Oncotarget

Research Papers:

A generic cycling hypoxia-derived prognostic gene signature: application to breast cancer profiling

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Oncotarget. 2014; 5:6947-6963. https://doi.org/10.18632/oncotarget.2285

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Romain Boidot, Samuel Branders, Thibault Helleputte, Laila Illan Rubio, Pierre Dupont and Olivier Feron _

Abstract

Romain Boidot1,*, Samuel Branders2,*, Thibault Helleputte2, Laila Illan Rubio1, Pierre Dupont2 and Olivier Feron1

1 Institut de Recherche Expérimentale et Clinique (IREC), Pole of Pharmacology and Therapeutics (FATH), Université catholique de Louvain, Brussels, Belgium

2 Machine Learning Group, Institute of Information and Communication Technologies, Electronics and Applied Mathematics (ICTEAM), Université catholique de Louvain, Louvain-la-Neuve, Belgium

* These authors contribued equally to this work

Correspondence:

Olivier Feron, email:

Keywords: hypoxia, breast cancer, biomarker, gene signature, prognosis

Received: June 09, 2014 Accepted: July 31, 2014 Published: July 31, 2014

Abstract

Background: Temporal and local fluctuations in O2 in tumors require adaptive mechanisms to support cancer cell survival and proliferation. The transcriptome associated with cycling hypoxia (CycHyp) could thus represent a prognostic biomarker of cancer progression.

Method: We exposed 20 tumor cell lines to repeated periods of hypoxia/reoxygenation to determine a transcriptomic CycHyp signature and used clinical data sets from 2,150 breast cancer patients to estimate a prognostic Cox proportional hazard model to assess its prognostic performance.

Results: The CycHyp prognostic potential was validated in patients independently of the receptor status of the tumors. The discriminating capacity of the CycHyp signature was further increased in the ER+ HER2- patient populations including those with a node negative status under treatment (HR=3.16) or not (HR=5.54). The CycHyp prognostic signature outperformed a signature derived from continuous hypoxia and major prognostic metagenes (P<0.001). The CycHyp signature could also identify ER+HER2 node-negative breast cancer patients at high risk based on clinicopathologic criteria but who could have been spared from chemotherapy and inversely those patients classified at low risk based but who presented a negative outcome.

Conclusions: The CycHyp signature is prognostic of breast cancer and offers a unique decision making tool to complement anatomopathologic evaluation.


INTRODUCTION

Hypoxia is nowadays described as a hallmark of tumors [1, 2]. Tumor angiogenesis and glycolytic metabolism are two extensively studied responses of cancer cells to a deficit in oxygen [1]. The building of new blood vessels to bring O2 and the respiration-independent metabolism to survive under low O2 are actually complementary responses of tumors to hypoxia [1, 2]. These somehow opposite modes of adaptation account for local and temporal heterogeneities in tumor O2 distribution. The terms ‘intermittent hypoxia’ or ‘cycling hypoxia’ were settled to describe this phenomenon of fluctuating hypoxia in tumors [3, 4]. As a corollary, the extent of cycling hypoxia reflects tumor plasticity and thus measures the capacity of tumor cells to survive and proliferate in a hostile environment [3].

Although we and others have contributed to demonstrate the existence of cycles of hypoxia and/or ischemia in mouse, canine and human tumors [see [5, 6] for review], technologies aiming to routinely measure tumor O2 fluctuations in the clinics are not (yet) available despite important progresses in the in vivo imaging of hypoxia [7-11]. In the absence of readily accessible monitoring strategies, the analysis of the transcriptome associated with this phenomenon could represent a prognostic biomarker of cancer progression. Indeed, although mutations and defects in tumor suppressor genes directly influence the whole genetic profile of a given tumor cell clone, cycling hypoxia could be envisioned as a supra-oncogenic phenomenon influencing gene expression [3]. In other words, independently of the genetic background of tumor cells, cycling hypoxia has the potential to lead to common alterations in the expression of some transcripts, and thus to a possible clinically exploitable signature.

Clinical data sets derived from breast cancer patients could be used to evaluate the performance of such cycling hypoxia-related gene signature. The clinical and genetic heterogeneities of this disease and the very large panel of data sets available represent indeed good opportunities to evaluate new prognostic gene expression signatures [12]. Whole genome analysis already provided several molecular classifications for breast cancer beyond standard clinicopathologic variables [12-21]. The latter include tumor size, presence of lymph node metastasis and histological grades [22] but also encompass three predictive markers of response, namely expression of oestrogen (ER), progesterone (PR) and HER2 receptors [12]. Treatment guidelines are nowadays still largely based on algorithms integrating these informations such as the Notthingham Prognostic Index [22, 23] or Adjuvant! Online [24]. Accordingly, for early-stage breast cancer, adjuvant chemotherapy is recommended for most patients with ER-negative or HER2-positive tumors [13, 25-27]. The challenge actually resides in selecting patients with ER-positive HER2-negative disease who could benefit from chemotherapy.

In this study, we derived a transcriptomic signature of cycling hypoxia (CycHyp) using 20 cell lines derived from various human tumors and characterized by a large variety of distinct genetic anomalies. We then validated the capacity of the CycHyp signature to optimize patient stratification. In particular, we showed how the CycHyp signature could identify ER-positive node-negative breast cancer patients at high risk based on conventional NPI (and who could have been spared from chemotherapy) and inversely those patients classified at low risk but who could have drawn benefits of chemotherapy.

RESULTS

Identification of the CycHyp signature

Tumor cells covering a large diversity of tissues (Suppl. Table 1) were submitted to cycling hypoxia (CycHyp) for 24 hours, maintained under normoxic conditions or exposed to continuous hypoxia (ContHyp) for the same period of time (Figure 1A). Corresponding mRNA samples were analysed by hybridization using Human Gene 1.0 ST Affymetrix microarrays. Gene expression profiles of each cell type under normoxia vs. cycling hypoxia (CycHyp) were produced to identify the most differentially expressed probesets. The CycHyp signature was determined as the top 100 probesets with the lowest FDR-corrected p-values averaged over 200 resamplings (Table 1); a ContHyp signature was also determined in parallel (Table 2). The heatmaps made with the 100 probe sets of the CycHyp signature confirmed its excellent potential of discrimination between cycling hypoxia and either normoxia (Figure 1B) or continuous hypoxia (Figure 1C). Moreover, Gene Set Enrichment Analysis (GSEA) [28] indicated that when considering differentially expressed probesets (after FDR correction), only 2 gene sets were significantly enriched in the CycHyp signature (Suppl. Table 2) whereas we identified 52 gene sets enriched in the ContHyp signature, including 17 directly related to hypoxia (Suppl. Table 3). Also, when using the MSigDB molecular signature database referring to hypoxia or HIF (www.broadinstitute.org), we found 13 hypoxia gene sets sharing, on average, only 1.4 gene with CycHyp (Suppl. Table 4) whereas 44 hypoxia gene sets showed overlap with ContHyp with an average of 6.6 (1-27) common genes (Suppl. Table 5). We also compared the CycHyp signature to 13 other hypoxia-derived signatures described by Seigneuric et al. [29] and Starmans et al. [30]. The CycHyp signature was again far from those signatures with an average of only 1 gene in common. The overlap was larger between ContHyp and those signatures with an average of 6 genes in common (Suppl. Table 6). Finally, using TFactS [31] to analyse transcription factors regulating expression of genes associated to either signature, HIF-1α was only found as positively associated with the ContHyp signature.

Table 1: Gene list of the CycHyp signature

Probe

Entrez ID

GenBank

Symbol

Gene Title

1

8018860

332

NM_001168

BIRC5

baculoviral IAP repeat containing 5

2

8064156

84619

NM_032527

ZGPAT *

zinc finger, CCCH-type with G patch domain

3

8138912

23658

NM_012322

LSM5§

LSM5 homolog, U6 small nuclear RNA associated (S. cerevisiae)

4

7921786

5202

NM_012394

PFDN2

prefoldin subunit 2

5

8165011

2219

NM_002003

FCN1

ficolin (collagen/fibrinogen domain containing) 1

6

7964262

4666

NM_001113201

NACA*

nascent polypeptide-associated complex alpha subunit

7

7949792

5790

NM_005608

PTPRCAP #

protein tyrosine phosphatase, receptor type, C-associated protein

8

8034101

11018

NM_006858

TMED1

transmembrane emp24 protein transport domain containing 1

9

8168087

3476

NM_001551

IGBP1

immunoglobulin (CD79A) binding protein 1

10

7963575

1975

NM_001417

EIF4B§

eukaryotic translation initiation factor 4B

11

8124397

3006

NM_005319

HIST1H1C #

histone cluster 1, H1c

12

7975989

81892

NM_031210

SLIRP§

SRA stem-loop interacting RNA binding protein

13

8127692

3351

NM_000863

HTR1B

5-hydroxytryptamine (serotonin) receptor 1B

14

8127087

2940

NM_000847

GSTA3

glutathione S-transferase alpha 3

15

7941122

29901

NM_013299

SAC3D1

SAC3 domain containing 1

16

7998692

4913

NM_002528

NTHL1

nth endonuclease III-like 1 (E. coli)

17

8073623

758

NM_001044370

MPPED1

metallophosphoesterase domain containing 1

18

8014865

4761

NM_006160

NEUROD2 *

neurogenic differentiation 2

19

8005726

3768

NM_021012

KCNJ12

potassium inwardly-rectifying channel, subfamily J, member 12

20

7966631

64211

NM_022363

LHX5 *

LIM homeobox 5

21

8037853

54958

NM_017854

TMEM160

transmembrane protein 160

22

8104136

3166

NM_018942

HMX1*

H6 family homeobox 1

23

7948606

746

NM_014206

C11orf10 #

chromosome 11 open reading frame 10

24

8044773

8685

NM_006770

MARCO

macrophage receptor with collagenous structure

25

7947015

7251

NM_006292

TSG101

tumor susceptibility gene 101

26

7931553

8433

NM_003577

UTF1 *

undifferentiated embryonic cell transcription factor 1

27

7956876

84298

NM_032338

LLPH

LLP homolog, long-term synaptic facilitation (Aplysia)

28

8117372

8334

NM_003512

HIST1H2AC#

histone cluster 1, H2ac

29

8001329

869

NM_004352

CBLN1

cerebellin 1 precursor

30

8027205

51079

NM_015965

NDUFA13

NADH dehydrogenase (ubiquinone) 1 alpha subcomplex, 13

31

8042896

3196

NM_016170

TLX2 *

T-cell leukemia homeobox 2

32

7911532

54998

NM_017900

AURKAIP1

aurora kinase A interacting protein 1

33

8039923

54998

NM_017900

AURKAIP1

aurora kinase A interacting protein 1

34

7992043

65990

BC001181

FAM173A

family with sequence similarity 173, member A

35

8063074

90204

NM_080603

ZSWIM1 *

zinc finger, SWIM-type containing 1

36

7992191

23430

NM_012217

TPSD1

tryptase delta 1

37

8108435

7322

NM_181838

UBE2D2

ubiquitin-conjugating enzyme E2D 2

38

8165309

8721

NM_003792

EDF1 *

endothelial differentiation-related factor 1

39

7946267

63875

NM_022061

MRPL17

mitochondrial ribosomal protein L17

40

7945536

51286

NM_016564

CEND1

cell cycle exit and neuronal differentiation 1

41

8159609

8636

NM_003731

SSNA1 #

Sjogren syndrome nuclear autoantigen 1

42

8005471

6234

NM_001031

RPS28 #,§

ribosomal protein S28

43

8025395

6234

NM_001031

RPS28

ribosomal protein S28

44

7942824

6234

NM_001031

RPS28

ribosomal protein S28

45

8170753

26576

NM_014370

SRPK3

SRSF protein kinase 3

46

8032718

1613

NM_001348

47

7967067

8655

NM_001037495

48

8159654

25920

NM_015456

COBRA1 *

cofactor of BRCA1

49

8011212

6391

NM_003001

SDHC

succinate dehydrogenase complex, subunit C, integral membrane protein, 15kDa

50

8011968

51003

NM_016060

MED31 *

mediator complex subunit 31

51

7977440

9834

NR_026800

KIAA0125

KIAA0125

52

8016508

11267

NM_007241

SNF8 *

SNF8, ESCRT-II complex subunit, homolog (S. cerevisiae)

53

8168567

5456

NM_000307

POU3F4 *

POU class 3 homeobox 4

54

8086317

64689

NM_031899

GORASP1

golgi reassembly stacking protein 1, 65kDa

55

8052834

54980

BC005079

C2orf42

chromosome 2 open reading frame 42

56

8073334

9978

NM_014248

RBX1 #

ring-box 1, E3 ubiquitin protein ligase

57

7915846

8569

NM_003684

MKNK1

MAP kinase interacting serine/threonine kinase 1

58

8071920

6634

NM_004175

SNRPD3 §

small nuclear ribonucleoprotein D3 polypeptide 18kDa

59

8032371

81926

NM_031213

FAM108A1

family with sequence similarity 108, member A1

60

7924884

8290

NM_003493

HIST3H3

histone cluster 3, H3

61

8006845

6143

NM_000981

RPL19 §

ribosomal protein L19

62

7946812

6207

NM_001017

RPS13 #,§

ribosomal protein S13

63

7949015

65998

NM_001144936

C11orf95 *

chromosome 11 open reading frame 95

64

8009784

51081

NM_015971

MRPS7 §

mitochondrial ribosomal protein S7

65

8174509

2787

NM_005274

GNG5

guanine nucleotide binding protein (G protein), gamma 5

66

7906235

5546

NM_005973

PRCC §

papillary renal cell carcinoma (translocation-associated)

67

8020179

57132

NM_020412

CHMP1B

chromatin modifying protein 1B

68

7947450

4005

NM_005574

LMO2

LIM domain only 2 (rhombotin-like 1)

69

8064370

6939

NM_004609

TCF15 *

transcription factor 15 (basic helix-loop-helix)

70

7955896

22818

NM_016057

COPZ1

coatomer protein complex, subunit zeta 1

71

8137805

8379

NM_003550

MAD1L1 #

MAD1 mitotic arrest deficient-like 1 (yeast)

72

8117334

8359

NM_003538

HIST1H4A #

histone cluster 1, H4a

73

8117368

8364

NM_003542

HIST1H4C #

histone cluster 1, H4c

74

7977507

85495

NR_002312

RPPH1§

ribonuclease P RNA component H1

75

7949410

378938

BC018448

MALAT1

metastasis associated lung adenocarcinoma transcript 1 (non-protein coding)

76

8150433

157848

NM_152568

NKX6-3 *

NK6 homeobox 3

77

8071168

29797

NR_024583

POM121L8P

POM121 membrane glycoprotein-like 8 pseudogene

78

7989611

84191

NM_032231

FAM96A

family with sequence similarity 96, member A

79

7980859

NM_001080113

80

8032782

126259

NM_144615

TMIGD2

transmembrane and immunoglobulin domain containing 2

81

8110861

64979

NM_032479

MRPL36 §

mitochondrial ribosomal protein L36

82

7901687

199964

NM_182532

TMEM61

transmembrane protein 61

83

7916130

112970

NM_138417

KTI12

KTI12 homolog, chromatin associated (S. cerevisiae)

84

8048712

440934

BC033986

LOC440934

hypothetical LOC440934

85

8018993

146713

NM_001082575

RBFOX3 §

RNA binding protein, fox-1 homolog (C. elegans) 3

86

8032601

84839

NM_032753

RAX2

retina and anterior neural fold homeobox 2

87

8010719

201255

NM_144999

LRRC45

leucine rich repeat containing 45

88

8036584

3963

NM_002307

LGALS7

lectin, galactoside-binding, soluble, 7

89

8133209

441251

NR_003666

SPDYE7P

speedy homolog E7 (Xenopus laevis), pseudogene

90

8159501

286256

NM_178536

LCN12

lipocalin 12

91

8028546

3963

NM_002307

LGALS7

lectin, galactoside-binding, soluble, 7

92

8065013

ENST00000427835

93

8018502

201292

NM_173547

TRIM65 *

tripartite motif containing 65

94

7903294

64645

NM_033055

HIAT1

hippocampus abundant transcript 1

95

7989473

388125

NM_001007595

C2CD4B

C2 calcium-dependent domain containing 4B

96

8054449

644903

AK095987

FLJ38668

hypothetical LOC644903

97

8081867

51300

NM_016589

TIMMDC1

translocase of inner mitochondrial membrane domain containing 1

98

7934544

118881

NM_144589

COMTD1

catechol-O-methyltransferase domain containing 1

99

7968260

219409

NM_145657

GSX1 *

GS homeobox 1

100

8022952

56853

NM_020180

CELF4 §

CUGBP, Elav-like family member 4

# common to the ContHyp signature; * regulators of transcription; § involved in RNA processing

Table 2: Gene list of the ContHyp signature

Probe

Entrez ID

GenBank

Symbol

Gene Title

1

7948606

746

NM_014206

C11orf10

chromosome 11 open reading frame 10

2

8043283

55818

NM_018433

KDM3A

lysine (K)-specific demethylase 3A

3

8025395

6234

NM_001031

RPS28

ribosomal protein S28

4

8139706

23480

NM_014302

SEC61G

Sec61 gamma subunit

5

7942824

6234

NM_001031

RPS28

ribosomal protein S28

6

8005471

6234

NM_001031

RPS28

ribosomal protein S28

7

8048489

55139

NM_018089

ANKZF1

ankyrin repeat and zinc finger domain containing 1

8

7994737

226

NM_000034

ALDOA

aldolase A, fructose-bisphosphate

9

7934278

5033

NM_000917

P4HA1

prolyl 4-hydroxylase, alpha polypeptide I

10

8102518

401152

NM_001170330

C4orf3

chromosome 4 open reading frame 3

11

8117334

8359

NM_003538

HIST1H4A

histone cluster 1, H4a

12

8074969

1652

NM_001355

DDT

D-dopachrome tautomerase

13

8044766

51141

NM_016133

INSIG2

insulin induced gene 2

14

7937476

6181

NM_001004

RPLP2

ribosomal protein, large, P2

15

8086961

5210

NM_004567

PFKFB4

6-phosphofructo-2-kinase/fructose-2,6-biphosphatase 4

16

8145454

665

NM_004331

BNIP3L

BCL2/adenovirus E1B 19kDa interacting protein 3-like

17

8113981

8974

NM_004199

P4HA2

prolyl 4-hydroxylase, alpha polypeptide II

18

8162142

81689

NM_030940

ISCA1

iron-sulfur cluster assembly 1 homolog (S. cerevisiae)

19

8007992

3837

NM_002265

KPNB1

karyopherin (importin) beta 1

20

7928308

54541

NM_019058

DDIT4

DNA-damage-inducible transcript 4

21

8073334

9978

NM_014248

RBX1

ring-box 1, E3 ubiquitin protein ligase

22

8124397

3006

NM_005319

HIST1H1C

histone cluster 1, H1c

23

8153459

65263

NM_023078

PYCRL

pyrroline-5-carboxylate reductase-like

24

7916568

AF263547

25

7955117

23519

NM_012404

ANP32D

acidic (leucine-rich) nuclear phosphoprotein 32 family, member D

26

8098604

353322

NM_181726

ANKRD37

ankyrin repeat domain 37

27

8121076

10957

NM_006813

PNRC1

proline-rich nuclear receptor coactivator 1

28

7921076

54865

NM_182679

GPATCH4

G patch domain containing 4

29

7908879

8497

NM_015053

PPFIA4

protein tyrosine phosphatase, receptor type, f polypeptide (PTPRF), interacting protein (liprin), alpha 4

30

8103518

23520

NM_012403

ANP32C

acidic (leucine-rich) nuclear phosphoprotein 32 family, member C

31

8050591

91942

NM_174889

NDUFAF2

NADH dehydrogenase (ubiquinone) 1 alpha subcomplex, assembly factor 2

32

8172154

6187

NM_002952

RPS2

ribosomal protein S2

33

7984846

1198

NM_001130028

CLK3

CDC-like kinase 3

34

7946812

6207

NM_001017

RPS13

ribosomal protein S13

35

7982531

8125

NM_006305

ANP32A

acidic (leucine-rich) nuclear phosphoprotein 32 family, member A

36

8119898

7422

NM_001025366

VEGFA

vascular endothelial growth factor A

37

8004331

9744

NM_014716

ACAP1

ArfGAP with coiled-coil, ankyrin repeat and PH domains 1

38

8159441

29085

NM_001135861

PHPT1

phosphohistidine phosphatase 1

39

8168500

5230

NM_000291

PGK1

phosphoglycerate kinase 1

40

7938890

10196

NM_005788

PRMT3

protein arginine methyltransferase 3

41

7930398

4601

NM_005962

MXI1

MAX interactor 1

42

7997740

81631

NM_022818

MAP1LC3B

microtubule-associated protein 1 light chain 3 beta

43

8004360

147040

NM_001002914

KCTD11

potassium channel tetramerisation domain containing 11

44

7909782

51018

NM_016052

RRP15

ribosomal RNA processing 15 homolog (S. cerevisiae)

45

7949792

5790

NM_005608

PTPRCAP

protein tyrosine phosphatase, receptor type, C-associated protein

46

8124385

8366

NM_003544

HIST1H4B

histone cluster 1, H4b

47

8117368

8364

NM_003542

HIST1H4C

histone cluster 1, H4c

48

8081241

84319

NM_032359

C3orf26

chromosome 3 open reading frame 26

49

8050079

246243

NM_002936

RNASEH1

ribonuclease H1

50

8005765

26118

NM_015626

WSB1

WD repeat and SOCS box containing 1

51

7924491

64853

NM_022831

AIDA

axin interactor, dorsalization associated

52

8133273

ENST00000455206

53

8124391

8335

NM_003513

HIST1H2AB

histone cluster 1, H2ab

54

8159609

8636

NM_003731

SSNA1

Sjogren syndrome nuclear autoantigen 1

55

7957890

27340

NM_014503

UTP20

UTP20, small subunit (SSU) processome component, homolog (yeast)

56

7933582

100287932

NM_006327

TIMM23

translocase of inner mitochondrial membrane 23 homolog (yeast)

57

8153002

10397

NM_001135242

NDRG1

N-myc downstream regulated 1

58

7926037

5209

NM_004566

PFKFB3

6-phosphofructo-2-kinase/fructose-2,6-biphosphatase 3

59

8082066

26355

NM_014367

FAM162A

family with sequence similarity 162, member A

60

8042962

9801

NM_014763

MRPL19

mitochondrial ribosomal protein L19

61

8090678

11222

NM_007208

MRPL3

mitochondrial ribosomal protein L3

62

7977507

85495

NR_002312

RPPH1

ribonuclease P RNA component H1

63

8007397

10197

NM_176863

PSME3

proteasome (prosome, macropain) activator subunit 3 (PA28 gamma/ Ki)

64

7998902

54985

NM_017885

HCFC1R1

host cell factor C1 regulator 1 (XPO1 dependent)

65

8117372

8334

NM_003512

HIST1H2AC

histone cluster 1, H2ac

66

7997230

5713

NM_002811

PSMD7

proteasome (prosome, macropain) 26S subunit, non-ATPase, 7

67

7915485

10969

NM_006824

EBNA1BP2

EBNA1 binding protein 2

68

8113873

3094

NM_005340

HINT1

histidine triad nucleotide binding protein 1

69

7958152

5223

NM_002629

PGAM1

phosphoglycerate mutase 1 (brain)

70

7947867

5702

NM_002804

PSMC3

proteasome (prosome, macropain) 26S subunit, ATPase, 3

71

7964460

1649

NM_004083

DDIT3

DNA-damage-inducible transcript 3

72

7928395

170384

NM_173540

FUT11

fucosyltransferase 11 (alpha (1,3) fucosyltransferase)

73

8163629

944

NM_001244

TNFSF8

tumor necrosis factor (ligand) superfamily, member 8

74

7965486

51134

NM_016122

CCDC41

coiled-coil domain containing 41

75

8136179

23008

AF277175

KLHDC10

kelch domain containing 10

76

8095870

901

NM_004354

CCNG2

cyclin G2

77

8127526

6170

NM_001000

RPL39

ribosomal protein L39

78

8174710

6170

NM_001000

RPL39

ribosomal protein L39

79

8137517

3361

NM_024012

HTR5A

5-hydroxytryptamine (serotonin) receptor 5A

80

7929624

5223

NM_002629

PGAM1

phosphoglycerate mutase 1 (brain)

81

8052331

87178

NM_033109

PNPT1

polyribonucleotide nucleotidyltransferase 1

82

8015969

7343

NM_014233

UBTF

upstream binding transcription factor, RNA polymerase I

83

8069168

386685

NM_198699

KRTAP10-12

keratin associated protein 10-12

84

7941087

5526

NM_006244

PPP2R5B

protein phosphatase 2, regulatory subunit B', beta

85

8026875

26780

NR_000012

SNORA68

small nucleolar RNA, H/ACA box 68

86

8027621

2821

NM_000175

GPI

glucose-6-phosphate isomerase

87

8130539

117289

NM_054114

TAGAP

T-cell activation RhoGTPase activating protein

88

8004691

92162

NM_203411

TMEM88

transmembrane protein 88

89

7962183

205

NM_001005353

AK4

adenylate kinase 4

90

8137805

8379

NM_003550

MAD1L1

MAD1 mitotic arrest deficient-like 1 (yeast)

91

8124388

8358

NM_003537

HIST1H3B

histone cluster 1, H3b

92

8083223

205428

NM_173552

C3orf58

chromosome 3 open reading frame 58

93

8113305

1105

NM_001270

CHD1

chromodomain helicase DNA binding protein 1

94

8169659

4694

NM_004541

NDUFA1

NADH dehydrogenase (ubiquinone) 1 alpha subcomplex, 1, 7.5kDa

95

8046408

5163

NM_002610

PDK1

pyruvate dehydrogenase kinase, isozyme 1

96

8053599

23559

NM_012477

WBP1

WW domain binding protein 1

97

8043377

23559

NM_012477

WBP1

WW domain binding protein 1

98

7960878

642559

GU480887

POU5F1P3

POU class 5 homeobox 1 pseudogene 3

99

7959023

643246

NM_001085481

MAP1LC3B2

microtubule-associated protein 1 light chain 3 beta 2

100

8073148

468

NM_001675

ATF4

activating transcription factor 4 (tax-responsive enhancer element B67)

The CycHyp and ContHyp signatures.

Figure 1: The CycHyp and ContHyp signatures. (A.) Flowchart of the signature determination from tumor cells exposed either to normoxia, cycling or continuous hypoxia. (B.) Heatmap depicting the transcripts from the CycHyp signature either underexpressed (green) or overexpressed (red) (centered to median values). Each column corresponds to a specific human Gene 1.0 ST probeset ; each line represents a specific cell line either maintained under normoxia (black label) or exposed to cycling hypoxia (red label); cells under normoxia and cycling hypoxia are perfectly separated in two distinct clusters, except for one cycling hypoxia sample in the normoxia cluster. (C.) Similarly, a heatmap depicting the relative expression of transcripts from the CycHyp signature in the cell lines maintained under continuous hypoxia (blue) or cycling hypoxia (red); only two cycling hypoxia samples are grouped with the continuous hypoxia samples.

The CycHyp signature predicts clinical outcome in breast cancer patients

To evaluate the prognostic value of the CycHyp signature, we focused on breast cancer because of the very large amounts of well-annotated clinical data sets available and a clearly identified need to discriminate between patients at low and high risks among subgroups determined on the basis of clinicopathologic criteria [12, 13]. Publicly available GEO data sets allowed us to collect information on the survival of 2,150 patients with primary breast cancer (see clinical features in Table 3).

Table 3: Breast Cancer Patient Demographics and Characteristics

All patients

n = 2150

No %

ER+/HER2-

n=1452

No %

ER+/HER2- Node neg.

n=899

No %

ER+/HER2- Node neg.

Untreated

n=590

No %

Age

≤50
>50
NA

649 30
945 44
556 26

388 27
649 45
415 28

218 24
367 41
314 35

190 32
237 40
163 28

Tumor size

≤2cm
>2cm
NA

742 35
473 22
935 43

537 37
326 22
589 41

474 53
210 23
215 24

424 72
158 28
8 1

Grade

0-1
2
3
NA

224 10
605 28
487 23
834 39

200 14
485 33
206 14
561 39

148 17
346 38
162 18
243 27

104 18
270 46
137 23
79 13

Node status

Negative
Positive

1329 62
821 38

899 62
553 38

899 100
0 0

590 100
0 0

Estrogen receptor

Negative
Positive
NA

443 21
1607 75
100 4

0 0
1452 100
0 0

0 0
899 100
0 0

0 0
590 100
0 0

HER2 status

Negative
Positive

1835 85
315 15

1452 100
0 0

899 100
0 0

590 100
0 0

Treatment

None
Chemotherapy
Hormonotherapy

901 42
691 32
558 26

590 41
410 28
452 31

590 66
73 8
236 26

590 100
0 0
0 0

Data obtained from GSE11121 (n=200), GSE17705 (n=298), GSE2034/5327 (n=344), GSE20685 (n=327), GSE21653 (n=253), GSE2990 (n=138), GSE3494 (n=178), GSE6532 (n=214), and GSE7390 (n=198). NA = Not Available.

In order to exploit these data sets, we first transferred the Gene 1.0ST datasets in the HU133 platform. We then used the VDX dataset (GSE2034 and GSE5327) as a reference because of its large number of node negative untreated patients [17]. This training dataset was used to estimate a prognostic multivariate Cox proportional hazard model built on the CycHyp signature (see Methods for details). The other eight datasets (see references in Table 3) were used according to the methodology described by Haibe-Kains and colleagues [32], to assess the prognostic performance of the CycHyp signature on independent samples. We first chose to evaluate our signature independently of the clinicopathological data. The prognostic potential of the CycHyp signature to discriminate between patients at low or high risk was confirmed with a HR=2.39 and a p-value = 1.13e-18 whathever the treatment and the tumor histology (Figure 2A). We then focused on the ER+ HER2- population which is known to be heterogeneous and thus difficult to treat [12, 13]. The discriminating capacity of the CycHyp signature remained strikingly high in the ER+ HER2- patient populations (HR = 2.47, p-value = 3.88e-13, Figure 2B). Finally, among this subpopulation of patients, we considered those with a node negative status (Figure 2C) and among the latter, those who did not receive any treatment (Figure 2D). Hazard ratios rose to 3.16 and 5.54 in these conditions (p-values = 2.85e-9 and 6.44e-10, respectively), further supporting the discriminating potential of the CycHyp signature. In particular, the data presented in Figure 2D allowed to exclude any confounding influence of the potential benefit arising from the treatment administered to these patients and thus clearly identified a population of patients who remained inadequately untreated.

Using the same methodology, we examined the prognostic capacity of the ContHyp signature (discriminating between normoxia and continuous hypoxia). The performance of the ContHyp signature was satisfactory on the ER+ HER2- untreated population (HR = 2.58, p-value = 1.46e-4, see Supplementary Fig. 1) but was significantly lower (p-value = 3.61e-8) than the CycHyp signature.

The CycHyp signature provides significant additional prognostic information to available multigene assays

To evaluate the performance of the CycHyp signature, we compared it with other well-established prognostic multigene assays for breast cancer, namely Gene70 or Mammaprint [14], Gene76 [17] and Oncotype Dx [15]. Using the same set of ER+ HER2- node negative patients as used in Figure 2D, we could determine the low vs. high risk patient stratification according to these signatures. The superior prognostic potential of the CycHyp signature could be captured from the Kaplan Meier curves obtained with the Gene 70, Gene76 and Oncotype DX signatures (compare Figure 3A with Figure 2D). Hazard ratios confirmed the net advantage of the CycHyp signature with a significantly higher value than the three other metagenes (Figure 3B). The concordance index, which is the probability of a high risk patient to relapse before a low risk patient, was also higher with the CycHyp signature (Figure 3B). Finally, the Balanced Classification Rate (BCR), which represents the average between sensitivity and specificity to discriminate between patients with progressing disease vs. disease-free at 5 years, was significantly higher for the CycHyp signature than the three other multigene assays (Figure 3B). The sensitivity of the CycHyp was above 80% and the specificity of the CycHyp signature was well above the level of the others (Figure 3B). Of note, the metrics corresponding to each data set taken separately is depicted in Suppl. Figure 2.

Kaplan-Meier survival curves of patients with primary breast cancer, as determined by using the CycHyp signature.

Figure 2: Kaplan-Meier survival curves of patients with primary breast cancer, as determined by using the CycHyp signature. (A) All patients. (B.) ER+/HER2- patients, (C.) node-negative ER+/HER2-, (D.) node-negative, untreated ER+/HER2- patients (DFS Mantel-Cox comparison); hazard ratio (HR), balanced classification rate (BCR) and concordance index (C-index) for the prediction in high risk vs. low risk groups are reported; HRs are presented with their associated p-values.

Importantly, to further validate the prognostic significance of the CycHyp signature, a comparison with random gene signatures was performed according to the methodology described by Venet et al. [33] and Beck et al. [34]. Figure 3C shows the distribution of the p-values (logrank test in log 10) for 1000 randomly generated signatures together with the p-values of the CycHyp and ContHyp signatures. The logrank test (or Mantel-Haenszel test) [35] is commonly used to assess whether there is a significant survival difference between risk groups. The discrimination between risk groups was significantly higher (P < 0.001) with the CycHyp signature as compared to each of the random signatures whereas the ContHyp signature was not significantly better (vs. random ones; P=0.141). The same analysis was carried out for the three other metrics (HR, CI and BCR) to assess the discrimination capability between risk groups and confirmed the significantly higher value of the CycHyp signature (vs. random signatures) (Suppl. Figure 3).

Comparison of the prognostic potential of the CycHyp signature vs.

Figure 3: Comparison of the prognostic potential of the CycHyp signature vs. Gene 70 (Mammaprint), Gene 76 and Oncotype Dx signatures. (A) Kaplan-Meier survival curves of node-negative, untreated ER+/HER2- patients, as determined by using the indicated signature (DFS Mantel-Cox comparison); hazard ratio (HR), balanced classification rate (BCR) and C-index for the prediction in high risk vs. low risk groups are reported; HR are presented with their associated p-values. (B.) Forest plots of the hazard ratio (HR), Concordance index (CI), balance classification rate (BCR), sensitivity and specificity for the prediction in high risk vs. low risk groups; p-values refer to the comparisons of CycHyp vs. Gene 70 (Mammaprint), Gene 76 and Oncotype Dx. (C.) Graph represents the power of discrimination in high vs. low risk groups (expressed as the logarithm of the p-values of the logrank) of the ContHyp and CycHyp signatures (see red dots) versus 1,000 randomly generated signatures (yellow shapes depicting their distribution).

The CycHyp signature in association with NPI offers a powerful prognostic tool

We then aimed to determine whether the CycHyp signature could improve the Nottingham Prognostic Index (NPI) for better predicting the survival of operable breast cancers. The NPI algorithm combines nodal status, tumour size and histological grade and allows to model a continuum of clinical aggressiveness with 3 subsets of patients divided into good, moderate, and poor prognostic groups with 15-year survival [22, 23, 36]. Since few patients were assigned a poor index, we merged here the moderate and poor indices into a high risk group to facilitate the comparison with the CycHyp signature. We found that by integrating the CycHyp signature, an important proportion of patients could be reclassified to another risk group (Figure 4). 44.1% of patients classified at high risk using the NPI algorithm were identified at low risk when using the CycHyp signature and were confirmed to be “false positive” since they actually exhibited a profile of survival closer to the low risk NPI patient (Figure 4A). Inversely, using the CycHyp signature, we also identified in the patients at low risk based on the NPI criteria, 33.1% of patients with a risk profile closer to the patients with a negative outcome (Figure 4B). This increased discriminating potential remained highly relevant when considering all patients or patients with a ER+ HER2- status (and among the latter, those with a node negative status or the untreated ones) (see Suppl. Figure 4).

Kaplan-Meier survival curves of node-negative, untreated ER+/HER2- patients stratified by using the CycHyp signature to detect.

Figure 4: Kaplan-Meier survival curves of node-negative, untreated ER+/HER2- patients stratified by using the CycHyp signature to detect. (A.) false positive patients among those identified at high risk based on the NPI nomenclature and (B.) false negative patients among those identified at low risk based on the NPI nomenclature (DFS Mantel-Cox comparison).

DISCUSSION

This study demonstrates that a gene signature derived from the transcriptomic adaptation of tumor cells to cycling hypoxia is prognostic of breast cancer. The CycHyp signature that we have identified and validated in this study has not only prognostic value independently of molecular risk factors but also provides significant additional prognostic information to clinicopathologic criteria. Clinical outcome of breast cancer patients is nowadays largely based on histological grade and the status of ER, PR, and HER2 receptors [12, 13, 22]. In early breast cancer, a lack of expression of ER (and PR) will almost systematically lead to the administration of adjuvant chemotherapy in addition to locoregional treatment [12, 25, 26]. Also, for patients with a tumor expressing HER2, chemotherapy and/or trastuzumab represents the option the most likely to be beneficial based on current clinical knowledge [12]. The impact of chemotherapy is actually more difficult to anticipate for the rest of early-stage breast cancer patients, i.e. those diagnosed with a ER-positive and HER2-negative disease. These patients represent indeed a wide spectrum of different risk profiles: for women with high-risk disease, if chemotherapy is appropriate, others will derive little benefit from it. Our study therefore represents a significant advance for this population of patients, which consists of two third of all breast cancers. We have indeed demonstrated that the CycHyp signature outperforms the existing major prognostic gene expression signatures and offers a unique decision making tool to complement the discrimination of breast cancer patients based on anatomopathologic evaluation.

More generally, the excellent prognostic value of CycHyp confirms the link between cycling hypoxia and cancer aggressiveness [4, 5]. This gives credentials to the phenotypic adaptation of tumors resulting from heterogeneities in blood flow distribution as a trigger of cancer progression [3, 4]. Also, with the recent impetus in the understanding of tumor metabolism [37, 38], it has become obvious that the capacity of a given tumor cell to survive in both aerobic and anaerobic environments represents a critical advantage [39-41]. Interestingly, our study also documents the higher prognostic value of a transcriptomic signature derived from cycling hypoxia vs. continuous hypoxia. This confirms that although hypoxia is a frequent feature of poor-prognosis tumors and was reported to drive gene signature associated with negative outcome [42-45], prognostic markers integrating fluctuations in the hypoxic status of tumors (this study) introduce an additional layer of complexity that better fits the in vivo situation.

Whether the CycHyp signature encompasses genes that actively drive cancer progression or reflects a context of metabolic and hypoxic stress favorable to increased mutagenesis and genetic instability [3], warrants further studies. A few hints can however be gleaned from the comparison of the different signatures.

First, the comparison of the CycHyp and ContHyp signatures indicates that the cycling nature of hypoxia leads to specific alterations in mRNA expression since only 11 common transcripts were found in the two gene lists (see symbols # in Table 1). Furthermore, among these 11 genes, most encode for proteins involved in housekeeping functions such as chromatin packaging (HIST1H 1C, 2AC, 4A and 4C) and RNA processing (RPS13 and 28). The only gene common to the two signatures with a known function related to hypoxia is RBX1 or E3 ubiquitin ligase which mediates the ubiquitination and subsequent proteasomal degradation of target proteins [46], including the misfolded proteins known to accumulate under low pO2. Besides the RBX1 gene, the CycHyp signature does not actually contain genes known to be consistently regulated in response to chronic hypoxia. By contrast, the ContHyp signature contains 14 genes already reported to be overexpressed under low pO2 and even directly under the control of the transcription factor HIF-1α, including those coding for glucose metabolism enzymes (ALDOA, PFKB3, PFKB4, PGK1, PGAM1, GPI) and the angiogenic growth factor VEGFA. This HIF-dependent gene expression program of the ContHyp signature was actually confirmed in the GSEA and MSigBD analyses and was consistent with previously reported hypoxia-driven gene signatures [42, 44, 45]. More generally, these findings position the CycHyp signature far from the conventional hypoxia-derived signatures [29, 30] but instead as a biomarker of a distinct tumor biology process involving adaptation to fluctuations in the tumor microenvironment.

Second, a large amount of transcripts of the CycHyp signature encode for proteins themselves involved in the regulation of transcription. Data mining revealed that more than 18 transcripts of the CycHyp signature are transcription factors/regulators and 13 others are directly involved in RNA processing (see symbols * and § in Table 1, respectively). This represents one third of the genes comprising the CycHyp signature and reflects a major difference with the ContHyp signature. While hypoxia is usually associated with cell cycle arrest and mTOR inhibition, cycling hypoxia may be compatible with a maintained proliferation potential. This is further supported by the suppression of geroconversion (ie, the process leading from proliferative arrest to irreversible senescence) observed in response to hypoxia [47, 48] that offers tumor cells the opportunity to re-enter cell cycle when O2 is again available. Further studies are needed to compare the evolution of mTOR activity and mTOR-dependent genes (including those encoding for ribosomal proteins) during cycling and continuous hypoxia.

Finally, the in vitro conditions at the origin of the establishment of the CycHyp signature may actually have specific bearing on its robustness and applicability. Indeed, we previously documented that fluctuating oxygen levels could also directly impact endothelial cells within a tumor [49, 50] indicating that non-tumor cells may also contribute to the same transcriptomic adaptation as tumor cells, thereby reinforcing the relevance of the CycHyp signature. Also, although we have used the CycHyp signature as a prognostic biomarker for early-stage breast cancer, this signature was identified by integrating the information arising from tumor cells of various origins and characterized by various oncogenic alterations; the prognostic value of the CycHyp signature in other cancers is currently under investigation in our laboratory.

Altogether, the above findings indicate that the CycHyp signature represents a new generation of prognostic biomarker reflecting a generic environmental condition in tumors that differs from the conventional view of a static, continuous hypoxia occurring in tumors. When applied to breast cancer, the CycHyp signature has a powerful prognostic value independently of molecular risk factors but also offers a unique decision making tool to complement the discrimination of patients based on anatomopathologic evaluation. The CycHyp signature is distinct from conventional hypoxia-related gene signature but also from existing prognostic metagenes, and the rationale behind its discovery supports a potential broad applicability to evaluate cancer patient outcomes.

MATERIALS AND METHODS.

Tumor cells

Twenty cell lines derived from cancer patients (see Suppl. Table 1 for details) were submitted to cycling hypoxia (CycHyp), i.e. 24 cycles of 30 min incubation under normoxia and 30 min incubation under hypoxic (1% O2) conditions to reproduce tumor hypoxic fluctuations, as previously reported [5, 51]. We also considered control conditions of 24 h continuous exposure of tumor cells to either 21% O2 (Normoxia) or 1% O2 (ContHyp). For each culture condition, cells were immediately snap-frozen at the end of the last incubation period.

Identification of the signatures

mRNA extracts from each tumor cell cultured under the three above conditions (normoxia, cycling hypoxia and continuous hypoxia) were analysed by hybridization on Human Gene 1.0 ST Affymetrix microarrays (GEO access number: GSE42416):

http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?token=probzowmiyseqxm&acc=GSE42416

The extent of the resulting tumor cell datasets (20 samples in each of the three conditions) led us to resort on a resampling mechanism to increase the robustness of the signatures to be identified. For every resampling experiment, a subset of 90 % of the samples was chosen uniformly at random as a training set and the remaining 10% were used as validation set. Differentially expressed probesets (one probeset = a collection of probes designed to interrogate a given sequence) were assessed on each subset according to a t-test and the corresponding FDR corrected p-values were reported. The 100 probesets with the lowest corrected p-values, averaged over 200 resamplings [52-54], formed the CycHyp (Table 1) or ContHyp (Table 2) signatures. All such expression differences were highly significant (p<1e-4) after Benjamini-Hochberg FDR correction for the multiplicity of the test [55]. Of note, in each resampling, the 10 % data not used to select probesets allowed one to estimate the discrimination potential between (cycling or continuous) hypoxia versus normoxia conditions. The average classification accuracy over all resamplings amounted to 97.5 % for CycHyp and 94.3% for ContHyp.

The 100 HGU1.0 ST probesets forming the CycHyp signature corresponded to 94 unique Entrez GeneID in the NCBI database, out of which 69 genes were available on the HGU133a platform (i.e., the technology used in most clinical studies considered here). Those 69 genes were represented by 87 HGU133a probesets. The few datasets collected on HGU133plus2 were reduced to the probesets also present on HGU133a.

Patient data sets

All breast cancer expression data were summarized with MAS5 and represented in log2 scale (except for GSE6532 already summarized with RMA). Breast cancer subtypes (ER+/HER2-, ER-/HER2- and HER2+) were identified with the genefu R package [56] (see Supplementary R Package). Disease-free survival at 5 years was used as the survival endpoint. The data from all patients were censored at 10 years to have comparable follow-up times across clinical studies [32].

Prognostic models of the clinical outcome

The VDX dataset (GSE2034 and GSE5327 from the GEO database) was considered as a reference because of its large number of node-negative untreated patients [17]. This dataset formed the training set used to estimate a prognostic model of the clinical outcome. A risk score for each patient was computed from a penalized Cox proportional hazards model [57] implemented in the Penalized R package [58]; the parameters of the elastic net penalty were learned on the training set by cross-validation. Prediction into a high risk vs. low risk group resulted from a predefined threshold value on this risk score. The decision threshold was chosen on the training set to maximize the specificity and sensitivity of the discrimination between patients with progressing disease versus disease-free patients at 5 years. Following the methodology described by Haibe-Kains et al. [32], all other datasets were used as validations to assess the prognostic performances on independent samples, i.e. balanced classification rate (BCR), concordance index (CI) [59] and hazard ratio (HR) [60]. The survcomp R packages were used to test the significance of the HR and CI values [33] while a Z-test allowed to infer p-values for the BCR relying on an approximation by a normal distribution.

Prognostic performances of a penalized Cox model defined on the CycHyp signature were also compared with well-established prognosis models for breast cancer, namely Gene 70 (Mammaprint) [14], Gene 76 [17] and Oncotype DX [15] signatures. Those existing signatures were associated to specific prognostic models implemented in the genefu R package [56]. Comparison of CycHyp and ContHyp signatures was also carried out with random gene signatures of the same sizes, i.e. 87 and 123 probesets, respectively. One thousand signatures of each size were generated and analysed using the methodology described by Venet et al. [11]. The objective of those experiments was to assess to which extent the CycHyp and ContHyp signatures had a better discrimination power between risk groups than random signatures. Gene Set Enrichment Assay (GSEA) analysis was also performed using the molecular signature database (MSigDB) and the CycHyp and ContHyp signatures expanded to 2118 and 2065 differentially expressed genes, respectively (after FDR correction and averaged over all resamplings.

ACKNOWLEDGEMENTS

This work was supported by grants from the Fédération Wallonie-Bruxelles (WB Health program HypoScreen), the Fonds de la Recherche Scientifique (F.R.S-FNRS), the Télévie, the Belgian Foundation against cancer, the J. Maisin Foundation, the interuniversity attraction pole (IUAP) research program #UP7-03 from the Belgian Science Policy Office (Belspo) and an Action de Recherche Concertée (ARC 09/14-020), O. Feron and P. Dupont equally supervised this work.

CONFLICT OF INTEREST

The authors declare that they have no conflicts of interest relating to this study.

REFERENCES

1. Semenza GL. Oxygen sensing, homeostasis, and disease. N Engl J Med. 2011; 365: 537-547.

2. Bertout JA, Patel SA, Simon MC. The impact of O2 availability on human cancer. Nat Rev Cancer. 2008; 8: 967-975.

3. Bristow RG, Hill RP. Hypoxia and metabolism. Hypoxia, DNA repair and genetic instability. Nat Rev Cancer. 2008; 8: 180-192.

4. Dewhirst MW, Cao Y, Moeller B. Cycling hypoxia and free radicals regulate angiogenesis and radiotherapy response. Nat Rev Cancer. 2008; 8: 425-437.

5. Dewhirst MW. Relationships between cycling hypoxia, HIF-1, angiogenesis and oxidative stress. Radiat Res. 2009; 172: 653-665.

6. Yasui H, Matsumoto S, Devasahayam N, Munasinghe JP, Choudhuri R, Saito K, Subramanian S, Mitchell JB, Krishna MC. Low-field magnetic resonance imaging to visualize chronic and cycling hypoxia in tumor-bearing mice. Cancer Res. 2010; 70: 6427-6436.

7. Baudelet C, Cron GO, Ansiaux R, Crokart N, Dewever J, Feron O, Gallez B. The role of vessel maturation and vessel functionality in spontaneous fluctuations of T2*-weighted GRE signal within tumors. NMR Biomed. 2006; 19: 69-76.

8. Baudelet C, Ansiaux R, Jordan BF, Havaux X, Macq B, Gallez B. Physiological noise in murine solid tumours using T2*-weighted gradient-echo imaging: a marker of tumour acute hypoxia? Phys Med Biol. 2004; 49: 3389-3411.

9. Martinive P, De WJ, Bouzin C, Baudelet C, Sonveaux P, Gregoire V, Gallez B, Feron O. Reversal of temporal and spatial heterogeneities in tumor perfusion identifies the tumor vascular tone as a tunable variable to improve drug delivery. Mol Cancer Ther 2006; 5: 1620-1627.

10. Chitneni SK, Palmer GM, Zalutsky MR, Dewhirst MW. Molecular imaging of hypoxia. J Nucl Med. 2011; 52: 165-168.

11. Krishna MC, Matsumoto S, Yasui H, Saito K, Devasahayam N, Subramanian S, Mitchell JB. Electron paramagnetic resonance imaging of tumor pO(2). Radiat Res. 2012; 177: 376-386.

12. Reis-Filho JS, Pusztai L. Gene expression profiling in breast cancer: classification, prognostication, and prediction. Lancet. 2011; 378: 1812-1823.

13. Prat A, Ellis MJ, Perou CM. Practical implications of gene-expression-based assays for breast oncologists. Nat Rev Clin Oncol 2012; 9: 48-57.

14. van ‘t Veer LJ, Dai H, van de Vijver MJ, He YD, Hart AA, Mao M, Peterse HL, van der Kooy K, Marton MJ, Witteveen AT, Schreiber GJ, Kerkhoven RM, Roberts C, et al. Gene expression profiling predicts clinical outcome of breast cancer. Nature. 2002; 415: 530-536.

15. Paik S, Shak S, Tang G, Kim C, Baker J, Cronin M, Baehner FL, Walker MG, Watson D, Park T, Hiller W, Fisher ER, Wickerham DL, et al. A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer. N Engl J Med. 2004; 351: 2817-2826.

16. Sotiriou C, Neo SY, McShane LM, Korn EL, Long PM, Jazaeri A, Martiat P, Fox SB, Harris AL, Liu ET. Breast cancer classification and prognosis based on gene expression profiles from a population-based study. Proc Natl Acad Sci USA. 2003; 100: 10393-10398.

17. Wang Y, Klijn JG, Zhang Y, Sieuwerts AM, Look MP, Yang F, Talantov D, Timmermans M, Meijer-van Gelder ME, Yu J, Jatkoe T, Berns EM, Atkins D, et al. Gene-expression profiles to predict distant metastasis of lymph-node-negative primary breast cancer. Lancet. 2005; 365: 671-679.

18. Sparano JA, Paik S. Development of the 21-gene assay and its application in clinical practice and clinical trials. J Clin Oncol. 2008; 26: 721-728.

19. Sotiriou C, Pusztai L. Gene-expression signatures in breast cancer. N Engl J Med. 2009; 360: 790-800.

20. Liu JC, Egan SE, Zacksenhaus E. A Tumor initiating cell-enriched prognostic signature for HER2+:ERalpha- breast cancer; rationale, new features, controversies and future directions. Oncotarget. 2013; 4: 1317-1328.

21. Snijders AM, Langley S, Mao JH, Bhatnagar S, Bjornstad KA, Rosen CJ, Lo A, Huang Y, Blakely EA, Karpen GH, Bissell MJ, Wyrobek AJ. An interferon signature identified by RNA-sequencing of mammary tissues varies across the estrous cycle and is predictive of metastasis-free survival. Oncotarget. 2014; 5: 4011-4025.

22. Rakha EA, Reis-Filho JS, Baehner F, Dabbs DJ, Decker T, Eusebi V, Fox SB, Ichihara S, Jacquemier J, Lakhani SR, Palacios J, Richardson AL, Schnitt SJ, et al. Breast cancer prognostic classification in the molecular era: the role of histological grade. Breast Cancer Res. 2010; 12: 207.

23. Galea MH, Blamey RW, Elston CE, Ellis IO. The Nottingham Prognostic Index in primary breast cancer. Breast Cancer Res Treat. 1992; 22: 207-219.

24. Ravdin PM, Siminoff LA, Davis GJ, Mercer MB, Hewlett J, Gerson N, Parker HL. Computer program to assist in making decisions about adjuvant therapy for women with early breast cancer. J Clin Oncol. 2001; 19: 980-991.

25. Espinosa E, Vara JA, Navarro IS, Gamez-Pozo A, Pinto A, Zamora P, Redondo A, Feliu J. Gene profiling in breast cancer: time to move forward. Cancer Treat Rev. 2011; 37: 416-421.

26. Eng-Wong J, Isaacs C. Prediction of benefit from adjuvant treatment in patients with breast cancer. Clin Breast Cancer. 2010; 10 Suppl 1: E32-E37.

27. Ignatiadis M, Singhal SK, Desmedt C, Haibe-Kains B, Criscitiello C, Andre F, Loi S, Piccart M, Michiels S, Sotiriou C. Gene modules and response to neoadjuvant chemotherapy in breast cancer subtypes: a pooled analysis. J Clin Oncol 2012; 30: 1996-2004.

28. Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, Paulovich A, Pomeroy SL, Golub TR, Lander ES, Mesirov JP. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci USA. 2005; 102: 15545-15550.

29. Seigneuric R, Starmans MH, Fung G, Krishnapuram B, Nuyten DS, van EA, Magagnin MG, Rouschop KM, Krishnan S, Rao RB, Evelo CT, Begg AC, Wouters BG, et al. Impact of supervised gene signatures of early hypoxia on patient survival. Radiother Oncol. 2007; 83: 374-382.

30. Starmans MH, Chu KC, Haider S, Nguyen F, Seigneuric R, Magagnin MG, Koritzinsky M, Kasprzyk A, Boutros PC, Wouters BG, Lambin P. The prognostic value of temporal in vitro and in vivo derived hypoxia gene-expression signatures in breast cancer. Radiother Oncol. 2012; 102: 436-443.

31. Essaghir A, Demoulin JB. A minimal connected network of transcription factors regulated in human tumors and its application to the quest for universal cancer biomarkers. PLoS One 2012; 7: e39666.

32. Haibe-Kains B, Desmedt C, Sotiriou C, Bontempi G. A comparative study of survival models for breast cancer prognostication based on microarray data: does a single gene beat them all? Bioinformatics. 2008; 24: 2200-2208.

33. Venet D, Dumont JE, Detours V. Most random gene expression signatures are significantly associated with breast cancer outcome. PLoS Comput Biol. 2011; 7: e1002240.

34. Beck AH, Knoblauch NW, Hefti MM, Kaplan J, Schnitt SJ, Culhane AC, Schroeder MS, Risch T, Quackenbush J, Haibe-Kains B. Significance analysis of prognostic signatures. PLoS Comput Biol. 2013; 9: e1002875.

35. Mantel N, Haenszel W. Statistical aspects of the analysis of data from retrospective studies of disease. J Natl Cancer Inst. 1959; 22: 719-748.

36. Balslev I, Axelsson CK, Zedeler K, Rasmussen BB, Carstensen B, Mouridsen HT. The Nottingham Prognostic Index applied to 9,149 patients from the studies of the Danish Breast Cancer Cooperative Group (DBCG). Breast Cancer Res Treat. 1994; 32: 281-290.

37. Koppenol WH, Bounds PL, Dang CV. Otto Warburg’s contributions to current concepts of cancer metabolism. Nat Rev Cancer. 2011; 11: 325-337.

38. Feron O. Pyruvate into lactate and back: from the Warburg effect to symbiotic energy fuel exchange in cancer cells. Radiother Oncol. 2009; 92: 329-333.

39. Wise DR, Ward PS, Shay JE, Cross JR, Gruber JJ, Sachdeva UM, Platt JM, DeMatteo RG, Simon MC, Thompson CB. Hypoxia promotes isocitrate dehydrogenase-dependent carboxylation of alpha-ketoglutarate to citrate to support cell growth and viability. Proc Natl Acad Sci USA. 2011; 108: 19611-19616.

40. Sonveaux P, Vegran F, Schroeder T, Wergin MC, Verrax J, Rabbani ZN, De Saedeleer CJ, Kennedy KM, Diepart C, Jordan BF, Kelley MJ, Gallez B, Wahl ML, et al. Targeting lactate-fueled respiration selectively kills hypoxic tumor cells in mice. J Clin Invest. 2008; 118: 3930-3942.

41. Boidot R, Vegran F, Meulle A, Le Breton A, Dessy C, Sonveaux P, Lizard-Nacol S, Feron O. Regulation of monocarboxylate transporter MCT1 expression by p53 mediates inward and outward lactate fluxes in tumors. Cancer Res. 2012; 72: 939-948.

42. Chi JT, Wang Z, Nuyten DS, Rodriguez EH, Schaner ME, Salim A, Wang Y, Kristensen GB, Helland A, Borresen-Dale AL, Giaccia A, Longaker MT, Hastie T, et al. Gene expression programs in response to hypoxia: cell type specificity and prognostic significance in human cancers. PLoS Med. 2006; 3: e47.

43. Winter SC, Buffa FM, Silva P, Miller C, Valentine HR, Turley H, Shah KA, Cox GJ, Corbridge RJ, Homer JJ, Musgrove B, Slevin N, Sloan P, et al. Relation of a hypoxia metagene derived from head and neck cancer to prognosis of multiple cancers. Cancer Res. 2007; 67: 3441-3449.

44. 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.

45. Favaro E, Lord S, Harris AL, Buffa FM. Gene expression and hypoxia in breast cancer. Genome Med. 2011; 3: 55.

46. Micel LN, Tentler JJ, Smith PG, Eckhardt GS. Role of ubiquitin ligases and the proteasome in oncogenesis: novel targets for anticancer therapies. J Clin Oncol. 2013; 31: 1231-1238.

47. Leontieva OV, Blagosklonny MV. Hypoxia and gerosuppression: the mTOR saga continues. Cell cycle. 2012; 11: 3926-3931.

48. Leontieva OV, Natarajan V, Demidenko ZN, Burdelya LG, Gudkov AV, Blagosklonny MV. Hypoxia suppresses conversion from proliferative arrest to cellular senescence. Proc Natl Acad Sci USA. 2012; 109: 13314-13318.

49. Martinive P, Defresne F, Bouzin C, Saliez J, Lair F, Gregoire V, Michiels C, Dessy C, Feron O. Preconditioning of the tumor vasculature and tumor cells by intermittent hypoxia: implications for anticancer therapies. Cancer Res. 2006; 66: 11736-11744.

50. Daneau G, Boidot R, Martinive P, Feron O. Identification of cyclooxygenase-2 as a major actor of the transcriptomic adaptation of endothelial and tumor cells to cyclic hypoxia: effect on angiogenesis and metastases. Clin Cancer Res. 2010; 16: 410-419.

51. Dewhirst MW. Intermittent hypoxia furthers the rationale for hypoxia-inducible factor-1 targeting. Cancer Res. 2007; 67: 854-855.

52. Davis CA, Gerick F, Hintermair V, Friedel CC, Fundel K, Kuffner R, Zimmer R. Reliable gene signatures for microarray classification: assessment of stability and performance. Bioinformatics. 2006; 22: 2356-2363.

53. Abeel T, Helleputte T, Van de Peer Y, Dupont P, Saeys Y. Robust biomarker identification for cancer diagnosis with ensemble feature selection methods. Bioinformatics. 2010; 26: 392-398.

54. Bach FR. Bolasso: model consistent Lasso estimation through the bootstrap. Proceedings of the 25th international conference on Machine learning. 2008: 33-40.

55. Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc. 1995; 57: 289-300.

56. Haibe-Kains B, Desmedt C, Rothe F, Piccart M, Sotiriou C, Bontempi G. A fuzzy gene expression-based computational approach improves breast cancer prognostication. Genome Biol. 2010; 11: R18.

57. Simon N, Friedman J, Hastie T, Tibshirani R. Regularization paths for Cox’s proportional hazards model via coordinate descent. Journal of statistical software. 2011; 39.

58. Goeman JJ. L1 penalized estimation in the Cox proportional hazards model. BiomJ. 2010; 52: 70-84.

59. Harrell FE, Jr., Lee KL, Mark DB. Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Stat Med. 1996; 15: 361-387.

60. Cox D. Regression models and life-tables. J R Stat Soc. 1972; 34: 187-220.


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