Table 1 An overview of mass spectrometry imaging studies on ovarian, endometrial, and vulvar cancer and their results
From: Mass spectrometry imaging in gynecological cancers: the best is yet to come
Authors | Groups | Technique | Input material | Conclusions of the study |
|---|---|---|---|---|
Ovarian cancer | ||||
Wanja Kassuhn et al. [27] | HGSOC tissue samples (n = 279) | MALDI-MSI, nanoLC-MS/MS | FFPE | Selection of 135 peptides able to classify HGSOC subtypes (MALDI-derived predictive proteomic signature). Identification of 91 of these peptides as 56 proteins |
Dagmara Pietkiewicz et al. [41] | Low-grade serous borderline ovarian tumor (n = 1) and ovarian fibrothecoma (n = 1) tissue samples | MALDI-MSI | FF | Demonstration of the potential of the MALDI-MSI technique by showing regiospecific m/z values to improve the diagnosis of ovarian tumors, particularly in the most challenging cases |
Hua Zhang et al. [42] | Human laryngeal cancer tissue sample (n = 1) and ovarian cancer tissue sample (n = 1) | MALDI-LTQ-Orbitrap-MS | FFPE | Demonstration of the usefulness of the on-tissue labelling strategy coupled with MALDI-MSI for the sensitive spatial characterization of N-glycan expression within heterogeneous tissue samples |
Matthew T. Briggs et al. [26] | FIGO stage I (n = 3), and stage III (n = 3) serous ovarian cancer tissue samples | MALDI-MSI, PGC-LC–MS/MS | FFPE | Characterization of spatial distribution across tumor and non-tumor regions of 14 N‐glycans by MALDI‐MSI. Identification and structural characterization of 42 N‐glycans (including structural and compositional isomers) by LC–MS |
Arun V. Everest-Dass et al. [43] | FIGO stage III (n = 3) serous ovarian cancer tissue samples | MALDI-MSI, PGC-LC–MS/MS | FFPE | Characterization of the spatial distribution of N-glycan structures within particular regions of the ovarian cancer sections (e.g., tumor, stroma, adipose tissue and necrotic areas). Detection of 40 individual N-glycan masses (including structural and compositional isomers). Delineation of cancerous and noncancerous tissue regions based solely on N-glycan structure distribution |
Rémi Longuespée et al. [44] | Serous ovarian adenocarcinoma (n = 2), endometrioid ovarian adenocarcinoma (n = 2), and serous fallopian tube adenocarcinoma (n = 1) tissue samples | MALDI-MSI, LC-Orbitrap-MS | FFPE | Demonstration of a possible correlation between the serous ovarian adenocarcinoma and fallopian tubes (some biomarkers of ovarian cancer are actually fallopian tubes biomarkers). Proposing the origin of serous ovarian cancer as a consequence of metastasis from tumor cells derived from the fallopian tube |
Oliver Klein et al. [34] | Low-grade serous ovarian carcinoma (n = 14), HGSOC (n = 19), serous borderline tumors (n = 14), ovarian clear-cell (n = 20) tissue samples | MALDI-MSI | FFPE | Demonstration that MALDI‐MSI combined with machine learning algorithms can classify different subtypes of epithelial ovarian cancer |
Vivian Delcourt et al. [45] | Benign, tumor and necrotic/fibrotic regions of serous ovarian cancer biopsies (n = 18) | MALDI-MSI, nanoLC-MS/MS, | FF | Proposed approach might be useful for determination of protein changes in health and disease. Demonstration that 61 proteins are specific to the tumor region, 44 to the necrotic/fibrotic tumor region and 48 to the benign region |
Marta Sans et al. [25] | Normal ovarian tissues (n = 15), borderline ovarian tumors (BOT) (n = 15), HGSOC (n = 48) tissue samples | DESI-MSI | FF | Identification of predictive markers of cancer aggressiveness, which involved various metabolites, free fatty acids, and complex lipids such as ceramides, cardiolipins, glycerophosphoglycerols, and glycerophosphocholines |
Stephan Meding et al. [46] | Serous ovarian cancer (n = 31) tissue samples | MALDI-MSI, LC–MS/MS | FFPE | Detection of 3844 distinct peptide sequences (at a false discovery rate of 1%) in all samples (an average of 982 distinct peptide sequences per sample). Identification of a total of 840 proteins and, on average, 297 proteins per sample |
Mohamed El Ayed et al. [20] | MALDI-MSI: FIGO stage III and stage IV (n = 48) ovarian cancer tissue samples derived from 25 patients, and benign tumors (n = 23) tissue samples NanoLC-ESI MS: grade III and IV ovarian cancer (n = 10) samples, and benign tumors (n = 10) samples | MALDI-MSI, nanoLC-MS/MS | FF | Detection of markers of ovarian carcinoma such as orosomucoid and lumican, which were highly glycosylated (consistent with the mucinous phenotype of ovarian cancers). Identification of two new biomarkers: fragment C-terminal of the PSME1 and mucin-9 |
Kristina Schwamborn et al. [47] | Serous ovarian carcinoma (n = 24) tissue samples, and samples from patients with non-ovarian carcinoma (n = 19, including gastric adenocarcinomas (n = 11), cholangiocarcinomas (n = 3), pancreatic adenocarcinomas (n = 2), lung adenocarcinomas (n = 2), and one ductal carcinoma of the breast (n = 1)) | MALDI-MSI | FFPE | Demonstration that MALDI-MSI allows subtyping of malignant effusions to identify the origin of neoplastic cells. Identification of heat shock protein beta-1, tropomyosin, and cytokeratin-7 as significantly overexpressed in samples from serous ovarian carcinomas compared to other adenocarcinomas |
Maria Luisa Dória et al. [24] | Normal ovary (n = 15) samples derived from 13 patients, normal fallopian tube (n = 6), malignant serous (n = 65), endometroid (n = 7), and clear cell (n = 6) ovarian cancer tissue samples | DESI-MSI | FF | Demonstration of the ability of the DESI-MSI technique to characterize ovarian cancer tissue samples while overcoming existing limitations in classical histopathology. Identification of molecular features (lipidomic profile) discriminating between studied tissue types |
Endometrial cancer | ||||
Parul Mittal et al. [40] | TMA: Endometrial cancer tumor metastasized to pelvic lymph nodes (with LNM) (n = 16), and without LNM (n = 27) LC–MS/MS and MALDI-MSI: Endometrial cancer tumor with LNM (n = 5) and without LNM (n = 5) | MALDI-MSI TMA, MALDI-MSI, IHC, nanoLC-MS/MS | FFPE | Demonstration that annexin A2 and α actinin 4 protein expression correlate with lymph node metastasis in endometrial cancer. Identification of m/z values which are associated with lymph node metastasis in endometrial cancer (by MALDI-MSI). Proving that MALDI-MSI shows higher accuracy than immunohistochemistry in predicting lymph node metastasis in endometrial cancer |
Parul Mittal et al. [17] | Endometrial cancer tumor with LNM (n = 16) and without LNM (n = 27) | MALDI-MSI, LC–MS/MS | FFPE | Identification m/z values which can classify 88% of all tumors correctly (plectin and α-actin-2). These features may be used as potential markers for distinguishing endometrial cancer with and without LNM |
Parul Mittal et al. [33] | Endometrial cancer tumor with LNM (n = 8) and without LNM (n = 20) | MALDI-MSI | FFPE | Demonstration that N-linked glycan may be useful for differentiate cancerous endometrium from normal, and endometrial cancer with LNM from endometrial cancer without LNM |
Vulvar squamous cell carcinomas | ||||
Chao Zhang, et al. [39] | MALDI-MSI: Vulvar squamous cell carcinoma (n = 6) tissue samples IHC: Vulvar squamous cell carcinoma (n = 8) | MALDI-MSI, IHC, nanoLC-MS/MS | FFPE | Providing an insight into the molecular profile of the vulvar intraepithelial neoplasia that seems to be more closely related to the healthy epithelium than the VSCC. Revealing decreased levels of Cytokeratin 5 in VSCC compared to the precursor lesion differentiated vulvar intraepithelial neoplasia |