The past decade has witnessed the gradual and steady progress of adoptive T cell therapy in treating various types of cancer. In combination with gemcitabine and carboplatin chemotherapy, we previously conducted a clinical trial, NCT00690872, to treat Epstein-Barr virus (EBV)-positive nasopharyngeal carcinoma (NPC) patients with autologous EBV-expanded cytotoxic T lymphocytes (CTLs). While achieving a 2-year overall survival rate of 62.9%, this trial failed to induce an anti-tumor response in a sizable fraction of patients. Thus, the identification of benchmarks capable of evaluating CTL products and predicting clinical immunotherapeutic efficacy remains an urgent need. We conducted T cell receptor (TCR) repertoire sequencing to assess EBV-expanded infusion-ready CTL products. To depict the overall repertoire landscape, we evaluated the individual repertoire diversity by Shannon entropy, and, compared the inter-patient CDR3 similarity to estimate T cells expanded by common antigens. With a recently developed bioinformatics algorithm, termed Motif Analysis, we made a machine-learning prediction of structural regions within the CDR3 of TCRβ that associate with CTL therapy prognosis. We found that long term survivors, defined as patients surviving longer than two years, had a higher CTL repertoire diversity with reduced inter-patient similarity. Furthermore, TCR Motif Analysis identified 11 structural motifs distinguishing long term survivors from short term survivors. Specifically, two motifs with a high area under the curve (AUC) values were identified as potential predictive benchmarks for efficacious CTL production. Together, these results reveal that the presence of diverse TCR sequences containing a common core motif set is associated with a favorable response to CTL immunotherapy against EBV-positive NPC.