Predicting high-risk endometrioid carcinomas using proteins

Development of prediction models for lymph node metastasis in endometrioid endometrial carcinoma

BACKGROUND

In endometrioid endometrial cancer (EEC), current clinical algorithms do not accurately predict patients with lymph node metastasis (LNM), leading to both under- and over-treatment. We aimed to develop models that integrate protein data with clinical information to identify patients requiring more aggressive surgery, including lymphadenectomy.

METHODS

Protein expression profiles were generated for 399 patients using reverse-phase protein array. Three generalised linear models were built on proteins and clinical information (model 1), also with magnetic resonance imaging included (model 2), and on proteins only (model 3), using a training set, and tested in independent sets. Gene expression data from the tumours were used for confirmatory testing.

RESULTS

LNM was predicted with area under the curve 0.72-0.89 and cyclin D1; fibronectin and grade were identified as important markers. High levels of fibronectin and cyclin D1 were associated with poor survival (p = 0.018), and with markers of tumour aggressiveness. Upregulation of both FN1 and CCND1 messenger RNA was related to cancer invasion and mesenchymal phenotype.

CONCLUSIONS

We demonstrate that data-driven prediction models, adding protein markers to clinical information, have potential to significantly improve preoperative identification of patients with LNM in EEC.

Mst1/2 kinases restrain transformation in a novel transgenic model of Ras driven non-small cell lung cancer

Non-small cell lung cancer remains a highly lethal malignancy. Using the tamoxifen inducible Hnf1b:CreERT2 (H) transgenic mouse crossed to the LsL-KrasG12D (K) transgenic mouse, we recently discovered that an Hnf1b positive cell type in the lung is sensitive to adenoma formation when expressing a mutant KrasG12D allele. In these mice, we observe adenoma formation over a time frame of three to six months. To study specificity of the inducible Hnf1b:CreERT2 in the lung, we employed lineage tracing using an mTmG (G) reporter allele. This technique revealed recombined, GFP+ cells were predominantly SPC+. We further employed this technique in HKG mice to determine Hnf1b+ cells give rise to adenomas that express SPC and TTF1. Review of murine lung tissue confirmed a diagnosis of adenoma and early adenocarcinoma, a pathologic subtype of non-small cell lung cancer. Our expanded mouse model revealed loss of Mst1/2 promotes aggressive lung adenocarcinoma and large-scale proteomic analysis revealed upregulation of PKM2 in the lungs of mice with genetic deletion of Mst1/2. PKM2 is a known metabolic regulator in proliferating cells and cancer. Using a human lung adenocarcinoma cell line, we show pharmacologic inhibition of Mst1/2 increases the abundance of PKM2, indicating genetic loss or pharmacologic inhibition of Mst1/2 directly modulates the abundance of PKM2. In conclusion, here we report a novel model of non-small cell lung cancer driven by a mutation in Kras and deletion of Mst1/2 kinases. Tumor development is restricted to a subset of alveolar type II cells expressing Hnf1b. Our data show loss of Mst1/2 regulates levels of a potent metabolic regulator, PKM2.

Utility of a Reverse Phase Protein Array to Evaluate Multiple Biomarkers in Diffuse Large B-Cell Lymphoma

Purpose

Diffuse large B‐cell lymphoma (DLBCL), the most common non‐Hodgkin lymphoma, is a heterogeneous lymphoma with different clinical manifestations and molecular alterations, and several markers are currently being measured routinely for its diagnosis, subtyping, or prognostication by immunohistochemistry (IHC). Here, the utility of a reverse‐phase‐protein‐array (RPPA) as a novel supportive tool to measure multiple biomarkers for DLBCL diagnosis is validated.

Experimental design

The expression of seven markers (CD5, CD10, BCL2, BCL6, MUM1, Ki‐67, and C‐MYC) is analyzed by RPPA and IHC using 37 DLBCL tissues, and the correlation between the two methods is determined. To normalize tumor content ratio in the tissues, the raw RPPA values of each marker are adjusted by that of CD20 or PAX‐5.

Results

The CD20‐adjusted data for CD5, MUM1, BCL2, Ki‐67, and C‐MYC has better correlation with IHC results than PAX‐5‐adjusted data. Receiver operating characteristic (ROC) analysis reveals that CD5, MUM1, BCL2, and C‐MYC exhibit a better sensitivity and specificity >0.750. Furthermore, the CD20‐adjusted C‐MYC value strongly correlates with that of IHC, and has a particularly high specificity (0.882).

Conclusions and clinical relevance

Although further investigation using a large number of DLBCL specimens needs to be conducted, these results suggest that RPPA could be applicable as a supportive tool for determining lymphoma prognosis.

A global immune gene expression signature for human cancers

Background

Except for a few, the immune gene signatures for most cancer types are not available. We sought to identify a global immune gene signature that is applicable to all human cancers.

Results

We identified an immune gene signature that was intimately correlated with tumor immune characteristics of human cancers and consisted of 382 genes indicative of different immune cell types. The T helper type 1 and 2 cell activation pathway was most significantly enriched in this global immune gene set, while transcription factors, such as SPI1 and STAT family members, were the top regulators of this gene signature. Skin cutaneous melanoma with higher expression of this immune gene signature had significantly longer survival than those with lower immune gene expression. Breast cancer patients with higher immune gene signature were significantly associated with advanced-stage.

Methods

We analyzed the gene expression profiles of 10,062 tumor samples from 32 cancer types in The Cancer Genome Atlas Pan-Cancer data set. Hierarchical clustering analysis of previously-defined immune genes was performed to identify a pan-cancer immune gene signature. Pathway and upstream regulator analyses were used to identify significantly enriched signaling pathways and transcription factors. Kaplan-Meier analysis was used to evaluate the survival difference between dichotomic groups with different immune gene signatures. Correlation of immune gene signature with tumor stage was also examined.

Conclusions

Our identified immune gene signature is applicable in human cancers and can be used to characterize tumor immunogenicity within and across cancer types. Clinical implication of this immune gene set warrants future investigation.

Clinical significance of FBXO17 gene expression in high-grade glioma

BACKGROUND

High-grade gliomas (HGGs) exhibit marked heterogeneity in clinical behavior. The purpose of this study was to identify a novel biomarker that predicts patient outcome, which is helpful in HGG patient management.

METHODS

We analyzed gene expression profiles of 833 HGG cases, representing the largest patient population ever reported. Using the data set from the Cancer Genome Atlas (TCGA) and random partitioning approach, we performed Cox proportional hazards model analysis to identify novel prognostic mRNAs in HGG. The predictive capability was further assessed via multivariate analysis and validated in 4 additional data sets. The Kaplan-Meier method was used to evaluate survival difference between dichotomic groups of patients. Correlation of gene expression and DNA methylation was evaluated via Student's t-test.

RESULTS

Patients with elevated FBXO17 expression had a significantly shorter overall survival (OS) (P = 0.0011). After adjustment by IDH1 mutation, sex, and patient age, FBXO17 gene expression was significantly associated with OS (HR = 1.29, 95% CI =1.04-1.59, P = 0.018). In addition, FBXO17 expression can significantly distinguish patients by OS not only among patients who received temozolomide chemotherapy (HR 1.35, 95% CI =1.12-1.64, P = 0.002) but also among those who did not (HR = 1.48, 95% CI =1.20-1.82, P < 0.0001). The significant association of FBXO17 gene expression with OS was further validated in four external data sets. We further found that FBXO17 endogenous expression is significantly contributable from its promoter methylation.

CONCLUSION

Epigenetically modulated FBXO17 has a potential as a stratification factor for clinical decision-making in HGG.