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Updated: Jun 13, 2026
Testing Targeted Therapies in Cancer using Structural DNA Alteration Analysis and Patient-Derived Xenografts
Published on: July 25, 2020
From Pathway Tracing to Actionable Targets: Integrative Mendelian Randomization and Experimental Triangulation Map
Xinqi Wang1, Haoyu Wang1,2,3, Siyuan Hu1
1Department of Obstetrics and Gynecology, Pelvic Floor Research Centre of Hubei Province, Renmin Hospital of Wuhan University, No. 238 Jiefang Road, Wuchang District, Wuhan 430060, China.
Abstract:
Ovarian cancer (OC) comprises multiple histotypes with distinct mechanisms, molecular features, and clinical behavior. We used Mendelian randomization (MR) to map histotype-stratified metabolic pathways and connect them to drug targets, establishing a translatable target-metabolic node-histotype risk chain. We built a multi-stage MR framework using Integrative Epidemiology Unit (IEU) OpenGWAS summary statistics. After screening 1400 plasma metabolites against overall ovarian cancer in UK Biobank and Ovarian Cancer Association Consortium (OCAC) with KEGG enrichment, we traced a prespecified amino acid/energy-nitrogen axis using histotype-stratified univariable MR and pathway-restricted multivariable MR. We then performed cis drug-target MR for PPARG, DPP4, ABCC8/KCNJ11, and SLC5A2, integrated triangulation, colocalization, and mediation analyses, and experimentally interrogated the prioritized PPARG/ABCC8-KCNJ11-lactate-invasive mucinous ovarian cancer (IMOC) triangle. Screening nominated 55 and 72 metabolites in UK Biobank and OCAC, respectively (IVW p < 0.05), highlighting amino-acid nitrogen and central-carbon metabolism. Univariable Mendelian randomization (UVMR) showed marked heterogeneity: alanine increased low-grade serous ovarian cancer (LGSOC) risk, glutamate was protective for endometrioid OC, and lactate-related traits most consistently implicated the low-grade/borderline serous lineage. In multivariable Mendelian randomization (MVMR), tryptophan and lactate levels emerged as independent risk nodes for serous low-grade plus low malignant potential (LG + LMP). Drug-target MR prioritized PPARG as protective (OR = 0.18) and ABCC8/KCNJ11 as risk-increasing (OR = 7.50) for IMOC, with opposite target → lactate effects supporting a directionally symmetric target-lactate-IMOC triangle. Experimental perturbation in mucinous ovarian cancer models produced concordant reciprocal changes in lactate and malignant phenotypes, extending this triangle biologically. This integrative MR framework delineates histotype-specific metabolic drivers and links them to actionable targets, providing a roadmap from genetic prioritization to mechanistic and translational validation.
Insights
This study reveals how specific metabolic pathways and drug targets influence different ovarian cancer types. It identifies key metabolic drivers and actionable targets for ovarian cancer, offering a path for new treatments.
Area of Science:
- Metabolic pathways
- Genetics
- Ovarian cancer research
Background:
- Ovarian cancer (OC) has diverse subtypes with unique characteristics.
- Understanding metabolic differences is crucial for targeted therapies.
Purpose of the Study:
- To map histotype-stratified metabolic pathways in ovarian cancer.
- To connect these pathways to drug targets, creating a risk chain.
- To validate findings experimentally.
Main Methods:
- Used a multi-stage Mendelian randomization (MR) framework with Integrative Epidemiology Unit (IEU) OpenGWAS data.
- Screened 1400 plasma metabolites against ovarian cancer subtypes.
- Performed cis drug-target MR and integrated triangulation, colocalization, and mediation analyses.
Main Results:
- Identified amino-acid nitrogen and central-carbon metabolism as key areas.
- Found specific metabolites linked to different ovarian cancer histotypes (e.g., alanine and LGSOC, glutamate and endometrioid OC).
- Prioritized PPARG as protective and ABCC8/KCNJ11 as risk-increasing for invasive mucinous ovarian cancer (IMOC), linked via lactate.
Conclusions:
- An integrative MR framework successfully delineated histotype-specific metabolic drivers in ovarian cancer.
- Linked metabolic drivers to actionable drug targets, establishing a target-metabolic node-histotype risk chain.
- Provided a roadmap for translating genetic discoveries into mechanistic and therapeutic validation for ovarian cancer.
