Multiomic characterization of high-grade serous ovarian carcinoma: editorial commentary on recent application and consideration for future directions

Ovarian carcinoma represents a major burden of women’s morbidity and mortality. It consists of various histological subtypes and among them, high-grade serous carcinoma (HGSC) is most common in the United States (1). Effective approaches to treat HGSC of the ovary, fallopian tube, and peritoneum remain challenging. Within the last two decades, there has been significant progress in our understanding of the molecular landscape in HGSCs and its precursor lesions, due to genomic changes (e.g., gene sequence changes, copy number variations) as well as transcriptomic or proteomic alterations (1-8). Despite these advancements, direct translation of the results from those studies to benefit women with HGSC awaits to be realized as the 10-year survival rate is less than 15% (3). This has led investigators to incorporate more comprehensive analyses using the data generated from sophisticated techniques in order to better characterize the disease and develop risk strata that are reliable and relevant to clinical application. The report by Hollis and colleagues is a timely one aiming at better understanding clinical phenotypes, progression, and survival patterns among HGSC molecular subtypes (9). Through a multiomic approach, they identified protein markers, compared transcriptomic subtype profiles, and studied homologous recombination repair (HRR) pathway-related mutations and copy number changes to determine if molecular characters can distinguish different clinical subtypes of HGSC.

There are several implications from integrated omic studies including this new report. They validate the importance of genomic instability in HGSC initiation and progression, promise clinically meaningful molecular classifications, and emphasize the emerging roles of tumor microenvironment with specific focus on tumor immunogenicity in HGSC development and response to treatment. However, there are several challenges ahead in applying the results of multiomic biomarker studies to clinical practice.

This report integrates an HRR-centric algorithm that classifies HGSC tumors according to prognosis. The authors compare overall survival (OS) of patients with tumors of the following features: mutation in BRCA1 (BRCA1m), BRCA2 (BRCA2m), amplification of CCNE1 (CCNE1g), mRNA overexpression of genes located in chromosome 11q13.5 (region harboring EMSY, termed EMSY-overxp in this study) and HRR wildtype (HRRwt) (9). As expected, the BRCA2m group had the best OS while CCNE1g and HRRwt groups had the worst OS (9). Interestingly, the authors found a survival advantage in groups without BRCA1/2m, but with mRNA transcript overexpression of EMSY in chromosome 11q13.5 (9). EMSY encodes a protein that binds and inactivates BRCA2 proteins (10,11). It is intuitive that EMSY may likely function to contribute to HRR deficiency but whether EMSY is over-expressed in HGSC tissue at the protein level is certainly unclear. In fact, chromosome 11q13.5 harbors several other genes of which Rsf-1, just adjacent to EMSY, has been reported to be amplified and overexpressed at the protein levels in chromosome 11q13.5 amplified HGSCs and their precursor lesions, serous tubal intraepithelial carcinomas (12). Rsf-1 protein is involved in ISWI chromatin remodeling and Rsf-1 amplification was associated with shorter OS compared to those without (12).

The results from this report support an association between HRR genotype and immune-rich phenotype on a basis of transcription analysis. Although similar findings have been previously reported, the authors provide more details on such correlation. HGSCs with either BRCA2m or BRCA1m were associated with the Tothill C2 and immunoreactive The Cancer Genome Atlas (TCGA) subtypes (9). These authors also find three canonical pathways (i.e., polo-like kinase, BRCA1-mediated DNA response, and G2/M DNA damage checkpoints) are highly associated with and may contribute to the high levels of chromosomal disruption in HGSC (13). Another major finding from this study is that the results show immune cell infiltration burden as a prognosis marker. The authors find that higher proportion of CD3⁺ tumor infiltrating lymphocytes (TILs) were associated with prolonged survival and better response to chemotherapy, and this correlated with genomic variants BRCA1/2m as well as with the Tothill and TCGA transcriptomic subtypes, C2 and IMR, respectively (9). Although understanding the TME as it relates to the immunogenicity of HGSC is encouraging, application of these concepts clinically has been challenging since their response rates to immunotherapies have been modest (14,15). In the study, over half of the HGSC tumors were HRRwt (57%), with or without CCNE1g (9). The authors’ HRR-centric algorithm confirms that both HRRwt and CCNE1g are associated with worse OS and response to chemotherapy when compared to the HRR-deficient cases (9). Notably, the anti-angiogenic agent bevacizumab confers the most progression-free survival (PFS) benefit to HRRwt patients as well as those that would be trascriptomically classified as PRO or C5 subtype, corresponding to the TCGA and Tothill subtypes, respectively (9). Taken together, there seems to be reliable, clinically translatable knowledge gained when the status of the HRR pathway (or related genes) in HGSC tumors is discerned.

The paper also concludes that use of immunohistochemistry can provide reliable and specific (2,16,17) approach to help outcome prediction. In this report, the authors find that loss of RB engendered significantly better OS and more favorable response to chemotherapy, and that it was especially prevalent in HRR-deficient variants of HGSC (9). Additionally, this report now classifies PTEN-loss and RB-loss as non-mutually exclusive, with a co-occurrence rate of more than 20% (9). Whether the co-loss of both tumor suppressors is associated with any clinical sign awaits further study in a large HGSC cohort. A long-standing challenge in applying immunostaining in clinical setting is the concern of the specificity and sustainable source of the antibody, interpretation of the staining pattern, the intra-tumoral heterogeneity and, most importantly, whether a single or a small set of staining markers whould have the power for clinical correlation when tested in a large independent cohort.

We propose that future success in the application of multiomic characterization of HGSC will depend on several factors, many of which rely on a reproducible and facile workflow to diagnose and characterize varying clinical profiles. DNA and protein are considered relatively stable markers and are easier to reproduce (18). However, compared to DNA and protein, RNA expression has historically been considered a less stable marker due to biodegradation by RNase, an enzyme highly present in the everyday environment (18). Additionally, formalin-fixed paraffin embedded (FFPE) specimens, when compared to fresh frozen tissue, are associated with fragmented and chemically modified nucleic acids, which can potentially add challenges and introduce bias when assaying RNA, if fresh frozen tissue isn’t available (19). And, while more sophisticated transcriptomic analysis platforms have been increasingly available, the overall cost and turnaround time have limited our broader clinical use (20). This calls for improvements in technique, which may include having less coverage and more shallow reads to broaden throughput and surveillance of the transcriptome (20). This approach could potentially lower costs and increase accessibility. Integration of reliable and affordable transcriptome profiling combined with profiling known stable markers (DNA and protein) has potential for robust combinational approach to predicting HGSC prognosis and treatment response.

We would be remiss to mention the implications of a more granular precision medicine without also taking into consideration the potential effects of next-generation “-omic” profiling on disparate populations, especially those which are historically underserved. Studies have shown that adherence to the National Comprehensive Cancer Network (NCCN) treatment guidelines is a key determinant to survival disparities seen in minority populations (21). In fact, there were no differences in survival based on race or socioeconomic status after controlling for adherence to NCCN treatment guidelines (22). What this tells us is that guideline adherence differences between specific populations will undoubtedly perturb their access to multiomic diagnostics. Additional considerations include biases in big data, with scoring systems at risk of introducing new forms of discrimination, most of which are attributable to human bias (23). The populations from which samples are taken, inappropriate length of surveyed time, and neglect of relevant variables within the diagnostic algorithm can be sources of bias. Historically, differential access to skilled technology became the impetus for the “Digital Divide,” whereby social status (age, gender, race, income, and level of education) directly determines one’s likelihood of access (24). To ensure an equitable distribution of multiomic profiling opportunities for all patients, in addition to public health initiatives, we encourage continued diversity, equity, and inclusion (DEI) initiatives across institutions to increase awareness. Furthermore, we encourage patient recruitment of diverse populations, implementation of mandated data sharing, and intentional consideration of heterogenous populations in the design of technology and data management.

Acknowledgments

Funding: This study was supported by NIH/NCI (Nos. NIH/NCIP50CA228991, R01CA215483; to IMS and TLW) and NIH (No. NIH K12HD000849, to TNJ).

Footnote

Provenance and Peer Review: This article was commissioned by the editorial office, Translational Cancer Research. The article did not undergo external peer review.

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tcr.amegroups.com/article/view/10.21037/tcr-23-355/coif). TNJ received grants from NIH (NIH K12HD000849). IMS and TLW received grants from NIH/NCI (NIH/NCI P50CA228991; R01CA215483). The authors have no other 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. Shih IM, Wang Y, Wang TL. The Origin of Ovarian Cancer Species and Precancerous Landscape. Am J Pathol 2021;191:26-39. [Crossref] [PubMed]
2. Integrated genomic analyses of ovarian carcinoma. Nature 2011;474:609-15. [Crossref] [PubMed]
3. Lisio MA, Fu L, Goyeneche A, et al. High-Grade Serous Ovarian Cancer: Basic Sciences, Clinical and Therapeutic Standpoints. Int J Mol Sci 2019;20:952. [Crossref] [PubMed]
4. Vang R, Levine DA, Soslow RA, et al. Molecular Alterations of TP53 are a Defining Feature of Ovarian High-Grade Serous Carcinoma: A Rereview of Cases Lacking TP53 Mutations in The Cancer Genome Atlas Ovarian Study. Int J Gynecol Pathol 2016;35:48-55. [Crossref] [PubMed]
5. Wang Y, Huang P, Wang BG, et al. Spatial Transcriptomic Analysis of Ovarian Cancer Precursors Reveals Reactivation of IGFBP2 during Pathogenesis. Cancer Res 2022;82:4528-41. [Crossref] [PubMed]
6. Pisanic TR 2nd, Wang Y, Sun H, et al. Methylomic Landscapes of Ovarian Cancer Precursor Lesions. Clin Cancer Res 2020;26:6310-20. [Crossref] [PubMed]
7. Pisanic TR 2nd, Cope LM, Lin SF, et al. Methylomic Analysis of Ovarian Cancers Identifies Tumor-Specific Alterations Readily Detectable in Early Precursor Lesions. Clin Cancer Res 2018;24:6536-47. [Crossref] [PubMed]
8. Wu RC, Wang P, Lin SF, et al. Genomic landscape and evolutionary trajectories of ovarian cancer precursor lesions. J Pathol 2019;248:41-50. [Crossref] [PubMed]
9. Hollis RL, Meynert AM, Michie CO, et al. Multiomic Characterization of High-Grade Serous Ovarian Carcinoma Enables High-Resolution Patient Stratification. Clin Cancer Res 2022;28:3546-56. [Crossref] [PubMed]
10. Hollis RL, Churchman M, Michie CO, et al. High EMSY expression defines a BRCA-like subgroup of high-grade serous ovarian carcinoma with prolonged survival and hypersensitivity to platinum. Cancer 2019;125:2772-81. [Crossref] [PubMed]
11. Dansonka-Mieszkowska A, Szafron LM, Moes-Sosnowska J, et al. Clinical importance of the EMSY gene expression and polymorphisms in ovarian cancer. Oncotarget 2018;9:17735-55. [Crossref] [PubMed]
12. Shih IeM, Sheu JJ, Santillan A, et al. Amplification of a chromatin remodeling gene, Rsf-1/HBXAP, in ovarian carcinoma. Proc Natl Acad Sci U S A 2005;102:14004-9. [Crossref] [PubMed]
13. Cope L, Wu RC, Shih IeM, et al. High level of chromosomal aberration in ovarian cancer genome correlates with poor clinical outcome. Gynecol Oncol 2013;128:500-5. [Crossref] [PubMed]
14. Matulonis UA, Shapira-Frommer R, Santin AD, et al. Antitumor activity and safety of pembrolizumab in patients with advanced recurrent ovarian cancer: results from the phase II KEYNOTE-100 study. Ann Oncol 2019;30:1080-7. [Crossref] [PubMed]
15. Konstantinopoulos PA, Waggoner S, Vidal GA, et al. Single-Arm Phases 1 and 2 Trial of Niraparib in Combination With Pembrolizumab in Patients With Recurrent Platinum-Resistant Ovarian Carcinoma. JAMA Oncol 2019;5:1141-9. [Crossref] [PubMed]
16. Kuhn E, Kurman RJ, Vang R, et al. TP53 mutations in serous tubal intraepithelial carcinoma and concurrent pelvic high-grade serous carcinoma--evidence supporting the clonal relationship of the two lesions. J Pathol 2012;226:421-6. [Crossref] [PubMed]
17. Zhang Y, Cao L, Nguyen D, et al. TP53 mutations in epithelial ovarian cancer. Transl Cancer Res 2016;5:650-63. [Crossref] [PubMed]
18. Zogg H, Singh R, Ro S. Current Advances in RNA Therapeutics for Human Diseases. Int J Mol Sci 2022;23:2736. [Crossref] [PubMed]
19. Wehmas LC, Hester SD, Wood CE. Direct formalin fixation induces widespread transcriptomic effects in archival tissue samples. Sci Rep 2020;10:14497. [Crossref] [PubMed]
20. Mock A, Braun M, Scholl C, et al. Transcriptome profiling for precision cancer medicine using shallow nanopore cDNA sequencing. Sci Rep 2023;13:2378. [Crossref] [PubMed]
21. Chen F, Bailey CE, Alvarez RD, et al. Adherence to treatment guidelines as a major determinant of survival disparities between black and white patients with ovarian cancer. Gynecol Oncol 2021;160:10-5. [Crossref] [PubMed]
22. Bristow RE, Chang J, Ziogas A, et al. Sociodemographic disparities in advanced ovarian cancer survival and adherence to treatment guidelines. Obstet Gynecol 2015;125:833-42. [Crossref] [PubMed]
23. Favaretto M, De Clercq E, Elger BS. Big data and discrimination: perils, promises, and solutions. A systematic review. J Big Data 2019; [Crossref]
24. Witte J, Kiss M, Lynn R. The Internet and social inequalities in the U.S. In: Ragnedda M, Muschert GW. editors. The Digital Divide: The Internet and Social Inequality in International Perspective. London: Routledge; 2015:309-16.