
Two drugs that block cell division synergize to kill tumor cells - 2_listerine_pls
http://news.mit.edu/2019/cancer-mitosis-drugs-combine-0710
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JangoSteve
For what it's worth, I'm not sure about the actual paper itself, but the
posted article makes it seem like no one knew that TH588 was not primarily
acting as an MTH1-inhibitor.

> “This is an exciting paper for two reasons,” says David Pellman, associate
> director for basic science at Dana-Farber/Harvard Cancer Center, who was not
> involved in the study. “First, Yaffe and colleagues make an important
> advance for the rational design of drug therapy combinations. Second, if you
> like scientific mysteries, this is a riveting example of molecular
> sleuthing. A drug that was thought to act in one way is unmasked to work
> through an entirely different mechanism.”

Since my startup compiles all genomic knowledge from the published literature,
of course the first thing I did was search the literature. The first article
that comes up with a quick search for MTH1 and TH588 was from 2014 [0], which
basically says TH588 is a first-class MTH1-inhibitor effective in treating
certain cancer cells. Then in 2016, another article says that's not its
primary mechanism of effectiveness [1]:

> In particular, we identified tubulin as the primary target of TH287 and
> TH588 responsible for the antitumor effects despite the nanomolar
> MTH1-inhibitory activity in vitro.

Tubulin of course is part of the mitotic spindle which the posted article
claims was discovered as the actual target upon which TH588 acts.

I've found around 10 other papers between 2016 and 2018 that further conclude
that TH588 doesn't act primarily as an MTH1-inhibitor.

[0]
[https://www.ncbi.nlm.nih.gov/pubmed/24695224](https://www.ncbi.nlm.nih.gov/pubmed/24695224)
[1]
[https://www.ncbi.nlm.nih.gov/pubmed/27210421](https://www.ncbi.nlm.nih.gov/pubmed/27210421)

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sjg007
Just want to say that your company is really cool. Are you doing any knowledge
graph stuff?

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JangoSteve
Thanks, I appreciate that!

In the abstract sense, I would say we are, in that it's the primary benefit of
what we've built versus textual searching like you get with PubMed or Google
Scholar. For example, if you were to search for a specific variant like BRAF
V600E in Google Scholar or PubMed, you'd need to search for every way that the
variant can be described in text, such as p.V600E, p.Val600Glu, c.1798T>A,
C1798A, etc. Furthermore, you'd need to then read the results to determine
which are specifically referencing the V600E variant in the BRAF gene, versus
mentioning the V600E variant in one of the other genes mentioned in the paper.

We've already done that work ahead of time, figuring out which variants belong
to which genes in papers, normalizing all the different nomenclatures used for
variants so that you can do a single search for your variant and remove the
need to think about how authors could have referenced it.

We do the same with taking into account all the different ways authors can
talk about genes, diseases, clinical contexts, meanings and interpretations,
etc.

I don't know if that answers your question. I guess it depends on what you
mean by knowledge graph, since there seem to be a few different ideas of what
that entails.

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ghostbrainalpha
They are hoping its 2 years away from clinical trials.

I wonder if that's mostly about red tape, regulations and funding, or are
there actually two years of actual work to do to synthesize the treatment
based on this finding?

Like if a scientist working on the project had terminal cancer and only had a
few months to live, would she be able to test this on herself right now?

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dekhn
many scientists have tested substances on themselves. But, with sample size
n=1, testing on yourself, would you really learn anything?

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2_listerine_pls
Would you like to participate in a getting shot experiment? I mean, we can't
be sure the first participant died because He got shot given that n=1.

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th0ma5
Seems like this was observered in a dish?

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_Wintermute
According to the actual paper[0] they did a xenograph study in mice as well.
But yeah, MIT are renowned for their over-hyped press releases.

[0]: [https://www.cell.com/cell-
systems/fulltext/S2405-4712(19)301...](https://www.cell.com/cell-
systems/fulltext/S2405-4712\(19\)30190-5)

~~~
michaelhoffman
Thanks. Would really help if the PR folks would also link to the paper.

