Cancer vaccines targeting mutated tumor proteins are an emerging personalized medicine. In a number of clinical trials evaluating these therapies, including several at our institution, each patient's vaccine is individually formulated based on the unique mutations present in his or her tumor. As experimentally testing vaccine immunogenicity is infeasible in this setting, these therapies rely on computational prediction of vaccine immunogenicity. In this talk I will discuss recent work to accurately predict T cell epitopes, focusing on development of the MHCflurry software package for CD8+ T cell epitope prediction (https://github.com/openvax/mhcflurry). I will also touch on other, less studied modeling tasks that may help improve cancer vaccines in the future.
The Phase I Personalized Genomic Vaccine trial at Mount Sinai recently began enrolling patients to receive a long-peptide vaccine targeting tumor neoantigens identified by exome and RNA sequencing. Each patient's vaccine is the result of a computational pipeline encompassing somatic variant calling, coding sequence prediction, variant-specific isoform identification, sequence-based HLA typing, peptide-MHC binding prediction, and a final ranking procedure over vaccine peptides. This talk will review the design considerations that went into our pipeline and introduce three new open source packages we have developed for variant coding sequence prediction, vaccine peptide selection, and peptide-MHC binding affinity prediction.
ProImmune Antigen Characterization Summit Paul Mossamandacturner
Paul Moss, School of Cancer Sciences, Birmingham UK, presents at the ProImmune Antigen Characterization and Biomarker Discovery Summit, January 2011.
Cytomegalovirus and Cancer-specific Immunity
Sanja Selak of Intercell AG, Vienna, Austria, presents at the ProImmune Antigen Characterization and Biomarker Discovery Summit, January 2011.
Intercell develops vaccines for the prevention and treatment of infectious diseases
Gene Olinger, USAMRIID, Fort Detrick USA, presents at the ProImmune Antigen Characterization and Biomarker Discovery Summit, January 2011.
Protective Immune Reponses to Ebola Virus
ProImmune Antigen Characterization Summit Johanna Olweusamandacturner
Johanna Olweus, Dept Immunology, Institute for Cancer Research, Radiumshospitalet, Oslo, Norway, presents at the ProImmune Antigen Characterization and Biomarker Discovery Summit, January 2011.
Cancer immunotherapy: finding allies among the "allos"
Stem Cells in A New Era of Cell based Therapies - Creative BiolabsCreative-Biolabs
A stem cell can replicate itself or differentiate into cells that carry out the specific functions of the body. The application of stem cells in regenerative medicine and disease therapeutics is one of the most exciting advances in medical science today. In cell-based therapies, stem cells may play two roles. The first role is as drug-delivery vehicles. The second role is as therapeutic agents themselves. Stem cells also offer opportunities for scientific advances that go far beyond cell-based therapies. Creative Biolabs is dedicated to facilitate the research of stem cells in both basic science and therapeutics development. Please contact us if you are interested in our services or products.
Variant G6PD levels promote tumor cell proliferation or apoptosis via the STA...Enrique Moreno Gonzalez
Glucose-6-phosphate dehydrogenase (G6PD), elevated in tumor cells, catalyzes the first reaction in the pentose-phosphate pathway. The regulation mechanism of G6PD and pathological change in human melanoma growth remains unknown.
The Phase I Personalized Genomic Vaccine trial at Mount Sinai recently began enrolling patients to receive a long-peptide vaccine targeting tumor neoantigens identified by exome and RNA sequencing. Each patient's vaccine is the result of a computational pipeline encompassing somatic variant calling, coding sequence prediction, variant-specific isoform identification, sequence-based HLA typing, peptide-MHC binding prediction, and a final ranking procedure over vaccine peptides. This talk will review the design considerations that went into our pipeline and introduce three new open source packages we have developed for variant coding sequence prediction, vaccine peptide selection, and peptide-MHC binding affinity prediction.
ProImmune Antigen Characterization Summit Paul Mossamandacturner
Paul Moss, School of Cancer Sciences, Birmingham UK, presents at the ProImmune Antigen Characterization and Biomarker Discovery Summit, January 2011.
Cytomegalovirus and Cancer-specific Immunity
Sanja Selak of Intercell AG, Vienna, Austria, presents at the ProImmune Antigen Characterization and Biomarker Discovery Summit, January 2011.
Intercell develops vaccines for the prevention and treatment of infectious diseases
Gene Olinger, USAMRIID, Fort Detrick USA, presents at the ProImmune Antigen Characterization and Biomarker Discovery Summit, January 2011.
Protective Immune Reponses to Ebola Virus
ProImmune Antigen Characterization Summit Johanna Olweusamandacturner
Johanna Olweus, Dept Immunology, Institute for Cancer Research, Radiumshospitalet, Oslo, Norway, presents at the ProImmune Antigen Characterization and Biomarker Discovery Summit, January 2011.
Cancer immunotherapy: finding allies among the "allos"
Stem Cells in A New Era of Cell based Therapies - Creative BiolabsCreative-Biolabs
A stem cell can replicate itself or differentiate into cells that carry out the specific functions of the body. The application of stem cells in regenerative medicine and disease therapeutics is one of the most exciting advances in medical science today. In cell-based therapies, stem cells may play two roles. The first role is as drug-delivery vehicles. The second role is as therapeutic agents themselves. Stem cells also offer opportunities for scientific advances that go far beyond cell-based therapies. Creative Biolabs is dedicated to facilitate the research of stem cells in both basic science and therapeutics development. Please contact us if you are interested in our services or products.
Variant G6PD levels promote tumor cell proliferation or apoptosis via the STA...Enrique Moreno Gonzalez
Glucose-6-phosphate dehydrogenase (G6PD), elevated in tumor cells, catalyzes the first reaction in the pentose-phosphate pathway. The regulation mechanism of G6PD and pathological change in human melanoma growth remains unknown.
Neoantigens is a class of HLA-bound peptides that arise from tumor-specific mutations. They are highly immunogenic because they are not present in normal tissues, thus can be used as biomarkers differentiating cancer cells from normal cells. It has been shown that recognition of these individual-specific neoantigens opens a new door for cancer immunotherapy.
Slide deck used for video presentation to the OCTS conference. Her2-positive CNS metastases plague refractory breast cancer patients. Here we present our wholly novel approach to cell therapy for the treatment of these patients. #celltherapy #oncology #breastcancer
Use of Methylation Markers for Age Estimation of an unknown Individual based ...QIAGEN
Biological samples and traces collected at crime scenes have potential to be used for predicting
the age of the individuals from whom the samples originated. In no-suspect cases and cases with
no DNA profile match against a database, such information could be critical for providing additional intelligence for criminal investigations. Read more.
A talk presented at The Eighth International Conference on Oncolytic Virus Therapeutics, Oxford, UK,9-13 April 2014 describing phase 2 data with Cavatak in melanoma.
Data Science Salon: Machine Learning for Personalized Cancer VaccinesFormulatedby
Presented by Alex Rubinsteyn
Next DSS MIA Event - https://datascience.salon/miami/
A short introduction to cancer immunotherapy followed by several machine learning problems which arise from designing personalized cancer vaccines.
Neoantigens is a class of HLA-bound peptides that arise from tumor-specific mutations. They are highly immunogenic because they are not present in normal tissues, thus can be used as biomarkers differentiating cancer cells from normal cells. It has been shown that recognition of these individual-specific neoantigens opens a new door for cancer immunotherapy.
Slide deck used for video presentation to the OCTS conference. Her2-positive CNS metastases plague refractory breast cancer patients. Here we present our wholly novel approach to cell therapy for the treatment of these patients. #celltherapy #oncology #breastcancer
Use of Methylation Markers for Age Estimation of an unknown Individual based ...QIAGEN
Biological samples and traces collected at crime scenes have potential to be used for predicting
the age of the individuals from whom the samples originated. In no-suspect cases and cases with
no DNA profile match against a database, such information could be critical for providing additional intelligence for criminal investigations. Read more.
A talk presented at The Eighth International Conference on Oncolytic Virus Therapeutics, Oxford, UK,9-13 April 2014 describing phase 2 data with Cavatak in melanoma.
Data Science Salon: Machine Learning for Personalized Cancer VaccinesFormulatedby
Presented by Alex Rubinsteyn
Next DSS MIA Event - https://datascience.salon/miami/
A short introduction to cancer immunotherapy followed by several machine learning problems which arise from designing personalized cancer vaccines.
Naiyer Rizvi, MD, Omid Hamid, MD, Solange Peters, MD, PhD, Thomas Powles, MBBS, MRCP, MD, and Nadeem Riaz, MD, MSc, prepared useful Practice Aids pertaining to immuno-oncology for this CME activity titled "Emerging Biomarkers, New Targets, and Rational Combinations: Are We on the Verge of the Next Generation of Immuno-Oncology?" For the full presentation, monograph, complete CME information, and to apply for credit, please visit us at http://bit.ly/2H2s92Y. CME credit will be available until June 17, 2019.
Robert Anders, MD, PhD, Julie R. Brahmer, MD, MSc, and Christopher D. Gocke, MD, prepared useful Practice Aids pertaining to immunotherapy and biomarker testing for this CME/MOC/CC activity titled "Keeping Up With Advances in Cancer Immunotherapy and Biomarker Testing: Implications for Pathologists at the Forefront of the Emerging Precision Immuno-Oncology Era." For the full presentation, monograph, complete CME/MOC/CC information, and to apply for credit, please visit us at http://bit.ly/2L7zlSy. CME/MOC/CC credit will be available until May 2, 2020.
Proteogenomic analysis of human colon cancer reveals new therapeutic opportun...Gul Muneer
We performed the first proteogenomic study on a prospectively collected colon cancer cohort. Comparative proteomic and phosphoproteomic analysis of paired tumor and normal adjacent tissues produced a catalog of colon cancer-associated proteins and phosphosites, including known and putative new biomarkers, drug targets, and cancer/testis antigens. Proteogenomic integration not only prioritized genomically inferred targets, such as copy-number drivers and mutation-derived neoantigens, but also yielded novel findings. Phosphoproteomics data associated Rb phosphorylation with increased proliferation and decreased apoptosis in colon cancer, which explains why this classical tumor suppressor is amplified in colon tumors and suggests a rationale for targeting Rb phosphorylation in colon cancer. Proteomics identified an association between decreased CD8 T cell infiltration and increased glycolysis in microsatellite instability-high (MSI-H) tumors, suggesting glycolysis as a potential target to overcome the resistance of MSI-H tumors to immune checkpoint blockade. Proteogenomics presents new avenues for biological discoveries and therapeutic development.
Cancer cell metabolism: special Reference to Lactate PathwayAADYARAJPANDEY1
Normal Cell Metabolism:
Cellular respiration describes the series of steps that cells use to break down sugar and other chemicals to get the energy we need to function.
Energy is stored in the bonds of glucose and when glucose is broken down, much of that energy is released.
Cell utilize energy in the form of ATP.
The first step of respiration is called glycolysis. In a series of steps, glycolysis breaks glucose into two smaller molecules - a chemical called pyruvate. A small amount of ATP is formed during this process.
Most healthy cells continue the breakdown in a second process, called the Kreb's cycle. The Kreb's cycle allows cells to “burn” the pyruvates made in glycolysis to get more ATP.
The last step in the breakdown of glucose is called oxidative phosphorylation (Ox-Phos).
It takes place in specialized cell structures called mitochondria. This process produces a large amount of ATP. Importantly, cells need oxygen to complete oxidative phosphorylation.
If a cell completes only glycolysis, only 2 molecules of ATP are made per glucose. However, if the cell completes the entire respiration process (glycolysis - Kreb's - oxidative phosphorylation), about 36 molecules of ATP are created, giving it much more energy to use.
IN CANCER CELL:
Unlike healthy cells that "burn" the entire molecule of sugar to capture a large amount of energy as ATP, cancer cells are wasteful.
Cancer cells only partially break down sugar molecules. They overuse the first step of respiration, glycolysis. They frequently do not complete the second step, oxidative phosphorylation.
This results in only 2 molecules of ATP per each glucose molecule instead of the 36 or so ATPs healthy cells gain. As a result, cancer cells need to use a lot more sugar molecules to get enough energy to survive.
Unlike healthy cells that "burn" the entire molecule of sugar to capture a large amount of energy as ATP, cancer cells are wasteful.
Cancer cells only partially break down sugar molecules. They overuse the first step of respiration, glycolysis. They frequently do not complete the second step, oxidative phosphorylation.
This results in only 2 molecules of ATP per each glucose molecule instead of the 36 or so ATPs healthy cells gain. As a result, cancer cells need to use a lot more sugar molecules to get enough energy to survive.
introduction to WARBERG PHENOMENA:
WARBURG EFFECT Usually, cancer cells are highly glycolytic (glucose addiction) and take up more glucose than do normal cells from outside.
Otto Heinrich Warburg (; 8 October 1883 – 1 August 1970) In 1931 was awarded the Nobel Prize in Physiology for his "discovery of the nature and mode of action of the respiratory enzyme.
WARNBURG EFFECT : cancer cells under aerobic (well-oxygenated) conditions to metabolize glucose to lactate (aerobic glycolysis) is known as the Warburg effect. Warburg made the observation that tumor slices consume glucose and secrete lactate at a higher rate than normal tissues.
A brief information about the SCOP protein database used in bioinformatics.
The Structural Classification of Proteins (SCOP) database is a comprehensive and authoritative resource for the structural and evolutionary relationships of proteins. It provides a detailed and curated classification of protein structures, grouping them into families, superfamilies, and folds based on their structural and sequence similarities.
Richard's aventures in two entangled wonderlandsRichard Gill
Since the loophole-free Bell experiments of 2020 and the Nobel prizes in physics of 2022, critics of Bell's work have retreated to the fortress of super-determinism. Now, super-determinism is a derogatory word - it just means "determinism". Palmer, Hance and Hossenfelder argue that quantum mechanics and determinism are not incompatible, using a sophisticated mathematical construction based on a subtle thinning of allowed states and measurements in quantum mechanics, such that what is left appears to make Bell's argument fail, without altering the empirical predictions of quantum mechanics. I think however that it is a smoke screen, and the slogan "lost in math" comes to my mind. I will discuss some other recent disproofs of Bell's theorem using the language of causality based on causal graphs. Causal thinking is also central to law and justice. I will mention surprising connections to my work on serial killer nurse cases, in particular the Dutch case of Lucia de Berk and the current UK case of Lucy Letby.
Multi-source connectivity as the driver of solar wind variability in the heli...Sérgio Sacani
The ambient solar wind that flls the heliosphere originates from multiple
sources in the solar corona and is highly structured. It is often described
as high-speed, relatively homogeneous, plasma streams from coronal
holes and slow-speed, highly variable, streams whose source regions are
under debate. A key goal of ESA/NASA’s Solar Orbiter mission is to identify
solar wind sources and understand what drives the complexity seen in the
heliosphere. By combining magnetic feld modelling and spectroscopic
techniques with high-resolution observations and measurements, we show
that the solar wind variability detected in situ by Solar Orbiter in March
2022 is driven by spatio-temporal changes in the magnetic connectivity to
multiple sources in the solar atmosphere. The magnetic feld footpoints
connected to the spacecraft moved from the boundaries of a coronal hole
to one active region (12961) and then across to another region (12957). This
is refected in the in situ measurements, which show the transition from fast
to highly Alfvénic then to slow solar wind that is disrupted by the arrival of
a coronal mass ejection. Our results describe solar wind variability at 0.5 au
but are applicable to near-Earth observatories.
The increased availability of biomedical data, particularly in the public domain, offers the opportunity to better understand human health and to develop effective therapeutics for a wide range of unmet medical needs. However, data scientists remain stymied by the fact that data remain hard to find and to productively reuse because data and their metadata i) are wholly inaccessible, ii) are in non-standard or incompatible representations, iii) do not conform to community standards, and iv) have unclear or highly restricted terms and conditions that preclude legitimate reuse. These limitations require a rethink on data can be made machine and AI-ready - the key motivation behind the FAIR Guiding Principles. Concurrently, while recent efforts have explored the use of deep learning to fuse disparate data into predictive models for a wide range of biomedical applications, these models often fail even when the correct answer is already known, and fail to explain individual predictions in terms that data scientists can appreciate. These limitations suggest that new methods to produce practical artificial intelligence are still needed.
In this talk, I will discuss our work in (1) building an integrative knowledge infrastructure to prepare FAIR and "AI-ready" data and services along with (2) neurosymbolic AI methods to improve the quality of predictions and to generate plausible explanations. Attention is given to standards, platforms, and methods to wrangle knowledge into simple, but effective semantic and latent representations, and to make these available into standards-compliant and discoverable interfaces that can be used in model building, validation, and explanation. Our work, and those of others in the field, creates a baseline for building trustworthy and easy to deploy AI models in biomedicine.
Bio
Dr. Michel Dumontier is the Distinguished Professor of Data Science at Maastricht University, founder and executive director of the Institute of Data Science, and co-founder of the FAIR (Findable, Accessible, Interoperable and Reusable) data principles. His research explores socio-technological approaches for responsible discovery science, which includes collaborative multi-modal knowledge graphs, privacy-preserving distributed data mining, and AI methods for drug discovery and personalized medicine. His work is supported through the Dutch National Research Agenda, the Netherlands Organisation for Scientific Research, Horizon Europe, the European Open Science Cloud, the US National Institutes of Health, and a Marie-Curie Innovative Training Network. He is the editor-in-chief for the journal Data Science and is internationally recognized for his contributions in bioinformatics, biomedical informatics, and semantic technologies including ontologies and linked data.
Earliest Galaxies in the JADES Origins Field: Luminosity Function and Cosmic ...Sérgio Sacani
We characterize the earliest galaxy population in the JADES Origins Field (JOF), the deepest
imaging field observed with JWST. We make use of the ancillary Hubble optical images (5 filters
spanning 0.4−0.9µm) and novel JWST images with 14 filters spanning 0.8−5µm, including 7 mediumband filters, and reaching total exposure times of up to 46 hours per filter. We combine all our data
at > 2.3µm to construct an ultradeep image, reaching as deep as ≈ 31.4 AB mag in the stack and
30.3-31.0 AB mag (5σ, r = 0.1” circular aperture) in individual filters. We measure photometric
redshifts and use robust selection criteria to identify a sample of eight galaxy candidates at redshifts
z = 11.5 − 15. These objects show compact half-light radii of R1/2 ∼ 50 − 200pc, stellar masses of
M⋆ ∼ 107−108M⊙, and star-formation rates of SFR ∼ 0.1−1 M⊙ yr−1
. Our search finds no candidates
at 15 < z < 20, placing upper limits at these redshifts. We develop a forward modeling approach to
infer the properties of the evolving luminosity function without binning in redshift or luminosity that
marginalizes over the photometric redshift uncertainty of our candidate galaxies and incorporates the
impact of non-detections. We find a z = 12 luminosity function in good agreement with prior results,
and that the luminosity function normalization and UV luminosity density decline by a factor of ∼ 2.5
from z = 12 to z = 14. We discuss the possible implications of our results in the context of theoretical
models for evolution of the dark matter halo mass function.
Richard's entangled aventures in wonderlandRichard Gill
Since the loophole-free Bell experiments of 2020 and the Nobel prizes in physics of 2022, critics of Bell's work have retreated to the fortress of super-determinism. Now, super-determinism is a derogatory word - it just means "determinism". Palmer, Hance and Hossenfelder argue that quantum mechanics and determinism are not incompatible, using a sophisticated mathematical construction based on a subtle thinning of allowed states and measurements in quantum mechanics, such that what is left appears to make Bell's argument fail, without altering the empirical predictions of quantum mechanics. I think however that it is a smoke screen, and the slogan "lost in math" comes to my mind. I will discuss some other recent disproofs of Bell's theorem using the language of causality based on causal graphs. Causal thinking is also central to law and justice. I will mention surprising connections to my work on serial killer nurse cases, in particular the Dutch case of Lucia de Berk and the current UK case of Lucy Letby.
Seminar of U.V. Spectroscopy by SAMIR PANDASAMIR PANDA
Spectroscopy is a branch of science dealing the study of interaction of electromagnetic radiation with matter.
Ultraviolet-visible spectroscopy refers to absorption spectroscopy or reflect spectroscopy in the UV-VIS spectral region.
Ultraviolet-visible spectroscopy is an analytical method that can measure the amount of light received by the analyte.
Introduction:
RNA interference (RNAi) or Post-Transcriptional Gene Silencing (PTGS) is an important biological process for modulating eukaryotic gene expression.
It is highly conserved process of posttranscriptional gene silencing by which double stranded RNA (dsRNA) causes sequence-specific degradation of mRNA sequences.
dsRNA-induced gene silencing (RNAi) is reported in a wide range of eukaryotes ranging from worms, insects, mammals and plants.
This process mediates resistance to both endogenous parasitic and exogenous pathogenic nucleic acids, and regulates the expression of protein-coding genes.
What are small ncRNAs?
micro RNA (miRNA)
short interfering RNA (siRNA)
Properties of small non-coding RNA:
Involved in silencing mRNA transcripts.
Called “small” because they are usually only about 21-24 nucleotides long.
Synthesized by first cutting up longer precursor sequences (like the 61nt one that Lee discovered).
Silence an mRNA by base pairing with some sequence on the mRNA.
Discovery of siRNA?
The first small RNA:
In 1993 Rosalind Lee (Victor Ambros lab) was studying a non- coding gene in C. elegans, lin-4, that was involved in silencing of another gene, lin-14, at the appropriate time in the
development of the worm C. elegans.
Two small transcripts of lin-4 (22nt and 61nt) were found to be complementary to a sequence in the 3' UTR of lin-14.
Because lin-4 encoded no protein, she deduced that it must be these transcripts that are causing the silencing by RNA-RNA interactions.
Types of RNAi ( non coding RNA)
MiRNA
Length (23-25 nt)
Trans acting
Binds with target MRNA in mismatch
Translation inhibition
Si RNA
Length 21 nt.
Cis acting
Bind with target Mrna in perfect complementary sequence
Piwi-RNA
Length ; 25 to 36 nt.
Expressed in Germ Cells
Regulates trnasposomes activity
MECHANISM OF RNAI:
First the double-stranded RNA teams up with a protein complex named Dicer, which cuts the long RNA into short pieces.
Then another protein complex called RISC (RNA-induced silencing complex) discards one of the two RNA strands.
The RISC-docked, single-stranded RNA then pairs with the homologous mRNA and destroys it.
THE RISC COMPLEX:
RISC is large(>500kD) RNA multi- protein Binding complex which triggers MRNA degradation in response to MRNA
Unwinding of double stranded Si RNA by ATP independent Helicase
Active component of RISC is Ago proteins( ENDONUCLEASE) which cleave target MRNA.
DICER: endonuclease (RNase Family III)
Argonaute: Central Component of the RNA-Induced Silencing Complex (RISC)
One strand of the dsRNA produced by Dicer is retained in the RISC complex in association with Argonaute
ARGONAUTE PROTEIN :
1.PAZ(PIWI/Argonaute/ Zwille)- Recognition of target MRNA
2.PIWI (p-element induced wimpy Testis)- breaks Phosphodiester bond of mRNA.)RNAse H activity.
MiRNA:
The Double-stranded RNAs are naturally produced in eukaryotic cells during development, and they have a key role in regulating gene expression .
Therapeutic Cancer Vaccines: Where Predictive Models Matter
1. Therapeutic Cancer Vaccines: Where
Predictive Models Matter
Tim O’Donnell
NEC Laboratories Europe GmbH
Oct 15, 2021
1
2. The OpenVax Project
● Clinical Trials: help run personalized cancer vaccine trials at
Mount Sinai
● Software: open source tools for cancer genomics and
neoantigen prediction
○ www.github.com/openvax/
● Research: improve methods for predicting the immune
response to tumor antigens
2
Tim O’Donnell
Mount Sinai
Julia Kodysh
Mount Sinai
Alex Rubinsteyn
UNC Chapel Hill
Nina Bhardwaj
Mount Sinai
9. Short history of cancer immunotherapy
9
Alex Rubinsteyn
20th century
Dark Age of
radiation and
chemotherapy
1850s-1890s
Infection & fever =>
tumor regression?
1893
Coley’s Toxins
(complete response in
~10% of sarcomas)
2010s
~20 approved cancer
immunotherapies
10. Cancer immunotherapy
10
Alex Rubinsteyn
Checkpoint blockade Cellular therapies Vaccines
Disinhibit T-cells.
Antigens responsible for tumor
clearance typically unknown.
Success stories:
● 𝛂CTLA-4 (ipi)
● 𝛂PD-1 (pembro, nivo, cemi)
● 𝛂PD-L1 (atezo, ave, durva)
Ex-vivo expansion of patient T-cells
after receptor engineering and/or
selection.
Success stories:
● CAR T-cells for B-cell
malignancies (CD19, CD20,
CD22, BCMA)
Therapeutic vaccines against
specific tumor antigens, including
patient-specific mutated tumor
antigens.
Success stories:
● ???
● Hints of efficacy in neoantigen
vaccine trials
11. Anti-PD1 vs. chemotherapy in metastatic melanoma
11
Robert et al. Nivolumab in Previously Untreated Melanoma without BRAF Mutation. NEJM 2014
12. Combination checkpoint blockade in melanoma
12
Wolchok et al. Overall Survival with Combined Nivolumab and Ipilimumab in Advanced Melanoma. NEJM 2017
13. Combination checkpoint blockade in melanoma
13
Wolchok et al. Overall Survival with Combined Nivolumab and Ipilimumab in Advanced Melanoma. NEJM 2017
Additional
therapies needed
14. Cancer vaccines
● Elicit a new T cell response against tumor
antigens
● May also reinvigorate pre-existing
exhausted T cells
● Once tumor killing gets going, more T cell
can come along
14
Hu Z, Ott P, Wu C. Nature Reviews Immunology 2017
16. First generation shared antigen vaccines unsuccessful
16
Rosenberg et al. Cancer immunotherapy: moving beyond current vaccines. Nature Medicine 2004
30. MHC binding restricts the space of possible epitopes
30
Only about 5% of peptides bind to MHC
strongly enough to be presented.
31. Peptide binding to MHC can be measured in vitro
31
The binding preferences for hundreds of MHC alleles have been characterized using in vitro affinity
measurements
32. Mass spec is another source of MHC binding data
32
Purcell et al. Nature Protocols 2019
40. Can we do better?
Larger training datasets enable more sophisticated models
1. MHC binding prediction
2. Antigen processing prediction
40
Measurements
42. Motivation for peptide encoding
Binding predictor input encoding
42
O’Donnell et al. Cell Systems 2020
HLA-A*02:01
binding the 15-mer
peptide
FLNKDLEVDGHFVT
M
HLA-A*02:01
binding the 9-mer
peptide LLFGYPVYV
43. Binding predictor architecture
43
O’Donnell et al. Cell Systems 2020
Tricks
● Training loss: MSE with inequalities
● Pretrain on synthetic measurements from allele-
specific predictor (99 alleles)
● Random negatives to equalize number of non-
binder points for each peptide length per allele
● Early stopping
● Dropout after each dense layer (50%)
● Skip connections
● L1 regularization on dense layers
● Ensembles Training loss: MSE with inequalities
Mass spec hits are assigned “< 100nM”
49. Training set generation for AP predictor
49
Consider only peptides
predicted to be top 2% in
binding affinity
Model learns to predict which
ones are actually detected in
MS experiments
50. AP predictor convolutional neural network
50
Intuition: MHC I ligands
must be cleaved at their
termini but not at interior
residues
55. Antigen processing motif
55
Known bias:
Prefers C-terminal Y, F, L, R
Disfavors C-terminal D, E, N, S
Enriched
Depleted
Uebel et al PNAS 1997
TAP
56. Antigen processing motif
56
Cleaves after
● Chymotryptic: F, Y, L, W, but not G
● Tryptic: R, K
● Caspase: D, E
Enriched
Depleted
Nussbaum et al. 1998; Harris et al. 2001
Proteasome
57. Antigen processing motif
57
Known bias:
Unable to cleave the X-Proline bond. Can
trim until there is a P at the second
position
Enriched
Depleted
Serwold et al. Nature 2002
ERAP
62. Neoantigen prediction
62
Robbins et al. Nature Medicine 2013, Tran et al. Science 2015, Gros et al. Nature Medicine 2016, Koşaloğlu-Yalçın et al. Oncoimmunology 2018
Steve Rosenberg group (NCI)
● 18 patients with melanoma or
gastrointestinal cancers
● 2,841 mutations screened
● 52 identified CD8+ T cell epitopes
Unbiased: mutations screened without
use of MHC binding prediction
65. Evaluation on viral epitopes
● Evaluation of CD8+ T cell epitopes
deposited in IEDB
● 1,380 epitopes + 527 non-epitopes
● Non-epitopes derive from the same proteins
as the epitopes and were assayed in the
same studies
● Surprisingly, MHCflurry 2.0 BA outperforms
MHCflurry 2.0 PS
● Suggests that to some extent the learned
antigen processing signals may be specific to
self proteins
65
66. Conclusions
● Larger training datasets and better modeling have enabled more accurate prediction of peptides
presented on MHC class I
● Antigen processing can be learned from MHC-presented peptides identified by mass spec. The
resulting predictors show agreement with the known biases of key processing steps
● Integration of antigen processing prediction with MHC binding can improve prediction of MHC-
presented peptides and tumor neoantigens
● Still significant room for improvement in CD8+ T cell epitope prediction
66
68. Sources of tumor T cell antigens
● Viral antigens
● Highly expressed genes in tumor cells (TAAs)
● Bacterial antigens
● Cancer-cell specific aberrations in...
○ The genome (mutation derived neoantigens)
○ Regulation of transcription (cancer testis antigens, endogenous retroviruses)
○ Splicing (intron retention, exon skipping)
○ RNA editing and RNA modifications
○ Translation (W-bumps)
○ Post translational modifications (phosphopeptides)
○ Antigen processing (?)
○ Metabolism (?)
○ … sensitivity to drugs that impact any of the above
68
69. Sources of tumor T cell antigens
● Viral antigens
● Highly expressed genes in tumor cells (TAAs)
● Bacterial antigens
● Cancer-cell specific aberrations in...
○ The genome (mutation derived neoantigens)
○ Regulation of transcription (cancer testis antigens, endogenous retroviruses)
○ Splicing (intron retention, exon skipping)
○ RNA editing and RNA modifications
○ Translation (W-bumps)
○ Post translational modifications (phosphopeptides)
○ Antigen processing (?)
○ Metabolism (?)
○ … sensitivity to drugs that impact any of the above
69
Predictors
needed
70. Emerging data identifies new candidate antigens
70
Reference Antigens Sequencing Mass spec
Griffin, …, Bernstein Nature 2021 Transposable elements (esp. LTR)
de-repressed by SETDB1 KO
RNA-seq, ATAC-seq MHC I MS, whole cell
lysate MS
Cuevas, …, Yewdell Cell Reports
2021
Novel isoforms, lncRNAs,
frameshifts
RNA-seq, ribo-seq MHC I MS, whole cell
lysate MS
Ouspenskaia, ... , Regev Biorxiv
2020
lncRNAs, pseudogenes, UTRs RNA-seq, ribo-seq MHC I MS
Chong, …, Bassani-Sternberg
Nature Communications 2020
lncRNAs, pseudogenes, UTRs,
TEs
WES, RNA-seq, sc-
RNA-seq, ribo-seq
MHC I and II MS
Laumont, …, Perreault Science
Translational Medicine 2018
lncRNAs, endogenous
retroelements
RNA-seq MHC I MS
71. Personalized tumor antigen detection from RNA-seq
Given tumor RNA-seq, identify candidate tumor specific antigens
71
Patient
RNA-seq
Database of tumor-
specific translation
products
Predicted
translated
peptides
Search
Vaccine prioritization
(MHC binding prediction,
expression levels)
Vaccine
72. Personalized tumor antigen detection from RNA-seq
Given tumor RNA-seq, identify candidate tumor specific antigens
72
Patient
RNA-seq
Database of tumor-
specific translation
products
Predicted
translated
peptides
Search
Vaccine prioritization
(MHC binding prediction,
expression levels)
Vaccine
Building the database is the main effort here
Experimenting with: logistic regression on RNA-seq k-mers
for each possible antigen to predict translation
73. Perspectives
● MHC binding prediction is reasonably well solved, T cell epitope prediction is not
● Immune monitoring from cancer vaccine studies will be useful to improve T cell epitope prediction
● Emerging high throughput readouts of TCR/pMHC interaction will eventually enable models of
TCR/pMHC binding
● For peptide vaccines, peptide pharmacokinetics likely has a huge impact on immunogenicity
● Other vaccine platforms such as mRNA will likely outperform peptide vaccines
● Casting a wider net for additional kinds of tumor antigens is likely to enable a new generation of
cancer vaccines - including semi-personalized vaccines
73
74. Thank you!
74
Nina Bhardwaj (Mount Sinai)
Alex Rubinsteyn (UNC Chapel Hill)
Julia Kodysh (Mount Sinai)
https://github.com/openvax/mhcflurry