Abstract Immune repertoire deep sequencing allows profiling T-cell populations and enables novel approaches to diagnose and prognosticate diseases by identifying T-cell receptor sequence patterns associated with clinical phenotypes and outcomes. Our study objective is to develop a method to diagnose and prognosticate cancer using T-cell receptors sequenced from tissue biopsies. To determine how to profile the specificity of a T-cell receptor, we analyze 3D X-ray crystallographic structures of T-cell receptors bound to antigen. We observe a contiguous strip typically 4 amino acid residues in length from the complimentary determining region 3 (CDR3) lying in direct contact with the antigen. Based on this observation, we extract 4 residue long snippets from every receptor’s CDR3 and represent each snippet using biochemical features encoded by its amino acid sequence. The biochemical features are combined with information about the abundance of the snippet or the receptor and scored using a logistic regression model. Each logistic regression model is fitted and validated under the requirement that at least one positively labelled snippet appears per tumor and no positively labelled snippets appear in healthy tissue. Using a patient-holdout cross-validation, we fit logistic regression models to distinguish colorectal tumors from healthy tissue matched controls with 93% accuracy, breast tumors from healthy tissue matched controls with 94% accuracy, ovarian tumors from non-cancer patient ovarian tissue with 95% accuracy (80% accuracy on a blinded follow-up cohort), and regression of preneoplastic cervical lesions over 1 year in advance with 96% accuracy. In conclusion, immune repertoires can be used to diagnose and prognosticate disease.