Challenges In Progressing Biomarkers To Clinical Use Proteomic Experiences Chris Harbron Technical...
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![Page 1: Challenges In Progressing Biomarkers To Clinical Use Proteomic Experiences Chris Harbron Technical Lead For High Dimensional Data AstraZeneca FDA Industry.](https://reader034.fdocuments.in/reader034/viewer/2022042714/5514bce255034693478b459a/html5/thumbnails/1.jpg)
Challenges In Progressing Biomarkers To Clinical Use
Proteomic ExperiencesChris Harbron
Technical Lead For High Dimensional Data
AstraZeneca
FDA Industry Statistics Workshop
September 2006
![Page 2: Challenges In Progressing Biomarkers To Clinical Use Proteomic Experiences Chris Harbron Technical Lead For High Dimensional Data AstraZeneca FDA Industry.](https://reader034.fdocuments.in/reader034/viewer/2022042714/5514bce255034693478b459a/html5/thumbnails/2.jpg)
2Gap Between Published Biomarkers And
Biomarkers Being Approved For Use
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3 Why Might This Be?Challenges
• Pressures from the contextual environment• High quality data is essential
– These are new technologies - not simple to use or analyse– Robust study design including :– Consistent sample collection and processing– Need to understand reproducibility between & within labs & within
subjects
• Failure leads to poor data quality, frequently dominated by nuisance factors
• Rigorous validation is also essential– Occurs at many levels– Avoid overfitting data
• Omics may not do it alone– Applications will require combining -omics with other data types
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4
Example : Case-Control Study
• Interest in identifying a peptidomic profile that could predict an adverse event– Potential use as a personalised medicine predictive
marker
• Blood samples taken from subjects at start of treatment
• Subjects monitored for adverse event using a rigorous definition
• Subjects entered in cohorts• Samples processed in batches within cohorts• Analysed on a LC/MS-MS platform
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Rel
ativ
e A
bund
anc
e
690.81
1027.87
570.33 1156
.84599.13
635.85
1138.861122
.831251.79
371.25
799.93
1010.89242
.26727.23258
.19881.99
389.22
561.21
958.89
276.24
832.76
1269.83
286.28
1234.85
1107.00
1346.63
1252.9
579.3
643.8F
ragm
ent
Ion
inte
nsity
Mass / Charge Ratio
Ion
inte
nsity
Mas
s / C
harg
e R
atio
Retention Time
LC-MS/MS Proteomics
Clinical Plasma Samples
Peptides
Liquid Chromatography
Preparation& Digestion
Mass Spectrometry
MS/MS
Separation By Mass/ChargeMeasurement Of Intensity
ProteinIdentification
Separation By Retention Time
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6 Distribution Of Average Intensities
Retention Time
Mas
s-C
harg
e R
atio
High Intensity
LowIntensity
Distribution Of Average Intensities~5,500,000RT / MZ / IntensityMeasurementsPer Sample
~25,000Common PeaksPer Sample
Pre-Processing- Alignment Of Retention Times- Scaling- Binning
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7 Proteomic DataExploratory Analysis - PCAConsiderable batch to batch variation
Cohort 1
Cohort 2
Cohort 3
Cohort 4
ControlCaseNon-Index Case
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8 Proteomic DataExploratory Analysis - PCA
Within all batches withboth cases and controls, there is separation of cases and controls
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9 Univariate Analyses Within BatchesHistograms Of t-Test p-Values
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10 Global Test Of Agreement Between Batches Using A Permutation Test
Observed Permuted
Identify peaks where direction of effect agrees in all 3 batchesSummarise by maximum p-valueGlobal test of expected level due to multiple testing by permutation
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11 Typical Highly Significant Peak
CASE CONTROL NIC
Within each batch,cases are highly expressed compared to controls
Not possible to define a global cut-off between cases and controls
Inte
nsity
Batches
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12
Multivariate Analyses
• Identified consistent effect• BUT, may be difficult to use as a predictive
biomarker in a clinical setting due to batch variation
• Would a combination of markers, a peptidomic profile, work as a predictive biomarker?
• Use Random Forests to generate multivariate predictive models
• Assess predictive power using a nested cross-validation– Within and between batch prediction
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13
Modelling Process
Data
Analyse Each PeakWithin Each Batch
Take Maximum p-Value For Each Peak
Test SetTraining Set
Rank Peaks By p-Value
Build Model WithTop n Peaks
Test Model InTest Set
Mixed Case-Control batchesExclude Batches In TurnExclude Observations By LOO
Control Only batchesBatch excludedObservation excluded
Number Of Peaks
ObservationExcluded
BatchExcluded
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14 Leave One Out Cross ValidationProteomic Model Predictions
Leave One Out Training Set Batches CasesLeave One Out Training Set Batches ControlsOther Mixed Batch CasesOther Mixed Batch ControlsOther Batches - Controls
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15Mask Data By Restricting To High Quality
Regions Of Proteomic Space
Retention Time
Mas
s C
harg
e R
atio
TECHNICALLY• Region of focus for instrument
EMPIRICALLY• Lowest residual variability• Highest average intensity
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16
Analysis Of Unmasked Peaks
• Batch Effects Still Dominate• Consistent Case-Control Effect
Can Identify Peaks SeparatingCases & Controls Across Batches
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17 Cross-Validation PredictionsUnmasked Peaks
Leave One Out Same Batch – CasesLeave One Out Same Batch - ControlsOther Mixed Batch - CasesOther Mixed Batch - ControlsOther Batches - Controls
•Good Predictions Within Same Batch•Prediction Rate Falls When Extrapolated To Other Batches•Need To Prospectively Test In Another Set Of Patients
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18How To Combine Other Non-omic
Information Into A Biomarker?
• Combining different data types is challenging
• The “bigger” data type will dominate the modelling
• Greater signal in data, but doesn’t extrapolate as well
• Exploring options turning the random part of random forests to our advantage
Known Clinical PrognosticProteomic Peaks
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19 Proteomic Quality Control Consortium?
• MAQC recently reported a reproducibility study for microarrays– Wealth of valuable information– Mammoth effort
• Could we do the same for proteomics?– Less mature technology– Greater diversity of platforms– Diversity of pre-processing methodologies– Issues of identification making large scale
comparisons challenging
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20
Conclusions
• Complicated new technologies• Many challenges
– Technical, Data Quality, Data Analysis, Practical
• Essential role for statistics• Need to integrate statistical approaches with
understanding of technologies and biology• Great potential
– Better treatments for patients– Improved use of compounds– Greater biological understanding