TIGIT and PD-L1 co-blockade promotes clonal expansion of multipotent, non-exhausted anti-tumor T cells by facilitating costimulation

TIGIT and PD-L1 co-blockade promotes clonal expansion of multipotent, non-exhausted anti-tumor T cells by facilitating costimulation | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article TIGIT and PD-L1 co-blockade promotes clonal expansion of multipotent, non-exhausted anti-tumor T cells by facilitating costimulation Eugene Chiang, Katherine Nutsch, Karl Banta, Thomas Wu, Stephanie Mittman, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4201684/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 16 Dec, 2024 Read the published version in Nature Cancer → Version 1 posted You are reading this latest preprint version Abstract Blockade of the immune checkpoints PD-1 and TIGIT has demonstrated activity in mouse tumor models and human cancer patients. Although these coinhibitory receptors can restrict signaling in CD8 + T cells by regulating their associated costimulatory receptors CD28 and CD226, the functional consequences of combining PD-1 and TIGIT blockade remain poorly characterized. In mouse tumor models, combination blockade elicited CD226-driven clonal expansion of tumor antigen-specific CD8 + T cells. The expanded clones emerged from a population of stem-like cells in draining lymph nodes (dLN), entering the blood as a previously unidentified single-phenotype, multi-clonal population. Upon reaching the tumor, these tumor antigen-specific transiting cells expanded further and differentiated into effector or exhausted T cells, with combination blockade restricting entry into the exhaustion pathway by favoring costimulation. Thus, PD-1 and TIGIT inhibition helps shape the repertoire of tumor-reactive CD8 + T cells in dLN and determines their immunological fate in the tumor to enhance therapeutic benefit. Analysis of clinical trial samples suggests a similar mechanism may also occur in cancer patients. Biological sciences/Immunology/Tumour immunology Biological sciences/Cancer/Tumour immunology Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Clonotypically expanded effector-like or exhausted CD8 + T cells are often found in tumors, normal adjacent tissue, and peripheral blood of patients with various types of cancer 1 . Expanded clones in the blood likely originate outside the tumor, presumably in draining lymph nodes (dLN). The existence of peripherally expanded T cell clones may indicate an active anti-tumor immune response as this group of patients exhibit favorable responses to the anti-PD-L1 monoclonal antibody (mAb) atezolizumab in various clinical trials 1 . T cells in blood do not exhibit features of exhausted T cells (Tex) and are unlikely to be derived from exhausted tumor-infiltrating lymphocytes (TILs) 2 , suggesting that peripherally expanded cells do not reflect reversal of intratumoral Tex exhaustion following PD-1 blockade 3, 4, 5, 6 . However, the extent to which checkpoint blockade reprograms CD8 + T cells already committed to the exhaustion pathway or discourages developmental commitment to exhaustion remains a key unknown 7 . Although immunotherapies targeting the PD-1/PD-L1 pathway have shown promise in several different cancers, only ~ 30% of patients achieve durable responses, necessitating a search for new strategies such as combinations targeting multiple or novel immune checkpoint receptors 8 . TIGIT (T cell immunoreceptor with Ig and immunoreceptor tyrosine-based inhibitory domains) has garnered widespread attention due to efficacy in early clinical trials using blocking antibodies against both TIGIT and PD-L1 9 . Recent analysis of the randomized phase-2 CITYSCAPE trial (NCT01903993) evaluating atezolizumab versus anti-TIGIT mAb tiragolumab plus atezolizumab in non-small cell lung cancer (NSCLC) 10 revealed that high baseline intratumoral macrophages and regulatory T cells were associated with clinical benefit 11 . Although these results suggest that the TIGIT/PD-L1 reprograms the tumor microenvironment (TME), high levels of CD8 + effector T cells (Teff) were also associated with response. In CD8 + TILs, TIGIT and PD-1 expression are highly correlated 12 . Whereas PD-1 primarily regulates costimulation by CD28, TIGIT and PD-1 together regulate the function of CD226, the activating counterreceptor to TIGIT 13 . Co-expression may define distinct populations of “stem cell-like memory (Tscm)” cells 14, 15, 16, 17 . Tscm cells are believed to be primary targets of PD-1/PD-L1 blockade in both anti-tumor and anti-viral immunity 18, 19, 20 . Blocking PD-1 signaling may differentiate these progenitors into T cells with cytolytic effector activity against tumor cells, perhaps via a recently described transient population of T precursor exhausted cells (Tpex) 21, 22, 23, 24, 25 . Thus, PD-1 expression may reflect T cell activation status in addition to denoting exhaustion or commitment to exhaustion. It remains uncertain whether Tpex give rise to only Tex in the tumor, whether commitment to the Tex pathway begins in dLN, or whether Tex, Teff and memory (Tem) cells originate from separate precursors either prior to or following tumor arrival. Also uncharacterized is the role (if any) of TIGIT blockade in regulating these events, alone or in combination with PD-1 blockade. To inform these questions, we undertook a unique multicompartment, multi-omics single-cell approach, analyzing over 245,000 T cells. We examined not only the features of CD8 + T cells in dLN and tumor as has been done previously, but also in the blood. Sampling these three critical tissue compartments facilitated insight into the spatial and temporal effects of TIGIT and PD-1 blockade on T cell fate decisions. Results Combination treatment requires trafficking of lymphocytes from draining lymph nodes to tumor The observation that PD-1 and TIGIT coordinately regulate costimulatory signals in T cells suggests that both receptors may activate T cells at the same steps and anatomical sites 9 . Using the CT26 syngeneic mouse tumor model, we evaluated the role of dLN in TIGIT blockade by restricting trafficking of T cells with FTY720, an inhibitor of T cell egress from lymphoid organs 26 . Consistent with previous observations 12 , the combination of anti-TIGIT with anti-PD-L1 demonstrated therapeutic efficacy whereas anti-PD-L1 or anti-TIGIT monotherapies had only limited impact on tumor growth (Fig. 1a, Extended Data Fig. 1a). FTY720 reduced the activity of both single-agent anti-TIGIT and TIGIT/PD-L1 co-blockade (Fig. 1a). Similar results were observed in the EO771 tumor model (Extended Data Fig. 1b). Treatment with anti-PD-L1, anti-TIGIT, or both did not affect total numbers of CD8 + T cells, CD4 + T cells or regulatory T cells (Tregs) in CT26 dLN or tumor, either with or without FTY720 treatment (Extended Data Fig. 1c). We therefore asked if checkpoint blockade and FTY720 affected the abundance or distribution of CD8 + T cells that were specific for tumor antigens. We identified these cells using tetramers that bind T cell receptors (TCRs) specific for gp70, a tumor-associated, immunodominant retroviral antigen expressed by CT26 cells (Extended Data Fig. 2a) 27 . Anti-TIGIT in combination with anti-PD-L1 increased the fraction of gp70 + CD8 + T cells ( p = 0.0138) in dLN, whereas anti-PD-L1 or anti-TIGIT alone had little effect (Fig. 1b). The addition of FTY720 before combination treatment further increased the frequency of gp70 + CD8 + T cells in dLN ( p = 0.0472), likely reflecting their accumulation in dLN by preventing T cell egress. In blood, numbers of gp70 + CD8 + T cells were significantly increased with anti-TIGIT ( p = 0.0134) or combination treatment ( p < 0.001), but not in FTY720-treated animals (Fig. 1b). In tumors, only the combination of anti-TIGIT and anti-PD-L1 significantly increased the fraction of gp70 + CD8 + T cells ( p = 0.0098) (Fig. 1b). Since trafficking via blood was blocked, at least some expansion of intratumoral T cells was likely to have occurred locally. Although FTY720-treated mice exhibited a trend towards increased gp70 + CD8 + T cells in tumors, these presumably locally expanded cells appear to be of lower “quality” as they were unable to control tumor growth. We next asked if anti-tumor efficacy relied on the continuous recruitment of newly generated T cells from dLN and blood. Early administration of FTY720 blocked combination efficacy, whereas delaying the blockade of T cell egress until 7 days after combination treatment resulted in only slight impairment in anti-tumor efficacy (Fig. 1c). Thus, the efficacy of combination checkpoint blockade depends on the induction of tumor-specific CD8 + T cells in dLN that then traffic to and infiltrate tumors via the circulation. Once the newly mobilized T cells seeded tumors, they appeared sufficient to sustain therapeutic benefit in response to anti-TIGIT plus anti-PD-L1. CD226 has role in tumor-specific CD8 + T cell differentiation Since human TILs in NSCLC differentially express CD226 and CD28 in various CD8 + T cell clusters, combination treatment may be required to optimally activate the entire tumor-reactive TIL repertoire 13 . To evaluate the role of CD226 on tumor-specific CD8 + T cell subsets in the mouse tumor model, we segregated gp70 + CD8 + T cells based on CD226 expression. Anti-TIGIT alone or in combination with anti-PD-L1 increased the frequency of CD226 + gp70 + CD8 + T cells in both dLN and tumor, even with FTY720 treatment (Fig. 2a). Following combination blockade, CD226 + gp70 + CD8 + T cells were significantly more proliferative (Ki67 + ), but only in dLN (Fig. 2b, c, Extended Data Fig. 3a). CD226 – gp70 + CD8 + T cell proliferation was not affected by any treatment. Few CD226 + gp70 + CD8 + T cells in dLN were naïve as compared with the CD226– fraction (Fig. 2b, Extended Data Fig. 3b); combination treatment, but neither monotherapy, increased the frequency of CD226 + gp70 + CD8 + T cells with a Teff or Tem phenotype whereas no effects were observed in the CD226– population (Fig. 2b, Extended Data Fig. 3c). To further elucidate the effects of checkpoint blockade on activation and differentiation, we measured various markers of T cell states. Slamf6 and TCF1 co-expression are considered markers of Tscm or Tpex cells 7, 16 . In dLN, the frequency of these cells in the CD226 + fraction was not affected by any treatment, but anti-TIGIT alone or in combination with anti-PD-L1 significantly reduced frequencies in the CD226– subset (Fig. 2b; Extended Data Fig. 3d, p = 0.0014). By contrast, in tumor, anti-TIGIT and combination treatment increased frequencies of Slamf6 + TCF1 + cells in both CD226 + and CD226 − subsets (Fig. 2c; Extended Data Fig. 3d, p = 0.0014). As T cells differentiate from the Tscm or Tpex state, they express immune checkpoints such as Tim3. Combination treatment as well as anti-TIGIT alone increased the frequencies of both CD226 + and CD226– TCF1 + Tim3 + gp70 + CD8 + T cells in tumor whereas effects in the dLN were limited to the CD226 + subset; FTY720 largely abolished these effects (Fig. 2b,c; see Extended Data Fig. 3e for statistics). As T cells further differentiate, they lose expression of TCF1 although transcription of the Tcf7 gene appears to precede the loss of the TCF1 protein itself (compare to Fig. 3b). In the dLN, a significant increase in the frequency of TCF1– tumor specific CD8 + T cells is seen in the CD226 + fraction with anti-TIGIT or combination treatment; no effect was detected in CD226– cells (Fig. 2b; Extended Data Fig. 3f). Tox is a key transcriptional regulator of exhaustion programming and differentiation towards terminal exhaustion 4, 5 . Treatment with either anti-TIGIT alone or anti-TIGIT plus anti-PD-L1 markedly decreased Tox expression in CD226 + but not CD226– gp70 + CD8 + T cells in dLN, while decreased Tox expression was seen in both CD226 + and CD226– fractions in tumor; FTY720 appeared to diminish the combination effect on Tox expression in some cases (Fig. 2b,c; see Extended Data Fig. 3g for statistics). Similar effects were seen in the EO771 model, with combination treatment increasing the frequency of CD8 + T cells in tumors, promoting CD226 expression on tumor CD8 + T cells, and increasing the TCF1 + Tim3 + phenotype while reducing Tox + frequencies (Extended Data Fig. 3h-m). To assess the effector state of TILs responding to checkpoint blockade, we measured production of the proinflammatory effector cytokines IFN-g and TNF-a. Single-agent anti-TIGIT and combination treatment increased dual production of proinflammatory cytokines IFN-g and TNF-a in the CD226 + fraction of intratumoral CD8 + T cells relative to the CD226– fraction, with FTY720 eliminating this effect, suggesting that T cells derived from the periphery might possess superior effector function (Fig. 2d, e); assessment of cytokine production by tumor-specific TILs was not possible due to downregulation of TCR upon in vitro stimulation. As anti-TIGIT plus anti-PD-L1 appeared to have more pronounced effects on CD8 + T cells expressing CD226, particularly in dLN, we concurrently treated mice receiving the combination with CD226-blocking mAb. As we could not segregate gp70-specific CD8 + T cells on the basis of CD226 expression in the presence of the blocking mAb, we examined total gp70 + cells and could not discern effects on Slamf6 + TCF1 + cells (Fig. 2f, Extended Data Fig. 3n). However, anti-CD226 mAb impaired the ability of combination treatment to increase the frequency of TCF1 + Tim3 + tumor-specific CD8 + T cells in dLN and tumor (Fig. 2f, Extended Data Fig. 3o). CD226 blockade also showed a trend towards reducing the ability of combination treatment to drive differentiation to a Teff/Tem phenotype (Fig. 2f, Extended Data Fig. 3p). Anti-CD226 mAb prevented the reduction in Tox-expressing cells in dLN and to a greater extent in tumor (Fig. 2f; Extended Data Fig. 3q, p = 0.025 and 0.009 respectively). Taken together, addition of anti-TIGIT to PD-1/PD-L1 blockade initiated distinct differentiation pathways of Tscm or Tpex cells in dLN in a CD226-dependent fashion. These cells were further expanded in the tumor and were guided to develop into qualitatively better polyfunctional effectors. Similarly, upregulation of Tox characteristic of Tpex and Tex differentiation was prevented, again in a CD226-dependent manner. TIGIT and PD-L1 co-blockade promotes and expands different CD8 + T cell states in dLN, blood, and tumor We further examined how co-blockade affects the generation, phenotype, and trajectory of tumor-specific T cells using a multi-omics single-cell approach, performing single-cell RNA sequencing (scRNA-seq) and TCR sequencing (scTCR-seq) on T cells from tumor, dLN, and blood. These assays were supplemented by antibody-derived tag sequencing (ADT-seq) with tetramers against gp70 and cellular indexing of transcriptomes and epitopes (CITE-seq) using a panel of 18 cell surface proteins. Gene expression profiles of a large dataset of 245,675 T cells yielded 24 distinct clusters (Extended Data Fig. 4a), with contributions across treatment groups (Extended Data Fig. 4b), but with some clusters appearing selectively localized to dLN, blood, or tumor (Extended Data Fig. 4c). Effector status, as indicated by granzyme B expression, was confined primarily to CD8 + T cells that showed clonal expansion and high ADT counts, a measure of the number of gp70 tetramers bound (Extended Data Fig. 4d–g). CITE-seq provided a complementary characterization of T cell differentiation, effector, and memory states based on surface marker expression (Extended Data Fig. 4h). We obtained greater resolution of CD8 + T cell phenotypes by re-analyzing the T cells with high CD8a expression. These 155,496 CD8 + T cells comprise one of the largest datasets used for this type of analysis, enabling higher resolution clustering and unprecedented insight into the responses of CD8 + T cells to checkpoint inhibition. 20 distinct CD8 + clusters were identified (Fig. 3a,b; Extended Data Fig. 5; see Supplementary Table 1 for genes defining each cluster), with contributions consistent across individual mice (Extended Data Fig. 6a). As before, clusters belonged to specific tissues, and had contributions across experimental groups (Extended Data Fig. 6b). Clonal expansion and ADT counts were differentially distributed amongst clusters, with increases seen in non-Ccr7 clusters (Extended Data Fig. 6c). The clusters exhibited various phenotypes (Fig. 3a): (a) four Ccr7 clusters ("Ccr7.1-4") characterized by Ccr7 , a marker expressed by naïve, Tscm and central memory (Tcm) cells but low in cytotoxic CD8 + Teff and Tem cells 28 , as well as genes associated with Tscm cells such as Sell , Lef1 , and Tcf7 18 , and also high expression of ribosomal proteins; (b) a distinct cluster ("Early") characterized by expression of Cd69 and other markers of early T cell activation; (c) a distinct "Slamf6" cluster marked by high Slamf6 and Tcf7 expression representative of a Tscm population; (d) three Ifit clusters ("Ifit.1-3") with hallmarks of interferon response genes indicating activated T cells; (e) two Ccl5 clusters ("Ccl5.1-2") marked by this chemokine that can exert chemotactic effects on T cells and is associated with CD8 + T cell infiltration into tumors 29 ; (f) two Cytotox clusters ("Cytotox.1-2") exhibiting hallmarks of cytotoxic gene expression as well as genes associated with exhaustion such as Tox and checkpoint inhibitory checkpoint receptors; (g) three Cyt/Mit clusters ("Cyt/Mit.1-3") that represent proliferating cytotoxic cells as they express genes associated with cytotoxicity and mitosis; (h) two Mitotic clusters ("Mitotic.1-2") expressing genes associated with mitosis but not genes associated with effector function; and (i) two clusters representing dying cells ("Dying.1-2"). The Ccl5 clusters shared expression of a number of genes associated with the Cytotox or Cyt/Mit clusters, but did not have properties of exhaustion. CITE-seq analysis using various surface-expressed proteins corroborated this categorization by gene expression (Extended Data Fig. 6d–f). Both scRNAseq and CITE-seq analysis showed that CD226 expression was most characteristic of Ccl5.1 T cells. CD28 showed some overlapping expression with CD226 but also marked a few distinct clusters consistent with our previous findings for human NSCLC TILs 13 (Extended Data Fig. 6d–f). The Ccl5.1 cluster is of particular interest in that it was the only major non-naïve cell state found in the blood. Comparison of our clusters with reference gene signatures from published datasets 23, 30, 31, 32 showed general concordance albeit with more granularity due to the larger sample set used here (Fig. 3c). Of particular relevance, our Ccl5, Ifit.3, and Cytotox clusters shared strong similarities with the “better effectors” described by others in response to a combination of anti-PD-1 therapy with IL-2 agonists 31 . However, our Ccl5.1 cluster also corresponded with the "Stem-like cluster" in that study and with the "Transitory Tex cluster" by Huang and colleagues 23 . Our multi-omics dataset allowed us to convert spliced and unspliced mRNA counts to estimate RNA velocity measurements and infer differentiation trajectories. Although the directionality of cell traffic often cannot be assigned confidently from velocity-based trajectories 33 , visualization results from Li and colleagues using photoactivation have established the in vivo migration of T cells into and out of tumors 34 . By assigning our clusters to the Li et al . gene expression signatures (Extended Data Fig. 7a), we can ascertain directionality in our analysis. Using control-treated tumor-bearing mice as a reference, RNA velocity patterns differed in dLN and tumor (Fig. 3d). In dLN, a major trajectory originated from Early and Ccr7 clusters and yielded Slamf6 cells, which then differentiated into Ifit or Ccl5 cells. In tumors, differentiation progressed from Ccl5 cells through Cytotox cells to Cyt/Mit cells. From there, a second differentiation pathway generated Mitotic cells. RNA velocity patterns were similar across treatment groups, indicating that differentiation pathways were not fundamentally affected by the various treatments (Extended Data Fig. 7b,c). Combination treatment expanded tumor-specific CD8 + T cells marked by Ccl5 that transit from dLN to tumor via blood We then applied our scTCR-seq data to segregate T cells by the expansion of their parent clone, revealing striking differences across treatment groups, especially when using ADT-seq counts to distinguish gp70 + from gp70 − cells (Extended Data Fig. 6c). As shown in Fig. 4, cells in dLN were predominantly singletons (having only one cell expressing a given TCR clonotype) across each cluster, but showed evidence of clonal expansion in the Slamf6 and Ccl5.1 clusters following combination treatment. In contrast, cells in tumor were almost exclusively expanded clones. Although clones were specific to individual mice, these results were not attributable to any single mouse (Extended Data Fig. 8). At day 7, gp70 + CD8 + T cells were detected in the blood of mice treated with anti-TIGIT or combination treatment and were comprised of Ccl5.1 cells (Fig. 4, bars facing right). The absolute cell numbers were low, likely reflecting the transient residence of mobilized T cells in the blood. Their appearance was blocked by FTY720 treatment, indicating that expanded Ccl5.1 cells likely originated in dLN. This inference was supported by the accumulation of clonally expanded gp70 + Ccl5.1 cells in dLN. Some gp70 − CD8 + T cells were found in the Ccl5.1 cluster, but they were mostly in the immature Ccr7 clusters (Fig. 4, bars facing left). Since these cells were apparent at day 0 and in all treatment groups, they were not elicited by combination TIGIT/PD-L1 blockade. The gp70 − cells in the Ccl5.1 cluster, however, were significantly enhanced by the combination, and could include both bystanders and T cell clonotypes that were specific to tumor antigens other than gp70. Tumors, unlike the dLN or blood, contained relatively large numbers of both clonally expanded gp70 − and gp70 + TILs in all treatment groups. However, in mice treated with both anti-PD-L1 and anti-TIGIT, this increase was most pronounced for gp70 + T cells, which were found in the Ifit, Ccl5.2, Cytotox, and Cyt/Mit clusters (Fig. 4). The increase in gp70 + clones in the Ccl5.2 cluster was both most pronounced and selectively decreased by FTY720 treatment, strongly suggesting that these cells derived from the blood-borne Ccl5.1 population. Interestingly, in FTY720 treated mice, gp70 + clones expanded in the other clusters, indicating that these may pre-exist in tumor and expand and differentiate intratumorally in response to combined PD-L1/TIGIT blockade. Thus, in response to combination treatment, tumor antigen-specific (and possibly also non-specific) clonotypes expand in the dLN, exit as Ccl5.1 cells into the blood, and continued to expand after arrival in the tumor. Co-blockade of PD-L1 and TIGIT focuses the TCR clonal diversity of tumor antigen-specific CD8 + T cells We next compared the degree of clonal expansion in dLN, blood, and tumor at day 7 post-treatment, characterizing each clone by its majority cluster at each site (Fig. 5a, Extended Data Fig. 9a). Inhibiting both PD-L1 and TIGIT elicited strikingly coordinated clonal dynamics. Although only a few clones exhibited large expansions, they did so in each of the three tissue compartments (Fig. 5a). In dLN, expansion occured mostly in Ifit.3, Ccl5.1 and Cytotox.1 cells, while in the tumor Ccl5.2, Cytotox.1 or Cytotox.2 cells were preferentially expanded. Combination treatment also resulted in expansion in the blood (illustrated by the diameter of the circles shown in each plot, Fig. 5a; Extended Fig. 9a). Here, the expanded clonotypes were contained almost exclusively in the Ccl5.1 population (Fig. 4; Extended Data Fig. 9b,c), and these were shared with the corresponding clusters in dLN or the tumor (illustrated by the color of the circles in each plot, Fig. 5a; Extended Fig. 9a). Expansion due to single agent treatment occurred (to a greater extent following anti-TIGIT alone) but expansion was mostly limited to dLN or tumor. In the presence of FTY720, many clones exhibited dual expansion in dLN and tumor with relatively limited expansion in blood, suggesting that these dual-expanded clones arose independently in dLN and tumor. The most highly expanded clones following combination treatment were gp70 + , indicated by a high ADT count (blue/purple circles, Fig. 5b); little or no expansion occurred after anti-TIGIT or anti-PD-L1 alone. Most of the dual-expanded clones in the single-agent treatment groups had low or undetectable gp70 ADT counts, suggesting that they were either “bystander” non-tumor reactive T clones 35, 36 or specific for other tumor-associated antigens. In the presence of FTY720, high gp70 ADT counts were also detectable in dual-expanded clones, as expected if these cells represented pre-existing clones already present in dLN and tumor prior to treatment. Since the scatterplots (Fig. 5a,b) depict only the primary cluster type for each clone, we evaluated the composition of the 30 most expanded clones for each treatment group in tumor, and matched them to dLN and blood to study the distribution of individual clones across T cell clusters (Fig. 5c). The largest clones in tumor had measurable counterparts in dLN but only following combination treatment. In dLN, these clones consisted predominantly of the Ccl5.1, Cytotox.1 and Cytotox.2 populations. The same expanded TCR clones were also found in the blood, again contained almost exclusively in the Ccl5.1 population. FTY720 treatment prevented the appearance of this population. The picture was quite different following single-agent treatments. CD8 + T cells in the tumor following anti-PD-L1 had largely the same composition as the control group, comprised primarily of Cytotox.2 and Cyt/Mit clusters. Expansion of the Cytotox.2 cluster was more pronounced than with other treatments, suggesting that anti-PD-L1 drives T cell differentiation towards this specific state in tumor. Anti-TIGIT, in contrast, promoted a shift in the tumor towards the Ccl5.2 cluster. With single-agent treatment, none of the largest clones in tumor had appreciable counterparts in dLN or blood. When we examined clonal expansion separately in each tissue compartment, each treatment had distinct effects on T cell differentiation (Extended Data Fig. 9b, c). In dLN, anti-TIGIT and combination treatment, but not anti-PD-L1 alone, caused expansion of Ccl5.1 T cells and, to a lesser extent, Mitotic clusters. FTY720 treatment shifted the intralymphatic composition to almost exclusively Ccl5.1, suggesting that these cells accumulated in dLN since their egress into blood was inhibited. Combination treatment, with or without FTY720, resulted in reduced proportions of the Slamf6 cluster in dLNs, especially in the most expanded clones, reflecting the possibility that the Slamf6 (putative Tscm) cluster is the source from which Ccl5.1 T cells are mobilized. Anti-PD-L1 and anti-TIGIT differentially reshape differentiation and trajectories of CD8 + T cells in dLN and tumor We next probed the lineage relationships across CD8 + T cell clusters following various treatments. Although we previously evaluated cellular trajectories using RNA velocity (Fig. 3d), it is apparent that individual clones exhibit complex expansion behaviors. scTCR-seq unambiguously identifies lineages of T cells, which provides a complementary approach to infer kinetics and differentiation based on the co-occurrence of phenotypes in individual clonotypes within and across tissue compartments. We analyzed co-occurrences of cell phenotypes by tabulating numbers of intraclonal pairs over all clonotypes, plotting only pairs between different clusters (Fig. 6a–c). As with RNA velocity, we could use signatures derived from empirical observations 34 to interpret such co-occurrences as directional steps in differentiation. In dLN (Fig. 6a), control mice exhibited a predominant differentiation of Slamf6 to the Cytotox.1 phenotype, with little connection to other populations as illustrated by the absence of additional intercluster links. With single-agent treatment, increased differentiation from Slamf6 to the Ccl5.1 phenotype was observed, but with anti-TIGIT further increased differentiation of Ccl5.1 cells into Cytotox.1 and Mitotic.1 cells. Combination treatment produced an even more complex pattern of differentiation, with Ccl5.1 cells also differentiating to Ifit.3 cells, and those co-occurrences being shared across cytotoxic and mitotic clusters. FTY720 treatment resulted in most Slamf6 cells differentiating to Ccl5.1, but then a sharp reduction in Ccl5.1 cells differentiating to other clusters, as indicated by the absence of intercluster links. Intraclonal pairs in dLN were comprised of primarily gp70 + specificities across treatment groups, and some gp70– with control or single-agent treatment. Thus, although Slamf6 (Tscm) cells differentiated to cell states other than Ccl5.1 in dLN, only the Ccl5.1 population entered the blood, seeding tumors with new CD8 + T cells. Co-occurrence profiles were different in tumor compared to dLN (Fig. 6b). Intraclonal pairs in control tumors showed an origin from the Cytotox.2 phenotype to the Cyt/Mit.1 and Cyt/Mit.2 phenotypes. Anti-PD-L1 had a similar pattern, but with additional co-occurrence of Cytotox.2 with the Ifit.3 and Cytotox.1 clusters. In sharp contrast, anti-TIGIT exhibited an expansion of clones with Ccl5.2 cells that differentiated to Cyt/Mit.2, Cyt/Mit.1, and Cytotox.2 cells; these clones were largely gp70 − (blue lines), consistent with the largest clonotypes in that group being gp70 − (Fig. 5c). Combination treatment resembled anti-TIGIT monotherapy in terms of Ccl5.2 expansion, but those Ccl5.2 cells differentiated primarily to Cytotox.1 cells. FTY720 treatment produced a complex pattern of co-occurrences among Ccl5.2, Cytotox.1, Cytotox.2, Cyt/Mit.1, and Cyt/Mit.2 clusters, revealing the extent of differentiation within tumor. In contrast with anti-TIGIT treatment, the vast majority of intraclonal pairs in tumor with combination treatment were gp70 + . We then tabulated intraclonal pairs from across tissues to determine migration relationships, plotting only co-occurrences between different tissues, but otherwise showing co-occurrences between both same and different clusters (Fig. 6c). In contrast to single-agent therapy, the anti-PD-L1/TIGIT combination facilitated migration of Ccl5.1 cells from dLN to Ccl5.2 cells in tumor, presumably through blood Ccl5.1 cells, but with co-occurrences from dLN to blood less apparent because of its relatively low degree of clonal expansion in both compartments (Fig. 4). Co-occurrences were also seen from Ccl5.1 cells in dLN to Cytotox.1 and Cytotox.2 clusters in tumor, but these are presumably attributable to intratumor differentiation (Fig. 6b). The co-occurrences between Ccl5.1 in dLN and Ccl5.2 in tumor were also observed in the presence of FTY720, with an absence of blood involvement, indicating that combination treatment may act on preexisting TILs in tumor that had progenitors remaining in the dLN. To visualize these differentiation and migration patterns in the context of gene expression, we projected the co-occurrence data onto our previously computed UMAPs. From these plots (Fig. 6d,e), it is apparent that Slamf6 cells (putative Tscm) in dLN serve as progenitors for Cytotox.1 cells in control and anti-PD-L1 treated mice and for Ccl5.1 cells in other treated mice. These Ccl5.1 cells then migrate into blood, with more frequent migration occurring with anti-TIGIT and combination-treated mice in gp70 + clones (Fig. 6d) than gp70 − clones (Fig. 6e). In these groups, and especially with combination treatment, the migration links revealed a convergence of multiple clusters from dLN onto Ccl5.1 cells in blood, and then a divergence from these cells into multiple clusters in tumor. With anti-TIGIT, and to a greater extent with combination therapy, gp70 + Ccl5.1 cells in blood then migrated into tumor where they appeared to give rise to the Ccl5.2 phenotype. Ccl5.2 cells differentiated into Cytotox.2 cells, which then differentiated into other cytotoxic and mitotic (precursor exhausted) phenotypes. Differentiation from gp70 + Cytotox.2 cells to other phenotypes was greater for anti-PD-L1 and FTY720 treatment, compared with anti-TIGIT and combination treatment. These results suggest that anti-TIGIT and especially combination treatment promote an immune response characterized by an influx of tumor-specific Ccl5.1 T cells, whereas anti-PD-L1 and FTY720 treatment exhibit primarily the differentiation of Cytotox.2 T cells already existing in the tumor. Gene signatures derived from reference mouse CD8 + T cell clusters show association with response to tiragolumab plus atezolizumab in cancer patients To explore whether these observations inform the clinical setting, we analyzed scRNA-seq data of peripheral blood T cells from a phase 1b study of NSCLC patients treated with the combination of tiragolumab plus atezolizumab (T + A) (GO30103) 37 . We mapped human CD8 + T cells onto the nearest mouse reference CD8 + T cell cluster (Extended Data Fig. 10a,b). Patients with a clinical response, evaluated as either complete response (CR) or partial response (PR), compared with non-responders (stable disease, SD, or progressive disease, PD), had an increased frequency of CD8 + T cells mapping to the Ccl5.1 and Ccl5.2 clusters and a decreased frequency mapping to Ccr7.3 and Ccr7.4 clusters (Extended Data Fig. 10c). This finding is consistent with Ccl5 clusters in our mouse models predominating with effective treatment. To address whether gene signatures derived from the mouse CD8 + T cell clusters associated with improved overall survival (OS), we analyzed bulk RNA-seq data from baseline tumor samples from patients in CITYSCAPE 10 . The top 20 differentially expressed signature genes for each mouse CD8 + T cell cluster were used to derive orthologous human gene signature “scores” in CITYSCAPE samples (Supplementary Table 2) which compared patients treated with T + A or placebo plus atezolizumab (P + A). Ccr7.3, Slamf6, Ifit.1, Ifit.2, Ifit.3, Ccl5.2 and Cytotox.2 gene signature scores were significantly higher in CR and PR responders as compared with SD and PD non-responders (Fig. 7a). While all CD8 + T cell cluster signatures trended with favorable OS hazard ratio (HR) in patients treated with T + A compared to P + A (Extended Data Fig. 10d), high expression of Ccr7.3, Slamf6 and Ccl5.1 gene scores associated with significantly improved HR for OS (HR = 0.44 (95% CI: 0.22–0.91; p = 0.028), 0.46 (95% CI: 0.22–0.95; p = 0.036), and 0.45 (95% CI: 0.22–0.90; p = 0.025), respectively), as did low expression of Cytotox.1 and Cyt/Mit.2 (OS HR = 0.46 (95% CI: 0.23–0.90; p = 0.023) and HR = 0.48 (95% CI: 0.23–0.98; p = 0.045), respectively). Dichotomization of patients on the basis of high or low cluster gene signature score and by treatment showed that high expression of the Ccl5.2 gene signature trended with increased OS with T + A but not P + A (Extended Data Fig. 10e). Gene signatures predominantly associated with response to T + A were characterized by high expression of chemokines or chemokine receptors. We focused on CXCR3 , CXCR6 , and CCL5 , genes that were among the most highly expressed in each of the clusters (Supplementary Table 1). High expression of each of these individual genes was associated with response in patients treated with T + A (Fig. 7b), and high expression of CCL5 or CXCR3 was individually associated with favorable OS HR in T + A compared to P + A, outperforming CD8A (OS HR = 0.32 (95% CI: 0.14–0.73; p = 0.006), 0.41 (95% CI: 0.18–0.94; p = 0.035) and 0.43 (95% CI: 0.20–0.91; p = 0.027), respectively) (Fig. 7c). CXCR3 , CXCR6 , and CCL5 were associated with improved OS for T + A, again outperforming CD8A (Fig. 7d). We then generated a composite gene signature score comprised of the average expression of CCL5, CXCR3 , and CXCR6 . This gene signature score was significantly higher ( p = 0.012) in responder CITYSCAPE patients as compared with non-responders, (Fig. 7e). A high gene signature score was associated with favorable OS HR in patients treated with T + A (HR = 0.43, p = 0.035) compared with P + A, while a low signature score did not associate significantly with OS benefit (HR = 0.70, p = 0.277) (Fig. 7f). Segregation of patients on the basis of high or low gene signature scores showed that those treated with T + A who had high gene score expression had improved OS compared to patients with a low gene signature (Fig. 7g). The composite gene signature score was also associated with improved progression-free survival (PFS) and OS in the phase 3 OAK study (NCR02008227) of atezolizumab monotherapy in patients with locally advanced or metastatic, previously treated NSCLC 38 (Extended Data Fig. 10f). Thus, our analysis of patients treated with T + A largely recapitulates the findings of anti-TIGIT plus anti-PD-L1 in our mouse tumor studies, providing translational evidence that the events observed in dLN of tumor-bearing mice may also be detected in human blood and tumors. Furthermore, our study suggests that CD8 + T cell quality, as represented by cells newly arrived from dLN, rather than the mere presence of CD8 + T cells in the tumors supplied by the periphery at steady state 39 , may be more strongly predictive of response and clinical benefit. Discussion Despite the profound influence of checkpoint inhibitors on oncology practice and our understanding of tumor immunity, many key questions remain regarding their mechanisms of action. One important unknown is whether these inhibitors work primarily in dLN or at the tumor site, and on which populations of cells. We have elucidated the effects of anti-PD-L1 and anti-TIGIT on the differentiation and function of CD8 + T cells by employing one of the largest datasets assembled for this type of analysis. By considering T cells not only in dLN and tumor but also in the blood, we were able to demonstrate that PD-1 and TIGIT coinhibitory receptors act to direct T cell fate at both anatomical sites, with activation, expansion, and differentiation beginning in dLN, but with final determination of progression to effector or exhausted T cells occurring in tumor, challenging the notion that the trajectory to exhaustion is established at or near the time of priming 7, 16, 40 . One key to our conclusions was the direct analysis of T cells and clonotypes in peripheral blood. After mobilization with combination therapy, the blood compartment was found to exhibit predominantly a single CD8 + T cell population (Ccl5.1 cluster) that represented TCR clonotypes that had expanded in dLN and that were found in the tumor. Interestingly, in the tumor these clonotypes were distributed among multiple T cell states. Trajectory analysis based on RNA velocity and lineage tracing of TCR clonotypes suggest that peripheral blood Ccl5.1 cells differentiated into the closely related Ccl5.2 population after reaching the tumor. Thus, the polyclonal Ccl5.1 cells can be considered to be "transit cells" whose main function are to convey newly expanded T cells to the tumor. Once in the tumor, the transit cell progeny (Ccl5.2) differentiated along the Tex or Teff pathways, a decision that appears to be influenced or determined by the degree of costimulation available via the CD28 and CD226 costimulatory receptors. Indeed, CD226 signaling was required to block the expression of Tox in dLN, and especially in the tumor where nearly 80% of tumor antigen-specific CD8 + T cells were otherwise Tox + (shown in Fig. 2c, Extended Data Fig. 3g). The prevention of coinhibitory receptor suppression of costimulatory receptor signaling by anti-PD-L1 and anti-TIGIT may explain how combination therapy directs differentiation away from the exhaustion pathway. This mechanism is consistent with observations that CD28 and CD226 signaling is under the control of the PD-1 and TIGIT coinhibitory receptors 13 , thus providing an attractive functional link between checkpoint inhibition and the accumulation of Tex in the tumor. It seems likely that dendritic cells (DCs) present in the dLN and tumor help determine fate decisions between Teff and Tex, as DCs present both antigen and costimulatory ligands, consistent with recent work 41, 42 . However, the role of DCs in this proposed mechanism remains to be determined. A surprising finding of our study is that the effects of PD-1 and TIGIT inhibition appear to be distinct (Fig. 6). While both facilitated the differentiation of tumor-specific T cell trajectories from the Tscm (Slamf6 cluster) compartment to the Ccl5.1 transit cell population in dLN, anti-PD-L1 treatment showed differentiation also to the Cytotox.1 phenotype, whereas anti-TIGIT and combination treatment showed a second stage of extensive differentiation from the Ccl5.1 phenotype to other phenotypes. TIGIT blockade, alone or especially in combination with anti-PD-L1, produced far more emigration of tumor antigen-specific (gp70 + ) Ccl5.1 T cells into the blood than did anti-PD-L1 alone. Once in the tumor, anti-PD-L1 monotherapy showed differentiation mainly of the gp70 + Ccl5.2 T cells to Cytotox.2 and then to the more exhausted Cyt/Mit phenotypes, while anti-TIGIT monotherapy showed differentiation mainly of the gp70– T cells also toward exhausted phenotypes (Fig. 6b). That anti-TIGIT therapy alone appeared to preferentially affect the gp70– population may be a factor in its relative therapeutic ineffectiveness. Combination therapy showed a coordinated infiltration of gp70 + tumor-specific T cells from the blood and less exhaustion of T cells in tumor, suggesting a replenishment by newly arriving T cells into the tumor. Tscm or Tpex cells have been proposed as targets for PD-1/PD-L1-targeted immunotherapies 18, 21, 23, 24 , so it seems likely that they would also be targets for a PD-L1/TIGIT combination. Both are presumed to be precursor populations, which would be consistent with our results, but it is difficult to precisely map our subpopulations to these designations. Nevertheless, our scRNA-seq study has greater resolution relative to previous studies, given its large number of cells studied across a range of effective and ineffective treatments. Tscm cells were originally defined as a CXCR5 + /TCF1 + /Slamf6 + self-renewing compartment present in dLN that give rise to all subsequent T cells 7 . Tpex are generally defined as cells that have at least some of these features (Slamf6, TCF1) and also some, but not all, features of exhausted cells; evidence indicates that they are along a continuum of precursors of terminally differentiated Tex 7, 25, 40 . These two populations are often invoked interchangeably. Our evidence suggests that the anti-PD-L1/anti-TIGIT combination works on a precursor population, likely defined by our Slamf6 + cluster in dLN and subsequently the Ccl5.1 and Ccl5.2 clusters in the tumor. The effect of combination treatment, however, enables these populations to give rise to Teff, not just Tex, and it is these Teff cells that appear to correlate with effective treatment. Were it possible to conduct the experiments for longer periods, it seems likely that combination treatment would also favor the differentiation of Tem cells in addition to Teff. At this point, there is no single marker that unequivocally defines the Ccl5.1 or Ccl5.2 clusters, precluding experimental validation through methods such as in vivo adoptive cell transfer. Although it will ultimately be important to agree upon a common lexicon, our finding that the anti-PD-L1/anti-TIGIT combination influences CD8 + T cell trajectories in a manner dependent on costimulatory receptor signaling can be viewed within any of the existing frameworks. It is noteworthy that features of the T cell populations observed for combination treatment in mice appear to have counterparts in human cancer patients who respond to combination treatment with atezolizumab and tiragolumab. Specifically, markers associated with Ccl5 clusters in mice, which represent newly expanded T cell clones trafficking from dLN to tumors, were found to be associated with clinical benefit. If the differentiation trajectories influenced by blockade of PD-1/PD-L1 and TIGIT observed in mouse tumor models are also recapitulated in human cancer patients, then more persistent and durable responses with better survival outcomes may be attained by focusing our therapeutic efforts on generating higher quality tumor-reactive effector cells that are either resistant to exhaustion programming or replacements for terminally exhausted cells. Intriguingly, combination of PD-1 blockade with immunostimulatory cytokines such as IL-2 31, 43 , blockade of immunosuppressive cytokines such as TGF-b 44 , or costimulatory (e.g. 4-1BB) agonists 25 may also skew Tscm/Tpex differentiation trajectories towards effector and cytotoxic states and/or away from exhaustion. As combination of anti-TIGIT with anti-PD-L1 has the additional mechanism of action of reshaping the TME 11 , higher quality antitumor T cells generated in response to combination treatment will be able to exert their effector function in a less suppressive, more permissive environment. Leveraging combination therapy strategies such as anti-TIGIT with anti-PD-L1 that drive both mechanisms may potentially bring improved clinical benefit for more patients beyond anti-PD-(L)1 alone. Materials And Methods Mice. BALB/c or C57BL/6 mice were purchased from the Charles River Laboratories. All mice were housed and maintained at Genentech in accordance with American Association of Laboratory Animal Care guidelines. All experimental animal studies were conducted under the approval of the Institutional Animal Care and Use Committees of Genentech Lab Animal Research and were performed in an Association for the Assessment and Accreditation of Laboratory Animal Care (AAALAC)-accredited facility. Cell Lines. CT26 and EO771 cell lines (obtained from external vendor such as ATCC) were maintained at a dedicated internal cell line facility and tested to be mycoplasma-free. CT26 or EO771 cells were cultured in RPMI 1640 media supplemented with 10% FBS and 100 U/mL penicillin and 100 mg/mL streptomycin, and grown in a 37˚C humidified, 5% CO 2 incubator. Syngeneic tumor studies. CT26 tumor studies were performed by inoculating age-matched 6-8 week old BALB/c female mice with a sub-cutaneous injection of 0.1 x 10 6 CT26 cells in 100 µL Hank’s balanced solution (HBSS) and Matrigel (BD Biosciences, San Jose, CA). EO771 tumor studies were performed by inoculating age-matched 6-8 week old C57BL/6 female mice with an injection into the fifth mammary fat pad of 0.1 x 10 6 EO771 cells in 100 µL HBSS + Matrigel. Once tumors achieved a mean volume of 150-200 mm 3 , animals were apportioned into treatment groups and treated with isotype control (anti-gp120 mIgG2a), 10 mg/kg; anti-PD-L1.mIgG2a LALAPG mAb (clone 6E11), 10 mg/kg followed by 5 mg/kg; anti-TIGIT.mIgG2a mAb (clone 10A7), 10 mg/kg; or TIGIT.mIgG2a.LALAPG, 10 mg/kg, and administered intravenously for the first dose and subsequently intraperitoneally. For the tracking of tumor volume, doses were given three times a week for three weeks. For single-cell analyses, the mIgG2a version of anti-TIGIT was used, and three doses were given over the course of one week. To inhibit trafficking, FTY720 (Cayman Chemical Company, 1 mg/kg) was administered by daily oral gavage starting day -1 before indicated treatment, or where indicated, day 7 after treatment, and continued until end of study. Tumor volumes were measured and calculated twice per week using the modified ellipsoid formula: ½ x (length x width 2 ). For pharmacodynamic analyses, mice were euthanized at day 7 after initial treatment. Tumors were dissociated into single cell suspensions by using gentleMACS TM dissociator (Miltenyi Biotec) and enzymatically digested in a buffer containing collagenase D (2 mg/mL) and DNAse (40 U/mL, Roche). Single cell suspensions of draining lymph nodes were obtained by mechanical dissociation through 40 µm cell strainers and performing red blood cell lysis as needed. Blood was obtained by terminal cardiac puncture and collected in lavender Microtainer Blood Collection Tubes (BD Biosciences, 365974) and subjected to red blood cell lysis. Animals bearing tumors exceeding 2,000 mm3 or showing ulceration were euthanized following approved protocols. Flow cytometry and FACS sorting. Immune cell phenotyping by flow cytometry was performed on single cell suspensions from mouse draining lymph nodes, tumor, and blood obtained and described elsewhere. Briefly, dead cells were excluded by using a fixable viability dye. Cell surface markers were stained on ice after tetramer staining. The FoxP3 nuclear staining buffer set (Invitrogen) was then performed using recommended manufacturer’s instructions to detect intracellular or nuclear staining. For intracellular cytokine detection, cells with stimulated for 4 hours with Cell Stimulation Cocktail (Invitrogen, 00-4970-93) at 37˚C. After stimulation, cells were stained for surface markers and intracellular factors as described above. For obtaining cells for single cell analysis, tumors and dLNs were processed into single cell suspensions as described elsewhere, and subjected to first tetramer staining, then surface markers and CITE-seq antibodies together. Processing of blood samples at day 0 before any treatment or at day 7 were first stained with hashed-tagged antibodies, then stained with surface markers. Cells were purified by fluorescence-activated cell sorting (FACS) on a Becton Dickinson FACSAria Fusion cell sorter equipped with four lasers (405 nm, 488 nm, 561 nm and 638nm). A 70-μm nozzle running at 70 psi and 90 kHz was used as the setup for each sort session. FACSDiva (v.8.0.1) and FlowJo (v.10) were used to collect and analyse the flow cytometry data. Before gating on fluorescence, single cells were gated using forward scatter (FSC-A) and side scatter (SSC-A) (for intact cells) and SSC-W/SSC-H and FSC-W/FSC-H (to ensure that only singlets were sorted). FACS gates were drawn to include only live single cells based on Calcein Blue AM+ and Propidium iodide (Thermo Fisher Scientific). Antibodies used for flow cytometry, cell sorting by FACS or CITE-seq are shown in Supplementary Table 3. All samples were acquired on LSR-Fortessa, BD Symphony Instruments (BD Biosciences) or Cytek Aurora and analysed using FlowJo v10.5 or higher version software (Tree Star, Inc.). Single-cell RNA-seq and TCR V(D)J clonotype profiling. Processing for single-cell expression (scRNA-seq) and T cell receptor V(D)J clonotypes (scTCR-seq) was done using the Chromium Single Cell 5’ Library and Gel Bead Kit (10x Genomics), following manufacturer’s instructions. T cells were isolated from tumor, dLN and blood from 31 mice. Cell density and viability from each mouse tissue of FACS-sorted CD90 + T cells from tumor and blood, or CD90 + CD44 + T cells from draining lymph nodes, were determined by hemacytometer. Approximately 6,000-10,000 cells per sample were used for the reverse transcription mastermix. A total of 14 cycles of PCR amplification was performed to obtain sufficient cDNAs used for both RNA-seq library generation and TCR V(D)J targeted enrichment followed by V(D)J library generation after Gel Bead-in-Emulsion reverse transcription (GEM-RT) reaction and clean-up. TCR V(D)J enrichment was done per manufacturer’s user guide using Chromium Single Cell V(D) J Enrichment Kit, Human T cell (10x Genomics). Libraries for RNA-seq and V(D)J were prepared following the manufacturer’s user guide (10x Genomics), then profiled using Bioanalyzer High Sensitivity DNA kit (Agilent Technologies) and quantified with Qubit (Thermo Fisher Scientific). scRNA-seq libraries were sequenced in one lane of HiSeq4000 (Illumina). scTCR V(D)J libraries were tagged with a sample barcode for multiplexed pooling with other libraries, sequenced in both lanes of a HiSeq2500 machine (Illumina) using Rapid Run mode, and then demultiplexed. All sequencing was done according to the manufacturer’s specification (10x Genomics). Detailed information on mice, tissue isolation and batching of samples is provided in Supplementary Table 4. Pre-processing of single-cell data Sequencing files from Illumina assays were run through CellRanger version 6.1.1 against a transcriptome derived from ENSEMBL version 2.2.0 for the mouse genome GRCm38. The combined matrix files from the filtered_feature_bc_matrix directory for the RNA and ADT libraries were divided into separate submatrices for each sample, based on 52,636 genes for expression, 6 tetramer barcodes for ADT counts, 24 antibody measurements for CITE-seq, and 10 barcodes for multiplexing of the blood samples. Measurements corresponding to various alleles of T cell receptor genes (e.g., Trbv1 through Trbv31) were combined into a single gene measurement (Trbv). Since blood samples were pooled from several mice based on an encoding scheme that used two multiplex barcodes to identify each mouse, single cells were de-multiplexed using the two multiplex barcodes with highest counts. In cases of a tie for the second highest multiplex count (4.6% of cells), those single cells could not be assigned to a particular mouse using this method. TCR sequence data from the filtered_contig_annotations.csv files were processed using a custom script that identified clones across multiple tissues in each mouse, based on identical sets of alpha and beta sequences. To handle the blood cells that could not be assigned using the multiplex counts, blood cells with a TCR nucleotide sequence uniquely matching a cell from lymph node or tumor of a mouse in the pool were assigned to the corresponding mouse. ADT barcodes came from 12 distinct tetramers, of which 2 had gp70 antigens and the remaining 10 had a non-gp70 antigen (C28, UV, or C142). A cell was assigned to an antigen based on its ADT barcode with the highest count, and were not assigned in cases of ties. Integration of single-cell expression data Analysis was performed in the statistical language R version 4.2.0 and with scripts written for Perl version 5.16.3. The single-cell UMI count matrix for each tumor and lymph node sample, and for each pooled blood sample, was processed using scDblFinder version 1.12.0 to identify and remove doublets, or gel beads containing more than one cell. The remaining singlet count matrices were processed using Seurat version 4.1.1 using the SCTransform function (unless specified otherwise, Seurat functions were run using default parameters). All samples were merged into a single Seurat object, then subjected to a filtering process to remove anomalous or low-quality cells, where 10,584 genes were first identified as each being present in more than 1% of all cells, and then 245,675 of the 260,391 cells were retained because more than 99% of their UMI counts were represented by these genes. Counts of mitochondrial genes were not used for filtering, since such genes are present in T cells at the end of their lifespan due to apoptosis, and not necessarily an indicator of poor-quality cells. Since the mice in this study were taken from batches on two different dates, we performed batch correction using the Harmony package 0.1.1 with the batch date as the controlling variable. We calculated PCA cell embeddings following the procedure in https://cran.r-project.org/web/packages/harmony/vignettes/Seurat.html, where we processed the count matrix with the Seurat procedures NormalizeData; FindVariableFeatures using selection.method=”vst” and nfeatures=2000; ScaleData, and RunPCA on the variable genes with npcs=30. The dataset was then processed with the procedure RunHarmony and the Seurat procedures RunUMAP and FindNeighbors on the harmony reduction, and FindClusters to obtain 24 clusters of CD4 and CD8 T cell subtypes. The reason that we made two calls to SCTransform is as follows. The first call was performed on individual samples before integrating them, standard practice in Seurat protocols. The second call was required because we used Harmony, which excels at batch correction, rather than the Seurat integration procedure. Harmony requires a PCA, and this in turn requires finding variable genes and scaling the data, as described above. While SCTransform is essentially equivalent to NormalizeData, FindVariableGenes, and ScaleData, we used these three steps separately as it is recommended procedure for Harmony. Furthermore, we used the procedure FindVariableFeatures with the parameter selection.method=”vst” because it is recommended in the above referenced Web page, and the SCTransform method does not allow for this option. Isolation of CD8 expression data To obtain better resolution and a clustering that was not affected by the CD4 + T cells, we determined the mean Cd4 and Cd8a expression of the 24 clusters, and isolated the 155,496 single cells belonging to the 16 clusters where Cd8a expression was predominant (Supplementary Fig. 2a). We then performed a re-clustering of that data using the Harmony reduction to yield 20 phenotypic CD8 clusters, which represented a reformulation of the original clusters (Supplementary Fig. 2b). The overall process of doublet removal, quality control filtering, and CD8 isolation is summarized in Supplementary Fig. 2c. Correspondences with clusters from external single-cell datasets We obtained single-cell RNA-seq datasets generated or analyzed from five previous published datasets, using raw counts from NCBI GEO (Gene Expression Omnibus) unless specified otherwise: GSM5452712 and GSM5452714 from GSE180094 23 , GSE122712 45 , GSM5530561 and GSM5530563 from GSE182509 (processed data) 24 , and GSM4618806 from GSE152628 (Jun Huang, unpublished) for the analysis by Huang et al., 2022 23 ; the LCMV samples from GSE188666 30 ; E-MTAB-11773 from ArrayExpress 31 ; GSE199565 32 ; and GSE193654 34 . For the study Daniel et al., 2022 30 , we obtained metadata with cluster assignments of individual cell barcodes from the NCBI GEO repository. For the study Giles et al., 2022 32 , we used cell assignments from the Seurat object provided online. For all other studies, we obtained metadata by direct communication with the authors. We used the metadata to create centroids of each of the published clusters by normalizing each cell by its total count to yield a value in transcripts per million and adding 1 as a pseudocount (tpm); computing a trimmed mean of the tpm for each gene, rejecting 10% of measurements from each end of the range; and taking the logarithm base 2. These centroids were used as reference gene signatures to assign each cell from our dataset, where genes with zero expression across an entire sample were excluded, gene expression for each cell was converted to log2(tpm+1), and assignment was performed by the SingleR package in R, using default parameters. Assignments between the two clustering schemes were cross-tabulated, and normalized by the total counts for each of our clusters. Assignment of gp70 status The single-cell ADT assay provided measures for each cell on its binding to two tetramers for gp70 antigens, and ten for non-gp70 antigens (two for C28, five for UV, and three for C142). To determine whether a cell was gp70 + , we used the minimum value for the gp70 as a test statistic in a Poisson test where base rate was the maximum value for the non-gp70 antigens, using the poisson.test function in R. A cell was considered gp70 + if the one-sided p-value with alt=”greater” was less the 1e–6. A clone was considered gp70 + if any of its cells was gp70 + . Clonal co-occurrence analysis Co-occurrence matrices were tabulated by summing intraclonal pairs across all clones. Specifically, for a given set of samples, each clone with n cells, where n > 1 , contributed to the co-occurrence matrix with its outer product xx T , where the outer product represents the count of any two cluster/tissue pairs occurring in the same clone. For migration analysis, we performed the same computation, including all dLN, blood day 7, and tumor samples for each experimental group and keeping track of intraclonal pairs for each combination of cluster and tissue. The resulting co-occurrence matrices were plotted using the chordDiagram function from the circlize package 46 , version 0.4.15, in R, with the parameters transparency=0.2 and reduce=0. In the resulting plots, link widths are normalized by the total number of intraclonal pairs, which make up a full circumference. Same-cluster links, or same-tissue links for the migration analysis, were hidden using the link.visible parameter. In this co-occurrence analysis, clones contribute information according to their possible pairwise counts, so that singleton clones contribute no information and expanded clones contribute information according to the square of their size. For migration analysis in Fig. 6c, chord thicknesses are proportional to the square of the clone sizes between tissues. Since effective clones are highly expanded in tumor but less expanded in dLN and blood, lines may not be discernible between dLN and blood while reasonable line thicknesses will be seen between blood and tumor or dLN and tumor. We also characterized a clone as being gp70 + if any one of its cells was determined to be gp70 + , although the largest clones also biased these counts according to the square of their size. When projecting co-occurrence onto the UMAPs, such projections can be noisy because of the transitive nature of co-occurrence, where co-occurrence of cluster A and B and co-occurrence of clusters B and C necessarily implies co-occurrence of A and C. Therefore, to identify primary differentiation pathways, we applied a minimum spanning tree (MST) algorithm in R to the co-occurrence data within dLNs and within tumors, where links were processed in order from largest to smallest count of intraclonal pairs, and retaining links only if they did not create a cycle in the graph with links previously kept. Co-occurrence links were plotted with the same relative thicknesses as in the circular co-occurrence plots of Fig. 6a–c, normalized to the total number of intraclonal pairs, but with a relative multiplier of 3 for the migration links, since they are relatively sparse. RNA velocity analysis The paired-end FASTQ files from each sample were mapped using kallisto bustools (version 0.46.1) 47 to a transcriptome index from Ensembl version 90 on genome GRCm38. The transcriptome index was generated using kallisto with a read length of 90 nt and intronic sequences from BUSpaRse (Moses L, Pachter L (2021). BUSpaRse: kallisto | bustools R utilities. R package version 1.6.1, https://github.com/BUStools/BUSpaRse.). The resulting spliced and unspliced count matrices for each tissue sample from each mouse were filtered to correspond to the cells used in the Seurat-based analysis, and the Seurat-based UMAP coordinates for those cells were added to the data object. The cells for each tissue and experimental group were combined using the concatenate procedure with join=”outer”. The resulting object was processed by scvelo package 0.2.4 within Python version 3.7.3, using the commands "pp.filter_and_normalize", "pp.moments", “tl.recover_dynamics”, and "tl.velocity" with mode="dynamical". Velocity graphs were generated using the command “tl.velocity_graph” and “pl.velocity_embedding_stream”, with the parameter arrow_size=0.001 to hide arrows, which otherwise gave directions often inconsistent with one another and with empirically determined T cell behavior. Projection of human CD8 + T cells from a Ph1b scRNAseq dataset to a mouse reference Human genes from the Ph1b scRNAseq data were first converted to their mouse orthologs using babelgene (version 22.9). Human genes without mouse orthologs or with mouse orthologs not present in the mouse scRNAseq dataset were left unmodified without renaming. Human CD8 + T cells were then separated by patient and normalized with SCTransform in Seurat (version 4.2) using the default parameters. These samples were then integrated using reference-based integration to overcome the memory limits of canonical correlation analysis (CCA) integration. The second patient in the dataset was chosen at random as the integration reference. After integration, transfer anchors were identified between the query human CD8 + T cell dataset and the mouse CD8 + T cell reference. The MapQuery function in Seurat was used to transfer cell type labels, integrate embeddings, and to project the query data onto the reference UMAP. Gene signature scores for CITYSCAPE The top 20 differentially expressed genes in each of the mouse CD8 + T cell clusters identified from scRNAseq were converted to their human orthologs using babelgene (version 22.9) in R (4.2.0). Mouse genes that did not have human orthologs or with human orthologs that were not present in the CITYSCAPE dataset were removed. The final curated table of signature genes used for analysis are in Supplementary Table 2. Analysis of CITYSCAPE and OAK clinical trial data CITYSCAPE (NCT01903993) is a phase 2 trial investigating tiragolumab with atezolizumab compared to placebo with atezolizumab in patients with locally advanced or metastatic NSCLC 10 . Patients were treated until disease progression or loss of clinical benefit. Patient tumor samples were submitted for RNAseq and the average, log-normalized expression of the genes in Supplementary Table 2 or selected genes as indicated in the text was used to define gene signature scores. Objective response was categorized according to RECIST (version 1.1). For Kaplan-Meier (KM) survival curves and hazard ratios, patients in the CITYSCAPE trial were separated by treatment group and further sub-divided by high or low expression of individual genes or gene signatures, where high or low is defined as greater than or equal to, or less than, the global median expression, respectively, of that gene or gene signature score. The survminer package (version 0.4.9), survival package (version 3.4-0) and R (version 4.2.0) were used to generate the KM plot. A log-rank test was used for statistical testing on the survival data. A Cox proportional hazards regression model was fit on gene or gene signature score high or low data and the hazard ratio and 95% confidence interval for overall survival calculated and plotted for patients receiving tiragolumab with atezolizumab compared to patients receiving placebo with atezolizumab. Similarly, KM survival curves for PFS and OS were generated for the phase 3 OAK study (NCT02008227) evaluating atezolizumab versus chemotherapy in PD-L1-positive previously treated patients with advanced or metastatic NSCLC. Statistical Analysis. Data were analyzed using GraphPad Prism software version 9 (GraphPad, San Diego, CA). Measures between two groups were performed with a Student’s t test (two-tailed). Groups of three or more were analyzed by one-way or two-way analysis of variance (ANOVA) followed by Tukey’s post-testing for multiple comparisons, as appropriate. Declarations Reporting summary Further information on research design is available in the Nature Research Reporting Summary linked to this article. Data availability FASTQ files containing raw sequencing reads for the scRNA-seq, scTCR-seq, ADT-seq, and CITE-seq analyses have been deposited with the NCBI Short Read Archive under accession PRJNA911822. Processed output files from Cell Ranger, and metadata with cluster assignments, clonotypes, and ADT assignments have been deposited with the NCBI Gene Expression Omnibus under accession GSE220901. Code availability Computer code used to generate the single-cell analyses and figures in this paper are provided as a Supplementary File to the NCBI GEO accession GSE220901. Acknowledgments We thank the patients who kindly provided tumor samples for this study, as well as the investigators and staff involved in the CITSCAPE study. We thank Genentech’s FACS core facility for contributing their expertise and performing cell sorting. We thank the Genentech laboratory animal core groups for microinjection, animal care, and genotyping support. We thank Lili Adams for providing assistance with the pharmacodynamic studies. We thank Robert Johnston, Jane Grogan, Avantika Chitre and Soyoung Oh for thoughtful discussions. Author contributions K.N., K.L.B. and E.D. performed in vivo tumor studies, pharmacodynamic studies, bioinformatic studies, and data analysis; T.D.W., K.W. and Sö.M. performed mouse bioinformatic analyses; St.M. and Y.Q. managed and performed in vivo tumor studies; C.T., B.Y.N. and N.S.P. performed clinical trial bioinformatic analyses; K.N., K.L.B., E.Y.C. and I.M. conceived this work; E.Y.C. and I.M. supervised this work; K.N., K.L.B., T.D.W., E.Y.C. and I.M. wrote the manuscript. Declaration of interests The authors declare the following competing interests: all authors are employees of Genentech, a member of the Roche group, which develops and markets drugs for profit. Supplementary Information is available for this paper. Correspondence and requests for materials should be addressed to Eugene Y. Chiang (email: [email protected] ), or Ira Mellman (email: [email protected] ; mobile: 650-452-3894). Reprints and permissions information is available at www.nature.com/reprints. 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The authors declare the following competing interests: all authors are employees of Genentech, a member of the Roche group, which develops and markets drugs for profit. Supplementary Files NIA36960TNREditorialChecklist.pdf NIA36960TNRReportingSummary.pdf SupplementaryFigure.pdf SupplementaryTable1.pdf SupplementaryTable2.pdf SupplementaryTable3.pdf SupplementaryTable4.pdf Cite Share Download PDF Status: Published Journal Publication published 16 Dec, 2024 Read the published version in Nature Cancer → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4201684","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":292957172,"identity":"084ca792-db0f-42d2-9708-047d869cc69d","order_by":0,"name":"Eugene Chiang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9UlEQVRIiWNgGAWjYDACCcYGJF4F6VrOEKUFmcPYRoQO/tnNjZ8LfjHIGZxffOzBx3m1cubsDYwfPubgseTOwWbpmX0MxgY3nqUbztx23Niy5wCz5MxtuLUYSCQ2SPP2MCRuuHHGTJp32zEgI4GNmRe/lubfQC31G26c/yb9d86x+g33HxDU0ibN84MhweB8D5s0Y0NNgsENBvxaJG4ktlnzNkgYzrzBZibZc+yA4YYzic14/cI/I/3xbZ4/NvJ85w8/k/hRUydvcPzwwQ8f8WgBA8Y2YOxIJICYh0HcBgLqQeAPyL4DIFYdEapHwSgYBaNgpAEA5hxWQl/Wh/YAAAAASUVORK5CYII=","orcid":"","institution":"Genentech","correspondingAuthor":true,"prefix":"","firstName":"Eugene","middleName":"","lastName":"Chiang","suffix":""},{"id":292957173,"identity":"fd447449-d0de-4c6c-9416-1d0dce7a677e","order_by":1,"name":"Katherine Nutsch","email":"","orcid":"","institution":"Genentech","correspondingAuthor":false,"prefix":"","firstName":"Katherine","middleName":"","lastName":"Nutsch","suffix":""},{"id":292957174,"identity":"eef1ae0a-f5a7-4d15-9507-688217f386d5","order_by":2,"name":"Karl Banta","email":"","orcid":"","institution":"Genentech, Inc.","correspondingAuthor":false,"prefix":"","firstName":"Karl","middleName":"","lastName":"Banta","suffix":""},{"id":292957175,"identity":"b3119b46-d437-483d-be2b-8c9d886e47c8","order_by":3,"name":"Thomas Wu","email":"","orcid":"https://orcid.org/0000-0003-4505-4531","institution":"Genentech, Inc.","correspondingAuthor":false,"prefix":"","firstName":"Thomas","middleName":"","lastName":"Wu","suffix":""},{"id":292957176,"identity":"4fd96e00-948d-4b78-9b0a-b26de05d8044","order_by":4,"name":"Stephanie Mittman","email":"","orcid":"","institution":"Genentech","correspondingAuthor":false,"prefix":"","firstName":"Stephanie","middleName":"","lastName":"Mittman","suffix":""},{"id":292957177,"identity":"d22ed814-0c09-48aa-b6b0-fc08b9c03287","order_by":5,"name":"Ellen Duong","email":"","orcid":"","institution":"Genentech","correspondingAuthor":false,"prefix":"","firstName":"Ellen","middleName":"","lastName":"Duong","suffix":""},{"id":292957178,"identity":"ffc8170d-594f-4dd2-b874-9ed1afe8375b","order_by":6,"name":"Charles Tran","email":"","orcid":"https://orcid.org/0000-0001-5818-818X","institution":"Genentech","correspondingAuthor":false,"prefix":"","firstName":"Charles","middleName":"","lastName":"Tran","suffix":""},{"id":292957179,"identity":"e7cce77d-08d6-4cba-ac27-47a6d8ecaa99","order_by":7,"name":"Barzin Nabet","email":"","orcid":"https://orcid.org/0000-0002-4824-3533","institution":"Genentech","correspondingAuthor":false,"prefix":"","firstName":"Barzin","middleName":"","lastName":"Nabet","suffix":""},{"id":292957180,"identity":"ab9afcf5-1f2b-4012-9dcb-283a6030ce1c","order_by":8,"name":"Yan Qu","email":"","orcid":"","institution":"Genentech, Inc.","correspondingAuthor":false,"prefix":"","firstName":"Yan","middleName":"","lastName":"Qu","suffix":""},{"id":292957181,"identity":"69973f6a-af8d-4ead-89cf-11003a24550f","order_by":9,"name":"Katherine Williams","email":"","orcid":"https://orcid.org/0000-0001-8061-0105","institution":"Genentech Inc.","correspondingAuthor":false,"prefix":"","firstName":"Katherine","middleName":"","lastName":"Williams","suffix":""},{"id":292957182,"identity":"05704dd3-5412-4684-a8ce-55c81474ee68","order_by":10,"name":"Sören Müller","email":"","orcid":"","institution":"Genentech","correspondingAuthor":false,"prefix":"","firstName":"Sören","middleName":"","lastName":"Müller","suffix":""},{"id":292957183,"identity":"5cfcdecf-9a1e-4998-bcb6-1b81e74199b0","order_by":11,"name":"Namrata S. 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Tumour growth was monitored, and grouped analysis and growth curves for each individual animal (n = 10 per group) are shown. Tumour growth efficacy study is representative of three independent experiments. b, Frequency (dLN, tumour) or numbers (blood) of CD8+ T cells with positive staining for the gp70-specific tetramer. Pharmacodynamic data are representative of three independent experiments (n = 5 per group). p-values are indicated where differences between two groups were determined by two-way unpaired Student’s t-test to be statistically significant. c, FTY720 treatment after CD8+ T cells have egressed from dLN and trafficked to tumour does not affect anti-TIGIT and anti-PD-L1 combination efficacy. FTY720 was administered at day 0, one day prior to initiation of therapy, or on day 7 after one week of therapy. Tumour growth was monitored, and grouped analysis and growth curves for each individual animal (n = 10 per group) are shown. Tumour growth efficacy study is representative of three independent experiments.\u003c/p\u003e","description":"","filename":"Fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-4201684/v1/31dd9f822a61feb313bdfebb.png"},{"id":55338678,"identity":"67402fcc-547b-4001-9c60-d1a555a3a751","added_by":"auto","created_at":"2024-04-26 01:41:27","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":50761,"visible":true,"origin":"","legend":"\u003cp\u003eCD226 expression is a determinant of tumor-specific CD8+ T cell differentiation state. a, Fractions of gp70+ CD8+ T cells expressing CD226 from dLN (top) and tumor (bottom) of CT26 tumor-bearing mice treated with anti-PD-L1, anti-TIGIT, or combination with or without FTY720. p-values are indicated where differences between two groups were determined to be statistically significant by ordinary one-way ANOVA with Tukey’s multiple comparisons test. b, c, Proportions of gp70+CD8+ T cells in dLNs (b) or tumor (c) having various biomarkers, separated by CD226+ (left) or CD226– (right) status, specifically, Ki67, naïve phenotype, Teff/Tem phenotype, Slamf6 and TCF1 co-expression, TCF1 and Tim3 co-expression, non-expression of TCF1, or Tox expression. Each row represents an individual mouse. d, Frequencies of CD226+ (top) and CD226- (bottom) TILs expressing combinations of IFNg and TNFa. e, Individual data and statistics for fractions of IFNg+ TNFa+ cells in CD226+ (left) and CD226- (right) TILs. f, Proportions of gp70+ CD8+ T cells in dLN (left) and tumor (right) having various biomarkers, under control and combination treatment, without and with anti-CD226 treatment.\u003c/p\u003e","description":"","filename":"Fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-4201684/v1/9b4c868a7976297f7270a7f7.png"},{"id":55338680,"identity":"db2dc954-a7ea-4d5d-8929-f8496f02132e","added_by":"auto","created_at":"2024-04-26 01:41:28","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":724000,"visible":true,"origin":"","legend":"\u003cp\u003eSingle-cell RNA-seq identifies distinct CD8+ T cell clusters in CT26 tumour, dLN and blood. a, UMAP of 155,496 CD8+ T cells colored by cluster. UMAP includes CD8+ T cells from all tissues. b, Heatmap of relative average expression of selected marker genes associated with CD8+ T cell phenotype, function or differentiation state in each cluster identified in UMAP. c, CD8+ T cell cluster correspondence with reference gene signatures. Heat maps show cross-labelling of CD8+ T cell clusters (rows) to reference gene signatures (columns), taken from the analyses of Huang et al., Deak et al., Daniel et al. and Giles et al., with intensities indicating normalized frequency. d, RNA velocity projections on UMAP for dLN and tumor from control treatment group.\u003c/p\u003e","description":"","filename":"Fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-4201684/v1/454f24f9b6ca40cea60df75e.png"},{"id":55338684,"identity":"2e7630d0-480d-4fe6-afe6-cad22ccbc9f9","added_by":"auto","created_at":"2024-04-26 01:41:29","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":37959,"visible":true,"origin":"","legend":"\u003cp\u003eTreatment effects on CD8+ T cell cluster and clonal composition in CT26 tumor-bearing mice. Stacked bar graphs of CD8+ T cell cluster composition in each tissue under each treatment condition. Specificity for gp70 was determined by comparing ADT counts for gp70 tetramers, requiring that they be higher than control tetramer count by a Poisson test with a one-sided p-value \u0026lt; 1x10-6. In each stacked bar, open bar denotes singletons, solid bar denotes numbers for clones with less than 100 cells, and hatched bar denotes numbers for clones with 100 or more cells. p-values were determined by post hoc Fisher’s exact test for the indicated category relative to the rest of the contingency table, and denoted by asterisks: *, \u0026lt; 1x10-5; **, \u0026lt; 1x10-10; ***, \u0026lt;1x10-20; ****, \u0026lt; 1x10-50.\u003c/p\u003e","description":"","filename":"Fig5.png","url":"https://assets-eu.researchsquare.com/files/rs-4201684/v1/f554e818118da0e6f5af2b9c.png"},{"id":55338686,"identity":"d59d3ec7-77ed-413d-a140-ca36eec0cf7e","added_by":"auto","created_at":"2024-04-26 01:41:29","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":367030,"visible":true,"origin":"","legend":"\u003cp\u003eEffects of anti-PD-L1, anti-TIGIT and combination treatment on clonal diversity and dual expansion in dLN and tumor. a, Scatterplots showing primary clusters of each individual clonotype in dLN (upper panels) or tumour (lower panels). Color of circles denote cluster designation. Size of circles is representative of clonotype numbers detected in blood at day 7. b, Scatterplots showing gp70 specificity and ADT count for individual clones. c, Cluster composition of the top 30 largest clones in tumor with matching clonotypes, based on identical TCR usage, in dLN and blood, in absolute numbers. Clonotypes from individual mice within each treatment group are identified by the color legend at the top of the tumor bar graphs. Individual mice are labeled as S”group number”.”mouse number”. gp70+ clones are identified by black symbol at the top of the bar graphs. Data shows that all mice have clonotypes represented in the top 30 largest clones in tumor.\u003c/p\u003e","description":"","filename":"Fig6.png","url":"https://assets-eu.researchsquare.com/files/rs-4201684/v1/3dc4d2aba25195f0424d7085.png"},{"id":55338688,"identity":"7719e7ee-67e2-473c-95c0-34c5da409cb6","added_by":"auto","created_at":"2024-04-26 01:41:29","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":777473,"visible":true,"origin":"","legend":"\u003cp\u003eCD8+ T cell cluster relationships within and across tissues of CT26 tumor-bearing mice following anti-TIGIT and/or anti-PD-L1 treatment. a, b, Chord diagrams showing numbers of intraclonal pairs between clusters in dLN (a) or tumor (b). Intraclonal pairs count all pairwise combinations of cluster phenotypes summed over all clones. Lines are shown for all intraclonal pairs between cells with different clusters, with their thickness representing the number of pairs relative to the total number of pairs constituting the full circle. Regions around the circumference without lines represent intraclonal pairs between cells with the same cluster. Red lines denote gp70+ clones; blue lines denote gp70– clones. Singleton clones do not have intraclonal pairs and are therefore not represented in this analysis. c, Intraclonal pairs shared between dLN, blood, and tumor. Each chord diagram contains clusters from blood (Bl), dLN and tumor, separated by gaps. Lines are shown for all intraclonal pairs between cells from different tissues. d, e, Cluster co-occurrence links for gp70+ (d) or gp70– (e) clones in dLN, blood and tumor, projected onto UMAP plots. UMAP plots show the cells of the given tissue and gp70 specificity for each experimental condition. Thickness of lines denotes relative strength of co-occurrence and correlates with line thickness shown in chord diagrams, with an additional multiplier of 3 for migration links between tissues. Lines within dLN and tumor were pruned by a minimum spanning tree (MST) algorithm to show primary relationships. Migration lines from dLN to blood and from blood to tumor were not subjected to MST.\u003c/p\u003e","description":"","filename":"Fig8.png","url":"https://assets-eu.researchsquare.com/files/rs-4201684/v1/fed149bd28dd52fc74e9f54c.png"},{"id":55338681,"identity":"a7fc2094-5bd7-481a-8b3d-fca2cf6fe940","added_by":"auto","created_at":"2024-04-26 01:41:28","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":383040,"visible":true,"origin":"","legend":"\u003cp\u003eAssociations of human CD8+ T cell clusters and gene signatures with clinical response to tiragolumab plus atezolizumab. a. Human gene signature scores in baseline tumor bulk RNA-seq samples from Phase 2 CITYSCAPE NSCLC trial. Patients, irrespective of treatment arm, were separated on the basis of clinical response (CRPR, complete response/partial response; SDPD, stable disease/progressive disease). p-values are indicated for statistically significant differences by two-tailed t-test. b. Individual human gene expression in baseline tumor bulk RNA-seq samples from Phase 2 CITYSCAPE NSCLC trial patients that were treated with tiragolumab plus atezolizumab (T+A) or placebo plus atezolizumab (P+A), separated on the basis of clinical response. c, Forest plot comparing high or low expression of indicated gene associated with overall survival (OS) hazard ratio (HR) in T+A or P+A treatment groups. Mean HR with 95% confidence intervals and p-values are shown. d. Kaplan-Meier (K-M) curves showing the probability of OS in P+A or T+A treatment groups dichotomized on the basis of high or low expression of indicated gene. e. Gene score calculated using the average expression of the CD8 gene panel comprised of CXCR3, CXCR6 and CCL5 in tumor bulk RNA-seq samples from patients treated with tiragolumab plus atezolizumab separated on the basis of clinical response. f, Forest plot comparing high or low expression of composite gene score associated with OS HR in T+A or P+A treatment groups. Mean HR with 95% confidence intervals and p-values are shown. g, K-M curves showing the probability of OS in P+A or T+A treatment groups dichotomized on the basis of high or low composite gene score. For K-M plots, p-value is from log-rank test with null hypothesis that there is no difference between the groups.\u003c/p\u003e","description":"","filename":"Fig10.png","url":"https://assets-eu.researchsquare.com/files/rs-4201684/v1/c8317d3018c83529d459b328.png"},{"id":71619996,"identity":"7fab7b6b-7dd1-4861-938e-c301771ba980","added_by":"auto","created_at":"2024-12-17 08:06:42","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3541901,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4201684/v1/d7b74f3f-dcbd-4577-8ed0-c177eac36379.pdf"},{"id":55338679,"identity":"b58ff7a2-8604-45ba-a77b-55ba2a14a811","added_by":"auto","created_at":"2024-04-26 01:41:28","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1682469,"visible":true,"origin":"","legend":"","description":"","filename":"NIA36960TNREditorialChecklist.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4201684/v1/606a4e43e05cd5459f96fc7f.pdf"},{"id":55338677,"identity":"d3839a97-f25c-49de-ba82-3844fdbbeb91","added_by":"auto","created_at":"2024-04-26 01:41:27","extension":"pdf","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":1672155,"visible":true,"origin":"","legend":"","description":"","filename":"NIA36960TNRReportingSummary.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4201684/v1/aec6d99164c1a2c9891d45f6.pdf"},{"id":55338683,"identity":"72864005-0d14-4df2-8ada-65b32a50520b","added_by":"auto","created_at":"2024-04-26 01:41:28","extension":"pdf","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":229728,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigure.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4201684/v1/6ae36b9fe567ba1b5d3130ea.pdf"},{"id":55339423,"identity":"422ebd47-1add-4eef-aa46-ee983da860ec","added_by":"auto","created_at":"2024-04-26 01:49:29","extension":"pdf","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":59688,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4201684/v1/a7aa9fa0cd5531000b782287.pdf"},{"id":55338685,"identity":"681fb574-bdda-4049-bb78-56c684daf324","added_by":"auto","created_at":"2024-04-26 01:41:29","extension":"pdf","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":50843,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4201684/v1/db29b2d594d94270613f5a12.pdf"},{"id":55338690,"identity":"e97bcd71-7d7e-47f9-907b-58007f4e72d5","added_by":"auto","created_at":"2024-04-26 01:41:30","extension":"pdf","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":62655,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable3.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4201684/v1/7e928a94911e0d37aea54bb4.pdf"},{"id":55338689,"identity":"2402be56-0ad8-45b5-aa02-bad8e76daca9","added_by":"auto","created_at":"2024-04-26 01:41:30","extension":"pdf","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":49212,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable4.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4201684/v1/5810f5ecc4884d3569844381.pdf"}],"financialInterests":"\u003cb\u003eYes\u003c/b\u003e there is potential Competing Interest.\nThe authors declare the following competing interests: all authors are employees of Genentech, a member of the Roche group, which develops and markets drugs for profit.","formattedTitle":"TIGIT and PD-L1 co-blockade promotes clonal expansion of multipotent, non-exhausted anti-tumor T cells by facilitating costimulation","fulltext":[{"header":"Introduction","content":"\u003cp\u003eClonotypically expanded effector-like or exhausted CD8\u003csup\u003e+\u003c/sup\u003e T cells are often found in tumors, normal adjacent tissue, and peripheral blood of patients with various types of cancer\u003csup\u003e1\u003c/sup\u003e. Expanded clones in the blood likely originate outside the tumor, presumably in draining lymph nodes (dLN). The existence of peripherally expanded T cell clones may indicate an active anti-tumor immune response as this group of patients exhibit favorable responses to the anti-PD-L1 monoclonal antibody (mAb) atezolizumab in various clinical trials\u003csup\u003e1\u003c/sup\u003e. T cells in blood do not exhibit features of exhausted T cells (Tex) and are unlikely to be derived from exhausted tumor-infiltrating lymphocytes (TILs)\u003csup\u003e2\u003c/sup\u003e, suggesting that peripherally expanded cells do not reflect reversal of intratumoral Tex exhaustion following PD-1 blockade\u003csup\u003e3, 4, 5, 6\u003c/sup\u003e. However, the extent to which checkpoint blockade reprograms CD8\u003csup\u003e+\u003c/sup\u003e T cells already committed to the exhaustion pathway or discourages developmental commitment to exhaustion remains a key unknown\u003csup\u003e7\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAlthough immunotherapies targeting the PD-1/PD-L1 pathway have shown promise in several different cancers, only\u0026thinsp;~\u0026thinsp;30% of patients achieve durable responses, necessitating a search for new strategies such as combinations targeting multiple or novel immune checkpoint receptors\u003csup\u003e8\u003c/sup\u003e. TIGIT (T cell immunoreceptor with Ig and immunoreceptor tyrosine-based inhibitory domains) has garnered widespread attention due to efficacy in early clinical trials using blocking antibodies against both TIGIT and PD-L1\u003csup\u003e9\u003c/sup\u003e. Recent analysis of the randomized phase-2 CITYSCAPE trial (NCT01903993) evaluating atezolizumab versus anti-TIGIT mAb tiragolumab plus atezolizumab in non-small cell lung cancer (NSCLC)\u003csup\u003e10\u003c/sup\u003e revealed that high baseline intratumoral macrophages and regulatory T cells were associated with clinical benefit\u003csup\u003e11\u003c/sup\u003e. Although these results suggest that the TIGIT/PD-L1 reprograms the tumor microenvironment (TME), high levels of CD8\u003csup\u003e+\u003c/sup\u003e effector T cells (Teff) were also associated with response.\u003c/p\u003e \u003cp\u003eIn CD8\u003csup\u003e+\u003c/sup\u003e TILs, TIGIT and PD-1 expression are highly correlated\u003csup\u003e12\u003c/sup\u003e. Whereas PD-1 primarily regulates costimulation by CD28, TIGIT and PD-1 together regulate the function of CD226, the activating counterreceptor to TIGIT\u003csup\u003e13\u003c/sup\u003e. Co-expression may define distinct populations of \u0026ldquo;stem cell-like memory (Tscm)\u0026rdquo; cells\u003csup\u003e14, 15, 16, 17\u003c/sup\u003e. Tscm cells are believed to be primary targets of PD-1/PD-L1 blockade in both anti-tumor and anti-viral immunity\u003csup\u003e18, 19, 20\u003c/sup\u003e. Blocking PD-1 signaling may differentiate these progenitors into T cells with cytolytic effector activity against tumor cells, perhaps via a recently described transient population of T precursor exhausted cells (Tpex)\u003csup\u003e21, 22, 23, 24, 25\u003c/sup\u003e. Thus, PD-1 expression may reflect T cell activation status in addition to denoting exhaustion or commitment to exhaustion. It remains uncertain whether Tpex give rise to only Tex in the tumor, whether commitment to the Tex pathway begins in dLN, or whether Tex, Teff and memory (Tem) cells originate from separate precursors either prior to or following tumor arrival. Also uncharacterized is the role (if any) of TIGIT blockade in regulating these events, alone or in combination with PD-1 blockade.\u003c/p\u003e \u003cp\u003eTo inform these questions, we undertook a unique multicompartment, multi-omics single-cell approach, analyzing over 245,000 T cells. We examined not only the features of CD8\u003csup\u003e+\u003c/sup\u003e T cells in dLN and tumor as has been done previously, but also in the blood. Sampling these three critical tissue compartments facilitated insight into the spatial and temporal effects of TIGIT and PD-1 blockade on T cell fate decisions.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003eCombination treatment requires trafficking of lymphocytes from draining lymph nodes to tumor\u003c/h2\u003e\n \u003cp\u003eThe observation that PD-1 and TIGIT coordinately regulate costimulatory signals in T cells suggests that both receptors may activate T cells at the same steps and anatomical sites\u003csup\u003e9\u003c/sup\u003e. Using the CT26 syngeneic mouse tumor model, we evaluated the role of dLN in TIGIT blockade by restricting trafficking of T cells with FTY720, an inhibitor of T cell egress from lymphoid organs\u003csup\u003e26\u003c/sup\u003e. Consistent with previous observations\u003csup\u003e12\u003c/sup\u003e, the combination of anti-TIGIT with anti-PD-L1 demonstrated therapeutic efficacy whereas anti-PD-L1 or anti-TIGIT monotherapies had only limited impact on tumor growth (Fig.\u0026nbsp;1a, Extended Data Fig.\u0026nbsp;1a). FTY720 reduced the activity of both single-agent anti-TIGIT and TIGIT/PD-L1 co-blockade (Fig.\u0026nbsp;1a). Similar results were observed in the EO771 tumor model (Extended Data Fig.\u0026nbsp;1b).\u003c/p\u003e\n \u003cp\u003eTreatment with anti-PD-L1, anti-TIGIT, or both did not affect total numbers of CD8\u003csup\u003e+\u003c/sup\u003e T cells, CD4\u003csup\u003e+\u003c/sup\u003e T cells or regulatory T cells (Tregs) in CT26 dLN or tumor, either with or without FTY720 treatment (Extended Data Fig. 1c). We therefore asked if checkpoint blockade and FTY720 affected the abundance or distribution of CD8\u003csup\u003e+\u003c/sup\u003e T cells that were specific for tumor antigens. We identified these cells using tetramers that bind T cell receptors (TCRs) specific for gp70, a tumor-associated, immunodominant retroviral antigen expressed by CT26 cells (Extended Data Fig. 2a)\u003csup\u003e27\u003c/sup\u003e. Anti-TIGIT in combination with anti-PD-L1 increased the fraction of gp70\u003csup\u003e+\u003c/sup\u003eCD8\u003csup\u003e+\u003c/sup\u003e T cells (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0138) in dLN, whereas anti-PD-L1 or anti-TIGIT alone had little effect (Fig.\u0026nbsp;1b). The addition of FTY720 before combination treatment further increased the frequency of gp70\u003csup\u003e+\u003c/sup\u003eCD8\u003csup\u003e+\u003c/sup\u003e T cells in dLN (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0472), likely reflecting their accumulation in dLN by preventing T cell egress.\u003c/p\u003e\n \u003cp\u003eIn blood, numbers of gp70\u003csup\u003e+\u003c/sup\u003eCD8\u003csup\u003e+\u003c/sup\u003e T cells were significantly increased with anti-TIGIT (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0134) or combination treatment (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), but not in FTY720-treated animals (Fig.\u0026nbsp;1b). In tumors, only the combination of anti-TIGIT and anti-PD-L1 significantly increased the fraction of gp70\u003csup\u003e+\u003c/sup\u003eCD8\u003csup\u003e+\u003c/sup\u003e T cells (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0098) (Fig.\u0026nbsp;1b). Since trafficking via blood was blocked, at least some expansion of intratumoral T cells was likely to have occurred locally. Although FTY720-treated mice exhibited a trend towards increased gp70\u003csup\u003e+\u003c/sup\u003eCD8\u003csup\u003e+\u003c/sup\u003e T cells in tumors, these presumably locally expanded cells appear to be of lower \u0026ldquo;quality\u0026rdquo; as they were unable to control tumor growth.\u003c/p\u003e\n \u003cp\u003eWe next asked if anti-tumor efficacy relied on the continuous recruitment of newly generated T cells from dLN and blood. Early administration of FTY720 blocked combination efficacy, whereas delaying the blockade of T cell egress until 7 days after combination treatment resulted in only slight impairment in anti-tumor efficacy (Fig.\u0026nbsp;1c). Thus, the efficacy of combination checkpoint blockade depends on the induction of tumor-specific CD8\u003csup\u003e+\u003c/sup\u003e T cells in dLN that then traffic to and infiltrate tumors via the circulation. Once the newly mobilized T cells seeded tumors, they appeared sufficient to sustain therapeutic benefit in response to anti-TIGIT plus anti-PD-L1.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003eCD226 has role in tumor-specific CD8\u003csup\u003e+\u003c/sup\u003e T cell differentiation\u003c/h2\u003e\n \u003cp\u003eSince human TILs in NSCLC differentially express CD226 and CD28 in various CD8\u003csup\u003e+\u003c/sup\u003e T cell clusters, combination treatment may be required to optimally activate the entire tumor-reactive TIL repertoire\u003csup\u003e13\u003c/sup\u003e. To evaluate the role of CD226 on tumor-specific CD8\u003csup\u003e+\u003c/sup\u003e T cell subsets in the mouse tumor model, we segregated gp70\u003csup\u003e+\u003c/sup\u003e CD8\u003csup\u003e+\u003c/sup\u003e T cells based on CD226 expression. Anti-TIGIT alone or in combination with anti-PD-L1 increased the frequency of CD226\u003csup\u003e+\u003c/sup\u003egp70\u003csup\u003e+\u003c/sup\u003eCD8\u003csup\u003e+\u003c/sup\u003e T cells in both dLN and tumor, even with FTY720 treatment (Fig. 2a). Following combination blockade, CD226\u003csup\u003e+\u003c/sup\u003egp70\u003csup\u003e+\u003c/sup\u003eCD8\u003csup\u003e+\u003c/sup\u003e T cells were significantly more proliferative (Ki67\u003csup\u003e+\u003c/sup\u003e), but only in dLN (Fig.\u0026nbsp;2b, c, Extended Data Fig.\u0026nbsp;3a). CD226\u003csup\u003e\u0026ndash;\u003c/sup\u003egp70\u003csup\u003e+\u003c/sup\u003eCD8\u003csup\u003e+\u003c/sup\u003e T cell proliferation was not affected by any treatment. Few CD226\u003csup\u003e+\u003c/sup\u003egp70\u003csup\u003e+\u003c/sup\u003eCD8\u003csup\u003e+\u003c/sup\u003e T cells in dLN were na\u0026iuml;ve as compared with the CD226\u0026ndash; fraction (Fig. 2b, Extended Data Fig. 3b); combination treatment, but neither monotherapy, increased the frequency of CD226\u003csup\u003e+\u003c/sup\u003egp70\u003csup\u003e+\u003c/sup\u003eCD8\u003csup\u003e+\u003c/sup\u003e T cells with a Teff or Tem phenotype whereas no effects were observed in the CD226\u0026ndash; population (Fig. 2b, Extended Data Fig. 3c).\u003c/p\u003e\n \u003cp\u003eTo further elucidate the effects of checkpoint blockade on activation and differentiation, we measured various markers of T cell states. Slamf6 and TCF1 co-expression are considered markers of Tscm or Tpex cells\u003csup\u003e7, 16\u003c/sup\u003e. In dLN, the frequency of these cells in the CD226\u003csup\u003e+\u003c/sup\u003e fraction was not affected by any treatment, but anti-TIGIT alone or in combination with anti-PD-L1 significantly reduced frequencies in the CD226\u0026ndash; subset (Fig. 2b; Extended Data Fig. 3d, p\u0026thinsp;=\u0026thinsp;0.0014). By contrast, in tumor, anti-TIGIT and combination treatment increased frequencies of Slamf6\u003csup\u003e+\u003c/sup\u003eTCF1\u003csup\u003e+\u003c/sup\u003e cells in both CD226\u003csup\u003e+\u003c/sup\u003e and CD226\u003csup\u003e\u0026minus;\u003c/sup\u003e subsets (Fig. 2c; Extended Data Fig. 3d, p\u0026thinsp;=\u0026thinsp;0.0014).\u003c/p\u003e\n \u003cp\u003eAs T cells differentiate from the Tscm or Tpex state, they express immune checkpoints such as Tim3. Combination treatment as well as anti-TIGIT alone increased the frequencies of both CD226\u003csup\u003e+\u003c/sup\u003e and CD226\u0026ndash; TCF1\u003csup\u003e+\u003c/sup\u003eTim3\u003csup\u003e+\u003c/sup\u003e gp70\u003csup\u003e+\u003c/sup\u003eCD8\u003csup\u003e+\u003c/sup\u003e T cells in tumor whereas effects in the dLN were limited to the CD226\u003csup\u003e+\u003c/sup\u003e subset; FTY720 largely abolished these effects (Fig. 2b,c; see Extended Data Fig. 3e for statistics). As T cells further differentiate, they lose expression of TCF1 although transcription of the \u003cem\u003eTcf7\u003c/em\u003e gene appears to precede the loss of the TCF1 protein itself (compare to Fig. 3b). In the dLN, a significant increase in the frequency of TCF1\u0026ndash; tumor specific CD8\u003csup\u003e+\u003c/sup\u003e T cells is seen in the CD226\u003csup\u003e+\u003c/sup\u003e fraction with anti-TIGIT or combination treatment; no effect was detected in CD226\u0026ndash; cells (Fig. 2b; Extended Data Fig. 3f).\u003c/p\u003e\n \u003cp\u003eTox is a key transcriptional regulator of exhaustion programming and differentiation towards terminal exhaustion\u003csup\u003e4, 5\u003c/sup\u003e. Treatment with either anti-TIGIT alone or anti-TIGIT plus anti-PD-L1 markedly decreased Tox expression in CD226\u003csup\u003e+\u003c/sup\u003e but not CD226\u0026ndash; gp70\u003csup\u003e+\u003c/sup\u003eCD8\u003csup\u003e+\u003c/sup\u003e T cells in dLN, while decreased Tox expression was seen in both CD226\u003csup\u003e+\u003c/sup\u003e and CD226\u0026ndash; fractions in tumor; FTY720 appeared to diminish the combination effect on Tox expression in some cases (Fig. 2b,c; see Extended Data Fig. 3g for statistics).\u003c/p\u003e\n \u003cp\u003eSimilar effects were seen in the EO771 model, with combination treatment increasing the frequency of CD8\u003csup\u003e+\u003c/sup\u003e T cells in tumors, promoting CD226 expression on tumor CD8\u003csup\u003e+\u003c/sup\u003e T cells, and increasing the TCF1\u003csup\u003e+\u003c/sup\u003eTim3\u003csup\u003e+\u003c/sup\u003e phenotype while reducing Tox\u003csup\u003e+\u003c/sup\u003e frequencies (Extended Data Fig. 3h-m).\u003c/p\u003e\n \u003cp\u003eTo assess the effector state of TILs responding to checkpoint blockade, we measured production of the proinflammatory effector cytokines IFN-g and TNF-a. Single-agent anti-TIGIT and combination treatment increased dual production of proinflammatory cytokines IFN-g and TNF-a in the CD226\u003csup\u003e+\u003c/sup\u003e fraction of intratumoral CD8\u003csup\u003e+\u003c/sup\u003e T cells relative to the CD226\u0026ndash; fraction, with FTY720 eliminating this effect, suggesting that T cells derived from the periphery might possess superior effector function (Fig. 2d, e); assessment of cytokine production by tumor-specific TILs was not possible due to downregulation of TCR upon \u003cem\u003ein vitro\u003c/em\u003e stimulation.\u003c/p\u003e\n \u003cp\u003eAs anti-TIGIT plus anti-PD-L1 appeared to have more pronounced effects on CD8\u003csup\u003e+\u003c/sup\u003e T cells expressing CD226, particularly in dLN, we concurrently treated mice receiving the combination with CD226-blocking mAb. As we could not segregate gp70-specific CD8\u003csup\u003e+\u003c/sup\u003e T cells on the basis of CD226 expression in the presence of the blocking mAb, we examined total gp70\u003csup\u003e+\u003c/sup\u003e cells and could not discern effects on Slamf6\u003csup\u003e+\u003c/sup\u003eTCF1\u003csup\u003e+\u003c/sup\u003e cells (Fig. 2f, Extended Data Fig. 3n). However, anti-CD226 mAb impaired the ability of combination treatment to increase the frequency of TCF1\u003csup\u003e+\u003c/sup\u003eTim3\u003csup\u003e+\u003c/sup\u003e tumor-specific CD8\u003csup\u003e+\u003c/sup\u003e T cells in dLN and tumor (Fig. 2f, Extended Data Fig. 3o). CD226 blockade also showed a trend towards reducing the ability of combination treatment to drive differentiation to a Teff/Tem phenotype (Fig. 2f, Extended Data Fig. 3p). Anti-CD226 mAb prevented the reduction in Tox-expressing cells in dLN and to a greater extent in tumor (Fig. 2f; Extended Data Fig. 3q, p\u0026thinsp;=\u0026thinsp;0.025 and 0.009 respectively).\u003c/p\u003e\n \u003cp\u003eTaken together, addition of anti-TIGIT to PD-1/PD-L1 blockade initiated distinct differentiation pathways of Tscm or Tpex cells in dLN in a CD226-dependent fashion. These cells were further expanded in the tumor and were guided to develop into qualitatively better polyfunctional effectors. Similarly, upregulation of Tox characteristic of Tpex and Tex differentiation was prevented, again in a CD226-dependent manner.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eTIGIT and PD-L1 co-blockade promotes and expands different CD8\u003c/strong\u003e \u003csup\u003e\u0026nbsp;\u003cstrong\u003e+\u003c/strong\u003e\u0026nbsp;\u003c/sup\u003e \u003cstrong\u003eT cell states in dLN, blood, and tumor\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eWe further examined how co-blockade affects the generation, phenotype, and trajectory of tumor-specific T cells using a multi-omics single-cell approach, performing single-cell RNA sequencing (scRNA-seq) and TCR sequencing (scTCR-seq) on T cells from tumor, dLN, and blood. These assays were supplemented by antibody-derived tag sequencing (ADT-seq) with tetramers against gp70 and cellular indexing of transcriptomes and epitopes (CITE-seq) using a panel of 18 cell surface proteins.\u003c/p\u003e\n \u003cp\u003eGene expression profiles of a large dataset of 245,675 T cells yielded 24 distinct clusters (Extended Data Fig.\u0026nbsp;4a), with contributions across treatment groups (Extended Data Fig.\u0026nbsp;4b), but with some clusters appearing selectively localized to dLN, blood, or tumor (Extended Data Fig.\u0026nbsp;4c). Effector status, as indicated by granzyme B expression, was confined primarily to CD8\u003csup\u003e+\u003c/sup\u003e T cells that showed clonal expansion and high ADT counts, a measure of the number of gp70 tetramers bound (Extended Data Fig. 4d\u0026ndash;g). CITE-seq provided a complementary characterization of T cell differentiation, effector, and memory states based on surface marker expression (Extended Data Fig. 4h).\u003c/p\u003e\n \u003cp\u003eWe obtained greater resolution of CD8\u003csup\u003e+\u003c/sup\u003e T cell phenotypes by re-analyzing the T cells with high \u003cem\u003eCD8a\u003c/em\u003e expression. These 155,496 CD8\u003csup\u003e+\u003c/sup\u003e T cells comprise one of the largest datasets used for this type of analysis, enabling higher resolution clustering and unprecedented insight into the responses of CD8\u003csup\u003e+\u003c/sup\u003e T cells to checkpoint inhibition. 20 distinct CD8\u003csup\u003e+\u003c/sup\u003e clusters were identified (Fig. 3a,b; Extended Data Fig. 5; see Supplementary Table 1 for genes defining each cluster), with contributions consistent across individual mice (Extended Data Fig. 6a). As before, clusters belonged to specific tissues, and had contributions across experimental groups (Extended Data Fig. 6b). Clonal expansion and ADT counts were differentially distributed amongst clusters, with increases seen in non-Ccr7 clusters (Extended Data Fig. 6c).\u003c/p\u003e\n \u003cp\u003eThe clusters exhibited various phenotypes (Fig.\u0026nbsp;3a):\u003c/p\u003e\n \u003cp\u003e(a) four Ccr7 clusters (\u0026quot;Ccr7.1-4\u0026quot;) characterized by \u003cem\u003eCcr7\u003c/em\u003e, a marker expressed by na\u0026iuml;ve, Tscm and central memory (Tcm) cells but low in cytotoxic CD8\u003csup\u003e+\u003c/sup\u003e Teff and Tem cells\u003csup\u003e28\u003c/sup\u003e, as well as genes associated with Tscm cells such as \u003cem\u003eSell\u003c/em\u003e, \u003cem\u003eLef1\u003c/em\u003e, and \u003cem\u003eTcf7\u003c/em\u003e\u003csup\u003e18\u003c/sup\u003e, and also high expression of ribosomal proteins;\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(b) a distinct cluster (\u0026quot;Early\u0026quot;) characterized by expression of \u003cem\u003eCd69\u003c/em\u003e and other markers of early T cell activation;\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(c) a distinct \u0026quot;Slamf6\u0026quot; cluster marked by high \u003cem\u003eSlamf6\u003c/em\u003e and \u003cem\u003eTcf7\u003c/em\u003e expression representative of a Tscm population;\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(d) three Ifit clusters (\u0026quot;Ifit.1-3\u0026quot;) with hallmarks of interferon response genes indicating activated T cells;\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(e) two Ccl5 clusters (\u0026quot;Ccl5.1-2\u0026quot;) marked by this chemokine that can exert chemotactic effects on T cells and is associated with CD8\u003csup\u003e+\u003c/sup\u003e T cell infiltration into tumors\u003csup\u003e29\u003c/sup\u003e;\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(f) two Cytotox clusters (\u0026quot;Cytotox.1-2\u0026quot;) exhibiting hallmarks of cytotoxic gene expression as well as genes associated with exhaustion such as \u003cem\u003eTox\u003c/em\u003e and checkpoint inhibitory checkpoint receptors;\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(g) three Cyt/Mit clusters (\u0026quot;Cyt/Mit.1-3\u0026quot;) that represent proliferating cytotoxic cells as they express genes associated with cytotoxicity and mitosis;\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(h) two Mitotic clusters (\u0026quot;Mitotic.1-2\u0026quot;) expressing genes associated with mitosis but not genes associated with effector function; and\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(i) two clusters representing dying cells (\u0026quot;Dying.1-2\u0026quot;).\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eThe Ccl5 clusters shared expression of a number of genes associated with the Cytotox or Cyt/Mit clusters, but did not have properties of exhaustion. CITE-seq analysis using various surface-expressed proteins corroborated this categorization by gene expression (Extended Data Fig.\u0026nbsp;6d\u0026ndash;f). Both scRNAseq and CITE-seq analysis showed that CD226 expression was most characteristic of Ccl5.1 T cells. CD28 showed some overlapping expression with CD226 but also marked a few distinct clusters consistent with our previous findings for human NSCLC TILs\u003csup\u003e13\u003c/sup\u003e (Extended Data Fig. 6d\u0026ndash;f). The Ccl5.1 cluster is of particular interest in that it was the only major non-na\u0026iuml;ve cell state found in the blood.\u003c/p\u003e\n \u003cp\u003eComparison of our clusters with reference gene signatures from published datasets\u003csup\u003e23, 30, 31, 32\u003c/sup\u003e showed general concordance albeit with more granularity due to the larger sample set used here (Fig. 3c). Of particular relevance, our Ccl5, Ifit.3, and Cytotox clusters shared strong similarities with the \u0026ldquo;better effectors\u0026rdquo; described by others in response to a combination of anti-PD-1 therapy with IL-2 agonists\u003csup\u003e31\u003c/sup\u003e. However, our Ccl5.1 cluster also corresponded with the \u0026quot;Stem-like cluster\u0026quot; in that study and with the \u0026quot;Transitory Tex cluster\u0026quot; by Huang and colleagues\u003csup\u003e23\u003c/sup\u003e.\u003c/p\u003e\n \u003cp\u003eOur multi-omics dataset allowed us to convert spliced and unspliced mRNA counts to estimate RNA velocity measurements and infer differentiation trajectories. Although the directionality of cell traffic often cannot be assigned confidently from velocity-based trajectories\u003csup\u003e33\u003c/sup\u003e, visualization results from Li and colleagues using photoactivation have established the \u003cem\u003ein vivo\u003c/em\u003e migration of T cells into and out of tumors\u003csup\u003e34\u003c/sup\u003e. By assigning our clusters to the Li \u003cem\u003eet al\u003c/em\u003e. gene expression signatures (Extended Data Fig.\u0026nbsp;7a), we can ascertain directionality in our analysis. Using control-treated tumor-bearing mice as a reference, RNA velocity patterns differed in dLN and tumor (Fig.\u0026nbsp;3d). In dLN, a major trajectory originated from Early and Ccr7 clusters and yielded Slamf6 cells, which then differentiated into Ifit or Ccl5 cells. In tumors, differentiation progressed from Ccl5 cells through Cytotox cells to Cyt/Mit cells. From there, a second differentiation pathway generated Mitotic cells. RNA velocity patterns were similar across treatment groups, indicating that differentiation pathways were not fundamentally affected by the various treatments (Extended Data Fig.\u0026nbsp;7b,c).\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eCombination treatment expanded tumor-specific CD8\u003c/strong\u003e \u003csup\u003e\u0026nbsp;\u003cstrong\u003e+\u003c/strong\u003e\u0026nbsp;\u003c/sup\u003e \u003cstrong\u003eT cells marked by Ccl5 that transit from dLN to tumor via blood\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eWe then applied our scTCR-seq data to segregate T cells by the expansion of their parent clone, revealing striking differences across treatment groups, especially when using ADT-seq counts to distinguish gp70\u003csup\u003e+\u003c/sup\u003e from gp70\u003csup\u003e\u0026minus;\u003c/sup\u003e cells (Extended Data Fig. 6c). As shown in Fig. 4, cells in dLN were predominantly singletons (having only one cell expressing a given TCR clonotype) across each cluster, but showed evidence of clonal expansion in the Slamf6 and Ccl5.1 clusters following combination treatment. In contrast, cells in tumor were almost exclusively expanded clones. Although clones were specific to individual mice, these results were not attributable to any single mouse (Extended Data Fig. 8).\u003c/p\u003e\n \u003cp\u003eAt day 7, gp70\u003csup\u003e+\u003c/sup\u003e CD8\u003csup\u003e+\u003c/sup\u003e T cells were detected in the blood of mice treated with anti-TIGIT or combination treatment and were comprised of Ccl5.1 cells (Fig. 4, bars facing right). The absolute cell numbers were low, likely reflecting the transient residence of mobilized T cells in the blood. Their appearance was blocked by FTY720 treatment, indicating that expanded Ccl5.1 cells likely originated in dLN. This inference was supported by the accumulation of clonally expanded gp70\u003csup\u003e+\u003c/sup\u003e Ccl5.1 cells in dLN.\u003c/p\u003e\n \u003cp\u003eSome gp70\u003csup\u003e\u0026minus;\u003c/sup\u003e CD8\u003csup\u003e+\u003c/sup\u003e T cells were found in the Ccl5.1 cluster, but they were mostly in the immature Ccr7 clusters (Fig. 4, bars facing left). Since these cells were apparent at day 0 and in all treatment groups, they were not elicited by combination TIGIT/PD-L1 blockade. The gp70\u003csup\u003e\u0026minus;\u003c/sup\u003e cells in the Ccl5.1 cluster, however, were significantly enhanced by the combination, and could include both bystanders and T cell clonotypes that were specific to tumor antigens other than gp70.\u003c/p\u003e\n \u003cp\u003eTumors, unlike the dLN or blood, contained relatively large numbers of both clonally expanded gp70\u003csup\u003e\u0026minus;\u003c/sup\u003e and gp70\u003csup\u003e+\u003c/sup\u003e TILs in all treatment groups. However, in mice treated with both anti-PD-L1 and anti-TIGIT, this increase was most pronounced for gp70\u003csup\u003e+\u003c/sup\u003e T cells, which were found in the Ifit, Ccl5.2, Cytotox, and Cyt/Mit clusters (Fig. 4). The increase in gp70\u003csup\u003e+\u003c/sup\u003e clones in the Ccl5.2 cluster was both most pronounced and selectively decreased by FTY720 treatment, strongly suggesting that these cells derived from the blood-borne Ccl5.1 population. Interestingly, in FTY720 treated mice, gp70\u003csup\u003e+\u003c/sup\u003e clones expanded in the other clusters, indicating that these may pre-exist in tumor and expand and differentiate intratumorally in response to combined PD-L1/TIGIT blockade.\u003c/p\u003e\n \u003cp\u003eThus, in response to combination treatment, tumor antigen-specific (and possibly also non-specific) clonotypes expand in the dLN, exit as Ccl5.1 cells into the blood, and continued to expand after arrival in the tumor.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eCo-blockade of PD-L1 and TIGIT focuses the TCR clonal diversity of tumor antigen-specific CD8\u003c/strong\u003e \u003csup\u003e\u0026nbsp;\u003cstrong\u003e+\u003c/strong\u003e\u0026nbsp;\u003c/sup\u003e \u003cstrong\u003eT cells\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eWe next compared the degree of clonal expansion in dLN, blood, and tumor at day 7 post-treatment, characterizing each clone by its majority cluster at each site (Fig.\u0026nbsp;5a, Extended Data Fig.\u0026nbsp;9a). Inhibiting both PD-L1 and TIGIT elicited strikingly coordinated clonal dynamics. Although only a few clones exhibited large expansions, they did so in each of the three tissue compartments (Fig.\u0026nbsp;5a). In dLN, expansion occured mostly in Ifit.3, Ccl5.1 and Cytotox.1 cells, while in the tumor Ccl5.2, Cytotox.1 or Cytotox.2 cells were preferentially expanded. Combination treatment also resulted in expansion in the blood (illustrated by the diameter of the circles shown in each plot, Fig.\u0026nbsp;5a; Extended Fig.\u0026nbsp;9a). Here, the expanded clonotypes were contained almost exclusively in the Ccl5.1 population (Fig.\u0026nbsp;4; Extended Data Fig.\u0026nbsp;9b,c), and these were shared with the corresponding clusters in dLN or the tumor (illustrated by the color of the circles in each plot, Fig.\u0026nbsp;5a; Extended Fig.\u0026nbsp;9a). Expansion due to single agent treatment occurred (to a greater extent following anti-TIGIT alone) but expansion was mostly limited to dLN or tumor.\u003c/p\u003e\n \u003cp\u003eIn the presence of FTY720, many clones exhibited dual expansion in dLN and tumor with relatively limited expansion in blood, suggesting that these dual-expanded clones arose independently in dLN and tumor.\u003c/p\u003e\n \u003cp\u003eThe most highly expanded clones following combination treatment were gp70\u003csup\u003e+\u003c/sup\u003e, indicated by a high ADT count (blue/purple circles, Fig.\u0026nbsp;5b); little or no expansion occurred after anti-TIGIT or anti-PD-L1 alone. Most of the dual-expanded clones in the single-agent treatment groups had low or undetectable gp70 ADT counts, suggesting that they were either \u0026ldquo;bystander\u0026rdquo; non-tumor reactive T clones\u003csup\u003e35, 36\u003c/sup\u003e or specific for other tumor-associated antigens. In the presence of FTY720, high gp70 ADT counts were also detectable in dual-expanded clones, as expected if these cells represented pre-existing clones already present in dLN and tumor prior to treatment.\u003c/p\u003e\n \u003cp\u003eSince the scatterplots (Fig.\u0026nbsp;5a,b) depict only the primary cluster type for each clone, we evaluated the composition of the 30 most expanded clones for each treatment group in tumor, and matched them to dLN and blood to study the distribution of individual clones across T cell clusters (Fig.\u0026nbsp;5c). The largest clones in tumor had measurable counterparts in dLN but only following combination treatment. In dLN, these clones consisted predominantly of the Ccl5.1, Cytotox.1 and Cytotox.2 populations. The same expanded TCR clones were also found in the blood, again contained almost exclusively in the Ccl5.1 population. FTY720 treatment prevented the appearance of this population.\u003c/p\u003e\n \u003cp\u003eThe picture was quite different following single-agent treatments. CD8\u003csup\u003e+\u003c/sup\u003e T cells in the tumor following anti-PD-L1 had largely the same composition as the control group, comprised primarily of Cytotox.2 and Cyt/Mit clusters. Expansion of the Cytotox.2 cluster was more pronounced than with other treatments, suggesting that anti-PD-L1 drives T cell differentiation towards this specific state in tumor. Anti-TIGIT, in contrast, promoted a shift in the tumor towards the Ccl5.2 cluster. With single-agent treatment, none of the largest clones in tumor had appreciable counterparts in dLN or blood.\u003c/p\u003e\n \u003cp\u003eWhen we examined clonal expansion separately in each tissue compartment, each treatment had distinct effects on T cell differentiation (Extended Data Fig.\u0026nbsp;9b, c). In dLN, anti-TIGIT and combination treatment, but not anti-PD-L1 alone, caused expansion of Ccl5.1 T cells and, to a lesser extent, Mitotic clusters. FTY720 treatment shifted the intralymphatic composition to almost exclusively Ccl5.1, suggesting that these cells accumulated in dLN since their egress into blood was inhibited. Combination treatment, with or without FTY720, resulted in reduced proportions of the Slamf6 cluster in dLNs, especially in the most expanded clones, reflecting the possibility that the Slamf6 (putative Tscm) cluster is the source from which Ccl5.1 T cells are mobilized.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eAnti-PD-L1 and anti-TIGIT differentially reshape differentiation and trajectories of CD8\u003c/strong\u003e \u003csup\u003e\u0026nbsp;\u003cstrong\u003e+\u003c/strong\u003e\u0026nbsp;\u003c/sup\u003e \u003cstrong\u003eT cells in dLN and tumor\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eWe next probed the lineage relationships across CD8\u003csup\u003e+\u003c/sup\u003e T cell clusters following various treatments. Although we previously evaluated cellular trajectories using RNA velocity (Fig. 3d), it is apparent that individual clones exhibit complex expansion behaviors. scTCR-seq unambiguously identifies lineages of T cells, which provides a complementary approach to infer kinetics and differentiation based on the co-occurrence of phenotypes in individual clonotypes within and across tissue compartments.\u003c/p\u003e\n \u003cp\u003eWe analyzed co-occurrences of cell phenotypes by tabulating numbers of intraclonal pairs over all clonotypes, plotting only pairs between different clusters (Fig.\u0026nbsp;6a\u0026ndash;c). As with RNA velocity, we could use signatures derived from empirical observations\u003csup\u003e34\u003c/sup\u003e to interpret such co-occurrences as directional steps in differentiation.\u003c/p\u003e\n \u003cp\u003eIn dLN (Fig.\u0026nbsp;6a), control mice exhibited a predominant differentiation of Slamf6 to the Cytotox.1 phenotype, with little connection to other populations as illustrated by the absence of additional intercluster links. With single-agent treatment, increased differentiation from Slamf6 to the Ccl5.1 phenotype was observed, but with anti-TIGIT further increased differentiation of Ccl5.1 cells into Cytotox.1 and Mitotic.1 cells. Combination treatment produced an even more complex pattern of differentiation, with Ccl5.1 cells also differentiating to Ifit.3 cells, and those co-occurrences being shared across cytotoxic and mitotic clusters. FTY720 treatment resulted in most Slamf6 cells differentiating to Ccl5.1, but then a sharp reduction in Ccl5.1 cells differentiating to other clusters, as indicated by the absence of intercluster links. Intraclonal pairs in dLN were comprised of primarily gp70\u003csup\u003e+\u003c/sup\u003e specificities across treatment groups, and some gp70\u0026ndash; with control or single-agent treatment. Thus, although Slamf6 (Tscm) cells differentiated to cell states other than Ccl5.1 in dLN, only the Ccl5.1 population entered the blood, seeding tumors with new CD8\u003csup\u003e+\u003c/sup\u003e T cells.\u003c/p\u003e\n \u003cp\u003eCo-occurrence profiles were different in tumor compared to dLN (Fig.\u0026nbsp;6b). Intraclonal pairs in control tumors showed an origin from the Cytotox.2 phenotype to the Cyt/Mit.1 and Cyt/Mit.2 phenotypes. Anti-PD-L1 had a similar pattern, but with additional co-occurrence of Cytotox.2 with the Ifit.3 and Cytotox.1 clusters. In sharp contrast, anti-TIGIT exhibited an expansion of clones with Ccl5.2 cells that differentiated to Cyt/Mit.2, Cyt/Mit.1, and Cytotox.2 cells; these clones were largely gp70\u003csup\u003e\u0026minus;\u003c/sup\u003e (blue lines), consistent with the largest clonotypes in that group being gp70\u003csup\u003e\u0026minus;\u003c/sup\u003e (Fig. 5c). Combination treatment resembled anti-TIGIT monotherapy in terms of Ccl5.2 expansion, but those Ccl5.2 cells differentiated primarily to Cytotox.1 cells. FTY720 treatment produced a complex pattern of co-occurrences among Ccl5.2, Cytotox.1, Cytotox.2, Cyt/Mit.1, and Cyt/Mit.2 clusters, revealing the extent of differentiation within tumor. In contrast with anti-TIGIT treatment, the vast majority of intraclonal pairs in tumor with combination treatment were gp70\u003csup\u003e+\u003c/sup\u003e.\u003c/p\u003e\n \u003cp\u003eWe then tabulated intraclonal pairs from across tissues to determine migration relationships, plotting only co-occurrences between different tissues, but otherwise showing co-occurrences between both same and different clusters (Fig.\u0026nbsp;6c). In contrast to single-agent therapy, the anti-PD-L1/TIGIT combination facilitated migration of Ccl5.1 cells from dLN to Ccl5.2 cells in tumor, presumably through blood Ccl5.1 cells, but with co-occurrences from dLN to blood less apparent because of its relatively low degree of clonal expansion in both compartments (Fig.\u0026nbsp;4). Co-occurrences were also seen from Ccl5.1 cells in dLN to Cytotox.1 and Cytotox.2 clusters in tumor, but these are presumably attributable to intratumor differentiation (Fig.\u0026nbsp;6b). The co-occurrences between Ccl5.1 in dLN and Ccl5.2 in tumor were also observed in the presence of FTY720, with an absence of blood involvement, indicating that combination treatment may act on preexisting TILs in tumor that had progenitors remaining in the dLN.\u003c/p\u003e\n \u003cp\u003eTo visualize these differentiation and migration patterns in the context of gene expression, we projected the co-occurrence data onto our previously computed UMAPs. From these plots (Fig.\u0026nbsp;6d,e), it is apparent that Slamf6 cells (putative Tscm) in dLN serve as progenitors for Cytotox.1 cells in control and anti-PD-L1 treated mice and for Ccl5.1 cells in other treated mice. These Ccl5.1 cells then migrate into blood, with more frequent migration occurring with anti-TIGIT and combination-treated mice in gp70\u003csup\u003e+\u003c/sup\u003e clones (Fig. 6d) than gp70\u003csup\u003e\u0026minus;\u003c/sup\u003e clones (Fig. 6e). In these groups, and especially with combination treatment, the migration links revealed a convergence of multiple clusters from dLN onto Ccl5.1 cells in blood, and then a divergence from these cells into multiple clusters in tumor. With anti-TIGIT, and to a greater extent with combination therapy, gp70\u003csup\u003e+\u003c/sup\u003e Ccl5.1 cells in blood then migrated into tumor where they appeared to give rise to the Ccl5.2 phenotype. Ccl5.2 cells differentiated into Cytotox.2 cells, which then differentiated into other cytotoxic and mitotic (precursor exhausted) phenotypes. Differentiation from gp70\u003csup\u003e+\u003c/sup\u003e Cytotox.2 cells to other phenotypes was greater for anti-PD-L1 and FTY720 treatment, compared with anti-TIGIT and combination treatment. These results suggest that anti-TIGIT and especially combination treatment promote an immune response characterized by an influx of tumor-specific Ccl5.1 T cells, whereas anti-PD-L1 and FTY720 treatment exhibit primarily the differentiation of Cytotox.2 T cells already existing in the tumor.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eGene signatures derived from reference mouse CD8\u003c/strong\u003e \u003csup\u003e\u0026nbsp;\u003cstrong\u003e+\u003c/strong\u003e\u0026nbsp;\u003c/sup\u003e \u003cstrong\u003eT cell clusters show association with response to tiragolumab plus atezolizumab in cancer patients\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eTo explore whether these observations inform the clinical setting, we analyzed scRNA-seq data of peripheral blood T cells from a phase 1b study of NSCLC patients treated with the combination of tiragolumab plus atezolizumab (T\u0026thinsp;+\u0026thinsp;A) (GO30103)\u003csup\u003e37\u003c/sup\u003e. We mapped human CD8\u003csup\u003e+\u003c/sup\u003e T cells onto the nearest mouse reference CD8\u003csup\u003e+\u003c/sup\u003e T cell cluster (Extended Data Fig. 10a,b). Patients with a clinical response, evaluated as either complete response (CR) or partial response (PR), compared with non-responders (stable disease, SD, or progressive disease, PD), had an increased frequency of CD8\u003csup\u003e+\u003c/sup\u003e T cells mapping to the Ccl5.1 and Ccl5.2 clusters and a decreased frequency mapping to Ccr7.3 and Ccr7.4 clusters (Extended Data Fig. 10c). This finding is consistent with Ccl5 clusters in our mouse models predominating with effective treatment.\u003c/p\u003e\n \u003cp\u003eTo address whether gene signatures derived from the mouse CD8\u003csup\u003e+\u003c/sup\u003e T cell clusters associated with improved overall survival (OS), we analyzed bulk RNA-seq data from baseline tumor samples from patients in CITYSCAPE\u003csup\u003e10\u003c/sup\u003e. The top 20 differentially expressed signature genes for each mouse CD8\u003csup\u003e+\u003c/sup\u003e T cell cluster were used to derive orthologous human gene signature \u0026ldquo;scores\u0026rdquo; in CITYSCAPE samples (Supplementary Table 2) which compared patients treated with T\u0026thinsp;+\u0026thinsp;A or placebo plus atezolizumab (P\u0026thinsp;+\u0026thinsp;A). Ccr7.3, Slamf6, Ifit.1, Ifit.2, Ifit.3, Ccl5.2 and Cytotox.2 gene signature scores were significantly higher in CR and PR responders as compared with SD and PD non-responders (Fig. 7a). While all CD8\u003csup\u003e+\u003c/sup\u003e T cell cluster signatures trended with favorable OS hazard ratio (HR) in patients treated with T\u0026thinsp;+\u0026thinsp;A compared to P\u0026thinsp;+\u0026thinsp;A (Extended Data Fig. 10d), high expression of Ccr7.3, Slamf6 and Ccl5.1 gene scores associated with significantly improved HR for OS (HR\u0026thinsp;=\u0026thinsp;0.44 (95% CI: 0.22\u0026ndash;0.91; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.028), 0.46 (95% CI: 0.22\u0026ndash;0.95; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.036), and 0.45 (95% CI: 0.22\u0026ndash;0.90; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.025), respectively), as did low expression of Cytotox.1 and Cyt/Mit.2 (OS HR\u0026thinsp;=\u0026thinsp;0.46 (95% CI: 0.23\u0026ndash;0.90; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.023) and HR\u0026thinsp;=\u0026thinsp;0.48 (95% CI: 0.23\u0026ndash;0.98; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.045), respectively). Dichotomization of patients on the basis of high or low cluster gene signature score and by treatment showed that high expression of the Ccl5.2 gene signature trended with increased OS with T\u0026thinsp;+\u0026thinsp;A but not P\u0026thinsp;+\u0026thinsp;A (Extended Data Fig.\u0026nbsp;10e).\u003c/p\u003e\n \u003cp\u003eGene signatures predominantly associated with response to T\u0026thinsp;+\u0026thinsp;A were characterized by high expression of chemokines or chemokine receptors. We focused on \u003cem\u003eCXCR3\u003c/em\u003e, \u003cem\u003eCXCR6\u003c/em\u003e, and \u003cem\u003eCCL5\u003c/em\u003e, genes that were among the most highly expressed in each of the clusters (Supplementary Table 1). High expression of each of these individual genes was associated with response in patients treated with T\u0026thinsp;+\u0026thinsp;A (Fig. 7b), and high expression of \u003cem\u003eCCL5\u003c/em\u003e or \u003cem\u003eCXCR3\u003c/em\u003e was individually associated with favorable OS HR in T\u0026thinsp;+\u0026thinsp;A compared to P\u0026thinsp;+\u0026thinsp;A, outperforming \u003cem\u003eCD8A\u003c/em\u003e (OS HR\u0026thinsp;=\u0026thinsp;0.32 (95% CI: 0.14\u0026ndash;0.73; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.006), 0.41 (95% CI: 0.18\u0026ndash;0.94; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.035) and 0.43 (95% CI: 0.20\u0026ndash;0.91; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.027), respectively) (Fig. 7c). \u003cem\u003eCXCR3\u003c/em\u003e, \u003cem\u003eCXCR6\u003c/em\u003e, and \u003cem\u003eCCL5\u003c/em\u003e were associated with improved OS for T\u0026thinsp;+\u0026thinsp;A, again outperforming \u003cem\u003eCD8A\u003c/em\u003e (Fig. 7d).\u003c/p\u003e\n \u003cp\u003eWe then generated a composite gene signature score comprised of the average expression of \u003cem\u003eCCL5, CXCR3\u003c/em\u003e, and \u003cem\u003eCXCR6\u003c/em\u003e. This gene signature score was significantly higher (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.012) in responder CITYSCAPE patients as compared with non-responders, (Fig. 7e). A high gene signature score was associated with favorable OS HR in patients treated with T\u0026thinsp;+\u0026thinsp;A (HR\u0026thinsp;=\u0026thinsp;0.43, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.035) compared with P\u0026thinsp;+\u0026thinsp;A, while a low signature score did not associate significantly with OS benefit (HR\u0026thinsp;=\u0026thinsp;0.70, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.277) (Fig.\u0026nbsp;7f). Segregation of patients on the basis of high or low gene signature scores showed that those treated with T\u0026thinsp;+\u0026thinsp;A who had high gene score expression had improved OS compared to patients with a low gene signature (Fig.\u0026nbsp;7g). The composite gene signature score was also associated with improved progression-free survival (PFS) and OS in the phase 3 OAK study (NCR02008227) of atezolizumab monotherapy in patients with locally advanced or metastatic, previously treated NSCLC\u003csup\u003e38\u003c/sup\u003e (Extended Data Fig. 10f).\u003c/p\u003e\n \u003cp\u003eThus, our analysis of patients treated with T\u0026thinsp;+\u0026thinsp;A largely recapitulates the findings of anti-TIGIT plus anti-PD-L1 in our mouse tumor studies, providing translational evidence that the events observed in dLN of tumor-bearing mice may also be detected in human blood and tumors. Furthermore, our study suggests that CD8\u003csup\u003e+\u003c/sup\u003e T cell quality, as represented by cells newly arrived from dLN, rather than the mere presence of CD8\u003csup\u003e+\u003c/sup\u003e T cells in the tumors supplied by the periphery at steady state \u003csup\u003e39\u003c/sup\u003e, may be more strongly predictive of response and clinical benefit.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eDespite the profound influence of checkpoint inhibitors on oncology practice and our understanding of tumor immunity, many key questions remain regarding their mechanisms of action. One important unknown is whether these inhibitors work primarily in dLN or at the tumor site, and on which populations of cells. We have elucidated the effects of anti-PD-L1 and anti-TIGIT on the differentiation and function of CD8\u003csup\u003e+\u003c/sup\u003e T cells by employing one of the largest datasets assembled for this type of analysis. By considering T cells not only in dLN and tumor but also in the blood, we were able to demonstrate that PD-1 and TIGIT coinhibitory receptors act to direct T cell fate at both anatomical sites, with activation, expansion, and differentiation beginning in dLN, but with final determination of progression to effector or exhausted T cells occurring in tumor, challenging the notion that the trajectory to exhaustion is established at or near the time of priming\u003csup\u003e7, 16, 40\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eOne key to our conclusions was the direct analysis of T cells and clonotypes in peripheral blood. After mobilization with combination therapy, the blood compartment was found to exhibit predominantly a single CD8\u003csup\u003e+\u003c/sup\u003e T cell population (Ccl5.1 cluster) that represented TCR clonotypes that had expanded in dLN and that were found in the tumor. Interestingly, in the tumor these clonotypes were distributed among multiple T cell states. Trajectory analysis based on RNA velocity and lineage tracing of TCR clonotypes suggest that peripheral blood Ccl5.1 cells differentiated into the closely related Ccl5.2 population after reaching the tumor. Thus, the polyclonal Ccl5.1 cells can be considered to be \"transit cells\" whose main function are to convey newly expanded T cells to the tumor.\u003c/p\u003e \u003cp\u003eOnce in the tumor, the transit cell progeny (Ccl5.2) differentiated along the Tex or Teff pathways, a decision that appears to be influenced or determined by the degree of costimulation available via the CD28 and CD226 costimulatory receptors. Indeed, CD226 signaling was required to block the expression of Tox in dLN, and especially in the tumor where nearly 80% of tumor antigen-specific CD8\u003csup\u003e+\u003c/sup\u003e T cells were otherwise Tox\u003csup\u003e+\u003c/sup\u003e (shown in Fig.\u0026nbsp;2c, Extended Data Fig.\u0026nbsp;3g). The prevention of coinhibitory receptor suppression of costimulatory receptor signaling by anti-PD-L1 and anti-TIGIT may explain how combination therapy directs differentiation away from the exhaustion pathway. This mechanism is consistent with observations that CD28 and CD226 signaling is under the control of the PD-1 and TIGIT coinhibitory receptors\u003csup\u003e13\u003c/sup\u003e, thus providing an attractive functional link between checkpoint inhibition and the accumulation of Tex in the tumor. It seems likely that dendritic cells (DCs) present in the dLN and tumor help determine fate decisions between Teff and Tex, as DCs present both antigen and costimulatory ligands, consistent with recent work\u003csup\u003e41, 42\u003c/sup\u003e. However, the role of DCs in this proposed mechanism remains to be determined.\u003c/p\u003e \u003cp\u003eA surprising finding of our study is that the effects of PD-1 and TIGIT inhibition appear to be distinct (Fig.\u0026nbsp;6). While both facilitated the differentiation of tumor-specific T cell trajectories from the Tscm (Slamf6 cluster) compartment to the Ccl5.1 transit cell population in dLN, anti-PD-L1 treatment showed differentiation also to the Cytotox.1 phenotype, whereas anti-TIGIT and combination treatment showed a second stage of extensive differentiation from the Ccl5.1 phenotype to other phenotypes. TIGIT blockade, alone or especially in combination with anti-PD-L1, produced far more emigration of tumor antigen-specific (gp70\u003csup\u003e+\u003c/sup\u003e) Ccl5.1 T cells into the blood than did anti-PD-L1 alone. Once in the tumor, anti-PD-L1 monotherapy showed differentiation mainly of the gp70\u003csup\u003e+\u003c/sup\u003e Ccl5.2 T cells to Cytotox.2 and then to the more exhausted Cyt/Mit phenotypes, while anti-TIGIT monotherapy showed differentiation mainly of the gp70\u0026ndash; T cells also toward exhausted phenotypes (Fig.\u0026nbsp;6b). That anti-TIGIT therapy alone appeared to preferentially affect the gp70\u0026ndash; population may be a factor in its relative therapeutic ineffectiveness. Combination therapy showed a coordinated infiltration of gp70\u003csup\u003e+\u003c/sup\u003e tumor-specific T cells from the blood and less exhaustion of T cells in tumor, suggesting a replenishment by newly arriving T cells into the tumor.\u003c/p\u003e \u003cp\u003eTscm or Tpex cells have been proposed as targets for PD-1/PD-L1-targeted immunotherapies \u003csup\u003e18, 21, 23, 24\u003c/sup\u003e, so it seems likely that they would also be targets for a PD-L1/TIGIT combination. Both are presumed to be precursor populations, which would be consistent with our results, but it is difficult to precisely map our subpopulations to these designations. Nevertheless, our scRNA-seq study has greater resolution relative to previous studies, given its large number of cells studied across a range of effective and ineffective treatments. Tscm cells were originally defined as a CXCR5\u003csup\u003e+\u003c/sup\u003e/TCF1\u003csup\u003e+\u003c/sup\u003e/Slamf6\u003csup\u003e+\u003c/sup\u003e self-renewing compartment present in dLN that give rise to all subsequent T cells\u003csup\u003e7\u003c/sup\u003e. Tpex are generally defined as cells that have at least some of these features (Slamf6, TCF1) and also some, but not all, features of exhausted cells; evidence indicates that they are along a continuum of precursors of terminally differentiated Tex\u003csup\u003e7, 25, 40\u003c/sup\u003e. These two populations are often invoked interchangeably. Our evidence suggests that the anti-PD-L1/anti-TIGIT combination works on a precursor population, likely defined by our Slamf6\u003csup\u003e+\u003c/sup\u003e cluster in dLN and subsequently the Ccl5.1 and Ccl5.2 clusters in the tumor. The effect of combination treatment, however, enables these populations to give rise to Teff, not just Tex, and it is these Teff cells that appear to correlate with effective treatment. Were it possible to conduct the experiments for longer periods, it seems likely that combination treatment would also favor the differentiation of Tem cells in addition to Teff. At this point, there is no single marker that unequivocally defines the Ccl5.1 or Ccl5.2 clusters, precluding experimental validation through methods such as \u003cem\u003ein vivo\u003c/em\u003e adoptive cell transfer. Although it will ultimately be important to agree upon a common lexicon, our finding that the anti-PD-L1/anti-TIGIT combination influences CD8\u003csup\u003e+\u003c/sup\u003e T cell trajectories in a manner dependent on costimulatory receptor signaling can be viewed within any of the existing frameworks.\u003c/p\u003e \u003cp\u003eIt is noteworthy that features of the T cell populations observed for combination treatment in mice appear to have counterparts in human cancer patients who respond to combination treatment with atezolizumab and tiragolumab. Specifically, markers associated with Ccl5 clusters in mice, which represent newly expanded T cell clones trafficking from dLN to tumors, were found to be associated with clinical benefit. If the differentiation trajectories influenced by blockade of PD-1/PD-L1 and TIGIT observed in mouse tumor models are also recapitulated in human cancer patients, then more persistent and durable responses with better survival outcomes may be attained by focusing our therapeutic efforts on generating higher quality tumor-reactive effector cells that are either resistant to exhaustion programming or replacements for terminally exhausted cells. Intriguingly, combination of PD-1 blockade with immunostimulatory cytokines such as IL-2\u003csup\u003e31, 43\u003c/sup\u003e, blockade of immunosuppressive cytokines such as TGF-b\u003csup\u003e44\u003c/sup\u003e, or costimulatory (e.g. 4-1BB) agonists\u003csup\u003e25\u003c/sup\u003e may also skew Tscm/Tpex differentiation trajectories towards effector and cytotoxic states and/or away from exhaustion. As combination of anti-TIGIT with anti-PD-L1 has the additional mechanism of action of reshaping the TME\u003csup\u003e11\u003c/sup\u003e, higher quality antitumor T cells generated in response to combination treatment will be able to exert their effector function in a less suppressive, more permissive environment. Leveraging combination therapy strategies such as anti-TIGIT with anti-PD-L1 that drive both mechanisms may potentially bring improved clinical benefit for more patients beyond anti-PD-(L)1 alone.\u003c/p\u003e"},{"header":"Materials And Methods","content":"\u003cp\u003e\u003cstrong\u003eMice.\u0026nbsp;\u003c/strong\u003eBALB/c or C57BL/6 mice were purchased from the Charles River Laboratories. All mice were housed and maintained at Genentech in accordance with American Association of Laboratory Animal Care guidelines. All experimental animal studies were conducted under the approval of the Institutional Animal Care and Use Committees of Genentech Lab Animal Research and were performed in an Association for the Assessment and Accreditation of Laboratory Animal Care (AAALAC)-accredited facility.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCell Lines.\u0026nbsp;\u003c/strong\u003eCT26 and EO771 cell lines (obtained from external vendor such as ATCC) were maintained at a dedicated internal cell line facility and tested to be mycoplasma-free. CT26 or EO771 cells were cultured in RPMI 1640 media supplemented with 10% FBS and 100 U/mL penicillin and 100 mg/mL streptomycin, and grown in a 37˚C humidified, 5% CO 2 incubator.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSyngeneic tumor studies.\u0026nbsp;\u003c/strong\u003eCT26 tumor studies were performed by inoculating age-matched 6-8 week old BALB/c female mice with a sub-cutaneous injection of 0.1 x 10\u003csup\u003e6\u003c/sup\u003e CT26 cells in 100 \u0026micro;L Hank\u0026rsquo;s balanced solution (HBSS) and Matrigel (BD Biosciences, San Jose, CA). EO771 tumor studies were performed by inoculating age-matched 6-8 week old C57BL/6 female mice with an injection into the fifth mammary fat pad of 0.1 x 10\u003csup\u003e6\u003c/sup\u003e EO771 cells in 100 \u0026micro;L HBSS + Matrigel. Once tumors achieved a mean volume of 150-200 mm\u003csup\u003e3\u003c/sup\u003e, animals were apportioned into treatment groups and treated with isotype control (anti-gp120 mIgG2a), 10 mg/kg; anti-PD-L1.mIgG2a LALAPG mAb (clone 6E11), 10 mg/kg followed by 5 mg/kg; anti-TIGIT.mIgG2a mAb (clone 10A7), 10 mg/kg; or TIGIT.mIgG2a.LALAPG, 10 mg/kg, and administered intravenously for the first dose and subsequently intraperitoneally. \u0026nbsp;For the tracking of tumor volume, doses were given three times a week for three weeks. \u0026nbsp;For single-cell analyses, the mIgG2a version of anti-TIGIT was used, and three doses were given over the course of one week. \u0026nbsp;To inhibit trafficking, FTY720 (Cayman Chemical Company, 1 mg/kg) was administered by daily oral gavage starting day -1 before indicated treatment, or where indicated, day 7 after treatment, and continued until end of study. Tumor volumes were measured and calculated twice per week using the modified ellipsoid formula: \u0026frac12; x (length x width\u003csup\u003e2\u003c/sup\u003e). For pharmacodynamic analyses, mice were euthanized at day 7 after initial treatment. Tumors were dissociated into single cell suspensions by using gentleMACS\u003csup\u003eTM\u003c/sup\u003e dissociator (Miltenyi Biotec) and enzymatically digested in a buffer containing collagenase D (2 mg/mL) and DNAse (40 U/mL, Roche). Single cell suspensions of draining lymph nodes were obtained by mechanical dissociation through 40 \u0026micro;m cell strainers and performing red blood cell lysis as needed. Blood was obtained by terminal cardiac puncture and collected in lavender Microtainer Blood Collection Tubes (BD Biosciences, 365974) and subjected to red blood cell lysis. Animals bearing tumors exceeding 2,000 mm3 or showing ulceration were euthanized following approved protocols.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFlow cytometry and FACS sorting.\u0026nbsp;\u003c/strong\u003eImmune cell phenotyping by flow cytometry was performed on single cell suspensions from mouse draining lymph nodes, tumor, and blood obtained and described elsewhere. Briefly, dead cells were excluded by using a fixable viability dye. Cell surface markers were stained on ice after tetramer staining. The FoxP3 nuclear staining buffer set (Invitrogen) was then performed using recommended manufacturer\u0026rsquo;s instructions to detect intracellular or nuclear staining. For intracellular cytokine detection, cells with stimulated for 4 hours with Cell Stimulation Cocktail (Invitrogen, 00-4970-93) at 37˚C. After stimulation, cells were stained for surface markers and intracellular factors as described above. For obtaining cells for single cell analysis, tumors and dLNs were processed into single cell suspensions as described elsewhere, and subjected to first tetramer staining, then surface markers and CITE-seq antibodies together. Processing of blood samples at day 0 before any treatment or at day 7 were first stained with hashed-tagged antibodies, then stained with surface markers. Cells were purified by fluorescence-activated cell sorting (FACS) on a Becton Dickinson FACSAria Fusion cell sorter equipped with four lasers (405 nm, 488 nm, 561 nm and 638nm). A 70-\u0026mu;m nozzle running at 70 psi and 90 kHz was used as the setup for each sort session. FACSDiva (v.8.0.1) and FlowJo (v.10) were used to collect and analyse the flow cytometry data. Before gating on fluorescence, single cells were gated using forward scatter (FSC-A) and side scatter (SSC-A) (for intact cells) and SSC-W/SSC-H and FSC-W/FSC-H (to ensure that only singlets were sorted). FACS gates were drawn to include only live single cells based on Calcein Blue AM+ and Propidium iodide (Thermo Fisher Scientific). Antibodies used for flow cytometry, cell sorting by FACS or CITE-seq are shown in Supplementary Table 3. All samples were acquired on LSR-Fortessa, BD Symphony Instruments (BD Biosciences) or Cytek Aurora and analysed using FlowJo v10.5 or higher version software (Tree Star, Inc.).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSingle-cell RNA-seq and TCR V(D)J clonotype profiling.\u0026nbsp;\u003c/strong\u003eProcessing for single-cell expression (scRNA-seq) and T cell receptor V(D)J clonotypes (scTCR-seq) was done using the Chromium Single Cell 5\u0026rsquo; Library and Gel Bead Kit (10x Genomics), following manufacturer\u0026rsquo;s instructions. T cells were isolated from tumor, dLN and blood from 31 mice. Cell density and viability from each mouse tissue of FACS-sorted CD90\u003csup\u003e+\u003c/sup\u003e T cells from tumor and blood, or CD90\u003csup\u003e+\u003c/sup\u003eCD44\u003csup\u003e+\u0026nbsp;\u003c/sup\u003eT cells from draining lymph nodes, were determined by hemacytometer. Approximately 6,000-10,000 cells per sample were used for the reverse transcription mastermix. A total of 14 cycles of PCR amplification was performed to obtain sufficient cDNAs used for both RNA-seq library generation and TCR V(D)J targeted enrichment followed by V(D)J library generation after Gel Bead-in-Emulsion reverse transcription (GEM-RT) reaction and clean-up. TCR V(D)J enrichment was done per manufacturer\u0026rsquo;s user guide using Chromium Single Cell V(D) J Enrichment Kit, Human T cell (10x Genomics). Libraries for RNA-seq and V(D)J were prepared following the manufacturer\u0026rsquo;s user guide (10x Genomics), then profiled using Bioanalyzer High Sensitivity DNA kit (Agilent Technologies) and quantified with Qubit (Thermo Fisher Scientific). scRNA-seq libraries were sequenced in one lane of HiSeq4000 (Illumina). scTCR V(D)J libraries were tagged with a sample barcode for multiplexed pooling with other libraries, sequenced in both lanes of a HiSeq2500 machine (Illumina) using Rapid Run mode, and then demultiplexed. All sequencing was done according to the manufacturer\u0026rsquo;s specification (10x Genomics). Detailed information on mice, tissue isolation and batching of samples is provided in Supplementary Table 4.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePre-processing of single-cell data\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSequencing files from Illumina assays were run through CellRanger version 6.1.1 against a transcriptome derived from ENSEMBL version 2.2.0 for the mouse genome GRCm38. The combined matrix files from the filtered_feature_bc_matrix directory for the RNA and ADT libraries were divided into separate submatrices for each sample, based on 52,636 genes for expression, 6 tetramer barcodes for ADT counts, 24 antibody measurements for CITE-seq, and 10 barcodes for multiplexing of the blood samples. Measurements corresponding to various alleles of T cell receptor genes (e.g., Trbv1 through Trbv31) were combined into a single gene measurement (Trbv). Since blood samples were pooled from several mice based on an encoding scheme that used two multiplex barcodes to identify each mouse, single cells were de-multiplexed using the two multiplex barcodes with highest counts. In cases of a tie for the second highest multiplex count (4.6% of cells), those single cells could not be assigned to a particular mouse using this method. TCR sequence data from the filtered_contig_annotations.csv files were processed using a custom script that identified clones across multiple tissues in each mouse, based on identical sets of alpha and beta sequences. To handle the blood cells that could not be assigned using the multiplex counts, blood cells with a TCR nucleotide sequence uniquely matching a cell from lymph node or tumor of a mouse in the pool were assigned to the corresponding mouse. ADT barcodes came from 12 distinct tetramers, of which 2 had gp70 antigens and the remaining 10 had a non-gp70 antigen (C28, UV, or C142). A cell was assigned to an antigen based on its ADT barcode with the highest count, and were not assigned in cases of ties.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIntegration of single-cell expression data\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAnalysis was performed in the statistical language R version 4.2.0 and with scripts written for Perl version 5.16.3. The single-cell UMI count matrix for each tumor and lymph node sample, and for each pooled blood sample, was processed using scDblFinder version 1.12.0 to identify and remove doublets, or gel beads containing more than one cell. The remaining singlet count matrices were processed using Seurat version 4.1.1 using the SCTransform function (unless specified otherwise, Seurat functions were run using default parameters). All samples were merged into a single Seurat object, then subjected to a filtering process to remove anomalous or low-quality cells, where 10,584 genes were first identified as each being present in more than 1% of all cells, and then 245,675 of the 260,391 cells were retained because more than 99% of their UMI counts were represented by these genes. \u0026nbsp;Counts of mitochondrial genes were not used for filtering, since such genes are present in T cells at the end of their lifespan due to apoptosis, and not necessarily an indicator of poor-quality cells.\u003c/p\u003e\n\u003cp\u003eSince the mice in this study were taken from batches on two different dates, we performed batch correction using the Harmony package 0.1.1 with the batch date as the controlling variable. \u0026nbsp;We calculated PCA cell embeddings following the procedure in https://cran.r-project.org/web/packages/harmony/vignettes/Seurat.html, where we processed the count matrix with the Seurat procedures NormalizeData; FindVariableFeatures using selection.method=\u0026rdquo;vst\u0026rdquo; and nfeatures=2000; ScaleData, and RunPCA on the variable genes with npcs=30. \u0026nbsp;The dataset was then processed with the procedure RunHarmony and the Seurat procedures RunUMAP and FindNeighbors on the harmony reduction, and FindClusters to obtain 24 clusters of CD4 and CD8 T cell subtypes.\u003c/p\u003e\n\u003cp\u003eThe reason that we made two calls to SCTransform is as follows. The first call was performed on individual samples before integrating them, standard practice in Seurat protocols. The second call was required because we used Harmony, which excels at batch correction, rather than the Seurat integration procedure. Harmony requires a PCA, and this in turn requires finding variable genes and scaling the data, as described above. While SCTransform is essentially equivalent to NormalizeData, FindVariableGenes, and ScaleData, we used these three steps separately as it is recommended procedure for Harmony. Furthermore, we used the procedure FindVariableFeatures with the parameter selection.method=\u0026rdquo;vst\u0026rdquo; because it is recommended in the above referenced Web page, and the SCTransform method does not allow for this option.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIsolation of CD8 expression data\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo obtain better resolution and a clustering that was not affected by the CD4\u003csup\u003e+\u003c/sup\u003e T cells, we determined the mean \u003cem\u003eCd4\u003c/em\u003e and \u003cem\u003eCd8a\u003c/em\u003e expression of the 24 clusters, and isolated the 155,496 single cells belonging to the 16 clusters where \u003cem\u003eCd8a\u003c/em\u003e expression was predominant (Supplementary Fig. 2a). \u0026nbsp;We then performed a re-clustering of that data using the Harmony reduction to yield 20 phenotypic CD8 clusters, which represented a reformulation of the original clusters (Supplementary Fig. 2b). \u0026nbsp;The overall process of doublet removal, quality control filtering, and CD8 isolation is summarized in Supplementary Fig. 2c.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorrespondences with clusters from external single-cell datasets\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe obtained single-cell RNA-seq datasets generated or analyzed from five previous published datasets, using raw counts from NCBI GEO (Gene Expression Omnibus) unless specified otherwise: GSM5452712 and GSM5452714 from GSE180094 \u003csup\u003e23\u003c/sup\u003e, GSE122712 \u003csup\u003e45\u003c/sup\u003e, GSM5530561 and GSM5530563 from GSE182509 (processed data) \u003csup\u003e24\u003c/sup\u003e, and GSM4618806 from GSE152628 (Jun Huang, unpublished) for the analysis by Huang et al., 2022 \u003csup\u003e23\u003c/sup\u003e; the LCMV samples from GSE188666 \u003csup\u003e30\u003c/sup\u003e; E-MTAB-11773 from ArrayExpress \u003csup\u003e31\u003c/sup\u003e; GSE199565 \u003csup\u003e32\u003c/sup\u003e; and GSE193654 \u003csup\u003e34\u003c/sup\u003e. \u0026nbsp; For the study Daniel et al., 2022 \u003csup\u003e30\u003c/sup\u003e, we obtained metadata with cluster assignments of individual cell barcodes from the NCBI GEO repository. For the study Giles et al., 2022 \u003csup\u003e32\u003c/sup\u003e, we used cell assignments from the Seurat object provided online. For all other studies, we obtained metadata by direct communication with the authors. \u0026nbsp; We used the metadata to create centroids of each of the published clusters by normalizing each cell by its total count to yield a value in transcripts per million and adding 1 as a pseudocount (tpm); computing a trimmed mean of the tpm for each gene, rejecting 10% of measurements from each end of the range; and taking the logarithm base 2. \u0026nbsp;These centroids were used as reference gene signatures to assign each cell from our dataset, where genes with zero expression across an entire sample were excluded, gene expression for each cell was converted to log2(tpm+1), and assignment was performed by the SingleR package in R, using default parameters. \u0026nbsp;Assignments between the two clustering schemes were cross-tabulated, and normalized by the total counts for each of our clusters.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAssignment of gp70 status\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe single-cell ADT assay provided measures for each cell on its binding to two tetramers for gp70 antigens, and ten for non-gp70 antigens (two for C28, five for UV, and three for C142). To determine whether a cell was gp70\u003csup\u003e+\u003c/sup\u003e, we used the minimum value for the gp70 as a test statistic in a Poisson test where base rate was the maximum value for the non-gp70 antigens, using the poisson.test function in R. A cell was considered gp70\u003csup\u003e+\u003c/sup\u003e if the one-sided p-value with alt=\u0026rdquo;greater\u0026rdquo; was less the 1e\u0026ndash;6. A clone was considered gp70\u003csup\u003e+\u003c/sup\u003e if any of its cells was gp70\u003csup\u003e+\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClonal co-occurrence analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCo-occurrence matrices were tabulated by summing intraclonal pairs across all clones. \u0026nbsp;Specifically, for a given set of samples, each clone with \u003cstrong\u003en\u003c/strong\u003e cells, where \u003cstrong\u003en \u0026gt; 1\u003c/strong\u003e, contributed to the co-occurrence matrix with its outer product \u003cstrong\u003exx\u003csup\u003eT\u003c/sup\u003e\u003c/strong\u003e, where the outer product represents the count of any two cluster/tissue pairs occurring in the same clone. For migration analysis, we performed the same computation, including all dLN, blood day 7, and tumor samples for each experimental group and keeping track of intraclonal pairs for each combination of cluster and tissue. The resulting co-occurrence matrices were plotted using the chordDiagram function from the circlize package\u003csup\u003e46\u003c/sup\u003e, version 0.4.15, in R, with the parameters transparency=0.2 and reduce=0. In the resulting plots, link widths are normalized by the total number of intraclonal pairs, which make up a full circumference. Same-cluster links, or same-tissue links for the migration analysis, were hidden using the link.visible parameter.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn this co-occurrence analysis, clones contribute information according to their possible pairwise counts, so that singleton clones contribute no information and expanded clones contribute information according to the square of their size. For migration analysis in Fig. 6c, chord thicknesses are proportional to the square of the clone sizes between tissues. Since effective clones are highly expanded in tumor but less expanded in dLN and blood, lines may not be discernible between dLN and blood while reasonable line thicknesses will be seen between blood and tumor or dLN and tumor. We also characterized a clone as being gp70\u003csup\u003e+\u003c/sup\u003e if any one of its cells was determined to be gp70\u003csup\u003e+\u003c/sup\u003e, although the largest clones also biased these counts according to the square of their size.\u003c/p\u003e\n\u003cp\u003eWhen projecting co-occurrence onto the UMAPs, such projections can be noisy because of the transitive nature of co-occurrence, where co-occurrence of cluster A and B and co-occurrence of clusters B and C necessarily implies co-occurrence of A and C. Therefore, to identify primary differentiation pathways, we applied a minimum spanning tree (MST) algorithm in R to the co-occurrence data within dLNs and within tumors, where links were processed in order from largest to smallest count of intraclonal pairs, and retaining links only if they did not create a cycle in the graph with links previously kept. Co-occurrence links were plotted with the same relative thicknesses as in the circular co-occurrence plots of Fig. 6a\u0026ndash;c, normalized to the total number of intraclonal pairs, but with a relative multiplier of 3 for the migration links, since they are relatively sparse.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRNA velocity analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe paired-end FASTQ files from each sample were mapped using kallisto bustools (version 0.46.1) \u003csup\u003e47\u003c/sup\u003e\u0026nbsp; to a transcriptome index from Ensembl version 90 on genome GRCm38. The transcriptome index was generated using kallisto with a read length of 90 nt and intronic sequences from BUSpaRse (Moses L, Pachter L (2021). BUSpaRse: kallisto | bustools R utilities. R package version 1.6.1, https://github.com/BUStools/BUSpaRse.).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe resulting spliced and unspliced count matrices for each tissue sample from each mouse were filtered to correspond to the cells used in the Seurat-based analysis, and the Seurat-based UMAP coordinates for those cells were added to the data object. \u0026nbsp;The cells for each tissue and experimental group were combined using the concatenate procedure with join=\u0026rdquo;outer\u0026rdquo;. \u0026nbsp;The resulting object was processed by scvelo package 0.2.4 within Python version 3.7.3, using the commands \u0026quot;pp.filter_and_normalize\u0026quot;, \u0026quot;pp.moments\u0026quot;, \u0026ldquo;tl.recover_dynamics\u0026rdquo;, and \u0026quot;tl.velocity\u0026quot; with mode=\u0026quot;dynamical\u0026quot;. \u0026nbsp;Velocity graphs were generated using the command \u0026ldquo;tl.velocity_graph\u0026rdquo; and \u0026ldquo;pl.velocity_embedding_stream\u0026rdquo;, with the parameter arrow_size=0.001 to hide arrows, which otherwise gave directions often inconsistent with one another and with empirically determined T cell behavior.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eProjection of human CD8\u003csup\u003e+\u003c/sup\u003e T cells from a Ph1b scRNAseq dataset to a mouse reference\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHuman genes from the Ph1b scRNAseq data were first converted to their mouse orthologs using babelgene (version 22.9). Human genes without mouse orthologs or with mouse orthologs not present in the mouse scRNAseq dataset were left unmodified without renaming. Human CD8\u003csup\u003e+\u003c/sup\u003e T cells were then separated by patient and normalized with SCTransform in Seurat (version 4.2) using the default parameters. These samples were then integrated using reference-based integration to overcome the memory limits of canonical correlation analysis (CCA) integration. The second patient in the dataset was chosen at random as the integration reference. After integration, transfer anchors were identified between the query human CD8\u003csup\u003e+\u003c/sup\u003e T cell dataset and the mouse CD8\u003csup\u003e+\u003c/sup\u003e T cell reference. The MapQuery function in Seurat was used to transfer cell type labels, integrate embeddings, and to project the query data onto the reference UMAP.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGene signature scores for CITYSCAPE\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe top 20 differentially expressed genes in each of the mouse CD8\u003csup\u003e+\u003c/sup\u003e T cell clusters identified from scRNAseq were converted to their human orthologs using babelgene (version 22.9) in R (4.2.0). Mouse genes that did not have human orthologs or with human orthologs that were not present in the CITYSCAPE dataset were removed. The final curated table of signature genes used for analysis are in Supplementary Table 2.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAnalysis of CITYSCAPE and OAK clinical trial data\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCITYSCAPE (NCT01903993) is a phase 2 trial investigating tiragolumab with atezolizumab compared to placebo with atezolizumab in patients with locally advanced or metastatic NSCLC\u003csup\u003e10\u003c/sup\u003e. Patients were treated until disease progression or loss of clinical benefit. Patient tumor samples were submitted for RNAseq and the average, log-normalized expression of the genes in Supplementary Table 2 or selected genes as indicated in the text was used to define gene signature scores. Objective response was categorized according to RECIST (version 1.1). For Kaplan-Meier (KM) survival curves and hazard ratios, patients in the CITYSCAPE trial were separated by treatment group and further sub-divided by high or low expression of individual genes or gene signatures, where high or low is defined as greater than or equal to, or less than, the global median expression, respectively, of that gene or gene signature score. The survminer package (version 0.4.9), survival package (version 3.4-0) and R (version 4.2.0) were used to generate the KM plot. A log-rank test was used for statistical testing on the survival data. A Cox proportional hazards regression model was fit on gene or gene signature score high or low data and the hazard ratio and 95% confidence interval for overall survival calculated and plotted for patients receiving tiragolumab with atezolizumab compared to patients receiving placebo with atezolizumab. Similarly, KM survival curves for PFS and OS were generated for the phase 3 OAK study (NCT02008227) evaluating atezolizumab versus chemotherapy in PD-L1-positive previously treated patients with advanced or metastatic NSCLC.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical Analysis.\u0026nbsp;\u003c/strong\u003eData were analyzed using GraphPad Prism software version 9 (GraphPad, San Diego, CA). Measures between two groups were performed with a Student\u0026rsquo;s t test (two-tailed). Groups of three or more were analyzed by one-way or two-way analysis of variance (ANOVA) followed by Tukey\u0026rsquo;s post-testing for multiple comparisons, as appropriate.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eReporting summary\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFurther information on research design is available in the Nature Research Reporting Summary linked to this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFASTQ files containing raw sequencing reads for the scRNA-seq, scTCR-seq, ADT-seq, and CITE-seq analyses have been deposited with the NCBI Short Read Archive under accession PRJNA911822. Processed output files from Cell Ranger, and metadata with cluster assignments, clonotypes, and ADT assignments have been deposited with the NCBI Gene Expression Omnibus under accession GSE220901.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eComputer code used to generate the single-cell analyses and figures in this paper are provided as a Supplementary File to the NCBI GEO accession GSE220901.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank the patients who kindly provided tumor samples for this study, as well as the investigators and staff involved in the CITSCAPE study. We thank Genentech\u0026rsquo;s FACS core facility for contributing their expertise and performing cell sorting. We thank the Genentech laboratory animal core groups for microinjection, animal care, and genotyping support. We thank Lili Adams for providing assistance with the pharmacodynamic studies. We thank Robert Johnston, Jane Grogan, Avantika Chitre and Soyoung Oh for thoughtful discussions.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eK.N., K.L.B. and E.D. performed in vivo tumor studies, pharmacodynamic studies, bioinformatic studies, and data analysis; T.D.W., K.W. and S\u0026ouml;.M. performed mouse bioinformatic analyses; St.M. and Y.Q. managed and performed in vivo tumor studies; C.T., B.Y.N. and N.S.P. performed clinical trial bioinformatic analyses; K.N., K.L.B., E.Y.C. and I.M. conceived this work; E.Y.C. and I.M. supervised this work; K.N., K.L.B., T.D.W., E.Y.C. and I.M. wrote the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare the following competing interests: all authors are employees of Genentech, a member of the Roche group, which develops and markets drugs for profit.\u003c/p\u003e\n\u003cp\u003eSupplementary Information is available for this paper.\u003c/p\u003e\n\u003cp\u003eCorrespondence and requests for materials should be addressed to Eugene Y. Chiang (email: [email protected]), or Ira Mellman (email: [email protected]; mobile: 650-452-3894).\u003c/p\u003e\n\u003cp\u003eReprints and permissions information is available at www.nature.com/reprints.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWu, T.D.\u003cem\u003e et al.\u003c/em\u003e Peripheral clonal expansion of T lymphocytes associates with tumour infiltration and response to cancer immunotherapy. \u003cem\u003eNature\u003c/em\u003e \u003cstrong\u003e579\u003c/strong\u003e,274-278 (2020).\u003c/li\u003e\n\u003cli\u003eEvrard, M.\u003cem\u003e et al.\u003c/em\u003e Single-cell protein expression profiling resolves circulating and resident memory T cell diversity across tissues and infection contexts. \u003cem\u003eImmunity\u003c/em\u003e \u003cstrong\u003e56\u003c/strong\u003e,P1664-1680.E1669 (2023).\u003c/li\u003e\n\u003cli\u003eWherry, E.J. \u0026amp; Kurachi, M. 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Although these coinhibitory receptors can restrict signaling in CD8\u003csup\u003e+\u003c/sup\u003e T cells by regulating their associated costimulatory receptors CD28 and CD226, the functional consequences of combining PD-1 and TIGIT blockade remain poorly characterized. In mouse tumor models, combination blockade elicited CD226-driven clonal expansion of tumor antigen-specific CD8\u003csup\u003e+\u003c/sup\u003e T cells. The expanded clones emerged from a population of stem-like cells in draining lymph nodes (dLN), entering the blood as a previously unidentified single-phenotype, multi-clonal population. Upon reaching the tumor, these tumor antigen-specific transiting cells expanded further and differentiated into effector or exhausted T cells, with combination blockade restricting entry into the exhaustion pathway by favoring costimulation. Thus, PD-1 and TIGIT inhibition helps shape the repertoire of tumor-reactive CD8\u003csup\u003e+\u003c/sup\u003e T cells in dLN and determines their immunological fate in the tumor to enhance therapeutic benefit. Analysis of clinical trial samples suggests a similar mechanism may also occur in cancer patients.\u003c/p\u003e","manuscriptTitle":"TIGIT and PD-L1 co-blockade promotes clonal expansion of multipotent, non-exhausted anti-tumor T cells by facilitating costimulation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-26 01:41:21","doi":"10.21203/rs.3.rs-4201684/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-cancer","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"natcancer","sideBox":"Learn more about [Nature Cancer](http://www.nature.com/natcancer/)","snPcode":"","submissionUrl":"","title":"Nature Cancer","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature Research","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"499a259a-4328-4cea-a6a8-54413fd8e949","owner":[],"postedDate":"April 26th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":30870777,"name":"Biological sciences/Immunology/Tumour immunology"},{"id":30870778,"name":"Biological sciences/Cancer/Tumour immunology"}],"tags":[],"updatedAt":"2024-12-17T08:06:35+00:00","versionOfRecord":{"articleIdentity":"rs-4201684","link":"https://doi.org/10.1038/s43018-024-00870-6","journal":{"identity":"nature-cancer","isVorOnly":false,"title":"Nature Cancer"},"publishedOn":"2024-12-16 05:00:00","publishedOnDateReadable":"December 16th, 2024"},"versionCreatedAt":"2024-04-26 01:41:21","video":"","vorDoi":"10.1038/s43018-024-00870-6","vorDoiUrl":"https://doi.org/10.1038/s43018-024-00870-6","workflowStages":[]},"version":"v1","identity":"rs-4201684","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4201684","identity":"rs-4201684","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}