Cancer cells use reprogrammed metabolic pathways to grow and resist stress encountered in the tumor microenvironment. These reprogrammed pathways facilitate malignant transformation and enable tumor progression. Identifying such pathways will allow us to better understand the biology of cancer and uncover new therapeutic targets. We use metabolomics, metabolic flux analysis, cell biology, and animal models of cancer to study how tumor cells generate energy, build macromolecules, and maintain redox balance. We seek to identify the processes, both intrinsic and extrinsic to the cancer cell, that affect tumor metabolism and to discover context-specific metabolic vulnerabilities that might provide a basis for new treatments.

As an example of our approach, we identified a new mechanism by which the MYC oncoprotein coordinates cell growth in small cell lung cancer (see figure, Journal of Clinical Investigation 131:e139929, 2021). MYC transcriptionally activates many pathways, including well-known pathways of nucleotide and ribosome biogenesis, that need to be synchronized to culminate in cell proliferation. We discovered that MYC’s ability to activate guanosine triphosphate (GTP) synthesis is required to activate the ribosome program. These two processes are mechanistically linked by the small GTP-binding proteins GPN1 and GPN3, both of which are MYC targets and whose GTPase activity helps guide RNA Polymerase I to rDNA loci to initiate ribosome biogenesis. Our findings provide an example of how MYC coordinates multiple biosynthetic programs in parallel to induce cell growth. Of note, reliance on this mechanism is prominent in chemotherapy-resistant small cell lung cancers, which are among the most treatment-refractory tumors but tend to display MYC activation. We report that these tumors can be treated in mice with clinically-available inhibitors of GTP synthesis.

A major challenge is understanding which metabolic pathways promote cancer growth and progression in live tumors growing in a native microenvironment. We established clinical protocols combining multiparametric, preoperative imaging with intra-operative infusions of isotope-labeled nutrients (e.g., ¹³C-glucose) to address this challenge (Cell 164, 681-94, 2016 and Nature Protocols 16, 5123-5145, 2022). Our approach allows us to assess metabolism in living, human tumors at multiple organ sites and compare isotope labeling features between tumor and adjacent tissues or between primary and metastatic tumors. This has allowed us to identify metabolic activities that could not have been predicted by experiments confined to cultured cells, and to determine which activities correlate with cancer progression and poor outcomes in patients. We have adapted these isotope labeling techniques to mouse models of cancer, allowing us to test hypotheses arising from observations in human cancer.

One discovery arising from this approach is that lactate provides a carbon source for some human tumors (see figure). Lactate, the end product of glycolysis, is traditionally viewed as a metabolic waste of cancer cells, and that its production and elimination from the cell are required to sustain energy production and redox balance. In some human non-small cell lung cancers, however, isotope labeling patterns indicate that lactate is taken up from the blood and provides a fuel for the tricarboxylic acid cycle (Cell 171, 358-371, 2017). Interestingly, patients whose tumors display labeling hallmarks of lactate uptake have worse outcomes than patients whose tumors lack these hallmarks. Moving this observation into mouse models, we found that blocking lactate transport by melanoma cells suppresses metastasis, the leading cause of cancer-associated mortality (Nature 577, 115-120, 2020). Altogether, these findings demonstrate that we can use intra-operative isotope tracing in patients to identify unexpected metabolic activities that drive cancer progression.

We are interested in a large category of Mendelian diseases called inborn errors of metabolism (IEMs). These diseases are caused by pathogenic genomic variants (i.e., mutations) in genes encoding metabolic enzymes, nutrient transporters, and other components of the metabolic network. We are developing approaches to make it easier to recognize these disorders in patients by combining an unbiased analysis of the metabolome with genome/exome sequencing. Integrating metabolomics data with gene sequencing makes it possible to infer the impact of genomic variants of uncertain significance (see figure, Cell 185, 2678-2689, 2022). Variants suspected of causing disease can then be subjected to functional analyses in cells or mice to understand how specific metabolic anomalies cause tissue dysfunction. We have used this approach to characterize rare Mendelian disorders and discover entirely new ones (Cell Reports 27, 1376-1386, 2019 and Genetics in Medicine 23, 900-908, 2021), with the long-term goal of developing better diagnostic techniques and treatments. Our clinical cohort of over 1,000 individuals provides a powerful resource to classify and explore metabolic variation in humans.

It has long been known that metabolic demands evolve during different stages of mammalian embryogenesis and that metabolic anomalies (e.g., nutritional deficiencies in the mother) can interfere with organogenesis. We want to decode the relationships between particular metabolic activities and particular developmental events, and IEMs provide a valuable starting point to this challenge. Because IEMs are monogenic disorders, all aspects of these diseases arise from mutation of a single node in the metabolic network. Some IEMs cause defects in organogenesis, indicating that particular metabolic activities are required for specific aspects of development. We are interested in malformations and defects in lineage commitment that arise downstream of human IEM mutations. As a system to study these mutations in mice, we developed an approach to observe metabolic activities across time and location during mid-gestation in utero (see figure, Nature 604, 349-353, 2022). This has allowed us to detect evolving, tissue-specific patterns of nutrient utilization in the developing embryo, and to pinpoint how IEMs perturb these patterns.