Nature MetabolismCancer BiologyPeer-reviewed journal article
Mitochondrial metabolism is a key determinant of chemotherapy sensitivity in Colorectal Cancer
Deborah Y. Moss, Connor N. BrowniD, Andrew M. ShawiD, Chris McCann, Nikita LewisiD, Matilda Downs, Rebecca Wurelly, Ciara Cunningham, Aaron Philips, Niamh DohertyiD, Sarah Gallagher, William J. McDaidiD, Andrew J. RoeiD, Aisling Y. CoughlaniD, Brenton CavanaghiD, Callum Ormsby, Fiammetta Falcone, Rachel McCole, Scott Monteith, Emily Rogan, Sudhir B. MallaiD, Alexandra J Emerson, Letitia Mohamed-Smith, Shaun Sharkey, Aoife Leonard, , Emma M. KerriD
- Citations
- 9
- FWCI
- 15.55
- Field percentile
- top 1%
Abstract
Therapy resistance is attributed to over 80% of cancer deaths per year emphasizing the urgent need to overcome this challenge for improved patient outcomes. Despite its widespread use in colorectal cancer (CRC) treatment, resistance to 5-fluorouracil (5FU) remains poorly understood. Here, we investigate the transcriptional responses of CRC cells to 5FU treatment, revealing significant metabolic reprogramming towards heightened mitochondrial activity. Utilizing CRC models, we demonstrate sustained enhancement of mitochondrial biogenesis and function following 5FU treatment, leading to resistance in both in vitro and in vivo settings. Furthermore, we show that targeting mitochondrial metabolism, specifically by inhibiting Complex I (CI), sensitizes CRC cells to 5FU, resulting in delayed tumour growth and prolonged survival in preclinical models. Additionally, our analysis of patient data suggests that oxidative metabolism signatures may predict responses to 5FU-based chemotherapy. These findings shed light on mechanisms underlying 5FU resistance and propose a rational strategy for combination therapy in CRC, emphasizing the potential clinical benefit of targeting mitochondrial metabolism to overcome resistance and enhance patient outcomes.
- organism: human
- assay: bulk RNA-seq
- research focus: drug response / drug discovery
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: experimental assay
- approach: statistical model
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