Consistent metagenes from cancer expression profiles yield agent specific predictors of chemotherapy response

PLK4 initiates crosstalk between cell cycle, cell proliferation and macrophages infiltration in gliomas

Tumor immune microenvironment plays an important role in tumorigenesis and metastasis. Polo-like kinases 4 (PLK4) is a crucial regulatory factor in the process of cell cycle, and its abnormal regulation often leads to a variety of diseases including tumorigenesis. We have previously explored the function of PLK4 in sensitizing chemotherapy in glioma, but there are few studies on the correlation between PLK4 and tumor immune microenvironment. PLK4 was found to be highly expressed in various types of cancers, including glioma and closely related to histological and genetic features in public databases. Kaplan-Meier survival analysis and Cox regression analysis revealed that higher PLK4 expression is associated with poorer prognosis. GO and KEGG functional enrichment analysis showed that PLK4 expression level was significantly correlated with regulation of immune microenvironment, cell cycle and genomic instability. Immune infiltration analysis showed that high expression of PLK4 resulted in reduced infiltration of macrophages. M1 macrophage infiltration assays showed that PLK4 knockdown GBM cell lines promoted the recruitment of M1-type macrophages via altering expression of chemokines. And in intracranial tumor mouse models, PLK4 inhibition increased tumor-infiltrating M1 macrophages. In summary, our results demonstrated the correlation between high PLK4 expression level and malignant progression of gliomas, and the possible involvement of PLK4 in regulation of cell cycle, cell proliferation and macrophages infiltration in gliomas.

Biasogram: visualization of confounding technical bias in gene expression data

Gene expression profiles of clinical cohorts can be used to identify genes that are correlated with a clinical variable of interest such as patient outcome or response to a particular drug. However, expression measurements are susceptible to technical bias caused by variation in extraneous factors such as RNA quality and array hybridization conditions. If such technical bias is correlated with the clinical variable of interest, the likelihood of identifying false positive genes is increased. Here we describe a method to visualize an expression matrix as a projection of all genes onto a plane defined by a clinical variable and a technical nuisance variable. The resulting plot indicates the extent to which each gene is correlated with the clinical variable or the technical variable. We demonstrate this method by applying it to three clinical trial microarray data sets, one of which identified genes that may have been driven by a confounding technical variable. This approach can be used as a quality control step to identify data sets that are likely to yield false positive results.

Homogeneous datasets of triple negative breast cancers enable the identification of novel prognostic and predictive signatures

Background

Current prognostic gene signatures for breast cancer mainly reflect proliferation status and have limited value in triple-negative (TNBC) cancers. The identification of prognostic signatures from TNBC cohorts was limited in the past due to small sample sizes.

Methodology/Principal Findings

We assembled all currently publically available TNBC gene expression datasets generated on Affymetrix gene chips. Inter-laboratory variation was minimized by filtering methods for both samples and genes. Supervised analysis was performed to identify prognostic signatures from 394 cases which were subsequently tested on an independent validation cohort (n = 261 cases).

Conclusions/Significance

Using two distinct false discovery rate thresholds, 25% and <3.5%, a larger (n = 264 probesets) and a smaller (n = 26 probesets) prognostic gene sets were identified and used as prognostic predictors. Most of these genes were positively associated with poor prognosis and correlated to metagenes for inflammation and angiogenesis. No correlation to other previously published prognostic signatures (recurrence score, genomic grade index, 70-gene signature, wound response signature, 7-gene immune response module, stroma derived prognostic predictor, and a medullary like signature) was observed. In multivariate analyses in the validation cohort the two signatures showed hazard ratios of 4.03 (95% confidence interval [CI] 1.71–9.48; P = 0.001) and 4.08 (95% CI 1.79–9.28; P = 0.001), respectively. The 10-year event-free survival was 70% for the good risk and 20% for the high risk group. The 26-gene signatures had modest predictive value (AUC = 0.588) to predict response to neoadjuvant chemotherapy, however, the combination of a B-cell metagene with the prognostic signatures increased its response predictive value. We identified a 264-gene prognostic signature for TNBC which is unrelated to previously known prognostic signatures.