Cancer ‘vaccine’ eliminates tumors in mice

February 05, 2018

Injecting minute amounts of two immune-stimulating agents directly into solid tumors in mice can eliminate all traces of cancer in the animals, including distant, untreated metastases, according to a study by researchers at the Stanford University School of Medicine.

The approach works for many different types of cancers, including those that arisespontaneously, the study found.

The researchers believe the local application of very small amounts of the agents could serve as a rapid and relatively inexpensive cancer therapy that is unlikely to cause the adverse side effects often seen with bodywide immune stimulation.

“When we use these two agents together, we see the elimination of tumors all over the body,” said Ronald Levy, MD, professor of oncology. “This approach bypasses the need to identify tumor-specific immune targets and doesn’t require wholesale activation of the immune system or customization of a patient’s immune cells.”

One agent is currently already approved for use in humans; the other has been tested for human use in several unrelated clinical trials. A clinical trial was launched in January to test the effect of the treatment in patients with lymphoma.

Levy, who holds the Robert K. and Helen K. Summy Professorship in the School of Medicine, is the senior author of the study, which was published Jan. 31 in Science Translational Medicine. Instructor of medicine Idit Sagiv-Barfi, PhD, is the lead author.

‘Amazing, bodywide effects’

Levy is a pioneer in the field of cancer immunotherapy, in which researchers try to harness the immune system to combat cancer. Research in his laboratory led to the development of rituximab, one of the first monoclonal antibodies approved for use as an anticancer treatment in humans.

Some immunotherapy approaches rely on stimulating the immune system throughout the body. Others target naturally occurring checkpoints that limit the anti-cancer activity of immune cells. Still others, like the CAR T-cell therapy recently approved to treat some types of leukemia and lymphomas, require a patient’s immune cells to be removed from the body and genetically engineered to attack the tumor cells. Many of these approaches have been successful, but they each have downsides — from difficult-to-handle side effects to high-cost and lengthy preparation or treatment times.

“All of these immunotherapy advances are changing medical practice,” Levy said. “Our approach uses a one-time application of very small amounts of two agents to stimulate the immune cells only within the tumor itself. In the mice, we saw amazing, bodywide effects, including the elimination of tumors all over the animal.”

Cancers often exist in a strange kind of limbo with regard to the immune system. Immune cells like T cells recognize the abnormal proteins often present on cancer cells and infiltrate to attack the tumor. However, as the tumor grows, it often devises ways to suppress the activity of the T cells.

Levy’s method works to reactivate the cancer-specific T cells by injecting microgram amounts of two agents directly into the tumor site. (A microgram is one-millionth of a gram). One, a short stretch of DNA called a CpG oligonucleotide, works with other nearby immune cells to amplify the expression of an activating receptor called OX40 on the surface of the T cells. The other, an antibody that binds to OX40, activates the T cells to lead the charge against the cancer cells. Because the two agents are injected directly into the tumor, only T cells that have infiltrated it are activated. In effect, these T cells are “prescreened” by the body to recognize only cancer-specific proteins.

Cancer-destroying rangers

Some of these tumor-specific, activated T cells then leave the original tumor to find and destroy other identical tumors throughout the body.

The approach worked startlingly well in laboratory mice with transplanted mouse lymphoma tumors in two sites on their bodies. Injecting one tumor site with the two agents caused the regression not just of the treated tumor, but also of the second, untreated tumor. In this way, 87 of 90 mice were cured of the cancer. Although the cancer recurred in three of the mice, the tumors again regressed after a second treatment. The researchers saw similar results in mice bearing breast, colon and melanoma tumors.

Mice genetically engineered to spontaneously develop breast cancers in all 10 of their mammary pads also responded to the treatment. Treating the first tumor that arose often prevented the occurrence of future tumors and significantly increased the animals’ life span, the researchers found.

Finally, Sagiv-Barfi explored the specificity of the T cells by transplanting two types of tumors into the mice. She transplanted the same lymphoma cancer cells in two locations, and she transplanted a colon cancer cell line in a third location. Treatment of one of the lymphoma sites caused the regression of both lymphoma tumors but did not affect the growth of the colon cancer cells.

“This is a very targeted approach,” Levy said. “Only the tumor that shares the protein targets displayed by the treated site is affected. We’re attacking specific targets without having to identify exactly what proteins the T cells are recognizing.”

The current clinical trial is expected to recruit about 15 patients with low-grade lymphoma. If successful, Levy believes the treatment could be useful for many tumor types. He envisions a future in which clinicians inject the two agents into solid tumors in humans prior to surgical removal of the cancer as a way to prevent recurrence due to unidentified metastases or lingering cancer cells, or even to head off the development of future tumors that arise due to genetic mutations like BRCA1 and 2.

“I don’t think there’s a limit to the type of tumor we could potentially treat, as long as it has been infiltrated by the immune system,” Levy said.

The work is an example of Stanford Medicine’s focus on precision health, the goal of which is to anticipate and prevent disease in the healthy and precisely diagnose and treat disease in the ill.

The study’s other Stanford co-authors are senior research assistant and lab manager Debra Czerwinski; professor of medicine Shoshana Levy, PhD; postdoctoral scholar Israt Alam, PhD; graduate student Aaron Mayer; and professor of radiology Sanjiv Gambhir, MD, PhD.

Levy is a member of the Stanford Cancer Institute and Stanford Bio-X.

Gambhir is the founder and equity holder in CellSight Inc., which develops and translates multimodality strategies to image cell trafficking and transplantation.

The research was supported by the National Institutes of Health (grant CA188005), the Leukemia and Lymphoma Society, the Boaz and Varda Dotan Foundation and the Phil N. Allen Foundation.

Stanford’s Department of Medicine also supported the work.

This article was originally published by:


Tony Wyss-Coray: How young blood might help reverse aging.

August 27, 2015

Tony Wyss-Coray studies the impact of aging on the human body and brain. In this eye-opening talk, he shares new research from his Stanford lab and other teams which shows that a solution for some of the less great aspects of old age might actually lie within us all.

Scientists create ‘evolved’ protein that may stop cancer from spreading

September 28, 2014


A team of Stanford researchers has developed a protein therapy that disrupts the process that causes cancer cells to break away from original tumor sites, travel through the blood stream and start aggressive new growths elsewhere in the body.

This process, known as metastasis, can cause to spread with deadly effect.

“The majority of patients who succumb to cancer fall prey to metastatic forms of the disease,” said Jennifer Cochran, an associate professor of bioengineering who describes a new therapeutic approach in Nature Chemical Biology.

Today doctors try to slow or stop metastasis with chemotherapy, but these treatments are unfortunately not very effective and have severe side effects.

The Stanford team seeks to stop metastasis, without side effects, by preventing two proteins – Axl and Gas6 – from interacting to initiate the spread of cancer.

Axl proteins stand like bristles on the surface of cancer cells, poised to receive biochemical signals from Gas6 proteins.

When two Gas6 proteins link with two Axls, the signals that are generated enable cancer cells to leave the original tumor site, migrate to other parts of the body and form new cancer nodules.

To stop this process Cochran used protein engineering to create a harmless version of Axl that acts like a decoy. This decoy Axl latches on to Gas6 proteins in the blood stream and prevents them from linking with and activating the Axls present on .

In collaboration with Professor Amato Giaccia, who heads the Radiation Biology Program in Stanford’s Cancer Center, the researchers gave intravenous treatments of this bioengineered decoy protein to mice with aggressive breast and ovarian cancers.

Mice in the breast cancer treatment group had 78 percent fewer metastatic nodules than untreated mice. Mice with had a 90 percent reduction in metastatic nodules when treated with the engineered decoy protein.

“This is a very promising therapy that appears to be effective and non-toxic in pre-clinical experiments,” Giaccia said. “It could open up a new approach to cancer treatment.”

Giaccia and Cochran are scientific advisors to Ruga Corp., a biotech startup in Palo Alto that has licensed this technology from Stanford. Further preclinical and animal tests must be done before determining whether this therapy is safe and effective in humans.

Early but promising tests in lab mice suggest that a bioengineered ‘decoy’ protein, administered intravenously, can halt the spread of cancer from the original tumor site. Years of subsequent tests lie ahead. But this approach might one day provide …more

Greg Lemke, of the Molecular Neurobiology Laboratory at the Salk Institute, called this “a prime example of what bioengineering can do” to open up new therapeutic approaches to treat .

“One of the remarkable things about this work is the binding affinity of the decoy protein,” said Lemke, a noted authority on Axl and Gas6 who was not part of the Stanford experiments.

“The decoy attaches to Gas6 up to a hundredfold more effectively than the natural Axl,” Lemke said. “It really sops up Gas6 and takes it out of action.”

Directed Evolution

The Stanford approach is grounded on the fact that all biological processes are driven by the interaction of proteins, the molecules that fit together in lock-and-key fashion to perform all the tasks required for living things to function.

In nature proteins evolve over millions of years. But bioengineers have developed ways to accelerate the process of improving these tiny parts using technology called directed evolution. This particular application was the subject of the doctoral thesis of Mihalis Kariolis, a bioengineering graduate student in Cochran’s lab.

Using genetic manipulation, the Stanford team created millions of slightly different DNA sequences. Each DNA sequence coded for a different variant of Axl.

The researchers then used high-throughput screening to evaluate over 10 million Axl variants. Their goal was to find the variant that bound most tightly to Gas6.

Kariolis made other tweaks to enable the bioengineered decoy to remain in the bloodstream longer and also to tighten its grip on Gas6, rendering the decoy interaction virtually irreversible.

Yu Rebecca Miao, a postdoctoral scholar in Giaccia’s lab, designed the testing in animals and worked with Kariolis to administer the decoy Axl to the lab mice. They also did comparison tests to show that sopping up Gas6 resulted in far fewer secondary cancer nodules.

Irimpan Mathews, a protein crystallography expert at the SLAC National Accelerator Laboratory, joined the research effort to help the team better understand the binding mechanism between the Axl decoy and Gas6.

Protein crystallography captures the interaction of two proteins in a solid form, allowing researchers to take X-ray-like images of how the atoms in each protein bind together. These images showed molecular changes that allowed the bioengineered Axl decoy to bind Gas6 far more tightly than the natural Axl protein.

Next steps

Years of work lie ahead to determine whether this protein therapy can be approved to treat cancer in humans. Bioprocess engineers must first scale up production of the Axl decoy to generate pure material for clinical tests. Clinical researchers must then perform additional animal tests in order to win approval for and to conduct human trials. These are expensive and time-consuming steps.

But these early, hopeful results suggest that the Stanford approach could become a non-toxic way to fight metastatic cancer.

Glenn Dranoff, a professor of medicine at Harvard Medical School and a leading researcher at the Dana-Farber Cancer Institute, reviewed an advance copy of the Stanford paper but was otherwise unconnected with the research. “It is a beautiful piece of biochemistry and has some nuances that make it particularly exciting,” Dranoff said, noting that tumors often have more than one way to ensure their survival and propagation.

Axl has two protein cousins, Mer and Tyro3, that can also promote metastasis. Mer and Tyro3 are also activated by Gas6.

“So one therapeutic decoy might potentially affect all three related proteins that are critical in cancer development and progression,” Dranoff said.

Explore further: Scientists identify how immune cells use two critical receptors to clear dead cells from the body

More information: An engineered Axl ‘decoy receptor’ effectively silences the Gas6-Axl signaling axis, Nature Chemical Biology, DOI: 10.1038/nchembio.1636

Scientists create circuit board modeled on the human brain

April 29, 2014


Human brain and circuits illustration (stock image). Stanford scientists have developed faster, more energy-efficient microchips based on the human brain — 9,000 times faster and using significantly less power than a typical PC. Credit: © agsandrew / Fotolia

Boahen and his team have developed Neurogrid, a circuit board consisting of 16 custom-designed “Neurocore” chips. Together these 16 chips can simulate 1 million neurons and billions of synaptic connections. The team designed these chips with power efficiency in mind. Their strategy was to enable certain synapses to share hardware circuits. The result was Neurogrid — a device about the size of an iPad that can simulate orders of magnitude more neurons and synapses than other brain mimics on the power it takes to run a tablet computer.

Stanford scientists have developed faster, more energy-efficient microchips based on the human brain — 9,000 times faster and using significantly less power than a typical PC. This offers greater possibilities for advances in robotics and a new way of understanding the brain. For instance, a chip as fast and efficient as the human brain could drive prosthetic limbs with the speed and complexity of our own actions.

Stanford scientists have developed a new circuit board modeled on the human brain, possibly opening up new frontiers in robotics and computing.

For all their sophistication, computers pale in comparison to the brain. The modest cortex of the mouse, for instance, operates 9,000 times faster than a personal computer simulation of its functions.

Not only is the PC slower, it takes 40,000 times more power to run, writes Kwabena Boahen, associate professor of bioengineering at Stanford, in an article for the Proceedings of the IEEE.

“From a pure energy perspective, the brain is hard to match,” says Boahen, whose article surveys how “neuromorphic” researchers in the United States and Europe are using silicon and software to build electronic systems that mimic neurons and synapses.

Boahen and his team have developed Neurogrid, a circuit board consisting of 16 custom-designed “Neurocore” chips. Together these 16 chips can simulate 1 million neurons and billions of synaptic connections. The team designed these chips with power efficiency in mind. Their strategy was to enable certain synapses to share hardware circuits. The result was Neurogrid — a device about the size of an iPad that can simulate orders of magnitude more neurons and synapses than other brain mimics on the power it takes to run a tablet computer.

The National Institutes of Health funded development of this million-neuron prototype with a five-year Pioneer Award. Now Boahen stands ready for the next steps — lowering costs and creating compiler software that would enable engineers and computer scientists with no knowledge of neuroscience to solve problems — such as controlling a humanoid robot — using Neurogrid.

Its speed and low power characteristics make Neurogrid ideal for more than just modeling the human brain. Boahen is working with other Stanford scientists to develop prosthetic limbs for paralyzed people that would be controlled by a Neurocore-like chip.

“Right now, you have to know how the brain works to program one of these,” said Boahen, gesturing at the $40,000 prototype board on the desk of his Stanford office. “We want to create a neurocompiler so that you would not need to know anything about synapses and neurons to able to use one of these.”

Brain ferment

In his article, Boahen notes the larger context of neuromorphic research, including the European Union’s Human Brain Project, which aims to simulate a human brain on a supercomputer. By contrast, the U.S. BRAIN Project — short for Brain Research through Advancing Innovative Neurotechnologies — has taken a tool-building approach by challenging scientists, including many at Stanford, to develop new kinds of tools that can read out the activity of thousands or even millions of neurons in the brain as well as write in complex patterns of activity.

Zooming from the big picture, Boahen’s article focuses on two projects comparable to Neurogrid that attempt to model brain functions in silicon and/or software.

One of these efforts is IBM’s SyNAPSE Project — short for Systems of Neuromorphic Adaptive Plastic Scalable Electronics. As the name implies, SyNAPSE involves a bid to redesign chips, code-named Golden Gate, to emulate the ability of neurons to make a great many synaptic connections — a feature that helps the brain solve problems on the fly. At present a Golden Gate chip consists of 256 digital neurons each equipped with 1,024 digital synaptic circuits, with IBM on track to greatly increase the numbers of neurons in the system.

Heidelberg University’s BrainScales project has the ambitious goal of developing analog chips to mimic the behaviors of neurons and synapses. Their HICANN chip — short for High Input Count Analog Neural Network — would be the core of a system designed to accelerate brain simulations, to enable researchers to model drug interactions that might take months to play out in a compressed time frame. At present, the HICANN system can emulate 512 neurons each equipped with 224 synaptic circuits, with a roadmap to greatly expand that hardware base.

Each of these research teams has made different technical choices, such as whether to dedicate each hardware circuit to modeling a single neural element (e.g., a single synapse) or several (e.g., by activating the hardware circuit twice to model the effect of two active synapses). These choices have resulted in different trade-offs in terms of capability and performance.

In his analysis, Boahen creates a single metric to account for total system cost — including the size of the chip, how many neurons it simulates and the power it consumes.

Neurogrid was by far the most cost-effective way to simulate neurons, in keeping with Boahen’s goal of creating a system affordable enough to be widely used in research.

Speed and efficiency

But much work lies ahead. Each of the current million-neuron Neurogrid circuit boards cost about $40,000. Boahen believes dramatic cost reductions are possible. Neurogrid is based on 16 Neurocores, each of which supports 65,536 neurons. Those chips were made using 15-year-old fabrication technologies.

By switching to modern manufacturing processes and fabricating the chips in large volumes, he could cut a Neurocore’s cost 100-fold — suggesting a million-neuron board for $400 a copy. With that cheaper hardware and compiler software to make it easy to configure, these neuromorphic systems could find numerous applications.

For instance, a chip as fast and efficient as the human brain could drive prosthetic limbs with the speed and complexity of our own actions — but without being tethered to a power source. Krishna Shenoy, an electrical engineering professor at Stanford and Boahen’s neighbor at the interdisciplinary Bio-X center, is developing ways of reading brain signals to understand movement. Boahen envisions a Neurocore-like chip that could be implanted in a paralyzed person’s brain, interpreting those intended movements and translating them to commands for prosthetic limbs without overheating the brain.

A small prosthetic arm in Boahen’s lab is currently controlled by Neurogrid to execute movement commands in real time. For now it doesn’t look like much, but its simple levers and joints hold hope for robotic limbs of the future.

Of course, all of these neuromorphic efforts are beggared by the complexity and efficiency of the human brain.

In his article, Boahen notes that Neurogrid is about 100,000 times more energy efficient than a personal computer simulation of 1 million neurons. Yet it is an energy hog compared to our biological CPU.

“The human brain, with 80,000 times more neurons than Neurogrid, consumes only three times as much power,” Boahen writes. “Achieving this level of energy efficiency while offering greater configurability and scale is the ultimate challenge neuromorphic engineers face.”

Story Source:

The above story is based on materials provided by Stanford University. The original article was written by Tom Abate. Note: Materials may be edited for content and length.

Journal Reference:

  1. Ben Varkey Benjamin, Peiran Gao, Emmett McQuinn, Swadesh Choudhary, Anand R. Chandrasekaran, Jean-Marie Bussat, Rodrigo Alvarez-Icaza, John V. Arthur, Paul A. Merolla, Kwabena Boahen. Neurogrid: A Mixed-Analog-Digital Multichip System for Large-Scale Neural Simulations. Proceedings of the IEEE, 2014; 1 DOI: 10.1109/JPROC.2014.2313565