Literature DB >> 36161512

Radiogenomic markers enable risk stratification and inference of mutational pathway states in head and neck cancer.

Clemens P Spielvogel1,2, Stefan Stoiber1,3, Laszlo Papp4, Denis Krajnc4, Marko Grahovac2, Elisabeth Gurnhofer3, Karolina Trachtova1,2,5, Vojtech Bystry5, Asha Leisser2, Bernhard Jank6, Julia Schnoell6, Lorenz Kadletz6, Gregor Heiduschka6, Thomas Beyer4, Marcus Hacker2, Lukas Kenner7,8, Alexander R Haug1,2.   

Abstract

PURPOSE: Head and neck squamous cell carcinomas (HNSCCs) are a molecularly, histologically, and clinically heterogeneous set of tumors originating from the mucosal epithelium of the oral cavity, pharynx, and larynx. This heterogeneous nature of HNSCC is one of the main contributing factors to the lack of prognostic markers for personalized treatment. The aim of this study was to develop and identify multi-omics markers capable of improved risk stratification in this highly heterogeneous patient population.
METHODS: In this retrospective study, we approached this issue by establishing radiogenomics markers to identify high-risk individuals in a cohort of 127 HNSCC patients. Hybrid in vivo imaging and whole-exome sequencing were employed to identify quantitative imaging markers as well as genetic markers on pathway-level prognostic in HNSCC. We investigated the deductibility of the prognostic genetic markers using anatomical and metabolic imaging using positron emission tomography combined with computed tomography. Moreover, we used statistical and machine learning modeling to investigate whether a multi-omics approach can be used to derive prognostic markers for HNSCC.
RESULTS: Radiogenomic analysis revealed a significant influence of genetic pathway alterations on imaging markers. A highly prognostic radiogenomic marker based on cellular senescence was identified. Furthermore, the radiogenomic biomarkers designed in this study vastly outperformed the prognostic value of markers derived from genetics and imaging alone.
CONCLUSION: Using the identified markers, a clinically meaningful stratification of patients is possible, guiding the identification of high-risk patients and potentially aiding in the development of effective targeted therapies.
© 2022. The Author(s).

Entities:  

Keywords:  Artificial intelligence; Biomarkers; Cancer genomics; Head and neck cancer; Machine learning; Radiomics

Year:  2022        PMID: 36161512     DOI: 10.1007/s00259-022-05973-9

Source DB:  PubMed          Journal:  Eur J Nucl Med Mol Imaging        ISSN: 1619-7070            Impact factor:   10.057


  39 in total

1.  Squamous cell carcinoma of the oral cavity, larynx, oropharynx and hypopharynx: EHNS-ESMO-ESTRO Clinical Practice Guidelines for diagnosis, treatment and follow-up.

Authors:  J-P Machiels; C René Leemans; W Golusinski; C Grau; L Licitra; V Gregoire
Journal:  Ann Oncol       Date:  2020-10-23       Impact factor: 32.976

Review 2.  The changing therapeutic landscape of head and neck cancer.

Authors:  John D Cramer; Barbara Burtness; Quynh Thu Le; Robert L Ferris
Journal:  Nat Rev Clin Oncol       Date:  2019-06-12       Impact factor: 66.675

Review 3.  Radiomics: the bridge between medical imaging and personalized medicine.

Authors:  Philippe Lambin; Ralph T H Leijenaar; Timo M Deist; Jurgen Peerlings; Evelyn E C de Jong; Janita van Timmeren; Sebastian Sanduleanu; Ruben T H M Larue; Aniek J G Even; Arthur Jochems; Yvonka van Wijk; Henry Woodruff; Johan van Soest; Tim Lustberg; Erik Roelofs; Wouter van Elmpt; Andre Dekker; Felix M Mottaghy; Joachim E Wildberger; Sean Walsh
Journal:  Nat Rev Clin Oncol       Date:  2017-10-04       Impact factor: 66.675

Review 4.  Review of emerging biomarkers in head and neck squamous cell carcinoma in the era of immunotherapy and targeted therapy.

Authors:  Jason Chia-Hsun Hsieh; Hung-Ming Wang; Min-Hsien Wu; Kai-Ping Chang; Pei-Hung Chang; Chun-Ta Liao; Chi-Ting Liau
Journal:  Head Neck       Date:  2019-10       Impact factor: 3.147

Review 5.  The molecular landscape of head and neck cancer.

Authors:  C René Leemans; Peter J F Snijders; Ruud H Brakenhoff
Journal:  Nat Rev Cancer       Date:  2018-03-02       Impact factor: 60.716

Review 6.  Novel prognostic clinical factors and biomarkers for outcome prediction in head and neck cancer: a systematic review.

Authors:  Volker Budach; Ingeborg Tinhofer
Journal:  Lancet Oncol       Date:  2019-06       Impact factor: 41.316

7.  Validation of A Method to Compensate Multicenter Effects Affecting CT Radiomics.

Authors:  Fanny Orlhac; Frédérique Frouin; Christophe Nioche; Nicholas Ayache; Irène Buvat
Journal:  Radiology       Date:  2019-01-29       Impact factor: 11.105

Review 8.  Prognostic versus predictive value of biomarkers in oncology.

Authors:  C N A M Oldenhuis; S F Oosting; J A Gietema; E G E de Vries
Journal:  Eur J Cancer       Date:  2008-04-07       Impact factor: 9.162

Review 9.  Interplay between epigenetics and metabolism in oncogenesis: mechanisms and therapeutic approaches.

Authors:  C C Wong; Y Qian; J Yu
Journal:  Oncogene       Date:  2017-01-16       Impact factor: 9.867

10.  CADD: predicting the deleteriousness of variants throughout the human genome.

Authors:  Philipp Rentzsch; Daniela Witten; Gregory M Cooper; Jay Shendure; Martin Kircher
Journal:  Nucleic Acids Res       Date:  2019-01-08       Impact factor: 16.971

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