Immunology

Clinical MedicineImmunologyPulmonology Free access | 10.1172/JCI128654 The alveolar immune cell landscape is dysregulated in checkpoint inhibitor pneumonitis Karthik Suresh,1 Jarushka Naidoo,2,3 Qiong Zhong,1 Ye Xiong,1 Jennifer Mammen,4 Marcia Villegas de Flores,5 Laura Cappelli,5 Aanika Balaji,2 Tsvi Palmer,1 Patrick M. Forde,2,3 Valsamo Anagnostou,2,3 David S. Ettinger,2 Kristen A. Marrone,2,3 Ronan J. Kelly,2,3 Christine L. Hann,2,3 Benjamin Levy,2,3 Josephine L. Feliciano,2,3 Cheng-Ting Lin,6 David Feller-Kopman,1 Andrew D. Lerner,1 Hans Lee,1 Majid Shafiq,1 Lonny Yarmus,1 Evan J. Lipson,3,4 Mark Soloski,5 Julie R. Brahmer,2,3 Sonye K. Danoff,1 and Franco D’Alessio1 First published July 16, 2019 - More info Abstract BACKGROUND. Checkpoint inhibitor pneumonitis (CIP) is a highly morbid complication of immune checkpoint immunotherapy (ICI), one which precludes the continuation of ICI. Yet, the mechanistic underpinnings of CIP are unknown. METHODS. To better understand the mechanism of lung injury in CIP, we prospectively collected bronchoalveolar lavage (BAL) samples in ICI-treated patients with (n = 12) and without CIP (n = 6), prior to initiating first-line therapy for CIP (high-dose corticosteroids). We analyzed BAL immune cell populations using a combination of traditional multicolor flow cytometry gating, unsupervised clustering analysis, and BAL supernatant cytokine measurements. RESULTS. We found increased BAL lymphocytosis, predominantly CD4+ T cells, in patients with CIP. Specifically, we observed increased numbers of BAL central memory T cells, evidence of type I polarization, and decreased expression of cytotoxic T lymphocyte–associated protein 4 and programmed cell death protein 1 in BAL Tregs, suggesting both activation of proinflammatory subsets and an attenuated suppressive phenotype. CIP BAL myeloid immune populations displayed enhanced expression of IL-1β and decreased expression of counterregulatory interleukin-1 receptor antagonist. We observed increased levels of T-cell chemoattractants in the BAL supernatant, consistent with our proinflammatory, lymphocytic cellular landscape. CONCLUSION. We observe several immune cell subpopulations that are dysregulated in CIP, which may represent possible targets that could lead to therapeutics for this morbid immune-related adverse event. FUNDING. NIH, Department of Defense, and the Bloomberg~Kimmel Institute for Cancer Immunotherapy. Graphical Abstract Introduction With recent clinical trials demonstrating clear efficacy for immunotherapy in patients with locally advanced and advanced-stage non–small-cell lung cancer (NSCLC) as well as other tumors, the use of immune checkpoint inhibitors (ICIs) for the treatment of NSCLC has rapidly increased (1–3), becoming the standard of care. ICIs, however, are associated with a constellation of toxicities termed immune-related adverse events (irAEs). These toxicities include arthritis, colitis, endocrinopathies, and lung injury; the last is termed checkpoint inhibitor pneumonitis (CIP) (4, 5). Clinically, patients with CIP present with acute to subacute onset of dyspnea, hypoxemia, and pulmonary infiltrates similar to that seen in patients with lung injury from acute respiratory distress syndrome (6). Although CIP can result in high morbidity, it was previously thought to be an uncommon complication of ICI therapy, with an incidence of around 3% to 5% (7, 8) based on clinical trial data. Recent evidence from our group and others suggests, however, that the occurrence of CIP may be higher in real-world settings (9, 10). For instance, using a multidisciplinary, standardized approach (11), we recently observed an incidence of 19% in a cohort of 205 patients with NSCLC treated with ICI (12). In addition, we also observed an association between CIP development and increased mortality rates in patients with NSCLC treated with ICI (13). Despite the rising incidence of CIP and its association with increased mortality, the current paradigms for diagnosis and treatment of CIP are largely based on anecdotal evidence, primarily because fundamental knowledge of CIP pathobiology is lacking (14). CIP is diagnosed by the presence of compatible symptoms (shortness of breath, hypoxia, cough), new radiographic infiltrates, which can be either unilateral or bilateral (15, 16), typically with ground glass and consolidative components (Figure 1), and the exclusion of infectious etiologies (with sputum cultures or bronchoalveolar lavage [BAL]). There are currently no diagnostic biomarkers for CIP, so the diagnosis remains largely one of exclusion. Once diagnosed, clinical severity is used to determine CIP grade (Supplemental Table 1; supplemental material available online with this article; https://doi.org/10.1172/JCI128654DS1). For CIP grade 2 (i.e., symptomatic patients with compatible radiographic infiltrates) and higher, ICI therapy is immediately discontinued, and empiric high-dose steroids are initiated. More targeted, disease-specific therapy is not instituted as first-line treatment for CIP in part because there are currently no available data on the mechanism of lung injury in CIP. Due to the lack of diagnostic and therapeutic options, patients diagnosed with CIP are typically also not eligible for further ICI; this is particularly disadvantageous in individuals with ongoing tumor response. Figure 1 Radiographic presentation of CIP. Representative computed tomography images of an ICI-treated NSCLC patient (A) prior to development of CIP, (B) at the time of CIP diagnosis, and (C) after 3 weeks of steroid treatment. *Denotes area of preexisting post-radiotherapy changes that were stable before initiation of ICI. As part of a multidisciplinary immune-related toxicity (irTox) team (11) engaged in diagnosis, management, and study of irAEs following ICI therapy, we prospectively collected BAL fluid (BALF) specimens from patients treated with ICI who have no evidence of CIP as well as those with suspected CIP. Clinical, laboratory, and radiographic data of patients suspected of having CIP were subsequently reviewed by the irTox team, and a determination was made as to whether the presenting symptoms were due to CIP or another etiology. Using these specimens, we performed multiparametric flow cytometric analysis on BALF samples to better understand the landscape of immune dysregulation in CIP. In part due to the lack of available data on the biology of lung injury in CIP, we utilized unbiased clustering analytic techniques to examine our flow cytometric results. Such approaches have the advantage of detecting changes in small cell populations that may otherwise be excluded with manual gating. Importantly, the control group comprised patients who also received ICI but did not exhibit any clinical evidence of CIP at the time of bronchoscopy. Results BAL lymphocytosis is a hallmark for CIP. Study design as well as baseline clinical characteristics for the patients enrolled in this observational study are shown in Figure 2 and Table 1, respectively. Clinical grade, management, and outcomes data for the 12 patients with CIP are presented in Supplemental Table 2. We first manually counted BAL cell differentials in a subset of control and CIP samples. We found a relative increase in lymphocytes with a concomitant decrease in monocytes in CIP (Supplemental Figure 2) compared with patients without CIP. Notably, BAL neutrophils were not abundant in patients with CIP. To further characterize subsets of BAL immune cells, we performed multiparametric flow cytometric analysis using optimized T cell and monocyte panels (Supplemental Table 3). We initially analyzed these data using traditional gating methods, and similar to our manual cell differentials, found an increase in the percentage of T lymphocytes in patients with CIP (Figure 3). Specifically, we found an increase in CD3+CD4+ cells (Figure 3A; P = 0.04) and a possible association with increased CD3+CD8+ cells (Figure 3B; P = 0.073). We also noted a decrease in monocytes, specifically CD3–CD19–CD14+ cells (Figure 3C; P = 0.04). We did not observe any differences in the percentage of Tregs (CD3+CD4+CD127loCD25+Foxp3+) among patients who were CIP+ and CIP– (data not shown). Figure 2 Study design and participating patients. Consort diagram showing study enrollment and adjudication of patients into control and CIP groups. *Pertinent clinical, radiographic, laboratory and microbiologic (including BAL culture when available) data were reviewed by the immune-related toxicity (irTox) team before and (in cases of suspected CIP) after bronchoscopy. At both time points, patients with suspected CIP with an alternative etiology for symptoms were excluded from the CIP group (n = 2). ICI, immune checkpoint inhibitor; VATS, video-assisted thoracic surgery; CIP, checkpoint inhibitor pneumonitis. Figure 3 BAL lymphocytosis in patients with CIP. Scatter plots showing number of (A) CD4+, (B) CD8+, (C) CD14+, and (D) CD16+ T cells (A and B) and monocytes (C and D), respectively, in control and CIP samples. n = 6 (CIP–), 12 (CIP+). Comparisons between groups performed using Mann-Whitney test. Table 1 Baseline characteristics Unsupervised clustering reveals differential T cell subpopulations in CIP. To understand immune cell subpopulations in our samples in more granular detail, we next turned to unsupervised clustering analysis. The total numbers of cells per condition used for our unsupervised analyses are shown in Supplemental Table 4. We represent the results of our clustering analysis using star charts. As shown in Supplemental Figure 1, groups of cells that share similar cytokine profiles are identified as a node and represented by a circle. The diameter of the circle reflects the number of cells present within that subpopulation. The cell surface or intracellular mean fluorescence intensity (MFI) for each fluorophore is expressed as a wedge within the circle; the radius of the wedge segment represents the expression level of that particular marker. For instance, in Supplemental Figure 1B, a node with very high PD-1, CD45RA, and CD127 expression is shown. Topologically, nodes are arranged by similarity to each other in a cluster map (Supplemental Figure 1C). Cell subsets occupy distinct areas within a map; for instance, in the T cell cluster map, as expected, CD4+ and CD8+ cells are clustered together in opposite ends because they are very distinct from each other (Supplemental Figure 1, D and E). In control, unstimulated T cells, we observed clustering around 2 cell populations: CD4+ cells with high PD-1 expression (Figure 4A) and CD8+ cells with moderate PD-1 expression (Figure 4A). Unstimulated CIP samples exhibited increased CD8+ cell populations compared with unstimulated controls (Figure 4B) as well as a local shift in CD4+ Treg populations (Figure 4B), as discussed in more detail in the information to follow. Figure 4 T cell populations in CIP. Unsupervised clustering of T cells in BALF samples of patients without (control, n = 6) and with CIP (n = 12). Cluster maps showing distribution of T cell subpopulations in (A) unstimulated controls and (B) unstimulated CIP. Within each cluster map, larger cell populations distinct to that particular condition are highlighted (square boxes). To better understand the specific T cell subsets that were up/downregulated in patients with CIP, we examined the differential cluster map of T cell subsets, which highlights only clusters where the magnitude of difference between groups was greater than 95%. As shown in Figure 5, in CIP, we observed a significant increase in CD4+CD45RA+CD25– cells that also expressed CD62L. Because this cytokine profile resembled that of central memory T cells (Tcms), a non-Treg (i.e., conventional) T cell subpopulation characterized by high CD62L and low CD45RA expression, we performed manual gating for Treg and non-Treg subpopulations (Supplemental Figure 3) and observed a significantly higher percentage of Tcm in CIP samples (P = 0.01). As mentioned earlier, we observed a shift in CD4+FoxP3+ cells between unstimulated control and CIP cluster maps. Closer examination of these clusters revealed that while clusters of PD-1loCTLA-4lo Tregs were similarly expressed in both CIP and controls, a subpopulation of Tregs with high PD-1 and CTLA-4 expression was only seen in controls, and these effector molecules were downregulated in alveolar Tregs in CIP (Figure 5). Compared with controls, multiple CD8+TNF-αhi subpopulations were upregulated at baseline in CIP (Figure 5). Ex vivo stimulation of CIP samples polarized T cells toward a type 1 phenotype with increased TNF-α and IFN-γ production across multiple cell subsets with varying degrees of CD8 expression; these cell populations were not increased in control cells following stimulation (Supplemental Figure 4). Figure 5 Abnormal T cell subsets in CIP. Differential cluster map (center) shows clusters where the number of cells within the cluster were increased by 95% in controls (red, n = 6) or CIP (cyan, n = 12). Cytokine profile (inset) and scatter plot of relevant cytokines showing MFI in the selected clusters (red) compared with MFI across all clusters (black) in (counterclockwise): (i) CD4+FoxP3loCD25–CD62LhiCD45RAlo cluster increased in CIP; (ii) PD-1hiCTLA-4hi clusters of Tregs increased in controls, scatter plot showing PD-1/CTLA-4 MFI in selected clusters; (iii) similar (i.e., <95% difference) expression of PD-1loCTLA-4lo Treg clusters in CIP and controls, scatter plot showing PD-1/CTLA-4 MFI in selected clusters; (iv) a CD3+CD4lo CD8–TNF-αhi population increased in CIP, scatter plot showing CD4/TNF-α MFI in selected clusters; (v) CD8+TNF-αhiPD-1hi clusters increased in CIP, scatter plot showing CD8/TNF-α MFI in selected clusters; and (vi) a second set of CD8+TNF-αhi clusters increased in CIP. In summary, these findings suggest multiple dysregulated T cell subsets in patients with CIP. At baseline, we observe in CIP: (a) increased Tcms, (b) loss of PD-1hi/CTLA-4hi CD4+ Tregs and (c) upregulation of proinflammatory (i.e., TNF-αhi, IFN-γhi) CD8+ cells. With stimulation, we observe an increase in numbers of CD8+ TNF-αhi subsets and the amount of TNF-α expression in stimulated CIP samples compared with controls. Upregulation of IL-1βhi monocytes in CIP and IL-1RA–expressing B cells in controls. Next, we sought to examine population differences in non–T (i.e., CD3–) cells. Similar to our T cell analyses, we represented the results in cluster maps where the distinct cell populations (e.g., CD14+ monocytes, CD16+ monocytes, B cells) occupy various regions within the map (Supplemental Figure 5). We observed clear differences between unstimulated control and CIP samples (Figure 6). As shown in Figure 6, A and B, and in closer detail in Figure 7, two reciprocal populations were upregulated in controls and CIP, respectively. In controls, we observed a large increase in several clusters corresponding to IL-1RA–expressing CD86+ B cells (CD19+). While this cluster was downregulated in CIP, a different cluster of IL-1βhiTNF-αhiCD-11bhi myeloid cells (CD19–, CD14int/CD16int) was significantly upregulated in CIP. Similar to our T cell analysis, we confirmed the presence of a TNF-αhiIL-1βhiCD11bhi population in CIP samples with manual gating (Supplemental Figure 6). Unlike T cells, we did not observe significant differences in cluster profiles between unstimulated and stimulated cells either in the control or CIP condition (Supplemental Figure 7). Figure 6 Monocyte populations in CIP. Unsupervised clustering of non–T cells (singlet, live, CD3–) in BALF samples of patients without (control, n = 6) and with CIP (n = 12). Cluster maps showing distribution of myeloid subpopulations in (A) unstimulated controls and (B) unstimulated CIP. Figure 7 Abnormal monocyte subsets in CIP. Differential cluster map of myeloid cells showing clusters that are increased by at least 95% between unstimulated controls (n = 6) and unstimulated CIP (n = 12) samples. Cytokine profiles and scatter plot of relevant cytokines showing MFI in the selected clusters (red) compared with MFI across all clusters (black) showing: (i) population of IL-1RAhi B cells (CD19+) increased in controls and (ii) large population of related clusters of IL-10hiIL-1βhi myeloid cells (CD14loCD16loCD19–) increased in CIP. We also compared the subpopulations identified previously as being significantly different in controls or CIP to the results of a meta-clustering analysis, to determine whether the subpopulations selected to be differentially upregulated in our prior analyses were also identified as distinct populations using an autogating strategy. As shown in Supplemental Figure 8, meta-clustering identified the clusters previously examined in our T cell and monocyte/B cell cluster maps (Figure 4 and Figure 6) as distinct subpopulations. Upregulation of lymphocyte chemoattractants in the BALF of patients with CIP. To determine whether BALF cytokines were promoting the cellular phenotypes observed in our flow cytometry data, we measured key cytokines in the cell-free BAL supernatant (Figure 8, A–C, and Supplemental Table 5). Surprisingly, despite observing an increased number of IL-1βhi cells in our flow analysis, we observed decreased levels of IL-1β in CIP BAL supernatants. We observed no differences in TNF-α levels, but discovered increased levels of the type 1 skewing cytokine IL-12p40. We also measured levels of cytokines involved in the recruitment of inflammatory cells to the alveolus. We observed lower levels of IL-8, the classical neutrophil chemoattractant, in CIP. Although no differences were seen in levels of monocyte chemoattractant proteins 1 or 4, we observed lower levels of macrophage inflammatory protein-3α (MIP-3α), a significant increase in levels of the IFN-γ–induced protein 10 (IP-10, or CXCL-10) and a trend toward increased levels of T cell chemoattractant protein TARC (also known as CCL17; P = 0.06). Figure 8 BALF cytokine analysis. (A) Heatmap showing expression of various cytokines in control and CIP BAL supernatant samples. Cytokines are scaled, centered, and hierarchically clustered (using the Euclidean distance). (B) Box-and-whisker plots showing median, minimum, and maximum with individual data point overlay (dots) for select cytokines involved in alveolar inflammation and immune cell skewing (B) or inflammatory cell recruitment/chemotaxis (C). *Denotes significant difference from control BALF samples (Mann-Whitney, P < 0.05). Discussion In this study, we describe multiple baseline and functional abnormalities in both lymphoid and myeloid alveolar cell types in patients who developed CIP. These abnormalities involve both upregulation of proinflammatory subsets and downregulation of the counterregulatory antiinflammatory process in both T cells and myeloid cells (Figure 9). In healthy adults, the BAL is composed primarily of macrophages (>85%) and lymphocytes (10%) (17). These percentages are similar to the pattern seen in our control samples (i.e., patients who received ICI but did not have CIP at the time of bronchoscopy), suggesting that ICI therapy alone does not appear to significantly alter the alveolar immune cell pattern. In contrast, we observed lymphocytosis of greater than 20% in most of our CIP+ BAL samples. BAL lymphocytosis has been reported in other conditions such as sarcoidosis, hypersensitivity pneumonitis, cryptogenic organizing pneumonia, nonspecific interstitial pneumonia, and radiation pneumonitis. Our finding of lymphocytosis in the BALF of patients with CIP argues for the use of BAL cell count differentials and flow cytometry for CD4+/CD8+ cells as part of the clinical evaluation scheme during BAL in patients with suspected CIP. As no biomarker currently exists for this disease, this discovery represents a translational application of our current findings. Figure 9 Summary of dysregulated immune cell phenotypes in CIP. Our unbiased clustering approach identified several subpopulations of T cells that are likely to be playing key roles in the pathobiology of CIP. First, CD4+ central memory subsets (Tcms, CD4+CD45RA–CD62L+) were increased in CIP. Tcms have been shown to be more resistant to steroid-induced apoptosis than other conventional T cells, such as effector memory T cells. Moreover, CD62L+ cells play an important role in adhesion to inflammatory sites and can perpetuate injury (18). Increased Tcm in CIP might explain why some patients fail high-dose steroid therapy. We recently reported steroid-refractory disease in up to 40% of patients with CIP in our cohort (10); from a lung injury standpoint, this feature of CIP is unique compared with other lymphocytic pneumonitides, which generally tend to be steroid responsive. The incidence of CIP is significantly higher in patients with underlying NSCLC than other cancers, and we have shown (5) that within patients with NSCLC, tumor histology further stratifies CIP incidence and risk. These findings, coupled with our current data, suggest that Tcm could be responding to tumor-specific antigens. T cell receptor sequencing of the T cell subsets in CIP samples will be useful in this regard. Second, a subpopulation of CD4+ cells skewed toward a type I phenotype with high IFN-γ and TNF-α production is upregulated in CIP. Type I lymphocytes have been linked to several lung diseases including sarcoidosis, hypersensitivity pneumonitis, and lung allograft rejection (19–21). Thus, the combination of “sticky” lung CD4+ T cells (i.e., CD62L+ CD4+ cells) and type I skewing may be synergistically contributing to lung injury seen in patients with CIP. Third, we observed decreased CTLA-4 and PD-1 expression within Treg (i.e., FoxP3+) populations, suggesting an attenuated Treg suppressive phenotype. One explanation for our findings is that, in CIP, loss of Treg suppression may be promoting exuberant Th1 T cell responses. We have shown that alveolar Tregs play a pivotal role orchestrating resolution of lung inflammation and are present in humans with lung injury (22), while others have shown that PD-1+ Tregs are more suppressive to control CD8+ T cells (23). In addition to PD-1, the lack of CTLA-4 may further impair Treg ability to control conventional T cell (such as Tcm) and macrophage proinflammatory responses (24). Overall, our findings suggest highly activated alveolar T cells with loss of a regulatory, antiinflammatory Treg suppressive phenotype contributing to unchecked immune dysregulation seen in CIP. Interestingly, while we observed decreased numbers of CD14+ monocytes based on traditional gating methods, our clustering data show additional dramatic shifts in myeloid populations between controls and patients with CIP, such as a significant increase in CD11bhiIL-1βhi, myeloid cells with varying degrees of CD14/CD16 expression. This is accompanied by a loss of IL-1RA+CD19+ cells in patients with CIP, reflected in the cluster maps as a relative upregulation of these cells in controls. These findings suggest that an imbalance in IL-1 signaling, along with overexuberant TNF-α signaling may be contributing to the pathobiology of lung injury in patients with CIP. The concomitant presence of increased Tcms, as discussed earlier, may also serve to augment T cell and monocyte inflammation. Our BALF cytokine results also point toward a proinflammatory, chemoattractant cytokine milieu. Interestingly, we observed a decrease in soluble IL-1β, while an increase in IL-1β–expressing monocyte subsets was observed in flow cytometry. The dynamics of IL-1β production and release is complex, however, and thought to be related to the strength of the inflammatory stimulus (25). Thus, one possibility is that, in CIP, the underlying source of inflammation promotes IL-1β translation and endosomal storage, but not membrane release. Another possibility is that soluble IL-1β release occurs earlier in injury and is decreased by the time our samples are obtained (generally 2 to 3 days at a minimum, after symptom onset). This lack of time resolution in our BALF data may also explain why TNF-α levels were not significantly different. Another explanation is that, although our controls did not have CIP, they underwent bronchoscopy prior to tumor sampling/resection; this bias may be skewing our control IL-1β results. Despite these findings, our IL-12p40 and CXCL-10 (IP-10) data further implicate CD4+ cells in the pathobiology of CIP. IL-12 is a known orchestrator of tissue inflammation and type I polarization. IL-12p40 can form heterodimers with IL-12p70 and IL-23 (26); however, neither of these cytokines was elevated in the BALF of subjects with CIP (Supplemental Table 5). Thus, we postulate that the increased IL-12p40 observed in CIP constitutes the monomeric form. This secreted form has been reported to be 10- to 20-fold in excess compared with IL-12p70 in stimulated human peripheral blood cells (27) and has been known to be elevated in patients with asthma during airway inflammation (28). Additionally, IP-10 is known to guide Tcm lymphocytes (a T cell subset seen to be upregulated in our flow cytometry data) to their destination within lymph nodes (29). Therapeutically, antibody-mediated blockade of IL-12p40 and CXCL10 has been used to treat inflammatory diseases (30, 31). Our chemotactic cytokine data collectively reflect a lack of neutrophil chemoattraction to the lung (decreased IL-8). Similarly, MIP-3α, which is decreased in patients with CIP BALF, has been previously observed in the context of airway infections (32), is thought to have antimicrobial properties. This observation, along with our IL-8 data and lack of significant neutrophil predominance in our BAL cell differentials (Supplemental Figure 2) further supports the notion that CIP may not be a bacterial infection–triggered phenomenon. Lastly, our finding of increased CCL17 levels correlates with our flow cytometric finding of increased CD11bhi populations of myeloid cells; CD11b+ cells have been previously identified as a key source of the CCL17-honing chemokine in the lung (33). Our findings suggest several targets for therapeutic consideration in patients with steroid-refractory CIP. We note upregulation of several TNF-αhi subsets (lymphoid and myeloid) at baseline in CIP; this finding provides some tissue-specific rationale for the use of infliximab for steroid-refractory CIP, although our BAL cytokine data suggest that timing of TNF-α inhibition may need to be further explored. Importantly, our data also identify several potentially novel populations upregulated in CIP (such as CD62Lhi Tcms and IL-1β–expressing monocytes) that could be targeted using existing therapies. Anti-CD62L antibodies or small-molecule inhibitors have been used to attenuate models of lung injury (34, 35), although these inhibitors are not currently approved for any clinical indication. Biological agents against IL-1β (e.g., anakinra or canakinumab) are currently either in trials or in use, and thus, further validation of these results could provide the rationale for testing these therapies either as first-line adjuncts or as salvage therapies for high-grade CIP. It is known that transient expression of IL-1β can induce lung inflammation, increase TNF-α, and contribute to progressive tissue fibrosis (36); hence, targeting IL-1β could represent an attractive target in treating CIP. CCL-17 (TARC) the ligand for CCR4 is usually considered a selective chemoattractant for type 2 cells, although it has been shown to be elevated in sarcoidosis, a classical type I–mediated lung disease (37). Blocking TARC or its receptor CCR4 could decrease T cell infiltration into the inflamed CIP lungs. Alternatively, transiently enhancing Treg suppressive function could lead to multiple beneficial effects, such as improving control of exuberant type I responses and limiting proliferation, abrogating macrophage proinflammatory responses and ultimately orchestrating lung repair (22, 38). For instance, we have previously shown that a short-course administration of the DNA methyltransferase inhibitor decitabine can potently augment endogenous Tregs and mediate resolution of lung inflammation and promote lung repair (39). Analysis of CIP rates in ongoing trials utilizing ICI/DNA methyltransferase inhibitor combinations could provide further insight into a potential beneficial effect for these agents from a CIP standpoint. Although our data provide insight into potential pathobiologic mechanisms in CIP, CIP is unique in comparison to other irAEs regarding incidence (across cancer types) (40) and relationship to overall survival (OS). CIP is much more common in lung cancers compared with other cancers, and although other irAEs have been associated with improved OS, we did not observe a similar association with CIP (13). Thus, we do not believe that our results are necessarily generalizable to other irAEs. There are several limitations to this study. First, due to the logistical challenges associated with identifying and promptly performing lavage in patients with suspected CIP before antibiotic or steroid administration, our sample sizes are low and thus preclude adjustment for clinical comorbidities (such as chronic obstructive pulmonary disease) that may confound our results. Second, while only patients with a negative infectious work-up were included in the CIP cohort, it is possible that BAL cultures did not identify a focus of infection in patients thought to have CIP. Third, although BAL of CIP infiltrates were performed in areas not previously affected by tumor, it is possible that presence of malignancy in the nearby airways could have influenced our results. In conclusion, our data provide several hypothesis-generating insights into the dysregulated alveolar immune dysregulation in patients with CIP. In the absence of a preclinical model for CIP, our findings provide the first rigorous report to our knowledge of immunological mechanisms underlying CIP. In addition to validation in larger clinical cohorts, these data could inform the design of preclinical and translational studies aimed at further understanding the mechanistic basis of CIP, so that targeted therapies can be developed for this morbid complication of immunotherapy. Methods Study population. Patients were enrolled in this prospective observational study if they were (a) diagnosed with NSCLC and (b) treated with ICIs. Patients who received neoadjuvant ICI underwent bronchoscopy with the collection of BALF prior to surgery. Otherwise, BALF was collected whenever patients underwent bronchoscopy. If CIP was suspected, the BALF sampled was categorized as “CIP” if (a) the sample was obtained before initiating steroids and antibiotics and (b) a clinical diagnosis of CIP was adjudicated by the multidisciplinary irTox team (information to follow). After adjudication, patients with CIP were treated with high-dose steroids (1 mg/kg prednisone). Second-line agents (infliximab, i.v. immunoglobulin, or mycophenolate mofetil) were added at the discretion of the treating team if no improvement was noted after 72 hours, as described previously (12). CIP diagnosis. CIP was defined as (a) shortness of breath, decreased exercise tolerance, exertional desaturation, and/or cough along with (b) the presence of new radiographic infiltrates and (c) lack of evidence of infection (negative cultures on BAL, negative respiratory viral swab) or alternate etiologies (diffuse alveolar hemorrhage, heart failure). Radiographic assessment was performed based on response evaluation criteria in solid tumors (RECIST); cases where the new infiltrates were deemed to represent tumor progression were excluded from both control and CIP groups. A diagnosis of CIP was adjudicated following review and discussion of the pertinent microbiologic and radiographic (11, 12) data by the primary oncologist, a second oncologist (JN), 2 pulmonologists (KS, SD), and a radiologist (CTL), with additional input from other members of the immune-related toxicity team (11) (such as radiation oncology or infectious disease), as needed. Patients in whom clinical equipoise regarding infection was present (e.g., clinical presence of fever, purulent sputum, sick contacts, elevated bands on complete blood count differential) were not adjudicated as CIP even if the BAL cultures were negative. BAL. In control patients, the middle lobe was lavaged. In patients with CIP, an area with new infiltrates not previously known to be associated with tumor was lavaged. The volume of instilled and returned saline was abstracted from the BAL procedure note. BAL specimens were processed with ammonium chloride–potassium lysis solution. Cells were then counted following trypan blue staining to exclude dead cells. Manual cell differentials. BALF cells were stained with Diff-Quik (Thermo Fisher) and equal numbers of total cells (n = 500) were counted per specimen by 2 investigators blinded to the sample group classification as previously described (22). BAL cytokine measurements. BAL supernatant was collected following centrifugation of the cellular components and stored at –80° until further processing. Cytokine measurements were performed using the Mesoscale Discovery platform. Values were normalized to total volume of BAL fluid returned, as noted during bronchoscopy. Flow cytometry. After thawing samples at 37°C, cells were stained for flow cytometry. Approximately 1 × 106 cells per sample were stained with violet LIVE/DEAD (Invitrogen). Cells were incubated with human IgG (Rockland Immunochemicals) to block Fc receptors. Cells were then surface stained with BD Biosciences–Pharmingen antibodies: BV510-conjugated anti-CD3 (UCHT1), BUV395-conjugated anti-CD4 (RPA-T4), allophycocyanin-Cy7–conjugated anti-CD25 (M-A251), BUV737-conjugated anti-CD8 (SK1), PE-CF594–conjugated anti-CD62L (DREG-56), BV421-conjugated anti-CD127 (HIL-7R-M21), BV650-conjugated anti-CD45RA (HI100), PE-Cy7–conjugated anti-CD14 (MoP9), BV711-conjugated anti–PD-1 (EH12), BB700-conjugated anti-CD19 (SJ25C1), PE-Cy7–conjugated anti-CD80 (L307), APC-Cy7–conjugated anti–HLD-DR (G46-6), BV650-conjugated anti-CD11b (M1/70), BV711-conjugated anti-C86 (2331-FUN-1), APC-R700–conjugated anti-CD274 (MIH1), BV786-conjugated anti-CD206 (19.2), PE-conjugated anti–IL-1RA (AS-17), and BUV395-conjugated anti-CD16 (3G8), and intracellularly stained with allophycocyanin-conjugated anti-Foxp3 (PCH101; eBioscience). The following intracellular antibodies from BD Biosciences were also used: Alexa-488–conjugated anti–IL-4 (8D4-8), PE-conujgated anti–IL-10 (JES3-9D7), APC-R700–conjugated anti–IL-17A (N49-653), BV605-conjugated anti–IFN-γ (B27), BV750-conjugated anti–TNF-α (Mab11), BV786-conjugated anti–CTLA-4 (BNI3), PE-CF594–conjugated anti–TGF-β (TW4-9E7), BV510-conjugated anti–IL-8 (G265-8), and BV421-conjugated anti–IL-1β (H1b-98; BioLegend). A UV-excitable LIVE/DEAD discrimination assay (Invitrogen) was applied. Cells were then analyzed on a FACSAria (BD Biosciences). Data were analyzed using either FlowJo (TreeStar, Inc.) for traditional gating analyses or R/Bioconductor for unsupervised clustering, as detailed in the information to follow. BAL cell ex vivo stimulation. Cells were resuspended in a 96-well U-bottom plate using Iscove’s modified Dulbecco medium (Thermo Fisher) (10% heat-inactivated fetal bovine serum, 1% sodium pyruvate, 1% HEPES, 2 mM GlutaMax, 100 U/mL penicillin/streptomycin, and 50 μM β-mercaptoethanol). For lymphocyte stimulation, cells were stimulated with PMA (40 ng/mL) and ionomycin (500 ng/mL) for a total for 4 hours, and GolgiStop and GolgiPlug (BD Biosciences) were added the last 3 hours. For myeloid stimulation, cells were stimulated with LPS (1 μg/mL) and IFN-γ (100 ng/mL) for a total of 4 hours, with GolgiStop and GolgiPlug added for the last 3 hours. BAL biomarker measurements using Vplex immunoassays. BAL supernatants were used to measure C-reactive protein, eotaxin, eotaxin-3, FGF (basic), GM-CSF, ICAM-1, IFN-γ, IL-1α, IL-1β, IL-2, IL-4, IL-5, IL-6, IL-7, IL-8, IL-8 (HA), IL-10, IL-12 (p40), IL-12 (p70), IL-13, IL-15, IL-16, IL-17A, IL-21, IL-22, IL-23, IL-27, IL-31, IP-10, MCP-1, MCP-4, MDC, MIP-1α, MIP-1β, MIP-3α, PlGF, SAA, TARC, Tie-2, TNF-α, TNF-β, VCAM-1, VEGF-A, VEGF-C, VEGF-D, and VEGFR-1/Flt-1 using Vplex immunoassays (Meso-Scale Discovery), according to the manufacturer’s instructions. Samples were run in duplicate. All BAL supernatants were diluted equivalently, the cytokine results were normalized for the amount of BALF recovered, and the results are thus expressed as micrograms per milliliter of recovered BALF, as per the guidelines for measurement of acellular BALF components (17, 41). Statistics. Unsupervised clustering analysis was conducted using the FlowSOM and flowCore packages in R/Bioconductor (42). Briefly, scaled, transformed MFIs for each cell are used as the coordinates for a data point in n-dimensional space, where n is the number of fluorophores. A self-organizing map of nodes in this space was to maximize similarity within each node. The distances between nodes reflects the degree of similarity between groups of cells. Importantly, MFI is treated as a continuous variable, thus allowing visualization and analysis of cell subsets where a surface marker expression may be intermediate. Further, because this method of analyzing flow cytometry data incorporates the MFIs for each fluorophore for each cell, it allows for greater resolution of differences in cytokine expression in a multiparametric flow cytometric data set. A graphical abstract of the algorithm is provided in Supplemental Figure 1A. All MFI values are compensated, scaled, and transformed as previously described (43, 44). For both T cells (i.e., singlet, live, CD3+ cells) and monocytes/B cells (i.e., singlet, live, CD3–), the cluster map was first constructed on a concatenated data set composed of unstimulated control, stimulated control, unstimulated CIP, and stimulated CIP samples. This concatenated set represents the sum total of biological replicates (n = 18; 6 controls and 12 CIP cases). Next, group comparisons between controls and CIP as well as unstimulated and stimulated samples were made by generating group-specific cluster maps as well as a differential cluster map, where a node was considered to be upregulated if there was a greater than 95% difference between groups. As the initial conditions for the clustering algorithm is randomly chosen, the map shape can differ slightly with each run; each analysis was rerun 5 times to ensure that the same clusters were upregulated across multiple runs (map stability). Lastly, a meta-clustering analysis was performed. This represents an auto-gating strategy where the algorithm attempts to classify cell populations across clusters based on their cytokine profile. The code used to generate the clustering analysis and high-resolution copies of cluster maps are provided in the supplemental data. Scaled, centered values were used to generate the heatmap for BALF cytokine data (gplots package, R). Cytokine profiles were hierarchically clustered (using complete linkage clustering and Euclidean distance). Individual cytokine comparisons were plotted and compared using GraphPad Prism. Two-tailed nonparametric tests (Mann-Whitney) were used to compare mean differences between control and CIP cytokine values. A P value less than 0.05 was accepted as significant. Study approval. IRB and ethical approval as well as consent was obtained for all participants in this study. All human work was approved by the IRB at the Johns Hopkins Hospital. Author contributions KS was responsible for flow cytometric and multiplex experimental design, data processing and statistical analysis, manuscript writing and review, and figure preparation. JN was responsible for study design, IRB and clinical database design/administration, clinical data analysis, and manuscript writing and review. KS, FD, JN, JRB, SKD, M Shafiq, PMF, JM, MVF, LC, M Soloski, and VA were responsible for study design. KS, FD, QZ, YX, TP, AB, JM, MVF, LC, VA, DSE, KAM, RJK, CLH, BL, JLF, CTL, DFK, ADL, HL, M Shafiq, LY, M Soloski, and EJL were responsible for conducting experiments/adjudication of patient data/data acquisition. QZ, YX, TP, KS, FD, and AB were responsible for data analysis. KS, FD, JN, and SKD were responsible for writing the manuscript. All authors reviewed and edited the manuscript. Supplemental material Acknowledgments We wish to thank Naresh Punjabi, Larissa Shimoda, and Mahendra Damarla for their comments on design and interpretation of unsupervised clustering analyses, the Bayview Immunomics Core for their technical expertise with BALF cytokine measurement, and the Bloomberg~Kimmel Institute for Cancer Immunotherapy for research and administrative support. This research supported in part by the National Institutes of Health (NIH HL132055, HL131812), Department of Defense (DoDW81XWH-16-1-0510) and the Bloomberg~Kimmel Institute for Cancer Immunotherapy. Footnotes Conflict of interest: The authors have declared that no conflict of interest exists. Copyright: © 2019, American Society for Clinical Investigation. Reference information: J Clin Invest. https://doi.org/10.1172/JCI128654. References Lipson EJ, Forde PM, Hammers HJ, Emens LA, Taube JM, Topalian SL. Antagonists of PD-1 and PD-L1 in cancer treatment. Semin Oncol. 2015;42(4):587–600. 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Scientists now report that Treg cells, a type of regulatory lymphocyte, may be protecting babies in the womb from getting infected with the HIV virus when the mother is infected.

Abstract

Immunotherapy with immune checkpoint blockers (ICBs) has revolutionized cancer treatment. Unfortunately, ICB therapy usually benefits <15% of patients and causes immune-related adverse events in a substantial number of patients. Another immunotherapy—engineered chimeric antigen receptor (CAR) T cells that specifically target tumor-associated antigens—has transformed the treatment of multiple hematological cancers and exhibited therapeutic potential in solid tumors. Emerging data show that the function of blood vessels associated with tumors is critical in the response to these immunotherapies (1, 2). The vasculature in tumors is abnormal, which impairs blood flow and limits the delivery of oxygen, nutrients, and therapeutics, including antibodies and immune cells (3) (see the figure). The resulting hypoxia (low oxygen concentration) and low pH can in turn induce the production of immunosuppressive molecules, such as transforming growth factor–β (TGFβ), vascular endothelial growth factor (VEGF), and adenosine in the tumor microenvironment (TME). Some of these molecules lower the expression of adhesion molecules on tumor vascular endothelium, the lining of vessel walls that normally supports immune cell adhesion and transmigration into tissues. Reducing the expression of adhesion molecules therefore interferes with the ability of immune cells to adhere to and migrate across the vessel wall, preventing their entry into the tumor. In addition, pro-angiogenic molecules produced by cancer, stromal, and immune cells up-regulate Fas ligand on endothelial cells and thereby induce the apoptosis of infiltrating immune cells. VEGF can hinder maturation of dendritic cells (DCs), which are needed to present tumor-associated antigens and activate effector immune cells. Furthermore, hypoxia directly up-regulates the expression of immune checkpoint molecules [e.g., programmed cell death protein ligand 1 (PD-L1)], which suppress effector T cell activation, on various cells in the TME, including myeloid-derived suppressor cells (MDSCs) and DCs. Hypoxia also promotes the recruitment of immunosuppressive regulatory T lymphocytes (Treg cells) and reprograms tumor-associated macrophages (TAMs) from an antitumor to a protumor phenotype. Notably, the immunosuppressive molecules generated in the TME can enter the circulation and cause systemic immunosuppression. Given the consequences of malfunctioning blood vessels, multiple therapeutic strategies to improve the function of the tumor vasculature have been developed (4). Because excess VEGF contributes to vascular abnormalities, it was proposed that the judicious use of anti-VEGF agents could normalize tumor vessels and thereby improve oxygenation and drug delivery. This was supported by experiments showing that low-dose anti–VEGF receptor 2 (VEGFR2) treatment enhances trafficking of immune cells to tumors, reprograms TAMs to an antitumor phenotype, and improves outcomes of vaccine therapy in mouse breast cancer models (5). A number of preclinical studies have now shown the benefit of combining anti-VEGF agents with various immunotherapies, including ICBs and adoptively transferred engineered CAR T cells (3). Anti-VEGF–induced vascular normalization is transient, which limits efficacy. Thus, a number of strategies are emerging to extend the window of normalization. One such strategy is to target angiopoietin-2 (ANG-2), which begins to increase toward the end of the normalization window. Importantly, dual blockade of VEGF and ANG-2 prolonged the duration of normalization and survival in a number of mouse tumor models, compared with inhibiting either molecule alone. Relevant for immunotherapies, VEGF and ANG-2 dual targeting reprogrammed TAMs to an antitumor phenotype (6) and enhanced tumor response to ICBs (7). Furthermore, circulating ANG-2 concentrations are initially elevated or increase in melanoma patients with an unfavorable response to ICBs. Moreover, blood ANG-2 concentrations inversely correlate with response to ICBs with or without anti-VEGF therapy in melanoma patients (3), providing additional rationale for VEGF and ANG-2 dual inhibition. A number of other agents that target endothelial cells and/or pericytes, which support the structure and function of blood vessels, have been shown to transiently normalize tumor vessels. These targets include the receptor tyrosine kinase TIE-2, Notch signaling, endothelial cell metabolism, and oxygen sensors (4). Other strategies that can induce vessel normalization—either directly or indirectly—include restoring perivascular nitric oxide gradients, some chemotherapeutics such as eribulin, and aerobic exercise. Notably, two recent studies showed that ICBs can normalize tumor vessels in breast cancer models in mice (8, 9), which is intriguing because it implies that T cells can directly affect vascular function. These studies showed that the increased vessel perfusion by ICB is positively correlated with its efficacy (9). Future efforts will determine whether these agents can be combined with particular ICBs in specific cancer types. Physical forces generated by cells and the extracellular matrix (ECM) in tumors can also impair the function of blood and lymphatic vessels through compression (4). This is particularly the case in desmoplastic tumors, which are characterized by excessive fibrosis and activated stroma [e.g., pancreatic ductal adenocarcinoma (PDAC) and triple-negative breast cancer] and which generally have abundant carcinoma-associated fibroblasts (CAFs) and ECM. Killing cells with cytotoxic agents or depleting specific ECM components by using enzyme-based drugs can reopen these vessels. However, as cancer cells become resistant to cytotoxic agents and begin to proliferate, vessels collapse again. Similarly, enzymes that target a single ECM component (e.g., hyaluronan), become ineffective when other ECM components (e.g., collagen I) begin to contribute to compressive forces. Agents that reprogram CAFs so that they stop producing multiple ECM components can overcome this compression. For example, angiotensin system inhibitors (ASIs) are a class of molecules commonly used to treat hypertension that also reprogram CAFs and have been shown to improve the function of tumor vessels as well as the delivery and efficacy of various therapeutics (10). Moreover, these widely used drugs can activate both innate and adaptive antitumor immune responses in mouse models and patients (10). On the basis of a successful phase II clinical trial, this approach is now being tested in a randomized clinical trial in patients with locally advanced PDAC (NCT03563248). Because ASIs can cause hypotension, new formulations are needed to lower the systemic exposure of these drugs. Recently, valsartan—an ASI linked to pH-sensitive polymers—was shown to reprogram CAFs in the acidic tumor environment to alleviate antitumor immunosuppression and improve T lymphocyte activity without adverse hypotension. This approach improved the response to ICBs in mice bearing primary and metastatic breast tumors (2). Other strategies are also emerging for targeting CAF-mediated desmoplasia and vessel compression. For example, blocking C-X-C motif chemokine 12 (CXCL12)-C-X-C chemokine receptor 4 (CXCR4) signaling in mouse models of primary and metastatic breast cancer reduces fibrosis, vessel compression, and hypoxia while alleviating immunosuppression, which enhances the efficacy of ICBs (1). Similarly, the activity of tumor-associated immunosuppressive cells and the highly desmoplastic stroma in PDACs are fueled by cancer cell focal adhesion kinase (FAK) activity. Selective FAK inhibitors normalized the fibrotic TME, improved T cell infiltration of the tumor, and increased survival of tumor-bearing mice (11). Endothelial cells present in tertiary lymphoid organs (organized structures consisting of lymphocytes and stromal cells that form outside of the spleen or lymph nodes) within tumors can also regulate antitumor immunity. By combining VEGFR2 and PD-L1 antibodies, it is possible to induce the formation of specialized vessels called high endothelial venules (HEVs) in some tumor models, which improved survival (12). HEVs facilitate the entry of immune cells into lymph nodes and might serve a similar function in tumors. Moreover, depletion of Treg cells can induce the formation of HEVs outside of lymph nodes and the spleen, where they are normally found. When HEVs form in tumors, they can support lymphocyte infiltration and subsequent tumor cell killing by cytotoxic T lymphocytes (13). Lymphatic vessels are the routes from tissue to lymph nodes and back to the blood circulation. Therefore, lymphatic endothelial cells (LECs) are among the first cells to contact antigens that drain from tissue. LECs can control DC maturation and migration as well as directly present antigens on major histocompatibility complex class I (MHC I) and MHC II molecules to T cells (14). They also produce cytokines that modulate the immune response. Numerous lines of evidence demonstrate that LECs help limit and resolve effector T lymphocyte responses, modulate leukocyte function, and maintain peripheral tolerance, which prevents autoimmune responses (14). In cancer, LECs also suppress immune cell antitumor responses (14). Paradoxically, tumors that contain LECs (even in the absence of functional lymphatic vessels) show better responses to ICBs. Tumors with more intratumoral LECs have more T cells, but they become quiescent. These dysfunctional yet antigen-educated T cells can be reawakened by ICBs to produce antitumor immunity. Immune-related toxicities are a major problem with ICB and CAR–T cell immunotherapies. However, discontinuing or reducing the dose of these agents can reduce or even abrogate these toxicities. Improving the function of tumor blood vessels can improve the delivery of antibodies and cells and thus may lower the required immunotherapy dose. Such approaches hold promise for reducing toxicities while improving efficacy. However, one of the biggest challenges in realizing this goal is the lack of validated biomarkers for guiding the dose and schedule of drugs that directly or indirectly target blood vessels. This is further complicated by the recently described “paradoxical effects of obesity.” Obesity is known to fuel tumor progression, desmoplasia, hypoxia, immunosuppression, and resistance to various therapies, including chemotherapies and anti-VEGF therapies (3). Yet in mouse models of melanoma and lung cancer, and in various human cancers, obesity is associated with improved response to ICBs (15). Given the epidemic of obesity, a better understanding of the mechanisms underlying these paradoxical effects are likely to yield strategies to improve immunotherapy further. Combining strategies that improve function of tumor vessels with ICBs holds promise. For example, three recent phase III trials have shown the benefit of combining PD1 or PD-L1 antibodies with anti-VEGF or anti-VEGFR agents, leading to U.S. Food and Drug Administration approvals for the combination in lung and kidney cancers (NCT02684006, NCT02853331, and NCT02366143). However, the relative contribution to patient survival from immunomodulatory versus nonimmunomodulatory effects of VEGF blockade with ICBs in these trials is not known. The ongoing trial that aims to test the role of adding losartan—a safe, inexpensive, and widely prescribed antihypertensive ASI shown to decompress tumor blood vessels—to chemo-radiation and PD1 antibody therapy will reveal the potential of this approach in improving the treatment outcome in patients with PDAC (NCT03563248). http://www.sciencemag.org/about/science-licenses-journal-article-reuse This is an article distributed under the terms of the Science Journals Default License. References and Notes ↵ I. X. Chen et al., Proc. Natl. Acad. Sci. U.S.A. 116, 4558 (2019). ↵ V. P. Chauhan et al., Proc. Natl. Acad. Sci. U.S.A. 116, 10674 (2019). ↵ D. Fukumura et al., Nat. Rev. Clin. Oncol. 15, 325 (2018).OpenUrlCrossRefPubMed ↵ J. D. Martin et al., Annu. Rev. Physiol. 81, 505 (2019).OpenUrl ↵ Y. Huang et al., Proc. Natl. Acad. Sci. U.S.A. 109, 17561 (2012). ↵ J. Kloepper et al., Proc. Natl. Acad. Sci. U.S.A. 113, 4476 (2016). ↵ M. Schmittnaegel et al., Sci. Transl. Med. 9, eaak9670 (2017). ↵ L. Tian et al., Nature 544, 250 (2017).OpenUrlCrossRef ↵ X. Zheng et al., J. Clin. Invest. 128, 2104 (2018).OpenUrl ↵ M. Pinter, R. K. Jain, Sci. Transl. Med. 9, eaan5616 (2017).OpenUrl ↵ H. Jiang et al., Nat. Med. 22, 851 (2016).OpenUrlCrossRefPubMed ↵ E. Allen et al., Sci. Transl. Med. 9, eaak9679 (2017). ↵ C. F. Sautès-Fridman et al., Nat. Rev. Cancer 19, 307 (2019).OpenUrl ↵ K. Maisel et al., Adv. Drug Deliv. Rev. 114, 43 (2017).OpenUrl ↵ Z. Wang et al., Nat. Med. 25, 141 (2019).OpenUrl Acknowledgments: L.L.M. and R.K.J. thank Z. Amoozgar, D. G. Duda, D. Fukumura, Y. Huang, and T. Padera for helpful comments. R.K.J. received honorarium from Amgen; consultant fees from Chugai, Enlight, Merck, Ophthotech, Pfizer, SPARC, and SynDevRx; owns equity in Enlight, Ophthotech, and SynDevRx; and serves on the Boards of Trustees of Tekla Healthcare Investors, Tekla Life Sciences Investors, Tekla Healthcare Opportunities Fund, and Tekla World Healthcare Fund. The Massachusetts General Hospital has applied for patents related to concepts discussed in this article. L.L.M. owns equity in Bayer AG.

Visualizing TGF-β1 regulation by GARP
Regulatory T cells (Tregs) can suppress immune responses through a variety of mechanisms. One such mechanism involves the activation of a surface-bound latent form of the cytokine transforming growth factor–β1 (TGF-β1). Within the cell, newly synthesized pro-TGF-β1 homodimers form disulfide bonds with the transmembrane protein GARP, which acts to chaperone and orient the cytokine for activation at the cell surface. Liénart et al. reveal how GARP interacts with TGF-β1, using a crystal structure in which the complex was stabilized using a Fab fragment from a monoclonal antibody (MHG-8) that binds to the complex. In so doing, they also demonstrate how MHG-8 prevents membrane-associated TGF-β1 release. These structural and mechanistic insights may inform treatments of diseases with altered TGF-β1 functionality and dysfunctional Treg activity, including cancer immunotherapy.

IV. THE ORIGIN OF DARWINIAN IMMUNITY IN VERTEBRATES Above, we argued that the origin of Darwinian immunity constitutes a major transition in evolution. We now speculate on how it might have happened in the lineage of vertebrates. We propose that the transition occurred only once, before the split between jawed and jawless vertebrates, and explain why we believe that the transition was limited by a difficult evolutionary innovation, rather than the presence or absence of selection pressure for Darwinian immunity. We offer a hypothesis on the nature of the limiting innovation, and outline possible routes of stepwise evolution once the bottleneck had been passed. (1) A single origin There are good reasons to believe that the Darwinian immune systems of jawless and jawed vertebrates can be traced back to a common root, and thus that the major transition occurred only once, in a common ancestor of the two lineages. Lampreys have three distinct classes of lymphocytes that provide cellular and humoral immunity, resembling both major lineages of T cells and B cells of jawed vertebrates, respectively (Guo et al., 2009; Hirano et al., 2013). The similarities between not only functions, but also gene expression profiles suggest that the three kinds of lymphocytes are homologous between the two groups and pre‐date the divergence of jawed and jawless vertebrates (Flajnik, 2014; Kasahara & Sutoh, 2014). In addition, jawless fish have thymus‐like lympho‐epithelial structures (‘thymoids’) that are thought to serve as the sites of lymphocyte development (Bajoghli et al., 2011), and express the lamprey orthologue of the gene encoding forkhead box N1 (Foxn1) transcription factor, a marker of the thymopoietic microenvironment in jawed vertebrates. Finally, receptor diversity in jawless fish is generated by the action of enzymes that are closely related to the gnathostome activation‐induced cytosine deaminase (AID) (Rogozin et al., 2007), which is active in the diversification of B‐cell receptors. The apparent homology of multiple components of clonal selection‐based immunity between jawed and jawless vertebrates strongly suggests that the roots of the system originated in the common ancestor of all vertebrates. (2) Chance or necessity The apparently unique origin of Darwinian immunity can be explained in two possible ways. Either, the transition involved a difficult (i.e. low‐probability) event that occurred only once, ‘by chance’, in a common ancestor of all vertebrates [classifying this transition as ‘variation‐limited’ (Számadó & Szathmáry, 2006)]; or, the selective forces that favour the emergence of Darwinian immunity appeared first (and only) in vertebrates and have then driven, ‘by necessity’, the stepwise evolution of the system [in the frame of a ‘selection‐limited’ transition (Számadó & Szathmáry, 2006)]. Historically, the discovery of the intricate molecular mechanisms of V(D)J recombination (the only mechanism of somatic receptor diversity then known) led researchers to favour the first alternative, assuming a once‐only low‐probability event for the origin of this system. Marchalonis & Schluter (1990, p. 16) termed this event ‘a “Big Bang” because sophisticated rearranging systems consisting of multiple elements appear in a fully functional form without foreshadowing in the antecedent species’. This notion was further strengthened by the recognition that the molecular machinery of V(D)J recombination likely arose by the integration of recombination‐activating genes (RAGs) into the vertebrate genome by horizontal gene transfer (Bernstein et al., 1996; Fugmann, 2010). However, subsequent discoveries have challenged the key role of V(D)J recombination in the origin of Darwinian immunity. First, the discovery of RAG1/2 in sea urchins (Fugmann et al., 2006) suggested that the original horizontal gene transfer event must have preceded the origin of vertebrates. Second, we now know that RAG‐mediated V(D)J recombination [enhanced with non‐templated nucleotide addition diversity (Kallenbach et al., 1992)] is far from being the only mechanism that can generate somatic receptor diversity. Jawless fish generate receptor diversity by RAG‐independent gene conversion (Nagawa et al., 2007), subsets of immunoglobulin (Ig) genes in some jawed vertebrates (sharks, birds, rabbits, sheep) rely heavily on gene conversion and hypermutation to generate antibody diversity (Flajnik & Kasahara, 2010), and invertebrate systems of shotgun immunity generate somatic diversity by gene conversion, alternative splicing, RNA editing, post‐translational modifications, and possibly even somatic recombination (Ghosh et al., 2011). Mechanisms of somatic diversity have thus evolved multiple times independently, and are unlikely to be a limiting ‘bottleneck’ in the evolution of Darwinian immunity. Although it cannot be ruled out that the evolution of V(D)J recombination in particular might have been triggered by a second (intragenomic) transposition event that inserted the RAG transposon into a variable innate immune receptor gene (Koonin & Krupovic, 2015), this can no longer be regarded as ‘the Big Bang’ of adaptive immunity, but rather as one of several ‘smaller bangs’ (Bartl et al., 2003; Flajnik, 2014). Kasahara (1997, 1998) argued that the triggering event of the ‘Big Bang’ might have been the one or two rounds of whole‐genome duplication (WGD) that occurred close to the origin of vertebrates (Smith et al., 2013; Smith & Keinath, 2015). This event duplicated many genes related to immunity, and it ‘might have provided unique opportunities to create many accessory and effector molecules of the adaptive immune system’ (Kasahara et al., 1997, p. 92). We will return to this idea in Section IV.4, proposing possible scenarios as to how the WGD event might have triggered the origin of Darwinian immunity. In turn, several studies have argued against the key role of a single triggering event. Klein & Nikolaidis (2005, p. 174) [along the lines of an earlier argument by Bartl et al. (2003)] favour gradual evolution that ‘consisted initially of changes unrelated to immune response that were selected to serve other functions’ and that, by chance, attained a combination that integrated the elements into a new function giving rise to adaptive immunity. Litman, Rast & Fugmann (2010) also emphasized co‐option and redirection of pre‐existing systems as the main source of innovation, at the same time perceiving ‘no reason to assume that vertebrates require a complex immune system any more than do complex invertebrates’ (Litman et al., 2010, p. 552). However, if the origin of Darwinian immunity is not dependent on a ‘difficult’ (i.e. low‐probability) transition, then vertebrates must have some specific traits that favour Darwinian immunity in this group, but are absent from others. (3) Selective scenarios: not exclusive to vertebrates Long lifespan (Klein, 1989; Lee, 2006) and slow reproduction (Flajnik, 1998; Lee, 2006; Flajnik & Kasahara, 2010), high metabolic intensity (Rolff, 2007; Sandmeier & Tracy, 2014), efficient closed circulation (van Niekerk, Davis & Engelbrecht, 2015), low population density (Klein, 1989) and large (Klein, 1989; Flajnik & Kasahara, 2010) or morphologically complex (Boehm, 2012) bodies have been invoked as factors favouring (Darwinian) adaptive immunity. However, these traits are not exclusive to vertebrates, and, in fact, the last common chordate ancestor (and therefore also the ancestral vertebrate) was probably a lancelet‐(amphioxus‐)like creature (Lowe et al., 2015): small, not particularly long‐lived, and rather inconspicuous. The most extensive phylogenetic analysis so far estimated that the lineages of jawed vertebrates and jawless fish diverged about 650 million years ago (Blair & Hedges, 2005). While molecular clock estimates might be sensitive to assumptions on the tempo and mode of evolution, fossil evidence of two distinct types of jawless fish dated to around 520 million years ago (Shu et al., 1999) confirms that the split must have occurred before or shortly after the Cambrian Explosion: Darwinian immunity must therefore have provided a selective advantage already in the Precambrian or early Cambrian world of small body sizes and simple body plans. Many extant invertebrates very likely surpass the last common ancestor of vertebrates in both size and life expectancy, and yet (to our current knowledge) lack Darwinian immunity. Cephalopods can have large bodies and long lifespan, but Darwinian immunity (clonal selection acting on heritable somatic receptor diversity) has not been found in the group (Castellanos‐Martínez & Gestal, 2013). It must nonetheless be noted that the species investigated so far have been octopuses that have short lifespans; studies of immunity in Nautilus species that can live for several decades (Saunders, 1984) are much awaited. Some further ancestral traits of vertebrates might also have facilitated or favoured the evolution of Darwinian immunity. A closed circulatory system, which seems to be an ancestral chordate character (Stach, 2008), may well be a prerequisite of effective immune surveillance by lymphocytes; however, cephalopods also have a closed circulation. Filter feeding seems to be an ancestral trait for deuterostomes (Gans & Northcutt, 1983; Yu & Holland, 2009; Lowe et al., 2015), and is present in echinoderms (sea urchins, sea cucumbers), tunicates (sea squirts), and also cephalochordates (amphioxus), which are thought to most closely resemble the common ancestor of vertebrates (Gans & Northcutt, 1983; Yu & Holland, 2009). The evolution of this lifestyle probably generated selection pressure for improved immunity (to fight pathogens, and to avoid unnecessary or harmful responses to the myriad harmless microorganisms in the filtrate). Echinoderms (Hibino et al., 2006), amphioxus (Huang et al., 2008) and, independently, also mussels (Gerdol & Venier, 2015) and sponges (Degnan, 2015), the most ancient group of filter‐feeding organisms, took the path of expanding their repertoire of innate pattern‐recognition receptors. While expanded innate receptors indeed imply selection pressure for improved immunity, the defining traits of Darwinian immunity have not been found in any of these groups to date. It has also been noted that vertebrates harbour more complex microbiomes than invertebrates, which tend to have either relatively simple microbial communities or rely on microbial partners that are shielded from immunity within the cells or in compartments enclosed in physical barriers (McFall‐Ngai, 2007). Managing a complex microbiome has been invoked as a selection pressure that may have driven the evolution of Darwinian immunity specifically in vertebrates (Pancer & Cooper, 2006; Weaver & Hatton, 2009; Lee & Mazmanian, 2010; Boehm, 2012). However, this explanation only leads one step back, to another question: why would vertebrates be special in terms of needing a complex microbiome? We find it more plausible that Darwinian immunity evolved for another reason (a rare event that opened up a difficult evolutionary path), and could then enable the acquisition of a more complex microbiome – which then might have provided an evolutionary edge to vertebrates. A further hypothesis has been proposed by Pancer & Cooper (2006, p. 512), who posited that novel selection pressure might have arisen at the origin of vertebrates because a large arsenal of innate receptors ‘presented serious autoimmunity problems at a time of rapid developmental and morphologic innovation’, and rapid changes in the endosymbiotic communities might also have occurred. As a consequence, the complexity of the innate immune system might have been reduced, creating increased selection pressure for the evolution of an alternative system. However, innate receptors, even those belonging to complex families, tend to target classes of molecules that are not present in the host, and the complexity of the vertebrate body plan increased not so much by expanding the set of molecular building blocks, but rather by regulatory and organizational complexity (Heimberg et al., 2008; Lowe et al., 2011). Such an evolutionary trajectory would not have raised the risk of autoimmunity by innate recognition. It is also unclear why the evolution of vertebrate characteristics would have generated a selection pressure for rapid shifts in the microbiome, sufficiently strong to compensate for drastically reduced (innate) immune defence against pathogens. The discoverer of clonal selection, Burnet himself entertained the idea that it might have been the increased developmental flexibility of vertebrates that created the selection pressure for adaptive immunity (Burnet, 1968). He argued that flexible development resulted in an increased risk of cancer, and the threat from the ‘modified self’ of tumours called for a mechanism that was itself variable and adaptable. However, Darwinian immunity requires reliable mechanisms of immune tolerance to be able to target patterns that are similar to those found in the host self. As we will explain in later sections, it is likely to have started targeting motifs that showed relatively small similarity to host motifs, and could expand to riskier targets only as gradual evolution improved the specificity of targeting and the capacity for antigen‐specific tolerance. Distinguishing tumours from normal self is likely to be the most challenging task for Darwinian immunity that could only be added at advanced stages of its evolution – it cannot have been the initial trigger. Finally, we note that extant vertebrates encompass huge diversity in terms of lifestyles, body size (from shrews to the blue whale) and lifespan (from weeks to >100 years), and while some species have lost or simplified elements of adaptive immunity, the presence of clonal selection‐based Darwinian immunity seems ubiquitous across this dizzying diversity of size and form. (Moreover, the species with reduced adaptive immunity do not seem to follow any discernible pattern of size or lifestyle: these examples may simply reflect stochastic loss in some lineages). Considering that most components of vertebrate Darwinian immunity appear to be scalable in terms of diversity, and a higher diversity of innate immune recognition would probably be quite straightforward to re‐evolve [indeed Atlantic cod (Gadus morhua) have lost MHC class II and have expanded their innate Toll‐like receptor (TLR) repertoire (Star et al., 2011)], the ubiquitous maintenance of Darwinian immunity in vertebrates suggests that this type of adaptive immune defence provides benefits across a very wide range of life‐history parameters. It is hard to see how this wide range also would not cover the lifestyles of a large number of invertebrate species. To conclude, while a number of life‐history traits likely exerted selection pressure on the ancestral vertebrate to develop sophisticated immunity, and some features of the vertebrate body plan might have acted as necessary pre‐adaptations, none of these selection pressures and physical traits seem to be exclusive to this group, and Darwinian immunity would likely be beneficial for many invertebrates as well. We therefore argue that a key piece of the puzzle is still missing: there must have been a difficult evolutionary innovation that emerged, as far as we know, only in vertebrates. (4) Immunological Big Bang 2.0 What had to be invented for the transition from the invertebrate immunity of an amphioxus‐like ancestor to Darwinian vertebrate immunity? The necessary components for the somatic generation of receptor diversity were all in place: amphioxus has RAG1 (Huang et al., 2014; Zhang et al., 2014) and proto‐MHC (Abi‐Rached et al., 2002); sea urchins have RAG1/2 (Fugmann et al., 2006); and the presence of orthologous ancestral genes in both jawed and jawless vertebrates indicates that the vertebrate ancestor had both BCR/TCR and VLR precursors (Flajnik & Kasahara, 2010). In addition, lymphocyte‐like cells have been found in amphioxus (Huang et al., 2007), along with homologues of several genes that are active in immune signalling in the Darwinian immunity of vertebrates (Yu et al., 2005), and recently discovered innate lymphoid cells in mammals perform many functions associated with T cells without expressing T‐cell receptors (Walker, Barlow & McKenzie, 2013). These cells can be induced by microbial products, and NK cells that bear germline‐encoded antigen receptors (specific, e.g. for conserved structures of viruses) establish immune memory by the survival of an amplified cell population (O'Sullivan et al., 2015), possibly constituting a system of proto‐Darwinian immunity. Similar lymphocyte‐like cells bearing germline‐encoded receptors might have existed in the ancestral vertebrate [innate lymphoid cells might be present in jawless fish, as well (Eberl, Di Santo & Vivier, 2015)], and might already have possessed both the genetic circuitry required for pathogen‐induced proliferation and antimicrobial effector mechanisms. In addition to these pre‐existing components, clonal selection‐based Darwinian immunity requires two key properties (Du Pasquier, 2006). First, as recognized very early by Burnet (1970), monoallelic (or at most oligoallelic) expression of the somatically generated, clonally heritable antigen receptors is needed to allow for specific amplification (clonal selection) of an appropriate response. Stable expression and clonal heritability are required to maintain targeting specificity over time and across cell divisions; monoallelic expression is necessary to prevent the simultaneous presence of useful and useless or harmful receptors on the same cell, which would greatly abrogate the efficiency of clonal selection. Second, antigen‐specific immune tolerance is needed to avoid autoimmunity when a somatically generated receptor responds to a molecular pattern of the host (‘self’). We propose that the evolution of antigen‐specific immune tolerance is a difficult (low‐probability) transition that requires major innovations in gene regulation, and therefore imposes a critical bottleneck in the evolution of Darwinian immunity. We argue that in the evolution of vertebrates this transition was made possible by an abrupt increase in regulatory complexity [precipitated by a WGD event and a series of segmental genome duplications (Smith et al., 2013; Smith & Keinath, 2015)] before the divergence of jawless and jawed vertebrates, and once this difficult transition had been achieved, pre‐existing mechanisms of somatic receptor diversity could quickly be co‐opted for clonal selection. We term this concept the ‘Immunological Big Bang 2.0’, and below provide further arguments in its support. Of the two components of the transition, monoallelic expression of receptor genes does not seem to be particularly difficult to evolve. In addition to the antigen receptors of lymphocytes in jawed and jawless vertebrates (Pancer et al., 2004), monoallelic expression occurs in many mammalian genes not associated with immunity (Nag et al., 2013), while inhibitory receptors on mammalian NK cells (Cichocki, Miller & Anderson, 2011) are characterized by the stochastic expression of a subset of receptor genes from a larger germline‐encoded repertoire, and the 185/333 immune‐response genes expressed in sea urchin coelomocytes (a type of immune cell) display near‐monoallelic expression from a set of germline‐encoded alleles (Majeske et al., 2014). However, amplifying lymphocytes with ‘random’ (i.e. somatically generated) receptors carries the risk of autoimmunity – which brings us to the necessity of antigen‐specific tolerance for clonal selection‐based Darwinian immunity. Whereas an autoreactive response without amplification inflicts damage analogous to a fixed dose of a toxic substance, an amplifiable response is analogous to an infectious agent that can multiply and do great harm even at a very low initial dose. As soon as clonal amplification extends to immune recognition motifs that can potentially target self patterns, protective mechanisms are needed to neutralize effector cells based on their self‐reactive targeting specificity. Two main mechanisms operate in jawed vertebrates: clonal deletion (‘negative selection’) removes autoreactive cells during the maturation of lymphocytes (Palmer, 2003) to enable ‘recessive tolerance’ (tolerance by the absence of autoreactivity); by contrast, regulatory T cells (Tregs) enable ‘dominant tolerance’ by actively downregulating autoreactive immune responses in the targeted tissues (Coutinho et al., 2001; Sakaguchi, 2004). Both mechanisms are based on intricate gene regulation mechanisms that are likely to be difficult to evolve, and the (near) simultaneous appearance of both systems is highly unlikely. We propose that the ‘Big Bang’ of vertebrate immunity might have been triggered by the evolution of Treg‐mediated dominant tolerance, facilitated by the greatly increased potential for regulatory complexity following the WGD event that gave rise to vertebrates. Below we explain why dominant, rather than recessive tolerance might have been the key innovation, and show that its main genomic components probably originated at or near the WGD event. We argue that reliable immune tolerance can be achieved by Treg‐mediated dominant tolerance, but not by negative selection alone. Both mechanisms are necessarily imperfect (and must have been even less efficient in the beginning), but there is an important difference in the way the two mechanisms can ‘fail’. Imperfect negative selection is imperfect in terms of coverage: some auto‐reactive clones escape selection; imperfect dominant tolerance is imperfect in terms of degree: all autoimmune reactions are affected, but the degree of control is limited. In the former case, a single escaped clone could wreak havoc without additional control by Tregs in the peripheries, because repeated rounds of clonal expansion would induce exponential growth of the autoimmune reaction. By contrast, imperfect dominant tolerance can afford mistakes, because a self‐reactive clone activated by a stochastic glitch in tolerance could still be brought under control later: negative selection has one chance to act, dominant tolerance has many. To suffice alone, negative selection should be perfect; dominant tolerance just needs to be ‘good enough’ to have a statistically high chance of bringing self‐reactive clones under control before they can do too much damage. We therefore argue (in agreement with Janeway, 2001) that the evolution of regulatory T cells (dominant tolerance) was probably necessary for the emergence of Darwinian immunity. Once dominant tolerance jumpstarted the evolution of Darwinian immunity, the evolution of mechanisms for negative selection against major self‐antigens could provide an economical advantage, removing highly autoreactive cells before they had their first chance to expand. If dominant immune tolerance was a necessary innovation to achieve Darwinian immunity, it certainly cannot have been an easy one. Foxp3 acts as a central switch: it forms complexes with hundreds of genes (Rudra et al., 2012), and affects the expression of more than 2000 genes in mouse T cells (Xie et al., 2015). The task is indeed not trivial. Foxp3+ Tregs often have to respond in the opposite reaction compared with conventional (non‐regulatory) effector T cells: TCR signalling (with co‐stimulation) induces effector functions in conventional T cells, but repressor functions acting on neighbouring T cells in Tregs. In addition, regulatory activity must strike a delicate balance between too little regulation resulting in runaway autoimmunity, and too much, which could downregulate useful responses against pathogens (self antigens are also presented in the vicinity of pathogen invasion). To achieve this complex functionality, Foxp3 acts not only as a repressor of activation‐associated genes, but also upregulates a large number of genes (Zheng et al., 2007), and is likely to operate a bistable autoregulatory loop to maintain a stable identity of regulatory cell clones (Rubtsov et al., 2010). The complexity and difficulty of the task supports the notion that Treg‐mediated tolerance might indeed constitute the major bottleneck towards Darwinian immunity that, in vertebrates, could only be passed by a rare burst of regulatory complexity. Phylogenetic evidence is compatible with the origin of Treg‐mediated dominant tolerance in the vertebrate common ancestor. Foxp3, the key regulatory gene for the development of regulatory T cells (Hori, Nomura & Sakaguchi, 2003), belongs to the ancient eukaryotic family of Forkhead box (Fox) transcription factors. Remarkably, the Foxp class of the family has a single orthologue in invertebrates (including sea urchin), but four members in most vertebrates (Andersen, Nissen & Betz, 2012), which is consistent with the origin of the class at the WGD event [followed by segmental duplication involving Foxp loci (Smith & Keinath, 2015)]. The analysis of the sea lamprey (Petromyzon marinus) genome identified homologues of Foxp1, 2 and 4, but did not find Foxp3 (Smith et al., 2013). However, Foxp3 is most closely related to Foxp4, and both were created by the last gene duplication in the family (Santos et al., 2011). The Foxp4 ortholog identified in lamprey might therefore be homologous to the common ancestor of Foxp3 and Foxp4 [a situation with known precedents among duplicated transcription factors (Kasahara & Sutoh, 2014)], and might perform the regulatory role of Foxp3 in jawless fish. While Foxp3 is at the top of the regulatory cascade of dominant tolerance, the evolution of this complex regulatory function likely required the involvement of a whole suite of regulatory genes – which may have depended on the sudden availability of duplicated genes in the ancestral vertebrate. Of note, the transcription factors Helios and GATA‐3, which are key interacting partners of Foxp3 in the orchestration of the regulatory phenotype (Rudra et al., 2012; Kim et al., 2015), both belong to gene families that were duplicated in the WGD event (Gillis et al., 2009; John, Yoong & Ward, 2009). Another member of the Foxp class, Foxp1 is involved in the regulation of B‐ and T‐cell development and homeostasis (Hu et al., 2006; Feng et al., 2010), and further classes of duplicated regulatory genes might also have contributed to the expanding genetic circuitry of immune cell fates (Rothenberg & Pant, 2004; John et al., 2009). In addition to duplicated transcription factors, the increased regulatory complexity of vertebrates arose partly from a massive increase in microRNAs (miRNAs) in the stem lineage of vertebrates (preceding the split between jawless and jawed vertebrates), both due to genome duplication and to the acquisition of new miRNA families (Heimberg et al., 2008, 2010). miRNAs play multiple complex roles in the development and control of vertebrate adaptive immunity (Xiao & Rajewsky, 2009; Mehta & Baltimore, 2016), including mechanisms of both central and peripheral tolerance (reviewed in Simpson & Ansel, 2015). In particular, the selective disruption of miRNAs in Tregs results in autoimmune pathology closely resembling that caused by deficiency in Foxp3 (Zhou et al., 2008), while the selective knockout of miRNAs in thymic epithelial cells compromises promiscuous gene expression (Ucar et al., 2013) that is crucial for the thymic induction of tolerance against peripheral self‐antigens. By contrast, V(D)J recombination does not seem to require miRNA control (Xiao & Rajewsky, 2009). Compatible with our scenario, the operation of specific immunological tolerance depends on regulatory complexity acquired at the origin of vertebrates, but the generation of receptor diversity does not. Thus many components of the genetic circuitry (transcription factors, miRNAs) seem to have appeared in the series of genomic duplications that occurred at the root of the vertebrate lineage. Since duplicated genes tend to get inactivated then lost unless they acquire new functions, the integration of a large number of elements, duplicated within a short time frame, is consistent with a rapid, ‘Big Bang’ like episode of evolution. Conversely, the construction of the highly complex genetic circuitry of dominant tolerance might have depended on the simultaneous presence of a large number of recently duplicated elements. The analysis of the gene regulatory networks (Martinez‐Sanchez et al., 2015) might eventually elucidate how the duplicated regulatory elements might have triggered the evolution of a Treg cell phenotype. We have thus argued that dominant immune tolerance might be a necessary condition for Darwinian immunity, that the regulatory circuitry required for this function might be very difficult to evolve, and that in vertebrates the origin of the involved genetic machinery apparently goes back to the rare burst of genomic innovation that gave rise to the lineage. Thus, while Burnet (1968) believed that the greater flexibility of development in vertebrates created the selection pressure for adaptive (Darwinian) immunity, we suggest that it created not the need, but the opportunity. However, we note that the presence of Tregs in jawless fish still needs to be demonstrated, and while it is plausible to assume a crucial role of dominant tolerance in Darwinian immunity, the evidence is not unequivocal. We argued that both somatic receptor diversity and clonal selection might have had pre‐existing components, and it was the linking of the two that required a difficult evolutionary innovation: specific (and probably dominant) immune tolerance. However, while the existence of multiple mechanisms of somatic diversity has clearly been demonstrated, clonal selection (amplification) has not been described in any invertebrate to date. It is possible that the machinery for clonal amplification by itself is difficult to evolve, and we cannot exclude that it was this step that imposed a bottleneck for the evolution of Darwinian immunity (L. Du Pasquier, personal communication) that could only be passed by the increased regulatory complexity of vertebrates. We also note that the ‘burst of regulatory complexity’ at the root of the lineage is not quite straightforward to explain. WGDs have occurred rarely, but still multiple times in animals, and much more frequently in plants (Otto & Whitton, 2000). However, while some of these events have given rise to successful new clades and/or duplicated regulatory factors, the origin of vertebrates appears to be unique with respect to the number of regulatory elements retained, and the abrupt increase in regulatory complexity and developmental flexibility that accompanied it. It remains to be elucidated what additional factors (selection pressures, pre‐adaptations, low‐probability genomic events) might have contributed to the rare constellation of conditions that allowed for the rapid increase in regulatory complexity that very likely laid the foundations for the evolutionary success of vertebrates, and opened the trajectory towards Darwinian immunity. To summarize, the lack of a selective scenario specific to vertebrates argues very strongly for a ‘Big Bang’‐type origin of Darwinian immunity, limited by a difficult evolutionary innovation; the abrupt increase in regulatory complexity at the origin of vertebrates was very likely a prerequisite (and possible trigger) to passing this bottleneck; and antigen‐specific dominant immune tolerance is a plausible (but not the sole) candidate for the limiting evolutionary innovation. (5) The chicken and egg problem of Darwinian immunity Beyond the initial bottleneck for Darwinian immunity, an apparent chicken and egg problem arises. Clonal amplification of immune responses with stochastic (somatically diversified) targeting is unsafe without specific (dominant) tolerance; however, specific tolerance might not make much sense without stochastic immune targeting. We argued previously that the emergence of dominant tolerance might have been the key to the evolution of Darwinian immunity in vertebrates – but what drove it in the first place? If specific tolerance evolved against the backdrop of innate or shotgun immunity that did not allow for the clonal amplification of somatically diversified immune responses, what was then the initial selective advantage? There are two ways to resolve this apparent paradox. First, some limited form of clonal amplification, involving immune responses with a limited scope of diversified targeting, might be beneficial even without specific tolerance, if the benefits of improved defence outweigh the costs (including some limited auto‐immunity). Below we shall discuss possible incremental stages in the evolution of randomized immune targeting: it is not impossible that the very first steps could be taken without dominant immune tolerance. In this scenario, a slightly enhanced form of proto‐Darwinian immunity (with restricted somatic diversification, and amplification limited in both space and time) might have preceded the emergence of dominant immune tolerance, and mitigating the low‐level auto‐immunity associated with the former might have provided an immediate evolutionary benefit. If this is true, this level of proto‐Darwinian immunity should eventually be found in extant invertebrates [the near‐monoclonally expressed 185/333 immune‐response genes of sea urchins (Majeske et al., 2014) might constitute a candidate system]. Note that while this scenario somewhat blurs the line between shotgun immunity and Darwinian immunity, a large gap still remains, and bridging that gap very likely required the evolution of specific immune tolerance. Alternatively, Treg‐mediated dominant tolerance might have evolved first to afford specific tolerance to beneficial symbiotic bacteria (Weaver & Hatton, 2009). Innate and shotgun immunity tend to target broad classes of conserved microbial patterns and cannot discriminate and selectively spare potentially beneficial species. By providing this function (downregulating innate mechanisms with narrow targeting), specific tolerance, even in early rudimentary forms, might have provided an immediate benefit even in the absence of somatically diversified immune effector targeting. Remarkably, the gut of extant vertebrates (mice), which holds the largest diversity and biomass of the microbiome, is enriched in Tregs that are reactive to commensal microbes and are essential for the maintenance of immune tolerance against these (Chai, Zhou & Hsieh, 2014; Sefik et al., 2015). Improved microbiome management might afford a huge metabolic benefit (McFall‐Ngai, 2007), and is thought to have been a major driver of immune evolution from the earliest animals (Bosch, 2014). In this scenario, Tregs might even have been the first cell type to evolve somatically diversified targeting, which could then be co‐opted for effector targeting, as the broadening scope of specific tolerance allowed it. Because the generation of regulatory cells depends on an education period when they encounter antigens under non‐inflammatory conditions, the scope of specific tolerance under this scenario could easily be extended to cover self‐antigens. (6) Stepwise evolution of Darwinian immunity after the ‘Big Bang’ After the emergence of an early form of specific immune tolerance, the subsequent evolution of vertebrate Darwinian immunity could proceed in small incremental steps, increasing the potential of somatic receptor diversification and clonal amplification to match and drive further the improving capacity of specific tolerance. We consider in turn how the scope and potential of somatic receptor diversification, clonal amplification and specific immune memory, and specific tolerance might have evolved through a series of gradual improvements. Receptor targeting might have evolved in terms of broadening epitope coverage, shifting from germline‐encoded receptors to increasing somatic diversification, and towards higher specificity. Mechanisms of receptor diversification very likely existed even before the immunological ‘Big Bang’ (Loker et al., 2004), either to generate shotgun immunity [e.g. by somatic hypermutation (Du Pasquier et al., 1998; Lee et al., 2002)] or expressed in the germline to generate variation rapidly across generations. LRR‐ and RAG/Ig‐based systems of gene assembly might also have had their origin at this stage, e.g. the sea urchin homologues of Rag1/2 are expressed in coelomocytes that perform immune functions (Fugmann et al., 2006), and RAG transposition still appears to play a role in generating germline‐encoded receptor diversity across generations in sharks (Lee et al., 2000; Hsu et al., 2006). Such pre‐existing mechanisms of receptor diversity could then be conveniently co‐opted for clonally selected lymphocytes once specific tolerance had appeared. Initially, the germline‐encoded receptors must have targeted safe molecular patterns that were reliably associated with potential pathogens but were absent from the host species (Ohno, 1990), and the scope of somatic diversification in the frame of shotgun immunity must have been optimized (limited) to keep the repertoire within these safe boundaries. Then, after the ‘Big Bang’, the gradual improvements of specific tolerance allowed these safe boundaries to expand, and the mechanisms of somatic receptor diversity had multiple ways to take advantage of this opportunity and expand accordingly (Fig. 4). First, germline‐encoded receptor genes (or their modular components) might have expanded by gene duplication and divergence to allow the targeting of novel domains in the ‘epitope space’ of possible targeting motifs. Second, the extent of somatic diversification (the possible distance from the germline‐encoded target specificities) might also have increased gradually, e.g. by increasing the rate of hypermutation or by expanding the genomic regions affected. The first mechanism could create new foci of epitope targeting, while the second could increase the action radius of existing foci in epitope space. Both would allow immune targeting to expand gradually into domains of epitope space that used to carry a high risk of autoimmunity, but were becoming safe due to improving specific tolerance. Mechanisms based on gene assembly also offer multiple ‘scalable’ solutions for both aspects of expanding epitope coverage. The number of genomic segments is freely scalable, and the set and probability of possible combinations can also be regulated. For example, Ig genes of cartilaginous fish are still characterized by the (probably) ancestral cluster organization of V(D)J miniloci, which involves very limited numbers of gene segments within each locus, and rearrangements are allowed only within the miniloci (Hsu et al., 2006) (Fig. 5A). This genomic arrangement constrains the possible foci of epitope targeting. By contrast, most Ig genes in tetrapods feature translocon organization, in which multiple gene segments are allowed to recombine, generating much greater combinatorial diversity (Fig. 5B). Remarkably, teleost fish have both cluster and translocon organization in different Ig genes or in different species, underlining the flexibility of gradual evolution towards increasing (or decreasing) combinatorial diversity (Hsu et al., 2006). Furthermore, even with translocon organization, ‘random’ somatic recombination does not necessarily imply that all possible combinations (specificities) are produced with the same probability. The generation of V(D)J recombinants can be skewed (Jackson et al., 2013; Elhanati et al., 2014), and some lymphocyte subsets [e.g. several types of unconventional T cells (Godfrey et al., 2015)] are characterized by a highly focused receptor repertoire with limited gene combinations and diversity. The degrees of freedom in combinatorial diversity might have evolved gradually with improving specific tolerance, and if some parts of ‘receptor space’ were more likely to be useful, regulatory mechanisms could apparently evolve to ensure the skewed production of these predictably useful specificities. There is also no reason why additional mechanisms of somatic receptor diversity could not be fine‐tuned towards generating broader or more constrained diversity. For example, some vertebrates first generate a limited repertoire relying on somatic recombination only, and switch on the expression of terminal deoxynucleotidyl transferase (TdT; responsible for nucleotide addition diversity) only at later stages of ontogeny (Schwager et al., 1991; Bogue et al., 1992). Knock‐out mice lacking TdT display reduced lymphocyte receptor diversity (Gilfillan, Benoist & Mathis, 1995), but are also less prone to autoimmune disease (Conde et al., 1998). The extent of TdT‐mediated junctional diversity could probably be flexibly tuned during evolution to match the evolving capabilities of tolerance mechanisms, and the same is likely to be true for the additional mechanisms of hypermutation and gene conversion. In jawed vertebrates, MHC restriction of adaptive immune responses offers a further scalable solution for the coverage of somatic receptor diversity. Most peptide antigens are able to elicit an immune response only when presented on the surface of a cell bound to an MHC molecule. MHC presentation requires the successive steps of proteasomal cleavage (for class I MHC only), translocation into the lumen of the endoplasmic reticulum (where MHC molecules are loaded), and binding to an MHC molecule. Each of these steps are selective (Hoof et al., 2012), and the degree of selectivity can be fine‐tuned by the substrate specificity of cleavage and translocation, and the number and binding specificity of MHC alleles. The analysis of the highly conserved genome of the elephant shark (Callorhinchus milii) (Venkatesh et al., 2014) suggests that MHC alleles were originally in genetic linkage with the genes of the antigen receptors that could bind to them. Such an arrangement might have facilitated the control of the set of peptides involved in MHC presentation, and might also have allowed somatic diversity to get started without thymic positive selection of lymphocytes (because coupled MHC–TCR pairs could be selected for binding over generations). Tissue‐specific restriction offers a further solution to restricting autoimmune collateral damage when specific tolerance is not (yet) efficient. Of note, unconventional T cells tend to recognize antigens in the context of non‐polymorphic antigen‐presenting molecules, some of which are expressed in a tissue‐specific manner (Godfrey et al., 2015). Clonal amplification and specific memory might also have evolved in incremental steps, contributing to the stepwise co‐evolution of the effector and regulatory arms of Darwinian immunity. Clonal amplification can be safe even without specific tolerance for effector cells bearing germline‐encoded receptors that are selected for safe targeting across generations (Boehm, 2006), and the genetic circuitry for inducible expansion might have evolved prior to the origins of Darwinian immunity for such cell types (as in NK cells). Then clonal amplification might have been co‐opted for cell types using a limited repertoire of somatically diversified receptors [focused on patterns typically associated with pathogens, similar to some classes of unconventional T cells (Godfrey et al., 2015) in extant organisms], and finally also for cells with the broadest diversity of targeting. The evolving genetic circuitry of programmed cell expansion and contraction also incorporated transcription factors that were created in the ancient vertebrate genome duplication event (Rothenberg & Pant, 2004). In addition to the breadth of targeting involved in clonal amplification, the extent and durability of the amplification could also evolve in gradual steps. In particular, if an immune reaction is short‐lived and no memory cells survive, then collateral damage is limited to the time span of the primary immune reaction (launched against an invading pathogen), and this one‐time cost might be outweighed by the benefit of efficient defence against the pathogen. That immune effector cells cross‐reactive to self would be induced against potentially dangerous non‐self antigens, but not to self tissues in the first place, could be ensured by the dependence of clonal amplification on danger signals from the very beginning of Darwinian immunity. The use of danger signals was probably easy to evolve: the new effector mechanisms simply needed to be built on top of the original (innate) decision cascades, co‐opting pre‐existing inducers of innate immunity as ‘danger signals’ for evolving Darwinian immunity. This way, self‐reactive cells inflicted only limited collateral damage during acute immune responses, and starting from such a situation, any (initially imperfect) measure of specific tolerance would have been useful and favoured by selection. The scalability of clonal amplification and specific immune memory can still be observed in the immune systems of extant vertebrates. For example, in sharks ‘the memory response is clearly inferior to that of the higher vertebrates’ (Flajnik & Kasahara, 2010, p. 50), and repeated challenge with an antigen cannot boost the antibody response beyond the peak of the initial response (Dooley & Flajnik, 2005). Even mammals have several lymphocyte subsets that display limited receptor diversity, tend to target conserved microbial structures, and are able to launch very rapid responses, but generate limited immune memory (Baumgarth, Tung & Herzenberg, 2005; Godfrey et al., 2015). This combination of characteristics may reconstitute (or preserve) the early stages of the evolution of Darwinian immunity, in that restricted somatic diversity and limited memory allow for safe responses without strict check‐points (that delay the response of highly diverse classes of lymphocytes) and advanced mechanisms of immune tolerance. The efficiency of tolerance mechanisms is also likely to have evolved in a stepwise manner. Genome duplication created surplus copies of regulatory factors, but wiring these into a genetic circuitry for regulatory T cells must have taken considerable evolutionary time, and each improvement in regulatory function could further potentiate the evolution of the effector components of Darwinian immunity. Of note, the deletion of Foxp3 in zebrafish results in only a moderate inflammatory phenotype (in contrast to the fatal autoimmune disease observed in Foxp3‐deficient mice) (Sugimoto et al., 2017), which is compatible with the view that the capacity of both effector and regulatory immune mechanisms has improved gradually during the evolution of vertebrates. The action of Treg cells could then also be complemented by the evolution of negative selection, improving not only the reliability, but also the cost efficiency of immune tolerance, by neutralizing autoreactive cells before they had their first chance to expand. In principle, some simple form of negative selection might even have preceded Treg‐mediated dominant tolerance in the frame of proto‐Darwinian immunity with restricted germline‐encoded receptor diversity. In a possible extant analogy, mammalian NK cells go through a period of ‘education’ early in their development, during which they are able to tune their responsiveness according to the level of inhibitory and stimulatory ligands in their environment (Orr & Lanier, 2010). However, we note that although NK cells are traditionally regarded as components of innate immunity, they are still embedded in the higher regulatory complexity of vertebrates, and it is unclear whether such fine‐tuned regulation had been possible before the ‘Big Bang’ of the WGD event. We cannot rule out that the ‘Big Bang’ of increasing regulatory complexity opened the way simultaneously to both Treg‐mediated dominant tolerance and recessive tolerance by negative selection; remarkably, Foxn1 transcription factor, a marker of the thymopoietic microenvironment, also originated at the WGD event (Singh, Arora & Isambert, 2015). Then, at least in jawed vertebrates, the evolution of the intricate mechanism of promiscuous gene expression in dedicated cells of the thymus (Derbinski et al., 2001) could extend the education of thymocytes (and thereby improve the efficiency of tolerance) to self antigens that are normally restricted to specific tissues. The gene of the transcription factor Aire, the central orchestrator of promiscuous gene expression in the thymus, has been found in the elephant shark (Venkatesh et al., 2014), an ancestral jawed vertebrate, but not yet in lamprey (Smith et al., 2013). Recent studies in mice indicate that promiscuous gene expression promotes the generation of Treg cells involved in dominant tolerance to tissue‐specific antigens (Aschenbrenner et al., 2007; Yang et al., 2015); in a recurring theme of immune evolution, new components of immunity tend to evolve interdependencies with pre‐existing components. As the increasing capacity for somatic receptor variability and clonal amplification allowed for increasing repertoire diversity, targeting could also evolve towards higher specificity. This allowed the targeting of variable (not evolutionarily conserved) patterns of potential pathogens, and facilitated the differential recognition of not only self and non‐self (Borghans, Noest & De Boer, 1999), but also of distinct pathogens that can be controlled by different effector mechanisms (Borghans & De Boer, 2002). The evolutionary scenario (including pre‐‘Big Bang’ pre‐adaptations and selection pressures) for the evolution of Darwinian immunity in vertebrates is shown in Fig. 6. (7) Darwinian immunity as a key driver of vertebrate evolution Finally, we argue that the origin and evolution of Darwinian immunity might have played a crucial role at several stages in the evolution of vertebrates. There are no known vertebrates without Darwinian immunity. Thus, either the innovation was necessary for the subsequent evolution of the vertebrate body plan, or the evolutionary advantage was so large that all other forms without it were outcompeted and went extinct without descendants. The latter possibility becomes highly unlikely once considerable adaptive radiation has occurred, so the emergence of the fundamental framework of vertebrate Darwinian immunity must have happened either shortly after the adaptive radiation of early vertebrates, or even before it, possibly contributing to the evolutionary success of vertebrates. The situation is somewhat analogous to the origin of eukaryotes and mitochondria. All extant eukaryotes either possess mitochondria or are derived from ancestors that had them. While it is unclear whether it was the acquisition of mitochondria that triggered the burst of evolutionary innovations that led to the last common eukaryotic ancestor (Poole & Gribaldo, 2014), the symbiogenetic event conferred sufficient selective advantage to drive all other protoeukaryotic lineages to extinction. Darwinian immunity evolved along with a whole package of evolutionary innovations triggered by the WGD event. While the exact contribution of Darwinian immunity to the evolutionary success of vertebrates cannot be directly estimated, the apparent extinction of several intermediate stages of its evolutionary trajectory argues that it must have been a major driver of vertebrate evolution. As argued in previous sections, the selective advantage provided by Darwinian immunity might have included improved cost efficiency of defence against pathogens and/or improved microbiome management. In addition, the pattern of two alternative implementations of Darwinian immunity in jawless and jawed vertebrates is far from straightforward to explain, and may have further implications for the evolution of vertebrates. Assuming that the evolution of Darwinian immunity was indeed initiated by the establishment of a framework for specific immune tolerance in the common ancestor of all vertebrates, two alternative scenarios can explain the extant pattern of two unrelated implementations of receptor diversity. In the first scenario, one of the two systems (VLR in jawless fish; TCR/BCR in jawed vertebrates) evolved first in the common ancestor of both lineages, but was then replaced by the other system in one of the lineages. It has been speculated that VLR might have evolved first, because all the required genes seem to have been present in the last common vertebrate ancestor, while the horizontal gene transfer that inserted RAG genes into an ancestral TCR/BCR‐like gene locus occurred after the split, in the jawed vertebrate lineage (Kato et al., 2012; Kasahara & Sutoh, 2014). Alternatively, the two systems might have arisen independently, each in the common ancestor of one of the lineages, over the background of some form of shotgun and/or proto‐Darwinian immunity. The first scenario (replacement) would imply that the more recent of the two systems had, already in its early rudimentary form, a selective advantage over the more ancient system, which at that time had already undergone some period of adaptive evolution. If VLR is indeed more ancient (Kato et al., 2012; Kasahara & Sutoh, 2014), then the BCR/TCR system must be more efficient, and it is tempting to speculate that it might have contributed to the much greater evolutionary success of jawed versus jawless vertebrates. Under the replacement scenario (irrespective of which system appeared first), the evolution of the second, more powerful system might have been helped by the presence of the tolerance mechanisms that co‐evolved with the first system of somatic diversity. In turn, the alternative scenario of independent origins of both systems from shotgun immunity would imply that two vertebrate lineages that acquired Darwinian immunity remained successful to this day, while all ancestral lineages without it have (apparently) been lost. Finally, we note that while at the moment, Darwinian immunity is practically synonymous with vertebrate adaptive immunity, independently evolved systems of Darwinian immunity might yet be found in invertebrates. The lessons from vertebrates suggest that higher developmental complexity and, in particular, extensive genome duplications might be prerequisites for the emergence of Darwinian immunity, while filter‐feeding and/or reliance on symbiotic microorganisms might give rise to particularly strong selection pressure for improved immunity: invertebrate groups displaying combinations of these traits should be investigated with particular scrutiny. If the last decade has taught us anything, it was that the diversity and ingenuity of invertebrate immune systems is far greater than previously thought: we are certain that the explosive growth of comparative immunology will not fail to deliver further surprises. We list some of the outstanding questions below. (8) Outstanding questions of Darwinian immunity What conditions (life‐history traits) favour Darwinian immunity over other types of adaptive and innate immunity? Has Darwinian immunity evolved in any invertebrate taxa? Is the monoallelic expression of variable immune receptors in sea urchins associated with clonal selection? Is there clonal selection (based on clonally stable receptor identity) in NK cells? Has Darwinian immunity been lost completely in any vertebrate? How exactly did the genomic duplication(s) at the origin of vertebrates facilitate the emergence of specific immune tolerance? What drove the exceptional increase in regulatory complexity, in contrast to other genome duplication events? How do species that appear to have no homologues of Foxp3 [some birds (Andersen et al., 2012); possibly sea lamprey] operate dominant immune tolerance? Do they have a divergent form of the gene (Denyer et al., 2016), or an alternative mechanism has taken over its function? Is the LRR‐based somatic receptor diversity of jawless vertebrates the ancestral vertebrate condition, or did both LRR‐based and Ig/RAG‐based somatic diversity evolve after the split of jawless and jawed vertebrates? Is MHC restriction a fortuitous ‘complication’ in jawed vertebrates, or is this function necessary (inevitable) beyond some level of complexity or potency of Darwinian immunity? In the latter case, are jawless fish below this level, or do they have an analogous system to perform this function?