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Updated: Aug 9, 2025
Visualizing DNA Damage Repair Proteins in Patient-Derived Ovarian Cancer Organoids via Immunofluorescence Assays
Published on: February 24, 2023
The DNA damage response in advanced ovarian cancer: functional analysis combined with machine learning identifies
Thomas D J Walker1, Zahra F Faraahi2, Marcus J Price1
1Division of Cancer Sciences, Faculty of Biology, Medicine & Health, University of Manchester, M13 9WL, Manchester, UK.
Background:
Ovarian cancers are hallmarked by chromosomal instability. New therapies deliver improved patient outcomes in relevant phenotypes, however therapy resistance and poor long-term survival signal requirements for better patient preselection. An impaired DNA damage response (DDR) is a major chemosensitivity determinant. Comprising five pathways, DDR redundancy is complex and rarely studied alongside chemoresistance influence from mitochondrial dysfunction. We developed functional assays to monitor DDR and mitochondrial states and trialled this suite on patient explants.
Methods:
We profiled DDR and mitochondrial signatures in cultures from 16 primary-setting ovarian cancer patients receiving platinum chemotherapy. Explant signature relationships to patient progression-free (PFS) and overall survival (OS) were assessed by multiple statistical and machine-learning methods.
Results:
DR dysregulation was wide-ranging. Defective HR (HRD) and NHEJ were near-mutually exclusive. HRD patients (44%) had increased SSB abrogation. HR competence was associated with perturbed mitochondria (78% vs 57% HRD) while every relapse patient harboured dysfunctional mitochondria. DDR signatures classified explant platinum cytotoxicity and mitochondrial dysregulation. Importantly, explant signatures classified patient PFS and OS.
Conclusions:
Whilst individual pathway scores are mechanistically insufficient to describe resistance, holistic DDR and mitochondrial states accurately predict patient survival. Our assay suite demonstrates promise for translational chemosensitivity prediction.
Insights
New assays predict ovarian cancer survival by analyzing DNA damage response (DDR) and mitochondrial function. Holistic analysis of these pathways offers better patient preselection for chemotherapy, improving outcomes.
Area of Science:
- Oncology
- Molecular Biology
- Genetics
Background:
- Ovarian cancers exhibit chromosomal instability, necessitating improved patient preselection for therapies.
- Therapy resistance and poor survival highlight the need for better predictive markers.
- DNA damage response (DDR) impairment is a key determinant of chemosensitivity, but its complexity and interaction with mitochondrial dysfunction are understudied.
Purpose of the Study:
- To develop and validate functional assays for monitoring DNA damage response (DDR) and mitochondrial states in ovarian cancer.
- To assess the relationship between DDR and mitochondrial signatures and patient outcomes (PFS, OS) in platinum-treated ovarian cancer patients.
Main Methods:
- Functional assays were developed to monitor DDR and mitochondrial states.
- DDR and mitochondrial signatures were profiled in cultures from 16 primary ovarian cancer patients receiving platinum chemotherapy.
- Statistical and machine-learning methods were used to correlate explant signatures with patient progression-free survival (PFS) and overall survival (OS).
Main Results:
- Wide-ranging DDR dysregulation was observed, with defective homologous recombination (HR) and non-homologous end joining (NHEJ) being nearly mutually exclusive.
- HR-proficient patients showed a higher incidence of perturbed mitochondria compared to HR-deficient patients.
- All patients who relapsed had dysfunctional mitochondria, and explant signatures accurately predicted patient survival (PFS and OS).
Conclusions:
- Holistic assessment of DDR and mitochondrial states, rather than individual pathway scores, accurately predicts patient survival in ovarian cancer.
- The developed assay suite shows promise for translational application in predicting chemosensitivity and guiding patient treatment selection.
