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

ABSTRACT: 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.

SUBMITTER: Carnielli CM

PROVIDER: S-EPMC6125363 | biostudies-literature | 2018 Sep

REPOSITORIES: biostudies-literature

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

Carnielli Carolina Moretto CM Macedo Carolina Carneiro Soares CCS De Rossi Tatiane T Granato Daniela Campos DC Rivera César C Domingues Romênia Ramos RR Pauletti Bianca Alves BA Yokoo Sami S Heberle Henry H Busso-Lopes Ariane Fidelis AF Cervigne Nilva Karla NK Sawazaki-Calone Iris I Meirelles Gabriela Vaz GV Marchi Fábio Albuquerque FA Telles Guilherme Pimentel GP Minghim Rosane R Ribeiro Ana Carolina Prado ACP Brandão Thaís Bianca TB de Castro Gilberto G González-Arriagada Wilfredo Alejandro WA Gomes Alexandre A Penteado Fabio F Santos-Silva Alan Roger AR Lopes Márcio Ajudarte MA Rodrigues Priscila Campioni PC Sundquist Elias E Salo Tuula T da Silva Sabrina Daniela SD Alaoui-Jamali Moulay A MA Graner Edgard E Fox Jay W JW Coletta Ricardo Della RD Paes Leme Adriana Franco AF

Nature communications 20180905 1

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 differen ...[more]

PMID: 30185791

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Project description:Purpose HER2 gene amplification or protein overexpression (HER2+) defines a clinically challenging subgroup of breast cancer with variable prognosis and response to therapy. We aimed to investigate the heterogeneous biological appearance and clinical behavior of HER2+ tumors using molecular profiling. Materials and Methods Hierarchical clustering of gene expression data from 58 HER2-amplified tumors of various stage, histological grade and estrogen receptor (ER) status was used to construct a HER2-derived prognostic predictor that was further evaluated in several large independent breast cancer data sets. Results Unsupervised analysis identified three subtypes of HER2+ tumors with mixed stage, histological grade and ER-status. One subtype had a significantly worse clinical outcome. A prognostic predictor was created based on differentially expressed genes between the subtype with worse outcome and the other subtypes. The predictor was able to define patient groups with better and worse outcome in HER2+ breast cancer across multiple independent breast cancer data sets and identify a sizable HER2+ group with long disease-free survival and low mortality. Significant correlation to prognosis was also observed in basal-like, ER−, lymph node positive or high-grade tumors, irrespective of HER2-status. The predictor included genes associated to immune response, tumor invasion and metastasis. Conclusion The HER2-derived prognostic predictor provides further insight into the heterogeneous biology of HER2+ tumors and may become useful for improved selection of patients that need additional treatment with new drugs targeting the HER2 pathway. Array comparative genomic hybridization (aCGH) identified 58 breast tumors with amplification of HER2 from a larger cohort of approx 500 tumors breast. Global gene expression profiles were obtained using 70-mer oligonucleotide microarrays. Unsupervised hierarchical clustering of the 58 tumors, using Pearson correlation and complete linkage, identified three main clusters. One cluster showed significantly poorer clinical outcome. Significance of microarray (SAM) analysis was performed to identify 158 genes separating the poor outcome cluster compared to the other two clusters. Gene expression centroids, based on the 158 genes, were created for each cluster for validation in independent breast cancer data sets.

Dataset Information

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

Publications

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

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