CytoGenetic Pattern Sleuth: A systematized platform to discover chromosomal aberration patterns in patient cohorts.

1. CytoGPS (CytoGenetic Pattern Sleuth)
Arka Pattanayak
Zachary Abrams
Informatics Research & Development,
Dept. of Biomedical Informatics at The Ohio State
University
08/28/2013

2. 2
• Complex chromosomal aberration
data – structure and knowledge.
• Inherently descriptive grammar –
International System for human
Cytogenetic Nomenclature (ISCN).
MOTIVATION : DATA
SOLUTION : CytoGPS
APPLICATIONS : MULTIPLE
• Parse karyotypes using Context-
Free Grammar (CFG) rules.
• Extract morphological phrases.
• Map phrases to abstract biological
meta-model.
• Discovery of important, obfuscated
patterns in cytogenetic data.
• Targeted Treatment.
• In-silico drug studies.
CytoGPS: 3-month status report

3. MOTIVATION Existing cytogenetic data:
• Structured.
• ISCN-conformant.
• Multi-dimensional.
Minimal exploitation due to its
informational complexity:
• Syntactic variability.
• Information density.
• Human error.
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CytoGPS: 3-month status report

4. 4
The Rulebook
CytoGPS: 3-month status report

5. CytoGPS Platform
Smart Parser
of Karyotypes
EBNF
Grammar
Rules
Parser
Generators
and Parse
Tree
Visitors
Biologically
Abstracted
Meta-Model
Mapper DSL
Genetic
Pattern
Matching
ML
Algorithms
Phenotype-
phenotype
Matching
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CytoGPS: CytoGenetic Pattern Sleuth
CytoGPS: 3-month status report

6. Parsing Complex Karyotypes using SPoK
(Smart Parser of Karyotypes)
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CytoGPS: 3-month status report

7. SPoK (Smart
Parser of
Karyotypes)
• Enables in-silico analyses of complex
karyotypes.
• Based on well-studied fundamentals in
computational parsing (CFG, EBNF).
• Disease-agnostic.
• Multi-disciplinary effort - Biomedical
Informatics, Cytogenetics, Hematology.
• ~76% of 3000 publicly available ISCN
2009 karyotypes were successfully parsed
with this method.
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CytoGPS: 3-month status report

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SPoK: Context-Free Grammar Rules
CytoGPS: 3-month status report

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SPoK: Parser Generation
Deterministic Parser
ANTLR
CytoGPS: 3-month status report

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CytoGPS: 3-month status report
SPoK: A Parse Tree Showing the
Morphological Deconstruction of a Complex
Karyotype
46,XY,del(17)(p12),t(12;15)(p13;q20)

11. Functional Abstraction using
LossGainFusion (Biologically Abstracted
Meta-Model)
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CytoGPS: 3-month status report

12. LGF(Biologicall
y Abstracted
Meta-Model)
• Abstraction of ISCN aberrations observed
in chromosomal bands to their biologically
functional outcomes.
• Using a custom Domain-Specific
Language (DSL)
• Karyotype complexity-agnostic.
• Human-readable karyotypes to machine-
readable construct.
• ~90% of parsed karyotypes were
successfully mapped using this model.
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CytoGPS: 3-month status report

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LGF: Understanding Oncogenic Effects with
an Abstracted Meta-Model
CytoGPS: 3-month status report

14. del(17)(p12)
del 17p12
del1:L
46,XY,del(17)(p12),t(12;15)(p13;q20)
t(12;15)(p13;q20)
t 12p13 15q20
t2:F,F
1.Complete karyotype
2.Chromosomal
aberrations
3.ID and chromosomal
locations
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CytoGPS: 3-month status report
LGF: Morphological Decomposition of
Karyotypes

15. der(4)t(4;13)(p14;p18)
der(4) t(4;13)(p14;p18)
t 4p14 13p18der 4
A B C D E
A+C=F
B,D,E add up to 3
F3:B,D,E
We don’t need B so we don’t put a annotation at
that location. We need to put the biological response for
D and E in there respective locations.
F3:,FL,FG
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CytoGPS: 3-month status report
LGF: Morphological Decomposition of
Karyotypes (more complex example)

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LGF: Domain-Specific Language for
Mapping ISCN Aberrations to the Meta-
Model.
CytoGPS: 3-month status report

17. Genetic Pattern Matching
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CytoGPS: 3-month status report

18. GPM: Genetic Pattern Matching using
C.A.R.T. (Classification And Regression
Tree) Algorithm
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Features-band locations on X-axis
Karyotypes
on Y-axis
1p36.3 1p36.2 1p36.1 … 1q44 2p25 … yp12
1 1 0 0 0 0 0 0
1 1 0 0 0 0 0 0
1 1 0 0 0 0 0 0
0 0 1 0 0 0 0 0
0 0 1 0 0 0 0 0
0 0 1 0 0 0 0 0
0 0 1 0 0 0 0 0
0 0 0 0 1 1 0 0
0 0 0 0 1 1 0 0
First cut
Second cut

19. Applied Biomedical Informatics using
CytoGPS: A Case Study
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CytoGPS: 3-month status report

20. Case Study: In-silico Drug Studies
 Raw ISCN Karyotypes.
 Parse
 Machine-readable Construct
 Map ISCN aberration to gene-set
 Map gene-set to known chemical reagent databases.
 An end-to-end in-silico solution for Drug Studies
 Significant cost savings.
 Rapid.
 Flatter learning curve to operate such a system
compared to wet-lab testing.
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CytoGPS: 3-month status report

21. Case Study: Map ISCN Aberration to Gene-
Set
 Ensembl @see:
http://beta.rest.ensembl.org/do
cumentation/info/feature_regio
n
 RESTful web service
endpoint
 Speaks JSON
 RESTful request looks like
this:
http://beta.rest.ensembl.org/
feature/region/human/17:15
700000-
16000000?feature=gene;co
ntent-type=application/json
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CytoGPS: 3-month status report

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Case Study: Extracting Genetic Information
CytoGPS: 3-month status report

23. Zachary
Abrams
Lori Dalton,
PhD
Philip R. O.
Payne, PhD
Arka
Pattanayak
Raj
Muthusamy,
PhD
Nyla
Heerema,
PhD
William
Kenworthy
Sarah
Yousef
Alex Mysiw
Yuxiang
Kou
Michael
Berkovich
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CytoGPS: 3-month status report

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CytoGPS: 3-month status report

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CytoGPS: 3-month status report