Abstract
The cellular processes that govern tumor resistance to immunotherapy
remain poorly understood. To gain insight into these processes, here we
perform a genome-scale CRISPR activation screen for genes that enable
human melanoma cells to evade cytotoxic T cell killing. Overexpression
of four top candidate genes (CD274 (PD-L1), MCL1, JUNB, and B3GNT2)
conferred resistance in diverse cancer cell types and mouse xenografts.
By investigating the resistance mechanisms, we find that MCL1 and JUNB
modulate the mitochondrial apoptosis pathway. JUNB encodes a
transcription factor that downregulates FasL and TRAIL receptors,
upregulates the MCL1 relative BCL2A1, and activates the NF-κB pathway.
B3GNT2 encodes a poly-N-acetyllactosamine synthase that targets >10
ligands and receptors to disrupt interactions between tumor and T cells
and reduce T cell activation. Inhibition of candidate genes sensitized
tumor models to T cell cytotoxicity. Our results demonstrate that
systematic gain-of-function screening can elucidate resistance pathways
and identify potential targets for cancer immunotherapy.
Subject terms: Cell biology, Cancer, Immunology
__________________________________________________________________
Loss-of-function CRISPR-based screens have identified several genes
associated with cancer resistance to T cell-induced cytotoxicity. Here
the authors perform a genome-scale, gain-of-function CRISPR screen and
identify candidate genes, including the poly-N-acetyllactosamine
synthase B3GNT2, whose overexpression confers tumor cell resistance to
T cell cytotoxicity
Introduction
By harnessing cytotoxic T cells of the immune system to eliminate
cancer cells, cancer immunotherapy has transformed the foundation of
cancer treatment and achieved notable clinical successes^[44]1.
Nevertheless, resistance to immunotherapy is a major
challenge^[45]2–[46]4, and elucidating the cellular pathways that
confer resistance is critical for developing alternative and auxiliary
strategies to expand the scope of immunotherapy. Small-scale studies
have identified a small number of genes, including CD274 (PD-L1), that
enable tumors to evade the immune system, and PD-L1 inhibition in
particular has been the focus of ongoing clinical
development^[47]4–[48]9. More recently, large-scale, loss-of-function
genetic screens using CRISPR have identified additional genes that
mediate resistance to T cell-induced cytotoxicity in the antigen
presentation, interferon-γ (IFNγ)-sensing, tumor necrosis factor (TNF),
and autophagy pathways^[49]10–[50]14. However, in loss-of-function
screens, candidate genes that can be inhibited to sensitize tumors
against immunotherapy are depleted. As depletion screens have a lower
dynamic range than enrichment screens^[51]15, a more tractable approach
is to perform a gain-of-function screen to enrich for genes that confer
resistance upon upregulation^[52]16 and could theoretically be
inhibited to sensitize tumors against immunotherapy.
Here, we perform a genome-scale CRISPR activation (CRISPRa) screen for
resistance against T cell cytotoxicity. Our screen identifies four
candidate genes (CD274 (PD-L1), MCL1, JUNB, and B3GNT2) that, upon
upregulation, enable human melanoma cells to evade T cell killing in
diverse cancer cell types and mouse xenografts. We elucidate the
mechanisms of candidate genes and find that MCL1 and JUNB modulate the
mitochondrial apoptosis pathway to promote resistance. JUNB encodes a
transcription factor that downregulates FasL and TRAIL receptors,
upregulates the MCL1 relative BCL2A1, and activates the NF-κB pathway.
We find that B3GNT2, encoding a poly-N-acetyllactosamine synthase,
operates in an orthogonal pathway to target >10 ligands and receptors
to disrupt interactions between tumor and T cells and reduce T cell
activation. Inhibition of candidate genes render tumor models more
susceptible to T cell cytotoxicity. Together, our results show the
feasibility of using systematic gain-of-function genetic screening to
elucidate resistance pathways and identify potential therapeutic
targets to expand the efficacy of cancer immunotherapy.
Results
CRISPR activation screen for T cell cytotoxicity resistance
We first established a T cell cytotoxicity assay for measuring
immunotherapy resistance. We transduced human primary CD4^+ and CD8^+ T
cells with a T cell receptor (TCR) specific for the NY-ESO-1 antigen
(NY-ESO-1:157-165 epitope) presented in an HLA-A*02-restricted manner
(ESO T cells)^[53]17. When A375 (NY-ESO-1^+, HLA-A2^+) human melanoma
cells were exposed to ESO T cells, we observed cytotoxicity that was
specific to the presence of the NY-ESO-1 antigen and NY-ESO-1 TCR
(Supplementary Fig. [54]1a–c). Cytotoxicity correlated with the
effector to target (E:T) ratio (Supplementary Fig. [55]1b, c). We then
transduced A375 cells with a genome-scale CRISPRa single-guide RNA
(sgRNA) library consisting of 70,290 sgRNAs targeting every coding
isoform from the RefSeq database (23,430 isoforms) to systematically
identify genes that enable tumor cells to evade T cell killing upon
upregulation^[56]18 (Fig. [57]1a). We tested two T cell exposure
strategies: acute (E:T ratio of 3 for 18 h) and chronic (E:T ratio of 2
for 3 days with three rounds of screening selection), in independent
screens. We deep-sequenced the sgRNA library distribution in the
surviving cells with or without ESO T cell exposure (Fig. [58]1a and
Supplementary Fig. [59]1d–g). In the chronic exposure screen, we
observed that the skew of the distribution increased after each round
of screening selection (Supplementary Fig. [60]1e, g).
Fig. 1. Genome-scale CRISPR activation screen identifies four candidate genes
that confer resistance to T cell cytotoxicity.
[61]Fig. 1
[62]Open in a new tab
a Schematic of the CRISPRa screen. NY-ESO-1^+ and HLA-A2^+ A375
melanoma cells were transduced with the pooled sgRNA library targeting
more than 23,000 coding isoforms. A375 cells were exposed to primary
CD4^+ and CD8^+ T cells expressing the T cell receptor (TCR) specific
for the NY-ESO-1 antigen. Deep sequencing identified candidate genes. b
Average MAGeCK analysis P-values for the acute and chronic exposure
screens. Top candidate genes are annotated and the two most enriched
genes from each screening strategy are highlighted in red. c Most
significant pathways enriched among the 576 candidate genes. d Heatmap
showing Pearson’s correlation between expression of the top four
candidate genes and cytolytic activity across patient tumors from TCGA.
Only significant (FDR < 0.05) correlations are shown. e Box plots
showing single-sample Gene Set Enrichment Analysis (ssGSEA)^[63]60 of
576 candidate genes in 308 patient tumor samples^[64]24–[65]29. Patient
samples were collected prior to treatment with checkpoint inhibitors
and classified as responders (n = 83) or nonresponders (n = 225) to
immunotherapy. Box plots indicate median (middle line), 25th, 75th
percentile (box), and 5th and 95th percentile (whiskers). Two-tailed t
tests were performed. f Cell survival of A375 cells transduced with
ORFs encoding candidate genes against ESO T cell cytotoxicity at
different effector to target (E:T) ratios. Cell survival was measured
using a luminescent cell viability assay and normalized to paired
control cells that were not cultured with T cells. T cells were derived
from three donors used in the CRISPRa screen, with n = 4 replicates per
donor for n = 12 total. All values are mean ± s.e.m. Two-tailed t tests
with adjustments for multiple comparisons were performed. Source data
are provided in Source Data 1.
We performed MAGeCK^[66]19 and FDR analyses to identify candidate genes
that were enriched in cells cultured with ESO T cells relative to
control (Fig. [67]1b, Supplementary Fig. [68]1h–k, and Supplementary
Data [69]1–[70]3). Both acute and chronic screening strategies
exhibited high variability between replicates, as co-culture screens,
particularly those using primary cells from different donors, are often
less well correlated than other types of screens. Indeed, comparable
screens in a previous loss-of-function study^[71]13 showed even higher
variability (<10% overlap between top 1000 genes from two replicates
compared to 30–60% overlap in our study; Supplementary Fig. [72]1h–k).
Pathway analysis on 576 genes prioritized by MAGeCK (top 1% of multiple
screening replicates combining the acute and chronic screens) revealed
pathways were significantly enriched (FDR < 0.05) within these top
candidates, including many that have been previously shown to be
important for tumor immune evasion, such as lipopolysaccharide
response, extrinsic apoptosis signaling, NF-κB activation, JAK-STAT
signaling, antigen presentation, and Wnt signaling^[73]10–[74]14,[75]20
(Fig. [76]1c and Supplementary Data [77]4). This analysis also
highlighted pathways with previously underappreciated roles in
regulating tumor response to T cell cytotoxicity, including
glycosaminoglycan metabolism and carbohydrate catabolism, perhaps
because we performed a gain-of-function, rather than loss-of-function,
screen (Fig. [78]1c and Supplementary Data [79]4). Several candidate
genes have been previously shown to mediate tumor immune evasion, such
as ATG3^[80]10, DKK2^[81]21, UHRF1^[82]22, and CDYL^[83]22
(Supplementary Data [84]2), further indicating that our screen enriched
for meaningful biological candidates.
To assess whether expression of the 576 candidate genes nominated by
the screens was associated with local immune cytolytic activity in
patient tumors (quantified using granzyme A and perforin 1 bulk
transcriptome data)^[85]23, we analyzed gene expression of 33 tumor
types from The Cancer Genome Atlas (TCGA). We found that expression of
501 candidate genes positively correlated (FDR < 0.05) with cytolytic
activity in at least 1 tumor type and 166 candidate genes were
correlated across >25% of tumor types (Fig. [86]1d and Supplementary
Fig. [87]2a, b). This is consistent with known immunotherapy resistance
mediators, as high cytolytic activity selects for the emergence of
evading tumor subclones (Supplementary Fig. [88]2c, d)^[89]23. Other
candidate genes may have context-specific impacts across different
types of cancers. We sought to evaluate whether the expression of
candidate genes is associated with clinical outcome by analyzing 308
patient transcriptomes collected prior to immune checkpoint blockade
therapy^[90]24–[91]29. In this analysis, we found expression of
candidate genes was significantly higher in nonresponders
(Fig. [92]1e). MAGeCK analysis for genes that generally affect A375
cell fitness in the absence of ESO T cell co-culture showed that the
MYC pathway governs fitness, with MYC and its antagonist MXI1 as the
top genes promoting or inhibiting cell fitness respectively
(Supplementary Fig. [93]2e, f and Supplementary Data [94]5). Out of 576
candidate genes, 5 generally drive A375 cell fitness and 19 repress it
(ranking in the top 1%; Supplementary Data [95]5).
Validation of four top candidate genes
To narrow our focus for further analysis, we selected the two most
enriched genes from each screening strategy: CD274 and MCL1 from the
acute screen, and JUNB and B3GNT2 from the chronic exposure screen
(Fig. [96]1b). Of these four candidates, CD274 (PD-L1) is known to play
a role in immune evasion, and it is currently the focus of immune
checkpoint blockade therapies, supporting the design of our
study^[97]1. We validated the four candidate genes by individually
expressing three sgRNAs targeting each gene in A375 cells. For each
candidate gene, at least two sgRNAs significantly increased survival
against ESO T cells, verifying the screening results (P < 0.05;
Supplementary Fig. [98]2g, h). For JUNB and B3GNT2, sgRNAs that
produced higher target gene expression were more enriched in the screen
and conferred more resistance, suggesting that resistance mediated by
these genes depends on expression level (Supplementary Fig. [99]2g, h).
Overexpression of ORFs encoding each of the four candidate genes
increased survival against T cell cytotoxicity, excluding the
possibility of potential CRISPRa off-target genes contributing to
resistance (Fig. [100]1f).
We sought to further assess the clinical relevance of these top four
candidate genes. By examining patient tumor samples from TCGA, we found
that expression of B3GNT2 was significantly higher than matched normal
samples for 9 out of 31 types of cancer (Supplementary Fig. [101]3a).
Focal copy number gain of MCL1 and B3GNT2 occurred more frequently than
losses across tumor types (in 95 and 81% of total cases respectively)
(Supplementary Fig. [102]3b). Our TCGA analysis suggests that increases
in both the expression of B3GNT2 as well as copy number of MCL1 and
B3GNT2 may generally promote tumor initiation or progression in the
absence of immunotherapy. In melanoma patients treated with anti-PD-1
immunotherapy, higher B3GNT2 expression was associated with poorer
clinical response (Supplementary Fig. [103]3c, d)^[104]27. A different
patient cohort showed that expression of CD274 and MCL1 significantly
increased over the course of checkpoint blockade therapy in patients
that did not respond to immunotherapy, suggesting that upregulation of
these genes may contribute to poor clinical outcome (Supplementary
Fig. [105]3e)^[106]29. Further analysis of the Riaz et al. dataset
showed expression of all four candidate genes significantly correlated
with immune cytolytic activity, consistent with our analysis of TCGA
data (Supplementary Fig. [107]3f). Together, these results suggest that
increased expression of MCL1 and B3GNT2 may promote tumor progression
and immune evasion in patients, whereas JUNB is not as clinically
relevant across diverse tumor types.
Candidate genes promote resistance in diverse contexts
Next, we evaluated whether our screening results were generalizable to
other contexts by testing different T cells and co-culture conditions.
Overexpression of all candidate genes in A375 cells conferred
resistance against ESO T cells from two additional donors that were not
used in the CRISPRa screens (Supplementary Fig. [108]4a). We verified
that candidate gene overexpression promoted resistance over time in an
alternative T cell cytotoxicity assay based on secreted Gaussia
luciferase (Supplementary Fig. [109]4b). In the absence of T cell
cytotoxicity, upregulation of candidate genes did not consistently
affect cell proliferation across the two cytotoxicity assays
(Supplementary Fig. [110]4c, d). We investigated how expression level
affects resistance by titrating the expression of candidate genes and
found that expression correlated with resistance at lower levels of
induction (extremely high expression levels of any protein, including
GFP, reduced cell fitness, and sensitized cells to T cell cytotoxicity)
(Supplementary Fig. [111]4e). The expression threshold above which we
observed increased resistance corresponded to the baseline expression
of 5–31% of cell lines from the Cancer Cell Line Encyclopedia^[112]30,
which suggested that the expression threshold for resistance is
physiologically relevant (Supplementary Fig. [113]4f). We tested
whether resistance conferred by candidate genes was specific to CD4^+
or CD8^+ T cells and found that candidate genes conferred resistance to
both types of T cells (Supplementary Fig. [114]4g). To determine
whether the resistance against T cells expressing the NY-ESO-1 TCR also
applies to those expressing chimeric antigen receptors (CARs), we
introduced candidate gene ORFs into A375 (AXL^+) cells and co-cultured
the cells with AXL-targeting CAR T cells^[115]31. Overexpression of
each of the four candidate genes increased resistance against
AXL-targeting CAR T cell cytotoxicity (Supplementary Fig. [116]4h).
These results show that candidate genes confer resistance against
different cytotoxic T cells and across co-culture conditions.
Next, we evaluated whether our screening results were generalizable to
other types of cancers. We assayed candidate genes in seven additional
cancer cell lines derived from five additional tissues [H1793
(NY-ESO-1^+, HLA-A2^−) and H1299 (NY-ESO-1^+, HLA-A2^−) non-small cell
lung carcinomas, SW1417 (NY-ESO-1^−, HLA-A2^−) colorectal
adenocarcinoma, OAW28 (NY-ESO-1^+, HLA-A2^−) ovarian
cystadenocarcinoma, A2058 (NY-ESO-1^−, HLA-A2^−) melanoma, LN-18
(NY-ESO-1^+, HLA-A2^+) glioblastoma, and SK-N-AS (NY-ESO-1^+, HLA-A2^−)
neuroblastoma]. Five of these cell lines expressed the NY-ESO-1 antigen
endogenously, at varying levels (Supplementary Fig. [117]5a), and those
that did not naturally express HLA-A2 or NY-ESO-1 were transduced with
the appropriate expression vectors. We found that ORF overexpression of
all four candidate genes significantly increased survival against
T cell cytotoxicity in at least two additional cancer types
(Fig. [118]2a, b and Supplementary Fig. [119]5b–h). Overexpression of
CD274 was not universally protective and did not confer resistance in
cell lines with higher baseline expression, despite robust upregulation
(Fig. [120]2b and Supplementary Fig. [121]5h). The effects of MCL1 and
B3GNT2 overexpression could be generalized to six and seven of the
additional cell lines respectively, demonstrating the broad
applicability of these candidate genes to other cancer types and
supporting the patient tumor analyses (Supplementary Fig. [122]3a, b).
Fig. 2. Candidate gene overexpression mediates resistance in other cell types
and in vivo.
[123]Fig. 2
[124]Open in a new tab
a Cell survival against ESO T cell cytotoxicity of H1793 (NY-ESO-1^+,
HLA-A2^−) non-small cell lung adenocarcinoma transduced with HLA-A2 and
ORFs encoding candidate genes. N = 12. Two-tailed t tests with
adjustments for multiple comparisons were performed. b Heatmap
summarizing results from ESO T cell cytotoxicity assays for eight cell
lines derived from different tissues. Each value represents the
significance of the difference between the survival of each ORF and GFP
control. Two-tailed t tests with adjustments for multiple comparisons
were performed. c Schematic of the in vivo experiments to test the
response of A375 xenografts overexpressing candidate genes to adoptive
cell transfer (ACT) in NSG mice. d, e Tumor growth in mice receiving
ACT of ESO T cells. Data are representative of two independent
experiments. N = 12. d Tumor volume is shown. Two-tailed t tests with
adjustments for multiple comparisons were performed. e Overall survival
is shown. Mantel–Cox log-rank tests were performed. All values are
mean ± s.e.m. Source data are provided in Source Data 2.
To test the relevance of candidate genes for immunotherapy in vivo, we
transduced A375 melanoma cells with dox-inducible candidate genes and
subcutaneously engrafted these cells in immunocompromised NSG mice
(Fig. [125]2c). At 2 days after subcutaneous tumor injection, we
induced overexpression of candidate genes, and at 7 days we treated
A375 xenografts with adoptive transfer of ESO T cells (Fig. [126]2c).
In untreated control mice, we did not observe significant differences
in tumor growth or host survival between the candidate genes and GFP
control (Supplementary Fig. [127]5i, j). However, in mice treated with
ESO T cells, overexpression of all four candidate genes significantly
diminished the efficacy of adoptive cell transfer as measured by tumor
growth and host survival (Fig. [128]2d, e). JUNB overexpression
resulted in largely ineffective treatment, as JUNB-overexpressing
xenografts displayed similar growth kinetics with and without ESO
T cell treatment (Fig. [129]2d, e and Supplementary Fig. [130]5i, j).
Mechanistic investigation of candidate genes
We proceeded to investigate the mechanisms by which the candidate genes
conferred resistance. As CD274 has been extensively studied^[131]1, we
focused our mechanistic studies on the other three candidate genes.
MCL1 encodes a BCL-2 family protein that inhibits apoptosis by
regulating mitochondrial outer membrane permeabilization, and MCL1
overexpression is generally correlated with poor prognosis and
resistance to most cancer therapeutics^[132]32,[133]33. JUNB encodes a
transcription factor that has been previously shown to downregulate an
NKG2D ligand and mediate resistance against natural killer cells in
mice^[134]34. B3GNT2 encodes a
beta-1,3-N-acetylglucosaminyltransferase. Although there are other
B3GNT family members with overlapping functions, we did not identify
any other B3GNT enzymes on our screen. This may be because B3GNT2 has
the strongest poly-LacNAc synthesis activity in vitro relative to other
B3GNT enzymes and is therefore considered the main
poly-N-acetyllactosamine (poly-LacNAc) synthase^[135]35,[136]36. In the
immune system, B3GNT2 is upregulated in T cells upon activation and
B3GNT2 knockout mice have lower poly-LacNAc on B and T cells, resulting
in hyperactivity^[137]35,[138]37. Single nucleotide polymorphisms that
reduced expression of B3GNT2 have been associated with autoimmune
diseases^[139]38–[140]40. To begin to understand the pathways related
to each candidate gene, we performed RNA sequencing (RNA-seq) on A375
cells overexpressing each gene to characterize transcriptome changes.
JUNB overexpression resulted in 632 differentially expressed genes with
an absolute log fold change >1, compared to <15 genes for the other
candidate genes, which is consistent with the role of JUNB in
transcriptional regulation (Supplementary Fig. [141]6a and
Supplementary Data [142]6). As the targets of JUNB and B3GNT2 are
relatively unknown compared to MCL1, we generated FLAG-tagged ORFs of
both genes for immunoprecipitation assays (Supplementary Fig. [143]6b).
Chromatin immunoprecipitation sequencing (ChIP-seq) of JUNB and
co-immunoprecipitation (co-IP) of B3GNT2 followed by mass spectrometry
nominated 3517 and 414 targets, respectively (Supplementary Data [144]7
and [145]8).
To narrow down the possible pathways to those that affect tumor immune
evasion, we assayed the effects of candidate gene overexpression on
secretion and sensing of various cytokines involved in T cell
cytotoxicity. We quantified IFNγ released by T cells in the
cytotoxicity assay using ELISA and found that upregulation of CD274 and
B3GNT2 reduced IFNγ secretion by T cells (Supplementary Fig. [146]6c).
Overexpression of CD274 reduced the tumor response to IFNγ, as
indicated by reduced phosphorylation of STAT1, potentially resulting
from a negative feedback mechanism (Supplementary
Fig. [147]6d)^[148]41. We challenged A375 cells overexpressing each of
the candidate genes with cytokines that mediate cytotoxicity, FasL,
TRAIL, or TNF. We found that MCL1 and JUNB overexpression significantly
increased survival against FasL- and TRAIL-induced cell death, whereas
B3GNT2 overexpression reduced survival against TRAIL (Fig. [149]3a and
Supplementary Fig. [150]7a).
Fig. 3. MCL1 and JUNB mediate resistance to FasL- and TRAIL-induced cell
death through the mitochondrial apoptosis pathway.
[151]Fig. 3
[152]Open in a new tab
a Caspase 8 activity measured using a colorimetric cleavage assay in
A375 cells overexpressing candidate genes after treating with 500 ng/µL
of FasL or TRAIL for 3 h. N = 3. Repeated measures ANOVA with
adjustments for multiple comparisons were performed. b Dox-induction of
genes in the mitochondrial apoptosis pathway in A375 cells
overexpressing MCL1 or GFP. Cell survival against ESO T cell
cytotoxicity (n = 8) and expression of MCL1 interaction partners
(n = 4) were measured at different Dox concentrations. Two-tailed t
tests with adjustments for multiple comparisons were performed. c
Expression of cell surface FasL or TRAIL receptor, FAS or TNFRSF10B,
measured by antibody staining and flow cytometry in A375 cells
overexpressing candidate genes. Data are displayed as histograms (top),
median fluorescence intensity (MFI; bottom left), and percent cells
expressing receptor with gating at the gray dashed line (bottom right).
N = 3. Two-tailed t tests were performed. d Cell survival against
500 ng/µL of FasL- or TRAIL-induced cell death in A375 cells
overexpressing JUNB or GFP with BCL2A1 knocked down. N = 4. KD
knockdown, NT non-targeting. Two-tailed t tests were performed. e
Expression of the NF-κB pathway genes overlapping JUNB ChIP-seq and
RNA-seq. Values represent fold change in A375 cells overexpressing JUNB
relative to GFP control. N = 3. Two-tailed t tests with adjustments for
multiple comparisons were performed. f Western blots of phosphorylated
or total p65 (RELA) protein in A375 cells overexpressing candidate
genes. Data are representative of two independent experiments. g
Schematic describing the pathways MCL1 and JUNB are involved in to
mediate resistance to T cell cytotoxicity. All values are mean ± s.e.m.
ns not significant. Source data are provided in Source Data 3.
MCL1 and JUNB modulate the mitochondrial apoptosis pathway
We examined components of the FasL and TRAIL signaling pathways that
could contribute to MCL1- and JUNB-mediated resistance. For MCL1, the
potential interaction partners involved in FasL and TRAIL resistance
have been identified in the previous studies^[153]32. We induced
expression of these interaction partners in MCL1-overexpressing A375
cells and measured survival against T cell cytotoxicity. Induction of
genes that more directly interact with MCL1, such as BID, PMAIP1
(NOXA), and BAX, could offset resistance conferred by MCL1
(Fig. [154]3b and Supplementary Fig. [155]7b). For JUNB, the extensive
list of target genes overlapping the ChIP- and RNA-seq datasets
(Supplementary Data [156]7) suggests there could be multiple components
involved, necessitating a more systematic approach. We first assayed
cell surface expression of FasL and TRAIL receptors and found that JUNB
overexpression moderately reduced expression of both receptors, FAS and
TNFRSF10B, with TNFRSF10B expression reduced in 20% of the cells
(Fig. [157]3c). We found that the JUNB target gene BCL2A1, which
encodes an anti-apoptotic BCL-2 family protein that operates in
parallel to MCL1^[158]32, was upregulated 45-fold and included in the
set of 576 candidate genes from the CRISPRa screen, suggesting that
BCL2A1 may also contribute to FasL and TRAIL resistance (Supplementary
Data [159]2 and [160]7). CRISPR inhibition (CRISPRi) knockdown of
BCL2A1 in JUNB-overexpressing cells significantly decreased survival
against cytotoxicity induced by FasL, TRAIL, and T cells (Fig. [161]3d
and Supplementary Fig. [162]7c–e). In addition, JUNB alters expression
of many different genes to activate the NF-κB pathway, including
components of the NF-κB complex (RELA, RELB, NFKB1, and NFKB2), NF-κB
activators (IKBKB and IKBKE), and NF-κB inhibitors
(IFRD1)^[163]42–[164]44 (Fig. [165]3e). Perturbation of these genes by
JUNB results in activation of the NF-κB pathway, as indicated by
phosphorylation of p65 (RELA) (Fig. [166]3f). Similar to JUNB, NF-κB
activation upregulates BCL2A1, thus creating a feed-forward loop for
BCL2A1 upregulation^[167]45,[168]46. Our results show that MCL1 and
JUNB counteract FasL- and TRAIL-induced cell death by inhibiting the
mitochondrial apoptosis pathway, further supporting the importance of
the death receptor signaling pathway in immunotherapy^[169]47,[170]48
(Fig. [171]3g).
B3GNT2 adds poly-LacNAc to >10 ligands and receptors
Next, we turned to the resistance mechanism for B3GNT2. B3GNT2
overexpression in A375 cells increased intra- and extra-cellular
poly-LacNAc as measured by tomato lectin staining (Supplementary
Fig. [172]8a). As B3GNT2 adds poly-LacNAc to both N- and O-linked
glycosylation^[173]35, pretreating cells with either N- or O-linked
glycosylation inhibitors, kifunensine or
benzyl-2-acetamido-2-deoxy-α-d-galactopyranoside (BAG) respectively,
reduced poly-LacNAc added by B3GNT2 in a dosage-dependent manner
(Supplementary Fig. [174]8a). As T cells that were co-cultured with
B3GNT2-overexpressing A375 cells secreted less IFNγ (Supplementary
Fig. [175]6c), we tested whether glycosylation inhibition could restore
T cell activation. We found that pretreating B3GNT2-overexpressing A375
cells with both glycosylation inhibitors reversed the effects of B3GNT2
overexpression, resulting in increased T cell IFNγ secretion and
reduced A375 survival, with kifunensine having a stronger effect
(Fig. [176]4a and Supplementary Fig. [177]8b). As interaction between
T cell and tumor cell surface ligands and receptors triggers IFNγ
secretion, we assayed 21 ligands and receptors that were expressed in
A375 for modifications by B3GNT2. We found that many of these proteins
showed higher and broader ranges of molecular weights on western blots,
potentially indicating increased presence of poly-LacNAc (Supplementary
Fig. [178]8c, d). Enzymatic deglycosylation of the proteins confirmed
that the increased molecular weights represented glycosylation, not
other post-translational modifications (Supplementary Fig. [179]8d).
Immunoprecipitation using tomato lectin or FLAG-tagged B3GNT2 further
verified that B3GNT2 adds poly-LacNAc to ten ligands and receptors
(CD276, CD70, CD58, NECTIN2, HLA-A, TNFRSF1A, IFNGR2, FAS, IFNAR1,
MICB) (Fig. [180]4b and Supplementary Fig. [181]8e). At baseline, all
ten ligands and receptors had some poly-LacNAc modifications, which
increased in proportion and length upon overexpression of B3GNT2
(Fig. [182]4b). Pretreating A375 cells overexpressing B3GNT2 with
either kifunensine or BAG showed that these ten ligands and receptors
are primarily N-glycosylated, aligning with our finding that
kifunensine treatment had a stronger effect on T cell IFNγ secretion
and tumor cell survival (Fig. [183]4c). We found that these ten ligands
and receptors had increased presence of poly-LacNAc at baseline in
SW1417 colorectal adenocarcinoma cells, which express higher levels of
endogenous B3GNT2 than A375 cells (Supplementary Fig. [184]8f).
Fig. 4. B3GNT2 disrupts ligand–receptor interactions between tumor and T
cells.
[185]Fig. 4
[186]Open in a new tab
a Cell survival against T cell cytotoxicity (top) and T cell IFNγ
secretion (bottom) in A375 cells overexpressing B3GNT2 or GFP that have
been treated with different concentrations of kifunensine. Kifunensine
was used to pretreat A375 cells and was present during co-culture with
T cells at E:T ratio of 3. Kifunensine-treated cells that were
co-cultured with ESO T cells were compared to kifunensine-treated cells
cultured in parallel without T cells to determine percent survival.
N = 6. Two-tailed t tests were performed. b Tomato lectin IP of A375
cells overexpressing GFP or B3GNT2 followed by western blot for
different B3GNT2 target proteins. 2% of the input and no lectin IP
controls are shown. Data are representative of two independent
experiments. c Western blots of A375 cells overexpressing B3GNT2 or
GFP that were treated with kifunensine (KIF) or
benzyl-2-acetamido-2-deoxy-α-D-galactopyranoside (BAG) to remove N- or
O-glycosylation respectively. Data are representative of two
independent experiments. d Histograms (top) and corresponding median
fluorescence intensity (MFI; bottom) showing binding of recombinant
T cell proteins to A375 cells measured by flow cytometry. A375 cells
were overexpressing GFP or B3GNT2 and treated with KIF or BAG. N = 3.
Two-tailed t tests were performed. e Schematic showing the tumor cell
surface ligands and receptors modified by B3GNT2 to disrupt
interactions with T cells that mediate cytotoxicity. All values are
mean ± s.e.m. ns not significant. Source data are provided in Source
Data 4.
We sought to determine whether increased poly-LacNAc on the B3GNT2
targets affected ligand–receptor interactions between tumor and T cells
that facilitate T cell activation and subsequent cytotoxicity. By
measuring binding of a panel of ten recombinant T cell proteins to A375
cells overexpressing B3GNT2, we found that binding of four T cell
proteins [CD2, 4-1BB, TREML2 (TLT2), and NKG2D] was significantly
reduced and binding of an antibody specific for HLA-A2:NY-ESO-1 was
slightly reduced (Supplementary Fig. [187]9a). Treating
B3GNT2-overexpressing A375 cells with kifunensine rescued the reduction
in binding for all four T cell proteins, whereas BAG treatment only
rescued NKG2D binding, consistent with our finding that most cell
surface proteins targeted by B3GNT2 are N-glycosylated (Fig. [188]4c,
d). In the case of HLA-A2:NY-ESO-1 antibody binding, kifunensine
further reduced binding, potentially because antigen presentation was
disrupted (Supplementary Fig. [189]9b). For T cell proteins CD2 and
NKG2D as well as HLA-A2:NY-ESO-1 antibody, we have demonstrated that
their known tumor interaction partners, CD58, MICB, and HLA-A
respectively, are B3GNT2 target proteins^[190]49,[191]50 (Fig. [192]4c,
d). However, for 4-1BB (TNFRSF9), the mechanism was not immediately
clear because its known interaction partner, 4-1BBL (TNFSF9), was not
modified by B3GNT2^[193]51 (Supplementary Fig. [194]8c). CRISPR
knockout of 4-1BBL resulted in a slight reduction of 4-1BB binding,
potentially because of residual 4-1BBL protein (Supplementary
Fig. [195]9c–e). These results suggest two possibilities: (1) increased
poly-LacNAc on other cell surface ligands and receptors targeted by
B3GNT2 disrupts the 4-1BB/4-1BBL binding; (2) 4-1BB binds to other
unknown ligands that are targeted by B3GNT2. For TREML2, though some
studies have suggested that TREML2 interacts with the B3GNT2 target
protein CD276^[196]52, CRISPR knockdown of CD276 did not affect binding
to TREML2 (Supplementary Fig. [197]9f–h). Our finding aligns with a
previous study showing that the interaction between TREML2 and CD276
does not occur in humans^[198]53. Taken together, we have shown that
B3GNT2 overexpression confers resistance against T cell cytotoxicity by
adding poly-LacNAc on numerous proteins to interfere with
ligand–receptor interactions between tumor and T cells, possibly by
providing a survival advantage that outweighs increased sensitivity to
TRAIL (Figs. [199]3a and [200]4e and Supplementary Fig. [201]7a).
Candidate gene inhibition sensitizes tumors to immunotherapy
To test whether inhibition of candidate genes could produce the
opposite effect and render tumors more susceptible to T cell
cytotoxicity, we designed CRISPR sgRNAs to knock down or knock out the
four candidate genes and measured tumor survival against the T cell
killing (Supplementary Fig. [202]10a, b). In SW1417 colorectal
adenocarcinoma cells, which express relatively high levels of our
candidate genes (Supplementary Fig. [203]5h), knockdown of all four
candidate genes significantly decreased cell survival when cells were
co-cultured with HER2 CAR or ESO T cells (Fig. [204]5a and
Supplementary Fig. [205]10c). In A375 melanoma cells, knockdown of
CD274, MCL1, and JUNB decreased cell survival, and in OAW28 ovarian
cystadenocarcinoma cells, knockdown of MCL1 and JUNB decreased cell
survival (Supplementary Fig. [206]10d, e). Knockdown of B3GNT2 did not
affect survival in A375 and OAW28 cell lines, potentially because
B3GNT2 is expressed at relatively low levels in these cell lines
(Supplementary Fig. [207]10a). We observed comparable results for
candidate gene knockout in SW1417 and A375 cells (Supplementary
Fig. [208]10f–h). In addition to CRISPR perturbation, we tested the
chemical inhibition of MCL1 and B3GNT2. Selective MCL1 inhibitors are
already undergoing testing in clinical trials^[209]33, and the dosage
of these inhibitors could be adjusted to preferentially target
MCL1-dependent tumor cells. The resistance mechanism of B3GNT2 suggests
that inhibition of extracellular poly-LacNAc could bolster
immunotherapy. We therefore inhibited MCL1 using selective
small-molecule inhibitors and B3GNT2 using kifunensine to generally
inhibit N-linked glycosylation. Both chemical inhibition approaches
reduced survival of A375 and SW1417 cells, as well as primary
patient-derived melanoma and pancreatic adenocarcinoma models, against
T cell cytotoxicity (Fig. [210]5b–e and Supplementary Fig. [211]10i,
j). Our CRISPR and chemical inhibition results indicate that inhibition
of candidate genes in tumor cells enhances T cell killing and might be
combined with current immunotherapy strategies to improve efficacy.
Fig. 5. Inhibition of candidate genes sensitizes tumors to T cell
cytotoxicity.
[212]Fig. 5
[213]Open in a new tab
a Cell survival against HER2 CAR T cell cytotoxicity in SW1417 (HER2^+)
colorectal adenocarcinoma with different candidate genes knocked down
using CRISPRi and 2 sgRNAs per gene. KD, knockdown. NT non-targeting.
b–e Cell survival against T cell cytotoxicity in tumor cells treated
with MCL1 or B3GNT2 small-molecule inhibitors, [214]S63845 and
kifunensine, respectively. Heatmaps show the significance of
statistical analyses for each condition. b A375 (NY-ESO-1^+, HLA-A2^+)
melanoma against ESO T cells. c SW1417 (HER2^+) colorectal
adenocarcinoma against HER2 CAR T cells. d CCLF_MELM_0011_T (AXL^+)
primary patient-derived melanoma model against AXL CAR T cells. e
CCLF_PANC_0014_T (HER2^+) primary patient-derived pancreatic
adenocarcinoma against HER2 CAR T cells. All values are mean ± s.e.m
with n = 8. ns not significant. Two-tailed t tests with adjustments for
multiple comparisons were performed. Source data are provided in Source
Data 5.
Discussion
More generally, our results suggest that inhibition of B3GNT2 and BCL-2
family proteins, MCL1 and BCL2A1, could enhance the efficacy of
immunotherapy and improve patient response. The high cross-validation
rate of MCL1 and B3GNT2 across different cancer cell types and their
frequency in patient tumor types suggest that the resistance effects
are relatively cell-type independent. The distinct pathways of the
candidate genes may have contributed to their respective differences in
resistance to TCR and CAR T cell cytotoxicity. MCL1 and JUNB
overexpression may result in higher resistance against CAR-expressing
T cell cytotoxicity because CAR-mediated killing may rely more on the
mitochondrial apoptosis pathway for cytotoxicity^[215]47,[216]48. By
contrast, B3GNT2 overexpression produces higher resistance against T
cells expressing TCR than CAR because B3GNT2 confers resistance by
disrupting interactions between tumor and T cells to reduce T cell
activation. As the CAR design includes multiple intracellular
co-stimulatory domains that promote T cell activation^[217]31, CAR
function is not as affected by these disruptions. Characterizing
resistance mechanisms thus might have the potential to inform the
choice between TCR- and CAR-based immunotherapy.
We have shown here that genome-scale, gain-of-function genetic screens
can discover genes involved in different biological processes that
confer resistance to T cell cytotoxicity. We focused on the top four
candidates and showed that overexpression of candidate genes conferred
resistance in diverse types of cancers. Mechanistic investigation
revealed that MCL1 and JUNB overexpression modulate the mitochondrial
apoptosis pathway to mediate resistance to FasL- and TRAIL-induced cell
death. JUNB downregulates FasL and TRAIL receptors, upregulates BCL2A1,
and activates the NF-κB pathway. B3GNT2 promotes resistance through an
orthogonal pathway by increasing poly-LacNAc on at least ten tumor
ligands and receptors to reduce T cell activation, highlighting the
importance of poly-LacNAc in immuno-oncology. Furthermore, inhibition
of these genes sensitized both tumor cell lines and primary
patient-derived tumor models to T cell killing. Our study complements
results from previous loss-of-function screens and advances our
understanding of the pathways that govern tumor immunotherapy.
Moreover, our screening results serve as a starting point for further
understanding different pathways in tumor immune evasion and
mechanistic studies to validate their roles. While additional studies
will need to be conducted to demonstrate that inhibition of other
candidate genes found in this screen can enhance current approaches to
immunotherapy, our results provide a strong foundation for such
translational research.
Methods
Research compliance
The designs of animal studies and procedures were approved by the
Institutional Animal Care and Use Committee (IACUC) of the Broad
Institute. Ethical compliance with IACUC protocols and institute
standards was maintained. All human samples were obtained with informed
consent and following institutional guidelines under protocols approved
by the Institutional Review Board (IRB) at the Massachusetts General
Hospital (MGH).
Sequences and cloning
The plasmids lenti dCAS-VP64_Blast (Addgene 61425), lenti
sgRNA(MS2)_zeo backbone (Addgene 61427), and lentiMPHv2 (Addgene 89308)
were used for CRISPR-Cas9 activation. The human SAM CRISPR activation
library (Addgene 1000000057) was used for CRISPR-Cas9 activation
screening. LentiCRISPRv2 (Addgene 52961) was used for CRISPR-Cas9
knockout. The Cas9 in lentiCRISPRv2 was replaced with dCas9-KRAB
(Addgene 46911) and the Puromycin resistance gene was replaced with
Blasticidin resistance gene (Addgene 75112) for CRISPR-Cas9 knockdown.
Single-guide RNA (sgRNA) spacer sequences used in this study are listed
in Supplementary Table [218]1, and were cloned into the respective
vectors^[219]54. The NY-ESO-1 T cell receptor (TCR) clone 1G4^[220]17,
AXL chimeric antigen receptor (CAR)^[221]31, and HER2 CAR^[222]31 were
synthesized and cloned into the pHR TCR vector (Addgene 89347). The
respective ORFs of candidate genes [CD274 ([223]NM_014143), MCL1
([224]NM_021960), JUNB ([225]NM_002229), and B3GNT2 ([226]NM_006577)]
were synthesized and cloned into the plasmid pLX_TRC209 (Broad Genetic
Perturbation Platform) for overexpression. HLA-A2 (Addgene 85162),
ESO:HLA-A2, and Gaussia luciferase were cloned into pLX_TRC209 for
stable expression. For dox-inducible upregulation, the EF1a promoter in
pLX_TRC209 was replaced with the pTight promoter (Addgene 31877) and
the plasmid pUltra-puro-RTTA3 (Addgene 58750) was used for rtTA.
Cell culture
HEK293FT cells (Thermo Fisher Scientific [227]R70007) were maintained
in high-glucose DMEM with GlutaMax and pyruvate (Thermo Fisher
Scientific 10569010) supplemented with 10% fetal bovine serum (VWR
97068-085) and 1% penicillin/streptomycin (Thermo Fisher Scientific
15140122). Cells were passaged every other day at a ratio of 1:4 or 1:5
using TrypLE Express (Thermo Fisher Scientific 12604021).
All cancer cell lines [A375 melanoma (NY-ESO-1^+, HLA-A2^+; Millipore
Sigma 88113005-1VL), H1793 non-small cell lung adenocarcinoma
(NY-ESO-1^+, HLA-A2^−; ATCC CRL-5896), H1299 non-small cell lung
carcinoma (NY-ESO-1^+, HLA-A2^−; ATCC CRL-5803), LN-18 glioblastoma
(NY-ESO-1^+, HLA-A2^+; ATCC CRL-2610), SK-N-AS neuroblastoma
(NY-ESO-1^+, HLA-A2^−; ATCC CRL-2137), A2058 melanoma (NY-ESO-1^−,
HLA-A2^−; ATCC CRL-11147), OAW28 ovarian cystadenocarcinoma
(NY-ESO-1^+, HLA-A2^−; Millipore Sigma 85101601-1VL), and SW1417
colorectal adenocarcinoma (NY-ESO-1^−, HLA-A2^−; ATCC CCL-238)] were
maintained in RPMI 1640 with Glutamax (Thermo Fisher Scientific
61870127) supplemented with 10% fetal bovine serum and 1%
penicillin/streptomycin. Cells were passaged every other day at a ratio
of 1:3 to 1:6 using TrypLE Express.
Leukopaks from anonymous human healthy normal donors were purchased
from the MGH blood bank under an IRB protocol of MGH. Leukopaks were
processed using the Ficoll-based RosetteSep Human T Cell Enrichment
Cocktail (StemCell Technologies 15061). Isolated CD4^+ and CD8^+ T
cells were frozen in FBS with 10% DMSO with 20–50 × 10^6 cells per
vial. Once thawed, T cells were maintained in RPMI 1640 with Glutamax
(Thermo Fisher Scientific 61870127) supplemented with 10% fetal bovine
serum, 1% penicillin/streptomycin, and 20 IU/mL IL-2 (Stemcell
Technologies 78036.3). T cells were activated and expanded for 1 week
using CD3/CD28 Dynabeads (Thermo Fisher Scientific 11132D). Beads were
removed with two rounds of magnetic separation and T cells were frozen
down (for in vitro cytotoxicity assays) or cultured for 1 week without
beads (for adoptive cell transfer). CD4^+ or CD8^+ T cells were further
purified using EasySep selection kits (StemCell Technologies 17852 and
17853, respectively) to assess the resistance of candidate genes
against cytotoxicity produced from each T cell type. Experiments with T
cells were performed using T cells derived from two to four unique
donors with n = 3 or 4 biological replicates per donor.
Lentivirus production and transduction
One day prior to transfection, HEK293FT cells were seeded at ~40%
confluency in T25, T75, or T225 flasks (Thermo Fisher Scientific
156367, 156499, or 159934). Cells were transfected the next day at
~90–99% confluency. For each T25 flask, 3.4 μg of the plasmid
containing the vector of interest, 2.6 μg of psPAX2 (Addgene 12260),
and 1.7 μg of pMD2.G (Addgene 12259) were transfected using 17.5 μL of
Lipofectamine 3000 (Thermo Fisher Scientific L3000150), 15 μL of P3000
Enhancer (Thermo Fisher Scientific L3000150), and 1.25 mL of Opti-MEM
(Thermo Fisher Scientific 31985070). Transfection parameters were
scaled up linearly with flask area for T75 and T225 flasks. Media was
changed 5 h after transfection. Virus supernatant was harvested 48 h
post-transfection, filtered with a 0.45-μm PVDF filter (Millipore Sigma
SLHV013SL), and concentrated when necessary via ultracentrifugation at
88,000 × g for 2 h at 4 °C^[228]54. Virus supernatant was then
aliquoted and stored at −80 °C.
Cancer cell lines were transduced by spinfection or mixing^[229]54. For
spinfection, 3 × 10^6 cells were seeded per well in a 12-well plate
with 8 μg/mL Polybrene (Millipore Sigma TR-1003-G) and the appropriate
volume in lentivirus. Cells were spinfected by centrifugation at
1000 × g for 2 h at 33 °C. Cells were replated into T75 flasks with the
appropriate antibiotic after 24 h. For mixing, 3 × 10^6 cells were
seeded in a T75 flask with 8 μg/mL Polybrene (Millipore Sigma
TR-1003-G) and the appropriate volume in lentivirus. After 1 day, media
was refreshed with the appropriate antibiotic, and cells were
maintained under antibiotic selection for 5 days. Concentrations for
selection agents were determined using a kill curve: 300 μg/mL
Hygromycin (Thermo Fisher Scientific 10687010), 10 μg/mL Blasticidin
(Thermo Fisher Scientific A1113903), 300 μg/mL Zeocin (Thermo Fisher
Scientific [230]R25001), and 1 μg/mL Puromycin (Thermo Fisher
Scientific A1113803). T cells were transduced after 1 day of activation
by mixing 1 × 10^6 cells in 1 mL media with 8 μg/mL Polybrene and
lentivirus in each well of a 24-well plate (Millipore Sigma
CLS3527-100EA). The transduction efficiency of T cells was measured by
sorting 1 × 10^6 cells for GFP expression on the TCR vector after 7
days of activation. T cells used for experiments had transduction
efficiencies of 80–90%.
T cell cytotoxicity assays
Expanded T cells were thawed and maintained in culture media for 8–10 h
before incubation with cancer cells. Cancer cells were seeded in
96-well plates and allowed to attach for 3–4 h before T cells were
added at the appropriate effector to target cell (E:T) ratio. Paired
controls with no T cells added were included for each condition. After
18 h, cancer cells were washed twice with PBS to remove T cells,
passaged, and cultured for 2 days. Primary patient-derived cell models
were not passaged after T cell co-culture. Viability was measured using
CellTiter-Glo (Promega G7571). For each E:T ratio, percent survival was
calculated as the viability of the cells incubated with T cells divided
by the viability of the paired control that was not incubated with T
cells. For example, CD274-overexpressing melanoma cells that were
co-cultured with ESO T cells were compared to CD274-overexpressing
melanoma cells that were cultured without T cells in parallel. Cells
treated with small-molecule inhibitors that were co-cultured with ESO T
cells were compared to cells treated with small-molecule inhibitors
cultured in parallel without T cells. As an alternative cytotoxicity
assay, A375 cells stably expressing Gaussia luciferase were co-cultured
with ESO T cells. At each time point, 10% of cell culture media was
used for the Gaussia luciferase assay (Targeting Systems GAR-2B) to
directly measure cytotoxicity.
CRISPRa screen for resistance to T cell cytotoxicity
A375 melanoma cells stably integrated with dCas9-VP64 (Addgene 61425)
and MS2-P65-HSF1 (Addgene 61426) were transduced with the pooled
CRISPRa sgRNA library (Addgene 1000000057) as described above at an MOI
of 0.3, with a minimal representation of 500 transduced cells per sgRNA
in each replicate. For the acute exposure screen, A375 cells were
co-cultured with T cells expressing the NY-ESO-1 TCR, unmodified T
cells, or no T cells at E:T ratio of 3. Each screen contained two
replicates with T cells from different donors. After 18 h of
co-culture, cells were washed twice with PBS to remove T cells,
passaged, and cultured for 2 days before genomic DNA was harvested. For
the chronic exposure screen, A375 cells were co-cultured with T cells
expressing the NY-ESO-1 TCR or no T cells at E:T ratio of 2. Screening
replicates used T cells from the same donor and each round of screening
selection used T cells from different donors. After 3 days of
co-culture, cells were washed twice with PBS to remove T cells,
passaged, and cultured for 2 days before seeding for the next round of
screening selection. After 3 rounds of screening selection, genomic DNA
was harvested. MAGeCK RRA analysis^[231]19 was used to analyze the
screens and identify candidate genes. A set of 576 candidate genes that
ranked in the top 1% and overlapped at least two screening replicates
(combining the acute and chronic exposure screens) were used for
pathway and cytolytic activity analyses. The FDR of screening results
was estimated using a set of 311 negative control housekeeping genes
consisting of ribosomal proteins, RNA polymerases, translation factors,
mitochondrial ribosomal proteins, GAPDH, and ACTB (Supplementary
Data [232]2). For each screening replicate, the FDR of each candidate
gene was measured as the fraction of negative control genes with higher
average sgRNA enrichment than the candidate gene. To validate the top
four candidate genes from the screens, sgRNAs targeting candidate genes
from the genome-scale library were individually cloned and transduced
into A375 cells. Validation was performed using T cell cytotoxic assays
at an E:T ratio of 3 as described above.
Pathway enrichment analysis
Pathway enrichment analysis of the top 576 candidate genes was
performed using g:Profiler^[233]55. GO:BP pathways with between 5 and
200 genes that were significantly enriched (FDR < 0.05) were included.
To identify nonoverlapping pathways, the enriched pathways were sorted
by FDR and any pathway that had more than 30% genes overlapping a
different pathway with lower FDR was excluded.
The Cancer Genome Atlas (TCGA) analysis
TCGA copy number variation and RNA-seq data were downloaded from the
Firehose Broad GDAC ([234]http://gdac.broadinstitute.org/) using the
TCGA2STAT package for R^[235]56. The RNA-seq data was normalized using
RSEM and log2 transformed. Local tumor immune cytolytic activity was
determined as the geometric mean of granzyme A (GZMA) and perforin 1
(PRF1) RNA-seq expression^[236]13,[237]23. For each gene in the TCGA
RNA-seq dataset, Pearson’s correlation between cytolytic activity and
expression was calculated. Significance was evaluated using Fisher
transformation of Pearson’s correlation followed by Benjamini–Hochberg
procedure to determine the FDR. For visualization, heatmaps with
hierarchical clustering using Ward’s linkage were generated using
Python’s Seaborn clustermap ([238]https://github.com/mwaskom/seaborn/).
For the prevalence of increased expression and copy number of the top
four candidate genes, TCGA RNA-seq data
([239]https://www.cancer.gov/tcga) was analyzed using GEPIA^[240]57.
TCGA tumor samples were matched with TCGA normal and GTEx data and
filtered for
[MATH: log2(foldchange)≥1 :MATH]
. Genes were considered significantly differentially expressed if the P
value was greater than 0.05 FDR correction. Copy number variation was
reported using the NCI Genomic Data Commons^[241]58.
Single-sample gene set enrichment analysis (ssGSEA)
A total of 308 unique patient tumor transcriptomes that were collected
prior to immunotherapy were used for ssGSEA^[242]24–[243]29. As
processed data was not available for the Gide et al. dataset^[244]26,
fastq files were downloaded and expression levels were estimated using
RSEM v1.3.1^[245]59 as described below. Expression values for
replicates from the same patient were averaged. ssGSEA^[246]60 as
implemented by GSEAPY v0.10.4 was performed on each sample using
default parameters to determine the normalized enrichment score of the
576 candidate genes. The z-score of the normalized enrichment scores
was calculated on each dataset and aggregated. Patients were classified
as responders (i.e., RECIST categories of complete response or partial
response, clinical benefit, and no tumor progression) or nonresponders
(i.e., RECIST categories of stable disease or progressive disease, no
clinical benefit, and tumor progression) based on the reported response
to subsequent anti-PD-1 or anti-CTLA-4 checkpoint blockade therapy.
Indel analysis
Cells plated in 96-well plates were grown to 60–80% confluency and
assessed for indel rates^[247]54. Genomic DNA was harvested from cells
using QuickExtract DNA Extraction kit (Lucigen QE09050). The genomic
region flanking the site of interest was amplified using NEBNext High
Fidelity 2× PCR Master Mix (New England BioLabs M0541L), first with
region-specific primers (Supplementary Table [248]2) for 15 cycles and
followed by barcoded primers for 15 cycles. PCR products were sequenced
on the Illumina MiSeq platform (>10,000 reads per condition), and indel
rates were determined using a published Python script^[249]54.
qPCR quantification of transcript expression
Cells were seeded in 96-well plates and grown to 60–90% confluency
prior to RT-qPCR^[250]54. Cells were lysed by adding Lysis Buffer
[4.8 mM Tris pH 8.0 (Thermo Fisher Scientific AM9855G), 4.8 mM Tris pH
7.5 (Thermo Fisher Scientific 15567027), 0.5 mM MgCl[2] (Thermo Fisher
Scientific AM9530G), 0.44 mM CaCl[2] (Millipore Sigma 21115), 10 μM DTT
(Promega P1171), 0.1% wt/vol Triton X-114 (Millipore Sigma X-114),
6 U/mL Proteinase K (Millipore Sigma P2308), 300 U/mL DNAse I
(Millipore Sigma D2821)] and mixing. After 6–12 min at room
temperature, lysis was terminated by adding Stop Lysis Buffer [1 mM
Proteinase K inhibitor (Millipore Sigma 539470), 90 mM EGTA (Millipore
Sigma E3889), 113 μM DTT (Promega P1171)] and mixing. RNA in cell
lysate was reverse transcribed into cDNA using the RevertAid RT Reverse
Transcription Kit (Thermo Fisher Scientific K1691) with 3.5 μM oligo dT
(Integrated DNA Technologies; TTTTTTTTTTTTTTTTTTTTNN). The reverse
transcription reaction was run with the following cycle conditions:
25 °C for 10 min, 37 °C for 1 h, and 95 °C for 5 min. TaqMan qPCR was
performed on the cDNA using TaqMan Fast Advanced Master Mix (Thermo
Fisher Scientific 4444557) with custom [Integrated DNA Technologies;
B3GNT2-Fwd (GGGCAGGCTCTCCAATATAAG), B3GNT2-probe
(/56-FAM/TGAACTACT/Zen/GCGAACCTGACCTGA/3IABkFQ/), B3GNT2-Rev
(GGCATCTCAAATACAGCAGAAAG)] or readymade probes [Thermo Fisher
Scientific; CD274 (Hs00204257_m1), MCL1 (Hs01050896_m1), JUNB
(Hs00357891_s1), BID (Hs00609632_m1), PMAIP1 (Hs00560402_m1), BBC3
(Hs00248075_m1), BAD (Hs00188930_m1), BAX (Hs00180269_m1), BAK1
(Hs00832876_g1), BCL2A1 (Hs06637394_s1), CD276 (Hs00987207_m1)].
Adoptive cell transfer and in vivo validation
Specific pathogen-free facilities at the Broad Institute was used for
the storage and care of all mice. Mice were housed at a temperature of
67–73 °F, relative humidity of 30–60%, and maintained in a 12 h
light–dark cycle. Female NSG mice (strain 005557) aged 4–6 weeks were
purchased from The Jackson Laboratory and used for tumor induction
experiments. A375 cells were transduced with dox-inducible candidate
genes. NSG mice were subcutaneously injected with 1 × 10^6 A375 cells.
After 2 days of tumor xenograft implantation, mice were switched to
1000 mg/kg doxycycline diet (Envigo TD.05298). At 7 days after tumor
implantation, for the adoptive cell transfer conditions, 2 × 10^7 ESO T
cells were intravenously injected in a blinded manner. Each tumor was
measured every 2 days beginning on day 7 after ACT until the survival
endpoint was reached. Measurements were assessed manually using the
longest dimension (length) and the longest perpendicular dimension
(width). Tumor volume was estimated with the formula: (L × W^2)/2. The
maximal tumor size permitted by the IACUC of the Broad Institute was
2000 mm^3. Mice with tumor volumes greater than 2000 mm^3 were
euthanized. CO[2] inhalation was used to euthanize mice. No statistical
methods were used to predetermine sample size. The sample size was
determined based on prior knowledge of the variability of experiments
with ACT. Animals were randomized before treatment and no blinding was
performed for tumor measurements.
Bulk RNA sequencing and data analysis
RNA from cells plated in 24-well plates and grown to 60–90% confluency
was harvested using the RNeasy Plus Mini Kit (Qiagen 74134). RNA-seq
libraries were prepared using NEBNext Ultra RNA Library Prep Kit for
Illumina (New England Biolabs E7530S) and deep-sequenced on the
Illumina NextSeq platform (>9 million reads per biological replicate).
Bowtie^[251]61 index was created based on the human hg38 UCSC genome
and RefSeq transcriptome. Next, RSEM v1.3.1^[252]59 was run with
command-line options “--estimate-rspd --bowtie-chunkmbs 512
--paired-end” to align paired-end reads directly to this index using
Bowtie and estimate expression levels in transcripts per million (TPM)
based on the alignments.
To identify genes that were differentially expressed as a result of ORF
overexpression, RSEM’s TPM estimates for each transcript were
transformed to log-space by taking log[2](TPM + 1). Transcripts were
considered detected if their expression level was equal to or above 10.
All genes detected in at least three libraries were used to find
differentially expressed genes. The Student’s t test was performed on
the ORF overexpression condition against GFP control condition. Only
genes that were significant (P value pass 0.01 FDR correction) were
reported.
Chromatin immunoprecipitation with sequencing (ChIP-seq)
Cells were plated in 10-cm cell culture dishes and grown to 60–80%
confluency. For each condition, two biological replicates were
harvested for ChIP-seq. Formaldehyde (Millipore Sigma 252549) was added
directly to the growth media for a final concentration of 1% and cells
were incubated at 37 °C for 10 min to initiate chromatin fixation.
Fixation was quenched by adding 2.5 M glycine (Millipore Sigma G7126)
in PBS for a final concentration of 125 mM glycine and incubated at
room temperature for 5 min. Cells were then washed with ice-cold PBS,
scraped, and pelleted at 1000 × g for 5 min.
Cell pellets were prepared for ChIP-seq using the Epigenomics
Alternative Mag Bead ChIP Protocol v2.0^[253]62. Briefly, cell pellets
were resuspended in 100 μL of lysis buffer (1% SDS, 10 mM EDTA, 50 mM
Tris-HCL pH 8.1) containing protease inhibitor cocktail (Millipore
Sigma 05892791001) and incubated for 10 min at 4 °C. Then 400 μL of
dilution buffer (0.01% SDS, 1.1% Triton X-100, 1.2 mM EDTA, 16.7 mM
Tris-HCl pH 8.1, and 167 mM NaCl) containing protease inhibitor
cocktail was added. Samples were pulse sonicated with two rounds of
10 min (30 s on-off cycles, high frequency) in a rotating water bath
sonicator (Diagenode Bioruptor) with 5 min on ice between each round.
10 μL of sonicated sample was set aside as input control. Then 500 μL
of dilution buffer (0.01% SDS, 1.1% Triton X-100, 1.2 mM EDTA, 16.7 mM
Tris-HCl pH 8.1, and 167 mM NaCl) containing protease inhibitor
cocktail and 1 μL of anti-FLAG (Millipore Sigma F3165-1MG) was added to
the sonicated sample. ChIP samples were rotated end-over-end overnight
at 4 °C.
For each ChIP, 50 μL of Protein A/G Magnetic Beads (Thermo Fisher
Scientific 88802) was washed with 1 mL of blocking buffer (0.5% TWEEN
and 0.5% BSA in PBS) containing protease inhibitor cocktail twice
before resuspending in 100 μL of blocking buffer. ChIP samples were
transferred to the beads and rotated end-over-end for 1 h at 4 °C. ChIP
supernatant was then removed and the beads were washed twice with
200 μL of RIPA low salt buffer (0.1% SDS, 1% Triton x-100, 1 mM EDTA,
20 mM Tris-HCl pH 8.1, 140 mM NaCl, 0.1% DOC), twice with 200 μL of
RIPA high salt buffer (0.1% SDS, 1% Triton x-100, 1 mM EDTA, 20 mM
Tris-HCl pH 8.1, 500 mM NaCl, 0.1% DOC), twice with 200 μL of LiCl wash
buffer (250 mM LiCl, 1% NP40, 1% DOC, 1 mM EDTA,10 mM Tris-HCl pH 8.1),
and twice with 200 μL of TE (10 mM Tris-HCl pH 8.0, 1 mM EDTA pH 8.0).
ChIP samples were eluted with 50 μL of elution buffer (10 mM Tris-HCl
pH 8.0, 5 mM EDTA, 300 mM NaCl, 0.1% SDS). In total, 40 μL of water was
added to the input control samples. 8 μL of reverse cross-linking
buffer (250 mM Tris-HCl pH 6.5, 62.5 mM EDTA pH 8.0, 1.25 M NaCl,
5 mg/ml Proteinase K, 62.5 μg/ml RNAse A) was added to the ChIP and
input control samples and then incubated at 65 °C for 5 h. After
reverse cross-linking, samples were purified using 116 μL of SPRIselect
Reagent (Beckman Coulter [254]B23318).
ChIP samples were prepared for NGS with NEBNext Ultra II DNA Library
Prep Kit for Illumina (New England Biolabs E7645S) and deep-sequenced
on the Illumina NextSeq platform (>60 million reads per condition).
Bowtie 1.2.3^[255]61 was used to align paired-end reads to the human
hg38 UCSC genome with command-line options q -X 300 --sam --chunkmbs
512”. Next, biological replicates were merged and Model-based Analysis
of ChIP-seq (MACS)^[256]63 was run with command-line options “-g hs -B
-S --mfold 6,30” to identify TF peaks. HOMER^[257]64 was used to
discover motifs in the TF peak regions identified by MACS. TFs were
considered potential regulators of a candidate gene if the TF peak
region identified by MACS overlapped with the promoter region of the
candidate gene. Promoter regions were defined as 2000 bp upstream and
500 bp downstream of RefSeq transcriptional start sites.
Co-immunoprecipitation (co-IP) and mass spectrometry
Cells were plated in 10-cm cell culture dishes and grown to 60–80%
confluency. For each condition, two biological replicates were
harvested for co-IP. Cells were washed with PBS and 4 mL of lysis
buffer (20 mM HEPES, 1% Triton X-100, 150 mM NaCl, 1 mM EDTA, and 10%
glycerol) containing protease inhibitor cocktail was added. Cells were
scraped, and the lysate was incubated at 4 °C under rotary agitation
for 1 h. The lysate was centrifuged at 14,000 × g for 10 min at 4 °C.
The supernatant was transferred to a new tube, and an aliquot was taken
as the input. The remaining lysate was split into two tubes for the
FLAG and IgG control conditions. For mass spectrometry, 10 µg/mL mouse
Anti-FLAG (Millipore Sigma F3165-1MG) and IgG control (Millipore Sigma
12-371) were added to the respective conditions. For tomato lectin IP
western blots, 20 µg/mL biotinylated tomato lectin (Vector Laboratories
B1175) was added. For co-IP western blots, 10 µg/mL chicken anti-FLAG
(Aves labs ET-DY100) and IgY control (R&D Systems AB-101-C) antibodies
were biotinylated (Thermo Fisher Scientific 90407) and added to the
respective conditions. Lysates with antibodies were incubated at 4 °C
under rotary agitation overnight. For each mL of lysate, 50 µL of
Pierce Protein A/G Magnetic Beads (Mass spectrometry; Thermo Fisher
Scientific 88803) or Pierce Streptavidin Magnetic Beads (Western blot;
Thermo Fisher Scientific 88817) was washed twice with lysis buffer.
Lysates with antibodies were added to the beads and incubated at 4 °C
under rotary agitation for 4 h. Beads were washed with lysis buffer
three times and resuspended in lysis buffer for storage.
Magnetic beads were resuspended in 100 mM Tris pH 7.8, reduced,
alkylated, and digested with trypsin at 37 °C overnight. This solution
was subjected to solid-phase extraction to concentrate the peptides and
remove unwanted reagents followed by injection onto a Shimadzu HPLC
with a fraction collector. Eight fractions were collected, and after
concentration, were injected on a Waters NanoAcquity HPLC equipped with
a self-packed Aeris 3-µm C18 analytical column 0.075 mm by 20 cm
(Phenomenex). Peptides were eluted using standard reverse-phase
gradients. The effluent from the column was analyzed using a Thermo
Orbitrap Elite mass spectrometer (nanospray configuration) operated in
a data-dependent manner for 54 min. The resulting fragmentation spectra
were correlated against the known database using Proteome Discover 1.4
(Thermo Fisher Scientific). Scaffold Q + S (Proteome Software) was used
to provide consensus reports for the identified proteins.
Cytokine assays
To challenge cells with cytokines, cells were incubated with
Interferon-γ (IFNγ; Cell Signaling Technology 80385S), FasL (AdipoGen
AG-40B-0130-3010), TRAIL (R&D Systems 375-TL-010), or TNF-α (AdipoGen
AG-40B-0019-3010) for 24 h. TRAIL was crosslinked by incubating with
anti-His Tag antibody (Thermo Fisher Scientific [258]MA121315, 1:500)
for 15 min at room temperature. Cell viability was measured using
CellTiter-Glo (Promega G7571) and protein was harvested for western
blots. For evaluating Caspase 8 activity, cells were incubated with
FasL or crosslinked TRAIL for 3 h and harvested for Caspase 8
colorimetric assay (R&D Systems K113-100). IFNγ in the cell culture
media of the T cell cytotoxic assay was quantified using an ELISA kit
(Thermo Fisher Scientific KHC4021).
Small-molecule inhibition
For glycosylation inhibition, cells were pretreated with 20 µg/mL
Kifunensine (Cayman Chemical 10009437) or 2 mM
Benzyl-2-acetamido-2-deoxy-alpha-d-galactopyranoside (BAG; Millipore
Sigma B4894-100MG) for 48 h to inhibit N- and O-glycosylation,
respectively unless otherwise indicated before incubation with T cells.
For MCL1 inhibition, cells were pretreated with 1–10 µM of [259]S63845
(Selleck Chemicals S8383) or AZD5991 (Selleck Chemicals S8643) for 4 h
before incubation with T cells. Both glycosylation and MCL1 inhibitors
were maintained at indicated concentrations during co-culture with T
cells.
Western blot
Protein lysates were harvested with RIPA lysis buffer (Cell Signaling
Technologies 9806S) containing protease inhibitor cocktail (Millipore
Sigma 05892791001). Samples were standardized for protein concentration
using the Pierce BCA protein assay (VWR 23227), and incubated at 70 °C
for 10 min under reducing conditions. To determine the presence of
glycosylation, samples were treated with Protein Deglycosylation Mix II
(O- and N-deglycosylation; New England Biolabs P6044S) or PNGase F
(N-deglycosylation; New England Biolabs P0704L). After denaturation,
samples were separated by Bolt 4–12% Bis-Tris Plus Gels (Thermo Fisher
Scientific NW04125BOX) and transferred onto a PVDF membrane using iBlot
Transfer Stacks (Thermo Fisher Scientific IB401001).
Blots were blocked with 5% BLOT-QuickBlocker (G Biosciences 786-011) in
TBST for 1 h at room temperature. Blots were then probed with different
primary antibodies [phospho- NF-κB p65 Ser536 (Cell Signaling
Technology 3033S, 1:1000), NF-κB p65 (Santa Cruz Biotechnology sc-8008,
1:200), phospho-STAT1 Tyr701 (Cell Signaling Technology 9167S, 1:1000),
STAT1 (Cell Signaling Technology 9172S, 1:1000), CD276 (R&D Systems
AF1027, 1:200), CD70 (Santa Cruz Biotechnology sc-365539, 1:200), CD58
(Thermo Fisher Scientific MA5800, 1:200), NECTIN2 (R&D Systems AF2229,
1:2000), HLA-A (Abcam ab52922, 1:5000), TNFRSF1A (Santa Cruz
Biotechnology sc-8436, 1:200), IFNGR2 (R&D Systems AF773, 1:200), FAS
(Santa Cruz Biotechnology sc-8009, 1:200), IFNAR1 (Santa Cruz
Biotechnology sc-7391, 1:100), TNFRSF10B (Novus Biologicals
NB100-56618, 1:200), MICB (R&D Systems MAB1599-100, 1:500), TNFRSF10A
(R&D Systems AF347, 1:200), PVR (R&D Systems MAB25301, 1:500), MICA
(R&D Systems MAB1300-100, 1:500), HMGB1 (Abcam ab18256, 1:1000), 4-1BBL
(TNFSF9; R&D Systems AF2295, 1:200), NT5E (Abcam ab175396, 1:1000),
ULBP2 (R&D Systems AF1298, 1:2000), IFNGR1 (R&D Systems MAB6731,
1:500), ULBP3 (R&D Systems AF1517, 1:2000), CD39 (Abcam ab108248,
1:1000), FLAG (Millipore Sigma F7425, 1:1000), or GAPDH (Cell Signaling
Technology 2118L, 1:1000)] in 2.5% BLOT-QuickBlocker (G Biosciences
786-011) in TBST overnight at 4 °C. Blots were washed with TBST before
incubation with secondary antibodies [Anti-rabbit IgG-HRP (Cell
Signaling Technology 7074S, 1:5000), Anti-mouse IgG-HRP (Cell Signaling
Technology 7076S, 1:5000), anti-goat IgG-HRP (Santa Cruz Biotechnology
sc-2354, 1:5000)] in 2.5% BLOT-QuickBlocker (G Biosciences 786-011) in
TBST for 1 h at room temperature. Blots were washed with TBST and
imaged using chemiluminescent substrate [Pierce ECL (Thermo Fisher
Scientific 32209), SuperSignal West Pico PLUS (Thermo Fisher Scientific
34577), or SuperSignal West Femto (Thermo Fisher Scientific 34096)] on
the ChemiDox XRS + (Bio-Rad).
Flow cytometry assays
Per condition, 5 × 10^5 cells were pelleted at 200 × g for 5 min and
washed once with PBS. Cell were fixed in 4% formaldehyde in PBS at 4 °C
for 10 min. Cells were washed twice with PBS and resuspended in PBS
with 25 µg/mL recombinant Fc chimera proteins [PVRIG (R&D Systems
9365-PV-050), CD226 (R&D Systems 666-DN-050), NKG2D (R&D 1299-NK-050),
TREML2 (R&D Systems 3259-TL-050), CD2 (R&D Systems 1856-CD-050), CD96
(R&D Systems 9360-CD-050), TIGIT (BPS Bioscience 71186), CD27 (BPS
Bioscience 71176), or 4-1BB (TNFRSF9; Sino Bio 10041-H03H)], 0.1 µg/mL
HLA-A2:NY-ESO-1 Fab^[260]65, 5 µg/mL Fas antibody (Millipore Sigma
05-201), 25 µg/mL TNFRSF10B antibody (Novus Biologicals NB100-56618,
1:200), or Dylight 649 labeled Tomato Lectin (Vector Laboratories
DL-1178, 1:100). Cells were incubated at 4 °C for 1 h. Cells were
washed twice with PBS and resuspended in PBS with the appropriate
secondary antibody [IgG Fc PE (Thermo Fisher Scientific 12-4998-82,
1:50), His Tag Alexa Fluor 647 (Thermo Fisher Scientific MA121315A647,
1:500), mouse Alexa Fluor 568 (Thermo Fisher Scientific A-11031,
1:400), or rabbit Alexa Fluor 647 (Thermo Fisher Scientific A-21244,
1:400)]. Cells stained with Tomato Lectin were not incubated with
additional secondary antibodies. Cells were incubated at 4 °C for
30 min. Cells were washed twice with PBS. For each sample, 10,000 cells
were analyzed on a CytoFLEX Flow Cytometer (Beckman Coulter) and
quantified with FlowJo 10.8.1. For each experiment, median fluorescence
intensities for three biological replicates were compared to determine
statistical significance.
Primary patient-derived cell models
CCLF_MELM_0011_T melanoma tumor tissue and CCLF_PANC_0014_T pancreatic
tumor tissue were obtained from Dana-Farber Cancer Institute hospital
with informed consent and the cancer cell model line generation was
approved by the ethical committee. Both tumor tissues were freshly
received into the lab, rinsed with 95–100% ethanol very quickly, and 1×
PBS twice. Tissue was transferred to a sterile Petri dish and the
tissue was minced into small 1–2 mm fragments. Dissected tissues were
dissociated in a collagenase/hyaluronidase (StemCell technologies
07912) medium for 1 h. The red blood cells were further depleted by
adding Ammonium Chloride Solution (StemCell technologies 07800).
CCLF_MELM_0011_T dissociated cells were plated with smooth muscle
growing medium-2 (Lonza CC-3181) into a six-well plate, media was
changed every 2–3 days, and cells were split when confluency of 80% was
reached. A 1:3 ratio was used when splitting CCLF_MELM_0011_T.
CCLF_PANC_0014_T dissociated cells were plated into a 24-well plate
with a 50:50 mix of Clevers pancreas organoid media^[261]66: Propagenix
Conditioned media (Propagenix 256-100) and split when confluency of 80%
was reached. Media was changed every 3–4 days. A 1:2 ratio was used
when splitting CCLF_PANC_0014_T which is a mixed population of
suspension and adherent cells. Both lines were passaged five times
before a pellet was taken for sequencing verification. The confirmed
melanoma cell model and confirmed pancreatic adenocarcinoma cell model
were propagated for another 10–15 passages and their cryovials
preserved. CCLF_MELM_0011_T passage 11 cells and CCLF_PANC_0014_T
passage 20 cells were used for this study.
Statistics
Statistical tests were applied with the sample size listed in the text
and figure legends. The sample size represents the number of
independent biological replicates. Data supporting main conclusions
represent results from at least two independent experiments. All graphs
with error bars report mean ± s.e.m. values. PRISM was used for basic
statistical analysis and plotting ([262]http://www.graphpad.com), and
the R language and programming environment
([263]https://www.r-project.org) was used for the remainder of the
statistical analysis.
Reporting summary
Further information on research design is available in the [264]Nature
Research Reporting Summary linked to this article.
Supplementary information
[265]Supplementary Information^ (9.1MB, pdf)
[266]Reporting Summary^ (381KB, pdf)
[267]Peer Review File^ (3.2MB, pdf)
[268]41467_2022_29205_MOESM4_ESM.pdf^ (65.6KB, pdf)
Description of Additional Supplementary Files
[269]Supplementary Data 1^ (28.7MB, xlsx)
[270]Supplementary Data 2^ (31.6KB, xlsx)
[271]Supplementary Data 3^ (2.3MB, xlsx)
[272]Supplementary Data 4^ (14.5KB, xlsx)
[273]Supplementary Data 5^ (1.1MB, xlsx)
[274]Supplementary Data 6^ (301.7KB, xlsx)
[275]Supplementary Data 7^ (170.1KB, xlsx)
[276]Supplementary Data 8^ (29.7KB, xlsx)
Acknowledgements