(95% confidence interval is 0.294-0.887, wide but not too wide, n=157, to be expected for phase 2).
How they work is also completely fucking insane. Intismeran autogene is personalized for every patient via sequencing their tumor DNA. That's sci-fi shit. If you're not impressed by that, you should be. Fast and scalable DNA sequencing, neoantigen identification, RNA synthesis, none of this is easy and all of it relies on recent innovations across multiple fields.
The first proofs of concept for personalized vaccines like this date back to 2017[1] or 2015[2]. The process for designing the vaccines requires a machine learning algorithm first published in 2020[3]. Details of the algorithm aren't available, but it validated against data published in 2019[4], and there have been many recent advancements in algorithms and datasets for biotech ML that it likely relied on. As you might already know, mRNA vaccines were first tested in humans around the 2010s[5].
[1] https://www.nature.com/articles/nature22991 [2] https://pubmed.ncbi.nlm.nih.gov/25837513/ [3] https://aacrjournals.org/cancerres/article/80/16_Supplement/... [4] https://pmc.ncbi.nlm.nih.gov/articles/PMC7138461/ [5] https://pubmed.ncbi.nlm.nih.gov/26082837/
That people aren't actually living longer with cancer, they're living longer while we know they have cancer.
Is there any truth to that?
Long answer, it's a variable you need to consider when doing data analysis, and it depends on what exactly you're talking about, but it's absolutely not true for improvements in cancer survival general. One alternative method is to look at per-capita death rates, for example:
Reduction in US and UK childhood cancer death since 2000 https://ourworldindata.org/grapher/cancer-death-rates-in-chi...
Reduction in several countries' age-standardized breast cancer death since 2000 (Why did it increase in South Africa? I'm not sure, maybe socioeconomic factors) https://ourworldindata.org/grapher/breast-cancer-death-rate-...
Reduction in global age-standardized cancer death rate since 2000 (Scroll down to second graph. Since the population is getting older, age-standardization makes a fairer comparison) https://ourworldindata.org/grapher/cancer-death-rates
2000 is an arbitrary year I picked for clear visual changes without needing to haggle over statistics. If you want to feel optimistic, switch the childhood cancer death graph to 1960-now.
This method has different possible failure points. It could be that less people are getting cancer, or that people who would get cancer are dying of other causes, or reporting of cause of death has changed, though this is very unlikely for some figures, such as leukemia death rates for children in the US. Statistics is hard. Overall though, the evidence is very good that cancer survival has improved a lot due to better treatments since 2000.
If you have a more specific claim you're dubious about, I'd be willing to look into it for you. I'm very enthusiastic about this topic.
Another way to come at it would be mortality data. But that has a bunch of its own problems.
Everything is changing at once, it makes this kind of science so hard.
A friend of mine, aged 50, has worked in pediatric oncology her entire (nursing) career. The ratio of surviving kids has flipped from 30/70 to 70/30 during her tenure.
What’s your prediction for the next five years?
it was available for [some] UCSF patients more than 5 years ago