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.
A recent study in The Lancet Oncology showed that AI found 20 percent more cancers on mammogram screens than radiologists alone.
Published in The Lancet Oncology, the study analyzed the scans of 80,000 Swedish women with a moderate hereditary risk of breast cancer who had undergone a mammogram between April 2021 and July 2022. Half of these scans were read by AI and then a radiologist to double-check the findings. The second group of scans was read by two researchers without the help of AI. (Currently, the standard of care across Europe is to have two radiologists analyze a scan before diagnosing a patient with breast cancer.)
The study showed that the AI group detected cancer in 6 out of every 1,000 scans, while the radiologists detected cancer in 5 per 1,000 scans. In other words, AI found 20 percent more cancers than the highly-trained radiologists.
Scientists have been using MRI images (like the ones pictured here) to train artificial intelligence to detect cancers earlier and with more accuracy. Here, MIT's AI system, MIRAI, looks for patterns in a patient's mammograms to detect breast cancer earlier than ever before. news.mit.edu
But even though the AI was better able to pinpoint cancer on an image, it doesn’t mean radiologists will soon be out of a job. Dr. Laura Heacock, a breast radiologist at NYU, said in an interview with CNN that radiologists do much more than simply screening mammograms, and that even well-trained technology can make errors. “These tools work best when paired with highly-trained radiologists who make the final call on your mammogram. Think of it as a tool like a stethoscope for a cardiologist.”
AI is still an emerging technology, but more and more doctors are using them to detect different cancers. For example, researchers at MIT have developed a program called MIRAI, which looks at patterns in patient mammograms across a series of scans and uses an algorithm to model a patient's risk of developing breast cancer over time. The program was "trained" with more than 200,000 breast imaging scans from Massachusetts General Hospital and has been tested on over 100,000 women in different hospitals across the world. According to MIT, MIRAI "has been shown to be more accurate in predicting the risk for developing breast cancer in the short term (over a 3-year period) compared to traditional tools." It has also been able to detect breast cancer up to five years before a patient receives a diagnosis.
The challenges for cancer-detecting AI tools now is not just accuracy. AI tools are also being challenged to perform consistently well across different ages, races, and breast density profiles, particularly given the increased risks that different women face. For example, Black women are 42 percent more likely than white women to die from breast cancer, despite having nearly the same rates of breast cancer as white women. Recently, an FDA-approved AI device for screening breast cancer has come under fire for wrongly detecting cancer in Black patients significantly more often than white patients.
As AI technology improves, radiologists will be able to accurately scan a more diverse set of patients at a larger volume than ever before, potentially saving more lives than ever.