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Vasculature analysis of patient derived tumor xenografts using species-specific PCR assays: evidence of tumor endothelial cells and atypical VEGFA-VEGFR1/2 signalings

  • Ivan Bieche1, 2,
  • Sophie Vacher1,
  • David Vallerand3, 4,
  • Sophie Richon5, 6,
  • Rana Hatem1,
  • Ludmilla De Plater3,
  • Ahmed Dahmani3,
  • Fariba Némati3,
  • Eric Angevin7,
  • Elisabetta Marangoni3,
  • Sergio Roman-Roman3,
  • Didier Decaudin3, 8 and
  • Virginie Dangles-Marie3, 9, 10Email author
BMC Cancer201414:178

DOI: 10.1186/1471-2407-14-178

Received: 29 September 2013

Accepted: 27 January 2014

Published: 13 March 2014

Abstract

Background

Tumor endothelial transdifferentiation and VEGFR1/2 expression by cancer cells have been reported in glioblastoma but remain poorly documented for many other cancer types.

Methods

To characterize vasculature of patient-derived tumor xenografts (PDXs), largely used in preclinical anti-angiogenic assays, we designed here species-specific real-time quantitative RT-PCR assays. Human and mouse PECAM1/CD31, ENG/CD105, FLT1/VEGFR1, KDR/VEGFR2 and VEGFA transcripts were analyzed in a large series of 150 PDXs established from 8 different tumor types (53 colorectal, 14 ovarian, 39 breast and 15 renal cell cancers, 6 small cell and 5 non small cell lung carcinomas, 13 cutaneous melanomas and 5 glioblastomas) and in two bevacizumab-treated non small cell lung carcinomas xenografts.

Results

As expected, mouse cell proportion in PDXs -evaluated by quantifying expression of the housekeeping gene TBP- correlated with all mouse endothelial markers and human VEGFA RNA levels. More interestingly, we observed human PECAM1/CD31 and ENG/CD105 expression in all tumor types, with higher rate in glioblastoma and renal cancer xenografts. Human VEGFR expression profile varied widely depending on tumor types with particularly high levels of human FLT1/VEGFR1 transcripts in colon cancers and non small cell lung carcinomas, and upper levels of human KDR/VEGFR2 transcripts in non small cell lung carcinomas. Bevacizumab treatment induced significant low expression of mouse Pecam1/Cd31, Eng/Cd105, Flt1/Vegfr1 and Kdr/Vefr2 while the human PECAM1/CD31 and VEGFA were upregulated.

Conclusions

Taken together, our results strongly suggest existence of human tumor endothelial cells in all tumor types tested and of both stromal and tumoral autocrine VEGFA-VEGFR1/2 signalings. These findings should be considered when evaluating molecular mechanisms of preclinical response and resistance to tumor anti-angiogenic strategies.

Keywords

Tumor vasculature Patient-derived xenografts Species-specific PCR assays Endothelial markers VEGFA-VEGFR1/2 signalings

Background

Tumor vasculature, a crucial feature in cancer development and progression, is based on angiogenesis and vasculogenesis driven by VEGF signalings [13] but also on tumor endothelial transdifferentiation and vascular mimicry [4]. The VEGFR1 and VEGFR2 tyrosine kinase receptors are primarily expressed by endothelial cells. Recent studies, however, suggest that tumor-derived VEGF provides not only paracrine survival cues for endothelial cells, but may also autocrine processes in tumor cells expressing VEGFRs and play a role in tumor resistance to existing anti-angiogenic therapies [57].

Growth of patient tumor fragments into immunodeficient mice allows an accurate depiction of human tumor biological characteristics and are considered to represent the heterogeneity of human cancers (for review [8]). These patient-derived tumor xenografts (PDX) are greatly helpful to evaluate fundamental issues in cancer and chemosensitivity response, including characteristics of angiogenesis, tumor-stroma interactions and response to antiangiogenic therapies. As real-time quantitative RT-PCR is highly specific, species-specific primer sets can allow to discriminating between mouse/stromal and human/cancer gene expression in PDX models.

To obtain further insight into tumor vascularization and VEGFR expression by cancer and non-tumor cells, we used real-time qRT-PCR to quantify species-specific mRNAs of PECAM1/CD31, ENG/CD105, FLT1/VEGFR1, KDR/VEGFR2 and VEGFA genes in a large series of 150 xenografts from different tumor types. We also validated clinical relevance of species-specific PCR assays for in vivo evaluation of anti-angiogenesis therapy in two non small cell lung carcinoma models. We showed human PECAM1/CD31 and ENG/CD105 expression in all tumor types, supporting existence of human tumor endothelial cells in all tumor types. In addition, the VEGFR expression profiles led to involvement of both stromal and tumoral autocrine VEGFA-VEGFR1/2 signalings in tumors.

Results and discussion

First, the proportion of mouse cells was estimated in a panel of 8 different PDX types, using a real-time qRT-PCR assay combining primers specific for mouse Tbp RNA and primers able to amplify a common sequence on both human and mouse TBP transcripts. (Additional file 1: Table S1). As this gene encoding the TATA box-binding protein is a robust house-keeping gene [9] with similar amplification efficiency for the 2 primer sets, the ratio reflects the percentage of mouse cells within xenograft as validated in a standard curve of mouse and human cDNA mixtures (data not shown).

In an initial series of 157 human xenografts, the proportion of mouse cells was 100% in 7 tumors. These 7 tumor samples probably originated from spontaneous mouse lymphoma, frequently observed in immunodeficient mice [10].

In the 150 other xenografts, mouse host cells were found in all specimens with a median proportion of mouse cells of 9%, ranged between 3.3% in SCLC and 20% in NSCLC (p < 0.05, Table 1). To note, all the xenografts used here, have been passaged at least 5 times in mice, leading to a replacement of human stroma by mouse components [8].
Table 1

Normalized gene expression for each of the 150 PDX samples, classified by tumor type (noted in bold)

Sample nature

Derived from primary tumor or metastatis

% of mouse cells

PECAM1

ENG

VEGFR1

VEGFR2

VEGFA

% of mVegfavs human + mouse VEGFA transcripts

   

Hs

Mm

Hs

Mm

Hs

Mm

Hs

Mm

Hs

Mm

 

Pure human control

 

0%

1265

0

796

0

2610

0

157

0

287

0

 

Pure mouse control

 

100%

0

1176

0

736

0

303

0

879

0

790

 

Colorectal carcinoma PDX

             

CRC#1

Primary

11%

0

894

2

492

23

453

0

405

4010

212

5%

CRC#2

Primary

5%

0

917

3

398

9

383

0

309

4912

51

1%

CRC#3

Metastasis

21%

1

2380

34

893

14

843

0

803

4642

628

12%

CRC#4

Primary

17%

0

836

<1

285

0

368

0

299

2876

302

10%

CRC#5

Metastasis

8%

0

813

0

492

3

337

0

374

3552

109

3%

CRC#6

Primary

9%

46

458

217

326

77

196

<1

176

1866

84

4%

CRC#7

Metastasis

8%

17

553

27

272

65

292

0

210

5230

251

5%

CRC#8

Primary

14%

0

1193

469

614

3

349

0

689

2999

92

3%

CRC#9

Primary

8%

0

967

8

550

3

475

0

379

7973

204

2%

CRC#10

Primary

10%

0

733

<1

409

176

246

0

284

3463

124

3%

CRC#11

Metastasis

9%

1

1083

<1

481

300

567

0

410

5461

135

2%

CRC#12

Metastasis

4%

48

479

0

182

26

274

0

230

4937

106

2%

CRC#13

Metastasis

4%

3

356

5

135

289

163

0

168

3606

145

4%

CRC#14

Primary

2%

<1

260

7

139

305

119

0

143

5085

76

1%

CRC#15

Primary

17%

<1

1287

<1

715

51

530

0

419

6541

311

5%

CRC#16

Metastasis

5%

<1

477

44

237

89

197

0

219

3406

196

5%

CRC#17

Primary

17%

21

1067

49

539

42

382

0

323

3674

555

13%

CRC#18

Primary

14%

4

1078

81

550

33

370

<1

356

2016

262

12%

CRC#19

Primary

4%

3

288

<1

162

<1

120

0

135

4258

111

3%

CRC#20

Metastasis

22%

4

1580

19

754

10

584

<1

684

5604

391

7%

CRC#21

Metastasis

17%

10

1336

373

749

10

656

0

639

4894

432

8%

CRC#22

Primary

18%

0

2315

322

1081

32

908

0

1262

4671

1244

21%

CRC#23

Metastasis

8%

0

446

407

406

42

202

0

173

2360

155

6%

CRC#24

Primary

12%

0

981

5

581

13

508

0

331

4773

233

5%

CRC#25

Primary

5%

0

622

36

329

0

246

0

285

2643

68

3%

CRC#26

Primary

11%

0

1245

569

480

112

375

0

296

3607

237

6%

CRC#27

Primary

14%

4

1789

3

895

83

682

0

581

3101

891

22%

CRC#28

Carcinosis

5%

3

526

1

326

1

215

0

268

2545

29

1%

CRC#29

Primary

11%

5

1000

2

541

0

364

0

344

3172

391

11%

CRC#30

Primary

7%

0

753

11

332

22

282

0

258

2247

231

9%

CRC#31

Metastasis

10%

<1

629

1

294

29

241

0

216

2896

210

7%

CRC#32

Primary

16%

0

1073

304

556

28

357

0

469

1731

166

9%

CRC#33

Primary

7%

4

563

<1

277

7

202

0

218

1253

129

9%

CRC#34

Primary

13%

2

749

379

530

15

306

0

390

4293

157

4%

CRC#35

Primary

9%

0

958

3

484

9

329

0

318

2206

212

9%

CRC#36

Primary

21%

1

991

0

504

32

388

<1

436

3296

140

4%

CRC#37

Primary

19%

6

1978

16

840

10

391

0

668

2692

182

6%

CRC#38

Primary

8%

2

1114

8

446

2

320

0

367

1889

218

10%

CRC#39

Metastasis

12%

0

1156

478

523

40

366

0

418

4034

214

5%

CRC#40

Primary

10%

<1

547

94

356

49

199

0

242

1848

142

7%

CRC#41

Carcinosis

16%

0

1552

3

762

7

325

0

457

918

228

20%

CRC#42

Primary

31%

0

1786

<1

922

94

447

0

599

2710

493

15%

CRC#43

Primary

10%

0

1024

75

459

249

358

2

431

4126

272

6%

CRC#44

Carcinosis

15%

<1

938

159

565

1

285

0

364

2523

269

10%

CRC#45

Primary

12%

1654

807

512

388

9

215

1

332

969

124

11%

CRC#46

Primary

3%

<1

412

3

158

2

139

0

168

1865

61

3%

CRC#47

Metastasis

6%

0

521

2

252

<1

173

0

195

1662

68

4%

CRC#48

Carcinosis

10%

0

843

<1

417

0

252

0

252

1705

227

12%

CRC#49

Metastasis

6%

1

379

426

274

11

248

0

267

4587

149

3%

CRC#50

Metastasis

18%

31

1697

0

690

23

485

0

421

5271

299

5%

CRC#51

Primary

23%

0

1294

2

662

67

476

0

375

6660

583

8%

CRC#52

Primary

38%

14

3265

398

1126

640

736

0

836

7517

953

11%

CRC#53

Metastasis

19%

0

1657

0

566

15

430

0

430

4014

209

5%

Median

 

10.6%

0.7

958

7

484

22

349

0

344

3463

210

6%

Ovarian carcinoma PDX

             

OVC#1

Metastasis

28%

42

2575

0

1498

89

1191

159

867

10390

459

4%

OVC#2

Metastasis

5%

4

565

99

350

2

439

34

259

6391

88

1%

OVC#3

Metastasis

21%

1

1427

304

809

69

406

<1

583

3133

710

18%

OVC#4

Primary

6%

26

709

9

474

0

259

4

272

1528

144

9%

OVC#5

Primary

7%

16

974

81

807

0

802

45

525

14226

95

1%

OVC#6

Primary

12%

3

2052

97

593

19

734

101

528

2628

427

14%

OVC#7

Primary

8%

0

762

4

470

0

270

32

278

6156

266

4%

OVC#8

Primary

3%

2

219

30

119

6

88.8

3

59.2

652

37

5%

OVC#9

Primary

8%

5

1795

2

674

3

518

5

372

2981

184

6%

OVC#10

Primary

4%

1

444

16

288

0

204

22

141

2812

52

2%

OVC#11

Primary

20%

24

1586

54

1036

0

482

2

648

2781

493

15%

OVC#12

Primary

13%

3

877

177

487

0

259

12

285

1720

127

7%

OVC#13

Primary

3%

17

550

207

263

2

196

<1

224

1134

16

1%

OVC#14

Primary

5%

0

332

<1

255

21

238

<1

164

19239

62

0%

Median

 

7%

3.7

819

42

480

2

338

9

281

2896

136

5%

Glioblastoma PDX

             

GBM#1

Primary

8%

22

712

2051

457

378

559

8

186

18822

241

1%

GBM#2

Primary

13%

1

1351

1143

819

0

799

378

328

17084

296

2%

GBM#3

Primary

13%

1

2372

422

1184

0

1325

0

1237

8452

131

2%

GBM#4

Primary

5%

55

870

321

328

0

503

0

372

5923

78

1%

GBM#5

Primary

15%

0

2600

268

1389

28

1361

294

1413

15443

100

1%

Median

 

13%

1.4

1351

422

819

0

799

8

372

15443

131

1%

Breast cancer carcinoma PDX

             

BC#1

Primary

2%

<1

222

204

113

3

89.4

2

80.7

637

114

15%

BC#2

Primary

8%

0

666

177

335

19

289

0

162

2997

310

9%

BC#3

Metastasis

10%

0

679

286

447

5

539

36

259

4961

334

6%

BC#4

Primary

15%

46

803

91

498

5

366

0

222

3547

447

11%

BC#5

Primary

1%

<1

116

0

61.9

33

89.1

4

31.9

15066

77

1%

BC#6

Metastasis

15%

2

1351

289

634

29

719

150

439

17360

357

2%

BC#7

Primary

22%

8

1908

442

887

0

1370

0

739

27659

365

1%

BC#8

Primary

6%

1

810

149

412

18

327

9

280

8360

160

2%

BC#9

Metastasis

10%

0

713

6

322

13

420

0

279

1020

294

22%

BC#10

Primary

6%

3

370

460

233

0

325

17

134

7447

154

2%

BC#11

Primary

6%

6

993

347

403

29

461

68

325

14282

256

2%

BC#12

Primary

8%

6

1005

466

543

28

664

3

391

25794

363

1%

BC#13

Primary

7%

0

575

92

256

8

266

15

189

6174

132

2%

BC#14

Primary

8%

654

745

45

413

0

279

2

253

3294

71

2%

BC#15

Metastasis

11%

4

912

50

461

1

311

0

286

3458

186

5%

BC#16

Primary

2%

0

199

188

94.6

4

73.6

2

69.7

610

100

14%

BC#17

Primary

4%

13

413

346

134

66

173

32

101

2131

197

8%

BC#18

Primary

10%

<1

1545

168

743

3

788

11

382

6550

167

2%

BC#19

Metastasis

16%

<1

2304

167

1188

5

1049

10

771

6004

280

4%

BC#20

Primary

17%

0

1967

340

959

1

709

20

634

11533

357

3%

BC#21

Primary

9%

0

730

334

332

2

476

91

202

8166

520

6%

BC#22

Primary

6%

0

598

451

222

10

334

90

124

5088

264

5%

BC#23

Primary

4%

0

377

331

179

2

195

19

83.3

1742

51

3%

BC#24

Primary

22%

1

1128

858

982

79

999

<1

495

21363

668

3%

BC#25

Primary

10%

0

1165

666

573

94

627

12

474

14542

244

2%

BC#26

Primary

10%

0

1446

429

572

0

685

230

510

5771

257

4%

BC#27

Primary

14%

2

880

4

452

94

415

<1

222

1505

299

17%

BC#28

Primary

5%

<1

182

91

113

7

119

6

80.3

1221

50

4%

BC#29

Primary

9%

<1

656

530

469

32

532

9

473

50360

255

1%

BC#30

Primary

7%

3

823

94

341

247

403

34

244

9097

373

4%

BC#31

Primary

4%

0

345

166

216

0

161

3

145

1085

79

7%

BC#32

Primary

7%

<1

629

13

276

4

237

19

194

1544

198

11%

BC#33

Primary

9%

<1

725

397

428

232

549

6

231

5414

144

3%

BC#34

Primary

14%

5

1061

245

557

0

457

0

308

3866

176

4%

BC#35

Primary

5%

2

484

103

358

3

506

50

185

23896

360

1%

BC#36

Primary

13%

0

1085

221

544

0

333

13

347

1153

231

17%

BC#37

Primary

4%

0

376

193

149

<1

144

13

120

1081

289

21%

BC#38

Primary

6%

2

776

90

415

8

326

12

281

4683

178

4%

BC#39

Primary

14%

0

961

5

731

83

691

4

606

4829

152

3%

Median

 

7.9%

0.7

745

193

413

5

403

10

253

5088

244

4%

Cutaneous melanoma PDX

             

CM#1

Metastasis

11%

0

1725

2985

825

0

953

83

1026

10386

1401

12%

CM#2

Metastasis

7%

27

544

1391

426

768

360

0

184

10696

315

3%

CM#3

Metastasis

4%

1

282

257

180

0

122

1

155

599

102

14%

CM#4

Primary

20%

0

3306

784

1178

427

883

38

836

9188

718

7%

CM#5

Metastasis

8%

9

936

872

363

15

587

0

289

5590

201

3%

CM#6

Metastasis

3%

<1

342

648

196

6

188

5

236

960

23

2%

CM#7

Metastasis

1%

2

176

382

83.6

2

135

<1

84.3

5962

37

1%

CM#8

Primary

10%

9

4760

876

705

0

2230

5

841

16732

239

1%

CM#9

Metastasis

1%

0

118

284

61.6

20

125

14

73.6

3704

24

1%

CM#10

Primary

10%

0

876

756

300

0

248

279

285

387

126

25%

CM#11

Metastasis

8%

2

641

1102

427

0

355

1

309

8837

266

3%

CM#12

Primary

5%

0

530

112

186

0

440

0

423

683

26

4%

CM#13

Metastasis

2%

<1

243

145

101

<1

116

1

82.2

466

31

6%

Median

 

7.1%

0.9

544

756

300

0.8

355

1

285

5590

126

3%

Renal cell carcinoma PDX

             

RCC#1

Primary

17%

0

1179

569

1002

0

680

2

473

19769

1513

7%

RCC#2

Primary

12%

0

3362

16

1929

0

1934

0

3043

27096

54

0%

RCC#3

Primary

27%

0

5431

411

2376

0

1866

0

1974

25792

211

1%

RCC#4

Metastasis

11%

120

2117

256

1430

4

1512

0

1337

13968

89

1%

RCC#5

Primary

16%

5

2906

33

1942

3

1624

0

2102

25817

87

0%

RCC#6

Primary

1%

1

341

2

125

2

157

47

119

609

13

2%

RCC#7

Primary

21%

0

768

549

1908

0

1292

0

1324

27232

157

1%

RCC#8

Metastasis

17%

1

842

410

778

0

466

0

286

1756

930

35%

RCC#9

Metastasis

13%

17

2024

230

1258

3

827

1

904

37839

55

0%

RCC#10

Primary

11%

0

2010

856

1359

0

1070

2

672

37217

83

0%

RCC#11

Primary

5%

2

597

907

350

0

487

0

253

5091

136

3%

RCC#12

Metastasis

14%

0

2546

257

1132

0

871

0

1040

16952

61

0%

RCC#13

Primary

21%

0

4963

38

3466

0

3281

0

3966

30645

155

1%

RCC#14

Primary

6%

330

1338

364

602

0

661

2

343

26952

52

0%

RCC#15

Primary

6%

77

565

1036

293

0

368

0

291

2210

59

3%

Median

 

12.9%

1.2

2010

364

1258

0

871

0

904

25792

87

1%

Lung carcinoma PDX

             

Small cell lung carcinoma

             

SCLC#1

Primary

8%

0

1030

3

387

0

250

0

196

419

43

9%

SCLC#2

Primary

3%

2

632

0

238

5

189

0

185

1300

52

4%

SCLC#3

Primary

4%

4

591

0

259

1

232

2

232

1117

49

4%

SCLC#4

Primary

3%

7

395

0

222

0

166

0

162

1498

46

3%

SCLC#5

Metastasis

2%

0

309

1

153

0

160

2

122

893

56

6%

SCLC#6

Primary

7%

2

670

9

221

471

208

72

192

954

86

8%

Median

 

3.3%

1.7

612

0

230

1

198

1

189

1035

51

5%

Non small cell lung carcinoma

            

NSCLC#1

Primary

28%

3

1969

61

941

2

1145

14

637

18440

794

4%

NSCLC#2

Primary

8%

0

1270

0

611

0

511

335

639

5911

98

2%

NSCLC#3

Primary

22%

95

1590

31

1438

124

961

930

669

18346

429

2%

NSCLC#4

Primary

5%

2

686

5

339

4

212

59

221

875

85

9%

NSCLC#5

Primary

20%

3

1363

667

1387

184

896

3106

652

10612

688

6%

Median

 

20%

2.7

1363

31

941

4

896

335

639

10612

429

4%

Mouse cells encompass here a wide range of stromal cell types, including fibroblasts, inflammatory and immune cells, smooth muscle cells, and endothelial cells. We further focused on endothelial cells using expression of mouse Pecam1/Cd31 and Eng/Cd105 genes (hereinafter referred to as mCd31 and mCd105, respectively) to evaluate their proportion within xenografts. Vwf gene encoding von Willebrand factor was also preliminary selected but not kept because of a lower expression rate in the mouse and human controls (Ct > 30, data not shown).

As expected, all samples, collected from large xenografts without necrotic centre, expressed mCd31 and mCd105 genes. Nevertheless, mCd31 and mCd105 mRNA levels widely varied between the samples (Table 1), but remained highly correlated to each other (p < 10-7; Table 2). Noteworthy, mCd31 and mCd105 expression levels were highly correlated with the proportion of mouse cells (Table 2), suggesting that the relative amount of endothelial cells remains stable within diverse stromal cell populations, whatever the density of stroma component and the cancer type.
Table 2

Relationships between mouse (m) and human (h) mRNA levels in the 150 human tumor xenografts

 

hCD31

mCd31

hCD105

mCd105

hVEGFR1

mVegfr1

hVEGFR2

mVegfr2

hVEGFA

mVegfa

mCd31

0.0251

         
 

0.762

         

hCD105

0.043

0.121

        
 

0.60

0.14

        

mCd105

0.040

0.928

0.189

       
 

0.63

<0.0000001

0.02

       

hVEGFR1

0.065

0.022

-0.076

0.004

      
 

0.43

0.79

0.35

0.96

      

mVegfr1

0.076

0.851

0.305

0.877

0.006

     
 

0.35

<0.0000001

<0.0002

<0.0000001

0.94

     

hVEGFR2

0.010

-0.029

0.232

-0.036

-0.036

0.070

    
 

0.91

0.72

<0.005

0.66

0.66

0.40

    

mVegfr2

0.003

0.912

0.173

0.919

-0.017

0.858

-0.090

   
 

0.98

<0.0000001

<0.05

<0.0000001

0.83

<0.0000001

0.27

   

hVEGFA

0.095

0.477

0.319

0.563

0.090

0.726

0.131

0.517

  
 

0.25

<0.0000001

<0.0002

<0.0000001

0.27

<0.0000001

0.11

<0.0000001

  

mVegfa

0.031

0.505

0.194

0.524

0.304

0.514

0.062

0.413

0.328

 
 

0.70

<0.0000001

<0.05

<0.0000001

<0.0002

<0.0000001

0.45

<0.0000001

<0.00005

 

% mouse cells

-0.016

0.828

0.113

0.865

0.154

0.715

-0.145

0.797

0.364

0.666

 

0.84

<0.0000001

0.17

<0.0000001

0.06

<0.0000001

0.08

<0.0000001

<0.000005

<0.0000001

Results, expressed as N-fold differences in target gene expression relative to the mouse and human TBP genes (both the mouse and human TBP transcripts) and termed “Ntarget”, were determined as Ntarget = 2∆Ctsample , where the ∆Ct value of the sample was determined by subtracting the average Ct value of target gene (human or mouse) from the average Ct value of ‘Total-TBP’ gene). The Ntarget values of the tumor samples were subsequently normalized such that the value for mRNA level was 1 when Ct=35. Target mRNA levels that were total absence or very low (Ct > 38) in tumor samples were scored ‘0’ for non expressed. As for calculation of % of mouse cells, specific mouse Tbp gene expression and the expression of both the mouse and the human TBP genes were studied by real-time qRT-PCR using the mouse Tbp as target gene and the ‘Total-TBP’ as endogenous RNA control. Results, expressed as N-fold differences in specific mouse Tbp gene expression (using mouse Tbp primers) relative to the sum of the mouse and the human TBP gene expression (using ‘Total-TBP’ primers), termed NMm-TBP, are determined by theformula: NMm-TBP = 2DCtsample. The DCt value of the sample is determined by subtracting the Ct value of the mouse TBP gene from the Ct value of the Total TBP gene. The NMm-TBP values of the samples are subsequently normalized such that the median of NMm-TBP values of 4 mouse tissues was 100. As TBP is a ubiquitously expressed housekeeping gene, showing similar expression in our human and mouse tissues (Ct=27 for 5 ng cDNA), the final result (normalized NMm-TBP value) gives an estimate of the proportion of mouse cell content for a given xenograft. 1Spearman correlation coefficient, 2p value of Spearman rank correlation test, in bold when p is significant.

While numerous pro-angiogenic factors have been characterized, the VEGFA ligand has been identified as a predominant regulator of tumor angiogenesis and binds to VEGFR1 and VEGFR2 expressed on vascular endothelial cells. It mediates numerous changes within the tumor vasculature, including endothelial cell proliferation, migration, invasion, survival, chemotaxis of bone marrow-derived progenitor cells, vascular permeability and vasodilatation [1, 2]. VEGFA expression by cancer cells is up-regulated by altered expression of oncogenes, a variety of growth factors and also hypoxia [2].

Unsurprisingly, we observed high levels of mouse Flt1/Vegfr1, mouse Kdr/Vegfr2 (hereby denominated mVegfr1 and mVegfr2) and human VEGFA (hVEGFA) transcripts, which correlated all with mCd31 and mCd105 RNA levels (Table 2). These strong positive correlations underline classical paracrine VEGFA-VEGFR1/2 signaling in tumorigenesis and crosstalk between the human ligand and mouse receptors. Expression of mVegfr1, mVegfr2 and hVEGFA however varied widely in the different tumor types. RCC, glioblastoma and NSCLC xenografts showed transcript level median of these three genes at least 2 times higher than in the 5 other tumor xenograft types (Table 1, Figure 1). According to the expression level of mCd105, mCd31, mVegfr1, mVegfr2 and hVEGFA (Figure 1), the most angiogenic PDXs are then renal cell carcinoma, glioblastoma, and NSCLs, tumor types well-known to be the most angiogenic tumors in patients [11], underlying the interest of PDX models to mimic patient tumors.
https://static-content.springer.com/image/art%3A10.1186%2F1471-2407-14-178/MediaObjects/12885_2013_Article_5220_Fig1_HTML.jpg
Figure 1

Gene expression levels of mouse endothelial markers and hVEGFA in the 8 human tumor xenograft types. Box-and-whisker diagrams showing the expression of mouse endothelial marker genes (mCd31, mCd105, mVegfr1, mVegfr2), plot on left Y axis and hVEGFA gene plot on right Y axis. The box indicates the interquartile range, the centre horizontal line the median value and the black dots are outliers.

Surprisingly, we observed also marked level of mVegfa transcripts ranged from 50.7 (median in SCLC xenografts) to 429 (median in NSCLC xenografts). Individually, some xenografts showed more than 20% of the total VEGFA transcripts of mouse origin (Table 1). While VEGFA production by cancer cells is commonly reported, significant VEGFA expression has been also observed by fibroblasts and immune cells that surround and invade the tumor mass [12]. As reported by others [13], great attention has to be paid to mouse stromal VEGFA when anti-VEGF agents displaying specific human activity are tested in xenograft preclinical models.

Angiogenesis and vasculogenesis, mediated by angiogenic factors such as VEGFA are commonly accepted to support tumor vasculature. Vascular mimicry (ability of tumor cells to form functional vessel-like networks, devoid of endothelial cells) and cancer stem cell transdifferentiation into tumor endothelial cells are also two mechanisms recently reported in different tumors, including melanoma, breast, renal, ovarian cancer and glioblastoma [1418] in which tumor cells directly participate in vascular channels. The presence of tumor-derived endothelial cells (TDECs) is usually investigated through the detection of CD31+ and CD105+ tumor cells [1518]. TDEC cells are generally rare events and their identification needs highly sensitive methods (flow cytometry or confocal microscopy). Likewise, another approach to improving the detection of TDEC is to enhance the TDEC frequency by implanting into mice cancer stem cell enriched population. This prior enrichment could be done by culturing cells as tumor spheres [19, 20] or by cell sorting for putative cancer stem cell markers [15, 21]. Only one recent publication attempted to immunostain human CD31 directly in 3 human tumor xenografts, with no preliminary step of TDEC or CSC enrichment [22]. This study did not detect human CD31 and led the authors to conclude that endothelial cells in human hepatocellular carcinoma xenografts are of mouse rather than human origin, but did not allow them to absolutely exclude this possibility. Consequently, we apply in our PDX panel the real-time qRT-PCR method, known for its very high sensitivity, using human-specific PECAM1/CD31 (hCD31) and ENG/CD105 (hCD105) to gain more insight into TDECs.

Surprisingly, we detected hCD31 and hCD105 transcripts in all types of PDXs, suggesting that TDECs can exist in virtually all types of cancer. The possibility of human endothelial marker signals due to very rare remaining human stroma cells can not be ignored, although the whole human stroma in tumor xenografts is reported to be eventually replaced by stroma of mouse origin [8, 23, 24]. But depending upon the types, the range of expression of hCD31 and hCD105 transcripts largely varied (Figure 2a-b). All tested samples of cutaneous melananoma and GBM highly expressed hCD105 gene (NHs-ENG >100). Literature indeed reports a large expression of CD105, a member of the transforming growth factor beta receptor family, on normal and neoplastic cells of the melanocytic lineage, including melanoma cell lines, and an up-regulation in gene signature of aggressive cutaneous melanoma in patients [14]. Likewise, CD105 is highly expressed in glioblastoma but essentially absent in normal brain [21]. RCC xenografts displayed a great proportion of samples expressed high levels of hCD31 or hCD105. These results fit with the literature that identified TDECs in patients mainly in glioblastoma and renal cancer [16, 21]. By contrast, SCLCs show very low levels of both hCD31 and hCD105 mRNAs. A striking point is that hCD31 and hCD105 RNA levels did not correlate to each others (Table 2), even if their expression is analyzed for each cancer type (data not shown). It could be explained by different expression profiles for these 2 endothelial molecules: CD31 is considered as a pan-endothelial marker, whereas CD105 is a cell membrane glycoprotein predominantly expressed on cellular lineages within the vascular system, and over-expressed on proliferating endothelial cells [25]. These data underline that combination of markers is required to study the TDEC population.
https://static-content.springer.com/image/art%3A10.1186%2F1471-2407-14-178/MediaObjects/12885_2013_Article_5220_Fig2_HTML.jpg
Figure 2

Variations of human hCD31 (a), hCD105 (b), hVEGFR1 (c) and hVEGFR2 (d) gene expression within the 8 human tumor xenograft types. Results are expressed for each cancer type as percent of PDX specimens showing normalized Ntarget values in the following categories: no expression, 0 to 1, 1 to 10, 10 to 100 or more than 100.

Initially, VEGFRs were thought to be expressed only on endothelial cells, but these receptors may also be expressed on tumor cells and play a role in tumor resistance to existing therapies [57]. The present species-specific real-time qRT-PCR assays combined with our series of 150 PDXs represents a powerful tool to obtain further insight into autocrine and paracrine VEGFA-VEGR1/2 signaling in tumorigenesis. We indeed observed human VEGFR expression in xenografts with a profile that varied widely according to tumor types (Table 1, Figure 2c-d): High levels of hVEGFR1 transcripts mainly observed in colon cancers and in NSCLCs; high levels of hVEGFR2 transcripts in NSCLCs. Individually, 2 out of 5 NSCLC xenografts (i.e.: NSCLC#3 and #5) showed more hVEGFR2 transcripts than mVegfr2 transcripts (Table 1). Conversely, SCLCs showed low levels of hVEGFR1 and hVEGFR2 transcripts and CRCs showed very low levels of hVEGFR2 transcripts (Absence in 89% of the 53 CRC xenografts). These results identified NSCLC as an attractive cancer type for anti-VEGFR2 treatment. Small-molecule inhibitors as Sunitinib and Sorafenib are oral multikinase inhibitors, including VEGFR2 among their targets. The development of antibodies that can selectively block VEGFR2 could potentially result in improved potency or tolerability [3].

Whereras mVegfr1 and mVegfr2 expressions were extremely correlated to mouse endothelial markers (p < 10-7), human VEGFR profiles did not correlate highly with neither hCD31 nor hCD105. Non exclusive hypotheses could explain this observation: i) human tumor cells expressing endothelial markers lead to VEGF- independent tumor vascularization with no expression of VEGFR1/2 [20]; ii) VEGFRs could be also expressed on carcinoma and participate to an essential autocrine/paracrine process for cancer cell proliferation and survival [1].

Collectively, VEGFA/VEGFR analyses suggest several autocrine and paracrine VEGFA-VEGFR1/2 signalings. In additional to the classical paracrine human tumoral VEGFA/mouse stromal VEGFR signalling, our data identified 3 others potential VEGFA-VEGFR signalings: a human cancer autocrine VEGFA/VEGFR signaling, an autocrine or paracrine mouse stromal VEGFA/VEGFR signaling, and a paracrine mouse stromal VEGFA/ human tumoral VEGFR signaling. It is noteworthy that the human cancer autocrine VEGFA/VEGFR signaling could occur intracellular, as well as by VEGFA secretion [6], limiting the quantity of extracellular VEGFA. Thus, VEGFR small-molecule inhibitors might be a more attractive therapy than VEGFA inhibitors which aim to sequestering free VEGFA.

To further investigate the potential value of species-specific PCR assays for in vivo evaluation of anti-angiogenesis therapy in PDX models, we analyzed in the same manner as described above, 2 NSCLC xenograft models after treatment with bevacizumab, a recombinant humanized monoclonal antibody to VEGF, approved for cancer therapy, including in NSCLC patients. These both models highly responded to one week-bevacizumab treatment in monotherapy: no tumor shrinkage but tumor stabilization throughout the experiment (Additional file 2: Figure S1).

As expected, the levels of mCd31, mCd105, mVegfr1 and mVegfr2 transcripts were significantly lower in the two bevacizumab-treated NSCLC xenografts as compared to matched non-treated xenografts (Table 3). Indeed, even if bevacizumab is able to bind and inhibit human VEGFA but unable to neutralize murine VEGFA, VEGFA in these 2 xenografts is produced by human cancer cells rather than by mouse stroma cells. It is noteworthy that one of the two xenografts (NSCLC#3) showed a significant upregulation of hVEGFA gene. More interestingly, the levels of hCD31, hCD105, hVEGFR1 and hVEGFR2 transcripts were not inferior in the two bevacizumab-treated NSCLC xenografts but on the contrary, hCD31 was upregulated by 3 times (p < 0.05 for NSCLC#3) in both bevacizumab-treated xenografts. These data suggest that the mouse endothelial cells are more sensitive to anti-VEGFA therapy than human cells. Indeed, cancer cells are able to take advantage of autocrine intracellular VEGFA/VEGFR signalling [6] while bevacizumab is directed against free fraction of VEGFA. Furthermore, transdifferentiation of tumor cells into endothelial cells has been reported to be VEGF-independent but induced by HIF-1α [20]. Finally, bevacizumab induces hypoxia through mouse endothelial cells destruction, which may lead in turn to TDEC expansion. These latter results are of interest to apprehend molecular mechanisms of bevacizumab resistance.
Table 3

Target mRNA levels in 2 NSCLC xenografts after bevacizumab treatment

 

NSCLC#3

NSCLC#5

  

Control (n=5)

After bevacizumab reatment (n=5)

p-value 1

Control (n=5)

After bevacizumab treatment (n=5)

p-value 1

PECAM1/CD31 mRNA

Human

18.1 (7.34-43.1)

57.6 (31.8-64.2)

<0.05

2.38 (0.00-9.21)

6.70 (2.41-17.1) NS

 
 

Mouse

863 (686-1790)

578 (483-847)

<0.05

2 334 (1 538-4 363)

856 (699-980)

<0.05

ENG/CD105 mRNA

Human

29.1 (3.59-47.2)

38.2 (15.1-71.4)

NS

57.64 (38.8-90.86)

57.50 (47.2 - 84.4)

NS

 

Mouse

619 (580-1098)

414 (328-619)

<0.05

1 519 (1120-1813)

821 (610-860)

<0.05

FLT1/VEGFR1 mRNA

Human

59.6 (56.7-90.6)

88.9 (62.3-118)

NS

3.84 (0.00-24.8)

9.11 (3.87-20.3)

NS

 

Mouse

589 (470-909)

274 (212-362)

<0.05

938 (633-1163)

305 (216-344)

<0.05

KDR/VEGFR2 mRNA

Human

507 (361-622)

545 (488-643)

NS

220 (140-274)

574 (213-834)

NS

 

Mouse

466 (386-800)

204 (196-298)

<0.05

1 175 (698-1 211)

328 (316-349)

<0.05

VEGFA mRNA

Human

20 503 (19162-24600)

32 160 (30 331-35 680)

<0.05

11 984 (5 368-13 961)

12 235 (7 088-14 042)

NS

 

Mouse

160 (119-495)

307 (184-614)

NS

262 (170-680)

267 (240-360)

NS

Results are expressed as normalized N-fold differences in target gene expression relative to the ‘Total-TBP’ expression. These Ntarget values of the tumor samples were normalized such that the value for the ’basal mRNA level‘ (Ct = 35) was 1Target mRNA levels that were total absence or very low (Ct > 38) in tumor samples were scored ‘0’ for non expressed.

Median and range in () are given for each gene in the different experimental conditions. 1Mann Whitney Test; NS, not significant; in bold, significant.

Conclusions

The screening of a large panel of xenografts established from various tumor types is appropriate to identify the human tumor types that are likely to benefit from a new targeted therapy, and next to identify predictive biomarkers for the response to this targeted therapy. Human tumor xenografted models, closely mimicking clinical situations in terms of biological features and response to treatment [8], will also provide the necessary experimental conditions to evaluate fundamental issues in cancer, including characteristics of metastasis, angiogenesis, and tumor-stroma interactions. The present approach combining species-specific real-time qRT-PCR assays with a large cohort of patient-derived xenografts identified tumor endothelial cells in the all 8 tumor types tested and also revealed a complex pattern of both stroma and tumoral and both autocrine and paracrine VEGFA-VEGFR1/2 signalings. These both findings should be taken into account when evaluating molecular mechanisms of resistance to tumor anti-angiogenic strategies.

Methods

Patient-derived xenografts

Tumor xenografts have been established directly from patient tumors and were routinely passaged by subcutaneous engraftment in Crl:NU(Ico)-Foxn1nu or CB17/Icr-Prkdcscid/IcrCrl [23, 24, 2631] purchased from Charles River Laboratories (Les Arbresles, France), with protocol and animal housing in accordance with national regulation and international guidelines [32]. Xenografts were harvested here, after 5 to 12 passages into mice, when they reached around 2,000 mg in size.

Bevacizumab (Avastin, Roche) was given i.p. twice a week, one week, at 15 mg/kg in 0.9% NaCl. Omalizumab (Xolair, Novartis) is given as isotypic control. Lung carcinoma xenografts were transplanted into female 8-week-old Crl:NU(Ico)-Foxn1nu mice. Mice with tumors of 60–200 mm3 were randomly assigned to control or treated groups. Tumor growth was evaluated by measurement of two perpendicular tumor diameters with a caliper twice a week. Individual tumor volumes were calculated: V = a × b2/2, a being the largest diameter, b the smallest. Mice were ethically sacrificed when the tumor volume reached 2 500 mm3 for control groups or at D29 and D50 after first injection of bevacizumab for NSCLC#2 and NCSCLC#3, respectively.

Real-time RT-PCR

RNA extraction, cDNA synthesis and PCR conditions were previously described [33]. The precise amount and quality of total RNA in each reaction mix are both difficult to assess. Therefore, transcripts of the TBP gene encoding the TATA box-binding protein (a component of the DNA-binding protein complex TFIID) were quantified as an endogenous RNA control. The endogenous TBP control was selected due to the moderate prevalence of its transcripts and the absence of known TBP retropseudogenes (retropseudogenes lead to coamplification of contaminating genomic DNA and thus interfere with RT-PCR, despite the use of primers in separate exons) [9].

Quantitative values were obtained from the cycle number (Ct value) (Perkin-Elmer Applied Biosystems, Foster City, CA), according to the manufacturer’s manuals.

The gene primers (Additional file 1: Table S1) were chosen using the Oligo 6.0 program (National Biosciences, Plymouth, MN). The mouse and the human target genes primer pairs were selected to be unique when compared to the sequence of their respective orthologous gene. By contrast, a primer pair, referred as to ‘Total-TBP’ primer pair, was selected to amplify both the mouse and the human TBP genes. dbEST and nr databases were scanned to confirm the total gene specificity of the nucleotide sequences chosen for the primers and the absence of single nucleotide polymorphisms. To avoid amplification of contaminating genomic DNA, one of the two primers was always placed at the junction between two exons. Agarose gel electrophoresis was used to verify the specificity of PCR amplicons. For each human-specific primer pair validation, we performed no-template control (NTC), no-human-reverse-transcriptase control (human RT negative), mouse-reverse-transcriptase control (mouse RT positive from a pool of normal and tumoral mouse RNAs extracted from various tissues types) assays, which produced negligible signals (Ct >40), suggesting that primer–dimer formation, genomic DNA contamination and cross species contamination effects were negligible. Same controls were realized for each mouse-specific primer pair.

Statistical analysis

The distributions of mRNA levels were characterized by their median values and ranges. Relationships between mRNA levels of the different target genes were identified using nonparametric tests (GraphPad Prism 4.00, GraphPad Software, San Diego, CA).

Abbreviations

CRC: 

Colorectal cancer

CSC: 

Cancer stem cell

GBM: 

Glioblastoma

NSCLC: 

Non small cell lung carcinoma

PDX: 

Patient-derived tumor xenograft

RCC: 

Renal cell carcinoma

SCLC: 

Small cell lung carcinoma

TDEC: 

Tumor-derived endothelial cell

mCd31: 

Mouse Pecam1 gene encoding mouse CD31

mCd105: 

Mouse Eng gene encoding mouse CD105

mVegfr1: 

Mouse Flt1 gene encoding mouse VEGFR1

mVegfr2: 

Mouse Kdr gene encoding mouse VEGFR2

hCD31: 

Human PECAM1 gene encoding human CD31

hCD105: 

Human ENG gene encoding human CD105

hVEGFR1: 

Human FLT1 gene encoding human VEGFR1

hVEGFR2: 

Human KDR gene encoding human VEGFR2.

Declarations

Acknowledgments

We thank Ludovic Bigot, Ludovic Lacroix, Franck Assayag and Dalila Labiod for the management of RNA, PDX tissues or PDX engrafted mice. We are grateful to Chantal Martin and Isabelle Grandjean for housing and care of mice in the animal facility of IMTCE and Institut Curie, respectively.

This work was supported by the Comité départemental des Hauts-de-Seine de la Ligue Nationale Contre le Cancer, the Conseil régional d'Ile-de-France, the Cancéropôle Ile-de-France and the Association pour la recherche en cancérologie de Saint-Cloud (ARCS), Genevieve and Jean-Paul Driot Transformative Research Grant, Philippe and Laurent Bloch Cancer Research Grant, Hassan Hachem Translational Medicine Grant and Sally Paget-Brown Translational Research Grant.

Authors’ Affiliations

(1)
Laboratoire d’Oncogénétique, 35 rue Dailly, Institut Curie - Hôpital Rene Huguenin
(2)
INSERM UMR745, Sorbonne Paris Cité
(3)
Département de Recherche Translationnelle, Laboratoire d’Investigation Préclinique
(4)
Roche SAS, 30, cours de l'Ile Seguin
(5)
IFR71, Sorbonne Paris Cité
(6)
CNRS, UMR 144, Centre de Recherche, Institut Curie
(7)
Institut de Cancérologie Gustave Roussy
(8)
Département d’Oncologie Médicale, Institut Curie
(9)
Université Paris Descartes, Sorbonne Paris Cité
(10)
Research Center, Institut Curie

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  34. Pre-publication history

    1. The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1471-2407/14/178/prepub

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