Top

Published in:

Open Access 01-05-2021 | Esophageal Cancer | Gastrointestinal

Addition of HER2 and CD44 to ¹⁸F-FDG PET–based clinico-radiomic models enhances prediction of neoadjuvant chemoradiotherapy response in esophageal cancer

Authors: Roelof J. Beukinga, Da Wang, Arend Karrenbeld, Willemieke P. M. Dijksterhuis, Hette Faber, Johannes G. M. Burgerhof, Véronique E. M. Mul, Riemer H. J. A. Slart, Robert P. Coppes, John Th. M. Plukker

Published in: European Radiology | Issue 5/2021

Abstract

Objectives

To assess the complementary value of human epidermal growth factor receptor 2 (HER2)-related biological tumor markers to clinico-radiomic models in predicting complete response to neoadjuvant chemoradiotherapy (NCRT) in esophageal cancer patients.

Methods

Expression of HER2 was assessed by immunohistochemistry in pre-treatment tumor biopsies of 96 patients with locally advanced esophageal cancer. Five other potentially active HER2-related biological tumor markers in esophageal cancer were examined in a sub-analysis on 43 patients. Patients received at least four of the five cycles of chemotherapy and full radiotherapy regimen followed by esophagectomy. Three reference clinico-radiomic models based on ¹⁸F-FDG PET were constructed to predict pathologic response, which was categorized into complete versus incomplete (Mandard tumor regression grade 1 vs. 2–5). The complementary value of the biological tumor markers was evaluated by internal validation through bootstrapping.

Results

Pathologic examination revealed 21 (22%) complete and 75 (78%) incomplete responders. HER2 and cluster of differentiation 44 (CD44), analyzed in the sub-analysis, were univariably associated with pathologic response. Incorporation of HER2 and CD44 into the reference models improved the overall performance (R²s of 0.221, 0.270, and 0.225) and discrimination AUCs of 0.759, 0.857, and 0.816. All models exhibited moderate to good calibration. The remaining studied biological tumor markers did not yield model improvement.

Conclusions

Incorporation of HER2 and CD44 into clinico-radiomic prediction models improved NCRT response prediction in esophageal cancer. These biological tumor markers are promising in initial response evaluation.

Key Points

• A multimodality approach, integrating independent genomic and radiomic information, is promising to improve prediction of γpCR in patients with esophageal cancer.

• HER2 and CD44 are potential biological tumor markers in the initial work-up of patients with esophageal cancer.

• Prediction models combining ¹⁸ F-FDG PET radiomic features with HER2 and CD44 may be useful in the decision to omit surgery after neoadjuvant chemoradiotherapy in patients with esophageal cancer.

Available only for authorised users

van Hagen P, Hulshof MC, van Lanschot JJ et al (2012) Preoperative chemoradiotherapy for esophageal or junctional cancer. N Engl J Med 366:2074–2084. https://doi.org/10.1056/NEJMoa1112088CrossRefPubMed

Kwee RM (2010) Prediction of tumor response to neoadjuvant therapy in patients with esophageal cancer with use of ¹⁸F FDG PET: a systematic review. Radiology 254:707–717. https://doi.org/10.1148/radiol.09091324CrossRefPubMed

Dexter DL, Leith JT (1986) Tumor heterogeneity and drug resistance. J Clin Oncol 4:244–257. https://doi.org/10.1200/JCO.1986.4.2.244CrossRefPubMed

O'Connor JP, Rose CJ, Waterton JC, Carano RA, Parker GJ, Jackson A (2015) Imaging intratumor heterogeneity: role in therapy response, resistance, and clinical outcome. Clin Cancer Res 21:249–257. https://doi.org/10.1158/1078-0432.CCR-14-0990CrossRefPubMed

Harris AL (2002) Hypoxia--a key regulatory factor in tumour growth. Nat Rev Cancer 2:38–47. https://doi.org/10.1038/nrc704CrossRefPubMed

Yang Z, He B, Zhuang X et al (2019) CT-Based radiomic signatures for prediction of pathologic complete response in esophageal squamous cell carcinoma after neoadjuvant chemoradiotherapy. J Radiat Res 60:538–545. https://doi.org/10.1093/jrr/rrz027CrossRefPubMedPubMedCentral

Hou Z, Ren W, Li S et al (2017) Radiomic analysis in contrast-enhanced CT: predict treatment response to chemoradiotherapy in esophageal carcinoma. Oncotarget 8:104444–104454. https://doi.org/10.18632/oncotarget.22304CrossRefPubMedPubMedCentral

Jin X, Zheng X, Chen D et al (2019) Prediction of response after chemoradiation for esophageal cancer using a combination of dosimetry and CT radiomics. Eur Radiol 29:6080–6088. https://doi.org/10.1007/s00330-019-06193-wCrossRefPubMed

van Rossum PS, Fried DV, Zhang L et al (2016) The incremental value of subjective and quantitative assessment of ¹⁸F-FDG PET for the prediction of pathologic complete response to preoperative chemoradiotherapy in esophageal cancer. J Nucl Med 57:691–700. https://doi.org/10.2967/jnumed.115.163766CrossRefPubMed

10.

Beukinga RJ, Hulshoff JB, Mul VEM et al (2018) Prediction of response to neoadjuvant chemotherapy and radiation therapy with baseline and restaging ¹⁸F-FDG PET imaging biomarkers in patients with esophageal cancer. Radiology 287:983–992. https://doi.org/10.1148/radiol.2018172229CrossRefPubMed

11.

Tixier F, Le Rest CC, Hatt M et al (2011) Intratumor heterogeneity characterized by textural features on baseline ¹⁸F-FDG PET images predicts response to concomitant radiochemotherapy in esophageal cancer. J Nucl Med 52:369–378. https://doi.org/10.2967/jnumed.110.082404CrossRefPubMed

12.

Tan S, Kligerman S, Chen W et al (2013) Spatial-temporal [¹⁸F]FDG-PET features for predicting pathologic response of esophageal cancer to neoadjuvant chemoradiation therapy. Int J Radiat Oncol Biol Phys 85:1375–1382. https://doi.org/10.1016/j.ijrobp.2012.10.017CrossRefPubMed

13.

Yip SS, Coroller TP, Sanford NN, Mamon H, Aerts HJ, Berbeco RI (2016) Relationship between the temporal changes in positron-emission-tomography-imaging-based textural features and pathologic response and survival in esophageal cancer patients. Front Oncol 6:72–82. https://doi.org/10.3389/fonc.2016.00072CrossRefPubMedPubMedCentral

14.

Beukinga RJ, Hulshoff JB, van Dijk LV et al (2017) Predicting response to neoadjuvant chemoradiotherapy in esophageal cancer with textural features derived from pretreatment ¹⁸F-FDG PET/CT imaging. J Nucl Med 58:723–729. https://doi.org/10.2967/jnumed.116.180299CrossRefPubMed

15.

Nakajo M, Jinguji M, Nakabeppu Y et al (2017) Texture analysis of ¹⁸F-FDG PET/CT to predict tumour response and prognosis of patients with esophageal cancer treated by chemoradiotherapy. Eur J Nucl Med Mol Imaging 44:206–214. https://doi.org/10.1007/s00259-016-3506-2CrossRefPubMed

16.

Wang D, Plukker JT, Coppes RP (2017) Cancer stem cells with increased metastatic potential as a therapeutic target for esophageal cancer. Semin Cancer Biol. https://doi.org/10.1016/j.semcancer.2017.03.010

17.

Bollschweiler E, Hölscher AH, Schmidt M, Warnecke-Eberz U (2015) Neoadjuvant treatment for advanced esophageal cancer: response assessment before surgery and how to predict response to chemoradiation before starting treatment. Chin J Cancer Res 27:221–230. https://doi.org/10.3978/j.issn.1000-9604.2015.04.04CrossRefPubMedPubMedCentral

18.

Tao CJ, Lin G, Xu YP, Mao WM (2015) Predicting the response of neoadjuvant therapy for patients with esophageal carcinoma: an in-depth literature review. J Cancer 6:1179–1186. https://doi.org/10.7150/jca.12346CrossRefPubMedPubMedCentral

19.

Vallböhmer D, Brabender J, Grimminger P, Schröder W, Hölscher AH (2011) Predicting response to neoadjuvant therapy in esophageal cancer. Expert Rev Anticancer Ther 11:1449–1455. https://doi.org/10.1586/era.11.126CrossRefPubMed

20.

Gowryshankar A, Nagaraja V, Eslick GD (2014) HER2 status in Barrett’s esophagus & esophageal cancer: a meta analysis. J Gastrointest Oncol 5:25–35. https://doi.org/10.3978/j.issn.2078-6891.2013.039CrossRefPubMedPubMedCentral

21.

Miller S, Hung M (1995) Regulation of her2/neu gene-expression (review). Oncol Rep 2:497–503. https://doi.org/10.3892/or.2.4.497CrossRefPubMed

22.

Bartley AN, Washington MK, Colasacco C et al (2017) HER2 testing and clinical decision making in gastroesophageal adenocarcinoma: Guideline from the College of American Pathologists, American Society for Clinical Pathology, and the American Society of Clinical Oncology. J Clin Oncol 35:446–464. https://doi.org/10.1200/JCO.2016.69.4836CrossRefPubMed

23.

Prins MJ, Ruurda JP, van Diest PJ, van Hillegersberg R, Ten Kate FJ (2013) The significance of the HER-2 status in esophageal adenocarcinoma for survival: an immunohistochemical and an in situ hybridization study. Ann Oncol 24:1290–1297. https://doi.org/10.1093/annonc/mds640CrossRefPubMed

24.

Wang D, Nagle PW, Wang HH et al (2019) Hedgehog pathway as a potential intervention target in esophageal cancer. Cancers 11:821. https://doi.org/10.3390/cancers11060821CrossRefPubMedCentral

25.

Gros SJ, Kurschat N, Drenckhan A et al (2012) Involvement of CXCR4 chemokine receptor in metastastic HER2-positive esophageal cancer. PLoS One 7:e47287. https://doi.org/10.1371/journal.pone.0047287CrossRefPubMedPubMedCentral

26.

Bao W, Fu HJ, Xie QS et al (2011) HER2 interacts with CD44 to up-regulate CXCR4 via epigenetic silencing of microRNA-139 in gastric cancer cells. Gastroenterology 141:2076–2087.e6. https://doi.org/10.1053/j.gastro.2011.08.050CrossRefPubMed

27.

Rice TW, Blackstone EH, Rusch VW (2010) 7th edition of the AJCC cancer staging manual: esophagus and esophagogastric junction. Ann Surg Oncol 17:1721–1724. https://doi.org/10.1245/s10434-010-1024-1CrossRefPubMed

28.

Mandard AM, Dalibard F, Mandard JC et al (1994) Pathologic assessment of tumor regression after preoperative chemoradiotherapy of esophageal carcinoma. clinicopathologic correlations. Cancer 73:2680–2686. https://doi.org/10.1002/1097-0142(19940601)73:11<2680::aid-cncr2820731105>3.0.CO;2-CCrossRefPubMed

29.

Boellaard R, Delgado-Bolton R, Oyen WJG et al (2015) FDG PET/CT: EANM procedure guidelines for tumour imaging: version 2.0. Eur J Nucl Med Mol Imaging 42:328–354. https://doi.org/10.1007/s00259-014-2961-xCrossRefPubMed

30.

Zwanenburg A, Vallières M, Abdalah MA et al (2020) The image biomarker standardization initiative: standardized quantitative radiomics for high-throughput image-based phenotyping. Radiology:328–338. https://doi.org/10.1148/radiol.2020191145

31.

Hofmann M, Stoss O, Shi D et al (2008) Assessment of a HER2 scoring system for gastric cancer: results from a validation study. Histopathology 52:797–805. https://doi.org/10.1111/j.1365-2559.2008.03028.xCrossRefPubMed

32.

Tubbs RR, Pettay JD, Roche PC, Stoler MH, Jenkins RB, Grogan TM (2001) Discrepancies in clinical laboratory testing of eligibility for trastuzumab therapy: apparent immunohistochemical false-positives do not get the message. J Clin Oncol 19:2714–2721. https://doi.org/10.1200/JCO.2001.19.10.2714CrossRefPubMed

33.

Honing J, Pavlov KV, Mul VE et al (2015) CD44, SHH and SOX2 as novel biomarkers in esophageal cancer patients treated with neoadjuvant chemoradiotherapy. Radiother Oncol 117:152–158. https://doi.org/10.1016/j.radonc.2015.08.031CrossRefPubMed

34.

Steyerberg EW, Vickers AJ, Cook NR et al (2010) Assessing the performance of prediction models: a framework for traditional and novel measures. Epidemiology 21:128–138. https://doi.org/10.1097/EDE.0b013e3181c30fb2CrossRefPubMedPubMedCentral

35.

Vallböhmer D, Kuhn E, Warnecke-Eberz U et al (2008) Failure in downregulation of intratumoral survivin expression following neoadjuvant chemoradiation in esophageal cancer. Pharmacogenomics 9:681–690. https://doi.org/10.2217/14622416.9.6.681CrossRefPubMed

36.

Smit JK, Faber H, Niemantsverdriet M et al (2013) Prediction of response to radiotherapy in the treatment of esophageal cancer using stem cell markers. Radiother Oncol 107:434–441. https://doi.org/10.1016/j.radonc.2013.03.027CrossRefPubMed

37.

Liang JW, Zhang JJ, Zhang T, Zheng ZC (2014) Clinicopathological and prognostic significance of HER2 overexpression in gastric cancer: a meta-analysis of the literature. Tumour Biol 35:4849–4858. https://doi.org/10.1007/s13277-014-1636-3CrossRefPubMed

38.

Fang M, Wu J, Lai X et al (2016) CD44 and CD44v6 are correlated with gastric cancer progression and poor patient prognosis: evidence from 42 studies. Cell Physiol Biochem 40:567–578. https://doi.org/10.1159/000452570CrossRefPubMed

39.

Shiri I, Rahmim A, Ghaffarian P, Geramifar P, Abdollahi H, Bitarafan-Rajabi A (2017) The impact of image reconstruction settings on ¹⁸F-FDG PET radiomic features: multi-scanner phantom and patient studies. Eur Radiol 27:4498–4509. https://doi.org/10.1007/s00330-017-4859-zCrossRefPubMed

40.

van Velden FH, Kramer GM, Frings V et al (2016) Repeatability of radiomic features in non-small-cell lung cancer [¹⁸F]FDG-PET/CT studies: impact of reconstruction and delineation. Mol Imaging Biol 18:788–795. https://doi.org/10.1007/s11307-016-0940-2CrossRefPubMedPubMedCentral

41.

Pfaehler E, Beukinga RJ, de Jong JR et al (2019) Repeatability of ¹⁸F-FDG PET radiomic features: a phantom study to explore sensitivity to image reconstruction settings, noise, and delineation method. Med Phys 46:665–678. https://doi.org/10.1002/mp.13322CrossRefPubMed

Title: Addition of HER2 and CD44 to 18F-FDG PET–based clinico-radiomic models enhances prediction of neoadjuvant chemoradiotherapy response in esophageal cancer
Authors: Roelof J. Beukinga
Da Wang
Arend Karrenbeld
Willemieke P. M. Dijksterhuis
Hette Faber
Johannes G. M. Burgerhof
Véronique E. M. Mul
Riemer H. J. A. Slart
Robert P. Coppes
John Th. M. Plukker
Publication date: 01-05-2021
Publisher: Springer Berlin Heidelberg
Keywords: Esophageal Cancer
Cancer Biomarker
Esophageal Cancer
Positron Emission Tomography
Published in: European Radiology / Issue 5/2021
Print ISSN: 0938-7994
Electronic ISSN: 1432-1084
DOI: https://doi.org/10.1007/s00330-020-07439-8

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Addition of HER2 and CD44 to ¹⁸F-FDG PET–based clinico-radiomic models enhances prediction of neoadjuvant chemoradiotherapy response in esophageal cancer

Abstract

Objectives

Methods

Results

Conclusions

Key Points

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Abstract

Objectives

Methods

Results

Conclusions

Key Points

Please log in to get access to this content

Other articles of this Issue 5/2021

Multiparametric MRI-based radiomics signature for preoperative estimation of tumor-stroma ratio in rectal cancer

Frequency and risk factors for major complications after stereotactic radiofrequency ablation of liver tumors in 1235 ablation sessions: a 15-year experience

Wallerian degeneration in cervical spinal cord tracts is commonly seen in routine T2-weighted MRI after traumatic spinal cord injury and is associated with impairment in a retrospective study

Chest radiograph at admission predicts early intubation among inpatient COVID-19 patients

Diffusion and perfusion MRI radiomics obtained from deep learning segmentation provides reproducible and comparable diagnostic model to human in post-treatment glioblastoma

Comparison of virtual computed tomography enteroscopy using carbon dioxide with small-bowel enteroclysis and capsule endoscopy in patients with small-bowel tuberculosis