Metabolomics in Oncology: A Look at AACR 2023 and ASCO 2023

Metabolomics in Oncology: A Look at AACR 2023 and ASCO 2023

By Ranga Sarangarajan, Ph.D., Chief Scientific Officer, Metabolon

It is now about the halfway point between AACR and ASCO, the two major oncology-focused meetings connecting the basic science/early clinical development at AACR to the progress and outcome reporting of mature oncology clinical programs at ASCO. Returning to physical attendance at AACR after a few years of hiatus was a great opportunity to reconnect on the science, catch up with old friends, and make new connections. In keeping up with the past, the plenary sessions, symposium, and poster sessions covered a breadth of scientific advances that is hard to keep track of outside the scope of this event. In the midst of the scientific grandiosity, all things gene therapy dominated the presentations and news cycles compared to the previous year’s focus on immuno-oncology, providing an acute sense of things to follow for the immediate future (eg, ASCO and beyond).

With Metabolon being the leading provider of comprehensive metabolomics solutions, one of the first orders of business was to visit presentations and/or posters that mentioned the word, the use thereof, and/or would have most likely used metabolomics for scientific insights. The reality, however, was that metabolomics had very little visibility in the mainstream oncology represented at AACR2023, quite disheartening given that “cancer cell metabolism” (with its many faces) and “phenotypic plasticity” are considered the next generation and most recent additions to the cancer hallmarks.1,2 Ironically, both metabolism (almost exclusively) and phenotypic plasticity (to a large extent) are ideally profiled using metabolomics, yet a large percentage of efforts in cancer is focused on the use of gene-based technologies to understand the biological underpinnings of these two phenotypic hallmarks.

“Why Metabolomics?”

The use of a metabolomic platform enables a functional readout of any biological state at any given moment in time (functional phenotype). Metabolomic profiling involves the identification of a fairly comprehensive list of endogenous small molecules that fill gaps in pathways during the execution of a biological event and associated endpoint (ie, a phenotype). Simply stated, metabolomics is an important readout of the “effect or consequence,” generally referring to a phenotype, directly associated with a “genetic cause.” This cause-and-effect relationship between gene and metabolome has been central to understanding the impact of metabolic variances on human health in large population studies, particularly in metabolic diseases.3,4 Metabolomic profiling is also routinely used to diagnose rare diseases, particularly in situations where no clear underlying genetic causality is present.5 In contrast, my general assessment, based on the last 18 months in this space, is the appearance or perception that metabolomics has not made significant inroads into oncology due to any number of reasons. Metabolomic applications in oncology are clearly still in the early stages of adoption, prime for exponential growth with a wide range of emerging utility and value propositions.6

In oncology, genetic mutations driving cancer influence bioenergetics and metabolism, insights which are supported by decades of scientific exploration where the inclusion of metabolomics has been pivotal in establishing a linkage between the genetic aberration (cause) and the phenotypic observations (effects) associated with the disease. In fact, from the earliest chemotherapies, targeting folate synthetic pathways to the more recent understanding of oncometabolite (eg, IDH1 mutations driving 2-hydroxyglutarate formation) or the role of glutaminase enzyme supporting cancer growth required metabolomics to delineate metabolic vulnerabilities driving cancer as the basis for drug development.7 A recent article provides a summary of drugs in clinical development targeting cancer metabolism,8 almost all of which should benefit from the use of metabolomic profiling, in my humble opinion.

Metabolomics, both technology and science, has made significant advances over the past decade (in parallel to other forms of molecular profiling). We have been at the center of these advancements, contributing and supporting basic science and clinical initiatives in a wide range of scientific disciplines in the pharmaceutical industry, including oncology. Our technology enables the capture of a comprehensive list of metabolites produced or influenced by the microbiome, recognized as a major contributor to causality and outcomes in various diseases, including oncology. Furthermore, the technology underlying metabolomic profiling has matured for mainstream adoption as an “omic” linking genetic causality to phenotypic endpoints. Metabolomics (or circulating small molecules) is conserved across species, amenable for nearly seamless translation from preclinical non-human models to the human condition. Metabolomic profiling enables the opportunity for the discovery and validation of blood-based biomarkers in drug discovery and clinical development to progressively de-risk and improve the success of drug programs.

My hope is that this rationale for the inclusion of metabolomic profiling in your respective study designs was sufficiently compelling to explore available options. Contact the team now for an introduction and details on how we can help in your adoption of metabolomics into your study. 


  1. Hanahan D and Weinberg RA. Hallmarks of Cancer: The Next Generation.  (2011) 144(5):646-74. (doi: 10.1016/j.cell.2011.02.013).
  2. Hanahan D. Hallmarks of Cancer: New Dimensions. Cancer Discov.  (2022) 12(1):31-46. (doi: 10.1158/2159-8290.CD-21-1059).
  3. Iliou A, Mikros E, Karaman I, Elliott F, Griffin JL, Tzoulaki I, Elliot P. Metabolic phenotyping and cardiovascular disease: an overview of evidence from epidemiological settings.  Heart (2021) 107:1123-1129. (doi:10.1136/heartjnl-2019-315615).
  4. Surendran P, Stewart ID, Au Yeung VPW, Pietzner M et al. Rare and common genetic determinants of metabolic individuality and their effects on human health.  Nature Medicine (2022) 28:2321-2332. (doi: 10.1038/s41591-022-02046-0).
  5. Liu N, Xiao J, Gijavanekar C, Pappan KL et al. Comparison of untargeted metabolomic profiling vs traditional metabolic screening to identify Inborn Errors of Metabolism.  JAMA Netw Open (2021) 4(7):e2114155. doi:10.1001/jamanetworkopen.2021.14155.
  6. Schmidt DR, Patel R, Krisch DG, Lewis GA, Vander Heiden MG, Locasale JW. Metabolomics in cancer research and emerging applications in clinical oncology.  CA: A Cancer J Clinicians. (2021) 71(4):333-358. (doi: 10.3322/caac.21670).
  7. Stine ZE, Schug ZT, Salvino JM, Dang CV. Targeting cancer metabolism in the era of precision oncology.  Nature Rev Drug Discov. (2022) 21:141-162. (doi:10.3322/caac.21670).
  8. Lemberg KM, Gori SS, Tsukamoto T, Rais R, Slusher BS. Clinical development of metabolic inhibitors for oncology.  J Clin Invest.  (2022) 132:e148550. (doi:10.1172/JCI148550).
Ranga Sarangarajan, Ph.D.
Ranga leads Metabolon’s R&D teams to deliver metabolomics data and insights that expand and accelerate the impact of life sciences research in all its applications, including biopharma and diagnostics.


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