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Supporting Information

Patient population. 130 fresh frozen, surgically resected lung SCC samples from 129

individual patients (LS-71 and LS-136 were duplicate samples from different areas of the

same tumor) from all stages of squamous cell lung carcinoma were evaluated in this

study. These samples were collected from patients from the University of Michigan

Hospital between October 1991 and July 2002 with patient consent and Institutional

Review Board approval. Portions of the resected lung carcinomas were sectioned and

evaluated by the study pathologist by routine H&E staining. Samples chosen for analysis

contained greater than 70% tumor cells. Seventy-three patients had stage I SCC. Follow-

up data was available for all patients. The mean patient age was 68 10 (range 42-91)

with approximately 45% of patients 70 years or older. Supplementary Table 1 lists the

clinical data associated with all lung SCC samples used in this study. The 86 lung

adenocarcinoma samples have previously been described (1). Table 1 shows the clinical

information associated with both adenocarcinoma and SCC samples used for identifying

and testing the prognostic signatures. Patients were censored from statistical analysis if

they were alive but had less than three years of clinical follow up. The independent

NSCLC dataset comprised 36 lung adenocarcinoma (27 Stage I) and 36 lung SCC

samples (25 Stage I) with at least 3 years follow up (Gene Omnibus dataset: GSE3141)

(2).

Microarray Analysis. Total RNA was extracted from fresh frozen tissue using the Trizol

method (Gibco BRL). RNA quality was checked using the Agilent Bioanalyzer. The

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mean ribosomal ratio (28s/18s) for all samples was 1.5 (range: 1.0 - 2.1). Four

micrograms of total RNA was amplified, labeled and aRNA was fragmented and

hybridized to the Affymetrix U133A GeneChip as previously described (3). Microarray

data was extracted using the Affymetrix MAS 5 software. Global gene expression was

scaled to an average intensity of 600 units. The data was then normalized using a spline

quantile normalization method. 128 of the 130 microarrays gave good data (% present >

40, scaling factor < 10) while the remaining 2 samples (LS78, LS82) gave acceptable

results (% present > 30, scaling factor < 15). All samples were included in the analyses.

The CEL files of the external validation datasets were downloaded from

http://data.cgt.duke.edu/oncogene.php (2) and the data were extracted using Affymetrix

MAS 5 software and were globally scaled to an average intensity of 600 units. Since the

validation datasets were from external sources on different platforms (U133Plus 2.0),

analysis of variance (ANOVA) was used to normalize the batch effect such as different

sample preparation methods, different RNA extraction methods, different hybridization

protocols, and different scanners. Array data from the duplicate samples LS-71 and LS-

136 was averaged for unsupervised and supervised analyses. The SCC data discussed in

this publication have been deposited in NCBIs Gene Expression Omnibus (GEO,

http://www.ncbi.nlm.nih.gov/geo/) and are accessible through GEO Series accession

number GSE4573.

The 50 probe sets from the adenocarcinoma classifier were identified on the

Affymetrix HU6800 GeneChip (1). For validation of this classifier using the Duke data

set it was necessary to map the HU6800 probe sets to the U133Plus GeneChip. To this

end, probe sets on HU6800 were mapped to U133Plus through the U95 GeneChip based

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on the Affymetrix Array Comparison Spreadsheets (Best Match). For those probe sets

that could not be mapped by the Affymetrix documentation, a BLAST was performed on

the target sequence from HU6800 to identify the corresponding probe sets on U133Plus.

Using this approach 47 of the 50 probe sets were mapped to the U133Plus GeneChip.

Real-Time Quantitative RTPCR. Total RNA samples were normalized by

OD260. Quality testing includedanalysis by capillary electrophoresis using a Bioanalyzer

(Agilent). For aRNA, the RibobeastTM 1- Round Aminoallyl-aRNA amplification kit

(Epicentre) was used. All first-strand cDNA synthesis, second-strand cDNA synthesis, in

vitro transcription of aRNA, DNase treatment, purification and other steps were

performed according to the manufactures protocol. For each sample aRNA was reverse

transcribed into first-stand cDNA and used for real-time quantitative RT-PCR. The first-

strand cDNA synthesis reaction contained, 100 ng of aRNA, 1 L of 50 ng/L T7-

Oligo(dT) primer, 0.25 L of 10 mM dNTPs, 1 L of 5 X SuperscriptTM III Reverse

Transcriptase Buffer, 0.25 L of 200 U/L SuperscriptTM III Reverse Transcriptase

(Invitrogen Corp), 0.25 L of 100 mM DTT and 0.25 L of 0.3 U/ul RNase Inhibitor

(Epicentre) in a total reaction volume of 5 L. Real-time quantitative RTPCR analyses

were performed on the ABI Prism 7900HT sequence detection system (Applied

Biosystems). Eachreaction contained 10 L of 2X TaqMan Universal PCR Master Mix

(AppliedBiosystems), 5 L of cDNA template, and 1ul of 20 X Assays-on-Demand

Gene Expression Assay Mix (AppliedBiosystems) in a total reaction volume of 20 L.

The PCR consisted of an UNG activation step at 50C for 2 min and initial enzyme

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activation step at 95Cfor 10 min, followed by 40 cycles of 95C for 15 sec, 60C

for 1

min.

Statistical Analyses

Univariate Cox proportional hazards regression was used to identify gene

transcripts whose expression levels were correlated to patient disease-free time. To

reduce the effect of multiple testing and to test the robustness of the selected gene

transcripts, the Cox model was performed with bootstrapping of the patients in the

training set. Briefly, 400 bootstrap samples of the training set were constructed, each

containing the same number of patients as the training set but randomly chosen with

replacement. Cox modeling was run on each of the bootstrap samples. A bootstrap score

was created for each gene transcript by averaging the inverses of the bootstrap p-values

with the top and bottom 5% of p-values removed. This score was used to rank the gene

transcripts. To determine the minimum number of gene transcripts used to construct the

signature, combinations of gene expression markers were tested by adding one gene at a

time according to the rank order. For each signature with increasing number of genes,

Receiver Operating Characteristic (ROC) analysis using death within 3 years as the

defining point was performed, in 100 5-fold cross validations, to calculate the average

area under the curve (AUC). The optimal number of gene transcripts was determined

when the average AUC reached plateau.

A maximum 3 years follow-up was employed since the majority of patients who

will relapse in this population will do so within 3 years (4). Also many of these patients

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were aged and death due to non-cancer related illnesses would likely increase after 3

years (4). This rationale was also employed when performing Cox modeling.

Cox score was used to determine each patients risk of survival. For lung SCC, it

was defined as the sum of the linear combination of weighted log2 expression intensity

with the average standardized Cox regression coefficient from 400 bootstrapping samples

as the weight. For lung adenocarcinoma, the Cox score was defined in the previous study

(2). The threshold for risk stratification was determined in another 100 5-fold cross

validations to test a series of cutoffs (percentile of risk index for the patients in the

training set) and the optimal cutoff was chosen to give the minimum overall error rate.

Because the Cox scores were from the two tumor types (i.e. adenocarcinoma and SCC),

they were normalized to have the their corresponding thresholds set at zero and to have

the same variance in order to combine them together to create the final score for each

patient. Patients whose scores were equal to or greater than 0 were classified in the high

risk of survival group while patients whose scores were less than 0 were predicted as the

low risk of survival group. The gene expression signature and the cutoff were validated in

the independent testing set (2). Kaplan-Meier survival plots and log-rank tests were used

to assess the differences in survival of the predicted high and low risk groups. Patients

were censored from statistical analysis if they were alive but had less than three years of

clinical follow up. Sensitivity was defined as the percent of the patients who died within

3 years that were predicted correctly by the gene expression signature, and specificity

was defined as the percent of the patients who survived for at least 3 years that were

predicted correctly by the classifier. All the statistical analyses were performed using S-

Plus 6 software (Insightful, VA).

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Hierarchical clustering was performed with GeneSpring 7.0 (Silicon Genetics) to

identify major clusters of patients and to investigate their association with patient co-

variates. Prior to clustering, genes that had a coefficient of variation (CV) smaller than

0.3 (arbitrarily chosen) were removed to reduce the impact of genes that displayed

minimal change in expression across the dataset. Thus a dataset with 11,101 gene

transcripts was created for this clustering analysis. The signal intensity of each gene

transcript was divided by the median expression level of that gene from all patients.

Samples were clustered using Pearson correlation as measurement of similarity. Genes

were clustered in the same way.

Pathway analysis was performed by first mapping the gene transcripts on the

Affymetrix U133A GeneChip to the Biological Process categories of Gene Ontology

(GO). The categories that had at least 10 genes on the U133A chip were used for

subsequent pathway analyses. Genes that were selected from data analysis were mapped

to the GO Biological Process categories. Then the hypergeometric distribution

probability of the genes was calculated for each category. A category that had a p-value

less than 0.05 and contained at least two genes was considered over-represented in the

selected gene list.

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Supplementary Table 1. Patient sample data.

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Supplementary Table 2. 121 probe sets differentially expressed between two major SCC

clusters.

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Supplementary Table 3. Significantly represented Gene Ontology groups between SCC

subgroups.

GO ID Gene Count GO Class Gene Number on U133A p-value8544 17 epidermal differentiation 56 7.31E-12

6325 3 chromatin architecture 12 2.75E-04

7586 3 digestion 15 7.08E-04

7156 4 homophilic cell adhesion 39 0.0049

7148 3 cell shape and cell size control 28 0.0079

7565 3 pregnancy 28 0.0079

165 2 MAPKKK cascade 15 0.0082

6805 2 xenobiotic metabolism 15 0.0082

7169 3 receptor tyrosine kinase signaling 41 0.0293

6832 2 small molecule transport 29 0.0493

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Supplementary Table 4. 50 lung SCC prognostic probe sets.

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Supplementary Table 5. 47 of 50 lung adenocarcinoma prognostic probe sets that were

used in this study.

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Supplementary Figure 1. Overall survival of patients with SCC lung cancer as stratified

by stage.

Stage I

Stage II

Stage III

months

0 10 20 30

0.0

0

.2

0.4

0.6

0.8

1.0

P= 0.027

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Supplementary Figure 2. Validation of Affymetrix gene expression using Taqman RT-

PCR of 4 genes. NTRK2, FGFR2, KRT13 were randomly chosen among the 121 genes

that significantly differentiate the unsupervised clusters of lung SCC cancers.

NTRK2 - Affy vs TaqMan FGFR2 - Affy vs TaqMan

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

0 5 10 15Taqman dCT (FGFR2-ACTB)

Affylogexpression

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

-5 0 5 10 15Taqman dCT (NTRK2-ACTB)

Affylog

expression

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

-10 -5 0 5 10 15

Taqman dCT (KRT13-ACTB)

Affy

log

expression

KRT13 - Affy vs TaqMan

2.52.7

2.93.13.33.53.73.94.1

0 5 10

VEGF - Affy vs TaqMan

Taqman dCT (VEGF-ACTB)

Affy

logexpression

R = 0.96 R = 0.83

R = 0.71 R = 0.92

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Supplementary Figure 3. IHC of select proteins using SCC lung tissue microarrays.

FGFR2

High

low

Expression

KRT19 KRT17

KRT5

High

low

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Supplementary Figure 4. Identification of a cutoff from the percentile of the risk index inthe training set. Panel A shows the results for the SCC training set and Panel B shows the

results for the adenocarcinoma training set.

0.0 0.2 0.4 0.6 0.8 1.0

0.4

4

0.4

6

0.4

8

0.5

0

0.52

0.5

4

0.5

6

percentile of scores

meanof5-foldcross-valid

ationerrorrate

0.0 0.2 0.40.0 0.2 0.4 0.6 0.8 1.0

0.4

4

0.4

6

0.4

8

0.5

0

0.52

0.5

4

0.5

6

percentile of scores

meanof5-foldcross-valid

ationerrorrate

A

0.0 0.2 0.4 0.6 0.8 1.0

0.4

5

0.5

0

0.5

5

percentile of scores

meanof5-foldcross-valid

ationerrorrate

0.0 0.2 0.40.0 0.2 0.4 0.6 0.8 1.0

0.4

5

0.5

0

0.5

5

percentile of scores

meanof5-foldcross-valid

ationerrorrate

B

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Supplementary Figure 5. Performance of 50-gene adenocarcinoma classifier in Duke samples. Panel A sh

adenocarcinoma samples from Duke University. Panel B shows the Kaplan-Meier analysis using a

training set (1). Panel C shows ROC analysis in 27 stage I samples only and Panel D shows the associate

A B

C D

1-specificity

sensitivity

0.0 0.2 0.4 0.6 0.8 1.0

0.0

0.2

0.4

0.6

0.8

1.0

AUC = 0.83

Overa

llSurvival

Years

0 1 2 30.0

0.2

0.4

0.6

0.8

1.0

log rank P= 0.0008HR (95% CI): 8.33 (1.89-36.6)

Good (n=21)

Poor (n=15)

1-specificity

sensitivity

0.0 0.2 0.4 0.6 0.8 1.0

0.0

0.2

0.4

0.6

0.8

1.0

AUC = 0.85

OverallSurvival

Years

0 1 2 30.0

0.2

0.4

0.6

0.8

1.0

log rank P= 0.0025HR (95% CI): 12.2 (1.55-95.7)

Good (n=12)

Poor (n=15)

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References

1. Beer DG, Kardia SL, Huang CC, et al. Gene-expression profiles predict survivalof patients with lung adenocarcinoma. Nat Med 2002;8:816-824.

2. Bild AH, Yao G, Chang JT, et al. Oncogenic pathway signatures in humancancers as a guide to targeted therapies. Nature 2006;439:353-357.3. Wang Y, Jatkoe T, Zhang Y, et al. Gene expression profiles and molecular

markers to predict recurrence of Dukes' B colon cancer. J Clin Oncol

2004;22:1564-1571.

4. Kiernan PD, Sheridan MJ, Byrne WD, et al. Stage I non-small cell cancer of thelung results of surgical resection at Fairfax Hospital. Va Med Q 1993;120:146-

149.