Balogh,3David R. Spigel,4Edward B. Garon,5Meghan E. Bushway,3 Asaf Poran,3Joong Hyuk Sheen,3Victoria Kohler,3Ekaterina Esaulova,3John Srouji,3Suchitra Ramesh,3 Rohit Vyasamneni,3Binisha Karki,5Tracey E. Sciuto,3Himanshu Sethi,6Jesse Z. Dong,3Melissa A. Moles,3 Kelledy Manson,3Michael S. Rooney,3Zakaria S. Khondker,3Mark DeMario,3Richard B. Gaynor,3,* and Lakshmi Srinivasan3,7,* 1Dana Farber Cancer Institute, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA 2Washington University School of Medicine, St. Louis, MO, USA 3BioNTech US, Cambridge, MA, USA 4Tennessee Oncology, Nashville, TN, USA 5David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA 6Natera, Austin, TX, USA 7Lead contact *Correspondence: richard.gaynor@biontech.us (R.B.G.), lakshmi.srinivasan1@external.biontech.us (L.S.) https://doi.org/10.1016/j.ccell.2022.08.003 SUMMARY Neoantigens arising from mutations in tumor DNA provide targets for immune-based therapy. Here, we report the clinical and immune data from a Phase Ib clinical trial of a personalized neoantigen-vaccine NEO-PV-01 incombination with pemetrexed, carboplatin, and pembrolizumab as ﬁrst-line therapy for advanced non-squa-mous non-small cell lung cancer (NSCLC). This analysis of 38 patients treated with the regimen demonstrated no treatment-related serious adverse events. Multiple parameters including baseline tumor immune inﬁltra- tion and on-treatment circulating tumor DNA levels were highly correlated with clinical response. De novo neoantigen-speciﬁc CD4 +and CD8+T cell responses were observed post-vaccination. Epitope spread to non-vaccinating neoantigens, including responses to KRAS G12C and G12V mutations, were detected post-vaccination. Neoantigen-speciﬁc CD4+T cells generated post-vaccination revealed effector and cyto- toxic phenotypes with increased CD4+T cell inﬁltration in the post-vaccine tumor biopsy. Collectively, these data support the safety and immunogenicity of this regimen in advanced non-squamous NSCLC. INTRODUCTION It has been recognized increasingly that somatic mutations in tu- mor cells can lead to the presentation of neoantigens, which canbe recognized by the host immune system and directly lead to tu- mor cell killing ( Ott et al., 2017 ;Keskin et al., 2019 ;Hu et al., 2021 ). Numerous clinical trials have attempted to harness this mecha-nism of inducing tumor control by using personalized neoanti- gen-based vaccination, particularly in the setting of solid tumors with a high neoantigen load, with some promising data, especiallyin the adjuvant setting ( Ott et al., 2017 ;Sahin et al., 2017 ;Keskin et al., 2019 ;Hilf et al., 2019 ). Studies have shown that a high neo- antigen burden is associated with favorable clinical responsesand increased progression-free survival (PFS) in patients with solid tumors, including non-small cell lung cancer (NSCLC) treated with anti-programmed cell death protein 1 (PD-1) ( Rizvi et al., 2015 ;Luksza et al., 2017 ;Hellmann et al., 2018 ).We have previously reported on a similar personalized neoan- tigen-based vaccine trial in combination with anti-PD-1 in pa- tients with advanced melanoma, NSCLC, and transitional cellcarcinoma (TCC) of the bladder. In this study, patients weretreated with a personalized peptide vaccine of up to 20 unique peptides (NEO-PV-01) targeted toward high-quality neoepitopes identiﬁed using a bioinformatics algorithm as described previ-ously ( Ott et al., 2020 ). The study demonstrated the safety and feasibility of this therapy, as well as the ability to deeply char- acterize the immune responses generated against the vaccinepeptides ( Ott et al., 2020 ). Immune checkpoint inhibitors targeting PD-1 or PD-L1 are well established as a ﬁrst-line standard of care for NSCLC,both in combination with cytotoxic chemotherapy regimens or as monotherapy ( Gandhi et al., 2018 ;Reck et al., 2019 ;Borghaei et al., 2020 ;Awad et al., 2021 ;Rodrı ´guez-Abreu et al., 2021 ). A dual checkpoint inhibitor regimen targeting PD-1 and CTLA-4 1010 Cancer Cell 40, 1010–1026, September 12, 2022 ª2022 Elsevier Inc.ll has also demonstrated improved outcomes over chemotherapy and is an additional approved ﬁrst-line treatment ( Hellmann et al., 2019 ;Paz-Ares et al., 2021 ). Despite these major ad- vances, there is still a need for additional options to furtherextend survival in this patient population. The addition of a neoantigen vaccine to a regimen of chemotherapy and pem- brolizumab presents an opportunity to induce and expandtumor-speciﬁc immune responses. We report here data from a Phase Ib clinical trial combining NEO-PV-01, pemetrexed, carboplatin, and pembrolizumab in ﬁrst-line metastatic non-squamous NSCLC (NCT03380871). Inaddition to demonstrating that this treatment regimen was safe and well tolerated, we performed detailed molecular and im- mune analyses of both the tumor microenvironment (TME) andperipheral blood from these patients. The analyses presented here suggest a robust effect of peptide vaccination in combina- tion with chemotherapy and anti-PD-1, particularly on CD4 + T cells, supporting further investigation of this treatment in addi-tional clinical trials. RESULTS Treatment with NEO-PV-01 plus chemotherapy and anti- PD-1 is feasible and safe in ﬁrst-line non- squamous NSCLC The study was designed as a single-arm study to assess thesafety and immunogenicity of NEO-PV-01 in combination with chemotherapy and pembrolizumab in ﬁrst-line non-squamous NSCLC. Patients were enrolled at four clinical sites in the UnitedStates. The protocol speciﬁed no prior systemic treatment formetastatic disease and no prior immunotherapy with anti-PD-1 or PD-ligand 1 (PD-L1) antibodies. Tumor PD-L1 status was as- sessed for all of the patients, but enrollment was not restrictedbased on PD-L1 status ( STAR Methods ). The primary objective of this Phase Ib trial was to evaluate the safety of administering NEO-PV-01 with pembrolizumab/chemotherapy; secondary ob-jectives included determination of antitumor activity such as objective response rate (ORR), PFS, and overall survival (OS). In addition, exploratory endpoints included a comprehensive im-mune analysis in blood and tumor ( STAR Methods ;Data S1 ). Production of NEO-PV-01 was performed as previously described ( Ott et al., 2020 ). Brieﬂy, tumor mutations were ﬁrst identiﬁed by whole-exome and RNA sequencing of each patient’sformalin-ﬁxed tumor and matched normal cells from blood as outlined in the vaccine manufacturing schema ( Figure 1 A). Epi- topes were then selected based on bioinformatic algorithms aspreviously described in Ott et al. (2020 ) and incorporated into peptide-based neoantigen vaccines ( Nielsen and Andreatta 2016 ;Abelin et al., 2017 ). Each neoantigen vaccine consisted of up to 20 unique peptides that were formulated in up to 4 distinct pools with adjuvant poly-ICLC (polyinosinic-polycytidylic acid stabilized with polylysine and carboxymethylcellulose). Pa-tients received a combination of pembrolizumab, pemetrexed,and carboplatin administered every 3 weeks (a cycle) for four cy- cles during vaccine production. Pemetrexed maintenance ther- apy was not administered in this study. Following this initial triplettherapy, starting at week 12, NEO-PV-01 was administered intra- muscularly in four separate anatomical locations. As reported previously ( Ott et al., 2020 ), ﬁve priming and two booster dosesof NEO-PV-01 were given over a period of the next 12 weeks. Pembrolizumab was continued both during the vaccine and post-vaccine time periods up to 2 years in the absence of unac- ceptable toxicities or disease progression ( Figure 1 B). Between May 2018 and April 2019, 38 patients were enrolled and received at least one dose of pembrolizumab and were deﬁned as the intention-to-treat (ITT) set ( Figure 1 C). Among these patients, 3 (8%) had prior therapy, with 2 of the 3 patientsreceiving cisplatin plus pemetrexed for 3 months as an adjuvant regimen. The third patient had 3 prior therapies that included car- boplatin plus paclitaxel for 3 months, followed by erlotinib for 2years, and a vaccine therapy ( a1,3 galactosyltransferase) for 4 months as adjuvant regimens. Most of the patients (87%) in our study presented with stage IV disease ( Table 1 ). Seventeen of the 38 patients (45%) in the ITT set were not vaccinated due to early study termination for reasons including the inability to manufacture vaccine due to inadequate tumor and/or sufﬁcientnumber of neoantigens (10 patients), adverse events (2 patients),patient consent withdrawn (2 patients), progressive disease, an investigator decision, or administration of a study-prohibited concomitant medication (1 patient each). Therefore, the vacci-nated patient subset (VAX) included 21 patients, of which 16 completed vaccination (full VAX), with ﬁve discontinuing treat- ment due to progressive disease during the vaccine regimen(Figure 1 C). Analysis of the mutational status for common muta- tions observed in NSCLC among the ITT patient set of 38 pa- tients revealed 19 patients with KRAS mutations, 15 patients with TP53 mutations, 6 patients with KEAP1 mutations, and no STK11 mutations ( Table S1 ). Depending on the patient’s human leukocyte antigen (HLA) alleles and predicted binding afﬁnity ofthe driver mutants, these common mutations were eitherincluded as part of the vaccine or were monitored for epitope spread analysis ( Table S1 ). A TP53 and a KEAP1 mutation were included in the vaccine for the corresponding patients.The reasons that driver mutations were not included in the vac- cine or monitored for epitope spread were either very low immu- nogenicity scores as deﬁned by our prediction pipeline or thepredicted immunogenic peptides had poor solubility and failed the manufacturing process. The primary objective of the study was to evaluate the safety and tolerability of NEO-PV-01 in combination with pembrolizu-mab, pemetrexed, and carboplatin ( Table S2 ). The regimen was well tolerated, consistent with the pembrolizumab plus pe- metrexed/carboplatin safety proﬁle. The most frequent relatedadverse events (AEs) included nausea, emesis, diarrhea, fatigue, neutropenia, and anemia. The only related AE with clear increased incidence in the VAX group were injection site reac-tions, with 6 patients (29%) experiencing transient, low-grade events. Treatment-related serious adverse events (SAEs) were uncommon, reported in 5 patients, only 1 of whom was in theVAX group (<5%). Treatment discontinuations due to related AEs were also uncommon (2 patients, 5.3%), and no grade 5 events were observed. In summary, NEO-PV-01 in combinationwith pembrolizumab, pemetrexed, and carboplatin was safe andwell tolerated, with a safety proﬁle similar to that previously re- ported in metastatic NSCLC ( Ott et al., 2020 ). ORR (RECIST version 1.1, patients who achieve complete response [CR] or partial response [PR], clinical beneﬁt rate [CBR, patients who achieve CR, PR or stable disease [SD]),ll Article Cancer Cell 40, 1010–1026, September 12, 2022 1011 PFS and OS were analyzed with a minimum follow-up of 24 months as of the August 2021 data cutoff. Figures 2 A and 2B summarize the radiographic response proﬁles for each pa-tient (change in sum of target lesions) for the VAX set and a sub- set of the ITT set, respectively. Only patients with at least one post-baseline RECIST assessment are shown in Figure 2 B. A B C Figure 1. NEO-PV-01 vaccine generation, clinical study design, and patient disposition (A) Schematic for sequencing of patients’ tumors, prediction of neoantigens restricted to MHC class I molecules, and generation of the synthetic lon g peptides included in the personalized neoantigen vaccines (tumor cell in blue, T cell in green).(B) Treatment scheme. Treatment with pembrolizumab and pemetrexed plus carboplatin was initiated at week 0; NEO-PV-01 was then administered betwee n weeks 12 and 24, with pembrolizumab continuing for up to 2 years.(C) Study patient disposition. Seventeen of the 38 patients (45%) in the ITT set were not vaccinated due to early study termination for reasons includi ng inability to manufacture vaccine due to inadequate tumor and/or insufﬁcient number of neoantigens (10 patients), adverse events (2 patients), patient consent w ithdrawn (2 patients), progressive disease, an investigator decision, or administration of a study-prohibited concomitant medication (1 patient each).See also Tables S1 and S2.ll Article 1012 Cancer Cell 40, 1010–1026, September 12, 2022 The pair of nested bar plots ( Figure 2 A) for the vaccinated patients allows for the comparison of the best response beforevaccine initiation (darker shade) and the best response overall (lighter shade). The difference indicates a further reduction in tumor size following the initiation of vaccine. Among the 21patients who received at least one dose of NEO-PV-01, two pa- tients had a further decrease in the sum of target lesion size of more than 30% post-vaccination ( Figure 2 A). A Swimmer’s plot for all of the patients in the ITT set provides detailed information on treatment schedules for each patient, as well as time of progression, and indicates any case in which a patient achieveda post-vaccination reduction of tumor size by at least 30% (Figure 2 C). The ORR/CBR (with 95% conﬁdence interval [CI]) for the ITT and VAX sets were 34%/71% (19.6–51.4/54.1–84.6) and 69%/100% (41.3–89.0/79.4–100), respectively. Kaplan-Meier esti- mates for PFS and OS in both the ITT and VAX cohorts are shown inFigure 2 D. Median PFS (95% CI) for ITT patients was 6.3 months (5.6–14.7) and for VAX patients was 7.2 months (5.7–14.7). Median OS for ITT patients was 16.8 months and for VAX patients was 20 months (11.6, NR ITT set, 11.5, NRVAX group). Tumor PD-L1 status for patients in the VAX group is shown in Figure S1 A. Consistent with checkpoint therapy (Hellmann et al., 2018 ), patients with higher tumor PD-L1 staining had better clinical outcomes. Among the VAX set of 21 patients, 4 of 5 patients (80%) who progressed during vaccination phase and 5 of 16 patients (31.25%) who completed vaccination (fullVAX set) had no PD-L1 expression on tumor cells at the pre-treatment time point. Levels of circulating tumor DNA (ctDNA) in the peripheral blood plasma have been shown to be indicative of overall tumorburden and to predict relapse and response in multiple solid tu- mor indications ( Abbosh et al., 2017 ;Corcoran and Chabner 2018 ;Snyder et al. 2019 ). In this study, two personalized and tu-mor-informed ctDNA assays ( Powles et al., 2021 ) were designed and tested for each patient to measure the levels of ctDNA. Of these 32 variants, 16 were predicted to be high-quality neoepi- topes based on our internal bioinformatics algorithms previouslydescribed by Ott et al. (2020 )(Figure S1 B). The other set of 16 variants may not have been included in the vaccination, depend- ing on the overlap between the Natera and our variant algorithmselection output, but they were shown to be tumor speciﬁc ( Fig- ure S1 C). For this post hoc analysis, we used an exploratory endpoint deﬁned by lack of progression at 9 months (PFS-9) post-initiation of treatment with pembrolizumab and chemo-therapy. A median PFS of 8.8 months was observed previously with pembrolizumab plus chemotherapy in ﬁrst-line metastatic non-squamous NSCLC patients ( Gandhi et al., 2018 ). Overall, the mean neoantigen change of all variants tested per patient re- mained undetectable for patients who achieved PFS-9 (9 pa- tients), while the majority of patients who did not achievePFS-9 (no PFS-9, 8 patients) showed stable or increasingamounts of neoantigen-speciﬁc ctDNA molecules ( Figure 2 E). Figure 2 F provides an example of the data at the individual neo- antigen level for a patient who did not achieve PFS-9 (2L3), andFigure 2 G provides an example of a patient who did achieve PFS-9 (2L5). Interestingly, the overall pattern of change in ctDNA levels is consistent across all mutations measured for these twopatients, suggesting that no one neoantigen is able to escape under conditions of tumor control, and in the case of progressive disease, all of the previously detected neoantigens are presentduring a period of tumor regrowth. Correlates with clinical response are observed pre- treatment in the TME, including T cell inﬁltration, major histocompatibility complex (MHC) class II expression, and T cell receptor (TCR) diversityIt has been shown previously that the inﬁltration of T and B cells into the tumor correlates with responses to checkpoint therapy (Helmink et al., 2020 ;Petitprez et al., 2020 ;Cabrita et al., 2020 ). We also observed multiple immune inﬁltrate readouts to correlate with clinical responses in our study. First, we observed that inﬁltration of both CD4 +and CD8+T cells into the TME at the pre-treatment time point correlates with clinical response, sug-gesting that the immune state of the tumor before the patient un- dergoes treatment can predict response to the treatment regimen ( Figure 3 A). Analysis of the speciﬁc localization of CD4 +and CD8+T cells revealed that inﬁltration of CD4+T cells into areas with tumor cells correlates positively with response, whereas the localization of CD8+T cells outside of the tumor areas correlates positively with response ( Figure 3 A). An example of a patient who achieved PFS-9 (2L5) and a patient who did not achieve PFS-9 (2L3) is shown in Figure 3 B. Second, we observed that HLA class II gene expression in the tumor bi- opsies at the pre-treatment time point correlates positively with clinical outcome ( Figure 3 C). This is further supported by analysis for MHC class II expression at the protein level by multipleximmunoﬂuorescence. We observed strong MHC class II staining (HLA-DR) speciﬁcally in cells of the monocyte lineage (CD14, CD11c) in the TME for patient 2L5 who achieved PFS-9 whencompared to a patient who did not achieve PFS-9 (2L3) ( Fig- ure 3 D). MHC class II expression by cells of the monocytic line- age in the TME suggest the presence of monocyte-derivedTable 1. Patient demographics and baseline disease characteristics ITT (N = 38) VAX (N = 21) Median age, y 62.5 (56–73) 63 (56–69) Sex, M/F, n (%) 15 (39)/23 (61) 7 (33)/14 (67) Prior therapy, n (%) 3 (8%) 0% Stage IV, n (%) 33 (87) 21 (100) Median TMB (range)a141 (2–776) 279 (96–776) Smokers (current or prior), %82 95 Tumor PD-L1 expression, n (%)b <1% 11 (42) 9 (43) R1% – < 50% 12 (46) 10 (47) R50% 3 (12) 2 (10) Median sum of target lesions, cm8.6 8.3 ECOG 0/1, n (%) 11 (29)/27 (71) 7 (33)/14 (67) aTMB is represented as count of non-silent mutations detected in exonic regions. bTumor PD-L1 expression NA for 12 of 38 ITT patients.ll Article Cancer Cell 40, 1010–1026, September 12, 2022 1013 A C D EF GB (legend on next page)ll Article 1014 Cancer Cell 40, 1010–1026, September 12, 2022 dendritic cells (DCs) and macrophages that may provide targets for neoantigen-speciﬁc CD4+T cells, leading to better tumor control for patients with high expression of MHC II. The TCR repertoire in the pre-treatment TME was also assessed both bycalculating the normalized Shannon’s entropy ( Jia et al., 2015 ; Hanson et al., 2020 ), as well as overall complementarity deter- mining region (CDR3) unique amino acid (aa) sequences. Thisanalysis revealed a positive correlation between PFS and TCRdiversity ( Figure 3 E). We were also able to monitor the changes in TCR diversity over time in a smaller subset of post-treatment tumor biopsies. We observed an overall pattern towardincreased or stable diversity during the course of the study for patients who achieved PFS-9, while those who did not achieve PFS-9 generally show decreasing TCR diversity over the courseof the study ( Figure 3 F). NEO-PV-01 plus chemotherapy and anti-PD-1 induces neoantigen-reactive T cell responses that are neo- epitope speciﬁc, persistent, and show cytotoxicpotential The overall immunogenicity of NEO-PV-01 plus chemotherapy and anti-PD-1 was assessed in patients’ peripheral blood bymeasuring interferon g(IFN- g) secretion in response to recall with the vaccinating peptides. In total, T cell responses were as- sessed for 204 vaccinating peptides across 13 patients with available samples. Consistent with our previous report ( Ott et al., 2020 ), the percentage of vaccine peptides generating an immune response in this assay was minimal at the pre-vaccine time point and increased across all 13 patients assessed at thepost-vaccine time point (8 weeks after the ﬁrst dose of vaccine) (Figures 4 A and S2A). We observed an average of 55% of vac- cine epitopes generating any T cell response using this assay(Figure 4 B). Of these responses, 49% were detectable ex vivo in an overnight assay with added peptide, and the remaining re- sponses were seen after a 5-day stimulation with added peptide. Overall, 100% of patients elicited at least one measurableresponse ex vivo (Figure 4 B). A total of 94% of the immune re- sponses to the vaccine peptides were seen only in the post- vaccination time point. The percentage of epitopes generating either a CD4 +or CD8+ T cell response for each patient was measured across the 13 pa-tients analyzed, resulting in 39% of epitopes generating a CD4 + T cell responses, while 31% generated CD8+T cell responses (Figure 4 B). The speciﬁcity of these immune responses to the neo-epitope as compared to the wild-type epitope was alsotested across a range of peptide concentrations. Stronger reac-tivity to the neo-epitope was observed in 18 of 19 epitopestested (95%) across 5 patients ( Figure 4 C;Table S3 ). These data are consistent with the speciﬁcity of the immune response for neoantigens as previously reported ( Ott et al., 2017 ,2020 ;Hu et al., 2021 ). Persistence of the vaccine-induced immune responses was also assessed for a subset of patients at 52 weeks after initiating the study (29 weeks after the ﬁnal dose of vaccine). Of the 34 epi-topes tested at week 52, 85.3% still had a response present atthis late time point ( Figures 4 D and S2B). We also observed re- sponses at week 52 that were not present at the week 20 post-vaccination time point for patients 2L13 (IM14), 2L14(IM25), and 2L16 (IM23), indicating the potential for delayed gen- eration of vaccine-induced responses ( Figure S2 B, green). A preexisting immune response was also observed for p53R213L (IM14) neoantigen in patient 2L16 that was also observed at the week 52 time point ( Figure S2 B). These data suggest that overall, the immune responses generated in this study persistedfor up to at least 1 year. The cytotoxic potential of the T cell responses generated against the vaccinating peptides was assessed by measuring the level of co-expression of IFN- gand CD107a (a marker of degranulation and cytotoxic potential; Betts et al., 2003 ) expres- sion on CD4 +and CD8+T cells, when assayed in the presence of neoantigen peptide ( Figure 4 E). In total, 92 vaccinating peptides were tested across 12 patients. Co-expression of IFN- gand CD107a was observed for 56 of the 92 (61%) peptides tested, with 11 of 12 patients showing at least one cytotoxic response(92%) ( Figures 4 F,S3A, and S3B). Interestingly, when each cyto- toxic response was characterized as either a CD4 +or CD8+T cell response based on the subset of T cells that showed increasedco-expression when compared to the DMSO-treated controlcells, 93% of responses were characterized solely as CD4 +re- sponses, and the remaining 7% showed a response in both the CD4+and CD8+compartments ( Figure 4 F). NEO-PV-01 plus chemotherapy and anti-PD-1 induces epitope spread responses in most patients analyzed, with mutant KRAS responses observed in multiple patientsGiven the cytotoxic phenotype of many of the T cell responses against the neoantigen vaccine epitopes, we speculated that tu- mor cell killing by these T cells could reveal additional epitopes,boosting the overall immune response to the patients’ tumor. This concept, known as epitope spread, has been reported in a number of studies assessing T cell responses to vaccines ( Cor- biere et al., 2011 ;Kreiter et al., 2015 ;Ott et al., 2020 ;Hu et al., 2021 ). To quantitate epitope spread in this study, we analyzed Figure 2. Rates and durability of responses following treatment with NEO-PV-01 plus chemotherapy and anti-PD-1 (A) Best radiographic change (%) in sum of target lesions for each patient who received at least 1 dose of vaccine (VAX set). The dark-shaded narrow bars represent the best change pre-NEO-PV-01; the light-shaded wider bars represent the best overall change for patients who received at least 1 dose of NE O-PV-01 plus anti-PD-1. Red indicates progressive disease, gray indicates stable disease, and blue indicates partial response.(B) Radiographic changes (%) in target lesions after initiation of pembrolizumab treatment for each patient (colors are the same as in A). Included ar e 30 of 38 ITT patients who had at least 1 post-baseline RECIST assessment.(C) Swimmer’s plot summarizing all of the patients in study. Each bar represents 1 subject in the study; bar length represents time on study.(D) Kaplan-Meier estimates of PFS (top) and OS (bottom) for both the ITT (left) and vaccinated (right) patient sets.(E–G) Measurement of ctDNA in the peripheral blood of a subset of patients as measured by the percent change in mean tumor molecules per milliliter of bl ood; open circles represent non-detectable levels of ctDNA. (E) Example of quantiﬁcation of individual mutant ctDNA molecules detected in a no-PFS-9 pat ient (F) and a PFS-9 patient (G).See also Figure S1 .ll Article Cancer Cell 40, 1010–1026, September 12, 2022 1015 A DE FB C (legend on next page)ll Article 1016 Cancer Cell 40, 1010–1026, September 12, 2022 additional circulating T cell responses to highly ranked neoepi- topes that were not included in the vaccine (referred to as ‘‘non-immunizing peptides,’’ or ‘‘NIMs’’). Immune responses that were detected only at the post-vaccine time point (week20) were considered to be epitope spread responses. Across 13 patients analyzed, 9 patients (69%) demonstrated epitope spread ( Figures 5 A and 5B). Of note were the epitope spread re- sponses that were seen to the driver mutations. Of 19 patientswho harbored KRAS mutations in the study, immune responses were measured for 7 of them. Three patients had an epitope spread response to G12C and one patient had a response to aG12V epitope ( Figure 5 C;Table S1 ). Of the 4 patients who had epitope spread to a KRAS mutation, 3 of them achieved PFS-9 on the study. The three patients whose epitope spread to mutantKRAS was not observed harbored the mutations G12V, G12C, and G12A respectively. Stronger reactivity to the neo-epitope when compared to wild type was demonstrated in all 4 patientsin whom an epitope spread response to a KRAS mutant epitope was observed ( Figure 5 D). We additionally assessed the cyto- toxic potential of these epitope spread responses by measuring co-expression of IFN- gand CD107a as in Figure 4 E. Of the 26 NIMs tested, 11 (42%) were cytotoxic, with 3 of 8 patients showing at least 1 cytotoxic response (38%). All of the cytotoxic responses observed were CD4 +responses, further supporting the importance of antitumor CD4+responses in this study (Figures S4 A and S4B). Interestingly, the KRAS G12C speciﬁc CD4+response (NIM101) from patient 2L15 showed cytotoxic potential in this assay ( Figure 5 E). The presence of KRAS muta- tions in our study was positively associated with improved PFS (Figure S4 C), suggesting the immunogenicity to KRAS mutations may in part be driving better clinical outcomes with our treatmentregimen. Persistence of these epitope spread responses was also assessed at the week 52 time point for three patients with available samples ( Figures S4 D and S4E). Of the 10 epi- topes tested, 80% were also detected at this later time point, consistent with the observation made to the vaccinating epi- topes in Figure 4 D. For patient 2L16, an additional epitope spread response (NIM12) was observed at week 52 that was not detectable at week 20, suggesting that epitope spread to additional antigens may continue beyond the vaccination timeperiod and could be an effect of continued pembrolizumab treat-ment beyond the vaccination phase ( Figure S4 D, green).Neoantigen-speciﬁc CD4 +T cell responses generated post-vaccination have an activated effector phenotype To further elucidate the role of vaccine-induced CD4+Tc e l l s induced in the patients, we generated a detailed transcriptomicand clonotype atlas of these cells by using a combination of sin- gle-cell sequencing techniques, including TCR- sequencing and cellular indexing of transcriptomes and epitopes by sequencing(CITE-seq). Using these methodologies, we were able to overlaydetailed single-cell-level TCR-seq data with protein and gene expression analysis comparing neoantigen-speciﬁc CD4 +Tc e l l s (tetramer+)w i t hb u l k( t e t r a m e r/C0)C D 4+T cells for ﬁve patients from peripheral blood at the post-vaccination time point (Figures 6 Aa n d S5A). The addition of protein expression analysis using CITE-seq to standard gene expression strengthened theclustering analysis ( Figures S5 B and S5C). Three of these patients achieved PFS-9 on the study and two did not. We chose CD4 + T cell responses representative of all of the responses in a patient based on availability of class II tetramers and the magnitude of theresponse yielding enough cells for detailed analysis. To perform these analyses, neoantigen-speciﬁc CD4 +T cells were sorted using MHC class II multimers as shown in Figure 6 A( r i g h t ) .U n s u - pervised clustering of all CD4+T cells across the ﬁve patients re- vealed commonly observed T cell subsets determined based on the expression proﬁles, such as naive CD4+T cells, central mem- ory, T regulatory cells (Treg), and effector CD4+Tc e l l s( Figure 6 B). Additional analysis demonstrated that the tetramer+cells for each of the 5 patients analyzed cluster separately from the tetramer/C0 CD4+T cell population, indicating a difference in transcriptomic and protein proﬁles ( Figure 6 C). Indeed, tetramer+CD4+Tc e l l s fall into the ‘‘effector activation/exhaustion’’ (eff act/exh), ‘‘effectorproliferation’’ (eff prolif), and ‘‘cytotoxic’’ clusters at a greater fre-quency than the tetramer /C0CD4+T cells, while tetramer/C0cells fall into clusters of ‘‘naive,’’ ‘‘central memory,’’ and ‘‘effector mem- ory’’ (eff memory) cells ( Figure 6 D). Differential gene expression analysis further underscored the difference in cluster distribution and phenotypes between tetramer+and tetramer/C0populations. We found that ZEB2 ,PDCD1 ,TOX,TIGT ,CXCR3 ,ITGB1 , GZMA ,a n d ICOS were all within the top 100 genes signiﬁcantly upregulated in the tetramer+(neoantigen-speciﬁc) CD4+Tc e l l populations ( Tables S4 and S5). In addition, when the clonality of the TCR repertoire was compared across the tetramer+and tet- ramer/C0populations as measured by the Gini coefﬁcient ( Kirsch Figure 3. Correlates with clinical response are observed pre-treatment in the tumor microenvironment (TME), including T cell inﬁltration, MHC class II expression, and TCR diversity (A) Correlation of CD4+(top) and CD8+(bottom) T cells per square millimeter either outside the tumor (left) or inside the tumor (right) in patient tumor biopsy ma- terial by multiplex immunoﬂuorescence at the pre-treatment time point with PFS in months. Pearson’s correlation coefﬁcient (R) and the associated p value are indicated.(B) Representative images of immunoﬂuorescence analysis of CD4 +and CD8+T cells in patient tumor biopsy material are shown for 1 patient with no PFS-9 (2L3) and 1 patient with PFS-9 (2L5) stained with DAPI (blue), CD3 (red), CD4 (green), CD8 (white), and PanCK (cyan). White arrows indicate CD8+T cells around the tumor area, and yellow arrows indicate CD4+T cells inﬁltrating into the TME. (C) Correlation of HLA class II gene expression in the tumor biopsy at the pre-treatment time point, with PFS in months. Pearson’s correlation coefﬁci ent (R) and the associated p value are indicated.(D) Representative images of MHC class II expression by multiplex immunoﬂuorescence in patient tumor biopsy material are shown for 1 patient with no P FS-9 (2L3) and 1 patient with PFS-9 (2L5) stained with HLA-DR/DP/DQ (red), PanCK (green), DAPI (blue), CD11c (white), and CD14 (yellow). Individual chann el images for patient 2L5 are shown below multiplex images for HLA-DR/DP/DQ, CD14, CD11c, and PanCK. White arrows denote HLA-DR/DP/DQ +CD11c+CD14+cells, and yellow arrow denote HLA-DR/DP/DQ+CD11c/C0CD14+cells. (E) Analysis of Shannon’s entropy (left) and unique amino acid (UniAA) count (right) in the tumor biopsy at the pre-treatment time point and correlati on with PFS in months. Pearson’s correlation coefﬁcient (R) and the associated p value are indicated.(F) Analyses of Shannon’s entropy (left) and UniAA count (right) at the pre-treatment (week 0), pre-vaccine (week 12), and post-vaccine (week 24) tim e points in tumor biopsy material are shown, with PFS-9 patients represented in green, and no-PFS-9 patients represented in blue.ll Article Cancer Cell 40, 1010–1026, September 12, 2022 1017 A C EFDB Figure 4. NEO-PV-01 plus chemotherapy and anti-PD-1 induces neoantigen-reactive T cell responses that are neo-epitope speciﬁc, persis- tent, and show cytotoxic potential (A) Percentage of all NEO-PV-01 vaccinating peptides that elicited IFN- gresponses in serial peripheral blood mononuclear cells (PBMCs) at the indicated time points.(B) Table summarizing overall immune responses detected for the 13 patients (pts) analyzed, also characterized as percent CD4 +or CD8+responses. (C and D) Speciﬁcity of immune responses as measured by IFN- gELISpot assay to mutant peptides (green) versus wild-type peptides (red) across a range of peptide concentrations. Representative responses from patient 2L7 (IM13 and IM04) and 2L3 (IM03 and IM15) are shown. Aggregate data are represented as means ± SEMs. (D) Persistence of immune responses induced by IM peptides, as measured by IFN- gELISpot assay in PBMCs collected at week 52 after the initiation of chemotherapy plus anti-PD-1 therapy. The data are represented as stacked columns for individual patients, with responses detecte d at both weeks 20 and 52 shown in light green, and responses detected only at week 20 shown in dark green.(E and F) Cytotoxic potential of NEO-PV-01-generated immune response as measured by the surface expression of the marker CD107a in combination withintracellular IFN- gexpression at the post-vaccine time point. (E) Representative ﬂow plots for patient 2L7 are shown, comparing peptide recall (bottom) with DMSO recall (top) in both the ex vivo (left) and 5-day stimulation (right) assay design. The data are summarized in (F) and represented as stacked columns for individual patients, with positive (cytotoxic potential) responses shown in dark green, and negative (no cytotoxic potential) responses shown in l ight green. The table below summarizes the responses detected for all of the patients analyzed, and categorizes responses as either CD4 +, CD8+or both. See also Figures S2 and S3and Table S3 .ll Article 1018 Cancer Cell 40, 1010–1026, September 12, 2022 et al. 2015 ;Poran et al., 2020 ), we observed increased clonality of the neoantigen-speciﬁc tetramer+populations for each of the 5 patients when compared to the tetramer/C0CD4+Tc e l lp o p u l a t i o n (Figure 6 E).To better understand the difference in phenotype of effector cells speciﬁcally within the vaccine-generated neoantigen-spe- ciﬁc CD4+T effector cell (tetramer+) and other effector cell (tet- ramer/C0) populations, all of the effector cells were identiﬁed as A CD EB Figure 5. NEO-PV-01 plus chemotherapy and anti-PD-1 induces epitope spread responses in most patients analyzed, with mutant KRAS re- sponses observed in multiple patients (A) Epitope spread was measured in 13 patients. Reactivity of the post-vaccine PBMCs against a range of 10–25 predicted neoantigen peptides that were not included in the vaccine were tested by IFN- gELISpot assay. Responses characterized as epitope spread were detected only at the post-vaccine time point (week 20) and were not detected at the pre-vaccine time point (week 10). (B) T cell responses to individual non-immunizing (NIM) peptides across 9 patients at the post-vaccination time point are shown. NIM peptides that di d not elicit reactivity post-vaccination are not shown. Each NIM peptide was tested for the generation of an immune response using overlapping assay peptides, an d the assay peptide that generated the maximum response in either the ex vivo assay or 5-day assay is shown. Responses with green stars denote ex vivo responses. (C) Four patients elicited mut KRAS -speciﬁc epitope spread responses as determined by IFN- gELISpot assay. PFS-9 status of each patient is denoted below the graph, and speciﬁc G12 mutation denoted above the graph. Responses with green stars denote ex vivo responses. (D) Speciﬁcity of immune responses as measured by IFN- gELISpot assay to mutant peptides (solid lines) versus wild-type peptides (dotted lines) across a range of peptide concentrations for each of the 4 patients where an epitope spread response to mut KRAS was observed. (E) Surface expression of the cytolytic marker CD107a (x axis) and intracellular expression of IFN- g(y axis) is shown by ﬂuorescence-activated cell sorting (FACS) analysis for the mut KRAS -speciﬁc epitope spread response observed for patient 2L15. Individual plots depict control (DMSO) on the left and NIM peptide on the right. Positivity in this assay was determined as a >1.5-fold stimulation over DMSO control in the double-positive gate. Parent gates are indicated b elow the pair of FACS plots. Aggregate data in (B)–(D) are represented as means ± SEMs.See also Figure S4 and Table S1 .ll Article Cancer Cell 40, 1010–1026, September 12, 2022 1019 A B D G HE FC Figure 6. Neoantigen-speciﬁc CD4+T cell responses share an activated effector phenotype in the periphery following vaccination (A) Table summarizing the 5 patients analyzed using a combination of multimer-based sorting of neoantigen-speciﬁc CD4+T cells (representative ﬂow panel at right), CITE-seq, TCR-seq, and gene expression analysis. (legend continued on next page)ll Article 1020 Cancer Cell 40, 1010–1026, September 12, 2022 being CD45RO+CD45RA/C0CD62LloCCR7lo, and subject to unsu- pervised clustering ( Figures S5 D and S5E). This analysis re- vealed 5 unique clusters, with the tetramer/C0cells falling mainly in clusters 1 and 2, and tetramer+cells falling mainly in clusters 0, 3, and 4 ( Figures S5 E and S5F). Expression proﬁles of each cluster revealed that tetramer/C0effector cells demonstrate an effector memory phenotype ( TCF7 ,IL7R ,CCR7 ), whereas the tetramer+/vaccine-speciﬁc CD4+T effector cells demonstrate activation/exhaustion ( PDCD1 ,ICOS ,TIGIT ,TOX) or cytotoxic (PFR1 ,GNLY ) phenotypes ( Figure S5 G). Additional analysis of CD4+neoantigen-speciﬁc T cell re- sponses was performed using a combination of MHC class II multimer staining and phenotypic analysis by ﬂow cytometry for 10 patients ( Figure S6 ). Overall, the ﬂow analysis was consis- tent with the CITE-seq analysis described in Figures 6 A–6C, in which the majority of neoantigen-speciﬁc CD4+T cells demon- strate an effector phenotype when compared to tetramer/C0 CD4+T cells. Clustering analysis of only the tetramer+CD4+T cells was per- formed to determine whether there are phenotypic differences in the neoantigen-speciﬁc CD4+T cell clones both within an indi- vidual patient and between patients ( Figure S7 A). Markers repre- sentative of each identiﬁed cluster and T cell phenotype are shown in the corresponding uniform manifold approximationand projection (UMAP) ( Figure S7 B). Gene and protein expres- sion proﬁles used for clustering analysis are shown in Figures S7 C and S7D, respectively. The abundance of each phenotype observed on a per-patient level is shown in Fig- ure S7 E. Furthermore, we assessed the phenotypes of T cells belonging to the most abundant TCR clones coming from thetop 30% of each patient’s tetramer +TCR repertoire. We observed consistency in phenotypes across clones within a pa- tient, but varying phenotypes when comparing across patients. (Figure S7 F). To conﬁrm speciﬁcity of the CD4+TCR clones to the predicted peptide:MHC class II allele combination, four of the top TCRs identiﬁed from each single-cell TCR sequencing result across four patients were validated by cloning the individ-ual TCR into the CD4 +Jurkat cell line, followed by co-culture with patient-matched antigen-presenting cells (APCs) loaded with either the mutated neoantigen peptide corresponding to the vac-cine or the relevant wild-type peptide. Cloned TCRs for patients2L7, 2L8, and 2L16 (recognizing the vaccine peptides IM13, IM04, and IM26, respectively) resulted in secretion of interleukin-2 (IL-2) only in response to the mutated neoantigenpeptide, and not the corresponding wild-type peptide ( Fig-ure S7 G). No wild-type peptide was available as a control for patient 2L11, in which the vaccine peptide (IM18) targets a frameshift mutation ( Figure S7 G). To validate that the neoantigen-speciﬁc tetramer +CD4+Tc e l l s being observed were present in the patient’s peripheral blood only after vaccination, we performed bulk TCR sequencing of CD3+-enriched patient PBMCs at the pre-treatment, pre-vaccine, and post-vaccine time points and quantiﬁed the abundance of theTCR clones covering the top 30% of the repertoire in concor- dance with the single-cell TCR sequencing discussed previously (Figure S7 A). We detected each of these dominant clones only at the post-vaccination time point ( Figure 6 F). We were not able to assess any of the top 4 clones for patient 2L8 in the bulk TCR sequencing due to the lack of available patient material. In addi-tion, immunohistochemical analysis of longitudinal tumor bi- opsies obtained at pre-treatment, pre-vaccine, and post-vaccine time points was performed where available, revealing a signiﬁcantincrease in the presence of CD4 +T cells at the post-vaccine time point when compared to the pre-treatment time point ( Figures 6 G and 6H). This increase was observed only in CD4+Tc e l l sa n dn o t in CD8+T cells ( Figure S7 H). The increase in CD4+T cells was also seen at the pre-vaccine time point and continued to increase at the post-vaccine time point. Representative images from the three time points from patient 2L7 show a marked increase inCD4 +T cell inﬁltration into the tumor following the initiation of treatment in both the pre-vaccine and post-vaccine time points (Figure 6 H). To address whether vaccine-induced T cells can be found in the post-treatment biopsy, we looked for the TCR clones that were exclusively found in the post-vaccine biopsy of patient 2L7 and 2L9 and compared the presence of these clones in theperiphery across the three time points of pre-treatment, pre-vaccination, and post-vaccination. This identiﬁed two distinct sets of clones, ones that expanded upon vaccination in the pe- riphery ( Figure S7 I, red) and ones that were detected exclusively at the post-vaccination time point in the periphery ( Figure S7 I, blue), suggesting that vaccine-induced T cells in the periphery can trafﬁc to the post-vaccination tumor. Although the speciﬁc-ities of these tumor-penetrating clones are currently unknown, we presume a majority of these clones to be induced by the vac- cine due to their expansion/detection only upon vaccination. DISCUSSION In this report of a Phase I study in non-squamous NSCLC, we have further tested the utility of targeting neoantigens in the (B) Unsupervised clustering (left) and heatmap (right) of normalized expression used for clustering analysis of tetramer+and tetramer/C0CD4+T cell samples from the 5 patients.(C) Unsupervised clustering plots separated to visualize tetramer /C0cells (top) and tetramer+cells (bottom) from the 5 patients. (D) Proportions of each UMAP cluster comparing tetramer+and tetramer/C0populations. (E) Measurement of the Gini coefﬁcient (as an indicator of TCR repertoire clonality) for both tetramer+and tetramer/C0populations. Each dot represents an in- dividual patient.(F) Measurement of clonotypes covering the top 30% of the TCR repertoire of tetramer +CD4+T cells that were detected by single-cell TCR sequencing using bulk TCR-seq data across the pre-treatment, pre-vaccine, and post-vaccine timepoints.(G) Measurement of the number of CD3 +CD4+T cells per square millimeter of tumor biopsy tissue using multiplex immunoﬂuorescence at the pre-treatment, pre- vaccine, and post-vaccine time points when available.(H) Representative immunoﬂuorescence images for patient 2L7 (PFS-9) at the pre-treatment (left), pre-vaccine (center), and post-vaccine (right) time points are shown, stained with DAPI (blue), CD3 (red), CD4 (yellow), and PanCK (green). Longitudinal biopsies were from the same lung lesion for this patient. Ag gregate data in (D), (E), and (G) are represented as means ± SEMs.See also Figures S5–S7 and Tables S4 and S5.ll Article Cancer Cell 40, 1010–1026, September 12, 2022 1021 context of immune checkpoint and chemotherapy combination as ﬁrst-line treatment. Despite the recent advances in the treat- ment of metastatic NSCLC using immunochemotherapy ( Gandhi et al., 2018 ;Gadgeel et al., 2020 ;Rodrı ´guez-Abreu et al., 2021 ), the median OS of metastatic NSCLC remains less than 3 years (Wang et al., 2021 ), warranting the need to improve upon the cur- rent standard of care. Our report of personalized neoantigenvaccine in this setting evaluates the safety, immunogenicity,and potential clinical beneﬁt in this patient group. The clinical data from this study indicate that a personalized neoantigen-based vaccination strategy is feasible and safe as aﬁrst-line treatment in NSCLC in combination with standard of care chemotherapy plus pembrolizumab. The single-arm design of the trial does not allow attribution of clinical re-sponses speciﬁcally to the vaccine since it was administered in combination with anti-PD-1 and chemotherapy. A study that could serve as a historical comparator is KEYNOTE 189(Gandhi et al., 2018 ;Gadgeel et al., 2020 ). However, this study could not be directly compared to our study, given the differ- ences in the patient populations and the treatment regimens. Patients in the KEYNOTE 189 study received maintenancechemotherapy, while patients in the NT002 study received NEO-PV-01 alone after the initial four cycles of chemotherapy and checkpoint inhibitor. In addition, the NT002 vaccinatedstudy cohort had a higher ratio of female to male patients and lower relative tumor PD-L1 expression, each of which can be associated with inferior clinical outcomes to the combinationof immunotherapy with chemotherapy ( Conforti et al., 2018 ; Gandhi et al., 2018 ). Approximately 45% of the enrolled patients were unable to proceed to vaccination due to progressive disease, the inabilityto manufacture vaccine due to low tumor content in the biopsy, or insufﬁcient mutations leading to a low number of high-quality neoantigens. Steps to mitigate such failures would includeimproved vaccine production timelines, evaluation of tumor mutational burden (TMB) using some of the emerging technolo- gies from cell-free DNA, and using a vaccine format such asmRNA that may be faster to manufacture and administer as well as overcome the solubility limitation of synthetic long peptides. With respect to the immunogenicity of the personalized vac- cine in the context of immunochemotherapy, we did not observe it to be different from our previously reported immunogenicity of NEO-PV-01 in combination with anti-PD-1 alone ( Ott et al., 2020 ). It is encouraging to see that despite receiving an immuno- suppressive chemotherapy regimen before vaccination, a robust immune response was generated to the vaccine in all of the pa-tients analyzed, with both CD4 +and CD8+responses being generated. Interestingly, a large majority of the vaccine-induced cytotoxic responses were CD4+T cells. This is in contrast to our previous study, in which we observed both CD4+and CD8+cyto- toxic T cells generated in response to NEO-PV-01 vaccination (Ott et al., 2020 ) in combination with nivolumab alone. The differ- ences observed with the previous study could be attributed tothe differences in treatment combination or may be because this was a cohort of exclusively ﬁrst-line NSCLC cancer patients. In addition, we observe robust MHC II expression in the TME ofthis patient cohort, particularly at the post-vaccination time point. This suggests the presence of a microenvironment in favorof developing CD4 +T cell responses. Skewing toward CD4+ T cell responses has been reported previously in the setting of neoantigen vaccine as well ( Ott et al., 2017 ;Sahin et al., 2017 ). The role of tumor-speciﬁc cytolytic CD4+T cells has only begun to emerge, with some of the early data focused on identi- ﬁcation of these cells in the context of pre-treatment biopsies and its predictive value for response to checkpoint therapy ( Ki- tano et al., 2013 ;Kagamu et al., 2020 ;Oh et al., 2020 ). The use of single-cell sequencing technologies and multiparametric ﬂow-based analysis has further enabled the detailed character- ization and deﬁnition of gene expression patterns associatedwith such cytotoxic CD4 +T cells ( Patil et al., 2018 ;Ma et al., 2021 ;Nicolet et al. 2021 ). Our data demonstrate the generation of such effector and cytolytic CD4+T cells upon vaccination with a personalized vaccine. Of note, the CD4+neoantigen T cells generated in the study were not of the Treg phenotype (they did not express Foxp3) and instead had a distinct effectorcytotoxic phenotype that could mediate tumor cell killing. Inaddition, the increase in CD4 +T cells and not CD8+T cells post-initiation of treatment emphasizes a critical role for CD4+ T cell compartment in tumor control. Another aspect of our study is the generation of epitope spread responses in multiple patients. Of particular interest are the epitope spread responses seen toward common driver mu-tations KRAS G12C and KRAS G12V across multiple patients. Preexisting immune responses to KRAS G12V and KRAS G12D mutation have been reported in a cohort of patients fromcolorectal cancer and a NSCLC patient previously ( Cafri et al., 2019 ;Veatch et al., 2019 ). Identiﬁcation of such immune re- sponses in our study provides additional data on KRAS muta- tion-speciﬁc T cells that can be further developed for T cell orvaccine-based therapies. A limitation of our study is its single-arm design. Since the vac- cine was administered in combination with anti-PD-1 andchemotherapy, the clinical responses, epitope spread, and immunogenicity cannot be solely attributed to the vaccine alone. Randomized trials of neoantigen therapy in combination withanti-PD-1 and chemotherapy versus immunochemotherapy alone will be necessary to conﬁrm these ﬁndings and determine the speciﬁc contribution of the personalized vaccine. In summary, we demonstrate the safety, feasibility, and gener- ation of desired immune responses upon treatment with person- alized neoantigen-based vaccine in combination with anti-PD-1 and chemotherapy as a ﬁrst-line treatment in NSCLC. Identiﬁca-tion of potential tumor biomarkers holds promise for selecting patients in further randomized trials. These trials will be required to further reﬁne the use of personalized neoantigen vaccine incombination with immunochemotherapy as a ﬁrst-line treatment in NSCLC. STAR+METHODS Detailed methods are provided in the online version of this paper and include the following: dKEY RESOURCES TABLE dRESOURCE AVAILABILITY BLead contact BMaterials availabilityll Article 1022 Cancer Cell 40, 1010–1026, September 12, 2022 BData and code availability dEXPERIMENTAL MODEL AND SUBJECT DETAILS BNT-002 study design BTreatment regimen BEligibility BClinical assessments BPatient samples dMETHOD DETAILS BGeneration of NEO-PV-01 BPeptide synthesis for immunological assays BPersonalized ctDNA assays BDetection of neoantigen-speciﬁc immune responses BIFNgELISpot assay BCharacterization of CD4+/CD8+T cell responses BCD107a mobilization assay BMHCII protein expression, puriﬁcation, and peptide ex- change BMultimer generation and staining BPhenotyping of multimer+CD4+T cells BRNA extraction from FFPE tumors and PBMCs BGene expression analysis BTCR library preparation and sequencing BTCR repertoire generation BTCR repertoire diversity/clonality analysis BSingle-cell RNA sequencing of neoantigen-speciﬁc CD4+T cells using HLA class II multimer staining BSingle-cell RNA sequencing data analysis BCloning of neoantigen-speciﬁc TCRs BMultiOmyx staining BMultiplex IHC staining protocol BMultispectral analysis BStatistical analysis dADDITIONAL RESOURCES SUPPLEMENTAL INFORMATION Supplemental information can be found online at https://doi.org/10.1016/j. ccell.2022.08.003 .