Association between CT-texture-derived tumor heterogeneity, outcomes, and BRCA mutation status in patients with high-grade serous ovarian cancer

Andreas Meier, Harini Veeraraghavan, Stephanie Nougaret, Yulia Lakhman, Ramon Sosa, Robert A Soslow, Elizabeth J Sutton, Hedvig Hricak, Evis Sala, Hebert A Vargas, Andreas Meier, Harini Veeraraghavan, Stephanie Nougaret, Yulia Lakhman, Ramon Sosa, Robert A Soslow, Elizabeth J Sutton, Hedvig Hricak, Evis Sala, Hebert A Vargas

Abstract

Purpose: To assess the associations between inter-site texture heterogeneity parameters derived from computed tomography (CT), survival, and BRCA mutation status in women with high-grade serous ovarian cancer (HGSOC).

Materials and methods: Retrospective study of 88 HGSOC patients undergoing CT and BRCA mutation status testing prior to primary cytoreductive surgery. Associations between texture metrics-namely inter-site cluster variance (SCV), inter-site cluster prominence (SCP), inter-site cluster entropy (SE)-and overall survival (OS), progression-free survival (PFS) as well as BRCA mutation status were assessed.

Results: Higher inter-site cluster variance (SCV) was associated with lower PFS (p = 0.006) and OS (p = 0.003). Higher inter-site cluster prominence (SCP) was associated with lower PFS (p = 0.02) and higher inter-site cluster entropy (SE) correlated with lower OS (p = 0.01). Higher values of all three metrics were significantly associated with lower complete surgical resection status in BRCA-negative patients (SE p = 0.039, SCV p = 0.006, SCP p = 0.02), but not in BRCA-positive patients (SE p = 0.7, SCV p = 0.91, SCP p = 0.67). None of the metrics were able to distinguish between BRCA mutation carrier and non-mutation carrier.

Conclusion: The assessment of tumoral heterogeneity in the era of personalized medicine is important, as increased heterogeneity has been associated with distinct genomic abnormalities and worse patient outcomes. A radiomics approach using standard-of-care CT scans might have a clinical impact by offering a non-invasive tool to predict outcome and therefore improving treatment effectiveness. However, it was not able to assess BRCA mutation status in women with HGSOC.

Keywords: BRCA mutation status; High-grade serous ovarian cancer; Radiomics; Texture analysis; Tumor heterogeneity.

Conflict of interest statement

Conflict of Interest:

The authors declare that they have no conflict of interest.

Figures

Fig. 1
Fig. 1
a Survival prediction using SE with regard to PSF b Survival prediction using SE with regard to OS
Fig. 2
Fig. 2
a Survival prediction using SCV with regard to PSF b Survival prediction using SCV with regard to OS
Fig. 3
Fig. 3
a Survival prediction using SCP with regard to PSF b Survival prediction using SCP with regard to OS
Fig. 4
Fig. 4
Boxplots displaying the similarity between BRCA positive mutation status versus BRCA negative mutation status for SE (a), SCV (b) and SCP (c).

References

    1. Siegel RL, Miller KD, Jemal A: Cancer statistics, 2017. CA: A Cancer Journal for Clinicians 2017. January 5;67:7–30.
    1. Prat J, FIGO Committee on Gynecologic Oncology: Staging classification for cancer of the ovary, fallopian tube, and peritoneum. Int J Gynaecol Obstet 2014. January;124:1–5.
    1. Bolton KL, Chenevix-Trench G, Goh C, Sadetzki S, Ramus SJ, Karlan BY, et al.: Association between BRCA1 and BRCA2 mutations and survival in women with invasive epithelial ovarian cancer. JAMA 2012. January 25;307:382–390.
    1. Hyman DM, Zhou Q, Iasonos A, Grisham RN, Arnold AG, Phillips MF, et al.: Improved survival for BRCA2-associated serous ovarian cancer compared with both BRCA-negative and BRCA1-associated serous ovarian cancer. Cancer 2012. August 1;118:3703–3709.
    1. Petrillo M, Marchetti C, De Leo R, Musella A, Capoluongo E, Paris I, et al.: BRCA mutational status, initial disease presentation, and clinical outcome in high-grade serous advanced ovarian cancer: a multicenter study. Am J Obstet Gynecol 2017. September;217:334.e1–334.e9.
    1. Parsons DW, Jones S, Zhang X, Lin JC-H, Leary RJ, Angenendt P, et al.: An integrated genomic analysis of human glioblastoma multiforme. Science 2008. September 26;321:1807–1812.
    1. Varela I, Tarpey P, Raine K, Huang D, Ong CK, Stephens P, et al.: Exome sequencing identifies frequent mutation of the SWI/SNF complex gene PBRM1 in renal carcinoma. Nature 2011. January 27;469:539–542.
    1. Bailey P, Chang DK, Nones K, Johns AL, Patch A-M, Gingras M-C, et al.: Genomic analyses identify molecular subtypes of pancreatic cancer. Nature 2016. March 3;531:47–52.
    1. Network TCGAR: Integrated genomic analyses of ovarian carcinoma. Nature 2011. June 1;474:609–615.
    1. Campbell PJ, Yachida S, Mudie LJ, Stephens PJ, Pleasance ED, Stebbings LA, et al.: The patterns and dynamics of genomic instability in metastatic pancreatic cancer. Nature 2010. October 28;467:1109–1113.
    1. Gerlinger M, Rowan AJ, Horswell S, Larkin J, Endesfelder D, Gronroos E, et al.: Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. N Engl J Med 2012. March 8;366:883–892.
    1. Gillies RJ, Kinahan PE, Hricak H: Radiomics: Images Are More than Pictures, They Are Data. Radiology 2016. February;278:563–577.
    1. Nougaret S, Lakhman Y, Gonen M, Goldman DA, Micco M, D’Anastasi M, et al.: High-Grade Serous Ovarian Cancer: Associations between BRCA Mutation Status, CT Imaging Phenotypes, and Clinical Outcomes. Radiology 2017. November;285:472–481.
    1. Gilks CB, Ionescu DN, Kalloger SE, Köbel M, Irving J, Clarke B, et al.: Tumor cell type can be reproducibly diagnosed and is of independent prognostic significance in patients with maximally debulked ovarian carcinoma. Human Pathology 2008. August;39:1239–1251.
    1. Soslow RA: Histologic Subtypes of Ovarian Carcinoma. International Journal of Gynecological Pathology 2008. February;PAP:1–14.
    1. Reyes MC, Arnold AG, Kauff ND, Levine DA, Soslow RA: Invasion patterns of metastatic high-grade serous carcinoma of ovary or fallopian tube associated with BRCA deficiency. Modern Pathology 2014. October;27:1405–1411.
    1. Yushkevich PA, Piven J, Hazlett HC, Smith RG, Ho S, Gee JC, et al.: User-guided 3D active contour segmentation of anatomical structures: significantly improved efficiency and reliability. Neuroimage 2006. July 1;31:1116–1128.
    1. Haralick RM, Shanmugam K: Textural features for image classification. Systems 1973;
    1. Vargas HA, Veeraraghavan H, Micco M, Nougaret S, Lakhman Y, Meier AA, et al.: A novel representation of inter-site tumour heterogeneity from pre-treatment computed tomography textures classifies ovarian cancers by clinical outcome. Eur Radiol 2017;38:1–11.
    1. Eisenhauer EA, Therasse P, Bogaerts J, Schwartz LH, Sargent D, Ford R, et al.: New response evaluation criteria in solid tumours: Revised RECIST guideline (version 1.1). European Journal of Cancer 2009. January;45:228–247.
    1. Rustin GJ, Marples M, Nelstrop AE, Mahmoudi M, Meyer T: Use of CA-125 to define progression of ovarian cancer in patients with persistently elevated levels. J Clin Oncol 2001. October 15;19:4054–4057.
    1. Safra T, Lai WC, Borgato L, Nicoletto MO, Berman T, Reich E, et al.: BRCA mutations and outcome in epithelial ovarian cancer (EOC): experience in ethnically diverse groups. Ann Oncol 2013. November 1;24:viii63–viii68.
    1. Sun C, Li N, Ding D, Weng D, Meng L, Chen G, et al.: The role of BRCA status on the prognosis of patients with epithelial ovarian cancer: a systematic review of the literature with a meta-analysis. PLoS ONE 2014;9:e95285.

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