apathy

You could use transmissible preleukemia (eg CHIP in allo transplants) as an example if you wished.

Direct unassisted clonal transmission in humans seems likely but, as you noted, it hasn’t been documented to the extent that Tasmanian Devil facial tumors have.

Warts are a corner case. I’m not sure whether it’s been determined if some hosts end up increasing the fitness of the shed cells. If so, that’s quickly heading towards a globally transmitted precursor lesion.

HTLV, EBV, HPV

you better believe we have! It’s just rare (thank goodness).

Indeed, this is why hydrogen sulfide is toxic in smallish doses

Independent. The 60th vote was an independent who endorsed John McCain in preference to Obama.

How can one person manage to be so consistently incorrect?

Remind us who tanked the public option in the ACA?

Hint: he was Al Gore’s running mate a decade prior. And he was never a republican.

China tends to buy light crude instead of light sweet crude, hence the oil and derived fuels have more sulfur than is typical for European or American vehicles, industry, etc.

This is something of a problem when a country with as many people as China and an economy as growth-oriented as China's is, uh, firing on all cylinders.

Oh good grief you’re right. This is doubly sad because using an ensemble metric for per-author eigenfactor seems like it would be tractable.

Carl Bergstrom is a smart guy so I suppose the practical implementation of the above must have some wrinkles, but with enough brute force it seems tractable. What I despise more than anything is the gaming that takes place for “impact factor”.

I do OK by standard metrics but would very much like to know where I stand by less easily gamed metrics of influence.

Aka eigenfactor

As a machine learning / probabilistic programming friend put it, (journal) peer review is boosting with three weak learners.

Better than nothing, but very far from ironclad. Only replication really verifies scientific findings. Everything else is just window dressing.

(I say this as a regular reviewer; for whatever reason, this particular week I’m reviewing for both Science and Nature.)

Timeless wisdom.

At least for lineage switching malignancies (5yo female was MLLr), I’m not sure anything other than synthetic lethal attacks on the clonal driver will help.

MLL rearrangements in young children have a particularly vile habit of swapping cell surface markers wholesale. They’re almost certainly more potent when they transform fetal liver progenitors, which seem to retain maximum plasticity. AA died prior to the introduction of second and third generation CAR-T, but stan riddell was part of her attending team, so I have to assume all the stops were pulled out.

MG was different, 26yo Philadelphia-like ALL with 300k WBC refractory to everything. She should have been on ruxolitinib then transplanted. Her disease simply outran her T cells’ ability to divide. It was unreal. And she was a single mom.

I will never forget either case. Even as just an “attached” fellow, it’s hard to watch. More so (for me at least) than older patients where you can reasonably guess what’s coming next. It just feels so unjust.

Anyways. Let’s hope I’m wrong and 2nd/3rd gen auto and allow CAR-T can put these kinds of cases into durable remission for the rest of their, hopefully long and healthy, lives. Because right now, nothing does.

Not mentioned here: the same results can in many cases be had from BiTEs at lower cost and without having to ship leukapheresis samples to the US in liquid nitrogen

Having seen both ALL and AML patients relapsing through CAR-T, I don’t think people appreciate either the systemic load or the possibility of treatment failure that exists in real patient populations. Neither was elderly; 26yo female ALL and 5yo female AML. Both dead, both crushing disappointments after the initial excitement.

(For CAR-T therapy, the common remark is “you know it’s working when the patient goes to the ICU” from tumor lysis; but in both cases the patient’s disease outran their engineered T cells)

I bought one of these to teach a one-shot class on experimental design and statistics.

To say it served the purpose would be an understatement. We blew through the CLT and derivation of statistical power in 10 minutes, leaving the other 110 minutes for the students to present research papers. One of the best $35 I’ve ever spent (don’t have the Amazon link handy but there are some great versions there). Highly recommended if you teach.

woo

This paper is a PR gold mine but a methodological disaster, which is par for the course in "EWAS" (epigenome-wide association studies). At one point I had to remind a coauthor that pointing out structural problems in fashionable studies such as these is a quick way to lose (more) faith in modern peer review; for some reason she thought that EWAS enthusiasts gave a flying crap whether the "genes" they were studying were subject to structural variation (e.g. amplification and deletion). Ha! Ha! Ha!

Anyways. Their functional enrichment analysis is uncorrected for the known bias of the platform (something that has been repeatedly addressed by multiple authors since 2012), and no attempt appears to have been made to correct for cryptic stratification (i.e. structural polymorphisms, which are rampant in human populations, and particularly among so-called metabolic genes), though in the study population that may not be a major issue.

Quantile normalization is only appropriate if one can reasonably assert that the overall distribution of measurements is roughly the same between individuals and groups; this assumption has been shown to be invalid in the absence of positive and negative controls for gene expression, whence its original propagation, and more so for DNA methylation under various conditions. The batch correction approach used here is notorious for squashing real signal, although paradoxically that may have moderated some of the other methods choices.

Moreover, the paper demonstrates that a particular sample of high-SES vs. low-SES individuals in Cebu in the Philippines demonstrates some (fairly tiny) differences in DNA methylation at a relatively small number of CpGs (about 2000 out of 485000 or so measured and 110000 or so tested), without particular note as to whether the sites are clustered, functional, or otherwise of interest. The functional impact of these changes are difficult to interpret, partly because of the bias in the functional analysis (something that has been established for nearly a decade; the authors clearly went shopping for methods in a "confirmatory" style).

We shan't even bother to discuss the effect of [mal]nutrition on metabolism and thereby upon DNA methylation and cell composition (both intertwined, although an attempt was made to correct for the interaction), which further muddies the waters w/r/t SES as opposed to individual-level effects. The analysis is done with a fixed-effects model assuming unstructured shrinkage, which of course is a bit odd considering that the measurements have a relatively easily determined correlation structure (their sample size is sufficient to estimate this) and thus variance decomposition could have been highly informative. This is doubly odd for a population "epigenetics" study, given that variance components were literally invented in population genetics.

In conclusion, while it's a lovely piece for a PR department, the actual relevance of either the measurements or the phenomena to actual humans and public policy is quite difficult to interpret. Perhaps that was the point...