Among many others:
- CAR T therapy going from lab to oncology suite (first launch 2017, but use rapidly growing)
- Approval of Keytruda and similar for many additional forms of cancer (see the 2021-2026 milestones here: https://www.drugs.com/history/keytruda.html )
- Liquid biopsy going from lab to PCP's office - starting with Grail Galleri and moving from there (yes, the NIH results were weak, but the idea of a liquid biopsy at all would be laughed off 10 years ago)
- Move of Atezolizumab and Tecentriq from infusion (hour) to injection (minutes) to increase availability
- Lower dose CT scanning for lung cancer, including for non-smokers
And a long line of immunotherapies that are making the leap from lab to chair right now.
The last 5 years have probably been the most exciting in cancer research since the launch of the monoclonal antibodies in the early 2010s. There is still incredibly far to go, but the trend is in the right direction: https://employercoverage.substack.com/p/decline-in-cancer-mo...
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.
(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/
What’s your prediction for the next five years?
[0] https://acsjournals.onlinelibrary.wiley.com/doi/10.3322/caac...
The breakthroughs happening now will benefit average patients later. It's frustrating, but it's not because we've run out of innovations.
So average person with cancer does better when any individuals cancer treatment improves and it keeps compounding over time. This doesn’t mean everyone with cancer gets a slight improvement, often it’s specific types or stages that improve without impacting others. Where general progress comes from is it’s not the same improvements year after year.
Though one thing that I might think researchers might not want is people may be too sick to recover even if their cancer disappeared tomorrow.
Here's an insightful blog series about Jake Seliger's experience participating in clinical trials. He was a regular HackerNews user who passed away in 2024: https://bessstillman.substack.com/p/please-be-dying-but-not-...
[1] https://www.science.org/content/article/joining-cancer-trial...
There must be informed consent, no reasonable alternatives (which, in cases we deem terminal, is often the case), and some evidence pointing to the treatment possibly being helpful. It's an excellent ethical program that gives patients a choice and advances science.
The biggest exception is oncology. Since everyone knows that chemotherapy is hell, cancer drugs tend to get a pass and pre-approval companies are (slightly) more willing to work with compassionate use exemptions.
When she was diagnosed with leukemia she was able to get into a research study herself that gave us 10 more years together.
One of the horrible but necessary parts of trials is the control group, who receives placebo. This is only done in a few of the trial phases but is essential in measuring efficacy. If someone wants to throw their brainpower and a little bit of AI/tech at the problem, you could end up eliminating a lot of suffering.
"When we systemically administered our nanoagent in mice bearing human breast cancer cells, it efficiently accumulated in tumors, robustly generated reactive oxygen species and completely eradicated the cancer without adverse effects ..."
So it kills human cancer and doesn't harm the mouse in the process.
Mice models of cancer are useful, but you should never be too surprised when something that works in mice doesn't work in the clinic, xenografting or no. Cancer is complicated.
In terms of where _prices_ are set, that negotiation is a function of efficacy relative to other things in the market right? If it ends up treating cancers that each already have a reasonably effective treatment, maybe the pricing isn't that high -- but if it is effective in cases where currently there are no options, the price should be high?
But for something that potentially works against a range of cancers, should we expect to see a sequence of more specific trials (i.e. one phase 1 for basic safety, a bunch of phase 2s for efficacy on specific cancer types, a sequence of phase 3s in descending order of estimated market value? And in 10 years, Alice and Bob with different cancers will pay radically different amounts for almost exactly the same treatment but with small variations in some aspect of the formulation so they can be treated as distinct products?
They have entire teams of people who figure out the viability and pricing of therapeutics before the first dollar is spent, with estimates getting refined the further you get along in the cycle.
Other countries use insurance, so once again the end cost is essentially irrelevant.
This is one of the issues with the modern cancer cures, thst they are very specific to the cancer, the patient, need one off lab work for each patient and this makes them very expensive and not affordable to many. Despite having public healthcare the managers of it still need to decide what to spend their limited funds on.
I think it matters because oftentimes insurance companies won't cover treatments if a cheaper form of treatment exists. It doesn't matter if the old treatment is less effective or a much worse outcome for a patient. This is especially true for "new" treatments.
Someone who needs to ask an LLM will not be helpful in trying to point out something they missed.
The average person in this thread, however, would probably be better informed by asking an LLM for context. They'd be even better informed by taking a few weeks to work through a textbook on cancer biology, but realistically they won't.
My horse in the race is that I'm annoyed by overenthusiastic comments that display a lack of understanding of the history of cancer treatment, and I'm going to be even more annoyed in a few months when the rounds of "haven't we had 1000 cures to cancer posted to HN??? why aren't we using any of them???" start showing up again. I'd rather encourage informed, skeptical optimism.
• If there's blood supply, then (A) it can't be a much higher pressure than the blood pressure (unless there's some Rube Goldberg machine involving active transport), and (B) the tumour is reachable by treatments like this;
• And if there isn't blood supply, then the tumour's core is necrotic, and a treatment to kill the dead cells wouldn't do anything anyway. (Sure, killing the tissue that isolates a lump of necrotic flesh from the rest of the body might cause new and exciting problems, but somehow I think those might be preferable to incurable breast cancer.)
The second is just not a relevant criticism. The third, if it's an actual issue, can probably be worked around by tweaking the molecule slightly – and if not, suppressing the immune system isn't that difficult (it's a known side-effect of many chemotherapies). The first, if it's an issue, can be avoided by injecting the medicine near the target site.
I agree that this treatment might not work in humans, but all the AI's done is taken a generic list of potential concerns, and inserted technobabble to try to make it match the scenario. If you want generic criticism, see https://news.ycombinator.com/item?id=47209076: at least that's true.
The problem of high interstitial pressure (not blood pressure) interfering with drug delivery in tumors is basic cancer biology. If you don't believe me, here's:
A review published in a reputable oncology journal, with over 100 citations, entirely about targeting interstitial pressure, with an abstract leading with "Tumor interstitial pressure is a fundamental feature of cancer biology. Elevation in tumor pressure affects the efficacy of cancer treatment." https://aacrjournals.org/cancerres/article/74/10/2655/592612...
Another review, also a reputable oncology journal, 1000 citations, about tumor stroma more generally, which lists high interstitial pressure as a mechanism by which tumors limit drug access and includes a nice diagram (Figure 2a). https://www.nature.com/articles/s41571-018-0007-1
That's how basic this fact is. 1000 citation reviews in Nature have beautiful fucking diagrams of it. I'm pretty sure it was in the textbook of my undergraduate biology class.
If you don't know shit, don't talk shit. People will criticize LLMs for being overconfident while writing essays from their ass.
This is perhaps the best targeted method devised as it seems to collect basically entirely in tumors. Chemo and Radio therapy just aren't that targeted.