Molecular & Cellular Proteomics

Deciphering the Impact of AKT1 Pathogenic Variants in Juvenile Granulosa Cell Tumors Using a Drosophila Model

Juvenile-type granulosa cell tumors (JGCTs) manifest during the prepubertal period as precocious pseudo-puberty and/or dysmenorrhea. We have previously identified pathogenic variants in AKT1 in JGCTs. This study aims to understand how these variants affect cellular function at the phenotypic and molecular levels using a Drosophila model. Transgenic Drosophila models expressing WT AKT1 and four pathogenic variants were created under the control of tissue-specific promoters. Phenotypic effects were studied by assessing Drosophila wings for cell division and growth using wing surface and trichome density and ovarian follicular cells were examined for subcellular localization and morphology. Molecular analyses included mass spectrometry to identify differentially expressed proteins (DEPs) and phospho-peptides, along with RNA-Seq to characterize transcriptomic changes. Wings expressing mutated AKT1 showed increased surface area and reduced trichome density, indicating larger cells. In ovarian follicular cells, WT AKT1 localized primarily to the cytoplasm, while mutated AKT1 variants were associated with the plasma membrane, leading to morphological abnormalities and increased cell size. Mass spectrometry revealed numerous DEPs and phospho-peptides, highlighting changes in pathways such as glycolysis and Rho GTPase signaling. Transcriptomics demonstrated a clear gain of function for mutated AKT1 in activating a subset of genes. However, several genes upregulated by WT AKT1 were less effectively activated by the mutants, indicating a potential loss-of-function in transcriptional regulation for this subset, revealing an unexpected mechanistic complexity. Network analysis of interactions involving DEPs, phosphorylated proteins, and transcription factors suggests these elements mediate the observed proteomic and transcriptional alterations. Taken together, the results underscore the utility of Drosophila models in unraveling the biological relevance of AKT1 pathogenic variants in cancer.

An Integrated Approach to Protein Discovery and Detection From Complex Biofluids

Ovarian cancer, a leading cause of cancer-related deaths among women, has been notoriously difficult to screen for and diagnose early, as early detection significantly improves survival. Researchers and clinicians seek routinely usable and noninvasive screening methods; however, available methods (i.e., biomarker screening) lack desirable sensitivity/specificity. The most fatal form, high-grade serous ovarian cancer, often originate in the fallopian tube; therefore, sampling from the vaginal environment provides more proximal sources for tumor detection. To address these shortcomings and leverage proximal sampling, we developed an untargeted mass spectrometry microprotein profiling method and identified cystatin A, which was validated in an animal model. To overcome the limits of detection inherent to mass spectrometry, we demonstrated that cystatin A is present at 100 pM concentrations using a label-free microtoroid resonator and translated our workflow to patient-derived clinical samples, highlighting the potential utility of early stage detection where biomarker levels would be low.

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 of 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.

Sulfation of O-glycans on Mucin-type Proteins From Serous Ovarian Epithelial Tumors

Despite sulfated O-linked glycans being abundant on ovarian cancer (OC) glycoproteins, their regulation during cancer development and involvement in cancer pathogenesis remain unexplored. We characterized O-glycans carrying sulfation on galactose residues and compared their expression with defined sulfotransferases regulated during OC development. Desialylated sulfated oligosaccharides were released from acidic glycoproteins in the cyst fluid from one patient with a benign serous cyst and one patient with serous OC. Oligosaccharides characterized by LC-MS

Quantitative Proteomics and Phosphoproteomics Analysis of Patient-Derived Ovarian Cancer Stem Cells

High-grade serous ovarian carcinoma (HGSOC) is the deadliest gynecologic cancer. Key to the progression and ultimate lethality of this subtype is the intra-tumoral heterogeneity, which is defined as the coexistence of different cell types and populations within a single tumor. Among those, ovarian cancer stem cells (OCSCs) are a distinct subpopulation of tumor cells endowed with stem-like properties, which can survive current standard therapies, resulting in tumor recurrence. Here, we generated ex vivo primary OCSC-enriched three-dimensional (3D) spheres from 10 distinct treatment naive patient-derived adherent (2D) cultures. We used state-of-the-art quantitative mass spectrometry to characterize the molecular events associated with OCSCs by analyzing their proteome and phosphoproteome. Our data revealed a stemness-related protein signature, shared within a heterogeneous patient cohort, which correlates with chemo-refractoriness in a clinical proteomics dataset. Moreover, we identified targetable deregulated kinases and aberrant PDGF receptor activation in OCSCs. Pharmacological inhibition of PDGFR in adherent OC cells reduced the stemness potential, measured by sphere formation assay. Overall, we provide a valuable resource to identify new OCSC markers and putative targets for OCSC-directed therapies.

Proteomic and Phosphoproteomic Reprogramming in Epithelial Ovarian Cancer Metastasis

Epithelial ovarian cancer (EOC) is a high-risk cancer presenting with heterogeneous tumors. The high incidence of EOC metastasis from primary tumors to nearby tissues and organs is a major driver of EOC lethality. We used cellular models of spheroid formation and readherence to investigate cellular signaling dynamics in each step toward EOC metastasis. In our system, adherent cells model primary tumors, spheroid formation represents the initiation of metastatic spread, and readherent spheroid cells represent secondary tumors. Proteomic and phosphoproteomic analyses show that spheroid cells are hypoxic and show markers for cell cycle arrest. Aurora kinase B abundance and downstream substrate phosphorylation are significantly reduced in spheroids and readherent cells, explaining their cell cycle arrest phenotype. The proteome of readherent cells is most similar to spheroids, yet greater changes in the phosphoproteome show that spheroid cells stimulate Rho-associated kinase 1 (ROCK1)-mediated signaling, which controls cytoskeletal organization. In spheroids, we found significant phosphorylation of ROCK1 substrates that were reduced in both adherent and readherent cells. Application of the ROCK1-specific inhibitor Y-27632 to spheroids increased the rate of readherence and altered spheroid density. The data suggest ROCK1 inhibition increases EOC metastatic potential. We identified novel pathways controlled by Aurora kinase B and ROCK1 as major drivers of metastatic behavior in EOC cells. Our data show that phosphoproteomic reprogramming precedes proteomic changes that characterize spheroid readherence in EOC metastasis.

Proteomic Discovery of Plasma Protein Biomarkers and Development of Models Predicting Prognosis of High-Grade Serous Ovarian Carcinoma

Ovarian cancer is one of the most lethal female cancers. For accurate prognosis prediction, this study aimed to investigate novel, blood-based prognostic biomarkers for high-grade serous ovarian carcinoma (HGSOC) using mass spectrometry-based proteomics methods. We conducted label-free liquid chromatography-tandem mass spectrometry using frozen plasma samples obtained from patients with newly diagnosed HGSOC (n = 20). Based on progression-free survival (PFS), the samples were divided into two groups: good (PFS ≥18 months) and poor prognosis groups (PFS <18 months). Proteomic profiles were compared between the two groups. Referring to proteomics data that we previously obtained using frozen cancer tissues from chemotherapy-naïve patients with HGSOC, overlapping protein biomarkers were selected as candidate biomarkers. Biomarkers were validated using an independent set of HGSOC plasma samples (n = 202) via enzyme-linked immunosorbent assay (ELISA). To construct models predicting the 18-month PFS rate, we performed stepwise selection based on the area under the receiver operating characteristic curve (AUC) with 5-fold cross-validation. Analysis of differentially expressed proteins in plasma samples revealed that 35 and 61 proteins were upregulated in the good and poor prognosis groups, respectively. Through hierarchical clustering and bioinformatic analyses, GSN, VCAN, SND1, SIGLEC14, CD163, and PRMT1 were selected as candidate biomarkers and were subjected to ELISA. In multivariate analysis, plasma GSN was identified as an independent poor prognostic biomarker for PFS (adjusted hazard ratio, 1.556; 95% confidence interval, 1.073-2.256; p = 0.020). By combining clinical factors and ELISA results, we constructed several models to predict the 18-month PFS rate. A model consisting of four predictors (FIGO stage, residual tumor after surgery, and plasma levels of GSN and VCAN) showed the best predictive performance (mean validated AUC, 0.779). The newly developed model was converted to a nomogram for clinical use. Our study results provided insights into protein biomarkers, which might offer clues for developing therapeutic targets.