Leaders in Pharmaceutical Business Intelligence (LPBI) Group

Plenary Keynotes TUESDAY | AUGUST 30 4:00PM – 5:30PM @CHI’s IMMUNO-ONCOLOGY SUMMIT, Marriott Long Wharf Hotel in Boston

http://www.immuno-oncologysummit.com/uploadedFiles/Immuno_Oncology_Summit/Agenda/16/2016-The-Immuno-Oncology-Summit-Brochure.pdf

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Plenary Keynotes TUESDAY | AUGUST 30

4:00 Personalized, NeoantigenBased Immunotherapy

Edward Fritsch, Ph.D., Chief Technology Officer, Neon Therapeutics, Inc.

Multiple lines of evidence have demonstrated the critical role that Neoantigens have in the immune response to cancer and the availability of next-generation sequencing to identify personal, neoantigen-creating mutations has opened the door to directly enhance the power and breadth of host immunity to overcome this deadly disease.

• Yervoy approved for melanoma ipilimumab
• ipilimumab and Nivolumab combination
• Cancer Vaccine for infections disease – PREVENTIVE NOT TREATMENT HPV
• CLASSES OF TUMOR ANTIGENS CT many tumors: Methylation pattern
• Selectively expressed: Melanomas
• Over expressed antigens – some tumor

Personalized

Scientific Advance I:

• Tumor DNA sequences: Kras, PIK3CA, FBXW7
• Somatic mutations potential to generate neoantigens
• Neoantigents: Native antigens (mage) vs NEOANTIGENS 0 tumor specific Antigens

Scientific Advance II:

• Ipilimumab
• anti PL-1
• single neoantigen reactive CD4+ T cell clone mediates tumor rejection in adoptive therapy

A Neoantigen Vaccine – The Opportunity

• T -cell capable of tumor infiltration

DFCI/Broad Institute: Tumor procurement, Target selection Personal caccine manufacturing Vaccine administration

• Identify targets: accuracy, epitope features multipla epitopes
• Personalised GMP manufacturing – 20 long peptide regulatory acceptance, time and cost

FDA approval:

• High risk Melanoma (IIIB/C; IVM1a)
• cancer with documented immune responsiveness
• 12 patients enrolled – Vaccine prepared for 8, 6 dosed
• Immunological Responses: DeNovo – detectable directly ex-vivo: ICS Primarily CD4+ Poly-functional
• CD8+ Responses – 1 Pre-stim: Pre vs 16 weeks after
• Immunizing peptide (IMP)

NEON Therapeutics, Inc.:

• Personalized Neoantigens vs Shared Neoantigens
• Vaccines vs T-Cells
• Moving into Advanced Metastatic disease — COmbination Checkpoint blockade

1. immediate response
2. synergies by combination therapy
3. Neoantigets

 

4:30 Merck Sasso 

Emerging Innate Immune Targets for enhancing 

• Additional component of the Immune system

1. Combination of Checkpoint inhibitors – Targeting functionality of T-Cells
2. Standard of care moving to Goal State by Check point blocade
3. Monoclonal antibodies blocking PD-1 and CTLA4
4. Immunogenic cell death
5. Effectors that promote cross presentation of antigens
6. recruitment of T cells
7. reversing the pathways driving a repressing tumor environment

MERCK – Keytruda

Study with Dinociclib – Immunogenic Cell Death – induced by Radiation, combined with CTLA4  – median response overall survival – 20 month

MORE OPTIONS:

• OV therapies – enhance tumor Combination:

1. TVEC +Keytruda – disease control 68%
2. CAVATAK +Keytruda
3. CAVATAK + pembrolizumab

• TLR agonism

1. Two Phase I in melanoma
2. TC-1 Anti-IL-10 MK-1966 induced by SD-101
3. Innate Immune Triggers against Pathogens and Damaged selt

 

• STING agonism

1. DNA Sensing cGAS/STING – another approach to viral mechanism for Cancer
2. Non-nuclear dsDNA is a ‘danger
3. DMXAA – can’t stimulate HUman only moth STING pathway

• RNA – RIG like receptors –

1. leveraging anti-viral Mechanisms to eliminate Tumors 
2. Activation of RIG-like receptor

SHARED MECHANISMS: INFgamma, TNF alpha

• TLR9
• RIG-I
• STING
• Oncolytic viruses

Summary

• Merck – Keytruda – will be combined with different strategies to leverage innate immunity in combination with traditional T cell approaches
• expend beyond T cell
• limitation of checkpoint blockade therapies can be due to aberrant T cell localization and the suppressive microenvironment of the tumor

 

5:00 Reading Tea Leaves: The Dilemma of Prediction and Prognosis in Immunotherapy

Morganna Freeman, D.O., Associate Director, Melanoma & Cutaneous Oncology Program, The Angeles Clinic and Research Institute

With the rapid expansion of immunotherapeutics in oncology, scientifically significant advances have been made with both the depth and duration of antitumor responses. However, not all patients benefit, or quickly relapse, thus much scientific inquiry has been devoted to appropriate patient selection and how such obstacles might be overcome. While more is known about potential biomarkers, accurate prognostication persists as a knowledge gap, and efforts to bridge it will be discussed here.

• CD8+ T cell – Tumor — Imune Priming — CHeckpoint Inhibition  CD8+
• execution is complex
• predict who will benefit from what treatment
• Patient and Tumor Profiling

1. Tcell Prining: TILs, PDL1, IDO, Tcell anergy Treg- Ovarian cancer: CD8+ TILs
2. microenvironment – immuno-scoring
3. Immune competence -flow, biomarkers CyTOF – Mass Flow Cytometry
4. mutation burden – chemo + Vaccine – longer time to progression
5. mictoenvironment – Tumor Profiling
6. tumor adaptation – serum ULBP2 NKligand – independent predictor of prognosis in Stage I-III
7. Predictive Genomic Analysis – Immune SIgnature of Response to CHeckPoint Blockage – liquid biopsy
8. Neoantigens: Allows analysis of T cell
9. Multispectral imaging – Immune cell phenotypes visualized and quantified simultaneously – improve TME immune suppression, TIL harvest potential, location of the T celle impact prognosis
10. Immune monitoring: Pre intervation vs Post Intervention
11. Tumor heterogeneity: Cancer progression and metastasis, clinical resistance
12. Intervention Assessments: Tissue marker of Blood which one is the best to use
13. Cloonal Tracking: Quantifying Tumor
14. ImmunoPET – anti CD8 immune-PET sensitive of tumor infiltrating
15. Transcriptomic Signature: IPRES (innate PD-1 resistence) can be induced by MAPKi, furthe account for poor response – due to immune depletion
16. Tests and immuno-toxicity, Translation to POC
17. Data assimilation
18. Ideal BioMarkers related to Mechanism of Action – multivariant scoring systems
19. Gender differences, BMI differences, Age difference  — IN RESPONDING TO IMMUNOTHERAPY IN IMMUNO ONCOLOGY