Despite a dramatic increase in T-cell receptor (TCR) sequencing, few approaches biologically parse the data in a fashion that both helps yield new information about immune responses and may guide immunotherapeutic interventions. To address this issue, we developed a method, ImmunoMap, that utilizes a sequence analysis approach inspired by phylogenetics to examine TCR repertoire relatedness. ImmunoMap analysis of the CD8 T-cell response to self-antigen (Kb-TRP2) or to a model foreign antigen (Kb-SIY) in naïve and tumor-bearing B6 mice showed differences in the T-cell repertoire of self- versus foreign antigen-specific responses, potentially reflecting immune pressure by the tumor, and also detected lymphoid organ–specific differences in TCR repertoires. When ImmunoMap was used to analyze clinical trial data of tumor-infiltrating lymphocytes from patients being treated with anti–PD-1, ImmunoMap, but not standard TCR sequence analyses, revealed a clinically predicative signature in pre- and posttherapy samples. Cancer Immunol Res; 6(2); 151–62. ©2017 AACR.
The advent of high-throughput immune sequencing has allowed scientists and clinicians to understand antigen-specific interactions of the immune response with various pathologies from a systems-like perspective. Its initial applications showed the depth and breadth of the T-cell receptor (TCR) and B-cell receptor (BCR) repertoire (1–4). Further applications of immune deep sequencing have contributed to vaccine development and to tracking disease progression in malignancies (5–7). Sequencing efforts have improved our understanding of immune responses to cancer, characterizing the TCR repertoire of circulating as well as tumor-infiltrating lymphocytes (TIL; refs. 8, 9).
With the abundance of new “big data” sets, a need has arisen to develop biologically meaningful techniques for analysis of TCR repertoire sequences. Current analysis methods fall short of providing an intuitive understanding of the immune system repertoire for two reasons. First, as purely mathematical constructs, they focus on diversity defined as a function of the number of different sequences and their respective frequencies, Shannon entropy, and ignore sequence relatedness (10, 11). Second, methods that compare different repertoires apply stringent criteria by only comparing exact TCR clonotypes (whether at the nucleotide or amino acid level) to assess similarity (9, 12, 13).
However, biological sequence similarity, not identity, is the relevant parameter. Ignoring sequence relatedness is a significant omission, because TCRs with similar, homologous sequences likely recognize related MHC/peptide targets. Several groups have sought to understand the structural aspects of the underlying TCR repertoire through a variety of techniques that cluster homologous CDR3 sequences, showing that, indeed, TCRs that recognize the same antigen have highly homologous sequences (14–16). Although this work highlights the relevance and rationale behind analyzing TCR sequence repertoire data via clustering methods, we sought to create structural diversity metrics for whole TCR repertoires. To address this, we developed ImmunoMap, which visualizes and quantifies immune repertoire diversity in a holistic fashion. ImmunoMap not only enables assessment of similarity between TCR sequences, but displays the scope of diversity among different repertoires. Our approach combines information about the frequency and relatedness of TCR sequences by using a sequence analysis inspired by phylogenetics to determine relatedness among cells of an antigen-specific T-cell response, as well as the similarities of a particular TCR repertoire to other repertoires.
We initially trained ImmunoMap on the TCRs used by T cells responding to Kb-TRP2, a shared self-peptide tumor antigen, and Kb-SIY, a model foreign antigen, in a model of murine melanoma. ImmunoMap analysis showed that in naïve animals, the response to Kb-SIY was highly conserved, with many TCR sequences having high sequence homology, a biological observation that we missed when using Shannon entropy calculations. In contrast, the bulk of the self-antigen response was comprised of fewer and more distantly related sequences. The presence of tumor had a differential effect on the shaping of the repertoire in the model foreign and self-antigen responses, greatly altering the TCR repertoire of the self-antigen response, with a smaller effect on the response to the foreign antigen. To understand the clinical utility of ImmunoMap, we compared ImmunoMap with Shannon entropy analysis of TILs from melanoma patients on anti–PD-1 therapy. Whereas Shannon entropy calculations did not find any clinically relevant correlates, ImmunoMap found clinically relevant, predicative TCR signatures in patients who responded to anti–PD-1 therapy after just 4 weeks on therapy. Thus ImmunoMap proved more effective in finding a clinically useful parameter that another repertoire analysis technique could not.
Materials and Methods
C57BL/6j mice were purchased from The Jackson Laboratory. All mice were maintained according to Johns Hopkins School of Medicine IACUC. Mice (4–5 mice, gender and age matched) were used and pooled for each stimulation condition, based on previous T-cell expansion experiments, and each stimulation and sequencing run was performed once. Mice were randomly selected for naïve or tumor-bearing treatments, and the principal investigator was blinded to which mice received tumors. Murine experiments for naïve and tumor-bearing spleens were duplicated in separate pools of animals to demonstrate reproducibility of antigen-specific repertoire characteristics (Supplementary Fig. S7).
Preparation of MHC-Ig dimers and nano-aAPC
Mouse lymphocytes were obtained from homogenized mouse spleens after hypotonic lysis of RBCs. Cytotoxic lymphocytes were isolated using a CD8 magnetic enrichment column from Miltenyi Biotec following the manufacturer's instructions. Lymphocytes from lymph nodes were obtained from homogenized inguinal lymph nodes and enriched with nano-aAPCs and plated for 7 days. For tumor-bearing animals, murine melanoma cell line B16-SIY, obtained with the consent of Tom Gajewski (The University of Chicago, Chicago, IL) through Charles Drake (Columbia University Medical Center, New York, NY) in 2011, and reauthenticated in the past year by flow cytometry, was injected subcutaneously after 5 days of culture, measured by calipers, and harvested when tumors reach over 50 mm2. The B16-SIY cell line is a tumor model modified to express SIY, a completely foreign epitope to the murine B6 background. In naïve mice setting experiments, it was used as a model foreign antigen such as would be a viral epitope and in tumor-bearing animals serves as a tumor antigen. Tumor-infiltrating lymphocytes were obtained from tumors by manual digestion and washing, a density gradient centrifugation (Lympholyte Cell Separation Media, Mouse), and then, tumor cells plated for 3 hours at 37°C and lymphocytes washed off and plated with nano-aAPCs (1.25 × 109 particles/mL). All cell lines underwent testing for mycoplasma contamination.
Enrichment and expansion
Nano-aAPCs were stored at a concentration of 8.3 nmol/L (5 × 1012 particles/mL), and all volumes refer to particles at this concentration. Ten million CD8+-enriched lymphocytes at approximately 108 cells/mL were incubated with 10 μL of nano-aAPCs for 1 hour at 4°C, for an approximate bead:cell concentration of 5,000:1. Cell–particle mixtures were subsequently passed through a magnetic enrichment column; the negative fraction was collected and the positive fraction eluted. Positive fractions were mixed and cultured in 96-well round-bottom plates for 7 days in complete RPMI1640 medium supplemented l-glutamine, nonessential amino acids, vitamin solution, sodium pyruvate, β-mercaptoethanol, 10% FBS, ciprofloxacin, and 1% T-cell growth factor, a cytokine cocktail derived from stimulated PBMCs as described in the literature (19), in a humidified 5% CO2, 37°C incubator for 1 week. Specificity of cytotoxic T lymphocytes (CTL) was monitored on day 7, by FACS analysis following LIVE/DEAD cell stain (Thermo Fisher Scientific), anti-CD8 (BD Pharmingen, cat# 553035, 53-6.7), and dimeric MHC-Ig staining. The number of antigen-specific cells was calculated by multiplying the number of total T cells by the fraction of CD8+ and antigen-specific T cells; the fraction of antigen-specific cells was calculated after subtracting the noncognate MHC staining from cognate MHC staining.
Sorting and sequencing of antigen-specific CD8+ T cells
Following LIVE/DEAD cell stain (Thermo Fisher Scientific), anti-CD8 (BD Biosciences), and dimeric MHC-Ig staining, cells were sorted by gating on cells with cognate Dimeric MHC-Ig staining over noncognate staining. Antigen-specific CD8+ T cells were sent directly for CDR3 β-chain sequencing by Adaptive Biotechnologies.
In vitro nano-aAPC functionality assay
Seven days following enrichment and expansion, antigen specificity was confirmed by intercellular cytokine staining. Briefly, RMA-S, given by Michael Edidin (Johns Hopkins University, Baltimore, MD) in 1996 (reauthenticated in the past year by peptide stabilization assays and cultured for 7 days prior to use), were peptide pulsed (10 μmol/L) overnight at room temperature with relevant or no peptide and mixed 1:2 RMAS:T-cell ratio with expanded T cells. Unpulsed RMAS cells were used as background stimulation. After 6 hours, cells were washed twice with FACS wash buffer and then stained with viability dye and anti-CD8 for 20 minutes. Cells were then fixed and permeabilized with the Cytofix/Cytoperm Kit (BD Biosciences) following the manufacturer's protocol. Anti-TNFα (BioLegend, cat# 506324, MR6-XT22) was added to the cells and stained for an hour.
Precursor frequency assessment
On day 0 following CD8+ T-cell isolation from splenic cells, CD8+ T cells were stained with LIVE/DEAD cell stain (Thermo Fisher Scientific), anti-CD8 (BD Biosciences), and dimeric MHC-Ig staining viability stain with either unloaded or peptide-loaded MHC-Ig. Cells were gated on live cells and anti-CD8a+ staining.
Collection of TILs from patients undergoing α-PD-1 therapy
Eighty-five patients, providing written consent, were accrued to a multiarm, multi-institutional, Institutional Review Board–approved, prospective study (BMS-038) to investigate the pharmacodynamic activity of nivolumab. All patients received nivolumab (3 mg/kg every 2 weeks) until progression for a maximum of 2 years. Tumor samples were collected prior to and 4 weeks after initiation of nivolumab therapy. The samples were stored in RNAlater (Ambion). Thirty-four patients permitted TCR sequencing, and DNA was extracted and submitted to Adaptive Biotechnologies for survey level TCR β-chain sequencing (3, 20). Clinical response was assessed via CT scan after 24 weeks of therapy.
Because of the fact that animals were pooled together on day 0 prior to expansion of antigen-specific cells, there was only one sequencing run. To determine the variance of calculated indicators of the repertoire, the reads from the sequencing file were randomly distributed into the number of bins corresponding to the number of animals that went into the experiment. This method of random deconvolution assured that the variance of the indicator by random chance was not greater than the difference observed between conditions.
Weighted repertoire dendrograms
For the antigen-specific sequencing, productive sequences with a frequency >0.01% were taken for analysis. For anti–PD-1 clinical trial analysis, Adaptive Biotechnologies' files were first filtered to only include sequences with reads greater than or equal to 5 and then top 40% of response was taken for analysis. Sequence distances were calculated on the basis of sequence alignments scores using a PAM10 scoring matrix and gap penalty of 30. Distance matrix was used to create a dendrogram using the Bioinformatics toolbox in MATLAB. Circles were overlaid at the end of the branches corresponding to the CDR3 sequences with diameters proportional to the frequency of the sequence. When using the terminology “weighted repertoire dendrogram,” this does not infer that the distance matrix used to create the dendrogram is weighted; rather, the dendrogram is visually “weighted” by frequency.
Dominant motif analysis
Using the cluster function in MATLAB toolbox, dendrogram was divided into homologous clusters using a homology threshold obtained from analyzing an unexpanded adult CD8+ T-cell population from a C57BL/6 animal (Supplementary Fig. S1). Clusters whose average sequence distance within cluster ≤ threshold and met a certain frequency cutoff (3%, Supplementary Fig. S1) were denoted as “dominant motifs.” Cluster frequency was lowered to 1% for α-PD-1 clinical trial analysis but held consistent across all patients due to the fact that this was not a single antigen-specific population of cells.
Singular and novel clone analysis
To define singular clones, a matrix was setup to calculate the mapped sequence distance of every unique combination of sequences in the repertoire. Using standard matrix operations within MATLAB, a singular clone was defined as a clone whose frequency was 10 times the sum of all other homologous clones. Homologous clones were those who had a sequence distance determined from the dominant motif analysis. To define novel clones, the same approach was used, but the matrix was setup in that it calculated the mapped sequence distance of every unique combination of sequences between the two repertoires being compared. A novel clone was defined as a clone whose frequency was 10 times the sum of all homologous clones in the other sample.
TCR diversity score
This measurement of diversity was calculated in a similar method as the singular and novel clone analysis. An initial matrix is created where the mapped sequence distance is calculated for every unique combination of sequences in the repertoire. Then, the average of the unique combination calculations is taken, weighted by reads, and reported as the TCR diversity score. Additional details of the algorithm behind this calculation are shown in Supplementary Fig. S6.
Shannon entropy calculations
Calculation of Shannon entropy was completed by the following formula where pi represents the frequency of each amino acid sequence and n represents the total number of sequences present in the response:
No specific statistical method was used to determine sample size for the stimulation cohorts. Two-tailed t tests were used as provided by GraphPad Prism 5 software for all comparative statistics given we expect normal distributions across all experiments.
To use the ImmunoMap algorithms, we have developed a MATLAB-based Graphical User Interface (GUI) that can be found along with the source code at https://github.com/sidhomj/ImmunoMap. Supplementary Figure S8 demonstrates the use of the GUI.
TCR β-chain sequencing raw data for the murine experiments are provided in Supplementary Material.
Overview of ImmunoMap algorithms
Weighted repertoire dendrograms.
To visualize the immune response, we created weighted dendrograms, combining information about sequence relatedness with information about sequence frequency. We initially applied this analysis to data (from the Adaptive Biotechnologies Data Portal; ref. 21) on the response of tetramer-sorted human CD8+ T cells to cytomegalovirus (CMV; Fig. 1A). The distance from the end of the dendrogram branches denotes distance in terms of sequence homology; the size of the circles at the ends of the branches denotes frequency of the sequence, and color denotes Vβ usage. Sequence distance is determined as a function of global alignment scores [Needleman–Wunsch (22), PAM10 scoring matrix (23), gap penalty = 30] between all unique combination of sequences as follows:
Dominant motif analysis.
To parse the many sequences that are detected in antigen-specific CTL expansion, we sought to perform hierarchical clustering to determine structural motifs that dominated the response. Thresholds for sequence homology and frequency were set by analyzing the sequences of the naïve B6 CD8+ repertoire, taken from the Adaptive Biotechnologies Data Portal (Supplementary Fig. S1; ref. 24). We used these thresholds to define homology clusters based on sequence distance and then examined clusters that met a predefined frequency threshold and termed them “dominant motifs” (Fig. 1B).
Singular and novel structural clones analysis.
We also defined a “singular structural clone” as one that has expanded 10 times more than the summation of all other homologous clones in a sample, representing a singular solution in “sequence space” (Fig. 1C). When comparing two separate CMV-specific sequencing samples, from different individuals, we defined a “novel structural clone” as one that has expanded 10 times more than the summation of all homologous clones to it in another sequencing sample, representing a newly expanded structural clone (Fig. 1D).
TCR diversity score.
To quantify the diversity of the entire TCR repertoire, we created a metric to quantify the relatedness of an entire sample, defined as the average mapped sequence distance of all unique combinations of sequences in a sample, weighted by number of reads per sequence.
The TCR diversity score is bounded between 0 and 1, in which a score of 0 would correspond to all TCRs in a response being identical and 1 would correspond to all TCRs being infinitely different (full details of algorithms to calculate TCR diversity score are provided in Supplementary Fig. S6).
Naïve TCR repertoires against model tumor antigens
To understand the clonal diversity of antigen responses, CD8+ T cells from naïve B6 mice were pooled and expanded against a model foreign antigen Kb-SIY, or against a self-tumor antigen, Kb-TRP2 (180–188), as described previously (17, 25). Briefly, CD8+ T cells were enriched and stimulated with nanoparticle artificial antigen-presenting cells (aAPC) containing peptide-MHC-Ig molecules and cultured in vitro for 7 days (Fig. 2A). The resultant CD8+ T-cell cultures were antigen specific by both peptide-MHC-Ig staining and cytokine analysis, confirming their functional specificity (Supplementary Fig. S2). Initial precursor frequency was also measured in the endogenous repertoire and, even though T cells that recognize either antigen could be expanded from naïve animals, Kb-SIY antigen–specific T cells had a higher naïve precursor frequency (Supplementary Fig. S2). Antigen-specific populations were sorted and the CDR3 region of the TCR Vβ chain was sequenced.
ImmunoMap analysis of Kb-SIY–specific and Kb-TRP2–specific TCRs (Fig. 2B) visualized unique aspects of the polyclonal response for both antigens. Kb-SIY CD8+ T cells consisted of clones with homologous TCR sequences; however, the naïve response to Kb-TRP2 was more clonal in nature (more high frequency clones) and used more unrelated sequences, each creating a distinct clonal variant for antigen recognition.
Dominant motif analysis showed that anti-Kb-SIY TCR had fewer, yet richer (more sequences per motif), dominant motifs than Kb-TRP2 (Fig. 2C and D). Kb-TRP2–specific T cells had a higher percentage of clones representing singular structural T-cell expansions, and they took up a larger portion of the overall TRP-2 antigen-specific response (Fig. 2D, bottom). Comparing the TCR diversity scores, Kb-SIY stimulated a more homologous response, whereas Kb-TRP2 had a more diverse response. The response to Kb-SIY had a more conserved Vβ usage, predominantly using Vβ13, whereas the response to Kb-TRP2 exhibited a more diverse use of Vβ segments (Supplementary Fig. S3).
To demonstrate the advantages of the ImmunoMap analysis over traditional analytic methods, we calculated Shannon entropies for Kb-SIY versus Kb-TRP2 responses (Fig. 2E). Shannon entropies revealed the diversity of the Kb-SIY response to be higher than that of the Kb-TRP2 response. However, because the Shannon entropy is largely determined by the number of sequences that are present in the Kb-SIY response and not their relatedness, it missed the fact that although more sequences responded to Kb-SIY, they were more convergent than the fewer sequences that responded to Kb-TRP2. Thus, the ImmunoMap TCR diversity score and dominant motif analyses reflected novel relatedness information that could not be seen by conventional Shannon entropy calculations.
Tumor exerts differential expansion pressure on antigen-specific repertoire
Although we know that tumors exert pressure on the immune response, it is not clear how this alters the repertoire of responding T cells. The ImmunoMap approach can provide insight into the biological impact of tumors on T-cell responses and TCR usage by studying TCR repertoire changes in the presence of tumor (B16-SIY; ref. 26). Visualization of the TCR repertoire by ImmunoMap analysis (Fig. 3A) showed differential effects of tumors on the repertoire of pooled splenic T cells specific for Kb-SIY or Kb-TRP2. The Kb-SIY CD8+ T-cell repertoire was largely unaltered in response to tumors. In contrast, as seen by ImmunoMap, the Kb-TRP2 response was not only more clonal, but also used TCR sequences that had minimal sequence homology to the TCRs seen in the naïve C57BL/6 response.
Dominant motif analysis showed that the presence of tumors increased the number of dominant motifs in the Kb-SIY response (Fig. 3B). In contrast, the presence of tumors decreased the number of dominant motifs in the Kb-TRP2 response, suggesting directed immune pressure on the self versus foreign antigens in the context of tumor. The dominant Kb-SIY motifs were conserved (Fig. 3C). In contrast, no common dominant motifs were shared in the Kb-TRP2 response in tumor-bearing animals compared with the naïve response. When examining novel structural clones (Fig. 3D), the Kb-TRP2 response in tumor-bearing mice had more structurally novel sequences that, combined, were a larger portion of the response as compared with the naïve response. Selective pressure by tumors on the immune response was also seen in analyzing the Vβ usage between naïve and tumor-bearing animals (Fig. 3E). We saw the elimination of the use of Vβ16 in the Kb-TRP2 response, and an increased use of Vβ5. In contrast, Vβ usage was conserved in the Kb-SIY response between naïve and tumor-bearing animals (Supplementary Fig. S4).
In addition, when examining the effect of tumors on Shannon entropy (Fig. 3F), we see that although maintenance of entropy in the Kb-SIY response and its decrease in the Kb-TRP2 response generally complement the ImmunoMap dominant motif analysis, Shannon entropies are uninformative about the conservation, or lack thereof, of TCR sequence structure in response to the tumor.
Lymphoid organ–dependent differences in TCR repertoires in tumor-bearing mice
We hypothesized that the influence of tumors on the repertoire may also vary depending upon the relationship of the lymphoid organ to the tumor site. This was studied by analyzing antigen-specific TCR repertoires in the spleen versus draining lymph node (dLN), and TILs in pooled tumor-bearing mice lymph nodes and tumors. ImmunoMap analysis revealed that the Kb-SIY repertoire selects for effective structural motifs as one probes compartments closer to the tumor site. This is seen as the richness of dominant motifs decreases, the response contributed by singular clones increases, and the TCR diversity score drops as one moves from the spleen toward the tumor (Fig. 4B). In addition, the structural clones expanded in the spleen, dLN, and TILs are generally conserved, as can be visualized by the dendrograms (Fig. 4A) and by tracking dominant motifs in the three lymphoid compartments (Fig. 4C). In contrast, the opposite trend was seen in the Kb-TRP2 response. In addition, dominant motifs between the spleen and dLN were not conserved; we were unable to expand any Kb-TRP2 specific cells from the TILs in multiple experiments (Fig. 4A–C).
Analysis of anti–PD-1 clinical trial data reveals indicators of response
Recent studies have implicated changes in T-cell responses as important in clinical outcomes to checkpoint blockade. We therefore applied ImmunoMap analysis to clinical trial data (BMS-038) from patients with metastatic melanoma undergoing anti–PD-1 therapy (nivolumab). For this analysis, formalin-fixed, paraffin-embedded scrapings were taken from 34 patients, the percentage of TILs estimated as per adaptive protocol (Materials and Methods) and CDR3 regions of Vβ-chains sequenced before and while on therapy (Fig. 5A). The number of TCRs sequenced in all samples analyzed was not significantly different (Supplementary Fig. S5).
ImmunoMap was used to compare the TCR repertoire before and after 4 weeks of anti–PD-1 therapy (all ImmunoMap metrics in BMS038Results.xlsx). Weighted repertoire dendrograms (Fig. 5B) revealed distinct differences between responders and nonresponders. Dominant motif analysis (Fig. 5C) showed that patients who had more dominant motifs prior to initiation of therapy had more favorable responses to therapy. In addition, those patients who had a decrease in their TCR diversity score (Fig. 5C) on therapy had more favorable outcomes to therapy. In contrast, no clinically relevant signature could be found by Shannon entropy calculations (Fig. 5D). Thus, ImmunoMap analysis was superior in its ability to reveal repertoire characteristics that could predict response to therapy after only 4 weeks of treatment.
Here, we introduce ImmunoMap, a bioinformatics approach to analyze TCR repertoire sequence data, and used it to characterize repertoire changes in responses to model murine tumors and in patients undergoing immunotherapy for melanoma. By combining information about sequence relatedness and frequency, ImmunoMap allows an intuitive appreciation of TCR repertoire characteristics that reconciles the structure and function of the repertoire.
ImmunoMap analysis comparing foreign (Kb-SIY) and self- (Kb-TRP2) antigens showed distinct differences in the naïve repertoire to these two different antigens. Although interesting, the conclusions of this analysis cannot be expanded to all foreign versus self-antigens. The presence of more dominant motifs in the Kb-TRP2 response in combination with greater clonality suggests that central and peripheral tolerance mechanisms limited clonal responses, with more distinct clones occupying a larger portion of the TRP2-specific repertoire. Self-reactive clones, with TRP2-specific TCRs, would either be removed during central thymic development or tolerized in the periphery, explaining the inability to find more numerous TCR sequences per dominant motif (27–29). Because our analysis was conducted on expanded antigen-specific populations, our results demonstrate the “expansion potential” of the antigen-specific T-cell repertoire for a model foreign and shared tumor antigen in the setting of both naïve and tumor-bearing animals. It is possible that the limited TCR relatedness of TRP2 responses could be due to the lower precursor frequency in naïve animals, and the T cells that have the ability to expand do not cluster in the same dominant motif due to lower initial cell frequency. The impact of pooling animals prior to expansion and sequencing must also be considered. In this scenario, one could be selecting for “public” clones and possibly enriching for these parts of the repertoire over “private” clones, unique to each animal. Although individual mice are genetically identical, VDJ recombination occurs as an independent process in each animal and the primary TCR repertoire capable of responding to a given antigen could vary between individual animals. Therefore, the effects on shaping of the repertoire may be most relevant to “public” or conserved sequences. Finally, the higher TCR diversity score of Kb-TRP2 alongside with the higher number of dominant motifs suggests that the immune system has to reach further to find solutions to bind the cognate antigen/MHC complex.
Although prior work on TCR clustering has focused on understanding the structural aspects that confer antigen specificity (14, 15), the effects of perturbations to the immune system on antigen-specific responses has not been studied. With ImmunoMap, we studied the changes in repertoire in response to tumor. We observed that the effects of tumor on the anti–self-Kb-TRP 2 peptide repertoire indicate that tumors exert greater pressure on the self- than on the foreign antigen. Not only did the presence of the tumors correlate with an increase the clonality of the response to self, via decreases in the number of dominant motifs and increases in their contribution to the net response, but tumor-bearing mice could shift their response to different, presumably suboptimal, motifs. In addition, the differences in repertoire characteristics among various lymphoid organs for the two different model antigens indicate that tumors effectively eliminated the expansion of certain clones from its microenvironment. The consequences of these findings are relevant to both antigen discovery and targeting for immune therapies related to treating cancer. Because of limitations of personalized antigen-specific therapy, targeting shared antigens like MART1, a self-antigen specific for melanocytes, has been a mainstay of antigen-specific cancer immunotherapy (30–32). This approach has typically relied on TCR transgenic models in which a single TCR clone is chosen as the source for the antigen-specific receptor (33–35). Given our analysis, several problems with this approach become apparent: (i) antigen-specific expansion not only generates a diversity of TCR sequences but one that spans the entire sequence distance of the naïve repertoire; (ii) self-antigen expansion represents a limited repertoire and arsenal against a given epitope due to effects of tolerance; and (iii) the tumor can exert pressure on the self-antigen–specific immune response in a more profound way than in the case of a foreign antigen. Our findings call into question the approach of using self or overexpressed antigens as targets for immune therapy and highlight the importance of exploring responses to neoantigens, novel MHC-specific epitopes that arise from mutations in a patient's individual malignancy (36–40).
We also have used ImmunoMap algorithms to understand mechanisms of successful immune responses to cancer against 4T1, a murine breast cancer model (41). In that model, when analyzing TILs from animals treated with anti–CTLA-4, radiation, or the combination of these therapies, we found that the TCR structural repertoire before therapy from TILs was highly conserved, seemingly targeting a single antigen, whereas after combination therapy, the structural response broadened within the TILs and each individual animal developed its own uniquely expanded repertoire (41).
Finally, we used ImmunoMap to study TILs from clinical trial specimens to determine whether structural diversity is an important parameter in determining successful immune responses to cancer immunotherapy. Our analysis revealed that patients who had more dominant motifs prior to therapy responded more favorably to therapy. In addition, the change in TCR diversity suggests that patients who respond to therapy converge on a solution of successful TCR sequences, and thus, their repertoire is actually less diverse after therapy. In contrast to previous work by Madi and colleagues that demonstrated a structural broadening of the peripheral repertoire to anti–CTLA-4 therapy in melanoma patients, but did not correlate this finding with response, we focused our analysis on studying changes in the repertoire within the TILs and could determine structural signatures of response (16). Although our findings are significant, we note the scope of the clinical trial was limited, which impacted the distribution of clinical responses. Nevertheless, taken together, ImmunoMap analysis revealed that patients with a broader repertoire prior to therapy have a higher probability of expanding effective TCR sequences and converging on them.
ImmunoMap not only has the potential for clinical monitoring of patients on therapy, through predictions of their likelihood to respond, but enables the acquisition of biological insights about antigen-specific immune responses that could alter current immune therapies.
Disclosure of Potential Conflicts of Interest
J.P. Schneck has ownership interest (including patents) in and is a consultant/advisory board member for NexImmune. No potential conflicts of interest were disclosed by the other authors.
Conception and design: J.-W. Sidhom, C.A. Bessell, T.A. Chan, J.P. Schneck
Development of methodology: J.-W. Sidhom
Acquisition of data (provided animals, acquired and managed patients, provided facilities, etc.): J.-W. Sidhom, C.A. Bessell, J.J. Havel, A. Kosmides, T.A. Chan
Analysis and interpretation of data (e.g., statistical analysis, biostatistics, computational analysis): J.-W. Sidhom, C.A. Bessell, J.J. Havel, T.A. Chan, J.P. Schneck
Writing, review, and/or revision of the manuscript: J.-W. Sidhom, C.A. Bessell, T.A. Chan, J.P. Schneck
Administrative, technical, or material support (i.e., reporting or organizing data, constructing databases): J.-W. Sidhom, T.A. Chan
Study supervision: J.P. Schneck
The authors thank the JHU-Coulter Translational Partnership, the TEDCO Maryland Innovation Initiative, The Troper Wojcicki Foundation, and the NIH (R01- AI44129, CA108835, and U01 – AI113315). Animal and clinical protocol images were reproduced under a Creative Commons License from Servier Medical Art (http://www.servier.com/Powerpoint-image-bank). This work was funded by Bristol Myers-Squibb, the Pershing Square Sohn Cancer Research Foundation (T.A. Chan), the PaineWebber Chair (T.A. Chan), Stand Up To Cancer (T.A. Chan), and the STARR Cancer Consortium (T.A. Chan). Partial Support was provided by the MSK Core Grant (P30 CA008748). Research supported by a Stand Up To Cancer - American Cancer Society Lung Cancer Dream Team Translational Research Grant (Grant Number: SU2C-AACR-DT17-15). Stand Up To Cancer is a program of the Entertainment Industry Foundation. Research grants are administered by the American Association for Cancer Research, the scientific partner of SU2C.
In addition, we thank each patient and their families for participation in this study and for their willingness to accept on-treatment biopsies to facilitate scientific advancement in cancer immunotherapy. Editorial assistance was provided by Michael Edidin and Drew Pardoll.
The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.