In two weeks, the Nobel Committee at the Karolinska Institute will award the 2020 Nobel Prize in Medicine/Physiology.
Who will win? We don’t know for sure - but I think that we can make some educated guesses.
Science is dominated by a phenomenon called “the Matthew effect”. In short, the rich get richer. Getting one grant makes it more likely you’ll get the next. Winning one prize makes it more likely you’ll win another.
I looked back at the last 20 years of Nobel Prizes in Med/Phys.
83% of them had won at least one of three prizes before the Nobel: the Lasker, the Gairdner, or the Horwitz Prize.
So, let’s define the pool of the most likely Nobel Laureates as people who have won one or more of those three prizes. Using data on these prizes from Wikipedia, that creates a set of ~190 candidates.
Who’s at the top?
5 people have recently won all 3 - Allison, Varshavsky, Horwich, Ambros, Ruvkun.
Allison got the Nobel in 2018. Varshavsky, Ambros, and Ruvkun made pathbreaking discoveries, but they work close to fields that have already been awarded Nobels, which may leave them out of luck.
That leaves Art Horwich, pioneer of protein folding and chaperones.
Gairdner, 2004
Horwitz, 2008
Lasker, 2011
So that’s guess #1.
Who else could win it? I think that we can narrow down the list by looking at fields of biology that haven’t been recognized recently. In particular, it’s been a long time since we’ve had a pure *genetics* prize. (Not since telomeres, 2009?)
I think that a prize related to gene sequencing, gene editing, or gene regulation may be likely.
Gene regulation - epigenetics is due! Likely David Allis (winner of both the Lasker and Gairdner).
Gene editing - CRISPR will win. It’s a question of when, not if. Zhang/Doudna/Charpentier/Horvath/Barrangou shared the Gairdner. Pick 2 or 3 of them?
My guess is that CRISPR will get the Nobel after the Lasker, but a win for CRISPR will never be surprising.
Gene sequencing - this is tough, and could go in multiple directions. The Human Genome Project could win (Collins/Venter/Lander won the Gairdner and Hood won the Lasker).
I could also imagine a mapping prize (Botstein/Collins) or a tech prize (Klenerman/Balasubramanian/Hood).
So - those are my guesses. Who do you think will win?
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One week from today, the Nobel Prize in Medicine/Physiology will be announced.
Here are the 79 most likely awardees, each of whom has won two or more pre-Nobel “predictor” prizes:
My top picks: Horwich/Hartl for their work on chaperone-mediated protein folding. Their discoveries changed how we think about protein structure and has had significant ramifications for our understanding of neurodegenerative diseases.
Klenerman/Balasubramanian for the development of next-gen sequencing (this could be a Chem prize too). NGS has revolutionized multiple areas of medicine and medical research, and the committee likes recognizing tool/technique development (PCR, CRISPR, monoclonal antibodies, etc).
The new class of HHMI investigators average 3.9 papers as corresponding author in Cell, Nature, or Science. 26 out of 26 members of this group previously trained with a PI who is in the National Academy of Sciences or who was an HHMI investigator themselves.
To back up, I have a longstanding interest in understanding the trajectories of academic careers and uncovering “hidden” factors that influence success. Some of my published work on this topic:
Recently, there has been a push for funding bodies to look more closely at preprints and put less emphasis on journal names. However, if you look at data that I collected from HHMI’s competition in 2018, you can see that the results are pretty similar:
Check out our new study in @ScienceMagazine, where we take on a 100-year-old debate: what’s the role of aneuploidy in cancer?
We discovered that genetically removing extra chromosomes blocks cancer growth - a phenomenon we call “aneuploidy addiction”. science.org/doi/10.1126/sc…
In the 19th century, pathologists observing cancer cells under a microscope noticed that they frequently underwent weird mitoses. The chromosome bodies visible in these cells were not equally divided between daughter nuclei - in other words, they were aneuploid.
Early pathologists like Theodor Boveri proposed that it was this aneuploidy that actually caused cancer. But, there was no way to test it. Eventually, this theory fell out of favor - researchers discovered oncogenes and showed the impact that point mutations could have in cancer.
Very excited to share a new paper from my lab: using a set chromosome-engineering tools, we show that cancers are “addicted” to aneuploidy. If you genetically eliminate single aneuploid chromosomes, cancer cells totally lose their malignant potential! biorxiv.org/content/10.110…
To back up, for many years researchers have used the standard tools of molecular genetics to learn about the function of individual oncogenes and tumor suppressors. We can easily over-express, mutate, or knockout genes like KRAS and TP53 to study their biology.
Chromosome gain events are exceptionally common in cancer, but the genetic tools that allow us to manipulate individual genes don’t work for these chromosome-scale copy number changes. You can’t package a whole chromosome in a lentivirus to over-express it.
If you choose to transfer a manuscript between Nature-family journals, you can consult a web page that lists the acceptance rates for 124 journals published by the Springer Nature Group.
I haven’t seen this data circulated before, so I copied it to share here:
According to this data, "Nature" is not actually the most selective journal. Nature Med, Cancer, and Human Behavior all have lower acceptance rates.
This could be Simpson’s paradox. Maybe a cancer paper has a 2% acceptance rate at Nature and a 4% acceptance rate at Nature Cancer, but Nature also loves to accept ML papers, which increases the overall acceptance rate?
Westermann and colleagues were studying a gene believed to regulate YAP1 expression. They made two CRISPR knockout clones in the gene. Unexpectedly, they found that one KO clone upregulated YAP1 while one downregulated YAP1!
They then proceeded to assess YAP1 expression across a panel of wild-type clones that were not modified with CRISPR, and they saw similar variability.