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31-05-2016 | Treatment | Book chapter | Article

1. Enhancing the Efficacy of Checkpoint Blockade Through Combination Therapies

Authors: Vikram R. Juneja, Martin W. LaFleur, Robert T. Manguso, Arlene H. Sharpe

Publisher: Springer International Publishing

Abstract

Antibodies targeting coinhibitory receptors on T cells (“checkpoint blockade”) have emerged as some of the most promising therapies for a broad range of malignancies, including melanoma, non-small cell lung cancer, renal cell carcinoma, Hodgkin’s lymphoma, and bladder cancer. These coinhibitory molecules include CTLA-4, PD-1, LAG-3, TIM-3, and others. The anti-CTLA-4 antibody ipilimumab was approved in 2011 and the anti-PD-1 antibodies pembrolizumab and nivolumab were approved in 2014 for patients with advanced melanoma. Single agent checkpoint blockade is associated with 20–40 % objective response rates in advanced melanoma with improved overall survival. The combination of anti-CTLA-4 and anti-PD-1 antibodies leads to an increased durable response rate compared to either antibody alone, supporting the concept that combination therapy may result in increased clinical benefit. An important goal in the field is to combine checkpoint blockade with other immunotherapies and other types of therapy (e.g., radiation, targeted therapy, chemotherapy, surgery) to increase the fraction of patients that have objective and durable responses. Here, we discuss the current understanding of the mechanisms underlying checkpoint blockade and the rationale for combination therapy. We then discuss potential immunotherapeutic and non-immunotherapeutic combination therapies. Finally, we discuss critical issues that need to be addressed in order to develop combination strategies to induce long-term clinical responses in patients with cancer.

1 Introduction

As the role of the immune system in cancer has become increasingly clear, so too has the potential for therapies that aim to restore the balance in favor of antitumor immunity and away from immunosuppression (Dougan and Dranoff 2009; Vanneman and Dranoff 2012). One of the most exciting therapies to emerge is “checkpoint blockade,” in which antibodies are used to disrupt pathways that suppress T cell responses to tumors (Pardoll 2012; Topalian et al. 2015). This approach has led to some of the most striking clinical trial results in many years, with 20–50 % response rates across a range of cancers and unprecedented durability (Table 1.1). The search is on to find combination therapies with the many other tools in the clinician’s toolbox to improve response rates while maintaining the durability of response to checkpoint blockade with acceptable toxicity (Sharma and Allison 2015; Brahmer et al. 2010; Fox et al. 2011). To make this search efficient and practical, there needs to be a rational approach to identify potentially synergistic combinations.
Table 1.1
Selected checkpoint blockade clinical trials
Pathway
Cancer types
Phase
Year
Response rates
Survival
CTLA-4
         
Tremelimumab a
Metastatic Melanoma (Camacho et al. 2009)
1/2
2009
9.8 % (10 mg/kg)
9.3 % (15 mg/kg)
9.97 months (10 mg/kg)
11.53 months (15 mg/kg)
Tremelimumab
Adva nced Melanoma (Kirkwood et al. 2010)
2
2010
6.6 %
10.0 months
Ipilimumab b,*
Stage 3 or 4 Melanoma (Hodi et al. 2010)
3
2010
5.7 % (Ipi + gp100)
10.9 % (Ipi)
1.5 % (gp100)
10.0 months (Ipi + gp100)
10.1 months (Ipi)
6.4 months (gp100)
Ipilimumab *
Advanced Melanoma with Brain Metastases (Margolin et al. 2012)
2
2012
10 %
13.3 months
Tremelimumab vs. Physician Choice Chemotherapy
Advanced Melanoma (Ribas et al. 2013)
3
2013
10.7 % (Tremel)
9.8 % (Chemo)
12.6 months (Tremel)
10.7 months (Chemo)
Ipilimumab
Metastatic Uveal Melano ma (Zimmer et al. 2015)
2
2015
0.0 %
6.8 months
PD-1
         
Nivolumab c,*
Metastatic Melanoma, Colorectal, CR-Prostate, NSCLC, RCC (Brahmer et al. 2010)
1
2010
100 % (RCC), 10 % (Melanoma), 7.1 % (Colorectal)
Not evaluated
Nivolumab *
Melanoma, NSCLC, Colorectal, RCC, CR-Prostate (Topalian et al. 2012)
1
2012
18 % (NSCLC), 28 % (Melanoma), 27 % (RCC)
Not evaluated
Pembrolizumab d,*
Advanced Melanoma (Hamid et al. 2013)
1
2013
38 %
Not reached
Nivolumab *
Advanced Melanoma (Topalian et al. 2014)
1
2014
31 %
16.8 months
Pembrolizumab *
Advanced Melanoma (Robert et al. 2014)
1
2014
26 %
Not evaluated
Nivolumab *
Refractory Squamous NSCLC (Rizvi et al. 2015b)
2
2015
14.5 %
8.2 months
Nivolumab **
Relapsed/Refractory Hodgkin’s Lymphoma (Ansell et al. 2015)
1
2015
87 %
Not reached
Nivolumab vs. Dacarbazine *
Untreated Metastatic Melanoma (Robert et al. 2015a)
3
2015
40.00 % (Nivo)
13.9 % (Dacarb)
Not reached (Nivo)
10.8 months (Dacarb)
Pembrolizumab * vs. Investigator Choice Chemotherapy
Ipilimumab Refractory Melanoma (Dummer et al. 2015)
2
2015
21 % (Pembro 2 mg/kg), 25 % (Pembro 10 mg/kg), 4 % (Chemo)
Not evaluated
Pembrolizumab **
NSCLC (Garon et al. 2015)
1
2015
19.4 %
Not reached
Pembrolizumab * vs. Ipilimumab *
Advanced Melanoma (Robert et al. 2015b)
3
2015
Pembro (2 wk)-33.7 %
Pembro (3 wk)-32.9 %
Ipi-11.9 %
Not reached
Nivolumab vs. Docetaxel *
Advanced Squamous NSCLC (Brahmer et al. 2015)
3
2015
20 % (Nivo)
9 % (Doce)
9.2 months (Nivo)
6.0 months (Doce)
Pembrolizumab
Progressive Metastatic Colorectal Cancer (Le et al. 2015a)
2
2015
40 % (MR deficient)
0 % (MR proficient)
Not reached (MR deficient)
5.0 months (MR proficient)
PD-L1
         
BMS-936559 e
Melanoma , NSCLC, Colorectal, Pancreatic, Gastric, Breast, Ovarian, and RCC (Brahmer et al. 2012)
1
2012
17.3 % (Melanoma)
11.8 % (RCC)
10.2 % (NSCLC)
5.9 % (Ovarian)
Not evaluated
MPDL3280A f,**
Metastatic bladder (Powles et al. 2014)
1
2014
43 % (IHC2/3)***
11 % (IHC0/1)***
Not evaluated
LAG-3
         
IMP-321 g
Advanced Renal Cell Carcinoma (Brignone et al. 2009)
1
2009
0 %
Not evaluated
Combination therapies
         
IMP-321 + Paclitaxel *
Metastatic Breast Carcinoma (Brignone et al. 2010)
1/2
2010
90 %
Not evaluated
Ipilimumab * + Dacarbazine *
Stage 3 or 4 Melanoma (Robert et al. 2011)
3
2011
15.2 % (Dacarb + Ipi)
10.3 % (Dacarb)
11.2 months (Dacarb + Ipi)
9.1 months (Dacarb)
Ipilimumab * + Fotemustine *
Stage 3 or 4 Melanoma (Di Giacomo et al. 2012)
2
2012
29.1 %
13.3 months
Ipilimumab + Poxviral Vaccine
Metastatic Prostate (Madan et al. 2012)
1
2012
58 %
34.4 months
Tremelimumab + IFNα *
Stage 4 Melanoma (Tarhini et al. 2012)
2
2012
24 %
21 months
Ipilimumab * + Nivolumab *
Stage 3 or 4 Melanoma (Wolchok et al. 2013)
1
2013
40.00 %
Not evaluated
Ipilimumab * + Sargramostim *
Metastatic Melanoma (Hodi et al. 2014b)
2
2014
15.5 % (Sargra + Ipi), 14.8 % (Ipi)
17.5 months (Sargra + Ipi)
12.7 months (Ipi)
Pidilizumabh + Rituximab *
Relapsed Follicular Lymphoma (Westin et al. 2014)
2
2014
66 %
Not evaluated
Ipilimumab * + Radiation
Stage 4 Melanoma (Twyman-Saint Victor et al. 2015)
1
2015
18 %
10.7 months
Nivolumab + Ipilimumab vs. Ipilimumab
Untreated BRAF WT Advanced Melanoma (Postow et al. 2015)
2
2015
61 % (Nivo + Ipi)
11 % (Ipi)
Not evaluated
Nivolumab + Ipilimumab vs. Nivolumab vs. Ipilimumab
Untreated Melanoma (Larkin et al. 2015)
3
2015
57.6 % (Nivo + Ipi)
43.7 % (Nivo)
19.0 % (Ipi)
Not evaluated
MR mismatch repair
*FDA-Approved
**FDA Breakthrough Status
***IHC0/1: <5 % PD-L1 +, IHC2/3: >5 % PD-L1 +
aTremelimumab also known as CP-675,206 or Ticilimumab [Human Monoclonal IgG2]
bIpilimumab also known as MDX010, MDX101, or Yervoy [Human Monoclonal IgG1]
cNivolumab also known as MDX-1106, Opdivo, BMS-936558, or ONO-4538 [Human Monoclonal IgG4]
dPembrolizumab also known as MK-3475, Keytruda, Lambrolizumab [Humanized Mouse Monoclonal IgG4]
eBMS-936559 [Human Monoclonal Ig G4]
fMPDL3280A [Human Monoclonal with Engineered Fc for avoiding ADCC]
gIMP-321 [LAG-3 Ig Fusion]
hPidilizumab also known as CT-011 [Humanized Mouse Monoclonal IgG1]
Checkpoint blockade aims to block the interactions between coinhibitory receptors on T cells and their ligands, which can be expressed on many cell types (Table 1.2). Ligation of coinhibitory receptors (e.g., PD-1, CTLA-4, TIM-3, LAG-3, TIGIT) can diminish T cell activation, homing to tissue, and effector function, and can affect the function of suppressive regulatory T cells (Schietinger and Greenberg 2014; Wherry 2011). Under physiologic conditions, these pathways are in place to prevent autoimmunity (T cells attacking self) and tissue damage (resolution of inflammation). However, the upregulation of coinhibitory pathways can prevent effective immunity; such is the case in many cancers (Schietinger and Greenberg 2014; Wherry 2011). Tumor-infiltrating T cells often express multiple coinhibitory receptors on their surface, and tumor cells and other cells in the tumor microenvironment often express their ligands (Ahmadzadeh et al. 2009). Blockade of these interactions in the clinic can have profound effects, though the specific mechanisms underlying these effects remain unclear. An understanding of these mechanisms is critical for the principled combination of checkpoint blockade with other therapies to increase the fraction of patients who respond to therapy.
Table 1.2
Costimulatory and coinhibitory pathways under investigation for tumor immunotherapies
Molecule
Expression profile
Role
Ligand
Expression profile
Costimulatory
CD28
T cells
Priming, cell growth, survival, memory
B7-1 (CD80)
B7-2 (CD86)
ICOSL (human only)
T cells, B cells, dendritic cells, macrophages, monocytes
ICOS (CD278)
T cells, NKT cells, ILC2
Cell growth, differentiation, effector function, survival, memory
ICOSL
T cells, B cells, dendritic cells, macrophage, monocytes, granulocytes
CD27
T cells, B cells, NK cells
Priming, cell growth, differentiation, effector function, survival, memory
CD70
T cells, B cells
CD137 (4-1BB)
T cells, B cells, macrophages, monocytes, epithelial cells
Cell growth, effector function, survival, memory
CD137L (4-1BBL)
T cells, B cells, macrophages, monocytes, dendritic cells, epithelial cells
Galectin-9
All cells
OX40
T cells, B cells
Cell growth, differentiation, effector function, survival, memory
OX40L
B cells, dendritic cells, T cells, endothelial cells
CD226
T cells, B cells, NK cells, macrophages, monocytes, stem cells, platelets
Cell growth, differentiation, effector function
CD112
Stem cells, dendritic cells, macrophages, monocytes, endothelial cells, platelets, epithelial cells, T cells, B cells
CD155
Stem cells, dendritic cells, macrophages, monocytes, endothelial cells, platelets, epithelial cells, T cells, B cells
GITR
T cells, macrophages, monocytes
Cell growth, effector function
GITRL
B cells, macrophages/monocytes, dendritic cells
Coinhibitory
CTLA-4 (CD152)
T cells, B cells
Cell growth, effector function, survival, memory
B7-1 (CD80)
B7-2 (CD86)
T cells, B cells, dendritic cells, macrophages, monocytes
PD-1 (CD279)
T cells, B cells, macrophages, monocytes
Cell growth, effector function, survival, memory
PD-L1 (B7-H1, CD274)
T cells, B cells, dendritic cells, NK cells, macrophages, monocytes, epithelial cells, endothelial cells, stromal cells
PD-L2 (B7-DC, CD273)
Mainly dendritic cells, macrophages
LAG-3 (CD223)
T cells, NK cells, B cells, dendritic cells
Cell growth, effector function
MHCII
B cells, dendritic cells, macrophages, monocytes, endothelial cells
TIM-3
T cells, dendritic cells, macrophages, monocytes
Cell growth, differentiation, effector function, memory
Galectin-9
All cells
Phosphatidylserine
All cells
HMGB1
All cells
TIGIT
T cells, NK cells
Cell growth, effector function
CD112
Stem cells, dendritic cells, macrophages, monocytes, endothelial cells, platelets, epithelial cells, T cells, B cells
CD155
Stem cells, dendritic cells, macrophages, monocytes, endothelial cells, platelets, epithelial cells, T cells, B cells
BTLA
T cells, B cells, dendritic cells, macrophages, monocytes
Cell growth, effector function, survival, memory
HVEM
T cells, B cells, dendritic cells, NK cells, macrophages, monocytes, epithelial cells
CD200R
Macrophages, monocytes, T cells, NK cells, dendritic cells
Priming, effector function
CD200
B cells, dendritic cells, macrophages, monocytes, endothelial cells
CEACAM1 (CD66a)
T cells, granulocytes, epithelial cells, NK cells
Proliferation, effector function
Not known
Not known
VISTA
Macrophages, monocytes, granulocytes, T cells
Activation, effector function
Not known
Not known
There is already strong evidence that combining checkpoint blockade agents with each other and with other therapeutic strategies can lead to synergies. Clinical trials studying the combination of antibodies targeting PD-1 and CTLA-4 have shown survival benefits beyond that of either agent alone (Larkin et al. 2015; Wolchok et al. 2013; Postow et al. 2015). Multiple clinical studies of checkpoint blockade in combination with different classes of therapeutics (including other immunotherapies, targeted therapies, vaccines, radiation and chemotherapy) are underway (Table 1.1).
The goal of this chapter is to provide an overview of checkpoint blockade and combination therapy strategies . We first discuss the current understanding of the mechanisms underlying checkpoint blockade, with a focus on the two most clinically relevant pathways to date, the PD-1 and CTLA-4 pathways. We then present an overview of the combinations that are being examined preclinically and clinically. Finally, we explore important questions that need to be addressed to design combination therapies involving checkpoint blockade in a principled manner.

2 Current Understanding of Mechanisms

2.1 CTLA-4 Blockade

CTLA-4 (Cytotoxic T Lymphocyte Antigen 4) was the first T cell co-receptor identified as inhibitory, and has a critical role in maintaining immune tolerance (Tivol et al. 1995; Waterhouse et al. 1995; Brunet et al. 1987). CTLA-4 also was the first coinhibitory receptor to show therapeutic promise in mouse models of cancer when blocked with a monoclonal antibody (Leach et al. 1996). Accordingly, CTLA-4 was the first coinhibitory receptor to be targeted in clinical trials and eventually approved for clinical use (Hodi et al. 2010). It is perhaps therefore surprising that the mechanism(s) by which CTLA-4 controls T cell responses remains controversial.
CTLA-4 is inducibly expressed upon activation of naive T cells (CD4 +FoxP3 and CD8 +) and constitutively expressed on suppressive regulatory T cells (CD4 +FoxP3 +, Tregs) (Alegre et al. 1996). CTLA-4 has both cell-intrinsic and cell-extrinsic functions (Grosso and Jure-Kunkel 2013). It binds to the same ligands (B7-1 and B7-2) as the costimulatory receptor CD28, but with a higher affinity (van der Merwe et al. 1997; Freeman et al. 1992; Freeman et al. 1993a; Freeman et al. 1993b). CTLA-4 inhibits the activation of naïve T cells and is a critical mediator of Treg cell suppressive function (Wing et al. 2008). CTLA-4 can inhibit T cell activation intrinsically, either by outcompeting CD28 or by recruiting phosphatases to the cytoplasmic domain of CTLA-4 upon ligation, leading to decreased TCR and CD28 signaling (Marengère et al. 1996; Grohmann et al. 2002). CTLA-4 also may inhibit activation of other T cells in a cell extrinsic fashion by leading to downregulation of B7-1 and B7-2 on antigen-presenting cells (APCs) , either indirectly (through cytokines such as IL-10) or directly (transendocytosis) (Grohmann et al. 2002; Chen et al. 1998; Qureshi et al. 2011). Whereas other coinhibitory molecules seem to exert their influence primarily at the site of immune response, CTLA-4 is critical for initial activation of T cells in secondary lymphoid organs. Mice that lack CTLA-4 develop a fatal systemic inflammatory phenotype within 2–4 weeks of birth, characterized by uncontrolled T cell expansion (Tivol et al. 1995; Waterhouse et al. 1995). Analogously, humans with heterozygous CTLA-4 mutations have increased susceptibility to severe immune dysregulation (Kuehn et al. 2014; Schubert et al. 2014; Topalian and Sharpe 2014). Therefore, although questions remain about mechanisms, it is clear that CTLA-4 plays a critical role in attenuating T cell responses.
The seminal work demonstrating that CTLA-4 blockade can promote antitumor immune responses in mouse tumor models was performed in James Allison’s lab in the mid-1990s (Leach et al. 1996). Since then, the basic science exploring the specific mechanisms underlying this efficacy has proceeded in parallel with the clinical development of antibodies that have a similar effect. Blockade of CTLA-4 on both effector T cells and regulatory T cells is necessary for optimal antitumor immunity, suggesting multiple modes of action (Peggs et al. 2009). In some, but not all mouse models, CD4 + T cells are necessary for therapeutic benefit with CTLA-4 blockade, whereas CD8 + T cells appear to always be necessary (Hurwitz et al. 1998; van Elsas et al. 1999). This is likely because cytotoxic CD8 + T cells are responsible for killing tumor cells, an important mode of antitumor immunity . Recent reports suggest that depletion of intratumoral regulatory T cells (the highest expressors of CTLA-4) may be a critical part of the therapeutic mechanism (Bulliard et al. 2013; Selby et al. 2013; Simpson et al. 2013).
The relevance of many of these findings to human cancer patients is an active area of investigation. Because of the challenges of obtaining fresh tumor samples from patients, many clinical studies have focused on peripheral blood samples. A recent study of blood samples from patients undergoing anti-CTLA-4 therapy showed an increase in new tumor-reactive T cell clones, but no change in preexisting clones (Kvistborg et al. 2014). This suggests that anti-CTLA-4 has a major effect on T cell priming (i.e., in the lymph nodes). Although studies such as this are informative, the phenotype of tumor-infiltrating lymphocytes (TILs) can differ drastically from peripheral sites (draining lymph node, blood, etc.) (Ahmadzadeh et al. 2009). Investigators are now designing protocols to obtain serial tumor biopsies before and during treatment. Initial studies of this sort show that anti-CTLA-4 increases T cell responses within the tumor (Cooper et al. 2014). In addition, mutational load in tumors prior to therapy has been correlated with response to anti-CTLA-4 in patients, suggesting that this therapy requires an immunogenic tumor (i.e., it can be recognized by T cells) (Snyder et al. 2014).
It is important to note that, while CTLA-4 blockade can induce striking antitumor immunity in some patients, many patients also experience immune-related adverse events (irAEs) , generally manifesting as tissue-specific inflammation (Teply and Lipson 2014). This can be managed by administration of immunosuppressive drugs, and temporally or permanently stopping anti-CTLA-4 therapy. A better understanding of the mechanism of action of anti-CTLA-4 antibodies is needed to minimize the associated toxicities and increase the fraction of responding patients.

2.2 PD-1 Pathway Blockade

The PD-1 (programmed death 1) pathway plays a critical role in regulating peripheral T cell tolerance and protects healthy tissues from inflammatory tissue damage (Topalian et al. 2015; Flies et al. 2011; Francisco et al. 2010a; Ostrand-Rosenberg et al. 2014). Similarly to CTLA-4, PD-1 is induced on naïve T cells upon activation, and engagement by either of its ligands PD-L1 (B7-H1) or PD-L2 (B7-DC) sends inhibitory signals into T cells (Keir et al. 2008). This can attenuate the initial activation of T cells in the lymphoid organs, or it can diminish effector T cell functions in tissue (Keir et al. 2007). In the setting of an acute viral infection, PD-1 is upregulated on T cells but returns to baseline upon viral clearance (Petrovas et al. 2006). In contrast, in the setting of a chronic infection and cancer , T cells that are continuously exposed to antigen express high levels of PD-1 and eventually become dysfunctional (Wherry 2011). PD-1 is also constitutively expressed on Tregs, and affects their suppressive capacity (Francisco et al. 2010b). Expression of PD-1 ligands dampens the local immune response and controls resolution of inflammation after clearance of microbes. PD-L1 is constitutively expressed on many immune cell types and its expression can be induced on many nonimmune cells (including epithelial cells, vascular endothelial and stromal cells) by pro-inflammatory cytokines (e.g., Type I and II IFNs, TNFα, VEGF) (Keir et al. 2007). PD-L2 is expressed primarily by APCs, induced by many of the same cytokines as PD-L1, but IL-4 and GM-CSF are the most potent stimuli for PD-L2 expression (Francisco et al. 2010b). Tumor cells can express one or both PD-1 ligands, as do many of the other cells in the tumor microenvironment (e.g., fibroblasts, endothelial cells, immune cells) (Flies et al. 2011). Multiple and reinforcing mechanisms drive PD-L1 and PD-L2 expression on tumor cells, including oncogenic signaling (e.g., AKT), amplifications and/or translocations of chromosome 9p24 (which contains PD-L1 and PD-L2), and Epstein Barr Virus latent membrane protein 1 (LMP1). This constitutive expression of PD-L1 is termed “innate immune resistance .” In addition, PD-L1 expression can be induced on tumor cells in response to T cells producing immunostimulatory cytokines (such as IFNs) (Spranger et al. 2013). This is termed “adaptive immune resistance ,” and represents a mechanism by which tumor cells attempt to evade immune mediated antitumor responses (Spranger et al. 2013; Spranger et al. 2015; Taube et al. 2012). Thus, there are multiple means by which PD-1 signaling may contribute to the immunosuppressive tumor environment.
Ligation of PD-1 negatively regulates T cells in several ways. First, engagement of PD-1 diminishes the signals downstream of TCR stimulation , and facilitates the downregulation of the TCR itself, leading to decreased activation and cytokine production (Karwacz et al. 2011; Sheppard et al. 2004). Second, PD-1 ligation can induce genes that impair T cell proliferation and cytokine production (e.g., the transcription factor batf) (Quigley et al. 2010), and can decrease anti-apoptotic gene expression and increase pro-apoptotic gene expression (e.g., bcl-xl), reducing T cell survival (Gibbons et al. 2012; Parry et al. 2005). Third, PD-1 signaling can decrease the production of cytotoxic molecules by T cells, decreasing their killing capacity (Azuma et al. 2008; Barber et al. 2006). Fourth, the PD-1 pathway can promote the induction of regulatory T cells from naïve or Th cells (iTregs) , which suppress effector T cells (Francisco et al. 2009). Therefore, PD-1 signaling can modulate T cells in multiple ways that synergize to suppress immune responses.
Many important questions remain about the mechanisms that underlie antitumor immunity induced by PD-1 blockade. As a monotherapy, PD-1 blockade works in ~20–50 % of patients, but we do not understand the molecular nature of an effective antitumor response, nor know how to identify patients who will respond to PD-1 blockade. Furthermore, we do not understand why patients fail to respond to anti-PD-1/PD-L1. This knowledge is needed for rational combination of PD-1 pathway blockade with other therapies to treat patients who do not respond to anti-PD-1 or anti-PD-L1 alone.
Recent work has begun to address these questions. Several papers have noted that PD-1 is more frequently expressed on tumor-specific TILs than on the bulk TIL population, and that the TILs from patients who respond to PD-1 blockade are more clonal prior to therapy than in patients that do not respond (Ahmadzadeh et al. 2009; Gros et al. 2014; Tumeh et al. 2014). TIL clonality is often used as a measure for antigen-specific T cell responses, as T cells proliferate in response to their cognate antigen (e.g., clonal expansion). Similar to CTLA-4 blockade, multiple studies have shown that a higher mutational burden—specifically, non-synonymous mutations —is associated with a better response to PD-1 blockade (Messina et al. 2012; Le et al. 2015a). These nonsynonymous mutations likely give rise to neoantigens and induce tumor-specific T cells. Similarly, multiple studies have shown that response to PD-1 pathway blockade is more frequent in the setting of PD-L1 expression in the tumor microenvironment (on either tumor cells or nontumor cells, or both) (Larkin et al. 2015; Taube et al. 2012; Garon et al. 2015). It is important to note, however, that responses to PD-1 checkpoint blockade can occur even when PD-L1 expression is not observed, and that PD-L1 expression is not needed for response to combined PD-1 and CTLA-4 therapy . It is likely that a high mutation rate and PD-L1 expression are in fact linked, as a high mutation rate correlates with a higher CD8 + T cell infiltration into tumors, which produce cytokines that can lead to increased PD-L1 expression in the tumor (i.e., adaptive immune resistance) (Spranger et al. 2013). Indeed, initial observations support the link between mutation rate and PD-L1 expression, but further work is needed to explore this concept (Le et al. 2015a).

2.3 Additional Coinhibitory Pathways

The success of anti-CTLA-4 and anti-PD-1 in the clinic has led to the search for other checkpoint molecules that can be targeted, especially in cases where anti-CTLA-4 or anti-PD-1 immunotherapy has little or no effect. There are many known checkpoint molecules (Table 1.2), and understanding the immunoregulatory roles of these molecules in cancer is an active area of investigation. Therapeutic targeting of several of these molecules has progressed to clinical trials (Shin and Ribas 2015).
One promising target is LAG-3 (lymphocyte-activation gene-3) (Goldberg and Drake 2011). LAG-3 is expressed on effector and regulatory T cells, B cells, NK cells and plasmacytoid dendritic cells, and binds with high affinity to MHCII to negatively regulate T cell responses (Shin and Ribas 2015). Whereas mice deficient in LAG-3 alone or PD-1 alone are generally healthy, mice lacking both LAG-3 and PD-1 develop systemic autoimmunity , highlighting the synergy between these two pathways in controlling T cell tolerance (Okazaki et al. 2011; Woo et al. 2012). T cells in tumors can co-express PD-1 and LAG-3, and combined blockade of PD-1 and LAG-3 in tumor models has a greater therapeutic benefit than blockade of either alone (Woo et al. 2012; Matsuzaki et al. 2010). These findings have given impetus to clinical trials with anti-LAG-3 antibodies (Shin and Ribas 2015).
Another promising target is TIM-3 (T-cell immunoglobulin and mucin-domain containing-3) , which is expressed on T cells (Th1 and Tc1 cells) as well as on monocytes, macrophages and dendritic cells (Anderson 2014). TILs that express PD-1 can also express TIM-3; the CD8+PD-1 +TIM-3 + population is thought to be more dysfunctional than the CD8+ PD-1 +TIM-3 population (Fourcade et al. 2010; Sakuishi et al. 2010). TIM-3 binds to galectin 9, HMGB1, and phosphatidylserine (Chiba et al. 2012; DeKruyff et al. 2010; Zhu et al. 2005). Tumor cells and other cells in the tumor microenvironment, such as myeloid-derived suppressor cells (MDSCs) , can express galectin-9 (Compagno et al. 2013). Signals through TIM-3 can lead to T cell death or tolerance (Zhu et al. 2005). In addition to impairing effector T cell function, TIM-3 expression on Tregs may enhance their suppressive capacity (Gautron et al. 2014) and TIM-3 on dendritic cells may decrease their ability to present antigens to stimulate T cells (Nakayama et al. 2009). Thus, TIM-3 may regulate innate and adaptive cells in the tumor microenvironment. Blockade of TIM-3 alone can induce antitumor immunity in some mouse models, and has shown potent synergy with PD-1 blockade (Sakuishi et al. 2010).
Another emerging coinhibitory receptor in cancer immunotherapy is TIGIT (T cell immunoreceptor with Ig and ITIM domains) , which is expressed on T cells upon activation, as well as on NK cells (Shin and Ribas 2015). TIGIT shares the ligands CD155 and CD112 with CD226, but TIGIT ligation leads to T cell inhibition while CD226 ligation leads to T cell stimulation (Lozano et al. 2012). A recent study showed that co-blockade of TIGIT and PD-L1 led to tumor regression in a tumor model whereas blockade of either alone had minimal effect, suggesting a synergy between these pathways (Johnston et al. 2014).

3 Combination Therapies Involving Checkpoint Blockade

3.1 Overview of Goals of Rational Combinations with Checkpoint Blockade

The major goal of combining checkpoint blockade with other therapies is to increase the fraction of patients who respond to therapy, while maintaining the durability of response and minimizing the toxicity. In order to analyze the infinite space of potential combination strategies (i.e., therapy, timing, dosing) with a finite set of resources (i.e., money and clinical trials), it is imperative to take a principled approach to testing combinations. It is likely that the optimal combination will differ between different cancers and even between patients with the same clinical diagnosis. Thus, it is critical to gain mechanistic insights into why certain combinations succeed or fail, rather than simply evaluating response. These insights will allow clinicians to confidently choose specific combinations for each patient.
The goals of checkpoint blockade are to either stimulate new antitumor T cells to attack tumors or to relieve suppression of existing antitumor T cells, or both (Fig. 1.1). In cases where there is a lack of clinical response to checkpoint blockade monotherapy, there is likely a failure in one or both of these goals, or it may be that antitumor T cells alone are not sufficient to control the tumor. This may be the case in particularly proliferative tumors, such that the growth rate outcompetes the rate of killing by T cells, in tumors with a relatively low mutation burden so there are few antigens to drive T cell responses, or in tumors that create a particularly suppressive microenvironment. Thus, combining checkpoint blockade with therapies that overcome these three possible failure modes may lead to synergies (Fig. 1.2). Therapies to combine with checkpoint blockade should: (1) increase the number of antitumor T cells (i.e., increase antigenicity of tumor, induce immunogenic cell death), (2) enhance the intrinsic function of antitumor T cells (i.e., block other suppressive pathways, upregulate stimulatory pathways), (3) extrinsically support antitumor T cell responses to tumors (i.e., stimulate other immune cell types that can promote antitumor immunity), and/or (4) simply debulk the tumor to allow the immune response to catch up to tumor growth.
Over the last decade, many studies have examined the effect of therapies not traditionally thought to affect the immune system on immune responses (Table 1.3). Many therapies can affect the immune system including targeted therapies (epigenetic modifiers, antiangiogenic agents, small molecules targeted at specific mutations), radiation therapy, and chemotherapy. These findings suggest that multiple types of cancer therapies have the potential to synergize with checkpoint blockade. In this section, we will discuss the principles behind specific combinations that are being studied, as well as summarize insights gained from preclinical and clinical studies of these combinations.
Table 1.3
Effects of non-immunologic cancer therapies on the immune system
Therapeutic agent
Effect on tumor
Effect on immune system
Chemotherapies
Anthracyclines (Doxorubicin, Daunorubicin, Idarubicin, etc)
Kills tumor cells by intercalating DNA/RNA
–Causes immunogenic cell death of cancer cells leading to ATP and HMGB1 release that stimulates DCs and increases T cell priming (Fucikova et al. 2011; Ghiringhelli et al. 2009; Ma et al. 2013; Sistigu et al. 2014)
Oxaliplatin (Eloxatin)
Kills tumor cells by crosslinking DNA and inhibiting DNA synthesis
–Causes calreticulin exposure and HMGB1 release by dying tumor cells, which stimulates DCs via TLR4 to prime T cell immunity (Apetoh et al. 2007; Tesniere et al. 2010)
Cyclophosphamide (Endoxan, Cytoxan, Neosar, Procytox, Revimmune, Cycloblastin)
Kills tumor cells by alkylating DNA and inhibiting replication
–Causes immunogenic cell death resulting in calreticulin exposure and HMGB1 release. Selectively depletes Tregs and restores T cell and NK cell effector functions (Ghiringhelli et al. 2004; Ghiringhelli et al. 2007; Lutsiak et al. 2005; Schiavoni et al. 2011)
Gemcitabine (Gemzar)
A nucleoside analog that inhibits DNA replication
–Selectively depletes myeloid-derived suppressor cells and restores T cell and NK cell activity (Suzuki et al. 2005; Vincent et al. 2010)
5-Fluorouracil (Adrucil, Carac, Efudex, Efudix)
A pyrimidine analog that inhibits thymidylate synthase, thus inhibiting DNA replication
–Selectively kills myeloid-derived suppressor cells, reducing inhibition of T cells (Vincent et al. 2010)
Surgery and radiation therapy
Surgical resection
Debulking of tumor or complete removal of primary tumor mass
–Restores T and B cell antitumor activity; reduces the number of MDSCs (Danna et al. 2004; Salvadori et al. 2000)
Radiotherapy
Kills tumor cells by causing severe DNA damage and eventually cell death. Can be targeted to deliver a higher dose or radiation to tumor than to surrounding tissue
–Causes immunogenic cell death of tumor cells, resulting in HMGB1 release that stimulates DCs via TLR4 to prime T cell immunity (Jing et al. 2015; Soukup and Wang 2015)
–Causes DCs to secrete IFN-beta after the sensing of tumor cell DNA by the STING-cGAS pathway (Burnette et al. 2011; Deng et al. 2014; Woo et al. 2014)
–Increases TCR repertoire diversity of tumor infiltrating CD8 + T cells (Twyman-Saint Victor et al. 2015)
Small molecule inhibitors
Vemurafenib (Zelboraf)
Selective inhibitor of the BRAF-V600E mutant
–Increases expression of melanocyte differentiation antigens, decreases melanoma cell expression of IL-6, IL-10 and VEGF, increases clonal expansion of T cells in tumor, and causes an upregulation of PD-L1 on tumor cells (Cooper et al. 2014; Sumimoto et al. 2006; Boni et al. 2010; Cooper et al. 2013a)
–Concurrent treatment with PD-1 blockade is synergistic (Cooper et al. 2014)
Imatinib (Gleevec)
Inhibitor of oncogenic tyrosine kinases such as ABL and KIT
–Improves antitumor immunity in gastrointestinal stromal tumors by blocking IDO and increasing NK cell activation (Balachandran et al. 2011; Menard et al. 2009)
–Decreases the number and suppressive capacity of Tregs (Larmonier et al. 2008)
–Increases B cell production of tumor-specific antibodies (Ozao-Choy et al. 2009)
Sunitinib (Sutent)
Inhibitor of multiple tumor-associated kinases such as PDGFR and VEGFR
–Decreases the numbers of tumor-infiltrating Tregs and MDSCs (Ozao-Choy et al. 2009; Ko et al. 2009)
–Decreases PD-1 and CTLA-4 expression on T cells and PD-L1 expression on MDSCs and pDCs (Ozao-Choy et al. 2009)
–Blocks STAT3 and promotes in vivo expansion of T cells (Kujawski et al. 2010)
HDAC inhibitors (Vorinostat, Romidepsin)
Block the silencing of gene expression through epigenetic repression. Mechanisms not completely understood
–Suppresses Tregs and MDSCs, leading to enhanced CTL effector function and synergy with PD-1 blockade (Bridle et al. 2013; Kim et al. 2014; Shen et al. 2012)
Biologics
Trastuzumab (Herclon, Herceptin)
Blocks signaling through HER2
–Increases DC cross-presentation and priming of T cells and synergizes with anti-PD-1 (Stagg et al. 2011; Disis et al. 2009; Park et al. 2010)
Bevacizumab (Avastin)
Inhibits angiogenesis by blocking VEGF
–Increases DC maturation and infiltration of antigen-specific CD8 + T cells (Shrimali et al. 2010; Yang et al. 2009)
–Reduces expression of checkpoint pathway molecules including PD-1, CTLA-4, Lag-3, and Tim-3 on T cells and synergizes with PD-1 blockade (Voron et al. 2015)
Cetuximab (Erbitux)
Blocks growth signals through EGFR
–Enhances DC-mediated phagocytosis and priming of T cell responses (Correale et al. 2012)
–Increases infiltration of NK cells and ADCC (Marechal et al. 2010)
–Increases complement-dependent tumor cell lysis (Dechant et al. 2008; Hsu et al. 2010)

3.2 Combining Checkpoint Blockade with Other Immunotherapies

One approach is to combine checkpoint blockade with other immunotherapies. Multiple immunotherapies (e.g., vaccines, T cell stimulatory agents, T cell transfer) have been extensively studied, and many may target one or more of the three failure modes of checkpoint blockade. However, without understanding the precise mechanisms underlying checkpoint blockade, it is currently difficult to predict whether these therapeutics will be synergistic or redundant, or perhaps even negate one another. Importantly, targeting multiple axes of the immune response may lead to an increased frequency or severity of autoimmune side effects. Thus, careful preclinical evaluation of these combinations is critical.

3.2.1 Targeting Multiple Checkpoint Blockade Agents

In cases where checkpoint blockade monotherapy fails because a single agent is not enough to overcome T cell suppression, it may be beneficial to target multiple checkpoints. This approach may be considered when a large fraction of TILs express multiple coinhibitory receptors or display other features of T cell dysfunction (i.e., diminished proliferation, cytokine production, or cytotoxicity). Not surprisingly, the most extensively studied combination of checkpoint blockade agents is anti-CTLA-4 plus anti-PD-1 antibodies. Preclinical studies show impressive synergies between these two approaches, even in situations where monotherapy has a relatively weak effect (Curran et al. 2010; Duraiswamy et al. 2013; Mangsbo et al. 2010; Spranger et al. 2014). Similarly, clinical combination of anti-CTLA-4 plus anti-PD-1 shows clinical response rates (specifically, durable response rates) above either agent alone, which further supports nonredundant roles for these antibodies (Larkin et al. 2015; Wolchok et al. 2013; Postow et al. 2015). However, this increased efficacy comes at the cost of increased frequency of irAEs, and thus might only be an option for otherwise relatively healthy patients who can tolerate these effects. Importantly, the synergy between anti-PD-1 and anti-CTLA-4 suggests that combinations of either of these antibodies with other checkpoint blockade agents may lead to increased response rates, and perhaps fewer additional irAEs . As discussed previously, preclinical studies targeting the PD-1 pathway in combination with LAG-3, TIM-3, TIGIT, or VISTA (Liu et al. 2015) have shown synergies, giving the impetus for clinical trials with these combinations (Woo et al. 2012; Sakuishi et al. 2010; Johnston et al. 2014).

3.2.2 Targeting T Cell Stimulation

Though most agents to reach the clinic so far have targeted coinhibitory molecules on T cells, there is rationale for targeting costimulatory molecules (using agonistic rather than blocking antibodies) as well. Sending positive signals into T cells may allow them to overcome suppressive signals induced by coinhibitory molecules (Table 1.2). Importantly, this can be done without knowing the dominant coinhibitory pathways driving T cell suppression in a particular tumor. Costimulatory targets include the 4-1BB, OX40, ICOS, GITR, CD40, and CD27 receptors, and many are in clinical trials as single agents (Pardoll 2012; Sharma and Allison 2015; Richman and Vonderheide 2014). Preclinical studies combining costimulatory receptor agonists with checkpoint inhibitors (particularly CTLA-4 or PD-1) have shown enhanced T cell responses to many tumors (Chen et al. 2015; Curran et al. 2011; Marabelle et al. 2013; Redmond et al. 2014). Design and enrollment of clinical trials that test these same combinations in patients are underway.
T cell responses also can be enhanced with cytokines, which have been used in cancer immunotherapy for decades, albeit with generally modest effects. Pro-inflammatory cytokines (e.g., IL-2, IL-15, IL-21) not only can lead to enhanced T cell function, but can also stimulate APCs to present more tumor antigens. Analogously, blockade of immunosuppressive cytokines (e.g., IL-10, TGF) can enhance T cell activation. Thus, clinical efforts to combine these two clinically validated approaches (checkpoint inhibitors and cytokine therapy) are underway (Yu et al. 2010; West et al. 2013; Vanpouille-Box et al. 2015; Brooks et al. 2008).

3.2.3 T Cell Transfer and Vaccines

In cases where checkpoint blockade fails because the underlying T cell response to the tumor is minimal or absent, therapies that can quickly induce or increase this response are needed. One approach is by adoptive transfer of tumor-specific T cells, either harvested from the tumor itself or naïve cells genetically modified to recognize the tumor using TCRs or chimeric antigen receptors (CARs) (Rosenberg and Restifo 2015). This approach has had striking success in blood cancers, and is being actively studied in solid tumors. A major barrier that these cells face upon entry into a solid tumor is the suppressive tumor microenvironment, which may be overcome by combination with checkpoint blockade. In fact, since these approaches involve manipulating T cells in vitro, multiple groups have taken the additional step to further engineer the cells to lack expression of coinhibitory molecules or even modify their cytoplasmic tails so they provide stimulatory rather than inhibitory signals upon ligation, as well as remove the need for antibody administration (Frigault et al. 2015).
Another way to increase T cell responses to a tumor is by therapeutic vaccination. The fundamental goal of tumor vaccines is to activate DCs and induce them to present tumor antigens, in turn stimulating a T cell response against tumors . Several approaches have been tested: administering DCs loaded with tumor antigens, coupling tumor antigens to adjuvant, or injecting irradiated tumor cells modified to promote DC recruitment (GVAX) (Palucka and Banchereau 2013). Preclinical studies showing synergy between GVAX (or derivatives of GVAX) and checkpoint inhibitors have given impetus for clinical trials studying these same combinations (Curran et al. 2010; Duraiswamy et al. 2013; Le et al. 2015b).

3.2.4 Targeting Immune Cells Other than T Cells

The tumor microenvironment is complex, with multiple immunosuppressive cell types that are, by definition, outcompeting any antitumor immune cells (Hanahan and Coussens 2012; Gajewski et al. 2013). There are multiple therapeutic strategies that aim to either diminish immunosuppressive cell types or enhance the antitumor immune cells. Immunosuppressive cells in the tumor microenvironment include Tregs and myeloid-derived suppressor cells (MDSCs) , and stromal cells such as cancer-associated fibroblasts (CAFs) . Macrophages in the tumor microenvironment can have either antitumor or pro-tumor roles, and the role of B cells in the tumor microenvironment is still unclear. Therapies that deplete or manipulate immunosuppressive cells are emerging. Preclinical work has shown that depletion of MDSCs or CAFs synergizes with checkpoint blockade, as does stimulation of macrophages in the tumor microenvironment (Feig et al. 2013; Highfill et al. 2014; Zhu et al. 2014). CD40 agonistic antibodies, which both enhance T cell stimulation by APCs and enhance macrophage activation, have shown synergy with checkpoint blockade in preclinical models and are being studied in clinical trials (Zippelius et al. 2015).

3.3 Combining Checkpoint Blockade with Non-immunotherapies

It has become increasingly clear that therapies initially thought to target tumor cells specifically also can profoundly affect immune responses (Table 1.3). This understanding provides rationale for combining these therapies with checkpoint blockade in certain settings. In addition, since checkpoint blockade can take weeks to months to have a clinical effect, in some situations it is necessary to start with another therapy that can debulk the tumor, or at least slow tumor growth.

3.3.1 Therapies Targeting Tumor-Specific Mutations

Agents that target specific genetic defects in tumor cells can have profound effects on tumor growth in high percentages of patients with targeted mutations, although the responses are generally not durable. Initially designed to inhibit tumor cells intrinsically, these agents can have tumor cell-extrinsic effects as well, which can elicit a stronger immune response against treated tumors. Perhaps the most well-studied example is agents that target the BRAFV600E mutation, the oncogenic driver commonly found in melanoma and other cancers (Sumimoto et al. 2006). In metastatic melanoma BRAF inhibitor therapy initially leads to an increase in expression of melanocyte differentiation antigens and decreased immunosuppressive cytokine production, coupled with an initial increase in CD8 + T cell infiltration into the tumor (Boni et al. 2010; Cooper et al. 2013a). However, as with other targeted therapies, the response to BRAF inhibitors is often transient. BRAF inhibitor resistance is associated with decreased T cell infiltration and increased PD-L1 expression (Frederick et al. 2013). These effects on the immune response, along with the high response rate but low durability (the inverse of what is seen with checkpoint inhibitors) provide rationale for combining targeted therapies with checkpoint inhibitors (Cooper et al. 2013b). However, clinical studies that combine some BRAF inhibitors with anti-CTLA-4 antibodies have shown increased toxicity, especially when combined with MEK inhibitors (Harding et al. 2012). Preclinical studies have shown synergy between BRAF inhibitors and anti-PD-1 antibodies, and this is now being evaluated clinically (Cooper et al. 2014).

3.3.2 Epigenetic Modifiers and Antiangiogenic Agents

Cancer is now appreciated to be both a genetic and epigenetic disease, and epigenetic changes in tumor cells can be reversed with the goal of returning tumor cells to a normal state (e.g., turn back on a tumor suppressor). Multiple drugs have been developed to target epigenetic modifications in tumor cells, such as methylation and histone modifications. Epigenetic modifiers may increase the expression of tumor antigens that have been repressed, thus allowing either preexisting T cells or new T cells to respond (Heninger et al. 2015). Epigenetic modifiers also can inhibit suppressive Tregs and MDSCs, thereby promoting an antitumor immune response (Bridle et al. 2013; Kim et al. 2014; Shen et al. 2012). The combination of epigenetic modifiers with checkpoint blockade improves T cell function during chronic viral infection in mice, suggesting that this approach may promote antitumor immunity (Zhang et al. 2014). Indeed, anecdotal reports of patients responding to checkpoint inhibitors after receiving epigenetic modifiers have led to the design of clinical trials exploring these combinations more systematically.
Similarly to epigenetic modifiers, antiangiogenic agents can support antitumor immune responses. As tumors grow, they induce the formation of a highly abnormal blood vessel supply. The original motivation for antiangiogenic agents was to prevent the formation of these blood vessels, thereby preventing the supply of nutrients to the tumor. At low doses, antiangiogenic agents can “normalize” this vasculature, which allows for better delivery of agents into the tumor and the infiltration of immune cells (Shrimali et al. 2010; Yang et al. 2009). However, tumor-derived VEGF, a potent angiogenic molecule, can upregulate checkpoint molecules on T cells and induce T cell dysfunction, providing rationale for combining agents that target VEGF with checkpoint blockade (Voron et al. 2015). Moreover, studies of tumor samples from patients treated with ipilimumab suggest that ipilimumab modulates blood vessel formation as part of its therapeutic mechanism (Yuan et al. 2014). Thus, clinical trials are underway to study combined ipilimumab and antiangiogenic therapy (Hodi et al. 2014a).

3.3.3 Radiation Therapy

Radiation therapy accomplishes two tasks that may synergize with checkpoint blockade. First, it debulks and remodels the tumor, potentially allowing the T cell response more time to develop. Second, it leads to immunogenic cell death, potentially increasing tumor-antigen presentation. Multiple mechanisms have been proposed for this immunogenic cell death, including the release of HMGB1 to stimulate DCs via TLR4 or the sensing of tumor cell DNA by the STING-cGAS pathway (Burnette et al. 2011; Deng et al. 2014; Jing et al. 2015; Soukup and Wang 2015; Woo et al. 2014). Radiotherapy seems to result in increased activation of new antitumor T cells. A recent study showed that radiation therapy synergized with blockade of both CTLA-4 and PD-L1; the authors suggested that radiation therapy led to an increased breadth of T cell responses to the tumor, accompanied by the relief of T cell exhaustion from PD-1 blockade and Treg depletion from CTLA-4 blockade (Twyman-Saint Victor et al. 2015).

3.3.4 Chemotherapy

Perhaps most surprising are the strong beneficial effects that conventional chemotherapeutics can have on the antitumor immune response. These drugs are thought to directly kill cancer cells by affecting DNA replication, thereby preventing tumor cell proliferation. However, multiple other modes of action have been elucidated, mostly centered on the stimulation of strong antitumor immune responses (Table 1.3). Similarly to radiation therapy, multiple chemotherapeutic agents induce immunogenic cell death of tumor cells, acting as an endogenous vaccine to stimulate DCs to activate antitumor T cells (Apetoh et al. 2007; Fucikova et al. 2011; Ghiringhelli et al. 2009; Ghiringhelli et al. 2004; Ghiringhelli et al. 2007; Kepp et al. 2014; Lutsiak et al. 2005; Ma et al. 2013; Schiavoni et al. 2011; Sistigu et al. 2014; Tesniere et al. 2010). Furthermore, some chemotherapeutic agents selectively deplete suppressive cells in the tumor microenvironment, such as MDSCs and Tregs (Messina et al. 2012; Ghiringhelli et al. 2004; Ghiringhelli et al. 2007; Schiavoni et al. 2011; Suzuki et al. 2005; Vincent et al. 2010). Interestingly, although chemotherapeutic agents can induce lymphodepletion, this may actually promote antitumor immunity, as suppressed T cells may regain their effector function during the ensuing homeostatic proliferation (i.e., outgrowth to fill the lymphocyte niche). These observations provide motivation for combining chemotherapeutic agents with checkpoint blockade, although clearly the dosing and timing of these therapies (and specific agents) are very critical for realizing their synergistic potential. Several clinical trials of these combinations are underway, with more planned in the near future.

4 Advancing Combination Checkpoint Therapies

4.1 Necessary Next Steps for Advancing Combination Checkpoint Therapies

Many combination checkpoint therapies are making their way to the clinic. Due to the large number of FDA-approved cancer therapies and the vast number currently in clinical trials, it is imperative that the choice of combination therapies to test in patients be rational. A failure to do so may lead to numerous disappointing trial results and exhaust resources and patient populations necessary for finding effective combinations. Towards this goal, we suggest an approach for testing combinations that is based on data from currently ongoing immunotherapy studies and grounded in a sound molecular understanding of the pathways to be targeted: (1) identify the patients most likely to benefit from combination therapy, (2) characterize the mechanisms by which tumors resist immunotherapy, and (3) determine strategies to target these resistance pathways effectively.
Optimal combination therapies will require analyses of the tumor microenvironment of each patient. The emerging principle that checkpoint blockade likely has efficacy only where there is already an ongoing (albeit failing) anti-immune response pre-therapy suggests that this is a primary aspect of the tumor that should be studied. Analytic approaches are needed to distinguish between a lack of an immune response and an active suppression of an immune response. The former type of patients would likely benefit from therapies that promote an immune response (e.g., vaccination, induction of immunogenic cell death), whereas the latter would benefit from therapies that remove suppression (e.g., combination checkpoint blockade, depletion of suppressive cells). These analytic strategies may also benefit patients who develop resistance to checkpoint blockade.

4.2 Understanding Mechanisms of Efficacy of Checkpoint Blockade

A major impediment to the principled combination of checkpoint blockade antibodies with other therapies is an incomplete understanding of their mechanisms of action. Rapid progress is being made in this area. In the clinical setting, tumor biopsies and resections obtained before and after treatment from the same patient are more valuable than equivalent samples obtained from different patients (Cooper et al. 2014). Collaborative efforts (between clinicians and scientists) have the potential to maximize the information obtained from these samples. In the preclinical setting, care must be taken in selection of animal models for studying therapeutic mechanisms of action. Many mouse tumor models do not respond to checkpoint blockade monotherapy (Grosso and Jure-Kunkel 2013). It is important to understand the differences between models that respond and those that do not, and utilize the appropriate models to study mechanism of response and resistance to checkpoint blockade.
For CTLA-4 and PD-1 checkpoint blockade, several fundamental questions remain. The relative effects of anti-CTLA-4 and anti-PD-1 on effector versus Treg cells versus other PD-1 expressing cells (NK cells, myeloid cells) are incompletely understood, and this knowledge may suggest approaches for combination therapy. For example, whereas blockade of PD-1 enhances the function of effector T cells, genetic loss of PD-1 in mouse models actually increases the suppressive capacity of at least a subset of Tregs (Sage et al. 2013), which could in turn inhibit antitumor immunity. Similarly, dissecting the role of PD-L1 and PD-L2 on specific cell types (e.g., immune cells versus tumor cells versus stromal) may lead to mechanistic insights as well as biomarkers that predict response. Future studies also should address the relative effects of checkpoint blockade in the periphery (lymph node, blood) versus the tumor microenvironment, as this information not only will inform the search for biomarkers, but also suggest combination therapeutics. Finally, a molecular understanding of the changes that occur in T cells following checkpoint blockade will greatly refine the combination therapies being explored to enhance T cell function.
Multiple checkpoint blockade antibodies are now available to clinicians but to optimize their therapeutic impact, a better understanding of the similarities and differences between coinhibitory pathways, as well as mechanisms of synergy between coinhibitory pathways is needed. Studying biopsy samples for the relative expression of coinhibitory molecules on T cells and their ligands in the tumor microenvironment will likely help inform the choice of which agent(s) to use, although how to make this choice is still an open question. Insights may come from determining whether synergies between coinhibitory pathways (e.g., PD-1 plus CTLA-4 vs. PD-1 plus LAG-3 vs. PD-1 plus TIM-3) affect similar or different molecular pathways.
While the majority of studies have focused on mechanisms of therapeutic efficacy, relatively little is known about mechanisms of durability and thus questions remain about the necessary length of therapy. Ipilimumab is given in only four doses over 12 weeks, but anti-PD-1 agents are given either every 2 weeks or every 3 weeks indefinitely . Despite the finite length of therapy, the response to ipilimumab seems to be durable, as responders from the original clinical trial remain in remission or with stable disease for many years (Schadendorf et al. 2015). However, because blockade of CTLA-4 and PD-1 likely work by different mechanisms, the durability seen with CTLA-4 blockade does not imply that blockade of PD-1 will have a similar durability. Further work is needed to understand how CTLA-4 and PD-1 blockade affect the generation, function and maintenance of memory T cell subsets. Updates on patients from early clinical trials will begin to answer the question of durability.

4.3 Understanding Mechanisms of Resistance to Checkpoint Blockade

The identification of resistance mechanisms to checkpoint blockade and the development of strategies to circumvent resistance are important steps toward increasing the proportion of patients that respond to therapy. Studies in mouse tumors models in which checkpoint blockade induces tumor regression in certain settings but not others (e.g., larger tumor burden) should provide insights into general principles of therapeutic nonresponsiveness. The potential of such work is illustrated by a recent study showing that elevated beta-catenin signaling in melanoma tumors suppresses spontaneous T cell infiltration into tumors, which is likely a prerequisite for a strong response to checkpoint blockade monotherapy (Spranger et al. 2015). Temporal studies of patient samples (tumor biopsies from patients with recurrence and blood samples from all patients) obtained at multiple time points during checkpoint blockade (during response versus resistance) will be valuable for investigating resistance mechanisms.
Moreover, analyses of immune alterations within human tumor cells and the tumor microenvironment have the potential to identify resistance mechanisms. For example, recently published data from The Cancer Genome Atlas (TCGA) project suggest that tumors may harbor mutations that increase the likelihood of resistance to immunotherapy. Tumors with an increased infiltration of cytolytic effector cells (CD8 + T cells, NK cells) are also more likely to harbor mutations in MHC-I complex subunits, which may impair antigen presentation or in molecules of the extrinsic cell death pathway, which may limit effectiveness of cytolytic effector cells (Rooney et al. 2015). Mutations such as these may be driving the incomplete responses observed in patients and would thus represent a tumor in which another therapy should be pursued.
Additionally, immunophenotyping of patient tumors that fail to respond completely could reveal the increased presence of suppressive cell populations that are preventing the successful reinvigoration of T cells. For example, class-I HDAC inhibitors deplete myeloid-derived suppressor cell (MDSC) populations disproportionately both in vitro and in vivo and synergize with checkpoint blockade, thus representing a strategy for specifically depleting suppressive populations in the tumor microenvironment (Kim et al. 2014).

4.4 Principled Combination of Checkpoint Blockade with Other Immunotherapies

Since immune cell types in addition to T cells can modulate the tumor microenvironment, there is great potential for strategies that combine manipulation of these immune cell types with checkpoint blockade. Here we will discuss principled combination of dendritic cells (DCs) , natural killer (NK) cells, and myeloid cell modulation with checkpoint blockade.
Studies of DCs have focused mainly on their use in vaccines, as well as approaches to manipulate endogenous DC populations. The goal of both approaches is a more potent CD8 + T cell response to the tumor, as discussed above. Recent advances in rapid prediction and synthesis of tumor-specific peptide sequences following sequencing of a patient’s tumor should enable development of more effective tumor vaccines (Rajasagi et al. 2014). Further work is needed to develop strategies to enhance recruitment and maturation of DCs following peptide vaccination, as well as to improve tumor peptide formulations to promote epitope spreading, the process by which an ongoing immune response to a particular antigen results in “spreading” of the targeted immune response to other antigens. One recent advance involved the preconditioning of a vaccine site with tetanus toxoid in order to enhance DC migration to the lymph node upon vaccination (Mitchell et al. 2015). This approach improved survival of patients with glioblastoma multiforme in a clinical trial. Approaches such as these should be considered in tumors with relatively few infiltrating T cells to induce an immune response that can be supported by combination with checkpoint blockade therapy.
Natural killer (NK) cells are another immune population under intense investigation since NK cells can directly kill tumor cells and mediate the effects of antibody-dependent cellular cytotoxicity. NK cells are particularly important in tumors that downregulate MHCI in an attempt to avoid CD8 + T cell responses, as NK cells attack cells with low MHCI expression. Although NK cells express many of the same coinhibitory receptors as T cells, little is known about the contribution of NK cells to the tumor immunity induced by checkpoint blockade. Intriguingly, a recent study demonstrated that Cbl-b was responsible for regulating NK cell mediated antitumor immunity and that knockdown of Cbl-b induced impressive spontaneous rejection of tumors (Paolino et al. 2014). Further work is need to determine how to modulate NK cell proliferation and effector function alone or in combination with antibodies designed to enhance ADCC or with T cell immunotherapies.
Myeloid cells are the most common immune cell subset in many tumors and often associated with an immunosuppressive M2 macrophage phenotype or myeloid derived suppressor cell phenotype (Lemke and Rothlin 2008; Ruffell et al. 2012). Myeloid derived suppressor cells are a Gr-1 + immature macrophage or dendritic cell subset characterized by their expression of ARG1 and iNos (Youn and Gabrilovich 2010). Myeloid cells in the tumor microenvironment can exert suppressive effects through the expression of PD-L1 as well as the production of immunosuppressive molecules such IL-10, TGFβ, and nitric oxide (Youn and Gabrilovich 2010). There are a number of strategies under investigation to overcome immunosuppressive effects of myeloid cells including repolarizing them to an M1 phenotype (as is seen in the setting of CSF1R blockade) (Zhu et al. 2014), or blocking their recruitment to the tumor (e.g., by disrupting the CXCR2: CXCL8 axis) (Highfill et al. 2014). In addition, macrophages have an underappreciated role in interfacing with endothelial cells, angiogenesis, and the maintenance of tumor cell vasculature. Tie2 + macrophages promote the production of new endothelial cells, which aids in the process of angiogenesis. Perturbation of these interactions decreases tumor cell vasculature and the ability of tumor cells to thrive in a hypoxic environment. Macrophages also can scavenge dead/stressed tumor cells in a non-immunogenic fashion, and strategies to increase the immunogenicity of the tumor through the accumulation of tumor-derived HMGB1 and ATP, two well known immunostimulatory signals in the tumor microenvironment, are under investigation. Lastly, the modulation of the local humoral response may provide a distinct means to promote tumor cell killing by skewing B cells to produce antibodies that participate in complement fixation, ADCC, or FcR-mediated phagocytosis. These approaches are likely to synergize with PD-1 blockade given the distinct mechanisms of action.

4.5 Principled Combination of Checkpoint Blockade with Non-immunotherapies

The effects of non-immunotherapeutic approaches on the immune response must be closely examined in order to determine how to optimally combine them with checkpoint blockade. Chemotherapy, targeted therapies, and radiotherapy are often not as effective at inducing tumor regression in the absence of functional immunity (Cooper et al. 2014; Zitvogel et al. 2008). It is clear that innate and adaptive immunity play a critical role in mediating the effects of various non-immunologic cancer therapies (Table 1.3). Traditional therapeutics that elicit immunogenic cell death and increase priming of adaptive immunity, such as chemotherapy with anthracyclines or radiotherapy may synergize with checkpoint blockade. Surgical resection can relieve systemic immunosuppression and perhaps restore T cell function (Danna et al. 2004; Salvadori et al. 2000). A better understanding of the effect of surgical resection of accessible lesions on the immune response to other lesions is needed.

4.6 Using Biomarkers to Predict Response and Stratify Patients

Current data indicate that when a patient responds to checkpoint blockade therapy, the response is most often durable (McDermott et al. 2014). However, across different cancer types, sizeable fractions of patients do not respond (Table 1.2). Thus, it is imperative to develop biomarkers that help stratify patients and predict whether a given patient is likely respond to monotherapy, should receive some type of combination therapy, or receive other therapies entirely. To date, three types of biomarkers have been studied intensively, particularly in the context of PD-1 blockade: a high degree of immune infiltrate in the tumor—specifically CD8 + T cells, a high mutational burden/predicted neoepitopes, and expression of PD-L1 by tumor cells or tumor immune cell infiltrates (Snyder et al. 2014; Tumeh et al. 2014; Le et al. 2015a; Herbst et al. 2014; Rizvi et al. 2015a).
Work of Jerome Galon demonstrated the benefit of the immunoscore on patient survival (Anitei et al. 2014). The immunoscore is being investigated for its predictive value for a patient’s response to checkpoint blockade. Moreover, with the development of multicolor multiplexed IHC, it is now possible to evaluate CD8 + T cell numbers, as well as their functional status and location within the tumor simultaneously. This approach may lead to identification of biomarkers that will predict whether checkpoint blockade monotherapy will be sufficient to induce an objective response. Ectopic lymphoid follicles are highly correlated with an increase in overall survival in melanoma, breast, and colorectal cancer patients (Messina et al. 2012; Bindea et al. 2013; Gu-Trantien et al. 2013; Huang et al. 2015). The association of these structures with an objective response to checkpoint blockade remains to be investigated.
A higher mutational burden within tumor cells correlates with a better response to checkpoint blockade (Snyder et al. 2014; Le et al. 2015a; Rizvi et al. 2015a). This was initially observed by comparing response rates and mutational burden across different types of cancers, but also is seen in patients with the same type of cancer (e.g., non-small cell lung cancer, colorectal cancer), but with different mutational burdens (Le et al. 2015a; Rizvi et al. 2015a). Together, these data further the hypothesis that tumors that elicit a strong CD8 + T cell response to tumor antigens (e.g., neoantigens) respond better to checkpoint blockade monotherapy than tumors without many mutations and therefore without many tumor antigens. If this is indeed the case, then analysis of the mutational burden of tumor cells, as well as the T cell repertoire in the tumor prior to therapy, may be useful for identifying patients more likely to respond to checkpoint blockade and patients who likely need another intervention prior to checkpoint therapy.
High expression of PD-L1 on tumor cells increases the likelihood of a response to PD-1 pathway blockade, but durable responses are seen in patients with little or no PD-L1 expression. It remains unclear whether optimization of PD-L1 immunohistochemistry will enable better predictive value of PD-L1 expression. Further work is needed to investigate intratumoral heterogeneity of PD-L1 expression, as well as relative expression of PD-L1 in primary and metastatic tumors.
New biomarkers may result from other approaches under investigation. Quantification and characterization of circulating tumor cells in the blood might allow for less invasive analysis of tumor cells, along with real-time monitoring or response to therapy. Similarly, more sensitive tumor DNA detection techniques allow for the real time monitoring of tumor burden in plasma (Anderson 2014). These methods may provide a means to probe the tumor microenvironment via a blood-based assay and enable rapid determination of the efficacy of checkpoint blockade in patients.

4.7 Potential Next-Generation Therapeutic and Diagnostic Strategies

The major goal of combining checkpoint blockade with other therapies is to increase the fraction of patients that have a durable response while minimizing irAEs. The increased rate of irAEs seen with the combination of nivolumab (anti-PD-1) with ipilimumab (anti-CTLA-4) may limit this combination to only otherwise healthy patients, despite the increased durable response rate seen with this therapy. New strategies that target multiple immunosuppressive pathways in parallel and that target these pathways in specific cells, as well as new strategies to study the antitumor immune response are under development and may overcome these limitations.
One potential method for reducing irAEs that result from systemic activation of the immune response is to use targeted antibodies, such as bispecific antibodies or antibodies with modified affinity, both with the goal of targeting specific immune cell populations. Bispecific antibodies have two targets rather than one (as with traditional antibodies). Bispecific antibodies have been used to bring T cells and tumor cells together, but they could be designed to bring T cells and dendritic cells together, draw NK cells to the tumor, and to more specifically target a cell (e.g., a CD8β/PD-1 bispecific antibody that blocks PD-1 specifically on CD8 + T cells to increase their effector function). Another avenue for more specific antibody targeting of immune cells in the tumor involves development of antibodies with a modified affinity such that they only bind cells expressing very high levels of a target molecule.
Many efforts are underway to target multiple coinhibitory pathways (e.g., PD-1, CTLA-4, TIM-3, and LAG-3 in pairwise combinations) in preclinical mouse models and clinical trials. A better understanding of the molecular pathways triggered by coinhibitory receptors is needed to determine shared and unique signaling nodes. This knowledge may reveal new therapeutic targets and strategies. Small molecule inhibitors that target a shared node might potentially replace administration of multiple checkpoint antibodies; this approach may efficiently invigorate dysfunctional CD8 + T cells and potentially decrease the side effects associated with multiple checkpoint inhibition, in addition to being easier to administer and likely less expensive. Furthermore, in engineered T cells (e.g., CAR T cells or adoptive cell transfer) these nodes could be targeted genetically (e.g., using the CRISPR/Cas9 or TALEN systems). In addition, CD8 + T cells could be manipulated to recruit other immune cell populations to the tumor, break down the stromal architecture of the tumor, disrupt tumor vasculature, or potentially support formation of memory CD8 + T cells.
Another way to limit systemic activation of immune cells is to take advantage of advances in drug delivery: both delivery to specific sites and to specific cells. Polymer scaffolds engineered to recruit effector cells and prime them with proper cytokines are being tested in animal models. Such scaffolds could potentially also incorporate checkpoint antibodies. In addition, intratumoral injection approaches may provide a means to stimulate an immune response locally. The potential for this approach is illustrated by studies showing that intratumoral injection of anti-CTLA-4, anti-OX40 plus CpG led to a curative immune response and required 1/100 of the dosage of antibody in mouse tumor models. Directed delivery of checkpoint antibodies to specific cells by targeted nanoparticles or bispecific antibodies may provide even greater specificity by targeting effector cells.
Finally, improvements in cellular and tissue analysis may enable finer methods for evaluating the tumor microenvironment and developing more meaningful immunoscores to more accurately predict if a patient will respond to checkpoint blockade. CyTOF technology coupled with barcoding approaches provides a novel means to identify the spatial location of cells within the tumor and to reconstruct tissue architecture computationally overlaid with quantitation of up to 50 proteins. This approach should make possible functional characterization of immune cell infiltrates in the tumor while maintaining spatial relationships in the tumor. Laser capture microscopy followed by single cell RNAseq provides a complementary approach to investigate the entire transcriptome while preserving tissue structure. These two techniques are powerful tools for investigating clonal heterogeneity of both the immune cells and tumor cells during tumor evolution and determining why tumors regress or progress. This knowledge should aid development of better biomarkers for predicting which patients will respond to checkpoint therapy, monitoring responses to checkpoint blockade and determining if additional interventions are needed to promote tumor eradication.

5 Future Directions

The story of the path of checkpoint blockade antibodies from the lab into the clinic is both exciting and inspirational, requiring the collaboration of physicians, scientists and patients. Checkpoint blockade is now established as a mainstay of medical oncology , but further work is needed to increase the efficacy of checkpoint blockade, and explore the infinite space of potential combination therapies towards the goal of increasing the number of cancer patients who can benefit from checkpoint blockade. New approaches for delivery of checkpoint blockade agents in a targeted manner may help minimize adverse events associated with stimulating the immune system. Multidisciplinary studies of patient biopsies are needed to achieve a mechanistic understanding of checkpoint blockade efficacy and resistance. A better understanding of the effects of non-immunotherapies on the immune response to tumors is needed to design rational combinations. This knowledge will lead to better animal models for studying the vast array of potential combinations, and more personalized combination therapies for patients.
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