A simple ten-minute universal cancer test that can be detected by the human eye or an electronic device - published in Nature Communications (Dec 2018) by the Trau lab at the University of Queensland. Red indicates the presence of cancerous cells and blue doesn't.
Matt Trau, a professor of chemistry at the University of Queensland, stunned the science world back in December when the prestigious journal Nature Communications published his lab's discovery about a unique property of cancer DNA that could lead to a simple, cheap, and accurate test to detect any type of cancer in under 10 minutes.
No one believed it. I didn't believe it. I thought, "Gosh, okay, maybe it's a fluke."
Trau granted very few interviews in the wake of the news, but he recently opened up to leapsmag about the significance of this promising early research. Here is his story in his own words, as told to Editor-in-Chief Kira Peikoff.
There's been an incredible explosion of knowledge over the past 20 years, particularly since the genome was sequenced. The area of diagnostics has a tremendous amount of promise and has caught our lab's interest. If you catch cancer early, you can improve survival rates to as high as 98 percent, sometimes even now surpassing that.
My lab is interested in devices to improve the trajectory of cancer patients. So, once people get diagnosed, can we get really sophisticated information about the molecular origins of the disease, and can we measure it in real time? And then can we match that with the best treatment and monitor it in real time, too?
I think those approaches, also coupled with immunotherapy, where one dreams of monitoring the immune system simultaneously with the disease progress, will be the future.
But currently, the methodologies for cancer are still pretty old. So, for example, let's talk about biopsies in general. Liquid biopsy just means using a blood test or a urine test, rather than extracting out a piece of solid tissue. Now consider breast cancer. Still, the cutting-edge screening method is mammography or the physical interrogation for lumps. This has had a big impact in terms of early detection and awareness, but it's still primitive compared to interrogating, forensically, blood samples to look at traces of DNA.
Large machines like CAT scans, PET scans, MRIs, are very expensive and very subjective in terms of the operator. They don't look at the root causes of the cancer. Cancer is caused by changes in DNA. These can be changes in the hard drive of the DNA (the genomic changes) or changes in the apps that the DNA are running (the epigenetics and the transcriptomics).
We don't look at that now, even though we have, emerging, all of these technologies to do it, and those technologies are getting so much cheaper. I saw some statistics at a conference just a few months ago that, in the United States, less than 1 percent of cancer patients have their DNA interrogated. That's the current state-of-the-art in the modern medical system.
Professor Matt Trau, a cancer researcher at the University of Queensland in Australia.
(Courtesy)
Blood, as the highway of the body, is carrying all of this information. Cancer cells, if they are present in the body, are constantly getting turned over. When they die, they release their contents into the blood. Many of these cells end up in the urine and saliva. Having technologies that can forensically scan the highways looking for evidence of cancer is little bit like looking for explosives at the airport. That's very valuable as a security tool.
The trouble is that there are thousands of different types of cancer. Going back to breast cancer, there's at least a dozen different types, probably more, and each of them change the DNA (the hard drive of the disease) and the epigenetics (or the RAM memory). So one of the problems for diagnostics in cancer is to find something that is a signature of all cancers. That's been a really, really, really difficult problem.
Ours was a completely serendipitous discovery. What we found in the lab was this one marker that just kept coming up in all of the types of breast cancers we were studying.
No one believed it. I didn't believe it. I thought, "Gosh, okay, maybe it's a fluke, maybe it works just for breast cancer." So we went on to test it in prostate cancer, which is also many different types of diseases, and it seemed to be working in all of those. We then tested it further in lymphoma. Again, many different types of lymphoma. It worked across all of those. We tested it in gastrointestinal cancer. Again, many different types, and still, it worked, but we were skeptical.
Then we looked at cell lines, which are cells that have come from previous cancer patients, that we grow in the lab, but are used as model experimental systems. We have many of those cell lines, both ones that are cancerous, and ones that are healthy. It was quite remarkable that the marker worked in all of the cancer cell lines and didn't work in the healthy cell lines.
What could possibly be going on?
Well, imagine DNA as a piece of string, that's your hard drive. Epigenetics is like the beads that you put on that string. Those beads you can take on and off as you wish and they control which apps are run, meaning which genetic programs the cell runs. We hypothesized that for cancer, those beads cluster together, rather than being randomly distributed across the string.
Ultimately, I see this as something that would be like a pregnancy test you could take at your doctor's office.
The implications of this are profound. It means that DNA from cancer folds in water into three-dimensional structures that are very different from healthy cells' DNA. It's quite literally the needle in a haystack. Because when you do a liquid biopsy for early detection of cancer, most of the DNA from blood contains a vast abundance of healthy DNA. And that's not of interest. What's of interest is to find the cancerous DNA. That's there only in trace.
Once we figured out what was going on, we could easily set up a system to detect the trace cancerous DNA. It binds to gold nanoparticles in water and changes color. The test takes 10 minutes, and you can detect it by eye. Red indicates cancer and blue doesn't.
We're very, very excited about where we go from here. We're starting to test the test on a greater number of cancers, in thousands of patient samples. We're looking to the scientific community to engage with us, and we're getting a really good response from groups around the world who are supplying more samples to us so we can test this more broadly.
We also are very interested in testing how early can we go with this test. Can we detect cancer through a simple blood test even before there are any symptoms whatsoever? If so, we might be able to convert a cancer diagnosis to something almost as good as a vaccine.
Of course, we have to watch what are called false positives. We don't want to be detecting people as positives when they don't have cancer, and so the technology needs to improve there. We see this version as the iPhone 1. We're interested in the iPhone 2, 3, 4, getting better and better.
Ultimately, I see this as something that would be like a pregnancy test you could take at your doctor's office. If it came back positive, your doctor could say, "Look, there's some news here, but actually, it's not bad news, it's good news. We've caught this so early that we will be able to manage this, and this won't be a problem for you."
If this were to be in routine use in the medical system, countless lives could be saved. Cancer is now becoming one of the biggest killers in the world. We're talking millions upon millions upon millions of people who are affected. This really motivates our work. We might make a difference there.
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.
