NantCell, Inc.

Systems and methods for tracking biological samples are presented. Intrinsic and extrinsic biological sample data and/or intrinsic and extrinsic patient data may be used to label a biological sample obtained at a point-of-care facility, which may be sent to a processing facility, wherein the labeled biological sample undergoes analysis and/or processing into a labeled therapeutic. The labeled therapeutic is delivered back to the point-of-care facility, wherein the labeled therapeutic is tested for the presence of the additives prior to administration to a patient. Multi-factor validation is performed, including validating the identity of the patient to a container, validating the identity of the labeled biological sample/therapeutic to the container, and thus, validating the identity of the labeled therapeutic to the patient. The entire life cycle of a biological sample used to generate a personalized therapeutic is validated, and ensures that mix-ups or errors in administration are reduced or eliminated.

Cancer is treated using coordinated treatment regimens that uses various compounds and compositions that drive a tumor from the escape phase of cancer immunoediting to the elimination and equilibrium phase of cancer immunoediting.

Multimodal cancer immunotherapy is a combination of cancer immunotherapies used to treat cancer in patients. T cell receptor diversity is used as a component of a method to treat cancer involving cancer therapy, including multimodal cancer immunotherapy.

Contemplated cancer therapies comprise co-administration of aldoxorubicin with an immune therapeutic composition that preferably comprises a vaccine component and a cytotoxic cell component.

Systems and methods are presented that provide for improved NK cell function. In preferred aspects, NK-92 cells express recombinant er/LSP-IL-15 to so render the NK-92 cells independent of exogenous cytokines and to provide extracellular immune stimulation.

An IL-15 super agonist (IL-15N72D:IL-15RαSU/IgG1Fc; N-803) increases circulating NK cells, effector memory and effector memory RA cells in post-allogeneic hematopoietic stem cell transplant patients (HCT). Methods of treatment include administration of N-803 to subjects in need of such treatment.

Techniques are provided for determining pan-HLA binding of viral proteins. A trained classifier model is operable to determine, independently per HLA, at least one of (a) an average binding prediction of overlapping peptides at each position of a viral protein, (b) a maximum value of a binding prediction of overlapping peptides at each position of the viral protein, (c) standard deviation of a binding prediction of overlapping peptides at each position of the viral protein, and (d) a combination of one or more of (a)-(c). A classification engine uses the classifier model to determine average binding predictions of overlapping peptides at each position of the viral protein independently for test HLA-I and HLA-II functional groupings, where a peptide is classified as a binder when an average binding prediction corresponding to the peptide satisfies a binding value threshold.

Systems and methods for tracking biological samples are presented. Intrinsic and extrinsic biological sample data and/or intrinsic and extrinsic patient data may be used to label a biological sample obtained at a point-of-care facility, which may be sent to a processing facility, wherein the labeled biological sample undergoes analysis and/or processing into a labeled therapeutic. The labeled therapeutic is delivered back to the point-of-care facility, wherein the labeled therapeutic is tested for the presence of the additives prior to administration to a patient. Multi-factor validation is performed, including validating the identity of the patient to a container, validating the identity of the labeled biological sample/therapeutic to the container, and thus, validating the identity of the labeled therapeutic to the patient. The entire life cycle of a biological sample used to generate a personalized therapeutic is validated, and ensures that mix-ups or errors in administration are reduced or eliminated.

Techniques are provided for determining pan-HLA binding of viral proteins. A trained classifier model is operable to determine, independently per HLA, at least one of (a) an average binding prediction of overlapping peptides at each position of a viral protein, (b) a maximum value of a binding prediction of overlapping peptides at each position of the viral protein, (c) standard deviation of a binding prediction of overlapping peptides at each position of the viral protein, and (d) a combination of one or more of (a)-(c). A classification engine uses the classifier model to determine average binding predictions of overlapping peptides at each position of the viral protein independently for test HLA-I and HLA-II functional groupings, where a peptide is classified as a binder when an average binding prediction corresponding to the peptide satisfies a binding value threshold.

Contemplated cancer therapies comprise co-administration of aldoxorubicin with an immune therapeutic composition that preferably comprises a vaccine component and a cytotoxic cell component.