Fallopian tube lesions as potential precursors of early ovarian cancer: a comprehensive proteomic analysis

Abstract

Ovarian cancer is the leading cause of death from gynecologic cancer worldwide. High-grade serous carcinoma (HGSC) is the most common and deadliest subtype of ovarian cancer. While the origin of ovarian tumors is still debated, it has been suggested that HGSC originates from cells in the fallopian tube epithelium (FTE), specifically the epithelial cells in the region of the tubal-peritoneal junction. Three main lesions, p53 signatures, STILs, and STICs, have been defined based on the immunohistochemistry (IHC) pattern of p53 and Ki67 markers and the architectural alterations of the cells, using the Sectioning and Extensively Examining the Fimbriated End Protocol. In this study, we performed an in-depth proteomic analysis of these pre-neoplastic epithelial lesions guided by mass spectrometry imaging and IHC. We evaluated specific markers related to each preneoplastic lesion. The study identified specific lesion markers, such as CAVIN1, Emilin2, and FBLN5. We also used SpiderMass technology to perform a lipidomic analysis and identified the specific presence of specific lipids signature including dietary Fatty acids precursors in lesions. Our study provides new insights into the molecular mechanisms underlying the progression of ovarian cancer and confirms the fimbria origin of HGSC.

Fig. 1. Workflow for spatially resolved proteomic using IHC tissue section.

A Protocol based on tissue from SEE-FIM protocol. After IHC against p53 and Ki-67, the coverslip glass and the mounting medium are removed to access the tissue section. Digestion of the lesion is performed directly on the tissue section and a droplet-based liquid extraction is performed to recover the peptides before MS-based proteomics analysis. B Pre-neoplastic lesions found in the fallopian tube are defined by p53 positivity and Ki-67 index. For p53 signature: accumulation of p53 in at least 12 cells without morphological abnormalities and low Ki-67 index; STIL: same accumulation of p53 in more than 20 cells with some morphological abnormalities and a higher Ki-67 proliferating index (10–40%); STIC: high p53 and Ki-67 index and cells atypical morphology (carcinoma-like).

Fig. 2. Comparison of the different lesions based on their proteomic signature.

A Correlation analysis based on the whole proteome. Ven diagrams showing the distribution of identified proteins among (B) normal tissue and the different lesions and (C) with the addition of HGSC data. Principal component analysis using (D) normal tissue and the pre-neoplastic lesions and (D’) with addition to HGSC. E Hierarchical clustering of the most variable proteins (ANOVA with permutation-based FDR < 0.05) including HGSC data.

Fig. 3. Proteomic analysis of the pre-neoplastic lesions.

A Hierarchical clustering of the most variable proteins between normal tissue, p53 signature, STIL and STIC (n = 4 for each category, ANOVA with permutation-based FDR < 0.05); B Subnetwork Enrichment Analysis was done to highlight altered biological and functional pathways in the different clusters of proteins.

Fig. 4. Annotation analysis of gene ontology terms.

A Biological process, B Transcription factor and C Cellular component for the clusters of proteins. D Biological process, E Biological pathway and F Molecular function of the proteins overexpressed in each lesion. (hypergeometric test against all annotated gene/protein list of Funrich database, the p-value is represented by stars: ***p < 0.001, **p < 0.01, *p < 0.05 no star for p > 0.05).

Fig. 5. Comparison between p53 signature and normal tissue.

A Visualization of a t-test in form of a volcano plot comparing normal to p53 lesion (proteins with p < 0.05 in red.) B Hierarchical clustering of the significant variable proteins between normal tissue and p53 signature (t-test with p < 0.05), C IHC of p53, STIL and STIC lesions with anti-FBLN5 and anti-Emilin2 antibodies, the arrow shows the p53 lesion on the tissue. The graphs represent the quantification of the fluorescence intensity of each marker in the corresponding tissue relative to the fluorescence intensity in the normal FTE.

Fig. 6. Potential mutations and Alternative proteins analysis.

A Annotated MS/MS spectra showing the mutated amino acid in red. B Hierarchical clustering of the most variable alternative proteins between the lesion (a) or with adjunction of HGSC (b).

Fig. 7. SpiderMass ex vivo real-time analyses of tissues section.

A Mass spectra obtained by SpiderMass from P52, STIL, and STIC preneoplasia lesion in fimbria, B The built PCA-LDA classification model based on 3 preneoplasia lesion stages; P53, STIL and STIC. C LDA representation of the 3-class PCA-LDA (right). The table (right) represents the “leave-one-patient-out” cross-validation results of the built classification model. D Specific markers detected in P53, or STIC. (****p < 0.0001, ***p < 0.001, **p < 0.01, *p < 0.05) no star for p > 0.05).