Integrated Multi-Tumor Radio-Genomic Marker of Outcomes in Patients with High Serous Ovarian Carcinoma

Radioterapia Oncologica ed Ematologica, Dipartimento Diagnostica per Immagini, Area Diagnostica per Immagini, Radiologica Diagnostica e Interventistica Generale, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Roma, Italy

⁵

Columbia University Medical Center, New York, NY 10032, USA

⁶

National Cancer Institute, Rockville, MD 20850, USA

⁷

Laura and Issac Perlmutter Cancer Center, New York University Langone Health, New York, NY 10016, USA

⁸

Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA

⁹

Department of Medicine, Weill Cornell Medical College, New York, NY 10065, USA

¹⁰

Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA

Author to whom correspondence should be addressed.

^†

These two authors contributed equally as co-first authors.

^‡

These two authors contributed equally as co-senior authors.

^§

Translational Oncology, Merck & Company, Kenilworth, NJ 07033, USA.

^‖

Department of Radiology and CRUK Cambridge Center, Cambridge Biomedical Campus, Cambridge CB2 0AX, UK.

Cancers 2020, 12(11), 3403; https://doi.org/10.3390/cancers12113403

Received: 28 September 2020 / Revised: 6 November 2020 / Accepted: 11 November 2020 / Published: 17 November 2020

(This article belongs to the Special Issue Radiomics/Radiogenomics in Cancer)

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Simple Summary

Clinical responses to the initial treatment of high grade serous ovarian cancer (HGSOC) vary greatly. Widespread intra-site and inter-site genomic heterogeneity presents significant challenges for the development of predictive biomarkers based on pre-treatment sampling of select individual tumors. Non-invasive stratification of patients with HGSOC by risk of outcome could facilitate a higher level of intervention for those with the highest risk of a poor outcome. We developed and validated a machine learning-based integrated marker of HGSOC outcomes to standard chemotherapy that combines a previously developed intra-site and inter-site CT radiomics measure called cluster dissimilarity (cluDiss) with clinical and genomic measures using two retrospective cohorts of internal and external institution datasets. Our approach was more accurate than conventional clinical and average radiomics measures for prognosticating progression-free survival and platinum resistance.

Abstract

Purpose: Develop an integrated intra-site and inter-site radiomics-clinical-genomic marker of high grade serous ovarian cancer (HGSOC) outcomes and explore the biological basis of radiomics with respect to molecular signaling pathways and the tumor microenvironment (TME). Method: Seventy-five stage III-IV HGSOC patients from internal (N = 40) and external factors via the Cancer Imaging Archive (TCGA) (N = 35) with pre-operative contrast enhanced CT, attempted primary cytoreduction, at least two disease sites, and molecular analysis performed within TCGA were retrospectively analyzed. An intra-site and inter-site radiomics (cluDiss) measure was combined with clinical-genomic variables (iRCG) and compared against conventional (volume and number of sites) and average radiomics (N = 75) for prognosticating progression-free survival (PFS) and platinum resistance. Correlation with molecular signaling and TME derived using a single sample gene set enrichment that was measured. Results: The iRCG model had the best platinum resistance classification accuracy (AUROC of 0.78 [95% CI 0.77 to 0.80]). CluDiss was associated with PFS (HR 1.03 [95% CI: 1.01 to 1.05], p = 0.002), negatively correlated with Wnt signaling, and positively to immune TME. Conclusions: CluDiss and the iRCG prognosticated HGSOC outcomes better than conventional and average radiomic measures and could better stratify patient outcomes if validated on larger multi-center trials. View Full-Text

Keywords: machine learning; radiomics; high grade serous ovarian cancer; computed tomography; chemotherapy response prognostication; intra-site and inter-site radiomic heterogeneity

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MDPI and ACS Style

Veeraraghavan, H.; Vargas, H.A.; Jimenez-Sanchez, A.; Micco, M.; Mema, E.; Lakhman, Y.; Crispin-Ortuzar, M.; Huang, E.P.; Levine, D.A.; Grisham, R.N.; Abu-Rustum, N.; Deasy, J.O.; Snyder, A.; Miller, M.L.; Brenton, J.D.; Sala, E. Integrated Multi-Tumor Radio-Genomic Marker of Outcomes in Patients with High Serous Ovarian Carcinoma. Cancers 2020, 12, 3403. https://doi.org/10.3390/cancers12113403

AMA Style

Veeraraghavan H, Vargas HA, Jimenez-Sanchez A, Micco M, Mema E, Lakhman Y, Crispin-Ortuzar M, Huang EP, Levine DA, Grisham RN, Abu-Rustum N, Deasy JO, Snyder A, Miller ML, Brenton JD, Sala E. Integrated Multi-Tumor Radio-Genomic Marker of Outcomes in Patients with High Serous Ovarian Carcinoma. Cancers. 2020; 12(11):3403. https://doi.org/10.3390/cancers12113403

Chicago/Turabian Style

Veeraraghavan, Harini, Herbert A. Vargas, Alejandro Jimenez-Sanchez, Maura Micco, Eralda Mema, Yulia Lakhman, Mireia Crispin-Ortuzar, Erich P. Huang, Douglas A. Levine, Rachel N. Grisham, Nadeem Abu-Rustum, Joseph O. Deasy, Alexandra Snyder, Martin L. Miller, James D. Brenton, and Evis Sala. 2020. "Integrated Multi-Tumor Radio-Genomic Marker of Outcomes in Patients with High Serous Ovarian Carcinoma" Cancers 12, no. 11: 3403. https://doi.org/10.3390/cancers12113403

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Integrated Multi-Tumor Radio-Genomic Marker of Outcomes in Patients with High Serous Ovarian Carcinoma

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