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

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Presentation on theme: "Immunomodulation and cancer: Different relationships across diseases and disease states? Rafael Ponce Sept 27, 2012."— Presentation transcript:

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

2 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)

3 Immunity and cancer paradigms 1.Immunosurveillance model 2.Inflammation model 3.Lymphomagenesis model 4.Oncogenic virus model All models have experimental and epidemiological support How can we understand the role of immunity and cancer for specific cases?

4 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)

5 1. Immunosurveillance model


7 Cancer immunosurveillance IL-13, IL-6 TGF-  Tumor Parenchyma Anti-tumor adaptive immune response Tumor supportive environment IDO TGF-  IL-10 PGE 2 Treg pDC IL-35IDO IL-10TGF-  PD-L1PGE 2 Imm DC MDSC CD8 + T Eff Tumor escape Tumor elimination MM PD-L1 B7-H1 B7-H3 B7x HLA-G HLA-E VEGF-C/D TH 17 IL-23 IFN-  Perforin B cell NKT Cell IL-12, IFN-  GalCer IL-6IL-1  TGF-  TNF-  NK Cell Perforin TRAIL IL-12 MM DC CD4 + T H PGE 2

8 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

9 2. Inflammation model Also, Mantovani et al (2008) Nature 454:436-444

10 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 c hronic B cell activation (infection, alloantigen (graft), self-antigen (autoimmunity))

11 B- cell development 3. Lymphomagenesis model

12 B- cell development requires DNA recombination

13 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

14 Errors arising in hyper-mutable stages of B-cell development drives lymphoma Klein and Dalla-Favera (2008) Nat Rev Immunol 8:22

15 3. Lymphomagenesis model

16 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

17 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%)

18 4. Oncogenic virus model: EBV B-cell transformation by EBV

19 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?

20 Grulich et al (2007) Lancet 370:59

21 Relative risk of cancer with immunomodulation >1-3x5-10x10-20x>20x HIV/AIDS (CD4+) Organ transplant 1° Immuno- deficiency Autoimmunity Hodgkin’s Thyroid NHL Kidney Penis Hodgkin’sNHL Anal cancer Kaposi’s sarcoma Non-melanoma skin Lip Genital cancers Gynecological cancers Liver Vulva/vagina Stomach Cervix Oro-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 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

22 EBV differentially contributes to lymphoma burden across patient populations Disease% EBV+ TumorsCitation Lymphoma with no known immunosuppression2-10% (Kamel et al., 1999; Hoshida et al., 2007)Kamel et al., 1999Hoshida et al., 2007 Hodgkin’s lymphoma40-50% 80% (Macsween et al., 2003; Swerdlow, 2003; Young et al., 2003; Thorley- Lawson et al., 2004; Young et al., 2004; Balandraud et al., 2005)Macsween et al., 2003Swerdlow, 2003Young et al., 2003Thorley- Lawson et al., 2004Young 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)Macsween et al., 2003Young et al., 2003Young et al., 2004 NHLPost-transplantation (<1yr) >90% (Macsween et al., 2003)Macsween et al., 2003 Post-transplantation (>1yr) 50% (Young et al., 2004)Young et al., 2004 HIV patientsNHL28-66% (Rabkin, 2001; Macsween et al., 2003; Balandraud et al., 2005)Rabkin, 2001Macsween et al., 2003 Balandraud et al., 2005 Burkitt’s25% (Macsween et al., 2003)Macsween et al., 2003 CNS Lymphoma100% (Rabkin, 2001; Macsween et al., 2003)Rabkin, 2001Macsween et al., 2003 Primary Immunodeficiency Lymphoma/BPLD ¶ Lymphoma Lymphoma (mucosal- associated) 31% # 0% (Filipovich et al., 1994)Filipovich et al., 1994 (Gompels et al., 2003)Gompels et al., 2003 (Cunningham-Rundles et al., 2002)Cunningham-Rundles et al., 2002 RA Patients2% 3% 15% 27% 11% 26% 17% 12% (Kamel et al., 1999) (Staal et al., 1989) (Mariette et al., 2002) (Hoshida et al., 2007)Kamel et al., 1999Staal et al., 1989Mariette et al., 2002Hoshida et al., 2007 (Askling et al., 2005)Askling et al., 2005 (Dawson et al., 2001)Dawson et al., 2001 (Baecklund et al., 2003)Baecklund et al., 2003 (Baecklund et al., 2006)Baecklund et al., 2006

23 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?

24 NHL 4, 3 Kidney 1 Penis 4 Hodgkin’s 3, 4NHL 3, 4 Anal cancer 4, 1 Kaposi’s sarcoma 4 Gynecological cancers 4, 1 Liver4/1? NHL 3 (4?) T cell lymphoma ? NHL 3 Stomach 2 Leuk (WAS, AT) --- Stomach (CVID) 2Breast (AT) --, 1 Kaposi’s sarcoma 4 Nonmelnma skin 1 Lip 1, 4 Genital cancers 4 5-10x10-20x>20x HIV/AIDS (CD4+) Organ transplant 1° Immuno- deficiency Autoimmunity Hodgkin’s 4, 3 Thyroid 1 1.Immunosurveillance model 2.Inflammation model 3.Lymphomagenesis model 4.Oncogenic virus model Which paradigm explains cancer in patient populations with altered immunity? RR

25 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

26 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)

27 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

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