Circulating biomarkers in classical Hodgkin lymphoma
Plattel, Wouter Johannes
DOI:
10.33612/diss.97631424
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Plattel, W. J. (2019). Circulating biomarkers in classical Hodgkin lymphoma. Rijksuniversiteit Groningen.
https://doi.org/10.33612/diss.97631424
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The studies described in this thesis were financially supported by the Dutch Cancer Society (Grant no. RUG 2010-4860) and the Dutch Organization of Scientific Research (NWO ZonMW Grant no. 92003569).
© Copyright by Wouter Plattel, 2019. All rights reserved. Cover design and layout: © evelienjagtman.com
Proefschrift
ter verkrijging van de graad van doctor aan de Rijksuniversiteit Groningen
op gezag van de
rector magnificus prof. dr. C. Wijmenga en volgens besluit van het College voor Promoties.
De openbare verdediging zal plaatsvinden op woensdag 16 oktober 2019 om 16.15 uur
door
Wouter Johannes Plattel
geboren op 26 januari 1985 te
Prof. dr. J.H.M. van den Berg Prof. dr. J.C. Kluin-Nelemans Copromotores Dr. G.W. van Imhoff Dr. A. Visser Beoordelingscommissie Prof. dr. M.J. Kersten Prof. dr. D. de Jong Prof. dr. G. de Haan
Chapter 1 Introduction and review of circulating biomarkers in Hodgkin Lymphoma
In preparation
7
PART 1
Chapter 2 Plasma Thymus and Activation-Regulated Chemokine as an early response marker in classical Hodgkin lymphoma
Haematologica. 2012 Mar;97(3):410-5
31
Chapter 3 Biomarkers for evaluation of treatment response in classical Hodgkin lymphoma: comparison of sGalectin-1, sCD163 and sCD30 with TARC
Br J Haematol. 2016 Dec;175(5):868-875
53
Chapter 4 Interim TARC versus interim 18F-FDG-PET in classical Hodgkin
lymphoma response evaluation Submitted
71
PART 2
Chapter 5 The role of microRNAs in Hodgkin lymphoma In MicroRNAs in Medicine, C. H. Lawrie (Ed.). 2013
83 Chapter 6 Pre-analytical, analytical and post-analytical challenges in
circulating miRNA studies
99 Chapter 7 Circulating miRNAs in serum of patients with classical Hodgkin
Lymphoma
117
SUMMARY AND DISCUSSION
Chapter 8 Summary, general discussion and future perspectives 133
Chapter 9 Nederlandse samenvatting 149
APPENDICES Dankwoord List of publications Curriculum Vitae 161 169 171
Modified from: Circulating biomarkers in classical Hodgkin Lymphoma
Wouter J. Plattel, Anke van den Berg, Lydia Visser, Hanneke C. Kluin-Nelemans, Gustaaf W. van Imhoff, and Arjan Diepstra
In preparation
CHAPTER 1
Introduction and review of circulating
1
Introduction and scope of the thesis
Epidemiology and pathobiology
Hodgkin lymphoma (HL), formally known as Hodgkin’s disease, is a malignant disease named after Thomas Hodgkin who first described this entity in 1832.1 HL has an age-standardized
incidence rate of 3 per 100.000 per year and accounts for 15-25% of all lymphoma patients in the western world.2 Remarkably, about half of the patients are adolescents or young adults.
HL is characterized by the presence of large malignant cells, which contain two nuclei or nuclear lobes. Together with its mononuclear variants, these cells are called Hodgkin Reed-Sternberg (HRS) cells and are typical for classical HL (cHL).2 Classical HL accounts for approximately 95%
of all HL cases and the remaining 5% consist of the non-classical subtype nodular lymphocyte predominant Hodgkin lymphoma (NLPHL). This non-classical variant is regarded as a distinct entity based on its distinct histopathological features, clinical presentation and clinical behavior. In this thesis we will focus on the cHL subtype only.
Classical HL has a unique and fascinating pathobiology. First, the malignant cells are greatly outnumbered by a massive inflammatory component which is unique not only among lymphomas but also among solid cancers (Figure 1). Second, HRS are of B-cell origin, but they have lost their entire B-cell phenotype including surface immunoglobulin expression.3
Last, about 30% of cHL cases contain the Epstein Barr Virus (EBV), which is involved in the pathogenesis of these cases.2
Figure 1. Histopathological pictures of cHL. The scarcity of the HRS cells with its surrounding
microenvironment (A) and an example of the interaction between HRS cells and its microenvironment as demonstrated by HRS cell specific TARC staining (blue) and its receptor CCR4 (brown) on lymphocytes (B). H = Hodgkin cells; RS = Reed Sternberg cells; T = T-cells; H = histiocytes; E = eosinophils; P = plasma cells.
Normal immature B-cells undergo somatic recombination in germinal centers to transform into plasma cells or memory B-cells containing a B-cell receptor (BCR) with high affinity to a certain antigen. B-cells that lack a high affinity BCR or express a BCR with high affinity to self-antigens are either clonally deleted, undergo receptor editing or end in an anergic state. The lack of a BCR or a functional BCR as characteristic for HRS cells would normally lead to apoptosis. However, HRS cells have found a way to escape from apoptosis during the germinal center reaction. Constitutive activation of the canonical and non-canonical nuclear factor kappa-B (NFkB) pathways are a main survival mechanism by upregulating anti-apoptotic genes like c-FLIP and XIAP.4 The canonical NFkB signaling can be activated via tumor necrosis receptor (TNFR) family
members like CD30, CD40, RANK and TNFR1 that are expressed on the cell membrane of HRS cells. The non-canonical pathway can be activated by both autocrine and exocrine signaling by HRS cells and surrounding immune cells, respectively.5 Latent membrane protein 1 (LMP-1) is
an EBV- derived protein that mimics CD40 receptor stimulation, whereas LMP2 mimics BCR signaling. Together these signals can also induce activation of the NFkB pathway in EBV+ cHL.6,7
The extensive micro-environment of cHL consists of varying numbers of plasma cells, granulocytes, histiocytes, eosinophils, mast cells and macrophages, but the main cell type present in this reactive infiltrate is the T-cell (Figure 2).8 Remarkably, this reactive infiltrate is
unable to induce an effective immune response against the HRS cells. Moreover, the HRS cells need the reactive infiltrate to survive. This protective micro-environment is for a main part self-orchestrated by the HRS cells by various mechanisms. Constitutive activation of the NFkB and Janus kinase–signal transducer and activator of transcription (JAK-STAT) signaling pathways are central players in this aberrant interaction. Genetic alterations including copy number gains and activating mutations have been found in components of both the NFkB and JAK-STAT pathways in 53% and 90% of cases, respectively.9 Moreover, HRS cells directly suppress a Th1 response
by producing IL-10 and TGF-β and by expressing cell surface molecules including Fas ligand and galectin-1 that are associated with decreased amounts of effector T-cells surrounding the HRS cells. On the other hand, HRS cells promote and attract T-helper 2 cells (Th2) and regulatory T-cells (Treg) type immune cells by secreting CCL17 (also known as thymus and activation regulated chemokine, TARC), CCL5, CCL22 chemokines and IL-7, the latter of which enables differentiation of naïve CD4+ T-cells into Treg cells.10 The T-cells in the reactive infiltrate mainly
consist of CD4+ T-cells and they produce Th2 stimulatory cytokines like 4 and 5 and IL-10. The inhibition of an effective Th1 response is illustrated by the scarcity of CD8+ cytotoxic T-lymphocytes (CTLs) and natural killer (NK) cells in the direct vicinity of the tumor cells. In addition, the CD4+ T-cells directly surrounding the HRS cells seem to be in an anergic state as a result of several mechanisms including lack of expression of the T-cell activation marker CD26 and variable expression of immune check-point molecules CTLA-4 and PD-1 in these T-cells, as well as increased PD-1 ligand expression by HRS cells and the abundant production of IL-10 and TGF-β.11
1
The composition of the reactive infiltrate and stroma is the basis of a further histopathological separation of cHL into four subtypes: nodular sclerosis cHL, mixed cellularity cHL, lymphocyte rich cHL and lymphocyte depleted cHL. Nodular sclerosis cHL is the most common subtype and represents about 60% of the cHL cases in the western world, whereas mixed cellularity is more common in developing countries and is frequently associated with presence of EBV (about 70%).2 Although these subtypes have shown differences in gene-expression, cytokine
production and also clinical behavior, this distinction does not result in different treatment approaches. Galec tin-1 TGFβ , IL-10 CCL28 CCL28, IL -6 TNFα CCL17, C CL22 NK Th2 Th2 T Treg T
Growth Factors Immune Escape
CD40L CD30L,CD40L IL-3 IL-3 IL3R CD30 CD40 RTK IL9R IL6R IL7R IL15R IL13R IL-13 CCL17, C CL22 CD30L,CD40L Eosinophil Mast cell
IL-9, IL-15, IL-6, IL-13, IL-7
IL-7 HLA-G HLA TCR LAG3 PD-1 IL-10 IDO PD-L1, PD-L2 EBI3 IL-12 IL-7 Eosinophil Shaping the environment
Plasma Th1 Exhausted T Macrophage T Treg HRS cell JAK STAT FasL fibroblast fibroblast
Figure 2. Cellular composition of cHL and crosstalk between HRS cells and its micro-environment. HRS
cell = Hodgkin Reed-Sternberg cell; T = T-cell; NK = Natural killer cell; Treg = regulatory T-cell; Th1 = T-helper 1 cell; plasma = plasma cell.
Staging, treatment and prognostic factors
After the introduction of multi-agent chemotherapy regimens in the 1960s and improvements in radiotherapy, cHL can now be cured in more than 80% of all newly diagnosed patients (stage I-IV).12-14 Current treatment algorithms are based on a combination of clinical and laboratory
factors. The Ann Arbor staging system separates early stage from advanced stage patients based on number and location of the involved lymph nodes and presence of extranodal disease. Several large study groups further classify early stage patients for allocation of treatment into
early favorable and early unfavorable risk groups using slightly different criteria (Table 1). For advanced stage disease, a more widely accepted scoring system, the International Prognostic Score (IPS), has been developed based on six factors with independent significance for freedom of progression of disease in multivariate analysis.15 However, the clinically utility of the IPS to
allocate treatment is limited.16
Table 1. Clinical prognostic risk classification for early stage cHL
EORTC# favorable GHSG$ favorable NCCN& favorable
Age < 50 Not included Not included
ESR and B-symptoms < 50 mm/hr no B-symptoms or < 30 mm/hr + B-symptoms < 50 mm/hr no B-symptoms or < 30 mmh/hr + B-symptoms <50 mm/hr no B-symptoms
Large mediastinal mass Absent Absent (advanced stage if present)
Absent
No. involved sites <4 <3 <4
Extranodal disease Not included Absent Not included
#European Organisation of Research and Treatment of Cancer; $German Hodgkin Study Group; &National
Comprehensive Cancer Network
With current treatment regimens over 90% of early stage patients can be cured with the combination of 2-4 courses of chemotherapy (Adriamycin, Bleomycin, Vinblastine and Dacarbazine (ABVD)) followed by involved node radiotherapy.17,18 The drawbacks of this highly
curative combined modality treatment are the long term cardiovascular toxicities and secondary malignancies that can for an important part be attributed to former extensive radiotherapy fields and anthracycline-based therapies.19,20 Advanced stage patients can be treated with 6 cycles of
ABVD chemotherapy or 4-6 cycles of dose intensified chemotherapy (Bleomycin, Etoposide, Adriamycin, Cyclophosphamide, Oncovin, Procarbazine, Prednisone (escalated BEACOPP)) followed by radiotherapy on residual active disease sites.21 With these treatments about 75 and
90% of advanced stage patients can be cured, respectively. Especially for patients treated with regimens such as escBEACOPP, the risk of death due to short and long-term treatment-related toxicities is approaching the risk of dying of cHL itself.22 EscBEACOPP is also strongly associated
with infertility in both men and women.23
The main challenge in cHL management is therefore to minimize both overtreatment and undertreatment by better tailoring treatment to the prognosis of the individual patient. Recognition of patients at high risk of treatment failure before or early during treatment is therefore of utmost importance.
1
FDG-PET guided treatmentFunctional imaging with Fluorodeoxyglucose Positron Emission Tomography (FDG-PET)-scans combined with computer tomography (FDG-PET/CT) has become the standard tool for staging and response assessment in cHL. FDG-PET/CT is more sensitive than conventional imaging and led to significant upstaging in cHL.24,25 Staging in the era of FDG-PET/CT has also made
routine bone marrow biopsy unnecessary because of its higher sensitivity for bone marrow involvement.26,27
Response evaluation during treatment using FDG-PET scanning (interim FDG-PET (iPET)) enables evaluation of early metabolic changes rather than the morphologic changes visualized by CT occurring later during and after completion of treatment. Several studies using iPET after two or three cycles of ABVD have shown that early metabolic changes are predictive of the final treatment response and PFS.28-30 Based on these studies, which were mainly performed among
advanced stage patients, several cooperative groups have incorporated iPET imaging in their trials to reduce treatment exposure in responding patients to prevent overtreatment and/or to intensify treatment in case of non-responsiveness. Very recently this strategy of treatment escalation or de-escalation based on iPET has shown to improve progression free survival or safely decrease treatment exposure in early- as well as advanced stage patients.17,18,31,32
Nevertheless, iPET does not perfectly predict final outcome. In patients with a negative iPET after two cycles of ABVD, failure rates were observed ranging from 10% up to 25% in early- and advanced stage patients, respectively.17,33 On the other hand, 25% of advanced stage patients
treated with ABVD who are iPET positive become FDG-PET negative after completion of ABVD treatment and experience durable remissions.34 Also, advanced stage patients treated
with escBEACOPP show an excellent prognosis after completion of treatment despite iPET positivity.32 Although current efforts are being made to improve iPET adapted strategies by
investigating different timing and interpretation methods, there is still a need for better early stratification of high and low risk patients.
Biomarkers
A plethora of biomarkers derived from patient tissue or from peripheral blood have shown to correlate with prognosis and survival in cHL. These can be grouped into two categories, one being general factors related to inflammation / the abundant reactive infiltrate; the second being tumor cell-specific biomarkers. In addition, the biomarkers can be grouped based on being tissue-based or blood-tissue-based markers. At this moment, only the blood-tissue-based factors such as elevated erythrocyte sedimentation rate, anemia, leukocytosis, lymphocytopenia and hypoalbuminia are included in clinical prognostic models. Excellent reviews on prognostic factors in cHL have been published during the last years, mainly focusing on tissue markers.35-38 A disadvantage of almost
all published tissue markers is the lack of reproducibility due to differences in tissue fixation, staining, often difficult scoring methods and impractical cut-offs.
Tumor cell specific biomarkers that correlate with treatment response have the potential to be used in the same way as FDG-PET imaging with additional benefits of low-costs, being more patient-friendly, and potentially more specific. Blood-based biomarkers have the advantage over tissue markers that cell numbers, protein levels or DNA copy numbers can usually be quantified with higher reproducibility using standardized assays. From a clinical view, blood-based biomarkers have the great advantage that they can be sampled not only before but also during and after treatment. During the last 40 years an extensive number of studies have been published on blood-based biomarkers either focusing on prognosis (progression or disease free survival) or on evaluating treatment response. In the remainder of this chapter we summarize both prognostic blood-based biomarkers identified at diagnosis and blood-based biomarkers that can be used as treatment response biomarkers.
Circulating biomarkers
Blood cell counts and systemic inflammatory markers
Leukocytes: leukocytosis, lymphocytopenia, monocytosis
Leukocytosis defined as leukocyte count >15x103/mm3 is a common feature in cHL patients
with an incidence of about 10-20% at diagnosis (see Table 2).15,39 It is included in the IPS as
an independent prognostic factor, but it is not consistently observed as such in recent, albeit smaller studies.40,41 Leukocytosis was shown to have prognostic value when other prognostic
factors including leukocyte subsets were not taken into account.42,43
Lymphocytopenia was already identified in 1971 as an adverse prognostic factor in cHL.44 This
finding was later confirmed in multiple studies. The Hasenclever study incorporated it into the IPS.15,45
Lymphocytopenia, either absolute (<0,6x103/mm3) or relative (<8% of total WBC) is observed in
about one fifth of the patients at diagnosis. It is hypothesized that lymphocytopenia reflects impaired host immune homeostasis or even depletion of lymphocytes by infiltration into the tumor.39,46
Monocytosis was more recently recognized as a negative prognostic factor in cHL.39 It has been
hypothesized that monocyte counts might reflect number of tumor-associated macrophages, which originate from monocytes.39 A monocyte count of >0,9 x103/mm3 was found to correlate
with adverse progression-free, disease-free and overall survival. The lymphocyte/monocyte
ratio further increased the prognostic impact of monocyte counts. A ratio of <1.1 correlated with
very poor prognosis both in early and advanced stage patients and was independent of IPS. The independent prognostic value of this ratio was confirmed for overall survival in a non-Western cohort, for both progression free and overall survival in a very large study involving patients from Italy and Israel and in multivariate analysis with interim FDG-PET result.41,47,48 In conclusion, there
1
Hemoglobin level and iron metabolism
Low Hemoglobin level or anemia has been identified as a negative prognostic factor in many
malignancies, including HL.15,45 A hemoglobin level below 10.5 g/dl in both males and females is
included in the IPS as a negative prognostic factor, which is present in about 40% of cHL patients at diagnosis.15 Anemia as seen in cHL patients mirrors anemia of patients with chronic diseases
and correlates with interleukin (IL)-6 and hepcidin levels. IL-6 can induce hepcidin, which in turn inhibits release of iron stores from the mononuclear phagocyte system and from the intestine and results in elevated levels of ferritin. Both IL-6 and ferritin correlate with prognosis as has been shown decades ago for ferritin and has been confirmed in multiple recent studies.40,49-53
Clinically, ferritin is mainly used as a reflection of actual iron stores, but ferritin also acts as an acute phase protein. Indeed, ferritin levels highly correlate with inflammatory parameters like erythrocyte sedimentation rate (ESR) and C-reactive protein (CRP).53 In cHL patients, anemia
probably reflects an active immune state and impaired iron metabolism. It is currently not known whether anemia, serum IL-6 or ferritin is the most potent prognostic factor or whether they have independent prognostic value since a direct comparison of those markers is lacking.
Other circulating cells
In a search for peripheral blood biomarkers that reflect tumor micro-environment or host immune response, there are two smaller studies that have found new prognostic markers. The first study showed that higher levels of circulating CD34+ myeloid-derived suppressor
cells (immature MDSCs) correlate with adverse prognosis.43 Multivariate analysis in this iPET
treatment adapted cohort, revealed that elevation of CD34+ MDSCs was the only remaining significant parameter for survival and outperformed iPET. Another study found the CD4/CD19
ratio to be a negative prognostic factor independent of iPET and stage.42
Systemic inflammatory markers
Systemic inflammatory markers are detected in about half of the patients with cHL and correlate with tumor burden. These markers mainly reflect the abundant micro-environment characteristic of cHL. The most well-known non-specific inflammatory biomarker is the ESR. Elevated ESR (>30 or >50 mm) is present in about half of the patients and is included as one of the risk factors for unfavorable early stage disease by major study groups, i.e., the European Organisation for Research and Treatment of Cancer (EORTC), the German Hodgkin Study Group (GHSG) and the National Comprehensive Cancer Network (NCCN) (see Table 1). The initial finding of a correlation of ESR with clinical outcome in both untreated and treated patients published more than four decades ago was confirmed in subsequent papers.54,55 In a more recent paper the ESR remained an
important factor in currently applied early stage risk classification.56 However, the prognostic value
of ESR is only modest or even absent in multivariate analysis with other prognostic biomarkers.57,58
This can probably be explained by the fact that the ESR is influenced by many other prognostic factors like erythrocyte count, fibrinogen levels, presence of acute phase proteins or increased
gamma globulins. The studies investigating these individual factors as well as C-reactive protein are generally small or show correlation only in univariate analysis.59-61
Low albumin is a negative prognostic factor in cHL. Albumin is the most abundant plasma protein
and accounts for about 15% of the protein producing capacity of the liver. Albumin levels inversely correlate with inflammatory status and inflammatory proteins.62 This can be explained by inhibition
of albumin synthesis by molecules associated with inflammatory states like tumor necrosis factor (TNF), IL-11 and IL-6 that shift the protein production of the liver to production of acute phase proteins.63 Low albumin was the only negative prognostic factor in a large cohort of cHL patients
treated from 1970 until 1980 in a model with ESR, hemoglobulin, alkaline phosphatase and lactate dehydrogenase.57 This international study confirmed the prognostic impact of low albumin levels
with a cut off at 4.0 g/dl and was included in the IPS as a negative prognostic factor.15
Table 2. Blood cell counts and systemic inflammatory markers with adverse prognostic value. % at diagnosis HR for PFS RR (from ref 15)
cut off level Status#
Hemoglobulin 15-30 1.35 <10.5 g/dl Established15, 45
Leukocytosis 15-20 1.41 >15x109/mm3 Established 15,39
Lymphopenia (L) 15-25 1.38 <600/mm3 or <8% Established15,45
Monocytosis (M) 35 1.8 >700-900 cells/ul Potential39
L/M ratio ~35 2.9 - 3.8 <1.1 / <2.1 High potential 41,47,48
ESR 35-50 1.5 -1.6* 30-50 mm/1sthr Established 54-58
Albumin 40-60 1.49 4 g/dl Established 15,57
Ferritin 30-40 4.0 350 ug/l Potential40,49-53
HR = hazard ratio; PFS = progression free survival; RR = relative risk
* Odds ratio for treatment failure within 2.5 years after diagnosis in early stage patients only
# Definitions used for status: established = biomarkers included in currently applied prognostic models;
potential = one or limited number of papers indicating prognostic value; high potential = multiple reports indicating high prognostic impact
Tumor and microenvironment-derived markers
Cytokines, chemokines and soluble receptors
Multiple studies addressed the prognostic value of soluble levels of cytokines, chemokines and their receptors. IL-6 is a pro-inflammatory cytokine produced by HRS cells and by cells from the microenvironment including lymphocytes, macrophages and fibroblasts (see Figure 2). IL-6 levels are elevated in about 20-30% of the patients depending on the applied cut-off.52,62 The
prognostic value of IL-6 was confirmed in a study including 30 cytokines, whereas in another study with a case-control design, IL-6 did not have independent prognostic value, showing that thus far the value of IL-6 is inconclusive.52,64
1
IL-10 is produced by HRS cells and regulatory T-cells and inhibits a Th1 antitumor response.IL-10 levels are elevated in about 40-60% of patients and correlate positively with tumor cell EBV status.65 The prognostic value of IL-10 is controversial with multiple studies showing a
prognostic value of IL-10 independent from clinical parameters, whereas other studies did not find any independent prognostic value.52,59,62,64,66-69 An explanation for these findings might be
that in the latter studies multiple cytokines have been included.
The soluble form of the alpha chain of the receptor of IL-2 (sIL-2R, sCD25) is another immunomodulatory molecule that correlates with prognosis in cHL.52,70 sCD25 is derived
by proteolytic cleavage of CD25 by Matrix metallopeptidase 9 (MMP9) produced by tumor-associated macrophages. Levels of sCD25 correlate with the presence of tumor-tumor-associated macrophages in the microenvironment of NHL.71 sCD25 is a marker for activated B- and
T-cells and is considered to be a marker of regulatory T-cells. Upon activation by IL-2, CD25 induces FOXP3 expression in CD4+ T-cells thereby promoting a regulatory T-cell phenotype in the tumor microenvironment.72 A negative prognostic value of high levels of sCD25 was first
reported in 1987.70 These findings were confirmed in several subsequent studies,73-76 but could
not in be confirmed in other studies.59,77-79
Soluble IL-1 receptor antagonist (sIL-1RA) is produced by various types of immune cells and is
elevated in about 35% of cHL patients.80 IL-1RA competes with type I and type II IL-1R and can
partly neutralize the inflammatory effects of IL-1 secreted by HRS cells. Interestingly, secretion of sIL1RA is dependent on IL-6, IL-13 and IL-10 and the prognostic value of these cytokines might be interdependent.62
Soluble CD30 (sCD30) is considered to be derived from HRS cells and thus presents a tumor
cell-derived marker. CD30 belongs to the TNFR family and can be actively shed from the membrane resulting in sCD30. High sCD30 serum levels were first described as a negative prognostic marker in 1990.81 This has been confirmed in multiple studies and was shown to be
independent of clinical features. or other soluble molecules.59,62,68,73,74,78,82-85
A few attempts have been made to include a set of these prognostic markers in a new prognostic model. Casasnovas et al. found that the combination of IL-6, sIL-1RA and sCD30 in a three cytokine/soluble receptor model had better prognostic value than the IPS.62
Unfortunately, no multivariate analysis with individual factors of the IPS was shown. Marri et al. performed a multi-cytokine study in which a two-cytokine model containing IL-6 and sCD25 had the best prognostic value. In this study, sCD30 did not maintain prognostic value in multivariate analysis.52 Unfortunately, sCD25 was not analyzed in the study by
In the last decade, Thymus and Activation Regulated Chemokine (TARC) or CCL17 has gained interest as a prognostic marker and treatment response marker. TARC is produced by HRS cells at very high levels and is responsible for the attraction of CCR4 positive cells, which are mainly regulatory T-cells and Th2 cells (Figures 1 and 2).86 Thus, TARC is for an important
part responsible for the immunosuppressive direct micro-environment of the HRS cells. In line with the high production, extremely high plasma or serum levels have been observed in cHL patients.87,88 TARC levels are elevated in >85% of patients at diagnosis and high levels correlate
with negative prognostic features and higher disease stage.89 In line with the fact that stage
of disease itself is a potent prognostic factor in cHL, high TARC levels at diagnosis correlated with adverse prognosis.90 This was confirmed in a large study in which a prognostic model that
included both TARC and clinical features showed strong prognostic value.58 However, in a
smaller study no correlation with prognosis was found for TARC.52 Since elevation of TARC is
strikingly high compared to healthy controls, TARC has the ideal features to serve as a biomarker for treatment response.
There are several other protein biomarkers like soluble Galectin-1, sCD163, M-CSF, sCD8, sICAM-1, CA125, B-lymphocyte stimulator and polyclonal free light chains that might serve as prognostic biomarkers since they correlate with adverse clinical or disease characteristics. Future studies are needed to elucidate whether these markers have real prognostic value independent of established markers within current treatment era. Soluble Galectin-1 (sGal-1) and soluble CD163 (sCD163) are of special interest since sGal-1 is thought to be derived from tumor cells itself and sCD163 is the soluble form of the M2 macrophage marker CD163. Tumor-associated macrophages are associated with adverse prognosis in tissue of patients with cHL. 91,92
Table 3. Cytokines, chemokines and soluble receptors related to prognosis % patients
with marker
cut off level Adverse prognosis for high/low levels
Status#
IL6 20-30 30 pg/ml High Controversial52,64
IL-10 40-60 10 pg/ml High Controversial52,59,62,64,66-69
sIL2R 60-75 ~1000 U/ml High Controversial59,73-79
sIL1RA 35 668 pg/ml High Potential62
sCD30 40-80 20-200 U/ml High High potential59,62,68,73,74,78,82-85
TARC 70-90 500-10,000 pg/ml High Controversial52,58,59
# Definitions used for status: controversial = papers showing contradictory results; potential = one or
limited number of papers indicating prognostic value; high potential = multiple reports indicating high prognostic impact
1
Markers of cell or membrane turnover
Beta-2-microglobulin (B2M) is a component of the HLA class I complex. Serum levels are
thought to reflect membrane turnover and have been correlated with adverse prognosis in several smaller studies with different treatment regimens.93-99 Several reports showed an
independent negative prognostic value for high serum lactate dehydrogenase (LDH).45,97,100-102
However, in the International Consortium on Prognostic factors study, LDH levels did not have a significant prognostic value.15 Also elevated alkaline phosphatase levels have been linked
with adverse prognosis in cHL, but did not remain significant in multivariate analyses in the Hasenclever study.15,40
Molecular markers
High levels of circulating Epstein Barr Virus DNA (EBV-DNA) were found to closely correlate with tumor EBV-status as detected by EBER in situ hybridization (EBER-ISH) on tissue samples.103 Patients with EBV positive disease have detectable EBV DNA levels in their
circulation in about 65% of cases.104-108 An adverse prognostic value of the presence of plasma
EBV-DNA independent of IPS was shown in a relatively large cohort of cHL patients.103 This is in
line with the finding that EBV positivity in tumor tissue has an adverse outcome among elderly cHL patients.109,110 The subtype of cHL in elderly patients is more often of the mixed cellularity
subtype and the HRS cells more often harbor EBV. Unfortunately, the study by Kanakry et al. did not specify the prognostic value of plasma EBV in relation with age. Jones et al. showed that patients with high pre-treatment EBV-DNA copy numbers showed a decrease in copy numbers rapidly after start of treatment, showing its potential to serve as treatment response biomarker among patients with EBV positive disease.92
Circulating microRNAs (miRNAs) have also received increasing attention as non-invasive
biomarkers for diagnosis or prognosis in cancer. MiRNAs are small non-coding RNAs of about 20-25 nucleotides that regulate expression of protein coding genes at the post-transcriptional level. In cHL, two miRNAs were found to correlate with treatment response.111 However,
differences in miRNAs disappeared when levels were normalized to a cellular small RNA (RNA U6), indicating possible blood cell origin of the miRNAs. A more recent paper isolated extracellular vesicles from plasma and identified several miRNAs that were enriched in plasma of cHL patients compared to healthy controls.112 A first analysis showed correlation between a
decrease of these vesicle-derived miRNAs and a metabolic response upon treatment.
Studies investigating genomic aberrations have always been challenging because of the scarcity of tumor cells in cHL tissue (see Figure 1). Analysis of circulating tumor DNA (ctDNA) levels showed to be feasible in cHL and allows non-invasive detection of tumor cell specific genomic aberrations including mutations and copy number alterations (CNAs). Genomic aberrations in cell-free DNA were initially detected in a pregnant woman, who was later diagnosed with
early stage cHL, indicating the presence of ctDNA.113 Subsequent testing on nine additional
cHL patients revealed the presence of 2p and 9p gains, two commonly observed aberrations in tumor cells of cHL, in ctDNA of seven and five of the nine cHL patients, respectively. A larger scale study that was carried out more recently showed that mutations detected in ctDNA mirror the genetics of cHL tumor cells.114 Due to the small sample size and heterogeneity of the
patient cohort no conclusions could be made on any predictive or prognostic value. Sequential samples collected from a small number of patients showed that persistence of high ctDNA levels correlated with treatment failure or relapse. This indicates its potential to serve as a biomarker for treatment response.
In conclusion, there are multiple circulating biomarkers that have potential to be applied at diagnosis and during treatment to optimize decision making upfront and during treatment. Large multicenter studies are needed to define the optimal set of prognostic biomarkers that can be applied at diagnosis given current treatment regimens and the known prognostic value of iFDG-PET imaging. Research on treatment response biomarkers in cHL is more limited, but several biomarkers have high potential to improve on or to be applied next to FDG-PET imaging.
Scope of the thesis
This thesis aims to address the relevance of selected circulating biomarkers. In the first part of this thesis we study the application of TARC as a biomarker for treatment response and compare TARC with galectin-1, sCD163 and sCD30, and interim FDG-PET imaging. In Chapter 2 we studied the correlation of TARC with clinical response in a cohort of 63 patients from the University Medical Center Groningen (UMCG). In Chapter 3 we added three other potential treatment response biomarkers to compare with TARC, i.e. galactin-1, sCD163 and sCD30 in a larger cohort of 103 patients. In Chapter 4 we compared TARC during treatment (interim TARC) with interim FDG-PET imaging for their ability to predict for modified progression-free survival.
In the second part of this thesis we focus on the applicability of circulating microRNAs as biomarkers in cHL. In Chapter 5 we summarize the current knowledge on mircroRNAs in cHL. In
Chapter 6 we address the pre-analytical, analytical, and post-analytical challenges in circulating
microRNA studies. Finally, in Chapter 7 we investigated the expression profiles of circulating microRNAs in serum of a cHL patient cohort from Vancouver.
In Chapter 8, all results are summarized and discussed, followed by a general perspective on the future application of biomarkers in cHL.
1
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CHAPTER 2
Plasma Thymus and Activation-Regulated
Chemokine as an early response marker
in classical Hodgkin lymphoma
Wouter J. Plattel, Anke van den Berg, Lydia Visser, Anne-Marijn van der Graaf, Jan Pruim, Hans Vos, Bouke Hepkema, Arjan Diepstra, and Gustaaf W. van Imhoff
Abstract
INTRODUCTION:
Plasma Thymus and Activation-Regulated Chemokine (TARC) is a potential biomarker for classical Hodgkin lymphoma. To define the value of plasma TARC as a marker to monitor treatment response, we correlated serial plasma TARC levels with clinical response in newly diagnosed and relapsed classical Hodgkin lymphoma patients.
DESIGN AND METHODS:
Plasma was collected from 60 (39 early stage and 21 advanced stage) newly diagnosed classical Hodgkin lymphoma patients before, during and after treatment and from 12 relapsed patients before and after treatment. Plasma TARC levels were determined by enzyme-linked immunosorbent assay and were related to pre-treatment metabolic tumor volume as measured by quantification of 2-[18F]fluoro-2-deoxyglucose positron emission tomography images and to treatment response.
RESULTS:
Baseline plasma TARC levels correlated with stage of disease and bulky disease and more closely with metabolic tumor volume. Response to treatment was observed among 38/39 early stage and 19/21 advanced stage patients. Reduction in plasma TARC to normal range levels could be observed as early as after one cycle of chemotherapy in all responsive patients, while plasma TARC remained elevated during and after treatment in the three non-responsive patients. Plasma TARC was elevated in all 12 relapsed patients at time of relapse and remained elevated after salvage treatment in the four non-responsive patients only.
CONCLUSIONS:
Baseline plasma TARC levels correlate with classical Hodgkin lymphoma tumor burden and serial plasma TARC levels correlate with response to treatment in patients with classical Hodgkin lymphoma.
2
Introduction
In classical Hodgkin Lymphoma (cHL) the neoplastic Hodgkin Reed-Sternberg (HRS) cells are vastly outnumbered by cells in the surrounding reactive infiltrate. This infiltrate is of major importance for the proliferation and survival of HRS cells. Different chemokines and cytokines produced by HRS cells and cells in the reactive infiltrate are responsible for the formation and maintenance of this reactive environment.1-4
The CC chemokine ligand 17 (CCL17) or Thymus and Activation-Regulated Chemokine (TARC) is highly expressed by HRS cells in cHL, but not by the tumor cells of nodular lymphocyte predominant Hodgkin lymphoma or other B-cell derived non-Hodgkin lymphomas.5,6 TARC binds
specifically to the CC chemokine receptor 4 (CCR4). CCR4 is mainly expressed on regulatory T and Th2 cells that are both abundantly present in the reactive infiltrate of cHL.6-8 Approximately
90% of the cHL patients show positive TARC staining in HRS cells by immunohistochemistry (IHC) and about 85% have significantly elevated levels of TARC in their pre-treatment serum or plasma sample compared to healthy controls.9,10 Although patients with active atopic
diseases can also have elevated plasma TARC levels, this is only a modest elevation which is in a significantly lower range than the high plasma TARC levels observed in cHL.11 Pre-treatment
serum TARC levels correlate with stage of disease, erythrocyte sedimentation rate, leukocyte and lymphocyte counts in cHL.9,10 Niens et al.9 reported TARC levels within the normal range
after successful treatment in seven cHL patients and persistent elevated TARC in a single non-responsive patient. Weihrauch et al.10 reported diminished survival rates among patients with
higher TARC levels after treatment. However, nothing is known about TARC dynamics during treatment in relation to clinical treatment response.
We therefore prospectively collected serial plasma samples from newly diagnosed and relapsed cHL patients. The aim of the current study was to correlate plasma TARC levels with tumor burden at time of diagnosis and to correlate serial plasma TARC levels during and after treatment with cHL treatment response.
Design and Methods
Patient inclusion and treatmentSerial plasma samples were prospectively collected from all newly diagnosed and relapsed cHL patients that were treated in the University Medical Center Groningen (UMCG) from January 2006 until June 2011.
Inclusion criteria for both newly diagnosed and relapsed cHL patients were (1) receipt of standard treatment regimens, (2) availability of a plasma sample before start of treatment and one or more plasma samples during or after treatment as well as (3) confirmation of TARC expression in diagnostic tissue by immunohistochemistry or by elevated baseline plasma TARC if diagnostic tissue was not available. From 78 newly diagnosed patients treated in the UMCG, 60 were included, while 18 were excluded (1 because of patient refusal, 2 because of receiving palliative treatment, 9 because of lack of a pre-treatment plasma sample, 3 with negative tissue TARC staining and 3 with no available tissue and normal pre-treatment plasma TARC levels). From 17 relapsed patients, 12 patients eligible for DHAP salvage treatment followed by autologous stem cell transplantation were included, while 5 patients were excluded (4 because of receiving only palliative treatment and one because of lack of a plasma sample after treatment).
Permission for this study was obtained from the institutional review board of the UMCG and all participating patients and healthy controls signed informed consent. Routine staging of patients at diagnosis or at relapse included diagnostic Computer Tomography (CT) imaging, ‘whole body’ 2-[18F]fluoro-2-deoxyglucose positron emission tomography (FDG-PET) imaging, and bone marrow biopsy. Presence of bulky disease was defined as presence of a mediastinal mass greater than one third of the thoracic diameter on chest X-ray (on level Th5-Th6) and/or a nodal mass of more than 10 cm CT imaging. Response to treatment was evaluated according to the revised International Working Group response criteria.12 Evaluation included FDG-PET/CT scanning
which was interpreted according to the International Harmonization Project criteria (IHP).13
FDG-PET/CT scanning was performed using a Siemens Biograph PET/CT mCT 64 scanner in the vast majority of patients
Patients were treated according to clinical trial (European Organisation for Research and Treatment of Cancer (EORTC)) protocols. Table 1 shows the patient characteristics and data on chemo- and radiotherapy regimens. Briefly, standard treatment for stage I/II (early stage) patients consisted of 3-6 cycles of ABVD chemotherapy with or without 30-36 Gy involved node radiotherapy (IN-RT) according to the EORTC (20051) H10 trial.14 Standard treatment for stage
III/IV (advanced stage) patients consisted of 6-8 cycles of ABVD without radiotherapy or in case of participation in the EORTC 20012 trial, patients randomized between 8 cycles of ABVD and
