How Different Types of Cancer Respond to Nanoparticle Drug Delivery


Using nanoparticles to deliver cancer drugs offers a way to hit tumors with large doses of drugs while avoiding the harmful side effects that often accompany chemotherapy. However, so far only a handful of nanoparticle-based cancer drugs have been approved by the FDA.

A new study from MIT and the Broad Institute by MIT and Harvard researchers could help overcome some of the barriers to developing nanoparticle-based drugs. The team’s analysis of the interactions between 35 different types of nanoparticles and nearly 500 types of cancer cells revealed thousands of biological traits that influence whether those cells absorb different types of nanoparticles.

The results could help researchers better tailor their drug-delivery particles to specific types of cancer or design new particles that take advantage of the biological characteristics of particular types of cancer cells.

“We are excited about our findings because this is really just the beginning – we can use this approach to determine which types of nanoparticles are best for targeting certain cell types, from cancer to immune cells and other types of healthy and diseased organ cells. We learn how surface chemistry and other properties of materials play a role in targeting,” says Paula Hammond, a professor at the MIT Institute, head of the department of chemical engineering and fellow at MIT’s Koch Institute for Integrative Cancer Research.

Hammond is the lead author of the new study, which appears in Science. The lead authors of the paper are Natalie Boehnke, a postdoctoral fellow at MIT who will soon join the faculty at the University of Minnesota, and Joelle Straehla, clinical researcher Charles W. and Jennifer C. Johnson at the Koch Institute, professor at Harvard. Medical School, and pediatric oncologist at the Dana-Farber Cancer Institute.

Cell-particle interactions

Hammond’s lab has already developed many types of nanoparticles that can be used to deliver drugs to cells. Studies in his lab and others have shown that different types of cancer cells often react differently to the same nanoparticles. Boehnke, who was studying ovarian cancer when she joined Hammond’s lab, and Straehla, who was studying brain cancer, also noticed this phenomenon in their studies.

The researchers hypothesized that the biological differences between the cells could be the cause of the variation in their responses. To understand what these differences might be, they decided to pursue a large-scale study in which they could examine a large number of different cells interacting with many types of nanoparticles.

Straehla had recently heard about the Broad Institute’s PRISM platform, which was designed to allow researchers to rapidly screen thousands of drugs on hundreds of different cancer types at the same time. With the instrumental collaboration of Angela Koehler, associate professor of biological engineering at MIT, the team decided to try to adapt this platform to screen for cell-nanoparticle interactions instead of cell-drug interactions.

“Using this approach, we can begin to wonder if there is something about a cell’s genotypic signature that predicts how many nanoparticles it will take up,” Boehnke says.

For their screening, the researchers used 488 cancer cell lines from 22 different tissues of origin. Each cell type is “barcoded” with a unique DNA sequence that allows researchers to identify cells later. For each cell type, large datasets are also available on their gene expression profiles and other biological characteristics.

On the nanoparticle side, the researchers created 35 particles, each of which had a core made of either liposomes (particles made up of many fatty molecules called lipids), a polymer called PLGA, or another polymer called polystyrene. The researchers also coated the particles with different types of protective or targeting molecules, including polymers such as polyethylene glycol, antibodies and polysaccharides. This allowed them to study the influence of both core composition and particle surface chemistry.

Working with scientists from the Broad Institute, including Jennifer Roth, director of the PRISM lab, the researchers exposed pools of hundreds of different cells to one of 35 different nanoparticles. Each nanoparticle had a fluorescent tag, so researchers could use a cell sorting technique to separate cells based on how much fluorescence they gave off after a four or 24 hour exposure.

Based on these measurements, each cell line was assigned a score representing its affinity for each nanoparticle. The researchers then used machine learning algorithms to analyze these scores along with all other biological data available for each cell line.

This analysis yielded thousands of characteristics, or biomarkers, associated with affinity for different types of nanoparticles. Many of these markers were genes that code for the cellular machinery needed to bind particles, bring them into a cell, or process them. Some of these genes were already known to be involved in nanoparticle trafficking, but many others were new.

“We found markers that we expected, and we also found many more that really haven’t been explored. We hope that other people can use this dataset to expand their view of how nanoparticles and cells interact,” says Straehla.

Particle absorption

The researchers chose one of the biomarkers they identified, a protein called SLC46A3, for further study. The PRISM screen had shown that high levels of this protein correlated with very low absorption of lipid-based nanoparticles. When the researchers tested these particles in mouse models of melanoma, they found the same correlation. The results suggest that this biomarker could be used to help doctors identify patients whose tumors are more likely to respond to nanoparticle therapies.

Now researchers are trying to uncover the mechanism of how SLC46A3 regulates nanoparticle uptake. If they could find new ways to decrease cellular levels of this protein, it could help make tumors more sensitive to drugs carried by lipid nanoparticles. The researchers are also working to further explore some of the other biomarkers they have found.

This screening approach could also be used to study many other types of nanoparticles that the researchers did not examine in this study.

“The sky’s the limit in terms of what other undiscovered biomarkers are out there that we just haven’t captured because we haven’t looked at them,” Boehnke said. “I hope this is an inspiration for others to start looking at their nanoparticle systems in the same way.”

Reference: Boehnke N, Straehla JP, Safford HC, et al. A massively parallel pooled screen reveals the genomic determinants of nanoparticle delivery. Science. 2022;377(6604):eabm5551. doi:10.1126/science.abm5551

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