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.
Avalanche rescue dogs train to find and dig out people buried in snow slides
By the time he is three, Lume, like Huckleberry, will be a fully trained avy pup and will join seven other avy dogs on Breckenridge ski patrol team. Some of the team members, both human and canine, are also certified to work with Colorado Rapid Avalanche Deployment, a coordinated response team that works with the Summit County Sheriff’s office for avalanche emergencies outside of the ski slopes’ boundaries.
There have been 19 avalanche deaths in the U.S. this season, according to avalanche.org, which tracks slides; eight in Colorado. During the entirety of last season there were 17. Avalanche season runs from November through June, but avalanches can occur year-round.
High tech and high stakes
Complementing avy dogs’ ability to smell people buried in a slide, avalanche detection, rescue and recovery is becoming increasingly high tech. There are transceivers, signal locators, ground scanners and drones, which are considered “games changers” by many in avalanche rescue and recovery
For a person buried in an avalanche, the chance of survival plummets after 20 minutes, so every moment counts.
A drone can provide thermal imaging of objects caught in a slide; what looks like a rock from far away might be a human with a heat signature. Transceivers, also known as beacons, send a signal from an avalanche victim to a companion. Signal locators, like RECCO reflectors which are often sewn directly into gear, can echo back a radar signal sent by a detector; most ski resorts have RECCO detector units.
Research suggests that Ground Penetrating Radar (GPR), an electromagnetic tool used by geophysicists to pull images from inside the ground, could be used to locate an avalanche victim. A new study from the Department of Energy’s Sandia National Laboratories suggests that a computer program developed to pinpoint the source of a chemical or biological terrorist attack could also be used to find someone submerged in an avalanche. The search algorithm allows for small robots (described as cockroach-sized) to “swarm” a search area. Researchers say that this distributed optimization algorithm can help find avalanche victims four times faster than current search mechanisms. For a person buried in an avalanche, the chance of survival plummets after 20 minutes, so every moment counts.
An avy dog in training is picking up scent
Sarah McLear
While rescue gear has been evolving, predicting when a slab will fall remains an emerging science — kind of where weather forecasting science was in the 1980s. Avalanche forecasting still relies on documenting avalanches by going out and looking,” says Ethan Greene, director of the Colorado Avalanche Information Center (CAIC). “So if there's a big snowstorm, and as you might remember, most avalanches happened during snowstorms, we could have 10,000 avalanches that release and we document 50,” says Greene. “Avalanche forecasting is essentially pattern recognition,” he adds--and understanding the layering structure of snow.
However, determining where the hazards lie can be tricky. While a dense layer of snow over a softer, weaker layer may be a recipe for an avalanche, there’s so much variability in snowpack that no one formula can predict the trigger. Further, observing and measuring snow at a single point may not be representative of all nearby slopes. Finally, there’s not enough historical data to help avalanche scientists create better prediction models.
That, however, may be changing.
Last year, an international group of researchers created computer simulations of snow cover using 16 years of meteorological data to forecast avalanche hazards, publishing their research in Cold Regions Science and Technology. They believe their models, which categorize different kinds of avalanches, can support forecasting and determine whether the avalanche is natural (caused by temperature changes, wind, additional snowfall) or artificial (triggered by a human or animal).
With smell receptors ranging from 800 million for an average dog, to 4 billion for scent hounds, canines remain key to finding people caught in slides.
With data from two sites in British Columbia and one in Switzerland, researchers built computer simulations of five different avalanche types. “In terms of real time avalanche forecasting, this has potential to fill in a lot of data gaps, where we don't have field observations of what the snow looks like,” says Simon Horton, a postdoctoral fellow with the Simon Fraser University Centre for Natural Hazards Research and a forecaster with Avalanche Canada, who participated in the study. While complex models that simulate snowpack layers have been around for a few decades, they weren’t easy to apply until recently. “It's been difficult to find out how to apply that to actual decision-making and improving safety,” says Horton. If you can derive avalanche problem types from simulated snowpack properties, he says, you’ll learn “a lot about how you want to manage that risk.”
The five categories include “new snow,” which is unstable and slides down the slope, “wet snow,” when rain or heat makes it liquidly, as well as “wind-drifted snow,” “persistent weak layers” and “old snow.” “That's when there's some type of deeply buried weak layer in the snow that releases without any real change in the weather,” Horton explains. “These ones tend to cause the most accidents.” One step by a person on that structurally weak layer of snow will cause a slide. Horton is hopeful that computer simulations of avalanche types can be used by scientists in different snow climates to help predict hazard levels.
Greene is doubtful. “If you have six slopes that are lined up next to each other, and you're going to try to predict which one avalanches and the exact dimensions and what time, that's going to be really hard to do. And I think it's going to be a long time before we're able to do that,” says Greene.
What both researchers do agree on, though, is that what avalanche prediction really needs is better imagery through satellite detection. “Just being able to count the number of avalanches that are out there will have a huge impact on what we do,” Greene says. “[Satellites] will change what we do, dramatically.” In a 2022 paper, scientists at the University of Aberdeen in England used satellites to study two deadly Himalayan avalanches. The imaging helped them determine that sediment from a 2016 ice avalanche plus subsequent snow avalanches contributed to the 2021 avalanche that caused a flash flood, killing over 200 people. The researchers say that understanding the avalanches characteristics through satellite imagery can inform them how one such event increases the magnitude of another in the same area.
Avy dogs trainers hide in dug-out holes in the snow, teaching the dogs to find buried victims
Sarah McLear
Lifesaving combo: human tech and Mother Nature’s gear
Even as avalanche forecasting evolves, dogs with their built-in rescue mechanisms will remain invaluable. With smell receptors ranging from 800 million for an average dog, to 4 billion for scent hounds, canines remain key to finding people caught in slides. (Humans in comparison, have a meager 12 million.) A new study published in the Journal of Neuroscience revealed that in dogs smell and vision are connected in the brain, which has not been found in other animals. “They can detect the smell of their owner's fingerprints on a glass slide six weeks after they touched it,” says Nicholas Dodman, professor emeritus at Cummings School of Veterinary Medicine at Tufts University. “And they can track from a boat where a box filled with meat was buried in the water, 100 feet below,” says Dodman, who is also co-founder and president of the Center for Canine Behavior Studies.
Another recent study from Queens College in Belfast, United Kingdom, further confirms that dogs can smell when humans are stressed. They can also detect the smell of a person’s breath and the smell of the skin cells of a deceased person.
The emerging avalanche-predicting human-made tech and the incredible nature-made tech of dogs’ olfactory talents is the lifesaving “equipment” that Leffler believes in. Even when human-made technology develops further, it will be most efficient when used together with the millions of dogs’ smell receptors, Leffler believes. “It is a combination of technology and the avalanche dog that will always be effective in finding an avalanche victim.”
