identifying and validating biomarkers for clinical trials · development: •r35gm126943...
TRANSCRIPT
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Identifying and Validating Biomarkers for Clinical Trials
Hector R. Wong, MD
Division of Critical Care Medicine
Cincinnati Children’s Hospital Medical Center
Cincinnati Children’s Research Foundation
Canada Critical Care ForumNovember 7, 2018
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Disclosures• Funding from National Institute of General Medical Sciences and
Eunice Kennedy Shriver National Institute of Child Health and Human Development: • R35GM126943• R21HD092869• R01GM108025• T32GM008478• R43GM125418.
• Sepsis biomarker-related patents issued and pending.
• Scientific advisory board:• Inflammatix• Eccrine Systems• Endpoint Health
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Biomarkers for Clinical Trials
Purpose?
Selecting a population(s) more likely to benefit from your trial intervention
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Enrichment• The use of any patient characteristic to select a population in
which an intervention is more likely to be effective, compared to an unselected population.
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Prognostic Enrichment• Selection of a patient population more likely to have a disease
related event (e.g. mortality).
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Prognostic EnrichmentEvent-based
study sample size
Effect Size
Event Rate
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Prognostic EnrichmentDecreased sample size
Effect Size
↑ Event Rate
No change in relative risk, but an increase in absolute effect size
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Prognostic EnrichmentDecreased sample size
Effect Size
↑ Event Rate
Placebo Group10% mortality
Intervention Group5% mortality
50% relative risk ↓5% absolute risk ↓
N = 948
Placebo Group50% mortality
Intervention Group25% mortality
50% relative risk ↓25% absolute risk ↓
N = 132
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Predictive Enrichment• Selection of a patient population in which an intervention is
more likely to have an effect based on a biological or physiological mechanism.
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Predictive EnrichmentEvent-based
study sample size
Effect Size
Event Rate
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Predictive EnrichmentDecreased sample size
↑ Effect Size
Event Rate
Increase of both relative and absolute effect size
Increased feasibility and enhanced benefit-risk relationship
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Prognostic and Predictive Enrichment
Prognostic Enrichment
Predictive Enrichment
Sample size ↓ ↓
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Prognostic and Predictive Enrichment
Prognostic Enrichment
Predictive Enrichment
Sample size ↓ ↓
Absolute Effect Size ↑ ↑
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Prognostic and Predictive Enrichment
Prognostic Enrichment
Predictive Enrichment
Sample size ↓ ↓
Absolute Effect Size ↑ ↑
Relative Effect Size No change ↑
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Prognostic and Predictive Enrichment
Prognostic Enrichment
Predictive Enrichment
Sample size ↓ ↓
Absolute Effect Size ↑ ↑
Relative Effect Size No change ↑
Event Rate ↑ No change
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Prognostic and Predictive Enrichment
Prognostic Enrichment
Predictive Enrichment
Sample size ↓ ↓
Absolute Effect Size ↑ ↑
Relative Effect Size No change ↑
Event Rate ↑ No change
Benefit-Risk Ratio ↑ ↑
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Qualifiers, Questions, and Caveats
• Primarily used prior to randomization or to inform enrollment.
• But, could also be used in an adaptive trial design.
• Generalizability and applicability of results:• Can the enrichment strategy be used in practice?• Is the intervention beneficial in a non-enriched population?
• Sensitivity and specificity of the enrichment strategy?
• Should enrichment be used primarily in initial “proof-of-concept” studies, and then broadened to a more general populations?
• We’ve already tried this, and it hasn’t worked!
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JAMA 288:862, 2002 • Predictive enrichment via ACTH stimulation test: “responders” and “non responders.”
• “Non responders”: CIRCI• No efficacy for the entire cohort.• Survival benefit among “non responders”
treated with hydrocortisone and fludrocortisone.
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N Engl J Med 358:111, 2008
JAMA 288:862, 2002 • Predictive enrichment via ACTH stimulation test: “responders” and “non responders.”
• “Non responders”: CIRCI• No efficacy for the entire cohort.• Survival benefit among “non responders”
treated with hydrocortisone and fludrocortisone.
• Not replicated in a subsequent study.
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Crit Care Med 45:278, 2017
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Crit Care Med 32:2173, 2004
• Prognostic/predictive enrichment via a rapid IL6 assay.
• Patients stratified into 2 groups:IL6 > 1,000 pg/ml → “IL6 positive”IL6 < 1,000 pg/ml → “IL6 negative”
• No efficacy in the overall cohort and in the IL6 negative cohort.
IL6 Positive Cohort
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JAMA 320:1455, 2018
• Predictive enrichment via a rapid endotoxin activity assay.
• Enrollment restricted to those with endotoxin activity assay level ≥ 0.6.
• No efficacy.
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Qualifiers, Questions, and Caveats
• Primarily used prior to randomization or to inform enrollment.
• But, could also be used in an adaptive trial design.
• Generalizability and applicability of results:• Can the enrichment strategy be used in practice?• Is the intervention beneficial in a non-enriched population?
• Sensitivity and specificity of the enrichment strategy?
• Should enrichment be used primarily in initial “proof-of-concept” studies, and then broadened to a more general populations?
• We’ve already tried this, and it hasn’t worked!
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Prognostic and Predictive Enrichment– HORRIBLE IDEAS!Fake News!
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We need to innovate and develop more robust prognostic and predictive enrichment biomarkers
Single biomarker approaches are appealing and pragmatic, but don’t seem to capture the biological complexity of sepsis.
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Development of prognostic and predictive biomarkers for critical illness
Knowledge-Based Approach
Identify a clinical
phenotype, e.g. immune suppression
Search for differentiating
biomarkers and characteristics
Potentially biasedLimited by current “knowledge”
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Discovery-Based Approach
High throughput “omics” to discover differentially regulated genes, proteins, etc.
“BIG DATA”
How are the data associated with phenotype and what are the biological links?
Development of prognostic and predictive biomarkers for critical illness
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Identification of sepsis biomarkers for prognostic enrichment
Whole genome expression profilingTranscriptomics
Bioinformatics to identify candidate prognostic genes/biomarkers
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HSPA1B ≤ 3.27E6 N = 207
Outcome Number Rate
Death 8 0.039
Survived 199 0.961
ROOT N = 335
Outcome Number Rate
Death 41 0.115
Survived 314 0.885
CCL3 ≤ 160 N = 234
Outcome Number Rate
Death 14 0.060
Survived 220 0.940
CCL3 > 160 N = 121
Outcome Number Rate
Death 27 0.223
Survived 94 0.777
HSPA1B > 3.27E6 N = 27
Outcome Number Rate
Death 6 0.222
Survived 21 0.778
IL8 ≤ 507 N = 55
Outcome Number Rate
Death 5 0.091
Survived 50 0.909
IL8 ≤ 829 N = 174
Outcome Number Rate
Death 2 0.011
Survived 172 0.989
IL8 > 829 N = 33
Outcome Number Rate
Death 6 0.182
Survived 27 0.818
IL8 > 507 N = 66
Outcome Number Rate
Death 22 0.333
Survived 44 0.667
GZMB > 55 N = 36
Outcome Number Rate
Death 17 0.472
Survived 19 0.528
GZMB ≤ 55 N = 30
Outcome Number Rate
Death 5 0.167
Survived 25 0.833
MMP8 > 47513 N = 15
Outcome Number Rate
Death 4 0.267
Survived 11 0.733
MMP8 ≤ 47513 N = 40
Outcome Number Rate
Death 1 0.025
Survived 39 0.975
Age ≤ 0.5 years N = 8
Outcome Number Rate
Death 5 0.625
Survived 3 0.375
Age > 0.5 years N = 22
Outcome Number Rate
Death 0 0.000
Survived 22 1.000
PERSEVERE
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The Pediatric Sepsis Biomarker Risk Model(PERSEVERE)
• Decision tree composed of 5 biomarkers• CC chemokine ligand 3 (a.k.a. MIP1α)• Interleukin 8• Heat shock protein 72• Granzyme B• Matrix metalloproteinase 8
• Serum proteins measured at the time admission with sepsis.
• Estimates baseline mortality probability.
• Multiple validations.
• Calibrated over time: PERSEVERE II.
• AUC: 0.88 to 0.92 (sensitivity >90%).
• Potential tool for prognostic enrichment.
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Whole genome expression profilingTranscriptomics
Bioinformatics to identify candidate predictive enrichment genes
Discovery of septic shock “endotypes”
Identification of sepsis biomarkers for predictive enrichment
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“Endotypes”: subclasses of diseases or syndromes, as defined by a biological mechanism(s).
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Am J Respir Crit Care Med. 191:309, 2015.
Endotype A Endotype B
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Endotype A Endotype B
• The 100 endotype-defining genes reflect:• Adaptive immunity• Glucocorticoid receptor signaling
• Repressed in endotype A.
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Endotype A Endotype B
• Endotype A subjects have greater mortality and organ failure burden.
•After accounting for illness severity, age, and co-morbidity burden:• Allocation to endotype A independently
associated with increased mortality.
• Corticosteroid prescription independently associated with increased mortality among endotype A subjects.
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Endotype A Endotype B
• The 100 endotype-defining genes reflect:• Adaptive immunity• Glucocorticoid receptor signaling
• Repressed in endotype A.
• Predictive enrichment tool?
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Endotype A Endotype B
Adaptive Immunity and Glucocorticoid Signaling
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Endotype A Endotype B
Adaptive Immunity and Glucocorticoid Signaling
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Prognostic Enrichment
Predictive Enrichment
“PERSEVERE”: A multi-biomarker stratification tool to assign a baseline mortality probability
Endotype A Endotype B
Endotype B patients have higher expression of glucocorticoid receptor pathway genes
Proof (some) of Concept: Can we identify patients who may benefit from corticosteroids using prognostic and predictive enrichment?
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Endotype BIntermediate to high
baseline mortality risk+
>10 fold decreased risk for poor outcome when prescribed corticosteroids.O.R. 0.0795% CI: 0.10-0.48P = 0.007
Increased expression of GCR genes.Predictive enrichment
Higher likelihood of disease-related event.Prognostic enrichment
Proof (some) of Concept: Can we identify patients who may benefit from corticosteroids using prognostic and predictive enrichment?
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Take home messages…• “Biomarkers for Clinical Trials” can translate to “Biomarkers for Prognostic
and Predictive Enrichment”
• Prognostic and predictive enrichment strategies can increase trial efficiency.
• Prognostic and predictive enrichment can potentially increase our ability to demonstrate efficacy of trial interventions.
• We’ve tried this before…and it hasn’t worked.
• This could reflect well intended, simple, and pragmatic approaches, that nonetheless failed to capture the biological complexity of critical illness.
• We need to consider alternative approaches to biomarker discovery for critically ill patients.
• Large scale collaborations are needed.
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Thank You
Endotype A Endotype B