PD-1 antibody-bound progenitor-exhausted CD8^+ T cells in lymph nodes boost PD-1-blockade anti-tumor immunity in gastrointestinal cancer

Domain: neuroscience
Published: 2026-04-08
Editorial outcome: major_revision

Abstract
While progenitor-exhausted T cells (Tpex) expressing TCF1 and PD-1 are crucial for the therapeutic effect of immune checkpoint inhibitors (ICIs) with therapeutic anti–PD-1 antibodies (aPD-1), the dynamics of ICI-bound Tpex are not fully understood. In this study, we investigate ICI-bound T cells in detail using combined sequencing analysis at the single-cell level. By analyzing samples from gastrointestinal cancer patients with or without ICI treatment, we find that Tpex are enriched in proximal lymph nodes (LNs) and proliferate at a high rate after ICI treatment. Importantly, aPD-1 high–bound Tpex in LNs share T-cell receptor clonotypes with intratumoral exhausted CD8+ T cells (Tex), suggesting their migration to tumor sites after ICI treatment. This study thus provides new insights into how ICIs enhance anti-tumor immunity by acting on Tpex in LNs, deepening our understanding of the cellular mechanisms underlying ICIS therapy.

Introduction
The therapeutic applications of immune checkpoint inhibitors (ICIs) have expanded in several cancer types, including gastrointestinal cancers. However, their response rates are limited1–9. Given that preoperative ICI treatment is not currently recommended for gastrointestinal cancers, mechanistic analysis of the anti-tumor immune response with human samples after ICI treatment is of great importance, since elucidation of these mechanisms may improve therapeutic efficacy10.

Cancer antigen–specific T cells were detected among PD-1+ T cells in tumors and peripheral blood of cancer patients11,12, and response to ICI treatment is associated with the abundance of PD-1–expressing exhausted T cells (Tex) in tumors13–16. Accordingly, anti–PD-1 antibodies are thought to exert their therapeutic effects by binding to PD-1 molecules on immune cells. Recently, progenitor-exhausted T cells (Tpex) expressing TCF7 along with PD-1 have been identified in various organs, which have the potential to be bound by anti–PD-1 therapeutic antibodies (aPD-1). Indeed, these populations also correlated with a response to ICI treatment17–21. However, it remains unclear which population of Tex or Tpex is activated by ICIs and whether the induction of tumor-reactive CD8+ T cell responses occurs primarily in the tumor microenvironment (TME) or in the tumor-draining lymph nodes (TDLNs). Therefore, this study addressed the questions of which cell populations are tumor-reactive T cells activated by the binding of aPD-1, and where these cells are located.

A problem is that aPD-1 bind to surface PD-1 molecules on immune cells, making it impossible to detect these cells with conventional PID-1 antibodies and necessitating a new method to detect these cells ex vivo. We therefore developed a method to detect ICI-bound T cells using an anti-IgG4 antibody and reported the importance of increased CD103 expression on blood ICI-bound CD8+ T cells with a favorable prognosis in gastric cancer patients22. Similar methods were used in some reports with the prognostic significance of increased proliferative activity of blood ICI-bound T cells after ICI treatment23,24. However, there are no reports demonstrating the dynamics of ICI-bound T cells in organs other than the blood.

Paired single-cell RNA and T-cell receptor sequencing (scRNA/TCR-seq) has enabled deep profiling of T cells in the context of their clonal lineage, phenotypic heterogeneity, and tissue distribution25. We combined cellular indexing of transcriptomes and epitopes sequencing (CITE-seq) with scRNA/TCR-seq (scRNA/TCR/CITE-seq) to analyze the dynamics of ICI-bound T cells in more detail, by detecting IgG4 antibody at the single-cell level. Indeed, this analysis of ICI-bound T cells allowed us to reveal region-dependent transcriptional programs of T-cell clonotypes. In addition, we systematically profiled the regional distribution and region-dependent cell states of ICI-high and ICI-low bound T cells.

Here, we demonstrate that ICIs preferentially engage CD8-Tpex cells in LNs, driving their proliferation and clonal expansion, followed by their migration to tumors and differentiation into CD8-Tex cells. This finding provides a clonally resolved framework for understanding how aPD-1 orchestrates tumor-specific T-cell responses across tissues.

Results
Multi-regional scRNA-seq of T cells in gastrointestinal cancer samples after ICI treatment
To profile the characteristics of infiltrating T cells in various organs after anti–PD-1 therapy, we first analyzed a discovery cohort of 2 patients with metastatic esophagogastric junction (EGJ) cancer who received aPD-1 combined with chemotherapy and underwent surgical resection within a month after completion of the therapy (Fig. 1a, Supplementary Fig. 1). We obtained 4 samples of tumor, metastatic LN, metastasis-free LN, and PBMC from patient 1, and 3 samples of liver metastases, metastasis-free LN, and PBMC from patient 2 (Supplementary Table 1a). CD3+ T cells in these samples were sorted for scRNA-seq analysis. We then performed scRNA-seq to characterize their molecular profiles. After quality control, 19,388 CD3+ T cells were retained.

Clustering analysis of the scRNA-seq data identified 23 clusters, which we segregated into CD8+ (12 clusters), conventional CD4+ (8 clusters), regulatory CD4+ (Treg cells) (2 clusters), and doublet (1 cluster) cells (Fig. 1b). These clusters were annotated with reference to differentially expressed genes and marker genes (Fig. 1c, Supplementary Fig. 2a, b)21. In these clusters, exhausted T cells (Tex; TCF7–, PDCD1+, TOX+), progenitor-exhausted T cells (Tpex; TCF7 +, PDCD1+), follicular helper T cells (Tfh; TCF7+, PD-1+, CXCR5+, BCL6+), effector T cells (GZMK+, GNLY+, FGFBP2+), regulatory T cells (Treg; FOXP3+, CTLA4+, CCR8+), naive T cells (CCR7+, TCF7+), central memory T cells (Tcm; CCR7+), and mucosal-associated invariant T cells (MAIT; SLC4A10+) were identified (Fig. 1b, c, Supplementary Fig. 2a). Tissue specificity was also detected in the clusters (Fig. 1d, e, Supplementary Fig. 3a-c). Among CD8+ T cell clusters, tumor and liver metastases were enriched for CD8-Tex (C04, C06.0, C17.0, C17.1) and CD8-Tpex (C12) cells, while LNs were enriched for CD8-Tpex (C05) and CD8-naive (C11) cells. Blood was enriched for CD8-naive (C10) and CD8-effector (C08) cells. Likewise, among CD4+ T cell clusters, tumor and liver metastases were enriched for CD4-Tex (C06.1, C17.2) and CD4-Treg effector (C07) cells, while LNs were enriched for CD4-Tfh (C09), CD4-naive (C00, C15), CD4-central memory (C03), and CD4-Treg (C13) cells. Blood was enriched for CD4-naive (C02) and CD4-central memory (C01) cells. The transcriptomic similarity of clusters was observed mainly according to tissue, whereas CD8-Tpex (C05) in LNs was similar to CD8-Tex (C04, C06.0) in tumor and liver metastases, and CD8-effector (C08) in PBMCs, despite these clusters exhibiting differential expression of multiple genes (Fig. 1e, Supplementary Fig. 3d). CD4-Tfh (C09) in LNs was also similar to CD4-Tex (C06.1) in tumor and liver metastases (Fig. 1e). The canonical gating strategy of TCF7+PDCD1+CD8+ for CD8-Tpex overlapped with C0520,26–29, and TCF7+PDCD1+CD4+ for CD4-Tfh overlapped with C09, which is consistent with previous studies29,30 (Fig. 1f). Importantly, C05 cells expressed T follicular marker CXCR5. As CXCR5+CD8+ T cells are reported to act on Tfh and exhibit antitumor effects, C05 cells may interact with C09 cells31. In summary, the regional distribution of T-cell subtypes was similar based on each tissue, and Tex and Tpex were coordinately enriched in tumor and LNs.

The C05 cluster (CD8-Tpex) was correlated with the therapeutic effect of ICI treatment.
It has been reported that CD8-Tpex cells proliferate following ICI treatment17–20, that CD8-Tpex cells in tumors correlate with the efficacy of ICI treatment21, and that LNs act as reservoirs for these cells32–34. Therefore, we hypothesized that CD8-Tpex (C05) cells play a critical role in the efficacy of ICI treatment. To test this hypothesis, we analyzed a previously reported scRNA-seq dataset of tumor-infiltrating lymphocytes from 15 surgically resected tumors after ICI treatment35 to identify cell populations that affect the response to ICI treatment. The single-cell data on mutation-associated neoantigens were projected to our data set using Symphony, in which gene expression profiles showed a good projection of this gene set and cluster-level similarities between the datasets were observed in tissue-specific comparative analyses (Supplementary Fig. 4a, b, 5). Comparison of the proportion of cells in each cluster between patients with the major pathologic response (MPR) and the non-major pathologic response (non-MPR) showed a significantly higher proportion of CD8-Tpex (C05) and CD8-effector (C08) in the MPR group than in the non-MPR group, indicating that abundant intratumoral CD8-Tpex and CD8-effector were associated with a favorable therapeutic effect of ICI (Supplementary Fig. 4c). Following these results, we focused further on the CD8-Tpex (C05) cells that were enriched in LNs in our scRNA-seq data.

Transcriptional signature of CD8-Tpex and CD4-Tfh
To examine the tissue-specific differences among CD8-Tpex cells, we investigated the characteristic genes of CD8-Tpex (C05) cells in LNs by comparing the gene expression of CD8-Tpex (C05) cells with that of CD8-Tex (C04, C06.0, C17.0, C17.1) and CD8-Tpex (C12) cells in tumor and liver metastases, and with that of CD8-naive (C11) cells in LNs (Fig. 1g, h). CD8-Tpex cells in LNs expressed naive/progenitor-associated genes (TCF7, KLF2, IL7R, CCR7, GPR183, SELL) at high levels compared with CD8-Tex/Tpex cells in tumor and metastasis. CD8-Tpex cells in LNs also had high expression of genes of effector transcripts (GZMA, GZMK), co-stimulatory molecules (TIGIT, KLRG1, LAG3, SLAMF7, KLRD1), IFN response (IFNG, TNF), and chemokine ligands and receptors (CCL4, CCL5, CXCR3, CXCR4) compared with CD8-naive cells. These data suggest that CD8-Tpex cells have already acquired migratory and effector functions in LNs while maintaining progenitor cell characteristics. We also investigated the characteristically expressed genes of CD4-Tfh (C09) cells in LNs by comparing them with the gene expression of CD4-Tex (C06.1, C17.2) cells in tumor and liver metastases, and with that of CD4-naive (C00) and CD4-Tcm (C03) cells in LNs (Supplementary Fig. 6). CD4-Tfh cells in LNs had high expression of naive/progenitor-associated genes (TCF7, IL7R, CCR7, LEF1, KLF2, SELL, GPR183) and chemokine receptors (CXCR4, CXCR5) compared with CD4-Tex cells in tumor and liver metastases. CD4-Tfh cells in LNs also had high expression of genes encoding co-stimulatory molecules (TIGIT, LAG3, TNFRSF4, TNFRSF18), a gene related to T-cell exhaustion (TOX), proliferation genes (DUSP4, DUSP6), and chemokine receptor genes (CXCL13, CXCR3) compared with CD4-naive/Tcm cells in LNs. These data suggest that CD4-Tfh cells have proliferative functions in LNs, possibly interacting with CD8-Tpex cells, while still maintaining progenitor cell characteristics31.

Relationship between intratumoral immune cell clusters and prognosis in ICI-naive gastric cancer patients from the Cancer Genome Atlas
We next examined whether intratumoral CD8-Tpex cells correlate with prognosis in ICI-naive patients, using the Cancer Genome Atlas (TCGA) dataset of gastric adenocarcinoma. Traditional clustering methods often categorize cells into discrete groups. These may fail to capture the continuum of cellular states, especially in dynamic or transitional populations, where many subsets exhibit overlapping gene expression profiles and functional states. Therefore, we applied non-negative matrix factorization (NMF), which is useful for profiling T cells with ambiguous boundaries36,37. We analyzed our scRNA-seq dataset to extract ten gene programs (Fig. 2a), in which the top 8 genes of each cluster by NMF are presented (Fig. 2b). In these ten clusters, NMF8 corresponded with CD8-Tpex, NMF3 with CD8-Tex, NMF2 with CD4-Tfh, and NMF0 with CD4-Treg (Supplementary Table 2). The pathway analysis of the top 100 characterized genes for NMF of CD8-Tpex revealed that the featured pathways of NMF8 were "Interferon signaling", "Translocation of ZAP-70 to immunological synapse", and "PD-1 signaling" (Fig. 2c, Supplementary Fig. 7). We projected these gene programs to the TCGA dataset of gastric adenocarcinoma. When the association between the abundance of each cluster and overall survival was examined, only NMF0, a cluster related to CD4-Treg, correlated with prognosis, while no significant associations were observed for the other clusters, including NMF8/NMF3, which reflected the Tpex/Tex ratio (Fig. 2d, e). TCGA evaluates gene expression of tumors treated without ICI. Thus, the result indicated that the intratumoral accumulation of CD8-Tpex and Tpex/Tex ratio would not affect the prognosis of ICI-naive gastric cancer.

The abundance of CD8-Tpex in LNs was correlated with better prognosis in patients with ICI-naive gastric cancer
Since there are few published data on the function and prognostic impact of Tpex cells in LNs of human samples, we next collected surgically dissected tissue samples of tumor (n = 39), metastatic LNs (n = 24), and metastasis-free LNs (n = 193: proximal 134, distal 59) from 55 patients with ICI-naive gastric cancer, and analyzed immune cells by flow cytometry (Fig. 3a, Supplementary Fig. 8, Supplementary Table 3). The LNs were defined by location as proximal LNs (pLN) and distal LNs (dLN), based on the Japanese Gastric Cancer Treatment Guidelines38. First, the expression frequency of various checkpoint molecules, such as PD-1, Tim-3, and CD103 among CD8+ T cells in each tissue was analyzed (Fig. 3b). The expression frequency of each molecule was high on intratumoral CD8+ T cells, and it was higher in pLNs than in dLNs within metastasis-free LNs, suggesting that anti-tumor immunity was induced in LNs close to the tumor. Although no difference in cytokine production between T cells in dLN and pLN was observed, we noted higher cytokine production by PD-1+ T cells than by PD-1− T cells in metastasis-free LNs, both in CD8+ and CD4+ T cells (Supplementary Fig. 89a, b). PD-1+ T cells are further divided into three subpopulations by the presence or absence of TCF1 expression based on previous reports: Tpex (TCF1+PD-1+ T cells), Tex (TCF1−PD-1+ T cells), and PD-1− T cells (Fig. 3c)20,27,28. Among CD8+ T cells, the frequency of CD8-Tpex was higher in pLN than in the other three regions, whereas the frequency of CD8-Tex was highest in tumors39 (Fig. 3d). The frequency of CD8-Tpex in metastasis-free LNs was lower in advanced tumor stages (Fig. 3d). Although CD8-Tpex in metastasis-free LNs did not express Ki-67 and GZMB at high levels (Fig. 3e), a higher CD8-Tpex/Tex ratio in metastasis-free LNs was associated with a better prognosis (Fig. 3f, Supplementary Fig. 10a, b). Multivariate analysis revealed that a high CD8-Tpex/Tex ratio in metastasis-free LNs tended to be a statistically significant prognostic factor (Supplementary Table 4). Regarding CD4+ T cells, the expression frequency of PD-1 and Tim-3 was high in intratumoral CD4+ T cells, and it was higher in pLNs than in dLNs within metastasis-free LNs. Although intratumoral CD103+CD4+ T cells have been reported to exhibit high cytokine production and are associated with a good prognosis40,41, CD103 was barely detected in CD4+ T cells of LNs (Fig. 3g). CD4-Tfh cells were abundant in pLN than dLNs (Fig. 3h), but CD4-Tfh cells in metastasis-free LNs did not express high Ki-67 and GZMB (Fig. 3i). In contrast, CD4-Tex cells in metastasis-free LNs had relatively high GZMB expression among CD4+ T cells, although the GZMB expression in CD4+ T cells was considerably lower than that in CD8+ T cells (Fig. 3i). A higher CD4-Tfh/Tex ratio in metastasis-free LNs was not associated with a better prognosis (Fig. 3j). In summary, CD8-Tpex cells in metastasis-free LNs are less proliferative and less cytotoxic, but are associated with a favorable prognosis, suggesting that they may act as reservoirs of tumor-reactive T cells.

The flow cytometry profiling of tissue-infiltrating T cells after ICI treatment
As it was suggested that Tpex cells in LNs may act as reservoirs of tumor-reactive T cells in ICI-naive cancer patients, we then proceeded to evaluate tissue-infiltrating T cells after ICI treatment, using samples from 14 ICI-treated patients with gastrointestinal cancer. Since PD-1+ T cells could not be detected by conventional anti–PD-1 antibodies after ICI treatment due to the binding of aPD-1 to T-cell surface PD-1 molecules, we developed methods to identify ICI-bound T cells using anti-IgG4 antibodies, which detect T-cell-bound aPD-1 (Fig. 4a)22. This approach was validated in vitro to accurately detect ICI-bound T cells (Supplementary Fig. 11). The method detected ICI-bound T cells in tumor, LNs, and blood, consistent with previous reports of ICI-bound T cells in blood (Fig. 4b)22–24. Here, we encountered a case of gastric cancer treated with ICI followed by gastrectomy. The patient initially had multiple liver and LN metastases and had been treated with three regimens of chemotherapy for 18 months. After progression of the primary tumor, LNs, and liver metastases after chemotherapy, the patient received anti–PD-1 treatment as fourth-line, which resulted in shrinkage of the tumor and metastases. However, the primary tumor subsequently regrew, with the liver metastases diagnosed as complete response by imaging, and a gastrectomy was performed (Fig. 4c). The flow cytometry analysis demonstrated high proliferative activity of ICI-bound CD8+ T cells compared with ICI-non-bound CD8+ T cells in all metastasis-free LNs (Fig. 4d). These results prompted us to further investigate the dynamics of ICI-bound T cells in ICI treatment.

ICI-bound Tpex in metastasis-free LNs were highly proliferative after ICI treatment
To evaluate the effect of ICI treatment on the function of Tpex in LNs, we collected surgically dissected tissue samples of tumor (n = 13), metastatic LNs (n = 6), metastasis-free LNs (n = 55), PBMC (n = 12), and liver metastases (n = 2) from 14 patients with gastrointestinal cancers (3 gastric, 7 esophageal, 3 EGJ, and 1 colorectal) treated with ICI, in which infiltrating immune cells were analyzed by flow cytometry (Supplementary Table 5). Since preoperative ICI treatment for gastrointestinal cancer is not approved worldwide, all subjects had initially unresectable cancer which then became resectable. First, the comparison between ICI-bound and ICI-non-bound CD8+ T cells revealed that ICI-bound CD8+ T cells exhibited higher CD103 expression and lower TCF1 expression than did ICI-non-bound CD8+ T cells across all tissues, suggesting that ICI-bound CD8+ T cells possess a memory phenotype, whereas ICI-non-bound CD8+ T cells retain a naive phenotype (Supplementary Fig. 1112a, b). Next, the expression frequencies of Ki-67 and GZMB in Tpex, Tex, and PD-1−CD8+ T cells were evaluated by flow cytometry in tumor and metastatic and metastasis-free LNs, and compared with those in ICI-naive gastric cancer. The results showed that the proliferative activity of Tpex in metastasis-free LNs significantly increased after ICI treatment, along with increased proliferative activity of other subgroups (Tex, PD-1− T cells) in metastasis-free LNs after ICI treatment (Fig. 4e). On the other hand, no increase in cytotoxicity, as evaluated by GZMB expression, was observed in any T cell subpopulation after ICI treatment (Fig. 4f). Regarding CD4+ T cells, the proliferative activity of CD4+ T cells in metastatic and metastasis-free LNs significantly increased after ICI treatment (Fig. 4g), and GZMB expression in Tex cells from metastasis-free LNs increased after ICI treatment, although the GZMB expression levels in CD4+ T cells remained much lower than those in CD8+ T cells (Fig. 4h). Although not statistically significant, patients with higher Ki-67 expression in CD8-Tpex within metastasis-free LNs tended to show better survival (Supplementary Fig. 13). Thus, ICI treatment primarily enhanced the proliferative potential of CD8+ and CD4+ T cells in LNs rather than their cytotoxicity. Notably, ICI treatment greatly increases the proliferative potential of CD8-Tpex cells in metastasis-free LNs.

scRNA/TCR/CITE-seq analysis identified and characterized ICI-bound T cells
We confirmed that the proliferative activity of ICI-bound Tpex cells was boosted in metastasis-free LNs after ICI treatment, but it was still unknown whether these cell populations exert anti-tumor immunity at the tumor site. To investigate the impact of ICI treatment on the distribution of cell populations, we performed integrated scRNA-seq on five patients, incorporating one additional ICI-treated (patient 3) and two ICI-naive cases (patient 4,5) as a validation cohort. In the analysis, CD8-Tpex cells in LNs (C5.0, C5.1), CD4-Tfh cells (C7), and intratumoral CD8-Tex cells (C6, C10) were identified. Notably, the distribution of these clusters across tissues did not differ substantially between ICI-treated and ICI-naive groups, suggesting that the effects of ICI treatment may depend more on the functional state of the cells rather than on changes in the size or distribution of cell populations (Supplementary Fig. 14). To uncover the dynamics of T cells after ICI treatment, we investigated the TCR repertoire at the single-cell level in combination with CITE-seq detecting anti-IgG4 antibody with the scRNA-seq dataset of 19 samples from 3 ICI-treated patients and 2 ICI-naive patients (Fig. 5a, Supplementary Table 1a, b). We thought this analysis might allow us to detect ICI-bound T cells at the single-cell level and simultaneously evaluate the TCRs on the ICI-bound T cells. Indeed, the comparative analysis between ICI-treated and ICI-naive patients demonstrated that the anti-IgG4 antibody in CITE-seq identified ICI-bound T cells in samples from ICI-treated patients, while these cells were hardly detected in samples from ICI-naive patients (Fig. 5b). According to the median value of IgG4 binding levels, clusters with more than half of the cells being IgG4-positive cells were classified as IgG4-high clusters (Fig. 5c, Supplementary Table 6). Consequently, we observed abundant binding of aPD-1 to Tex and Tpex in both tumor and LNs (Fig. 5b-d). These ICI-bound T cells showed moderate to high levels of PDCD1 mRNA expression, and IgG4 binding levels were proportional to PDCD1 mRNA levels, meaning that all PD-1-expressing T cells at any site in the body were bound by aPD-1, depending on the level of surface PD-1 expression (Fig. 5b, Supplementary Fig. 15). In the comparison of gene expression between ICI-bound PD-1⁺CD8⁺ T cells from ICI-treated patients and PD-1⁺CD8⁺ T cells from ICI-naive patients, genes associated with antigen recognition and T-cell activation, such as HLA-DRB1 and HLA-DQA1, were upregulated in tumor of ICI-treated patients, whereas genes related to cell migration and interferon signaling, including CCL3L1, CCL4L2, and GBP5 were elevated in LNs. In contrast, no significant differences in exhaustion-related genes were observed in the blood, suggesting that the effects of ICIs are most pronounced in LNs and tumors (Supplementary Fig. 16a). In a comparison of gene expression between ICI-bound and ICI-non-bound CD8+ T cells in tumors from ICI-treated patients, ICI-bound CD8+ T cells expressed CXCL13, PDCD1, TIGIT, and LAG3, while ICI-non-bound CD8+ T cells expressed IL7R and LTB (Supplementary Fig. 16b), indicating that ICI-bound CD8+ T cells exhibit an exhausted phenotype, whereas ICI-non-bound CD8+ T cells retain a naive phenotype. In addition, paired TCR data with phenotypic analysis showed that these ICI-bound T cells expanded clonally, with the top 20 overlapping clones between LNs and tumor or liver metastases occupied by these ICI-bound T cells (Fig. 5e, f).

STARTRAC analysis unveiled the clonal link between ICI-bound CD8-Tpex in LNs and intratumoral ICI-bound CD8-Tex
To analyze the dynamic relationships among T-cell clusters with distinct gene signatures and clonalities, we analyzed the scRNA-seq data from 3 ICI-treated (patient 1,2,3) and 2 ICI-naive patients (patient 4,5) using the RNA sequencing and TCR tracking (STARTRAC) algorithm42 (Supplementary Fig. 15). This algorithm incorporates several indices, including distribution, expansion, migration, and transition, to quantitatively describe tissue distribution, clonal expansion, migration, and developmental transition or differentiation, respectively. First, the comparison of STARTRAC results between ICI-treated and ICI-naive patients demonstrated that the expansion score was increased in CD8-Tex (C04, C17.0), CD8-Tpex (C05, C12), and CD4-Tex (C06.1) cells after ICI treatment (Fig. 6a). The cross-tissue migration score was increased in CD8-Tex (C04, C06.0, C17.0), CD8-Tpex (C05), CD8-effector (C08), and CD4-Tex (C06.1) cells. Conversely, the transition score was decreased in CD8-Tex (C04, C06.0, C17.0), CD8-Tpex (C12), and CD8-effector (C08) cells. Thus, increased expansion and migration scores, along with decreased transition score after ICI treatment, were predominantly observed in IgG4-high clusters. In the analysis with 3 ICI-treated patients, a high expansion score was observed in CD8-Tex (C04, C06.0, C19), CD8-Tpex (C05, C12), and CD8-effector (C08) cells (Supplementary Fig. 15b). A high cross-tissue migration score was observed in CD8-Tex (C04, C06.0, C17.0), CD8-Tpex (C05, C12) and CD8-effector (C08) cells. A high transition score was observed in CD8-Tex (C04, C06.0, C17.0, C17.1) cells. Thus, CD8-Tpex cells in LNs and tumors had a high potential to expand and migrate, and intratumoral CD8-Tex cells had a high potential to expand, migrate and differentiate. All of these T cells with high expansion, migration, and transition scores were derived from IgG4-high clusters, except for C08. The high scores in the C08 cluster may be attributed to the presence of PD-1-negative recently activated effector memory (TEMRA) T cells within C08 cells, which have been reported to exhibit high proliferation, migration, and transition scores, as well as strong migratory potential between blood and organs, even without ICI treatment42. In contrast, CD4-Tfh in LNs showed limited potential for clonal expansion after ICI treatment (Fig. 5e, Supplementary Table 7). Moreover, STARTRAC analysis revealed that these cells exhibit low levels of migration and transition (Fig. 6a, b). Overall, these findings suggest that ICI-bound T cells play a leading role after ICI administration, possibly by the enhanced expansion and migration of PD-1+CD8+ T cells.

Regarding migration, the most observed route of migration was between LN and tumor (Supplementary Fig. 15c). The LN-enriched CD8-Tpex (C05) had a moderate expansion score and a high cross-tissue migration score (Supplementary Fig. 15b), and exhibited the highest migration potential between LN and tumor among all clusters (Fig. 5f, Supplementary Fig. 15c). Notably, this migration potential of C05 between LN and tumor increased after ICI treatment (Fig. 6b). Importantly, in ICI-treated cases, TCR clonotypes within CD8-Tex (C04, C06.0) in tumor were highly shared with CD8-Tpex (C05, C12) in LNs or tumor, in contrast to ICI-naive cases (Fig. 6c, Supplementary Fig. 17). Conversely, TCR clonotypes within the C05 cluster were highly shared with CD8-Tex (C04, C06.0) and CD8-Tpex (C12) in tumor and CD8-effector (C08) in blood, with 89.8% in patient 1, 97.9% in patient 2, and 53.1% in patient 3 (Supplementary Fig. 18a). Moreover, the C05 consisted of two subgroups with high and low IgG4 binding. After categorizing clonotypes based on high and low IgG4 binding, ICI high–bound T cells in the C05 cluster shared clones with CD8-Tex (C04, C06.0) and CD8-Tpex (C12) cells. Whereas, ICI low–bound T cells in the C05 cluster shared clones with CD8-effector (C08) cells in blood (Fig. 6d, Supplementary Fig. 18b). On the other hand, compared with C05 cells, ICI high- and low-bound T cells in the C08 cluster shared fewer clones with CD8-Tex (C04, C06.0) and CD8-Tpex (C12) cells in tumor (Supplementary Fig. 18c). Interestingly, ICI low-bound T cells in the C08 cluster exhibited higher expansion, migration, and transition scores than ICI high-bound T cells (Supplementary Fig. 19). This may be because TEMRA cells with low PD-1 expression were included in ICI low-bound T cells within the C08 cluster. Indeed, it has been reported that the clonotypes of blood TEMRA cells were mutually exclusive with those of intratumoral Tex cells42, which may explain why the clonal similarity of C08 cells to intratumoral CD8-Tex cells was relatively lower than that of C05 cells.

Additionally, pseudotime analysis within C05, C12, and C04 clusters across organs revealed a differentiation flow from C05 to C12 and then to C04, suggesting that CD8+ T cells differentiate sequentially from CD8-Tpex in LNs to CD8-Tpex in tumor, and ultimately to CD8-Tex in tumor (Supplementary Fig. 20). Regarding CD4+ T cells, TCR similarity between CD4-Tfh in LN (C09) with intratumoral CD4-Tex (C06.1) appears to be relatively low compared with that of CD8+ T cells (Fig. 6c, Supplementary Fig. 21). Overall, these findings suggest that ICI high–bound Tpex cells in LNs serve as a source of Tex cells in tumor, possibly containing cancer antigen–specific T cells, whereas ICI low–bound Tpex cells in LNs may have a different antigen specificity.

Finally, the comparison of gene expression between the ICI high– and low–bound CD8-Tpex in LNs showed that the ICI low–bound T cells expressed high levels of naive/progenitor-associated genes (TCF7, KLF2, IL7R, CCR7, SELL) (Fig. 6e). On the other hand, the ICI high–bound T cells expressed high levels of genes encoding effectors (GZMA, GZMK), co-stimulatory molecules (TIGIT, LAG3, SLAMF6, ICOS, PRDM1, TNFSF9, CD82, CD27), an IFN response gene (IFNG), chemokine ligands and receptors (CCL4), and differentiating molecules (DUSP4, DUSP5). Taken together, these data support the hypothesis that ICIs target Tpex in LNs, leading to their proliferation and migration to tumor, where these cells differentiate into Tex exerting anti-tumor immunity.

Methods
Patients and Samples
Fifty-five patients with pathologically diagnosed ICI-naive gastric cancer, who underwent surgery at Osaka University Hospital from November 2019 to February 2022, were included for flow cytometry analysis (Supplementary Table 3). Survival analysis and univariate and multivariate analysis were performed in 49 of these cases with data on metastasis-free LNs (Fig. 3f, j, Supplementary Table 4). Fourteen patients with gastrointestinal cancer (3 gastric, 7 esophageal, 3 EGJ, and 1 colorectal) who underwent surgery within one month of ICI treatment from September 2020 to October 2024 were included for flow cytometry analysis (Supplementary Table 5). TNM status was recorded according to the Union for International Cancer Control TNM Classification of Malignant Tumors (7th edition)55. Two ICI-treated patients (patient 1, 2) with EGJ cancer (1 adenocarcinoma, and 1 squamous cell carcinoma) were included for initial single-cell analysis, and a total of 7 samples (patient 1: PBMC, tumor, metastatic LN, metastasis-free LN; patient 2: PBMC, liver metastases, metastasis-free LN) were collected from 2 patients (Fig. 1, Supplementary Table 1a). One additional ICI-treated patient (patient 3) with gastric adenocarcinoma and two ICI-naive patients (patient 4, 5) with gastric cancer were included for scRNA/TCR/CITE-seq, and a total 12 samples (patient 3: tumor, two metastasis-free LNs, PBMC; patient 4: tumor, metastatic LN, metastasis-free LN, PBMC; patient 5: tumor, two metastasis-free LNs, PBMC) were collected from 3 patients (Fig. 5-6, Supplementary Table 1b). Demultiplexing was performed using HashSolo56 with the default parameters. Next, the data were integrated with the discovery cohort reference using symphonypy. Tumor tissues, LN tissues, and peripheral blood were collected at the time of surgery. Multiple LN samples were taken from a single patient. LNs were divided into halves, and one was subjected to flow cytometry analysis while the other was submitted to histopathological examination to determine the presence or absence of metastasis. The collected samples were subdivided by gentleMACS Dissociator (Miltenyi Biotec, Bergisch Gladbach, Germany, #130-093-235) and purified by cell strainer (BD Biosciences, San Jose, USA, #352340), and mononuclear cells were extracted. Blood samples were centrifuged to extract mononuclear cells using Ficoll-Paque (Cytiva, #17144002). PBMCs were isolated from blood samples and stored in the nitrogen bank. To collect tissue-infiltrating lymphocytes, fresh tumor and LN tissues were minced and treated with the gentleMACS Dissociator. Extracted lymphocytes were stored in the nitrogen bank and analyzed with an LSR Fortessa cytometer (BD Biosciences) after thawing and washing. All donors provided written informed consent before sampling in accordance with the Declaration of Helsinki. This study was approved by the institutional ethics committees of Osaka University (approval number; #13266 Osaka, Japan).

Antibodies and reagents
The fluorescence-labeled antibodies used in the present study were the following: anti-CD3 (1/100, clone UCHT1; BioLegend, San Diego, CA, #300424), CD4 (1/100, clone OKT4; BioLegend, #317440), CD8 (1/100, clone RPA-T8; BioLegend, #301048/ clone SK1; BD Biosciences, #565310), CD45RA (1/100, clone HI100; BioLegend, #304148), PD-1 (1/100, clone EH12.1; BD Biosciences, #561272), T-cell immunoglobulin mucin domain 3 (1/100, Tim-3, clone F38-2E2; BioLegend, #345012), CD25 (1/100, clone BC96; BioLegend, #302638), CD103 (1/100, clone Ber-ACT8; BioLegend, #350218), Ki-67 (1/50, clone Ki-67; BioLegend, #350506), TCF1/TCF7 (1/100, clone C63D9; Cell Signaling Technology, #37636), Granzyme B (1/100, clone QA16A02; BioLegend, #372212), IFN-γ (1/100, clone 4S.B3; BioLegend, #502506), TNFα (1/100, clone MAb11; BioLegend, #502950). ICI-bound T cells were detected by use of a biotinylated anti-human IgG4 antibody (1/4000, clone HP6025; Invitrogen, #A-10663) followed by secondary staining with streptavidin (1/200, BioLegend, #405204). Dead cells were identified using the Live/Dead Fixable Red Dead Cell Stain Kit (1/1000, Invitrogen, #L34993). Corresponding isotype control antibodies were purchased from the same manufacturer.

Surface marker and intracellular staining
Thawed cells were incubated with antibodies at 4 °C for 30 min after washing. Cells washed with phosphate-buffered saline with 2% fetal calf serum were stained with CD3, CD4, CD8, CD25, and CD45RA antibodies, and fixable viability dye (Invitrogen). After washing, cells were analyzed with an LSR Fortessa (BD Biosciences), and the data obtained were analyzed using BD FACSDiva software. For intracellular staining, cells were washed and stained for surface markers. Cells were permeabilized with fix/perm solution (BD Biosciences, #554714) at 4 °C for 15 min. Cells were then stained with antibodies for intracellular molecules such as transcription factors (TCF1), Ki-67, and cytotoxic granules (GZMB) at 4 °C for 30 min. After washing, the cells were analyzed with an LSR Fortessa instrument.

IgG4 staining method
We have previously established a method to identify PD-1+ T cells to which aPD-1 bind by binding a secondary antibody, anti-IgG4, which binds directly to aPD-1 (Fig. 4a)22. Briefly, cells were stained with biotinylated anti-IgG4 antibody at 4 °C for 30 min to detect administered aPD-1 (human IgG4) bound to PD-1 molecules on the T-cell surface. The cells were washed twice and then incubated with streptavidin (PE) at room temperature for 15 min, followed by flow cytometry analysis. To confirm this method was detecting ICI-bound T cells correctly, the proportion of anti–IgG4 antibody in T cells after in vitro culture with aPD-1 was compared with that of anti–PD-1 antibody in T cells prior to culture, using PBMC samples from 29 ICI-naive gastric cancer patients (Supplementary Fig. 11). Following in vitro culture with aPD-1 (1 µg/ml) at 4°C for 30 minutes, the anti–PD-1 antibody could barely detect PD-1+ T cells (<1%), and there was a strong correlation between the proportion of anti–PD-1 and anti-IgG4 antibodies before and after in vitro culture of the aPD-1, for both CD8+ and CD4+ T cells, respectively (R2=0.79 [CD8+ T cells] and R2=0.81 [CD4+ T cells]).

Intracellular cytokine staining assay
Cells were stimulated with 50 ng/ml PMA (Sigma-Aldrich, Saint Louis, MO, #P1585), 1 μg/ml ionomycin (Sigma-Aldrich, #I0634), and Golgi Plug reagent (BD Biosciences, #555029) at 37 °C under 5% CO2 for 5 hours. Harvested cells were washed and stained for surface markers (mAbs specific for CD3, CD4, CD8, and CD45RA, and fixable viability dye). Cells were permeabilized with fix/perm solution (BD Biosciences) at 4 °C for 15 min, and stained with IFN-γ and TNF-α antibodies at 4 °C for 30 min. After washing, the cells were analyzed with an LSR Fortessa instrument.

Cell preparation and sequencing of scRNA-seq
Mononuclear cells were extracted from samples collected. Cells were incubated with FcBlock and LiveDead for 15 min at room temperature, then a biotinylated anti-IgG4 antibody was added and incubated for 30 min at 4°C. Streptavidin for CITE-Seq (BioLegend, Barcode sequence: AACCTTTGCCACTGC, #405261) was added and incubated at room temperature for 15 min. Anti-CD3, anti-CD45, and TotalSeqC antibodies (PD-1 [clone EH12.2H7; BioLegend, Barcode sequence: ACAGCGCCGTATTTA, #329963) were then added, incubated at 4°C for 30 min, and cell sorting was performed by fluorescence-activated cell sorting. The sorted cells were loaded onto a Chromium Next GEM Chip G on a Chromium Controller (both from 10x Genomics) for barcoding and cDNA synthesis. Amplification of the cDNA and the library construction were performed using a Chromium Next GEM Single-Cell 5' Kit v2, Chromium Single-Cell Human TCR Amplification Kit, and 5' Feature Barcode Kit (all 10x Genomics) for 5', VDJ, and antibody profiling according to the manufacturer’s protocol. The libraries were sequenced on NovaSeq6000 (Illumina) and DNBSEQ (BGI).

Single-cell immune profiling for gene expression and TCR repertoire
The sorted cells were mixed with reagents of the Chromium Next GEM Single-Cell 5’ Library & Gel Bead Kit v1.1 and loaded onto a Chromium Next GEM Chip G on a Chromium Controller (all 10x Genomics) for barcoding and cDNA synthesis. Amplification of the cDNA, targeted enrichment of TCR sequences, and library construction were performed using a Chromium Single-Cell V(D)J Enrichment Kit, Human T Cell and Chromium Single-Cell 5’ Library Construction Kit (both 10x Genomics) according to the manufacturer's protocol. The libraries were sequenced on a NovaSeq 6000 (Illumina).

Bioinformatics analysis of scRNA-seq
Gene expression was quantified by Cell Ranger (v6.0.0) count with pre-built reference refdata-gex-GRCh38-2020-A downloaded from the 10x Genomics website. TCRs were called by Cell Ranger (v6.0.0) vdj with pre-built reference refdata-cellranger-vdj-GRCh38-alts-ensembl-4.0.0 downloaded from the 10x Genomics website. Quantified gene expression levels were preprocessed and visualized using Scanpy v1.9.158. and Python 3.8.0. Demultiplexing was performed using HashSolo56 with the default parameters. Inferred singlets with TCR chains were retained. The TRAV, TRBV, TRAJ, and TRBJ genes were removed for the clustering and embedding analysis in order to remove the effect by clonal expansion. The data were preprocessed by sc.pp.normalize_per_cell (counts_per_cell_after = 1e4), sc.pp.log1p, sc.pp.highly_variable_genes (min_mean = 0.0125, max_mean = 3, min_disp = 0.5), retaining highly variable genes. The inference of the cell cycle was performed using the sc.tl.score_genes_cell_cycle function following the tutorial (https://nbviewer.jupyter.org/github/theislab/scanpy_usage/blob/master/180209_cell_cycle/cell_cycle.ipynb). Unwanted variations ('total_counts', 'pct_counts_mt', 'S_score', 'G2M_score') were regressed out using sc.pp.regress_out, and the expression was scaled using sc.tl.scale. Then, principal components were computed using sc.tl.pca. The batch effect of samples on principal components was removed by use of the Harmony algorithm59. Lastly, cells were embedded in two-dimensional space by uniform manifold approximation and projection (UMAP) using sc.tl.umap, clustered using sc.tl.leiden, and manually annotated using the following criteria. Pseudotime was calculated using sc.tl.dpt.

Bioinformatics analysis of Non-negative Matrix Factorization (NMF)
To extract gene programs from the single-cell data, we applied NMF following the tutorial (https://github.com/yyoshiaki/NMFprojection/blob/main/PBMC.ipynb). For each factor in NMF, we calculated the enriched pathways for the top 100 genes with the highest feature values using the enrichPathway function from the R package ReactomePA60, and visualized them using the cnetplot and dotplot functions. NMF is extracted from the highly expressed variation by factor. As the correlation plots are influenced by the large variation, local features such as NMF4 can be underestimated. The gene expression data of tumors and clinical information for TCGA-STAD were downloaded using TCGAbiolinks (2.24.3). The NMF scores were calculated using NMFproj (0.0.1_220610, https://github.com/yyoshiaki/NMFprojection). A Cox proportional-hazards model was applied to infer the influences of the NMF features and the stage using lifelines61 (0.27.4). For this analysis, the stage was defined as a number in the range 1–4. All values were standardized.

Integration of published dataset with our dataset
To assess the effect of ICI, we integrated a published dataset (GSE173351) with our dataset using the Symphony algorithm62 implemented in Symphonypy (https://github.com/potulabe/symphonypy). Cell abundances were statistically assessed using single-cell compositional data analysis (scCODA)63 with the default parameters. In scCODA, Bayesian estimation is used to calculate the posterior probability of cell frequencies, and a qualitative assessment is made as to whether the difference is greater than the significance level.

TCR analysis
TCR analysis was performed using Scirpy64 (0.10.1). Clonotypes were defined using ir.tl.define_clonotypes (receptor_arms="all", dual_ir="any"). A same clone was defined as one in which the DNA sequence of CDR3 of VD and VDJ matched. For the assigned clones, STARTRAC42 (0.1.0) analysis was performed to infer the expansion, migration, and transition status. For the clone network construction, we used the ir.tl.repertoire_overlap function to calculate the Jaccard index, which we then employed as a measure of clonotype similarity (Fig. 6c). There are clones in PBMCs that exhibit strong clonal expansion, which can disproportionately affect visualization. Therefore, to reduce their influence and to focus on clones present in the tumor, metastasis, and LN, the Jaccard index was calculated using only the clones found in those three compartments.

Statistics
Differences between the two groups in each experiment were analyzed using the Student’s t-test or Mann–Whitney U test, where appropriate. Categorical variables and continuous variables were compared as indicated in the figure captions. Non-parametric comparisons involving three or more groups were performed using the two-sided Kruskal–Wallis test followed by Dunn’s post hoc test. Univariate and multivariate Cox regression analyses were performed. Hazard ratios (HRs) and 95% confidence intervals (CIs) were calculated for each factor. The variables with univariate regression P-value < 0.1 were included in multivariate regression analysis. Survival curves were calculated using the Kaplan–Meier method and differences were assessed using the log-rank test. The high and low groups were divided by the median values of each factor. A value of P < 0.05 was considered to be significant. All statistical analyses were performed using JMP Pro 17 Discovery (SAS Institute Inc., Cary, NC), R (4.3.0) and python (3.9.16).

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "This study aims to understand the dynamics of progenitor-exhausted T cells (Tpex) in cancer patients treated with immune checkpoint inhibitors (ICIs), addressing a fundamental question since Tpex are crucial for the effectiveness of ICIs. The key findings of the manuscript demonstrate that ICIs primarily target Tpex in lymph nodes, prompting their proliferation and migration to tumors. This have been suggested in recent studies in mice (https://www.science.org/doi/10.1126/sciimmunol.abg7836?url_ver=Z39.88-2003&rfr_id=ori:rid:crossref.org&rfr_dat=cr_pub 0pubmed) and human (https://www.cell.com/cell/fulltext/S0092-8674(23)00164-2). However, overall, clarity needs enhancement, transitions should be smoother for better flow, and a more thorough analysis is necessary to provide additional context.\nMajor concerns:\nWhile the paper reads nicely and is well written, it lacks the depth and detail required to provide the reader with a complete understanding of the experimental setup and the significance of the results. Additionally, the logic behind each cohort and experiment needs to be clarified, as well as the connections between the figures, to ensure the reader understands how each part of the study contributes to the overall findings.\nThe authors seek to answer how ICI-bound CD8 respond in comparison to non-ICI bound CD8, a really important question that will be critical for understanding how to better improve/target new therapies. Nonetheless, the authors provide inadequate details about the method. Furthermore, there is no validation within the paper that confirms this method performs in a specific manner. It would be nice to have (1) more controls that corroborates the effectiveness of the method and (2) explanation of ICI-non-bound CD8 – the implication is that these cells are exhausted and ICI therapy is just not working for them, it would help to determine whether these cells are tumor specific, obviously this is challenging not knowing the specific antigen but at least marker of exhaustion could be helpful (e.g. CD39, TOX, TCF7, PD-1, Tim3)?\nThe major finding of the paper concludes that C5 Tpex in the LN have higher expansion, migration, transition upon ICI treatment. Yet, we see in Figure 5e that C12 appear to have higher expansion, migration, transition but no ICI-bound CD8 T cells (according to text). That said, figure 5H shows high IgG4 clones and they appear to be located in cluster 12 Tpex, so it remains unclear how the authors claim with certainty that ICI treatment is expanding Tpex in LN and not meta-LN. Also, cluster 12 has most Tpex within the meta-LN or tumor, where one would expect Tpex to respond better. This reviewer feels that there is not evidence provided to determine if ICI truly expands Tpex within LN instead of meta-LN. This point needs further expansion. Do the authors have non-ICI treated patients they could compare cluster 12 to, to see whether the increased expansion, migration, transition pattern seen in cluster 12 Tpex is truly independent of ICI?\nMinor concerns:\nFigure 1: needs better explanation for cluster assignments. For example, authors assigned effectors based on (GrzmK) expression, but in ext fig 1a – highest level of Grzmk was in Tpex. This should be explained in more depth and more markers should be added to the text.\nWhy did the authors not exclude doublet clusters from their analysis? This could compromise the interpretation of the results.\nExt data fig 1A: This representation is quite confusing and not intuitive. Also missing important markers like Tcf7\nFig2. it would significantly help if the authors provided more details about the bioinformatics analysis and for example why they choose non-negative matrix factorization (NMF) analysis for their analzsis rather than saying Non-negative matrix factorization (NMF) is useful for profiling T cells with ambiguous boundaries… what dose it mean ambiguous boundaries here ?\nFigure 2C: why is NMF4 missing?\nFigure 3: Include gating tree\nFigure 4E-H: It would help significantly if we had number of samples used in 5E-H (especially for Tumor and Meta LN samples that look like there are only 2 samples there)\nFigure 5C: what is 1, 2,   3? Is this TCR sequences that were found more than 3 times?\nFigure 5G-H: explain better. Is this TCR sequences (alpha, beta chain), or is this amino acid sequence specifying similar binding properties? How do they define a clonotype – matching TCR sequence? How do they define clonotype matched to C5 cluster – T cells that share the exact same TCR sequence? Or similar amino acid sequence?\nAuthors may comment the result btw different figure for example why there is no significant of Tpex with better prognosis in gastric cancer (Fig2 ) and why there is better prognosis in gastric in fig 3f and J, this is because of they include LN…", "reviewer_2": "In this manuscript, the authors aim to characterize the anti-tumor immunity of progenitor exhausted T cells (CD8-Tpex) in gastrointestinal cancers. They also used anti-IgG4 antibody combined with epitopes sequencing (CITE-seq) with scRNA/TCR-seq (scRNA/TCR/CITE-seq) to study the ICI-bound T cells in tumors, lymph nodes (LNs), and blood from one ICI-treated gastric cancer patient. They showed that there was a clonal link between ICI-bound CD8-Tpex in LNs and intratumoral ICI-bound CD8-Tex.\nAlthough the mechanistic analysis of ICI-bound T cells in ICI-treated cancer patients is interesting and important, I am confused by the flow of the manuscript. The manuscript starts by analyzing the T cells from tumors, metastatic and metastasis-free LNs, and blood from two esophagogastric junction (EGJ) cancer patients who had received chemotherapy and anti-PD-1 antibody therapy. They then projected published scRNAseq data of tumors from 15 ICI-treated lung cancer patients on to their EGI dataset and claimed that a CD8-Tpex T cell subset from the LNs of the two EGJ patients is associated with better therapy response. They redefined the T cell subsets from the two EGJ patients using non-negative matrix factorization (NMF) and tested their association with survival in the TCGA dataset of gastric adenocarcinoma, finding no association between Tpex abundance and patient survival.\nSubsequently, they analyzed the LN Tpex from 55 ICI-naive gastric cancer patients using flow cytometry and showed that the ratio of Tpex/Tex in metastasis-free LNs was associated with better survival outcomes. They then compared the LN Tpex from 12 ICI-treated gastrointestinal cancer patients with the ICI-naive gastric cancer patients and found that ICI treatment potentially increases the proliferative activity of the LN Tpex but not their cytotoxicity. Finally, they characterized the ICI-bound T cells in two(?) ICI-treated gastric cancer patient and found that ICI-bound CD8-Tpex had a higher cross-tissue migration potential and high clone overlaps with exhausted CD8 T cells (CD8-Tex) in the tumor.\nOverall, the observations of the scRNAseq and flow cytometry data from the ICI-naive and ICI-treated gastric cancer patients are diverse and, many times, do not converge on concrete conclusions. Please find the specific comments below.\nComments:\n1. As the authors noted, Figure 1b shows that all the CD3+ T cells sampled from various locations/organs exhibit significant organ specificity. However, the authors provided the same annotation for these cells from distinct clusters originating from different organs (Fig 1b). This is confusing and raises doubts about the correctness of the classification. For instance, the authors defined Tpex cells by the high expression of genes TCF7, PDCD1, and CXCL13. However, among the two annotated CD8-Tpex clusters (C05 and C12), C12 clearly lacks the expression of CXCL13.\n2. The authors later mentioned that \"The transcriptomic similarity of clusters was observed mainly according to tissue\". Is this an actual difference (i.e. is it true that the naive T cell populations in LN and PBMC are very distinct)? The UMAP and the correlation analysis show that C2 and C10 are separated from C0 and C11 - I am not sure if this is true difference or there is some batch effect that was not accounted for here. Also, C05 and C08 are correlated in Fig 1E but they are much further in the UMAP. Why is that?\n3. C09 is the population of CD4 T folicular helper which is known to express CXCR5, PD1, TOX, and BCL6 - as the authors mentioned later in Fig 1F. There is no indication that these are CD4 Tpex.\nA part of C5 \"Tpex\" may have CD8+ CXCR5+ population reported previously (PMID: 31028278) - this population resembles the CD4 Tfh in their markers and these seems to be found in the LN.\n4. The authors stated that “a significantly higher proportion of CD8-Tpex (C05) and CD8-effector (C08) in the MPR group than in the non-MPR group, indicating that abundant intratumoral CD8-Tpex and CD8-effector were associated with a favorable therapeutic effect of ICI (Fig. 1G).”\nHowever, neither C05 nor C08 were tumor-infiltrating T cells in Fig 1B dataset: C05 were found mainly in lymph nodes (LNs) and C08 in blood.\nAre the projected T cells from the lymph nodes of the lung cancer patients (in GSE173351)?\nIf they are not from the LN but from the tumor, how did the authors correlate these non-tumor T cell clusters (C5 and C8) to the tumor-infiltrating T cells from lung cancers and draw conclusions on their prognostic power? Furthermore, in Figure 1g, no statistical significance of the comparison was mentioned. What is the meaning of “Credible”?\n5. What is the data supporting this statement (page 5, ine 154-155) \"CD4-Tpex cells have acquired migratory and proliferative functions in LNs, possibly working on CD8-Tpex cells, while still maintaining progenitor cell characteristics\"?\nThe reviewer still thinks that the CD4 Tpex is a population of Tfh that may or may not be related to the CD4 Tex. Did the author check if the TCR of CD4 Tpex and CD4 Tex highly overlap?\n6. The NMF analysis of their scRNAseq dataset (Fig 2) appears inconsistent with their clustering analysis (Fig 1b) and the similarity analysis of these clusters (Fig 1e). For example, NMF4 includes C0, C11, C1, C2, and C10 (Fig 2a, Fig 1b). C0 and C11 are from lymph nodes (LNs), which do not share high similarity with C1, C2, and C10, which are from blood (Fig 1e). This raises doubts about the robustness of the NMF analysis or the initial clustering analysis in Fig 1.\n7. Furthermore, it is unclear how the biological processes represented by NMF1 in Fig 2c are relevant to Tpex cell state and ICI response.\n8. The absence of any significant correlation between the NMF1 or NMF8 gene program enrichment in TCGA does not imply that the Tpex accumulation is not important in the prognosis of ICI-naive gastric cancer. The authors has not established that the enrichment of NMF1 or NMF8 onto a bulk RNAseq dataset of TCGA is specific to the T cell and not other cell types in the tumor.\n9. On Page 5, lines 184-188, why did the authors think that Extended Figures 5a and 5b support their hypothesis that “anti-tumor immunity was induced in LNs close to the tumor”? Extended Figure 5 compares the frequency of IFN-γ+ and TNF-α+ CD8 T cells among CD8+PD1+ vs. CD8+PD1- cells across pathological tumor stages. It was expected that CD8+PD1+ T cells would have higher IFN-γ and TNF-α production than CD8+PD1- T cells, since PD1 itself is a T cell activation marker. However, I do not understand how this data is related to Figure 3b and the authors’ claim that “anti-tumor immunity was induced in LNs close to the tumor.” The authors should show that IFNy and TNF expression data in the same grouping as Fig 3b and not by pathological tumor stage.\n10. \"The frequency of CD8-Tpex in metastasis-free LNs was lower in advanced tumor stages (Fig. 3d).\" Is this analyis only done in the proximal metastasis free LN?\n11. \"The expression frequency of molecules among CD4 + T cells in each tissue was similar to that of CD8 + T cells (Fig. 3g).\" The reviewer is unsure of the relevance of Tim3 and CD103 in CD4 T helper populations. Furthermore, Fig 3i also show an increase in GZMB expressing CD4 Tex - what is this population?\n12. In Figure 3d, metastasis-free LNs from Stage III seem to have a lower Tpex/Tex ratio compared to Stages I and II. I suggest that the authors perform a multivariate Cox hazard analysis (as they did in Figure 2d) to determine if Tpex/Tex is an independent prognostic marker or if it is just reflecting tumor stages (the ratio correlates with stages in Fig 3d and 3h). Additionally, did the authors test the survival association of Tpex, Tex frequency, or the Tpex/Tex ratio in tumors and metastatic LNs?\n13. \"On the other hand, cytotoxicity evaluated by GZMB was not significantly different between each T-cell subset in patients with ICI-naive and ICI-treated cancers (Fig. 4f)\" - there seems to be one statistically significant difference in Fig 4f. More puzzlingly, there is a uniform, statistically significant upregulation of GZMB across all CD4+ T cell groups in mets free LNs in Fig 4h but the authors stated: \"These results were consistent with CD4 + T cells (Fig. 4g,h). Thus, ICI treatment does not affect the cytotoxicity of Tpex in metastasis-free LNs, but greatly increases their proliferative potential\".\n14. Is it correct that both CD4 Tpex (of Tfh) and Tex are not clonally expanding as shown in Fig 5c? The authors have been drawing parallel conclusions across the CD8 and CD4 Tpex and Tex populations but most of the data, including this TCR data, suggest that they are different.\n15. The reviewer noted that most of the analysis in Fig 5 is based on only two patients, which limits the ability to generalize any conclusions. Additionally, the authors should provide some supporting data (maybe as supplementary) that the clonal expansion, migration and transition across the organs and/or T cell clusters are consistent between the two patients to ensure we are not looking at a pattern dominated by a single patient.\n16. \"A high cross-tissue migration score was observed in CD8-Tpex (C05, C12) and CD8-effector (C08) cells\" - are the T cells in C08 clonally related to C05 and C12? WHat is the authors opinion on the sequence of the migration of tumor infiltrating Tex T cell clones? Is it starting in the proximal LN?\nIn line 263, the authors stated \"Regarding migration, the most observed route of migration was between LN and tumor (C04, C05, C06.0, C08) (Fig. 5f).\" It is noted that C08 is from PBMC and not from LN. The reviewer is also curious that the PBMC C08 clones are not discussed as much as LN Tpex C05 despite a strong clonal overlap with tumors and a high expansion, migration and transition indices.\n17. The Tex clusters have high transition scores and the authors stated \"intratumoral CD8-Tex cells had a high potential to differentiate and expand\"- To the reviewer's understanding, the Tex are exhausted T cells that are not undergoing further state transition.\n18.\"All of these T cells were bound by ICIs except for C12, suggesting that ICI-bound T cells play a leading role after ICI administration.\" I am not sure if this is what I observe from Fig 5a - there are some IgG4 signal in C12 and the mRNA expression in C12 is similar to C5 (Fig 5b). This seems to be a rather incoherent description of the data. The implication \"suggesting that ICI-bound T cells play a leading role after ICI administration\" is even more confusing. What is the basis of this statement?\n19. \"Although some cells in the C05 migrated between LNs and PBMCs, most cells migrated between LNs and tumor, and clonotypes within this cluster were highly shared with the tumor-enriched CD8-Tex clusters (C04, C06.0) (Fig. 5f,g).\" This qualitative approach comparing \"some cells\" with \"most cells\" without quantification is concerning.\n20. \"Importantly, clonotypes with high IgG4 binding (ICI high–bound T cells) shared clones with the tumor-enriched CD8-Tex clusters (C04, C06.0), whereas clonotypes with low IgG4 binding (ICI low–bound T cells) shared clones with the PBMC-enriched CD8-effector (C08) (Fig. 5h), indicating the possibility that ICI high–bound Tpex in LNs contains cancer antigen–specific T cells, whereas ICI low–bound Tpex in LNs may have a different antigen specificity.\"\nThis statement needs to be supported by a quantification of clone overlap between IgG4 high clones in C05 with the tumor (and mets) compared to the overlap with C8 PBMC clones.\n21. Based on Fig 5f, C08 has a strong pairwise migration index from PBMC to the tumor (almost as strong as C05 in LN - Tumor comparison). Can the authors generate the same analysis focusing the clones in C08 and separating them by IgG4 high vs. low? This is important to confirm the source of Tex in the tumor (whether they all come from Tpex in LN or not). Either way these obervations are important to be reported.\n22. \"We also discovered that PDCD1+ TCF7+ Tpex presence within tumors correlates with the successful outcome of ICI treatment. Interestingly, this correlation did not extend to the prognosis of ICI-naive gastric cancers. In contrast, abundant Tpex presence in LNs was linked to favorable prognosis in ICI-naive gastric cancer, and the proliferation activity of Tpex in LNs significantly increased after ICI treatment.\"\nI am not sure the projection of lung tumors on the scRNAseq of gastric cancer can reliably define that the PDCD1+ TCF7+ T cell in the TIL is the same as the Tpex in the LN. This convoluted sentence is probably unnecessary and is confusing to the reader. It is adequate to state the positive correlation in gastrointestinal cancers.\n23. In line 339, the authors stated \"In our data, abundant intratumoral Tpex were associated with better response to ICI treatment\". Is this pointing to the lung cancer data? It is confusing. I really suggest the authors to remove the lung cancer data analysis altogether (GSE173351) - it is hard to see why it is related to this study.", "reviewer_3": "The study conducted by Takuro Saito’s team investigates the impact of therapeutic PD-1 antibodies on TCF1 and PD1-expressing progenitor exhausted CD8 T cells (Tpex) by analyzing tumor tissues, peripheral blood, and lymph node samples from gastrointestinal cancer patients both before and after ICI treatment. The authors found that Tpex cells are enriched in the proximal lymph nodes of non-metastatic tumors and then proliferate and migrate to the tumor site post-ICI treatment, possibly differentiating into exhausted CD8 T cells (Tex) to exert anti-tumor effects. However, I have several major concerns listed as below.\n1. The authors mention that several studies have already demonstrated the association of TCF1 and PD1-expressing Tpex in tumors and peripheral blood with the response and prognosis of cancer patients to ICI treatment. A study published in Cell 2023 titled \"Dynamic CD8+ T cell responses to cancer immunotherapy in human regional lymph nodes are disrupted in metastatic lymph nodes\" also reported a high presence of TCF1+PD1+Tpex in the uninvolved lymph nodes of head and neck squamous cell carcinoma patients, and verified the clonal correlation between these Tpex and Tex in the tumor microenvironment. This study already provided a potential explanation for the anti-tumor mechanism of Tpex post-ICI treatment. Additionally, in another Cancer Cell paper published in 2023 entitled \"Progenitor-like exhausted SPRY1+CD8+ T cells potentiate responsiveness to neoadjuvant PD-1 blockade in esophageal squamous cell carcinoma.\" revealed that the abundance of SPRY1+ CD8+ Tpex in the digestive tract tumor microenvironment can serve as a marker for patient treatment response and prognosis to immunotherapy. Given the existing research discussing the presence and functions of Tpex in improving ICI treatment, the novelty and translational value of this study are limited.\n2. The authors performed multi-sample single-cell sequencing on two cancer patients who underwent chemotherapy combined with anti-PD-1 treatment, and multi-sample flow cytometry analysis on 12 gastrointestinal tumor patients who received ICI treatment. However, a larger number of samples from more patients is required to provide more convincing evidence.\n3. The authors utilized CITE-seq, TCR sequencing, and TCR tracking algorithms (STARTRAC) to analyze the clonal correlation between lymph node CD8+ Tpex and tumor CD8+ Tex, and their potential migration pathways at the single-cell level. However, showing the analysis of bioinformatics is not enough and indirect. This study lacks dynamic monitoring of lymph node Tpex migration in animal models, failing to clearly elucidate the migration and differentiation process of Tpex. Therefore, the study does not provide strong evidence for the hypothesis that lymph node Tpex migrate to the tumor and differentiate into Tex.\n4. The authors analyzed the relationship between the Tpex/Tex ratio in the lymph nodes of 12 patients without ICI treatment and prognosis, finding that a high Tpex/Tex ratio correlates with better prognosis. This finding suggests that Tpex could serve as a prognostic predictor and a novel cell therapy approach, holding significant clinical translational value. However, the authors need to conduct validation in large external cohorts to enhance the reliability of this finding."}, "author_response": ""}, {"round": 2, "reviewer_comments": {"reviewer_1": "The authors have completed a thorough revisions and have provided a detailed response to my comments. This has significantly improved the manuscript. There are just a few minor points I would suggest adding:\nTpex → Tex transition: The pseudotime and clonal data are of high quality, but lack functional validation. Acknowledging this point as a limitation in the discussion would be beneficial.\nTOX in CD4-Tpex: These cells display both TOX and progenitor markers such as TCF7 and IL7R. It might be useful to add a line in the discussion addressing the potential plasticity of these cells, considering their dual marker expression.", "reviewer_2": "The revised manuscript by Nose et al is not addressing most of the concern of the reviewer. In fact, Supp Fig 1 shows how complicated the flow of this paper is. Fig 1 start with scRNAseq analysis of T cells in blood, LN and tumor of ICI treated EGJ cancer and the author applied the NMF-derived PBMC and blood gene signatures on ICI naive TCGA gastric cancer (why?) and say that these signatures does not predict survival (can it be the problem of the signature?), then they show using flow cytometry that the Tpex/Tex ratio in LN (a value not related to the NMF signatures) is associated with RFS and OS. Then, the authors show that they can identify anti-PD1-bound T cells using anti-IgG4 (the isotype of the therapeutic anti-PD1) and suggest that the anti-PD-1 bound T cells in the LN is more proliferative. Then, they went back into their original scRNAseq analysis and added additional samples with scTCRseq and CITEseq (1 ICI-treated and 2 ICI naive) and this time added the layer of scTCRseq and CITEseq analysis. The finding was that anti-PD-1 bound CD8 Tpex cells are clonally expanding and share the same clones as the Tex in the tumor.\nPai et al (ref 25) and others have reported the clonal linkage between Tpex in tumor draining LN and the intratumoral CD8 T cell. The novel result that the Authors reported here is the analysis of the anti-PD-1 bound T cells. The Authors show the anti-PD-1 bound T cells seem to be proliferating more in the LN and have mostly exhausted phenotype in the tumor. Thus, instead of first showing scRNAseq analysis of 2 ICI-treated patients, the authors should directly start with analysis of all 5 scRNAseq/scTCRseq/CITEseq samples (3 ICI treated and 2 ICI naive). The Authros should present the phenotype(s) of the anti-PD-1-bound T cells and how they are different from the similar T cells from ICI naive patients. The Authors should separate analyses on intratumoral T cells vs T cells in LN vs. T cells in PBMC unless they can be integrated into unified phenotypes across organs. The Authors highlighted the patterns of T cell clone sharing in the anti-PD-1 bound T cells vs non-bound T cells (in ICI treated patients) vs. T cells from ICI-naive patients across the tumor, LN and PBMC, which is novel and important. Is there any clonal sharing between TFH in LN with the CD4 in the tumor?\nAny intratumoral anti-PD-1 bound T cell population identified in the ICI treated gastric tumor/mets samples can be compared to those found in lung tumor/mets sample from GSE173351 to see if there are any similarity. T cell populations from the LN of gastric tumor patients should be compared with those from LN of the lung tumor patients in the GSE173351 dataset (instead of what is done now where the authors are mixing the clusters from blood, LN and tumor and compare them against a mixture of T cells from lung tumor, adjacent normal lung, and LN).\nThe flow cytometry analysis should be focused on that in Figure 4 where the authors compare the effect of ICI in the Tpex and other T cell populations. The effect of anti-PD-1 treatment in increasing the proliferation of TCF1+ PD1+ Tpex in LN. The Authors should demonstrate if the fraction of Ki67+ TCF1+ PD1+ Tpex in LN is associated with survival in patients with gastric cancer. The importance of anti-PD-1 bound TFH in LN should also be supported with a similar analysis.\nFinally, the Reviewer could not see any significant implication or conclusion from the TCGA analysis.\nThe Reviewer also has other specific concerns with the analysis as listed below.\nSpecific comments:\n1. The single cell RNAseq analysis in Fig 1b shows a strong organ-driven batch effect (not patient driven batch effect). The Authors should really try to integrate the data using data integration method like CCA, RPCA, bbknn etc and observe whether such organ effect can be mitigated. Looking at the method description of the scRNAseq analysis in line 579-580 of the main text, the Reviewer noticed that the Authors did not integrate the profiles from different samples (different organs and different patients).\nFor an example of multi sample integration using scanpy, please see https://scanpy-tutorials.readthedocs.io/en/latest/integrating-data-using-ingest.html or https://nbisweden.github.io/workshop-archive/workshop-scRNAseq/2020-01-27/labs/compiled/scanpy/scanpy_03_integration.html.\nFor instance, the integration may be able to cluster together the naive T cell populations in the LN (C0 and C11) and PBMC (C2 and C10).\n2. The scRNAseq analysis should be done on all 5 scRNAseq samples right from the start rather than analyzing two sample first (pt 1 and 2) and projecting the scRNAseq of Pt 3,4,5 on to the clustering of Pt 1 and 2 using symphonypy.\n3. If the authors still find separate CD4 and CD8 T cell populations after integration step, then the Authors must ensure that analysis of different T cell populations are grouped by organ. For instance, to analyze the correlation between intratumoral T cell populations in TCGA gastric cancer dataset, the Authors should only test gene signatures representing intratumoral T cell populations (e.g. C4, C6.0, C6.1, C7, C12, C14, C17.0, C17.1, C19 - based on Fig 1d). If the authors must do NMF analysis, then please only test NMF signatures that included intratumoral T cells (NMF0,3,5,6,7). NMF8 is clearly not strongly related to intratumoral T cells clusters (see Fig 1d and Fig 2b).\n4. (related to response to Reviewer 1 minor comment #5, Reviewer 2 comment #6 #7 #8) The gene signatures of each NMF group need not be enriching for some reactome gene set (reactome is a pathway database with groups of proteins in a pathway, not a gene signature database with list of genes changing in expression in response to perturbation like the MsigDB). NMF0 clearly enriches for Treg markers (Fig 2b). NMF4 enriches naive or central memory T cell genes (Fig 2b). Looking at the genes in NMF1 and NMF8 (Fig 2b), there is no indication that they are related to CD8 Tpex. Please provide paper reference(s) showing that SLC2A3 (GLUT2), NR4A2, GZMK, FGFBP2, CST7 and interferon signaling are in any way related to Tpex. Also, are TCF7, SELL, TOX, PDCD1 included in the gene signatures of NMF1 and 8?\n5. (related to response to Reviewer 2 comment #4) In Suppl Fig 4, the Authors tests if any of the T cell clusters in Fig 1b is enriched in the T cells in a neoadjuvant PD-1 inhibitor-treated lung cancer patients. The lung cancer patient-derived T cells are coming from lung tumor/mets or adjacent normal lung or the LN (this is described in the Figure caption). The authors are lumping all of them together into their existing clustering, which is also a mixture of T cells from tumor, LN and PBMC of gastric cancer patients. The Authors then reported that higher proportion of T cells in C5 (Tpex in LN) and C8 (T eff in PBMC) is associated with major pathological response (MPR) in the lung cancer patients receiving neoadjv anti-PD1. Are the T cells projected to C5 from the lung cancer dataset from the LN T cells (there are 3 patients with T cells from LN in the dataset)? Are the T cells projected to C8 from the lung cancer dataset from the blood T cells (there is only 1 patient with T cells from blood)?\nIn their response, the Authors stated that \"there is currently no scRNA-seq dataset that directly evaluates Tpex in LNs after ICI treatment in human samples and examines its association with ICI treatment efficacy or prognosis.\" This does not justify checking the Tpex cluster in LN across T cells from multiple organs. What the Authors can do is to test if any of the intratumoral T cell population in Fig 1b is enriched in the the intratumoral T cell population of lung cancer patients with MPR after neoadjuvant anti-PD1.\n6. (related to response to Reviewer 2 comment #3 and #5) The whole discussion of \"CD4 Tpex\" in this paper totally ignores the known fact that LNs enriches for CD4 Tfh and that C09 matches the phenotype of Tfh very clearly (icluding the expression of TCF7, TOX, TOX2, PDCD1, TIGIT, CXCL13, CXCR5, BCL6 shown in Supp Fig 5) and that C09 is the ONLY cluster matching CD4 Tfh in this whole dataset. So C09 is Tfh and there is no need to invent a new CD4 Tpex population here. What is NOT known is whether aPD-1 binds to Tfh in the tumor draining LN (answer: yes based on Fig 5a), whether they are activated by aPD-1 (yes, their proliferation is induced in Fig 4g) and whether they migrate into the tumor (answer: probably no based on Fig 5d,e,g; more specific analysis is needed).\n7. Analysis in Fig 3 is to support the importance of CD8 Tpex and Tfh fraction in proximal (presumably tumor draining) LNs. The Authors used a ratio between Tpex and Tex to correlate with relapse free or overall survival. Does the fraction of Tpex in the LN not correlated with RFS/OS? Also, the difference in p-value significance between CD8 Tpex/Tex ratio in LN vs. CD8 Tpex/Tex ratio in tumor may simply reflect the difference in the sample numbers (LN n=49, tumor n=23) - the KM plots look quite comparable between the two.\n8. (related to response to Reviewer 2 comment #9) \"First, The comparison between ICI-bound and ICI-non-bound CD8+ T cells revealed that ICI-bound CD8+ T cells exhibited higher CD103 expression and lower TCF1 expression than did ICI-non-bound CD8+ T cells across all tissues, suggesting that ICI-bound CD8+ T cells possess an exhausted phenotype, whereas ICI-non-bound CD8+ T cells retain a naive phenotype (Supplementary Fig. 11a, b).\" CD103 is not a known marker of exhaustion - it is reported as a marker of tissue resident T cells that can also be expressed on intratumoral T cells (see for example https://rupress.org/jem/article/218/4/e20201605/211911/Tissue-resident-memory-T-cells-in-tumor-immunity). The authors should include citation of supporting papers defining CD103 as marker of exhaustion, especially for Tpex in the LN?\n9. (Fig 4e-h) are the samples from ICI-treated T cells stained for PD-1 using the indirect method (i.e. using anti-IgG4) while the ICI-naive stained using direct method (i.e. using anti-PD1)? If yes, cells from the same T cell subsets from the same organ in the ICI-treated and ICI-naive may not be comparable. This is because the antibodies used to define the subsets are not exactly the same and hence may bind the PD1+ T cells at different rates. Indeed, Supp Fig 10 shows that although the correlation between PD-1 staining and IgG4 staining is highly positive, the slope of the fitted linear curve is not equal to 1 (the %IgG4+ is smaller than %PD-1+).\nWhat the authors can do is comparing the relative abundance of TCF1+ within PD1+ subset (for ICI naive) or IgG4+ subset (for ICI-treated).\n10. The STARTRAC analysis in the main Figure 5 should highlight the differences between the ICI-naive vs. ICI-treated patients (now Supp Fig 14). The Authors should highlight the subsets with differences. The important observations include ICI increases expansion in Tpex (C5 and C12) and increased migration from LN to tumor only in C5 (which may make sense since the Tpex in C12 is already in the tumor). It is important to highlight that with ICI, the transition of intratumor Tex and Tpex are lower -- does this mean they slow their transition to terminally exhausted phenotype? These are important points to highlight. Finally, the STARTRAC analysis should be performed only on the IgG4+ subset of the ICI-treated samples to show the patterns of expansion, migration and transition in the anti-PD-1 bound T cells.\nTo aid the visualization of the TCR overlap between different pairs of clusters, the author should visualize the percentage overlap in a heatmap format rather than listing them in the text. This should be done in ICI treated vs ICI naive (and perhaps in IgG4+ T cell subset).", "reviewer_3": "fter carefully evaluating the author's revised manuscript and supplementary data in response to the review comments, I find that the author has provided detailed and substantive responses to the key concerns, including research innovation, sample size, limitations in animal model validation and prognostic analysis. While certain constraints remain due to practical limitations, the author has made notable improvements to the paper, addressing several critical issues and enhancing its overall rigor.\nThe authors has integrated matched tumor, lymph node, and blood samples from patients after ICI treatment and utilized scRNA/TCR/CITE-seq technologies to distinguish ICI-bound and non-bound T cells, demonstrating a certain level of technical innovation. Furthermore, the study focuses on the activation of Tpex cells in non-metastatic LNs, their migration into tumors, and their association with prognosis, providing unique insights into the dynamic migration mechanism of Tpex cells.\nIn addition, the author has expanded the sample size and supplemented single-cell sequencing data, enhancing the reliability of the results. Although the study lacks dynamic monitoring of lymph node Tpex migration in animal models due to practical constraints, the author provides evidence of Tpex migration to some extent through multi-omics data and clonal correlation analysis, while explicitly acknowledging this limitation in the discussion, making the response scientifically sound and reasonable.\nFinally, validating the prognostic value of the Tpex/Tex ratio in a larger cohort is of significant reference value. However, the author indicates that the difficulty in obtaining surgical samples from ICI-treated patients limits further validation of the correlation between the Tpex/Tex ratio and prognosis in a larger cohort, which is indeed an objective limitation.\nIn conclusion, this study systematically reveals the dynamic role of lymph node Tpex in ICI treatment, providing a new perspective for optimizing immunotherapy strategies. I recommend accepting the manuscript."}, "author_response": "1\nResponses to the reviewers’ comments\n2\n4\n6\nin cancer patients treated with immune checkpoint inhibitors (ICIs), addressing a\n7\nfundamental question since Tpex are crucial for the effectiveness of ICIs. The key\n8\nfindings of the manuscript demonstrate that ICIs primarily target Tpex in lymph\n9\nnodes, prompting their proliferation and migration to tumors. This have been\n10\nsuggested in recent studies in mice\n11\n(https://www.science.org/doi/10.1126/sciimmunol.abg7836?url_ver=Z39.88-\n12\n2003&rfr_id=ori:rid:crossref.org&rfr_dat=cr_pub 0pubmed) and human\n13\n(https://www.cell.com/cell/fulltext/S0092-8674(23)00164-2). However, overall, clarity\n14\nneeds enhancement, transitions should be smoother for better flow, and a more\n15\nthorough analysis is necessary to provide additional context.\n16\n18\nby the reviewer that “the paper reads nicely and is well written” was highly encouraging to\n19\nus. We have responded to each of the comments one by one.\n20\n22\n23\n1) While the paper reads nicely and is well written, it lacks the depth and detail\n24\nrequired to provide the reader with a complete understanding of the experimental\n25\nsetup and the significance of the results. Additionally, the logic behind each cohort\n26\nand experiment needs to be clarified, as well as the connections between the figures,\n27\nto ensure the reader understands how each part of the study contributes to the\n28\noverall findings.\n29\n31\nhave created an overview summarizing the samples and experimental methods for each\n32\nfigure to clarify the overall connections in the study (Supplementary Fig. 1). Additionally, we\n33\nhave added some introductory text at the beginning of each section in the results to\n34\nenhance readability (page 4 lines 87-88, page 5 lines 128-131, page 5 line 147, page 6\n35\nlines 172-176, page 7 lines 197-198, page 8 lines 237-238, page 9 lines 289-290).\n37\nincluding the IgG4 detecting method in flow cytometry (page 16 lines 520-534,\n39\n297, Supplementary Fig. 12), and NMF analysis (page 18 lines 584-596). We expect that\n40\nthese revisions will contribute to improving the readability of the manuscript.\n41\n42\n2) The authors seek to answer how ICI-bound CD8 respond in comparison to non-ICI\n43\nbound CD8, a really important question that will be critical for understanding how to\n44\nbetter improve/target new therapies. Nonetheless, the authors provide inadequate\n45\ndetails about the method. Furthermore, there is no validation within the paper that\n46\nconfirms this method performs in a specific manner. It would be nice to have (1)\n47\nmore controls that corroborates the effectiveness of the method and (2) explanation\n48\nof ICI-non-bound CD8 – the implication is that these cells are exhausted and ICI\n49\ntherapy is just not working for them, it would help to determine whether these cells\n50\nare tumor specific, obviously this is challenging not knowing the specific antigen but\n51\nat least marker of exhaustion could be helpful (e.g. CD39, TOX, TCF7, PD-1, Tim3)?\n52\n54\nthe methodological details shown in the query 1.\n1\n56\nvalidate the IgG4 methods with both flow cytometry and scRNA-seq analysis. A flow\n57\ncytometry method was validated to detect ICI-bound T cells using an anti-IgG4 antibody\n58\nwith PBMCs from 29 newly added ICI-naive gastric cancer patients. We compared the\n59 positivity of anti–PD-1 and anti-IgG4 antibodies for PD-1+ T cells before and after in vitro\n60\nculture with anti–PD-1 therapeutic antibodies (Supplementary Fig. 10). The results show\n61\nthat the anti-PD-1 antibody positivity before culture was consistent with the anti-IgG4\n62\nantibody positivity after culture, clearly indicating that the IgG4 method with flow cytometry\n63\ndetected ICI-bound T cells (page 8 lines 239-244).\n65\nanti–PD-1 antibodies in one new ICI-treated and two ICI-naive cancer patients to confirm\n66\nthe ability of the IgG4 methods to detect ICI-bound T cells in single-cell sequencing\n67\nanalysis (Supplementary Fig. 12b,c, 14). The anti-IgG4 antibody was detected in ICI-\n68\ntreated patients (Supplementary Fig. 12b-c right), whereas it was barely detected in ICI-\n69\nnaive patients (Supplementary Fig. 12b-c left). These experiments further validated the\n71\nmethods section (page 15 lines 477-484, page 16-17 lines 542-554) and described these\n72\nresults in the manuscript (page 9-10 lines 291-300).\n74 non-bound CD8+ T cells, we analyzed scRNA-seq data from tumor samples of three ICI-\n75\ntreated cancer patients to compare gene expression between ICI-bound and ICI-non-bound\n78 genes such as CXCL13, PDCD1, HAVCR2 (Tim-3), TIGIT, and LAG3, indicating that ICI-\n79 bound CD8+ T cells exhibit an exhausted phenotype while ICI-non-bound CD8+ T cells\n80\nshow a naive phenotype (Supplementary Fig. 13). Flow cytometry analysis further\n81\nconfirmed these findings, showing lower CD103 and higher TCF1 expression in ICI-non-\n82 bound CD8+ T cells compared with ICI-bound CD8+ T cells across all tissues\n83\n(Supplementary Fig. 11a, b). These results collectively demonstrate that ICI-non-bound\n85\nsection (page 9 lines 264-269, page 10 lines 305-310).\n86\n87\n88\n91\ncytometry. The frequency of CD103 and TCF1 expression among ICI-bound and ICI-non-\n92 bound CD8+ T cells was shown in each tissue. Error bars indicate the median ±\n93\ninterquartile range. The significance of the differences between ICI-bound CD8 and ICI-\n94\nnon-bound CD8 in the same tissue was calculated using the nonparametric Wilcoxon\n95\nmatched-pairs signed-rank test (*P < 0.05, **P < 0.01, ***P < 0.001). Tumor: n=13, meta-\n2\n97\n98\n3) The major finding of the paper concludes that C5 Tpex in the LN have higher\n99\nexpansion, migration, transition upon ICI treatment. Yet, we see in Figure 5e that C12\n100\nappear to have higher expansion, migration, transition but no ICI-bound CD8 T cells\n101\n(according to text). That said, figure 5H shows high IgG4 clones and they appear to\n102\nbe located in cluster 12 Tpex, so it remains unclear how the authors claim with\n103\ncertainty that ICI treatment is expanding Tpex in LN and not meta-LN. Also, cluster\n104\n12 has most Tpex within the meta-LN or tumor, where one would expect Tpex to\n105\nrespond better. This reviewer feels that there is not evidence provided to determine\n106\nif ICI truly expands Tpex within LN instead of meta-LN. This point needs further\n107\nexpansion. Do the authors have non-ICI treated patients they could compare cluster\n108\n12 to, to see whether the increased expansion, migration, transition pattern seen in\n109\ncluster 12 Tpex is truly independent of ICI?\n110\n112\nthe scRNA/TCR/CITE-seq analysis was not predetermined, we set it as the median of\n113\n1.079 (Fig. 5b, Supplementary Fig. 12b bottom), defining clusters with 50% or more IgG4-\n114\npositive cells as IgG4-high clusters. On the basis of this threshold, C12 (73.4%) was\n115\nclassified as an IgG4-high cluster (page 10 lines 298-300)(Fig. 5b, Supplementary Fig.\n116\n12c). Accordingly, Fig. 5b was revised to reflect the updated IgG4-high clusters, and the\n117\nphrase “except for C12” was removed from the manuscript (page 10 line 329). As for\n118\nannotations of Fig. 1d, e, “Meta” indicates liver metastases and “LN” indicates metastatic\n119\nand metastasis-free LNs, as described in the legend of Fig. 1d, e. Therefore, CD8-Tpex\n120\n(C12) are derived from liver metastases and tumors, not metastatic LNs.\n122\npatients along with one new ICI-treated cancer patient for RNA/TCR/CITE-seq analysis.\n124\nthree ICI-treated cancer patients (Supplementary Fig. 12, 14). The STARTRAC algorithm\n125\nwas used to compare clonal expansion between ICI-treated and ICI-naive samples. The\n126\nresults showed that the expansion score increased after ICI treatment in C04, C05, C06.1,\n129\n(Supplementary Fig. 14a). These changes—higher expansion and migration scores and\n130\nlower transition scores—were primarily observed in IgG4-high clusters. The C12 cluster\n131\nshowed increased expansion and transition scores after ICI treatment, consistent with\n132\ntrends observed in other IgG4-high clusters. The C08 cluster, the only IgG4-low cluster\n133\nexhibiting changes after ICI treatment, demonstrated an increased migration score and\n134\ndecreased transition score, aligning with trends in the IgG4-high clusters. Regarding\n135\nmigration potential, LN–tumor migration potential increased in C05, while LN–PBMC\n136\nmigration potential increased and PBMC–tumor migration potential decreased in C08\n137\n(Supplementary Fig. 14b). Thus, STARTRAC analysis comparing the ICI-treated and ICI-\n138\nnaive patients, confirmed the enhanced expansion and migration potential of ICI-bound T\n139\ncells after ICI treatment, particularly an increased LN–tumor migration potential in the LN-\n142\nand C04 clusters across organs, we conducted a pseudotime analysis using scRNA-seq\n143\ndata from three ICI-treated patients. A flow from C05 (CD8-Tpex)→C12 (CD8-Tpex)→C04\n144\n(CD8-Tex) was observed, suggesting that CD8-Tpex in C05 represents the initiation of\n146\n(Supplementary Fig. 17).\n148\nexpansion and migration potential of ICI-bound T cells and boosts LN–tumor migration\n149\npotential in the LN-Tpex population (C05). C12 was also an IgG4-high cluster, exhibiting\n150\nincreased expansion scores after ICI treatment, but did not show a change in migration\n3\n151\npotential. It likely represents the immediate stage of ICI-bound Tpex in the tumor. We\n152\nadded these results and discussed the results in the revised manuscript (page 10 lines\n153\n322-329, page 11 lines 346-356, page 11 lines 364-367).\n154\n155\n158\n(CD8-Tpex). Pseudotime was calculated using sc.tl.dpt.\n159\n160\n162\n163\n1) Figure 1: needs better explanation for cluster assignments. For example, authors\n164\nassigned effectors based on (GrzmK) expression, but in ext fig 1a – highest level of\n166\nshould be added to the text.\n167\n169\nadded details of annotation for all clusters in Supplementary Fig. 2b. Additionally, we\n170 included new canonical genes related to effector function, such as TGFB1, GNLY, GZMH,\n171 and FGFBP2, to refine the annotations (Fig. 1c). In particular, GNLY and FGFBP2 were\n172\nshown to be more highly expressed in CD8-effector cells (C08) than in CD8-Tpex cells\n173\n(C05). We have updated Fig. 1c and incorporated the corresponding description into the\n175\n176\n2) Why did the authors not exclude doublet clusters from their analysis?\n178\n180\npredicts a “doublet” population that is close to the simulated doublet cell. While this method\n181\nworks well when applied to entire tissues and PBMCs, if it is applied only to T cells, the\n182\ndoublet score for some intermediate cells will be high. To avoid this bias, we did not\n183\nperform automatic doublet filtering in this study. Re-embedding clusters other than cluster\n184\n18 may be useful, but we left the doublet in place to avoid making clusters harder to identify\n185\ndue to the shuffle caused by re-embedding.\n186\n187\n3) Ext data fig 1A: This representation is quite confusing and not intuitive. Also\n188\nmissing important markers like Tcf7.\n189\n191\nautomatic marker gene detection, which we have previously reported to be insufficient (ref\n192\n36). The key canonical marker genes are listed in Fig. 1c, while the full set of marker genes\n4\n193\nidentified by automatic marker gene detection is shown in Supplementary Fig. 2a.\n195\nannotations for clustering (Supplementary Fig. 2a, b).\n196\n197\n4) Fig2. it would significantly help if the authors provided more details about the\n198\nbioinformatics analysis and for example why they choose non-negative matrix\n199\nfactorization (NMF) analysis for their analysis rather than saying Non-negative matrix\n200\nfactorization (NMF) is useful for profiling T cells with ambiguous boundaries… what\n201\ndoes it mean ambiguous boundaries here ?\n202\n205\ntype identity and cellular activity programs, thereby addressing the profiling of complex cell\n206\npopulations. By “ambiguous boundaries,” we refer to the challenges in defining clear\n207\ndistinctions between T cell subsets, as many subsets exhibit overlapping gene expression\n208\nprofiles and functional states. Traditional clustering methods often assign cells to discrete\n209\ngroups that may not fully capture the continuum of cellular states, especially in dynamic or\n210\ntransitional populations. We have added the details about NMF analysis in the results and\n211\nmethods section (page 6 lines 176-178, page 18 lines 584-596).\n212\n213\n5) Figure 2C: why is NMF4 missing?\n214\n216\ncould be detected. We have added this explanation in the legend for Fig. 2c.\n217\n218\n6) Figure 3: Include gating tree\n219\n222\n223\n7) Figure 4E-H: It would help significantly if we had number of samples used in 5E-H\n224\n(especially for Tumor and Meta LN samples that look like there are only 2 samples\n225\nthere)\n226\n228\nsamples to the legend for Fig. 4e-h.\n229\n230\n8) Figure 5C: what is 1, 2,   3? Is this TCR sequences that were found more than 3\n231\ntimes?\n232\n234\npanel in Fig. 5c shows the actual clone numbers, indicating how many cells share the same\n236\nwith clone size 1, 2, and greater than 3. We have added a detailed explanation of clonal\n237\nexpansion in the legend for Fig. 5c.\n238\n239\n9) Figure 5G-H: explain better. Is this TCR sequences (alpha, beta chain), or is this\n240\namino acid sequence specifying similar binding properties? How do they define a\n241\nclonotype – matching TCR sequence? How do they define clonotype matched to C5\n242\ncluster – T cells that share the exact same TCR sequence? Or similar amino acid\n243\nsequence?\n244\n246\n(0.10.1) (ref 64). Clonotypes were defined using ir.tl.define_clonotypes\n5\n247\n(receptor_arms=\"all\", dual_ir=\"any\")\n248\n[https://scirpy.scverse.org/en/latest/generated/scirpy.tl.define_clonotypes.html]. The same\n249\nclone was defined as the one in which the DNA sequence of CDR3 of VD and VDJ\n250\nmatched. For the assigned clones, STARTRAC (0.1.0) analysis (ref 42) was performed to\n251\ninfer the expansion, migration, and transition status. For the clone network construction, we\n252\nused the ir.tl.repertoire_overlap function to calculate the Jaccard index, which was then\n253\nemployed as a measure of clonotype similarity. “Jaccard Index” was used for the similarity\n254\nmetric. We have added this explanation in the Methods section (page 18-19 lines 604-614)\n255\nand the legend for Fig. 5g.\n256\n257\n10) Authors may comment the result btw different figure for example why there is no\n258\nsignificant of Tpex with better prognosis in gastric cancer (Fig2) and why there is\n259\nbetter prognosis in gastric in fig 3f and J, this is because of they include LN…\n260\n263\nwas assessed in tumor, while an abundant Tpex infiltration in metastasis-free LNs was\n264\ncorrelated with a favorable prognosis in ICI-naive gastric cancer patients (Fig. 3f), since\n266\n217-219, page 12 lines 388-391).\n267\n268\n270\n272\nprogenitor exhausted T cells (CD8-Tpex) in gastrointestinal cancers. They also used\n273\nanti-IgG4 antibody combined with epitopes sequencing (CITE-seq) with scRNA/TCR-\n274\nseq (scRNA/TCR/CITE-seq) to study the ICI-bound T cells in tumors, lymph nodes\n275\n(LNs), and blood from one ICI-treated gastric cancer patient. They showed that there\n276\nwas a clonal link between ICI-bound CD8-Tpex in LNs and intratumoral ICI-bound\n278\n280\nis interesting and important, I am confused by the flow of the manuscript. The\n281\nmanuscript starts by analyzing the T cells from tumors, metastatic and metastasis-\n282\nfree LNs, and blood from two esophagogastric junction (EGJ) cancer patients who\n283\nhad received chemotherapy and anti-PD-1 antibody therapy. They then projected\n284\npublished scRNAseq data of tumors from 15 ICI-treated lung cancer patients on to\n285\ntheir EGI dataset and claimed that a CD8-Tpex T cell subset from the LNs of the two\n287\nsubsets from the two EGJ patients using non-negative matrix factorization (NMF)\n288\nand tested their association with survival in the TCGA dataset of gastric\n289\nadenocarcinoma, finding no association between Tpex abundance and patient\n290\nsurvival.\n291\n293\nusing flow cytometry and showed that the ratio of Tpex/Tex in metastasis-free LNs\n294\nwas associated with better survival outcomes. They then compared the LN Tpex\n295\nfrom 12 ICI-treated gastrointestinal cancer patients with the ICI-naive gastric cancer\n296\npatients and found that ICI treatment potentially increases the proliferative activity of\n297\nthe LN Tpex but not their cytotoxicity. Finally, they characterized the ICI-bound T\n298\ncells in two(?) ICI-treated gastric cancer patient and found that ICI-bound CD8-Tpex\n299\nhad a higher cross-tissue migration potential and high clone overlaps with\n300\nexhausted CD8 T cells (CD8-Tex) in the tumor.\n6\n301\n303\nnaive and ICI-treated gastric cancer patients are diverse and, many times, do not\n304\nconverge on concrete conclusions. Please find the specific comments below.\n305\n307\nstatement by the reviewer that “the mechanistic analysis of ICI-bound T cells in ICI-treated\n308\ncancer patients is interesting and important” is highly encouraging to us. Accepting the\n309\nreviewer's suggestion, we created an overview summarizing the samples and experimental\n310\nmethods for each figure, to enhance the understanding of the overall connections of this\n311\nstudy (Supplementary Fig. 1).\n312\n314\n315\n1) As the authors noted, Figure 1b shows that all the CD3+ T cells sampled from\n316\nvarious locations/organs exhibit significant organ specificity. However, the authors\n317\nprovided the same annotation for these cells from distinct clusters originating from\n318\ndifferent organs (Fig 1b). This is confusing and raises doubts about the correctness\n319\nof the classification. For instance, the authors defined Tpex cells by the high\n320\nexpression of genes TCF7, PDCD1, and CXCL13. However, among the two annotated\n322\n324\nin cell characteristics was observed. However, we performed clustering in a unified\n325\nrepresentation, as described in previous studies that simultaneously evaluated populations\n326\ncommon across tissues (ref 35, 42). Consequently, many clusters were derived from\n327\nmultiple tissues, as demonstrated in Supplementary Fig. 3a. This approach enables\n328\nconsistent cell classification and facilitates analyses, such as STARTRAC, by leveraging\n329\nthe advantage of clustering across tissues.\n331\nexplanations of all clusters in Supplementary Fig. 2b. According to this definition, CD8-Tpex\n332 was characterized by the expression of TCF7 and PDCD1. While C05 expressed CXCL13,\n334\ndefinition of CD8-Tpex in the Manuscript (page 4 line 102).\n335\n336\n2) The authors later mentioned that \"The transcriptomic similarity of clusters was\n337\nobserved mainly according to tissue\". Is this an actual difference (i.e. is it true that\n338\nthe naive T cell populations in LN and PBMC are very distinct)? The UMAP and the\n339\ncorrelation analysis show that C2 and C10 are separated from C0 and C11 - I am not\n340\nsure if this is true difference or there is some batch effect that was not accounted for\n341\nhere. Also, C05 and C08 are correlated in Fig 1E but they are much further in the\n343\n345\ngenes between C02+C10 (naive T cell in PBMC) and C00+C11 (naive T cell in LNs)\n346 (Figure shown below). The results showed that the expression of BATF, which encodes a\n347\ntissue-specific transcription factor, was upregulated in C00+C11 (LNs), while the\n348 expression of CCR7 and S1PR genes involved in circulation was upregulated in C02+C10\n349\n(PBMC), indicating C02 and C10 are distinct from C00 and C11 in terms of gene\n350\nexpression. We also analyzed the genes that were differentially expressed between C05\n351\nand C08 (Supplementary Fig. 3b). The results showed that the expression of AP1 family\n352 genes (CD69) was upregulated in C05, while the expression of S1PR1 and TGFB1 was\n353\nupregulated in C08, indicating that C05 is genetically distinct from C08 (page 4 lines 113-\n7\n354\n116). This is consistent with the gene expression program found to be elevated in tissues in\n355\nour previous paper (ref 36).\n357\na batch effect. C05 and C08 are separated by reasonable distances because UMAP is a\n358\nmethod to reduce the dimensions to two while maintaining the topology and does not\n359\ncompletely maintain the gene expression distance. Therefore, there are cases where C05\n360\nand C08 are separated by UMAP, as in this case. We included Fig.1e to clarify this issue.\n362\n363\n364\n367\n368\n3) C09 is the population of CD4 T folicular helper which is known to express CXCR5,\n370\nindication that these are CD4 Tpex.\nA part of C5 \"Tpex\" may have CD8+ CXCR5+ population reported previously (PMID: 31028278) - this population resembles the\n372\n375\n(progenitor T cells) are related to the therapeutic effect of ICIs and interact to exert clinical\n376 effect. Among these, Tfh cells express CXCL13, BTLA, CD200, IL21, TCF7, and IL6ST,\n377 while progenitor T cells express CXCL13, XCL1, and Tfh-associated marker genes\n378 (CD200, BTLA, GMG4). Therefore, we consider C09 to be a population with characteristics\n379 similar to those of CD4+ Tfh cells, based on gene expression markers such as CXCL13 and\n381 31) indicates that CXCR5+CD8+ T cells interact with Tfh cells in LNs to enhance antitumor\n382 immunity. In our data, C05 and C09 exhibited high expression of CXCR5, PDCD1, TCF1,\n384 may interact with Tfh cells in LNs. However, in this study, we designated the CD4+ Tfh\n385 population as “CD4-Tpex” solely on the basis of the expression of CD4, PDCD1, and TCF7.\n387\nreference (ref 31).\n388\n8\n389\n4) The authors stated that “a significantly higher proportion of CD8-Tpex (C05) and\n391\nabundant intratumoral CD8-Tpex and CD8-effector were associated with a favorable\n392\ntherapeutic effect of ICI (Fig. 1G).” However, neither C05 nor C08 were tumor-\n393\ninfiltrating T cells in Fig 1B dataset: C05 were found mainly in lymph nodes (LNs)\n394\nand C08 in blood.\nAre the projected T cells from the lymph nodes of the lung cancer patients (in GSE173351)?\nIf they are not from the LN but from the tumor, how did the authors correlate these non-tumor T cell clusters (C5 and C8) to the tumor-infiltrating T cells from lung cancers and draw conclusions on their prognostic power?\nFurthermore, in Figure 1g, no statistical significance of the comparison was\n395\nmentioned. What is the meaning of “Credible”?\n396\n398\ncells were derived from lung cancer tumors (GSE173351). We used this dataset because\n399\nwe were unable to identify datasets other than that from the study (ref 35) that investigated\n402\nsamples (ref 25, 29), and these studies did not examine the correlation between the\n403\nabundance of the Tpex population in LNs and ICI treatment efficacy or prognosis. Thus,\n404\nthere is currently no scRNA-seq dataset that directly evaluates Tpex in LNs after ICI\n405\ntreatment in human samples and examines its association with ICI treatment efficacy or\n406\nprognosis. To infer this association, we used the dataset from the previous paper (ref 35)\n407\nand concluded that “a significantly higher proportion of CD8-Tpex (C05) and CD8-effector\n408\n(C08) in the MPR group than in the non-MPR group indicates that abundant intratumoral\n411\ntreatment correlates with a favorable response to ICI from this result (Supplementary Fig.\n412\n4c), we acknowledge that it is difficult to draw conclusions regarding the correlation\n413\nbetween the Tpex population in LNs and treatment efficacy or prognosis. Therefore, in line\n414\nwith the reviewer’s suggestion, we have decided to relocate this data to Supplementary Fig.\n415\n4c, temper our conclusions about this figure, and strengthen our logic focusing on Tpex in\n417\n“Credible” means significantly changed, since a cell population that is considered\n418\nsignificantly (FDR <0.05) different in the single-cell compositional data analysis (scCODA)\n419\nframework was described as “credible” in a previous report (ref 63) (Supplementary Fig.\n420\n4c). In scCODA, Bayesian estimation is used to calculate the posterior probability of cell\n421\nfrequencies, and a qualitative assessment is made as to whether the difference is greater\n422\nthan the significance level. We have added this explanation in the Methods section (page\n423\n18 lines 597-603).\n424\n425\n5) What is the data supporting this statement (page 5, line 154-155) \"CD4-Tpex cells\n426\nhave acquired migratory and proliferative functions in LNs, possibly working on\nThe reviewer\n428\nstill thinks that the CD4 Tpex is a population of Tfh that may or may not be related to\n429\nthe CD4 Tex. Did the author check if the TCR of CD4 Tpex and CD4 Tex highly\n430\noverlap?\n431\n433\nthought to overlap with the previously reported Tfh. The TCR similarity between C09 (CD4-\n437\nhigh expression of genes encoding co-stimulatory molecules (TIGIT, LAG3), a gene related\n438\nto T-cell exhaustion (TOX), proliferation-associated genes (DUSP4, DUSP6), and\n9\n439\nchemokine receptor genes (CXCL13, CXCR3) compared with CD4-naive/Tcm cells in LNs.\n441\n(TCF7, CCR7) relative to CD4-Tex cells in tumors and liver metastases, suggesting that\n443\nprogenitor-like characteristics. The referenced study (ref 31), which the reviewer\n444\nmentioned, demonstrated that CXCR5+CD8+ T cells (a subset of CD8-Tpex) interact with\n448\nreference (ref 31) was added.\n449\n450\n6) The NMF analysis of their scRNAseq dataset (Fig 2) appears inconsistent with\n451\ntheir clustering analysis (Fig 1b) and the similarity analysis of these clusters (Fig 1e).\n453\nare from lymph nodes (LNs), which do not share high similarity with C1, C2, and C10,\n454\nwhich are from blood (Fig 1e). This raises doubts about the robustness of the NMF\n455\nanalysis or the initial clustering analysis in Fig 1.\n456\n458\nreported previously (ref 36, PMID 39418984). NMF is extracted from the highly expressed\n459\nvariation by factor. As the correlation plots are influenced by the large variation, local\n460\nfeatures such as NMF4 can be underestimated. However, since this can be misleading as\n461\nthe reviewer mentioned, we have added an explanation of NMF analysis in the Methods\n462\nsection (page 18 lines 584-596).\n463\n464\n7) Furthermore, it is unclear how the biological processes represented by NMF1 in\n466\n468\nwith CD8-Tpex. NMF1 was associated with the naive population, defined by the expression\n469\nof NR4A2 and SLC2A3, and correlated with interferon signaling (Fig. 2a-c). NMF8 was\n470\nassociated with the Tpex population, defined by the expression of GZMK, FGFBP2, and\n472\nlymphoid and non-lymphoid cells (Fig. 2a-c). As this dataset was derived from TCGA of\n473\npatients without ICI treatment, we are unable to evaluate ICI responses based on these\n474\nfigures (page 6 lines 183-186).\n475\n476\n8) The absence of any significant correlation between the NMF1 or NMF8 gene\n477\nprogram enrichment in TCGA does not imply that the Tpex accumulation is not\n478\nimportant in the prognosis of ICI-naive gastric cancer. The authors has not\n479\nestablished that the enrichment of NMF1 or NMF8 onto a bulk RNAseq dataset of\n481\n483\ndefined gene programs in T cells are not necessarily T-cell specific. To verify the activity of\n484\nthese gene programs across different cell types, we established a method to assess their\n485\nactivation by calculating the explained variance (Evar) (ref 36). We evaluated the Evar of\n486\nthe gene programs defined in this study across various cell populations using public\n487\nscRNA-seq data from gastric cancer samples (GSE206785, PMID 36550535), as shown\n488\nbelow. Our findings revealed that NMF1 exhibited high Evar in CD4+ T cells and mast cells,\n489\nwhile NMF8 had high Evar in CD8+ T cells and NK/innate lymphoid cells. These\n490\nobservations suggest that NMF1 and NMF8 are, indeed, T cell-specific gene programs.\n491\n10\n492\n493\n495\nacross different cell types in gastric cancer. Each column is normalized individually.\n497\n498\n9) On Page 5, lines 184-188, why did the authors think that Extended Figures 5a and\n499\n5b support their hypothesis that “anti-tumor immunity was induced in LNs close to\n500\nthe tumor”? Extended Figure 5 compares the frequency of IFN-γ+ and TNF-α+ CD8 T\n501\ncells among CD8+PD1+ vs. CD8+PD1- cells across pathological tumor stages. It was\n502\nexpected that CD8+PD1+ T cells would have higher IFN-γ and TNF-α production than\n504\nunderstand how this data is related to Figure 3b and the authors’ claim that “anti-\n505\ntumor immunity was induced in LNs close to the tumor.” The authors should show\n506\nthat IFNy and TNF expression data in the same grouping as Fig 3b and not by\n507\npathological tumor stage.\n508\n510\npathological stage and evaluated the frequencies of IFN-γ and TNF-α expression in PD-1+\n511\nand PD-1− T cells in metastatic LNs, proximal LNs, and distal LNs (Supplementary Fig. 8).\n513\nin proximal and distal LNs. This may be attributed to the cytokine production being\n514\nassessed using forced T-cell stimulation with PMA and ionomycin, which is not antigen-\n515\nspecific. However, PD-1+ T cells exhibited significantly higher cytokine production\n516\ncompared with PD-1− T cells in both proximal and distal LNs. Moreover, checkpoint\n517\nmolecules, including PD-1 and CD103, were expressed at higher levels in pLNs than in\n518\ndLNs. Several studies have reported that checkpoint molecules such as PD-1, CD103,\n520\n31892342, 36792124, 36574773). Taken together, these findings suggest that anti-tumor\n521\nimmunity is induced in LNs proximal to the tumor. We have revised the description in the\n523\n524\n10) \"The frequency of CD8-Tpex in metastasis-free LNs was lower in advanced tumor\n525\nstages (Fig. 3d).\" Is this analysis only done in the proximal metastasis free LN?\n526\n528\n3d, right), encompassing both proximal and distal LNs. The same analysis was conducted\n11\n529\nsolely on proximal LNs, revealing that the frequency of CD8-Tpex among CD8+ T cells was\n530\nsignificantly lower in Stage III, as shown below.\n531\n532\n533\n535\namong CD8+ T cells in proximal metastasis-free lymph nodes according to tumor\n536\nstage.\n537\n538\n11) \"The expression frequency of molecules among CD4+ T cells in each tissue was\n539\nsimilar to that of CD8+ T cells (Fig. 3g).\" The reviewer is unsure of the relevance of\n541\nincrease in GZMB expressing CD4 Tex - what is this population?\n542\n544 have been shown to be present in PD-1+CD4+ T cells but not in PD-1−CD4+ T cells (PMID\n545 31609250). Regarding Tim-3 expression in the CD4+ T cell population, studies on infectious\n546\ndiseases have demonstrated that Tim-3 is co-expressed with PD-1 in exhausted T cells\n547\nduring antimicrobial responses (PMID 21697013). In tumors that progress following an\n548\ninitial response to anti-PD-1 therapy, Tim-3 upregulation has been observed in PD-1\n549\nantibody–bound T cells, and blockade of Tim-3 after PD-1 inhibition failure has shown a\n550\nsurvival advantage (PMID 26883990). Therefore, PD-1 and Tim-3 are considered\n551\nexhaustion markers in CD4+ T cells, similar to CD8+ T cells. Although CD103+CD4+ T cells\n552\nin tumors have been reported to be a marker of high cytokine production and good\n553\nprognosis (ref 40, 41), there are limited reports on their presence in LNs and peripheral\n554\nblood. The results showed very low expression of CD103 in CD4+ T cells in pLN and dLN,\n555\nwith no significant differences (Fig. 3g). As the reviewer pointed out, GZMB expression in\n558\nbiological significance. We have revised the relevant sentences in the Results section\n559\n(page 7-8 lines 221-226, page 8 lines 229-231).\n560\n561\n12) In Figure 3d, metastasis-free LNs from Stage III seem to have a lower Tpex/Tex\n562\nratio compared to Stages I and II. I suggest that the authors perform a multivariate\n564\nindependent prognostic marker or if it is just reflecting tumor stages (the ratio\n565\ncorrelates with stages in Fig 3d and 3h). Additionally, did the authors test the\n12\n566\nsurvival association of Tpex, Tex frequency, or the Tpex/Tex ratio in tumors and\n567\nmetastatic LNs?\n568\n570\nanalyses for recurrence-free survival (RFS) in 49 ICI-naive gastric cancer patients\n571\n(Supplementary Table 3). Univariate analysis identified sex, pStage, and Tpex/Tex ratio in\n572\nmetastasis-free LNs as significant factors. Multivariate analysis revealed that a high CD8-\n574\nfactor for RFS (P=0.081) (page 7 lines 219-221, page 14 lines 467-469).\n576\ncompared between patients with high and low Tpex/Tex ratios in tumor and metastatic LNs\n577\n(Supplementary Fig. 9). No significant differences in RFS or OS were observed between\n578\nthese groups in tumor and metastatic LNs, although patients with high Tpex/Tex ratio had\n579\nbetter survival both in tumor and metastatic LNs. We have added Supplementary Fig. 9 and\n580\nhave described the corresponding results in the Results section (page 7 lines 217-219).\n581\n582\n13) \"On the other hand, cytotoxicity evaluated by GZMB was not significantly\n583\ndifferent between each T-cell subset in patients with ICI-naive and ICI-treated\n584\ncancers (Fig. 4f)\" - there seems to be one statistically significant difference in Fig 4f.\n586\nacross all CD4+ T cell groups in meta free LNs in Fig 4h but the authors stated:\n587\n\"These results were consistent with CD4 + T cells (Fig. 4g,h). Thus, ICI treatment\n588\ndoes not affect the cytotoxicity of Tpex in metastasis-free LNs, but greatly increases\n589\ntheir proliferative potential\".\n590\n592\nnoted, GZMB expression was lower in Tpex and PD-1 CD8 T cells within metastasis-free\n594\nsignificantly higher in Tex of metastasis-free and PD-1⁻CD4⁺ T cells of metastatic LNs after\n597\nlimited. In contrast, Ki-67 expression was significantly elevated across CD8 T cell subsets\n598\nwithin⁺ metastasis-free LNs and CD4 T cell subsets within all LNs. Accordin⁺gly, we stated\n599\nthat “ICI treatment does not affect the cytotoxicity of Tpex in metastasis-free⁺ LNs, but\n600\ngreatly increases their proliferative p⁺otential.” We have revised this sentence in the\n601\nmanuscript (page 9 lines 274-284).\n602\n603\n14) Is it correct that both CD4 Tpex (of Tfh) and Tex are not clonally expanding as\n604\nshown in Fig 5c? The authors have been drawing parallel conclusions across the\n606\ndata, suggest that they are different.\n607\n609\nexpansion of CD4+ T cells in detail, we fully agree with the reviewer’s opinion. The\n610\nfrequencies of clonally expanded cells in CD4-Tex (C06.1/C17.2) were 83% and 55%,\n611\nrespectively, while that of CD4-Tpex (C09) was 36% (Fig. 5c, Supplementary Table 6). In\n612\ncontrast, the frequencies of clonally expanded cells in CD8-Tex (C04/C06.0/C17.0/C17.1)\n613\nwere 88%, 93%, 89%, and 71%, respectively, and in CD8-Tpex (C05/C12), it was 62% and\n614\n72%, respectively. Based on these results, it appears that CD8-Tpex and CD8-Tex were\n615\nclonally expanded following ICI treatment, whereas CD4-Tpex showed limited clonal\n616\nexpansion after ICI treatment. We have described this point in the Results section (page 11\n617\nlines 333-334).\n618\n13\n619\n15) The reviewer noted that most of the analysis in Fig 5 is based on only two\n620\npatients, which limits the ability to generalize any conclusions. Additionally, the\n621\nauthors should provide some supporting data (maybe as supplementary) that the\n622\nclonal expansion, migration and transition across the organs and/or T cell clusters\n623\nare consistent between the two patients to ensure we are not looking at a pattern\n624\ndominated by a single patient.\n625\n627\n(patient 3) and two ICI-naive gastric cancer patients (patient 4,5) to the previous\n628\nscRNA/TCR/CITE-seq analysis. In total, we performed single-cell RNA/TCR/CITE-seq in\n629\ntwo ICI-naive cancer patients and three ICI-treated cancer patients (Fig. 5, Supplementary\n632\nobserved within each cluster (Fig. 5e, f, Supplementary Fig. 14). Thus, we confirmed that\n633\nthe values in clonal expansion, migration, and transition were consistent across T-cell\n634\nclusters.\n635\n636\n16) \"A high cross-tissue migration score was observed in CD8-Tpex (C05, C12) and\nIn line 263, the authors stated\n640\n\"Regarding migration, the most observed route of migration was between LN and\n641\ntumor (C04, C05, C06.0, C08) (Fig. 5f).\" It is noted that C08 is from PBMC and not\n642\nfrom LN. The reviewer is also curious that the PBMC C08 clones are not discussed\n643\nas much as LN Tpex C05 despite a strong clonal overlap with tumors and a high\n644\nexpansion, migration and transition indices.\n645\n647\nwith CD8-Tex (C04/C06.0) and CD8-Tpex (C12) cells in tumor was 19.6% and 7.0% in\n648\nhigh- and low-IgG4 clones in patient 1, 64.9% and 50.9% in patient 2, and 7.7% and 16.9%\n649\nin patient 3 (Supplementary Fig. 15c). In contrast, the percentage of TCR sharing of C08\n650\ncells with CD8-Tpex (C05) cells in LNs was 75.3% and 79.1% in high- and low-IgG4 clones\n651\nin patient 1, 30.9% and 47.3% in patient 2, and 50.8% and 33.8% in patient 3\n652\n(Supplementary Fig. 15c). These results indicate that while C08 cells in patient 2 exhibited\n653\nnotable TCR similarity with C04/06.0/12 cells, C08 cells generally shared a higher\n654\nproportion of TCR clones with C05 cells, regardless of IgG4 binding status.\n656\nbased on ICI high- and low-bound T cells within the C08 cluster (Supplementary Fig. 16).\n658\nand transition scores than ICI high-bound T cells, while the pairwise migration index\n659\nremained similar between the two groups. This may be attributed to the fact that C08\n660\nclones contain recently activated effector memory (TEMRA) T cells (ref 42), which are\n661\nclonally expanded (Fig. 5c, Supplementary Table 6). A previous study on STARTRAC\n662\nanalysis using T cells from tumors, adjacent normal tissues, and blood (ref 42) reported\n663\nthat TEMRA cells express numerous effector molecules such as PRF1, GZMB, and GZMH,\n664\nbut lack Tex cell markers such as PDCD1 and HAVCR2. The study also demonstrated that\n666\ntransition scores, as well as the highest migration potential between blood and tumor, blood\n667\nand normal tissue, and normal tissue and tumor. Thus, it is thought that TEMRA cells\n668\nmatch ICI low-bound T cells within C08 cells. Given that TEMRA cells do not express PD-1,\n669\nthey are likely unaffected by ICI treatment. Therefore, the high expansion, migration, and\n670\ntransition scores detected after ICI treatment in our data likely reflect the inherently high\n671\nproliferative and migratory potential of TEMRA cells within the C08 cluster.\n14\n673\nbetween C05 and C08 cells, the percentages of TCR sharing of C05 cells with CD8-Tex\n674\n(C04/C06.0) and CD8-Tpex (C12) were 43.2% and 7.8% in high- and low-IgG4 clones,\n675\nrespectively, in patient 1, 94.0% and 59.3% in patient 2, and 33.0% and 17.3% in\n676\npatient 3 (Supplementary Fig. 15b). Compared with the percentage of TCR similarity between C08\n677\ncells and C04/06.0/12 cells (lines 651-654 in response letter) (Supplementary Fig. 15c),\n679\nthan did C08 cells. Importantly, the clonotypes in blood TEMRA cells with low PD-1\n680\nexpression were mutually exclusive with those in tumor Tex cells in the previous study (Ref\n681\n42). Thus, the high TCR similarity between C08 and C04/06.0/12 was likely a result of the\n682\ncombined high TCR similarity between C08 and C05, as well as between C05 and\n687\ntransitional potential of TEMRA cells within the C08 cluster. We have described this\n688\nexplanation for the C08 cluster in the manuscript (pages 10-11 lines 329-333, page 11 lines\n689\n356-364).\n690\n691\n17) The Tex clusters have high transition scores and the authors stated \"intratumoral\n693\nunderstanding, the Tex are exhausted T cells that are not undergoing further state\n694\ntransition.\n695\n697\ncells as Tex, but this is a rather broad definition previously reported to include intermediate\n698\nand terminally exhausted types, with the former transitioning to the latter (PMID 32396847,\n699\n37968405, 37820583). It has also been reported that it is not Tpex but Tex (especially\n700\nintermediate Tex) that are involved in cytotoxicity or have an effector function (ref 19, PMID\n701\n37968405, 37820583). Thus, exhausted T cells (Tex) have different status which include\n702\nprogenitor, intermediate, and terminally exhausted T cells. Among these, terminally\n703\nexhausted T cells do not undergo further state transitions, whereas other forms of\n704\nexhausted T cells have the potential to differentiate and expand.\n705\n706\n18) \"All of these T cells were bound by ICIs except for C12, suggesting that ICI-\n707\nbound T cells play a leading role after ICI administration.\" I am not sure if this is what\n709\nin C12 is similar to C5 (Fig 5b). This seems to be a rather incoherent description of\n710\nthe data. The implication \"suggesting that ICI-bound T cells play a leading role after\n712\n714\nin the scRNA/TCR/CITE-seq analysis was not predetermined, we set it as the median of\n715\n1.079, defining clusters with 50% or more IgG4+ cells as IgG4-high clusters (Fig. 5b,\n718\nto reflect the updated IgG4-high clusters, and removed the phrase “except for C12” from\n719\nthe manuscript (page 10 line 329).\n721\npatients and two ICI-naive patients (Fig 5, Supplementary Fig. 12, Supplementary Fig. 14).\n724\ntreatment in C04, C05, C06.1, C12, and C17.0; the migration score increased in C04, C05,\n15\n727\ntransition scores—were primarily observed in IgG4-high clusters, with opposite trends\n728\nrarely detected. The C12 cluster showed increased expansion and transition scores after ICI\n729\ntreatment, consistent with trends observed in other IgG4-high clusters. Regarding\n730\nmigration potential, LN–tumor migration potential increased in C05, while LN–PBMC\n731\nmigration potential increased and PBMC–tumor migration potential decreased in C08,\n732\nthough C08 cells, compared with C05 cells, less frequently shared clones with CD8-Tex\n733\nand CD8-Tpex in the tumor, which was shown in query 16 (Supplementary Fig. 14b). Thus,\n736\n(C05).\n741\nafter ICI treatment, with C12 as the intermediate stage in the tumor.\n743\nexpansion and migration potential of ICI-bound T cells and boosts LN–tumor migration\n744\npotential in the LN-Tpex population (C05). Although C12 exhibited increased expansion\n745\nand transition scores post-ICI treatment, it likely represents the intermediate stage of ICI-\n746\nbound Tpex in the tumor. We have added Supplementary Fig. 17 and have discussed the\n747\nresults in the revised manuscript (page 11 lines 364-367).\n748\n749\n750\n753\n(CD8-Tpex). Pseudotime was calculated using sc.tl.dpt.\n754\n755\n19) \"Although some cells in the C05 migrated between LNs and PBMCs, most cells\n756\nmigrated between LNs and tumor, and clonotypes within this cluster were highly\n757\nshared with the tumor-enriched CD8-Tex clusters (C04, C06.0) (Fig. 5f,g).\" This\n758\nqualitative approach comparing \"some cells\" with \"most cells\" without\n759\nquantification is concerning.\n760\n762\nother clusters in each patient (Supplementary Fig. 15a). The percentages of TCR sharing\n763\nof C05 cells with CD8-Tex (C04/C06.0), CD8-Tpex (C12) and and CD8-effector (C08) cells\n764\nwere 89.8% in patient 1, 97.9% in patient 2, and 53.1% in patient 3 (Supplementary Fig.\n765\n15a). Thus, TCR clonotypes within the C05 cluster were highly shared with the tumor-\n766\nenriched CD8-Tex clusters (C04, C06.0) and CD8-Tpex (C12) clusters and the blood-\n767\nenriched CD8-effector (C08) cells.\n16\n769\nfound that ICI high–bound T cells in the C05 cluster predominantly shared clones with the\n770\ntumor-enriched CD8-Tex and CD8-Tpex (C04, C06.0, C12) cells. whereas, ICI low–bound\n772\n(Supplementary Fig.15b). Specifically, the percentages of TCR sharing of C05 cells with\n774\nclones, respectively, in patient 1, 94.0% and 59.3% in patient 2, and 33.0% and 17.3% in\n775\npatient 3. Meanwhile, the percentages of TCR sharing of C05 cells with CD8-effector (C08)\n776\nwere 41.8% and 74.4% in high- and low-IgG4 clones, respectively, in patient 1, 3.6% and\n777\n35.6% in patient 2, and 12.2% and 40.4% in patient 3. In summary, the ICI high–bound\n778\ncells in the C05 cluster had a high degree of consistency with TCRs of intratumoral CD8-\n780\nconsistency with TCRs of CD8-effector cells in PBMC. In response to this result, we have\n781\nincorporated the quantification results into Supplementary Fig. 15 and revised the\n782\ncorresponding sentences in the Results section (page 11 lines 346-356).\n783\n784\n786\npatients. The percentage of TCR sharing of high and low-IgG4 clones in C05 with the\n787\ntumor-enriched CD8-Tex (C04, C06.0) and CD8-Tpex (C12) and the blood-enriched CD8-\n788\neffector (C08) are shown below.\n789\n790\n20) \"Importantly, clonotypes with high IgG4 binding (ICI high–bound T cells) shared\n791\nclones with the tumor-enriched CD8-Tex clusters (C04, C06.0), whereas clonotypes\n792\nwith low IgG4 binding (ICI low–bound T cells) shared clones with the PBMC-enriched\n795\nmay have a different antigen specificity.\" This statement needs to be supported by a\n796\nquantification of clone overlap between IgG4 high clones in C05 with the tumor (and\n797\nmets) compared to the overlap with C8 PBMC clones.\n798\n800\nquery in query 19.\n801\n802\n21) Based on Fig 5f, C08 has a strong pairwise migration index from PBMC to the\n803\ntumor (almost as strong as C05 in LN - Tumor comparison). Can the authors\n804\ngenerate the same analysis focusing the clones in C08 and separating them by IgG4\n805\nhigh vs. low? This is important to confirm the source of Tex in the tumor (whether\n17\n806\nthey all come from Tpex in LN or not). Either way these observations are important\n807\nto be reported.\n808\n810\nquery in query 16.\n811\n812\n22) \"We also discovered that PDCD1+ TCF7+ Tpex presence within tumors correlates\n813\nwith the successful outcome of ICI treatment. Interestingly, this correlation did not\n814\nextend to the prognosis of ICI-naive gastric cancers. In contrast, abundant Tpex\n815\npresence in LNs was linked to favorable prognosis in ICI-naive gastric cancer, and\n816\nthe proliferation activity of Tpex in LNs significantly increased after ICI treatment.\"\n818\nreliably define that the PDCD1+ TCF7+ T cell in the TIL is the same as the Tpex in the\n820\nreader. It is adequate to state the positive correlation in gastrointestinal cancers.\n821\n824\nanalyzed T cells that migrate between tissues after ICI treatment and also infiltrate tumors.\n826\ngastrointestinal cancers, it is difficult to obtain surgical specimens from patients treated with\n828\nare limited. Therefore, we used the datasets of these lung cancer patients (ref 35),\n829\ninvestigating the correlation between T cell populations in tumors after ICI treatment,\n830\nanalyzed by scRNA-seq, and the treatment efficacy of ICI. However, as the reviewer\n831\nmentioned, we acknowledge that our data included T cells in different types of cancer and\n832\nalso in non-tumor LNs and PBMC. Thus, we have decided to relocate this data to\n834\nfocusing on Tpex in LNs by citing several studies (page 5 lines 128-130, 142–144).\n835\n836\n23) In line 339, the authors stated \"In our data, abundant intratumoral Tpex were\n837\nassociated with better response to ICI treatment\". Is this pointing to the lung cancer\n838\ndata? It is confusing. I really suggest the authors to remove the lung cancer data\n839\nanalysis altogether (GSE173351) - it is hard to see why it is related to this study.\n840\n842\nrevised manuscript (page 14 line 442-443).\n843\n844\n846\n849\n(Tpex) by analyzing tumor tissues, peripheral blood, and lymph node samples from\n850\ngastrointestinal cancer patients both before and after ICI treatment. The authors\n851\nfound that Tpex cells are enriched in the proximal lymph nodes of non-metastatic\n852\ntumors and then proliferate and migrate to the tumor site post-ICI treatment,\n853\npossibly differentiating into exhausted CD8 T cells (Tex) to exert anti-tumor effects.\n855\n856\n1) The authors mention that several studies have already demonstrated the\n857\nassociation of TCF1 and PD1-expressing Tpex in tumors and peripheral blood with\n858\nthe response and prognosis of cancer patients to ICI treatment. A study published in\n18\n860\nregional lymph nodes are disrupted in metastatic lymph nodes\" also reported a high\n861\npresence of TCF1+PD1+Tpex in the uninvolved lymph nodes of head and neck\n862\nsquamous cell carcinoma patients, and verified the clonal correlation between these\n864\npotential explanation for the anti-tumor mechanism of Tpex post-ICI treatment.\n866\nexhausted SPRY1+CD8+ T cells potentiate responsiveness to neoadjuvant PD-1\n867\nblockade in esophageal squamous cell carcinoma.\" revealed that the abundance of\n869\nmarker for patient treatment response and prognosis to immunotherapy. Given the\n870\nexisting research discussing the presence and functions of Tpex in improving ICI\n871\ntreatment, the novelty and translational value of this study are limited.\n872\n874\nseveral studies have already demonstrated the association between Tpex in tumors and\n876\n17–21). Accordingly, recent studies suggest that T-cell responses to ICIs may originate\n877\noutside the tumor and depend on the recruitment of peripheral T cells (Refs. 14, 43-44, 47–\n878\n48), with LNs potentially serving as a reservoir for ICI-responsive, tumor-reactive T cells\n879\n(Refs. 25, 32, 34, 49–50). However, a closer examination of the literature revealed that the\n880\nfirst paper cited by the reviewer suggested that Tpex may differentiate into Tex within\n881\nmetastasis-free LNs following ICI treatment and subsequently migrate to the tumor via the\n882\nbloodstream. Notably, this study did not include matched LN and tumor samples. The\n883\nsecond paper, which analyzed paired esophageal cancer tumor samples before and after\n885\nexhibits a progenitor exhausted T-cell (Tpex) phenotype and correlates with a complete\n886\nresponse to ICI. Yet, this study did not examine LNs. Given these limitations, few studies\n887\nhave analyzed the dynamic changes in T cells after ICI treatment using matched human\n888\nsamples from tumors, LNs, and blood.\n890\nbound T cells across various organs using flow cytometry and scRNA/TCR/CITE-seq, we\n891\nwere able to investigate the dynamics of both ICI-bound and ICI-non-bound T cells. Indeed,\n893\nevidence showing that ICI-bound Tpex are activated in LNs, migrate to tumors, and\n894\ndifferentiate into Tex within tumors. Importantly, no previous study has investigated T-cell\n895\ndynamics based on the distinction between ICI-bound and ICI-non-bound cells. We have\n896\nemphasized this point in the manuscript (page 13 lines 434-436). We believe that our study\n897\nprovides novel insights into the crucial role of ICI-bound T cells in determining ICI treatment\n898\nefficacy.\n899\n900\n2) The authors performed multi-sample single-cell sequencing on two cancer\n901\npatients who underwent chemotherapy combined with anti-PD-1 treatment, and\n902\nmulti-sample flow cytometry analysis on 12 gastrointestinal tumor patients who\n903\nreceived ICI treatment. However, a larger number of samples from more patients is\n904\nrequired to provide more convincing evidence.\n905\n908\ntreated patient to enhance the data in Fig. 5 and compare ICI-treated and naive patients.\n910\npatients and three ICI-treated cancer patients (Fig. 5, Supplementary Fig. 12,\n912\nbetween ICI-treated and ICI-naive samples. The results showed that the expansion score\n913\nincreased after ICI treatment in C04, C05, C06.1, C12, and C17.0; the migration score\n914\nincreased in C04, C05, C06.0, C06.1, C08, C14, and C17.0; and the transition score\n915\ndecreased in C04, C06.0, C08, C12, C17.0, and C18 (Supplementary Fig. 14a). These\n916\nchanges—higher expansion and migration scores and lower transition scores—were\n917\nprimarily observed in IgG4-high clusters, with opposite trends rarely detected (page 11\n918\nlines 334-337). The C12 cluster showed increased expansion and transition scores after ICI\n919\ntreatment, consistent with trends observed in other IgG4-high clusters. Regarding migration\n920\npotential, LN–tumor migration potential increased in C05, while LN–PBMC migration\n921\npotential increased and PBMC–tumor migration potential decreased in C08\n922\n(Supplementary Fig. 14b). Thus, STARTRAC analysis confirmed the enhanced expansion\n923\nand migration potential of ICI-bound T cells, particularly increased LN–tumor migration\n924\npotential in the LN-Tpex population (C05).\n928\nproliferation activity of Tpex in LNs was significantly increased after ICI treatment.\n930\nthe clonal link between ICI-bound CD8-Tpex in LNs and intratumoral ICI-bound CD8-Tex\").\n932\n933\n3) The authors utilized CITE-seq, TCR sequencing, and TCR tracking algorithms\n934\n(STARTRAC) to analyze the clonal correlation between lymph node CD8+ Tpex and\n935\ntumor CD8+ Tex, and their potential migration pathways at the single-cell level.\n937\nstudy lacks dynamic monitoring of lymph node Tpex migration in animal models,\n938\nfailing to clearly elucidate the migration and differentiation process of Tpex.\n940\nnode Tpex migrate to the tumor and differentiate into Tex.\n941\n943\nhuman tissue samples alone is insufficient to fully capture the true kinetics of immune cells.\n945 previous study (ref 32) demonstrated that precursor-like TCF1+CD8+ T cells migrate from\n947\nfrom LNs to tumors. Such models could indeed provide important insights. However, even\n948\nwith these models, it remains challenging to examine the dynamics of ICI-bound T cells\n949\nfrom LNs to tumors in mouse models. Thus, at present, we have not established a suitable\n950\nmouse model for verifying ICI-bound T cells. Given the time constraints of the revision\n951\nperiod, we were unable to conduct such studies and have decided to address this issue in\n952\nfuture research. Nevertheless, we believe the findings of this study, which comprehensively\n953\nanalyzes ICI-bound T cells in humans, provide significant insights. This limitation is now\n954\nexplicitly stated in the revised manuscript's limitations section (page 14 lines 450-453). We\n955\nwould be glad if the reviewer kindly accepted our viewpoint.\n956\n957\n4) The authors analyzed the relationship between the Tpex/Tex ratio in the lymph\n958\nnodes of 12 patients without ICI treatment and prognosis, finding that a high\n960\ncould serve as a prognostic predictor and a novel cell therapy approach, holding\n961\nsignificant clinical translational value. However, the authors need to conduct\n962\nvalidation in large external cohorts to enhance the reliability of this finding.\n963\n965\nsurvival analysis of 49 ICI-naive gastric cancer patients with metastasis-free LNs, not ICI-\n966\ntreated patients. Additionally, we demonstrated the significance of Tpex cells in metastasis-\n967\nfree LNs in 14 ICI-treated cancer patients but were unable to establish a relationship\n20\n968\nbetween these cells and prognosis. As the reviewer noted, we fully recognize the need to\n969\nconfirm the correlation between Tpex cells in LNs and prognosis in ICI-treated cancer\n970\npatients using larger cohorts. However, since preoperative ICI treatment is not yet a\n971\nstandard approach for gastrointestinal cancers, obtaining surgical samples, including LNs,\n972\nfrom ICI-treated patients remains challenging in routine clinical practice. Consequently,\n973\nopportunities to investigate the prognostic impact of the Tpex/Tex ratio in LNs in ICI-treated\n974\npatients are currently limited. Instead, we conducted analyses of T cells in tumors and LNs\n975\nafter ICI treatment using flow cytometry and scRNA/TCR/CITE sequencing in a limited\n976\nnumber of cases. In the future, as opportunities for such investigations become more\n977\nfeasible, we intend to further explore the prognostic significance of the Tpex/Tex ratio in\n979\nmanuscript's limitations section (page 14 lines 456-458).\n980\n21\n2\n4\n6 response to my comments. This has significantly improved the manuscript. There\n7 are just a few minor points I would suggest adding:\n8\n10\n11 1) Tpex → Tex transition: The pseudotime and clonal data are of high quality, but\n12 lack functional validation. Acknowledging this point as a limitation in the discussion\n13 would be beneficial.\n14\n16 functional validation as a limitation in the revised manuscript (page 14, lines 470-471).\n17\n18 2) TOX in CD4-Tpex: These cells display both TOX and progenitor markers such as\n20 potential plasticity of these cells, considering their dual marker expression.\n21\n23 follicular helper T cells (Tfh) based on its predominant derivation from lymph nodes and its\n24 expression of CD4, TCF7, PDCD1, CXCL13, TOX, CXCR5, and BCL6, the master\n25 transcriptional regulator of Tfh cells, and we have revised the annotation accordingly (page\n26 4, line 99). Furthermore, we provided an explanation regarding the co-expression of TOX\n27 and TCF7 observed in the C09 cluster, consistent with a previous study (PMID: 37322116)\n28 reporting that TOX and TCF7 are co-expressed in Tfh cells, and another study (PMID:\n29 31844658) demonstrating that TOX functions as a key transcriptional regulator in Tfh cells\n30 by promoting the expression of multiple molecules essential for Tfh biology, including\n32\n34\n36 reviewer. In fact, Supp Fig 1 shows how complicated the flow of this paper is. Fig 1\n37 start with scRNAseq analysis of T cells in blood, LN and tumor of ICI treated EGJ\n38 cancer and the author applied the NMF-derived PBMC and blood gene signatures on\n40 survival (can it be the problem of the signature?), then they show using flow\n41 cytometry that the Tpex/Tex ratio in LN (a value not related to the NMF signatures) is\n42 associated with RFS and OS. Then, the authors show that they can identify anti-PD1-\n43 bound T cells using anti-IgG4 (the isotype of the therapeutic anti-PD1) and suggest\n44 that the anti-PD-1 bound T cells in the LN is more proliferative. Then, they went back\n45 into their original scRNAseq analysis and added additional samples with scTCRseq\n46 and CITEseq (1 ICI-treated and 2 ICI naive) and this time added the layer of\n47 scTCRseq and CITEseq analysis. The finding was that anti-PD-1 bound CD8 Tpex\n48 cells are clonally expanding and share the same clones as the Tex in the tumor.\n49\n51 including stage as a covariate, treating NMF8/NMF3 as Tpex/Tex ratio. No significant\n52 association was observed between NMF8/NMF3 and prognosis. We have added a new\n53 figure (Fig. 2d) and revised the manuscript accordingly (page 6, line 178/ pages 6-7, lines\n54 186-190).\n1\n55\n56\n59 incorporates NMFs and cancer stages as covariates.\n60\n61 1) Pai et al (ref 25) and others have reported the clonal linkage between Tpex in\n62 tumor draining LN and the intratumoral CD8 T cell. The novel result that the authors\n63 reported here is the analysis of the anti-PD-1 bound T cells. The authors show the\n64 anti-PD-1 bound T cells seem to be proliferating more in the LN and have mostly\n65 exhausted phenotype in the tumor. Thus, instead of first showing scRNAseq\n66 analysis of 2 ICI-treated patients, the authors should directly start with analysis of all\n67 5 scRNAseq/scTCRseq/CITEseq samples (3 ICI treated and 2 ICI naive).\n68\n70 together is valuable for assessing the generalizability of our findings. To address this, we\n71 performed an integrated single-cell RNA-seq data analysis of all five cases and identified\n72 distinct clusters using UMAP (Supplementary Fig.13). Based on gene expression profiles,\n73 clusters C5.0 and C5.1 were defined as CD8-Tpex cells, while C7 was classified as T\n74 follicular helper (Tfh) cells. IgG4 expression was observed in CD8-Tpex [LN] (C5.0, C5.1),\n76 treated patients. Notably, clonal expansion was particularly enriched in C5.0, C5.1, C6, and\n78 1, Fig. 5). In contrast, the spatial distribution of clusters across tumor, LN, and blood did not\n79 differ substantially between ICI-treated and ICI-naive groups (Supplementary Fig. 13f).\n81 state of T cells rather than on changes in their frequency or anatomical distribution.\n83 gastrointestinal (GI) cancers. In this study, we obtained tissue samples from two rare cases\n84 of initially unresectable gastroesophageal cancer with distant metastases that became\n85 eligible for conversion surgery after preoperative ICIs (patients 1 and 2). We then included\n86 one resectable gastric cancer case treated with ICI as part of neoadjuvant chemotherapy\n87 (patient 3), as well as two ICI-naive gastric cancer cases (patients 4 and 5). Because the\n88 clinical context of ICI treatment was different between patients 1/2 (conversion surgery) and\n89 patient 3 (neoadjuvant therapy), we designated the two rare ICI-treated conversion cases\n90 as the discovery cohort, and subsequently included the additional ICI-treated and two ICI-\n91 naive cases as the validation cohort. Reanalyzing the data by combining all five cases\n92 would require regenerating Figures 1, 2, 5, S2, S3, S4, S5, S6, S12, S13, S14, S15, S16,\n93 and S17, which is currently not feasible due to limitations in available personnel resources.\n95 acceptable.\n2\n97 13) and revised the manuscript accordingly (page 4, line 86; page 9, lines 282-289).\n98\n99 2) The authors should present the phenotype(s) of the anti-PD-1-bound T cells and\n100 how they are different from the similar T cells from ICI naive patients.\n101\n104 bound PD-1⁺ CD8⁺ T cells from ICI-naive cases (Patients 4,5), across each tissue type. In\n105 tumors, genes involved in antigen recognition and T cell activation, such as HLA-DRB1 and\n107 inflammatory signaling, including CCL3L1 and CCL4L2, were elevated. In contrast, no\n108 significant differences in exhaustion-related genes were observed in the blood, suggesting\n109 that the effects of ICIs are most pronounced in lymph nodes and tumors. Based on these\n110 findings, we incorporated additional explanations into the main text (page 10, lines 305-\n111 312) and Supplementary Fig. 15a.\n112\n113\n114\n116 cases and PD-1+CD8+ T cells in ICI-naive cases. Volcano plot showing the differences in\n117 gene expression between ICI-bound PD-1+CD8+ T cells in ICI-treated cases (Patients 1-3)\n118 and ICI-non-bound PD-1+CD8+ T cells in ICI-naive patients (Patients 4,5) per tissue. Genes\n119 related to sex differences were excluded.\n3\n120\n121 3) The authors should separate analyses on intratumoral T cells vs T cells in LN vs. T\n122 cells in PBMC unless they can be integrated into unified phenotypes across organs.\n123\n125 the Supplementary Fig. 3b, there is tissue-specific bias in the distribution of T cells. T cell\n126 populations from LN are present in tumor, Meta, and blood, indicating that T cells from all\n127 tissue sources are generally well represented. However, circulating and tissue-resident T\n128 cells generally exhibit distinct transcriptional programs. For example, tissue-derived T cells\n129 specifically express genes such as CD69, NR4A1, and CD2, which are known to be\n130 upregulated in tissue contexts (Supplementary Fig. 3c). Therefore, the separation of tissue-\n131 resident and circulating T cells into different clusters reflects true biological differences\n132 rather than a batch effect.\n134 annotation, and similar approaches have been employed in previously published studies\n135 (PMID: 32611125, 36423636) to investigate how the same types of immune cells function\n136 across different tissues, to better understand the tumor immune microenvironment, and to\n137 evaluate transitions in cell states. Conversely, without applying consistent clustering across\n138 tissues, many downstream analyses—such as STARTRAC—cannot be effectively\n139 performed. To demonstrate the robustness of cross-tissue analysis to the reader, we have\n140 added the figure showing the tissue-wise distribution of clusters (Supplementary Fig. 3b, c).\n141\n142 4) The authors highlighted the patterns of T cell clone sharing in the anti-PD-1 bound\n144 patients across the tumor, LN and PBMC, which is novel and important. Is there any\n145 clonal sharing between TFH in LN with the CD4 in the tumor?\n146\n148 in LN (C09) and other clusters, including CD4-Tex in tumor (C06.1 and C17.2), appears\n149 limited. When we created a figure for CD4-Tfh in LNs, we observed some degree of TCR\n150 similarity with C6.1 (CD4-Tex in tumor) (Supplementary Fig. 20a). However, this similarity\n151 varied among individual cases, and the extent of clonal expansion was lower than that\n152 observed in CD8+ T cells (Fig. 5e). Therefore, TCR similarity between CD4-Tfh in LN with\n153 the CD4+ T cells in the tumor appears to be relatively low compared with that of CD8+ T\n154 cells. This explanation has been incorporated into the main text (page 12, line 377-379),\n155 and the corresponding figure has been added (Supplementary Fig. 20).\n156\n157\n159 that are shared with CD4-Tfh in LNs (C09). The dots represent only the tumor and meta\n160 compartments. Clusters not shown indicate zero cells with TCR sharing. The percentage of\n162\n163 5) Any intratumoral anti-PD-1 bound T cell population identified in the ICI treated\n164 gastric tumor/mets samples can be compared to those found in lung tumor/mets\n165 sample from GSE173351 to see if there are any similarity. T cell populations from the\n4\n167 tumor patients in the GSE173351 dataset (instead of what is done now where the\n168 authors are mixing the clusters from blood, LN and tumor and compare them against\n169 a mixture of T cells from lung tumor, adjacent normal lung, and LN).\n170\n172 cells from tumor, adjacent normal lung tissue, TDLN, and a resected brain metastasis were\n173 jointly clustered (Fig. 1,2). As the reviewer pointed out, we additionally performed separate\n174 analyses for (a) LNs and (b) tumors (note that the GSE173351 dataset does not contain\n175 blood samples, and thus comparisons involving blood were not possible). In the LN\n176 comparison, cluster C05 (CD8-Tpex) from our dataset corresponded mainly to CD8-effector\n177 cells, whereas cluster C09 (CD4-Tfh) aligned with CD4-Tfh cells, indicating that C05 and\n179 dataset. Additionally, in the tumor comparison, CD8-Tex clusters (C04, C06.0, C17.0,\n181 associations: C04 and C06.0 corresponded primarily to CD8-TRM cells, C06.1 matched\n183 and C17.2 matched CD4-Treg cells. Thus, consistent similarities were observed in tissue-\n184 specific comparative analyses.\n185\n186 (a) Lymph nodes (LNs)\n187\n188\n189\n5\n190\n191 (b) Tumor\n192\n193\n194\n195\n6\n196\n197\n199 lymph nodes and tumor. Uniform manifold approximation and projection (UMAP) plot of T\n200 cells in single-cell RNA-seq colored by patients, clusters, and marker genes. Heatmap\n201 comparing clusters per tissue.\n202\n203 6) The flow cytometry analysis should be focused on that in Figure 4 where the\n204 authors compare the effect of ICI in the Tpex and other T cell populations. The effect\n205 of anti-PD-1 treatment in increasing the proliferation of TCF1+ PD1+ Tpex in LN.\nThe\n206 authors should demonstrate if the fraction of Ki67+ TCF1+ PD1+ Tpex in LN is\n207 associated with survival in patients with gastric cancer. The importance of anti-PD-1\n208 bound TFH in LN should also be supported with a similar analysis.\n209\n211 significant, patients with higher Ki-67 expression in CD8-Tpex within metastasis-free LNs\n212 tended to show better RFS. (There was no difference in the frequency of Ki-67 expression\n213 in CD4-Tfh cells within metastasis-free LNs) These findings suggest that Ki-67 expression\n214 after ICI treatment may be associated with subsequent prognosis. However, as this\n215 analysis included only a small number of cases and various cancer types, future studies will\n216 require a larger cohort with a uniform cancer type. We have added Supplementary Fig. 12\n217 and described this point in the main text (page 9, lines 273-275).\n218\n221 outcomes. Kaplan–Meier curves for recurrence-free survival rate (RFS) were compared\n222 between high and low groups of the Ki-67 percentage in CD8-Tpex (a) or CD4-Tfh (b)\n7\n223 among metastasis-free lymph nodes (n=13). The two groups were divided based on the\n224 median value. P-values were calculated by using the log-rank test.\n225\n226 7) Finally, the Reviewer could not see any significant implication or conclusion from\n227 the TCGA analysis.\n228\n230 cell function and its association with prognosis. However, scRNA-seq analysis could not be\n231 used for prognostic evaluation in either ICI-treated or ICI-naive cases, due to the lack of\n232 available prognostic data. In ICI-naive cases, TCGA data allowed for prognostic analysis,\n233 but because bulk RNA-seq data were used, NMF analysis was used in this study. This\n234 approach demonstrated that intratumoral CD8-Tpex cells did not contribute to prognosis. In\n235 the future, as scRNA-seq datasets of lymphocytes from tumors or LNs become publicly\n236 available, more direct analyses of infiltrating lymphocytes and their relationship to\n237 prognosis will be possible. We kindly ask for the reviewer’s understanding in this regard.\n238\n239 1. The single cell RNAseq analysis in Fig 1b shows a strong organ-driven batch\n240 effect (not patient driven batch effect). The authors should really try to integrate the\n241 data using data integration method like CCA, RPCA, bbknn etc and observe whether\n242 such organ effect can be mitigated. Looking at the method description of the\n243 scRNAseq analysis in line 579-580 of the main text, the reviewer noticed that the\n244 authors did not integrate the profiles from different samples (different organs and\n245 different patients). For an example of multi sample integration using scanpy, please\n246 see https://scanpy-tutorials.readthedocs.io/en/latest/integrating-data-using-\n247 ingest.html or https://nbisweden.github.io/workshop-archive/workshop-\n248 scRNAseq/2020-01-27/labs/compiled/scanpy/scanpy_03_integration.html.\n250 populations in the LN (C0 and C11) and PBMC (C2 and C10).\n251\n253 established method for batch correction, as previously reported (PMID: 31948481). As the\n254 reviewer suggested, we also evaluated other commonly used methods such as BBKNN,\n256 reviewer). As mentioned in Supplementary Fig. 3b,c, although naive T cells from LNs and\n258 due to tissue-specific transcriptional programs, including the expression of genes such as\n260\n262 method (BBKMN, MNN, and Scanorama).\n263\n265 at the donor + site level would further remove tissue-specific differences (Figures only for\n266 reviewer). In that case, LN (C0 and C11) and PBMC (C2 and C10) would be represented\n267 as a single population. That said, there is no clear consensus on how strictly to separate\n268 cell populations and cell states in the context of batch correction, and treating naive T cells\n8\n269 from PBMC and LNs as distinct clusters does not pose a problem for downstream\n270 analyses. We kindly ask for the reviewer’s understanding, as updating the embedding and\n271 clustering to reflect these changes would require regenerating all related figures, which is\n272 not feasible given our current personnel capacity.\n273\n274\n275\n277 colored by Leiden cluster (left) and tissue of origin (right).\n278\n279 2. The scRNAseq analysis should be done on all 5 scRNAseq samples right from the\n280 start rather than analyzing two sample first (pt 1 and 2) and projecting the scRNAseq\n281 of Pt 3,4,5 on to the clustering of Pt 1 and 2 using symphonypy.\n282\n284 1).\n285\n286 3. If the authors still find separate CD4 and CD8 T cell populations after integration\n287 step, then the authors must ensure that analysis of different T cell populations are\n288 grouped by organ. For instance, to analyze the correlation between intratumoral T\n289 cell populations in TCGA gastric cancer dataset, the authors should only test gene\n290 signatures representing intratumoral T cell populations (e.g. C4, C6.0, C6.1, C7, C12,\n292 please only test NMF signatures that included intratumoral T cells (NMF0,3,5,6,7).\n295\n297 38981439, 34450029) have not specifically separated CD8+ and CD4+ T cells, and we\n298 believe it is not necessary in our analysis either. In NMF analysis, it is important to project a\n299 wide range of data without making arbitrary selections, as irrelevant factors will simply have\n300 values close to zero. Therefore, we projected clusters from all tissues, not just tumor data.\n302 subsets such as Tregs, to a single NMF component. In particular, Tpex cells represent an\n303 intermediate state, and as the reviewer pointed out, it is indeed difficult to conclude that\n305 limitation about this point in the discussion part (page 14, lines 461-465).\n306\n307 4. (related to response to Reviewer 1 minor comment #5, Reviewer 2 comment #6 #7\n308 #8) The gene signatures of each NMF group need not be enriching for some\n309 reactome gene set (reactome is a pathway database with groups of proteins in a\n310 pathway, not a gene signature database with list of genes changing in expression in\n311 response to perturbation like the MsigDB). NMF0 clearly enriches for Treg markers\n312 (Fig 2b). NMF4 enriches naive or central memory T cell genes (Fig 2b). Looking at the\n313 genes in NMF1 and NMF8 (Fig 2b), there is no indication that they are related to CD8\n9\n317\n320 weights of TCF7, SELL, TOX, and PDCD1 expression in each NMF program\n321 (Supplementary Table 2) shows that NMF2, which corresponds to CD4-Tfh cells,\n322 successfully captured key Tfh features such as TCF7 and PDCD1. However, marker genes\n323 of CD8-Tpex cells were not well represented in NMF1 or NMF8. This may be explained by\n324 the nature of NMF, which linearly decomposes gene expression data and is therefore\n325 particularly well suited for identifying terminal and well-defined gene programs such as\n326 those of Treg, Th1, or Th17 cells (PMID: 38359792). In contrast, Tpex cells possess an\n327 intermediate phenotype and may exhibit behaviors that deviate from expectations in this\n328 context. Accordingly, in the main text, we moderated the description specifically regarding\n330 reported (PMID: 38510246). Furthermore, rather than CD8-Tpex cells intrinsically exhibiting\n331 a strong interferon signaling signature, it is possible that they are influenced by other cell\n332 populations included in NMF1 and NMF8.\n333\n336 subsets—NMF proved highly effective in extracting biologically meaningful transcriptional\n337 programs in our TIL dataset, as previously demonstrated (PMID: 38359792). Given its\n338 strengths in integrating across datasets and generating reusable gene programs, NMF\n339 remains an indispensable tool for our analysis. While no single tool offers a perfect solution\n340 at present, we hope this limitation is understood in the appropriate context. These points\n341 have been added as a limitation (page 14, lines 461-465).\n342\n343 5. (related to response to Reviewer 2 comment #4) In Suppl Fig 4, the authors test if\n344 any of the T cell clusters in Fig 1b is enriched in the T cells in a neoadjuvant PD-1\n345 inhibitor-treated lung cancer patients. The lung cancer patient-derived T cells are\n346 coming from lung tumor/mets or adjacent normal lung or the LN (this is described in\n347 the Figure caption). The authors are lumping all of them together into their existing\n348 clustering, which is also a mixture of T cells from tumor, LN and PBMC of gastric\n349 cancer patients. The authors then reported that higher proportion of T cells in C5\n350 (Tpex in LN) and C8 (T eff in PBMC) is associated with major pathological response\n351 (MPR) in the lung cancer patients receiving neoadjuvant anti-PD1. Are the T cells\n352 projected to C5 from the lung cancer dataset from the LN T cells (there are 3 patients\n353 with T cells from LN in the dataset)? Are the T cells projected to C8 from the lung\n354 cancer dataset from the blood T cells (there is only 1 patient with T cells from\n355 blood)? In their response, the authors stated that \"there is currently no scRNA-seq\n356 dataset that directly evaluates Tpex in LNs after ICI treatment in human samples and\n357 examines its association with ICI treatment efficacy or prognosis.\" This does not\n358 justify checking the Tpex cluster in LN across T cells from multiple organs. What the\n359 authors can do is to test if any of the intratumoral T cell population in Fig 1b is\n360 enriched in the intratumoral T cell population of lung cancer patients with MPR after\n361 neoadjuvant anti-PD1.\n362\n364 separated the tumor and LN samples in the GSE173351 dataset and compared them with\n365 our dataset. Accordingly, our data showed that LN-derived C05 (CD8-Tpex) and C09 (CD4-\n367 the GSE dataset, respectively. In response to the question of whether the T cells projected\n10\n368 to C8 in the lung cancer dataset originate from blood, we are unable to make this\n369 determination due to the absence of blood sample data in the GSE173351 dataset. Finally,\n370 in the comparison across tumors, we observed that among the tumor-infiltrating exhausted\n372 and C06.0 primarily corresponded to CD8-TRM cells, while C06.1 aligned with CD4-Tfh(2)\n373 cells. In addition, C17.0 and C17.1 matched CD8-proliferating and CD8-TRM cells, and\n375\n377 tumor/metastasis data from our reference dataset and performed reference mapping with\n379 clusters C04 and C05 exhibited statistically significant differences (FDR < 0.05), with higher\n380 proportions of these clusters being associated with favorable major pathological response\n381 (MPR). Taken together, both the analysis of all tissues and that restricted to\n382 tumor/metastasis samples suggest that the C05 cluster plays a role in mediating MPR.\n383\n385 the major pathologic response (MPR) and the non-major pathologic response (non-\n388 single-cell RNA-seq data of tumor/metastases were projected on our reference using\n390 single-cell compositional data analysis (scCODA) framework is described as credible.\n391\n392 6. (related to response to Reviewer 2 comment #3 and #5) The whole discussion of\n393 \"CD4 Tpex\" in this paper totally ignores the known fact that LNs enriches for CD4\n395 expression of TCF7, TOX, TOX2, PDCD1, TIGIT, CXCL13, CXCR5, BCL6 shown in\n398 is NOT known is whether aPD-1 binds to Tfh in the tumor draining LN (answer: yes\n399 based on Fig 5a), whether they are activated by aPD-1 (yes, their proliferation is\n400 induced in Fig 4g) and whether they migrate into the tumor (answer: probably no\n401 based on Fig 5d,e,g; more specific analysis is needed).\n402\n404 we annotated this cluster as follicular helper T cells (Tfh) (page 4, line 99). The C09\n405 population is bound by ICI (Fig. 5c, d), and increases Ki-67 expression with ICI treatment\n406 (Fig. 4g). However, STARTRAC analysis revealed that these cells exhibit low levels of\n407 migration and transition (Fig. 6a,b), and TCR sharing between CD4-Tfh in LN (C09) and\n408 other clusters appears limited (Fig. 6c). This explanation has been cooperated in the main\n409 text (page 11, lines 349-350).\n410\n411 7. Analysis in Fig 3 is to support the importance of CD8 Tpex and Tfh fraction in\n412 proximal (presumably tumor draining) LNs. The authors used a ratio between Tpex\n11\n413 and Tex to correlate with relapse free or overall survival. Does the fraction of Tpex in\n414 the LN not correlated with RFS/OS? Also, the difference in p-value significance\n415 between CD8 Tpex/Tex ratio in LN vs. CD8 Tpex/Tex ratio in tumor may simply reflect\n416 the difference in the sample numbers (LN n=49, tumor n=23) - the KM plots look quite\n417 comparable between the two.\n418\n422 than the proportion of Tpex alone, may more accurately reflect patient survival. As the\n423 reviewer pointed out, it is possible that a significant difference could emerge in tumors if the\n424 sample size were increased. Therefore, we have removed the statement in the manuscript\n425 indicating that “no difference was observed in the tumor or metastatic LNs (page 7, lines\n426 214-215).\n427\n428\n429\n430\n432 overall survival (OS: right) among ICI-naive gastric cancer patients. The upper panel\n433 shows the comparison of the percentage of Tpex in CD8+ T cells from metastasis-free\n434 lymph nodes between the two groups. The lower panel shows the percentage of Tfh in\n436 groups were divided based on the median value. P-values were calculated by using the\n437 log-rank test.\n438\n439 8. (related to response to Reviewer 2 comment #9) \"First, The comparison between\n441 exhibited higher CD103 expression and lower TCF1 expression than did ICI-non-\n442 bound CD8+ T cells across all tissues, suggesting that ICI-bound CD8+ T cells\n443 possess an exhausted phenotype, whereas ICI-non-bound CD8+ T cells retain a\n12\n444 naive phenotype (Supplementary Fig. 11a, b).\" CD103 is not a known marker of\n445 exhaustion - it is reported as a marker of tissue resident T cells that can also be\n446 expressed on intratumoral T cells (see for example\n447 https://rupress.org/jem/article/218/4/e20201605/211911/Tissue-resident-memory-T-\n448 cells-in-tumor-immunity). The authors should include citation of supporting papers\n449 defining CD103 as marker of exhaustion, especially for Tpex in the LN?\n450\n452 as an exhaustion marker. Accordingly, we have revised the phrase “exhausted” phenotype”\n453 to “memory” phenotype” in the manuscript (page 9, line 261).\n454\n455 9. (Fig 4e-h) are the samples from ICI-treated T cells stained for PD-1 using the\n456 indirect method (i.e. using anti-IgG4) while the ICI-naive stained using direct method\n457 (i.e. using anti-PD1)? If yes, cells from the same T cell subsets from the same organ\n458 in the ICI-treated and ICI-naive may not be comparable. This is because the\n459 antibodies used to define the subsets are not exactly the same and hence may bind\n460 the PD1+ T cells at different rates. Indeed, Supp Fig 10 shows that although the\n461 correlation between PD-1 staining and IgG4 staining is highly positive, the slope of\n462 the fitted linear curve is not equal to 1 (the %IgG4+ is smaller than %PD-1+).\n464 subset (for ICI naive) or IgG4+ subset (for ICI-treated).\n465\n467 performed using both anti-IgG4 and anti–PD-1 antibodies. The method employing the IgG4\n468 antibody is considered equivalent to a secondary detection method using a biotinylated\n469 antibody and streptavidin, and we believe that nearly all cells bound by therapeutic anti–\n471 expression is unlikely to change markedly over a short period, suggesting that the anti–PD-\n472 1 and anti-IgG4 antibodies detect nearly identical PD-1⁺ cell populations (Supplementary\n475 determination (Supplementary Fig. 10b).\n476\n478 antibodies takes approximately 30–40 weeks (Osa, JCI Insight 2018). Therefore, in the\n479 early treatment phase analyzed in this study, we consider that nearly all PD-1⁺ cells in any\n480 tissue remain bound by therapeutic anti–PD-1 antibodies and are consequently detected by\n481 the anti-IgG4 antibody as well (Fig. 4b). In addition, ex vivo scRNA-seq data demonstrated\n482 that the expression levels of PDCD1 mRNA and anti-IgG4 were well correlated, indicating\n483 concordance between PD-1 expression and anti-IgG4 detection (Fig. 5b).\n484\n486 antibody treatment in patients, nearly all PD-1⁺ cells across tissues are bound by anti-IgG4\n487 antibodies. Although the inability to trace PD-1⁺ cells before and after treatment is a\n488 limitation of this study, no such method currently exists. Considering the observed\n489 concordance between PDCD1 mRNA expression and anti-IgG4 detection, classification of\n491 therefore hope the reviewer understands that, given this limitation, our current approach\n492 represents the most feasible method for flow cytometric comparison between ICI-naive and\n494\n495 10. The STARTRAC analysis in the main Figure 5 should highlight the differences\n496 between the ICI-naive vs. ICI-treated patients (now Supp Fig 14). The authors should\n497 highlight the subsets with differences. The important observations include ICI\n13\n498 increases expansion in Tpex (C5 and C12) and increased migration from LN to tumor\n499 only in C5 (which may make sense since the Tpex in C12 is already in the tumor).\n501 lower -- does this mean they slow their transition to terminally exhausted\n502 phenotype? These are important points to highlight. Finally, the STARTRAC analysis\n503 should be performed only on the IgG4+ subset of the ICI-treated samples to show the\n504 patterns of expansion, migration and transition in the anti-PD-1 bound T cells.\n505\n507 author should visualize the percentage overlap in a heatmap format rather than\n508 listing them in the text. This should be done in ICI treated vs ICI naive (and perhaps\n509 in IgG4+ T cell subset).\n510\n512 between ICI-treated and ICI-naive cases in the STARTRAC analysis, we have restructured\n513 the original Figure 5 into new Figures 5 and 6. As noted by the reviewer, the transition\n514 scores of CD8-Tpex (C12) and CD8-Tex subsets (C04, C06.0, C17.0, C17.2) within tumors\n515 were lower in ICI-treated cases compared with ICI-naive cases. This may suggest that ICI\n516 treatment does not promote the differentiation from Tpex to Tex. We have added a\n517 discussion on this point in the revised manuscript (page 11, lines 333-336).\n518\n520 independently for each cluster, extracting only the IgG4⁺ clusters would not alter the\n521 results. For further details, please refer to the STARTRAC methodology (PMID: 30479382).\n523 exhibit higher scores than the IgG4⁻ clusters, indicating their importance. However, as\n524 explained in the main text, we believe that performing STARTRAC analysis and\n525 comparisons across all clusters, rather than focusing solely on IgG4⁺ clusters, is essential\n526 to highlight the significance of IgG4-bound clusters after ICI treatment in this study.\n527\n529 overlap across clusters, separately for ICI-treated and ICI-naive cases. As expected, this\n530 analysis further demonstrates that, in ICI-treated cases compared with ICI-naive cases,\n532 that ICI treatment enhances TCR similarity between Tpex cells in LNs and Tex cells in\n533 tumors. We have added this as Supplementary Fig. 16 and described the corresponding\n534 results in the main text (page 11, lines 356-357).\n535\n14\n537 and ICI-naive cases.\n538\n539\n541\n543 response to the review comments, I find that the author has provided detailed and\n544 substantive responses to the key concerns, including research innovation, sample\n545 size, limitations in animal model validation and prognostic analysis. While certain\n546 constraints remain due to practical limitations, the author has made notable\n547 improvements to the paper, addressing several critical issues and enhancing its\n548 overall rigor. The authors has integrated matched tumor, lymph node, and blood\n549 samples from patients after ICI treatment and utilized scRNA/TCR/CITE-seq\n550 technologies to distinguish ICI-bound and non-bound T cells, demonstrating a\n551 certain level of technical innovation. Furthermore, the study focuses on the\n552 activation of Tpex cells in non-metastatic LNs, their migration into tumors, and their\n553 association with prognosis, providing unique insights into the dynamic migration\n554 mechanism of Tpex cells. In addition, the author has expanded the sample size and\n555 supplemented single-cell sequencing data, enhancing the reliability of the results.\n557 animal models due to practical constraints, the author provides evidence of Tpex\n558 migration to some extent through multi-omics data and clonal correlation analysis,\n559 while explicitly acknowledging this limitation in the discussion, making the response\n560 scientifically sound and reasonable. Finally, validating the prognostic value of the\n562 author indicates that the difficulty in obtaining surgical samples from ICI-treated\n563 patients limits further validation of the correlation between the Tpex/Tex ratio and\n564 prognosis in a larger cohort, which is indeed an objective limitation. In conclusion,\n565 this study systematically reveals the dynamic role of lymph node Tpex in ICI\n566 treatment, providing a new perspective for optimizing immunotherapy strategies. I\n567 recommend accepting the manuscript.\n568\n570 which have greatly helped us to improve our manuscript.\nReviewer #2\nThe revised manuscript of Nose et al has included some important updates. The\nmanuscript keeps most of the original figures intact and most of the updates is added into\nnew supplementary figures. The new manuscript feels heavy and a little convoluted to my\npreference but I appreciate the efforts taken to address my concerns.\n1, First, the Authors tested several methods of integration and demonstrated that T cells\nfrom different organs are different transcriptomically from one another (Response to\nReviewer 2 comment #1). However, I noted that the integration (i.e. batch effect removal)\nis based on removing the effect of different donors. Indeed, when the batch effect removal\nis performed based on donors+site, much of the site specific differences disappear. I agree\nthat the Authors have freedom to choose to analyze the T cells without removing the organ-\nspecific differences. However, if that is the Authors' choice, then when the Authors\nprojected GSE173351 single cell data of lung cancer to their UMAP in Fig 1B, they have to\ncheck if the organ of origins of the mapped GSE173351 T cells should match the organ\nsite of the Authors' own clusters. For example, GSE173351's \"CD8-effectors (and also CD8\nTRMs)\" seem to match with lymph node-enriched C05 (CD8-Tpex). Are these CD8 T\neffector/TRM cells from the lymph node in GSE173351? If yes - it is useful to highlight this.\nIf the CD8 T effector/TRM cells in GSE173351 are mostly intratumoral, the Authors should\ncompare them with C12, which is the intratumoral Tpex in Figure 1B and 1E.\n→We thank the reviewer for the constructive comments. As noted in our previous response\n(Response to Reviewer 2, comment #1), we explicitly evaluated the effect of batch correction\nusing Harmony with donor and site as covariates. Even under this more stringent correction,\ntissue-specific differences among T cells were not eliminated but remained evident (Figure for\nReviewer #2).\nImportantly, these residual differences were not arbitrary or driven by technical distortion. Across\nintegration strategies, clusters retained highly consistent marker gene expression profiles, and\neach cluster could be defined by essentially the same canonical markers as described in the main\ntext. This indicates that the integration did not collapse biologically distinct T cell states nor\nartificially generate site-specific clusters.\nRather, tissue-specific effects manifested primarily as differences in the relative abundance of\nshared transcriptional states across organs, with substantial overlap in embedding space. Taken\ntogether, these analyses support that organ-associated transcriptional differences reflect genuine\nbiological variation rather than incomplete batch correction. Consequently, our donor-based\nintegration strategy, which yields highly similar clustering and marker patterns, provides a valid\nand biologically interpretable framework for downstream analyses.\n(Top) UMAP plot showing tissues after batch correction by donors and sites. (Bottom) UMAP plots\nshowing the marker genes.\nFurthermore, as described in our previous response to Reviewer 2 (comment #5), we performed\ncomparative analyses between our dataset and the GSE173351 dataset separately for lymph\nnodes and tumor (Supplementary Fig. 5). In the LN comparison, cluster C05 (CD8-Tpex) in our\ndataset mainly corresponded to CD8-effector cells, whereas cluster C09 (CD4-Tfh) aligned with\nCD4-Tfh cells, indicating that C05 and C09 in our dataset closely matched the LN-derived subsets\nin the GSE173351 dataset (Supplementary Fig. 5a). Additionally, in the tumor comparison, CD8-\nTex clusters (C04, C06.0, C17.0, C17.1) and CD4-Tex clusters (C06.1, C17.2) from our dataset\nshowed the following associations: C04 and C06.0 primarily corresponded to CD8-TRM cells,\nC06.1 matched CD4-Tfh(2) cells, C17.0 and C17.1 corresponded to CD8-proliferating and CD8-\nTRM cells, and C17.2 matched CD4-Treg cells (Supplementary Fig. 5b). Thus, consistent\nsimilarities were observed in tissue-specific comparative analyses.\n2, Second, the discussion of CD4 Tfh is redundant (page 6). They are enriched in LNs and\nhave well known function as B cell helper and have well defined markers that are different\nfrom naive CD4 T or CD4 Tcm. The fact that very little CD4 clones in the tumor (or PBMC)\nhave TCR overlap with these Tfh suggest that they are NOT migratory. The Authors should\nconsider removing this part to avoid confusion.\n→We thank the reviewer for this valuable comment. Indeed, as shown in our data, TCR similarity\nbetween CD4-Tfh cells in lymph nodes and CD4-Tex cells in tumors is not high (Figure 6c,\nSupplementary Fig. 21). These findings suggest that lymph node–resident Tfh cells are unlikely\nto migrate into the tumor microenvironment, but rather exert their functions primarily within the\n2\nlymph nodes. According to the reviewer’s advice, we have revised and partly removed the related\ndescriptions of CD4-Tfh cells (page 6, lines 168-172).\n3, Third, following the same vein, the discussion on CD4 Tfh/Tex ratio in Fig 3g,h,i,j may\nnot be relevant. The CD4 Tfh and CD4 Tex populations are not connected clonally and it is\nnot clear why the ratio between them matters. Also, the log-rank p-values are all > 0.1. I\nalso noted that Fig 3J still uses the “Tpex” label for the Tfh.\n→Thank you for this important comment. As the reviewer pointed out, CD4-Tfh and CD4-Tex\npopulations are not clonally connected, and no significant prognostic difference was observed\nbased on the CD4-Tfh/Tex ratio. Accordingly, we have moderated our discussion on the CD4-\nTfh/CD4-Tex ratio and instead emphasized the importance of CD8-Tpex/Tex populations (pages\n7-8, lines 226-228, 231-232). In addition, as suggested, we have corrected the labeling in Figure\n3J and its legend.\n3"}]

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