New study in journal Cell is prime example why utilization of sophisticated high-throughput methods and computer technologies does not guarantee generation of clinically useful results. I imagine the only reason this study was even accepted in Cell was the fact that list of authors included many well-known scientists with links to both academia and silicon valley (Stanford University School of Medicine, Chan Zuckerberg Biohub, Parker Institute for Cancer Immunotherapy).
Idea of this study was to develop techniques to quickly identify tumor-specific antigens (most likely mutated antigens) that could be used in immunotherapy (though there is no evidence that any cancer vaccines based on mutant protein sequences actually work in humans using available practices).
For this task, the authors took advantage of yeast-display library expressing random peptide covalently linked to the HLA-A*02:01 molecule, an allele which is present in up to 50% of a number of populations. The authors estimated that "approximately 400 million unique peptides ranging from 8 to 11 amino acids are represented in the combined [yeast-display] libraries."
To validate this approach, they used three recombinant 'blinded' positive control TCRs derived from a melanoma patient (their antigen specificity had been identified independently by exome sequencing, tetramer staining and binding prediction algorithms). However, antigen-specificity of only 1 TCR (NKI 2) could be validated using their yeast-display library. As the authors said "targets of NKI 1 and NKI 3 could not be unambiguously identified through this blinded validation."
Of note, in these validation experiments with NKI 2 (specific for ALDPHSGHFV, a peptide neoantigen derived from CDK4 and other DMF5 TCR specific for EAAGIGILTV derived from the MART-1 melanoma antigen, successful validation [specific enrichment + TCR staining] occurred when HA tagged 10-mer epitope library were used.
The authors anyway went ahead with this "less than perfect" approach to try to identify tumor antigen specificity of T cells derived from 2 patients with colorectal adenocarcinoma and homozygous for the HLA-A*02 allele. The authors focused on 20 TCR most enriched in tumor tissues (based on frequency of occurrence of the same TCR genes).
Out of these 20, only 4 TCRs could enrich peptide from the library (only with c-Myc tagged 9-mer epitope library) and only 3 TCR could stain yeast samples.
Next, the authors try to identify epitopes from potential landscape of sequences for each TCR. Several algorithms were deployed (at least 3 or more such as a modified variant of the previous statistical method using a position weight matrix and a method utilizing a two-layer convolutional neural network). They found 1 peptide sequence EYGVSYEW, which closely matches the peptide motif for TCR 1A, however, neither this exome peptide or the anchor-modified exome peptide (EMGVSYEM), nor the human peptide predictions stimulated the cell line modified to express the TCR 1A. TCR 4B was stimulated with several peptides and as the authors write "true in vivo specificity cannot be unambiguously identified without additional tumor information". Regarding TCR 2A and 3B, only 1 peptide stimulated cell line expressing these TCRs. This peptide was MMDFFNAQM, which is derived from U2AF2, a protein involved in an RNA splicing complex. However, in both patients, no mutations were found in U2AF2.
In summary, the authors wrote "although we cannot definitively determine an immune response targeting the peptide derived from U2AF2, the evidence from the yeast-display screen, prediction algorithm, and in vitro stimulation identify this peptide as the likely target". However, when reading this study it is clear that none of the components worked: yeast-display screen performed suboptimally, prediction algorithms provide little clue and in vitro stimulation made it even more confusing. So, what have we learned from all of these? I would say maybe don't do what they did.
posted by David Usharauli
No comments:
Post a Comment