On April 18, 2018 Genocea Biosciences, Inc. (NASDAQ:GNCA), a biopharmaceutical company developing neoantigen cancer vaccines, reported highlights from its scientific presentations at the 2018 Annual Meeting of the American Association for Cancer Research (AACR) (Free AACR Whitepaper) (AACR 2018), taking place April 14-18, 2018 in Chicago, IL (Press release, Genocea Biosciences, APR 18, 2018, View Source [SID1234525509]).

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Jessica Flechtner, Ph.D., Genocea’s chief scientific officer commented on the AACR (Free AACR Whitepaper) presentations: "We continue to generate data that demonstrate the versatility of our ATLAS platform. As the studies presented at AACR (Free AACR Whitepaper) indicate, ATLAS is a differentiator for Genocea – allowing us to do what in silico approaches cannot – to both identify and characterize neoantigens for use in personalized cancer vaccines. We believe that our ability to find stimulatory and inhibitory antigens during the neoantigen selection process combined with our capacity to explore mechanisms of inhibitory antigens in a murine model, may enable us to help cure cancer by pioneering next-generation cancer vaccines."

Summary of AACR (Free AACR Whitepaper) Poster #730, "Empirical neoantigen identification using the ATLAS platform across thousands of mutations and multiple tumor types highlights advantages over algorithmic prediction methods":

ATLAS enables identification of biologically relevant CD4+ and CD8+ T cell neoantigens in subjects in an unbiased manner, by using subjects’ own antigen-presenting cells (APCs) and T cells rather than predictive algorithms to identify and characterize T cell responses to all candidate neoantigens.
Neoantigen screening was performed on 23 individuals across eight tumor types with mutational burden ranging from 9 to 319 unique mutations.
Empiric identification of neoantigens derived from somatic mutations from each patient’s tumor independently of HLA type and without predictions resulted in the following observations:
ATLAS identified stimulatory neoantigens of both CD4+ and CD8+ T cells, which Genocea believes confirms the importance of including antigens of relevance for both T cell subsets in neoantigen vaccines;
There is little overlap between CD4+ and CD8+ T cell neoantigens; fewer than 2% of empirically confirmed neoantigens were shared between T cell subsets;
Prediction algorithms missed up to 69% of ATLAS-identified neoantigens, with only 2% of CD8+ neoantigens and 24% of CD4+ neoantigens accurately predicted;
The major histocompatibility complex (MHC) class I algorithm appeared to better predict CD4+, not CD8+, neoantigens;
ATLAS also identified inhibitory neoantigens of both CD4+ and CD8+ T cells
Inhibitory neoantigens outnumbered stimulatory neoantigens more than three-fold in aggregate in the screened patients;
Inhibitory antigens currently cannot be identified using in silico approaches.
Summary of Poster #5718, "ex vivo ATLASTM identification of neoantigens for personalized cancer immunotherapy in mouse melanoma":

The B16F10 mouse melanoma model was utilized to characterize neoantigens. More than 1,600 tumor-specific mutations (possible neoantigens) were interrogated using the ATLAS technology and CD8+ T cells from tumor-bearing C57BL/6 mice.
Similar to human neoantigen screens, mouse ATLAS (mATLAS) identified both stimulatory and inhibitory neoantigens:
99% of mutations identified using whole exome sequencing were screened;
68 stimulatory (4% of total mutations) and 57 inhibitory (3% of total mutations) neoantigens were identified.
NetMHCPan, a MHC-binding prediction algorithm, failed to identify the majority of mATLAS-identified neoantigens:
Only 2% of B16F10 neoantigens predicted by algorithms were empirically confirmed to be stimulatory antigens;
91% of stimulatory neoantigens empirically identified with mATLAS were not predicted;
6% of algorithm-predicted neoantigens were inhibitory.
These data demonstrate that inhibitory antigens can be identified in mouse models, allowing for future research into the mechanism of ATLAS-identified inhibitory responses and their relationship to stimulatory neoantigens in mediating tumor control.