Pinpointing the tumor-specific T cells via TCR clusters
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Curated by eLife
Evaluation Summary:
This manuscript presents a computational approach to identify T-cells which can mount an immune response against tumors. The authors examine the presence of clusters of T cells with similar sequence as a surrogate for tumor antigen specific responses. The identification of tumor-specific responses within the background of bystander T cell infiltration is an area of great current interest. This study provides solid support for the concept that T cell sequence clustering can be used to quantify the tumor specific response in vivo and in vitro.
(This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. All reviewers agreed to share their name with the authors.)
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Abstract
Adoptive cell transfer (ACT) is a promising approach to cancer immunotherapy, but its efficiency fundamentally depends on the extent of tumor-specific T cell enrichment within the graft. This can be estimated via activation with identifiable neoantigens, tumor-associated antigens (TAAs), or living or lysed tumor cells, but these approaches remain laborious, time-consuming, and functionally limited, hampering clinical development of ACT. Here, we demonstrate that homology cluster analysis of T cell receptor (TCR) repertoires efficiently identifies tumor-reactive TCRs allowing to: (1) detect their presence within the pool of tumor-infiltrating lymphocytes (TILs); (2) optimize TIL culturing conditions, with IL-2 low /IL-21/anti-PD-1 combination showing increased efficiency; (3) investigate surface marker-based enrichment for tumor-targeting T cells in freshly isolated TILs (enrichment confirmed for CD4 + and CD8 + PD-1 + /CD39 + subsets), or re-stimulated TILs (informs on enrichment in 4-1BB-sorted cells). We believe that this approach to the rapid assessment of tumor-specific TCR enrichment should accelerate T cell therapy development.
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Author Response:
Reviewer #1 (Public Review):
The authors investigate a clustering-based method to find reactive T cells based on their TCR (T cell receptor) sequences following ACT (Adoptive T cell transfer). This method, which was previously implemented as ALICE, find reactive T cell clones in samples by looking for overrepresented clusters of T cells with similar TCR sequences.
By applying the method on published data from Melanoma patients, the authors show an increase in the number of clusters following anti-PD1 immunotherapy. They also find in those clusters many TCRs known to be reactive to melanoma antigens. Clusters are also found in CD39+PD1+ activated T cells.
Overall, the paper shows strong indications that clusters are indeed enriched for tumor reactive TCRs. The overall number of reactive TCRs in the clusters, on …
Author Response:
Reviewer #1 (Public Review):
The authors investigate a clustering-based method to find reactive T cells based on their TCR (T cell receptor) sequences following ACT (Adoptive T cell transfer). This method, which was previously implemented as ALICE, find reactive T cell clones in samples by looking for overrepresented clusters of T cells with similar TCR sequences.
By applying the method on published data from Melanoma patients, the authors show an increase in the number of clusters following anti-PD1 immunotherapy. They also find in those clusters many TCRs known to be reactive to melanoma antigens. Clusters are also found in CD39+PD1+ activated T cells.
Overall, the paper shows strong indications that clusters are indeed enriched for tumor reactive TCRs. The overall number of reactive TCRs in the clusters, on the other hand, is not known (and hard to estimate). Specifically, it is not clear how many of the TCRs in the clusters found using this method are indeed reactive against the tumor cells. However, the ones found are excellent candidates for functional assays that determine reactivity. The functional analysis presented in the paper, which involved sorting on CD137, doesn't link the TCRs in the clusters with activation very strongly.
The paper makes a strong case to the usefulness of cluster-based analysis for measuring tumor related response and finding possible reactive TCRs. However, stronger functional validation methods are needed to assess the quality of the TCRs found in the clusters. Further work would investigate this relation in more depth, mainly to pinpoint and improve the sensitivity and accuracy of the inferred tumor related clones.
Thank you so much for your thorough work and your warm words.
Concerning the functional confirmation - we did our best within the scopes of this manuscript. We will definitely continue this work that hopefully expand on other TAA and neoantigens in further investigations.
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Evaluation Summary:
This manuscript presents a computational approach to identify T-cells which can mount an immune response against tumors. The authors examine the presence of clusters of T cells with similar sequence as a surrogate for tumor antigen specific responses. The identification of tumor-specific responses within the background of bystander T cell infiltration is an area of great current interest. This study provides solid support for the concept that T cell sequence clustering can be used to quantify the tumor specific response in vivo and in vitro.
(This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. All reviewers agreed to share their name with the authors.)
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Reviewer #1 (Public Review):
The authors investigate a clustering-based method to find reactive T cells based on their TCR (T cell receptor) sequences following ACT (Adoptive T cell transfer). This method, which was previously implemented as ALICE, find reactive T cell clones in samples by looking for overrepresented clusters of T cells with similar TCR sequences.
By applying the method on published data from Melanoma patients, the authors show an increase in the number of clusters following anti-PD1 immunotherapy. They also find in those clusters many TCRs known to be reactive to melanoma antigens. Clusters are also found in CD39+PD1+ activated T cells.
Overall, the paper shows strong indications that clusters are indeed enriched for tumor reactive TCRs. The overall number of reactive TCRs in the clusters, on the other hand, is not …
Reviewer #1 (Public Review):
The authors investigate a clustering-based method to find reactive T cells based on their TCR (T cell receptor) sequences following ACT (Adoptive T cell transfer). This method, which was previously implemented as ALICE, find reactive T cell clones in samples by looking for overrepresented clusters of T cells with similar TCR sequences.
By applying the method on published data from Melanoma patients, the authors show an increase in the number of clusters following anti-PD1 immunotherapy. They also find in those clusters many TCRs known to be reactive to melanoma antigens. Clusters are also found in CD39+PD1+ activated T cells.
Overall, the paper shows strong indications that clusters are indeed enriched for tumor reactive TCRs. The overall number of reactive TCRs in the clusters, on the other hand, is not known (and hard to estimate). Specifically, it is not clear how many of the TCRs in the clusters found using this method are indeed reactive against the tumor cells. However, the ones found are excellent candidates for functional assays that determine reactivity. The functional analysis presented in the paper, which involved sorting on CD137, doesn't link the TCRs in the clusters with activation very strongly.
The paper makes a strong case to the usefulness of cluster-based analysis for measuring tumor related response and finding possible reactive TCRs. However, stronger functional validation methods are needed to assess the quality of the TCRs found in the clusters. Further work would investigate this relation in more depth, mainly to pinpoint and improve the sensitivity and accuracy of the inferred tumor related clones.
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Reviewer #2 (Public Review):
Identifying and quantifying the tumor specific T cell response continues to be an area of great interest in the context of cancer immunotherapy. The presence of sets of T cells with similar sequence (clusters) is becoming increasingly accepted as a key feature of the antigen-specific T cell response. In this study the authors explore this hypothesis in the context of melanoma. The study uses the bioinformatics tool ALICE which identifies TCR clusters, taking into account "background" clustering which might be expected by chance.
The study reports three key findings: 1. the number of clusters in TILs increases following anti-PD1 immunotherapy. 2. the number of clusters is enriched in the CD39+PD1+ positive TIL fraction. 3. the number of clusters can be used as an indication of the success of in vitro …
Reviewer #2 (Public Review):
Identifying and quantifying the tumor specific T cell response continues to be an area of great interest in the context of cancer immunotherapy. The presence of sets of T cells with similar sequence (clusters) is becoming increasingly accepted as a key feature of the antigen-specific T cell response. In this study the authors explore this hypothesis in the context of melanoma. The study uses the bioinformatics tool ALICE which identifies TCR clusters, taking into account "background" clustering which might be expected by chance.
The study reports three key findings: 1. the number of clusters in TILs increases following anti-PD1 immunotherapy. 2. the number of clusters is enriched in the CD39+PD1+ positive TIL fraction. 3. the number of clusters can be used as an indication of the success of in vitro expansions of tumor specific T cells.
Overall, the data provided are convincing, although the number of samples studied is small. The explanation of the figures is occasionally hard to follow, which in its current form lessens the impact of the paper. But the study will certainly be of interest to all those interested in characterising the tumor-specific T cell response in humans, and could form the basis for further more extensive studies to validate the results, and apply them to well-characterised clinical cohorts. -
Reviewer #3 (Public Review):
Goncharov et al provide a clear example of the importance of immune receptor repertoire profiling to identify responding tumor-specific T cells. They show that a previously proposed algorithm (ALICE) can be a useful tool to characterize tumor-specific T cell enrichment in grafts and to optimize TIL cultures. These results have the potential to accelerate clinical development of adoptive T cell transfer techniques and are of interest for the community.
The paper is concise and to the point. The conclusions are well supported by the data and the analyses. Some additional explanations regarding the computational analysis can help readability and clarity.
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