Cancer DNA is detectable in blood years before diagnosis

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.

> 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.

The child of a friend of mine has PTEN-Hamartom-Tumor-Syndrom, a tendency to develop tumors throughout life due to a mutation in the PTEN gene. The poor child gets whole body MRIs and other check-ups every half year. As someone in biological data science, I always tell the parents how difficult it will be to prevent false positives, because we don't have a lot of data on routine full body check-ups on healty people. We just know the huge spectrum on how healthy/ok tissue looks like.

Hopefully gene therapy can fix this sort of problem.

is it even possible for gene therapy to just rewrite all the existing DNA in a body? can't you only do that to cells that are dividing or whatever?

They've managed to treat sickle cell.

>CRISPR/Cas9 can be directed to cut DNA in targeted areas, enabling the ability to accurately edit (remove, add, or replace) DNA where it was cut. The modified blood stem cells are transplanted back into the patient where they engraft (attach and multiply) within the bone marrow...

https://www.fda.gov/news-events/press-announcements/fda-appr...

> 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.

I wonder if our current research product is only considered the gold standard because doing things in a probabilistic way is the only way we can manage the complexity of the human body to date.

It’s like me running an application many, many times with many different configurations and datasets, while scanning some memory addresses at runtime before and after the test runs, to figure out whether a specific bug exists in a specific feature.

Wouldn’t it be a lot easier if I could look at the relevant function in the source code and understand its implementation to determine whether it was logically possible based on the implementation?

We currently don’t have the ability to decompile the human body, or understand the way it’s “implemented”, but that is something that tech is rapidly developing tools that could be used for such a thing. Either a way to corroborate enough information aggregated about the human body “in mind” than any person can in one lifetime and reason about it, or a way to simulate it with enough granularity to be meaningful.

Alternatively, the double-blindedness of a study might not be as necessary if you can continually objectively quantify the agreement of the results with the hypothesis.

Ie if your AI model is reporting low agreement while the researchers are reporting high agreement, that could be a signal that external investigation is warranted, or prompt the researchers to question their own biases where they would’ve previously succumbed to confidence bias.

All of this is fuzzy anyway - we likely will not ever understand everything at 100% or have perfect outcomes, but if you can cut the overhead of each study down by an order of magnitude, you can run more studies to fine-tune the results.

Alternatively, you can have an AI passively running studies to verify reproducibility and flag cases where it fails, whereas now the way the system values contributions makes it far less useful for a human author to invest the time, effort, and money. Ie improve recovery from a bad study a lot quicker rather than improve the accuracy.

EDIT: These are probably all ideas other people have had before, so sorry to anyone who reaches the end of my brainstorming and didn’t come out with anything new. :)