GEN-IsoPlexis_RT_Nov22_Single-CellAdvances - 6

ROUND T ABLE
" pools. " This is an important insight because then immunotherapy
cannot be applied based on only the pre-treatment
situation of a tumor, but also needs to account for how
the tumor response evolves after treatment. Unfortunately,
transcriptomics alone is not going to help in understanding
dynamic (functional) tumor responses to immunotherapy, so
we need functional proteomics to better inform experimental
choices, which is what we are working on in my lab.
GEN: How can single-cell functional
proteomics help shed light on the drivers of
disease progression?
Abhishek Garg:
This is a question quite close to my heart because when
I started my own lab in 2020, one of the clinically relevant
points that we were dealing with was the reverse translational
gap between mouse and human data. Being more of a translational
immunologist, it was necessary for me to design projects
that strive to bridge the gap between genotype and phenotype.
That is currently a major bottleneck in immuno-oncology
since clinical progress is sometimes exceeding fundamental
understanding, which is really exceptional for any field in
biological sciences. For example, immunotherapies targeting
some of the molecules I mentioned above (e.g., LAG3, TIGIT,
TIM3, VISTA) are already being tested in multiple clinical
studies, yet the immunological mechanistic insights surrounding
these proteins is more limited than required for successful
translation of such therapies.
Indeed, the drivers of disease progression exist on a
phenotypic level. The genotype is perhaps the gateway
to the identity, but not necessarily the functionality of
such drivers. What we have repeatedly seen is that no
matter how good a single-cell analysis you do on that level
where you get thousands of genes' worth of information,
your ultimate analyses bottleneck is always pre-existing
databases. For people who are aware of how these databases
are constructed and used, they know that they are rarely
updated and/or the pathways are incomplete or sometimes
6 | GENengnews.com
too big to give any kind of clear idea.
When you are determining drivers of disease, based on
such databases, you are basically always getting the same
result back because when you depend on these databases to
give your drivers of progression, they are sometimes saying
it doesn't change depending on the context. Whether today
you use it for pancreatic cancer, glioblastoma, or melanoma,
these driver pathways are the same. If your transcriptomic
data clicks it will give you back that same driver.
Now, if you stop there and you don't do mouse experiments
or functional analysis, which is what you conclude about those
pathways, i.e., here's my single-cell map and a list of drivers
of disease. But in the background, they are plugging back to a
database that's not tailored for the disease at all. It is a broad
analysis, and this is a recurring pattern if functional analysis
is not done. At the end of the cycle, the mouse is absolutely
necessary in some of these cases because there is only so
much correlative multi-omics you can do, but still functional
proteomics is indeed better to have as a mitigating task.
Luca Gattinoni:
Single-cell functional proteomics is a way to mitigate the
risk when you are evaluating potential patterns involved in
disease progression. It is a valuable screening method that
helps to cut down the hypotheses that you can formally test
in animal models.
Peggy Sotiropoulou:
Durable clinical responses of adoptive cell therapies or
immunotherapies in general depend on the interactions
between the diverse cell types/states of the infused T cell
product and the dynamic state of the host immune system and
the tumor cells. Comprehensive analysis of the tumor cells
and the tumor microenvironment is crucial to understand the
difference between responding and nonresponding tumors.
Single-cell functional proteomics enables parallel systemwide
profiling of infiltrating engineered cells, intratumor host
immune cells, tumor cells, and cells of the tumor microenvironment.
By assessing, at the single-cell level, the interactions of the
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GEN-IsoPlexis_RT_Nov22_Single-CellAdvances

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