Early detection of cancer using identification of volatile metabolites

Cancer is a disease which more than a third of the world population will get in their life. Different types of cancers have different prevalence and mortality rates, and it has been shown that advances in screening and diagnostic tools reduce mortality. We now live in an era of 'precision health and wellness' where everyone of us are different and the manner in which we age, live or become ill is unique, but predicting an individual's fate is notoriously difficult. However, new technology offers us a possible solution: by accurately and precisely measuring biomolecules, it may be possible to provide a 'way in' to quantifiably profiling the underlying biochemistry of health and aging. The earlier cancer is detected, the better the chances of survival.
It has been shown that many types of cancer are characterised by altered metabolic regimens, for example some cancers can be detected by dogs using their olfactory sense, indicating that specific volatile metabolites must be produced or increased in cancer. In this project, we will use metabolic models of two solid cancer types, ovarian and prostate cancer, and assess them in comparison to their healthy tissue counterparts using constraint-based metabolic analysis techniques. This will allow us to predict alterations in metabolic flux distributions between healthy and cancer tissues. We will then map reactions to the production of metabolites in order to determine compounds whose production is either unique or significantly increased in cancer tissues. Next, these predictions will be tested and validated using metabolomics and other experimental analyses in prostate and ovarian cancer samples.

We will have access to primary and in vitro samples via the Manchester Cancer Research Centre Biobank; this includes novel ovarian cancer cultures which act as a mini ovary, along with their paired matched stromal cell cultures. These have been isolated from patients that have been clinically stratified, treated and monitored in real time in the real world. We anticipate that the cultures will have unique metabolic fluxes aiding their molecular characterisation and offers an additional level of 'omic detail which will help us deconstruct their responses to treatment.

In addition, we have a series of prostate cancer cells in the lab that represents the disease-progression pathway, encompassing normal, benign prostate hyperplasia, through to high-grade aggressive cancer. Determining their metabolic fluxes will generate a fingerprint of aggression that may identify novel metabolic pathways which we can target for prediction, earlier diagnosis but also treatment. We will also asses the cells by multi 'omics (proteomics, lipidomics etc). By combining this information and mapping against data sets we have from human prostate cancer, we will be able to 'omically characterise disease prediction, progression and help guide treatment options. In vitro we can take these various metabolic flux pathways and analyse cultures in terms of drug toxicity, modulation and survival proliferation.

Together, ovary and prostate cancer which are hormonally driven, are difficult to detect and control and this project will add detailed metabolic characterisation which will help correlate and accurately 'map' onto clinical data.