exciting bioinformatics adventures limsoon wong institute for infocomm research

Exciting Bioinformatics

Adventures

Limsoon WongInstitute for Infocomm

Research

Plan

• Treatment optimization of childhood ALL• Treatment prognosis of DLBC lymphoma• Prediction of translation initiation site• Prediction of vaccine target• Reliability Assessment of Y2H expts

Treatment Optimization of

Childhood Leukemia

Image credit: FEER

Copyright © 2004, 2005 by Jinyan Li and Limsoon Wong

Childhood ALL

• Major subtypes are: T-ALL, E2A-PBX, TEL-AML, MLL genome rearrangements, Hyperdiploid>50, BCR-ABL

• Diff subtypes respond differently to same Tx

• Over-intensive Tx – Development of

secondary cancers– Reduction of IQ

• Under-intensiveTx – Relapse

• The subtypes look similar

• Conventional diagnosis– Immunophenotyping– Cytogenetics– Molecular diagnostics

• Unavailable in most ASEAN countries


Image credit: Affymetrix

Single-Test Platform ofMicroarray & Machine

Learning


Multidimensional Scaling Plot Subtype Diagnosis


Is there a new subtype?

• Hierarchical clustering of gene expression profiles reveals a novel subtype of childhood ALL

Conclusions

Conventional Tx:• intermediate intensity to everyone 10% suffers relapse 50% suffers side effects costs US$150m/yr

Our optimized Tx:• high intensity to 10%• intermediate intensity to 40%• low intensity to 50%• costs US$100m/yr


•High cure rate of 80%• Less relapse

• Less side effects• Save US$51.6m/yr

References

• E.-J. Yeoh et al., “Classification, subtype discovery, and prediction of outcome in pediatric acute lymphoblastic leukemia by gene expression profiling”, Cancer Cell, 1:133--143, 2002

Treatment Prognosis for DLBC

Lymphoma

Image credit: Rosenwald et al, 2002

Diffuse Large B-Cell Lymphoma

• DLBC lymphoma is the most common type of lymphoma in adults

• Can be cured by anthracycline-based chemotherapy in 35 to 40 percent of patients

DLBC lymphoma comprises several diseases that differ in responsiveness to chemotherapy

• Intl Prognostic Index (IPI) – age, “Eastern Cooperative

Oncology Group” Performance status, tumor stage, lactate dehydrogenase level, sites of extranodal disease, ...

• Not very good for stratifying DLBC lymphoma patients for therapeutic trials

Use gene-expression profiles to predict outcome of chemotherapy?

Copyright © 2005 by Limsoon Wong. Adapted from Huiqing Liu

Knowledge Discovery from Gene Expression of “Extreme” Samples

“extreme”sampleselection:< 1 yr vs > 8 yrs

knowledgediscovery from gene expression

240 samples

80 samples26 long-

term survivors

47 short-term survivors

7399genes

84genes

T is long-term if S(T) < 0.3

T is short-term if S(T) > 0.7 Copyright © 2005 by Jinyan Li, Huiqing Liu, and Limsoon Wong

p-value of log-rank test: < 0.0001Risk score thresholds: 0.7, 0.3

Kaplan-Meier Plot for 80 Test Cases

Copyright © 2005 by Jinyan Li, Huiqing Liu, and Limsoon Wong

(A) IPI low, p-value = 0.0063

(B) IPI intermediate,p-value = 0.0003

Improvement Over IPI


(A) W/o sample selection (p =0.38) (B) With sample selection (p=0.009)

No clear difference on the overall survival of the 80 samples in the validation group of DLBCL study, if no training sample selection conducted

Merit of “Extreme” Samples


References

• H. Liu et al, “Selection of patient samples and genes for outcome prediction”, Proc. CSB2004, pages 382--392

Protein Translation Initiation Site Recognition

299 HSU27655.1 CAT U27655 Homo sapiensCGTGTGTGCAGCAGCCTGCAGCTGCCCCAAGCCATGGCTGAACACTGACTCCCAGCTGTG 80CCCAGGGCTTCAAAGACTTCTCAGCTTCGAGCATGGCTTTTGGCTGTCAGGGCAGCTGTA 160GGAGGCAGATGAGAAGAGGGAGATGGCCTTGGAGGAAGGGAAGGGGCCTGGTGCCGAGGA 240CCTCTCCTGGCCAGGAGCTTCCTCCAGGACAAGACCTTCCACCCAACAAGGACTCCCCT............................................................ 80................................iEEEEEEEEEEEEEEEEEEEEEEEEEEE 160EEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEE 240EEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEE

A Sample cDNA

• What makes the second ATG the TIS?

Copyright © 2005 by Limsoon Wong

Approach

• Training data gathering• Signal generation

– k-grams, distance, domain know-how, ...

• Signal selection– Entropy, 2, CFS, t-test, domain know-how...

• Signal integration– SVM, ANN, PCL, CART, C4.5, kNN, ...

Copyright © 2005 by Limsoon Wong

Amino-Acid Features


Amino Acid K-grams Discovered (by entropy)


Validation Results (on Hatzigeorgiou’s)

• Using top 100 features selected by entropy and trained on Pedersen & Nielsen’s dataset


ATGpr

Ourmethod

Validation Results (on Chr X and Chr 21)

• Using top 100 features selected by entropy and trained on Pedersen & Nielsen’s


References

• L. Wong et al., “Using feature generation and feature selection for accurate prediction of translation initiation sites”, GIW 13:192--200, 2002

Image credit: Asif Khan

Vaccine Target Prediction

T-Cell Epitope Prediction

• Why?– Only 1%-5% of peptides

from a protein bind to any one HLA molecule

– Traditional approaches are slow, & inapplicable to large-scale screening

Computer Modeling – Enable systematic

screening for HLA binders

– Minimize number of expts

– Reduce cost 10x

• Challenges:– There are ~2000

variants of HLA classified in ~20 supertypes

– Relatively small number of expt data on peptides that bind HLA molecules

– for majority of HLA molecules expt data do not exist

H1 H4H3H2

P1P2P3P4

Promiscuous peptides

One supertype

Copyright © 2005 by Limsoon Wong. Adapted from Asif Khan.

Multipred Approach

Copyright © 2005 by Asif Khan, Guanglan Zhang, Vladimir Brusic

FP

FN

DR supertype

Cut-offThreshold

HCV IB protein sequence


Expt Validation

Accuracy of Multipred

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

0.90

1.00

ANN

HMM

SVM

ANN 0.87 0.76 0.88 0.93 0.91 0.87

HMM 0.93 0.73 0.92 0.94 0.88 0.88

SVM 0.90 0.81 0.93 0.97 0.85 0.89

A-0201 A-0202 A-0204 A-0205 A-0206 avearage


Conclusions

• Computer models are necessary to aid in identification of vaccine targets

• Prediction models built are both sensitive and specific

• MULTIPRED can identify promiscuous peptides and immunological hot-spots which are useful for vaccine design

• Hot-spots are ideal for development of epitope-based vaccines

References

• K.N. Srinivasan, et al. “Predictions of Class I T-cell epitopes: Evidence of presence of immunological hot spots inside antigens”, Bioinformatics, 20:i297-i302, 2004.

% of TP based on co-localization% of TP based on shared cellular role (I = 1)% of TP based on shared cellular role (I = .95)

TP = ~50% Image credit: Sprinzak et al, 2003

Assessing Reliability

of Protein-Protein Interaction Expts

Large disagreement betw methods

Copyright © 2005 by Limsoon Wong. Adapted from Sprinzak et al, 2003

Some Protein Interaction Data Sets

• Can we find a way to rank candidate interacting pairs according to their reliability?

Copyright © 2005 by Limsoon Wong. Adapted from Chen et al, 2004

Some “Reasonable” Speculations

• A true interacting pair is often connected by at least one alternative path (reason: a biological function is performed by a highly interconnected network of interactions)

• The shorter the alternative path, the more likely the interaction (reason: evolution of life is through “add-on” interactions of other or newer folds onto existing ones)

Existence of a strong short alternative path connecting an interacting pair indicates that the interaction is “reliable”

Interaction Pathway Reliability


The number of pairs not in theintersection of Ito & Uetz is notchanged much wrt the ipr valueof the pairs

The number of pairs in theintersection of Ito & Uetzincreases wrt the ipr valueof the pairs

Evaluation wrt Reproducible Interactions

• “ipr” correlates well to “reproducible” interactions

• “ipr” seems to work


At the ipr thresholdthat eliminated 80%of pairs, ~85% of theof the remaining pairshave common cellularroles

Evaluation wrt Common Cellular Role, etc

• “ipr” correlates well to common cellular roles, localization, & expression


Evaluation wrt “Many-few” Interactions

• Number of “Many-few” interactions increases when more “reliable” IPR threshold is used to filter interactions

• Consistent with the Maslov-Sneppen prediction

Part of the network of physical interactions reported byIto et al., PNAS, 2001

Copyright © 2005 by Limsoon Wong. Adapted from Chen et al., 2004

Evaluation wrt “Cross-Talkers”

• A MIPS functional cat:– | 02 | ENERGY – | 02.01 | glycolysis and gluconeogenesis – | 02.01.01 | glycolysis methylglyoxal

bypass – | 02.01.03 | regulation of glycolysis &

gluconeogenesis

• First 2 digits is top cat• Other digits add more

granularity to the cat Compare non-co-

localized high- & low- IPR pairs to find number that fall into same cat. More high-IPR pairs in same cat, then IPR works

• For top cat– 148/257 high-IPR pairs

are in same cat– 65/260 low-IPR pairs are

in same cat

• For fine-granularity cat– 135/257 high-IPR pairs

are in same cat.37/260 low-IPR pairs are in same cat

IPR works IPR pairs that are not

co-localized are real cross-talkers!

Copyright © 2005 by Limsoon Wong.

Conclusions

• There are latent local & global “motifs” that indicate the likelihood of protein interactions

• These motifs can be exploited in computational elimination of false positives from high-throughput Y2H expts

Copyright © 2005 by Limsoon Wong.

References

• J. Chen et al, “Mining high-throughput experimental data for reliable protein interaction data using using network”, 16th IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2004), Florida, November 15-17, 2004

Acknowledgements

• Childhood ALL: – Jinyan Li, Huiqing Liu– Allen Yeoh

• DLBC Lymphoma:– Jinyan Li, Huiqing Liu

• Translation Initiation: – Fanfan Zeng, Roland

Yap– Huiqing Liu

• T-Cell Epitopes: – Vladimir Brusic, Asif

Khan, Guanglan Zhang– Tom August, KN

Srinivasan

• Protein Interaction Reliability: – Jin Chen, Mong Li Lee,

Wynne Hsu– See-Kiong Ng– Prasanna Kolatkar, Jer-

Ming Chia

exciting bioinformatics adventures limsoon wong institute for infocomm research

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