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PSB 2018 presentation
1. Samir Rachid Zaim, Qike Li, A. Grant Schissler, and Yves A. Lussier
Emergence of pathway-level composite biomarkers
from converging gene set signals
of heterogeneous transcriptomic responses
http://lussiergroup.org/publications/PathwayMarker
@lussiergroup
@UA_CB2
#PrecisionMedicine
2. Problem: unproductive assumptions
for discovery of transcriptome biomarkers in common diseases
• 30,000 NIH “biomarker” grants in 25 yrs (> $2.5 billion/year) [1]
o unproductive: only 12 FDA-approved cancer biomarkers
(2012-2017)
o limited success in clinical practice
• Conventional transcriptome biomarker discovery designed for
the average patient:
o single biomolecule assumed concordantly altered across
patients
o patient-specific biomarker signal remains undetected
[1] Ptolemy, A.S. and N. Rifai, Scand J Clin Lab Invest Suppl, 2010. 242: p. 6-14.
3. Single-subject (SS) pathway-level studies emerging cross-subject pathway signal
• Hypothesis: pathway-level signal emerges from heterogeneous dysregulated genes in each
patient (responsive genes), as they coordinate to alter a multi-gene function (e.g., pathway)
• Pathway Biomarker Framework:
o Identify responsive genes (red & blue below) and altered pathways in each subject
(single-subject studies)
o Followed by cross-subject pathway-level statistics
Figure: Three SS studies. Same altered pathway in each patient,
discoverable in each single-subject study
Patient 1 = SS study 1 Patient 2 = SS study 2 Patient 3 = SS study 3
Simulation parameters:
20% responsive genes
50% up regulated genes
4. Enabling precision medicine using transcriptomes:
pathway-level interpretation in a single subject (one study per subject)
.
5. .
Background: single-subject transcriptome analyses of altered pathways
Vitali F., Li Q., et. al., Briefings in bioinformatics, 2017.
Pathifier (PNAS 2013;110:6388);
IndividPath (Brief Bioinform
2016;17:78);
iPAS (Bioinform2014;30:I422)
N-of-1-pathways methods:
(Lussier Group).
Wilcoxon: JAMIA 2014;21:1015;
Mahalanobis Distance:
Bioinformatics 2015;31:i293;
ClusterT: Statistical Methods in
Medical Research 2017;
MIxEnrich: BMC Medical Genomics
2017;10:27
kMEn: J Biomed Inform 2017;66:32
Methods
6. Simulation Study
Simulation Overview
1. Simulation parameters
2. Conventional cohort-based analysis from the transcriptome
3. Single-subject analysis followed by pathway-level analysis across subjects
4. Evaluation: contrasting approaches
7. 1. Simulation Parameters
Model a variety of biological and clinical conditions
Noise distributed according to negative binomial distribution
(parameters estimated from real TCGA data)
Understand the impact of bidirectional expression (p)
Assess the power of conventional methods in small samples vis-à-vis single subjects
Table 3: Simulation Parameters, 54 combinations x 1000 simulations = 54,000 datasets
8. 2. Conventional cohort-based analysis from the transcriptome
Normal Tumor
Gene 1
Patie
n
t 1
Patie
n
t 2
Patie
n
t N
Normal Tumor
Gene M
Normal Tumor
Gene 2
9. Normal Tumor
Gene 1
Patie
n
t 1
Patie
n
t 2
Patie
n
t N
Normal Tumor
Gene M
Normal Tumor
Gene 2
Paired t-test
2. Conventional cohort-based analysis from the transcriptome
10. Normal Tumor
Gene 1
Patie
n
t 1
Patie
n
t 2
Patie
n
t N
Normal Tumor
Gene M
Normal Tumor
Gene 2
Paired t-test
2. Conventional cohort-based analysis from the transcriptome
11. Normal Tumor
Gene 1
Patie
n
t 1
Patie
n
t 2
Patie
n
t N
Normal Tumor
Gene M
Normal Tumor
Gene 2
Paired t-test
2. Conventional cohort-based analysis from the transcriptome
17. 4. Evaluation
Gene set size = 40
N = 10 patients
Legend:
Black = SS-anchored discovery
Red = Conventional discovery
Recall
Precision
18. Take Home Message
Individual response of transcriptomes can contain
valuable pathway-level biomarkers
• Undetectable by conventional cohort-based
studies except with a large fraction of transcripts
concordantly responsive
• Unveiled by paired transcriptome analysis in each
single subject, then detectable across subjects
19. Acknowledgement
Yves A. Lussier, MD
Francesca Vitali, PhDColleen Kenost, EdD
@lussiergroup
@UA_CB2
#PrecisionMedicine
Helen Zhang, PhD
Acknowledgement: This study was supported in part by The University of Arizona Cancer Center, The
University of Arizona BIO5 Institute, The University of Arizona Center for Biomedical Informatics and
Biostatistics, and the University of Arizona Health Sciences Center.
Samir Rachid Zaim A. Grant Schissler, PhD
Haiquan Li, PhDJoanne Berghout, PhD
20. 5.1 Evaluation
Gene set size = 200
N = 30 patients
Legend:
Black = SS-anchored discovery
Red = Conventional discovery
Precision
Recall
21. 2. Generation of 54,000 simulated datasets
1. Estimate patient-specific distributions using brain tissue data from GTeX (method of
moments estimation)
2. Select a combination of simulation parameter combination
3. Select a random ‘patient-specific’ distribution (i.e., select a (μi,δi) pair of parameter
estimates at random)
4. Simulate normal and tumor transcriptomes
5. Repeat 1000 simulations x 54 combinations of parameters
Table 4: Simulated Paired Transcriptomes
Gene subject1 subject1
A1BG 214 298
A1CF 0 0
A2M 2827 5372
A2ML1 625 474
A3GALT2 4 1
22. Problem Statement
Gene mu delta
1 CTAGE15 0.4 1.9
2 GATA5 0.7 3.1
3 KLK11 3.8 0.2
4 TSSC4 1253.8 0.1
5 EIF1B 4486.4 0.2
6 MOK 1041.2 0.3
7 RAX2 20.7 1.5
8 KRT15 18.2 0.0
9 PAMR1 932.0 0.3
10 ANKRD24 1570.1 1.5
• Table 1: Method of Moments
Negative Binomial parameter
estimates for brain-tissue RNA-Seq
data1
• Assuming isogenic conditions, all
patients’ gene expression
distributions are identical, differing
only by random chance
• Differing baseline risks or levels of
variability masked when everyone
gets clumped together
1. https://gtexportal.org/home/datasets
Table 1: Isogenic MME Parameter Estimates
s 2
= m +dm2
25. Each SS pathway-level signals emerging cross-subject pathway signal
• SS Hypothesis: pathway-level signal
emerges from heterogeneous
dysregulated genes in each patient
(responsive genes), as they
coordinate to alter a multi-gene
function (e.g., pathway)
• Pathway biomarker framework:
o Identify responsive genes and
altered pathways in each
subject (single-subject studies)
o Followed by cross-subject
pathway-level statistics
Editor's Notes
- That’s precisely what we are trying to accomplish with our method—N-of-1-pathways MixEnrich. It’s a single-subject method to discover the dynamic changes of transcriptomes.
- This presentation highlights the main ideas and results from our proceedings paper.
- Our group specialize in translational medicine, we focus on translating clinical and genomic big data to the realm of precision medicine.
Ptolemy, A.S. and N. Rifai, What is a biomarker? Research investments and lack of clinical
integration necessitate a review of biomarker terminology and validation schema. Scand J
Clin Lab Invest Suppl, 2010. 242: p. 6-14.
The text of the figure to the right should be increased – won t be visible to the rear, remove the two top cohorts (keep one row and three columns. Modify legent to include the name gene besides upregulated and downregulated and the left axis to be bigger and add gene to responsive and upregulated
Corollary: heterogeneous transcript alterations across patients leads to undiscoverable biomarkers by conventional assumptions (e.g. single transcript biomarkers or enriched pathways of differentially expressed genes in a cohort)
Transgresses assumptions of coordinated transcript-level methods
Inconsistent and unstable cross-subject transcript alterations
Precision Medicine: beyond analytics of an average patient:
“The right treatments, at the right time, every time to the right person”
Precision Medicine: beyond analytics of an average patient:
“The right treatments, at the right time, every time to the right person”
Change add responsive gene to each ”responsive” term in the table.
This is not good enough, it needs to show pathways coming out
This is not good enough, it needs to show pathways coming out
This is not good enough, it needs to show pathways coming out
This is not good enough, it needs to show pathways coming out
Responsive should be ”altered” here
Responsive should be ”altered” here
Responsive should be ”altered” here
The text of the figure to the right should be increased – won t be visible to the rear, remove the middle cohort (keep two rows and three columns