A retrovirus illustration.
Even with groundbreaking advances in cancer treatment and research over the past two centuries, the problem remains that some cancer does not respond to treatment. A subset of patients experience recurrence or metastasis, even when the original tumor is detected at an early stage.
"Why do some tumors evolve into metastatic disease that is then capable of spreading, while other tumors do not?"
Moreover, doctors are not able to tell in advance which patients will respond to treatment and which will not. This means that many patients endure conventional cancer therapies, like countless rounds of chemo and radiation, that do not ultimately increase their likelihood of survival.
Researchers are beginning to understand why some tumors respond to treatment and others do not. The answer appears to lie in the strange connection between human life at its earliest stages — and retroviruses. A retrovirus is different than a regular virus in that its RNA is reverse-transcribed into DNA, which makes it possible for its genetic material to be integrated into a host's genome, and passed on to subsequent generations.
Researchers have shown that reactivation of retroviral sequences is associated with the survival of developing embryos. Certain retroviral sequences must be expressed around the 8-cell stage for successful embryonic development. Active expression of retroviral sequences is required for proper functioning of human embryonic stem cells. These sequences must then shut down at the later state, or the embryo will fail to develop. And here's where things get really interesting: If specific stem cell-associated retroviral sequences become activated again later in life, they seem to play a role in some cancers becoming lethal.
"Eight to 10 million years ago, at the time when we became primates, the population was infected with a virus."
While some retroviral sequences in our genome contribute to the restriction of viral infection and appear to have contributed to the development of the placenta, they can also, if expressed at the wrong time, drive the development of cancer stem cells. Described as the "beating hearts" of treatment-resistant tumors, cancer stem cells are robust and long-living, and they can maintain the ability to proliferate indefinitely.
This apparent connection has inspired Gennadi V. Glinsky, a research scientist at the Institute of Engineering in Medicine at UC San Diego, to find better ways to diagnose and treat metastatic cancer. Glinsky specializes in the development of new technologies, methods, and system integration approaches for personalized genomics-guided prevention and precision therapy of cancer and other common human disorders. We spoke with him about his work and the exciting possibilities it may open up for cancer patients. This interview has been edited and condensed for clarity.
What key questions have driven your research in this area?
I was thinking for years that the major mysteries are: Why do some tumors evolve into metastatic disease that is then capable of spreading, while other tumors do not? What explains some cancer cells' ability to get into the blood or lymph nodes and be able to survive in this very foreign, hostile environment of circulatory channels, and then be able to escape and take root elsewhere in the body?
"If you detect conventional cancer early, and treat it early, it will be cured. But with cancer involving stem cells, even if you diagnose it early, it will come back."
When we were able to do genomic analysis on enough early stage cancers, we arrived at an alternative concept of cancer that starts in the stem cells. Stem cells exist throughout our bodies, so in the case of cancer starting in stem cells you will have metastatic properties … because that's what stem cells do. They can travel throughout the body, they can make any other type of cell or resemble them.
So there are basically two types of cancer: conventional non-stem cell cancer and stem cell-like cancer. If you detect conventional cancer early, and treat it early, it will be cured. But with cancer involving stem cells, even if you diagnose it early, it will come back.
What causes some cancer to originate in stem cells?
Cancer stem cells possess stemness [or the ability to self-renew, differentiate, and survive chemical and physical insults]. Stemness is driven by the reactivation of retroviral sequences that have been integrated into the human genome.
Tell me about these retroviral sequences.
Eight to 10 million years ago, at the time when we became primates, the population was infected with a virus. Part of the population survived and the virus was integrated into our primate ancestors' genome. These are known as human endogenous retroviruses, or HERVs. The DNA of the host cells became carriers of these retroviral sequences, and whenever the host cells multiply, they carry the sequences in them and pass them on to future generations.
This pattern of infection and integration of retroviral sequences has happened thousands of times during our evolutionary history. As a result, eight percent of the human genome is derived from these different retroviral sequences.
We've found that some HERVs are expressed in some cancers. For example, 10-15 percent of prostate cancer is stem cell-like. But at first it was not understood what this HERV expression meant.
Gennadi V. Glinsky, a research scientist at the Institute of Engineering in Medicine at UC San Diego.
(Courtesy)
How have you endeavored to solve this in your lab?
We were trying to track down metastatic prostate cancer. We found a molecular signature of prostate cancer that made the prostate tumors look like stem cells. And those were the ones likely to fail cancer therapy. Then we applied this signature to other types of cancers and we found that uniformly, tumors that exhibit stemness fail therapy.
Then in 2014, several breakthrough papers came out that linked the activation of the retroviral sequences in human embryonic stem cells and in human embryo development. When I read these papers, it occurred to me that if these retroviral sequences are required for pluripotency in human embryonic stem cells, they must be involved in stem cell-resembling human cancer that's likely to fail therapy.
What was one of the biggest aha moments in your cancer research?
Several major labs around the U.S. took advantage of The Cancer Genome Anatomy Project, which made it possible to have access to about 12,000 individual human tumors across a spectrum of 30 or so cancer types. This is the largest set of tumors that's ever been made available in a comprehensive and state of the art way. So we now know all there is to know about the genetics of these tumors, including the long-term clinical outcome.
"When we cross-referenced these 10,713 human cancer survival genes to see how many are part of the retroviral network in human cells, we found that the answer was 97 percent!"
These labs identified 10,713 human genes that were associated with the likelihood of patients surviving or dying after [cancer] treatment. I call them the human cancer survival genes, and there are two classes of them: one whose high expression in tumors correlates with an increased likelihood of survival and one whose high expression in tumors correlates with a decreased likelihood of survival.
When we cross-referenced these 10,713 human cancer survival genes to see how many are part of the retroviral network in human cells, we found that the answer was 97 percent!
How will all of this new knowledge change how cancer is treated?
To make cancer stem cells vulnerable to treatment, you need to interfere with stemness and the stemness network. And to do this, you would need to identify the retroviral component of the network, and interfere with this component therapeutically.
The real breakthrough will come when we start to treat these early stage stem cell-like cancers with stem cell-targeting therapy that we are trying to develop. And with our ability to detect the retroviral genome activation, we will be able to detect stem cell-like cancer very early on.
How far away are we from being able to apply this information clinically?
We have two molecule [treatment] candidates. We know that they efficiently interfere with the stemness program in the cells. The road to clinical trials is typically a long one, but since we're clear about our targets, it's a shorter road. We would like to say it's two to three years until we can start a human trial.
Artificial Intelligence is already helping to grow some of the food we eat.
The chef-farmers choose from among 45 types of herb and leafy-greens seeds, plop them into grow trays, and a few weeks later they pick and serve. While success is predicated on at least a small amount of these humans’ care, the systems are autonomously surveilled round-the-clock from Babylon’s base of operations. And artificial intelligence is helping to run the show.
Babylon piloted the use of specialized cameras that take pictures in different spectrums to gather some less-obvious visual data about plants’ wellbeing and alert people if something seems off.
Imagine consistently perfect greens and tomatoes and strawberries, grown hyper-locally, using less water, without chemicals or environmental contaminants. This is the hefty promise of controlled environment agriculture (CEA) — basically, indoor farms that can be hydroponic, aeroponic (plant roots are suspended and fed through misting), or aquaponic (where fish play a role in fertilizing vegetables). But whether they grow 4,160 leafy-green servings per year, like one Babylon farm, or millions of servings, like some of the large, centralized facilities starting to supply supermarkets across the U.S., they seek to minimize failure as much as possible.
Babylon’s soilless hydroponic growing system
Courtesy Babylon Micro-Farms
Here, AI is starting to play a pivotal role. CEA growers use it to help “make sense of what’s happening” to the plants in their care, says Scott Lowman, vice president of applied research at the Institute for Advanced Learning and Research (IALR) in Virginia, a state that’s investing heavily in CEA companies. And although these companies say they’re not aiming for a future with zero human employees, AI is certainly poised to take a lot of human farming intervention out of the equation — for better and worse.
Most of these companies are compiling their own data sets to identify anything that might block the success of their systems. Babylon had already integrated sensor data into its farms to measure heat and humidity, the nutrient content of water, and the amount of light plants receive. Last year, they got a National Science Foundation grant that allowed them to pilot the use of specialized cameras that take pictures in different spectrums to gather some less-obvious visual data about plants’ wellbeing and alert people if something seems off. “Will this plant be healthy tomorrow? Are there things…that the human eye can't see that the plant starts expressing?” says Amandeep Ratte, the company’s head of data science. “If our system can say, Hey, this plant is unhealthy, we can reach out to [users] preemptively about what they’re doing wrong, or is there a disease at the farm?” Ratte says. The earlier the better, to avoid crop failures.
Natural light accounts for 70 percent of Greenswell Growers’ energy use on a sunny day.
Courtesy Greenswell Growers
IALR’s Lowman says that other CEA companies are developing their AI systems to account for the different crops they grow — lettuces come in all shapes and sizes, after all, and each has different growing needs than, for example, tomatoes. The ways they run their operations differs also. Babylon is unusual in its decentralized structure. But centralized growing systems with one main location have variabilities, too. AeroFarms, which recently declared bankruptcy but will continue to run its 140,000-square foot vertical operation in Danville, Virginia, is entirely enclosed and reliant on the intense violet glow of grow lights to produce microgreens.
Different companies have different data needs. What data is essential to AeroFarms isn’t quite the same as for Greenswell Growers located in Goochland County, Virginia. Raising four kinds of lettuce in a 77,000-square-foot automated hydroponic greenhouse, the vagaries of naturally available light, which accounts for 70 percent of Greenswell’s energy use on a sunny day, affect operations. Their tech needs to account for “outside weather impacts,” says president Carl Gupton. “What adjustments do we have to make inside of the greenhouse to offset what's going on outside environmentally, to give that plant optimal conditions? When it's 85 percent humidity outside, the system needs to do X, Y and Z to get the conditions that we want inside.”
AI will help identify diseases, as well as when a plant is thirsty or overly hydrated, when it needs more or less calcium, phosphorous, nitrogen.
Nevertheless, every CEA system has the same core needs — consistent yield of high quality crops to keep up year-round supply to customers. Additionally, “Everybody’s got the same set of problems,” Gupton says. Pests may come into a facility with seeds. A disease called pythium, one of the most common in CEA, can damage plant roots. “Then you have root disease pressures that can also come internally — a change in [growing] substrate can change the way the plant performs,” Gupton says.
AI will help identify diseases, as well as when a plant is thirsty or overly hydrated, when it needs more or less calcium, phosphorous, nitrogen. So, while companies amass their own hyper-specific data sets, Lowman foresees a time within the next decade “when there will be some type of [open-source] database that has the most common types of plant stress identified” that growers will be able to tap into. Such databases will “create a community and move the science forward,” says Lowman.
In fact, IALR is working on assembling images for just such a database now. On so-called “smart tables” inside an Institute lab, a team is growing greens and subjects them to various stressors. Then, they’re administering treatments while taking images of every plant every 15 minutes, says Lowman. Some experiments generate 80,000 images; the challenge lies in analyzing and annotating the vast trove of them, marking each one to reflect outcome—for example increasing the phosphate delivery and the plant’s response to it. Eventually, they’ll be fed into AI systems to help them learn.
For all the enthusiasm surrounding this technology, it’s not without downsides. Training just one AI system can emit over 250,000 pounds of carbon dioxide, according to MIT Technology Review. AI could also be used “to enhance environmental benefit for CEA and optimize [its] energy consumption,” says Rozita Dara, a computer science professor at the University of Guelph in Canada, specializing in AI and data governance, “but we first need to collect data to measure [it].”
The chef-farmers can choose from 45 types of herb and leafy-greens seeds.
Courtesy Babylon Micro-Farms
Any system connected to the Internet of Things is also vulnerable to hacking; if CEA grows to the point where “there are many of these similar farms, and you're depending on feeding a population based on those, it would be quite scary,” Dara says. And there are privacy concerns, too, in systems where imaging is happening constantly. It’s partly for this reason, says Babylon’s Ratte, that the company’s in-farm cameras all “face down into the trays, so the only thing [visible] is pictures of plants.”
Tweaks to improve AI for CEA are happening all the time. Greenswell made its first harvest in 2022 and now has annual data points they can use to start making more intelligent choices about how to feed, water, and supply light to plants, says Gupton. Ratte says he’s confident Babylon’s system can already “get our customers reliable harvests. But in terms of how far we have to go, it's a different problem,” he says. For example, if AI could detect whether the farm is mostly empty—meaning the farm’s user hasn’t planted a new crop of greens—it can alert Babylon to check “what's going on with engagement with this user?” Ratte says. “Do they need more training? Did the main person responsible for the farm quit?”
Lowman says more automation is coming, offering greater ability for systems to identify problems and mitigate them on the spot. “We still have to develop datasets that are specific, so you can have a very clear control plan, [because] artificial intelligence is only as smart as what we tell it, and in plant science, there's so much variation,” he says. He believes AI’s next level will be “looking at those first early days of plant growth: when the seed germinates, how fast it germinates, what it looks like when it germinates.” Imaging all that and pairing it with AI, “can be a really powerful tool, for sure.”
Researchers from the University of Cambridge have laid the foundations for growing synthetic embryos that could develop a beating heart, gut and brain.
The organoid revolution
In 1981, scientists discovered how to keep stem cells alive. This was a significant breakthrough, as stem cells have notoriously rigorous demands. Nevertheless, stem cells remained a relatively niche research area, mainly because scientists didn’t know how to convince the cells to turn into other cells.
Then, in 1987, scientists embedded isolated stem cells in a gelatinous protein mixture called Matrigel, which simulated the three-dimensional environment of animal tissue. The cells thrived, but they also did something remarkable: they created breast tissue capable of producing milk proteins. This was the first organoid — a clump of cells that behave and function like a real organ. The organoid revolution had begun, and it all started with a boob in Jello.
For the next 20 years, it was rare to find a scientist who identified as an “organoid researcher,” but there were many “stem cell researchers” who wanted to figure out how to turn stem cells into other cells. Eventually, they discovered the signals (called growth factors) that stem cells require to differentiate into other types of cells.
For a human embryo (and its organs) to develop successfully, there needs to be a “dialogue” between these three types of stem cells.
By the end of the 2000s, researchers began combining stem cells, Matrigel, and the newly characterized growth factors to create dozens of organoids, from liver organoids capable of producing the bile salts necessary for digesting fat to brain organoids with components that resemble eyes, the spinal cord, and arguably, the beginnings of sentience.
Synthetic embryos
Organoids possess an intrinsic flaw: they are organ-like. They share some characteristics with real organs, making them powerful tools for research. However, no one has found a way to create an organoid with all the characteristics and functions of a real organ. But Magdalena Żernicka-Goetz, a developmental biologist, might have set the foundation for that discovery.
Żernicka-Goetz hypothesized that organoids fail to develop into fully functional organs because organs develop as a collective. Organoid research often uses embryonic stem cells, which are the cells from which the developing organism is created. However, there are two other types of stem cells in an early embryo: stem cells that become the placenta and those that become the yolk sac (where the embryo grows and gets its nutrients in early development). For a human embryo (and its organs) to develop successfully, there needs to be a “dialogue” between these three types of stem cells. In other words, Żernicka-Goetz suspected the best way to grow a functional organoid was to produce a synthetic embryoid.
As described in the aforementioned Nature paper, Żernicka-Goetz and her team mimicked the embryonic environment by mixing these three types of stem cells from mice. Amazingly, the stem cells self-organized into structures and progressed through the successive developmental stages until they had beating hearts and the foundations of the brain.
“Our mouse embryo model not only develops a brain, but also a beating heart [and] all the components that go on to make up the body,” said Żernicka-Goetz. “It’s just unbelievable that we’ve got this far. This has been the dream of our community for years and major focus of our work for a decade and finally we’ve done it.”
If the methods developed by Żernicka-Goetz’s team are successful with human stem cells, scientists someday could use them to guide the development of synthetic organs for patients awaiting transplants. It also opens the door to studying how embryos develop during pregnancy.
This article originally appeared on Big Think, home of the brightest minds and biggest ideas of all time.
