TY - JOUR
T1 - Combining discovery and targeted proteomics reveals a prognostic signature in oral cancer
AU - Carnielli, Carolina Moretto
AU - Macedo, Carolina Carneiro Soares
AU - De Rossi, Tatiane
AU - Granato, Daniela Campos
AU - Rivera, César
AU - Domingues, Romênia Ramos
AU - Pauletti, Bianca Alves
AU - Yokoo, Sami
AU - Heberle, Henry
AU - Busso-Lopes, Ariane Fidelis
AU - Cervigne, Nilva Karla
AU - Sawazaki-Calone, Iris
AU - Meirelles, Gabriela Vaz
AU - Marchi, Fábio Albuquerque
AU - Telles, Guilherme Pimentel
AU - Minghim, Rosane
AU - Ribeiro, Ana Carolina Prado
AU - Brandão, Thaís Bianca
AU - de Castro, Gilberto
AU - González-Arriagada, Wilfredo Alejandro
AU - Gomes, Alexandre
AU - Penteado, Fabio
AU - Santos-Silva, Alan Roger
AU - Lopes, Márcio Ajudarte
AU - Rodrigues, Priscila Campioni
AU - Sundquist, Elias
AU - Salo, Tuula
AU - da Silva, Sabrina Daniela
AU - Alaoui-Jamali, Moulay A.
AU - Graner, Edgard
AU - Fox, Jay W.
AU - Coletta, Ricardo Della
AU - Paes Leme, Adriana Franco
N1 - Publisher Copyright:
© 2018, The Author(s).
PY - 2018/12/1
Y1 - 2018/12/1
N2 - 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.
AB - 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.
KW - Biomarkers, Tumor
KW - Carcinoma, Squamous Cell
KW - Clinical Decision-Making
KW - Female
KW - Follow-Up Studies
KW - Humans
KW - Immunohistochemistry
KW - Lymphatic Metastasis
KW - Machine Learning
KW - Male
KW - Middle Aged
KW - Mouth Neoplasms
KW - Neoplasm Recurrence, Local
KW - Peptides
KW - Predictive Value of Tests
KW - Prognosis
KW - Proteomics
KW - Retrospective Studies
KW - Saliva
KW - Survival Rate
UR - http://www.scopus.com/inward/record.url?scp=85053009368&partnerID=8YFLogxK
U2 - 10.1038/s41467-018-05696-2
DO - 10.1038/s41467-018-05696-2
M3 - Article
C2 - 30185791
AN - SCOPUS:85053009368
SN - 2041-1723
VL - 9
SP - 1
EP - 17
JO - Nature Communications
JF - Nature Communications
IS - 1
M1 - 3598
ER -