STAT5 Confers Lactogenic Properties in Breast Tumorigenesis and Restricts Metastatic Potential

Signal transducer and activator of transcription 5 (STAT5) promotes cell survival and instigates breast tumor formation, and in the normal breast it also drives alveolar differentiation and lactogenesis. However, whether STAT5 drives a differentiated phenotype in breast tumorigenesis and therefore impacts cancer spread and metastasis is unclear. We found in two genetically engineered mouse models of breast cancer that constitutively activated Stat5a (Stat5aca) caused precancerous mammary epithelial cells to become lactogenic and evolve into tumors with diminished potential to metastasize. We also showed that STAT5aca reduced the migratory and invasive ability of human breast cancer cell lines in vitro. Furthermore, we demonstrated that STAT5aca overexpression in human breast cancer cells lowered their metastatic burden in xenografted mice. Moreover, RPPA, Western blotting, and studies of ChIPseq data identified several EMT drivers regulated by STAT5. In addition, bioinformatic studies detected a correlation between STAT5 activity and better prognosis of breast cancer patients. Together, we conclude that STAT5 activation during mammary tumorigenesis specifies a tumor phenotype of lactogenic differentiation, suppresses EMT, and diminishes potential for subsequent metastasis

Wilcoxon rank-sum test still outperforms dearseq after accounting for the normalization impact in semi-synthetic RNA-seq data simulation

AbstractIn this response to the correspondence by Hejblum et al. [1], we clarify the reasons why we ran the Wilcoxon rank-sum test on the semi-synthetic RNA-seq samples without normalization, and why we could only run dearseq with its built-in normalization, in our published study [2]. We also argue that no normalization should be performed on the semi-synthetic samples. Hence, for a fairer method comparison and using the updated dearseq package by Hejblum et al., we re-run the six differential expression methods (DESeq2, edgeR, limma-voom, dearseq, NOISeq, and the Wilcoxon rank-sum test) without normalizing the semi-synthetic samples, i.e., under the “No normalization” scheme in [1]. Our updated results show that the Wilcoxon rank-sum test is still the best method in terms of false discovery rate (FDR) control and power performance under all settings investigated

Exaggerated false positives by popular differential expression methods when analyzing human population samples.

When identifying differentially expressed genes between two conditions using human population RNA-seq samples, we found a phenomenon by permutation analysis: two popular bioinformatics methods, DESeq2 and edgeR, have unexpectedly high false discovery rates. Expanding the analysis to limma-voom, NOISeq, dearseq, and Wilcoxon rank-sum test, we found that FDR control is often failed except for the Wilcoxon rank-sum test. Particularly, the actual FDRs of DESeq2 and edgeR sometimes exceed 20% when the target FDR is 5%. Based on these results, for population-level RNA-seq studies with large sample sizes, we recommend the Wilcoxon rank-sum test

DORGE: Discovery of Oncogenes and tumoR suppressor genes using Genetic and Epigenetic features.

Data-driven discovery of cancer driver genes, including tumor suppressor genes (TSGs) and oncogenes (OGs), is imperative for cancer prevention, diagnosis, and treatment. Although epigenetic alterations are important for tumor initiation and progression, most known driver genes were identified based on genetic alterations alone. Here, we developed an algorithm, DORGE (Discovery of Oncogenes and tumor suppressoR genes using Genetic and Epigenetic features), to identify TSGs and OGs by integrating comprehensive genetic and epigenetic data. DORGE identified histone modifications as strong predictors for TSGs, and it found missense mutations, super enhancers, and methylation differences as strong predictors for OGs. We extensively validated DORGE-predicted cancer driver genes using independent functional genomics data. We also found that DORGE-predicted dual-functional genes (both TSGs and OGs) are enriched at hubs in protein-protein interaction and drug-gene networks. Overall, our study has deepened the understanding of epigenetic mechanisms in tumorigenesis and revealed previously undetected cancer driver genes

DORGE: Discovery of Oncogenes and tumoR suppressor genes using Genetic and Epigenetic features.

Data-driven discovery of cancer driver genes, including tumor suppressor genes (TSGs) and oncogenes (OGs), is imperative for cancer prevention, diagnosis, and treatment. Although epigenetic alterations are important for tumor initiation and progression, most known driver genes were identified based on genetic alterations alone. Here, we developed an algorithm, DORGE (Discovery of Oncogenes and tumor suppressoR genes using Genetic and Epigenetic features), to identify TSGs and OGs by integrating comprehensive genetic and epigenetic data. DORGE identified histone modifications as strong predictors for TSGs, and it found missense mutations, super enhancers, and methylation differences as strong predictors for OGs. We extensively validated DORGE-predicted cancer driver genes using independent functional genomics data. We also found that DORGE-predicted dual-functional genes (both TSGs and OGs) are enriched at hubs in protein-protein interaction and drug-gene networks. Overall, our study has deepened the understanding of epigenetic mechanisms in tumorigenesis and revealed previously undetected cancer driver genes