Immunomodulation and cancer: Different relationships across diseases and disease states? Rafael Ponce Sept 27, 2012.

Immunomodulation and cancer: Different relationships across diseases and disease states?

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 growth/development• Drives lymphoma development (chronic B

cell activation)

• Immunosuppression• Used by tumor to escape surveillance• Increased risk of oncogenic virus activity• Increased risk of unresolved infection

• Immune escape mechanisms• Perception of ‘self’ in the absence of

‘danger’, Ignorance: Peripheral tolerance, Down-regulation of MHC class I

• Active immunosuppression, induced tolerance

Need to break tolerance

• Evolve 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)

Immunity and cancer paradigms

1. Immunosurveillance model2. Inflammation model3. Lymphomagenesis model4. Oncogenic virus model

All models have experimental and epidemiological supportHow can we understand the role of immunity and cancer for specific cases?

1. Immunosurveillance model

• Innate 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)

1. Immunosurveillance model

1. Immunosurveillance model

Cancer immunosurveillance

IL-13, IL-6TGF-b

TumorParenchyma

Anti-tumor adaptive immune response

Tumor supportive environment

IDOTGF-bIL-10PGE2

Treg

pDC

IL-35 IDOIL-10 TGF-bPD-L1 PGE2

Imm DC

MDSC

CD

8 +

TEff

Tumor escape

Tumor elimination

M

PD-L1B7-H1B7-H3B7xHLA-GHLA-E

VEGF-C/D

TH17

IL-23

IFN-gPerforin

B cell

NKT Cell

IL-12, IFN- , -g a GalCer

IL-6 IL-1bTGF-b TNF-a

NK Cell

PerforinTRAIL

IL-12

M

DC

CD4 + TH

PGE2

2. Inflammation model

• Chronic inflammation can – induce cell transformation (reactive

oxygen/nitrogen spp),– promote cell proliferation and increase the risk of

spontaneous mutations, and– create a permissive environment for tumor growth

and spread

2. Inflammation model

Also, Mantovani et al (2008) Nature 454:436-444

3. Lymphomagenesis model• B 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 genes– Leads to inappropriate expression of genes at reciprocal breakpoints that

regulate a variety of cellular functions• gene transcription, cell cycle, apoptosis, and tumor progression

• Lymphomas promoted by chronic B cell activation (infection, alloantigen (graft), self-antigen (autoimmunity))

B- cell development

3. Lymphomagenesis model

3. Lymphomagenesis model

B- cell development requires DNA recombination

B- 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

Errors arising in hyper-mutable stages of B-cell development drives lymphoma

Klein and Dalla-Favera (2008) Nat Rev Immunol 8:22

3. Lymphomagenesis model

4. 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, DC

• Seven identified human oncogenic viruses– EBV: B cell lymphoma– Hepatitis B, C viruses: hepatocellular carcinoma– HTLV-1: T cell leukemia/lymphoma– HHV8 (KSHV): Kaposi’s sarcoma– HPV: Cervical cancer, anogenital cancers, oropharyngeal cancers– Merkel cell polyomavirus: Merkel cell carcinoma

Role of oncogenic viruses

• Variable 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%)

4. Oncogenic virus model: EBV

B-cell transformation by EBV

Relating paradigm to cancer in patient populations with altered immunity

• Which patient populations provide useful information?– Congenital (Primary) immunodeficiency– Organ transplant recipients– Acquired immunodeficiency (HIV)– Autoimmunity

• What forms of cancer prevail in these populations?

Grulich et al (2007) Lancet 370:59

Relative risk of cancer with immunomodulation

>1-3x 5-10x 10-20x >20x

HIV/AIDS (CD4+)

Organ transplant

1° Immuno-deficiency

Autoimmunity

Hodgkin’sThyroid

NHLKidneyPenis

Hodgkin’s NHLAnal cancerKaposi’s sarcoma

Kaposi’s sarcomaNon-melanoma

skinLipGenital cancers

Gynecological cancers

LiverVulva/vagina

StomachCervixOro-pharynx

Leukemia, 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 agammaglobulinemiaSCID: 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)

1Breast, ProstateColon/rectumOvaryThyroid

Breast, ProstateOvary, Brain, Testes

RR

EBV differentially contributes to lymphoma burden across patient populationsDisease % EBV+ Tumors CitationLymphoma 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¶

LymphomaLymphoma (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)

Relating paradigm to cancer in patient populations with altered immunity: A proposal

1. Is cancer associated with oncogenic virus etiology identified at increased rates?– What proportion of tumors evidence viral DNA?

2. Is there evidence/risk of inflammation?– Unresolved infection?– Autoimmunity?

3. Are pathways associated with tumor antigen detection and adaptive immunity affected?

NHL 4, 3Kidney 1Penis 4

Hodgkin’s 3, 4 NHL 3, 4Anal cancer 4, 1Kaposi’s sarcoma 4

Gynecological cancers 4, 1

Liver 4/1?

NHL 3 (4?)T cell lymphoma ?

NHL 3

Stomach 2Leuk (WAS, AT) ---

Stomach (CVID) 2Breast (AT) --, 1

Kaposi’s sarcoma 4Nonmelnma skin 1Lip 1, 4Genital cancers 4

5-10x 10-20x >20x

HIV/AIDS (CD4+)

Organ transplant

1° Immuno-deficiency

Autoimmunity

Hodgkin’s 4, 3Thyroid 1

1. Immunosurveillance model2. Inflammation model3. Lymphomagenesis model4. Oncogenic virus model

Which paradigm explains cancer in patient populations with altered immunity?

RR

So 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 status• Nature of inciting antigen• Concomitant unresolved infection, autoimmunity• Contributing conditions (AT/DNA repair error)

• Challenges broad generalizations

Case 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)

Conclusions

• Our 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 needs

• A combination of mechanistic studies, clinical data, and epidemiology results will be necessary to ‘validate’ and refine our models