Abstract
Neoantigen-specific T cell receptors (neoTCRs) promise safe,
personalized anti-tumor immunotherapy. However, detailed assessment of
neoTCR-characteristics affecting therapeutic efficacy is mostly
missing. Previously, we identified diverse neoTCRs restricted to
different neoantigens in a melanoma patient. In this work, we now
combine single-cell TCR-sequencing and RNA-sequencing after
neoantigen-specific restimulation of peripheral blood-derived CD8^+ T
cells of this patient. We detect neoTCRs with specificity for the
previously detected neoantigens and perform fine-characterization of
neoTCR-transgenic (tg) T cells in vitro and in vivo. We describe a
heterogeneous spectrum of TCR-intrinsic activation patterns in response
to a shared neoepitope ranging from previously detected more highly
frequent neoTCRs with moderate activation to rare ones with initially
stronger activation. Experimental restimulation of adoptively
transferred neoTCR-tg T cells in a xenogeneic rechallenge tumor model
demonstrates superior anti-tumor responses of moderate neoTCR-tg T
cells upon repeated tumor contact. These insights have significant
implications for the selection of TCRs for therapeutic engineering of
TCR-tg T cells.
Subject terms: Cancer immunotherapy, Immunotherapy, Cytotoxic T cells,
Tumour immunology, T-cell receptor
__________________________________________________________________
Mutations in cancers can generate neoepitopes which can be recognized
by different specific T cell receptors (TCR) with varying functional
avidity. Here the authors show using xenogenic mouse models that
antigen reactive TCR-engineered T cells expressing TCRs from a cancer
patient with differing functional avidity have differential persistence
and function in vivo.
Introduction
Immunotherapeutic regimens have revolutionized anti-tumor therapy of
multiple malignancies, especially advanced by the efficacy of
immune-checkpoint inhibition (ICI)^[58]1. ICI treatment is especially
based on unleashing T cells, specifically recognizing tumor cells.
However, the exact cellular interplay is often multi-faceted and
requires deeper understanding to improve therapeutic response^[59]2.
Besides ICI, T cell-based adoptive cellular therapy (ACT) approaches
using tumor-infiltrating lymphocytes (TILs) or T cells genetically
engineered to express T cell receptors (TCRs) or chimeric antigen
receptors (CARs) have shown promising results^[60]3–[61]5. Since an
overall challenge of adoptive cellular transfer lies in attacking
mutant cells without targeting healthy tissues^[62]6,[63]7, neoantigens
arising from somatic, tumor-restricted mutations promise a safe,
precise, and highly personalized target structure. In fact, tumor
mutations correlate with response to ICI treatment^[64]8 and represent
prognostic biomarkers for successful immunotherapy, emphasizing the
importance of neoantigens and neoantigen-specific T cells for
anti-tumor response^[65]9. Moreover, targeting neoantigens with TILs or
TCR-transgenic (tg) ACT has been shown to confer deep, durable
responses in various cancer entities^[66]10–[67]12. However, the
discovery of neoantigen-specific TCRs (neoTCRs) relied on
labor-intensive functional T cell assays or sorting of reactive T cells
in the past^[68]13,[69]14. Recently, single-cell sequencing-based
identification approaches led to a less biased discovery to some
extent^[70]15–[71]20. Yet, low frequency in peripheral blood and
dysfunction of TILs still pose major challenges for neoTCR
identification^[72]21.
Recently, a clinical trial showed feasibility of adoptive transfer of
individual neoTCRs in a small number of patients with diverse
malignancies, although clinical efficacy was limited^[73]22.
Optimization of this approach, including neoTCR selection, will require
in-depth characterization of neoantigen-specific T cell phenotype and
neoTCR functionality as well as understanding of mechanisms affecting
sustained TCR-reactivity versus T cell dysfunction^[74]23,[75]24.
Generally, the heterogeneous functional states of tumor-specific T
cells are known to range from strong effector to dysfunctional
phenotypes, yet their effects on short- and long-term tumor control
remain largely unclear^[76]25–[77]27. So far, a small number of
approaches combined neoTCR identification in tumor-derived TILs across
different entities with transcriptomic characterization of the whole
neoTCR-population, although limited focus has been put on individual
TCR-clonotype properties and functional patterns^[78]16–[79]20.
Meanwhile, few preclinical models – tumor- or infection-based – aimed
at deciphering the impact of TCR-stimulation strength on T cells and
within the TCR repertoire of oligo-/polyclonal T cell responses, so
far, with limited translational significance for engineering T
cells^[80]28–[81]30. Despite attempts to transfer such results into
patient datasets, translational assessment of persistence of different
patient-neoTCRs in tumor settings with chronic antigen presence is
missing^[82]31.
In this case study, we build on previous work, where we identified
mutated peptide ligands by mass spectrometry (MS) and in-silico
prediction in melanoma patient Mel15. Subsequently, we investigated
TILs and PBMCs from Mel15 and discovered six neoTCRs targeting the two
neoantigens KIF2C^P13L and SYTL4^S363F ^[83]14,[84]31. We now combine
single-cell transcriptome sequencing (scRNA-seq) and single-cell TCR
sequencing (scTCR-seq) and thereby identify two further
KIF2C^P13L-specific neoTCRs. These two neoTCRs differ substantially in
their precursor frequency in the patient and transcriptomic activation
profile from the previously known TCRs with identical
KIF2C^P13L-specificity and thereby reveal a broad functional repertoire
of neoTCRs recognizing a common neoantigen. We show that diverse
activation patterns detected in scRNA- and scTCR-seq of primary T cells
are reinforced by in vitro and in vivo functionality of neoTCR-tg T
cells. Moreover, including an in vivo xenograft model for repeated
tumor re-challenge, we also provide evidence for substantial
differences in maintaining the functional capacity of engineered T
cells expressing defined neoTCRs depending on their stimulation
signatures. We here demonstrate that upon repeated antigen encounter in
vivo, neoTCR-tg T cells harboring an initially moderate activation
pattern outperform initially more strongly activated TCR-T cells. These
data correlate with different TCR frequencies in the patient and
suggest the inclusion of such TCRs in the selection and modification of
tumor-reactive TCRs for their application in ACT.
Results
Sensitive identification of neoTCRs via scRNA-seq
We previously reported about the neoantigens SYTL4^S363F and KIF2C^P13L
identified in a melanoma patient (Mel15) using our proteogenomic
approach as published before^[85]14,[86]32. We subsequently detected
reactive T cell clones and neoTCRs derived from peripheral blood
mononuclear cells (PBMCs) or TILs of the patient with specificity for
SYTL4^S363F or KIF2C^P13L. Functional characterization of these neoTCRs
revealed that TCR clonotypes of comparably lower avidity
(KIF2C^P13L-reactive) in comparison to those with higher functional
avidity (SYTL4^S363F-reactive) showed high frequencies within tumor,
lymph node and blood of the patient and surprisingly demonstrated equal
reactivity upon initial tumor encounter in vivo within a xenogeneic
murine tumor model^[87]14,[88]31.
To further understand qualitative transcriptomic differences between
these previously described neoTCRs and potentially identify additional
clonotypes, we performed scTCR- and scRNA-seq on a peripheral blood
sample of stage IV melanoma patient Mel15 at the time he was treated
with Pembrolizumab in a setting of no further evidence of
disease^[89]14. By enriching for CD137^+ activated T cells following
specific stimulation with the neoantigens SYTL4^S363F and KIF2C^P13L
and employing scTCR-seq (Fig. [90]1a), we aimed to increase the
sensitivity for detection of less frequent neoTCRs. As a reference for
expansion rates, we compared the specifically stimulated and enriched
blood sample to a freshly thawed, unstimulated sample from the same
time point. Indeed, upon antigen-specific stimulation of enriched and
expanded T cells, we observed an increase in peptide-specific T cells
in the enriched population with significantly upregulated CD137
expression (Fig. [91]S1a) and increased Interferon-y (IFN-γ) secretion
(Fig. [92]S1b).
Fig. 1. Identification of neoTCRs via scTCR-seq of CD8^+ T cells from
melanoma patient Mel15.
[93]Fig. 1
[94]Open in a new tab
a Schematic experiment setting. b Increase in total number of cells of
known (KIF-P1 and -P2, SYT-T1, -T2, -P1, -P2) and additionally
identified (KIF-sc1 and -sc2) TCRs upon antigen-specific stimulation
and CD137-enrichment with dominance of KIF-P1 and -P2 harboring high
precursor frequency. The two additionally identified KIF2C-TCRs were
selected based on fold change of TCR frequency and highest absolute
frequency in the stimulated sample. c Assessment of antigen-specific
IFN-γ-secretion for the two identified TCRs KIF-sc1 and -sc2 in
comparison to the known TCR KIF-P2. Cytokine secretion was measured by
IFN-γ-ELISA upon 24 h of co-culture of TCR-tg T cells from one
representative donor with Mel15-LCL transgenic for the mutated
KIF2C^P13L minigene (mut mg) and the wildtype KIF2C minigene (wt mg) as
well as pulsed for 2 h at 37 °C with the mutated and wildtype peptide
(mut pep and wt pep). An irrelevant peptide (irr peptide), target cells
(LCL only) or T cells alone (T cell only) served as negative controls.
Mean and SD of technical triplicates depicted. Data representative for
two different donors. d Frequency of KIF-sc1 and -sc2 in relation to
the previously identified TCR-sequences^[95]31 identified by deep
sequencing of the TCR-β-chain in intestinal (M[Int]) and lung
metastases (M[Lung]) as well as corresponding non-malignant draining
lymph nodes (M[Int]-LN1, M[Int]-LN2 and M[Lung]-LN) of patient Mel15.
Non-td non-transduced.
The diversity of TCR clonotypes with one defined alpha and one defined
beta chain in our samples decreased throughout stimulation, from 1832
different clonotypes in the unstimulated to 279 in the restimulated
sample. When including clonotypes with only one defined alpha or beta
chain, the numbers decreased from 2657 different clonotypes in the
unstimulated to 362 clonotypes in the restimulated sample
(Supplementary Data 1). All six previously known reactive receptors
ranged amongst the most expanded TCRs, suggesting high efficacy of the
CD137^+-selection step for these TCRs, KIF-P1, KIF-P2, SYT-T1, SYT-T2,
SYT-P1 and SYT-P2 (Fig. [96]S1c–e and Supplementary Table [97]1).
KIF-P1 accounted for 69.0% of the restimulated clonotypes, with a high
baseline frequency of 3.2% before enrichment and thereby greatly
exceeded all other receptors in total frequency (Fig. [98]S1c, d).
Besides, we sought to identify additional clonotypes with defined
specificity by comparing abundance in the unstimulated and restimulated
conditions (Fig. [99]1b and Supplementary Data 1). Two previously
unknown clonotypes were identified—KIF-sc1 and KIF-sc2—demonstrating
specific reactivity towards the mutated epitope KIF2C^P13L
(Fig. [100]1c). Surprisingly, their binding motifs were significantly
different from the previously identified KIF2C^P13L-reactive TCRs with
no matching human protein hits containing the recognition motifs
(Fig. [101]S1f and Supplementary Table [102]2). In the patient, the two
additionally identified KIF2C^P13L-reactive TCRs could furthermore be
detected at different frequencies in several compartments: both were
detectable below the previously described high frequencies of KIF-P1
and KIF-P2 in lung and intestinal metastases as well as the respective
lymph nodes (Fig. [103]1d and Supplementary Table [104]3). While
KIF-sc1 was less frequent in the intestinal metastasis as well as the
draining lymph nodes than KIF-sc2, the opposite was true for the lung
metastasis and its draining lymph node (Fig. [105]1d and Supplementary
Table [106]3). In conclusion, by combining CD137^+-enrichment and
frequency comparison of clonotypes identified from scTCR-seq we could
enrich all previously discovered neoTCRs and identify two further
neoTCRs from peripheral blood. In comparison to the previously
identified, the additional receptors showed significantly different
binding motifs as well as varying frequencies in metastases and lymph
nodes.
Negatively regulated and proliferative transcriptomic signatures in ex vivo
restimulated, patient-derived T cells
In order to understand if qualitative differences in T cell activation
and proliferative capacity determine clonotype frequency as previously
hypothesized^[107]31, we combined scTCR-seq with transcriptome analysis
via scRNA-seq upon neoantigen-specific stimulation using the described
ex vivo TCR-centered restimulation model detached from the tumor
microenvironment (TME). To specifically focus on stimulation-dependent
effects, we performed unbiased clustering using differential gene
expression of the CD137^+ enriched, repeatedly neoantigen-stimulated
cells compared to freshly thawed CD8^+ T cells reflecting the mainly
native TCR repertoire of patient Mel15 (Fig. [108]2a). These analyses
revealed eleven clusters according to our experimental setting
(Fig. [109]2a, b). As expected, unstimulated (mainly clusters 1–6,
partly 7–9) and restimulated cells (mainly clusters 7–9 and partly also
5 and 6) clustered differently (Fig. [110]2a).
Fig. 2. Heterogeneous spectrum of transcriptomic activation patterns for
different neoTCRs between cytotoxic, less inhibitory (KIF-P1 and -P2) and
inflammation-related, negatively regulated activation (SYTL4^S363F-specific
TCRs).
[111]Fig. 2
[112]Open in a new tab
a UMAP of 5764 unstimulated and 6007 restimulated CD8^+, sorted T cells
after QC with color code indicating 11 different clusters named after
one of the most differentially expressed genes each (except for MAIT1
and MAIT2). UMAPs of unstimulated (blue, lower left graph) and
restimulated (red, lower right graph), enriched (single alive) CD8^+ T
cells next to the UMAP with all identified clusters. b Dot plot showing
the five most differentially expressed genes per cluster. Size of each
dot indicates percentage of gene-expressing cells per cluster; color
indicates scaled average fold expression of the corresponding gene
within the cluster. c UMAPs of unstimulated and restimulated CD8^+
cells showing distribution of single known TCR-specificities from both,
the stimulated (red) and unstimulated (blue) sample. Non-assigned cells
are depicted in gray for stimulated and unstimulated sample. d Bar-plot
indicating percentual distribution of neoTCR clonotypes per cluster.
Only conditions surpassing the threshold for minimal cell numbers (>25)
were included. e Heatmap showing scaled average differential
transcriptomic gene expression comparing all KIF2C^P13L-specific
neoTCRs (separating KIF-P1 and -P2 from the identified KIF-sc1 and
-sc2) and all SYTL4^S363F-specific neoTCRs (all SYTL4-TCRs). f Volcano
plot indicating fold changes and p values of differential
transcriptomic gene expression comparing all KIF2C^P13L- (all
previously and all additionally identified TCRs) and
SYTL4^S363F-specific TCRs. Wilcoxon rank sum test with Bonferroni
correction for p value adjustment was used for statistical testing. g
Heatmap showing scaled average differential transcriptomic gene
expression comparing the same neoTCR-groups as in (e), ranked with
focus on highest expressed genes in SYTL4^S363F-specific TCRs.
Within this approach, we focused on the overall distribution of T cell
phenotypes across these defined clusters irrespective of their
clonotype (Fig. [113]S2a–f). A naїve-like, antigen-inexperienced
transcriptional state (expressing CCR7, LEF1, NELL) could be identified
in the unstimulated sample, mainly within clusters 1, 2 and 3
(Fig. [114]S2a, b). A smaller fraction of the unstimulated as well as
parts of the restimulated cells mainly clustering in 5 and 6 (partly
also 8) could be assigned to an effector-like phenotype (expressing
CX3CR1, GNLY, GZMH, FGFBP2, FCGR3A, PLEK, ADGRG1, PRF1), however,
missing expression of proliferative genes (Fig. [115]S2b, c).
Meanwhile, the upregulation of inhibitory surface receptors (most
dominantly LAG3, but also TIGIT and HAVCR2) was a particular feature of
clusters 6 and 7 mostly comprising stimulated cells (Fig. [116]S2d, e).
In contrast, stimulated T cells in clusters 8 and 9 had vastly
initiated proliferative processes upregulating typical genes such as
MKI67, HIST1H4C, HSPD1, NME1, SP90AB1, ENO1, EIF4A1 (Fig. [117]S2e, f).
Within the mainly negatively regulated, inhibitory cluster 7 pathways
indicating TCR signaling and cytokine-mediated response to the cognate
antigen were highly upregulated, yet proliferative processes and cell
cycle G2/M-phase transition were negatively regulated (Fig. [118]S2g).
Clusters 8 and 9 are prominently reflected in cell cycle phases
(Fig. [119]S2h) and high numbers of features and expanded cells of
these clusters (Fig. [120]S2i, j). Overall, as projected by trajectory
analyses, the dynamic evolution of the differentiation state starting
at cluster 1 (most naïve) with the lowest and ending at cluster 9 with
the highest pseudotime score (Fig. [121]S2k, l) also confirmed
successful initiation of T cell stimulation within our experimental
setup.
Heterogeneous gene expression patterns in neoTCRs with shared and divergent
specificities
Regarding stimulation patterns of each individual neoTCR, we then
analyzed the distribution of all known clonotypes within these clusters
(Fig. [122]2a, c). Regarding the cluster-related composition of
different neoTCR-clonotypes, neoTCRs showed marked differences in their
effector, inhibitory or proliferative state (Fig. [123]2d). From all
unstimulated cells, only KIF-P1 and -P2 could be included in the
comparison of cluster distribution surpassing the subset-analysis
threshold of 25 cells after quality control. Both clonotypes were
mostly present in the FGFBP2-effector cluster 5 (Fig. [124]2a–d).
Considering only the stimulated T cell population, all four
KIF2C^P13L-specific TCRs were mostly present in a proliferative state
(clusters 8 and 9), whereas SYT-P2, -T1 and -T2 were distinguished by a
high percentage of cells from the inhibitory LAG3-cluster (cluster 7).
SYT-P1 clustered more similarly to the KIF2C^P13L-specific pattern,
raising the question about further heterogeneity within the
SYTL4^S363F-TCRs. It has to be noted, however, that the absolute number
of cells compared per TCR differed substantially, likely associated
with TCR frequencies before stimulation, among other factors
(Fig. [125]1b and Supplementary Table [126]1). In conclusion, these
transcriptome analyses supported the notion of heterogeneity within
activation patterns of KIF2C^P13L- and SYTL4^S363F-specific TCRs.
An unbiased look at the differentially expressed genes of KIF2C^P13L-
versus SYTL4^S363F -specific TCRs within the stimulated population
further reflected the patterns described. On the one hand,
KIF2C^P13L-specific TCRs upregulated genes of cytotoxic effector
functions (GZMA), antigen presentation (MHC class II-genes and CD74)
and TCR-signaling (ANXA5, AHNAK, S100A6, S100A10, LIME1) among which
many are involved in calcium-dependent processes (Fig. [127]2e, f). On
the other hand, SYTL4^S363F-specific TCRs diverged from this activation
pattern upon stimulation. SYTL4^S363F-TCRs highly expressed genes
correlated with chemokine profiles and proinflammatory pathways (e.g.,
XCL1, XCL2, CD27, CCR3, CCL3; Figs. [128]2f, g and [129]S3a, b). At the
same time, inhibitory receptors like LAG3 and TIGIT (potentially also
TNFRSF18), but also DUSP4 and PTPN7, two MAP-Kinase inhibitors, were
upregulated in SYTL4^S363F-TCRs implicating simultaneous inhibitory
regulation. In contrast, the significantly upregulated genes for
KIF2C^P13L-TCRs did not include such indicators of inhibitory signaling
(Fig. [130]2e, f).
Differential gene expression revealed further insights on
KIF2C^P13L-specific TCRs, indicating a distinct state for KIF-sc1 and
-sc2 differing from both previously described qualitatively contrasting
activation signatures of KIF2C^P13L- versus SYTL4^S363F-TCRs. This is
displayed by a gradient detectable in the expression level of MHC class
II genes, CD74 and GZMA from KIF-P1 and -P2 over -sc1 and -sc2 towards
SYTL4^S363F-TCRs (Fig. [131]2e). A heterogeneity between
KIF2C^P13L-specific profiles was further supported by the direct
comparison of KIF-sc1/-sc2 versus SYTL4^S363F-TCRs showing only the
upregulation of genes associated with TCR signaling like LIME1 and
S100A10 in KIF-sc1/-sc2 in contrast to upregulation of negative
regulators like PTPN7 and DUSP4 only in SYTL4^S363F-TCRs
(Fig. [132]S3c). MHC class II genes, however, were not differentially
upregulated between KIF-sc1/-sc2 and SYTL4^S363F-TCRs (Fig. [133]S3c).
Regarding unbiased analysis of unstimulated neoTCRs, again, only KIF-P1
and -P2 transcriptomes comprising sufficient cell counts could be
analyzed. Comparing both TCR clonotypes with all other unstimulated T
cell clones, cytotoxic markers, including FGFBP2, GZMB, GZMH, GNLY and
NKG7, were predominantly upregulated (Fig. [134]S3d).
Overall, we describe a spectrum of TCR-dependent T cell activation
patterns in this scRNA-seq dataset from an ex vivo restimulation
setting from patient-derived neoantigen-specific CD8^+ T cells. We
detected cytotoxic, proliferative and less inhibitory T cell activation
patterns, especially for KIF-P1 and -P2 and comparatively higher
expression of inflammation- and chemokine-related as well as inhibitory
genes for SYTL4^S363F-specific TCRs. KIF-sc1 and -sc2 shared features
of both patterns.
TCR-construct-inherent differences in surface expression of Mel15’s neoTCRs
in retrovirally and orthotopically TCR-engineered T cells
All cells originating from patient PBMCs possess a certain
differentiation state at the time of blood collection due to multiple
variables, including potential previous encounters with their cognate
antigen as well as therapeutic regimens. To circumvent potential bias
between T cell populations with different previous fates in the
patient, we further compared the different neoTCRs after genetic
transfer via retroviral transduction into activated CD8^+ T cells of
several healthy donors in independent experiments. This in vitro
analysis enabled antigen dose-titrated T cell stimulation and,
moreover, helped to decouple TCR-intrinsic features from
patient-specific cellular differentiation within the narrow spectrum of
determined functional avidities^[135]31. Expressing the neoTCRs under
the retroviral promotor (Fig. [136]S4a), we observed notable
differences in extra- and intracellular TCR expression
(Fig. [137]S4b–d), with KIF-sc1 showing the highest TCRmu^+ expression
rates but also the highest TCR density as determined by gMFI
(Fig. [138]S4c, d). Of note, the relative differences in TCR surface
expression between constructs were neither entirely reflected by the
absolute quantity of TCR transcripts (Fig. [139]S4e) nor insertions
(Fig. [140]S4f). High surface expression of KIF-sc1, however, was
associated with the highest number of RNA transcripts detected (3-fold
higher than the endogenous human TCR-β chain; Fig. [141]S4e).
To rule out expression differences only based on the retroviral
CMV-promotor, we further employed CRISPR/Cas9 for orthotopic TCR
replacement (OTR) of the endogenous TCR-α chain by our TCR-constructs
in the TRAC locus (Fig. [142]S4g–k)^[143]33,[144]34. Comparing the
retroviral (RV) with the OTR system, we detected a similar level of
TCRmu-surface expression per cell within the TCR-tg population in both
systems for KIF-P2 and -sc1 after enrichment by FACS-sorting and in
vitro expansion. This indicated similar construct-inherent surface
levels under both promotors for these two TCRs, with KIF-P2 showing
overall lower surface expression likewise in both engineering systems
(Fig. [145]S4k). In contrast, surface expression of KIF-P1 and KIF-sc2
markedly increased in the OTR system (Fig. [146]S4k), suggesting
altered expression characteristics of both TCRs under retroviral gene
expression and potentially higher dependence on the expression system.
Inflammation-related, inhibitory neoTCR-transcriptome signatures correspond
to overall stronger activation of virally engineered TCR-tg T cells in vitro
In line with several current clinical ACT protocols, we further focused
our analyses on retrovirally engineered T cells with the highest
expression of neoTCRs: KIF2C^P13L-specific TCRs KIF-P2, -sc1 and -sc2
in direct comparison to SYT-T1. We investigated the effect of different
stimulation strengths by cytokine secretion and expression of
activation and inhibitory markers in response to target cells pulsed
with ascending peptide concentrations (Figs. [147]3a–d and [148]S5a–d).
This illustrated stronger, more sensitive activation of SYT-T1-tg T
cells after 24 h, reflected by quantitative IFN-γ secretion compared to
KIF-P2 as previously described^[149]31. KIF-sc1 and -sc2 showed
intermediate responses between those two diverse reactivity patterns
(Fig. [150]3a–d), as also confirmed by EC[50]-value measurement in the
viral expression system (Fig. [151]S5e and Supplementary Table [152]4).
These differences in activation patterns were similarly reflected by
the expression of the activation marker CD137 (Figs. [153]4b and
[154]S5b) as well as, during early activation, the inhibitory receptors
PD-1 and LAG-3 (Figs. [155]3c, d and [156]S5c, d); the latter being the
most prevalent gene in the inhibitory signature of the transcriptome
analysis (Fig. [157]2a, f). Besides functional avidity, structural
avidity was only recently described as an important feature for
TCR-functionality and T cell tumor tropism^[158]35. Comparing KIF-P2,
-sc1 and -sc2, we did not detect any significant differences in k[off]
rates (Fig. [159]S5f).
Fig. 3. Moderate versus strong activation patterns are transferable to CD8
neoTCR-tg T cells from healthy donors during in vitro co-cultures with
different cell lines.
[160]Fig. 3
[161]Open in a new tab
Color code for neoTCRs in a–f indicated next to h: SYT-T1 red, KIF-sc1
light green, KIF-sc2 dark green, KIF-P2 blue. a–d Mel15 LCL were pulsed
with titrated peptide concentrations (2 h, 37 °C) and co-incubated with
TCR-tg T cells with subsequent ELISA-based assessment of
IFN-γ-secretion within 24 h of co-culture (a). The cellular activation
level was determined after 24 h by FACS staining of the extracellular
level of CD137 (b), PD-1 (c) and LAG-3 (d) expression (reflected by
geometric mean of all CD3^+CD8^+/TCRmu^+ cells). Wildtype control
depicts only the highest peptide concentration (100 µM wt peptide). The
mean for ELISA data is depicted for technical triplicates of one
representative of four donors; triplicates from the same donor have
been pooled prior to EC FACS-staining. E:T = 1:1 (15,000 tg T
cells:15,000 tumor cells). EC FACS staining at different time points
after co-culture setup displays temporal dynamics of T cell activation
marker CD137 (e, f) and inhibitory receptor LAG-3 (g, h) for TCR-tg T
cells upon co-culture with JJN3-B27 peptide-pulsed target cells. A weak
(0.01 µM for peptide pulsing; e, g) versus a strong (1 µM for peptide
pulsing; f, h) stimulus were compared. E:T = 1:1 (10,000 tg T
cells:10,000 tumor cells). gMFI-values of all TCRmu^+ cells are shown.
i Annexin-V/PI-staining was employed for detection of activation
induced cell death (AICD) after 20 h of co-culture upon strong
stimulation with 1 µM mut-peptide pulsed Mel15 LCL (early
apoptotic = AnnexinV^+PI^−, late apoptotic = AnnexinV^+PI^+). E:T = 1:1
(30,000 tg T cells:30,000 tumor cells). j Representative FACS plot of a
healthy donor of CTV-analysis for all TCRmu^+ cells depicted after 4
days of co-culture with 1 µM mut-peptide pulsed Mel15 LCL (colors were
chosen according to (b–e); representative wt mg-control depicted in
gray). E:T = 1:1 (30,000 tg T cells:30,000 tumor cells). For all
co-cultures, TCRmu^+ rates were adjusted by addition of non-transduced
T cells to equalize TCRmu^+ cell frequencies for all neoTCRs. For all
co-cultures in e–j technical triplicates per donor were pooled prior to
staining; mean and SD for biological replicates from three different
human donors are shown.
Fig. 4. Despite distinct activation patterns neoTCR-tg T cells demonstrate
entity-independent, comparable in vivo tumor rejection upon first tumor
encounter.
[162]Fig. 4
[163]Open in a new tab
a Schematic setting of xenograft first-encounter tumor rejection
experiment with newly transduced neoTCR-tg T cells. b Tumor growth
kinetics in a lymphoma model are displayed as tumor area (in cm^2) for
U698M-mut-mg-tumor-bearing NSG-mice comparing neoTCR-tg T cells to
irrelevant TCR 2.5D6 until day 20. 30 × 10^6 TCR-tg T cells were
injected per mouse. Mean and SD display rejection dynamics of
tumor-bearing mice as biological replicates (n = 6). Parts of this
dataset were published before^[164]31. Here, tumor rejection kinetics
of KIF-sc1 and -sc2 analyzed in the same experiment are additionally
shown. c Kaplan–Meier-survival curve is displayed for
U698M-tumor-bearing mice injected with 30 × 10^6 neoTCR-tg T cells
(Mantel–Cox test). d Tumor growth kinetics for
U698M-mut-mg-tumor-bearing NSG-mice for KIF-P2, -sc1 and 2.5D6 after
injection of 5 × 10^6 TCR-tg T cells per mouse (n = 4 biological
replicates). e Kaplan–Meier-survival curve displayed for
U698M-tumor-bearing mice injected with 5 × 10^6 neoTCR-tg T cells
(Mantel–Cox test; KIF-P2 and KIF-sc1 p = 0.0067). f Tumor growth
kinetics in a melanoma model for mut-mg-A2058-tumor-bearing NSG-mice
after injection of 5 × 10^6 TCR-tg T cells per mouse (n = 5 biological
replicates). g Kaplan–Meier-survival curve displayed for
A2058-tumor-bearing mice injected with 5 × 10^6 neoTCR-tg T cells
(Mantel–Cox test, KIF-P2 and KIF-sc1 p = 0.0026). For d–g mean and SEM
display rejection dynamics.
While the detected spectrum of activation strengths for different
neoTCRs remained stable across different effector-to-target
(E:T)-ratios (Fig. [165]S6), we also analyzed temporal dynamics of
activation kinetics at different stimulation strengths (Figs. [166]3e–h
and [167]S7). Surface staining of CD137 over the course of 48 h on
different TCR-tg populations upon co-culture with two different peptide
concentrations (0.01 μM and 1 μM) consistently showed maximal
expression after 12 h with a similar spectrum of activation patterns
over time as observed before (Figs. [168]3e, f and [169]S7a–c). Higher
peptide concentration, nevertheless, prolonged the time of CD137
expression on a population level for all TCRs and, moreover, increased
CD137 levels, particularly for the KIF2C^P13L-reactive TCRs. The same
pattern was detected when stimulating TCR-tg T cells with another tumor
cell line (Fig. [170]S7d–i). We detected maximal upregulation of LAG-3
after 24 h, especially on SYT-T1- and KIF-sc1-tg T cells for both
stimuli, while KIF-P2 and -sc2 upregulated LAG-3 only upon the strong
stimulus (Figs. [171]3g, h and [172]S7j–l). Similar trends could also
be shown for PD-1 levels despite overall lower expression as compared
to LAG-3 (Fig. [173]S7m–o). In addition, the stronger TCR activation
patterns of SYT-T1 and KIF-sc1 were linked to a slightly increased
percentage of apoptotic (Annexin V^+) cells in co-culture with diverse
cell lines pulsed with 1 μM peptide (Figs. [174]3i and [175]S8a–f).
Meanwhile, no significant proliferative differences could be detected
for these neoTCRs in vitro throughout the first four days after
stimulation (Figs. [176]3j and [177]S8g–i). Thus, despite higher levels
of inhibitory receptors, proliferative dysregulation in vitro did not
appear to be a key feature of TCR-tg T cells with strong activation
patterns upon this first in vitro stimulation.
In summary, KIF-sc1-tg T cells show patterns of stronger activation
more similar to SYTL4^S363F-specific TCRs, while KIF-sc2 showed
comparably more moderate activation closer to the KIF-P2-pattern. This
indicates a level of heterogeneity in activation patterns of TCRs with
identical neoantigen/peptide-HLA-specificity. Overall, T cells
transduced with TCRs associated with proinflammatory, negatively
regulated transcriptomic signatures performed more sensitively in the
applied viral expression system. They reached higher overall levels of
cytokine secretion and activation markers but also increased inhibitory
receptor expression upon first antigen encounter in vitro. This pattern
of stronger activation could be described for SYT-T1 and partly
KIF-sc1. In comparison, KIF-P2 and KIF-sc2 appeared with a more
moderate activation signature.
NeoTCR-tg T cells demonstrate comparable tumor rejection upon first in vivo
encounter despite different activation patterns
To assess functionality in vivo, we investigated the anti-tumor
reactivity of neoTCR-tg T cell populations, including the additional
clonotypes in a previously established in vivo xenograft tumor model
with the HLA-matched B cell lymphoma cell line U698M expressing
minigenes encoding KIF2C^P13L and SYTL4^S363F (mut mg). We initially
used a model designed for highest efficacy in tumor rejection
(Fig. [178]4a–c), revealing comparably potent rejection kinetics for
neoTCRs—previously known and now additionally identified—compared to
the irrelevant, MPO-specific TCR-control (2.5D6)^[179]31. We have
already published part of the data from this experiment, including
KIF-P2, SYT-T1 and 2.5D6^[180]31, and now additionally show data for
KIF-sc1 and -sc2, which were included in the same experiment. In this
setting, the two additionally identified neoTCRs KIF-sc1 and -sc2
performed equally well compared to those previously known and reached
complete tumor rejection in all mice with significantly prolonged
survival (Fig. [181]4b, c).
Subsequently, to investigate differences within our observed spectrum
of activation, we focused on two TCRs with shared
neoantigen-specificity, HLA-restriction as well as similar behavior of
surface expression in different engineering systems, yet different
activation patterns: moderate (KIF-P2) versus strong (KIF-sc1).
Lowering effector cell numbers, both neoTCRs still demonstrated equally
potent in vivo rejection in the lymphoma model (Fig. [182]4d, e).
However, to return from our entity agnostic-approach to patient Mel15’s
entity, we also tested in vivo anti-tumor response against the
endogenously HLA-A03-expressing melanoma cell line A2058 transgenic for
the same neoantigen-encoding minigene (mut mg). Again, we observed
potent anti-tumor response in all tumor-bearing hosts for both neoTCRs
(Fig. [183]4f, g). Both cell lines selected for our in vivo model
covered different levels of neoantigen surface expression: Notably,
U698M expresses a 4.1- to 4.8-fold lower level of HLA-A03 compared to
other tumor cell lines (Fig. [184]S9a) and its surface level of
KIF2C^P13L, measured by MS, ranked overall lowest compared to A2058 and
Mel15 LCL (Fig. [185]S9b). Thereby, MS analysis revealed comparable
antigen levels resulting from minigene expression and in vitro peptide
pulsing (0.1 µM and 1 µM) for all three different cell lines
(Fig. [186]S9b). In fact, the level of T cell activation after
co-culture with minigene-expressing or peptide-pulsed targets
correlated with the level of antigen since much higher concentrations
of peptide were needed to achieve comparable activation between the mut
mg and pulsed conditions for U698M compared to A2058. However,
regarding Mel15 LCL in comparison to the other cell lines, it becomes
evident that this response also seemed dependent on other determinants
of the tumor entity (Fig. [187]S9c–e).
Tumor-infiltrating T cells were characterized in both models at day 5
after T cell injection (Fig. [188]S10) for their composition
(Fig. [189]S10b–e, l–o), activation status (Fig. [190]S10f–i, p–s) and
phenotype (Fig. [191]S10j, k, t, u). Compatible with increased TCRmu^+
T cell enrichment of KIF-P2 at the tumor site, we observed
significantly higher percentages of TCRmu^+ KIF-P2 than KIF-sc1 T cells
in both models (Fig. [192]S10c–e, m–o). However, despite clear signs of
activation of T cells at the tumor site compared to those residing in
the spleen (Fig. [193]S10f–i, p–s), no further significant differences
comparing the two neoTCRs could be observed (Fig. [194]S10f–k, p–u).
Overall, the observed in vitro differences in neoTCR activation
patterns did neither translate into significant differences in killing
capacity nor TIL activation status upon first in vivo encounter of the
tumor.
Moderate TCR-signal associates with superior tumor control upon repeated
neoantigen challenge in vivo
Aiming to understand the impact of the detected slight differences in
activation between neoTCRs on long-term T cell functionality, we next
challenged our setting by investigating repeated in vivo tumor
challenge. Therefore, we generated TIL products (TIL-P) from
tumor-bearing TIL-P-treated mice and after ex vivo expansion reinjected
these cells (TIL-P-KIF-sc1-tg or TIL-P-KIF-P2-tg) into other
tumor-bearing recipients. In parallel, we compared the performance of
these TIL-P with a new transduction of the same two TCRs on freshly
isolated CD8^+ T cells from the same donor (NEW) as control groups
(Fig. [195]5a).
Fig. 5. Moderate activation pattern of KIF-P2 T cells associates with
sustained anti-tumor response upon in vivo rechallenge in contrast to
strongly activated KIF-sc1.
[196]Fig. 5
[197]Open in a new tab
a Setting of xenograft neoTCR-TIL-P rechallenge experiment. b Tumor
growth kinetics of U698M-mut mg displayed as tumor area (in cm^2) on
day 17 after second injection of in total 5 × 10^6 neoTCR-tg T cells
(55% TCRmu^+ for all groups). For TIL-P-groups, TIL-P from two mice per
TCR were pooled. Mean and SEMs for each group display rejection
dynamics (n = 5 experimental groups, n = 3 2.5D6; one 2.5D6-control
sacrificed earlier). Statistical significance calculated for tumor area
on day 17 with one-way ANOVA and Tukey’s multiple comparison test
(adjusted p values of TIL-P-KIF-P2 versus 2.5D6: ***p = 0.0001 and
TIL-P-KIF-P2 versus -KIF-sc1: ****p < 0.0001). c Kaplan–Meier-survival
curve displayed for U698M-tumor-bearing mice injected with TCR-tg T
cells (n = 5 for experimental groups, n = 4 for 2.5D6-control;
Mantel–Cox test, p = 0.0019). d, e Log2(fold change) is depicted for
the ratio KIF-P2:KIF-sc1 for cytokines secreted within 20 h of in vitro
co-culture on the day of (re)injection of TIL-P or NEW T cell
conditions (d0 of survival experiment) with U698M-mut-mg
(E:T = 50,000:50,000). Ratios depicted for NEW (d) and TIL-P (e) cells
from three human donors (A, B and C). Mean and min-to-max-range
depicted. f Heatmap showing transcriptional expression for selected
cytokines detected in bulk-RNAseq on CD8^+-enriched neoTCR-tg T cells
of donor B normalized to 2.5D6-control (12 h or 24 h stimulation with
U698M mut mg or wt mg). Technical triplicates pooled prior to
CD8^+-enrichment. g Tumor growth kinetics of A2058-mut mg displayed as
tumor area (in cm^2) until day 15 after injection of 5 × 10^6 neoTCR-tg
T cells (55% TCRmu^+ for all groups). Mean values and SEMs display
rejection dynamics (n = 5 for experimental groups, n = 7 for TIL-P
KIF-P2 and n = 4 for 2.5D6; one 2.5D6-control sacrificed earlier).
Statistical significance was calculated for tumor area on day 14 with
one-way ANOVA and Tukey’s multiple comparison test (adjusted p values
of TIL-P-KIF-P2 versus 2.5D6: **p = 0.0018 and TIL-P-KIF-P2 versus
-KIF-sc1: **p = 0.0075). h Kaplan–Meier-survival curve for
A2058-tumor-bearing mice injected with TCR-tg T cells (n = 5, n = 7 for
TIL-P KIF-P2; Mantel–Cox test, p values of TIL-P-KIF-P2 versus 2.5D6:
***p = 0.0005 and TIL-P-KIF-P2 versus -KIF-sc1: *p = 0.0138).
While the newly transduced TCR-tg T cells conferred complete tumor
rejection with both TCRs in all mice until day 18 as previously
described, we observed clear dysfunction of TIL-P-KIF-sc1 upon
rechallenge in vivo (Figs. [198]5b and [199]S11). Tumors in
TIL-P-KIF-sc1 mice could not be controlled by the T cell product
applied as observed for animals receiving the non-specific T cell
product (TCR 2.5D6). TIL-P-KIF-P2, meanwhile, reached potent tumor
rejection in all mice and performed equally efficiently compared to the
newly transduced (NEW) T cells (Figs. [200]5b, c and [201]S11). These
distinct response patterns upon repeated antigen challenge were
observed albeit with incomplete tumor rejection in two additional
independent experiments using T cells from different healthy human
donors (Fig. [202]S11).
During in vitro expansion of TIL-P of the individual mice, we again
detected differences in TCRmu^+ frequencies (Fig. [203]S12a–c) and
overall superior expansion of TCRmu^+ KIF-P2 conditions despite overall
comparable growth of the CD8^+ fraction (Fig. [204]S12d–i). This
suggests superior preservation of proliferative capacity and
TCR-expression for the moderate TCR KIF-P2. Prior to reinjection of
TIL-P, we performed ex vivo co-cultures with the U698M-mut mg tumor
cell line to compare T cell functionality. Multiplex analysis of
several traditional CD8/natural killer (NK)-cytokines on a protein
level did not reveal differences between both neoTCRs in the NEW
conditions (Fig. [205]5d). Meanwhile, the comparison of both TIL-P
revealed that secretion of classical CD8 effector cytokines (IFN-γ,
IL-2 and TNF) was highly heterogeneous between donors and, therefore,
not causative for the shared in vivo phenotype (Fig. [206]5e). While
furthermore, no differences for cytokines linked to killing capacity
(GzmA, GzmB and Perforin) were detected between both TCRs, it was
interesting that secretion of the inhibitory, anti-inflammatory
cytokine IL-10 was significantly upregulated in TIL-P KIF-P2 of all
three donors (Figs. [207]5e and [208]S13a–c) potentially linked to a
protective role of this cytokine for these T cells. For one donor, we
moreover investigated the transcriptome in a CD8^+-purified fraction
after in vitro stimulation via bulk-RNA-sequencing, which clearly
separated stimulated from unstimulated cells as seen by principal
component analysis (PCA) (Fig. [209]S13d). Thus, we could also confirm
an upregulation of IL-10 transcripts in the KIF-P2 TIL-P CD8^+ cells
(Fig. [210]5f).
Rechallenging the selected neoTCRs in the melanoma model, we detected
the same patterns of tumor rejection albeit with a smaller survival
advantage for KIF-P2 (Figs. [211]5g, h and [212]S14a–c). Moreover,
among the secreted cytokines for TIL-P generated in response to the
melanoma cell line, IL-10 secretion was among the cytokines increased
for TIL-P KIF-P2 compared to KIF-sc1 (Fig. [213]S14d, e). Altogether,
these findings strengthen entity-independence of the resilience
patterns described and underline their dependence on the TCR in our
model. We furthermore lowered the effector cell number of TIL-P to
5×10^5 TCRmu^+ cells per mouse and included the two other
KIF2C^P13L-specific TCRs into our rechallenge setting to cover the
whole spectrum of identified KIF2C^P13L-neoTCRs. The previously
detected activation spectrum in vitro ranging from moderately to
strongly activated was translated into tumor growth dynamics in the
rechallenge model during the first 14 days after T cell injection:
KIF-P1 and -P2 significantly slowed down tumor growth, while KIF-sc2
and particularly KIF-sc1 did not (Fig. [214]S15a, b). Thus, only KIF-P1
and -P2 were able to significantly improve survival despite the low
effector cell dose upon tumor rechallenge (Fig. [215]S15b).
The potent in vivo capacity of TIL-P KIF-P1 upon rechallenge was
particularly surprising as significantly lower effector cell numbers
were used for TIL-generation due to the inferior surface expression
capacity of KIF-P1 in the RV system. In vitro, this TCR showed low
activation levels and functional avidity in the RV system
(Fig. [216]S15c–f) and was not directly comparable to all other neoTCRs
due to very low TCRmu^+ frequencies in this system (Fig. [217]S4b–d).
Compared to the very similar structural avidities of the other three
KIF2C^P13L-specific TCRs, KIF-P1 exhibited a 4.6-fold increase in
structural avidity (Fig. [218]S15g), suggesting a potential
compensation mechanism for low TCR surface expression. KIF-P1
demonstrated a strong in vitro killing capacity comparable with all
other TCRs (Fig. [219]S15h), indicating distinct qualities of this
neoTCR compared to the other neoTCRs.
Orthotopically engineered T cells confirm increased resilience of KIF-P2 upon
rechallenge
As described earlier, KIF-P1 profited substantially from the OTR-based
expression (Fig. [220]S4k). To investigate functional differences of
Mel15’s neoTCRs based on engineering systems in vivo, we continued to
functionally test all KIF2C^P13L-reactive neoTCRs after orthotopic
insertion into the TRAC locus via CRISPR/Cas9 (Fig. [221]S4g–k). Due to
substantially lower TCR-knock-in frequencies compared to viral
expression (Fig. [222]S4g–k), an intensive expansion protocol
(Fig. [223]S4g) was applied to OTR- as well as RV-engineered cells for
all following experiments (Fig. [224]S16). Interestingly, higher
surface expression of OTR-KIF-P1 led to increased activation levels but
no functional advantage compared to RV-KIF-P1 (Fig. [225]S16a–f), while
increased structural avidity (9-fold increase in k[off] rate) remained
similarly detectable (Fig. [226]S16e). Meanwhile, OTR-KIF-P2 still
demonstrated moderate T cell activation (Fig. [227]S16a–d) linked to
slightly increased killing and higher residual numbers of OTR-KIF-P2
neoTCR-T cells already upon first co-culture with neoantigen-expressing
tumor cells (Fig. [228]S16f–h). OTR and RV engineered T cells
expressing KIF-P2 or -sc1 were injected into tumor-bearing mice, which
were sacrificed on day 5 for TIL-P generation (Fig. [229]S16i–m).
KIF-P2 showed slightly improved in vivo tumor control upon first tumor
encounter until day 5 (Fig. [230]S16i, j). Following TIL-P generation
of the OTR-engineered T cells according to the previously established
protocol (Fig. [231]5a), in vitro (Fig. [232]S16n–u) and particularly
in vivo rechallenge (Fig. [233]S16v, w) substantiated significantly
improved tumor control of the more moderate neoTCR KIF-P2 upon
rechallenge compared to OTR-KIF-sc1 (in vitro: Fig. [234]S16n, r; in
vivo: Fig. [235]S16v, w) and resulted in significantly prolonged
survival of tumor-bearing mice (Fig. [236]S16w).
Overall, based on our results we conclude more preserved functional
activity and resilience upon tumor rechallenge in TIL-P-KIF-P2-tg T
cells with primarily more moderate activation. In addition, we observed
an enhanced expression of the anti-inflammatory cytokine IL-10
potentially associated to this reactivity pattern. In contrast, we show
functional impairment for initially more strongly activated
TIL-P-KIF-sc1-tg T cells upon antigen-specific T cell rechallenge and
subsequently ineffective anti-tumor activity upon restimulation in
vivo. Data from an orthotopic non-viral expression system strengthened
these findings by highlighting the improved persistence of
KIF-P2-engineered T cells. This oligoclonal neoantigen-defined
TCR-repertoire highlights the complexity of TCR-intrinsic structural
features influencing long-term anti-tumor functionality of
TCR-engineered T cells.
Discussion
To date, first clinical studies for adoptive transfer of highly
personalized neoTCR-T cells prove clinical feasibility although
therapeutic efficiency is still limited^[237]22. We are convinced that
better understanding of neoTCR-inherent qualities is required for an
optimal benefit from this promising approach. Neoantigen-reactive T
cell clones typically represent minor fractions among TILs and comprise
scarce populations in human blood^[238]16,[239]21,[240]31. Therefore,
identification and characterization of neoTCRs still pose a major
bottleneck for selecting T cells and TCRs with favorable
characteristics for effective ACT. Several approaches already aimed at
enrichment of tumor-reactive T cells, exemplarily by sorting for
CD137^+ or PD-1^+ T cells^[241]13,[242]36. In contrast to other recent
studies on TIL-derived neoTCRs^[243]16,[244]18,[245]37, we used
peripheral blood-derived T cells of a metastatic melanoma patient under
ICI treatment with known neoantigen-specific T cell reactivity. We
present a restimulation-dependent single-cell sequencing approach
detached from the TME for identification of neoTCRs and subsequent
in-depth fine-characterization of these TCRs in vitro and in vivo.
The sequential approach of specific stimulation of blood-derived CD8^+
T cells with MS-approved epitopes^[246]14,[247]32, magnetic enrichment
of CD137^+ cells and in vitro restimulation enabled sensitive detection
of T cell clones specific for the two known neoantigens SYTL4^S363F and
KIF2C^P13L despite partially very low precursor frequencies. Direct
comparison with the native TCR repertoire thereby enabled a ranked
quantification of T cell expansion rates after neoantigen-specific
stimulation. Beyond detection of all six previously described
neoTCRs^[248]14,[249]31, two additional neoantigen-reactive TCRs with
specificity for KIF2C^P13L were identified. This suggests peripheral
blood as a valuable, easily accessible source for detection of potent
neoTCRs independent from the TME providing potential advantages
compared to neoantigen-specific TILs which are often either not present
or in an exhausted and dysfunctional state^[250]16,[251]38,[252]39. In
fact, markers for dysfunction, such as CXCL13, CD39 or CD69, have been
proposed as bio- or selection markers for neoantigen-specific TILs with
potential for diagnostic or therapeutic exploitation^[253]16–[254]20.
Of note, we did not observe notable transcriptomic upregulation of such
markers among patient-derived, non-restimulated T cells. In contrast,
we aimed at the dissection of neoantigen-specific T cells upon early
(re)activation with focus on a head-to-head comparison between
clonotypes with known specificity.
Since T cell effector functions are defined by distinct activation
properties associated to intrinsic TCR-associated determinants, we went
beyond a static signature of patient-derived neoantigen-specific
TILs^[255]16–[256]20 by restimulating peripheral blood-derived T cells
of Mel15 with defined mutated peptide ligands. Of note, upon specific
in vitro restimulation we observed a heterogeneous pattern in
neoTCR-dependent transcriptomics of these patient-derived T cells
revealing qualitative differences between the identified neoTCRs. In
synopsis with analyses on TCR-tg cells mainly in a retroviral
expression system, we identified on one end of the spectrum more
strongly activated but simultaneously inhibitory activation patterns
especially within SYTL4^S363F-specific T cells, which harbored slightly
higher functional avidities^[257]31. These cells were characterized by
strong transcriptomic upregulation of proinflammatory markers and
chemokines, e.g., the inflammatory chemokines XCL1 and XCL2, both
regularly expressed by natural killer cells and activated CD8^+ T
cells^[258]40,[259]41. Simultaneously, these cells also significantly
upregulated inhibitory receptors (LAG3, TIGIT, HAVCR2) throughout the
first 24 h of stimulation. Furthermore, SYTL4^S363F-specific T cells
showed upregulation of DUSP4 and PTPN7, negative regulators of the
mitogen activated protein kinase (MAPK)^[260]42–[261]44. Interestingly,
these neoantigen-specific T cells were found at comparably low
frequencies in the patient potentially associated to defects in MAPK
phosphorylation and subsequent proliferation as previously described
for TCRs with higher signaling strength^[262]45.
On the other end, KIF-P1 and -P2, neoTCRs with notably higher
frequencies in the patient, demonstrated a distinct, in conjunction
with functional data later defined moderate activation pattern with
lower negative regulation. The marked transcriptomic upregulation of
GZMA suggested cytotoxic capacity^[263]46, while the presentation of
HLA-class II molecules and CD74 may be associated with T cell-mediated
antigen-presentation and proliferation^[264]47–[265]49. The expression
of genes related to calcium-dependent TCR-signaling, such as
ANXA5^[266]50, AHNAK^[267]51, S100A6, S100A10 (S100 calcium binding
proteins)^[268]52 and with a lesser extent of Ca2^+-dependency
LIME1^[269]53, further supported qualitative differences in signaling
cascades. Both of these identified TCRs, KIF-sc1 and -sc2, seem to be
in between those opposite transcriptional patterns.
To distinguish TCR-intrinsic features from those potentially imprinted
by previous antigen encounter or other patient-specific properties, we
retrovirally transduced T cells from healthy donors with defined
neoTCRs and investigated functional patterns of these TCRs. Our
analyses in neoTCR-tg T cells largely reflected the activation spectrum
determined by transcriptomic signatures and added further in vitro
distinction between KIF-sc2 (closer to KIF-P1 and -P2) and KIF-sc1
(closer to SYTL4^S363F-reactive TCRs). These in vitro findings
strengthen TCR-inherence of the heterogenous activation patterns of the
patient-derived neoTCRs rather than patient-imprinted differentiation.
Despite general accordance between activation signatures of patient and
TCR-engineered T cells, bias introduced through the artificial
expression system cannot be fully excluded. Slightly higher functional
avidity and activation were associated with higher TCR surface
expression under a CMV promoter. However, similarly to the RV system,
expression of KIF-P2 remained on a lower level compared to KIF-sc2 and
KIF-sc1 under the endogenous TCR-α-chain-promotor in the OTR
setting^[270]33,[271]34. Thus, this difference in KIF-P2 TCR surface
expression can be considered construct-inherent. The expression
differences between OTR and RV, particularly highlighting a special
role for KIF-P1, suggest that each engineering system likely may
contribute to the performance patterns of each TCR individually.
Substantially more aggressive in vitro expansion in the OTR-system
might account for significant differences between TCR-T cells of
different engineering modalities. However, the repeatedly shown more
moderate activation and eventually superior tumor control of KIF-P2
upon rechallenge in both systems, further substantiates our findings.
At the same time, it remains important to keep in mind that features of
TCR-engineered T cells cannot be inferred directly on natural,
patient-inherent neoTCR-expressing T cell clones.
Based on the strong clonotype distinction in patient-derived
neoantigen-specific T cells (cluster 7 in our scRNAseq), we focused
more closely on inhibitory regulation. Strong expression of LAG-3 and
PD-1 upon specific stimulation in vitro upon early T cell stimulation
corroborated the distinction between more strongly activated but
simultaneously inhibitory patterns (SYT-T1 and KIF-sc1) and otherwise
moderate expression of activation markers (KIF-sc2 and -P2) with
limited inhibitory marker expression. Our observations are in line with
previous reports accounting for a threshold of stimulation for the
initiation of inhibitory programs as a protective rheostat mechanism
during early T cell activation^[272]54,[273]55. Currently, upregulation
of inhibitory receptors is mostly understood as dysfunction and
exhaustion^[274]16,[275]18,[276]24, most likely resulting from chronic
antigen encounter or overstimulation early during
tumorigenesis^[277]56. In our approach, simultaneously high levels of
canonical activation as well as inhibitory receptors upon early
activation could be observed alongside stronger induction of AICD as
part of the strong activation pattern. This suggested TCR-driven
dysfunction associated to hyperresponsivity, which, however, could not
be observed functionally upon first tumor challenge in vivo for
retrovirally engineered T cells.
To understand potential TCR-dysfunction in the context of chronic
stimulation, we investigated the persistence and resilience of T cells
tg for KIF-sc1 and KIF-P2 representative for the two opposing response
patterns recognizing the identical antigen in a rechallenge model. To
mimic repeated antigen encounter we adapted our in vivo model and
restimulated TIL-P from xenograft tumor explants in vitro as well as in
vivo. In this setting, we detected significant functional impairment of
KIF-sc1, the TCR with stronger initial activation. Meanwhile, KIF-P2,
the more moderate neoTCR with higher frequencies in the patient and
lower inhibitory regulation, revealed potent in vivo tumor rejection,
especially upon repeated stimulation independently from the tumor
entity. Of note, the engineered antigen expression in our model cell
lines does not reflect the heterogeneous neoepitope presentation
expected in a primary tumor. Thus, further investigation of T cell
resilience in response to varying antigen densities will be crucial to
elucidate effects in different tumors with distinct mutational burden
and intra-tumoral heterogeneity. Functionally, no traditional effector
cytokine such as IFN-y, IL-2, TNF or GzmB was significantly differently
regulated between TIL-P KIF-P2 and -sc1 across healthy donors. Instead,
the superior TIL-P from moderate TCR KIF-P2 upregulated secretion of
the anti-inflammatory cytokine IL-10^[278]57. IL-10 secretion from
CD8^+ T cells is known to have a protective function in acute viral
infection^[279]58. At the same time, IL-10-receptor (IL10R) signaling
plays an important role in sustaining non-exhausted T cell phenotypes
in anti-tumor immunity^[280]59. In fact, co-expression of IL-10 and
CARs has recently been demonstrated to increase preservation of T cell
functionality and improve tumor control^[281]60. While more elaborated
models beyond the highly artificial TME in NSG mice will be necessary
to elucidate the role of CD8^+-driven IL-10 secretion after moderate,
but not strong initial T cell stimulation, this finding complements the
picture of a more stable, persistent functionality of TCRs with an
initially moderate activation profile.
Consequently, we hypothesize from our findings, that (1) TCR-intrinsic
features qualitatively determining activation have an enduring impact
on the functional state, and (2) more strongly activated T cell
reactivity patterns are associated with functional impairment upon
repeated stimulation.
Other reports recently associated moderate rather than overly strong T
cell stimulation to beneficial proliferation and longevity of T
cells^[282]45,[283]61,[284]62, lower TCR avidity to a more
effector-like, and less exhausted phenotype with increased persistence
upon murine chronic viral infection^[285]63 and intermediate levels of
TCR signal strength to superior anti-tumor efficacy in a murine model
system^[286]28. Our results complement these findings with reverse
translation of human data from a tumor patient and question the current
understanding to exploit mainly high avidity T cells and TCRs for
ACT^[287]29,[288]64–[289]67. Since factors like antigen density and
tumor burden crucially affect T cell activation, it will be important
to further test such response patterns in other tumor models with
diverse tumor microenvironments.
The herein proposed picture of diverse neoantigen reactivity bases on
the immune repertoire of one single patient and thus, in-depth analysis
of other cases will contribute essentially to the definition of factors
rendering T cell responses and respective neoTCRs as significant for
individualized therapeutic approaches. The TCRs identified in Mel15
covered an only small range of functional avidity rather at the lower
end of functional avidity scales in other recent publications for human
neoTCRs^[290]29,[291]35. Yet, we want to stress the substantial
differences in maintained anti-tumor reactivity upon rechallenge
suggesting further influences on T cell persistence beyond the slight
differences in functional avidity. Recently, structural avidity was
highlighted to improve prediction on tumor tropism of tumor-specific T
cells^[292]35, which could account for the surprising phenotype of
KIF-P1 across all experiments despite low surface expression. However,
very similar values between KIF-P2, -sc1 and -sc2 cannot explain the
differences seen in their rechallenge response. This suggests
association of individual neoTCR activation patterns to other
structural determinants, binding properties or inherent signaling
differences of TCR-peptide-MHC-interaction. NeoTCR activation patterns
here appear as a complex equation of different variables which might
compensate for each other and in their diversity be fittest within
different settings.
In a synopsis, experimental outcomes like these and ours, have
implications for T cell engineering and vaccination
strategies^[293]68,[294]69 currently mainly focusing on enhancing
co-stimulatory receptor interactions^[295]70,[296]71, reducing
inhibitory signals^[297]66 or TCR affinity maturation^[298]64,[299]72.
We show that individual TCR-intrinsic characteristics play a major role
in determining T cell activation and sustained functionality in
addition to peptide-HLA-complex density, antigen expression,
co-signaling interactions and immunosuppressive factors in the TME. The
question arising for future investigations therefore is, whether TCRs
with qualitatively distinct activation profiles are necessary to
complement each other in ACT. Whereas strongly activating TCRs might
play a role in initial tumor debulking (under adequate ICI modulation),
we hypothesize a substantial role for TCRs exhibiting more moderate
stimulation patterns in sustained and resilient long-term tumor
control.
Methods
This research and all experiments align with the regulations and
approval of the institutional review board (Ethics Commission, Faculty
of Medicine, project nr. 5722/13, 193/17S and 521/18S) of Technical
University Munich and are in accordance with principles put forth in
the Declaration of Helsinki. Informed consent of all participants in
this study was granted in written form. All animal studies were
approved by the Regierung von Oberbayern (Government of Upper Bavaria;
ROB-55.2-2532.Vet_02-19-125).
Primary patient material and cell lines
The clinical course of melanoma patient Mel15 was previously described
in detail^[300]31. The identification of neoantigens resulting from
somatic mutations (SYTL4^S363F and KIF2C^P13L) by MS and in
silico-prediction were previously reported^[301]14,[302]31,[303]32. The
PBMC sample of Mel15 used for single-cell-sequencing was selected based
on previously confirmed reactivities within tested primary material at
the specified time point^[304]31, i.e., 966 days after first Ipilimumab
application and 41 days after start of therapy with Pembrolizumab in a
stage IV without evidence of disease.
PBMCs were isolated using density-gradient centrifugation
(Ficoll-Paque) from either EDTA-anticoagulated blood of patient
Mel15^[305]31, EDTA-anticoagulated blood or leukapheresis products from
healthy donors. PBMCs were either immediately included in further
downstream assays or stored in freezing medium (90% FCS and 10% DMSO)
in liquid nitrogen. Feeder cells used in this study included pools of
irradiated healthy-donor PBMCs.
Cell lines used in this study included as target cell lines: Mel15
lymphoblastoid cell line (LCL) generated from Mel15 B cells by
infection with Epstein-Barr Virus (EBV)-containing supernatant, T2
somatic cell hybrid (American Type Culture Collection - ATCC cat.
CRL-1992; purchased from ATCC in 2005; RRID:CVCL_2211), U698M B cell
lymphoma cell line (DSMZ cat. ACC-4, RRID:CVCL_0017) endogenously
HLA-A03:01^+ and HLA-B27:05^+ as well as stably transduced with the
mutated (mut mg) or wildtype (wt mg) tandem-minigene^[306]31 and a
fluorescent marker (Discosoma red fluorescent protein (dsRed) or green
fluorescent protein (GFP)), JJN3-B27 multiple myeloma cell line (DSMZ
cat. ACC-541, RRID:CVCL_2078), endogenously HLA-A03^+ and stably
retrovirally transduced with HLA-B27 as described earlier and A2058
melanoma cell line (ATCC cat. CRL-3601, RRID: CVCL_1059), endogenously
HLA-A03^+ and stably retrovirally transduced with mutated (mut mg) or
wildtype (wt mg) tandem-minigene and a fluorescent marker (Discosoma
red fluorescent protein (dsRed))^[307]31. For retroviral transduction
the embryonal kidney cell line 293Vec-RD114 (BioVec Pharma, Québec,
Canada) stably expressing gag/pol and env was employed. For mouse
experiments NS0-IL15 cells, kindly provided by S. R. Riddell in 2011,
were used.
T cells were cultivated as previously reported^[308]14. Target cell
lines were cultivated in RPMI 1640 supplemented with 10% FCS,
glutamine, non-essential amino acids, sodium pyruvate, and
Penicillin/Streptomycin (Mel15 LCL, U698M, T2), DMEM supplemented with
10% FCS and Penicillin/Streptomycin (A2058) or 40% DMEM + 40% IMDM
supplemented with 20% FCS and Penicillin/Streptomycin (JJN3 B27). RD114
cells were cultivated in DMEM supplemented with 10% FCS and
Penicillin/Streptomycin. Growth and morphology of cultivated cells were
checked routinely. Absence of mycoplasma infection in cell lines and
media was regularly confirmed by PCR or a cellular-based detection
assay (PlasmoTest, Mycoplasma Detection Kit, cat. rep-pt1).
CD137 enrichment, rapid expansion and restimulation
To enrich PBMCs from patient Mel15 for KIF2C^P13L- and
SYTL4^S363F-specific TCRs, we adapted our previously described method
for identification of neoantigen-specific TCRs^[309]14,[310]31. Both
neoepitopes arose from a non-synonymous point mutation, resulting in
naturally presented ligands on HLA-A03:01 for peptide KIF2C^P13L (amino
acid sequence RLFLGLAIK) and HLA-B27:05 for SYTL4^S363F (GRIAFFLKY).
Briefly summarized, PBMCs from Mel15 were cultivated in AIM-V
supplemented with cytokines. After 24 h, both neoepitope peptide
ligands, KIF2C^P13L and SYTL4^S363F (0.1 μM) were added to the culture.
Another 24 h later, reactive T cells were separated using magnetic
labeling and positive selection with the CD137 MicroBead Kit (Miltenyi,
cat. 130-093-476). CD137^+ enriched cells were then co-incubated with
irradiated feeder cells in T cell medium (TCM) with supplements and
expanded for eleven days.
After expansion, T cells were stimulated again with mutated KIF2C^P13L
and SYTL4^S363F peptides using autologous antigen-presenting cells.
Therefore, Mel15 LCL were pulsed either with 0.1 µM KIF2C^P13L or
SYTL4^S363F and irradiated with 30 Gy. Expanded T cells and irradiated
LCL were co-cultured at a ratio of 10:1 (T cells:LCL) for 24 h before
preparing cells for single cell sequencing.
IFN-γ release of T cells was assessed before and after enrichment using
ELISpot assay as described before^[311]14. Briefly, ELISpot plates were
coated with IFN-γ capture antibody 1-DK1 (Mabtech, cat. 3420-3-250) and
incubated with cells. After removal of cells, anti-IFN-γ 7-B6-1
(biotinylated, Mabtech, cat. 3420-6-250) as well as
streptavidin-horseradish complex was added for visualization.
CD8 isolation, scRNA-seq and scTCR-seq
CD8^+ T cells were negatively isolated from the enriched, restimulated
as well as an unstimulated Mel15-PBMC sample from the same time point
using the Dynabeads™ Untouched™ Human CD8 T Cells Kit (Invitrogen, cat.
11348D). Single, alive (Propidium Iodide (PI)-negative) cells were
sorted, 25 × 10^3 cells from each sample were loaded onto one lane of a
Chromium Next GEM Chip G (10x Genomics, cat. 1000263) and used for
library prep using the Chromium next GEM Single Cell VDJ V1.1, Rev D)
workflow (10x Genomics) as per company protocols. A high-sensitivity
dsDNA was used for quality control and analyzed on a Bioanalyzer 2100.
Quantity of dsDNA was measured using a Qubit dsDNA HS kit (Life
Technologies, cat. [312]Q32854). Libraries were sequenced on an
Illumina NovaSeq 6000 using read lengths of 26 + 8 + 0 + 91 for
combined assessment of single cell RNA sequencing (scRNA-seq) and TCR
sequencing (TCR-seq) information.
Single-cell sequencing data bioinformatic analyses
Samples were converted from BCL to FASTQ using bcl2fastq
(demultiplexed).
Raw paired-end sequencing files of the GEX and VDJ libraries were
aligned to the human reference genome (refdata-gex-GRCh38-2020-A) and
VDJ reference (refdata-cellranger-vdj-GRCh38-alts-ensembl-4.0.0)
respectively, using 10x Genomics Cell Ranger (v4.0.0). Subsequently, we
used the R package Seurat (v. 4.1.0)^[313]73 to further analyze the
transcriptome- and TCR-based data. Only the genes detected in at least
three cells were included in the raw counts matrix of the object. We
retrieved only cells containing at least 200 genes and fewer than 6000
genes. To avoid possible dead cells contamination, we excluded cells
with a fraction of mitochondrial genes higher than 18%. In the next
step, the corresponding TCR data was added to the meta.data slot of the
Seurat object. Raw gene counts were log-normalized, and variable
features were detected with the vst method. Subsequently, canonical
correlation analysis (CCA) integration was used to leverage the batch
effects between two experimental setups combined in one Seurat object.
After that, we newly determined the variable features using the
integrated assay and scaled the expression matrix with regression on
the number of UMIs and fraction of mitochondrial genes per cell.
Unbiased calculation of k-nearest neighbors was done, and using UMAP,
neighborhood graph and embedding were generated. After the UMAP
construction, we retrieved only cells containing the TCR information
and clonotypes expressing more than one alpha or beta chain were
removed. Previously identified neoTCRs from our index patient^[314]31
were detected using their CDR3 region, and corresponding clonotypes in
our samples were assigned to the respective TCR group. The final cell
numbers in our linked TCR-transcriptome data set were 5764 cells in the
unstimulated and 6007 cells in the restimulated sample. Cell cycle
stage was determined with the CellCycleScoring function of the Seurat R
package. The FindAllMarkers function was used to calculate
differentially expressed genes in each cluster and the corresponding
upregulated genes were retrieved for the subsequent pathway enrichment
analysis using the enrichR (v. 3.0) R package. Seurat clusters were
annotated manually by analyzing the expression of upregulated genes on
the UMAP. The gathered signature expression score was generated by
using AddModuleScore function. Subsequently, the Seurat object was
converted into.h5ad format, and the pseudotime score with corresponding
diffusion maps was generated using the scanpy library implemented in
Python^[315]74. For pseudotime score calculation, cluster 1_CCR7 (most
naїve) was set as a starting point.
For the differential gene expression analysis between the TCR groups,
we used the FindMarkers function of the Seurat package by plotting the
results using the ggplot2 (v. 3.3.5) R package.
V(D)J analysis and selection of TCRs for TCR transduction
For subsequent TCR selection a meta data.csv was exported after initial
QC (see above). Only clonotypes expressing exactly one productive alpha
and one beta chain were considered to allow for precise identification
of TCRs. The total number of this refined TCR set was 4182 in the
unstimulated and 4913 in the restimulated sample. To select new
neoTCRs, clonotypes that had previously been identified were excluded
and the frequencies of remaining clonotypes were compared. We
considered two metrics: highest fold change of TCR frequency before and
after stimulation as well as greatest absolute frequency of clonotypes
in the restimulated sample. We selected four new TCRs for investigation
of specificity and functionality, two of them demonstrated specificity
for KIF2C^P13L, later termed TCR KIF-sc1 and -sc2.
Engineering of KIF-sc1 and -sc2 TCRs
α- and β-chain-sequences of clonotypes identified as potential
neoantigen-reactive TCRs were submitted to IMGT to obtain comprehensive
information on respective V-(D-)J sequences
([316]https://www.imgt.org/IMGT_vquest/vquest). Full-length TCR
sequences were reconstructed using Ensembl database and subsequently in
silico optimized throughout insertion of a cysteine bridge,
murinization of the constant region and codon
optimization^[317]75–[318]77. β- and α-chain were linked by a P2A
element and tandem gene products were synthesized (BioCat). Each TCR
candidate was cloned into MP71 retroviral vector and subsequently used
for transduction into healthy donor T cells.
Retroviral transduction of healthy donor CD8+ T cells with neoTCRs
CD8^+ T cells used for transduction were obtained by magnetic negative
selection from healthy donor-derived PBMCs (EasySep™ Human CD8^+ T Cell
Isolation Kit, Stemcell, cat. 17953) and activated for 48 h with 30
U/ml human IL-2 and anti-CD3-anti-CD28-beads (Dynabeads™ human
T-Activator CD3/CD28, Thermo Fisher, cat. 11131D). Retroviral packaging
cells RD114 were seeded to reach a confluency of 60% on the day of
transfection and subsequently transfected with plasmids containing the
neoTCR-α- and -β-chain-sequences using TransIT®-293 (MirusBio, cat. MIR
2700). Transfected cells were incubated for 48 h and supernatants
subsequently filtered and used for spin infection of activated CD8^+ T
cells. Transduced T cells were cultivated with IL-7 and IL-15 for 10
days as described before^[319]14. Transduction efficacies were
determined via fluorescence activated cell sorting (FACS) staining with
TCRmu antibody (anti-mouse TCR-β-chain, FITC, BD Biosciences,
RRID:AB_394683) against the murine-β-chain of engineered TCR-constructs
in comparison to non-transduced T cell populations.
Orthotopic T cell receptor replacement via CRISPR/Cas9 Knock-in
CRISPR/Cas9-mediated TCR engineering was done as described
before^[320]33,[321]34. In brief, isolated PBMCs were activated at a
density of 1 × 10^6 cells/ml for 48 h with CD3/CD28 Expamer (Juno
Therapeutics), 300 IU/ml IL-2, 5 ng/ml IL-7 and 5 ng/ml IL-15 in RPMI.
Expamer stimulation was stopped by 20 min incubation with 1 mM
D-biotin. Single guide RNAs (sgRNAs) were generated by annealing CRISPR
RNA (crRNA) (80 μM; Integrated DNA Technologies) with trans-activating
crRNA (tracrRNA) (80 μM; Integrated DNA Technologies) for 5 min at
95 °C. Ribonucleoproteins (RNPs) were then assembled by incubating
sgRNAs with high-fidelity Cas9 (24 μM; Integrated DNA Technologies) at
final concentrations of 12 μM Cas9 and 20 μM gRNA for 15 min at room
temperature. Fifteen million cells were electroporated with 15 µl RNPs
per target, 15 µg HDR-DNA template and 20 μM electroporation enhancer
(Integrated DNA Technologies) in P3 buffer (Lonza) using the 4D
Nucleofector X unit, pulse code EH-100 (Lonza) and the corresponding
electroporation cuvettes.
After five days of cultivation, OTR and RV modified cells were enriched
for TCRmu^+ cells on an Astrios cell sorter (Beckman Coulter). Cells
were then expanded with irradiated feeder cells in RPMI supplemented
with 5% human serum, 180 IU/mL IL-2 and 1 μg/mL phytohaemagglutinin
(PHA). Latest five days before experiments no more PHA was added and
IL-2 reduced to 50 IU/ml. The following crRNA sequences were used: TRAC
5’-AGAGTCTCTCAGCTGGTACA-3’; TRBC 5’-GGAGAATGACGAGTGGACCC-3’.
K[off] rates of TCRs using pMHC-multimers
TCR:pMHC k[off]-rates were determined as previously described^[322]78.
Atto488-conjugated monomeric pMHCs for StrepTamer staining were
generated by in vitro refolding of the peptide of interest with
HLA-A*03:01 heavy chain and β2 microglobulin as previously
described^[323]79. pMHC-StrepTamer were generated by incubating 1 µl
StrepTactin-APC backbone (IBA, cat. 6-5010-001) with 1 µg
Atto488-conjugated pMHC in a final volume of 50 µl FACS buffer (PBS 1x,
0.5 % (w/v) BSA, pH 7.45) for 30 min on ice in the dark. Up to 5 × 10^6
cells were stained with 50 µl multimer for 45 min on ice, in the dark.
20 min before the end of the StrepTamer staining, additional surface
antibody staining was added. Cells were stained with PI for live/dead
discrimination just before the acquisition. The final volume was
adjusted to 1 ml with FACS buffer to allow an acquisition for up to
20 min. Acquisition was performed at 4 °C on a Cytoflex S (Beckman
Coulter). Upon 30 s initial acquisition, 1 ml cold 2 mM D-biotin was
added to the cell suspension whilst monitoring the dissociation
kinetics. Analysis of the k[off]-rates was performed with FlowJo and
GraphPad Prism. t[1/2] were calculated by fitting of a one-phase
exponential decay curve. FACS antibodies used for analyses: aCD45-PO
(Exbio, clone HI30, RRID:AB_10952114), aCD45-PB (DAKO / Agilent,
T29/33, RRID:AB_579532), aCD45-ECD (Beckman Coulter, J33,
RRID:AB_130855), aCD45-PerCP (ThermoFisher, MEM-28, RRID:AB_11152976),
amTRBC-APCeF780 (biolegend, H57-597, RRID: AB_2629697), aCD8a-PE
(eBioscience, OKT8, RRID:AB_10732344).
In vitro assessment of reactivity and activation patterns in TCR-tg or OTR
engineered T cells
The subsequently described functional and phenotypic aspects were
assessed within co-culture settings using retrovirally tg or OTR
engineered CD8^+ T cells from different healthy donors and different
target cells. Cell lines were either transgenic for the tandem minigene
(mutated minigene (mut mg) versus wildtype minigene (wt mg)) or pulsed
with different concentrations of peptides KIF2C^P13L and SYTL4^S363F,
their wildtype form or peptide derivates containing single amino acid
substitutions with alanine and threonine at all possible positions as
described before^[324]31. FACS- as well as ELISA-based readout was
performed at different timepoints after co-culture setup as indicated.
In selected experiments, varying transduction efficiencies (between
donors and transductions) were equalized by diluting to the lowest rate
per assay with a minimum at 10% of TCRmu^+ cells with non-transduced
CD8^+ T cells obtained from the same donor. TCR-tg or OTR engineered
TCRmu^+ T cells were considered effector cells for all E:T-ratios
unless indicated otherwise.
Extra- and intracellular FACS staining
FACS staining was performed in FACS buffer (PBS with 1% FCS and 2 mM
EDTA) in 96well-u-bottom plates. Cells from in vitro co-cultures or
tumor lysates were washed in FACS buffer and for in vitro co-cultures
experimental triplicates were pooled prior to staining. Unspecific
binding sites were blocked with 30% human serum in FACS buffer for
20 min at 4 °C before extracellular (EC) staining with diverse
antibodies diluted in FACS buffer at 4 °C for 30 min. Live/dead stains
were either directly added to the EC-antibody mix (Hoechst, Thermo
Fisher) or added directly prior to measurement (PI, 7-AAD).
In addition to EC-staining subsequent intracellular (IC)-staining was
performed for several analyses. Prior to EC-staining, a fixable
live-dead stain (Zombie UV or Zombie NIR, biolegend) was stained in
PBS. After EC-staining, cells were washed and fixed (fixation buffer,
biolegend) for 20 min at room temperature (RT) (protected from light).
Afterwards, perm buffer (biolegend) diluted in deionized water was used
for permeabilization according to manufacturer’s protocol. IC-staining
antibody-mix in perm buffer was added afterwards for 40 min at RT
(protected from light), followed by further washing steps.
FACS antibodies used for analyses: aCD137-APC (RRID:AB_830671) and
aCD137-APC-Cy7 (RRID: AB_2629645, all biolegend, clone 4B4-1),
aCD137-PE (RRID:AB_314782), anti-murine TCR-β-FITC (BD Biosciences,
H57-597, RRID:AB_394683), anti-murine TCR-β-PE (BD Biosciences,
H57-597, RRID:AB_10563767), aCD3-AF700 (biolegend, UCHT1,
RRID:AB_493740), aCD3-PerCP/Cy5.5 (biolegend, UCHT1, RRID: AB_893301),
aCD2-BV785 (biolegend, RPA-2.10, RRID: AB_2800717), aCD45RA (biolegend,
H100, RRID:AB_10708880), aCD45RO-APC (biolegend, UCHL1,
RRID:AB_314426), aCD8-PE-Cy7 (BD, clone RPA-T8, RRID: AB 396852),
aCD8-PerCP (biolegend, SK1, RRID:AB_2890877), aHLA-A03-APC (Miltenyi,
REA950, RRID:AB_2727171), aIFN-γ-APC (biolegend, 4S.B3,
RRID:AB_315237), aIL-2-BV785 (biolegend, MQ1-17H12, RRID:AB_2566471),
aTNF-PE-Cy7 (biolegend, Mab11, RRID:AB_2204079), aLAG-3-BV605
(biolegend, 11C3C65, RRID: AB_2721541), aLAG-3-BV650 (biolegend,
11C3C65, RRID: AB_2632951), aPD-1-BV785 (biolegend, EH12.2H7,
RRID:AB_11218984), aPD-1-APC-Cy7 (biolegend, EH12.2H7, RRID: AB
10900982). Sample analysis was performed at an LSRII (BD Biosciences,
RRID:SCR_002159) or LSR Fortessa (BD Biosciences, RRID:SCR_019601).
FACS data was analyzed using Flow Jo_v10.8.1.
Activation induced cell death (AICD) assessment: Annexin-V staining
Cells stained extracellularly with FACS antibodies were stained in
Annexin-V binding buffer diluted in water (Thermo Fisher) with AnnexinV
(APC, biolegend) and PI for 20 min at RT prior to analysis.
Proliferation assessment: cell trace violet (CTV)-staining
TCR-tg CD8^+ T cells from three healthy donors were labeled with CTV
Dye (Thermo Fisher) according to manufacturer’s guidelines. On day 4 of
co-culture, cells were stained extracellularly with FACS antibodies and
afterwards the percentage of TCRmu^+ T cells per division was
determined via flow cytometric readout.
Quantitative analysis of the murine TCR-β-chain on RNA and DNA level
RNA and gDNA isolation
For the isolation of RNA, snap-frozen cell pellets were first
homogenized using the QIAshredder Homogenizer (Qiagen, cat. 79656).
Then, RNA was isolated with the RNeasy mini kit (Qiagen, cat. 74104)
including an on-column DNA digestion step using the RNase-free DNase
set (Qiagen, cat. 79254) according to the manufacturer’s instructions.
RNA was eluted with 25 μl DEPC-H[2]O and quantified with a NanoDrop
1000 spectrophotometer (Thermo Fisher). Genomic DNA (gDNA) was
extracted from snap-frozen cell pellets with the DNA blood and tissue
kit (Qiagen, cat. 69504) and eluted using 30 µL DEPC-H[2]O. To prevent
co-purification of RNA, the RNA was removed using 4 µL Monarch RNAse A
(20 mg/mL, NEB).
Reverse transcription of RNA
1 µg RNA was reversely transcribed to cDNA using the AffinityScript
Multiple Temperature cDNA Synthesis Kit (Agilent Technologies, cat.
200436) following the manufacturer’s protocol.
Real time (RT) PCR
The RT-PCR was performed in a QuantStudioTM 5 Real-Time-PCR-System
(Applied Biosystems). The assay was carried out in a 20 µL reaction
volume using 2 µL of 1:10 diluted cDNA or 50 ng gDNA, 0.6 µM of each,
forward and reverse primer, and 10 µL PowerUp SYBR Green Master Mix
(Applied Biosystems). The cycling conditions used were the following:
50 °C for 2 min, 95 °C for 10 min, and then 40 cycles of 95 °C for 15 s
and 65 °C for 1 min. After the run, a melt curve analysis was performed
to determine the specificity of the primers. For the absolute
quantification of the TCRs on the RNA and the gDNA level, standard
curves were generated using serial dilutions of the respective vector
that was used for the retroviral transduction ranging from 10^6 to 10
copies. For normalization, additionally a control vector encoding the
constant region of the human TCRβ chain was used. Primers used for
RT-PCR:
KIF-P1-fwd AGCAAAGAGACTCCGCAATG, -rev CTTTGTACGCCTGTGGATCC;
KIF-P2-fwd CGGACAAGGGTGAGGTATCT, -rev GAATCCTCGGGCCAAACAAA;
KIF-sc1-fwd TCAATAACAACGTGCCTATCGA, -rev AGGTGTCACATTCCTCAGGT;
KIF-sc2-fwd TACAGACAGTTCCCCAAGCA, -rev TTCTCAGATCCTCCACCACG;
2.5D6-fwd CTGATGGCTACAACGTGTCC, -rev CACCAAGACAGTTCCACGTG;
huTCRb-fwd GAAGCAGAGATCTCCCACAC, -rev CCCGTAGAACTGGACTTGAC.
In vitro real-time monitoring of TCR-mediated cytotoxicity
Killing of adherent target cells by T cells was measured with the
xCELLigence® RTCA eSight-System of using the technique of
impedance-based real-time cell analysis as described before. Briefly,
culture media measured in 96 well E-Plates (OLS) for background
impedance assessment. A2058 were seeded as target cells (30,000/well)
and were incubated for 24 h to reach a growth plateau. Impedance was
measured every 15 min with the xCELLigence® system. Measurement was
paused for addition of TCR-tg T cells in a 1:1 E:T-ratio and the
analysis was continued every 15 min for further 30 h. The number of
effector cells was equalized according to their retroviral transduction
efficiency.
To allow direct comparison of killing mediated by different neoTCR-tg T
cells, cytolysis was calculated with normalized Cell Indexes (CI) by
using the following formula (1):
[MATH:
specifi<
mi>ccytolysis%=100−CIxCI
non−transducedx100 :MATH]
1
Mass spectrometry (MS)-based measurement of neoepitope KIF2CP13L abundance
Preparation of cells
The cell lines U698M, A2058, Mel15 LCL, U698M-mut mg, A2058-mut mg and
Mel15 LCL-mut mg were expanded to reach sufficient cell numbers. The
cell lines U698M, A2058 and Mel15 LCL were pulsed with 0.1 or 1 µM of
KIF2C^P13L in AIM-V at a cell concentration of 5 × 10^6 cells/ml while
rotating for 2 h at 37 °C. The cells were washed two times with cold
PBS before 1.5 × 10^8 cells of each condition were snap-frozen.
Immunoprecipitation of HLA peptide complexes and purification of HLA peptides
For the purification of pan-HLA class I peptides from cell lines, the
cells were first lysed in 4 mL lysis buffer (PBS 1x, 1% (w/v)
Ocytl-β-D-Glucopyranoside, 0.25% (w/v) Na-Deoxycholate, 1 mM EDTA, 1 mM
phenylmethylsulfonyl fluoride (PMSF), pH 8.0) for 2 h at 4 °C.
Meanwhile, 1 mg mouse IgG2a W6/32 per condition was coupled to 0.5 mL
Pierce Protein G Agarose beads (ThermoFisher) through incubation for
2 h at 4 °C while rotating. The lysates were cleared by centrifugation
at 20,000 g for 20 min at 4 °C. Subsequently, the antibody-coupled
beads were transferred to the cleared lysates and immunoprecipitation
was performed O.N. at 4 °C while rotating. The beads were washed
sequentially with 5 mL of 20 mM Tris-HCl buffer, pH 8, that contained
varying concentrations of NaCl (150 mM, 400 mM, 150 mM, 0 mM). HLA
peptides were then eluted from the beads together with the IP antibody
and the MHC complex in three subsequent elutions with 1 mL 200 mM
Glycine buffer, pH 2.5. Between elutions, the beads were incubated in
elution buffer for 5 min at RT while rotating. The proteins were
separated from the HLA peptides by using 10 kDa molecular weight
cut-off columns (Millipore). The volume of the <10 kDa fraction was
then reduced to 200 µL using vacuum centrifugation in order to purify
the HLA peptides using C18 SPE-StageTips (3 M). The elution of HLA
peptides was performed using 50 µL 60% acetonitrile (ACN) in 0.1%
formic acid (FA). The peptides were finally dried using vacuum
centrifugation before they were used for mass spectrometry analysis.
Mass spectrometry analysis
HLA peptide samples were resuspended in 0.1% formic acid (FA) and
analyzed by LC-MS/MS (liquid chromatography tandem mass spectrometry).
Peptides were chromatographically separated using a Dionex Ultimate
3000 RSLCnano system (Thermo Fisher Scientific) coupled to an Orbitrap
Eclipse mass spectrometer (Thermo Fisher Scientific). Peptides were
loaded to a trap column (75 μm i.d. × 2 cm, packed in-house with 5 μm
of ReproSil-Pur 120 ODS-3 beads, Dr. Maisch) using 0.1% FA at a flow
rate of 5 μL/min for 10 minutes. Subsequently, peptides were
transferred to an analytical column (75 μm i.d. × 40 cm, packed
in-house with 1.9 μm ReproSil-Pur C18-AQ beads, Dr. Maisch) at a flow
rate of 300 nL/min and chromatographically separated using an 80 min
linear gradient from 4% to 32% of solvent B (0.1% FA, 5% DMSO in ACN)
and solvent A (0.1% FA, 5% DMSO in water). The total measurement time
for each sample was 90 min. The Orbitrap Eclipse was operated in data
dependent mode, automatically switching between MS1 and MS2 spectra.
MS1 survey spectra were recorded in the Orbitrap from 360 to 1800 m/z
at a resolution of 120 K (automatic gain control (AGC) target value of
100%, maximum injection time (maxIT) of 50 ms). Peptide fragmentation
was performed via higher energy collisional dissociation (normalized
collision energy of 30%), and MS2 spectra were recorded in the Orbitrap
at 30 K resolution via sequential isolation of the 15 most abundant
precursors (isolation window 1.3 m/z, AGC target value of 400%, maxIT
of 54 ms, and dynamic exclusion of 35 s). To enhance coverage, mass
ranges were specified for each charge state as follows: 360–1800 m/z
for charge 2–4+, and 700–1800 m/z for charge 1+. The acquisition method
also integrated an “inclusion list” containing the theoretical mass of
the doubly charged HLA peptide RLFLGLAIK, 515.8422 m/z.
Raw mass spectrometry data were processed using the FragPipe software
(version 21.1) with its built-in search engine MSFragger version
4.0^[325]80. Spectra were searched against the human UniProtKB database
UP000005640 (82,507 entries downloaded on 04.2024), supplemented with
the translated open reading frame of the KIF2C gene, containing the
mutated sequence RLFLGLAIK. Default parameters for a nonspecific-HLA
search were employed, with a defined precursor tolerance of 20 ppm and
no enzyme specificity for database digest. After peptide-to-spectrum
matches (PSM) rescoring via MSBoster and percolator^[326]81,
identifications were adjusted to 1% false discovery rate (FDR) at the
peptide and PSM levels, whereas protein FDR was not applied.
IonQuant^[327]82 was used to perform MS1-based quantification of the
detected peptide features, with the match-in-between runs option
enabled.
The mass spectrometry proteomics data have been deposited in the
ProteomeXchange Consortium via the PRIDE partner repository^[328]83
with the dataset identifier PXD051734.
In vivo tumor rejection potential in a xenograft model
NOD.CG-Prkdcscid IL2rgtm1Wjl/SzJ (NSG; The Jackson Laboratory, RRID:
IMSR_JAX:021885) were maintained according to the institutional
guidelines and approval of local authorities (Regierung von Oberbayern;
ROB-55.2-2532.Vet_02-19-125). A xenograft murine model was established
as previously described^[329]84,[330]85. Animal well-being was assessed
daily and tumor growth was monitored in vivo by external measurements
with digital caliper until endpoint criteria as regulated in
ROB-55.2-2532.Vet_02-19-125 were achieved. Mice were euthanized by
isoflurane and cervical dislocation upon achievement of endpoint
criteria or end of experiment.
Tumor rejection potential of TCR-tg T cells
The capacity of primary tumor control was assessed as described
before^[331]31. Briefly, male and female NSG mice at the age of six to
nineteen weeks were subcutaneously injected with U698M-mut mg cells or
A2058-mut mg cells (10 × 10^6 cells/flank). As tumors reached an area
of ca. 20 mm^2, T cells transduced with neoTCRs (KIF-P2, KIF-sc1,
KIF-sc2, SYT-T1) or T cells transduced with an irrelevant TCR (2.5D6
targeting MPO^[332]84) were injected intravenously. For the initial
setting, a total of 2 × 10^7 neoTCR-tg T cells (3.2 × 10^7 absolute T
cells including non-transduced cells) administered to each individual
of 6 mice per group (n = 6). Injection was split on two subsequent
days. In this initial setting, IL-15-producing NSO cells were injected
intraperitoneally after T cell administration two times per week
(irradiated with 80 Gy). To challenge our setting in subsequent
experiments, a total of 5 × 10^6 neoTCR-tg T cells (KIF-P2 versus
KIF-sc1) was administered intravenously into either U698M-mut mg- or
A2058-mut mg-tumor bearing mice. Male and female animals as well as
animals of different age were distributed evenly across all treatment
groups. Tumor growth kinetics were monitored daily for up to 12 weeks
with digital caliper until experiment endpoint criteria were reached.
Ex vivo analysis of TILs on day 5 after T cell injection
On day 5 after T cell injection, animals were sacrificed and tumors as
well as spleen explanted. Minced tumors were enzymatically digested for
90 min at 37 °C (Human tumor dissociation kit, Miltenyi Biotec, cat.
130-095-929) and passed through a cell strainer (100 μm) afterwards in
parallel to the spleen. Both lysates from tumors as well as spleen were
directly used for further analysis.
Rechallenge model: tumor rejection potential of TIL products generated from
transgenic T cells
For the generation of TIL products, male and female NSG mice at the age
of seven to twelve weeks were subcutaneously injected with U698M-mut mg
cells or A2058-mut mg cells (10×10^6 cells). As tumors reached an area
of 20 mm^2, T cells of different healthy donors (A, B and C) transduced
with neoTCRs (mostly KIF-P2 and KIF-sc1 or in later experiments also
KIF-P1 and -sc2) were injected intravenously. 8 × 10^6 transduced T
cells (in total 11 to 32 × 10^6 including non-transduced cells) were
administered to five to six mice per group at equalized transduction
rates for both groups (KIF-P2 versus KIF-sc1 in U698M: 70% for donor A,
60% for donor B, 62% for donor C, KIF-P2 versus KIF-sc1 in A2058-model:
25% for donor A, all KIF-TCRs in U698M-model: 25% for donor A). Tumor
growth kinetics were monitored daily for 5 days with digital caliper.
On day 5, before tumor regression was measurable, animals were
sacrificed and tumors as well as spleens explanted. Minced tumors were
enzymatically digested for 90 min at 37 °C (Human tumor dissociation
kit, Miltenyi Biotec, cat. 130-095-929) and passed through a cell
strainer (100 μm) afterwards in parallel to the spleen. Partly, tumor
lysate was used for immediate downstream applications as described
above (FACS staining or co-culture). Further parts of the tumor
material were cultivated with irradiated (70 Gy) feeder cells, 1000
U/ml human IL-2 and 30 ng/ml anti-CD3 antibody (OKT3) in TCM for 21
days. IL-2 was supplemented on days 7, 11 and 15 (300 U/ml). Efficacy
of TIL generation was assessed by FACS staining (CD8 and TCRmu) and
TILs of those mice with the highest rate and count of TCRmu^+ CD8-TILs
per TCR pooled to reach equal transduction rates for all subsequent
experiments wherever possible.
For the initial experiments comparing KIF-P2 and -sc1 in U698M, we
injected 5 × 10^6 transgenic T cells (transduction rate of 55% (donor
A), 45% (donor B) or 50% (donor C)) into U698M-tumor bearing mice
(equal distribution of male and female, age between 7 and 15 weeks per
group). For those experiments, we applied KIF-P2 and KIF-sc1 TCR-tg T
cells each for the TIL-P-conditions (injected on day 21 after tumor
explant; 43 days after blood donation) as well as a new batch of
transgenic T cells (NEW) from the same donor (17 days after blood
donation). For the transfer of this model into the melanoma cell line
A2058, we generated TIL-P in the described fashion from A2058-tumors
and injected 4 × 10^6 KIF-P2 and KIF-sc1 TIL-P (transduction rates 22%
for KIF-P2 and 15% for KIF-sc1) in parallel to NEW conditions into
A2058-tumor bearing hosts. For the comparison of all four
KIF2C^P13L-specific neoTCRs in the U698M-model, we injected 5 × 10^5
transgenic T cells of TIL-P from all four TCRs (transduction rates: 2%
KIF-sc1, 4.5% KIF-sc2, 8% KIF-P2, 1% KIF-P1) into U698M-tumor bearing
mice. In both settings, due to very different TCRmu^+ rates of TIL-P
conditions no equalized transduction rate was possible; therefore we
injected the same amount of TCR-tg, but different absolute amounts of T
cells. In all rechallenge experiments, we compared neoTCRs with
2.5D6-tg T cells as negative control. Tumor growth kinetics were
monitored daily by blinded measurement regarding T cell condition for
up to 12 weeks with digital caliper until experiment endpoint criteria
were reached.
Rechallenge model: in vitro co-cultures on the day of reinjection
Multiplex analysis
In parallel to the injection of TIL-P (and NEW) T cells into
tumor-bearing hosts, in vitro co-cultures of TIL-P cells were set up
with U698M-mut mg tumor cells. TCR-tg T cells from either the TIL-P or
the NEW condition were co-cultured for 24 h with U698M-mut mg tumor
cells. Supernatant was collected after 24 h and 13-plex legendplex
(biolegend, cat. 741186) analysis of human CD8/NK-cytokines
(Granulysin, Perforin, GzmB, GzmA, IFN-γ, sFasL, sFas, TNF, IL-17A,
IL-6, IL-10, IL-4 and IL-2) was performed for comparison of KIF-P2
versus -sc1.
BulkRNA-sequencing and data analysis
For transcriptomic analysis of TIL-P (KIF-P2, KIF-sc1) and NEW (KIF-P2
and KIF-sc1, 2.5D6) of donor B on the day of reinjection, in vitro
co-cultures of 1 × 10^6 TIL-P cells (50% TCRmu^+) were set up with 1 ×
10^6 U698M-mut mg or U698M-wt mg tumor cells for 12 h and 24 h in
triplicates. At each timepoint, triplicates were pooled and CD8^+ T
cells purified by CD8-MACS (Miltenyi Biotec). CD8^+ T cells were
immediately snap frozen and total RNA was isolated for all co-culture
conditions as well as unstimulated T cells using the RNeasy Mini Kit
(Quiagen, cat. 74104) according to the manufacturer’s instructions.
Library preparation for bulk‐sequencing of poly(A)‐RNA was performed as
described previously^[333]86. In brief, barcoded cDNA was generated
with a Maxima RT polymerase (Thermo Fisher) using oligo‐dT primer
containing barcodes, unique molecular identifiers (UMIs) and an
adapter. 5′‐Ends of the cDNAs were extended by a template switch oligo
(TSO) and full‐length cDNA was amplified with primers binding to the
TSO site and the adapter. The NEB Ultra II FS kit (NEB, cat. E6177) was
used to fragment cDNA. After end repair and A‐tailing, a TruSeq adapter
was ligated, and 3′‐end‐fragments were amplified using primers with
Illumina P5 and P7 overhangs. In comparison with Parekh et al. the P5
and P7 sites were exchanged to allow sequencing of the cDNA in read1
and barcodes and UMIs in read2 to achieve a better cluster recognition.
The library was sequenced on a NextSeq 500 (Illumina) with 57 cycles
for the cDNA in read1 and 16 cycles for the barcodes and UMIs in read2.
Data were processed using the published Drop‐seq pipeline (v1.0) to
generate sample‐ and gene‐wise UMI tables^[334]87. Reference genome
(GRCh38) was used for alignment. Transcript and gene definitions were
used according to the GENCODE version 38.
The raw gene counts were normalized through the vst method of the
DESeq2^[335]88 (v. 1.30.1) R package for the subsequent principal
component analysis (PCA) calculation. The first two PCA dimensions were
used for the visualization. For the expression comparison, the raw
counts values were normalized through the median of ratios method of
the DESeq2 package by dividing the counts by sample-specific size
factors determined by the median ratio of gene counts relative to the
geometric mean per gene. Subsequently, the normalized values were
divided by the expression values of the 2.5D6 T cell clones per group
by adding the pseudo-counts values, thus eliminating unspecific
signaling signatures due to cell culture-specific or processing issues.
All plots were generated with the help of the ggplot2 (v. 3.4.2) R
package.
Statistics
Significance of differences between TCRmu^+ frequencies of transgenic T
cells, TCRmu transcripts, EC[50]-values, half-life of k[off]-rates as
well as TCR-TIL-P products on the day of TIL reinjection (FACS analysis
and IL-10 secretion) were investigated by one-way analysis of variance
(ANOVA) and Tukey’s multiple comparison test. Significance of
differences between TCRmu^+ frequencies and count in the tumor or
spleen on day 5 of sacrifice was calculated by Student’s t-test.
Regarding the rechallenge model, differences in tumor growth were
calculated for the tumor area on day 14 to 17 with ordinary one-way
ANOVA and Tukey’s multiple comparison test or for the rechallenge of
OTR-TIL-P on day 15 with two-tailed, unpaired t-test. Statistical
comparison of survival was performed using the Mantel-Cox test.
Statistical analyses were performed with GraphPad Prism V.9.3.1
software.
Reporting summary
Further information on research design is available in the [336]Nature
Portfolio Reporting Summary linked to this article.
Supplementary information
[337]Supplementary Information^ (13.2MB, pdf)
[338]Peer Review File^ (1.3MB, pdf)
[339]41467_2024_53911_MOESM3_ESM.pdf^ (138.7KB, pdf)
Description of Additional Supplementary Files
[340]Supplementary Data 1^ (1.9MB, xlsx)
[341]Reporting Summary^ (45.3KB, pdf)
Source data
[342]Source data^ (5.3MB, zip)
Acknowledgements