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Presentation for BIOF 501 at the University of British Columbia. ManChon, U., et al. "Prediction and Prioritization of Rare Oncogenic Mutations in the Cancer Kinome Using Novel Features and Multiple Classifiers." PLoS computational biology 10.4 (2014): e1003545.
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Prediction and prioritization of rare oncogenicmutations in the cancer kinome using novel features
and multiple classifiersPresentation by Rosemary McCloskey
ManChon U1 Eric Talevich2 Samiksha Katiyar3 KhaledRasheed1 Natarajan Kannan3,4
1Department of Computer Science, University of Georgia
2Department of Dermatology, University of California San Francisco
3Department of Biochemistry and Molecular Biology, University of Georgia
4Institute of Bioinformatics, University of Georgia
September 4, 2014
U et al. Oncogenic kinome mutations September 4, 2014 1 / 11
Driver vs. passenger mutations
driver passengers
How to distinguish driver from passenger mutations?
U et al. Oncogenic kinome mutations September 4, 2014 2 / 11
Driver vs. passenger mutations
driver passengers
How to distinguish driver from passenger mutations?
U et al. Oncogenic kinome mutations September 4, 2014 2 / 11
Driver vs. passenger mutations
driver passengers
How to distinguish driver from passenger mutations?
U et al. Oncogenic kinome mutations September 4, 2014 2 / 11
Driver vs. passenger mutations
driver passengers
How to distinguish driver from passenger mutations?
U et al. Oncogenic kinome mutations September 4, 2014 2 / 11
Driver vs. passenger mutations
driver
passengers
How to distinguish driver from passenger mutations?
U et al. Oncogenic kinome mutations September 4, 2014 2 / 11
Driver vs. passenger mutations
driver passengers
How to distinguish driver from passenger mutations?
U et al. Oncogenic kinome mutations September 4, 2014 2 / 11
Driver vs. passenger mutations
driver passengers
How to distinguish driver from passenger mutations?
U et al. Oncogenic kinome mutations September 4, 2014 2 / 11
Driver vs. passenger mutations
driver passengers
How to distinguish driver from passenger mutations?
U et al. Oncogenic kinome mutations September 4, 2014 2 / 11
Protein kinases
Enzymes which transfer POH− from ATP to protein.
Regulate cellular processes.
Kinase mutations implicated in cancer.
518 human protein kinase genes (Manning et al. 2002).
8 groups (plus “other“ and “atypical”), 133 families.
11 structurally conserved regions among all kinases(“subdomains”).
U et al. Oncogenic kinome mutations September 4, 2014 3 / 11
Training and validation data
˝ˆ ¨
birds
ˇY 7
peoplem
what is this?
bird
Supervised machine learning approach.
Known oncogenic: Catalogue of Somatic Mutations in Cancer(COSMIC) database.
I Filtered for protein kinases.I Two sets, one excluding mutations reported only once.
Known benign: protein kinase SNP’s (SNP@Domain).
U et al. Oncogenic kinome mutations September 4, 2014 4 / 11
Training and validation data
˝
ˆ ¨birds
ˇY 7
people
mwhat is this?
bird
Supervised machine learning approach.
Known oncogenic: Catalogue of Somatic Mutations in Cancer(COSMIC) database.
I Filtered for protein kinases.I Two sets, one excluding mutations reported only once.
Known benign: protein kinase SNP’s (SNP@Domain).
U et al. Oncogenic kinome mutations September 4, 2014 4 / 11
Training and validation data
˝
ˆ ¨birds
ˇY 7
peoplem
what is this?
bird
Supervised machine learning approach.
Known oncogenic: Catalogue of Somatic Mutations in Cancer(COSMIC) database.
I Filtered for protein kinases.I Two sets, one excluding mutations reported only once.
Known benign: protein kinase SNP’s (SNP@Domain).
U et al. Oncogenic kinome mutations September 4, 2014 4 / 11
Training and validation data
˝
ˆ ¨birds
ˇY 7
peoplem
what is this?
bird
Supervised machine learning approach.
Known oncogenic: Catalogue of Somatic Mutations in Cancer(COSMIC) database.
I Filtered for protein kinases.I Two sets, one excluding mutations reported only once.
Known benign: protein kinase SNP’s (SNP@Domain).
U et al. Oncogenic kinome mutations September 4, 2014 4 / 11
Training and validation data
˝
ˆ ¨birds
ˇY 7
peoplem
what is this?
bird
Supervised machine learning approach.
Known oncogenic: Catalogue of Somatic Mutations in Cancer(COSMIC) database.
I Filtered for protein kinases.I Two sets, one excluding mutations reported only once.
Known benign: protein kinase SNP’s (SNP@Domain).
U et al. Oncogenic kinome mutations September 4, 2014 4 / 11
Training and validation data
˝
ˆ ¨birds
ˇY 7
peoplem
what is this?
bird
Supervised machine learning approach.
Known oncogenic: Catalogue of Somatic Mutations in Cancer(COSMIC) database.
I Filtered for protein kinases.I Two sets, one excluding mutations reported only once.
Known benign: protein kinase SNP’s (SNP@Domain).
U et al. Oncogenic kinome mutations September 4, 2014 4 / 11
Mutation features
Identified 29 mutation features.
Kinase group and family classification.
Conservation of position within all kinases, family, and group.
Amino acid properties: charge, polarity, mass, etc.
Structural and functional: kinase sub-domain, binding site,posttranslational modification.
Which of these is important?
U et al. Oncogenic kinome mutations September 4, 2014 5 / 11
Mutation features
Identified 29 mutation features.
Kinase group and family classification.
Conservation of position within all kinases, family, and group.
Amino acid properties: charge, polarity, mass, etc.
Structural and functional: kinase sub-domain, binding site,posttranslational modification.
Which of these is important?
U et al. Oncogenic kinome mutations September 4, 2014 5 / 11
Mutation features
Identified 29 mutation features.
Kinase group and family classification.
Conservation of position within all kinases, family, and group.
Amino acid properties: charge, polarity, mass, etc.
Structural and functional: kinase sub-domain, binding site,posttranslational modification.
Which of these is important?
U et al. Oncogenic kinome mutations September 4, 2014 5 / 11
Mutation features
Identified 29 mutation features.
Kinase group and family classification.
Conservation of position within all kinases, family, and group.
Amino acid properties: charge, polarity, mass, etc.
Structural and functional: kinase sub-domain, binding site,posttranslational modification.
Which of these is important?
U et al. Oncogenic kinome mutations September 4, 2014 5 / 11
Mutation features
Identified 29 mutation features.
Kinase group and family classification.
Conservation of position within all kinases, family, and group.
Amino acid properties: charge, polarity, mass, etc.
Structural and functional: kinase sub-domain, binding site,posttranslational modification.
Which of these is important?
U et al. Oncogenic kinome mutations September 4, 2014 5 / 11
Mutation features
Identified 29 mutation features.
Kinase group and family classification.
Conservation of position within all kinases, family, and group.
Amino acid properties: charge, polarity, mass, etc.
Structural and functional: kinase sub-domain, binding site,posttranslational modification.
Which of these is important?
U et al. Oncogenic kinome mutations September 4, 2014 5 / 11
Mutation features
Identified 29 mutation features.
Kinase group and family classification.
Conservation of position within all kinases, family, and group.
Amino acid properties: charge, polarity, mass, etc.
Structural and functional: kinase sub-domain, binding site,posttranslational modification.
Which of these is important?
U et al. Oncogenic kinome mutations September 4, 2014 5 / 11
Feature selection
5 feature selection algorithms.
10-fold “cross-validation” for each algorithm.I Ran 10 times, each time excluding 10% of data.I Averaged results over all 10 folds.
17 features chosen by 3/5 selectors were retained.
Kinase family and group were most important by a wide margin.
U et al. Oncogenic kinome mutations September 4, 2014 6 / 11
Feature selection
5 feature selection algorithms.
10-fold “cross-validation” for each algorithm.I Ran 10 times, each time excluding 10% of data.I Averaged results over all 10 folds.
17 features chosen by 3/5 selectors were retained.
Kinase family and group were most important by a wide margin.
U et al. Oncogenic kinome mutations September 4, 2014 6 / 11
Feature selection
5 feature selection algorithms.
10-fold “cross-validation” for each algorithm.I Ran 10 times, each time excluding 10% of data.I Averaged results over all 10 folds.
17 features chosen by 3/5 selectors were retained.
Kinase family and group were most important by a wide margin.
U et al. Oncogenic kinome mutations September 4, 2014 6 / 11
Feature selection
5 feature selection algorithms.
10-fold “cross-validation” for each algorithm.I Ran 10 times, each time excluding 10% of data.I Averaged results over all 10 folds.
17 features chosen by 3/5 selectors were retained.
Kinase family and group were most important by a wide margin.
U et al. Oncogenic kinome mutations September 4, 2014 6 / 11
Training and cross-validation
11 machine learning methods.
10-fold cross-validation for each (train on 90%, test on 10%).
Quantify accuracy of each method by
F − measure =2 × recall × precision
recall + precision,
where
precision =identified true oncogenic
all identified oncogenic
recall =identified true oncogenic
all true oncogenic.
U et al. Oncogenic kinome mutations September 4, 2014 7 / 11
Training and cross-validation
11 machine learning methods.
10-fold cross-validation for each (train on 90%, test on 10%).
Quantify accuracy of each method by
F − measure =2 × recall × precision
recall + precision,
where
precision =identified true oncogenic
all identified oncogenic
recall =identified true oncogenic
all true oncogenic.
U et al. Oncogenic kinome mutations September 4, 2014 7 / 11
Training and cross-validation
11 machine learning methods.
10-fold cross-validation for each (train on 90%, test on 10%).
Quantify accuracy of each method by
F − measure =2 × recall × precision
recall + precision,
where
precision =identified true oncogenic
all identified oncogenic
recall =identified true oncogenic
all true oncogenic.
U et al. Oncogenic kinome mutations September 4, 2014 7 / 11
EGFR mutations
Combined trained classifiers toanalyse rare mutations inepidermal growth factorreceptor.
L861R, G724S most likelyoncogenic.
T725M, L858Q middle-rankedbut unknown functionalimpact.
E746K low ranked.
U et al. Oncogenic kinome mutations September 4, 2014 8 / 11
EGFR mutations
Combined trained classifiers toanalyse rare mutations inepidermal growth factorreceptor.
L861R, G724S most likelyoncogenic.
T725M, L858Q middle-rankedbut unknown functionalimpact.
E746K low ranked.
U et al. Oncogenic kinome mutations September 4, 2014 8 / 11
EGFR mutations
Combined trained classifiers toanalyse rare mutations inepidermal growth factorreceptor.
L861R, G724S most likelyoncogenic.
T725M, L858Q middle-rankedbut unknown functionalimpact.
E746K low ranked.
U et al. Oncogenic kinome mutations September 4, 2014 8 / 11
EGFR mutations
Combined trained classifiers toanalyse rare mutations inepidermal growth factorreceptor.
L861R, G724S most likelyoncogenic.
T725M, L858Q middle-rankedbut unknown functionalimpact.
E746K low ranked.
U et al. Oncogenic kinome mutations September 4, 2014 8 / 11
In vitro verification
Effect of each mutation onEGFR autophosphorylationand Akt phosphorylation.
L861R, T725M, E746K showedincreased EGFRautophosphorylation.
I Up-regulation of cellproliferation.
G724S, L858Q showedincreased Akt (protein kinaseB) phosphorylation.
I Blocks apoptosis.
U et al. Oncogenic kinome mutations September 4, 2014 9 / 11
In vitro verification
Effect of each mutation onEGFR autophosphorylationand Akt phosphorylation.
L861R, T725M, E746K showedincreased EGFRautophosphorylation.
I Up-regulation of cellproliferation.
G724S, L858Q showedincreased Akt (protein kinaseB) phosphorylation.
I Blocks apoptosis.
U et al. Oncogenic kinome mutations September 4, 2014 9 / 11
In vitro verification
Effect of each mutation onEGFR autophosphorylationand Akt phosphorylation.
L861R, T725M, E746K showedincreased EGFRautophosphorylation.
I Up-regulation of cellproliferation.
G724S, L858Q showedincreased Akt (protein kinaseB) phosphorylation.
I Blocks apoptosis.
U et al. Oncogenic kinome mutations September 4, 2014 9 / 11
Conclusions
Used machine learning to classify rare EGFR mutations.
Kinase group and family were most important predictors.
Identified T725M, L861R as likely cancer-associated with anobvious mechanism (activating EGFR).
L858Q, G724S also likely oncogenic, but less obvious mechanism(Akt?).
U et al. Oncogenic kinome mutations September 4, 2014 10 / 11
Conclusions
Used machine learning to classify rare EGFR mutations.
Kinase group and family were most important predictors.
Identified T725M, L861R as likely cancer-associated with anobvious mechanism (activating EGFR).
L858Q, G724S also likely oncogenic, but less obvious mechanism(Akt?).
U et al. Oncogenic kinome mutations September 4, 2014 10 / 11
Conclusions
Used machine learning to classify rare EGFR mutations.
Kinase group and family were most important predictors.
Identified T725M, L861R as likely cancer-associated with anobvious mechanism (activating EGFR).
L858Q, G724S also likely oncogenic, but less obvious mechanism(Akt?).
U et al. Oncogenic kinome mutations September 4, 2014 10 / 11
Conclusions
Used machine learning to classify rare EGFR mutations.
Kinase group and family were most important predictors.
Identified T725M, L861R as likely cancer-associated with anobvious mechanism (activating EGFR).
L858Q, G724S also likely oncogenic, but less obvious mechanism(Akt?).
U et al. Oncogenic kinome mutations September 4, 2014 10 / 11
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