A scientist works on a blood test in the Ajay Goel Lab, one of many labs that are developing blood tests to screen for different types of cancer.
Soon it may be possible to find different types of cancer earlier than ever through a simple blood test.
Among the many blood tests in development, researchers announced in July that they have developed one that may screen for early-onset colorectal cancer. The new potential screening tool, detailed in a study in the journal Gastroenterology, represents a major step in noninvasively and inexpensively detecting nonhereditary colorectal cancer at an earlier and more treatable stage.
In recent years, this type of cancer has been on the upswing in adults under age 50 and in those without a family history. In 2021, the American Cancer Society's revised guidelines began recommending that colorectal cancer screenings with colonoscopy begin at age 45. But that still wouldn’t catch many early-onset cases among people in their 20s and 30s, says Ajay Goel, professor and chair of molecular diagnostics and experimental therapeutics at City of Hope, a Los Angeles-based nonprofit cancer research and treatment center that developed the new blood test.
“These people will mostly be missed because they will never be screened for it,” Goel says. Overall, colorectal cancer is the fourth most common malignancy, according to the U.S. Centers for Disease Control and Prevention.
Goel is far from the only one working on this. Dozens of companies are in the process of developing blood tests to screen for different types of malignancies.
Some estimates indicate that between one-fourth and one-third of all newly diagnosed colorectal cancers are early-onset. These patients generally present with more aggressive and advanced disease at diagnosis compared to late-onset colorectal cancer detected in people 50 years or older.
To develop his test, Goel examined publicly available datasets and figured out that changes in novel microRNAs, or miRNAs, which regulate the expression of genes, occurred in people with early-onset colorectal cancer. He confirmed these biomarkers by looking for them in the blood of 149 patients who had the early-onset form of the disease. In particular, Goel and his team of researchers were able to pick out four miRNAs that serve as a telltale sign of this cancer when they’re found in combination with each other.
The blood test is being validated by following another group of patients with early-onset colorectal cancer. “We have filed for intellectual property on this invention and are currently seeking biotech/pharma partners to license and commercialize this invention,” Goel says.
He’s far from the only one working on this. Dozens of companies are in the process of developing blood tests to screen for different types of malignancies, says Timothy Rebbeck, a professor of cancer prevention at the Harvard T.H. Chan School of Public Health and the Dana-Farber Cancer Institute. But, he adds, “It’s still very early, and the technology still needs a lot of work before it will revolutionize early detection.”
The accuracy of the early detection blood tests for cancer isn’t yet where researchers would like it to be. To use these tests widely in people without cancer, a very high degree of precision is needed, says David VanderWeele, interim director of the OncoSET Molecular Tumor Board at Northwestern University’s Lurie Cancer Center in Chicago.
Otherwise, “you’re going to cause a lot of anxiety unnecessarily if people have false-positive tests,” VanderWeele says. So far, “these tests are better at finding cancer when there’s a higher burden of cancer present,” although the goal is to detect cancer at the earliest stages. Even so, “we are making progress,” he adds.
While early detection is known to improve outcomes, most cancers are detected too late, often after they metastasize and people develop symptoms. Only five cancer types have recommended standard screenings, none of which involve blood tests—breast, cervical, colorectal, lung (smokers considered at risk) and prostate cancers, says Trish Rowland, vice president of corporate communications at GRAIL, a biotechnology company in Menlo Park, Calif., which developed a multi-cancer early detection blood test.
These recommended screenings check for individual cancers rather than looking for any form of cancer someone may have. The devil lies in the fact that cancers without widespread screening recommendations represent the vast majority of cancer diagnoses and most cancer deaths.
GRAIL’s Galleri multi-cancer early detection test is designed to find more cancers at earlier stages by analyzing DNA shed into the bloodstream by cells—with as few false positives as possible, she says. The test is currently available by prescription only for those with an elevated risk of cancer. Consumers can request it from their healthcare or telemedicine provider. “Galleri can detect a shared cancer signal across more than 50 types of cancers through a simple blood draw,” Rowland says, adding that it can be integrated into annual health checks and routine blood work.
Cancer patients—even those with early and curable disease—often have tumor cells circulating in their blood. “These tumor cells act as a biomarker and can be used for cancer detection and diagnosis,” says Andrew Wang, a radiation oncologist and professor at the University of Texas Southwestern Medical Center in Dallas. “Our research goal is to be able to detect these tumor cells to help with cancer management.” Collaborating with Seungpyo Hong, the Milton J. Henrichs Chair and Professor at the University of Wisconsin-Madison School of Pharmacy, “we have developed a highly sensitive assay to capture these circulating tumor cells.”
Even if the quality of a blood test is superior, finding cancer early doesn’t always mean it’s absolutely best to treat it. For example, prostate cancer treatment’s potential side effects—the inability to control urine or have sex—may be worse than living with a slow-growing tumor that is unlikely to be fatal. “[The test] needs to tell me, am I going to die of that cancer? And, if I intervene, will I live longer?” says John Marshall, chief of hematology and oncology at Medstar Georgetown University Hospital in Washington, D.C.
Ajay Goel Lab
A blood test developed at the University of Texas MD Anderson Cancer Center in Houston helps predict who may benefit from lung cancer screening when it is combined with a risk model based on an individual’s smoking history, according to a study published in January in the Journal of Clinical Oncology. The personalized lung cancer risk assessment was more sensitive and specific than the 2021 and 2013 U.S. Preventive Services Task Force criteria.
The study involved participants from the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial with a minimum of a 10 pack-year smoking history, meaning they smoked 20 cigarettes per day for ten years. If implemented, the blood test plus model would have found 9.2 percent more lung cancer cases for screening and decreased referral to screening among non-cases by 13.7 percent compared to the 2021 task force criteria, according to Oncology Times.
The conventional type of screening for lung cancer is an annual low-dose CT scan, but only a small percentage of people who are eligible will actually get these scans, says Sam Hanash, professor of clinical cancer prevention and director of MD Anderson’s Center for Global Cancer Early Detection. Such screening is not readily available in most countries.
In methodically searching for blood-based biomarkers for lung cancer screening, MD Anderson researchers developed a simple test consisting of four proteins. These proteins circulating in the blood were at high levels in individuals who had lung cancer or later developed it, Hanash says.
“The interest in blood tests for cancer early detection has skyrocketed in the past few years,” he notes, “due in part to advances in technology and a better understanding of cancer causation, cancer drivers and molecular changes that occur with cancer development.”
However, at the present time, none of the blood tests being considered eliminate the need for screening of eligible subjects using established methods, such as colonoscopy for colorectal cancer. Yet, Hanash says, “they have the potential to complement these modalities.”
Giving robots self-awareness as they move through space - and maybe even providing them with gene-like methods for storing rules of behavior - could be important steps toward creating more intelligent machines.
Now roboticists are going a step further, training their creations to do even better: understand their own image in space and interact with humans like humans do. Today, there are already robot-teachers like KeeKo, robot-pets like Moffin, robot-babysitters like iPal, and robotic companions for the elderly like Pepper.
But even these reasonably intelligent creations still have huge limitations, some scientists think. “There are niche applications for the current generations of robots,” says professor Anthony Zador at Cold Spring Harbor Laboratory—but they are not “generalists” who can do varied tasks all on their own, as they mostly lack the abilities to improvise, make decisions based on a multitude of facts or emotions, and adjust to rapidly changing circumstances. “We don’t have general purpose robots that can interact with the world. We’re ages away from that.”
Robotic spatial self-awareness – the achievement by the team at Columbia – is an important step toward creating more intelligent machines. Hod Lipson, professor of mechanical engineering who runs the Columbia lab, says that future robots will need this ability to assist humans better. Knowing how you look and where in space your parts are, decreases the need for human oversight. It also helps the robot to detect and compensate for damage and keep up with its own wear-and-tear. And it allows robots to realize when something is wrong with them or their parts. “We want our robots to learn and continue to grow their minds and bodies on their own,” Chen says. That’s what Zador wants too—and on a much grander level. “I want a robot who can drive my car, take my dog for a walk and have a conversation with me.”
Columbia scientists have trained a robot to become aware of its own "body," so it can map the right path to touch a ball without running into an obstacle, in this case a square.
Jane Nisselson and Yinuo Qin/ Columbia Engineering
Today’s technological advances are making some of these leaps of progress possible. One of them is the so-called Deep Learning—a method that trains artificial intelligence systems to learn and use information similar to how humans do it. Described as a machine learning method based on neural network architectures with multiple layers of processing units, Deep Learning has been used to successfully teach machines to recognize images, understand speech and even write text.
Trained by Google, one of these language machine learning geniuses, BERT, can finish sentences. Another one called GPT3, designed by San Francisco-based company OpenAI, can write little stories. Yet, both of them still make funny mistakes in their linguistic exercises that even a child wouldn’t. According to a paper published by Stanford’s Center for Research on Foundational Models, BERT seems to not understand the word “not.” When asked to fill in the word after “A robin is a __” it correctly answers “bird.” But try inserting the word “not” into that sentence (“A robin is not a __”) and BERT still completes it the same way. Similarly, in one of its stories, GPT3 wrote that if you mix a spoonful of grape juice into your cranberry juice and drink the concoction, you die. It seems that robots, and artificial intelligence systems in general, are still missing some rudimentary facts of life that humans and animals grasp naturally and effortlessly.
How does one give robots a genome? Zador has an idea. We can’t really equip machines with real biological nucleotide-based genes, but we can mimic the neuronal blueprint those genes create.
It's not exactly the robots’ fault. Compared to humans, and all other organisms that have been around for thousands or millions of years, robots are very new. They are missing out on eons of evolutionary data-building. Animals and humans are born with the ability to do certain things because they are pre-wired in them. Flies know how to fly, fish knows how to swim, cats know how to meow, and babies know how to cry. Yet, flies don’t really learn to fly, fish doesn’t learn to swim, cats don’t learn to meow, and babies don’t learn to cry—they are born able to execute such behaviors because they’re preprogrammed to do so. All that happens thanks to the millions of years of evolutions wired into their respective genomes, which give rise to the brain’s neural networks responsible for these behaviors. Robots are the newbies, missing out on that trove of information, Zador argues.
A neuroscience professor who studies how brain circuitry generates various behaviors, Zador has a different approach to developing the robotic mind. Until their creators figure out a way to imbue the bots with that information, robots will remain quite limited in their abilities. Each model will only be able to do certain things it was programmed to do, but it will never go above and beyond its original code. So Zador argues that we have to start giving robots a genome.
How does one do that? Zador has an idea. We can’t really equip machines with real biological nucleotide-based genes, but we can mimic the neuronal blueprint those genes create. Genomes lay out rules for brain development. Specifically, the genome encodes blueprints for wiring up our nervous system—the details of which neurons are connected, the strength of those connections and other specs that will later hold the information learned throughout life. “Our genomes serve as blueprints for building our nervous system and these blueprints give rise to a human brain, which contains about 100 billion neurons,” Zador says.
If you think what a genome is, he explains, it is essentially a very compact and compressed form of information storage. Conceptually, genomes are similar to CliffsNotes and other study guides. When students read these short summaries, they know about what happened in a book, without actually reading that book. And that’s how we should be designing the next generation of robots if we ever want them to act like humans, Zador says. “We should give them a set of behavioral CliffsNotes, which they can then unwrap into brain-like structures.” Robots that have such brain-like structures will acquire a set of basic rules to generate basic behaviors and use them to learn more complex ones.
Currently Zador is in the process of developing algorithms that function like simple rules that generate such behaviors. “My algorithms would write these CliffsNotes, outlining how to solve a particular problem,” he explains. “And then, the neural networks will use these CliffsNotes to figure out which ones are useful and use them in their behaviors.” That’s how all living beings operate. They use the pre-programmed info from their genetics to adapt to their changing environments and learn what’s necessary to survive and thrive in these settings.
For example, a robot’s neural network could draw from CliffsNotes with “genetic” instructions for how to be aware of its own body or learn to adjust its movements. And other, different sets of CliffsNotes may imbue it with the basics of physical safety or the fundamentals of speech.
At the moment, Zador is working on algorithms that are trying to mimic neuronal blueprints for very simple organisms—such as earthworms, which have only 302 neurons and about 7000 synapses compared to the millions we have. That’s how evolution worked, too—expanding the brains from simple creatures to more complex to the Homo Sapiens. But if it took millions of years to arrive at modern humans, how long would it take scientists to forge a robot with human intelligence? That’s a billion-dollar question. Yet, Zador is optimistic. “My hypotheses is that if you can build simple organisms that can interact with the world, then the higher level functions will not be nearly as challenging as they currently are.”