Quantitative Proteomic Analysis of Oral Brush Biopsies Identifies Secretory Leukocyte Protease Inhibitor as a Promising, Mechanism-Based Oral Cancer Biomarker

<div><p>A decrease in the almost fifty percent mortality rate from oral cancer is needed urgently. Improvements in early diagnosis and more effective preventive treatments could affect such a decrease. Towards this end, we undertook for the first time an in-depth mass spectrometry-based quantitative shotgun proteomics study of non-invasively collected oral brush biopsies. Proteins isolated from brush biopsies from healthy normal tissue, oral premalignant lesion tissue (OPMLs), oral squamous cell carcinoma (OSCC) and matched control tissue were compared. In replicated proteomic datasets, the secretory leukocyte protease inhibitor (SLPI) protein stood out based on its decrease in abundance in both OPML and OSCC lesion tissues compared to healthy normal tissue. Western blotting in additional brushed biopsy samples confirmed a trend of gradual decreasing SLPI abundance between healthy normal and OPML tissue, with a larger decrease in OSCC lesion tissue. A similar SLPI decrease was observed <i>in-vitro</i> comparing model OPML and OSCC cell lines. In addition, exfoliated oral cells in patients’ whole saliva showed a loss of SLPI correlated with oral cancer progression. These results, combined with proteomics data indicating a decrease in SLPI in matched healthy control tissue from OSCC patients compared to tissue from healthy normal tissue, suggested a systemic decrease of SLPI in oral cells correlated with oral cancer development. Finally, <i>in-vitro</i> experiments showed that treatment with SLPI significantly decreased NF-kB activity in an OPML cell line. The findings indicate anti-inflammatory activity in OPML, supporting a mechanistic role of SLPI in OSCC progression and suggesting its potential for preventative treatment of at-risk oral lesions. Collectively, our results show for the first time the potential for SLPI as a mechanism-based, non-invasive biomarker of oral cancer progression with potential in preventive treatment.</p></div

Western blotting results against SLPI.

<p>A. Verification of decreased SLPI in tissues collected by brush biopsy. B. SLPI abundance levels measured in exfoliated cells from whole saliva. C. SLPI abundance levels measured in model cell lines. Positive control is a normal healthy human saliva sample; Rhek cells are from a normal epithelial cell line, MSK is a model OPML cell line (MSK-Leuk1) and CA9-22 is a model OSCC cell line.</p

Effect of SLPI peptide treatment on NF-κB activity in an MSK-Leuk1 cell line at two different doses.

<p>Fold-change of an NF-κB reporter gene assay relative to vehicle control is plotted at different time points after treatment.</p

Differentially abundant proteins in OPML and OSCC lesion tissues compared to healthy control tissues.

<p>*fold-change for each OPML or OSCC tissue replicate compared to healthy normal tissue.</p

Average SLPI relative abundance levels relative to healthy normal tissue in OSCC matched healthy tissue and OSCC lesion tissue.

<p>Average SLPI relative abundance levels relative to healthy normal tissue in OSCC matched healthy tissue and OSCC lesion tissue.</p

Path analysis model used to estimate the effect of smoking duration on SS and the role of potential confounding factors.

<p>The standardized path coefficients, significant odds ratios (OR) and p-values (in bold) are shown next to each path. A. Effect of smoking duration on SS, conditional on socioeconomic status; B. Model used to estimate the effect of smoking duration on SS, conditional on body mass index.</p

Logistic Regression models exploring the effect of covariates of smoking on the risk of classification as Sjögren’s Syndrome.

<p>The only significant predictor was smoking duration.</p

Study design.

<p>To evaluate genetic factors involved in the dysregulation of type I IFN signaling in SS, we first compared transcriptional profiles between anti-Ro/SSA positive SS cases and controls to identify genes that make up the IFN signature in SS. We then performed genetic association analysis for variants in the regions of the differentially expressed genes. By integrating transcriptome data with genotype data, <i>cis</i>-eQTL analysis was performed for SS-associated SNPs to evaluate their role in gene dysregulation. This genomic convergence approach resulted in increased power to identify and prioritize disease susceptibility genes for further genetic replication and functional studies.</p

Composition of independent cohorts used in the genetic association analyses.

<p>Composition of independent cohorts used in the genetic association analyses.</p

Differentially expressed transcripts between 115 anti-Ro/SSA positive SS cases and 56 controls identified through transcriptome profiling.

<p>(A) We identified 73 genes (represented by 83 probes on the heatmap) differentially expressed between anti-Ro/SSA positive SS cases and healthy controls (absolute FC >2 and <i>q</i><0.05). Among the differentially expressed genes, 57 were type I IFN-regulated genes (black bar on right) and formed an IFN signature where most genes were overexpressed in SS patients (yellow indicates overexpressed genes compared to controls). (B) The 57 differentially expressed type I IFN-regulated genes were re-clustered in anti-Ro/SSA positive SS cases using <i>k</i>-means (<i>k</i> = 3) algorithm and heterogeneity of the IFN signature levels in anti-Ro/SSA positive SS cases was observed.</p