Abstract
Therapeutic resistance to immune checkpoint blockers (ICBs) in melanoma
patients is a pressing issue, of which tumor loss of IFN-γ signaling
genes is a major underlying mechanism. However, strategies of
overcoming this resistance mechanism have been largely elusive.
Moreover, given the indispensable role of tumor-infiltrating T cells
(TILs) in ICBs, little is known about how tumor-intrinsic loss of IFN-γ
signaling (IFNγR1^KO) impacts TILs. Here, we report that IFNγR1^KO
melanomas have reduced infiltration and function of TILs. IFNγR1^KO
melanomas harbor a network of constitutively active protein tyrosine
kinases centered on activated JAK1/2. Mechanistically, JAK1/2
activation is mediated by augmented mTOR. Importantly, JAK1/2
inhibition with Ruxolitinib selectively suppresses the growth of
IFNγR1^KO but not scrambled control melanomas, depending on T cells and
host TNF. Together, our results reveal an important role of
tumor-intrinsic IFN-γ signaling in shaping TILs and manifest a targeted
therapy to bypass ICB resistance of melanomas defective of IFN-γ
signaling.
Subject terms: Cancer immunotherapy, Cancer immunotherapy, Cancer
microenvironment
__________________________________________________________________
Tumor loss of IFN-γ signalling is a major mechanism of resistance to
immune checkpoint blockers. Here the authors report that melanoma cells
with knockout of IFNγR1 show constitutive JAK1/2 activation and that
the JAK1/2 inhibitor ruxolitinib can overcome resistance to anti-CTLA-4
therapy.
Introduction
ICBs such as anti-CTLA-4 and anti-PD-1/L1 induce unprecedented clinical
benefits in patients with various types of advanced cancer and are
revolutionizing the field of cancer treatment^[50]1–[51]3. Over the
past decade or so, more than 70 approvals have been granted to ICBs by
the FDA^[52]2–[53]7, some of which are for first-line use, establishing
ICBs as a major pillar of cancer care. Notwithstanding these
transformative clinical successes, the overall efficacy of ICBs is
limited to a small subset of cancer patients due to frequently
encountered therapeutic resistance^[54]8. Using a cohort of advanced
melanoma, we found that ~75% of melanoma patients did not respond to
anti-CTLA-4 therapy and their tumors harbored losses of IFN-γ signaling
genes^[55]9. Similar findings were reported for anti-PD-1
therapy^[56]10 and subsequently corroborated by a series of seminal
studies in melanoma and colon cancer^[57]11–[58]14. Together, these
studies reveal that tumor loss of IFN-γ signaling is a major mechanism
of resistance to ICBs^[59]9–[60]14. However, therapeutic approaches to
overcome this ICB resistance have remained largely unknown.
ICBs, by blocking immune checkpoints (namely, CTLA-4, PD-1, and PD-L1)
hijacked by tumor cells to evade immunosurveillance, enhance the
effector function (e.g., IFN-γ production)^[61]15,[62]16 and decrease
the abundance of immunosuppressive FoxP3^+ regulatory T cells (T[reg])
in TILs^[63]17, leading to tumor rejection. In support of this, we
found that the interactive loop of IFN-γ and IL-7 signaling in T cells
dictates the therapeutic efficacy of anti-CTLA-4 and anti-PD-1^[64]18.
Although both T cell- and tumor-intrinsic IFN-γ signaling are required
for ICB response, surprisingly, our original characterizations of TILs
isolated from melanomas with knockdown of the essential IFN-γ receptor
1 (IFNγR1^KD) did not reveal overt changes of the CD8^+/T[reg]
ratio^[65]9, a commonly used index of TILs’ effector function. While
this suggests that tumor IFN-γ signaling may not impart TILs, a caveat
is that IFNγR1^KD melanoma still has residual IFN-γ signaling and is
not an ideal model to assess how the loss of IFN-γ signaling in tumor
cells modulates TILs.
In this study, to circumvent the partial attenuation of IFN-γ signaling
in IFNγR1^KD melanoma and to unequivocally evaluate how tumor IFN-γ
signaling affects TILs, we generate the B16 melanoma model with Ifngr1
knocked out by CRISPR-Cas9 (hereafter, IFNγR1^KO). In contrast to
IFNγR1^KD melanomas, IFNγR1^KO melanomas show a reduced abundance of
CD8^+ T cells at the baseline and lack increased infiltration and
functional rejuvenation of TILs upon anti-CTLA-4 therapy. Bioinformatic
analyses of human melanomas with impaired IFN-γ signaling also reveal
reduced expression of T cell signature genes. Interestingly, our
multi-omics studies inform a network of constitutively active PTKs
centered on activated JAK1/2, downstream of the heightened mTOR
signaling pathway in IFNγR1^KO cells. In direct correlation, human
melanomas with reduced IFN-γ signaling or ICB resistance exhibit
upregulation of target genes in the mTOR and JAK1/2 pathways,
indicative of their activation. Targeting activated JAK1/2 with Ruxo
selectively suppresses IFNγR1^KO but not scrambled control melanomas,
coupled with enhanced effector functions (e.g., TNF production) and
reduced T[reg] frequency in TILs. Subsequently, deletion of T cells and
host TNF signaling completely abolish therapeutic effects of Ruxo,
highlighting an indispensable role of T cells and host TNF signaling in
this process. Collectively, we demonstrate that tumor-intrinsic IFN-γ
signaling actively regulates infiltration and function of TILs; our
results support Ruxo as a potential “targeted” therapy for
ICB-resistant IFNγR1^KO melanoma. Since Ruxo is clinically approved,
this study may lead to a rapid repurposing of Ruxo to treat melanomas
lacking IFN-γ signaling.
Results
Creation of a “clean” melanoma model lacking IFN-γ signaling
Our previous work using the syngeneic IFNγR1^KD melanoma model
identified a theretofore unreported role of tumor-intrinsic IFN-γ
signaling in anti-CTLA-4 response^[66]9. However, IFNγR1^KD melanoma
still retained some degree of IFN-γ signaling, evidenced by significant
upregulation of inducible PD-L1 by IFN-γ (Supplementary Fig. [67]1a,
the right panel), preventing us from explicitly assessing how tumor
loss of IFN-γ signaling modulates TILs and ICB response. To
circumvent this, we created the IFNγR1^KO B16-BL6 melanoma model using
CRISPR-Cas9 technology (Fig. [68]1a). Unlike IFNγR1^KD cells, IFNγR1^KO
cells were completely resistant to IFN-γ stimulation, indicated by the
lack of IFN-γ-induced p-JAK2 (Fig. [69]1b), no transcriptional
upregulation of Irf1 (a direct downstream target of IFN-γ signaling,
Fig. [70]1c), as well as no upregulation of PD-L1 (Fig. [71]1d), MHC II
(Fig. [72]1e), and MHC I (Supplementary Fig. [73]1b). Furthermore,
IFN-γ did not induce overt cell death in IFNγR1^KO cells, assessed by
7-AAD and Annexin V staining (Supplementary Fig. [74]1c), neither did
it suppress cell proliferation, indicated by no dilution of CellTrace
Violet (CTV, a cell proliferation dye) (Supplementary Fig. [75]1d).
Consequently, total numbers of viable IFNγR1^KO cells were not reduced,
contrasting a drastic decrease of scrambled control cells in response
to IFN-γ (Fig. [76]1f).
Fig. 1. Generation and characterization of IFNγR1^KO melanoma model lacking
functional IFN-γ signaling.
[77]Fig. 1
[78]Open in a new tab
B16-BL6 cells were transduced with specific single guide RNAs (sgRNAs)
against exon #1 of mouse Ifngr1 or scrambled sgRNAs. a IFNγR1
expression in scrambled control and IFNγR1^KO clones by flow cytometry
(FACS strategy 1). b p-JAK2 in scrambled control and IFNγR1^KO clones
untreated (UnTx) or treated with IFN-γ (100 U/mL for 15 min) by Western
blot. β-actin was the loading control. Experiments were repeated twice
with similar results. c mRNA expression of Irf1 in scrambled control
(n = 3) and IFNγR1^KO cells (n = 3) treated with 1000 U/mL of IFN-γ for
90 min. ***p = 0.0007 by two-sided Student’s t-test. d–f Scrambled
control and IFNγR1^KO cells were untreated (UnTx) or treated with
100 U/mL IFN-γ for 24 h to detect surface expression of PD-L1 (d) and
MHC II (e) by flow cytometry (FACS strategy 1), or for 48 h to count
live cells (f) (n = 4 per group). ****p = 0.00005 (Ctrl vs IFN-γ groups
for the same cell type), by two-sided Student’s t-test. g Tumor growth
of scrambled control (n = 5) and IFNγR1^KO (n = 5) melanomas in
Rag-1^−/− mice. h Tumor growth of scrambled control and IFNγR1^KO
melanomas in B6 mice treated with anti-CTLA-4 or isotype control
(UnTx). N = 5 for Scrambled UnTx/Anti-CTLA-4; n = 4 for IFNγR1^KO
UnTx/Anti-CTLA-4. ***p = 0.0007, by two-way ANOVA with Dunnett’s
multiple comparisons test (with adjustment). i, j Surface expression of
PD-L1 (i) and MHC II (j) on isolated tumor cells (CD45^−) by flow
cytometry (FACS strategy 3). N = 10 for Scrambled UnTx, n = 9 for each
of the other three groups. In i, *p = 0.0415 and **p = 0.0082; in j,
*p = 0.0283 and ***p = 0.0006, by two-way ANOVA with Tukey’s multiple
comparisons test (with adjustment). Representative data from two
independent experiments are shown. The scatter plots and line graphs
depict means ± SEM. Source data are provided in the Source Data file.
To examine whether IFNγR1^KO affected tumor formation in vivo, we
inoculated Rag-1^−/− mice lacking mature T and B cells with scrambled
control and IFNγR1^KO cells. Consistent with a previous report showing
comparable growth of melanomas lacking other important genes in the
IFN-γ signaling^[79]13, we did not observe overt growth defect of
IFNγR1^KO melanoma (Fig. [80]1g). We also did not find altered growth
kinetics of IFNγR1^KO tumor in immunocompetent B6 mice, in the absence
of ICBs (Fig. [81]1h). In keeping with reported ICB resistance in
tumors with impaired IFN-γ signaling^[82]9–[83]14, IFNγR1^KO melanomas
did not respond to anti-CTLA-4 treatment and continued to grow, whereas
scrambled control melanomas were suppressed by anti-CTLA-4
(Fig. [84]1h). In line with our in vitro data, direct analyses of
IFNγR1^KO tumor cells (CD45^−) did not show upregulation of PD-L1
(Fig. [85]1i) and MHC II (Fig. [86]1j) upon anti-CTLA-4, in contrast to
marked upregulation in scrambled control melanoma cells. In aggregate,
IFNγR1^KO melanomas lack functional IFN-γ signaling and are completely
resistant to ICBs and IFN-γ stimulation, presenting a “clean” system to
interrogate how tumor-intrinsic loss of the IFN-γ signaling imparts
TILs.
Reduced infiltration and function of TILs in IFNγR1^KO melanoma
In line with an essential role of tumor IFN-γ signaling in tumor
antigen presentation^[87]14, we noticed a drastic reduction of MHC
molecules in IFNγR1^KO cells (Fig. [88]1j), suggesting an inefficient
process of T cell cross-priming in IFNγR1^KO melanomas. However, our
previous analysis of IFNγR1^KD melanomas did not unveil altered ratios
of CD8^+/T[reg]^[89]9, a widely accepted indication of TILs’ function.
Considering IFNγR1^KD melanomas still possessed IFN-γ signaling (albeit
weaker) (Supplementary Fig. [90]1a), we revisited this issue by
analyzing TILs isolated from the “clean” IFNγR1^KO melanomas.
Appallingly, unlike IFNγR1^KD melanomas^[91]9, IFNγR1^KO melanomas had
markedly reduced CD8^+ T cells at the baseline and no increased T cell
infiltration upon anti-CTLA-4 therapy, as compared to scrambled control
melanomas (Fig. [92]2a). In addition, anti-CTLA-4 failed to deplete
intratumoral T[reg] (Fig. [93]2b), did not increase the CD8^+/T[reg]
ratio (Fig. [94]2b), and did not promote the production of effector
cytokines by CD8^+ (Figs. [95]2c and [96]S2b) and CD4^+ TILs
(Supplementary Fig. [97]2a). Increasing trends of IFN-γ production by
CD8^+ (Supplementary Fig. [98]2c) and CD4^+ (Supplementary Fig. [99]2d)
TILs were noticed in scrambled control but not IFNγR1^KO melanomas upon
anti-CTLA-4. Similarly, anti-CTLA-4 increased the expression of T cell
activation marker PD-1 on both CD8^+ (Fig. [100]2d) and CD4^+ TILs
(Supplementary Fig. [101]2e) and concurrently reduced CD73 expression,
an immunosuppressive ectoenzyme that catalyzes immunostimulatory ATP to
potent immunosuppressive adenosine^[102]19, only in scrambled control
melanoma. Taken together, these data indicate that melanomas with
dysfunctional IFN-γ signaling have reduced infiltration and function of
TILs, pointing to an important role of tumor IFN-γ signaling in shaping
TILs.
Fig. 2. Tumor-intrinsic IFN-γ signaling shapes tumor-infiltrating T cells.
[103]Fig. 2
[104]Open in a new tab
Isolated tumor-infiltrating lymphocytes (TILs) from scrambled control
and IFNγR1^KO melanomas treated with or without (UnTx) anti-CTLA-4 were
analyzed for the abundance of CD4^+ and CD8^+ T cells (a) (*p = 0.0173;
**p = 0.0044), FoxP3^+ cells among CD4^+ TILs (b) (**p = 0.0025;
***p = 0.0004), TNF and perforin production by CD8^+ TILs after a brief
stimulation with PMA and ionomycin (c) (**p = 0.0043), and surface
expression of PD-1 and CD73 on unstimulated CD8^+ TILs (d) (*p = 0.033;
***p = 0.0005; ****p = 0.00004). The scatter plots in a–d depict
representative data (means ± SEM) from two independent experiments.
N = 5 for Scrambled UnTx/Anti-CTLA-4, n = 4 for IFNγR1^KO UnTx
/Anti-CTLA-4 in a, c, d. N = 10 for Scrambled UnTx, n = 9 for Scrambled
Anti-CTLA-4, IFNγR1^KO UnTx and IFNγR1^KO Anti-CTLA-4 groups in b.
One-way ANOVA with Tukey’s multiple comparisons test (with adjustment)
was used for statistical analyses in a and b, and two-way ANOVA with
Šídák’s multiple comparisons test (with adjustment) in c and d. FACS
strategy 3 was applied in a–d. e Skin cutaneous melanomas (SKCMs) in
the TCGA database were grouped into IFNGR1^High (n = 101) and
IFNGR1^Low (n = 150) according to IFNGR1 expression in melanoma cells
(after deconvolution using a panel of melanoma-specific genes).
Comparisons of T cell signature genes in the bulk (without
deconvolution) IFNGR1^High vs IFNGR1^Low SKCMs were presented as
boxplots. f Expression of IFNGR1 (after deconvolution) and T cell
signature genes (without deconvolution) in SKCMs (n = 251) vs uveal
melanomas (UVMs) (n = 58) from the TCGA database. The boxes in e, f
depict the first (lower) quartile, median (center line), and the third
(upper) quartile, and the vertical lines indicate the minimum and
maximum values. The statistical analyses in e, f were calculated using
R with Mann–Whitney U-test. *p < 0.05; **p < 0.01; ***p < 0.001;
****p < 0.0001. Source data as well as exact p values for e, f are
provided in the Source Data file.
We previously reported that patients with advanced melanoma harboring
loss of IFN-γ signaling genes were resistant to anti-CTLA-4
therapy^[105]9. However, how IFN-γ signaling in human melanomas
regulates TILs has not been reported. Inspired by our preclinical
findings, we posited that human melanomas with attenuated IFN-γ
signaling would have reduced expression of T cell signature genes,
including prototypical surface markers for T cells (CD3, CD4, and CD8),
effector molecules (IFNG, GZMB, perforin (PRF1), and TNF), and MHC
molecules (MHC I: HLA-A, HLA-B, and HLA-C; MHC II: HLA-DRA). Since our
previously published database^[106]9 was derived from whole exome
sequencing and did not contain gene expression data, we were unable to
address this using that dataset. To circumvent this, we first analyzed
the TCGA database of human skin cutaneous melanomas (SKCMs) (n = 458).
Specifically, we grouped SKCMs into IFNGR1^High vs IFNGR1^Low using the
median expression of IFNGR1 in melanoma cells after deconvolution of
the bulk samples with a panel of melanoma-specific genes^[107]20. We
reasoned that IFNGR1^Low SKCMs would have attenuated IFN-γ signaling
and thus reduced expression of T cell signature genes. Indeed, we
observed significantly reduced expression of CD3, CD4, CD8, HLA-DRA,
GZMB, IFNG, and TNF in bulk IFNGR1^Low SKCMs, while the others (HLA-A,
HLA-B, HLA-C, and PRF1) were also reduced (although not significant)
(Fig. [108]2e). Interestingly, in correlation with their lower T cell
signature, IFNGR1^Low SKCMs had worse survival probabilities
(p = 0.0039) (Supplementary Fig. [109]2f), suggesting weaker anti-tumor
responses in these patients. Secondly, unlike SKCMs being responsive to
ICBs, uveal melanomas (UVMs) have been known to be resistant to
ICBs^[110]21. We, therefore, assessed IFNGR1 expression in UVMs vs
SKCMs after the aforementioned deconvolution and found significantly
reduced IFNGR1 expression in UVMs (Fig. [111]2f), suggestive of weaker
IFN-γ signaling in UVMs than SKCMs. Importantly, bulk UVMs also had
decreased expression of most T cell signature genes (except for just
one: HLA-A) (Fig. [112]2f). These data suggest that human melanomas
with attenuated IFN-γ signaling have decreased expression of T cell
signature genes, reflective of reduced T cell infiltration and
function, corroborating our preclinical findings. Noteworthily,
dysfunctional IFN-γ signaling (IFNγR1^KO) is required to impart TILs in
murine melanomas, as TILs in IFNγR1^KD melanoma are largely
unaltered^[113]9. However, in human melanomas, attenuated IFN-γ
signaling as in IFNGR1^low SKCMs and in UVMs (lower IFNGR1 expression
than SKCMs) is sufficient to induce appreciable effects on TILs,
implying that TILs in human melanomas are more sensitive to the
dysregulation of tumor IFN-γ signaling. Despite this gradient
discrepancy between murine and human melanoma, our results nevertheless
highlight an important role of tumor IFN-γ signaling in shaping TILs.
Constitutively active JAK1/2 in IFNγR1^KO melanoma
Although tumor loss of IFN-γ signaling has been defined as a major
mechanism of resistance to anti-CTLA-4 (Fig. [114]1h)^[115]9 and
anti-PD-1^[116]10–[117]14, little effort has been devoted to overcome
this ICB resistance. We thus attempted to uncover therapeutic targets
that can be harnessed to treat ICB-resistant melanomas lacking
functional IFN-γ signaling. Considering the important role of PTKs in
coordinating the IFN-γ signaling cascade, we conducted a global kinase
activity analysis (kinomics). Because PTK inhibitors are readily
available for pharmacological targeting, we specifically focused on
activated PTKs that have positive Mean Kinase Statistics (MKS, a
readout for extent and direction of change) and Mean Final Scores (MFS,
indicative of specificity) greater than 0.5. Following these criteria,
we found 26 activated PTKs in IFNγR1^KO cells (Supplementary
Table [118]1), including receptor tyrosine kinases (RTKs such as Ephrin
receptor A and B (EphA/B)) as well as non-receptor tyrosine kinases
(NRTKs: spleen tyrosine kinase (Syk) and ZAP70) that are known to be
involved in carcinogenesis^[119]22. To our surprise, we also observed
activated JAK1 and JAK2, essential downstream components of the IFN-γ
signaling pathway^[120]23. More intriguingly, when these constitutively
activated PTKs were integrated for annotated network modeling, a
JAK1/2-centric network emerged (Fig. [121]3a), highlighting a central
role of active JAK1/2 in the rewiring of these kinases. To directly
confirm this finding, we analyzed phosphorylation of JAK1 and JAK2
(p-JAK1 and p-JAK2) by Western blot (WB) in cells cultured under
normoxia (21% O[2]) and hypoxia (1% O[2], mimicking hypoxic tumor
microenvironment [TME]). Consistent with our kinomic data, p-JAK1 and
p-JAK2 were increased in IFNγR1^KO cells (Fig. [122]3b). Similarly,
basal p-JAK1 and p-JAK2 were increased in IFNγR1^KD cells
(Supplementary Fig. [123]3a). We also assessed the three kinases (Syk,
ZAP70, and EphA3) with high MFS from our kinomic study (Supplementary
Table [124]1) by WB. Of note, basal p-Syk (Supplementary Fig [125]3b)
and p-ZAP70 (Supplementary Fig. [126]3c) were very low in these cells.
Although p-EphA3 was detectable (Supplementary Fig. [127]3c), they did
not show significant increases in IFNγR1^KO cells. Given these results
and the central role of JAK1/2 in the PTK network, we dedicated our
subsequent efforts on JAK1/2.
Fig. 3. Constitutive activation of JAK1/2 in IFNγR1^KO melanoma cells.
[128]Fig. 3
[129]Open in a new tab
a Identification of a JAK1/2-centric network of activated protein
tyrosine kinases in IFNγR1^KO cells by kinomic analysis. Input nodes
(kinases) with large blue circles around them and smaller red circles
on the top right corner indicate increased activity in IFNγR1^KO cells.
Arrowheads denote the direction of interaction and colors of the lines
indicate the type of interaction (yellow: positive; red: negative;
gray: context-dependent). b Scrambled and IFNγR1^KO cells were cultured
under normoxic (21% O[2]) or tumor microenvironment-mimicking hypoxic
(1% O[2]) culture conditions, followed by Western blot (WB) analyses of
p-JAK1/2 and total-JAK1/2. c p-STAT3 and total-STAT3 in scrambled and
IFNγR1^KO cells by WB. d, e Scrambled and IFNγR1^KO cells were
transduced with control lentiviruses (Ctrl) or lentiviruses encoding
mouse IFNγR1 for re-expression (IFNγR1^R). Successfully transduced
cells were analyzed for IFNγR1 expression by flow cytometry (FACS
strategy 1) (d) and p-JAK1/2 by WB (e). β-actin was used as a loading
control in WB. Experiments were repeated twice with similar results in
b, c, and e. Source data are provided in the Source Data file.
A classical downstream event of activated JAK1/2 is tyrosine
phosphorylation of STATs, particularly STAT1 and STAT3^[130]23. We thus
examined p-STAT1/3 by WB. Surprisingly, we could not detect p-STAT1,
even with a substantial amount of protein loading and prolonged film
exposure times, indicating a low level of basal p-STAT1 in melanoma. On
the other hand, although the basal level of p-STAT3 was also low, it
was detectable and increased in IFNγR1^KO cells, suggesting that STAT3
is a preferential target of activated JAK1/2 in IFNγR1^KO cells
(Fig. [131]3c). Because we used single IFNγR1^KO clones but not
mixtures in this study to avoid interference from cells with
inefficient/partial deletion of Ifngr1 by CRISPR-Cas9, a potential
concern would be that activated JAK1/2 may occur merely by chance in
single clones rather than a direct outcome of deletion of IFN-γ
signaling. To address this, we re-expressed Ifngr1 in scrambled control
and IFNγR1^KO cells to comparable levels (IFNγR1^R) (Fig. [132]3d),
using lentiviruses encoding mouse Ifngr1. Compellingly, IFNγR1^R
greatly reduced p-JAK1/2 in IFNγR1^KO cells and largely rescued the
overly increased p-JAK1/2 (Fig. [133]3e), directly linking lack of
IFN-γ signaling to aberrant JAK1/2 activation in melanoma.
JAK1/2 activation in IFNγR1^KO melanoma is unlikely mediated by extrinsic
signals
Next, we wanted to shed light on how the JAK1/2 were activated in
IFNγR1^KO cells. As we recently reviewed^[134]23, the JAK-STAT pathway
is a rapid membrane-to-nucleus signaling module regulated by a wide
array of extracellular signals, including cytokines and growth
hormones. In addition to IFN-γ, type I interferons such as
IFN-α/β^[135]23 and IL-6^[136]24 are among the major extrinsic signals
that engage the JAK-STAT pathway. To determine whether JAK1/2
activation in IFNγR1^KO cells could be due to enhanced IL-6 signaling,
we analyzed the expression of Il6 and Il6r, both of which were
significantly upregulated (Fig. [137]4a). To evaluate whether this
enhanced IL-6 signaling mediated JAK1/2 activation, we blocked IL-6 and
IL-6R with anti-IL-6 and anti-IL-6R antibodies, respectively, at
concentrations that were sufficient to inhibit IL-6-induced p-STAT3 in
melanoma cells (Supplementary Fig. [138]4a). Unfortunately, blocking
IL-6 (Fig. [139]4b) and IL-6R (Fig. [140]4c) did not restore increased
p-JAK1/2 in IFNγR1^KO cells, suggesting IL-6 signaling is not involved
in JAK1/2 activation.
Fig. 4. Activation of JAK1/2 in IFNγR1^KO cells is not mediated by extrinsic
signals.
[141]Fig. 4
[142]Open in a new tab
a mRNA expression of Il6 (*p = 0.0153) and Il6r (*p = 0.0216) in
scrambled control (n = 3) and IFNγR1^KO (n = 3) cells by real-time
RT-PCR. Representative data from two independent experiments are shown
as means ± SEM. b, c p-JAK2 in scrambled control and IFNγR1^KO cells
pretreated with various doses of blocking antibodies against IL-6 (b)
or IL-6R (c), analyzed by Western blot (WB). Experiments were repeated
twice with similar results. d mRNA expression of Ifnar1 in scrambled
control (n = 3) and IFNγR1^KO (n = 3) cells by real-time RT-PCR.
Representative data from two independent experiments are shown as
means ± SEM. ***p = 0.0005. e–g Scrambled and IFNγR1^KO cells were
transduced with different sgRNAs against mouse Ifnar1. Successfully
transduced cells were analyzed for IFNαR1 expression in untreated cells
(e) and PD-L1 expression after stimulation with 100 ng/mL IFN-α for
48 h (f) by flow cytometry (FACS strategy 1) and p-JAK1/2 in untreated
cells by WB (g). h p-JAK2 in scrambled and IFNγR1^KO cells incubated
with supernatants (SN) harvested from scrambled or IFNγR1^KO cultures
for 24 h, analyzed by WB. β-actin was used as a loading control in WB.
All the experiments were repeated twice with similar results. A
two-sided Student’s t-test was used for statistical analyses in a, d.
Source data are provided in the Source Data file.
We then asked if type I interferon signaling contributes to JAK1/2
activation. To this end, we analyzed IFNαR1, the essential receptor for
IFN-α/β, and found it was significantly upregulated in IFNγR1^KO cells
(Fig. [143]4d). We interrogated if IFNαR1 upregulation would lead to
greater IFN-α signaling. To this end, we stimulated scrambled control
and IFNγR1^KO cells with various doses of IFN-α, followed by an
examination of p-STAT1 and p-STAT3, which did not show greater
increases in IFNγR1^KO cells (Supplementary Fig. [144]4b). Also,
IFNγR1^KO cells did not show enhanced sensitivity to IFN-α-induced
killing (Supplementary Fig. [145]4c). While these results suggested
that JAK1/2 activation in IFNγR1^KO cells may not be due to enhanced
IFN-α signaling, to explicitly rule out this, we deleted Ifnar1 in
scrambled control and IFNγR1^KO cells using CRISPR-Cas9 with different
single guide RNAs (sg1 and sg2) (Fig. [146]4e). We confirmed the
ablation of the IFN-α signaling in these cells, evidenced by no
inducible PD-L1 upregulation after IFN-α stimulation (Fig. [147]4f).
Importantly, this ablation of IFNαR1 did not rescue JAK1/2 activation
(Fig. [148]4g), indicating a dispensable role of IFN-α signaling in
JAK1/2 activation. Lastly, to explore the potential regulation of
JAK1/2 activation by other extrinsic factors secreted by IFNγR1^KO
cells into the supernatant (SN) (cytokines, growth factors,
extracellular vesicles, etc.), we treated scrambled control cells with
SNs harvested from IFNγR1^KO cultures for 24 h. This did not induce
increased p-JAK2 (Fig. [149]4h), suggesting a nonessential role of
extrinsic factors in JAK1/2 activation. Of note, increased p-JAK2 in
IFNγR1^KO cells persisted, irrespective of the SNs (IFNγR1^KO or
scrambled control) used, indicating that JAK1/2 activation is more of a
cell-intrinsic event.
Augmented mTOR pathway mediates JAK1/2 activation in IFNγR1^KO melanoma
In addition to extracellular signals (IFN-α, IL-6, etc.), constitutive
activation of JAK1/2 can result from cell-intrinsic alterations (i.e.,
enhanced intracellular signaling^[150]25). To gain a global idea of
this, we performed a whole transcriptome analysis of scrambled control
and IFNγR1^KO cells, which identified 265 downregulated genes and 332
upregulated genes (Fig. [151]5a). We performed a signaling pathway
enrichment analysis using these differentially expressed genes (DEGs).
This unsupervised analysis revealed a wide array of pathways that were
significantly affected (Fig. [152]5b), including essential
intracellular pathways in tumor aggression and therapeutic resistance
(e.g., PI3K-Akt, p53, FoxO, MAPK, and mTOR pathways^[153]26–[154]29),
pathways important in tumor cell growth and proliferation (e.g., cell
cycle^[155]26, glutathione metabolism^[156]30, arginine, proline
metabolism^[157]31, etc.), as well as pathways involved in the
formation of various types of cancer (e.g., prostate cancer, breast
cancer, colorectal cancer, melanoma, gastric cancer, etc.). This
confirms a widespread impact of IFN-γ signaling loss in tumor cells on
tumor progression and therapy response, including its role in ICB
resistance^[158]9.
Fig. 5. Heightened PI3K-AKT-mTOR axis in IFNγR1^KO cells mediates JAK1/2
activation.
[159]Fig. 5
[160]Open in a new tab
a, b Upregulated and downregulated genes in scrambled (n = 3) and
IFNγR1^KO (n = 3) cells by RNA-Seq (a) and top hits of altered
signaling pathways in IFNγR1^KO cells (b). The gene expression analyses
were performed using DESeq2 (version 1.34.0). The Wald test was used to
calculate the p values and log2 fold changes. Genes with an adjusted p
value < 0.05 and absolute log2 fold change >1 were considered as
differentially expressed genes (DEGs). A volcano plot was used to show
all upregulated and downregulated DEGs using the ggplot2 R package.
Enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways of the
DEGs were identified by enrichr package. Significant terms of the KEGG
pathways were selected with a p value < 0.05. c Cell lysates of
scrambled control (n = 4) and IFNγR1^KO (n = 4) cells were subjected to
mass spectrometry-based phosphoproteomic analysis. Phosphorylation
sites known to be mediated by experimentally defined kinases were shown
in the heatmap. Blue and red colors indicate low and high expression
levels, respectively. d p-AKT, total-AKT, and p-4E-BP1 in scrambled and
IFNγR1^KO cells were analyzed by Western blot (WB). e p-JAK2 in
scrambled and IFNγR1^KO B16-BL6 cells untreated (Ctrl) or pretreated
with rapamycin (1 μM) for 3 h, analyzed by WB. f Scrambled and
IFNγR1^KO cells were transduced with lentiviruses expressing
nonspecific shRNAs (shCtrl) or mTOR shRNAs (shmTOR), followed by
analyses of mTOR, p-JAK1/2 by WB. β-actin was the loading control in
WB. Experiments were repeated twice (f) or thrice (d, e) with similar
results. Source data are provided in the Source Data file.
To directly detect the activities of these intracellular signaling
pathways, we conducted phosphoproteomic studies with a special focus on
serine/threonine kinases, considering their intricate interactions with
PTKs^[161]22. Our analysis identified 7529 phosphosites, of which 217
showed significantly increased phosphorylation in IFNγR1^KO cells
(deposited to massive.ucsd.edu and also included in the source data
file). We paid special attention to the ones catalyzed by
experimentally well-defined kinases (Fig. [162]5c); targeted proteins
and phosphosites were listed on the right. Uniprot IDs (mouse) for
these kinases were then used to map to KEGG IDs for pathway enrichment
analyses, which defined 23 signaling pathways (Supplementary
Table [163]2). Because the same phosphopeptides can be mediated by
different kinases, it is rare to have definitive cognate
phosphopeptides for individual kinases. We, therefore, reason that if
the activation of kinases in one pathway can explain most of the
phosphorylation events, a great level of confidence can be reached to
conclude that that pathway is activated. Following this logic, we
sorted the 23 signaling pathways according to the number of identified
phosphorylation sites known to be catalyzed by their kinase members,
which identified the top five pathways as PI3K-Akt, growth hormone
synthesis, ErbB, mTOR, and EGFR tyrosine kinase inhibitor resistance.
Considering that our above results did not support an important role of
extrinsic factors (such as cytokines and growth hormones) in JAK1/2
activation and the fact that EGFR tyrosine kinase inhibitor resistance
was not relevant to our study, we dedicated our efforts to the other
three signaling pathways. Notably, these pathways are intimately
interconnected with each other in cancer, as ErbB signaling feeds into
PI3K-Akt^[164]32 and mTOR is a major downstream module of
PI3K-Akt^[165]27. Importantly, our RNA-seq and phosphoproteomic
analysis converged on the PI3K-Akt and mTOR pathways, highlighting
their essential roles in our system. Of note, the JAK-STAT pathway was
not identified by our phosphoproteomic and RNA-seq analyses; this is
likely due to the preferential enrichment of peptides with serine
and/or threonine phosphorylation by the TiO2-based sample preparation
for phosphoproteomics, the fact that JAK-STAT proteins are primarily
activated by tyrosine phosphorylation, very low basal levels of
p-STAT3/1, and the dependence of these omics analysis on protein
abundance. To directly test if the PI3K-Akt-mTOR axis is activated in
IFNγR1^KO melanoma cells, we analyzed p-AKT and p-4E-BP1, functional
readouts of mTOR action, and found both were increased (Fig. [166]5d).
Given the co-activation of JAK1/2 and mTOR in IFNγR1^KO cells and a
recent study showing a positive mutual regulatory relationship between
them in colorectal tumor cells^[167]33, we assessed how they interact
and regulate one another in melanoma. First, we took a pharmacological
approach by treating cells with rapamycin (Rapa), a well-established
inhibitor for mTOR and found that Rapa profoundly suppressed p-JAK2
(Fig. [168]5e); conversely, inhibition of JAK1/2 with Ruxo did not
change p-4E-BP1 in IFNγR1^KO cells (Supplementary Fig. [169]5a),
placing mTOR upstream of JAK1/2. To directly assess the role of mTOR in
JAK1/2 activation, we knocked down mTOR using shRNAs (mTOR^KD)
(Fig. [170]5f). Similar to mTOR inhibition by Rapa, mTOR^KD also
significantly reduced p-JAK1/2 and at least partially rescued JAK1/2
activation in IFNγR1^KO cells (Fig. [171]5f). Collectively, these
results establish that augmentation of mTOR pathway is a major upstream
regulator of JAK1/2 activation in melanoma cells lacking functional
IFN-γ signaling.
To establish the clinical relevance of our findings, we rationalized
that IFNGR1^Low SKCMs with impaired IFN-γ signaling and patient
melanomas resistant to ICBs would house activated mTOR and JAK1/2 to
some extent. Because phosphorylation data of JAK1/2 and mTOR were not
available in the TCGA database and in the published database of
melanoma patients treated with ICB ([172]GSE78220)^[173]34, precluding
a direct examination of their activation, as an alternative approach,
we constructed a list of genes that were reported to be direct
downstream targets of mTOR and JAK1/2 in various tumor types, including
bladder cancer, breast cancer, liver cancer, lymphoma, and
chondrosarcoma. These genes encompass tumor promoter genes (ENO1, FASN,
FKBP4, ODC1, JUNB, and VEGFA)^[174]35–[175]42 and tumor suppressor gene
(GADD45A)^[176]43. Notably, activation of mTOR and/or JAK-STAT leads to
upregulation of tumor promoter genes (ENO1: α-Enolase, an important
glycolytic enzyme; FASN: fatty acid synthase, a major enzyme for de
novo fatty acids synthesis; FKBP4: FK506-binding protein 4, an
HSP90-associated co-chaperone; ODC1: ornithine decarboxylase, the first
biosynthetic enzyme of the polyamine pathway; JUNB: a key member in the
activator protein (AP-1) family with an important role in cell cycle
progression; VEGFA: vascular endothelial growth factor-A, a key
regulator of angiogenesis) but downregulation of GADD45A (the founding
member of the growth arrest and DNA damage-inducible 45 families with
important function in promoting cell cycle arrest and apoptosis),
consistent with their prominent roles in tumor
formation^[177]27,[178]44. To specifically assess their expression in
human melanomas, we deconvoluted the TCGA and [179]GSE78220 databases
derived from bulk tumor samples, as described above. While
understandably not all the genes showed significant changes in
melanoma, we did observe upregulation of ENO1, FASN, and FKBP4, as well
as downregulation of GADD45A in IFNGR1^Low SKCMs (Supplementary
Fig. [180]5b); on the other hand, patient melanomas resistant to
anti-PD-1 exhibited significant increases of ENO1, FKBP4, ODC1, and
VEGFA (Supplementary Fig. [181]5c). The other genes exhibited expected
increases/decrease, which did not reach statistical significance
(Supplementary Fig. [182]5b, c). In spite of the differences in
affected genes between the TCGA and [183]GSE78220 databases, two genes
(ENO1 and FKBP4) were consistently upregulated in both IFNGR1^Low SKCMs
and ICB non-responders, suggesting that they may be more sensitive to
attenuation of IFN-γ signaling and ICB resistance. Taken together, our
RNA-seq, phosphoproteomic analysis, bioinformatic analysis, as well as
pharmacological and genetic modulations of the mTOR pathway establish
that malfunction of IFN-γ signaling engages the mTOR-JAK1/2 axis in
melanoma cells, which may represent an attractive target for
therapeutic interventions to bypassing ICB resistance in melanomas
lacking functional IFN-γ signaling.
Selective suppression of IFNγR1^KO melanomas by JAK inhibition
To test this, we employed Ruxo, an FDA-approved JAK1/2 inhibitor for
myeloproliferative neoplasms (MPN), which is also being tested
preclinically^[184]45,[185]46 and clinically in solid tumors^[186]47,
as well as in overcoming chemotherapy resistance^[187]48,[188]49.
However, its utility in ICB resistance has not been explored. To this
end, we treated B6 mice bearing scrambled control and IFNγR1^KO
melanomas with Ruxo. Whereas Ruxo did not result in growth suppression
of scrambled control melanoma (Fig. [189]6a), it potently inhibited
IFNγR1^KO melanoma growth (Fig. [190]6b, c), highlighting a selective
suppressive effect of Ruxo in the latter. Given that JAK1/2 were
activated in IFNγR1^KO cells at the baseline (Fig. [191]3), we asked if
IFNγR1^KO cells were more sensitive to Ruxo-induced cell killing. To
this end, we first titrated out effective doses of Ruxo at suppressing
JAK1/2 in scrambled control and IFNγR1^KO cells, based on suppression
of p-STAT1/3 derived from a brief stimulation of IFN-α (Note: this was
necessary for a ready detection of p-STAT1/3, given the low basal level
of p-STAT1/3 in these cells). As shown in Supplementary Fig. [192]6a,
Ruxo already showed significant suppression of p-STAT1/3 at 10 nM and
at 1 μM, completely blocked induced p-STAT1/3 by IFN-α. However, no
appreciable killing of scrambled control and IFNγR1^KO cells by Ruxo
(10 nM–1 μM) was observed (Supplementary Fig. [193]6b), neither did it
cause differential suppression of colony formation between these two
cell types in a 7-day colony forming assay (Supplementary
Fig. [194]6c). These data indicate that the selective suppression of
IFNγR1^KO melanoma by Ruxo is unlikely a result of the preferential
killing of IFNγR1^KO cells by Ruxo.
Fig. 6. Ruxo suppresses IFNγR1^KO but not scrambled control melanomas.
[195]Fig. 6
[196]Open in a new tab
a Growth of scrambled control melanomas in B6 mice treated with vehicle
(UnTx, n = 5) or with Ruxo (90 mg/kg by oral gavage twice daily)
(n = 5) for 10 days. b–e B6 mice bearing IFNγR1^KO melanoma were
treated as in a. b Tumor growth: n = 8 per group; ****p = 0.0002 by
two-way ANOVA with Šídák’s multiple comparisons test. c Tumor weights
at euthanization (n = 8 per group; *p = 0.0422). Isolated TILs from
these mice were analyzed for frequency of FoxP3^+ T[reg] (d) (n = 8 per
group; ****p = 0.0003) and cytokine production of TNF, IFN-γ, Perforin,
and IL-2 in CD4^+ TILs (e) (n = 5 per group; *p = 0.0233 for TNF;
*p = 0.011 for IFN-γ; *p = 0.0311 for Perforin; *p = 0.0319 for IL-2)
after a brief stimulation with PMA and ionomycin. f, g Isolated TILs
were cultured with 100 U/mL IL-2, ±1 μM Ruxo, for 3 days, to analyze
FoxP3^+ T[reg] (f) (n = 3 per group; *p = 0.0346; ****p = 0.00009) and
IFN-γ/TNF production (g) (n = 3 per group; *p = 0.0488) in CD4^+ TILs
after a brief stimulation with PMA and ionomycin by flow cytometry
(FACS strategy 3). A two-sided Student’s t-test was used in c–g for
statistical analyses. The scatter plots and line graphs depict
means ± SEM. Source data are provided in the Source Data file.
Next, we wondered if Ruxo treatment of IFNγR1^KO melanoma could render
TILs more functional. To this end, single-cell suspensions prepared
from untreated and Ruxo-treated IFNγR1^KO melanomas were analyzed. In
line with the fact that Ruxo is a well-established JAK1/2 inhibitor, we
observed the expected suppression of p-JAK2 and p-STAT3 in tumor
(CD45^−) cells by Ruxo (Supplementary Fig. [197]6d). Interestingly,
Ruxo resulted in a pronounced reduction of T[reg] in CD4^+ TILs
(Fig. [198]6d) and a milder but still significant reduction in CD4^+
splenocytes (Supplementary Fig. [199]6e), consistent with previously
reported Ruxo suppression of T[reg] in humans^[200]50 and mice^[201]51.
Moreover, Ruxo increased TNF, IFN-γ, perforin, and IL-2 production by
CD4^+ TILs (Fig. [202]6e), essential effector molecules in anti-tumor
immunity; similar increases of IFN-γ (Supplementary Fig. [203]6f),
perforin (Supplementary Fig. [204]6g), and GzmB (Supplementary
Fig. [205]6h) were also noticed in CD8^+ TILs. Intrigued by these
prominent in vivo Ruxo effects on TILs, we asked if Ruxo could directly
reprogram TILs in vitro. To this end, TILs isolated from untreated
melanomas were cultured with 100 U/mL IL-2, ±1 μM Ruxo (a concentration
with potent suppression of p-STAT1/3 in vitro) for 3 days and then
analyzed for FoxP3 expression (Fig. [206]6f) and production of
IFN-γ/TNF (Fig. [207]6g). Although not as striking as the in vivo
effects, this in vitro Ruxo regimen nevertheless reduced FoxP3
expression and enhanced effector function of TILs. Considering the
reported on-target suppressive effects of Ruxo on MPN-associated
splenomegaly that could ensue potential toxicity on mature T
cells^[208]52, we assessed the abundance of CD4^+ and CD8^+ T cells in
the spleens and did not observe overt reduction (Supplementary
Fig. [209]6i), suggesting negligible toxicity from this short-term Ruxo
therapy. Because our results revealed minimal direct killing of tumor
cells and substantial modulation of TILs by Ruxo, we posit that Ruxo
relies on TILs to mediate its efficacy.
T cells and host TNF signaling control Ruxo efficacy
To directly assess the importance of T cells in Ruxo therapy, we
treated IFNγR1^KO melanoma-bearing mice with anti-CD4 and anti-CD8
neutralizing antibodies prior to and during Ruxo therapy. Strikingly,
deletion of either CD4^+ or CD8^+ T cells completely abolished Ruxo
efficacy (Fig. [210]7a), supporting a pivotal role of T cells in
orchestrating therapeutic effects of Ruxo. Next, we wanted to delineate
the molecular mechanism(s) underscoring Ruxo efficacy. To this end, we
focused on TNF for the following considerations: (1) TNF has long been
regarded as an important effector molecule in mediating tumor
necrosis^[211]53 and has been previously shown to be important in
anti-tumor immune responses^[212]54. (2) TNF has been reported to
suppress T[reg] in both mouse and human systems^[213]55,[214]56, which
coincides with the prominent effect of Ruxo therapy (Fig. [215]6d and
[216]S6e), implying an intricate connection between Ruxo and TNF. (3)
Both Ruxo and anti-CTLA-4 induced prominent production of TNF by TILs
(Figs. [217]2c, [218]S2a, and Fig. [219]6e). Because Ruxo was
systemically administered in our study, we further assessed if Ruxo
impacted TNF production by other immune cells such as intratumoral
CD8^+ T cells (Supplementary Fig. [220]7a), dendritic cells (DCs:
CD11c^+MHC-II^+, Supplementary Fig. [221]7b), and macrophages
(CD11b^+F4/80^+, Supplementary Fig. [222]7c). Interestingly, no
increase of TNF production by these immune cells was induced by Ruxo,
suggesting a selective promotion of TNF production by Ruxo in CD4^+
TILs. Despite these seemingly dispensable effects of Ruxo on TNF
production in these immune cells, they (in particular, CD8^+ TILs and
macrophages, and likely, other immune cells) still produce an abundant
amount of TNF, highly comparable to that of CD4^+ TILs (Fig. [223]6e),
which can contribute to the overall T cell-dependent anti-tumor
responses elicited by Ruxo therapy. To directly examine how the host
TNF signaling affects Ruxo efficacy, we inoculated TNF^−/− mice lacking
TNF in host cells, including immune cells (T cells, myeloid cells,
etc.), with IFNγR1^KO melanoma cells, followed by Ruxo treatment. In
contrast to the significant suppression of IFNγR1^KO melanomas by Ruxo
in B6 mice (Fig. [224]6b), Ruxo was unable to suppress IFNγR1^KO
melanoma in TNF^−/− mice (actually, reversed) (Fig. [225]7b, c),
highlighting a crucial role of host TNF signaling in this process. To
assess whether TNF deficiency abrogates Ruxo modulatory effects on
TILs, we analyzed TILs from TNF^−/− mice treated with Ruxo and did not
observe Ruxo-driven depletion of T[reg] (Fig. [226]7d). Also, there was
no increase of IFN-γ production by CD4^+ TILs (Fig. [227]7e) and CD8^+
TILs (Supplementary Fig. [228]7d). Similar findings were noticed for
IL-2 production by CD4^+ (Fig. [229]7f) and CD8^+ TILs (Supplementary
Fig. [230]7e). Considering the potentially detrimental effects from
chronic TNF deficiency in TNF^−/− mice, we took a complementary
approach by temporarily blocking TNF with in vivo anti-TNF neutralizing
antibodies. We treated mice before tumor inoculation and throughout the
duration of Ruxo therapy. As shown in Supplementary Fig. [231]7f, like
TNF^−/− mice, in vivo neutralization of TNF also largely abolished the
therapeutic effects of Ruxo. Lastly, considering the well-recognized
role of TNF in inducing tumor necrosis, we determined if TNF could
induce greater killing of IFNγR1^KO melanoma cells as an additional
underlying mechanism, in addition to the aforementioned
immunomodulatory effects. To this end, both scrambled control and
IFNγR1^KO cells were treated with TNF in vitro. Surprisingly, no
obvious killing was seen, even when TNF was used at a
supraphysiologically high dose (10,000 U/mL) (Supplementary
Fig. [232]7g), suggesting that direct killing of tumor cells by TNF may
not be important for Ruxo efficacy. In sum, these results indicate that
Ruxo selectively suppresses the growth of IFNγR1^KO melanoma in a T
cell and TNF-dependent manner.
Fig. 7. Ruxo-induced suppression of IFNγR1^KO melanomas relies on T cells and
host TNF.
[233]Fig. 7
[234]Open in a new tab
a Growth of IFNγR1^KO melanomas in B6 mice treated with Ruxo,
±neutralizing antibodies against CD4^+ (α-CD4) or CD8^+ (α-CD8) T cells
(n = 5 for UnTx, n = 5 for Ruxo, n = 10 for α-CD4, n = 10 for
α-CD4+Ruxo, n = 9 for α-CD8, n = 5 for α-CD8+Ruxo). ****p = 0.00007 by
two-way ANOVA with Tukey’s multiple comparisons test (with adjustment).
b–f TNF^−/− mice bearing IFNγR1^KO melanoma were treated with vehicle
(UnTx, n = 4) or Ruxo (n = 4) (90 mg/kg by oral gavage twice daily) for
10 days. b Tumor growth (***p = 0.0007 by two-way ANOVA with Šídák’s
multiple comparisons test with adjustment). c Tumor weights at
euthanization (*p = 0.0391) were shown. Isolated CD4^+ TILs from these
mice were analyzed for FoxP3^+ T[reg] frequencies (d) and production of
IFN-γ (e) (*p = 0.0473) and IL-2 (f) after a brief PMA and ionomycin
stimulation by flow cytometry (FACS strategy 3). A two-sided Student’s
t-test was used in c–f for statistical analyses. Representative results
from two independent experiments are shown as means ± SEM in the
scatter plots and line graphs. Source data are provided in the Source
Data file.
Discussion
Paradigm-shifting ICBs have brought great promises to patients with
advanced melanoma, a tumor type that had been largely incurable until
the approval of anti-CTLA-4 in 2011. However, therapeutic resistance to
ICBs is common^[235]8 and the loss of IFN-γ signaling in melanoma cells
has been reported to be a major mechanism of resistance^[236]9–[237]14.
Given this key information, little is known about why this resistance
occurs and how to overcome it. Here, we identify that melanomas
defective of IFN-γ signaling are not only resistant to IFN-γ-induced
cell death but also have reduced infiltration of CD8^+ T cells and lack
of anti-CTLA-4 induced functional rejuvenation of TILs, posing a dual
resistance to ICBs. Surprisingly, IFNγR1^KO melanomas harbor an
aberrantly active mTOR-JAK1/2 axis, which, when targeted with an
FDA-approved JAK1/2 inhibitor Ruxo, results in potent and selective
suppression of IFNγR1^KO but not scrambled control melanomas, in a T
cell and host TNF-dependent fashion. Moreover, human melanomas with
attenuated IFN-γ signaling or ICB resistance exhibit reduced expression
of T cell signature genes and alteration of target genes downstream of
mTOR and JAK1/2 pathways, suggestive of their activation. Our results
herein establish an important role of tumor IFN-γ signaling in
modulating TILs and manifest a potential “targeted” therapy for
ICB-resistant IFNγR1^KO melanomas.
Tumors lacking functional IFN-γ signaling have been shown to evade
endogenous immunosurveillance^[238]57–[239]59 and anti-tumor immunity
elicited by ICBs^[240]9,[241]10. However, it is unknown whether
tumor-intrinsic IFN-γ signaling modulates TILs. On one hand, IFN-γ, by
upregulating MHC molecules and activating tumor antigen processing and
presentation machinery^[242]60–[243]64, promotes anti-tumor immunity;
on the other hand, it can also suppress anti-tumor immunity by inducing
various regulatory mechanisms such as PD-L1 upregulation in stromal and
tumor cells^[244]65. We observed a pronounced reduction of both MHC
molecules and PD-L1 in IFNγR1^KO melanoma, albeit the former being more
pronounced. Our study corroborates an early pioneering study by Bob
Schreiber and colleagues, which demonstrated that IFNγR1 truncation in
methA fibrosarcoma decreased tumor immunogenicity and responsiveness to
LPS therapy^[245]59. Although our results suggest that lack of
inducible PD-L1 upregulation in IFNγR1^KO melanomas has a seemingly
nonessential role in promoting TILs, this is likely a context-dependent
finding, as incongruous results have been reported for the importance
of tumor PD-L1 in anti-tumor immunity^[246]66–[247]68. Given these
findings of reduced T cell infiltration and function in IFNγR1^KO
melanoma, it would be interesting to delineate the specific molecular
and biochemical mechanisms underlying the immunomodulation of TILs by
tumor IFN-γ signaling in the future. For example, what is the role of
MHC downregulation in this process? How would tumor cell-intrinsic
IFN-γ signaling regulate stemness, survival, and metabolic fitness of
tumor cells, as these features have been associated with therapeutic
resistance^[248]45 and suppression of TILs’ function^[249]69? To this
end, a recent study showed that melanoma cells defective of IFN-γ
signaling outgrew wild-type tumor cells when treated with
anti-PD-1^[250]70, indicating a survival advantage of IFNγR1^KO cells.
We identified a JAK1/2-centered network of constitutively active PTKs
in IFNγR1^KO melanomas, which offers a “personalized” therapeutic
target that can be harnessed to treat these ICB-resistant melanomas.
Indeed, short-term Ruxo therapy selectively suppressed IFNγR1^KO
melanomas, coupled with improved TILs’ effector function and reduced
frequency of intratumoral T[reg]. Our results established an essential
role of T cells and host TNF signaling in governing Ruxo efficacy.
Although we observed that Ruxo selectively promoted TNF production by
CD4^+ TILs but not by CD8^+ TILs and myeloid cells (i.e., macrophages
and DCs), it is noteworthy to mention that those immune cells (esp.,
CD8^+ TILs and macrophages) produced abundant and comparable amount of
TNF to that of CD4^+ TILs (if not higher), which can in turn act on
TILs, in an autocrine or paracrine manner, to mediate therapeutic
effects of Ruxo. Additionally, other immune cells such as γδ T cells,
iNKT, NK cells, and innate lymphoid cells (ILCs) can also produce an
ample amount of TNF that can be regulated by Ruxo. Additional
mechanistic studies using mice with selective deletion of TNF in
different immune cell populations (e.g., CD4^+ T cells, CD8^+ T cells,
DCs, macrophages, and other immune cells) are needed to explicitly
pinpoint the major cellular sources of TNF that underscore Ruxo
efficacy. Importantly, Ruxo has been utilized preclinically to treat
solid tumors, with promising effects reported in ovarian cancer by
suppressing stemness^[251]45, in aggressive carcinoma by antagonizing
TGF-β-induced production of leukemia inhibitory factor^[252]46, and in
KRAS-driven lung adenocarcinoma by decreasing tumor-promoting
chemokines, cytokines, as well as immunosuppressive myeloid-derived
suppressor cells^[253]71. Here, we report that Ruxo can be also
utilized to overcome ICB resistance derived from tumor loss of IFN-γ
signaling. Currently, Ruxo is being clinically tested in patients with
advanced solid tumors ([254]NCT02646748), non-small cell lung cancer
([255]NCT02917993), and triple-negative breast cancer
([256]NCT02876302)^[257]47. Our results justify further testing of Ruxo
in patients with advanced melanoma that are resistant to ICBs, which
accounts for ~75% of all patients^[258]9. Although our short-term Ruxo
therapy was effective and did not incite overt immunosuppressive
toxicity, we argue that it likely needs to be combined with other
therapeutic modalities to achieve a long-term cure. To this end,
preclinical studies have shown that JAKi can improve the therapeutic
efficacy of radiotherapy^[259]72–[260]75, and when rationally combined
with other chemotherapies or oncolytic virus immunotherapy, induce
synergistic effects in different types of cancer^[261]76–[262]78.
Interestingly, a similar counterintuitive reactivation of the JAK-STAT
pathway was previously identified in MPN cells that were chronically
treated with JAK2 inhibitor^[263]49. Perhaps, long-term JAK inhibition
and the chronic functional deficiency (as in IFNγR1^KO melanoma) would
engage other mechanisms to reactivate this essential pathway to sustain
crucial functions such as cell division and differentiation^[264]23. We
show here that the augmented mTOR pathway represents such a key
compensatory mechanism, resulting in JAK1/2 activation in IFNγR1^KO
melanoma. However, how IFNγR1^KO activates the PI3K-Akt-mTOR axis
remains to be delineated. In a patient with myelodysplastic syndrome,
the constitutively active fusion protein TEL-Syk is associated with
activated PI3K-AKT^[265]79. And, ectopic knock-in of TEL-Syk or
overexpression of Syk in various lymphoma cells^[266]80 directly leads
to activation of mTOR. Although our kinomic studies revealed active
Syk, unfortunately, additional WB analyses showed that p-Syk was
extremely low and did not show significant differences between
scrambled control and IFNγR1^KO cells. Future studies with genetic
knockdown/knockout of Syk may be worth pursuing to directly pinpoint
its involvement in mTOR activation. In addition, our phosphoproteomic
studies identified activation of ErbB signaling as a top hit in
IFNγR1^KO melanoma, which is known to feed signals into the
PI3K-Akt-mTOR pathway^[267]32 and may represent a potential underlying
mechanism of mTOR activation. As we recently described^[268]23, as
principal gatekeepers of various cellular signaling pathways, JAK1/2
are delicately regulated at different levels, including
post-translational modifications, inhibitory function of the
pseudokinase domain, as well as many regulators such as phosphatases,
Protein Inhibitors of Activated STAT (PIAS) that inhibit STAT-DNA
binding, and suppressor of cytokine signaling (SOCS)^[269]81. It would
be interesting to investigate how the IFNγR1^KO-mTOR axis affects these
regulatory mechanisms in the future, especially the activity of protein
tyrosine phosphatases (PTPs) to mediate activation of JAK1/2.
In summary, we demonstrate that ICB-resistant melanomas lacking IFN-γ
signaling have reduced infiltration and effector function of TILs but
exhibit an aberrantly active mTOR-JAK1/2 axis. Inhibiting activated
JAK1/2 with Ruxo induces selective suppression of IFNγR1^KO melanomas,
providing a “targeted” therapy to treat these ICB-resistant melanomas.
Ruxo relies on T cells and host TNF signaling but not direct killing of
tumor cells to exert its selective efficacy. Since Ruxo is clinically
approved to treat MPN and is actively being tested preclinically and
clinically in solid tumors^[270]47, our findings lay a solid foundation
for additional clinical testing of Ruxo in patients with advanced
melanoma resistant to ICBs, which can be repurposed to overcome ICB
resistance, a pressing unmet medical need.
Methods
Mice and cell lines
Seven-week-old C57BL/6 (Stock No: 000664), Rag-1^−/− (Stock No:
002216), and TNF^−/− (Stock No: 005540) mice were purchased from The
Jackson Laboratory (Bar Harbor, ME) and housed in specific
pathogen-free conditions in the animal facility of The University of
Alabama at Birmingham (UAB) under 12 h/12 h light/dark cycle, ambient
room temperature (22 °C) with 40–70% humidity. The animal protocol
(APN-21945) was approved by Institutional Animal Care and Use Committee
at UAB. All tumor-bearing mice were humanely euthanized prior to their
tumors reaching the maximally allowed tumor size (20 mm in diameter) in
our animal protocol. The B16-BL6 murine melanoma cells were kindly
provided by Dr. I. Fidler at MD Anderson Cancer Center and cultured
with MEM supplemented with 10% FBS, 2 mM l-glutamine, 1 mM sodium
pyruvate, 1% nonessential amino acids, 1% vitamin, 100 units/mL of
penicillin and 100 µg/mL of streptomycin (all from Invitrogen) in a
humidified 37 °C incubator with 5% CO[2]. B16-BL6 IFNγR1^KD and
scrambled control cells were similarly maintained and used as we
previously described^[271]9. All cells were regularly tested using the
MycoAlert detection kit (Lonza, LT07-118) and kept free of mycoplasma.
Generation of genetically engineered cell lines
Gene knockout cell lines were generated using CRISPR-Cas9 technology,
as we previously described in ref. [272]82. Briefly, single guide RNA
sequences (sgRNAs) were inserted into the lentiCRISPR v2 plasmid
(Addgene, #52961). Lentiviruses were packaged by co-transfecting 293 T
cells with lentiCRISPR v2, pMD2.G (Addgene, #12259), and psPAX2
(Addgene, #12260). B16-BL6 cells were then transduced with lentiviruses
containing scramble sgRNAs (5′-GCACTACCAGAGCTAACTCA-3′, targeting GFP)
or sgRNAs against genes of interest. Cells were then selected with
2 µg/mL of puromycin and then seeded on 96-well-plates at ~1 cell per
well. The grown single clones were then screened based on PD-L1
expression after IFN-γ and IFN-α stimulation for IFNγR1^KO and
IFNαR1^KO, respectively, with further confirmation of their IFNγR1 and
IFNαR1 expression by flow cytometry. Used sgRNAs against mouse Ifngr1
were sgRNA #2 (5′-TGGAGCTTTGACGAGCACTG-3′) and sgRNA #5
(5′-AGCTGGCAGGATGATTCTGC-3′). Used sgRNAs against mouse Ifnar1 were
sgRNA #1 (5′-TCAGTTACACCATACGAATC-3′) and sgRNA #2
(5′-GCTTCTAAACGTACTTCTGG-3′). For mTOR knockdown, lentiviruses
containing shRNAs against mouse mTOR or scramble shRNA were purchased
from Santa Cruz Biotechnology (#sc-35410-V). Transduction of scrambled
control or IFNγR1^KO B16-BL6 cells were performed following the
manufacturer’s instructions. Briefly, cells were seeded to 6-well-plate
and cultured until ~70% confluency. Ten microliters of scramble or
shmTOR lentivirus were added to the medium containing 8 μg/mL of
polybrene from Santa Cruz Biotechnology (#sc-134220). Forty-eight hours
later, cells were transferred to a 10-cm plate and selected with
2 μg/mL puromycin until no further cell death was observed with
puromycin selection. Successfully transduced cells are maintained in a
medium containing 1 μg/mL puromycin. mTOR knockdown was confirmed by
WB. For IFNγR1 restoration, we subcloned mouse Ifngr1 cDNA to pLenti
CMV GFP Puro between BamH I and Sal I restriction enzyme sites.
Lentiviruses were packaged by co-transfection with pMD2.G and psPAX2.
Scrambled control and IFNγR1^KO cells were transduced with
lentiviruses, selected under 2 μg/mL puromycin, and maintained in
medium containing 1 μg/mL puromycin. In some experiments, scrambled
control and IFNγR1^KO B16-BL6 cells were seeded on a six-well-plate,
left untreated, or treated with 10 and 50 μg/mL of anti-IL-6 (Bio X
Cell, clone MP5-20F3, #BE0046) or anti-IL-6R (Bio X Cell, clone 15A7,
#BE0047,) antibodies for 48 h, and then lysed for WB analysis of
phospho-JAK2 (see WB section below). To prove effective blocking with
anti-IL-6 and anti-IL-6R, we treated B16-BL6 cells with IL-6
(100 ng/mL; Biolegend, #575702) in the presence or absence of 10 μg/mL
anti-IL-6/IL-6R antibodies; harvested cell lysates were analyzed for
phospho-STAT3 (see WB section below).
In vivo tumor inoculation and treatment
Seven-week-old C57BL/6 or Rag-1^−/− mice were shaved and inoculated in
the right flanks with 1.25 × 10^5 of B16-BL6 cells intradermally on day
0. Mice were left untreated or treated with anti-CTLA-4 (Bio X Cell,
clone 9H10, #BE0131) intraperitoneally (i.p.) on days 3, 6, and 9 with
200, 100, and 100 µg per mouse, concurrently with vaccination using
GVAX (GM-CSF-expressing B16-BL6 cells irradiated for 150 Gy), as we
previously reported in ref. [273]9. C57BL/6 and TNF^−/− mice bearing
palpable melanoma were treated with Ruxolitinib (LC Laboratories,
#R-6600) by oral gavage (reconstituted evenly in ORA-Plus Suspending
Vehicle), twice daily at 90 mg/kg for 10 days. In vivo TNF blocking
(Bio X Cell, clone XT3.11, #BE0058) was initiated 1 day before tumor
inoculation at a dose of 250 µg per mouse by i.p. and repeated every
three days until mice were euthanized. In vivo neutralizing antibodies
against CD4 (Bio X Cell, clone GK1.5, #BE0003-1) and CD8 (Bio X Cell,
clone 2.43, #BE0061) was given at a dose of 250 µg per mouse by i.p. 1
day prior to tumor inoculation and on days 1, 3, and 10 post tumor
inoculation. Tumors were measured by caliper every other day starting
from day 6 and tumor volumes (mm^3) were calculated using the formula
(0.52 × length × width^2). The tumor-bearing mice were sacrificed when
the tumor reached 20 mm in diameter. Tumors and spleens were collected
at indicated times, and tumor weights were recorded.
TILs isolation and splenocyte preparation
Tumors were collected in ice-cold RPMI 1640 containing 2% FBS and
minced into fine pieces, followed by digestion with 400 U/mL
collagenase D (Worthington Biochemical Corporation, #[274]LS004186) and
20 µg/mL DNase I (Sigma, #10104159001) at 37 °C for 40 min with
periodic shaking. EDTA (Sigma, #1233508) was then added to the final
concentration of 10 mM to stop digestion. Cell suspensions were
filtered through 70 µM cell strainers, and TILs were obtained by
collecting the cells in the interphase after Ficoll (MP Biomedicals,
#091692254). Spleens were collected in ice-cold HBSS containing 2% FBS
to prepare single-cell suspensions after lysis of red blood cells and
filtering with 70 µM nylon mesh. Both TILs and splenocytes were
resuspended in complete Click’s culture medium (Irvine Scientific,
#9195-500 mL) for flow cytometric analyses. In some experiments,
isolated TILs were cultured with 100 U/mL IL-2, with or without 1 μM
Ruxo for 3 days and analyzed for FoxP3 expression and production of
IFN-γ/TNF by flow cytometry, as described below.
Flow cytometric analysis
Surface staining of TILs and splenocytes was done in DPBS containing 2%
BSA for 30 min on ice. To analyze FoxP3, following surface staining,
cells were fixed using the Foxp3/Transcription Factor Staining Buffer
Set (Invitrogen, #00-5523-00) and stained for FoxP3, according to the
manufacturer’s instructions. To detect intracellular cytokines, cells
were briefly stimulated for 4–5 h with Phorbol 12-myristate 13-acetate
(PMA, final concentration: 50 ng/mL; Sigma, #P8139-5MG) plus ionomycin
(final concentration: 1 μM; Sigma, #I0634-1MG) in the presence of
monensin (BD Biosciences, #51-2092KZ) (for the last 2 h). Stimulated
cells were stained with surface markers, fixed using the BD
Cytofix/Cytoperm Plus Fixation/Permeabilization Kit (BD Biosciences,
#554715), and stained for cytokines according to the manufacturer’s
instructions. Antibodies used include Aqua fixation LIVE/DEAD™ Fixable
Aqua Dead Cell Stain Kit (1:200, Thermo Fisher, #[275]L34966),
CD4-BV421 (1:200, clone RM4-5, BioLegend, #100544), CD8-BV786 (1:200,
clone 53-6.7, BD Biosciences, #563332), CD45- PerCP-Cyanine5.5 (1:200,
clone 30-F11, Thermo Fisher, #45-0451-82), CD11b-PE (1:200, clone
M1/70, BioLegend, #101208), CD11c-APC (1:200, clone N418, BioLegend,
#117310), F4/80-BV785 (1:200, clone BM8, BioLegend, #123141), TCRβ-APC
Cy7 (1:200, clone H57-597, BioLegend, #109220), CD3-BV711 (1:200, clone
145-2C11, BioLegend, #100349), IFNγR1-BV605 (1:200, clone GR20, BD
Biosciences, #745111), IFNαR1-APC (1:200, clone MAR1-5A3, BioLegend,
#127313), PD-L1-APC (1:200, clone 10 F.9G2, BioLegend, #124312), MHC
I-BV650 (1:200, clone SF1-1.1, BD Biosciences, #742434), MHC II-BV785
(1:200, clone M5/114.15.2, BioLegend, #107645), FoxP3-eFluor™ 450
(1:100, clone FJK-16s, Thermo Fisher, #48-5773-82), Perforin-PE (1:100,
clone S16009A, BioLegend, #154306), TNF-APC Cy7 (1:100, MP6-XT22,
BioLegend, #506344), PD-1-APC (1:100, clone RMP1-30, Thermo Fisher, #
17-9981-82), CD73-BV605 (1:200, clone TY/11.8, BioLegend, #127215),
Granzyme B-FITC (1:100, clone QA16A02, BioLegend, #372206), IFN-γ-BV650
(1:100, clone XMG1.2, BioLegend, #505832), IL-2-BV711 (1:100, clone
JES6-5H4, BioLegend, #503837), phospho-JAK2 (Tyr 1007/Tyr 1008)-APC
(1:100, clone E132, Abcam, #ab200340) and phosphor-STAT3 (Tyr705)-FITC
(1:100, clone LUVNKLA, Thermo Fisher, #11-9033-42). For cell apoptosis
analysis, cells treated with or without IFN-γ (100 U/mL), IFN-α
(100 ng/mL), Ruxo (10–1000 nM), and TNF (100–10,000 U/mL) were washed
once with DPBS and then washed again with 1× Annexin V binding buffer.
Afterward, cells resuspended in the Annexin V binding buffer were
stained with Annexin V (1:50, Thermo Fisher, #17-8007) and 7-AAD
(1:200, Sigma, #129935) for 30 min at room temperature. For cell
proliferation analysis, cells were pre-labeled with 4 μM CellTrace
Violet (CTV, Thermo Fisher, #[276]C34557) by incubating for 20 min with
periodic mixing. After incubation, cells were washed twice with a
complete culture medium to remove soluble CTV. CTV-labeled tumor cells
(10,000 cells) were seeded onto a six-well plate to evaluate cell
proliferation (CTV dilution) after being cultured in a hypoxic (1%
O[2]) and a normoxic (21% O[2]) incubator for 72 h, in the absence and
presence of IFN-γ (100 U/mL). All the flow cytometric data were
acquired using the built-in software of the Attune NxT Flow Cytometer
(Invitrogen, A24860) from Thermo Fisher. Flow cytometric data were
analyzed using FlowJo (version 10.8.1).
Western blot (WB)
Western blot was performed, as previously described in ref. [277]82.
Briefly, 0.5 millions of scrambled and IFNγR1^KO B16-BL6 tumor cells
were seeded onto a 6-cm-plate and cultured for 24 h. Cells were washed
with cold DPBS twice before lysed with M-PER buffer (Thermo Scientific,
#78501) containing proteinase inhibitors cOmplete (Roche, #11836170001)
and phosphatase inhibitors (Sigma, P2850, and P5726) directly on the
plate. Lysates were then collected and transferred to 1.5 mL Eppendorf
tubes and briefly sonicated. Protein concentration was determined by
BCA quantification (Thermo Scientific, #23225). Fifty µg of total
proteins were loaded onto each lane of a 10% SDS-PAGE gel; after
electrophoresis, proteins on the gel were transferred to 0.22 µm of
nitrocellulose membrane (Bio-Rad, #1620112) in a sponge sandwich.
Membranes were then blocked with 5% of non-fat milk (Bio-Rad,
#170-6404) and probed with primary antibodies overnight on a shaker in
a cold room. After that, membranes were washed and incubated with
HRP-conjugated secondary antibodies at room temperature for 1 h. The
membranes were then incubated with Western HRP substrate (Millipore,
WBLUR0500) for 2–5 min before imaging with an X-ray film. For p-STAT1/3
detection, substantially more total proteins (100 µg and above) were
loaded onto each lane of the gel and membranes were exposed for a much
longer time (20 min or longer) to enhance the signals. About 100 U/mL
IFN-γ was added for the last 15 min for JAK-STAT signaling activation
and phospho-JAK2 was detected. Cells were treated with or without 10 μM
of Ruxo for 30 min or 1 h, 0–1000 nM of Ruxo for 2.5 h and 10 ng/mL of
IFN-α for the last 15 min, 1 μM of Rapamycin for 3 h, or 0–100 ng/mL of
IFN-α for 15 min as indicated in individual experiments. For
supernatant treatment experiments, supernatants collected from ~70%
confluent cultures of scrambled control and IFNγR1^KO cells were spun
down, filtered with 0.22 μm PVDF membrane, and used to treat cells for
24 h. The antibodies used for WB are: phospho-JAK1 (Tyr 1022) (1:1000,
Santa Cruz Biotechnology, polyclonal, #sc-101716), total-JAK1 (1:1000,
Santa Cruz Biotechnology, clone HR-785, #sc-277), phospho-JAK2 (Tyr
1007/Tyr 1008) (1:1000, Santa Cruz Biotechnology, polyclonal,
#sc-16566-R), total-JAK2 (1:1000, Santa Cruz Biotechnology, clone C-10,
#sc-390539), phospho-AKT (Ser473) (1:1000, Cell Signaling Technology,
polyclonal, #9271), total-AKT (1:1000, Cell Signaling Technology,
polyclonal, #9272), phospho-4EBP1 (Thr37/46) (1:5000, Cell Signaling
Technology, clone 236B4, #2855), phospho-STAT1 (Tyr701) (1:1000, Cell
Signaling Technology, clone 58D6, #9167), total-STAT1 (1:1000, Cell
Signaling Technology, polyclonal, #9172), phospho-STAT3 (Tyr705)
(1:1000, Cell Signaling Technology, D3A7, #9145), total-STAT3 (1:1000,
Cell Signaling Technology, 79D7, #4904), phospho-Syk (Tyr525/526)
(1:1000, Cell Signaling Technology, C87C1, #2710), phospho-ZAP70
(Tyr493) (1:1000, Cell Signaling Technology, polyclonal, #2704 T),
phospho-EphA3 (Tyr779) (1:1000, Cell Signaling Technology, D10H1,
#8862 S), mTOR (1:1000, Cell Signaling Technology, clone 7C10, #2983),
and β-actin (1:10000, Santa Cruz Biotechnology, #sc-47778 HRP). β-actin
was run on the same blot with proteins of interest. Uncropped and
unprocessed scans of all blots were provided in the Source Data file.
RT-PCR
Total RNAs were extracted from scrambled control and IFNγR1^KO cells
using the RNeasy Plus Mini kit (QIAGEN, #74136). First-strand cDNAs
were synthesized by SuperScript III reverse transcriptase (Invitrogen,
# 11752250). Quantitative RT-PCR was performed on Bio-Rad One-step with
primers synthesized by IDT. Primers used were Irf-1 (Forward:
5′-CAGAGGAAAGAGAGAAAGTCC-3′; Reverse: 5′-CACACGGTGACAGTGCTGG-3′), Il-6
(Forward: 5′-CTGCAAGAGACTTCCATCCAG-3′; Reverse:
5′-AGTGGTATAGACAGGTCTGTTGG-3′), Il-6r (Forward:
5′-GCCCAAACACCAAGTCAACT-3′; Reverse: 5′-TATAGGAAACAGCGGGTTGG-3′),
IFNαR1 (Forward: 5′-CATGTGTGCTTCCCACCACT-3′; Reverse:
5′-TGGAATAGTTGCCCGAGTCC-3′). β-actin was used as the housekeeping gene
(Forward: 5′- CATTGCTGACAGGATGCAGAAGG-3′; Reverse:
5′-TGCTGGAAGGTGGACAGTGAGG-3′). The gene expression level was calculated
using the 2^−∆∆CT method.
Colony formation assay
Three hundred scrambled control and IFNγR1^KO B16-BL6 cells per well
were seeded on six-well plates; triplicates were set up for each
condition. About 100 nM or 100 nM of Ruxo or an equal volume of solvent
(DMSO) were added to cells after seeding. Cells were cultured for 7
days following crystal violet staining. Stained cells were washed with
DPBS and dried on filter paper for photographs.
Kinomic analysis
Kinomic profiling was performed in the UAB Kinome Core. Scrambled
control and IFNγR1^KO B16-BL6 cells were lysed on ice as described in
sample preparation for WB. Lysates were loaded at 15 μg per array. Each
array had a porous 3D surface imprinted with tethered phosphorylatable
targets. These 12–15 amino acid targets (as listed in the attached
array layout file) were imprinted as “spots” in a 12 × 12 grid. Each
one of these spots had thousands of identical peptide targets, with
residues that could be phosphorylated as lysates were pumped through
the porous array, with phosphorylation detected with phosphor-specific
FITC conjugated antibodies. After each pumping cycle, the lysate itself
was pumped behind an opaque membrane, and an image of the array was
captured over multiple exposure times (10, 20, 50, 100, and 200 ms).
Gridding of whole array images was done with Evolve 2 image analysis
software prior to import into BioNavigator, where signals by exposure
slopes were calculated, multiplied by 100, and log2 transformed to
generate single values per peptide, per sample. These values were used
for upstream kinase identification. Specifically, peptides with
acceptable curve fit and signal were used to identify upstream kinases
using BioNavigator Upkin PTK v6.0. Scores derived from Kinexus
([278]www.phosphonet.ca) for each phosphorylatable peptide residue
(links in array layout file), with amino acid sequences with greater
than 90% homology were queried. Kinases with PhosphoNET V2 scores
greater than 300 and rank ordered in the top 12 were retained.
Individually in vitro identified peptide targets of kinases on-chip
from PamGene’s proprietary database were given a rank order of 0. For
each kinase (ALK), a difference between experimental groups (T; mean
kinase statistic [MKS]) was calculated. The sample mean
[MATH: p¯ij :MATH]
and variance
[MATH:
sij
2 :MATH]
of peptide i in each comparative group. A significance score was based
on permutations of samples and measured how much T depends on the
experimental grouping of the samples. A specificity score was based on
the permutation of peptides and measured how much τ depended on the
peptide to kinase mapping.
[MATH:
τKINA<
/mi>SE=1
n∑i=1n=9
P¯i1<
mo>−P¯i2<
/mrow>si12+si22 :MATH]
1
The combined overall or mean final score (MFS) was either specificity
(if singlicate) or the sum of significance and specificity. Kinases
identified were uploaded as seed nodes by UniProt ID to GeneGo
MetaCore, where they were overlaid on literature annotated
interactions, in an auto-expand network model where sub-networks were
generated from the seed node list, expanded iteratively with preference
given to objects with more connectivity to the initial seed nodes. The
expansion was halted when the sub-networks intersected or when the
network reached a selected size (n < 50 nodes). Networks were named by
their most centric (interconnected) node.
RNA-seq analysis
Scrambled control and IFNγR1^KO B16 melanoma cells were seeded
overnight as triplicates before RNA extraction. Cells were directly
lysed on the plate and total RNA was extracted immediately by RNeasy
Plus Mini Kit from QIAGEN, Inc. Standard RNA-seq was performed by
GENEWIZ, Inc. Briefly, total RNA was enriched with Poly A selection and
sequencing was performed on the Illumina platform. For RNA-seq data
analysis, paired-end transcriptome sequences were mapped to the Mus
musculus GRCm38 reference genome available on ENSEMBL using the STAR
aligner (version 2.7.5a. Read counts per gene were calculated using
htseq-count in the HTseq package (version 0.11.2)^[279]83. Then the
read counts per gene were used for downstream differential gene
expression analysis and pathway enrichment analysis. The analysis of
differentially expressed genes (DEGs) between the scrambled control and
IFNγR1^KO samples was performed using DESeq2 (version 1.34.0)^[280]84
in R (version 3.6.0). The Wald test was used to calculate the p values
and log2 fold changes. Genes with an adjusted p value < 0.05 and
absolute log2 fold change > 1 were considered as DEGs. A volcano plot
was used to show all upregulated and downregulated DEGs using the
ggplot2 package (version 3.3.6) (ggplot2: Elegant Graphics for Data
Analysis. Springer-Verlag New York. ISBN 978-3-319-24277-4,
[281]https://ggplot2.tidyverse.org). Enriched Kyoto Encyclopedia of
Genes and Genomes (KEGG) pathways^[282]85 of the DEGs were identified
by enrichr package^[283]86 (version 3.0), a comprehensive gene set
enrichment analysis tool. Significant terms of the KEGG pathways were
selected with a p value < 0.05.
Multiplexed phosphoproteomic analysis
Cells collected at 90–95% confluence were washed with ice-cold DPBS
thrice and lysed in 8 M urea buffer. The protein concentration was
measured with the Bradford method using Pierce™ Coomassie Plus Assay
Reagent (Thermo Fisher, #23238). For each sample, 1 mg of protein was
digested by TPCK-trypsin at the ratio of 50:1 (w/w) overnight at
37 ^oC. The peptide concentration was quantified using Pierce™
Quantitative Colorimetric Pierce Quantitative Colorimetric Peptide
Assay Kit (Thermo Fisher, #23275). From each quantified peptide sample,
70 μg of peptides was labeled using TMTpro™ 16plex Label Reagent Set
(Thermo Fisher, #[284]A44520) according to the manufacturer’s manual.
Labeled peptides were pooled (4 samples/group × 4 groups) and dried by
Speedvac. Dried peptides were then dissolved in 0.1% trifluoracetic
acid (TFA), with pH values adjusted to 3.5 using 5% TFA. Phosphorylated
peptides were enriched using TiO[2] beads as described
previously^[285]87. Enriched phosphopeptides were then fractionated
using the Pierce Reversed-Phase Peptide Fractionation Kit (Thermo
Fisher, #84868). The fractions of total phosphopeptides were dried by
Speedvac and purified using Millipore ZipTip with 0.6 µL C18 resin
(Thermo Fisher, #ZTC18S096) according to the manufacturer’s manual.
Purified peptides were analyzed using the SPS-MS3 approach with the
Orbitrap Fusion Lumos mass spectrometer^[286]88. Maxquant (version
1.6.17.0) was used to search against mouse protein databases that were
downloaded from uniprot.org. Protein phosphosites were compared among
groups based on corrected reporter ion intensities. The
phosphoproteomic data generated in this study have been deposited in
the Mass Spectrometry Interactive Virtual Environment (MassIVE)
database under accession ID [287]MSV000087796.
Bioinformatic analysis
Gene expression data of The Cancer Genome Atlas (TCGA), Skin Cutaneous
Melanoma (SKCM), and Uveal Melanoma (UVM) were downloaded from National
Cancer Institute Genomics Data Commons (GDC)
[[288]https://gdc.cancer.gov/about-data/publications/pancanatlas]. The
clinical information for each patient in TCGA was obtained from Genomic
Data Commons (GDC) Data Portal [[289]https://portal.gdc.cancer.gov/].
The gene expression profiles of published pretreatment melanomas
undergoing anti-PD-1 therapy transcriptome data^[290]89 were retrieved
from the gene expression omnibus database (GEO) using the accession
number [291]GSE78220. The SKCM samples were grouped into IFNGR1^High
and IFNGR1^Low groups based on the median expression of IFNGR1
expression in tumor cells of all samples. The statistical significances
for gene expressions in IFNGR1^High vs IFNGR1^Low SKCMs, SKCMs vs UVMs,
and anti-PD-1 responders vs non-responders were calculated using R with
the Mann–Whitney U-test. To identify malignant cells from the TCGA and
[292]GSE78220 datasets, CIBERSORTx tool^[293]20 was used to assess the
cell type abundance from the transcriptomes of the bulk tumor tissues.
Specifically, a matrix of reference gene expression signatures was
provided as an input of CIBERSORTx (deconvolution), which were
collectively used to estimate the proportions of melanoma cells and
other stromal cells, including immune cells. The permutation was set as
1000, and the B-mode of batch correction was applied. Samples with p
value < 0.05 were considered successful deconvoluted samples. For the
TCGA cohort, tumor-dominant samples were identified as the samples that
had a relative signature score of the malignant cell (melanoma cell)
>80%. For the [294]GSE78220 cohort, tumor-dominant samples were those
with a relative signature score of the malignant cell (melanoma cell)
>60%.
Statistical analysis
For animal experiments, five mice were included in each group; for in
vitro studies with cells, triplicates were set up to ensure consistency
and reproducibility. All experiments were repeated for two to five
times. Preclinical results were expressed as mean ± SEM. Data were
analyzed using a two-sided Student’s t-test, one-way ANOVA, or two-way
ANOVA after confirming their normal distribution. The log-rank test was
used to analyze survival data from the preclinical studies. All
analyses were performed using Prism 9.4.0 (GraphPad Software, Inc.) and
p < 0.05 was considered statistically significant. TCGA data and
[295]GSE78220 data were expressed as boxplots, with the box depicting
the first (lower) quartile, median, and the third (upper) quartile, and
the lines indicating minimum score and maximum score. To assess the
overall survival of patients with clinical information from TCGA, the
survival time was calculated based on their vital status. The overall
survival of patients with IFNGR1^High or IFNGR1^Low SKCMs was estimated
with Kaplan-Meier analysis and the differences between the cohorts were
assessed with a log-rank test using the “Surv” function in the R
package “Survival” (version 3.2.13). A p value threshold of 0.05 was
used to identify the significantly different survival rates between
groups.
Reporting summary
Further information on research design is available in the [296]Nature
Research Reporting Summary linked to this article.
Supplementary information
[297]Supplementary Information^ (2.7MB, pdf)
[298]Peer Review File^ (815.2KB, pdf)
[299]Reporting Summary^ (449KB, pdf)
Acknowledgements