Download 1 - Clinical Cancer Research

Supplementary Methods
BCON cohort
Histopathology
One 4 µm haemotoxylin and eosin (H&E)-stained section from each FFPE block was
analysed. Clinical staging followed TNM AJCC/UICC classifications and grading was according
to the World Health Organization guidelines (1973). Percent tumour material and presence
(any amount) or absence of tumour necrosis was scored by a consultant pathologist.
Samples with less than 10% viable tumour material were excluded from analysis.
RNA extraction and quality control
RNA was extracted from FFPE samples (three 20 µm sections) using the RecoverAll Total
Nucleic Acid Isolation Kit (Life Technologies, Warrington, UK), which included DNase I
treatment. RNA integrity number was measured using a Bioanalyser (Agilent Technologies
Ltd, Santa Clara, CA, USA). RNA concentration and 260/230 and 260/280 ratios were
determined using a spectrophotometer (NanoDrop 1000; Thermo Scientific, Loughborough,
UK). Nanodrop-quantified cDNA yield was also recorded. RNA purity was assessed using the
260/280 ratio and all samples fell with the range of 1.8-2.1 as recommended by the
manufacturer. RNA quality control parameters: RIN, 260/230 ratio and 260/280 ratio were
not used to screen samples [1]. Data are summarised in Supplementary Table S2. Minimum
requirements for hybridisation to Exon arrays were 100 ng total RNA that amplifies to give a
yield of ≥3.8 µg cDNA.
Exon array hybridisation
1
Total RNA (100 ng) was amplified using NuGen WT-Ovation FFPE v2 kit (NuGen
Technologies, San Carlos, CA, USA). The WT-Ovation Exon Module V1.0 was used to
generate ST-DNA, and 3.8 to 4.0 µg was hybridised to the human 1.0 exon ST arrays. Further
details and raw data (CEL files) are GSE39067.
Transcriptomic data normalisation
Samples from BCON cohort were profiled using human exon 1.0 ST array. Raw CEL files were
downloaded and RMA normalised with aroma package (http://www.aroma-project.org/).
Chip definition file “HuEx-1_0-st-v2,coreR3,A20071112,EP.cdf” was downloaded from
aroma official website for annotation. Expression summarisation was performed at the level
of transcript cluster. For TCGA cohort where raw data files were not publicly available, RNA
sequencing version 2 data were downloaded for gene expression. As a large and multiinstitutional project, TCGA cohort consists of tumour samples which were collected and
processed in multiple batches. Batch effect introduced by different tissue source centres
was corrected with parametric empirical Bayes. For external validation cohorts including
GSE5287 and GSE31684, raw CEL files were downloaded from GEO database and processed
with GC-RMA. Probe level intensities were summarised to the level of probe sets. Probe sets
mapping to multiple genes were removed and median expression of all probe sets mapping
to the same genes were computed as gene expression. For the other four cohorts including
GSE13507, GSE32894, GSE19915 and GSE1827, expression data were downloaded directly
from GEO database. For GSE19915 and GSE1827, where missing expression values were
present, gene expression imputation was performed with R package “impute”.
2
A seed-based co-expression network method to generate hypoxia signature in bladder
cancer
As previously suggested [2, 3] we considered a number of commonly regulated hypoxia
genes, collecting them from the literature as candidate genes. However, here we exploited a
more comprehensive list of genes instead of limiting the number to the 10 seed genes
initially used [2, 3]. This identified 611 genes from a recent review [4] (table 1). Note that we
only included genes shown to be hypoxia regulated in cancer cell lines or previous hypoxia
signature genes validated in clinical cohorts. To determine the hypoxia specificity of these
candidate genes in bladder cancer, Spearman correlation was calculated for each pair of
candidate genes in TCGA cohort, representing the strength of co-expression. A bladder
carcinoma-specific hypoxia co-expression network was constructed by pooling together
positive gene-gene interactions with strength above a threshold value (set as 0.5 in this
work). The co-expression network, consisting of 168 seed genes and 458 interactions,
provided a system level view of the interactions between known hypoxia genes. A candidate
gene is likely to be hypoxia regulated in bladder cancer if highly co-expressing with multiple
candidate genes.
The second step of the approach identified signature genes from the bladder carcinoma
hypoxia gene network. Most of the 611 curated known hypoxia genes are up-regulated
under hypoxia across different tumour sites and we further selected genes where upregulations were associated with poor prognosis in TCGA cohort (Cox regression P < 0.05
and HR > 1). This was based on the evidence that tumour hypoxia is associated with
aggressive tumour phenotype and poor prognosis. The final signature consisted of 24 genes
3
and a hypoxia gene signature score can be calculated for each tumour as the median of the
(log 2 transformed) expression levels over all signature genes.
4
Supplementary Table S1. Clinicopathologic details compared to original trial
Variable
N
RT
N=75
RT+CON
N=76
Gender
Male
Female
115
36
73 (79)
27 (21)
79 (80)
21 (20)
Median Age (years)
151
75 (74)
75 (74)
T stage
T1
T2
T3
T4a
9
108
30
4
0 (9)
72 (63)
25 (24)
3 (4)
12 (10)
71 (68)
14 (18)
3 (4)
Grade
2
3
1
150
0 (13)
100 (87)
2 (15)
98 (85)
TURBT
Biopsy
Partial
Complete
No data
33
58
55
5
20 (28)
32 (26)
43 (40)
5 (6)
24 (25)
44 (35)
31 (37)
1 (3)
Median Hb (g/L)
No data
149
2
14 (14)
1 (2)
14 (14)
1 (1)
Data for each variable are % samples, with the exception of Age and Hb. Numbers in brackets are the %
samples recorded in the original trial.
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Supplementary Table S2. FFPE sample details by randomisation arm
Variable
RT
N=75
116.6 (29.6-430.8)
RT+CON
N=76
116.4 (29.5 to 502.5)
0.87
260/230
1.5 (0.3-3.1)
1.3 (0.2-9.5)
0.04
260/280
2.0 (1.7-3.1)
2.0 (0.2-2.6)
0.62
RIN
2.4 (1.0-2.8)
2.5 (1.0-4.4)
0.22
DNA yield (µg)
4.8 (3.6-8.2)
4.7 (3.0-7.9)
0.44
Percent tumour
80% (15%-95%)
80% (15%-100%)
0.59
RNA concentration (ng/µl)
P
Data represent median (range) or n (%)
Abbreviations: RT, radiotherapy; CON, carbogen and nicotinamide; RIN, RNA integrity number.
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Supplementary Table S3 Predictive significance of the literature signatures for LPFS analysis
of BCON cohort
Signature
Ragnum et al.
Buffa et al.
Winter et al.
Betts et al.
Toustrup et al.
Toustrup et al.a
Chi et al.
Lendahl et al.
Mitra et al.
Riester et al.
Sanchez-Carbayo et al.
Kim et al.
Kim et al.
a
BCON (low-hypoxia patients)
Prediction of Benefit
0.601
0.278
0.611
0.694
0.402
0.349
0.702
0.483
0.980
0.650
0.970
0.458
0.191
Classification of samples into more or less hypoxic using the centroid method described in Toustrup et al.
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Supplementary Table S4 Bladder cancer-specific hypoxia signature genes
Gene symbol
CAV1
COL5A1
ITGA5
P4HA2
SLC16A1
TGFBI
DPYSL2
SRPX
TRAM2
SYDE1
LRP1
PDLIM2
SAV1
AHNAK2
CAD
CYP1B1
DAAM1
DSC2
SLC2A3
FUT11
GLG1
GULP1
LDLR
THBS4
Description
Caveolin 1
Collagen Type V Alpha 1
Integrin Subunit Alpha 5
Prolyl 4-Hydroxylase Subunit Alpha 2
Solute Carrier Family 16 Member 1
Transforming Growth Factor Beta Induced
Dihydropyrimidinase Like 2
Sushi Repeat Containing Protein, X-Linked
Translocation Associated Membrane Protein 2
Synapse Defective Rho GTPase Homolog 1
LDL Receptor Related Protein 1
PDZ And LIM Domain 2
Salvador Family WW Domain Containing Protein 1
AHNAK Nucleoprotein 2
Carbamoyl-Phosphate Synthetase 2, Aspartate
Transcarbamylase, And Dihydroorotase
Cytochrome P450 Family 1 Subfamily B Member 1
Dishevelled Associated Activator Of Morphogenesis 1
Desmocollin 2
Solute Carrier Family 2 Member 3
Fucosyltransferase 11
Golgi Glycoprotein 1
GULP, Engulfment Adaptor PTB Domain Containing 1
Low Density Lipoprotein Receptor
Thrombospondin 4
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Supplementary Table S5. Distribution of clinicopathological factors by hypoxia signature
scores
Factor
Gender
Male
Female
Age (year)
< 75
≥ 75
Stage
1
2
3
4a
Turbt
Complete
Partial
Biopsy
Necrosis
Absent
Present
Growth pattern
Papillary
Solid
Both
CIS
Absent
Present
Hb (g dl-1)
< 13.7
≥ 13.7
HIF-1α
Median
CAIX
0
>0
GLUT1
Median
High hypoxia
Low hypoxia
P
53
23
62
13
0.09
33
43
43
32
0.12
1
53
20
2
8
55
10
2
0.03
35
23
15
30
25
18
0.82
31
45
40
35
0.17
11
33
32
21
29
25
0.12
57
19
60
15
0.59
45
31
31
44
0.04
23.1 (0-169.4)
16.8 (0-111.30)
0.54
20
47
21
39
0.67
126.3 (0-300)
84.7 (0-278.5)
0.29
Abbreviations: CAIX= carbonic anhydrase IX; CIS= carcinoma in situ; Hb= haemoglobin; HIF-1α = hypoxiainducible factor-1α; TURBT= transurethral resection of bladder tumour.
a
Mann-Whitney test.
9
Supplementary Figure S1. BCON consort diagram.
10
Supplementary Figure S2. Bladder cancer-specific hypoxia gene co-expression network. A
network was constructed by pooling together generic hypoxia genes strongly correlated in
bladder carcinoma. Thickness of the edge is proportional to the correlation strength.
11
Supplementary Figure S3. Fixed effect meta-analysis of the hazard ratios of the 24-gene
hypoxia signature developed in this work on 6 independent cohorts. HR of high hypoxia
patients were calculated for each cohort and combined into a meta HR. Inverse variance
weighting was used for pooling the data.
12
Supplementary Figure S4. Distribution of 24-gene hypoxia signature score in tumours with
high or low protein expressions. A) CA9, B) HIF-1a.
13
Supplementary Figure S5. Kaplan-Meier plot for BCON patients receiving radiotherapy only.
Patients were stratified into four groups based on both 24-gene hypoxia signature and
Riester signature.
14
Supplementary Figure S6. Kaplan-Meier plot for BCON patients having high 24-gene hypoxia
signature scores and high Riester risk signature scores. Patients receive RT plus CON.
15
Supplementary Figure S7. Kaplan-Meier plot for BCON patients receiving radiotherapy only.
Patients were stratified into four groups based on both 24-gene hypoxia signature and
Lendahl signature.
16
Supplementary Figure S8. Kaplan-Meier plot for BCON patients having high 24-gene hypoxia
signature scores and high Lendahl risk signature scores. Patients receive RT plus CON.
17
Reference
1.
Hall JS, Taylor J, Valentine HR et al. Enhanced stability of microRNA expression facilitates
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107: 684-694.
2.
Buffa FM, Harris AL, West CM, Miller CJ. Large meta-analysis of multiple cancers reveals a
common, compact and highly prognostic hypoxia metagene. Br J Cancer 2010; 102: 428-435.
3.
Winter SC, Buffa FM, Silva P et al. Relation of a hypoxia metagene derived from head and
neck cancer to prognosis of multiple cancers. Cancer Research 2007; 67: 3441-3449.
4.
Harris BHL, Barberis A, West CML, Buffa FM. Gene expression signatures as biomarkers of
tumour hypoxia. Clinical Oncology 2015; 27: 547-560.
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