Search bioRxiv⌕ Search

Biology subjects

Bakayoko, A.

Publications and source records attributed to Bakayoko, A..

2 recordsLinked to original sources

Chemoresistance of TP53 mutant AML requires the mevalonate byproduct, GGPP, for regulation of ROS and induction of a mitochondria stress response

Acute myeloid leukemia (AML) with mutations in the tumor suppressor gene, TP53 (TP53mut AML), is fatal with a median survival of only 6 months. RNA sequencing on purified AML patient samples show TP53mut AML has higher expression of mevalonate pathway genes. We retrospectively identified a survival benefit in TP53mut AML patients who received chemotherapy concurrently with a statin, which inhibits the mevalonate pathway. Mechanistically, TP53mut AML resistance to standard AML chemotherapy, cytarabine (AraC), correlates with increased mevalonate pathway activity and a mitochondria stress response with increased mitochondria mass and oxidative phosphorylation. Pretreatment with a statin reverses these effects and chemosensitizes TP53mut AML cell lines and primary samples in vitro and in vivo. Mitochondria-dependent chemoresistance requires the geranylgeranyl pyrophosphate (GGPP) branch of the mevalonate pathway and novel GGPP-dependent synthesis of glutathione to manage AraC-induced reactive oxygen species (ROS). Overall, we show that the mevalonate pathway is a novel therapeutic target in TP53mut AML. SignificanceChemotherapy-persisting TP53mut AML cells induce a mitochondria stress response that requires mevalonate byproduct, GGPP, through its novel role in glutathione synthesis and regulation of mitochondria metabolism. We provide insight into prior failures of the statin family of mevalonate pathway inhibitors in AML. We identify clinical settings and strategies to successfully target the mevalonate pathway, particularly to address the unmet need of TP53mut AML.

cancer biology↗

Pharmaco-Pheno-Multiomic Integration Reveals Biomarker Profiles and Therapeutic Response Prediction Models in Leukemia and Ovarian Cancer

Despite considerable progress made in improving therapeutic strategies, the overall survival for patients diagnosed with various cancer types remains low. Further, patients often cycle through multiple therapeutic options before finding an effective regimen for the specific malignancy being treated. A focus on building enhanced computational models, which prioritize therapeutic regimens based on a tumors complete molecular profile, will improve the patient experience and augment initial outcomes. In this study, we present an integrative analysis of multiple omic datasets coupled with phenotypic and therapeutic response profiles of Cytarabine from a cohort of primary AML tumors, and Olaparib from a cohort of Patient-Derived Xenograft (PDX) models of ovarian cancer. These analyses, termed Pharmaco-Pheno-Multiomic (PPMO) Integration, established novel complex biomarker profiles that were used to accurately predict prospective therapeutic response profiles in cohorts of newly profiled AML and ovarian tumors. Results from the computational analyses also provide new insights into disease etiology and the mechanisms of therapeutic resistance. Collectively, this study provides proof-of-concept in the use of PPMO to establish highly accurate predictive models of therapeutic response, and the power of leveraging this method to unveil cancer disease mechanisms.

cancer biology↗