Odontogenic tumors show considerable morphologic heterogeneity and at times the diagnosis can be challenging. Ameloblastoma, the most common odontogenic tumor, can have morphologic similarity to some salivary gland tumors and therefore we sought to identify biomarkers that might aid in the diagnosis by performing transcriptome wide gene expression profiling of 80 odontogenic and salivary gland neoplasms. These data identified the FOXP1/SOX10 expression profile as characteristic of many odontogenic tumors including ameloblastoma but largely absent in salivary gland tumors. We then assessed 173 salivary gland tumors and 108 odontogenic tumors by immunohistochemistry for FOXP1 and SOX10 expression and found that 34/35 (97%) cases of ameloblastomas were diffusely positive for FOXP1 but completely negative for SOX10. None of the basaloid salivary neoplasms (basal cell adenoma, adenoid cystic carcinoma, polymorphous adenocarcinoma, and myoepitheloma) demonstrated FOXP1/SOX10 expression pattern. Taken together, the results of this study suggest that the FOXP1/SOX10 immunophenotype is common in odontogenic tumors including ameloblastoma and might be useful distinguishing these from similar appearing basaloid salivary gland tumors.

Introduction

Screening mammography has contributed to a significant increase in the diagnosis of ductal carcinoma in situ (DCIS), raising concerns about overdiagnosis and overtreatment. Building on prior observations from lineage evolution analysis, we examined whether measuring genomic features of DCIS would predict association with invasive breast carcinoma (IBC). The long-term goal is to enhance standard clinicopathologic measures of low- versus high-risk DCIS and to enable risk-appropriate treatment.

Methods

We studied three common chromosomal copy number alterations (CNA) in IBC and designed fluorescence in situ hybridization-based assay to measure copy number at these loci in DCIS samples. Clinicopathologic data were extracted from the electronic medical records of Stanford Cancer Institute and linked to demographic data from the population-based California Cancer Registry; results were integrated with data from tissue microarrays of specimens containing DCIS that did not develop IBC versus DCIS with concurrent IBC. Multivariable logistic regression analysis was performed to describe associations of CNAs with these two groups of DCIS.

Results

We examined 271 patients with DCIS (120 that did not develop IBC and 151 with concurrent IBC) for the presence of 1q, 8q24 and 11q13 copy number gains. Compared to DCIS-only patients, patients with concurrent IBC had higher frequencies of CNAs in their DCIS samples. On multivariable analysis with conventional clinicopathologic features, the copy number gains were significantly associated with concurrent IBC. The state of two of the three copy number gains in DCIS was associated with a risk of IBC that was 9.07 times that of no copy number gains, and the presence of gains at all three genomic loci in DCIS was associated with a more than 17-fold risk (P = 0.0013).

Conclusions

CNAs have the potential to improve the identification of high-risk DCIS, defined by presence of concurrent IBC. Expanding and validating this approach in both additional cross-sectional and longitudinal cohorts may enable improved risk stratification and risk-appropriate treatment in DCIS.

Background

Tumors comprise a complex microenvironment of interacting malignant and stromal cell types. Much of our understanding of the tumor microenvironment comes from in vitro studies isolating the interactions between malignant cells and a single stromal cell type, often along a single pathway.

Result

To develop a deeper understanding of the interactions between cells within human lung tumors, we perform RNA-seq profiling of flow-sorted malignant cells, endothelial cells, immune cells, fibroblasts, and bulk cells from freshly resected human primary non-small-cell lung tumors. We map the cell-specific differential expression of prognostically associated secreted factors and cell surface genes, and computationally reconstruct cross-talk between these cell types to generate a novel resource called the Lung Tumor Microenvironment Interactome (LTMI). Using this resource, we identify and validate a prognostically unfavorable influence of Gremlin-1 production by fibroblasts on proliferation of malignant lung adenocarcinoma cells. We also find a prognostically favorable association between infiltration of mast cells and less aggressive tumor cell behavior.

Conclusion

These results illustrate the utility of the LTMI as a resource for generating hypotheses concerning tumor-microenvironment interactions that may have prognostic and therapeutic relevance.