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Metabolic Reprogramming by Ribitol Expands the Therapeutic Window of BETi JQ1 against Breast Cancer
Ravi Doddapaneni1, Jason D Tucker1, Pei J Lu1
1McColl-Lockwood Laboratory for Muscular Dystrophy Research, Atrium Health Musculoskeletal Institute, Wake Forest University School of Medicine, 1000 Blythe Blvd., Charlotte, NC 28231, USA.
Abstract:
Many cancer patients still lack effective treatments, and pre-existing or acquired resistance limits the clinical benefit of even the most advanced medicines. Recently, much attention has been given to the role of metabolism in cancer, expanding from the Warburg effect to highlight unique patterns that, in turn, may improve diagnostic and therapeutic approaches. Our recent metabolomics study revealed that ribitol can alter glycolysis in breast cancer cells. In the current study, we investigate the combinatorial effects of ribitol with several other anticancer drugs (chrysin, lonidamine, GSK2837808A, CB-839, JQ1, and shikonin) in various breast cancer cells (MDA-MB-231, MCF-7, and T-47D). The combination of ribitol with JQ1 synergistically inhibited the proliferation and migration of breast cancer cells cell-type dependently, only observed in the triple-negative MDA-MB-231 breast cancer cells. This synergy is associated with the differential effects of the 2 compounds on expression of the genes involved in cell survival and death, specifically downregulation in c-Myc and other anti-apoptotic proteins (Bcl-2, Bcl-xL, Mcl-1), but upregulation in p53 and cytochrome C levels. Glycolysis is differentially altered, with significant downregulation of glucose-6-phosphate and lactate by ribitol and JQ1, respectively. The overall effect of the combined treatment on metabolism and apoptosis-related genes results in significant synergy in the inhibition of cell growth and induction of apoptosis. Given the fact that ribitol is a metabolite with limited side effects, a combined therapy is highly desirable with relative ease to apply in the clinic for treating an appropriate cancer population. Our results also emphasize that, similar to traditional drug development, the therapeutic potential of targeting metabolism for cancer treatment may only be achieved in combination with other drugs and requires the identification of a specific cancer population. The desire to apply metabolomic intervention to a large scope of cancer types may be one of the reasons identification of this class of drugs in a clinical trial setting has been delayed.
Insights
Ribitol combined with JQ1 synergistically inhibits triple-negative breast cancer cell growth by altering metabolism and apoptosis pathways. This combination shows promise for targeted cancer therapy with potentially fewer side effects.
Area of Science:
- Oncology
- Metabolomics
- Cancer Metabolism
Background:
- Cancer treatment faces challenges due to drug resistance.
- Metabolism's role in cancer is increasingly recognized for therapeutic potential.
- Ribitol was previously found to alter glycolysis in breast cancer cells.
Purpose of the Study:
- To investigate the combinatorial effects of ribitol with other anticancer drugs.
- To evaluate the synergistic potential of ribitol and JQ1 in breast cancer cells.
- To elucidate the molecular mechanisms underlying the observed synergy.
Main Methods:
- Tested combinations of ribitol with chrysin, lonidamine, GSK2837808A, CB-839, JQ1, and shikonin.
- Utilized breast cancer cell lines (MDA-MB-231, MCF-7, T-47D).
- Analyzed gene expression, cell proliferation, migration, and metabolic pathways.
Main Results:
- Ribitol and JQ1 synergistically inhibited proliferation and migration in MDA-MB-231 cells (triple-negative).
- Synergy involved downregulation of c-Myc, Bcl-2, Bcl-xL, Mcl-1, and upregulation of p53 and cytochrome C.
- Combined treatment differentially altered glycolysis, downregulating glucose-6-phosphate and lactate.
Conclusions:
- Combination therapy of ribitol and JQ1 demonstrates significant synergy in inhibiting breast cancer growth and inducing apoptosis.
- Targeting cancer metabolism, especially in combination, is crucial for effective treatment.
- Identifying specific cancer populations is key for successful clinical application of metabolomic interventions.
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