1 / 27

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

Immunomodulation and cancer: Different relationships across diseases and disease states?. Rafael Ponce Sept 27, 2012. Immunomodulation and cancer. Virus. Immune function. Tumor. Immune escape mechanisms

hani
Télécharger la présentation

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

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

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

  2. Immunomodulation and cancer Virus Immunefunction 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 • 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 status in the tumor microenvironment drives balance of response (tolerance vs immunity)

  3. Immunity and cancer paradigms • 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?

  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

  6. 1. Immunosurveillance model

  7. Cancer immunosurveillance Tumor supportive environment Anti-tumor adaptive immune response B cell M IL-12 M Perforin TRAIL PGE2 VEGF-C/D NK Cell IL-23 Tumor Parenchyma TH17 PD-L1 B7-H1 B7-H3 B7x HLA-G HLA-E IL-12, IFN-g, a-GalCer NKT Cell IL-6 IL-1b TGF-b TNF-a IDO TGF-b IL-10 PGE2 IFN-g Perforin DC Treg CD8+TEff CD4+ TH IL-35 IDO IL-10 TGF-b PD-L1 PGE2 IL-13, IL-6 TGF-b MDSC pDC Imm DC Tumor escape Tumor elimination

  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 chronic B cell activation (infection, alloantigen (graft), self-antigen (autoimmunity))

  11. 3. Lymphomagenesis model B- cell development

  12. 3. Lymphomagenesis model B- cell development requires DNA recombination

  13. B- cell development requires DNA recombination Class switch recombination Somatic hypermutation V(D)J recombination Process for altering effector activity of heavy chain via recombination of Fc heavy chain Process for altering antibody specificity via point mutations, deletions, duplications Process for assembling gene segments coding variable region of antibody molecule to generate Ab diversity

  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 RR 1 >1-3x 5-10x 10-20x >20x HIV/AIDS (CD4+) Breast, Prostate Colon/rectum Ovary Thyroid Leukemia, Lip, Stomach, Non-melanoma skin, Oro-pharynx Gynecological cancers Liver Vulva/vagina NHL Anal cancer Kaposi’s sarcoma Hodgkin’s 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 1° Immuno-deficiency Breast (CVID) Breast (AT) Stomach (CVID) NHL (CVID, SCID, AT, WAS, XLD) Stomach (XLA) Leukemia (AT, WAS) Autoimmunity 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

  22. EBV differentially contributes to lymphoma burden across patient populations

  23. Relating 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?

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

  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 Similar EBV rates (as RA patients) High rate of EBV (greater than that for RA patients) Use of anti-TNFs is not increasing EBV-mediated tumors (increase anti-TNF use to suppress autoimmune-mediated lymphoma) • Use of anti-TNFs increasing rate of virally-related tumors (maintain warning label)

  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

More Related