Background
T cells that recognize tumor antigens are the foundation of current immunotherapy, on which the efficacy of checkpoint inhibitors (CPIs) relies. CPIs have revolutionized the treatment of many cancers, most notably malignant melanomas [
1,
2]. Although many patients experience deep and durable clinical responses to CPIs, unfortunately, many patients progress and require additional treatment. CPIs work by inhibiting axes that restrict T cell activation and proliferation, releasing spontaneously primed antitumor immune responses [
3]. Therefore, predictive biomarkers for CPI efficacy are largely related to tumor immunogenicity, such as tumor mutational burden [
4], neoantigen burden [
5], PD-L1 positivity [
6], tumor-infiltrating lymphocytes [
7], and IFN-γ gene signature [
8]. Conversely, a lack of CPI efficacy is associated with inadequate antitumor T cell responses and immunosuppressive factors in the tumor microenvironment (TME) [
9]. Although incremental benefits are achieved by simultaneously inhibiting multiple immune checkpoints [
10], the quality and quantity of T cells specific for tumor antigens remains a limiting factor for further advancements in immunotherapy.
Therapeutic cancer vaccines (TCVs) aim to direct the expansion of T cells targeting relevant tumor antigens, providing a new wave of tumor-specific T cells to the TME [
11]. This approach thereby serves to supplement and reinforce the anti-tumor immune response, synergizing with immunotherapies that depend on the presence of tumor-specific T cell responses (e.g. CPIs). Strategies applied to TCVs include targeting neoantigens and shared tumor-associated antigens (TAAs). Tumor-specific somatic mutations may give rise to aberrant peptides that are sufficiently different from their normal counterparts, allowing recognition of these neoantigens by the patient’s T cells. In contrast, TAAs are non-mutated antigens with a selective expression pattern that is preferentially limited to tumors. Personalized neoantigen cancer vaccines require tumor tissue harvesting and next-generation sequencing for in silico neoantigen prediction, and subsequent personalized vaccine production. TAA-based vaccines, however, can forego personalized production and can be delivered directly off-the-shelf to the patient, assuming that the tumor expresses the target antigen. TAA-based vaccines may also be more relevant in cancers with low tumor mutational burden, where fewer neoantigens are presented.
Telomerase reverse transcriptase (hTERT) is a TAA activated in 85–90% of all tumor types [
12,
13]. Telomerase is expressed by cancer cells to maintain telomere replication supporting unconstrained cancer cell proliferation and metastasis [
14,
15]. Therefore, telomerase activation is considered a hallmark of cancer [
16]. Melanomas frequently harbor hTERT promoter mutations and gene copy number amplification which are associated with increased hTERT expression [
17‐
19]. These genomic aberrations collectively lead to an increased antigen presence providing a scientific rationale for targeting hTERT in melanomas. High tumor telomerase activity is a well-established negative prognostic factor across multiple cancer indications [
20‐
24], whereas anti-telomerase CD4 T cell immune responses are emerging as independent positive prognostic factors validated in several malignancies [
25‐
27]. Based on these characteristics, hTERT is considered a promising TAA for therapeutic vaccination [
28].
UV1 is a TCV composed of three synthetic long peptides derived from the active site of hTERT and has been proven to establish robust, long-lasting T cell responses across an HLA-unselected population in three completed phase I clinical trials [
29]. Immune responses induced by UV1 have been identified as CD4 Th1-polarized effector memory cells with inflammatory cytokine profiles (tumor necrosis factor-α and IFN-γ). Expanding a population of CD4 Th1 cells targeting a shared tumor antigen could lead to intratumoral re-activation of these cells, inducing an inflammatory TME and immune-mediated cancer cell death [
30‐
32]. CD4 T cells enhance antitumor immunity by licensing dendritic cells for effective antigen presentation and by secretion of inflammatory cytokines, promoting immune cell infiltration, and effector functions.
Immune checkpoints maintain immune responses within a desired physiological spectrum, and their blocking is expected not only to disinhibit spontaneously primed anti-tumor T cell responses, but also de novo T cell responses induced by vaccination. This provides a therapeutic rationale for combining checkpoint inhibition with TCVs. The cytotoxic T lymphocyte-associated protein 4 (CTLA-4) immune checkpoint competitively inhibits the binding of CD28 on T cells with CD80/CD86 on antigen-presenting cells, thereby reducing T cell activation by preventing the co-stimulation of primed T cells. Ipilimumab is a monoclonal antibody that blocks this immune checkpoint and disrupts negative regulation imposed by CTLA-4. We wanted to explore whether combining ipilimumab with the UV1 vaccine would lead to synergy in terms of expanding vaccine-specific T cells, improving anti-tumor immune responses, and clinical outcomes.
We have previously reported safety and feasibility data of this phase I/IIa clinical trial evaluating combined UV1 vaccination and ipilimumab in patients with metastatic melanoma [
33]. A parallel phase 4 trial evaluating ipilimumab monotherapy at Norwegian hospitals during the same period has since been published, demonstrating clinical outcomes aligned with previously reported data on ipilimumab monotherapy [
34,
35]. Considering the combination study yielded comparatively superior progression-free survival, overall survival, and objective responses rate, we sought to further investigate whether this cohort comprised patients with favorable baseline characteristics and explore the dynamics of the vaccine-induced immune response. Herein, we report an updated survival analysis and extensive translational research from this clinical trial.
Methods
Study design, patients, and treatments
The study design, eligibility criteria, and treatments have been previously described [
33]. The UV1/hTERT-MM study was an open-label, single-arm, single-center, phase I/IIa clinical trial (NCT02275416). The primary objective of this study was to assess the safety of ipilimumab combined with UV1 vaccination in patients with malignant melanoma. The secondary objectives included immune response assessment, objective response rate (ORR) per RECIST 1.1, overall survival (OS), and progression-free survival (PFS). Key eligibility criteria were age ≥ 18 years and a histologically confirmed diagnosis of unresectable stage III/IV cutaneous malignant melanoma. An Eastern Cooperative Oncology Group (ECOG) performance status ≤ 1 and any previous therapies for melanoma were permitted. The study participants provided written informed consent prior to enrolment. The study was approved by the competent regulatory authority and independent ethics committee.
UV1 consists of three synthetic long peptides derived from the active site of telomerase reverse transcriptase (hTERT 660–689 termed p719-20; hTERT 691–705 termed p725; hTERT 651–665 termed p728). A total of 300 µg of lyophilized peptides in equimolar amounts was reconstituted in water for injection and administered intradermally to the lower abdomen. Granulocyte–macrophage colony-stimulating factor (GM-CSF, sargramostim) (Leukine, Sanofi Aventis, Bridgewater, NJ, US) was used as a vaccine adjuvant at 75 µg and was injected intradermally at the same site 10–15 min before UV1. Ipilimumab (3 mg/kg) was administered according to the label for up to 4 infusions. Patients received up to nine UV1 vaccinations, initiated one week prior to the first dose of ipilimumab.
Immune response assessment
Peripheral blood mononuclear cells (PBMCs) were isolated from whole blood samples (50 ml in acid dextrose tubes) at baseline and at frequent intervals during the treatment and long-term follow-up periods, as previously described [
29]. Briefly, vaccine-specific T cell immune responses were assessed using a standard proliferation assay (
3H-Thymidine incorporation). PBMCs were pre-stimulated in vitro for 10–12 days with a mixture of vaccine peptides at 10 μM. After pre-stimulation, the cells were re-stimulated for 48 h with or without 10 μM vaccine peptides using irradiated autologous PBMCs as antigen-presenting cells, and tested in triplicate for proliferation by
3H-thymidine incorporation. The stimulation index (SI) was calculated by dividing the mean proliferation count in vaccine peptide-stimulated wells by the mean proliferation count in unstimulated wells. A three-fold increase in proliferation towards any of the three peptides, or a mixture of these, was considered an immune response-positive sample. Staphylococcus aureus enterotoxin C3 (SEC3) was used as the positive control in the immune response assay. The reported SI values were those observed during the treatment period, defined as up to 16 weeks after the last vaccination.
TCR sequencing
DNA was extracted from PBMC samples of all patients at baseline and up to two time points thereafter using the GenElute Mammalian Genomic DNA Kit (Sigma Aldrich), according to the manufacturer´s instructions (Additional file
1: Table S1). In addition, DNA extraction was performed on stimulated PBMC samples after a 10-day in vitro stimulation (N01, N02, N07, and N09) and biopsies from patients N02 and N03.
The T cell receptor beta (TRB) locus was amplified from up to 250 ng of genomic DNA, as described previously [
36,
37]. Briefly, a TRB repertoire library was generated using two consecutive polymerase chain reactions. First, the rearranged TRB locus was amplified and sample-specific barcodes were added to the amplicons. Library concentrations and sizes were determined using a Qubit (Thermo Fisher) and Bioanalyzer (Agilent), respectively. The final library was sequenced on an Illumina MiSeq with the MiSeq Reagent Kit v3 (600-cycles) chemistry.
TCR profiling and processing
Sequencing quality was assessed before and after repertoire sequencing data processing with IGX Inspect (IGX Platform 3.0.6 August 2021), a quality control application designed for immune receptor sequencing data. Checks included standard fastq quality metrics, such as average read quality, Q30 scores, as well as V and J gene alignment distributions and read fate, and a percentage breakdown of the receptor extraction status of raw reads. All the samples had good sequence quality and a high percentage of reads with successfully extracted receptors (all > 95%, except for one sample with 90% and one with 70%).
Raw fastq files were processed using the IGX Profile (IGX Platform 3.0.6 August 2021), a tool that parses immune receptor structural components by aligning germline genes and adaptively correcting errors based on the overall sample quality. The IGX Profile provided receptor annotation with complementarity-determining region 3 (CDR3) sequences, V and J gene assignments, functionality, alignment scores, and quality information. All receptors with the same CDR3 amino acid sequence were considered instances of the same clone and all analyses were performed at the clone level.
Count normalization of TCR repertoires was performed by downsampling. Identification of significantly expanded clones was performed using EdgeR [
38]. The expanded clones already present in the baseline sample were filtered out. A more detailed description of these methods is provided in the Additional file
1.
Multiplex immunofluorescence staining
Biopsies were harvested at baseline from nine patients and at week 12–15 from five patients (Additional file
1: Table S2). One part of the biopsy was snap-frozen in liquid nitrogen and stored at − 80 °C, whereas the other was formalin-fixed and paraffin-embedded (FFPE). FFPE biopsies were used for multiplex immunofluorescence staining. Biopsy Sects. (4 µm thick) were stained using a custom-based 5-color IHC kit (Akoya Biosciences, Marlborough, MA, USA) and the fully automated Leica Bond RXm (Leica Biosystems, Buffalo Grove, IL, USA). The slides were deparaffinized, rehydrated, and rinsed with distilled H
2O. Antigen retrieval and removal of antibodies from the previous cycles were performed by boiling at 95 °C at pH 9 (first cycle) or pH 6 (all remaining cycles).
For multiplex immunofluorescence staining, a panel of immune markers was developed using antibodies against CD4 (rabbit/ERP6855, Abcam, 1:80), CD8α (mouse/144B, Invitrogen/MA5-13,473, 1:100), PD-L1 (rabbit/E1L3N, Akoya, ready to use), and TERT (rabbit/ab230527, Abcam, 1:400). A cocktail of two antibodies was used to identify the melanoma cells: anti-Sox10 (rabbit/EP268-1, Akoya, ready to use) and anti-S100 (mouse/4C4.9, Akoya, ready to use). Staining was developed using amplification HRP-polymer systems and Opal fluorophore dyes (see Additional file
1: Table S3). To visualize the cell nuclei, the tissue was stained with 4′,6-diamidino-2-phenylindole (Spectral DAPI, Akoya). The slides were mounted with Prolong Diamond Antifade Mountant (Thermo Fisher, Waltham, MA, USA) and imaged at × 20 magnification using the Vectra® Polaris™ Automated Quantitative Pathology Imaging System (Akoya Biosciences, Marlborough, MA, USA). Each image was manually reviewed and curated by a pathologist to exclude artifacts and staining defects.
Whole-exome and RNA sequencing and downstream analyses
Snap-frozen biopsies were disrupted on a TissueLyser LT, followed by DNA extraction using the AllPrep DNA/RNA/miRNA Universal Kit (Qiagen, Hilden, Germany). RNA extraction was performed using a GenElute™ Total RNA Purification Kit (Merck). Biopsy DNA and RNA extracts were obtained from nine patients at baseline and five at week 12–15.
Whole-exome sequencing (WES) was performed as previously described in Aamdal et al. [
33]. Briefly, 1 µg of DNA was used as the starting material for exome library preparation using the Agilent AllExome V5 kit, according to the manufacturer’s protocol. Sequencing was performed pair-ended, generating approximately 90 M PE reads per tumor and 40 M PE reads per normal, using sequencing by synthesis chemistry on a HiSeq4000 system. Variant calling was performed as previously described [
33]. Tumor mutational burden (TMB) was defined as the number of non-synonymous variants with an allelic frequency of > 5% per megabase.
RNA samples were processed using an Illumina TruSeq stranded mRNA kit with 100 ng as the starting material. RNA sequencing was performed on the NextSeq500 using two HighOutput flow cells with 75 bp single-read sequencing. Hierarchical clustering of the genes included in the IFN-γ signature [
8] was performed using Euclidean distance with the Morpheus tool (
https://software.broadinstitute.org/morpheus). HLA class I expression was assessed for HLA-A, -B, and -C, and HLA-DP, -DQ, and -DR for class II, as previously described [
39]. Differentially expressed genes post-treatment compared to baseline were assessed using the NOISeq tool [
40] and were calculated for each patient with available biopsies (N01, N02, N03, and N07). Gene set enrichment analysis of the differentially expressed genes was performed using WebGestalt [
41] and Gene Ontology mapping to the Biological Processes functional database.
The artificial intelligence (AI) prediction platform used for immunogenic neoantigen prediction was the NEC Immune Profiler (NIP) [
42]. The NIP software predicted each of the key determinants of antigen presentation (AP) for each somatic mutation, by predicting the potential of all tumor-specific mutated peptides to be efficiently presented by each of the patients Class I HLA-A and -B alleles.
Statistics
The sample size (n) represents the number of patients or samples analyzed. 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.
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 translational 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 monotherapy 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 ipilimumab 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 immunotherapy 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 therapeutic vaccine that has demonstrated HLA-independent induction of vaccine-specific T cell responses in patients treated across three completed phase I/IIa clinical trials [
29]. Effective induction of robust T cell responses is a prerequisite for the potential clinical activity of a TCV. 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 trafficking after peripheral priming and their interaction with the tumor microenvironment.
Tumor hTERT protein expression was confirmed in all evaluable biopsies using combined hTERT and melanoma 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 relevant 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 mechanism 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 stimulation, 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 potentially render the UV1-specific immune response redundant. 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 associated 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 harvesting, and intratumoral heterogeneity. As the median time to clinical response was 30.2 weeks, tumor tissue sampling at weeks 12–15 may be too early to describe clinically 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 clinically 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 proliferation 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 restrictions 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 superior 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 clinical trial evaluating ipilimumab monotherapy at Norwegian 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 vaccination. Anti-telomerase immune responses were established in 91% of patients, and clinical responses were observed in patients with otherwise less favorable baseline genomic, transcriptomic, and tumor microenvironmental features predictive of CPI efficacy. Currently, five randomized phase II clinical trials are evaluating UV1 in combination with various CPIs across multiple indications (NCT05075122, NCT04742075, NCT04382664, NCT04300244, and NCT05344209).
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