Combining discovery and targeted proteomics reveals a prognostic signature in oral cancer.
Carolina Moretto CarnieliCarolina Carneiro Soares MacedoTatiane De RossiDaniela Campos GranatoCésar RiveraRomênia Ramos DominguesBianca Alves PaulettiSami YokooHenry HeberleAriane Fidelis Busso-LopesNilva Karla CervigneIris Sawazaki-CaloneGabriela Vaz MeirellesFábio Albuquerque MarchiGuilherme Pimentel TellesRosane MinghimAna Carolina Prado RibeiroThaís Bianca BrandãoGilberto de CastroWilfredo Alejandro González-ArriagadaAlexandre GomesFabio PenteadoAlan Roger Santos-SilvaMárcio Ajudarte LopesPriscila Campioni RodriguesElias SundquistTuula SaloSabrina Daniela da SilvaMoulay A Alaoui-JamaliEdgard GranerJay W FoxRicardo Della ColettaAdriana Franco Paes LemePublished in: Nature communications (2018)
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.
Keyphrases
- lymph node metastasis
- machine learning
- mass spectrometry
- squamous cell carcinoma
- papillary thyroid
- end stage renal disease
- cancer therapy
- label free
- newly diagnosed
- chronic kidney disease
- high throughput
- type diabetes
- deep learning
- lymph node
- artificial intelligence
- risk assessment
- binding protein
- genome wide
- prognostic factors
- free survival
- high speed
- human health
- drug delivery
- glycemic control