chrisamiller

I don't mean this to sound snarky, but if a blog doesn't have a good ratio of signal to noise, you just unsubscribe from the feed.

The solution is to be okay with missing some things instead of trying to drink from the firehose.

Our middle school has banned phones during school hours, and it's been great. Even though most kids own phones and have them in their backpacks, they aren't seen. There are less problems during classes and kids have to talk to each other during lunch instead of going head down in their phones. Parental pushback has been really minimal - most parents love it, and it's good for the kids.

Making no phones a blanket policy in all schools should be an absolute no-brainer.

At any given time, I'm working on like 10 different projects. tmux lets me set up sessions for each of these projects, so that when I leave one and come back in a week, all the context is there (multiple windows, bash history, working directories I was in, etc)

Since I work with big biological data, most of my work takes place on our university cluster, which means my laptop is just a dumb terminal and all of the action takes place on the server. IME, tmux is especially powerful with coupled with mosh, which gives a persistent SSH connection. That means I can be in the middle of a project, close my laptop lid, go home, then later that evening, open my lid and everything is reconnected and just right there. Same if I reboot my laptop - one command to reconnect my terminal with mosh, and I'm back in the middle of my complicated multi-window project.

Are you claiming that declining fertility is due to plastic, rather than to increasing wealth, education of women, and access to contraception? If so, then I'm going to have to ask you for a) a source and b) a plausible mechanism.

Absolutely. We're not there yet, but it's not inconceivable.

The short answer is, we sequence their genomes, identify mutations that change a protein sequence and are highly expressed, then run those all through suites of algorithms that predict how well they'll be presented to the immune system. (mostly neural networks trained on far-too-sparse experimental data). This prediction is the hard part right now - we still don't understand enough about how the immune system identifies and interacts with these altered peptides to do really accurate predictions of which ones will be most effective. Throw in that these tumors are actively suppressing the immune system in various ways, and it's complicated! There is lots of research going on, though, and lots of promising early results, like this one

Look, I get the impulse, and might have agreed with you 10 years ago. At the end of the day, though, we have to work with non-computationally savvy people who (reasonably) want to look at their data sometimes. Not every lab tech or PI can or should learn Python/R/your favorite scripting language, and frameworks like Galaxy take time to set up and maintain, etc etc. Our job is to meet the users where they are and push the biology forward. Any time that I can push them onto a better path, I'll do that, but sometimes the right move is to tell them: "Yeah, go ahead and play with the data in excel, then tell me what you find", and I'll code something proper up afterwards to verify it, get solid stats and a make a pretty visualization.

These are ballpark numbers to be sure, and yeah, my off the cuff comment didn't get that exactly right. I also simplified things quite a bit to try to get the broader point across - Thanks for following up!

This is a great example of where cancer treatment is headed and why it's so hard - namely that cancer isn't one disease, it's many thousands of diseases.

This is a drug that targets lung cancer (~12% of cancers) and only one type of lung cancer (non-small cell lung cancer, ~80% of cases). It targets a particular mutated gene that occurs in about 30% of that subtype. And then, about 50% of those patients respond.

So do that math, and you end up seeing that treating one of the most common mutations in one of the most common cancers with what is considered very high efficacy still only helps with about 1.4% of all cancers. This is actually an enormous number for this kind of treatment, and there is a long tail of rare cancers that are going to be much harder to find targeted therapies for.

That all said, this currently appears to be an enormous success story, and the kind of treatment options that have been enabled by genomic sequencing of cancers, followed by many years of drug development and clinical trials. It's fantasically exciting to see us continue to chip away at the problem but by bit and grant people longer lives as a result!

I do cancer research, including using mouse models, so believe me, I know the difficulty :-)

Getting promising drugs into humans is -hard-, and this research is interesting, but this press release is still kind of shitty.

I hate to be that guy, because it seems like there is some interesting science behind this press release hype. There are, however, many many miles between "effects in a mouse model" and "human therapeutic", let alone "cure". I wish them the best of luck as they consider safety and efficacy in clinical trials.

It's true that the whole problem of cancer therapy is "how do I target these cells over here, while avoiding the rest of your healthy cells, when they look mostly the same" And you're right that this is a really exciting and promising approach!

A partial list of the "catches" at present are

1) tumors are really good at down-regulating the immune system. If they weren't, they would have been cleared already

2) we're still fairly bad at knowing which of the many mutations in a tumor are going to be good neoantigens - that is, which ones the immune system will be good at recognizing and ramping up against

3) It's expensive to design personalized therapies for every patient, but may be necessary (see some of my other comments below)

4) Tumors aren't single cells, they're collections of them that can have different characteristics. Just like with other therapies, sometimes immunotherapies kill 99% of the tumor, but leave that 1% to come roaring back. (same idea as antibiotic resistance)

So yeah, lots of hurdles to overcome, but overall, I'm genuinely excited about the rapid progress we're making in the area of immunotherapies. It's impossible to overstate the effect that sustained research funding has had on the rate of advances in this field.