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An empirical framework

for genome-wide

single nucleotide

polymorphism-basedpredictive modeling

Charalampos S. Floudas, MD, PhD, MS

Jeya Balaji Balasubramanian, MS

Marjorie Romkes, PhD

Vanathi Gopalakrishnan, PhD

Department of Biomedical Informatics

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A workflow for prediction in cancer

Predicting Risk of early recurrence in

early stage non-small cell lung cancer

(NSCLC)

SNPR workflow

Genome-wide Single Nucleotide

Polymorphisms (SNP)

Bayesian rule learning (BRL) system

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Translational Bioinformatics

Includes prediction of clinical outcomes

from available genomic data

Genomic data:

High-dimensional

Many modalities

Different aspects of disease

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Translational Bioinformatics

Multiple clinical outcomes

Combinations of datasets and outcomes

Collaborative effort

Many tools available

Workflows

Flexibility of design

Reproducibility of research

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Core elements

Subjects: 86 early stage NSCLC patients

University of Pittsburgh Cancer Institute

Lung SPORE cohort

Importance

Predictors dataset: Affymetrix SNP Array

6.0, 1 million SNPs

Outcome: categorical disease freesurvival (DFS), good vs. poor, 1952 days

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Workflow tools

Affymetrix Genotyping Console

Quality control (QC), genotype calling

After QC: 67 samples (50 poor DFS, 17 good)

PLINK

QC of Genotypes (MAF, etc.) feature selection

(2) for BRL and export of features

BRL system

Predictive rules (sets of SNPs) and metrics

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BRL system elements

Rule learner (RL)

Bayesian Rule Learner (BRL)

Bayesian scoring induces Bayesian networks

Rule models

Global (GBRL): full

Local (LBRL): decision tree representation

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Workflow tools

SQLite

Fine selection of datasets and clinical parameters

Unix command line tools

Operations on datasets (Affymetrix genotypes to

PLINK, PLINK selected features to BRL)

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Results - feature selection

100 SNPs from PLINK 2

44 intragenic -> 33 genes

Functional analysis (Ingenuity IPA)

most significantly associated disease is

cancer (9 of 33 genes)

most significantly associated biological

function cell-to-cell signaling andinteraction (8 of 33 genes)

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Results - feature selection

CHODL (chondrolectin) gene

associated with shorter survivalin NSCLC

CDH13 (cadherin 13) gene

hypermethylated in NSCLC

CHST11 (carbohydrate (chondroitin 4)

sulfotransferase 11) gene

associated with lung colonization in breast

cancer

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Results BRL prediction

5 fold cross

validation

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Conclusions

Our empirical workflow (SNPR)

Efficiently overcomes challenges of

prediction using high-dimensional datasets

Achieves biological relevance and goodpredictive performance

Can be generalized and adapted

Other experimental platforms, data miningtasks

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Limitations

Small sample size

No independent testing cohort

Categorization of survival instead of

time-to-event analysis

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Acknowledgments

Cancer Biomarkers Facility of the University ofPittsburgh Cancer Institute, award

P30CA047904

Grant support: National Cancer Institute Award Number

P50CA090440

National Library of Medicine Award Number

R01LM010950

National Institute of General Medical Sciences

Award Number R01GM100387

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Thank you

chf35@pitt.edu