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.
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.
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.
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...
Dubious — cellular senescence (whether proliferation- or oncogene-induced) is the usual failsafe for avoiding proliferation of damaged cells. It relies heavily upon TP53, RB1, and CDKN2A, all of which are routinely deleted in tumors. When anti-aging researchers refer to senolytic drugs, usually they’re referring to drugs that clear senescent cells.
Oddly, the drugs tend to clear out tumor cells in many cases, as senescence bypass is a critical step in carcinogenesis.
Not oddly, the inflammatory paracrine (secreted) profile of senescent cells tends to engage the immune system in clearing them out. Immunosenescence gets in the way of this and also of clearing precancerous cells, hence it is a risk factor for both age-related frailty and cancer.
Also worth noting: this is further evidence that cancer, like many other diseases, is in part an immune disease.
When central tolerance is too lax, tumor cells can more easily survive a trip through the bloodstream to seed metastasis (the eventual cause of nearly all cancer deaths, aside from treatment sequelae and thrombosis).
By contrast, if central tolerance is overly tight, then you see autoimmune diseases (severe aplastic anemia is a classic example) where the immune system wipes out the competition from healthy progenitors, and mutant clones better able to survive the onslaught seed cancers. This is one reason why both immunosuppressive therapies and immunostimulatory agents can both increase cancer risk.
It’s also worth noting just how different the mutational profiles of pediatric tumors are versus adults. To grossly oversimplify, peds tumors tend to carry mutations (typically gene fusions, amplifications, or deletions) that confer a developmental-stage-specific advantage in proliferation, such that no normal progenitor could ever hope to keep up. By contrast, the most frequently observed point mutations in adult cancer (to TP53, in particular, although DNMT3A in leukemia is another example) confer stress resistance to the mutant clones. They are nearly absent from tumors seen in children. Even Li-Fraumeni syndrome, where people carry deleterious TP53 variants inherited from their parents, does not begin to show a huge risk differential until adolescence. So there are evolutionary, developmental, and immune differences that shape the genesis, selection, and growth of different tumors in different age groups, and tend to indicate different treatment.
The standard chemo regimens for pediatric ALL (acute lymphoblastic leukemia, the most common cancer in kids) would kill many adults, and despite over 90% cure rates in kids, far less than half of adults with the “same” disease will survive it. (In quotes, because as with every other tumor that spans the full range of age groups, the drivers in adults are different from those in kids for almost all instances).
Similarly, immune checkpoint inhibitors can generate miraculous responses in adults tumors, though these are seldom seen in pediatric patients. With the benefit of hindsight, it’s more obvious why this is so (the random accumulation of mutations over decades in adult tumors is more likely to generate immune-recognized non-self proteins), but it took a long, long time to get here. (Look up “Cooley’s Toxins” if you think immunotherapy is new :-/)
I still find the fields fascinating, despite having enough ghosts on my conscience to stock a mausoleum. Cancer is part of our evolutionary heritage; the best we can do in adults is usually try to control its spread and cut out enough of it for the immune system to mop up the rest. Kids are different, but that’s another story for another time. It’s a great period in history to be working on understanding these things and they interact.
I download the illustrations (duh?). That’s the value. I don’t actually care about the service per se. Once the illustrations are in the paper/talk, the value has been realized, for my lab.
Nothing in this world lasts forever. I’m ok with that. Software undergoes bit rot, and services shut down. For us, the value is simply that we can communicate complicated experimental designs and results, clearly and effectively, without a lot of application training or other bullshit. That’s worth a LOT to my lab.
We release almost everything open source, and are militant open data proponents. We also like to remind people “if it breaks, you get to keep the pieces”. The value of this service isn’t in the pieces.
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.