The proteome of clear cell ovarian carcinoma

The "Road" to Malignant Transformation from Endometriosis to Endometriosis-Associated Ovarian Cancers (EAOCs): An mTOR-Centred Review

Ovarian cancer is an umbrella term covering a number of distinct subtypes. Endometrioid and clear-cell ovarian carcinoma are endometriosis-associated ovarian cancers (EAOCs) frequently arising from ectopic endometrium in the ovary. The mechanistic target of rapamycin (mTOR) is a crucial regulator of cellular homeostasis and is dysregulated in both endometriosis and endometriosis-associated ovarian cancer, potentially favouring carcinogenesis across a spectrum from benign disease with cancer-like characteristics, through an atypical phase, to frank malignancy. In this review, we focus on mTOR dysregulation in endometriosis and EAOCs, investigating cancer driver gene mutations and their potential interaction with the mTOR pathway. Additionally, we explore the complex pathogenesis of transformation, considering environmental, hormonal, and epigenetic factors. We then discuss postmenopausal endometriosis pathogenesis and propensity for malignant transformation. Finally, we summarize the current advancements in mTOR-targeted therapeutics for endometriosis and EAOCs.

Proteomic profiling of ovarian clear cell carcinomas identifies prognostic biomarkers for chemotherapy

Clear cell ovarian carcinoma (CCOC) is a relatively rare subtype of ovarian cancer (OC) with high degree of resistance to standard chemotherapy. Little is known about the underlying molecular mechanisms, and it remains a challenge to predict its prognosis after chemotherapy. Here, we first analyzed the proteome of 35 formalin-fixed paraffin-embedded (FFPE) CCOC tissue specimens from a cohort of 32 patients with CCOC (H1 cohort) and characterized 8697 proteins using data-independent acquisition mass spectrometry (DIA-MS). We then performed proteomic analysis of 28 fresh frozen (FF) CCOC tissue specimens from an independent cohort of 24 patients with CCOC (H2 cohort), leading to the identification of 9409 proteins with DIA-MS. After bioinformatics analysis, we narrowed our focus to 15 proteins significantly correlated with the recurrence free survival (RFS) in both cohorts. These proteins are mainly involved in DNA damage response, extracellular matrix (ECM), and mitochondrial metabolism. Parallel reaction monitoring (PRM)-MS was adopted to validate the prognostic potential of the 15 proteins in the H1 cohort and an independent confirmation cohort (H3 cohort). Interferon-inducible transmembrane protein 1 (IFITM1) was observed as a robust prognostic marker for CCOC in both PRM data and immunohistochemistry (IHC) data. Taken together, this study presents a CCOC proteomic data resource and a single promising protein, IFITM1, which could potentially predict the recurrence and survival of CCOC.

Characterization of a fluorescence imaging probe that exploits metabolic dependency of ovarian clear cell carcinoma

The purpose of this study is to clarify the metabolic dependence of ovarian clear cell carcinoma (CCC) by comparing normal tissues and to examine the applicability of fluorescence imaging probe to exploit these metabolic differences. Enhanced glutathione synthesis was supported by the increased uptake of related metabolites and elevated expression levels of genes. Accumulation of intracellular iron and lipid peroxide, induction of cell death by inhibition of the glutathione synthesis pathway indicated that ferroptosis was induced. The activation of γ-glutamyl hydroxymethyl rhodamine green (gGlu-HMRG), a fluorescent imaging probe that recognizes γ-glutamyl transferase, which is essential for the synthesis of glutathione, was investigated in fresh-frozen surgical specimens. gGlu-HMRG detected extremely strong fluorescent signals in the tumor lesions of CCC patients, compared to normal ovaries or endometrium. These results revealed that CCC occurs in the stressful and unique environment of free radical-rich endometrioma, and that glutathione metabolism is enhanced as an adaptation to oxidative stress. Furthermore, a modality that exploits these metabolic differences would be useful for distinguishing between CCC and normal tissues.

Clinicopathological and Prognostic Values of Telomerase Reverse Transcriptase (TERT) Promoter Mutations in Ovarian Clear Cell Carcinoma for Predicting Tumor Recurrence, Platinum Resistance and Survival

BACKGROUND/AIM

A small subset of patients with ovarian clear cell carcinoma (OCCC) harbors telomerase reverse transcriptase promoter (TERTp) mutations. We aimed to analyze the clinicopathological and molecular characteristics of TERTp-mutant OCCC and investigate whether TERTp mutations are associated with the clinicopathological characteristics and outcomes of patients with OCCC.

PATIENTS AND METHODS

We included 11 OCCC cases in our study. Targeted sequencing was performed with a thorough review of pathology slides and electronic medical records.

RESULTS

Eleven OCCCs harbored two hotspot TERTp mutations: c.1-146C>T (6/11) and c.1-124C>T (5/11). All patients (11/11) who underwent postoperative adjuvant chemotherapy experienced tumor recurrence, and eight of them were classified as platinum-resistant. TERTp-mutant OCCC showed significantly higher frequencies of postoperative recurrence and relapse within six months of chemotherapy. TERTp mutations significantly predicted disease-free survival (DFS) in patients with OCCC.

CONCLUSION

We demonstrate that TERTp mutations have significant prognostic value for predicting tumor recurrence, platinum resistance, and worse DFS in patients with OCCC.

Mass Spectrometry-based Proteomics of Epithelial Ovarian Cancers: a Clinical Perspective

Increasing proteomic studies focused on epithelial ovarian cancer (EOC) have attempted to identify early disease biomarkers, establish molecular stratification, and discover novel druggable targets. Here we review these recent studies from a clinical perspective. Multiple blood proteins have been used clinically as diagnostic markers. The ROMA test integrates CA125 and HE4, while the OVA1 and OVA2 tests analyze multiple proteins identified by proteomics. Targeted proteomics has been widely used to identify and validate potential diagnostic biomarkers in EOCs, but none has yet been approved for clinical adoption. Discovery proteomic characterization of bulk EOC tissue specimens has uncovered a large number of dysregulated proteins, proposed new stratification schemes, and revealed novel targets of therapeutic potential. A major hurdle facing clinical translation of these stratification schemes based on bulk proteomic profiling is intra-tumor heterogeneity, namely that single tumor specimens may harbor molecular features of multiple subtypes. We reviewed over 2500 interventional clinical trials of ovarian cancers since 1990, and cataloged 22 types of interventions adopted in these trials. Among 1418 clinical trials which have been completed or are not recruiting new patients, about 50% investigated chemotherapies. Thirty-seven clinical trials are at phase 3 or 4, of which 12 focus on PARP, 10 on VEGFR, 9 on conventional anti-cancer agents, and the remaining on sex hormones, MEK1/2, PD-L1, ERBB, and FRα. Although none of the foregoing therapeutic targets were discovered by proteomics, newer targets discovered by proteomics, including HSP90 and cancer/testis antigens, are being tested also in clinical trials. To accelerate the translation of proteomic findings to clinical practice, future studies need to be designed and executed to the stringent standards of practice-changing clinical trials. We anticipate that the rapidly evolving technology of spatial and single-cell proteomics will deconvolute the intra-tumor heterogeneity of EOCs, further facilitating their precise stratification and superior treatment outcomes.

Screening and Identification of a Prognostic Model of Ovarian Cancer by Combination of Transcriptomic and Proteomic Data

The integration of transcriptome and proteome analysis can lead to the discovery of a myriad of biological insights into ovarian cancer. Proteome, clinical, and transcriptome data about ovarian cancer were downloaded from TCGA's database. A LASSO-Cox regression was used to uncover prognostic-related proteins and develop a new protein prognostic signature for patients with ovarian cancer to predict their prognosis. Patients were brought together in subgroups using a consensus clustering analysis of prognostic-related proteins. To further investigate the role of proteins and protein-coding genes in ovarian cancer, additional analyses were performed using multiple online databases (HPA, Sangerbox, TIMER, cBioPortal, TISCH, and CancerSEA). The final resulting prognosis factors consisted of seven protective factors (P38MAPK, RAB11, FOXO3A, AR, BETACATENIN, Sox2, and IGFRb) and two risk factors (AKT_pS473 and ERCC5), which can be used to construct a prognosis-related protein model. A significant difference in overall survival (OS), disease-free interval (DFI), disease-specific survival (DSS), and progression-free interval (PFI) curves were found in the training, testing, and whole sets when analyzing the protein-based risk score (p < 0.05). We also illustrated a wide range of functions, immune checkpoints, and tumor-infiltrating immune cells in prognosis-related protein signatures. Additionally, the protein-coding genes were significantly correlated with each other. EMTAB8107 and GSE154600 single-cell data revealed that the genes were highly expressed. Furthermore, the genes were related to tumor functional states (angiogenesis, invasion, and quiescence). We reported and validated a survivability prediction model for ovarian cancer based on prognostic-related protein signatures. A strong correlation was found between the signatures, tumor-infiltrating immune cells, and immune checkpoints. The protein-coding genes were highly expressed in single-cell RNA and bulk RNA sequencing, correlating with both each other and tumor functional states.