Abstract SY11-02: Antigen-independent de novo prediction of cancer-associated immune repertoire

Abstract

Cancer-associated T cells play a critical role in mediating immune responses in the anti-tumor immunity. However, due to the complex nature of cancer antigens, and the limited experimental approaches for collecting antigen-specific T cells, it remains a difficult task in cancer immunology to detect cancer-associated T cells. In the past we developed TRUST for de novo assembly of TCR hypervariable CDR3 regions from the tumor RNA-seq data. Application of TRUST to the TCGA samples resulted in calling over 1.5 million cancer-related TCRs. From this dataset, we trained a classifier to distinguish cancer vs non-cancer CDR3s, independent of cancer antigens, and developed a method, TCRboost, for the prediction of cancer-associated TCR repertoire. TCRboost assigns a 9cancer score9 to a given immune repertoire, as an estimation of its probability of being derived from a cancer patient. We applied TCRboost to study over 1,100 TCR repertoire sequencing samples from 15 study cohorts covering healthy donors, viral infections, autoimmune disorders and 10 types of malignancies of both early and late stages. Surprisingly, we observed consistently and significantly higher cancer scores using the cancer patients’ immune repertoire data, while none of the non-cancer repertoire was significant compared to healthy donors. We therefore used repertoire cancer score as a single predictor for cancer status to distinguish cancer patients from healthy donors, and observed high prediction power measured by area under the ROC (AUROC) curves. The AUROC reached 0.90 for early breast cancer patients, which is better than a number of current early prediction methods based on cancer biomarkers, such as PSA, CA-125, CEA, etc. Additional analysis of TCRboost on a longitudinal cohort of healthy individuals suggested that the cancer scores are robust against random fluctuations in the immune repertoire. Therefore, it is unlikely to predict a healthy donor to be a cancer patient due to random sampling, and vice versa. Furthermore, we investigated two cohorts of late-stage cancer patients treated with anti-CTLA4 mAb (melanoma and prostate), where TCRboost predicted cancer scores are predictive of the patient outcome. These results indicate that it is potentially feasible to use biomarkers derived blood repertoire to track clinical responses to checkpoint blockade therapies. Finally, since cancer score is a quantity derived from the immune repertoire, it is an independent criterion to the existing methods based on cancer-related materials, such as ctDNA, CTC, cfDNA, cancer antigens, or imaging-based approaches detecting lesions of tumor. This quality makes it legitimate to be combined with any existing approach to increase the detection power and accuracy. We anticipate cancer score to serve as a potential powerful tool to facilitate cancer diagnosis and immunotherapy prognosis. Citation Format: Bo Li. Antigen-independent de novo prediction of cancer-associated immune repertoire [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr SY11-02.