The CD4^+ T cell population partners with Tpex CD8^+ T cells to mediate antitumor immunity in the tumor microenvironment

Domain: biology
Published: 2026-03-28
Editorial outcome: major_revision

Abstract
CD4⁺ T cells support the priming, expansion, and function of CD8⁺ T cells through dendritic cells. Precursor exhausted T cells (Tpex) maintain self-renewal and supply cytotoxic CD8⁺ T cells in the tumor
microenvironment (TME), but the identity of their CD4⁺ T-cell partners remains unclear. Here, we perform
scRNA-seq, scTCR-seq, and mass cytometry analysis on peripheral blood, tumor, and lymph nodes
primarily from lung cancer patients and, in part, renal cell carcinoma. We identify an IL-7Rhigh CCR6⁺ Th1-
like CD4⁺ T cell-population, named Th7R, that is numerically and spatially partnered with Tpex. Th7R
cells express lymphotoxin-β and CXCL13, correlate with high endothelial venules, and co-localize with
Tpex in tertiary lymphoid structures. Th7R cell abundance correlates with Tpex numbers in the TME and
lymph nodes, and adoptive transfer of Th7R increases Tpex in a preclinical mouse model. Intratumoral
Th7R and Tpex associate with improved response to neoadjuvant PD-1 blockade therapy. These results
suggest that Th7R cells act as partners of Tpex to sustain antitumor T-cell immunity.

Introduction
Precursor-exhausted CD8+ T cells (Tpex) were first reported in a chronic viral infection model as
programmed cell death-1 (PD-1)-expressing exhausted T cells that also express CXCR5 and TCF7, possess
self-renewal capacity, and play a critical role in controlling persistent viral infections 1-3. Tpex function as
the starting point for the proliferation burst of CD8+ T cells induced by PD-1 blockade therapy 2. Single-
cell RNA sequencing of tumor tissue revealed that Tpex are a subset of stem-like memory T cells with self-
renewal capacity, sustaining long-term antitumor immunity in the tumor microenvironment (TME) while
providing GZMB+ exhausted CD8+ T cells4,5. Tpex infiltrate TME via high-endothelial venules (HEVs),
and localize within tertiary lymphoid structures (TLS) 6,7.
Ferris et al. reported that dendritic cells that have taken up tumor antigens first present these antigens
to CD4⁺ T cells. Dendritic cells that have been licensed through interaction with CD4⁺ T cells can then
induce the proliferation of CD8⁺ T cells 8. Primed CD8+ T cells also require antigen presentation from
dendritic cells licensed by CD4+ T cells to acquire cytotoxic function9. Thus, the helper function of CD4+
T cells is essential for the priming, proliferation, and induction of cytotoxic function in CD8+ T cells.
However, the role of CD4+ T cells in the generation and maintenance of Tpex has not been addressed.
Recently, it has been reported that CD8⁺ T cells exhausted by repeated antigen stimulation can undergo
epigenetic changes through the formation of a triad—a direct contact between CD4⁺ T cells, dendritic cells,
and CD8⁺ T cells—leading to the expression of IL-7 receptor (IL-7R) and TCF7, which are characteristic
of Tpex 10. Magen et al. reported that in TME of hepatocellular carcinoma that responded to preoperative
PD-1 blockade therapy, both CXCL13+ CD4+ T cells and GZMK+ CD8+ T cells clonally proliferated and
played crucial roles in antitumor immunity. They concluded that intratumoral niches containing CXCL13+
CD4+ T cells control the differentiation of tumor-specific progenitor exhausted CD8+ T cells through the
formation of triad with dendritic cells 11. CD4⁺ T cells are also involved in the formation of ectopic HEVs,
which are essential for Tpex to infiltrate the tumor microenvironment 12. Although CD4+ T cells seem to
play an important role in situation that require persistent immune responses, such as cancer, the specific
CD4⁺ T cell subset that assists Tpex remains poorly defined.
Upon priming, CD4⁺ T cells differentiate into functionally distinct subsets 13. While type1 helper T
cells (Th1) are traditionally linked with antitumor immunity through IFN-γ and T-bet expression, recent
analyses of immune checkpoint inhibitor (ICI) responses have identified Th1-like, non-canonical CD4⁺ T
cells in tumors. We previously described a unique CD4⁺ T-cell population, Th7R, which expresses TCF7
and IL-7R and is associated with better outcomes in lung cancer patients treated with ICIs or surgery 14,15.
Based on their shared molecular features with Tpex, such as IL-7R and TCF7 expression, we
hypothesize that Th7R cells may function as CD4⁺ T-cell partners of Tpex. In this study, we analyze TME
and peripheral blood using scRNA-seq, scTCR-seq, and imaging mass cytometry to define the relationship
between Th7R cells and Tpex in antitumor immunity. By identifying Th7R cells as key CD4⁺ T-cell partners
of Tpex, this study bridges fundamental T-cell differentiation biology with clinically relevant antitumor
immune dynamics. Our findings suggest that CD4⁺ T-cell–mediated support of Tpex represents a critical
axis governing durable responses to immune checkpoint blockade and tumor control. This conceptual
advance opens new avenues for patient stratification and for therapeutic strategies that selectively enhance
or restore CD4⁺–CD8⁺ T-cell cooperation in cancer.

Results
Cells with the same TCR clonotypes and gene expression signature as Th7R cells in the peripheral
blood are present in the tumor microenvironment.
Th7R is a novel Th1-like CD4+ T-cell cluster identified by scRNA-seq analysis of peripheral blood
from advanced-stage lung cancer patients who responded to immune checkpoint inhibitor (ICI) therapy 14.
To clarify whether Th7R cells are also present in the tumor microenvironment (TME), we analyzed the
scRNA-seq and scTCR-seq data of CD4+ T cells derived from the peripheral blood and tumor tissues of
eight stage Ⅰ-Ⅱ lung cancer patients (5 males, 3 females, aged 62-80 years) who had undergone surgery and
examined whether CD4+ T-cell clusters with the same gene expression patterns and TCR clonotypes as
peripheral blood Th7R cells are present in lung cancer tissues. A total of 79,130 CD4+CD3D+ cells (65,572
PB-CD3D+CD4+ cells and 13,558 TIL-CD3D+CD4+ cells) were subjected to unsupervised clustering on the
basis of 2,000 genes with highly variable expression, generating 27 clusters (Fig. 1a, Supplementary Table
1, 2, Supplementary Fig. 1 a, b). Since Th7R cells were discovered in a cluster of effector T cells with
low SELL expression in the peripheral blood, we first focused on the SELL low-expression clusters 13, 14,
18, 21, and 26 (Fig. 1b). The SELL low-expression clusters contained many clonally expanded T cells (Fig.
1c, Supplementary Table 3). Cluster 8 and 11, which contained many multicellular clonotypes with high
SELL expression in the TME, was thought to include regulatory T cells (Tregs) with high FoxP3 expression
(Fig. 1d).
The SELLlow effector CD4+T T cells in the peripheral blood included canonical Th1-type cells, Th17-
type cells and Th7R cells, and chemokine receptor expression patterns revealed that these cells were
CXCR3+ CCR4- CCR6- Th1 cells, CXCR3- CCR4+CCR6+ Th17 cells, and CXCR3± CCR4- CCR6+ Th7R
cells, respectively14. Next, we examined the signature genes that could discriminate among Th1, Th17, and
Th7R cells. First, CXCR3+ CCR4- CCR6-, CXCR3- CCR4+ CCR6+, and CXCR3± CCR4- CCR6+ T cells
sorted from the peripheral blood of CD62Llow CD4+ T cells from lung cancer patients (sorted Th cohort,
n=3) were analyzed by scRNA-seq (Supplementary Table 1). Differential expression analysis identified 64
genes that were differentially expressed among these subsets, including both upregulated and
downregulated genes, thereby enabling the characterization of gene expression signatures associated with
the Th1, Th7R, and Th17 phenotypes. (Supplementary Data 1). We refined the 64-gene set to 58 genes by
applying support vector machine-based recursive feature elimination (SVM-RFE) for feature selection
(Supplementary Fig. 2 a-c). By using the expression data for the 58 selected genes from sorted Th1, Th7R,
and Th17 cells, we trained an SVM classifier and applied it to discriminate the Th subtypes of the cell
clusters shown in Fig. 1a. As a result, cluster 13 and 26 were predicted to be Th1 cells, cluster 14 was
predicted to be Th17 cells, and cluster 18 and 21 were predicted to be Th7R cells (Supplementary Fig. 2 d,
e). We evaluated the similarity of the sorted subsets and SVM-predicted clusters using an expression
heatmap of 58 genes combined with cosine similarity analysis. This approach revealed strong concordance
indicating that the cluster 13+26 cluster was Th1 and that the cluster 18+21 was Th7R (Fig. 1e). Robustness
of this SVM-prediction was examined using scRNA-seq data obtained from other 34 lung cancer patients
other than the 8 surgery patients, a public dataset of 135 healthy Japanese, 3 renal cell cancer patients, and
a public dataset of 4 melanoma patients (Supplementary Table 1, Supplementary Fig. 3 a-o). In all cohorts,
the clusters of Th1-type, Th17-type, and Th7R-type gene signature were consistently found in SELLlow
region of UMAP and the cosine similarity and the prediction probabilities indicated good concordance with
sorted Th.
Next, the independence of the TIL-13, TIL-14, TIL-18, TIL-21 and TIL-26 clusters was examined by
analyzing TCR sharing in multicellular TCR clonotypes, which included 2 or more T cells in each TIL
cluster (Fig. 1f). Among the Th1-type clusters, only TIL-13 contained multicellular clonotypes. The TIL-
13 clonotypes were only shared with the peripheral blood (PB)-13 cluster, indicating that the T cells
belonging to PB-13 do not significantly change their gene expression patterns even after infiltration into
the TME. TCR sharing with TIL-21 cells was found not only for the PB-21 cluster but also for the TIL-18
cluster. On the other hand, the clonotypes belonging to TIL-18 were mostly found in the PB-18 cluster.
Thus, T cells in the PB-21 cluster are likely to change their gene expression patterns to the cluster 18 state
after infiltrating the TME, suggesting that clusters 18 and 21 exhibit two gene expression states of one type
of differentiated T cell. Accordingly, clusters 18 and 21 were considered two closely related transcriptional
states of the same Th7R subset. TCR clonotypes belonging to the TIL-14 cluster were rarely detected in
other clusters. The total cell counts and TCR clone sizes for each cluster are shown in Fig. 1g. Cluster13
had greatly expanded TCR clones in the peripheral blood but lacked expanded TCR clones in the TME. It
is likely that the abundant multicellular clonotypes in the PB-13 cluster reflect clonal expansion in the
secondary lymphoid organs and that some of the expanded cells infiltrate the TME. In contrast, cluster 18
exhibited few expanded TCR clonotypes in the peripheral blood but the greatest number of expanded TCR
clonotypes in the TIL-18 cluster, suggesting clonal expansion in the TME rather than in the secondary
lymphoid organs.
Furthermore, to investigate whether this gene expression pattern is derived from epigenetic changes,
we used the 10x Genomics Multiome platform to perform both single-nucleus RNA-seq (snRNA-seq) and
single-cell ATAC-seq (scATAC-seq) of the same nuclei (Fig. 1h, Supplementary Fig. 4a). Among the
clusters obtained on the WNN-UMAP diagram, clusters 4, 6, 11, 14, and 20 were considered SELL low-
expression clusters (Supplementary Fig. 4b, c). On the basis of SVM-based prediction of Th subtypes using
the 58 signature genes, clusters 6 and 20 were classified as Th1 cells, cluster 14 was classified as Th17 cells,
and clusters 4 and 11 were classified as Th7R cells (Fig.1i, Supplementary Fig. 4d-f). The overall gene
expression patterns of the clusters classified as Th1, Th17, and Th7R cells were very similar to those of
sorted Th cells (Fig. 1j). The gene activity (GA) scores of 54 of the 58 signature genes obtained from DNA
chromatin information also matched the gene expression patterns of each Th subset (Fig. 1k). These results
indicate that Th7R cells have a characteristic gene expression pattern that differs from those of Th1 and
Th17 cells and that they have a DNA chromatin state that supports this gene expression profile.
Th7R cell gene expression in the TME
The SVM classifier was applied to the SELLlow clusters of TIL and PB to evaluate prediction
probabilities (Supplementary Fig. 5a). For #13+26 Th1 and #14 Th17 clusters, the peripheral blood
(prediction probability of 1.000, 1.000) and the TIL (0.999, 0.842), showed good predictive agreement. In
contrast, the #18+21 Th7R cluster had a greater probability in the peripheral blood (0.993) but a lower
probability in TILs (0.557), suggesting altered gene expression in the TME. However, the prediction
probability of the TIL Th7R cluster being classified as Th1 (0.171) or Th17 (0.272) was low, suggesting
that Th7R cells have a unique gene expression pattern in the TME. Therefore, we compared the gene
expression profile of the Th7R cluster between peripheral blood and TILs and found that 115 genes were
upregulated more than 2-fold in TILs and 18 genes were downregulated more than 2-fold in TILs (Fig. 2a,
b, Supplementary Data 2, Supplementary Fig. 5b). The genes upregulated in the TME included CCL4,
CXCL13, CREM, CCL4L2, GZMB, CTLA4, CXCR4, IFNG, ID2, XCL1, CXCR6, CCL3 and ICOS. Next,
we investigated the differences in gene expression between Th7R and Th1 or Th17 cells in the TME (Fig.
2c, Supplementary Fig. 5c). The genes whose expression was more than 2-fold greater in Th7R cells than
in canonical Th1 cells in the TME included LTB, CXCR6, CTLA4, and CXCL13 (Fig. 2d, Supplementary
Data 3). The genes whose expression was more than 2-fold greater in Th7R cells than in Th17 cells
included CCL4, CCL4L2, CXCL13, CCL5, GZMK, GZMB, IFNG, and CCL3.
Relationship between Th7R cells and HEV formation in the TME
CXCL13 was found to be upregulated in Th7R cells in the TME, with a significant difference from the
expression observed in Th1 and Th17 cells. Th7R cells also expressed more LTB; LTβ and CXCL13 are
known to be essential for the formation of ectopic HEVs and TLSs 16. To investigate the relationship
between Th7R cells and ectopic HEV formation, immunohistochemical (IHC) analysis and Hyperion
analysis were performed (n=56, Supplementary Table 1). Sections were stained with antibodies against the
normal vascular marker CD31 and the HEV marker peripheral node addressin (PNAd), and the ratio of
PNAd-positive areas to CD31-positive areas (the MECA index) was calculated to assess HEV density and
its relationship with the proportion of Th7R cells in the peripheral blood (Fig. 3a). The threshold of the
MECA index was determined using the value that yielded the highest likelihood ratio in ROC analysis for
distinguishing recurrent patients from non-recurrent patients. Consistent with previous reports, significantly
better disease-free survival (DFS) was observed in patients with more HEV formation in the TME, and a
MECA index of 0.044 was found to predict better DFS (Fig. 3b). A chi-square test using a Th7R percentage
of 3.05, which was used as a threshold for predicting better DFS after surgery for lung cancer in a previous
study, and a MECA index of 0.044 showed that HEV density was associated with Th7R abundance (P =
0.0080, Fig. 3c) 15.
Tpex as a source of Tex in the TME
Tpex have self-renewal ability and contribute to a sustained supply of CD8+ T cells to maintain long-
term antitumor immunity through stem cell-like asymmetric cell division 7. To analyze the relationship
between Th7R cells and Tpex in the TME, we attempted to identify Tpex in lung cancer tissues. We
performed scRNA-seq analysis of 25,728 CD3D+CD8+ cells (17,997 PB-CD3D+CD8+ cells and 7,731 TIL-
CD3D+CD8+ cells) derived from 8 surgery patients. Unsupervised clustering was performed on the basis of
the expression of 2,000 highly variable genes, generating 16 clusters, as shown in the UMAP plot (Fig. 4a,
Supplementary Fig. 6a, b). We analyzed the gene expression of GZMK and GZMB as candidate markers
for discriminating among Tpex, exhausted CD8+ T cells (Tex), and unexhausted cytotoxic T lymphocytes
(CTLs) (Fig. 4b) 4,5,17. GZMK expression was observed in clusters 1, 2, 4, 8, 10, 11, 13 and 14 adjacent to
the SELLhighCD45RA+ naive clusters (6, and 15) and the SELLhighCD45RA- central memory (CM) cluster
(3, 7, and 12) (Fig. 4c, Supplementary Fig. 6c). GZMB expression was observed in cluster 5, which was
farthest from the naive clusters, and clusters 1, 4, 8, 11, and 13. Cluster 9 was CCR6+ and was considered
mucosa-associated invariant T (MAIT) cells. Next, we assessed the expression of signature genes thought
to characterize Tpex in each TIL cluster (Fig. 4d) 7. GZMK+GZMB- clusters 2, 10, and 14
expressed TCF7, IL-7R, PDCD1, SELL, and CCR7, matching the characteristics for Tpex. Tpex is known
to undergo asymmetric division, containing a population that remains in a more stem-like state and a
population acquiring effector functions 5. Although the GZMK+GZMB- cluster comprises multiple sub-
clusters, the entire cluster will be referred to as GZMK+GZMB- Tpex in this study. On the other hand,
GZMK-GZMB+ cluster 5 was considered a CTL cluster because it expressed no exhaustion genes and highly
expressed PRF1, GZMB, GNLY, FGFBP2, and NKG7. The GZMK+GZMB+ clusters 1, 4, 8, 11, and 13
were considered Tex, as they did not express IL-7R or TCF7 but highly expressed exhaustion genes such
as PDCD1, CTLA-4, ENTPD1, and HAVCR2. These results indicate that Tpex, Tex, and CTLs can be
distinguished on the basis of their GZMB and GZMK expression patterns.
When the cell density of each pixel in the PB-UMAP plot was subtracted from the cell density of each
pixel in the TIL-UMAP plot, clusters 1, 2, 10 and 13 presented a distribution with a TME-dominance (Fig.
4e, Supplementary Table 4). The direction of the state change for each cluster in the TME and peripheral
blood was also examined by velocity analysis (Fig. 4f). In the peripheral blood, changes to the Tex clusters
starting from cluster 5, which is a cluster of CTLs that have not been exhausted, were observed, and it was
thought that CTLs that proliferated in the secondary lymphoid organs changed their state in response to
repeated antigen stimulation. In contrast, TIL velocity analysis revealed that cluster 2, which was included
in GZMK+GZMB- Tpex, was the source of the surrounding clusters, including Tex. Considering that
clusters 1 and 2 were dominant in the TME, the main progression of T-cell state changes is thought to be
from the GZMK+GZMB- Tpex cluster 2 to the terminally exhausted cluster 1, which has the highest
expression of HAVCR2, CTLA4, and ENTPD1. TIL-1 and 2 are the clusters containing the highest number
of clonally expanded cells in TME. The fact that many TCR clonotypes belonging to the TIL-2 cluster were
shared with TIL-1 suggests that the cells in TIL-2 is transformed into the TIL-1 phenotype (Supplementary
Fig. 6d, e). These results indicate that Tex, which have cytotoxic activity, are supplied mainly by Tpex,
which replicate in the TME.
Spatial relationship between Th7R cells and GZMK+GZMB- Tpex in TLSs
IHC analysis revealed that CXCR3+CCR6+ CD4+ T cells were located in TLSs (Supplementary Fig.
7). Next, we performed a neighborhood analysis of Th7R cells and GZMK+GZMB- Tpex on the basis of
the molecular expression information obtained using Hyperion (Fig. 5a). We used software that can segment
and annotate individual cells on the basis of their molecular expression patterns (Fig. 5b, Supplementary
Fig. 8). GZMB+ cells, which constitute a population with cytolytic functions, were found in the cancer cell
nest. In contrast, GZMB-GZMKT+ CD8+ T cells, which are considered Tpex, were found to be abundant
within the B-cell aggregates considered TLSs. CXCR3+ CCR6+ CD4+ T cells, which are considered Th7R
cells, were found near GZMK+GZMB- Tpex within TLSs (Fig. 5c).
Correlations between Th7R and GZMK+GZMB- Tpex numbers
Next, we examined whether the percentages of CTLs and GZMK+GZMB- Tpex among total CD8+ T
cells were correlated with the percentages of Th1 and Th7R cells among total CD4+ T cells in cluster 12 in
the draining lymph nodes (the hilar lymph nodes) and peripheral blood derived from lung cancer patients
(Fig. 6a, b). In both locations, Th1 cell abundance was positively correlated with CTL abundance, and Th7R
abundance was positively correlated with GZMK+GZMB- Tpex abundance. To eliminate overlapping
relationships and clarify the relationships between cells, a quantitative network analysis of the proportions
of clusters in CD4+ T cells and CD8+ T cells relative to CD3+ cells in the peripheral blood was performed
(Fig. 6c). Notably, the only CD4+ T-cell cluster whose abundance directly correlated with that of
GZMK+GZMB- Tpex was Th7R cells, with a connection established in 1000 out of 1000 interferences.
These results support the hypothesis that Th7R cells are involved in the intratumoral infiltration and
proliferation of Tpex.
Th7R cell therapy in a mouse tumor model
To determine whether increased Th7R abundance in the TME affects CD8+ T-cell counts, we analyzed
TILs from mice subjected to adoptive transfer of Th7R cells. Th7R cells were sorted as CXCR3±CCR4-
CCR6+ CD4+ T cells and Th1 as CXCR3+CCR4-CCR6- CD4+ T cells from inguinal lymph nodes draining
a methylcholanthrene-induced fibrosarcoma MCA205 skin tumor, and after ex vivo culture, they were
adoptively transferred into mice bearing MCA205 skin tumors. Four million of Th1 or Th7R exhibited
antitumor therapeutic efficacy against 8-day tumors (Fig. 7a). The antitumor reactivity of Th7R is likely
tumor-antigen specific, since antitumor therapeutic efficacy was not observed in Th7R derived from LNs
draining B16 melanoma (Fig. 7b). To obtain TIL, seven days after tumor implantation, mice received
adoptive transfer of Th1 or Th7R cells. Six days post-transfer, tumors were harvested from four mice per
group for TIL isolation. TILs were dissociated with Liberase TL, enriched by Percoll density-gradient
centrifugation, and analyzed by mass cytometry. Both Th1 and Th7R transfer increased intratumoral CD8⁺
T-cell numbers. Notably, only Th7R transfer significantly expanded Tpex (PD-1⁺IL-7R⁺ CD8⁺ or PD-
1⁺TCF-1⁺ CD8⁺) and Tex (PD-1⁺TIM-3⁺ CD8⁺) populations (Fig. 7c, d). In the Th1 group, these populations
showed an upward trend that did not reach statistical significance, whereas CTL (PD-1⁻GZMB⁺ CD8⁺)
tended to increase. Therefore, Th7R cells likely promote asymmetric cell division of Tpex, resulting in
increases in Tpex as abundance and the supply of Tex. The effect of increased Tpex in the tumor
microenvironment induced by Th7R adoptive transfer was not observed in Th7R derived from LNs draining
B16 melanoma but was only observed in Th7R derived from LNs draining MCA205. This suggests the
effect occurs in an antigen-specific manner (Supplementary Fig. 9a, b).
Clinical significance of Th7R and Tpex
To evaluate whether Th7R contribute to antitumor immunity, we investigated their associations with
disease free progression (DFS) after surgery (n = 55, Supplementary Table 1) 18. In stage I-II lung cancer
patients with an observation period of 5 years or more, the patients with preoperative peripheral blood Th7R
≥ 3.05% demonstrated favorable DFS with a hazard ratio of 0.121 (95% CI 0.0306-0.479), P = 0.0002 (Fig.
8a). This Th7R threshold value was obtained in previous studies15. In contrast, preoperative Th1 levels
could not distinguish cases with favorable DFS. Furthermore, only Th7R significantly decreased after
resection of lung cancer tissues, while Th1 and Th17 remained unchanged (Fig. 8b). These results suggest
that Th7R numbers are driven by cancer antigens. Next, to evaluate whether Th7R cells and Tpex in the
TME contribute to antitumor immunity, we investigated their associations with those responses to
preoperative neoadjuvant therapy including anti-PD-1 antibody (n = 20, Supplementary Table 1) 18. There
was a significantly greater abundance of Tpex in the patients who achieved a RECIST partial response by
imaging. In addition, Th7R abundance was significantly greater in patients who achieved a complete
pathological response or major pathological response (Fig. 8c). Thus, Th7R cells and Tpex in the TME
likely play important roles as partners in antitumor immunity.

Methods
Patients
TIL and peripheral blood samples for single-cell RNA sequencing (scRNA-seq, n = 8), along with
tissue samples for IHC analysis (n = 56, Supplementary Table 1) and Hyperion analysis, were collected
from patients with stage I–II non-small cell lung cancer (NSCLC) who underwent radical surgery between
October 2017 and October 2019 at Saitama Medical University International Medical Center (Hidaka city,
Saitama pref., Japan).
TIL samples for mass cytometry analysis were collected from patients (n = 20, Supplementary Table
1) with stage II-IIIA NSCLC who underwent surgery after 3 courses of neoadjuvant therapy, including
nivolumab and chemotherapy, between August 2023 and October 2024 at Saitama Medical University
International Medical Center. The response to treatment was assessed using Response Evaluation Criteria
in Solid Tumors (RECIST), version 1.1. Pathological response was independently assessed by pathologists.
All the samples were collected after written informed consent was obtained from the patients. The
Internal Review Board of Saitama Medical University International Medical Center approved the study
protocol, in accordance with the Declaration of Helsinki (ethical approval Nos. 17-084, 15-221, and 2023-
011).
Blood samples
Peripheral blood samples were collected using heparinized CPT Vacutainer tubes (Becton Dickinson
Vacutainer Systems, Franklin Lakes, NJ, USA), as previously described27. The samples were preserved
using Cellbanker2 (Nippon Zenyaku Kogyo Co., Koriyama, Japan) in a liquid nitrogen tank. For T-cell
subset analyses, the cells were incubated for 32–48 h in RPMI 1640 medium supplemented with 10% fetal
calf serum (FCS) in a 5% CO incubator at 37 °C.
2
TIL samples
To extract TILs, the collected tumors were sectioned into 2–3-mm pieces and incubated with
Dulbecco's modified Eagle’s medium supplemented with Dri Tissue & Tumor Dissociation Reagent (BD
horizon: 661563; BD Biosciences, Franklin Lakes, NJ, USA) or with RPMI 1640 medium supplemented
with Liberase TL Research Grade (05401020001; Roche, Basel, Switzerland) following the manufacturer’s
instructions. Erythrocytes were removed using a biotin anti-human CD235ab antibody (306617; BioLegend,
San Diego, CA, USA) and streptavidin nanobeads (480016; BioLegend, San Diego, CA, USA) according
to the manufacturer’s instructions. Tissue dissociation and cell preparation were initiated immediately after
surgical resection, and the procedure was advanced to the point where processing could be safely paused
within the same day.
Multiplex immunohistochemical staining, and imaging mass cytometry image acquisition and data
analysis for tissue samples
The tumor samples were formalin fixed and paraffin embedded (FFPE), and the three tumors with the
largest areas of viable tumor cells were selected (Supplementary Table 1). Five-micron-thick sections were
deparaffinized and rehydrated with xylene and ethanol for multiplex immunohistochemical staining. All
the slides were sequentially treated with 0.3% hydrogen peroxide in methanol for 30 min to block
endogenous peroxidase activity. To expose the antigens, the sections were autoclaved in 10 mM sodium
citrate buffer (pH 6.0) for 20 min, heated in a microwave at 98 °C for 15 min, and then cooled for 30 min.
After rinsing in 0.05 M Tris-buffered saline containing 0.1% Tween 20, the sections were incubated
with various antibodies: mouse monoclonal CD4 (Leica Biosystems, clone 4B12, 1:300, high pH, retrieval),
mouse monoclonal CD8 (DAKO, clone C8/144B, 1:150, high pH, retrieval), mouse monoclonal CD31
(DAKO, clone JC70A, 1:400, pH 6, retrieval), rabbit monoclonal CCR6 (Abcam, clone EPR22259, 1:1000,
high pH, retrieval), rat monoclonal peripheral node addressin (Novus, clone MECA79, 1:200, pH 6,
retrieval), mouse monoclonal CXCR3 (BioLegend, clone G025H7, 1:300, pH 6, retrieval), CD20 (Akoya,
clone L26, 1:200, high pH, retrieval), and mouse monoclonal pan cytokeratin (Abcam, clone AE1/AE3,
1:100, pH 6, retrieval). All the slides were stained using an OPAL 7-color IHC kit (Akoya Biosciences
NEL811001KT, MA, USA). Immunofluorescence analysis was performed on the Mantra imaging platform
(Akoya Biosciences) using Mantra Snap software. Color separation was conducted with inForm Software
v2.5.1 (Akoya Biosciences, MAT) to extract image data. Multiplex immunohistochemical staining and data
analysis were performed according to previously described procedures28.
After heat-induced epitope retrieval, the tumor samples were subjected to Hyperion analysis. The
sections were incubated with conjugated monoclonal antibodies overnight at 4 °C (Supplementary Table
5). Following antibody incubation and washing, selected regions of interest (ROIs) were ablated with a
laser at a resolution of 1 µm, and the vaporized particles were analyzed. The acquired data were visualized
and analyzed using MCD Viewer and MCD Smart Viewer software (Standard BioTools). Pseudo-colored
composite images were generated for each marker to assess tissue morphology and marker expression
patterns. All images were reviewed at high resolution, and representative ROIs were selected for figure
preparation.
Cell sorting for scRNA-seq and scTCR-seq
For peripheral blood scRNA-seq analyses, CD3+ T cells were isolated from frozen PBMCs that had
been incubated for 36 hrs in CM with 5% CO at 37 °C, using Dynabeads Untouched Human T Cells
2
(11344D; Invitrogen). For TIL scRNA-seq analysis, samples were used after erythrocyte removal. For
scRNA-seq analysis of sorted Th cohort, CD4+ T cells were isolated as follows. CD62Llow CD4+ cells were
separated from cultured frozen PBMCs using Dynabeads. Untouched Human CD4 T Cells (11346D;
Invitrogen) and Human CD62L MicroBeads (130-091-758; Miltenyi Biotec, Bergisch Gladbach, Germany)
were used. For sorting, CD62LlowCD4+ T cells were stained for 30 min at room temperature with CXCR3
(353715, 353716; BioLegend), CCR4 (353429, 353430; BioLegend), and CCR6 (359410; BioLegend)
antibodies, each diluted 1:40, and subsequently using a Cell Sorter SH800Z (Sony Imaging Products &
Solutions Inc., Tokyo, Japan) prior to single-cell library construction (Supplementary Fig. 10a).
Library construction and sequencing for scRNA-seq and scTCR-seq of surgery cohort and sorted Th
cohort
All single-cell RNA-seq and scTCR-seq libraries were constructed using the Chromium controller and
Chromium Single-Cell Immune Profiling v2 kit (10x Genomics Inc., Pleasanton, CA, USA) to analyze gene
expression differences and T-cell receptor (TCR) repertoires among T-cell subpopulations. Several
TotalSeq-C antibody types (BioLegend Inc., San Diego, CA, USA) were used as feature barcodes to
separate T-cell subpopulations (Supplementary Table 6). All the libraries were constructed according to the
manufacturers’ standard protocols. The quality and quantity of the libraries were evaluated using a
Bioanalyzer High-Sensitivity Kit (Agilent Technologies, CA, USA). All libraries were sequenced on
DNBSEQ-G400 sequencers (MGI, Shenzhen, China) in paired-end mode (read 1:28 bp; read 2:90 bp)
according to the manufacturer’s standard protocol. The sequencing depth was targeted at 50,000 reads/cell
for gene expression libraries and 10,000 reads/cell for feature barcoding and TCR libraries. Details of the
libraries and sequence results are summarized in Supplementary Data 4.
Data preprocessing and quality control for scRNA-seq and scTCR-seq of surgery cohort and sorted
Th cohort
The sequenced reads were processed using Cell Ranger 6 and 7 software (10x Genomics Inc.,
Pleasanton, CA, USA) with the GRCh38 reference dataset (version 2020-A for gene expression and 5.0.0
(samples from surgery cohort) or 7.1.0 (sorted Th cohort) for the TCR repertoire) obtained from 10x
Genomics. This processing generated the gene expression/feature barcode (TotalSeq-C) unique molecular
identifier (UMI) count matrix and TCR repertoire data. Quality control, statistical analysis, and graphical
representation were performed using the Loupe browser (10x Genomics, Inc.) and the Seurat 5.2.1 package
in R software (version 4.4.1) (https://satijalab.org/seurat/)29,30. The gene expression library and feature
barcoding library from the same sample were processed simultaneously using the ‘cellranger count’
command. TCR libraries were processed using the ‘cellranger vdj’ command for each sample. TCR
repertoires from different samples (i.e., TILs and PBMCs) from the same patient were aggregated using the
‘cellranger aggr’ command to identify shared clonotypes across different libraries. Quality control of the
scRNA-seq data was executed as follows. cells with a valid TCR clonotype within the CD3D+ cluster were
selected using the Loupe browser. Genes expressed in fewer than three cells were excluded from the
analysis. Cells with either too few (≤200) or too many (>2,500-3000) expressed genes were excluded to
remove empty or multiple GEMs. Cells with a high percentage of mitochondrial genes (≥5–8%) were also
excluded to eliminate dead cells, and those with excessively high TotalSeq-C counts were removed to
minimize the impact of aggregated antibodies. These cutoff thresholds were set on the basis of the tail of
the distribution. Prior to analysis, the UMI gene expression counts were log-normalized using Seurat's
‘LogNormalize’ function (scale factor = 10,000).
scRNA-seq and scTCR-seq data integration and analysis of CD4+ and CD8+ T cells derived from
surgery cohort
The filtered CD3D+ T cells derived from surgical patients were categorized into CD4+CD3D+ T cells
and CD8+CD3D+ T cells on the basis of the RNA and surface protein expression of CD4 and CD8A prior
to integration (Supplementary Fig. 10b). CD3D⁺ T cells were annotated as CD4⁺ or CD8⁺ at the clonotype
level based on CD4 and CD8A expression in both RNA and surface protein data. For each clonotype, the
total number of CD4⁺ and CD8A⁺ cells (RNA⁺ + protein⁺) was compared, and the clonotype was assigned
according to the predominant signal, using a simple rule-based approach to handle variability across
modalities. The integration of these CD4+ and CD8+ T cells was conducted following the standard
integration workflow of Seurat version 5, utilizing the reciprocal PCA integration method. Initially, 2,000
highly variable genes (excluding TCR-related genes) were identified using the 'FindVariableFeatures'
function with the vst method. The expression of these variable genes was then scaled and centered using
the 'ScaleData' function. Principal component analysis was performed using the 'RunPCA' function,
followed by batch correction among samples using the reciprocal PCA method with the 'IntegrateLayers'
function. Unsupervised clustering of the integrated gene expression matrix was carried out using the
'FindNeighbors' (with the parameter “dims=1:30”) and 'FindClusters' functions of Seurat on the basis of the
shared nearest neighbor modularity optimization clustering algorithm31. The resolution parameter of
'FindClusters' was optimized on the basis of the number of expected clusters and the stability of the
clustering results. Visualization via a UMAP plot was performed with the 'RunUMAP' function.
Differential expression analysis was conducted using the MAST package in R software 32, implemented in the
'FindMarkers' function of the Seurat package. Genes with an adjusted P value < 0.05 and log -fold change
2
> 1 were considered significant and are indicated by red dots on volcano plots drawn with the
EnhancedVolcano package in R33. The RNA velocity was estimated using the velocyto 0.17.17 package
and scVelo 0.2.5 in Python 3.634. The Loom file with UMAP embedding calculated via Seurat analysis was
merged with the Loom file containing RNA velocity data from velocyto, and merged data were filtered and
normalized using the 'scvelo.pp.filter_and_normalize' function (min_shared_counts=5, n_top_genes=2000).
First- and second-order moments were computed for each cell across its nearest neighbors using the
'scvelo.pp.moments' function (n_pcs=30, n_neighbors=100). Finally, RNA velocities were estimated in the
dynamic model using the 'scvelo.tl.recover_dynamics' and 'scvelo.tl.velocity' functions. The estimated
velocity was visualized as a stream plot using the 'scvelo.pl.velocity_embedding_stream' function.
scRNA-seq analysis of sorted Th cohort
Preparation and quality control of scRNA-seq libraries from CD4+ T cells from sorted Th cohort were
performed in a manner similar to that used for PBMCs from surgical specimens, except that feature
barcoding technology with TotalSeq was not utilized. For cells that passed quality control, log-normalized
count data were merged for each patient, and differential expression gene analysis was conducted among
the four cell types, Th1, Th17, CXCR3+ Th7R, and CXCR3- Th7R, on a round-robin basis using the
‘FindMarkers’ function with the MAST method. This statistical test was applied to genes that were
expressed in more than 10% of the cells and exhibited a minimum log -fold change of 0.25. Genes with an
2
adjusted P value < 0.05 were considered differentially expressed. The 64 DEGs common to all three patients
were selected as candidate signature genes (Supplementary Data 1).
To identify CD4 signature genes and construct a predictive model for CD4 cell subtypes, we employed
support vector machines (SVMs). To mitigate the influence of variability in RNA expression at the single-
cell level, we created mini-clusters by averaging the gene expression profiles of 10 randomly sampled cells.
For each sorted population—Th1, Th7R, and Th17—we generated 4,000 mini-clusters and used them for
downstream analysis.
Feature selection was conducted using the 'rfeControl' function in the caret R package in combination
with a radial kernel SVM implemented using the e1071 package. On the basis of this feature selection
process, 58 genes were selected as relevant markers for classification.
By using the expression profiles of these 58 genes, we built an SVM-based classifier to distinguish
among the Th1, Th7R, and Th17 CD4 subtypes. Of the 4,000 mini-clusters generated per subtype, 2,000
were used for training, and the remaining 2,000 were used for testing to evaluate the model’s predictive
performance.
To annotate SELL low-expression clusters, the classifier was trained on all 12,000 mini-clusters (4,000
per subtype) generated from the sorted reference samples.
scRNA-seq analysis of PBMC samples from scRNA-seq cohort 2
PBMC samples were collected prior to treatment from 34 patients with lung cancer. scRNA-seq
libraries were generated using the Chromium Single Cell 5′ Gene Expression platform (10x Genomics).
Due to differences in sample collection time points, two versions of the chemistry were employed:
Chromium Single-Cell Immune Profiling v1.1 and v2 kits. Libraries were constructed and sequenced
according to the manufacturer’s instructions. Sequence data were processed using the Cell Ranger pipeline
(10x Genomics, versions 4.0.0 to 7.1.1) with default settings. Data preprocessing and quality control were
performed using the same workflow as for the other cohorts. Potential batch effects arising from differences
in kit chemistry and Cell Ranger versions were corrected using the standard Seurat integration workflow
described above. A summary of the sequencing results is summarized in Supplementary Data 5.
scRNA-seq analysis of PBMC from healthy Japanese donors derived from public database
To further validate our findings in an independent dataset, we utilized publicly available scRNA-seq
data from the Human Cell Atlas project (https://www.humancellatlas.org/), the Asian Immune Diversity
Atlas (AIDA) 35. We downloaded the AIDA Phase 1 Data Freeze v2 dataset in h5ad format from the
CELLxGENE portal (https://cellxgene.cziscience.com/collections/ced320a1-29f3-47c1-a735-
513c7084d508). Given the large size of the dataset, we restricted our analysis to samples collected in Japan
from self-reported Japanese donors (134 donors). Based on the author-provided annotations, we extracted
84,065 cells classified as CD4+_T, CD4+_T_CM, CD4+_T_cyt, CD4+_T_EM, CD4+_T_naive, and Treg.
Ensembl gene identifiers were converted to HGNC gene symbols (gene_name field) using the Annotation
Hub package (3.14.0) with EnsDb.Hsapiens.v98. The processed raw UMI count data were then imported
into Seurat and analyzed using the standard integration workflow as applied to our other cohorts, ensuring
methodological consistency.
scRNA-seq analysis of PBMC samples from 3 renal cancer patients
Preparation and quality control of scRNA-seq libraries from CD4+ T cells from 3 renal cancer patients were
performed in a manner similar to that used for PBMCs from surgical specimens, except that we applied the
CCA-based integration method in Seurat because the sample number was limited, whereas RPCA was used
for surgical datasets with larger sample sizes. A summary of the sequencing results is summarized in
Supplementary Data 5.
scRNA-seq analysis of PBMC samples from melanoma patients
PBMC-derived CD4 T-cell samples from melanoma patients were obtained by downloading scRNA-seq
count data and scTCR-seq data from four pretreatment (day 0) cases (p134, p145, p196 and p326) in the
NCBI GEO dataset GSE307261 36. Cells with excessively high nFeature_RNA values (> 2,000-2,500) and
excessively high percentage of mitochondrial genes (> 5-8 %) and those lacking valid TCR information
were excluded. The four samples were then integrated and analyzed following the same workflow used for
CD4 T-cell samples from surgical cases.
Single-nucleus multiomic profiling of gene expression and chromatin accessibility analysis
After a two-day culture of thawed frozen cells, CD4 staining was performed, and CD4+ cells were
sorted using a SH800Z cell sorter (Sony). Nuclei were extracted according to the 10x Genomics protocol
(CG000365, Rev C). The nuclear density was assessed by microscopy using the BZ-X700 system
(Keyence). All multiome libraries were constructed using the Chromium controller and Chromium Single
Cell Multiome ATAC + Gene Expression Kit (10x Genomics, Inc.) following the 10x Genomics protocol
(CG000338, Rev F). All multiome libraries were sequenced on DNBSEQ-G400 sequencers (MGI)
according to the manufacturer’s standard protocol.
The sequenced reads were processed using Cell Ranger ARC 2.0.2 software (10x Genomics, Inc.) with
the GRCh38 reference dataset (version 2020-A-2.0.0) obtained from 10x Genomics. Quality control,
statistical analysis, and graphical representation were performed using the Loupe browser (10x Genomics,
Inc.), Seurat 5.2.1, and the Signac1.14 package in R software (version 4.4.1) 29,30,37. Quality control was
executed as follows. Clusters enriched for CD3D expression were identified using the Loupe Browser (10x
Genomics) from 'cloupe' files generated by Cell Ranger ARC and subsequently imported into Seurat for
further analysis. Cells with either too few (≤500) or too many (>2,500) expressed genes were excluded to
remove empty or multiple GEMs. Cells with either too few (≤1500) or too many (>10,000) ATAC counts
were also excluded. Cells with a high percentage of mitochondrial genes (≥15%) were excluded to eliminate
dead cells. Cells with a nucleosome signal that was too high (> 2) or a TSS enrichment score that was too
low (≤1) were excluded, as these characteristics are indicative of compromised chromatin structure or
degraded nuclei. Following quality control and normalization, dimensionality reduction was performed
separately for each modality, with principal component analysis (PCA) applied to the RNA data and latent
semantic indexing (LSI) to the ATAC data. Integration of the two modalities was achieved using the
weighted nearest neighbor (WNN) approach, which enables joint clustering and visualization of cellular
states in a unified embedding using the standard Seurat and Signac multiome analysis workflow.
Unsupervised clustering of the integrated data was performed using the 'FindMultiModalNeighbors'
function (reduction.list = list (“pca”, “lsi”), dims.list=list(1:50, 2:50)) and the 'FindClusters' function
(resolution = 1.5). To relate chromatin accessibility to gene expression, gene activity scores were computed
using the 'GeneActivity' function (extend.upstream = 2000, extend.downstream = 0) on the basis of gene
annotations from Ensembl Hsapiens version 98. To visualize chromatin accessibility signals and peak
distributions around specific genes of interest, we generated coverage plots using the 'CoveragePlot'
function implemented in Signac.
Mass cytometry
The monoclonal antibodies used for Helios mass cytometry analysis are listed in Supplementary Table
7. Cell preparation and measurements were performed according to the manufacturer’s instructions for
Helios (Standard BioTools, San Francisco, CA, USA). Briefly, 5.0 × 106 cells were stained with mass
cytometric antibodies. For intracellular staining, samples were prepared using Maxpar Nuclear Antigen
Staining Buffer working solution prior to staining. After being washed twice with Maxpar Cell Staining
Buffer, the samples were fixed with Maxpar Fix and Perm Buffer supplemented with 125 nM iridium
nucleic acid intercalator (Standard BioTools). Following fixation, the cells were washed once with Maxpar
cell-staining buffer, twice with Maxpar water, and resuspended in Maxpar water. Over 200,000 cells per
sample were analyzed using Helios and Cytobank (https://www.cytobank.org) software. The gating strategy
is detailed in Supplementary Fig. 10c.
Associations between T-cell subpopulations
Network analysis was performed to evaluate associations between T-cell subpopulations in the
peripheral blood. Network analysis eliminates false (pseudo)correlations due to confounding effects
between multiple variables. In addition, the modified path consistency (PC) algorithm, a modification of
the original path consistency algorithm, was used to address the redundant data that frequently occur in
biological data 38,39. The PC algorithm requires variable selection when calculating partial correlation
coefficients (associations among three or more variables) of the first order or higher. Therefore, the
estimation is repeated several times to reduce the dependence on variable selection, and edges with a high
frequency of occurrence are selected. In the present analysis, the estimation was repeated 1,000 times, and
edges inferred more than 500 times were chosen as thresholds.
Mice
Female C57BL/6J (B6) mice were purchased from CLEA Japan (Tokyo, Japan, JAX stock no. 000664).
They were maintained in a specific pathogen-free environment and used for experiments at 8–12 weeks of
age. Animals were housed with a 12 h light–dark cycle (lights on at 8:00). The temperature was maintained
at 23 °C (21–25 °C) with a relative humidity of 50% (30–70%). All animal experiments were approved by
the Institutional Animal Care and Use Committee of Saitama Medical University (Approval No. 4081 and
4191).
Tumor model
MCA 205 is a fibrosarcoma of B6 origin induced by i.m. injection of 3-methylcholanthrene. The tumor
has been maintained in vivo by serial s.c. transplantation in syngeneic mice and was used within the 9th
transplantation generation. Single cell suspensions were prepared from solid tumors by digestion with a
mixture of 1 mg/ml collagenase type Ⅳ, 50 µg/ml DNaseⅠ, and 100 µg/ml hyaluronidase for 2 hours. The
cells were filtered through a 70-μm cell strainer, washed, and suspended in HBSS for inoculations. B16
melanoma were cultured in complete medium (CM) at 37 °C and 5% CO : RPMI, 10% FBS, 1 % Penicillin-
2
Streptomycin, 1 % Non-Essential Amino Acids, 1 mM Sodium Pyruvate, 2 mM L-Alanyl-L-glutamine,
10mM HEPES. Tumor cells were routinely tested for mycoplasma contamination using MycoStrip tests
(InvivoGen).
T cell sorting and culture
Mice were subcutaneously injected in the lateral flank with 1 × 10⁶ B16 or 2 × 10⁶ MCA205 tumor
cells. On days 10 to 12 after tumor inoculation, inguinal lymph nodes were harvested, mechanically
dissociated using a syringe plunger, and passed through a 40 μm cell strainer to prepare single-cell
suspensions. CD4+ T cells were purified using Dynabeads FlowComp Mouse CD4 (Thermo Fisher). For
sorting, cells were stained for 30 min at room temperature with PE-conjugated anti–mouse CD194 (CCR4)
and Brilliant Violet 421–conjugated anti–mouse CD196 (CCR6) monoclonal antibodies (both at a 1:40
dilution), and APC-conjugated anti–mouse CD183 (CXCR3) monoclonal antibody (1:100 dilution)
(BioLegend). Th7R cells were defined as CXCR3±CCR4−CCR6+ CD4+ T cells, and Th1 cells as
CXCR3+CCR4−CCR6− CD4+ T cells and were sorted using a SONY SH800Z cell sorter (Supplementary
Fig. 11a). Sorted Th1 and Th7R cells were stimulated with Dynabeads Mouse T-Activator CD3/CD28
(Thermo Fisher) and cultured in CM supplemented with IL-2 (15 IU/mL). Lymphocytes were cultured for
7–14 days and used for adoptive cell transfer (ACT).
Adoptive T cell transfer and TIL analysis
MCA205 cells (2 × 106) were inoculated subcutaneously along the abdominal midline of B6 mice. One
day before ACT, cyclophosphamide (50 mg/kg) was administered intraperitoneally as a single dose; on the
following day, cultured Th1 or Th7R cells were injected via the tail vein at the indicated cell numbers. No
blinding was performed. Experimental and control mice were housed under identical SPF conditions and
bred separately. Tumor size was measured on 2–3 days per week with calipers in two orthogonal dimensions,
and tumor area (mm²) was calculated as the product of the two perpendicular diameters. For survival
analysis, the endpoint was defined as tumor area exceeding 100 mm2 or upon the appearance of tumor
ulceration. Euthanasia was carried out by cervical dislocation under inhalation anesthesia with isoflurane.
All experiments were conducted in accordance with institutional guidelines, and the maximum allowed
tumor size or tumor burden was not exceeded in any experiment.
For TIL analyses, MCA205 (2 × 106 cells) were inoculated subcutaneously into the left flank of B6
mice. After the same cyclophosphamide preconditioning, ACT was performed, and tumors were harvested
on day 6 post-ACT. Tumor tissues were digested in RPMI containing Liberase TL (25 µg/mL for MCA205;
100 µg/mL for B16) at 37 °C for 30 min with gentle inversion. Cell suspensions were passed through a 40-
µm cell strainer, enriched by 40/80% Percoll density-gradient centrifugation, and used for mass cytometry
analysis (Supplementary Fig. 11b). The monoclonal antibodies used for mass cytometry analysis are listed
in Supplementary Table 7.
Statistical analyses
GraphPad Prism 10 (GraphPad Software, San Diego, CA, USA) was used for statistical analysis. The
data are presented as the means ± standard errors of the means unless otherwise specified. Differences
between two groups were evaluated using Student’s t test. Comparisons across multiple groups were
conducted using one-way analysis of variance (ANOVA) with Tukey’s post hoc test. Survival curves were
generated using the Kaplan–Meier method, with differences and hazard ratios assessed using the log-rank
(Mantel‒Cox) test. All P values were two-sided, and values < 0.05 were considered to indicate statistical
significance.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "In this manuscript, Dr. Takei and coworkers provide compelling data of the identification of a specific CD4+T cell population (Th7R) in the tumour microenvironment of lung cancer patients that are postulated to support precursor exhausted CD8+ T cells (Tpex) as good predictors of immunotherapy response. Although overall the results are very interesting and convincing, some major concerns arise during the study of the manuscript that from my point of view need to be addressed before I could recommend the publication of the manuscript.\nMajor comments:\n1.- The origin of the data in the multiple analysis results confusing and need to be better clarified. In section 1 of the results (lines 118-136), a group of sorted Tcells is used to generate signatures to differentiate major CD4+ T cell populations. These signatures are used first to identify Tcell clusters identified in scRNA-seq analysis of 5 patients (lines 83-117). Nevertheless, in the second section of the results (lines 152-175), these signatures are used to further characterize “ three additional TIL and PB scRNA-seq analyses”. The origin of this data is not stated in the manuscript. Are they new patients or public data? Additionally, further in this section (lines 152-175), identified Th7R populations of TME and PB are used to determine differential expression, what data is used for this? , Only the new “three additional studies”? Why is not all the data merged to identify more robust differentially expressed genes?\n2.- In the section in where Th7R abundance is associated with treatment response, it is stated that 20 patients have been used. Nevertheless, the characteristics of these patients is not stated. How does response is evaluated in these patients? Have all the patients treated in the same conditions? How many patients are in each response group? Table S8 shows aggregated data on these and don’t offer a lot of information regarding this. Additionally, 20 patients seem a quite small group to determine strong response associated Would it be possible to validate these observations in a larger patient cohort?\n3.- In mouse transplant experiments (lines 241-253), purified Th7R cells are injected in mice to evaluate their potential to generate Tpex numbers. Which is the control group here? According to methods, non-injected mice are used as control group. Nevertheless, non-purified CD4+T cells or any other population should be injected in the control group to properly assess and affirm that the excess of Tpex numbers cannot be a general unspecific response to Tcell injection.\nMinor comments:\n1.- A better methods section is necessary. Number and characteristics of the patients, origins of the data, etc should be clearly stated in the methods section along with the used methodology.\n2.- Some figures are highly overcrowded with redundant information. Figures 1 and 2 particularly should be simplified for clarity. In figure 2, an excess of volcano and violin plots with quite redundant information is overwhelming. In both figures, the same UMAP plots are plotted several times with very similar information. I better effort is necessary to reduce the number of graphs for clarity trying to show different layers of information in the same plot.", "reviewer_2": "This study addresses IL-7RhighCCR6+ Th1-like CD4 T cells (termed Th7R) and their association with progenitor exhausted CD8 T cells (Tpex cells )in tumors, peripheral blood and lymph nodes from NSCLC lung tumor patients using scRNAseq/ scTCRseq, and spatial analyses. Additionally, the authors correlate better response to neoadjuvant PD-1 checkpoint blockade in patients with higher Th7R and Tpex. Some findings are also investigated in a non-lung cancer cell mouse model. Th7R cells have been previously identified and characterized in NSCLC patients by the authors and correlated with response rate to immunotherapy (ref 11) even though mostly in PBMCs, but colocalization with DCs/Tpex in triads or TLCs and correlation with PD-1 response rate has been described before by others (refs. 7-8). More importantly, there are several concerns with data shown resulting in lack of support for the conclusions presented in this study.\nMajor points of criticism:\n(1) Data are descriptive and correlative and often lack details to enable reader to understand analysis or data shown. Small patient numbers for single-cell datasets (and even pooled data was analyzed?)\n(2) Are the authors proposing that Th7R cells are tumor specific? Do they express PD-1, TOX, CXCL13? But then how to reconcile abundance in blood? Results in this study should be analyzed and appropriately discussed with respect to and in the context of other recent studies on this topic.\n(3) Definition of T cell populations is not consistent with studies of other groups and authors do not provide enough evidence to support the proposed annotations.\n(4) Markers used to identify key cell types for spatial analysis are insufficient.\n(5) The mouse experiment in its current form does not add much value to this study.\nAdditional Concerns\n(1) The introduction is very short and needs to be revised for accuracy.\n(2) Initial sentence that Tpex were identified by scRNAseq is incorrect and should be adjusted (original papers had identified those cells in mouse models using flow cytometry based on TCF1/CXCR5 expression, scRNA seq confirmed the unique identify of those cells and their presence in patient tumors). Also, Tpex are a subset of exhausted T cells (NOT effector T cells, cf. line 59) and functionally/phenotypically different from effector T cells.\n(3) “Unlike cytotoxic T lymphocytes (CTLs), which lose lack TCF7 expression”\n(4) “CD4⁺ T-cell help is essential for inducing effective CD8⁺ T-cell responses, including Tpex 6.” This statement is incorrect, and the cited manuscript does not address this question (Tpex are induced without CD4-help).\n(5) “It has been shown that CD4⁺ T-cell-mediated signals, particularly in the presence of antigen-presenting dendritic cells, prevent deep exhaustion of CD8⁺ T cells and promote TCF7 and IL-7 receptor expression 7,8.”, this is also incorrect, CD4⁺ T-cell-mediated signals promote differentiation of Tpex into effector-like cells, not TCF7 and IL7R expression.\n(6) References 11 and 13 are duplicates\n(7) Even though mentioned in the Method section, giving a brief description of what kind of patient samples (lung cancer cell type, location, prior treatment, gender/age of patients etc) at the beginning of the results section would be appreciated. Alternatively, a summarizing table could be referenced. Overall methods should be more detailed. For example, were TILs cryopreserved before scRNA seq analysis? Were the 5 patients pooled together? Or ran separately or barcoded? Number of cells per patient should be provided and not just the total number of CD4 in PBMC and TILs. If samples were pooled how can TCR sharing be appropriately accessed?\n(8) In Fig 1A, “UMAP plots of CD3D+CD4+ cells generated with integrated gene expression data from single-cell RNA sequencing (scRNA-seq) of peripheral blood (PB) and tumor-infiltrating lymphocytes (TILs) derived from 5 stage I-II non-small cell lung cancer patients. All 58,687 CD3D+CD4+ cells (50,608 PB-CD3D+CD4+ cells and 8,079 TIL-CD3D+CD4+ cells)”. Are these data from sorted CD3+ CD4+ cells from PBMC and TIL? According to methods it seems that “For peripheral blood scRNA-seq analyses, CD3+ T cells were isolated from frozen PBMCs that had been incubated for 36 hrs in CM with 5% CO2 at 37 °C, using Dynabeads Untouched Human T Cells (11344D; Invitrogen). For TIL scRNA-seq analysis, samples were used after erythrocyte removal.” Therefore, were the CD4 T cells “enriched” based on CD4 mRNA expression detected in 10x? If the later, CD4 mRNA detection has a very high dropout rate, how did the authors circumvent this important issue?\n(9) Fig1/S1: cluster 18 has the highest CXCL13 expression and CXCL13 has been described as a marker of tumor-specific T cells. Similarly, cluster 9 and 15 have the highest PD-1 expression. TOX is high on cluster 8 (Treg), 9, 12,13 (Treg), 15 and 18. Yet cluster 9, 15 and 18 are left out of the subsequent analysis based on SELL expression.\n(10) Fig. 1E/F: frequency of CD4 T cell should also be provided, since a lot more CD4 T cells from PBMC were sequenced than from TILs. Or data from PBMC should be down sampled. Are some of these clusters more expanded in PBMC than tumor (e.g. 12)? How can the authors know this is not blood contamination in the tissue? Tumor-specific T cells will be much more frequent in the tissue than blood. If these clones are more abundant in blood, then they are not tumor specific. The authors then state “T cells in the PB-19 cluster are likely to change their gene expression patterns to the cluster 16 state after infiltrating the TME, suggesting that clusters 16 and 19 exhibit two gene expression states of one type of differentiated T cell. Accordingly, clusters 16 and 19 were considered a single cluster.” TCR sharing between clusters does not exactly justify grouping them into the same cluster.\n(11) S2D: how about the high similarity of cluster 3,5 and 21 with Th7R?\n(12) Fig. 2A: why is cluster 26 in different positions in the UMAP of PB and TILs? Number 26 seems out of place in TIL UMAP, as it also does not overlap with SELL low area. Also, clusters 3 and 15 are also SELL low.\n(13) Fig. 2A and 1A show similar analysis. 2A even include all data already presented in 1A, just adding 3 patients. Why show 1A then?\n(14) S1 had shown that CXCL13 expression was highest in clusters not highlighted to have low SELL, yet in Fig2, the authors emphasize that Th7R in the TME have higher CXCL13 than Th7R in PB or other SELL low CD4 clusters in the TME, although CXCL13 expression is not shown across all clusters (in S5A). LTB is high in all CD4 clusters, except 13 and 26 (Th1).\n(15) Fig. 3 B, how was the ratio of MECA79 to CD31 as 0.044 chosen?\n(16) “GZMK expression was observed in clusters 1, 2, 3, 4, 8, 10, 11, 13 and 14 adjacent to the SELLhighCD45RA+ naive clusters (6, 12, and 15) and the SELLhighCD45RA- central memory (CM) cluster (7) (Fig. 4C).” How can the authors highlight CD45RA expression status based on scRNAseq?\n(17) “Next, we assessed the expression of signature genes thought to characterize Tpex in each cluster (Fig. 4D) 5.” The genes depicted are not sufficient to characterize Tpex. Published signatures can be included, but Naïve T-cell and and central memory T-cell clusters should be included in Fig. 4D. Add XCL1/XCL2, TIGIT, and CXCL13. Precursor of exhausted CD8 T cells should not be found in PB. Are they clonally expanded and share TCR with more exhausted CD8 subsets?\nAlso, exhausted CD8 T cell states would only constitute a minute fraction of PB, if they are at all detected.\n(18) S7: how were TLS defined?\n(19) Fig 5 legend is very abbreviated and lacks critical information. Yet it seems that it represents analysis by Hyperion and antibodies are listed in Table 9. Without Granz K, B or CXCR3, CCR6 how did the authors annotate the cell types of interest?\n(20) Fig 6. What would happen if another population of antigen experienced CD4 T cells was isolated from tumor draining LNs and transferred? Is Th7R phenotype even maintained after ex vivo expansion? Is 6A data from pooled tumor analysis (no biological replicates)? How were immune cell population defined? CD8 cell numbers/phenotypes and outcome should only be compared between mice of equivalent tumor stage to avoid different antigen loads becoming a driving factor for differences\n(21) In general, figure font sizes for axis labeling are extremely small and, even when fully zoomed in, are hard to read. IF and imaging CYTOF images are too small to really see the indicated cells/structures. Scales need to be included.", "reviewer_3": "The topic is highly relevant and challenging in the context of immune-oncology.\nThe presumed involvement of T CD4 help in the generation and/or maintenance of Tpex is a recent hypothesis emerging from recent articles cited partly by the authors (see Humbling et al Immunity 2025 in press). However, the CD4 partner has not been clearly identified.\nThis study was conducted in a cohort of NSCLC patients undergoing surgery.\nThe methodology approach is robust and state-of-the-art including Sc RNAseq, TCR seq, mass cytometry (Hyperion), and multiplex IHC OPAL technology).\nThe authors have therefore identified both in blood and TME, a new subpopulation called Th7R which is thought to be a \"close cousin\" of Th1 as the partner of Tpex. These Th7R cells express CXCR3± CCR4- CCR6+, TCF7+ and also characterized by LTB CXCR6, CTLA4, and CXCL13 expression. Th7R are found in TLS where they co-localize with Tpex in the TME and lymph nodes. The presence of Th7R and Tpex in TME was associated with better response to neoadjuvant PD-1 blockade therapy. Furthermore, adoptive transfer of Th7R increased Tpex in a MCA-induced sarcoma in mouse.\nThe results are robust although they mainly show correlations, and are therefore often descriptive rather than demonstrative.\nThe article is well written and well discussed.\nHere are my main comments\n- The presence of this Th7R subset should be validated in other human cancers, at least in a second cancer model (in blood, lymph nodes and TIL if available). In addition, the transcriptomic signature associated with this subpopulation should also be investigated in other cancers using TCGA.\n- Th7Rs are found in zone B of TLS. This raises the following question: are Th7R cells different from Tfh? How can the authors differentiate them from Tfh , which also express CXCL13?\n- What STATs are expressed by Th7R cells? Given that they are RORC+, one would expect STAT3 to be expressed.\n- The mouse model is not really conclusive. This adoptive transfer experiment should be replicated using separately, the 3 sub-populations defined by the authors such as CXCR3+ CCR4- CCR6- (Th1 cells); CXCR3- CCR4+CCR6+ (Th17 cells), and CXCR3± CCR4- CCR6+ Th7R cells. It should also be validated in a second tumor model in mice. For example B16-OVA and OTI/OT II cells if possible. I suggest moving this paragraph on mouse experiments in another section after the data on patients.\nMinor points\nLines 186-191: fig. 3A and fig 3B are inversed. Please change in the right sense\nlines 244-249: discrepancy between text in the results section, legend of Fig 6A and method; Please make them harmonious."}, "author_response": "RESPONSE TO REVIEWERS’ COMMENTS\nReviewer #1 (Remarks to the Author):\nMajor comments:\n1.- The origin of the data in the multiple analysis results confusing and need to be better clarified.\nIn section 1 of the results (lines 118-136), a group of sorted Tcells is used to generate signatures\nto differentiate major CD4+ T cell populations. These signatures are used first to identify Tcell\nclusters identified in scRNA-seq analysis of 5 patients (lines 83-117). Nevertheless, in the second\nsection of the results (lines 152-175), these signatures are used to further characterize “ three\nadditional TIL and PB scRNA-seq analyses”. The origin of this data is not stated in the manuscript.\nAre they new patients or public data? Additionally, further in this section (lines 152-175),\nidentified Th7R populations of TME and PB are used to determine differential expression, what\ndata is used for this? , Only the new “three additional studies”? Why is not all the data merged to\nidentify more robust differentially expressed genes?\n→We appreciate your constructive suggestion. Patient background information for all cohorts\nanalyzed is explicitly documented in Supplementary Table 1. In revised manuscript, initial\nanalysis was performed on 8 early-stage stageⅠ-Ⅱ lung cancer patients who underwent surgery\nyielding both TILs derived from lung cancer tissue and peripheral blood, with annotation based\non single-cell RNA sequencing analysis of sorted Th cells (Fig. 1a-f). Furthermore, to\ndemonstrate the robustness of this analysis, annotation was performed using peripheral blood\nscRNA-seq data from independent 34 lung cancer patients with locally advanced- or advanced-\nstage disease (Supplementary Fig. 3a, b), and public scRNA-seq data from 135 healthy\nindividuals (the Human Cell Atlas project, the Asian Immune Diversity Atlas) (Supplementary\nFig. 3e, f). Results showed that the SELLlow effector-type cluster group consistently contained\nclusters exhibiting Th1, Th17, and Th7R gene expression patterns, achieving good prediction\nprobabilities and cosine similarity indices (Supplementary Fig. 3c, d, g, h).\n2.- In the section in where Th7R abundance is associated with treatment response, it is stated that\n20 patients have been used. Nevertheless, the characteristics of these patients is not stated. How\ndoes response is evaluated in these patients? Have all the patients treated in the same conditions?\nHow many patients are in each response group? Supplementary Table8 shows aggregated data on\nthese and don’t offer a lot of information regarding this. Additionally, 20 patients seem a quite\nsmall group to determine strong response associated Would it be possible to validate these\nobservations in a larger patient cohort?\n→In response to the reviewer’s suggestion, we analyzed and added DFS data for stage I-II lung\ncancer (n=55) with an observation period of 5 years or more (Fig. 8A). The patients with high\npreoperative peripheral blood Th7R levels showed significantly better DFS with HR=0.121 (95%\nCI 0.0306-0.479), P = 0.0002. In contrast, Th1 did not demonstrate such DFS predictive\nperformance.\n3.- In mouse transplant experiments (lines 241-253), purified Th7R cells are injected in mice to\nevaluate their potential to generate Tpex numbers. Which is the control group here? According to\nmethods, non-injected mice are used as control group. Nevertheless, non-purified CD4+T cells or\nany other population should be injected in the control group to properly assess and affirm that the\nexcess of Tpex numbers cannot be a general unspecific response to Tcell injection.\n→ As the reviewer suggested, repeated mouse experiments were performed, and figures have\nbeen revised (Fig. 7a-c). Fig. 7a and b demonstrate that Th7R possesses antitumor effects\nequivalent to those of Th1. However, the CD8+ T cells responsible for this antitumor effect differ\nin the tumor microenvironments (Fig. 7c). In Th1-transfered mice, CD8+ T cells with a phenotype\ncorresponding to CTLs (PD-1-GZMB+) were primarily increased. In contrast, in Th7R-transfered\nmice, CD8+ T cells with Tpex-type phenotypes—PD-1+IL-7R+ CD8+ and PD-1+TCF7+ CD8+—\nwere significantly increased.\nMinor comments:\n1.- A better methods section is necessary. Number and characteristics of the patients, origins of\nthe data, etc should be clearly stated in the methods section along with the used methodology.\n→As the reviewer suggested, to avoid confusion, patient background information for all cohorts\nanalyzed is explicitly documented in Supplementary Table 1.\n2.- Some figures are highly overcrowded with redundant information. Figures 1 and 2 particularly\nshould be simplified for clarity. In figure 2, an excess of volcano and violin plots with quite\nredundant information is overwhelming. In both figures, the same UMAP plots are plotted several\ntimes with very similar information. I better effort is necessary to reduce the number of graphs\nfor clarity trying to show different layers of information in the same plot.\n→As the reviewer suggested, we increased the number of figures from six to eight and reduced\nthe amount of information in each individual figure.\nReviewer #2 (Remarks to the Author):\nMajor comments:\n1.- Data are descriptive and correlative and often lack details to enable reader to understand\nanalysis or data shown. Small patient numbers for single-cell datasets (and even pooled data was\nanalyzed?)\n→To avoid confusion in the data, the patient demographics for all analyzed cohorts are explicitly\nlisted in new Supplementary Table1. In the revised manuscript, initial analysis was performed on\n8 early-stage lung cancer patients who underwent surgery yielding both TILs from lung cancer\ntissue and peripheral blood (Fig. 1a-g, Supplementary Fig.1a, b). Each patient's samples were\nprocessed separately, and single-cell libraries were generated individually. Next, we performed\nintegrated analysis using the standard Seurat workflow (Stuart et al., Cell 2019) to correct for\nbatch effects between patients. Therefore, our analysis relies on an established integration\napproach that preserves patient-level information, rather than simply pooling all cells. As a result,\na total of 205,260 cells were analyzed.\nTo demonstrate the robustness of this analysis, additionally we performed CD4+ T cells using\nperipheral blood scRNA-seq data from 34 patients, including locally advanced and advanced lung\ncancers (Supplementary Fig.3a, b), and peripheral blood scRNA-seq data from 135 healthy\nindividuals obtained from public data (the Human Cell Atlas project\n(https://www.humancellatlas.org/), the Asian Immune Diversity Atlas (AIDA)) (Fig. 3e, f).\nResults showed that the SELLlow effector-type cluster group consistently contained clusters\nexhibiting Th1, Th17, and Th7R gene expression patterns, achieving good prediction probabilities\nand cosine similarity indices using 58 signature genes (Supplementary Fig.3c, d, g, h).\n2.- Are the authors proposing that Th7R cells are tumor specific? Do they express PD-1, TOX,\nCXCL13? But then how to reconcile abundance in blood? Results in this study should be analyzed\nand appropriately discussed with respect to and in the context of other recent studies on this topic.\n→Annotation based on 58 signature gene expression using publicly available data from healthy\nindividuals revealed that a certain number of Th7R cells are present even in healthy individuals\n(Supplementary Fig.3). Therefore, it is unlikely that all Th7R cells are tumor-specific. However,\nin lung cancer surgical cases, Th7R cells were the only CD4+ T cells that significantly decreased\nafter lung cancer tissue resection (Fig. 8b). This suggests that the CD4+ T cells most increased by\nthe presence of cancer are Th7R. Furthermore, our prior analysis showed that the proportion of\nTh7R cells was highest in the lymph node closest to the lung cancer #1. These results suggest that\nTh7R cells exhibit dynamics consistent with the cancer immunity cycle theory; they proliferate\nin tumor-draining lymph nodes driven by lung cancer-associated antigens, migrate into the\nperipheral blood, and infiltrate the tumor. We believe that Th7R is induced together with Tpex in\nsituations requiring sustained T cell immunity, such as in cancer and chronic infections.\nAs shown in Fig.2a, Supplementary Fig.1b, and Supplementary Table 4, Th7R expresses PD-\n1, TOX, and CXCL13.\n#1 Yanagihara, A., et al. A Th1-like CD4(+) T-cell Cluster That Predicts Disease-free Survival in\nEarly-stage Lung Cancer. Cancer Res Commun3, 1277-1285 (2023).\n3.- Definition of T cell populations is not consistent with studies of other groups and authors do\nnot provide enough evidence to support the proposed annotations.\n→ We appreciate the reviewer’s comment regarding the annotation of T cell subsets. We agree\nthat the definition of T cell populations in single-cell studies is not always uniform across different\ngroups, reflecting both biological heterogeneity and methodological differences. Nevertheless,\nthe use of low expression of SELL (CD62L) as an indicator of effector T cell differentiation is a\nwell-established and widely accepted criterion in T cell immunology. Furthermore, within effector\nCD4+ T cells, the classification of CXCR3⁺CCR4⁻CCR6⁻ cells as Th1 and CXCR3⁻CCR4⁺CCR6⁺\ncells as Th17 is also widely recognized and has been consistently adopted in both bulk and single-\ncell studies #2. By contrast, the remaining effector CD4+ T-cell populations that do not fit into\nthese canonical Th1 or Th17 categories are the subject of ongoing debate, with multiple studies\nproposing novel subsets or intermediate states. In this context, our annotation strategy reflects one\nreasonable and literature-based interpretation of these heterogeneous populations. A similar\nsituation applies to CD8+ T cells, where definitions of subsets are also variable and continue to\nevolve. However, in the present manuscript, our conclusions regarding CD8+ T cell populations\nare not intended to make any overreaching or novel claims, but rather to provide descriptive\nannotations consistent with widely used markers. Importantly, our interpretation remains aligned\nwith the current consensus in the field, and we have refrained from proposing nonstandard or\nspeculative classifications.\nTo further support the validity of our CD4+ T cell annotations, we additionally analyzed PBMC\nsamples from 34 independent patients as well as PBMC datasets derived from Japanese healthy\ndonors available in a public database (Supplementary Fig.3a-h). Both analyses yielded results\nconsistent with our original findings, thereby reinforcing the robustness of our annotation strategy.\n#2 Sallusto, F. Heterogeneity of Human CD4(+) T Cells Against Microbes. Annu Rev Immunol\n34, 317-334 (2016).\n4.- Markers used to identify key cell types for spatial analysis are insufficient.\n→For Hyperion analysis, we employed the Maxpar Human Tumor-infiltrating Lymphocytes IMC\nPanel Kit, Maxpar Human Tissue Architecture IMC Panel Kit, and MaxPar IMC Cell\nSegmentation Kit, recommended by Standard Biotool. For Th7R and Tpex detection, tissue\nstaining was performed using anti-CXCR3, CCR6, GZMB, and GZMK antibodies\n(Supplementary Table8). Annotation for known cell types was performed based on staining\npatterns according to the manufacturer's instructions.\n5.- The mouse experiment in its current form does not add much value to this study.\n→As the reviewer suggested, repeated mouse experiments were conducted, and the figures were\nrevised (Fig. 7a-c). Th7R and Th1 cells isolated by sorting from lymph nodes draining MCA205\nskin tumors were i.v. infused into MCA205 skin tumor-bearing mice after ex vivo culture (each\ngroup contains 4 mice). Th7R cells exhibited antitumor therapeutic efficacy comparable to Th1\ncells (Fig. 7a), but induced distinct CD8⁺ T-cell phenotypes in the tumor microenvironment (Fig.\n7c) (each group contains 4 mice). Th1 transfer increased PD-1⁻GZMB⁺ cytotoxic CD8⁺ T cells,\nwhereas Th7R transfer resulted in significant increase of PD-1⁺IL-7R⁺ and PD-1⁺TCF7⁺Tpex-\nlike CD8⁺ T cells. Moreover, Th7R cells derived from melanoma-draining lymph nodes did not\nmediate therapeutic effects against MCA205 tumors, demonstratingtumor antigen\nspecificity(Fig. 7b) (each group contains 5 mice).\nAdditional Concerns\n1.- The introduction is very short and needs to be revised for accuracy.\n→As the reviewer suggested, we have revised the Introduction (P.3 line40-62).\n2.- Initial sentence that Tpex were identified by scRNA-seq is incorrect and should be adjusted\n(original papers had identified those cells in mouse models using flow cytometry based on\nTCF1/CXCR5 expression, scRNA seq confirmed the unique identify of those cells and their\npresence in patient tumors). Also, Tpex are a subset of exhausted T cells (NOT effector T cells,\ncf. line 59) and functionally/phenotypically different from effector T cells.\n→Based on the reviewer's suggestion, we have revised the opening paragraph of the\nintroduction as follows (P.3, lines 40-48). We have also cited the original paper (ref. 1-3).\nPrecursor-exhausted CD8+ T cells (Tpex) were first reported in a chronic viral infection\nmodel as programmed cell death-1 (PD-1)-expressing exhausted T cells that also express CXCR5\nand TCF7, possess self-renewal capacity, and play a critical role in controlling persistent viral\ninfections 1-3. Tpex function as the starting point for the proliferation burst of CD8+ T cells induced\nby PD-1 blockade therapy 2.Single-cell RNA sequencing of tumor tissue revealed that Tpex are\na subset of stem-like memory T cells with self-renewal capacity, sustaining long-term antitumor\nimmunity in the tumor microenvironment (TME) while providing GZMB+ exhausted CD8+ T\ncells4,5.Tpex infiltrate TME via high-endothelial venules (HEVs), and localize within tertiary\nlymphoid structures (TLS) 6,7.\n1. He, R., et al. Follicular CXCR5- expressing CD8(+) T cells curtail chronic viral infection.\nNature537, 412-428 (2016).\n2. Im, S.J., et al. Defining CD8+ T cells that provide the proliferative burst after PD-1\ntherapy. Nature537, 417-421 (2016).\n3. Utzschneider, D.T., et al. T Cell Factor 1-Expressing Memory-like CD8(+) T Cells\nSustain the Immune Response to Chronic Viral Infections. Immunity45, 415-427 (2016).\n4. Kallies, A., Zehn, D. & Utzschneider, D.T. Precursor exhausted T cells: key to successful\nimmunotherapy? Nat Rev Immunol20, 128-136 (2020).\n5. Liu, B., et al. Temporal single-cell tracing reveals clonal revival and expansion of\nprecursor exhausted T cells during anti-PD-1 therapy in lung cancer. Nat Cancer3, 108-\n121 (2022).\n6. Im, S.J., et al. Characteristics and anatomic location of PD-1(+)TCF1(+) stem-like CD8\nT cells in chronic viral infection and cancer. Proc Natl Acad Sci U S A120, e2221985120\n(2023).\n7. Gebhardt, T., Park, S.L. & Parish, I.A. Stem-like exhausted and memory CD8(+) T cells\nin cancer. Nat Rev Cancer23, 780-798 (2023).\n3.- “Unlike cytotoxic T lymphocytes (CTLs), which lose lack TCF7 expression”\n→Our data showed that the GZMB+GZMK- CD8+ T cell cluster (#5 cluster in Fig. 4a, d),\nconsidered to be effector CTL, did not express TCF7 or IL-7 receptor. This result is consistent\nwith previous reports#3,#4.\n#3 Kallies, A., Zehn, D. & Utzschneider, D.T. Precursor exhausted T cells: key to successful\nimmunotherapy? Nat Rev Immunol20, 128-136 (2020).\n#4 Williams, M.A. & Bevan, M.J. Effector and memory CTL differentiation. Annu Rev Immunol\n25, 171-192 (2007).\n4.- “CD4⁺ T-cell help is essential for inducing effective CD8⁺ T-cell responses, including Tpex 6.”\nThis statement is incorrect, and the cited manuscript does not address this question (Tpex are\ninduced without CD4-help).\n→As the reviewer pointed out, the presence of CD4+ T cells involved in Tpex induction and\nmaintenance has not been clearly established. On the other hand, it has been demonstrated that\ndendritic cells that have taken up cancer antigens require licensing from CD4+ T cells to present\nantigens to CD8+ T cells. This study was conducted to resolve this question.\nTherefore, we have revised the second paragraph of the Introduction as follows (P. 3 line 49-62).\nFerris et al. reported that dendritic cells that have taken up tumor antigens first present\nthese antigens to CD4⁺ T cells. Dendritic cells that have been licensed through interaction with\nCD4⁺ T cells can then induce the proliferation of CD8⁺ T cells 8. Primed CD8+ T cells also\nrequire antigen presentation from dendritic cells licensed by CD4+ T cells to acquire cytotoxic\nfunction9. Thus, the helper function of CD4+ T cells is essential for the priming, proliferation,\nand induction of cytotoxic function in CD8+ T cells. However, the role of CD4+ T cells in the\ngeneration and maintenance of Tpex has not been addressed in detail. Recently, it has been\nreported that CD8⁺ T cells exhausted by repeated antigen stimulation can undergo epigenetic\nchanges through the formation of a triad—a direct contact between CD4⁺ T cells, dendritic\ncells, and CD8⁺ T cells—leading to the expression of IL-7 receptor and TCF7, which are\ncharacteristic of Tpex 10. CD4⁺ T cells are also involved in the formation of ectopic HEVs,\nwhich are essential for Tpex to infiltrate the tumor microenvironment 11. Although CD4+ T\ncells seem to play an important role in situation that require persistent immune responses, such\nas cancer, the specific CD4⁺ T cell subset that assists Tpex remains poorly defined.\n8. Ferris, S.T., et al. cDC1 prime and are licensed by CD4(+) T cells to induce anti-tumour\nimmunity. Nature584, 624-629 (2020).\n9. Tomita, Y., et al. Identification of promiscuous KIF20A long peptides bearing both CD4+\nand CD8+ T-cell epitopes: KIF20A-specific CD4+ T-cell immunity in patients with\nmalignant tumor. Clin Cancer Res19, 4508-4520 (2013).\n10. Espinosa-Carrasco, G., et al. Intratumoral immune triads are required for\nimmunotherapy-mediated elimination of solid tumors. Cancer Cell42, 1202-1216 e1208\n(2024).\n11. Asrir, A., et al. Tumor-associated high endothelial venules mediate lymphocyte entry into\ntumors and predict response to PD-1 plus CTLA-4 combination immunotherapy. Cancer\nCell40, 318-334 e319 (2022).\n5.- “It has been shown that CD4⁺ T-cell-mediated signals, particularly in the presence of antigen-\npresenting dendritic cells, prevent deep exhaustion of CD8⁺ T cells and promote TCF7 and IL-7\nreceptor expression 7,8.”, this is also incorrect, CD4⁺ T-cell-mediated signals promote\ndifferentiation of Tpex into effector-like cells, not TCF7 and IL7R expression.\n→As reviewers suggest, CD4+ T cell signaling may drive Tpex toward effector-like cell\nproliferation. Our experiments also demonstrate that Th7R transplantation increases GZMB+ Tex-\nlike cells in the TME (Fig. 7b). However, this is accompanied by Tpex-type cell proliferation.\nThe enhanced expression of the IL-7 receptor and TCF7 gene via triad formation is thought to\nsimultaneously promote a self-replicating mechanism that prevents loss of Tpex and the\nproliferation of GZMB+ effector-type CD8+ T cells. Otherwise, it would be difficult to maintain\npersistent T-cell responses against chronic viral infections or cancer.\n6.- References 11 and 13 are duplicates\n→Thank you for pointing it out. We have corrected.\n7.- Even though mentioned in the Method section, giving a brief description of what kind of\npatient samples (lung cancer cell type, location, prior treatment, gender/age of patients etc) at the\nbeginning of the results section would be appreciated. Alternatively, a summarizing table could\nbe referenced. Overall methods should be more detailed. For example, were TILs cryopreserved\nbefore scRNA seq analysis? Were the 5 patients pooled together? Or ran separately or barcoded?\nNumber of cells per patient should be provided and not just the total number of CD4 in PBMC\nand TILs. If samples were pooled how can TCR sharing be appropriately accessed?\n→As the reviewer suggested, we explicitly listed the patient demographics for all analyzed\ncohorts in Supplementary Table1 and added short description the manuscript (P. 4 line 80-81).\nWe would like to clarify that each patient sample was processed independently, and single-cell\nlibraries were generated separately using the 10x Genomics barcoding system, which assigns\nunique molecular identifiers to each cell. Therefore, samples were never pooled prior to library\npreparation, and TCR clonotypes can be accurately assigned to each patient. Tumor-infiltrating\nlymphocytes (TILs) samples were processed on the day of surgery. Tissue dissociation and cell\npreparation were initiated immediately after surgical resection, and the procedure was advanced\nto the point where processing could be safely paused within the same day. These details have now\nbeen explicitly described in the Methods section. Per-sample cell numbers were included in\nSupplementary Table10, which serves as a summarizing table for all analyzed cohorts. We\nbelieve these additions provide the necessary details to fully understand our experimental design\nand analysis.\n8.- In Fig 1A, “UMAP plots of CD3D+CD4+ cells generated with integrated gene expression data\nfrom single-cell RNA sequencing (scRNA-seq) of peripheral blood (PB) and tumor-infiltrating\nlymphocytes (TILs) derived from 5 stage I-II non-small cell lung cancer patients. All 58,687\nCD3D+CD4+ cells (50,608 PB-CD3D+CD4+ cells and 8,079 TIL-CD3D+CD4+ cells)”. Are\nthese data from sorted CD3+ CD4+ cells from PBMC and TIL? According to methods it seems\nthat “For peripheral blood scRNA-seq analyses, CD3+ T cells were isolated from frozen PBMCs\nthat had been incubated for 36 hrs in CM with 5% CO2 at 37 °C, using Dynabeads Untouched\nHuman T Cells (11344D; Invitrogen). For TIL scRNA-seq analysis, samples were used after\nerythrocyte removal.”\nTherefore, were the CD4 T cells “enriched” based on CD4 mRNA expression detected in 10x?\nIf the later, CD4 mRNA detection has a very high dropout rate, how did the authors circumvent this\nimportant issue?\n→ As described in method section, “CD3D+ T cells derived from surgical patients were\ncategorized into CD4+CD3D+ T cells and CD8+CD3D+ T cells based on the RNA and surface\nprotein expression of CD4 and CD8A prior to integration”. In addition, to further demonstrate\nthis point, we have now included Supplementary Fig.9d, which shows the expression levels of\nCD4 and CD8A surface proteins measured by TotalSeq-C at the single-cell level in one\nrepresentative sample. Furthermore, CD3D⁺ T cells were annotated as CD4⁺ or CD8⁺ at the\nclonotype level based on CD4 and CD8A expression in both RNA and surface protein data. For\neach clonotype, the total number of CD4⁺ and CD8A⁺ cells (RNA⁺ + protein⁺) was compared, and\nthe clonotype was assigned according to the predominant signal, using a simple rule-based\napproach to handle variability across modalities. These details have now been explicitly described\nin the Methods section.\n9.- Fig1/S1: cluster 18 has the highest CXCL13 expression and CXCL13 has been described as a\nmarker of tumor-specific T cells. Similarly, cluster 9 and 15 have the highest PD-1 expression.\nTOX is high on cluster 8 (Treg), 9, 12,13 (Treg), 15 and 18. Yet cluster 9, 15 and 18 are left out\nof the subsequent analysis based on SELL expression.\n→ We appreciate the reviewer’s observation regarding the expression of CXCL13, PDCD1, and\nTOX across several clusters. While it is indeed possible to detect the expression of such markers\nin subsets of SELLhigh cells, these likely reflect transitional or heterogeneous states. As our study\nis focused on SELLlow effector populations, we did not pursue these further. Future studies will be\nneeded to fully characterize these SELLhigh populations, which may provide additional insights\ninto tumor-reactive or exhausted T cell states.\n10.- Fig. 1E/F: frequency of CD4 T cell should also be provided, since a lot more CD4 T cells\nfrom PBMC were sequenced than from TILs. Or data from PBMC should be down sampled. Are\nsome of these clusters more expanded in PBMC than tumor (e.g. 12)? How can the authors know\nthis is not blood contamination in the tissue? Tumor-specific T cells will be much more frequent\nin the tissue than blood. If these clones are more abundant in blood, then they are not tumor\nspecific. The authors then state “T cells in the PB-19 cluster are likely to change their gene\nexpression patterns to the cluster 16 state after infiltrating the TME, suggesting that clusters 16\nand 19 exhibit two gene expression states of one type of differentiated T cell. Accordingly, clusters\n16 and 19 were considered a single cluster.” TCR sharing between clusters does not exactly justify\njustifying grouping them into the same cluster.\n→ Frequency: Frequency of CD4+ T cells per cluster was summarized in Supplementary Table2.\nWe would like to clarify that this figure does not display the overall frequency of CD4+ T cells in\nPBMC versus TILs. Rather, Fig. 1e in previous submission and revised Fig. 1f shows the\nfrequencies of PBMC cells that share the same clonotypes as multicellular clonotypes observed\nin TILs. The purpose of this analysis was to track tumor-infiltrating clonotypes in the peripheral\nblood, not to compare absolute CD4+ T cell proportions between PBMC and TILs. For this reason,\ndown-sampling of PBMC or presentation of total CD4+ frequencies is not directly relevant to the\ninterpretation of this figure.\nBlood contamination: Prior to analysis, adherent blood and vessels were removed from tumor\ntissue samples as much as possible. However, it is widely recognized that the preparation of\ntumor-infiltrating lymphocytes (TILs) inevitably contains a certain proportion of circulating T\ncells present within tumor vasculature. This does not, however, prevent meaningful analysis.\nIndeed, peripheral blood analysis data and TIL analysis data from the same patient show\nsignificant differences in cell frequency within each cluster and the degree of clonal expansion.\nWe believe this reflects characteristics observed in TIL that are not seen in peripheral blood.\nclusters more expanded in PBMC than tumor: #13 cluster (#12 in the previous submission)\ncorresponds to canonical Th1 cells, which are indeed more abundant in PBMC than in TILs\n(Fig.1f). In PBMC, we observed evidence of large-scale clonal expansion within this cluster,\nwhereas in TILs such expansion was not detected. This pattern suggests that many of the cluster12\ntype cells that clonally expanded in the lymph nodes are circulating in the blood toward targets\nother than the tumor, such as viral infection. In contrast, cluster 18 (#16 in the previous\nsubmission) was found to harbor more expanded TCR clonotypes within the tumor than in\nperipheral blood, suggesting it possesses the features of tumor-specific T cells as noted by the\nreviewer.\nclusters 16 and 19: We did not intend to suggest that clusters 16 and 19 (clusters 18 and 21 in\nthe revised manuscript) were forcibly merged into a single cluster at the computational level.\nRather, our point was that both clusters independently emerged from the analysis but represent\nthe same biological subtype, namely Th7R. Accordingly, our statement that “clusters 16 and 19\nwere considered a single cluster” referred to their biological interpretation, not to the clustering\nresult itself.\n11.- S2D: how about the high similarity of cluster 3,5 and 21 with Th7R?\n→Thank you for pointing it out. These clusters represent middle-range SELL expression, which\nare clearly distinct from SELLhigh naïve / central memory T cells, and from the SELLlow effector T\ncells used to train our SVM classifier. In the initial submission, we included them in the analysis\nbecause of their intermediate position toward the SELLlow state; however, this created unnecessary\nambiguity. In the revised version, we have restricted the classifier analysis strictly to SELLlow\npopulations, which were the intended scope from the beginning. Therefore, any apparent\nsimilarity of such clusters (#3, 5, and 21 in the previous version) to Th7R in the classifier output\nreflects extrapolation outside the training domain and should not be interpreted as evidence of\nbiological identity.\n12.- Fig. 2A: why is cluster 26 in different positions in the UMAP of PB and TILs? Number 26\nseems out of place in TIL UMAP, as it also does not overlap with SELL low area. Also, clusters\n3 and 15 are also SELL low.\n→ We would like to clarify that the seemingly “out of place” position of cluster 26 in the TIL\nUMAP arises from the very small number of cells assigned to this cluster (7 cells in TILs). It is\nwell known that clusters composed of very few cells can be positioned variably in low-\ndimensional embeddings such as UMAP, without implying a biological discrepancy. Therefore,\nthe unusual placement of cluster 26 in the TIL UMAP should be interpreted as a technical artifact\nof dimensionality reduction due to limited cell numbers, rather than a true biological difference\nfrom the PBMC map.\nThe expression levels of SELL in clusters 3 and 15, especially in PB, were higher than those in\nSELLlow clusters (Fig.1b, Supplementary Fig.1). As noted above, our revised analysis strategy\nfocuses on clearly defined SELLlow effector subsets, and therefore not included in the downstream\ninterpretation to avoid ambiguity arising from transitional states.\n13.- Fig. 2A and 1A show similar analysis. 2A even include all data already presented in 1A,\njust adding 3 patients. Why show 1A then?\n→Thank you for the suggestion. In the original submission, Fig. 1A was based on the 5 patients\ninitially available, and Fig. 2A expanded the analysis after an additional 3 patients were obtained,\nresulting in a total of 8. This sequential presentation reflected the timeline of data acquisition,\nbut we agree that it led to redundancy between the figures. In the revised version, all analyses\nhave been re-performed on the complete set of 8 patients from the outset, and the figures have\nbeen updated accordingly to avoid duplication (Fig. 1a-f). To further support the validity of our\nCD4+ T cell annotations, we additionally analyzed PBMC samples from independent 34 lung cancer\npatients including locally advanced and advanced disease and PBMC datasets derived from\nhealthy donors available in a public database (Human Cell Atlas,Asian Immune Diversity\nAtlas)(Supplementary Fig.3a-h). Both analyses yielded results consistent with our original\nfindings, thereby reinforcing the robustness of our annotation strategy.\n14.- S1 had shown that CXCL13 expression was highest in clusters not highlighted to have low\nSELL, yet in Fig2, the authors emphasize that Th7R in the TME have higher CXCL13 than Th7R\nin PB or other SELL low CD4 clusters in the TME, although CXCL13 expression is not shown\nacross all clusters (in S5A). LTB is high in all CD4 clusters, except 13 and 26 (Th1).\n→The purpose of Fig. 2 is to highlight differential expression analysis about Th7R cells,\ncomparing TIL versus PBMC, and also comparing Th7R against other clusters within TILs. By\ncontrast, Supplementary Fig.1 already provides the violin plots of CXCL13 (and other marker\ngenes) across all CD4 clusters, showing that CXCL13 expression can be detected in multiple\nsubsets. Supplementary Fig.5A, on the other hand, displays the 58 signature genes used for our\nSVM classifier. This classifier was trained on PBMC data, in which CXCL13 expression is nearly\nabsent, and therefore CXCL13 was not included among the classifier genes.\n15.- Fig. 3 B, how was the ratio of MECA79 to CD31 as 0.044 chosen?\n→ The threshold of the ratio of MECA79 to CD31 was determined using the value that yielded\nthe highest likelihood ratio in ROC analysis for distinguishing recurrent patients from non-\nrecurrent patients (P.7 line205-P. 8 line207, Fig1. for the reviewer).\nFig. 1 for the reviewer\n16.- “GZMK expression was observed in clusters 1, 2, 3, 4, 8, 10, 11, 13 and 14 adjacent to the\nSELLhighCD45RA+ naive clusters (6, 12, and 15) and the SELLhighCD45RA- central memory\n(CM) cluster (7) (Fig. 4C).” How can the authors highlight CD45RA expression status based on\nscRNAseq?\n→As the reviewer pointed out, CD45RA expression status cannot be obtained by scRNA-seq.\nWe would like to clarify that CD45RA status was determined based on surface protein measurements.\nSpecifically, CD45RA protein expression was directly assessed using the 10x Genomics Feature\nBarcoding technology (TotalSeq-C antibodies). To address this oversight, we have now added\nSupplementary Fig.6c, which shows the distribution of CD45RA protein expression across the\nrelevant clusters.\n17.- “Next, we assessed the expression of signature genes thought to characterize Tpex in each\ncluster (Fig. 4D) 5.” The genes depicted are not sufficient to characterize Tpex. Published\nsignatures can be included, but Naïve T-cell and central memory T-cell clusters should be included\nin Fig. 4D. Add XCL1/XCL2, TIGIT, and CXCL13. Precursor of exhausted CD8 T cells should\nnot be found in PB. Are they clonally expanded and share TCR with more exhausted CD8 subsets?\nAlso, exhausted CD8 T cell states would only constitute a minute fraction of PB, if they are at all\ndetected.\n→ We thank the reviewer for these comments. In response to the reviewer’s suggestion, we have\nadded XCL1, XCL2, TIGIT, and CXCL13 to Fig. 4d, as well as naïve and central memory clusters\nfor reference. These additions are consistent with our conclusions and do not change our\ninterpretation that TIL CD8 subsets can be reasonably classified into CTL (GZMB⁺GZMK⁻),\nTpex (GZMB⁻GZMK⁺), Tex (GZMB⁺GZMK⁺), and other minor subsets (GZMB⁻GZMK⁻). As a\nresult of this updated comparison, cluster #3 showed stronger similarity to the naïve/central\nmemory group than to the effector CD8 group, and was therefore reannotated as CM. This\nrevision does not alter the overall interpretation of the data.\nIn Fig. 4f, we performed RNA velocity analysis and observed a trajectory from cluster 2 toward\ncluster 1 within TILs. To further support this finding with clonotype information, we have now\nadded a supplementary figure (Supplementary Fig.6d, e) showing the distribution of cells across\nTIL clusters that belong to clonotypes found in TIL-#2. This analysis demonstrates that cluster 2\nand cluster 1 are strongly interconnected at the clonotype level, with extensive sharing between\nthese two clusters, whereas other clusters contribute little or not at all. These findings reinforce\nour interpretation that clusters 2 and 1 represent two transcriptional states within the same\ndifferentiation lineage.\n18.- S7: how were TLS defined?\n→ Hematoxylin and eosin staining is known to enable the detection of both mature TLS with a\ngerminal center and immature TLS as lymphocytic aggregates lacking a germinal center#5. In\nthis study, TLS were identified using H&E staining on serial sections prior to performing\nHyperion and IHC analyses.\n#5 Sautes-Fridman, C., Petitprez, F., Calderaro, J. & Fridman, W.H. Tertiary lymphoid\nstructures in the era of cancer immunotherapy. Nat Rev Cancer19, 307-325 (2019).\n19.- Fig 5 legend is very abbreviated and lacks critical information. Yet it seems that it represents\nanalysis by Hyperion and antibodies are listed in Table 9. Without Granz K, B or CXCR3, CCR6\nhow did the authors annotate the cell types of interest?\n→ As the reviewer suggested, the legend for the Hyperion analysis diagram has been corrected\nas follows.\na-c Lung cancer tissues were stained with hematoxylin and eosin, revealing lymphocyte\naggregates presumed to be TLS. Antibody staining was performed centered on this region of\ninterest (ROI). Using the Maxpar Human Tumor-infiltrating Lymphocytes IMC Panel Kit,\nMaxpar Human Tissue Architecture IMC Panel Kit, and MaxPar IMC Cell Segmentation Kit\npurchased from Standard BioTools, single-cell segmentation and annotation via\nHyperionTManalysis were performed according to the manufacturer's instructions (a, b)\n(Supplementary Table 8). For Th7R and Tpex detection, tissues were also stained with anti-\nCXCR3, CCR6, GZMB, and GZMK antibodies. In this analysis, the CXCR3+CCR6+ CD4+\nCD3+cluster was considered Th7R, and the GZMKhighGZMBlow CD8+ CD3+cluster was\nconsidered Tpex. Red circles indicate TLS (b). Spatial relationship analysis between\nannotated cells (including Tpex cells and Th7R cells) is shown (c).\nAdditionally, CXCR3, CCR6, GZMB, and GZMK were missing from the table of antibodies\nused, so we have added the antibody information (Supplementary Table8).\n20.- Fig 6. What would happen if another population of antigen experienced CD4 T cells was\nisolated from tumor draining LNs and transferred? Is Th7R phenotype even maintained after ex\nvivo expansion? Is 6A data from pooled tumor analysis (no biological replicates)? How were\nimmune cell population defined? CD8 cell numbers/phenotypes and outcome should only be\ncompared between mice of equivalent tumor stage to avoid different antigen loads becoming a\ndriving factor for differences\n→ As the reviewer suggested, we repeated animal experiments (Fig. 7). Fig. 7ademonstrates that\nTh7R possesses antitumor effects equivalent to those of Th1. However, the CD8+T cells\nresponsible for this antitumor effect differ in the tumor microenvironments (Fig. 7c). In Th1-transfered mice, CD8+T cells with a phenotype corresponding to CTLs (PD-1-GZMB+) were\nprimarily increased. In contrast, in Th7R-transfered mice, CD8+T cells with Tpex-type\nphenotypes—PD-1+IL-7R+CD8+and PD-1+TCF7+CD8+—were significantly increased. As\nshown in Fig. 7b, tumor regression was observed in MCA205 skin tumors treated with the Th7R\nderived from MCA205 tumor-draining lymph nodes but not with the Th7R derived from B6\nmelanoma-draining lymph nodes. Thus, it is likely that Th7R cells mediate antitumor reactivity\nin an antigen-specific manner.\nAfter sorting Th7R, Th1, and Th17 cells based on their CXCR3, CCR4, and CCR6 expression\npatterns, they were stimulated with anti-CD3/CD28 beads and cultured in the presence of IL-2.\nFollowing more than tenfold proliferation, flow cytometry analysis was performed. Th cells\nundergo changes in the expression patterns of several chemokine receptors during culture; CCR4\nis upregulated in all proliferating CD4+ T cells #3. In our culture experiments, CD4+ T cells sorted\nas CXCR3+CCR4-CCR6- Th1 cells expressed CCR4 after culture, but CCR6 expression remained\nat 14.1%. No CXCR3-positive cells emerged from the CXCR3-CCR4+CCR6+ Th17 cells. In\ncontrast, CXCR3+CCR4-CCR6+ Th7R cells remained CXCR3-positive and maintained 89.5%\nCCR6 positivity. Thus, the Th7R phenotype was considered to be preserved after culture (Fig. 2\nfor the reviewer).\n#3 Sallusto, F., et al. Switch in chemokine receptor expression upon TCR stimulation reveals\nnovel homing potential for recently activated T cells. Eur J Immunol29, 2037-2045 (1999).\nFig. 2 for the reviewer\n21.- In general, figure font sizes for axis labeling are extremely small and, even when fully\nzoomed in, are hard to read. IF and imaging CYTOF images are too small to really see the\nindicated cells/structures. Scales need to be included.\n→We apologize that the IF and Hyperion images were small and low-resolution. In the initial\nsubmission, we were required to paste all figures into the Word file, which resulted in the low-\nresolution figures. As the reviewer requested, we have added scales to the tissue images.\nReviewer #3 (Remarks to the Author):\nHere are my main comments\n- The presence of this Th7R subset should be validated in other human cancers, at least in a second\ncancer model (in blood, lymph nodes and TIL if available). In addition, the transcriptomic\nsignature associated with this subpopulation should also be investigated in other cancers using\nTCGA.\n→In the revised manuscript, initial analysis was performed on 8 early-stage lung cancer patients\nwho underwent surgery yielding both TILs from lung cancer tissue and peripheral blood (Fig. 1a-\ng, Supplementary Fig.1a, b). To demonstrate the robustness of this analysis, additionally we\nperformed CD4+ T cells using peripheral blood scRNA-seq data from 34 patients, including\nlocally advanced and advanced lung cancers (Supplementary Fig.3a, b), and peripheral blood\nscRNA-seq data from 135 healthy individuals obtained from public data (the Human Cell Atlas\nproject (https://www.humancellatlas.org/), specifically the Asian Immune Diversity Atlas\n(AIDA)) (Fig. 3e, f). Results showed that the SELLlow effector-type cluster group consistently\ncontained clusters exhibiting Th1, Th17, and Th7R gene expression patterns, achieving good\nprediction probabilities and cosine similarity indices using 58 signature genes (Supplementary\nFig.3c, d, g, h). While Th7R was present in healthy individuals, the only CD4+ T cell cluster\nsignificantly decreased after surgical resection of cancer was Th7R (Fig. 8b). Therefore, Th7R\nwas suggested to be induced by cancer antigens.\nAs the reviewer suggested, we analyzed data obtained from three renal cell carcinoma (RCC)\npatients (Fig. for reviewer 1). Consistent with the analyses of the initial eight stage I–II lung\ncancer patients (Fig. 1), the independent cohort of 34 stage III–IV lung cancer patients\n(Supplementary Fig. 3a–d), and 135 healthy individuals from public datasets (Supplementary Fig.\n3e–h), unsupervised clustering of total CD4⁺ T cells in the RCC samples identified a distinct\ncluster exhibiting the Th7R-type 58-gene signature. This cluster was clearly separated from Th1-\nand Th17-type clusters and showed high cosine similarity with sorted Th7R cells.\nThe reason we did not include RCC analysis in the current revised manuscript was that results\nfrom healthy individual data from human cell Atlas-AIDA demonstrated the presence of Th7R\ncells even in non-cancerous states, indicating the broader universality. Moreover, the independent\nvalidation using 34 stage III–IV lung cancer patients provided stronger robustness than the small\nRCC cohort (n = 3). Nevertheless, we would be pleased to include the RCC analysis results as\nadditional supplementary material should the reviewers consider it appropriate.\nFig. for reviewer 1\n- Th7Rs are found in zone B of TLS. This raises the following question: are Th7R cells different\nfrom Tfh? How can the authors differentiate them from Tfh , which also express CXCL13?\n→Thank you for the suggestion. We examined the distribution of BCL6, CXCR5, and IL-21—\ntranscription factors, chemokine receptors, and cytokines characteristic of Tfh—on the UMAP\nderived from TIL scRNA-seq analysis (Fig. for reviewer 2). This revealed cells highly expressing\nCXCR5 within the SELLhigh cluster group (in the middle of the UMAP). However, we could not\nidentify specific clusters highly expressing BCL6 and IL-21 genes. The Th7R cluster (#18+21)\nexpressed IL-21 to some extent, but its expression was not particularly high compared to other\nclusters. For CXCR5 and BCL6, we observed only relatively low gene expression. These results\nindicated that typical Tfh cells present in secondary lymph nodes are not found in tumor tissues,\nand that Th7R cells do not share properties with Tfh cells beyond CXCL13 expression.\nFig. for the reviewer 2\n- What STATs are expressed by Th7R cells? Given that they are RORC+, one would expect STAT3\nto be expressed.\n→As the reviewer suggested, we re-analyzed the data. STAT3 gene expression was observed in\nthe Th7R cluster (#18, #21), suggesting higher expression in the TME than in peripheral blood\n(Fig. 2a, b, Supplementary Table5). However, no particularly high trend was observed\ncompared to the Th1 cluster (#13, #26) or the Th17 cluster (#14) (Supplementary Fig.1a, b,\nSupplementary Table6). Volcano plot analysis between Th cell types also failed to reveal\nsignificantly different STAT gene expression levels (Fig. 2c, Supplementary Fig.5b). We\nspeculate that this may be because gene expression does not directly reflect protein levels or\nphosphorylation levels.\n- The mouse model is not really conclusive. This adoptive transfer experiment should be\nreplicated using separately, the 3 sub-populations defined by the authors such as CXCR3+ CCR4-\nCCR6- (Th1 cells); CXCR3- CCR4+CCR6+ (Th17 cells), and CXCR3± CCR4- CCR6+ Th7R\ncells. It should also be validated in a second tumor model in mice. For example B16-OVA and\nOTI/OT II cells if possible. I suggest moving this paragraph on mouse experiments in another\nsection after the data on patients.\n→As the reviewer suggested, repeated mouse experiments were conducted, and the figures were\nrevised in Fig. 7a-c. Th7R and Th1 cells isolated by sorting from lymph nodes draining MCA205\nskin tumors were i.v. infused into MCA205 skin tumor-bearing mice after ex vivo culture (each\ngroup contains 4 mice). Th7R cells exhibited antitumor therapeutic efficacy comparable to Th1\ncells (Fig. 7a), but induced distinct CD8⁺ T-cell phenotypes in the tumor microenvironment (Fig.\n7c) (each group contains 4 mice). Th1 transfer increased PD-1⁻GZMB⁺ cytotoxic CD8⁺ T cells,\nwhereas Th7R transfer resulted in significant increase of PD-1⁺IL-7R⁺ and PD-1⁺TCF7⁺Tpex-\nlike CD8⁺ T cells. Moreover, Th7R cells derived from melanoma-draining lymph nodes did not\nmediate therapeutic effects against MCA205 tumors, demonstratingtumor antigen\nspecificity(Fig. 7b) (each group contains 5 mice).\nMinor points\nLines 186-191: fig. 3A and fig 3B are inversed. Please change in the right sense\nlines 244-249: discrepancy between text in the results section, legend of Fig 6A and method;\nPlease make them harmonious.\n→Thank you for pointing it out. I have made the correction."}, {"round": 2, "reviewer_comments": {"reviewer_1": "I consider that most of my concerns have been correctly addressed in the current version of the manuscript:\n1.- The details of the patients used for the analysis have been singificantly clarified in the current version.\n2.- A better description of the methods is included in the manuscript.\n3.- The new experiments with mice clarify my original doubts about the conclussions extracted from the authors.\n4.- Figures are much clearer now.\nMinor points:\n1.-I huge amount of methodologic information is included in the figure legends. One clear example is the legend of Figure 7 that is overcrowded with methodological information that is already included in the methods section. All figure legends should be reviewed in order to point out what is clearly shown in the figure and what are the observations made whithout repeating information from the manuscript method section.\n2.- It is not completely clear to me yet what is the conclussion of Figure 7. Th7R lympocytes improve anti-tumoral response versus what? all lympocytes? PBS injection? no treatment at all? If the authors claim that the presence of Th7R lymphocytes increase anti-tumoral response I still think that a non-purified general lymphocyte injection is necessary. For the new experiments, the authors can claim that the fact that purified Th7R lymphocytes from other tumour type doesn't increase antitumoral behaviour (Th7R from B6 Cells) but I think that this should be better stated in the respective section of the manuscript and in the figure legend.", "reviewer_2": "Takei et al resubmitted an improved study.\nWith regards to the major criticisms raised:\n(1) The authors have clarified origin of the data and analysis performed.\n(2) While I appreciate the author’s attempt to improve the murine data, the new results were unable to confirm that Th7R are better than other CD4 subsets. The authors claim that even though both Th7R and Th1 control tumors, the quality of the CD8 T cell response is different. However, this is not supported by the data: there are no significant differences between CD8 T cells in mice that received Th1 or Th7R. “adoptive transfer of Th7R increased Tpex in a mouse model” should be removed from abstract. In my view, these experiments do not add much value to this study. The extensive in vitro expansion may have altered CD4 phenotypes.\n(3) I am still concerned with the authors’ definitions of T cell populations and markers used to identify key cell types.\nCD4: there have been multiple reports that CXCL13+ CD4 T cells are the tumor-specific cells in patients. The authors cannot ignore “SELLhigh cells” without properly showing that PD-1hi Tox+ CXCL13+ CD4 with features of chronic antigen stimulation are only among SELLlow. For example, cluster 10, which seems to also be highly clonally expanded has high CXCL13, TOX. With the rich data the authors present, it seems unreasonable to narrow down analysis based on a single marker to be able to match data to previous publications from this group on analysis of PBMC. At a minimum, the authors should show that TCR sharing of Th7R cells in PBMC with all CD4 clusters in TILS. Is it really restricted to the SELL low? As the authors pointed out, cells can change gene expression depending on the environment (PBMC -> TME), the TCR would have been a better strategy to identify the Th7R in the TME.I understand SELL-low is an established marker of effector cells, yet there are still expanded clonotypes in clusters that were not identified as SELL low by the authors, so it is clearly not capturing all non-naïve cells. Maybe some of SELL+ clusters with expanded clonotype are memory cells (cluster 15?), but the authors need to clarify this, showing their gene signature and abundance in PBMC vs TME to justify focus on SELL low clusters\nCD8: For example, identification of clusters of Tpex in Fig 4d and definition as Gzmb- Gzmk+. Tpex may be among these clusters, but it is very unlikely that all Gzmb- Gzmk+ are Tpex. Many studies have described PD-1+ Gzmk+ CD8 T cells as a more effector-like phenotype and not Tpex. According to reference 5 “GZMK+NR4A2–/+ cells shared the highest similarities with intermediate Tex cells defined by Beltra et al.16 and transitory Tex cells by Hudson et al.15. GZMK+NR4A2–/+ cells also showed high similarity with Texprog2 cells defined by Beltra et al., although GZMK+NR4A2– cells had stronger enrichment in comparison with GZMK+NR4A2+ cells.” I understand there is no real consensus in the field, but based on the gene expression show here (low XCL1/XCL2), the clusters named as Tpex by the authors seem more like intermediate Tex cells. In figure 5, TCF-1 (and PD-1) detection need to be used to identify Tpex. Maybe the authors just need to recognize that their Tpex definition may also include intermediate states between Tpex and differentiated Tex.\nAdditional points:\nThere are still overstatements that need to be corrected:\nAbstract “The OPTIMAL priming, expansion, and function of CD8⁺ T cells depend on CD4⁺ T-cell help via dendritic cells.\nINTRO “Thus, the helper function of CD4+ T cells is essential for OPTIMAL priming, proliferation, and induction of cytotoxic function in CD8+ T cells.”", "reviewer_3": "Please find my comments below my previous questions and a new major comment\n- The presence of this Th7R subset should be validated in other human cancers, at least in a second cancer model (in blood, lymph nodes and TIL if available). In addition, the transcriptomic signature associated with this subpopulation should also be investigated in other cancers using TCGA.\n→In the revised manuscript, initial analysis was performed on 8 early-stage lung cancer patients who underwent surgery yielding both TILs from lung cancer tissue and peripheral blood (Fig. 1a-g, Supplementary Fig.1a, b). To demonstrate the robustness of this analysis, additionally we performed CD4+ T cells using peripheral blood scRNA-seq data from 34 patients, including locally advanced and advanced lung cancers (Supplementary Fig.3a, b), and peripheral blood scRNA-seq data from 135 healthy individuals obtained from public data (the Human Cell Atlas project (https://www.humancellatlas.org/), specifically the Asian Immune Diversity Atlas (AIDA)) (Fig. 3e, f). Results showed that the SELLlow effector-type cluster group consistently contained clusters exhibiting Th1, Th17, and Th7R gene expression patterns, achieving good prediction probabilities and cosine similarity indices using 58 signature genes (Supplementary Fig.3c, d, g, h). While Th7R was present in healthy individuals, the only CD4+ T cell cluster significantly decreased after surgical resection of cancer was Th7R (Fig. 8b). Therefore, Th7R was suggested to be induced by cancer antigens.\nReviewer #3: these additional data address the issue\nAs the reviewer suggested, we analyzed data obtained from three renal cell carcinoma (RCC) patients (Fig. for reviewer 1). Consistent with the analyses of the initial eight stage I–II lung cancer patients (Fig. 1), the independent cohort of 34 stage III–IV lung cancer patients (Supplementary Fig. 3a–d), and 135 healthy individuals from public datasets (Supplementary Fig. 3e–h), unsupervised clustering of total CD4⁺ T cells in the RCC samples identified a distinct cluster exhibiting the Th7R-type 58-gene signature. This cluster was clearly separated from Th1- and Th17-type clusters and showed high cosine similarity with sorted Th7R cells.\nThe reason we did not include RCC analysis in the current revised manuscript was that results from healthy individual data from human cell Atlas-AIDA demonstrated the presence of Th7R cells even in non-cancerous states, indicating the broader universality. Moreover, the independent validation using 34 stage III–IV lung cancer patients provided stronger robustness than the small RCC cohort (n = 3). Nevertheless, we would be pleased to include the RCC analysis results as additional supplementary material should the reviewers consider it appropriate.\nReviewer #3 : Of course, if you found this Th7R subpopulation in RCC, you should mention this in the article as supplementary data even if the number of patients is small. Otherwise, you should adjust the title of the article to specify ‘in non small cell lung cancer’.\n- Th7Rs are found in zone B of TLS. This raises the following question: are Th7R cells different from Tfh? How can the authors differentiate them from Tfh , which also express CXCL13?\n→Thank you for the suggestion. We examined the distribution of BCL6, CXCR5, and IL-21—transcription factors, chemokine receptors, and cytokines characteristic of Tfh—on the UMAP derived from TIL scRNA-seq analysis (Fig. for reviewer 2). This revealed cells highly expressing CXCR5 within the SELLhigh cluster group (in the middle of the UMAP). However, we could not identify specific clusters highly expressing BCL6 and IL-21 genes. The Th7R cluster (#18+21) expressed IL-21 to some extent, but its expression was not particularly high compared to other clusters. For CXCR5 and BCL6, we observed only relatively low gene expression. These results indicated that typical Tfh cells present in secondary lymph nodes are not found in tumor tissues, and that Th7R cells do not share properties with Tfh cells beyond CXCL13 expression.\nReviewer #3 : the suggested response is appropriate\n- What STATs are expressed by Th7R cells? Given that they are RORC+, one would expect STAT3 to be expressed.\n→As the reviewer suggested, we re-analyzed the data. STAT3 gene expression was observed in the Th7R cluster (#18, #21), suggesting higher expression in the TME than in peripheral blood (Fig. 2a, b, Supplementary Table5). However, no particularly high trend was observed compared to the Th1 cluster (#13, #26) or the Th17 cluster (#14) (Supplementary Fig.1a, b, Supplementary Table6). Volcano plot analysis between Th cell types also failed to reveal significantly different STAT gene expression levels (Fig. 2c, Supplementary Fig.5b). We speculate that this may be because gene expression does not directly reflect protein levels or phosphorylation levels.\nReviewer #3 : I agree\n- The mouse model is not really conclusive. This adoptive transfer experiment should be replicated using separately, the 3 sub-populations defined by the authors such as CXCR3+ CCR4- CCR6- (Th1 cells); CXCR3- CCR4+CCR6+ (Th17 cells), and CXCR3± CCR4- CCR6+ Th7R cells. It should also be validated in a second tumor model in mice. For example B16-OVA and OTI/OT II cells if possible. I suggest moving this paragraph on mouse experiments in another section after the data on patients.\n→As the reviewer suggested, repeated mouse experiments were conducted, and the figures were revised in Fig. 7a-c. Th7R and Th1 cells isolated by sorting from lymph nodes draining MCA205 skin tumors were i.v. infused into MCA205 skin tumor-bearing mice after ex vivo culture (each group contains 4 mice). Th7R cells exhibited antitumor therapeutic efficacy comparable to Th1 cells (Fig. 7a), but induced distinct CD8⁺ T-cell phenotypes in the tumor microenvironment (Fig. 7c) (each group contains 4 mice). Th1 transfer increased PD-1⁻GZMB⁺ cytotoxic CD8⁺ T cells, whereas Th7R transfer resulted in significant increase of PD-1⁺IL-7R⁺ and PD-1⁺TCF7⁺ Tpex-like CD8⁺ T cells. Moreover, Th7R cells derived from melanoma-draining lymph nodes did not mediate therapeutic effects against MCA205 tumors, demonstrating tumor antigen specificity (Fig. 7b) (each group contains 5 mice).\nReviewer #3 : these results are more consistent and reinforce the relevance of the article\nAdditional new comments from reviewer 3 :\nThe authors did not mention a major article on this field.\nIndeed, Myriam Merad's group reported in Nat Med 2023 that response to immune checkpoint inhibitor (ICI) in hepatocell carcinoma correlated with the clonal expansion of intratumoral CXCL13+CH25H+IL-21+PD-1+CD4+ T helper cells (“CXCL13+ TH”) and Granzyme K+ PD-1+ effector-like CD8+ T cells, whereas terminally exhausted CD39hiTOXhiPD-1hiCD8+ T cells dominated in nonresponders. PD-1+TCF-1+ (Progenitor-exhausted) CD8+ T cells shared clones mainly with effector-like cells in responders or terminally exhausted cells in nonresponders. Notably the authors also showed that progenitor CD8+ T cells interact with CXCL13+ TH within cellular triads around dendritic cells enriched in maturation and regulatory molecules, or “mregDC”. These results suggest that CXCL13+ TH control the differentiation of tumor-specific Progenitor exhasuted CD8+ T cells following ICI (Magen & Hamon et al. Nat Med 2023).\nThis article should be cited in the introduction section and discussed in the context of the present manuscript.\nIn my view, the CXCL13+ TH population shares phenotypic, functional and topographical similarities with Th7R. How can the authors differentiate between these two subpopulations of CD4 T cells( their own and that identified by Merad's group)?"}, "author_response": "RESPONSE TO REVIEWERS’ COMMENTS\nReviewer #1:\nMinor points:\n1.-I huge amount of methodologic information is included in the figure legends. One clear\nexample is the legend of Figure 7 that is overcrowded with methodological information that is\nalready included in the methods section. All figure legends should be reviewed in order to point\nout what is clearly shown in the figure and what are the observations made whithout\nrepeating information from the manuscript method section.\n→As the reviewer suggested, we have corrected the figure legends (Fig. 3, 5, 6, 7).\n2.- It is not completely clear to me yet what is the conclusion of Figure 7. Th7R lympocytes\nimprove anti-tumoral response versus what? all lympocytes? PBS injection? no treatment at all?\nIf the authors claim that the presence of Th7R lymphocytes increase anti-tumoral response I still\nthink that a non-purified general lymphocyte injection is necessary. For the new experiments,\nthe authors can claim that the fact that purified Th7R lymphocytes from other tumour type\ndoesn't increase antitumoral behaviour (Th7R from B16 Cells) but I think that this should be\nbetter stated in the respective section of the manuscript and in the figure legend.\n→Thank you for the reviewer’s important comments regarding the mouse experimental results.\nWe have previously reported that adoptive transfer of all T cells derived from MCA205 tumor-\ndraining lymph nodes does not yield sufficient antitumor effects 1. While it was known that\nCD62Llow CD4+ T cells contained cells responsible for antitumor effects, their Th type remained\nunclear 2,3.\nNumerous past mouse experiments have demonstrated that Th1 cells play the central role in\ncellular immunity among CD4+ T cells. Thus, we consider the experimental results showing that\nTh7R alone mediated antitumor effects equivalent to those of Th1, compared to the untreated\ncontrol, to be highly significant.\nDifferences were observed in the CD8+ T cells that increased within the tumor following each\nACT. Th7R primarily increased Tpex, whereas Th1 increased fresh CTL. To illustrate this clearly,\nthe Tpex/CTL ratio is shown in Fig. 7d. This result suggests that Th1 activate the cancer immunity\ncycle, represented by the 7-step model, while Th7R activates the sub-cycle within the updated\ncancer immunity cycle, specifically the cycle maintaining the intratumoral CD8+ T cells.\nFurthermore, as suggested by the reviewer, the antitumor effect of Th7R is antigen-specific, and\nwe have added this description to the main text (p10, line 287-291). We have also added\nsupplementary data showing that Th7R increased Tpex within tumors in a tumor antigen-specific\nmanner in this experiment (Supplementary Fig. 9).\np10, line 287-291\nThe effect of increased Tpex in the tumor microenvironment induced by Th7R adoptive transfer\nwas not observed in Th7R derived from LNs draining B16 melanoma, but was only observed in\nTh7R derived from LNs draining MCA205. This suggests the effect occurs in an antigen-specific\nmanner (Supplementary Fig. 9a, b).\n1. Kagamu, H., Touhalisky, J.E., Plautz, G.E., Krauss, J.C. & Shu, S. Isolation based on L-\nselectin expression of immune effector T cells derived from tumor-draining lymph nodes.\nCancer Res 56, 4338-4342 (1996).\n2. Kagamu, H. & Shu, S. Purification of L-selectin(low) cells promotes the generation of\nhighly potent CD4 antitumor effector T lymphocytes. J Immunol 160, 3444-3452 (1998).\n3. Hiura, T., et al. Both regulatory T cells and antitumor effector T cells are primed in the\nsame draining lymph nodes during tumor progression. J Immunol 175, 5058-5066 (2005).\nReviewer 2\n（2）\n→We appreciate the reviewer's comments regarding the mouse experimental results.\nTo clarify the difference in CD8+ T cells within the TME following Th7R administration, we\nadded the Tpex/CTL ratio (Fig. 7d). We also added TIL analysis results obtained from an antigen-\nspecificity experiment, which is an independent experiment (Supplementary Figure 9a, b). A\nsignificant increase in the proportion of PD-1+IL-7R+CD8+ T cells within TILs compared to\nuntreated mice was observed only in mice administered Th7R from lymph nodes draining\nMCA205, but not with Th7R from lymph nodes draining B16 melanoma. The statement in the\nabstract the reviewer pointed out, “Adoptive transfer of Th7R increased Tpex in the mouse\nmodel,” directly reflects these two consistent experimental results.\nOn the other hand, as the reviewer pointed out, the statement “Both Th7R and Th1 control tumors,\nbut the quality of the CD8+ T cell response differs” was exaggerated and has been deleted.\n（3）\n→As the reviewer suggested, we present data analyzing TCR sharing between the Th7R cluster\nin peripheral blood and TIL-clusters, along with a 58-signature gene expression heatmaps for all\nclusters in both peripheral blood and TILs (Fig. 1 for the reviewer #2, Supplemental Fig. 2e).\nThe TCR clonotype belonging to the peripheral blood Th7R cluster showed the highest sharing\nwith the same Th7R clusters in TIL. Meanwhile, a certain degree of TCR sharing was also\nobserved with clusters #3 and #7 in the SELL+ group. Clusters #3 and #7 are predicted as the\nTh7R type by the SVM classifier. This indicates that, aside from their SELL expression, these\nclusters share a transcriptional pattern similar to that of Th7R. This suggests, as the reviewer\npointed out, that after SELLlow Th7R infiltrated the tumor, it may have re-expressed SELL.\nThe significance of SELL- versus SELL+ lies in whether they possess the homing property to SLO.\nTherefore, I agree with the reviewer's point that SELL+ T cells can exist in an activated state with\neffector function. On the other hand, the analysis in this study aims to characterize the CD4+ T-\ncell clusters migrating to the TME rather than SLO. Regarding the 58 genes identified via machine\nlearning to characterize Th7R, since these were derived from Th1, Th17, and Th7R populations\npurified from CD62L (SELL)low CD4+ T cells, we consider their application to SELL+ cell\npopulations to be an over-extrapolation. Therefore, rather than denying the presence of anti-tumor\nimmune-competent CD4+ T cells in the SELL+ group, we aim to demonstrate that a distinct CD4+\nT cell population called Th7R, differing from Th1 and Th17, exists among SELLlow T cells\nmigrating toward the TME. This population infiltrates the TME, is located near Tpex within TLS,\ncorrelates numerically with Tpex, and plays a role in anti-tumor immunity. To clarify the\nlimitation of this study, we have added a statement (p14, line384-394).\np14, line384-394\nThis study has a limitation regarding the annotation of SELL⁺ CD4⁺ T cells. Our analyses\nprimarily focused on SELLlow CD4⁺ T cells that home toward the tumor site according to the\ncancer-immunity cycle framework. Because the 58 signature genes were derived from scRNA-\nseq analyses of Th1, Th17, and Th7R cells sorted specifically from CD62Llow CD4⁺ T cells,\napplying this signature to annotate effector-type CD4⁺ T cells within the SELL⁺ cluster may lead\nto overinterpretation. Most SELL⁺ CD4⁺ T cells exhibited low expression of functional effector\nmolecules and limited clonal expansion, consistent with naïve or central memory phenotypes.\nAlthough expression patterns of the 58-gene signature suggested that clusters #3, and #7 may\ncorrespond to Th7R-type cells, we consider such annotation within the SELL⁺ compartment to be\ninappropriate with the current data. A more dedicated analysis focusing on potential effector-type\nSELL⁺ CD4⁺ T cells will be required in future studies.\nAs the Reviewer pointed out, cluster #10 expresses TOX,\nPDCD1, and CXCL13 in the tumor microenvironment\nwhile also expressing SELL. However, within the TME,\nwe detected TCR clonotypes consisting of at most five\ncells, with 94.5% being singletons (Fig. 2 for Reviewer\n#2), indicating minimal clonal expansion. In addition,\ncluster #10 lacks expression of chemokine receptors\ntypically associated with Th1-, Th17-, or Tfh/Th7R-type\ncells—namely CXCR5, CXCR3, and CCR6—and instead\nexpresses only CCR4. Based on the classical\nclassification by Sallusto et al., this chemokine receptor\nprofile is most consistent with a Th2 phenotype 1.\nI agree with the reviewer's perspective that the entire GZMK+GZMB-CD8+ T cell cluster does\nnot represent a homogeneous Tpex population. On the other hand, the concept of precursor\nexhausted CD8+ T cells can be considered as a population of pre-exhausted cells expressing\nTCF7, IL-7R, etc., and retaining self-renewal capacity, which gradually\ntransitions towards terminally exhausted CD8+ T cells that undergo\nexhaustion and cell death. Reference 5 also demonstrates that among\nGZMK+ CD8+ T cells, there exist populations expressing NR4A2 and\nthose not expressing it. NR4A2- cells are more stem cell-like than\nNR4A2+. Within this context, they termed NR4A2- GZMK+ CD8+ T\ncells as Texp1 and NR4A2+ GZMK+ CD8+ T cells as Texp2,\ndemonstrating that continuous changes occur even within the population considered precursor\nexhausted CD8+ T cells. To clarify the terminology used in this paper, we refer to\nGZMK+GZMB- CD8+ T cell clusters as GZMK+GZMB- Tpex (p10, line 287-291). Consistent\nwith the reference #5, among the clusters included in GZMK+GZMB- Tpex, cluster #10 and #14\nshowed low NR4A2 expression, while NR4A2 expression was observed in clusters #2 (Figure\n3 for Reviewer #2).\np8, line 215-220\nGZMK+GZMB- clusters 2, 10, and 14 expressed TCF7, IL-7R, PDCD1, SELL, and CCR7,\nmatching the characteristics for Tpex. Tpex is known to undergo asymmetric division, containing\na population that remains in a more stem-like state and a population acquiring effector functions.\nAlthough the GZMK+GZMB- cluster comprises multiple sub-clusters, the entire cluster will be\nreferred to as GZMK+GZMB- Tpex in this study.\nAdditional points:\nAbstract “The OPTIMAL priming, expansion, and function of CD8⁺ T cells depend on CD4⁺ T-\ncell help via dendritic cells.\nINTRO “Thus, the helper function of CD4+ T cells is essential for OPTIMAL priming,\nproliferation, and induction of cytotoxic function in CD8+ T cells.”\n→As the reviewer suggested, the sentences have been corrected according to the references 2-5\n(p2, line 27-28, p3 line 53-54).\np2, line 27-28\nAbstract “The priming, expansion, and function of CD8⁺ T cells are helped by CD4⁺ T cells via\ndendritic cells”.\np3 line 53-54\nINTRO “Thus, the helper function of CD4+ T cells is considered to give license for priming,\nproliferation, and induction of cytotoxic function in CD8+ T cells”.\n1. Sallusto, F. Heterogeneity of Human CD4(+) T Cells Against Microbes. Annu Rev\nImmunol 34, 317-334 (2016).\n2. Melief, C.J. \"License to kill\" reflects joint action of CD4 and CD8 T cells. Clin Cancer Res\n19, 4295-4296 (2013).\n3. Ferris, S.T., et al. cDC1 prime and are licensed by CD4(+) T cells to induce anti-tumour\nimmunity. Nature 584, 624-629 (2020).\n4. Magen, A., et al. Intratumoral dendritic cell-CD4(+) T helper cell niches enable CD8(+)\nT cell differentiation following PD-1 blockade in hepatocellular carcinoma. Nat Med 29,\n1389-1399 (2023).\n5. Espinosa-Carrasco, G., et al. Intratumoral immune triads are required for\nimmunotherapy-mediated elimination of solid tumors. Cancer Cell 42, 1202-1216 e1208\n(2024).\nReviewer #3\nOf course, if you found this Th7R subpopulation in RCC, you should mention this in the article\nas supplementary data even if the number of patients is small. Otherwise, you should adjust the\ntitle of the article to specify ‘in non small cell lung cancer’.\n→As suggested by the reviewer, we have added the results of signature gene expression analysis\nusing scRNA-seq data from RCC patients to the supplementary Fig. 3. Additionally, following\nthe editors' recommendation, we have also added scRNA-seq analysis data from melanoma\npatients. Across the entire cohort, we identified clusters corresponding to Th1, Th17, and Th7R\ntypes within the SELLlowCD4+ T cell population according to the 58 signature gene expression\npatterns.\nAdditional new comments from reviewer 3 :\nThe authors did not mention a major article on this field.\nIndeed, Myriam Merad's group reported in Nat Med 2023 that response to immune checkpoint\ninhibitor (ICI) in hepatocell carcinoma correlated with the clonal expansion of intratumoral\nCXCL13+CH25H+IL-21+PD-1+CD4+ T helper cells (“CXCL13+ TH”) and Granzyme K+ PD-\n1+ effector-like CD8+ T cells, whereas terminally exhausted CD39hiTOXhiPD-1hiCD8+ T cells\ndominated in nonresponders. PD-1+TCF-1+ (Progenitor-exhausted) CD8+ T cells shared clones\nmainly with effector-like cells in responders or terminally exhausted cells in nonresponders.\nNotably the authors also showed that progenitor CD8+ T cells interact with CXCL13+ TH within\ncellular triads around dendritic cells enriched in maturation and regulatory molecules, or\n“mregDC”. These results suggest that CXCL13+ TH control the differentiation of tumor-specific\nProgenitor exhasuted CD8+ T cells following ICI (Magen & Hamon et al. Nat Med 2023).\nThis article should be cited in the introduction section and discussed in the context of the present\nmanuscript.\n→We appreciate the reviewer's important suggestion. We have incorporated the paper by Myrian\nMerad et al. as a reference and made the corresponding revisions (p3, line 59-64).\np3, line 59-64\nMagen et al. reported that in TME of hepatocellular carcinoma that responded to preoperative\nPD-1 blockade therapy, both CXCL13+ CD4+ T cells and GZMK+ CD8+ T cells clonally\nproliferated and played crucial roles in antitumor immunity. They concluded that intratumoral\nniches containing CXCL13+ CD4+ T cells control the differentiation of tumor-specific\nprogenitor exhausted CD8+ T cells through the formation of triad with dendritic cells 7.\nReferences\n11. Magen, A., et al. Intratumoral dendritic cell-CD4(+) T helper cell niches enable CD8(+)\nT cell differentiation following PD-1 blockade in hepatocellular carcinoma. Nat Med 29,\n1389-1399 (2023).\nIn my view, the CXCL13+ TH population shares phenotypic, functional and topographical\nsimilarities with Th7R. How can the authors differentiate between these two subpopulations of\nCD4 T cells( their own and that identified by Merad's group)?\n→As the reviewer suggests, the CXCL13+ CD4+ T cells identified by Myriam Merad's group\nexpress PDCD1, CTLA-4, DUSP4, and others compared to CXCL13- CD4+ T cells. Given their\npresence near Tpex in the TME and support their differentiation and proliferation, they likely\nform a cluster close to Th7R. However, their lower expression of IL-7R, TBX21, and CCL5\ndistinguishes them from Th7R. We compared Th7R with Th1 and Th17 within the SELLlow\neffector CD4+ T cell clusters, while Myriam Merad's group presented results comparing\nCXCL13+ CD4+ T cells with all other CXCL13- CD4+ T cells within the TME. Therefore, we\ncannot make more precise statements about the identity of gene expression patterns."}, {"round": 3, "reviewer_comments": {"reviewer_1": "I think that the current version of the manuscript has successfully addressed all my previous concerns. I would strongly recommend to review again the legends of all figures because in some ocassions, a full methodology description is included instead of a description of which is shown in the figure. One example is Figure 7c. Legend says \"In each group of the experiment shown in Fig. 7a, TILs were collected from tumors on 974 day 14 after ACT and analyzed by mass cytometry\". Whereas a correct description might be \"Total quantification of CD45 positive cells, analyzed by mass cytometry, is shown in each experimental group\". Similar examples can be found in Figure 5 a-c, etc. A careful review of all figure legends is necessary from my point of view.", "reviewer_2": "The authors responded to criticism by adding some new data and modifying the text.\nSpecifically with regards to concerns raised by this reviewer:\n1. “To clarify the difference in CD8+ T cells within the TME following Th7R administration, we\nadded the Tpex/CTL ratio (Fig. 7d).”\nok\n2. “We also added TIL analysis results obtained from an antigenspecificity\nexperiment, which is an independent experiment (Supplementary Figure 9a, b). A\nsignificant increase in the proportion of PD-1+IL-7R+CD8+ T cells within TILs compared to\nuntreated mice was observed only in mice administered Th7R from lymph nodes draining\nMCA205, but not with Th7R from lymph nodes draining B16 melanoma.”\nSure, but control Th1 or CD4 T cells should also have been included to show a Th7R specific effect compared to other known populations. The new data do not show that Th7R are better than other CD4 subsets.\nNevertheless the authors can still emphasize the point raised for reviewer #1 “Numerous past mouse experiments have\ndemonstrated that Th1 cells play the central role in cellular immunity among CD4+ T cells. Thus, we consider the experimental results showing that\nTh7R alone mediated antitumor effects equivalent to those of Th1, compared to the untreated control, to be highly significant.”\n3. “The significance of SELL- versus SELL+ lies in whether they possess the homing property to SLO. ..On the other hand, the analysis in this study aims to characterize the CD4+ Tcell clusters migrating to the TME rather than SLO.”\nI disagree with the logic presented by the authors. Many tumors have TLS with HEVs and thus ability to attract SELL+ T cells. The authors even state in their intro “CD4⁺ T cells are also involved in the formation of ectopic HEVs, which are essential for Tpex to infiltrate the tumor microenvironment”. Therefore, this argument does not support focusing only on SELL- CD4 T cells.\nNevertheless, the authors do “demonstrate that a distinct CD4+ T cell population called Th7R, differing from Th1 and Th17, exists among SELLlow T cells migrating toward the TME. This population infiltrates the TME, is located near Tpex within TLS,\ncorrelates numerically with Tpex, and plays a role in anti-tumor immunity..”\nI believe the study limitations described in “p14, line384-394” are appropriate.\n4. Very odd that CXCL13+ CD4 presented such a low clonal expansion, since this was the most expanded CD4-helper subset in tumors in several studies.\n5. I agree with reviewer #3 that the authors need to better place their findings in context to other key studies in the field (e.g. Magen et al) and the authors should have had a better attempt to compare other studies to their own. For example, showing similarities vs differences in gene signatures.", "reviewer_3": "I appreciated the authors' efforts to address all of my concerns.\nMinor point: the difference between Th7R and CXCL13+ Th subset described by Magen et al. should also be included in the discussion section."}, "author_response": "RESPONSE TO REVIEWERS’ COMMENTS\nReviewer #1 (Remarks to the Author):\nI think that the current version of the manuscript has successfully addressed all my previous\nconcerns. I would strongly recommend to review again the legends of all figures because in some\nocassions, a full methodology description is included instead of a description of which is shown\nin the figure. One example is Figure 7c. Legend says \"In each group of the experiment shown in\nFig. 7a, TILs were collected from tumors on 974 day 14 after ACT and analyzed by mass\ncytometry\". Whereas a correct description might be \"Total quantification of CD45 positive cells,\nanalyzed by mass cytometry, is shown in each experimental group\". Similar examples can be\nfound in Figure 5 a-c, etc. A careful review of all figure legends is necessary from my point of\nview.\n→We thank the reviewer for this helpful suggestion. We carefully revised all figure legends\nthroughout the manuscript to ensure that they clearly describe the data shown in each figure\nrather than providing detailed methodological explanations. For example, the legend for Figure\n3, 5, 6, 7, and 8 have been revised. We believe that these changes have improved the clarity and\nreadability of the figure legends.\nReviewer #2 (Remarks to the Author):\n1. “To clarify the difference in CD8+ T cells within the TME following Th7R administration, we\nadded the Tpex/CTL ratio (Fig. 7d).”\nok\n→ We thank the reviewer for this helpful suggestion.\n2. “We also added TIL analysis results obtained from an antigenspecificity experiment, which is\nan independent experiment (Supplementary Figure 9a, b). A significant increase in the proportion\nof PD-1+IL-7R+CD8+ T cells within TILs compared to untreated mice was observed only in mice\nadministered Th7R from lymph nodes draining MCA205, but not with Th7R from lymph nodes\ndraining B16 melanoma.”\nSure, but control Th1 or CD4 T cells should also have been included to show a Th7R specific\neffect compared to other known populations. The new data do not show that Th7R are better than\nother CD4 subsets. Nevertheless the authors can still emphasize the point raised for reviewer #1\n“Numerous past mouse experiments have demonstrated that Th1 cells play the central role in\ncellular immunity among CD4+ T cells. Thus, we consider the experimental results showing that\nTh7R alone mediated antitumor effects equivalent to those of Th1, compared to the untreated\ncontrol, to be highly significant.”\n→We agree with the Reviewer that inclusion of Th1 or other CD4⁺ T-cell subsets as comparators\nis important to rigorously demonstrate Th7R-specific superiority. Importantly, in our primary\nadoptive transfer experiments (Fig. 7), Th1 cells were included as controls, and only Th7R\ntransfer resulted in a statistically significant expansion of Tpex (PD-1⁺IL-7R⁺ or PD-1⁺TCF7⁺\nCD8⁺ T cells), whereas Th1 cells preferentially increased CTL-like populations. Thus, rather than\noutperforming Th1 in bulk antitumor efficacy, Th7R appear to exert a qualitatively distinct helper\nfunction by selectively supporting the Tpex compartment that underlies durable antitumor\nimmunity.\n3. “The significance of SELL- versus SELL+ lies in whether they possess the homing property to\nSLO. ..On the other hand, the analysis in this study aims to characterize the CD4+ Tcell clusters\nmigrating to the TME rather than SLO.”\nI disagree with the logic presented by the authors. Many tumors have TLS with HEVs and thus\nability to attract SELL+ T cells. The authors even state in their intro “CD4⁺ T cells are also\ninvolved in the formation of ectopic HEVs, which are essential for Tpex to infiltrate the tumor\nmicroenvironment”. Therefore, this argument does not support focusing only on SELL- CD4 T\ncells.\nNevertheless, the authors do “demonstrate that a distinct CD4+ T cell population called Th7R,\ndiffering from Th1 and Th17, exists among SELLlow T cells migrating toward the TME. This\npopulation infiltrates the TME, is located near Tpex within TLS,\ncorrelates numerically with Tpex..”\nI believe the study limitations described in “p14, line384-394” are appropriate.\n→We agree with the Reviewer that SELL+ T cells may home not only to secondary lymphoid\norgans (SLOs) but also to tertiary lymphoid structures (TLS) within the tumor\nmicroenvironment. Traditionally, CD62L-positive T cells have been regarded as naïve or central\nmemory populations that preferentially traffic to SLOs. However, given the critical role of high\nendothelial venules (HEVs) and TLS in tumors, it is plausible that a subset of CD62L-positive T\ncells represents effector or differentiated populations that are actively recruited to TLS via\nHEVs.\nAs noted in the Limitations section, although the identification and functional characterization\nof CD62L-positive T cells distributed to TLS through HEVs is an important and biologically\nintriguing question, a comprehensive analysis of this population is beyond the scope of the\npresent study. Our current analyses therefore focused on SELL^low effector-type CD4⁺ T cells\nthat are clearly enriched in the tumor microenvironment.\nWe intend to pursue this question in future studies, with the aim of defining the phenotype,\ndifferentiation state, and functional contribution of CD62L-positive T cells that localize to TLS,\nand determining how they may cooperate with other immune cell subsets in sustaining\nantitumor immunity.\n4. Very odd that CXCL13+ CD4 presented such a low clonal expansion, since this was the most\nexpanded CD4-helper subset in tumors in several studies.\n→In Fig. 1a, the #18 cluster, which constitutes Th7R cells, exhibits the most prominent clonal\nexpansion within the tumor microenvironment, as shown in Fig. 1g. In contrast, although the #13\ncluster—annotated as Th1—contains large expanded clones in the peripheral blood, it shows only\nlimited clonal expansion in the TME. Furthermore, CXCL13 expression is markedly enriched in\nTh7R cells compared with Th1 cells within the TME (Fig. 2).\n5. I agree with reviewer #3 that the authors need to better place their findings in context to other\nkey studies in the field (e.g. Magen et al) and the authors should have had a better attempt to\ncompare other studies to their own. For example, showing similarities vs differences in gene\nsignatures.\n→As the reviewer suggested, we added a discussion addressing the differences between Th7R\nidentified in this study and the CXCL13⁺ CD4⁺ T-cell subset described by Magenet al.\nP13 line365-380\nPrevious studies have identified CXCL13⁺ CD4⁺ T cells as key cellular partners associated with\nthe maintenance and expansion of progenitor exhausted CD8⁺ T cells through high expression of\nTCF711. Consistent with these findings, Th7R cells in our study also exhibited elevated TCF7\nexpression and upregulated CXCL13 upon localization to the tumor microenvironment,\nsuggesting that Th7R cells may fall within the broader CXCL13⁺ CD4⁺ T-cell compartment\ndescribed previously. Notably, however, unsupervised clustering of intratumoral CD4⁺ T cells\nidentified a CXCL13-expressing cluster that was transcriptionally distinct from Th7R cells. This\nobservation indicates that CXCL13 expression alone does not define a single CD4⁺ T-cell lineage\nwithin tumors. Rather, CXCL13⁺ CD4⁺ T cells likely comprise multiple transcriptionally and\nfunctionally heterogeneous populations, among which Th7R represents one subset with a distinct\ndifferentiation state and immunological role.\nReviewer #3 (Remarks to the Author):\nI appreciated the authors' efforts to address all of my concerns.\nMinor point: the difference between Th7R and CXCL13+ Th subset described by Magen et al.\nshould also be included in the discussion section.\n→We thank the reviewer for this helpful suggestion. We have now added a discussion addressing\nthe differences between Th7R identified in this study and the CXCL13⁺ CD4⁺ T-cell subset\ndescribed by Magenet al.\nP13 line365-380\nPrevious studies have identified CXCL13⁺ CD4⁺ T cells as key cellular partners associated with\nthe maintenance and expansion of progenitor exhausted CD8⁺ T cells through high expression of\nTCF711. Consistent with these findings, Th7R cells in our study also exhibited elevated TCF7\nexpression and upregulated CXCL13 upon localization to the tumor microenvironment,\nsuggesting that Th7R cells may fall within the broader CXCL13⁺ CD4⁺ T-cell compartment\ndescribed previously. Notably, however, unsupervised clustering of intratumoral CD4⁺ T cells\nidentified a CXCL13-expressing cluster that was transcriptionally distinct from Th7R cells. This\nobservation indicates that CXCL13 expression alone does not define a single CD4⁺ T-cell lineage\nwithin tumors. Rather, CXCL13⁺ CD4⁺ T cells likely comprise multiple transcriptionally and\nfunctionally heterogeneous populations, among which Th7R represents one subset with a distinct\ndifferentiation state and immunological role."}]

Decision letter