Predictive value of co-expression patterns of immune checkpoint molecules for clinical outcomes of hematological malignancies

Abstract

Co-expression of immune checkpoint (IC) molecules can exacerbate T cell exhaustion in patients with hematological malignancies (HMs) and contribute to the immune escape of tumor cells, which is related to poor clinical outcome. It is worth establishing and optimizing an ideal prediction model based on the co-expression patterns of IC molecules to evaluate the immune status of HM patients and predict their clinical outcome. In this perspective, we summarize the co-expression patterns of IC molecules and their importance as biomarkers that predict the prognosis of patients with different HMs, providing new insights for designing dual IC blockades (ICBs).

Introduction

Immune escape is an important factor leading to progression and poor clinical outcomes (1-4). The primary reason for immune escape is downregulation of immune cell function, which is mainly manifested as immune cell exhaustion (2,5-7). This exhaustion included high expression of immune checkpoint (IC) molecules as well as high expression of IC ligands on tumor cells (2,8,9). Therefore, the immune suppression status can be evaluated by detecting the expression levels of IC molecules, which may be potential immune biomarkers that predict the prognosis of patients with hematological malignancies (HMs) and guide the design of immunotherapies (2,10-12). IC molecules include programmed cell death 1 (PD-1), PD ligand 1 (PD-L1), PD ligand 2 (PD-L2), cytotoxic T-lymphocyte associated protein 4 (CTLA-4), T cell immunoglobulin domain and mucin domain-3 (TIM-3), lymphocyte-activation gene 3 (LAG-3), and T cell immunoreceptor with Ig and ITIM domains (TIGIT), CD276, etc. (13-16). IC molecules have been confirmed to be up-regulated in acute myeloid leukemia (AML), myelodysplastic syndrome (MDS), B-cell acute lymphocytic leukemia (B-ALL) and B-cell lymphoma (BCL) and have an association with clinical outcomes (2,10,17,18). Thus, the expression status of IC molecules may be an immune biomarker for HMs. Therefore, in this perspective, we comprehensively analyzed differences in IC molecule expression and their optimal combination for predicting the prognosis of patients with different HMs and discussed the advantages and limitations of different analyses and detection methods.

Clinical implications of abnormal IC molecule expression in HMs

Upregulation of IC proteins on immune cells or tumor cell surfaces will lead to an increase in the exhausted immune cell number and reduce effective anti-tumor immune responses in patients with HMs, thereby inducing immune tolerance (19-21). IC mRNA expression has been detected by quantitative real-time polymerase chain reaction (qRT-PCR), flow cytometry, and transcriptome sequencing, and its high expression was significantly associated with adverse clinical outcomes for patients with HMs (10,11,17,18). For example, higher PD-1 expression concurrent with exhausted CD4+/CD8+ T cells exhibits a reduced anti-leukemia response in patients with de novo AML, and bone marrow (BM) contains more exhausted T cells than peripheral blood (PB) (11,22,23). Moreover, high expression of PD-1, PD-L1, PD-L2, CTLA4, or LAG-3 is significantly associated with poor overall survival (OS) in AML patients (10,24). After allogeneic hematopoietic stem cell transplantation (allo-HSCT), IC proteins can serve a critical role in the anti-leukemic immune response in AML (25). For example, higher expression of TIGIT was associated with poor OS and progression-free survival (PFS) in AML after allo-HSCT (25). Moreover, up-regulated PD-1, PD-L1, PD-L2, CTLA4, TIGIT, or CD47 predicts adverse clinical outcomes for MDS patients (17,26). In patients with B-ALL, two heterogeneous exhausted T cell populations, characterized by upregulation of TIGIT, PD-1, HLADRA, LAG-3, and CTLA4, were identified, and this may be correlated with poor prognosis and the effectiveness of immune-based therapies targeting B-ALL (18). In addition, abnormal expression of PD-1, TIM-3, and CD244 in CD3+, CD4+, Treg and CD8+ T cells may contribute to T cell exhaustion and impair anti-tumor T cell activity in B-cell lymphoma (27). Similarly, high expression of PD-1, PD-L1, LAG-3, or TIM-3 may predict poor clinical outcomes for patients with peripheral T-cell lymphoma (PTCL) (28). These findings suggest that IC molecules can serve as biomarkers for prognostic prediction, and more importantly, they may further provide a reference for the application of immune checkpoint inhibitors (ICIs) based on the alteration of immunosuppressive receptors.

Recently, increasing numbers of anti-tumor immunotherapy clinical trials based on the expression of IC molecules are being performed (9,29-31). PD-1/PD-L1 or CTLA-4 blockade has shown clinical benefit in only certain types of HMs, such as Hodgkin lymphoma (HL) and follicular lymphoma (FL) with an objective response rate (ORR) of 64%−87% (32-36). However, patients with relapsed or refractory PTCL, multiple myeloma (MM), AML, diffuse large B cell lymphoma (DLBCL), mycosis fungoides, and Sèzary syndrome have a limited reactivity response to PD-1/PD-L1 or CTLA-4 blockade with an ORR less than 45% in phase I/II trials (32,37-41). Furthermore, in a phase 1/1b multicenter trial, 23% of patients had a complete response, 9% had a partial response, and 27% had decreased tumor burden when CTLA-4 blockade was used in patients with relapsed HMs after allo-HSCT (40). Therefore, beyond PD-1 and CTLA-4 blockade, clinical trials are ongoing to evaluate the efficacy and safety of LAG-3, TIM-3, and TIGIT blockade in HMs (32). Because ICI monotherapy in HMs has limited clinical benefit, co-expression of IC proteins and the anti-tumor activity of ICI combinations are currently being explored. Therefore, the detection of the IC protein expression pattern may guide the design of combined immunotherapy.

Co-expression of IC molecules predicts clinical outcome for patients with HMs

The detection of co-expressing IC molecules mainly includes analysis of RNA and protein levels (10,11). The mRNA of IC genes is detected by qRT-PCR and transcriptome sequencing, and IC protein levels on the surface of T cells are mainly detected by flow cytometry and immunohistochemistry (IHC) (Table 1) (10,18,42,43). qRT-PCR assesses the mRNA expression level of co-expressing IC proteins by detecting preserved RNA or cDNA and has the advantage of being fast, convenient, and cost-effective, but it cannot accurately determine which T cell subpopulation expresses each IC RNA (10). Transcriptome sequencing can comprehensively identify the internal co-expression of all IC genes or co-expression relationships with other genes, while its disadvantages include a longer time to results, requiring subsequent qRT-PCR validation, and it also cannot identify which cell subpopulation expresses the IC genes (10,17,24). Flow cytometry and IHC can relatively accurately detect the co-expression levels of IC proteins on T cell subpopulations, but the disadvantages of flow cytometry include high cost and the requirement of fresh samples (11,42,44). The disadvantages of IHC are mainly applicable to BCL and T cell lymphoma (TCL) (Table 1) (45-47). Overall, detection of the mRNA and protein expression levels of IC genes can complement and verify each other.

Table 1. Advantages and disadvantages of qRT-PCR, flow cytometry, and IHC.

Methods	Advantages	Disadvantages
qRT-PCR, quantitative real-time polymerase chain reaction; IHC, immunohistochemistry; IC, immune checkpoint; BCL, B-cell lymphoma; TCL, T-cell lymphoma.
qRT-PCR	1. Fast 2. Convenient 3. Cost-effective	Cannot identify the T cell subpopulation
Transcriptome sequencing	1. Comprehensively observe the internal co-expression of all IC 　genes 2. Co-expression IC genes with other genes	1. Longer time to results 2. Requires qRT-PCR validation 3. Cannot identify the T cell 　subpopulation
Flow cytometry	1. Detect the co-expression of IC proteins on T cell 　subpopulations	1. High cost 2. Fresh sample requirement
IHC	Detect the co-expression of IC proteins on T cell subpopulations	Mainly applicable to BCL and TCL

The co-expression levels of IC molecules may be different in patients with newly diagnosed disease or in patients with HMs after treatment, and this may guide the risk stratification and prediction of clinical outcomes through their detection at different treatment stages. For example, increased co-expression of PD-1/CTLA-4 or PD-L2/CTLA-4 correlated with poor OS in patients newly diagnosed with AML by detecting the co-expression levels of IC mRNA in BM samples using transcriptome sequencing and qRT-PCR (Table 2) (10). These findings are further supported by an ongoing phase 2 clinical trial on PD-1/CTLA-4 dual blockade in which the data demonstrated that the combination of nivolumab and ipilimumab can improve OS for relapsed or refractory AML patients with a median OS time of 7.6 months (48,49). Moreover, increased PD-1+TIM-3+, PD-1+TIGIT+ or CTLA-4+LAG-3+ exhausted T cells in newly diagnosed patients detected by flow cytometry were associated with adverse clinical outcomes, and these might be biomarkers for designing dual blockade strategies (Table 2) (42,44,50,51). In addition, cells with an exhausted phenotype, such as BM CD8+ T cells with PD-1, CTLA-4, and TIM-3 upregulation, were detected in a relapsed AML subgroup, indicating that combining ICIs with HSCT is a strategy for preventing recurrence based on the dynamic detection of IC protein expression in AML patients after allo-HSCT (52,53). PD-1/TIGIT, PD-1/CD47, or TIGIT/CD47 as detected by qRT-PCR in BM samples from newly diagnosed MDS patients was optimal combinations for predicting poor OS in patients (Table 2) (17). These findings provide novel insights into designing combination ICI strategies. Similarly, V-domain Ig suppressor of T cell activation (VISTA) and PD-L1 synergistically predicts poor OS and PFS in patients with extranodal natural killer/T-cell lymphoma (NK/TCL) and provides a rationale for designing dual blockade strategies for targeting VISTA and PD-L1 (Table 2) (43).

Table 2. Optimal combination of IC proteins in patients with HMs.

IC combination	Stage	Detection method	HM patients
IC, immune checkpoint; HM, hematological malignancy; PD-1, programmed cell death 1; CTLA-4, cytotoxic T-lymphocyte associated protein 4; PD-L2, PD ligand 2; TIM-3, T cell immunoglobulin domain and mucin domain-3; TIGIT, T cell immunoreceptor with Ig and ITIM domains; LAG-3, lymphocyte-activation gene 3; VISTA, V-domain Ig suppressor of T cell activation; qRT-PCR, quantitative real-time polymerase chain reaction; IHC, immunohistochemistry; AML, acute myeloid leukemia; MDS, myelodysplastic syndrome; NK/TCL, natural killer/T-cell lymphoma.
PD-1/CTLA-4	De novo	qRT-PCR	AML
PD-L2/CTLA-4	De novo	qRT-PCR	AML
PD-1/TIM-3	De novo	Flow cytometry	AML
PD-1/TIGIT	De novo	Flow cytometry	AML
CTLA-4/LAG-3	De novo	Flow cytometry	AML
PD-1/TIGIT	De novo	qRT-PCR	MDS
PD-1/CD47	De novo	qRT-PCR	MDS
TIGIT/CD47	De novo	qRT-PCR	MDS
VISTA/PD-L1	De novo	IHC	Extranodal NK/TCL

Although the IC co-expression patterns detected by qRT-PCR, flow cytometry, and single cell or bulk transcriptome sequencing provide evidence for predicting clinical outcomes for patients with HMs, another validation dataset is needed to improve the accuracy and feasibility of prediction (10,54). However, different validation methods have different credibility of the results. Validation using multiple datasets is more conclusive than that using data from a single clinical center. Importantly, validation results from multicenter clinical trials, particularly randomized controlled studies from multiple countries, have great value for clinical application and guide clinicians to accurately perform risk stratification and choice-making for patients with HMs (32,54).

Conclusions and further directions

Increasing co-expression of IC molecules can exacerbate T cell exhaustion, which is related to poor clinical outcomes in patients with HMs. Different models for co-expression patterns are establishing and modifying for predicting clinical outcomes and potential for disease relapse in different HMs and guide the development of a ICB combination in clinical trials (48). However, clinical trials for the synergistic application of ICB in MDS and extranodal NK/TCL as well as B-ALL, T-ALL and BCL are little known. To better guide the clinical application of dual ICBs and select the best IC combinations from prognostic information, it is important to provide evidence from preclinical application in mice and clinical trials involving dual ICBs in patients with HMs (Figure 1).

Figure 1.

IC molecule co-expression predicts poor clinical outcomes for patients with HMs and guides preclinical application in mice and clinical trials for dual ICBs. HM, hematological malignancy; PD-1, programmed cell death 1; PD-L1, PD ligand 1; PD-L2, PD ligand 2; CTLA-4, cytotoxic T-lymphocyte associated protein 4; IC, immune checkpoint; ICB, IC blockade; TCR, T cell receptor; MHC, major histocompatibility complex.