I actually know a little about this through my work. Cell-free DNA (CfDNA) Has been known about for a few decades, but has become more of a focus in recent years because of the advent of immunotherapies, which are often highly targeted drugs. CfDNA has also been used in "liquid biopsies" i.e, a simple blood draw, because it can help you profile the tumor and location of the cancer.
In my field, we all think that CfDNA testing will eventually become a standard thing that will go along with your annual physical's blood test, because it has predictive/preventative abilities.
The big secret is that they could detect cancer very early in most people, but the health care companies don't want to pay for the screening. You can pay out of pocket for these procedures. I was told this by a cancer researcher
EDIT:
Adding these caveats:
1. There is a ton of nuance in the diagnosis, since most people have a small amount of cancer in their blood at all times
2. The screenings are 5-10k + follow up appointments to actually see if its real cancer
3. All in cost then could be much higher per person
4. These tests arent something that are currently produced to be used at mass scale
Some thoughts on this as someone working on circulating-tumor DNA for the last decade or so:
- Sure, cancer can develop years before diagnosis. Pre-cancerous clones harboring somatic mutations can exist for decades before transformation into malignant disease.
- The eternal challenge in ctDNA is achieving a "useful" sensitivity and specificity. For example, imagine you take some of your blood, extract the DNA floating in the plasma, hybrid-capture enrich for DNA in cancer driver genes, sequence super deep, call variants, do some filtering to remove noise and whatnot, and then you find some low allelic fraction mutations in TP53. What can you do about this? I don't know. Many of us have background somatic mutations speckled throughout our body as we age. Over age ~50, most of us are liable to have some kind of pre-cancerous clones in the esophagus, prostate, or blood (due to CHIP). Many of the popular MCED tests (e.g. Grail's Galleri) use signals other than mutations (e.g. methylation status) to improve this sensitivity / specificity profile, but I'm not convinced its actually good enough to be useful at the population level.
- The cost-effectiveness of most follow on screening is not viable for the given sensitivity-specificity profile of MCED assays (Grail would disagree). To achieve this, we would need things like downstream screening to be drastically cheaper, or possibly a tiered non-invasive screening strategy with increasing specificity to be viable (e.g. Harbinger Health).
Here's what may seem like an unrelated question in response: how can we get 10^7+ bits of information out of the human body every day?
There are a lot of companies right now trying to apply AI to health, but what they are ignoring is that there are orders of magnitude less health data per person than there are cat pictures. (My phone probably contains 10^10 bits of cat pictures and my health record probably 10^3 bits, if that). But it's not wrong to try to apply AI, because we know that all processes leak information, including biological ones; and ML is a generic tool for extracting signal from noise, given sufficient data.
But our health information gathering systems are engineered to deal with individual very specific hypotheses generated by experts, which require high quality measurements of specific individual metrics that some expert, such as yourself, have figured may be relevant. So we get high quality data, in very small quantities -a few bits per measurement.
Suppose you invent a new cheap sensor for extracting large (10^7+ bits/day) quantities of information about human biochemistry, perhaps from excretions, or blood. You run a longitudinal study collecting this information from a cohort and start training a model to predict every health outcome.
What are the properties of the bits collected by such a sensor, that would make such a process likely to work out? The bits need to be "sufficiently heterogeneous" (but not necessarily independent) and their indexes need to be sufficiently stable (in some sense). What is not required if for specific individual data items to be measured with high quality. Because some information about the original that we're interested in (even though we don't know exactly what it is) will leak into the other measurements.
I predict that designs for such sensors, which cheaply perform large numbers of low quality measurements are would result in breakthroughs what in detection and treatment, by allowing ML to be applied to the problem effectively.
The sensitivity challenge is compounded by the signal-to-noise ratio problem at ultra-low allelic fractions (<0.1%), where technical artifacts from library preparation and sequencing can mask true variants.
Would you say ctDNA tools are sensitive and specific enough now to be able to make a decision about post op adjuvant therapies? “Now that I’ve had surgery, did the R0 resection get it all, or do I need to do chemo and challenging medication like mitotane?”
This sort of thing is exactly like preventative whole body MRI scans. It's very noisy, very overwhelming data that is only statistically useful in cases we're not even sure about yet. To use it in a treatment program is witchcraft at this moment, probably doing more harm than good.
It COULD be used to craft a pipeline that dramatically improved everyone's health. It would take probably a decade or two of testing (an annual MRI, an annual sequencing effort, an annual very wide blood panel) in a longitudinal study with >10^6 people to start to show significant reductions in overall cancer mortality and improvements in diagnostics of serious illnesses. The diagnostic merit is almost certainly hiding in the data at high N.
The odds are that most of the useful things we would find from this are serendipitous - we wouldn't even know what we were looking at right now, first we need tons of training data thrown into a machine learning algorithm. We need to watch somebody who's going to be diagnosed with cancer 14 years from now, and see what their markers and imaging are like right now, and form a predictive model that differentiates between them and other people who don't end up with cancer 14 years from now. We [now] have the technology for picking through complex multidimensional data looking for signals exactly like this.
In the meantime, though, you have to deal with the fact that the system is set up exclusively for profitable care of well-progressed illnesses. It would be very expensive to run such a trial, over a long period of time, and the administrators would feel ethically bound to unblind and then report on every tiny incidentaloma, which completely fucks the training process.
This US is institutionally unable to run this study. The UK or China might, though.
Long term the goal should be to find a treatment that is safe enough and with so small side effects that it can be used for any suspicious mutations even though it may be decades away from killing you.
How about tracking deltas between blood draws starting ant youngish age when things are on average presumed to be in a good state? When a new feature turns up in a subsequent blood draw, could it then be something more concerning?
Sadly health insurance in the US is unlikely to pay for most preventative care because the followup costs of false-positives and that they are betting that down the line someone else will pick up the tab when you get sick decades later (like the government).
It's kind of why I'm favor of universal option to align financial incentives. Like given how sick the US population is, it probably makes sense to put a lot more people of GPL-1s and invest in improving their efficacy and permanence. Like nationalize-the-patent COVID-operational-warp-speed level urgency. There are over 100M Americans that are pre-diabetic, the cost of treating a diabetic is about 20k/yr. So $4 trillion in new costs, on top of the misery and human suffering.
US private healthcare insurance is required to pay for “medically necessary” treatments and generally does pay for medicine where they are unlikely to see the benefits (see statins).
Any bureaucratic system is going to be inefficient. You see it in countries with universal healthcare. In Canada, some provinces now have wait times of over a year from referral to treatment. Many European countries face similar access issues, though France and the Netherlands perform somewhat better.
The U.S. is a different kind of mess. It’s a patchwork of heavy government restrictions, large public programs like Medicare, and for-profit corporations, all thrown together without a coherent design. It’s no surprise it’s expensive. In 2023, healthcare spending was nearly 18% of GDP. Another factor could simply be wealth: higher per capita GDP tends to correlate with higher healthcare spending. To be fair to the U.S. healthcare system, it is highly capitalized, with much higher concentrations of diagnostic equipment like MRI machines than other OECD countries, and it does have some of the highest five-year survival rates for cancer and heart disease.
Even so, all of these healthcare systems are heavily dysfunctional in many ways.
In contrast to all of this, cosmetic surgery and laser eye surgery are the only fields of medicine where prices have actually fallen in inflation-adjusted terms, which is extraordinary, as prices in healthcare in general have increased much faster than inflation. The superior performance of these fields is because of basic market dynamics. People pay out of pocket, so they’re price conscious, and providers compete. There are also fewer regulatory restrictions since these fields aren’t tied up in government programs like Medicare.
Innovation is the only thing that reliably drives prices down. But in most of healthcare, it moves slowly. Devices often take 10-30 years to cycle out. Compare that to consumer electronics, where turnover happens every 1-2 years.
If it were up to me, I’d make restrictions on medical providers much lighter. Anyone could offer medical procedures as long as they disclose they’re uncertified and include a government-mandated warning. That kind of freedom is necessary to solve hard problems. You can’t regiment innovation and industry development. Gatekeeping in the name of consumer safety is the worst thing that can be done to any industry, and unfortunately, there is heavy gatekeeping in healthcare.
That is not to say that I am opposed to government intervention in general. I think it can play a critical role in advancing healthcare. Where government intervention creates the most value is in funding research for the public domain: drug designs, medical procedures, and open datasets. These investments have enormous returns and are best handled by governments. If the private sector focused on delivery and innovation, with governments making strategic contributions in foundational research, healthcare would see revolutionary improvements generation after generation.
Full text is paywalled, and no mention in abstract of false positive rate in control group. Has this test actually been independently verified? No mention of that important fact in the press release.
A relative had this or a similar test come back positive. This sounds like a helpful signal on paper, but in reality it's not always actionable.
They assumed their previous cancer had survived and metastasized. Doctors couldn't find the source. It turned into a waiting game, where they lived with a sword of Damocles over their head. They were retested every few months and monitored. Then after a year the tests the levels dropped off. And the end result was nothing came of it so far.
It's normal to have some amount of pre-cancerous cells get naturally removed by your immune system. And this catches those too.
Seems this newfound ability to detect cancers earlier than we thought possible could be used to develop better treatments to boost the body's innate ability to eliminate cancerous cells before they turn malign: 1) identify thousands of people with traces of cancerous DNA that are too weak to merit immediate action and who are willing to participate in a trial. 2) Divide them into two groups, one group gets for one month a daiiy dose of auricularia auricula fungus extract or whatever that is believed to possibly prevent cancers from developing, the other group gets a placebo. 3) Run the early-detection test again at the end of the month to see whether or not there is a difference in cancerous DNA signal strength between the two groups.
How to use such test results? I propose your GP knows your test results but you don't. You see the GP about a mole or something and the GP (not you) gets to decide on further tests / treatment, or sends you home saying don't worry about it.
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[ 5.1 ms ] story [ 40.8 ms ] threadAgeless also provides many other longevity therapies.
In my field, we all think that CfDNA testing will eventually become a standard thing that will go along with your annual physical's blood test, because it has predictive/preventative abilities.
EDIT:
Adding these caveats:
1. There is a ton of nuance in the diagnosis, since most people have a small amount of cancer in their blood at all times
2. The screenings are 5-10k + follow up appointments to actually see if its real cancer
3. All in cost then could be much higher per person
4. These tests arent something that are currently produced to be used at mass scale
- Sure, cancer can develop years before diagnosis. Pre-cancerous clones harboring somatic mutations can exist for decades before transformation into malignant disease.
- The eternal challenge in ctDNA is achieving a "useful" sensitivity and specificity. For example, imagine you take some of your blood, extract the DNA floating in the plasma, hybrid-capture enrich for DNA in cancer driver genes, sequence super deep, call variants, do some filtering to remove noise and whatnot, and then you find some low allelic fraction mutations in TP53. What can you do about this? I don't know. Many of us have background somatic mutations speckled throughout our body as we age. Over age ~50, most of us are liable to have some kind of pre-cancerous clones in the esophagus, prostate, or blood (due to CHIP). Many of the popular MCED tests (e.g. Grail's Galleri) use signals other than mutations (e.g. methylation status) to improve this sensitivity / specificity profile, but I'm not convinced its actually good enough to be useful at the population level.
- The cost-effectiveness of most follow on screening is not viable for the given sensitivity-specificity profile of MCED assays (Grail would disagree). To achieve this, we would need things like downstream screening to be drastically cheaper, or possibly a tiered non-invasive screening strategy with increasing specificity to be viable (e.g. Harbinger Health).
There are a lot of companies right now trying to apply AI to health, but what they are ignoring is that there are orders of magnitude less health data per person than there are cat pictures. (My phone probably contains 10^10 bits of cat pictures and my health record probably 10^3 bits, if that). But it's not wrong to try to apply AI, because we know that all processes leak information, including biological ones; and ML is a generic tool for extracting signal from noise, given sufficient data.
But our health information gathering systems are engineered to deal with individual very specific hypotheses generated by experts, which require high quality measurements of specific individual metrics that some expert, such as yourself, have figured may be relevant. So we get high quality data, in very small quantities -a few bits per measurement.
Suppose you invent a new cheap sensor for extracting large (10^7+ bits/day) quantities of information about human biochemistry, perhaps from excretions, or blood. You run a longitudinal study collecting this information from a cohort and start training a model to predict every health outcome.
What are the properties of the bits collected by such a sensor, that would make such a process likely to work out? The bits need to be "sufficiently heterogeneous" (but not necessarily independent) and their indexes need to be sufficiently stable (in some sense). What is not required if for specific individual data items to be measured with high quality. Because some information about the original that we're interested in (even though we don't know exactly what it is) will leak into the other measurements.
I predict that designs for such sensors, which cheaply perform large numbers of low quality measurements are would result in breakthroughs what in detection and treatment, by allowing ML to be applied to the problem effectively.
It COULD be used to craft a pipeline that dramatically improved everyone's health. It would take probably a decade or two of testing (an annual MRI, an annual sequencing effort, an annual very wide blood panel) in a longitudinal study with >10^6 people to start to show significant reductions in overall cancer mortality and improvements in diagnostics of serious illnesses. The diagnostic merit is almost certainly hiding in the data at high N.
The odds are that most of the useful things we would find from this are serendipitous - we wouldn't even know what we were looking at right now, first we need tons of training data thrown into a machine learning algorithm. We need to watch somebody who's going to be diagnosed with cancer 14 years from now, and see what their markers and imaging are like right now, and form a predictive model that differentiates between them and other people who don't end up with cancer 14 years from now. We [now] have the technology for picking through complex multidimensional data looking for signals exactly like this.
In the meantime, though, you have to deal with the fact that the system is set up exclusively for profitable care of well-progressed illnesses. It would be very expensive to run such a trial, over a long period of time, and the administrators would feel ethically bound to unblind and then report on every tiny incidentaloma, which completely fucks the training process.
This US is institutionally unable to run this study. The UK or China might, though.
It's kind of why I'm favor of universal option to align financial incentives. Like given how sick the US population is, it probably makes sense to put a lot more people of GPL-1s and invest in improving their efficacy and permanence. Like nationalize-the-patent COVID-operational-warp-speed level urgency. There are over 100M Americans that are pre-diabetic, the cost of treating a diabetic is about 20k/yr. So $4 trillion in new costs, on top of the misery and human suffering.
The U.S. is a different kind of mess. It’s a patchwork of heavy government restrictions, large public programs like Medicare, and for-profit corporations, all thrown together without a coherent design. It’s no surprise it’s expensive. In 2023, healthcare spending was nearly 18% of GDP. Another factor could simply be wealth: higher per capita GDP tends to correlate with higher healthcare spending. To be fair to the U.S. healthcare system, it is highly capitalized, with much higher concentrations of diagnostic equipment like MRI machines than other OECD countries, and it does have some of the highest five-year survival rates for cancer and heart disease.
Even so, all of these healthcare systems are heavily dysfunctional in many ways.
In contrast to all of this, cosmetic surgery and laser eye surgery are the only fields of medicine where prices have actually fallen in inflation-adjusted terms, which is extraordinary, as prices in healthcare in general have increased much faster than inflation. The superior performance of these fields is because of basic market dynamics. People pay out of pocket, so they’re price conscious, and providers compete. There are also fewer regulatory restrictions since these fields aren’t tied up in government programs like Medicare.
Innovation is the only thing that reliably drives prices down. But in most of healthcare, it moves slowly. Devices often take 10-30 years to cycle out. Compare that to consumer electronics, where turnover happens every 1-2 years.
If it were up to me, I’d make restrictions on medical providers much lighter. Anyone could offer medical procedures as long as they disclose they’re uncertified and include a government-mandated warning. That kind of freedom is necessary to solve hard problems. You can’t regiment innovation and industry development. Gatekeeping in the name of consumer safety is the worst thing that can be done to any industry, and unfortunately, there is heavy gatekeeping in healthcare.
That is not to say that I am opposed to government intervention in general. I think it can play a critical role in advancing healthcare. Where government intervention creates the most value is in funding research for the public domain: drug designs, medical procedures, and open datasets. These investments have enormous returns and are best handled by governments. If the private sector focused on delivery and innovation, with governments making strategic contributions in foundational research, healthcare would see revolutionary improvements generation after generation.
https://aacrjournals.org/cancerdiscovery/article-abstract/do...
Full text is paywalled, and no mention in abstract of false positive rate in control group. Has this test actually been independently verified? No mention of that important fact in the press release.
They assumed their previous cancer had survived and metastasized. Doctors couldn't find the source. It turned into a waiting game, where they lived with a sword of Damocles over their head. They were retested every few months and monitored. Then after a year the tests the levels dropped off. And the end result was nothing came of it so far.
It's normal to have some amount of pre-cancerous cells get naturally removed by your immune system. And this catches those too.