Completed Cancer Genetics & Molecular Biology

The metabolic determinants of cancerous transformation

In plain English

AI plain-English summary

A single mutated metabolic enzyme—fumarate hydratase—can trigger an aggressive, metastatic kidney cancer in people who inherit a faulty copy of the gene. This project tackles a fundamental puzzle in cancer biology: why a metabolic glitch in one enzyme causes cancer only in specific tissues, and why those cancers vary so dramatically in severity. In the rare inherited condition HLRCC, patients develop benign skin and uterine tumours alongside a lethal form of renal cancer. The researchers will use this unique human model to trace exactly how a broken metabolic switch rewires cell behaviour from the very first step of transformation. If successful, the work could reveal early metabolic markers that flag disease onset years before symptoms appear, enabling screening and patient stratification. It may also expose metabolic vulnerabilities unique to these cancer cells—weak spots that could be targeted with drugs. The project is fundamentally curiosity-driven, seeking to understand how metabolism itself can initiate cancer. But that mechanistic understanding is the necessary foundation for any future diagnostic test or therapy aimed at the earliest stages of tumour formation.

View original technical description
Mutations in enzymes including the Tricarboxylic acid (TCA) cycle enzymes Succinate Dehydrogenase (SDH), Fumarate Hydratase (FH), and Isocitrate Dehydrogenase (IDH) have been shown to cause hereditary and sporadic forms of cancer. These recent discoveries provide evidence of an unanticipated cancer-causing role of mutated metabolic enzymes. Yet, the contribution of dysregulated metabolism and the mechanisms underpinning its link to carcinogenesis remain poorly understood. FH mutations cause Hereditary Leiomyomatosis and Renal Cell Cancer (HLRCC), a cancer predisposition syndrome characterised by benign tumours of smooth muscle in the skin and uterus, plus an aggressive and highly metastatic form of renal cancer. HLRCC patients inherit a mutant copy of FH and cancer formation is caused by the loss of the wild type allele (loss of heterozygosity, LOH). A significant question in cancer biology is why the loss of the metabolic enzyme FH should predispose to cancer in specific tissues, with distinct severity and progression. Our laboratory seeks to understand the contribution of dysregulated metabolism to tissue-specific carcinogenesis using HLRCC as a unique model system. Indeed, while rare, HLRCC provides a tractable paradigm where it is clear that a metabolic event initiates cancer. Furthermore, HLRCC individuals represent an unmet need in terms of cancer prevention and management. Our work has multiple implications. (1) it will provide a mechanistic understanding of the role of metabolism and small molecule metabolites in the early phases of cancer transformation and how metabolism contributes to tissue-specific tumorigenesis. (2) it will generate experimental and computation tools to identify metabolic vulnerabilities in cancer cells that we can use as pharmacological targets for cancer therapy. (3) it will apply metabolomics and multi-omics analyses, to mouse and human models to identify metabolic markers of disease initiation for clinical application in early detection and for patient stratification.

View the original record at the funder ↗

Researchers

Christian Frezza (Principal Investigator)

Related Research

Grants with similar aims, by meaning.

Integrating the tissue-specificity and chronology of hereditary renal cancer predisposition
Investigation of the role of metabolism in tumorigenesis
The role of fumarate-mediated 2-oxoglutarate-dependent oxygenase inhibition in hereditary renal cancer.
Identifying and targeting metabolic vulnerabilities of cancer cells
The central role of cytoplasmic fumarate hydratase in both nitric oxide metabolism and tumorigenesis

Original classification

Research Grant

Plain English summaries and category classifications on this site are generated by AI and may not perfectly reflect the original research.