p53, transcriptional, and drug sensitivity: fresh perspectives on an old activity

RBL2/DREAM-mediated repression of the Aurora kinase A/B pathway determines therapy responsiveness and outcome in p53 WT NSCLC

Wild-type p53 is a stress-responsive transcription factor and potent tumor suppressor. P53 activates or represses genes involved in cell cycle progression or apoptosis in order to arrest the cell cycle or induce cell death. Transcription repression by p53 is indirect and requires repressive members of the RB-family (RB1, RBL1, RBL2) and formation of repressor complexes of RB1-E2F and RBL1/RBL2-DREAM. Many aurora kinase A/B (AURKA/B) pathway genes are repressed in a p53-DREAM-dependent manner. We found heightened expression of RBL2 and reduced expression of AURKA/B pathway genes is associated with improved outcomes in p53 wild-type but not p53 mutant non-small cell lung cancer (NSCLC) patients. Knockdown of p53, RBL2, or the DREAM component LIN37 increased AURKA/B pathway gene expression and reduced paclitaxel and radiation toxicity in NSCLC cells. In contrast, pharmacologic inhibition of AURKA/B or knockdown of AURKA/B pathway components increased paclitaxel and IR sensitivity. The results support a model in which p53-RBL2-DREAM-mediated repression of the AURKA/B pathway contributes to tumor suppression, improved tumor therapy responses, and better outcomes in p53 wild-type NSCLCs.

Computational Modeling of Gene-Specific Transcriptional Repression, Activation and Chromatin Interactions in Leukemogenesis by LASSO-Regularized Logistic Regression

Many physiological and pathological pathways are dependent on gene-specific on/off regulation of transcription. Some genes are repressed, while others are activated. Although many previous studies have analyzed the mechanisms of gene-specific repression and activation, these studies are mainly based on the use of candidate genes, which are either repressed or activated, without simultaneously comparing and contrasting both groups of genes. There is also insufficient consideration of gene locations. Here we describe an integrated machine learning approach, using LASSO-regularized logistic regression, to model gene-specific repression and activation and the underlying contribution of chromatin interactions. LASSO-regularized logistic regression accurately predicted gene-specific transcriptional events and robustly detected the rate-limiting factors that underlie the differences of gene activation and repression. An example was provided by the leukemogenic transcription factor AML1-ETO, which is responsible for 10-15 percent of all acute myeloid leukemia cases. The analysis of AML1-ETO has also revealed novel networks of chromatin interactions and uncovered an unexpected role for E-proteins in AML1-ETO-p300 interactions and a role for the pre-existing gene state in governing the transcriptional response. Our results show that logistic regression-based probabilistic modeling is a promising tool to decipher mechanisms that integrate gene regulation and chromatin interactions in regulated transcription.