An Introduction to Biomarkers for Statisticians: A Brief Guide to Utilizing

2. An Introduction to Biomarkers for Statisticians: A Brief Guide to Utilizing the Most Appropriate Analytical Tool Ena Bromley and Lin Li

3. What are Biomarkers? A defined characteristic that is measured as an indicator of normal biological processes, pathogenic processes, or responses to an exposure or intervention, including therapeutic interventions.FDA (2016) BEST (Biomarkers, EndpointS, and other Tools) Proprietary

4. Types of Biomarkers EGFR PDL1 Stain Tumor size

5. Why are Biomarkers Important? “The application of using biomarkers in drug development can help lower development costs and improve efficiency of drug development programs, including potentially reducing the sample size needed to achieve statistical significance to demonstrate clinical effect or identifying potential safety signals earlier.” Scott Gottlieb, MD, former FDA commissioner December 11, 2018 Draft Guidance Biomarker Quantification:https://www.federalregister.gov/documents/2018/12/12/2018-26900/biomarker-qualification-evidentiary-framework-draft-guidance-for-industry-and-food-and-drug Proprietary

6. Categories of Biomarkers Proprietary

7. Diagnostic Biomarkers • A diagnostic biomarker refers to a biological parameter that aids the diagnosis of a disease (subtype) and may serve in determining disease progression and/or success of treatment. • It may be a laboratory, radiological, genetic, anatomical, physiological or other finding that helps to differentiate one disease from others (MEpedia). • Gene expression profiling may be used as a diagnostic biomarker to segregate patients with diffuse large B-cell lymphoma into subgroups with different tumor cell of origin signatures (Scott et al. 2014). Proprietary

8. Diagnostic Biomarker Utility • Enables accurate diagnosis of diseases and conditions. • Provides a critical evaluation whether a patient should be enrolled into a study, i.e. accurate definition of disease. • Identifies subtypes with different prognoses or responses to a specific treatment. • Could be used to predict responders versus non-responders, especially for cancers. Proprietary

9. Diagnostic Biomarker Considerations • Perfect world: 100% sensitivity – all patients with the disease are detected and 100% specificity – no patients without the disease would be diagnosed. • Limitations: Clinical sensitivity and analytical performance of the measurement method. • Analytical considerations: • Is reference diagnosis accurate? • Characterize biomarker within intent-to-diagnose population • Use qualified sites and operators to run the same diagnostic biomarker test to obtain concordance Proprietary

10. Safety Biomarkers • A biomarker measured before or after an exposure to a medical product or an environmental agent to indicate the likelihood, presence, or extent of toxicity as an adverse effect. Examples: Neutrophil count (evaluating cytotoxic chemotherapy)HLA-B*1502 allele (serious to fatal skin reaction to carbamazepine treatment) Proprietary

11. Safety Biomarker Considerations • Efficient safety biomarkers should be able to accurately detect or predict adverse drug or exposure effects. • Toxicity could be signaled by the detection of or change in a safety biomarker allowing for a dose modification • Safety biomarkers could signal need for treatment, developing toxicity (drug induced organ injury), or identification of patients with a safety risk (DMET). Proprietary

12. Response (pharmacodynamic ) biomarkers • A biomarker used to show that a biological response has occurred in an individual who has been exposed to a medical product or an environmental agent. Examples: • Blood pressure when evaluating patients with hypertension, to assess response to an antihypertensive agent or sodium restriction (James et al. 2014). • Viral load when evaluating response to antiretroviral treatment (DHHS Panel on Antiretroviral Guidelines for Adults and Adolescents 2016). Proprietary

13. Response/PD Biomarker Considerations • Is a biomarker whose level changes in response to an exposure to a medical product or environmental agent. • Limitations: Do not necessarily reflect the effect of an intervention on a future clinical event (i.e. not always a good surrogate endpoint). • Benefit/Utility: Gauges the level of response so that individual doses can be altered. Allow of more specific dosing. • Analytical Considerations: It is often difficult to power clinical studies to demonstrate a meaningful change in clinical outcome due to a PD biomarker. Proprietary

14. Susceptibility/Risk Biomarkers • A biomarker that indicates the potential for developing a disease or medical condition in an individual who does not currently have clinically apparent disease or the medical condition. Examples: BRCA1/2, APOE • Note: Susceptibility (genetic) biomarkers often indicate whether an individual has an increased likelihood of developing disease later in life. Prognostic biomarkers indicate an increased likelihood of a specific clinical event in an individual already diagnosed with a disease or medical condition, and diagnostic biomarkers, which may confirm whether a disease is actually present.  Proprietary

15. Susceptibility/Risk Biomarker Considerations • Main utility in clinical practice is to guide preventative strategies. • Estimate likelihood of developing disease given a specific biomarker (BRCA1). • Can be very useful in clinical trial enrichment in identifying patients who are more likely to develop aa particular disease. • Analytical Considerations:If patient already has disease = Prognostic biomarkerIf patient is otherwise healthy = Susceptibility/risk biomarker Proprietary

16. Monitoring Biomarkers • A biomarker measured serially for assessing status of a disease or medical condition or for evidence of exposure to (or effect of) a medical product or an environmental agent. • Examples: HCV-RNA when assessing treatment response in patients with chronic Hepatitis C. CA125 to assess disease burden during and after treatment in patients with ovarian cancer Proprietary

17. Monitoring Biomarker Considerations • Measure presence, status or extend of a disease or medical condition. • Provide evidence of exposure to medical product or environmental agent. • Useful to measure compliance to drugs, or epidemiological studies to measure environmental exposure. • Analytical Considerations: Think repeated measures Consider when, how often, till when? Proprietary

18. Predictive Biomarkers • Predictive biomarker gives information about the effect of a therapeutic intervention. • To identify a predictive biomarker, there generally should be a comparison of a treatment to a control in patients with and without the biomarker.  • Guide in the development/use of tailored therapies. • Useful link for strategies to design clinical studies to identify predictive biomarkers: https://www.sciencedirect.com/science/article/pii/S0305737216301530 Proprietary

19. Prognostic Biomarkers • A prognostic biomarker provides information about the patients overall cancer outcome, regardless of therapy. • A biomarker used to identify likelihood of a clinical event, disease recurrence or progression in patients who have the disease or medical condition of interest (BEST). • Guide other aspects of care and clinical trial planning, i.e. prognostic markers can be considered as covariates for stratification.  • Chromosome 17p deletions and TP53 mutations (chronic lymphocytic leukemia), to assess the likelihood of death (Gonzalez et al. 2011; Shanafelt et al. 2006).  Proprietary

20. Predictive vs. Prognostic Biomarkers What statistical analysis can you think of to assess predictive and prognostic biomarkers? Proprietary

21. Predictive vs. Prognostic Biomarkers Accelerating Drug Disc thu Precision Medicine and Innovative Designs

22. Predictive vs. Prognostic Biomarkers Useful references: • BEST Description of predictive versus prognostic biomarkers https://www.ncbi.nlm.nih.gov/books/NBK402284/ • R Code to distinguish predictive versus prognostic biomarkers https://academic.oup.com/bioinformatics/article/34/19/3365/4991984 Proprietary

23. Biomarkers in Cancer Proprietary

24. Biomarkers in Precision Medicine Forbes, March 2017 Proprietary

25. Benefits of Biomarkers in Precision Medicine • Removes the need for guesswork, variable diagnosis and generalized treatment strategies. • Utilize data from direct and indirect sources to provide a holistic view for an individualized patient. • Mitigates inefficiencies due to false positives, false negatives, unnecessary treatment, over/under medication. Proprietary

26. General Considerations when Developing Biomarkers Draft Guidance: Biomarker Quantification Evidentiary Framework Proprietary

27. Evidence to Support Quantification • Biologic rationale • Data supporting relationship between the biomarker and clinical outcome of interest • Analytical performance • Should inform the type and level of evidence needed to support quantification Proprietary

28. Statistical Considerations • Any biomarker analytic test should be robust, sensitive and specific • Goal: Evaluate the degree and certainty of association between a biomarker and outcome of interest. • Considerations: • Sources of data • Statistical design of studies contributing data • Statistical tools used Proprietary

29. Sources of Data in Biomarker Development • Randomized controlled trial • Single-arm/historical control trial • Cohort study • Case-control study • Cross-sectional study • Case series or case reports • Registry information • Meta-analysis Proprietary

30. Sound Statistical Practice in Analysis of Biomarker Data • Carefully designed prospective studies are more likely to support evidence for association between a biomarker and outcome. • Design and power studies to appropriately assess the association between a biomarker and outcome • Know and address potential methodological limitations that could lead to overestimation of actual associations:1) lack of proper controls (bias)2) confounding3) multiplicity • Independent verification increases credibility of results. https://www.fda.gov/news-events/fda-brief/fda-brief-fda-issues-guidance-facilitate-efficient-qualification-novel-biomarkers-can-help-advance Proprietary

31. Follow ICH E9 • Sample size should be sufficient • Control for multiplicity in analysis plan (consider false positives)Remember: Multiplicity should be considered when analyzing multiple-candidate biomarkers. • Avoid over reliance on p-values • Appropriately quantify the relationship between the biomarker and the outcome of interest.Available on the FDA Drugs guidance web page at https://www.fda.gov/Drugs/GuidanceComplianceRegulatoryInformation/Guidances/default.htm. Proprietary

32. Avoid Bias • Develop a biomarker analysis plan (BAP) as soon as possible • If initial data did not consider biomarkers, is it similar to data used to investigate associations between biomarkers and outcome? • Analysis intended to support biomarker qualification should be specified in the BAP Proprietary

33. Data • Are sample and data collection methods sound? • What is the effect of missing data? • Is collection of biomarker data from a subset of clinical sites, groups or treatments? • Would these subsets yield unbiased estimates? • How is the outcome of interest being defined? Proprietary

34. Statistical Tools • Binary outcomes (presence or absence of disease):- Clinical sensitivity and specificity- PPV and NPV- ROC • Continuous outcomes (disease progression):- Regression models- Change from baseline- Repeated measures Proprietary

35. Continuous of Dichotomized? • Expressing biomarker measures quantitatively increases statistical power compared to dichotomization. • Once a relationship between and biomarker and outcome has been established, several cutoffs can be considered. • Most appropriate cutoffs can be selected by comparing clinical outcomes of at risk subjects at each biomarker cutoff. • Choose cutoff based on clinical sensitivity or clinical specificity Proprietary

36. Criteria for Determining Relationship between Biomarker and Outcome • No set criteria • Most criteria based on parameters used to quantify relationship:- threshold values for sensitivity or specificity- change in clinical performance as a function of biomarker quantity • Findings need to be relevant, reliable and statistically robust • Is there a strong biological rationale for supporting a statistical finding? Proprietary

37. Central Dogma of Molecular Biology “The central dogma of molecular biology deals with the detailed residue-by-residue transfer of sequential information. It states that such information cannot be transferred back from protein to either protein or nucleic acid. ” – Francis Crick, Nature 1970, 227(5258) Proprietary

38. Biomarkers and Drug Development Process Genomics Adapted from: Bilello (2005) Current Molecular Medicine Proprietary

39. Biomarkers and Drug Development Process Transcriptomics Adapted from: Bilello (2005) Current Molecular Medicine Proprietary

40. Biomarkers and Drug Development Process Proteomics Adapted from: Bilello (2005) Current Molecular Medicine Proprietary

41. A Brief Introduction of Genetics Genetics is the science of how living organisms (humans, mice, plants, viruses) inherit a trait (e.g. hair/eye color, disease risk) from their ancestors and how differences in genetic architectures between individuals as well as environmental influences lead to phenotypic variation (e.g. different hair/eye colors, differences in disease risks). Proprietary

42. Pharmacogenetics / Pharmacogenomics • Genetics: The science of genes, heredity and variation in organisms • Pharmacogenetics: original term used to describe the translational research area that investigates the relationship between genetic variation and drug response • Pharmacogenomics: defined later to cover the ’omics era’ of high throughput study of the relationship between the whole genome and drug response • Terms are often used interchangeably and abbreviated by “PGx”, which can be confusing: • Pharmacogenetic: applicable to single gene, candidate panel, or genome • Pharmacogenomic: only applicable to whole-genome data Proprietary

43. The Building Blocks of the Human Genome Nucleotides 22 pairs of autosomal chromosomes 2 sex chromosomes Proprietary

44. Gene Structure • Gene region consists of regulatory regions (promoter regions, UTRs) as well as protein coding regions Proprietary

45. Proteins • Proteins are translated from mRNA in the Cytoplasm of a cell • Proteins are responsible for carrying out body functions Proprietary

46. Single Nucleotide Polymorphism • Single Nucleotide Polymorphism • A DNA sequence variation occurring when a single nucleotide (A, C, G, or T) differs between members of a biological species or paired chromosomes in a human • Example: C and T are called alleles [locus] • Sequence in majority of people: TTGCCTAGTG • Sequence in minority of people: TTGCTTAGTG • SNPs are mutations with population frequency reaches at least 1% after a large number of generations • Account for 90% of variation in the genome TTGCCTAGTG TTGCCTAGTG TTGCCTAGTG TTGCTTAGTG TTGCCTAGTG TTGCCTAGTG TTGCTTAGTG TTGCCTAGTG TTGCCTAGTG TTGCTTAGTG Proprietary

47. Allele Frequency • The frequency of an allele at a locus is termed as “allele frequency” • The allele that is more common (at that locus) in the population is termed “major allele” (common allele) • The allele that is less common (at that locus) in the population is termed “minor allele” (rare allele) => its frequency is minor allele frequency (MAF) • If a SNP’s MAF=0 in a certain population, the SNP is termed “monomorphic” in that population Proprietary

48. Hardy-Weinberg Equilibrium • A tendency for the population allele frequencies to remain invariant across generations, with genotype probabilities that are a particular function of the population allele frequency (Thomas, 2004) • Reference: http://cyberbridge.mcb.harvard.edu/evolution_4.html

49. Hardy-Weinberg Equilibrium Test • Hardy-Weinberg Equilibrium for a genomic locus can be tested in terms of a 1 d.f. test :: Population is in Hardy-Weinberg equilibrium at the locus: Population is not in Hardy-Weinberg equilibrium at the locus • Consider a population of 100 individuals: • reject

50. Quality Control of SNP Data • Sample call rate: exclude individuals with a SNP call rate => low call rate for a sample may indicate low DNA quality or problem with the sample handling • SNP call rate across samples:remove markers with a call rate => low call rate for a marker may indicate problem with the probe assaying the marker • MAF: remove markers with for single SNP analyses => significant association results may be driven by a few individuals for low MAF in small sample sizes • HWE: flag markers with deviations from HWE (e.g. HWE p-value) => can be indicative of a genotyping or genotype calling error (removal desired) => can also be indicative selection and consequently of a disease locus associated with disease (removal undesired) Note: thresholds here are only ‘rules of thumb’ and may vary