Salivary alpha-1-antitrypsin and macrophage migration inhibitory factor may be potential prognostic biomarkers for oncologic treatment-induced severe oral mucositis.

AIMS: Evaluate the abundance of the selected targets, alpha-1-antitrypsin (A1AT) and macrophage migration inhibitory factor (MIF), and correlate these findings with the risk of developing severe oral mucositis (OM). MATERIALS AND METHODS: Head and neck squamous cell carcinoma (HNSCC) patients submitted to radiotherapy (RT) or chemoradiotherapy (CRT) were assessed. OM grade and pain were evaluated daily during treatment. Two protein targets, A1AT and MIF, were evaluated, using selected reaction monitoring-mass spectrometry (SRM-MS), in whole saliva, collected prior to oncologic treatment. The results obtained from the targeted proteomic analysis were correlated with OM clinical outcomes. RESULTS: A total of 27 patients were included, of whom 21 (77.8%) had locally advanced disease (clinical stage III or IV). Most patients (70.4%) received CRT. OM grades 2 (40.8%) and 3 (33.3%) were the most prevalent during RT with a mean highest reported OM-related pain of 3.22 through the visual analogue scale (VAS). The abundance of A1AT and MIF correlated significantly with severe (grades 3 or 4, p < 0.02) compared with moderate-low (grades 1 or 2, p < 0.04) OM grade. CONCLUSIONS: There is a correlation between the abundance of salivary A1AT and MIF and oncologic treatment-induced OM. The correlation of MIF expression with severe OM appears to be compatible with its physiological pro-inflammatory role. These results open up great possibilities for the use of salivary MIF and A1AT levels as prognostic markers for effective therapeutic interventions, such as photobiomodulation therapy, patient-controlled analgesia, or personalized medicaments.

Combining discovery and targeted proteomics reveals a prognostic signature in oral cancer.

Different regions of oral squamous cell carcinoma (OSCC) have particular histopathological and molecular characteristics limiting the standard tumor-node-metastasis prognosis classification. Therefore, defining biological signatures that allow assessing the prognostic outcomes for OSCC patients would be of great clinical significance. Using histopathology-guided discovery proteomics, we analyze neoplastic islands and stroma from the invasive tumor front (ITF) and inner tumor to identify differentially expressed proteins. Potential signature proteins are prioritized and further investigated by immunohistochemistry (IHC) and targeted proteomics. IHC indicates low expression of cystatin-B in neoplastic islands from the ITF as an independent marker for local recurrence. Targeted proteomics analysis of the prioritized proteins in saliva, combined with machine-learning methods, highlights a peptide-based signature as the most powerful predictor to distinguish patients with and without lymph node metastasis. In summary, we identify a robust signature, which may enhance prognostic decisions in OSCC and better guide treatment to reduce tumor recurrence or lymph node metastasis.