Download Genome-Scale Neoantigen Screening Using ATLAS™ Prioritizes

Poster 374
GENOME-SCALE NEOANTIGEN SCREENING USING ATLAS PRIORITIZES
CANDIDATE ANTIGENS FOR IMMUNOTHERAPY IN A NON-SMALL CELL LUNG CANCER PATIENT
Lila Ghamsari1, Emilio Flano1, Judy Jacques1, Biao Liu1, Jonathan J. Havel2, Vladimir Makarov2, Taha Merghoub2,
Jedd D. Wolchok2, Matthew D. Hellman2, Timothy A. Chan2, and Jessica B. Flechtner1
1Genocea
Biosciences, 100 Acorn Park Drive, Cambridge, MA 02140; 2Memorial Sloan Kettering Cancer center, 1275 York Avenue, New York, NY 10065
Background
Tumoricidal potential of T cells have been demonstrated using infusion of ex vivo expanded tumor-infiltrating lymphocytes and most recently using trials of T cell checkpoint molecule blocking antibodies. These advances in tumor
immunotherapy has shown that the functional targets of these therapies are tumor-specific, mutation-derived novel protein sequences or neoantigens. Despite substantial promise, most treated patients fail to respond to checkpoint
therapies, therefore, application of neoantigens may be important for the development of next-generation of cancer immunotherapies. Current approaches to rank patient-specific neoantigens, as therapies or therapeutic targets,
rely on in silico epitope predication algorithms or proteomics. These methods fail to provide functional evidence of T cell reactivity and specificity. We used our proprietary ATLASTM platform to screen T cell responses to the landscape
of detected neoantigens in a non-small cell lung cancer (NSCLC) patient successfully treated with anti PD-L1 therapy pembrolizumab.
Construction of Personalized Neoantigen Expression Library
Shine-Dalgarno 6 x His
T7 Promoter
attR1
ATLASTM Expression construct
Neoantigen
SIINFEKL T7 terminator
attR2
Ampicillin
Concentration (pg/μL)
Expression level (% of Positive control)
 Whole exome sequences were
obtained from patient DNA
isolated from PBMCs and tumor
biopsy samples, and led to the
identification of 201 tumorspecific somatic mutations1.
120.00
Personalized ATLASTM Identifies Neoantigen Inducers of
CD8+ T Cells Pre- and Post-pembrolizumab Therapy
6204
1
Type of mutations in the library
100.00
198 Single missense mutation
2 Multiple missense mutations
2 Loss of Stop codon
60.00
40.00
20.00
 Individual DNA sequences spanning each mutation site (coding 133 amino acids) were
synthesized and cloned into the ATLAS™ bacterial expression construct.
 Each neoantigen was co-expressed with listeriolysin O to facilitate MHC class I presentation2.
0.00
Neoantigen clones ordered by expression level
TM
ATLAS :
A Platform to Rank Patient-Specific Neoantigens
Concentration (pg/μL)
Personalized
 The expression level of each neoantigen was detected using a surrogate T cell assay
that identifies C-terminal fusion tag SIINFEKL (OVA257-264 class I epitope).
Concentration (pg/μL)
80.00
Neoantigen
 Neoantigens were identified as clones that induced a cytokine response that exceeded three
standard deviations from the mean of the negative control wells (represented by the yellow bars).
 One neoantigen was identified as T cell target using IFN-γ both pre- and post-therapy (Un075) and
two neoantigens were identified by both IFN-γ and TNF-α cytokines (Un131 and Un198, pre- and
post-therapy, respectively).
 Validation of the positive hits and screening of the rest of the library is in progress.
 We identified twelve neoantigens associated with downregulation of IFN-γ and TNF-α.
The possible role of these neoantigens in modulating T cell responses is under study.
 Peripheral blood mononuclear cells (PBMC) that were collected pre- and post-pembrolizumab therapy were thawed. CD14+
monocytes were enriched and derived into dendritic cells (APC) and CD8+ T cells were sorted and expanded non-specifically
using microbeads.
 E. coli expressing individual neoantigens were pre-arrayed in 384 well plates. The arrayed library was added to the
screening plates containing APCs, then incubated with expanded CD8+ T cells overnight.
 The supernatant from each individual well was collected and the levels of IFN-γ and TNF-α cytokines were measured using
Meso Scale Discovery (MSD).
 E. coli expressing Neon Green were used as negative control. Ten percent of the wells of the screening plate contained
negative controls to define true T cell responses.
 Only 103 neoantigens in the library were assayed due to the limitations in the yield of dendritic cells. Further work is
in progress to screen the rest of the expressed clones in the library.
ATLAS Versus Prediction Algorithms
1
5
3
1
3
 MHC class I epitopes were predicted for all screened neoantigens
using three commonly used algorithms: NetMHC, NetCTLpan and
IEDB, and using patient-specific haplotypes HLA-A*02:01/*32:01,
HLA-B*40:01:02/*45:01:01, HLA-C*06:02/*03:041.
 All of the unique epitopes identified using these algorithms carry
the tumor somatic mutation.
 Using the defined cutoffs for both ATLASTM and the prediction tools,
we did not obtain any biological evidence on immunogenic
potential of the majority of candidates predicted by the algorithms.
Conclusions
References
1. Rizvi, et al. Mutational landscape determines sensitivity to PD-1 blockade in non–small cell lung cancer (2015) Science. 348(6230): 124-8.
2. Hu, et al. Escherichia coli expressing recombinant antigen and listeriolysin O stimulate class I-restricted CD8+ T cells following uptake by
human APC (2004) J Immunol 172(3): 1595-601.
Acknowledgments
The authors wish to thank the patient and their family who consented to participate in this study.
 We provide data that personalized neoantigens can be comprehensively identified using functional
evidence of T cell responses through ATLASTM.
 The potential unique neoantigens identified using ATLASTM that were not identified in silico, and
the disagreement between prediction tools, reinforces the need to develop biological assays to
identify tumor neoantigens and to improve prediction algorithms.
 These data offer the opportunity to develop next-generation personalized T cell immunotherapies
with biologically evidenced neoantigens to improve outcomes for patients for whom current
therapies are ineffective.