Research ArticleExperimental Studies

Tumor-infiltrating ICOS+ Effector Regulatory T-Cells in Oral Squamous Cell Carcinoma as a Promising Biomarker for Prognosis and ‘Hot’ Tumor

HITOMI KAJIKAWA, MICHINARI HIRATA, MIYA HARUNA, AZUMI UEYAMA, KATSUTOSHI HIROSE, ATSUNARI KAWASHIMA, KOTA IWAHORI, KAZUHIDE MATSUNAGA, SATORU TOYOSAWA, NARIKAZU UZAWA and HISASHI WADA
Anticancer Research May 2022, 42 (5) 2383-2393; DOI: https://doi.org/10.21873/anticanres.15717

Abstract

Background: Tumor immunity in the tumor microenvironment is activated in patients with feasible clinical responses to immune checkpoint inhibitors. The immunological profile of tumor-infiltrating lymphocytes (TILs) obtained from patients with oral squamous cell carcinoma (OSCC) was examined in relation to their prognosis. Materials and Methods: Surface antigens, including immune checkpoint molecules, on TILs from 31 patients with primary OSCC were analyzed by flow cytometry. The activation status of TILs was examined through a heatmap analysis and unsupervised clustering classified patients into groups with activated or inactivated TILs. A supervised machine-learning algorithm for single-cell analyses in relation to prognosis was run using the Cluster Identification, Characterization, and Regression (CITRUS) program. Results: None of surface antigens were related to prognosis. The CITRUS program revealed a relationship between CD45RA−CD4+ CD25high inducible T-cell co-stimulator (ICOS)+ TILs and recurrence, and also identified a similar fraction significantly specific to the group with activated TILs. The disease-free survival rate for patients with ≥95% ICOS+ TILs was significantly lower than that for those with <95% ICOS+ TILs. Furthermore, a review of clinicopathological factors related to prognosis identified the percentage of ICOS+ TILs to be an independent prognostic factor for patients with OSCC. Conclusion: CD25highICOS+ regulatory T-cells in TILs have potential as a biomarker for predicting recurrence after surgical treatment and clinical responses to immune checkpoint inhibitors in patients with OSCC.

Key Words:

Among head and neck squamous cell carcinomas, oral squamous cell carcinoma (OSCC) is the most common malignant neoplasm originating in the oral cavity, with an estimated incidence of 6.3 and 3.2 per 100,000 males in Northern America and South-Eastern Asia, respectively, in 2018 (1-3). Surgical resection is the basic treatment for OSCC; however, decision-making regarding adjuvant therapy relies on risk factors identified in a pathological examination using resected specimens, e.g., the depth of invasion, extranodal extension, and pathological TNM staging (4-6). Recent advances in morphological analyses of the prognosis of patients has led to the identification of several factors, e.g., tumor budding, tumor-infiltrative growth pattern (INF), Y-K classification, and an immunohistochemical analysis of infiltrating immune-related cells (7-11).

Due to the success of immune checkpoint inhibitors (ICIs) in recent years, tumor-infiltrating immune-related cells have been attracting increasing interest. Greater CD3+ or CD8+ T-cell infiltration being associated with a better prognosis was proposed as an ‘immunoscore’ by Galon et al. using immuno-histochemistry (12), and subsequent studies pathologically detected different grades of immune activation in the tumor microenvironment (TME) in cases showing feasible clinical responses to ICIs (13-16). TME can be classified based on three characteristics, which are further subclassified into three categories: Components (prevalence of effectors/equilibrium/prevalence of suppressors), location (inflamed/excluded/‘desert’), and T-cell activation (activated effectors/equilibrium/exhausted effectors). Non-responders to ICIs often have TMEs with characteristics of prevalence of suppressors, immune desert, and exhausted effectors; tumors with these characteristics are considered ‘cold’, as opposed to ‘hot’ ones, with activated tumor immunity (16-22).

Comprehensive gene-based studies to identify predictors of outcomes to ICIs have also been performed using pretreatment tumor biopsies. Due to the difficulties associated with identifying a single factor, a profile analysis using multiple factors is often applied to biomarker research for ICI responders and some clusters, e.g., the regulation of mesenchymal transition, cell adhesion, and angiogenesis, have been identified (16, 17, 19). Immune-related signatures, particularly that associated with tumor-infiltrating lymphocytes (TILs), have been also targeted based on gene expression. Although there are few reports of TIL analysis by flow cytometry due to the difficulty of purifying TILs from small samples of fresh tumor tissue, Kumagai et al. reported that the expression of immune-related proteins was generally strongly positive in activated T-cell clusters, which appeared to be more remarkable in TILs from responders to ICIs (23). Such profile analysis of the expression signature of TILs has also been applied to the prediction of the general prognosis of patients with cancer, such as in the ‘immunoscore’ (12, 14, 21).

CD4+ regulatory T-cells (Tregs) expressing the critical transcriptional factor, forkhead box protein 3 (FOXP3) play an important role in maintaining immune homeostasis. In tumor immunity, the induction, proliferation, recognition, and responses of T-cells in a tumor-specific manner are suppressed through the consumption of interleukin-2 (IL2), binding of cytotoxic T-lymphocyte-associated protein 4, and combined immune-inhibitory functions with other molecules, e.g., transforming growth factor β, IL10, and T-cell immunoreceptor with Ig and ITIM domains, resulting in an environment that is suitable for the survival and proliferation of tumor cells (21, 24-28). Since effector Tregs (eTregs) with strong immunosuppressive functions express high levels of FOXP3 and CD25, previous studies examined markers specific to the FOXP3/CD25high eTreg fraction and successfully identified some, including inducible co-stimulatory receptor (ICOS), C-C chemokine receptor types 4 and 8 (29, 30).

In this study, to investigate the immunological profile of the TME in relation to patient prognosis, we investigated the immune-related molecules on TILs purified from fresh OSCC using multicolor flow cytometry.

Materials and Methods

Patients and tissue samples. Fresh tumor tissues were obtained from 31 treatment-naïve patients with OSCC at surgery as standard therapy between December 2018 and February 2021. Tissue-infiltrating T-cells were purified using a Tumor Dissociation Kit for humans (Miltenyi Biotec, Bergisch Gladbach, Germany) and gentleMACS Dissociator (Miltenyi Biotec) according to the manufacturer’s instructions. The present study was approved by the Osaka University Dental Hospital Ethics Committee (H30-E49-3, Osaka, Japan), and written informed consent was obtained from each patient according to the Declaration of Helsinki.

Antibodies. The following antibodies were purchased for flow cytometry analysis: CD45RA-fluorescein isothiocyanate (clone HI100), CD25-phycoerythrin (PE) (BC96), tumor necrosis factor (TNF) receptor superfamily member 9 (4-1BB/CD137)-BV421 (4B4-1), CD8-BV510 (RPA-T8), CD103-BV605 (Ber-ACT8), CD4-BV711 (OKT4), CD45-BV786 (HI30), T-cell immunoglobulin and mucin domain-containing protein 3 (TIM3)-allophycocyanin, (F38-2E2), CD3-Alexa Fluor 700 (UCHT1), and IgG1 isotype control (MOPC-21) from BioLegend (San Diego, CA, USA), ICOS-peridinin-chlorophyll-protein eFluor 710 (ISA-3) and IgG1 isotype control (P3.6.2.8.1) from eBioscience (San Diego, CA, USA), and TNF receptor superfamily member 4 (OX40/CD134)-PE-CF594 (ACT35), programmed cell death 1 (PD1)-PE cyanine7 (EH12.1), and IgG1 isotype control (X40) from BD Bioscience (San Jose, CA, USA).

Flow cytometry. Cells were stained with fluorophore-conjugated antibodies after a Fc receptor block (Human TruStain FcX Fc Receptor-blocking solution; BioLegend) and incubated with the Live/Dead Fixable Yellow Dead Cell Stain Kit (Life Technologies, Carlsbad, CA, USA) at 4°C for 30 min. Stained cells were analyzed by LSR Fortessa (BD Biosciences), and the frequencies of cell populations were obtained and analyzed with DiVA software (BD Biosciences). To assess positive staining, an isotype control of the primary antibody conjugated with each fluorophore was used.

Cluster Identification, Characterization and Regression (CITRUS). CITRUS in the Cytobank program (Beckman Coulter, Brea, CA, USA) is a supervised machine-learning algorithm for single-cell analyses. The CITRUS algorithm clusters and identifies cell populations that are significantly different to distinguish outcome groups (31). The CITRUS algorithm was run using the raw data of 10 cell-surface markers (CD4, CD8, CD45RA, CD25, PD1, TIM3, CD103, ICOS, 4-1BB, and OX40) obtained from flow cytometric analyses of patients with >1,600 CD3+ cells.

Statistical analysis. The significance of differences in each experimental data set between two groups was assessed using the Kruskal–Wallis test, Fisher’s exact test, and Pearson’s chi-squared test. The disease-free survival (DFS) rate was analyzed by the Kaplan–Meier method and compared using the log-rank test. Survival time was calculated from the time of surgery until June 2021. Univariate and multivariate analyses were performed using Cox regression analysis. The expression profiles of T-cell surface markers were visualized by heat mapping after individual data were transformed to Z-scores for standardization, and a hierarchical clustering algorithm was performed using Ward’s method. All statistical analyses were performed using JMP Pro 15 (SAS Institute, Cary, NC, USA). To detect the optimal cut-off value, the area under the receiver operating characteristic (ROC) curve was calculated. A value of p<0.05 was considered to be significant.

Results

Clinicopathological factors in enrolled patients with OSCC. Thirty-one patients with OSCC were enrolled in the present study (Table I). Their median age at surgery was 72 years, and pathological stages were diagnosed as I/II in 15 patients and III/IV in 16 patients. After curative surgical treatment, six patients were diagnosed with recurrence during the observation period. When clinicopathological factors, including tumor stage, tumor grade, and INF, were compared between the six patients with and 25 without recurrence, no significant differences were observed.

View this table:
Table I.

Patient characteristics.

Analysis of the expression of surface molecules on TILs in relation to prognosis. The characteristics of CD4+ and CD8+ TILs purified from surgically resected specimens were evaluated by the expression of surface molecules representing activation (CD45RA and CD103), immune checkpoints (PD1, TIM3, ICOS, 4-1BB, and OX40), and eTregs (CD25). To investigate marker candidates that predict the prognosis of patients, the frequency of CD4+ and CD8+ in CD3+ cells; CD45RA+, PD-1+, TIM3+, ICOS+, 4-1BB+, OX40+, CD25+, and CD103+ cells in CD4+ or CD8+ cells; and CD45RA−CD25high cells in CD4+ cells among populations of live, singlet, CD45+, and CD3+ cells were compared between patients with and without recurrence. For each marker, patients with and without recurrence showed similar patterns of expression (Figure 1). A profiling analysis with the CITRUS algorithm using the raw data of all markers obtained from flow cytometric analyses was performed to identify specific populations to patients with recurrence. The CITRUS analysis identified cluster 40119 (Figure 2A and B), and T-cells in this population showed clear expression of CD4, strong expression of CD25 and ICOS, and relatively higher expression of OX40 and 4-1BB than all TILs as the control (Figure 2C and Supplementary Figure 1). A scatter plot of the flow cytometric analyses revealed that most of the CD4+ TILs expressing OX40 and 4-1BB were included in the CD4+ CD25high ICOS+ population in all patients (Figure 2D). When the percentage of ICOS+CD4+ CD25high cells analyzed by flow cytometry was compared between patients with and without recurrence, it was non-significantly higher in the former than in the latter (p=0.117, Figure 2E). Furthermore, when patients with OSCC were divided based on the median percentage of ICOS+ cells in CD4+CD25high cells (82.1%), five out of six patients with recurrence were classified into the group with a higher proportion of ICOS+ cells (compared with 11/25 patients without recurrence p=0.0834).

Figure 1.
Figure 1.

Relationship between recurrence and the expression of surface markers on tumor-infiltrating lymphocytes. The percentages of CD8+, CD4+, CD45RA+, programmed cell death 1 (PD1)+, T-cell immunoglobulin and mucin domain-containing protein 3 (TIM3)+, inducible T-cell co-stimulator (ICOS)+, tumor necrosis factor receptor superfamily member 9 (4-1BB/CD137)+, TNF receptor superfamily member 4 (OX40/CD134)+, CD25+, CD103+, and CD45RA−CD25high cells in CD3+ (A), CD8+ (B), and CD4+ (C) T-cells were compared between patients with and without recurrence.

Figure 2.
Figure 2.

A profiling analysis with the CITRUS algorithm identified a T-cell population specific to patients with recurrence. A: The CITRUS analysis identified cluster 40119. B: The frequency of T-cells in cluster 40119 was significantly higher in patients with than in those without recurrence. C: Expression patterns on T-cells in cluster 40119 in patients with recurrence relative to those in all tumor-infiltrating lymphocytes (TILs). D: Most of the CD4+ TILs expressing TNF receptor superfamily member 4 (OX40/CD134) and TNF receptor superfamily member 9 (4-1BB/CD137) were part of the population of CD4+ inducible T-cell co-stimulator (ICOS)+ CD25high TILs. A representative result is shown. E: The percentage of ICOS+ in CD4+CD25high TILs was compared between patients with (mean±standard deviation= 87.9±9.2%) and without (75.7±21.4%) recurrence (p=0.117). PD1: Programmed cell death 1; TIM3: T-cell immunoglobulin and mucin domain-containing protein 3.

Identification of ‘hot’ and ‘cold’ tumors using a heatmap analysis. The status of T-cells in tumor tissue was examined by a heatmap analysis using the frequencies of CD4+ and CD8+ in CD3+cells; CD45RA+, PD1+, TIM3+, ICOS+, 4-1BB+, OX40+, CD25+, and CD103+ cells in CD4+ and CD8+ cells, and CD45RA−CD25high in CD4+ cells. Unsupervised clustering classified 31 OSCC TILs into two clusters: Cluster A with immunologically inactivate TILs and cluster B with immunologically activated TILs (Figure 3). Twenty patients in cluster B with activated TIL profiles had a significantly higher maximum standardized uptake value (SUVmax) by 18F-fluoro-deoxyglucose positron-emission tomography imaging, a stronger INF, and shorter DFS than patients in cluster A (Table II).

Figure 3.
Figure 3.

Profiling and clustering analyses with a heatmap analysis. The activation status of tumor-infiltrating lymphocytes (TILs) was examined by a heatmap analysis using 31 sets (ORC No.) of TILs and the expression frequencies of 19 surface markers. A hierarchical clustering algorithm showed two clusters, A and B. Recurrence is shown under the heatmap. 4-1BB: Tumour necrosis factor receptor superfamily member 9 (CD137); ICOS: Inducible T-cell co-stimulator; N: no recurrence; OX40: TNF receptor superfamily member 4 (CD134); PD1: programmed cell death 1; R: recurrence; TIM3: T-cell immunoglobulin and mucin domain-containing protein 3.

View this table:
Table II.

Clinicopathological factors of patients with oral squamous cell carcinoma in clusters A and B defined by the heatmap analysis.

A profiling analysis with the CITRUS algorithm using the raw data of all markers was performed to examine specific populations of clusters. Two clusters abundant in cluster B, 40129 and 40140, and two clusters, abundant in cluster A, 40118 and 40144, were identified (Figure 4A-C and Supplementary Figure 2). T-Cells in cluster 40129 were CD4+, CD25+ and ICOS+, and had high expression of OX40 and 4-1BB, and this expression pattern was similar to that identified in the prognostic analysis. In the flow cytometric analysis, % ICOS+ in CD4+ CD25high was significantly higher in Cluster B than in Cluster A (p=0.03, Figure 4D). T-Cells in cluster 40140 had clear expression of CD4, intermediate expression of CD25 and ICOS, but did not express OX40 or 4-1BB (Figure 4C).

Figure 4.
Figure 4.

A profiling analysis with the CITRUS algorithm identified a population specific to cluster B. A: The CITRUS analysis identified clusters 40129, 40140, 40118, and 40144. B: The frequency of T-cells in clusters 40129 and 40140 was significantly higher in patients of cluster B than in patients of cluster A. Clusters 40118 and 40144 were abundant in cluster A. C: Expression patterns of clusters 40129, 40140, 40118 and 40144 in patients of cluster B relative to those in all patients. D: The percentage of inducible T-cell co-stimulator (ICOS)+ cells in CD4+ CD25high tumor-infiltrating lymphocytes were compared between patients of cluster A (mean±standard deviation=68.8±25.7) and cluster B (83.1±14.7) (p=0.03). 4-1BB: Tumour necrosis factor receptor superfamily member 9 (CD137); ICOS: Inducible T-cell co-stimulator; OX40: tumor necrosis factor receptor superfamily member 4 (CD134); PD1: programmed cell death 1; TIM3: T-cell immunoglobulin and mucin domain-containing protein 3.

ICOS expression in CD4+ CD25high T-cells and prognosis. Since the present results indicated that the percentage of ICOS+ cells in CD4+CD25high T-cells (mean±standard deviation=78.0±20.1%, median=82.1%) was closely associated with the activation of T-cells in the TME, we examined its relevance to the prognosis of these patients with OSCC. No significant difference was observed in DFS when analyzed by the log-rank test (p=0.095, Figure 5A) when the 31 patients were divided by the median percentage of ICOS+ cells of CD4+ CD25high cells. However, DFS was significantly shorter in patients with ≥95% ICOS+ in CD4+CD25high cells, the threshold selected by the ROC curve (Figure 5B and C) (p=0.0157). The percentages of different cell populations did not correlate with DFS even in re-evaluation using each respective ROC. We then reviewed clinicopathological factors in relation to prognosis, including the ICOS+ population. Even after the re-evaluation of age and SUVmax using ROC curves (data not shown), the percentage of ICOS+ cells in CD4+ CD25high TILs was identified as an independent prognostic factor for OSCC patients in univariate and multivariate analyses (Table III).

Figure 5.
Figure 5.

Disease-free survival curves for patients with oral squamous cell carcinoma according to the percentage of inducible T-cell co-stimulator (ICOS)+ cells in CD4+ CD25high tumor-infiltrating lymphocytes (TILs). A: Patients were divided into two groups based on the median percentage of ICOS+ cells (82%). Log-rank test showed disease-free survival was not significantly different between the two groups. B: A receiver operating characteristics analysis was performed to detect the optimal cut-off value for disease-free survival, which was found to be 95% ICOS+ cells in CD4+ CD25high TILs (area under the curve=0.713, sensitivity=50%, specificity=92%). C: Partitioning the patients by the value derived from the receiver operating characteristics analysis showed disease-free survival to be significantly better for those with <95% ICOS+CD4+CD25high TILs.

View this table:
Table III.

Univariate and multivariate analyses of prognostic factors for disease-free survival of patients with oral squamous cell carcinoma.

Discussion

CD25+CD4+ T-cells have been identified as Tregs. Miyara et al. detected eTregs with immunosuppressive functions in the CD25high fraction, while others reported the co-expression of ICOS and tumor necrosis factor receptor superfamily members, including OX40 and 4-1BB (29, 30). The fraction of cells with intermediate expression of CD25 has also been identified as non-Tregs, a heterogenous population with low or no immunosuppressive activity. The populations in cluster 40119 in Figure 2 and Cluster 40129 in Figure 4 may be regarded as eTregs because of the high expression of CD25, ICOS, OX40 and 4-1BB (32, 33). On the other hand, the population in cluster 40140 in Figure 4 may be non-Tregs with intermediate expression of CD25, low expression of ICOS, and no expression of OX40 or 4-1BB. Abundant expression of CD25, high affinity IL2 receptor, plays an important role in the immunosuppressive functions of eTregs because the consumption of IL2 by eTregs induces a deficiency in IL2 and affects T-cell antitumor activity around eTregs in the TME (24, 25).

ICOS has been identified as a surface molecule that is induced and activated by T-cell receptors, and is highly expressed by TILs, particularly eTregs in the TME (26, 34). Interactions with its counterpart ICOS-ligand mediate the induction, proliferation, and immunosuppressive functions of ICOS+ eTregs. In our previous studies on gastric cancer and colorectal cancer, we demonstrated that ICOS+ Tregs were unique to tumor tissues, were induced by ICOS-ligand+ plasmacytoid dendritic cells following stimulation with Helicobacter pylori through toll-like receptor 7 in the gastric mucosa, produced the highly immunosuppressive cytokine IL10 and were identified as a biomarker for a poor prognosis, while a very small population of effector T-cells expressed ICOS (27, 35, 36). ICOS+ Tregs have also been detected in tumor tissue from many cancer types, including melanoma, head and neck cancer, and breast cancer (26). Several clinical trials using anti-ICOS to target eTregs in TME as cancer immunotherapy are now being actively conducted (NCT02520791, NCT03829501, and NCT02723955). An antibody developed for clinical use achieved depletion of ICOShigh eTregs by antibody-dependent cellular cytotoxicity and, simultaneously, activated effector T-cells with low expression of ICOS (34).

Our CITRUS analysis revealed that abundant ICOS+ eTregs reflected the phenomenon of ‘hot’ OSCC tumors, which has been reported as a feasible immune status for ICI therapy. A biomarker other than the expression of PD-L1 that has already been established and is clinically applied is microsatellite instability (MSI)-high. Frequent mutations in tumor cells induce the MSI-high status and produce immunogenic proteins, so-called neoantigens, that elicit strong antitumor immune responses (14, 37-39). However, the sustained presence of tumors in patients indicates that the tumor immunity elicited does not function, due to several mechanisms including immunosuppressive pathways, such as PD1/PD-L1, and immunosuppressive cells such as Tregs. This is why we anticipate the effectiveness of combination therapy of ICOS antibodies and ICIs, such as through PD1/PD-L1 pathway blockade.

The results showing that CD8+ and CD4+ T-cells in CD3+ cell populations were not associated with prognosis, and that CD8+ T cells in CD3+ cells were not clearly distinguishable between the ‘hot’ and ‘cold’ tumors in the heatmap analysis were of interest (Figure 3). The activated state of T-cells identified by multivariate analysis, such as heatmapping, may be independent of the frequency of CD8+ cells, and this hypothesis is supported by recent studies by Galon et al. that added CD45RO as an activation marker for immunoscore analysis (12, 14). To overcome the limitations in the present study of the frequency, but not numbers, of T-cells and prognosis of DFS, but not overall survival, we intend to conduct an immunohistochemical analysis of archived OSCC specimens. We will also accumulate a larger number of cases including ICI-treated patients to confirm the present results.

In conclusion, we identified ICOS+ eTregs as a potential biomarker for the prediction of recurrence after surgery and activation status of TILs in patients with OSCC. A clinical study of ICOS antagonist in combination with the blockade of the PD1/PD-L1 pathway for patients with head and neck cancer is currently being carried out (NCT02723955), therefore, we are paying close attention to the results for patients with OSCC and hope that assay of ICOS may become a companion diagnostic method.

Acknowledgements

This study was supported by Grants-in-Aid for Scientific Research (B) grant no. 19H03729 from the Ministry of Education, Culture, Sports, Science and Technology of Japan. The Authors thank all the patients who participated. We thank Junko Higuchi for her excellent assistance.

Footnotes

  • Authors’ Contributions

    HK, NU, and HW designed the study; HK, Mhi, Mha, and AU performed the assays; AK and KI performed the statistical analysis; HK, KH, KM, ST, and NU collected samples and obtained clinical data; HK, NU, and HW analyzed the data; HK and HW wrote the article; HK, NU, and HW participated in article editing and discussions.

  • Supplementary Material

    Supplementary Figure 1 is available at: https://doi.org/10.6084/m9.figshare.19388423.v1

    Supplementary Figure 2 is available at: https://doi.org/10.6084/m9.figshare.19391159

  • Conflicts of Interest

    The Department of Clinical Research in Tumor Immunology is a collaborating laboratory of Osaka University and Shionogi Co., Ltd. The Authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this article.

  • Received March 1, 2022.
  • Revision received March 22, 2022.
  • Accepted March 24, 2022.

References

  1. ↵
    1. Bray F,
    2. Ferlay J,
    3. Soerjomataram I,
    4. Siegel RL,
    5. Torre LA and
    6. Jemal A
    : Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 68(6): 394-424, 2018. PMID: 30207593. DOI: 10.3322/caac.21492
    OpenUrlCrossRefPubMed
    1. Du M,
    2. Nair R,
    3. Jamieson L,
    4. Liu Z and
    5. Bi P
    : Incidence trends of lip, oral cavity, and pharyngeal cancers: Global Burden of Disease 1990-2017. J Dent Res 99(2): 143-151, 2020. PMID: 31874128. DOI: 10.1177/0022034519894963
    OpenUrlCrossRefPubMed
  3. ↵
    1. Almangush A,
    2. Mäkitie AA,
    3. Triantafyllou A,
    4. de Bree R,
    5. Strojan P,
    6. Rinaldo A,
    7. Hernandez-Prera JC,
    8. Suárez C,
    9. Kowalski LP,
    10. Ferlito A and
    11. Leivo I
    : Staging and grading of oral squamous cell carcinoma: An update. Oral Oncol 107: 104799, 2020. PMID: 32446214. DOI: 10.1016/j.oraloncology.2020.104799
    OpenUrlCrossRefPubMed
  4. ↵
    1. Brierley JD,
    2. Gospodarowicz MK and
    3. Wittekind C
    (eds.): TNM Classification of Malignant Tumours. Wiley-Blackwell, 2017.
    1. Huang SH and
    2. O’Sullivan B
    : Overview of the 8th Edition TNM Classification for Head and Neck Cancer. Curr Treat Options Oncol 18(7): 40, 2017. PMID: 28555375. DOI: 10.1007/s11864-017-0484-y
    OpenUrlCrossRefPubMed
  6. ↵
    1. Colevas AD,
    2. Yom SS,
    3. Pfister DG,
    4. Spencer S,
    5. Adelstein D,
    6. Adkins D,
    7. Brizel DM,
    8. Burtness B,
    9. Busse PM,
    10. Caudell JJ,
    11. Cmelak AJ,
    12. Eisele DW,
    13. Fenton M,
    14. Foote RL,
    15. Gilbert J,
    16. Gillison ML,
    17. Haddad RI,
    18. Hicks WL,
    19. Hitchcock YJ,
    20. Jimeno A,
    21. Leizman D,
    22. Maghami E,
    23. Mell LK,
    24. Mittal BB,
    25. Pinto HA,
    26. Ridge JA,
    27. Rocco J,
    28. Rodriguez CP,
    29. Shah JP,
    30. Weber RS,
    31. Witek M,
    32. Worden F,
    33. Zhen W,
    34. Burns JL and
    35. Darlow SD
    : NCCN Guidelines insights: Head and neck cancers, version 1.2018. J Natl Compr Canc Netw 16(5): 479-490, 2018. PMID: 29752322. DOI: 10.6004/jnccn.2018.0026
    OpenUrlAbstract/FREE Full Text
  7. ↵
    1. Lugli A,
    2. Kirsch R,
    3. Ajioka Y,
    4. Bosman F,
    5. Cathomas G,
    6. Dawson H,
    7. El Zimaity H,
    8. Fléjou JF,
    9. Hansen TP,
    10. Hartmann A,
    11. Kakar S,
    12. Langner C,
    13. Nagtegaal I,
    14. Puppa G,
    15. Riddell R,
    16. Ristimäki A,
    17. Sheahan K,
    18. Smyrk T,
    19. Sugihara K,
    20. Terris B,
    21. Ueno H,
    22. Vieth M,
    23. Zlobec I and
    24. Quirke P
    : Recommendations for reporting tumor budding in colorectal cancer based on the International Tumor Budding Consensus Conference (ITBCC) 2016. Mod Pathol 30(9): 1299-1311, 2017. PMID: 28548122. DOI: 10.1038/modpathol.2017.46
    OpenUrlCrossRefPubMed
    1. Almangush A,
    2. Pirinen M,
    3. Heikkinen I,
    4. Mäkitie AA,
    5. Salo T and
    6. Leivo I
    : Tumour budding in oral squamous cell carcinoma: a meta-analysis. Br J Cancer 118(4): 577-586, 2018. PMID: 29190636. DOI: 10.1038/bjc.2017.425
    OpenUrlCrossRefPubMed
    1. Mäkitie AA,
    2. Almangush A,
    3. Rodrigo JP,
    4. Ferlito A and
    5. Leivo I
    : Hallmarks of cancer: Tumor budding as a sign of invasion and metastasis in head and neck cancer. Head Neck 41(10): 3712-3718, 2019. PMID: 31328847. DOI: 10.1002/hed.25872
    OpenUrlCrossRefPubMed
    1. Yamamoto E,
    2. Miyakawa A and
    3. Kohama G
    : Mode of invasion and lymph node metastasis in squamous cell carcinoma of the oral cavity. Head Neck Surg 6(5): 938-947, 1984. PMID: 6724960. DOI: 10.1002/hed.2890060508
    OpenUrlCrossRefPubMed
  11. ↵
    1. Shimizu S,
    2. Miyazaki A,
    3. Sonoda T,
    4. Koike K,
    5. Ogi K,
    6. Kobayashi JI,
    7. Kaneko T,
    8. Igarashi T,
    9. Ueda M,
    10. Dehari H,
    11. Miyakawa A,
    12. Hasegawa T and
    13. Hiratsuka H
    : Tumor budding is an independent prognostic marker in early stage oral squamous cell carcinoma: With special reference to the mode of invasion and worst pattern of invasion. PLoS One 13(4): e0195451, 2018. PMID: 29672550. DOI: 10.1371/journal.pone.0195451
    OpenUrlCrossRefPubMed
  12. ↵
    1. Galon J,
    2. Mlecnik B,
    3. Bindea G,
    4. Angell HK,
    5. Berger A,
    6. Lagorce C,
    7. Lugli A,
    8. Zlobec I,
    9. Hartmann A,
    10. Bifulco C,
    11. Nagtegaal ID,
    12. Palmqvist R,
    13. Masucci GV,
    14. Botti G,
    15. Tatangelo F,
    16. Delrio P,
    17. Maio M,
    18. Laghi L,
    19. Grizzi F,
    20. Asslaber M,
    21. D’Arrigo C,
    22. Vidal-Vanaclocha F,
    23. Zavadova E,
    24. Chouchane L,
    25. Ohashi PS,
    26. Hafezi-Bakhtiari S,
    27. Wouters BG,
    28. Roehrl M,
    29. Nguyen L,
    30. Kawakami Y,
    31. Hazama S,
    32. Okuno K,
    33. Ogino S,
    34. Gibbs P,
    35. Waring P,
    36. Sato N,
    37. Torigoe T,
    38. Itoh K,
    39. Patel PS,
    40. Shukla SN,
    41. Wang Y,
    42. Kopetz S,
    43. Sinicrope FA,
    44. Scripcariu V,
    45. Ascierto PA,
    46. Marincola FM,
    47. Fox BA and
    48. Pagès F
    : Towards the introduction of the ‘Immunoscore’ in the classification of malignant tumours. J Pathol 232(2): 199-209, 2014. PMID: 24122236. DOI: 10.1002/path.4287
    OpenUrlCrossRefPubMed
  13. ↵
    1. Tumeh PC,
    2. Harview CL,
    3. Yearley JH,
    4. Shintaku IP,
    5. Taylor EJ,
    6. Robert L,
    7. Chmielowski B,
    8. Spasic M,
    9. Henry G,
    10. Ciobanu V,
    11. West AN,
    12. Carmona M,
    13. Kivork C,
    14. Seja E,
    15. Cherry G,
    16. Gutierrez AJ,
    17. Grogan TR,
    18. Mateus C,
    19. Tomasic G,
    20. Glaspy JA,
    21. Emerson RO,
    22. Robins H,
    23. Pierce RH,
    24. Elashoff DA,
    25. Robert C and
    26. Ribas A
    : PD-1 blockade induces responses by inhibiting adaptive immune resistance. Nature 515(7528): 568-571, 2014. PMID: 25428505. DOI: 10.1038/nature13954
    OpenUrlCrossRefPubMed
  14. ↵
    1. Galon J,
    2. Costes A,
    3. Sanchez-Cabo F,
    4. Kirilovsky A,
    5. Mlecnik B,
    6. Lagorce-Pagès C,
    7. Tosolini M,
    8. Camus M,
    9. Berger A,
    10. Wind P,
    11. Zinzindohoué F,
    12. Bruneval P,
    13. Cugnenc PH,
    14. Trajanoski Z,
    15. Fridman WH and
    16. Pagès F
    : Type, density, and location of immune cells within human colorectal tumors predict clinical outcome. Science 313(5795): 1960-1964, 2006. PMID: 17008531. DOI: 10.1126/science.1129139
    OpenUrlAbstract/FREE Full Text
    1. Giraldo NA,
    2. Becht E,
    3. Vano Y,
    4. Petitprez F,
    5. Lacroix L,
    6. Validire P,
    7. Sanchez-Salas R,
    8. Ingels A,
    9. Oudard S,
    10. Moatti A,
    11. Buttard B,
    12. Bourass S,
    13. Germain C,
    14. Cathelineau X,
    15. Fridman WH and
    16. Sautès-Fridman C
    : Tumor-infiltrating and peripheral blood T-cell immunophenotypes predict early relapse in localized clear cell renal cell carcinoma. Clin Cancer Res 23(15): 4416-4428, 2017. PMID: 28213366. DOI: 10.1158/1078-0432.CCR-16-2848
    OpenUrlAbstract/FREE Full Text
  16. ↵
    1. Hugo W,
    2. Zaretsky JM,
    3. Sun L,
    4. Song C,
    5. Moreno BH,
    6. Hu-Lieskovan S,
    7. Berent-Maoz B,
    8. Pang J,
    9. Chmielowski B,
    10. Cherry G,
    11. Seja E,
    12. Lomeli S,
    13. Kong X,
    14. Kelley MC,
    15. Sosman JA,
    16. Johnson DB,
    17. Ribas A and
    18. Lo RS
    : Genomic and transcriptomic features of response to anti-PD-1 therapy in metastatic melanoma. Cell 165(1): 35-44, 2016. PMID: 26997480. DOI: 10.1016/j.cell.2016.02.065
    OpenUrlCrossRefPubMed
  17. ↵
    1. Lizotte PH,
    2. Ivanova EV,
    3. Awad MM,
    4. Jones RE,
    5. Keogh L,
    6. Liu H,
    7. Dries R,
    8. Almonte C,
    9. Herter-Sprie GS,
    10. Santos A,
    11. Feeney NB,
    12. Paweletz CP,
    13. Kulkarni MM,
    14. Bass AJ,
    15. Rustgi AK,
    16. Yuan GC,
    17. Kufe DW,
    18. Jänne PA,
    19. Hammerman PS,
    20. Sholl LM,
    21. Hodi FS,
    22. Richards WG,
    23. Bueno R,
    24. English JM,
    25. Bittinger MA and
    26. Wong KK
    : Multiparametric profiling of non-small-cell lung cancers reveals distinct immunophenotypes. JCI Insight 1(14): e89014, 2016. PMID: 27699239. DOI: 10.1172/jci.insight.89014
    OpenUrlCrossRefPubMed
    1. Chen PL,
    2. Roh W,
    3. Reuben A,
    4. Cooper ZA,
    5. Spencer CN,
    6. Prieto PA,
    7. Miller JP,
    8. Bassett RL,
    9. Gopalakrishnan V,
    10. Wani K,
    11. De Macedo MP,
    12. Austin-Breneman JL,
    13. Jiang H,
    14. Chang Q,
    15. Reddy SM,
    16. Chen WS,
    17. Tetzlaff MT,
    18. Broaddus RJ,
    19. Davies MA,
    20. Gershenwald JE,
    21. Haydu L,
    22. Lazar AJ,
    23. Patel SP,
    24. Hwu P,
    25. Hwu WJ,
    26. Diab A,
    27. Glitza IC,
    28. Woodman SE,
    29. Vence LM,
    30. Wistuba II,
    31. Amaria RN,
    32. Kwong LN,
    33. Prieto V,
    34. Davis RE,
    35. Ma W,
    36. Overwijk WW,
    37. Sharpe AH,
    38. Hu J,
    39. Futreal PA,
    40. Blando J,
    41. Sharma P,
    42. Allison JP,
    43. Chin L and
    44. Wargo JA
    : Analysis of immune signatures in longitudinal tumor samples yields insight into biomarkers of response and mechanisms of resistance to immune checkpoint blockade. Cancer Discov 6(8): 827-837, 2016. PMID: 27301722. DOI: 10.1158/2159-8290.CD-15-1545
    OpenUrlAbstract/FREE Full Text
  19. ↵
    1. Cristescu R,
    2. Mogg R,
    3. Ayers M,
    4. Albright A,
    5. Murphy E,
    6. Yearley J,
    7. Sher X,
    8. Liu XQ,
    9. Lu H,
    10. Nebozhyn M,
    11. Zhang C,
    12. Lunceford JK,
    13. Joe A,
    14. Cheng J,
    15. Webber AL,
    16. Ibrahim N,
    17. Plimack ER,
    18. Ott PA,
    19. Seiwert TY,
    20. Ribas A,
    21. McClanahan TK,
    22. Tomassini JE,
    23. Loboda A and
    24. Kaufman D
    : Pan-tumor genomic biomarkers for PD-1 checkpoint blockade-based immunotherapy. Science 362(6411): eaar3593, 2018. PMID: 30309915. DOI: 10.1126/science.aar3593
    OpenUrlAbstract/FREE Full Text
    1. Sharma P and
    2. Allison JP
    : The future of immune checkpoint therapy. Science 348(6230): 56-61, 2015. PMID: 25838373. DOI: 10.1126/science.aaa8172
    OpenUrlAbstract/FREE Full Text
  21. ↵
    1. Galon J and
    2. Bruni D
    : Approaches to treat immune hot, altered and cold tumours with combination immunotherapies. Nat Rev Drug Discov 18(3): 197-218, 2019. PMID: 30610226. DOI: 10.1038/s41573-018-0007-y
    OpenUrlCrossRefPubMed
  22. ↵
    1. Galluzzi L,
    2. Chan TA,
    3. Kroemer G,
    4. Wolchok JD and
    5. López-Soto A
    : The hallmarks of successful anticancer immunotherapy. Sci Transl Med 10(459): eaat7807, 2018. PMID: 30232229. DOI: 10.1126/scitranslmed.aat7807
    OpenUrlFREE Full Text
  23. ↵
    1. Kumagai S,
    2. Togashi Y,
    3. Kamada T,
    4. Sugiyama E,
    5. Nishinakamura H,
    6. Takeuchi Y,
    7. Vitaly K,
    8. Itahashi K,
    9. Maeda Y,
    10. Matsui S,
    11. Shibahara T,
    12. Yamashita Y,
    13. Irie T,
    14. Tsuge A,
    15. Fukuoka S,
    16. Kawazoe A,
    17. Udagawa H,
    18. Kirita K,
    19. Aokage K,
    20. Ishii G,
    21. Kuwata T,
    22. Nakama K,
    23. Kawazu M,
    24. Ueno T,
    25. Yamazaki N,
    26. Goto K,
    27. Tsuboi M,
    28. Mano H,
    29. Doi T,
    30. Shitara K and
    31. Nishikawa H
    : The PD-1 expression balance between effector and regulatory T cells predicts the clinical efficacy of PD-1 blockade therapies. Nat Immunol 21(11): 1346-1358, 2020. PMID: 32868929. DOI: 10.1038/s41590-020-0769-3
    OpenUrlCrossRefPubMed
  24. ↵
    1. Wing JB,
    2. Tanaka A and
    3. Sakaguchi S
    : Human FOXP3+ regulatory T cell heterogeneity and function in autoimmunity and cancer. Immunity 50(2): 302-316, 2019. PMID: 30784578. DOI: 10.1016/j.immuni.2019.01.020
    OpenUrlCrossRefPubMed
  25. ↵
    1. Tanaka A and
    2. Sakaguchi S
    : Targeting Treg cells in cancer immunotherapy. Eur J Immunol 49(8): 1140-1146, 2019. PMID: 31257581. DOI: 10.1002/eji.201847659
    OpenUrlCrossRefPubMed
  26. ↵
    1. Li DY and
    2. Xiong XZ
    : ICOS+ Tregs: A functional subset of tregs in immune diseases. Front Immunol 11: 2104, 2020. PMID: 32983168. DOI: 10.3389/fimmu.2020.02104
    OpenUrlCrossRefPubMed
  27. ↵
    1. Nagase H,
    2. Takeoka T,
    3. Urakawa S,
    4. Morimoto-Okazawa A,
    5. Kawashima A,
    6. Iwahori K,
    7. Takiguchi S,
    8. Nishikawa H,
    9. Sato E,
    10. Sakaguchi S,
    11. Mori M,
    12. Doki Y and
    13. Wada H
    : ICOS+ Foxp3+ TILs in gastric cancer are prognostic markers and effector regulatory T cells associated with Helicobacter pylori. Int J Cancer 140(3): 686-695, 2017. PMID: 27756099. DOI: 10.1002/ijc.30475
    OpenUrlCrossRefPubMed
  28. ↵
    1. Saito T,
    2. Nishikawa H,
    3. Wada H,
    4. Nagano Y,
    5. Sugiyama D,
    6. Atarashi K,
    7. Maeda Y,
    8. Hamaguchi M,
    9. Ohkura N,
    10. Sato E,
    11. Nagase H,
    12. Nishimura J,
    13. Yamamoto H,
    14. Takiguchi S,
    15. Tanoue T,
    16. Suda W,
    17. Morita H,
    18. Hattori M,
    19. Honda K,
    20. Mori M,
    21. Doki Y and
    22. Sakaguchi S
    : Two FOXP3(+)CD4(+) T cell subpopulations distinctly control the prognosis of colorectal cancers. Nat Med 22(6): 679-684, 2016. PMID: 27111280. DOI: 10.1038/nm.4086
    OpenUrlCrossRefPubMed
  29. ↵
    1. Miyara M,
    2. Yoshioka Y,
    3. Kitoh A,
    4. Shima T,
    5. Wing K,
    6. Niwa A,
    7. Parizot C,
    8. Taflin C,
    9. Heike T,
    10. Valeyre D,
    11. Mathian A,
    12. Nakahata T,
    13. Yamaguchi T,
    14. Nomura T,
    15. Ono M,
    16. Amoura Z,
    17. Gorochov G and
    18. Sakaguchi S
    : Functional delineation and differentiation dynamics of human CD4+ T cells expressing the FoxP3 transcription factor. Immunity 30(6): 899-911, 2009. PMID: 19464196. DOI: 10.1016/j.immuni.2009.03.019
    OpenUrlCrossRefPubMed
  30. ↵
    1. Sakaguchi S,
    2. Miyara M,
    3. Costantino CM and
    4. Hafler DA
    : FOXP3+ regulatory T cells in the human immune system. Nat Rev Immunol 10(7): 490-500, 2010. PMID: 20559327. DOI: 10.1038/nri2785
    OpenUrlCrossRefPubMed
  31. ↵
    1. Bruggner RV,
    2. Bodenmiller B,
    3. Dill DL,
    4. Tibshirani RJ and
    5. Nolan GP
    : Automated identification of stratifying signatures in cellular subpopulations. Proc Natl Acad Sci USA 111(26): E2770-E2777, 2014. PMID: 24979804. DOI: 10.1073/pnas.1408792111
    OpenUrlAbstract/FREE Full Text
  32. ↵
    1. Freeman ZT,
    2. Nirschl TR,
    3. Hovelson DH,
    4. Johnston RJ,
    5. Engelhardt JJ,
    6. Selby MJ,
    7. Kochel CM,
    8. Lan RY,
    9. Zhai J,
    10. Ghasemzadeh A,
    11. Gupta A,
    12. Skaist AM,
    13. Wheelan SJ,
    14. Jiang H,
    15. Pearson AT,
    16. Snyder LA,
    17. Korman AJ,
    18. Tomlins SA,
    19. Yegnasubramanian S and
    20. Drake CG
    : A conserved intratumoral regulatory T cell signature identifies 4-1BB as a pan-cancer target. J Clin Invest 130(3): 1405-1416, 2020. PMID: 32015231. DOI: 10.1172/JCI128672
    OpenUrlCrossRefPubMed
  33. ↵
    1. Shitara K and
    2. Nishikawa H
    : Regulatory T cells: a potential target in cancer immunotherapy. Ann N Y Acad Sci 1417(1): 104-115, 2018. PMID: 29566262. DOI: 10.1111/nyas.13625
    OpenUrlCrossRefPubMed
  34. ↵
    1. Sainson RCA,
    2. Thotakura AK,
    3. Kosmac M,
    4. Borhis G,
    5. Parveen N,
    6. Kimber R,
    7. Carvalho J,
    8. Henderson SJ,
    9. Pryke KL,
    10. Okell T,
    11. O’Leary S,
    12. Ball S,
    13. Van Krinks C,
    14. Gamand L,
    15. Taggart E,
    16. Pring EJ,
    17. Ali H,
    18. Craig H,
    19. Wong VWY,
    20. Liang Q,
    21. Rowlands RJ,
    22. Lecointre M,
    23. Campbell J,
    24. Kirby I,
    25. Melvin D,
    26. Germaschewski V,
    27. Oelmann E,
    28. Quaratino S and
    29. McCourt M
    : An antibody targeting ICOS increases intratumoral cytotoxic to regulatory T-cell ratio and induces tumor regression. Cancer Immunol Res 8(12): 1568-1582, 2020. PMID: 32999002. DOI: 10.1158/2326-6066.CIR-20-0034
    OpenUrlAbstract/FREE Full Text
  35. ↵
    1. Urakawa S,
    2. Yamasaki M,
    3. Makino T,
    4. Kurokawa Y,
    5. Yamamoto K,
    6. Goto K,
    7. Haruna M,
    8. Hirata M,
    9. Morimoto-Okazawa A,
    10. Kawashima A,
    11. Iwahori K,
    12. Mizushima T,
    13. Sato E,
    14. Mori M,
    15. Doki Y and
    16. Wada H
    : The impact of ICOS+ regulatory T cells and Helicobacter pylori infection on the prognosis of patients with gastric and colorectal cancer: potential prognostic benefit of pre-operative eradication therapy. Cancer Immunol Immunother 70(2): 443-452, 2021. PMID: 32803278. DOI: 10.1007/s00262-020-02696-4
    OpenUrlCrossRefPubMed
  36. ↵
    1. Kawashima A,
    2. Kanazawa T,
    3. Goto K,
    4. Matsumoto M,
    5. Morimoto-Okazawa A,
    6. Iwahori K,
    7. Ujike T,
    8. Nagahara A,
    9. Fujita K,
    10. Uemura M,
    11. Nonomura N and
    12. Wada H
    : Immunological classification of renal cell carcinoma patients based on phenotypic analysis of immune check-point molecules. Cancer Immunol Immunother 67(1): 113-125, 2018. PMID: 28975380. DOI: 10.1007/s00262-017-2060-5
    OpenUrlCrossRefPubMed
  37. ↵
    1. Le DT,
    2. Uram JN,
    3. Wang H,
    4. Bartlett BR,
    5. Kemberling H,
    6. Eyring AD,
    7. Skora AD,
    8. Luber BS,
    9. Azad NS,
    10. Laheru D,
    11. Biedrzycki B,
    12. Donehower RC,
    13. Zaheer A,
    14. Fisher GA,
    15. Crocenzi TS,
    16. Lee JJ,
    17. Duffy SM,
    18. Goldberg RM,
    19. de la Chapelle A,
    20. Koshiji M,
    21. Bhaijee F,
    22. Huebner T,
    23. Hruban RH,
    24. Wood LD,
    25. Cuka N,
    26. Pardoll DM,
    27. Papadopoulos N,
    28. Kinzler KW,
    29. Zhou S,
    30. Cornish TC,
    31. Taube JM,
    32. Anders RA,
    33. Eshleman JR,
    34. Vogelstein B and
    35. Diaz LA Jr.
    : PD-1 blockade in tumors with mismatch-repair deficiency. N Engl J Med 372(26): 2509-2520, 2015. PMID: 26028255. DOI: 10.1056/NEJMoa1500596
    OpenUrlCrossRefPubMed
    1. Le DT,
    2. Durham JN,
    3. Smith KN,
    4. Wang H,
    5. Bartlett BR,
    6. Aulakh LK,
    7. Lu S,
    8. Kemberling H,
    9. Wilt C,
    10. Luber BS,
    11. Wong F,
    12. Azad NS,
    13. Rucki AA,
    14. Laheru D,
    15. Donehower R,
    16. Zaheer A,
    17. Fisher GA,
    18. Crocenzi TS,
    19. Lee JJ,
    20. Greten TF,
    21. Duffy AG,
    22. Ciombor KK,
    23. Eyring AD,
    24. Lam BH,
    25. Joe A,
    26. Kang SP,
    27. Holdhoff M,
    28. Danilova L,
    29. Cope L,
    30. Meyer C,
    31. Zhou S,
    32. Goldberg RM,
    33. Armstrong DK,
    34. Bever KM,
    35. Fader AN,
    36. Taube J,
    37. Housseau F,
    38. Spetzler D,
    39. Xiao N,
    40. Pardoll DM,
    41. Papadopoulos N,
    42. Kinzler KW,
    43. Eshleman JR,
    44. Vogelstein B,
    45. Anders RA and
    46. Diaz LA Jr.
    : Mismatch repair deficiency predicts response of solid tumors to PD-1 blockade. Science 357(6349): 409-413, 2017. PMID: 28596308. DOI: 10.1126/science.aan6733
    OpenUrlAbstract/FREE Full Text
  39. ↵
    1. Overman MJ,
    2. McDermott R,
    3. Leach JL,
    4. Lonardi S,
    5. Lenz HJ,
    6. Morse MA,
    7. Desai J,
    8. Hill A,
    9. Axelson M,
    10. Moss RA,
    11. Goldberg MV,
    12. Cao ZA,
    13. Ledeine JM,
    14. Maglinte GA,
    15. Kopetz S and
    16. André T
    : Nivolumab in patients with metastatic DNA mismatch repair-deficient or microsatellite instability-high colorectal cancer (CheckMate 142): an open-label, multicentre, phase 2 study. Lancet Oncol 18(9): 1182-1191, 2017. PMID: 28734759. DOI: 10.1016/S1470-2045(17)30422-9
    OpenUrlCrossRefPubMed
Back to top

In this issue

Print
Download PDF
Article Alerts
Email Article
Citation Tools
Reprints and Permissions
Tumor-infiltrating ICOS+ Effector Regulatory T-Cells in Oral Squamous Cell Carcinoma as a Promising Biomarker for Prognosis and ‘Hot’ Tumor
HITOMI KAJIKAWA, MICHINARI HIRATA, MIYA HARUNA, AZUMI UEYAMA, KATSUTOSHI HIROSE, ATSUNARI KAWASHIMA, KOTA IWAHORI, KAZUHIDE MATSUNAGA, SATORU TOYOSAWA, NARIKAZU UZAWA, HISASHI WADA
Anticancer Research May 2022, 42 (5) 2383-2393; DOI: 10.21873/anticanres.15717
Twitter logo Facebook logo Mendeley logo

Jump to section

Keywords