Scientists are studying cancer genomes to more precisely diagnose their patients' diseases - offering hope for targeted drug treatments.
Next, he recalls, he felt ushered into “the jaws of the medical system very quickly.” He spent a couple of days meeting with a team of doctors at Beth Israel Deaconess Medical Center in nearby Boston. One of them was from a medical field he hadn’t even known existed, a pulmonary interventionist, who would perform a biopsy on the mass in his lung.
“Knowing there was a medicine for my particular type of cancer was like a weight lifted off my shoulders."
A week later he and his wife Allison returned to meet with the oncologist, radiologist, pulmonary interventionist – his medical team. They confirmed his initial diagnosis: Stage 4 metastatic lung cancer that had spread to several parts of his body. “We just sat there, stunned,” he says. “I felt like I was getting hit by a wrecking ball over and over.”
An onslaught of medical terminology about what they had identified flowed over the shocked couple, but then the medical team switched gears, he recalls. They offered hope. “They told me, ‘Hey, you’re not a smoker, so that’s good,’” Reiner says. “‘There’s a good chance that what’s driving this disease for you is actually a genetic mutation, and we have ways to understand more about what that could be through some simple testing.’”
They told him about Foundation Medicine, a company launched in neighboring Cambridge, MA, in 2009 that develops, manufactures, and sells genomic profiling assays. These are tests that, according to the company’s website, “can analyze a broad panel of genes to detect the four main classes of genomic alterations known to drive cancer growth.” With these insights, certain patients can be matched with therapies targeted specifically for the genetic driver(s) of their cancer. The company maintains one of the largest cancer genomic databases in the world, with more than 500,000 patient samples profiled, and they have more than 65 biopharma partners.
According to Foundation Medicine, they are the only company that has FDA-approved tests for both tissue- and blood-based comprehensive genomic profiling tests. One other company has an FDA-approved biopsy test, and several other companies offer tissue-based genomic profiling. Additionally, several major cancer centers like Memorial Sloan Kettering in New York and Anderson Cancer Center in Texas have their own such testing platforms.
Currently, genomic profiling is more accessible for patients with advanced cancer, due to broader insurance coverage in later stages of disease.
“Right now, the vast majority of patients either have cancers for which we don’t have treatments or they have genetic alterations that are not known,” says Jorge Garcia, MD, Division Chief, Solid Tumor Oncology, UH Cleveland Medical Center, which has its own CGP testing platform. “However, a significant proportion of patients with advanced cancer have alterations that we can tap for therapeutic purposes.”
Foundation Medicine estimates that in 2017, just over 5 percent of advanced solid cancer patients in the U.S. received CGP testing. In 2021, they estimate that number is between 25 to 30 percent of advanced solid cancer patients in the U.S., which doesn’t include patients who are tested with small (less than 50 genes) panels. Their panel tests for more than 300 cancer-related genes.
“The good news is the platforms we are developing are better and more comprehensive, and they’re going to continue to be larger data sets,” Dr. Garcia adds.
In Reiner’s case, his team ordered comprehensive genetic profiling on both his tissue and blood, from Foundation Medicine.
At this point, Reiner still wasn’t sure what genetic mutations were or how they factored into cancer or what comprehensive genomic profiling entailed. That day, though, his team ushered the Reiners into the world of precision oncology that placed him on much more sure footing to learn about and fight the specific lung cancer that had been troubling him for more than a year.
What genetic alterations were driving his cancer? Foundation Medicine’s tests were about to find out.
At the core of these tests is next generation sequencing, a DNA sequencing technology. Since 2009, this has revolutionized genomic research, according to the National Center for Biotechnology Information, because it allows an entire human genome to be sequenced within one day. Cancer genomics posits that cancer is caused by mutations and is a disease of the genome. Now, cancer genomes can be systemically studied in their entirety. For cancer patients such as Reiner, NGS can provide a more precise diagnosis and classification of the disease, more accurate prognosis, and potentially the identification of targeted drug treatments. Ultimately, the technology can provide the basis of personalized cancer management.
The detailed reports supply patients and their oncologists with extensive information about the patient’s genomic profile and potential treatment options that they can discuss together. Reiner trusted his doctors that this approach was worth the two- or three-week wait to receive the Foundation Medicine report and the specifically targeted treatment, rather than immediately jump into a round of chemotherapy. He is especially grateful now, he says, because the report delivered a great deal of relief from his previously exhausting and growing anxiety about having cancer.
Reiner and his team learned his lung cancer contained the epidermal growth factor receptor (EGFR) mutation. That biomarker enabled his oncologist to prescribe Tagrisso (osimertinib), a medication developed to directly target that genetic mutation.
“Knowing there was a medicine for my particular type of cancer was like a weight lifted off my shoulders,” he says. “It only took a week or two before my cough finally started subsiding. This pill goes right after the particular piece of genetic material in the tumor that’s causing its growth.”
Dr. Jerry Mitchell, director field medical oncology, Foundation Medicine, in Columbus, Ohio, explains that genomic profiling is generating substantial impacts today. “This is a technology that is the standard of care across many advanced malignancies that takes patients from chemotherapy-only options to very targeted options or immunotherapy options,” he says. “You can also look at complex biomarkers, and these are not specific genetic changes but different genes across the tumor to get a biomarker.”
According to Dr. Mitchell, Foundation Medicine’s technology can test more than 324 different cancer-related genes in a single test. Thus, a growing number of patients are benefitting from comprehensive genetic profiling, due to the rapidly growing number of targeted therapies. While not all of the cancers are treatable yet, the company uses that information to partner with researchers to find new potential therapies for patient groups that may have rare mutations.
Since his tumor’s diagnosis, Reiner has undergone chemotherapy and a couple surgeries to treat the metastatic cancer in other parts of his body, but the drug Tagrisso has significantly reduced his lung tumor. Now, having learned so much during the past couple of years, he is grateful for precision oncology. He still reflects on the probability that, had the Tagrisso pill not been available in May 2019, he might have only survived for another six months or a year.
“Comprehensive Genomic Profiling is not some future state, but in both the U.S. and Europe, it is a very standard, accepted, and recommended first step to knowing how to treat your cancer,” says Dr. Mitchell, adding that he feels fortunate to be an oncologist in this era. “However, we know there are still people not getting this recommended testing, so we still have opportunities to find many more patients and impact them by knowing the molecular profile of their cancer.”
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.
