Abstract: Disclosed are nucleotides, cells, and methods associated with the compositions including their use as vaccines, including vectors and methods for a homologous prime/boost vaccination strategy, such as adenoviral vectors for use in a homologous prime/boost vaccination strategy.

Abstract: Disclosed herein are compositions that include antigen-encoding nucleic acid sequences and/or antigen peptides. Also disclosed are nucleotides, cells, and methods associated with the compositions including their use as vaccines, including vectors and methods for a heterologous prime/boost vaccination strategy.

Abstract: Methods and compositions for monitoring mutation burden, cancer status, vaccine efficacy using cell-free DNA sequencing are disclosed.

Abstract: Provided herein are target HLA-PEPTIDE antigens, e.g., HLA-PEPTIDE neoantigens and shared tumor HLA-PEPTIDE antigens, and antigen binding proteins (ABPs) that bind the target HLA-PEPTIDE antigens. Also disclosed are methods for identifying target HLA-PEPTIDE antigens as well as identifying one or more antigen binding proteins that bind a given HLA-PEPTIDE target antigen.

Abstract: Disclosed herein are compositions that include antigen-encoding nucleic acid sequences and/or antigen peptides. Also disclosed are nucleotides, cells, and methods associated with the compositions including their use as vaccines, including vectors and methods for a heterologous prime/boost vaccincation strategy.

Abstract: A method for identifying T-cells that are antigen-specific for at least one neoantigen that is likely to be presented by class II MHC alleles on surfaces of tumor cells of a subject. Peptide sequences of tumor neoantigens are obtained by sequencing the tumor cells of the subject. The peptide sequences are input into a machine-learned presentation model to generate presentation likelihoods for the tumor neoantigens, each presentation likelihood representing the likelihood that a neoantigen is presented by the class II MHC alleles on the surfaces of the tumor cells of the subject. A subset of the neoantigens is selected based on the presentation likelihoods. T-cells that are antigen-specific for at least one of the neoantigens in the subset are identified. These T-cells can be expanded for use in T-cell therapy. TCRs of these identified T-cells can also be sequenced and cloned into new T-cells for use in T-cell therapy.

Abstract: Methods are provided to separately isolate antigen-binding T cells and antigen-activated T cells derived from a starting population of peripheral blood mononuclear cells, and to identify overlapping T cell receptor clonotypes. Antigens include personal and shared neoantigens as well as cancer-testis antigens. The T cell receptor clonotypes can be further used to develop cancer treatment therapies.

Abstract: A method for identifying T-cells that are antigen-specific for at least one neoantigen that is likely to be presented on surfaces of tumor cells of a subject. Peptide sequences of tumor neoantigens are obtained by sequencing the tumor cells of the subject. The peptide sequences are input into a machine-learned presentation model to generate presentation likelihoods for the tumor neoantigens, each presentation likelihood representing the likelihood that a neoantigen is presented by an MHC allele on the surfaces of the tumor cells of the subject. A subset of the neoantigens is selected based on the presentation likelihoods. T-cells that are antigen-specific for at least one of the neoantigens in the subset are identified. These T-cells can be expanded for use in T-cell therapy. TCRs of these identified T-cells can also be sequenced and cloned into new T-cells for use in T-cell therapy.