠𝑖𝑡𝑖𝑣𝑖𝑡𝑦= 𝑃𝑒𝑜𝑝𝑙𝑒 𝑑𝑖𝑎𝑔𝑛𝑜𝑠𝑒𝑑 𝑐𝑜𝑟𝑟𝑒𝑐𝑡𝑙𝑦/𝐴𝑙𝑙 𝑝𝑒𝑜𝑝𝑙𝑒 ℎ𝑎𝑣𝑖𝑛𝑔 𝑡ℎ𝑒 𝑑𝑖𝑠𝑒𝑎𝑠𝑒 

!   Don't make a healthy person sick! ! 𝑆𝑝𝑒𝑐𝑖𝑓𝑖𝑐𝑖𝑡𝑦 = 𝑃𝑒𝑜𝑝𝑙𝑒 𝑑𝑒𝑐𝑙𝑎𝑟𝑒𝑑 𝑡𝑜 𝑏𝑒 ℎ𝑒𝑎𝑙𝑡ℎ𝑦 𝑐𝑜𝑟𝑟𝑒𝑐𝑡𝑙𝑦/𝐴𝑙𝑙 ℎ𝑒𝑎𝑙𝑡ℎ𝑦 𝑝𝑒𝑜𝑝𝑙𝑒 

!   Sensitivity and specificity indicate the quality of a diagnostic test and both should be close to 100%

But are these criteria – alone – meaningful?

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The “100% Assay”

!   "All of you have cancer!" ! Everyone who has cancer is diagnosed to have cancer ! => 100% sensitivity

–  No disease is overlooked, because no one is declared as healthy

!   "None of you have cancer!" ! Everyone who is healthy is declared to be healthy ! => 100% specificity

–  No false diagnosis, because no diagnosis is made at all

Yes, this is nonsense, because the value of a diagnostic assay depends on much more than sensitivity or specificity.

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Predictive values

Predictive value - If we make an observation, how likely is it that it is correct? !   Sensitivity and specificity are always given !   Predictive values are often overlooked and their usefulness is underestimated If we see it, is it real? – Important for treatment decisions !   Positive predictive value

! 𝑃𝑃𝑉= 𝑇𝑟𝑢𝑒 𝑝𝑜𝑠𝑖𝑡𝑖𝑣𝑒𝑠/𝑇𝑒𝑠𝑡 𝑜𝑢𝑡𝑐𝑜𝑚𝑒 𝑝𝑜𝑠𝑖𝑡𝑖𝑣𝑒𝑠 

If we don't see it, does this mean it isn't there? – Important for screening exams !   Negative predictive value

! 𝑁𝑃𝑉= 𝑇𝑟𝑢𝑒 𝑛𝑒𝑔𝑎𝑡𝑖𝑣𝑒𝑠/𝑇𝑒𝑠𝑡 𝑜𝑢𝑡𝑐𝑜𝑚𝑒 𝑛𝑒𝑔𝑎𝑡𝑖𝑣𝑒𝑠 

!   PPV and NPV quantify the reliability of assay results in actual populations.

PPV and NPV depend on prevalence.

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Biomarker X in 5% of patients

Number of patients

'X' present 5

'X' absent 95

Assay positive ('X' detected)

True positives 4.85

False positives 1.9

PPV 0.72

Assay negative ('X' not detected)

False negatives 0.15

True negatives 93.1

NPV 0.998

Sensitivity 0.97

Specificity 0.98

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Purple numbers = Given All other colors = Derived Interpretation: Although NPV is almost perfect, PPV is not satisfactory. PPV 0.72 means that 28% of patients with a positive assay don't actually have the Biomarker X target. Apparent responder rate of patients treated with new drug is 60%.


Biomarker X in 50% of patients

Number of patients

'X' present 50

'X' absent 50

Assay positive ('X' detected)

True positives 48.5

False positives 1

PPV 0.98

Assay negative ('X' not detected)

False negatives 1.5

True negatives 49

NPV 0.97

Sensitivity 0.97

Specificity 0.98

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Purple numbers = Given All other colors = Derived Interpretation: Properties of assay and of drug unchanged; only prevalence is changed. Now, both NPV and PPV are almost perfect. Apparent responder rate is now ~79%, very close to the theoretically best 80%.


How to make it more complicated

!   Previous scenarios were based on the simplified assumption that Biomarker X predicts only the response to a new drug.

In real life, however: !   Biomarkers are not only drug targets, but are associated with

a better or worse prognosis, even under standard of care !   A new drug is targeting biomarker X, but it may show some

efficacy also in patients who do not carry X

The next slide shows which probabilities must be modified... !   to simulate biomarker X prognosis without new drug !   to simulate drug efficacy in patients without biomarker X

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Modified decisional algorithm tree 15

Biomarker-associated poor prognosis. New drug with off-target efficacy.

Outcome changed, not always detectable. (Compare with previous tree on slide #7.)

Biomarker X really present?




Effect of standard?


Effect of standard?

Yes

No 0.03

0.97

Yes

No

0.95

0.05

No

Yes

0.20

0.80

Yes

No

0.02

0.98

Responder ~ 4%

Non-responder ~ 1%

Normal course ~ 84%

Effect < 0.1%

Responder ~ 0.5%

Non-responder ~ 1%

Normal course ~ 0.1%

Effect ~ 9%

No

Yes

0.95

0.05

No

Yes

0.75

0.25

No

Yes

0.90

0.10

Biomarker (invisible)

Apparent interpretation Assay outcome Therapy outcome


What to do if you have a companion diagnostic...

!   Check out the prevalence of the biomarker in your target population!

!   Calculate predictive values!

!   Work through the tree of conditional probabilites and see if it makes sense!

!   Calculate different plausible scenarios (sensitivity analysis – "What if?")

!   If PPV (positive predictive value) is troublesome, utilize a 2nd diagnostic test ! ...sequentially (2nd only if 1st is positive) ! ...parallel (outcome is positive only if both are positive)

!   A 2nd diagnostic should be complementary to the 1st ! ...one with high PPV ! ...the other with high NPV

!   Avoid raising false expectations! Write the best hypothesis in your study protocol!

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THANK YOU!

Dr. Stephan de la Motte Chief Medical Advisor

www.SynteractHCR.com

Global Headquarters 5759 Fleet Street, Suite 100 Carlsbad, CA 92008 +1 760 268 8200

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