Revisiting neoantigen depletion signal in the untreated cancer genome
Wang, S.; Wang, X.; Wu, T.; He, Z.; Li, H.; Sun, X.; Liu, X.-S.
Show abstract
This study is arising from Van den Eynden J. et al. Nature Genetics. Lack of detectable neoantigen depletion signals in the untreated cancer genome. Van den Eynden J. et al. tried to address a very important scientific question: could the immune system eliminate cancer cells with immunogenic mutations in untreated situation? Van den Eynden J. et al. first annotated the human exome into "HLA-binding regions" and "non HLA-binding regions" based on the predicted binding affinity of nonapeptides translated from the un-mutated reference coding genome with type I HLA alleles. They hypothesized that if neoantigen depletion signal exist, the nonsynonymous mutations in "HLA-binding regions" will be negatively selected during cancer evolution, while nonsynonymous mutation in "non HLA-binding regions" will not be negatively selected. This will lead to decreased nonsynonymous vs synonymous mutation ratio (n/s) in "HLA-binding regions" compared with "non HLA-binding regions". They defined HLA-binding mutation ratio (HBMR) as the ratio of n/s in "HLA-binding regions" to "non HLA-binding regions", and reported that HBMRs are close to 1 in different types of cancer after background corrections, meaning neoantigen depletion signals are not detectable in different types of cancer. The fundamental problem of their hypothesis lies in that the actual neoantigens with immunogenicity do not overlap with their defined "HLA-binding regions". Actually, most neoantigens with immunogenicity are not located in "HLA-binding regions", when dissimilarity between mutant and wild type peptide are considered. It is the neoantigen with immunogenicity, but not nonsynonymous mutation in their defined "HLA-binding regions" undergo immunoediting based negative selection. Thus the results reported in that study are fundamentally flawed, and at this current stage we could not draw a solid conclusion as to whether the neoantigen depletion signal exists or not.
Matching journals
The top 8 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- NeoPrecis: Enhancing Immunotherapy Response Prediction through Integration of Qualified Immunogenicity and Clonality-Aware Neoantigen Landscapes 95%
- MetaTiME: Meta-components of the Tumor Immune Microenvironment 94%
- Deep learning predictions of TCR-epitope interactions reveal epitope-specific chains in dual alpha T cells 94%
Similar papers in this journal
Similar papers in this journal
Similar papers in this journal
- Sliding Window INteraction Grammar (SWING): a generalized interaction language model for peptide and protein interactions 94%
- Morphological map of under- and over-expression of genes in human cells 94%
- Reproducible single cell annotation of programs underlying T-cell subsets, activation states, and functions 93%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.