Presented by Nuraini Aguse

Slides:



Advertisements
Similar presentations
Brief introduction on Logistic Regression
Advertisements

Epitope Selection Rational Vaccine design. Why? Therapeutic vaccines Therapeutic vaccines Treatment of viral infections (e.g., HIV, HCV), and resistant.
Advanced Cancer Topics Journal Review 4/16/2009 AD.
Models and Algorithms for Complex Networks Power laws and generative processes.
New Treatments. New drug treatments Chronotherapy Lung cancer vaccine Summary.
Bayesian Analysis and Applications of A Cure Rate Model.
Immunotherapy Sara Engh & Tenzin Yiga. Role of the Immune system ➔ Defends against pathogens such as bacteria, fungi, and viruses that enter the body.
RNA Structure Prediction
The Structure, Function, and Evolution of Vascular Systems Instructor: Van Savage Spring 2010 Quarter 3/30/2010.
HMMs for alignments & Sequence pattern discovery I519 Introduction to Bioinformatics.
We obtained breast cancer tissues from the Breast Cancer Biospecimen Repository of Fred Hutchinson Cancer Research Center. We performed two rounds of next-gen.
Basic Immunology of the Mouse (and Human) Nicholas P. Restifo, MD October 19, 2015.
Statistical Methods. 2 Concepts and Notations Sample unit – the basic landscape unit at which we wish to establish the presence/absence of the species.
Progress in Cancer Therapy Following Developments in Biopharma
Cancer immunotherapy: an update
UNIVERSITA’ DEGLI STUDI NAPOLI FEDERICO II DOTTORATO IN INGEGNERIA DEI MATERIALI E DELLE STRUTTURE Brunella Corrado Filomena Gioiella Bernadette Lombardi.
Immuno and Epigenetic Therapies Xiaole Shirley Liu STAT115, STAT215, BIO298, BIST520.
Immune Epitope Database assays. Standard immune epitope definition Classical (textbook) definition: An epitope, also known as antigenic determinant, is.
Cancer immunotherapy: an update
Lecture Outline Antigens Definition Exogenous Endogenous
Cancer Vaccine Design Ion Mandoiu
Chapter 43 The Immune System.
Biologic Medicines.
Immunoinformatics Approach for Non-Small Cell Lung Cancer
Unit 3 Autoimmunity Part 1 Introduction
Dr. Peter John M.Phil, PhD Atta-ur-Rahman School of Applied Biosciences (ASAB) National University of Sciences & Technology (NUST)
Socializing Individualized T-Cell Cancer Immunotherapy
Therapeutic vaccines and immune-based therapies for the treatment of chronic hepatitis B: Perspectives and challenges  Marie-Louise Michel, Qiang Deng,
Prognosis of younger patients in non-small cell lung cancer
Melanoma Cell-Intrinsic PD-1 Receptor Functions Promote Tumor Growth
Quantifying the Impact of HIV Escape from CTL
MHC Tetramer Creative Biolabs is a world leading biotechnological company in research and development of agents in the realm of immunology. With years.
The Immune System. The Immune System Adaptive Immune Response.
Immunotherapy for GI Cancer: Pipe Dream or Dream Come True?
by Jennifer Couzin-Frankel
Discussion Outline Cells of the Immune System.
Figure 2 Neoantigen presentation in the tumour microenvironment
Volume 67, Issue 4, Pages (April 2015)
Tumor Evolution: A Problem of Histocompatibility
Emergence of resistance to immune checkpoint blockade is associated with elimination of mutation-associated neoantigens by LOH and a more diverse T-cell.
Applications of Immunogenomics to Cancer
Volume 2, Issue 1, Pages (January 2016)
Volume 22, Issue 1, Pages e4 (July 2017)
Therapeutic vaccines and immune-based therapies for the treatment of chronic hepatitis B: Perspectives and challenges  Marie-Louise Michel, Qiang Deng,
Volume 165, Issue 1, Pages (March 2016)
Targeting T Cell Co-receptors for Cancer Therapy
Figure 1. The evolution of tumor mutation burden as an immunotherapy biomarker. Major studies that are important in the ... Figure 1. The evolution of.
Neoantigen-specific TCR expansion in stimulated T-cell cultures.
Volume 2, Issue 1, Pages (January 2016)
Nat. Rev. Endocrinol. doi: /nrendo
Nat. Rev. Urol. doi: /nrurol
Volume 39, Issue 1, Pages (July 2013)
George Weiner, MD Director, Holden Comprehensive Cancer Center
Chung-Han Lee, Roman Yelensky, Karin Jooss, Timothy A. Chan 
Utilizing NGS-Data to Evaluate Anti-PD-1 Treatment
Cancer Evolution during Immunotherapy
Volume 9, Pages (November 2018)
Volume 39, Issue 1, Pages (July 2013)
Current Status of Biomarkers for Immune Checkpoint Inhibitors
Fig. 2 Estimate of the neoantigen repertoire in human cancer.
Combining Immunotherapy and Chemotherapy in NSCLC
by Jennifer Couzin-Frankel
Immune signatures of patients with short-, medium-, and long-term survival. Immune signatures of patients with short-, medium-, and long-term survival.
Primary, Adaptive, and Acquired Resistance to Cancer Immunotherapy
Human cancer immunotherapy strategies targeting B7-H3 A, blockade of B7-H3 with blocking mAbs neutralizes inhibitory signaling in its unidentified receptor(s)
Ten most frequent TCRs in the bulk 12TILs comprise >99% of the TIL population, and the neoantigens are shown to be the most dominant clones. Ten most frequent.
Nienke Vrisekoop, João P. Monteiro, Judith N. Mandl, Ronald N. Germain 
Strategies for personalized precision immunotherapy.
Volume 28, Issue 4, Pages e6 (July 2019)
A. B. C. D. Above median: Below median:
Presentation transcript:

Presented by Nuraini Aguse A neoantigen fitness model predicts tumour response to checkpoint blockade immunotherapy Marta Łuksza, Nadeem Riaz, Vladimir Makarov, Vinod P. Balachandran, Matthew D. Hellmann, Alexander Solovyov, Naiyer A. Rizvi, Taha Merghoub, Arnold J. Levine, Timothy A. Chan, Jedd D. Wolchok & Benjamin D. Greenbaum Presented by Nuraini Aguse

Content Brief biology background Introduction Methods Results Conclusion

Brief biology background Cancer cell MHC class 1 Neoantigen (produced by certain mutations) T-cell receptor (TCR) T-cell T-cells detect foreign cells and destroy them

Brief biology background Cancer cell MHC class 1 Neoantigen T-cell receptor T-cell T-cells detect foreign cells and destroy them Destroy this cell!

Introduction Cancer patients who underwent immunotherapy respond differently to the treatment Previous studies show link between number of neoantigens and immunotherapy response But, due to heterogeneity of tumors, the neoantigen load does not provide sufficient information Main biological question: How does the characteristicsof neoantigen predict immunotherapy response? Approach – mathematical fitness model of tumor-immune interactions This model predicts the evolutionary dynamics of cancer cell population after immunotherapy

Methods Evolutionary dynamics of a cancer cell population in a tumor The fitness of a cancer cell is its expected replication rate The total population size is the sum over all clones Evolved relative effective population size n(τ) = N(τ) / N(0) Initial frequency of clone α Xα = Nα(0) / N(0) Then, we obtain equation 1

Methods Neoantigen recognition-based fitness cost for a tumor clone. Fitness cost  recognition potential  likelihood that neoantigen is recognized by TCR Fitness of a clone α is defined by maximum A x R of each neoantigen Replacing F in equation 1, we get Cancer cell Max AxR

Methods MHC amplitude (A) Probability that neoantigen is displayed More precise: the ratio of relative probability between mutant neoantigen and wild type In terms of dissociation constants After some adjustments

Methods TCR recognition (R) Probability that a neoantigen will be recognized by TCR Align the neoantigen to epitopes that are known to be recognized by TCR These epitopes are obtained from Immune Epitope Database and Analysis Resource (IEDB) Probability R depends on the alignment score Need to perform parameter training to determine a and k

Methods TCR recognition (R) Alignments to IEDB epitopes TCR recognition (R) Probability that a neoantigen will be recognized by TCR Align the neoantigen to epitopes that are known to be recognized by TCR These epitopes are obtained from Immune Epitope Database and Analysis Resource (IEDB) Probability R depends on the alignment score Need to perform parameter training to determine a and k

Methods Parameter training Select parameters that maximize the log-rank test scores of the survival analysis of patient cohorts Patient cohort is split into high and low fitness groups They used a cohort of 64 patients with melanoma to train parameters for another 103 patients with melanoma cohort and vice versa. They uses the total score of both cohorts to train parameters for a cohort of 34 patients with lung cancer

Survival analysis score landscape as a function of model parameters

Methods Model selection Multiple other models are selected, all of them results in a loss of predictive power Examples of models A only R only Constant fitness across all neoantigens

Recap a, Clones are inferred from the genealogical tree of each tumour. We predict n(τ), the future effective size of the cancer cell population, relative to its size at the start of therapy (equation (1)) by evolving clones under the model over a fixed timescale, τ. Application of therapy can decrease the fitness of clones depending on their neoantigens. Clones with strongly negative fitness have greater loss of population size than more fit ones. b, Our model accounts for the presence of dominant neoantigens within a clone, α, by modelling presentation and recognition of inferred neoantigens, assigning fitness to a clone, Fα.

Results Neoantigen fitness model is predictive of survival after checkpoint blockade immunotherapy

Results Neoantigen fitness model is predictive of survival after checkpoint blockade immunotherapy

Results Predicted evolutionary dynamics in cohorts

Conclusions The model formalizes a method of determining what makes tumors immunologically different from its host Immunological difference determines the tumor’s fitness The model predicts the survivability of the patient based on the tumor’s fitness The model can inform the choice of targets for tumor vaccine The model can be improved by advances in predicting proteosomal processing and stability of neoantigen-MHC binding