et al. Efficacy and safety of nivolumab plus ipilimumab in patients with advanced hepatocellular carcinoma previously treated with sorafenib: the CheckMate 040 randomized clinical trial. JAMA Oncol. 6, e204564 (2020). Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. © The Author(s) 2024 Mark Yarchoan 1 , Edward J. Gane2, Thomas U. Marron 3, Renzo Perales-Linares4, Jian Yan 4, Neil Cooch4, Daniel H. Shu1, Elana J. Fertig 1,5,6, Luciane T . Kagohara 1, Gabor Bartha7, Josette Northcott7, John Lyle7, Sarah Rochestie4, Joann Peters4, Jason T . Connor8,9, Elizabeth M. Jaffee 1, Ildiko Csiki4, David B. Weiner 10, Alfredo Perales-Puchalt 4 & Niranjan Y . Sardesai 4 1Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA. 2New Zealand Liver Transplant Unit, University of Auckland, Auckland, New Zealand. 3Early Phase Trials Unit, Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA. 4Geneos Therapeutics, Philadelphia, PA, USA. 5Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA. 6Department of Applied Mathematics and Statistics, Johns Hopkins University Whiting School of Engineering, Baltimore, MD, USA. 7Personalis, Inc., Fremont, CA, USA. 8ConfluenceStat, Cooper City, FL, USA. 9University of Central Florida College of Medicine, Orlando, FL, USA. 10Vaccine and Immunotherapy Center, The Wistar Institute, Philadelphia, PA, USA. e-mail: mark.yarchoan@jhmi.edu; sardesai@geneostx.com Nature Medicine Article https://doi.org/10.1038/s41591-024-02894-yMethods Trial design and treatment plan We conducted a 36-patient, phase 1/2, multicenter, open-label trial of a PTCV (GNOS-PV02 and pIL12) plus pembrolizumab in patients with advanced HCC who progressed or were intolerant to first-line therapy with an mTKI. The individualized patient demographic information is shown in Supplementary Table 1. Methods for tumor sequencing and PTCV manufacturing are described below. All selected neoantigens (up to 40) were assembled into a single patient-specific vaccine plasmid (GNOS-PV02) and manufactured for each patient while they were receiving first-line systemic therapy. The PTCV (GNOS-PV02 (1 mg) and pIL12 (0.34 mg)) was administered intradermally using a CELLECTRA 2000 electroporation device (INOVIO Pharmaceuticals) into two loca - tions (deltoid area of both arms) Q3w for four doses, followed by Q9w until year 2 and Q12w thereafter. Pembrolizumab was administered at the standard dose of 200 mg intravenously Q3w for up to 2 years per label recommendation. Therapy was continued until the progression of disease, development of unacceptable toxicity, withdrawal of consent or end of the study. Eligibility criteria included age ≥18 years, a confirmed diagnosis of HCC, BCLC stage B or C disease, Child–Pugh class A, a predicted life expectancy of >6 months, a performance status of 0 or 1 using the ECOG performance scale, and measurable disease based on RECIST 1.1. Key exclusion criteria were the use of prior systemic therapy for HCC other than sorafenib or lenvatinib and active autoimmune disease. The full eligibility and exclusion criteria are provided in the study protocol (Supplementary Information). Study oversight The protocol of the GT-30 clinical study was approved by the institu- tional review board or ethics committees at each participating institu - tion. Written informed consent was obtained from each patient. The study was registered at https://clinicaltrials.gov/ under the identifier NCT04251117 . The trial was conducted in accordance with the princi- ples of the Declaration of Helsinki. Data were collected by the study investigators and analyzed by employees of Geneos Therapeutics. Endpoints The primary endpoint was safety, graded using the Common Terminol - ogy Criteria for Adverse Events version 5.0. The coprimary endpoint was immune response, assessed by quantifying IFNγ-secreting T lym- phocytes in PBMCs by ELISpot. Secondary endpoints included ORR according to RECIST 1.1, PFS and OS. Response data were provided by local sites and were not centrally reviewed. This analysis included all patients enrolled in the cohorts who received at least one dose of the PTCV. The exploratory endpoints included the evaluation of tumor and immune biomarkers and their association with the treatment outcome. The data cutoff date was August 18, 2023. PTCV design Next-generation sequencing of patient-specific tumor samples was performed using the ACE technology (Personalis). DNA samples from matched normal tissue were also prepared for germline whole-exome sequencing. Sequence alignments, variant discovery and annota - tion, and comprehensive analysis were performed to identify all targ - etable neoantigens for vaccine design. Targetable neoantigens were identified based on somatic, nonsynonymous nucleotide changes (single-nucleotide variants, indels, fusions) with a DNA allelic fraction of >0.05 and RNAseq FPKM (fragments per kilobase of transcript per million mapped reads) of >1. The resulting peptides were filtered for duplicates and self-similarity and ranked by HLA class I-binding affinity (nM, high to low, NetMHCpan 4.0). All targetable neoantigens (up to 40) were included in each patient’s PTCV. Where >40 potential neoantigens were identified, the 40 neoantigens with the strongest HLA-binding affinity (lowest nM kd) were included in the PTCV. Patient-specific DNA vaccine constructs were designed to consist of a string of epitopes with flanking sequences with a center-embedded CD8 epitope separated by synthetic furin cleavage sites for efficient epitope presentation during processing18,24. Each neoepitope contained the somatic variant and its flanking sequence so that each is about 33 amino acids in length. The final assembled cassettes were codon and RNA optimized, syn - thesized and subcloned into the vaccine expression vector pGX0001 (GenScript). Details of the PTCV design and vaccine plasmid construc - tion are shown in Supplementary Fig. 6. IL-12 DNA consisted of a single plasmid containing a dual-promoter system for the expression of both the IL-12 p35 and p40 genes necessary for the production of the active heterodimeric IL-12 protein25,46. All plasmids were sequence verified and manufactured under current good manufacturing practice conditions (VGXI) and met all acceptance criteria for release. Blood collection and PBMC isolation for immunology assessments Whole blood samples were collected before treatment, Q3w after treatment until week 12, and then Q9w for immunological analyses. PBMCs from whole blood were isolated, counted and cryopreserved in CryoStor CS10 (part no. 210102, STEMCELL Technologies) according to standardized protocols. Cryopreserved cells were thawed, washed, counted and rested overnight before use in immunological assays. Peptides Custom-made, recombinant, lyophilized peptides specific to each patient were produced (GenScript). Peptides were reconstituted at 100 mg ml−1 per peptide in sterile dimethylsulfoxide (DMSO) (cat. no. BDH1115-1LP, VWR International), aliquoted and stored at −80 °C. A single-neoepitope peptide pool (1 mg ml−1) consisted of four peptides covering the entire 33-mer neoepitope, each containing 15 amino acids, overlapping by eight amino acids. Additionally, a 9-mer predicted CD8 epitope was synthesized and included in the single-neoepitope pool. To make two large peptide pools covering all vaccine-encoded patient-specific neoepitopes, we pooled (at a concentration of 1 mg ml−1) all peptides covering the first half of the vaccine-encoded neoepitopes into pool 1 and pooled (at a concentration of 1 mg ml−1) the remaining peptides covering the second half of the vaccine-encoded neoepitopes into pool 2. The B*40:01-restricted NY-ESO-1 epitope, EFTVSGNIL (CTA1), was used as a nonspecific stimulus at 10 µg ml−1. IFNγ ELISpot assay The ELISpot assay was performed (FlowMetric) using the standard ELIS - pot protocol47 and the human IFNγ single-color ELISpot kit (all reagents and plates included; stock keeping unit no. hIFNgp-1M, Cellular Tech- nology Limited). Briefly, PBMCs from each patient (3 × 105 cells per well) were placed in ELISpot multiscreen plates precoated with antihuman IFNγ capture antibody, stimulated with the matching peptide pools (single-neoepitope pool or larger pools with multiple neoepitopes) at a concentration of 10 µg ml−1 for 18–24 h. No cytokine stimulation was performed. An equivalent amount of DMSO was added to control wells. PBMCs from each patient were set in duplicate or triplicate for peptide stimulations and controls. After stimulation, cells were removed and a biotinylated secondary antibody was added. After 2 h of incubation, the plates were washed, added with streptavidin-conjugated alkaline phosphatase and further incubated for 1 h. The plates were devel - oped by adding 5-bromo-4-chloro-3-indolyl phosphate/nitroblue tetrazolium as the substrate. ELISpot plates were analyzed on the CTL ImmunoSpot S6 Ultimate-V analyzer (Cellular Technology Limited) using ImmunoSpot software version 5.1. The cell viability and counts upon thawing of the PBMC vials are reported in Supplementary Fig. 7. A T cell response to a specific epitope at a time point was con - sidered positive if it met each of the three criteria to assure with 95% confidence that the response could be attributed to the specific pep - tide. The epitope-specific response had to be (1) at least 2 s.d. above Nature Medicine Article https://doi.org/10.1038/s41591-024-02894-ythe corresponding unstimulated control sample (background), (2) at least twofold above the corresponding unstimulated control sample (background) and (3) at least 5 SFU. The same criteria were used to evaluate pretreatment samples (for preexisting neoepitope responses) and on-treatment samples. Data are presented as SFU per 106 PBMCs. For the calculation of the number and percentage of responding epitopes, an epitope was counted if it resulted in a positive ELISpot response, as defined above, at any time point. For the analysis of the magnitude of response, upon determina - tion of a positive response, each sample was background corrected by subtracting the average value of the negative control peptide wells. The background-subtracted responses of each positive epitope were summed for the pretreatment baseline and for each available on-treatment time point to determine the cumulative ELISpot response for that time point. The postvaccination response for each patient is the ‘best’ cumulative response (highest magnitude) for that patient across the available time points. In vitro stimulation and intracellular staining The patients’ PBMCs (2.5 × 105 cells) were cultured in a growth medium (RPMI with 10% FBS) supplemented with a cocktail of the IL-2 (20 IU ml−1), IL-4 (10 ng ml−1) and IL-7 (10 ng ml−1) cytokines and enriched for neoantigen-specific T cells using 10 µg ml−1 of epitope stimuli. Three days later, cells were washed and the supplemented growth medium was replaced. On day 4, epitope stimuli (10 µg ml−1) or controls were added, followed by incubation for 1 h. Then, anti-CD107a-APC (clone H4A3, BioLegend) antibody and Protein Transport Inhibitor Cocktail (1:500 dilution, Invitrogen) were added. After a 5-h incubation, cells were stained using fluorescently labeled surface marker antibodies: anti-CD3-BV711 (clone UCHT1, BD Biosciences), anti-CD4-BUV395 (clone RPA-T4, BD Biosciences), anti-CD8-BUV805 (clone RPA-T8, BD Biosciences), anti-CD69-BV421 (clone FN50, BD Biosciences), anti-CD137-BV605 (clone 4B4-1, BioLegend) and dump markers including anti-CD14/-CD16/-CD19-APC-H7 (clone MΦP9, 3G8, SJ25CI, respectively) (all from BD Biosciences). Dead cells were stained using Live/Dead Blue solution (1:1,000 dilution, Thermo Fisher Scientific), followed by overnight fixation and permeabilization using fixation/ permeabilization buffers (cat. nos. 00-5123-43 and 00-5223-56, eBio - science) according to the manufacturer’s instructions. Cells were then stained intracellularly in eBioscience permeabilization buffer (cat. no. 00-8333-56) with anti-IFNγ-BV786 (clone 4S.B3, BD Biosciences), anti-IL-2-FITC (clone MQ1-17H12, BD Biosciences), anti-Ki67-AF700 (clone B56, BD Biosciences), anti-GZMA-PerCP/Cy5.5 (clone CB9, Bio - Legend), anti-perforin-PE/Dazzle 594 (clone dG9, BioLegend) and anti-TNF-PE/Cy7 (clone MAb11, BD Biosciences) antibodies. The gating strategy is shown in Supplementary Fig. 8. Flow cytometry data were acquired on the LSRFortessa analyzer (BD Biosciences) using FACSDiva software version 8.0.1 and analyzed using FlowJo version 10.4 or later. TCR variable β-chain sequencing Immunosequencing of the CDR3 regions of human TCRβ chains was performed using the immunoSEQ assay (Adaptive Biotechnologies). Extracted genomic DNA (500 ng) was amplified from each patient’s pair-matched PBMC and tumor biopsy samples in a bias-controlled multiplex PCR followed by high-throughput sequencing. Sequences were collapsed and filtered to identify and quantify the absolute abun - dance of each unique TCRβ CDR3 region for further analysis, as previ-ously described 48–50. TCRseq analysis Raw sequence reads were demultiplexed according to Adaptive’s pro - prietary barcode sequences. Demultiplexed reads were then further processed to remove adapter and primer sequences and to identify and remove primer dimer, germline and other contaminant sequences. The filtered data were clustered using both the relative frequency ratio between similar clones and a modified nearest-neighbor algorithm to merge closely related sequences to correct for technical errors intro - duced through PCR and sequencing. The resulting sequences were sufficient to allow the annotation of the V, D and J genes and the N1 and N2 regions constituting each unique CDR3 and the translation of the encoded CDR3 amino acid sequence. Gene definitions were based on annotation in accordance with the IMGT database ( www.imgt.org). Data were analyzed using the immunoSEQ analyzer toolset. Single-cell RNAseq/TCRseq and digital gene expression T cells were isolated and enriched from the week 12 posttreatment PBMC samples of patients 6, 7 and 8 using a human pan T cell isolation kit (cat. no. 130-096-535, Miltenyi Biotec), following the manufacturer’s instructions. For patient 8, two samples from the same time point were sequenced. Next-generation sequencing libraries were prepared using the 10x Genomics Chromium Single-Cell 5′ Reagent kit v2 per the manufacturer’s instructions. Libraries were uniquely indexed using the Chromium Dual Index kit, pooled and sequenced on an Illumina NovaSeq 6000 sequencer in a paired-end, dual-indexing run. Sequenc - ing for each library targeted 20,000 mean reads per cell. Single-cell data preprocessing, quality control and analysis Cell Ranger version 6.0.0 was used to demultiplex FASTQ reads, per- form sequence alignment to the GRCh38 (Genome Reference Consor - tium Human Build 38) transcriptome and extract unique molecular identifier barcodes. Single-cell gene expression matrices were analyzed using the R package Seurat version 4.1.3 (ref. 51 ). Quality control was performed by excluding genes found in fewer than three cells, cells with <200 or >4,000 expressed genes, and cells with ≥25% mitochondrial RNA content. To avoid clonotype bias, we removed the TCRα and TCRβ genes from the count data52. The Seurat function SCTransform was used to normalize raw count data to a gamma–Poisson generalized linear model, perform variance stabilization, identify highly variable features, and scale features 53,54. Integration of individual samples was performed using the Seurat functions FindIntegrationAnchors and IntegrateData. Cells were projected into the first 30 principal compo - nents using the RunPCA function in Seurat and further reduced into a two-dimensional visualization space using the RunUMAP function. Initial cell-type assignment was performed using the Seurat function MapQuery to perform reference mapping to an annotated human PBMC dataset. Cluster identities were then manually assigned by iden - tification of differentially expressed genes using the MAST hurdle model, as implemented in the Seurat FindAllMarkers function with a log(fold-change) threshold of 0.25 and minimum fractional expression threshold of 0.25 (ref. 55 ). Canonical marker genes used for cluster identity included the following: CD4+ naive (CCR7 ), CD4+ T central memory (IL7R, LT B ), CD4+ TEM (GZMK, CCL5), CD4+ CTL (GZMH, NGK7, GNLY), CD4+ proliferating (MKI67, TOP2A), CD8+ naive (CD8B, CCR7), CD8+ T central memory (CD8A, IL7R), CD8+ TEM (CCL5, GZMK, GZMH), CD8+ proliferating (MKI67, CD8B), double-negative (NUCB2, FXYD2), γδ T (TRGV9 , TRDV2), mucosal-associated invariant T (KLRB1, RORA), NK ( NKG7 , TYROBP ) and T regulatory ( FOXP3 , RTNK2 ) cells. For single- cell VDJ sequencing, only cells with full-length sequences were retained. Integration of the single-cell TCRseq data into the single-cell RNAseq data was performed using the R package scRepertoire56. Integration of Adaptive bulk TCRβ sequencing data and single-cell data was per - formed by comparing matching TCRβ CDR3 amino acid sequences between datasets. TCR-engineered constructs High-frequency T cell clones were identified in patient-derived PBMC samples after vaccination by TCRseq and single-cell RNAseq analy - ses. Patient-specific clonal TCR sequences were gene optimized and inserted into the pMXs-IRES-GFP retroviral plasmid vector containing the viral packaging signal, transcriptional and processing elements, Nature Medicine Article https://doi.org/10.1038/s41591-024-02894-yand the GFP reporter gene (GenScript). TCRβ and TCRα were posi - tioned in sequence separated by a P2A (2A peptide derived from porcine teschovirus-1) cleavage site (TCRβ-P2A-TCRα). Retroviral particles encoding TCR constructs were generated by transfecting Phoenix-AMPHO cells (American Type Culture Collection) using Lipo - fectamine 3000 (Thermo Fisher Scientific) following the manufactur - er’s instructions. Unvaccinated (pretreatment) patient-derived PBMCs (1 × 106 cells) were retrovirally transduced to express the selected TCRs, as previously described57. Cells were cultured in RPMI medium sup - plemented with 10% FBS, 50 U ml−1 IL-2 and 1 ng ml−1 IL-7 (Peprotech) in a 5% CO2 humidified incubator for 10 days. The cell culture medium was refreshed every 2–3 days. TCR-engineered T cells were stimulated in vitro for 6 h with neoantigen pools (10 µg ml−1). Then, cells were evaluated by flow cytometry for T cell activation. ctDNA extraction, sequencing and analysis Whole blood samples were collected when feasible in cell-free DNA (cfDNA) BCT Streck tubes (cat. no. 218997, Streck) at baseline (pre - treatment), on week 3, and then Q3w until week 9 and Q9w thereafter. Plasma was separated from the cellular component and clarified using a double-spin protocol (1,600g for 10 min, 3,200g for 10 min). cfDNA was purified from plasma samples using the QIAamp circulating nucleic acid kit (cat. no. 55114, Qiagen). cfDNA was quantified using the cfDNA ScreenTape assay (Agilent). cfDNA samples from 13 patients who had baseline samples were batched and analyzed using personalized ctDNA assays. Up to 50 ng cfDNA was used as input for library preparation before enrichment and deep sequencing. Somatic mutation calls were made using Personalis ACE exome data from tumor tissue biopsy sam - ples. Capture probe panels were designed for personalized targets. Advanced noise suppression, mutation calling, aggregate tumor track - ing and measurable residual disease calling were performed using Personalis ctDNA technology. Statistical analysis This study aimed to enroll 36 patients with advanced HCC. This article details results, as of the data cutoff date, from the full cohort of 36 patients enrolled. With a null hypothesis of an ORR of 16.9%, a sam - ple size of 36 patients provides 80% power to detect an alternative hypothesis of at least 33.1% using a one-sided α of 0.10. No data were excluded from the analyses, and there was no randomization as part of the trial design. The key secondary analysis was performed with a one-sided exact binomial test, and results are reported with a one-sided 90% CI. Otherwise, descriptive statistics of counts and rates are used for categorical outcomes, and median times to events are used for time-to-event outcomes. Reporting summary Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article. Data availability Single-cell RNA sequencing data are deposited in the Gene Expres - sion Omnibus under accession number GSE255830 . TCRβ sequencing data can be found in the open-access immuneACCESS database under Digital Object Identifier https://doi.org/10.21417/RP2024NM. The TCR constructs used to evaluate T cell vaccine specificity are depos - ited in GenBank under accession numbers PP316119 (patient 8_TCR1), PP316120 (patient 8_TCR2), PP316121 (patient 8_TCR3), PP316116 (patient 5_c3-1), PP316117 (patient 5_c3-2) and PP316118 (patient 5_c6). Deidentified individual participant clinical data that underlie the results reported in this article are available for transfer. Interested investigators can obtain and certify the data transfer agreement and submit requests to the corresponding author (N. Y .S.). Investigators and institutions who consent to the terms of the data transfer agree- ment form, including but not limited to the use of these data for a specific project and only for research purposes, and to protect the confidentiality of the data and limit the possibility of identification of participants in any way whatsoever for the duration of the agreement will be granted access. Geneos will then facilitate the transfer of the requested deidentified data. This process is expected to be through a Geneos Secure File Transfer Service, but Geneos reserves the right to change the specific transfer method at any time, provided appropriate levels of access authorization and control can be maintained. Code availability All custom code used to generate the results in this study has been deposited in a GitHub repository at https://github.com/FertigLab/ GeneosCollaboration. References 46. De Rosa, S. C.