NEO-STIM advances personalized neoantigen-specific adoptive T cell therapy

Domain: neuroscience
Published: 2026-02-05
Editorial outcome: major_revision

Abstract
Neoantigen-based adoptive T cell therapies (ACTs) represent a promising avenue in cancer
immunotherapy due to their exquisite tumor specificity. The first cell-based immunotherapy for a
solid tumor, comprising tumor-infiltrating lymphocytes, recently received FDA approval. Building
on this, we designed a distinct ACT approach, where T cell responses against personalized
neoantigens are systematically generated from autologous peripheral blood. Here we report the
establishment of NEO-STIM, an ex vivo induction process to prime and expand pre-existing
memory and de novo CD8+ and CD4+ T cell responses, thereby highlighting critical parameters
for generating potent neoantigen-specific T cell responses. The drug products comprise mutant-
reactive, polyfunctional, and cytotoxic CD8+ and CD4+ T cells, able to recognize autologous tumor
material. Following infusion, T cell responses are detected in tumor and blood of a patient, and
display activated/exhausted and cytotoxic phenotypes. A first-in-human clinical trial
(NCT04625205) recently further validated proof-of-concept, supporting continued development
of this ACT approach.

Introduction
Cancer immunotherapy has become an established treatment modality in oncology. Adoptive T
cell therapies (ACTs) in particular have advanced rapidly, resulting in recent FDA approval of a
tumor-infiltrating lymphocyte (TIL) product defining a major milestone for personalized adoptive
cell therapy1. While early ACT approaches targeted primarily tumor-associated antigens (TAAs)
on liquid tumors2,3, more recently the focus has shifted to solid tumors and targeting neoantigens4-
8. Neoantigens are peptides derived from mutant proteins generated as a consequence of genomic
mutations in tumor cells. They are unique for each patient and thus require a personalized
approach. Neoantigen recognition has been shown to be a major component in TIL therapy, where
TILs are obtained from the tumor, expanded ex vivo and then re-infused into the patient following
a lymphodepleting regimen.

Some of the key challenges in TIL therapy are i) only a relatively low number of tumor neoantigens
(0.5-2.3%) being generally recognized by T cells9-14, ii) the necessity for a sizeable and resectable
amount of tumor tissue, and iii) the relatively high proportion of phenotypically exhausted T cells
used for TIL (compared to blood-derived T cells) 15.

To address these challenges, we designed a neoantigen-specific T cell therapy and established
NEO-STIM, an ex vivo T cell induction platform to STIMulate T cells from peripheral blood to
generate responses against tumor NEOantigens. We envisioned a streamlined, scalable process
that would generate autologous T cell drug products (DP) with the following characteristics: i)
inducing polyclonal de novo responses (see below for definition), in addition to expanding T cells
from the memory compartment, with the objective to broaden the diversity of the responses and
reduce the risk of immune evasion16,17; ii) containing T cell responses of both CD8+ and CD4+
subtype, capable of direct cytotoxicity or production of cytokines upon challenge with neoantigen-
expressing cells9,18,19; iii) containing T cell responses with high specificity for the mutant epitope
to minimize off-target side effects.

NEO-STIM builds on a previously reported vaccination approach where clinical benefit was seen
in patients treated with personalized neoantigen-based peptides in combination with immune
checkpoint blockade20. It takes that approach one step further: Instead of vaccinating in vivo, T
cell responses against personalized neoantigens are induced ex vivo under tailored and optimized
conditions to avoid immunosuppressive mechanisms by removing the cells from the inhibitory
niches within the tumor microenvironment (TME) seen in cancer patients21.

Here, we report the successful design, optimization, and scale-up of NEO-STIM, first using model
antigens in healthy donors (HDs), then using peptides representing personalized neoantigens from
patients with melanoma. We describe critical parameters for the generation of potent T cell
responses and for robust and scalable manufacturing. Phenotypic characterization of T cells from
the DP and from tumor and blood of a patient pre-and post-infusion provides insights into the
biology of the T cell response in the context of this ACT approach.

Results
Development of NEO-STIM to generate neoantigen-specific T cells
The overarching goal was to develop a personalized, autologous ACT DP from peripheral blood
(Fig. 1A). To facilitate platform development, NEO-STIM was initially carried out using HD
peripheral blood mononuclear cells (PBMCs) and model antigens that either always (MART-1),
frequently or sporadically (HIV-derived epitopes, and previously identified neoantigens) were able
to prime T cell responses in vitro22-24 (see Supplementary Table 1). The size of the TCR repertoire
and epitope characteristics are key aspects that impact the generation of a T cell response. As such,
dividing model antigens into both highly and weakly immunogenic categories allowed us to
systematically analyze their distinct response dynamics and growth kinetics, ensuring
experimental decisions were tailored to the unique characteristics of each epitope class. Model
antigens were matched with HD material expressing relevant human leukocyte antigen (HLA)
restriction elements to investigate the expansion of memory-like responses (MART-1) and the
induction of responses from the naïve compartment (neoantigens and HIV-derived) 22-24. Finally, T
cell products were generated from patient derived PBMCs and their relative potency was measured
against engineered and natural tumor targets.

To optimize cell type composition for T cell induction, NEO-STIM is initiated with the isolation
of PBMCs and depletion of inhibitory cell subsets. The immunogens are then added, the antigen
presenting cells (APCs) are matured, and finally antigen-specific T cells are primed and expanded
(1o culture). Unstimulated PBMCs are thawed, and a secondary (2o) culture is prepared and
stimulated analogous to the 1o culture in a staggered way, and parallel cell cultures are combined
and expanded to a final DP. The restimulation of primary cultures with autologous depleted
PBMCs aims to further expand already primed antigen-specific T cell responses and prime
additional T cell responses with the objective of producing a drug product that is enriched for
multiple neoantigen-specific T cell responses. Fig. 1B illustrates the steps of the final process, and
the following critical process parameters were optimized to finalize this: choice of APCs (see next
paragraph), starting cell number, culturing time, and aspects of the restimulation process (cell
number and ratio, timing, and overall number of restimulations). The respective optimization
experiments and results are described in detail in Supplementary Fig. 1 and 2. When optimizing
the timing between 1o and 2o culture, we observed that the time difference between starting the 2o
culture and restimulation had an impact on the balance between new priming events and expansion
of already primed events, in addition to the frequency of neoantigen-specific cells. In general,
decisions to implement process steps into NEO-STIM were driven both by biology (phenotype,
number, and magnitude of induced responses) and by feasibility of good manufacturing practices
(GMP) manufacturing (logistics, costs, and time). The final optimized protocol is described in the
caption of Fig. 1B, and yielded T cell responses against the memory-like antigen MART-1 as well
as de novo responses against high and low immunogenic model viral and neoantigens
(Supplementary Fig. 1E, 2D, E). De novo responses were defined as responses that were not
detected in the pre-NEO-STIM sample. This definition is practical for the purpose of this study,
as responses could either originate from T cells in the naïve compartment or from very small
memory clones that were below the detection limit of the assay. However, here the responses are
most likely derived from the naïve T cell compartment, given that HD-derived T cells were primed
with antigens that the donor has not encountered previously.

Naturally occurring dendritic cells drive T cell priming in NEO-STIM
With the aim of streamlining T cell priming, we set out to systematically investigate the starting
PBMCs to pinpoint critical cell subsets. T cells and APCs are key cell types required for efficient
induction of T cell responses from PBMCs. However, cell subsets that potentially prohibit efficient
T cell priming, such as monocytic myeloid-derived suppressor cells (MDSCs) and T regulatory
(Treg) cells, are also present25-27. Therefore, CD25+ T cells, of which the majority were Treg cells,
and CD14+ monocytes, for which the short priming period was likely insufficient to differentiate
tiate
them to mature dendritic cells (DCs), were depleted from the PBMC starting material.

Moreover, since the antigen presentation step is critical for effective T cell priming, we wanted to
understand which APC population supports the NEO-STIM process best. DCs are generally
considered professional APCs. At the high level, there are three sources of DCs from peripheral
blood: classical/conventional DCs (cDCs), plasmacytoid DCs (pDCs) and monocyte-derived DCs
(moDCs), with moDCs frequently being used in the context of the manufacturing of TAA-targeted
T cell therapy2,3,28-31. We compared the priming efficiency of these traditionally used moDCs with
that of naturally circulating DCs (nDCs; Supplementary Fig. 3A). Fold expansion, hit rate,
frequency, diversity and functionality of CD8+ responses, and additionally the ability to generate
CD8+ responses to long peptides (typically used to induce CD4+ responses) were evaluated. nDCs,
both classical DCs (cDCs) 1 and 2, showed upregulation of maturation markers (CD40, CD60,
CD83 and CD86, Supplementary Fig. 3B) and most parameters were more favorable using the
nDCs (Supplementary Fig. 3C-G). Notably, CD8+ responses to long peptides were only seen with
nDCs.

To further characterize T cell priming efficiency between cDCs and plasmacytoid DCs (pDCs),
we depleted either CD11b+ cells or CD11c+ cells or both, in addition to CD14+ and CD25+ cells.
cDCs are CD11c+ and CD11b negative to low positive (depending on subtype) while pDCs are
double-negative32,33.

Efficient depletion (90-98.5%) for both markers was achieved in both single- and double-depleted
conditions (Supplementary Fig. 4A-D). Depletion of CD11c+ DCs resulted in a significant decline
in total number of antigen-specific cells or hit rate for memory-like MART-1-specific responses
and de novo model antigen responses, respectively (Supplementary Fig. 4E, F). This confirms that
the CD11c+ cell fraction (primarily cDCs) is critical for T cell priming in NEO-STIM. CD11b
depletion had more variable results, consistent with the variability of CD11b expression among
the cDC subtypes. pDCs may still be contributing to NEO-STIM, but are not the key stand-alone
drivers, based on these results.

These interesting biological findings had practical implications and we conclude that: i) CD14-
depletion does not remove any critical DC populations; ii) there is no strong rationale for an
additional CD11b depletion step, which would increase the costs of manufacturing; and most
importantly iii) nDCs are able to more efficiently prime T cells in NEO-STIM compared to
moDCs, which implies that a separate manufacturing workstream of isolating CD14+ cells and
maturing them to DCs is neither needed nor beneficial. These conclusions positively impacted
workload, time, and costs of NEO-STIM manufacturing.

NEO-STIM with patient-derived PBMCs efficiently generates responses against
personalized neoantigens
To evaluate whether NEO-STIM could efficiently generate T cell responses against personalized
tumor antigens, we applied the optimized process to PBMCs from patients with melanoma or
ovarian cancer (OVC), indications with a mid-to-high mutational load34. This required additional
steps, i.e. identifying neoantigens in each patient and then manufacturing the selected personalized
peptides to be used as immunogens. Neoantigens were identified and selected by sequencing the
patient-specific mutanome and performing epitope predictions in the context of the patients’ HLA
alleles (both major histocompatibility complex (MHC) class I and MHC class II). The
bioinformatic approach considers RNA expression, predicted binding affinity to patient-specific
MHC molecules, and predicted proteasomal processing to generate a score and rank the predicted
neoantigens20,35,36. Additionally, PT3, PT07, PT08, PT09 each had 1-2 pools of previously
identified neoantigens8,20,22-24,37,38 added to NEO-STIM process.

Research-scale experiments were performed using PBMCs from seven patients, four with
melanoma and three with ovarian cancer (see Supplementary Data S1, Supplementary Table 2).
After identification of personalized neoantigens, up to 60 synthetic neoantigen peptides per patient
were manufactured and divided into six or seven pools. Each peptide pool was used in triplicate
(melanoma) or quadruplicate (OVC) to assess reproducibility. After applying NEO-STIM, we
investigated both CD8+ and CD4+ responses in the DPs of all seven patients.

To understand what epitopes were immunogenic, we first measured CD8+ responses using pMHC
tetramers in a combinatorial coding strategy (Fig. 2A, Supplementary Fig. 5, 6A). Fig. 2A shows
five examples of CD8+ responses. One of the CD8+ responses was detected prior to NEO-STIM
(SRSF1 33:E>K, 4.5% of CD8+ cells) showing that pre-existing responses from the memory
compartment can be further expanded. This pre-existing response also showed the highest
magnitude in a single well, 72.1% of CD8+ cells (range 51.3-72.1% across three wells), which
represented a 16-fold expansion compared to pre-NEO-STIM cells (Fig. 2B). Overall, all seven
patients generated multiple CD8+ responses (median 6, range (2-13)) and the magnitude of
neoantigen-specific CD8+ cells ranged from 0.04% to 72.1% with a median of 4% (Fig. 2C,
Supplementary Fig. 5A, 6B, Supplementary Data S1). Reproducibility between cultures was
consistent across biological replicates, in particular for higher frequency responses
(Supplementary Data S1).

Next, CD4+ responses were identified by measuring interferon (IFN)γ and/or tumor necrosis factor
(TNF)α in response to autologous peptide-loaded vs unloaded dendritic cell (DC) exposure (Fig.
2D). A functional readout was used with the goal to identify clinically relevant CD4+ T cell
responses. All, but one patient, generated multiple CD4+ responses (median 6, range 1-13, Fig. 2E,
Supplementary Fig. 6C, D). The magnitude of neoantigen-specific CD4+ cells ranged from 1.1-
74%, with a median of 7.3%) (Supplementary Data S1).

Taken together, NEO-STIM generated a polyclonal T cell product containing multiple neoantigen-
specific CD8+ and CD4+ T cell responses using PBMCs from patients with advanced melanoma
and ovarian cancer.

NEO-STIM responses are polyfunctional and recognize mutant epitopes
Prior studies with ACTs showed that a polyfunctional T cell response, i.e. the ability of a T cell to
produce multiple cytokines, is correlated with improved clinical outcome39,40. We therefore
measured the expression of IFNγ, TNFα, and CD107a mobilization in a recall response assay upon
stimulation of T cells with their cognate antigen.

Twenty out of 27 CD4+ and CD8+ melanoma responses tested showed an increase in all three
functions (IFNγ, TNFα, and CD107a). Overall functionality, i.e. increase in any of the three
functions or any combination thereof was detected in 100% of evaluable CD8+ and CD4+ responses
(examples shown in Fig. 2F). Polyfunctionality was also observed in one additional ovarian
carcinoma patient tested (Supplementary Fig. 6E - G).

Given that neoantigen epitopes can differ from their wild-type (WT) counterparts by as little as a
single amino acid, and single point mutations can either reduce or enhance the likelihood of the
epitope to be processed and presented41, we evaluated whether the induced T cell responses could
differentiate between mutant (MT) and WT peptides. DCs loaded with varying concentrations of
either peptide were co-cultured with the induced T cells. Subsequently, the fraction of
responding T cells was quantified based on functional marker expression in response to both
peptides. Both of the two evaluable CD8+ responses (SRSF1 33:E>K and ARAP1 828:Y>H) were
mutant-specific, which we defined as having a statistically significant reactivity over the no-
peptide control (datapoint at concentration 0) without any significant WT reactivity (Fig.
2G,Source Data file, tab Figure 2g). The one CD4+ T cell response tested at this point of protocol
development (TPCN1 413:K>E) was WT cross-reactive: significant reactivity to MT and WT with
no significant increase of MT over WT reactivity (Supplementary Fig. 7B, yellow header,
Source Data file, tab Figure 7a).

NEO-STIM responses are cytotoxic and recognize/respond to autologous tumor
The assessment of cytotoxicity towards antigen-expressing targets is a relevant proxy for in vivo
killing capacity. To understand the ability of NEO-STIM-induced CD8+ T cells to kill targets with
naturally processed and presented epitopes, we transduced cells of the human melanoma cell line
A375 with the associated HLA type and either the MT or WT epitope with flanking natural
sequence, positioning the mutation towards the center of the sequence. We then co-cultured the
transduced target A375 cells with effector CD8+ T cells from the DP and measured upregulation
of CD107a on T cells, which is a surrogate marker for cell killing42. In addition, upregulation of
active caspase 3, a marker of early apoptosis, was measured on the A375 tumor cells.

Fig. 2H illustrates that in four cytotoxic responses (out of five evaluable responses tested),
significant upregulation of both CD107a and caspase 3 was observed, when comparing MT-
transduced and WT-transduced cells. This showed that the majority of NEO-STIM-induced T cell
responses were able to specifically kill mutant antigen-expressing targets.

Finally, the key test for functionality of cytotoxic CD8+ T cells is the ability to recognize and
respond to autologous tumor. We were able to obtain a single-cell suspension from a tumor of one
patient (PT04). The induced T cells from this patient contained a sizeable CD8+ T cell response
towards LRBA 2198:S>L after a single stimulation (5.2% tet+ of CD8+ T cells). After co-culture of
these T cells with the autologous tumor we measured IFNγ expression and CD107a mobilization,
and observed that the LRBA 2198:S>L-specific CD8+ T cells were able to recognize autologous tumor
(9.4% IFNγ+ and/or CD107a+ of tet+ CD8+ cells, Fig. 2I). This demonstrates that NEO-STIM-
induced CD8+ T cells can recognize cells derived from autologous tumor and can respond to
physiologically relevant levels of presented epitope.

Polyclonal neoantigen-specific responses show an increased transcriptional activation
signature
To assess the phenotypes of NEO-STIM-induced T cells, single-cell RNA/TCR sequencing
analysis was performed for a patient (PT01) at four time points: pre-NEO-STIM (day 0),
stimulation 1 (STIM1, day 14), stimulation 2 (STIM2, day 21), and stimulation 3 (STIM3, day
28). PT01 had three CD8+ T cell responses in the DP, one of them pre-existing and two de novo,
based on tetramer staining (Fig. 2C). We chose the pre-existing (SRSF1 33:E>K) and one of the de
novo responses (ARAP1 828:Y>H) for this analysis. Single-cell TCR sequencing confirmed that
SRSF1 33:E>K was indeed a pre-existing response and that ARAP1 828:Y>H was a de novo response
(Fig. 3A, B). Functional avidity data were generated for four TCRs (SRSF1: n=3; ARAP1: n=1)
and showed mutant-specific binding in the nano- to micromolar range (Supplementary Fig. S8).

At day 0, the percentage of neoantigen-specific cells was extremely low, so the bulk T cells at day
0 were considered by and large as bystanders. For the other timepoints we sorted the cells based
on tetramer staining prior to phenotyping. Cells from each time point were co-cultured overnight
with DCs loaded with relevant peptide (Pep) or without peptide (NP). Sequences from all
conditions were analyzed together and uniform manifold approximation and projections (UMAP)
analysis revealed the transcriptional differences between different experimental groups (Fig. 3C,
D and Supplementary Fig. 9A, B). Phenotypes were assigned based on expression of known
markers (Supplementary Fig. 9C-F).

Upon peptide challenge, ARAP1 828:Y>H neoantigen-specific cells shifted drastically, to comprise
more effector type cells. Cells with effector phenotypes were of the IFNG type at STIM1, but then
expressed increasing levels of PRDM1, a gene involved in T cell differentiation43, at STIM2 and
STIM3 (Fig. 3E, F). In contrast, phenotypic shifts in SRSF1 33:E>K-specific T cells upon peptide
challenge were detectable, but less pronounced and consisted mainly in the appearance of a small
IFNG+ effector cluster. Overall, the expression of genes such as IFNG, CCL3, and TNFRSF9 in
cells rechallenged with antigen suggests that these T cells induced through NEO-STIM were
functional and have effector function upon rechallenge.

To understand the activation profile of the neoantigen-specific cells upon rechallenge, we used a
published T cell activation signature (Fuchs activation score) 44 to compare cells rechallenged with
and without cognate peptide. We observed a significant increase in activation scores following
peptide stimulation, which was more pronounced in ARAP1 828:Y>H-specific T cells, particularly at
STIM1 (Fig. 3G, H). At the clonotype level, we observed that within both responses all clonotypes
had a homogenous expression profile showing an increased fraction of T cells shifting to a “partial
effector” population (IFNGmedTNFRSF9- effector cells) with time in culture (Fig. 3I, J), in line
with what was observed at the response level.

In summary, two different patterns of phenotypic shift were seen for ARAP1 828:Y>H (de novo) and
SRSF1 33:E>K (pre-existing). The former demonstrated a dramatic shift to effector phenotypes
already in STIM1, and from there a gradual shift to a more differentiated effector phenotype
(PRDM1) in STIM2 and -3 (Fig. 3E), while the latter showed a gradual and less pronounced
increase of partial effector and effector phenotypes (Fig. 3F). We analyzed stem-like (CCR7,
SELL) and exhaustion markers (LAG3, PDCD1, PRDM1) in both responses (Supplementary Fig.
9G). CCR7 and SELL showed minimal expression at all timepoints, while LAG3 and PRDM1
increased over time with stable PDCD1 levels. This pattern was consistent across SRSF1 and
ARAP1 T cell responses. Considering that PRDM1 has been shown to be negatively correlated
with persistence in CAR-T cells43 and the fact that the benefits of STIM3 for the pre-existing
response (SRSF1 33:E>K) were minor, only one restimulation was adopted for the final protocol to
keep the overall manufacturing time as short as possible.

Therapeutic-scale manufacturing process generates reproducible results
To support further development for clinical studies, it was critical to scale up NEO-STIM from a
small-scale research process to a semi-closed therapeutic-scale process using GMP-grade reagents.
The therapeutic-scale process followed the optimized protocol with two stimulations (except for
one pilot run, which used three stimulations). Peptides were again divided into at most six distinct
pools to avoid overgrowth of dominant clones.

DPs were successfully manufactured across two HDs and three patients with melanoma
(Supplementary Data S1 and Supplementary Table 2). Total cell yields ranged from 3.6x109 to
10.2x109 cells and had a high viability (≥79%) (Fig. 4A). The majority of cells in all the
manufactured DPs were CD3+ T cells (≥60%), with the remaining cells being mostly B cells and
NK cells (Supplementary Fig. 10A). Additionally, all DPs passed the quality control testing
(sterility, mycoplasma, and endotoxin testing). We observed a strong correlation of initial CD8+
frequency with hit rate (R2= 0.87 for HD and R2= 0.98 for patient samples) consistent with a similar
finding in initial process establishment (Supplementary Fig. 1B), as well as consistency between
research scale and therapeutic-scale samples, when comparable processes were used (Fig. 4B).
This further highlights the importance of depleting CD14+ monocytes and CD25+ regulatory T
cells on day 0 to optimize neoantigen-specific T cell priming.

Therapeutic-scale DPs are polyclonal, polyfunctional, specific and cytotoxic
Next, we sought to confirm the research-scale results at therapeutic scale. After scale-up, we
observed that NEO-STIM can generate multiple T cell responses. Interestingly, variations in
growth kinetics across vessels and stimulation with related peptides (e.g., 9mer and 10mer from
the same mutation) can lead to non-uniform dilution and, in some cases, unexpectedly varying
frequencies of antigen-specific T cells in the final DPs, as observed TENM3 1243:S>L in PT05
(Fig. 4C, Supplementary Fig. 5B and Supplementary Data S1). The majority of cells displayed an
effector memory phenotype (Supplementary Fig. 10B) and all showed to be polyfunctional
(Supplementary Fig. 10C for examples). Specificity for MT epitopes was tested for 16 therapeutic-
scale responses. MT-specificity and WT-cross-reactivity was defined above (section “NEO-STIM
responses are polyfunctional”); mutant-selective responses were defined as responses with
significant reactivity towards MT and WT peptide, but with a significant difference between
mutant and wildtype peptide (together with MT-specific responses labelled as mutant-reactive).
Representative plots for each category are shown in Fig. 4D. All of the tested CD8+ responses
(n=3) and 85% of the tested CD4+ responses (n=13) were mutant-reactive (Supplementary Fig.
7B; Supplementary Table 3, tab Figure 7b). Given the infrequent observation of WT-reactive
responses during development, the release specifications of the drug product did not include the
exclusion of WT-reactive clones, thus resulting in the infusion of bulk products. Additionally, all
tested neoantigen-specific CD8+ responses were cytotoxic against antigen-expressing tumor lines
(Supplementary Fig. 10D, E).

As we showed recognition of autologous tumor at research scale (Fig. 2I), we tested two additional
responses (based on sample availability) from a therapeutic-scale DP (ITPR3 616:E>K and
TENM3 1243:S>L) with and without addition of cognate peptide. Tumor-derived single-cell
suspension cells and CD8+ T cells from the DP were co-cultured, and cytokine production and
degranulation was measured. We observed that the ITPR3 616:E>K-specific cells were functional in
response to autologous tumor only when additional exogenous mutant peptide was added.
Importantly, the TENM3 response showed an even stronger increase over the negative
control, which was statistically significant. This recognition was observed without addition of
exogenous peptide and thus means TENM3 1243:S>L-specific cells were able to recognize and
respond to physiologically relevant levels of antigen on autologous tumor (Fig. 4E).

Overall, we were able to evaluate autologous tumor recognition by three distinct T cell responses
derived from two patients (Fig. 2I and 4E). Two out of the three responses were able to recognize
autologous tumor (TENM3 1243 and LRBA 2198). Interestingly, while a high variant allele
frequency (VAF) and high variant RNA expression did not correspond to observed
immunogenicity in our system (Supplementary Data S1), they are likely to be relevant with respect
to tumor recognition. Indeed, we observed that the VAF and RNA expression of the mutant ITPR3
616 variant (corresponding to the T cell response that was unable to recognize autologous tumor
without exogenous peptide loading) was the lowest of the three (Supplementary Data S1). While
sample availability was limited in this study, testing the correlation of VAF and RNA expression
with tumor recognition in additional samples would be valuable to further confirm these results.

Neoantigen-specific T cells with distinct phenotypes are recovered from blood and tumor
post-infusion of DP
Early clinical proof of concept has been established with T cell products manufactured through
NEO-STIM, and translational data from this trial showed that neoantigen-specific responses can
be tracked at the antigen level in blood and tumor pre- and post-infusion45. Additional
translational analysis was performed here for one patient, NAC0445, evaluating persistence post-
infusion down to the level of individual TCR clones. We found that among the pre-existing
responses initially detected at the antigen level, some were in fact de novo at the clonotype level,
suggesting that a subset of clonotypes may have been induced de novo through NEO-STIM45.
Across two responses which initially were considered pre-existing, three out of eight subclones
were de novo. One of those three was detected post-infusion, compared to five out of five which
were pre-existing (Fig. 5A, B). A similar observation of clonal heterogeneity regarding pre-
existing and de novo responses was made in another patient from the same trial, NAC01
(Supplementary Fig. 11).

T cells from the peripheral blood of patient NAC04 were single-cell sorted using barcoded
tetramer staining and, alongside a post-infusion tumor biopsy digest sample, subjected to single-
cell RNA & VDJ sequencing to characterize neoantigen-specific T cell responses. Neoantigen-
specificity was defined as peripheral or tumor VDJ sequences matching the validated clonotype
from the DP. T cells carrying these responses (NeoAg) were further phenotyped and compared to
other cells (Other) from the same sample. We observed an increase in activated/exhausted
phenotypes (e.g. CD86, TNFRSF9, PDCD1, LAG3, HAVCR2) and cytotoxic phenotypes (e.g.
GZMB, IFNG, PRF1, NKG7) in neoantigen-specific T cells (NeoAg) versus other cells (Other)
in both blood and tumor. In the tumor we also observed increased CXCL13 mRNA expression on
neoantigen-specific T cells, which has been described as a marker for neoantigen-reactive cells
and shown to promote tumor infiltration46-48 (Fig. 5C, D).

The activation status of neoantigen-specific cells versus other cells could only be determined
from the blood sample, since the tumor sample did not yield a sufficient number of neoantigen-
specific cells to perform this analysis. We observed a significant increase in the activation status
of neoantigen-specific T cells compared to other cells in the blood (Fig. 5E).

Taken together, de novo and pre-existing T cell responses were detected post-infusion in both
tumor and peripheral blood of a patient, and these responses displayed both activated/exhausted
and cytotoxic phenotypes.

Methods
Ethics statement
The research described in this study complies with all relevant ethical regulations, see section
Patient and healthy donor material for detailed board approval information.

Study Design
With the goal of developing a robust process to induce de novo and expand existing T cell
responses from autologous patient material, the NEO-STIM method was established as follows:
Initial setup and optimization was carried out with PBMCs from 22 HDs, using model antigens
as immunogens (Supplementary Table 1). Optimized parameters and final protocol are shown in
Fig. 1B. The resulting process was then applied to PBMCs from four patients with melanoma
(Supplementary Data S1, Supplementary Table 2) at research scale, using patient-specific
neoantigens as immunogens. A scale-up of this personalized drug product generation process
was achieved with PBMCs from two HDs and three patients with melanoma (Supplementary
Data S1, Supplementary Table 2). The experiments at research scale were carried out in
biological triplicates (melanoma) or biological quadruplicates (ovarian cancer) for up to seven
pools of peptides and at therapeutic scale in single biological replicates for up to six vessels (one
vessel per peptide pool). Output parameters to test the performance of the final protocol were
number and quality of induced T cell responses and phenotype of resulting T cells. Post-infusion
analysis was performed on material from two patients from a clinical trial using NEO-STIM to
generate T cell DPs45.

Patient and healthy donor material
Leukapheresis was obtained from six individual patients with metastatic melanoma, three patients
with ovarian carcinoma and 22 healthy individuals. Among the six patients for process
development, three patients (PT01-PT03) were enrolled under the trial NT001 (NCT02897765)
conducted by Neon Therapeutics, USA. The study was approved by the Institutional Review Board
at each participating site. Six patients (PT04-PT09) participated in the N16NEON trial (CCMO
ID: NL57848.031.16) under an institutional review board (IRB)-approved protocol conducted at
Netherlands Cancer Institute (NKI). The IRB was the Medisch-Ethische Toetsingscommissie AVL
(METC AVL). The two patients for post-infusion analysis were treated at NKI in a phase I study
(NCT04625205) approved by the Central Committee on Research Involving Human Subjects
(approval number NL72301.000.19) and DP, PBMC or tumor single cell digest aliquots were
provided for this study. All studies were conducted in accordance with the Declaration of Helsinki.
All patients signed an informed consent form. Patients did not receive reimbursement for the study
participation. HD material was sourced from AllCells, USA or HemaCare, USA.

Processing of leukapheresis and tumor material
Leukaphereses collected from patients and HDs were processed under universal precautions.
PBMCs from NT001 participants and HDs were prepared by Ficoll-paque (GE Healthcare)
density-gradient centrifugation and cryopreserved in either Recovery Cell Culture Freezing
Medium (Invitrogen) 20 or Human serum (Sigma) with 10% DMSO. Material collected from
N16NEON patients was washed using wash media (4% w/v human serum albumin [HSA] and
0.4% w/v sodium citrate in NaCl) and cell pellets were resuspended in 4% w/v sodium citrate in
HSA that was frozen by adding an equal amount of freezing media (20% v/v DMSO, 4% w/v
sodium citrate in NaCl). Tumor material collected from N16NEON patients was digested into
single-cell suspension and frozen using freezing media into cryovials. All processed material was
shipped and stored at <-140⁰C until use. Post-infusion tumor resection or biopsy was collected,
processed and stored as described previously45.

Identification of patient-specific neoantigens, peptide synthesis
A detailed method of identifying patient-specific neoantigens was described previously20. For each
patient a set of induction peptides (used for NEO-STIM) and a set of assay peptides (used for read-
outs) were synthesized using solid phase peptide synthesis and purified by reverse phase HPLC.
Preparative HPLC fractions that met purity and identity criteria based on UPLC-UV/MS analysis
were pooled and dried using parallel centrifugal evaporators or lyophilizers. These peptides were
resuspended in DMSO and pooled together for experimental use. Synthesis and purification took
place at Creosalus, at PPL or at BioNTech.

Induction peptides were either short peptides (8-12 amino acids [aa]) predicted to bind to MHCI
or long peptides (25 aa) predicted to bind to MHCII. Induction peptides were either patient-specific
neoantigens (up to 40 short and up to 20 long peptides) or model antigens (Supplementary Table
1). Assay peptides (20 aa) were synthesized as overlapping (at least 9 aa overlap) to cover the
sequence of the long mutated induction peptide.

Rapid induction protocol
A rapid induction protocol was used to evaluate the impact of various APC populations on the
priming of T cells in NEO-STIM. PBMCs were thawed and resuspended in AIM-V media (Gibco,
12055-083), incubated with Benzonase at 250U/mL (Sigma-Aldrich 70746) for 30 minutes at
37°C, and then depleted to remove monocytes (CD14+) and T regulatory cells (CD25+) according
to the manufacturer’s protocol (Miltenyi Biotec, Inc 130-050-201, 130-092-983). For research
experiments where additional populations were depleted, CD11c-biotin (clone Bu15; Biolegend
#337232), CD11b-biotin (clone ICRF44; Miltenyi #301304), and Anti-Biotin microbeads
(Miltenyi #130-090-485) were used according to the manufacturer’s instructions. Post-depletion
cells were cultured in AIM-V media with FLT3L (CellGenix 1415-050) at 50 ng/mL plated in low
volume (2 mL) in a 24-well G-Rex plate (Wilson Wolf, 80192M) for 18-24h in six biological
replicates. Previously identified neoantigens (Supplementary Table 1) in the form of peptides (0.4
µM final concentration) were incubated for 1 hour and the cultures were matured using maturation
cocktail with TNF-α (1000 U/mL; CellGenix 1406-050), IL-1β (10 ng/mL; CellGenix 1411-050),
Prostaglandin-E 1 (PGE-1; 0.5 µg/mL; Cayman Chemical, S745-65-3), and IL-7 (0.5 ng/mL;
CellGenix 1410-050). The cells were left in culture for 14 days in total with the addition of human
serum (HS) 18-24h post maturation, the addition of media on day 5 with IL-7 (CellGenix 1410-
050) and IL-15, (CellGenix 1413-050) and a media replacement on day 12. Exception: For
comparison with the moDC protocol, medium was changed on day 5 to AIM-V/RPMI (18% AIM-
V + 71% RPMI1640 glutamax + 10% Human serum + 1% PenStrep, all v/v), with IL7 and IL15
additions every two to three days.

NEO-STIM process using nDCs
Preparation of PBMCs, stimulation and expansion until day 14 were carried out as in the rapid
induction protocol with two modifications: Plating was either as 2 mL in a 24-well plate in
biological triplicates (research scale) or as 9 mL in a 6-well plate or 10M or 100 M G-Rex vessel
(therapeutic scale; Wilson Wolf), and peptide incubation was 2h. For the therapeutic-scale
experiments, cells were grown in one large culture vessel per peptide pool (10M or 100M G-Rex
vessel), up to six distinct pools per patient. The 2o culture (as well as 3o culture) was generated by
thawing unstimulated PBMCs and preparing them as done for the 1o culture. For the process with
one restimulation (two stimulations total), the 2o culture was initiated on day 12 (unless otherwise
specified) and added to 1o culture on day 14 for a total culture time of 26 days. For the process
with three stimulations, the 2o culture cells were initiated on day 12 and combined with 1o culture
cells on day 14; 3o culture cells were initiated on day 19 and combined with 1o/2o cultures on day
21, with a 28-day total culture time. (Exception: When comparing nDC-based and moDC based
protocols, first restimulation was on day 15, second restimulation on day 22 and harvest on day 29
to be able to compare the two protocols side-by-side). For the large-scale semi-closed system
process, the depletion was carried out using a CliniMACS system, and the cells were cultured in
G-Rex 10M or 100M vessels. The success of the manufacturing process was evaluated using the
following acceptance criteria: Cell count 109-1010 cells; Viability ≥70% as set forth by FDA
guidance53; T cell purity/identity (CD3+ cells) ≥40%; Mycoplasma negative; Endotoxin ≤1.25
EU/mL; Sterility: no growth.

moDC preparation and use in moDC-based NEO-STIM
Monocyte-derived dendritic cells were prepared from PBMCs to stimulate T cells and expand
antigen-specific cells. Monocytes were isolated from PBMCs using CD14 microbeads according
to the manufacturer’s protocol (Miltenyi Biotec, Inc 130-050-201). Isolated monocytes were
resuspended in DC medium (CellGenix 20801-0500) and plated with GM-CSF (800 U/mL;
CellGenix 1412-050) and IL4 (400 U/mL; CellGenix 1403-050). After 5 days, immature DCs were
harvested and incubated with neoantigens in the form of peptides (0.4 µM final concentration) for
1 hour and the cultures were matured using maturation cocktail with TNF-α (10 ng/mL; CellGenix
1406-050), IL-1β (10 ng/mL; CellGenix 1411-050), IL6 (10 ng/mL; CellGenix 1404-050) and
Prostaglandin-E 1 (PGE-1; 0.5 µg/mL; Cayman Chemical, 745-65-3) for 2 days. Post maturation
differentiated DCs were then cultured in AIM-V/RPMI medium (18% AIM-V + 71% RPMI1640
glutamax + 10% Human serum + 1% PenStrep, all v/v). APCs were used in moDC based NEO-
STIM at a ratio of 1:10 (DC to T cells) to stimulate CD14/CD25-depleted PBMCs on days 0, 13
and 20 of the process.

Dendritic cell analysis
For measurement of costimulatory markers on DCs, DCs were matured as described above and
after 2 days were harvested and stained with surface antibodies for 30 min at 4°C. Cells were
washed and resuspended in FACS buffer at 4°C. To characterize the dendritic cells, they were
gated on: Cells, Singlets, Live all, CD3-/CD19-, then mDCs were gated against CD11c+ and HLA-
DR+; cDC1: HLA-DR+, CD11c+, CD141+; and cDC2: HLA-DR+, CD11c+, CD1c+. The fraction
of CD40+, CD80+, CD83+ and CD86+ expressing cells was quantified, as well as the MFI of those
markers using FlowJo software (v07-10).

Corresponding flow panel: Dendritic cell analysis panel (Supplementary Data S2)

pMHC multimer analysis using tetramers
A combinatorial coding approach using pMHC tetramers to identify neoantigen-specific CD8+ T
cells was applied, as described previously54. Briefly, cells were thawed, washed, and resuspended
in phosphate-buffered saline and then stained with the tetramers associated with respective pMHC
molecules, in addition to a number of surface markers. This allowed for quantification of the
fraction of tetramer+ CD8+ T cells.

In the pMHC tetramer analysis, cells of interest were gated as previously described45 i) Lineage:
Forward scatter and side scatter gates were applied to filter for singlets of the target cell population,
PBMC. Dead cells were excluded with a live/dead marker. Cells were then gated for positive CD8
staining and negative CD4/CD14/CD16 and CD19 staining. ii) Single positives: CD8+ cells stained
with the single fluorophores, which were used for the tetramer labeling, were gated. iii)
Combicoding: Peptide – MHC complexes were labeled with two different fluorophores to make
tetramers. T cells that interacted with a defined combination of two and only two of the
fluorochromes assigned to a specific peptide-MHC complex and satisfied additional specificity
criteria54 were considered specific to the respective neoantigen.

Corresponding flow panel: Class I tetramer panel (Supplementary Data S2)

Investigation of cell expansion using proliferation dyes
The proliferation of the cells of interest was investigated using the CellTrace CFSE Cell
Proliferation Kit (Thermofisher Scientific # C34554) and the Tag-IT violet proliferation and cell
tracking dye (Biolegend, # 425101) at final concentrations of 0.5 µM and 2.5 µM respectively. For
labeling, the cells were washed and incubated with proliferation dyes for 5 minutes at 37⁰C and
later quenched with five times the volume of 20/80 media (18% AIM-V + 71% RPMI + 10% HS
+ 1% Penstrep) at room temperature in the dark for 5 minutes. The cells were then washed again
and cultured in the NEO-STIM process to track multiple generations of antigen-specific cells,
using dye dilution, by flow cytometry. 1⁰ culture cells were harvested on day 14 and labeled with
Cell trace CFSE before adding to 2⁰ cultures labeled with Tag-IT violet for restimulation. The
antigen-specific cells were identified using combinatorial coding technology (as described in the
previous section) and gated on FITC (CFSE) or BV421 (Tag-IT violet) to understand the
proliferative capacity of specific populations.

Corresponding flow panel: Proliferation panel (Supplementary Data S2)

Phenotypic flow cytometry characterization
The subpopulations in starting material, in-process-samples and post-NEO-STIM samples were
analyzed using flow cytometry. All samples were subjected to the following gating strategy:
live/dead marker−, CD14−/CD16−/CD19−, CD8+ (either bulk or tetramer+ subset) or CD4+.
Subsequent analysis of phenotypic markers (CD62L and CD45RA) using FlowJo software (v07-
10) was performed to delineate subpopulations based on differentiation status of the T cells as
mentioned below,
CD62L+ CD45RA+: Naïve T cells, CD62L+ CD45RA-: Central memory T cells, CD62L- 
CD45RA-: Effector memory T cells, CD62L- CD45RA+: Effector T cells.

Corresponding flow panel: Differentiation status panel (Supplementary Data S2)

Recall response assay
A multi-color flow cytometric panel in combination with an overnight antigen recall assay in
which the NEO-STIM-induced cultures were co-cultured with peptide-loaded APCs was used at a
ratio of 1:10 (DC to T cells) to identify neoantigen-specific CD4+ T cell responses. Here,
monocyte-derived dendritic cells were used as APCs which were differentiated over seven days
from CD14+ monocytes using GMCSF (Stock 800U/µL) and IL-4 (Stock 400U/µL). During the
differentiation process, immature DCs were loaded with respective neoantigens in the form of
assay peptides at a final concentration of 0.8 µM and matured with TNFα (10 ng/mL), IL1β (10
ng/mL), IL-6 (10 ng/mL), and PGE-1 (0.5 mg/mL) 45. Pre-NEO-STIM and induced T cells (in
process or final NEO-STIM or DP samples) were co-cultured overnight with or without the
antigens of interest in the format of overlapping 20 aa assay peptides at a final concentration of
0.8 µM each. After the overnight incubation, a neoantigen-specific CD4+ T cell response was
defined by the increased expression of IFNγ and/or TNFα in the presence of target antigen
compared to the negative control (co-cultured with unloaded DCs). In the flow cytometric readout,
cells of interest were gated as follows: live/dead marker−, CD14−/CD16−/CD19−, CD4+.
Subsequently, production of IFNγ or TNFα or both (IFNγ and TNFα) by the CD4+ T cells was
assessed for identifying antigen-specific responses and the difference in frequency of response
over background was plotted. For the polyfunctionality profiles, the production of IFNγ, TNFα
and CD107a in response to assay peptide-loaded APCs was assessed. FlowJo software (v07-10) was used.

To understand the polyfunctionality profile of the antigen-specific CD8+ T cell responses, cells
were stained with pMHC tetramer molecules and then assessed for the production of IFNγ, TNFα
and CD107a in response to assay peptide-loaded APCs. To describe antigen specificity, the
responses were categorized based on differential reactivity towards MT or WT neoantigen
peptides, measured at different concentrations (0, 0.05, 0.2, 0.8 & 3.2 µM). Significance of
differences between peptide-stimulated cells and no peptide control, as well as between MT and
WT reactivity was calculated using paired t-test with FDR correction for adjusted P value or
Tukey multiple comparison test or Sidak’s multiple comparison test, as indicated in figure
captions. Exact P values were recorded for all comparisons in the Source Data file.

Corresponding flow panel: Recall response panel (Supplementary Data S2)

Cytotoxicity assay
Neoantigen-expressing tumor lines were generated by transducing A375 tumor cells using
pCDH_CMV_MART1-T2A-eGFP vector with the relevant HLA (https://www.ebi.ac.uk/ipd/), as
well as with neoantigen- (or corresponding WT-) epitope, and flanking sequence. By transducing
a sequence that includes the flanking region, the epitope can only be presented if the antigen
presentation machinery of the cell is capable of processing the sequence. Separately, for a subset
of the specificities tested, A375 tumor cells with the HLA of interest were loaded with the relevant
peptide. The induced T cells were then co-cultured with these tumor lines. After 6 hours, the tumor
and T cells were analyzed by flow cytometry using FlowJo software (v07-10). The upregulation
of active caspase 3 (an early apoptotic marker on tumor cells), and mobilization of CD107a
(expressed when T cells degranulate) were measured. The apoptotic target cells were gated on
live/dead marker−, Cas3+ and gating for CD8+ T cells: live/dead marker−, CD3+, CD8+ CD107a+.

Corresponding flow panel: Cytotoxicity assay panel (Supplementary Data S2)

Tumor recognition assay
A single-cell suspension of autologous tumor corresponding to the patients PT04 & PT05 and T
cells generated through NEO-STIM from each of these patient samples were co-cultured
overnight. In the flow cytometric readout, cells of interest were gated as follows: live/dead
marker−, CD14−/CD16−/CD19−, CD8+ and pMHC+. Subsequently, functional markers CD107a,
TNFα, and IFNγ were quantified among antigen-specific (pMHC+) CD8+ T cells using FlowJo
software (v07-10) .

Corresponding flow panel: Recall response panel (Supplementary Data S2)

Functional Avidity Assay
Functional avidity of neoantigen-specific TCR clones was determined using an NFAT reporter
Jurkat model as described previously45. Briefly, E6.1 Jurkat T cells (ATTC; TIB-152) engineered
with an NFAT luciferase reporter were used to detect T cell activation by electroporating them with
the respective TCRα and TCRβ chain RNA. TCR-expressing Jurkat cells and peptide-loaded A375
cells (ATTC; CRL-1619) were co-cultured at 1:1 effector-to-target ratio. After overnight co-culture
luminescence was determined and NFAT activation per condition was calculated by normalizing
luminescence to a negative control reaction containing no peptide. Estimated EC50 is reported
based on fit curve analysis for cases, where two peptide concentrations showed significant NFAT
activation.

Single-cell RNA sequencing and TCR seq analysis
Preparation of neoantigen-specific T cells from NEO-STIM, post-infusion blood and tumor
samples for single-cell RNA/TCR sequencing
Neoantigen-specific CD8+ T cells from NEO-STIM samples were activated through a recall
response assay and were sorted on BD Aria using MHC class I tetramers. Single-cell RNA and
TCR libraries were prepared using the Single-cell 5’ VDJ Reagent kit (10x Genomics). The library
preparation process was performed according to the associated 10x Genomics protocol, including
GEM Generation & Barcoding, Post GEM-RT Cleanup, cDNA Amplification, Target Enrichment
from Amplified cDNA, 5’ Gene Expression Library Construction, Enriched Library Construction,
and Sequencing Libraries. The sequencing was performed on a NovaSeq 6000 system (Illumina).

Post-infusion patient PBMCs were thawed and subjected to T cell enrichment by using a human
Pan T Cell Isolation Kit (Miltenyi Biotec #130-096-535). Isolated T cells were counted and treated
with Benzonase and 50 nM Dasatinib for 20 minutes at 37 °C in RPMI media containing 10%
FBS. Enriched T cells were centrifuged at 1500 RPM for 5 minutes, washed once with FACS
buffer (1X PBS + 0.5% w/v BSA) supplemented with 50nM dasatinib, and plated in a 96-well
plate (3x106 cells/well). Cells were stained with the barcoded tetramers associated with a pooled
tetramer mix, followed by surface markers staining (BV711 anti-human CD3 (BD) antibody), and
LIVE/DEAD Fixable Near-IR Dead Cell Stain Kit (Thermo Fisher #L34975), allowing for
quantification of the fraction of neoantigen-specific T cells. After 30-minutes of incubation at 4°C,
cells were washed twice in PBS with 1% BSA and sorted using an MA900 cell sorter (Sony
Biotechnology). For a given patient sample, two different T cell populations were sorted:
Tetramer- (Live CD3+) and tetramer+ (Live CD3+ dual tetramer+). After sorting, labeled cells were
resuspended at ~1x103 cells/ml in PBS supplemented with 2% FBS and submitted to subsequent
single-cell RNA sequencing (scRNA-Seq) processes. Sample processing for single-cell gene
expression (GEX) and TCR V(D)J clonotypes (VDJ) libraries was performed following the
manufacturer’s protocol (Chromium GEM-X Single-Cell 5' Reagent Kits v3, 10X Genomics). All
libraries were incorporated with Sample Index sequences (Dual Index Kit TT Set A) and sequenced
on the NovaSeq 6000 system (Illumina). The sequencing parameters were: Read 1 of 28 cycles, i7
Index of 10 cycles, i5 Index of 10 cycles, and Read 2 of 90 cycles.

Tumor biopsy sample was kept on ice and enzymatically and physically dissociated using a Human
Tumor Dissociation Kit (Miltenyi, 130-095-929) following manufacturer’s protocol. Briefly,
tumor biopsy fragments were minced into small pieces using scalpels and mixed with an enzyme
mix (Enzyme H, R, and A) into a gentleMACS C Tube (Miltenyi). The C tube was then placed
onto the sleeve of the GentleMACS Dissociator and an appropriate GentleMACS Program was
chosen according to tumor type. After dissociation, the sample solution was filtered through a 70
μm cell strainer to collect the dissociated cells with complete RPMI 1640 passing through the 70
μm cell strainer. Filtered tumor cells were then rested in complete RPMI medium containing 10%
FBS and 1% penicillin/streptomycin for 3 hours at 37°C. After resting, cells were washed two
times, counted, resuspended in PBS, supplemented with 2% FBS, and submitted to subsequent
single-cell RNA sequencing processes. Sample processing for single-cell GEX and VDJ libraries
was performed following the manufacturer’s protocol as described above.

Corresponding flow panel: Sequencing sort panel-NEO-STIM, Sequencing sort panel-Post-
infusion, CITE panel (Supplementary Data S2)

Computational analysis of single-cell RNA/TCR sequencing data
Data was aligned using Cell Ranger (10x Genomics) with the following versions and reference
assemblies. NEO-STIM samples were aligned with Cell Ranger 6.0.1, GEX reference GRCh38-
2020-A, and VDJ reference GRCh38-alts-ensembl-7.0.0, using the 'multi' option. Post-infusion
blood samples were aligned with Cell Ranger 8.0.1, GEX reference GRCh38-2024-A, VDJ
reference GRCh38-alts-ensembl-7.1.0, and a custom CITE panel of antibodies using the 'multi'
option. Tumor samples were aligned using Cell Ranger 7.1.0 (GEX reference GRCh38-2020-A,
'count' option) and Cell Ranger 7.0.1 (VDJ reference GRCh38-alts-ensembl-7.1.0, 'vdj' option).
Python package Scanpy 1.9.155 was used for downstream analysis of data acquired from Cell
Ranger.

Filtering was performed as follows: We removed cells that had (1) more than 10% mitochondrial
gene content, (2) fewer than 1000 UMI (NEO-STIM, tumor) or 2000 UMI (post-infusion) from
RNA, and (3) fewer than 300 genes (NEO-STIM, tumor) or 1500 genes (post-infusion) from RNA.
For blood samples, cells were also removed if they had (4) fewer than 800 CITE UMI or (5) fewer
than 20 CITE markers detected. For blood and NEO-STIM samples, we also filtered to include
only protein-coding genes, as annotated in Gencode Basic 4.2, that were expressed in at least 0.1%
of all cells.

RNA libraries were log2-normalized following library-size normalization with a scale factor of
104; with the exception of tumor digests, which were ln(X+1) normalized using sc.pp.log1p. These
values were used for visualization of gene expression, including heatmaps and UMAP plots, and
identification of highly variable genes. The 4000 most variable genes were detected by the
sc.pp.highly_variable_genes function (flavor=Seurat), with the tumor analysis utilizing a
`min_mean=0.02`, `max_mean=4`, and a `min_dispersion=0.5`. For blood and NEO-STIM
samples, these 4000 genes were overlaid with 10x Genomics Immunological panel (1056 genes)
to de-noise the list.

Downstream processing was performed on each patient individually to limit batch and donor
effects. For integration across samples within each patient, data was normalized as follows. RNA
libraries were natural-log-normalized following library-size normalization with a scale factor of
104, then scaled with the sc.pp.scale function using a max value of 10. Total counts were regressed
out using the sc.pp.regress_out function. CITE libraries were CLR normalized and then scaled
with the sc.pp.scale function using a maximum value of 10. Principal component analysis (PCA) was
performed using the sc.tl.pca function. For blood and NEO-STIM samples, we performed
multi-omics factor analysis (MOFA) using the mu on library function mu.tl.mofa with 10 factors
to integrate the RNA and protein data after restricting to highly variable genes as defined above56,57.
Nearest neighbors were found using the sc.pp.neighbors function: for blood and NEO-STIM, we
utilized the 10 factors from MOFA and for tumor digest we utilized the top 25 principal
components. Then, UMAP dimensionality reduction was performed with sc.tl.umap to obtain a
two-dimensional representation of the cell states. For clustering, we used the Scanpy sc.tl.leiden
function.

TCR library preparation and bulk TCR-seq
Library preparation and sequencing was performed as described previously45. In brief, TCRβ
libraries were prepared from isolated RNA derived from the snap-frozen T cell pellets (1 million
pan T cells each) or tumor. Amp-RepSeq was used for PBMC, DP and tumor samples. Libraries
were sequenced using a NextSeq Reagent Kit v2 300 cycles (Illumina) according to the
manufacturer’s protocol.

TCR repertoire generation
TCR repertoires were generated by applying MiXCR 4.3.2 software on the paired-end raw
sequencing FASTQ files. TCRβ CDR3 clonotypes were filtered by removal of non-functional
sequences (out-of-frame sequences, those containing stop codons or those of length ≤5 aa). Clonal
frequency was calculated based on the read count for each clone out of the total read count.

Calculation of T cell activation scores
We used a published T cell activation signature44, Table S5 (tab ‘TOP50 separators expression’)
representing the top 50 genes upregulated in tetramer-sorted antigen-specific CD8+ T cells after
stimulation with cognate antigen (influenza or CMV). Each cell was assigned an activation score
representing the average Z-score for the gene set using log2(library-size normalized) expression
values.

Statistical Analysis
For continuous variables, comparisons of means between groups were made using t-tests or
ANOVA, unless stated otherwise in Figure captions or respective tabs in the Source Data File.
Correlation analysis between two quantitative variables was performed using simple linear
regression and measured based on R2 value to determine the goodness of fit. All analyses were
performed using GraphPad versions 7.04-9.5.1 or Python version 3.9.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "This manuscript provides a detailed description of optimization of a process for generating tumor antigen reactive T cells by in vitro sensitization of peripheral blood cells for use in clinical adoptive cell transfer trials. In addition, characterization of the cells administered to a single melanoma patient and the corresponding cells that appeared to persist following ACT provides data was informative. There were several points, however, that we not adequately addressed in the manuscript.\n1. While infused TIL products may in some cases only appear to contain a small percentage of tumor reactive T cells, the frequencies of those cells are much higher than the 0.5% in the cited references, for example in Parkhurst et al. Cancer Discov. 9:1022-1035, 2019.\n2. The logic of combining a culture receiving a 10 peptide stimulation with a 20 culture or even a 30 stimulation was not clear. It appears that a portion of the 10 culture was pulsed with the appropriate peptide in a 20 stimulation and then the pulsed cells were mixed at varying ratios with the remaining cells from the 10 culture. Was this done in an attempt to minimize the activation induced cell death that may have occurred in the re-stimulated cultures or was there another reason for employing this somewhat complex protocol? Why weren’t unstimulated autologous PBMC pulsed with peptide and used for the 20 and 30 stimulations? Also does the hit rate correspond to the frequency of reactive T cells or the % of antigen reactivities that were generated?\n3. While the protocol that was developed appears to have utilized an initial 14 day stimulation period followed by 1 or 2 stimulations for 7 days each, were studies carried out to determine the optimal timing of the subsequent stimulations?\n4. Responses against MELANA are highly atypical in that they can readily be generated in the majority of A*02:01+ patients and healthy donors and so should not be used to model neoantigen responses.\n5. The criteria used to categorize antigens as highly or weakly immunogenic should be provided. In addition, an explanation of the reasoning behind dividing antigens into these 2 categories should be provided.\n6. The experiments evaluating the effectiveness of DCs in stimulating T cells were carried out by depleting CD11b and CD11c populations but not by enriching these populations. If CD11c cells are the most effective at priming T responses then positively selected cells should be tested, as these would then presumably be the most effective at generating T cells responses. In addition, the data demonstrating that CD11c+ cells were needed to generate class I responses when stimulating with long peptides should have been shown.\n7. The tetramer staining results for some of the specificities shown in Fig. S5 need additional explanations. For example, the single tetramer populations for DMN2 S88:P>V, NLRP1 123:E>K, ZNF226 777:H>Y and PANCM 2031:D>H appeared to be comparable to or higher than the double positive populations. Given the fact that a substantial proportion of the cells were positive with a single fluorophore, how confident can you be that the double positive populations were antigen specific?\n8. The sentence beginning on line 154 states that ‘Responses that were155 not detected prior to NEO-STIM were called de novo responses, although we cannot rule out that at least some of those responses were present in the starting material at low frequencies, below the detection limit of the assay.’ This is not accurate, as T cells must have been present at some frequency prior to stimulation. The T cells may have been expanded from the naïve or memory T cell pool, which may have been what was meant by this statement, but this can be determined by separating naïve and memory T cells prior to deep TCR sequencing, which can be carried out in 106 or more PBMC. Another question is whether T cells have been expanded in a patient relative to a healthy donor bearing the same HLA restriction element, but given the differences in the T cell repertoire present in multiple individual, this could probably only carried out using tetramer analysis, which would be difficult due to the extremely low frequencies of the majority of reactive T cells.\n9. The evaluation of CD4 reactivity by evaluating intracellular cytokine responses may have under-represented the frequency of reactive T cells. Additional assays such as measurement of CD4 reactivity should have been carried out by evaluating upregulation of 4-1BB, OX-40 or CD154.\n10. The results of the analysis of specificity for some of the mutant epitopes were unclear. In Fig. 2G and 2H there was essentially no difference between the response of ARAP 828:Y>H T cells in response to cells pulsed with the mutant vs. the wild type peptide or tumor cells transduced with the mutant or wild type construct and the frequencies of cells reactive with the mutant peptide and construct were quite low. The specificity of the CSNK1A1 27:S>L reactive T cells was also not clearly demonstrated by the results shown in Fig. 2H where only a minimal difference was observed between responses to tumor cells transduced with the mutant and wild type constructs. In addition, it is not correct to claim that the response of ITPR3 616:E>K responses are tumor specific, as there was no significant increase in the IFN-g response when cells were cultured with tumor cells.\n11. It is not clear what was meant by the statement beginning on line 266 that ‘This further highlights the importance of cell subset depletion at the beginning of the process.’ Were CD4+ and CD8+ T cells separated before that peptide stimulation was carried out?\n12. The criteria used to define neoantigens as being mutant specific, mutant selective or wild-type cross-reactive are unclear. The reactivity against the CD8 neoepitope TENM3 1243:S>L appears to be mutant specific, as the baseline or reactivity was ~20% and the response to the wild type peptide was only slightly elevated at the highest peptide concentration. Responses such as the CD4 response against the ATP2C1 427: E>K CD4 epitope should not be considered when evaluating these responses as they are unlikely to recognize any naturally processed and presented antigen.\n13. An explanation is needed for the disparities between the relatively high frequencies of some of the CD8 reactivities shown in Fig. 2 and Fig. S5 and the data shown in Fig. 4 where all the frequencies of all of the reactivities, with the exception of 1, presumably that directed against the SRSF1 33:E>K neoepitope, are below 2%. The data presented in Fig. 4C also needs further explanation. Why are all of the points for CD4 reactivities filled but only 1 of the CD8 specificities filled? Do the unfilled points represent the final concentration of these reactivities in the pooled product, and why are some reactivities shown as being in the T cell product before pooling but not in the final product, as all of them should have been present unless they were not pooled? An explanation is also needed for the data in the supplementary table marked ‘Therapeutic scale’ (should this be labeled Supplementary Table 2?). What is meant by ‘vessel’, or ‘well’, and why are the frequencies sometimes higher in the vessel than in the DP whereas sometimes the reverse is true? How was the final DP generated – were all of the wells containing neoantigen reactive T cells combined, and were there significant differences between the expansions carried out using the different peptide pools? Since multiple wells containing the same peptide pools were set up, were all of the wells pooled, or only the wells that showed effective expansion of reactive T cells pooled, and if so what criteria were used?\n14. In general, did de novo/pre-existing reactivity correspond to specificity categories of mut specific/selective, wt-reactive? For DYNC2H1 or RNF103 reactivities, were multiple TCR clonotypes observed in the scRNA data (Fig 5C/5D)? If so, were there distinctions in phenotypic state distribution within or among clusters and did these differ by de novo or pre-existing categories?", "reviewer_2": "This manuscript describes the development of NEO-STIM, a platform that induces de novo and expands memory T cell responses to patient-specific neoantigens ex vivo from peripheral blood. The authors convincingly demonstrate that the resulting drug products (DPs) are polyclonal, polyfunctional, mutant-selective, and cytotoxic, with clear recognition of autologous tumor. Their platform overcomes several limitations of TIL-based ACT, including reliance on resectable tumors and exhausted phenotypes, and is supported by preclinical and early clinical feasibility data.\nAll major conclusions are well supported, and the methodological detail is commendable. However, several revisions would improve clarity and reproducibility:\nMajor Concerns:\n1. Please explicitly state which subsets are depleted (e.g., CD14⁺ monocytes, CD25⁺ Tregs), whether CD11c⁺ DCs are enriched, and what maturation cocktail is used. Consider including a side-by-side comparison of DC phenotypes and costimulatory marker expression after maturation to validate the superiority of naturally circulating DCs over moDCs.\n2. The magnitude of neoantigen-specific responses varies widely across patients. If feasible, correlate DP response magnitude with pre-culture CD8⁺ T cell frequency or baseline TCR repertoire richness. At minimum, please discuss whether predictive biomarkers (e.g., mutation burden, CD8⁺/CD4⁺ ratio, memory subset composition) might inform response yield and product quality.\n3. The authors report a minority of responses with WT reactivity. Please summarize the percentage of tested responses falling into each specificity category (mutant-specific, selective, WT-crossreactive). If possible, add data on functional avidity (e.g., EC50 curves) for at least one WT-crossreactive versus mutant-specific clone to support safe discrimination. Clarify whether WT-reactive clones would be excluded from clinical release criteria.\n4. PRDM1 induction after repeated stimulation suggests terminal effector skewing. The authors might consider comparing CD62L⁺CCR7⁺ memory phenotypes across 1× vs 2× stimulation DPs to assess whether reduced stimulation could preserve less-differentiated states. A brief analysis of exhaustion marker expression (e.g., PD-1, LAG-3) in the final product would also be informative.\n5. In the patient with tracked TCR clonotypes, the activated/exhausted phenotype is well described. If possible, include a quantitative comparison of persistence or expansion between pre-existing and de novo clonotypes. This could clarify the relative durability of newly induced versus expanded memory clones.\n6. Would this approach work in tumors with fewer mutations (e.g. some epithelial cancers or pediatric cancers)? It likely can, as long as at least a handful of neoantigens are identifiable, but the effort (60 peptides per patient) might yield fewer hits. A sentence discussing this (perhaps noting that even low-mutational-load tumors can still have a few strong neoantigens to target) would be useful to frame the breadth of this therapy.\n7. Additionally, while the focus is lab/process development, the authors did treat at least one patient and mention an ongoing Phase I trial. If possible, the clinical context should be expanded slightly: For example, the authors state BNT221 showed early signals of efficacy – without divulging too much beyond what is allowed, can they say what signals? (Tumor regression in some patients? Durable T cell persistence?) Even a general statement like “early signs of anti-tumor activity were observed in the trial, such as objective tumor responses in some participants” would underline the importance. If such details are not yet public, it’s understandable, but then perhaps emphasize that a formal clinical trial is underway to assess safety/efficacy, which is a strength of this work’s translational readiness.\n8. I recommend adding a sentence on how this approach might integrate with other therapies – for instance, will patients receive checkpoint inhibitors alongside NEO-STIM T cells? This could be relevant since ex vivo expanded cells might still benefit from checkpoint blockade post-transfer. Even if not part of this study, acknowledging combination strategies (as is common with TIL therapy + IL-2 or checkpoints) would show awareness of the clinical landscape.\nMinor Concerns:\n9. It’s excellent that each peptide pool was run in triplicate to test process reproducibility. The supplemental data likely show high correlation (the text notes R2=0.98 between some replicates). The authors might consider mentioning in the Results that reproducibility between parallel cultures was high, as this will give readers confidence that the process yields consistent results. A brief mention of the replicate consistency (perhaps referencing Supplementary Fig. S5 or S1B as appropriate) would suffice.\n10. In the main figures, some data (e.g., Fig 2A examples, 2H cytotoxicity, 2I tumor recognition) seem to highlight one or two patients. It would be helpful if the authors could ensure that data from all four patients are reported, at least in aggregate. For example, Fig. 2C, 2E summarize the number of responses per patient – presumably each dot is one patient’s result. Clarify in the figure caption if those are per-patient medians or individual responses. Similarly, for cytotoxicity and tumor recognition, if not all patients were tested, state the number of patients or responses evaluated. Transparency about n for each assay will help readers gauge the consistency across the cohort.\n11. Overall, the figures are well-designed. A few suggestions: (i) Figure 1B – if this is a schematic of the process, ensure that all steps are labeled clearly (e.g., “Deplete PBMC subsets,” “Primary stimulation (Stim1) day 0–14,” etc.). Currently it’s referenced that Fig. 1B shows process steps; just make sure it’s readable and perhaps include an explanation in the legend for any icons or abbreviations. (ii) Figure 2 – panels C, E (showing range and medians of responses) should indicate whether the percentages shown are fraction of total CD8 or CD4 T cells that are neoantigen-specific (I assume yes). Adding “% of CD8^+ T cells” in the axis label or caption would help. Also, define “tet+” in the caption for non-specialist readers (T cells detected by peptide-MHC tetramers). (iii) Figure 3 – the UMAP plots and heatmaps from scRNA-seq are complex. The authors might add a brief description in the legend about what the major clusters represent (activated effector vs memory, etc., as derived from Fig. S7). A simplified summary of the key takeaway (that ARAP1 de novo clone shifts to effector state early, vs the SRSF1 memory clone which shifts gradually) could guide the reader through panel E/F.\n12. Please ensure all abbreviations are defined on first use. For instance, “DP” (drug product) is defined in the Introduction – that’s good. Also define Stim1/2/3 clearly as rounds of stimulation. The term “research scale” vs “therapeutic scale” is used; perhaps clarify that “research scale” refers to small-scale lab process used for initial 4 patients, and “therapeutic scale” is a full-scale manufacture (if I understood correctly). Minor point: “WT” and “MT” (wild-type vs mutant) should be defined in figure legends or methods for clarity.\n13. The authors cite relevant literature on TIL therapy and neoantigen vaccines. One suggestion is to cite recent clinical successes: for instance, the FDA approval of Lifileucel for melanoma in 2024, which they allude to in the introduction, could be explicitly referenced as a milestone that underscores the importance of advancing neoantigen-targeted T cell therapies. Additionally, if there are any comparable approaches (perhaps a recent study attempting to prime T cells from blood for adoptive therapy, if any), mentioning those would position this work in the landscape. It appears this approach is quite unique, but for completeness, the authors might consider if groups have tried similar “prime-and-expand” T cell therapies (I recall some early trials expanding T cells to tumor-associated antigens or virus antigens ex vivo). A sentence distinguishing NEO-STIM from TCR-engineered cell therapies would also help: e.g., emphasize this does not require genetic modification, making it potentially faster and less expensive once neoantigen peptides are identified.", "reviewer_3": "This study develops and validates NEO-STIM, an ex vivo method to generate personalized neoantigen-specific T-cell responses under optimized conditions that avoid immunosuppression.\nLenkala et al. validated the protocol using model antigens with healthy donor PBMCs expressing relevant HLA types. They enhanced T cell priming by depleting inhibitory cell subsets and identified the best APC populations by assessing expansion, diversity, and functionality of CD8+ responses—including those to long peptides that usually induce CD4+ T cells. Applying the method to PBMCs from four melanoma patients, they detected both pre-existing and newly induced CD8+ responses, via combinatorial pMHC tetramer staining – as wells as CD4+ T cell responses. The generated DP T cells were further phenotypically and functionally characterized. Finally, the protocol was optimized for therapeutic-scale manufacturing while maintaining functional activity.\nThe study addresses an important and unmet clinical need with a promising approach. The idea is compelling and has strong potential for personalized immunotherapy. However, the work feels preliminary and would benefit from more extensive analysis and deeper characterization to strengthen its conclusions and clinical relevance.\nMajor concerns:\n- Limited donor and patient diversity: Proof-of-concept experiments use only two healthy donors (one with weak de novo reactivity), with very low response frequencies (<1%), raising questions about functional relevance. The clinical cohort is restricted to melanoma patients, nearly all previously treated with immunotherapy, limiting generalizability and potentially confounding immune responses.\n- Lack of tumor antigen expression and clonality data: The manuscript does not report expression levels or clonal distribution of the selected neoantigens on patient tumors, which is critical for evaluating clinical impact.\n- Insufficient functional validation and controls: Functional assays with autologous tumor cells are limited (only two responses tested), and killing assays are lacking. Healthy autologous cells were not used as specificity controls. Many experiments rely on single replicates, and controls often lack APC plus non-mutated peptide conditions, weakening robustness and specificity assessment.\n- Use of artificial tumor models: Validation frequently relies on tumor cells transduced to express the desired neoantigens, which may not reflect physiological expression or clinical relevance. Confirmation with authentic autologous tumor cells is necessary.\n- Incomplete phenotypic and CD4+ T cell characterization: Phenotypic analysis is largely restricted to CD8+ T cells and compares only one pre-existing and one de novo response. There is no thorough characterization of other CD8+ or CD4+ T cell responses. Additionally, no pre-protocol assessment of CD4+ responses was conducted to distinguish pre-existing from induced de novo populations, limiting interpretation of efficacy.\nMinor concerns:\n- Proof-of-concept responses should be shown separately for high and low immunogenic antigens to support claims on low immunogenic antigen recognition.\n- Activation markers should be shown separately for clarity.\n- Supplementary Figure S1E is hard to interpret and uses different donors than the previous experiments, making it difficult to get an overall picture."}, "author_response": "Response to Referees\nContents\nReviewer 1 feedback: ................................................................................................................... 2\nReviewer 2 feedback: ................................................................................................................. 17\nReviewer 3 feedback: ................................................................................................................. 27\nReferences: ............................................................................................................................... 31\nThis manuscript provides a detailed description of optimization of a process for generating\ntumor antigen reactive T cells by in vitro sensitization of peripheral blood cells for use in clinical\nadoptive cell transfer trials. In addition, characterization of the cells administered to a single\nmelanoma patient and the corresponding cells that appeared to persist following ACT provides\ndata was informative. There were several points, however, that we not adequately addressed in\nthe manuscript.\nQuestion 1:\nWhile infused TIL products may in some cases only appear to contain a small percentage of\ntumor reactive T cells, the frequencies of those cells are much higher than the 0.5% in the cited\nreferences, for example in Parkhurst et al. Cancer Discov. 9:1022-1035, 2019.\nIn the paragraph starting in line 54 (all line references refer to the updated clean versions) we\noriginally stated that one of the key challenges of TIL therapy is “the relatively low number of\npre-existing responses against only 0.5% of tumor neoantigens on average”. The cited 0.5%\nfrequency refers to the fraction of gene products encoded by somatic nonsynonymous\nmutations that were naturally immunogenic in the context of melanoma, not the percentage of\ntumor reactive T cells in TIL. We recognize that this number can be higher in other tumor\nindications, and we revised the manuscript accordingly, including the Parkhurst reference.\n> Change (lines 54-55):\n“only a relatively low number of tumor neoantigens (0.5-2.3%) being generally recognized by T\ncells9-14.”\nQuestion 2:\nThe logic of combining a culture receiving a 10 peptide stimulation with a 20 culture or even a 30\nstimulation was not clear. It appears that a portion of the 10 culture was pulsed with the\nappropriate peptide in a 20 stimulation and then the pulsed cells were mixed at varying ratios\nwith the remaining cells from the 10 culture. Was this done in an attempt to minimize the\nactivation induced cell death that may have occurred in the re-stimulated cultures or was there\nanother reason for employing this somewhat complex protocol? Why weren’t unstimulated\nautologous PBMC pulsed with peptide and used for the 20 and 30 stimulations? Also does the hit\nrate correspond to the frequency of reactive T cells or the % of antigen reactivities that were\ngenerated?\nWe confirm that unstimulated autologous PBMCs pulsed with peptide were used to restimulate\nand expand the primary and/or secondary cultures (see below, updated Figure 1B and updated\nmethods). The restimulation of primary cultures with autologous depleted PBMCs aims to\nfurther expand already primed antigen specific T cell responses and prime additional T cell\nresponses with the objective to produce a drug product that is enriched for multiple neoantigen\nspecific T cell response.\nThe hit rate is used to quantify the proportion of tested epitopes that elicited a response during\nthe NEO-STIM process. For example, a hit rate of 12% means that of the 60 neoantigens\nincluded in a clinical manufacturing scale process, T cell responses were detected towards\n2\nseven neoantigens in the final drug product. Hit rate does not refer to the frequency of reactive T\ncells, which is quantified as a percentage of tet+ CD8+ cells. Hit rate is defined in the respective\nFigure captions: Suppl. Fig. 3C-G, Suppl Fig 4E-F. Additionally, we added a definition to Suppl.\nFig. 1A.\n> Changes:\nWe have adjusted the main text (paragraph starting in line 93), the Methods (paragraph starting\nline 709) and caption of Figure 1B (lines 1003-1004) to clarify the protocol, and we added a\ndefinition for hit rate to the caption of Suppl. Fig. 1A (line S-33).\nQuestion 3:\nWhile the protocol that was developed appears to have utilized an initial 14 day stimulation\nperiod followed by 1 or 2 stimulations for 7 days each, were studies carried out to determine the\noptimal timing of the subsequent stimulations?\nTo clarify, the final protocol described in this manuscript (Figure caption 1B) is a 14-day 10\nculture, which is combined with a two day old 20 culture and further cultured for 12 additional\ndays, resulting in a total culture time of 26 days. The changes described in the previous\nquestion are intended to make this clearer.\nAs to optimization of time frames, the antigen specific fraction was monitored over time for\nboth the 10 and 20 culture (Suppl. Fig. 1D and Suppl. Fig. 2D, E), and the following was observed:\n3\n- 10 culture: While MART-1 (a model antigen we used to mimic the expansion of memory T cell\nresponses, due to its high precursor frequency) reached its highest magnitude 12 days after\nthe start of culture, the T cell responses induced towards high and low immunogenic\npeptides continued expanding up to the end of the experiment (day 14; Suppl. Fig. 1D, left\nand right, respectively).\n- 20 culture: The frequency and total number of antigen-specific cells peaked at day 26, i.e. 12\ndays post restimulation, for MART-1 and also for some of the responses to high and low\nimmunogenic peptides (Suppl. Fig. 2D, E, respectively).\nTaken together, these data supported the 14 d (1o culture) plus 12 d (post restimulation culture)\n= 26 d (total culture) times. For increased clarity, we changed the axis labels to read “Days after\nstart of culture”.\n> Changes:\nSupplemental Figure 1D: x-axis label, and Supplemental Figure 2D, E: x-axis labels and caption\nof Suppl. Fig. 2D-E.\nResponses against MELANA are highly atypical in that they can readily be generated in the\nmajority of A*02:01+ patients and healthy donors and so should not be used to model\nneoantigen responses.\nWe appreciate the reviewer's comment regarding the HLA-A02:01 restricted MELANA/MART-1\nresponses in HLA-A02:01+ healthy donors. We agree with the assessment that responses to\nMELANA / MART-1 can be readily generated, since both healthy donors and patients often have\nMELANA-specific T cells. Since we wanted to look at both pre-existing (memory) and de novo\nresponses, we used MELANA/MART-1 to model memory responses.\nOur interest in pre-existing (memory) T cell responses towards neo-antigens is based on the\nfact that they are sometimes observed in patients1-5 and may play an important role in the anti-\ntumor response4,5. Therefore, we wanted to ensure that our process is equipped to both expand\npre-existing T cell responses and induce de novo T cell responses.\nThe frequency of MELANA+ cells has proven to be a valuable benchmark, as it aligns with the\nfrequencies observed during expansion of the memory response SRSF1 (PT01, 72.1% of\n33:E>K\nCD8+ T cells, Fig. 2B, C).\nQuestion 5:\nThe criteria used to categorize antigens as highly or weakly immunogenic should be provided.\nIn addition, an explanation of the reasoning behind dividing antigens into these 2 categories\nshould be provided.\nWe appreciate the reviewer’s request for clarification regarding the criteria used to categorize\nantigens as highly or weakly immunogenic, as well as the rationale behind this classification.\nModel epitopes were initially selected based on published evidence demonstrating that T cell\nresponses were detected in patients expressing the mutated or viral antigens from which these\nepitopes are derived6-8. These references show that these antigens are naturally processed and\npresented in vivo, making them suitable candidates for the development of NEO-STIM.\nTo refine this selection, we conducted in vitro priming experiments, which revealed distinct\ndifferences in the ability of these epitopes to elicit T cell responses. Specifically, epitopes were\ncategorized as ‘high immunogenic’ if they consistently induced robust T cell responses across\nthe majority of experimental replicates. Conversely, epitopes were classified as ‘low\nimmunogenic’ if they elicited modest T cell responses sporadically across replicates.\nThe decision to divide antigens into these two categories was driven by the need to better\nunderstand the functional differences between epitopes with varying immunogenic potential.\nThis classification enabled us to systematically analyze the growth kinetics and response\ndynamics associated with each category, ensuring that experimental decisions were optimized\nfor the unique characteristics of highly and weakly immunogenic epitopes. By stratifying\nantigens in this manner, we aimed to provide a more nuanced framework for evaluating their\nimmunological behavior and therapeutic potential.\n> Change: This information has been added in the manuscript (lines 86-89).\nThe experiments evaluating the effectiveness of DCs in stimulating T cells were carried out by\ndepleting CD11b and CD11c populations but not by enriching these populations. If CD11c cells\nare the most effective at priming T responses then positively selected cells should be tested, as\nthese would then presumably be the most effective at generating T cells responses. In addition,\nthe data demonstrating that CD11c+ cells were needed to generate class I responses when\nstimulating with long peptides should have been shown.\nWe thank the reviewer for their question. The reason we chose depletion experiments over\nenrichment experiments is that there is overlap in CD11b and CD11c expression. In our work\nwe were considering moDCs and conventional DCs (cDCs). cDCs primarily consist of three\nmain subtypes that express both markers to varying extents as laid out in the Table below9-11.\nWith this overlap in expression, enrichment experiments result in the inclusion of the other\nsubset, which is not the case for depletion experiments. While additional human DC markers\nwere used to evaluate the presence of each DC subset, enrichment experiments based on\nthose were out of scope given the low frequencies of them in peripheral blood.\ncDC1 cDC2 pDC\nCD11b Negative > low Low expression Negative\nexpression\nCD11c Low expression Positive Negative\nDespite the abovementioned caveat we still performed the suggested experiment where\nCD11c+ cells were enriched (n=3 healthy donors). The results are shown here and corroborate\nour claims in the manuscript.\nDescription of experiment:\nCD11c+ cells were positively selected from the starting material as follows: Freshly thawed\nPBMCs were depleted of CD14+ and CD25+ cells using Miltenyi microbeads (#130-050-201,\n130-092-983 respectively). The CD14-CD25- PBMC were subjected to CD11b+ positive selection\n(Miltenyi 130-049-601). The resulting CD14-CD25-CD11b- flow through material was used to\nselect CD11c+ cells (Miltenyi 130-113-586, 130-090-485). The CD11c+ fraction which was\nisolated through positive selection was used to increase or “enrich” the CD11c+ proportion of\ncells in the CD14-CD25- material, through addition to a new aliquot of CD14/CD25 depleted\ncells, such that 0.5 million cells from this fraction were added to 5 million CD14-CD25- cells.\nThe NEO-TIM protocol was carried out for base condition (CD14/25 depleted) and for the\nenriched condition (CD14/25 depleted and CD11c+ enriched).\nResults:\nIndeed, the hit rate for the high and low immunogenic antigens increased in two out of three\ndonors, when adding CD11c+ cells. However, the MART-1 responses did not benefit from adding\nadditional CD11c+ cells, as quantified by number of antigen specific cells. This may be due to\nthe already high precursor frequency of MART-1-specific T cells in PBMCs of healthy donors,\n6\nwhich could mean neither the precursor T cells nor the DCs are a limiting factor for MART-1\nresponses.\nA, Calculated frequency of CD11+ cells in the base condition and after addition of the CD11c+-\nenriched fraction; B, Absolute number of MART-1-specific peptides after NEO-STIM for both\nconditions (the hit rate was 100%); C, Hit rate for High and Low immunogenic peptides after\nNEO-STIM for both conditions.\nWith respect to the criticality of CD11c+ cells for the generation of class I responses with long\npeptides: these data were included in the original manuscript in the following places:\n- Supplementary Figure 3G, (panel F in original manuscript), row 7\n- Supplementary Data S1 under the tab Research scale. Note: A filter function in this Excel\nfile under the headers “Responses detected in well” (previously “CD4+ responses detected\nin well”) and “Additional CD8+ responses” allows for easy retrieval.\nFor clarity, the previous header “CD4+ responses detected in well” has been changed to\n“Responses detected in well”, since CD8+ responses are also listed under the same header.\n>Change: Supplementary Data S1, tab Research scale: Change header “CD4+ responses\ndetected in well” to “Responses detected in well”\nQuestion 7:\nThe tetramer staining results for some of the specificities shown in Fig. S5 need additional\nexplanations. For example, the single tetramer populations for DMN2 S88:P>V, NLRP1 123:E>K,\nZNF226 777:H>Y and PANCM 2031:D>H appeared to be comparable to or higher than the\ndouble positive populations. Given the fact that a substantial proportion of the cells were\npositive with a single fluorophore, how confident can you be that the double positive\npopulations were antigen specific?\nWe appreciate the reviewer’s concern regarding the specificity of the tetramer staining results\nand the confidence in identifying antigen-specific CD8+ T cells. To ensure the robustness and\naccuracy of our data interpretation, we employed a combinatorial coding strategy, as described\nby Andersen et al.12. This approach utilizes a boolean gating method, where each peptide-MHC\n(pMHC) tetramer is uniquely labeled with a pair of fluorophores, creating a distinct “barcode”\nfor each epitope. Only T cells staining positively for both fluorophores - and exclusively those\nfluorophores - are considered antigen-specific.\nTo further validate the identification of bona fide antigen-specific responses, we applied the\nfollowing three criteria, as specified by Andersen et al.12:\n1. The frequency of double-positive cells must reach a threshold of at least 0.005% of CD8+ T\ncells.\n2. A minimum of 10 events must fall within the double-positive gate.\n3. Visual inspection of the staining ensures that the mean fluorescence intensity (MFI) of both\npMHC multimer reagents is approximately equal, resulting in cells consistently aligning\nalong the diagonal in flow cytometry plots.\nSingle-positive staining derived from T cell responses that share a specific pMHC-fluorophore\nare also plotted alongside double-positive populations to support visual inspection and confirm\nstaining specificity. This rigorous approach ensures that the tetramer staining exclusively\nidentifies antigen-specific CD8+ T cells, providing confidence in the data interpretation.\nThis is described in Methods, line 784-786. The Figure below from Borgers et al.13 further\nillustrates the gating strategy.\nSupplementary Figure 8 from Borgers et al.13:\n8\n“Lineage: Forward scatter and side scatter gates are applied to filter for singlets of the target cell\npopulation PBMC. Dead cells are excluded with a live/dead marker. Cells are then gated for\npositive CD8 staining and negative CD4/CD14/CD16 and CD19 staining.\nSingle positives: CD8+ cells stained with the single fluorophores, which are used for the\ntetramer labelling, are gated.\nCombicoding: Peptide – MHC complexes are labelled with two different fluorophores to make\ntetramers. Double positivity for both fluorophores that have been assigned to a specific peptide\nin the combinatorial coding process are considered positive for the respective neoantigen.\nExample:” 0.29% of CD8+ cells are specific for peptide 3, and “2.45% of CD8+ cells are specific\nfor peptide #7.”\n>Change:\nPoint to additional specificity criteria in Methods line 785-786: “T cells that interacted with a\ndefined combination of two and only two of the fluorochromes assigned to a specific peptide-\nMHC complex and satisfied additional specificity criteria50 were considered specific to the\nrespective neoantigen.”\nQuestion 8:\nThe sentence beginning on line 154 states that ‘Responses that were not detected prior to NEO-\nSTIM were called de novo responses, although we cannot rule out that at least some of those\nresponses were present in the starting material at low frequencies, below the detection limit of\nthe assay.’ This is not accurate, as T cells must have been present at some frequency prior to\nstimulation. The T cells may have been expanded from the naïve or memory T cell pool, which\nmay have been what was meant by this statement, but this can be determined by separating\nnaïve and memory T cells prior to deep TCR sequencing, which can be carried out in 106 or more\nPBMC. Another question is whether T cells have been expanded in a patient relative to a healthy\ndonor bearing the same HLA restriction element, but given the differences in the T cell\nrepertoire present in multiple individual, this could probably only carried out using tetramer\nanalysis, which would be difficult due to the extremely low frequencies of the majority of\nreactive T cells.\nWe appreciate the reviewer’s insightful comments to highlight the lack of precision for the\ndefinition of de novo responses. We now define this more clearly at first use in the Results (line\n114-117):\n“De novo responses were defined as responses that were not detected in the pre-NEO-STIM\nsample. This definition is practical for the purpose of this study is an operational definition, as\nresponses could either originate from T cells in the naïve compartment or from very small\nmemory clones that were below the detection limit of the assay.”\nTo expand on the role the assay sensitivity plays in this, we also adjusted the discussion. In this\nstudy, we attempted to detect CD8+ responses pre-NEO-STIM using pMHC tetramers in a\ncombinatorial fashion; additionally, in our clinical study13, bulk TCR sequencing was employed,\nwhich is also very sensitive with a detection limit of 1:335,000. Based on the observations of\nboth datasets we believe a subset of responses that are not detected prior to NEO-STIM stem\nfrom the naïve compartment. Accordingly, we changed the text in the Discussion (lines 375-\n381) to:\n“While “de novo response” is a practical definition for the purpose of the study, a subset of\nthose responses likely stem from the naïve compartment, as healthy donors were able to raise\nresponses against neoantigens they had not encountered before. While in the melanoma\npatients presented here we only detected one CD8+ response and zero CD4+ responses prior to\nNEO-STIM (likely explained by the assay sensitivity), in another study more than half of CD8+\nresponses were defined as de novo, using a highly sensitive detection method (bulk TCR\nsequencing)13.“\nFollowing the reviewer’s proposal to sort cells into naïve and non-naïve prior to bulk TCR\nsequencing, we executed such an experiment with cells from one patient, NAC01, from the\nclinical trial13.\nHowever, given that only a subset of T cells can be analysed from a human subject, there is the\nfollowing caveat: We likely can only substantiate that responses which were labelled as pre-\nexisting indeed stem from the memory compartment. On the other hand, it is unlikely to\nrobustly detect a naïve T cell from a pre-determined response from 106 PBLs due to the scarcity\nof naïve T cells, which in 95% of cases have a clone size of one14,15.\nAccordingly, we learned from the experiment that all five responses tested (all of which had\nbeen labelled as pre-existing) appeared to stem from the memory compartment. In addition,\nwe confirmed heterogeneity at the clonal level, i.e. a combination of de novo and pre-existing\nclones within a neoantigen-response, as we already had described in Figure 5.\nDescription of the experiment:\nBulk TCR sequencing in naïve and non-naïve T cells was carried out for one patient, NAC01. In\nthis patient, we had detected eight CD8+ T cell responses in the drug product (DP)13, as shown\nbelow. Five of these responses (MRPS30 173:S>F; EP400 3016:A>G; TNPO3 428:K>I; S100A6\n62:R>W; LIMA1 401:E>A) were sufficiently robust to facilitate high quality single cell sequencing\nand TCR reconstruction. All of those had originally been labelled as pre-existing.\nData from Borgers et al.13: Excerpt from Supplementary Figure 2\nThree samples from this patient were subjected to bulk TCR sequencing: 1x106 bulk cells from\nthe DP and two 1.28x106 samples of pre-NEO-STIM PBMCs (naïve and non-naïve). To separate\nnaïve from non-naïve T cells, we chose bead-based separation over flow cytometry-based\nsorting because large cell numbers can be rapidly processed and commercial (validated) kits\n10\nare available for separation. We first isolated pan-T cells, then used the CD3+ fraction derived\nfrom this, to positively select non-naïve cells, leaving naïve cells in the flow through (Miltenyi\nBiotec kits 130-096-535; 130-097-095). The caveats are that – particularly during the step where\nnon-naïve cells are bead-isolated – cells could potentially be lost. Further, the naïve vs non-\nnaïve separation is based on CD45RO expression, so exhausted T cells and T cells of the\nSCM\nrespective clone could show up in the naïve-labelled fraction.\nThe sequencing data demonstrated high quality, identifying approximately 2,500, 25,000, and\n25,000 TCR clonotypes in the DP, naïve, and non-naïve PBMCs, respectively, with robust\ncoverage indicated by approximately 9k reads per sample.\nResults:\nThe results were as expected. Five out of five neoantigen-responses were detected in the non-\nnaïve compartment. For response S100A6 all subclones of the response were detected in the\nnon-naïve compartment, validating the methodology we chose. Two of those were also seen in\nthe naïve compartment, likely exhausted cells of the respective subclone. For MRSP30, only\ntwo of the eight subclones were detected in the non-naïve compartment, which labels the\nremaining six as de novo response subclones per our definition, and corroborates results\nshown in Figure 5 (i.e. some subclones of a response are pre-existing and some de novo).\nBulk TCR sequencing results for drug product (DP), naïve and no- naïve fractions (separated\nbased on CD45RO expression).\nThe sequencing data was included in the manuscript as Suppl. Fig. 11.\nWe understand that the second question the reviewer raises relates to whether patients would\nraise similar responses as healthy donors with the same HLA restriction element. We took a\nsimilar approach in this study, by using a patient mutanome to perform epitope predictions in\nthe context of the HLA restriction elements of the healthy donor. Those predicted epitopes were\nthen used in NEO-STIM to raise T cell responses towards neoantigens in healthy donors who\nwere not previously exposed to them.\nGenerally speaking, both donor types efficiently raised responses, and for both a strong\ncorrelation was observed between hit rate and fraction of CD8+ T cells in the starting material.\nHowever, higher hit rates were observed overall with the HD-derived samples (Fig. 4B).\nmutant KRAS in the context of common HLA’s. These data show that a modified NEO-STIM\nprotocol can prime and expand T cells to KRAS G12 neoantigens effectively from healthy donor\nmaterial and that TCRs cloned from the process are comparable to clinically efficacious,\npatient derived benchmarks (Fig. 4B and Fig. 6 in Conn et al.16).\n>Changes:\nAdd definition of de novo response at first use in Results (lines 114-117); reword discussion for\nde novo and pre-existing response (lines 375-381); addition of Suppl. Fig. 11\nQuestion 9:\nThe evaluation of CD4 reactivity by evaluating intracellular cytokine responses may have under-\nrepresented the frequency of reactive T cells. Additional assays such as measurement of CD4\nreactivity should have been carried out by evaluating upregulation of 4-1BB, OX-40 or CD154.\nWe appreciate the reviewer’s suggestion regarding alternative methods for assessing CD4\nreactivity. While we acknowledge that activation-induced marker (AIM) assays may identify a\nbroader fraction of antigen-specific CD4 T cells17,18, our approach prioritized functional\ncytokine-producing subsets2, as these are most likely to provide critical support to CD8 T cells\nin the tumor microenvironment or lymphoid compartments. Additionally, a third methodology,\nusing pMHC multimers remains challenging to produce for the diverse range of MHCII alleles,\nlimiting their feasibility19. Our focus on functional readouts aligns with the goal of identifying\nclinically relevant CD4 T cell responses.\n> Change:\nThe following sentence was added to the manuscript (lines 193-194): “A functional readout was\nused with the goal to identify clinically relevant CD4+ T cell responses.”\nQuestion 10:\nThe results of the analysis of specificity for some of the mutant epitopes were unclear. In Fig. 2G\nand 2H there was essentially no difference between the response of ARAP 828:Y>H T cells in\nresponse to cells pulsed with the mutant vs. the wild type peptide or tumor cells transduced\nwith the mutant or wild type construct and the frequencies of cells reactive with the mutant\npeptide and construct were quite low. The specificity of the CSNK1A1 27:S>L reactive T cells\nwas also not clearly demonstrated by the results shown in Fig. 2H where only a minimal\ndifference was observed between responses to tumor cells transduced with the mutant and\nwild type constructs. In addition, it is not correct to claim that the response of ITPR3 616:E>K\nresponses are tumor specific, as there was no significant increase in the IFN-g response when\ncells were cultured with tumor cells.\nWe thank the reviewer for their thoughtful comments and have revised the manuscript to\nimprove transparency regarding the specificity analysis.\n1. ITPR3 616:E>K Tumor Specificity:\nWe acknowledge that ITPR3 616:E>K-specific T cells required exogenous mutant peptide to\nrecognize autologous tumor cells, which we have further clarified in the text (lines 328-329).\n12\n2. ARAP1 828:Y>H and CSNK1A1 27:S>L Responses:\nThe low response magnitudes in Figures 2G and 2H reflect the use of bulk drug products\nand gating on bulk CD8+ T cells, which resulted in low E:T ratios. Despite this, statistically\nsignificant differences between mutant and wild-type responses were observed,\nunderscoring the antigen specificity of these T cells even under suboptimal conditions. The\nbulk nature is highlighted in the caption of Figure 2G (“…then IFN-γ+ and/or TNFα+ and/or\nCD107a+ secretion of total CD8+ T cells was measured).\n> Changes:\nAdded clarification (lines 328-329): “We observed that the ITPR3 K specific cells were\n616:E>\nfunctional in response to autologous tumor cells only when additional exogenous mutant\npeptide was added.”\nQuestion 11:\nIt is not clear what was meant by the statement beginning on line 266 that ‘This further\nhighlights the importance of cell subset depletion at the beginning of the process.’ Were CD4+\nand CD8+ T cells separated before that peptide stimulation was carried out?\nWe appreciate the reviewer’s question and have clarified this statement. The reference to cell\nsubset depletion pertains to the removal of CD14⁺ monocytes and CD25⁺ regulatory T cells\nprior to peptide stimulation on day 0. This step is critical for enhancing priming efficiency by\nreducing inhibitory cell populations and increasing the frequency of CD8⁺ T cells in culture. As\nshown in Fig. 4B, this positively correlates with the successful priming and expansion of\nantigen-specific T cell responses.\n> Change (lines 303 - 305): “This further highlights the importance of depleting CD14+ monocytes\nand CD25+ regulatory T cells on day 0 to optimize neoantigen-specific T cell priming.”\nQuestion 12:\nThe criteria used to define neoantigens as being mutant specific, mutant selective or wild-type\ncross-reactive are unclear. The reactivity against the CD8 neoepitope TENM3 1243:S>L appears\nto be mutant specific, as the baseline or reactivity was ~20% and the response to the wild type\npeptide was only slightly elevated at the highest peptide concentration. Responses such as the\nCD4 response against the ATP2C1 427: E>K CD4 epitope should not be considered when\nevaluating these responses as they are unlikely to recognize any naturally processed and\npresented antigen.\nWe appreciate the reviewer’s comments and have clarified the criteria used to categorize\nneoantigen responses as mutant-specific, mutant-selective, or wild-type cross-reactive. These\ncategorizations were based on pre-defined statistical criteria, which are explicitly included\nbelow, in the Methods section (line 834-839), the Results section (215-219 and 315-319) and in\nthe caption of Suppl. Fig. 7 (formerly 6), lines S210-213.\n• Mutant-specific (MT-specific): Responses were classified as mutant-specific when T cell\nclones demonstrated significant reactivity to the mutant peptide compared to the unloaded\ndendritic cell (DC) control and showed no reactivity to the wild-type peptide.\n13\n• Mutant-selective (MT-selective): Clones were considered mutant-selective if they exhibited\nsignificant reactivity to the mutant peptide (over the unloaded DC control), but also displayed\nsome reactivity to the wild-type peptide compared to the unloaded DC control\n• Wild-type cross-reactive (WT cross-reactive): These responses were defined by significant\nreactivity to both mutant and wild-type peptides, with no significant difference in magnitude\nbetween responses to the two peptides.\nTENM3 1243:S>L :\nA slight, but statistically significant increase at 3.2 µM in response to the wild-type peptide\ncompared to the observed baseline reactivity assigns this response to the MT-selective\ncategory.\nATP2C1 427:E>K:\nThe observation that a significant difference was observed between 3.2 µM and baseline in\nresponse to both the mutant and the wildtype peptides, but no difference between mutant and\nwildtype peptide at 3.2 µM results in categorizing this response as wild type cross-reactive.\nQuestion 13:\nAn explanation is needed for the disparities between the relatively high frequencies of some of\nthe CD8 reactivities shown in Fig. 2 and Fig. S5 and the data shown in Fig. 4 where all the\nfrequencies of all of the reactivities, with the exception of 1, presumably that directed against\nthe SRSF1 33:E>K neoepitope, are below 2%.\nThe disparities in CD8 reactivity frequencies between Fig. 2/S5 and Fig. 4 are primarily due to\ndifferences in the number of restimulations. Research-scale experiments included two\nrestimulations, while therapeutic-scale manufacturing involved only one (except for HD17).\nThis reduction in restimulation was a deliberate choice to simplify the process and minimize\nPRDM1 expression, which negatively correlates with ACT persistence20. Despite lower\nfrequencies, the therapeutic-scale data align with our Nature Medicine dataset13, supporting\nconsistency across development phases.\nThe data presented in Fig. 4C also needs further explanation. Why are all of the points for CD4\nreactivities filled but only 1 of the CD8 specificities filled? Do the unfilled points represent the\nfinal concentration of these reactivities in the pooled product, and why are some reactivities\nshown as being in the T cell product before pooling but not in the final product, as all of them\nshould have been present unless they were not pooled?\nFig. 4C shows the detected antigen specific T cell responses (CD8 top, CD4 bottom) for each\nrun at therapeutic scale. The identification and quantification of the CD4 T cell responses were\nonly performed at the vessel level prior to pooling (filled circle) and not at the pooled final drug\nproduct (unfilled circle), this has been included in the legend (lines 1103-1104). For CD8\nresponses the filled circles are prior to pooling and the open circles are from the DP. All vessels\ncontributed to the pooled final drug product, but the concentration for a given response could\nbe lower in the pooled product due to the dilution effect at pooling. In the original Figure we had\nonly shown CD8+ responses at the vessel level, for cases where there was not a response to\nreport at the DP level. We understand how this can become confusing, and changed the\n14\npresentation to showing all responses, both at the vessel level and at the DP level (see also\nbelow).\nAn explanation is also needed for the data in the supplementary table marked ‘Therapeutic\nscale’ (should this be labeled Supplementary Table 2?). What is meant by ‘vessel’, or ‘well’,\nand why are the frequencies sometimes higher in the vessel than in the DP whereas sometimes the\nreverse is true? How was the final DP generated – were all of the wells containing neoantigen\nreactive T cells combined, and were there significant differences between the expansions\ncarried out using the different peptide pools? Since multiple wells containing the same peptide\npools were set up, were all of the wells pooled, or only the wells that showed effective\nexpansion of reactive T cells pooled, and if so what criteria were used?\nAt research scale NEO-STIM was performed by inducing multiple replicate wells with an\nidentical peptide set to assess reproducibility, data on each individual well is shared in Suppl.\nData S1, first tab of excel file ‘Research scale’. No pooling was performed at research scale.\nAt therapeutic scale (Suppl. Data S1, second tab of excel file ‘Therapeutic scale’), six parallel\nvessels with unique peptide sets were used for the manufacturing process. Pooling these six\nvessels generated the final drug product. The growth kinetics in each vessel vary. As a\nconsequence of that, responses do not get exactly diluted 1:6. Additionally, some vessels are\nstimulated with related peptides (e.g. a 9mer and a 10mer derived from the same mutation),\nraising T cell responses that can recognize both peptides. In these instances the frequency in\nthe final DP could unexpectedly be higher, as for example is the case for ENG02\nTENM3 1243:S>L (see updated Fig. 4C and Suppl. Data S1, second tab ‘Therapeutic scale’).\n> Changes:\n- Fig. 4C has been updated to separate out pooled vs pre-pooled frequencies for the CD8 T cell\nresponses.\n- The caption for Fig. 4C has been updated to reflect that CD4 responses were only analyzed\nprior to pooling (lines 1103-1104).\n- Lines 308 – 312 have been updated to now include an explanation for the observed variation:\n“Interestingly, variations in growth kinetics across vessels and stimulation with related\npeptides (e.g., 9mer and 10mer from the same mutation) can lead to non-uniform\ndilution and, in some cases, unexpectedly varying frequencies of antigen-specific T\ncells in the final drug product, as observed for ENG02 TENM3 1243:S>L (Fig. 4C,\nSupplementary Fig. 5B and Supplementary Data S1).”\nQuestion 14:\nIn general, did de novo/pre-existing reactivity correspond to specificity categories of mut\nspecific/selective, wt-reactive? For DYNC2H1 or RNF103 reactivities, were multiple TCR\nclonotypes observed in the scRNA data (Fig 5C/5D)? If so, were there distinctions in phenotypic\nstate distribution within or among clusters and did these differ by de novo or pre-existing\ncategories?\nreactive categories. Since both the number of pre-existing responses as also the number of\nwild-type reactive clones was quite low, it would be hard to establish a correspondence.\nMultiple TCR clonotypes were detected for DYNC2H1 and RNF103, but the small number of\ncells limited detailed analysis of phenotypic state distribution within or among clusters. The\ndata showed relatively consistent distribution (UMAPs, first and third plots), and for clarity, we\ncollapsed the data in the manuscript (UMAPs, first and second plot) to focus on high-level\nconclusions about the transcriptional profiles of these cells.\nThis manuscript describes the development of NEO-STIM, a platform that induces de novo and\nexpands memory T cell responses to patient-specific neoantigens ex vivo from peripheral\nblood. The authors convincingly demonstrate that the resulting drug products (DPs) are\npolyclonal, polyfunctional, mutant-selective, and cytotoxic, with clear recognition of\nautologous tumor. Their platform overcomes several limitations of TIL-based ACT, including\nreliance on resectable tumors and exhausted phenotypes, and is supported by preclinical and\nearly clinical feasibility data.\nAll major conclusions are well supported, and the methodological detail is commendable.\nHowever, several revisions would improve clarity and reproducibility:\nMajor Concerns:\nQuestion 1:\nPlease explicitly state which subsets are depleted (e.g., CD14⁺ monocytes, CD25⁺ Tregs),\nwhether CD11c⁺ DCs are enriched, and what maturation cocktail is used. Consider including a\nside-by-side comparison of DC phenotypes and costimulatory marker expression after\nmaturation to validate the superiority of naturally circulating DCs over moDCs.\nWe appreciate the reviewer’s request for clarification. CD14⁺ monocytes and CD25⁺ Tregs are\ndepleted on Day 0, as specified in the manuscript (Results: lines 125-127; Methods: line 712; all\nline references refer to the updated clean version). CD11c⁺ DCs are utilized at their natural\nabundance following depletion, without further enrichment. This is now clarified in the updated\nFigure caption of Fig. 1B, as well as in the Methods (lines 713-714: “For research experiments\nwhere additional populations were depleted, …“. The maturation cocktail components and\nprocedures are detailed in the Methods section (lines 720-723) and have also been added to\nupdated Figure caption 1B.\nTo address the reviewer‘s suggestion, we measured DC costimulatory markers for conventional\nDC subsets (cDC1 and cDC2) and moDCs. A direct side by side comparison of these is difficult,\nas the protocols differ. However, as expected, moDCs show high expression of CD40, CD80,\nCD83 and CD86 two days post maturation. Likewise, naturally occurring cDCs show robust\ncostimulatory marker expression two days post maturation.\nWe have included these data in the manuscript as Suppl. Fig. 3B, see below.\nto streamline and shorten the manufacturing protocol, rather than identifying a superior subset.\nIn the first iteration of the protocol, PBMCs were depleted of CD14+ cells for the reasons\nindicated in the manuscript (removal of potentially inhibitory cells), and the CD14+ cell\npopulation was used to prepare moDCs, which were used to facilitate T cell priming and\nexpansion. We hoped to avoid this separate workstream by using nDCs. Interestingly, most\nparameters tested were comparable or superior which led us to continue with nDCs. The robust\nexpression of costimulatory markers on cDCs is in line with this decision. We tried to avoid\ngeneral claims of nDC superiority by stating in several locations that nDC preference is seen as\npart of streamlining NEO-STIM (lines 120, 128-129, 160, 162).\n>Changes:\nAdd statement that CD11c+ cells stem from the CD14/CD25 depleted cell pool, and details on\nmaturation cocktail to caption of Fig. 1B (lines 1000-1003): “25-50 million depleted cells,\ncontaining CD11c+ naturally circulating dendritic cells, were cultured overnight in the presence\nof FMS-like tyrosine kinase 3-ligand (FLT3L), then peptide loaded, matured with maturation\ncocktail (TNF-α, interleukin (IL)-1β, Prostaglandin-E 1 and IL-7), and cultured for 14 days with IL-\n7 and IL-15.”\nClarify in Methods, that depletion of CD11b+ or CD11c+ cells was only performed for dedicated\nresearch experiments (lines 713-714).\nQuestion 2:\nThe magnitude of neoantigen-specific responses varies widely across patients. If feasible,\ncorrelate DP response magnitude with pre-culture CD8⁺ T cell frequency or baseline TCR\nrepertoire richness. At minimum, please discuss whether predictive biomarkers (e.g., mutation\nburden, CD8⁺/CD4⁺ ratio, memory subset composition) might inform response yield and\nproduct quality.\nWe appreciate the reviewer’s suggestion regarding predictive biomarkers. In our current\ndataset, CD8⁺ T cell frequency positively correlates with neoantigen-specific response yield, as\nshown by the hit rate metric (Fig. 4B). This highlights CD8⁺ abundance as a potential predictor\nof response yield.\nAs our dataset grows, we will continue to evaluate additional biomarkers, such as mutation\nburden, CD8⁺/CD4⁺ ratio, memory subset composition, and TCR repertoire richness, to refine\ninclusion criteria and optimize manufacturing decisions. Similarly, in the context of our recent\npublication in Nature Medicine13 we performed an analysis to try and correlate various\ncharacteristics (as mentioned by the reviewer) with clinical outcome. We did not find any\npredictors here but notably observed tumor reduction in patients treated with very comparable\ndrug products and sometimes low neo-antigen specific T cell content. Identifying robust\nbiomarkers remains a key priority to enhance product quality and therapeutic outcomes.\nQuestion 3:\nThe authors report a minority of responses with WT reactivity. Please summarize the percentage\nof tested responses falling into each specificity category (mutant-specific, selective, WT-\ncrossreactive). If possible, add data on functional avidity (e.g., EC50 curves) for at least one WT-\n18\ncrossreactive versus mutant-specific clone to support safe discrimination. Clarify whether WT-\nreactive clones would be excluded from clinical release criteria.\nWe thank the reviewer for this question.\nIn our dataset, most T cell responses were mutant-specific (7/19; 36.8%) or mutant-selective\n(9/19; 47.4%), with a minority showing WT cross-reactivity (3/19; 15.8%). This is illustrated in\nSuppl. Fig 7 (formerly Suppl. Fig. 6) and summarized at the high level in lines 319-321.\nFunctional avidity data were generated for four TCRs (SRSF1: n=3, one TCR with 2 clonotypes;\nARAP1: n=1), and showed mutant-specific binding with avidities in the nano- to micromolar\nrange (New Suppl. Fig. 8).\nIn the clinical setting, we similarly observed predominantly tumor-specific responses with\ncomparable EC values (Borgers et al.13, Extended Data Fig. 4). Given the infrequent\n50\nobservation of WT-reactive responses during development, the clinical approach involved\ninfusing bulk products and assessing cross-reactivity post-infusion. Importantly, no safety\nsignals directly attributable to neoantigen-specific products were observed in treated\npatients13.\n> Change:\nA new Suppl: Fig. 8 has been created which shows the generated EC data for four TCRs\n50\nderived from this study. Lines 251 – 252 of the manuscript have been updated to reflect this.\nQuestion 4:\nPRDM1 induction after repeated stimulation suggests terminal effector skewing. The authors\nmight consider comparing CD62L⁺CCR7⁺ memory phenotypes across 1× vs 2× stimulation DPs\nto assess whether reduced stimulation could preserve less-differentiated states. A brief\nanalysis of exhaustion marker expression (e.g., PD-1, LAG-3) in the final product would also be\ninformative.\nIn conjunction with the upregulation of PRDM1 over time, we also show that the ‘partial effector\ncells’ (low/absent levels of CCL3, IFNG and TNFRSF9) express exhaustion markers (PDCD1,\nTIGIT and TOX) (Suppl. Fig. 9E, formerly Suppl Fig. 7E).\nAdditionally, as suggested by the reviewer, we examined the expression of stem-like (CCR7,\nSELL) and exhaustion markers (LAG3, PD1CD1 & PRDM1) at different times of the protocol.\nLittle expression (no more than in bystander cells) was observed for CCR7 and SELL. LAG3 and\nPRDM1 both increased expression over time, with stable PDCD1 expression. This pattern was\nconsistent across both the SRSF1 and ARAP1 T cell responses. These findings support the\ninterpretation that repeated stimulation drives terminal effector differentiation and support our\ndecision to manufacture a product with only two stimulations to preserve a drug product with a\nless differentiated state.\n> Change:\nThe above data has been added as panel G to Suppl. Fig. 9, with the following text added to the\nmanuscript (lines 281-285): “We analyzed stem-like (CCR7, SELL) and exhaustion markers (LAG3,\nPDCD1, PRDM1) in both responses (Supplementary Fig. 9G). CCR7 and SELL showed minimal\nexpression at all timepoints, while LAG3 and PRDM1 increased over time with stable PDCD1 levels.\nThis pattern was consistent across SRSF1 and ARAP1 T cell responses.”\nQuestion 5:\nIn the patient with tracked TCR clonotypes, the activated/exhausted phenotype is well\ndescribed. If possible, include a quantitative comparison of persistence or expansion between\npre-existing and de novo clonotypes. This could clarify the relative durability of newly induced\nversus expanded memory clones.\nAcross this manuscript and the clinical study13 quantitative analyses indicate that the more\nrobust persistence and expansion following infusion is associated with pre-existing clonotypes.\nThis is likely explained due to their higher abundance within the infused product, rather than an\nintrinsic phenotypic advantage, given the phenotypic similarity between those two groups:\n- This manuscript (lines 344-346): “Across two responses which initially were considered pre-\nexisting, three out of eight subclones were de novo. One of those three was detected post-\ninfusion, compared to five out of five which were pre-existing (Figure 5A, B).”\n20\n- Borgers et al.13 (Fig. 5C, D): Consistent results were observed in the clinical context. Two out\nof six clonotypes from a pre-existing response appeared to be de novo at the clonotype\nlevel. Neither was detected in blood or tumor post-treatment, compared to three out of four\npre-existing clonotypes.\n- Additionally, in Borgers et al.13, Ext. Fig 2C, D, we found that both pre-existing and de novo\nclonotypes exhibited a diverse array of phenotypes, with no clear phenotypic distinction\nbetween the two groups. However, pre-existing responses were more abundant in the drug\nproduct, both by frequency and by the number of detected clonotypes. This higher\nabundance within the infused product may explain why de novo responses, despite\nphenotypic similarity, are not always detected post-treatment and their lower initial\nabundance may make expansion to detectable levels less likely.\nQuestion 6:\nWould this approach work in tumors with fewer mutations (e.g. some epithelial cancers or\npediatric cancers)? It likely can, as long as at least a handful of neoantigens are identifiable, but\nthe effort (60 peptides per patient) might yield fewer hits. A sentence discussing this (perhaps\nnoting that even low-mutational-load tumors can still have a few strong neoantigens to target)\nwould be useful to frame the breadth of this therapy.\nWe appreciate the interest in the application of NEO-STIM in other indications with fewer\nmutations. We performed NEO-STIM in three additional patients with ovarian cancer (OVC),\nwith a tumor mutational burden (TMB) ranging from 25 – 88. This resulted in 40-57 predicted\nneo-antigens to raise responses towards. In addition to the patient-specific mutations, an\nadditional set of model antigens was added in one (PT07 and PT08) or two (PT09) pools for each\npatient in case fewer hits were observed, as suggested by the reviewer. Consistent with the\nresults obtained in PBMCs derived from melanoma patients, these studies demonstrate that\nNEO-STIM at research scale:\n• Generates polyclonal drug products in patients with low TMB that recognize patient-\nspecific mutations as well as model antigens:\n2-13 CD8+ T cell responses and 1-13 CD4+ T cell responses (n=3 donors)\n• Induced neoantigen-specific T cell responses produce IFN, TNF and CD107a in\nresponse to cognate peptide.\n> Changes:\nThe data derived from NEO-STIM in PBMCs derived from OVC patients is included in the new\nSuppl. Fig. 6 shown below. Text in the manuscript has been updated to reflect the addition, and\nresponse numbers were updated in the two Results sections starting in line 160 and 200 (see\nalso Redlines of the manuscript).\ncarcinoma patients.\nQuestion 7:\nAdditionally, while the focus is lab/process development, the authors did treat at least one\npatient and mention an ongoing Phase I trial. If possible, the clinical context should be\nexpanded slightly: For example, the authors state BNT221 showed early signals of efficacy –\nwithout divulging too much beyond what is allowed, can they say what signals? (Tumor\nregression in some patients? Durable T cell persistence?) Even a general statement like “early\nsigns of anti-tumor activity were observed in the trial, such as objective tumor responses in\nsome participants” would underline the importance. If such details are not yet public, it’s\nunderstandable, but then perhaps emphasize that a formal clinical trial is underway to assess\nsafety/efficacy, which is a strength of this work’s translational readiness.\nWe appreciate the reviewer request on sharing some safety and efficacy data of BNT221 to\nfurther strengthen the work’s translational readiness. As reported in Borgers et al.13, the early\nsafety and efficacy data of BNT221 in heavily pretreated patients are encouraging. We have\nupdated the manuscript to briefly describe the observed signals and refer to the reported data\nto underscore the translational aspect of BNT22113.\n> Change:\nWe have updated the Discussion to include additional detail from the clinical evaluation (lines\n432 - 434: “Of the nine treated patients, six had stable disease (SD) as their best overall\nresponse and four of these patients with SD showed tumor regression after infusion. The drug\nproduct characteristics observed in the FIH study are consistent with the findings described\nhere.”\nI recommend adding a sentence on how this approach might integrate with other therapies – for\ninstance, will patients receive checkpoint inhibitors alongside NEO-STIM T cells? This could be\nrelevant since ex vivo expanded cells might still benefit from checkpoint blockade post-transfer.\nEven if not part of this study, acknowledging combination strategies (as is common with TIL\ntherapy + IL-2 or checkpoints) would show awareness of the clinical landscape.\nWe appreciate the reviewer’s comment regarding the potential integration of NEO-STIM-\ngenerated drug products with combination therapies, particularly checkpoint inhibitors.\nIndeed, translational data from the FIH study demonstrated robust PD-1 upregulation on\nmultiple post-infusion neoantigen-specific T cell responses (Borgers et al.13, Fig. 4D),\nhighlighting a compelling rationale for PD-1 blockade as a complementary strategy. This\nobservation aligns with the broader clinical landscape, where checkpoint inhibition has proven\nsynergistic with adoptive cell therapies, further underscoring the potential of NEO-STIM in\ncombination approaches to enhance therapeutic efficacy.\n> Change:\nWe added this perspective to the Discussion (lines 434-436): “Furthermore, evaluation of\ncombination strategies with for example checkpoint inhibitors and/or cytokines is warranted to\nenhance the potency, expansion and persistence of neoantigen specific cells further.”\nMinor Concerns:\nQuestion 9:\nIt’s excellent that each peptide pool was run in triplicate to test process reproducibility. The\nsupplemental data likely show high correlation (the text notes R2=0.98 between some\nreplicates). The authors might consider mentioning in the Results that reproducibility between\nparallel cultures was high, as this will give readers confidence that the process yields\nconsistent results. A brief mention of the replicate consistency (perhaps referencing\nSupplementary Fig. S5 or S1B as appropriate) would suffice.\nWe appreciate the reviewers’ suggestion to strengthen the paper and have incorporate this in\nthe Results.\n> Change (lines 188-190): “Reproducibility between cultures was consistent across biological\nreplicates, in particular for higher frequency responses (Supplementary Data S1).”\nQuestion 10:\nIn the main figures, some data (e.g., Fig 2A examples, 2H cytotoxicity, 2I tumor recognition)\nseem to highlight one or two patients. It would be helpful if the authors could ensure that data\nfrom all four patients are reported, at least in aggregate. For example, Fig. 2C, 2E summarize the\nnumber of responses per patient – presumably each dot is one patient’s result. Clarify in the\nfigure caption if those are per-patient medians or individual responses. Similarly, for cytotoxicity\nand tumor recognition, if not all patients were tested, state the number of patients or responses\nevaluated. Transparency about n for each assay will help readers gauge the consistency across\nthe cohort.\nWe appreciate the reviewer’s request for transparent reporting.\nFig 2A shows 1-2 examples for CD8+ tetramer staining per patient and tetramer staining graphs\nfor all tested responses are shown in Suppl. Fig. 5. We have added a reference to Suppl. Fig. 5 in\nthe caption of Fig. 2A.\nFig. 2B and D show two examples each to illustrate the method, and the respective aggregate\ndata is shown in Fig 2C, E. This is explained in the Figure caption. Groups of responses are\nshown per patient as defined in the Figure caption. Datapoints show all responses per patient,\nas listed in the caption Fig. 2C, and this is now also added in the caption of Fig. 2E.\nFor Fig. 2F-I we now clarified in the Figure caption that only responses with sufficient sample\navailability and magnitude of response were tested.\nFig 2F shows representative examples as listed in the caption; the total n is given in the caption\nand in the main text (line 205).\nFig. 2G shows example graphs as specified in the caption and we added a reference in the\ncaption to Supplementary Fig. 7, which shows all responses. Results on all samples tested are\ngiven in the main text (lines 214-218).\nFig 2H shows all responses that were positive, as described in the text (line 229), where also the\ntotal number of responses tested is given.\nFor Figure 2I we explain in the text, why only one patient was investigated at this point (lines\n234-235): “We were able to obtain a single-cell suspension from a tumor of one patient (PT04).”\nFigure 4D shows examples, for which a high-level summary is given in the table below the\ngraphs, and graphs for all specificity plots are provided in Suppl. Fig. 7 (formerly 6). We added a\nnote to refer the reader to Suppl. Fig. 7 in the Figure caption of Fig. 4D (line 1111). Total sample\nnumber and number of specific responses is also given in the main text (lines 314-315; 319-\n321).\nFor Fig 4E, we added the sample availability limitation in the main text (line 325). This\nconsideration is also reflected in the Reporting Summary under the section “Sample size”\n(Header “Life Sciences Study Design”).\nSuppl Figure 10C (formerly 8C) shows representative examples for 26 samples tested; we\nadded the word “representative” to the Figure caption for clarity. We clarified the overall result\nin the Figure caption and in the main text (line 313).\nSuppl Fig. 10D, E states in the Fig caption (lines S265-2669: “Six responses tested and found\ncytotoxic, of which five are shown here.”\n> Changes:\nAdded information to Figure legends of Fig 2A, 2E, 2F-I, 2G, 4D, Suppl. Fig. 10C and to main text\n(lines 313, 325).\nOverall, the figures are well-designed. A few suggestions:\n(i) Figure 1B – if this is a schematic of the process, ensure that all steps are labeled clearly (e.g.,\n“Deplete PBMC subsets,” “Primary stimulation (Stim1) day 0–14,” etc.). Currently it’s referenced\nthat Fig. 1B shows process steps; just make sure it’s readable and perhaps include an\nexplanation in the legend for any icons or abbreviations.\nFigure 1B has been updated as suggested.\n>Change: Update Figure 1B and its caption.\n(ii) Figure 2 – panels C, E (showing range and medians of responses) should indicate whether\nthe percentages shown are fraction of total CD8 or CD4 T cells that are neoantigen-specific (I\nassume yes). Adding “% of CD8^+ T cells” in the axis label or caption would help. Also, define\n“tet+” in the caption for non-specialist readers (T cells detected by peptide-MHC tetramers).\nThe definition of tet+ has been included in the legend of this figure at first use. The suggested\nlabel for the axis / caption was used.\n>Change: Figure caption 2A\n(iii) Figure 3 – the UMAP plots and heatmaps from scRNA-seq are complex. The authors might\nadd a brief description in the legend about what the major clusters represent (activated effector\nvs memory, etc., as derived from Fig. S7). A simplified summary of the key takeaway (that\nARAP1 de novo clone shifts to effector state early, vs the SRSF1 memory clone which shifts\ngradually) could guide the reader through panel E/F.\nWe thank the reviewer for this recommendation and have included the following in the caption\nof Fig. 3 E-F (lines 1071-1075): “Tem subsets are characterized by expression of IL7R, effector\nsubsets by expression of TNFRSF9, and partial effectors by low levels of TNFRSF9 combined\nwith exhaustion markers (see Supplementary Fig. 9). Note the larger phenotypic shifts in\nresponse to antigen for the ARAP1 de novo clonotypes than the SRSF1 memory clonotypes,\nespecially at the first stimulation (STIM1) timepoint.”\n>Change: Add explanation of UMAP results in figure caption 3E-F (lines 1071-1075)\nQuestion 12:\nPlease ensure all abbreviations are defined on first use. For instance, “DP” (drug product)\nis defined in the Introduction – that’s good. Also define Stim1/2/3 clearly as rounds of stimulation.\nThe term “research scale” vs “therapeutic scale” is used; perhaps clarify that “research scale”\nrefers to small-scale lab process used for initial 4 patients, and “therapeutic scale” is a full-\nscale manufacture (if I understood correctly). Minor point: “WT” and “MT” (wild-type vs mutant)\nshould be defined in figure legends or methods for clarity.\nWe have incorporated these suggestions and added explanations for the acronyms shown\nbelow.\nWT and MT is explained in Figure captions Fig. 2 and 4, Suppl. Fig. 7 (formerly 6) at first use (i.e.\nnot in every subpanel), and we have spelled it out in Suppl. Fig. 10 (formerly 8).\nhas been clarified in the Methods (lines 731-733): “Plating was either as 2 mL in a 24-well plate\nin triplicates (research scale) or as 9 mL in a 6-well plate or 10M or 100 M G-Rex vessel\n(therapeutic scale; Wilson Wolf),…”\n> Changes:\nSpell out additional abbreviations at first use: TAA (line 47), TME (line 73), PBMCs (line 84), HLA\n(line 90), GMP (lines 110-111), MHC (line 167-168), pre-NEO-STIM and STIM 1,2,3 (line 245-247);\nwild-type and mutant spelled out in caption of Suppl. Fig. 10 (formerly 8); research scale vs\ntherapeutic scale clarified in Methods (lines 731-733)\nQuestion 13:\nThe authors cite relevant literature on TIL therapy and neoantigen vaccines. One suggestion is\nto cite recent clinical successes: for instance, the FDA approval of Lifileucel for melanoma in\n2024, which they allude to in the introduction, could be explicitly referenced as a milestone that\nunderscores the importance of advancing neoantigen-targeted T cell therapies. Additionally, if\nthere are any comparable approaches (perhaps a recent study attempting to prime T cells from\nblood for adoptive therapy, if any), mentioning those would position this work in the landscape.\nIt appears this approach is quite unique, but for completeness, the authors might consider if\ngroups have tried similar “prime-and-expand” T cell therapies (I recall some early trials\nexpanding T cells to tumor-associated antigens or virus antigens ex vivo). A sentence\ndistinguishing NEO-STIM from TCR-engineered cell therapies would also help: e.g., emphasize\nthis does not require genetic modification, making it potentially faster and less expensive once\nneoantigen peptides are identified.\nWe appreciate the reviewers’ suggestions to strengthen the manuscript and have adopted this\nto enhance the positioning of our work in the landscape.\n> Changes:\nAdded the following statement (line 46-47): “…resulting in FDA approval of a tumor-infiltrating\nlymphocyte (TIL) product, defining a major milestone for personalized adoptive T cells\ntherapies.”\nAdded reference (Khateb et al.21) to line 48-49, and expanded on the landscape\nThis study develops and validates NEO-STIM, an ex vivo method to generate personalized\nneoantigen-specific T-cell responses under optimized conditions that avoid immunosuppression.\nLenkala et al. validated the protocol using model antigens with healthy donor PBMCs expressing\nrelevant HLA types. They enhanced T cell priming by depleting inhibitory cell subsets and identified\nthe best APC populations by assessing expansion, diversity, and functionality of CD8+ responses—\nincluding those to long peptides that usually induce CD4+ T cells. Applying the method to PBMCs\nfrom four melanoma patients, they detected both pre-existing and newly induced CD8+ responses,\nvia combinatorial pMHC tetramer staining – as wells as CD4+ T cell responses. The generated DP T\ncells were further phenotypically and functionally characterized. Finally, the protocol was\noptimized for therapeutic-scale manufacturing while maintaining functional activity.\nThe study addresses an important and unmet clinical need with a promising approach. The idea is\ncompelling and has strong potential for personalized immunotherapy. However, the work feels\npreliminary and would benefit from more extensive analysis and deeper characterization to\nstrengthen its conclusions and clinical relevance.\nMajor concerns:\nQuestion 1:\nLimited donor and patient diversity: Proof-of-concept experiments use only two healthy donors\n(one with weak de novo reactivity), with very low response frequencies (<1%), raising questions\nabout functional relevance. The clinical cohort is restricted to melanoma patients, nearly all\npreviously treated with immunotherapy, limiting generalizability and potentially confounding\nimmune responses.\nWe agree with the reviewer that donor and patient diversity is critical for demonstrating the\nrobustness and generalizability of NEO-STIM. The platform development and therapeutic validation\nincluded data from 22 healthy donors and six melanoma patients, as presented across the main\nand supplementary figures. Additionally, we extended NEO-STIM to three ovarian cancer patients\nwith low tumor mutational burden (TMB 25–88), generating polyclonal drug products with multiple\neffector functions, consistent with results from melanoma-derived PBMCs. Furthermore, a recent\npublication from our group describing a modified NEO-STIM protocol, optimized for mutant\nKRAS16, demonstrated reproducible induction of T cell responses against this shared neoantigen\nacross 47 KRAS-MHC combinations in healthy donor PBMCs, underscoring the applicability of\nNEO-STIM beyond melanoma and its broader translational potential.\n> Changes:\nThe data derived from NEO-STIM in PBMCs derived from OVC patients is included in the new Suppl.\nFig. 6. Text in the manuscript has been updated to reflect the addition, and response numbers were\nupdated in the two Results sections starting in line 160 and 200 (all line references refer to the\nupdated clean version). See also Redlines of the manuscript for updates of numbers.\nLack of tumor antigen expression and clonality data: The manuscript does not report expression\nlevels or clonal distribution of the selected neoantigens on patient tumors, which is critical for\nevaluating clinical impact.\nWe agree that this data would be informative. We have now included this data in Supplemental\nData S1, i.e. mutant allele fractions (column P ‘variant_dna_allele_fraction’) and mutant-allele-\nspecific RNA-Seq expression (in TPM, column O ‘variant_rna_expression_tpm’) on tabs “Research\nscale” and “Therapeutic scale”. In some cases, model antigens (neoantigens or TAAs) were\nadditionally used in peptide pools 6-8,22-24Given that these antigens were not part of the patient’s own\nmutational landscape, mutant allele fraction and mutant-allele-specific RNA-Seq expression were\nmarked as N/A in those cases.\n>Changes: Addition of mutant allele fractions and mutant-allele-specific RNA-Seq expression in\nSupplemental Data S1 (columns P and O)\nQuestion 3:\nInsufficient functional validation and controls: Functional assays with autologous tumor cells are\nlimited (only two responses tested), and killing assays are lacking. Healthy autologous cells were\nnot used as specificity controls. Many experiments rely on single replicates, and controls often lack\nAPC plus non-mutated peptide conditions, weakening robustness and specificity assessment.\nWe respectfully disagree with the reviewer’s assessment regarding the robustness and specificity of\nour functional validation. Autologous tumor material was indeed limited, but we prioritized this for\ndemonstration of tumor recognition in a physiologically relevant setting, evaluating three T cell\nresponses across two patients (Fig. 2I and 4E, n=3 technical replicates).\nDuring development each peptide pool was run in triplicate (and for Ovarian carcinoma in\nquadruplicate) to test process reproducibility. Data is provided in Suppl. Data S1 and the high\nreproducibility is now specifically called out in the main text.\nCytotoxic capacity was determined by quantifying the fraction of active caspase 3 positivity against\nA375 tumor lines transduced with the HLA restriction element and a construct encoding 200 amino\nacids of mutant or wildtype sequence, such that processing and presentation must occur for tumor\nrecognition and killing. Evaluation of the mutant and wildtype (i.e. non-mutated, representative of\nhealthy cells) constructs, and assessment against the parental line with expression of the relevant\nHLA only, allowed us to assess specificity (Fig. 2H, n=3 technical replicates).\nFinally, specificity was also confirmed through a co-culture assay, where APCs loaded with either\nthe wildtype (i.e. non-mutated) or mutant peptide were co-cultured with the neoantigen specific T\ncell responses (Fig. 2G and Supplementary Fig. S6, n=3 technical replicates). The observed\nfunctionality profile showed that 84.2% (16 out of 19 responses tested) preferentially recognized\nthe mutant epitope.\nCollectively, these experiments incorporated appropriate controls, technical replicates, and\nspecificity assessments, generating robust and reproducible data that validate the functionality\nand cytotoxic capacity of the neoantigen-specific T cell responses.\n28\n>Change: Added statement on reproducibility in main text (lines 188-190): “Reproducibility\nbetween cultures was consistent across biological replicates, in particular for higher frequency\nresponses (Supplementary Data S1).”\nQuestion 4:\nUse of artificial tumor models: Validation frequently relies on tumor cells transduced to express the\ndesired neoantigens, which may not reflect physiological expression or clinical relevance.\nConfirmation with authentic autologous tumor cells is necessary.\nWe agree with the reviewer that autologous tumor cells represent the most physiologically relevant\ntarget for validation in killing assays. However, the availability of such material is inherently\nchallenging, particularly in the context of a non-clinical study. For this manuscript, we leveraged\nautologous tumor material from two patients to evaluate three T cell responses (Fig. 2I and 4E, n=3\ntechnical replicates), but no additional material was accessible.\nImportantly, the clinical efficacy data for BNT221 shown in Borgers et al. further supports the\npreclinical findings described here. In the FIH study, neoantigen-specific drug products\nmanufactured according to the process outlined in this manuscript demonstrated tumor\nreductions post-infusion in heavily pre-treated patients, accompanied by infiltration of neoantigen-\nspecific T cells into the tumor. These clinical observations substantiate the relevance and\ntranslational potential of our approach13.\nQuestion 5:\nIncomplete phenotypic and CD4+ T cell characterization: Phenotypic analysis is largely restricted\nto CD8+ T cells and compares only one pre-existing and one de novo response. There is no\nthorough characterization of other CD8+ or CD4+ T cell responses. Additionally, no pre-protocol\nassessment of CD4+ responses was conducted to distinguish pre-existing from induced de novo\npopulations, limiting interpretation of efficacy.\nCD4+ T cell responses were characterized using recall response assays, which included pre-NEO-\nSTIM PBMCs as a baseline control (this is now specifically included in the Methods, line 819). No\nCD4+ responses were observed at baseline, supporting the interpretation that these responses\nwere induced de novo. As noted in the discussion (line 379), the recall assay does not achieve the\nsame specificity threshold as pMHC multimers used for CD8+ T cells, which may partially account\nfor the absence of detectable pre-existing CD4+ responses. Unfortunately, the lack of suitable\nmultimer reagents for CD4+ T cells limited our ability to perform sequence-based analyses for\ndeeper characterization. Despite these technical constraints, the recall assay provided meaningful\ninsights into the induction of CD4+ T cell responses through NEO-STIM.\n> Changes: to enhance clarity, we specified the use of pre–NEO-STIM PBMCs in the Methods, line\n819: “Pre–NEO-STIM PBMCs and induced T cells...”\nQuestion 6:\nProof-of-concept responses should be shown separately for high and low immunogenic antigens to\nsupport claims on low immunogenic antigen recognition.\nWe understand the reviewer’s request to categorize antigens into high and low immunogenicity\ngroups, as was done for several Supplementary Figures using model antigens (Suppl Fig 1E, Suppl\n3F).\nHowever, for the therapeutic-scale experiments performed with patient samples in this manuscript,\nprivate patient-specific neoantigens were used, and by-and-large, no prior data on their\nimmunogenicity was available, reflecting the clinical context. As such, classification of these\nantigens into high or low immunogenicity categories was not feasible within the experimental\nframework. This approach mirrors real-world scenarios, where neoantigen immunogenicity is often\nunknown prior to therapeutic application. For a subset of patients (PT3, PT7, PT8 and PT9) 1-2\npools\nof previously identified neo-antigens were included6-8,22-24, Supplemental Data Set1 displays\nwhether or not responses were raised towards those for each peptide.\nQuestion 7:\nActivation markers should be shown separately for clarity.\nWe have taken the reviewers’ suggestion and have grouped the expression profile in various\ncategories, including activation markers.\n> Change: Add labels for activation and other markers in Fig. 5C, D\nQuestion 8:\nSupplementary Figure S1E is hard to interpret and uses different donors than the previous\nexperiments, making it difficult to get an overall picture.\nDuring the development of NEO-STIM materials derived from a total of 22 healthy donors and six\nmelanoma patients were used. While certain parts of the process (i.e. priming vs expansion) have\nbeen optimized with a different set of healthy donors, each experiment was internally controlled.\nThe final proof of concept experiments were run through the same process across multiple patient\nsamples.\ncell therapy with tumor-infiltrating lymphocytes in melanoma. Journal of Clinical\nInvestigation 132 (2022). https://doi.org/10.1172/jci150535\nantigens by CD4+ T cells in human melanoma. Nat Med 21, 81-85 (2015).\nhttps://doi.org/10.1038/nm.3773\nGastrointestinal Cancers. Cancer Discov 9, 1022-1035 (2019).\nhttps://doi.org/10.1158/2159-8290.CD-18-1494\nby adoptively transferred tumor-reactive T cells. Nat Med 19, 747-752 (2013).\nhttps://doi.org/10.1038/nm.3161\nScience 350, 1387-1390 (2015). https://doi.org/10.1126/science.aad1253\nneoepitopes in humans - PubMed. Cancer immunology research 2 (2014).\nhttps://doi.org/10.1158/2326-6066.CIR-13-0227\nProportion of Human Histocompatibility Leukocyte Antigen (Hla)-A2 Individuals. The Journal\nof Experimental Medicine 190, 705-716 (1999). https://doi.org/10.1084/jem.190.5.705\n8 van Buuren MM, C. J., Schumacher TN. High sensitivity of cancer exome-based CD8 T cell\nneo-antigen identification - PubMed. Oncoimmunology 3 (2014).\nhttps://doi.org/10.4161/onci.28836\n(2018). https://doi.org/10.1111/imm.12888\nPubMed. Cytometry. Part A : the journal of the International Society for Analytical Cytology\n99 (2021 Mar). https://doi.org/10.1002/cyto.a.24269\nlineage priming at the common DC progenitor stage in the bone marrow. Nat Immunol 16,\n718-728 (2015). https://doi.org/10.1038/ni.3200\ncombinatorial encoding of MHC multimers. Nat Protoc 7, 891-902 (2012).\nhttps://doi.org/10.1038/nprot.2012.037\nmetastatic melanoma: a phase 1 trial. Nature Medicine (2025).\nhttps://doi.org/10.1038/s41591-024-03418-4\n14 de Greef, P. C. et al. The naive T-cell receptor repertoire has an extremely broad distribution\nof clone sizes. Elife 9 (2020). https://doi.org/10.7554/eLife.49900\nMemory Subsets Using an Integrated Experimental and Computational Pipeline Which Is\nRobust, Economical, and Versatile. Front Immunol 8, 1267 (2017).\nhttps://doi.org/10.3389/fimmu.2017.01267\ncell therapy or TCR discovery. Cell Reports Methods 5, 101049 (2025).\nhttps://doi.org/10.1016/j.crmeth.2025.101049\n31\nassay. Cell Rep Methods 4, 100690 (2024). https://doi.org/10.1016/j.crmeth.2023.100690\nreliable Ag-specific CD4+ T cell characterization by AIM assay. Science\nAdvances 11, eadv3491 (2025). https://doi.org/doi:10.1126/sciadv.adv3491\nspecific CD4(+) T cells. Cell Rep Methods 3, 100388 (2023).\nhttps://doi.org/10.1016/j.crmeth.2022.100388\nefficacy of adoptive immunotherapy. Blood 139, 2156-2172 (2022).\nhttps://doi.org/10.1182/blood.2021012714\nfrom peripheral blood. bioRxiv (2025). https://doi.org/10.1101/2025.05.11.653315\nNature 547, 217-221 (2017). https://doi.org/10.1038/nature22991\nPatients with Advanced Melanoma, Non-small Cell Lung Cancer, or Bladder Cancer. Cell\n183, 347-362 e324 (2020). https://doi.org/10.1016/j.cell.2020.08.053\nSchrörs B, Omokoko T, Vormehr M, Albrecht C, Paruzynski A, Kuhn AN, Buck J, Heesch S,\nSchreeb KH, Müller F, Ortseifer I, Vogler I, Godehardt E, Attig S, Rae R, Breitkreuz A, Tolliver\nC, Suchan M, Martic G, Hohberger A, Sorn P, Diekmann J, Ciesla J, Waksmann O, Brück AK,\nWitt M, Zillgen M, Rothermel A, Kasemann B, Langer D, Bolte S, Diken M, Kreiter S, Nemecek\nR, Gebhardt C, Grabbe S, Höller C, Utikal J, Huber C, Loquai C, Türeci Ö. Personalized RNA\nmutanome vaccines mobilize poly-specific therapeutic immunity against cancer - PubMed.\nNature 547 (2017). https://doi.org/10.1038/nature23003\nThe majority of issues raised by the reviewers were addressed in the revised manuscript; however,\nthere were a few additional points that need to be clarified.\nIn response to question 5 from reviewer 1, the distinction between highly immunogenic and weakly\nimmunogenic still seems artificial. Highly immunogenic antigens might simply possess a larger\nrepertoire of reactive clonotypes and therefore might elicit a more robust response. While this is of\nsome interest, the most important point regarding tumor antigens is their relative potency against\nnatural tumor antigen targets, as this can vary widely depending on the target.\nWe agree with the reviewer that the size of the TCR repertoire can play a role. We have included this\nas a possible explanation in the text. Additionally, we are referencing the section using patient\nspecific targets as a point of value to assess relative potency against natural tumor antigen targets.\n> Changes:\n- lines 86 – 88: “The size of the TCR repertoire and epitope characteristics are key aspects that\nimpact the generation of a T cell response.”\n- lines 93 – 95: “Finally, T cell products were generated from patient derived PBMCs and their\nrelative potency was measured against engineered and natural tumor targets.”\nIn response to question 12 from reviewer 1, responses were defined as ‘mutant-selective’ if there\nwas significant reactivity against the mutant epitope but ‘some’ reactivity against the wild type\nepitope. This is not a clear definition, and some criteria should be used such as a 10-fold or greater\ndifference between the response to the mutant and wild type peptide. It should also be pointed out\nthat wild-type cross-reactive T cells will be ineffective at tumor control as these presumably\nrepresent targets that are not naturally processed and presented at levels on tumor cells.\nWe appreciate the reviewer’s request for additional clarity on this. The criteria we have\nimplemented to define a mutant selective response is as defined below. This does not change the\nnumber of responses that fall into each category that was described previously:\n“Clones that showed significant reactivity to the MT peptide over the unloaded DC control, and\nsignificant reactivity to the WT peptide compared to the unloaded DC control, additionally, a\nsignificant difference should be observed between MT and WT reactivities”\n> Change: We have addressed this by updating the language in the following places. Main file: lines\n323 – 324; lines 851 – 85. Supplemental file: lines 208 – 218.\nRegarding the assertion that wildtype cross-reactive T cells are ineffective in tumor control, we\npropose an alternative perspective. Single point mutations can influence the processing and\npresentation of peptides in either direction, either reducing or enhancing their likelihood of being\nprocessed and presented. When these mutations lead to more efficient peptide presentation, they\nmay also enhance T cell recognition1. We have clarified this in the text.\n> Change (lines 214 – 216): “Given that neoantigen epitopes can differ from their wild-type (WT)\ncounterparts by as little as a single amino acid, and single point mutations can either reduce or\n1\nenhance the likelihood of the epitope to be processed and presented1, we evaluated whether the\ninduced T cell responses could differentiate between mutant (MT) and WT peptides.”\nThe response to question 7 from reviewer 2 was unclear. The statement that was added to the text\nindicated that six had stable disease as their best overall response but four of these patients\nshowed tumor regression. Why was the best overall response of these four patients not designated\ntumor regression? Was tumor regression based upon RECIST criteria, and what was the duration of\nresponses seen in these patients?\nWe thank the reviewer for their request for clarification. Indeed, RECIST 1.1 criteria were used to\ndefine the observed best overall responses (BOR). The observed tumor regression (up to -20%\nreduction in target lesions) did not meet criteria for partial response (which is defined as at least\n-30% decrease in the sum of target lesions, per RECIST 1.1). Therefore, the best overall response is\ndefined as SD. The duration of the response of these four patients was between 12 and 36 weeks.\nWe have updated the language in the discussion to incorporate these edits.\n> Change (lines 445 – 448): “Of the nine treated patients, six had stable disease (SD) as their best\noverall response, including up to 36 weeks for one patient. Four of these patients with SD showed\ntumor regression (up to -20% according to Response Evaluation Criteria in Solid Tumors (RECIST)\nversion 1.1) after infusion.”\nReviewer #2 (Remarks to the Author):\nThe revision and rebuttal are careful, specific, and, on the whole, responsive. Most of my major and\nminor concerns are fully or substantially addressed with added methods detail, clearer figure\nlegends, new supplemental analyses (specificity categories; functional avidity), and expanded\nclinical context and discussion. Only two items remain partially addressed (noted below). Explicit\nclinical release criteria for WT-cross-reactive clones, and breadth of claims for low-TMB tumors.\nOtherwise, the manuscript is materially clearer and stronger.\n1. The authors now summarize the distribution mutant-specific 7/19 (36.8%), mutant-selective\n9/19 (47.4%), WT-cross-reactive 3/19 (15.8%) and provide full profiles in Suppl. Fig. 7, with example\npanels and a summary table in Fig. 4D. They also added functional avidity data for four TCRs in\nSuppl. Fig. 8, which was exactly the kind of orthogonal support I suggested. However, my request to\nclarify whether WT-reactive clones are excluded from clinical release is not answered explicitly; the\nrebuttal focuses on bulk-product infusion and post-hoc assessments. The new data are strong; a\nsingle sentence stating the release criterion will fully close this point.\nWe thank the reviewer for requesting the additional claim and we have included this in the\nmanuscript.\n> Change (lines 327 – 329): “Given the infrequent observation of WT-reactive responses during\ndevelopment, the clinical approach2 involved infusing bulk products without the exclusion of WT-\nreactive clones.”\n2\n2. The new ovarian cancer data demonstrate feasibility beyond melanoma and are presented\ntransparently; however, the reported TMB 25–88 spans moderate–high mutational loads, so it\ndoesn’t fully speak to the lowest TMB contexts I had in mind. Still, adding these data and updating\ncounts strengthens generalizability.\nWe appreciate that the reviewer recognizes the value of the additional data generated in the context\nof OVC. For transparency purposes, we have included a reference to Alexandrov et al.3 that\ndescribes OVC and melanoma as indications with a moderate-to-high mutational load.\n> Change (lines 167 – 168): “…we applied the optimized process to PBMCs from melanoma\npatients and ovarian cancer (OVC) patients, indications with a mid-to-high mutational load3.”\nReviewer #3 (Remarks to the Author):\nThe authors have provided a careful and detailed rebuttal, and several of my earlier points have\nbeen appropriately addressed. In particular, the revisions related to donor inclusion and diversity\n(Question 1), antigen immunogenicity classification (Question 6), and figure and marker clarity\n(Questions 7 and 8) improve the manuscript’s transparency and readability. These efforts\ndemonstrate the authors’ commitment to strengthening the presentation and completeness of\ntheir work.\nHowever, several core scientific concerns remain insufficiently resolved.\nRegarding Question 2, the inclusion of tumor antigen expression and clonality data represents only\na minimal and largely cosmetic fix. The new data are not analyzed or discussed, merely appended\nto a supplementary spreadsheet. There is no examination of how antigen expression correlates\nwith immune responses, nor any commentary on tumor purity or clonal context. While this addition\nimproves transparency, it adds little biological or clinical insight.\nWe appreciate the reviewer's request for additional insights from this data. We have not\nemphasized such data in our manuscript because we believe that the relationships we observe are\nmore influenced by the design of NEO-STIM and the principles of our target selection framework\nthan by inherent biological processes.\nIt is well documented that high antigen expression correlates with the ability to raise immune\nresponses in patients4, our in vitro system does not display this correlation (see figure below).\nThis is not unexpected as we add equimolar amounts of the target peptides for stimulation,\nrendering the natural gene expression level of the target gene irrelevant when raising T cell responses\nwith NEO-STIM. This is important because our data shows that most of the responses we observe\nare induced de novo during manufacturing rather than expanding out pre-existing responses.\nNaturally, high expression of antigen is expected to remain relevant for anti-tumor reactivity upon\ninfusion of the manufactured drug product.\nRegarding clonality/variant allele fraction (VAF), it is important to note that we cannot assess the\nimmunogenicity of all mutations/epitopes in our system, so we employ a target selection\nframework. Through the computational algorithm, we prioritize peptides based on primary\ncharacteristics like binding affinity, antigen expression, and proteasomal cleavage potential5, an\napproach that has been shown to allow for the discrimination of immunogenic peptides in a large\nconsortium as well4. We complement this by secondary characteristics such as VAF for refining\nepitope selection5. This approach emphasizes more clonal targets due to their clinical significance\n6,7 but also ensures that lower clonality targets with robust primary traits remain in the pool.\nTherefore, the observed phenomenon that immunogenic peptides exhibit a lower median VAF\ncompared to non-responsive ones (see figure below) is likely a reflection of the selection process\nrather than intrinsic biological factors. Again, high VAF is expected to be relevant for anti-tumor\nreactivity and prevention of antigen escape upon infusion of the manufactured drug product.\nFor Question 3, the key issue of functional validation remains. The authors continue to rely\nprimarily on engineered A375 tumor cells, which are artificial targets that do not fully reflect native\ntumor antigen processing or presentation. No new functional data were added, and the rebuttal\nmainly reinterprets existing results rather than addressing the underlying experimental limitation.\nSimilarly, for Question 4, the justification provided for the use of artificial tumor models is\npragmatic but not a solution. Citing another study from the group offers useful context but does not\ncompensate for the lack of validation within this work. The connection is indirect and reads more as\nan appeal to external support than as additional evidence. For a mechanistic or preclinical study,\nthis remains a significant shortfall.\nWe share the reviewer's commitment to using physiologically relevant models. In the context of a\npersonalized neoantigen-specific product, the unique HLA background and mutational landscape\nof each patient mean that only autologous tumor material can truly reflect native tumor antigen\nprocessing and presentation. This manuscript includes recognition of autologous tumors from two\npatients for whom we had access to this type of material, 1 T cell response was tested from PT04, n\n= 3 replicates; and 2 T cell responses were tested for PT05, n = 3 replicates each). While we\nacknowledge that the A375 tumor cells used in our study require engineering of the HLA and the\nimmunogen components, which do not fully capture the physiological situation in patients, we\nbelieve that they still offer valuable insights as presented in this manuscript. Furthermore, we\nremain confident that the reference to the clinical data generated with this manufacturing process\nsupports the functional validation presented in this preclinical manuscript.\nrecognition:\n> Change (lines 409 – 414): “Finally, the manufactured drug products (DPs) demonstrated cytotoxic\npotential against tumor cells engineered to express the patients’ specific HLA and neoantigens.\nAlthough these targets are engineered cell lines, we additionally observed recognition of\nautologous tumor material for two patients, further supporting the relevance of our findings.”\nWith respect to Question 5, the clarification regarding CD4⁺ T-cell baseline controls is appreciated,\nbut no new data have been added. The CD4⁺ component remains underexplored. While the\nlimitation of available multimer reagents is understandable, reviewers would generally expect\nalternative or creative approaches to strengthen this aspect in a high-impact submission.\nWe respectfully disagree that the CD4+ component remains underexplored. With the addition of\nthe ovarian cancer data (Supplementary Figure 6), we strengthened the manuscript with an\nadditional 18 identified CD4+ T cell responses. We further show functional validation that these\ninduced CD4+ T cell responses also produce multiple effector functions (CD107a degranulation,\nTNFα, and IFNγ production).\nOverall, while the revised manuscript is clearer and more complete in presentation, the\nexperimental depth and validation required to fully support the central claims are still lacking.\nNat\nCommun 15, 2140 (2024). https://doi.org/10.1038/s41467-024-46367-9\nmetastatic melanoma: a phase 1 trial. Nat Med 31, 881-893 (2025).\nhttps://doi.org/10.1038/s41591-024-03418-4\n415-421 (2013). https://doi.org/10.1038/nature12477\nConsortium Approach Improve Neoantigen Prediction. Cell 183, 818-834 e813 (2020).\nhttps://doi.org/10.1016/j.cell.2020.09.015\nPatients with Advanced Melanoma, Non-small Cell Lung Cancer, or Bladder Cancer. Cell\n183, 347-362 e324 (2020). https://doi.org/10.1016/j.cell.2020.08.053\nimmune checkpoint blockade. Science 351, 1463-1469 (2016).\nhttps://doi.org/10.1126/science.aaf1490\nhttps://doi.org/10.1084/jem.20220846\n5"}, {"round": 2, "reviewer_comments": {"reviewer_1": "The majority of issues raised by the reviewers were addressed in the revised manuscript; however, there were a few additional points that need to be clarified.\nIn response to question 5 from reviewer 1, the distinction between highly immunogenic and weakly immunogenic still seems artificial. Highly immunogenic antigens might simply possess a larger repertoire of reactive clonotypes and therefore might elicit a more robust response. While this is of some interest, the most important point regarding tumor antigens is their relative potency against natural tumor antigen targets, as this can vary widely depending on the target.\nIn response to question 12 from reviewer 1, responses were defined as ‘mutant-selective’ if there was significant reactivity against the mutant epitope but ‘some’ reactivity against the wild type epitope. This is not a clear definition, and some criteria should be used such as a 10-fold or greater difference between the response to the mutant and wild type peptide. It should also be pointed out that wild-type cross-reactive T cells will be ineffective at tumor control as these presumably represent targets that are not naturally processed and presented at levels on tumor cells.\nThe response to question 7 from reviewer 2 was unclear. The statement that was added to the text indicated that six had stable disease as their best overall response but four of these patients showed tumor regression. Why was the best overall response of these four patients not designated tumor regression? Was tumor regression based upon RECIST criteria, and what was the duration of responses seen in these patients?", "reviewer_2": "The revision and rebuttal are careful, specific, and, on the whole, responsive. Most of my major and minor concerns are fully or substantially addressed with added methods detail, clearer figure legends, new supplemental analyses (specificity categories; functional avidity), and expanded clinical context and discussion. Only two items remain partially addressed (noted below). Explicit clinical release criteria for WT‑cross‑reactive clones, and breadth of claims for low‑TMB tumors. Otherwise, the manuscript is materially clearer and stronger.\n1. The authors now summarize the distribution mutant‑specific 7/19 (36.8%), mutant‑selective 9/19 (47.4%), WT‑cross‑reactive 3/19 (15.8%) and provide full profiles in Suppl. Fig. 7, with example panels and a summary table in Fig. 4D. They also added functional avidity data for four TCRs in Suppl. Fig. 8, which was exactly the kind of orthogonal support I suggested. However, my request to clarify whether WT‑reactive clones are excluded from clinical release is not answered explicitly; the rebuttal focuses on bulk‑product infusion and post‑hoc assessments. The new data are strong; a single sentence stating the release criterion will fully close this point.\n2. The new ovarian cancer data demonstrate feasibility beyond melanoma and are presented transparently; however, the reported TMB 25–88 spans moderate–high mutational loads, so it doesn’t fully speak to the lowest TMB contexts I had in mind. Still, adding these data and updating counts strengthens generalizability.", "reviewer_3": "The authors have provided a careful and detailed rebuttal, and several of my earlier points have been appropriately addressed. In particular, the revisions related to donor inclusion and diversity (Question 1), antigen immunogenicity classification (Question 6), and figure and marker clarity (Questions 7 and 8) improve the manuscript’s transparency and readability. These efforts demonstrate the authors’ commitment to strengthening the presentation and completeness of their work.\nHowever, several core scientific concerns remain insufficiently resolved. Regarding Question 2, the inclusion of tumor antigen expression and clonality data represents only a minimal and largely cosmetic fix. The new data are not analyzed or discussed, merely appended to a supplementary spreadsheet. There is no examination of how antigen expression correlates with immune responses, nor any commentary on tumor purity or clonal context. While this addition improves transparency, it adds little biological or clinical insight. For Question 3, the key issue of functional validation remains. The authors continue to rely primarily on engineered A375 tumor cells, which are artificial targets that do not fully reflect native tumor antigen processing or presentation. No new functional data were added, and the rebuttal mainly reinterprets existing results rather than addressing the underlying experimental limitation. Similarly, for Question 4, the justification provided for the use of artificial tumor models is pragmatic but not a solution. Citing another study from the group offers useful context but does not compensate for the lack of validation within this work. The connection is indirect and reads more as an appeal to external support than as additional evidence. For a mechanistic or preclinical study, this remains a significant shortfall. With respect to Question 5, the clarification regarding CD4⁺ T-cell baseline controls is appreciated, but no new data have been added. The CD4⁺ component remains underexplored. While the limitation of available multimer reagents is understandable, reviewers would generally expect alternative or creative approaches to strengthen this aspect in a high-impact submission.\nOverall, while the revised manuscript is clearer and more complete in presentation, the experimental depth and validation required to fully support the central claims are still lacking."}, "author_response": ""}, {"round": 3, "reviewer_comments": {"reviewer_2": "I am fully satisfied with the responses to my critiques, and I have no further comments."}, "author_response": ""}]

Decision letter