Cetuximab Responses in Patients with HNSCC Correlate to Clonal Expansion Feature of Peripheral and Tumor-Infiltrating T Cells with Top T-Cell Receptor Clonotypes

About 90% of head and neck cancers are head and neck squamous cell carcinoma (HNSCC; refs. 1, 2). HNSCCs have a high morbidity and mortality rate, with only 50%–60% patients surviving 5 years (3). The risk factors for HNSCCs include carcinogens, such as alcohol and tobacco use, or oncogenic human papillomavirus (HPV; refs. 2, 4); thus, HNSCCs can be categorized into two main subtypes as HPV⁻ or HPV⁺ HNSCCs, according to distinct etiology. HNSCCs often overexpress EGFR (5), and agents have been approved by FDA to target EGFR in HNSCCs, including cetuximab, a mAb against EGFR. Response rate to cetuximab in HNSCCs remains modest [<15% in recurrent/metastatic (R/M) HNSCCs; ref. 5] and markers correlated to clinical response remain poorly characterized.

T cells play an essential role in mediating antitumor immunity by recognizing tumor-specific or tumor-associated antigens via clonotypic T-cell receptors (TCR). TCRs are generated through a somatic DNA recombination process, termed V(D)J recombination (6, 7), occurring in a random and stochastic manner. Most conventional T cells express TCRs consisting of an alpha (α) chain and a beta (β) chain, encoded by TRA and TRB, respectively, and linked by disulfide bonds. T cells can be grouped into different “clonotypes” based on their unique TCRα and/or TCRβ chains, constituting specific V(D)J gene segments and complementarity-determining region 3 (CDR3). The latter is of particular interest for T-cell function because antigen specificity of each TCR is largely determined by CDR3 that encompasses the highly divergent junction of V(D)J recombination and encodes for the antigen-binding pocket of TCR. Therefore, clonal diversity representing how many unique clones per sample and expansion features of the TCR repertoire can be assessed by analyzing TCRα and/or TCRβ CDR3 sequences of a given patient sample using high-throughput sequencing (8, 9). Moreover, CDR3 analysis allows for assessing dynamic changes for a given TCR clone between pretreatment and posttreatment in a patient-specific manner and for identifying difference or similarity between peripheral and tumor-infiltrating T cells in their TCR repertoires. Hence, characterizing dynamic changes in the TCR repertoire of the peripheral blood mononuclear cells (PBMC) and tumor tissue may facilitate developing minimally invasive markers for immunotherapy efficacy, including clinical responses to cetuximab treatment.

We previously showed that cetuximab can significantly enhance antigen presentation and priming of adaptive immune responses in tumor-infiltrating lymphocytes (TIL; ref. 10). To further delineate the actual changes in adaptive immunity in relation to cetuximab response, we examined the effect of cetuximab on the TCR repertoires of peripheral and tumor-infiltrating T cells from 14 patients with HNSCC before and after treatment, taking advantage of a prospective “window of opportunity” clinical trial of neoadjuvant cetuximab therapy (NCT01218048; ref. 11). Some uncertainty and controversy exist about whether TCR dynamic changes correlate with clinical response to immunotherapy such as anti-EGFR. Further studies are needed to evaluate different analytic methods to overcome technical variations in the number of total productive TCR sequences obtained in different samples.

In the current study, we applied a new analytic method to TCR sequencing data and tested different normalization approaches that account for the variations in the number of total productive TCR sequences obtained in different samples. Using this method, we did not detect differences between responders and nonresponders in TCR richness, defined by the number of unique productive TCR sequences. However, we did identify a few parameters that correlated with cetuximab response, including the expansion rate of top TCR clonotypes in PBMC or TIL and the overlapping probability of expanded top TCR clonotypes between PBMC and TIL. We propose that these parameters may serve as useful markers for predicting clinical response to cetuximab. Our study also highlights the importance of data normalization for TCR repertoire analysis, based on total number of productive TCR sequences amplified in a given sample.

Detailed information for patients with HNSCC and the clinical trial was described previously (11). Briefly, patients with treatment-naïve stage III/IV HNSCC who were candidates for surgical resection were recruited to the prospective phase II “window of opportunity” clinical trial (NCT01218048). The clinical trial was started in 2011 and completed in 2017. The prior study was approved by the Institutional Review Board (IRB) at the University of Pittsburgh (Pittsburgh, PA) and conducted in accordance with Good Clinical Practice guidelines and the ethical principles outlined in the Declaration of Helsinki. Written informed consent was obtained from all subjects before enrollment (11). Anti-EGFR treatment composed of three to four weekly doses of neoadjuvant cetuximab, followed by surgical resection within 36–48 hours of the last cetuximab dose received. Radiological response was evaluated according to RECIST. The basic clinical characteristic of patients is included in Supplementary Data S1. Patient sample collection was performed in the prior study (11). Briefly, tumor tissue and peripheral blood samples were collected at initial screening and again at the time of surgery under the IRB-approved tissue banking protocol (HCC 99-069). PBMCs were isolated from peripheral blood samples as described previously (11) and immediately used for experiments or stored at −80°C for further analysis. TILs were isolated as described previously and used for TCRβ sequencing with ImmunoSEQ platform by Adaptive Biotechnologies. Clinical samples were collected upon approval of IRB at the University of Pittsburgh (HCC 99-069) in a prior study. The current study only involves analysis of existing dataset.

Each patient has four samples sequenced including pre- and post-PBMC and pre- and post-TIL (Tissue) before and after 4 weeks of cetuximab treatment. In total, 56 samples were sequenced for 14 patients with HNSCC. ImmunoSEQ Analyzer was used to retrieve, process, and track the TCRβ sequencing data. The data retrieved from immunoSEQ analyzer contained two columns that are relevant to TCR repertoire analysis, “Total Productive Templates” representing the number of total productive TCR DNA sequences obtained per sample and “Productive Rearrangements” representing the number of unique productive TCR DNA sequences in each sample. We combined the productive rearrangements that encode the same CDR3 amino acid (a.a.) and contain the same V, D, J gene usage into the same TCR clonotypes using R. TCR richness was defined as the number of unique productive TCR a.a. sequences in each sample. Normalized TCR richness was calculated by dividing the number of unique productive TCR a.a. sequences with the number of total productive TCR DNA sequences. Differences between two independent groups (e.g., pre-PBMC vs. post-PBMC) were compared by the Wilcoxon signed-rank tests for the number of total productive TCR sequences, TCR richness or normalized TCR richness.

Expansion rate was calculated by dividing the frequency of a given TCR sequence between post- and pre-treatment samples (expansion rate for PBMC = % in post-PBMC/% in pre-PBMC; expansion rate for TIL = % in post-TIL/% in pre-TIL). If expansion rate is >1 for a given TCR clonotype, it will be counted as an expanded clone. Singleton clonotype was defined as the TCR sequences that only occurred once in a given PBMC or TIL sample. The sum of a singleton clonotype frequency was calculated by adding up the clonal frequency of all singleton clonotypes in a given sample. Normalized expansion rate was calculated in two different ways: (i) using clonal frequency of a singleton clonotype, |$\big(\frac{{\% \,in\,post\,PBMC}}{{singleton\% \,in\,post\,PBMC}}\big)$|/|$\big(\frac{{\% \,in\,pre\,PBMC}}{{singleton\% \,in\,pre\,PBMC}}\big)$| or |$\big(\frac{{\% \,in\,post\,TIL}}{{singleton\% \,in\,post\,TIL}}\big)$|/|$\big(\frac{{\% \,in\,pre\,TIL}}{{singleton\% \,in\,pre\,TIL}}\big)$|⁠; (ii) using the sum (∑) of total singleton clonotype frequency, |$\big(\frac{{\% \,in\,post\,PBMC}}{{\sum\nolimits_{i = 1}^n {singleton\% \,in\,post\,PBMC}}}\big)$|/|$\big(\frac{{\% \,in\,pre\,PBMC}}{{\sum\nolimits_{i = 1}^n {singleton\% \,in\,pre\,PBMC}}}\big)$| or |$\big(\frac{{\% \,in\,post\,TIL}}{{\sum\nolimits_{i = 1}^n {singleton\% \,in\,post\,TIL}}}\big)$|/|$\big(\frac{{\% \,in\,pre\,TIL}}{{\sum\nolimits_{i = 1}^n {singleton\% \,in\,pre\,TIL}}}\big)$| (n is the total number of singleton clonotypes). Differences between two independent groups (responder vs. nonresponder) were compared by the Wilcoxon rank-sum test for expansion rate or normalized expansion rate. Data analyses were carried out using the R version 4.2.0. All tests are two sided and a P value of 0.05 or less is considered statistically significant.

Adaptive ImmunoSEQ sequencing data are available via https://clients.adaptivebiotech.com/pub/ge-2022-ccr. The immuneACCESS DOI is https://doi.org/10.21417/HG2022CCR. Additional analysis information is available upon request to Dr. Jing H. Wang at [email protected].

We found that the number of total productive TCR sequences, which could serve as a surrogate for how many T cells were sequenced per sample, varied significantly in different samples; in particular, it appeared to be predominantly higher in post-PBMC samples (Fig. 1A), regardless of responder versus nonresponder status (Fig. 1B and C). Consistently, we showed that the number of total productive TCR sequences was significantly higher in post-PBMC than pre-PBMC samples (P = 0.0006; Fig. 1D). These data suggest that there were many more T cells sequenced or many more productive TCR sequences obtained in post-PBMC group, raising a question about whether variations in the number of total productive TCR sequences may influence TCR richness analysis.

Our current analysis revealed that TCR richness demonstrated a supporting strong positive association with the number of total productive TCR sequences (Fig. 1E), meaning that if a given sample has more productive TCR sequences, it will have more unique productive TCR sequences. To reduce the influence of such sampling variation, we normalized the number of unique productive TCR sequences by dividing it by the number of total productive TCR sequences, defined as “normalized TCR richness.” After applying the normalization step, we did not detect a strong association between normalized TCR richness and the number of total productive TCR sequences (Fig. 1F). These data suggest that we have minimized the effect of sampling variation on TCR richness analysis.

After normalizing TCR richness with the number of total productive TCR sequences, we did not detect differences between pre-PBMC and post-PBMC samples in normalized TCR richness (Fig. 2A). However, we found that the normalized TCR richness was significantly lower in post-TIL than pre-TIL samples (Fig. 2B; P = 0.05), indicating that clonal diversity was reduced in post-TIL, consistent with the notion that TCRs underwent clonal expansion at tumor site after cetuximab treatment. We did not observe significant differences in normalized TCR richness between responders and nonresponders for pre-PBMC, post-PBMC, pre-TIL, and post-TIL (Fig. 2C and D). We also detected no significant differences in normalized TCR richness within responder or nonresponder group by comparing pre-PBMC versus post-PBMC or pre-TIL versus post-TIL (data not shown). Finally, we compared HPV⁺ versus HPV⁻ samples and did not identify significant differences for all paired comparison between HPV⁺ and HPV⁻ or within HPV⁺ or HPV⁻ samples by comparing pre- versus post-PBMC or pre- versus post-TIL (data not shown). In summary, we did not detect significant differences in normalized TCR richness in paired comparison among all samples except between pre-TIL and post-TIL.

There are two levels of clonal expansion, and the first one is clonal expansion within a given sample. Each patient has four samples: pre-PBMC, post-PBMC, pre-TIL, and post-TIL. We can calculate the clonotype frequency for each clone within a given sample, for instance, the clonotype of CASTRTGYPAGPHNEQFF has its frequency as 1.033% in pre-TIL sample and 2.401% in post-TIL sample of patient 16 (Table 1). We can determine the extent of their clonal expansion based on their relative clonal frequency against the frequency of a singleton (see Materials and Methods) and their ranking in the entire TCR repertoire of each patient's PBMC or TIL samples. However, the number of total productive TCR sequences of pre-TIL versus post-TIL of patient 16 varied significantly (Fig. 1C), which may influence the baseline of expansion potential within each sample. Hence, we may have to normalize samples when comparing the extent of clonal expansion between different samples.

The second one is clonal expansion between samples by comparing pretreatment versus posttreatment, which is likely induced by cetuximab treatment as shown in Table 1. Given the evidence of clonal expansion in TCR repertoire, we next examined how TCR clones underwent expansion by comparing pre-PBMC versus post-PBMC or pre-TIL versus post-TIL samples. Expanded clones were defined as a given TCR clone whose clonal frequency was higher in posttreatment than pretreatment samples. We found that the number of expanded clones was not significantly different between responders and nonresponders for both PBMC and TIL samples (Fig. 3A and B), likely due to sampling variation.

Next, we focused on the top 20 TCR clones ranked by their individual clonal frequency within each sample, because we envision that these top ranked clones may contribute more to antitumor immunity than relatively rare clones. We first compiled top 20 clones in posttreatment samples including post-PBMC and post-TIL from each responder or nonresponder and identified the expanded TCR clonotypes by comparing clonal frequency between pretreatment versus posttreatment samples, defined as “expanded clonotypes from top 20 clones,” for simplicity, termed “expanded top 20 clonotypes.” Of note, not all of the top 20 ranked TCR clones were expanded between pretreatment and posttreatment samples (see details below in Fig. 5). The expansion rate was calculated to reflect the magnitude of clonal expansion between pre- versus post-PBMC or between pre- versus post-TIL for a given TCR clonotype (see details in Materials and Methods). We found that the expansion rate of expanded top 20 clonotypes was significantly higher in responder PBMC samples than nonresponder ones (Fig. 3C), even without baseline normalization for expansion potential. In contrast, we did not detect significant differences in the expansion rate of expanded top 20 clonotypes between responder and nonresponder TIL samples without baseline normalization for expansion potential (Fig. 3D).

Because the baseline of expansion potential varied markedly in different groups, we first normalized the expansion rate with the clonal frequency of a given singleton clonotype (see details in Materials and Methods). After applying this normalization approach, we found that the normalized expansion rate was significantly higher in both PBMC and TIL samples in responders than nonresponders (Fig. 3E and F). However, we noticed that the clonal frequency of a given singleton clonotype reversely correlated to log10(the number of total productive TCR sequences) (Fig. 3G). For instance, if a sample has 5,000 productive TCR sequences, the singleton frequency would be 1/5,000 = 0.02%; if a sample has 10,000 productive TCR sequences, the singleton frequency would be 1/10,000 = 0.01%. Thus, this normalization approach will again be influenced by the number of total productive TCR sequences, namely, sampling variation.

To better quantify the expansion potential of expanded top 20 clonotypes, we divided the clonal frequency of each expanded top 20 clonotype with the sum of a singleton clonotype frequency within a given sample, then calculated the normalized expansion rate between pretreatment and posttreatment samples (see details in Materials and Methods). We chose the sum of a singleton clonotype frequency to calibrate expansion potential between different samples due to the following reasons: (i) we found that this parameter did not correlate with the number of total productive TCR sequences (Fig. 4A), thus, was not affected by sampling variation; (ii) we did not detect any significant differences in the sum of a singleton clonotype frequency between responders and nonresponders for all groups including pre-PBMC, post-PBMC, pre-TIL, and post-TIL (Fig. 4B). Hence, this parameter was not affected by sample size or sample type; (iii) in a certain sense, the sum of a singleton clonotype frequency sets the baseline for expansion potential. Basically, it represents the percentage of all nonexpanded clones. After applying this normalization approach, we found that the expansion rate of expanded top 20 clonotypes was still significantly higher in responder PBMC (Fig. 4C) and, especially, in responder TIL samples (Fig. 4D) than nonresponder ones. Taken together, we conclude that the expansion rate of expanded top 20 clonotypes was significantly higher in responder PBMC regardless of normalization and remarkably higher in responder TIL upon normalization.

We also tested whether HPV status affected the extent of TCR clonotype expansion. We failed to detect any significant correlation between HPV status and the number of all expanded clones or the expansion rate of top 20 TCR clonotypes (Supplementary Fig. S1A–S1C). However, the sample size is rather small, and additional studies are needed to validate the findings.

Because we identified expanded TCR clonotypes in PBMC or TIL samples, we next asked whether the expanded top TCR clonotypes were shared between PBMC and TIL samples in a given patient. We first compiled the top 20 TCR clonotypes of post-PBMC or post-TIL from responders (n = 5; Fig. 5A, top) or nonresponders (n = 9; Fig. 5A, bottom), identified the expanded clones in PBMC or TIL, then overlapped the two groups of the expanded clones to find the shared expanded TCR clonotypes between PBMC and TIL samples. To define an overlapping expanded TCR clonotype, we calculated the ratio of (Freq in post-PBMC)/(Freq in pre-PBMC) and the ratio of (Freq in post-TIL)/(Freq in pre-TIL), both of which had to be more than 1 (Table 1). Using the TCR clonotype of CASSLSGGGEQYF as an example, the ratio of post-TIL/pre-TIL is 3.027 and the ratio of post-PBMC/pre-PBMC is 1.871 (Table 1), showing that this particular TCR clonotype was expanded in both PBMC and TIL after cetuximab treatment. Overall, we identified 11 TCR clonotypes shared between PBMC and TIL samples (Table 1); furthermore, nine of 11 clonotypes were found in responders (Fig. 5A; Table 1). Notably, 4 of 5 responders were found to harbor nine shared TCR clonotypes, whereas only 2 of 9 nonresponders contained two shared TCR clonotypes (Fig. 5A and B; Table 1). Clinical responders showed a significantly higher chance to harbor a shared expanded top 20 TCR clonotype between PBMC and TIL than nonresponders (Fig. 5C). Of note, the TCR clonotypes can distribute into eight different categories based on their expansion and overlapping status in PBMC and/or TIL (Fig. 5D, see details in figure legend for category a–h). The only category that had a higher number of clonotypes in responders is category c (PBMC Expanded and TIL Expanded), while all others showed higher numbers in nonresponders likely due to random chance because nonresponders had a higher number of total clones (n = 180 for nonresponders vs. n = 100 for responders).

Next, we extended our analysis to the top 50 TCR clonotypes identified in posttreatment samples (Fig. 6A). We found that all responders harbored shared expanded top TCR clonotypes, while 6 of 9 nonresponders harbored such TCR clonotypes (Fig. 6B). Similar to what we observed for top 20 TCR clonotypes, the responders still showed a significantly higher chance to harbor a shared, expanded top TCR clonotype between PBMC and TIL than did nonresponders (Fig. 6C). Again, we observed that only category c showed a higher number of TCR clonotypes in responders than nonresponders (21 vs. 13), whereas all other categories showed higher numbers in nonresponders (Fig. 6D), likely due to larger sample size (n = 450 for nonresponders vs. n = 250 for responders). These data strongly suggest that the shared expanded top TCR clonotypes occurred much more frequently in responders against random chance. When all expanded TCR clonotypes were included, we did not find the overlapping expanded TCR clonotypes occurred more frequently in responders, instead, they occurred more frequently in nonresponders (Supplementary Fig. S2), likely due to random chance because nonresponder samples contained more productive TCR clonotypes. Thus, we conclude that the expanded top TCR clonotypes can be shared between PBMC and TIL samples, which occurred much more frequently in responders than nonresponders.

Next, we examined whether the top 20 TCR clonotypes of pre-treatment samples at baseline also expanded in posttreatment samples, namely, whether the information of preexisting TCR clonotypes can help to predict the dynamic changes of TCR clonotypes upon cetuximab treatment. We first compiled the top 20 TCR clonotypes of pre-PBMC or pre-TIL from responders or nonresponders, identified the clones that expanded upon treatment in PBMC and TIL, then overlapped the two groups of the expanded clones to find the shared expanded TCR clonotypes between pre-PBMC and pre-TIL samples (Supplementary Fig. S3A). We noticed that the percentage of expanded TCR clonotypes in pretreatment samples appeared to be less than that in corresponding posttreatment samples, for example, by comparing top 20 TCRs in pre-TIL of responders versus post-TIL of responders (35 expanded of 100 in total in Supplementary Fig. S3A vs. 85 expanded of 100 in total in Fig. 5A). These data suggest that if a given TCR ranked among top 20 in post-TIL samples, it appeared to have a higher chance to be a treatment-expanded TCR clone. We found that 3 of 5 responders harbored shared expanded top TCR clonotypes, while only 1 of 9 nonresponders did so (Supplementary Fig. S3B; Supplementary Table S1). Similar to what we observed for posttreatment samples, the responders still showed a significantly higher chance to harbor a shared, expanded top TCR clonotype between pre-PBMC and pre-TIL than did nonresponders (Supplementary Fig. S3C). A higher number of TCR clonotypes in category c was detected in responders than nonresponders (9 vs. 1; Supplementary Fig. S3D). Of note, 8 of 9 shared expanded top TCR clonotypes identified between pre-PBMC and pre-TIL (Supplementary Table S1) were identical to their counterparts identified between post-PBMC and post-TIL (Table 1), suggesting that the shared expanded top TCR clonotypes existed prior to treatment. Next, we extended our analysis to the top 50 TCR clonotypes identified in pre-PBMC and pre-TIL samples (Supplementary Fig. S4A–S4D). We observed similar findings when top 50 TCR clonotypes were analyzed, although the level of significance was less (Supplementary Fig. S4C). These data strongly suggest that the shared expanded top TCR clonotypes occurred much more frequently in responders against random chance.

We applied new analytic methods to the TCR sequencing dataset containing 56 samples from 14 patients with cetuximab-treated HNSCC comparing pretreatment versus posttreatment as well as responders versus nonresponders. Using this highly unique data set, for each patient, we sequenced four types of samples: pre-PBMC, post-PBMC, pre-TIL and post-TIL. By examining the differences between pretreatment and posttreatment samples of patients with HNSCC, we uncovered dynamic changes of the TCR repertoires that correlated with cetuximab clinical responses (tumor shrinkage), including the expansion rate of top TCR clonotypes in PBMC or TIL and the overlapping probability of expanded top TCR clonotypes between PBMC and TIL. Our current analysis revealed that TCR richness strongly associated with the number of total productive TCR sequences, with the latter varying significantly in different samples. After normalizing TCR richness with the number of total productive TCR sequences, we did not detect differences in normalized TCR richness in paired comparison among all samples except between pre- and post-TIL. While these observations may be distinct from our prior study (11) but not inconsistent, this is because different analytic methods were employed taking into account or not the influence of the number of total productive TCR sequences.

Prior studies characterized the TCRβ clonotypes in locoregionally advanced HNSCC patient samples (12) or compared HPV⁺ versus HPV⁻ HNSCC samples (13), without linking TCR clonotypes with treatment outcomes. Our data showed that the expansion rate in top TCR clonotypes was significantly higher in responder PBMC than nonresponder even without normalization. These results suggest that the expansion rate between pre- and post-PBMC samples may serve as a minimally invasive and readily accessible marker predicting clinical response to cetuximab. In line with our observation, a recent study also showed that peripheral TCR repertoire changes correlated with response to anti-PD-1 treatment in renal cell carcinoma (14), in which the ratio of post-ICI/pre-ICI TCR diversity index and the number of expanded TCR clones were used for comparison between responders and nonresponders. However, it is unclear whether these parameters are influenced by sampling variation and whether they have been normalized to reduce the influence of such variation. Nevertheless, we did not observe significant differences in the number of expanded TCR clones between responders and nonresponders (Fig. 3A) or in the ratio of posttreatment/pretreatment TCR diversity index (data not shown). Taken together, our current study highlights the importance of data normalization for TCR repertoire analysis.

After testing and applying different normalization methods, the expansion rate of responder TIL became remarkably higher than nonresponder for the top TCR clonotypes. Assuming the expanded top TCRs are tumor reactive, at least some of them, these results suggest that cetuximab may function as an immunotherapy and promote a much stronger T cell–mediated antitumor immune response in responding patients with HNSCC. These results also suggest that combining cetuximab and anti-PD-1 may synergize in a fraction of patients whose antitumor immunity can be promoted via TCR expansion, thereby broadening the spectrum of anti-PD-1 responding patients. Consistent with this notion, a recent trial reported that pembrolizumab combined with cetuximab showed promising clinical activity for R/M HNSCCs with the overall response rate as 45% by 6 months (15). In contrast, such a combination of anti-PD-1/cetuximab may not be effective in the patients who do not contain useful TCRs (16) that can be expanded significantly upon treatment, similar to what we present here for cetuximab nonresponders. Another study examined the peripheral blood TCR repertoires of patients with HNSCC treated with concurrent cetuximab and nivolumab and reported that the peripheral TCR repertoires from pre-PBMC might be developed as biomarkers for predicting outcome of combined treatment of cetuximab and nivolumab (17).

We found that the expanded top TCR clonotypes overlapped between PBMC and TIL which occurred much more frequently in responders than nonresponders. These data suggest that TIL clones may migrate into peripheral blood after their expansion in the tumor microenvironment (TME), thus, can be detected in the post-PBMC samples. However, another possibility is that certain TCR clones expanded in post-PBMC, then migrated into the TME, similar to the “clonal replacement” concept proposed in a prior study (18). It is also possible that these clones are already prominent before treatment and their shared expansion after treatment is the result of homeostasis between peripheral blood and tumor tissue. Notably, the frequency of overlapping expanded TCR clonotypes is significantly higher in responders than nonresponders only when we count the top 20 or top 50 clonotypes. If we included all expanded TCR clonotypes, we do not detect a preferential association between response and overlapping frequency. These data suggest that the dynamic changes in the top ranking TCR clonotypes might be more relevant to therapeutic responses, whereas other clonotypes with limited expansion do not contribute substantially.

A limitation of our current study is that paired pretreatment and posttreatment samples were only available from 14 patients, with 5 responders versus 9 nonresponders. Nevertheless, we were able to perform analyses on paired peripheral blood and TIL samples, which identified potentially novel biomarkers of response including the expansion rate of top TCR clonotypes in PBMC or TILs and their overlapping probability between PBMC and TIL. Future studies are needed to confirm our initial findings. Our study also highlights the importance of data normalization for TCR repertoire analysis given that the number of total productive TCR sequences varies remarkably in different patient samples, based on specimen size and degree of T-cell infiltration. We propose that future TCR repertoire analysis needs to take into consideration sampling variation.

R.L. Ferris reports personal fees from Adagene Incorporated, Aduro Biotech, Bicara Therapeutics Inc, Brooklyn Immunotherapeutics, Catenion, Everest Clinical Research Corp, F. Hoffman-La Roche Ltd, Genocea Biosciences Inc, Kowa Research Institute Inc, Mirati Therapeutics, Nanobiotix, Novartis Pharmaceutical Corp, PPD Development LB, Sanofi, and Zymeworks, Inc.; grants from AstraZeneca/Medimmune and Tesaro; grants and other support from Bristol Myers Squibb and Merck; other support from Coherus BioSciences, Inc, Hookipa Biotech GmbH, Instil Bio Inc, Lifescience Dynamics LTD, MacroGenics Inc, MeiraGTx LLC, Mirror Biologics Inc, Numab Therapeutics AG, OncoCyte Corporation, Pfizer, Rakuten Medical Inc, Seagen Inc, SIRPant Immunotherapeutics, Inc, and Vir Biotechnology, Inc; and grants, personal fees, and other support from Novasenta during the conduct of the study. J.H. Wang reports grants from NIH/NIDCR during the conduct of the study. No disclosures were reported by the other authors.

H. Ge: Formal analysis, visualization, methodology, writing–original draft. R.L. Ferris: Conceptualization, resources, funding acquisition, investigation, writing–original draft. J.H. Wang: Conceptualization, supervision, funding acquisition, writing–original draft.

This work was supported by UPMC Hillman Cancer Center startup fund to J.H. Wang; NIH R01-DE027329, P50-CA261605, and R01-DE028420, to J.H. Wang; R01-DE031947, to R.L. Ferris and J.H. Wang; and P30-CA047904, P50-CA097190, and R01-CA206517, to R.L. Ferris. We thank Dr. Lazar Vujanovic for helpful comments on the article. This research was partially supported by the University of Pittsburgh Center for Research Computing (CRC) through the resources provided. We apologize to those whose work was not cited because of length restrictions.

The publication costs of this article were defrayed in part by the payment of publication fees. Therefore, and solely to indicate this fact, this article is hereby marked “advertisement” in accordance with 18 USC section 1734.