Abstract

   Chimeric antigen receptor (CAR) T cell therapy is used in treating
   human hematological malignancies, but its efficacy is limited by T cell
   exhaustion (T[EX]). T[EX] arises at the expense of central memory T
   cells (T[CM]), which exhibit robust antitumor efficacy. Reduction of
   the TET2 gene led to increased T[CM] differentiation in a patient with
   leukemia who experienced a complete remission. We show that loss of
   TET2 led to increased chromatin accessibility at exhaustion regulators
   TOX and TOX2, plus increased expression of TOX2. Knockdown of TOX
   increased the percentage of T[CM]. However, unexpectedly, knockdown of
   TOX2 decreased T[CM] percentage and reduced proliferation.
   Consistently, a T[CM] gene signature was reduced in the TOX2 knockdown,
   and TOX2 bound to promoters of numerous T[CM] genes. Our results thus
   suggest a role for human TOX2, in contrast to exhaustion regulator TOX,
   as a potentiator of central memory differentiation of CAR T cells, with
   plausible utility in CAR T cell cancer therapy via modulated TOX2
   expression.
     __________________________________________________________________

   TOX2 positively regulates central memory CAR T cell differentiation
   unlike the related exhaustion regulator TOX.

INTRODUCTION

   The cancer immunotherapy field has produced notable results that the
   adoptive transfer of genetically engineered T cells can mediate durable
   and complete remissions in patients with a variety of refractory
   hematologic cancers ([38]1, [39]2). Accordingly, chimeric antigen
   receptor (CAR) T cells have powerful antitumor effects in leukemia,
   lymphoma, and myeloma ([40]3–[41]5). However, despite this great
   clinical potential, in many patients, CAR T cells do not proliferate,
   durably persist, or elicit effective antitumor activity ([42]6). A
   certain level of CAR T cell clonal expansion and persistence appears
   necessary for initial and durable benefit, but the mechanisms remain
   unclear. Poor persistence and loss of cytotoxicity, both indicators of
   dysfunction, are major determinants of resistance against CAR T cells
   ([43]6). One important finding is that T cell exhaustion often occurs
   at the expense of differentiation into stem-like or central memory
   cells, which have superior antitumor activity and prolonged persistence
   in preclinical CAR T cell models and in patients treated with CAR T
   cells ([44]7). These findings suggest that modulation of
   transcriptional regulators to enforce T cell memory–associated programs
   will increase persistence and antitumor efficacy.

   One factor that regulates CAR T cell function is the DNA demethylase
   Tet methylcytosine dioxygenase 2 (TET2) ([45]7). We profiled a patient
   with chronic lymphocytic leukemia (CLL) who had a complete and durable
   response to CAR T cell therapy and found that the CAR construct (which
   is inserted into the genome by a lentivirus) had disrupted the TET2
   locus, rendering it nonfunctional. The patient had a missense mutation
   in the second allele of TET2 that compromised its demethylase activity,
   culminating in biallelic disruption of the gene. Postinfusion
   sequencing revealed that CAR T cells with this TET2 disruption were
   positively selected, had undergone a large clonal expansion, and led to
   a higher CAR T cell proportion differentiating into central memory T
   cells (T[CM]), a memory subset that persist for long periods of time in
   the body and have greater self-renewal potential than other later-stage
   memory subtypes. In vitro knockdown proved that reduction of TET2 was
   sufficient to increase the frequency of T[CM] and to improve
   proliferation in response to antigen-presenting cells, which is a
   common measure of CAR T cell antitumor efficacy ([46]7).

   However, while the disruption of TET2 improves CAR T cell therapy, its
   use as a therapeutic strategy may be limited because TET2 is a tumor
   suppressor. Thus, there may be detrimental long-term effects of
   reducing TET2 function in human CAR T cells, as it may safeguard
   against aberrant lymphoproliferation. Alternative strategies that
   target specific factors without broad functions like TET2 may be more
   beneficial for CAR T therapy.

   Previous studies reveal up-regulation of thetranscription factor
   Thymocyte selection-associated high mobility group box protein (TOX) in
   T cell exhaustion with a crucial role in promoting T cell exhaustion
   ([47]8). Mouse models of CAR T therapy also show increased expression
   of TOX in tumor-infiltrating lymphocytes (TILs), as well as the closely
   related transcription factor, TOX2 ([48]9). Mouse models of chronic
   infection show up-regulation of both TOX and TOX2 in exhausted T cells,
   with TOX playing a direct role in regulating transcription of TOX2.
   Simultaneous loss of TOX and TOX2 improves the antitumor potency of
   TILs, but loss of TOX2 alone is not sufficient to significantly reduce
   tumor burden, suggesting that, in mouse models, the contribution of
   TOX2 to exhaustion is weaker compared to TOX ([49]9).

   Outside T cell exhaustion, TOX2 can improve function in other cellular
   contexts. TOX2 is required for proper maturation of cytotoxic human
   natural killer (NK) cells, which are positively regulated by TOX2,
   inducing expression of the gene TBX21 ([50]10). TOX2 also up-regulates
   Tbx21 in mouse type I invariant NK T (iNKT1) cells; defects in iNKT1
   cells are associated with diseases such as rheumatoid arthritis
   ([51]11). In addition, TOX2 is a key regulator of the differentiation
   of mouse T follicular helper (Tfh) cells. In Tfh cells, TOX2 has been
   consistently found to improve function by suppressing
   inflammation-related gene pathways ([52]12). Thus, the precise function
   of TOX2 appears to be highly cell context and T cell type dependent.

   CAR T cell therapy is typically administered as a mixed population of
   CD4^+ and CD8^+ T cells, conventionally considered helper cells and
   cytotoxic cells, respectively. They are distinct subsets with different
   functional and transcriptional characteristics, including variations in
   ability to proliferate and persist in vivo following in vitro expansion
   and adoptive transfer ([53]2). Previous studies of TET2 and other
   factors that affect CAR T cell cytotoxicity and persistence have mainly
   focused on CD8^+ T cells ([54]7). However, we recently found that CAR
   T–treated patients with CLL have a preponderance of clonally expanded
   CD4^+ CAR T cells with cytotoxic function that correlates with
   long-term durable remission ([55]2). This highlights the importance of
   considering both CD4^+ and CD8^+ T cells in the context of CAR T cell
   therapy, as both subsets contribute to the overall treatment efficacy.

   In this study, we aimed to gain a deeper understanding of TOX compared
   to TOX2 in human CD4^+ and CD8^+ CAR T cells, prompted by our
   observation of chromatin opening at both genes in the patient with CLL
   with disrupted TET2. Our results show that TOX2 plays a critical role
   in the development of T cell memory by increasing T[CM]
   differentiation, while, in contrast, TOX promotes T cell exhaustion by
   reducing the generation of T[CM]. These findings have important
   implications for potential human CAR T therapy.

RESULTS

TET2 loss promotes TOX and TOX2 chromatin accessibility

   To investigate TOX and TOX2 in human CAR T cell fate and function, we
   first analyzed the regulation of these genes in a CAR T cell context
   with robust antitumor patient response. Our previous study identified
   biallelic disruption of the TET2 gene in expanded CAR T cells from a
   patient who had responded completely to CAR T therapy ([56]7).
   Furthermore, we found that in vitro loss of TET2 is sufficient to
   increase the frequency of T[CM] cells and to improve proliferation in
   response to antigen ([57]7). Using available assay for transposase
   accessible-chromatin using sequencing (ATACseq) data generated from our
   study of these patient-derived TET2-disrupted postinfusion CD8^+ CD19
   CAR T cells, we compared the TOX and TOX2 loci to a negative control of
   patient-matched CD8^+ T cells that did not express the CAR
   ([58]Fig. 1A).

Fig. 1. TET2 loss induces chromatin changes at TOX and TOX2.

   [59]Fig. 1.
   [60]Open in a new tab

   (A) Our prior study ([61]7) examined the chromatin accessibility of
   postinfusion T cells from a patient (n = 1) who responded completely to
   chimeric antigen receptor (CAR) T therapy using assay for transposase
   accessible-chromatin using sequencing (ATACseq) to compare CAR^+ to
   CAR^− cells. This patient had biallelic disruption of TET2. (B) ATACseq
   browser tracks for CAR^− (n = 2 technical replicates) and CAR^+ (n = 2
   technical replicates), shaded boxes indicate MACS2 ([62]28) called
   peaks with log[2]fold change > 1 (green) or <−1 (pink), tracks
   visualized through [63]http://genome.ucsc.edu, showing the TOX locus
   and (C) TOX2 locus. (D) Schematic of RNA-seq comparing healthy human
   donor T cells treated with nontargeting short hairpin RNA (shRNA) as a
   control (n = 2) versus shRNA targeting TET2 (n = 2). (E) Volcano plot
   showing log[2]fold change versus −log[10](P value) of all genes in
   CD8^+ CAR^+ T cells for shTET2 versus control, with P[adj] < 0.18 and
   fold change > 0.25 [calculated using DESeq ([64]43)] considered
   significant. (F) Gene set enrichment analysis (GSEA) in CD8^+ shTET2 (n
   = 2) versus control (n = 2) T cell counts of a gene set down-regulated
   in exhausted T cells, normalized enrichment score (NES) = 1.12, false
   discovery rate (FDR) q = 0.05. (G) RNA-seq normalized tags per million
   from the FANTOM dataset showing expression in various tissues and
   organs of TOX2, (H) TOX, and (I) TET2.

   We found that the TOX locus included seven ATACseq peaks with a
   log[2]–fold change greater than one ([65]Fig. 1B) and the TOX2 locus
   included five such peaks ([66]Fig. 1C). The chromatin opening of TOX
   and TOX2 in these CAR T cells from a robustly responding patient is
   unexpected because they are both linked to T cell exhaustion in mouse.
   To examine the transcription state, we used knockdown of TET2 in donor
   human CAR T cells ([67]Fig. 1D) to recapitulate the loss of TET2
   observed in the patient. Using gene set enrichment analysis (GSEA), we
   checked for enrichment in the TET2 knockdown versus the control groups
   of gene sets associated with T cell exhaustion ([68]13) to determine
   whether the increased chromatin accessibility at TOX and TOX2 in the
   patient data correlated with an increase in T cell exhaustion in the
   donor human CAR T cells. We found that the RNA sequencing (RNA-seq)
   data following shTET2 treatment was significantly enriched for a gene
   set that is down-regulated in T cell exhaustion ([69]Fig. 1E),
   suggesting that TET2 loss does not promote exhaustion. Upon examining
   transcriptional targets of TET2 loss, we found that TOX2 ranked 71st on
   the list of most significantly up-regulated genes in the shTET2,
   whereas TOX was not significantly up-regulated ([70]Fig. 1F). We more
   closely examined the locations of the ATACseq peaks from the patient
   and found that two of the opened peaks at the TOX2 locus overlapped
   with enhancers previously found to interact with the promoter of TOX2
   ([71]14), potentially explaining the increased TOX2 transcription. In
   contrast, an enhancer that normally interacts with the promoter of TOX
   ([72]14) was closed in the CAR T cells, suggesting decreased
   transcription. Together, this suggests that TOX2 is up-regulated by
   loss of TET2, and it highlights that TOX2 can be up-regulated
   separately from TOX in non–T cell exhaustion settings.

   Considering concerns about the potential lack of specificity when
   directly manipulating TET2 in CAR T cells and TET2’s role in cancer, we
   compared the tissue-specific expression levels of TET2, TOX, and TOX2.
   Using published data ([73]15–[74]17), we found that the expression of
   TOX and TOX2 is at least fourfold higher in the thymus than in any
   other tissue type ([75]Fig. 1, G and H), underscoring their role in T
   cell biology. However, TET2 is widely expressed throughout the body
   ([76]Fig. 1I). Thus, although TET2 loss has been clinically beneficial
   ([77]7), TOX2 is a potentially superior target for manipulation in CAR
   T cells because of its specificity within the T cell compartment and
   increased chromatin accessibility in a patient who responded completely
   to CAR T therapy.

TOX2 is necessary for T[CM] differentiation

   To address the specific roles of TOX and TOX2 in CAR T cells, we
   altered their expression. We hypothesized that modulation of TOX and
   TOX2 levels would affect the memory differentiation of CAR T cells,
   similar to loss of TET2 ([78]7). To test this, we used short hairpin
   RNA (shRNA) knockdown of TOX2 in human CAR T cells and examined their
   immunophenotype ([79]Fig. 2A, Expt. 1). We used a previously
   established T cell differentiation panel that identifies memory
   subtypes based on the expression of cell surface markers CCR7 (high in
   T[CM]) and CD45RO [high in both T[CM] and effector memory T cells
   (T[EM])] ([80]7). We found that loss of TOX2 significantly reduced
   CD45RO^+CCR7^+ T[CM] cells ([81]Fig. 2B, top right; quantified in
   [82]Fig. 2C and table S1), while it significantly increased the
   CD45RO^+CCR7^− (T[EM]) population ([83]Fig. 2B, bottom right;
   quantified in [84]Fig. 2D and table S1). We also validated this using a
   second marker, CD27, which showed decreased T[CM] cells via a drop in
   the CD45RO^+ CD27^+ population (fig. S1A and table S2). These data
   indicate that TOX2 may regulate human CAR T cell differentiation via
   enriching T[CM] cells, which have robust antitumor potency ([85]18).

Fig. 2. TOX2 is necessary for maximal central memory T cell (T[CM])
formation.

   [86]Fig. 2.
   [87]Open in a new tab

   (A) Schematic of experimental design. (B) Representative flow cytometry
   (n = 1) comparing day 14 in vitro–expanded human CAR19 T cells treated
   with nontargeting control short hairpin RNA (shRNA) versus shRNA
   targeting TOX2, plotted as CD45RO versus CCR7. (C) Percentage of
   CD45RO^+CCR7^+CAR19^+ T cells in seven human donors over four separate
   experiments, 12 samples in total, paired Student’s t test, P = 4.38 ×
   10^−5. (D) Percentage of CD45RO^+CCR7^−CAR19^+ T cells in seven human
   donors over four separate experiments, 12 samples in total, paired
   Student’s t test, P = 6.54 × 10^−7. (E) Population doublings after 7,
   12, 17, and 22 days for control versus shTOX2 CAR19^+ T cells
   cocultured with CD19-presenting K562 cells (n = 3, paired Student’s t
   test, P = 0.03). (F) Population doublings after 7, 12, 17, and 22 days
   for control versus shTOX2 CAR19^+ T cells cocultured with
   mesothelin-presenting K562 cells (n = 3, paired Student’s t test, P >
   0.05, n.s., not significant). (G) Numbers of genes up-regulated in
   shTOX2 (n = 4) versus control (n = 4) in both CD4^+ T cells and CD8^+ T
   cells, plus genes down-regulated in shTOX2 versus control in both CD4^+
   T cells and CD8^+ T cells. Hypergeometric test, P = 2.91 × 10^−161 and
   P = 3.7 × 10^−81. (H) Heatmap of z scores from normalized RNA-seq
   counts from the 137 genes up-regulated in both CD4^+ and CD8^+ shTOX2
   versus control, clustered by row. (I) Gene set enrichment analysis
   (GSEA) in CD4^+ shTOX2 versus control T cell counts of a gene set
   up-regulated in effector memory T cells (T[EM]) versus T[CM], NES =
   1.23, FDR q = 0.09. (J) GSEA in CD8^+ shTOX2 versus control T cell
   counts of a gene set up-regulated in effector memory cells versus
   T[CM], normalized enrichment score (NES) = 1.18, false discovery rate
   (FDR) q = 0.13. (K) Sets of genes down-regulated or up-regulated in the
   shTET2 versus control from [88]Fig. 1D, plotted as box plots of their
   log[2]–fold change in the shTOX2 versus control, unpaired Student’s t
   test, P = 0.002.

   To investigate whether the reduction of TOX2 that impairs T[CM] cell
   outgrowth can decrease the proliferative potency of CAR T cells, we
   performed a serial stimulation experiment using CAR T cells and
   irradiated K562 chronic myeloid leukemia (CML) cells engineered to
   express the CD19 extracellular domain. This “stress test” assay closely
   mimics features of antigen-induced exhaustion during long-term
   coculture ([89]19). The restimulation assay accurately predicts the
   proliferative potency of CAR T cells in patients, which has a direct
   and strong correlation with clinical outcome ([90]20, [91]21). We
   found, after 22 days of chronic antigen challenge, that cells
   containing the shTOX2 had significantly fewer cell divisions than CAR T
   cells with a scrambled control shRNA ([92]Fig. 2E and table S3). To
   confirm that this reduction in cell number was antigen dependent, we
   cocultured the manipulated CAR T cells with mesothelin-expressing K562
   cells as an irrelevant control, which do not interact with the
   CD19-targeting CAR. There was no difference in cell number between the
   control and shTOX2 ([93]Fig. 2F and table S3), indicating that reduced
   proliferation caused by TOX2 loss was antigen dependent. These results
   demonstrate that TOX2 is necessary for maximal CAR T cell growth in the
   presence of antigen.

   Similar to CAR T therapy, our coculture experiment used a mixed
   population of CD4^+ and CD8^+ T cells. To investigate whether TOX2
   knockdown affected CD4^+ cells differently from CD8^+ T cells, we
   performed RNA-seq on sorted CD4^+ and CD8^+ CAR T cells. Levels of TOX2
   were significantly reduced in both CD4^+ and CD8^+ shTOX2 samples
   compared to controls, although we noted that endogenous levels of TOX2
   were higher in CD4^+ cells, and the reduction in TOX2 with the shRNA
   was larger (fig. S1B). We identified 1074 changing genes in CD4^+ T
   cells (fig. S1C) but only 312 in CD8^+ T cells (fig. S1D). A majority
   of differentially expressed genes in CD8^+ T cells were also changing
   in CD4^+ T cells (68.5%), whereas the CD4^+ T cells had many more
   differentially expressed genes that were unaltered in CD8^+ T cells
   ([94]Fig. 2G). These results suggest that TOX2 loss had a greater
   effect on CD4^+ T cells in our experimental system. Even for the 137
   genes up-regulated in both CD4^+ T cells and CD8^+ T cells with the
   TOX2 knockdown, a heatmap showed a trend toward stronger, more
   consistent up-regulation in CD4^+ T cells ([95]Fig. 2H). Although the
   average fold change of the TOX2 knockdown over the control for these
   genes was higher in CD8^+ than CD4^+ cells (fig. S1E), the normalized
   expression levels of the genes were higher in CD4^+ cells (fig. S1F).
   These findings indicate that, in addition to regulating more genes in
   CD4^+ CAR T cells, TOX2 loss leads to higher expression of individual
   genes.

   Using GSEA, we checked for enrichment in the TOX2 knockdown versus the
   control groups of gene sets associated with various T cell states
   (table S4) ([96]22). We found that a previously described set of genes
   up-regulated in T[EM] compared to T[CM] ([97]22) were significantly
   enriched in the shTOX2 in both CD4^+ cells ([98]Fig. 2I) and CD8^+
   cells ([99]Fig. 2J). The reverse was also true in CD4^+ cells, in that
   genes down-regulated in T[EM] compared to T[CM] ([100]22) cells were
   enriched in the control sample (table S4). These results indicate that
   loss of TOX2 drives progressive, antigen-dependent CAR T cell
   differentiation from the T[CM] to T[EM] state, particularly in CD4^+ T
   cells. In addition, we speculate that TOX2 may promote long-term
   maintenance of CD4^+ T cells, as they can exhibit longer persistence in
   CAR T cell therapy ([101]2).

   To further predict the consequences of TOX2 loss on clinical CAR T
   therapy, we compared transcriptional gene targets of TOX2 loss to those
   of TET2 loss. We examined mRNA levels from the shTET2 in [102]Fig. 1.
   We grouped all genes either into down-regulated (N = 7000) or
   up-regulated (N = 7007) and then plotted as log[2]–fold changes in our
   shTOX2 cells ([103]Fig. 2K). We found that the genes up-regulated in
   the TET2 loss had, on average, a significantly more negative fold
   change in the TOX2 loss than the genes down-regulated in TET2 loss (P =
   0.002; [104]Fig. 2K). These results are consistent with the
   interpretation that some of the positive gene expression gained in TET2
   loss would be eliminated upon TOX2 loss, and thus, certain positive
   effects of losing TET2 may be dependent on the presence of TOX2.

TOX2 is sufficient to increase central memory differentiation but not to
improve CAR T cell function

   Thus, loss of TOX2 led to reduction of T[CM]. We hypothesized that, if
   TOX2 is sufficient to drive the T[CM] phenotype, increasing the levels
   of TOX2 would shift differentiation toward the T[CM] state and lead to
   improvement in CAR T cell function. To test this, we overexpressed
   TOX2-FLAG in human CAR T cells ([105]Fig. 2A, Expt. 2). We used flow
   cytometry as described above to separate memory subtypes based on
   CD45RO and CCR7 expression. TOX2 overexpression significantly increased
   the proportion of CD45RO^+CCR7^+ T[CM] (top right quadrant in
   [106]Fig. 3A; quantified in [107]Fig. 3B and table S5) and
   correspondingly decreased the proportion of CD45RO^+CCR7^− T[EM]
   (bottom right quadrant of [108]Fig. 3A; quantified in [109]Fig. 3C and
   table S5). It also trended toward increasing the proportion of
   CD45RO^+CD27^+ T[CM] (fig. S2A and table S2). These results demonstrate
   that elevated expression of TOX2 alters the differentiation path of
   human CAR T cells in favor of a longer-lasting, central memory
   phenotype.

Fig. 3. TOX2 is sufficient to increase central memory phenotype but not to
improve chimeric antigen receptor (CAR) T cell function.

   [110]Fig. 3.
   [111]Open in a new tab

   (A) Representative flow cytometry (n = 1) comparing day 14 in
   vitro–expanded human CAR19 T cells treated with empty control virus
   versus TOX2 cDNA virus, plotted as CD45RO versus CCR7. (B) Percentage
   of CD45RO^+CCR7^+CAR19^+ T cells in five human donors over four
   separate experiments, seven samples total, paired Student’s t test, P =
   0.005. (C) Percentage of CD45RO^+CCR7^− CAR19^+ T cells in five human
   donors over four separate experiments, seven samples total, paired
   Student’s t test, P = 0.016. (D) Population doublings after 7, 12, 17,
   22, and 27 days for control versus TOX2OE CAR19^+ T cells cocultured
   with CD19-presenting K562 cells (n = 3, paired Student’s t test, P >
   0.05; n.s., not significant). (E) Representative flow cytometry (n = 1)
   comparing day 0 and day 7 control versus TOX2OE CAR19^+ T cells from
   (D), plotted as PD-1 versus CCR7. (F) Percentage of PD-1^+CCR7^+CAR19^+
   T cells from three human donors at days 0 and 7, control (n = 3) versus
   TOX2OE (n = 3), paired Student’s t test, P > 0.05 and P = 0.002. (G)
   Gene set enrichment analysis (GSEA) in CD8^+ TOX2OE versus control (n =
   3) RNA-seq normalized counts of a gene set up-regulated in T cell
   exhaustion–precursor (T[PEX]) cells, normalized enrichment score (NES)
   = 1.79, false discovery rate (FDR) q = 0.001.

   In previous studies, an outgrowth of CAR T[CM] cells after infusion
   correlates with improvement in CAR T cell function in patients
   ([112]7). Therefore, we tested whether increasing TOX2 levels would
   improve CAR T cell proliferation in response to antigen. The CAR^+ T
   cells were sorted and then cocultured for 27 days with the
   CD19-presenting K562 cell line. Unexpectedly, there was no change in
   cell number doublings between the empty vector and the TOX2
   overexpression ([113]Fig. 3D and table S6). Hence, while TOX2 is
   necessary for optimal CAR T cell proliferation ([114]Fig. 2) and TOX2
   overexpression increases T[CM] differentiation ([115]Fig. 3, A to C),
   TOX2 overexpression is not sufficient to increase growth above the
   level of the control.

   Next, we investigated the impact of TOX2 overexpression on CAR T cell
   function and growth because our observations above showed that TOX2
   overexpression increased the frequency of CAR T[CM] cells but did not
   increase cell proliferation. We performed flow cytometry after 7 days
   of coculture and found that the percentage of programmed cell death
   protein 1 (PD-1)^+ cells was significantly increased in the group with
   TOX2 overexpression [[116]Fig. 3E (bottom right quadrant); quantified
   in fig. S2B and table S7]. Many of these PD-1^+ cells were also CCR7^+
   ([117]Fig. 3E; quantified in [118]Fig. 3F and table S7). This
   population of cells, known as T cell exhaustion–precursor cells
   (T[PEX]), complicates traditional distinctions between memory T cell
   subsets and shows reduced proliferation and cytokine secretion compared
   to other T cell types ([119]23). Thus, our results suggest that TOX2
   overexpression promotes T[PEX] differentiation, which may explain the
   lack of improvement in CAR T cell function despite an increase in
   beneficial T[CM].

   Using RNA-seq, we further investigated whether T[PEX] cells develop in
   the TOX2 overexpression state. GSEA revealed that a set of genes
   up-regulated in T[PEX] compared to T[CM] ([120]23) was enriched with
   TOX2 overexpression ([121]Fig. 3G). Exhaustion-related genes were
   increased, such as PDCD1 (encoding PD-1), NKG7, and GZMK, and these
   were also up-regulated in T[PEX] (fig. S2C, top). In addition, TOX2
   overexpression down-regulated many genes (including IL6ST, MYC, DPP4,
   and SATB1) that are also down-regulated in T[PEX] (fig. S2C, bottom).
   Hence, TOX2 overexpression leads not only to increased T[CM] signature
   but also to increased T[PEX] signature [as in ([122]23)], which likely
   explains the limited overall improvement in T cell function.

TOX2 promotes both central memory and exhaustion gene signatures

   Many studies of exhaustion focus on CD8^+ T cells because the loss of
   CD8^+ cytotoxicity is thought to underlie the negative effects of
   exhaustion in contexts like CAR T therapy. However, as mentioned above,
   evidence suggests that clonal expansion of CD4^+ CAR T cells is linked
   to long-term remission in patients ([123]2). While our
   immunophenotyping assays assessed a combined population of CD4^+ and
   CD8^+ cells, we can examine these cells separately through RNA-seq. We
   thus sorted the TOX2 overexpressing CAR^+ cells into CD4^+ and CD8^+
   subgroups, performed RNA-seq, and confirmed that TOX2 levels were
   greatly increased in both CD4^+ and CD8^+ cells (fig. S2D). We
   identified 655 changing genes in CD4^+ cells (fig. S2E) and 404
   changing genes in CD8^+ cells (fig. S2F), indicating that overexpressed
   TOX2 regulates more genes in CD4^+ than CD8^+ cells, consistent with
   our shTOX2 results ([124]Fig. 2G). We found 95 genes down-regulated by
   TOX2 overexpression in both CD4^+ and CD8^+ cells. In CD4^+ cells, the
   mean log[2]–fold change of these 95 genes was −1.21, compared to −0.92
   in CD8^+ cells (P = 1.43 × 10^−7; fig. S2G), showing that TOX2
   overexpression leads to larger changes in average gene expression in
   CD4^+ cells.

   Having detected the presence in TOX2 overexpression of increased
   signatures of both T[CM] and T[PEX], we then examined whether there was
   transcriptional specificity between CD4^+ and CD8^+ cells for the two
   cell types. We performed the same GSEA as in [125]Fig. 2. In CD4^+
   cells, but not CD8^+, the control sample was enriched for genes
   up-regulated in T[EM] versus T[CM] ([126]22), suggesting that the
   beneficial T[CM] memory function of TOX2 may be stronger in CD4^+ T
   cells. Note that, at baseline, TOX2 was more highly expressed in CD4^+
   T cells compared to CD8^+ (fig. S1A). In addition, there were
   consistently a greater number of differentially expressed genes in
   CD4^+ T cells compared to CD8^+ (figs. S1, B and C, and S2, D and E).
   In CD8^+ cells, but not CD4^+, the control samples were enriched for
   genes down-regulated in exhaustion pathways (table S8) ([127]13). Thus,
   the CD4^+ T cells may be contributing more strongly to memory function,
   whereas the CD8^+ T cells may be contributing more to the T[PEX]
   phenotype.

   We then overexpressed TOX2 in a non–T cell context to investigate
   whether TOX2 can invoke T[CM] and T[EX] gene signatures in a completely
   unrelated cell context, as activation in an unrelated system is a
   persuasive way to show that a factor has a dominant transcriptional
   function. We constitutively expressed FLAG-tagged TOX2 in the diploid
   human IMR90 lung fibroblast cell line. RNA-seq showed strong expression
   of TOX2 ([128]Fig. 4A). GSEA showed enrichment for genes associated
   with T[CM] over naive (T[N]) cells ([129]24) with TOX2 overexpression
   ([130]Fig. 4B), consistent with TOX2 overexpression in T cells
   increasing the percentage of T[CM] ([131]Fig. 3B). Furthermore, we
   observed that the TOX2 overexpression samples were also enriched for a
   gene set up-regulated in T[EX] ([132]Fig. 4C) ([133]13), and the
   control samples were enriched for a gene set down-regulated in T[EX]
   ([134]Fig. 4D) ([135]13). These results support our assertion above
   that TOX2 may regulate the intermediate phenotype T[PEX].

Fig. 4. TOX2 promotes both central memory and exhaustion gene signatures.

   [136]Fig. 4.
   [137]Open in a new tab

   (A) Normalized RNA-seq counts of TOX2 in overexpression of FLAG-TOX2 (n
   = 2) versus expression of FLAG-empty (n = 2) in human IMR90
   fibroblasts. (B) Gene set enrichment analysis (GSEA) in IMR90 TOX2OE (n
   = 3) versus control RNA-seq (n = 3) normalized counts of a gene set
   up-regulated in central memory T cells (T[CM]) versus naive T cells
   (T[N]), normalized enrichment score (NES) = 1.06, false discovery rate
   (FDR) q = 0.76. (C) GSEA in IMR90 TOX2OE (n = 3) versus control (n = 3)
   RNA-seq normalized counts of a gene set up-regulated in exhausted T
   cells (T[EX]), NES = 0.99, FDR q = 0.56. (D) GSEA in IMR90 TOX2OE (n =
   3) versus control (n = 3) RNA-seq normalized counts of a gene set
   down-regulated in T[EX], NES = −0.79, FDR q = 0.89. (E) Heatmap of z
   scores from normalized RNA-seq counts from the 195 genes changing in
   the TOX2OE (n = 3) and shTOX2 (n = 4) compared to their respective
   controls, clustered by row and column. (F) Pathway enrichment analysis
   by ConsensusPathDB for the 81 genes up-regulated by TOX2OE.

TOX2 regulates cell cycle genes

   Thus, TOX2 may positively regulate memory gene signatures in CD4^+ T
   cells. To reveal the most important genetic targets that may underlie
   the positive effects on memory, we combined the RNA-seq data from the
   TOX2 overexpression in [138]Fig. 3 with data from the shTOX2 in
   [139]Fig. 2. We focused on 81 genes up-regulated by TOX2 overexpression
   and down-regulated by shTOX2, as genes activated by TOX2 [[140]Fig. 4E
   (top half) and table S9], and 114 genes down-regulated by TOX2
   overexpression and up-regulated by shTOX2, as genes repressed by TOX2
   [[141]Fig. 4E (bottom half) and table S9]. Pathway analysis revealed
   that the genes activated by TOX2 are primarily enriched for cell cycle
   pathways ([142]Fig. 4F), suggesting that TOX2 functions in cell growth
   and is consistent with the necessity of TOX2 for maximal CAR T cell
   proliferation in response to antigen ([143]Fig. 2E). The genes
   repressed by TOX2 were not significantly enriched for any pathways of
   obvious interest. Together, we conclude that, while knockdown of TOX2
   shows that it is necessary for CAR T cell proliferation in response to
   antigen, overexpression of TOX2 leads to simultaneous promotion of the
   T[PEX] state, thus preventing improvement in proliferation.

TOX is necessary for T[EM] differentiation

   Similar to the TOX2 gene locus, the TOX locus exhibited many ATACseq
   peaks of increased chromatin accessibility in the patient who responded
   to CAR T therapy ([144]Fig. 1B) ([145]7). We therefore directly
   compared the effects of TOX and TOX2 on memory differentiation in CAR T
   cells ([146]Fig. 2A, Expt. 3). We knocked down TOX in human CAR T cells
   and examined the effect on T cell memory differentiation
   ([147]Fig. 5A). In contrast to the effects of knockdown of TOX2, which
   lowered T[CM], knockdown of TOX led to increased percentages of
   CD45RO^+CCR7^+ T[CM] ([148]Fig. 5A, top right quadrant; quantified in
   [149]Fig. 5B and table S10) and decreased frequencies of CD45RO^+CCR7^−
   T[EM] ([150]Fig. 5A, bottom right quadrant; quantified in [151]Fig. 5C
   and table S10). In addition, knockdown of TOX led to increased
   percentages of CD45RO^+CD27^+ T[CM] in some donors (fig. S3A and table
   S2). This demonstrates that TOX plays a key role in differentiation of
   T[EM], in contrast to our observations with TOX2, which enforces the
   central memory state at the expense of T[EM] differentiation.

Fig. 5. TOX is necessary for effector memory T cells (T[EM]) differentiation.

   [152]Fig. 5.
   [153]Open in a new tab

   (A) Representative flow cytometry (n = 1) comparing day 14 in
   vitro–expanded human CAR19 T cells treated with nontargeting control
   short hairpin RNA (shRNA) versus shRNA targeting TOX, plotted as CD45RO
   versus CCR7. (B) Percentage of CD45RO^+CCR7^+CAR19^+ T cells in three
   human donors, paired Student’s t test, P = 0.02. (C) Percentage of
   CD45RO^+CCR7^−CAR19^+ T cells in three human donors, paired Student’s t
   test, P = 0.03. (D) Gene set enrichment analysis (GSEA) in CD4^+ shTOX
   (n = 3) versus control (n = 3) T cell counts of a gene set up-regulated
   in T[EM] versus central memory T cells (T[CM]), normalized enrichment
   score (NES) = −1.82, false discovery rate (FDR) q = 0. (E) GSEA in
   CD8^+ shTOX versus control T cell counts of a gene set up-regulated in
   T[EM] versus T[CM], NES = −1.78, FDR q = 0. (F) Normalized RNA-seq
   counts for TOX2 in CD4^+ and CD8^+ CAR19^+ T cells, control (n = 3)
   versus shTOX (n = 3). (G) Overlap of genes up- or down-regulated in the
   shTOX versus shTOX2 CD4^+ T cells. (H) Heatmap of z scores from
   normalized RNA-seq counts from the 44 genes up-regulated in shTOX and
   down-regulated in shTOX2, clustered by row and column. (I) Pathway
   enrichment analysis by ConsensusPathDB for the 44 genes shown in (H).

   We performed RNA-seq on CD4^+ and CD8^+ T cells separately in the shTOX
   and found that the levels of TOX decreased in both cell populations
   (fig. S3B). GSEA revealed that the gene set associated with increased
   T[EM] compared to T[CM] ([154]22) was enriched in the control samples
   of both CD4^+ ([155]Fig. 5D) and CD8^+ T cells ([156]Fig. 5E),
   suggesting that TOX promoted differentiation of T[EM] cells compared to
   T[CM] cells. Previous work has suggested that a higher proportion of
   T[EM] cells compared to T[CM] cells leads to less effective tumor
   clearance during CAR T therapy ([157]25), so these results are
   consistent with the idea that TOX is a driver of exhaustion in T cells,
   which also negatively affects the function of CAR T cells. However, our
   findings are not consistent with previous studies in mouse models,
   which suggest that TOX functions in a similar manner as TOX2 ([158]9).

   We observed that TOX2 was significantly up-regulated upon knockdown of
   TOX ([159]Fig. 5F), which could, in part, underlie increases in T[CM]
   observed with knockdown of TOX. The up-regulation was stronger in CD4^+
   cells compared to CD8^+ cells ([160]Fig. 5F), suggesting that TOX may
   repress TOX2 specifically in CD4^+ T cells. Overall, loss of TOX had a
   stronger effect on CD4^+ T cells, similar to TOX2: A total of 1745
   genes changed in CD4^+ shTOX cells (fig. S3C), whereas 715 genes
   changed in CD8^+ shTOX cells (fig. S3D).

   We compared the RNA-seq results from TOX knockdown versus TOX2
   knockdown and found that the majority of genes were uniquely regulated
   by either TOX or TOX2 ([161]Fig. 5G). We were most interested in 44
   genes that were down-regulated by TOX2 knockdown and up-regulated by
   TOX knockdown ([162]Fig. 5G, outlined in red); that is, given that
   T[CM]are increased by gain of TOX2 ([163]Fig. 3B) and increased by loss
   of TOX ([164]Fig. 5B), this core set of 44 genes ([165]Fig. 5H) could
   be key to positive regulation of T[CM]. These genes were significantly
   enriched for interleukin-2 (IL-2) signaling pathways through the
   regulation of JAK3 and CCND2 ([166]Fig. 5I). Recent studies show that
   IL-2 signaling can be harnessed to improve CAR T cell expansion,
   activation, and antitumor activity ([167]26, [168]27). Furthermore, in
   our experiments, negative effects on CAR T function of loss of TOX2
   could be due to loss in IL-2 signaling, which does not occur upon loss
   of TOX. Overall, we conclude that TOX and TOX2 provide differential
   gene regulation leading to different functional consequences in human
   CAR T cells.

TOX2 binds to DNA at memory-related genes

   Although TOX2 is classified as a transcription factor, little is known
   of how it functions at the level of chromatin. We investigated whether
   the location of TOX2 binding sites correlated with target genes
   regulated by TOX2. We performed chromatin immunoprecipitation with
   sequencing (ChIPseq) to determine TOX2 binding in the genome of human
   CAR T cells using T cells from two donors; in one set, we mapped
   endogenous levels of TOX2, and in the second set, we mapped
   overexpressed TOX2. Example browser tracks for both are shown at VWA5A
   and CLUAP1 genes ([169]Fig. 6A), which are both up-regulated in T[CM]
   cells ([170]24). In both ChIPseq datasets, numerous discrete binding
   peaks were identified using MACS2 (heatmaps and metaplots shown in
   [171]Fig. 6B) ([172]28). Over 90% of the 7755 peaks in the TOX2
   endogenous condition were replicated in the TOX2 overexpression
   condition (fig. S4A), and TOX2 overexpression led to an additional
   20,364 peaks (fig. S4A). Further analyses were based on the consensus
   7016 peaks in both samples ([173]Fig. 6B, bottom). Compared to the
   entire genome ([174]Fig. 6C, left), TOX2-bound peaks were highly
   enriched at promoter-TSS (transcription start site) regions
   ([175]Fig. 6C, right). Furthermore, there was strong correlation
   between peak amplitude score and distance to TSS in that the strongest
   TOX2 peaks were closest to TSSs (fig. S4B), supporting an activation
   role for TOX2 bound at TSSs. Overall, the proximity of TOX2 binding to
   promoters strongly implicate TOX2 as a transcription factor.

Fig. 6. TOX2 binds to and activates central memory genes.

   [176]Fig. 6.
   [177]Open in a new tab

   (A) Chromatin immunoprecipitation with sequencing (ChIPseq) browser
   tracks for wild-type (WT) TOX2 (n = 1) and overexpressed TOX2 (n = 1)
   binding at the VWA5A and CLUAP1 loci, called peaks shaded in teal.
   Tracks visualized through [178]http://genome.ucsc.edu. (B) Metaplot and
   heatmap of WT TOX2 (n = 1) and TOX2OE (n = 1) binding scores over the
   total number of peaks called for each sample alone and for their shared
   peaks. (C) Pie charts showing the makeup of the whole genome and the WT
   TOX2-bound sites, annotated by genomic region type. (D) Venn diagram
   showing the genes bound by TOX2 (n = 4238) and genes up- and
   down-regulated by TOX2 (n = 1843), with some memory- and
   exhaustion-related genes highlighted. (E) ChIPseq and RNA-seq browser
   tracks for control and overexpressed TOX2 at the CCND2 locus, with
   MACS2-called ChIPseq peaks shaded in teal and exons shaded in gray.
   Tracks were visualized through [179]http://genome.ucsc.edu. (F) Barplot
   of predicted number of TOX2 binding peaks versus actual number of TOX2
   binding peaks for four gene sets of interest. Predictions calculated by
   multiplying the percentage of total promoters bound by TOX2 (4238 of
   51794 = 8.1%) by the number of genes in each gene set (187, 49, 520,
   and 568, respectively), hypergeometric test P = 1.01 × 10^−16, 0.001,
   1.67 × 10^−14, and 7.52 × 10^−103. (G) Barplot of predicted number of
   TOX2 binding peaks versus actual number of TOX2 binding peaks for the
   open or closed assay for transposase accessible-chromatin using
   sequencing (ATACseq) peaks from the patient described in [180]Fig. 1.
   Predictions calculated by multiplying the percentage of total changing
   ATAC peaks bound by TOX2 (222 of 32518 = 0.68%) by the number of genes
   in each gene set (7286 open peaks and 25,233 closed peaks),
   hypergeometric test, P = 1.9 × 10^−5 and 9.9 × 10^−6. (H) Probability
   density functions for hypergeometric tests shown in (G), comparing
   number of predicted TOX2 binding sites (tan) to actual number of
   TOX2-binding sites (red lines) for the peaks opened or closed in the
   patient.

   We found that numerous genes regulated by TOX2 were also bound by TOX2.
   There were 1843 genes changed by either the TOX2 knockdown, TOX2
   overexpression, or both; a total of 418 of these genes were bound by
   TOX2 within 5 kb of their promoters ([181]Fig. 6D), including ATP1A1,
   BAG2, and NOC3L, all of which are up-regulated in T[CM] over T[EM]
   cells ([182]24). Of the TOX2 peaks near promoters, 239 genes were
   activated by TOX2; some of these were also repressed by TOX, such as
   CCND2 and JAK3 ([183]Fig. 6E), indicating that the functions of TOX2
   that distinguish it from TOX involve specific genes bound by TOX2.

   TOX2 also bound to 179 genes that it repressed, including EOMES and
   PARP8 ([184]Fig. 6D), which are expressed most highly in T[EM] compared
   to other memory subsets, as well as up-regulated in T cell exhaustion
   ([185]24). Thus, TOX2 may function as an activator at some genes and as
   a repressor at others. To delve deeper into the activity of TOX2 as an
   activator versus repressor, we analyzed all genes with TOX2 peaks
   within 1 kb of their promoters and compared the mean log[2]-fold change
   of these genes in the TOX2 knockdown condition relative to the TOX2
   overexpression condition. TOX2 overexpression led to a larger fold
   change, and these genes showed a trend toward activation by TOX2 (fig.
   S4C). This indicates that proximity of TOX2 binding to a gene promoter
   may positively correlate with TOX2-mediated activation of that gene.

   To narrow our focus to genes of interest, we examined TOX2 binding at
   memory and exhaustion-related targets. TOX2 bound within 5 kb of 4238
   distinct promoters, which is approximately 8.1% of total annotated
   promoters. We separated these 8.1% of promoters into four gene sets
   (T[CM], T[EM], exhaustion-up, and exhaustion-down) and then compared
   the predicted number to the actual number of TOX2-bound peaks. We found
   that although all four gene sets showed more TOX2 peaks than expected,
   the significance of the increase varied ([186]Fig. 6F). Notably, T[CM]
   genes and exhaustion-down genes showed much larger increases than T[EM]
   genes and exhaustion-up genes ([187]Fig. 6F), consistent with our
   earlier finding that a TOX2 peak near a promoter is more likely to lead
   to an activated gene (fig. S4C). Thus, the major targets of TOX2
   activation are T[CM] genes and genes that decrease in T[EX].

   Although it appears that TOX2 is more likely to bind to T[CM] genes
   than exhaustion-up genes ([188]Fig. 6F), this analysis was based on
   consensus peaks observed in both the endogenous TOX2 ChIPseq and the
   overexpressed TOX2 ChIPseq. Considering the acquisition of exhaustion
   gene expression that we observed with TOX2 overexpression
   ([189]Fig. 3G), we investigated whether overexpressed TOX2 bound more
   often to exhaustion-related genes than endogenous TOX2. For endogenous
   TOX2, the number of TOX2 peaks at exhaustion-up genes is barely higher
   than predicted by chance (fig. S4D), whereas for overexpressed TOX2, it
   is significantly higher (fig. S4E). This increase in binding to
   exhaustion genes may explain our findings that overexpressed TOX2
   induces a T[PEX] signature.

   We compared our consensus TOX2 binding data to the ATACseq data from
   the patient described in [190]Fig. 1, who responded completely to CAR T
   therapy. We found that TOX2 is much more likely than predicted by
   chance to bind to regions open in the patient ([191]Fig. 6, G and H).
   In contrast, the number of TOX2 peaks at closed regions was less than
   predicted by chance ([192]Fig. 6, G and H). Together, these data show
   that TOX2 binding is enriched at regions of open chromatin in a patient
   who responded to CAR T therapy and suggest that TOX2 may play a role in
   increasing T[CM] differentiation during the antitumor response.

DISCUSSION

   Our results show that a remarkable patient response to CAR T cell
   therapy ([193]7) correlates with an increase in chromatin accessibility
   at both TOX and TOX2 ([194]Fig. 1D). This is unexpected, given that, in
   preclinical mouse studies, they are identified as redundant regulators
   of T cell exhaustion ([195]9). Our in vitro stress test model,
   incorporating human CAR T cells, showed that TOX2 contributes to the
   formation and proliferation of T[CM] in response to antigen
   ([196]Fig. 2, C and E), which predict robust CAR T cell function.
   However, we find that, while high levels of TOX2 expression are
   sufficient to increase central memory CAR T cell differentiation
   ([197]Fig. 3B), they do not improve proliferation ([198]Fig. 3D),
   possibly because of the up-regulation of exhaustion precursor pathways
   (T[PEX]; [199]Fig. 3, F and G). In addition, we have found that TOX2
   binds chromatin at many of the genes it regulates, including key
   central memory and exhaustion-related genes ([200]Fig. 6D). In clear
   contrast, our results show that TOX works differently, wherein loss of
   TOX leads to increases in T[CM] and their accompanying transcriptomic
   programs ([201]Fig. 5, B, D, and E).

   Hence, we identify a role for TOX2 distinct from TOX as a regulator of
   memory differentiation in human CAR T cells. First, TOX2 is necessary
   for maximal differentiation of central memory cells ([202]Fig. 2B),
   while loss of TOX leads to an increase in central memory cells
   ([203]Fig. 5B), suggesting that TOX2 promotes central memory cell
   differentiation, whereas TOX promotes exhausted T cells. Thus, while
   TOX and TOX2 are both HMG box transcription factors and are highly
   similar in protein sequence ([204]29), they are not functionally
   redundant in human T cells; their highly differential effects on
   central memory cells have implications for the design of CAR T
   therapies. Notably, our findings also indicate that despite their high
   sequence similarity, TOX and TOX2 drive expression of different genes,
   which likely explain their distinct functions. The mechanisms of DNA
   binding and protein interactions underlying distinct function of TOX
   and TOX2 remain to be investigated.

   Unlike previous studies of exhausted T cells that focused primarily on
   CD8^+ T cells, we investigated CD4^+ and CD8^+ T cells. We found that
   the effects of TOX2 knockdown and overexpression were greater in CD4^+
   compared to CD8^+ ([205]Fig. 2, H to J, and fig. S1, B and C). Thus,
   together with our recent findings that CD4^+ cells persist throughout
   prolonged patient remission after CAR T therapy ([206]2), these results
   highlight the importance of analyzing CD4^+ cells in the context of CAR
   T therapy.

   There is strong consensus from various studies that TOX is required for
   the formation of exhausted T cells ([207]8, [208]9, [209]30, [210]31);
   however, mechanisms by which TOX regulates memory-related gene
   expression are not fully understood. Strangely, in a mouse model of
   chronic viral infection, Tox^−/− CD8^+ T cells show down-regulation of
   memory-related genes, such as Sell, Ccr7, Tcf7, and Lef1 ([211]8). This
   suggests that some function of TOX may be required for central memory
   cells. However, our findings in human CAR T cells show TOX knockdown
   up-regulating SELL, CCR7, TCF7, and LEF1 (fig. S3E), which is
   consistent with our findings that loss of TOX increases central memory
   cells ([212]Fig. 5B). Thus, TOX may function beyond exhaustion
   regulation, and these functions may be context dependent. Most notably,
   past studies in mouse show TOX loss down-regulating Tox2 ([213]8),
   whereas we detect loss of TOX up-regulating TOX2 ([214]Fig. 5F),
   further emphasizing TOX2 function in human T[CM].

   In conclusion, our research highlights the importance of TOX2 as a key
   regulator of T[CM] differentiation in human CAR T therapy. Our results
   show that TOX2 promotes formation of T[CM], which are key predictors of
   robust CAR T cell function. However, TOX2 overexpression does not
   result in improved CAR T function, likely because of the emergence of
   exhaustion precursor cells. Our findings thus indicate that there is an
   optimal level of TOX2 expression that might be achievable via
   manipulating transgene delivery to harness the benefits of TOX2 while
   avoiding the activation of exhaustion pathways. These results provide
   insights into how TOX2 can be used to improve the effectiveness of CAR
   T therapy and pave the way for further research in this area.

MATERIALS AND METHODS

ATACseq data analysis

   Raw data were downloaded from the Gene Expression Omnibus (accession
   number [215]GSE112494). Data were trimmed of adapter contamination
   using Cutadapt ([216]32). They were aligned to GRC37/hg19 using bowtie2
   ([217]33). After removing mitochondrial reads, polymerase chain
   reaction (PCR) duplicates were removed with Picard. Peaks were called
   with MACS2 ([218]28). Browser tracks were made with bedtools ([219]34)
   and visualized using the UCSC Genome Browser ([220]35). Differential
   peaks were assessed using the Bioconductor package DiffBind ([221]36,
   [222]37). Peaks were annotated using annotatePeaks.pl from Homer
   ([223]38).

Plasmids

   We used the CD19BBζ CAR plasmid, and lentiviral packaging plasmids were
   provided by the Center for Cellular Immunotherapies at the Perelman
   School of Medicine. The shTOX2 and shTOX plasmids were designed and
   made in the Product Development Laboratory at the Center for Cellular
   Immunotherapies. The pMSCV-IRES-mCherry plasmid was also provided by
   the Center for Cellular Immunotherapies, and the TOX2 sequence was
   cloned into it. The retroviral packaging plasmids were provided by
   C. June. The TOX2 sequence was generated by PCR amplification of cDNA
   that was reverse-transcribed from mRNA isolated from human 293T cells.
   The pLPC-FLAG plasmid, described previously ([224]39), was used to
   clone the same TOX2 cDNA sequence.

Cell lines and culture conditions

   293T, IMR90, and SupT1 cells were purchased from the American Type
   Culture Collection. K562-CD19 and K562-mesothelin cells were provided
   by J. Fraietta. 293T cells were cultured in Dulbecco’s modified Eagle’s
   medium (DMEM; Gibco), supplemented with 10% fetal bovine serum (FBS;
   Gibco) and 5% penicillin-streptomycin (Gibco) in a humidified incubator
   at 37°C. SupT1 and K562 cells were cultured in RPMI 1640 (Gibco)
   supplemented with 10% FBS and 5% penicillin-streptomycin, incubated as
   above. IMR90 cells were cultured in DMEM supplemented with 10% FBS and
   5% penicillin-streptomycin, cultured under physiological oxygen (3%).

Lentiviral vector packaging

   Log-phase 293T cells were seeded at 1 × 10^7 cells per 175-cm^2 flask.
   After 24 hours, 15 μg of vector plasmid, 18 μg of pRSV.REV (encoding
   Rev), 18 μg of pMDLg/p.RRE (encoding Gag/Pol), and 7 μg of pVSV-G
   (encoding vesicular stomatitis virus glycoprotein) were mixed with 90
   μl of Lipofectamine 2000 (Invitrogen) and 1.5 ml of Opti-MEM I Reduced
   Serum Medium (Invitrogen) and incubated at room temperature for 30 min
   and then added to the 293T cells. After 24 hours, supernatant was
   harvested, passed through a 0.45-μm filter, and concentrated by
   overnight centrifugation at 8500 rpm. Fresh medium was added to the
   293T cells, and supernatant was harvested, filtered, and concentrated
   again after another 24 hours. The concentration of lentivirus was
   determined by infecting SupT1 cells at concentrations of viral
   supernatant ranging from 1:3 to 1:6561 and then staining for the
   vector, identifying the percent positively stained by flow cytometry,
   and calculating the titer at each concentration using the formula:
   Titer (TU/ml) = (% positive/100) × 2 × 10^4 × 20 × dilution. The titer
   from the highest dilution with less than 20% positive cells was used to
   calculate multiplicity of infection (MOI).

Generation of human CAR T cells

   Human peripheral blood mononuclear cells (PBMCs) from healthy adult
   donors were purchased from the Abramson Cancer Center’s Human
   Immunology Core. CD3^+ T cells were isolated from PBMCs using the
   EasySep Human T Cell Isolation Kit (STEMCELL Technologies), following
   the manufacturer’s instructions. Cells were placed in Opt5 media [5%
   heat-inactivated GemCell Human Serum AB (Gemini Bio) and 2 mM GlutaMAX
   (Gibco) in CTS OpTmizer T Cell Expansion SFM (Gibco)] at a
   concentration of 1 × 10^6 cells/ml and activated with Dynabeads Mouse
   T-Activator CD3/CD28 (Gibco) at a ratio of 3:1 beads to cells and human
   recombinant IL-2 (100 IU/ml; PeproTech Inc.). One day after activation,
   cells were infected with CAR19 containing lentivirus at an MOI of 2. On
   days 5, 7, 9, and 12, cells were counted using a Countess automated
   cell counter; then, Opt5 with IL-2 was added to bring the cell
   concentration down to 5 × 10^5 cells/ml. Cells were harvested on day
   14.

shRNA knockdown

   CAR T cells were manufactured as above. One day after activation, cells
   were infected with shRNA-containing lentivirus at an MOI of 3 and then
   expanded as above.

Retroviral overexpression in CAR T cells

   Log-phase 293T cells were seeded at 8 × 10^6 cells per 15-cm plate.
   After 24 hours, 40 μg of pMSCV vector plasmid, 20 μg of plasmid
   encoding retrovirus-specific Gag/Pol, and 20 μg of plasmid encoding
   RD114 envelope protein were mixed with 90 μl of Lipofectamine 2000 and
   3 ml of Opti-MEM and incubated at room temperature for 30 min and then
   added to the 293T cells. After 2 days, viral supernatant was harvested
   and passed through a 0.45-μm filter, and fresh medium was added.
   Twenty-four hours later, the final viral supernatant was harvested and
   filtered. Nontissue-culture treated six-well plates were filled with
   RetroNectin (20 μg/ml; Takara Bio) in phosphate-buffered saline (PBS)
   and then left at 4°C overnight. The plates were blocked with 2% bovine
   serum albumin in PBS for 30 min and then washed with PBS. Viral
   supernatant was added, and plates were centrifuged for 2 hours at
   2000g. CAR T cells were generated as above. Four days after activation,
   they were added to the prepared viral plates. After two more days, the
   T cells were placed in fresh viral plates for a second round of
   infection and then expanded until day 14 as above.

Flow cytometry

   All flow cytometry experiments were performed on an LSRFortessa (BD
   Biosciences). For each panel, 1 × 10^6 cells were stained with
   LIVE/DEAD Fixable Aqua Dead Cell Stain Kit (Thermo Fisher Scientific)
   in 100 μl of PBS for 15 min at room temperature in the dark. Cells were
   washed with 200 μl of fluorescence-activated cell sorting (FACS) buffer
   (0.5% Human Serum AB and 1 mM EDTA in PBS). Cells were stained with
   antibodies in 100 μl of FACS buffer for 20 min at room temperature in
   the dark, washed with 200 μl of FACS buffer, resuspended, and then
   analyzed. The CD19-CAR was stained with biotin-conjugated anti-mouse
   F(ab′)[2] antibody (Jackson ImmunoResearch), washed, and then labeled
   with APC-streptavidin (BioLegend). Other marks were assessed using the
   following antibodies: CD27-phycoerythrin (PE)/cyanine7 (clone O323),
   PD-1–Brilliant Violet (BV)421 (clone EH12.2H7), CD45RO-BV570 (clone
   UCHL1), CD8a-BV650 (clone RPA-T8), and CD4-BV785 (clone OKT4) (from
   BioLegend) and CCR7–fluorescein isothiocyanate (clone 150503) and
   CD3-allophycocyanin(APC)-H7 (clone SK7) (from BD Pharmingen).

Fluorescence-activated cell sorting

   All sorting was performed on a FACSAria II (BD Biosciences). Up to 1 ×
   10^8 frozen CAR T cells were thawed and washed in sterile FACS buffer,
   then resuspended in 1 ml of FACS and stained with antibodies, and then
   sorted. For the antigen restimulation experiments, the staining was
   carried out in sterile conditions: stained with LIVE/DEAD Fixable Aqua
   for 15 min in the dark, washed in 1 ml of FACS buffer, stained with
   biotin-conjugated F(ab′)[2] for 20 min, washed, stained with APC-H7-CD3
   and APC-streptavidin, washed, resuspended in 1 ml of FACS buffer, and
   sorted. For the RNA-seq experiments, the staining was carried out as
   above, with the addition of CD4-BV785 and CD8a-BV650 in the final
   antibody staining step. Sorting was performed with a 100-μm nozzle, and
   cells were collected into a solution of 50% Human Serum AB and 50%
   OpTmizer.

Antigen restimulation

   Either 5 × 10^5 or 1 × 10^6 sorted CAR T cells were cocultured in
   cytokine-free Opt5 media at a 1:1 ratio with gamma-irradiated K562-CD19
   cells or gamma-irradiated K562-mesothelin cells, with a total cell
   concentration of 1 × 10^6 per ml. After 7 days, cells were counted with
   a Countess automated cell counter, gating for cells between 3 and 15 μm
   in diameter. Then, 5 × 10^5 cells were reseeded in 1 ml of fresh Opt5
   with 5 × 10^5 target cells. This process was repeated on days 12, 17,
   22, and 27. At each time point, cell count, size, and viability were
   recorded.

Retroviral overexpression in IMR90 cells

   Phoenix retroviral packaging cells were seeded at 1.6 × 10^6 cells per
   6-cm plate. After 24 hours, 2.5 μg of vector plasmid was mixed with 500
   μl of Opti-MEM and 7 μl of Lipofectamine 2000, incubated for 20 min,
   and then added to the cells. After 12 hours, viral supernatant was
   harvested, and fresh medium was added. This was repeated after 24
   hours, and log-phase IMR90 cells were seeded at 2 × 10^5 cells per well
   of a six-well plate. After 18 hours, the final viral supernatant was
   harvested, and all harvests were pooled and passed through a 0.45-μm
   filter. Four milliliters of virus plus 1:1000 polybrene (Millipore)
   were added to each well of IMR90 cells for 12 hours, followed by 12
   hours in fresh medium. This cycle of infection was repeated two more
   times. Four days after the first infection, cells were treated with
   puromycin (500 ng/μl) and then grown until noninfected control cells
   were all dead.

RNA sequencing

   Cells were frozen at −80°C in TRIzol (Ambion); then, RNA was extracted
   using the RNA Clean and Concentrator kit (Zymo Research). mRNA was
   isolated using the NEBNext Poly(A) mRNA Magnetic Isolation Module
   (NEB), and RNA-seq libraries were made using NEBNext Ultra II
   Directional Library Prep Kit for Illumina (NEB). Library sizes were
   determined on a Bioanalyzer, and concentrations were calculated with
   the NEBNext Lib Quant Kit (NEB). Libraries were pooled and sequenced on
   an Illumina NextSeq 550 using paired-end sequencing of 42 bases per
   read.

RNA-seq data analysis

   RNA-seq data were aligned to the reference human genome assembly,
   GRC37/hg19, using STAR ([225]40). Reads for each gene with RefSeq
   annotations were counted using htseq-counts or featureCounts ([226]41,
   [227]42). Counts were normalized, and significant differences were
   calculated with DESeq2 ([228]43).

ChIPseq

   A total of 2.5 × 10^7 T cells were cross-linked for 15 min at room
   temperature with 1% formaldehyde in PBS. Cross-linking was quenched
   with 125 mM glycine. Nuclei were isolated by lysing cells for 10 min at
   4°C in 50 mM Hepes, 140 mM NaCl, 1 mM EDTA, 10% glycerol, 0.5% NP-40,
   and 0.25% Triton X-100 and then pelleting and lysing again in 10 mM
   tris-HCl (pH 8.0), 200 mM NaCl, 1 mM EDTA, and 0.5 mM EGTA. After
   pelleting again, nuclei were lysed in 10 mM tris-HCl (pH 8.0), 100 mM
   NaCl, 1 mM EDTA, 0.5 mM EGTA, 0.1% Na-deoxycholate, and 0.5%
   N-lauroylsarcosine, and chromatin was sheared using a Covaris S220
   sonicator. After decross-linking a small sample at 65°C overnight, DNA
   was extracted using the QIAquick Gel Extraction Kit (QIAGEN) and
   quantified on a Qubit Fluorometer. For each immunoprecipitation, 30 μl
   of Dynabeads Protein G were incubated rotating overnight at 4°C with 5
   μg of TOX2 antibody (Invitrogen, PA5-62084). After washing, the beads
   were combined with a quantity of lysate that contained 5 μg of DNA and
   rotated overnight at 4°C. After five washes with
   radioimmunoprecipitation assay buffer, DNA was eluted from the beads by
   incubation at 65°C for 30 min into 50 mM tris-HCl (pH 8.0), 10 mM EDTA,
   and 1% SDS. After reversing the cross-linking, DNA was purified by
   phenol-chloroform extraction followed by ethanol precipitation. ChIPseq
   libraries were made from 3 μg of the immunoprecipitated DNA and input
   lysis controls with the NEBNext Ultra II DNA Library Prep Kit (NEB).
   Library sizes were determined on a Bioanalyzer, and concentrations were
   calculated with the NEBNext Lib Quant Kit (NEB). Libraries were pooled
   and sequenced on an Illumina NextSeq 550 using paired-end sequencing of
   42 bases per read.

ChIPseq data analysis

   ChIPseq data were trimmed of adapter contamination using Cutadapt
   ([229]32). They were aligned to GRC37/hg19 using bowtie2 ([230]33).
   After removing mitochondrial reads, PCR duplicates were removed with
   Picard. Peaks were called with MACS2 ([231]28) using the input files as
   background. Input-subtracted bigWig files were made with bedtools
   genomeCoverageBed and bedGraphtoBigWig ([232]34). Tracks were
   visualized using the UCSC Genome Browser ([233]35). Metaplots and
   heatmaps were produced with deeptools ([234]44). Peaks were annotated
   using annotatePeaks.pl (including the -genomeOntology option for
   comparisons to the entire genome) from Homer ([235]38).

Statistical analysis

   For two-sample comparisons of means, a paired Student’s t test was
   used, and significance was established by a P value ≤0.05. For GSEA,
   significance was established by a false discovery rate (FDR q value)
   ≤0.25, per the developer’s instructions. To assess the enrichment of
   ChIPseq binding at various peak sets (both ChIPseq and ATACseq peaks),
   the R package phyper was used to perform hypergeometric tests, and
   significance was established by a P value ≤0.05.

Acknowledgments