Rojas, Zachary Sethna. ✉e-mail: greenbab@mskcc.org; balachav@mskcc.org Methods Trial design, treatments, oversight and conduct We administered atezolizumab, autogene cevumeran and mFOL - FIRINOX sequentially to measure how each immunotherapy mod - ulated neoantigen-specific T cells. To establish clinical feasibility, we set the following benchmarked times to treatment after surgery (Fig. 1a): (1) one 1,200 mg intravenous dose of atezolizumab on week 6; (2) nine 25 µg intravenous doses of autogene cevumeran given as seven weekly priming doses beginning on week 9, an eighth dose at week 17 and a ninth booster dose at week 46; (3) 12 cycles of mFOLFIRINOX beginning on week 21. As the half-life of atezolizumab is 27 days, with receptor occupancy persisting for several months39,40, we hypothesized that this dosing scheme would allow sufficient PD-L1 receptor occupancy to support proficient T cell activation by autogene cevumeran. Additional details are in provided in the protocol in Supplementary Data 1. As we treated the first patient before the COVID-19 pandemic, patients received SARS-CoV-2 vaccines as they became available either before, interspersed during or following completion of individual experimental treat - ments. We conducted the study in accordance with the Declaration of Helsinki and good clinical practice guidelines. The study was approved by the institutional review board at MSK, the US Food Drug Adminis - tration and was registered on ClinicalTrials.gov ( NCT04161755 ). All participants provided written informed consent. Immune response assays We investigated peripheral blood samples using two independent assays. Both, per design, detect high-magnitude T cell responses to vaccines without distinguishing CD8+ from CD4+ T cell responses. IFNγ ELISpot was performed ex vivo (that is, without previous expansion in culture to capture T cells below the assay threshold) to map the induc- tion of neoantigen-specific T cell responses for each vaccine target used across all patients. Patients were classified as responders to autogene cevumeran if the IFNγ ELISpot assay detected T cell reactivity against at least one vaccine neoantigen. To confirm IFNγ ELISpot results using an orthogonal technique, we used TCR Vβ sequencing-based CloneTrack to detect greater than twofold in vivo expansion of T cell clones to vaccines in a non-antigen- specific manner. Clones that expanded at different treatment times (before and after atezolizumab; before and after autogene cevumeran) further distinguished atezolizumab from autogene- cevumeran-expanded T cell clones. For 6 ELISpot-identified immuno - dominant neoantigens in 4 out of 8 (50%) autogene cevumeran respond - ers, we used TCR Vβ sequencing of in vitro neopeptide-stimulated T cells to validate the specificity of in vivo vaccine-expanded T cell clones. Patients We enrolled patients with an Eastern Cooperative Oncology Group performance status of 0–1 with single, radiographically suspicious, surgically resectable PDACs, no distant metastases and ≥5 neoantigens. We excluded patients with metastatic, borderline or locally unresect-able PDACs, and patients who received neoadjuvant therapy. After surgery, we included patients with pathologically confirmed PDAC with R0/R1 margins. Additional eligibility criteria and ethical study conduct information are in the protocol (Supplementary Data 1). We aimed to accrue 20 evaluable patients. Surgery Patients underwent open pancreaticoduodenectomy or either open or laparoscopic distal pancreatectomy and splenectomy at MSK. We then transported tumour blocks with the most (minimum ≥10%) histological tumour content with matched blood to BioNTech.End points The primary end point was safety (Extended Data Table 1). Secondary end points were 18-month RFS and 18-month OS. We defined recur - rence as new lesions on the basis of response evaluation criteria in solid tumours (v.1.1), and RFS from either the date of surgery (RFS) or from the date of the last autogene cevumeran priming dose (landmark RFS) to the date of recurrence or death, whichever occurred first. We censored patients without events at the last known date they were recurrence-free. We defined OS from the date of surgery to the date of death. As exploratory end points, we measured immune response and feasibility as actual compared with benchmarked treatment times. Data cut-off was 1 April 2022, extending the median follow-up beyond the prespecified 18-month RFS secondary end point. Mutation identification and neoantigen selection DNA was extracted from PBMCs. DNA and RNA were extracted from tumours. Expressed non-synonymous mutations and HLA type were identified by whole-exome sequencing of patient-specific tumour– normal pairs and tumour RNA sequencing. Neoantigens were bioin - formatically predicted and ranked by immunogenicity as previously described16. mRNA vaccine neoantigen characteristics are detailed in Supplementary Table 5. Somatic and germline mutation testing For the detection of somatic tumour mutations in key cancer genes, we used MSK-IMPACT, a previously published targeted tumour-sequencing test that covers 468 cancer genes41. For the detection of germline mutations, we utilized the MSK-IMPACT panel to detect pathogenic germline variants. In brief, gDNA was enriched for targeted regions using a hybridization-based protocol and sequenced on an Illumina HiSeq instrument. Sequence reads were aligned to the GRCh37/hg19 reference human genome and variants (SNVs, small indels, and copy number variants encompassing one or more exons) were called using publicly available and in-house devel - oped bioinformatics tools. Variants were classified according to the American College of Medical Genetics and Genomics guidelines42. Only variants classified as pathogenic or likely pathogenic are reported. The list of mutations analysed in the MSK-IMPACT germline panel are provided in Supplementary Table 4. Production benchmarks We set the following a priori benchmarks from surgery to vaccine manu - facture: (1) transport specimen from operating room to pathology in ≤5 min; (2) fix specimen in formalin and embed in paraffin in ≤15 min; (3) select blocks for vaccine production in ≤2 days; (4) ship to BioNTech in ≤72 h; (5) produce vaccines in ≤6 weeks; (6) administer first dose of the vaccine in ≤9 weeks. Autogene cevumeran manufacturing For every patient, individualized mRNA neoantigen vaccines were manufactured under good manufacturing practice conditions contain - ing two uridine-based mRNA strands with noncoding sequences opti - mized for superior translational performance43, 44. Each strand encoded up to 10 MHCI and MHCII neoepitopes, formulated in approximately 400 nm diameter lipoplex nanoparticles20 comprising the synthetic cationic lipid (R )-N ,N,N-trimethyl-2,3-dioleyloxy-1-propanaminium chloride (DOTMA) and the phospholipid 1,2-dioleoyl-sn-glycero-3- phosphatidylethanolamine (DOPE) to enable intravenous delivery. Cell culture We purified patient PBMCs from blood samples by density centrifuga - tion over Ficoll-Paque Plus (GE Healthcare). We purified healthy donor PBMCs from buffy coats (New York Blood Center) and isolated T cells using a Pan-T cell isolation kit (Miltenyi Biotech). We activated T cells Articlewith CD3/CD28 beads (Thermo Fisher) with IL-7 (3,000 IU ml–1) and IL-15 (100 IU ml–1) (Miltenyi Biotec), and transduced T cells on day 2 after activation. Virus-producing cell lines (H29 and RD114-envelope producers) were as previously described45,46. We cultured T cells and K562 cells in RPMI medium supplemented with 10% FBS (Nucleus Biologics), 100 U ml–1 penicillin–streptomycin (Thermo Fisher Gibco) and 2 mM l -glutamine (Thermo Fisher Gibco). We cultured patient PBMCs with RPMI medium supplemented with 10% FBS, 1 mM sodium pyruvate, 2 mM l -glutamine, non-essential amino acids and 2-mercaptoethanol (MSK medium preparation core facility). We cul - tured H29, RD114-envelope producers and Phoenix-AMPHO in DMEM medium supplemented with 10% FBS (Nucleus Biologics), 100 U ml–1 penicillin–streptomycin (Thermo Fisher Gibco) and 2 mM l -glutamine (Thermo Fisher Gibco). Immune response Ex vivo IFNγ ELISpot. Multiscreen filter plates (Merck Millipore), pre - coated with antibodies specific for IFNγ (Mabtech), were washed with PBS and blocked with X-VIVO 15 (Lonza) containing 2% human serum albumin (CSL-Behring) for 1–5 h. Next, 3 × 105 effector cells per well were stimulated for 16–20 h with pools of 15-amino-acid-long peptides ( JPT Peptide Technologies) overlapping by 11 amino acids covering the length of each target. Cryopreserved PBMCs were subjected to ELIS - pot after a resting period of 2–5 h at 37 °C. All tests were performed in duplicate and included anti-CD3 (Mabtech) as a positive control. Bound IFNγ was visualized using a secondary antibody directly conjugated with alkaline phosphatase (ELISpotPro kit, Mabtech). Next, plates were incubated with BCIP/NBT (5-bromo-4-chloro-3′-indolyl phosphate and nitro blue tetrazolium) substrate (ELISpotPro kit, Mabtech). Plates were scanned using an AID Classic Robot ELISPOT reader and analysed using AID ELISPOT 7.0 software (AID Autoimmun Diagnostika). A sam - ple was deemed positive if the IFNγ ELISpot count exceeded a minimum threshold of 7 spots per 300,000 PBMCs. A post-vaccination PBMC sample was deemed positive on the basis of a significant increase in ELISpot count compared with a negative control (medium alone, as no IFNγ ELISpot responses were detected in pre-vaccination samples). To account for varying sample quality reflected in the number of spots in response to anti-CD3 antibody stimulation, we applied a normali - zation method that enabled direct comparison of spot counts and strength of response between individuals, as described previously24,47,48. Statistical significance was determined based on two statistical tests (distribution-free resampling)47,48. TCR Vβ clone tracking (CloneTrack). For TCR Vβ sequencing, we prepared gDNA from bulk PBMCs or purified T cells using a Qiagen DNA extraction kit according to the manufacturer’s instructions. We quantified samples using Dropsense 96 and diluted to standard con - centrations for library preparation. We generated sample data using an immunoSEQ Assay (Adaptive Biotechnologies). In brief, the somatically rearranged TCRB CDR3 was amplified49,50 from gDNA using a two-step, amplification bias-controlled multiplex PCR approach. The first PCR consists of forward and reverse amplification primers specific for every known V and J gene segment, and amplifies the hypervariable CDR3 of the immune receptor locus. The second PCR adds a proprietary barcode sequence and Illumina adapter sequences51. In addition, reference gene primers were included in the PCR reaction to quantify total nucle - ated cells that can be sequenced and to accurately measure the frac - tion of T cells in each sample. CDR3 and reference gene libraries were sequenced on an Illumina instrument according to the manufacturer’s instructions. Raw sequence reads were demultiplexed according to Adaptive’s proprietary barcode sequences. Demultiplexed reads were 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 fre - quency ratio between similar clones and a modified nearest-neighbour algorithm to merge closely related sequences to correct for technical errors introduced through PCR and sequencing. The resulting sequences were sufficient to annotate 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 (https://www. imgt.org). The set of observed biological TCRB CDR3 sequences were normalized to correct for residual multiplex PCR amplification bias and quantified against a set of synthetic TCRB CDR3 sequence analogues50. T cell clone definition. We identified and tracked T cell clones by their β chain sequence (TRB), defined as the nucleotide CDR3 sequence (including the conserved C and F residues) and a deterministic V and J gene alignment. For T cells identified by single-cell sequencing, we similarly defined clones by the TRB sequence to map clones to paired TCR Vβ sequencing. Owing to the higher entropy of the nucleotide CDR3 sequence generation probability distribution52, we used nucleo - tide instead of amino acid CDR3 sequences to minimize the chance of conflating two different T cell clones (different original VDJ recombi-nation events). This becomes crucial to differentiate clones that may have different α chain (TRA) sequences, which are unobserved in the bulk TCR Vβ sequencing step. We used the provided deterministic V and J alignments from Adaptive Biotechnologies (for bulk TCR Vβ sequencing) and 10x (for single-cell sequencing). T cell clone frequency estimation. For a given sample of bulk TRB sequences, we estimated the total number of effective cells sequenced, N, as the summation of all productive (in-frame, no stop codons) reads. We excluded non-productive reads, as they must necessarily be recom - bined CDR3s from silenced alleles (we did not model the fraction of productive reads from silenced alleles and assumed them to be a small correction). We estimated a T cell clone x’s cell count nx in a sample as the number of reads corresponding to the clone as defined above (V and J gene and nucleotide CDR3 sequence). We therefore estimated the frequency of clone x as f=xn Nx. For the purpose of visualization, if we did not observe a clone in a sample, we used a pseudo-frequency of N1 3 (if plotting a trajectory with multiple samples, we used the largest N over the samples) and indicated this observation threshold as dotted black lines. We computed the aggregate frequency of several clones x X∈ in a similar fashion using an aggregate count n n =∑Xx Xx∈ and used the same convention for a pseudo-frequency if n=0X . T cell clone significance determination. We took a statistically conserv - ative approach to minimize the false-positive identification of expanded T cell clones. To this end, to calculate treatment-expanded T cell clones, we used a significance threshold of Padjusted < 0.001, where the P value is adjusted using Bonferroni correction (Padjusted = P × no. of T cell clones) to account for the large number of T cell clones that were screened. T cell expansion criteria. To identify treatment-expanded T cell clones, we used an adapted Fisher’s exact test and computed P values for expanded T cell clones using a two-tailed adapted exact Fisher’s test for a twofold increase in a T cell clone between any two samples. We implemented this by rescaling the repertoire size of the initial sample by half, to effectively reduce the sample size and the number of cells not belonging to the clone in question. We computed this as a Fisher’s exact test (implemented from scipy.stats.fisher_exact) using the categorical table supplied in Extended Data Table 2. Clones that had a fold change <2 (that is, <2 ×m Mn Nxx) were assigned a P value of 1. These P values were then adjusted using Bonferroni cor- rection: ∪ PP NM =×adjusted /uni2223/uni2223 , where ∪NM| | designates the number of unique clones in the union of the two samples. We applied this T cell clone expansion P value in the following two contexts to determine whether atezolizumab or autogene cevumeran expanded T cell clones. Atezolizumab response: to determine whether atezolizumab expanded T cell clones (Extended Data Fig. 3), we compared the num- ber of cells of a particular T cell clone in a blood sample taken on the day of but before atezolizumab administration to the number of cells of that T cell clone in a blood sample taken on the day of but before the first dose of autogene cevumeran. We then considered that a patient had a response to atezolizumab if any T cell clone was found to be sig - nificantly expanded (Padjusted < 0.001) according to the above outlined expansion criterion. Autogene cevumeran response with priming doses: to determine whether autogene cevumeran expanded T cell clones, we imposed two criteria: (1) a quality control requirement that a T cell clone must have a minimum of three reads in at least two samples, and (2) the T cell clone must not be observed before vaccination (0 cells in all samples taken until the day of but before the first dose of autogene cevumeran). Then, for clones that passed these criteria (as defined above), we compared the number of cells of a particular T cell clone in a blood sample taken after atezolizumab and before vaccination to the number of cells of that T cell clone in any blood sample taken until the day of but before the first dose of mFOLFIRINOX. We further assigned an expan- sion P value as defined as the minimum adjusted expansion P value for all samples, further adjusted by Bonferroni correction for the number of samples the expansion P values were computed for. We then considered that a patient had a response to autogene cevu - meran if any T cell clone significantly expanded (Padjusted < 0.001) accord - ing to the above outlined expansion criteria. For 50% of the responders (n = 4 patients), we further examined whether the autogene-cevumeran-expanded T cell clones included neoantigen-specific in vitro clones (see the section ‘In vitro stimulation and T cell clone specificity to peptides’ below). Autogene cevumeran response with a booster dose: to determine whether an autogene-cevumeran-expanded clone further expanded after the booster dose, we used a standard Fisher’s exact test, with no additional fold change criteria, to compare the clone sizes in samples taken immediately before booster administration to samples from the first follow-up after the booster. As we had previously identified these clones and assessed each one independently, we did not use any multiple hypothesis adjustment and set a significance threshold of Padjusted < 0.01 for boost expansion. Boost expansion was analysed in all patients with identified autogene-cevumeran-expanded clones, except for patient 5 who did not receive a booster. In vitro stimulation and T cell clone specificity to peptides We resuspended neopeptides (Genscript) in DMSO at 10 mg ml–1 and a SARS-CoV-2 peptide pool (Mabtech) following the manufacturer’s instructions. We stored all peptides at –80 °C. We restimulated PBMCs with peptides in vitro as previously described with minor modifica - tions2. In brief, we cultured 1 × 106 PBMCs in a 48-well plate with indi - vidual (10 µg ml–1) or pooled (1–5 µg ml–1 per peptide) peptides on day 1. We added IL-2 (100 U ml–1) and IL-15 (10 ng ml–1) on day 2 and every subsequent 2–3 days. On day 7, we restimulated cells with peptides and added a CD107a antibody (clone H4A3, PE, BD Biosciences) for 1 h at 37 °C. After 1 h, we added a protein transport inhibitor containing monensin (BD Biosciences) and incubated for 4 h at 37 °C. We then stained the cells for additional surface or intracellular markers as per the manufacturer guidelines, and either analysed or purified cells based on CD107a surface expression, or analysed cells based on intracellular cytokine expression. To determine whether a T cell clone was specifically stimulated by the peptide pool, we sorted and identified T cell clones in CD107a– and CD107a+ fractions after peptide stimulation as described above. We then determined a peptide-specificity stimulation P value for each T cell clone using a one-tailed binomial test P value (implementing the scipy.stats.binom_test) with a 0.2 threshold (specifically, significance with respect to at least 20% of a clone being CD107a+ as opposed to CD107a–). We adjusted P values using Bonferroni correction and deter - mined significance at a Padjusted < 0.001 threshold. We included DMSO as a control to identify nonspecifically stimulated T cell clones. Of all patients tested, only one nonspecific clone was identified (patient 10) as nonspecifically stimulated in DMSO and both screened peptide pools. This clone was therefore excluded as a peptide-specific clone. HLA cloning and transduction We cloned the HLA alleles into an SFG γ-retroviral vector53 and sequence- verified all plasmids (Genewiz). We transfected Phoenix-AMPHO cells with the plasmids using MegaTran 2.0 (OriGene). We collected virus-containing supernatants 48 h after transfection, added Polybrene (EMD Millipore) and spinoculated K562 cells for 2 h at 2,400 r.p.m. at 33 °C. Seventy-two hours after transduction, we sorted HLA+ K562 cells using an Aria Cell sorter (BD Biosciences). TCR cloning, transduction and peptide stimulations We constructed TCR fragments as previously described54. In brief, we isolated TRB V-D-J and TRA V-J sequences from purified, sequenced single T cells and fused the TRB V-D-J and TRA V-J sequences to modi- fied mouse constant TRB and TRA chain sequences54, respectively (gift from A. Gros) to prevent mispairing of transduced TCRs with the endogenous TCRs55. In brief, we joined TRB and TRA chains with a furin SGSG P2A linker, cloned the TCR constructs into a SFG γ-retroviral vector53 and sequence-verified all plasmids (Genewiz). We transfected H29 cells (gpg29 fibroblasts) with retrovirus vectors using calcium phosphate and produced VSV-G pseudo-typed retroviruses45. We next used Polybrene (Sigma) and virus-containing supernatants to generate stable RD114-enveloped producer cell lines46. We collected and con - centrated virus-containing supernatants using a Retro-X Concentrator (Takara). We then coated non-tissue-culture treated 6-well plates with Retronectin (Takara), plated a titrated virus quantity to 3 × 106 activated T cells per well, centrifuged cells for 1 h at room temperature at 300g and used transduced T cells between day 7 and 14 after transduction or cryopreserved them for future use. To stimulate TCR-transduced T cells with peptides, we pulsed 5 × 104 (effector:target ratio 1:1) or 2.5 × 105 (effector:target ratio of 1:5) HLA-transduced K562 cells (antigen presenting cells) in a 96-well U-bottom plate for 1 h at 37 °C with the indicated peptides at the indicated concentrations. After 1 h, we washed the peptide by centrifugation and added 5 × 104 TCR or mock (control) transduced T cells per well. We then measured CD137 (also known as 4-1BB) expression on CD8+ mouse TCR+ T cells 24 h later. Immunophenotyping by flow cytometry and optical impedance For flow cytometry, PBMCs from patients were rested overnight at 37 °C and 5% CO2 before staining55. We defined TCR-transduced CD8+ T cells as live, CD3+, CD8+, mouse TCR+ cells. We stained cells with the follow - ing antibodies: from BioLegend, CD62L (clone DREG-56, BV510), CD56 (clone HCD56, BV605), CD4 (clone OKT4, BV650), CD19 (clone HIB19, BV711), FOXP3 (clone 206D, PE), CD3 (clone SK-7, PE-Cy7), CD8 (clone SK1, FITC or Alexa Fluor 700), CD45RA (clone HI100, APC), CD45 (clone 2D1, Alexa Fluor 700), CD39 (clone A1, BV421), LAG-3 (clone 11C3C65, PerCP-Cy5.5), CD366 (clone F38-2E2, APC-Cy7) CD11c (clone S-HCL-3, BV421), HLA-DR (clone L243, BV785), CD14 (clone HCD14, PE), CD11b (clone ICRF44, APC), IFNγ (clone 4S.B3, BV421), mouse TRB (clone H57- 597, PE-Cy5), CD137 (clone 4B4-1, PE), HLA-A,B,C (clone W6/23, APC) and a Zombie Red Fixable Viability kit (423110); from BD Biosciences, PD-1 (clone EH12.1, BV786), TNF (clone MAb11, APC), CD107a (clone H4A3, PE), CD56 (clone NCAM16.2, BV786) and DAPI (564907); from ThermoFisher Scientific, Ki-67 (clone SolA15, PE-Cy5). We stained cells using antibody cocktails in the dark at 4 °C, washed and analysed on a FACS LSR Fortessa (BD Biosciences) using FACSDiva (v.8.0.1) software (BD Biosciences). To examine the expression levels of intracellular markers, we surface-stained, fixed, permeabilized and stained the cells for intracellular proteins using a Fixation and Permeabilization ArticleBuffer kit per the manufacturer’s recommendations (Invitrogen). We used appropriate FMO controls as indicated. We analysed the data using FlowJo (v.10, Tree Star). We used the following definitions (all pre-gated on live, CD45+ cells): regulatory T cells, CD3+CD56–CD8– CD4+FOXP3+; dendritic cells, CD3–CD56–CD19–CD14–CD11c+HLA– DR+; monocytes, CD3–CD56–CD19–CD11b+CD14+; natural killer cells, CD3–CD56+; B cells, CD3–CD19+; CD8+ T cells, CD3+CD56–CD8+CD4–; CD4+ T cells, CD3+CD56–CD8–CD4+; natural killer T cells, CD3+CD56+. To identify frequencies of peripheral blood neutrophils, eosinophils and basophils, we measured respective cell frequencies by optical impedance on a clinical-grade Sysmex analyser. T cell sorting We sorted bulk T cells from patient PBMC samples immediately after thawing on a BD FACS Aria flow cytometer (BD Biosciences). We sorted CD107a– and CD107a+ CD8+ T cells after 7 days of peptide stimulation. We used the sorted T cell samples for TCR Vβ sequencing, single-cell RNA/TCR sequencing, or single-cell TCR sequencing as indicated. Single-cell RNA/TCR sequencing Library preparations for single-cell immune profiling, sequencing and post-processing of the raw data were performed at the Epigenomics Core at Weill Cornell Medicine. Sample preparation. Single-cell RNA sequencing libraries were pre - pared according to 10x Genomics specifications (Chromium Single Cell V(D)J User Guide PN-1000006, 10x Genomics). Each cellular suspension (>90% viability), at a concentration between 700 and 1,000 cells per µl, was loaded onto to a 10x Genomics Chromium platform to generate Gel Beads-in-Emulsion (GEM), targeting about 10,000 single cells per sample. After GEM generation, polyA cDNA barcoded at the 5′ end by the addition of a template switch oligonucleotide (TSO) linked to a cell barcode and unique molecular identifiers (UMIs) was generated by incubation at 53 °C for 45 min in a C1000 Touch Thermal cycler with a 96-Deep Well Reaction module (Bio-Rad). GEMs were broken and the single-strand cDNA was cleaned up using DynaBeads MyOne Silane Beads (Thermo Fisher Scientific). The cDNA was amplified for 13 cycles (98 °C for 45 s; 98 °C for 20 s, 67 °C for 30 s, 72 °C for 1 h). Quality and quantity of the cDNA was assessed using an Agilent Bioanalyzer 2100, obtaining a product of about 1,600 bp. For generation of 5P expres - sion libraries, an aliquot of the cDNA (about 50 ng) was enzymatically fragmented, end repaired, A-tailed, subjected to a double-sided size selection with SPRI select beads (Beckman Coulter) and ligated to adap - tors provided in the kit. A unique sample index for each library was introduced through 14 cycles of PCR amplification using the indexes provided in the kit (98 °C for 45 s; 98 °C for 20 s, 54 °C for 30 s, and 72 °C for 20 s × 14 cycles; 72 °C for 1 min; held at 4 °C). Indexed libraries were subjected to a second double-sided size selection, and libraries were then quantified using Qubit fluorometric quantification (Thermo Fisher Scientific). The quality was assessed on an Agilent Bioanalyzer 2100, obtaining an average library size of 430 bp. For generation of full-length TCR VDJ regions, an aliquot of the cDNA (about 5 ng) was subjected to nested PCR amplification with specific VDJ outer and inner primer pairs (98 °C for 45 s; 98 °C for 20 s, 67 °C for 30 s, and 72 °C for 20 s × 8 cycles; 72 °C for 1 min; held at 4 °C), and one-sided size selection using SPRI select beads. Quality and quantity of the VDJ region was assessed using an Agilent Bioanalyzer 2100. The average library size was 620 bp. Sequencing and post-processing of data. 5P expression and TCR libraries were clustered on an Illumina NovaSeq pair-end read flow cell and sequenced for 28 cycles on R1 (10x barcode and the UMIs), followed by 8 cycles of I7 Index (sample Index), and 91 bases on R2 (transcript), obtaining about 250 million clusters for 5P expression and 50 million for TCR libraries. Primary processing of sequencing images was done using Illumina’s Real Time Analysis software (RTA). 10x Genomics Cell Ranger Single Cell Software suite (https://support.10xgenomics.com/ single-cell-gene-expression/software/pipelines/latest/what-is-cell- ranger was used to perform sample) was used for demultiplexing, align - ment (hg19), filtering, UMI counting, single-cell 5′ end gene counting, TCR assembly, annotation of paired VDJ and performing quality control using the manufacturer’s parameters. Analysis. Filtered gene expression matrices generated from 10x Cell - Ranger for five samples were matched to paired TCR sequences using the python package Scirpy56. All five samples were aggregated into a single unnormalized counts matrix and all downstream analyses were performed using GeneVector57. Batch-effect correction was applied over all cells using the samples as batch labels. Cells were first clas - sified as either CD4 or CD8 T cells using the respective gene marker. CD8+ T cells were further classified into four phenotypes (effector, memory, naive and dysfunctional) using previously published gene markers25,58. A probability distribution over phenotypes was gener - ated for each cell, and phenotype assignment corresponded to the maximum probability. Vaccine-specific T cells were identified by exact match of the associated nucleotide sequence. Uniform manifold approximation and projection visualizations were constructed using the python library Scanpy59. Clonality Whole-exome sequence reads of tumour–normal paired samples of patients were aligned to the reference human genome (hg19) using the Burrows–Wheeler alignment tool (bwa mem v.0.7.17) and samtools (v.1.6). Duplicates were marked with picard-2.11.0 MarkDuplicates (http://broadinstitute.github.io/picard). Indel realignments were done using the Genome Analysis toolkit (GenomeAnalysisTK-3.8-1-0-gf - 15c1c3ef) RealignerTargetCreator and IndelRealigner60 using 1000 genome phase1 indel (1000G_phase1.indels.b37.vcf) and Mills indel calls (Mills_and_1000G_gold_standard.indels.b37.vcf) as references. Base calls were recalibrated using BaseRecalibrator and dbSNP (v.138). Both tumour samples were covered at 378× and normal samples at 346× on average on its target regions. MuTect 1.1.7 and Strelka 1.0.15 were used to call SNV and indels on pre-processed sequencing data. For the MuTect calls, dbSNP 138 and CosmicCodingMuts.vcf (v.86)61 were used as reference files. For the Strelka calls, we set “isSkipDepthFilters = 1” to prevent filtering-out of mutation calls from exome sequencing due to exome-sequencing mapping breadth. Unbiased normal and tumour read counts for each SNV and indel call were then assigned with the bam-readcount software 0.8.0-unstable-6-963acab-dirty (commit 963acab-dirty) (https://github.com/genome/bam-readcount ). A minimum base quality filter was set with the “-b 15” flag. The reads were counted in an insertion-centric way with the “-i” flag, so that reads overlapping with insertions were not included in the per-base read counts. We then used the normal and tumour read counts to filter mutations. The fol- lowing filtering criteria were used: (1) total coverage for tumour ≥10; (2) variant allele frequency for tumour ≥2%; (3) number of reads with alternative allele ≥5 for tumour; (4) total coverage for normal ≥7; and (5) variant allele frequency for normal ≤1% at a given mutation. Filtered mutation sets were annotated using SnpEff (v.4.3t). 23 Dbsnp138 (b37) was used for snp-pileup. To infer clonality of vaccine targets, we extracted missense and frameshift mutations from the filtered VCF files, and these muta - tions were put into the PhyloWGS software package (https://github. com/morrislab/phylowgs, v.1.0-rc2, branch: 681df79) along with copy number variant calls for phylogeny reconstruction. Among 10,000 trees from PhyloWGS, we took the top five trees based on the likelihood and computed the average entropy level to measure tumour heterogeneity. For a given tree, we computed exclusive clone frequencies such as ∑ xX X =−αα βD αβ ∈( ) where Dα() is the set of clones that are direct descendants of clone α in the given tree, and Xα is the cellular cancer fraction of clone α. Based on exclusive clone frequencies, we computed Shannon’s entropy as a measure of tumour heterogeneity as follows: ∑ Sx x =− log( ) αταα τ∈ where ⟨.⟩τ is the arithmetic average operator from top five trees ( τ). Neoantigen quality and vaccine response To model neoantigen quality, we adapted our previously described model2,3 that identified spontaneously targeted neoantigens in tumours to identify immunogenic neoantigens for vaccination. Specifically, according to our model2,3 the immunogenicity (or quality) of a neo - antigen is the product of two components. The first component— the non-self-recognition potential, R , of a neoantigen—is the inher - ent immunogenicity of the neopeptide. The second component—the self-discrimination potential, D—models whether the cognate T cells of a neoantigen avoid negative thymic selection to therefore render neoantigen recognition less constrained by self-toleration. Previous versions of our quality model2,3 estimated the non-self- recognition potential R of a neopeptide using sequence homology (as determined by soft max rescaling of BLAST alignment) to the immu - nogenic infectious disease-derived epitopes in the Immune Epitope Database (IEDB). Self-discrimination was estimated as a sum of two free discrimination energies between the neoantigen (MT) and its wild-type (WT) peptide, one for differential MHC presentation, the other for differential T cell cross reactivity: DK K=log +l ogEC ECdWT dMT50MT 50WT     where Kd is the HLA specific peptide–MHC affinity (as estimated by net- MHC 3.4), and EC50 is the concentration for 50% activation for an avidity curve with the neopeptide and its cognate T cell clone2,3. Furthermore, in previous studies, we restricted our definition of minimal epitopes to consider to only 9-mers, the most common length of MHCI-bound peptides, predicted to bind to the HLA of the patient with a cut-off of 500 nM. To now extend the notion of the cross-reactivity or epitope dis - tance beyond the single substitution case as previously described2,3, we now made an independent site approximation by modelling the cross-reactivity distance, dC, between two 9-mer epitopes, pA and pB as follows: ∑ dp pd Mp p(, )= ≈l ogEC () EC ()iip p CAB =19 ,50A 50B iiAB   where di is a scaling weight for position i and M is the substitution matrix as inferred from ref. 3 . This extension allowed us to replace the esti - mation of the non-self-recognition potential R of a neopeptide from sequence homology using BLAST with epitope distance. We now took as our two components, in the context of vaccination, how far a neopeptide is from the germline and how close it is to known antigenic IEDB epitopes. For a given 9-mer minimal neoepitope, pMT we defined the quality of the 9-mer as follows: Qp dp pd pp () =( ,) −m in (, ) pPMT CMT WT ∈CMT a a IEDB where PIEDB is the collection of all 9-mers sequences and subsequences of IEDB epitopes.We defined the quality of a neopeptide as the average quality over the two highest quality 9-mer subsequences that include the substi - tuted residue and are predicted binders (threshold of 4,000 nM) to the HLA type of the individual. As a vaccine can induce neoantigen expression in excess of endogenous expression in a tumour, we dropped the differential MHC presentation term and relaxed our MHC binding cut-off. To determine whether neoantigen quality correlated with immu - nogenicity of neoantigenic peptides included in the vaccines used in this study, we classified the neopeptides from the n = 8 immune responders as derived from immunogenic or non-immunogenic neoan - tigens according to the ELISpot assay. Individual immunogenicity was unable to be established for 7 out of the neoantigens from patient 25 and were excluded from the analysis. We used neoantigens only from immune responders to ensure that lack of an immunological response to a neoantigen reflected non-immunogenicity and not general vaccine failure. This generated 23 immunogenic neoantigens out of a total of 99 screened neoantigens from n = 8 immune responders. After excluding neoantigens with no predicted minimal epitope binders, we had a final cohort of 22 reactive neopeptides out of a total of 79. Immunofluorescence Automated double immunofluorescence was conducted using the Leica Bond BX staining system. Paraffin-embedded tissues were sec - tioned at 5 µm and baked at 58 °C for 1 h. Slides were loaded in Leica Bond and staining was performed as follows. Samples were pretreated with EDTA-based epitope retrieval ER2 solution (Leica, AR9640) for 20 min at 95 °C. The double antibody staining and detection were conducted sequentially. Primary antibodies against CD3 (0.6 µg ml–1, rabbit, Dako, A0452) and CD8 (1/10, rabbit, Ventana (Roche), 790- 4460) were used. The Leica Bond Polymer anti-rabbit HRP second - ary antibody (Leica Biosystems, DS9800) was applied followed by Alexa Fluor tyramide signal amplification reagents (Life Technolo - gies, B40953) or CF dye tyramide conjugates (Biotium, 92174) for detection. After CD3 staining, epitope retrieval was performed for denaturation of primary and secondary antibodies before CD8 anti - body was applied. After the run was finished, slides were washed in PBS and incubated in 5 µg ml–1 4′,6-diamidino-2-phenylindole (DAPI) (Sigma Aldrich) in PBS for 5 min, rinsed in PBS and mounted in Mowiol 4–88 (Calbiochem). Slides were kept overnight at −20 °C before imag - ing. Slides were scanned on a Panoramic scanner (3DHistech) using a ×20/0.8 NA objective. Whole tissues were annotated in CaseViewer (3DHistech) and converted to Tiff images. ImageJ was used to segment cells based on DAPI and to quantify whether a given cell is single, double or null positive. Humoral responses to SARS-CoV-2 We measured anti-SARS-CoV-2 spike IgG antibody titres with a chemi- luminescent microparticle immunoassay (AdviseDx SARS-CoV-2 IgG II assay; Abbott). In brief, we combined serum samples with paramagnetic particles coated with recombinant SARS-CoV-2 protein specific for the receptor-binding domain of the S1 protein, followed by incubation, washing and addition of a conjugate and chemiluminescent substrate. We then measured the resulting chemiluminescent reaction as a relative light unit, with a direct relationship between the amount of IgG antibod - ies to SARS-CoV-2 in the sample and the relative light unit detected by the system optics (Architect i2000 analyzer). We used a 4 Parameter Logistic Curve fit data reduction method (4PLC, Y-weighted) to gener - ate calibration, with a positivity cut-off of 50.0 AU ml–1. Circulating tumour DNA We measured circulating tumour DNA using MSK-ACCESS62, a high- depth next generation sequencing assay with molecular barcoding technology for the detection of very low frequency somatic alterations in 129 key cancer-associated genes within the plasma cell-free DNA Article(cfDNA) fraction in peripheral blood. In brief, cfDNA MSK-ACCESS raw sequence data were demultiplexed and processed as previously described62. cfDNA samples were sequenced to a median raw cover - age of 25,465× (range 5,007×–49,869×); after collapsing, the median duplex coverage was 1,088× (range 187×–1,783×). Variant calling was performed in a matched tumour-informed manner (“genotyping”) using GetBaseCountMultiSample (https://github.com/msk-access/ GetBaseCountsMultiSample) and required at least 1 duplex consensus read or 2 simplex consensus reads, to call a somatic SNV or short indel at a site known to be altered in the matched tumour sample from a given patient, as previously described62. Digital droplet PCR DNA extraction. Formalin-fixed paraffin-embedded (FFPE) curls col- lected in AutoLys M tubes (Thermo Fisher, A38738) were digested with protease solution. DNA was extracted using a MagMAX FFPE DNA/RNA Ultra kit (Thermo Fisher, A31881) on a KingFisher Flex purification system (Thermo Fisher) according to the manufacturer’s protocol. Samples were eluted in 55 µl elution solution. Detection of TP53R175H mutation by digital droplet PCR. TP53 assays were ordered from Bio-Rad (assay identifiers dHsaCP2000105 for TP53 p.R175H c.524G>A; dHsaCP2000106 for TP53 WT). Cycling conditions were tested to ensure optimal annealing and extension temperatures and optimal separation of positive from empty droplets. Optimization was done using a known positive control. After PicoGreen quantification, 9 ng of gDNA was combined with locus-specific primers, FAM-labelled and HEX-labelled probes, HaeIII and digital PCR Supermix for probes (no dUTP). All reactions were per - formed on a QX200 ddPCR system (Bio-Rad, 1864001), and each sample was evaluated in technical duplicates. Reactions were partitioned into a median of around 22,000 droplets per well using a QX200 droplet generator. Emulsified PCR assays were run on a 96-well thermal cycler using cycling conditions identified during the optimization step (95 °C for 10 min; 40 cycles of 94 °C for 30 min and 55 °C for 1 min; 98 °C for 10 min; 4 °C hold). Plates were read and analysed using QuantaSoft software to assess the number of droplets positive for mutant DNA, WT DNA, both or neither. Statistical analyses Safety end points are presented descriptively as percentages. Sam - ple sizes (n) represent the number of patients, tumours, T cell clones or neoantigens. We analysed feasibility as the statistical equivalence between benchmarked and achieved treatment times. Here, we defined a delay of <1 week as the zone of clinical indifference and defined the achieved time to be statistically equivalent to the benchmarked time if the 90% confidence interval of the achieved time was within the zone of clinical indifference. We analysed survival curves using log-rank (Mantel Cox) test, compared two groups using unpaired two-tailed Mann–Whitney test and categorical variables using Chi square test. We compared longitudinal clonal expansion using two-tailed Fisher’s exact test, and in vitro clonal activation using bino - mial test with Bonferroni correction. P < 0.05 was considered signifi - cant. All analyses were performed using GraphPad Prism (v.9.3.1) or Python (v.3.4). Reporting summary Further information on research design is available in the Nature Port - folio Reporting Summary linked to this article. Data availability The clinical protocol approved by the institutional review board is pro - vided in Supplementary Data 1. All single-cell sequencing data are avail - able at the Gene Expression Omnibus (accession number GSE222011 ). De-identified individual participant data reported in the paper will be shared under data use agreements upon reasonable request. Requests must be made to balachav@mskcc.org. Source data are provided with this paper. Code availability The following codes are available at GitHub: to infer clonality, https:// github.com/genome/bam-readcount, https://github.com/mskcc/ facets and https://github.com/morrislab/phylowgs (with details out- lined in the Methods); to construct and apply the neoantigen quality model, https://github.com/LukszaLab/NeoantigenEditing; and for CloneTrack, https://github.com/zsethna/CloneTrack. 39.