1. © 2014 SynteractHCR. All rights reserved. SHARED WORK. SHARED VISION. Pitfalls in Companion Diagnostics Don't underestimate the power of conditional probabilities

2. © 2014 SynteractHCR. All rights reserved. SHARED WORK. SHARED VISION. 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 2

3. © 2014 SynteractHCR. All rights reserved. SHARED WORK. SHARED VISION. 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. 3

4. © 2014 SynteractHCR. All rights reserved. SHARED WORK. SHARED VISION. 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? 4

5. © 2014 SynteractHCR. All rights reserved. SHARED WORK. SHARED VISION. 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? 5

6. © 2014 SynteractHCR. All rights reserved. SHARED WORK. SHARED VISION. 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. 6

7. © 2014 SynteractHCR. All rights reserved. SHARED WORK. SHARED VISION. Decisional algorithm tree 7 Biomarker X really present? Assay pos., new drug? Assay pos., new drug? Response to new drug? Effect of standard? Response to new drug? 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 outcomeTherapy outcome

8. © 2014 SynteractHCR. All rights reserved. SHARED WORK. SHARED VISION. What we see is not what is real! 8

9. © 2014 SynteractHCR. All rights reserved. SHARED WORK. SHARED VISION. Sensitivity and Specificity 9

10. © 2014 SynteractHCR. All rights reserved. SHARED WORK. SHARED VISION. 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. 10

11. © 2014 SynteractHCR. All rights reserved. SHARED WORK. SHARED VISION. Predictive values 11

12. © 2014 SynteractHCR. All rights reserved. SHARED WORK. SHARED VISION. 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 12 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%.

13. © 2014 SynteractHCR. All rights reserved. SHARED WORK. SHARED VISION. 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 13 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%.

14. © 2014 SynteractHCR. All rights reserved. SHARED WORK. SHARED VISION. 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 14

15. © 2014 SynteractHCR. All rights reserved. SHARED WORK. SHARED VISION. 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? Assay pos., new drug? Assay pos., new drug? Response to new drug? Effect of standard? Response to new drug? 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 outcomeTherapy outcome

16. © 2014 SynteractHCR. All rights reserved. SHARED WORK. SHARED VISION. 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! 16

17. © 2014 SynteractHCR. All rights reserved. SHARED WORK. SHARED VISION. THANK YOU! Dr. Stephan de la Motte Chief Medical Advisor www.SynteractHCR.com

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