PSI Biomarker Special Interest Group

1. PSI Biomarker Special Interest Group Please get in touch if you would like to get involved! • Join the mailing list or linked-in group in • Help on the SIG committee • Review papers, training, discussion groups… • Attend one of our free meetings /or our conference sessions • Go to www.psiweb.org and click on “committees and sigs” in the menu to find our webpage with more information

2. Agenda – Day 1 • Introduction - Nigel Dallow • What is a biomarker? • What use can biomarkers give us? • What form might they take? • Different types of biomarker – prognostic, predictive, pharmacodynamic • Biomarker development and practical issues in implementation - Viswanath Devanarayan • Preparatory work • Methodology studies • Mixed models and sources of variation • Standard curve calibration • Practical issues in implementation – missing data & confounding • Pharmacodynamic use of biomarkers in early development • Developing a biomarker based classifier - Viswanath Devanarayan • Pre-processing of large datasets • ROC curves and prediction statistics • Multiplicity considerations • Considering associations between biomarkers • Unsupervised methods to explore and understand the data • Selection of important biomarkers • Independent validation, cross-validation, resampling

3. Agenda – Day 2 • Practical session in R - Athula Herath • Summarising statistical properties of biomarkers • Dimension reduction and graphical visualization • Deriving and validating a multivariate index • Synthesizing additional pathway evidence • Designing a trial for a prognostic/predictive biomarker - Marc Buyse • Considering the strength of existing evidence • Potential designs for a confirmatory trial • Interaction analyses • Testing of multiple hypotheses • Statistics for diagnostic development

4. Introduction to BiomarkersDe-Mystifying Terminology Nigel Dallow GlaxoSmithkline

5. Definitions • Biomarker: A characteristic that is objectively measured and evaluated as an indicator of normal biological processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention • Clinical endpoint: A characteristic or variable that reflects how a patient feels, functions, or survives • Surrogate endpoint: A biomarker that is intended to substitute for a clinical endpoint. A surrogate endpoint is expected to predict clinical benefit *Downing, GJ (NIH Biomarker Definition working Group), Clin Pharmacol Ther 2001; 69:89-95

6. What is a biomarker? • Many endpoints could be considered as biomarkers • Commonly thought of in terms of biological / tissue samples (e.g. Blood, sputum), imaging techniques or even examinations • Biomarkers include a broad range of biological characteristics, measurement techniques/platforms, analysis methods • Does not define one purpose and so a clear objective and terminology can be helpful • Appear at many stages throughout the development program Caution: The term “biomarker” can mean different things to different people

7. Types of biomarker

8. Terminology

9. Prognostic Biomarker • A biomarker that provides information on the likely course of the disease in an untreated individual. • Measured before treatment to indicate long term outcome for untreated patients • Can be used to identify patients with poor prognosis to target. • Provide some ability to "predict" the patient'sfuture, potentially irrespective of therapy • E.g. patients with multiple myeloma with chromosome 17 deletions have a worse • Her2 positivity in breast cancer has similar negative prognostic implications.

10. Predictive Biomarker • A biomarker which can be used to identify subpopulations of patients who are most likely to respond, or suffer harm, to a given therapy. • With predictive biomarkers it should be possible to select the therapy with the highest likelihood of efficacy to the individual patient. • Thus, predictive biomarkers are the basis for individualized or tailor-made treatment. • Measured at prior to treatment (baseline/screening) to select/identify patients

11. Example Trial Designs for Predictive Biomarker Major focus here though will be on the demonstrating the clinical utility of the biomarker. Increasingly this needs to be via prospective clinical trials, rather than purely retrospective analyses Buyse et al 2011, Integrating biomarkers in clinical trials http://www.expert-reviews.com/doi/abs/10.1586/erm.10.120?url_ver=Z39.88-2003&rfr_id=ori:rid:crossref.org&rfr_dat=cr_pub%3dpubmed

12. Pharmacodynamic Biomarker • Measured Post-Treatment to measures whether and by how much a particular biological response has occurred • Treat like any other efficacy measure of interest. • BUT: need to understand collection process • More later

13. Pharmacodynamic biomarkers Biomarker to measure target engagement Hit Target Affect downstream pathway Biomarker to measure downstream pathway Link here commonly not well known Impact disease progression

14. Surrogate Biomarker • A biomarker intended to substitute for a clinical endpoint’ which can be used to predict effect on clinical endpoint. • Measured post-treatment • Useful in settings where the primary clinical endpoint takes large, long term trials • e.g. early breast and prostate cancer Caution: I see the term surrogate validation widely stated but rarely done well

15. Some examples • Genetics • Study of variability of trait due to hereditary factors (genetic code) • Gene Expression (Transcriptomics) • Study of the process by which a gene’s code is converted into a functional product (eg a protein) • Proteomics • Study of protein abundance, structure and activity • Metabolomics • Study of breakdown products (eg from a protein) • Imaging endpoints (fMRI, PET, EEG) • Receptor Occupancy, Cell counts (e.g. Blood Eosinophils) etc.

16. Biomarker Examples

17. Why are we interested? • Trials with clinical outcomes often have a long duration and are expensive. • Biomakers can give early readout of engagement of mechanism or disease • Biomarkers could help identify population of interest/responders • Surrogate endpoints may reduce the cost/development, shorten the duration and improve compliance.

18. Some Key Questions that can be addressed using biomarkers • Confirmation of Primary Pharmacology • Target engagement • Predictions from pre-clinical models • Dose, Dose Frequency and Formula optimisation • PK/PD • PoM/Go / no-go investment decisions • Predictions of efficacy • Surrogate endpoint • Population Enrichment • Bridging Studies • Early Safety Signals • Identify Indication (e.g. tumour type)

19. What characteristics are we interested in? • It is generally advisable to learn as much as possible about a biomarker prior to it’s application for decision-making • The science behind the choice of biomarker should be supported prior to initiating studies • How was the biomaker derived? Understand objectivity, possible values, whether naturally continuous or ordinal • The practical feasibility of using the marker should be examined • The statistical properties of the biomarker are of interest • Statistical properties of interest include • Variability estimates (and components of variability, within and between subjects) • Effect sizes (using a positive control, challenge model, animal model or other data sources) and the likely dynamic range • Distributions and scaling approaches required • Appropriate analysis methods considered

20. Developing Biomarkers: Methodology studies • Feasibility / Methodology studies can be used to learn the characteristics of an endpoint, but also the practicalities • Feasibility of multiple assessments • Acceptability to the patient, degree of dropout • The same design and analysis methodology should be used as is envisaged for the subsequent clinical trial • Information learned can aid in designing or sizing of trial. Repeat measurements without intervention can be used to study variation • Work carried out should be fit-for-purpose enough to ensure that there is sufficient confidence that decisions can be made based upon the biomarker, given it’s intended use. • Further studies, reviews or meta-analyses can aid in this if required.

21. “Good” Biomarker Examples • Serum cholesterol level for coronary artery disease. • Replaces cardiovascular event • Reduction of elevated arterial blood pressure -> reduced risk for stroke, congestive heart failure and cardiovascular death. • Replaces clinical outcome: death • HIV viral load and CD4 cell counts • Substitute for death and occurrence of opportunistic infections

22. Example 1Mechanistic Biomarker – Pharmacodynamic MarkerUse:1. Target Engagement2. Dose selection3. Dose frequency

23. 3 hr 12 hr 27hr

24. PK/PD Target trough conc at close to max inhibition (e.g EC90)

25. Example 1Mechanistic PD Biomarker And predictive biomarker

26. Mepolizumab:Impact on mechanism & predictive biomarker Pharmacodynamic Marker: Predictive Biomarker: The lancet Vol 380 August 18, 2012, Pavord

27. Example – Safety Biomarker

28. Daily Dose [μg] 0 0 500 1000 1500 2000 2500 3000 -10 -20 -30 -40 Cortisol Suppression from Baseline [%] -50 -60 -70 -80 -90 -100 Example: Serum Cortisol Model Drug B Marketed drug (asthmatics) Drug A (Asthmatics) Marketed drug (HVTs) Drug A (HVTs)

29. Example Safety biomarker, collaborative consortium In the case of a safety biomarker, this will often be of wide applicability and mutual benefit to all – more suited to qualification situations. Early qualification procedure examples were related to non-clinical kidney toxicity biomarkers for example (Predictive Safety Testing Consortium) Several aspects of interest in validation: Distribution and Variation in healthy cases Dynamic response to injury Sensitivity and specificity vs gold standard (?) Differential effects by severity Time course of effects Return to normal upon recovery Improvement over existing methods PSTC: http://www.nature.com/nbt/journal/v28/n5/full/nbt0510-444.html EMA opinion: http://www.ema.europa.eu/docs/en_GB/document_library/Regulatory_and_procedural_guideline/2010/11/WC500099359.pdf

30. Sources of Biomarkers

31. Sources of biomarkers

32. Plates • Often used to measure blood samples • Used to detect the present of particular analytes • In an ELISA antibodies to this analyte result in colour change • Standard curve used to convert level of colour change into the amount of analyte detected • 96 well plates often used, but other sizes exist • Samples are usually split into 2 replicates, so use up 2 wells • Important to collect well info in case of spurious results. • E.g. contamination often occurs in corner wells • Depending on study design, subjects may be split across plates so as to guard against batch variation, spatial aspects etc

33. Wells Well A01 • Each well: • Contains: • Subject data or • Control data or • Standard Curve data • Can analyse up to 10 analytes IL2 TNFa IL13 IL12p70 IL5 IL4 IL10 IL1b

34. Example Plate Layout Subjects: 1 2 3 4 5 6 7 8 9 10 11 12 Standard curve Negative control Positive control

35. Standard Curve Data: Uses an artificial media (i.e. no real ‘matrix’) Used for assay validation and derivation of LLOQ and LLOD • 10-point Standard Curve Data • Varying concentrations including zero • Generally 2 reps for each concentration

36. Control Data: Uses an equivalent matrix to the subject data Generally used for assay validation • Negative Controls • Other names: ‘Blanks’, ‘Basal’, ‘Not-spiked’ • Wouldn’t expect these to give a zero signal since they contain matrix which is likely to contain a small amount of analyte • Values unlikely to be >3 times signal observed without matrix • Positive Controls • Other names: ‘Spiked’ • A known amount of analyte is added (one that is expected to sit slightly above top of standard curve) • Values should be higher/not much lower than signal observed at top of standard curve

37. Signal Standard curve data Subject data ‘background’: zero concentration values Concentration 0 0.85 2.54 7.62 22.86 68.59 205.76 617.28 Calculated Concentration 0 0.85 2.54 7.62 22.86 68.59 205.76 617.28

38. Tissue Samples • Tissue biopsies are collected and “fixed” and embedded in paraffin to prevent deterioration of the sample. This must be done promptly • “Immunohistochemistry” used detect antigens (proteins) by staining the tissue with antibodies • Image is then scored manually by a pathologist (eg H-score) or using automated methods (eg of area or % of cells stained) • Measurements can be made using the whole block or a TMA • Cores of a few mm wide from samples can be placed in a single block to form a Tissue MicroArray to allow high-throughput work & re-use. Often several cores per sample are included

39. Imaging • Imaging used to either identify the presence / size / structure of an abnormality (eg tumour size) or to assess functional aspects (such as blood flow, brain activity etc) • Depending on the technique and purpose, results may be obtained in an automated fashion or from a manual read • May be a detailed scoring algorithm which considers how to combine different regions of interest (eg multiple tumours or joints) • If manually read, key consideration is whether images are read at each site or centrally by an independent expert group (recommended) • Inter-reader reproducibility often assessed

40. Key Areas of Importance when using Biomarkers

41. Determining & Understanding Sources of variation Much biomarker work is accompanied by similar challenges around controlling sources of variation and confounders: • Patient-related factors • Individual biological factors: genetics, race, age, comorbidities • Environmental factors: smoking, physical fitness, diet • Natural biological variation: within patient variation, diurnal effects • External factors: • Pre-analytical variation: sample collection and fixation practices • Analytical variation: between laboratories, readers and batches, technical precision of the assay

42. Controlling variation The level of within subject variation will affect the power of a future study. Including more samples or averaging over several replicates will increase precision In some cases this may also be sensible to give some allowance for missing data from individual sample replicates Fig 1 shows the power for an example cohort of 200 patients with 130 observed events. The study was planned to detect a hazard ratio of 1.3 at a 5% significance level. There would be 85% power if the marker was measured with complete precision. e1 is a measure of heterogeneity ( the proportion of the total variance which is explained by the within patient component of variation) Heterogeneity and Power in Clinical Biomarker Studies, Pintilie et al. Journal ClinOncol, 2009; 27(9): 1517-1521

43. Sources of bias Bias can arise, especially in open-label studies, for example: • Verification bias because of the choice of locally available methods • Patient selection bias, for example, patients with breast cancer family history, may not consent to BRCA DNA testing • Treatment allocation bias, if group assignment is based on a subjective assessment, as in some histo-pathological endpoints • Publication bias Being asked the same question repeatedly, for example in a pain challenge model, could induce false differences If a scoring method is subjective, then measures should be taken to minimize bias, for example, with the use of scripted questions, blinding and randomisation of the order of image scoring • Blinded assessors Values below limit of Quantification can introduce bias if not treated carefully.

44. Sources of missing data Biomarker studies can suffer from missing data issues, especially in exploratory situations or where repeat samples are requested: • Consent rates will generally be lower for optional samples • Poor quality of fixation in archival samples can mean degraded samples cannot be used • Novel technologies may fail for some patients giving no result • Inaccessibility of tissues (e.g. lung cancer) It may be fair to assume that some of these data are missing completely at random if purely due to technology or operational aspects. However some cases may be informative: • Lack of a sample due to complete response or small tumour • Lack of consent because of a patient’s current condition Likely sources of missing data should be considered in advance so as to minimise implications and make appropriate assumptions

45. Reproducibility Comparisons of observers and rescore rounds for the same observer

46. Important to understand how biomarker was measured

47. Standard Curves • Many assays use dyes or stains to identify the presence of an analyte. Measures such as the fluorescence or absorbance then acts as a “signal” of the level of analyte in the sample. But we need a way to translate between this signal and the actual concentration of the analyte • To do this known amounts of an analyte are ‘spiked’ into the ‘artificial media’ to generate a concentration response (signal) relationship. • A 4 parameter logistic model is fitted to create the ‘Standard Curve’ from which we can read off the concentration for our samples using the signals from each one • Care must be taken not to use the curve at points which have resulted purely from extrapolation away from these spiked results – hence limits of quantification

48. Issues can arise!Don’t just take data you are given!

49. Biomarker Validation

50. Biomarker qualification • Formal biomarker qualification process have been introduced: • FDA (qualification process for drug development tools) • EMA (qualification of novel methodologies and biomarkers) • Examples on EMA and FDA websites • These work alongside usual scientific advice routes and are a way to seek regulatory opinion on acceptability of a marker for a given use • This is not mandatory and so is usually not required, but may be an advantageous step, particularly for a marker with wide applicability (For example for collaborative groups seeking to develop a new endpoint, particularly for toxicity) • Diagnostic biomarkers would need to follow the in-vitro diagnostic regulatory processes