Detection of high dimensional biomarkers in non-clinical and clinical microbiome studies NCS conference 2018
October, 4 2018 Dea Putri, Rudradev Sengupta, Ziv Shkedy, Nolen Joy Perualila, Luc Bijnens
Gut microbiota and human health
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The human microbiome
Estimated 1013 bacterial cells ≈ human cells (Sender, Fuchs, and Milo 2016).
More than 3 × 106 genes provided by our gut microbiome.
Different microbiome at different body sites.
Microbiome can contribute to link between genetic variation and disease (Grice and Segre 2012).
Source: Cho and Blaser 2012
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Research questions
2. What is the identity of the microbes that populate their host?
1. What are these microbes doing?
4. How is the host responding to them?
3. What are the forces that maintain equilibrium among the populations?
5. What are the unique characteristics of each individual?
6. Connect microbes with other responses like weight/IgA etc.
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Interdependence in the gut
Factors
Microbiota
Clinical response
dysregulate
The biomarker setting
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Overview: the biomarker setting
Factors
Microbiome
Clinical response
dysregulate
Main goal: • Explore how intervention factors
influence the microbiome.
• Study the association between microbiome and immunity, taking the intervention into account.
• Development of model to identify microbiome biomarkers.
Intervention factors: Trigger to the change that we observed in microbiome and immunity
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Feature-level biomarker setting
Data setting per time point: 𝑌 = clinical response (IgA level, CDAI, etc.) (𝑦1,𝑦2,⋯ ,𝑦𝑛)
𝑿 = microbiome data (e.g. OTU count)
𝑍 = factor (e.g. antibiotic use) (𝑧1, 𝑧2,⋯ , 𝑧𝑛)
Factors
Microbiome
Clinical response
dysregulate
Y
Z
𝑋𝑗
Group
Clinical response
𝑗th OTU 𝑗 = 1,⋯ ,𝑚
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Joint modeling approach: an OTU-specific model
=Σ
×+
×+
2
2
,~YXY
XYXX
i
ijj
i
ijj
YZZ
NYX
σσσσ
βµ
αµMicrobiome data
Clinical response
𝛼𝑗: the group effect on the jth microbiome data 𝛽 : the group effect on the clinical response 𝜌𝑗 adjusted association: the correlation between the jth microbiome data and clinical response, adjusted for group effect
A/B
Clinical response
jth microbiome data
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Illustration: JM parameters
The intervention study Trial in animal model: transPAT experiment
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TransPAT studies
PAT: Pulsed Antibiotic Treatment (PAT) model of pediatric exposures.
Hypothesis: a series of short, therapeutic-dose pulses of antibiotic administered early in life will perturb the intestinal microbiota and lead to long-lasting alterations in metabolic and immune profiles.
transPAT – tylosin only at one dose level.
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TransPAT studies
Research question:
Is the (PAT)-altered microbiota sufficient to alter intestinal immunity?
Measurements: 1. Microbiome (OTU counts) 2. Immune response (IgA level)
Longitudinal measurement of IgA (left) and boxplot of IgA at day 20.
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Microbiome data structure
15 mice: 8 control and 7 PAT
355 Operational Taxonomic Unit (OTU)
Per time point:
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Association over time
Post transplant: development of the immune system as microbiome colonizes the gut
Low IgA level could mean the immune system is under stressed or compromised
Day 20: largest observed difference (impaired host immunity for PAT-perturbed microbiota).
Can OTU abundance from day t-1 be predictive to IgA at day 20?
ln (O
TU c
ount
s)
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Results: OTU 264734
β
α
OTU
IgA Results at day 20
The association is derived by the treatment effect
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Results: OTU 206324
β
αOTU
IgA
Results at day 20
Treatment effect and correction. But… the effect of zero counts.
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Results: OTU 276629
significantly associated from day 12
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Analysis of overall diversity (richness estimates)
The number of OTUs in a sample with non-zero counts
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Observed diversity vs IgA
The intervention study Clinical trial: CERTIFI
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CERTIFI studies
Patients with moderate-to-severe Crohn’s disease that was resistant to TNF (anti-tumor necrosis factor) antagonists had an increased rate response to induction with ustekinumab, as compared with placebo (Sandborn, et al. 2012).
FDA approved ustekinumab (UST; STELARA®*) has a potential impact on the microbiota.
Hypothesis: response to UST could be influenced by difference in subjects’ gut microbiota and UST treatment may alter the fecal microbiota.
Phase 2 clinical trial on randomly assigned 526 patients to receive intravenous UST (306 patients with fecal samples).
*ClinicalTrial.gov identifier: NCT00771667
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CERTIFI studies
Patient’s were scored using CDAI for their response to therapy.
The black arrows indicate treatment administration Abbreviation: R, randomization; IV, intravenous; PE, primary endpoint; RR, rerandomization (only for subjects receiving UST induction therapy); SC, subcutaneous.
306 patients: 256 UST and 144 placebo treated
2353 Operational Taxonomic Unit (OTU)
CDAI
Placebo / treated
jth microbiome data
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CERTIFI data structure
=Σ
×+
×+
2
2
,~YXY
XYXX
i
ijj
i
ijj
YZZ
NYX
σσσσ
βµ
αµOTU counts
CDAI (Crohn’s Disease Activity
Index)
𝛼𝑗: the treatment effect on the jth OTU counts 𝛽 : the treatment effect on the CDAI score 𝜌𝑗 adjusted association: the correlation between the jth OTU counts and clinical response, adjusted for treatment effect
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Association of baseline microbial signatures with treatment remission (Doherty, et. al 2018)
Therapeutic remission: CDAI of < 150.
Hypothesis: the composition of the baseline fecal microbiota could predict therapeutic remission 6 weeks after induction.
Random forest used to predict which subjects would be in remission 6 weeks after induction treatment based on relative abundance of the fecal microbiota on baseline.
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Association of baseline microbial signatures with CDAI: JM
OTU1 OTU7
𝛼 = -0.5672 (0.9736; 1)* 𝜌 = 0.0183
𝛼 = -0.3595 (0.9800; 1)* 𝜌 = -0.0066
*(raw p-value, adj.p-value)
OTU counts OTU counts
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Association of microbial signatures on Week 4 with CDAI: JM
OTU1 OTU7
𝛼 = 11.5085 (0.4697; 1)* 𝜌 = -0.0035
𝛼 = -22.9548 (0.1034; 1)* 𝜌 = -0.0094
*(raw p-value, adj.p-value)
OTU counts OTU counts
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Association of microbial signatures on Week 6 with CDAI: JM
OTU1 OTU7
𝛼 = -16.5572 (0.3460; 1)* 𝜌 = 0.0327
𝛼 = -9.3697 (0.3305; 1)* 𝜌 = -0.1106
*(raw p-value, adj.p-value)
OTU counts OTU counts
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Discussion
Joint Modeling Approach Analysis at each time point:
– Can be applied to any kind of intervention studies with different clinical response
– Different microbiome measurement: raw counts, relative abundance, diversity index
– Non-parametric approach – Method for zero-inflated count data.
The method provides insight on how the clinical endpoint (IgA and CDAI) is related to both treatment and the microbiome.
Output of the analysis: – Top OTUs based on the significance of adjusted association can tell us which
OTUs are associated with a biological response independent of treatment. – OTUs that are associated with the response due to treatment effect
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Discussion
Joint model for longitudinal data – Joint models for any clinical endpoint and the microbiome measurement – Evolution of treatment effects over time
It’s a foundation to everything that is done in surrogacy setting at the moment.
Considerations Degree of zero-inflation Small sample size Different distributional assumption
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Research Team
UHasselt – Ziv Shkedy – Olivier Thas
UGhent – Stijn Hawinkel
New York School of Medicine
– Martin Blaser – Joyce Ying – Victoria Ruiz – Xuesong Zhang – Thomas Battaglia
Industry – Luc Bijnens (JnJ) – Rudradev Sengupta (JnJ) – Thierry van Effelterre (JnJ) – David Sargsyan (JnJ) – Mathilda Hsieh (JnJ) – Kathy Mutambanengwe (Open
Analytics) – Nolen Joy Perualila (JnJ)
