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Omics, Biomarkers, Personalized Medicine: A New Era, or More of the Same? Klaus Lindpaintner Roche Genetics/Roche Center for Medical Genomics Differential drug efficacy Same symptoms Same findings Same disease (?) Same Drug…. Genetic Differences Different Effects ? Possible Reasons: Non-Compliance… Drug-drug interactions… Chance… Or…. 2 Pharmacotherapy: State-of-the-Art Group Incomplete/absent efficacy AT2-antag SSRI ACE -I Beta blockers Tricycl. AD HMGCoAR-I Beta-2-agonists 10-25% 10-25% 10-30% 15-25% 20-50% 30-70% 40-70% • Inter-individual differences in drug efficacy • Significant incidence of serious adverse effects among elderly hospitalized patients (US) Serious Lethal 6.7% 0.3% 2 M cases 100,000 cases JAMA 98;279:1200 3 Pharmacogenetics and Personalized Medicine • AnKnowledge altogether new concept? of inter-individual differences wrt metabolism as old as civilization: 6th century B.C. Pythagoras observes that ingestion of fava beans is harmful to some individuals yet innocuous to others • Finding the optimal treatment for every patient is as old as medicine: differential diagnosis • Tailoring treatments to drug-specific test results is nothing new. Example: antibiotics • • • Gram-positive bacteria: e.g. penicillin derivatives Gram-negative bacteria: e.g. aminoglycosides M. tuberculosis: isoniazid/rifampin/pyrazinamide 4 Bridging a Historical Divide clinical diagnosis tissue / organ physiology-pathology molecular diagnosis cell-biology protein RNA DNA protein RNA DNA cell-biology protein RNA DNA protein RNA DNA drugs 6 Pharmacogenetics, Pharmacogenomics • • • Glossary of Terms Pharmacogenetics: • • • • • a concept to provide more patient/disease-specific health care* based on the effects of inherited (or acquired) genetic variants assessed primarily by sequence determination (or single gene expression) one drug – many genomes (patients) focus: patient variability Pharmacogenomics (1): • • • • • a concept to provide more patient/disease-specific health care based on the effects acquired (or inherited) genetic variants assessed primarily by expression profiles (many mRNAs) one drug – many genomes (patients) focus: patient variability Pharmacogenomics (2): • • • a tool for compound selection/drug discovery many drugs – one genome (inbred animal/chip) focus: compound variability *as conceptualized by Motulsky (1957), Vogel (1959), Kalow (1962) and endorsed in the 2003 Nuffield Council’s Report on Pharmacogenetics 7 2 Major Classes of Pharmacogenetics – Both Resulting in Patient Stratification • Strictly affecting drug response – not predictive of disease risk: “Differentiating people” (“classical” pgx: Archibald Garrod) • • • • Related to molecular subclass of clinical diagnosis: “Differentiating disease” (“molecular differential diagnosis”) • • • Pharmacokinetics (not only M, but also AADE) Pharmacodynamics Has not had much impact Inherently linked to disease mechanism/prognosis Likely increasing impact in indications where we begin to treat causally – oncology, inflammatory disease Both are conceptually rather different (and arguably the second should not be included) but have practically the same consequence: Patient stratification according to novel, DNA-based parameters 8 Omeprazole response rate and CYP2C19 A/A – SLOW 100 90 A/B B/B – FAST response frequency (%) 80 70 60 50 40 30 20 10 0 gastric ulcer duodenal ulcer 9 Drug metabolism Inherited differences affect drug effects Pharmacogenetics = molecular DD Case Study: Herceptin® Bimodal response: 2/3 of patients: addition of Herceptin® to chemoRx no benefit 1/3 of patients: addition of Herceptin® to chemoRx 50% survival time increased by factor 1.5 (20 29 weeks) High HER2 Low HER2 11 Xeloda® (capcitabine) Patient stratification based on enzyme patterns Xeloda susceptibility vs tumor TP/DPD TP/DPD in 24 xenografts TS (dThdPase/DPD) Xeloda P = 0.0015 100 TP 5-DFUR DPD 5-FU inactive metabolites S: susceptible R: refractory 10 1 0.1 S R 12 Biomarkers What’s new – and why now? • Availability of powerful, highly parallel new screening methods (omics) makes looking for new biomarkers a reasonable proposition. • Paradigm shift(?): maturation of these basic cell and molecular biology tools makes them newly applicable to later-stage R&D • • Opportunities: personalized medicine Challenges: technical, scientific (clinical-epidemiological) economical, ethical • CAVEAT 1: Association ≠ Causality • Good news and bad news 13 Caveat 2 “Responders” & “NonResponders” Reality Check FDA benchmark: 35% improvement/response 80 70 response (%) 60 22% 50 43% 40 31% 30 20 10 0 individual patients 14 Single Gene Disease Environment SNPs in other genes Mutation intermediate phenotype health outcome Heritability: h2 ≈ 1 Deterministic … possible stigma 15 M onogenic Diseases CCD Common Complex Diseases 16 Single Gene Disease Environment SNPs in other genes Mutation intermediate intermediate phenotype phenotype health health outcome outcome Common Complex Disease Environment SNPs in other genes SNP intermediate phenotype health outcome Probabilistic, not deterministic - no reason for stigma. 17 Complex Common Disease: Nature and Nurture environment genes Colon, Hemobreast ApoE4 philia Cancer AD CF P53, BRCA HD nutrition Stroke MI Diabetes Asthma Lung cancer tobacco --- asbestos MVA GSW P450 18 Heritability estimates in CCD Disorder or phenotype Preeclampsia NIDDM Hypertension Osteoarthritis Stroke Asthma Obesity Depression Other dementia Blood pressure BMI Rheumatoid arthritis Death from heart disease Coronary heart disease IGT SLE Alzheimer’s (sporadic) Protracted/recurrent otitis media Heritability h2 0.2-0.35 0.26-0.50 0.28-0.73 0.3-0.46 0.32 0.36-0.47 0.4-0.7 0.41-0.66 0.43 0.5 0.5-0.7 0.53-0.65 0.55 0.56 0.61 0.66 0.72 0.72 19 Heritability estimates in cancer Malignancy Thyroid Endocrine glands Breast Testis Cervix invasive Melanoma Nervous system: age <15 years Colon Cervix in situ Rectum Nervous system Non-Hodgkin lymphoma Leukemia: age <15 years Lung Kidney Urinary bladder Leukemia Stomach \ Heritability h2 0.53 (0.52–0.53) 0.28 (0.27–0.28) 0.25 (0.23–0.27) 0.25 (0.15–0.37) 0.22 (0.14–0.27) 0.21 (0.12–0.23) 0.13 (0.06–0.20) 0.13 (0.12–0.18) 0.13 (0.06–0.15) 0.12 (0.08–0.13) 0.12 (0.10–0.18) 0.10 (0.08–0.10) 0.09 (0.09–0.16) 0.08 (0.05–0.09) 0.08 (0.07–0.09) 0.07 (0.02–0.11) 0.01 (0.00–0.01) 0.01 (0.01–0.06) Czene et al, Int J Cancer 99:260; 2002 20 Medical Progress: Evolution or Revolution? Historic Drivers of Medical Progress Clinical expertise …Genetics Differential diagnosis Risk assessment - prevention Classical epidemiology More differentiated, molecular understanding of pathology and drug action Clinical Disease Definition Clinical Diagnosis Molecular Disease Definition Molecular Diagnosis in-vitro Diagnostics 21 Consumption Phlebotomy Tuberculosis Tuberculosis Cancer Cancer Antibacterials Cytostatics Heart Heart Failure Failure ACE Inhibitors Breast Ca Colon Ca Lung Ca HER-2-negative (2/3) HER-2-positive (1/3) Mean survival 7 yrs Mean survival 3 yrs Cytostatics Cytostatics + humMAb 22 Pharmacogenetics vs. other Markers A useful distinction? Normal Pharmacogenetics Pharmacogenomics DNA DNA* DNA * mRNA mRNA* mRNA* primary protein primary protein* processed protein, small molecule response to medicine Pharmacogenomics PharmacoPharmacoproteomics metabonomics DNA DNA mRNA* mRNA mRNA primary protein* primary protein* primary protein* primary protein processed protein, small molecule* processed protein, small molecule* processed protein, small molecule* processed protein, small molecule* processed protein, small molecule* altered response to medicine* altered response to medicine* altered response to medicine* altered response to medicine* altered response to medicine* DNA * alteration germ-line in origin – heritable * alteration somatic – acquired (environment, life-style) 23 Pharmacogenetics and beyond: Biomarkers • Key concept: More targeted medicines (“personalized medicine”) • • • • Based on a better understanding of inter-individual differences among patients • • • More effective Safer More cost-effective (?) Inherited (the “classical” pharmacogenetics) Acquired (“flavors” of disease, underlying molecular heterogeneity of any one clinical diagnosis: molecular differential diagnosis) Paradigm: carry out specific test that point to one or another medicine as optimal for the patient before prescribing it. What does not matter: Nature of test (DNA, RNA, protein, other) What does matter: Information content 24 Biomarker tests in medical practice Two sets of considerations • Test performance • • Analytical performance – QC and accreditation of labs Clinical performance • • • Clinical validity – retrospective/observation studies Clinical utility – prospective intervention trials Note: Prior probability: critical for test performance, esp. screens (sensitivity/specificity, PPV/NPV) • Nature of illness • Serious (life-threatening) illness Default: ”don’t withhold in error”; If in doubt: “treat” • Less serious illness Default: “don’t treat in error”; If in doubt: “don’t treat” 25 EGFR Mutants Much ado about…? 26 EGRF-R variants Colocation with ATP-binding domain 27 Regulators are Taking Note 28 Interpretation? Consequences? • NEJM • • • 8/9 responders 7/7 non-responders 2 of 25 untreated • Pre-testing will increase response rate to 100% among those who test + Pre-testing will result in denial of treatment to 11% of who would responders • • Pao et al, MSKCC (PNAS) + for mutation – for mutation + for mutation • • • • 7/10 responders 8/8 non-responders 4/81 NSCLC smokers 7/15 non-smoker, adeno-Ca + – + + for mutation for mutation for mutation for mutation • Pre-testing will result in denial of treatment to 30% of who would be responders 29 EGF-R variants and Drug Response • Gefitinib (IRESSA) Response in Caucasians Prevalence of variants in Boston patients 10% 2/25 (NEJM) • Gefitinib (IRESSA) Response in Japanese Prevalence of variants in Japanese patients 28% 26% (Science) • Erlotinib (TARCEVA) Monotherapy in NSCLS EGFR Mutratoin prevalence Response Rate 12% 42% 30 Analytical Performance: Metrology Aything but straight-forward • Precision • • Repeatability under same conditions, precision in a series of measurement in the same run; and Reproducibility under different conditions, which are usually specified, e.g. day-to-day or lab-to lab • Trueness • • the closeness of agreement of an average value from a large series of measurements with a "true value" or an accepted reference value. Numerical value: bias • Accuracy – • • referring to a single measurement and comprising both random and systematic influences. Numerical value: total error of measurement. 31 Biomarker tests in medical practice Two sets of considerations • Test performance • • Analytical performance – QC and accreditation of labs Clinical performance • • • Clinical validity – retrospective/observation studies Clinical utility – prospective intervention trials Note: Prior probability: critical for test performance, esp. screens (sensitivity/specificity, PPV/NPV) • Nature of illness • Serious (life-threatening) illness Default: ”don’t withhold in error”; If in doubt: “treat” • Less serious illness Default: “don’t treat in error”; If in doubt: “don’t treat” 32 Analytical performance The dirty (not so) little secret • Multiple complex variables: • • • • • Tissue heterogeneity Limited sample quantity and quality (FFPE) LCDM/macro-dissection commonly necessary PCR-pre-amplification 4 exons x 2 amplification runs each 33 Analytical performance: EGFR sequencing Sometimes, far from it… unambiguous wt wt vs. mut wt vs. mut vs. artifact wt? wt vs. mut? unambiguous known mut known mut vs. new mut vs. both? mut? wt vs. mut vs. artifact? known mut vs. new mut vs. both vs. indet? unambiguous new mut new mut? wt? known mut? new mut? unambiguous unknown 34 EGFR mutation analysis analytical performance The dirty (not so) little secret • Multiple complex variables: • • • • • Tissue heterogeneity Limited sample quantity and quality (FFPE) LCDM/macro-dissection PCR-pre-amplification 4 exons x 2 amplification runs each • • • Algorithm 1: Algorithm 2: Algorithm 3: • How to deal with “drop-outs”? • How to deal with non-replicated mutations – artifact or quantitative manifestation of relative abundance of mutation? • None of current publications disclose this difficulty • Own experience – using different “calling” algorithms: 6.1% (13 mut / 200 wt / 94 indeterminate) 7.5% (15 mut / 186 wt / 106 indeterminate) 9.9% (23 mut / 210 wt / 74 indeterminate) 35 EGFR-Mutations, Erlotinib, and Survival The picture is more complex… 36 Biomarker tests in medical practice Two sets of considerations • Test performance • • Analytical accuracy – QC and accreditation of labs Clinical performance • • • Clin validity – retrospective/observation studies Clinical utility – prospective intervention trials Note: Prior probability: critical for test performance, esp. screens (sensitivity/specificity, PPV/NPV) • Nature of illness • Serious (life-threatening) illness Default: ”don’t withhold in error”; If in doubt: “treat” • Less serious illness Default: “don’t treat in error”; If in doubt: “don’t treat” 37 Optimizing Sensitivity vs. Specificity Target Product Profile Definition is Essential sensitivity 100% 0% 0% 1-specificity 100% Note: Sliding the ROC-cutoff value may be more difficult with (categorical) genotype data than with other (quantitative) biomarker data 38 Biomarker performance Up and down the ROC curve Serious illness: don’t withhold inappropriately + test - test + response true positive false negative - response false positive true negative Efficacy marker: High sensitivity + test - test + adverse event true positive false negative - adverse event false positive true negative Safety marker: High specificity Less serious illness: don’t prescribe inappropriately + test - test + response true positive false negative - response false positive true negative Efficacy marker: High specificty + test - test + adverse event true positive false negative - adverse event false positive true negative Safety marker: High sensitivity 39 Case-in-point: Herceptin/HerCepTest The search for new biomarkers – and its implications Status quo, 66% success rate no potential responder denied Rx + response - response + new BM test 19 true + 5 false + 24 79% response among treated Her2+/BM+ - new BM test 1 false - 5 true - 7 16% response among Her2-/BM- Sensitivity: Specificity: true+/(true+ + false-) 19/(19+1)=0.95 true-/(true- + false+) 5/5+5=.5 95% sensitive 50% (94%*) specific + response - response + new BM test 16 true + 2 false + 18 88% response among treated Her2+/BM+ - new BM test 4 false - 8 true - 12 33% response among Her2-/BM- Sensitivity: Specificity: true+/(true+ + false-) 16/(16+4)=0.8 true-/(true- + false+) 8/1+9=0.9 80% sensitive 80% (98%*) specific 30 *Specificity of combined Her2 and new BM tests + response - response +Her2 test 20 true + 10 false + 30 66% response among treated Her2+ - Her2 test 0 false - 70 true - 70 presumed 0% response among Her2(NB: anecdotal data) Sensitivity: Specificity: true+/(true+ + false-) 20/(20+0)=1 true-/(true- + false+) 70/70+10=0.875 100% sensitive 88% specific 100 Add-on-BM scenario 1 78% success rate 5% of would-be responders denied Rx Add-on-BM scenario 2 88% success rate 20% of would-be responders denied Rx 30 40 Not all that glitters is gold: TPMT Thiopurine-treated patients with adverse drug reactions Total n 25 17 7 15 41 8 Patients without deficient TPMT-allele n % 20 16 4 14 29 6 80 94 57 93 70 75 Patients with one or two deficient TPMT-alleles n % 5 1 3 1 12 2 20 6 43 7 30 25 Reference Black et al. 1998 Naughton et al. 1999 Ishioka et al. 1999 Dubinsky et al. 2000 Colombel et al. 2000 Ando et al. 2001 sensitivity positive test predicts, but negative tests by no means excludes SAE 299 negative tests for every one positive test 41 Economic considerations How far is segmentation of markets feasible? “Exhaustive pharmacogenetic research efforts have narrowed your niche market down to Harry Finkelstein of Newburg Heights here.” 42 Emergence of sub-critically small segments A self-limited proposition • Retrospectively: Given biomedical variance, biomarker-defined segments are unlikely to be recognizable unless they represent a significant share of the overall patient population. • Prospectively: Small segments known to exist will either not be addressed for lack of business case, or under Orphan Drug Guidelines 43 The Tightening Reimbursement Climate Biomarker strategies may be essential Strategy No test Chemo-Rx alone Positive HerCep Test Chemo-Rx and Herceptin No test Chemo-Rx and Herceptin Life-months Incr. QALYs Incr. Cost UK £ Incr Cost/QUALY UK £ 28.02 1.28 26,919 21,030 29.30 1.36 33,376 24,541 29.41 1.37 49,211 35,920 Elkin et al; J Clin Oncol 2004; 22:854 ff ($/£ conv. rate 1/1/2003, not PPP-adjusted) NB: National Institute for Clinical Excellence’s (NICE) threshold for approving reimbursement through NHS believed to be ~UK £ 30,000 per QUALY (quality-adjusted life year) 44 Biomarkers – likely outcome: • The concept applies potentially to most compounds • It will in fact, however, become reality only for some/few compounds… but we will have to look at all to find the few! • (We will likely see more examples of “pathology-related” biomarkerbased stratification (Herceptin-paradigm) that advance efficacy; and most likely in oncology and inflammatory/autoimmune disease) • Multifactorial algorithms likely to emerge, rather than simple, onevariable models – but highly complex algorithms unlikely. • Essential: Define Target-Product-Profile • Key: Modesty, Realism, robust Optimism: we will not have perfect medicines BUT we will have increasingly better medicines 45 No 1-on-1 custom tailoring, but towards a much better fit … 377/8 38 39 39½ 39¾ 40 Remember: All medical decisions/knowledge are based on group-derived (aggregate) data analysis. “Data” on individuals (Harry Finkelstein) are anecdotal and (largely) medically/clinically meaningless 46 Without information, the doctor cannot act. With information, he cannot but act. HL Mencken’s Law Every complex problem has a simple solution. And it is always wrong. 48