1

REVIEW OF LITERATURE

I. Diffuse Large B-cell Lymphoma

Classification

Diffuse large B-cell lymphoma (DLBCL) includes a variety of

different intermediate-to high-grade lymphomas derived from mature B

cells. All DLBCL cases share a diffuse growth pattern and predominant

large tumor cell size, defined as being at least twice as large as a normal

lymphocyte or with a nuclear size equal to or greater than that of a normal

macrophage. Large cell lymphomas of T cell, natural killer cell, or unclear

lineage are classified separately. Other terms used for DLBCL include

centroblastic or immunoblastic lymphoma, lymphosarcoma, and

reticulosarcoma. In the recent WHO classification 2008 of hematopoietic

malignancies, specific subtypes of DLBCL are defined based on the cell of

origin, presumed pathogenesis (e.g., viral association), or their predominant

site of involvement (Table 1), whereas those that do not fit any of the

individual entities are considered DLBCL, not otherwise specified (NOS).

The recognized subtypes of DLBCL in the current WHO classification

represent a fraction of the heterogeneity observed in this common tumor, so

further changes in subclassification will likely occur in the coming years

(Swerdlow, 2008).

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Incidence, Demographics, and Clinical Features

DLBCL is the most common subtype of adult lymphoma

worldwide, comprising 30–40% of cases in Western countries and

approximately 50% of lymphomas in China and many parts of Asia.

DLBCL arises in all age groups, but occurs most commonly in the elderly,

with approximately 25,000 new cases per year in the United States

(Rueffer, 2001). DLBCL represents about 54.67% of NHL cases in

national cancer institute (NCI), cairo (Mokhtar et al, 2007). Along with the

Burkitt lymphoma (BL), DLBCL comprises the majority of all childhood

B-cell lymphomas and is also the most common type of lymphoma

associated with genetic and acquired immunodeficiency states. DLBCL can

also arise rarely as a therapy-associated malignancy, most commonly

following breast cancer or Hodgkin’s lymphoma (Rueffer, 2001).

Table (1). Subclassification of diffuse large B-cell lymphoma.

Categories in the 2008 WHO classification: Alternative classification:

1)DLBCL, NOS

2)DLBCL, specific histogenic variants

Primary mediastinal DLBCL

T-cell/histiocyte-rich DLBCL

ALK+ DLBCL

1)Morphologic variants

Centroblastic

Immunoblastic

Anaplastic

Plasmablastic

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Continued table (1)

3)DLBCL, distinctive extranodal variants

Primary DLBCL of the CNS

Primary cutaneous DLBCL, leg type

Intravascular DLBCL

4)DLBCL, primarily associated with viral

infection

EBV-associated DLBCL of the elderly

Lymphomatoid granulomatosis

DLBCL associated with chronic

inflammation

Plasmablastic lymphoma

Primary effusion lymphoma

Large B-cell lymphoma arising from

HHV8+ multicentric Castleman disease

5)DLBCL, unclassifiable

intermediate between DLBCL and Burkitt

lymphoma

intermediate between DLBCL and

classical Hodgkin lymphoma

2)Immunophenotypic variants

GCB-like

Non-GCB

3)Molecular subgroups

Lymphoma: GCB-like vs.

ABC-like

Stroma: Immune response

vs.angiogenic types

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DLBCL can present with localized or generalized lymphadenopathy

and is also the most common lymphoma type in nearly every extranodal

site. Symptoms are highly dependent on the site(s) of presentation. Routine

clinical evaluation requires anatomic staging, commonly using the Ann

Arbor system, which is combined with laboratory data to derive the

International Prognostic Index (IPI) (Table 2) (Armitage, 2005). DLBCL is

clinically aggressive, but potentially curable due to its high proliferation

rate, especially in those tumors presenting at a low stage with a low IPI

score. However, each DLBCL subtype has variable patterns of treatment

response, relapse, and progression. Multi-agent combination chemotherapy

with cyclophosphamide, doxorubicin, vincristine, and prednisone (CHOP)

is most commonly used for DLBCL and shows long-term remission rates

in up to 40% of patients. The addition of the anti-CD20 antibody

(Rituximab) has improved overall survival by 10–15%. However, DLBCL

still accounts for nearly 10,000 cancer deaths per year in the United States

(Dong, 2010). Extensive molecular stratification and risk prediction

modeling of DLBCL have been actively investigated in recent years in an

attempt to predict therapy response and relapse as well as to better define

mechanisms of transformation. DLBCL may also arise as large cell

transformation of a low-grade B-cell malignancy, such as follicular

lymphoma (FL), marginal zone lymphoma (MZL), nodular lymphocyte

predominant Hodgkin lymphoma (NLP-HL), classical Hodgkin lymphoma

(CHL), or chronic lymphocytic leukemia/small lymphocytic lymphoma

(CLL/SLL). Such transformed DLBCL shares many of the same genetic

features as de novo DLBCL and is usually treated similarly. In some cases,

the underlying low-grade lymphoproliferative neoplasm may be

unrecognized at diagnosis, but can be suspected if cytogenetic studies

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reveal the genetic characteristics of a particular low-grade B-cell

lymphoma (Swerdlow, 2008).

Table (2): Clinical parameters affecting prognosis in lymphoma.

International Prognostic Index (IPI)

Unfavorable prognostic factors

Age >60 yrs

Poor performance status (ECOG ≥2)

Extranodal involvement ≥2 sites

High Ann Arbor stage (III or IV)

High lactate dehydrogenase (LDH)

Risk group Score (5 factors)

All patients > 60yrs

Low

Low-intermediate

High-intermediate

High

0 or 1

2

3

4 or 5

0

1

2

3-4

Eastern Cooperative Oncology Group (ECOG) performance status grading

0 Fully active without restriction

1 Restricted in physically strenuous activity

2 Ambulatory and capable of all self-care but unable to carry out any work

activities.

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3 Capable of only limited self-care, confined to bed or chair more than 50% of

waking hours.

Continued table (2)

4 Completely disabled. Cannot carry on any self-care. Totally confined to bed or

chair.

Ann Arbor staging system for lymphoma

I Involvement of a single lymph node region or lymphoid structure (eg, spleen,

thymus, Waldeyer’s ring)

II Involvement of two or more lymph node regions on the same side of the

diaphragm

III Involvement of lymph regions or structures on both sides of the diaphragm

IV Involvement of extranodal site(s) beyond that designated E

For all stages

A No symptoms

B Fever (>38oC), drenching sweats, weight loss (10% body weight over 6

months)

For stages I to III

E Involvement of a single, extranodal site contiguous or proximal to known nodal

site.

Morphological Evaluation of DLBCL

Nodal DLBCL usually partially or completely effaces the

architecture with frequent extra-capsular extension and grows as either

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sheets or as individual cells dispersed in a dense background of non-

neoplastic/inflammatory cells. Various degrees of sclerosis (in slowly

growing tumors) and necrosis (in rapidly-growing tumors) are present.

Extra-nodal DLBCL diffusely invades and replaces normal structures. In

cytologic preparations, such as fine needle aspirate (FNA) smears and

touch imprints, DLBCL can be readily recognized because of its large cell

size, prominent nucleoli, and irregular nuclear contours with nose-like

protrusions or indentations (Dong, 2008). Morphology subtypes of DLBCL

include centroblastic, immunoblastic, plasmablastic and anaplastic.

Centroblastic DLBCL, the most common morphologic variant, resembles

GC centroblasts with their round nuclei, vesicular, fine chromatin, and 2–4

distinct membrane-bound nucleoli, and discernible amphophilic cytoplasm.

DLBCL with polylobate nuclei are often grouped with these cases are more

common at certain extranodal sites, such as bone. Immunoblastic DLBCL

are those where at least 90% of tumor cells resemble reactive

immunoblasts with their prominent single, central nucleolus, and moderate

amounts of amphophilic cytoplasm. Plasmablastic DLBCL has eccentric

nuclei with prominent nucleoli, and abundant, often basophilic cytoplasm.

Anaplastic DLBCL shows large polygonal, spindle- or bizarre-shaped

tumor cells and often angulated or multilobated nuclei. Reed–Sternberg

(RS)-like cells are common in this variant as well as atypical mitoses and

frequently admixed inflammatory cells raising the differential diagnosis of

CHL (Swerdlow, 2008).

Immunophenotyping

Pan-B cell markers used for diagnosis of DLBCL by flow cytometry

(FCM) include CD19, CD20, CD22, and CD79a, whereas CD20, CD79a,

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and PAX5 are commonly used for immunohistochemistry (IHC) in fixed

sections. Clonality can be confirmed by demonstrating immunoglobulin

(Ig) light chain restriction, defined as Igκ + cells greater than 4X the

number of Igλ + cells, or Igλ + cells more than twice the number Igκ+

cells, using FCM, or by in situ hybridization (ISH) or IHC in fixed sections

in some cases. Absence or dim expression of one or more pan-B antigens is

a characteristic feature of some DLBCL subtypes at diagnosis or status-

post treatment, particularly CD20 loss after use of rituximab. For these

cases, other markers such as Ig heavy and/or light chains, CD22, CD79a,

PAX5, and CD138 may help. CD138 is particularly useful in detecting

plasmablastic DLBCL (which often lack CD20) and these cases often also

show cytoplasmic Ig light chain restriction detectable by IHC or ISH.

Given its crisp nuclear staining pattern, the B-cell marker PAX5 can

highlight tumor cases in cases with marked sclerosis or crush artifact, such

as primary mediastinal B-cell lymphoma (PMBL). However, in DLBCL

with extensive necrosis, CD20 immunostaining is usually retained, whereas

nuclear markers, including PAX5, BCL1/cyclin D1, BCL6, MUM1, and

Ki-67, will generally fail (Dunphy, 2010). Many other markers that are

routinely used in IHC are variably expressed in DLBCL. Strong CD30

expression is characteristic of DLBCL with a sinusoidal distribution, those

associated with EBV, and cases with anaplastic morphology. About 10% of

de novo DLBC are positive for CD5 and DLBCL may also aberrantly

express T/NK cell antigens, such as CD2, CD7, CD8, and CD56. DLBCL

associated with EBV may be identified by in situ hybridization for EBV-

encoded RNAs (EBER), or expression of EBV latent membrane protein-1

(LMP1) (Rosenwald, 2002).

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Molecular Testing

Molecular genetic assays may be useful in demonstrating B-cell

clonality in difficult cases of DLBCL. However, although essentially all

cases of DLBCL have clonally rearranged immunoglobulin genes, IGH

polymerase chain reaction (PCR) assays can give a false-negative result in

up to 15–20% of cases due to somatic mutations in the IGH primer binding

sites which prevent amplification. The monoclonal nature of DLBCL can

also be established by detecting chromosomal translocations involving IGH

and/or BCL6 genes that occur at high frequency in DLBCL. Translocations

involving the BCL6 gene at chromosome 3q27 occur in about 30% of

DLBCL, but they are not subtype specific since they occur in 5–10% of

follicular lymphoma and rarely in T-cell lymphomas (Sahai et al, 2011).

About 30% of DLBCL have the t (14;18)(q32;q21) translocation producing

the IGH/BCL2 fusion, which may or may not be related to transformation

from an underlying follicular lymphoma. Finally, MYC translocations

involving the immunoglobulin loci, while present in all cases of BL, also

occur in 10–15% of DLBCLs or aggressive B-cell lymphomas with mixed

features. Use of "break-apart" FISH probes which detect IGH, MYC, or

BCL6 translocations irrespective of the partner genes increases the

detection frequency for these translocations and may be considered as an

alternative clonality assay, especially when the IGH PCR fails to detect

clonality (Dong, 2008).

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Molecular and Immunophenotypic Subclassification

Gene expression profiles (GEP) established using large-scale gene

expression arrays have led to recognition of several DLBCL subtypes with

distinct clinical outcomes. The best studied are the germinal center B cell

(GCB)-like and the activated B cell (ABC)-like patterns (Rosenwald,

2002). The GCB and ABC signatures reflect fundamental differences in

expression of hundreds of genes that largely match GCB and post-GC

states of B-cell maturation. The GCB-like type highly expresses many

genes that are also expressed in non-neoplastic GCB, and usually shows

ongoing immunoglobulin gene somatic hypermutation (Lossos, 2000),

whereas the ABC-like type shows expression of growth factor and

signaling molecules seen in post-GC B cells where somatic hypermutation

has ceased. DLBCL with plasmacytoid differentiation and most EBV-

associated DLBCL have ABC-like features, whereas primary mediastinal

large B-cell lymphoma (PMBL) has a distinct GEP pattern. De novo CD5+

DLBCL may have an either GCB- or ABC-like GEP. As a group, the

GCB-like DLBCL has a significantly better clinical outcome than the

ABC-like type among patients treated with CHOP, with 5-year survival

rates of 60 and 35%, respectively (Rosenwald, 2002). Some recent studies

have shown that these outcome differences also apply to R-CHOP-treated

patients (Lenz, 2008). In addition, GCB- and ABC-like DLBCL correlate

with particular genomic changes reflective of distinct patterns of oncogenic

progression For example, a subset of GCB cases show IGH/BCL2

rearrangements or amplification of the REL locus at chromosome 2q15,

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features not seen in ABC-like DLBCL. In contrast, ABC-like cases display

trisomy 3 and amplification of a small region on chromosome19,

corresponding to upregulation of the ABC-associated genes FOXP1 and

SPIB. These cases may also exhibit gains of chromosome 18q spanning the

BCL2 and MALT1 loci, as well as deletions of chromosome 9p involving

the CDKN2A (p16/INK4a) locus (Lenz, 2008). A number of studies have

reported IHC-based correlates of the GCB vs. ABC molecular schema for

DLBCL, most often using stains for CD10, BCL6, and MUM1 in the

commonly used Hans scoring system (Hans et al, 2004). GCB-like cases

are positive for CD10 (with or without BCL6) and negative for MUM1,

whereas ABC-like DLBCLs express MUM1 and lack CD10. Most studies

agree that MUM1 (which is expressed in approximately 35% of DLBCL) is

mutually exclusive with CD10 expression. BCL6 can be expressed in either

GCB or ABC cases as a result of an activating promoter mutation or a

chromosomal translocations and in those cases its expression will not

reflect cell of origin. Likely for these reasons and due to differences in the

quality and scoring criteria of the immunostains, IHC studies have not

clearly shown differences in outcome stratification of DLBCL. Recent

studies have added additional GCB-like markers such as HGAL (Lossos,

2003) and LMO2 (Natkunam et al. 2008) and other ABC-like markers

such as FOXP1(Banham, 2005) which significantly improved outcome

prediction in patients treated with either CHOP or R-CHOP. Nevertheless,

there is not yet a consensus on the routine use of immunophenotyping for

treatment stratification or on what markers would constitute the best

routine IHC panel to determine prognosis and guide therapy selection. GEP

studies of DLBCL also identified distinct molecular signatures related to

tumor microenvironment that predict survival independently (Sahai et al,

2011). Using cohorts of both CHOP and R-CHOP-treated patients, a

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“stroma-1” signature, enriched for genes responsible for increased host

response, predicted a significantly better prognosis reminiscent of GCB-

like DLBCL. It was also associated with histologic evidence of increased

deposition of extracellular proteins and a prominent histiocytic infiltrate. A

“stroma-2” signature rich in genes highly expressed in endothelial cells and

in those encoding angiogenic factors and regulators predicted a poor

prognosis and correlated with increased angiogenesis seen in the biopsies

(Lenz, 2008).

Prognostic Factors in DLBCL

In addition to an ABC-like GEP and IPI clinical factors, high-risk

genetic changes in DLBCL include complex karyotype, MYC

translocation, and P53 loss (or p53 mutation). DLBCL with a MYC/IGH

translocation do worse than those carrying a MYC translocation with other

partner genes. Relapsed DLBCL frequently acquires additional genetic

abnormalities and unstable karyotypes. Relapsed DLBCL that has been

previously treated with rituximab may show a higher false-negative rate in

nuclear medicine scans (Shipp et al. 2002).

Differential Diagnosis

DLBCL with predominantly medium-sized cells, especially if

cytoplasmic vacuoles are present on smears should be distinguished from

Burkitt lymphoma (BL), and cases with an anaplastic morphology may

mimic Classic Hodgkin lymphoma (CHL) and/or anaplastic large cell

lymphoma (ALCL). None of the commonly used pan-B markers are

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specific for B-cell tumors. CD20 and CD79a can be expressed in some T-

cell lymphomas. Neuroendocrine carcinomas (Merkel cell carcinoma and

small cell carcinoma) and CD19+ acute myeloid leukemias express PAX5.

MUM1 is expressed in CHL and T-cell lymphoma, and CD138 is

expressed in a number of non-hematopoietic neoplasms (Dunphy, 2010).

Histogenetic Variants of DLBCL:

1) Primary Mediastinal Large B-cell Lymphoma (PMBL):

PMBL comprises 6–10% of DLBCL and it is twice as common in

woman (median age, 35years). PMBL is believed to arise from a

specialized population of thymic B cells and warrants separate recognition

because of its distinct clinical, morphologic, and genetic features.

Lymphomas presenting with concurrent involvement of distant lymph

nodes or bone marrow are excluded from this entity. It usually responds

well to initial treatment and has a relatively good prognosis. PMBL is

composed of intermediate to large cells with frequently irregular or

multilobate nuclear contours and moderate amounts of clear cytoplasm

embedded in fibrosis with a reticulated or alveolar pattern. Sclerosis may

be absent in rapidly growing tumors, whereas zonal necrosis is a common

finding. PMBL cells express CD20, PAX5, and BCL6, with MUM1

positivity in up to 75% supporting an origin from post-GC activated B cells

in most cases. More than 50% of cases show at least focal CD30 reactivity,

but the staining is usually less uniform compared to CHL. Furthermore, the

transcription factors BOB1 and OCT2 are positive, whereas they are

usually negative or dim in CHL. CD10 and CD15 are usually negative, and

EBER is always negative. FCM has only limited diagnostic value because

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of the tissue sclerosis and the frequent absence of surface immunoglobulin

on the tumor cells. (Winter et al. 2006).

2) T-cell/Histiocyte-Rich Large B-cell Lymphoma (TCHR-LBCL):

TCHR-LBCL is a DLBCL variety characterized by numerous non-

neoplastic T cells and/or histiocytes. It comprises less than 10% of DLBCL

and is most common in middle-aged men (median age, 49 years). It often

involves deep lymph nodes in the retroperitoneum, as well as the liver,

spleen, and bone marrow. Patients usually have high clinical stage at

presentation, and tumors are largely refractory to R-CHOP chemotherapy.

In lymph node, TCHR-LBCL shows diffuse effacement of the architecture,

resulting in an abnormal appearance even on needle core biopsy. Whether

in lymph node or extranodal sites, the neoplastic cells typically comprise

less than 10% of the cellularity and are dispersed amid dense populations

of small T cell and/or sheets of large epithelioid histiocytes. Small B cells,

eosinophils, plasma cells, and neutrophils are usually completely absent.

Tumor cell morphology is variable, sometime resembling Reed–Sternberg

cells, with other cases having multilobate nuclei resembling the popcorn

cells of NLPHL. Given the T-cell predominance, tumor cells may be

missed by FCM so IHC is recommended in all cases (Boudova, 2003).

CD20 and BCL6 are uniformly positive, MUM1 is variable, and CD5,

CD10, CD30, and BCL2 are expressed in only a minority of cases. TCHR-

LBCL is likely a heterogeneous disorder, with some cases develop as a

transformation of preexisting NLPHL, and the two tumors can even coexist

in the same lymph node. Therefore, distinction of these two entities can be

difficult whenever NLPHL acquires a diffuse growth pattern and has few

associated small B cells. Spread of NLPHL outside of lymph nodes to liver

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or bone marrow is a sign of transformation to TCHR-LBCL (Swerdlow,

2008).

3) DLBCL Expressing ALK:

This is an extremely rare lymphoma comprising much less than 1% of

DLBCL that has also been reported as ALK+ plasmablastic lymphoma. It

occurs in all age groups, but is most common in young to middle-aged

men. Despite having been reported at extranodal sites, it is generally an

aggressive nodal lymphoma and is insensitive to rituximab due to lack of

CD20 expression. ALK+ DLBCL displays a sinusoidal distribution or

forms large tumor nodules both grossly and microscopically mimicking

carcinoma and melanoma. The lymphoma displays monotonous

immunoblast-like cytology, but the cell size is much larger than regular

immunoblasts. Like other lymphomas with sinusoidal localization, ALK+

DLBCL may have prominent membrane villous projections. ALK+

DLBCL is negative for B-cell or T-cell markers, as well as CD30 (unlike

ALK + ALCL) and CD45, but expresses granular cytoplasmic ALK and

plasma cell antigens such as CD138, MUM1, and EMA (Linderoth et al,

2003).

4) Extranodal Variants of DLBCL:

a. Primary DLBCL of the Central Nervous System (CNS):

DLBCLs may present in the CNS from systemic spread (often from

PMBL), in immunosuppressed patients (often HIV+), or in

immunocompetent patients. The latter is known as primary DLBCL of the

CNS. Extracranial metastasis and bone marrow involvement are very rare.

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Biopsy usually shows peri-vascular parenchymal infiltration by clusters of

tumor cells and subtle meningeal spread, but the limited sampling afforded

by brain biopsies will sometimes show only necrosis, foamy histiocytes,

and rare CD20+ cells. Tumor cells are positive for BCL2, MUM1 (~90%),

and BCL6 (60–100%) with mutated IGH consistent with a post-GC origin

(Montesinos-Rongen, 2009).

b. Primary Cutaneous DLBCL, Leg Type:

Most DLBCLs involving the skin are localized to the dermis (Stage

IE), have an indolent course and can be easily treated with excision and/or

radiotherapy. However, there is an aggressive variant of primary skin

DLBCL typically presenting as bluish nodules on the lower extremities (in

contrast to the trunk, head, and neck involvement seen more commonly in

indolent cases), which has been named “leg type”. Such cases comprise

about 20% of primary cutaneous B-cell lymphoma and commonly occur in

elderly women having a 5-year survival of only 50%. The presence of

multiple tumor nodules and rapid systemic spread are adverse risk factors.

Both immunoblastic and centroblastic morphology are common, and

epidermal or adnexal involvement is absent. Tumor cells display molecular

signatures consistent with ABC-like DLBCL (Hoefnagel, 2005) and

express BCL2, BCL6, MUM1, and FOXP1, but lack CD10 expression

commonly seen in most of the cutaneous DLBCL of GCB origin. Deletions

of chromosome 9p21 spanning the CDKN2A tumor suppressor are seen in

67% of cases. Translocations involving IGH, BCL6, and MYC may be

seen (Dunphy, 2010).

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c. Intravascular DLBCL:

This is an exceedingly rare type of extranodal DLBCL with a

characteristic intravascular pattern of growth that can be diagnosed

incidentally during biopsy for other reasons. It often presents with

symptoms related to vasculitis and thrombosis in various organs such as

brain (stroke and infarct), lung (pneumonia and pulmonary embolism),

kidney (infarction or renal insufficiency), adrenal (endocrine disorders), or

skin. Although there is often preferential involvement of some organs, it is

a systemic disease with bone marrow involvement in nearly all cases that

can be demonstrated by immunostaining. Delays in diagnosis due to the

subtlety and focal nature of the intravascular infiltrates often lead to a

terminal disease with death before chemotherapy is initiated. Most cases

have an immunoblastic or anaplastic appearance and express BCL2 and

MUM1, consistent with a post-GC/ABC-like origin (Dong, 2008).

5) Variants of DLBCL Associated with Viral Infections:

a. The Role of Herpes viruses in DLBCL:

Viral-associated DLBCL are the most common malignancies in

patients with an underlying immunodeficiency, whether it is related to

primary immunodeficiencies, chronic iatrogenic immunosuppression,

human immunodeficiency virus (HIV) infection and other chronic

infections, transplantation, or age-related declines in immune function. The

two primary viral agents involved in lymphomagenesis are the gamma

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herpesviruses, EBV and human herpesvirus 8 (HHV8). EBV is endemic

among all populations worldwide, with sero-positivity rates of over 90% in

adults in the United States. However, geographic differences in genetic

strains of the virus, age of primary infection, and patient socioeconomic

status lead to differences in the profile of EBV-associated malignancies in

different countries. HHV8 infects up to 5% of the population in the United

States, with higher percentages in Europe, and up to 40–70% sero-

positivity rates in some parts of Africa. HHV8-associated malignancies are

rare in Western countries, except in the context of severe

immunodeficiency (Dunphy, 2010). Nearly all EBV- and HHV8-

associated DLBCLs are MUM1+ and CD10-, consistent with a post-

GC/ABC-like origin. Plasma cell differentiation is also very common.

Most of these lymphomas are clinically aggressive with median survivals

of only months to 1–2 years. EBER (small nuclear RNAs associated with

the EBV) detected by ISH is the most useful marker to demonstrate EBV

infection in the tumor cells, and latency associated nuclear antigen

(LANA)-1 detected by IHC is the most useful marker of HHV8 infection.

Detection of LMP1 (EBV latent membrane protein 1) by IHC in EBV +

lymphomas is typically associated with reactivation of EBV replication

(Dong, 2008).

b. Acquired Immunodeficiency Syndrome (AIDS)-Associated

DLBCL:

Prior to the advent of highly active anti-retroviral therapy, there was

a 110- to 200-fold increase in the incidence of lymphoma in HIV-infected

patients, the majority of which were EBV-associated DLBCL and BL.

Currently, in the United States, there has been a marked reduction in the

incidence of AIDS-associated lymphomas, which are now limited to

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patients who are refractory to, or noncompliant with, therapy. Involvement

of various extranodal sites is frequently seen, and tumor cell morphology

ranges from polymorphous B-cell infiltrates with oligoclonal IGH gene

rearrangements to mono-morphous high-grade immunoblastic DBLCL

similar to the range of features seen in post-transplant tumors (Dunphy,

2010).

c. Lymphomatoid Granulomatosis (LyG):

LyG is an extranodal EBV + B-cell lymphoproliferative disorder that

characteristically presents with multifocal angiocentric lesions, most

commonly involving lung (bilateral pulmonary nodules in middle and

lower lobes), skin (multiple nodules or ulcers), CNS, and less commonly

kidney and liver. Although most common in middle-aged men, it affects a

wide age range and is associated with an immuno-compromised state in

many patients. The EBV + large cells typically have a perivascular

distribution with vascular invasion, and are regularly positive for CD30 as

well as both EBER and LMP1. Prognosis of LyG depends on histological

grade (Table 3), with disease progression manifested by an increased

number of EBV + tumor cells and a decreased number of reactive CD4+ T

cells (Dong, 2008).

Table (3): Histological grading of lymphomatoid granulomatosis.

Features Grade 1 Grade 2 Grade 3

Polymorphic

background

Dominant Significant Focal

Necrosis Focal Frequent Extensive

Tumor cells Rare (need IHC) Occasional, may be Frequent, may be

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< 5/hpf in Clusters 5–20/hpf,

may be up to 50/hpf

in aggregates

> 50/hpf

Outcome Wax & wane, may

Regress

May have durable

response to therapy

Same as EBV +

DLBCL

d. DLBCL Associated with Chronic Inflammation:

The outgrowth of an EBV + DLBCL in response to unremitting

chronic suppurative inflammation was first recognized in Japan in patients

who had a history of pyothorax. Such pyothorax-associated lymphoma

(PAL) typically occurred after 10–20 years of persistent inflammation.

Similar lymphomas have now been reported following chronic

osteomyelitis, chronic skin ulcers, and around protheses and metallic

implants. The tumor infiltrates dense fibrotic linings or capsule of cavities

and joint spaces and can show just focal nests of EBER + and LMP1+ large

B cells. Surgical debulking with complete tumor resection has been

reported to improve survival, but the overall prognosis is poor, possibly

due to delays in diagnosis (Dong, 2005).

e. Plasmablastic Lymphoma:

Plasmablastic lymphoma is typically an EBV + extranodal,

extramedullary lymphoma with immunoblastic or plasmablastic

morphology that is negative for pan-B-cell antigens. Some patients may

have a low-level serum para-protein (usually IgG kappa or lambda), but the

tumor cells only infrequently exhibit cytoplasmic immunoglobulin

expression by IHC. Nodal involvement is very rare and tends to be found in

elderly patients without an identifiable immunodeficiency state.

Plasmablastic lymphoma should be distinguished from plasmablastic

transformation of myeloma, as the latter usually has the characteristic triad

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of lytic bone lesions, plasmacytosis, and prominent monoclonal

gammopathy, as well as expression of cytoplasmic immunoglobulin, and is

typically negative for EBV (Dong, 2005).

f. Primary Effusion Lymphoma (PEL):

PEL, also known as body-cavity lymphoma, is a highly aggressive

extranodal tumor invariably associated with HHV8 infection. A vast

majority of cases are also positive for EBV. PEL presents with massive

serous effusions in one or more of the large body cavities (i.e., pleura

space, pericardium, or peritoneum) without localized masses, adenopathy,

or organomegaly (Nador, 1996). PEL cells exhibit immunoblastic or

anaplastic morphology and are often extremely pleomorphic. They lack

most pan-B-cell markers, but express CD45, MUM1, and CD138. CD30,

EMA, and CD79a are variably expressed (Dong, 2008).

g. EBV + DLBCL of the Elderly:

There is an increasing recognition that age-related declines in T-cell

surveillance can lead to emergence of nodal and extranodal systemic EBV

+ DLBCL in elderly patients, and are termed senile EBV-associated

lymphoproliferative disorders. While this entity is defined in the 2008

WHO classification as a disease of patients > 65 year-old, it does

occasionally occur in younger patients. The boundaries of this EBV-

associated DLBCL are currently under investigation, but the diagnosis

should only be made when a tumor does not fit other entities. This

lymphoma should be suspected whenever a DLBCL has areas containing

Reed-Sternberg-like cells, polymorphic infiltrates with plasmacytoid

features, and zonal necrosis (Oyama, 2003).

22

h. Large B-cell Lymphoma Arising from HHV8+ Multicentric

Castleman Disease (MCD):

This is primarily a nodal IgM plasmablastic lymphoma arising from

MCD and is the only known lymphoma subtype exclusively associated

with HHV8 without EBV co-infection. In a minority of patients, it may

disseminate to the GI tract, liver, lungs, or evolve into a leukemic phase.

The early lesions manifest as monotypic IgM + Igl + plasmablastic

proliferations localized to the mantle zones of MCD follicles, which then

evolve into microscopic aggregates (micro-lymphoma) and eventually

frank lymphoma with confluent sheets of tumor cells. Unlike EBV +

extranodal plasmablastic lymphoma, HHV8+ plasmablastic lymphoma

arising from MCD exhibit intense cytoplasmic expression of IgM and

lambda light chain (rarely kappa). The tumor cells express CD20 (weak)

and MUM1, but lack PAX5 and CD138 (Du MQ, 2001).

6) B-cell Lymphoma, Unclassifiable, with Features Intermediate

Between DLBCL and Burkitt lymphoma (BL):

This category comprises aggressive B-cell lymphomas that are

difficult to classify. They display features closely resembling BL but vary

in their morphology, immunophenotype and genetics. For example, a BL-

like lymphoma may show more variable cytomorphology, such as

increased number of large cells and more irregular nuclear contours. Cases

with morphology typical of BL may have strong BCL2 expression and

harbor both t(14;18) and t(8;14), that may be either de novo or represent

23

transformation of follicular lymphoma. Indeed, when routine FISH analysis

is performed, up to 2.5% of DLBCL may have both t(8;14) and t(14;18),

and such "double-hit" cases have very poor outcome. Rare B-ALL/BL

hybrid cases may have MYC translocation and classical BL morphology,

but display a mixed phenotype (e.g. surface Ig+ and CD10+ but with TdT

expression). Cases with typical BL morphology and immunophenotype but

no identifiable MYC translocation may be diagnostically challenging,

though they might be best considered as BL, especially in young patients

who might be expected to benefit from the intensive chemotherapy given

for BL (Dong, 2008).

7) B-cell Lymphoma, Unclassifiable, with Features Intermediate

Between DLBCL and CHL:

This category covers so-called gray-zone lymphoma and large B-cell

lymphoma with Hodgkin-like features. It mostly reflects the diagnostic

overlaps between PMBL and CHL in young adults presenting with a

mediastinal mass though similar lymphomas have been reported at other

sites as well. The classification difficulties arise when there is a sheet-like

proliferation of large lymphocytes that show a CHL-like immunophenotype

(CD20−,CD15+), or conversely the histologic appearance of nodular

sclerosis CHL but with tumor cells positive for CD20, CD79a, and/or

CD45, and negative for CD15. In most cases, the tumor cells will be larger

and more pleomorphic than is typical for PMBL but lack a classical Reed-

Sternberg appearance, and may strongly express the B-cell transcription

factors PAX5, BOB1, and OCT2 (like PMBL) along with CD30. In some

cases that have separate areas resembling CHL and DLBCL, the tumor may

be better classified as composite lymphoma. These borderline cases

probably reflect a shared biology, since microarray studies have shown

24

overlap in the molecular signature between CHL and PMBL (Traverse-

Glehen, 2005).

Treatment of DLBCL:

Diffuse large B-cell lymphoma (DLBCL) is an aggressive NHL in

which survival without treatment is measured in months. For treatment

purposes, patients with DLBCL are generally classified as having either

limited stage disease or advanced stage disease based upon whether or not

the tumor can be contained within one irradiation field (Swerdlow et al,

2008).

Limited stage disease (usually Ann Arbor stage I or II)

Limited stage DLBCL can be contained within one irradiation field.

This population accounts for 30-40 % of patients with DLBCL. Limited

stage DLBCL is treated primarily with combined modality therapy

consisting of abbreviated systemic chemotherapy (three cycles), the

recombinant anti-CD20 antibody rituximab, and involved field radiation

therapy. Alternatively, full course (six to eight cycles) systemic

chemotherapy plus rituximab without radiation therapy may be used (Sehn

et al. 2005).

Advanced stage disease (usually Ann Arbor stage III or IV)

Advanced stage DLBCL cannot be contained within one irradiation

field. This population accounts for 60-70 % of patients with DLBCL.

Advanced stage DLBCL is treated primarily with systemic

chemotherapy plus the recombinant anti-CD20 antibody rituximab.

25

Patients with bulky (>10 cm) stage II disease and patients with stage

IIB disease have a less favorable prognosis than those with non-bulky stage

II disease without systemic B symptoms. Many clinicians treat such

patients in a similar fashion to those with advanced stage disease.

Rituximab containing regimens

With the advent of combination chemotherapy with

cyclophosphamide, doxorubicin, vincristine, and prednisone (CHOP) or

CHOP-like regimens, disease-free survival rates of 35 to 45 % at four years

have been realized (Fisher et al. 1993).

Survival has been further improved with the addition of rituximab to

standard CHOP-based therapy (R-CHOP). The addition of rituximab to

CHOP-based therapy results in an approximately 10 to 15 % overall

increase in survival beginning at one year from initiation of therapy in

patients of all ages with almost no increase in toxicity (Sehn et al. 2005).

In addition to stage of disease, survival with CHOP-based therapy

varies with the presence or absence of certain clinical features. The

International Prognostic Index (IPI) provides information on patient

outcomes with CHOP-based therapy based upon the patient's age,

performance status, stage, number of extranodal sites, and LDH level. For

patients treated with R-CHOP, the original IPI classifies patients into four

risk categories with three-year overall survival rates of 91, 81, 65, and 59

% for patients with IPI scores of 0-1, 2, 3, or 4-5, respectively (Ziepert et

al. 2010).

26

II. DNA methyaltion and epigenetics in cancer

Classic genetics alone cannot explain the diversity of phenotypes

within a population. Monozygotic twins despite their identical DNA

sequences can have different phenotypes and different susceptibilities to a

disease. The concept of epigenetics offers a partial explanation of these

phenomena. It was first introduced by C.H. Waddington in 1939 to name

"the causal interactions between genes and their products, which bring the

phenotype into being". Epigenetics was later defined as heritable changes

in gene expression that are not due to any alteration in the DNA sequence

(Fraga et al, 2005).

The best-known epigenetic mechanism is DNA methylation. The

initial finding of global hypomethylation of DNA in human tumors was

soon followed by the identification of hypermethylated tumor-suppressor

genes, and then, more recently, the discovery of inactivation of micro RNA

(mi RNA) genes by DNA methylation. These and other demonstrations of

how epigenetic changes can modify gene expression have led to human

epigenome projects and epigenetic therapies. Moreover, DNA methylation

occurs in a complex chromatin network and is influenced by the

modifications in histone structure that are commonly disrupted in cancer

cells (Siato and Jones, 2006).

Epigenetic in normal cells:

27

DNA methylation has critical roles in the control of gene activity and

the architecture of the nucleus of the cell. In humans, DNA methylation

occurs in cytosines that precede guanines; these are called dinucleotide

CpGs. There are CpG-rich regions known as CpG islands, which span the

5′ end of the regulatory region of many genes. These islands are usually not

methylated in normal cells. The methylation of particular subgroups of

promoter CpG islands can, however, be detected in normal tissues

(Herman, 2005).

DNA methylation is one of the layers of control of certain tissue-

specific genes, such as MASPIN (a member of the serine protease inhibitor

family) and germ-line genes such as the MAGE genes (silent in almost all

tissues except malignant tumors). Genomic imprinting also requires DNA

hypermethylation at one of the two parental alleles of a gene to ensure

monoallelic expression, and a similar gene-dosage reduction is involved in

X-chromosome inactivation in females (Feinberg et al, 2002).

The hypermethylation of repetitive genomic sequences probably

prevents chromosomal instability, translocations, and gene disruption

caused by the reactivation of transposable DNA sequences. Cells that lack

the stabilizing effect of DNA methylation because they have spontaneous

defects in DNA methyltransferases (DNMTs) or experimentally disrupted

DNMTs have prominent nuclear abnormalities (Espada et al. 2007).

DNA methylation occurs in the context of chemical modifications of

histone proteins. Histones are not merely DNA-packaging proteins, but

molecular structures that participate in the regulation of gene expression

(Esteller, 2007). They store epigenetic information through such post-

translational modifications as lysine acetylation, arginine and lysine

28

methylation, and serine phosphorylation. These modifications affect gene

transcription and DNA repair. It has been proposed that distinct histone

modifications form a "Histone code". Acetylation of histone lysines, for

example, is generally associated with transcriptional activation (Jenuwein,

2006). The functional consequences of the methylation of histones depend

on the type of residue –lysine (K) or arginine- and the specific site that the

methylation modifies (e.g., K4, K9, or K20). Methylation of H3 at K4 is

closely linked to transcriptional activation, whereas methylation of H3 at

K9 or K27 and of H4 at K20 is associated with transcriptional repression.

What emerges from these findings is a flexible but precise pattern of DNA

methylation and histone modification that is essential for the physiologic

activities of cells and tissues (Karpf, 2006).

DNA Hypomethylation in Tumors

The low level of DNA methylation in tumors as compared with the

level of DNA methylation in their normal tissue counterparts was one of

the first epigenetic alterations to be found in human cancer. The loss of

methylation is mainly due to hypomethylation of repetitive DNA sequences

and demethylation of coding regions and introns regions of DNA that allow

alternative versions of the messenger RNA (mRNA) which is transcribed

from a gene. A recent large-scale study of DNA methylation with the use

of genomic microarrays has detected extensive hypomethylated genomic

regions in gene-poor areas. During the development of a neoplasm, the

degree of hypomethylation of genomic DNA increases as the lesion

progresses from a benign proliferation of cells to an invasive cancer (Fraga

et al, 2007).

29

Three mechanisms have been proposed to explain the contribution of

DNA hypomethylation to the development of a cancer cell: generation of

chromosomal instability, reactivation of transposable elements, and loss of

imprinting. Hypomethylation of the DNA can favor mitotic recombination,

leading to deletions and translocations, and it can also promote

chromosomal rearrangements. This mechanism was seen in experiments in

which the depletion of DNA methylation by the disruption of DNMTs

caused aneuploidy. Hypomethylation of DNA in malignant cells can

reactivate intragenomic endoparasitic DNA, such as L1 (long interspersed

nuclear elements), and Alu (recombinogenic sequence) repeats. These

undermethylated transposons can be transcribed or translocated to other

genomic regions, thereby further disrupting the genome (Eden, 2003).

The loss of methyl groups from DNA can also disrupt genomic

imprinting. In the hereditary Beckwith–Wiedemann syndrome (a syndrome

characterized by exomphalos, macroglossia, and gigantism), for example,

there is loss of imprinting of IGF2 (the insulin-like growth factor gene) and

an increased risk of cancer. Loss of imprinting of IGF2 is also a risk factor

for colorectal cancer, and disrupted genomic imprinting contributes to the

development of Wilms’ tumor. In animal models, mice with a loss of

imprinting of IGF2 or overall defects in imprinting have an increased risk

of cancer (Kaneda and Feinberg, 2005).

Normally, certain testis specific genes (genes that encode melanoma

antigens, or specific proliferation-linked genes) are silent in somatic cells

because promoter-regions are methylated. In some cancer cells, by

contrast, these promoter regions undergo demethylation, and the usually

repressed genes become expressed. Two notable examples of the

30

hypomethylation mechanism are the activation of PAX2 (a gene that

encodes a transcription factor involved in proliferation and other important

activities of cells) and the activation of the let-7a-3 miRNA gene, which

has been implicated in endometrial and colon cancer (Sakatani et al.

2001).

The hypomethylation of DNA can have unpredictable effects. The

progeny of a mouse deficient in DNA methylation and a Min mouse

(Mouse strain which has a genetic defect in the adenomatous polyposis coli

"APC" gene and is prone to colon adenoma) have fewer tumors than one

would expect; by contrast, another DNMT-defective mouse strain has an

increased risk of lymphoma (Gaudet, 2003). Moreover, hypomethylation

suppresses the later stages of intestinal tumorigenesis but promotes early

precancerous lesions in the colon and liver through genomic deletions

(Yamada, 2007).

Hypermethylation of Tumor-Suppressor Genes

Hypermethylation of the CpG islands in the promoter regions of

tumor-suppressor genes is a major event in the origin of many cancers. The

initial reports of hyper-methylation of the CpG islands in the promoter

region of the retinoblastoma tumor- suppressor gene (Rb) were followed

by the findings that hypermethylation of the CpG island was a mechanism

of inactivation of the tumor-suppressor genes VHL, p16INK4a

, hMLH1 (a

homologue of MutL E. coli), and BRCA1 (breast-cancer susceptibility

gene1) (Herman, 2005).

31

Hypermethylation of the CpG-island promoter can affect genes

involved in the cell cycle, DNA repair, the metabolism of carcinogens, cell-

to-cell interaction, apoptosis, and angiogenesis, all of which are involved in

the development of cancer. It can occur at different stages in the

development of cancer and in different cellular networks, and it interacts

with genetic lesions. Such interactions can be seen when hypermethylation

inactivates the CpG island of the promoter of the DNA-repair genes

hMLH1, BRCA1, MGMT (O6-methylguanine–DNA methyltransferase),

and the gene associated with Werner’s syndrome (WRN) (a very rare,

autosomal recessive disorder characterized by the appearance of premature

aging and telomere instability). In each case, silencing of the DNA-repair

gene blocks the repair of genetic mistakes, thereby opening the way to

neoplastic transformation of the cell (Agrelo et al. 2006).

The profiles of hypermethylation of the CpG islands in tumor-

suppressor genes are specific to the cancer type (Agrelo and Wutz, 2009).

Each tumor type can be assigned a specific, defining DNA

“hypermethylome.” Such patterns of epigenetic inactivation occur not only

in sporadic tumors but also in inherited cancer syndromes, in which

hypermethylation can be the second lesion in Knudson’s two-hit model of

how cancer develops. Recently devised epigenomic techniques have

revealed maps of hypermethylation of the CpG islands that suggest the

occurrence of 100 to 400 hypermethylated CpG islands in the promoter

regions of a given tumor (Esteller, 2007).

The mechanism through which CpG islands become

hypermethylated in some types of cancer but not in others is still unclear.

Inactivation of a particular gene by methylation could give certain tumor

32

types a growth advantage. CpG islands can have a location within a

particular nucleotide sequence that allows them to become

hypermethylated (Weber et al, 2005), or they can be located in a

chromosomal region that is subject to large-scale epigenetic dysregulation

(Esteller, 2007). In addition, there is a mechanism in which modifications

of histones mark a gene for hypermethylation. This marking occurs in the

binding site of the methyltransferase enhancer of zeste drosophila

homologue 2 (EZH2)( a component of the polycomb family of gene-

silencing proteins) (Hellebrekers et al, 2006), in histones of stem cells with

unmethylated gene promoters (Widschwendter et al, 2007) and in the

histone-associated silencing of p16INK4a

in colon-cancer cells ( (Bachman,

2003).

Histone Modifications in Cancer Cells

The most reliable method for detecting changes in histones is mass

spectrometry, which is highly specialized but time-consuming (Esteller,

2007). Moreover, histone modifications occur in different histone proteins,

histone variants (e.g., H3.3), and histone residues such as lysine, arginine,

and serine. These modifications also involve different chemical groups

(e.g., methyl, acetyl, and phosphate) and have different degrees of

methylation (e.g., monomethylation, dimethylation, and trimethylation).

Acetylation and methylation of histones have direct effects on a variety of

nuclear processes, including gene transcription, DNA repair, DNA

replication, and the organization of chromosomes. Generally, histone

acetylation is associated with transcriptional activation but the effect of

33

histone methylation depends on the type of amino acid and its position in

the histone tail (Bernstein, 2007). Hyper-methylation of the CpG islands

in the promoter regions of tumor-suppressor genes in cancer cells is

associated with a particular combination of histone markers: deacetylation

of histones H3 and H4, loss of H3K4 trimethylation, and gain of H3K9

methylation and H3K27 trimethylation (Jones, 2007).

Epigenetic Factors and miRNA

“miRNAs” are short, 22-nucleotide, non-coding RNAs that regulate

gene expression by sequence-specific base pairing in the 3′ untranslated

regions of the target mRNA. The result is mRNA degradation or inhibition

of translation. Patterns of miRNA expression are tightly regulated and play

important roles in cell proliferation, apoptosis, and differentiation (He and

Hannon, 2004). The number of human genes known to lose activity as a

result of the binding of a miRNA to the untranslated regions of the mRNA

is growing rapidly (Bueno et al, 2008).

The miRNA expression profiles differ between normal tissues and

tumor tissues and among tumor types (Lu J, 2005). Down-regulation of

subgroups of miRNAs, a common finding, implies a tumor-suppressor

function for miRNAs, as in the examples of downregulated let-7 and miR-

15/miR-16, which target the RAS and BCL2 oncogenes, respectively

(Johnson et al, 2008). DNA hypermethylation in the miRNA 5′ regulatory

region is a mechanism that can account for the down-regulation of miRNA

in tumors. In colon-cancer cells with disrupted DNMTs, hypermethylation

of the CpG island does not occur in miRNAs. The methylation silencing of

miRNA-124a also causes activation of the cyclin D–kinase 6 oncogene

34

(CDK6), and it is a common epigenetic lesion in tumors (Saito and Jones,

2006).

Epigenetics in Cancer Management

The DNA-methylation and histone-modification patterns associated

with the development and progression of cancer have potential clinical use.

DNA hyper-methylation markers are under study as complementary

diagnostic tools, prognostic factors, and predictors of responses to

treatment. For instance, the glutathione S-transferase gene (GSTP1) is

hypermethylated in 80 to 90% of patients with prostate cancer (Cairns and

Adams, 2004), but it is not hypermethylated in benign hyperplastic prostate

tissue. Thus, the detection of GSTP1 methylation could help to distinguish

between prostate cancer and a benign process (Costa et al, 2007).

Hypermethylation of CpG islands can be a marker of cancer cells in

all types of biologic fluids and biopsy specimens, making detection of

GSTP1 methylation in urine, a possible clinical application. Analysis of

hypermethylation of the CpG island has potential diagnostic applicability

for carriers of high-penetrance mutations in tumor-suppressor genes. For

example, identification of DNA hyper-methylation in a breast-biopsy

specimen from a carrier of a BRCA1 mutation could be useful when the

pathological diagnosis is uncertain, because hypermethylation of the CpG

island is an early event in the development of cancer (Esteller, 2007).

Analysis of several hypermethylated genes detects twice as many

tumor cells in breast ductal fluids as conventional cytologic analysis (

Mehrotra et al, 2006), and hypermethylated genes can be found in

35

exfoliated cells at different stages in the development of cervical cancer

(Feng et al. 2006). The application of DNA-hypermethylation markers as

tumor markers in routine clinical practice will require rapid, quantitative,

accurate, and cost-effective techniques and objective criteria for selection

of the genes that are applicable to different tumor types. Hypermethylation

of a tumor-suppressor gene and DNA hypermethylome profiles can be

indicators of the prognosis in patients with cancer. Hypermethylation of the

death-associated protein kinase (DAPK), p16INK4a, and epithelial

membrane protein 3 (EMP3) has been linked to poor outcomes in lung,

colorectal, and brain cancer, respectively (Esteller, 2007).

Prognostic dendrograms similar to those used in gene-expression

microarray analyses, with the use of a combination of hypermethylated

markers and CpG-island microarrays, have been developed. These

epigenomic profiles are complementary to profiles of gene-expression

patterns and can be developed with DNA extracted from archived material

(Laird et al. 2004). The hypermethylation of particular genes is potentially

a predictor of the response to treatment. The methylation-associated

silencing of the gene for the DNA-repair protein MGMT in gliomas is an

example (Esteller and Herman, 2002). MGMT reverses the addition of

alkyl groups by the alkylating agents to the guanine base of DNA. Two

studies have shown that the hypermethylation of MGMT is an independent

predictor of a favorable response of gliomas to carmustine (BCNU) or

temozolomide (Hegi et al, 2009).

The potential of the methylation status of MGMT and other DNA-

repair genes to predict the response to chemotherapy has also been seen

with cyclophosphamide (with the MGMT gene), cisplatin (with the

hMLH1 gene)(Strathdee, 2007), methotrexate (with the reduced folate

36

carrier [RFC] gene), and irinotecan (with the WRN gene) (Agrelo et al,

2006).

Epigenetic Therapy of cancer

Unlike mutations, DNA methylation and histone modifications are

reversible. Epigenetic alterations allow the cancer cell to adapt to changes

in its micro-environment, but dormant, hypermethylated tumor-suppressor

genes can be re-activated with drugs. It is possible to re-express DNA

methylated genes in cancer cell lines by using demethylating agents and to

rescue their functionality (Yoo and Jones, 2006). DNA demethylating

drugs in low doses have clinical activity against some tumors. Two such

agents, 5-azacytidine (Vidaza) and 5-aza-2′-deoxycytidine (decitabine),

have been approved as treatments for the myelodysplastic syndrome and

leukemia (Oki and Issa, 2007). However, these demethylating agents have

not yet been shown to have clinical activity against solid tumors.

Histone deacetylase (HDAC) inhibitors can induce differentiation,

cell-cycle arrest, and apoptosis in vitro, although it has not been possible to

pinpoint a specific mechanism that explains these effects (Ropero et al.

2004). The first drug of this type, suberoylanilide hydroxamic acid

(vorinostat), has been approved by the Food and Drug Administration

(FDA) for the treatment of cutaneous T-cell lymphoma (Marks et al,

2009). The efficacy of HDAC inhibitors in the treatment of other tumors is

limited. The nonspecific effects of DNA demethylating agents and HDAC

inhibitors could have unintended consequences with regard to gene

expression, and as a paradoxical result, they could have growth-promoting

effects on a tumor. However, there are prospects for directed epigenetic-

specific therapy with the use of transcription factors that target particular

37

gene promoters (Moore and Ullman, 2003). For instance, the engineered

zinc finger proteins target unique sequences in the MASPIN promoter;

these proteins not only reactivate the epigenetically silenced gene but also

inhibit tumor growth in vitro (Beltran et al, 2008).

DNA methylation in Non-Hodgkins lymphoma

Hematological neoplasms are known to have different

hypermethylation profiles than those of other solid tumors. The three major

forms of lymphoid/hematopoietic malignancies; non-Hodgkin’s

lymphomas, Hodgkin lymphoma & multiple myeloma show overlapping

but individual patterns of methylation (Takahashi1, 2004). To date, there

haven’t been extensive studies about aberrant promoter methylation of

tumor suppresser genes (TSGs) in NHL; only a limited numbers of TSGs

have been tested and their analysis has been restricted to certain types of

NHLs (Esteller, 2007). In a recent study, the prevalence of aberrant

promtor methylation was explored. The selected eight TSGs are known to

be involved in cell cycle regulation (p16, COX2), DNA repair (MGMT),

apoptosis (DAPK, RUNX3), angiogenesis inhibitor (THBS1), invasion and

metastasis (CDH1) and cell proliferation (MT1G). The methylation status

was examined in all the enrolled lymphoma cases and this was analyzed

specifically according to the cellular origins (B-cells or T/NK-cells) of the

NHLs. This study suggests that aberrant CpG island methylation is a

frequent event in NHLs, and diffuse large B-cell lymphomas show

overlapping but distinct methylation profiles (Kim1 et al, 2008).

38

Methods of detection of DNA methylation

DNA methylation can be detected by the following assays currently used in

scientific research:

Methylation-Specific PCR (MSP): MSP is based on a chemical

reaction of sodium bisulfite with DNA that converts unmethylated

cytosines of CpG dinucleotides to uracil or UpG, followed by

traditional PCR. However, methylated cytosines will not be converted

in this process, and primers are designed to overlap the CpG site of

interest, which allows one to determine methylation status as

methylated or unmethylated (Herman et al, 1996).

The MethyLight method: is based on MSP, but provides a

quantitative analysis using real-time PCR. Methylated-specific

primers are used, and a methylated-specific fluorescence reporter

probe is also used that anneals to the amplified region. Quantitation

is made in reference to a methylated reference DNA. A modification

to this protocol to increase the specificity of the PCR for successfully

bisulfite-converted DNA (ConLight-MSP) uses an additional probe

to bisulfite-unconverted DNA to quantify this non-specific

amplification (Rand et al. 2002).

Melting curve analysis (Mc-MSP): This method amplifies

bisulfite-converted DNA with both methylated-specific and

unmethylated-specific primers, and determines the quantitative ratio

of the two products by comparing the differential peaks generated in

a melting curve analysis. A high-resolution melting analysis method

that uses both real-time quantification and melting analysis has been

39

introduced, in particular, for sensitive detection of low-level

methylation (Kristensen et al. 2008).

Whole genome bisulfite sequencing: also known as BS-Seq, which

is a high-throughput genome-wide analysis of DNA methylation. It is

based on aforementioned sodium bisulfite conversion of genomic DNA,

which is then sequencing on a Next-generation sequencing platform.

The sequences obtained are then re-aligned to the reference genome to

determine methylation states of CpG dinucleotides based on

mismatches resulting from the conversion of unmethylated cytosines

into uracil (Yuanxin Xi and Wei Li, 2009).

The HELP assay: which is based on restriction enzymes' differential

ability to recognize and cleave methylated and unmethylated CpG DNA

sites (Batbayar et al, 2006).

ChIP-on-chip assays: which is based on the ability of commercially

prepared antibodies to bind to DNA methylation-associated proteins

like MeCP2 (Pillai and Chellappan, 2009).

Methylated DNA immunoprecipitation (MeDIP): analogous to

chromatin immunoprecipitation. Immunoprecipitation is used to isolate

methylated DNA fragments for input into DNA detection methods such

as DNA microarrays (MeDIP-chip) or DNA sequencing (MeDIP-seq)

(Mohn et al, 2009).

Pyrosequencing of bisulfite treated DNA: This is sequencing of an

amplicon made by a normal forward primer but a biatenylated reverse

primer to PCR the gene of choice. The Pyrosequencer then analyses the

sample by denaturing the DNA and adding one nucleotide at a time to

the mix according to a sequence given by the user. If there is a mis-

match, it is recorded and the percentage of DNA for which the mis-

40

match is present is noted. This gives the user a percentage methylation

per CpG island (Tost and Gut, 2007).

Molecular break light assay for DNA adenine methyltransferase

activity: an assay that relies on the specificity of the restriction enzyme

DpnI for fully methylated (adenine methylation) GATC sites in an

oligonucleotide labeled with a fluorophore and quencher. The adenine

methyltransferase methylates the oligonucleotide making it a substrate

for DpnI. Cutting of the oligonucleotide by DpnI gives rise to a

fluorescence increase (Yan et al, 2007).

Methyl Sensitive Southern Blotting: is similar to the HELP assay,

although uses Southern blotting techniques to probe gene-specific

differences in methylation using restriction digests. This technique is

used to evaluate local methylation near the binding site for the probe

(Moore, 2009).

41

III. DAPK and MT1G genes

DAPK (death-associated protein kinase 1)

Death-associated protein kinase (DAPK) is a tumor suppressor gene

(mediator of apoptotic systems). DAPK was discovered in the mid 1990s

during genetic screening in which an antisense library was used to identify

genes necessary for interferon (IFN)-induced death in HeLa cells (Deiss et

al, 1995). Subsequent sequence and activity analysis indicated that DAPK

encoded a Ca2+/calmodulin (CaM) regulated Ser/Thr kinase, with a

catalytic domain highly homologous to that of myosin light chain kinase

(MLCK). This 160 kDa protein bears an interesting multi-domain structure,

including ankyrin repeats and the death domain (Figure 1). DAPK is

necessity for cell death and not limited to IFN-signaling; numerous studies

have demonstrated that DAPK activity is required for the induction of cell

death by multiple death signals, including those generated by death

receptors, cytokines, matrix detachment, and hyperproliferation (Jang et al,

2002). Transcriptional silencing of death-associated protein kinase

(DAPK) occurs in many diverse types of human cancers through promoter

hypermethylation at prevalences ranging from 7% in liver tumors to 84%

42

in B-cell non-Hodgkin’s lymphoma (Lehmann et al, 2009). The ubiquitous

silencing of this gene implies a critical role for it in cancer development.

Loss of expression of DAPK confers a selective growth advantage for

cancer cells that may drive tumor aggressiveness and progression. This

gene has a CpG island extending 2500 bp from the translational start site;

however, studies characterizing its transcriptional regulation have not been

conducted. Two transcripts for DAPK were identified that code for a single

protein, while being regulated by two promoters. The previously identified

DAPK transcript designated as exon 1 transcript was expressed at levels 3-

fold greater than the alternate exon 1b transcript. Deletion constructs of

promoter 1 identified a 332 bp region containing a functional CP2-binding

site important for expression of the exon 1 transcript. While moderate

reporter activity was seen in promoter 2, the region comprising intron 1 and

containing a HNF3B- binding site sustained expression of the alternate

transcript (Pullin et al, 2009). Sequencing the DAPK CpG island in tumor

cell lines revealed dense, but heterogenous methylation of CpGs that

blocked access of the CP2 and HNF3B proteins that in turn, was associated

with loss of transcription that was restored by treatment with 5-aza-2-

deoxycytidine (Toyooka et al, 2003).

Figure (1): Schematic diagram of DAP-kinase protein structure. The 160

kDa actin microfilament-associated Ca2+/calmodulin (CaM)-regulated

Serine/Threonine kinase bears a multiple domain structure. The catalytic

43

and the calmodulin regulatory domains determine substrate specificity and

regulation of kinase catalytic activity, respectively. The non-catalytic

association domains, involved in subcellular localization or interactions

with other proteins, include the 8 ankyrin repeats, two nucleotide-binding

P-loops, a cytoskeleton-binding region, and a death domain.

Phosphorylation by RSK at Ser289 triggers a suppression of DAPK

proapoptotic function (Anjum et al., 2005). The autophosphorylation site

was mapped to Ser308 within the CaM-regulatory domain (Shohat et al.,

2002). ERK phosphorylates DAPK at Ser735, which stimulates DAPK-

mediated apoptosis (Chen et al., 2005).

Metallothionein 1G

Zinc is an essential trace element as a component of several

metalloenzymes involved in critical physiologic processes, including cell

growth and proliferation, osteogenesis, immunity, and antioxidant activity

(Platz and Helzlsouer, 2001). The bioavailability of zinc is controlled by

metallothioneins, a class of low molecular weight proteins with metal-

binding and antioxidant properties (Andrews, 2000). Human

metallothioneins are encoded by a family of genes located at chromosome

16q13 and some of the isoforms seem expressed in an organ-dependent

manner (Coyle et al. 2002). The MT-IF and MT-IG genes are

differentially regulated in two human hepatoma cell lines ( HepG2 and

Hep3B2) and a human lymphoblastoid cell line ( WI-L2 ) in response to

the heavy metals cadmium, zinc and copper, and glucocorticoids

(Gedamu et al, 1987). MT1G hypermethylation is more frequent in

prostate cancer that spread beyond the prostate capsule. MT1G promoter

hypermethylation was found in 29 of 121 prostate cancer, 5 of 39 HGPIN

(High grade PIN), 3 of 29 benign prostatic hyperplasia, and 0 of 13

44

normal prostate tissue samples (Henrique et al, 2005). Melatonin has

been shown to bind to the MT1G protein-coupled receptor (GPCR) in

MCF-7 breast cancer cells to modulate the estrogen response pathway

suppressing estrogen-induced estrogen receptor alpha (ER alpha)

transcriptional activity, blunting ER/DNA binding activity and

suppressing cell proliferation ( Kiefer et al, 2005). Loss of expression of

MT1G is accompanied by hypermethylation in the 5' regions of these

genes (Huang et al, 2003). Hypermethylation, but not LOH, is associated

with the low expression of MT1G and CRABP1 in papillary thyroid

carcinoma (Huang et al, 2003). VEGF stimulation also led to the

increased acetylation E2F1 as well as the histones in the hMT1G

promoter region (Joshi et al, 2005). Combined treatment with the DNA

methyltransferase inhibitor 5-aza-2'-deoxycytidine (5-Aza-dC) and the

histone deacetylase inhibitor trichostatin A (TSA) resulted in

demethylation and re-expression of the MT1G gene in the cell line K2

(Huang et al, 2003). Human metallothionein 1G (hMT1G) promoter is

upregulated by E2F1 upon VEGF stimulation of human aortic endothelial

cells (Joshi et al, 2005).The MTI-F and MT-IG gene promoters were also

functional in human chondrocytes (su et al, 1996).

45

MATERIAL AND METHODS

I) Study material:

This retrospective study included 70 cases of diffuse large B-cell

NHL lymph node biopsies, which were obtained from the Pathology

Department, NCI, Cairo during the period from 2003-2009. Paraffin blocks

of the studied cases were recruited and those with scanty or exhausted

material were excluded. All relevant clinico-pathologic data of the patients

were obtained from the pathology reports & clinical records including age,

sex, type of specimen, diagnosis, number of extra-nodal sites, PS, B-

symptoms, LDH level, stage, IPI, type of treatment, response and survival.

From each paraffin block, one H&E slide was prepared to

confirm the diagnosis and to assess the neoplastic to non-neoplastic cell

ratio. Only cases with more than 80% neoplastic cells in the examined

sections were included in the study.

46

II) Immunohistochemistry:

Three sections (5um thick each) were cut from the paraffin blocks

onto positive charged slides and stained for CD10 (Clone 56C6, ready to

use, Dako, Denmark), BCL6 (clone PG-B6p, dilution range 1:10-1:20,

Dako, Denmark) and MUM1 (clone MUM1p, dilution range 1:25-1:50,

Dako, Denmark) in order to sub-classify the cases into germinal center

(GCB) and non-germinal center B-cell (Non-GCB) prognostic types (Hans

et al, 2004). This was done according to the following algorithm (Figure 2).

A case of reactive lymphoid hyperplasia was used as a positive control.

Figure (2): The immunohistochemical algorithm for identification of

prognostically important subgroups of DLBCL proposed by Hans et al

2004.

Immunostaining Procedures

The Avidin-Biotin immunoperoxidase technique (ABC) was used.

The following steps were done (Hsu et al, 1981):

47

1-Deparaffinization and rehydration: paraffin embedded tissues were de-

paraffinized in xylene 3 times for 5-10 minutes each, and re-hydrated

through graded alcohol series (100%, 90 %, 70% ) for 5 minutes each,

then immersed in tap water for 5 minutes.

2-Antigen retrieval: Tissue sections were treated in a microwave oven for

5minutes at 700w, in antigen retrieval citrate buffer (PH 6), and then

sections were left to cool.

3- Endogenous peroxidase blocking: The slides were immersed in 3%

hydrogen peroxide solution for 10 minutes, and then washed in PBS (PH

7.2) for 2 x 5 minutes.

4-Primary antibody: Slides were then put in a wet chamber; two drops of

the primary antibodies were added on each slide to cover the entire tissue

section. They were incubated for one hour at room temperature and then

washed in PBS for 3 x 5 minutes.

5- Secondary antibody (Link): Tissues were covered with 2 drops of

UltraVision biotinylated goat anti-polyvalent secondary antibody,

incubated at room temperature for 10 minutes and then washed in PBS for

3x3 minutes.

6- Streptavidin/ Peroxidase (Label): Drops of streptavidin peroxidase

were added to cover the sections, incubated at room temperature for 10

minutes, and then washed in PBS for 2x5 minutes.

7-Substrate/ Chromogen: Drops of chromogen (DAB: 3′3-

diaminobenzidine HCl) were added to cover the entire sections, incubated

at room temperature for 10 minutes, and then washed in PBS for 5 minutes.

8-Counterstaining: The slides were placed in hematoxylin bath for 1

48

minute, and then washed in a water bath 5 times and tap water for 5

minutes.

9- Cover slipping: Sections were dehydrated through 70%, 90%,

100% alcohol and then put in xylene for 5 minutes. Few drops of

DPX (Di-N-Butyle Phthalate in Xylene) were added and slides were

covered by cover slips, and allowed to dry for few minutes.

Cases were considered positive for CD10, BCL6 & MUM1 if

30% or more of the tumor cells in the section were stained with the

antibody. The intensity of staining was not used to determine

positivity because the variability in tissue fixation and processing

appeared to affect the intensity of staining (Hans et al, 2004).

III) Molecular studies:

1) DNA extraction:

DNA extraction was done for each case according to Shi et al (2002)

as follows:

Three sections 10 microns thick were taken from each paraffin block

in an autoclaved plastic micro-tube (1.5ml).

One ml xylene was added to the micro-tube containing the tissue

sections for 30 min for two changes.

Ethanol (100% and 75%) was added for 30 min (two changes).

Washing with PBS for 15 min was done with two changes.

49

50 µl, Lysis buffer were added (Proteinase K 20 mg/ml, 1 M Tris-

HCl solution 10 µl, 0.5 M EDTA 2 µl, 10% SDS 100 µl), and

completed with distilled water to a final volume of 500ul in each

tube.

Overnight incubation was done at 52C until all tissue fragments

were dissolved completely.

Phenol: chloroform: isopropanol (25:24:1) was added to the de-

waxed tissue as 500 ul in each tube.

Mixing with vortex.

Centrifugation at 12,000 X g for 10 min.

The supernatant fluid was transferred to another autoclaved micro

tube using a 100-µl pipette.

An equal (One) volume of chloroform was added to the supernatant

& mixed by vortexing.

Centrifugation was done at 12,000 x g for 5 min.

The upper aqueous supernatant was carefully removed of to another

micro tube.

0.1 volume of 3 M sodium acetate was added to the tube followed by

(two volumes) 100% ice-cold ethanol to precipitate the DNA.

The tube was incubated at -20˚C overnight.

The DNA was precipitated by centrifugation at 12,000 x g at 4˚C.

50

The supernatant fluid was discarded and the DNA was washed once

with 75% ethanol, left to dry on a filter paper.

The final yield of DNA was dissolved in 50 µl of the storage buffer

(10mM Tris HCL + 1mM EDTA, PH 8) or distilled water after

complete drying in a hood.

The DNA concentration was calculated by Spectrophotometry

through the following equation:

(Unknown DNA) mg/ml = 50 mg/ml x Measured A260 x dilution

factor.

2) Bisulfite Modification:

The methylation status of a DNA sequence can best be determined

using sodium bisulfite. Incubation of the target DNA with sodium bisulfite

results in conversion of unmethylated cytosine residues into uracil, leaving

the methylated cytosines unchanged. Therefore, bisulfite treatment gives

rise to different DNA sequences for methylated and unmethylated DNA.

Bisulfite modification was done for each sample according to Herman et al,

1996:

1. DNA (1ug) in a volume of 50 ul was denatured by NaOH (final

concentration, 0.2 M) for 10 min at 37°C.

2. 30 ul of 10 mM hydroquinone (Sigma) and 520 ul of 3 M sodium

bisulfite (Sigma) at pH 5, both freshly prepared, were added and mixed,

and samples were incubated under mineral oil at 50°C for 16 hr.

51

3. Modified DNA was purified using the Wizard DNA purification resin

according to the manufacturer (Promega) and eluted into 50 ul of water.

4. Modification was completed by NaOH (final concentration, 0.3 M)

treatment for 5 min at room temperature, followed by ethanol

precipitation.

5. DNA was resuspended in water and used immediately or stored at -

20°C.

3) Methylation specific PCR (MSP):

The modified DNA was used for methylation specific PCR reaction

for detection of DAPK and MT1G promoter methylation using the

primer sequences, PCR conditions and the product size for both genes

as illustrated in the (Table 5). All the PCR amplifications were

performed in the PTC 200 thermal cycler (MJ research, Waltham, MA,

USA) using:

Bisulphite modified DNA (30-50 ng)

primers (10 p mol each)

dNTPs (1mM each)

10X standard PCR buffer (Qiagen)

0.5 U of Hot Start Taq plus DNA polymerase (Qiagen) in a volume of

20.

PCR cycles:

52

The reactions were hot-started at 95C for 5 min

Followed by 35 cycles of

94 C for 30 sec

60 C for 30sec

72 C for 30 sec

A final extension step 72 C for 10 min

The PCR products were electrophori zed in 2% ethidium bromide-

stained agarose gels and were then visualized in the photo-documentation

system. For each MSP reaction, both positive (a sample known to harbor

promoter methylation of the tested gene) and negative (distilled water

without template DNA) controls were used.

Table (4): Primer sequences and PCR conditions for methylation-

specific PCR analysis.

Primer

Name

Primer sequence (5′-3′) Produc

t

Size

(pb)

Annealing

Temp. (ºc) Forward Reverse

DAPK M GGATAGTCGGATCGAGTTAACGTC CCCTCCCAAACGCCGA 98 60

U GGAGGATAGTTGGATTGAGTTAATGTT CAAATCCCTCCCAAACACCAA 98 60

MT1G M TGCGAAAGGGGTCGTTTTGC GCGATCCCGACCTAAACTATACG 93 59

U GTGAGTTGGTGTGAAAGGGGTT CCACACCACCCACAATCCCA 113 59

. m, methylated sequence; u, unmethylated sequence.

53

IV) Statistical methods:

The data was coded and entered using the statistical package SPSS

version 15. The data was summarized using descriptive statistics:

mean, standard deviation, minimal and maximum values for

quantitative variables and number and percentage for qualitative

values. Statistical differences between groups were tested using Chi

square test for qualitative variables, independent sample t test for

quantitative normally distributed variables while Non-parametric

Mann Whitney test and kruskal-Wallis test were used for quantitative

variables which aren’t normally distributed. Correlations were done

to test for linear relations between variables. Kaplan-Meier Survival

Analysis was used. P-values less than or equal to 0.05 were

considered statistically significant.

54

RESULTS

linical features:C-1

Age and sex:

The age of patients showed a wide distribution range (between 20 and

85 years). The median age was 54 years with a standard deviation of 13.91.

Twenty two percent of the patients were above 60 years (Figure 3). Males

represented 58.6% of cases (41/70) and females represented 41.4% (29/70).

Male to female ratio was 1.4.

Figure (3): The age distribution in DLBCL patients.

B-symptoms:

B-symptoms were present in 38/53 (71.7%) of the cases and

absent in 15/53(28.3%). Seventeen cases had no data as regards B-

symptoms (Figure 4).

55

Figure (4): The B-symptoms occurrence in the DLBCL cases.

Performance status (PS):

As regards PS, 37/63 (58.7%) of the cases showed good PS (<2) and

26/63 (41.3%) showed poor PS (≥2) according to ECOG performance

status criteria (Figure 5), with missing data in 7 cases.

Figure (5): The performance status in the DLBCL cases.

LDH level:

Most of cases 38/55 (69.1%) showed high LDH serum level and 15/55

(30.9%) showed normal LDH serum level (Figure 6), with missing data in

15 cases.

56

Figure (6): The LDH level in the DLBCL cases.

Stage:

Forty nine out of the sixty five cases (74.2%) who have

presented in advanced stages (III and IV) in contrast to 16/65 (24.2%)

in early stages (I and II) (Figure 7). Five cases had missing data.

Figure (7): The stage distribution in the DLBCL cases.

57

Number of extra-nodal sites affection:

The number of extra-nodal sites (including soft tissue, hepatic,

mesenteric, bone marrow and others) affection was <2 in 60/66

(90.9%) and ≥ 2 in 6/66 (9.1%) of cases with missing data in 4 cases.

International prognostic index (IPI):

As regards the international prognostic index, Cases with low/low-

intermediate IPI (0-2) and high-intermediate/high IPI represented (3-5)

represented 32.1% and 67.9% of cases, respectively (Figure 8).

32.10%

67.90%

0.00%

10.00%

20.00%

30.00%

40.00%

50.00%

60.00%

70.00%

80.00%

Low IPI High IPI

Figure (8): The international prognostic index (IPI) in the cases.

:Histopathologic features-2

All cases were DLBCL, NOS category. The centroblastic, anaplastic,

immunoblastic and plasmablastic morphologic variants were 88.5%, 7.5%,

4% and 0%, respectively (Figure 9).

58

Figure(9): DLBCL cases: (A) a case of DLBCL with dominant

centroblastic morphologic variant (H&E, 40X), (B) a case of

DLBCL with dominant immunoblastic morphology (H&E, 40X), (C)

a case of DLBCL with high turn-over & tangible-body macrophages

forming starry-sky appearance (H&E, 40X) & (D) a case of DLBCL

with anaplastic morphology showing giant cells (H&E, 20X)

59

:Immunophenotypic classes andmmunophenotyping I -3

CD10

Positive membranous immunostaining for CD10 was reported in

20/65 (30.8%) of the cases whereas 45/65 (69.2%) were negative.

Bcl6

Positive nuclear stain for BCL6 was detected in 39/65 (60%) of

the cases and negative stain was reported in 26/65 (40%) of cases.

Mum1

Positive nuclear stain for MUM1 was detected in 41/65 (63%) of the

cases while 24/65 (37%) were negative (Figure 11). Five cases were

excluded from immunostaining due to inconclusive results.

The non-germinal center B-cell phenotypic class predominate in the

cases as 38/61 (62.3%) showed immunostaining results consistent with

non-GCB pattern according to Hans algorithm, whereas 23/61 (37.7%) of

the cases showed germinal center B-cell pattern (Figure 10). Nine cases

were unclassifiable.

62.3%

37.7%

Class

Non-GCB

GCB

Figure (10): The phenotypic classes; germinal-center B-cell (GCB) &

Non-Germinal center B-cell (Non-GCB) subtypes.

60

Figure (11): Immunostain cases (A & B) two cases of DLBCL shows

positive nuclear stain with MUM1 (40X). (C) a case of DLBCL

shows positive nuclear stain for BCL6 (20X).

61

:methylation patternpromoter hyperDNA -4

By methylation-specific PCR, 29/70 (41.4%) of the cases showed

DAPK PM. On the other hand, 33/70 (47.1%) showed evidence of MT1G

PM. No PM detected for DAPK and MT1G in the control cases (Figures 12-

15).

Figure (12): DAPK methylation pattern.

Figure (13): MT1G methylation pattern

62

Figure (14): A photomicrograph for an ethidium bromide-stained

4% agarose gel showing: A) DAPK PM: case 1 & case 2 show pure

methylated DAPK gene & case 3 shows mixed pattern (methylated

& unmethyated) DAPK gene. (B)MT1G PM: case 1 & case 3 show

mixed methylated & unmethylated MT1G gene & case 2 shows pure

methylated MT1G gene (Gel electrophoresis, photoducmentation)

Figure (15): Summary of the

methylation analysis of

DAPK (D) and MT1G (M) in

DLBCL cases. The filled

boxes indicate the presence

of methylation and the open

boxes indicate the abscence

of methylation.

63

Treatment and response: -5

CHOP was given to 61/64 (95.3%) and no treatment was given in

3/64 (4.7%). No data was available about the treatment in 6 cases.

Complete remission (CR) occurred in 28/45 (62.2%) of the cases and non-

complete remission (Non-CR) (including partial remission (21.7%),

sustained disease (13%) and progressive disease (2.1%)) occurred in 17/45

(37.8%) of the cases with missing data in 25 cases (Figure 16).

62.2%

37.80%

0%

20%

40%

60%

80%

CR Non-CR

Response

Response

Figure (16): The treatment response in the cases (CR: complete

remission & Non-CR including PR: partial remission, SD: sustained

disease & PD: progressive disease).

vival:up and sur-Follow -6

Relapse occurred in 27.5% of the cases (Figure 17). After two years

follow-up, 41/58 (70.7%) of cases were alive and 17/58 (29.3%) were

dead. The cause of death in all cases, except one, was disease related (16

disease related and 1 unrelated). Twelve cases had lost follow up. The

overall survival (OS) ranged from 1-80 months. The two years OS was

51.5% (Figure 18). The disease free survival (DFS) ranged from 2-75

months. The two years DFS was 60% (Figure 19).

64

Figure (17): The occurrence of relapse in the DLBCL cases.

Figure (18): overall survival (OS) among DLBCL cases.

65

Figure (19): Disease free survival (DFS) among DLBCL cases

with complete remission (CR).

7- Correlations and relationships:

a) Univariate analysis:

1) Correlation between studied DAPK and MT1G promotor

hypermethylation:

Both genes were methylated in 20% of cases and both were

unmethylated in 31.4% of cases (P=0.873) (Table 6).

66

Table (5): Correlation of MT1G and DAPK promotor

hyermethylation

DAPK Total P-value

U M

MT1G U 22(31.4%) 15(21.4%) 37 0.873

M 19(27.1%) 14(20%) 33

Total 41 29 70

(U: unmethylated- M: methylated).

2) Correlations DAPK and MT1G PM and phenotypic classes with the

clinical parameters:

As regards DAPK promoter hypermethylation, a statistically significant

correlation was detected with resistance to therapy, P-value was 0.005

(Table7).

As regards the MT1G promoter hypermethylation, statistically

significant correlation was detected with stage IV & occurrence of relapse

(P-value 0.016 and 0.049, respectively) (Table 8).

As regards phenotypic classification of DLBCL (GCB & Non-GCB);

the Non-GCB was significantly correlated with poor IPI and occurrence of

relapse (P-values 0.024 and 0.030, respectively) (Table 9).

3) Survival correlations:

The overall survival was significantly correlated with age groups, B-

symptoms, number of extra-nodal sites, PS and IPI. The disease free

survival is significantly correlated with age, B-sypmtoms and number of

extra-nodal sites (Table 10).

67

Table (6): Correlation of DAPK methylation pattern with other

clinical & pathological parameters.

P-value

DAPK

Methylated

No (%)

Unmethylated No (%)

0.327

22(84.6)

4 (15.4)

30 (73.2)

11 (26.8)

Age:

≤60

˃60

0.107

9(30)

20(70)

20(41.4)

21(58.6)

Sex:

Female

Male

0.432

6(30.8)

16(69.2)

11(33.3)

22(66.7)

LDH:

normal

high

0.500

9(36)

16(64)

6(21.4)

22(75.6)

B-symptoms: absent

present

0.083

23(92)

2(8)

37(90.2)

4(9.8)

Extra-nodal sites: ≤1

≥2

0.092

5(20)

20(80)

11(26.8)

30(73.2)

Stage:

Early(I,II)

Advanced(III,IV)

0.591

13(59)

9(41)

24(58.5)

17(41.5)

PS:

<2

≥2

0.553

7(33.3)

14(66.7)

10(45.5)

22(54.5)

IPI:

Low/Low intermediate

High/High intermediate

0.005

12(60)

8(40)

5(20)

20(80)

Response:

Non-CR

CR

0.597

12(63.2)

7(36.8)

13(61.9)

8(38.1)

Relapse:

No relapse

Relapsed

0.704

17(65.4)

9(34.6)

28(73.7)

10(26.3)

CD10:

negative

positive

0.822

11(42.3)

15(57.7)

14(36.8)

24(63.2)

BCL6:

negative

positive

0.338

12(46.2)

14(53.8)

11(28.9)

27(71.1)

MUM1: negative

positive

0.194

13(48.1)

14(51.9)

10(26.3)

28(73.7)

Class: GCB

Non-GCB

Significant level (P-value<0.05).

68

Table (7): Correlation of MT1G methylation pattern with other

clinical & pathological parameters.

P-value

MT1G

Methylated

No (%)

Unmethylated

No (%)

0.350

26(81.2)

6(18.8)

26(74.3)

9(25.7)

Age:

≤60

˃60

0.333

16(48.5)

17(51.5)

13(35.1)

24(64.9)

Sex:

Female

Male

0.607

8(30.8)

18(69.2)

9(31)

20(69)

LDH:

normal

high

0.500

9(34)

16(64)

6(21.4)

22(75.6)

B-symptoms: absent

present

0.277

26(86.7)

4(13.3)

34(94.4)

2(5.6)

Extra-nodal sites: <2

≥2

0.315

9(30)

21(70)

7(19.5)

29(80.5)

Stage:

Early(I,II)

Advanced(III,IV)

0.016

17(56.7)

10(28.6) Stage: IV

0.318

15(51.7)

14(48.3)

22(64.7)

12(35.3)

PS:

<2

≥2

0.311

10(59)

17(61)

7(27)

19(73)

IPI:

Low/Low intermediate

High/High intermediate

0.278

6(28.6)

15(71.4)

11(45.8)

13(54.2)

Response:

Non-CR

CR

0.049

10(50)

10(50)

15(75)

5(25)

Relapse:

No relapse

Relapsed

0.254

18(62)

11(38)

27(77.1)

8(22.9)

CD10:

negative

positive

0.567

12(41.4)

17(58.6)

13(37.1)

22(62.9)

BCL6:

negative

positive

0.456

9(31)

20(69)

14(40)

21(60)

MUM1: negative

positive

0.106

13(44.8)

16(55.2)

10(27.8)

26(72.2)

Class: GCB

Non-GCB

Significant level (P-value<0.05).

69

Table (8): Correlation of DLBCL phenotypes with other clinical &

pathological parameters.

P-value

Immunophenotypic class

Non-GCB

No (%)

GCB

No (%)

0.104

27(73)

10(27)

19(90.5)

2(9.5)

Age:

≤60

˃60

0.387

15(40.5)

22(59.5)

11(47.8)

12(52.2)

Sex:

Female

Male

0.130

7(24.1)

22(75.9)

8(44.4)

10(55.6)

LDH:

normal

high

0.588

10(31.2)

22(68.8)

5(27.8)

13(72.2)

B-symptoms: absent

present

0.395

34(94.4)

2(5.6)

21(100)

0 (0.0)

Extra-nodal sites: ≤1

≥2

0.077

6(17.1)

29(82.9)

8(38.1)

13(61.9)

Stage:

Early(I,II)

Advanced(III,IV)

0.353

19(57.6)

14(42.4)

14(66.7)

7(33.3)

PS:

<2

≥2

030.0

5(16)

26(84)

10(59)

7(41)

IPI:

Low/Low intermediate

High/High intermediate

0.293

11 (47.8)

12(52.2)

5(33.3)

10(66.7)

Response:

Non-CR

CR

0.030

9(50)

9(50)

13(86.7)

2(13.3)

Relapse:

No relapse

Relapsed

0.151

24(65)

13(35)

11(47.8)

12(52.2)

DAPK

Unmethylated

methylated

0.206

23(62.2)

14(37.8)

11(47.8)

12(52.2)

MT1G

Unmethylated

methylated

Significant level (P-value<0.05).

70

Table (9): Correlation of OS and DFS with other parameters.

Disease free survival (DFS) Overall survival (OS)

P-value Median 2years DFS P-value Median 2years OS

550.0

080.0

Age:

28.1±4.6 68.8% 20±3.725

57.7% ≤60

6±0.82 0% 5±1.309 16.7% ˃60

0.407

0.472 Sex:

28.1±8.3 75% 15±6.103 47% Female

21.7±7.275 44.4% 18±1.363 50% Male

0.643

0.764

LDH:

19.4±14.6 66.7% 32.8±4.372 66.7% Normal

27.2±5.7 66.7% 18±0.074 50.7% High

0.002

0.01

B-symptoms: 36±7.1 87.5% 34.7±11.010 63.6% absent

20±1.992 33.3% 18±3.566 39% present

0.001

0.046

Extra-nodal sites: 27.2±5.1 64.7% 20±4.6

57.1% ≤1

2±0.01 0% 15±10.954 0% ≥2

0.713

0.125 Stage:

22.4±9.3 66.7% 27±6.67

71.4% Early(I,II)

21.7±7.9 58.3% 16±2.191 41.7% Advanced(II,IV)

0.465

060.0

PS:

19.4±7.7 45.5% 25.6±5.85

71.4% <2

28.1±3.82 83.3% 12±7.255 31.3% ≥2

0.954

210.0

IPI:

30±2.286 100% 30.133±2.383 83.3% Low

22.4±6.27 60% 25.567±7.071 36% High

0.603

0.501 Class:

27.2±6.285 75% 17.9±12.8

55.6% GCB

28.1±6.267 70% 18±3.2 47.7% Non-GCB

0.195

0.989 DAPK:

27.2±7.2 46.2% 21±6.042 52.6% Unmethylated

21.7±3.404 100% 16±4.137 41.7% Methylated

0.247

0.283 MT1G:

28.1±4.954 71.4% 18±0.111 53.8% Unmethylated

22.4±9.107 50% 18±4.577 44.4% Methylated

71

b) Multivariate analysis:

1) Correlations DAPK and MT1G PM, phenotypic class and IPI with

response to treatment:

DAPK and MT1G PM, phenotypic class and IPI were together

significantly correlated with response to treatment. DAPK PM was

independently significantly correlated with the response.

Table (10): Correlation of DAPK, MT1G, phenotypic class and IPI

with response.

Response

P-value

DAPK

MT1G

Phenotypic class

IPI

0.007

0.872

0.294

0.203

Constant 0.004

2) Correlations DAPK and MT1G PM, phenotypic classes and IPI

with occurance of relapse:

DAPK and MT1G PM, phenotypic class and IPI were together

significantly correlated with relapse. Phenotypic class independently

showed border line significant correlation with relapse.

72

Table (11): Correlation of DAPK, MT1G, phenotypic class and IPI

with relapse.

Relapse

P-value

DAPK

MT1G

Phenotypic class

IPI

0.133

0.823

0.060

0.155

Constant 0.020

3) Correlations DAPK and MT1G PM, phenotypic class and IPI with

overall survival (OS):

DAPK and MT1G PM, phenotypic class and IPI were together non-

significantly correlated with OS. IPI independently showed border line

significant correlation with OS.

Table (12): Correlation of DAPK, MT1G, phenotypic class and IPI

with OS.

Overall survival (OS)

P-value

DAPK

MT1G

Phenotypic class

IPI

0.591

0.893

0.231

0.058

Constant 0.329

73

4) Correlations DAPK and MT1G PM, phenotypic class and IPI

with disease free survival (DFS):

DAPK and MT1G PM, phenotypic class and IPI were together non-

significantly correlated with DFS but non of the former independently

correlated with DFS.

Table (13): Correlation of DAPK, MT1G, phenotypic class and IPI

with DFS.

Disease free survival (DFS)

P-value

DAPK

MT1G

Phenotypic class

IPI

0.456

0.644

0.950

0.327

Constant 0.550

5) Correlations DAPK and MT1G PM and phenotypic class and

IPI with IPI:

DAPK and MT1G PM and phenotypic class were together

significantly correlated with IPI. Phenotypic class independently

showed significant correlation with IPI.

74

Table (14): Correlation of DAPK, MT1G and phenotypic class with

IPI.

International prognostic index (IPI)

P-value

DAPK

MT1G

Phenotypic class

0.499

0.613

0.002

Constant 0.025

75

DISCUSSION

Diffuse large B-cell lymphoma (DLBCL) comprises the largest

subtype of non-Hodgkin’s lymphomas (NHL), accounting for

approximately 32% of all lymphomas seen throughout the world (Xu et al,

2001). DLBCL represents about 54.67% of NHL cases in national cancer

institute (NCI), cairo (Mokhtar et al, 2007).

DLBCL is characterized by a significant spectrum of morphologic,

immunophenotypic and molecular genetic features. The treatment of

patients with DLBCL has been guided traditionally by clinical parameters

such as the Ann Arbor Staging and International Prognostic Index (IPI).

Although the IPI represents the most widely accepted prognostic model,

there is still a marked variability in patient's outcome within identical IPI

subgroups, reflecting the heterogeneity of this malignancy. Recent

application of DNA microarray, real-time reverse transcription polymerase

chain reaction, and tissue array immunohistochemistry makes the

development of new classifications possible based on molecular profiling.

The molecular classification of DLBCL may lead to grouping of specific

disease entities sharing similar biologic features, clinical behavior, and

outcome (Morgensztern and Lossos, 2005).

Beyond genetic alterations, identification of novel epigenetic

alterations, such as aberrant promotor methylation may yield better

76

diagnostic, prognostic and therapeutic information. (Jones and Baylin,

2007).

The profiles of promotor hypermethylation of the CpG islands in

tumor-suppressor genes are specific to the tumor type (Agrelo and Wutz,

2009). Thus, each tumor type can be assigned a specific, defining DNA

“hypermethylome.” Such patterns of epigenetic inactivation occur not only

in sporadic tumors but also in inherited cancer syndromes, in which

hypermethylation can be the second lesion in Knudson’s two-hit model of

how cancer develops. Recently devised epigenomic techniques have

revealed maps of hypermethylation of the CpG islands that suggest the

occurrence of 100 to 400 hypermethylated CpG islands in the promoter

regions of a given tumor (Esteller, 2007).

Hematological neoplasms are known to have different

hypermethylation profiles than those of other solid tumors ( Takahashi1,

2004). In a recent study, the prevalence of aberrant promtor methylation of

eight TSGs that are known to be involved in cell cycle regulation (p16,

COX2), DNA repair (MGMT), apoptosis (DAPK, RUNX3), inhibition of

angiogenesis (THBS1), invasion and metastasis (CDH1) and cell

proliferation (MT1G). This study demonstrated that aberrant CpG island

methylation is a frequent event in NHLs especially diffuse large B-cell

lymphomas with overlapping but distinct methylation profiles (Kim1 et al.

2008).

77

DAPK and MT1G genes were the most commonly affected gene by

promoter hypermethylation in DLBCL according to Kim et al 2008. Thus

we attempted in the current study to determine the prevalence of DAPK

and MT1G PM in a well characterized cohort of DLBCL from Egypt (70

cases) and to assess their prognostic and predictive value.

Methylation specific PCR was done using high molecular weight DNA

extracted from formalin fixed, paraffin embedded tissues. Inspite of the fact

that DNA extract from fresh tissue is better in quality and quantity, but the

absence of tissue banking system in NCI makes availability of fresh tissue

is difficult. Another disadvantage of using paraffin embedded tissue is that

purification of DNA extracted from paraffin embedded sections is limited

by the length of fixation, with longer fixation times leading to a steep

increase in cross-links between proteins and nucleic acid. The result of

extensive cross-linking is often evidenced by limitations of purification of

the final DNA product. However, using of paraffin embedded tissue is

widely used all over the word due to conveniently & availability and to

allow retrospective studies with full data and long follow-up.

We chose the methylation specific PCR method as it is the cheapest

and the most widely used method that fits our limited resources. Bisulfite

modification is a principle step in this technique. The latter requires low

PH (PH 5) to be accomplished which again affect the integrity of the DNA

increasing the possibility of false negative results. We tried to overcome

this problem by increasing the starting amount of DNA entering the

78

bisulfite modification reaction, avoiding prolonged time of DNA in low PH

and finally by re-introducing of the PCR products of negative cases to

another PCR reaction simulating semi-nested PCR in which some cases

which were false negative in the first PCR reaction turned into positive in

the second PCR.

The available clinical data were collected from the clinical records but

we faced a major problem due to the large fraction of missing data as

regards clinical parameters and survival rates. We tried to solve these

statistical problems by compiling possible parameters as early (I&II) and

advanced (III&IV) stages, low/low intermediate IPI and high/high

intermediate IPI and CR and non-CR. OS and DFS were divided into above

and below median to allow for multivariate analysis. Missing data were

excluded from calculation.

The median age of our studied cases was 54 year, 77.6% of the Cases

were ≤60 years and 22.4% of the cases were >60 years. The median age

reported in the current study is slightly higher than that reported in an early

study performed in NCI (Cairo) by Abdel Aty et al (2004) including 90

cases. In this study, the median age was 49 years, 90% of cases were ≤60

years and 10% of cases were >60 years. Similarly, the NCI pathology

registry showed comparable median age (47.2 years), Mohktar et al (2007).

This slight increase in the median age suggests a shift in the incidence of

DLBCL cases to an older age group. On the other hand, the median age of

DLBCL is much higher in western countries since the WHO (2008)

79

reported a median age in the 7th

decade which is in accordance with the

median age reported by Ott et al (2010) (69 years) in a large series of 949

cases.

Our study shows a slight male predilection with a male to female ratio

of 1.4:1. Similar finding was detected in the study of Abdel Aty et al

(2004) as well as in the NCI pathology registry (2007). However, some

studies showed a reversed ratio with slight female sex predilection (1:1.25),

Broyde et al (2009).

B-symptoms were reported in 28% of our studied cases. This is in

agreement with Amara et al (2008) who reported B-symptoms in 26% of

their studied cases. However, Abdel Aty et al (2004) and Mosaad et al

(2008) from the NCI, Cairo, reported higher percentages (54.4% and 55%,

respectively). However, lower percentage (20%) was reported in the WHO

(2008).

A high LDH serum level was reported in 69.1% of the cases in the

current study. The results of Abdel Aty et al (2004) are lower than our

study since they found high LDH serum level in 57.2% of cases. The

literature shows high LDH serum level in variable percentages including

higher percentage (84.8%) in the study of Amara et al (2008) and lower

percentage (38%) reported by Shu-Nan et al, (2009).

80

The majority of our cases (74.2%) had advanced stages (stages III &

IV). This finding is comparable to Liu et al (2008), who reported 77.9% of

advanced stages in DLBCL cases from China. Other studies performed on

Egyptian patients from the NCI, Cairo, Abdel Aty et al (2004) showed

higher percentage (95.6%) while Mosaad et al (2008) showed lower

percentage (57%) in their studied cases.

Out of the 70 studied cases, 41.3% had poor performance status

(PS≥2), which is comparable with other studies (Abdel Aty et al, 2004),

(Amara et al, 2008) and (Broyde et al, 2009).

The number of extra-nodal sites affected were 2 in 9.1% of the

studied cases, which is in agreement with Abdel Aty et al (2004) (10%).

However, higher frequencies were reported by Amara et al (2008) (15.5%)

and Broyde et al (2009) (30%).

As regards the international prognostic index, cases with low/low-

intermediate IPI and high-intermediate/high IPI represented 32.1% and

67.9%, respectively. Our data were comparable to those of Abdel Aty et al

(2004) in which low/low-intermediate IPI and high-intermediate/high IPI

represented 21.1% and 78.9%, respectively. Low/low-intermediate IPI and

high-intermediate/high IPI represented 71.7% and 28.3%, respectively,

according to Amara et al (2008), 46% and 54%, respectively, as reported

by Liu et al (2008) and 56.2% & 43,8%, respectively as shown by Broyde

81

et al (2009). Thus, our study and the study of Abdel Aty et al (2004)

showed the highest percentage of high-intermediate/high IPI (67.9%) and

this is related to high percentages of advanced stages and poor PS in

Egyptian patients.

Our data shows that CD10, BCL6 and MUM1 were positive in 30.8%,

60% and 63% of our cases, respectively. According to the

immunophenotyping results, we subclassified our studied cases into

germinal center B-phenotypic class (GCB) and non- germinal center B-

phenotypic class (Non-GCB). The GCB class represented 37.7% (23/61) of

the cases. Hans et al 2004 reported a comparable percentages 27.6%

(42/152), 55.9% (85/152) and 46.7% (71/152) as regarding CD10, BCL6 &

MUM1, respectively. Xu et al 2001 showed higher percentage 43.4%

(23/53) positive to CD10. Joanaa et al 2010 found positive CD10 in lower

percentage 13.6% (9/66) and comparable percentages 53% (35/66) and

54.5 (36/66) for BCL6 and MUM1, respectively.

The Percentage of GCB class shows variation in the literatures ranging

from 22% (Chen et al, 2010) up to 73% (Joanna et al, 2010). Some

studies showed racial difference in the phenotypic class distribution, in this

regard, Chen et al (2010) compared distribution of GCB DLBCL and Non-

GCB DLBCL in Chinese and Western cases. They found that Chinese

cases showed lower percentage of GCB class 22% (27/124) in contrast to

Western cases 52.6% (60/114).

82

The current study addresses the possible role of DAPK and MT1G

promotor hypermethylation (PM) in the development and progression of

DLBCL. DAPK PM was detected in 41.4% (29/70) of the studied cases but

not in the control cases.

Literature review shows variable incidence of DAPK PM ranging

from 22.2% up to 94.3%. In hematolymphoid malignancies, Gutierrez et al

(2003) reported lower percentage (30%) of DAPK PM in acute

lymphoblastic leukemia (ALL) cases while higher percentages of DAPK

PM were reported by Huang et al (2007) (55%) in gastric lymphoma,

Amara et al (2007) (74%) in Tunisian DLBCL cases, Kim et al (2008)

(76.1%) (35/46) in Korean DLBCL cases and much higher percentage

(94.3%) was reported by Choung et al (2012) in Korean ocular MALT

lymphoma. This variation in DPAK PM may be related to: 1) differences in

the detection methods, 2) differences in the nature of specimen (fresh or

paraffin embedded tissues), 3) differences in tumor type and subtypes as

well as 4) population difference that includes variable etiologic,

carcinogenic and genetic factors.

Other non-hematologic malignancies show variable percentages of

DAPK PM. So, Kato et al (2008) reported DAPK PM in 22.2% of gastric

carcinoma, Xian-Lan et al (2008) reported DAPK PM in 65.4% cervical

carcinoma cases and Hee Jung Park et al (2010) reported 28.1% of DAPK

PM in urothelial carcinoma cases.

83

The current study showed MT1G PM in 47.1% (33/70) of the studied

cases but not in the control cases. The available data in the literature show

variable incidences of MT1G PM ranging from 24% up to 76.1%. There

are only two published manuscripts reporting on MT1G PM in lymphoma;

Kim et al (2008) reported higher percent (76.1%) of MT1G PM in Korean

DLBCL cases while Choung et al (2012) reported comparable percentage

(48.6%) of MT1G PM in Korean ocular MALT lymphoma cases.

In other non-hematologic malignancies, Henrique et al (2005) reported

MT1G PM in 24% of prostatic carcinoma cases, Ferrario et al (2008)

reported MT1G PM in 60% of papillary carcinoma cases, Kanda et al

(2009) reported MT1G PM in 60.4% of hepatocellular carcinoma cases and

Luis et al (2010) reported MT1G PM in 55% of hepatoblastoma cases.

Correlation of the promoter hypermehtylation status with the standard

clinico-pathological prognostic factors of the patients was attempted when

there is available clinical data. DAPK PM showed significant correlation in

both univariate and multivariate analysis with resistance to therapy as non-

CR was reported in 60% of the cases with DAPK PM compared to 20% in

the unmethylated cases. On the other hand, CR reported in 40% of cases

with DAPK PM compared to 80% in the unmethylated cases. Amara et al

(2008) showed comparable results regarding response to therapy where

81% of DLBCL cases resistant to chemotherapy showed DAPK PM. The

study of Amara et al (2008) represents the only study which correlated

DAPK PM with patients response to treatment in lymphoma, however,

84

other studies on different solid tumors revealed comparable results

including the study of Kato et al (2008) who reported a response rate to

chemotherapy of 21.4% and 44.8% in gastric carcinoma with methylated

and unmethylated DAPK, respectively. All these results confirm that

DAPK PM seems to play a significant role in the acquisition of resistance

to chemotherapy. This could be explained by the fact that DAPK has a pro-

apoptotic function which is blocked by its PM which in turn increases the

resistance to chemotherapy. Therefore, DAPK PM could be considered a

possible predictive factor that can predict response to treatment in hemato-

lymphoid and other solid malignancies.

As regards the correlation between survival rates and DAPK PM, we

found that the median OS in the current study for methylated and

unmethylated DAPK cases was 16 months and 21 months, respectively,

while the median DFS was 21 months and 27 months, respectively.

Although in our study DAPK PM was correlated with shortened OS and

DFS, this correlation did not reach significant level. This might be related

to the large fraction of cases with lost follow up (20/70 cases) and the short

period of follow-up (2 years). In multivariate analysis by Amara et al

(2008) DAPK PM was found to be an independent prognostic factor in

predicting shortened OS (P=0.001) and DFS (P=0.024). Similarly,

Hoffmann, et al. (2009) reported that preoperative measurement of

methylated DAPK in peripheral blood DNA may contribute to better

estimate of postoperative survival chances of patients with esophageal

carcinoma, especially adenocarcinoma. Therefore, we recommend a larger

sample size and a longer follow-up period (5years) to get more accurate

results.

85

On the other hand, DAPK PM in our cases showed no significant

correlation with the other clinic-pathologic features of the patients

including age, sex, LDH level, B-symptoms, IPI, Ann Arbor Stage, number

of extra nodal sites affection, PS, relapse and phenotypic class. Our data in

this regard are comparable to Amara et al (2008).

As regards MT1G PM; there were significant correlations in

univariate and multivariate analysis with stage IV and the incidence of

relapse. Stage IV was reported in 56.7% of methylated MT1G and in

28.6% of unmethylated MT1G. Similarly, the incidence of Relapse was

50% in methylated MT1G and 25% in unmethylated MT1G. MT1G PM is

not widely studied in hematologic malignancies thus comparison of our

data with other studies was not valid, however all previous correlations

considering other solid malignancies confirm that MT1G PM increases

tumor aggressiveness. For instance in prostatic carcinoma, significant

correlation was detected in higher stages by Henrique et al (2005) as stages

T3 & T4 were 65.5% (19/29) in methylated MT1G & 42.4% (39/92) in

unmethylated MT1G (P=0.049). Similarly, Sakamoto et al (2010) reported

that MT1G PM showed a significant correlation with poor prognosis of

patients with hepatoblastoma. Kanda et al (2009) reported that MT1G PM

indicating a poorer prognosis in hepatocellular carcinoma than the negative

group, although the difference was not significant (p<0.0978).

On the other hand, MT1G PM showed no significant correlation with

age, sex, LDH level, B-symptoms, IPI, extra-nodal sites affection, PS,

response to therapy, phenotypic class or survival rates.

The DLBCL phenotypic classes in the current study showed significant

correlations with IPI and relapse rate (in both univariate and multivariate

86

analysis). A high/high intermediate IPI (3-5) was reported in 84% of non-

GCB class and 41% of GCB class, whereas, low/low intermediate IPI (0-2)

was reported in 16% of Non-GCB class and in 59% of GCB class. Relapse

rate in non-GCB and GCB classes was 50% and 13.3%, respectively. On

the other hand, phenotypic classes showed near significant correlation

(P=0.077) with stage since advanced stages (Stages III&IV) were reported

in 83% and 62% of Non-GCB and GCB, respectively, while early stages

(Stages I&II) were reported in 17% and 38% of Non-GCB and GCB,

respectively.

No significant correlations were detected with age, sex, LDH level, B-

symptoms, PS, response, DAPK, MT1G or survival rates. Comparing our

data regarding the phenotypic classes with those of Joanna et al (2010), the

later showed significant correlation of DLBCL phenotypic classes with

stage, IPI, response and overall survival as follows: Early stages (I&II) in

GCB and Non-GCB were 89% (16/18) and 42% (20/48), respectively,

while advanced stages (III&IV) in GCB and Non-GCB were 11% (2/18)

and 58% (28/48), respectively (P= 0.001). Similarly, a low IPI in GCB and

non-GCB were 72% (13/16) and 40.5% (17/42), respectively. High IPI was

detected in 17% (3/16) of the GCB class and in 59.5% (25/42) of the non-

GCB class (P=0.008). CR in GCB and non-GCB were 94% (17/18) and

54% (26/48), respectively. Non-CR in GCB and non-GCB were 6% (1/18)

and 46% (22/48), respectively (P=0.003). The 5-year OS for the GCB

group was 83% compared with only 30% for the non-GCB group. No

significant correlation detected with age groups, sex, B-symptoms, LDH

level and number of extra-nodal sites. Our study showed no correlation

with response to therapy and survival in relation to phenotypic class which

may be related to the large fraction of cases with lost follow up (20 out of

87

70 cases) and the short period of available follow up (2years) in contrast to

Joanna et al (2010) (5years survival).

In the current study, 62.2% of our cases showed complete remission

after treatment. Our result in this context is comparable to Xu et al (2001)

(66.7%), Abdel Aty et al (2008) (61.1%) and Amara et al (2008) (56%) but

slightly lower than Mosaad et al (2008) in which CR was reported in 78.6%

of cases.

Similarly, the high rate of relapse (27.5%) in our studied cases after CR

is higher than Xu et al (2001) who reported a much lower rate of relapse

(7.7%) but our finding is comparable to Abdel Aty et al (2008) (34.5%).

The two years overall survival (OS) reported in the current study

(51.5%) is lower than those reported by Abdel Aty et al (2004) 80%,

Amara et al (2008) 55% and Mosaad et al (2008) 63%.

Our two years DFS (60%) is comparable to those reported by Abdel

Aty et al (2004) 54.4%, Amara et al (2008) 46% and Mosaad et al (2008)

60%. The low OS in the current study could be attributed to the high

percent of high IPI cases and cases with non-GCB phenotypic class.

88

In our study, the OS was significantly correlated in the current study

with age groups, B-symptoms, number of extra-nodal sites, PS and IPI.

Disease free survival was significantly correlated with age, B-symptoms

and number of extra-nodal sites only.

89

CONCLUSIONS

Our work is a preliminary study which needs to be verified in a

larger study containing more cases with complete clinical data and longer

follow-up. However, according to this study we can conclude the

following:

1) Promotor methylation of DAPK and MT1G can be used as molecular

prognostic and predictive markers in specific cases of DLBCL. DAPK PM

can predict resistance to chemotherapy in DLBCL, whereas MT1G PM can

predict the incidence of relapse.

3) The Egyptian DLBCL shows high percentage of Non-GCB class

(62.3%) using the standard immunophenotyping panel (CD10, BCL6

and MUM1) denoting an aggressive behavior.

3) The IPI is still considered the strongest prognostic factor in DLBCL in

prediction of overall survival.

90

RECOMMENDATIONS

Studying of PM in a larger panel of genes using recent profiling

technique in order to identify more reliable predictive biomarkers.

Implementation of tissue banking system to provide fresh tissue for

molecular research.

Improvement of the hospital filling to provide complete set of

clinical data.

91

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