In a recent paper published in Nature, researchers used positron emission tomography imaging (PET) and electron microscopy to create 3D ultra-resolution maps of mitochondrial networks in lung tumours of genetically engineered mice. They discovered that the diverse bioenergetic phenotypes and metabolic dependencies identified in non-small cell lung cancer (NSCLC) tumours align with distinct structural organization of mitochondrial networks present. This sheds light on the complexity of NSCLC and highlights the importance of developing personalized treatment strategies that take into account the heterogeneity of these tumours.
The powerhouse of the cell
Mitochondria play a critical role in the metabolism and energy production of cancer cells, including non-small cell lung cancer (NSCLC). NSCLC is a heterogeneous disease with varying histological, genetic, and metabolic characteristics. Mitochondria are essential regulators of cellular energy and metabolism, sustaining tumour cell growth and survival. However, our understanding of how mitochondrial networks are structurally and functionally regulated in NSCLC at an in vivo level is limited. In order to better elucidate this, the research team from UCLA Jonsson Comprehensive Cancer Center used PET and electron microscopy to create 3D ultra-resolution maps of mitochondrial networks in lung tumours of genetically engineered mice.
“Our study represents a first step towards generating highly detailed three-dimensional maps of lung tumours using genetically engineered mouse models,” said David Shackelford, senior author of the study. “Using these maps, we have begun to create a structural and functional atlas of lung tumours, which has provided us valuable insight into how tumour cells structurally organize their cellular architecture in response to the high metabolic demands of tumour growth. Our findings hold promise to inform and improve current treatment strategies while illuminating new directions from which to target lung cancer.”
Building a detailed 3D map of lung cancer
The researchers used an integrated platform consisting of PET imaging, respirometry, and three-dimensional scanning block-face electron microscopy to analyse mitochondrial networks and bioenergetic phenotypes in NSCLC. They discovered that the diverse bioenergetic phenotypes and metabolic dependencies identified in NSCLC tumours aligned with the distinct structural organization of mitochondrial networks present. The researchers used deep learning to categorize tumours based on mitochondrial activity and other factors. They found that two subtypes of non-small cell lung cancer (NSCLC), adenocarcinomas (LUAD) and squamous-cell carcinomas (LUSC), had distinct subpopulations of mitochondrial networks within them.
They found that mitochondrial networks are compartmentalized into distinct subpopulations that govern the bioenergetic capacity of tumours. In tumours with high rates of oxidative phosphorylation (OXPHOSHI) and fatty acid oxidation, the researchers identified peri-droplet mitochondrial networks wherein mitochondria contact and surround lipid droplets. On the other hand, in tumours with low rates of OXPHOS (OXPHOSLO), high glucose flux regulated perinuclear localization of mitochondria, structural remodelling of cristae, and mitochondrial respiratory capacity.
“Our study has uncovered a novel finding in the metabolic flux of lung tumours, revealing that their nutrient preference may be determined by the compartmentalization of their mitochondria with other organelles, either relying on glucose (“sugar”) or free fatty acids (“fat”),” said Mingqi Han, first-author of the study. “This discovery has important implications for developing effective anticancer therapies that target tumour-specific nutrient preferences. Our multi-modality imaging approach has enabled us to uncover this previously unknown aspect of cancer metabolism, and we believe that it can be applied to other types of cancer, paving the way for further research in this area.”
The impact of this research
This finding that diverse bioenergetic phenotypes and metabolic dependencies identified in NSCLC tumours aligns with distinct structural organization of mitochondrial networks sheds light on the complexity of NSCLC and highlights the importance of developing personalized treatment strategies that take into account the heterogeneity of these tumours. This has major implications for developing effective anticancer therapies that target tumour-specific nutrient preferences.
The study’s multi-modality imaging approach enabled the researchers to uncover previously unknown aspects of cancer metabolism, revealing that nutrient preference may be determined by the compartmentalization of mitochondria with other organelles. This approach can be applied to other types of cancer, paving the way for further research in this area and ultimately leading to better treatment outcomes for patients.
