Effort to identify tumor-specific antigens: The University Industrial Complex study results

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    

From microbiome-association studies to causal microbe identification

As new Nature study says "the general inability to move beyond correlations and address causation has been the Achilles heel of microbiome research." Well said. Indeed, notwithstanding of so many microbiome studies, the number of identified microbiota species specifically and reproducibly linked to a particular [medical] condition is still zero.  

Is there a way to overcome this challenge? Maybe one could compare microbiome content of the hosts displaying variable clinical phenotype [DSS colitis, in this case] and find one microbial species, if you are lucky, that control that phenotype in every host? But isn't this approach exactly what all other studies have done all along? 

And actually, how strong is the data in support of this approach? The authors showed that germ-free mice and germ-free mice colonized with mouse microbiota derived from SPF mice (MMb mice) were highly sensitive to DSS colitis induction compared to SPF mice or germ-free mice colonized with human microbiota (HMb mice). Of note, observed difference in survival between MMb and SPF is strange since both should have SPF microbiota.

When the authors compared several pairs of mice strains housed separately or co-housed, they found that sensitivity to DSS colitis segregated with presence of Lachnospiraceae species (Clostridium immunis).       

Indeed, colonization of germ-free mice with human microbiota enriched with Clostridium immunis could improve mice survival in DSS colitis model.

However, again, survival data between different mouse strains did not correlate with level of Lachnospiraceae species, questioning simplicity of one-on-one relationship between Clostridium immunis and colitis score (HMb and SPF have similar survival curve but vastly differ in Lachnospiraceae content).

In summary, without more data and confirmation by other labs I will view these data as very preliminary and unverified with lots of caveats. Surely, not a Nature material in my opinion.

posted by David Usharauli

Dual TCR expressing T cells could drive autoimmunity

An article in Cell Host and Microbe (CHM) caught my attention. In proposed that dual TCR expressing T cells are responsible for autoimmune phenotype in K/BxN mice. 

K/BxN mice develop spontaneous arthritis thought to driven by Vβ6+ KRN T cells recognizing glucose-6-phosphate isomerase (GPI), the self-Ag presented by MHC class II A^g7 molecules. Ordinarily, it is thought that pathogen cross-reactive to self antigen, in this case GPI, could initiate autoimmune disease (theory of molecular mimicry).

However, recently the role of endogenous microbiota in driving autoimmune arthritis received new attention. Here, the authors showed segmented filamentous bacteria (SFB) was required to initiate autoimmune arthritis in K/BxN.

However, since SFB-derived peptide recognized by T cells required Vβ14+ TCR it was unclear how SFB was mediating autoimmunity against GPI recognized by completely different TCR made of Vβ6+ chain. Further analysis showed that some T cells in K/BxN express dual TCRs expressing both Vβ6+ and Vβ14+ chains. Indeed, sorted T cells expressing dual TCR, but not Vβ6+ chain alone, recognized SFB-derived peptide (A6).

In vivo, adoptive transfer of monoclonal KRN T cell population on RAG KO background that prevents expression of other Vβ or Vα chains (only expressing Vβ6+ KRN T cells), could not mediate arthritis in T cell-deficient host (harbor normal B cells also required for arthritis development). Of note, other paper in 1999 however found no difference in arthritis development between WT and RAG KO KRN T cells.

In summary, the authors think that first dual TCR T cells get activated by SFB-derived epitope via Vβ14+ TCR, indirectly prime Vβ6+ TCRs, on the same T cells, that then actually mediates autoimmunity against self antigen GPI. However, it is not clear from this study whether endogenous Vα chains could contribute to cross-reactivity between SFB and GPI when recombined with Vβ6+ or Vβ14+ chains in WT KRN T cells.

posted by David Usharauli