Perelman School of Medicine (PSOM) professors and Penn Bioengineering Graduate Group members Carl June and Avery Posey are leading the charge in T cell therapy and the fight against cancer.
Avery Posey, PhDCarl June, MD
Advances in genome editing through processes such as CRISPR, and the ability to rewire cells through synthetic biology, have led to increasingly elaborate approaches for modifying and supercharging T cells for therapy. Avery Posey, Assistant Professor of Pharmacology, and Carl June, the Richard W. Vague Professor in Immunotherapy, explain how new techniques are providing tools to counter some of the limitations of current CAR T cell therapies in a recent Nature feature.
The pair were also part of a team of researchers from PSOM, the Children’s Hospital of Philadelphia (CHOP), and the Corporal Michael J. Crescenz VA Medical Center to receive an inaugural $8 million Therapy ACceleration To Intercept CAncer Lethality (TACTICAL) Award from the Prostate Cancer Foundation. Their project will develop new clinic-ready CAR T cell therapies for Metastatic Castrate-Resistant Prostate Cancer (mCRPC).
Engineers in the Center for Precision Engineering for Health (CPE4H) are focusing on innovations in diagnostics and delivery, cellular and tissue engineering, and the development of new devices that integrate novel materials with human tissues. Below is an excerpt from “Going Small to Win Big: Engineering Personalized Medicine,” featuring the research from the laboratory of Michael Mitchell, J. Peter and Geri Skirkanich Assistant Professor of Innovation in Bioengineering.
The Challenge
Solid tumors evade the immune system’s ability to attack them in part due to the tumors’ tough, fibrous biological barriers that circulating immune cells can’t cross. Researchers need to identify ways to deliver individualized treatments that can better target these tumors without causing damage to healthy tissues or affecting overall quality of life.
The Status Quo
Current cancer treatments typically involve surgery, radiation or chemo- therapy to eliminate solid tumors. These treatments are invasive and can cause numerous negative downstream effects. Newer treatments involve engineering a patient’s immune system to recognize and fight cancerous cells, but are so far only effective against certain “liquid” cancers, where the mutated cells circulate freely in the blood and bone marrow and are small enough to be picked off by the patient’s upgraded T cells. Additionally, existing methods can also require that the cell engineering take place in a lab rather than directly inside the body.
The Mitchell Lab’s Fix
Members of the lab of Michael Mitchell, J. Peter and Geri Skirkanich Assistant Professor of Innovation in Bioengineering, are looking to utilize nanoparticle delivery technology developed by their lab to engineer a different type of immune cell, the macrophage, in order to fight solid- tumor cancers from the inside.
The Mitchell lab is using lipid nanoparticles (LNPs) to carry mRNA and DNA sequences inside of macrophages, a type of immune cell that can consume tumor cells if engineered correctly. In theory, a patient would receive an injection carrying the LNP payload, and the macrophages, whose name literally means “big eaters,” would take up the genetic sequence, alter their function and be able to recognize a patient’s own unique tumor cells in the body.
Because of the way macrophages operate, they could cross the tumor’s biological barrier and attack the cells, destroying the tumor from the inside. An added benefit of the Mitchell Lab’s technology is that the destroyed tumor cells would then also allow other immune cells to present their antigens to circulating T cells, which could then learn to fight those same cancer cells in the future.
“One of the longstanding challenges that we face in the context of cancer and immunotherapies is that every tumor has unique antigens that are specific to patients,” says Mitchell. “This is why we’ve had a lot of trouble developing targeted therapies. Personalizing an approach by harnessing an individual’s immune system gives each patient a greater chance of a positive outcome.”
Engineers in the Center for Precision Engineering for Health (CPE4H) are focusing on innovations in diagnostics and delivery, cellular and tissue engineering, and the development of new devices that integrate novel materials with human tissues. Below is an excerpt from “Going Small to Win Big: Engineering Personalized Medicine,” featuring the research from the laboratory of Jenny Jiang, J. Peter and Geri Skirkanich Associate Professor of Innovation in Bioengineering.
The Challenge
In order to create personalized immune therapies, researchers need to untangle what is happening between an individual patient’s immune cells and the antigens that they interact with on a molecular level. Immune cell-antigen interactions need to be understood in four different areas in order to create a full picture: the unique genetic sequence of the T cell’s antigen receptors, the antigen specificity of that cell, and both the gene and protein expression of the same cell.
The Status Quo
Prior methods of understanding interactions between T cells and antigens could only get a picture of one or two of these four elements because of technology constraints. Other roadblocks included that cells cultured or engineered in a laboratory setting are not in a natural environment so they won’t express genes or proteins in the way T cells would in the body, and technologies that assess the antigen specificity of T cells were not cost-effective for looking at large numbers of antigens.
The Jiang Lab’s Fix
The lab of Jenny Jiang, J. Peter and Geri Skirkanich Associate Professor of Innovation in Bioengineering, developed a technology called TetTCR-SeqHD, which solves these problems. Using this technology, scientists can now simultaneously profile samples of large numbers of single T cells in the four dimensions using high- throughput screening.
The Jiang Lab’s technology is essentially a method for getting a “full-body scan” of an individual’s T cells and creates a catalog of the different types of T cells and the antigens they respond (or don’t respond) to, paving the way for the ability to better target immune therapies to an individual patient.
“Individual T cells are unique, and that’s the challenge of using one treatment to fit all,” says Jiang. “Identifying antigen specificity and creating therapies that target that specificity in an individual’s T cells will be key to truly personalizing immune therapies in the future.”
Researchers at Penn and colleagues have developed a tool to analyze single cells that assesses both the patterns of gene activation within a cell and which sibling cells shared a common progenitor.
Arjun Raj of the School of Engineering and Applied Science and the Perelman School of Medicine, former postdoc Lee Richman, now of Brigham and Women’s Hospital, and colleagues have developed a new analysis tool that combines a cell’s unique gene expression data with information about the cell’s origins. The method can be applied to identify new cell subsets throughout development and better understand drug resistance.
Recent advances in analyzing data at the single-cell level have helped biologists make great strides in uncovering new information about cells and their behaviors. One commonly used approach, known as clustering, allows scientists to group cells based on characteristics such as the unique patterns of active or inactive genes or by the progeny of duplicating cells, known as clones, over several generations.
Although single-cell clustering has led to many significant findings, for example, new cancer cell subsets or the way immature stem cells mature into “specialized” cells, researchers to this point had not been able to marry what they knew about gene-activation patterns with what they knew about clone lineages.
Now, research published in Cell Genomics led by University of Pennsylvania professor of bioengineering Arjun Raj has resulted in the development of ClonoCluster, an open-source tool that combines unique patterns of gene activation with clonal information. This produces hybrid cluster data that can quickly identify new cellular traits; that can then be used to better understand resistance to some cancer therapies.
“Before, these were independent modalities, where you would cluster the cells that express the same genes in one lot and cluster the others that share a common ancestor in another,” says Lee Richman, first paper author and a former postdoc in the Raj lab who is now at Brigham and Women’s Hospital in Boston. “What’s exciting is that this tool allows you to draw new lines around your clusters and explore their properties, which could help us identify new cell types, functions, and molecular pathways.”
Researchers in the Raj Lab use a technique known as barcoding to assign labels to cells they are interested in studying, particularly useful for tracking cells, clustering data based on cells’ offspring, and following lineages over time. Believing they could parse more valuable information out of this data by incorporating the cell’s unique patterns of gene activation, the researchers applied ClonoCluster to six experimental datasets that used barcoding to track dividing cells’ offspring. Specifically, they looked at the development of chemotherapy resistance and of stem cells into specialized tissue types.
Members of the research team include (from left to right) Xuexiang Han, Michael J. Mitchell, Ningqiang Gong, Lulu Xue, Sarah J. Shepherd, and Rakan El-Mayta.
Since the success of the COVID-19 vaccine, RNA therapies have been the object of increasing interest in the biotech world. These therapies work with your body to target the genetic root of diseases and infections, a promising alternative treatment method to that of traditional pharmaceutical drugs.
Lipid nanoparticles (LNPs) have been successfully used in drug delivery for decades. FDA-approved therapies use them as vehicles for delivering messenger RNA (mRNA), which prompts the cell to make new proteins, and small interfering RNA (siRNA), which instruct the cell to silence or inhibit the expression of certain proteins.
The biggest challenge in developing a successful RNA therapy is its targeted delivery. Research is now confronting the current limitations of LNPs, which have left many diseases without an effective RNA therapy.
Liver fibrosis occurs when the liver is repeatedly damaged and the healing process results in the accumulation of scar tissue, impeding healthy liver function. It is a chronic disease characterized by the buildup of excessive collagen-rich extracellular matrix (ECM). Liver fibrosis has remained challenging to treat using RNA therapies due to a lack of delivery systems for targeting activated liver-resident fibroblasts. Both the solid fibroblast structure and the lack of specificity or affinity to target these fibroblasts has impeded current LNPs from entering activated liver-resident fibroblasts, and thus they are unable to deliver RNA therapeutics.
To tackle this issue and help provide a treatment for the millions of people who suffer from this chronic disease, Michael Mitchell, J. Peter and Geri Skirkanich Assistant Professor of Innovation in the Department of Bioengineering, and postdoctoral fellows Xuexiang Han and Ningqiang Gong, found a new way to synthesize ligand-tethered LNPs, increasing their selectivity and allowing them to target liver fibroblasts.
Lulu Xue, Margaret Billingsley, Rakan El-Mayta, Sarah J. Shepherd, Mohamad-Gabriel Alameh and Drew Weissman, Roberts Family Professor in Vaccine Research and Director of the Penn Institute for RNA Innovation at the Perelman School of Medicine, also contributed to this work.
Penn Engineering’s newly established ASSET Center aims to make AI-enabled systems more “safe, explainable and trustworthy” by studying the fundamentals of the artificial neural networks that organize and interpret data to solve problems.
ASSET’s first funding collaboration is with Penn’s Perelman School of Medicine (PSOM) and the Penn Institute for Biomedical Informatics (IBI). Together, they have launched a series of seed grants that will fund research at the intersection of AI and healthcare.
Teams featuring faculty members from Penn Engineering, Penn Medicine and the Wharton School applied for these grants, to be funded annually at $100,000. A committee consisting of faculty from both Penn Engineering and PSOM evaluated 18 applications and judged the proposals based on clinical relevance, AI foundations and potential for impact.
Artificial intelligence and machine learning promise to revolutionize nearly every field, sifting through massive amounts of data to find insights that humans would miss, making faster and more accurate decisions and predictions as a result.
Applying those insights to healthcare could yield life-saving benefits. For example, AI-enabled systems could analyze medical imaging for hard-to-spot tumors, collate multiple streams of disparate patient information for faster diagnoses or more accurately predict the course of disease.
Given the stakes, however, understanding exactly how these technologies arrive at their conclusions is critical. Doctors, nurses and other healthcare providers won’t use such technologies if they don’t trust that their internal logic is sound.
“We are developing techniques that will allow AI-based decision systems to provide both quantifiable guarantees and explanations of their predictions,” says Rajeev Alur, Zisman Family Professor in Computer and Information Science and Director of the ASSET Center. “Transparency and accuracy are key.”
“Development of explainable and trustworthy AI is critical for adoption in the practice of medicine,” adds Marylyn Ritchie, Professor of Genetics and Director of the Penn Institute for Biomedical Informatics. “We are thrilled about this partnership between ASSET and IBI to fund these innovative and exciting projects.”
Seven projects were selected in the inaugural class, including projects from Dani S. Bassett, J. Peter Skirkanich Professor in the Departments of Bioengineering, Electrical and Systems Engineering, Physics & Astronomy, Neurology, and Psychiatry, and several members of the Penn Bioengineering Graduate Group: Despina Kontos, Matthew J. Wilson Professor of Research Radiology II, Department of Radiology, Penn Medicine and Lyle Ungar, Professor, Department of Computer and Information Science, Penn Engineering; Spyridon Bakas, Assistant Professor, Departments of Pathology and Laboratory Medicine and Radiology, Penn Medicine; and Walter R. Witschey, Associate Professor, Department of Radiology, Penn Medicine.
Optimizing clinical monitoring for delivery room resuscitation using novel interpretable AI
Elizabeth Foglia, Associate Professor, Department of Pediatrics, Penn Medicine and the Children’s Hospital of Philadelphia
Dani S. Bassett, J. Peter Skirkanich Professor, Departments of Bioengineering and Electrical and Systems Engineering, Penn Engineering
This project will apply a novel interpretable machine learning approach, known as the Distributed Information Bottleneck, to solve pressing problems in identifying and displaying critical information during time-sensitive clinical encounters. This project will develop a framework for the optimal integration of information from multiple physiologic measures that are continuously monitored during delivery room resuscitation. The team’s immediate goal is to detect and display key target respiratory parameters during delivery room resuscitation to prevent acute and chronic lung injury for preterm infants. Because this approach is generalizable to any setting in which complex relations between information-rich variables are predictive of health outcomes, the project will lay the groundwork for future applications to other clinical scenarios.
With OCTOPUS, Dan Huh’s team has significantly advanced the frontiers of organoid research, providing a platform superior to conventional gel droplets. OCTOPUS splits the soft hydrogel culture material into a tentacled geometry. The thin, radial culture chambers sit on a circular disk the size of a U.S. quarter, allowing organoids to advance to an unprecedented degree of maturity.
When it comes to human bodies, there is no such thing as typical. Variation is the rule. In recent years, the biological sciences have increased their focus on exploring the poignant lack of norms between individuals, and medical and pharmaceutical researchers are asking questions about translating insights concerning biological variation into more precise and compassionate care.
What if therapies could be tailored to each patient? What would happen if we could predict an individual body’s response to a drug before trial-and-error treatment? Is it possible to understand the way a person’s disease begins and develops so we can know exactly how to cure it?
Dan Huh, Associate Professor in the Department of Bioengineering at the University of Pennsylvania’s School of Engineering and Applied Science, seeks answers to these questions by replicating biological systems outside of the body. These external copies of internal systems promise to boost drug efficacy while providing new levels of knowledge about patient health.
An innovator of organ-on-a-chip technology, or miniature copies of bodily systems stored in plastic devices no larger than a thumb drive, Huh has broadened his attention to engineering mini-organs in a dish using a patient’s own cells.
Penn Medicine researchers laud the early results for CAR T therapy in lupus patients, which point to broader horizons for the use of personalized cellular therapies.
Penn Medicine’s Carl June and Daniel Baker.
Engineered immune cells, known as CAR T cells, have shown the world what personalized immunotherapies can do to fight blood cancers. Now, investigators have reported highly promising early results for CAR T therapy in a small set of patients with the autoimmune disease lupus. Penn Medicine CAR T pioneer Carl June and Daniel Baker, a doctoral student in cell and molecular biology in the Perelman School of Medicine, discuss this development in a commentary published in Cell.
“We’ve always known that in principle, CAR T therapies could have broad applications, and it’s very encouraging to see early evidence that this promise is now being realized,” says June, who is the Richard W. Vague Professor in Immunotherapy in the department of Pathology and Laboratory Medicine at Penn Medicine and director of the Center for Cellular Immunotherapies at the Abramson Cancer Center.
T cells are among the immune system’s most powerful weapons. They can bind to, and kill, other cells they recognize as valid targets, including virus-infected cells. CAR T cells are T cells that have been redirected, through genetic engineering, to efficiently kill specifically defined cell types.
CAR T therapies are created out of each patient’s own cells—collected from the patient’s blood, and then engineered and multiplied in the lab before being reinfused into the patient as a “living drug.” The first CAR T therapy, Kymriah, was developed by June and his team at Penn Medicine, and received Food & Drug Administration approval in 2017. There are now six FDA-approved CAR T cell therapies in the United States, for six different cancers.
From the start of CAR T research, experts believed that T cells could be engineered to fight many conditions other than B cell cancers. Dozens of research teams around the world, including teams at Penn Medicine and biotech spinoffs who are working to develop effective treatments from Penn-developed personalized cellular therapy constructs, are examining these potential new applications. Researchers say lupus is an obvious choice for CAR T therapy because it too is driven by B cells, and thus experimental CAR T therapies against it can employ existing anti-B-cell designs. B cells are the immune system’s antibody-producing cells, and, in lupus, B cells arise that attack the patient’s own organs and tissues.
Penn Integrates Knowledge Professor Kevin Johnson takes the stage at 24th Engaging Minds. (Image: Ben Asen)
This past weekend in New York City, the University of Pennsylvania showcased its 24th Engaging Minds event, the first in person since 2019. It was hosted by Penn Alumni.
Three Penn Integrates Knowledge University Professors — Kevin Johnson, Lance Freeman and Dolores Albarracín, — each discussed their research. The audience, at least 600 in person and remote, heard about using city planning to promote racial equity, about how conspiracy theories come to life and propagate, and about the need for physicians to communicate effectively with patients and families.
Following brief remarks from Penn Alumni President Ann Reese, University President Liz Magill introduced the event. “As many of you know, I’ve been thinking a lot and speaking often about what makes Penn Penn,” she said. “What are our distinctive strengths? What are the unique contributions to society that we have made in the past and can make in the future? And where do we go from the extraordinary position we are in now?”
Magill went on to express gratitude for the speakers and invited the audience to think about how the researchers’ work and expertise furthered what she described as the “twin principles of truth and opportunity.”
He took the audience through his family history, education and training, pausing at a point on the timeline when he was a young physician-scientist who had just explained a new medical topic to a journalist. “I felt really good about the conversation — and then the article came out,” Johnson said.
In the piece, he had been cast as saying that the medical community was over-treating this condition, “which is not what I said.” He realized in that moment that as a physician, he had been taught to communicate what a study finds, not how to act based on those findings. That experience shifted his thinking on how to communicate scientific topics, and he has spent decades trying to move the needle on how others in his field perceive this.
“As scientists we face obstacles. We face the obstacle of scale, so, small projects that we’re asked to generalize. We face the issue of trust. And then we face the issue of values,” Johnson said. “I’ll add a fourth, which is format; the way we choose to reach specific audiences will be different.”
Read more about the 24th Engaging Minds at Penn Today.
Kevin Johnson is the David L. Cohen University of Pennsylvania Professor in the Departments of Biostatistics, Epidemiology and Informatics and Computer and Information Science. As a Penn Integrates Knowlegde (PIK) University Professor, Johnson also holds appointments in the Departments of Bioengineering and Pediatrics, as well as in the Annenberg School of Communication.
The Heilmeier Award honors a Penn Engineering faculty member whose work is scientifically meritorious and has high technological impact and visibility. It is named for the late George H. Heilmeier, a Penn Engineering alumnus and member of the School’s Board of Advisors, whose technological contributions include the development of liquid crystal displays and whose honors include the National Medal of Science and Kyoto Prize.
Bassett, who also holds appointments in Physics & Astronomy in Penn Arts & Sciences and in Neurology and Psychiatry in the Perelman School of Medicine, is a pioneer in the field of network neuroscience, an emerging subfield which incorporates elements of mathematics, physics, biology and systems engineering to better understand how the overall shape of connections between individual neurons influences cognitive traits. They lead the Complex Systems lab, which tackles problems at the intersection of science, engineering and medicine using systems-level approaches, exploring fields such as curiosity, dynamic networks in neuroscience, and psychiatric disease.
Bassett will deliver the 2022-23 Heilmeier Award Lecture in Spring 2023.
