Immunomodulation and Cancer

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

Immunomodulation and cancer Virus

Immune function • 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

Tumor • 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

Immune status in the tumor microenvironment drives balance of response (tolerance vs immunity)

Immunity and cancer paradigms 1. 2. 3. 4.

Immunosurveillance model Inflammation model Lymphomagenesis model Oncogenic virus model

All models have experimental and epidemiological support How 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 Tumor supportive environment

M IL-12

M

PGE2 IL-23

VEGF-C/D

TH17 IL-6 IL-1b TGF-b TNF-a

Anti-tumor adaptive immune response

B cell

IDO TGF-b IL-10 PGE2

PD-L1 B7-H1 B7-H3 B7x HLA-G HLA-E

Tumor Parenchyma

Perforin TRAIL

NK Cell

IL-12, IFN-g, a-GalCer

NKT Cell

IFN-g Perforin

DC Treg IL-35 IDO IL-10 TGF-b PD-L1 PGE2

IL-13, IL-6 TGF-b

MDSC Imm DC

Tumor escape

pDC

Tumor elimination

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))

3. Lymphomagenesis model B- cell development

3. Lymphomagenesis model B- cell development requires DNA recombination

B- cell development requires DNA recombination

V(D)J recombination

Process for assembling gene segments coding variable region of antibody molecule to generate Ab diversity

Class switch recombination

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 RR

1

>1-3x

5-10x

10-20x

>20x

HIV/AIDS (CD4+)

Breast, Prostate Colon/rectum Ovary Thyroid

Leukemia, Lip, Stomach, Nonmelanoma skin, Oro-pharynx

Gynecological cancers Liver Vulva/vagina

Hodgkin’s

NHL Anal cancer Kaposi’s sarcoma

Organ transplant

Breast, Prostate Ovary, Brain, Testes

Stomach Cervix Oro-pharynx

Hodgkin’s Thyroid

NHL Kidney Penis

Kaposi’s sarcoma Non-melanoma skin Lip Genital cancers

Breast (CVID)

Breast (AT)

Stomach (CVID)

NHL (CVID, SCID, AT, WAS, XLD) Stomach (XLA) Leukemia (AT, WAS)

NHL (RA) Other solid organ (RA) Leukemia (RA) Hodgkin’s (RA)

NHL (Sjogren’s, SLE, Celiac) T cell lymphoma (AHA, celiac disease)

1° Immunodeficiency

Autoimmunity

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

EBV differentially contributes to lymphoma burden across patient populations Disease Lymphoma with no known immunosuppression

% EBV+ Tumors 2-10%

>90%

(Kamel et al., 1999; Hoshida et al., 2007) (Macsween et al., 2003; Swerdlow, 2003; Young et al., 2003; ThorleyLawson et al., 2004; Young et al., 2004; Balandraud et al., 2005) (Macsween et al., 2003; Young et al., 2003; Young et al., 2004) (Macsween et al., 2003)

50%

(Young et al., 2004)

Hodgkin’s lymphoma

40-50% 80%

Burkitt’s lymphoma (developed world)

15-25%

NHL

HIV patients

Primary Immunodeficiency

RA Patients

Post-transplantation (1yr) NHL Burkitt’s CNS Lymphoma Lymphoma/BPLD¶ Lymphoma Lymphoma (mucosalassociated)

Citation

28-66% 25% 100% 31%# 0% 0% 2% 3% 15% 27% 11% 26% 17% 12%

(Rabkin, 2001; Macsween et al., 2003; Balandraud et al., 2005) (Macsween et al., 2003) (Rabkin, 2001; Macsween et al., 2003) (Filipovich et al., 1994) (Gompels et al., 2003) (Cunningham-Rundles et al., 2002) (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?

Which paradigm explains cancer in patient populations with altered immunity? RR HIV/AIDS (CD4+) Organ transplant 1° Immunodeficiency

Autoimmunity

5-10x

10-20x

Gynecological Hodgkin’s cancers 4, 1 Liver 4/1? Hodgkin’s Thyroid

Breast (AT)

4, 3 1

NHL Kidney Penis

>20x 3, 4

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

4, 3 Kaposi’s sarcoma 4 1 Nonmelnma skin 1 4 Lip 1, 4 Genital cancers 4

--, 1 Stomach (CVID) 2 NHL Stomach Leuk (WAS, AT)

NHL 3 (4?) T cell lymphoma ?

3 2 ---

1. Immunosurveillance model 2. Inflammation model 3. Lymphomagenesis model 4. Oncogenic virus model

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 antiTNFs? 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 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)

High rate of EBV (greater than that for RA patients) Use of anti-TNFs increasing rate of virally-related tumors (maintain warning label)

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