Top Tweets for #NeoantigenQualityModel
3. Now, prevailing belief ->#PDAC = few mutations = fewer mutation-derived NAs = NAs unlikely #PDAC T cell antigens.
BUT...our #NeoantigenQualityModel(estimates the ~1-2% of immunogenic NAs)
found that long term #PDACsurvivors ∝ more “high quality” NAs ∝longer survival.

@manjuggm Thank you @manjuggm! We focus on #PDAC but we think the concepts in the #NeoantigenQualityModel as demonstrated in this paper could be extended to other cancers as well, including #colorectalcancer.
Definitely worth testing..!
@MaverickNY @Nature Thanks @MaverickNY! Yes, the #NeoantigenQualityModel does factor clonality of mutations (so is the neoantigen in a clone vs. subclone).
We found it interesting that, despite evolving over much longer times, #PDACs in long-term survivors were paradoxically more clonal (Fig. 2a).
And implications for #immunotherapy
#NeoantigenQualityModel could allow rational antigen selection for vaccines (for cancers + possibly other diseases)
What are the implications?
Well, implications for biology
We think the #NeoantigenQualityModel captures the features that the immune system recognizes to preserve the integrity of host genome.
So, take home messages are
1. We think this = evidence that immunoediting occurs in human cancer
(we found it interesting that this is revealed in #PDAC, the classic “immune cold” tumor)
2. Editing targets high quality neoantigens defined by the #NeoantigenQualityModel
With this #NeoantigenQualityModel v2.0, we found that the long-term #PDAC survivors in fact evolved tumors with far fewer high quality neoantigens (arguing for immunoediting)

Interesting..
but are these neoantigens that are not acquired in long-term #PDAC survivors just
any neoantigens/mutations (arguing against immunoediting)
or
immunogenic “high quality neoantigens (arguing for immunoediting)?
So, we developed #NeoantigenQualityModel v2.0…
With #NeoantigenQualityModel v1.0, we showed long-term #PDAC survivors had tumors enriched in high quality neoantigens.
Essentially, that classifying tumors (#PDAC, #lungcancers, #melanomas https://t.co/Sr1nN31aXg) based on “neoantigenicity” correlated with prognosis.

So, in 2017, we proposed our #NeoantigenQualityModel v1.0.
Here, we estimate if a neoantigen is immunogenic based on features of
a. Non-selfness – by neoantigen similarity to known antigens
b. Selfness – by how the neoantigen differentially binds the MHC vs. its wild type

And if we can capture the features that T cells recognize in such errors, through what we called the “#NeoantigenQualityModel”, we could both understand the underlying biology and potentially apply it in therapies (eg: rationally select antigens for vaccines).
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