Highlights
•A prospective trial reveals molecular actions of anti-PD-1 therapy
•Anti-PD-1 therapy induces changes in the mutational burden of tumors
•Distinct changes in gene expression programs associate with clinical response
•Shifts in the TCR repertoire occur following immune checkpoint blockade
Summary
The mechanisms by which immune checkpoint blockade modulates tumor evolution during therapy are unclear. We assessed genomic changes in tumors from 68 patients with advanced melanoma, who progressed on ipilimumab or were ipilimumab-naive, before and after nivolumab initiation (CA209-038 study). Tumors were analyzed by whole-exome, transcriptome, and/or T cell receptor (TCR) sequencing. In responding patients, mutation and neoantigen load were reduced from baseline, and analysis of intratumoral heterogeneity during therapy demonstrated differential clonal evolution within tumors and putative selection against neoantigenic mutations on-therapy. Transcriptome analyses before and during nivolumab therapy revealed increases in distinct immune cell subsets, activation of specific transcriptional networks, and upregulation of immune checkpoint genes that were more pronounced in patients with response. Temporal changes in intratumoral TCR repertoire revealed expansion of T cell clones in the setting of neoantigen loss. Comprehensive genomic profiling data in this study provide insight into nivolumab’s mechanism of action.
Introduction
Immune checkpoint inhibitors have demonstrated improved overall survival (OS) and progression-free survival (PFS) in the treatment of many different tumor types (Brahmer et al., 2015, Ferris et al., 2016, Hodi et al., 2016, Motzer et al., 2015, Robert et al., 2015). The underlying genomic features of a tumor can contribute to its response to checkpoint blockade, and increased tumor mutation load associates with survival benefits from both anti-CTLA-4 and anti-PD-1 therapy in multiple malignancies (Hugo et al., 2016, Le et al., 2015, Rizvi et al., 2015, Rosenberg et al., 2016, Snyder et al., 2014, Van Allen et al., 2015). High tumor mutation load may increase the probability of generating immunogenic neoantigens, which facilitate recognition of a tumor as foreign (Riaz et al., 2016a, Schumacher and Schreiber, 2015). Thus, tumors with a high number of clonal neoantigens may be more likely to elicit effective immune responses (McGranahan et al., 2016).
Features of the tumor microenvironment (TME) also associate with response to checkpoint inhibitor therapy. Expression of PD-L1 in the TME associates with clinical response to anti-PD-1/PD-L1 therapies in multiple tumor types (Herbst et al., 2014, Topalian et al., 2012). Baseline levels of tumor-infiltrating CD8+ T cells correlate with the likelihood of response and may increase during therapy in responding, but not progressing, tumors (Topalian et al., 2016, Tumeh et al., 2014). Further, the location of CD8+ T cells at the invasive margin of tumors may indicate an effective immune response (Chen et al., 2016, Spranger et al., 2015, Tumeh et al., 2014). The TME may limit extravasation of effector T cells into the tumor, diminish T cell expansion, or reduce the viability of tumor-infiltrating lymphocytes (TILs) (Joyce and Fearon, 2015).
How checkpoint inhibitor-mediated immune activation modulates the mutational landscape of the tumor and the TME remains poorly understood. To characterize genomic changes, we performed comprehensive genomic analyses on melanoma samples pre- and post-nivolumab (Nivo; anti-PD-1 agent) therapy.
Results
Genomic Characteristics of Tumors before Nivo Treatment
Pre-therapy biopsies from 68 patients were assessed by whole-exome sequencing (WES) at 150× (mean depth: 168; range: 121–237) (Table S1). Thirty-five patients had previously progressed on ipilimumab (Ipi) therapy (Ipi-P); 33 patients were Ipi-naive (Ipi-N) (Table S2). In the patients with WES data, rates of response (RECIST v1.1-defined complete response [CR] or partial response [PR]) to Nivo were comparable in Ipi-N (21%) and Ipi-P (22%) patients. Median mutation load in the patient cohort was 183 mutations (range: 1–7,360; interquartile range: 44–433) (Tables S2 and S3) and did not differ significantly between Ipi-N and Ipi-P patients (Figure 1A). Mutational subtypes of melanoma as defined by The Cancer Genome Atlas (TCGA) (Cancer Genome Atlas Network, 2015) did not differ in response to Nivo. There were more triple wild-type (WT) patients in the Ipi-P cohort than the Ipi-N cohort (57% versus 33%, p = 0.06; Fisher’s exact test) (Figure 1A).
Tumor and Clonal Mutation Load Are Associated with OS and Response in Nivo-Treated Ipi-N Patients
Tumor mutation load associated with OS in Ipi-N but not Ipi-P patients (Figures 1B and S1A), and Ipi-P patients tended to have lower numbers of clonal mutations (p = 0.08) (Figure S1B). Stratification of patients by the number of clonal mutations improved the ability to predict survival and response of Ipi-N but not Ipi-P patients (Figures 1B, S1C, and S1D). Mutation signatures may play a role in response to checkpoint blockade in NSCLC (Rizvi et al., 2015). However, here, no relationship between the proportion of mutations due to any of the known melanoma mutation signatures (e.g., UV or aging) (Alexandrov et al., 2013), and response to therapy was observed (Figure 1A).
No single gene mutations were significantly associated with response or resistance to therapy. SERPINB3/B4 gene mutations in melanoma samples associate with response to anti-CTLA-4 therapy (Riaz et al., 2016b), and here, five of six patients with SERPINB3/B4 mutations had disease control (CR/PR or stable disease [SD]), however, this was not statistically significant, likely due to small numbers (p = 0.21; Fisher’s exact test). One patient with PR had a frameshift alteration in B2M with corresponding loss of heterozygosity, alterations previously associated with acquired resistance to anti-PD-1 therapy (Zaretsky et al., 2016). JAK1 and JAK2 mutations were not associated with resistance in this cohort (Figure 1A). No recurrent copy-number alterations as determined by TCGA associated with response; nor did copy-number alterations in interferon (IFN) genes on chromosome 9p (Figures S1E and S1F). However, the frequency of global genomic instability (Davoli et al., 2017) associated with OS in Ipi-P but not Ipi-N patients (Figure S1G).
Evolution of Tumors during Nivo Treatment
To determine if Nivo therapy affects tumor mutation load and intratumor heterogeneity, WES was performed on a subset (n = 41) of paired pre- and on-therapy biopsy samples (Table S4). Significant differences between pre- and on-therapy biopsies correlated with response (p = 5.87e–5; Mann-Whitney test for CR/PR versus progressive disease [PD]) (Figures 1C, S1H, and S1I). Among Ipi-N and Ipi-P responders, a reduction in mutation and neoantigen load was observed 4 weeks after initiation of Nivo, perhaps consistent with immunoediting (Tables S5A and S5B). To ensure that these observations were not solely due to changes in tumor purity after therapy, further deep sequencing (300×) was performed on responding tumors. In each case, many of the mutations seen in the pre-therapy samples were still detectable in on-therapy samples (Figures S2A–S2C). The proportion of mutations that remained detectable varied depending on response: the mean fraction of variants in on-therapy samples was 19% for CR/PR (range: 1%–99%), 82% for SD (range: 2%–140%), and 101% for PD (range: 33%–205%) (values >100% indicate additional mutations beyond those in pre-therapy samples were detected). Power calculations, assuming a global 5-fold decrease in the variant allele frequency on-therapy (see STAR Methods), demonstrated that the magnitude in change of mutation load could not be explained by changes in tumor purity alone (Figures S2D–S2F). There were four cases of focal loss of CDKN2A that appeared in on-therapy samples, all in patients with PD. In three of these patients, chromosome 9p deletions also included the nearby IFN gene cluster.
Subsequently, we examined association of changes in the clonal compositions of paired pre- and on-therapy tumor samples with response. The fraction of tumor cells carrying a variant (cancer cell fraction [CCF]) in both pre- and on-therapy samples was estimated (see STAR Methods) (Roth et al., 2014). The temporally related changes that occurred in the clonal composition of tumors differed between response groups. Patients with CR/PR had high frequencies of tumor clonal and subclonal variants that decreased in prevalence after Nivo therapy and, in many cases, were not detected on-therapy (mutational contraction) (Figure 2A; STAR Methods). The relative frequencies of single nucleotide variations (SNVs) undergoing mutational contraction were significantly lower in patients with PD than SD (p = 0.01). In addition, the relative frequencies of novel SNVs detectable on-therapy were significantly higher on a per-sample basis in patients with PD than SD (p = 0.02), consistent with presumed mutational expansion and/or genetic drift (Figure 2B). Net genomic changes (defined as the difference of variants representing mutational contraction and mutational persistence [see STAR Methods]) per sample strongly associated with response and OS, and this metric was superior to the temporal change in mutation load in predicting response (Figure 2C). Tumors from patients with SD were identified as an intermediate molecular phenotype between those with CR/PR and PD: 26% (5 of 19) of PD samples had >50% of SNVs with variant gain (SD: 0 of 13), while 38% (5 of 13) of SD samples had >50% of SNVs under variant loss (PD: 0 of 19) (Figure 2A).
Excluding patient 3, all patients with CR/PR consistently lost one or more clones on-therapy; conversely, patients with SD and PD gained novel sets of mutations on-therapy (Figures 2A and 2D). For example, patient 27 (PD) and patient 10 (SD) both had a dominant clone, at least one smaller subclone at initiation of therapy, and the emergence of a novel subclone in the on-therapy sample. In addition, patient 10 had a subclone that was lost after treatment (Figure 2D). Losses were more common in patients with SD than PD. On aggregate, novel subclonal variants were often due to mutational signature 11, which has previously been associated with melanoma and exposure to temozolomide (Alexandrov et al., 2013) and in this setting, suggests that a specific type of repair process becomes dysfunctional due to immunotherapy-mediated stress (Figure S2G).
Transcriptome Analysis and Changes during Treatment
Pre-therapy Expression Analysis Identified Pre-existing Immune Programs in Responders and Expression Footprints of “Hot” versus “Cold” Tumors Based on Prior Immunotherapy Exposure
Baseline transcriptional programs using RNA sequencing (RNA-seq) were characterized, and associations with clinical response were investigated (n = 45) (Table S4). Analysis of differentially expressed genes (DEGs) between patients with CR/PR and PD identified 189 DEGs (q < 0.20) (Figure 3A). Highly expressed genes were immune-related, which suggests pre-existing immune recognition of the tumor; however, Gene Ontology (GO) analysis using the Reactome database only identified high-level categories such as T cell activation and lymphocyte aggregation as enriched (q < 0.1) (Figure S3A). Notably, this group included IL17RE, IL17RC, and FGFR3 (Table S6A), which modulate the immune environment (Sweis et al., 2016). Next, pre-existing signatures of immune response were evaluated by immune deconvolution (see STAR Methods). A pre-existing immunologically active or “hot tumor” environment was observed in all Ipi-P patients with CR/PR, whereas variable immunological activity was observed in Ipi-N patients with CR/PR (Figures 3B and S3B). No association between the previously reported IPRES gene signature and response was seen in this cohort or in another cohort previously described (Hugo et al., 2016) (Figure S3C).
On-Therapy Genomic Contraction Phenotype Correlated with Pre-existing Immunity in Responders
We hypothesized that a molecular phenotype of response, such as tumor genomic contraction/persistence, ascertained at an early time-point, such as 4 weeks (Figure 2C), may more strongly correspond to underlying biological changes than would clinical assessments of response. Differential gene expression analysis between patients who had genomic contraction and genomic persistence was performed to examine pre-existing differences in immunity pre-therapy. In the cohort of 26 patients with paired WES and RNA-seq, 695 DEGs were observed (q < 0.10) (Figure 3C; Table S6B), of which 565 had a fold change greater than two. Clustering analysis of all patients and other checkpoint-blockade-treated cohorts demonstrated that this set of immune-related genes stratified patient survival (Figure 3D).
Gene set enrichment analysis demonstrated enrichment of genes involved in PD-1 signaling, co-stimulation of the CD28 family, downstream T cell receptor (TCR) signaling, interferon (IFN)-γ, and interleukin (IL)-2 signaling (q < 0.1) (Figure S3D). Although PD-1 signaling was enriched, neither PDCD1 (PD-1) nor CD274 (PD-L1) were differentially expressed. However, components of the TCR immunological synapse were enriched (e.g., CD3D/E/G, PTPN6, CD247, CD28, CD86). Notably, several HLA class II alleles were differentially regulated between the two groups in addition to genes associated with PI3K-γ signaling by G protein-coupled receptors (Figures S3D and S3E).
On-Therapy Analysis of Tumor Transcriptome
We subsequently hypothesized that anti-PD-1 therapy can induce tumor transcriptional and microenvironmental sculpting associated with response and therefore evaluated how the expression landscape of melanoma is altered during Nivo therapy. To identify expression changes indicative of a “pharmacologic response” to Nivo, expression of genes that significantly change on-therapy, regardless of response, were analyzed. 475 DEGs were identified in on-therapy samples (q < 0.20) (Figure 4A; Table S6C), most of which were associated with immune regulation as determined by ingenuity pathway analysis (IPA) (Figure S4A). Many immune checkpoint genes increased in expression, regardless of response to therapy, including PDCD1 (PD-1), CD274 (PD-L1), CTLA-4, CD80 (CTLA-4-L), ICOS, LAG3, and TNFRSF9 (4-1BB).
Matched pre- and on-therapy samples were examined to determine whether relative differences in gene expression changes on-therapy could distinguish between patients whose disease was controlled and patients with PD. 2670 DEGs were identified between pre- and on-therapy samples of responders and non-responders (q < 0.20) (Figure 4B; Table S6D). Upregulated genes in responders involved a broader spectrum of immune-related genes than genes solely identified in pre-therapy samples (Figures S4B and S4C) including additional checkpoint-related genes (TNFRSF4 [OX40], TIGIT, HAVCR2 [TIM-3], and C10orf54 [VISTA]) and genes involved in lymphocyte activation, chemotaxis and cytokine signaling, and immune cytolytic activity, consistent with previous findings (Das et al., 2015). Downregulated genes in responders involved pathways related to tumor growth, including neural and melanin pathways, cell-cycle regulation, mitotic division, and translation (Figure 4C). Significantly more immune-related genes were selectively upregulated in responders than in non-responders (p = 6.17e–3 and p = 4.61e–4 for depletion in the inflammatory response and cytokine-mediated signaling pathways from GO, respectively).
Changes in immune subpopulations between pre-therapy and on-therapy samples were assessed by immune-deconvolution analysis (see STAR Methods) and numerous changes in immune response among different tumors were observed. An increase in number of CD8+ T cells and NK cells, and a decrease in M1 macrophages, was associated with response to therapy (Figure 4D).
T Cell Repertoire Analysis and Immune Checkpoint Therapy
How Nivo therapy influences T cell repertoires was examined by analysis of changes in intratumoral T cell abundance, activation, and diversity. We also evaluated how these anti-PD-1-induced changes are affected by prior immunotherapy exposure. To study the dynamics of T cell infiltration and repertoire diversity in response to Nivo, next-generation deep sequencing of TCR β-chain complementarity determining regions (CDR3s) (TCR-seq) was performed on tumor samples pre- and 4 weeks post-Nivo initiation (n = 34) (Table S4). From these nucleotide sequences, the repertoire of amino acid motifs that determine the specificity of antigen-binding and their relative abundances were tabulated (see STAR Methods). Due to the limited number of samples for which TCR-seq data were available, we grouped patients as those with benefit (CR, PR, and SD) or no benefit (PD).
Changes in T Cell Tumor Infiltration and Activation Associated with Prior Treatment Status and Clinical Response
The fraction of TILs present within each tumor (the proportion of sample that is infiltrated by lymphocytes) was assessed by both TCR-seq and immunohistochemical (IHC) staining for CD3 on mostly non-overlapping cohorts of patients (Table S4). By both approaches, an increase in the fraction of TILs upon Nivo therapy was significantly greater among benefiting than non-benefiting patients in the Ipi-N, but not Ipi-P, cohort (TCR-seq, p = 0.040; IHC, p = 0.023). Despite the differences in T cell infiltration, increased cytolytic pathway genes (Rooney et al., 2015) as measured by RNA-seq were associated with benefit in both Ipi-P and Ipi-N cohorts (p = 0.043 and p = 0.005) (Figure 5A). Notably, there was no significant difference in TIL abundance pre-therapy between patients with benefit and no benefit, or between Ipi-N and Ipi-P cohorts (Figure S5A).
On-Therapy Changes in Intratumoral T Cell Repertoire Diversity Associated with Response Differently in Ipi-P and Ipi-N Patients
The diversity of the CDR3 repertoire can be characterized by the Shannon entropy metric, which has two components: the number of unique CDR3s (richness) and their equality of distribution (evenness) (see STAR Methods). Pre-therapy, no significant difference in either of these metrics was observed between cohorts or response status (Figures S5B and S5C). On-therapy, the median fold change in the number of unique CDR3 sequences (richness) was significantly associated with benefit in Ipi-P but not Ipi-N patients (p = 0.016 versus p = 0.489) (Figure 5B). In contrast, the median change in T cell evenness on-therapy was associated with benefit in Ipi-N but not Ipi-P patients (p = 0.036 versus p = 0.594) (Figure 5B).
To refine interpretation of these findings with respect to the antigen-binding properties of the TCR repertoire, the diversity of the CDR3 amino acid sequences encoded by a single VJ cassette combination was analyzed individually for every observed VJ combination (see STAR Methods). Notably, a significant decrease in median CDR3 evenness per VJ group on-therapy was observed in Ipi-N but not Ipi-P patients (p = 0.006 versus p = 0.600) (Figures 5C and 5D) and in benefiting but not non-benefiting patients (p = 0.003 versus p = 0.636) (Figures 5D and S5D). When stratified by both prior treatment status and response, the fold change in CDR3 richness per VJ combination was associated with benefit in Ipi-P but not Ipi-N patients (p = 0.014 versus p = 0.287) (Figure S5E), while a significant decrease in CDR3 evenness per VJ pair was observed in Ipi-N but not Ipi-P benefiting patients (p = 0.020 versus p = 0.131) (Figure S5D). These results, derived from CDR3 subsets grouped by VJ combination, are mostly consistent with the trends of bulk CDR3 populations (Figure 5B). This suggests, due to association with CDR3 amino acid sequences rather than VJ cassette identities, that T cell population diversity dynamics are driven substantially by antigen recognition.
To visualize these changes in CDR3 diversity within each VJ combination, CDR3 evenness per VJ versus number of CDR3s per VJ were plotted for every VJ cassette group pre- and on-therapy as kernel density plots (Figure 5D). In the Ipi-N cohort, rightward shifts along the x axis represent increases in CDR3 richness per VJ combination on-therapy, and downward shifts along the y axis represent decreases in CDR3 evenness per VJ combination on-therapy. Notably, the shifts in evenness and richness per VJ are less prominent in the Ipi-P cohort.
Integrated T Cell Abundance and Diversity Metrics Associated with Response
Due to the diversity of the TCR repertoire in circulating blood (Zarnitsyna et al., 2013), increased T cell infiltration is usually concomitant with an increase in the observed diversity of that TIL repertoire. To understand whether the changes observed in the TIL CDR3 repertoires during Nivo therapy were primarily a function of the degree of infiltration or were due to clonotype distribution changes, indicative of selection and expansion of T cell clonotypes, the minimum percentage of unique CDR3 sequences accounting for 90% of the sequencing reads (D90) was calculated for each TIL sample. D90 is an indicator of evenness in which lower values indicate a more skewed distribution. While the evenness of most Ipi-P TIL repertoires did not change on-therapy, evenness varied widely among Ipi-N patients (Figure 5E). D90 value versus the level of TIL infiltration showed that in Ipi-N patients, disease control was greater among patients with lower TIL D90, including several with low TIL infiltration, such as patients 10, 89, and 94 (Figure 5E). This likely reflects the expansion of specific clonal populations in Nivo responders. This behavior contrasted with that of the Ipi-P cohort, in which high fractional TIL levels during Nivo therapy were a strong indicator of disease control, but CDR3 diversity varied relatively little (Figures 5E and S5A). Furthermore, usage of CD8-associated V-segments (Emerson et al., 2013) was significantly correlated with response (p = 0.005; ordinal regression), while CD4-associated V-segments were not (p = 0.329; ordinal regression) (Figure 5F).
Integrative Analysis of Tumor and T Cell Dynamics
Changes in T Cell Diversity Associate with Tumor Neoantigen Landscape
We next assessed how the underlying T cell clonal dynamics related to the clonal dynamics of the tumor. Changes in T cell repertoire evenness were directly proportional to changes in the fraction of clonal mutations in patients with CR/PR and PD. Notably, there was a trend for a positive relationship in responders (p = 0.07) but a negative relationship in patients with PD (p = 0.08) (Figure 6A). Futhermore, in patients with CR/PR, we detected a linear relationship between the number of expanded T cell clones and the number of neoantigens that became undetectable on-therapy (p = 0.03) (Figure 6B; Table S5B). Notably, this relationship was not observed in patients with SD or PD, suggesting a qualitative difference in the T cell response in these tumors. Similar results were obtained when considering clonal mutations that became undetectable on-therapy, pre-therapy mutation load, or genomic contraction/persistence cases, supporting the view that T cell expansion is related to the underlying genetic profile of the tumor (Figures S6A–S6C).
Selective Depletion of Antigenic Mutations On-Therapy in Responding Patients
To investigate whether mutations that became undetectable on-therapy were more likely to be neoantigens or missense mutations than nonantigenic or synonymous mutations, the ratios of mutations that produce predicted neoantigens to those that do not were compared between mutations detected solely on-therapy and those detected solely pre-therapy. We hypothesized that the ratio of neoantigen-producing mutations would be higher pre-therapy versus on-therapy in patients with an active immune response, indicating selective pressure against the generation of antigenic mutations. Patients with CR/PR had lower neoantigen ratios on-therapy than patients with PD (p = 0.03; Wilcoxon rank-sum test), and patients with SD had a borderline association (p = 0.11; Wilcoxon rank-sum test) (Figures 6C and S6D).
To evaluate the possibility of selective depletion of putative neoantigens within each individual patient, the pre-therapy number of neoantigens per synonymous mutation was determined, and the expected number of neoantigens on-therapy was computed using the measured number of on-therapy synonymous mutations (see STAR Methods). In patients with CR/PR, the observed number of neoantigens was lower than the expected value (p < 0.05) (Figure S6E), suggesting that T cells were effective in eliminating tumor cells expressing immunogenic neoantigens.
Discussion
Previous reports indicate that increased tumor mutation load is associated with response to immune checkpoint therapy and that this relationship improves with assessment of clonality (McGranahan et al., 2016, Rizvi et al., 2015, Snyder et al., 2014). We observed an association of pre-therapy tumor mutation load and clonal mutation load with survival and response; however, this observation was limited to the Ipi-N subset of patients, consistent with previous findings (Weber et al., 2016a). Ipi-P patients had significantly more subclonal mutations present, and neither of these genomic markers correlated with response to therapy in these patients. These findings suggest that current biomarkers used to determine which patients will respond to immunotherapy may be more useful for Ipi-N patients than Ipi-P patients, although larger studies will be necessary to confirm these findings.
After 4 weeks of Nivo therapy, we observed a marked decrease in detectable mutations among patients with CR/PR and a moderate decrease in patients with SD. Clonality analysis identified that Nivo therapy affects the evolutionary landscape of tumors in patients with CR/PR, leading to the collapse of whole clonal populations, while in patients with SD, Nivo may shift the landscape in favor of specific subclones. Notably, in several patients with SD, some subclones became undetectable while others remained. In addition, we demonstrated genomic evidence of effective immune elimination of tumor cells containing non-synonymous mutations and neoantigens on-therapy in responding patients as a group and in a subset of responding patients individually. Moreover, T cell clones expanded in proportion to the number of neoantigenic mutations that became undetectable on-therapy. These observations agree with evidence of immune-mediated genetic loss of patient-specific mutations, such as after adoptive T cell therapy (Verdegaal et al., 2016). Our genetic data are consistent with immunoediting and are derived from a larger group of patients than previously studied (Verdegaal et al., 2016). However, we cannot make definitive conclusions due to computational limitations, namely, unambiguous identification of specific neoantigens driving this effect.
Therapy-induced clonal evolution has been reported following other cancer therapies such as cytotoxic treatments in glioblastoma (GBM) and chronic lymphocytic leukemia, and our observation of CDKN2A loss in four patients with PD agrees with reported changes in GBM tumors at progression (Johnson et al., 2014, Landau et al., 2015). The appearance of new mutations on-therapy could represent genetic drift, or alternatively, could be consistent with a model in which certain subclonal populations are selected under immunologic pressure. However, on-therapy expression analysis indicates that an immune ignorance or immune exclusion mechanism of resistance is likely operative rather than the evolution of an intrinsic genetic mechanism mediating resistance. The early collapse in clonal populations among responding patients is consistent with previous findings (Landau et al., 2015, Wang et al., 2016) and suggests that clonal composition undergoes significant changes after cytotoxic therapy. Our results may potentially be affected by variation in the anatomic location from which the tissue was taken, by intratumoral heterogeneity, and by decreased purity in on-therapy biopsies. However, biopsies targeted the same site and, although purity did decrease on-therapy, the magnitude of change was not large enough to explain observations herein.
Expression analysis of pre-therapy tumor samples identified a small set of upregulated immune-related genes in responders, consistent with prior reports demonstrating an IFN-γ signature associated with response (Taube et al., 2012, Taube et al., 2015). However, these expression changes were only marginally predictive of response. It is notable that consistent with previous findings (Larkin et al., 2017, Weber et al., 2016b) of patients who received prior immunotherapy, only those with PD-L1-expressing or immunologically “hot tumors” appeared to respond whereas significant responses were observed among PD-L1 low to no expression subgroups among Ipi-N patients. These findings may again be limited due to the size of the cohort but, like mutation load and tumor clonality, suggest that the importance of a “hot tumor” may depend on prior therapy received.
Surprisingly, stratification based on a molecular phenotype of response (i.e., genomic contraction/persistence) demonstrated a more pronounced difference in pre-existing immunity between molecular responders and non-responders. Genes differentially expressed between groups could predict survival in other patients in this dataset and in other immunotherapy-treated cohorts, signifying their biologic relevance and suggesting that delays in clinical response may be secondary to immune infiltration (Wolchok et al., 2009). This highlights the difficulty in relying solely on clinical and radiographic response to understand the underlying biologic mechanisms of response to therapy in human tumors. Notably, this analysis identified numerous HLA class II genes, along with other genes, as differentially expressed and this is similar to a recently reported signature indicating differences in regulation of macrophages (Kaneda et al., 2016). Changes in macrophages on-therapy also associated with clinical response (Figure 4D), suggesting that macrophages may play an important role in response (Gordon et al., 2017).
Analysis of on-therapy expression changes revealed marked upregulation of a multitude of immune pathways that were more pronounced in responding patients and included additional checkpoints and a broader set of DEGs than previously reported (Chen et al., 2016). Some of these newly identified gene products may be considered as candidate targets for future combination immunotherapy trials. Most patients with PD had minimal immune response on-therapy, and differential gene analysis did not identify a dominant or single method of tumor-intrinsic immune evasion, suggesting an immune ignorance or immune exclusion mechanism rather than an adaptive resistance mechanism (Salerno et al., 2016, Spranger et al., 2013) (Figure 4B). In contrast, many patients with SD appeared to have modest immune response induction, suggesting that an adaptive mechanism of resistance may have a more important role in these patients (Taube et al., 2012, Taube et al., 2015).
Increased cytolytic activity, indicative of T cell activation, associated with response. Both cytolytic activity and response were observed at similar rates regardless of prior immune therapy exposure. However, analysis of T cell repertoires suggested that anti-PD-1 response is associated with different patterns of T cell diversity dynamics in Ipi-N versus Ipi-P patients. We infer that decreased evenness without a significant change in the total number of CDR3s (richness) observed in Ipi-N responders is consistent with expansion and accumulation of specific T cell clonotypes in response to the detection of tumor antigens. We speculate that TILs of Ipi-P patients may represent a binding repertoire already differentially selected by the tumor antigenic landscape during Ipi treatment but subsequently exhausted through PD-1/PD-L1 signaling. The predictive value of exhaustion marker expression for responsiveness in Ipi-P but not Ipi-N patients is consistent with this model (Figure 3B). Thus, we speculate that in Ipi-P patients, anti-PD-1 therapy functions mainly by alleviating exhaustion among the extant distribution of TIL clonotypes, while in Ipi-N patients, anti-PD-1 therapy facilitates selective intratumoral expansion of tumor-reactive clonotypes (Figure 6D).
We hypothesize that changes in T cell repertoire diversity are associated with anti-PD-1-induced tumor clonal architecture changes, reflecting the cytolytic anti-tumor effect of expanded tumor antigen-specific T cell clonotypes. Of note, changes in T cell repertoire evenness were directly proportional to changes in the fraction of clonal mutations in responders and patients with PD. There was a trend for a positive and negative relationship in responders and non-responders, respectively, suggesting that in responders, as T cell clones expand, clonal mutations are targeted and eliminated. Possible explanations for the negative relationship in non-responders include: (1) T cell clonotypes that expand may target subclonal mutations instead of clonal mutations, resulting in a futile T cell response (Figure S6F), or (2) decreased T cell repertoire evenness could be due to clonal expansion of regulatory T cells, which may enhance an immunosuppressive environment.
In conclusion, we performed extensive immunogenomic analyses on melanoma samples treated with anti-PD-1 therapy and characterized how tumor genomic and microenvironmental features changed over time. Assessment of the genomic landscape on-therapy demonstrated clonal evolution consistent with therapy-dependent immunoediting. T cell repertoire analysis identified that T cell clonotypes expand in proportion to the number of neoantigens that become undetectable in responding patients. Gene expression analysis revealed the changing transcriptional and microenvironmental alterations induced by anti-PD-1 therapy and identified a broad spectrum of immune checkpoint-related genes that were upregulated. These data have important implications for understanding the mechanism of action of checkpoint inhibitors and for the design of future immune checkpoint blockade trials. Based on our observations, we propose a model of tumor evolution and its microenvironment in response to anti-PD-1 therapy (Figure 6D).
