Survival analyses were performed using the Kaplan–Meier method. All statistical analyses were performed using GraphPad Prism version 9.2.0. (GraphPad Software). Statistical significance was set at p < 0.05. Page 5 of 13 Ellingsen et al. Journal of Translational Medicine (2022) 20:419 Results Clinical outcome of combined ipilimumab and UV1 treatment We have previously presented patient demographics and 5-year clinical follow-up data [33]. Between Janu - ary and October 2015, 12 patients with stage IV mel - anoma were enrolled in this study. Patients received a mean of 5.5 UV1 vaccinations (range 3–9) and 3.2 courses of ipilimumab (range 1–4). UV1 was consid - ered safe and well-tolerated, with most adverse events being grade 1–2 injection site reactions. With up to 85.5 months of clinical follow-up, the median PFS was 6.7 months (Fig. 1A), and the median OS was 66.3 months (Fig. 1B). The ORR was 33% (three partial responses and one complete response) (Fig. 1C). The median time to clinical response was 30.2 weeks (range 16.4–155.3), and the median duration of response was 64.1 weeks (range 12-not reached) (Fig. 1D). One patient (N09) had a sustained complete response last - ing more than 5.5 years. A vaccine-induced anti-UV1 immune response was demonstrated in 10/11 evalu - able patients (91%). The median of maximum in vitro T cell proliferation response (stimulation index) across all patients was 11.5 (range 2.3–60.0). Patient N02 exhib - ited the strongest immune response, with a 60-fold increase in T cell proliferation response to in vitro vac - cine peptide stimulation (Fig. 1 E). Baseline tumor microenvironment and TCR repertoire status There was no apparent association between baseline tumor mutational burden (TMB) or neoantigen load and response to therapy (Fig. 2A). One patient had a mis - sense mutation in hTERT (Glu429Lys). However, the mutation did not map to epitopes corresponding to UV1 peptides and did not persist in the post-treatment biopsy (Additional file 1: Figure S1). Alternative lengthening of telomeres (ALT) has been described as an hTERT-inde - pendent telomere maintenance mechanism that may limit the efficacy of hTERT-targeting T cells. Therefore, we examined the presence of loss-of-function mutations in two genes, ATRX and DAXX, known to induce the ALT phenotype [43]. No nonsense or frameshift muta - tions were detected; however, we observed missense mutations in ATRX in patients N09 (Met352Ile) and N01 (Glu2105Lys), and both ATRX and DAXX mutations in patient N06 (Asp2048Asn, Ser377Phe). The ATRX muta - tion present at baseline in patient N01 was not detected in the biopsy specimen harvested at week 12 (Additional file 1: Figure S1). Fig. 1 Treatment outcomes and anti-hTERT immune response induction in patients treated with UV1 and ipilimumab. A Progression-free survival and B overall survival in all patients enrolled (n = 12). C Maximum change in the sum of longest diameter in RECIST 1.1 evaluable patients (n = 9). D Duration of response (n = 4). E Maximum T cell proliferation response in terms of stimulation index (SI) in evaluable patients (n = 11). The dotted line represents positivity threshold (SI ≥ 3). CR complete response, PR partial response, SD stable disease, PD progressive disease, IR immune response Page 6 of 13 Ellingsen et al. Journal of Translational Medicine (2022) 20:419 Two patients had tumors polyploid of the hTERT gene. Patient N02 had seven copies of hTERT at baseline, whereas patient N13 had three copies (Fig. 2B). We fur - ther investigated copy number alterations in HLA and TAP genes, as their loss have been described as a mecha - nism of resistance to immunotherapy [44]. Most patients were diploid for HLA and TAP , and we did not observe any loss of heterozygosity for these genes (Fig. 2B). Melanoma cell hTERT expression was determined by co-staining for Sox10/S100 and hTERT and was con - firmed in all evaluable biopsies (baseline sample from patient N11 was not evaluable because of a lack of Sox10/ S100 stained cells). The median fraction of hTERT-posi - tive melanoma cells was 72.7% (range 5.2–99.3) (Fig. 2C). Two patients (N02 and N13) that were polyploid for the hTERT gene also had the highest hTERT + Sox10/S100 density (Fig. 2E). Interestingly, patient N02 displayed the highest in vitro T cell proliferation response against UV1 peptides, but patient N13 did not demonstrate a vaccine- specific T cell response. However, patient N13 had only one post-vaccination sample for the immune response assessment at week 4. There was no overall associa - tion between tumor hTERT density and vaccine-specific peripheral T cell proliferation response (Fig. 2E). Baseline PD-L1 expression and infiltration of CD4 or CD8 T cells were not significantly different between responders and non-responders (Fig. 2F). However, the baseline hTERT intensity in Sox10/S100 positive cells was significantly lower in the clinical responders (Fig. 2F). We performed RNA sequencing of the available biop - sies and found no apparent association between the baseline IFN-γ gene signature and response to therapy (Fig. 2G). Patient N06, with a JAK1 mutation, demon - strated relatively low expression of the IFN-γ signature. Baseline HLA class I and II expression levels were not significantly different between the clinical responders and non-responders (Additional file 1: Figure S2). Across the clinical response categories, we observed a trend towards an inverse relationship between base - line PBMC and biopsy T cell receptor (TCR) repertoire diversity. The complete response patient (N09) had the highest intratumoral TCR diversity at baseline, and com - paratively low PBMC diversity. The diversity and clonal - ity of PBMC samples at baseline were fairly even across clinical response categories (Fig. 2H and I). Evolution of the TCR repertoire and tracking of vaccine‑enriched clonotypes We matched the baseline and post-treatment (week 14–15) tumor biopsy TCR sequencing data available from two patients (N02 and N03, both with PD as BOR). Most intratumoral TCR clonotypes changed between baseline and post-treatment (Fig. 3A). The overlapping TCRs did not have significantly different relative abun - dances after treatment, indicating that treatment did not lead to intratumoral expansion of the T cells (Fig. 3B). Although there was an increase in total unique TCR clo - notypes post-treatment for patient N02, intratumoral TCR clonality increased (Fig. 3C), indicating that fewer TCR clonotypes constituted a larger proportion of the intratumoral TCR repertoire. The opposite was observed for patient N03. We investigated the PBMC TCR rep - ertoire for TCRs unique to the post-treatment biopsies, observing an increasing number of these clonotypes upon treatment, indicating peripheral expansion of these clonotypes. However, these TCRs did not constitute a considerably larger fraction of the total TCR repertoire over time (Fig. 3D). From week 7 PBMC samples, TCR sequencing was performed before and after 10 days in vitro stimulation with the vaccine peptides in patients N01, N02, N07, and N09. We assessed whether vaccine-enriched TCR clo - notypes were detectable in unstimulated PBMCs and a biopsy at later time points (only patient N02 had avail - able calculated vaccine-enriched TCRs and a post-treat - ment biopsy) (Fig. 3E). In patient N02, we observed three clones expanding from week 7 to week 18 (Fig. 3F), and a single vaccine-enriched TCR clone was detected in the biopsy post-treatment (Fig. 3G). For patients N01, N07, and N09, we observed similar findings, with single clones (See figure on next page.) Fig. 2 Baseline tumor microenvironment. A Clinical outcome and genomic characteristics of patients with available biopsies at baseline. Genes in red font are related to immunotherapy resistance [45], and genes in blue telomerase expression. Green squares indicate non-synonymous mutations. B Gene copy alterations of TERT, HLA, and TAP . C Fraction of Sox10/S100 cells also positive for hTERT based on immunofluorescence staining (dotted line represents the median, 72.7%). D Representative immunofluorescence staining of tumor biopsies. CD4 is stained cyan, CD8 is stained blue, hTERT is stained red, and Sox10/S100 is stained green. Image 1 is from patient N04 at baseline, and image 2 from patient N06 at baseline. The hTERT dense area in the top right of image 2 is likely a hair follicle. E Linear regression analysis of immune response (maximum stimulation index (SI)) and hTERT + Sox10/S100 + cell density based on immunofluorescence staining. The association is not significant (Pearson’s correlation, p = 0.47). F Baseline CD4, CD8, and PD-L1 density in responders (R) and non-responders (NR) (Mann–Whitney test, CD4 p = 1.0; CD8 p = 0.71; PD-L1 p = 0.90). Baseline hTERT intensity in melanoma cells was significantly lower in clinical responders (Mann–Whitney test, p = 0.04). G Hierarchical clustering of baseline IFN-γ signature using the Euclidean distance. The grey box indicates missing data (HLA-DQA1 only). H Baseline PBMC and tumor TCR repertoire diversity and I clonality according to RECIST 1.1 response categories. BOR, best overall response; ns, not significant; ND, not detected. *Patient N04 only had week 12 biopsy available for whole-exome and RNA sequencing. The BRAF status was based on a diagnostic biopsy for this patient Page 7 of 13 Ellingsen et al. Journal of Translational Medicine (2022) 20:419 Fig. 2 (See legend on previous page.) Page 8 of 13 Ellingsen et al. Journal of Translational Medicine (2022) 20:419 expanded in later PBMC samples, reaching up to 12% of the entire repertoire (Additional file 1: Figure S3). To assess whether in vitro stimulation induced the expansion of a few single T cell clones, we compared the TCR clonality of the unstimulated and stimulated PBMC samples (Fig. 3H) and observed no trend towards increased clonality despite strong T cell proliferation responses in the same sample, especially evident in patient N02 (Fig. 3I). Furthermore, there was no overall correlation between in vitro T cell proliferation responses and TCR clonality in the unstimulated samples (Fig. 3J). Evolution of the tumor microenvironment There was no significant overall increase in tumor-infil - trating lymphocytes (TILs) or PD-L1 expression in the five patients with available baseline and post-treatment (week 12–15) biopsy immunofluorescence staining. An increase in CD8 density was observed in patients N01, N02, and N07 (Fig. 4A), and we observed a non-signif - icant trend towards increased delta CD8 density with increased vaccine-specific peripheral T cell responses (Fig. 4B). No loss of heterozygosity for either the HLA/TAP or hTERT genes was observed in post-treatment biop - sies (Fig. 4C). A relative increase in the expression of both HLA class I and II genes was observed in patient N07 post-treatment, and class II only in patient N02 (Fig. 4D). For the genes included in the IFN-γ signature, we observed a relative increase in expression post-treat - ment in responding patient N07, and to a lesser degree in patient N01 (Fig. 4E). The two progressors did not show considerable changes in IFN-γ signature upon treatment. A similar expression pattern was observed for genes related to T cell function and activation, immune check - point molecules, and cytokine activity (Additional file 1: Figure S4). We evaluated mutational contraction and expansion in four patients with available baseline and post-treatment biopsy whole-exome sequencing data. The responding patients (N07 and N01) exhibited a drastic reduction in total single nucleotide variants (SNVs), with only a few overlapping between the two time points. Conversely, the two progressors had a high degree of overlapping SNVs, indicating a limited impact of the treatment and negligible killing of cancer cells harboring these muta - tions (Fig. 4F). The four neoantigens present at baseline in responding patient N01 were not observed post-treat - ment, whereas most neoantigens persisted in non- responding patient N03 (Fig. 4G). Gene set enrichment analysis was performed on dif - ferentially expressed genes post-treatment for the four patients with matching biopsies, observing enriched gene sets related to immune responses in the responding patients, such as “T cell activation” , “Leukocyte prolifera - tion” , although with an FDR > 0.05 (Additional file 1: Fig - ure S5). The two progressors did not show enrichment in gene sets related to adaptive immune responses. Discussion The anti-CTLA-4 monoclonal antibody ipilimumab was the first checkpoint inhibitor (CPI) to receive Food and Drug Administration approval for the treatment of metastatic malignant melanoma. Here, we report trans - lational research and updated clinical follow-up of 12 patients with malignant melanoma enrolled in a clinical trial evaluating ipilimumab and the TCV candidate UV1. Since the completion of this clinical trial, PD-1 inhibitors, either as single agents or in combination with a CTLA-4 or a LAG-3 inhibitor, have replaced ipilimumab mono - therapy as the standard of care for metastatic melanoma. While ipilimumab led to a median OS of approximately 10 months [35], the combination of nivolumab and ipili - mumab further improved clinical outcomes, exhibiting a 6-year overall survival rate of approximately 50% [2]. Despite these advancements, insufficient T cell responses remain a limiting factor for the efficacy of immunother - apy in the treatment of melanoma. TCVs represent a promising approach for boosting T cell responses against tumor antigens without significantly aggravating toxicity. Therapeutic cancer vaccines aiming to mount anti- hTERT immune responses have been evaluated with several platforms, including peptide, mRNA, and DNA- based approaches [28]. UV1 is a multipeptide therapeu - tic vaccine that has demonstrated HLA-independent induction of vaccine-specific T cell responses in patients treated across three completed phase I/IIa clinical tri - als [29]. Effective induction of robust T cell responses is a prerequisite for the potential clinical activity of a TCV. Fig. 3 Evolution of the TCR repertoire on treatment. A Overlapping intratumoral TCR clonotypes at baseline and post-treatment for patient N02 and N03. B Relative abundance (normalized read count) for persisting intratumoral TCRs between baseline and post-treatment. C Intratumoral TCR clonality at baseline and post-treatment. D The number of clonotypes unique to the post-treatment biopsy also detected in PBMC samples, and their fraction of the TCR repertoire. The TCR repertoire fraction was calculated by summing the normalized read count for each TCR and dividing by the total read count for the same sample. E Volcano plot illustrating enriched TCRs after a 10-day in vitro peptide stimulation of PBMCs. Orange dots indicate TCRs with a log fold change above 5, unadjusted p < 0.05, and red dots adjusted p < 0.05. F Vaccine-enriched TCRs identified in unstimulated PBMC samples. TCR clonotypes are labeled according to their rank in terms of log fold change after stimulation. G Vaccine-enriched TCRs identified in tumor biopsies. H Sample clonality pre and post-10-day in vitro vaccine peptide stimulation. I T cell proliferation responses from the same samples. J Unstimulated PBMC sample clonality vs. in vitro T cell proliferation response(See figure on next page.) Page 9 of 13 Ellingsen et al. Journal of Translational Medicine (2022) 20:419 Fig. 3 (See legend on previous page.) Page 10 of 13 Ellingsen et al. Journal of Translational Medicine (2022) 20:419 Fig. 4 Evolution of the tumor microenvironment on treatment. A CD4, CD8, and PD-L1 density in baseline and post-treatment biopsies. Circles indicate clinical responders and squares non-responders. The difference between baseline and post-treatment was not significant (Mann–Whitney test, CD4 p = 0.69; CD8 p = 0.69; PD-L1 p = 0.84). B Linear regression analysis of difference in CD8 density between post-treatment and baseline versus maximum T cell proliferation response (Pearson’s correlation, p = 0.22). C Copy number status of HLA/TAP and hTERT genes post-treatment. D HLA class I and II expression in baseline and post-treatment biopsies. The p values represent unpaired t test for difference between baseline and post-treatment expression levels. E IFN-γ signature in baseline vs. post-treatment biopsies. F Overlapping single nucleotide variants and G predicted neoantigens between baseline and post-treatment biopsies. IR immune response, TIL tumor-infiltrating lymphocytes, R clinical responder, NR clinical non-responder, BL baseline, PT post-treatment, NS not significant, TPM transcripts per million, ND not detected Page 11 of 13 Ellingsen et al. Journal of Translational Medicine (2022) 20:419 While T cell responses after therapeutic vaccination have been well documented in peripheral blood, there is still a need to further elucidate vaccine-specific T cell traffick - ing after peripheral priming and their interaction with the tumor microenvironment. Tumor hTERT protein expression was confirmed in all evaluable biopsies using combined hTERT and mela - noma cell immunofluorescence staining. The relatively high fraction of hTERT positive melanoma cells (median 72.7%) supports the concept of hTERT being a relevant tumor antigen also in otherwise heterogenous tumors. As hTERT activation serves essential tumorigenic functions, the hTERT negative melanoma cells may be bystander cells contributing less to metastasis and are thus less rel - evant for clinical progression. The intensity of hTERT staining in melanoma cells was significantly higher in clinical progressors. The increased staining intensity may be related to a higher tumoral hTERT activity, which is a well-described negative prognostic factor [20–24]. Copy number amplification of the hTERT gene is a mecha - nism of tumor hTERT activation and associates with high tumor hTERT expression [18, 19]. Two biopsies (N02 and N13) were polyploid for the hTERT gene, and interestingly, these two biopsies had the highest hTERT- Sox10/S100 density based on immunofluorescence. Furthermore, patient N02 demonstrated the strongest T cell proliferation response to in vitro peptide stimula - tion, possibly indicating tumoral boosting of the immune response. Regrettably, we had only one PBMC sample (week 4) for immune response assessment of patient N13, which did not show a positive immune response. We did not observe mutations in the UV1 region of hTERT, either at baseline or post-treatment, which could poten - tially render the UV1-specific immune response redun - dant. Inducing immune responses towards epitopes in the hTERT active site theoretically limits tumor immune escape, as mutations in this region could negatively affect telomerase activity and thus impede tumor growth. We observed mutations in ALT-related DAXX and ATRX genes. However, these missense mutations did not induce the ALT phenotype, as hTERT expression was confirmed by immunofluorescence staining of the same biopsies. Baseline CD4 or CD8 T cell infiltration was not asso - ciated with clinical response, and we did not observe a significant influx of TILs in post-treatment samples. The limitations of our study include the small number of patients with evaluable samples, timing of tissue harvest - ing, and intratumoral heterogeneity. As the median time to clinical response was 30.2 weeks, tumor tissue sam - pling at weeks 12–15 may be too early to describe clini - cally relevant T cell infiltration, although TIL influx after ipilimumab treatment of melanoma has been observed after 18 weeks in other studies [46]. We observed a non-significant trend towards tumor CD8 influx with increased peripheral vaccine-specific T cell responses (Fig. 4B). This observation may fit well with the proposed mechanism of action of a therapeutic cancer vaccine, whereby vaccination promotes the infiltration of T cells into the tumor. Nevertheless, this correlation requires further testing in larger cohorts. Increased expression of the IFN-γ gene signature and genes related to T cell activation and cytokine activity was observed in clini - cally responding patient N07 (Fig. 4 and Additional file 1: Figure S4). Conversely, the two non-responding patients exhibited relatively higher expression of the immune checkpoints CD276 and VTCN1 (B7-H3 and B7-H4), the latter being upregulated post-treatment. TCR sequencing is emerging as an important tool for characterizing T cell dynamics and tissue trafficking [47]. By sequencing the rearranged TRB locus, we aimed to elucidate how ipilimumab and hTERT vaccination affected the overall TCR repertoire and whether vaccine- enriched TCR clonotypes were detectable in peripheral blood and tumor biopsies. Our strategy for identifying TCRs related to vaccination consisted of paired TCR sequencing of PBMC samples before and after a 10-day in vitro vaccine peptide stimulation. The TCR clonality of the sample did not increase after the 10-day in vitro stimulation, despite exhibiting strong T cell prolifera - tion responses to vaccine-peptide stimulation. These findings support the concept that the long UV1 vaccine peptides contain multiple epitopes eliciting a diverse T cell response in each patient, rather than single vaccine- specific clonotypes. This hypothesis is further supported by previously published data on diversity among immune responder HLA genotypes and the various HLA restric - tions and epitope specificities of vaccine-specific T cell clones [29]. Nevertheless, we identified TCR clonotypes that were significantly enriched after in vitro stimulation and subsequently detected them in unstimulated PBMCs and tumor tissue. Alternative strategies, such as peptide- MHC multimer or IFN-γ positivity sorting, may be supe - rior for accurately detecting vaccine-specific T cell clones and validate our current approach in future studies. The clinical read-out of our study yielded an ORR of 33%, mPFS of 6.7 months, and mOS of 66.3 months. The clinical outcomes of patients enrolled in a phase 4 clini - cal trial evaluating ipilimumab monotherapy at Norwe - gian hospitals during the same period as our study were recently published (n = 151) [34], demonstrating an ORR of 9%, mPFS of 2.7 months, and mOS of 12.1 months. Conclusions Although the small sample size and lack of a control arm limits interpretations of clinical efficacy, our study provides support for further clinical evaluation of UV1 Page 12 of 13 Ellingsen et al. Journal of Translational Medicine (2022) 20:419 vaccination. Anti-telomerase immune responses were established in 91% of patients, and clinical responses were observed in patients with otherwise less favora - ble baseline genomic, transcriptomic, and tumor microenvironmental features predictive of CPI effi - cacy. Currently, five randomized phase II clinical tri - als are evaluating UV1 in combination with various CPIs across multiple indications (NCT05075122, NCT04742075, NCT04382664, NCT04300244, and NCT05344209). Abbreviations CPI: Checkpoint inhibitor; PD-1: Programmed cell death protein 1; PD-L1: Programmed death-ligand 1; IFN-γ: Interferon-γ; TME: Tumor microenviron- ment; TCV: Therapeutic cancer vaccine; TAA : Tumor-associated antigen; hTERT: Human telomerase reverse transcriptase; HLA: Human leukocyte antigen; CTLA-4: Cytotoxic T-lymphocyte-associated protein 4; Th1: T helper 1; ORR: Objective response rate; OS: Overall survival; PFS: Progression-free survival; RECIST 1.1: Response Evaluation Criteria in Solid Tumors 1.1.; ECOG: Eastern Cooperative Oncology Group; GM-CSF: Granulocyte–macrophage colony- stimulating factor; PBMC: Peripheral blood mononuclear cell; SI: Stimulation index; SEC3: Staphylococcus aureus enterotoxin C3; TRB: T cell receptor beta; CDR3: Complementarity-determining region 3; FFPE: Formalin-fixed and paraffin-embedded; WES: Whole-exome sequencing; TIL: Tumor-infiltrating lymphocyte; NR: Not reached; BOR: Best overall response; CR: Complete response; PR: Partial response; SD: Stable disease; PD: Progressive disease; SNV: Single nucleotide variant; ALT: Alternative lengthening of telomeres. Supplementary Information The online version contains supplementary material available at https:// doi.