A Pan-Cancer Signature Catalog to Classify Tumor Mixtures:
Application to Recognition of Metastatic Disease in Prostate Cancer
Kiley GraimUC Santa Cruz
Motivation
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TCGA has many high quality primary tumor samples,
but metastasis kills
Which primaries will metastasize?
Image courtesy of wikimedia commons
3 Possible Scenarios
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Primary Subtype Metastatic Subtype
If true, may use signature as an early sign of aggressive disease.
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Do a restricted subset of primary subtypes share gene expression signatures with metastatic disease?
Multiple Datasets to Define Primary and Metastatic Gene Expression Signatures• n Dataset #
Normal# Primary # Metastatic # Genes
Cai (2011) 0 22 29 10,523Chandran (2007) 0 10 21 14,997
Grasso (2012) 28 59 32 15,830GTEx (2014) 42 0 0 13,256
Monzon (2007) 52 65 25 9,383Taylor (2010) 29 131 19 19,923
TCGA 21 246 0 20,500Joint 172 533 126 4,895
4831 Samples (659 Tumor)
Removal of Batch and Dataset Effects
• << new before/after combat PCA plots >>• << add indicat
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Before After
Batch effect removal via COMBAT (R package ‘sva’)
Removal of Batch and Dataset Effects
• << new before/after combat PCA plots >>• << add indicat
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Before After
Batch effect removal via COMBAT (R package ‘sva’)
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4 Primary Subtypes Identified from
Multiple Datasets (Including TCGA)
K = 4
Subtype 1 vs. Not
Subtype 2 vs. Not
Subtype 3 vs. Not
Subtype 4 vs. Not
Primary Subtype Predictors• Multinomial elastic
net to predict primary subtypes
• Trained using primary data
• Leave-one-out cross-validation
• Apply to metastatic samples
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Samples
SubtypeNot Subtype
How Robust Are the Predictors?
Balanced Success Rate = 0.991 9
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K = 3
3 Met Subtypes Identified from
Multiple Datasets (None TCGA)
Predicted Primary Cluster 11
The Majority of Mets Are Predicted to Be Primary Subtype 2
Met-like primaries
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Met-Like Primaries Have Higher Gleason and Higher Tumor Grade
pval = 0.0e-3
FDR = 0.0e-3
pval = 0.0e-3
FDR = 0.0e-3
Met-like primaries
Predisposition for Movement and Metastasis
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pval = 0.0e-3
FDR = 0.0e-3
pval = 0.0e-3
FDR = 0.0e-3
MILI_PSEUDOPODIA_HAPTOTAXIS_UP BIDUS_METASTASIS_UP
Are There Networks that Distinguish Met-like Primaries from the Others?
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PathMark Overview of Distinguishing Networks of Met-Like Primaries
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Proliferation-Related Subnetwork
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MYB/MYC Subnetwork
Acknowledgements
Yulia Newton Adrian Bivol Robert Baertsch Artem Sokolov Christina Yau (Buck Institute) Joshua M. Stuart
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Subtype Pipeline
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TCGA
Taylor
Joint primaries, mets, normals
CombatBatch effectadjusted joint
MetsPrimaries
ConsensusClustering
Normalizedjoint
ExponentialNormalization
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