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Novel Biomarker Approaches in Classic Hodgkin Lymphoma

Aoki, Tomohiro, MD, PhD; Steidl, Christian, MD

doi: 10.1097/PPO.0000000000000334
Review Articles

Classic Hodgkin lymphoma (cHL) is one of the most common lymphomas in the Western world. Advances in the management of cHL have led to high cure rates exceeding 80%. Nevertheless, relapse or refractory disease in a subset of patients and treatment-related toxicity still represents unsolved clinical problems. The introduction of targeted treatments such as PD-1 blockade and the CD30 antibody drug conjugate, brentuximab vedotin, has broadened treatment options in cHL, emphasizing the critical need to identify biomarkers with the goal to provide rationales for treatment selection, increase effective drug utilization, and minimize toxicity. The unique biology of cHL featuring low abundant tumor cells and numerous nonmalignant immune cells in the tumor microenvironment can provide various types of promising biomarkers related to the tumor cells directly, tumor microenvironment cross-talk, and host immune response. Here, we comprehensively review novel biomarkers including circulating tumor DNA and gene expression–based prognostic models that might guide the ideal management of cHL in the future.

From the Department for Lymphoid Cancer Research, BC Cancer Research Centre; and Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, British Columbia, Canada.

The authors have disclosed that they have no significant relationships with, or financial interest in, any commercial companies pertaining to this article.

Author Contribution: T.A. and C.S. reviewed the literature and wrote the paper.

Reprints: Christian Steidl, MD, BC Cancer Research Centre, 675 West 10th Ave, Room 12-110, Vancouver, British Columbia, Canada V5Z 1L3. E-mail:

Hodgkin lymphoma (HL) accounts for 50% of all lymphomas in children and young adults and is one of the most common lymphomas in all age groups in the Western world.1 The HL is characterized by an extensively dominant microenvironment, composed of different types of noncancerous normal immune cells, such as several types of T cells, B cells, eosinophils, and M1/M2 macrophages, with rare (~1%) clonal malignant Hodgkin and Reed-Sternberg (HRS) cells in classic Hodgkin lymphoma (cHL) and lymphocyte predominant cells in nodular lymphocyte predominant Hodgkin lymphoma.2 The HL can be subdivided into cHL and nodular lymphocyte predominant Hodgkin lymphoma based on histopathologic criteria with cHL representing more than 95% of all HL cases. This review mainly focuses on cHL due to the prevalence of biology and biomarker studies in this subtype.

ABVD (doxorubicin, bleomycin, vinblastine, and dacarbazine) polychemotherapy with or without radiotherapy has been standard first-line treatment for more than 30 years in cHL,3 and gradual advances in the management of cHL have led to achieving high cure rates exceeding 80% across all patients. The ideal number of cycles of first-line treatment and radiation dose are currently determined based on disease stage and a German Hodgkin Study Group risk classification system using risk factors such as extranodal disease, the presence of a large mediastinal mass, high erythrocyte sedimentation rate, and B symptoms.4–6 Nevertheless, approximately 20% to 30% of cHL patients still suffer from refractory disease, and a significant proportion of these patients eventually die of their disease.7 Moreover, treatment-related late effects including secondary cancers and cardiac toxicity represent serious morbidities for long-term survivors.8–12 Recent advances in targeted treatments have broadened the treatment options in cHL. At the same time, the emergence of novel drugs emphasizes the need for identification of effective biomarkers that can provide rationales for treatment selection.

In this review, we distinguish 2 types of biomarkers, classified as “predictive” and “prognostic” biomarkers. If outcome using alternative treatments is different for biomarker-positive patients compared with biomarker-negative patients, a biomarker is predictive, whereas a prognostic biomarker has association with outcome independent of treatment.13 The unique biology of cHL, which is characterized by a mixture of low abundant tumor cells and numerous nonmalignant immune cells in the tumor microenvironment (TME), can provide various types of potential biomarkers. These biomarkers are related to malignant cell biology, TME cross-talk, and the host immune system.13 Here, we comprehensively review (1) molecular biology hallmarks, (2) targeted treatments, and (3) novel biomarkers with the aim to guide treatment decisions in the future.

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The HRS cells are generally considered to be of germinal center B cell origin14,15 showing a similar gene expression pattern with CD30-positive extrafollicular B cells.16 However, a striking feature of HRS cells is the “lost B-cell phenotype.” Hodgkin and Reed-Sternberg cells usually lack common B cell phenotypic markers including CD19, CD20, the B cell receptor, as well as important transcriptional factors for B cell function and differentiation, such as Oct-2 and Bob-1.17,18 Recent progress in molecular techniques, in particular genome-wide analysis methods, contributed to a more refined characterization of HRS cells and their molecular hallmarks.19–24 Constitutive activation of the Janus kinase–signal transducer and activator of transcription (JAK/STAT) signaling is one of the major characteristics of HRS cells, and a high frequency (~90%) of mutations in the JAK/STAT pathways (e.g., SOX1, PTPN1, STAT3, STAT5, and STAT6) was reported by whole-exome sequencing in enriched HRS cells.22–24 Other recurrent genetic alternations detected in HRS cells affect the nuclear factor κB (NF-κB) (e.g., TNFAIP3 and IKBKB), PI3K-AKT (e.g., ITPKB and GNA13), and NOTCH (e.g., SPEN and FBXW7) signaling pathways, which regulate antiapoptotic factors, proinflammatory cytokines, and B cell reprogramming. Constitutive activation of these pathways plays a central role in the pathogenesis of cHL. However, these pathways can also be extrinsically activated, for example, by cytokines/chemokines and ligands to interleukin and tumor necrosis factor receptors expressed in the TME. Thirty percent to 40% of cHLs are associated with Epstein-Barr virus (EBV),25 and viral oncoprotein latent membrane protein 1 (LMP-1) can activate the NF-κB pathway to support HRS cell survival in EBV-related cHL.26,27

Another major principle that emerged from past studies in cHL is the concept of “acquired immune privilege” that postulates that the malignant HRS cells evade immune surveillance and are shaping a 'pro-tumor' microenvironment by acquisition of specific gene mutations (e.g., CIITA, PDL1/2, PTPN1, and B2M).20,22,28–30 These genetic alterations underlie TME interactions that result in immune evasion of HRS cells from an effective immune response.20,31–33 The HRS cells often exhibit overexpression of programmed death receptor 1 (PD-1) ligands, programmed death receptor 1 ligand (PD-L1), and PD-L2 on chromosome 9p24.1 through copy number alteration of 9p24.1 including gain and amplification, EBV infection, and chromosomal rearrangement.20,31–34 These complementary mechanisms of frequent PD-L1 overexpression in HRS cells highlight genetically fixed immune escape characteristics and provide the rationale for usage of PD-1 blockade for therapeutic benefit in cHL. In solid tumors, the activation of CD8-positive cytotoxic T cells in the TME was described as the major mechanism of antitumor effects of PD-1 blockade35–38 consistent with CD8-positive T cells recognizing tumor peptides presented by major histocompatibility complex (MHC) class I molecules. However in cHL, MHC class I expression is often absent in HRS cells in part because of the frequent functional mutation and copy number loss of β2-microglobulin,22,39 indicating an alternate mechanism of antitumor effects attributable to PD-1 blockade in cHL. Recently, Carey et al.40 reported important insights related to PD-1 biology in the TME of cHL. First, they found that macrophages were the majority of cells that express PD-L1 in the TME. Using systematic imaging analysis of multicolor immunohistochemistry (IHC), they successfully visualized the spatial relationship of constituent cell types in the TME. In particular, PD-L1+ macrophages were localized in closer proximity to HRS cells than PD-L1 macrophages, and PD-L1+ macrophages and HRS cells were found to have direct contact with PD-1+ CD4+ T cells. Of interest, they found that PD-1+ CD4+ T cells had more frequent contact with PD-L1+ HRS cells than PD-1+ CD8+ T cells. The finding that CD4+ T cells are more enriched for HRS cell contact is consistent with previous results that HRS cells more frequently express MHC class II than MHC class I,39,41 although MHC class II loss of expression is also not uncommon (~40%) and was found to be associated with outcome.41,42 Mass cytometry has also recently added more texture to the literature that characterizes immunophenotypic markers of the immune infiltrate and the microenvironment composition in cHL.43 For example, Cader et al.43 noted, in a series of 7 patients, that CD4+ TH1-polarized regulatory T cell and PD-1+ TH1 T cell are characteristic components of the T cell–rich microenvironment in cHL.

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The recent deeper understanding of the molecular characteristics of HRS cells and their TME contributed to the development of targeted treatments in cHL (Fig. 1). CD30 is a member of the tumor necrosis factor receptor family and is universally expressed by HRS cells. The antibody-drug conjugate, brentuximab vedotin, is composed of a monoclonal antibody directed against the CD30 antigen joined to several molecules of monomethyl aurostatin E, a potent microtubule disruptor. Brentuximab vedotin showed impressive single-agent activity with overall response rates (ORRs) of 68% to 75% in relapse or refractory cHL.44,45 In addition, the large phase III ECHELON-1 study demonstrated significantly longer modified progression-free survival (PFS) (82.1% vs. 77.2%; hazard ratio, 0.77; P = 0.03) in the treatment arm with brentuximab vedotin + AVD (doxorubicin, vinblastine, and dacarbazine) compared with the standard arm of ABVD in patients with previously untreated advanced-stage cHL.46 Another breakthrough in targeted therapy in cHL came with immune checkpoint blockade such as the PD-1 inhibitors nivolumab and pembrolizumab. Early-phase clinical trials of nivolumab and pembrolizumab demonstrated dramatic responses with ORR of 65% to 87% in relapsed or refractory cHL.47–52 Of note, some cHL patients also achieved prolonged remissions (> 2 years) after discontinuation of nivolumab.53 The clinical studies of brentuximab vedotin and immune checkpoint blockade have already demonstrated significant clinical impact when adding these novel agents to standard therapy in cHL, and clinical trials testing combination use of these 2 drugs and with other agents (e.g., ipilimumab)54,55 are ongoing.56



Transforming growth factor β (TGF-β) production in the TME has been recently proposed as a novel target in relapsed cHL.57 The TGF-β not only promotes tumor growth, but also inhibits growth and function of effector T cells and antigen-presenting cells.58 Bollard et al.57 demonstrated a favorable safety profile and raised the possibility to overcome TGF-β–mediated immune escape mechanisms by using TGF-β–resistant tumor-specific T cells directed to the EBV latency-associated antigens, LMP-1 and LMP-2,59 in 8 patients with relapsed EBV- associated cHL. This treatment induced persistent (>3 years) complete remission (CR) in a cHL patient with resistant disease.

T cells that were engineered with chimeric antigen receptors (CARs) have also been studied as a potential treatment approach in patients with relapsed cHL.60,61 CAR T cells targeting CD30 have been evaluated in a small number of cHL patients with uncertain success in a clinical trial, with 1 patient achieving long-lasting CR (>2 years). A more promising CAR T cell approach might be using CD123 as a novel target in cHL.62 Approximately 60% of HRS cells and immune cells in the TME including tumor-associated macrophage were reported to express CD123, and CD123-recognizing CAR T cells displayed potent therapeutic activity in vivo.62,63 Although loss of target antigen may allow immune escape and cause treatment resistance in CAR T cells,64 targeting a surface antigen via a treatment mechanism that is independent of MHC-based antigen presentation is a major advantage of CAR T cells in the context of cHL that often lacks surface MHC classes I and II molecules.22,65,66

The bispecific anti-CD30/CD16A antibody, AFM13, can induce antitumor effects by recruiting natural killer cells via binding to CD16A.67 Although the results of the phase I study demonstrated limited efficacy with an ORR of 11.5%,68 further clinical trials optimizing dose and treatment duration may increase efficacy. Moreover, the JAK inhibitors, pacritinib, ruxolitinib, and itacitinib, were considered rational targeted therapies in cHL based on the finding of constitutive activation and the high frequency of mutations in the JAK/STAT pathway. However, so far, the results were rather disappointing with ORR less than 10% by the single agents.69,70 Considering limited toxicity, these drugs may have the potential to be used in combination therapy.70

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The main goal of treatment in cHL is the achievement of high cure rates while maintaining minimal toxicity. Establishment of reliable criteria to discriminate refractory patients from responders as early as possible, that is, before or during the early phase of standard treatment, is considered ideal to adapt treatment modality and intensity. The International Prognostic Score has been a widely recognized tool for estimating prognosis and potential risk stratification in advanced cHL.71 However, the clinical utility of the International Prognostic Score as a predictive biomarker is limited, and therefore routine application has been lacking. Thus, identification of clinically useful biomarkers to guide treatment selection is still urgently needed to achieve durable treatment success with favorable toxicity profiles. An overview about the most promising biomarkers in cHL, categorized by assay type, is provided in Figure 2.



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Imaging Biomarkers

The functional imaging assessment using [18F]-fluoro-2-deoxy-D-glucose (FDG) positron emission tomography (PET)/computed tomography (CT) has been introduced as a promising guiding tool to evaluate chemosensitivity and outcome in cHL.72–77 Numerous nonmalignant immune cells in the TME of cHL and their interaction with HRS cells demonstrated high glycolytic activity, resulting in high FDG uptake in cHL.78 Positron emission tomography can be used at different time points: for staging at diagnosis (“baseline”), for response assessment during the treatment (“interim”), and at the end of treatment (“response assessment”). As a dynamic biomarker assay, interim FDG-PET during first-line treatment can predict relapse or refractory disease with high sensitivity (0.8; 95% confidence interval, 0.72–0.89) and specificity (0.97; 95% confidence interval, 0.94–0.99).79 Based on these findings, PET-guided treatment strategies have been investigated in clinical trials in both early- and advanced-stage cHL.80–85 The results of these studies have shown the potential of PET response-adapted strategies to provide escalation treatment for chemorefractory patients to improve outcome and de-escalation treatment that might contribute to reduction of treatment-related toxicity for early responders. Recently, 3-dimensional measurements of disease burden such as metabolic tumor volume (MTV) and total lesion glycolysis evaluated by PET/CT have been proposed as novel methods to measure tumor burden of cHL.86,87 Although further validation in prospective trials is required to determine predictive biomarker properties, prognostic significance of quantitative measurement of MTV harbors the potential to improve risk stratification for clinical decision making in cHL. The combination of MTV values and baseline/interim PET is an intriguing approach88 and is the subject of current ongoing and planned future clinical trials. Regardless of its high potential as an imaging biomarker, PET/CT has several limitations. First, the false-negative rate of interim PET in cHL is considerable; for example, it was reported as 13% to 19% across multiple studies.79,84,89 This means that a significant proportion of patients are at risk of relapse or refractory disease despite negative PET/CT results. Second, the response is generally evaluated using a scoring system, the Deauville 5-point scale for PET/CT,90 which is not always easily reproducible. For example, in the RATHL study, the scoring discordance between local investigators and consensus reviewers was more than 30%.91 Moreover, there is currently no universal consensus for the definition of MTV. Because correct and universal risk stratification of patients is preferred at diagnosis or the early phase of treatment, other approaches need to be investigated to improve on the risk stratification system, potentially in synergy with PET/CT.

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Minimally Invasive Biomarkers

Peripheral blood is one of the ideal, minimally invasive, and easy-to-access biomarker sources. Several malignant cell–, TME-, and immune response–related markers, such as neutrophil-to-lymphocyte ratio, lymphocyte-to-monocyte ratio, serum thymus and activation-regulated chemokine (TARC), galectin 1, micro-RNAs, serum CD163, and serum CD30, have been proposed as tools to predict outcome using peripheral blood taken at diagnosis (“pretreatment”) or during treatment (“interim”) in cHL.92–98 Interestingly, serum TARC was correlated with disease status and can be predictive for PET/CT results in cHL, indicating that serum TARC has strong potential as a biomarker for monitoring tumor dynamics.99 These biomarkers likely in part reflect TME or immune biology, such as infiltrating lymphocytes and tumor-associated macrophage; however, detailed insight into the complex ecosystem of the TME of cHL-involved lymphatic tissue might not be possible. Another major limitation of these biomarkers is that optimal cutoff values have not yet been determined for each marker.

In recent years, circulating tumor DNA (ctDNA) has been investigated as a novel tool from peripheral blood that enables dynamic monitoring of tumor biology.100–104 The ctDNA contains DNA fragments that are released from apoptotic and necrotic cancer cells105 and can be detected and quantified by next-generation sequencing. As proof of concept, tumor-specific immunoglobulin gene segments were detected in peripheral blood of cHL patients.106 Moreover, Vandenberghe et al.107 assessed genomic imbalances in 9 cHL patients using ctDNA, and HRS cell–characteristic gains of chromosomes 2p and 9p were detected and validated by fluorescence in situ hybridization. Notably, these abnormalities could not be detected after 2 to 6 weeks of chemotherapy, suggesting clinical utility of ctDNA as a dynamic biomarker for therapy response. Furthermore, Camus and colleagues108 investigated the prognostic value of ctDNA using highly sensitive digital polymerase chain reaction for XPO1 E571K mutations that were previously reported in approximately 10% to 25% of cHL patients.22,108 Although the number of cHL patients with XPO1 E571K mutations in this study was low (n = 22), cHL patients with a detectable XPO1 mutation showed a trend toward shorter PFS when compared with cHL patients without XPO1 mutations (2-year PFS: 57.1% vs. 90.5%, P = 0.0601, respectively). The results of these studies indicated that ctDNA might be useful for monitoring disease dynamics including treatment response and minimal residual disease.

Spina et al.23 recently reported the utility of ctDNA as (1) a novel biomarker to predict chemorefractory patients in cHL patients treated with standard chemotherapy and (2) a reasonable source for genotyping HRS cells by deep targeted sequencing in retrospective study designs. They analyzed ctDNA of previously untreated (n = 80) and relapsed or refractory (n = 32) cHL as well as ctDNA from plasma collected during ABVD treatment (on day 1 of cycle 2) and at the end of treatment (n = 24). Nonsynonymous somatic mutations were detected in 81.2% of patients (an average of 5 mutations per case) with 5.5% (range, 0.29%–74.0%) mean variant allele frequency. Recurrently mutated genes (>20%) detected from ctDNA of the 80 cHL patients included STAT6, TNFAIP3, and ITPKB, findings that are consistent with well-described activation of signaling pathways in cHL such as JAK/STAT, NF-κB, and PI3K-AKT. In a small subset of patients, longitudinal ctDNA samples, including at the time of relapse or after salvage treatment, were genotyped and demonstrated the possibility to assess clonal evolution patterns. In addition, the concentration of ctDNA can be used as a surrogate marker of tumor load. Indeed, pretreatment ctDNA concentration was correlated with stage and outcome groups of cHL in this study. Specifically, a drop of 100-fold in ctDNA levels after 2 cycles of ABVD treatment was associated with favorable treatment outcome and CR status, similar to investigations in DLBCL.103 Importantly, the quantification of ctDNA might be useful in addition to interim PET/CT results that are known to produce false-positive results after immune checkpoint blockade (“pseudo progression”109) as the current ctDNA study by Spina and colleagues,23 indicating that incorporation of both biomarkers (ctDNA and PET/CT) can predict outcome more precisely. Further evaluation of the clinical utility of ctDNA analysis is required in the context of prospective trials with focus on response-adapted treatment and monitoring of clonal evolution and treatment resistance mechanisms.

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Biomarkers Using Tumor Biopsies

Tumor biopsy samples have been used as a major source for biomarker development for more than 30 years. In 1987, Ree and Kadin110 found increased macrophages in the TME was a poor prognostic biomarker using agglutinin staining. Many following studies used IHC to evaluate protein expression on either the tumor cells and/or the immune cells in the TME.111–113 Considering the well-referenced immune-suppressive function of the TME, mainly due to regulatory immune cell infiltration, the characterization of TME signatures emerged as a major focus for biomarker studies and attempts to establish biomarkers for clinical testing. Importantly, the complex interaction between tumor cells and the TME likely plays a pivotal role in acquiring immune privilege phenotypes of HRS cells. Thus, analyzing single genes or proteins might be insufficient, and more comprehensive approaches are preferable. Gene expression profiling is one of the approaches using recent technology advances to interrogate sample resources generated during routine diagnostic procedures, such as formalin-fixed and paraffin-embedded biopsies.114 Because of the paucity of the HRS cells in the whole-tissue biopsies of cHL, analysis of whole-tissue sections is largely a reflection of TME composition and function in cHL-involved tissues. Therefore, most studies aiming to find biomarkers using gene expression profiling have focused on the TME.115–119 Among these studies, a hallmark discovery was the association of the prevalence of macrophages with patient outcomes in previously untreated cHL.118–120 A follow-up study using the NanoString platform successfully expanded this finding to a more reliable predictive model that comprised the expression levels of 23 genes for overall survival in newly diagnosed advanced-stage cHL treated with ABVD.119 The 23 genes in the predictive model included genes that were reflective of not only a macrophage signature, but also TH1 response, cytotoxic T cells, cytokines, and natural killer cells, indicating that this model captured complex TME biology and tumor-host interactions. However, the 23 gene expression assay did not predict clinical outcome in the RATHL and S0816 trials,121,122 possible due to the response-adapted treatment strategy in these trials.

Despite these advances in biomarker studies for newly diagnosed cHL, biology and biomarker studies at the time point of relapse are rare, and until recently, no reproducible, well-validated biomarkers for relapsed or refractory patients were available.113,123–130 Considering the unsatisfactory relapse rate of approximately 50% after standard salvage treatment with high-dose therapy followed by autologous stem cell transplantation (ASCT),131 establishment of risk-stratified treatment strategies using reliable biomarkers at the time of relapse are needed. Recently, Chan and colleagues132 established a novel gene expression–based prognostic model of post-ASCT outcomes (RHL30) in relapsed or refractory cHL. Using digital gene expression analysis of 71 cHL patients with matched paired primary and relapse /refractory formalin-fixed and paraffin-embedded specimens, biologic factors including TME composition differences between primary and relapse specimen were investigated. Interestingly, approximately a quarter of patients displayed low correlation between the gene expression profiles of their primary and relapse specimens, and a subset of these patients with low correlation exhibited inferior failure-free survival (FFS) when compared with patients with high correlation (5-year FFS: 38.4% vs. 68.4%, P = 0.005 respectively), indicating evolving TME dynamics in relapse and refractory cHL biology. The most striking TME variation between primary and relapse specimens was reflected in an inverse correlation of relative changes in macrophage and B cell signatures (Spearman r = −0.796; P < 0.001). These findings were validated using IHC (Spearman r = −0.645; P < 0.001) using CD20 and CD68. In addition, the study revealed that interrogation of relapse specimens was superior to diagnostic/pretreatment biopsies in predicting post-ASCT outcomes. Finally, using 18 genes associated with post-ASCT outcomes including B-cell, macrophage, HRS-cell, and drug resistance components, a novel gene expression–based prognostic model on the NanoString platform, called RHL30, was established. RHL30 stratified relapse cHL patients into 2 risk groups (high risk and low risk) according to post-ASCT FFS, and the prognostic power was validated in 2 independent external cohorts. Of note, in multivariable testing, RHL30 was an independent prognostic marker compared with known other biomarkers including IHC and PET, indicating that RHL30 has the potential to be used for treatment modification or selection in relapsed or refractory cHL. The AETHERA trial demonstrated that the early consolidation therapy with brentuximab vedotin after ASCT improved ASCT outcome in patients with unfavorable risk relapsed or refractory cHL.133 However, there is currently no universal definition of “unfavorable risk” patients who might benefit most from brentuximab vedotin consolidation in relapsed or refractory cHL. Although PET results before ASCT can be used for this purpose,87,134–136 PET was not routinely evaluated in the AETHERA trial. Considering the significant and independent power of RHL30 to predict treatment failure after ASCT, RHL30 might add to the accuracy of risk-stratification tools to guide treatment decisions, including consolidation or maintenance therapy. Future clinical trials will be needed to investigate the utility of RHL30 as a predictive biomarker in this setting.

The introduction of PD-1 checkpoint inhibitors, nivolumab and pembrolizumab, has dramatically changed the treatment strategy of relapsed or refractory cHL with high ORR exceeding 60%.47–52 Roemer et al.42 evaluated biomarkers that can predict outcome of PD-1 blockade in relapsed or refractory cHL treated with nivolumab using specimens from the CheckMate 205 study.42,51 They evaluated 9p24.1 alterations by fluorescence in situ hybridization and expression of PD-L1, β2-microglobulin, MHC class I, and MHC class II by IHC. The patients whose tumors had lower levels of 9p24.1 copy number gains and less PD-L1 expression on HRS cells showed inferior PFS (P < 0.001 and P = 0.026, respectively). Notably, the expression of MHC class II on HRS cells was predictive for CR, whereas the expression of β2-microglobulin and MHC class I on HRS cells was not predictive for CR or PFS. In the subset of patients who were treated with nivolumab, after more than 12 months the expression of MHC class II on HRS cells was predictive for PFS (P = 0.014). Unlike in solid tumors, MHC class II and not MHC class I may play a major role in the mechanism of action of PD-1–blocking antibodies. Although these novel and potentially predictive biomarkers (PD-L1 and MHC class II expression on HRS cells) have emerged in the context of PD-1 blockade in cHL, it remains unclear which cells are the most important targets of PD-1 antibodies and which components are most relevant for the immune escape phenotype in cHL, suggesting that further comprehensive investigations of interaction with HRS cells and TME components are needed. Such studies should ideally include further characterization of PD-L1+ macrophages and CD4+ T cells with additional markers (e.g., exhaustion markers such as LAG3 and PIM3).

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Recent advances in molecular and genetic studies as well as the emergence of novel treatments targeting TME biology have provided a new framework for biomarker development and assessment of clinical utility (Fig. 2). Novel biomarkers such as ctDNA, MHC class II expression on HRS cells, and RHL30 have already shown promise to guide treatment selection or response-adapted treatment algorithms with the goal to achieve favorable long-term outcomes.23,42,132 Adding to well-established imaging techniques, gene expression–based analysis platforms and ctDNA measurements have also demonstrated the capability of capturing tumor dynamics. However, most biomarkers were developed by retrospective analyses in small sample cohorts and should therefore be prospectively validated in clinical trials. The complex interactions between HRS cells and their TME remain only partially understood, indicating the potential for identification of more powerful biomarkers in cHL in the future.

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This work was supported by JSPS Overseas Research fellowships (T.A.). CS is the recipient of a Michael Smith Foundation for Health Research Career Investigator award. We would like to thank Dr Katsuyoshi Takata for providing valuable pathology insight.

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1. Bazzeh F, Rihani R, Howard S, et al. Comparing adult and pediatric Hodgkin lymphoma in the Surveillance, Epidemiology and End Results Program, 1988–2005: an analysis of 21 734 cases. Leuk Lymphoma. 2010;51:2198–2207.
2. Swerdlow SH, Campo E, Pileri SA, et al. The 2016 revision of the World Health Organization classification of lymphoid neoplasms. Blood. 2016;127:2375–2390.
3. Bonadonna G, Zucali R, Monfardini S, et al. Combination chemotherapy of Hodgkin's disease with adriamycin, bleomycin, vinblastine, and imidazole carboxamide versus MOPP. Cancer. 1975;36:252–259.
4. Lister TA, Crowther D, Sutcliffe SB, et al. Report of a committee convened to discuss the evaluation and staging of patients with Hodgkin's disease: Cotswolds meeting. J Clin Oncol. 1989;7:1630–1636.
5. Eich HT, Diehl V, Gorgen H, et al. Intensified chemotherapy and dose-reduced involved-field radiotherapy in patients with early unfavorable Hodgkin's lymphoma: final analysis of the German Hodgkin Study Group HD11 trial. J Clin Oncol. 2010;28:4199–4206.
6. Engert A, Plutschow A, Eich HT, et al. Reduced treatment intensity in patients with early-stage Hodgkin's lymphoma. N Engl J Med. 2010;363:640–652.
7. Connors JM. Risk assessment in the management of newly diagnosed classical Hodgkin lymphoma. Blood. 2015;125:1693–1702.
8. Swerdlow AJ, Barber JA, Hudson GV, et al. Risk of second malignancy after Hodgkin's disease in a collaborative British cohort: the relation to age at treatment. J Clin Oncol. 2000;18:498–509.
9. van Leeuwen FE, Klokman WJ, Veer MB, et al. Long-term risk of second malignancy in survivors of Hodgkin's disease treated during adolescence or young adulthood. J Clin Oncol. 2000;18:487–497.
10. Aleman BM, van den Belt-Dusebout AW, De Bruin ML, et al. Late cardiotoxicity after treatment for Hodgkin lymphoma. Blood. 2007;109:1878–1886.
11. Schaapveld M, Aleman BM, van Eggermond AM, et al. Second cancer risk up to 40 years after treatment for Hodgkin's lymphoma. N Engl J Med. 2015;373:2499–2511.
12. Sud A, Thomsen H, Sundquist K, et al. Risk of second cancer in Hodgkin lymphoma survivors and influence of family history. J Clin Oncol. 2017;35:1584–1590.
13. Ballman KV. Biomarker: predictive or prognostic? J Clin Oncol. 2015;33:3968–3971.
14. Kuppers R, Rajewsky K, Zhao M, et al. Hodgkin disease: Hodgkin and Reed-Sternberg cells picked from histological sections show clonal immunoglobulin gene rearrangements and appear to be derived from B cells at various stages of development. Proc Natl Acad Sci U S A. 1994;91:10962–10966.
15. Kanzler H, Kuppers R, Hansmann ML, et al. Hodgkin and Reed-Sternberg cells in Hodgkin's disease represent the outgrowth of a dominant tumor clone derived from (crippled) germinal center B cells. J Exp Med. 1996;184:1495–1505.
16. Weniger MA, Tiacci E, Schneider S, et al. Human CD30+ B cells represent a unique subset related to Hodgkin lymphoma cells. J Clin Invest. 2018;128:2996–3007.
17. Re D, Muschen M, Ahmadi T, et al. Oct-2 and Bob-1 deficiency in Hodgkin and Reed Sternberg cells. Cancer Res. 2001;61:2080–2084.
18. Schwering I, Bräuninger A, Klein U, et al. Loss of the B-lineage-specific gene expression program in Hodgkin and Reed-Sternberg cells of Hodgkin lymphoma. Blood. 2003;101:1505–1512.
19. Steidl C, Telenius A, Shah SP, et al. Genome-wide copy number analysis of Hodgkin Reed-Sternberg cells identifies recurrent imbalances with correlations to treatment outcome. Blood. 2010;116:418–427.
20. Steidl C, Shah SP, Woolcock BW, et al. MHC class II transactivator CIITA is a recurrent gene fusion partner in lymphoid cancers. Nature. 2011;471:377–381.
21. Steidl C, Diepstra A, Lee T, et al. Gene expression profiling of microdissected Hodgkin Reed-Sternberg cells correlates with treatment outcome in classical Hodgkin lymphoma. Blood. 2012;120:3530–3540.
22. Reichel J, Chadburn A, Rubinstein PG, et al. Flow sorting and exome sequencing reveal the oncogenome of primary Hodgkin and Reed-Sternberg cells. Blood. 2015;125:1061–1072.
23. Spina V, Bruscaggin A, Cuccaro A, et al. Circulating tumor DNA reveals genetics, clonal evolution, and residual disease in classical Hodgkin lymphoma. Blood. 2018;131:2413–2425.
24. Tiacci E, Ladewig E, Schiavoni G, et al. Pervasive mutations of JAK-STAT pathway genes in classical Hodgkin lymphoma. Blood. 2018;131:2454–2465.
25. Schmitz R, Stanelle J, Hansmann ML, et al. Pathogenesis of classical and lymphocyte-predominant Hodgkin lymphoma. Annu Rev Pathol. 2009;4:151–174.
26. Vockerodt M, Yap LF, Shannon-Lowe C, et al. The Epstein-Barr virus and the pathogenesis of lymphoma. J Pathol. 2015;235:312–322.
27. Carbone A, Gloghini A, Caruso A, et al. The impact of EBV and HIV infection on the microenvironmental niche underlying Hodgkin lymphoma pathogenesis. Int J Cancer. 2017;140:1233–1245.
28. Mottok A, Steidl C. Genomic alterations underlying immune privilege in malignant lymphomas. Curr Opin Hematol. 2015;22:343–354.
29. Mottok A, Woolcock B, Chan FC, et al. Genomic alterations in CIITA are frequent in primary mediastinal large B cell lymphoma and are associated with diminished MHC class II expression. Cell Rep. 2015;13:1418–1431.
30. Twa DD, Chan FC, Ben-Neriah S, et al. Genomic rearrangements involving programmed death ligands are recurrent in primary mediastinal large B-cell lymphoma. Blood. 2014;123:2062–2065.
31. Juszczynski P, Ouyang J, Monti S, et al. The AP1-dependent secretion of galectin-1 by Reed Sternberg cells fosters immune privilege in classical Hodgkin lymphoma. Proc Natl Acad Sci U S A. 2007;104:13134–13139.
32. Green MR, Monti S, Rodig SJ, et al. Integrative analysis reveals selective 9p24.1 amplification, increased PD-1 ligand expression, and further induction via JAK2 in nodular sclerosing Hodgkin lymphoma and primary mediastinal large B-cell lymphoma. Blood. 2010;116:3268–3277.
33. Roemer MG, Advani RH, Ligon AH, et al. PD-L1 and PD-L2 genetic alterations define classical Hodgkin lymphoma and predict outcome. J Clin Oncol. 2016;34:2690–2697.
34. Green MR, Rodig S, Juszczynski P, et al. Constitutive AP-1 activity and EBV infection induce PD-L1 in Hodgkin lymphomas and posttransplant lymphoproliferative disorders: implications for targeted therapy. Clin Cancer Res. 2012;18:1611–1618.
35. Tumeh PC, Harview CL, Yearley JH, et al. PD-1 blockade induces responses by inhibiting adaptive immune resistance. Nature. 2014;515:568–571.
36. Im SJ, Hashimoto M, Gerner MY, et al. Defining CD8+ T cells that provide the proliferative burst after PD-1 therapy. Nature. 2016;537:417–421.
37. Kamphorst AO, Pillai RN, Yang S, et al. Proliferation of PD-1+ CD8 T cells in peripheral blood after PD-1-targeted therapy in lung cancer patients. Proc Natl Acad Sci U S A. 2017;114:4993–4998.
38. Kamphorst AO, Wieland A, Nasti T, et al. Rescue of exhausted CD8 T cells by PD-1–targeted therapies is CD28-dependent. Science. 2017;355:1423–1427.
39. Roemer MG, Advani RH, Redd RA, et al. Classical Hodgkin lymphoma with reduced β2M/MHC class I expression is associated with inferior outcome independent of 9p24.1 status. Cancer Immunol Res. 2016;4:910–916.
40. Carey CD, Gusenleitner D, Lipschitz M, et al. Topological analysis reveals a PD-L1–associated microenvironmental niche for Reed-Sternberg cells in Hodgkin lymphoma. Blood. 2017;130:2420–2430.
41. Diepstra A, Niens M, Vellenga E, et al. Association with HLA class I in Epstein-Barr-virus–positive and with HLA class III in Epstein-Barr-virus–negative Hodgkin's lymphoma. Lancet. 2005;365:2216–2224.
42. Roemer MGM, Redd RA, Cader FZ, et al. Major histocompatibility complex class II and programmed death ligand 1 expression predict outcome after programmed death 1 blockade in classic Hodgkin lymphoma. J Clin Oncol. 2018;36:942–950.
43. Cader FZ, Schackmann RCJ, Hu X, et al. Mass cytometry of Hodgkin lymphoma reveals a CD4+ exhausted T-effector and T-regulatory cell rich microenvironment. Blood. 2018;132:825–836.
44. Younes A, Gopal AK, Smith SE, et al. Results of a pivotal phase II study of brentuximab vedotin for patients with relapsed or refractory Hodgkin's lymphoma. J Clin Oncol. 2012;30:2183–2189.
45. Chen R, Palmer JM, Martin P, et al. Results of a multicenter phase II trial of brentuximab vedotin as second-line therapy before autologous transplantation in relapsed/refractory Hodgkin lymphoma. Biol Blood Marrow Transplant. 2015;21:2136–2140.
46. Connors JM, Jurczak W, Straus DJ, et al. Brentuximab vedotin with chemotherapy for stage III or IV Hodgkin's lymphoma. N Engl J Med. 2018;378:331–344.
47. Ansell SM. Nivolumab in the treatment of Hodgkin Lymphoma. Clin Cancer Res. 2017;23:1623–1626.
48. Armand P, Shipp MA, Ribrag V, et al. Pembrolizumab in patients with classical Hodgkin lymphoma after brentuximab vedotin failure: long-term efficacy from the phase 1b KEYNOTE-013 study. Blood. 2016;128:1108.
49. Moskowitz CH, Zinzani PL, Fanale MA, et al. Pembrolizumab in relapsed/refractory classical Hodgkin lymphoma: primary end point analysis of the phase 2 KEYNOTE-087 study. Blood. 2016;128:1107.
50. Timmerman JM, Engert A, Younes A, et al. Checkmate 205 update with minimum 12-month follow up: a phase 2 study of nivolumab in patients with relapsed/refractory classical Hodgkin lymphoma. Blood. 2016;128:1110.
51. Younes A, Santoro A, Shipp M, et al. Nivolumab for classical Hodgkin's lymphoma after failure of both autologous stem-cell transplantation and brentuximab vedotin: a multicentre, multicohort, single-arm phase 2 trial. Lancet Oncol. 2016;17:1283–1294.
52. Chen R, Zinzani PL, Fanale MA, et al. Phase II study of the efficacy and safety of pembrolizumab for relapsed/refractory classic Hodgkin lymphoma. J Clin Oncol. 2017;35:2125–2132.
53. Manson G, Herbaux C, Brice P, et al. Prolonged remissions after anti–PD-1 discontinuation in patients with Hodgkin lymphoma. Blood. 2018;131:2856–2859.
54. Diefenbach CS, Hong F, David KA, et al. Title: a phase I study with an expansion cohort of the combination of ipilimumab and nivolumab and brentuximab vedotin in patients with relapsed/refractory Hodgkin lymphoma: a trial of the ECOG-ACRIN Cancer Research Group (E4412 arms D and E). Blood. 2016;128:1106.
55. Diefenbach CS, Hong F, Cohen JB, et al. Preliminary safety and efficacy of the combination of brentuximab vedotin and ipilimumab in relapsed/refractory hodgkin lymphoma: a trial of the ECOG-ACRIN Cancer Research Group (E4412). Blood. 2015;126:585.
56. Herrera AF, Moskowitz AJ, Bartlett NL, et al. Interim results of brentuximab vedotin in combination with nivolumab in patients with relapsed or refractory Hodgkin lymphoma. Blood. 2018;131:1183–1194.
57. Bollard CM, Tripic T, Cruz CR, et al. Tumor-specific T-cells engineered to overcome tumor immune evasion induce clinical responses in patients with relapsed Hodgkin lymphoma. J Clin Oncol. 2018;36:1128–1139.
58. Massague J. TGF-beta signal transduction. Annu Rev Biochem. 1998;67:753–791.
59. Bollard CM, Gottschalk S, Torrano V, et al. Sustained complete responses in patients with lymphoma receiving autologous cytotoxic T lymphocytes targeting Epstein-Barr virus latent membrane proteins. J Clin Oncol. 2014;32:798–808.
60. Ramos CA, Ballard B, Zhang H, et al. Clinical and immunological responses after CD30-specific chimeric antigen receptor–redirected lymphocytes. J Clin Invest. 2017;127:3462–3471.
61. Wang CM, Wu ZQ, Wang Y, et al. Autologous T cells expressing CD30 chimeric antigen receptors for relapsed or refractory Hodgkin lymphoma: an open-label phase I trial. Clin Cancer Res. 2017;23:1156–1166.
62. Ruella M, Klichinsky M, Kenderian SS, et al. Overcoming the immunosuppressive tumor microenvironment of Hodgkin lymphoma using chimeric antigen receptor T cells. Cancer Discov. 2017;7:1154–1167.
63. Fromm JR, Sabath DE, Wood BL. Diagnostic usefulness of flow cytometry for immunophenotyping classical Hodgkin lymphoma. Am J Clin Pathol. 2011;136:157–158; author reply 8.
64. Gardner R, Wu D, Cherian S, et al. Acquisition of a CD19-negative myeloid phenotype allows immune escape of MLL-rearranged B-ALL from CD19 CAR–T-cell therapy. Blood. 2016;127:2406–2410.
65. Diepstra A, van Imhoff GW, Karim-Kos HE, et al. HLA class II expression by Hodgkin Reed-Sternberg cells is an independent prognostic factor in classical Hodgkin's lymphoma. J Clin Oncol. 2007;25:3101–3108.
66. Nijland M, Veenstra RN, Visser L, et al. HLA dependent immune escape mechanisms in B-cell lymphomas: implications for immune checkpoint inhibitor therapy? Oncoimmunology. 2017;6:e1295202.
67. Hombach A, Jung W, Pohl C, et al. A CD16/CD30 bispecific monoclonal antibody induces lysis of Hodgkin's cells by unstimulated natural killer cells in vitro and in vivo. Int J Cancer. 1993;55:830–836.
68. Rothe A, Sasse S, Topp MS, et al. A phase 1 study of the bispecific anti-CD30/CD16A antibody construct AFM13 in patients with relapsed or refractory Hodgkin lymphoma. Blood. 2015;125:4024–4031.
69. Younes A, Romaguera J, Fanale M, et al. Phase I study of a novel oral Janus kinase 2 inhibitor, SB1518, in patients with relapsed lymphoma: evidence of clinical and biologic activity in multiple lymphoma subtypes. J Clin Oncol. 2012;30:4161–4167.
70. Van Den Neste E, Andre M, Gastinne T, et al. A phase II study of the oral JAK1/JAK2 inhibitor ruxolitinib in advanced relapsed/refractory Hodgkin lymphoma. Haematologica. 2018;103:840–848.
71. Hasenclever D, Diehl V. A prognostic score for advanced Hodgkin's disease. International Prognostic Factors Project on Advanced Hodgkin's Disease. N Engl J Med. 1998;339:1506–1514.
72. Wiedmann E, Baican B, Hertel A, et al. Positron emission tomography (PET) for staging and evaluation of response to treatment in patients with Hodgkin's disease. Leuk Lymphoma. 1999;34:545–551.
73. Hutchings M, Mikhaeel NG, Fields PA, et al. Prognostic value of interim FDG-PET after two or three cycles of chemotherapy in Hodgkin lymphoma. Ann Oncol. 2005;16:1160–1168.
74. Gallamini A, Rigacci L, Merli F, et al. The predictive value of positron emission tomography scanning performed after two courses of standard therapy on treatment outcome in advanced stage Hodgkin's disease. Haematologica. 2006;91:475–481.
75. Hutchings M, Loft A, Hansen M, et al. FDG-PET after two cycles of chemotherapy predicts treatment failure and progression-free survival in Hodgkin lymphoma. Blood. 2006;107:52–59.
76. Kostakoglu L, Goldsmith SJ, Leonard JP, et al. FDG-PET after 1 cycle of therapy predicts outcome in diffuse large cell lymphoma and classic Hodgkin disease. Cancer. 2006;107:2678–2687.
77. Gallamini A, Hutchings M, Rigacci L, et al. Early interim 2-[18F]fluoro-2-deoxy-D-glucose positron emission tomography is prognostically superior to international prognostic score in advanced-stage Hodgkin's lymphoma: a report from a joint Italian-Danish study. J Clin Oncol. 2007;25:3746–3752.
78. Ma Y, Visser L, Roelofsen H, et al. Proteomics analysis of Hodgkin lymphoma: identification of new players involved in the cross-talk between HRS cells and infiltrating lymphocytes. Blood. 2008;111:2339–2346.
79. Terasawa T, Lau J, Bardet S, et al. Fluorine-18-fluorodeoxyglucose positron emission tomography for interim response assessment of advanced-stage Hodgkin's lymphoma and diffuse large B-cell lymphoma: a systematic review. J Clin Oncol. 2009;27:1906–1914.
80. Radford J, Illidge T, Counsell N, et al. Results of a trial of PET-directed therapy for early-stage Hodgkin's lymphoma. N Engl J Med. 2015;372:1598–1607.
81. Johnson P, Federico M, Kirkwood A, et al. Adapted treatment guided by interim PET-CT scan in advanced Hodgkin's lymphoma. N Engl J Med. 2016;374:2419–2429.
82. Johnson PW. Response-adapted frontline therapy for Hodgkin lymphoma: are we there yet? Hematology Am Soc Hematol Educ Program. 2016;2016:316–322.
83. Andre MPE, Girinsky T, Federico M, et al. Early positron emission tomography response-adapted treatment in stage I and II Hodgkin lymphoma: final results of the randomized EORTC/LYSA/FIL H10 trial. J Clin Oncol. 2017;35:1786–1794.
84. Borchmann P, Goergen H, Kobe C, et al. PET-guided treatment in patients with advanced-stage Hodgkin's lymphoma (HD18): final results of an open-label, international, randomised phase 3 trial by the German Hodgkin Study Group. Lancet. 2018;390:2790–2802.
85. Gallamini A, Tarella C, Viviani S, et al. Early chemotherapy intensification with escalated BEACOPP in patients with advanced-stage Hodgkin lymphoma with a positive interim positron emission tomography/computed tomography scan after two ABVD cycles: long-term results of the GITIL/FIL HD 0607 trial. J Clin Oncol. 2018;36:454–462.
86. Akhtari M, Milgrom SA, Pinnix CC, et al. Reclassifying patients with early-stage Hodgkin lymphoma based on functional radiographic markers at presentation. Blood. 2018;131:84–94.
87. Moskowitz AJ, Schoder H, Gavane S, et al. Prognostic significance of baseline metabolic tumor volume in relapsed and refractory Hodgkin lymphoma. Blood. 2017;130:2196–2203.
88. Meignan M, Itti E, Gallamini A, et al. FDG PET/CT imaging as a biomarker in lymphoma. Eur J Nucl Med Mol Imaging. 2015;42:623–633.
89. Press OW, Li H, Schoder H, et al. US Intergroup Trial of response-adapted therapy for stage III to IV Hodgkin lymphoma using early interim fluorodeoxyglucose–positron emission tomography imaging: Southwest Oncology Group S0816. J Clin Oncol. 2016;34:2020–2027.
90. Barrington SF, Mikhaeel NG, Kostakoglu L, et al. Role of imaging in the staging and response assessment of lymphoma: consensus of the International Conference on Malignant Lymphomas Imaging Working Group. J Clin Oncol. 2014;32:3048–3058.
91. Barrington SF, Kirkwood AA, Franceschetto A, et al. PET-CT for staging and early response: results from the Response-Adapted Therapy in Advanced Hodgkin Lymphoma study. Blood. 2016;127:1531–1538.
92. Jones K, Vari F, Keane C, et al. Serum CD163 and TARC as disease response biomarkers in classical Hodgkin lymphoma. Clin Cancer Res. 2013;19:731–742.
93. Jones K, Nourse JP, Keane C, et al. Plasma microRNA are disease response biomarkers in classical Hodgkin lymphoma. Clin Cancer Res. 2014;20:253–264.
94. Plattel WJ, Alsada ZN, van Imhoff GW, et al. Biomarkers for evaluation of treatment response in classical Hodgkin lymphoma: comparison of sGalectin-1, sCD163 and sCD30 with TARC. Br J Haematol. 2016;175:868–875.
95. Weihrauch MR, Manzke O, Beyer M, et al. Elevated serum levels of CC thymus and activation-related chemokine (TARC) in primary Hodgkin's disease: potential for a prognostic factor. Cancer Res. 2005;65:5516–5519.
96. Porrata LF, Ristow K, Colgan JP, et al. Peripheral blood lymphocyte/monocyte ratio at diagnosis and survival in classical Hodgkin's lymphoma. Haematologica. 2012;97:262–269.
97. Vassilakopoulos TP, Dimopoulou MN, Angelopoulou MK, et al. Prognostic Implication of the Absolute Lymphocyte to Absolute Monocyte Count Ratio in Patients With Classical Hodgkin Lymphoma Treated With Doxorubicin, Bleomycin, Vinblastine, and Dacarbazine or Equivalent Regimens. Oncologist. 2016;21:343–353.
98. Romano A, Parrinello NL, Vetro C, et al. Prognostic meaning of neutrophil to lymphocyte ratio (NLR) and lymphocyte to monocyte ration (LMR) in newly diagnosed Hodgkin lymphoma patients treated upfront with a PET-2 based strategy. Ann Hematol. 2018;97:1009–1018.
99. Farina L, Rezzonico F, Spina F, et al. Serum thymus and activation-regulated chemokine level monitoring may predict disease relapse detected by PET scan after reduced-intensity allogeneic stem cell transplantation in patients with Hodgkin lymphoma. Biol Blood Marrow Transplant. 2014;20:1982–1988.
100. Diehl F, Schmidt K, Choti MA, et al. Circulating mutant DNA to assess tumor dynamics. Nat Med. 2008;14:985–990.
101. Faham M, Zheng J, Moorhead M, et al. Deep-sequencing approach for minimal residual disease detection in acute lymphoblastic leukemia. Blood. 2012;120:5173–5180.
102. Bettegowda C, Sausen M, Leary RJ, et al. Detection of circulating tumor DNA in early- and late-stage human malignancies. Sci Transl Med. 2014;6:224ra24.
103. Kurtz DM, Green MR, Bratman SV, et al. Noninvasive monitoring of diffuse large B-cell lymphoma by immunoglobulin high-throughput sequencing. Blood. 2015;125:3679–3687.
104. Roschewski M, Dunleavy K, Pittaluga S, et al. Circulating tumour DNA and CT monitoring in patients with untreated diffuse large B-cell lymphoma: a correlative biomarker study. Lancet Oncol. 2015;16:541–549.
105. Jahr S, Hentze H, Englisch S, et al. DNA fragments in the blood plasma of cancer patients: quantitations and evidence for their origin from apoptotic and necrotic cells. Cancer Res. 2001;61:1659–1665.
106. Oki Y, Neelapu SS, Fanale M, et al. Detection of classical Hodgkin lymphoma specific sequence in peripheral blood using a next-generation sequencing approach. Br J Haematol. 2015;169:689–693.
107. Vandenberghe P, Wlodarska I, Tousseyn T, et al. Non-invasive detection of genomic imbalances in Hodgkin/Reed-Sternberg cells in early and advanced stage Hodgkin's lymphoma by sequencing of circulating cell-free DNA: a technical proof-of-principle study. Lancet Haematol. 2015;2:e55–e65.
108. Camus V, Stamatoullas A, Mareschal S, et al. Detection and prognostic value of recurrent exportin 1 mutations in tumor and cell-free circulating DNA of patients with classical Hodgkin lymphoma. Haematologica. 2016;101:1094–1101.
109. Cheson BD, Ansell S, Schwartz L, et al. Refinement of the Lugano Classification lymphoma response criteria in the era of immunomodulatory therapy. Blood. 2016;128:2489–2496.
110. Ree HJ, Kadin ME. Macrophage-histiocytes in Hodgkin's disease. The relation of peanut-agglutinin-binding macrophage-histiocytes to clinicopathologic presentation and course of disease. Cancer. 1985;56:333–338.
111. Kelley TW, Pohlman B, Elson P, et al. The ratio of FOXP3+ regulatory T cells to granzyme B+ cytotoxic T/NK cells predicts prognosis in classical Hodgkin lymphoma and is independent of bcl-2 and MAL expression. Am J Clin Pathol. 2007;128:958–965.
112. Tzankov A, Meier C, Hirschmann P, et al. Correlation of high numbers of intratumoral FOXP3+ regulatory T cells with improved survival in germinal center-like diffuse large B-cell lymphoma, follicular lymphoma and classical Hodgkin's lymphoma. Haematologica. 2008;93:193–200.
113. Casulo C, Arcila M, Bohn OL, et al. Tumor associated macrophages in relapsed and refractory Hodgkin lymphoma. Leuk Res. 2013;37:1178–1183.
114. Geiss GK, Bumgarner RE, Birditt B, et al. Direct multiplexed measurement of gene expression with color-coded probe pairs. Nat Biotechnol. 2008;26:317–325.
115. Devilard E, Bertucci F, Trempat P, et al. Gene expression profiling defines molecular subtypes of classical Hodgkin's disease. Oncogene. 2002;21:3095–3102.
116. Sanchez-Aguilera A, Montalban C, de la Cueva P, et al. Tumor microenvironment and mitotic checkpoint are key factors in the outcome of classic Hodgkin lymphoma. Blood. 2006;108:662–668.
117. Chetaille B, Bertucci F, Finetti P, et al. Molecular profiling of classical Hodgkin lymphoma tissues uncovers variations in the tumor microenvironment and correlations with EBV infection and outcome. Blood. 2009;113:2765–3775.
118. Steidl C, Lee T, Shah SP, et al. Tumor-associated macrophages and survival in classic Hodgkin's lymphoma. N Engl J Med. 2010;362:875–885.
119. Scott DW, Chan FC, Hong F, et al. Gene expression–based model using formalin-fixed paraffin-embedded biopsies predicts overall survival in advanced-stage classical Hodgkin lymphoma. J Clin Oncol. 2013;31:692–700.
120. Steidl C, Farinha P, Gascoyne RD. Macrophages predict treatment outcome in Hodgkin's lymphoma. Haematologica. 2011;96:186–189.
121. Burton CH, Scott D, Kirkwood AA, et al. Application of a gene expression–based model in combination with FDG-PET imaging to predict treatment response in advanced Hodgkin lymphoma in the RATHL Study (CRUK/07/033). Hematol Oncol. 2017;35:91–92.
122. Scott DW, Li H, Harvey Y, et al. The 23-gene gene expression–based assay does not predict interim PET scan results after ABVD in advanced stage classical Hodgkin lymphoma in the US Intergroup S0816 trial. Hematol Oncol. 2017;35:92–93.
123. Lohri A, Barnett M, Fairey RN, et al. Outcome of treatment of first relapse of Hodgkin's disease after primary chemotherapy: identification of risk factors from the British Columbia experience 1970 to 1988. Blood. 1991;77:2292–2298.
124. Brice P, Bastion Y, Divine M, et al. Analysis of prognostic factors after the first relapse of Hodgkin's disease in 187 patients. Cancer. 1996;78:1293–1299.
125. Josting A, Rueffer U, Franklin J, et al. Prognostic factors and treatment outcome in primary progressive Hodgkin lymphoma: a report from the German Hodgkin Lymphoma Study Group. Blood. 2000;96:1280–1286.
126. Bierman PJ, Lynch JC, Bociek RG, et al. The International Prognostic Factors Project score for advanced Hodgkin's disease is useful for predicting outcome of autologous hematopoietic stem cell transplantation. Ann Oncol. 2002;13:1370–1377.
127. Josting A, Engert A, Diehl V, et al. Prognostic factors and treatment outcome in patients with primary progressive and relapsed Hodgkin's disease. Ann Oncol. 2002;13(suppl 1):112–116.
128. Perz JB, Giles C, Szydlo R, et al. LACE-conditioned autologous stem cell transplantation for relapsed or refractory Hodgkin's lymphoma: treatment outcome and risk factor analysis in 67 patients from a single centre. Bone Marrow Transplant. 2007;39:41–47.
129. Koreishi AF, Saenz AJ, Persky DO, et al. The role of cytotoxic and regulatory T cells in relapsed/refractory Hodgkin lymphoma. Appl Immunohistochem Mol Morphol. 2010;18:206–211.
130. Gerrie AS, Power MM, Shepherd JD, et al. Chemoresistance can be overcome with high-dose chemotherapy and autologous stem-cell transplantation for relapsed and refractory Hodgkin lymphoma. Ann Oncol. 2014;25:2218–2223.
131. Schmitz N, Pfistner B, Sextro M, et al. Aggressive conventional chemotherapy compared with high-dose chemotherapy with autologous haemopoietic stem-cell transplantation for relapsed chemosensitive Hodgkin's disease: a randomised trial. Lancet. 2002;359:2065–2071.
132. Chan FC, Mottok A, Gerrie AS, et al. Prognostic model to predict post-autologous stem-cell transplantation outcomes in classical Hodgkin lymphoma. J Clin Oncol. 2017;35:3722–3733. JCO2017727925.
133. Moskowitz CH, Nademanee A, Masszi T, et al. Brentuximab vedotin as consolidation therapy after autologous stem-cell transplantation in patients with Hodgkin's lymphoma at risk of relapse or progression (AETHERA): a randomised, double-blind, placebo-controlled, phase 3 trial. Lancet. 2015;385:1853–1862.
134. Moskowitz AJ, Yahalom J, Kewalramani T, et al. Pretransplantation functional imaging predicts outcome following autologous stem cell transplantation for relapsed and refractory Hodgkin lymphoma. Blood. 2010;116:4934–4937.
135. Devillier R, Coso D, Castagna L, et al. Positron emission tomography response at the time of autologous stem cell transplantation predicts outcome of patients with relapsed and/or refractory Hodgkin's lymphoma responding to prior salvage therapy. Haematologica. 2012;97:1073–1079.
136. Gentzler RD, Evens AM, Rademaker AW, et al. F-18 FDG-PET predicts outcomes for patients receiving total lymphoid irradiation and autologous blood stem-cell transplantation for relapsed and refractory Hodgkin lymphoma. Br J Haematol. 2014;165:793–800.

biomarker; checkpoint inhibitor; Hodgkin lymphoma; immune biology; therapeutic targets; tumor microenvironment

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