Cannabidiol promotes apoptosis and downregulation of oncogenic factors
Greenwood, A. L.; Yamamoto, T.; Joshi, M. G.; Hutchison, K.; Bitler, B. G.
Show abstract
Patients with high-grade serous carcinoma of tubo-ovarian origin (HGSC) often experience significant side effects related to their disease and treatments, such as pain, discomfort, nausea, and vomiting. Over the last two decades, the use of cannabinoids (CBD) to manage pain and anxiety has become more mainstream. However, there is limited data on how CBD interacts with HGSC tumor cells or whether CBD impacts the effect of chemotherapy. Prior preclinical data has suggested the antitumor benefits of cannabinoids; however, the mechanism and data in ovarian cancer are limited. The objectives of this proposed research are to define the endocannabinoid system milieu in ovarian cancer, determine if CBD influences the growth of ovarian cancer cells, measure the cell viability when cannabinoids such as CBD are combined with standard-of-care therapies, and identify potential molecular pathways in which cannabinoids have a therapeutic effect. We conducted publicly available database searches, in vitro proliferation and apoptotic assays, functional protein signaling via reverse phase protein array analysis of CBD-treated cells using 2D cultured cells, and immunohistological analysis of ex vivo cultured patient-derived tumor slices treated with CBD. Our data suggests that CBD is unlikely to affect the growth of cancer cells at physiologic doses but promotes apoptosis and can have growth inhibitory effects at higher concentrations. The inhibitory effects seen at high dose concentrations are likely from the upregulation of apoptotic pathways and inhibition of oncogenic pathways. Overall, physiologic CBD levels have minimal impact on cancer cell growth or chemotherapy efficacy.
Matching journals
The top 9 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Suppression of Ovarian Cancer Cell Proliferation is Associated with Upregulation of Cell-Matrix Adhesion Programs and Integrin-β4-Induced Cell Protection from Cisplatin. 95%
- The MEK1/2 pathway as a therapeutic target in high-grade serous ovarian carcinoma 95%
- Synthetic lethality screening identifies FDA-approved drugs that overcome ATP7B-mediated tolerance of tumor cells to cisplatin 95%
Similar papers in this journal
- Selective Impact of ALK and MELK Inhibition on ERα Stability and Cell Proliferation in Cell Lines Representing Distinct Molecular Phenotypes of Breast Cancer 96%
- Periostin facilitates ovarian cancer recurrence by enhancing cancer stemness 96%
- Cannabidiol (CBD) as a novel inhibitor of HLA-G expression in human choriocarcinoma cell line (JEG-3) 95%
Similar papers in this journal
- A Priori Activation of Apoptosis Pathways of Tumor (AAAPT) Technology: Development of Targeted Apoptosis Initiators for Cancer Treatment. 96%
- Altering mammalian transcription networking with ADAADi: An inhibitor of ATP-dependent chromatin remodeling 95%
- Evaluation of deacetylase inhibition in metaplastic breast carcinoma using multiple derivations of preclinical models of a new patient-derived tumor 94%
Similar papers in this journal
- Targeting dormant ovarian cancer cells in vitro and in an in vivo model of platinum resistance 95%
- Anticancer efficacy of KRASG12C inhibitors is potentiated by PAK4 inhibitor KPT9274 in preclinical models of KRASG12C mutant pancreatic and lung cancers 94%
- Targeting aggressive B-cell lymphomas through pharmacological activation of the mitochondrial protease OMA1 93%
Similar papers in this journal
- Augmentation of extracellular ATP synergizes with chemotherapy in triple negative breast cancer 95%
- Activity of Estrogen Receptor β Agonists in Therapy-Resistant Estrogen Receptor-Positive Breast Cancer 94%
- A pro-oxidant combination of resveratrol and copper down-regulates hallmarks of cancer and immune checkpoints in patients with advanced oral cancer: Results of an exploratory study (RESCU 004) 94%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.