Vathany Kulasingam (Presenter)
University Health Network
Authorship: Felix Leung1, Marcus Q. Bernardini2, Kun Liang3, Marjan Rouzbahman1,4, Eleftherios P. Diamandis1,5,6, and Vathany Kulasingam1,6
1Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario, Canada 2Division of Gynecologic Oncology, Department of Obstetrics and Gynecology, University of Toronto, Toronto, Ontario 3University of Waterloo, Waterloo, Ontario, Canada 4Laboratory Medicine Program, University Health Network, Toronto, Ontario, Canada 5Department of Pathology and Laboratory Medicine, Mount Sinai Hospital, Toronto, Ontario, Canada 6Department of Clinical Biochemistry, University Health Network, Toronto, Ontario, Canada
| Short Abstract Ovarian cancer (OvCa) is made up of several distinct subtypes, including serous, endometrioid (EC), clear cell (CCC) and mucinous (MC). While serous OvCa mostly arises from the fallopian tube, the origins of the non-serous subtypes remain unclear. In light of this, we have deciphered the proteomes of non-serous OvCa tissues along with their suspected precursors. Overall, the expression profiles of EC and CCC were associated with endometriosis while those of MC were associated with gastrointestinal cancers. In addition, a subgroup of EC correlated well with MC suggesting that the current subtype model based on histopathology may not be sufficient and that proteomic profiling may enhance accurate diagnoses. Most importantly, this work will serve as the basis to unravel the underlying biology of the non-serous subtypes with respect to their extra-ovarian origins. |
Long Abstract
Introduction and Objectives
Ovarian cancer (OvCa) is not a single disease but is made up of several distinct subtypes, including serous, endometrioid, clear cell and mucinous. It is now widely accepted that the majority of serous OvCa, specifically of the high-grade type, arise from carcinomas of the fallopian tube secretory epithelium (1). Unfortunately, the origins of the endometrioid, clear cell and mucinous subtypes are not well-delineated and the pathobiology of these subtypes remain poorly understood. Genomic and morphologic studies have identified links between a subset of endometrioses that progress to endometrioid and clear cell OvCa (2,3), as well as possible gastrointestinal origins for mucinous OvCa (4). However, none of these associations have been well characterized at the proteomic level and the mechanisms driving tumourigenesis of these extra-ovarian precursors have yet to be identified. As such, comprehensive proteomic profiling would not only aid in substantiating the purported precursors of the non-serous subtypes but as well, reveal the underlying biology behind why these seemingly distinct diseases converge on the ovaries upon metastasis and clinical presentation.
Methods
Fresh, frozen tissues from patients diagnosed with endometrioid, clear cell, and mucinous ovarian carcinoma, as well as endometriosis, healthy endometrium and mucinous cystadenoma were subjected to an in-depth proteomic analysis using a label-free mass spectrometry method. A total of 34 tissues were collected with 7 endometrioid carcinoma, 6 clear cell carcinoma, 7 mucinous carcinoma, 3 endometriosis, 5 healthy endometrium and 6 mucinous cystadenoma patients. Tissues were first subjected to tryptic digestion followed by strong cation-exchange chromatography (SCX). The SCX fractions were then analyzed by liquid chromatography tandem mass spectrometry on the EASY-nLC system coupled online to the Q Exactive Plus mass spectrometer with a nanoelectrospray ionization source. Full MS1 scan was acquired from a scan range of 400-1500 m/z in the Orbitrap at a resolution of 70000, followed by the MS2 scans for the top 12 precursor ions at a resolution of 17500 in a data-dependent acquisition mode and isolation window of 1.6 m/z. The dynamic exclusion was enabled and unassigned, +1 and +4 to > 8 charge states were omitted from MS2 fragmentation.
Mass spectra were uploaded into MaxQuant (5) and searched against the UniProtKB/Swiss-Prot database with built-in Andromeda search engine (6) using the label free quantification (LFQ) function. Search parameters included fixed carbamidomethylation of cysteines and variable modifications of methionine oxidation and N-terminal acetylation. Data was initially searched against a “human first search” database with a parent tolerance of 20 ppm and a fragment tolerance of 0.5 Da in order to calculate and adjust the correct parent tolerance to 5 ppm for the search against the UniProtKB/Swiss-Prot database. During the search, the database was randomized and false detection rate was set to 1% at the peptide and protein levels with proteins being identified with a minimum of two unique peptides.
The produced MaxQuant output matrix was loaded onto the Perseus software to perform statistical and bioinformatics analyses (7). Specifically, LFQ data were logarithmically-transformed and imputed by creating a Gaussian distribution of random numbers with a standard deviation of 30% relative to the standard deviation of the measured values and one standard deviation down-shift of the mean to simulate low signal values. Hierarchical clustering of proteins was performed on logarithmized intensities and z-score normalization of the data using Euclidean distances. Principal component analyses were performed on logarithmized values using singular value decomposition in order to find the principal components.
The Human Proteome Atlas, Protein Analysis Through Evolutionary Relationships Classification System, and Ingenuity Pathway Analysis were also utilized to retrieve additional annotations on identified proteins such as molecular function, biological process, and overrepresented pathways.
Results and Discussion
Approximately 10,000 unique proteins were identified in this proteomic exercise across the 34 biological samples, with roughly 7000 unique proteins being identified within each patient cohort (endometrioid carcinoma, clear cell carcinoma, mucinous carcinoma, endometriosis, endometrium and mucinous cystadenoma). Using all of the quantitative data without any enrichment, PCA and hierarchical clustering revealed interesting intra- and inter-cohort patterns. The endometrioid and clear cell carcinomas clustered closely together indicating similar expression patterns (and possibly a common origin). Endometriosis and endometrium clustered closely, as expected, but did not cluster as closely to endometrioid and clear cell carcinoma. Mucinous carcinoma and mucinous cystadenoma displayed a higher degree of intra-cohort variability but both cohorts clustered relatively closely. Interestingly, one endometrioid carcinoma sample clustered with the mucinous cohorts, possibly suggesting that a subpopulation of endometrioid carcinomas may be more mucinous-like. Indeed, re-examination of the histopathology of this endometrioid carcinoma sample confirmed the presence of mucinous-like elements in the tissue. Thus, proteomic analysis may enhance or aid in accurate diagnoses of non-serous carcinomas.
Inspection of each of the cancer cohorts compared to their suspected benign precursors identified possible drivers of tumourigenesis and metastasis to the ovaries, unique to each of the subtypes. Through enriching for and annotating the most differentially-expressed proteins, overrepresented pathways and molecular mechanisms were identified for each of the subtypes. Endometrioid carcinoma displayed decreased expression of apoptosis signaling and increased expression of gonadotropin signaling, angiogenesis, and Wnt signaling when compared to endometriosis. At the molecular level, cyclin D1 and microphthalmia-associated transcription factor appeared to be central effectors in pathway analysis, both of which are associated with the Wnt/beta-catenin pathway. Clear cell carcinoma displayed increased expression of PTEN signaling and NF-kB signaling compared to endometriosis, which are canonical aberrant pathways in the majority of clear cell carcinomas. Central effectors included Smad and ZEB1, which although have not been demonstrated to be linked to clear cell ovarian carcinoma, are implicated in clear cell renal cell carcinoma, suggesting possible similarities between these two carcinomas. Finally, mucinous carcinoma displayed increased expression of p53 signaling and K-ras signaling, and decreased expression of DNA-damage response pathways when compared to mucinous cystadenoma. Furthermore, many of the overrepresented pathways have been identified as hallmark features of gastrointestinal cancers further suggesting the gastrointestinal origin of some mucinous carcinomas. The MAP kinases were identified as central effectors in pathway analysis as they are downstream targets of the K-ras pathway.
Conclusions
This discovery-phase study has generated a large warehouse of proteomic data for tissues of non-serous ovarian carcinomas and their suspected precursor lesions. Overall, our dataset is not only robust and comprehensive, it is also reflective of the molecular profiles of the various diseases. Clustering analysis displayed the unique expression patterns within each of the cohorts and even that proteomic profiling may serve as a more accurate method of subtype-classification than traditional histopathology. As well, functional network and pathway analyses have demonstrated that each of the subtypes is overrepresented by distinct pathways and molecular mechanisms. Such distinctions may be indicative of the heterogeneity of the non-serous subtypes and that they are unique in their etiological origins and pathogenic pathways that contribute to disease development and progression. With such information, future studies should expand on the use of proteome-based profiling as well as further unraveling insights into the etiology and pathogenesis of each subtype, which could eventually lead to novel molecular targets for therapeutics.
References & Acknowledgements:
References
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