miRNA-based signature for predicting epithelial ovarian cancer recurrence

We thank Ceppi and coauthors (1) for their comments on our recent paper on the identification of a miRNA-based signature predicting early relapse/progression in ovarian cancer patients (2). Epithelial ovarian cancer (EOC) is still the commonest cause of gynecological cancer-related death mainly due to the difficult early diagnosis and to the high frequency of patients eventually developing an incurable state of platinum-resistant disease (3). The selection of patients at higher risk of relapse is therefore an urgent need to improve the design of tailored therapies.

As pointed out by Ceppi and coauthors, in the era of next generation sequencing, we provided further evidences on the importance of expression studies for selecting prognostic biomarkers. In particular we have focused our attention to miRNAs, therefore to the non coding area of the genome. miRNAs, whose number is at least one order of magnitude lower than that of genes, act as a master layer of regulation for gene expression and can be considered attractive candidates as cancer biomarkers (4). We developed and validated our miRNA-based predictor of EOC early relapse/progression (MiROvaR) by analyzing the miRNA expression profiles of 894 EOC samples, which is the largest collection so far available. Necessarily, this samples collection included cohorts of patients from different clinical centers and with different clinical-pathological characteristics. Although this point may raise some concerns, we believe that the ability of MiROvaR in stratifying patients according to their risk of progression, regardless of the clinical-pathological characteristics of their tumors at presentation, is instead an added value. Indeed, concomitantly to our paper, also Calura et al. have shown that the prognostic contribution of miRNAs is shared across different histological subtypes in early stages disease (5). MiROvaR was developed on a training set (OC179) derived from a randomized clinical trial (see 2); samples for translational research purpose were collected retrospectively ending up with a population eligible for data analysis with a particularly favourable prognosis (2). Indeed, when compared to the two validation sets, OC263 and OC452 (derived from TCGA, the only publically available collection with fully annotated clinical data), OC179 showed a longer progression-free survival (PFS) time (22.8 months, 95% CI: 18–29 vs. 16 months, 95% CI: 13–21 vs. 17 months, 95% CI: 15–18 months, respectively) and OS [not yet reached (NYR); 95% CI: 63–NYR vs. 60 months, 95% CI: 46–77 vs. 49 months, 95% CI: 45–52 months]. The training set OC179 contained a greater proportion of early stages EOC as compared to the two validation sets with a significant number (72%) of early stage patients in the low risk group (2). By restricting the analysis to Type II or high grade serous cancer (HGSOC) included in OC179, the proportion of early stages raised up to 81% in low risk group further increasing the heterogeneity of our case materials arguing against additional subgroup analyses in OC179. However MiROvaR maintained its prognostic independency in multivariable analysis after adjusting for the two strongest prognostic factors in EOC, stage and residual disease after primary surgery in all the performed analyses (2). This unbalanced proportion of early stages as well as the lack of histological heterogeneity in the TCGA cohort, being composed of HGSOC only, may explain why the PFS advantage for the patients in the MiROvaR low risk group was 20 and 22 months in OC179 and OC263, respectively but only 4 months in the TCGA validation set. On the other end, the reproducibility of TCGA Ovarian cancer miRNA profiles has been recently questioned (6).

Our effort to globally analyze 894 EOC samples for miRNA expression profile implicated the need for an accurate data pre-processing to allow the best comparison among the different platforms and chip arrays used. We had to normalize microarray data from different tissue fixation materials and most importantly we had to correct by bioinformatic analysis the possible batch effect in microarray analysis (2). This is the reason why after data filtering and miRNAs re-annotation, we obtained a list of 385 unique miRNAs shared among the platforms, from which the 35 miRNA-based predictor of EOC risk of relapse was developed (2). By relying on the 385 miRNA shared by all the used platforms we may have lost other important miRNAs but we believe that our work is one of the few attempts in integrating the existing data trying to overcome the use of different platforms and different annotated lists typical of miRNA profiles. Although only a shared list of 385 miRNA could be tested, we expect that during MiROvaR further validations, in the case other pivotal miRNAs will be identified, they can be tested and integrated into the signature with the purpose to refine the number of miRNAs entering into a possible clinical-grade tool. The miRNA signature will be further corroborated by a concurrent gene expression analysis of our case materials to shed light in to the signaling pathways involved in the biological processes regulated by MiROvaR and apparently associated to epithelial mesenchymal transition (EMT). Indeed among the major contributors to MiROvaR performance there are miRNAs belonging to the miR-200 family, which are known EMT regulators and loss of miR-200c expression has been associated with relapse even in stage I EOC as reported by the authors of the commentary of our paper (7). Major contributors to MiROvaR are also miRNAs belonging to the miR-506 family that we have previously identified as down-modulated in early relapsing EOC patients (8) and involved in regulation of keys EMT nodes (9) and response to therapy (10).

Our study, by showing that the prognostic role of MiROvaR can be shared across different histological subtypes, represents an important step forward in highlighting the role of miRNAs as biomarkers in EOC and therefore warrants a further prospective validation for MiROvaR entering into clinical practice.

Acknowledgments

Funding: We thank the Italian Association for Cancer Research (AIRC: IG-10302, IG-5776, IG-17475, Special Program 12162) and CARIPLO Foundation (2013-0865) for partially supporting our study.

Footnote

Provenance and Peer Review: This article was commissioned and reviewed by the Section Editor Zheng Li (Department of Gynecologic Oncology, The Third Affiliated Hospital of Kunming Medical University (Yunnan Tumor Hospital), Kunming, China).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at http://dx.doi.org/10.21037/tcr.2017.02.44). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

References

1. Ceppi L, Marchini S, Fruscio R. miRNA based signature for predicting epithelial ovarian cancer relapse-progression: a step forward to prime time clinical adoption? Transl Cancer Res 2016;5:S664-7. [Crossref]
2. Bagnoli M, Canevari S, Califano D, et al. Development and validation of a microRNA-based signature (MiROvaR) to predict early relapse or progression of epithelial ovarian cancer: a cohort study. Lancet Oncol 2016;17:1137-46. [Crossref] [PubMed]
3. Jayson GC, Kohn EC, Kitchener HC, et al. Ovarian cancer. Lancet 2014;384:1376-88. [Crossref] [PubMed]
4. Iorio MV, Croce CM. Commentary on microRNA Fingerprint in Human Epithelial Ovarian Cancer. Cancer Res 2016;76:6143-5. [Crossref] [PubMed]
5. Calura E, Paracchini L, Fruscio R, et al. A prognostic regulatory pathway in stage I epithelial ovarian cancer: new hints for the poor prognosis assessment. Ann Oncol 2016;27:1511-9. [Crossref] [PubMed]
6. Wan YW, Mach CM, Allen GI, et al. On the reproducibility of TCGA ovarian cancer microRNA profiles. PLoS One 2014;9:e87782 [Crossref] [PubMed]
7. Marchini S, Cavalieri D, Fruscio R, et al. Association between miR-200c and the survival of patients with stage I epithelial ovarian cancer: a retrospective study of two independent tumour tissue collections. Lancet Oncol 2011;12:273-85. [Crossref] [PubMed]
8. Bagnoli M, De Cecco L, Granata A, et al. Identification of a chrXq27.3 microRNA cluster associated with early relapse in advanced stage ovarian cancer patients. Oncotarget 2011;2:1265-78. [Crossref] [PubMed]
9. Sun Y, Hu L, Zheng H, et al. MiR-506 inhibits multiple targets in the epithelial-to-mesenchymal transition network and is associated with good prognosis in epithelial ovarian cancer. J Pathol 2015;235:25-36. [Crossref] [PubMed]
10. Liu G, Yang D, Rupaimoole R, et al. Augmentation of response to chemotherapy by microRNA-506 through regulation of RAD51 in serous ovarian cancers. J Natl Cancer Inst 2015;107:djv108 [Crossref] [PubMed]