A cancer cell featuring metabolic uptake (in yellow) and vessels (in blue).
Photo credit: The University of Queensland
Scientific Frontline: "At a Glance" Summary
- Main Discovery: Cell metabolism within specific "neighbourhoods" of non-small cell lung carcinoma (NSCLC) acts as a critical determinant for patient response and resistance to immunotherapy.
- Methodology: Researchers employed machine learning algorithms and computational spatial biology to map cell interactions at cellular resolution, specifically profiling how individual cancer cells and tumor regions metabolize glucose.
- Key Data: While immunotherapy costs governments approximately $400,000 per patient annually, it is effective in only 20% to 30% of cases; higher glucose uptake was directly correlated with poorer patient outcomes.
- Significance: This profiling capability allows clinicians to identify non-responders early, preventing the use of ineffective, expensive treatments and facilitating the selection of patients who require combination or alternative therapies.
- Future Application: The findings will guide the development of metabolic inhibitors to enhance immunotherapy efficacy and are planned for expansion into clinical trials for head, neck, and aggressive skin cancers.
- Branch of Science: Oncology and Computational Biology
- Additional Detail: The research, published in Nature Communications, utilized technologies to visualize glucose processing heterogeneity within tumors to advance precision medicine.
University of Queensland researchers who mapped cancer cell ‘neighborhoods' in the most common type of lung cancer have found cell metabolism plays a critical role in determining how lung cancer patients will respond to immunotherapy.
Associate Professor Arutha Kulasinghe from UQ’s Frazer Institute said machine learning algorithms and computational approaches were used to examine cell interactions at cellular resolution in non-small cell lung carcinoma (NSCLC) to better understand why some patients don’t respond to immunotherapy treatment.
“Building on research published last year that mapped lung cancer cells. This study examined how cells interact and metabolize glucose, which is something we know cancer cells thrive on,” Dr Kulasinghe said.
“We were able to dive deep into the complex nature of cells – basically looking at the cells’ personal lives in the complex composition of a tumor – and found certain metabolic neighborhoods associated with response and resistance to immunotherapy.
“Immunotherapy is extremely costly and can cost the government more than $400,000 per patient per year, but it often only works in about 20-30 per cent of patients.
“It’s important to understand how to identify these patients, and those that might need combination or alternative therapies.”
Having the ability to predict whether cancer cells will respond to immunotherapy means doctors can provide more targeted treatment, which could lead to better outcomes for lung cancer, which causes around 20,000 deaths in Australia every year.
Lead author Dr James Monkman said the cutting-edge technologies paired with our computational analysis used in the study enabled researchers to see how each cell processed glucose.
“We know cancer cells love sugar, and we analyzed where glucose was being processed in the cells and where it wasn’t – you could have a region of a tumor processing glucose in a completely different way to another area of the tumor,” Dr Monkman said.
“Now we’re starting to understand how and where each cancer cell metabolizes sugars – and that higher glucose uptake in cancer cells leads to poorer outcomes.
“The next step is to design targeted treatments to make immunotherapy more effective, such as with metabolic inhibitors.
“Our end goal is precision medicine, where we can profile every cell in a patient’s tumour to determine what drug the patient needs based on their unique tumor profile.”
Associate Professor Kulasinghe hopes this research will be expanded to include other tumors including head and neck cancer and some aggressive skin cancers.
The next phase of research will examine how the approach can be incorporated into clinical trials.
UQ’s Frazer Institute is based at the Translational Research Institute (TRI).
Published in journal: Nature Communications
Authors: James Monkman, Aaron Kilgallon, Clara Lawler, Rafael Tubelleza, Thazin Nwe Aung, Jonathan H. Warrell, Ioannis Vathiotis, Ioannis P. Trontzas, Niki Gavrielatou, Nay Nwe Nyein Chan, Rotem Czertok, Shai Bookstein, Ken O’Byrne, Ettai Markovits, David L. Rimm, and Arutha Kulasinghe
Source/Credit: University of Queensland
Reference Number: ongy020426_01
.jpg)