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.

Center for Medical Physics and Biomedical Engineering Medical University of Vienna Vienna Austria

Centre for Molecular Medicine Central European Institute of Technology Brno Czech Republic

Christian Doppler Laboratory for Applied Metabolomics Vienna Austria

Clinical Institute of Pathology Medical University of Vienna Vienna Austria

Department of Biomedical Imaging and Image Guided Therapy Division of Nuclear Medicine Medical University of Vienna Vienna Austria

Department of Otorhinolaryngology Head and Neck Surgery Medical University of Vienna Vienna Austria

J Mol Diagn. 2019 Mar;21(2):261-273. doi: 10.1016/j.jmoldx.2018.09.008. PubMed

Zobrazit více v PubMed

Cramer JD, Burtness B, Le QT, Ferris RL. The changing therapeutic landscape of head and neck cancer. Nat Rev Clin Oncol. 2019;16(11):669–683. doi: 10.1038/s41571-019-0227-z. PubMed DOI

Pfister DG, Spencer S, Adelstein D, et al. Head and Neck Cancers, Version 2.2020, NCCN Clinical Practice Guidelines in Oncology. J Natl Compr Cancer Netw. 2020;18(7):873–898. doi: 10.6004/jnccn.2020.0031. PubMed DOI

Leemans CR, Snijders PJF, Brakenhoff RH. The molecular landscape of head and neck cancer. Nat Rev Cancer. 2018;18(5):269–282. doi: 10.1038/nrc.2018.11. PubMed DOI

Machiels JP, René Leemans C, Golusinski W, Grau C, Licitra L, Gregoire V. Squamous cell carcinoma of the oral cavity, larynx, oropharynx and hypopharynx: EHNS–ESMO–ESTRO Clinical Practice Guidelines for diagnosis, treatment and follow-up. Ann Oncol. 2020;31(11):1462–1475. doi: 10.1016/j.annonc.2020.07.011. PubMed DOI

Lambin P, Leijenaar RTH, Deist TM, et al. Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol. 2017;14(12):749–762. doi: 10.1038/nrclinonc.2017.141. PubMed DOI

Budach V, Tinhofer I. Novel prognostic clinical factors and biomarkers for outcome prediction in head and neck cancer: a systematic review. Lancet Oncol. 2019;20(6):e313–e326. doi: 10.1016/S1470-2045(19)30177-9. PubMed DOI

Hsieh JC, Wang H, Wu M, et al. Review of emerging biomarkers in head and neck squamous cell carcinoma in the era of immunotherapy and targeted therapy. Head Neck. 2019;41(S1):19–45. doi: 10.1002/hed.25932. PubMed DOI

Oldenhuis CNAM, Oosting SF, Gietema JA, de Vries EGE. Prognostic versus predictive value of biomarkers in oncology. Eur J Cancer. 2008;44(7):946–953. doi: 10.1016/j.ejca.2008.03.006. PubMed DOI

Wong CC, Qian Y, Yu J. Interplay between epigenetics and metabolism in oncogenesis: mechanisms and therapeutic approaches. Oncogene. 2017;36(24):3359–3374. doi: 10.1038/onc.2016.485. PubMed DOI PMC

Rentzsch P, Witten D, Cooper GM, Shendure J, Kircher M. CADD: predicting the deleteriousness of variants throughout the human genome. Nucleic Acids Res. 2019;47(D1):D886–D894. doi: 10.1093/nar/gky1016. PubMed DOI PMC

Orlhac F, Frouin F, Nioche C, Ayache N, Buvat I. Validation of a method to compensate multicenter effects affecting CT Radiomics. Radiology. 2019;291(1):53–59. doi: 10.1148/radiol.2019182023. PubMed DOI

Li H, Durbin R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics. 2009;25(14):1754–1760. doi: 10.1093/bioinformatics/btp324. PubMed DOI PMC

Kim S, Scheffler K, Halpern AL, et al. Strelka2: fast and accurate calling of germline and somatic variants. Nat Methods. 2018;15(8):591–594. doi: 10.1038/s41592-018-0051-x. PubMed DOI

Lai Z, Markovets A, Ahdesmaki M, et al. VarDict: a novel and versatile variant caller for next-generation sequencing in cancer research. Nucleic Acids Res. 2016;44(11):e108–e108. doi: 10.1093/nar/gkw227. PubMed DOI PMC

McLaren W, Gil L, Hunt SE, et al. The Ensembl Variant Effect Predictor. Genome Biol. 2016;17(1):122. doi: 10.1186/s13059-016-0974-4. PubMed DOI PMC

Kircher M, Witten DM, Jain P, O’Roak BJ, Cooper GM, Shendure J. A general framework for estimating the relative pathogenicity of human genetic variants. Nat Genet. 2014;46(3):310–315. doi: 10.1038/ng.2892. PubMed DOI PMC

Rentzsch P, Schubach M, Shendure J, Kircher M. CADD-Splice—improving genome-wide variant effect prediction using deep learning-derived splice scores. Genome Med. 2021;13(1):31. doi: 10.1186/s13073-021-00835-9. PubMed DOI PMC

Sukhai MA, Misyura M, Thomas M, et al. Somatic tumor variant filtration strategies to optimize tumor-only molecular profiling using targeted next-generation sequencing panels. J Mol Diagnostics. 2019;21(2):261–273. doi: 10.1016/j.jmoldx.2018.09.008. PubMed DOI

Auton A, Abecasis GR, Altshuler DM, et al. A global reference for human genetic variation. Nature. 2015;526(7571):68–74. doi: 10.1038/nature15393. PubMed DOI PMC

Karczewski KJ, Francioli LC, Tiao G, et al. The mutational constraint spectrum quantified from variation in 141,456 humans. Nature. 2020;581(7809):434–443. doi: 10.1038/s41586-020-2308-7. PubMed DOI PMC

Auer PL, Johnsen JM, Johnson AD, et al. Imputation of exome sequence variants into population-based samples and blood-cell-trait-associated loci in African Americans: NHLBI GO Exome Sequencing Project. Am J Hum Genet. 2012;91(5):794–808. doi: 10.1016/j.ajhg.2012.08.031. PubMed DOI PMC

Landrum MJ, Chitipiralla S, Brown GR, et al. ClinVar: improvements to accessing data. Nucleic Acids Res. 2020;48(D1):D835–D844. doi: 10.1093/nar/gkz972. PubMed DOI PMC

Kanehisa M, Goto S. KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 2000;28(1):27–30. doi: 10.1093/nar/28.1.27. PubMed DOI PMC

Itan Y, Shang L, Boisson B, et al. The mutation significance cutoff: gene-level thresholds for variant predictions. Nat Methods. 2016;13(2):109–110. doi: 10.1038/nmeth.3739. PubMed DOI PMC

Papp L, Pötsch N, Grahovac M, et al. Glioma survival prediction with combined analysis of in vivo 11C-MET PET features, ex vivo features, and patient features by supervised machine learning. J Nucl Med. 2018;59(6):892–899. doi: 10.2967/jnumed.117.202267. PubMed DOI

Papp L, Spielvogel CP, Grubmüller B, et al. Supervised machine learning enables non-invasive lesion characterization in primary prostate cancer with [68Ga]Ga-PSMA-11 PET/MRI. Eur J Nucl Med Mol Imaging. 2021;48(6):1795–1805. doi: 10.1007/s00259-020-05140-y. PubMed DOI PMC

Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP. SMOTE: synthetic minority over-sampling technique. J Artif Intell Res. 2002;16:321–357. doi: 10.1613/jair.953. DOI

Chicco D, Warrens MJ, Jurman G. The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation. PeerJ Comput Sci. 2021;7:e623. doi: 10.7717/peerj-cs.623. PubMed DOI PMC

Zwanenburg A, Leger S, Vallières M, Löck S. Image biomarker standardisation initiative. arXiv Prepr arXiv161207003. Published online 2016.

Van Griethuysen JJM, Fedorov A, Parmar C, et al. Computational radiomics system to decode the radiographic phenotype. Cancer Res. 2017;77(21):e104–e107. doi: 10.1158/0008-5472.CAN-17-0339. PubMed DOI PMC

Cottereau AS, Versari A, Loft A, et al. Prognostic value of baseline metabolic tumor volume in early-stage Hodgkin lymphoma in the standard arm of the H10 trial. Blood. 2018;131(13):1456–1463. doi: 10.1182/blood-2017-07-795476. PubMed DOI

Marusyk A, Polyak K. Tumor heterogeneity: causes and consequences. Biochim Biophys Acta - Rev Cancer. 2010;1805(1):105–117. doi: 10.1016/j.bbcan.2009.11.002. PubMed DOI PMC

Levine AJ, Ting DT, Greenbaum BD. P53 and the defenses against genome instability caused by transposons and repetitive elements. BioEssays. 2016;38(6):508–513. doi: 10.1002/bies.201600031. PubMed DOI PMC

Pisanic TR, Athamanolap P, Wang TH. Defining, distinguishing and detecting the contribution of heterogeneous methylation to cancer heterogeneity. Semin Cell Dev Biol. 2017;64:5–17. doi: 10.1016/j.semcdb.2016.08.030. PubMed DOI PMC

Huang J. Current developments of targeting the p53 signaling pathway for cancer treatment. Pharmacol Ther. 2021;220:107720. doi: 10.1016/j.pharmthera.2020.107720. PubMed DOI PMC

Hensley CT, Wasti AT, DeBerardinis RJ. Glutamine and cancer: cell biology, physiology, and clinical opportunities. J Clin Invest. 2013;123(9):3678–3684. doi: 10.1172/JCI69600. PubMed DOI PMC

Casero RA, Murray Stewart T, Pegg AE. Polyamine metabolism and cancer: treatments, challenges and opportunities. Nat Rev Cancer. 2018;18(11):681–695. doi: 10.1038/s41568-018-0050-3. PubMed DOI PMC

Ananieva E. Targeting amino acid metabolism in cancer growth and anti-tumor immune response. World J Biol Chem. 2015;6(4):281. doi: 10.4331/wjbc.v6.i4.281. PubMed DOI PMC

Kurmi K, Haigis MC. Nitrogen metabolism in cancer and immunity. Trends Cell Biol. 2020;30(5):408–424. doi: 10.1016/j.tcb.2020.02.005. PubMed DOI PMC

Pak K, Cheon GJ, Nam HY, et al. Prognostic value of metabolic tumor volume and total lesion glycolysis in head and neck cancer: a systematic review and meta-analysis. J Nucl Med. 2014;55(6):884–890. doi: 10.2967/jnumed.113.133801. PubMed DOI

Traverso A, Kazmierski M, Zhovannik I, et al. Machine learning helps identifying volume-confounding effects in radiomics. Phys Medica. 2020;71:24–30. doi: 10.1016/j.ejmp.2020.02.010. PubMed DOI

Calcinotto A, Kohli J, Zagato E, Pellegrini L, Demaria M, Alimonti A. Cellular senescence: aging, cancer, and injury. Physiol Rev. 2019;99(2):1047–1078. doi: 10.1152/physrev.00020.2018. PubMed DOI

Alimirah F, Pulido T, Valdovinos A, et al. Cellular senescence promotes skin carcinogenesis through p38MAPK and p44/42MAPK signaling. Cancer Res. 2020;80(17):3606–3619. doi: 10.1158/0008-5472.CAN-20-0108. PubMed DOI PMC

Grossmann P, Stringfield O, El-Hachem N, et al. Defining the biological basis of radiomic phenotypes in lung cancer. Elife. 2017;6:e23421. doi: 10.7554/eLife.23421. PubMed DOI PMC

Aerts HJWL, Velazquez ER, Leijenaar RTH, et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun. 2014;5(1):4006. doi: 10.1038/ncomms5006. PubMed DOI PMC

Preoperative detection of extraprostatic tumor extension in patients with primary prostate cancer utilizing [68Ga]Ga-PSMA-11 PET/MRI