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Swartz JE, Smits HJG, Philippens MEP, de Bree R, H A M Kaanders J, Willems SM. Correlation and colocalization of HIF-1α and pimonidazole staining for hypoxia in laryngeal squamous cell carcinomas: A digital, single-cell-based analysis. Oral Oncol 2022; 128:105862. [PMID: 35447566 DOI: 10.1016/j.oraloncology.2022.105862] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Revised: 03/31/2022] [Accepted: 04/12/2022] [Indexed: 11/25/2022]
Abstract
OBJECTIVE Tumor hypoxia results in worse local control and patient survival. We performed a digital, single-cell-based analysis to compare two biomarkers for hypoxia (hypoxia-inducible factor 1-alpha [HIF-1α] and pimonidazole [PIMO]) and their effect on outcome in laryngeal cancer patients treated with accelerated radiotherapy with or without carbogen breathing and nicotinamide (AR versus ARCON). MATERIALS AND METHODS Immunohistochemical staining was performed for HIF-1α and PIMO in consecutive sections of 44 laryngeal cancer patients randomized between AR and ARCON. HIF-1α expression and PIMO-binding were correlated using digital image analysis in QuPath. High-density areas for each biomarker were automatically annotated and staining overlap was analyzed. Kaplan-Meier survival analyses for local control, regional control and disease-free survival were performed to predict a response benefit of ARCON over AR alone for each biomarker. RESULTS 106 Tissue fragments of 44 patients were analyzed. A weak, significant positive correlation was observed between HIF-1α and PIMO positivity on fragment level, but not on patient level. A moderate strength correlation (r = 0.705, p < 0.001) was observed between the number of high-density staining areas for both biomarkers. Staining overlap was poor. HIF-1α expression, PIMO-binding or a combination could not predict a response benefit of ARCON over AR. CONCLUSION Digital image analysis to compare positive cell fractions and staining overlap between two hypoxia biomarkers using open-source software is feasible. Our results highlight that there are distinct differences between HIF-1α and PIMO as hypoxia biomarkers and therefore suggest co-existence of different forms of hypoxia within a single tumor.
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Affiliation(s)
- Justin E Swartz
- Department of Otorhinolaryngology - Head and Neck Surgery, University Medical Center Utrecht, Utrecht, the Netherlands; Department of Head and Neck Surgical Oncology, University Medical Center Utrecht, Utrecht, the Netherlands.
| | - Hilde J G Smits
- Department of Radiotherapy, University Medical Center Utrecht, Utrecht, the Netherlands
| | | | - Remco de Bree
- Department of Head and Neck Surgical Oncology, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Johannes H A M Kaanders
- Department of Radiation Oncology, University Medical Center Nijmegen, Nijmegen, the Netherlands
| | - Stefan M Willems
- Department of Pathology and Medical Biology, University Medical Center Groningen, Groningen, the Netherlands
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Sanduleanu S, Jochems A, Upadhaya T, Even AJG, Leijenaar RTH, Dankers FJWM, Klaassen R, Woodruff HC, Hatt M, Kaanders HJAM, Hamming-Vrieze O, van Laarhoven HWM, Subramiam RM, Huang SH, O'Sullivan B, Bratman SV, Dubois LJ, Miclea RL, Di Perri D, Geets X, Crispin-Ortuzar M, Apte A, Deasy JO, Oh JH, Lee NY, Humm JL, Schöder H, De Ruysscher D, Hoebers F, Lambin P. Non-invasive imaging prediction of tumor hypoxia: A novel developed and externally validated CT and FDG-PET-based radiomic signatures. Radiother Oncol 2020; 153:97-105. [PMID: 33137396 DOI: 10.1016/j.radonc.2020.10.016] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2019] [Revised: 10/09/2020] [Accepted: 10/12/2020] [Indexed: 12/14/2022]
Abstract
BACKGROUND Tumor hypoxia increases resistance to radiotherapy and systemic therapy. Our aim was to develop and validate a disease-agnostic and disease-specific CT (+FDG-PET) based radiomics hypoxia classification signature. MATERIAL AND METHODS A total of 808 patients with imaging data were included: N = 100 training/N = 183 external validation cases for a disease-agnostic CT hypoxia classification signature, N = 76 training/N = 39 validation cases for the H&N CT signature and N = 62 training/N = 36 validation cases for the Lung CT signature. The primary gross tumor volumes (GTV) were manually defined by experts on CT. In order to dichotomize between hypoxic/well-oxygenated tumors a threshold of 20% was used for the [18F]-HX4-derived hypoxic fractions (HF). A random forest (RF)-based machine-learning classifier/regressor was trained to classify patients as hypoxia-positive/ negative based on radiomic features. RESULTS A 11 feature "disease-agnostic CT model" reached AUC's of respectively 0.78 (95% confidence interval [CI], 0.62-0.94), 0.82 (95% CI, 0.67-0.96) and 0.78 (95% CI, 0.67-0.89) in three external validation datasets. A "disease-agnostic FDG-PET model" reached an AUC of 0.73 (0.95% CI, 0.49-0.97) in validation by combining 5 features. The highest "lung-specific CT model" reached an AUC of 0.80 (0.95% CI, 0.65-0.95) in validation with 4 CT features, while the "H&N-specific CT model" reached an AUC of 0.84 (0.95% CI, 0.64-1.00) in validation with 15 CT features. A tumor volume-alone model was unable to significantly classify patients as hypoxia-positive/ negative. A significant survival split (P = 0.037) was found between CT-classified hypoxia strata in an external H&N cohort (n = 517), while 117 significant hypoxia gene-CT signature feature associations were found in an external lung cohort (n = 80). CONCLUSION The disease-specific radiomics signatures perform better than the disease agnostic ones. By identifying hypoxic patients our signatures have the potential to enrich interventional hypoxia-targeting trials.
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Affiliation(s)
- Sebastian Sanduleanu
- The-D-Lab, Dpt of Precision Medicine, GROW - School for Oncology, Maastricht University Medical Centre+, The Netherlands.
| | - Arthur Jochems
- The-D-Lab, Dpt of Precision Medicine, GROW - School for Oncology, Maastricht University Medical Centre+, The Netherlands
| | - Taman Upadhaya
- Laboratory of Medical Information Processing (LaTIM), INSERM, UMR 1101, Univ Brest, France; Department of Radiation Oncology, University of California, 1600 Divisadero Street, CA 94115, San Francisco, United States
| | - Aniek J G Even
- The-D-Lab, Dpt of Precision Medicine, GROW - School for Oncology, Maastricht University Medical Centre+, The Netherlands
| | - Ralph T H Leijenaar
- The-D-Lab, Dpt of Precision Medicine, GROW - School for Oncology, Maastricht University Medical Centre+, The Netherlands
| | - Frank J W M Dankers
- Department of Radiation Oncology, Radboud University Nijmegen Medical Centre, The Netherlands
| | - Remy Klaassen
- Department of Medical Oncology, Cancer Center Amsterdam, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | - Henry C Woodruff
- The-D-Lab, Dpt of Precision Medicine, GROW - School for Oncology, Maastricht University Medical Centre+, The Netherlands; Department of Radiology and Nuclear Imaging, GROW - school for Oncology, Maastricht University Medical Centre+, The Netherlands
| | - Mathieu Hatt
- Laboratory of Medical Information Processing (LaTIM), INSERM, UMR 1101, Univ Brest, France
| | - Hans J A M Kaanders
- Department of Radiation Oncology, Radboud University Nijmegen Medical Centre, The Netherlands
| | - Olga Hamming-Vrieze
- Department of Radiation Oncology, Antoni van Leeuwenhoek - Netherlands Cancer institute, Amsterdam, The Netherlands
| | - Hanneke W M van Laarhoven
- Department of Medical Oncology, Cancer Center Amsterdam, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | - Rathan M Subramiam
- Boston University School of Medicine, United States; Division of Nuclear Medicine, Russell H Morgan Department of Radiology and Radiologic Sciences, Johns Hopkins Medical Institutions, Baltimore, United States
| | - Shao Hui Huang
- Department of Radiation Oncology, Princess Margaret Cancer Center, University of Toronto, Canada
| | - Brian O'Sullivan
- Department of Radiation Oncology, Princess Margaret Cancer Center, University of Toronto, Canada
| | - Scott V Bratman
- Department of Radiation Oncology, Princess Margaret Cancer Center, University of Toronto, Canada
| | - Ludwig J Dubois
- Department of Precision Medicine, The M-LAB, GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre+, The Netherlands
| | - Razvan L Miclea
- Department of Radiology and Nuclear Imaging, GROW - school for Oncology, Maastricht University Medical Centre+, The Netherlands
| | - Dario Di Perri
- Center of Molecular Imaging, Radiotherapy and Oncology (MIRO), Institut de Recherche Expérimentale et Clinique (IREC), Université catholique de Louvain, Belgium; Department of Radiation Oncology, Cliniques universitaires Saint-Luc, Brussels, Belgium
| | - Xavier Geets
- Center of Molecular Imaging, Radiotherapy and Oncology (MIRO), Institut de Recherche Expérimentale et Clinique (IREC), Université catholique de Louvain, Belgium; Department of Radiation Oncology, Cliniques universitaires Saint-Luc, Brussels, Belgium
| | - Mireia Crispin-Ortuzar
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, United States; Cancer Research UK Cambridge Institute, University of Cambridge, UK
| | - Aditya Apte
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, United States
| | - Joseph O Deasy
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, United States
| | - Jung Hun Oh
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, United States
| | - Nancy Y Lee
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, United States
| | - John L Humm
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, United States
| | - Heiko Schöder
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, United States
| | - Dirk De Ruysscher
- Department of Radiation Oncology (Maastro), GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre+, The Netherlands
| | - Frank Hoebers
- Department of Radiation Oncology (Maastro), GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre+, The Netherlands
| | - Philippe Lambin
- The-D-Lab, Dpt of Precision Medicine, GROW - School for Oncology, Maastricht University Medical Centre+, The Netherlands; Department of Radiology and Nuclear Imaging, GROW - school for Oncology, Maastricht University Medical Centre+, The Netherlands
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