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Leading Edge
Review
Immune checkpoint therapy—current perspectives
and future directions
Padmanee Sharma,1,2,3,4,9,* Sangeeta Goswami,1,2,9 Deblina Raychaudhuri,2 Bilal A. Siddiqui,1 Pratishtha Singh,2
Ashwat Nagarajan,1 Jielin Liu,1,5 Sumit K. Subudhi,1 Candice Poon,6 Kristal L. Gant,1 Shelley M. Herbrich,1
Swetha Anandhan,1,5 Shajedul Islam,7 Moran Amit,7 Gayathri Anandappa,8 and James P. Allison2,3,4
1Department of Genitourinary Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
2Department of Immunology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
3The Immunotherapy Platform, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
4James P. Allison Institute, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
5MD Anderson UT Health Graduate School of Biomedical Sciences, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
6Department of Neurosurgery, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
7Department of Head & Neck Surgery Research, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
8Department of Gastrointestinal Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
9These authors contributed equally
*Correspondence: padsharma@mdanderson.org
https://doi.org/10.1016/j.cell.2023.03.006
SUMMARY
Immune checkpoint therapy (ICT) has dramatically altered clinical outcomes for cancer patients and
conferred durable clinical benefits, including cure in a subset of patients. Varying response rates across
tumor types and the need for predictive biomarkers to optimize patient selection to maximize efficacy and
minimize toxicities prompted efforts to unravel immune and non-immune factors regulating the responses
to ICT. This review highlights the biology of anti-tumor immunity underlying response and resistance to
ICT, discusses efforts to address the current challenges with ICT, and outlines strategies to guide the development of subsequent clinical trials and combinatorial efforts with ICT.
INTRODUCTION
Immune checkpoint therapy (ICT) is designed to block inhibitory
signals of T cell activation to promote anti-tumor immune responses.1 Since its successful introduction as a treatment for
unresectable or metastatic melanoma in 2011, ICT offered
long-term clinical benefits in a large population of patients with
multiple tumor types, including cure in a subset of patients.2
Thus, the clinical success with ICT revolutionized the field of
cancer immunotherapy and led to ICT becoming a mainstay of
cancer therapy along with traditional treatment modalities such
as surgery, chemotherapy, and radiation therapy.
Despite the unprecedented improvement in the durability of
clinical responses with ICT, resistance to ICT is very common.
While patients with certain tumor types such as glioblastoma
and pancreatic cancer demonstrate de novo resistance to ICT,
a section of patients with ICT-sensitive tumors including
melanoma, bladder cancer, and renal cancer develop adaptive
resistance to ICT. Furthermore, ICT can lead to the development
of immune-related adverse events (irAEs) that can prove to be
fatal in a fraction of patients. In addition, there is a lack of predictive biomarkers for the efficient selection of patients who are
most likely to derive benefits from ICT. Hence, a thorough understanding of immune and non-immune regulators of response and
resistance to ICT is critical to address some of the critical challenges that limit the full potential of ICT.
1652 Cell 186, April 13, 2023 ª 2023 Elsevier Inc.
Here, in this review, we describe the mechanisms of action of
immune checkpoint molecules and the clinical advances in ICT,
including combination therapies in early and advanced stages of
cancer. Furthermore, in the later sections of this review, we
outline some of the critical challenges with ICT and precision approaches to overcome these challenges and broaden the clinical
utility of ICT.
ROLE OF T CELLS IN MEDIATING ANTI-TUMOR
IMMUNITY
T cells are the established lynchpins of anti-tumor immunity. The
classical cancer-immunity cycle provides a useful overview of
T cell-mediated anti-tumor immune responses including priming, activation of T cells in lymph nodes, trafficking into the tumor
microenvironment (TME), and tumor cell killing.3
T cell activation requires the following two major signals: (1)
binding of the T cell receptor (TCR) with the major histocompatibility complex (MHC) plus cognate peptide displayed on
antigen-presenting cells (APCs) and (2) ligation of the co-stimulatory receptor CD28 with B7 ligands present on APCs. T cell
activation leads to T cell proliferation, enhancement of effector
function, and generation of memory T cells.4 Concurrently,
inhibitory checkpoint molecules are upregulated to suppress
unrestrained T cell activation and prevent off-target damage.
Importantly, these checkpoint molecules, discussed in detail
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Review
below, are utilized by the tumor to evade T cell-mediated antitumor immunity.
T cell checkpoints
CTLA-4
Cytotoxic T lymphocyte-associated protein 4 (CTLA-4) is an
inhibitory checkpoint molecule highly expressed on activated
T cells and regulatory T cells (Tregs). Pioneering preclinical
studies by Dr. James Allison demonstrated that CTLA-4, a homolog of CD28 with higher affinity and avidity for B7 ligands, outcompetes CD28 ligation to inhibit T cell proliferation and IL-2
production.5 Indeed, CTLA-4 expression is induced following
T cell activation and restrains uncontrolled expansion of activated T cells. Importantly, the blockade of CTLA-4 engagement
using anti-CTLA-4 antibody to inhibit these ‘‘switch-off signals’’
in T cells induced durable T cell-mediated anti-tumor immune responses and tumor regression in murine models.1,6 Subsequent
preclinical studies attributed the anti-CTLA-4-induced improvement in tumor rejection to an increase in effector CD4 and CD8
T cells with a concomitant decrease in Tregs.7 Notably, the therapeutic efficacy of anti-CTLA-4 was not limited to a single tumor
model, and several studies conducted on diverse murine tumor
models demonstrated the broad efficacy of anti-CTLA-4 therapy.6 Clinical trials tested the efficacy of ipilimumab, a human
monoclonal anti-CTLA-4 antibody, that showed remarkable clinical efficacy and received Food and Drug Administration (FDA)
approval for the treatment of melanoma in 2011.8–10 Importantly,
a group of patients with advanced melanoma receiving this therapy demonstrated durable clinical responses and long-term
survival benefits lasting up to 10 years.10 Clinical approval of ipilimumab opened the gates to a mode of cancer immunotherapy
termed ICT. Since then, the field of ICT has made major strides
by offering durable clinical benefits including cure to patients
with various types of solid cancers and led to multiple FDA
approvals (Table S1).
PD-1
Programmed death 1 (PD-1) is another T cell inhibitory checkpoint molecule whose function as a checkpoint molecule in
T cells was elucidated following the discovery of its ligands programmed death ligand 1 (PD-L1) and PD-L2.11,12 Studies using
Pd1 / mice revealed that engagement of PD-1 with its ligands
maintains T cell tolerance in the periphery.13 PD-1 engagement
inhibits TCR signaling by recruiting the Src homology 2
domain-containing protein tyrosine phosphatase 1 (SHP-1) and
2 (SHP-2) tyrosine phosphatases that dephosphorylate molecules involved in TCR signalings such as CD3ε and ZAP-70.14
PD-1 is highly expressed in tumor-infiltrating T cells including
exhausted T cells. PD-L1 is expressed by tumor cells and other
intratumoral cell types including endothelial cells, epithelial cells,
and myeloid cells,15 whereas PD-L2 is mostly expressed by
APCs.16 Preclinical studies demonstrated that PD-1:PD-L1
ligation in the TME impairs anti-tumor T cell responses.17
Conversely, inhibition of this interaction using anti-PD-1/PD-L1
antibodies improved T cell-mediated anti-tumor response, leading to tumor regression in multiple murine tumor models.15 Clinical trials demonstrated the efficacy of anti-PD-1 and anti-PD-L1
antibodies in patients with multiple tumor types including melanoma, renal cell carcinoma (RCC), and non-small cell lung can-
cer (NSCLC).18 The FDA initially approved the use of monoclonal
anti-PD-1 antibodies for the treatment of patients with metastatic melanoma in 2014,19 following which multiple immune checkpoint inhibitors targeting the PD-1/PD-L1 pathway were FDA
approved for the treatment of different tumor types (Table S1).
Distinct mechanisms of action of CTLA-4 and PD-1
Although both anti-CTLA-4 and anti-PD-1/PD-L1 therapies exert
their anti-tumorigenic functions by suppressing signals that
dampen T cell function, they demonstrate different modes of
action and elicit distinct immune responses20 (Figure 1). Mechanistically, CTLA-4 blocks CD28-B7 interactions to regulate APCinduced T cell responses, whereas PD-1 acts downstream of
TCR signaling to regulate the effector phase of T cell response.
Anti-CTLA-4 mainly affects CD4 T cell clonal expansion and
trafficking.9,21 Conversely, anti-PD-1/PD-L1 mostly affects the
exhausted CD8 T cell compartment.20 Furthermore, anti-PD-1/
PD-L1 does not affect T cell clonal expansion or T cell trafficking.2 The distinct mechanisms of action by CTLA-4 and
PD-1 supports the potential of combinatorial targeting
compared with individual targeting.
Other immune regulatory molecules
In addition to CTLA-4 and PD-1, several other positive and negative immune regulatory molecules have been identified and
investigated in the past few years as potential targets for ICT.
Lymphocyte activation gene-3 (LAG-3), highly expressed on
activated T cells, binds to MHC class II, galectin-3, and a-synuclein and exerts immunosuppressive effects by augmenting Treg
function and inhibiting effector function of T cells.22 LAG-3 selectively recognizes stable MHC II:peptide complexes, and binding
of LAG-3 to stable MHCII:peptide complexes elicits inhibitory
signals through its intracellular domains.22 A recent study also
showed that the cytoplasmic tail of LAG-3 causes dissociation
of the tyrosine kinase, Lck, from CD4/CD8 co-receptors leading
to a loss of TCR signaling and T cell activation.23 Importantly,
administration of anti-LAG-3 antibody improves T cell-mediated
anti-tumor immunity in preclinical models.22
T cell immunoglobulin and mucin-domain containing-3 (TIM-3)
is another inhibitory immune checkpoint molecule highly expressed on dysfunctional/exhausted T cells. TIM-3 binds to
galectin-9, high-mobility group protein B1, phosphatidyl serine,
and carcinoembryonic antigen cell adhesion molecule 124,25
Importantly, co-targeting of the TIM-3 and PD-1 pathways has
shown remarkable efficacy in preclinical models of solid tumors.25
T cell immunoglobulin and immunoreceptor-tyrosine-basedinhibitory-motif domain (TIGIT) interacts with its ligands CD155
and CD112, whereas V-domain Ig suppressor of T cell activation
(VISTA) binds to VSIG-3 to inhibit T cell proliferation and cytokine
production.26,27 Importantly, TIGIT is expressed on dysfunctional or exhausted tumor-infiltrating T cells, whereas VISTA is
expressed on naive T cells, and these inhibitory receptors suppress T cell-mediated anti-tumor immunity.28–30 Blockade of
these receptors, alone and in combination, enhances anti-tumor
efficacy in murine tumor models28 and have led to the initiation of
multiple clinical trials testing the efficacy of combinatorial immune checkpoint therapies.
In contrast to inhibitory checkpoint molecules, co-stimulatory
molecules expressed on T cells improve T cell-mediated anti-tumor immune responses. ICOS is a co-stimulatory molecule that
Cell 186, April 13, 2023 1653
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Review
Figure 1. Schematic demonstrating the difference in mechanisms of action of CTLA-4 and PD-1
CTLA-4 regulates APC-induced T cell responses by outcompeting CD28 binding to B7, thus restraining uncontrolled expansion of activated T cells. PD-1
engagement with PD-L1 inhibits TCR signaling, thus regulating the effector phase of T cell responses. In addition, the blockade of CTLA-4 and PD-1 engagement using anti-CTLA-4 or anti-PD-1 antibody inhibits these suppressive signals in T cells leading to improved tumor cell eradication. APC, antigen-presenting
cell; CTLA-4, cytotoxic T lymphocyte-associated protein 4; MHC, major histocompatibility complex; PD-1, programmed death 1; PD-L1, programmed death
ligand 1; TCR, T cell receptor.
enhances effector functions of T cells.31 In addition, members of
the tumor necrosis factor (TNF) receptor family including glucocorticoid-induced TNFR-related gene (GITR), OX40, and 4-1BB
also act as co-stimulators of T cell effector function.32
4-1BB is highly expressed on primed T cells.32 4-1BB engagement with its ligand 4-1BBL upregulates the expression of antiapoptotic genes in T cells, enhancing durable memory responses of cytotoxic T cells.32 Indeed, overexpression of
4-1BBL and administration of agonistic monoclonal antibodies
targeting 4-1BB improved CD8 T cell-mediated anti-tumor responses and induced tumor rejection in preclinical models.32
OX-40 is another co-stimulatory receptor that is transiently expressed on T cells following activation.32 Engagement of OX-40
with OX-40L enhances T cell survival and memory formation.33 In
addition, it inhibits the function of Tregs and the generation of
induced Tregs.34 Notably, enhanced expression of OX-40L and
administration of agonistic anti-OX40 antibodies improved tumor rejection in multiple murine models.35
Similar to 4-1BB and OX-40, GITR expression is transiently
induced following T cell activation, and GITR-GITRL engagement
1654 Cell 186, April 13, 2023
enhances CD4 and CD8 T cell responses.32 GITR is also constitutively expressed on Tregs. Importantly, anti-GITR antibodies
deplete Tregs and enhance the rejection of established tumors.36
Ongoing preclinical studies and clinical trials are testing the
potential of these T cell co-stimulators as anti-tumor therapeutic
targets.
CLINICAL ADVANCES IN ICT
There are now more than eighty FDA approvals for ICTs in human
cancers, which are briefly discussed in this section and summarized in Table S1. The clinically approved ICTs have been
comprehensively reviewed elsewhere.2,37 This section provides
an overview of the clinical use of ICT and discusses emerging
ICT strategies.
Clinical use of single-agent ICT
Anti-CTLA-4
In 2011, ipilimumab (anti-CTLA-4 IgG1 antibody) was approved
by the FDA for the treatment of metastatic melanoma, with phase
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Review
III clinical trials indicating improved overall survival (OS)
compared with vaccine or chemotherapy.8,10 A second antiCTLA-4 antibody, tremelimumab (IgG2), has now been approved
in combination for the treatment of hepatocellular and
NSCLC.38,39 The activity of anti-CTLA-4 antibodies in patients
beyond the enhancement of effector T cell function have been
controversial, whereas anti-CTLA-4 antibodies selectively
deplete intratumoral Tregs using an Fc-dependent mechanism
in preclinical murine models, neither ipilimumab nor tremelimumab, which have different FcgR-dependent mechanisms,
depleted Tregs in patients across multiple tumor types.40 Novel
anti-CTLA-4 antibodies are therefore under development with
engineered Fc domains to promote Treg depletion and potentially enhance anti-tumor efficacy (NCT03860272).
Anti-PD-1/L1
Anti-PD-1/L1 has been approved by the FDA in specific cancer
types (Table S1). Anti-PD-1 antibodies have also demonstrated
efficacy in tumors with high microsatellite instability (MSI-H),
mismatch repair deficiency (dMMR), and high tumor mutation
burden (TMB-high), leading to tumor-agnostic FDA approvals.41,42
ICT combinations
CTLA-4 and PD-1/L1
Although durable responses can be seen with ICT monotherapies, only approximately 20% of the patients respond to single-agent treatment, prompting investigations into combination
therapies to improve efficacy. Clinically, combined checkpoint
inhibition with anti-CTLA-4 and anti-PD-1/L1 demonstrated
higher response rates than monotherapy with either agent alone
in multiple tumor types and has shown improved OS in certain
cancers, such as melanoma (Table S1).43,44 Anti-CTLA-4
and anti-PD-1 combination therapy has also demonstrated
anti-tumor activity in patients with anti-PD-(L)1 monotherapy
failure, suggesting that the combination may overcome mechanisms of adaptive resistance in selected patients.45 Finally, as
not all patients respond to anti-CTLA-4 and anti-PD-1/L1 combination therapy, identification of novel mechanisms of primary
and adaptive resistance will be necessary for rational combinations.
LAG-3 and PD-1
The success and limitations of CTLA-4 and PD-1/L1 blockade
invigorated the search for novel, non-redundant immune checkpoints. LAG-3 and PD-1 are frequently co-expressed and have
been shown to cooperate to suppress T cell function and promote cancer immune escape. Consistent with this potential
synergy between LAG-3 and PD-1, a phase II–III randomized trial
showed that combined checkpoint inhibition increased progression-free survival (PFS) compared with nivolumab alone in patients with previously untreated or unresectable melanoma.46
The combination also induced pathologic complete responses
in 57% of the patients with resectable melanoma.47
TIGIT and PD-1
Based on the clinical benefits observed with CTLA-4, PD-1/PDL1, and LAG-3, a fourth immune checkpoint, TIGIT, has been
evaluated. Both PD-1 and TIGIT inhibit co-stimulatory signaling
on CD8 T cells through the CD226 receptor but through different
mechanisms. However, despite promising phase II results,48 a
TIGIT antibody, tiragolumab, failed to meet its primary endpoint
of PFS when combined with atezolizumab (anti-PD-L1) in extensive-stage small-cell lung cancer.49 A second phase III clinical
trial in PD-L1 high NSCLC did not meet its co-primary endpoint
of PFS, and the analysis of data for the other co-primary
endpoint of OS is currently ongoing.50 Of note, multiple other
clinical trials are currently testing anti-TIGIT agents as monotherapy and in combination.
Novel immune checkpoint combinations
There is a pressing need for novel ICT combinations, particularly
for tumor types such as prostate cancer, pancreatic cancer, and
glioblastoma that have low response rates to ICT. There have
been multiple studies investigating the role of other inhibitory
checkpoint molecules such as TIM-3, VISTA, B and T lymphocyte attenuator (BTLA), sialic acid-binding immunoglobulin-like
lectin 15 (Siglec-15), and co-stimulatory molecules such as
inducible T cell costimulator (ICOS), CD40, 4-1BB, and OX-40
to improve the responses to ICT.51
In addition to the targeting of cell-surface molecules, emerging
cell-intrinsic immunomodulatory pathways such as metabolic
and epigenetic pathways are being explored to determine their
potential as therapeutic targets.51 Adenosine signaling pathways
and indoleamine 2,3-dioxygenase (IDO)-mediated tryptophan
metabolism pathways and glucose and fatty acid metabolisms
are some of the commonly targeted metabolic pathways under
clinical investigation. On the other hand, histone deacetylases
and histone methylases/demethylases are being explored as
epigenetic targets to modulate ICT responses. Immune cell subsets including dendritic cells, macrophages, and NK cells, are
also being targeted using small-molecule regulators and antibodies in the clinical setting to overcome resistance to ICT.
Furthermore, new modes of therapy such as the use of bi-specific agents for immune checkpoint inhibitors, for instance, PD1 3 CTLA-4 and PD-1 3 LAG-3, which represent a single-molecule form of combination therapy are also under investigation.52
The pleiotropic effects of cytokine therapy on the immune system makes it another attractive target to enhance ICT efficacy.
Historically, high-dose IL-2 monotherapy showed clinical efficacy in melanoma and renal cancer but had a significant toxicity
profile. The next-generation, pegylated form of IL-2 showed
some early encouraging clinical data and was well-tolerated.
However, the phase III trial with pegylated IL-2 (NKTR-214)
plus nivolumab vs. nivolumab alone failed to meet its primary
endpoints of PFS and objective response rate (ORR) in patients
with metastatic melanoma (NCT03635983).53 Biomarker analyses showed that despite the increase in peripheral CD8
T cells, there was no difference in CD8 T tumor-infiltrating lymphocytes (TILs) in both arms.53 The reasons for this failure could
be multifactorial ranging from non-specific effects of IL-2 on
Tregs in addition to effector T cells and the structural design of
NKTR-214. Cytokine therapy still holds promise with the exploration of other cytokines and cytokine payloads with better engineering methods.
In addition, the ability of conventional approaches to cancer
treatment such as chemotherapy and radiotherapy to regulate
immune response has provided a strong rationale for multiple
preclinical studies and clinical trials testing combinatorial
Cell 186, April 13, 2023 1655
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treatment regimens to boost response and/or overcome resistance to ICT.
Combination of immune checkpoint agents with
chemotherapy
Multiple combinatorial regimens involving ICT and chemotherapy are being tested for the treatment of various cancers in
clinical trials. In the recently concluded phase III CheckMate
648 trial with 970 patients having advanced, recurrent, or metastatic esophageal squamous cell carcinoma, nivolumab plus
chemotherapy treatment significantly improved OS compared
with chemotherapy alone.54 Similarly, clinical trials administering
the combination of ICT and chemotherapy to patients with
NSCLC have displayed significant survival advantage. The results from the CheckMate 816 trial conducted in patients with
resectable NSCLC showed that neoadjuvant nivolumab plus
platinum-based chemotherapy improved event-free survival
and pathological complete response in 24% of patients in the
combination group compared with only 2.2% of the patients
receiving chemotherapy alone.55 These findings provide a strong
rationale for using this combination as a first-line treatment
option for patients with advanced NSCLC. In addition, the combination of carboplatin and etoposide with anti-PD-L1 (atezolizumab) resulted in improved median OS from 10.3 (in the placebo
arm) to 12.3 months in the treatment arm leading to FDA
approval of the combination as first-line therapy in SCLC.56 Of
note, although atezolizumab received accelerated approval
from FDA as a frontline treatment for patients with metastatic urothelial carcinoma based on the results from the IMvigor210
trial,57 the recently published results from the phase III IMvigor130 trial (NCT02807636) showed that the combination of
atezolizumab and chemotherapy failed to improve OS compared
with chemotherapy alone. The immunosuppressive effect of
chemotherapy including the impact of dose and scheduling requires further evaluation in suitable tumor-specific preclinical
studies to optimize its combination with ICTs.
Combination of immune checkpoint treatments with
targeted therapy
The efficacy of the combination of angiogenesis inhibitors and
ICT has been demonstrated in several clinical trials in patients
with diverse cancer types including endometrial carcinoma and
RCC that has led to the FDA approval of the combination of levatinib and pembrolizumab.58,59 Furthermore, FDA approved the
combination of pembrolizumab with axitinib as a first-line treatment for patients with advanced RCC based on the findings
from the KEYNOTE 426 trial (NCT02853331).60 The combination
of nivolumab and cabozantinib, which inhibits c-Met, vascular
endothelial growth factor receptor 2 (VEGFR2), AXL Receptor
Tyrosine Kinase, and RET has also received approval as firstline treatment for patients with advanced RCC based on the
CHECKMATE-9ER trial (NCT03141177).61
In addition, the efficacy of anti-PD-1 and anti-PD-L1 therapy is
being tested in combination with V-Raf murine sarcoma viral
oncogene homolog B1 (BRAF) and mitogen-activated protein kinase kinase (MEK) inhibitors, poly (ADP-ribose) polymerases
(PARP) inhibitors, AXL/MER proto-oncogene, tyrosine kinase
(MERTK) inhibitors, and other targeted therapies extensively
reviewed elsewhere.62 However, drug combinations will need
optimization to improve responses. Preclinical studies have sug-
1656 Cell 186, April 13, 2023
gested that tyrosine kinase inhibitors (TKIs) can modulate the immune microenvironment, thus regulating the response to ICT.63
One of our recent clinical studies showed that the combination
of nivolumab with sitravatinib reduced immune-suppressive
myeloid cell subsets with durable clinical response observed
even in patients with liver metastasis.64 Additional clinical and
preclinical studies are needed to determine the immunomodulatory properties of TKIs.
Combination of immune checkpoint agents with
radiation
Radiation is another pillar of cancer care that may be enhanced
by immuno-oncologic means and vice versa. Radiotherapy not
only directly affects tumor cells but also remodels the immune
milieu in the TME by releasing tumor antigens, inducing a type
I IFN response and increasing the infiltration of effector CD8
T cells.62,65 Unfortunately, the clinical trials of patients with
metastatic NSCLC, metastatic head and neck squamous cell
carcinoma, and Merkel cell carcinoma failed to show clinical efficacy when combining radiotherapy with immune checkpoint
blockade.66,67 However, major pathological responses when
stereotactic body radiotherapy was added to neoadjuvant durvalumab were observed in patients with early-stage NSCLC,
suggesting that combination treatment with radiation may
depend on the stage of the disease and the timing of the immune
checkpoint blockade.68
Combination of immune checkpoint agents with
oncolytic viruses
Stimulating the immune response using different strategies
including oncolytic viruses (OVs) has gained traction as they provide a unique opportunity for precision targeting as a payload
that self-amplifies and selectively proliferates in cancer cells,
killing them and releasing tumor-specific antigens that induce
IFN signaling and create a pro-inflammatory TME.69,70 To date,
there is only one FDA-approved OV therapy talimogene laherparepvec (T-VEC), a herpes simplex virus 1 (HSV-1)-based therapy for the treatment of patients with metastatic melanoma.
Building on the results of the OPTiM study, the phase 1b study
combining T-VEC with anti-CTLA4 in unresectable melanoma
significantly improved the ORR compared with ipilimumab alone
(39% vs. 18%) with responses seen in the injected skin and
visceral lesions.71,72 However, the recent, larger phase 3
MASTERKEY-265 study comparing T-VEC with pembrolizumab
with single-agent pembrolizumab showed an improved ORR
(48.6% vs. 41.3%) but did not meet its dual primary endpoints
of PFS and OS.73 Deeper correlative analyses of the tissues to
assess immune modulation by OVs, the effect of chronic interferon stimulation, choice of ICT, and the clinical trial design are
all important considerations in understanding the reasons for
the lack of survival benefit despite responses seen with this
rational combination.
Combination of immune checkpoint agents with surgery
The use of systemic treatment to achieve pathologic responses
and facilitate surgical resection is well established in multiple tumor types, for example, in the use of neoadjuvant chemotherapy
for the treatment of breast cancer or urothelial carcinoma.
Furthermore, the neoadjuvant setting allows for the examination
of the entire, intact tumor specimen, providing rich tissue for
detailed immune monitoring to identify the mechanisms of
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response and resistance to therapy. In our presurgical clinical
trial of ipilimumab for patients with bladder cancer, ICOShiCD4
T cells were detected in tumor tissues and subsequently in the
systemic circulation, defining it as a pharmacodynamic
biomarker for anti-CTLA-4.9 Subsequent mechanistic studies
confirmed the functional contribution of the ICOS/ICOSL
pathway for response to anti-CTLA-4, providing a rationale for
combination therapy.74,75 Neoadjuvant ICT has demonstrated
promising activity in multiple tumor types, including high-risk,
human epidermal growth factor receptor 2 (HER2)-positive early
breast cancer, non-small cell lung cancer, and muscle-invasive
bladder cancer.76–78 Evidence suggests that the timing of ICT
with respect to surgery influences responses. For example, in
the murine models of spontaneous breast cancer metastasis,
neoadjuvant anti-PD-1 therapy drastically reduced metastases
unlike adjuvant immunotherapy through sustained tumor-specific CD8 T cell responses, which may be related to the tumor antigen release as a result of surgery.79 Nevertheless, ICT also
showed benefit in the adjuvant setting in several tumor types
such as melanoma and NSCLC, although data have been conflicting in other tumor types such as renal cancer.80–83 A recent
clinical trial in mismatch repair deficient locally advanced rectal
adenocarcinoma yielded a 100% clinical response rate, and no
patients required chemoradiotherapy or surgery.84 These findings raise the prospect of organ preservation enabled by ICTs,
with significant potential implications for patients’ long-term outcomes and quality of life.
THE CURRENT CHALLENGES WITH ICT
To broaden the clinical utility of ICT, it is important to understand
the key factors limiting their action. Resistance to immune
checkpoint inhibitors and the development of irAEs, both multifactorial and complex, are two of the critical challenges with
ICT (Figure 2).
Resistance to ICT
Based on the kinetics of response, the Society for Immunotherapy of Cancer (SITC) Immunotherapy Resistance Taskforce
has three consensus definitions of ICT resistance seen in the
clinic: primary resistance seen in the never-responders, secondary resistance that develops after a period of response, and third,
progression after treatment discontinuation.85 Biologically,
resistance to ICT can be attributed to both tumor cells intrinsic
factors and TME-associated extrinsic factors. In this section,
we provide a general overview of the tumor cell intrinsic and
extrinsic factors, which can be potentially leveraged to overcome resistance to ICT.
Tumor cell intrinsic factors
Tumor cell intrinsic factors that lead to ICT resistance include the
lack of immunogenicity due to tumor-specific genetic alterations, chromatin remodeling, and oncogenic signaling, among
others. Some of these factors are present at the initial presentation leading to de-novo resistance to ICT, whereas other pathways might evolve, subsequently leading to adaptive resistance
to ICT.
Defects in antigen presentation machinery
Genetic and epigenetic alterations in cancer cells causing a
loss of major histocompatibility complex (MHC)-I, including
the monoallelic loss can affect antigen processing and presentation, thereby evading recognition by T cells.86 In ICTresistant melanoma and NSCLC, truncating mutations or
homozygous loss of beta 2 microglobulin (B2M), leads to the
loss of MHC-I expression on the cell surface, thus limiting
T cell-mediated anti-tumor immunity.87 Furthermore, transcriptional suppression of human leukocyte antigen (HLA)
class I antigen processing and antigen presentation machinery
lead to deficiencies in CD8 T cell recognition and correlate
with patient response to ICT.88
Genetic alterations converging on the IFNg signaling
pathway
The binding of IFNg to its receptors results in the activation of the
Janus kinase/signal transducer and activator of transcription
(JAK/STAT) pathway, which modulates the transcription of IFNg-regulated genes culminating in T cell activation.89 Around 75%
of the patients with melanoma who did not respond to CTLA-4
blockade had copy number alterations (compared with 0% in responders) with the loss of copy number in the key IFNg pathway
genes and amplification of important IFNg pathway inhibitors.90
Loss-of-function mutations in the kinase domains of JAK1 and
JAK2 has also been demonstrated in primary and acquired resistance to ICT in melanoma.91
Lack of neoantigens
Tumor-specific alterations generate tumor neoantigens that
drive cytotoxic responses. Mutations in the mismatch repair machinery and other DNA repair pathways can give rise to hypermutator phenotypes that are sensitive to ICTs.92 The frequency
and relative potency of T cell responses to these neoantigens
are the key determinants of ICT effect in these tumors.93 Tumors
with low TMB lack neoantigens and are poorly immunogenic.
However, even in these, neoantigen quality influences patient
survival.94 In patients with prostate cancer with low TMB, those
with higher intratumoral CD8 density, high IFNg gene signature,
and antigen-specific T cell responses responded to CTLA-4
blockade.95 Efforts to combine ICTs with personalized neoantigen vaccines are currently underway.96
Oncogenic signaling pathways
Tumor intrinsic signaling pathways help maintain an immunosuppressive TME and facilitate immune escape. Several pathways including Wnt, PTEN/PI3K, and rat sarcoma virus (RAS)
have been recognized in different tumor types in abetting immune exclusion and dysfunction along with recruitment and differentiation of immunosuppressive cells.97 Wnt signaling has
been implicated in T cell exclusion in cancers of bladder, head
and neck, gastroesophageal, kidney, and others.98 PTEN loss
has been associated with a decreased number of tumor-infiltrating T cells and lack of responses to ICT in patients with melanoma,99 but interestingly, PD-L1 expression or expression of
class I MHC molecules did not correlate with PTEN status, suggesting alternate pathways of immune evasion in the context of
PTEN loss, including PI3K signaling. PI3K inhibitors have shown
an improvement in the responses to ICT in murine models and
are currently being tested in clinical trials.99,100 Finally, aberrant
RAS signaling upregulates PD-L1 expression, thus promoting
Cell 186, April 13, 2023 1657
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Figure 2. Precision approaches to address the current challenges with ICT
Longitudinal profiling of human samples using state-of-the-art high-throughput technologies such as spatial transcriptomics, whole genome and exome
sequencing, scRNA-seq, scATAC-seq, machine learning, and AI models will allow in-depth investigations of TME and host-derived factors such as microbiome,
neuronal, and hormonal signals. Furthermore, leveraging these tools to combine human data with appropriate preclinical models such as syngeneic, transgenic,
and humanized murine models as well as organoids and ex vivo human tumor slice cultures to identify targets will help accelerate rational therapy combinations to
improve clinical outcomes in patients.
immunosuppression.101 In patients with lung cancer, not only did
PD-L1 expression correlate with KRAS mutation, but co-mutations in other genes including STK11/LKB1, TP53, and
CDKN2A/B also affected responses to ICT.102 Alterations in
the RAS pathway also suppress IFNg-induced expression of
MHC molecules and reduce T cell infiltration in breast and colorectal cancers.103,104 Synergistic effects of KRAS G12C inhibitors plus anti-PD-1 in mouse models of NSCLC showed
increased CD8 T cell infiltration in the tumor.105 However, clinical
trials with pembrolizumab plus KRAS G12C inhibitor demonstrated a higher incidence of grade 3–4 treatment-related
adverse events (CodeBreaK 100/101 trial). Overall, targeting
oncogene addiction to enhance immune response is an important strategy that will require further optimization to develop
combinatorial strategies.
1658 Cell 186, April 13, 2023
Epigenetic reprogramming
Epigenetic reprogramming, a key enabling characteristic in tumor formation and progression, has also been shown to cause
ICT resistance.106 Epigenetic silencing of IFNg in tumor cells by
enhancer of zeste homolog 2 (EZH2)-mediated histone modification and DNA methylation by DNA methyltransferase1 (DNMT1)
reduces Th1-type chemokines to suppress effector T cell homing
to TME in in-vivo models and is associated with a reduction in
tumor-infiltrating CD8 T cells and patient outcomes in ovarian
cancers.107 In a study of global methylation patterns of TILs in
melanoma, DNA hypermethylation at CpG sites was associated
with low PD-L1 expression that limited ICT efficacy by inhibiting
anti-tumor interferon responses, whereas hypomethylation was
shown to promote the expression of PD-L1 and other inhibitory
cytokines and checkpoints, contributing to immune evasion,
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providing evidence on the impact of methylation status on ICT responses.108 These counter-regulatory yet convergent roles of the
epigenetic phenomenon are not fully understood and require
further exploration.
The tumor intrinsic pathways discussed above have been
correlated with ICT resistance in certain tumor types. How these
pathways orchestrate immune evasion leading to primary and
adaptive resistance to ICT in a tumor-specific manner requires
further investigation.
Extracellular matrix and stroma-derived factors
The stromal cells, vasculature, and the extracellular matrix in
the microenvironment interact in a concerted manner with tumor and immune cells that play a pivotal role in tumor
development and therapy resistance. Aberrant intratumoral
angiogenesis can inhibit optimal penetrance of ICIs, leading
to resistance.109 In addition, there is growing evidence that
cancer-associated fibroblasts (CAFs), a highly heterogeneous
group of cells, contribute to ICT resistance by T cell exclusion,
making them desirable therapeutic targets.110 However, targeting CAFs to alter the immune microenvironment remains a challenge, clinical benefit has been limited in patients, and initial
clinical trials have been unsuccessful.111,112 Nonetheless, novel
anti-TGF-b antibodies have shown promising results in the
early phase studies and offer a potential combinatorial strategy
with ICTs.113
Immune factors
Myeloid cells are the most abundant immune components of the
TME and correlate with poor prognosis in multiple tumor
types.114–117 In addition, we and others have linked immunesuppressive myeloid cells to ICT resistance.118–121 One myeloid
subset, which has been associated with immune-suppression in
the TME and resistance to ICT, is myeloid-derived suppressor
cells (MDSCs).122,123 Chronic inflammatory conditions present
in the TME induce the differentiation of immature granulocytic
and monocytic cells into MDSCs. Of note, considering the lack
of specific markers uniquely expressed by MDSCs, it is still
debatable whether MDSCs are a distinct myeloid lineage.124
Studies in human NSCLC and preclinical models identified a
cluster of dendritic cells (DCs) named mature DCs enriched in
immunoregulatory molecules (mregDCs), which are characterized by the high expressions of immune-suppressive genes
including immune checkpoint ligands PD-L1 and PD-L2.125
These findings suggest that although DCs have been conventionally associated with strong anti-tumor responses and
improved ICT efficacy, TME-derived signals may subvert DC
function to drive resistance to ICT and need to be taken into
consideration while developing combinatorial therapeutic
strategies.
Tumor-associated macrophages (TAMs) are another dominant immune-suppressive myeloid subset present in the
TME.114,116,119,120 Metabolites abundant in the TME such as
adenosine drive the recruitment and expansion of TAMs.120,126
In addition, intratumoral accumulation of cytokines and chemokines including CCL2, CCL5, and CSF recruits suppressive
monocytes and macrophages to the TME.127 TAMs express
high levels of anti-phagocytic cell-surface receptors such as
signal regulatory protein-a (SIRPa) and SIGLEC10. The binding
of these receptors to ligands, such as CD47 and CD24 expressed by cancer cells, suppresses macrophage-mediated
phagocytosis of cancer cells.128,129 Other myeloid-specific
phagocytic checkpoints such as LILRB1 also suppress the
function of intratumoral macrophages.130 Additionally, intratumoral macrophages also upregulate the expression of inhibitory
receptors, such as macrophage receptor with collagenous
structure (MARCO), common lymphatic endothelial and vascular
endothelial receptor-1 (CLEVER-1), and triggering receptor expressed on myeloid cells-2 (TREM-2) in response to the cues
derived from the TME.131–133
TAMs also produce chemokines such as CCL17 and CCL22
that lead to the recruitment of another major immunosuppressive subset—the Tregs.134 The frequency of Tregs in tumors
has been associated with poor prognosis and impaired
response to ICT across solid tumor types.135 Tregs contribute
to ICT resistance by contact-dependent mechanisms and
contact-independent mechanisms. Importantly, multiple preclinical studies have shown that Treg depletion enhances
anti-tumor immunity and response to ICT.136 For many years,
Tregs were considered a monolith—marked by the expression
of FoxP3.137 However, recent studies have revealed a notable
transcriptional diversity in intratumoral Tregs compared with
those in the periphery. Longitudinal profiling of intratumoral
Treg evolution in a preclinical model of lung adenocarcinoma
revealed that interferon-responsive Tregs are more abundant
in the early stages of tumor growth, whereas Tregs derived
from advanced tumor stages are marked by the expressions
of the IL-33 receptor-ST2. Furthermore, the deletion of ST2
in Tregs decreased the tumor burden. Thus, the longitudinal
evolution of Treg phenotypes has distinct effects during tumor
progression.138
Overall, the orchestration of resistance by distinct tumor
intrinsic and extrinsic pathways in different cancer types is multifactorial and nuanced. Understanding these mechanisms
requires a strategic approach of integrating clinical research
and discovery science to delineate resistance mechanisms in a
tumor-specific manner.
Development of irAEs
irAEs include more than 70 distinct pathologies that range from
mild to fatal in severity and can involve almost every organ.139,140
The irAEs present a unique clinical challenge, which has been
extensively reviewed elsewhere.139–141 Different ICT regimens
demonstrate distinct toxicity patterns. Therefore, understanding
the underlying mechanisms is of critical importance.141 Few
recent studies outline the role of immune cell subsets such as
neutrophils and tissue-resident effector memory CD8 T cells
and cytokines including IL-6 and IFNg in driving ICT-induced colitis.142–144 A separate study also found circulating the CD4
memory cells as a predictor of several irAEs in melanoma.145
Other mechanisms including the loss of self-tolerance, molecular mimicry, and complement-mediated inflammation responsible for the development of irAEs have also been delineated.24
Break of self-tolerance and aberrant activation of autoreactive
T cells bearing TCRs specific for peptides derived from a-myosin
have been observed in myocarditis.146 This phenomenon has
Cell 186, April 13, 2023 1659
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been further exacerbated by the release of self-antigens from
dying tumor cells into a pre-inflamed milieu, a process described
as epitope spreading.24
Glucocorticoids have been the mainstay of treating non-endocrine irAEs and hormonal replacement for endocrinopathies.
Intravenous immunoglobulins and plasma exchange are beneficial for neurological and hematological irAEs and steroid-sparing
immunosuppressive monoclonal antibodies such as infliximab in
the setting of steroid refractoriness and chronic irAEs. Approaches such as fecal microbiota transplantation are being
used to alleviate ICT-induced colitis.147
Published data have indicated a positive correlation between
the development of non-fatal irAEs and response to ICT.148
Therefore, it will be important to develop strategies for patient
stratification, probing the kinetics of irAE development and
timing of its resolution to determine who would benefit the
most from the continuation of ICT.
PREDICTIVE BIOMARKERS FOR OPTIMAL PATIENT
SELECTION
Durable clinical benefit is seen only in a subset of patients treated
with single-agent ICT and increased immune-related toxicities
noted with combination therapy, prompted significant efforts to
develop predictive biomarkers of response and toxicities. However, the dynamic interactions within the TME have been a
limiting factor in developing robust predictive biomarkers.
Single biomarkers
The field has historically focused on single biomarker(s) to predict the responses to ICT. MSI-H has been strongly associated
with improved response to ICT across different tumor types,
leading to the FDA approval of pembrolizumab for the treatment
of advanced pediatric and adult solid tumors.41,149 Similar to MSI
status, TMB has also been correlated with the response in
certain cancer types, most notably melanoma and NSCLC,
leading to a tumor-agnostic approval for pembrolizumab in
TMB-high cases.42 However, low TMB does not preclude the
generation of effective antigen responses nor does high TMB
guarantee responses to ICT. This highlights the limitation of using TMB as a single biomarker across different tumor types.
Similarly, immune-based biomarkers such as PD-L1, interferon signature, and density of TILs also demonstrated limitations as a single-predictive biomarker. Although PD-L1 expression has been correlated with improved response in certain
tumor types, substantial responses can be observed in some patients with PD-L1-negative tumors.18 This could be due to the
inducible nature of PD-L1 expression and epigenetic regulation
of its expression. Furthermore, the timing of the biopsy and significant variations in the tests have been the limiting factors.
Combinatorial biomarkers
As our understanding of determinants of anti-tumor immunity
has improved, it is clear that single biomarkers in isolation are
insufficient, and combinatorial biomarker strategies that capture
attributes of the host and tumor-immune ecosystem have better
predictive power. For example, TMB plus T cell gene expression
profile or TMB plus PD-L1 expression showed an improved pre-
1660 Cell 186, April 13, 2023
diction over single biomarkers.2 Such combinatorial biomarkers,
tested in prospective clinical trials, will be key for optimal patient
selection. It was also recently demonstrated that two novel biomarkers correlate with response to anti-PD(L)-1 therapy. A retrospective analysis of tumor tissue from patients with metastatic
urothelial cancer identified mutations in ARID1A in tumor tissues
and expression of the chemokine CXCL13 as predictive biomarkers for ICT in a discovery cohort.150 This was confirmed in
the preclinical studies of bladder tumor-bearing CXCL13 null
mice that demonstrated the lack of responses to anti-PD-1 therapy, whereas ARID1A knockdown tumor-bearing mice were
more sensitive to anti-PD-1 therapy.150 These findings were
confirmed in two independent clinical trial cohorts: importantly,
the combination of AT-rich interaction domain 1A (ARID1A) mutation plus CXCL13 was predictive of response to ICT than either
of the single biomarker. This study highlighted the importance of
integrating tumor mutational status with immune signatures in
the microenvironment to predict responses to ICT and has led
to a prospective clinical trial to test the utility of ARID1A mutation
plus CXCL13 as a combinatorial biomarker strategy for patient
selection.
In summary, the field needs to appreciate the dynamic interactions within the TME for the development of the predictive biomarkers rather than adopting a ‘‘silo approach.’’ Furthermore,
the standardization of assays will be critical and will need to be
tested in prospective clinical trials.
PRECISION APPROACHES TO ADDRESS THE CURRENT
CHALLENGES WITH ICT
Exploring the host-derived factors
Recent advances have highlighted the importance of hostderived non-immune factors, including host and tumor microbiome and neuronal and hormonal signals in shaping the efficacy
of ICT (Figure 2).
Microbiome
Preclinical mouse models of sarcoma, melanoma, and colon
cancer showed that the optimal responses to anti-CTLA-4 and
anti-PD-L1 depend on the presence of specific species of
commensal bacteria in the gut, demonstrating the association
between microbiome and response to ICT.151,152 This association was soon found to also exist in cancer patients with specific
microbial species affecting immune cell function and maturation
in lymph nodes or TME, thereby regulating responses to ICT.153
For example, Bacteroides fragilis can elicit Th1 responses in
lymph nodes and facilitate the maturation of intratumoral DC,
leading to improved anti-CTLA-4 responses, Bifidobacterium
can modulate DC activation and enhance tumor-specific effector
CD8 T cell function, whereas Akkermansia muciniphila can increase intratumoral infiltration of CCR9+CXCR3+CD4 T cells
and increase effector/regulatory CD4 T cell ratios.151,152,154
Numerous studies and clinical trials are currently being carried
out to explore the feasibility of modulating the microbiome to
improve ICT efficacy. Fecal microbial transplantation improves
ICT response and reduces ICT-related toxicity in patients.155,156
In addition, dietary intervention represents a potential strategy
to modulate the gut microbiome, and mounting preclinical
pieces of evidence have suggested its role in improving ICT
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response.157 In addition to the gut microbiome, intratumoral
microbes can also affect the ICT response. In patients with
pancreatic cancers, the abundance of intratumoral Megasphaera has been associated with better survival outcomes after
anti-PD-1 treatment.158 Of note, the metagenomic studies of
stool samples from multiple cohorts of patients with advanced
melanoma identified a cohort-dependent association of specific
microbial species with ICT response but did not have one single
consensus species as a common biomarker of ICT response
across all cohorts indicating the necessity of further research
to unravel the nuances of microbiome-mediated regulation of
the response to ICT.159
Neuronal signals
b2-adrenergic receptor-mediated signaling induces the expression of immune inhibitory molecules (PD-1, PD-L1, and Foxp3)
and leads to T cell exhaustion, wherein the inhibition of b2-adrenergic receptor signaling, particularly the b2-adrenergic receptor
blocker propranolol, suppresses tumor growth and induces the
infiltration of T cells into the TME.160,161 Accordingly, using propranolol in combination with the anti-PD-1 antibody pembrolizumab improved the anti-tumor immune response and raised the
possibility of targeting cancer, immune, and neuronal cells
together. Overall, the neuroimmune axis is an important player
in the TME and can directly accelerate cancer cell proliferation,
survival, invasion, and distant metastasis, underscoring its potential impact when considering ICT efficacy in future studies.
Hormonal signals
The immunological effects of AR inhibition prompted the investigation of ICTs with androgen receptor (AR)-targeted treatment in
prostate cancer.162 Strikingly, clinical trials in patients combining
ICTs with AR inhibition failed to show clinical benefit.163 The
discrepancy between the preclinical and clinical effects of AR
inhibition in combination with ICTs is likely explained by (1) the
dynamic nature of changes within the TME following AR inhibition and the diverse components of the TME that express
AR including immune cells, tumor cells, and stromal elements.
AR inhibition has a transient effect on T cells within the TME,
as T cell infiltration peaks within approximately 1 month and repolarizes over time from Th1 cells toward Th17 cells and
Tregs.164,165 Future studies will need to explore the cellular and
molecular underpinning of hormonal therapy to rationally
combine with ICT in a tumor-specific manner.
Longitudinal profiling of samples
Tumor evolution over space and time leads to intratumor heterogeneity (ITH) and underpins therapeutic resistance and disease
progression, including those to ICT.166 The heterogeneity of immune microenvironment within and between patients on a background of pervasive genetic diversity adds a further layer of
complexity and contributes significantly to clinical outcomes.167
Through immunoediting, the highly adaptive immune system
exerts a selection pressure during tumor evolution and selects
tumor clones expressing less immunogenic neoantigens facilitating tumor progression.168 In addition, ICTs exerts therapeutic
pressure and further shapes tumor evolution.166 Longitudinal
tissue sampling has provided valuable insights into these
evolutionary trajectories. For example, the TRACERx study, a
multi-sample cohort of >900 patients with early-stage NSCLC
has allowed the study of spatial-temporal heterogeneity of lung
cancer including those of TCR repertoire, the prognostic implications of the disparate immune infiltration, selection pressure from
the TME affecting neoantigen presentation, allele-specific HLA
loss leading to high subclonal neoantigen burden and immune
escape, and epigenetic mechanisms of immune evasion through
promoter hypermethylation of neoantigen coding genes.167
Single-time point tissue biopsies are routinely used to make
therapeutic decisions through treatment paradigms in the clinic.
These not only fail to capture ITH but are insufficient to comprehensively delineate the dynamic tumor-TME interactions. Paired
pre-treatment and on-therapy samples from patients with
advanced melanoma treated with nivolumab showed a reduction in clonal and subclonal variants and neoantigen load four
weeks after treatment in responders, with proportional changes
in the T cell repertoire and an increase in the number of TILs.
However, those with stable or progressive disease gained novel
sets of mutations on treatment, suggesting therapeutic modulation by immunoediting.169 In patients with metastatic clear cell
RCC, however, treatment with ICT not only maintained pre-treatment T cell clones in responders but also expanded pre-existing
CD8 T cells and induced a cytotoxic phenotype with higher expressions of GZMB, TCF7, CD39, TOX, and TIM3.170
Cumulatively, these findings underscore the importance of
longitudinal profiling of the TME to understand tumor evolution
and identify potential pathways of resistance. Currently, multiregional analysis of tumor tissue is still challenging in routine
clinical practice. Alternative, minimally invasive approaches
such as liquid biopsies can provide real-time readouts of the responses to ICT.171 In the adjuvant IMvigor010 phase III study in
patients with operable urothelial cancer, the detection of postoperative circulating tumor DNA (ctDNA) was prognostic of disease relapse, and treatment with atezolizumab improved both
disease-free survival and OS compared with the standard of
care observations alone.172 As the peripheral compartment
does not adequately reflect the interactions within the TME, a
combination of these approaches is needed to evaluate the dynamic temporal and spatial changes that occur during tumor
evolution for patient selection for treatment (Figure 2).
Reverse translational studies
To develop rational combination therapies, we need a better
understanding of the mechanism of action of each agent and
its impact on immune responses in a tumor-specific manner.
Reverse translational studies of individual agents are helpful in
this regard, whereby each agent can be studied in small cohorts
of patients, with longitudinal samples collected and analyzed
thoroughly to generate hypotheses regarding immunologic
mechanisms that can be tested in appropriate preclinical
models. Murine models are still the favored and invaluable model
systems in immunology to address focused mechanistic hypotheses. These model systems allow for hypothesis testing and
translation of the preclinical data back into new clinical trials.
Increasingly, this approach has helped in the design of rational
clinical trials and avoids the pitfalls of failed clinical trials.
The reverse translational model was successfully implemented by the Sharma team in 2006 with a neoadjuvant clinical
trial identifying ICOS, and subsequent, in-depth, preclinical
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studies evaluated the role of ICOS/ICOSL in mediating anti-tumor responses driven by ICT.9,21 In another example of reverse
translation, we found that increased EZH2 expression in T cells
in response to anti-CTLA-4 therapy acts as a T cell-intrinsic
mechanism of adaptive resistance to ICT, which led to the successful initiation of a clinical trial combining EZH1/2 inhibitor
(DS3201) with ipilimumab in patients with genitourinary malignancies with primary resistance to ICT (NCT04388852).173
Furthermore, to understand the reasons for failed anti-CTLA-4
treatment in patients with metastatic prostate cancer, using
pre and post- treatment samples from a small clinical trial, we
learned that although anti-CTLA-4 increased TILs, there was
additional compensatory increase in frequency of cells expressing PD-1/PD-L1 and VISTA within the TME, contributing to
adaptive resistance.174 In further examples, we observed the
enrichment of Th17 cells in the bone marrow samples of patients
with prostate cancers. Subsequent examinations of preclinical
models of prostate cancers demonstrated enrichments of Th17
cells in bone lesions, whereas subcutaneous tumors were
enriched in Th1 cells. Osteoclast-mediated IL-6 and TGF-b secretions in the bone lesion polarize CD4 T cells to Th17, contributing to ICT resistance. Cumulatively, these findings rationalized
the ongoing trial of ICT plus TGF-b blocking agent in prostate
cancers.175,176 Similarly, we noted CD73hi immunosuppressive
myeloid cells in glioblastoma, which is resistant to ICT, and these
cells persisted even after anti-PD-1 therapy.120 CD73 knockout
mice provided the ideal platform to demonstrate that the
absence of CD73 improves the responses to ICT, and this is
now being tested in clinical trials.177
In summary, based on the tumor type and biological question,
choosing preclinical models that offer mechanistic insights
is critical. Syngeneic, genetically engineered mouse models
(GEMMs) and humanized mouse model are all relevant, each
with their own strengths and drawbacks. Emerging model systems such as patient-derived micro-organospheres and ex vivo
organotypic tissue slice cultures have shown promise to study
immune pathways, but more data are needed to fully understand
their potential and limitations178,179 (Figure 2).
INCORPORATING HIGH-THROUGHPUT TECHNOLOGIES
AND COMPUTATIONAL BIOLOGY
To maximize the benefit of ICT, it is important to leverage computational approaches integrating high-throughput technologies,
systems biology, and machine-learning approaches. This is
particularly useful in reverse translational studies for biomarker
discovery and validation, understanding other immune-suppressive mechanisms and facilitating the design of combinatorial
therapy.112 Whole genome sequencing and/or whole exome
sequencing have been used to determine TMB and neoantigen
burden that have been correlated with response to ICT.180,181
The T and B cell dynamics derived from sequences of T and B
cell receptors can be used to study the receptor diversity and
clonality, which can also be utilized as biomarkers for ICT as
different studies have reported their correlation with response
and survival.182,183 Recent development in single-cell technologies such as single-cell RNA sequencing (scRNA-seq), cellular indexing of transcriptomes and epitopes (CITE) sequencing, and
1662 Cell 186, April 13, 2023
single-cell sequencing assay for transposase-accessible chromatin (scATAC-seq) can identify uncharacterized immune cell
populations and novel cellular states along with mechanistic
details at higher resolutions. In addition, T cell/B cell receptor clonotype analyses and trajectory analyses combined with RNA velocity could potentially quantify cell state transition providing
valuable immunological readouts.184 Further spatial transcriptomics methods including CO-detection by indexing (CODEX),
digital spatial profiling (DSP), and Visium aid in studying the cell
types and their interactions within the tumor.185–187 Incorporation
of single-cell technologies with clinical and preclinical studies using computational algorithms helps in the better characterization
of TME regulating anti-tumor responses. The surge in data from
clinical trials and translational studies is outrunning the ability to
analyze them with current computational methods to understand
the underlying biology. The incorporation of machine learning
and advanced artificial intelligence (AI) models will be important
for the field. Recent developments combining neural networkbased machine-learning models with diverse-omics layers to
improve the prediction of response to ICT may help bridge this
gap.188 Computational approaches combining transcriptomic,
epigenomics, and genomic data to identify the biomarkers of
response and resistance and robust algorithms to identify biologically relevant information will also help accelerate reverse
translational approaches (Figure 2).
Conclusion
Development of the fundamental understanding of mechanisms
of T cell function opened the field of ICT that has provided durable clinical benefits to many patients across different tumor
types. To achieve the goal where ICT can render cure to a
maximum number of patients with cancer, we will need to integrate clinical research with discovery science adopting reverse
translation strategies. In addition to improving our understanding
of T cell-mediated anti-tumor functions, we will need to delve
into the intricacies of other immune and non-immune pathways
that cumulatively impact the anti-tumor immunity. This will provide insights into developing rational tumor-specific combinatorial strategies with ICT. Currently, most of the approvals of ICT
are in the metastatic setting with few as adjuvant therapy.
Exploring the utility of ICT in earlier disease settings could potentially help with surgical downstaging and even organ preservation. Overall, clinical success of ICT underlines the importance
of developing biologically informed therapeutic strategies
to improve clinical outcomes for patients. The Allison Institute
plans to build on this model by integrating discovery science
research into mechanisms with translational research focused
on analyzing longitudinal patient samples after treatments that
target specific mechanisms.
SUPPLEMENTAL INFORMATION
Supplemental information can be found online at https://doi.org/10.1016/j.cell.
2023.03.006.
ACKNOWLEDGMENTS
S.G. is supported by the MD Anderson Physician Scientist Award, Khalifa
Physician Scientist Award, Andrew Sabin Family Foundation Fellows Award,
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Review
and Clinic and Laboratory Integration Program Award. P.Sharma and J.P.A.
are members of the Parker Institute for Cancer Immunotherapy. K.L.G. is supported by the CPRIT Training Program (RP210028). S.A. is supported by the
CPRIT Research Training Program (RP210028).
DECLARATION OF INTERESTS
P.Sharma reports consulting fees from Achelois, Adaptive Biotechnologies,
Affini-T, Apricity, BioAtla, BioNTech, Candel Therapeutics, Catalio, Codiak,
Constellation, Dragonfly, Earli, Enable Medicine, Glympse, Hummingbird,
ImaginAb, Infinity Pharma, Jounce, JSL Health, Lava Therapeutics, Lytix,
Marker, Oncolytics, PBM Capital, Phenomic AI, Polaris Pharma, Sporos, Time
Bioventures, Trained Therapeutix, Two Bear Capital, and Venn Biosciences;
and ownership of stocks for Achelois, Adaptive Biotechnologies, Affini-T, Apricity, BioAtla, BioNTech, Candel Therapeutics, Catalio, Codiak, Constellation,
Dragonfly, Earli, Enable Medicine, Glympse, Hummingbird, ImaginAb, Infinity
Pharma, Jounce, JSL Health, Lava Therapeutics, Lytix, Marker, Oncolytics,
PBM Capital, Phenomic AI, Polaris Pharma, Sporos, Time Bioventures, Trained
Therapeutix, Two Bear Capital, and Venn Biosciences. J.P.A. reports consulting
fees from Achelois, Adaptive Biotechnologies, Apricity, BioAtla, BioNTech, Candel Therapeutics, Codiak, Dragonfly, Earli, Enable Medicine, Hummingbird,
ImaginAb, Jounce, Lava Therapeutics, Lytix, Marker, PBM Capital, Phenomic
AI, Polaris Pharma, Time Bioventures, Trained Therapeutix, Two Bear Capital,
and Venn Biosciences; ownership of stocks for Achelois, Adaptive Biotechnologies, Apricity, BioAtla, BioNTech, Candel Therapeutics, Codiak, Dragonfly,
Earli, Enable Medicine, Hummingbird, ImaginAb, Jounce, Lava Therapeutics,
Lytix, Marker, PBM Capital, Phenomic AI, Polaris Pharma, Time Bioventures,
Trained Therapeutix, Two Bear Capital, and Venn Biosciences.
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