key: cord-0319230-mg71lzil
authors: Alvarez-Breckenridge, Christopher A.; Markson, Samuel C.; Stocking, Jackson H.; Nayyar, Naema; Lastrapes, Matthew; Strickland, Matthew R.; Kim, Albert E.; de Sauvage, Magali; Dahal, Ashish; Larson, Juliana M; Mora, Joana L.; Navia, Andrew W.; Kuter, Benjamin M.; Gill, Corey M.; Bertalan, Mia Solana; Shaw, Brian; Kaplan, Alexander; Subramanian, Megha; Jain, Aarushi; Kumar, Swaminathan; Danish, Husain; White, Michael; Shahid, Osmaan; Pauken, Kristen E.; Miller, Brian C.; Frederick, Dennie T.; Herbert, Christine; Shaw, McKenzie; Martinez-Lage, Maria; Frosch, Matthew P.; Wang, Nancy; Gerstner, Elizabeth R.; Nahed, Brian V.; Curry, William T.; Carter, Bob S.; Cahill, Daniel P.; Boland, Genevieve Marie; Izar, Benjamin; Davies, Michael; Sharpe, Arlene; Suvà, Mario L.; Sullivan, Ryan J.; Brastianos, Priscilla K.; Carter, Scott L.
title: Microenvironmental correlates of immune checkpoint inhibitor response in human melanoma brain metastases revealed by T cell receptor and single-cell RNA sequencing
date: 2021-08-27
journal: bioRxiv
DOI: 10.1101/2021.08.25.456956
sha: 450f738bafd0f020aef741ce125ebb868c8c4b65
doc_id: 319230
cord_uid: mg71lzil

Melanoma-derived brain metastases (MBM) represent an unmet clinical need due to central nervous system (CNS) progression as a frequent, end-stage site of disease. Immune checkpoint inhibition (ICI) represents a clinical opportunity against MBM; however, the MBM tumor microenvironment (TME) has not been fully elucidated in the context of ICI. To dissect unique MBM-TME elements and correlates of MBM-ICI response, we collected 32 fresh MBM and performed single cell RNA sequencing of the MBM-TME and T cell receptor clonotyping on T cells from MBM and matched blood and extracranial lesions. We observed myeloid phenotypic heterogeneity, most notably multiple distinct neutrophil states including an IL-8 expressing population that correlated with malignant cell epithelial-to-mesenchymal transition. Additionally, we observe significant relationships between intracranial T cell phenotypes and the distribution of T cell clonotypes intracranially and peripherally. We found that the phenotype, clonotype, and overall number of MBM-infiltrating T cells were associated with response to ICI, suggesting that ICI-responsive MBMs interact with peripheral blood in a manner similar to extracranial lesions. These data demonstrate unique features of the MBM-TME, which may represent potential targets to improve clinical outcomes for patients with MBM.

Brain metastases, arising most commonly from lung, breast and melanoma 1,2 , represent the most common type of intracranial tumor, occurring in 20-40% of patients diagnosed with cancer [2] [3] [4] [5] [6] [7] [8] . Intracranial progression frequently occurs even within the context of extracranial response to therapy and nearly half of patients with symptomatic brain metastases succumb to their disease 9 . Metastatic tumors disseminating into the central nervous system (CNS) are associated with a poor prognosis and have traditionally been relegated to surgical resection and radiotherapy. Within this standard of care, intracranial metastases have been associated with significant morbidity and median survival ranges from 3 to 27 months 10 . In the setting of their increasing prevalence, limited treatment options, and historical exclusion from clinical trials, brain metastases represent an unmet clinical need.

Immune checkpoint inhibitors (ICI) have revolutionized the treatment of cancer with approval in 19 cancer types and two tissue-agnostic indications 11 . With the growing clinical success of immune checkpoint modulation, attention has shifted towards the potential activity of ICI in treating brain metastases. Studies targeting CTLA-4 and PD-1 have demonstrated promising ICI mediated intracranial response rates for metastatic melanoma, with combination therapy achieving similar rates of response observed extracranially [12] [13] [14] [15] . Beyond melanoma brain metastases, intracranial efficacy has similarly been demonstrated with pembrolizumab in patients with renal cell carcinoma 16 and non-small-cell lung cancer (NSCLC) 13 .

Despite these promising clinical results, approximately half of all melanoma patients progress on ICI secondary to either innate or acquired resistance. While extracranial disease progression occurs through both tumor-intrinsic 17 and extrinsic 18 mechanisms, a paucity of data exists investigating determinants of intracranial response and/or resistance to ICI. The CNS features an immune specialized microenvironment [19] [20] [21] [22] and the interaction of these CNS-unique elements with ICI is not fully understood. Clinical evidence suggests that the presence of extracranial lesions influences immune based therapies for brain metastases 8, 15, 23 . Potential mechanisms for this process include T cell priming at extracranial sites of disease and T cell trafficking into the brain where shared intra-and extracranial tumor antigens are targeted.

Several features of response and resistance to ICI in the extracranial setting have been investigated. At a genomic level, the overall tumor mutational burden has been associated with clinical response to anti-PD1 therapy 24, 25 in addition to predicted neoantigen load at pre-treatment timepoints 26 . Additionally, many studies have investigated the role of T cell infiltration prior to ICI 27, 28 , the spatial distribution of CD8+ T cells along tumor margins 27 , and the extent of PD-L1 expression within the tumor 29, 30 .

However, the results of these findings do not provide a definitive link to ICI response.

With the advent of single cell sequencing techniques to dissect immune cell phenotypes, further insights have been made into phenotypic T cell states that are enriched within responding tumors 31 . In order to explore these features within the intracranial context, we utilized single cell RNA sequencing (scRNA-seq) from a cohort of immunotherapy naive and post-ICI treated melanoma-derived brain metastases (MBM) paired with T cell receptor sequencing (TCR-seq) from patient-matched blood and extracranial lesions to describe the diversity of immune and malignant cellular elements within the TME; to identify associations between those elements; and to suggest TME-related biomarkers of intracranial response to ICI.

In order to dissect the tumor microenvironment of MBM, we performed scRNA-seq on 32 sequentially collected MBM from 27 unique patients (Fig. 1a,b ; Supplementary Table   1 ). Two patients underwent simultaneous intracranial resections and longitudinal samples were obtained from 2 patients (one contributing 2 samples, the other 3). Of the resected tumors, 23 had been previously treated with ICI, while 9 were immunotherapy naive. Among the ICI-naive individuals, one went on to receive ICI and was a responder both intra-and extracranially. Although the 23 post-ICI patients ultimately experienced intracranial progression resulting in the need for craniotomy, they were defined as having either complete non-response (8) or partial response (15) Our analysis identified a total of 27 non-immune clusters (see Methods). As has previously been reported, malignant cells' RNA expression was highly patient and sample specific (Fig. 1c) [34] [35] [36] . In contrast, the remaining clusters demonstrated cell-type specific gene expression (Fig. 1c,d) 

Utilizing our CD45+ sorted cells, we identified a total of 1,266 neutrophil and 653 monocyte-derived cells (including macrophages and microglia) after QC. Highly expressed genes associated with the macrophage/microglia cluster included CD14, CD163, and CSF1R, while the gene expression profile of neutrophils featured S100A8, S100A9, and NCF2 (Fig 1d) . Each myeloid population included a cross section of patients from the cohort (Fig. S4a,b) .

Within monocyte-derived cells, we observe a cell cluster characterized by elevated relative expression of S100A8, S100A9, S100A12, MNDA and a separate cluster of microglia defined by expression of TREM2, APOE, C1QA, C1QC (Fig. 2a,b) . Canonical microglia markers TMEM19 and P2RY12 were additionally upregulated in this cluster ( Fig. S5a-d) . This latter population of cells has been described elsewhere as reactive microglia, with an elevated presence in the brain parenchyma of individuals with conditions such as multiple sclerosis and Alzheimer's disease 37, 38 .

A total of 1,266 neutrophils were identified in 23 of 27 patients' cells and were associated with one of four subclusters. One of these subclusters (representing 63 cells from 10 patients) clustered most distantly from the other three neutrophil groups (Fig.   S6a ). Separate clustering of these cells revealed four clusters (Fig. S6b) , with gene expression characteristic of eosinophils (CCR3), primary/azurophilic (MPO, AZU1, CTSG, DEFA1B, DEFA3, DEFA4), secondary/specific (LTF, CAMP), and tertiary degranulation (MMP9), respectively (Fig. S6c,d) . These eosinophils/degranulating neutrophils were removed prior to re-clustering the remaining neutrophils. Among the three remaining neutrophil clusters, one included a "calprotectin-high" group characterized by high expression of S100A8 and S100A9, jointly coding for the calprotectin heterodimer, and representing 52% of all identified neutrophils (Fig. 2c,d) . A second cluster was termed the "IFN-responsive" neutrophils, which were characterized by high expression of genes associated with interferon gamma response, including IFI6, IFIT2, ISG15, and TAP1 (Fig. 2c,d) . The final cluster was termed the "IL-8-high" neutrophils and was characterized by elevated expression of IL-8/CXCL8 and VEGFA ( Fig. 2c,d) . The calprotectin-high, IFN-responsive, and IL-8-high neutrophils were observed in 23, 15, and 15 patients, respectively (Fig. S4b ).

This neutrophil polarization is partially concordant with what has been seen in murine models and human in vitro studies 39, 40 . Previous work on heterogeneity of tumorassociated neutrophils (TANs) has subdivided neutrophils along an "N1" and "N2" polarization axis, corresponding to anti-tumor and pro-tumor phenotypes, respectively 39 . N1 neutrophils were characterized by higher levels of ICAM-1/CD54, FasR/CD95, and TNF-α, whereas N2 neutrophils expressed higher levels of CCL5, CCL2, VEGFA, arginase, and IL-8/CXCL8 39, 41 . IL-8-high neutrophils derived from our MBM cohort corresponded most closely to N2 neutrophils; however, they showed reduced expression of N2 markers MMP9, and ARG1, as well as elevated expression of the N1 markers CCL3, and ICAM1 (Fig. 2e) . Therefore, while our data recapitulates the heterogeneous nature of TANs, further studies will be necessary to delineate the precise markers of neutrophil states and their associated plasticity in the human tumoral context. Across multiple histologies, neutrophil gene signatures are associated with poor prognosis 42 , and the neutrophil-to-lymphocyte ratio (NLR) has been considered as a biomarker of poor survival outcomes in multiple therapies, including ICI [43] [44] [45] . This is consistent with our data, wherein the neutrophil fraction (of CD45+ cells) is significantly higher in post-treatment non-responders when compared to post-treatment partial responders (Fig. 2f) ; this is further demonstrated via histology from a non-responding patient (MEL022) (Fig. 2g) . However, calprotectin high, IL-8 high, and IFN-responsive neutrophils did not display a significantly larger representation in post-ICI nonresponders compared to partial responders (Fig. S7 ). CXCR2 and IL-8/CXCL8 have both been linked elsewhere to ICI resistance as well as to the neutrophil N2 phenotype [46] [47] [48] ; however, as CXCR2 (one of the two receptors for IL-8) was more highly expressed in calprotectin high neutrophils than in IL-8-high neutrophils (Fig. 2e) , it may be that the protumor roles of neutrophils are not isolated to a single phenotype, but rather to a spectrum whose protumor effects are context-dependent.

Multiple mechanisms for the negative prognostic association of neutrophils have been proposed, including an association with tumor necrosis (itself linked to poor prognosis in multiple histologies) [49] [50] [51] [52] [53] [54] [55] [56] [57] [58] [59] , as well as the induction of angiogenesis, epithelialmesenchymal transition (EMT), and neutrophil extracellular traps (NETosis) 60 .

Accordingly, we see a significant association (p=.0063) between samples' neutrophil fraction (of CD45+ cells) and evidence of tumor necrosis on pathology reports (Fig 2h, Supplementary Table 1) . Additionally, the presence of necrosis is particularly associated with the IL-8 high phenotype, although this association does not reach significance (Fig. 2h) . Our data support the hypothesis that angiogenesis in the TME is promoted by IL-8 producing neutrophils, based on the significantly higher expression of genes in the hallmark angiogenesis gene module (see Methods) compared to the two other primary neutrophil phenotypes (Fig. 2i) . We additionally see a significant correlation between patients' IL-8 neutrophil fraction and the EMT module score of patients' malignant cells (see Methods) (Fig. 2j) . Lastly, genes associated with NETosis 61 were most highly expressed in calprotectin-high neutrophils but were also observed, albeit in decreasing amounts, amongst the IFN-responsive, and IL-8-high neutrophils, respectively (Fig. 2k) . These collective findings suggest that neutrophils' protumor function is multifaceted involving a spectrum of phenotypic states, and that a given neutrophil may play certain, though not all, protumor roles at a given time.

We assessed the diversity of intracranial T cell phenotypes in the MBM-TME, and found that while our data recapitulated T cell phenotypic diversity observed extracranially, overall T cell infiltration did not reach statistical significance as a prognostic biomarker for ICI response in our cohort (Fig. 1e) . In extracranial melanoma, both T cell infiltration and specific T cell phenotypes have been implicated as prognostic factors for response to ICI 36, [62] [63] [64] [65] . We therefore performed a focused analysis of only the flow cytometryselected CD45+CD3+ cells within our scRNA-seq cohort (see Methods). Following a more stringent quality control screening via a cell complexity threshold of 2,000, a total of 2,974 cells were ultimately included for further analysis. Following unsupervised analysis, post-QC cells clustered into the following seven phenotypic populations, which we refer to as IFN-responsive, cycling, memory/naive, effector, exhausted, CD4/FOXP3, and NK/NKT cells (Fig. 3a,b) . Cells within the "IFN-responsive" cluster were predominantly CD8+ T cells with upregulation of interferon response pathways (IFIT1, IFIT2, IFIT3, ISG15). The "cycling" population consisted of primarily CD8+ T cells with expression of canonical cycling genes (MKI67, ZWINT, TOP2A). Cells within the "memory" cluster included both CD8+ T and CD4+ T cells enriched for IL7R, and CCR7. "Effector" cells were marked by cytotoxic genes including GNLY, GZMH, PRF1, and KLRG1, and lower levels of exhaustion-associated genes than the "exhausted" cluster. "Exhausted" cells, meanwhile, expressed high levels HAVCR2, PDCD1, CTLA4 and TIGIT.

The difference in phenotypes between the predominantly CD8+ "effector" and "exhausted" clusters are concordant with those observed in both murine models of melanoma ICI response 65 , as well as those observed in clinical extracranial melanoma samples 36 (Fig. 3c) , with the effector cluster corresponding to a progenitor exhausted phenotype/CD8_G, and the exhausted cluster corresponding to a terminally exhausted phenotype/CD8_B. However, while prior reports demonstrated that the ratio of "CD8_G" to "CD8_B" cells was associated with ICI response both before and following treatment 36 , this association was not significant in our data; rather, the fractions of exhausted and cycling cells was associated (p=0.0699 for both) with partial vs non-response among the post-therapy samples (Fig. 3d) . Among the post-therapy samples, only the CD4/FOXP3 fraction was significantly associated with partial over non-response (p=0.00279) (Fig.   3d ). Neither intracranial nor blood T cell fraction (as measured by the immunoSEQ assay, see Methods) was associated with partial vs non-response post-therapy. We did, however, observe that the "effector" T cell fraction was highest in the pre-treatment responder sample in our cohort (MEL-027) (Fig. 3d) , which is consistent with previous reports in the extracranial context 36, 65 .

Consistent with previous work 27, [66] [67] [68] [69] [70] we observed that T cell clonal expansion was associated with response to ICI. From our cohort's full-length transcript scRNA of freshly-resected metastases, we performed single cell T cell receptor (TCR) clonotyping via TraCeR 71 (see Methods) to quantify T cell clonal expansion within the brain tumor microenvironment in addition to the genomic DNA based immunoSEQ assay (see 72 , which was computed using both TraCeR-and immunoSEQ-derived TCR repertoires (see Methods). Using TraCeR results from within MBM, we found that patients who had demonstrated partial response to ICI with intracranial progression had a non-significant increase in their degree of clonal expansion compared to patients who were entirely non-responsive to ICI (p=0.241 via TraCeR, p=0.868 via immunoSEQ) ( Fig. 3f) . In contrast, sampling T cells from the blood demonstrated that clonal expansion tracked with partial response to ICI compared to the blood of patients who were non-responsive to ICI (P=0.014 via immunoSEQ) (Fig. 3f) . These findings are consistent with previous reports suggesting that T cells from the peripheral circulation may be sampled as a biomarker of systemic response to ICI 73 .

Given the association between clonal expansion and response to ICI, we assessed phenotypic correlates with clonal expansion. Of the 2,110 successfully clonotyped cells, 684 cells shared an α or β chain with one or more other T cells from the same patient, and were therefore regarded as being "detectably expanded" (448 of the 1,371 post-QC CD3+ T cells were detectably expanded). The remaining clonotyped cells we refer to as "non-detectably expanded" (Fig. 4a) . Differential expression across samples of cells which were "detectably expanded" revealed enrichment of exhaustion-and effectorassociated genes, including HAVCR2, TIGIT, PRF1, NKG7, and GZMB, in the detectably-expanded cells, whereas those cells which were clonotyped but not detectably expanded expressed higher levels of memory/naive-associated genes, including CCR7 and IL7R (Fig. 4b) . The majority of cells from two of the seven T cell clusters--"cycling" and "exhausted"--were detectably expanded (p=8.70e-7, 4.51e-59 respectively via Fisher's exact test, see Methods) ( Fig. 4c ). By contrast, "memory" cells were most significantly enriched in the non-expanded set (p=7.83e-36 via Fisher's exact test, φ coefficient=-0.3268, see Methods) ( Fig. 4c) . Thus, while clonal expansion is closely linked to response to ICI, we observed that clonally expanded cells at the site of the lesion were more likely to be exhausted.

In order to investigate the relationship between clonally expanded T cells within the blood and tumor microenvironment, we performed TCR clonotyping of the TCR-β chain via the immunoSEQ assay of genomic DNA (gDNA) from peripheral blood (see Methods). T cell clones from the intracranial TME were subsequently matched (based on shared CDR3) against clones found in the blood. Intracranial T cell CDR3's were therefore referred to as "blood-associated" T cell clones when they had a TraCeRdetected TCR-β CDR3 from the brain which were detected in the blood via the immunoSEQ assay. Those intracranial T cells with a TraCeR-detected TCR-β CDR3 which was not detected in the blood via immunoSEQ were denoted "bloodunassociated". We observed a significant divergence in the distribution of "blood-associated" and "blood-unassociated T cells in our CD45+CD3+ UMAP (Fig. 4d ).

Differential expression of "blood-associated" and "blood-unassociated" T cells revealed upregulation of progenitor/effector-like genes (e.g. TCF7, GNLY, and KLRG1) in the former group, and exhaustion-related genes (CTLA4, TIGIT, HAVCR2) in the latter (Fig.   4e ). Accordingly, we discovered a significant association between the "effector" population and blood-association (p=2.34e-12, Fisher's exact test, T cell clones with private CDR3s are more likely to be exhausted and are associated with partial response to ICI In order to explore the question of TCR tumor specificity in our cohort of intracranial melanoma metastases, we utilized a dataset of more than 500,000,000 clonotyped T cells across 1,486 samples from individuals with COVID-19 as a reference of presumably non-tumor specific clones (see Methods) and compared against our 2,110 clonotyped melanoma associated T cells 77 . A total of 68% of clonotyped post-QC CD45+CD3+ T cells from our melanoma brain metastasis cohort had CDR3s which were detected in the COVID-19 dataset suggesting that the majority of detectably expanded T cells within our cohort were not tumor specific. We subsequently categorized the melanoma T cell CDR3s as public, indicating that they were present in both our melanoma cohort and the COVID-19 data, or private, indicating that the T cell clones were solely identified in our cohort of MBM (Fig. 5a) ; as observed elsewhere [78] [79] [80] , these private CDR3s were significantly longer (two-sided Mann-Whitney U p-value=3.26e-11) than the public CDR3s (Fig. 5b) . Upon further investigation of the phenotypic distribution amongst private and public clones, exhausted T cell clones were significantly associated with private CDR3 (p=0.00198) while an effector phenotype was primarily associated with public clones (p=0.0324) (Fig. 5c ). These findings suggest that public T cell clones lack tumor specificity, and maintain an effector status without evidence of exhaustion. In contrast, private T cell clones appear tumor specific and may be driven to an exhausted phenotype by persistent antigenic stimulation.

In order to examine the clinical relevance of this finding, we stratified patients by their clinical response to ICI and quantified the fraction of private T cell clones per patient.

Within our cohort, we observed an association between a patient's percentage of private CDR3 clones and overall response (p=0.054, two-sided Mann-Whitney U-test) This finding was corroborated with T cell fraction quantification from brain tumor and blood using the immunoSEQ platform ( Fig. 5g , S9, see Methods). Across both sites, T cell fraction was above the median for the cohort (Fig. 5g, S9) . Similarly, amongst our MBM cohort, MEL027 demonstrated the greatest degree of clonal expansion within the brain and blood while also harboring the greatest proportion of effector T cells within the brain (Fig. 5g, S9) . Lastly, we explored the extent of T cell clonal overlap with clones from the COVID-19 dataset and observed that the fraction of private CDR3 clones for MEL027 in the blood was highest within the cohort and similarly elevated within the brain metastasis (Fig. 5g, S9 ). These findings are consistent with previously published reports that pre-treatment T cell infiltration is predictive of response to ICI while also integrating T cell phenotype, clonality, and tumor exclusivity.

With the documented success of immune checkpoint blockade for extracranial disease and emerging data demonstrating intracranial response rates across multiple histologies, increasing attention has been placed on the tumor intrinsic and microenvironmental factors that portend a favorable response to ICI 18 . Here, we utilize scRNA-seq from MBMs combined with TCR-seq from intracranial and extracranial samples to both characterize biomarkers of response to ICI, as well as to elucidate cross-compartmental correlates of malignant and immune phenotypes. We observe relationships between myeloid and malignant phenotypes, as well as relationships between T cells' phenotype,TCR distribution, and diversity. These findings have implications for both the therapy and monitoring of intracranial disease.

Melanoma metastases have been significantly associated with increased levels of tumor infiltrating lymphocytes. Beyond T cell infiltration, however, increasing evidence suggests that the spatial distribution within the tumor, T cell phenotypic plasticity, and T cell clonality all impact anti-tumor immune mediated responses 31, 69, 70, 81 . While single cell RNA sequencing has been used to characterize CD8+ T cell states in extracranial melanoma metastases, our cohort of MBM provides a unique opportunity to investigate the degree of phenotypic overlap within the brain metastatic TME. Prior studies identified naïve/memory, cytotoxic, and exhausted/dysfunctional T cells 33, 34, 36 . We observe T cell transcriptional signatures that are concordant with those seen in extracranial melanoma [34] [35] [36] . While the fraction of effector-like T cells was elevated in the one pre-ICI responder in our cohort, neither this nor other CD8+ T cell phenotypes were significantly associated with partial vs non-response in the post-treatment samples.

T cell clonality is a proxy for antigen-driven T cell expansion and has been associated with clinical benefit across multiple tumor types and therapies 27, 67, 68 . Increasing efforts have attempted to understand the dynamics of T cell clonotype modulation, infiltration within the tumor microenvironment, and the relationship between T cell clones in the blood and tumor 69, 73, [82] [83] [84] . Within our cohort, clonal expansion in post-treatment blood, but not in intracranial lesions, was significantly associated with partial-response over non-response. This may be explained by the strong association between T cell clone size and exhaustion observed intracranially, wherein intracranial clonally expanded T cells have lost effector capacity due to persistent antigen stimulation, and therefore have reduced prognostic significance.

Our results provide critical context within the setting of CNS metastatic disease. Rather than representing a unique, immune isolated environment, our findings further support the concept that the CNS tumor microenvironment is immune-specialized rather than immune-privileged. In contrast, we observed unique phenotypic differences between detectably expanded and non-expanded T cell clones within the intracranial tumors.

While exhausted and cycling T cell clones were predominantly expanded, CD4/FOXP3 T cells, memory CD8 T cells, and IFN-responsive CD8 T cells were associated with non-clonally expanded T cells. These results are concordant with findings in both murine and clinical models of extracranial melanoma 74, 75 , where there is an association between a T cell clone's phenotype and its shared presence in both the lesion and in the blood. This model of cross-talk between blood and extracranial tumors is similarly recapitulated within our cohort of melanoma brain metastases. With the finding of phenotypic divergence between blood-overlapping and tumor-exclusive T cell clones in MBM, this finding suggests a model where the CNS maintains features of the periphery with active cross-talk between the peripheral and intratumoral immune compartments with the blood acting as a reservoir of fresh, pre-antigen-stimulated T cells for MBMs, and that responding MBMs exist in an environment that is not fully immune-privileged 85 .

Future investigation is needed to explore the process of intracranial T cell trafficking, tumor antigen exposure, and clonal replacement in brain metastases and cervical lymph nodes compared to extracranial sites of disease, the involvement of blood-brain-barrier disruption in this process, and how this process is further modulated by exposure to immune checkpoint blockade 21, [86] [87] [88] .

In addition to identifying individual T cell states, deciphering the nature of T cell tumor reactivity is an increasingly pressing challenge. The MBM-TME is populated both by T cell clones that are reactive to tumor antigens, as well as by bystander clones that are non-cancer-specific. Amongst our cohort of MBM, we observed a phenotypic difference between intracranial T cells with CDR3 regions that were patient-specific, and therefore assumed to be more likely specific to that patient's tumor, in contrast to T cell clones with CDR3s found in public clonotype databases. Consistent with this finding, the greatest positive and negative associations with CDR3 privacy were observed in exhausted and effector cells respectively. The degree of CDR3 privacy was also associated with clinical benefit to ICI within the MBM context. Outside of MBM, increasing efforts have been made to understand features that identify tumor reactivity and delineate which phenotypic T cell states are most associated with response to ICI. Klemm et al. 94 and Friebel et al. 93 , we observed multiple monocyte phenotypes within MBM, including TREM2/APOE expressing reactive microglia 37 . TREM2 expression has been inversely correlated with overall survival across multiple histologies 95, 96 . Utilizing melanoma scRNA-seq datasets, Xiong et al. 97 has linked TREM2-expressing macrophages with complement activation, tumor-associated macrophage polarization, and ICI resistance 97 --a finding similarly observed by Molgora et al. within the context of in vivo sarcoma models 95 . While we observed TREM2+ reactive microglia in too few samples to evaluate whether they play a significant role in the context of MBM response to ICI (Fig. S4a) , further work is warranted to explore (1) the role of these cells in modulating the immune microenvironment within the context of melanoma brain metastases, (2) the relative contribution of TREM2-expressing monocyte in the intracranial and extracranial compartment, and (3) the potential of targeting this population to enhance immune checkpoint blockade for CNS metastatic disease.

Our analysis of the myeloid compartment provided unique insights into neutrophil heterogeneity within the MBM-TME, which recommend further studies with larger patient cohorts and myeloid cell numbers. We identified a significant association between an IL-8 expressing neutrophil subset and EMT transition in neutrophils. While this neutrophil subset has been previously identified, and may correspond to the N2 subset described in other contexts, they have not, to our knowledge, been previously observed in MBMs. Serum IL-8 levels have previously been reported to be associated with worse prognosis and reduced clinical benefit of ICIs 48, 98 . Due to the significantly higher infiltration of neutrophils in intracranial relative to extracranial tumors 99 , it is possible that these IL-8 expressing neutrophils play an even greater role in determining intracranial prognosis and ICI responsiveness than they do extracranially. We do note certain discrepancies between previously described N1 and N2 markers and markers observed in our scRNA-seq data, suggesting that the precise markers of different neutrophil phenotypes--and their association with neutrophil-mediated phenomena including angiogenesis and NETosis--are context-dependent. Further work is needed to dissect this cell population's phenotypic plasticity, link cell state with tumor supportive or suppressive roles, investigate their relative distribution across histologies, and study their differential role within intra-and extracranial metastases.

The field of immune checkpoint blockade has revolutionized the treatment of metastatic melanoma and is increasingly being applied to an array of other histologies with encouraging results. Unfortunately, the CNS is frequently a site of disease progression and ultimate patient mortality. While recent clinical trials exploring ICI for melanoma brain metastases have demonstrated encouraging results, a significant number of patients continue to experience CNS progression. The results of our study provide unique insights into the relationship between features of the tumor microenvironment of the brain and previous findings within the extracranial compartment. Moreover, as many myeloid phenotypes are specific to the CNS or elevated intracranially 99 , they may represent particularly attractive targets for increasing intracranial ICI efficacy. For example, the role of unique myeloid populations including TREM2/APOE expressing monocytes and CXCL8/VEGFA expressing neutrophils appear to be linked to tumoral plasticity and shifts in ICI responsiveness. Further investigation will be needed to explore the presence and longitudinal dynamics of these populations in the cranial and extracranial compartments for metastatic melanoma.

Within the T cell compartment, single cell immune profiling with associated TCR clonotyping provided clues regarding responsiveness in the brain metastases setting.

We find that multiple factors--T cell fraction, phenotype, and clonotype--play roles in determining intracranial MBM response to ICI, and recommend that future studies jointly consider these factors whenever possible. Our cohort also provided the unique opportunity to explore the TME of a treatment-naive individual (MEL-027) who went on to respond to ICI. From a histologic perspective, the tumor demonstrated robust lymphocyte infiltration throughout the tumor suggestive of an immunologically "hot" tumor. Additionally, features of the blood compartment were reflective of a baseline proinflammatory state with elevated T cell fraction, simpson index, and fraction of private T cell clonotypes. These features of the periphery were recapitulated within the brain metastasis with an elevated T cell fraction, abundance of effector T cell clones, robust clonal expansion, and concomitant elevation of private T cell clones within the tumor.

While our study provides insights into features defining the MBM-TME and potential factors that reflect response to ICI, our results have several limitations. Our cohort reflects a relatively small population of patients with both diverse treatment courses and varied responses to therapy across multiple sites of disease. Additionally, we were limited by a lack of matched pre-and post-ICI brain metastasis samples--a challenge inherent with this patient population. While we were able to analyze a single treatment naive patient who responded to ICI, a larger cohort of treatment naive individuals who subsequently are treated with ICI is needed to extend the applicability of those findings.

We were similarly limited by the ability to sample patient matched cranial and extracranial tumors in order to precisely decipher at a single cell resolution the intratumoral features that are unique to the MBM-TME.

Nevertheless, the implications of this unique cohort of patients provide intracranial context within the broader context of immunotherapy for metastatic melanoma. Our collective results emphasize (1) the critical role of T cell mediated response in the setting of ICI for MBM, (2) elucidate the relationship between T cells within the blood and intratumoral compartment, and (3) demonstrate that blood provides insights into ICI response not only for extracranial disease, but also disease within the brain. Moreover, the relationship between T cell clonal expansion and phenotypic states in the blood and brain has the potential of providing critical insight into the intracranial TME, which may be clinically advantageous when acquisition of brain tumor tissue is limited. Lastly, future work using larger cohorts of human specimens and murine models will be needed to fully understand the intracranial features of the tumor microenvironment that attenuate these initially robust intracranial responses.

This study was conducted in accordance with recognized ethical guidelines and patient samples Supplementary   Table 1 . Collection of solid tumor tissue and blood samples was performed to reduce the time between collection and processing. Approximately 20mL of blood is collected at any time point during the patient's surgery with coordination and permission of the anesthesiology team. Blood is collected in two 10mL EDTA tubes that we provide. Immediately after collection, a portion of the blood is used to separate plasma, and another portion of whole blood is saved for eventual gDNA extraction. Solid tumor tissue is collected via the coordination of lab technicians with the participant's surgical team. Lab technicians collect tissue directly from the operating room and bring it to the pathology team to approve a portion of the tissue to be used for research. The tissue is then immediately brought back to the lab to begin the dissociation workflow (described below).

After collection, the specimen was transferred to a sterile petri dish and mechanically were used to identify granularity and ensure to sort only singlets. We then used fluorescence staining to gate conservatively and specifically for live cell populations within the CD45+, CD45-, and/or CD45+ CD3+ cells clusters. The sorted plates were then spun down at 188g for 1 minute, flash frozen at -80°C and stored for future scRNA sequencing. Flow sorting analysis was completed with the FACSDiva (v. 8.0.1) and FlowJo (v. 10).

Single cell RNA sequencing was performed using the Smart-Seq 2 protocol to create an 8 plate gDNA extraction from blood 500uL of whole blood was used to extract gDNA using the Qiagen blood and tissue kit (Qiagen, cat. no. 69506). Blood was processed according to the manufacturer's guidelines no later than two weeks post collection, while being stored at 4ºC, and after extraction gDNA was stored at -20ºC until sequencing.

Between 25-35mg of fresh frozen tissue, stored at -80ºC, was first mechanically dissociated using an RNAse free homogenizer until the tissue was completely dissociated into solution.

gDNA was then extracted using the Qiagen AllPrep DNA/RNAmiRNA Universal kit (Qiagen, cat.

no. 80224) according to the manufacturer's guidelines. Samples were stored at -20ºC until sequencing.

One slide from each case was first stained with hematoxylin and eosin then evaluated by a collaborating pathologist to determine the location of tumor tissue on the paraffin slide. Slides were then scraped to strategically collect the tumor tissue into 1.5 mL eppendorf tubes containing deparaffinization solution. gDNA was then extracted using the Qiagen QIAmp DNA FFPE Tissue kit (Qiagen, cat. no. 56404) according to the manufacturer's guidelines. Samples were stored at -20ºC until sequencing.

The Pico green assay (invitrogen, cat. no. P11496) was used according to the manufacturer's protocol to quantify the concentration of DNA extracted from FFPE, fresh frozen tissue, and blood. 

The Adaptive Biotechnologies immunoSEQ human T-cell receptor beta (hsTCRB) v4b kit was used to identify and quantify the frequency of specific T-cell clones in extracted DNA. DNA that was extracted from FF, FFPE-preserved tissue, and blood samples, as explained earlier, was quantified with the PICO assay to use for TCR sequencing. DNA from FF samples were diluted to 83 ng/ul and run in duplicates, gDNA from blood samples were diluted to 44 ng/ul and also run in duplicates, while all extracted DNA from FFPE samples was divided and run in quadruplicates. A negative control is also included and run in parallel with the samples. After the two PCR amplification steps, given in the manufacturer's protocol, the multiplexed sample was 

TCR α and β chains were reconstructed using TraCeR (https://github.com/Teichlab/tracer) 71 .

The Simpson index was computed without replacement for TraCeR samples according to the Generated fastq files were aligned and corrected for PCR bias using the RSEM program 100 using version 8 of the smartseq2 workflow on Terra (app.terra.bio) provided by Cumulus 101 .

Transcripts were aligned using Bowtie 2 102 to human genome GRCh38, with gene annotation generated using human Ensembl 93 GTF.

All analyses were done using the panopticon package 103 Source code for the panopticon package can be found at https://github.com/scyrusm/panopticon, with additional documentation at https://panopticonsingle-cell.readthedocs.io/en/latest/.

Cells were initially filtered based on a minimum unique gene count of 1000. Clustering was then performed as described above. These clusters were then manually reviewed to assess whether they showed signs of contamination, or of being doublets. Results of this manual review, and justification for the inclusion or removal of cells, are given in Supplementary Table 2 .

Inferred copy number profiles were performed within cells from a single patient taken all FACS gating categories (CD45-, CD45+, CD3+) according to the procedure in Tirosh et al. 34 . These copy number profiles were than projected onto their first principal component (within a group of cells from a single patient). The quantile of cells' loading onto this component was denoted the "malignancy score," with the loading sign-adjusted such that the CD45-cells (per FACS) had a greater such mean score than the grouped CD45+/CD3+ cells. This score was used as a factor when considering cell quality control above (see Supplementary Table 2 ). Code for this procedure is implemented in panopticon.analysis.generate_malignancy_score.

DIfferential expression between sets was computed with the Mann-Whitney U test, as implemented in panopticon.analysis.cluster_differential_expression, between groups using log2(TP100k+1) gene expression values. Dot plots (Fig 2e,k) were computed via the function panopticon.visualization.plot_dotmap.

Module scores were as originally used in Tirosh et al. 34 33 .

These signatures are given in Supplementary Table 3 .

All calculations were performed using python v3. 4c) or blood-associated/non-associated, respectively (for Fig 4f) . Table elements c, d represent the sum of cells belonging to all other phenotypes which are detectably expanded/not-expanded, respectively (for Fig 4c) or bloodassociated/non-associated, respectively (for Fig 4f) . The Fisher's exact test p-value is computed in the usual way, namely where is the binomial coefficient. The same contingency table is used to compute the phi coefficient via the usual formula:

Throughout, kernel density estimate plots were computed via the seaborn.violinplot package using seaborn v0.11.0, with the argument "cut=0, inner='quartile'" all other parameters default.

Putative iNKT cells, MAIT cells were classified according to known TCR-α V/J allele combinations, or TCR-β V alleles that have been associated with these cells, according to Mori et al. (see Table 3 ) 106 .

A large cohort of TCR-β repertoires was obtained from the immuneCODE dataset (https://clients.adaptivebiotech.com/pub/covid-2020) 77 . TCR-β CDR3s detected via TraCeR d were classified as being private or public based on whether they were detected at any level in the immuneCODE dataset.

The genes-by-cells matrix and associated metadata for the current study, including all UMAPs for plots in this manuscript, are available via the Broad single-cell portal:

https://singlecell.broadinstitute.org/single_cell/study/SCP1493/microenvironmental-correlates-ofimmune-checkpoint-inhibitor-response-in-human-melanoma-brain-metastases-revealed-by-tcell-receptor-and-single-cell-rna-sequencing. Raw data, including fastq files from both scRNAseq and whole exome sequencing, are available on dbGAP (accession pending). TCR-seq data generated through the immunoSEQ assay is available through the immuneACCESS portal (link pending).

Panopticon v0.1.1 has been made publicly available (https://github.com/scyrusm/panopticon/). Notebooks used for figure creation available upon reasonable request. Days from Initial Brain Metastasis Diagnosis   MEL033  MEL019  MEL027  MEL005  MEL016  MEL031  MEL018  MEL029  MEL020  MEL011  MEL026  MEL023  MEL008  MEL006  MEL004  MEL022  MEL021  MEL028  MEL015  MEL014  MEL025  MEL001  MEL024  MEL002  MEL017  MEL010 HSD3B7  ANXA2  TUBB  GPNMB  CCL2  VWA5A LGALS1

LGALS3 EMP1 VAT1 PHLDA1 HS3ST2 FCN1 S100A9 S100A8

LGALS2 

Brain metastases

Emerging Immunotherapies in the Treatment of Brain Metastases

Incidence Proportions of Brain Metastases in Patients Diagnosed (1973 to 2001) in the Metropolitan Detroit Cancer Surveillance System

Epidemiology of Brain Metastases

Recent Trends in Epidemiology of Brain Metastases: An Overview

The Evolving Landscape of Brain Metastasis

A Blazing Landscape: Neuroinflammation Shapes Brain Metastasis

Immune Checkpoint Blockade -How Does It Work in Brain Metastases?

Clinical Discussion and Review of the Management of Brain Metastases

Genomic Characterization of Brain Metastases Reveals Branched Evolution and Potential Therapeutic Targets

Cancer Immunotherapy Update: FDA-Approved Checkpoint Inhibitors and Companion Diagnostics

Ipilimumab in patients with melanoma and brain metastases: an openlabel, phase 2 trial

Pembrolizumab for patients with melanoma or non-small-cell lung cancer and untreated brain metastases: early analysis of a non-randomised, open-label, phase 2 trial

Combination nivolumab and ipilimumab or nivolumab alone in melanoma brain metastases: a multicentre randomised phase 2 study

Combined Nivolumab and Ipilimumab in Melanoma Metastatic to the Brain

Safety and efficacy of nivolumab for metastatic renal cell carcinoma: realworld results from an expanded access programme

Tumour-intrinsic resistance to immune checkpoint blockade

Resistance Mechanisms to Immune-Checkpoint Blockade in Cancer: Tumor-Intrinsic and -Extrinsic Factors

Structural and functional features of central nervous system lymphatic vessels

Embracing rejection: Immunologic trends in brain metastasis

Immune Checkpoint Blockade -How Does It Work in Brain Metastases?

The blood-brain barrier and blood-tumour barrier in brain tumours and metastases

Pembrolizumab for management of patients with NSCLC and brain metastases: long-term results and biomarker analysis from a non-randomised, open-label, phase 2 trial

PD-1 Blockade in Tumors with Mismatch-Repair Deficiency

Mutational landscape determines sensitivity to PD-1 blockade in non-small cell lung cancer

Genomic and Transcriptomic Features of Response to Anti-PD-1 Therapy in Metastatic Melanoma

PD-1 blockade induces responses by inhibiting adaptive immune resistance

Analysis of Immune Signatures in Longitudinal Tumor Samples Yields Insight into Biomarkers of Response and Mechanisms of Resistance to Immune Checkpoint Blockade

Safety, Activity, and Immune Correlates of Anti-PD-1 Antibody in Cancer

Pembrolizumab for the Treatment of Non-Small-Cell Lung Cancer

CD8+ T cell states in human cancer: insights from single-cell analysis

Full-length RNA-seq from single cells using Smart-seq2

Dysfunctional CD8 T Cells Form a Proliferative, Dynamically Regulated Compartment within Human Melanoma

Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq

A Cancer Cell Program Promotes T Cell Exclusion and Resistance to Checkpoint Blockade

Defining T Cell States Associated with Response to Checkpoint Immunotherapy in Melanoma

TREM2 Lipid Sensing Sustains the Microglial Response in an Alzheimer's Disease Model

Single-Cell RNA Sequencing of Microglia throughout the Mouse Lifespan and in the Injured Brain Reveals Complex Cell-State Changes

Polarization of Tumor-Associated Neutrophil (TAN) Phenotype by TGF-β: "N1" versus "N2" TAN

An Attempt to Polarize Human Neutrophils Toward N1 and N2 Phenotypes in vitro

Tumor-associated neutrophils: friend or foe?

The prognostic landscape of genes and infiltrating immune cells across human cancers

Exploring the prognostic value of the neutrophilto-lymphocyte ratio in cancer

Neutrophil content predicts lymphocyte depletion and anti-PD1 treatment failure in NSCLC

Pretreatment neutrophil-to-lymphocyte ratio and mutational burden as biomarkers of tumor response to immune checkpoint inhibitors

Disruption of CXCR2-Mediated MDSC Tumor Trafficking Enhances Anti-PD1 Efficacy

Simultaneous inhibition of CXCR1/2, TGF-β, and PD-L1 remodels the tumor and its microenvironment to drive antitumor immunity

Elevated serum interleukin-8 is associated with enhanced intratumor neutrophils and reduced clinical benefit of immune-checkpoint inhibitors

Neutrophil-induced ferroptosis promotes tumor necrosis in glioblastoma progression

Pathologic findings from the national surgical adjuvant breast project protocol B-06 10-year pathologic and clinical prognostic discriminants

Tumour necrosis is an independent prognostic marker in non-small cell lung cancer: correlation with biological variables

Tumor Necrosis Correlates With Angiogenesis and Is a Predictor of Poor Prognosis in Malignant Mesothelioma*

Comparisons of Outcome and Prognostic Features Among Histologic Subtypes of Renal Cell Carcinoma

Prognostic factors in malignant gastrointestinal stromal tumors

Histopathological predictive factors in Ewing's sarcoma of bone and clinicopathological correlations

Tumor necrosis is an important hallmark of aggressive endometrial cancer and associates with hypoxia, angiogenesis and inflammation responses

Radiological assessment of necrosis in glioblastoma: variability and prognostic value

Prognostic significance of preoperative MRI scans in glioblastoma multiforme

Necrosis as a prognostic factor in glioblastoma multiforme

IL8, Neutrophils, and NETs in a Collusion against Cancer Immunity and Immunotherapy

Neutrophil extracellular traps in immunity and disease

T cell exclusion, immune privilege, and the tumor microenvironment

Density of tumor-infiltrating lymphocytes correlates with extent of brain edema and overall survival time in patients with brain metastases

Tumor infiltrating lymphocytes and PD-L1 expression in brain metastases of small cell lung cancer (SCLC)

Subsets of exhausted CD8 + T cells differentially mediate tumor control and respond to checkpoint blockade

Clonal expansion of antitumor T cells in breast cancer correlates with response to neoadjuvant chemotherapy

Clinical significance of T cell clonality and expression levels of immunerelated genes in endometrial cancer

Integrated molecular analysis of tumor biopsies on sequential CTLA-4 and PD-1 blockade reveals markers of response and resistance

Clonal replacement of tumor-specific T cells following PD-1 blockade

Immune-awakening revealed by peripheral T cell dynamics after one cycle of immunotherapy

T cell fate and clonality inference from single-cell transcriptomes

Homeostasis of T cell diversity

Peripheral T cell expansion predicts tumour infiltration and clinical response

Circulating clonally expanded T cells reflect functions of tumor-infiltrating T cells

Single-cell analyses identify circulating anti-tumor CD8 T cells and markers for their enrichment

Measurement of 'Overlap' in Comparative Ecological Studies

A large-scale database of T-cell receptor beta (TCRβ) sequences and binding associations from natural and synthetic exposure to SARS-CoV-2. Res. Sq. rs.3.rs-51964

Sharing of T cell receptors in antigen-specific responses is driven by convergent recombination

The molecular basis for public Tcell responses?

Overlap and Effective Size of the Human CD8+ T Cell Receptor Repertoire

TCR Repertoire Intratumor Heterogeneity in Localized Lung Adenocarcinomas: An Association with Predicted Neoantigen Heterogeneity and Postsurgical Recurrence

Improved Survival with T Cell Clonotype Stability After Anti-CTLA-4 Treatment in Cancer Patients

CTLA4 Blockade Broadens the Peripheral T-Cell Receptor Repertoire

Select sequencing of clonally expanded CD8+ T cells reveals limits to clonal expansion

Revisiting the Mechanisms of CNS Immune Privilege

Experimental manipulations of afferent immune responses influence efferent immune responses to brain tumors

Intranasally Induced Immunological Tolerance Is Determined by Characteristics of the Draining Lymph Nodes: Studies with OVA and Human Cartilage gp-39

Role of the cervical lymphatics in the Th2-type hierarchy of CNS immune regulation1

Co-expression of CD39 and CD103 identifies tumor-reactive CD8 T cells in human solid tumors

Bystander CD8 + T cells are abundant and phenotypically distinct in human tumour infiltrates

Myeloid-derived suppressor cells coming of age

Mapping microglia states in the human brain through the integration of high-dimensional techniques

Single-Cell Mapping of Human Brain Cancer Reveals Tumor-Specific Instruction of Tissue-Invading Leukocytes

Interrogation of the Microenvironmental Landscape in Brain Tumors Reveals Disease-Specific Alterations of Immune Cells

TREM2 Modulation Remodels the Tumor Myeloid Landscape Enhancing Anti-PD-1 Immunotherapy

Triggering Receptor Expressed on Myeloid Cells-2 (TREM2) Interacts With Colony-Stimulating Factor 1 Receptor (CSF1R) but Is Not Necessary for CSF1/CSF1R-Mediated Microglial Survival

A gene expression signature of TREM2hi macrophages and γ δ T cells predicts immunotherapy response

High systemic and tumor-associated IL-8 correlates with reduced clinical benefit of PD-L1 blockade

Molecular Profiling Reveals Unique Immune and Metabolic Features of Melanoma Brain Metastases

RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome

Cumulus provides cloud-based data analysis for large-scale single-cell and single-nucleus RNA-seq

Fast gapped-read alignment with Bowtie 2

Genomic and transcriptomic correlates of immunotherapy response within the tumor microenvironment of leptomeningeal metastases, scyrusm/panopticon: v0.1. (Zenodo, 2021)

Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

Molecular signatures database (MSigDB) 3.0

The Immunology of CD1-and MR1-Restricted T Cells

PKB and SLC were supported by the NIH (5R01CA227156-02, 1R21CA220253-0A01 and