Abstract
The poor prognosis of head and neck cancer (HNC) is associated with metastasis within the lymph nodes (LNs). Herein, the proteome of 140 multisite samples from a 59-HNC patient cohort, including primary and matched LN-negative or -positive tissues, saliva, and blood cells, reveals insights into the biology and potential metastasis biomarkers that may assist in clinical decision-making. Protein profiles are strictly associated with immune modulation across datasets, and this provides the basis for investigating immune markers associated with metastasis. The proteome of LN metastatic cells recapitulates the proteome of the primary tumor sites. Conversely, the LN microenvironment proteome highlights the candidate prognostic markers. By integrating prioritized peptide, protein, and transcript levels with machine learning models, we identify nodal metastasis signatures in blood and saliva. We present a proteomic characterization wiring multiple sites in HNC, thus providing a promising basis for understanding tumoral biology and identifying metastasis-associated signatures.
| Original language | English |
|---|---|
| Article number | 6725 |
| Pages (from-to) | 6725 |
| Journal | Nature Communications |
| Volume | 13 |
| Issue number | 1 |
| DOIs | |
| State | Published - Dec 2022 |
Bibliographical note
Funding Information:This work was supported by FAPESP under Grant numbers 2009/54067-3 [A.F.P.L.], 2010/19278-0 [A.F.P.L], 2016/07846-0 [A.F.P.L.], 2018/18496-6 [A.F.P.L.], 2015/19191-6 [A.F.B.L.], and 2019/21815-9 [A.F.B.L.], CNPq under Grant numbers 305851/2017-9 [A.F.P.L.] and 310392/2021-7 [A.F.P.L.], and ANID-FONDECYT Grant number 1190775 [W.A.G.A.]. This work was also partially supported/or had resources from the Brazilian Federal Government provided to the Center for Research in Energy and Materials (CNPEM), a private non-profit organization under the supervision of the Brazilian Ministry for Science, Technology, and Innovation (MCTI). The proteomics analysis was performed at the Mass Spectrometry Laboratory of the Brazilian Biosciences National Laboratory (LNBio), that is part of CNPEM. The Mass Spectrometry Laboratory staff are acknowledged for their assistance during the experiments (Proposal number MAS-22044). We also acknowledge Prof. Dr. Tsai Siu Mui, CENA, USP, for the use of the Leica Laser Microdissection System LMD6 (FAPESP Grant number 2009/53998-3), Waters Corporation for providing access to the Acquity UPLC-Class M system coupled with a Xevo TQ-XS triple quadrupole mass spectrometer, Prof. Guilherme Telles, IC, UNICAMP, for his support with the selection of proteotypic peptides for SRM-MS analysis, and the Laboratory for Integrative and System Biology (LaBIS) for the use of the LaBIS Cloud (FAPESP Grant numbers 2011/00417-3 and 2015/50612-8).
Funding Information:
This work was supported by FAPESP under Grant numbers 2009/54067-3 [A.F.P.L.], 2010/19278-0 [A.F.P.L], 2016/07846-0 [A.F.P.L.], 2018/18496-6 [A.F.P.L.], 2015/19191-6 [A.F.B.L.], and 2019/21815-9 [A.F.B.L.], CNPq under Grant numbers 305851/2017-9 [A.F.P.L.] and 310392/2021-7 [A.F.P.L.], and ANID-FONDECYT Grant number 1190775 [W.A.G.A.]. This work was also partially supported/or had resources from the Brazilian Federal Government provided to the Center for Research in Energy and Materials (CNPEM), a private non-profit organization under the supervision of the Brazilian Ministry for Science, Technology, and Innovation (MCTI). The proteomics analysis was performed at the Mass Spectrometry Laboratory of the Brazilian Biosciences National Laboratory (LNBio), that is part of CNPEM. The Mass Spectrometry Laboratory staff are acknowledged for their assistance during the experiments (Proposal number MAS-22044). We also acknowledge Prof. Dr. Tsai Siu Mui, CENA, USP, for the use of the Leica Laser Microdissection System LMD6 (FAPESP Grant number 2009/53998-3), Waters Corporation for providing access to the Acquity UPLC-Class M system coupled with a Xevo TQ-XS triple quadrupole mass spectrometer, Prof. Guilherme Telles, IC, UNICAMP, for his support with the selection of proteotypic peptides for SRM-MS analysis, and the Laboratory for Integrative and System Biology (LaBIS) for the use of the LaBIS Cloud (FAPESP Grant numbers 2011/00417-3 and 2015/50612-8).
Publisher Copyright:
© 2022, The Author(s).
