A Pan-Cancer Signature Catalog to Classify Tumor Mixtures: Application to Recognition of Metastatic...
Transcript of A Pan-Cancer Signature Catalog to Classify Tumor Mixtures: Application to Recognition of Metastatic...
![Page 1: A Pan-Cancer Signature Catalog to Classify Tumor Mixtures: Application to Recognition of Metastatic Disease in Prostate Cancer Kiley Graim UC Santa Cruz.](https://reader030.fdocuments.in/reader030/viewer/2022032523/56649d825503460f94a6807c/html5/thumbnails/1.jpg)
A Pan-Cancer Signature Catalog to Classify Tumor Mixtures:
Application to Recognition of Metastatic Disease in Prostate Cancer
Kiley GraimUC Santa Cruz
![Page 2: A Pan-Cancer Signature Catalog to Classify Tumor Mixtures: Application to Recognition of Metastatic Disease in Prostate Cancer Kiley Graim UC Santa Cruz.](https://reader030.fdocuments.in/reader030/viewer/2022032523/56649d825503460f94a6807c/html5/thumbnails/2.jpg)
Motivation
2
TCGA has many high quality primary tumor samples,
but metastasis kills
Which primaries will metastasize?
Image courtesy of wikimedia commons
![Page 3: A Pan-Cancer Signature Catalog to Classify Tumor Mixtures: Application to Recognition of Metastatic Disease in Prostate Cancer Kiley Graim UC Santa Cruz.](https://reader030.fdocuments.in/reader030/viewer/2022032523/56649d825503460f94a6807c/html5/thumbnails/3.jpg)
3 Possible Scenarios
3
Primary Subtype Metastatic Subtype
If true, may use signature as an early sign of aggressive disease.
?
?
?
?
Do a restricted subset of primary subtypes share gene expression signatures with metastatic disease?
![Page 4: A Pan-Cancer Signature Catalog to Classify Tumor Mixtures: Application to Recognition of Metastatic Disease in Prostate Cancer Kiley Graim UC Santa Cruz.](https://reader030.fdocuments.in/reader030/viewer/2022032523/56649d825503460f94a6807c/html5/thumbnails/4.jpg)
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)
![Page 5: A Pan-Cancer Signature Catalog to Classify Tumor Mixtures: Application to Recognition of Metastatic Disease in Prostate Cancer Kiley Graim UC Santa Cruz.](https://reader030.fdocuments.in/reader030/viewer/2022032523/56649d825503460f94a6807c/html5/thumbnails/5.jpg)
Removal of Batch and Dataset Effects
• << new before/after combat PCA plots >>• << add indicat
5
Before After
Batch effect removal via COMBAT (R package ‘sva’)
![Page 6: A Pan-Cancer Signature Catalog to Classify Tumor Mixtures: Application to Recognition of Metastatic Disease in Prostate Cancer Kiley Graim UC Santa Cruz.](https://reader030.fdocuments.in/reader030/viewer/2022032523/56649d825503460f94a6807c/html5/thumbnails/6.jpg)
Removal of Batch and Dataset Effects
• << new before/after combat PCA plots >>• << add indicat
6
Before After
Batch effect removal via COMBAT (R package ‘sva’)
![Page 7: A Pan-Cancer Signature Catalog to Classify Tumor Mixtures: Application to Recognition of Metastatic Disease in Prostate Cancer Kiley Graim UC Santa Cruz.](https://reader030.fdocuments.in/reader030/viewer/2022032523/56649d825503460f94a6807c/html5/thumbnails/7.jpg)
7
4 Primary Subtypes Identified from
Multiple Datasets (Including TCGA)
K = 4
![Page 8: A Pan-Cancer Signature Catalog to Classify Tumor Mixtures: Application to Recognition of Metastatic Disease in Prostate Cancer Kiley Graim UC Santa Cruz.](https://reader030.fdocuments.in/reader030/viewer/2022032523/56649d825503460f94a6807c/html5/thumbnails/8.jpg)
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
8
Samples
SubtypeNot Subtype
![Page 9: A Pan-Cancer Signature Catalog to Classify Tumor Mixtures: Application to Recognition of Metastatic Disease in Prostate Cancer Kiley Graim UC Santa Cruz.](https://reader030.fdocuments.in/reader030/viewer/2022032523/56649d825503460f94a6807c/html5/thumbnails/9.jpg)
How Robust Are the Predictors?
Balanced Success Rate = 0.991 9
![Page 10: A Pan-Cancer Signature Catalog to Classify Tumor Mixtures: Application to Recognition of Metastatic Disease in Prostate Cancer Kiley Graim UC Santa Cruz.](https://reader030.fdocuments.in/reader030/viewer/2022032523/56649d825503460f94a6807c/html5/thumbnails/10.jpg)
10
K = 3
3 Met Subtypes Identified from
Multiple Datasets (None TCGA)
![Page 11: A Pan-Cancer Signature Catalog to Classify Tumor Mixtures: Application to Recognition of Metastatic Disease in Prostate Cancer Kiley Graim UC Santa Cruz.](https://reader030.fdocuments.in/reader030/viewer/2022032523/56649d825503460f94a6807c/html5/thumbnails/11.jpg)
Predicted Primary Cluster 11
The Majority of Mets Are Predicted to Be Primary Subtype 2
Met-like primaries
![Page 12: A Pan-Cancer Signature Catalog to Classify Tumor Mixtures: Application to Recognition of Metastatic Disease in Prostate Cancer Kiley Graim UC Santa Cruz.](https://reader030.fdocuments.in/reader030/viewer/2022032523/56649d825503460f94a6807c/html5/thumbnails/12.jpg)
12
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
![Page 13: A Pan-Cancer Signature Catalog to Classify Tumor Mixtures: Application to Recognition of Metastatic Disease in Prostate Cancer Kiley Graim UC Santa Cruz.](https://reader030.fdocuments.in/reader030/viewer/2022032523/56649d825503460f94a6807c/html5/thumbnails/13.jpg)
Predisposition for Movement and Metastasis
13
pval = 0.0e-3
FDR = 0.0e-3
pval = 0.0e-3
FDR = 0.0e-3
MILI_PSEUDOPODIA_HAPTOTAXIS_UP BIDUS_METASTASIS_UP
![Page 14: A Pan-Cancer Signature Catalog to Classify Tumor Mixtures: Application to Recognition of Metastatic Disease in Prostate Cancer Kiley Graim UC Santa Cruz.](https://reader030.fdocuments.in/reader030/viewer/2022032523/56649d825503460f94a6807c/html5/thumbnails/14.jpg)
Are There Networks that Distinguish Met-like Primaries from the Others?
14
![Page 15: A Pan-Cancer Signature Catalog to Classify Tumor Mixtures: Application to Recognition of Metastatic Disease in Prostate Cancer Kiley Graim UC Santa Cruz.](https://reader030.fdocuments.in/reader030/viewer/2022032523/56649d825503460f94a6807c/html5/thumbnails/15.jpg)
15
PathMark Overview of Distinguishing Networks of Met-Like Primaries
![Page 16: A Pan-Cancer Signature Catalog to Classify Tumor Mixtures: Application to Recognition of Metastatic Disease in Prostate Cancer Kiley Graim UC Santa Cruz.](https://reader030.fdocuments.in/reader030/viewer/2022032523/56649d825503460f94a6807c/html5/thumbnails/16.jpg)
16
Proliferation-Related Subnetwork
![Page 17: A Pan-Cancer Signature Catalog to Classify Tumor Mixtures: Application to Recognition of Metastatic Disease in Prostate Cancer Kiley Graim UC Santa Cruz.](https://reader030.fdocuments.in/reader030/viewer/2022032523/56649d825503460f94a6807c/html5/thumbnails/17.jpg)
17
MYB/MYC Subnetwork
![Page 18: A Pan-Cancer Signature Catalog to Classify Tumor Mixtures: Application to Recognition of Metastatic Disease in Prostate Cancer Kiley Graim UC Santa Cruz.](https://reader030.fdocuments.in/reader030/viewer/2022032523/56649d825503460f94a6807c/html5/thumbnails/18.jpg)
Acknowledgements
Yulia Newton Adrian Bivol Robert Baertsch Artem Sokolov Christina Yau (Buck Institute) Joshua M. Stuart
18
![Page 19: A Pan-Cancer Signature Catalog to Classify Tumor Mixtures: Application to Recognition of Metastatic Disease in Prostate Cancer Kiley Graim UC Santa Cruz.](https://reader030.fdocuments.in/reader030/viewer/2022032523/56649d825503460f94a6807c/html5/thumbnails/19.jpg)
19
![Page 20: A Pan-Cancer Signature Catalog to Classify Tumor Mixtures: Application to Recognition of Metastatic Disease in Prostate Cancer Kiley Graim UC Santa Cruz.](https://reader030.fdocuments.in/reader030/viewer/2022032523/56649d825503460f94a6807c/html5/thumbnails/20.jpg)
Subtype Pipeline
…
TCGA
Taylor
Joint primaries, mets, normals
CombatBatch effectadjusted joint
MetsPrimaries
ConsensusClustering
Normalizedjoint
ExponentialNormalization
20
![Page 21: A Pan-Cancer Signature Catalog to Classify Tumor Mixtures: Application to Recognition of Metastatic Disease in Prostate Cancer Kiley Graim UC Santa Cruz.](https://reader030.fdocuments.in/reader030/viewer/2022032523/56649d825503460f94a6807c/html5/thumbnails/21.jpg)
21
![Page 22: A Pan-Cancer Signature Catalog to Classify Tumor Mixtures: Application to Recognition of Metastatic Disease in Prostate Cancer Kiley Graim UC Santa Cruz.](https://reader030.fdocuments.in/reader030/viewer/2022032523/56649d825503460f94a6807c/html5/thumbnails/22.jpg)
22