The present disclosure concerns genetically modified host cells that express an immunoproteasome in the absence of induction by or contact with a cytokine. The genetically modified stem cells are useful, for example, for vaccine production, and identification of new target antigens.

Systems and methods are presented that allow for selection of tumor neoepitopes that are filtered for various criteria. In particularly contemplated aspects, filtering includes a step in which the mutation leading to the neoepitope is ascertained as being located in a cancer driver gene.

The present invention provides for single-molecule profiling of combinatorial protein modifications and single-molecule profiling of combinatorial protein modifications combined with single-molecule sequencing of protein/nucleic acids complexes. High-throughput single-molecule imaging was applied to decode combinatorial modifications on millions of individual nucleosomes from pluripotent stem cells and lineage-committed cells. Applicants identified bivalent nucleosomes with concomitant repressive and activating marks, as well as other combinatorial modification states whose prevalence varies with developmental potency. Applying genetic and chemical perturbations of chromatin enzymes show a preferential affect on nucleosomes harboring specific modification states. The present invention also combines this proteomic platform with single-molecule DNA sequencing technology to simultaneously determine the modification states and genomic positions of individual nucleosomes. This novel single-molecule technology can be used to address fundamental questions in chromatin biology and epigenetic regulation leading to novel therapeutics and diagnostics.

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.

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.

The present disclosure concerns methods of treating cancer with neoantigenic peptides such that durable clinical benefits are obtained, and compositions and methods for determining whether DCB can be predicted or assessed for a patient to be treated with a therapeutic comprising neoantigen.

The invention concerns a device for tumour antigen identification and a method for tumour antigen identification; a tumour antigen identified following use of said device and/or method; a pharmaceutical composition comprising said tumour antigen; a method of treating cancer using said device and/or said method; a method of stratifying patients for cancer treatment using said device and/or said method; a treatment regimen involving stratifying patients for cancer treatment using said device and/or method and then administering a cancer therapeutic; and a tumour antigen identified using said device and/or said method for use as a cancer vaccine or immunogenic agent or cancer therapy.

A method for identifying neoantigens that are likely to be presented on a surface of tumor cells of a subject. Peptide sequences of tumor neoantigens are obtained by sequencing the tumor cells of the subject. The peptide sequence of each of the neoantigens is associated with one or more k-mer blocks of a plurality of k-mer blocks of the nucleotide sequencing data of the subject; The peptide sequences and the associated k-mer blocks 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.

The invention provides improved strategies, prognostic indicators, compositions, and methods for producing personalized neoplasia vaccines. More particularly, embodiments of the present disclosure relate to the identification of neoplasia-specific neo-epitopes to predict survival and to identify and design subject-specific neo-epitopes, further assessing the identified neo-epitopes encoded by said mutations to identify neo-epitopes that are known or determined, or predicted to engage regulatory T cells and/or other detrimental T cells (including T cells with potential host cross-reactivity and/or anergic T cells), and excluding such neo-epitopes that are known, determined, or predicted) to engage regulatory T cells and/or other detrimental T cells (including T cells with potential host cross-reactivity and/or anergic T cells) from the subject-specific neo-epitopes that are to be used in personalized neoplasia vaccines. The present disclosure further relates to a novel ranking system for determining the optimal subject-specific neo-epitopes that are to be used in personalized neoplasia vaccines.

The present invention relates to cancer immunotherapy, in particular to sequence variants of tumor-related antigenic epitope sequences. Namely, the present invention provides a method for identification of microbiota sequence variants of tumor-related antigenic epitope sequences. Such microbiota sequence variants are useful for the preparation of anticancer medicaments, since they differ from self-antigens and, thus, they may elicit a strong immune response. Accordingly, medicaments comprising microbiota sequence variants, methods of preparing such medicaments and uses of such medicaments are provided.