Many studies in recent years have expanded our knowledge of the metabolic landscape in cancer. Cancer cells rewire metabolism to be beneficial in changing conditions such as in hypoxia and new microenvironment of the metastatic niches. They also establish metabolic communication with other cell types such as fibroblasts and immune infiltrating cells (1). Inevitably, this complexity at the cellular level leads to the question, how different factors, especially metabolic therapies, can affect the organism as a whole and contribute to the metabolic tumour microenvironment. In order to address this question, there is an urgent need for developing methods that allow the study of cancer metabolism in vivo (e.g. metabolomics, intravital imaging) (2). Interestingly, metabolites are often screened in the blood plasma for diagnostic purposes, but assessment of tumour interstitial fluid (TIF), which reveals local tumour nutrient exchange levels, was so far rarely done due to technical limitations (3).
In this preprint Mark Sullivan et al. establish a new metabolomics approach in order to compare metabolite levels between tumour interstitial fluid and plasma from murine cancer models. Precision of such analysis was previously limited due to the presence of different matrix effects in these biological liquids. The authors overcome this problem by using internal standards for every labelled sample (12C to known amounts of added 13C).
By hierarchical clustering and principal component analysis they show in a mouse model of pancreatic ductal adenocarcinoma (PDAC) that TIF composition significantly differs from plasma, which represents local changes caused by the tumour microenvironment. Changes in the major metabolites are in line with previous findings: depletion of glucose in TIF matches its enhanced consumption by cancer cells, and depletion of particular amino acids can be caused by enhanced immune cell infiltration.
Excited by the abilities provided by the new method, the authors next sought to assess the role of several biological factors on TIF composition by manipulating one factor at a time. In particular, they assessed complex factors (as tumour size), cancer cell extrinsic (as anatomical location, mouse diet) and intrinsic factors (as tissue of origin, tumour genetics).
They found that two categories of tumour size (small vs large) did not possess significant difference in TIF composition. Anatomical location of a tumour, such as orthotopic vs subcutaneous, turned out to be crucial for defining the TIF metabolic profile. Different mouse diet (standard chow with whole protein vs defined diet with purified amino acids) lead to different TIF metabolic profiles as well. However, changes in TIF largely correlated with the changes in plasma levels, which highlights a systemic response of the whole organism to diet.
Interestingly, tumours comprised of cancer cells with different tissue of origin (lung vs pancreas, same genetic drivers) produced different TIF metabolic profiles. This, at least, proves previous findings that these cell types have different branched-chain amino acid metabolism. Finally, genetic knockout of tumour suppressor Keap1, expected to promote metabolic changes in lung adenocarcinoma, was not sufficient for changing TIF composition.
What I like
This study provides a great tool to address questions in tumour metabolism in a physiological context by analysing TIF metabolites. When plasma levels of certain metabolites can be used as a diagnostic tool, TIF composition can provide more precise information about tumour nutrition.
Certainly, the scope of the paper allows only to start answering certain questions about changes in TIF profiles. Nonetheless, authors use elegant set ups to pinpoint if intrinsic or extrinsic factors in cancer have a role in defining tumour metabolism as a whole.
As the next steps to this study, it is important to support findings in the PDAC model with similar analyses of TIFs in other cancer models and cells carrying other genetic alterations. Hypotheses explaining certain metabolite changes in TIFs can be confirmed by analysing tumour composition (e.g. presence of difference cell types), performing in parallel in vitro assays with extracted cancer cells, etc. In addition, it will be exciting to study TIFs of mice treated with metabolic therapy (e.g. including different delivery mechanisms) as it can provide more insight about in vivo effects of the drugs. Finally, TIFs from tumours with different metastatic potential can be compared to see if some metabolic changes of the tumour microenvironment can explain this behaviour. All this would complement interesting findings in this preprint.
Questions to authors
- Would a cancer model with controllable tumour size upon harvest be a better model for size-dependent TIFs profiling (e.g. xenografts)?
- Why do some of the tumours not yield any TIF? Is it just because they have a small size?
- Could we experimentally separate consumption and secretion effects of a tumour in TIF?
- How different you think can be tumour nutrition in case of studying cancer metabolism in immunocompetent vs immunocompromised mice?
- Wolpaw AJ, Dang CV. “Exploiting Metabolic Vulnerabilities of Cancer with Precision and Accuracy” Trends Cell Biol.2018; 28(3)201-212
- Muir A, Danai LV, Vander Heiden MG. “Microenvironmental regulation of cancer cell metabolism: implications for experimental design and translational studies” Disease Models & Mechanisms.2018; 11
- Wiig H, Tenstad O, Iversen PO, Kalluri R, Bjerkvig R. “Interstitial fluid: the overlooked component of the tumour microenvironment?” Fibrogenesis Tissue Repair. 2010; 3 12
Posted on: 11th February 2019