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Adaptive responses to mTOR gene targeting in hematopoietic stem cells reveal a proliferative mechanism evasive to mTOR inhibition

ABSTRACT: The mechanistic target of rapamycin (mTOR) is a central regulator of cell growth and an attractive anti-cancer target that integrates diverse signals to control cell proliferation. Previous studies using mTOR inhibitors have shown that mTOR targeting suppresses gene expression and cell proliferation. To date, however, mTOR targeted therapies in cancer have seen very limited efficacy, and one key issue is related to the development of evasive resistance. In this manuscript, through the use of a gene targeting mouse model, we have found that inducible deletion of mTOR in hematopoietic stem cells (HSCs) results in a loss of quiescence and increased proliferation. Adaptive to the mTOR loss, mTOR-/- HSCs increase chromatin accessibility and activate global gene expression, contrary to the effects of short-term inhibition by mTOR inhibitors. Mechanistically, such genomic changes are due to a rewiring and adaptive activation of the ERK/MNK/eIF4E signaling pathway that enhances the protein translation of RNA polymerase II (RNAP II), which in turn leads to increased c-Myc gene expression, allowing the HSCs to thrive despite loss of a functional mTOR pathway. This adaptive mechanism can also be utilized by leukemia cells undergoing long-term mTOR inhibitor treatment to confer resistance to mTOR drug targeting. The resistance can then be counteracted by MNK, CDK9, or c-Myc inhibition. These results provide new insights into the physiological role of mTOR in mammalian stem cell regulation and implicate a novel mechanism of evasive resistance in the context of mTOR targeting.

ORGANISM(S): Mus musculus

PROVIDER: GSE134316 | GEO | 2020/12/24

REPOSITORIES: GEO

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Project description:The mTOR (mammalian Target of Rapamycin) pathway is constitutively activated in Diffuse Large B-Cell Lymphoma (DLBCL). mTOR inhibition has been shown to have clinical activity in patients with DLBCL, although overall response rates remain low. We therefore evaluated differences in the transcriptome between DLBCL cell lines with differential sensitivity to the mTOR inhibitor Rapamycin, to (A) identify gene-expression patterns(GEP) capable of identifying sensitivity to Rapamycin, (B) understand the underlying mechanisms of resistance to Rapamycin in DLBCL and (C) identify bioactive molecules likely to synergize with mTOR inhibitors. Using Affymetrix HuGene ST 1.0 microarrays, we were able to identify a gene expression signature capable of accurately predicting sensitivity and resistance to Rapamycin in DLBCL cell lines. Pathway analysis identified the serine/threonine kinase Akt as central to the differentially-expressed gene network. Connectivity mapping of our datasets identified compounds targeting the AKT pathway with a high likelihood of reversing the GEP associated with resistance to Rapamycin. Specifically, we evaluated the HIV protease inhibitor (PI) Nelfinavir, which is known to have anti-cancer and Akt-inhibitory properties, as well as the small molecule Akt inhibitor MK-2206, for their potential to synergize with to Rapamycin in DLBCL. Nelfinavir and MK-2206 caused profound inhibition of cell viability in combination with Rapamycin in DLBCL cell lines. Low nanomolar concentrations of Rapamycin inhibited phosphorylation of Akt and also downstream targets of activated mTOR when used in combination with these Akt inhibitors. These findings have the potential to significantly improve patient selection for mTOR inhibitor therapy, and to improve rates and depths of response. More broadly, they support the use of global RNA expression and connectivity mapping to improve patient selection and identify synergistic drug combinations for cancer therapy. DLBCL cell lines were tested for Rapamycin sensitivity and classified as "sensitive" or "resistant." Genome-wide analysis of all cell lines were performed using the Affymetrix HuGene ST 1.0 Array Platform. Genes with differential expression between sensitive and resistant cell lines were analyzed using Statistical Analysis of Microarrays (SAM) software, and a signature of genes determnined. This signature was found to accurately predict sensitivity or resistance of other DLBCL cell lines, and to identify the protein kinase Akt as central to resistance.