Molecular immuno-imaging improves tumor detection in head and neck cancer.


Journal

FASEB journal : official publication of the Federation of American Societies for Experimental Biology
ISSN: 1530-6860
Titre abrégé: FASEB J
Pays: United States
ID NLM: 8804484

Informations de publication

Date de publication:
01 2022
Historique:
revised: 05 11 2021
received: 24 05 2021
accepted: 23 11 2021
entrez: 17 12 2021
pubmed: 18 12 2021
medline: 12 1 2022
Statut: ppublish

Résumé

Detection and accurate delineation of tumor is important for the management of head and neck squamous cell carcinoma (HNSCC) but is challenging with current imaging techniques. In this study, we evaluated whether molecular immuno-imaging targeting myeloperoxidase (MPO) activity, an oxidative enzyme secreted by many myeloid innate immune cells, would be superior in detecting tumor extent compared to conventional contrast agent (DTPA-Gd) in a carcinogen-induced immunocompetent HNSCC murine model and corroborated in human surgical specimens. In C57BL/6 mice given 4-nitroquinoline-N-oxide (4-NQO), there was increased MPO activity in the head and neck region as detected by luminol bioluminescence compared to that of the control group. On magnetic resonance imaging, the mean enhancing volume detected by the MPO-targeting agent (MPO-Gd) was higher than that by the conventional agent DTPA-Gd. The tumor volume detected by MPO-Gd strongly correlated with tumor size on histology, and higher MPO-Gd signal corresponded to larger tumor size found by imaging and histology. On the contrary, the tumor volume detected by DTPA-Gd did not correlate as well with tumor size on histology. Importantly, MPO-Gd imaging detected areas not visualized with DTPA-Gd imaging that were confirmed histopathologically to represent early tumor. In human specimens, MPO was similarly associated with tumors, especially at the tumor margins. Thus, molecular immuno-imaging targeting MPO not only detects oxidative immune response in HNSCC, but can better detect and delineate tumor extent than nonselective imaging agents. Thus, our findings revealed that MPO imaging could improve tumor resection as well as be a useful imaging biomarker for tumor progression, and potentially improve clinical management of HNSCC once translated.

Identifiants

pubmed: 34919761
doi: 10.1096/fj.202100864R
pmc: PMC9584652
mid: NIHMS1842296
doi:

Substances chimiques

4-nitroquinolone-1-oxide 0
Biomarkers, Tumor 0
Quinolones 0
4-Nitroquinoline-1-oxide 56-57-5

Types de publication

Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

e22092

Subventions

Organisme : NHLBI NIH HHS
ID : K25 HL150305
Pays : United States
Organisme : NINDS NIH HHS
ID : R01 NS103998
Pays : United States

Informations de copyright

© 2021 Federation of American Societies for Experimental Biology.

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Auteurs

Jing-Hui Li (JH)

Institute for Innovation in Imaging, Department of Radiology, and Center for Systems Biology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA.
Department of Magnetic Resonance Imaging, FuWai Hospital, Peking Union Medical College and Chinese Academy of Medical Science, Beijing, China.

Reza Forghani (R)

Institute for Innovation in Imaging, Department of Radiology, and Center for Systems Biology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA.
Augmented Intelligence & Precision Health Laboratory (AIPHL), Department of Radiology, Research Institute of the McGill University Health Centre, Montreal, Quebec, Canada.
Segal Cancer Centre and Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Quebec, Canada.

Lionel Bure (L)

Institute for Innovation in Imaging, Department of Radiology, and Center for Systems Biology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA.

Gregory R Wojtkiewicz (GR)

Institute for Innovation in Imaging, Department of Radiology, and Center for Systems Biology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA.

Yue Wu (Y)

Institute for Innovation in Imaging, Department of Radiology, and Center for Systems Biology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA.

Yoshiko Iwamoto (Y)

Institute for Innovation in Imaging, Department of Radiology, and Center for Systems Biology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA.

Muhammad Ali (M)

Institute for Innovation in Imaging, Department of Radiology, and Center for Systems Biology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA.

Anning Li (A)

Institute for Innovation in Imaging, Department of Radiology, and Center for Systems Biology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA.

Cuihua Wang (C)

Institute for Innovation in Imaging, Department of Radiology, and Center for Systems Biology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA.

Negin Jalali Motlagh (N)

Institute for Innovation in Imaging, Department of Radiology, and Center for Systems Biology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA.

Andreas I Papadakis (AI)

Department of Pathology, Jewish General Hospital & McGill University, Montreal, Quebec, Canada.

Marc P Pusztaszeri (MP)

Department of Pathology, Jewish General Hospital & McGill University, Montreal, Quebec, Canada.

Alan Spatz (A)

Department of Pathology, Jewish General Hospital & McGill University, Montreal, Quebec, Canada.

Hugh Curtin (H)

Department of Radiology, Massachusetts Eye and Ear Infirmary, Harvard Medical School, Boston, Massachusetts, USA.

Ying-Sheng Cheng (YS)

Department of Radiology, The Affiliated Sixth People's Hospital of Shanghai Jiao Tong University, Shanghai, China.

John W Chen (JW)

Institute for Innovation in Imaging, Department of Radiology, and Center for Systems Biology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA.

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Classifications MeSH