A multi-platform normalization method for meta-analysis of gene expression data.

Transcriptomic profiling is a mainstay of translational cancer research and is often used to identify cancer subtypes, stratify responders vs. non-responders patients, predict survival, and identify potential targets for therapeutic intervention. Analysis of gene expression data gathered by RNA sequencing (RNA-seq) and microarray is generally the first step in identifying and characterizing cancer-associated molecular determinants. The methodological advancements and reduced costs associated with transcriptomic profiling have increased the number of publicly available gene expression profiles for cancer subtypes. Data integration from multiple datasets is routinely done to increase the number of samples, improve statistical power, and provide better insight into the heterogeneity of the biological determinant. However, utilizing raw data from multiple platforms, species, and sources introduces systematic variations due to noise, batch effects, and biases. As such, the integrated data is mathematically adjusted through normalization, which allows direct comparison of expression measures among studies while minimizing technical and systemic variations. This study applied meta-analysis to multiple independent Affymetrix microarray and Illumina RNA-seq datasets available through the Gene Expression Omnibus (GEO) and The Cancer Gene Atlas (TCGA). We have previously identified a tripartite motif containing 37 (TRIM37), a breast cancer oncogene, that drives tumorigenesis and metastasis in triple-negative breast cancer. In this article, we adapted and assessed the validity of Stouffer's z-score normalization method to interrogate TRIM37 expression across different cancer types using multiple large-scale datasets.

Oncogenic TRIM37 Links Chemoresistance and Metastatic Fate in Triple-Negative Breast Cancer.

The majority of clinical deaths in patients with triple-negative breast cancer (TNBC) are due to chemoresistance and aggressive metastases, with high prevalence in younger women of African ethnicity. Although tumorigenic drivers are numerous and varied, the drivers of metastatic transition remain largely unknown. Here, we uncovered a molecular dependence of TNBC tumors on the TRIM37 network, which enables tumor cells to resist chemotherapeutic as well as metastatic stress. TRIM37-directed histone H2A monoubiquitination enforces changes in DNA repair that rendered TP53-mutant TNBC cells resistant to chemotherapy. Chemotherapeutic drugs triggered a positive feedback loop via ATM/E2F1/STAT signaling, amplifying the TRIM37 network in chemoresistant cancer cells. High expression of TRIM37 induced transcriptomic changes characteristic of a metastatic phenotype, and inhibition of TRIM37 substantially reduced the in vivo propensity of TNBC cells. Selective delivery of TRIM37-specific antisense oligonucleotides using antifolate receptor 1-conjugated nanoparticles in combination with chemotherapy suppressed lung metastasis in spontaneous metastatic murine models. Collectively, these findings establish TRIM37 as a clinically relevant target with opportunities for therapeutic intervention. SIGNIFICANCE: TRIM37 drives aggressive TNBC biology by promoting resistance to chemotherapy and inducing a prometastatic transcriptional program; inhibition of TRIM37 increases chemotherapy efficacy and reduces metastasis risk in patients with TNBC.