# Transcriptome-driven constraint-based modelling reveals metabolic targets for ovarian cancer

## TL;DR

This study uses metabolic modeling to identify new drug targets in ovarian cancer, focusing on a protein called TPI1 that is linked to cancer cell survival.

## Contribution

The study introduces an omics-integrated constraint-based modeling workflow to identify metabolic vulnerabilities in ovarian cancer.

## Key findings

- Transcriptome-driven metabolic models identified TPI1 as a candidate target for ovarian cancer.
- Experimental validation confirmed TPI1's role in ovarian cancer cell survival.
- The workflow could be broadly applicable to other cancers for therapeutic discovery.

## Abstract

Constraint-based modelling (CBM) is a powerful computational approach that reconstructs cellular metabolism by integrating ‘omics data with genome-scale metabolic models (GEMs), enabling in silico hypothesis generation and genetic engineering studies. Advances in high-throughput ‘omics technologies and the complete mapping of the human genome have expanded the application of CBM to human systems. Given that altered metabolism is a hallmark of cancer, this disease represents an ideal context for developing and applying CBM workflows. Despite the presence of well-characterised metabolic signatures and vulnerabilities in ovarian cancer, this tumour type remains under-explored within the CBM field. Meanwhile, the limited efficacy of current therapies and the frequent emergence of chemoresistance underscore the need for novel, mechanism-based approaches to therapeutic discovery. In this study, we constructed ovarian cancer-specific metabolic models using an ‘omics integration algorithm that incorporates transcriptomic data in a way that is directed by experimental proliferation measurements. Simulations identified multiple candidate molecules predicted to influence cancer cell proliferation. Among these, triosephosphate isomerase 1 (TPI1) was selected for experimental validation based on qualitative prioritisation criteria. Notably, model predictions were supported by RNA sequencing and colony-formation assays, implicating TPI1 in ovarian cancer cell survival. Our results provide novel insights into the metabolic dependencies of ovarian cancer and demonstrate an omics-integrated CBM workflow that may be broadly applicable for uncovering therapeutic vulnerabilities in other malignancies.

The online version contains supplementary material available at 10.1186/s40170-026-00425-6.