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Catastrophic ATP loss underlies a metabolic combination therapy tailored for MYCN-amplified neuroblastoma
Krista M Dalton1, Timothy L Lochmann1, Konstantinos V Floros1
1Philips Institute for Oral Health Research, VCU School of Dentistry and Massey Cancer Center, Virginia Commonwealth University, Richmond, VA 23298.
Abstract:
MYCN-amplified neuroblastoma is a lethal subset of pediatric cancer. MYCN drives numerous effects in the cell, including metabolic changes that are critical for oncogenesis. The understanding that both compensatory pathways and intrinsic redundancy in cell systems exists implies that the use of combination therapies for effective and durable responses is necessary. Additionally, the most effective targeted therapies exploit an "Achilles' heel" and are tailored to the genetics of the cancer under study. We performed an unbiased screen on select metabolic targeted therapy combinations and correlated sensitivity with over 20 subsets of cancer. We found that MYCN-amplified neuroblastoma is hypersensitive to the combination of an inhibitor of the lactate transporter MCT1, AZD3965, and complex I of the mitochondrion, phenformin. Our data demonstrate that MCT4 is highly correlated with resistance to the combination in the screen and lowly expressed in MYCN-amplified neuroblastoma. Low MCT4 combines with high expression of the MCT2 and MCT1 chaperone CD147 in MYCN-amplified neuroblastoma, altogether conferring sensitivity to the AZD3965 and phenformin combination. The result is simultaneous disruption of glycolysis and oxidative phosphorylation, resulting in dramatic disruption of adenosine triphosphate (ATP) production, endoplasmic reticulum stress, and cell death. In mouse models of MYCN-amplified neuroblastoma, the combination was tolerable at concentrations where it shrank tumors and did not increase white-blood-cell toxicity compared to single drugs. Therefore, we demonstrate that a metabolic combination screen can identify vulnerabilities in subsets of cancer and put forth a metabolic combination therapy tailored for MYCN-amplified neuroblastoma that demonstrates efficacy and tolerability in vivo.
Insights
A new metabolic therapy combining AZD3965 and phenformin shows promise for MYCN-amplified neuroblastoma. This combination targets key cancer cell energy pathways, offering a tolerable and effective treatment for this aggressive pediatric cancer.
Area of Science:
- Oncology
- Metabolic Pathways
- Pediatric Cancer Research
Background:
- MYCN-amplified neuroblastoma is a highly lethal pediatric cancer driven by metabolic alterations.
- Effective treatment requires combination therapies targeting cancer-specific vulnerabilities.
- Understanding cancer metabolism is key to developing targeted therapies.
Purpose of the Study:
- To identify effective metabolic combination therapies for MYCN-amplified neuroblastoma through an unbiased screen.
- To investigate the role of lactate transporters (MCTs) and their chaperones in treatment sensitivity.
- To evaluate the in vivo efficacy and tolerability of a promising drug combination.
Main Methods:
- Performed an unbiased screen of metabolic targeted therapy combinations across various cancer types.
- Correlated sensitivity with genetic subsets, focusing on MYCN-amplified neuroblastoma.
- Utilized mouse models to assess tumor response and toxicity of the identified combination therapy.
Main Results:
- MYCN-amplified neuroblastoma demonstrated hypersensitivity to a combination of AZD3965 (MCT1 inhibitor) and phenformin (complex I inhibitor).
- Low expression of MCT4 and high expression of CD147 correlated with sensitivity to this combination.
- The combination therapy led to simultaneous disruption of glycolysis and oxidative phosphorylation, causing cell death.
Conclusions:
- A metabolic combination screen successfully identified a targeted therapy for MYCN-amplified neuroblastoma.
- The combination of AZD3965 and phenformin is effective and tolerable in preclinical models.
- This approach highlights the potential of tailored metabolic therapies for specific cancer subsets.
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