RES EARCH
RESEARCH ARTICLE SUMMARY
◥
CLINICAL TRIALS
Combined JAK inhibition and PD-1 immunotherapy
for non–small cell lung cancer patients
Divij Mathew†, Melina E. Marmarelis†, Caitlin Foley, Joshua M. Bauml, Darwin Ye, Reem Ghinnagow,
Shin Foong Ngiow, Max Klapholz, Soyeong Jun, Zhaojun Zhang, Robert Zorc, Christiana W. Davis,
Maximillian Diehn, Josephine R. Giles, Alexander C. Huang, Wei-Ting Hwang, Nancy R. Zhang,
Adam J. Schoenfeld, Erica L. Carpenter, Corey J. Langer, E. John Wherry*, Andy J. Minn*
cancer (NSCLC) with programmed death ligand 1 (PD-L1) expression ≥50%. Patients were
administered 6 weeks of anti–PD-1 (PD-1, programmed cell death protein 1) immunotherapy,
followed by a combination of anti–PD-1 and
itacitinib [a selective Janus kinase 1 (JAK1)
inhibitor] for 6 weeks before continuing with
anti–PD-1 alone. To understand the immunological effects of transient JAK inhibition aimed
at interfering with persistent inflammation
occurring after anti–PD-1, we evaluated the association between clinical response and the evolution of CD8 T cell differentiation, immune
signaling, and inflammatory markers.
RATIONALE: Preclinical studies in mice have
demonstrated that blockade of type-one IFN
(IFN-I) signaling can improve immune function during chronic viral infections and enhance the efficacy of immunotherapy for cancer.
Moreover, tumors from patients with lung and
other cancer types that relapse after—or are
resistant to—immune checkpoint blockade
(ICB) can have high expression of IFN-stimulated
genes. Thus, we conducted a phase 2 clinical
trial for first-line metastatic non–small cell lung
RESULTS: In mice, the addition of itacitinib after
Cycle 1
Cycle 2
Cycle 3
anti–PD-1
the start of ICB improved response of interferonstimulated gene (ISG)–high resistant tumors
and increased the proportion of proliferating
precursor-like CD8 T cells in the periphery.
Use of an IFN-I–receptor blocking antibody
similarly improved ICB efficacy, suggesting
that blockade of IFN-I signaling is sufficient to
phenocopy the effects of delayed JAK inhibition. In humans, the combination of anti–PD-1
with delayed, transient, itactinib treatment in
Cycle 4
Cycle 5
anti–PD-1
JAK1 inhibition
Cycle 6
Cycle 7-10
anti–PD-1
Responder
High
plasticity
Progenitor
CD8 T cell differentiation
CD8 T cell plasticity
Terminal differentiation
Inflammation
Inflammation
IFN-I
Low
plasticity Terminal
anti–PD-1
JAK1 inhibition
anti–PD-1
Cycle 1
Cycle 2
Cycle 3
Cycle 4
Non Responder
CD8 T cell plasticity
Terminal differentiation
anti–PD-1
Cycle 5
Cycle 6
Cycle 7-10
JAK inhibitors to improve clinical and immune response to anti–PD-1 for lung cancer patients. In a clinical
trial of patients with NSCLC treated with anti–PD-1, mitigating persistent IFN-I and inflammation with a JAK inhibitor
decreased terminal differentiation of CD8 T cells and expanded fate-flexible CD8 T cell progenitors in responding
patients. Patients with progressive disease had high baseline inflammation that minimally changed with JAK1 inhibition.
Mathew et al., Science 384, 1314 (2024)
21 June 2024
CONCLUSION: As a therapeutic strategy to block
the immunosuppressive effects of persistent
IFN and/or chronic inflammation, JAK inhibition after initial anti–PD-1 is safe, feasible, and
associated with durable and high response rates
in NSCLC. JAK inhibition may be particularly
beneficial in patients with elevated inflammation who have poor CD8 T cell responses to
anti–PD-1 alone. In this study, JAK inhibition
enhanced CD8 T cell plasticity by decreasing
the inflammatory and IFN-I signals that drive
terminal differentiation of CD8 T cells. However, some patients with the highest baseline
levels of inflammation were largely unaffected
by JAK1 inhibition and had progressive terminal CD8 T cell differentiation and disease
progression. Our findings suggest that JAK inhibition can target chronic immunoregulatory
functions of cytokine signaling that contribute
to relapse during cancer immunotherapy and
is a strategy warranting further preclinical and
clinical investigation.
▪
The list of author affiliations is available in the full article online.
*Corresponding author. Email: andyminn@upenn.edu
(A.J.M.); wherry@pennmedicine.upenn.edu (E.J.W.)
†These authors contributed equally to this work.
Cite this article as D. Mathew et al., Science 384, eadf1329
(2024). DOI: 10.1126/science.adf1329
READ THE FULL ARTICLE AT
https://doi.org/10.1126/science.adf1329
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INTRODUCTION: Inflammation is a hallmark of
cancer but is also required to generate optimal
antitumor immune responses. Although short
exposure to cytokines such as interferon (IFN)
can enhance tumor immunity, prolonged exposure can result in immunosuppression. The
dual nature of inflammation makes it challenging to harness the beneficial effects of cytokine
activation during cancer immunotherapy while
avoiding detrimental consequences. Thus, a
therapeutical strategy to effectively modulate
these often-opposing functions of cytokine signaling could improve immunotherapy efficacy
and mitigate development of resistance.
patients with NSCLC led to an overall response rate of 67% and median progressionfree survival of 23.8 months. Patients were
categorized into three response groups based
on the timing of radiographic response: patients with clinical response within the first
two cycles of anti–PD-1 monotherapy (aPD1.R),
patients who responded only after a 6-week
course of concurrent itacitinib was added at the
start of the third cycle of anti–PD-1 (JAKi.R), or
patients who did not respond regardless of
treatment (NR). Each clinical response group
had distinctive immunological changes coupled
to inflammatory features. The aPD1.R patients
had low baseline inflammation and CD8 T cell
responses after anti–PD-1 alone. JAKi.R patients
had elevated inflammatory markers, poor CD8
T cell responses, and blunted immune signaling after anti–PD-1 alone. However, after addition
of itacitinib, subsequent clinical responses in
JAKi.R patients were associated with decreased
inflammatory signaling accompanied by an
increase in a “fate-flexible” CD8 T cell progenitorlike population. This fate-flexible population
was linked to features of greater CD8 T cell
plasticity. By contrast, NR patients had high
baseline inflammation refractory to JAK inhibition. This persistent inflammatory and IFN-I
signaling in NR patients was associated with
CD8 T cell terminal differentiation and treatment failure.
RES EARCH
RESEARCH ARTICLE
◥
CLINICAL TRIALS
Combined JAK inhibition and PD-1 immunotherapy
for non–small cell lung cancer patients
Divij Mathew1,2,3†, Melina E. Marmarelis4†, Caitlin Foley4,5,6, Joshua M. Bauml4, Darwin Ye7,5,3,6,
Reem Ghinnagow7,5,6, Shin Foong Ngiow1,2,3, Max Klapholz1,7,5,6, Soyeong Jun8, Zhaojun Zhang9,
Robert Zorc1, Christiana W. Davis4, Maximillian Diehn8, Josephine R. Giles1,2,3, Alexander C. Huang2,3,
Wei-Ting Hwang10, Nancy R. Zhang9, Adam J. Schoenfeld11, Erica L. Carpenter4, Corey J. Langer4,
E. John Wherry1,2,3,6*, Andy J. Minn7,2,5,3,6*
T
he administration of monoclonal antibodies to block the PD-1/PDL-1 inhibitory
signaling axis and reactivate antitumor
CD8 T cells has led to durable responses
to many cancer types (PD-1, programmed
cell death protein 1; PD-L1, programmed death
ligand 1). In non–small cell lung cancer (NSCLC),
the response rate to single-agent pembrolizumab
(anti–PD-1) is ~45% in the first-line metastatic
setting for patients with tumor PD-L1 expression
≥50% (1), making pembrolizumab the standard
of care for this patient population. However,
many patients fail to benefit from anti–PD-1
immunotherapy, and approximately two-thirds
1
Department of Systems Pharmacology and Translational
Therapeutics, Perelman School of Medicine, University of
Pennsylvania, Philadelphia, PA, USA. 2Institute for
Immunology and Immune Health, Perelman School of
Medicine, University of Pennsylvania, Philadelphia, PA, USA.
3
Parker Institute for Cancer Immunotherapy, Perelman
School of Medicine, University of Pennsylvania, Philadelphia,
PA, USA. 4Department of Medicine, Perelman School of
Medicine, University of Pennsylvania, Philadelphia, PA, USA.
5
Abramson Family Cancer Research Institute, Perelman
School of Medicine, University of Pennsylvania, Philadelphia,
PA, USA. 6Mark Foundation Center for Immunotherapy,
Immune Signaling, and Radiation, Perelman School of
Medicine, University of Pennsylvania, Philadelphia, PA, USA.
7
Department of Radiation Oncology, Perelman School of
Medicine, University of Pennsylvania, Philadelphia, PA, USA.
8
Department of Radiation Oncology and Stanford Cancer
Institute, Stanford University School of Medicine, Stanford,
CA, USA. 9Department of Statistics, The Wharton School,
University of Pennsylvania, Philadelphia, PA, USA.
10
Department of Biostatistics, Epidemiology and Informatics,
Perelman School of Medicine, University of Pennsylvania,
Philadelphia, PA, USA. 11Department of Medicine, Memorial
Sloan Kettering Cancer Center, New York, NY, USA.
*Corresponding author. Email: andyminn@upenn.edu (A.J.M.);
wherry@pennmedicine.upenn.edu (E.J.W.)
†These authors contributed equally to this work.
Mathew et al., Science 384, eadf1329 (2024)
of NSCLC patients who do initially respond will
relapse (2). Thus, developing new approaches
to induce durable clinical responses is an important goal for immune checkpoint blockade
(ICB) therapy.
Interferon (IFN) signaling, which uses the
Janus kinase (JAK) family, has well-recognized
roles in immune stimulation and promoting
antitumor immunity. However, IFN signaling
can also have immunoregulatory effects. For
example, in chronic lymphocytic choriomeningitis virus (LCMV) infection, high type-one
interferon (IFN-I) signaling can inhibit ongoing
immune responses and limit viral clearance (3).
Blocking the IFN-I receptor (IFNAR1) improves
viral control during chronic LCMV infection
(4, 5), prevents antigen-specific CD8 T cells from
becoming terminally exhausted, and preserves
the CXCR5+ TCF1+ exhausted progenitor subset
(6). In cancer, which is another disease characterized by chronic inflammation, high expression of a subset of interferon-stimulated genes
(ISGs) in cancer cells is associated with immunotherapy resistance for multiple human
tumor types, including in NSCLC after acquired resistance to anti–PD-1 (2, 7, 8). In CD8
T cells from patients with NSCLC and other
cancer types, high expression of ISGs is also
coupled to differentiation toward states that
include terminal CD8 T cell exhaustion (9, 10).
Moreover, tumor mutations of IFN pathway
genes in NSCLC and other cancers can predict
longer progression-free survival (PFS), and blocking IFN signaling in ISG-high mouse cancer
models either genetically or by administration
of JAK inhibitor (JAKi) can improve immune
21 June 2024
Delayed administration of a JAKi alters
proliferating CD8 T cells and improves
checkpoint blockade immunotherapy in mice
We previously demonstrated in mice that a
JAK1 and 2 inhibitor (ruxolitinib) given after
the start of ICB can resensitize ICB-resistant
tumors from multiple cancer types (12). To
examine whether the JAK1 selective inhibitor
itacitinib can similarly improve ICB response,
we used the Res 499 tumor model, a wellcharacterized ICB-resistant tumor derived
from B16-F10 melanoma (13). Administration
of itacitinib 7 days after the start of ICB by
either anti–PD-L1 plus anti-CTLA4 (Fig. 1, A
to C) or anti–PD-1 alone (fig. S1A) resulted in
improved tumor responses. Because JAKi can
inhibit numerous cytokine signaling pathways
in addition to IFN, we also compared the effects of itacitinib with those of anti-IFNAR1
(Fig. 1B, right). This comparison demonstrated
that anti-IFNAR1 largely phenocopied itacitinib,
suggesting that inhibiting IFN-I signaling is an
important property of—and sufficient for—the
JAKi effect.
Consistent with a role for IFN-I in dysfunction
of CD8 T cells (14), CD8 T cells were among the
leukocytes most significantly altered after addition of itacitinib or anti-IFNAR1 to anti–PD-L1
plus anti-CTLA4 (fig. S1, B and C). Therefore, we
focused on non-naïve CD8 T cells and systemically evaluated changes in the tumor, draining
lymph nodes (dLN), and spleen from treated
mice. By flow cytometric analysis, non-naïve
CD8 T cells were classified into 12 clusters (Fig.
1, D and E). To enrich for treatment-relevant
CD8 T cells, we next restricted analysis to Ki67+proliferating cells. These Ki67+-proliferating cells
differentially distributed between dLN, spleen,
and tumor (Fig. 1F, density plots; and fig. S1D).
We focused on Ki67+ CD8 T cells belonging to
clusters 5 and 11 because these were two clusters
that significantly or near-significantly changed
with the largest effect sizes in the dLN after JAKi
plus ICB versus ICB alone (fig. S1E). Cluster 5
comprised CD8 T cells expressing TCF1, Ly108,
low or intermediate PD-1 and TOX, and mixed
expression of CX3CR1, resembling memory
precursors and/or progenitor-like CD8 T cells
(TPRE/PROG-like) (Fig. 1, D and E). Cluster 11 had
high expression of PD-1 and TOX as well as
CX3CR1, KLRG1, Ki67, and GZMB, consistent
with an intermediate or circulatory subset of
exhausted CD8 T cells with effector-like features (PD-1hi TEX-INT-like) (15–17). As a proportion of Ki67+ cells, TPRE/PROG-like cluster 5 cells
increased in the dLN when JAKi was added to
ICB, with similar trends in the spleen (Fig. 1F,
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Persistent inflammation driven by cytokines such as type-one interferon (IFN-I) can cause
immunosuppression. We show that administration of the Janus kinase 1 (JAK1) inhibitor itacitinib
after anti–PD-1 (programmed cell death protein 1) immunotherapy improves immune function and
antitumor responses in mice and results in high response rates (67%) in a phase 2 clinical trial for
metastatic non–small cell lung cancer. Patients who failed to respond to initial anti–PD-1 immunotherapy
but responded after addition of itacitinib had multiple features of poor immune function to anti–PD-1
alone that improved after JAK inhibition. Itacitinib promoted CD8 T cell plasticity and therapeutic
responses of exhausted and effector memory–like T cell clonotypes. Patients with persistent
inflammation refractory to itacitinib showed progressive CD8 T cell terminal differentiation and
progressive disease. Thus, JAK inhibition may improve the efficacy of anti–PD-1 immunotherapy by
pivoting T cell differentiation dynamics.
function and ICB response (7, 11, 12). Thus,
persistent IFN signaling can have potent immunoregulatory effects. In cancer cells and
immune cells, chronic IFN-I signaling is linked
with ICB resistance in humans and impedes
efficacy of immunotherapy in mice.
RES EARCH | R E S E A R C H A R T I C L E
Fig. 1. Preclinical and
Tumor Growth
A
B
C Tumor Weight (Day 16)
NT
JAKi
αIFNAR1
phase 2 clinical trial
Study Design
2.0
2.0
2.0
p=NS
p=NS
**
1.5
1.5 vs NT
1.5 vs NT
results of anti–PD-1
No Treatment
***
1.5
1.0
1.0
1.0
αIFNAR1
****
immunotherapy and a
**
0.5
0.5
0.5
Itacitinib (JAKi)
JAK1 inhibitor for
0.0
0.0
0.0
1.0
αCTLA4 + αPDL1 (ICB)
11 13 15 17 19 22 25
11 13 15 17 19 22 25
11 13 15 17 19 22 25
NSCLC. (A) Preclinical
αIFNAR1
ICB
ICB + JAKi
ICB + αIFNAR1
αCTLA4 + αPDL1 (ICB)
2.0
2.0
2.0
p=0.006
p<0.001
p<0.001
Itacitinib
treatment regimen with
0.5
αCTLA4 + αPDL1 (ICB)
1.5 vs ICB
1.5 vs NT
1.5 vs ICB
ICB plus either itacitinib,
1.0
1.0
1.0
d5
d10
d15
0.5
0.5
0.5
0.0
a JAK1 inhibitor (JAKi), or
0.0
0.0
0.0
anti-IFNAR1 antibody
11 13 15 17 19 22 25
11 13 15 17 19 22 25
11 13 15 17 19 22 25
Day
Day
(aIFNAR1) for mice bearing
D
F
CD69
resistant B16-derived Res
KLRG1
Non−Naive CD8 T Cells
GZMB
499 tumors. (B) Mouse tumor
Ki67
Systemic Distribution of Ki67+ CD8 T Cells
1
growth curves in response
CX3CR1
NT
JAKi
ICB
ICB + JAKi
5
Cluster 5
Cluster 11
2
TIM3
0.85
to treatment strategy
0.05
TOX
0.80
p=0.02
5
5
5
5
PD1
0.04
p=0.069
7
outlined in (A). NT, no
0.75
Ly108
0.03
4 6
11
8
0.70
CD44
11
11
11
11
0.02
treatment. (C) Mouse
12
0.65
CD27
10
3
0.01
TCF1
0.60
9
tumor weights at day 16
0.00
CD127
0.3
CD62L
after the indicated treatment.
0.6
p=0.06
p=0.13
5
5
5
5
0.5
(D) Flow cytometry features
Scaled MFI
0.2
Cluster
0.4
−1 0 1 2
11
11
11
11
from non-naive CD8
E
0.1
0.3
PD-1
CX3CR1
TOX
TCF1
0.2
T cells from Res 499 mouse
0.6
0.6
tumors, the draining lymph
5
5
5
5
0.4
0.4
node (dLN), and spleen
11
11
11
11
p=0.075
0.2
0.2
projected on UMAP space.
CD62L
CD127
GZMB
Ki67
0.0
0.0
Shown are 12 FlowSOM
Density
NT
JAKi
clusters (left) along with
Treatment
0.05
0.20
ICB
ICB + JAKi
heat map of scaled median
fluorescence intensity (MFI)
for all marker proteins
Clinical Trial Schema (NCT03425006)
G
H
12-week and Best Overall Response
Pembrolizumab
arranged by hierarchical
Pembrolizumab
Treatment Pembrolizumab
0
Itacitinib
*
clustering (right). (E) MFI
Cycle
−20
C3
C4
C2
C5
C6
C7
C8
C9
C10
expression of select protein
(3 wks) C1
Best Overall Response
−40
Cytometry
markers overlaid on cluster
Complete Response (CR)
First-line Metastatic Proteomic
Partial Response (PR)
ctDNA
uniform manifold approxi−60
NSCLC
Stable Disease (SD)
scRNA
seq
PD-L1 50%
Progressive Disease (PD)
Biopsy
mation and projection
−80
Imaging
(UMAP). (F) Systemic
Patient
I
distribution of all Ki67+ CD8
Progression-free Survival
Progression-free Survival
Duration of Response
1.00 +
1.00
1.00
+
T cells compared across
αPD1.R
K
+
+
+ +
++
dLN, spleen, and tumor (left
0.75
0.75
0.75
+ + +
Baseline
12 weeks
6 weeks
JAKi.R
+
+
+
+
+
+
+ + +
density plots) overlaid
+ ++ +
0.50
0.50
0.50
+ ++ ++
on the UMAP from (D) (light
NR
0.25
0.25
0.25
αPD1.R
p = 0.041
+
gray dots). Locations for
0.00
0.00
0.00
clusters 5 and 11 are labeled
0
10
20
30
40
0
10
20
30
40
0
10
20
30
40
Months
Months
Months
on the density plot overlay.
J
The relative frequencies
Spider Plots of Tumor Response by Response Group
JAKi.R
αPD1.R
JAKi.R
NR
in each tissue compartment
Response (CR/PR) to
Response (CR/PR) to
No response (SD/PD) to
for cells belonging to cluster 5
αPD1 at 6 wks
αPD1 + JAKi at 12 wks
αPD1 + JAKi at 12 wks
150
or 11 are also shown (right
100
NR
dot plots). (G) Schema
50
of phase 2 clinical trial for
0
pembrolizumab and
Weeks
delayed administration of
itacitinib for first-line
metastatic NSCLC with tumor PD-L1 ≥50%. Times of treatment, sample collection,
responder (aPD1.R) if a complete response (CR) or partial response (PR) was
and response assessment by imaging are shown relative to each 3-week treatment
observed at 6 weeks after pembrolizumab but before itacitinib, a JAKi responder
cycle. ctDNA, circulating tumor DNA. (H) Waterfall plot of 12-week tumor response
(JAKi.R) if a CR or PR was not observed until 12 weeks after itacitinib, or a
for each patient. Patients are color-coded by best objective response (BOR)
nonresponder (NR) if a CR or PR was not observed at 12 weeks. Cycles when
JAKi was added to anti–PD-1 are highlighted in bisque. (K) Representative
that includes response with additional follow-up beyond 12 weeks. Asterisk
indicates a patient who clinically progressed prior to the 12-week assessment.
computerized tomography (CT) scan from aPD1.R, JAKi.R, and NR at baseline,
6 weeks, and 12 weeks. Significance for tumor growth was determined by a
(I) Survival curves for overall progression-free survival (PFS), PFS by response
group defined in (J), and overall duration of response. The 95% CIs are shaded.
mixed-effect regression model. For pairwise comparisons, a two-sided Wilcox test
or t test was used for nonparametric or parametric data, respectively. Survival
(J) Spider plots indicating change in tumor measurements from baseline for
patients in each response group. Patients were categorized as either an anti–PD-1
differences were determined by a log-rank test. Error bars represent SEM.
2.0
0.0 1.0
0.75 1.50
1
3
1
JA
Ki
IC
B
αI
FN I
ARCB
1
+ I
JA CB
Ki
5
% Change from Baseline
at 12-weeks
1 2
Spleen
dLN
0.5
Tumor
4
6
1
2
3
9
11
12
5
10
7
8
2.5
1.0
3
0
21 June 2024
48
72
10
2
42
24
30
36
18
12
6
42
48
72
10
ba 2
se
lin
e
24
30
36
18
6
12
48
72
10
ba 2
se
lin
e
36
42
30
18
24
e
lin
se
ba
Mathew et al., Science 384, eadf1329 (2024)
6
12
% Change from baseline
Probability
and
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Proportion of Ki67+ CD8 T Cells within each Compartment
+
αI
N
T
FN
AR
Weight (gram)
Tumor Growth (cm3)
Tumor Growth (cm3)
ns
RES EARCH | R E S E A R C H A R T I C L E
top and middle). At the same time, PD-1hi TEXINT-like cluster 11 cells trended to decrease in
the periphery but increased in the tumor both
proportionally and by absolute number of cells
per gram of tumor (Fig. 1F, middle and bottom; and fig. S1F). The combination of JAKi
and ICB also increased the total number of
non-naïve CD8 T cells per gram of tumor (fig.
S1G). In total, these preclinical data suggest
that itacitinib improves ICB efficacy in resistant tumors characterized by high ISGs
and functions through antagonizing IFN-I
signaling. Compared with ICB alone, addition of JAKi increased both the proportion of
proliferating cells in the periphery resembling
precursor-like CD8 T cells and the number of
intratumoral PD-1hi CD8 TEX-INT-like cells with
effector-like features.
Motivated by our preclinical findings, we initiated a phase 2 clinical trial of pembrolizumab
and delayed itacitinib for treatment-naïve metastatic NSCLC with tumor PD-L1 ≥50%. A total
of 21 patients were treated and evaluated to
examine the efficacy of adding itacitinib to
pembrolizumab (Fig. 1G). Patients first received two cycles of pembrolizumab and then
two cycles of itacitinib and pembrolizumab
(start of cycles 3 and 4). At the start of cycle 5,
patients were continued on pembrolizumab
without itacitinib until disease progression.
Imaging was performed after the first two
cycles of pembrolizumab (at week 6, cycle 3)
and then after itacitinib (at week 12, cycle 5).
Objective response rate (ORR) was defined as
the proportion of evaluable patients with a
complete response (CR) or partial response
(PR) on the week-12 scan, and the best overall
response (BOR) was defined as the best response at any time, including with additional
follow-up after 12 weeks.
Clinicopathological characteristics of the 21
evaluable metastatic NSCLC patients were
comparable to reported cohorts from other
large academic institutions (18) and to the
pembrolizumab arm of the KEYNOTE-24 multicountry randomized trial (1) (table S1). The
12-week ORR was 62%, and the BOR with
additional follow up after 12 weeks was 67%
(Fig. 1H). Only one patient had progression
of disease as a BOR. After a median followup time of 27.6 months, the median PFS was
23.8 months [95% confidence interval (CI) 4.9
to not applicable (NA)], and the median duration of response (DOR) has not been reached
(Fig. 1I, left and right; and fig. S1H). Although
these results cannot be directly compared with
other clinical trials, the ORR from randomized
studies and select US academic centers has
been reported to be ~44% with a median PFS
of 6.5 to 10.3 months, and a median DOR of
about 6 months (1, 18, 19).
Mathew et al., Science 384, eadf1329 (2024)
JAK inhibition is associated with clinical
response despite low initial CD8 T cell
proliferative burst to anti–PD-1 immunotherapy
Although a single-arm study does not allow for
a direct examination of the impact of JAKi on
anti–PD-1 efficacy, we sought to gain insight
into whether itacitinib may have altered the
response to anti–PD-1 by examining discordance between clinical outcome and predictions from biomarkers for disease progression.
In previous reports, approximately 60% of
NSCLC patients who failed to achieve a significant increase in the percentage of peripheral
Ki67+ CD8 T cells after the first one to two cycles
of anti–PD-1 showed progression of disease
(failed to achieve CR, PR, or SD) (20). To confirm
that poor CD8 T cell proliferative responses to
anti–PD-1 predicted disease progression, we first
examined a cohort of NSCLC patients treated
with anti–PD-1 monotherapy and used a previously defined 1.5-fold increase in Ki67+ CD8
T cells as a prediction threshold (20). Consistent
with earlier reports, 71% (five out of seven) of
patients with a CD8 T cell proliferative burst
below this 1.5-fold threshold after one to two
cycles of anti–PD-1 treatment had progression
of disease (Fig. 2A). This early proliferative burst
after one to two cycles of anti–PD-1 was also
observed in our trial of anti–PD-1 plus JAKi;
however, by contrast, none of the nine analyzable patients who fell below the 1.5-fold
threshold showed progression of disease
(Fig. 2B, P = 0.009 by Fisher’s exact test). As
an average across all patients, a second CD8
21 June 2024
T cell proliferative burst was observed after
the period of JAKi treatment (Fig. 2B, left) but
was rarely observed with continuous anti–PD-1
monotherapy (fig. S2A) (20–22). Thus, despite
the absence of an early CD8 T cell response to
anti–PD-1 in some patients, both clinical responses and a post-JAKi CD8 T cell proliferative
burst could be observed, suggesting a possible
clinical and immune effect when delayed JAKi
was added to anti–PD-1 immunotherapy.
JAKi modulates proliferating CD8
T cell subset composition
Because clinical responses occurred in multiple patients receiving JAKi despite a poor CD8
T cell proliferative burst to initial anti–PD-1, we
reasoned that CD8 T cell markers besides
Ki67 and/or the composition of treatmentresponsive Ki67+ CD8 T cells might provide insight into the effects of JAKi. Thus, to identify
CD8 T cell features that dynamically changed
after initial administration of anti–PD-1 (samples collected at the start of cycles 2 and 3),
during concurrent JAKi plus anti–PD-1 (start
of cycles 4 and 5), or after return to anti–PD-1
monotherapy (start of cycles 6 and following),
we used principal components analysis (PCA)
of manually gated flow cytometry data from
non-naïve peripheral CD8 T cells (fig. S2, B and
C). This analysis revealed an association between initial anti–PD-1 treatment and changes
in Ki67+ and PD-1+CD39+ CD8 T cells, between
concurrent JAKi plus anti–PD-1 therapy and
CD127+ and CXCR5+ CD8 T cell populations,
and a correlation between post-JAKi anti–PD-1
monotherapy and CD127+ and PD-1+ CD8 T cell
populations (fig. S2, D to F). Temporal changes
in the frequency of Ki67+, CD127+, and CXCR5+
CD8 T cells during therapy also revealed notable differences by response groups (Fig. 2C).
For example, after initial anti–PD-1, an increase
in Ki67+ CD8 T cells was observed in all response
groups except JAKi.R patients. Conversely, an
increase in CXCR5+ and CD127+ CD8 T cells was
only observed in JAKi.R patients and occurred
during the window of concurrent JAKi and
anti–PD-1 (cycle 4). This increase observed in
JAKi.R patients was then followed by a postJAKi increase in Ki67+ CD8 T cells. Thus, changes
in CD8 T cells expressing Ki67, PD-1, CXCR5,
and/or CD127 distinguished both effects of
JAKi treatment and patient response.
To quantify changes in the composition of
Ki67+ CD8 T cells during treatment, we first
defined T cell states from non-naïve peripheral
CD8 T cells. Unbiased clustering analysis revealed 12 clusters of circulating CD8 T cells
(Fig. 2D and fig. S3A). Cluster 12 comprised
Ki67+ PD-1hi CD39+ CD8 T cells that resembled known TEX intermediate populations
detectable in the blood (denoted as PD-1hi
TEX-INT-like Cl.12) (21–23). CD127 expression
was predominantly confined to clusters 1 and
2 and accompanied by variable expression of
3 of 13
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Clinical response to anti–PD-1 plus delayed
JAKi for metastatic lung cancer
In addition to the 12-week response assessment, we also assessed response at 6 weeks.
This analysis revealed an early (cycles 1 and
2) radiographic response to pembrolizumab
before the addition of itacitinib in five patients. By contrast, eight patients failed to
respond or had tumors that grew after initial
pembrolizumab but responded at week 12
after itacitinib (cycles 3 and 4). Six patients
remained nonresponders (failed to achieve
PR or CR) at 12 weeks, and one responder did
not have a 6-week scan, precluding assessment. On the basis of these response patterns
(Fig. 1, J and K), we classified patients as either
6-week anti–PD-1 responders (aPD1.R), 12-week
post-itacitinib responders (JAKi.R), or nonresponders (NR) at 12 weeks. These three
response groups had similar clinicopathological features (table S2), but the PFS stratified
by these groups was expectedly different (Fig.
1I, middle). In total, these findings suggested
that the delayed administration of itacitinib
after anti–PD-1 resulted in a high response
rate and durable responses in NSCLC patients
with tumor PD-L1 ≥50%. Of the patients with
clinical benefit, some responded early after
anti–PD-1, whereas others objectively responded
only after addition of JAKi to anti–PD-1. Only
one patient had progression of disease as a BOR.
RES EARCH | R E S E A R C H A R T I C L E
A
C
Ki67+ CD8 T Cell Kinetics of Anti-PD1 Monotherapy Trial
Ki67+
6
Frequency
PD
CR
PR
SD
4
2
1
2
3
4
5
1
2
3
4
5
Cycle
B
JAKi.R
αPD1.R
Response
0
Ki67+ CD8 T Cell Kinetics of Anti-PD1 + JAKi Trial
6
p=0.029
p=0.01
4
3
2
Response
0.03
p=0.003
0.05
0.04
0.03
0.02
0.01
CXCR5+
p=NS
p=0.002
p=0.01
(vs. c1)
CD127+
4
2
p=0.001
5
6
7
8
p=0.06
0.2
p=0.05
p=0.003
p=0.02 (vs. c1)
(vs. c1)
0.1
5
6
7
8
1 2−3 4
5
6
7
8
1 2−3 4
5
6
7
8
7
8
Cycle
FC<1.5 FC≥1.5
Cycle
D
E
Non−naive CD8 T Cells
7
2.0
1.5
1.0
0.5
0.0
2.0
1.5
1.0
0.5
0.0
5
6
CD127
CCR7
CX3CR1
1
11
2
2
1
0
1.0
0.5
0.0
1
0
0.5
0.4
0.3
0.2
0.1
0.0
NR
p=0.009
p=0.001
0.7
0.6 p<0.001
0.5
0.4
0.3
PD1hi TEX-INT-like
2
10
4
3
2
1
0
Proportion of Ki67+ Cells
3
JAKi.R
αPD1.R
9
8
Subset Composition of Ki67+ CD8 T Cells
CXCR5
CD127+ TEFF/MEM-like
12
4
PD1
Ki67
p=0.03
p=0.002
1 2-3 4
5
6
7
8
5
1 2-3 4
6
7
8
1 2-3 4
5
6
Cycle
F
+
+
hi
+
Diffusion Map of PD1 CXCR5 TPRE/PROG-like with PD1 TEX-INT-like and CD127 TEFF/MEM-like
CD127+ TEFF/MEM-like (Cl.1+2)
PD1hi TEX-INT-like (Cl.12)
hi
Path to PD1 TEX-INT-like
(Cl.12)
Cluster
1.0
0.5
0.0
DC1
CCR7, suggestive of a mixture of memory and
effector-memory CD8 T cell populations (CD127+
TEFF/MEM-like Cl.1+2). High expression of CXCR5
was almost exclusive to cluster 7, a relatively
rare population of CD8 T cells with a profile
(fig. S3B) that resembled a fate-flexible PD-1+
Mathew et al., Science 384, eadf1329 (2024)
0.04 p=NS
p=NS
p=0.001
(vs. c1)
0.02
0.01
p=0.001
(vs. c1)
0.00
1 2-3 4
5
6
7
8
1 2-3 4
5
6
7
8
1 2-3 4
5
6
7
8
Cycle
treatment cycles and faceted by treatment response. Markers were selected
based on PCA of manually gated flow cytometry features (fig. S2, B to F).
(D) UMAP and cluster assignment of peripheral non-naïve CD8 T cells analyzed
by flow cytometry (left) along with an overlay of the MFI values for the
indicated marker proteins (right). (E) Proportions of CD127+ TEFF/MEM-like clusters
1 and 2 and PDhi TEX-INT-like cluster 12 relative to all Ki67+ CD8 T cells (top) and
the frequency of PD-1+ CXCR5+ TPRE/PROG-like cluster 7 cells relative to non-naïve
CD8 T cells (bottom). (F) Trajectory analysis by diffusion mapping for the
indicated CD8 T cell subtypes (left) with pseudotime values and predicted paths
overlaid (right). For longitudinal data, significance was determined by beta
regression for frequency data and Dirichlet regression for compositional data.
Error bars represent SEM.
CXCR5+ precursor cells or progenitor CD8 TEX
(PD-1+ CXCR5+ TPRE/PROG-like Cl.7) reported in
mice (6, 24, 25) and NSCLC patients (26) that
can differentiate into antitumor CD8 TEX subsets (27) or early memory precursors (28). The
PD-1hi TEX-INT-like Cl.12 cells made up most of
21 June 2024
NR
p=0.001
0.03
DC1
Fig. 2. Patient responses after anti–PD-1 immunotherapy or JAK inhibition
are associated with longitudinal changes in CD8 T cells. (A) Fold change
in the percentage of Ki67+ CD8 T from a cohort of NSCLC patients treated with
anti–PD-1 monotherapy (Memorial Sloan Kettering Cancer Center cohort).
Patients are faceted by progression of disease (PD) or no progression (SD, PR,
or CR) with dotted lines representing baseline (black) or a 1.5-fold increase
over baseline (red). (B) (Left) Fold change (FC) in the percentage of Ki67+
CD8 T from all analyzable patients treated on a clinical trial (this study) of
anti–PD-1 + JAKi. Cycles when JAKi was added to anti–PD-1 are highlighted in
bisque. (Right) Responses for patients grouped by the status of a 1.5-fold
threshold change in Ki67+ CD8 T cells after cycle 1 and 2 of anti–PD-1.
(C) Frequency changes of Ki67+, CXCR5+, and CD127+ CD8 T cells across
JAKi.R
αPD1.R
Frequency
DC2
DC2
1.5
Diffusion
Pseudotime
PD1+ CXCR5+ TPRE/PROG-like(Cl.7)
PD1hi TEX-INT-like (Cl.12)
CD127+ TEFF/MEM-like (Cl.1+2)
EMRA (Cl.3)
EMRA (Cl.4)
other
PD1+ CXCR5+ TPRE/PROG-like (Cl. 7) Kinetics
Path to CD127+ TEFF/MEM-like
(Cl.1+2)
the Ki67+ CD8 T cells responding to treatment
along with a smaller Ki67+ fraction of CD127+
TEFF/MEM-like Cl.1+2 cells, which otherwise
were largely nonproliferating (Fig. 2E and fig.
S3C). The composition of Ki67+ CD8 T cells
from both of these populations dynamically
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4
p=NS
p=0.001
(vs. c1)
1 2−3 4
2−3
p=0.001
p=0.03
(vs. c1)
0.01
1
1
NR
p=0.03
0.02
PD
CR
PR
SD
Frequency
5
Fold-Change Ki67+
Fold−Change Ki67+
PCA of Selected CD8 T Cell Features (Manual Gating)
SD/PR/CR
Frequency
Fold-Change Ki67+
Progression (PD)
RES EARCH | R E S E A R C H A R T I C L E
Progenitor-like, effector-memory,
and exhausted CD8 T cell clonotypes
coordinately change after JAK inhibition
Because naïve T cells largely possess a distinctive T cell receptor (TCR), T cells that share the
same TCR (clonotypes) are developmentally
related. Thus, to more directly examine the impact of JAKi on T cell developmental relationships, we used single-cell RNA and TCR
sequencing (scRNA/TCR-seq) on patients (n =
2 to 3) from each clinical response group at
baseline (start of cycle 1), after anti–PD-1 (start
of cycle 2), during concurrent JAKi and anti–
PD-1 therapy (start of cycle 4), and after return
to anti–PD-1 monotherapy (start of cycle 6). A
reference CD8 T cell map for 40 samples was
created and comprised 13 clusters annotated
by expression of key genes and enrichment of
CD8 T cell–atlas gene sets (10, 23, 29) (Fig. 3, A
to C, and fig. S4A). Two clusters corresponding
to naïve cells and a rare and poorly characterized population (unannotated) were not
considered for downstream analysis. Using
this annotated reference, we mapped CD8 T
cells from all samples, keeping only T cells
with a single TCR (a rearranged a and b chain)
and a minimum annotation score [fig. S4B
and supplementary materials (SM), materials
and methods].
Using the clusters identified by scRNA/TCRseq, we sought to approximate the relationships with clusters defined by flow cytometry—
in particular, the PD-1hi TEX-INT-like Cl.12 cells, the
CD127+ TEFF/MEM-like Cl.1+2 cells, and the PD-1+
CXCR5+ TPRE/PROG-like Cl.7 cells. A CD127 (IL7R).
Mathew et al., Science 384, eadf1329 (2024)
Tm gene set (10) enriched in several scRNA-seq
clusters, including two memory and effector–
memory like clusters (cm.cd127 and em.cd127)
likely approximating the CD127+ TEFF/MEM-like
Cl.1+2 cells identified by flow cytometry. A
cluster of TEX cells denoted exh was identified
on the basis of enrichment for an Exhaustion
gene set for CD8 T cell exhaustion (23), high
expression of MKI67, and multiple TEX intermediate genes (e.g., GZMK, KLRG1, and
CD38) that likely reflect recently proliferating
PD-1hi TEX-INT like Cl.12 cells identified by flow
cytometry. A second rare cluster, denoted exh.
cm, also modestly enriched for the Exhaustion
gene set and for a CM gene set for central
memory CD8 T cells. However, partly owing to
sparsity of cells, low annotation scores (fig. S4B),
and TCR sharing primarily with an unannotated cluster (fig. S5B), this cluster was not
further examined. We next used two external
gene sets to identify progenitor-like CD8 T cells:
a CXCR5.Tem gene set for early effector-memory
CD8 T cells and a Exh.Prog gene set for CD8 TEX
progenitor cells. Indeed, both of these gene
sets identified peripheral CD8 T cells from a
previously described tumor T cell atlas (10)
that are highly enriched for CXCR5 and TCF7
(fig. S4C). In this study, the CXCR5.Tem and
Exh.Prog gene sets enriched in a cluster that
we denoted pre.prog. This pre.prog cluster
also had high expression of TCF7, CXCR5, GZMK,
EOMES, IFIT1, and multiple genes that characterize previously described TEX progenitor cells (17, 25, 30, 31) (Fig. 3B and fig. S4A).
This pre.prog cluster likely captures the fateflexible PD-1+ CXCR5+ TPRE/PROG-like Cl.7 cells
identified by flow cytometry, although cells
that do not express CXCR5 transcript (fig.
S4C) or protein (Fig. 2D) were also contained
in this cluster. Other dysfunctional and/or terminal CD8 T cell subtypes were identified by
using an EMRA gene set, including two EMRA
subtypes (emra and emra.nk), with emra.nk
expressing high levels of natural killer (NK)
receptors.
In our flow cytometry analysis, Ki67 was
used to define CD8 T cells responding to anti–
PD-1 and enriching for treatment-relevant
T cells. Similarly, treatment-relevant T cell
clonotypes were enriched by including only
TCR clonotypes that expanded after therapy
(fig. S4D and SM, materials and methods).
This approach identified 5867 distinct clonotypes from 124,534 CD8 T cells. Changes in the
cumulative frequencies of filtered clonotypes
that belonged to pre.prog, exh, and the em.
cd127 clusters approximated key patterns observed with PD-1+ CXCR5+ TPRE/PROG-like Cl.7,
PD-1hi TEX-INT-like Cl.12, and CD127+ TEFF/MEM-like
Cl.1+2 populations identified by flow cytometry
(Fig. 3, D and E). Namely, JAKi.R patients had a
blunted exh response at cycle 2 after anti–PD-1
and an increase in pre.prog and em.cd127 clonotypes during cycle 4 on JAKi. This pattern was
21 June 2024
followed by a post-JAKi increase in exh clonotypes that likely corresponded to the late CD8
T cell proliferative burst in JAKi.R patients
(Fig. 2C). Neither aPD1.R patients nor patients from a separate NSCLC cohort treated
with anti–PD-1 monotherapy (aPD-1.m) showed
this pattern. Thus, as with the flow cytometry analysis, coordinated changes in expanded
clonotypes from CD8 T cell populations that
resemble PD-1+ CXCR5+ TPRE/PROG-like, CD127+
TEFF/MEM-like, and PD-1hi TEX-INT-like populations were linked to effects of adding JAKi to
anti–PD-1 therapy.
Patient responses after JAK inhibition are
associated with expanded CD8 T cell
clonotypes with fate-flexible features
To begin assessing whether a developmental relationship between PD-1+ CXCR5+ TPRE/PROG-like,
CD127+ TEFF/MEM-like, and PD-1hi TEX-INT-like CD8
T cells might be altered by JAKi, we examined
clonotype expansion and TCR sharing between
these populations. In JAKi.R patients who responded after addition of JAKi to anti–PD-1,
not only did JAKi result in an increase in the
frequency of pre.prog clonotypes but also an
increase in clonality (decreased clonal diversity) that was not observed in other patient
response groups (Fig. 3F). This increase in
clonality suggested that expansion of the
pre.prog population may have been antigendriven rather than a consequence of homeostatic proliferation or selective survival. To
investigate whether this expansion of pre.
prog clonotypes during JAKi that occurred
together with an increased frequency in other
clonotypes might be due to a developmental
relationship, we used a previously described
pairwise transition index (pTrans-index) (32)
that measures the degree of TCR sharing between two T cell states. This analysis demonstrated that pre.prog CD8 T cells had a high
degree of developmental relatedness with
multiple different subtypes (Fig. 4A), which
is consistent with pre.prog having fate-flexible
properties. The degree of TCR sharing between pre.prog cells and either em.cd127 or
cm.cd127 (Path.Cd127) increased after addition of JAKi during cycle 4 in JAKi.R patients
but not in other patient response groups (Figs.
4, B and C, middle rows). Simultaneously, the
pTrans-index from pre.prog to exh or EMRA
subtypes (Path.Exh.EMRA) also increased on
JAKi but then decreased post-JAKi at cycle 6.
No significant changes in the pTrans-index
were observed with aPD1.R or NR patients or
patients treated with anti–PD-1 monotherapy.
Thus, these findings suggested that in JAKi.R
patients, fate-flexible pre.prog CD8 T cells underwent specific expansion when JAKi was
added, resulting in an increase in clonality
and frequency. During JAKi treatment, these
pre.prog clonotypes were developmentally
linked to expansion of em.cd127 clones but
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changed in opposite directions during treatment. In JAKi.R patients but not patients in
other response groups, the proportion of proliferating CD127+ TEFF/MEM-like Cl.1+2 cells increased, whereas that of Ki67+ PD-1hi TEX-INT-like
Cl.12 cells decreased during the window of JAKi
(Fig. 2E, top plot), a pattern also not observed in
a separate cohort of NSCLC patients treated
with anti–PD-1 monotherapy (fig. S3D). Furthermore, this reciprocal compositional change in
proliferating Ki67+ CD8 T cells during the JAKi
window also correlated with an increase in
the PD-1+ CXCR5+ TPRE/PROG-like Cl.7 population
(Fig. 2E, bottom). Indeed, trajectory analysis
predicted differentiation paths connecting
PD-1+ CXCR5+ TPRE/PROG-like Cl.7 cells with
both CD127+ TEFF/MEM-like Cl.1+2 and PD-1hi
TEX-INT-like Cl.12 populations (Fig. 2F and fig.
S3E). Thus, these findings suggest that in
JAKi.R patients, addition of JAKi increased
the frequency of PD-1+ CXCR5+ CD8 T cells
resembling fate-flexible precursor cells predicted to be developmentally related to the
CD127+ TEFF/MEM-like and/or PD-1hi TEX-INT-like
populations. JAKi may have impacted this
relationship by rebalancing the expansion of
CD127+ CD8 TEFF/MEM-like and PD-1hi TEX-INT-like
populations.
RES EARCH | R E S E A R C H A R T I C L E
A
B
C
CD8 T Cell Clusters
CD127 (IL7R).Tm
Gene Set Enrichment
Exh.Prog
Naive
CM
unannot
CD127 (IL7R).Tm
emra.nk
emra
naive
EM
Exh.Prog
pre.prog
early.cm
Exhaustion
EMRA
cm
exh.cm
CXCR5.Tem
Exhaustion
exh
cm.cd127
0.6
0.4
0.2
0.4
0.0
−0.4
CXCR5.Tem
em
1.25
1.00
0.75
Enrichment
Score
ive cm em cmd127h.cmd127.prog exh mrara.nknnot
e m na
naarly.
.c ex m.c pre
e u
e
cm
e
0.08
0.04
0.00
−0.04
1.0
0.8
0.6
em.cd127
Zheng, L., et al., Science, 2021
Giles, J.R., et al., Immunity, 2022
Giles, J.R., et al., Nat Immunol 2022
D
E
Cycle 2
Cycle 4 (+JAKi)
Baseline Expanded Clonotypes
Cluster-Specific Frequency
Cycle 1
Cycle 6
pre.prog
exh
em.cd127
0.100
0.03
cm.cd127
αPD1.R
0.03
0.05
0.075
0.04
0.02
0.02
0.03
0.050
0.01
0.025
0.01
JAKi.R
0.00
0.000
1
2
4
6
0.02
0.01
1
2
4
6
0.00
1
2
4
6
1
NR
αPD1.m
2
4
6
Cycle
Response Group
αPD1.m
Cluster-specific
Clonotype Frequency
0.05
0.10
cm.cd127
em.cd127
exh
pre.prog
JAKi.R
Clonotype Expansion of pre.prog Cluster
Clonotype Expansion
NR
F
αPD1.R
JAKi.R
αPD1.R
0.100
NR
p=0.024
0.075
0.050
p=0.04
0.025
0.000
2
4
6
2
4
6
2
4
6
Cycle
Fig. 3. Evolution in CD8 T cell clonotypes after anti–PD-1 immunotherapy
and JAK inhibition correlates with treatment and response. (A) UMAP
of peripheral CD8 T cell subtypes analyzed by scRNA/TCR-seq from the start
of cycles 1, 2, 4, and 6 from aPD1.R patients (n = 2), JAKi.R patients (n = 3),
NR patients (n = 3), and a separate cohort of patients treated with anti–PD-1
monotherapy (aPD1.m) (n = 2). (B) Gene set variation analysis (GSVA)
enrichment scores for each CD8 T cell cluster (x axis) using the indicated CD8
T cell subtype gene set (y axis). Source of the gene sets are indicated.
(C) Enrichment scores for select gene sets overlaid on the UMAP from (A).
(D) UMAP from (A) showing the frequency (expansion) of clonotypes belonging
also concomitantly gave rise to exh cells. These
effects are consistent with JAKi impeding
known functions of IFN-I signaling in driving
progenitor differentiation toward downstream
TEX subsets (9).
Response after addition of a JAKi is linked
to increased CD8 T cell plasticity
The ability of JAKi to impact the balance of
progenitor CD8 T cell differentiation would
Mathew et al., Science 384, eadf1329 (2024)
to the indicated color-coded CD8 T cell subtype (size of dot). Clonotypes
belonging to other subtypes are shown in gray. (E) Cumulative frequencies for
expanded TCR clonotypes belonging to the indicated CD8 T cell subtype. Data
for individual patients are pooled by treatment group. Cycles when JAKi was
added to anti–PD-1 are highlighted in bisque. (F) Clonotype expansion score
(measuring degree of clonality) for pre.prog CD8 T cells from each indicated
response group. Cycles when JAKi was added to anti–PD-1 are highlighted. For
longitudinal data, significance was determined with a repeated measures analysis
of variance (ANOVA) using a mixed effect model and post-hoc interaction
analysis. Error bars represent SEM.
be predicted to have broad consequences for
the fate of individual CD8 T cell clonotypes
after JAKi is added to anti–PD-1. Such an effect would result in changes in the subtype
composition of individual clonotypes to include more nonterminal and nonexhausted
T cell states. To test this hypothesis, we used
the pTrans-index to derive a plasticity score
(PS) for each CD8 T cell subtype. In this case,
a subtype has a high PS if there is relatively
21 June 2024
even TCR sharing with other subtypes and
T cell states, and it has a low PS if sharing is
more restricted to only a few subtypes or states
(Fig. 4D and fig. S5, A to C). By extension, the
clonotype PS (PSclono) is the average of the PS
values of all T cell subtypes that contribute to
the composition of a clonotype. For each expanded clonotype from non-naïve CD8 T cells,
we then determined the change in PSclono at
the start of cycles 2, 4, and 6 compared with
6 of 13
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Baseline Expanded Clonotypes
RES EARCH | R E S E A R C H A R T I C L E
A
B
pTrans Index (TCR sharing)
for pre.prog
pTrans for pre.prog with CD127+ or TEX-like and Terminal Paths
JAKi.R
pTrans Index
αPD1.R
-1 / log10 (pTrans)
2.0
pre.prog
1.5
1.0
pre.prog
Path.Exh.EMRA
Path.Cd127
Path.Cont
0.4
p=0.006
NR
αPD1.m
p=0.04
0.3
0.2
p=0.05
0.1
0.0
2
4
6
2
4
6
2
4
6
2
4
6
Cycle
C
D
pTrans for pre.prog with Other CD8 T Cell Subtypes
Cycle 2
Cycle 4 (+JAKi)
Legend for pTrans UMAP
Cycle 6
Low Clonotype Plasticity Score
emra.nk
TEMRA-NK
emra
pre.prog
cm cm.cd127
exh.cm
TEX
Tpre.prog
exh
em.cd127
High Clonotype Plasticity Score
TEX
TEMRA-NK
JAKi.R
Tpre.prog
Tpre.prog
Cluster-Specific
Clonotype Frequency
TEFF/MEM
0.05
n
0.10
Clonotype PS (PSclono) =
plasticity score
of cell k
sk
NR
Scaled
pTrans-Index
k =1
E
clono
F
Distribution of Expanded Clonotypes
Cycle 4 (+JAKi)
JAKi.R
αPD1.R
αPD1.R
= PSclono – Baseline PSclono
Plasticity Score Change of Expanded Clonotypes
Cycle 6
clono
Cycle 2
clono
2
1
0
−1
NR
80
200
p=NS
60
p=0.014
75
150
JAKi.R
40
50
20
25
0
0
100
NR
50
αPD1.m
αPD1.m
25
100
p=NS
p=NS
0
−25
−50
0
0 102
103
0 102
103
0 102
Plasticity Score Change from Baseline (
103
clono
)
Fig. 4. Response to combined JAK inhibition and anti–PD-1 immunotherapy is associated with alterations to CD8 T cell differentiation
dynamics and clonotype plasticity. (A) Pairwise transition (pTrans) index
values (measuring TCR sharing) from the pre.prog subtype to other subtypes
overlaid on the UMAP shown in Fig. 3A. (B) pTrans-index values between
pre.prog CD8 T cells and either exh and terminal EMRA (emra, emra.nk)
clusters (Path.Exh.EMRA, red) or em.cd127 and cm.cd127 clusters (Path.Cd127,
blue). For comparison, results to the unrelated cm and exh.cm clusters
(Path.Cont) are also shown. (C) pTrans-index values between the pre.prog
subtype and other subtypes (legend in right margin) overlaid on a UMAP (from
Fig. 3A) of expanded CD8 T cell clonotypes faceted by treatment cycle and
response group. Edges connecting nodes from the pre.prog subtype to other
Mathew et al., Science 384, eadf1329 (2024)
21 June 2024
−75
2
4
6
2
4
2
6
4
6
2
4
6
Cycle
subtypes are color-coded by the pTrans-index value (higher scores indicate
greater TCR sharing and hence developmental relatedness; the absence of
edges indicate no detectable sharing). Subtype-specific clonotype frequency is
represented by dot size. (D) Schema and derivation of the plasticity scores
(PS) using the pTrans-indices for each CD8 T cell subtype and the DPSclono
using the difference of PS values from baseline. (E) DPSclono of all expanded
clones colored by response group and faceted by treatment cycles. Positive
DPSclono represents an altered clonotype subtype composition resulting
from an increased plasticity. (F) Mean DPSclono for patients in each of the
indicated response groups. For longitudinal data, significance was determined
with a repeated measures ANOVA using a mixed effect model and post-hoc
interaction analysis. Error bars represent SEM.
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αPD1.R
early.cm em
RES EARCH | R E S E A R C H A R T I C L E
B
aPD1.R
JAKi.R
Olink plasma proteins
and cytokines
(n = 92)
Cy.kclust patterns
(41 proteins, 4 patterns)
Pathway Activity Score
1
2
1234567
1234567
3
4
1234567
1234567
0.5
0.0
−0.5
Cy.kclust
Pattern (bars)
1
2
3
4
Cytokine/Protein
Group (ribbons)
NR Cy.kclust.1
other
Temporal patterns by
K-means clustering
C
NR
Non-Responder Cy.kclust.1 Cytokine/Protein Group
Mean Scaled Expression
CS.kclust patterns
(18 pathways, 4 patterns)
Cycle
D
NR
Response
JAKi.R
αPD1.R
CytoSig pathway activity
in PBMCs or CD8 T cells
(n = 43)
JAKi.R
αPD1.R
Cycle
Features differing by
treatment cycle
and/or response
JAKi
Scaled Expression
Joint Profiling of Inflammatory Proteins and Signaling
aPD1
Cy.kclust Cytokine/Protein Temporal Patterns
NR
Freq. Cytokine/Protein
Plasma Protein Expression
A
2
NR
p=0.016
p<0.001
p=0.006
p<0.001
p=0.014
p=0.008
Select
Cytokine
IL6
CD274
CSF1
IL10
1
0
p=0.057
−1
Cycle 1
vs αPD1.R cycle 1: p<0.001
vs JAKi.R cycle 1: p<0.001
−2
2
4
6
2
4
6
2
4
6
Cycle
3
4
2.5
0.0
−2.5
−5.0
1 2 4 6
1 2 4 6
1 2 4 6
1 2 4 6
CS.kclust
Pattern (bars)
JAKi.R
αPD1.R
Cycle
E
F
CytoSig Activity in TEX-like and Terminal States
1
2
3
4
Cytokine Signaling
Group (ribbons)
NR CS.kclust.2
other
EGF
NR
Response
Non-Responder CS.kclust.2 Cytokine Signaling Group
JAKi.R
Mean Scaled Score
αPD1.R
10
NR
p<0.001
p<0.001
p<0.001
p=0.008
NS
NS
0
−5
Cycle 1
vs αPD1.R cycle 1: p<0.001
1
2
4
6
1
2
4
1
2
4
IL22
IL36
IL12
VEGFA
OSM
HGF
2
CD40L
TRAIL
MCSF
IL21
NO
IFN-I
–log10 (pval)
TGFB3
5
GMCSF
BMP4
1
IL17A
CXCL12
IL6
TGFB1
BDNF
TWEAK
IL27
TNFA
0.50
0.25
0.00
−0.25
−0.50
IL2
IL1B
IL1A
10
GDF11
IFNG
BMP6
IL15
15
IL10
FGF2
GCSF
BMP2
LTA
Activin A
WNT3A
IL13
1
2
Significance Cycle −log10(pval)
G
Cycle 1
vs αPD1.R cycle 1: p<0.001
vs JAKi.R cycle 1: p=0.006
6
IFNL
0
Select
Cytokine
IFN-I
IL10
CSF1
p=0.047
5
Avg. Correlation
with Pseudotime
IL4
3
0
p<0.001
IL3
LIF
Significance Response:Cycle
−log10(pval)
2
Freq. Cytokine Pathways
Scaled Score
1
IFN−I and TGFB1 CytoSig in exh.early
JAKi.R
αPD1.R
NR
CytoSig Score
IFN-I
6
1.2
0.8
0.4
0.0
−0.4
Cycle
TGFB1
I
Downloaded from https://www.science.org at Universite de Geneve on June 28, 2024
CS.kclust Cytokine Signaling Temporal Patterns
1
2
4
6
1
2
4
6
1
2
4
6
Cycle
Baseline Inflammation
Low
High
Coordinated
Discordant
H
Cytokine Signaling
TEFF/MEM
αPD1 Responder
TEX
TEMRA-NK
IFN-I
Tpre.prog
TEFF/MEM
IFN-I
Tpre.prog
JAKi Responder
TEX
TEMRA-NK
TEFF/MEM
IFN-I
Tpre.prog
TEX
TEMRA-NK
TEFF/MEM
Non-Responder
JAKi.R
αPD1.R
emra.nk
exh
Cycle 1
2
4
6
p=0.02
p<0.001
1
2
4
Mathew et al., Science 384, eadf1329 (2024)
21 June 2024
1
JAKi.R
0.2
0.20
4
6
NR
p=0.007
0.3
p=0.06
0.15
0.1
2
p=0.03
0.2
0.10
0.1
0.05
0.0
2
4
6
2
4
Cycle
Fig. 5. Refractoriness to JAK inhibition and persistent inflammation are
associated with terminal CD8 T cell differentiation and therapy failure.
(A) Schema for joint profiling of plasma cytokines and immune signaling activity
and their classification into temporal expression patterns. (B) Temporal
expression patterns for plasma cytokines/proteins (Cy.kclust, left bar plots)
along with their distribution in each response group (right alluvial plots). The
ribbon in the alluvial plot is color-coded to track how Cy.kclust.1 cytokines/
proteins from NR patients (tan-colored) change patterns in other response
groups. (C) Expression of cytokines/proteins belonging to the Cy.kclust.1 pattern
from NR patients [tan ribbon in alluvial plot from (B)] is shown for all response
6
0.25
p=0.02
Enrichment
0.12
0.10
0.08
0.06
NR
p=0.09
p=0.056
αPD1.R
Expansion Index
Tpre.prog
TEX
TEMRA-NK
ISGs and Clonotype Expansion of emra.nk and exh
JAKi
Refractory
ISG Expression
IFN-I
JAKi
anti-PD1
Clonotype Expansion
anti-PD1
6
2
4
6
Cycle:Response p=0.019
groups. Select suppressive cytokines are color-coded. P values are for
comparisons using all cytokines/proteins plotted (gray dots and lines). Cycles
when JAKi was added to anti–PD-1 are highlighted in bisque. (D) Temporal
expression patterns for cytokine signaling activity in immune cells (CS.kclust, left
bar plots) along with their distribution in each response groups (right alluvial plots).
The ribbon in the alluvial plot is color-coded to track how CS.kclust.2 pathways from
NR patients (blue-colored) change patterns in other response groups. (E) Activity
score of cytokine pathways belonging to the CS.kclust.2 pattern from NR patients
[blue ribbon in alluvial plot from (D)] is shown for all response groups. Select
suppressive cytokines and IFN-I are color-coded. P values are for comparisons
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RES EARCH | R E S E A R C H A R T I C L E
using all pathways plotted (gray dots and lines). Cycles when JAKi was added to
anti–PD-1 are highlighted in bisque. (F) Cytokine pathway activity associated
with CD8 T cell differentiation, treatment cycle, and response. Correlation
of CD8 T cell pathway activity with pseudotime from trajectory analysis using
all CD8 T cell subtypes are color-coded with circle size representing significance
of the correlation (small gray dots are nonsignificant). The significance of
changes in pathway activity in emra.nk and exh subtypes across treatment
cycles (main effect) is shown on the x axis, and the significance of whether changes
across cycles differ by response group (interaction effect) is shown on the y axis.
Dotted lines represent significance levels (P = 0.05 for main effect, P = 0.10 for
interaction effect), and gray upper-right quadrant shows pathways that significantly
Failure to respond to combined anti–PD-1
immunotherapy and JAK inhibition is coupled
to persistent inflammation
In contrast to patients who responded to anti–
PD-1 and JAKi, NR patients showed marginal
changes in features of CD8 T cell plasticity
(Fig. 4, E and F), suggesting that NR patients
may have been at least partially refractory to
JAK inhibition. To investigate this notion, we
profiled 92 plasma proteins by using Olink,
together with 43 cytokine signaling pathways
that we inferred from transcriptional changes
in immune cells by using CytoSig (33) (Fig. 5A).
For the plasma protein profiling, k-means clustering revealed four distinct temporal patterns (Cy.kclust) for 41 differentially expressed
proteins (Fig. 5B, left bar plots; and fig. S7A).
Compared with protein expression from aPD1.R
and JAKi.R patients, that from NR patients,
either group-averaged (fig. S7B) or examined
across individual patients (fig. S7C), largely fell
into pattern Cy.kclust.1 (Fig. 5B, gray stacked
bar in right alluvial plot). Cy.kclust.1 proteins
Mathew et al., Science 384, eadf1329 (2024)
from NR patients included suppressive cytokines and proteins such as CD274, interleukin6 (IL-6), IL-10, and CSF1, and they enriched for
pathways such as IL-23 and IL-27 (fig. S7D)
that have links to diseases of chronic inflammation (34). Expression of Cy.kclust.1 proteins
from NR patients was higher at baseline than
the other groups, modestly decreased on anti–
PD-1, but then stably increased during the JAKi
window (Fig. 5C). By contrast, most of these
proteins took on a Cy.kclust.2 pattern in JAKi.R
and aPD1.R patients (Fig. 5B, right-to-left path
of tan-colored ribbons in right alluvial plot).
Rather than having high baseline expression
that transiently dipped and then increased
during JAKi, these proteins in aPD1.R and
JAKi.R patients had lower baseline expression that further decreased during and after
JAKi (Fig. 5C). Thus, nonresponders displayed
a specific pattern of high baseline levels of circulating inflammatory and immunoregulatory
proteins that on average further increased
during and after JAKi.
To examine how persistently elevated cytokines might impact signaling in immune cells,
we next assessed the activity of signaling pathways in peripheral blood mononuclear cells
(PBMCs). As expected, this analysis revealed
significant alterations in many of the signaling
pathways known to use JAKs (JAK1, 2, and 3)
after JAKi treatment (fig. S8A). Next, using only
the signaling pathways that differed between
response groups before or after JAKi (n = 18
CytoSig pathways), we again applied k-means
clustering to determine temporal patterns
(CS.kclust) (Fig. 5D, left bar plots; and fig. S8B).
In NR patients, more than half of the signaling
pathways belonged to CS.kclust.2 (Fig. 5D, blue
stacked bar in right alluvial plot), which included
IFN-I as well as suppressive cytokine pathways
such as IL-10 and CSF1. Similarly to the expression of circulating inflammatory proteins,
CS.kclust.2 signaling pathways from NR patients
exhibited high relative baseline activity compared with their activity in aPD1.R and JAKi.R
patients, decreased on anti–PD-1, but then increased during JAKi (Fig. 5E). By contrast, these
pathways displayed a CS.kclust.3 pattern in aPD1.R
patients and largely a CS.kclust.4 pattern in
JAKi.R patients (Fig. 5D, right-to-left path of
blue-colored ribbons in right alluvial plot).
21 June 2024
Specifically, in aPD1.R patients NR CS.kclust.2
pathways had the lowest baseline activity that
increased after anti–PD-1 and then decreased
during JAKi. In JAKi.R patients, induction
after anti–PD-1 was not apparent but instead
observed post-JAKi (Fig. 5E). Thus, NR patients
were characterized not only by high inflammation refractory to JAKi but also by immune
signaling that appeared discordant and/or
hyporesponsive to treatment.
IFN signaling that is refractory to JAK
inhibition links progressive terminal CD8
T cell differentiation with treatment failure
Because IFN-I and other cytokines can promote differentiation of CD8 T cell progenitors
(6) and because clinical response after JAKi
was associated with enhanced features of CD8
T cell plasticity, the failure of JAKi to suppress
inflammatory features in nonresponders might
have contributed to progressive CD8 T cell exhaustion and/or terminal differentiation. Correlating all 43 CytoSig signaling pathway scores
from CD8 T cells with pseudotime values from
trajectory analysis (fig. S5A) revealed a strong
positive correlation between IFN-I and IFN-g
(IFNG) and the development of exh and emra.nk
terminally differentiated CD8 T cell states (Fig.
5F, brown-hued dots). Indeed, IFN-I signaling
(along with TGFB1) was among the few pathways that both significantly varied across treatment cycle and differed by response group
(Fig. 5F, upper-right shaded quadrant; and
Fig. 5G). Accordingly, ISGs were strongly induced on anti–PD-1 (cycle 2) in both emra.nk
and exh CD8 T cell subtypes in aPD1.R patients, inconsistently increased in JAKi.R patients, but remained unchanged in NR patients
(Fig. 5H, top; and fig. S9A). Only in NR patients
did JAKi fail to decrease ISG expression (cycle 4), resulting in continuously high relative
expression even post-JAKi. These differences
in ISGs were corroborated by flow cytometry
for ISG15 expression in PD-1hi and EMRA-like
CD8 T cells (fig. S9B). In this analysis, NR patients had inconsistent blunting of ISG15 on
JAKi and showed increasing expression postJAKi, whereas patients belonging to the aPD1.R
and JAKi.R groups showed significant decreases
of ISG15 during JAKi. These persistently elevated ISG patterns in TEX-INT-like and EMRA
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baseline (DPSclono). This analysis revealed an
increase in DPSclono in multiple clonotypes
during cycle 4 on JAKi and during cycle 6
post-JAKi in JAKi.R patients (Fig. 4, E and F).
Neither aPD1.R patients, NR patients, nor
the aPD-1.m patients from a separate cohort
treated with anti–PD-1 monotherapy showed
a significant difference in DPSclono across treatment cycles. For JAKi.R patients, restricting
analysis to clonotypes shared with the pre.
prog subtype provides specific examples of
how changes in individual clonotype composition increased DPSclono after JAKi (fig. S5,
D and E). Similarly, analysis of CD8 T cell
clonotypes shared by the tumor and blood
also provided examples of how JAKi possibly
influenced the plasticity and evolution of tumorrelevant clonotypes (fig. S6). Thus, the ability of
JAKi to potentially alter the balance of differentiation between PD-1+ CXCR5+ CD8 TPRE/PROG-like
cells, CD127+ TEFF/MEM-like, and PD-1hi TEX-INT-like
CD8 T cells may have broadly led to the evolution of CD8 T cells with less-committed and
exhausted subtypes. This effect was particularly
evident in JAKi.R patients, linking changes in
CD8 T cell differentiation dynamics to clinical
response after addition of JAKi to anti–PD-1.
differ by main and interaction effects. (G) Inferred CytoSig signaling activity for
IFN-I and TGFB1 in exh CD8 T cells across treatment cycles. Shown are averages for
each response group. Significance values are shown in (F). (H) (Top) Average ISG
expression in terminal exh and emra.nk subtypes for each response group across
treatment cycles. P values for the indicated comparisons are shown. (Bottom) Average
expansion index (measure of clonality) for these subtypes. (I) Model summarizing
relationship between inflammation, cytokine signaling in immune cells after anti–PD-1,
the impact of IFN-I on CD8 T cell differentiation toward either terminal or less-committed
states, and consequence of JAK inhibition. For longitudinal data, significance was
determined with a repeated measures ANOVA using a mixed effect model and posthoc interaction analysis. Error bars represent SEM.
RES EARCH | R E S E A R C H A R T I C L E
CD8 T cell subtypes in NR patients correlated
with increased clonality in exh and emra.nk
CD8 T cells (Fig. 5H, bottom), which is suggestive of progressive terminal differentiation in
these populations. Moreover, the elevated ISG
expression in such terminal CD8 T cells was
associated with hyporesponsiveness to direct
IFN-I stimulation in vitro compared with lesscommitted CD8 T cell subsets (fig. S9C), reminiscent of the overall immune signaling in NR
patients. Thus, persistent IFN-I signaling and
refractoriness to JAKi may sustain progressive
differentiation of CD8 T cells toward terminal
and dysfunction states, contributing to therapy failure.
Discussion
Mathew et al., Science 384, eadf1329 (2024)
21 June 2024
Materials and methods
Clinical trial
The clinical trial (ClinicalTrials.gov identifier:
NCT03425006) was approved by the institutional
review board at the University of Pennsylvania
and was completed in accordance with international standards of good clinical practice.
All patients provided written informed consent
at the time of enrollment. Patients with metastatic NSCLC who were treatment-naive with
PD-L1 ≥50% by TPS PD-L1 IHC 22C3 pharmDx
assay (Dako North America), eastern cooperative group performance status (ECOG PS)
0-1, RECIST v 1.1 measurable disease amenable
to biopsy, and no untreated brain metastases
were enrolled (n = 31 screened). Patients received pembrolizumab (200 mg every 21 days)
and itacitinib 200 mg daily by mouth was
started on cycle 3 day 1 of pembrolizumab and
continued for 6 weeks. Of the 31 patients
screened, 23 were enrolled between 16 October 2018 and 4 March 2021 and received at
least 1 cycle of pembrolizumab. Primary endpoints were: (i) ORR determined by RECIST
v1.1 PR and CR at 12 weeks, and (ii) toxicity of
pembrolizumab and itacitinib by common terminology criteria for adverse events (CTCAE)
v5.0 (table S3). Best ORR was defined as the
best response determined by RECIST v1.1 over
the study period. Secondary clinical objectives
included PFS and overall survival (OS) from
initiation of study therapy until disease progression or death due to any cause, respectively. DOR was defined as time from first
response at 12 weeks until disease progression by RECIST v1.1. Response at 6 weeks
was defined using a modified RECIST v1.1,
allowing for changes of near −30% reduction
to also be included as a PR. This resulted in a
clear demarcation between responders (range:
−27.3 to −61%) and nonresponders (range:
−19.5% to 150%). Patients were censored at
the date of last follow up if they came off trial
for reasons other than progression or death
and at data cutoff (1 December 2021) for those
on trial without disease progression or death.
Median PFS, DOR, OS were estimated using
Kaplan-Meier methodology. Paired blood and
tissue samples were collected for several translational and exploratory objectives.
Cell lines
The Res 499 melanoma cell line was derived
from a B16-F10 melanoma tumor that relapsed
after ICB-based immunotherapy and acquired
elevated expression of a subset of IFS. This cell
line was cultured as previously described (12)
and is available upon request.
Mice
Mice were maintained in a specific-pathogenfree facility at the University of Pennsylvania.
Experiments and procedures were performed
in accordance with the Institutional Animal
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The opposing functions of IFN rely on a temporal component that activates immune responses early but inhibits these responses at
later times, especially in the setting of chronic
inflammation. As shown here and in other
preclinical studies (12), blocking the inhibitory function of IFNs to improve antitumor
immunity is achievable in mice with delayed
administration of JAKi or anti-IFNAR1 antibodies. We have now extended this concept
to humans and demonstrated that in a phase
2 clinical trial for patients with metastatic
NSCLC with tumor PD-L1 ≥50%, this therapeutic strategy in combination with anti–
PD-1 is feasible and safe. Although proof of
improved clinical efficacy from JAKi addition will require larger prospective randomized studies, the high response rates (67%)
and long PFS (23.8 months) observed in this
study encourage further clinical investigation.
Additional goals for future clinical studies include investigating other settings amenable to
improvement with JAKi (e.g., NSCLC with low
PD-L1, other cancer types, and relapsed setting)
and understanding properties of JAK inhibition
required to complement immunotherapy (e.g.,
duration of treatment and JAKi selectivity).
In this study, we identified immune features
and immune cell types that distinguished the
patients who had clinical response after addition of JAKi to anti–PD-1. These immune response characteristics include a subset of CD8
T cells that express CXCR5 and/or exhibit
progenitor-like features. This population of
cells is flexible in their developmental fate and
can be modulated by JAKi. These CXCR5+ and
progenitor-like CD8 T cells share similarities
with memory precursor and exhausted progenitor CD8 T cells that can display a high degree of TCR sharing with multiple downstream
subsets, express ISGs as part of an activation
state, and circulate in the periphery (10, 24, 25).
Blocking IFN-I signaling may impede the differentiation of such precursor or progenitor
CD8 T cells toward terminal and exhausted
states, promote fate flexibility during an immune response, and rebalance the proportion
of nonexhausted to exhausted CD8 T cells to
impact ICB efficacy. Besides the impact on
CD8 T cells, JAK inhibition is also associated
with the reprogramming of suppressive myeloid
cells in anti–PD-1 refractory Hodgkin lymphoma patients who responded after the addition of the JAKi ruxolitinib to anti–PD-1, as
shown in work published together with ours
(35). Additional effects of JAKi on other CD8 T
subsets—including more rare populations in
the blood, or on other immune cells—cannot be
excluded and will require further investigation.
Furthermore, although the current findings
together with previous studies (12) strongly
implicate a role for impeding IFN signaling on
the effects from JAK inhibition, JAKi can also
impact signals downstream of many diverse
cytokines. Indeed, the contribution of compensatory effects [e.g., the effect of IL-15 on CD8
T cell homeostatic proliferation (36)] and
non-JAK cytokines [e.g, the effect of TGFB
on CD8 T cell stemness (37)] will also be important to explore in future studies. Moreover,
our previous work (7, 12) and recent genomewide genetic screens in mice highlight a role for
cancer-cell IFN signaling in ICB resistance (38),
which is consistent with elevated levels of ISGs
found in tumors from NSCLC patients that relapse after anti–PD-1 (2). Although detailed
mechanisms are not yet clear, persistent IFN
signaling in cancer cells can orchestrate immune suppression and impact CD8 T cell differentiation. Thus, some of the immune changes
and improved ICB response evoked by JAK
inhibition may indirectly result from inhibiting or preventing an IFN-driven resistant state
in cancer cells.
Whether JAKi can improve ICB may depend
on levels and duration of baseline inflammation and how cytokine signaling pathways, including stimulatory and inhibitory pathways,
are altered by chronic inflammation (Fig. 5I).
The cellular consequences of chronic IFN and
other cytokine pathways may involve changes
in signaling thresholds, an altered balance between positive and negative regulators, changes
in the composition of actively signaling cells,
and/or inflexible epigenetic states leading to
long-lasting changes in signaling behavior in
tumor-specific T cells, other immune cells, or
cancer cells (8, 39, 40). Indeed, immune cells
from patients that did not respond in our trial
appeared overtly refractory to JAKi or exhibited discordant signaling behavior that may
have resulted from at least some of these features. A deeper understanding of how chronic
inflammation alters cell behavior will provide
insight into context-dependent differences in
IFN and other cytokine pathways important
for ICB response (41). This understanding may
inform additional therapeutic strategies to
“reset” signaling pathways in immune cells,
cancer cells, or other cell types important for
antitumor immunity.
RES EARCH | R E S E A R C H A R T I C L E
Care and Use Committee (IACUC) of the University of Pennsylvania under protocol #803042.
Five- to seven-weeks-old female C57BL/6 mice
were obtained from Charles River Production.
For ICB studies, 50,000 Res 499 cells were
mixed 1:1 with reduced growth factor basement membrane extract (BME type 2) and injected into the hind limb of mice. Mice were
randomized at tumor injection and then assigned to treatment groups with five or more
mice per treatment group. Caliper measurements were started when palpable tumors were
observed at approximately day 11. Mice with
ulcerated tumors were censored. Endpoint for
survival studies were a tumor size of 15 mm or
greater in any dimension. Three independent
experiments were performed.
Tumors, spleens, and dLNs were harvested at
day 16 post-tumor implantation. For spleens
and dLNs, single-cell suspensions were prepared after RBC lysis with ACK Lysis Buffer
(Life Technologies). Tumors were weighed
prior to enzymatic digestion with Type 4 collagenase and DNAse 1 at 1mg/ml. After enzymatic digestion or ACK Lysis, all tissues were
filtered through 100 mm filters. Cells were
stained with Fc Block and Zombie Live/Dead
stain for 10 min prior to surface staining.
Surface staining was done for 30 min at room
temperature. Samples were fixed and permeabilized by incubating in 100 ml of Fix/Perm
buffer at room temperature for 30 min and
washed in Perm Buffer. Intracellular stains
were performed overnight at 4°C. Cell counting beads were spiked into each sample prior
to data acquisition. Data acquisition was done
on a FACSymphony A5 and analyzed using
OMIQ and/or R. See table S5 and table S7 for
list of antibodies and buffers.
Human antibody panels and staining
Approximately 1×106 to 5×106 frozen PBMCs
were used per patient per timepoint. PBMCs
were thawed into 10% complete RPMI with
DNase. Cells were stained with live/dead
and Fc block for 10 min at room temperature.
Chemokine receptors were stained at 37°C for
20 min followed immediately by surface staining for 30 min at room temperature. Samples
were fixed and permeabilized by incubating in 100 ml of Fix/Perm buffer at room
temperature for 30 min and washed in Perm
Buffer. Intracellular stains were performed
overnight at 4°C. Data acquisition was done
on a FACSymphony A5 and analyzed using
OMIQ and/or R. See table S4 and table S7 for
list of antibodies and buffers.
Flow cytometry feature clustering
For quantification and statistical analysis of
flow cytometry data, the flowCore and flowWorkspace R packages and custom R analysis
Mathew et al., Science 384, eadf1329 (2024)
Olink
Cytokines were measured from EDTA-plasma
using the Olink Extension Assay (PEA) to measure 92 unique analytes. In brief, oligonucleotide
labeled capture antibodies were used to bind
target cytokines and subsequently hybridized.
The hybridized product was then amplified
and measured by qPCR, allowing for parallel
detection of 92 cytokines within the same
sample.
pSTAT1 detection
Healthy donor PBMCs were plated overnight
in RPMI at 1×106 cells per 100 ul. The next
day, PBMCs were stimulated with 20ng/ml
IFNa2 (Biolegend #592704) for 15 min. Reactions were stopped with a final concentration
of 2% PFA and kept in methanol O/N at −80°C.
The following day, cells were washed with PBS
and stained with a pSTAT detection panel
for 1 hour at room temperature. Data acquisition was done on a FACSymphony A5 and
analyzed using OMIQ. See table S6 for list of
all antibodies.
Single-cell RNA and TCR sequencing and
processing
PBMCs from select patients were sorted for
total live cells or live CD8+ cells on a BD FACs
Aria II. Cells were sorted into GEMs using a
10x Chromium Controller and were made into
libraries following the Chromium Next GEM
Single Cell 5′ Reagent Kits v2 (Dual Index)
Protocol. Libraries were sequenced using a
NovaSeq 6000. Sequencing data was processed
using the CellRanger pipeline v5 (10x Genomics). BCL files were converted to FASTQ
and aligned to the human genome (GRCh38)
to generate count matrices. For TCR libraries,
BCL files were converted to 5 FASTQ and the
CellRanger VDJ pipeline was used for sequence
assembly and clonotype calling. Cells with
>10% mitochondrial DNA and/or <200 or
>2500 RNA features were filtered out and
21 June 2024
conditions were integrated using Seurat v3.2.0.
UMI barcodes were used to combine cell expression data with clonotype data.
Human CD8 T cell reference mapping
and annotation
Using the Seurat objects for scRNA/TCRsequencing data from patient PBMC-sorted
CD8 T cells from all study timepoints (start of
cycles 1, 2, 4, 6), 1000 cells with a rearranged
TCR were randomly sampled and a separate
Seurat object was created. Processing to filter
out cells with high mitochondrial DNA and
variations in UMI count were carried out as
described. Data were then log normalized, variable features identified, scaled, and integrated
using the RPCA reduction method from Seurat
with k.anchors = 20. Predicted cell doublets
were identified with the DoubletFinder R package (42). After dimensionality reduction by
PCA and UMAP, cell clustering was performed
by shared nearest-neighbor. Very sparse clusters or clusters consisting primarily of predicted doublets were removed along with any
remaining predicted cell doublets. Clusters
not considered to be independent CD8 T cell
subtypes but rather activation states (10) (e.g.,
clusters characterized by high ISG expression)
were merged with the subtype most resembling it by gene set enrichment and marker
gene expression. This resulted in a CD8 T cell
reference consisting of approximately 20,000
cells. After re-clustering, the reference was
annotated using marker gene expression and
enrichment of CD8 T cell subtype gensets by
GSVA (described below). This annotated CD8
T cell reference was then used to map CD8
T cells from scRNA/TCRsequencing of (i)
CD8 T cells sorted from PBMCs, and (ii) matching PBMCs from the same patient and timepoint. To extract CD8 T cells from the latter
sample type (the CD8 T cells from scRNA/TCRsequenced PBMCs), the PBMC data were
first mapped to the Azimuth Human PBMC
reference. CD8 T cells identified from the
level one predicted cell type annotation were
extracted to create a separate Seurat object
that was then merged with the Seurat object
of sorted CD8 T cells from the same sample
(i.e., same patient and same timepoint). All
mapping of cells to reference maps was performed using MapQuery from Seurat.
Gene set enrichment and scores
Gene sets for human CD8 T cells from PBMCs
and from a human pan-cancer T cell atlas were
used to aid in cluster annotation and analysis
(10, 23, 29). For the human PBMC CD8 T cell
gene sets, the top 250 genes from each set were
used. For the CD8 T cell gene sets from the
human pan-cancer T cell atlas, the top 100 genes
or all the genes in the gene set were used, whichever 6 were smaller. The scRNA-sequencing
data from all cells in a cluster were averaged to
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In vivo mouse lymphocyte studies
pipelines were used. A down-sampled feature
matrix was then created by equal random
sampling of cells from each FCS file. This downsampled data were then used for dimensionality reduction by UMAP, as implemented
in the umap R package, and clustering by selforganizing maps, as implemented in the
FlowSOM R package. The number of clusters
(K) was determined using random forest with
stratified sampling, as implemented in the
randomForestSRC R package, to estimate an
out-of-bag overall prediction error rate for a
range of K values. Then, a value for K was selected to maximize the number of clusters,
while keeping the out-of-bag error rate to
approximately 10% or below. Using this reference map, a random forest classifier was
then trained and used to predict cluster membership for all cells for all samples.
RES EARCH | R E S E A R C H A R T I C L E
create pseudo-bulk data. Then, an enrichment
score for each gene set of interest was determined using GSVA. To determine gene set
scores for individual cells, the AddModuleScore
function from Seurat was used.
Pseudotime trajectory analysis
Pseudotime trajectory analysis was carried out
using Monocle 3. The integrated gene expression data from the CD8 T cell reference map
were used as ordering genes to construct pseudotime trajectories. The naive CD8 T cell cluster
was selected as a starting root state. Assigned
pseudotime values for each cell were then
used in downstream analysis.
CD8 T cell clonotype filtering and analysis
TCR sharing, clonotype plasticity, and expansion
Assessment of clonotype sharing between two
CD8 T cell states was calculated using Startrac
(32) to determine the pairwise transition index (pTrans-index). The PS for each CD8 T cell
state or cluster (representing a subtype or activation state) was then defined as the reciprocal of the variance of its pTrans-index
with each of the other CD8 T cell states. A
clonotype plasticity score (PSclono) was defined as the average of the PS for all CD8 T cells
states comprising the clonotype. The DPSclono
is the difference between the PSclono at a given
seven-treatment cycle from the PSclono at
baseline (start of cycle 1). To assess clonotype
expansion, the expansion index from Startrac
(32) was used.
Mathew et al., Science 384, eadf1329 (2024)
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the CytoSig Python package to return the predicted cytokine signaling activity scores for
different immune cells or CD8 T cell subtypes.
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matrix using the unitize function from the
thresher R package. Then, we used k-means
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Statistical analysis
The significance of changes across treatment
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examined by a repeated measures ANOVA
using a mixed effect model implemented in
the nlme R package. If the main effect was
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To enrich for well-annotated and treatmentrelevant CD8 T cells and clonotypes, a set of
filtering criteria were applied to each sample.
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with: (i) a single rearranged TCR comprised
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reference mapping annotation score >0.50.
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sequences of the CDR3 region of TCR alpha
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T cells, (ii) had a relative frequency in the
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Adaptive TCR sequencing was matched to the
TCR beta chain from 10X Genomics sequencing with no requirement for a minimum frequency in the blood or increase above baseline.
Cytokine signaling activity score
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ACKN OWLED GMEN TS
Funding: This research was supported by the Mark Foundation
for Cancer Research (E.J.W. and A.J.M.), the Parker Institute for
Cancer Immunotherapy (E.J.W. and A.J.M.), the LUNGevity
Foundation (J.B.), Incyte (E.J.W. and A.J.M.), NIH grants AI155577,
AI115712, AI117950, AI108545, AI082630, and CA210944 (E.J.W.),
a National Cancer Institute (NCI)/NIH P30 CA008748 institutional
grant (A.J.S.), the American Association of Immunologists
Intersect Fellowship Program for Computational Scientists and
Immunologists (D.M.), the Parker Institute for Cancer
Immunotherapy (PICI) scholar fellowship (D.M. and D.Y.), and
by NIH grant 1P01CA210944-01 (A.J.M. and E.J.W.). Author
contributions: Conceptualization: A.J.M., E.J.W., and C.J.L.
Methodology: D.M., D.Y., J.M.B., and A.J.M. Validation: D.M., S.J.,
M.D., J.R.G., A.C.H., A.J.S., and A.J.M. Formal analysis: D.M.,
W.T.H., N.R.Z., and A.J.M. Investigation: D.M., C.F., D.Y., R.G.,
S.F.N., M.K., Z.Z., and R.Z. Resources: D.M., M.E.M., J.M.B., E.J.W.,
and A.J.M. Data curation: C.W.D., M.E.M., and J.M.B. Writing –
original draft: D.M., E.J.W., and A.J.M. Writing – review and editing:
D.M., E.J.W., and A.J.M. Visualization: D.M. and A.J.M. Supervision:
D.M., M.E.M., C.J.L., E.J.W., and A.J.M. Project administration:
C.W.D., M.E.M., C.J., and A.J.M. Funding acquisition: J.M.B., E.J.W.,
and A.J.M. Competing interests: A.J.M. has received research
funding from Merck. He has also received honoraria from Merck,
AstraZeneca, Pfizer, and Takeda. A.J.M. is an inventor on patents filed
or issued on the IFN pathway and modified CAR T cells. He is a
scientific founder for Dispatch Biotherapeutics and advisor for Related
21 June 2024
Sciences, Diagonal Therapeutics, and Xilio. E.J.W. is an advisor for
Danger Bio, Marengo, Janssen, NewLimit, Pluto Immunotherapeutics
Related Sciences, Santa Ana Bio, and Synthekine. E.J.W. is a
founder of and holds stock in Arsenal Biosciences. J.R.G. is a
consultant for Arsenal Biosciences and Cellanome. A.J.S. reports in
a consulting and advising role to J&J, KSQ Therapeutics, BMS,
Merck, Enara Bio, Perceptive Advisors, Oppenheimer and Co.,
Umoja Biopharma, Obsidian Therapeutics, Legend Biotech, Iovance
Biotherapeutics, Prelude Therapeutics, Immunocore, Lyell
Immunopharma, Amgen, and Heat Biologics. A.J.S. has received
research funding from GSK, PACT Pharma, Iovance Biotherapeutics,
Achilles Therapeutics, Merck, BMS, Harpoon Therapeutics, and
Amgen. M.E.M. has received research funding from Eli Lilly, Trizell,
AstraZeneca, Merck, and Genentech. MEM has a consulting role for
Astra Zeneca, Novocure, Boehringer Ingelheim, Janssen, Takeda,
Blueprint Pharmaceuticals, Bristol Myers Squibb, and Ikena. M.E.M.
also has stock in Gilead Sciences, Portola Pharmaceuticals, Merck,
Bluebird Bio, Johnson & Johnson, and Pfizer. J.M.B. has a consulting
or advisory role for Bristol-Myers Squibb, Merck, AstraZeneca,
Genentech, Celgene, Boehringer Ingelheim, Guardant Health, Takeda,
Novartis, Janssen, Ayala Pharmaceuticals, Regeneron, Inivata, and
Foundation Medicine. J.M.B. has received research funding from
Merck, Carevive Systems, Novartis, Incyte, Bayer, Janssen,
AstraZeneca, Takeda, Amgen, Pfizer, and Mirati Therapeutics. J.M.B.
was affiliated with the University of Pennsylvania for the duration
of this study but is now currently employed by Janssen Research &
Development. C.J.L. has received honoraria from Bristol-Myers
Squibb, Genentech/Roche, Lilly/ImClone, AstraZeneca, Takeda
Science Foundation, and Merck. C.J.L. has a consulting or advisory
role for Genentech/Roche, Lilly/ImClone, Merck, Abbott
Biotherapeutics, Bayer/Onyx, Clarient, Clovis Oncology, Celgene,
Cancer Support Community, Bristol-Myers Squibb, ARIAD, Takeda,
AstraZeneca, Pfizer, Novocure, and Gilead Sciences. C.J.L. has
received research funding from Merck, Advantagene, Clovis
Oncology, Celgene, Inovio Pharmaceuticals, Ariad, GlaxoSmithKline,
Genentech/Roche, Stem CentRx, Lilly, and Trizell. C.J.L. has other
relationships with Lilly, Amgen, Peregrine Pharmaceuticals, and Synta.
Data and materials availability: Raw and processed sequencing
data presented in this study are deposited at Gene Expression
Omnibus (GSE266219) (https://www.ncbi.nlm.nih.gov/geo/query/
acc.cgi?acc=GSE266219). License information: Copyright © 2024
the authors, some rights reserved; exclusive licensee American
Association for the Advancement of Science. No claim to original US
government works. https://www.science.org/about/science-licensesjournal-article-reuse
SUPPLEMENTARY MATERIALS
science.org/doi/10.1126/science.adf1329
Figs. S1 to S9
Tables S1 to S7
Reference (43)
MDAR Reproducibility Checklist
Submitted 30 September 2022; resubmitted 11 October 2023
Accepted 5 May 2024
10.1126/science.adf1329
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