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SUPERVISED METHODS CAN ONLY VALIDATE OR REJECT HYPOTHESES. CAN NOT LEAD TO DISCOVERY OF ... Bax, IGF-BP3, Fas, killer/DR5, Noxa, PIG3, p53AIP1, PIDD, Puma ...

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Advantages of SPC

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Basic methodologies1 l.jpg

Basic methodologies1

. UNSUPERVISED: EXPLORATORY ANALYSIS

  • NO PRIOR KNOWLEDGE IS USED

  • EXPLORE STRUCTURE OF DATA ON THE BASIS OF

  • CORRELATIONS AND SIMILARITIES

BASIC METHODOLOGIES OF ANALYSIS:

SUPERVISED ANALYSIS: HYPOTHESIS TESTING

USING CLINICAL INFORMATION (MLL VS NO TRANS.)

IDENTIFY DIFFERENTIATING GENES

SUPERVISED METHODS CAN ONLY VALIDATE OR REJECT HYPOTHESES. CAN NOT LEAD TO DISCOVERY OF UNEXPECTED PARTITIONS


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Advantages of SPC

  • Scans all resolutions (T)

  • Robust against noise and initialization -calculates collective correlations.

  • Identifies “natural” () and stable clusters (T)

  • No need to pre-specify number of clusters

  • Clusters can be any shape

  • Can use distance matrix as input (vs coordinates)


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stability

T

larger T - tighter, more stable cluster


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P53

p53 IS A CENTRAL PLAYER IN APOPTOSIS AND IN CELL

CYCLE CONTROL. IT IS A TRANSCRIPTION FACTOR.


Primary targets of p53 l.jpg

PRIMARY TARGETS OF P53

K. Kannan, D. Givol, G. Rechavi,... G. Getz, I. Kela, Oncogene 2001

TEMPERATURE SENSITIVE MUTANT P53, ACTIVATE - 32 C (t=0)

MEASURE EXPRESSION AT t=0,2,6,12,24 h (use t=0 as control)

REPEAT IN PRESENCE OF CYCLOHEXIMIDE (CHX)t=0,2,4,6,9,12

(CHX INHIBITS PROTEIN SYNTHESIS)

IDENTIFY UPREGULATED GENES USING FILTER:

AT LEAST 2.5 FOLD INCREASE AT 3 OR MORE TIME POINTS

(SEPARATELY IN EACH OF THE

TWO EXPTS, -CHX AND +CHX)

38 CANDIDATE PRIMARIES:

EFFECT OF FILTERING???

RELEASE FILTER FROM +CHX

CLUSTERING: 3847 (31)


Reduce effect of filtering by clustering l.jpg

REDUCE EFFECT OF FILTERING BY CLUSTERING

c

a

%candidate

primary

targets

K.Kannan et al, Oncogene

X – 38 candidate primary targets


Slide7 l.jpg

INHIBITION OF P53-INDUCED APOPTOSIS BY IL-6

Lotem…Rechavi, D. Givol, L. Sachs PNAS 2003

BY REDUCING TEMPERATURE TO 32 DEGREES,

P53 ASSUMES WILD-TYPE CONFORMATION, IS

ACTIVATED AND INDUCES APOPTOSIS

ADDING THE CYTOKINE IL-6

INHIBITS THE APOPTOTIC PROCESS

QUESTION: WHERE DOES IL-6

INTERFERE IN THE CASCADE

INITIATED BY P53?

AT TOP?AT BOTTOM?


Slide8 l.jpg

IL-6 ??

Apoptosis

IL-6 ??

QUESTION: WHERE DOES IL-6

INTERFERE IN THE CASCADE

INITIATED BY P53?

AT TOP?AT BOTTOM?

Activated

p53

Transactivation

Other activities

(C terminal = TFIIH binding?)

(N terminal = SH3 binding?)

p21/

Waf1

Bax, IGF-BP3, Fas, killer/DR5, Noxa, PIG3, p53AIP1, PIDD, Puma

Other genes

etc, etc, etc

??

Caspese cascade

Growth arrest


Slide9 l.jpg

333 GENES UPREGULATED BY P53 – NOT AFFECTED BY IL-6

309 GENES DOWNREGULATED BY P53 ALSO NOT AFFECTED


Slide10 l.jpg

IL-6 ??

Apoptosis

IL-6 ??

QUESTION: WHERE DOES IL-6

INTERFERE IN THE CASCADE

INITIATED BY P53?

AT TOP?AT BOTTOM?

ANSWER: AT BOTTOM!!

Activated

p53

Transactivation

Other activities

(C terminal = TFIIH binding?)

(N terminal = SH3 binding?)

p21/

Waf1

Bax, IGF-BP3, Fas, killer/DR5, Noxa, PIG3, p53AIP1, PIDD, Puma

Other genes

etc, etc, etc

??

Caspese cascade

Growth arrest


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Colon Cancer Data

COLON CANCER DATA

Alon,Barkai, Notterman, Gish, Ybarra, Mack, Levine:

PNAS 96, 6745 (1999)

AFFYMETRIX; 40 TUMOR, 22 NORMAL TISSUES

2000 (OUT OF 6500) GENES OF HIGHEST INTENSITY

Aij = EXPRESSION LEVEL OF GENE i IN TISSUE j


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Colon Cancer Data

COLON CANCER DATA:


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Two-way clustering

S1(G1)

G1(S1)

TWO-WAY

CLUSTERING:


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Two way clustering-ordered

TWO-WAY

CLUSTERING:

S1(G1)

G1(S1)


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2-way clustering - tissues

TWO-WAYCLUSTERING – TISSUES

1. IDENTIFY TISSUE CLASSES (TUMOR/NORMAL)


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2-way clustering –genes Erel

Ribosomal proteins

Cytochrome C

metabolism

HLA2

TWO-WAY CUSTERING – GENES - G1(S1)

2.FIND DIFFERENTIATING AND CORRELATED GENES EACH GENE = POINT IN 62 DIMENSIONAL SPACE


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Two-way clustering

TWO-WAY

CLUSTERING:

Can one improve?


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football


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C2WC - Motivation

COUPLED TWO-WAY CLUSTERING

MOTIVATION:

ONLY A SMALL SUBSET OF GENES PLAY A ROLE

IN A PARTICULAR BIOLOGICAL PROCESS; THE

OTHER GENES INTRODUCE NOISE, WHICH MAY

MASK THE SIGNAL OF THE IMPORTANT PLAYERS.

ONLY A SUBSET OF SAMPLES EXHIBIT THE

EXPRESSION PATTERNS OF INTEREST.

SHOULD USE A SUBSET OF GENES TO STUDY A

SUBSET OF THE SAMPLES (AND VICE VERSA)

PROBLEM: ENORMOUS NUMBER OF SUBMATRICES


C2wc method l.jpg

C2WC - method

COUPLED TWO-WAY CLUSTERING

PICK ONE STABLEGENE CLUSTER. REPRESENT

TISSUES BY THE EXPRESSION LEVELS OF THESE

GENESONLY. ANALYZE ALL TISSUE CLUSTERS

BY USING ALL GENE CLUSTERS, ONE AT A TIME.

LOOK FOR INTERNAL STRUCTURE, SUB-CLUSTERS.

USE ALL STABLE TISSUE CLUSTERS TO CLASSIFY

GENES; IDENTIFY GENE CLUSTERS THAT GOVERN

BIOLOGICAL PROCESSES.

ITERATE THE PROCEDURE UNTIL NO NEW STABLE

CLUSTERS EMERGE


Tissues 1 l.jpg

tissues 1

G4

G12

COUPLED TWO-WAY CLUSTERING OF COLON

CANCER: TISSUES

S1(G4)

S1(G12)


Ctwc colon cancer tissues l.jpg

CTWC colon cancer - tissues

Tumor

Normal

S17

Protocol A

Protocol B

COUPLED TWO-WAY CLUSTERING OF COLON

CANCER: TISSUES

S1(G4)

S1(G12)


Genes1 l.jpg

genes1

G1(S17)

S17


Ctwc of colon cancer genes l.jpg

CTWC of colon cancer - genes

G1(S17)

COUPLED TWO WAY CLUSTERING OF COLON

CANCER - GENES

USING ONLY THE TUMOR TISSUES TO CLUSTER

GENES, REVEALS CORRELATION BETWEEN TWO

GENE CLUSTERS; CELL GROWTH AND EPTHELIAL

G1(S1)

COLON CANCER - ASSOCIATED WITH EPITHELIAL CELLS


Glioblastoma l.jpg

glioblastoma

174 genes separate (at FDR of 5%)

PrGBM from LGA + ScGBM

S Godard, G Getz, H Kobayashi, P Farmer, M Delorenzi, M Nozaki,

A-C Diserens, M-F Hamou, P-Y Dietrich, J-G Villemure, R C. Janzer,

P Bucher, R Stupp, N de Tribolet, E Domany, M E. Hegi

GLIOBLASTOMA:

CLONTECH ARRAYS

1185 Genes, 36 Samples

12 Astrocytoma(II)

4 secondary GBM

17 Primary GlioBlastoMa

3 Cell Lines


Glioblastoma26 l.jpg

glioblastoma

FILTERING  358 HIGHLY VARYING GENES

GLIOBLASTOMA:

S3

S1(G1)

Coupled Two-Way Clustering (CTWC)

of 358 Genes and 36 Samples

S2

T

G12

GENES

G5

Astrocytoma(II)

Secondary GBM

Primary GlioBlastoMa

Cell Lines

G1(S1)


S1 g5 l.jpg

S1(G5)

Super-Paramagnetic Clustering of All Samples

Using Stable Gene Cluster G5

S1(G5)

S14

S13

S12

S11

S10

Fig. 2B


Validation l.jpg

validation

G5Ver


The genes of g5 l.jpg

THE GENES OF G5

THE GENES OF G5:

AB004904

STAT-induced STAT inhibitor 3

M32977

VEGF

M35410

IGFBP2

X51602

VEGFR1

M96322

gravin

AB004903

STAT-induced STAT inhibitor 2

PTN

X52946

J04111

c-jun

X79067

TIS11B

VEGF AND ITS RECEPTORS – INSTRUMENTAL IN

ANGIOGENESIS; INDUCED GROWTH OF BLOOD

VESSELS, ESSENTIAL FOR GROWTH BEYOND A

CRITICAL SIZE. THE COEXPRESSION OF IGFBP2

WAS INDEPENDENTLY VERIFIED; 1ST EVIDENCE

FOR POSSIBLE ROLE IN ANGIOGENESIS.


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Fig 6


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Analysis of cervical cancer data

‘g’ - good

‘b’ - bad

‘o’ - other

S 02 - 1 e g

‘S’ - sample

‘C’ - cell line

Batch

#1,2,3

‘a’ - adeno

‘e’ - epidermal

‘n’ - normal

Sample

number

C. Rosty, F. Radvanyi, N. Stransky …M. Sheffer,

D. Tsafrir, I. Tsafrir …X. Sastre, Oncogene (2005)

Total of 45 samples/chips:

  • 5 Cell lines.

  • 5 Normal samples.

  • 35 tumor samples, 5 of which are repeats.

    • 10 adenocarcinoma tumors: 4 are HPV-16 and 6 are HPV-18.

    • 20 epidermal carcinoma: 12 HPV-16, 6 HPV-18, 1 HPV-33 and 1 HPV-99.

MAIN AIM:

PREDICT

OUTCOME

AT DISCOVERY


Slide32 l.jpg

AIM: IDENTIFY GENES WHOSE EXPRESSION LEVEL,

MEASURED AT THE TIME OF DISCOVERY OF

THE MALIGNANCY, IS INDICATIVE OF OUTCOME


Slide33 l.jpg

WE USED STANDARD STATISTICAL TESTS LOOKING

FOR GENES WHOSE EXPRESSION LEVELS SEPARATE

PATIENTS WITH GOOD OUTCOME FROM PATIENTS

WITH BAD OUTCOME.

NO SUCH GENES WERE FOUND

PERHAPS TRY UNSUPERVISED METHODS

(E.G. CLUSTERING) ???


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S1(G1)

G1(S1)

Two-way Clusteringof cervical data

Two clustering operations:

  • 35 samples based on

    the expression of 5000 probes; S1(G1)

  • 5000 probes in 35

    dimensional space;

    G1(S1)


Slide35 l.jpg

S1(G7)

G7

G3

S1(G10)

G10

S1(G3)

Coupled Two-Way Clustering of Cervix Cancer

35 SAMPLES (REMOVE CELL LINES AND REPLICATES)

5000 GENES (PASSED VARIANCE FILTER)

FOCUS ON G3: CLUSTER OF 148 GENES (163 probe sets)


Slide36 l.jpg

“good”

normal

cell lines

S1(G7)

G7

G3

S1(G10)

G10

S1(G3)

Coupled Two-Way Clustering of Cervical Cancer

Getz et al PNAS 2000

FOCUS ON G3 (PROLIFERATION CLUSTER, GO):

1. Cluster samples using 163 probe sets;

2. SORT (using SPIN )


Good outcome sample cluster aacr 2004 l.jpg

S19-1noo

S28-1noo

S07-1noo

S35-1noo

S02-1noo

S29-3a6g

S26-2a8+

S20-2e8g

S03-1e8g

S34-2e8g

S23-1a8g

S13-1a8b

S31-3a6g

S08-1e6g

S23-2a8g

S10-1e6b

S18-1e8b

S04-1a8b

S12-1e8b

S05-1a8b

S11-1e3b

S25-3a6g

S22-1e6g

S27-1e6b

S32-2e6g

S17-3a6o

S33-1e6b

S15-2e6g

S09-1e6b

S18-2e8b

S06-1e6+

S14-1e6b

S33-2e6b

S15-1e6g

S21-2a8o

S24-1e6b

S01-1e6g

S14-3e6b

S30-2e8b

C01-3c8o

S16-1e9o

C06-3c8o

C07-3c6o

C03-3c6o

C05-3c8o

163 probes

‘Good outcome’ sample cluster(AACR 2004)

Low expression level

of the “Proliferation

Cluster”indicates

good outcome

High expression:

no prediction

Normal

samples

Good

outcome

Cell-line

samples

Validated by RT-PCR of

20 genes over 70 samples


Slide38 l.jpg

P53 and Rb control (restrain) proliferation (inactivating E2F)

Activity of P53 and Rb is controlled by E6/E7 Viral Protein Content.

E6/E7 Protein Concentration controlled by E6/E7 RNA Expression Level

use TF binding site

sequence information

to derive network

E7 RNA:

Corr=0.54,0.62

Ordered

Expression

Matrix of

20 proliferation

Genes

HPV16/HPV18

E7 DNA:

Corr=0.34,0.55

E6/E7 RNA Level controlled by E6/E7 DNA COPY NUMBER


Slide39 l.jpg

AIM: IDENTIFY GENES WHOSE EXPRESSION LEVEL,

MEASURED AT THE TIME OF DISCOVERY OF

THE MALIGNANCY, IS INDICATIVE OF OUTCOME

FINDING: A CUSTER OF 150 GENES, ASSOCIATED WITH

CELL PROLIFERATION, HAS RELATIVELY LOW

EXPRESSION LEVELS IN A SUBSET OF THE

“GOOD OUTCOME” PATIENTS. VALIDATION (PCR)

FINDING: CELL PROLIFERATION EXPRESSION LEVEL

IS CONTROLLED BY AMOUNT OF VIRAL PROTEINS

E6, E7, WHICH IS GOVERNED BY NUMBER OF DNA

COPIES THAT WERE INSERTED BY THE VIRUS

Rosty et al, Oncogene 2005


Signature algorithm l.jpg

signature algorithm

J. Ihmels, G. Friedlander,S. Bergmann,O. Sarig, Y Ziv, N. Barkai


Recurrence l.jpg

recurrence

yeast genome: 6400 genes, 1000 “conditions” (chips)

(

  • Ncore = 37,73,145 genes for ribosomal proteins

  • 132 genes for biosynthesis

  • Each used as input GIref, returns (nearly same) gene signature Sref

  • add Nrandrandomly picked genes

  • GIinput set of Ncore + Nrandgenes, returns gene signatures SI

  • Recurrence of Sref is measured by

  • Overlap = Fraction of shared genes by Sref and SI

  • (b) Use as GIrefsets of genes with shared regulatory sequences.

  • Only the truely coregulated ones are returned in Sref; recurrent.


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pathways

  • Tricarboxyl acid (TCA) cycle: known genes in E.coli,

  • find (34) homologues in yeast used as GI ; produce SIwhich

  • excludes the wrong genes and misses only few correct ones

  • (b,c) Identify two autonomous subparts of the cycle


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