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Иммунотерапия раковых опухолей: взгляд со стороны системной биологии. Максим Артемов
This document summarizes recent advances in cancer immunotherapy from the perspective of systems biology. It discusses how checkpoint blockade immunotherapy works by addressing the second co-inhibitory checkpoint signal needed for T cell activation. Computational methods are now able to identify tumor-specific neoantigens that can be targeted by immunotherapy. Mouse model studies showed that certain tumors are naturally rejected due to expression of a mutant antigen recognized by T cells, and that antigen-specific T cells are present before immunotherapy treatment. The high mutational load in melanoma makes it particularly responsive to checkpoint blockade. Early work in the 19th century by William Coley observed tumor regression following bacterial infection, which led to development of a toxin mixture that resembled modern vaccine formulations. Members of
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Иммунотерапия раковых опухолей: взгляд со стороны системной биологии. Максим Артемов
- 1. Иммунотерапия раковых опухолей сточки зрения системной биологии Максим Артёмов 25 мая 2015
- 2. T cell activationrequires 2 signals
- 3. CTLA4 and PD-1are checkpoint regulatory molecules
- 4. Immunotherapy addresses secondcheckpoint
- 5. Huge success bothin clinics and in academia 2011 - ipilimumab approved by FDA for melanoma 2013 - breakthrough of the year
- 6.
- 7. Mouse model oftumor rejection – panel of sarcomas Some mouse sarcomas are naturally rejected while others grow out Matsushita et al, Nature 2012
- 8. Mouse model oftumor rejection – panel of sarcomas Hypothesis: Tumor neoantigen is responsible for rejecion Matsushita et al, Nature 2012 compare mutational landscape of regressor vs progressor tumors
- 9. Only regressor tumorsexpress spnb2 mutant! Matsushita et al, Nature 2012
- 10. Only regressor tumorsexpress spnb2 mutant! Spnb2 mutant is also computationally strong binder to MHC! Matsushita et al, Nature 2012
- 11. Computational filtering allowsto go identify neoantigens
- 12. Computational filtering allowsto go identify neoantigens
- 13. Computational filtering allowsto go identify neoantigens
- 14. Computational filtering allowsto go identify neoantigens
- 15. Checkpoint blockade worksin progressor tumors aCTLA4/aPD1 treatments “cure” the mice Gubin et al, Nature 2014
- 16. Potential Antigens identifiedfor T3 tumor
- 17. Antigen-specific T-cells arepresent in tumor even before treatment!
- 18. Theraputic vaccination savesthe mouse!
- 19. Theraputic vaccination savesthe mouse!
- 20. Independent validation intwo additional tumors
- 21. Mutational load ispredictive of immunotherapy response Rizvi et al, Science 2015
- 22. This is whyfirst successes of checkpoint blockade are in melanoma!!
- 23. It all startedin 19th century Worked in New York Cancer Hospital (later became a part of Memorial Sloan Kettering Cancer Center) • Noticed that infection with erysipelas often leads to spontaneous regression of sarcomas • Started therapeutically infection patients with inoperable sarcomas
- 24. It all startedin 19th century Worked in New York Cancer Hospital (later became a part of Memorial Sloan Kettering Cancer Center) • Noticed that infection with erysipelas often leads to spontaneous regression of sarcomas • Started therapeutically infection patients with inoperable sarcomas
- 25. It turns outthat Streptococci alone is not enough! What we refer to as Coley’s toxins is combination of two components: • Streptococci – gram-positive bacterial infection (no endotoxins) • Serratia – gram-negative bacteria (endotoxins)
- 26. It turns outthat Streptococci alone is not enough! Note resemblance to classical vaccine formulation: Adjuvant + Adaptive Immunity Target What we refer to as Coley’s toxins is combination of two components: • Streptococci – gram-positive bacterial infection (no endotoxins) • Serratia – gram-negative bacteria (endotoxins)
- 27. Streptococci action issarcoma specific! Survival
- 28. Sarcomas have characteristicgenomic landscape
- 29. Fusion neoantigens aresimilar to Streptococci native protein epitopes!
- 30. Artyomov Lab members (andassociated members) Alex Predeus Katya Loginicheva Alexey Sergushichev Vicky Lampropoulou Mike Orzel Anton Alexandrov Pavel Zakharov Many thanks to lab members and collaborators Bob Schreiber Matt Gubin Jeff Ward (WashU) Ben Zeskind Fadi Towfic (Immuneering)