Leveraging Azure and AI to bridge the gap between cancer research and treatment

Leveraging Azure and AI to bridge the gap between cancer research and treatment


[MUSIC].>>The healthcare industry is at a cusp of a massive transformation. Thanks to the power of
cloud computing and AI, we are at a time where massive
amounts of data can be harnessed securely to deliver
actionable insight that leads to better and
more personalized care.>>What is exciting about what Princess Margaret Cancer Centre is
doing is that they are not only sharing their research
findings in the form of the data outcome
and the algorithms, but they are also bridging the gap
between clinical and research.>>Ultimately what we
want to do is to bring precision medicine and personalized
medicine closer to reality. That involves using
artificial intelligence techniques, as well as more traditional
statistical approaches to build those complex predictive
models that will ultimately help clinicians
make better decisions.>>In the laboratory, we are trained to design a new method using what we call
single cell sequencing trying to understand the genetic makeup of every single cell in a tumour sample, and use that very rich
information to actually predict the response of
each of those single cells and to optimize
the combination of the drugs that would kill most of them and
prevent the rise of resistance.>>So really we want to shift the timeline up quite
a bit for patients. We don’t want to wait
for them to come through a series of monotherapy trials. We want to really
hit this cancer with combination therapy
right at the start. CReSCENT is a newly funded project to essentially make
single cell genomics easy. It’s basically a place where basic researchers can put data
derived from individual cells, and have it run through a standardized set of
pipelines but also make it easy to share that data
amongst researchers. The long-term vision
is to essentially have all of Canada’s data
accessible in one place, with appropriate data governance and patient controls really
built right into the system. Why I’m excited about
single cell sequencing is we can now see all of this diversity
both in cancer cells and immune cells at the same time
from a single assay. >>Now that we’re investigating
drug combination we can imagine that with one thousand
compounds there’s literally hundreds of thousands of possible drug combinations that
you could investigate. This explosion of the number of possibilities in terms of
therapeutics is really challenging us to not only design efficient ways to store
and access the data, but also a very efficient way
to analyze the data. When we wanted to scale
up our research and attack those problems with
much more sophisticated tools, we were looking for trusted partners and that’s where we
got in touch with Microsoft who not only provided the technologies
to enable our research, but also the level of
support that we needed.>>Healthcare organizations
trust Microsoft because we’ve been the pioneer in
building a secure platform. This will enable and unlock
so many capabilities when you place your data in an
environment that you can leverage all the
machine learning tools and services that Microsoft provides.>>So this partnership was really crucial to not only get access to the technology but also the support for us to use
the technology correctly, and in a cost-efficient way. The use of Azure not only allows
us to access massive amounts of computational resources
and being able to scale up and scale down
our project on-demand, they also provide us
a common platform for multiple resource groups to share data as well as analytical tools. So we’re really in
this really very exciting time where DNA and RNA analysis
is really becoming a part of a patient’s medical record, but it’s not being done for every single patient at
every single centre. So this is really the opportunity through having large data
sharing networks to really enable really high
resolution genomic analysis really for every patient in Canada. I’m in still early days
and the few patients we have analyzed in great depth
using single cell sequencing, it’s really been
remarkable being able to see the effect of drugs
at the single cell level. So where we see
a cancer cell population with a mutation that
should respond to drug and we look at post-treatment, that’s the exact population
that disappeared.>>It’s amazing to see this
coming to life in Canada. At UHN, they are taking more data
than humans could ever make sense of and using AI and the cloud to
transform how care is delivered. We can’t wait to see
what the future holds.

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