The question is can we exploit this genomic data for cancer immunotherapy?
The important questions are can we characterize the TILs?
Can we identify the cancer antigens, neoantigens and
the cancer-germline antigens?
And of course, many people want to identify biomarkers and to inform choice
of immunotherapies and combination therapies.
This project has been designed by geneticists and they were mostly focusing
on using exome sequencing, whole genome sequencing and RNA sequencing
data to identify copy number variations, gene expression and also epigenetics.
But on top of this you can use this data also to extract immunologically relevant
parameters as shown here.
You can use this data, specifically exome sequencing data and RNA
sequencing data to estimate the HLA alleles which is then used to derive the
tumor antigens, but you can use also RNA sequencing data to estimate the tumor
infiltrating lymphocytes (TILs) and to a certain extent also the T
cell receptor repertoire.
One example how you can do this is you can collect a number of expression profiles
from purified immune cells, then also from normal tissue and
cancer cell lines.
And then you can filter these genes and you have a set of genes which are more or
less related to specific immune cell populations and then you can use either
deconvolution or gene set enrichment analysis and then estimate the TILs.
In order to do this, we first had to gather all these
expression profiles from purified immune cells for CD3 cells,
CD4 cells and all the subsets.
And down here you can see that these are the genes expressed in normal mucosa and
in colorectal cancer cell lines.
And we use these expression profiles then to derive TILs from bulk
RNA expression data.
Since this is a very challenging job, you need a lot of analytical pipelines,
we did all the calculations from the TCGA data for around 9,000 samples.
So, we took all this exome sequencing and RNA sequencing data, survival data,
SNP arrays, exome sequencing and RNA sequencing, and calculated what you see
here on the lower part.
We calculated the clonal and sub-clonal mutations, the neoantigens,
the cancer germline antigens and the TILs, as well as the immunophenotypes and
everything is available on this database which we call The Cancer
Immunome Atlas (TCIA.at).
So, you can either go here and look for your favorite immune cell type,
or your favorite cancer, or you can download all the data and do
the analysis with your specific molecules you are interested in.
And what we learned from this analysis is the following.
There are two major messages.
One, the mutational and the neoantigen landscapes are highly individual.
Here on the left-hand side, see the correlation between the
neoantigens and the mutations.
They are highly corrected, of course.
And what you see here, this is the matrix of the neoantigens and
it's extremely sparse.
This tells you there are very, very few neoantigens which are shared
in more than let's say 5% of the patients in one cancer entity.
This is not unexpected, but this is extremely sparse and that
tells you that you cannot use any off-the-shelf vaccine based
on the antigens.
So, all the vaccines you develop for treating cancer patients have to be
individualized for the specific patient.
But something which is intriguing is shown on this slide.
We analyzed also all the tumor infiltrating lymphocytes in all
the cancer types.
And here you can see an example for colon cancer.
One line represents one patient and the different colors of the different
immune cell types.
And you can see that the immune infiltrates are highly heterogeneous
in these cohorts - the TCGA cohorts for colorectal cancer.
But despite this heterogeneity, you can see some patterns
on the right-hand side.
Here are the microsatellite unstable (MSI-H) patients.
You see here different immune profiles for these patients.
Then in the middle, these are microsatellite stable,
but hypermutated (MSS^) tumors.
Again, different infiltrates.
And on the right-hand side you can see the microsatellite stable (MSS) patients,
very different from the MSI and MSS patients.
And you can carry on this analysis also for other molecular phenotypes,
for example methylated or non-methylated phenotypes.
So, based on the molecular phenotype we see very distinct immunophenotypes,
at least in colorectal cancer.
But then you might say okay, this is trivial, MSI tumors have a lot
of mutations and that's why they have a lot of TILs.
But we carried out also this analysis for other cancers.
This is a thyroid cancer (THCA).
You see here on the right-hand side, for thyroid cancer, very few mutations and
the two types, BRAF-mutated and the RAS-mutated thyroid cancers,
they have more or less the same number of mutations, but again,
very different immunophenotypes.
Down here you can see the BRAF-mutated vs.
RAS-mutated.
You see in the BRAF-mutated, the MHC molecules are upregulated and the
RAS-mutated we see infiltration of MDSCs and Tregs.
So, it appears that depending on the molecular phenotype,
different tumor escape mechanisms are in place which we don't understand why.