A data similarity-based strategy for meta-analysis of transcriptional profiles in cancer

Identification of a gene-expression predictor for diagnosis and personalized stratification of lupus patients

Systemic lupus erythematosus (SLE) is an autoimmune disease characterized by a wide spectrum of clinical manifestations and degrees of severity. Few genomic biomarkers for SLE have been validated and employed to inform clinical classifications and decisions. To discover and assess the gene-expression based SLE predictors in published studies, we performed a meta-analysis using our established signature database and a data similarity-driven strategy. From 13 training data sets on SLE gene-expression studies, we identified a SLE meta-signature (SLEmetaSig100) containing 100 concordant genes that are involved in DNA sensors and the IFN signaling pathway. We rigorously examined SLEmetaSig100 with both retrospective and prospective validation in two independent data sets. Using unsupervised clustering, we retrospectively elucidated that SLEmetaSig100 could classify clinical samples into two groups that correlated with SLE disease status and disease activities. More importantly, SLEmetaSig100 enabled personalized stratification demonstrating its ability to prospectively predict SLE disease at the individual patient level. To evaluate the performance of SLEmetaSig100 in predicting SLE, we predicted 1,171 testing samples to be either non-SLE or SLE with positive predictive value (97–99%), specificity (85%-84%), and sensitivity (60–84%). Our study suggests that SLEmetaSig100 has enhanced predictive value to facilitate current SLE clinical classification and provides personalized disease activity monitoring.

A Murine Model of K-RAS and β-Catenin Induced Renal Tumors Expresses High Levels of E2F1 and Resembles Human Wilms Tumor

PURPOSE

Wilms tumor is the most common renal neoplasm of childhood. We previously found that restricted activation of the WNT/β-catenin pathway in renal epithelium late in kidney development is sufficient to induce small primitive neoplasms with features of epithelial Wilms tumor. Metastatic disease progression required simultaneous addition of an activating mutation of the oncogene K-RAS. We sought to define the molecular pathways activated in this process and their relationship to human renal malignancies.

MATERIALS AND METHODS

Affymetrix® expression microarray data from murine kidneys with activation of K-ras and/or Ctnnb1 (β-catenin) restricted to renal epithelium were analyzed and compared to publicly available expression data on normal and neoplastic human renal tissue. Target genes were verified by immunoblot and immunohistochemistry.

RESULTS

Mouse kidney tumors with activation of K-ras and Ctnnb1, and human renal malignancies had similar mRNA expression signatures and were associated with activation of networks centered on β-catenin and TP53. Up-regulation of WNT/β-catenin targets (MYC, Survivin, FOXA2, Axin2 and Cyclin D1) was confirmed by immunoblot. K-RAS/β-catenin murine kidney tumors were more similar to human Wilms tumor than to other renal malignancies and demonstrated activation of a TP53 dependent network of genes, including the transcription factor E2F1. Up-regulation of E2F1 was confirmed in murine and human Wilms tumor samples.

CONCLUSIONS

Simultaneous activation of K-RAS and β-catenin in embryonic renal epithelium leads to neoplasms similar to human Wilms tumor and associated with activation of TP53 and up-regulation of E2F1. Further studies are warranted to evaluate the role of TP53 and E2F1 in human Wilms tumor.

Evaluation of public cancer datasets and signatures identifies TP53 mutant signatures with robust prognostic and predictive value

Background

Systematic analysis of cancer gene-expression patterns using high-throughput transcriptional profiling technologies has led to the discovery and publication of hundreds of gene-expression signatures. However, few public signature values have been cross-validated over multiple studies for the prediction of cancer prognosis and chemosensitivity in the neoadjuvant setting.

Methods

To analyze the prognostic and predictive values of publicly available signatures, we have implemented a systematic method for high-throughput and efficient validation of a large number of datasets and gene-expression signatures. Using this method, we performed a meta-analysis including 351 publicly available signatures, 37,000 random signatures, and 31 breast cancer datasets. Survival analyses and pathologic responses were used to assess prediction of prognosis, chemoresponsiveness, and chemo-drug sensitivity.

Results

Among 31 breast cancer datasets and 351 public signatures, we identified 22 validation datasets, two robust prognostic signatures (BRmet50 and PMID18271932Sig33) in breast cancer and one signature (PMID20813035Sig137) specific for prognosis prediction in patients with ER-negative tumors. The 22 validation datasets demonstrated enhanced ability to distinguish cancer gene profiles from random gene profiles. Both prognostic signatures are composed of genes associated with TP53 mutations and were able to stratify the good and poor prognostic groups successfully in 82%and 68% of the 22 validation datasets, respectively. We then assessed the abilities of the two signatures to predict treatment responses of breast cancer patients treated with commonly used chemotherapeutic regimens. Both BRmet50 and PMID18271932Sig33 retrospectively identified those patients with an insensitive response to neoadjuvant chemotherapy (mean positive predictive values 85%-88%). Among those patients predicted to be treatment sensitive, distant relapse-free survival (DRFS) was improved (negative predictive values 87%-88%). BRmet50 was further shown to prospectively predict taxane-anthracycline sensitivity in patients with HER2-negative (HER2-) breast cancer.

Conclusions

We have developed and applied a high-throughput screening method for public cancer signature validation. Using this method, we identified appropriate datasets for cross-validation and two robust signatures that differentiate TP53 mutation status and have prognostic and predictive value for breast cancer patients.

Electronic supplementary material

The online version of this article (doi:10.1186/s12885-015-1102-7) contains supplementary material, which is available to authorized users.

SPARCL1 suppresses metastasis in prostate cancer

PURPOSE

Metastasis, the main cause of death from cancer, remains poorly understood at the molecular level.

EXPERIMENTAL DESIGN

Based on a pattern of reduced expression in human prostate cancer tissues and tumor cell lines, a candidate suppressor gene (SPARCL1) was identified. We used in vitro approaches to determine whether overexpression of SPARCL1 affects cell growth, migration, and invasiveness. We then employed xenograft mouse models to analyze the impact of SPARCL1 on prostate cancer cell growth and metastasis in vivo.

RESULTS

SPARCL1 expression did not inhibit tumor cell proliferation in vitro. By contrast, SPARCL1 did suppress tumor cell migration and invasiveness in vitro and tumor metastatic growth in vivo, conferring improved survival in xenograft mouse models.

CONCLUSIONS

We present the first in vivo data suggesting that SPARCL1 suppresses metastasis of prostate cancer.