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

Carolina Moretto Carnielli, Carolina Carneiro Soares Macedo, Tatiane De Rossi, Daniela Campos Granato, César Rivera, Romênia Ramos Domingues, Bianca Alves Pauletti, Sami Yokoo, Henry Heberle, Ariane Fidelis Busso-Lopes, Nilva Karla Cervigne, Iris Sawazaki-Calone, Gabriela Vaz Meirelles, Fábio Albuquerque Marchi, Guilherme Pimentel Telles, Rosane Minghim, Ana Carolina Prado Ribeiro, Thaís Bianca Brandão, Gilberto de Castro, Wilfredo Alejandro González-ArriagadaAlexandre Gomes, Fabio Penteado, Alan Roger Santos-Silva, Márcio Ajudarte Lopes, Priscila Campioni Rodrigues, Elias Sundquist, Tuula Salo, Sabrina Daniela da Silva, Moulay A. Alaoui-Jamali, Edgard Graner, Jay W. Fox, Ricardo Della Coletta, Adriana Franco Paes Leme*

*Autor correspondiente de este trabajo

Resultado de la investigación: Contribución a una revistaArtículorevisión exhaustiva

71 Citas (Scopus)

Resumen

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.
Idioma originalInglés
Número de artículo3598
PublicaciónNature Communications
Volumen9
N.º1
DOI
EstadoPublicada - 1 dic. 2018

Nota bibliográfica

Funding Information:
This work was supported by FAPESP Grants 2009/54067-3, 2010/19278-0, 2013/16483-0, 2014/02288-0, 2016/07846-0, CNPq Grants 152619/2015-1, 470268/2013-1, 305432/ 2014-1. We acknowledge Prof. Dr. Tsai Siu Mui, CENA, USP for the use of the Leica Laser Microdissection Systems (FAPESP 2009/53998-3). We acknowledge Prof. Dr. Débora Lima Pereira for the assistance in histopathological images. We thank Dr. Ana Karina de Oliveira and Jamile de Oliveira Sá for all the assistance in primary cell culture experiments.

Publisher Copyright:
© 2018, The Author(s).

Palabras clave

  • Biomarkers, Tumor
  • Carcinoma, Squamous Cell
  • Clinical Decision-Making
  • Female
  • Follow-Up Studies
  • Humans
  • Immunohistochemistry
  • Lymphatic Metastasis
  • Machine Learning
  • Male
  • Middle Aged
  • Mouth Neoplasms
  • Neoplasm Recurrence, Local
  • Peptides
  • Predictive Value of Tests
  • Prognosis
  • Proteomics
  • Retrospective Studies
  • Saliva
  • Survival Rate

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