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Tumor neoantigenes:building a framework for personalized cancer immunotherapy
Matthew M. Gubin, Maxim N. Artyomov, Elaine R. Mardis, and Robert D. Schreiber
Academia Sinica, MIB Chen group
The Journal of Clinical Investigation, volume 125 , Number 9,
September 2015
Speaker :
Ting-Wei Lin
National Taiwan University, Genome and System Biology
Journal Club
2nd part
How Tumor-Specific antigen became important + How to predict it
Paving the way for TSA-based cancer immunotherapy
2005 2015
TSA(Tumor-specific antigens)
TAA(Tumor-associated antigen)
CTAs(Cancer-germline/Cancer testis antigens)
1943
Thomas Wölfel
Steven A. RosenbergPaul F. Robbins
patient tumor cell
+ lymphocyte
adoptive therapy
+ patient tumor regression
Gross
Foley
Prehn
Old
2008 2013
Steven A. Rosenberg
Human
Next Generation Sequencing
~2 weeks
Ugur Sahin
2012
Jan
Feb
Washington University School of Medicine
St. Louis, Missouri
translational research institute TRON
Bioinformatic
+ NGS
The response of autologous T cells to a human melanoma
2005
Thomas Wölfel
Lennerz, V.,… Wölfel, T. (2005).
The response of autologous T cells to a human melanoma is dominated by mutated neoantigens
PNAS, 102(44), 16013–8.
PBMC(Peripheral Blood Mononuclear Cells)
CTL(Cytolytic )
Mixed Lymphocyte-Tumor Cell MLTC
DT Melanoma-Reactive T cellsTumor-specific antigen
DT
Patient
IFN-𝜸 ELISPOT Assay
Kenneth Murphy, Casey Weaver., Appendix I, Janeway’s Immunology 9th, (2016 ), Garland Science
Cytokine-specific antibodies
second cytokine-specific antibody added
spot generated
activated T cell secreted cytokine
add the T cell + APC + antigen
IFN-𝜸
COS-7 cotransfected with HLA class I + antigens
HLA-A*2601, HLA-B*3801 ,HLA-B*3801
Enzyme-linked Immunospot Assay
use DT melanoma-reactive T cell to recognized tumor-specific antigen
PBMC(Peripheral Blood Mononuclear Cells)
CTL(Cytolytic )
Mixed Lymphocyte-Tumor Cell MLTC
MLTC(Mixed Lymphocyte-Tumor Cell)
MZ7-MEL
PBMC
(different time points)
+
MZ7-MEL
cDNA library+ RT-PCR
IFN-𝜸 ELISPOT Assay
Tumor-specific antigen
Reactivity of MLTC responders to known common antigenes
Tumor-specified antigen
Without prior stimulation, low fraction of anti tumor T
cell response
Lennerz, V., Fatho, M., Gentilini, C., Frye, R. a, Lifke, A., Ferel, D., … Wölfel, T. (2005).
The response of autologous T cells to a human melanoma is dominated by mutated neoantigens
Proceedings of the National Academy of Sciences of the United States of America, 102(44), 16013–8.
Tumor-specific antigenCommon tumor antigen
Complete Tumor Regression in a Melanoma Patient Receiving Adoptive
Cell Transfer Therapy
2005
Steven A. RosenbergPaul F. Robbins
Zhou, J., Dudley, M. E., Rosenberg, S. a, & Robbins, P. F. (2005).Journal of Immunotherapy, 28(1), 53–62. Nicholas P. Restifo, Mark E. Dudley & Steven A. Rosenberg,(2012), Nature Reviews Immunology 12, 269-281
6/13 patients response (2 nearly complete regression of metastasis)
Accelerating the immunotherapy by using bioinformatic and
next sequencing technology to predict epitope
NGS + Bioinformatics approach
The way to the automated bioinformatic epitope prediction
Next
Generation
Sequencing
Somatic Mutation
Detection
Epitope
prediction
bwa/bowtie
samtools
vcf
GATK(mutect)
wet dry
software introduction
Conquer the specimen archive problem in sequencing technology
1. obtaining information in a clinically relevant time frame
2. the small amounts of DNA/RNA available from a core biopsy procedure
3. tissue preservation in formalin and paraffin
4. data interpretation
Cabanski, C. R., Magrini, V., Griffith, M., Griffith, O. L., McGrath, S., Zhang, J., … Maher, C. A. (2014).
CDNA hybrid capture improves transcriptome analysis on low-input and archived samples. Journal of Molecular Diagnostics, 16(4), 440–451.
cDNA hybrid capture
Resources and softwares in timeline for the predicting epitope
SYFPEITHI
Rankpep
BIMAS
IEDB
NetMHC
SMM
SMMPMBEC
1999 20021994 20152004
NetMHCpan
2007 2008
IPD-IMGT/HLA
Boston University
2005 2009
UC San Diego
Technical University of Denmark
Center for Biological Sequence Analysis
2012 updated
University of Tübingen
2014 updated
Universidad Complutense de Madrid
2017 updated
Resources and softwares in timeline for the predicting epitope
SYFPEITHI
Rankpep
BIMAS
IEDB
NetMHC
SMM
SMMPMBEC
1999 20021994 20152004
NetMHCpan
2007 2008
IPD-IMGT/HLA
Boston University
2005 2009
UC San Diego
Technical University of Denmark
Center for Biological Sequence Analysis
2012 updated
University of Tübingen
2014 updated
Universidad Complutense de Madrid
2017 updated
Database
Resources and softwares in timeline for the predicting epitope
SYFPEITHI
Rankpep
BIMAS
IEDB
NetMHC
SMM
SMMPMBEC
1999 20021994 20152004
NetMHCpan
2007 2008
IPD-IMGT/HLA
Boston University
2005 2009
UC San Diego
Technical University of Denmark
Center for Biological Sequence Analysis
2012 updated
University of Tübingen
2014 updated
Universidad Complutense de Madrid
2017 updated
Artificial Neural Network
Position-Weight Matrices
Binding Coefficient model
Position Specific Scoring Matrics(PSSM)
Peptide:MHC binding
energy covariance matrix
Stabilized Matrix Method
Position Specific Scoring Matrics(PSSM)
The Evolving Concepts to deal with the peptide binding affinity
ANN/Deep learning
weight function
input data
NetMHC
NetMHCpan
SMM
SMMPMBEC
SYFPEITHI
Rankpep
How is artificial neural network work?
bias(error)weight
non-linear function
How to prioritize predicted Neoepitope for validation
1. p-MHCI binding affinity
2. epitope abundance
3. antigen processing
•protein degradation
•peptide transport
•quantitating RNA expression
•mass spectromy
Immunogenicity
How to prioritize predicted Neoepitope for validation
1. p-MHCI binding affinity
2. epitope abundance
3. antigen processing
•protein degradation
•peptide transport
•quantitating RNA expression
•mass spectromy
Immunogenicity
Tung, C. W., & Ho, S. Y. (2007).
POPI: Predicting immunogenicity of MHC class I binding peptides by mining informative physicochemical properties. Bioinformatics, 23(8), 942–949.
POPI
physicochemical properties
Jeff Hammerbacher
Jeff Hammerbacher
Epidisco
OCaml
Not extensively validation and use, MHC class II prediction
TEPITOPE
netMHCII
SMM-align
2009
1994
2007
http://datamining-iip.fudan.edu.cn/service/TEPITOPEpan/TEPITOPEpan.html
http://www.cbs.dtu.dk/services/NetMHCII/
http://tools.immuneepitope.org/mhcii/ IEDB
Technical University of Denmark
Center for Biological Sequence Analysis
Thank you!Thank you!
9mer
887 sequence 1 HLA type 9mer
Reference for the software and resource
SYFPEITHI
Rankpep
BIMAS
IEDB
NetMHC
SMM
SMMPMBEC
http://imed.med.ucm.es/Tools/rankpep.html
http://www.syfpeithi.de/bin/MHCServer.dll/Info.htm#General
http://www.iedb.org/
IMGT/HLA http://www.ebi.ac.uk/ipd/imgt/hla/index.html
https://zlab.bu.edu/SMM/

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Tumor neoantigenes:building a framework for personalized cancer immunotherapy

  • 1. Tumor neoantigenes:building a framework for personalized cancer immunotherapy Matthew M. Gubin, Maxim N. Artyomov, Elaine R. Mardis, and Robert D. Schreiber Academia Sinica, MIB Chen group The Journal of Clinical Investigation, volume 125 , Number 9, September 2015 Speaker : Ting-Wei Lin National Taiwan University, Genome and System Biology Journal Club 2nd part How Tumor-Specific antigen became important + How to predict it
  • 2. Paving the way for TSA-based cancer immunotherapy 2005 2015 TSA(Tumor-specific antigens) TAA(Tumor-associated antigen) CTAs(Cancer-germline/Cancer testis antigens) 1943 Thomas Wölfel Steven A. RosenbergPaul F. Robbins patient tumor cell + lymphocyte adoptive therapy + patient tumor regression Gross Foley Prehn Old 2008 2013 Steven A. Rosenberg Human Next Generation Sequencing ~2 weeks Ugur Sahin 2012 Jan Feb Washington University School of Medicine St. Louis, Missouri translational research institute TRON Bioinformatic + NGS
  • 3. The response of autologous T cells to a human melanoma 2005 Thomas Wölfel Lennerz, V.,… Wölfel, T. (2005). The response of autologous T cells to a human melanoma is dominated by mutated neoantigens PNAS, 102(44), 16013–8. PBMC(Peripheral Blood Mononuclear Cells) CTL(Cytolytic ) Mixed Lymphocyte-Tumor Cell MLTC DT Melanoma-Reactive T cellsTumor-specific antigen DT Patient
  • 4. IFN-𝜸 ELISPOT Assay Kenneth Murphy, Casey Weaver., Appendix I, Janeway’s Immunology 9th, (2016 ), Garland Science Cytokine-specific antibodies second cytokine-specific antibody added spot generated activated T cell secreted cytokine add the T cell + APC + antigen IFN-𝜸 COS-7 cotransfected with HLA class I + antigens HLA-A*2601, HLA-B*3801 ,HLA-B*3801 Enzyme-linked Immunospot Assay
  • 5. use DT melanoma-reactive T cell to recognized tumor-specific antigen PBMC(Peripheral Blood Mononuclear Cells) CTL(Cytolytic ) Mixed Lymphocyte-Tumor Cell MLTC MLTC(Mixed Lymphocyte-Tumor Cell) MZ7-MEL PBMC (different time points) + MZ7-MEL cDNA library+ RT-PCR IFN-𝜸 ELISPOT Assay Tumor-specific antigen
  • 6. Reactivity of MLTC responders to known common antigenes Tumor-specified antigen
  • 7. Without prior stimulation, low fraction of anti tumor T cell response Lennerz, V., Fatho, M., Gentilini, C., Frye, R. a, Lifke, A., Ferel, D., … Wölfel, T. (2005). The response of autologous T cells to a human melanoma is dominated by mutated neoantigens Proceedings of the National Academy of Sciences of the United States of America, 102(44), 16013–8. Tumor-specific antigenCommon tumor antigen
  • 8. Complete Tumor Regression in a Melanoma Patient Receiving Adoptive Cell Transfer Therapy 2005 Steven A. RosenbergPaul F. Robbins Zhou, J., Dudley, M. E., Rosenberg, S. a, & Robbins, P. F. (2005).Journal of Immunotherapy, 28(1), 53–62. Nicholas P. Restifo, Mark E. Dudley & Steven A. Rosenberg,(2012), Nature Reviews Immunology 12, 269-281 6/13 patients response (2 nearly complete regression of metastasis)
  • 9. Accelerating the immunotherapy by using bioinformatic and next sequencing technology to predict epitope NGS + Bioinformatics approach
  • 10. The way to the automated bioinformatic epitope prediction Next Generation Sequencing Somatic Mutation Detection Epitope prediction bwa/bowtie samtools vcf GATK(mutect) wet dry software introduction
  • 11. Conquer the specimen archive problem in sequencing technology 1. obtaining information in a clinically relevant time frame 2. the small amounts of DNA/RNA available from a core biopsy procedure 3. tissue preservation in formalin and paraffin 4. data interpretation Cabanski, C. R., Magrini, V., Griffith, M., Griffith, O. L., McGrath, S., Zhang, J., … Maher, C. A. (2014). CDNA hybrid capture improves transcriptome analysis on low-input and archived samples. Journal of Molecular Diagnostics, 16(4), 440–451. cDNA hybrid capture
  • 12. Resources and softwares in timeline for the predicting epitope SYFPEITHI Rankpep BIMAS IEDB NetMHC SMM SMMPMBEC 1999 20021994 20152004 NetMHCpan 2007 2008 IPD-IMGT/HLA Boston University 2005 2009 UC San Diego Technical University of Denmark Center for Biological Sequence Analysis 2012 updated University of Tübingen 2014 updated Universidad Complutense de Madrid 2017 updated
  • 13. Resources and softwares in timeline for the predicting epitope SYFPEITHI Rankpep BIMAS IEDB NetMHC SMM SMMPMBEC 1999 20021994 20152004 NetMHCpan 2007 2008 IPD-IMGT/HLA Boston University 2005 2009 UC San Diego Technical University of Denmark Center for Biological Sequence Analysis 2012 updated University of Tübingen 2014 updated Universidad Complutense de Madrid 2017 updated Database
  • 14. Resources and softwares in timeline for the predicting epitope SYFPEITHI Rankpep BIMAS IEDB NetMHC SMM SMMPMBEC 1999 20021994 20152004 NetMHCpan 2007 2008 IPD-IMGT/HLA Boston University 2005 2009 UC San Diego Technical University of Denmark Center for Biological Sequence Analysis 2012 updated University of Tübingen 2014 updated Universidad Complutense de Madrid 2017 updated Artificial Neural Network Position-Weight Matrices Binding Coefficient model Position Specific Scoring Matrics(PSSM) Peptide:MHC binding energy covariance matrix Stabilized Matrix Method
  • 15. Position Specific Scoring Matrics(PSSM) The Evolving Concepts to deal with the peptide binding affinity ANN/Deep learning weight function input data NetMHC NetMHCpan SMM SMMPMBEC SYFPEITHI Rankpep
  • 16. How is artificial neural network work? bias(error)weight non-linear function
  • 17. How to prioritize predicted Neoepitope for validation 1. p-MHCI binding affinity 2. epitope abundance 3. antigen processing •protein degradation •peptide transport •quantitating RNA expression •mass spectromy Immunogenicity
  • 18. How to prioritize predicted Neoepitope for validation 1. p-MHCI binding affinity 2. epitope abundance 3. antigen processing •protein degradation •peptide transport •quantitating RNA expression •mass spectromy Immunogenicity Tung, C. W., & Ho, S. Y. (2007). POPI: Predicting immunogenicity of MHC class I binding peptides by mining informative physicochemical properties. Bioinformatics, 23(8), 942–949. POPI physicochemical properties
  • 21. Not extensively validation and use, MHC class II prediction TEPITOPE netMHCII SMM-align 2009 1994 2007 http://datamining-iip.fudan.edu.cn/service/TEPITOPEpan/TEPITOPEpan.html http://www.cbs.dtu.dk/services/NetMHCII/ http://tools.immuneepitope.org/mhcii/ IEDB Technical University of Denmark Center for Biological Sequence Analysis
  • 22.
  • 24. 9mer 887 sequence 1 HLA type 9mer
  • 25. Reference for the software and resource SYFPEITHI Rankpep BIMAS IEDB NetMHC SMM SMMPMBEC http://imed.med.ucm.es/Tools/rankpep.html http://www.syfpeithi.de/bin/MHCServer.dll/Info.htm#General http://www.iedb.org/ IMGT/HLA http://www.ebi.ac.uk/ipd/imgt/hla/index.html https://zlab.bu.edu/SMM/