
Cancer: A Computational Disease that AI Can Cure [video] - caustic
http://videolectures.net/aaai2010_tenenbaum_cac/
======
hxrts
I'm a bioinformatician at a translational cancer research institution & have
some sense of the stumbling blocks and opportunities in this space.

First off, doing away with clinical trials as we perform them now would
certainly speed up pairing drugs with patients but it's not going to happen
unfortunately. Drug trials aren't designed to minimize search paths, they are
designed to minimize risk.

With terminal diseases like many cancers the rules are bent a little but
what's important to understand is that these are real people making the
decisions ultimately. Sometimes the most information can be gained by
withholding drug, and isolating another treatment's impact but if you are a
patient with a terminal prognosis, or the doctor trying to treat that patient,
are you going to choose not to take something that might help you?

This is a point of frustration for many in research fields because it means
clinical data is hugely noisy. Patients are often cycled through different
drugs quickly to find something that takes hold, while at the same time going
in for as much chemo/radiation treatment as they can bare.

I'm not saying these approaches aren't relevant, they just won't happen as a
grand reimagining of our drug approval system. What is starting to happen
however, is reclassification of cancer 'type' based on genetic profiles
meaning ovarian cancer may have certain genetic similarities to lung or
pancreatic so instead of treating melanoma, you can treat a cancer with a
disturbed MAP pathway.

All the same, I'm glad he's working on this problem because it's huge, and I
look forward to seeing the point of view of the HN community.

edit: Though obvious to some, I should also mention this problem is
complicated by the fact that genetic information is private and dissemination
is highly restricted. Patients can release this info but it does often inhibit
massive cross-patient research.

~~~
abrax3141
There are many new sorts of trials coming online that are closer to the model
he's proposing here. (Which, BTW, is more like A/B testing than like a
classical RCT.) See esp. adaptive trials, point-of-care trials, and global
cumulative trails. Some of these new models are really being run by real
clinical researchers.

~~~
hxrts
This is a great point and I think many doctors are pushing for new ways to
perform clinical trials but adaptive trials generally only change a single
parameter during the course of a trial (dosage for example). And POC trials
open up the number of patients that may be willing to participate by
administering tests at local sites but the state of clinical trials still has
a long way to go. Once it's firmly in drug companies' and insurance agencies'
best interest to open things up then we may see more rapid change.

~~~
dnautics
"opening things up" is definitely going to help, and is a huge part of the
philosophy of my nonprofit science research org (our first project will be
taking an anticancer through preclinical). There are, however, a ton ton ton
of biological issues that still are, open or not, difficult to surmount,
sometimes not even having to do with the cancer itself (bioavailability, side
effects, etc).

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melling
Interesting discussion. At around 7 minutes in, he postulates that maybe we've
already cured 20% of cancers. The problem is that we have cures that fail
clinical trials because cancer is so varied and not generic enough to have one
cure. One drug might only work in 10% of people while another cure might cure
another 10%, and so on.

These drugs all fail clinical trails and are lost.

~~~
dnautics
"One drug might only work in 10% of people while another cure might cure
another 10%, and so on."... I believe this, to some degree. But I don't think
it's that much. A lot of drugs get killed because of side effects, poor
bioavailibility, unexpected responses, interference with other drugs that are
common or necessary in the cohort of cancer patients, etc. Those things are
much harder to predict. It is embarassingly easy to find compounds that kill
cancer cells _in vitro_.

However, this:

"Cancer results from finite genomic mutations that biotechnology can easily
list."

Well the first part is true. The second part, not so much.

~~~
siganakis
Not even sure that the number of ways that a genome can be mutated is even
finite. Genomes can be mutated not just by the substitution of single base
pairs, but also by introducing copies, through deletions, translocations and
insertions.

There is even the phenomenon known as Chromothripsis [1], which has the entire
genome smashed into thousands or millions of pieces and then randomly put back
together.

[1]
[http://en.wikipedia.org/wiki/Chromothripsis](http://en.wikipedia.org/wiki/Chromothripsis)

~~~
bjterry
Pedantically, it would have to be finite, because there is a size limit.
Cancer couldn't transform your genome into a piece of DNA the size of the sun.

~~~
cam_l
Just to out-pedant you, the size of the sun is still quite finite, most (all?)
things are.

Perhaps colloquially, finite here is (not un-like _literally_ ) a metaphor, in
this case for not-unfathomably-large, or more precisely, measurable.

------
joe_the_user
"Time Travel: A temporal challenge faster than light travel can solve!"

Sorry to be snarky but my point is that it may be plausible that really strong
AI could solve the cancer problem but that it doesn't matter.

The point that both AI and cancer are much harder and much different problem
than even the optimistic researchers in the fields imagine (why they're still
optimistic).

Most of the time cancer cure articles are met here by someone who posts a link
describing how two side of even a single tumor will often involve
substantially different cells with substantially dynamics. And each of these
dynamics is a real challenge to the human (and the challenge varies because
each person physiology varies more than is common recognized[1]) . A strong
enough AI could be sending drugs separately to each side of the tumor, yes.
But the AI would have to figuring out the dynamics of those particular cells.
And I mean _those particular cells_ , not the other cells kind of like them in
some laboratory (yes, the presenter deals with this issue but I claim it's
harder, much harder than he claims).

Basically, there is no AI of sufficient quality to have the flexibility to
figure out each person's cancer. There's nothing flexible enough in theory so
there's nothing to apply. Modern AI can recognize a lot of patterns and can
use fixed rules to solve problems but it's far from the human ability to put
all these things together. And our human ability here is, itself, not up to
the challenge. You can have a fifty experts treating a single person's cancer
and the chance of success won't go that much, etc.

[1] See:
[http://www.anapsid.org/aboutmk/biochem.html](http://www.anapsid.org/aboutmk/biochem.html)
etc

------
guyinblackshirt
Imagine what NSA's mathematicians (and budget) could do, if they were focused
on these tasks, instead of spying!

~~~
Florin_Andrei
One could argue that all the brain resources currently used on Wall Street are
also wasted, when compared to the magnitude of the problems we're facing in
other places.

------
corruption
Hopelessly optimistic. Spoken like someone who has never taken an experimental
design course imho.

1) The search space is practically infinite dimensional. All methods suck when
trying to extract a causative model from an infinite search space. There's a
reason why in experimental design we change as little as possible.

2) SNP's are not the entire story. If it were this simple we would have
progressed much further already. See dismal failure of all other high
throughput sequencing and microarray technology. We still don't know how to
analyse this stuff properly, if it will ever be possible.

3) The metabolome is adaptive! While we each have different enzyme kinetics
due to slight differences in protein makeup, overall metabolic flux rates are
amazingly consistent. See Oliver Feihn et al for more details.

------
troels
There's an unsaid implication that it is possible to sub-type cancers based on
a patients genetic type. E.g. what makes a particular drug work on a
particular cancer form, is determined by that patients genetics. Is this a
generally accepted assumption?

~~~
hxrts
This technique is coming into prominence but genomic is still a very young
science with the cost of sequencing only recently coming down enough to have
it become regular practice. What we have now are essentially a shortlist of
known mutations that are seen across many cancers, a huge list of closely
associated mutations in the same pathway or impinging pathways that are
sometimes seen in conjunction with other mutations, sometimes not and then a
ton of noise from which researchers are trying to pick out possible
correlation. The vast majority of mutations are not cancer causing, so picking
out the ones that are is difficult. That being said, frequently broad
characterization can be done. This person has an effected PI3Kinase, or MAP
pathway. We only have drugs for a few of the major mutations so once the
cancer mutates to avoid drugging, patients are often out of luck.

To answer your second question, cancer is generally accepted as a genetic
disease ie mutations, copy number alterations, insertion and deletion of
genetic information, however, recent research has shown that epigenetic
processes (that is what your cells are doing with your genes such as
alternative splicing or DNA methylation) are also an important factor and
these can be effected by environmental factors such as diet, exercise, even
mood.

~~~
ekianjo
> This person has an effected PI3Kinase, or MAP pathway. We only have drugs
> for a few of the major mutations so once the cancer mutates to avoid
> drugging, patients are often out of luck.

Yeah, patients relapsing into cancer usually do not have much alternatives.
Combination therapies often help since they can attach several pathways at
once, but combo therapies are usually limited to 2 compounds at the same time.

Another huge problem is linked to medical practice. So many doctors treating
cancer patients are just NOT aware of the latest studies and developments in
terms of Standards of Care. If you have a cancer, the most relevant factor is
the necessarily the drug you take but who's treating you and how much he knows
about your condition.

Helping doctors make decisions may be another area for disruption, where
Software may help.

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utunga
Very, very impressive. I'm excited about the possibilities! FWIW using machine
learning approach to try and individualize treatments and treat people one at
a time (linking 'what works' for the individual rather than trying to find the
single cure for all) seems very similar to what we tried to do for autism
spectrum treatments with [http://autism360.org](http://autism360.org) (thought
that some on here may find this interesting).

------
stephth
Link to the company he founded:
[http://www.cancercommons.org](http://www.cancercommons.org)

~~~
dnautics
thanks for this. I didn't know about this... I'm looking for an "outside
scientific advisor" to audit my anticancer compound research project. I think
I'll be contacting these guys.

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ctdonath
I recall seeing a fascinating paper which did address some medical malady
purely as a programming problem, and offered a viable solution. Made a lot of
sense, seemed to work, am mentioning it in hopes somebody might remember it
too and find a link (I don't remember enough keywords).

~~~
dnautics
you may be thinking of emerald therapeutics and the work by Brian Frezza.

------
jostmey
The future of Cancer therapy could exist today! But why are do we still rely
on decades old medical technologies? I cannot say.

Check out this gene therapy called Gendicine approved in China to reverse
cancer.

[http://scholar.google.com/scholar?q=Gendicine+p53](http://scholar.google.com/scholar?q=Gendicine+p53)

Apparently, the treatment has been available for years, and yet the product
has failed to penetrate to the U.S. It would be funny if it were not true.

~~~
elemeno
Perhaps because there haven't been any wide scale clinical trials that prove
it's effectiveness? Of the papers I read through at the given link, most seem
to come to conclusion that Gendicide p53 is safe to give to humans and don't
comment on whether or not it's an effective therapy, or if it's more effective
than existing treatments.

