Abstract: Methods for identifying HERV-derived T cell epitopes associated with cancer, and peptides that are or that include epitopes identified by the methods, expression vectors encoding the peptides, cytotoxic T lymphocytes (CTLs) of a subject treated with the peptides or vectors and engineered T cells expressing T-cell receptors recognizing the peptides. Also, the peptides, expression vectors, CTLs or engineered T cells as a vaccine or a medicament, and in particular the use of the peptides, expression vectors, CTLs or engineered T cells for use in preventing or treating cancer in a subject in need thereof.

Abstract: A transcriptomic signature based on Human endogenous retroviruses (HERVs) expression to characterize leukemic stem cells. In particular, determining the presence of Leukemic Stem Cells (LSCs) in a patient. In an aspect, determining the presence of, or quantifying, LSCs in a patient. Also, gene-related methods for the identification of high-risk acute myeloid leukemia (AML) patients, methods of predicting response to treatment, methods to evaluate (minimal) residual disease during follow-up, methods to determine relapse risk, and methods of treatment of patients following implementation of the former methods.

Abstract: The use of a transcriptomic signature based on HERVs expression to characterize new AML subtypes, and a method to determine to which AML subtype a patient pertains. The method includes providing relationship between the 9 AML sub-types and HERVs characterized by their specific herv_id and their relationship with one of these AML subtypes, determining from a patient cell sample HERVs expression profile, determining which of the 9 AML subtypes is the most represented based on HERV expression in the cell sample, and attributing to the patient the most represented AML subtype among the 9 AML subtypes. The method allows identifying patients with medium good or bad prognosis and treating the same with a cancer therapy against AML.