For people with cancer, early detection can mean the difference between life and death. Detecting cancer in its earliest stages — before it has grown aggressively or spread to lymph nodes or other parts of the body — gives people the best chance of survival. The more advanced the cancer when it’s diagnosed, the lower the odds it can be conquered with surgery or therapies.

While scientists are constantly seeking better treatments for cancer, the real holy grail among oncology researchers has been developing an early-warning system. Routine screenings such as colonoscopies, Pap smears, or mammograms have been highly effective in detecting certain cancers earlier than if people waited until they had noticeable symptoms. But even these tools can’t spot the earliest signs of cancer. Also, they’re not available for every type of cancer.

In the genomics field, scientists have begun focusing on telltale cancer markers found in the blood. For example, tiny DNA fragments known as ctDNA are shed by tumors and circulate in the bloodstream. Many companies and research institutions have now developed tests to detect these fragments from a simple blood draw. They are used primarily to monitor existing cancers — offering important signals about how a tumor is responding to treatment or whether tumors have specific genetic variants that make them good targets for certain therapies — but they can also pick up the presence of cancer in people who have not yet been diagnosed.

Unfortunately, ctDNA fragments generally cannot be used to locate the source of the cancer; since they travel in the bloodstream, they could have originated almost anywhere. But now scientists are exploring another layer of information carried by these DNA fragments. Results from a new study indicate this may be the missing puzzle piece that finally allows for early cancer detection with the ability to map its origin.

In our bodies, DNA is the blueprint that guides everything — from building our organs to sending out a key hormone at a critical moment. But our DNA carries a second layer of code on top of it, known as methylation. This code regulates which parts of our DNA get turned into active marching orders by RNA and proteins at any given time, ensuring that our complex systems get only the information they need when they need it.

At the recent annual meeting of the American Society of Human Genetics, Nicholas Cheng from the Ontario Institute for Cancer Research presented data from a study that aimed to detect cancer by mining methylation data from DNA fragments found in blood samples. He and his team used more than 300 blood samples collected between 2009 and 2017 as part of Canadian Partnership for Tomorrow’s Health Project, a large-scale health project that follows hundreds of thousands of participants over several years.

The team focused on people who were healthy at enrollment but were later diagnosed with breast, prostate, or pancreatic cancer at some point during the study. They used blood samples collected prior to diagnosis — in some cases, many years prior — and applied machine learning tools to look for methylation patterns indicative of cancer. They also analyzed control samples from study participants who never had cancer to ensure that any findings were specific to the development of cancer.

Cheng and his colleagues found that their approach not only identified the presence of cancer, but also pinpointed its source using key methylation signatures. Best of all, the researchers found this worked up to seven years prior to diagnosis by conventional techniques. “Across cancer genomes there [tend] to be genome-wide disruptions in methylation patterns,” Cheng said in his conference presentation. “We’re able to identify discriminatory signatures indicative of cancers.”

Cheng believes that eventually this kind of tool could be validated sufficiently to be used alongside standard screening protocols in clinical practice; it could be useful with cancers for which there are good screening methods as well as cancers for which no screening exists, such as pancreatic cancer. While further study is needed, it’s a remarkably promising step toward earlier detection of cancer.

Suicide is a global epidemic, taking nearly 800,000 lives each year. And in the U.S., where suicide rates rose by 33 percent between 1999 and 2019, getting support and resources to the right people at the right time is a pressing need.

The problem is so critical that large groups of scientists around the world have banded together to study it. Recently, they mined genomic data from nearly a million people and identified specific genetic variants that appear to be associated with increased risk of suicide. Those discoveries could have important implications for getting useful medications to at-risk individuals, or for predictive diagnostic tools that would flag people who might need help.

Suicide is an incredibly complex phenomenon, and scientists don’t believe that genetic risk factors tell the whole story. Accurate prediction of which people would attempt suicide in a particular situation would require a sophisticated understanding of genetic, environmental, psychological, sociological, and other factors.

Still, the new genetic findings could be a major step forward. Results from in-depth analyses of attempted suicides in two large populations — including nearly 30,000 people tracked by the International Suicide Genetics Consortium and more than 14,000 in the Million Veteran Program, plus more than 900,000 people who had not attempted suicide as controls — were presented this month at the annual meeting of the American Society of Human Genetics. By comparing genetic differences between people who had made suicide attempts and those who hadn’t, scientists were able to create a list of DNA variants found only in individuals with recorded suicide attempts. Because veterans attempt suicide more often than civilians, including their data was key to these discoveries.

The scientists looked at which variants occurred most often among people who had attempted suicide, and investigated their biological function. Many were associated with traits known to be linked to suicide risk, such as oxytocin signaling, which is important for social bonding, or circadian rhythm, which could explain the higher sleep dysfunction reported in people who attempt suicide.

A particularly revealing sign to the scientists was that many of the findings dovetailed between the veteran and the civilian populations studied. “We’ve seen the same results in two large data sets,” says Elizabeth Hauser, a professor and biostatistician at Duke University who helped crunch the numbers. “That really gives us confidence [in these results].”

The genetic data offer some hopeful possibilities. By highlighting certain biological pathways that may be associated with suicide risk, the data could lead to new clinical treatments. Currently approved drugs that work on those same pathways might be repurposed for use in at-risk patients, and in the longer term, drug developers could create new treatments based on specific genetic variants.

In addition, further validation of the genetic variants could pave the way for a diagnostic test that would help to identify people who are at increased risk for attempting suicide — understanding, of course, that genetics is just one piece of the puzzle. “Just because you might have a particular genetic risk factor, that doesn’t necessarily mean that you will in fact go on to develop these suicidal behaviors,” says Allison Ashley-Koch, a professor and genetic epidemiologist at Duke who participated in these studies. “It just means you may benefit from some additional targeted interventions to help prevent it.”

Nate Kimbrel, a clinical psychologist at Duke and the Durham VA Medical Center who helped lead these studies, hopes that genetic data will ultimately be incorporated alongside more traditional risk factors to hone predictions of which people are most at risk. He and his colleagues previously established the Durham Risk Score, a checklist that helps clinicians identify chronic risk factors to understand a patient’s likelihood of attempting suicide. When high-risk people face acute stressors, such as a romantic breakup or sudden financial hardship, their clinician can offer more intensive support than might be needed for someone at low risk of suicide. If genetic data could be used to pinpoint a person’s risk with greater accuracy, it could give psychologists and psychiatrists a better opportunity to tailor interventions for each patient.

Of course, the possibility of using genetic data for this purpose raises concerns about privacy and each person’s willingness to get tested. “I would view it as something that people would always need to have control over,” Kimbrel says. “People need to be able to make that decision. I would see it being part of a conversation with your provider.”