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.”
Personalized nutrition apps could provide valuable data to people trying to eat healthier, though more research must be done to show effectiveness.
“We discovered that different foods spike people differently,” he says. “Some people spike to pasta, others to bread, others to bananas, and so on. It’s very personalized and our feeling was that building programs around these devices could be extremely powerful for better managing people’s glucose.”
Unbeknown to Snyder at the time, thousands of miles away, a group of Israeli scientists at the Weizmann Institute of Science were doing exactly the same experiments. In 2015, they published a landmark paper which used CGM to track the blood sugar levels of 800 people over several days, showing that the biological response to identical foods can vary wildly. Like Snyder, they theorized that giving people a greater understanding of their own glucose responses, so they spend more time in the normal range, may reduce the prevalence of type 2 diabetes.
The commercial potential of such apps is clear, but the underlying science continues to generate intriguing findings.
“At the moment 33 percent of the U.S. population is pre-diabetic, and 70 percent of those pre-diabetics will become diabetic,” says Snyder. “Those numbers are going up, so it’s pretty clear we need to do something about it.”
Fast forward to 2022,and both teams have converted their ideas into subscription-based dietary apps which use artificial intelligence to offer data-informed nutritional and lifestyle recommendations. Snyder’s spinoff, January AI, combines CGM information with heart rate, sleep, and activity data to advise on foods to avoid and the best times to exercise. DayTwo–a start-up which utilizes the findings of Weizmann Institute of Science–obtains microbiome information by sequencing stool samples, and combines this with blood glucose data to rate ‘good’ and ‘bad’ foods for a particular person.
“CGMs can be used to devise personalized diets,” says Eran Elinav, an immunology professor and microbiota researcher at the Weizmann Institute of Science in addition to serving as a scientific consultant for DayTwo. “However, this process can be cumbersome. Therefore, in our lab we created an algorithm, based on data acquired from a big cohort of people, which can accurately predict post-meal glucose responses on a personal basis.”
The commercial potential of such apps is clear. DayTwo, who market their product to corporate employers and health insurers rather than individual consumers, recently raised $37 million in funding. But the underlying science continues to generate intriguing findings.
Last year, Elinav and colleagues published a study on 225 individuals with pre-diabetes which found that they achieved better blood sugar control when they followed a personalized diet based on DayTwo’s recommendations, compared to a Mediterranean diet. The journal Cell just released a new paper from Snyder’s group which shows that different types of fibre benefit people in different ways.
“The idea is you hear different fibres are good for you,” says Snyder. “But if you look at fibres they’re all over the map—it’s like saying all animals are the same. The responses are very individual. For a lot of people [a type of fibre called] arabinoxylan clearly reduced cholesterol while the fibre inulin had no effect. But in some people, it was the complete opposite.”
Eight years ago, Stanford's Michael Snyder began studying how continuous glucose monitors could be used by patients to gain real-time updates on their blood sugar levels, transmitted directly to their phone.
The Snyder Lab, Stanford Medicine
Because of studies like these, interest in precision nutrition approaches has exploded in recent years. In January, the National Institutes of Health announced that they are spending $170 million on a five year, multi-center initiative which aims to develop algorithms based on a whole range of data sources from blood sugar to sleep, exercise, stress, microbiome and even genomic information which can help predict which diets are most suitable for a particular individual.
“There's so many different factors which influence what you put into your mouth but also what happens to different types of nutrients and how that ultimately affects your health, which means you can’t have a one-size-fits-all set of nutritional guidelines for everyone,” says Bruce Y. Lee, professor of health policy and management at the City University of New York Graduate School of Public Health.
With the falling costs of genomic sequencing, other precision nutrition clinical trials are choosing to look at whether our genomes alone can yield key information about what our diets should look like, an emerging field of research known as nutrigenomics.
The ASPIRE-DNA clinical trial at Imperial College London is aiming to see whether particular genetic variants can be used to classify individuals into two groups, those who are more glucose sensitive to fat and those who are more sensitive to carbohydrates. By following a tailored diet based on these sensitivities, the trial aims to see whether it can prevent people with pre-diabetes from developing the disease.
But while much hope is riding on these trials, even precision nutrition advocates caution that the field remains in the very earliest of stages. Lars-Oliver Klotz, professor of nutrigenomics at Friedrich-Schiller-University in Jena, Germany, says that while the overall goal is to identify means of avoiding nutrition-related diseases, genomic data alone is unlikely to be sufficient to prevent obesity and type 2 diabetes.
“Genome data is rather simple to acquire these days as sequencing techniques have dramatically advanced in recent years,” he says. “However, the predictive value of just genome sequencing is too low in the case of obesity and prediabetes.”
Others say that while genomic data can yield useful information in terms of how different people metabolize different types of fat and specific nutrients such as B vitamins, there is a need for more research before it can be utilized in an algorithm for making dietary recommendations.
“I think it’s a little early,” says Eileen Gibney, a professor at University College Dublin. “We’ve identified a limited number of gene-nutrient interactions so far, but we need more randomized control trials of people with different genetic profiles on the same diet, to see whether they respond differently, and if that can be explained by their genetic differences.”
Some start-ups have already come unstuck for promising too much, or pushing recommendations which are not based on scientifically rigorous trials. The world of precision nutrition apps was dubbed a ‘Wild West’ by some commentators after the founders of uBiome – a start-up which offered nutritional recommendations based on information obtained from sequencing stool samples –were charged with fraud last year. The weight-loss app Noom, which was valued at $3.7 billion in May 2021, has been criticized on Twitter by a number of users who claimed that its recommendations have led to them developed eating disorders.
With precision nutrition apps marketing their technology at healthy individuals, question marks have also been raised about the value which can be gained through non-diabetics monitoring their blood sugar through CGM. While some small studies have found that wearing a CGM can make overweight or obese individuals more motivated to exercise, there is still a lack of conclusive evidence showing that this translates to improved health.
However, independent researchers remain intrigued by the technology, and say that the wealth of data generated through such apps could be used to help further stratify the different types of people who become at risk of developing type 2 diabetes.
“CGM not only enables a longer sampling time for capturing glucose levels, but will also capture lifestyle factors,” says Robert Wagner, a diabetes researcher at University Hospital Düsseldorf. “It is probable that it can be used to identify many clusters of prediabetic metabolism and predict the risk of diabetes and its complications, but maybe also specific cardiometabolic risk constellations. However, we still don’t know which forms of diabetes can be prevented by such approaches and how feasible and long-lasting such self-feedback dietary modifications are.”
Snyder himself has now been wearing a CGM for eight years, and he credits the insights it provides with helping him to manage his own diabetes. “My CGM still gives me novel insights into what foods and behaviors affect my glucose levels,” he says.
He is now looking to run clinical trials with his group at Stanford to see whether following a precision nutrition approach based on CGM and microbiome data, combined with other health information, can be used to reverse signs of pre-diabetes. If it proves successful, January AI may look to incorporate microbiome data in future.
“Ultimately, what I want to do is be able take people’s poop samples, maybe a blood draw, and say, ‘Alright, based on these parameters, this is what I think is going to spike you,’ and then have a CGM to test that out,” he says. “Getting very predictive about this, so right from the get go, you can have people better manage their health and then use the glucose monitor to help follow that.”