cancers Article Activated Lymphocytes and Increased Risk of Dermatologic Adverse Events during Sorafenib Therapy for Hepatocellular Carcinoma Josep Corominas 1,2 , Victor Sapena 1,2 , Marco Sanduzzi-Zamparelli 1,2 , Cristina Millán 1,2 , Esther Samper 1,2 , Neus Llarch 1,2 , Gemma Iserte 1,2 , Ferràn Torres 3,4 , Leonardo G. Da Fonseca 1,2 , Sergio Muñoz-Martínez 1,2 , Alejandro Forner 1,2 , Jordi Bruix 1,2,5 , Loreto Boix 1,5, *,† and María Reig 1,2,5, *,† 1 Barcelona Clinic Liver Cancer (BCLC) Group, Liver Unit, Hospital Clinic, IDIBAPS, University of Barcelona, 08036 Barcelona, Spain; jcorominas@clinic.cat (J.C.); vsapena@clinic.cat (V.S.); msanduzzi@clinic.cat (M.S.-Z.); cristinamdp.2020@gmail.com (C.M.); esamper@clinic.cat (E.S.); nllarch@clinic.cat (N.L.); giserte@clinic.cat (G.I.); l.fonseca@fm.usp.br (L.G.D.F.); smunozm@clinic.cat (S.M.-M.); aforner@clinic.cat (A.F.); jbruix@clinic.cat (J.B.) 2 Fundació Clínic Recerca Biomèdica, 08036 Barcelona, Spain 3 Biostatistics Unit, Faculty of Medicine, Universitat Autònoma de Barcelona, 08193 Barcelona, Span; ferran.torres@uab.cat 4 Medical Statistics Core Facility, Clinical Pharmacology Department, IDIBAPS-Hospital Clínic de Barcelona, 08036 Barcelona, Spain 5 Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBERehd),  28028 Madrid, Spain  * Correspondence: lboix@clinic.cat (L.B.); mreig1@clinic.cat (M.R.) † Equal contribution as senior authors. Citation: Corominas, J.; Sapena, V.; Sanduzzi-Zamparelli, M.; Millán, C.; Samper, E.; Llarch, N.; Iserte, G.; Simple Summary: Hepatocellular carcinoma is the second cause of cancer-related death world- Torres, F.; Da Fonseca, L.G.; wide. Of those advanced-stage patients who are treated with sorafenib, those who develop early Muñoz-Martínez, S.; et al. Activated dermatologic adverse events have a better prognosis. These events are possibly immune-related. Lymphocytes and Increased Risk of Therefore, we analyzed the phenotype of 52 sorafenib-treated patients’ circulating lymphocytes Dermatologic Adverse Events during throughout treatment. We found that different co-stimulatory and immune exhaustion markers, such Sorafenib Therapy for Hepatocellular as Programmed cell death protein 1 (PD-1) and DNAX accessory molecule 1 (DNAM-1) amongst Carcinoma. Cancers 2021, 13, 426. others, correlate with the probability of developing these adverse events, both before and during https://doi.org/10.3390/ the treatment. We also compared the phenotype of those lymphocytes expressing DNAM-1 with cancers13030426 those that do not, and while NK DNAM-1-expressing cells have a co-stimulatory phenotype, T DNAM-1-expressing cells are immune-suppressors. Overall, we set a rationale for the combination Academic Editor: Matias A. Avila of sorafenib and immune-targeted therapies; and for the use of immune markers (such as DNAM-1) Received: 10 December 2020 Accepted: 18 January 2021 for patients’ prognosis evaluation. Published: 23 January 2021 Abstract: Advanced hepatocellular carcinoma patients treated with sorafenib who develop early Publisher’s Note: MDPI stays neutral dermatologic adverse events (eDAEs) have a better prognosis. This may be linked to immune with regard to jurisdictional claims in mechanisms, and thus, it is relevant to assess the association between peripheral immunity and the published maps and institutional affil- probability of developing eDAEs. Peripheral blood mononuclear cells of 52 HCC patients treated iations. with sorafenib were analyzed at baseline and throughout the first eight weeks of therapy. T, B, Natural Killer cells, and their immune checkpoints expression data were characterized by flow cytometry. Cytokine release and immune-suppression assays were carried out ex vivo. Cox baseline and time-dependent regression models were applied to evaluate the probability of increased risk Copyright: © 2021 by the authors. of eDAEs. DNAM-1, PD-1, CD69, and LAG-3 in T cells, plus CD16 and LAG-3 in NK cells, are Licensee MDPI, Basel, Switzerland. significantly associated with the probability of developing eDAEs. While NK DNAM-1+ cells express This article is an open access article activation markers, T DNAM-1+ cells induce immune suppression and show immune exhaustion. distributed under the terms and This is the first study to report an association between immune checkpoints expression in circulating conditions of the Creative Commons immune cells and the increased incidence of eDAEs. Our results support the hypothesis for an Attribution (CC BY) license (https:// off-target role of sorafenib in immune modulation. We also describe a novel association between creativecommons.org/licenses/by/ DNAM-1 and immune exhaustion in T cells. 4.0/). Cancers 2021, 13, 426. https://doi.org/10.3390/cancers13030426 https://www.mdpi.com/journal/cancers Cancers 2021, 13, 426 2 of 20 Keywords: hepatocellular carcinoma; sorafenib; early dermatologic adverse events; peripheral blood mononuclear cells; immune cell phenotyping; PD-1; DNAM-1; CD96 1. Introduction Hepatocellular carcinoma (HCC) is the most common form of liver cancer (>80%) [1] and the second most common cause of cancer-related death worldwide [2]. Moreover, the overall burden of liver cancer is increasing over time. Up to 30–35% of the patients present advanced disease at diagnosis in countries without nationwide surveillance pro- grams [3]. Currently, the combination of atezolizumab with bevacizumab has shown to be superior to sorafenib in first line [4], but not all HCC patients will be candidates for this option, and sorafenib and lenvatinib will remain the effective option for a relevant proportion of patients. While having a main anti-angiogenic function, sorafenib has off-target activities on the immune system. It can enhance lymphocyte-mediated anti-tumor activity through an increase in lymphocyte infiltration [5,6], by targeting lymphocyte-specific protein tyro- sine kinase (LCK) phosphorylation [7], and by inhibition of the major histocompatibility complex (MHC) shedding [8]. Early dermatologic adverse events (those arising in the first 60 days of treatment; eDAEs) predict a better outcome in HCC patients treated with sorafenib, with an overall survival of 18.2 months in patients with eDAEs vs. 10.1 months in patients without eDAEs [9]. The impact of eDAEs has been externally validated in sorafenib and regorafenib treated patients [10–12]. Skin adverse events have also been associated with better outcomes in other tumor indications treated with immune checkpoint inhibitors (ICIs) such as atezolizumab [13–15]. The underlying molecular mechanisms of eDAEs development, how they are trig- gered, and how these mechanisms contribute to a better response to sorafenib, are questions that have yet to be elucidated. The advent of immune checkpoint inhibition therapies has revolutionized the land- scape of cancer treatments over the last decade. These therapies are based on the use of monoclonal antibodies directed against inhibitory checkpoints, expressed by immune cells; or against their ligands, expressed by tumor cells. The disruption of these receptor/ligand interactions may revert the functional inhibition of these cells and restore an effective anti- tumor activity [16]. Since the discovery of Cytotoxic T-Lymphocyte Antigen 4 (CTLA-4) and Programmed Cell Death Protein 1 (PD-1) as therapeutic targets, the list of checkpoints has been increasing, and numerous efforts are focused on the discovery of new molecules that regulate the dynamics of the immune system [17]. Three PD-1 inhibitors (nivolumab, pem- brolizumab, and cemiplimab) and three PD-1-Ligand inhibitors (atezolizumab, avelumab, and durvalumab) are in the current list of agents approved by the FDA for the treatment of a wide range of malignancies [18]. In the case of HCC, nivolumab and pembrolizumab are approved for second-line treatment after sorafenib. Lymphocyte Activation Gene-3 (LAG-3) is another checkpoint marker associated with the inhibition of effector T cells and the promotion of regulatory T cells (Treg) through crosslinking with CD3, which inhibits T cell proliferation, cytokine production, and calcium flux [19]. Although initially described for lymphocyte T cells, further research has shown that other immune populations like Natural Killer (NK) cells also have their own set of checkpoint molecules. Some of these checkpoints prevent the activation of NK cells against healthy tissues, whilst others work to overcome the inhibition and elicit cytotoxic responses. The Natural Killer Group 2 member NKG2A recognizes the Human Leukocyte Antigen (HLA) and inhibits the NK cell response, whilst another member, NKG2D, recognizes induced self-proteins commonly expressed on stressed or malignant cells activating the NK cell response [20]. Cancers 2021, 13, 426 3 of 20 Low-Affinity IgG Fc Receptor Region Receptor III (CD16) is the most potent activating receptor expressed by NK cells [21]. Upon IgG-induced crosslinking of CD16 NK cells engage on antibody-dependent cell-mediated cytotoxicity, an adaptive immunity-like mechanism. Tactile (CD96), the T Cell Immunoreceptor with Ig and ITIM Domains (TIGIT) and DNAX Accessory Molecule 1 (DNAM-1) share the common ligands Poliovirus receptor (CD155) and Nectin-2 (CD112) and are part of the group of Immunoglobulin-superfamily members [22]. While DNAM-1 has been shown to boost NK cytotoxic responses, TIGIT and CD96 are thought to act as a counter-balance to DNAM-1 and inhibit the cytotoxic response by competitive binding against their common ligands [23]. Metabolic pathways can also contribute to immune cell regulation in cancer. Dur- ing inflammation, extracellular ATP undergoes phosphohydrolysis by ectonucleotidases (most prominently NTPDase 1, also known as CD39), culminating in the formation of high levels of adenosine within the tumor microenvironment (TME). Adenosine is an immunosuppressive metabolite that regulates tumor immunity, and targeting its pathway may provide therapeutic benefit [24]. Immune checkpoints are not restricted to one specific cell type, but rather can be ex- pressed in more than one cell population depending on the physiologic conditions or the anatomic compartment [25,26]. This is the case for PD-1 and DNAM-1, which are also ex- pressed in NK and T cell populations, respectively [27,28]. However, despite its co-stimulatory function in NK cells and cytotoxic T CD8+ cells, the role of DNAM-1 in other immune cells is not yet clear. Amongst the transcription factors that dictate the functional programs of immune cells, Eomesodermin (Eomes) and T-box Protein 21 (T-bet) have emerged as crucial for the development and maturation of lymphocytes, where they tip the balance between effector/memory/tolerant functions [29–32]. In this study, we have investigated the distribution and functional status of peripheral blood immune B, T, and NK lymphocyte populations of patients developing eDAEs under sorafenib treatment. Our results show that the expression of DNAM-1 and PD-1 on T cells, and in less measure, the expression of CD16 and LAG-3, correlate with the probability of eDAEs appearance. Further characterization of PBMCs DNAM-1+ reveals its association with CD96, TIGIT, and T-bet. Overall, the results confirm that the immune system plays a role in sorafenib-associated eDAEs and provide a new rationale for sorafenib plus ICI therapy combination. 2. Results 2.1. Patients A total of 52 patients were recruited for the study (Table 1). A median of 4 samples were extracted from every patient. Forty-four patients were men (84.62%) and 8 (15.38%) women, with a median age of 64.34 years. Twenty-two patients were staged as BCLC B (42.31%) and 30 (57.69%) as BCLC C, while 42 (80.77%) were Child-Pugh A and 7 (13.46%) were Child-Pugh B. Performance Status was 0 for 50 patients (96.15%) and 1 for 2 patients (3.85%). Twenty patients (38.46%) had vascular invasion and 22 (42.31%) had extrahepatic spread. Median sorafenib treatment duration was 5.1 months [2.5–9.6], the median follow-up was 9.6 months [3.9–19.2] and patients’ median overall survival was 26.4 months (95% CI: 10.7–42.2). Sixteen patients (30.8%) developed early dermatologic adverse events requiring dose modification, with a median overall survival of 26.4 months (95% CI: 17.4–35.5). Non- eDAEs patients had a median overall survival of 16.4 months (95% CI: 6.6-NE). Cancers 2021, 13, 426 4 of 20 Table 1. Patient characteristics. Patients N = 52 Age (Years), median [IQR] 64.34 (56.6–71.9) Gender (males), n (%) 44 (84.6) Arterial Hypertension (Yes), n (%) 28 (53.9) Diabetes (Yes), n (%) 21 (40.4) Child-Pugh Score (non-Cirrhotic * or A/B) 45 (86.5)/7 (13.5) Aetiology, n (%) HCV 13 (25) Alcohol 14 (26.9) HBV 3 (5.8) NASH 4 (7.7) More than one of previous aetiologies 9 (17.3) Others 6 (11.5) Not applicable 3 (5.8) Previous HCC treatment, n (%) Liver transplant 3 (5.8) Surgery 2 (3.9) Percutaneous 2 (3.9) Sequential 9 (17.3) TACE 13 (25) No previous treatment 23 (44.2) Ascites (Yes), n (%) 7 (13.5) Encephalopathy (No), n (%) 52 (100) BCLC stage (B/C), n (%) 22 (43.3)/30 (57.7) Vascular Invasion (Yes), n (%) 20 (38.5) ECOG-PS (0/1), n (%) 50 (96.1)/2 (3.9) Alpha-fetoprotein (ng/dL), median [IQR] 17 (8–292) Extra-hepatic spread (Yes), n (%) 22 (42.3) Haemoglobin (g/L), median [IQR] 13.1 (12.1–14.4) Prothrombin time (%), median [IQR] 79 (68–90) Total bilirubin (mg/dL), median [IQR] 0.9 (0.6–1.4) Conjugated bilirubin (mg/dL), median [IQR] 0.4 (0.3–0.6) ALT (IU/L), median [IQR] 44 (29–68) AST (UI/L), median [IQR] 54.5 (38–85) Alkaline phosphatase (IU/L), median [IQR] 151 (102–193) GGT (IU/L), median [IQR] 167 (99–265) Albumin (g/L), median [IQR] 40 (34–43) Follow-up (months), median [IQR] 9.6 (3.9–19.2) Treatment time (months), median [IQR] 5.1 (2.5–9.6) Overall survival (months), median (95% CI) 26.4 (10.7–42.2) eDAEs (Yes), n (%) 16 (30.8) Overall survival eDAEs (months), median (95% CI) 26.4 (17.4–35.5) Overall survival non-eDAEs (months), median (95% CI) 16.4 (6.6–NE) Decompensation (Yes), n (%) 11 (21.2) Exitus (Yes), n (%) 20 (38.5) NASH: Non-alcoholic steatohepatitis; HBV: Hepatitis B virus; HCV: Hepatitis C virus; TACE: transarterial chemo-embolization; BCLC: Barcelona Clinic Liver Cancer; NE: not estimable; *: 3 non-cirrhotic patients. 2.2. The Increase of B Cell Population during Sorafenib Treatment Increases the Probability of Developing eDAEs No lymphocyte population at baseline was associated with eDAEs development. To as- sess whether changes throughout the treatment, rather than the baseline values, are respon- sible for the eDAEs, we used Cox regression models generated with time-dependent data. These models consider the evolutionary values of the selected variables at each recorded time during the first 8 weeks of treatment, allowing us to take into account the changes of specific variables over time and accurately assess their influence on the outcome. These Cancers 2021, 13, 426 5 of 20 models show that an increase in B cells correlates with higher probability of developing eDAEs (Table 2). The correlation, although not statistically significant, between an increase in NK cells (Table 2) and a lower probability of developing eDAEs is also found. No asso- ciation was revealed between T cells or any of its subpopulations and the probability of developing eDAEs in neither of these models. Table 2. Time-dependent multivariate Cox regression models between B and NK lymphocyte percentages and the probability of developing early dermatologic adverse events (eDAEs) during the first 8 weeks of sorafenib treatment. Lymphocyte Population Baseline Adjusting Co-Factors HR (95% CI) p-Value none 1.06 (1–1.11) 0.044 BCLC stage 1.06 (1–1.12) 0.049 ECOG-PS 1.06 (1–1.11) 0.051 B cell Child-Pugh Score 1.05 (1–1.11) 0.06 BCLC stage|ECOG-PS 1.06 (1–1.12) 0.06 BCLC stage|Child-Pugh Score 1.05 (1–1.11) 0.06 none 0.91 (0.83–1.01) 0.06 BCLC stage 0.91 (0.83–1) 0.06 Child-Pugh Score 0.92 (0.84–1.01) 0.07 NK ECOG-PS 0.92 (0.83–1.01) 0.07 BCLC stage|ECOG-PS 0.92 (0.83–1.01) 0.07 BCLC stage|Child-Pugh Score 0.91 (0.83–1) 0.055 HR: Hazard Ratio, 95% CI: 95% Confidence Interval. 2.3. Lymphocytes Expressing DNAM-1 and PD-1 Are Significantly Associated with the Probability of Developing eDAEs, Both in Baseline and Time-Dependent Models We next considered whether the lymphocyte’s immune checkpoint expression could lead to the development of eDAEs. With this purpose in mind, we determined the number of cells positive for the immune checkpoints PD-1 and LAG-3, which act as immune sup- pressors; DNAM-1 and NKG2D, activators of the NK cell cytotoxic response; and CD16, which triggers the NK antibody-dependent cell-mediated cytotoxicity. We also assessed the expression of CD39, the rate-limiting enzyme in the conversion of ATP to immunomodu- latory adenosine; of CD69, a membrane-bound, type II C-lectin receptor that marks early activation of different immune subsets [33]; and of the IL-7 receptor subunit-α (CD127), associated with chronic inflammation and poorer outcome in cancer patients [34]. Figure 1 shows representative FACS dot plots to illustrate the gating strategy for all cell populations and cell markers analyzed in our study. Cox regression models using baseline information identified that T lymphocytes, either CD4+ or CD8+ , expressing DNAM-1 (DNAM-1+ ) correlated with a lower probability of developing eDAEs (Table 3). The same association was demonstrated for those NK cells with highest CD56 intensity (CD56+bright ) expressing PD-1 (PD-1+ ) (Table 3). In contrast, there was a significant relationship between T CD8+ cells expressing CD69 (CD69+ ) and a higher probability of developing eDAEs (Table 3). Time-dependent models also confirmed that the increase of T cells, either CD4+ or + CD8 , expressing DNAM-1 concurred with lower eDAEs probability (Table 3). The same applies for the increase of T CD4+ cells expressing PD-1 (Table 3). This model also revealed that the increase of NK CD56+bright cells expressing DNAM-1 or PD-1 was associated with a lower probability of developing eDAEs (Table 3). Time-dependent models also confirmed that the increase of T cells, either CD4+ or CD8+, expressing DNAM-1 concurred with lower eDAEs probability (Table 3). The same applies for the increase of T CD4+ cells expressing PD-1 (Table 3). This model also revealed Cancers 2021, 13, 426 6 of 20 that the increase of NK CD56+bright cells expressing DNAM-1 or PD-1 was associated with a lower probability of developing eDAEs (Table 3). Figure 1. Lymphocytes Figure gating strategy. (A) Flow chart 1. Lymphocytes depicting gating the order strategy. (A) for subsetting Flow the different chart depicting lymphocyte the order forpopulations. subsetting the dif- Events were initially gated on single events, then on lymphocytes by size and scatter. Living cells ferent lymphocyte populations. Events were initially gated on single events, were gated next.then Fromon there, lymphocytes the different populations were gated based on their markers. (B) Gating strategy for the different lymphocyte by size and scatter. Living cells were gated next. From there, the different populations were gated immune checkpoints. Representative basedexamples on theirformarkers. the gatings(B) of the different Gating checkpoints strategy for theused in the study. different LAG-3 and lymphocyte NKG2Dcheckpoints. immune could not be gated and were analyzed by MFI quantification instead. T CD4 + : T helper cells, T CD8+ : cytotoxic T cells, NK: Natural Representative examples for the gatings of the different checkpoints used in the study. LAG-3 and Killer cells, Treg: regulatory NKG2DT cells. could not be gated and were analyzed by MFI quantification instead. T CD4+: T helper cells, T CD8+: cytotoxic T cells, NK: Natural Killer cells, Treg: regulatory T cells. Cancers 2021, 13, 426 7 of 20 Table 3. Baseline and time-dependent multivariate Cox regression models between immune checkpoints expression in T and NK lymphocytes and the probability of developing eDAEs during the first 8 weeks of sorafenib treatment. Model Lymphocyte Population Baseline Adjusting Co-Factors HR (95% CI) p-Value TCD4+ DNAM-1+ BCLC Stage|Child-Pugh Score|ECOG-PS 0.94 (0.89–0.99) 0.01 T CD8+ DNAM-1+ BCLC Stage|Child-Pugh Score|ECOG-PS 0.94 (0.9–0.99) 0.02 T DNAM-1+ BCLC Stage|Child-Pugh Score|ECOG-PS 0.92 (0.87–0.97) 0.003 Baseline values NK CD56+bright PD-1+ BCLC Stage|Child-Pugh Score|ECOG-PS 0.47 (0.22–0.99) 0.05 T CD8+ CD69+ BCLC Stage 1.07 (1–1.14) 0.047 T CD8+ CD69+ Child-Pugh Score 1.07 (1–1.14) 0.03 T CD8+ CD69+ ECOG-PS 1.07 (1–1.14) 0.04 T CD4+ DNAM-1+ BCLC Stage|Child-Pugh Score|ECOG-PS 0.94 (0.9–0.98) 0.01 T CD8+ DNAM-1+ BCLC Stage|Child-Pugh Score|ECOG-PS 0.94 (0.9–0.99) 0.02 T DNAM-1+ BCLC Stage|Child-Pugh Score|ECOG-PS 0.93 (0.89–0.97) 0.002 NK CD56+bright DNAM-1+ BCLC Stage|Child-Pugh Score|ECOG-PS 0.91 (0.85–0.96) 0.002 Time-dependent NK CD56+bright PD-1+ BCLC Stage|Child-Pugh Score|ECOG-PS 0.57 (0.33–0.99) 0.047 values T CD4+ PD-1+ BCLC Stage|ECOG-PS 0.9 (0.82–0.99) 0.04 NK-like CD3+ CD16+ BCLC Stage|Child-Pugh Score|ECOG-PS 1.05 (1–1.08) 0.02 NK-like CD3+ LAG-3 MFI BCLC Stage|Child-Pugh Score|ECOG-PS 1.06 (1–1.11) 0.05 NK-like CD3+ PD-1 MFI BCLC Stage|Child-Pugh Score|ECOG-PS 2.11 (1.12–3.95) 0.02 HR: Hazard Ratio, 95% CI: 95% Confidence Interval, MFI: Mean Fluorescence Intensity. 2.4. CD16, PD-1, and LAG-3 Are Co-Expressed in NK-Like CD3+ Cells and Correlate with eDAEs Development Further analysis of the time-dependent Cox regression models showed that the in- crease over time of NK-like CD3+ cells expressing CD16 (CD16+ ) correlated with a higher probability of developing eDAEs (Table 3). The same effect was found for NK-like CD3+ cells with higher quantification of PD-1 and LAG-3 measured by Mean Fluorescence Inten- sity (MFI). Since in NK and NK-like CD3+ cells LAG-3 expression could not be qualitatively distinguished due to its low levels, MFI quantification was undertaken instead. As stated above, the increase of T cells expressing PD-1 correlated with a lower probability of developing eDAEs, while the increase of NK-like CD3+ cells expressing PD-1 correlated with a higher probability. Since the latter association is also found in NK-like CD3+ cells expressing CD16 or LAG-3, we evaluated whether the co-expression of PD-1 with CD16 and LAG-3 could explain the opposite results between NK-like CD3+ PD-1+ and T PD-1+ cells effects on eDAEs probability. In the first subset of patients (n = 15), we found that NK-like CD3+ cells expressing CD16 had higher PD-1 and LAG-3 expression measured by MFI at all timepoints of the first 8 weeks (Figure 2), suggesting that they are co-expressed (834.15 mean PD-1 MFI and 4110.64 mean LAG-3 MFI in CD16+ cells, versus 335.35 mean PD-1 MFI and 1424.17 mean LAG-3 MFI in CD16− cells). To assess how this co-expression changes over time, we used a correlation analysis including the whole set of patients and found a positive association for CD16, LAG-3, and PD-1 in those patients who develop eDAEs, while a negative relationship was found for those with no eDAEs (Table 4). Table 4. Correlation models between CD16 positive expression, PD-1 MFI, and LAG-3 MFI quantifi- cation in NK-like CD3+ cells of eDAEs and non-eDAEs developing patients. Cell Marker eDAEs (Yes/No) LAG-3 MFI PD-1 MFI yes $ r = 0.51 (p = 0.1) r = 0.62 (p = 0.04) CD16+ no & r = −0.16 (p = 0.4) r = −0.15 (p = 0.4) yes $ - r = 0.86 (p < 0.001) LAG-3 MFI no & - r = 0.16 (p = 0.4) $ n = 11, & n = 28. MFI: Mean Fluorescence Intensity, eDAEs: early dermatologic adverse events. Cancers Cancers 13,13, 2021, 2021, x 426 8 of 8 of 2120 Figure Figure 2. 2. PD-1 PD-1 and LAG-3 and LAG-3 cluster with cluster CD16 with CD16in in NK-like CD3 NK-like CD3 + cells + cells of patients developing of patients eDAEs. developing eDAEs. LAG-3 (top) and PD-1 (bottom) Mean Fluorescence Intensity (MFI) quantification inin NK-like CD3 + + LAG-3 (top) and PD-1 (bottom) Mean Fluorescence Intensity (MFI) quantification NK-like CD3 cells with cells with andandwithout withoutCD16 CD16 expression expression (CD16and (CD16 + + CD16 and − CD16 respectively) − at baseline respectively) andand at baseline 1, 4,1,and 8 4, and weeks (n =(n 8 weeks 15). Bar Bar = 15). height represents height the mean represents of each the mean group. of each MFI:MFI: group. Mean Fluorescence Mean Intensity. Fluorescence Intensity. **: p < 0.01, ***: p < 0.005. **: p < 0.01, ***: p < 0.005. 2.5. T DNAM-1+ Cells Have an Exhausted Phenotype Compared to DNAM-1− Counterparts; While NK DNAM-1+ Cells Have an Immune Activated Phenotype After determining the immune checkpoints associated with the development of eDAEs, we assessed the role of DNAM-1 in T cells and in NK cells. To this end, we compared the phenotype of DNAM-1-expressing cells (DNAM-1+ ) against cells without DNAM-1 expression (DNAM-1− ) in each patient using only the baseline samples. We compared the number of cells expressing PD-1 (PD-1+ ), CD127 (CD127+ ), CD39 (CD39+ ), CD69 (CD69+ ), and CD16 (CD16+ ) in each group and quantified the MFI of all these markers plus LAG-3 and NKG2D, whose expression could not be qualitatively distinguished. As previously described, PD-1+ , CD127+ , and CD39+ cells were more prevalent in T cells compared to NK cells, while CD16+ cells were only found in NK cells. CD69+ cells were found in both T CD8+ and NK cells (Figure 3). T DNAM-1+ cells contained a higher number of CD127+ , PD-1+ and CD39+ cells and lower numbers of CD69+ cells than T DNAM-1− cells (Figure 3A–E). No difference was seen in NK cells for CD127, PD-1, and CD69 expression (Figure 3A,D,E). However, un- like T cells, there were fewer CD39+ cells in the DNAM-1+ group than in the DNAM-1− Cancers 2021, 13, 426 9 of 20 Cancers 2021, 13, x group (Figure 3C). In the case of CD16+ cells, these were increased in the DNAM-1 10+ofgroup 21 − compared to the DNAM-1 group (Figure 3B). Figure Figure 3. DNAM-1 3. DNAM-1 +cells + cells characterization.Comparison characterization. Comparison between betweenCD127 CD127(A),(A),CD16 CD16(B), CD39 (B), CD39 (C), CD69 (C), (D),(D), CD69 andand PD-1 (E) (E) PD-1 expressing cells in DNAM-1 + and DNAM-1− subgroups of T CD4+, T CD8+, and NK cells of sorafenib treated patients at expressing cells in DNAM-1+ and DNAM-1− subgroups of T CD4+ , T CD8+ , and NK cells of sorafenib treated patients at baseline (n = 52). Bar height represents the mean of each group. *: p < 0.05, **: p < 0.01, ***: p < 0.005. baseline (n = 52). Bar height represents the mean of each group. *: p < 0.05, **: p < 0.01, ***: p < 0.005. CD127, LAG-3, CD39, CD69, PD-1 and NKG2D MFI quantification allowed us to confirm the results and define additional differences for LAG-3 and NKG2D expression (Figure 4). For T cells, both LAG-3 and NKG2D had higher MFI in the DNAM-1+ group Cancers 2021, 13, 426 10 of 20 than in the DNAM-1− group (Figure 4B,F). In NK cells, LAG-3 showed the opposite trend, Cancers 2021, 13, x with lower expression in DNAM-1+ cells than in the DNAM-1− ; and no differences 11 of 21were found for NKG2D (Figure 4B,F). Figure 4. DNAM-1+ cells characterization. Comparison between CD127 (A), LAG-3 (B), CD39 (C), CD69 (D), PD-1 (E), and Figure 4. DNAM-1+ cells characterization. +Comparison between CD127 (A),+ LAG-3+ (B), CD39 (C), CD69 (D), PD-1 (E), NKG2D (F) MFI quantification in DNAM-1 and+ DNAM-1- subgroups of T CD4 , T CD8 +, and NK+cells of sorafenib treated − subgroups andpatients NKG2Dat(F) MFI quantification in DNAM-1 and DNAM-1 of TMean baseline (n = 52). Bar height represents the mean of each group. MFI: CD4 Fluorescence , T CD8 , and NK cells Intensity. *: pof