Supplementary Results - lausanne.isb-sib.chpwirapat/bcfsib2007-1/supresults.pdf · 1 Survival data 1.1 Data collection and preprocessing Survival endpoints The original studies often

Supplementary Results

Pratyaksha J. Wirapati

April 19, 2007

Contents

1 Survival data 21.1 Data collection and preprocessing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21.2 Cohort-specific survival for all endpoints . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

2 Coexpression modules 32.1 Gene Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32.2 Number of genes selected and mapped to each dataset . . . . . . . . . . . . . . . . . . . . . . . . 32.3 Heatmaps for all datasets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42.4 Biological annotation of the modules . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

3 Module score distributions 83.1 Dot histograms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83.2 Tests of bimodality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

4 Three subtypes 104.1 Scatter plots of ERBB2 vs ESR1 module scores . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104.2 Tests of trimodality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

5 Additional survival analyses 135.1 Three subtypes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135.2 Three subtypes combined with high/low proliferation . . . . . . . . . . . . . . . . . . . . . . . . . 135.3 Separate metastasis-free survival and relapse-free survival . . . . . . . . . . . . . . . . . . . . . . . 13

6 Prognostic value of ER-status within type-2 (ERBB2+) tumors 14

7 Gene-by-gene survival analysis 147.1 Gene Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147.2 Agreement of gene associations with different survival endpoints . . . . . . . . . . . . . . . . . . . 14

8 Forest plots of signature performance for all survival endpoints 15

9 Concordance in risk classifications 169.1 Tables of pairwise concordance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 169.2 Combined prediction by pairs of signatures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 179.3 Patient classifications on proliferation-vs-subtype plots . . . . . . . . . . . . . . . . . . . . . . . . 18

1

1 Survival data

1.1 Data collection and preprocessing

Survival endpoints The original studies often do not give precise description of their endpoints. Thefollowing are the definitions we used:

• Relapse-free survival (RFS) The endpoints are any events including local (in the same breast orthe other side) or regional relapse, and distant metastasis (which is often implied by death). We alsoconsider as RFS endpoints described as disease-free survival (DFS) in the original studies.

• Metastasis-free survival (MFS) The endpoints are relapses to other organs, such as liver or brain.

• Overall survival (OS) The endpoints are patient’s deaths. Few studies specify “disease-specific sur-vival” to distinguish death due to breast cancer, as opposed to any death. When not specified, weassume all deaths are disease related.

Note that these endpoint types are “nested”. Death due to breast cancer implies distant metastasis shortlybefore (and often the time to metastasis is identical to the time to death). RFS typically includes MFSand OS. The events are therefore correlated. Although they obviously have different baseline hazard (RFSdrops faster than MFS, which in turn drops faster than OS) (See Supplementary Result 1.2 below), most ofour conclusions are concerned with relative survival between groups, which are valid for any type of events(Supplementary Result 1.2, 5.1, 5.2, 5.3, 6, 8). To save space (without affecting the conclusions), Figure 4c,dand Figure 6a,b,c in the main text are based on combined event types. Furthermore, we can not detect genesspecifically associated with one type of endpoint but not the others (see Supplementary Result 7).

Time-unit conversion and modifications All time units were converted to “days”. One year is consid-ered to be 365.25 days. “Months” and “‘weeks” were converted to “years” first. Patients with missing timewere excluded, but those with missing event indicator (very few) were considered censored. Median follow-uptime can vary greatly between studies (e.g. very short in STNO and very long in TRANSBIG; see the numbersat risk in Supplementary Result 1.2). Therefore all survival data were truncated to 10 years (i.e. patientswith longer follow-up are considered to be censored at this time point) in Cox regression analysis to ensurethat the meta-analytical results pertain to similar time range in all datasets. The truncation also deals withproblems with non-proportional hazard in long follow-up time (see Hilsenbeck et al. [1998] Time-dependenceof hazard ratios for prognostic factors in primary breast cancer. Breast Cancer Res. Treat. 52:227-237).

1.2 Cohort-specific survival for all endpoints

a b c

total 6311890 1415 1147 799 469

STNO 60115 39 10 1UNC 32128 33 10 4JRH1 4599 75 59 30 1MGH 2560 50 42 28 18NCH 47135 110 97 81 66NKI 121319 260 216 131 75TRANSBIG 101253 207 170 147 118UPP 88249 185 158 140 107JRH2 1561 55 44 38 33STOCK 40159 140 124 68EMC2 37180 164 149 94 40UCSF 20132 97 68 37 11

STNO

UNCJRH1MGH

NCH

NKITRANSBIGUPP

JRH2STOCK

EMC2UCSF

40

60

80

100

rela

pse-

free

sur

viva

l (%

)

group: number at risk: events

follow-up: 0 2.5 5 7.5 10 (year)

total 3061015 852 693 491 308

NKI 105319 266 223 135 82TRANSBIG 56253 225 197 177 149EMC 107286 226 180 127 44HPAZ 2596 80 49 16 2JRH2 1361 55 44 36 31

NKI

TRANSBIG

EMC

HPAZ

JRH2

40

60

80

100

met

asta

sis-

free

sur

viva

l (%

)


follow-up: 0 2.5 5 7.5 10 (year)

total 4842019 1628 1313 867 512

STNO 46115 54 14 3 1DUKE 43170 86 46 12 3UNC 22129 39 14 5JRH1 4599 85 70 32 1UCSF 37132 104 74 43 14NKI 74319 290 248 147 89STOCK 40159 148 130 64NCH 34135 122 111 96 81TRANSBIG 57253 240 212 190 154UPP 51232 198 173 152 122EMC2 23180 175 166 103 44HPAZ 1296 87 55 20 3

STNODUKE

UNC

JRH1

UCSFNKI

STOCKNCH

TRANSBIGUPPEMC2HPAZ

40

60

80

100

over

all s

urvi

val (

%)


follow-up: 0 2.5 5 7.5 10 (year)

Cohort-specific survival curves for relapse-free survival (a), metastasis-free survival (b) and overall survival(c). Panel c is the same as figure 1b in the main text.

2

2 Coexpression modules

2.1 Gene Tables

The genes identified by prototype-based coexpression analysis are listed in the file modules.txt, in a tab-delimited text file. For convenient browsing, open the file using spreadsheet program (e.g. MS Excel) andformat the column widths to fit the contents automatically. We identified 909 genes under the specifiedcriteria. The ordering of the genes in the table is the same as that in Figure 2b of the main text.

2.2 Number of genes selected and mapped to each dataset

---------------------------------------------------

modules

--------------------------------------

datasets ESR1 ERBB2 AURKA PLAU STAT1

---------------------------------------------------

NKI 250 20 278 154 159

EMC 248 18 270 151 156

UPP 268 21 294 160 166

STOCK 268 21 294 160 166

DUKE 182 14 204 130 134

UCSF 143 11 176 109 116

UNC 235 19 270 156 161

NCH 235 19 270 156 161

STNO 133 8 156 100 102

JRH1 100 8 135 90 99

JRH2 248 18 270 151 156

MGH 196 11 198 123 108

expO 268 21 294 160 166

TGIF1 248 18 270 151 156

BWH 268 21 294 160 166

TRANSBIG 11 1 50 2 2

EMC2 1 1 10 1 0

HPAZ 2 1 13 1 0

---------------------------------------------------

Gene union 268 21 294 160 166

---------------------------------------------------

3

2.3 Heatmaps for all datasets

NKI1 2 3

ESR1FOXA1SCUBE2ERBB4BCL2ERBB3AR

RARA

EGFRFABP7CRYABYBX1FOXC1

GATA3CA12XBP1MYBTFF3TFF1CCND1

KRT5KRT7KLF5KRT16PROM1CDH3LMO4

ERBB2GRB7

STARD3PERLD1

AURKACCNB2BUB1FOXM1CCNB1CDKN3MCM6CCNE2MCM2CCNA2MCM10CCNE1MCM3

TGFB3TGFBR2IGF1SPARCL1

PTTG1BIRC5TOP2AMYBL2PCNALMNB1MKI67CKS1BTK1TYMSE2F1

DPT

RECKDCN

PLAUMMP11CTSKPLAURCTSBMMP2MMP13TIMP3MMP1LAMB1TIMP2

COL11A1COL5A2COL10A1COL5A1COL12A1COL6A3COL6A2COL1A2COL3A1COL6A1

STAT1HLA-GHLA-FHLA-CHLA-BHLA-DRAHLA-DMAHLA-DQB1HLA-DPA1HLA-EHLA-DMB

MX1IFIT3IRF1IFITM1GZMACD38IFIT5

05

1015 fo

llow

-up

time

(yea

r)

censoredevent

EMC1 2 3


RARA


GATA3CA12SLC39A6XBP1MYBTFF3TFF1KRT18CCND1


ERBB2GRB7

STARD3PERLD1




DPT

RECKDCN


COL11A1COL5A2COL10A1COL5A1COL6A3COL6A2COL1A2COL3A1COL6A1



05

1015 fo

llow

-up

time

(yea

r)

censoredevent

TGIF112 3


RARA




ERBB2GRB7

STARD3PERLD1




DPT

RECKDCN





05

1015 fo

llow

-up

time

(yea

r)

censoredevent

JRH11 2 3

ESR1BCL2

RARAEGFRFABP7YBX1FOXC1

GATA3SLC39A6XBP1MYBTFF3KRT18KRT5KRT7PROM1CDH3

ERBB2GRB7

STARD3AURKABUB1FOXM1CCNB1CDKN3MCM6MCM2CCNA2CCNE1MCM3TGFB3TGFBR2IGF1SPARCL1

PTTG1BIRC5TOP2AMYBL2PCNALMNB1MKI67CKS1BTK1TYMSDPT


COL6A3COL6A2COL3A1COL6A1

STAT1HLA-GHLA-CHLA-DRAHLA-DMAHLA-DQB1HLA-DPA1HLA-EHLA-DMB

MX1IRF1IFITM1GZMACD38IFIT5

05

1015 fo

llow

-up

time

(yea

r)

censoredevent

STNO1 2 3 N

ESR1SCUBE2ERBB4BCL2ERBB3


GATA3SLC39A6XBP1MYBTFF3TFF1KRT18CCND1KRT5KRT7PROM1CDH3

ERBB2GRB7

STARD3AURKABUB1FOXM1CCNB1CDKN3MCM6MCM2CCNA2CCNE1MCM3TGFB3TGFBR2IGF1SPARCL1

PTTG1BIRC5TOP2AMYBL2PCNAMKI67CKS1BTYMSE2F1

DPT

PLAUCTSKPLAURCTSBMMP13TIMP3MMP1LAMB1TIMP2


STAT1HLA-CHLA-DRAHLA-DMAHLA-DQB1HLA-DPA1

MX1IRF1IFITM1CD38IFIT5

05

1015 fo

llow

-up

time

(yea

r)

censoredevent

UCSF1 2 3 N

ESR1BCL2ERBB3AR


GATA3SLC39A6MYBTFF3KRT18CCND1KRT5KRT7KLF5KRT16CDH3LMO4

ERBB2GRB7

STARD3

AURKABUB1FOXM1CCNB1CDKN3MCM6MCM2CCNA2CCNE1MCM3


PTTG1BIRC5TOP2AMYBL2PCNALMNB1MKI67CKS1BTK1TYMS

DPT


COL11A1COL5A1COL6A3COL6A2COL1A2COL3A1COL6A1

STAT1HLA-GHLA-FHLA-CHLA-DRAHLA-DMAHLA-DQB1HLA-DPA1

MX1IRF1IFITM1GZMACD38IFIT5

05

1015 fo

llow

-up

time

(yea

r)

censoredevent

4

UPP1 2 3

ESR1FOXA1SCUBE2ERBB4BCL2ERBB3

AR

RARA




ERBB2GRB7

STARD3PERLD1




DPT

RECKDCN




MX1IFIT3IRF1

IFITM1GZMACD38IFIT5

05

1015 fo

llow

-up

time

(yea

r)

censoredevent

STOCK1 2 3

ESR1FOXA1SCUBE2ERBB4BCL2ERBB3

AR

RARA




ERBB2GRB7

STARD3PERLD1




DPT

RECKDCN




MX1IFIT3IRF1

IFITM1GZMACD38IFIT5

05

1015 fo

llow

-up

time

(yea

r)

censoredevent

expO

1 2 3ESR1FOXA1SCUBE2ERBB4BCL2ERBB3

AR

RARA




ERBB2GRB7

STARD3PERLD1




DPT

RECKDCN




MX1IFIT3IRF1

IFITM1GZMACD38IFIT5

05

1015 fo

llow

-up

time

(yea

r)

censoredevent

TRANSBIG1 2 3

ESR1

SCUBE2

KRT18

AURKACCNB2BUB1

MCM6CCNE2

TGFB3

BIRC5

TK1

05

1015 fo

llow

-up

time

(yea

r)

censoredevent

HPAZ12 3

SCUBE2EGFR

ERBB2

MCM6

TGFB3

05

1015 fo

llow

-up

time

(yea

r)

censoredevent

EMC21 3

CCNE2

05

1015 fo

llow

-up

time

(yea

r)

censoredevent

Note Because of the small number of genes mapped, the vertical scale of the heatmaps for TRANSBIG,HPAZ and EMC2 is larger than that of other datasets.

5

UNC

1 2 3 NESR1FOXA1SCUBE2ERBB4ERBB3

AR

RARA



KRT5KLF5KRT16PROM1CDH3

ERBB2GRB7

STARD3PERLD1




DPT

RECKDCN

PLAUMMP11CTSKPLAURCTSBMMP2MMP13MMP1LAMB1TIMP2


STAT1HLA-GHLA-FHLA-BHLA-DRAHLA-DMAHLA-DQB1HLA-DPA1HLA-EHLA-DMB

MX1IFIT3IRF1

IFITM1GZMACD38IFIT5

05

1015 fo

llow

-up

time

(yea

r)

censoredevent

NCH

1 2 3ESR1FOXA1SCUBE2ERBB4ERBB3

AR

RARA



KRT5KLF5KRT16PROM1CDH3

ERBB2GRB7

STARD3PERLD1




DPT

RECKDCN

PLAUMMP11CTSKPLAURCTSBMMP2MMP13MMP1LAMB1TIMP2


STAT1HLA-GHLA-FHLA-BHLA-DRAHLA-DMAHLA-DQB1HLA-DPA1HLA-EHLA-DMB

MX1IFIT3IRF1

IFITM1GZMACD38IFIT5

05

1015 fo

llow

-up

time

(yea

r)

censoredevent

JRH2

12 3ESR1FOXA1SCUBE2ERBB4BCL2ERBB3AR

RARA




ERBB2GRB7

STARD3PERLD1




DPT

RECKDCN





05

1015 fo

llow

-up

time

(yea

r)

censoredevent

BWH

1 23NESR1FOXA1SCUBE2ERBB4BCL2ERBB3

AR

RARA




ERBB2GRB7

STARD3PERLD1




DPT

RECKDCN




MX1IFIT3IRF1

IFITM1GZMACD38IFIT5

05

1015 fo

llow

-up

time

(yea

r)

censoredevent

DUKE

1 2 3ESR1FOXA1ERBB4BCL2ERBB3AR

RARA


GATA3CA12XBP1MYBTFF3TFF1KRT18CCND1KRT5KRT7KLF5KRT16PROM1CDH3LMO4

ERBB2GRB7

STARD3

AURKACCNB2BUB1FOXM1CCNB1CDKN3MCM6CCNE2MCM2CCNA2CCNE1MCM3



DPT

RECK

PLAUMMP11CTSKPLAURMMP2MMP13TIMP3MMP1LAMB1TIMP2

COL11A1COL5A2COL10A1COL6A3COL6A2COL1A2COL3A1COL6A1

STAT1HLA-GHLA-FHLA-DRAHLA-DMAHLA-DQB1HLA-DPA1HLA-DMB


MGH

12 3ESR1ERBB4BCL2ERBB3

AR

EGFR

FOXC1

GATA3CA12SLC39A6XBP1MYBTFF3CCND1

KRT5KLF5KRT16PROM1

ERBB2

AURKACCNB2BUB1FOXM1CDKN3MCM6MCM2CCNA2MCM10MCM3


PTTG1MYBL2

CKS1BTYMSE2F1

DPTRECKDCN

PLAUMMP11CTSBMMP2

MMP1LAMB1TIMP2


STAT1HLA-FHLA-CHLA-BHLA-DPA1HLA-EHLA-DMB

MX1IFIT3IRF1

IFIT5

05

1015 fo

llow

-up

time

(yea

r)

censoredevent

6

2.4 Biological annotation of the modules

The genes were divided into groups according to the module and the direction of the correlation (i.e., thosecorrelated and anticorrelated with ESR1 were grouped separately; denoted by ESR1+ and ESR1-). OnlyESR1 and AURKA modules have negatively correlated genes.

Each of these grouped was compared against the genesets in GO ontology database (based on annotationin Entrez Gene database tables from ftp://ftp.ncbi.nih.gov/gene/; version 21 January 2007) and Molecu-lar Signature Database (MSigDB) version 2 from http://www.broad.mit.edu/gsea/msigdb/msigdb index.

html, Fisher’s exact test was used to score the association between the modules and a gene set. The top-ranking genesets were shown in the file go.txt and msigdb.txt. The tables are in tab-delimited text formatthat can be opened from spreadsheet programs. The files contain Fisher’s exact test p-values, odd-ratio ofenrichment, geneset identifiers, and brief description.

7

3 Module score distributions

3.1 Dot histograms

ESR1 module ERBB2 module AURKA module PLAU module STAT1 module

NKI -1 0 estrogen score

-1 0 1 2 ERBB2-amplification score

-1 -0.5 0 0.5 1 proliferation score

-1 0 1 invasion score

-1 0 1 immune-response score

EMC

-1 0 1 -1 0 1 2 -1 0 1 -2 -1 0 1 -1 0 1 2

UPP

-1 -0.5 0 0.5 -1 0 1 2 -0.5 0 0.5 1 -1 0 1 -1 0 1

STOCK

-1 -0.5 0 0.5 1 -1 0 1 2 -0.5 0 0.5 1 -1 0 1 -1 0 1

DUKE

-0.5 0 0.5 0 1 -0.5 -0.25 0 0.25 0.5 -0.5 0 0.5 1 -0.5 0 0.5 1 1.5

UCSF

-0.5 0 0.5 -0.5 0 0.5 1 1.5 -1 -0.5 0 0.5 1 -0.5 0 0.5 -0.5 0 0.5 1

UNC

-1 -0.5 0 0.5 1 0 1 2 -1 -0.5 0 0.5 1 -1 0 1 -1 0 1 2

NCH

-0.5 0 0.5 -0.5 0 0.5 1 -0.5 0 0.5 -1 -0.5 0 0.5 1 -1 0 1

STNO

-1 0 1 -1 0 1 2 -1 0 1 -1 0 1 -1 -0.5 0 0.5 1JRH1

-0.5 0 0.5 0 0.5 1 -0.25 0 0.25 -0.5 0 0.5 -0.5 0 0.5JRH2

-1 -0.5 0 0.5 0 1 2 -1 -0.5 0 0.5 1 -1 0 1 -0.5 0 0.5 1MGH

-1 -0.5 0 0.5 0 1 -0.5 0 0.5 -1 -0.5 0 0.5 1 -0.5 0 0.5 1

expO

-1 0 1 0 2 -1 0 1 -2 -1 0 1 -1 0 1 2TGIF1

-1 -0.5 0 0.5 -0.5 0 0.5 1 -0.5 0 0.5 -0.5 0 0.5 -1 -0.5 0 0.5 1BWH

-1 0 1 2 0 2 -2 -1 0 1 -1 0 1 -2 -1 0 1 2

TRANSBIG

-2.5 0 2.5 0 2 -2.5 0 2.5 -5 -2.5 0 2.5 -2.5 0 2.5 5

HPAZ-2.5 0 2.5 -2.5 0 2.5 5 -2 0 2 -5 -2.5 0 2.5

EMC2-2.5 0 2.5 5 -0.5 0 0.5 -2 -1 0 1 2 -5 -2.5 0

Note In datasets TRANSBIG, EMC2 and HPAZ (produced on custom diagnostic platforms), few genes werefound for modules other than proliferation (AURKA), and therefore the module score might be unreliable.None can be found for STAT1-module for EMC2 and HPAZ.

8

3.2 Tests of bimodality

Log likelihood ratio between two- and one-component Gaussian mixture models were used as the test statis-tics, (as suggested in McLachlan and Peel [2000] Finite Mixture Modules, Wiley, New York). However, theparametric bootstrap null distribution was generated from uniform distribution instead of unimodal normaldistribution (following Hartigan [1985] “The dip test of unimodality.” Ann. Statist. 13:30–84). This wasdone because the p-values based on unimodal-normal null distribution were too sensitive to non-normal, butstill unimodal, densities.

---------+-------------------------------------------------+

| p-values of bimodality test of module scores |

+---------+---------+---------+---------+---------+

dataset | ESR1 | ERBB2 | AURKA | PLAU | STAT1 |

---------+---------+---------+---------+---------+---------+

NKI | 0.001 * | 0.001 * | 0.560 | 0.990 | 0.068 |

EMC | 0.001 * | 0.001 * | 0.438 | 0.996 | 0.064 |

UPP | 0.002 * | 0.001 * | 0.047 | 0.061 | 0.061 |

STOCK | 0.001 * | 0.001 * | 0.083 | 0.083 | 0.083 |

DUKE | 0.069 | 0.001 * | 0.630 | 0.082 | 0.078 |

UCSF | 0.003 * | 0.001 * | 0.055 | 0.473 | 0.054 |

UNC | 0.005 * | 0.001 * | 0.146 | 0.087 | 0.085 |

NCH | 0.040 | 0.047 | 0.097 | 0.295 | 0.083 |

STNO | 0.689 | 0.001 * | 0.412 | 0.573 | 0.571 |

JRH1 | 0.113 | 0.003 * | 0.724 | 0.804 | 0.731 |

JRH2 | 0.003 * | 0.001 * | 0.119 | 0.531 | 0.010 * |

MGH | 0.064 | 0.076 | 0.929 | 0.965 | 0.070 |

expO | 0.021 | 0.001 * | 0.673 | 0.574 | 0.481 |

TGIF1 | 0.877 | 0.001 * | 0.088 | 0.875 | 0.919 |

BWH | 0.927 | 0.001 * | 0.020 | 0.926 | 0.889 |

TRANSBIG | 0.001 * | 0.001 * | 0.715 | 0.108 | 0.072 |

EMC2 | 0.692 | 0.081 | 0.753 | 0.001 * | nan |

HPAZ | 0.108 | 0.001 * | 0.109 | 0.097 | nan |

---------+---------+---------+---------+---------+---------+

* p <= 0.01

9

4 Three subtypes

4.1 Scatter plots of ERBB2 vs ESR1 module scores

EMC UNC NKI STOCK UPP

1

2

3-1

0

1

2

ER

BB

2-am

plifi

catio

n s

core

-1 0 1estrogen score

1

2

3-1

0

1

2

-1 -0.5 0 0.5 1estrogen score

1

2

3-1

0

1

2

-1.5 -1 -0.5 0 0.5 1estrogen score

1

2

3-1

0

1

2


1

2

3-1

0

1

2

-1 -0.5 0 0.5estrogen score

-1.5

-1

-0.5

0

0.5

1

prol

ifera

tion

sco

re

subtype1 2 3

-1

-0.5

0

0.5

1

subtype1 2 3 N

-1

-0.5

0

0.5

1

subtype1 2 3

-0.5

0

0.5

1

subtype1 2 3

-0.5

0

0.5

1

subtype1 2 3

NCH DUKE UCSF STNO JRH1

1

2

3-1

-0.5

0

0.5

1

ER

BB

2-am

plifi

catio

n s

core


1

2

3

0

1

2

17q-

ampl

ifica

tion

sco

re


1

2

3-0.5

0

0.5

1

1.5

ER

BB

2-am

plifi

catio

n s

core


1

2

3-1

0

1

2


1

2

3-0.5

0

0.5

1

ER

BB

2-am

plifi

catio

n s

core

-0.5-0.25 0 0.25 0.5 0.75estrogen score

-0.5

-0.25

0

0.25

0.5

0.75

prol

ifera

tion

sco

re

subtype1 2 3

-0.5

-0.25

0

0.25

0.5

prol

ifera

tion

sco

re

subtype1 2 3

-1

-0.5

0

0.5

1

prol

ifera

tion

sco

re

subtype1 2 3 N

-1

-0.5

0

0.5

1

subtype1 2 3 N

-0.2

0

0.2 pr

olife

ratio

n s

core

subtype1 2 3

10

JRH2 expO TGIF1 BWH

1

2

3-1

0

1

2

ER

BB

2-am

plifi

catio

n s

core


1

2

3-1

0

1

2

3

ER

BB

2-am

plifi

catio

n s

core


1

2

3-0.5

0

0.5

1

1.5

ER

BB

2-am

plifi

catio

n s

core


1

2

3-1

0

1

2

3

ER

BB

2-am

plifi

catio

n s

core

-1 0 1 2estrogen score

-1

-0.5

0

0.5

1

prol

ifera

tion

sco

re

subtype1 2 3

-1.5

-1

-0.5

0

0.5

1

1.5

prol

ifera

tion

sco

re

subtype1 2 3

-0.75

-0.5

-0.25

0

0.25

0.5

prol

ifera

tion

sco

re

subtype1 2 3

-2

-1

0

1

prol

ifera

tion

sco

re

subtype1 2 3 N

MGH TRANSBIG HPAZ EMC2

1

2

3-1

-0.5

0

0.5

1

1.5

ER

BB

2-am

plifi

catio

n s

core


1

2

3-1

0

1

2

3

ER

BB

2-am

plifi

catio

n s

core

-2.5 0 2.5estrogen score

1

2

3-2.5

0

2.5

5

ER

BB

2-am

plifi

catio

n s

core

-2.5 0 2.5estrogen score

12

3

-1

-0.5

0

0.5

ER

BB

2-am

plifi

catio

n s

core

-2.5 0 2.5 5estrogen score

-0.5

-0.25

0

0.25

0.5

0.75

prol

ifera

tion

sco

re

subtype1 2 3

-3

-2

-1

0

1

2

3

prol

ifera

tion

sco

re

subtype1 2 3

-2

-1

0

1

2

prol

ifera

tion

sco

re

subtype1 2 3

Notes

1. Dataset BWH contains entirely of high-grade tumors according to pathological data. It does not havepatient outcome data and therefore the difficulty in deciding high- or low-proliferation does not affectsurvival analysis.

2. Dataset MGH contains only one ER-negative, 3 ERBB2+ and 3 low-grade tumors (out of 60) accordingto pathological data.

3. Dataset EMC2 does not have enough genes to reliably determine the ER and ERBB2 module scores.The clusters can not be fitted well. In subsequent analysis, the pathological ER-status are used to assignthe subtype (ER+ is considered type 3, ER- is type 1).

11

4.2 Tests of trimodality

Log likelihood ratio between k- and (k−1)-component Gaussian mixture models were used as the test statistics,(as suggested in McLachlan and Peel [2000] Finite Mixture Modules, Wiley, New York). Parametric bootstrapwere used to general the null distribution, using the the parameters from the fitted null model.

---------+-------------------+

| p-values of tests|

| for the number |

| of components |

---------+---------+---------+

dataset | 3 vs 2 | 4 vs 3 |

---------+---------+---------+

NKI | 0.001 * | 0.978 |

EMC | 0.001 * | 0.024 |

UPP | 0.001 * | 0.325 |

STOCK | 0.001 * | 0.014 |

DUKE | 0.002 * | 0.007 * |

UCSF | 0.001 * | 0.012 |

UNC | 0.001 * | 0.054 |

NCH | 0.001 * | 0.589 |

STNO | 0.001 * | 0.026 |

JRH1 | 0.002 * | 0.030 |

JRH2 | 0.002 * | 0.088 |

MGH | 0.932 | 0.671 |

expO | 0.001 * | 0.002 * |

TGIF1 | 0.001 * | 0.268 |

BWH | 0.001 * | 0.025 |

TRANSBIG | 0.001 * | 0.035 |

EMC2 | 0.091 | 0.007 * |

HPAZ | 0.001 * | 0.905 |

---------+---------+---------+

* p <= 0.01

12

5 Additional survival analyses

5.1 Three subtypes

total 6311890 1365 1010 584

type 1 129361 219 172 88type 2 110224 119 87 57type 3 3921305 1027 751 439

type 1

type 2

type 3

0

20

40

60

80

100

rela

pse-

free

sur

viva

l (%

)


follow-up: 0 3 6 9 (year)

total 3061015 821 620 375

type 1 71211 151 125 70type 2 44119 83 60 34type 3 191685 587 435 271

type 1type 2

type 3

0

20

40

60

80

100

met

asta

sis-

free

sur

viva

l (%

)group: number at risk: events


total 4842019 1569 1122 644

type 1 122389 260 189 101type 2 100263 160 99 59type 3 2621367 1149 834 484

type 1type 2

type 3

0

20

40

60

80

100

over

all s

urvi

val (

%)



5.2 Three subtypes combined with high/low proliferation

total 6311890 1365 1010 584

1H 108305 184 147 761L 2156 35 25 122H 100196 99 75 472L 1028 20 12 103H 253654 477 335 2023L 139651 550 416 237

1H

1L2H

2L3H

3L

0

20

40

60

80

100

rela

pse-

free

sur

viva

l (%

)


total 3061015 821 620 375

1H 65192 138 114 651L 619 13 11 52H 38101 69 49 262L 618 14 11 83H 127344 272 188 1213L 64341 315 247 150

1H

1L

2H

2L

3H

3L

0

20

40

60

80

100

met

asta

sis-

free

sur

viva

l (%

)


total 4842019 1569 1122 644

1H 104324 216 162 861L 1865 44 27 152H 94231 138 83 472L 632 22 16 123H 189686 559 385 2293L 73681 590 449 255

1H

1L

2H

2L

3H

3L

0

20

40

60

80

100

over

all s

urvi

val (

%)


5.3 Separate metastasis-free survival and relapse-free survival

Figure 4c,d use metastasis-free survival, if available, or relapse-free survival otherwise. Below are the resultsfor the two types of endpoints separately.

total 3061015 821 620 375

1 71211 151 125 702 44119 83 60 343H 127344 272 188 1213L 64341 315 247 150

123H

3L

0

20

40

60

80

100

met

asta

sis-

free

sur

viva

l (%

)


poor(1+2+3H) vs. good(3L):5-year: 67% vs 89%HR = 2.28 [1.73, 3.01]


DUKEEMCEMC2HPAZJRH1JRH2MGHNKISTNOSTOCKUCSFUNCUPPNCHTRANSBIGTotal

0 25 50 75 100

5-year MFS (%)

poor: 1+2+3H good: 3L

hazard ratio

1 3 10 30

total 6311890 1365 1010 584

1 129361 219 172 882 110224 119 87 573H 253654 477 335 2023L 139651 550 416 237

1

23H

3L

0

20

40

60

80

100

rela

pse-

free

sur

viva

l (%

)


poor(1+2+3H) vs. good(3L):5-year: 64% vs 84%HR = 2.22 [1.84, 2.69]


DUKEEMCEMC2HPAZJRH1JRH2MGHNKISTNOSTOCKUCSFUNCUPPNCHTRANSBIGTotal

0 25 50 75 100

5-year RFS (%)

poor: 1+2+3H good: 3L

hazard ratio

1 3 10 30

13

6 Prognostic value of ER-status within type-2 (ERBB2+) tumors

Because pathological ER status is not available for all patients, here ER-status is based on estrogen scores(using as cutoff the midpoint between the cluster center of type 1 and type 3).

total 110224 119 87 57

2,ER+ 66133 78 56 302,ER- 4491 41 31 27

2,ER+2,ER-

0

20

40

60

80

100

rela

pse-

free

sur

viva

l (%

)


total 44119 83 60 34

2,ER+ 2879 57 41 232,ER- 1640 26 19 11

2,ER+2,ER-

0

20

40

60

80

100

met

asta

sis-

free

sur

viva

l (%

)follow-up: 0 3 6 9 (year)

total 100263 160 99 59

2,ER+ 57152 101 66 362,ER- 43111 59 33 23

2,ER+2,ER-

0

20

40

60

80

100

over

all s

urvi

val (

%)


7 Gene-by-gene survival analysis

7.1 Gene Tables

The top-ranking genes identified by meta-analytical gene-by-gene Cox regression are are listed in the filezsurvival.txt, in a tab-delimited text file. For convenient browsing, open in spreadsheet program (e.g. MSExcel) and format the column widths to fit the contents automatically.

7.2 Agreement of gene associations with different survival endpoints

Here we show that the choice of endpoints (OS: overall survival, MFS: metastasis-free survival, RFS: relapse-free survival) do not substantially affect gene-by-gene survival analysis. The lower Z-scores of MFS is duemainly to smaller sample size (MFS data are available only in 5 datasets, see Figure 1a of the main text).

-10 -5 0 5 10

-10

-5

0

5

10

Z-OS

Z-M

FS

corr = 0.60

-10 -5 0 5 10

-10

-5

0

5

10

Z-OS

Z-R

FS

corr = 0.87

-10 -5 0 5 10

-10

-5

0

5

10

Z-MFS

Z-R

FS

corr = 0.67

14

8 Forest plots of signature performance for all survival endpoints

NKI

EMC

NCH

UPP

STOCK

DUKE

UNC

STNO

UCSF

JRH1

JRH2

MGH

TRANSBIG

HPAZ

EMC2

Total for genomic datasets:

ZCOX

NKI

EMC

NCH

UPP

STOCK

DUKE

UNC

STNO

UCSF

JRH1

JRH2

MGH

TRANSBIG

HPAZ

EMC2


CCYC

NKI

EMC

NCH

UPP

STOCK

DUKE

UNC

STNO

UCSF

JRH1

JRH2

MGH

TRANSBIG

HPAZ

EMC2


GGI-128

NKI

EMC

NCH

UPP

STOCK

DUKE

UNC

STNO

UCSF

JRH1

JRH2

MGH

TRANSBIG

HPAZ

EMC2


CSR

NKI

EMC

NCH

UPP

STOCK

DUKE

UNC

STNO

UCSF

JRH1

JRH2

MGH

TRANSBIG

HPAZ

EMC2


p53-32

NKI

EMC

NCH

UPP

STOCK

DUKE

UNC

STNO

UCSF

JRH1

JRH2

MGH

TRANSBIG

HPAZ

EMC2


CON-52

NKI

EMC

NCH

UPP

STOCK

DUKE

UNC

STNO

UCSF

JRH1

JRH2

MGH

TRANSBIG

HPAZ

EMC2


NCH-70

NKI

EMC

NCH

UPP

STOCK

DUKE

UNC

STNO

UCSF

JRH1

JRH2

MGH

TRANSBIG

HPAZ

EMC2


EMC-76

NKI

EMC

NCH

UPP

STOCK

DUKE

UNC

STNO

UCSF

JRH1

JRH2

MGH

TRANSBIG

HPAZ

EMC2


NKI-70

NKI

EMC

NCH

UPP

STOCK

DUKE

UNC

STNO

UCSF

JRH1

JRH2

MGH

TRANSBIG

HPAZ

EMC2

Total for genomic datasets:ONC-16

50 60 70 80 90 1005-year survival (%)of low-risk group

hazard ratio.5 1 2 4 8 16

NKI

EMC

NCH

UPP

STOCK

DUKE

UNC

STNO

UCSF

JRH1

JRH2

MGH

TRANSBIG

HPAZ

EMC2


ZCOX

NKI

EMC

NCH

UPP

STOCK

DUKE

UNC

STNO

UCSF

JRH1

JRH2

MGH

TRANSBIG

HPAZ

EMC2


CCYC

NKI

EMC

NCH

UPP

STOCK

DUKE

UNC

STNO

UCSF

JRH1

JRH2

MGH

TRANSBIG

HPAZ

EMC2


GGI-128

NKI

EMC

NCH

UPP

STOCK

DUKE

UNC

STNO

UCSF

JRH1

JRH2

MGH

TRANSBIG

HPAZ

EMC2


CSR

NKI

EMC

NCH

UPP

STOCK

DUKE

UNC

STNO

UCSF

JRH1

JRH2

MGH

TRANSBIG

HPAZ

EMC2


p53-32

NKI

EMC

NCH

UPP

STOCK

DUKE

UNC

STNO

UCSF

JRH1

JRH2

MGH

TRANSBIG

HPAZ

EMC2


CON-52

NKI

EMC

NCH

UPP

STOCK

DUKE

UNC

STNO

UCSF

JRH1

JRH2

MGH

TRANSBIG

HPAZ

EMC2


NCH-70

NKI

EMC

NCH

UPP

STOCK

DUKE

UNC

STNO

UCSF

JRH1

JRH2

MGH

TRANSBIG

HPAZ

EMC2


EMC-76

NKI

EMC

NCH

UPP

STOCK

DUKE

UNC

STNO

UCSF

JRH1

JRH2

MGH

TRANSBIG

HPAZ

EMC2


NKI-70

NKI

EMC

NCH

UPP

STOCK

DUKE

UNC

STNO

UCSF

JRH1

JRH2

MGH

TRANSBIG

HPAZ

EMC2




NKI

EMC

NCH

UPP

STOCK

DUKE

UNC

STNO

UCSF

JRH1

JRH2

MGH

TRANSBIG

HPAZ

EMC2


ZCOX

NKI

EMC

NCH

UPP

STOCK

DUKE

UNC

STNO

UCSF

JRH1

JRH2

MGH

TRANSBIG

HPAZ

EMC2


CCYC

NKI

EMC

NCH

UPP

STOCK

DUKE

UNC

STNO

UCSF

JRH1

JRH2

MGH

TRANSBIG

HPAZ

EMC2


GGI-128

NKI

EMC

NCH

UPP

STOCK

DUKE

UNC

STNO

UCSF

JRH1

JRH2

MGH

TRANSBIG

HPAZ

EMC2


CSR

NKI

EMC

NCH

UPP

STOCK

DUKE

UNC

STNO

UCSF

JRH1

JRH2

MGH

TRANSBIG

HPAZ

EMC2


p53-32

NKI

EMC

NCH

UPP

STOCK

DUKE

UNC

STNO

UCSF

JRH1

JRH2

MGH

TRANSBIG

HPAZ

EMC2


CON-52

NKI

EMC

NCH

UPP

STOCK

DUKE

UNC

STNO

UCSF

JRH1

JRH2

MGH

TRANSBIG

HPAZ

EMC2


NCH-70

NKI

EMC

NCH

UPP

STOCK

DUKE

UNC

STNO

UCSF

JRH1

JRH2

MGH

TRANSBIG

HPAZ

EMC2


EMC-76

NKI

EMC

NCH

UPP

STOCK

DUKE

UNC

STNO

UCSF

JRH1

JRH2

MGH

TRANSBIG

HPAZ

EMC2


NKI-70

NKI

EMC

NCH

UPP

STOCK

DUKE

UNC

STNO

UCSF

JRH1

JRH2

MGH

TRANSBIG

HPAZ

EMC2




Relapse-free Survival Metastasis-free Survival Overall Survival

15

9 Concordance in risk classifications

9.1 Tables of pairwise concordance

Whole signatures:

NKI-70 ONC-16 EMC-76 NCH-70 CON-52 p53-32 GGI-128 CSR CCYC ZCOX

NKI-70 * 79.97 69.37 77.54 81.02 76.07 82.18 75.12 81.34 83.66

ONC-16 79.97 * 70.22 82.50 83.87 74.06 86.51 79.02 84.08 86.72

EMC-76 69.37 70.22 * 70.11 73.59 61.04 73.38 72.11 73.27 73.48

NCH-70 77.54 82.50 70.11 * 85.45 67.84 86.29 82.39 83.13 85.66

CON-52 81.02 83.87 73.59 85.45 * 69.11 91.04 82.29 88.82 91.14

p53-32 76.07 74.06 61.04 67.84 69.11 * 71.64 67.21 69.74 72.69

GGI-128 82.18 86.51 73.38 86.29 91.04 71.64 * 83.34 92.62 93.46

CSR 75.12 79.02 72.11 82.39 82.29 67.21 83.34 * 82.81 83.87

CCYC 81.34 84.08 73.27 83.13 88.82 69.74 92.62 82.81 * 91.25

ZCOX 83.66 86.72 73.48 85.66 91.14 72.69 93.46 83.87 91.25 *

Average pairwise concordance: 79.4% (84.5% if EMC-76 and p53-32 are excluded)

Proliferation-gene partial signatures:

ONC-16 NKI-70 EMC-76 NCH-70 CON-52 p53-32 CSR GGI-128 CCYC ZCOX

ONC-16 * 84.71 82.39 85.35 89.46 84.92 84.40 90.72 89.77 90.20

NKI-70 84.71 * 83.24 83.45 88.09 80.50 83.34 88.51 88.72 89.67

EMC-76 82.39 83.24 * 84.61 86.93 78.39 83.13 86.29 86.72 85.56

NCH-70 85.35 83.45 84.61 * 87.45 81.13 87.66 87.56 86.51 87.24

CON-52 89.46 88.09 86.93 87.45 * 83.66 86.72 93.15 91.78 92.30

p53-32 84.92 80.50 78.39 81.13 83.66 * 81.34 85.98 84.29 84.40

CSR 84.40 83.34 83.13 87.66 86.72 81.34 * 86.61 86.19 87.45

GGI-128 90.72 88.51 86.29 87.56 93.15 85.98 86.61 * 94.83 94.52

CCYC 89.77 88.72 86.72 86.51 91.78 84.29 86.19 94.83 * 93.67

ZCOX 90.20 89.67 85.56 87.24 92.30 84.40 87.45 94.52 93.67 *

Average pairwise concordance: 86.7%

Non-proliferation-gene partial signatures:

NKI-70 ONC-16 EMC-76 NCH-70 CON-52 p53-32 GGI-128 CSR CCYC ZCOX

NKI-70 * 67.00 59.30 65.21 62.78 69.43 69.30 65.42 62.05 73.22

ONC-16 67.00 * 58.36 63.73 60.15 68.90 65.29 62.68 59.20 70.48

EMC-76 59.30 58.36 * 59.52 60.46 54.98 61.85 64.47 56.88 57.62

NCH-70 65.21 63.73 59.52 * 66.16 58.78 64.18 65.74 56.04 65.00

CON-52 62.78 60.15 60.46 66.16 * 58.99 61.40 60.46 58.88 62.99

p53-32 69.43 68.90 54.98 58.78 58.99 * 65.07 61.52 58.67 69.74

GGI-128 69.30 65.29 61.85 64.18 61.40 65.07 * 65.74 57.40 69.97

CSR 65.42 62.68 64.47 65.74 60.46 61.52 65.74 * 62.05 67.53

CCYC 62.05 59.20 56.88 56.04 58.88 58.67 57.40 62.05 * 63.42

ZCOX 73.22 70.48 57.62 65.00 62.99 69.74 69.97 67.53 63.42 *

Average pairwise concordance: 63.1%

16

9.2 Combined prediction by pairs of signatures

Every distinct pair of signatures were combined in the following way:

1. Their continuous prediction scores were used as explanatory variables in Cox regression (stratified bydataset).

2. The fitted coefficients were used as weights in linear combination of the two prediction scores to producethe combined prediction score

3. The combined scores were used to rank patients. 33% in each dataset were classified as low-risk.

The results for the 45 pairs are shown below as the black bars to the right of the individual signatures. Theidentity of each combination is not shown (the ordering from left to right is [ONC-16 + NKI-70], [ONC-16 +EMC-76p], and so on), because we only want to demonstrate that their hazard ratios and survivals are similarand within the confidence interval of the some individual signatures. Note that the combined performance maybe slightly biased upward because the weights are estimated from the same data. Even so, the improvementis not clinically substantial.

0 10 20 30 40 50

0.5

1.0

2.0

5.0

Haz

ard

Rat

io

ONC.16NKI.70EMC.76pNCH.70

CON.52p53.32GGI.128CSR

CCYCZCOXcombinations

0 10 20 30 40 50

6070

8090

100

Sur

viva

l at 5

yea

rs

ONC.16NKI.70EMC.76pNCH.70

CON.52p53.32GGI.128CSR

CCYCZCOXcombinations

17

9.3 Patient classifications on proliferation-vs-subtype plots

All vertical axes correspond to proliferation score.

NKI EMC NCH UPP STOCK

ONC-16

-1

-0.5

0

0.5

1

subtype1 2 3

-1.5

-1

-0.5

0

0.5

1

subtype1 2 3

-0.5

-0.25

0

0.25

0.5

0.75

subtype1 2 3

-0.5

0

0.5

1

subtype1 2 3

-0.5

0

0.5

1

subtype1 2 3

NKI-70

-1

-0.5

0

0.5

1

subtype1 2 3

-1.5

-1

-0.5

0

0.5

1

subtype1 2 3

-0.5

-0.25

0

0.25

0.5

0.75

subtype1 2 3

-0.5

0

0.5

1

subtype1 2 3

-0.5

0

0.5

1

subtype1 2 3

EMC-76

-1

-0.5

0

0.5

1

subtype1 2 3

-1.5

-1

-0.5

0

0.5

1

subtype1 2 3

-0.5

-0.25

0

0.25

0.5

0.75

subtype1 2 3

-0.5

0

0.5

1

subtype1 2 3

-0.5

0

0.5

1

subtype1 2 3

NCH-70

-1

-0.5

0

0.5

1

subtype1 2 3

-1.5

-1

-0.5

0

0.5

1

subtype1 2 3

-0.5

-0.25

0

0.25

0.5

0.75

subtype1 2 3

-0.5

0

0.5

1

subtype1 2 3

-0.5

0

0.5

1

subtype1 2 3

CON-52

-1

-0.5

0

0.5

1

subtype1 2 3

-1.5

-1

-0.5

0

0.5

1

subtype1 2 3

-0.5

-0.25

0

0.25

0.5

0.75

subtype1 2 3

-0.5

0

0.5

1

subtype1 2 3

-0.5

0

0.5

1

subtype1 2 3

18


NKI EMC NCH UPP STOCK

p53-32

-1

-0.5

0

0.5

1

subtype1 2 3

-1.5

-1

-0.5

0

0.5

1

subtype1 2 3

-0.5

-0.25

0

0.25

0.5

0.75

subtype1 2 3

-0.5

0

0.5

1

subtype1 2 3

-0.5

0

0.5

1

subtype1 2 3

CSR

-1

-0.5

0

0.5

1

subtype1 2 3

-1.5

-1

-0.5

0

0.5

1

subtype1 2 3

-0.5

-0.25

0

0.25

0.5

0.75

subtype1 2 3

-0.5

0

0.5

1

subtype1 2 3

-0.5

0

0.5

1

subtype1 2 3

GGI-128

-1

-0.5

0

0.5

1

subtype1 2 3

-1.5

-1

-0.5

0

0.5

1

subtype1 2 3

-0.5

-0.25

0

0.25

0.5

0.75

subtype1 2 3

-0.5

0

0.5

1

subtype1 2 3

-0.5

0

0.5

1

subtype1 2 3

CCYC

-1

-0.5

0

0.5

1

subtype1 2 3

-1.5

-1

-0.5

0

0.5

1

subtype1 2 3

-0.5

-0.25

0

0.25

0.5

0.75

subtype1 2 3

-0.5

0

0.5

1

subtype1 2 3

-0.5

0

0.5

1

subtype1 2 3

ZCOX

-1

-0.5

0

0.5

1

subtype1 2 3

-1.5

-1

-0.5

0

0.5

1

subtype1 2 3

-0.5

-0.25

0

0.25

0.5

0.75

subtype1 2 3

-0.5

0

0.5

1

subtype1 2 3

-0.5

0

0.5

1

subtype1 2 3

19


DUKE UNC STNO UCSF JRH1

ONC-16

-0.5

-0.25

0

0.25

0.5

subtype1 2 3

-1

-0.5

0

0.5

1

subtype1 2 3 N

-1

-0.5

0

0.5

1

subtype1 2 3 N

-0.5

-0.25

0

0.25

0.5

subtype1 2 3 N

-0.2

0

0.2

subtype1 2 3

NKI-70

-0.5

-0.25

0

0.25

0.5

subtype1 2 3

-1

-0.5

0

0.5

1

subtype1 2 3 N

-1

-0.5

0

0.5

1

subtype1 2 3 N

-0.5

-0.25

0

0.25

0.5

subtype1 2 3 N

-0.2

0

0.2

subtype1 2 3

EMC-76

-0.5

-0.25

0

0.25

0.5

subtype1 2 3

-1

-0.5

0

0.5

1

subtype1 2 3 N

-1

-0.5

0

0.5

1

subtype1 2 3 N

-0.5

-0.25

0

0.25

0.5

subtype1 2 3 N

-0.2

0

0.2

subtype1 2 3

NCH-70

-0.5

-0.25

0

0.25

0.5

subtype1 2 3

-1

-0.5

0

0.5

1

subtype1 2 3 N

-1

-0.5

0

0.5

1

subtype1 2 3 N

-0.5

-0.25

0

0.25

0.5

subtype1 2 3 N

-0.2

0

0.2

subtype1 2 3

CON-52

-0.5

-0.25

0

0.25

0.5

subtype1 2 3

-1

-0.5

0

0.5

1

subtype1 2 3 N

-1

-0.5

0

0.5

1

subtype1 2 3 N

-0.5

-0.25

0

0.25

0.5

subtype1 2 3 N

-0.2

0

0.2

subtype1 2 3

20


DUKE UNC STNO UCSF JRH1

p53-32

-0.5

-0.25

0

0.25

0.5

subtype1 2 3

-1

-0.5

0

0.5

1

subtype1 2 3 N

-1

-0.5

0

0.5

1

subtype1 2 3 N

-0.5

-0.25

0

0.25

0.5

subtype1 2 3 N

-0.2

0

0.2

subtype1 2 3

CSR

-0.5

-0.25

0

0.25

0.5

subtype1 2 3

-1

-0.5

0

0.5

1

subtype1 2 3 N

-1

-0.5

0

0.5

1

subtype1 2 3 N

-0.5

-0.25

0

0.25

0.5

subtype1 2 3 N

-0.2

0

0.2

subtype1 2 3

GGI-128

-0.5

-0.25

0

0.25

0.5

subtype1 2 3

-1

-0.5

0

0.5

1

subtype1 2 3 N

-1

-0.5

0

0.5

1

subtype1 2 3 N

-0.5

-0.25

0

0.25

0.5

subtype1 2 3 N

-0.2

0

0.2

subtype1 2 3

CCYC

-0.5

-0.25

0

0.25

0.5

subtype1 2 3

-1

-0.5

0

0.5

1

subtype1 2 3 N

-1

-0.5

0

0.5

1

subtype1 2 3 N

-0.5

-0.25

0

0.25

0.5

subtype1 2 3 N

-0.2

0

0.2

subtype1 2 3

ZCOX

-0.5

-0.25

0

0.25

0.5

subtype1 2 3

-1

-0.5

0

0.5

1

subtype1 2 3 N

-1

-0.5

0

0.5

1

subtype1 2 3 N

-0.5

-0.25

0

0.25

0.5

subtype1 2 3 N

-0.2

0

0.2

subtype1 2 3

21


JRH2 MGH TRANSBIG HPAZ EMC2

ONC-16

-1

-0.5

0

0.5

1

subtype1 2 3

-0.5

-0.25

0

0.25

0.5

0.75

subtype1 2 3

-3

-2

-1

0

1

2

3

subtype1 2 3

-2

-1

0

1

2

subtype1 2 3

NKI-70

-1

-0.5

0

0.5

1

subtype1 2 3

-0.5

-0.25

0

0.25

0.5

0.75

subtype1 2 3

-3

-2

-1

0

1

2

3

subtype1 2 3

-2

-1

0

1

2

subtype1 2 3

-2

-1

0

1

2

subtype1 2

EMC-76

-1

-0.5

0

0.5

1

subtype1 2 3

-0.5

-0.25

0

0.25

0.5

0.75

subtype1 2 3

-2

-1

0

1

2

subtype1 2

NCH-70

-1

-0.5

0

0.5

1

subtype1 2 3

-0.5

-0.25

0

0.25

0.5

0.75

subtype1 2 3

-3

-2

-1

0

1

2

3

subtype1 2 3

-2

-1

0

1

2

subtype1 2 3

CON-52

-1

-0.5

0

0.5

1

subtype1 2 3

-0.5

-0.25

0

0.25

0.5

0.75

subtype1 2 3

-3

-2

-1

0

1

2

3

subtype1 2 3

-2

-1

0

1

2

subtype1 2 3

-2

-1

0

1

2

subtype1 2

22


JRH2 MGH TRANSBIG HPAZ EMC2

p53-32

-1

-0.5

0

0.5

1

subtype1 2 3

-0.5

-0.25

0

0.25

0.5

0.75

subtype1 2 3

CSR

-1

-0.5

0

0.5

1

subtype1 2 3

-0.5

-0.25

0

0.25

0.5

0.75

subtype1 2 3

-3

-2

-1

0

1

2

3

subtype1 2 3

-2

-1

0

1

2

subtype1 2 3

-2

-1

0

1

2

subtype1 2

GGI-128

-1

-0.5

0

0.5

1

subtype1 2 3

-0.5

-0.25

0

0.25

0.5

0.75

subtype1 2 3

-3

-2

-1

0

1

2

3

subtype1 2 3

-2

-1

0

1

2

subtype1 2 3

-2

-1

0

1

2

subtype1 2

CCYC

-1

-0.5

0

0.5

1

subtype1 2 3

-0.5

-0.25

0

0.25

0.5

0.75

subtype1 2 3

-3

-2

-1

0

1

2

3

subtype1 2 3

-2

-1

0

1

2

subtype1 2 3

-2

-1

0

1

2

subtype1 2

ZCOX

-1

-0.5

0

0.5

1

subtype1 2 3

-0.5

-0.25

0

0.25

0.5

0.75

subtype1 2 3

-3

-2

-1

0

1

2

3

subtype1 2 3

-2

-1

0

1

2

subtype1 2 3

-2

-1

0

1

2

subtype1 2

23

Documents

Supplementary Results - lausanne.isb-sib.chpwirapat/bcfsib2007-1/supresults.pdf · 1 Survival data 1.1 Data collection and preprocessing Survival endpoints The original studies often