Mutant KRAS vaccine with dual checkpoint blockade in resected pancreatic cancer: a phase I trial

Domain: neuroscience
Published: 2026-02-10
Editorial outcome: accept

Abstract
In this phase I study, we test a pooled synthetic long peptide vaccine targeting the six KRAS mutations (G12V, G12A, G12R, G12C, G12D, G13D) with ipilimumab and nivolumab in resected pancreatic adenocarcinoma. Co-primary endpoints include safety and maximal percent change of IFNγ-producing mutant KRAS T cell responses in the blood within 17 weeks. Secondary endpoints include disease-free survival, overall survival, and maximal percent change of IFNγ-producing mutant KRAS T cell responses at any time after vaccination. Vaccine-related adverse events are grade 1-2. 11/12 and 10/12 patients generate a significant increase in average T cell response to 6 mutant KRAS antigens and tumor-specific response, respectively. Immunophenotyping demonstrate Th1 CD4 central memory and effector memory T cells, and CD8 effector memory T cells at a lower frequency. The vaccine also generates cross-reactive T cells that recognize more than one mutant KRAS antigen. These findings support the safety and diverse anti-tumor immunity of mutant KRAS vaccines (NCT04117087).

Introduction
Pancreatic ductal adenocarcinoma (PDAC) has a dismal 5-year survival of 12%1. The poor prognosis is due to the lack of early detection, early micrometastatic spread, and resistance to chemotherapy. Given that the majority of patients present with advanced disease, only 20% are eligible for surgical resection at the time of diagnosis2. Even after surgery and adjuvant chemotherapy, up to 80% of patients develop disease recurrence leading to poor long-term survival3,4. Immune checkpoint inhibitors (ICIs) alone are not effective in PDAC mainly due to a low tumor mutational burden, lack of functional effector T cells, and a highly immunosuppressive tumor microenvironment (TME)5,6. Oncogenic mutations in KRAS (mKRAS) are attractive targets due to their high prevalence in up to 90% of PDACs as well as, importantly, in precancer lesions7,8. As a driver mutation that is expressed clonally, mKRAS antigen loss is unlikely to cause tumor escape. Small molecule mKRAS inhibitors have shown promise in advanced PDAC, but their use in the micrometastatic setting is unclear, and durability is limited by secondary resistance9. mKRAS has also been targeted with adoptive T cell therapies, but clinical benefit is restricted to patients with the few HLA subtypes in whom cognate T cells have been identified10,11. In contrast, mKRAS-targeted vaccines have the potential to broadly cover more HLA subtypes. Vaccines targeting mKRAS have been shown to slow tumorigenesis with a survival benefit in murine models of PDAC and lung cancer12,13. Prior clinical trials have tested mKRAS-targeted peptide vaccines14-16 or peptide-pulsed antigen-presenting cells17,18 but observed low immunological response rates (less than 50%) or were limited to single patient analyses. A more recent study using a lymph-node-trafficking mKRAS peptide vaccine showed positive peripheral immune responses but was limited to KRAS G12D and G12R19. To this point, little is known about the relative immunogenicity of the diverse mKRAS subtypes in broad patient populations. Furthermore, none of these studies have combined a pooled mKRAS peptide vaccine with ICIs that are known to reinvigorate exhausted T cells. Lastly, despite being the most common oncogene in human cancers, to our knowledge, ~25 mKRAS-specific T cell receptors across the antigen subtypes have been sequenced and validated to-date, limiting the field's understanding of their cognate T cell recognition.

Here, we report the development of a pooled peptide vaccine-termed mKRAS-VAX-targeting the six most common KRAS mutations, KRAS G12D, G12V, G12R, G12C, G12A, and G13D. This vaccine provides the broadest coverage against mKRAS in a vaccine yet, which is important for potentially heterogenous expression of mKRAS in micrometastatic lesions and critical for use in the preventative vaccination setting20. In a first-in-human phase I clinical trial (ClinicalTrials.gov: NCT04117087), we tested mKRAS-VAX in combination with nivolumab (anti-PD-1 antibody) and ipilimumab (anti-CTLA-4 antibody) in patients with resected PDAC. Our trial was designed to determine safety and immunogenicity of mKRAS-VAX in combination with ICIs. We find that this combinatorial therapy is safe, feasible, and effective in inducing mKRAS-specific T cells across all six mKRAS antigens. High-dimensional immune profiling revealed activated and polyfunctional mKRAS-specific CD4 and CD8 T cells with distinct phenotypic profiles. Through bulk and single cell TCR sequencing, we generated the largest known database of putative mKRAS-specific TCRs from the peripheral blood of the vaccinated patients. Furthermore, we validated a subset of these TCRs functionally that respond to a single or multiple mKRAS antigens. Finally, we identified a public mKRAS TCR in more than one patient. The safety, immunogenicity, and broad coverage of mKRAS-VAX have provided the foundation for vaccinating high-risk individuals (ClinicalTrials.gov: NCT05013216)21.

Results
mKRAS-VAX plus ICIs is safe and activates mKRAS-specific T cells
We enrolled and treated 12 patients (May 2020 to May 2023) with mKRAS-VAX, formulated with adjuvant poly-ICLC (Hiltonol®), in combination with ipilimumab and nivolumab (Fig. 1a, b, Supplementary Fig. 1a). Patients were eligible if they had undergone curative-intent surgical resection and standard-of-care adjuvant therapy within 6 months. Resected tumors were confirmed to have one of the six KRAS mutations. KRAS G12V was the most common mutation (n=6), followed by G12D (n=4) and G12R (n=2) (Supplementary Table 1). Perioperative chemotherapy was administered to 11/12 patients. Median time from the completion of standard-of-care therapy to first vaccination was 4.2 months (range: 1.6-6.0). Importantly, time to first vaccination was dependent on when the patient was referred by their treating oncologist after completion of standard of care therapy. Additional baseline characteristics are included in Supplementary Table 1.

We achieved an acceptable safety profile of the combinatorial therapy, with the most frequent vaccine-related AEs being pain (83.3%), erythema (33.3%), muscle cramps (16.7%) at the injection site, fatigue (33.3%), and fever (33.3%) (Supplementary Table 2). These vaccine-attributable AEs were low-grade (grade 1-2) without grade 3-5 AEs. As expected from dual checkpoint blockade, there were four grade 3 immune-related AEs, namely pneumonitis, myalgias, arthralgias, and adrenal insufficiency. These events occurred in 2/12 patients (16.7%), resulting in discontinuation of ICIs, while booster doses of vaccine were continued (Supplementary Table 3, 4).

We measured the magnitude of mKRAS-specific T cells in pre- and post-vaccine peripheral blood mononuclear cells (PBMCs) as the primary endpoint. Ex vivo restimulation with the six individual 21-mer synthetic mKRAS peptides or a control irrelevant fusion peptide was followed 24h later by IFNγ ELISPOT (Fig. 1c, Statistics in Supplementary Table 5). We detected mKRAS-specific T cells in post-vaccine PBMCs but not in pre-vaccination samples. By determining the maximal fold change in response to each mKRAS antigen relative to baseline (Supplementary Fig. 1b), we found a statistically significant increase in average mKRAS-specific T cell response across all six antigens in 11/12 patients (91.7%); these patients were classified as ‘immune responders’ per the pre-specified primary endpoint (Fig. 2a). Furthermore, we detected a significant response to all six antigens within 17 weeks post-vaccination (Fig. 2b). Notably, the magnitude of T cell activation to each mKRAS antigen varied, possibly due to diverse HLA subtypes within the cohort (Supplementary Table 6). 7/12 patients (58.3%) mounted a positive response to all six mKRAS antigens with 11/12 (91.6%) responding to at least three antigens (Fig. 2c). 10/12 patients (83.3%) patients mounted a significant response to their tumor KRAS mutation, while 2 patients with a G12D mutation did not (Fig. 2d). mKRAS G12V and G12R peptides induced significantly higher levels of IFNγ-producing T cells relative to mKRAS G12C, G12D, or G13D (Fig. 2e). Lastly, we observed that the response to mKRAS G13D was associated with positive responses to G12D and G12R (Supplementary Fig. 1c).

With a median follow up of 35.8 months (data cutoff 10/24/2024), 4/12 patients (33%) remained disease-free, and 8/12 (66.7%) had disease recurrence. Four of the five patients who remained disease-free at the end of primary treatment phase entered the optional extended phase of the trial at 52 weeks, during which they received vaccine only (Fig. 1a). Patients with disease recurrence and those who remained disease-free had similar characteristics, including time from completion of standard adjuvant chemotherapy to first vaccination on study (Supplementary Table 1). Median disease-free survival (DFS) and overall survival (OS) of the entire cohort were 6.35 months and 29.59 months, respectively, as defined from the time of first mKRAS-VAX dose (Supplementary Fig. 2a, b).

Notwithstanding the small sample size, we elected to perform an exploratory correlative analysis between mKRAS-specific T cell responses and clinical outcomes. Patients were stratified into two groups above and below the 25th percentile either of the maximal fold change of the pooled average or the tumor mutation-specific T cell responses. Using either approach, patients within the upper three quartiles of T cell response displayed longer DFS than patients within the lowest quartile (median 18.8 vs 2.76 months, P=0.024, hazard ratio (HR)=0.19 (95% CI: 0.04-0.95) (Supplementary Fig. 2c). This analysis provides initial support for the hypothesis that strong responses to mKRAS are clinically meaningful in the context of resected PDAC, consistent with other recent study of adjuvant vaccines in PDAC19,22. There was no correlation between DFS and lymph node status, primary tumor size, baseline CA19-9 levels, ctDNA positivity at baseline, neoadjuvant chemotherapy, splenectomy, or absolute lymphocyte counts at baseline (Supplementary Fig. 2e-k). Of note is that all patients with a mKRAS G12D mutation recurred; 2 out of 4 were in the lowest quartile of average mKRAS immune responders (Supplementary Fig. 2d). Those with a mKRAS G12D mutation also had a lower DFS compared with those with mKRAS G12V; this is consistent with the differences we noted in the immunogenicity for the respective antigens (Fig. 2e, Supplementary Fig. 2l). All four patients who remained disease-free at the time of data cutoff had mKRAS G12V or G12R tumors (Supplementary Fig. 2d). Finally, to determine if there was an association between baseline immunopathological features and DFS, we performed immunohistochemistry for CD4, CD8, MHC class II (HLA-DR, DP, DQ), and PD-L1 on the pre-treatment, primary resected tumors (Supplementary Fig. 3a). There was no correlation between DFS and the densities or proportions of CD4, CD8 T cells, macrophages (CD68+), or PD-L1 expression within the primary tumor (Supplementary Fig. 3b-f). However, patients with moderate levels of HLA-DR, DP, DQ on tumor cells had a significantly higher DFS relative to patients with high or low/negative expression (Supplementary Fig. 3g).

mKRAS-VAX plus ICIs activates polyfunctional Th1 CD4 and cytotoxic CD8 T cells
To phenotype the mKRAS-specific T cells, patient PBMCs were restimulated with individual mKRAS peptides followed by mass cytometry time of flight (CyTOF) using markers for T cell activation, effector function, exhaustion, and memory with analysis at pre-treatment (baseline), peak response (by IFNγ ELISPOT), and last collection (within the first 52 weeks) timepoints. Using unsupervised clustering, we identified CD4 and CD8 T cells within the total PBMC population, and activated populations enriched in mKRAS peptide restimulated PBMCs relative to a control peptide (Supplementary Fig. 4a-f). We next performed a refined clustering on both CD4 or CD8 T cells and identified two activated (Acv) mKRAS-responsive CD4 T cell populations and two Acv CD8 T cells populations (Fig. 3a). For all six neoantigens across all patients, mKRAS-specific CD4 T cells were the predominant population with responding CD8 T cells detected in a small proportion of patients for each antigen (Fig. 3b).

We quantified the percent of activated T cells relative to the control peptide stimulation at each timepoint. Within the CD4 compartment, we noted a significant increase in activated mKRAS-specific CD4 central memory (Tcm, CD45RO+ CD45RA+ CCR7+) and effector memory (Tem, CD45RO+, CD45RA-, CCR7-) populations at the peak and late timepoints relative to baseline (Fig. 3c). Notably, G12D and G13D had the lowest response magnitude, consistent with the ELISPOT data. The CD4 Tcm active population (Tcm Acv) was KI67+ and expressed the activation markers OX40, CD137, and CD25, with lower levels of Th1 effector cytokines (IL-2, IFNγ, TNFα) and exhaustion markers (CTLA4, PD1, TIM3, and LAG3). Conversely, CD4 Tem Acv cells expressed the highest levels of activation markers (OX40, CD25, and CD137) and cytokines (IL-2 and TNFα) but also displayed a higher exhaustion marker expression (CTLA4hi, PD1hi, TIM3hi, and LAG3hi) (Fig. 3a). Both CD4 Acv populations expressed low levels of Granzyme B. We found no increase in unstimulated PBMCs in either activated or resting CD4 or CD8 T cell populations in post-vaccine samples validating our identification of peptide-specific, activated T cells (Supplementary Fig. 4g, h). We further confirmed the predominance of Th1 cytokines by Luminex multiplex cytokine secretion assay of supernatants from the peptide restimulated PBMCs (Supplementary Fig. 5). We detected lower secretion of the Th2-associated cytokine IL-5 but not of IL-4. We also detected the Th17-associated cytokine IL-17A after restimulation with certain peptides relative to control or unstimulated samples (Supplementary Fig. 5).

Within the CD8 compartment, the activated effector population (Teff Acv, CD45ROhi, CD28hi) expressed highest levels of the activation markers CD137, OX40, KI67, cytokines IFNγ, IL-2, and granzyme B, as well as the exhaustion markers CTLA4, TIM3, and PD1. This Teff Acv population was detectable in a limited number of patients for single antigen stimulation and was not significant across the cohort (Fig. 3d, right). Thus, this population, which exists at a relatively low frequency, represents highly activated but possibly more exhausted CD8 T cells. Conversely, a significant increase in activated CD8 effector memory cells (Tem Acv, CD45RAlo, CD45ROlo, CCR7-) was detected post-vaccine for G12V, G12A, G12R, G12C, and G13D across patients. This population expressed CD137 and KI67, with higher granzyme B expression and lower levels of dysfunctional markers (PD1, CTLA4, and TIM3) (Fig. 3a, d). Flow cytometry of peptide-restimulated PBMCs revealed an increase in CD69 and CD137 activation markers and effector cytokines (Supplementary Fig. 6a-e). Using an antigen stimulation titration assay, we also confirmed a lack of responsiveness of the CD3+ compartment to wild-type KRAS relative to KRAS G12V (Supplementary Fig. 6f). Notably, in our CyTOF analysis, we detected the chemokine receptor CCR5 on both CD8 and CD4 mKRAS-responsive T cells, which has been associated with durable immunological memory and long-term therapeutic benefit in the context of ICIs (Fig. 3a)23.

The long-term durability of mKRAS-specific T cells was assessed in all four patients who had remained disease-free at the end of the primary treatment phase and opted to receive booster doses of mKRAS-VAX alone every three months in an extended treatment phase (Fig. 4a). Measurable mKRAS-specific T cell ELISPOT responses, albeit of a lower magnitude, persisted in the extended phase (Fig. 4a, Statistics in Supplementary Table 7). Importantly, CyTOF of peptide-restimulated PBMCs detected the persistence of the highly activated CD4 Tem population, and to a more variable extent, the CD4 Tcm and CD8 Tem populations (Fig. 4b, c). In vitro peptide expansion of PBMCs from the last timepoint for each patient (>week 52) showed rapid expansion of mKRAS-specific T cells for all four patients (Fig. 4d). Consistent with earlier timepoints, supernatants from peptide-restimulated PBMCs revealed predominantly Th1 cytokines, with lower but detectable Th2 and Th17 cytokines (Fig. 4e). In all, our comprehensive T cell phenotyping revealed the activation of distinct Th1 CD4 (predominant) and CD8 central and effector memory T cell populations in response to combinatorial therapy with vaccine and ICIs, and persistence of distinct populations with vaccine only.

Identification of mono-reactive, cross-reactive, and public mKRAS-specific T cell clonotypes
The identification of mKRAS-specific TCRs is critical to better understand the biological underpinnings of mKRAS immunological recognition. Despite the predominance of the mKRAS oncogene in PDAC (up to 90%) and other cancer types (30%), few mKRAS-specific T cell clones have been identified to-date. One case report showed that an infusion of two CD8+ mKRAS-specific T cell clones recognized and killed lung metastases in a patient with a KRAS-expressing PDAC tumor, highlighting the importance of identifying other functional TCRs11.

To isolate mKRAS-specific T cells, we performed in vitro peptide expansion with individual mKRAS peptides followed by bulk TCRβ sequencing in a subset of patients. To identify TCRβ sequences that expanded significantly in the presence of mKRAS peptides, we compared the frequency of TCRβ sequences present in each mKRAS expansion condition to those in their relevant peptide or vehicle-only control (Fig. 5a). T cell clonal expansion was confirmed in response to mKRAS peptides relative to the unstimulated control for each patient as an increase in Shannon equitability, a measure of TCR repertoire clonality (Supplementary Fig. 7a). Based on recurrence status, we detected a trend, although not significant, towards more clonal repertoire of tumor-specific and average mKRAS-expanded TCRs in patients who did not recur (Supplementary Fig. 7b). Using stringent expansion cut-offs of an odds ratio (OR) of >5-fold relative to control and a frequency cutoff of >0.1% of the expanded repertoire, we identified putative mKRAS TCRs for all four patients (Fig. 5b, Supplementary Fig. 7c, Supplementary Data 1). To confirm that mKRAS-specific T cells were not present in the pre-vaccine timepoint, we performed in vitro peptide-expansion assays with individual mKRAS peptides on PBMCs from the baseline sample followed by IFNγ ELISPOT for 3/4 patients (with sufficient baseline samples). No significant expansion of mKRAS-specific T cells was observed in baseline PBMC samples although significant expansion was detected in post-vaccine samples (Fig. 5c). Consistent with this, there was little overlap between the mKRAS-specific TCRs identified by expansion and bulk TCRβ sequencing of the resected primary tumors, despite the high numbers of productive TCRs sequenced for all tissue specimens (Supplementary Fig. 7d, e). Conversely, a single metastatic lung recurrence biopsied from patient J1994_5 had 80 unique mKRAS-specific TCR sequences overlapping with in the tissue representing almost 20% of the mKRAS-repertoire identified for this patient (Supplementary Fig. 7e). Of the mKRAS-specific TCRs in the tumor, 55% were specific for the patient’s tumor mutation (G12V) (Supplementary Fig. 7f). Thus, we reasoned TCRs present in an unstimulated baseline timepoint were unlikely to be mKRAS-specific and were filtered out of the expansion data; this further ensured the stringency of our inclusion criteria. Using this curated TCR repertoire, we observed that for 3 out of 4 patients, more than 10% of the enriched repertoire expanded to more than one mKRAS peptide, suggesting potential cross-reactivity of mKRAS-specific TCRs (Fig. 5d). For all three patients harboring cross-reactive TCRs, the most common antigen cross-reactivity was between mKRAS G12C, G12A, and G12V, and to a lesser extent, G12R (Fig. 5e).

We also identified TCRβ clonotypes that expanded in response to mKRAS peptides in multiple patients-termed ‘public TCRβ clonotypes’ (Supplementary Fig. 7g). Two public TCRβ clonotypes were also detected in a post-vaccine PBMC sample from a patient (J1994_8) with mKRAS positive metastatic colorectal cancer (CRC), who was also treated with mKRAS-VAX. These public TCRβ clonotypes expanded in response to at least one shared mKRAS antigen across patients, although some public TCRs expanded to different mKRAS antigens. Notably, public TCRβ06 (TCRBV28.01.01_CASRLGNTGELFF) was enriched in response to mKRAS G13D in two patients; this clonotype has been previously identified in tumor infiltrating lymphocytes as G13D-responsive in a patient harboring a G13D+ tumor24. In addition, public TCRβ06 and another public clonotype, TCRβ08 (TCRBV28.01.01_CASRRGNTGELFF), which significantly expanded in response to mKRAS G13D, use the same TCRΒV allele and differ in the antigen recognition CDR3 domain by a single amino acid, consistent with public “high similarity” (HS-TCRβ) clonotypes found to viral antigens. We asked if we could use HS-TCRβ comparisons to detect HS-mKRAS TCRβ clonotypes present in mKRAS positive PDAC tumor samples within the Cancer Genome Atlas (TCGA). Using our expanded TCRβ repertoire, we detected HS-TCR clonotypes with 1-2 amino acid differences, within the mKRAS-positive PDAC tumors, with 13 tumor samples having 10 or more HS-TCR clonotypes (Supplementary Fig. 7h, Supplementary Data 2). However, there was no correlation between the abundance of mKRAS HS-TCRs in a tumor sample and OS (Supplementary Fig. 7i). Overall, these data support the presence of public mKRAS-TCRβ clonotypes raised by mKRAS-VAX and imply the potential for these clonotypes to be present at baseline.

To determine the complete TCRαβ sequence of mKRAS-specific T cells in the four patients, we further performed scRNA/TCR sequencing on unstimulated PMBCs at pre- and post-vaccine timepoints. CD4 and CD8 T cell populations were identified based on cell-type marker expression (Fig. 6a, Supplementary Fig. 8a, b). No significant difference in post-vaccine TCR clonality was observed in patients who had recurred relative to those who had not recurred (Supplementary Fig. 8c). When looking at immune cells present pre- or post-vaccine, there was no significant change in the proportion of B cells, monocytes, dendritic cells, NK cells, or T cells across timepoints for all patients (Supplementary Fig. 8d, e). At a gene level, we observed changes broadly in genes associated with metabolism, cytoskeletal structure, and inflammation in the post-vaccine CD4 Tcm and CD8 Tem populations, although we did not observe any significant changes at the pathway level by KEGG pathway analysis (Supplementary Fig. 8f, Supplementary Data 3). At a transcription factor level, we observed a trend towards increased TBX21 (Tbet) but not GATA3 or RORC (RORγt) expression in the CD4 Tcm subset post-vaccination (Supplementary Fig. 8g). Recently ZNF683, encoding the transcription factor hobit, has been shown to be a marker of a tissue-residence phenotype associated with expanding T cells in response to ICI25. We found that ZNF683 expression in proliferating CD8 T cells was significantly upregulated post-vaccine, potentially highlighting an ICI-induced population (Supplementary Fig. 8g).

To distinguish mKRAS-specific T cells in this dataset, we mapped mKRAS-specific TCRβ chains identified by in vitro expansion onto the scRNA/TCR seq dataset. mKRAS-specific TCRs mapped predominantly to CD4 Tcm (CD3+ CD4+ TNFRSF4+ TCF7+ IL7R+) and CD8 Tem (CD3+ CD8+ ITGB1+ CCL5+ CST7+) populations, consistent with the populations identified by CyTOF (Fig. 6b, Supplementary Data 5). We noted that most CD8 T cell TCRs found in the adaptive sequencing datasets were present at a lower frequency (<0.1% of repertoire) compared to CD4 T cells; consistent with a predominant CD4 T cell response to our vaccine (Supplementary Fig. 8h). Therefore, to improve detection of potentially mKRAS-reactive CD8 T cells, we did not include a frequency cutoff and only considered TCRs that met the OR >5 in mapping mKRAS-reactive TCRs onto the single cell dataset (Supplementary Data 5). We compared the gene expression profiles of mKRAS-specific CD4 Tcm cells relative to all other CD4 Tcm cells in the dataset. We note that too few mKRAS-specific cells were detectable in the CD4 naïve, proliferating, or Tem populations to perform this analysis (Supplementary Fig. 8i, Supplementary Table 8). Five genes were significantly upregulated in the mKRAS-CD4 Tcm population, including PDCD, indicating higher activation/exhaustion levels; GPR25, whose homolog has been associated with T resident memory cells26; APOBEC3H, associated with inflammatory responses; TRBV28 demonstrating a V gene enrichment for mKRAS-reactive CD4 T cells; and TBX21 (Tbet), consistent with the trend towards increased expression in the broad CD4 Tcm population and the functional Th1 phenotype after antigen restimulation (Fig. 3). Similar expression levels of markers associated with central memory phenotypes and stem-like cell phenotypes, including LEF1 and TCF7, and senescence and dysfunction-associated markers, including FOXP3, KLRG1, LAG3, and CTLA4, were observed between mKRAS-specific CD4 Tcm and all other CD4 Tcm cells (Supplementary Fig. 8i).

Validation of antigen specificity of mutant KRAS TCRs
We sought to validate the antigen-specificity of the mKRAS CD4 and CD8 TCRs identified in the peptide expansion assay that were mapped onto the single cell dataset (Supplementary Data 5). For this, Jurkat TCR KO-NFAT-GFP cells were transduced with lentiviruses expressing mono-reactive, cross-reactive, or public putative mKRAS-reactive TCRs in our dataset and were flow sorted for TCR+ cells (Supplementary Fig. 9a, b). TCR+ cells were co-cultured overnight with patient-matched B lymphoblastoid cell lines (LCLs) and pulsed with individual mKRAS peptides, control peptide, or wild-type KRAS synthetic long peptides. GFP expression was quantified by flow cytometry to assess antigen recognition (Supplementary Fig. 9c). All 4 mono-reactive TCRs responded to their respective mKRAS antigen that elicited expansion and did not respond to other mKRAS mutations or wild-type KRAS (Fig. 6c). Cross-reactive TCR05 responded to G12V, G12A, G12C, and G13D with weak reactivity to WT KRAS observed (Fig. 6c). TCR03, a cross-reactive TCR that had expanded in vitro to KRAS G12V, G12A, and G12C responded significantly to these three antigens at the highest peptide concentration tested (10μM) but did not respond at lower concentrations of peptide (Fig. 6e). However, the avidity of this TCR for each mutation was notably weaker than the other TCRs. To determine the HLA-specificity for each TCR derived from patient J1994_2, HLA-null K562 cells were co-transfected with plasmids expressing the HLA-DR, DP, or DQ alpha and beta chain alleles and co-cultured with TCR transduced Jurkat cells. All mKRAS-specific TCRs recognized cognate peptide in the context of the HLA-DR*07:01 allele (Fig. 6e). Notably, TCR05 was indicated from the single cell dataset as a CD8 T cell; however, it responded to antigen in the context of this class II allele suggesting it is likely a CD4 T cell.

Additionally, we confirmed the antigen specificity and HLA-restriction of public TCRβ08, which was identified as significantly expanded to KRAS G13D in the bulk TCRβ sequencing for three patients (J1994_2, J1994_6, J1994_8) relative to control (Supplementary Fig. 7g). The paired alpha chain for this TCRβ sequence was identified only in the single cell dataset for patient J1994_2 and was therefore cloned using this alpha-beta chain pairing. Public TCRβ08 responded to KRAS G13D but not to control or wild-type KRAS peptide in the context of peptide pulsed LCLs for the three patients it was detected in (Fig. 6f). Between these three patients, HLA-DQB*03:01 was the only shared class II allele (Supplementary Table 6). To test if this allele was required for TCR recognition, we co-cultured Public TCRβ08 with peptide pulsed LCLs from a donor that did not have the DQB*03:01 allele. TCRβ08 failed to respond to G13D when co-cultured with this DQB*03:01 negative donor, while co-culture with LCLs from an irrelevant DQB*03:01 positive donor pulsed with G13D allowed for efficient TCR activation (Fig. 6f).

To determine if these mKRAS-specific T cells were capable of direct tumor recognition, we tested TCR02, TCR03, and TCR05 which recognize KRAS G12V in the context of HLA-DR*07:01 against the HLA-matched human pancreatic cancer line CAPAN1 which endogenously expresses KRAS G12V or the control line SUDHL10 expressing KRAS WT. We also tested recognition of public TCR08 (G13D-reactive) against target cell line MDA-MB-231 a KRAS G13D+ tumor line expressing HLA-DQB*03:01. We pre-treated the tumor cell lines with IFNγ for 48h to upregulate HLA class I and class II on the surface (Supplementary Fig. 9d), followed by co-culture with TCR transduced Jurkat reporter lines for 24h in the absence or presence of additional exogenous KRAS G12V peptide. High tumor recognition, measured by an increase in GFP expression from the reporter line after co-culture, was observed predominantly when exogenous G12V was supplied to the CAPAN1 co-cultures (Fig. 6g). This was consistent for public TCRβ08 when co-cultured with the KRAS G13D+ target cell line MD-MB-231 (Fig. 6g). None of the TCRs responded to the WT KRAS-expressing SUDHL10 control line, confirming a lack of recognition of healthy tissue. To determine the potential for clinical translation of these TCRs, and to test cross-reactivity of TCR05 against multiple tumor antigens presented on tumor cell lines, we generated a transgenic human TCR expressing line from healthy donor T cells in which endogenous TCR was knocked out followed by lentiviral overexpression of TCR05 (Supplementary Fig. 9e). After co-culture with KRAS G12V+ CAPAN1 cells pre-treated with IFNγ, strong activation was detected only when each mKRAS antigen was supplied exogenously to either CAPAN1 or SUDHL10 cells, which expresses the cognate HLA allele (Fig. 6h). To test if TCR05 can indirectly recognize target antigen, we pulsed patient matched LCLs with tumor cell lysate generated from control or CAPAN1 (G12V+) tumor cells (Supplementary Fig. 9f). Indeed, TCR05 expressing human T cells increased CD25 expression after co-culture with CAPAN1 but not control lysate pulsed APCs supporting a role for indirect recognition of tumor with these identified mKRAS-specific TCRs.

Methods
Study design, patient enrollment, and treatment schema
This study was conducted in accordance with the Declaration of Helsinki and good clinical practice guidelines. The trial protocol and amendments were approved by the Institutional Review Board at Johns Hopkins University and the US Food Drug Administration. This trial was registered on ClinicalTrials.gov (NCT04117087). All participants provided written informed consent. No financial incentive was provided. Participants were recruited from Johns Hopkins under IRB approved protocol. The study protocol is available in the Supplementary Information. Data collection was collected prospectively at the time of enrollment and during protocol-defined visits. Both male and female participants were included in this study. Sex and gender were recorded based on clinical documentation in the electronic record based on self-report. Sex was reported as a descriptive demographic variable. Analysis was not stratified on sex/gender because the study was not powered to detect sex-specific differences in outcomes. Demographic variables, disease characteristics, and treatment information were abstracted from the electronic medical record. Radiographic assessments were performed at pre-specified intervals. Laboratory and correlative studies were performed on prospectively collected biospecimens. All data were recorded in a secure, IRB-approved research database at Johns Hopkins.

The co-primary endpoints were safety and immunogenicity. Immunogenicity was defined as the maximal fold change in the pooled average mKRAS-specific T cell density across six mutation subtypes within 17 weeks post-vaccination using serial IFNγ ELISPOT assays of peripheral blood. Safety was assessed by the frequency and type of treatment-related adverse events per NCI Common Terminology Criteria for Adverse Events, version 5.0.

This single-arm, open-label, first-in-human phase I trial was conducted at the Johns Hopkins Sidney Kimmel Comprehensive Cancer Center to assess the safety and immunogenicity of a pooled synthetic long peptide mutant KRAS vaccine (mKRAS-VAX) combined with ipilimumab and nivolumab in patients with resected PDAC (NCT04117087) with a start date of 5/28/2020. Patients were eligible to participate following curative-intent surgery and completion of adjuvant chemotherapy and/or radiation therapy per standard-of-care. Molecular tumor testing must have confirmed the presence of one of six KRAS mutations included in the study vaccine. KRAS testing was performed locally in a CLIA certified laboratory using next generation sequencing (NGS) or external CLIA-certified laboratories. Only those patients who harbor one of the KRAS mutations were eligible to enroll.

On-study treatment was divided into prime and boost phases. In the prime phase, patients received mKRAS-VAX on days 1, 8, 15, and 22 with two doses of ipilimumab 1 mg/kg and four doses of nivolumab 3 mg/kg. In the boost phase, patients received mKRAS-VAX every 8 weeks with nivolumab 480 mg every 4 weeks, up to a total of 14 treatment cycles. In an optional extended phase, patients who remained disease-free were permitted to receive four additional booster vaccines every 3 months during a second year on study. Radiographic disease assessments by CT imaging were performed every 3 months.

Vaccine formulation and administration
The study vaccine consists of 21-mer synthetic long peptides corresponding to six of the most common KRAS mutation subtypes found in PDAC. Peptides were synthesized by JPT Technologies with sequences as follows: G12V- YKLVVVGAVGVGKSALTIQLI, G12C- YKLVVVGACGVGKSALTIQLI, G12A- YKLVVVGAAGVGKSALTIQLI, G12R- YKLVVVGARGVGKSALTIQLI, G12D- YKLVVVGADGVGKSALTIQLI, G13D- YKLVVVGAGDVGKSALTIQLI. These six peptides were admixed (0.3 mg/peptide, a total of 1.8 mg) with 0.5 mg TLR3 agonist poly-ICLC (Hiltonol; Oncovir) in saline prior to vaccine administration at the Investigational Drug Service Pharmacy at Johns Hopkins Hospital. The pooled vaccine consisting of all six peptides was administered subcutaneously at five injection sites. For in vitro assays, lyophilized peptides were dissolved in DMSO at 4 mg/ml and aliquots were stored at -80°C. The sequence for the control peptide used in in vitro stimulations corresponds to an irrelevant fusion gene product as follows: RKREIFDRYGEEVKEFLAKAKEDF. If stability of peptides dropped below 90% or if sterility did not meet criteria, peptides were excluded from vaccine timepoints as indicated (Supplementary Table 9).

Statistical analysis
Patients were considered evaluable for immunogenicity if they received at least one vaccination and had peripheral blood samples available at baseline and within 17 weeks post-vaccination. Descriptive statistics, boxplots, and plots over time were used to summarize mKRAS-specific T cell responses. Number of IFNγ spot forming unit (SFU) after peptide restimulation as a measure of T cell activation was normalized to an irrelevant control peptide stimulation at each timepoint. T cell measurements at each timepoint were reported using two approaches: 1) pooled average across six mKRAS subtypes and 2) response specific to the mKRAS subtype found in each patient’s tumor. Fold-change from baseline is calculated at each timepoint, and T cell response is defined as maximum fold-change within 17 weeks. To determine whether the change in T cell is a true change for individual patient and not due to random variation, the difference of T cell IFNγ SFU between pre- and post-vaccination was compared to the threshold of 2.77 times standard deviation (SD) of the baseline values across patients. For each patient, the observed change greater than 2.77 SD is deemed a statistically significant change at the two-sided significance level of 0.0548. Disease-free survival and overall survival were evaluated using the Kaplan-Meier method. Statistical analysis was performed using Rstudio version 4.3.0 (Rstudio Inc). For each patient, the observed change greater than 2.77 SD is deemed a statistically significant change at the two-sided significance level of 0.0548. Disease-free survival and overall survival were evaluated using the Kaplan-Meier method. The log-rank test was used to compare survival times between patient subgroups, and hazard ratio was estimate based on Cox model. Statistical analysis was performed using Rstudio version 4.3.0 (Rstudio Inc).

Genomics analyses and graphing were performed in R as described above, and graphing and statistical analysis for the experimental data was performed using GraphPad Prism 9 (Version 9.4.0). Multiple comparisons were analyzed using two-way ANOVA followed by Dunnett’s multiple comparisons, Sidak’s multiple comparisons, or Tukey’s multiple comparisons test where appropriate. Data is presented as group mean ± standard deviation or ± standard error of the mean where indicated. Statistical significance was set as ns = p>0.05, *p≤0.05, **p<0.005, ***p≤0.0005, ****p≤0.00001. Figures were prepared using Adobe Illustrator CC 2022.

ctDNA detection from plasma samples
ctDNA analysis was done in the Ludwig Center at Johns Hopkins. DNA was extracted from 5 mL of plasma per patient using cfPure MAX Cell-Free DNA Extraction Kit (BioChain, cat# K5011625MA), as specified by the manufacturer’s instructions. DNA from peripheral white blood cells (WBCs) was purified with the QIAsymphony DSP DNA Midi Kit (Qiagen, cat# 937255) according to manufacturer’s instructions. DNA was then made into SaferSeqS libraries and queried for common driver mutations49. ctDNA analysis was done in the Ludwig Center at Johns Hopkins. DNA was extracted from 5 mL of plasma per patient using cfPure MAX Cell-Free DNA Extraction Kit (BioChain, cat# K5011625MA), as specified by the manufacturer’s instructions. DNA from peripheral white blood cells (WBCs) was purified with the QIAsymphony DSP DNA Midi Kit (Qiagen, cat# 937255) according to manufacturer’s instructions. DNA was then made into SaferSeqS libraries and queried for common driver mutations in genes including TP53, KRAS, CDKN2A, SMAD4, and APC (ref. 49). Any mutation that was present in the matched WBCs from the patient was attributed to clonal hematopoiesis of indeterminate potential (CHIP) and therefore excluded.

Cell lines and cell culture
Fresh human PBMCs were cultured in RPMI (Gibco, cat# 11875093) supplemented with 5% human serum (GemCell, cat# 100-512), 12.5 mM HEPES (Gibco, cat# 15630080), 2 mM L-Glutamine (Gibco, cat# 25030061), and 1X Pen/Strep (Gibco, cat# 15140122). The jurkat report line pASP90 and pASP90-CD8 which are knocked out for endogenous TCR and express constitutive mCherry with GFP expression under the control of NFAT were a kind gift from Dr. Beatriz Careno39. Jurkat report cells, SUDHL10, and K562 cells were cultured in RPMI supplemented with 10% FBS (Gemini Bioproducts, cat# 100-106), 2 mM L-Glutamine, and 1X Pen/Strep. Patient-derived LCL cell lines were cultured in RPMI supplemented with 20% FBS, 2 mM L-Glutamine, and 1X Pen/Strep. 293Ta cells were cultured in DMEM (Gibco, cat# 11965092) supplemented with 10% FBS, 2 mM L-Glutamine, and 1 mM sodium pyruvate (Gibco, cat# 11360070). Tumor cell lines CAPAN1 (ATCC cat# HTB-79) and MDA-MB-231 (ATCC cat# CRM-HTB-26) were cultured in DMEM (Gibco cat# 11965092) supplemented with 10% FBS, 2 mM L-Glutamine, 1X Sodium pyruvate, and 1X Pen/Strep.

mKRAS-specific T cell tracking using IFNγ ELISPOT on fresh PBMC samples
The frequency of IFNγ secreting cells was determined using Human IFN-γ (HRP) precoated strip wells (Mabtech, cat# 3420-4HST-2) following the Johns Hopkins Immune Monitoring Core SOPs. The tissue culture medium was composed of RPMI-1640 (Gibco, cat# 11875-093) supplemented with L-glutamine (Mediatech, cat# 25-005-Cl), antibiotics (Quality Biological, cat# 120-095-721), 50 uM beta mercaptoethanol (Sigma, cat#) and 10% Fetal Calf Serum (Hyclone). The peptide pool control (CTL, cat# CTL-CEF-002) and KRAS peptides (JPT) were used at 2 ug/ml with 200,000 fresh PBMC responders per well and an 18-hour incubation time. Developed assays were read using an iSpot Spectrum Analyzer (AID, Strassburg Germany) and reported as SFU (Spot Forming Units) per million cells.

CyTOF of mKRAS peptide pulsed PBMCs
All antibodies used for CyTOF analysis are shown in Supplementary Table 10. Pre- and post-vaccine PBMCs were thawed at 37°C and resuspended in complete media. PBMCs were seeded at 2×10^6 cell/well in 48 well plates and rested overnight. DMSO (Sigma Aldrich, cat# 472301), control peptide, or mutant KRAS SLPs were added at 2 ug/mL. CD3/CD28 Dynabeads (Gibco, cat# 11161D) were used as a positive control. Cells with peptide were incubated for 48h at 37°C. eBioscienceTM protein transport inhibitor cocktail (Thermofisher, cat# 00-4980-93) was added to each well at 1X and cells were incubated for an additional 5h. Cells were collected and transferred to a 96 well U bottom plate for staining. Cells were stained for viability with 20 µM cis-platinum (Standard BioTools, cat# 201064) for 2.5 min at room temperature (RT), quenched with complete RPMI media, and washed twice in Maxpar Cell Staining Buffer (CSB, Standard BioTools, cat# 201068). To ensure robust analysis with minimized batch effects, we employed 30-plex barcoding to stain all stimulation and timepoint conditions at the same time for each patient. A 7-choose-3 (89Y, 110Cd, 111Cd, 112Cd, 113Cd, 114Cd, 116Cd) schematic was used based on CD45 to generate up to 35 unique barcodes. After staining for 25 min at RT, samples were washed three times in CSB and combined into batches via 40 µm-strainer into 5-ml flow tubes. Batches were then incubated in Fc block (Thermo Fisher Scientific, cat# 14-9161-73) in CSB for 10 min at RT, stained with an antibody cocktail in CSB against chemokine receptors for 10 min at 37°C, and then surface antibody cocktail in CSB for 30 min at RT. After two washes, batches were fixed/permeabilized using eBioscienceTM Fix/Perm (ThermoFisher Scientific, cat# 00-5123-43) for 30 min at RT. Cells were washed with eBioscienceTM Perm Buffer twice and stained with intracellular staining cocktail for 30 min at RT. Fully stained cells were washed with CSB and fixed with freshly prepared PierceTM paraformaldehyde 1.6% (ThermoFisher Scientific, cat# 28906) in Maxpar PBS. On the day of acquisition, all cells were labeled with 103Rh (1:500, cat# 201103, in Maxpar Fix & Perm Buffer, cat# 201067, Standard BioTools) for 30 min at RT. Data was acquired on HeliosTM at the Johns Hopkins Mass Cytometry Facility. Acquired data was preprocessed for normalization, randomization, and bead removal using CyTOF Software (v6.7, Standard BioTools). Gating for cell events, live cells, and debarcoding was performed using FlowJo (v10.9, BD). Processed FCS files were loaded into R (v4.0.2) for analysis. Expression levels were arcsine transformed with a cofactor of 5. Cells were clustered using FlowSOM algorithm and annotated based on canonical markers (e.g., CD3+ CD4+ CD8- CD45RA+ CCR7+ CD45RO- characterizes naïve helper T cells)50. Cluster abundances were calculated as proportions of CD45+ cells and were analyzed for significance relative to control peptide by one-way ANOVA followed by Dunnett’s multiple comparisons test. Expression levels were compared using Wilcoxon test.

Flow cytometry of mKRAS peptide pulsed PBMCs
PBMCs were thawed and rested overnight in complete media at 1×10^6 cell/well in a 96 well plate. The next day, DMSO control (unstimulated), the control peptide, or KRAS G12V, G12C, G12R, G12A, G12D, G13D 24-mer peptides were added to each well at a final concentration of 2 ug/mL. PBMCs were incubated for 48h. Six hours prior to collection, eBioscience protein transport inhibitor cocktail (Thermo Scientific, cat# 00-4980-03) was added at a final dilution of 1X. Cells were collected and washed twice with PBS. Samples were incubated in Zombie NIR viability stain (Biolegend, cat# 423105) for 15 min at room temperature in the dark. Samples were washed twice with FACS buffer containing 1x HBSS (Gibco, Serum 2%, Na Azide 0.1% and HEPES 0.1%). Cells were then stained with anti-CD3 (clone HIT3a Biolegend cat# 300306), anti-CD4 (clone RPA-T4, Biolegend cat# 300556), anti-CD8 (clone RPA-T8, Biolegend cat# 301036), anti-CD45RO (clone UCHL1, cat# 304218), anti-CD62L (clone DREG-56, Biolegend cat# 304823), anti-CD69 (clone FN50, Biolegend cat# 310905), anti-CD137 (clone 4B4-1, Biolegend cat# 309817) in FACS buffer. Samples were washed twice with FACS buffer and then permeabilized with BD Perm/Fix kit (BD Biosciences, cat# 554714) followed by staining with anti-IFNγ (clone 4S.B3, Biolegend cat# 502512), anti-IL-2 (clone MQ1-17H12, Biolegend cat# 500343), anti-TNFɑ (clone Mab11, Biolegend cat# 502938), anti-T2A (clone 3H4, Novus cat# 59627AF700). Samples were washed twice with 1X Perm/wash buffer and resuspended in FACS buffer. All samples were run on a Beckman Coulter Cytoflex cytometer. Data was analyzed in Flowjo version 10.8.1.

Luminex cytokine measurements from supernatants of peptide restimulated PBMCs
The Bioplex 200 platform (Biorad,) was used to determine the concentration (pg/mL) of multiple target cytokines in plasma or cell culture supernatants. Luminex bead-based immunoassays (Millipore) were performed following the Johns Hopkins Immune Monitoring Core SOPs and concentrations were determined using 5 parameter log curve fits (using Bioplex Manager 6.0) with vendor-provided standards and quality controls. The HCYTA-60K panel was used to detect cytokines (IL-2, IL-4, IL-5, IL-10, IL-17A, IFNγ). Concentrations which were outside of the standard curves values were categorized as “out of range” (OOR). For each cytokine, OOR < values were replaced with the lower limit of the standard curve of the assay while OOR > were replaced with the upper limit of the standard curve.

mKRAS-specific T cell in vitro expansion and TCR sequencing
PBMCs were thawed and rested overnight in complete media at 2×10^6 cell/well in a 48 well plate. The next day, DMSO control, the control peptide, or KRAS G12V, G12C, G12R, G12A, G12D, G13D 21-mer peptides were added to each well at a final concentration of 2 ug/mL and 100 IU/mL human IL-2 (Peprotech cat# 200-02) and incubated at 37°C for 7 days. Peptide expanded cells were collected, centrifuged at 1500 and genomic DNA was isolated using a DNeasy Blood and Tissue kit (Qiagen, cat# 69504) according to the manufacturer’s protocol. gDNA was quantified using a Nanodrop. TCRβ sequencing was performed by Adaptive BiotechnologiesTM.

Tumor Bulk TCR Sequencing
Tumor DNA was extracted either from either FFPE slides or frozen core biopsies. For FFPE slides, scalpels were used for manual microdissection using slides stained with haematoxylin and eosin (H&E) as a guide. DNA was then extracted using the QIAmp DNA FFPE Tissue Kit (Qiagen #56404). TCR CDR3β sequencing was performed by Adaptive Biotechnologies. Frozen core biopsies were directly sent to Adaptive Biotechnologies for DNA extraction prior to sequencing.

TCRβ repertoire analysis of expanded T cell populations
Shannon equitability for each expansion condition was calculated using the formula:
Shannon equitability = 1 - (Σ Pi log(Pi)) / log(n)
where Pi is the frequency of an individual species and n is the total number of species. This value ranges between zero and one, with zero representing maximally even distributions and one representing maximum clonality51,52. TCRs expanded to any of the six described mKRAS peptides or control peptide were identified through comparison against DMSO control. Expanded clonotypes were identified using a one-sided fisher’s exact test with false discovery rate correction using the Benjamini-Yekutieli procedure53 in a manner similar to prior studies. Expanded clonotypes were identified using a one-sided fisher’s exact test with false discovery rate correction using the Benjamini-Yekutieli procedure53 in a manner similar to prior studies53-55. Odds ratio (OR) is the absolute number of reads of a TCR clonotype found in an expansion condition over the absolute number of that respective TCRs in the control peptide condition53. Clonotypes were defined as the combination of TRBV and CDR3 amino acid sequence. Only expanded clonotypes with OR >= 5 over DMSO control and expansion frequency >= 0.1% were considered biologically meaningful and included for further analysis. Clones meeting expansion criteria for multiple mKRAS peptide stimulations were defined as cross-reactive. Expanded clones appearing in data for multiple subjects were considered public.

Single cell TCR/RNA sequencing of unstimulated PBMCs
PBMCs were thawed on ice, washed, and resuspended in PBS containing 1% BSA, and cell counts and viability were made using Trypan Blue staining (ThermoFisher) in the hemocytometer. scRNA library preparations were performed using the 10x Genomics ChromiumTM Single Cell system and ChromiumTM Single Cell 5′ Library & Gel Bead Kit v2 (10x Genomics). TCRs were enriched using the TCR Amplification kit (10x Genomics), following the manufacturer’s instructions. Initial cell input for each sample was 20,000 PBMCs to recover a total of 10,000 cells. Sequencing was performed on the NovaSeq platform (Illumina) using 10x Genomics recommended features and with a depth of 50,000 reads per cell. Samples were sequenced in two batches, with subject J1994_12 sequenced separately from remaining samples. Sequences were processed using the Cellranger 5.0.1 pipeline (10x Genomics) and mapped to the human reference genome (GRCh38).

Single-cell RNA and TCR-seq analysis
Raw feature-barcode matrices were imported into Seurat version 4.3.2 for processing56. Samples from each of two sequencing batches were analyzed separately. Cells were clustered following QC using the Seurat FindClusters function using resolution 0.3. T cell clusters were differentiated from other PBMCs by expression patterns of positive and negative markers including CD3E, CD4, CD8B, CD14, CD19, FCGR3A, the presence of VDJ reads, and predicted cell type using Azimuth version 0.5.0 (PBMC reference, level 2 resolution)56. Following preliminary annotation, T cells were isolated and re-clustered. Cluster markers were identified using a Wilcoxon Rank Sum test as implemented through the Seurat FindMarkers function with default arguments including significance level = 0.01. Manual inspection of cluster markers and Azimuth predictions were used to make final cell type determinations. T cells with matching TRBV and CDR3 beta amino acid sequence to an anti-mKRAS TCR identified in expansions were considered anti-KRAS with peptide reactivity corresponding to expansion data. Gene expression analyses performed using native Seurat functions and custom analysis in R version 4.3.1.

Generation of lentivirus and transduction of TCR expressing jurkat reporter cells
Complete paired TCRα and β sequences were compiled from the single cell dataset using StiTChR57. Third generation lentiviral vector plasmid (pLV) were designed to encode a TCRα-T2A-TCRβ expression cassette under control of the EF1α promoter. Plasmids were synthesized by VectorBuilder. To generate lentivirus, 3×10^6 293Ta were seeded in a 10 cm dish with 10 mL complete media and incubated overnight 37°C. 8 ug pLV-TCR constructed with co-transfected into the cells with 6 ug psPAX2 and 2 ug pMD2.g using Lipofectamine 3000 (Thermo Fisher, cat# L3000001). Forty-eight hours later, supernatant was collected, centrifuged at 1500 rpm for 5 min, and filtered through a 0.45 uM PES syringe filter (Celltreat, cat# 229749). Virus was then concentrated using lenti-X concentrator (Takara Bioscience, cat# 631231) and resuspended in PBS. Concentrated virus was snap frozen in liquid nitrogen and stored at -80°C.

To generate TCR expressing jurkat NFAT-GFP reporter cell lines, 1×10^6 cells were seeded in 0.5 mL complete media with 8 ug/mL polybrene (Sigma-Aldrich, cat# H9268). 100–200 ul of concentrated lentivirus was added to each well and cells were incubated overnight at 37°C. The next day, 1 mL complete media was added to each well and cells were incubated for another 24 h at 37°C.

Transduced cells were collected and washed twice with PBS. Cells were stained with Zombie NIR (1/1500 in PBS) and incubated for 15 min at room temperature in the dark. Cells were then washed twice in FACS buffer containing 1x PBS with 2% FBS. Anti-human TCRα/b-APC (clone IP26, Biolegend cat# 306718) stain was added in FACS buffer to each well and incubated for 20 min at 4°C. Cells were washed twice with FACS buffer and analyzed on a Beckman Coulter Cytoflex cytometer. Data was analyzed in Flowjo version 10.8.1. TCRα/β positive cells were then flow sorted on a BD Fushion sorter in sterile conditions.

T cell engineering
Engineered primary human T cells expressing TCRs under control of an EF1-α promoter were generated via transduction with TCR-expressing lentivirus followed by CRISPR-Cas-mediated knockout of endogenous TCR60. Transgenic TCR sequences were codon optimized at the TRAC/TRBC locus to prevent CRISPR-Cas recognition and editing. T cells were isolated by negative selection using immunomagnetic cell separation (EasySep Human T Cell Isolation Kit #17951) from cryopreserved healthy donor peripheral blood mononuclear cells collected via leukapheresis. Purified CD3+ T cells were activated with Dynabeads Human T-Activator CD3/CD28 beads with 100 IU/mL recombinant human IL-2 (Peprotech #200-02) and 5 ng/mL recombinant human IL-7 (Peprotech #200-07) at 37C, 5% CO2. The next day, T cells were spinoculated with 100-200 ul concentrated lentivirus at 920g for 90 min at room temperature in the presence of 8 ug/mL polybrene (Sigma-Aldrich, cat# H9268). The next day, cells were collected and CD3/CD28 beads were removed with a magnet. Cpf1 (Cas12a) ribonucleoprotein (RNP) targeting TRAC (GAGTCTCTCAGCTGGTACAC) as well as TRBC1 and TRBC2 (GCCCTATCCTGGGTCACTC) were assembled by mixing the appropriate crRNA (IDT) with Alt-R a.s. Cas12a (Cpf1) Ultra nuclease (IDT) and Cpf1 Electroporation Enhancer (IDT) and incubating the mixture at room temperature for 15 min. To edit activated T cells, 20 μL of T cells were resuspended in P3 buffer at 5×10^7 cells/mL (Lonza) and added to the electroporation mixture. Electroporation was performed with a 4D-Nucleofector X Unit (Lonza) in 16-well cuvettes using pulse code EH115. After electroporation, T cells were recovered by immediately adding 80 μL of warm, cytokine-free T cell media to the cuvettes and incubation at 37°C for 15 min. Then, T cells were diluted in T cell growth media containing 100 IU/mL recombinant human IL-2 and 5 ng/mL recombinant human IL-7. Cells were restimulated with Dynabeads Human T-Activator CD3/CD28 beads on day 8 and then expanded for 2 weeks replacing media with fresh cytokine media every 2 days for ~two weeks prior to use in functional assays.

Generation of patient matched LCL lines
3×10^6 patient PBMCs from pre-vaccine timepoint were resuspended in 500 ul human gammaherpesvirus 4 (HHV-4) (ATCC, cat# VR-1492), transferred to one well of a 24 well plate, and incubated for 2 h at 37°C. After incubation, the plate was centrifuged at 1000g for 10 min. 1.5 mL of LCL media was then added to each well. Cyclosporin A (Sigma-Aldrich, cat# C3662) was added to the cells at 0.1 ug/mL. Cultures were incubated for 2-4 weeks at 37°C to allow for LCL outgrowth. LCLs were phenotype by flow cytometry for expression of CD19, HLA class I, HLA class II, CD80, CD83, CD86, and CD137.

Validation of mKRAS-specific T cell reactivity
Patient matched LCLs were collected and resuspended in Jurkat T cell media at a concentration of 1×10^6 cell/mL DMSO, control peptide, mutant KRAS or wild type KRAS peptide (10 uM - 1 pM) was added and cell were incubated at 37C, 4 h. TCR expressing jurkat reporter cells were seeded at 1×10^5 cells/well in a 96 well plate. Peptide-pulsed LCLs were added to the jurkat cultures at a 1:1 APC: effector ratio. Cell stimulator cocktail was added to jurkat only cultures to determine the maximum GFP expression (%GFPmax). Co-cultures were incubated for 16–20 h at 37°C. Co-cultures were then stained with Zombie NIR viability stain (1/1500 dilution, Biolegend, cat# 423105) and analyzed by flow for mCherry and GFP expression as a marker for T cell recognition and activation. Co-cultured cells were analyzed for activation by GFP expression and normalized to specific activity of minimal (unpulsed LCL only) and maximal (PMA+Ionomycin) activation conditions.

Tumor cell-T cell coculture assays
Upregulation of HLA class I and class II were first confirmed by flow cytometry staining of hIFNγ pre-treated tumor cells at 24, 48, or 72 h post-treatment. Based on this data, tumor cell lines were pretreated with 100 IU/mL hIFNγ (peprotech #300-02) for 48 h. Cells were then washed with PBS and co-cultured with TCR transduced Jurkat cells or primary human T cells at an effector to target ratio of 2:1 for 24 h in the presence or absence of 2 ug/mL supplemented mKRAS peptide. After incubation, jurkat co-cultures were analyzed by flow cytometry for upregulation of GFP as a marker of antigen recognition. For primary human T cells, expression of activation markers using anti-CD25 (clone BC96, Biolegend #302625) and anti-CD137 anti-CD137 (clone 4B4-1, Biolegend cat# 309817) antibodies was assessed.

Immunohistochemistry of primary tumor tissue
Immunostaining was performed at the Oncology Tissue Services Core of Johns Hopkins University School of Medicine. Immunolabeling for PD-L1, CD4, CD8, and HLA-DR, DP, DQ was performed on formalin-fixed, paraffin embedded sections on a Ventana Discovery Ultra autostainer (Roche Diagnostics). Briefly, following dewaxing and rehydration onboard, epitope retrieval was performed using Ventana Ultra CC1 buffer (catalog# 6414575001, Roche Diagnostics) at 96°C for 64 min. The following stains were performed:
For PD-L1 staining, anti-PD-L1 (1:100, catalog# 13684, Cell Signaling Technology) was applied at 36oC for 16 min. Primary antibodies were detected using an anti-rabbit HQ detection system (catalog# 7017936001 and 7017812001, Roche Diagnostics) followed by Chromomap DAB IHC detection kit (catalog # 5266645001, Roche Diagnostics), counterstaining with Mayer’s hematoxylin, dehydration and mounting.
For CD4 and CD8 staining, primary antibody anti-CD8 (1:100 dilution; catalog# m7103, Dako) was applied at 36oC for 60 min. CD8 primary antibodies were detected using an anti-mouse HQ detection system (catalog# 7017936001 and 7017782001, Roche Diagnostics) followed by Chromomap DAB IHC detection kit (catalog # 5266645001, Roche Diagnostics). Following CD8 detection, primary and secondary antibodies from the first round of staining were stripped onboard using Ventana Ultra CC1 buffer at 93°C for 8 min. Primary antibody, anti-CD4 (1:200 dilution; catalog# ab133616, Abcam) was applied at 36°C for 20 min. CD4 primary antibodies were detected using an anti-rabbit HQ detection system (catalog# 7017936001 and 7017782001, Roche Diagnostics) followed by Discovery Teal Detection kit (catalog# 8254338001, Roche Diagnostics), counterstaining with Mayer’s hematoxylin, dehydration and mounting.
For MHC class II staining, primary antibody, Anti-HLA DR+DP+DQ antibody (1:1000 dilution; catalog# ab7856 [CR3/43], Abcam) was applied at 36°C for 60 min. Primary antibodies were detected using an anti-mouse HQ detection system (catalog# 7017936001 and 7017782001, Roche Diagnostics) followed by Chromomap DAB IHC detection kit (catalog # 5266645001, Roche Diagnostics), counterstaining with Mayer’s hematoxylin, dehydration and mounting. Level of HLA-DR, DP, DQ expression on tumor cells in patient primary tumor resections was categorized as low, medium, or high expression by review by two individual expert pathologists.

High similarity clonotypes in TCGA PDAC samples
To look for high similarity (HS-TCR) clonotypes, we used CDR3 amino acid sequences from PDAC tumor samples within the TCGA dataset reported in Thorsson et al.58. Using the expanded TCRβ repertoire from patients on this trial we detected TCR clonotypes with 2 or fewer amino acid differences using the stringdist R package (v0.9.12). The KRAS mutations for TCGA PDAC samples were extracted from the TCGA Multi-Center Mutation Calling in Multiple Cancers project59. For every mKRAS positive PDAC samples from TCGA, we summarized the number of HS-TCR clonotypes.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "In this important study, Huff and colleagues have conducted a trial of pooled synthetic long peptide (SLP_ vaccines against mKRAS in patients who underwent surgery for localized pancreatic cancer. Vaccines were administered agnostic to patient HLA and were initially combined with immune checkpoints (aPD1 + aCTLA4) for the first four doses. The majority of patients (10/12) generated significant tumor specific T cell responses in the periphery, which were dominated by a Th1 CD4 T cell response. The authors identified distinctions in the amplitude of immunogenic T cell responses based on mKRAS allelic variant, with G12V and G12R generating higher immunogenic responses than G12D or G13D (agnostic to the dominant allelic variant in the resected primary tumor). Importantly, patients generated both mono-reactive and cross-reactive T cell responses to the SLP vaccine. One critical and valuable resource developed by this manuscript is the identification of a large panel of mKRAS specific TCR sequences, both private (patient-specific) and public, which can be deployed in future for generating engineered T cells for adoptive therapy. Remarkably, four of 12 patients had recurrence free survival at 52 weeks of follow up at which point they entered an extended vaccine only phase of therapy. AT >100 weeks, all four patients had demonstrate mKRAS specific T cells in the circulation, with Cd4+ T cells dominating in the response. This study establishes the safety and feasibility, as well as early efficacy of a pooled mKRAS targeted SLP vaccine in resected pancreatic cancer. There is extensive phenotyping of T cell responses post vaccination (including in the long term responders) and cataloging of an extensive cache of mKRAS specific TCRs (a subset with functional validation). I only have some minor queries, mostly aligning this study in the context of previously published trials in this pace using mRNA and ampiphile vaccines.", "reviewer_2": "In this study, Huff et al conducted a phase 1 trial in 12 patients with resected pancreatic ductal adenocarcinoma (PDAC), administering a vaccine of a pool of six KRAS mutant long peptides (mKRAS) in combination with immune checkpoint inhibitors. They did not find significant vaccine-related adverse events and found that majority of patients generated a significant increase in average T cell response to the 6 mKRAS antigens included in the vaccine, and tumor-specific mKRAS response. Authors were able to identify the generation of CD4 and CD8 T cells with distinct phenotypes and both mono- and cross-reactive T cell responses against mKRAS antigens. The study is well conducted and elaborate, however, most of the reported findings lack novelty and highly predictable based on many vaccine trials conducted over the past 20 years. This include lack of novelty in the conduct of a vaccine trial with pool of peptides, the ability of the patients to generate CD8 or CD4 immune responses against each of the antigens separately, cloning of TCRs against specific ras antigenic epitopes have been reported and also used in T cell therapy and finally, the clinical findings are highly predictable. Furthermore, there are some concerns:", "reviewer_3": "The manuscript by Huff et al. is a comprehensive analysis of a Phase 1 study assessing the safety and immunogenicity following vaccination with a pool of synthetic long peptides targeting 6 KRAS subtypes administered with Poly ICLC in patients with resected pancreatic ductal adenocarcinoma. Overall, the trial is well-performed by experienced and outstanding investigators focused on immune therapies against PDAC. The paper demonstrates safety of the vaccine regimen in the context of combination immune-checkpoint blockade. The major strength of the paper is the detailed analysis of the immune responses over time using a variety of assays. As the trial was done in 12 patients, it was not powered to establish immune correlates with clinical outcome although there is an exploratory analysis of this."}, "author_response": "We thank the reviewers for their thoughtful and constructive comments and have provided a point-by-point response in blue. Revision in line call outs here refer to the mark up version of our revised manuscript.\n\nReviewer #1\n(Remarks to the Author)\nIn this important study, Huff and colleagues have conducted a trial of pooled synthetic\nlong peptide (SLP_ vaccines against mKRAS in patients who underwent surgery for\nlocalized pancreatic cancer. Vaccines were administered agnostic to patient HLA and\nwere initially combined with immune checkpoints (aPD1 + aCTLA4) for the first four\ndoses. The majority of patients (10/12) generated significant tumor specific T cell\nresponses in the periphery, which were dominated by a Th1 CD4 T cell response. The\nauthors identified distinctions in the amplitude of immunogenic T cell responses based on\nmKRAS allelic variant, with G12V and G12R generating higher immunogenic responses\nthan G12D or G13D (agnostic to the dominant allelic variant in the resected primary\ntumor). Importantly, patients generated both mono-reactive and cross-reactive T cell\nresponses to the SLP vaccine. One critical and valuable resource developed by this\nmanuscript is the identification of a large panel of mKRAS specific TCR sequences, both\nprivate (patient-specific) and public, which can be deployed in future for generating\nengineered T cells for adoptive therapy. Remarkably, four of 12 patients had recurrence\nfree survival at 52 weeks of follow up at which point they entered an extended vaccine\nonly phase of therapy. AT >100 weeks, all four patients had demonstrate mKRAS specific\nT cells in the circulation, with Cd4+ T cells dominating in the response.\nThis study establishes the safety and feasibility, as well as early efficacy of a pooled\nmKRAS targeted SLP vaccine in resected pancreatic cancer. There is extensive\nphenotyping of T cell responses post vaccination (including in the long term responders)\nand cataloging of an extensive cache of mKRAS specific TCRs (a subset with functional\nvalidation).\nI only have some minor queries, mostly aligning this study in the context of previously\npublished trials in this pace using mRNA and ampiphile vaccines.\nResponse: The authors are grateful for the reviewer’s thoughtful and thorough appraisal\nof our manuscript and for the insightful comments.\nA: It is unclear if neoadjuvant therapy versus upfront surgery had an effect on DFS. In Fig\n1B, 3 of the 4 long term responders (patients 1, 5 and 14) received neoadjuvant therapy.\nAs a corollary, please confirm that patient 2 does not harbor microsatellite instability or a\ngermline alteration like BRCA2 that might render the tumor more \"immunogenic\".\nResponse: We have performed an analysis on the impact of neoadjuvant chemotherapy\nand DFS and find no difference in outcomes in those who do versus those who do not\nreceive neoadjuvant chemotherapy. This is now added as a new Supplemental Figure 2i\nbelow. We also note that the benefit of neoadjuvant chemotherapy on long-term\noutcomes in resected disease is unclear with mixed data reported (PREOPANC, ESPAC-\n5F trials).\nNew Supplemental Figure 2i:\nThis is also referenced in the text on lines 169-172 as follows:\n“There was no correlation between DFS and lymph node status, primary tumor size, baseline\nCA19-9 levels, ctDNA positivity at baseline, neoadjuvant chemotherapy, splenectomy, or absolute\nlymphocyte counts at baseline (Supplemental Figure 2e-k).”\nAdditionally, we can confirm that patient 2 does not harbor microsatellite instability or a\npathogenic germline alteration, such as BRCA2. All patients had previously undergone\nnext-generation sequencing of their tumor tissue, and all of the tumors were TMB low.\nWe have added this information on TMB and MSI status into our new Supplemental Table\n1 for clarity:\nNew Supplemental Table 1: Patient demographics and clinical characteristics\nAll Patients (n Recurrence (n = No Recurrence P\nCharacteristic\n= 12) 7) (n = 5) Value*\nAge (years)\nMean (SD) 64.2 (10.5) 63.3 (8.9) 65.4 (13.5)\nMedian (range) 68.0 (42.0- 59.0 (53.0-76.0) 69.0 (42.0-75.0) 0.67\n76.0)\nSex\nMale 10 (83.3%) 7 (100%) 3 (60%) 0.15\nFemale 2 (16.7%) 0 (0%) 2 (40%)\nRace\nWhite 10 (83.3%) 5 (71.4%) 5 (100%) 0.47\nAsian 2 (16.7%) 2 (28.6%) 0\nTumor Location\nHead 7 (58.3%) 3 (42.9%) 4 (80%) 0.12\nBody 4 (33.3%) 4 (57.1%) 0\nTail 1 (8.3%) 0 1 (20%)\nTumor Size (cm)\nMean (SD) 2.78 (0.88) 3.1 (0.9) 2.4 (0.8)\nMedian (range) 2.50 (1.50- 3.0 (2.2-4.5) 2.4 (1.5-3.5) 0.25\n4.50)\nTumor Differentiation\nWell-to-moderately 1 (8.3%) 0 1 (20%) 0.046\ndifferentiated\nModerately differentiated 7 (58.3%) 6 (85.7%) 1 (20%)\nModerate-to-poorly 2 (16.7%) 1 (14.3%) 1 (20%)\ndifferentiated\nPoorly differentiated 2 (16.7%) 0 2 (40%)\nPathologic Stage\nIA 1 (8.3%) 0 1 (20%) 0.85\nIB 2 (16.7%) 1 (14.3%) 1 (20%)\nIIB 6 (50.0%) 4 (57.1%) 2 (40%)\nIII 3 (25.0%) 2 (28.6%) 1 (20%)\nLymph Node Status\nN0 3 (25.0%) 1 (14.3%) 2 (40%) 0.77\nN1 6 (50.0%) 4 (57.1%) 2 (40%)\nN2 3 (25.0%) 2 (28.6%) 1 (20%)\nResection Margins\nR0 11 (91.7%) 7 (100%) 4 (80%) 0.42\nR1 1 (8.3%) 0 1 (20%)\nNeoadjuvant Chemotherapy\n(m)FOLFIRINOX 6 (50%) 3 (42.9%) 3 (60%) >0.99\nNone 6 (50%) 4 (57.1%) 2 (40%)\nAdjuvant Chemotherapy\n(m)FOLFIRINOX 8 (66.7%) 5 (71.4%) 3 (60%) 0.82\nGemcitabine/nab-paclitaxel 1 (8.3%) 1 (14.3%) 0\nGemcitabine/cisplatin/nab- 1 (8.3%) 0 1 (20%)\npaclitaxel\nNone 2 (16.7%) 1 (14.3%) 1 (20%)\nPerioperative Radiation\nYes 3 (25%) 1 (14.3%) 2 (40%) 0.52\nNo 9 (75%) 6 (85.7%) 3 (60%)\nTMB >0.99\nLow (<10 mutations per Mbp) 12 (100%) 7 (100%) 5 (100%)\nHigh (>10 mutations per Mbp) 0 (0%) 0 (0%) 0 (0%)\nMicrosatellite instability (MSI)\nstatus\nMSS (microsatellite stable) 11 (100%) 6 (50%) 5 (100%) >0.99\nMSI- high 0 (0%) 0 (0%) 0 (0%)\nMSI- low 0 (0%) 0 (0%) 0 (0%)\nUnknown (not evaluable) 1 (8.3%) 1 (8.3%) 0 (0%)\nBaseline CA-19-9 (U/mL)\nMean (SD) 24.8 (21.2) 26.3 (24.8) 17.7 (17,0)\nMedian (range) 26.1 (0-69.9) 26.1 (0-69.9) 12.7 (0-42.1) 0.75\nKRAS Mutation\nG12D 4 (33.3%) 4 (57.1%) 0 0.086\nG12R 2 (16.7%) 1 (14.3%) 1 (20%)\nG12V 6 (50%) 2 (28.6%) 4 (80%)\nTime From Completion of\nAdjuvant Chemotherapy to First\nmKRAS-VAX (months)\nMean (SD) 3.9 (1.7) 3.5 (1.5) 4.4 (1.9)\nMedian (range) 4.2 (1.5-6.0) 3.4 (1.5-5.4) 5.0 (1.6-6.0) 0.25\nB: The 82% response rate of KRAS targeted T cell responses observed in this study\naligns almost perfectly with the responses observed in the amphiphile peptide vaccine\ntrial (Elicio, Pant et al Nat Med 2024), but both are higher than mRNA vaccines (~50%,\nRojas Nature 2023). While the small numbers make these comparisons fraught, do the\nauthors believe that peptide vaccines might be more immunogenic than mRNA vaccines.\nIn particular, the dominance of a CD4+ T cell response stands out here versus CD8+ T\ncell responses with mRNA vaccines.\nResponse: We appreciate this comment and agree that there is a need to compare the\nmRNA and peptide vaccine platforms head-to-head to assess differences in\nimmunogenicity, particularly as it relates to mutant KRAS neoantigens. We do note,\nhowever, in Rojas et al., that the subset of patients who were vaccinated with mutant\nKRAS antigens did not generate a T cell response to mKRAS. Additionally, we agree that\nvaccination with SLPs have consistently generated CD4 T cell responses (Ott et al.,\nNature 2017, Ott et al., Cell 2020, Pant et al., Nature Medicine 2024). We have added\nthese points to paragraph 4 of the discussion starting on line 464 as follows:\n“Cancer vaccines must not only generate a diverse activated T cell response for tumor killing but\nalso elicit memory T cells should recurrence occur. We show, through single cell proteomics and\ntranscriptomics, that mKRAS-VAX activates mKRAS-specific Th1 CD4 central and effector\nmemory T cells that express functional cytokines IL2, IFNγ and TNFα as well as cytotoxic effector\nmemory CD8 T cells which express the cytotoxic effector molecules GzmB. The Th1 CD4\nresponses observed are consistent with other peptide-based vaccine approaches and have\npreviously been associated with improved clinical outcomes31. Notably, and consistent with our\napproach, the amphiphile-peptide mKRAS vaccine also elicited predominantly Th1 CD4 T cells,\nwhile in contrast no mKRAS-specific T cells were detected by mRNA-based vaccine. Additionally,\nthe mRNA-based approach mounted predominantly CD8 T cells to the targeted personalized\nneoantigens, highlighting the need for more study of the differences of T cell populations activated\nby each platform. Lower levels of the Th2-associated cytokine IL-5, but not IL-4, were also\ndetected after mKRAS peptide stimulation in this study. While IL-5 function is still not entirely\nunderstood in the context of immunotherapy, recently, IL-5 secreting CD4 T cells have been\nassociated with efficacy of ICB by promoting eosinophil expansion, tumor infiltration, and thus\nintra-tumoral CD8 T cell activation in breast cancer32. In our study, we found that the activated T\ncell populations persisted and tracked with a durability marker CCR5 in the four patients who\nremained enrolled during the extended vaccine-only, no-ICI phase. The CD4 Tem population was\nthe most durable, likely owing to the trafficking of CD4 Tcm to the secondary lymphoid tissues\nover time due to CCR7 expression. Furthermore, granzyme B was detected in responding mKRAS-\nspecific CD4 T cells, questioning the potential for a role of CD4 T cells in direct tumor killing. In\nthe small subset of TCRs validated in our study, T cells recognized antigen when peptide was\nsupplied exogenously or when KRAS-mutation specific tumor cell lysate was pulsed onto APCs,\nsuggesting their function in controlling tumor growth may be more related to indirect mechanisms\nof tumor control as previously reported33-37. Notably, Alspach et al described the critical role of\nneoantigen-specific CD4 T cell-mediated control of tumor growth in tumors that lacked expression\nof MHCII, underscoring the potential importance of these T cell responses33-37. We also found that\nfour patients who remained recurrence free displayed moderate levels of HLA-DR, DP and DQ in\ntheir primary tumor tissues, again suggesting that CD4 T cells may directly recognize patient\ntumors. However, additional testing of mKRAS-specific CD4 T cells in our dataset and their ability\nto lyse tumor directly needs to be explored. Nonetheless, these data appear to be consistent with\nhigh levels of HLA class II associated with greater activation of T regulatory cells and a more\nimmunosuppressive TME38.”\nC: One plausible hypothesis that was proposed in the Rojas et al study was the possibility\nof splenectomy dampening T cell responses to the vaccine. Did the authors observe any\neffect of splenectomy on intensity or duration of responses.\nResponse: We thank the reviewer for this comment. We did not find any significant\nassociation between the magnitude of mKRAS-specific T cell responses and splenectomy\nstatus in this cohort. These data have been included in the manuscript as new\nSupplemental Figure 2j as follows:\nThis is also referenced in the text on lines 169-172 as follows:\n“There was no correlation between DFS and lymph node status, primary tumor size, baseline\nCA19-9 levels, ctDNA positivity at baseline, neoadjuvant chemotherapy, splenectomy, or absolute\nlymphocyte counts at baseline (Supplemental Figure 2e-k).”\nD: The differences in mKRAS allele specific T cell responses for G12V and G12R versus\nG12D are truly remarkable. Could the authors confirm that patients with a \"tumor\ndiscordant\" major T cell response (for example patient 18 - a G12D tumor with significant\nG12R and G12V responses) did not harbor these mutant clones as secondary mKRAS\nalterations within the resected tumor? And that this is simply an example of epitope\nspreading?\nResponse: We can confirm that we did not see any other secondary mKRAS alterations\nwithin the resected tumor. As part of our ctDNA analysis, we performed additional whole\nexome sequencing on 9 patient tumor samples for those with enough available tissue.\nThis analysis did include patient 18’s primary tumor, which only expressed a detectable\nKRAS G12D mutation as shown in Revision Table 1. While we cannot exclude epitope\nspreading, we do vaccinate with all six mKRAS antigens and thus would expect\ngeneration of T cell responses across the majority of vaccinated neoepitopes. As this data\nwas consistent with our CLIA-certified WES performed in the clinic (reported in\nSupplemental Table 1 and Supplemental Table 6 for each patient), we provide our\nadditional confirmation analysis as Revision Table 1, below:\nRevision Table 1: Summary of WES detection of KRAS mutations in primary tumor\nPatient ID KRAS mutations detected %MAF, WBC %MAF, tumor\n10 p.G12D 0% 32%\n9 p.G12V 0% 19%\n1 p.G12R 0% 9%\n2 p.G12V 0% 25%\n14 p.G12V 0% 3%\n3 p.G12D 0% 20%\n6 p.G12V 0% 8%\n7 p.G12V 0% 14%\n18 p.G12D 0% 13%\nE: As a corollary to the point above, one missing link here is the demonstration of whether\nthese mKRAS reactive clonotypes were present in the tumor tissue (not PBMCs) at\nbaseline. The entire study is based on baseline and post vaccine PBMCs, but excluding\nthe possibility of existence of these clonotypes with some form of bulk TCR assay on\ntumor tissue would be helpful to validate the vaccine is not expanding pre-existing clones.\nResponse: To assess if these mKRAS reactive\nclonotypes were present in tumor tissue at\nbaseline, we performed bulk TCRb sequencing on\nFFPE serial sections of the primary tumors from 3\nout of the 4 patients for whom sufficient tumor\nsample was available. We also performed TCRb\nsequencing on a recurrence lesion from one of the\npatients that was available. These data are\npresented in Supplemental Figure 7D-F. When\nmKRAS-specific TCRs were mapped onto the\ntumor TCR repertoires, few mKRAS-specific TCRs\nwere found in the primary tumors of these patients\nrelative to a recurrence biopsy sample available for\npatient 5. For example, only 1 unique mKRAS\nclonotype was observed in the baseline tumor\nsample for patient 5, while 80 unique clonotypes\nwere observed in the post-vaccination, recurrence\nlesion. To clarify this, we have added a figure that\nshows the percent of mKRAS TCRs found in the\ntumor tissue at baseline for patients 2, 5, and 6 as\nwell as a post-vaccine timepoint for patient 5. We\nobserve the majority of mKRAS TCRs identified\nare found only in the post-vaccine sample for\npatient 5. These data, in combination with our\nPBMC expansion data, offer support for the\nconclusion that the majority of the mKRAS\nclonotypes detected were de novo and not found\nin the blood or primary tumor samples of these\npatients prior to vaccination.\nNew Supplemental Figure 7D-F:\nThis is also referenced in the revised manuscript on lines 275-282 as follows:\n“Consistent with this, there was little overlap between the mKRAS-specific TCRs identified by\nexpansion and bulk TCRβ sequencing of the resected primary tumors, despite the high numbers\nof productive TCRs sequenced for all tissue specimens (Supplemental Figure 7d,e).\nConversely, a single metastatic lung recurrence biopsied from patient J1994_5 had 80 unique\nmKRAS-specific TCR sequences overlapping within the tissue representing almost 20% of the\nmKRAS-repertoire identified for this patient (Supplemental Figure 7e). Of the mKRAS-\nspecific TCRs in the tumor, 55% were specific for the patient’s tumor mutation (G12V)\n(Supplemental Figure 7f).”\nReviewer #2\n(Remarks to the Author)\nIn this study, Huff et al conducted a phase 1 trial in 12 patients with resected pancreatic\nductal adenocarcinoma (PDAC), administering a vaccine of a pool of six KRAS mutant\nlong peptides (mKRAS) in combination with immune checkpoint inhibitors. They did not\nfind significant vaccine-related adverse events and found that majority of patients\ngenerated a significant increase in average T cell response to the 6 mKRAS antigens\nincluded in the vaccine, and tumor-specific mKRAS response. Authors were able to\nidentify the generation of CD4 and CD8 T cells with distinct phenotypes and both mono-\nand cross-reactive T cell responses against mKRAS antigens.\nThe study is well conducted and elaborate, however, most of the reported findings lack\nnovelty and highly predictable based on many vaccine trials conducted over the past 20\nyears. This include lack of novelty in the conduct of a vaccine trial with pool of peptides,\nthe ability of the patients to generate CD8 or CD4 immune responses against each of the\nantigens separately, cloning of TCRs against specific ras antigenic epitopes have been\nreported and also used in T cell therapy and finally, the clinical findings are highly\npredictable. Furthermore, there are some concerns:\nResponse: We appreciate the reviewer comments and agree that prior KRAS mutation\ntargeted vaccines had been tested years ago before immune checkpoint inhibitors were\navailable, and many were against HLA-restricted peptides. While some of these did show\nT cell responses in the blood, the technologies to conduct TCR sequencing and multiplex\nanalyses were not available. Thus, the use of these technologies to obtain granular details\nof immune response separates our study from the prior ones. We also build upon previous\nstudies by:\n1. In this study, we are the first to directly compare the immunogenicity profile of the\n6 most common mutant KRAS antigens in a population of non-HLA restricted\npatients. Thus, we were able to directly identify the weaker immunogenicity of\nKRAS G12D relative to other KRAS antigens. These findings have an important\nbiological context for future vaccine designs targeting mKRAS antigens, as well\nas, broadly to the field studying the immune profile of these antigens.\n2. Our results establish the largest repertoire of mutant KRAS–specific T cells\nidentified across patients and against the 6 most KRAS mutations to date. This\ndataset has significant implications for further understanding of mutant KRAS T\ncell biology across the six most common KRAS driver neoantigens, and more\ngenerally, for the field of adoptive therapy targeting these antigens.\n3. Few, if any studies have shown the prevalence of common T cell receptors\nrecognizing the same antigen across patients treated with a neoantigen vaccine,\nor indeed, T cell receptor sequences that recognize more than one mutation within\nthe same original antigen. These data support the feasibility of a pan-KRAS “off–\nthe–shelf” adoptive T cell approach for future testing in patients with KRAS–\nmutation–driven tumors (about 20% of all cancers).\n4. Our study is the first to test a KRAS peptide vaccine plus anti–PD-1 and anti–\nCTLA-4 antibodies in an adjuvant setting for pancreatic adenocarcinoma (PDAC).\nBased on our prior preclinical studies, this combination is critical for inducing the\nhighest quality of neoantigen–specific T cells.\n5. While this study represents the first testing of this off–the–shelf vaccine including\nall 6 mKRAS antigens for immunogenicity in an adjuvant setting, the data provide\nthe basis for its testing as a preventative vaccine for patients who are at high risk\nof PDAC, currently underway [NCT04117087] – the latter representing the first\ntesting of any cancer vaccine in a PDAC high-risk population. For this, we were\nrequired to establish safety and immunogenicity in the therapeutic setting with this\nexact vaccine platform before vaccinating otherwise healthy individuals. As high–\nrisk patients do not yet have cancer, single mutant KRAS antigen vaccination\nwould not be an effective vaccine approach – thus our rationale for a pooled\npeptide approach that provides the broadest coverage against mKRAS antigens\nin a vaccine yet.\nMajor concerns:\nThe study is unable to draw any definitive conclusions about clinical efficacy (e.g., DFS\ndifferences between mutation subtypes). The safety of peptide vaccines has been\nexhaustively reported, and it was also published in a comprehensive review (Rahma et\nal. Clin Cancer Res. 2014). Furthermore, although the authors acknowledge these causal\ninferences regarding improved disease-free survival (DFS) remain speculative in a a)\nsingle arm, b) small sample size of 12 patients, c) in combination therapy and d) with a\nlimited follow-up duration. It's impossible to disentangle the Contribution of the vaccine\nversus natural immune recovery or checkpoint blockade effects.\nResponse: We agree with the reviewer that this is a small, phase I trial to establish safety\nand immune responses of our pooled peptide platform as a prelude to testing in a\nprevention setting in high-risk individuals. While the association between T cell responses\nand DFS are correlative, the signal we observed is consistent with recent studies in\nresected PDAC targeting either mKRAS neoantigens (Pant et al. Nature Medicine, 2024)\nor personalized neoantigens (Rojas et al. Nature, 2023). Furthermore, it is well known\nthat immune checkpoint blockade alone does not have efficacy in the vast majority of\nPDACs either as single or double agents. To address this, we have expanded on our\ndiscussion of the limitations of this study starting on line 428 as follows:\n“Patients were allowed to enroll within 6 months of completing adjuvant chemotherapy and\ntherefore joined the study at different timepoints within this window. Our exploratory analysis\nsuggests that patients in the top three quartiles of average tumor-specific T cell responses (by\nELISPOT) experienced greater DFS, which we have defined from the time of first vaccination.\nNotably, we did not observe a correlation of DFS with patient characteristics, tumor size, lymph\nnode status at resection, CA-19-9 levels, ctDNA positivity, neoadjuvant chemotherapy,\nsplenectomy, or lymphocyte count at baseline. Although perhaps informative for a potential\nmagnitude of response that is required for clinical benefit, these correlations should be interpreted\nwith caution given that the trial was not powered for clinical efficacy but appears to be consistent\nwith recent neoantigen vaccine studies in PDAC19,22. Our study was also limited in analysis of\npost-vaccine tumor samples which were unavailable in this cohort. Future studies which correlate\nmKRAS-specific T cell infiltration into tumors with clinical outcomes may also be more\ninformative.”\nWe have also provided clinical information from the cohort that all patients were TMB low\nand microsatellite stable supportive that ICB alone would likely be ineffective in this\nsetting. This data is now included in new Supplemental Table 1 as follows:\nAll Patients (n Recurrence (n = No Recurrence P\nCharacteristic\n= 12) 7) (n = 5) Value*\nAge (years)\nMean (SD) 64.2 (10.5) 63.3 (8.9) 65.4 (13.5)\nMedian (range) 68.0 (42.0- 59.0 (53.0-76.0) 69.0 (42.0-75.0) 0.67\n76.0)\nSex\nMale 10 (83.3%) 7 (100%) 3 (60%) 0.15\nFemale 2 (16.7%) 0 (0%) 2 (40%)\nRace\nWhite 10 (83.3%) 5 (71.4%) 5 (100%) 0.47\nAsian 2 (16.7%) 2 (28.6%) 0\nTumor Location\nHead 7 (58.3%) 3 (42.9%) 4 (80%) 0.12\nBody 4 (33.3%) 4 (57.1%) 0\nTail 1 (8.3%) 0 1 (20%)\nTumor Size (cm)\nMean (SD) 2.78 (0.88) 3.1 (0.9) 2.4 (0.8)\nMedian (range) 2.50 (1.50- 3.0 (2.2-4.5) 2.4 (1.5-3.5) 0.25\n4.50)\nTumor Differentiation\nWell-to-moderately 1 (8.3%) 0 1 (20%) 0.046\ndifferentiated\nModerately differentiated 7 (58.3%) 6 (85.7%) 1 (20%)\nModerate-to-poorly 2 (16.7%) 1 (14.3%) 1 (20%)\ndifferentiated\nPoorly differentiated 2 (16.7%) 0 2 (40%)\nPathologic Stage\nIA 1 (8.3%) 0 1 (20%) 0.85\nIB 2 (16.7%) 1 (14.3%) 1 (20%)\nIIB 6 (50.0%) 4 (57.1%) 2 (40%)\nIII 3 (25.0%) 2 (28.6%) 1 (20%)\nLymph Node Status\nN0 3 (25.0%) 1 (14.3%) 2 (40%) 0.77\nN1 6 (50.0%) 4 (57.1%) 2 (40%)\nN2 3 (25.0%) 2 (28.6%) 1 (20%)\nResection Margins\nR0 11 (91.7%) 7 (100%) 4 (80%) 0.42\nR1 1 (8.3%) 0 1 (20%)\nNeoadjuvant Chemotherapy\n(m)FOLFIRINOX 6 (50%) 3 (42.9%) 3 (60%) >0.99\nNone 6 (50%) 4 (57.1%) 2 (40%)\nAdjuvant Chemotherapy\n(m)FOLFIRINOX 8 (66.7%) 5 (71.4%) 3 (60%) 0.82\nGemcitabine/nab-paclitaxel 1 (8.3%) 1 (14.3%) 0\nGemcitabine/cisplatin/nab- 1 (8.3%) 0 1 (20%)\npaclitaxel\nNone 2 (16.7%) 1 (14.3%) 1 (20%)\nPerioperative Radiation\nYes 3 (25%) 1 (14.3%) 2 (40%) 0.52\nNo 9 (75%) 6 (85.7%) 3 (60%)\nTMB >0.99\nLow (<10 mutations per Mbp) 12 (100%) 7 (100%) 5 (100%)\nHigh (>10 mutations per Mbp) 0 (0%) 0 (0%) 0 (0%)\nMicrosatellite instability (MSI)\nstatus\nMSS (microsatellite stable) 11 (100%) 6 (50%) 5 (100%) >0.99\nMSI- high 0 (0%) 0 (0%) 0 (0%)\nMSI- low 0 (0%) 0 (0%) 0 (0%)\nUnknown (not evaluable) 1 (8.3%) 1 (8.3%) 0 (0%)\nBaseline CA-19-9 (U/mL)\nMean (SD) 24.8 (21.2) 26.3 (24.8) 17.7 (17,0)\nMedian (range) 26.1 (0-69.9) 26.1 (0-69.9) 12.7 (0-42.1) 0.75\nKRAS Mutation\nG12D 4 (33.3%) 4 (57.1%) 0 0.086\nG12R 2 (16.7%) 1 (14.3%) 1 (20%)\nG12V 6 (50%) 2 (28.6%) 4 (80%)\nTime From Completion of\nAdjuvant Chemotherapy to First\nmKRAS-VAX (months)\nMean (SD) 3.9 (1.7) 3.5 (1.5) 4.4 (1.9)\nMedian (range) 4.2 (1.5-6.0) 3.4 (1.5-5.4) 5.0 (1.6-6.0) 0.25\n2. Authors fail not find any correlation between DFS and the densities or proportions of\nCD4, CD8 T cells, macrophages, or PD-L1 expression within the primary tumor. However,\nin a recent study in pancreatic and colorectal cancer, an association between relapse-\nfree survival (RFS) and baseline absolute lymphocyte count was reported (Nature Med,\n2024, doi:10.1038/s41591-023-02760-3). Authors should discuss the potential reasons\nfor these differences between the two trials, especially the type of vaccine/formulation,\npatient selection criteria and how these may affect the prediction, treatment outcome or\nthe application of a particular vaccine in patients with mKRAS PDAC.\nResponse: We appreciate this comment, and, in response, we have performed an\nanalysis to assess a possible association between DFS, and baseline absolute\nlymphocyte count in the periphery, as reported in the recent manuscript (Pant et al, Nature\nMedicine 2024). In this manuscript, the authors suggest this correlation meant that\n“complete hematologic recovery from prior cytotoxic therapy may be required for optimal\nvaccine response”. In contrast, we had performed correlations by quantifying immune\nsubsets and markers on the primary tissue at surgery to exclude the possibility that\nincreased DFS could be attributed to greater immunogenicity of the tumor at baseline.\nWe have added here the analysis of baseline absolute lymphocyte count with DFS where\nwe observed no significance as new Supplemental Figure 2k as follows:\nThis is also referenced in the text on lines 169-172 as follows:\n“There was no correlation between DFS and lymph node status, primary tumor size, baseline\nCA19-9 levels, ctDNA positivity at baseline, neoadjuvant chemotherapy, splenectomy, or absolute\nlymphocyte counts at baseline (Supplemental Figure 2e-k).”\n3. The vaccine fails to consistently generate robust T cell responses to KRAS G12D, the\nmost common mutation in PDAC and colorectal cancer. Other studies have reported\nrobust responses against these epitopes. G12D and G13D antigens are highly common\nand pose important clinical relevance, furthermore, weaker response to G12D and G13D\nis concerning and undercuts the rationale of a pooled vaccine approach if key subtypes\nremain poorly immunogenic. Accordingly, the authors need to address such finding.\nResponse: We agree that G12D is the most prevalent in PDAC, and this study is one of\nthe first to report that KRAS G12D induces the lowest T cell responses in the context of\nvaccination. To our knowledge, prior studies testing mKRAS vaccines have not directly\ncompared the relative peripheral T cell immunogenicity across all six of the common\nmKRAS antigens and are thus not able to provide direct evidence of this. We agree,\nhowever, this brings to light important considerations for a pooled vaccination approach,\nand reason that the cross-reactive T cells across antigens may support such a pooled\napproach. In this case, vaccination with other antigens may stimulate cross-reactive\nresponses to the G12D antigen. We have acknowledged this point and expanded on this\non in the discussion on lines 441-462 as follows:\n“Little is known about the immunogenicity ranking of antigens included in vaccines, particularly\nwhen administered as a pooled vaccine. Here, we are the first to test a pooled vaccine against 6\ncommon KRAS mutations. Interestingly, we observed weaker immunogenicity of mKRAS G12D\nindependent of whether the patient’s tumor expressed this mutation. In contrast, T cell responses\nto mKRAS G12V and G12R were of a higher magnitude than mKRAS G12D and G13D, findings\nthat are consistent with preclinical models27. As KRAS G12D is the most common mutation\ndetected in PDAC tumors (~40%), this highlights the need to improve immunological responses\nagainst this mutation. One approach may be vaccination with a single tumor-specific peptide\nvaccine in the therapeutic setting, which could be explored in future studies. However, in the\npreventative vaccination setting where a potential KRAS mutation is unknown, single peptide\nvaccination limits immunological coverage across mutations. Additionally, another question\nremains if the cross-reactive T cells raised by the pooled vaccine in this cohort may also be\nadvantageous for providing broader immunological protection across antigens. The low\nimmunogenicity of mKRAS G12D also coincides with decreased DFS, suggesting that different\nmutations may have different rates and/or mechanisms of tumor growth and TME\nimmunosuppression28,29. Reduced OS has been shown for mKRAS G12D positive tumors\ncompared with G12R tumors, but not relative to G12V tumors. However, in our albeit 12 patient\ncohort, we observed a significant improvement in DFS for patients with G12V mutations relative\nto G12D positive patients30. Larger studies are required to further examine the clinical significance\nof this preliminary observation.”\n4. The significance of identifying “public” TCR clonotypes is an intriguing finding but not\nyet clinically actionable and there is insufficient discussion on how this information can\nguide patient selection, future vaccine design, or TCR-engineered therapy etc.\nResponse: We agree that this is one of the first reports of public KRAS TCR clonotypes\nand thus provides important novel biological information to the field in regard to our\nunderstanding of the basic immunology of responses to these antigens as well as defining\ntargetable patient populations for antigen-specific vaccines. We have expanded on the\nsignificance of these TCRs in greater detail in the Discussion on lines 511-525 as follows:\n“Importantly, we also identified public mKRAS TCRβ clonotypes within our dataset. One of these\nmKRAS TCRs identified in two patients on our trial was also detected in the tumor infiltrating\nlymphocyte population of a patient by Levin et al.24, further supporting the detection of public\nclonotypes across patients outside of our cohort. The alpha chains of the public TCRβ06 found in\na previous study and HS-public TCRβ08 found in our study were different; however, both\nclonotypes responded to KRAS G13D indicating a potential TCRβ chain-dominant interaction\nwith cognate peptide-HLA24. We confirmed TCRb08 had conserved reactivity to the G13D antigen\nin the context of DQB*03:01 identifying that patients with this mutation-HLA combination may\nbe an ideal population for vaccination with this antigen in future clinical settings. Additionally,\nthese public clonotypes may be ideal candidates for adoptive TCR therapies based on their\nconserved immunological relevance across patients. While public TCR clonotypes have been\nidentified against many pathogens47, little is known about the abundance and role of public or HS-\npublic clonotypes against cancer antigens. Thus, this work provides important basic\nimmunological context of shared T cell responses to these highly conserved neoantigens across\nhuman solid tumors. To our knowledge, this is the first report of public mKRAS-specific TCR\nclonotype detected in patients vaccinated with any mKRAS vaccine.”\n5. There is minimal evidence that the identified TCRs actually recognize endogenous\ntumor antigen in physiologic settings, mediating the direct anti-tumor activity in vivo. The\ndata reported in the manuscript demonstrated only weak responses in the form of antigen\nrecognition and T cell activation were seen in assays with Jurkat and cancer cells, largely\nreliant on exogenous peptide pulsing (peptide pulsing is artificial and may overestimate\nfunctional relevance).\nResponse: Given the predominance of CD4 T cell response, we prioritized reactivity\nvalidation of CD4 TCRs. We aimed to validate our novel findings in regard to the TCR\nbiology by including: 1) mKRAS reactive TCRs that were mono- and cross-reactive to\nantigens, and 2) identified public TCRs that could respond to antigen in all of the patients\nin whom the TCR was identified. While we did not find these CD4 T cells directly\nrecognized the tumor cell lines tested, this may demonstrate their potential role in indirect\ntumor cell clearance that has been reported in several clinical and preclinical studies\n(Alspach et al. Nature 2019, Kruse et al. Nature 2023, and Mumberg et al. PNAS 1999).\nTo support this, we have added experimental data showing TCR05 was able to recognize\npatient matched LCLs when pulsed with tumor cell lysate which expresses cognate KRAS\nmutation antigen as new Supplemental Figure 9F:\nAnd has been referred to in the results on lines 404-409 as follows:\n“To test if TCR05 can indirectly recognize target antigen, we pulsed patient matched LCLs with\ntumor cell lysate generated from control or CAPAN1 (G12V+) tumor cells (Supplemental Figure\n9f). Indeed, TCR05 expressing human T cells increased CD25 expression after co-culture with\nCAPAN1 but not control lysate pulsed APCs supporting a role for indirect recognition of tumor\nwith these identified mKRAS-specific TCRs.”\n6. The dominant vaccine-induced response reported by the authors is CD4 T cell based,\nwith CD8 Teff cell population detected only in a limited number of patients and was most\nlikely exhausted. Yet, effective tumor clearance typically relies on cytotoxic CD8+ T cell\nactivity, especially in poorly MHC-II expressing tumors like PDAC. The manuscript offers\nno compelling evidence or reasons for these findings and also does not offer any\nstrategies that may be used to overcome this while designing future vaccines.\nResponse: While the proportion of responding CD8 T cells was lower that responding\nCD4s, our predominant responding CD8 population, CD8 Tem Acv, does not have\nmarkers of terminal exhaustion (Figure 3A) as shown here:\nFigure 3A:\nFigure 3D:\nThe CD8 Tem Acv cell population which is increased across patients to most antigens\n(Figure 3D, left) express high levels of CD137 activation marker, functional cytolytic\ncytokine GzmB, and are highly proliferative by KI67 positivity. They express moderate\nlevels of PD1, consistent with canonical markers of contraction after activation, but do not\nexpress high levels of CTLA4, TIM3, or LAG3. The second activated CD8 population\nobserved, CD8 Teff Acv, does have higher expression of dysfunctional inhibitory receptors\nbut was a minor responding population across all patients or antigens and was not a\nsignificant response across the cohort (Figure 3D, right panel).\nRegarding the physiological role of responding CD4 T cells, several studies have shown\nthat CD4 T cells can mediate tumor cell clearance either through direct or indirect\nmechanisms. Additionally, we demonstrate that 9/12 patients in our cohort have MHCII+\ntumor at baseline (Supplemental Figure 3A) – thus, providing a feasible mechanism for\ntumor recognition of some CD4 clonotypes raised by the vaccine. Additionally, we have\nadded experimental evidence these T cells can function through indirect antigen\nrecognition as shown in reviewer point 5. We have clarified this in our discussion on lines\n464-493 as follows:\n“Cancer vaccines must not only generate a diverse activated T cell response for tumor killing but\nalso elicit memory T cells should recurrence occur. We show, through single cell proteomics and\ntranscriptomics, that mKRAS-VAX activates mKRAS-specific Th1 CD4 central and effector\nmemory T cells that express functional cytokines IL2, IFNγ and TNFα as well as cytotoxic effector\nmemory CD8 T cells which express the cytotoxic effector molecules GzmB. The Th1 CD4\nresponses observed are consistent with other peptide-based vaccine approaches and have\npreviously been associated with improved clinical outcomes31. Notably, and consistent with our\napproach, the amphiphile-peptide mKRAS vaccine also elicited predominantly Th1 CD4 T cells,\nwhile in contrast no mKRAS-specific T cells were detected by mRNA-based vaccine19.\nAdditionally, the mRNA-based approach mounted predominantly CD8 T cells to the targeted\npersonalized neoantigens, highlighting the need for more study of the differences of T cell populations activated\nby each platform.22 Lower levels of the Th2-associated cytokine IL-5, but not IL-4, were also\ndetected after mKRAS peptide stimulation in this study. While IL-5 function is still not entirely\nunderstood in the context of immunotherapy, recently, IL-5 secreting CD4 T cells have been\nassociated with efficacy of ICB by promoting eosinophil expansion, tumor infiltration, and thus\nintra-tumoral CD8 T cell activation in breast cancer32. In our study, we found that the activated T\ncell populations persisted and tracked with a durability marker CCR5 in the four patients who\nremained enrolled during the extended vaccine-only, no-ICI phase. The CD4 Tem population was\nthe most durable, likely owing to the trafficking of CD4 Tcm to the secondary lymphoid tissues\nover time due to CCR7 expression. Furthermore, granzyme B was detected in responding mKRAS-\nspecific CD4 T cells, questioning the potential for a role of CD4 T cells in direct tumor killing. In\nthe small subset of TCRs validated in our study, T cells recognized antigen when peptide was\nsupplied exogenously or when KRAS-mutation specific tumor cell lysate was pulsed onto APCs,\nsuggesting their function in controlling tumor growth may be more related to indirect mechanisms\nof tumor control as previously reported33-37. Notably, Alspach et al described the critical role of\nneoantigen-specific CD4 T cell-mediated control of tumor growth in tumors that lacked expression\nof MHCII, underscoring the potential importance of these T cell responses33-37. We also found that\nfour patients who remained recurrence free displayed moderate levels of HLA-DR, DP and DQ in\ntheir primary tumor tissues, again suggesting that CD4 T cells may directly recognize patient\ntumors. However, additional testing of mKRAS-specific CD4 T cells in our dataset and their ability\nto lyse tumor directly needs to be explored. Nonetheless, these data appear to be consistent with\nhigh levels of HLA class II associated with greater activation of T regulatory cells and a more\nimmunosuppressive TME38.”\n7. The study largely depends on surrogate markers (ELISPOT, cytokine profiling, CyTOF)\nand lacks functional anti-tumor assays. Direct tumor lysis by vaccine-induced T cells was\nnot shown, especially for CD4+ T cells, which as mentioned above was the major\nresponse induced by the vaccine; CD4 T cells alone are not sufficient for tumor clearance\nor sustained control.\nResponse: Please refer to response to point 5.\n8. Results in multiple instances are either missing or are inadequately described. This\ninclude but not limited to:\na. Suppl Fig. 8F: GSEA pathway analysis data is missing.\nResponse: We apologize for this omission. We have now added the data for our KEGG\npathway analysis performed on this dataset as new Supplemental Data 3 and have\nclarified this in the text on line 320 as follows:\n“At a gene level, we observed changes broadly in genes associated with metabolism, cytoskeletal\nstructure, and inflammation in the post-vaccine CD4 Tcm and CD8 Tem populations, although we\ndid not observe any significant changes at the pathway level by KEGG pathway analysis\n(Supplemental Figure 8f, Supplemental Data 3).”\nb. Figure 6c: Naive, proliferating, TEM CD4 T cells are also shown but are not described\nin the results in the text.\nResponse: We thank the reviewer for this point. The mKRAS-specific T cells mapped onto\nthe single cell dataset aligned predominantly to the CD4 Tcms and therefore focused on\nthis population. In the other populations (CD4 naive, proliferating, TEM), there were not\nenough cells to perform this statistical analysis with confidence in our phenotype analysis.\nWe clarified this in the manuscript on lines 338-340 as follows:\n“We compared the gene expression profiles of mKRAS-specific CD4 Tcm cells relative to all other\nCD4 Tcm cells in the dataset. We note that too few mKRAS-specific cells were detectable in the\nCD4 naïve, proliferating, or Tem populations to perform this analysis (Figure 6c, Supplemental\nTable 8).”\nc. Lines 349-351, Fig. 6e: Authors mention- TCR03, a cross-reactive TCR that had\nexpanded in vitro to KRAS G12V, G12A, and G12C responded significantly to these three\nantigens at the highest peptide concentration tested (10 microM). However, data at\ndifferent concentrations is not shown (similar to what is shown in 6d).\nResponse: The response to antigen by TCR03 was observed to be low relative to others\nTCRs when stimulated at the highest concentration. TCR03 did not respond at lower\nconcentrations, thus we were unable to make an avidity curve similar to those in 6d. We\nhave instead highlighted its weak response at high concentrations for clarity on lines362-\n364 as follows:\n“TCR03, a cross-reactive TCR that had expanded in vitro to KRAS G12V, G12A, and G12C\nresponded significantly to these three antigens at the highest peptide concentration tested (10 µM)\nbut did not respond at lower concentrations of peptide.”\nd. Fig. 6i: Please explain why T cells were also activated in SUDHL10 cell line when\nexogenous mKRAS peptides were provided.\nResponse: SUDHL10 cells express WT KRAS and also express HLA-DRB*07:01, the\ncognate HLA for the TCRs tested, and thus serve as antigen negative controls in the\nabsence of peptide in our co-culture system. We supplied exogenous mutant antigen to\ndemonstrate that, when the negative control which does express the correct HLA was\nsupplied with target antigen, this allowed recognition by cognate T cells further confirming\nspecificity of the TCR. We have clarified that in the results section on lines 403 as follows:\n“However, strong activation was detected when each mKRAS antigen was supplied exogenously\nto either CAPAN1 or SUDHL10 cells, which expresses the cognate HLA allele. (Figure 6h).”\nMinor comments:\nResponse: We thank the reviewer for these minor but important comments and have\namended these throughout the text.\n1. All figure numbers should be referred to in lower case both in the text and figure legends\n(same as in the figures).\n2. The figures are not referenced correctly in several instances:\na. Line 207: Fig. 3c should be 3a.\nb. Line 224: Refer Fig. 3a and Fig. 3d.\nc. Line 225: Correct to Suppl. Fig. 6a-e.\nd. Line 241: Fig. 4f should be 4e. Also correct this labeling in the figure and figure legend.\ne. Line 319: Suppl. Fig. 8h is not referred anywhere. Referred to on line 334.\nf. Suppl Fig. 9c: In the figure legend, b should be c and c should be b.\ng. Figure 6: 6g is mentioned two times in the figure legend (should be changed to 6e).\nAlso, 6f is not included in the figure legend. Therefore, it is not clear where TCR05 data\nis shown as mentioned in the text on lines 355 to 357.\nh. Line 361: Suppl Fig. 8d should be changed to Suppl Fig. 7g.\n3. Line 238: timepoint should be post 52 weeks as mentioned in the figure legend.\n4. Please proofread as several sentences are repeated or have some other errors, such\nas:\na. Line 248: Despite the predominance of the mKRAS oncogene in of PDAC……Remove\n“of”.\nb. Line 261: Add- “in patients who recurred” at the end of the sentence …..greater clonality\nof the tumor-specific……\nc. Line 265: Insert “in” to …..were not present the pre-vaccine……\nd. Line 285: space between oftentimes.\ne. Line 309: Remove: “in”.\nf. Fig. 5b legend: Replace “and” with “at”.\ng. Suppl. Fig. 9e: Correct figure legend- T cells from a healthy donor “were” (not with)\nisolated. Also correct: first endogenous TCR was KO and then TCR5 was KI (it is written\nthe other way round); same correction needed in Fig. 6i legend. This order is correct. We\nknock in the transgenic TCR of interest, then knock out endogenous. Our transgenic TCR\nconstruct has a codon optimized PAM site in the TRAC/TRBC locus that prevents editing\nof the transduced TCR construct allowing for this order which gives us optimal\ntransduction. This was clarified in the Methods.\nh. Fig. 6b legend: Insert space between conditions mapped.\ni. Fig. 6i legend: TCR06 should be TCR05.\nj. Fig. 6h legend: MDA-MD-231 should be MDA-MB-231.\nk. Lines 421-422: Repeated.\nl. Lines 425-426: Repeated.\nm. Lines 421-422: Repeated.\nn. Line 534: Correct to: spot forming unit.\no. Lines 547-550: Repeated.\np. Line 574: Correct SUDL10.\nq. Lines 679-682: Repeated.\nr. Lines 722-723: Repeated.\ns. Suppl. Fig. 3a legend: Remove extra ‘and’. The ‘and’ in Supp Fig 3a refers to the CD4\nand CD8 costain. We clarified this by indicating each other stain was a single stain in the\nfigure legend.\nt. Suppl. Fig. 4a legend: Remove extra ‘including’.\nu. Suppl. Fig. 8f legend: Replace T cell populations with “in”.\nv. Suppl. Fig. 8g legend: Insert “)”.\n5. Fig. 2a legend: provide info about immune responders and non-responders.\n6. Lines 318 to 321: Include the names of genes.\n7. Figure 6b: Do the colors of the bars match with the legend of 6a? Please provide the\nlegend in 6b or clarify.\n8. Lines 327-332: The 5th gene, APOBEC3H is not mentioned.\n9. Line 342: Correct that Jurkat cells were transduced with mKRAS-reactive TCRs (not T\ncells).\n10. Suppl Fig. 9b: It would be good to write what cherry+ represent in the figure legend\nand also on the Y-axis in Fig. 9b.\n11. Suppl Fig. 9d: Also describe results of MDA-MB-231 cells with G13D KRAS as shown\nin SF9d in the text.\nReviewer #3\n(Remarks to the Author)\nThe manuscript by Huff et al. is a comprehensive analysis of a Phase 1 study assessing\nthe safety and immunogenicity following vaccination with a pool of synthetic long peptides\ntargeting 6 KRAS subtypes administered with Poly ICLC in patients with resected\npancreatic ductal adenocarcinoma. Overall, the trial is well-performed by experienced and\noutstanding investigators focused on immune therapies against PDAC. The paper\ndemonstrates safety of the vaccine regimen in the context of combination immune-\ncheckpoint blockade. The major strength of the paper is the detailed analysis of the\nimmune responses over time using a variety of assays. As the trial was done in 12\npatients, it was not powered to establish immune correlates with clinical outcome although\nthere is an exploratory analysis of this.\nSummary of key results\n1. CD4 responses were generated in 11/12 patients. CD8 responses were low to\nundetectable. The authors need to highlight the limiting CD8 T cell responses in the\ndiscussion. Notwithstanding the data showing CD4 T cells can have an important\nprotective role against tumors, the data presented highlight that this vaccine approach\nwould be limited for CD8 T cell generation.\n2. Responses to mKRAS G12V were highest and G12D and G13D the lowest\n3. T cell cross reactivity against multiple KRAS antigens were generated.\n4. A public KRAS-specific TCR clonotype was discovered following vaccination.\nOriginality and significance\nThe paper uses a set of mKRAS peptides for vaccination which is likely new. The use of\nPoly ICLC as an adjuvant is not novel. The immune analysis is extensive although recent\nwork by Balachandran for personalized neoantigen cancer vaccines against PDAC is\nequally if not more detailed. The TCR analysis is more extensive than what has been\ndone but not novel.\nData and methodology: The techniques used (Cytof, elispots, flow cytomtery and\nrRNAseq are valid.\nStatistics- Seems valid- using fold change for a lot of their analysis can be less informative\nthan seeing the actual data.\nConclusions- Generally supported by the data.\nClarity- Some editing needs to be done. The manuscript is dense and there are areas for\nimproving clarity.\nMajor comments:\n1. vaccines were administered subcutaneously at 5 injection sites. Can the authors verify\nthat all 6 peptides were pooled and then given at the 5 different sites. Have prior studies\nbeen done to optimize the dose of Poly ICLC and the regimen (single peptide per site\nversus combining all peptides) for how to achieve optimal responses. The dose of 5ug of\nPoly ICLC seems relatively low compared to what is used in pre-clinical studies.\nResponse: We confirm that all six peptides were pooled and given at 5 different sites.\nGiven the hydrophobic nature of the peptides, we had to dilute our peptides to a volume\nthat required us to deliver the vaccine in 5 injections.\nThe Poly ICLC dose is a typo, and we will correct the dose to be 0.5mg. This same dose\nhas been used in a number of human vaccination trials with synthetic long peptides\n(including Ott et al, Nature 2017, Ott et al., Cell 2020). This dosage and vaccination\nschedule has been previously established to elicit immune responses in humans.\nWe have clarified our vaccination strategy in our Methods section on in the Vaccine\nformulation and administration section starting on line 569 and as follows:\n“The study vaccine consists of 21-mer synthetic long peptides corresponding to six of the most\ncommon KRAS mutation subtypes found in PDAC. Peptides were synthesized by JPT\nTechnologies with sequences as follows: G12V- YKLVVVGAVGVGKSALTIQLI, G12C-\nYKLVVVGACGVGKSALTIQLI, G12A- YKLVVVGAAGVGKSALTIQLI, G12R-\nYKLVVVGARGVGKSALTIQLI, G12D- YKLVVVGADGVGKSALTIQLI, G13D-\nYKLVVVGAGDVGKSALTIQLI. These six peptides were admixed (0.3mg/peptide, a total of\n1.8mg) with 0.5mg TLR3 agonist poly-ICLC (Hiltonol; Oncovir) in saline prior to vaccine\nadministration at the Investigational Drug Service Pharmacy at Johns Hopkins Hospital. The\npooled vaccine consisting of all six peptides was administered subcutaneously at five injection\nsites.”\n2. While the study was not designed to correlate DFS with immune analysis, the authors\nstratify the patients into two groups above and below the 25 percentiles of the maximal\nfold-change or average tumor specific T cells. Using fold-change may one way to present\ncomplex data, but it preferable to show that actual data for elispot responses across the\ndifferent peptides and denote whether they progressed. In Figure 2b, the symbols could\nbe changed to those that had DFS. This would more clearly highlight whether the 4\nhighest responders to G12V did not progress. The elispot response are generally\nbetween 10-50 for each of the mKRAS peptides which relatively low compared to viral\nvectors or prime-boost approaches. This may be a concern for durable\nimmunity/protection.\nResponse: We thank the reviewer for these points. While we agree the absolute\nmagnitude was observed to be relatively lower for some antigens in some patients, there\nhave been many instances of lower detectable IFNg responses ex vivo with clinical\noutcome. For example, in Ott et al. 2017, the two patients with clinical responses after\npersonalized neoantigen vaccination in their cohort had responses to antigens with less\nthan 100SFU, while clinical non-responders had higher immune responses overall (Ott,\nP et al, Nature 2017 - Figure 2B). Analysis of tissue specimens after vaccination may\nelucidate neoantigen-specific T cell trafficking and possibly account for these differences-\nwhich our studied was limited by. We have added this point to our discussion of the\nlimitations of our study analysis on lines 428-439 of the discussion as follows: “\n“Patients were allowed to enroll within 6 months of completing adjuvant chemotherapy and\ntherefore joined the study at different timepoints within this window. Our exploratory analysis\nsuggests that patients in the top three quartiles of average tumor-specific T cell responses (by\nELISPOT) experienced greater DFS, which we have defined from the time of first vaccination.\nNotably, we did not observe a correlation of DFS with patient characteristics, tumor size, lymph\nnode status at resection, CA-19-9 levels, ctDNA positivity, neoadjuvant chemotherapy,\nsplenectomy, or lymphocyte count at baseline. Although perhaps informative for a potential\nmagnitude of response that is required for clinical benefit, these correlations should be interpreted\nwith caution given that the trial was not powered for clinical efficacy but appears to be consistent\nwith recent neoantigen vaccine studies in PDAC19,22. Our study was also limited in analysis of\npost-vaccine tumor samples which were unavailable in this cohort. Future studies which correlate\nmKRAS-specific T cell infiltration into tumors with clinical outcomes may also be more\ninformative.”\nAdditionally, to address this, we have added symbols in Figure 2D that indicate patients\nwho recurred vs those who have not recurred as follows:\nNew Figure 2d:\n3. The authors use CyTof and Flow cytometry to characterize the phenotypic and\nfunctional cytokine responses. This is used to delineate the responses into various CD4\nand CD8 effector and memory populations. It would be clearer if they used a standard set\nof markers to define the subsets. CD45RA and CD45R0 is a more general way to divide\nthe T cell populations but lacks greater depth of the delineating the populations into\ndistinct phenotypic subsets. As they have CCR7 and CD62L in their analysis, they can\nuse these markers to better delineate the populations of the cells. The authors need to\ndefine what markers were used to define each of the T cell subsets in Figure 3.\nResponse: This is an important point, and we have added expression of CCR7 into the\nphenotyping heatmap used in new Figure 3A below. We apologize for this initial oversight.\nWhile we had CD62L on our flow cytometry panel, we did not have this marker on our\ncytof panel. We have better defined in the text the markers that were used to define each\ncell type in the results section on lines 202-204 and lines 219-225 as follows:\n“Within the CD4 compartment, we noted a significant increase in activated mKRAS-specific CD4\ncentral memory (Tcm, CD45RO+CD45RA+CCR7+) and effector memory (Tem, CD45RO+,\nCD45RA-, CCR7-) populations at the peak and late timepoints relative to baseline (Figure 3c).”\nAnd\n“Within the CD8 compartment, the activated effector population (Teff Acv, CD45ROhi, CD28hi)\nexpressed highest levels of the activation markers CD137, OX40, KI67, cytokines IFNγ, IL-2, and\ngranzyme B, as well as the exhaustion markers CTLA4, TIM3, and PD1….. Conversely, a\nsignificant increase in activated CD8 effector memory cells (Tem Acv, CD45RAlo, CD45ROlo,\nCCR7-) was detected post-vaccine for G12V, G12A, G12R, G12C, and G13D across patients.”\nNew Figure 3a:\n4. Line 205-206 says that Tem Acv cells express the highest level of cytokines (IL-2, TNF,\nIFN). This is not true for IFN.\nResponse: We apologize for this inconsistency and have corrected this statement in the\ntext on line 209.\n5. It is notable and somewhat surprising that the CD4 T cells produce IL-5. Also, the IL-\n10 is detected in the stimulated cultures (Supplemental Fig 5) which was not mentioned\nin the text. While the significance of these Th2 and regulatory cytokines are not clear,\nthere should be mention of this in the discussion (line 435) when discussing the responses\ngenerated.\nResponse: We agree this was an interesting and unexpected observation. We have\nadded a discussion of this observation on lines 474-478 as follows:\n“Lower levels of the Th2-associated cytokine IL-5, but not IL-4, were also detected after mKRAS\npeptide stimulation in this study. While IL-5 function is still not entirely understood in the context\nof immunotherapy, recently, IL-5 secreting CD4 T cells have been associated with efficacy of ICB\nby promoting eosinophil expansion, tumor infiltration, and thus intra-tumoral CD8 T cell\nactivation in breast cancer32”\n6. In Figure 4b, the authors present data as a normalization to the control following 48-\nhour stimulation with peptide. Do the authors have a kinetic of the immune responses\nwith the peptides. A 48hour stimulation with peptides is longer than what is standard for\nthe field in measuring responses and in other models, a long stimulation can lead\nbystander enhancement.\nResponse: Initial peptide stimulation kinetic studies performed did indicate that 48-hours\nwas the optimal time point for detecting responding mKRAS-specific T cells. Please see\nbelow for our initial characterization of cytokine expression from total T cell populations\nin Response Figure 1. As a similar comparison for detecting vaccine induced mKRAS-\nspecific T cell responses, Pant, S. et al Nat Med 2024, stimulated post-vaccine PBMCs\nfor 44h, consistent with our methods.\nResponse Figure 1:\n7. It seems counterintuitive that the only durable detectable responses are Tem Acv rather\nthan Tcem Acv.\nResponse: We agree this was an interesting finding. We reasoned that over time, the\nTcm population likely homed to lymphatic tissue due to the observed expression of CCR7\n(by CyTOF, Figure 3a) and therefore the remaining mKRAS-specific T cells in circulation\nat later time points were more Tem. We have pointed this out in this discussion on lines\n480-481 as follows:\n“The CD4 Tem population was the most durable, likely owing to the trafficking of CD4 Tcm to\nthe secondary lymphoid tissues over time due to CCR7 expression.”\n8. Line 323- Should this refer to Supplemental Figure 8E and if so, the figure shows that\nCD4 responses are higher in two of the patients, while CD4 and CD8 responses are\nsimilar in the other two.\nResponse: We thank the reviewer for identifying this discrepancy. This call out should\nrefer to Supplemental Figure 8H and was corrected in the new manuscript file on line 334\nas follows:\n“We noted that most CD8 T cell TCRs found in the adaptive sequencing datasets were present at\na lower frequency (<0.1% of repertoire) compared to CD4 T cells; this is consistent with a\npredominant CD4 T cell response to our vaccine (Supplemental Figure 8h).”\n9. The data in Figure 6c seems to break the flow. Perhaps the more interesting/important\ndata should be the TCR analysis and the differences in a small subset of genes be taken\nout or included in some of the supplemental data.\nResponse: As suggested, we have moved this figure to the supplemental section and is\nnow new Supplemental Figure 8i.\n10. While the TCR analysis is interesting and having additional KRAS specific TCR\nsequences may be important in using for T cell therapy, the functional data they show for\nG12V required exogenous peptide with limited reactivity against the endogenous KRAS\nG12V suggests that they may limited in functionality in vivo.\nResponse: To further support the potential in vivo function of the validated T cells outside\nof direct tumor recognition, we have provided new experimental data that demonstrate\nrecognition of antigen from APCs pulsed with whole tumor cell lysate- please see reviewer\n2, point 5 above.\n11. The discussion should be edited. From lines 432-449 the authors seem to make a\ncase for CD4 T cells in having a protective role. There is a lot of back and forth in this\nsection. I cannot understand how the data in the paper are consistent with greater\nactivation of T regulatory cells and a more suppressive TME.\nResponse: We apologize for the confusion in this paragraph. We aimed to highlight the\nidentification of the mKRAS-specific CD4 T cells which had a Th1-like phenotype and\nexpressed effector cytokines such as IFNg, IL2, and TNFa which may promote a pro-\nimmune/anti-tumor tumor microenvironment. We have clarified this paragraph in the\ndiscussion on lines 464-470 as follows:\n“Cancer vaccines must not only generate a diverse activated T cell response for tumor killing but\nalso elicit memory T cells should recurrence occur. We show, through single cell proteomics and\ntranscriptomics, that mKRAS-VAX activates mKRAS-specific Th1 CD4 central and effector\nmemory T cells that express functional cytokines IL2, IFNg and TNFa as well as cytotoxic\neffector memory CD8 T cells which express the cytotoxic effector molecules GzmB. The Th1\nCD4 responses observed here are consistent with other peptide-based vaccine approaches and\nhave previously been associated with improved clinical outcomes31.”\n12. The authors may want to include the reference by Bear et al. JCI 2024 based on the\nidentification of TCRs targeting KRAS G12V.\nResponse: We thank the reviewer for pointing this out. While we cited earlier literature\nfrom Bear et al., we missed adding this newer study. We have added this reference on\nline 495."}]

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