Radiomic profiles improve prognostication and reveal targets for therapy in cervical cancer.
Cluster analysis
Imaging genomics
Magnetic resonance imaging
Molecular targeted treatment
Uterine cervical neoplasms
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
17 May 2024
17 May 2024
Historique:
received:
23
02
2024
accepted:
03
05
2024
medline:
18
5
2024
pubmed:
18
5
2024
entrez:
17
5
2024
Statut:
epublish
Résumé
Cervical cancer (CC) is a major global health problem with 570,000 new cases and 266,000 deaths annually. Prognosis is poor for advanced stage disease, and few effective treatments exist. Preoperative diagnostic imaging is common in high-income countries and MRI measured tumor size routinely guides treatment allocation of cervical cancer patients. Recently, the role of MRI radiomics has been recognized. However, its potential to independently predict survival and treatment response requires further clarification. This retrospective cohort study demonstrates how non-invasive, preoperative, MRI radiomic profiling may improve prognostication and tailoring of treatments and follow-ups for cervical cancer patients. By unsupervised clustering based on 293 radiomic features from 132 patients, we identify three distinct clusters comprising patients with significantly different risk profiles, also when adjusting for FIGO stage and age. By linking their radiomic profiles to genomic alterations, we identify putative treatment targets for the different patient clusters (e.g., immunotherapy, CDK4/6 and YAP-TEAD inhibitors and p53 pathway targeting treatments).
Identifiants
pubmed: 38760387
doi: 10.1038/s41598-024-61271-4
pii: 10.1038/s41598-024-61271-4
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
11339Subventions
Organisme : Kreftforeningen
ID : 104484
Organisme : Kreftforeningen
ID : 190202
Organisme : Norges Forskningsråd
ID : 326348
Organisme : Helse Vest
ID : F-12542
Organisme : Helse Vest
ID : HV912263
Organisme : Trond Mohn stiftelse
ID : 809119
Organisme : Bergens Forskningsstiftelse
ID : BFS2018TMT06
Organisme : Norges forsknigsråd
ID : 311350
Informations de copyright
© 2024. The Author(s).
Références
Sung, H. et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J. Clin. 71(3), 209–249 (2021).
pubmed: 33538338
doi: 10.3322/caac.21660
Arbyn, M. et al. Estimates of incidence and mortality of cervical cancer in 2018: A worldwide analysis. Lancet Glob. Health. 8(2), e191–e203 (2020).
pubmed: 31812369
doi: 10.1016/S2214-109X(19)30482-6
Halle, M. K. et al. Clinicopathologic and molecular markers in cervical carcinoma: A prospective cohort study. Am. J. Obstet. Gynecol. 217, 432.e1 (2017).
pubmed: 28599900
doi: 10.1016/j.ajog.2017.05.068
Pecorelli, S., Zigliani, L. & Odicino, F. Revised FIGO staging for carcinoma of the cervix. Int. J. Gynaecol. Obstet. 105(2), 107–108 (2009).
pubmed: 19342051
doi: 10.1016/j.ijgo.2009.02.009
Bhatla, N. et al. Revised FIGO staging for carcinoma of the cervix uteri. Int. J. Gynaecol. Obstet. 145(1), 129–135 (2019).
pubmed: 30656645
doi: 10.1002/ijgo.12749
Manganaro, L. et al. Staging, recurrence and follow-up of uterine cervical cancer using MRI: Updated Guidelines of the European Society of Urogenital Radiology after revised FIGO staging 2018. Eur. Radiol. 31(10), 7802–7816 (2021).
pubmed: 33852049
doi: 10.1007/s00330-020-07632-9
Wagner-Larsen, K. S. et al. Interobserver agreement and prognostic impact for MRI-based 2018 FIGO staging parameters in uterine cervical cancer. Eur. Radiol. 32(9), 6444–6455 (2022).
pubmed: 35332408
pmcid: 9381622
doi: 10.1007/s00330-022-08666-x
Lura, N. et al. What MRI-based tumor size measurement is best for predicting long-term survival in uterine cervical cancer?. Insights Imaging. 13(1), 105 (2022).
pubmed: 35715582
pmcid: 9206052
doi: 10.1186/s13244-022-01239-y
Hricak, H. et al. Increasing access to imaging for addressing the global cancer epidemic. Radiology. 301(3), 543–546 (2021).
pubmed: 34581630
doi: 10.1148/radiol.2021211351
Ak, M., Toll, S. A., Hein, K. Z., Colen, R. R. & Khatua, S. Evolving role and translation of radiomics and radiogenomics in adult and pediatric neuro-oncology. AJNR Am. J. Neuroradiol. 43(6), 792–801 (2022).
pubmed: 34649914
pmcid: 9172943
doi: 10.3174/ajnr.A7297
Gordon, L. G. et al. Estimating the costs of genomic sequencing in cancer control. BMC Health Serv. Res. 20(1), 492 (2020).
pubmed: 32493298
pmcid: 7268398
doi: 10.1186/s12913-020-05318-y
Singh, G. et al. Radiomics and radiogenomics in gliomas: A contemporary update. Br. J. Cancer. 125(5), 641–657 (2021).
pubmed: 33958734
pmcid: 8405677
doi: 10.1038/s41416-021-01387-w
Hoivik, E. A. et al. A radiogenomics application for prognostic profiling of endometrial cancer. Commun. Biol. 4(1), 1363 (2021).
pubmed: 34873276
pmcid: 8648740
doi: 10.1038/s42003-021-02894-5
Halle, M. K. et al. A 10-gene prognostic signature points to LIMCH1 and HLA-DQB1 as important players in aggressive cervical cancer disease. Br. J. Cancer. 124(10), 1690–1698 (2021).
pubmed: 33723390
pmcid: 8110544
doi: 10.1038/s41416-021-01305-0
Hodneland, E. et al. Fully automatic whole-volume tumor segmentation in cervical cancer. Cancers. 14(10), 2372 (2022).
pubmed: 35625977
pmcid: 9139985
doi: 10.3390/cancers14102372
Cox, R. et al. A (Sort of) new image data format standard. NIfTI-1, Budapest, Hungary (2004).
Andersen, E. Imagedata: A python library to handle medical image data in NumPy array subclass series. J. Open Source Softw. 7(73), 4133 (2022).
doi: 10.21105/joss.04133
van Griethuysen, J. J. M. et al. Computational radiomics system to decode the radiographic phenotype. Cancer Res. 77(21), e104–e107 (2017).
pubmed: 29092951
pmcid: 5672828
doi: 10.1158/0008-5472.CAN-17-0339
Lee, J. et al. Radiomics feature robustness as measured using an MRI phantom. Sci. Rep. 11(1), 3973 (2021).
pubmed: 33597610
pmcid: 7889870
doi: 10.1038/s41598-021-83593-3
Yuan, J. et al. Quantitative assessment of acquisition imaging parameters on MRI radiomics features: A prospective anthropomorphic phantom study using a 3D–T2W-TSE sequence for MR-guided-radiotherapy. Quant. Imaging Med. Surg. 11(5), 1870–1887 (2021).
pubmed: 33936971
pmcid: 8047358
doi: 10.21037/qims-20-865
Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: A practical and powerful approach to multiple testing. J. R. Stat. Soc. Ser. B (Methodol.) 57(1), 289–300 (1995).
doi: 10.1111/j.2517-6161.1995.tb02031.x
Kaufman, L. & Fousseeuw, P. J. Partitioning Around Medoids (Program PAM). Finding Groups in Data, 68–125 (1990).
Subramanian, A. et al. A next generation connectivity map: L1000 platform and the first 1,000,000 profiles. Cell. 171(6), 1437–52.e17 (2017).
pubmed: 29195078
pmcid: 5990023
doi: 10.1016/j.cell.2017.10.049
Halle, M. K. et al. A gene signature identifying CIN3 regression and cervical cancer survival. Cancers 13(22), 5737 (2021).
pubmed: 34830895
pmcid: 8616457
doi: 10.3390/cancers13225737
Dysvik, B. & Jonassen, I. J-Express: Exploring gene expression data using Java. Bioinformatics. 17(4), 369–370 (2001).
pubmed: 11301307
doi: 10.1093/bioinformatics/17.4.369
Liberzon, A. et al. The Molecular Signatures Database (MSigDB) hallmark gene set collection. Cell Syst. 1(6), 417–425 (2015).
pubmed: 26771021
pmcid: 4707969
doi: 10.1016/j.cels.2015.12.004
Cancer Genome Atlas Research Network. Integrated genomic and molecular characterization of cervical cancer. Nature. 543(7645), 378–384 (2017).
doi: 10.1038/nature21386
Yoshihara, K. et al. Inferring tumour purity and stromal and immune cell admixture from expression data. Nat. Commun. 4, 2612 (2013).
pubmed: 24113773
doi: 10.1038/ncomms3612
Halle, M. K. et al. Genomic alterations associated with mutational signatures, DNA damage repair and chromatin remodeling pathways in cervical carcinoma. NPJ Genom. Med. 6(1), 82 (2021).
pubmed: 34620846
pmcid: 8497615
doi: 10.1038/s41525-021-00244-2
Lawrence, A. D. et al. Integrated analysis of TP53 gene and pathway alterations in the cancer genome atlas. Cell Rep. 28(5), 1370–84.e5 (2019).
doi: 10.1016/j.celrep.2019.07.001
Fang, M. et al. Multi-habitat based radiomics for the prediction of treatment response to concurrent chemotherapy and radiation therapy in locally advanced cervical cancer. Front. Oncol. 10, 563 (2020).
pubmed: 32432035
pmcid: 7214615
doi: 10.3389/fonc.2020.00563
Sun, C. et al. Radiomic analysis for pretreatment prediction of response to neoadjuvant chemotherapy in locally advanced cervical cancer: A multicentre study. EBioMedicine. 46, 160–169 (2019).
pubmed: 31395503
pmcid: 6712288
doi: 10.1016/j.ebiom.2019.07.049
Lucia, F. et al. Prediction of outcome using pretreatment (18)F-FDG PET/CT and MRI radiomics in locally advanced cervical cancer treated with chemoradiotherapy. Eur. J. Nucl. Med. Mol. Imaging. 45(5), 768–786 (2018).
pubmed: 29222685
doi: 10.1007/s00259-017-3898-7
Zhang, X. et al. MRI-based radiomics value for predicting the survival of patients with locally advanced cervical squamous cell cancer treated with concurrent chemoradiotherapy. Cancer Imaging. 22(1), 35 (2022).
pubmed: 35842679
pmcid: 9287951
doi: 10.1186/s40644-022-00474-2
Jiang, X. et al. MRI radiomics combined with clinicopathologic features to predict disease-free survival in patients with early-stage cervical cancer. Br. J. Radiol. 95(1136), 20211229 (2022).
pubmed: 35604668
pmcid: 10162065
doi: 10.1259/bjr.20211229
Fang, J. et al. Association of MRI-derived radiomic biomarker with disease-free survival in patients with early-stage cervical cancer. Theranostics. 10(5), 2284–2292 (2020).
pubmed: 32089742
pmcid: 7019161
doi: 10.7150/thno.37429
Takada, A. et al. A multi-scanner study of MRI radiomics in uterine cervical cancer: Prediction of in-field tumor control after definitive radiotherapy based on a machine learning method including peritumoral regions. Jpn. J. Radiol. 38(3), 265–273 (2020).
pubmed: 31907716
doi: 10.1007/s11604-019-00917-0
Wormald, B. W. et al. Radiomic features of cervical cancer on T2-and diffusion-weighted MRI: Prognostic value in low-volume tumors suitable for trachelectomy. Gynecol. Oncol. 156(1), 107–114 (2020).
pubmed: 31685232
pmcid: 7001101
doi: 10.1016/j.ygyno.2019.10.010
Keenan, K. E. et al. Challenges in ensuring the generalizability of image quantitation methods for MRI. Med. Phys. 49(4), 2820–2835 (2022).
pubmed: 34455593
doi: 10.1002/mp.15195
Traverso, A., Wee, L., Dekker, A. & Gillies, R. Repeatability and reproducibility of radiomic features: A systematic review. Int. J. Radiat. Oncol. Biol. Phys. 102(4), 1143–1158 (2018).
pubmed: 30170872
pmcid: 6690209
doi: 10.1016/j.ijrobp.2018.05.053
Manganaro, L. et al. Radiomics in cervical and endometrial cancer. Br. J. Radiol. 94(1125), 20201314 (2021).
pubmed: 34233456
pmcid: 9327743
doi: 10.1259/bjr.20201314
Moskowitz, C. S., Welch, M. L., Jacobs, M. A., Kurland, B. F. & Simpson, A. L. Radiomic analysis: Study design, statistical analysis, and other bias mitigation strategies. Radiology. 304(2), 265–273 (2022).
pubmed: 35579522
doi: 10.1148/radiol.211597
Huang, E. P. et al. Criteria for the translation of radiomics into clinically useful tests. Nat. Rev. Clin. Oncol. 20(2), 69–82 (2023).
pubmed: 36443594
doi: 10.1038/s41571-022-00707-0
Haldorsen, I. S., Lura, N., Blaakær, J., Fischerova, D. & Werner, H. M. J. What is the role of imaging at primary diagnostic work-up in uterine cervical cancer?. Curr. Oncol. Rep. 21(9), 77 (2019).
pubmed: 31359169
pmcid: 6663927
doi: 10.1007/s11912-019-0824-0
Yang, S. et al. Identification of a prognostic immune signature for cervical cancer to predict survival and response to immune checkpoint inhibitors. Oncoimmunology. 8(12), e1659094 (2019).
pubmed: 31741756
pmcid: 6844304
doi: 10.1080/2162402X.2019.1659094
Calses, P. C., Crawford, J. J., Lill, J. R. & Dey, A. Hippo pathway in cancer: Aberrant regulation and therapeutic opportunities. Trends Cancer. 5(5), 297–307 (2019).
pubmed: 31174842
doi: 10.1016/j.trecan.2019.04.001
Suski, J. M., Braun, M., Strmiska, V. & Sicinski, P. Targeting cell-cycle machinery in cancer. Cancer Cell. 39(6), 759–778 (2021).
pubmed: 33891890
pmcid: 8206013
doi: 10.1016/j.ccell.2021.03.010