Harnessing metabolomic methods to understand the molecular basis of cancer-associated cachexia

Primary supervisor: Mariam Jamal-Hanjani, UCL

Project

Cancer-associated cachexia (CAC) is a complex, paraneoplastic syndrome of muscle and fat wasting afflicting 50-80% of patients with metastatic disease, and accounting for 20% of cancer-related death [1,2]. A key driver of CAC involves metabolic derangements associated with advanced cancer [3]. Evidence for such derangements dates back almost 100 years, with Otto Warburg describing the propensity of tumours to produce lactate in the abundance of oxygen – denoted the Warburg Effect. Despite its prevalence, mortality, and morbidity, the mechanisms underpinning altered metabolism in CAC remain poorly understood. There is a need for biomarkers to enable early diagnosis of CAC, inform patient care, and facilitate development of disease-modifying agents – currently absent from clinical practice.

This project will leverage the TRACERx [4] (TRAcking Cancer Evolution through therapy (Rx)) study to characterise the metabolomic phenotype of CAC in non-small cell lung cancer (NSCLC). TRACERx is a multicentre, longitudinal study of >820 patients diagnosed with early-stage NSCLC. Patients are followed for 5-years from diagnosis, through surgical resection, to either cure or eventual cancer relapse and death. The study mandates prospective collection of clinical and epidemiological data, radiological scans, and pathological specimens, including multi-region tumour tissue biopsies.

We have shown that using a deep-learning imaging analysis pipeline that examines body composition in the form of skeletal muscle (SKM), subcutaneous adipose tissue (SAT) and visceral adipose tissue (VAT) from longitudinal computed tomography (CT) scans, we can identify patients who develop CAC5, and that this can be used for downstream analyses investigating the potential biological mechanisms associated with this phenotype [6]. We have found that patients with CAC have distinct tumour genomic and blood proteomic profiles that may be leveraged to identify novel therapeutic approaches in the management of CAC [5]. Given its associated catabolic state, there is an unmet need to understand the metabolic dysregulation associated with the cachexia cascade, and to this end this project will utilise techniques, including plasma and tissue mass spectrometry metabolomics and spatial metabolomic imaging, to define the cachexia-associated metabolome within TRACERx. There is scope to integrate ongoing parallel analysis of the tumour genomic and transcriptomic data to help delineate the tumour mutational landscape governing the metabolic drivers of CAC, in addition to existing data from analysis of the tumour microenvironment (TME) using imaging mass cytometry techniques. In some patients, matched metastatic tissue and blood are available through the PEACE (Posthumous Evaluation of Advanced Cancer Environment) national autopsy study led by our lab, which can be analysed to examine the evolution of the metabolome in the advanced disease setting in which extreme CAC phenotypes may be more prevalent.

The overall objectives are:

1. To expand an existing cohort of patients with longitudinal body composition in TRACERx using a semi-automated imaging pipeline to track the development of CAC throughout the natural history of NSCLC from early to late stage disease.
2. To establish a longitudinal metabolomic dataset derived from 1) plasma and tumour tissue mass spectrometry metabolomics constituting >1000 profiled metabolites, and 2) metabolic imaging techniques (e.g. MALDI-based mass spectrometry) applied to tumour tissue for the assessment of localised metabolite distribution.
3. To identify the molecular characteristics associated with the CAC phenotype by integrating body composition with the above metabolomic dataset, as well as tumour genomic, transcriptomic and TME analyses.

References

1. Fearon, K. et al. Definition and classification of cancer cachexia: an international consensus. The Lancet Oncology 12, 489-495 (2011)
2. von Haehling, S. & Anker, S. D. Prevalence, incidence and clinical impact of cachexia: facts and numbers-update 2014. J Cachexia Sarcopenia Muscle 5, 261-263 (2014)
3. Fearon, K. C. H., Glass, D. J. & Guttridge, D. C. Cancer Cachexia: Mediators, Signaling, and Metabolic Pathways. Cell Metabolism 16, 153-166 (2012)
4. Jamal-Hanjani, M. et al. Tracking the Evolution of Non-Small-Cell Lung Cancer. N Engl J Med 376, 2109-2121 (2017)
5. Al-Sawaf, O. et al...Jamal-Hanjani, M*., Swanton, C*. Body composition and lung cancer-associated cachexia in TRACERx. Nature Medicine, in press (2023)
6. Ma, D., Chow, V., Popuri, K. & Beg, M. F. Comprehensive Validation of Automated Whole Body Skeletal Muscle, Adipose Tissue, and Bone Segmentation from 3D CT images for Body Composition Analysis: Towards Extended Body Composition. Preprint at http://arxiv.org/abs/2106.00652 (2021)