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Published in: European Radiology 8/2020

Open Access 01-08-2020 | Computed Tomography | Urogenital

Integration of proteomics with CT-based qualitative and radiomic features in high-grade serous ovarian cancer patients: an exploratory analysis

Authors: Lucian Beer, Hilal Sahin, Nicholas W. Bateman, Ivana Blazic, Hebert Alberto Vargas, Harini Veeraraghavan, Justin Kirby, Brenda Fevrier-Sullivan, John B. Freymann, C. Carl Jaffe, James Brenton, Maura Miccó, Stephanie Nougaret, Kathleen M. Darcy, G. Larry Maxwell, Thomas P. Conrads, Erich Huang, Evis Sala

Published in: European Radiology | Issue 8/2020

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Abstract

Objectives

To investigate the association between CT imaging traits and texture metrics with proteomic data in patients with high-grade serous ovarian cancer (HGSOC).

Methods

This retrospective, hypothesis-generating study included 20 patients with HGSOC prior to primary cytoreductive surgery. Two readers independently assessed the contrast-enhanced computed tomography (CT) images and extracted 33 imaging traits, with a third reader adjudicating in the event of a disagreement. In addition, all sites of suspected HGSOC were manually segmented texture features which were computed from each tumor site. Three texture features that represented intra- and inter-site tumor heterogeneity were used for analysis. An integrated analysis of transcriptomic and proteomic data identified proteins with conserved expression between primary tumor sites and metastasis. Correlations between protein abundance and various CT imaging traits and texture features were assessed using the Kendall tau rank correlation coefficient and the Mann-Whitney U test, whereas the area under the receiver operating characteristic curve (AUC) was reported as a metric of the strength and the direction of the association. P values < 0.05 were considered significant.

Results

Four proteins were associated with CT-based imaging traits, with the strongest correlation observed between the CRIP2 protein and disease in the mesentery (p < 0.001, AUC = 0.05). The abundance of three proteins was associated with texture features that represented intra-and inter-site tumor heterogeneity, with the strongest negative correlation between the CKB protein and cluster dissimilarity (p = 0.047, τ = 0.326).

Conclusion

This study provides the first insights into the potential associations between standard-of-care CT imaging traits and texture measures of intra- and inter-site heterogeneity, and the abundance of several proteins.

Key Points

• CT-based texture features of intra- and inter-site tumor heterogeneity correlate with the abundance of several proteins in patients with HGSOC.
• CT imaging traits correlate with protein abundance in patients with HGSOC.
Appendix
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Literature
1.
go back to reference Siegel RL, Miller KD, Jemal A (2017) Cancer statistics, 2017. CA Cancer J Clin 67:7–30 Siegel RL, Miller KD, Jemal A (2017) Cancer statistics, 2017. CA Cancer J Clin 67:7–30
2.
go back to reference Winter WE 3rd, Maxwell GL, Tian C et al (2007) Prognostic factors for stage III epithelial ovarian cancer: a gynecologic oncology group study. J Clin Oncol 25:3621–3627 Winter WE 3rd, Maxwell GL, Tian C et al (2007) Prognostic factors for stage III epithelial ovarian cancer: a gynecologic oncology group study. J Clin Oncol 25:3621–3627
3.
go back to reference Agarwal R, Kaye SB (2005) Prognostic factors in ovarian cancer: how close are we to a complete picture? Ann Oncol 16:4–6CrossRef Agarwal R, Kaye SB (2005) Prognostic factors in ovarian cancer: how close are we to a complete picture? Ann Oncol 16:4–6CrossRef
4.
go back to reference Yoshihara K, Tsunoda T, Shigemizu D et al (2012) High-risk ovarian cancer based on 126-gene expression signature is uniquely characterized by downregulation of antigen presentation pathway. Clin Cancer Res 18:1374–1385CrossRef Yoshihara K, Tsunoda T, Shigemizu D et al (2012) High-risk ovarian cancer based on 126-gene expression signature is uniquely characterized by downregulation of antigen presentation pathway. Clin Cancer Res 18:1374–1385CrossRef
5.
go back to reference Kang J, D'Andrea AD, Kozono D (2012) A DNA repair pathway-focused score for prediction of outcomes in ovarian cancer treated with platinum-based chemotherapy. J Natl Cancer Inst 104:670–681CrossRef Kang J, D'Andrea AD, Kozono D (2012) A DNA repair pathway-focused score for prediction of outcomes in ovarian cancer treated with platinum-based chemotherapy. J Natl Cancer Inst 104:670–681CrossRef
6.
go back to reference Verhaak RG, Tamayo P, Yang JY et al (2013) Prognostically relevant gene signatures of high-grade serous ovarian carcinoma. J Clin Invest 123:517–525PubMed Verhaak RG, Tamayo P, Yang JY et al (2013) Prognostically relevant gene signatures of high-grade serous ovarian carcinoma. J Clin Invest 123:517–525PubMed
7.
go back to reference Parkinson CA, Gale D, Piskorz AM et al (2016) Exploratory analysis of TP53 mutations in circulating tumour DNA as biomarkers of treatment response for patients with relapsed high-grade serous ovarian carcinoma: a retrospective study. PLoS Med 13:e1002198CrossRef Parkinson CA, Gale D, Piskorz AM et al (2016) Exploratory analysis of TP53 mutations in circulating tumour DNA as biomarkers of treatment response for patients with relapsed high-grade serous ovarian carcinoma: a retrospective study. PLoS Med 13:e1002198CrossRef
8.
go back to reference Cancer Genome Atlas Research Network (2011) Integrated genomic analyses of ovarian carcinoma. Nature 474:609CrossRef Cancer Genome Atlas Research Network (2011) Integrated genomic analyses of ovarian carcinoma. Nature 474:609CrossRef
9.
go back to reference Ahmed AA, Etemadmoghadam D, Temple J et al (2010) Driver mutations in TP53 are ubiquitous in high grade serous carcinoma of the ovary. J Pathol 221:49–56CrossRef Ahmed AA, Etemadmoghadam D, Temple J et al (2010) Driver mutations in TP53 are ubiquitous in high grade serous carcinoma of the ovary. J Pathol 221:49–56CrossRef
10.
go back to reference Yang JY, Yoshihara K, Tanaka K et al (2013) Predicting time to ovarian carcinoma recurrence using protein markers. J Clin Invest 123:3740–3750PubMedPubMedCentral Yang JY, Yoshihara K, Tanaka K et al (2013) Predicting time to ovarian carcinoma recurrence using protein markers. J Clin Invest 123:3740–3750PubMedPubMedCentral
11.
go back to reference Zhang H, Liu T, Zhang Z et al (2016) Integrated proteogenomic characterization of human high-grade serous ovarian cancer. Cell 166:755–765CrossRef Zhang H, Liu T, Zhang Z et al (2016) Integrated proteogenomic characterization of human high-grade serous ovarian cancer. Cell 166:755–765CrossRef
12.
go back to reference Zhang H, Mao Y, Chen X et al (2019) Magnetic resonance imaging radiomics in categorizing ovarian masses and predicting clinical outcome: a preliminary study. Eur Radiol 29:3358–3371CrossRef Zhang H, Mao Y, Chen X et al (2019) Magnetic resonance imaging radiomics in categorizing ovarian masses and predicting clinical outcome: a preliminary study. Eur Radiol 29:3358–3371CrossRef
13.
go back to reference Danala G, Thai T, Gunderson CC et al (2017) Applying quantitative CT image feature analysis to predict response of ovarian cancer patients to chemotherapy. Acad Radiol 24:1233–1239CrossRef Danala G, Thai T, Gunderson CC et al (2017) Applying quantitative CT image feature analysis to predict response of ovarian cancer patients to chemotherapy. Acad Radiol 24:1233–1239CrossRef
14.
go back to reference Rizzo S, Botta F, Raimondi S et al (2018) Radiomics of high-grade serous ovarian cancer: association between quantitative CT features, residual tumour and disease progression within 12 months. Eur Radiol 28:4849–4859CrossRef Rizzo S, Botta F, Raimondi S et al (2018) Radiomics of high-grade serous ovarian cancer: association between quantitative CT features, residual tumour and disease progression within 12 months. Eur Radiol 28:4849–4859CrossRef
15.
go back to reference Lu H, Arshad M, Thornton A et al (2019) A mathematical-descriptor of tumor-mesoscopic-structure from computed-tomography images annotates prognostic- and molecular-phenotypes of epithelial ovarian cancer. Nat Commun 10:764CrossRef Lu H, Arshad M, Thornton A et al (2019) A mathematical-descriptor of tumor-mesoscopic-structure from computed-tomography images annotates prognostic- and molecular-phenotypes of epithelial ovarian cancer. Nat Commun 10:764CrossRef
16.
go back to reference Wei W, Liu Z, Rong Y et al (2019) A computed tomography-based radiomic prognostic marker of advanced high-grade serous ovarian cancer recurrence: a multicenter study. Front Oncol 9:255CrossRef Wei W, Liu Z, Rong Y et al (2019) A computed tomography-based radiomic prognostic marker of advanced high-grade serous ovarian cancer recurrence: a multicenter study. Front Oncol 9:255CrossRef
17.
go back to reference Vargas HA, Miccò M, Hong SI et al (2014) Association between morphologic CT imaging traits and prognostically relevant gene signatures in women with high-grade serous ovarian cancer: a hypothesis-generating study. Radiology 274:742–751CrossRef Vargas HA, Miccò M, Hong SI et al (2014) Association between morphologic CT imaging traits and prognostically relevant gene signatures in women with high-grade serous ovarian cancer: a hypothesis-generating study. Radiology 274:742–751CrossRef
18.
go back to reference Vargas HA, Veeraraghavan H, Micco M et al (2017) A novel representation of inter-site tumour heterogeneity from pre-treatment computed tomography textures classifies ovarian cancers by clinical outcome. Eur Radiol 27:3991–4001CrossRef Vargas HA, Veeraraghavan H, Micco M et al (2017) A novel representation of inter-site tumour heterogeneity from pre-treatment computed tomography textures classifies ovarian cancers by clinical outcome. Eur Radiol 27:3991–4001CrossRef
20.
go back to reference Pinker K, Chin J, Melsaether AN, Morris EA, Moy L (2018) Precision medicine and radiogenomics in breast cancer: new approaches toward diagnosis and treatment. Radiology 287:732–747CrossRef Pinker K, Chin J, Melsaether AN, Morris EA, Moy L (2018) Precision medicine and radiogenomics in breast cancer: new approaches toward diagnosis and treatment. Radiology 287:732–747CrossRef
21.
go back to reference Vargas HA, Huang EP, Lakhman Y et al (2017) Radiogenomics of high-grade serous ovarian cancer: multireader multi-institutional study from the cancer Genome Atlas Ovarian Cancer Imaging Research Group. Radiology 285:482–492CrossRef Vargas HA, Huang EP, Lakhman Y et al (2017) Radiogenomics of high-grade serous ovarian cancer: multireader multi-institutional study from the cancer Genome Atlas Ovarian Cancer Imaging Research Group. Radiology 285:482–492CrossRef
22.
go back to reference Veeraraghavan H, Vargas HA, Sanchez AJ et al (2019) Computed tomography measures of inter-site tumor heterogeneity for classifying outcomes in high-grade serous ovarian carcinoma: a retrospective study. bioRxiv. https://doi.org/10.1101/531046 Veeraraghavan H, Vargas HA, Sanchez AJ et al (2019) Computed tomography measures of inter-site tumor heterogeneity for classifying outcomes in high-grade serous ovarian carcinoma: a retrospective study. bioRxiv. https://​doi.​org/​10.​1101/​531046
23.
go back to reference Holback C, Jarosz R, Prior F et al (2016) Radiology data from The Cancer Genome Atlas Ovarian Cancer [TCGA-OV] collection. The Cancer Imaging Archive Holback C, Jarosz R, Prior F et al (2016) Radiology data from The Cancer Genome Atlas Ovarian Cancer [TCGA-OV] collection. The Cancer Imaging Archive
24.
go back to reference Clark K, Vendt B, Smith K et al (2013) The Cancer Imaging Archive (TCIA): maintaining and operating a public information repository. J Digit Imaging 26:1045–1057CrossRef Clark K, Vendt B, Smith K et al (2013) The Cancer Imaging Archive (TCIA): maintaining and operating a public information repository. J Digit Imaging 26:1045–1057CrossRef
25.
go back to reference Mongkolwat P, Kleper V, Talbot S, Rubin D (2014) The National Cancer Informatics Program (NCIP) Annotation and Image Markup (AIM) Foundation model. J Digit Imaging 27:692–701CrossRef Mongkolwat P, Kleper V, Talbot S, Rubin D (2014) The National Cancer Informatics Program (NCIP) Annotation and Image Markup (AIM) Foundation model. J Digit Imaging 27:692–701CrossRef
27.
go back to reference Fedorov A, Beichel R, Kalpathy-Cramer J et al (2012) 3D slicer as an image computing platform for the quantitative imaging Network. Magn Reson Imaging 30:1323–1341CrossRef Fedorov A, Beichel R, Kalpathy-Cramer J et al (2012) 3D slicer as an image computing platform for the quantitative imaging Network. Magn Reson Imaging 30:1323–1341CrossRef
28.
go back to reference Yoo TS, Ackerman MJ, Lorensen WE et al (2002) Engineering and algorithm design for an image processing Api: a technical report on ITK--the insight Toolkit. Stud Health Technol Inform 85:586–592PubMed Yoo TS, Ackerman MJ, Lorensen WE et al (2002) Engineering and algorithm design for an image processing Api: a technical report on ITK--the insight Toolkit. Stud Health Technol Inform 85:586–592PubMed
29.
go back to reference Dhillon IS, Guan Y, Kulis B (2004) Kernel k-means: spectral clustering and normalized cutsProceedings of the tenth ACM SIGKDD international conference on knowledge discovery and data mining. ACM, pp 551-556 Dhillon IS, Guan Y, Kulis B (2004) Kernel k-means: spectral clustering and normalized cutsProceedings of the tenth ACM SIGKDD international conference on knowledge discovery and data mining. ACM, pp 551-556
30.
go back to reference Jimenez-Sanchez A, Memon D, Pourpe S et al (2017) Heterogeneous tumor-immune microenvironments among differentially growing metastases in an ovarian cancer patient. Cell 170(927–938):e920 Jimenez-Sanchez A, Memon D, Pourpe S et al (2017) Heterogeneous tumor-immune microenvironments among differentially growing metastases in an ovarian cancer patient. Cell 170(927–938):e920
31.
go back to reference Cancer Genome Atlas Research Network (2011) Integrated genomic analyses of ovarian carcinoma. Nature 474:609–615CrossRef Cancer Genome Atlas Research Network (2011) Integrated genomic analyses of ovarian carcinoma. Nature 474:609–615CrossRef
32.
go back to reference Karnovsky A, Weymouth T, Hull T et al (2011) Metscape 2 bioinformatics tool for the analysis and visualization of metabolomics and gene expression data. Bioinformatics 28:373–380CrossRef Karnovsky A, Weymouth T, Hull T et al (2011) Metscape 2 bioinformatics tool for the analysis and visualization of metabolomics and gene expression data. Bioinformatics 28:373–380CrossRef
33.
go back to reference Cheung AKL, Ko JM, Lung HL et al (2011) Cysteine-rich intestinal protein 2 (CRIP2) acts as a repressor of NF-κB–mediated proangiogenic cytokine transcription to suppress tumorigenesis and angiogenesis. Proc Natl Acad Sci U S A 108:8390–8395CrossRef Cheung AKL, Ko JM, Lung HL et al (2011) Cysteine-rich intestinal protein 2 (CRIP2) acts as a repressor of NF-κB–mediated proangiogenic cytokine transcription to suppress tumorigenesis and angiogenesis. Proc Natl Acad Sci U S A 108:8390–8395CrossRef
34.
go back to reference Zhou L, Wang Y, Zhou M et al (2018) HOXA9 inhibits HIF-1alpha-mediated glycolysis through interacting with CRIP2 to repress cutaneous squamous cell carcinoma development. Nat Commun 9:1480CrossRef Zhou L, Wang Y, Zhou M et al (2018) HOXA9 inhibits HIF-1alpha-mediated glycolysis through interacting with CRIP2 to repress cutaneous squamous cell carcinoma development. Nat Commun 9:1480CrossRef
35.
go back to reference Yakirevich E, Sabo E, Lavie O, Mazareb S, Spagnoli GC, Resnick MB (2003) Expression of the MAGE-A4 and NY-ESO-1 cancer-testis antigens in serous ovarian neoplasms. Clin Cancer Res 9:6453–6460PubMed Yakirevich E, Sabo E, Lavie O, Mazareb S, Spagnoli GC, Resnick MB (2003) Expression of the MAGE-A4 and NY-ESO-1 cancer-testis antigens in serous ovarian neoplasms. Clin Cancer Res 9:6453–6460PubMed
36.
go back to reference Cheon DJ, Walts AE, Beach JA et al (2015) Differential expression of argininosuccinate synthetase in serous and non-serous ovarian carcinomas. J Pathol Clin Res 1:41–53CrossRef Cheon DJ, Walts AE, Beach JA et al (2015) Differential expression of argininosuccinate synthetase in serous and non-serous ovarian carcinomas. J Pathol Clin Res 1:41–53CrossRef
37.
go back to reference Coscia F, Watters KM, Curtis M et al (2016) Integrative proteomic profiling of ovarian cancer cell lines reveals precursor cell associated proteins and functional status. Nat Commun 7:12645CrossRef Coscia F, Watters KM, Curtis M et al (2016) Integrative proteomic profiling of ovarian cancer cell lines reveals precursor cell associated proteins and functional status. Nat Commun 7:12645CrossRef
38.
go back to reference Nicholson LJ, Smith PR, Hiller L et al (2009) Epigenetic silencing of argininosuccinate synthetase confers resistance to platinum-induced cell death but collateral sensitivity to arginine auxotrophy in ovarian cancer. Int J Cancer 125:1454–1463CrossRef Nicholson LJ, Smith PR, Hiller L et al (2009) Epigenetic silencing of argininosuccinate synthetase confers resistance to platinum-induced cell death but collateral sensitivity to arginine auxotrophy in ovarian cancer. Int J Cancer 125:1454–1463CrossRef
Metadata
Title
Integration of proteomics with CT-based qualitative and radiomic features in high-grade serous ovarian cancer patients: an exploratory analysis
Authors
Lucian Beer
Hilal Sahin
Nicholas W. Bateman
Ivana Blazic
Hebert Alberto Vargas
Harini Veeraraghavan
Justin Kirby
Brenda Fevrier-Sullivan
John B. Freymann
C. Carl Jaffe
James Brenton
Maura Miccó
Stephanie Nougaret
Kathleen M. Darcy
G. Larry Maxwell
Thomas P. Conrads
Erich Huang
Evis Sala
Publication date
01-08-2020
Publisher
Springer Berlin Heidelberg
Published in
European Radiology / Issue 8/2020
Print ISSN: 0938-7994
Electronic ISSN: 1432-1084
DOI
https://doi.org/10.1007/s00330-020-06755-3

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