IMMUNOSELECT-R™ IDENTIFY AND PRIORITIZE CANDIDATE

IMMUNOSELECT-R™
IDENTIFY AND PRIORITIZE CANDIDATE NEOANTIGENS FROM
CANCER EXOME SEQUENCING RESULTS WITH UNMATCHED ACCURACY
WHAT IS IMMUNOSELECT -R™?
Neoantigens are a class of immunogens based on the personal,
exquisitely tumor-specific mutations found uniquely in each
patient’s tumor. Combining PGDx’s highly accurate cancer exome
analyses (CancerXome™ ) with in silico neoantigen prediction,
ImmunoSelect-R™ identifies and prioritizes the most relevant
mutation-derived neoantigens to enable adoptive T-cell transfer,
cancer vaccine development, and prediction of clinical utility of
checkpoint inhibitors.
CANCER-IMMUNITY CYCLE
4
Priming and activation
(APCs & T cells)
3
CHECKPOINT INHIBITOR
(e.g. ANTI-CTLA-4)
blood
vessel
lymph
node
5
Infiltration of T cells
into tumors (CTLs,
endothelial cells)
6
Recognition of cancer
cells by T cells
(CTLs, cancer cells)
NEOANTIGEN ID
Cancer antigen
presentation
(dendritic cells/APCs)
IMMUNOSELECT-R™ DELIVERABLES
Trafficking of T cells
to tumors (CTLs)
•Unparalleled cancer exome sequencing accuracy to ensure
identification of true somatic mutations
— 95% sensitivity and 97% PPV down to 10% mutant allele
frequency at 150x coverage
• Accurate HLA typing using whole exome sequencing
•Prediction and prioritization of the most relevant neoantigens
from exome-based mutations and novel open-reading-frames
2
TUMOR
ADOPTIVE T CELL
THERAPIES
VACCINES
Release of cancer cell antigens
(cancer cell death)
7
1
Killing of cancer cells
(Immune and cancer cells)
CHECKPOINT INHIBITOR
(e.g. ANTI-PD-1)
SOURCE: “Oncology meets immunology: the cancer-immunity cycle”,
Immunity, 25 July 2013
IMMUNOSELECT-R™ PIPELINE
WT/Somatic Peptides (Mutation, neoORF)
HLA-information
1_BRAF ISQFWYDSY [F/T] SVPAFWQDY
1_HAUS3 SERFWVEYYS [CFTF/HSVP] WFSYRV
Generate Fasta of WT/Som
Peptide Pairs (8-11aa)
Expression
(mRNA or Protein)
Similarity to Known Ag
MHC Binding Affinity
* .fastq
HLA-A01: 01
HLA-A26: 01
In Silico
HLA-typing
HLA-A01: 01
HLA-A26: 01
Mutant Allele
Frequency
Self-Similarity
Antigen Processing
Candidate Neo-Ag (MHC-peptide IC50< 500nM)
Prioritized Neo-Ag for Experimental Follow-up
EXAMPLE REPORT OUTPUT
Patient ID
HLA Type
Gene Name
Peptide ID
MUT Peptide
MUT MHC Affinity
MUT CTL Class
Mean Exp in Tumors
Priority
Patient 1
HLA-A*02:01
GAS7
1_p09490_10
SLADEAEVYL
14.4
E
236
High
Patient 1
HLA-B*44:03
CSNK1A1
59_p06000_8
GLFGDIYL
5.72
E
4633
High
Patient 1
HLA-A*24:02
KIAA031
124_p11981_11
KLLQQLNGWYM
25.3
E
2063
High
Patient 1
HLA-A*02:01
RRP1B
197_p19240_11
FLPKPLFFFRA
16.63
E
1111
High
PERSONAL GENOME DIAGNOSTICS | 2809 BOSTON STREET, SUITE 503 | BALTIMORE, MARYLAND 21224 | TEL (888) 571-2163
IMMUNOSELECT-R™ IS FOR RESEARCH USE ONLY, NOT FOR DIAGNOSTIC PURPOSES.
PERSONAL GENOME DIAGNOSTICS
IMMUNOSELECT-R™ OVERVIEW
ONLY CANCERXOME ™ DELIVERS THE SENSITIVITY AND SPECIFICITY REQUIRED TO PREVENT
FALSE POSITIVE MUTATIONS FROM CONFOUNDING NEOANTIGEN IDENTIFICATION
NUMBER OF DNA BASE PAIRS VALIDATED WITH AN
INDEPENDENT METHOD USED FOR PIPELINE OPTIMIZATION
Using 100 to 1,000 times more independently validated data points to optimize the
bioinformatics pipeline ensures unmatched accuracy of PGDx exome sequencing.
IMPACT OF INCREASED SPECIFICITY ON EXOME SEQUENCING PPV
Pipelines that have not been validated against whole exome Sanger sequencing
using matched normal controls will be less than 99.99999% specific.
97%
100%
SMALL
COMMERCIAL
LABS
HOSPITAL
LABS
<1 Million
Mass spectrometry
100,000
Sanger sequencing of hot spots in key genes
<1,000
Targeted arrays
80%
PGDx CancerXome™ can reduce
false positive somatic mutation
calls by 50% to 90% over
competitor exome tests.
74%
PGDx PERFORMANCE
FOUNDATION
MEDICINE
90%
Positive Predictive Value
(Polymorphism is a True Somatic Variant)
PGDx
Hundreds of millions of DNA base pairs using
whole exome Sanger sequencing
70%
60%
50%
COMPETITOR
PERFORMANCE
40%
30%
22%
20%
3%
10%
0.3%
0%
99.9%
IMMUNOSELECT-R™ VALIDATION
99.99%
99.999%
99.9999%
99.99999%
IMMUNOSELECT-R™ CONSISTENTLY RANKED EXPERIMENTALLY VALIDATED NEOANTIGENS WITHIN
TOP 20% OF ALL NEOANTIGEN CANDIDATES DERIVED FROM WHOLE EXOME SEQUENCING
•Identified 18 out of 19 experimentally
validated neoantigens as being strong
neoantigen candidates, suggesting an
assay sensitivity of greater than 90%
Sample
#Mutations
#Neo-Ag
(IC50<500nM)
#Neo-Ag Post
Prioritization
Rank of Validated
Neo-Ag
Patient 1
504
128
55
1, 14, 15, 16
SOURCE OF DATA:
cancerimmunity.org/peptide/mutations/
Patient 2
257
277
30
1, 2, 3, 4, 15, 16
Patient 3
58
97
30
9, 10, 14, 15, 16
•Reproduced the tetra-peptide
signature predictive of clinical benefit
of CTLA-4 blockade in melanoma
SOURCE OF DATA: “Genetic Basis for Clinical
Response to CTLA-4 Blockade in Melanoma” New
England Journal of Medicine, 04 December 2014
SOURCE OF DATA: “Mining exomic sequencing data to identify mutated antigens recognized by adoptively
transferred tumor-reactive T cells” Nature Medicine, 05 May 2013
IMMUNOSELECT-R™ KEY PERFORMANCE ATTRIBUTES
•Unparalleled cancer exome sequencing accuracy to
ensure research is focused on true somatic mutations for
neoantigen prediction
— 95% sensitivity and 97% positive predictive value down to
10% mutant allele frequency at 150x coverage
• Works on FFPE or frozen tissue
•Matched patient normal (germline DNA) is required
for optimal results
•Accurate inference of HLA typing from
whole-exome sequencing
•Neoantigen analysis can be offered alone or in combination
with CancerXome™ and completed within 48 hours of tumor
mutation analysis completion
•Effective in silico pipeline to discover and prioritize candidate
neoantigens by incorporating the following:
—Comprehensive Identification of tumor-specific
mutated peptides and neo-ORF
—State-of-the-art prediction of HLA-types,
antigen processing and MHC-peptide binding
—Proprietary strategy to select most-relevant candidate
neoantigens for experimental validation
IMMUNOSELECT-R™ IS FOR RESEARCH USE ONLY, NOT FOR DIAGNOSTIC PURPOSES.
INNOVATIVE GENOMIC TECHNOLOGIES TO SOLVE FUNDAMENTAL CHALLENGES IN ONCOLOGY
LEARN MORE AT PERSONALGENOME.COM OR CALL FOR A QUOTE 443.602.8833