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G. Poste. The Next Era in Immuno-Oncology.

This document summarizes a presentation by Dr. George Poste on the next era of immuno-oncology. It discusses cancer as a complex adaptive system and the challenges of tumor heterogeneity and resistance. It outlines passive immunotherapies like antibodies and cell therapies, as well as active immunotherapies like checkpoint inhibitors and vaccines. Combination immunotherapies aim to overcome limitations of single agents. Challenges include toxicity, biomarkers, and the complex interactions between the immune system and tumor microenvironment. Next generation immunotherapies seek better responses, durability, tolerability through new targets and combination approaches.

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Dr. George Poste Chief Scientist, Complex Adaptive Systems Initiative and Del E. Webb Chair in Health Innovation Arizona State University george.poste@asu.edu www.casi.asu.edu The Next Era in Immuno-Oncology Presentation at Community Oncology Alliance Annual Meeting Orlando, FL April 15, 2016
Board of Directors Advisory/Consultancy Declared Interests -Caris Life Sciences - Monsanto - Exelixis - Bulletin Atomic Scientists - Life Sciences Foundation ˗ USG: Depts. of Defense and Homeland Security ˗ US Academy of Medicine Global Forum on Health - Synthetic Genomics - Human Longevity Inc. - University of Michigan, Alfred A. Taubman Medical Research Institute Scientific Advisory Boards Slides available @ http://casi.asu.edu/
Confronting the Clinical, Economic and Human Toll of Cancer US Cancer Deaths (2014) 580,000
US Cancer Prevalence Estimates 2010 and 2020 # People (thousands) % Site 2010 2020 change Breast 3461 4538 31 Prostate 2311 3265 41 Colorectal 1216 1517 25 Melanoma 1225 1714 40 Lymphoma 639 812 27 Uterus 588 672 15 Bladder 514 629 22 Lung 374 457 22 Kidney 308 426 38 Leukemia 263 240 29 All Sites 13,772 18,071 32 From: A.B. Mariotto et al. (2011) J. Nat. Cancer Inst. 103, 117
Cancer as a Complex Adaptive System: The Dynamic Interaction Between Host Immune Defenses and Relentless Emergence of Phenotypically Diverse Tumor Cell Clones Escape From Controls for Normal Tissue Architecture Genome Instability and Emergence of Clonal Variants Evasion of Clonal Detection/Destruction by Host Immune System Use of Host Systems to Promote Progression Invasion and Metastasis Emergence of Drug-Resistant Clones
Pembrolizumab and Therapy of Metastatic Melanoma in President J. Carter Saturation TV Advertising
Cancer Immunotherapy Investment by Big Pharma: Big Bucks, Big Risks, Big Payoffs?
The Rationale for Cancer Immunotherapy Overcoming the Tumor Cell Heterogeneity Problem? Circumventing the Omnipresent Resistance Problem in Chemotherapy and Targeted Therapies?
Cancer as a Complex Adaptive System The Relentless Emergence of Phenotypically Diverse Tumor Clones and Subclones During Progression Rx Resistance • intrinsic • acquired
The Extravagant Landscape of Inter-individual Genomic Alterations in Cancer (Cell 2012: 150, 1107 and 1121)  “malignant snowflakes”: each cancer carries multiple unique mutations and other genome perturbations  disturbing implications for therapeutic ‘cure’ and development of new Rx Mutations in Individual Non-small Cell Lung Cancers Drug Targets in Individual Non-Small Cell Lung Cancers
The Multi-Dimensional Matrix for Cancer Immunotherapy cellular and humoral multi-component system and complex regulatory networks tumor cell (epi) genetic and phenotypic heterogeneity and clonal diversification dynamic tumor-host cell interactions and complex immune activation/suppression pathways host immune system tumor tumor micro- environments
impact of therapy • emergence of resistance • immune functions The Multi-Dimensional Matrix for Cancer Immunotherapy dynamic tumor-host cell interactions and complex immune activation/suppression pathways host immune system tumor tumor micro- environments cellular and humoral multi-component system and complex regulatory networks tumor cell (epi) genetic and phenotypic heterogeneity and clonal diversification
Anti-Cancer Immunotherapies  passive therapies  active therapies  combination therapies
Passive Immunotherapy: Enhancement of Anti-Tumor Activities Without Direct Modification of Intrinsic Host Immune Functions  therapeutic anti-tumor antibodies  adoptive transfer of cytotoxic T lymphocytes (TILS, TCRs, CARs)  oncolytic viruses
Fineartamerica.com Passive Immunotherapy With Antibodies
FDA-Approved Immunotherapy Agents MOA Agent Year Indication CD52 Alemtuzumab 2001 CLL CD20 Ofatumumab 2009 CLL CD20 Rituximab 1997 NHL 2010 CLL CD38 Daratumumab 2015 Multiple Myeloma HER2 Trastuzumab 1998 Breast cancer 2010 Gastric cancer EGF Cetuximab 2004 Colorectal cancer 2011 Head/neck cancer CD20 ADC Y-Ibritumomab tiutexan 2002 NHL CD30 ADC brentuximab vedotin 2011 Hodgkin lymphoma, ALCL MOA Agent Year Indication CD3/CD19 Blinatumomab 2014 ALL monoclonal antibodies (mabs) BITE antibody constructs: Bi- and Multi-Specific Antibodies
Intrinsic Limitations of Passive Antibody Therapies Tumor Cell Antigenic Heterogeneity and Dynamic Emergence of New Antigenically Different Clones
Clone Wars Relentless Emergence of New Tumor Cell Clones During Tumor Progression and Immune Evasion versus Activation of Host T Lymphocyte Clones to Kill (Neo)Antigen-Specific Tumor Clones Active Immunotherapies
Rx1 Rx2 Rx3 Rx4 Rx5 Rx-resistant clones/ Rx refractory disease targeted drugs clones The Promise of Immunotherapy: Circumventing the Inevitable Drug Resistance Problem in Targeted Rx Therapy versus Restoration of Effective Immune Surveillance clones / tumor neoantigens Cytotoxic T cells immuo- therapeutic regimens adaptive evolution of immune response and expanded cytotoxic T cell responses NA1 NA2 NA3 NA4 NA5 NAn1 NAn2
Mapping the Molecular Control Pathways in Immune Responses for Rational Design of New Immunotherapeutics
Understanding Molecular Signaling (Information) Systems and Feedback Control in the Immune System
The Immunostat: The Constantly Shifting Balance Between Activation and Suppression
Active Immunotherapies  immunostimulatory cytokines  vaccine-induced expansion of cytotoxic T cells to cancer neoantigens  unanticipated immune-stimulation by targeted Rx/SOC activation of cytotoxic T cells blockade/inhibition of immunosuppressive pathways  immune checkpoint inhibitors (CTLA-4, PD-1, PD-L1)  inhibition of Tregs and myeloid-derived suppression cells  inhibition of immunosuppressive signals from non- immune cells in the tumor microenvironment
C.L. Batlevi; et. al. (2016) Nature Reviews │ Clinical Oncology 13,25 Cancer Immunotherapy
The Immune-Checkpoint Axis  complex networks of multiple negative checkpoint regulators to limit the scale and duration of activated immune reactions  maintain self-tolerance  prevent autoimmunity  limit cytokine release storms
Immune Checkpoint Inhibitors Timelines of FDA Approvals - ipilimumab: melanoma - pembrolizumab : melanoma March 2011 September 2014 - pembrolizumab: NSCLC October 2014 - nivolumab: melanoma December 2014 - nivolumab: NSCLC March 2015 - nivolumab: renal cancer October 2015
Combination Immunotherapies
Combination Immunotherapy  ipilimumab + nivolumab - melanoma 60% response versus single agent responses 44% (nivo), 19% (ipi) - 12% CR - 80% two year survival
Combination Immunotherapies Combination Therapy Mechanisms of Action Phase Indication Nivolumab + ipilimumab Anti-PD1 + anti-CTLA-4 I/II Gastric, TNBC, PA, SCLC, Bladder, Ovarian II/III Melanoma, RCC II SCLC, GBM, NSCLC Nivolumab + BMS-986016 Anti-PD1 + anti-LAG3 I Solid tumors Nivolumab + Viagenpumatucel-L Anti-PD1 + vaccine I NSCLC Nivolumab + urelumab Anti-PD1 + anti-4-1ββ I/II Solid Tumors, B-Cell NHL Atezolizumab + MOXR0916 Anti-PDL1 + anti-OX40 I Solid Tumors Atezolizumab + varlilumab Anti-PDL1 + anti-CD27 II RCC Atezolizumab + GDC-0919 Anti-PDL1 + IDO inhibitor I Solid Tumors Epacadostat + atezolizumab, durvalumab, or pembrolizumab IDO inhibitor + anti-PDL1 or anti-PD1 I/II Solid Tumors Pembrolizumab + T-Vec Anti-PD1 + vaccine III Melanoma Durvalumab + tremelimumab Anti-PDL1 + anti-CTLA-4 I/II Melanoma I/II/III SCCHN II Mesothelioma, UBC, TNBC, PA III NSCLC, Bladder Pidilizumab + dendritic cell/RCC fusion cell vaccine Anti-PD1 + vaccine II RCC
Immunotherapy Plus Chemotherapy Combination Therapy Mechanisms of Action Phase Indication Nivolumab + platinum doublet chemo Anti-PD1 + chemotherapy III NSCLC Pembrolizumab + cisplatin Anti-PD1 + chemotherapy III Gastric Pidilizumab + lenalidomide Anti-PD1 + chemotherapy I/II Multiple Myeloma Pidilizumab +sipuleucel-T + cyclophosphamide Anti-PD1 + vaccine + chemotherapy II Prostate Atezolizumab + carboplatin/paclitaxel +/- bevacizumab anti-PDL1 + chemotherapy +/- anti-VEGF III NSCLC
Immunotherapy Plus Targeted Therapy Combination Therapy Mechanisms of Action Phase Indication Atezolizumab + bevacizumab Anti-PDL1 + anti-VEGF II/III RCC Atezolizumab + cobimetinib Anti-PDL1 + MEK inhibitor I Solid Tumors Atezolizumab + vemurafenib Anti-PDL1 + BRAF inhibitor I Melanoma Atezolizumab + erlotinib or alectinib Anti-PDL1 =EGFR or ALK inhibitor I NSCLC Nivolumab + bevacizumab Anti-PD1 + anti-VEGF II RCC Pembrolizumab + pazopanib Anti-PD1 + tyrosine kinase inhibitor I RCC Pembrolizumab + dabrafenib + trametinib Anti-PD1 + BRAF inhibitor + MEK inhibitor I/II Melanoma Durvalumab + dabrafenib + trametinib Anti-PDL1 + BRAF inhibitor + MEK inhibitor I/II Melanoma Nivolumab + sunitinib, pazopanib or ipilimumab Anti-PD1 + RTK inhibitor, RTK inhibitor I RCC
Combination of PD-1, PDL-1 and CTLA-4 Blockade  higher clinical response rates than single agent - melanoma, NSCLC, head and neck  lower tolerability and higher discontinuation rates  management of toxicity in broad patient populations in community settings  cost  dosing and sequence  competition and cutting corners in dose optimization
Cell-Based Therapies
Adapted From: T. N. Schumacher and R. D. Schreiber (2015) Science 348, 69 Immunotherapeutic Strategies to Enhance Immune Responses to Patient-Specific Tumor Neoantigens Immune Checkpoint Modulation Cancer Neoantigen Vaccines Adoptive Cell Therapy TILs, TCRs, CARs
Adoptive T Cell Transfer in Cancer Immunotherapy • collect patient’s T cells • expand T cells ex vivo • +/- lymphodepletion/conditioning prior to reinfusion of expanded cells • no modification only expansion • transfection with genes for T cell receptors (TCRs) or chimeric antigen receptors (CARs) against specific tumor neoantigens TCRs and CARsTILs
Design of Chimeric Antigen Receptors for Cancer Immunotherapy: Engineered Combination of Elements of Antibody Structure and T Cell Receptors C.L. Batlevi et al. (2016) Nature Reviews Clinical Oncology 13,25
Design of Chimeric Antigen Receptors for Cancer Immunotherapy C.L. Batlevi et al. (2016) Nature Reviews Clinical Oncology 13,25
Design of Chimeric Antigen Receptors  incorporation of additional T cell activation mechanisms into CAR-T cells to counter immunosuppression in the tumor microenvironment ‘armored CARs’  integration of ‘kill switches’ (reversible/irreversible) to shut down CAR-T cells for better control of toxicities ‘switchable CARs’
Future Needs in the Evolution CAR Therapy  need to establish efficacy in solid tumors  lymphodepletion by preconditioning appears necessary for successful treatment and CAR-cell persistence  reduction of AEs and CRS - CRS is observed more frequently in patients with high tumor burden - merits of prior Rx tumor-debulking in improving safety profile?  dose selection is difficult since transferred cell expansion in vivo appears highly variable  reduce cost and complexity of ex vivo scale up of cells for reinfusion  ‘off-the-shelf’ use of allogeneic cells HLA matched to recipients
NK Cells: The Next Target for Selective Activation of Anti-Tumor Cell Responses?
The Next Generation of Immuno-Oncology Therapeutics Beyond CTLA-4 and PD-1/PD-L1 as Targets for Cancer Immunotherapeutics
Next Generation Immunotherapies  better response rates  extended durable clinical benefits  better tolerability  improved knowledge of how to best use I/O combinations or I/O plus SOC  predictive biomarkers for reliable stratification of responder and non-responder patients and monitoring treatment efficacy
The Complex Dynamics of the Host Immune System-Tumor Ecosystem  corrupted tumor microenvironment - protumor inflammatory responses and immunosuppressive signals  intrinsic immune checkpoint regulators (suppression) - CD28-CTLA-4, PD1-PD-L1, TIM-3, LAG  blockade of T cell infiltration  extrinsic checkpoint regulators (suppression) - regulatory T cells (Tregs), myeloid suppressor cells (MDSC)  T cell anergy and exhaustion (suppression)  immune evasion (escape) - antigen-deletion clones, neoantigens with low affinity
Negative Immune Checkpoint Regulators (NCRs) as New Targets for Next-Generation Immunotherapeutics  TIM-3 - T-cell immunoglobulin and mucin-containing protein 3  LAG-3 - lymphocyte-activated gene-3 (CD223)  TIGIT - T-cell immunoreceptor with Ig and ITIM domains  BTLA - B- and T-lymphocyte attenuator  VISTA - V-domain Ig suppressor or T cell activatin
The Immunosuppressive Tumor Microenvironment The New Frontier, A Wealth Of Targets From: K.M. Mahoney et al. (2015) Clinical Therapeutics 34, 764 / TDO
The Tumor Microenvironment and the “Stromagenic Switch”
The Stromagenic Switch  role of stroma surveillance mechanisms in preventing tumorigenesis or imposition of dormant states  transition of cancer-associated stromal cells (CASC) to protumorigenic drivers - inflammation - ECM remodeling - immunosuppressive signaling - M1 to M2 macrophage conversion - angiogenesis - invasion, EMT and metastasis  altered stromal elements as new Rx Targets
Predictive Identification of Responder and Non-Responder Patients
 melanoma  NSCLC  bladder  renal  head and neck  pancreatic  colorectal  ovarian More Responsive Less Responsive Why Are Some Cancer Types More Responsive to Immunotherapy?
Immunogenic Versus Non-Immunogenic Tumor Microenvironments?  ‘hot’  ‘inflamed’  ‘stimulatory’  ‘cold’  ‘non-inflamed’  ‘silent’ Immunogenic Non-Immunogenic
Immunogenic Versus Non-Immunogenic Tumor Microenvironments  ‘hot’  ‘inflamed’  ‘stimulatory’  high mutagenic burden  high tumor neoantigen expression  ‘cold’  ‘non-inflamed’  ‘silent’  low mutagenic burden  low tumor neoantigen expression Immunogenic Non-Immunogenic
Cancer Immunotherapy  in situ infiltration of activated T cells is critical for therapeutic response and tumor regression  not all immune infiltrates are equal  therapeutic success depends on the dynamics balance of immune activation/suppression factors in the tumor microenvironment
T-Cell Tumor Infiltration From: K. Wkatsuki et al. (2013) Spandidos Publications (DOI: 10.3892/or.2013.2302
Profiling Intratumoral Immune Cell Populations  cytotoxic T cells and memory-T cells  antigen-presenting cells  T regulatory cells (Treg)  Th2 helper T cells  myeloid-derived suppressor cells  M2 phenotype macrophages positive prognosis: immune activation dominant negative prognosis: immune suppression dominant
The Immunophenotype Biomarker Development for Immuno-Oncology Developing An Immunoscore for Individual Patients
The Paucity of Biomarkers to Identify Responder and Non-Responder Patients  major problem in patient selection and cost of futile Rx  conflicting data on relationship of PD-L1 expression and responsiveness to anti-PD1 therapy - KEYNOTE – 001: 45.2% of patients below predetermined PD-L1 cutoff still responded to pembrolizumab  use of different antibody assay platforms and PD-L1 cutoff levels in different clinical trials
PD-L1 Expression and Response Rate (RR) for Immune Checkpoint Modulation in Melanoma Agent Response Rate Median PFS Months PD-L1 none/low PD-L1 high PD-L1 none/low PD-L1 high iplimumab 18 21 3 4 nivolumab 41 57 5 14 iplimumab plus nivolumab 54 72 11 14 From: E.I. Buchbinder and F.S. Hodi (2016) Nature Rev. Oncol 13, 47
Immunophenotyping: Biomarkers for Evaluation of Immune System ‘States’ and Prediction of Responder: Non-Responder Cohorts for Immunotherapy  characterization of immune functions in three anatomic compartments - lymphoid organs/nodes, systemic circulation and neoplastic lesions  formation of international consortium to establish a classification metric designated TNM-I (TNM-Immune)
Profiling of Intratumoral Core (GZMB) Cytotoxic T Cells and Lymphatic Vessel Density at the Invasive Margin (PDPN) in 838 CRC Patients and Relationship to Overall Survival No Metastasis No Metastasis Metastasis Metastasis From: B. Mlecnik et al. (2016) Science Translational Medicine 8, 327ra26
The Tumor Mutational Landscape and Responses to Immunotherapy Agents  hypothesis that high(er) non-synonymous mutation burden generates neoantigens recognized by the immune system  patients with higher neoantigen burden exhibit higher durable clinical benefit (DCB)  ‘mutanome’ profiling - ID mutant nonamer peptides with <500nM binding affinity for patient-specific class I HLA alleles  combination with targeted anti-cancer agents - increase neoantigen release?
Adapted from: T. N. Schumacher and R. D. Schreiber (2015) Science 348, 69 and L. B. Alexandrov et al. (2013) Nature 500, 415 Estimates of Likelihood of Neoantigen Expression Based on Somatic Mutation Prevalence in Different Tumor Types
The Tumor Mutational Landscape and Response to Immunotherapy Agents  higher non-synonymous mutation burden correlates with improved objective response, PFS and durable clinical benefit  highest response rates in melanoma and NSCLC - chronic mutagen burden (UV, tobacco carcinogens)  high inter-patient variation in NSCLC - smokers vs non-smokers - paradoxical greater DCB in smokers to PD-1 blockade
Molecular ‘Smoking Signature’ in NSCLC and PFS in Patients Treated with Pembrolizumab From: N.A. Rizvi et al. (2016) Science 348, 124 (smokers) (non-smokers)
From: J. Zhang et al. (2014) Science 346, 256 Wagner Parsimony Profiling of Intratumoral Clonal Heterogeneity in 11 Lung Adenocarcinomas and Different Trunk (Blue), Branch (Green) and Private (Red) Branches
Neoantigen Clonal Architecture and Clinical Benefit of Immune Checkpoint Blockade (anti-PD1 pembrolizumab) From: N. McGranahan et al. (2016) Science DOI.10.1126/aaf490
Use of Combination Therapies to Increase Neoantigen Expression and Release
Lessons from Breast Cancer Trials of HER-2 Kinase Inhibitors  trastuzumab as a singular success story for HER-2 positive breast cancer  exploration of value of small molecular TKls - lapatinib (EGFR + HER2) afatinib (EGFR, HER2, HER4) neratinib (HER1, HER2, HER4) - inferior outcomes and higher toxicity  is consistent superiority of trastuzumab over other TKIs due to additional effects on immune responses? - tumors enriched for immune signatures benefit from trastuzumab - level of tumor-infiltrating lymphocytes predicts trastuzumab benefit - not all studies concordant
Potential Previously Unrecognized Immunostimulatory Effects of Conventional Chemotherapeutics?  low dose, metronomic administration schedule with immune checkpoint agents and enhanced responses?  off-target effects in activation of immune system directly? - 5-fluorouracil killing of tumor-associated myeloid suppressor cells  value in increasing mutagen burden and neoantigen expression as activation trigger for immune response?
Oncolytic Pipeline Biocentury 02/29/16
Science (2014) 345, 1254, 665
Immunogenetics: Individual Genetic Variation in Immune Responses  how does individual genetic variation affect the nature and intensity of T cell responses?  identification of single nucleotide polymorphisms that influence susceptibility/relative resistance to autoimmune diseases and responses to pathogens  wide individual variation and eQTLs polymorphisms for activation-induced cytokine levels  no information on how these parameters may link to individual variation in immunotherapy-induced anti-tumor responses
Imaging Endpoints for Immunotherapy Response Evaluation  limitations of traditional RECIST criteria due to ‘pseudo-progression’ caused by T cell infiltration/inflammation edema - tumor size and density  nivolumab CheckMate 057 trial - reported short PFS but significant prolongation of OS - superiority versus docetaxal at 9 months  development of irRECIST criteria
Need for New Minimally-Invasive Assays for Monitoring Patient Responses to Immunotherapy  ‘static’ snapshot of immune profile in resected lesions/biopsies versus longitudinal monitoring of dynamic changes with tumor progression /Rx responses  how far does the immune profile assayed in blood (liquid biopsy) mirror intratumoral events in anatomically dispersed metastases? - immune cell subsets? - cytokines? - ctDNA? - exosomes?
Does the Gastrointestinal Microbiome Affect Immunotherapy Efficacy?
A Role for the Microbiome in Regulating Systemic Cancer Risk, Immune Responses and Responses to Therapy?  gut microbiota dramatically impacted by many anti- neoplastic drugs  translocation of gut microbiota across intestinal epithelium and activation of DCs in lympho- depleting irradiation and improved responses to ACT  Bifidobacterium prevalence influences efficacy of anti-PD-1 and anti-CTLA-4 mAb therapy and efficacy reduced by antibiotic therapy
Immune-Medicated Colitis in Melanoma Patients Treated with CTLA-4 Blockade Correlates with Lower Levels of Bacteroides Phylum Families in the Gut Microbiome Adapted from: K. Dubin et al. (2016) Nature Communications 10391 Adundance Taxonomic Units Bacteroidaceae Rikenellaceae Bernesiellaceae
Could Selective Manipulation of Gut Microbiota Impact Cancer Risks and/or Improve Efficacy of Some Anti-Cancer Therapies?*  adverse impact of antibodies in eliminating ‘beneficial’ species?  use of antibiotics to reduce untoward bacterial species?  use of probiotics to optimize ‘beneficial’ species?  postbiotics: metabolic products from ‘beneficial’ species that exert therapeutically valuable effects? *L. Zitvogel et al. (2015) Sci. Trans. Med 7, 2741psl Cancer and the gut microbioata. an unexpected link
Price and Affordability!!!
• AML • An 18 month journey to remission • 3 approved drugs, 2 investigational drugs • 2 stem cell transplants • $4 million dollars Evan Johnson sits on a terrace at the Mayo Clinic Hospital, Methodist Campus in Rochester, Minn. during the summer of 2014. From: Winslow, R. (2016) Cancer Treatment's New Direction. WSJ The Cost of Complex Cancer Care
Is Widespread Adoption of Immunotherapy Economically Feasible?  direct Rx cost  indirect care cost  escalating cost of combination regimens (> $200K)  extravagant cost of cell-based therapies ($500K - $1.5 million)  complex clinical management challenges and compatibility with community oncology services April 2016
What Are We Willing to Pay for Added Months of Survival in Cancer? Lifetime cost above standard care If cancer is on par with other diseases ($150,000 per life year gained), months of added overall survival benefit needed Treating cancer as worthy of much higher reimbursement ($250,000 per life year gained), months of added overall survival benefit needed $50,000 4 months 2.4 months $100,000 8 months 4.8 months $150,000 12 months 7.2 months $200,000 16 months 9.6 months $250,000 20 months 12 months $300,000 24 months 14.4 months $350,000 28 months 16.8 months $400,000 32 months 19.2 months $450,000 36 months 21.6 months $500,000 40 months 24 months Source: Pink Sheet 13 Sept. 2010. Adapted from S. Ramsey FHCRC, ASCO 2010
Performance Comparison for New Anti-Cancer Drugs Approved 2002-2014 for Top Ten Pharmaceutical Companies From: T. Fojo et al. (2014) JAMA Otolaryngology–Head & Neck Surgery 140, 1225 Gains in Progression-Free Survival (PFS) and Overall Survival (OS) for 71 Drugs Approved by the FDA From 2002 to 2014 for Metastatic and/or Advanced and/or Refractory Solid Tumors PFS median 2.5 months OS median 2.1 months
Value-Based Rx Pricing of Oncology Therapeutics  outcomes-based payments  indication-specific pricing  reference pricing (maximum price for all drugs in a therapeutic class)
Deconvolution of the Multi-Dimensional Matrix of Immuno-Oncology Therapeutics tumor host immune response tumor microenvironment balance of stimulatory and suppressive factors • complex non-immune cell contributions to suppressive environment • localization of immune cells/ soluble mediators and impact of Rx • clonal • heterogeneity • mutagen burden • neoantigen profile
The Evolution of Cancer Immunotherapeutics  likely to become SOC in increasing number of indications  need for better informed rationale for combination regimens  identification of new I/O intervention points - Tregs, MDSC, NK cells, TME resistance mechanisms  risk of MDR and recurrence in long DOR patients?  improved immunophenotyping (immunoscore) of individual patients for predictive ID of responders and non-responders  intense competitive corporate landscape and massive financial investments  price and new pharmaco-economic-realities for approval and reimbursement
G. Poste. The Next Era in Immuno-Oncology.

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G. Poste. The Next Era in Immuno-Oncology.

  • 1.
    Dr. George Poste ChiefScientist, Complex Adaptive Systems Initiative and Del E. Webb Chair in Health Innovation Arizona State University george.poste@asu.edu www.casi.asu.edu The Next Era in Immuno-Oncology Presentation at Community Oncology Alliance Annual Meeting Orlando, FL April 15, 2016
  • 2.
    Board of DirectorsAdvisory/Consultancy Declared Interests -Caris Life Sciences - Monsanto - Exelixis - Bulletin Atomic Scientists - Life Sciences Foundation ˗ USG: Depts. of Defense and Homeland Security ˗ US Academy of Medicine Global Forum on Health - Synthetic Genomics - Human Longevity Inc. - University of Michigan, Alfred A. Taubman Medical Research Institute Scientific Advisory Boards Slides available @ http://casi.asu.edu/
  • 3.
    Confronting the Clinical,Economic and Human Toll of Cancer US Cancer Deaths (2014) 580,000
  • 4.
    US Cancer PrevalenceEstimates 2010 and 2020 # People (thousands) % Site 2010 2020 change Breast 3461 4538 31 Prostate 2311 3265 41 Colorectal 1216 1517 25 Melanoma 1225 1714 40 Lymphoma 639 812 27 Uterus 588 672 15 Bladder 514 629 22 Lung 374 457 22 Kidney 308 426 38 Leukemia 263 240 29 All Sites 13,772 18,071 32 From: A.B. Mariotto et al. (2011) J. Nat. Cancer Inst. 103, 117
  • 5.
    Cancer as aComplex Adaptive System: The Dynamic Interaction Between Host Immune Defenses and Relentless Emergence of Phenotypically Diverse Tumor Cell Clones Escape From Controls for Normal Tissue Architecture Genome Instability and Emergence of Clonal Variants Evasion of Clonal Detection/Destruction by Host Immune System Use of Host Systems to Promote Progression Invasion and Metastasis Emergence of Drug-Resistant Clones
  • 7.
    Pembrolizumab and Therapy ofMetastatic Melanoma in President J. Carter Saturation TV Advertising
  • 8.
    Cancer Immunotherapy Investmentby Big Pharma: Big Bucks, Big Risks, Big Payoffs?
  • 9.
    The Rationale forCancer Immunotherapy Overcoming the Tumor Cell Heterogeneity Problem? Circumventing the Omnipresent Resistance Problem in Chemotherapy and Targeted Therapies?
  • 10.
    Cancer as aComplex Adaptive System The Relentless Emergence of Phenotypically Diverse Tumor Clones and Subclones During Progression Rx Resistance • intrinsic • acquired
  • 11.
    The Extravagant Landscapeof Inter-individual Genomic Alterations in Cancer (Cell 2012: 150, 1107 and 1121)  “malignant snowflakes”: each cancer carries multiple unique mutations and other genome perturbations  disturbing implications for therapeutic ‘cure’ and development of new Rx Mutations in Individual Non-small Cell Lung Cancers Drug Targets in Individual Non-Small Cell Lung Cancers
  • 12.
    The Multi-Dimensional Matrixfor Cancer Immunotherapy cellular and humoral multi-component system and complex regulatory networks tumor cell (epi) genetic and phenotypic heterogeneity and clonal diversification dynamic tumor-host cell interactions and complex immune activation/suppression pathways host immune system tumor tumor micro- environments
  • 13.
    impact of therapy •emergence of resistance • immune functions The Multi-Dimensional Matrix for Cancer Immunotherapy dynamic tumor-host cell interactions and complex immune activation/suppression pathways host immune system tumor tumor micro- environments cellular and humoral multi-component system and complex regulatory networks tumor cell (epi) genetic and phenotypic heterogeneity and clonal diversification
  • 14.
    Anti-Cancer Immunotherapies  passivetherapies  active therapies  combination therapies
  • 15.
    Passive Immunotherapy: Enhancement ofAnti-Tumor Activities Without Direct Modification of Intrinsic Host Immune Functions  therapeutic anti-tumor antibodies  adoptive transfer of cytotoxic T lymphocytes (TILS, TCRs, CARs)  oncolytic viruses
  • 16.
  • 17.
    FDA-Approved Immunotherapy Agents MOAAgent Year Indication CD52 Alemtuzumab 2001 CLL CD20 Ofatumumab 2009 CLL CD20 Rituximab 1997 NHL 2010 CLL CD38 Daratumumab 2015 Multiple Myeloma HER2 Trastuzumab 1998 Breast cancer 2010 Gastric cancer EGF Cetuximab 2004 Colorectal cancer 2011 Head/neck cancer CD20 ADC Y-Ibritumomab tiutexan 2002 NHL CD30 ADC brentuximab vedotin 2011 Hodgkin lymphoma, ALCL MOA Agent Year Indication CD3/CD19 Blinatumomab 2014 ALL monoclonal antibodies (mabs) BITE antibody constructs: Bi- and Multi-Specific Antibodies
  • 18.
    Intrinsic Limitations ofPassive Antibody Therapies Tumor Cell Antigenic Heterogeneity and Dynamic Emergence of New Antigenically Different Clones
  • 19.
    Clone Wars Relentless Emergenceof New Tumor Cell Clones During Tumor Progression and Immune Evasion versus Activation of Host T Lymphocyte Clones to Kill (Neo)Antigen-Specific Tumor Clones Active Immunotherapies
  • 20.
    Rx1 Rx2 Rx3 Rx4 Rx5 Rx-resistant clones/ Rx refractory disease targeted drugs clones The Promiseof Immunotherapy: Circumventing the Inevitable Drug Resistance Problem in Targeted Rx Therapy versus Restoration of Effective Immune Surveillance clones / tumor neoantigens Cytotoxic T cells immuo- therapeutic regimens adaptive evolution of immune response and expanded cytotoxic T cell responses NA1 NA2 NA3 NA4 NA5 NAn1 NAn2
  • 21.
    Mapping the MolecularControl Pathways in Immune Responses for Rational Design of New Immunotherapeutics
  • 23.
    Understanding Molecular Signaling(Information) Systems and Feedback Control in the Immune System
  • 24.
    The Immunostat: The ConstantlyShifting Balance Between Activation and Suppression
  • 25.
    Active Immunotherapies  immunostimulatorycytokines  vaccine-induced expansion of cytotoxic T cells to cancer neoantigens  unanticipated immune-stimulation by targeted Rx/SOC activation of cytotoxic T cells blockade/inhibition of immunosuppressive pathways  immune checkpoint inhibitors (CTLA-4, PD-1, PD-L1)  inhibition of Tregs and myeloid-derived suppression cells  inhibition of immunosuppressive signals from non- immune cells in the tumor microenvironment
  • 26.
    C.L. Batlevi; et.al. (2016) Nature Reviews │ Clinical Oncology 13,25 Cancer Immunotherapy
  • 27.
    The Immune-Checkpoint Axis complex networks of multiple negative checkpoint regulators to limit the scale and duration of activated immune reactions  maintain self-tolerance  prevent autoimmunity  limit cytokine release storms
  • 28.
    Immune Checkpoint Inhibitors Timelinesof FDA Approvals - ipilimumab: melanoma - pembrolizumab : melanoma March 2011 September 2014 - pembrolizumab: NSCLC October 2014 - nivolumab: melanoma December 2014 - nivolumab: NSCLC March 2015 - nivolumab: renal cancer October 2015
  • 29.
  • 30.
    Combination Immunotherapy  ipilimumab+ nivolumab - melanoma 60% response versus single agent responses 44% (nivo), 19% (ipi) - 12% CR - 80% two year survival
  • 31.
    Combination Immunotherapies Combination TherapyMechanisms of Action Phase Indication Nivolumab + ipilimumab Anti-PD1 + anti-CTLA-4 I/II Gastric, TNBC, PA, SCLC, Bladder, Ovarian II/III Melanoma, RCC II SCLC, GBM, NSCLC Nivolumab + BMS-986016 Anti-PD1 + anti-LAG3 I Solid tumors Nivolumab + Viagenpumatucel-L Anti-PD1 + vaccine I NSCLC Nivolumab + urelumab Anti-PD1 + anti-4-1ββ I/II Solid Tumors, B-Cell NHL Atezolizumab + MOXR0916 Anti-PDL1 + anti-OX40 I Solid Tumors Atezolizumab + varlilumab Anti-PDL1 + anti-CD27 II RCC Atezolizumab + GDC-0919 Anti-PDL1 + IDO inhibitor I Solid Tumors Epacadostat + atezolizumab, durvalumab, or pembrolizumab IDO inhibitor + anti-PDL1 or anti-PD1 I/II Solid Tumors Pembrolizumab + T-Vec Anti-PD1 + vaccine III Melanoma Durvalumab + tremelimumab Anti-PDL1 + anti-CTLA-4 I/II Melanoma I/II/III SCCHN II Mesothelioma, UBC, TNBC, PA III NSCLC, Bladder Pidilizumab + dendritic cell/RCC fusion cell vaccine Anti-PD1 + vaccine II RCC
  • 32.
    Immunotherapy Plus Chemotherapy CombinationTherapy Mechanisms of Action Phase Indication Nivolumab + platinum doublet chemo Anti-PD1 + chemotherapy III NSCLC Pembrolizumab + cisplatin Anti-PD1 + chemotherapy III Gastric Pidilizumab + lenalidomide Anti-PD1 + chemotherapy I/II Multiple Myeloma Pidilizumab +sipuleucel-T + cyclophosphamide Anti-PD1 + vaccine + chemotherapy II Prostate Atezolizumab + carboplatin/paclitaxel +/- bevacizumab anti-PDL1 + chemotherapy +/- anti-VEGF III NSCLC
  • 33.
    Immunotherapy Plus TargetedTherapy Combination Therapy Mechanisms of Action Phase Indication Atezolizumab + bevacizumab Anti-PDL1 + anti-VEGF II/III RCC Atezolizumab + cobimetinib Anti-PDL1 + MEK inhibitor I Solid Tumors Atezolizumab + vemurafenib Anti-PDL1 + BRAF inhibitor I Melanoma Atezolizumab + erlotinib or alectinib Anti-PDL1 =EGFR or ALK inhibitor I NSCLC Nivolumab + bevacizumab Anti-PD1 + anti-VEGF II RCC Pembrolizumab + pazopanib Anti-PD1 + tyrosine kinase inhibitor I RCC Pembrolizumab + dabrafenib + trametinib Anti-PD1 + BRAF inhibitor + MEK inhibitor I/II Melanoma Durvalumab + dabrafenib + trametinib Anti-PDL1 + BRAF inhibitor + MEK inhibitor I/II Melanoma Nivolumab + sunitinib, pazopanib or ipilimumab Anti-PD1 + RTK inhibitor, RTK inhibitor I RCC
  • 34.
    Combination of PD-1,PDL-1 and CTLA-4 Blockade  higher clinical response rates than single agent - melanoma, NSCLC, head and neck  lower tolerability and higher discontinuation rates  management of toxicity in broad patient populations in community settings  cost  dosing and sequence  competition and cutting corners in dose optimization
  • 35.
  • 36.
    Adapted From: T.N. Schumacher and R. D. Schreiber (2015) Science 348, 69 Immunotherapeutic Strategies to Enhance Immune Responses to Patient-Specific Tumor Neoantigens Immune Checkpoint Modulation Cancer Neoantigen Vaccines Adoptive Cell Therapy TILs, TCRs, CARs
  • 37.
    Adoptive T CellTransfer in Cancer Immunotherapy • collect patient’s T cells • expand T cells ex vivo • +/- lymphodepletion/conditioning prior to reinfusion of expanded cells • no modification only expansion • transfection with genes for T cell receptors (TCRs) or chimeric antigen receptors (CARs) against specific tumor neoantigens TCRs and CARsTILs
  • 38.
    Design of ChimericAntigen Receptors for Cancer Immunotherapy: Engineered Combination of Elements of Antibody Structure and T Cell Receptors C.L. Batlevi et al. (2016) Nature Reviews Clinical Oncology 13,25
  • 39.
    Design of ChimericAntigen Receptors for Cancer Immunotherapy C.L. Batlevi et al. (2016) Nature Reviews Clinical Oncology 13,25
  • 40.
    Design of ChimericAntigen Receptors  incorporation of additional T cell activation mechanisms into CAR-T cells to counter immunosuppression in the tumor microenvironment ‘armored CARs’  integration of ‘kill switches’ (reversible/irreversible) to shut down CAR-T cells for better control of toxicities ‘switchable CARs’
  • 41.
    Future Needs inthe Evolution CAR Therapy  need to establish efficacy in solid tumors  lymphodepletion by preconditioning appears necessary for successful treatment and CAR-cell persistence  reduction of AEs and CRS - CRS is observed more frequently in patients with high tumor burden - merits of prior Rx tumor-debulking in improving safety profile?  dose selection is difficult since transferred cell expansion in vivo appears highly variable  reduce cost and complexity of ex vivo scale up of cells for reinfusion  ‘off-the-shelf’ use of allogeneic cells HLA matched to recipients
  • 42.
    NK Cells: TheNext Target for Selective Activation of Anti-Tumor Cell Responses?
  • 43.
    The Next Generationof Immuno-Oncology Therapeutics Beyond CTLA-4 and PD-1/PD-L1 as Targets for Cancer Immunotherapeutics
  • 44.
    Next Generation Immunotherapies better response rates  extended durable clinical benefits  better tolerability  improved knowledge of how to best use I/O combinations or I/O plus SOC  predictive biomarkers for reliable stratification of responder and non-responder patients and monitoring treatment efficacy
  • 45.
    The Complex Dynamicsof the Host Immune System-Tumor Ecosystem  corrupted tumor microenvironment - protumor inflammatory responses and immunosuppressive signals  intrinsic immune checkpoint regulators (suppression) - CD28-CTLA-4, PD1-PD-L1, TIM-3, LAG  blockade of T cell infiltration  extrinsic checkpoint regulators (suppression) - regulatory T cells (Tregs), myeloid suppressor cells (MDSC)  T cell anergy and exhaustion (suppression)  immune evasion (escape) - antigen-deletion clones, neoantigens with low affinity
  • 46.
    Negative Immune CheckpointRegulators (NCRs) as New Targets for Next-Generation Immunotherapeutics  TIM-3 - T-cell immunoglobulin and mucin-containing protein 3  LAG-3 - lymphocyte-activated gene-3 (CD223)  TIGIT - T-cell immunoreceptor with Ig and ITIM domains  BTLA - B- and T-lymphocyte attenuator  VISTA - V-domain Ig suppressor or T cell activatin
  • 47.
    The Immunosuppressive TumorMicroenvironment The New Frontier, A Wealth Of Targets From: K.M. Mahoney et al. (2015) Clinical Therapeutics 34, 764 / TDO
  • 48.
    The Tumor Microenvironmentand the “Stromagenic Switch”
  • 49.
    The Stromagenic Switch role of stroma surveillance mechanisms in preventing tumorigenesis or imposition of dormant states  transition of cancer-associated stromal cells (CASC) to protumorigenic drivers - inflammation - ECM remodeling - immunosuppressive signaling - M1 to M2 macrophage conversion - angiogenesis - invasion, EMT and metastasis  altered stromal elements as new Rx Targets
  • 50.
    Predictive Identification ofResponder and Non-Responder Patients
  • 51.
     melanoma  NSCLC bladder  renal  head and neck  pancreatic  colorectal  ovarian More Responsive Less Responsive Why Are Some Cancer Types More Responsive to Immunotherapy?
  • 52.
    Immunogenic Versus Non-Immunogenic TumorMicroenvironments?  ‘hot’  ‘inflamed’  ‘stimulatory’  ‘cold’  ‘non-inflamed’  ‘silent’ Immunogenic Non-Immunogenic
  • 53.
    Immunogenic Versus Non-Immunogenic TumorMicroenvironments  ‘hot’  ‘inflamed’  ‘stimulatory’  high mutagenic burden  high tumor neoantigen expression  ‘cold’  ‘non-inflamed’  ‘silent’  low mutagenic burden  low tumor neoantigen expression Immunogenic Non-Immunogenic
  • 54.
    Cancer Immunotherapy  insitu infiltration of activated T cells is critical for therapeutic response and tumor regression  not all immune infiltrates are equal  therapeutic success depends on the dynamics balance of immune activation/suppression factors in the tumor microenvironment
  • 55.
    T-Cell Tumor Infiltration From:K. Wkatsuki et al. (2013) Spandidos Publications (DOI: 10.3892/or.2013.2302
  • 56.
    Profiling Intratumoral ImmuneCell Populations  cytotoxic T cells and memory-T cells  antigen-presenting cells  T regulatory cells (Treg)  Th2 helper T cells  myeloid-derived suppressor cells  M2 phenotype macrophages positive prognosis: immune activation dominant negative prognosis: immune suppression dominant
  • 57.
    The Immunophenotype Biomarker Developmentfor Immuno-Oncology Developing An Immunoscore for Individual Patients
  • 58.
    The Paucity ofBiomarkers to Identify Responder and Non-Responder Patients  major problem in patient selection and cost of futile Rx  conflicting data on relationship of PD-L1 expression and responsiveness to anti-PD1 therapy - KEYNOTE – 001: 45.2% of patients below predetermined PD-L1 cutoff still responded to pembrolizumab  use of different antibody assay platforms and PD-L1 cutoff levels in different clinical trials
  • 59.
    PD-L1 Expression andResponse Rate (RR) for Immune Checkpoint Modulation in Melanoma Agent Response Rate Median PFS Months PD-L1 none/low PD-L1 high PD-L1 none/low PD-L1 high iplimumab 18 21 3 4 nivolumab 41 57 5 14 iplimumab plus nivolumab 54 72 11 14 From: E.I. Buchbinder and F.S. Hodi (2016) Nature Rev. Oncol 13, 47
  • 60.
    Immunophenotyping: Biomarkers for Evaluationof Immune System ‘States’ and Prediction of Responder: Non-Responder Cohorts for Immunotherapy  characterization of immune functions in three anatomic compartments - lymphoid organs/nodes, systemic circulation and neoplastic lesions  formation of international consortium to establish a classification metric designated TNM-I (TNM-Immune)
  • 61.
    Profiling of IntratumoralCore (GZMB) Cytotoxic T Cells and Lymphatic Vessel Density at the Invasive Margin (PDPN) in 838 CRC Patients and Relationship to Overall Survival No Metastasis No Metastasis Metastasis Metastasis From: B. Mlecnik et al. (2016) Science Translational Medicine 8, 327ra26
  • 62.
    The Tumor MutationalLandscape and Responses to Immunotherapy Agents  hypothesis that high(er) non-synonymous mutation burden generates neoantigens recognized by the immune system  patients with higher neoantigen burden exhibit higher durable clinical benefit (DCB)  ‘mutanome’ profiling - ID mutant nonamer peptides with <500nM binding affinity for patient-specific class I HLA alleles  combination with targeted anti-cancer agents - increase neoantigen release?
  • 63.
    Adapted from: T.N. Schumacher and R. D. Schreiber (2015) Science 348, 69 and L. B. Alexandrov et al. (2013) Nature 500, 415 Estimates of Likelihood of Neoantigen Expression Based on Somatic Mutation Prevalence in Different Tumor Types
  • 64.
    The Tumor MutationalLandscape and Response to Immunotherapy Agents  higher non-synonymous mutation burden correlates with improved objective response, PFS and durable clinical benefit  highest response rates in melanoma and NSCLC - chronic mutagen burden (UV, tobacco carcinogens)  high inter-patient variation in NSCLC - smokers vs non-smokers - paradoxical greater DCB in smokers to PD-1 blockade
  • 65.
    Molecular ‘Smoking Signature’in NSCLC and PFS in Patients Treated with Pembrolizumab From: N.A. Rizvi et al. (2016) Science 348, 124 (smokers) (non-smokers)
  • 66.
    From: J. Zhanget al. (2014) Science 346, 256 Wagner Parsimony Profiling of Intratumoral Clonal Heterogeneity in 11 Lung Adenocarcinomas and Different Trunk (Blue), Branch (Green) and Private (Red) Branches
  • 67.
    Neoantigen Clonal Architectureand Clinical Benefit of Immune Checkpoint Blockade (anti-PD1 pembrolizumab) From: N. McGranahan et al. (2016) Science DOI.10.1126/aaf490
  • 68.
    Use of CombinationTherapies to Increase Neoantigen Expression and Release
  • 69.
    Lessons from BreastCancer Trials of HER-2 Kinase Inhibitors  trastuzumab as a singular success story for HER-2 positive breast cancer  exploration of value of small molecular TKls - lapatinib (EGFR + HER2) afatinib (EGFR, HER2, HER4) neratinib (HER1, HER2, HER4) - inferior outcomes and higher toxicity  is consistent superiority of trastuzumab over other TKIs due to additional effects on immune responses? - tumors enriched for immune signatures benefit from trastuzumab - level of tumor-infiltrating lymphocytes predicts trastuzumab benefit - not all studies concordant
  • 70.
    Potential Previously UnrecognizedImmunostimulatory Effects of Conventional Chemotherapeutics?  low dose, metronomic administration schedule with immune checkpoint agents and enhanced responses?  off-target effects in activation of immune system directly? - 5-fluorouracil killing of tumor-associated myeloid suppressor cells  value in increasing mutagen burden and neoantigen expression as activation trigger for immune response?
  • 71.
  • 72.
  • 73.
    Immunogenetics: Individual Genetic Variationin Immune Responses  how does individual genetic variation affect the nature and intensity of T cell responses?  identification of single nucleotide polymorphisms that influence susceptibility/relative resistance to autoimmune diseases and responses to pathogens  wide individual variation and eQTLs polymorphisms for activation-induced cytokine levels  no information on how these parameters may link to individual variation in immunotherapy-induced anti-tumor responses
  • 74.
    Imaging Endpoints for ImmunotherapyResponse Evaluation  limitations of traditional RECIST criteria due to ‘pseudo-progression’ caused by T cell infiltration/inflammation edema - tumor size and density  nivolumab CheckMate 057 trial - reported short PFS but significant prolongation of OS - superiority versus docetaxal at 9 months  development of irRECIST criteria
  • 75.
    Need for NewMinimally-Invasive Assays for Monitoring Patient Responses to Immunotherapy  ‘static’ snapshot of immune profile in resected lesions/biopsies versus longitudinal monitoring of dynamic changes with tumor progression /Rx responses  how far does the immune profile assayed in blood (liquid biopsy) mirror intratumoral events in anatomically dispersed metastases? - immune cell subsets? - cytokines? - ctDNA? - exosomes?
  • 76.
    Does the GastrointestinalMicrobiome Affect Immunotherapy Efficacy?
  • 77.
    A Role forthe Microbiome in Regulating Systemic Cancer Risk, Immune Responses and Responses to Therapy?  gut microbiota dramatically impacted by many anti- neoplastic drugs  translocation of gut microbiota across intestinal epithelium and activation of DCs in lympho- depleting irradiation and improved responses to ACT  Bifidobacterium prevalence influences efficacy of anti-PD-1 and anti-CTLA-4 mAb therapy and efficacy reduced by antibiotic therapy
  • 78.
    Immune-Medicated Colitis inMelanoma Patients Treated with CTLA-4 Blockade Correlates with Lower Levels of Bacteroides Phylum Families in the Gut Microbiome Adapted from: K. Dubin et al. (2016) Nature Communications 10391 Adundance Taxonomic Units Bacteroidaceae Rikenellaceae Bernesiellaceae
  • 79.
    Could Selective Manipulationof Gut Microbiota Impact Cancer Risks and/or Improve Efficacy of Some Anti-Cancer Therapies?*  adverse impact of antibodies in eliminating ‘beneficial’ species?  use of antibiotics to reduce untoward bacterial species?  use of probiotics to optimize ‘beneficial’ species?  postbiotics: metabolic products from ‘beneficial’ species that exert therapeutically valuable effects? *L. Zitvogel et al. (2015) Sci. Trans. Med 7, 2741psl Cancer and the gut microbioata. an unexpected link
  • 80.
  • 81.
    • AML • An18 month journey to remission • 3 approved drugs, 2 investigational drugs • 2 stem cell transplants • $4 million dollars Evan Johnson sits on a terrace at the Mayo Clinic Hospital, Methodist Campus in Rochester, Minn. during the summer of 2014. From: Winslow, R. (2016) Cancer Treatment's New Direction. WSJ The Cost of Complex Cancer Care
  • 82.
    Is Widespread Adoptionof Immunotherapy Economically Feasible?  direct Rx cost  indirect care cost  escalating cost of combination regimens (> $200K)  extravagant cost of cell-based therapies ($500K - $1.5 million)  complex clinical management challenges and compatibility with community oncology services April 2016
  • 83.
    What Are WeWilling to Pay for Added Months of Survival in Cancer? Lifetime cost above standard care If cancer is on par with other diseases ($150,000 per life year gained), months of added overall survival benefit needed Treating cancer as worthy of much higher reimbursement ($250,000 per life year gained), months of added overall survival benefit needed $50,000 4 months 2.4 months $100,000 8 months 4.8 months $150,000 12 months 7.2 months $200,000 16 months 9.6 months $250,000 20 months 12 months $300,000 24 months 14.4 months $350,000 28 months 16.8 months $400,000 32 months 19.2 months $450,000 36 months 21.6 months $500,000 40 months 24 months Source: Pink Sheet 13 Sept. 2010. Adapted from S. Ramsey FHCRC, ASCO 2010
  • 84.
    Performance Comparison forNew Anti-Cancer Drugs Approved 2002-2014 for Top Ten Pharmaceutical Companies From: T. Fojo et al. (2014) JAMA Otolaryngology–Head & Neck Surgery 140, 1225 Gains in Progression-Free Survival (PFS) and Overall Survival (OS) for 71 Drugs Approved by the FDA From 2002 to 2014 for Metastatic and/or Advanced and/or Refractory Solid Tumors PFS median 2.5 months OS median 2.1 months
  • 85.
    Value-Based Rx Pricingof Oncology Therapeutics  outcomes-based payments  indication-specific pricing  reference pricing (maximum price for all drugs in a therapeutic class)
  • 86.
    Deconvolution of theMulti-Dimensional Matrix of Immuno-Oncology Therapeutics tumor host immune response tumor microenvironment balance of stimulatory and suppressive factors • complex non-immune cell contributions to suppressive environment • localization of immune cells/ soluble mediators and impact of Rx • clonal • heterogeneity • mutagen burden • neoantigen profile
  • 87.
    The Evolution ofCancer Immunotherapeutics  likely to become SOC in increasing number of indications  need for better informed rationale for combination regimens  identification of new I/O intervention points - Tregs, MDSC, NK cells, TME resistance mechanisms  risk of MDR and recurrence in long DOR patients?  improved immunophenotyping (immunoscore) of individual patients for predictive ID of responders and non-responders  intense competitive corporate landscape and massive financial investments  price and new pharmaco-economic-realities for approval and reimbursement