From single-cell transcriptomic to single-cell fluxomic: characterising metabolic dysregulations for breast cancer subtype classification

Despite the recent developments in breast cancer treatments, the high variability of cancer cells and their related drug resistance still pose a huge obstacle to improving clinical outcomes. It is now well-known that cancer cells must reprogram their cellular metabolism (chemical processes that occur within the cell to maintain life) to support rapid proliferation and promote acquired drug resistance. However, the underlying mechanisms regulating such biological changes are neither fully understood nor sufficiently treated. Only recently, with the advent of single cell analysis (a novel technique that allows the analysis of individual cancer cells), it has been possible to analyse changes at the cellular level that have helped in identifying four main breast cancer subtypes (i.e., Luminal A, Luminal B, TNB, and HER2 positive) and developing different treatment routes. However, patient survival remains low - especially for the most aggressive breast cancer subtypes - since cellular changes cannot be easily connected to an alteration in the metabolic state that promotes drug resistance. Moreover, the lack of specific tools to analyse a vast quantity of single cell metabolic profiles makes single-cell analysis at the metabolic level still impractical.

This proposal aims at initiating an international collaboration between Teesside University (UK) and Cornell University (US) to characterise the metabolic profile of 32 different breast cancer cell types (i.e., cell lines) from the four main breast cancer subtypes to identify metabolic dysregulations and allow informed treatment decisions. Advanced computation techniques (i.e., artificial intelligence) will be applied to identify the metabolic reactions and changes responsible for cancer progression in each cancer subtype. The final objective of the proposed collaboration will be to elucidate the main differences among breast cancer subtypes at the metabolic level to inform the development of targeted drugs and support clinical decisions.

First, mathematical techniques will be applied to develop metabolic models (through a set of mathematical equations) of 32 different breast cancer cell lines. These 32 models will mathematically describe the metabolic reactions taking place inside the different cancer cells. This will be achieved by integrating the expertise in metabolic modelling of the PI (Dr Occhipinti) with the knowledge of single cell analysis of the International Partner (Dr Betel).

Second, advanced computational techniques will be applied to identify the key features affecting the proliferation of each of the 32 cancer cell types. Such features will include a set of biological elements (i.e., information related to cancer metabolism) specific to each cell type, which can be used to predict cell-specific drug resistance or inform clinical decisions.

Finally, the selected key features (e.g. the metabolic reactions that are contributing the most to the growth of each cancer cell type) will be validated through computational and lab experiments and shared with breast cancer clinicians and experts through regular meetings and discussions that will be arranged during the project.

Specifically, the academic team will coordinate a wide range of activities, including regular meetings with breast cancer experts from the NHS, designed to provide feedback on the developed computational model through knowledge and skills exchange while promoting connectivity across different sectors both in the medical and computational areas.

The proposed project brings together academics from two centres of excellence in the healthcare sector (i.e., Weill Cornell Medicine at Cornell University and the National Horizon Centre at Teesside University), who have a strong track record in working with cell analysis, metabolic modelling, and artificial intelligence to better understand the metabolic mechanisms of cancer development and improve cancer outcomes.