Rafael Ponce Sept 27 2012 Immune function Tumor Inflammation immune activation Used by host to eliminate malignant cells immunosurveillance Used by tumor to create a permissive environment for growthdevelopment ID: 915736
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Slide1
Immunomodulation and cancer: Different relationships across diseases and disease states?
Rafael Ponce
Sept 27, 2012
Slide2Immune
function
Tumor
Inflammation, immune activation
Used by host to eliminate malignant cells (
immunosurveillance
)Used by tumor to create a permissive environment for growth/developmentDrives lymphoma development (chronic B cell activation)ImmunosuppressionUsed by tumor to escape surveillanceIncreased risk of oncogenic virus activityIncreased risk of unresolved infection
Immune escape mechanismsPerception of ‘self’ in the absence of ‘danger’, Ignorance: Peripheral tolerance, Down-regulation of MHC class IActive immunosuppression, induced toleranceNeed to break toleranceEvolve under selective pressure of immune response to acquire mechanisms for immune escape
Virus
Immunomodulation and cancer
Immune status in the tumor microenvironment drives balance of response (tolerance vs immunity)
Slide3Immunity and cancer paradigms
Immunosurveillance model
Inflammation modelLymphomagenesis modelOncogenic virus model
All models have experimental and epidemiological supportHow can we understand the role of immunity and cancer for specific cases?
Slide41. Immunosurveillance modelInnate and adaptive immune cells protect the host from transformed cells (elimination)
NK, NKT, CD4+ T cells, CD8+ T cells, DC
Transformed cells can adapt to immune surveillance, establish a fight for dominance (equilibrium)Transformed cells overcome immune surveillance, develop into clinically apparent tumors (escape)
Slide51. Immunosurveillance model
Slide61. Immunosurveillance model
Slide7Cancer immunosurveillance
IL-13, IL-6
TGF-
b
Tumor
P
arenchyma
Anti-tumor adaptive immune responseTumor supportive environmentIDOTGF-
bIL-10PGE2
Treg
pDC
IL-35 IDO
IL-10 TGF-
b
PD-L1 PGE
2
Imm
DC
MDSC
CD8
+
T
Eff
Tumor escape
Tumor elimination
M
PD-L1
B7-H1
B7-H3
B7x
HLA-G
HLA-E
VEGF-C/D
TH
17
IL-23
IFN-
g
Perforin
B cell
NKT Cell
IL-12, IFN-
g, a-
GalCer
IL-6 IL-1
b
TGF-
b
TNF-
a
NK Cell
Perforin
TRAIL
IL-12
M
DC
CD4
+
T
H
PGE
2
Slide82. Inflammation modelChronic inflammation can
induce cell transformation (reactive oxygen/nitrogen
spp),promote cell proliferation and increase the risk of spontaneous mutations, andcreate a permissive environment for tumor growth and spread
Slide92. Inflammation model
Also,
Mantovani et al (2008) Nature 454:436-444
Slide103. Lymphomagenesis modelB cell lymphomas occur at different steps of B-cell development and represent their malignant counterpart
Lymphomas arise from errors occurring at hyper-mutable stages of B cell development
Genetic hallmark is chromosomal translocations resulting from aberrant rearrangements of IG and B(or T) cell receptor genesLeads to inappropriate expression of genes at reciprocal breakpoints that regulate a variety of cellular functions
gene transcription, cell cycle, apoptosis, and tumor progressionLymphomas promoted by chronic B cell activation (infection, alloantigen (graft), self-antigen (autoimmunity))
Slide11B- cell development
3. Lymphomagenesis model
Slide123. Lymphomagenesis model
B- cell development requires DNA recombination
Slide13B- cell development requires DNA recombination
V(D)J recombination
Class switch recombination
Process for assembling gene segments coding variable region of antibody molecule to generate Ab diversity
Process for altering effector activity of heavy chain via recombination of Fc heavy chain
Somatic
hypermutation
Process for altering antibody specificity via point mutations, deletions, duplications
Slide14Errors arising in hyper-mutable stages of B-cell development drives lymphoma
Klein and
Dalla-Favera
(2008) Nat Rev Immunol 8:22
Slide153. Lymphomagenesis model
Slide164. Oncogenic virus model
Innate and adaptive immunity protects the host from active infection by oncogenic viruses
NK cells, CD8+ T cells, CD4+ T cells, granulocytes, DCSeven identified human oncogenic viruses
EBV: B cell lymphomaHepatitis B, C viruses: hepatocellular carcinomaHTLV-1: T cell leukemia/lymphomaHHV8 (KSHV): Kaposi’s sarcoma
HPV: Cervical cancer, anogenital cancers, oropharyngeal cancersMerkel cell polyomavirus: Merkel cell carcinoma
Slide17Role of oncogenic virusesVariable attribution of cancer to
oncoviruses
HPV and cervical cancer (~100%)CNS lymphoma and EBV (HIV patients, 100%)Merkel cell polyoma virus and MC carcinoma (80%)HTLV-1 and Adult T cell leukemia/lymphoma (?)
HHV8 and Kaposi’s sarcoma (~100%)EBV and Lymphoma (2 to >90%)
Slide184. Oncogenic virus model: EBV
B-cell transformation by EBV
Slide19Relating paradigm to cancer in patient populations with altered immunity
Which patient populations provide useful information?
Congenital (Primary) immunodeficiencyOrgan transplant recipientsAcquired immunodeficiency (HIV)AutoimmunityWhat forms of cancer prevail in these populations?
Slide20Grulich et al (2007) Lancet 370:59
Slide21Relative risk of cancer with immunomodulation
>1-3x
5-10x
10-20x
>20xHIV/AIDS (CD4+)
Organ transplant
1° Immuno
-deficiencyAutoimmunity
Hodgkin’sThyroid
NHLKidneyPenisHodgkin’s
NHLAnal cancerKaposi’s sarcoma
Kaposi’s sarcomaNon-melanoma skinLipGenital cancersGynecological cancersLiverVulva/vagina
StomachCervixOro-pharynxLeukemia, Lip, Stomach, Non-melanoma skin, Oro-pharynx
NHL (RA)Other solid organ (RA)Leukemia (RA)Hodgkin’s (RA)
NHL (
Sjogren’s
, SLE, Celiac)
T cell lymphoma (AHA, celiac disease)
AHA: Autoimmune hemolytic anemia; CVID: Common variable immunodeficiency; XLA: X-linked agammaglobulinemia
SCID: Severe combined immunodeficiency; AT: Ataxia telangiectasia; WAS: Wiscott-Aldrich syndrome; XLD: X-linked lymphoproliferative disorder
NHL (CVID, SCID, AT, WAS, XLD)
Stomach (XLA)
Leukemia (AT, WAS)
Stomach (CVID)
Breast (CVID)
Breast (AT)
1
Breast, Prostate
Colon/rectum
Ovary
Thyroid
Breast, Prostate
Ovary, Brain, Testes
RR
Slide22EBV differentially contributes to lymphoma burden across patient populations
Disease
% EBV+ Tumors
Citation
Lymphoma with no known immunosuppression
2-10%
(
Kamel et al., 1999
; Hoshida et al., 2007)Hodgkin’s lymphoma
40-50%
80%
(
Macsween et al., 2003; Swerdlow, 2003; Young et al., 2003;
Thorley-Lawson et al., 2004;
Young et al., 2004;
Balandraud et al., 2005
)
Burkitt’s lymphoma (developed world)
15-25%
(
Macsween et al., 2003
;
Young et al., 2003
;
Young et al., 2004
)
NHL
Post-transplantation (<1yr)
>90%
(
Macsween et al., 2003
)
Post-transplantation (>1yr)
50%
(
Young et al., 2004
)
HIV patients
NHL
28-66%
(
Rabkin, 2001
;
Macsween et al., 2003
;
Balandraud et al., 2005
)
Burkitt’s
25%
(
Macsween et al., 2003
)
CNS Lymphoma
100%
(
Rabkin, 2001
;
Macsween et al., 2003
)
Primary Immunodeficiency
Lymphoma/BPLD
¶
Lymphoma
Lymphoma (mucosal-associated)
31%
#
0%
0%
(
Filipovich et al., 1994
)
(
Gompels et al., 2003
)
(
Cunningham-Rundles et al., 2002
)
RA Patients
2%
3%
15%
27%
11%
26%
17%
12%
(
Kamel et al., 1999
)
(
Staal et al., 1989
)
(
Mariette et al., 2002
)
(
Hoshida et al., 2007
)
(
Askling
et al., 2005
)
(
Dawson et al., 2001
)
(
Baecklund et al., 2003
)
(
Baecklund et al., 2006
)
Slide23Relating paradigm to cancer in patient populations with altered immunity: A proposal
Is cancer associated with oncogenic virus etiology identified at increased rates?
What proportion of tumors evidence viral DNA?
Is there evidence/risk of inflammation?Unresolved infection?Autoimmunity?Are pathways associated with tumor antigen detection and adaptive immunity affected?
Slide24NHL 4, 3Kidney
1
Penis 4
Hodgkin’s 3, 4NHL 3, 4Anal cancer 4, 1Kaposi’s sarcoma
4Gynecological cancers 4, 1 Liver 4/1?
NHL 3 (4?)T cell lymphoma ?
NHL 3Stomach 2
Leuk (WAS, AT) ---Stomach (CVID) 2Breast (AT) --, 1Kaposi’s sarcoma 4Nonmelnma skin 1Lip 1, 4Genital cancers 4
5-10x10-20x>20xHIV/AIDS (CD4+)
Organ transplant1°
Immuno-deficiency
Autoimmunity
Hodgkin’s
4, 3
Thyroid
1
Immunosurveillance model
Inflammation model
Lymphomagenesis model
Oncogenic virus model
Which paradigm explains cancer in patient populations with altered immunity?
RR
Slide25So what does this tell us?Risk of immunomodulation and cancer differ across patient populations
Nature of immunomodulation
Which pathways?How many are affected? [Remove redundancy (immunologic reserve)]Underlying patient statusNature of inciting antigen
Concomitant unresolved infection, autoimmunityContributing conditions (AT/DNA repair error)Challenges broad generalizations
Slide26Case example: Treatment of RA
Use of anti-TNFs associated with increased lymphoma risk (labels)
Available epidemiology data suggests more severe RA associated with greater background lymphoma risk (not treatment related)
Question: Is lymphoma increasing in RA patients treated with anti-TNFs? Is this related to disease severity or infection?Test lymphomas from RA patients with and without clinical history of anti-TNF use for presence of EBV
Use of anti-TNFs increasing rate of virally-related tumors (maintain warning label)
High rate of EBV (greater than that for RA patients)
Similar EBV rates (as RA patients)
Use of anti-TNFs is not increasing EBV-mediated tumors (increase anti-TNF use to suppress autoimmune-mediated lymphoma)
Slide27ConclusionsOur ability to address concerns regarding immunomodulation and cancer depends on our ability to articulate discrete, experimentally evaluable hypotheses
As we move from broad-spectrum immunomodulation to targeted
immunotherapies, we will need to define experimental tools that address specific needsA combination of mechanistic studies, clinical data, and epidemiology results will be necessary to ‘validate’ and refine our models