New research optimizes body's own immune system to fight cancer
May 14, 2021
University of Minnesota
A new study shows how engineered immune cells used in new cancer therapies can overcome physical barriers to allow a patient's own immune system to fight tumors. The research could improve cancer therapies in the future for millions of people worldwide.
A groundbreaking study led by engineering and medical researchers at the University of Minnesota Twin Cities shows how engineered immune cells used in new cancer therapies can overcome physical barriers to allow a patient's own immune system to fight tumors. The research could improve cancer therapies in the future for millions of people worldwide.
The research is published in Nature Communications, a peer-reviewed, open access, scientific journal published by Nature Research.
Instead of using chemicals or radiation, immunotherapy is a type of cancer treatment that helps the patient's immune system fight cancer. T cells are a type of white blood cell that are of key importance to the immune system. Cytotoxic T cells are like soldiers who search out and destroy the targeted invader cells.
While there has been success in using immunotherapy for some types of cancer in the blood or blood-producing organs, a T cell's job is much more difficult in solid tumors.
"The tumor is sort of like an obstacle course, and the T cell has to run the gauntlet to reach the cancer cells," said Paolo Provenzano, the senior author of the study and a biomedical engineering associate professor in the University of Minnesota College of Science and Engineering. "These T cells get into tumors, but they just can't move around well, and they can't go where they need to go before they run out of gas and are exhausted."
In this first-of-its-kind study, the researchers are working to engineer the T cells and develop engineering design criteria to mechanically optimize the cells or make them more "fit" to overcome the barriers. If these immune cells can recognize and get to the cancer cells, then they can destroy the tumor.
In a fibrous mass of a tumor, the stiffness of the tumor causes immune cells to slow down about two-fold -- almost like they are running in quicksand.
"This study is our first publication where we have identified some structural and signaling elements where we can tune these T cells to make them more effective cancer fighters," said Provenzano, a researcher in the University of Minnesota Masonic Cancer Center. "Every 'obstacle course' within a tumor is slightly different, but there are some similarities. After engineering these immune cells, we found that they moved through the tumor almost twice as fast no matter what obstacles were in their way."
To engineer cytotoxic T cells, the authors used advanced gene editing technologies (also called genome editing) to change the DNA of the T cells so they are better able to overcome the tumor's barriers. The ultimate goal is to slow down the cancer cells and speed up the engineered immune cells. The researchers are working to create cells that are good at overcoming different kinds of barriers. When these cells are mixed together, the goal is for groups of immune cells to overcome all the different types of barriers to reach the cancer cells.
Provenzano said the next steps are to continue studying the mechanical properties of the cells to better understand how the immune cells and cancer cells interact. The researchers are currently studying engineered immune cells in rodents and in the future are planning clinical trials in humans.
While initial research has been focused on pancreatic cancer, Provenzano said the techniques they are developing could be used on many types of cancers.
"Using a cell engineering approach to fight cancer is a relatively new field," Provenzano said. "It allows for a very personalized approach with applications for a wide array of cancers. We feel we are expanding a new line of research to look at how our own bodies can fight cancer. This could have a big impact in the future."
In addition to Provenzano, the study's authors included current and former University of Minnesota Department of Biomedical Engineering researchers Erdem D. Tabdanov (co-author), Nelson J. Rodríguez-Merced (co-author), Vikram V. Puram, Mackenzie K. Callaway, and Ethan A. Ensminger; University of Minnesota Masonic Cancer Center and Medical School Department of Pediatrics researchers Emily J. Pomeroy, Kenta Yamamoto, Walker S. Lahr, Beau R. Webber, Branden S. Moriarity; National Institute of Biomedical Imaging and Bioengineering researcher Alexander X. Cartagena-Rivera; and National Heart, Lung, and Blood Institute researcher Alexander S. Zhovmer, who is now at the Center for Biologic Evaluation and Research.
The research was funded primarily by the National Institutes of Health (NIH) and University of Minnesota Physical Sciences in Oncology Center, which receives funding from NIH's National Cancer Institute. Additional funding was provided by the American Cancer Society and the Randy Shaver Research and Community Fund. The University of Minnesota Imaging Center provided additional staff expertise. Some of the researchers also are part of the University of Minnesota Center for Genome Engineering and the University's Institute for Engineering in Medicine.
- Erdem D. Tabdanov, Nelson J. Rodríguez-Merced, Alexander X. Cartagena-Rivera, Vikram V. Puram, Mackenzie K. Callaway, Ethan A. Ensminger, Emily J. Pomeroy, Kenta Yamamoto, Walker S. Lahr, Beau R. Webber, Branden S. Moriarity, Alexander S. Zhovmer, Paolo P. Provenzano. Engineering T cells to enhance 3D migration through structurally and mechanically complex tumor microenvironments. Nature Communications, 2021; 12 (1) DOI: 10.1038/s41467-021-22985-5
Light-carrying chips advance machine learning
International team of researchers uses photonic networks for pattern recognition
- January 6, 2021
- University of Münster
- Researchers found that so-called photonic processors, with which data is processed by means of light, can process information very much more rapidly and in parallel than electronic chips.
In the digital age, data traffic is growing at an exponential rate. The demands on computing power for applications in artificial intelligence such as pattern and speech recognition in particular, or for self-driving vehicles, often exceeds the capacities of conventional computer processors. Working together with an international team, researchers at the University of Münster are developing new approaches and process architectures which can cope with these tasks extremely efficient. They have now shown that so-called photonic processors, with which data is processed by means of light, can process information much more rapidly and in parallel -- something electronic chips are incapable of doing.
Background and methodology
Light-based processors for speeding up tasks in the field of machine learning enable complex mathematical tasks to be processed at enormously fast speeds (10¹² -10¹⁵ operations per second). Conventional chips such as graphic cards or specialized hardware like Google's TPU (Tensor Processing Unit) are based on electronic data transfer and are much slower. The team of researchers led by Prof. Wolfram Pernice from the Institute of Physics and the Center for Soft Nanoscience at the University of Münster implemented a hardware accelerator for so-called matrix multiplications, which represent the main processing load in the computation of neural networks. Neural networks are a series of algorithms which simulate the human brain. This is helpful, for example, for classifying objects in images and for speech recognition.
The researchers combined the photonic structures with phase-change materials (PCMs) as energy-efficient storage elements. PCMs are usually used with DVDs or BluRay discs in optical data storage. In the new processor this makes it possible to store and preserve the matrix elements without the need for an energy supply. To carry out matrix multiplications on multiple data sets in parallel, the Münster physicists used a chip-based frequency comb as a light source. A frequency comb provides a variety of optical wavelengths which are processed independently of one another in the same photonic chip. As a result, this enables highly parallel data processing by calculating on all wavelengths simultaneously -- also known as wavelength multiplexing. "Our study is the first one to apply frequency combs in the field of artificially neural networks," says Wolfram Pernice.
In the experiment the physicists used a so-called convolutional neural network for the recognition of handwritten numbers. These networks are a concept in the field of machine learning inspired by biological processes. They are used primarily in the processing of image or audio data, as they currently achieve the highest accuracies of classification. "The convolutional operation between input data and one or more filters -- which can be a highlighting of edges in a photo, for example -- can be transferred very well to our matrix architecture," explains Johannes Feldmann, the lead author of the study. "Exploiting light for signal transference enables the processor to perform parallel data processing through wavelength multiplexing, which leads to a higher computing density and many matrix multiplications being carried out in just one timestep. In contrast to traditional electronics, which usually work in the low GHz range, optical modulation speeds can be achieved with speeds up to the 50 to 100 GHz range." This means that the process permits data rates and computing densities, i.e. operations per area of processor, never previously attained.
The results have a wide range of applications. In the field of artificial intelligence, for example, more data can be processed simultaneously while saving energy. The use of larger neural networks allows more accurate, and hitherto unattainable, forecasts and more precise data analysis. For example, photonic processors support the evaluation of large quantities of data in medical diagnoses, for instance in high-resolution 3D data produced in special imaging methods. Further applications are in the fields of self-driving vehicles, which depend on fast, rapid evaluation of sensor data, and of IT infrastructures such as cloud computing which provide storage space, computing power or applications software.
- J. Feldmann, N. Youngblood, M. Karpov, H. Gehring, X. Li, M. Stappers, M. Le Gallo, X. Fu, A. Lukashchuk, A. S. Raja, J. Liu, C. D. Wright, A. Sebastian, T. J. Kippenberg, W. H. P. Pernice, H. Bhaskaran. Parallel convolutional processing using an integrated photonic tensor core. Nature, 2021; 589 (7840): 52 DOI: 10.1038/s41586-020-03070-1
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