RESUMEN
Despite documented evidence that ovarian cancer cells express immune-checkpoint molecules, such as PD-1 and PD-L1, and of a positive correlation between the presence of tumour-infiltrating lymphocytes and favourable overall survival outcomes in patients with this tumour type, the results of trials testing immune-checkpoint inhibitors (ICIs) in these patients thus far have been disappointing. The lack of response to ICIs can be attributed to tumour heterogeneity as well as inherent or acquired resistance associated with the tumour microenvironment (TME). Understanding tumour immunobiology, discovering biomarkers for patient selection and establishing optimal treatment combinations remains the hope but also a key challenge for the future application of immunotherapy in ovarian cancer. In this Review, we summarize results from trials testing ICIs in patients with ovarian cancer. We propose the implementation of a systematic CD8+ T cell-based immunophenotypic classification of this malignancy, followed by discussions of the preclinical data providing the basis to treat such immunophenotypes with combination immunotherapies. We posit that the integration of an accurate TME immunophenotype characterization with genetic data can enable the design of tailored therapeutic approaches and improve patient recruitment in clinical trials. Lastly, we propose a roadmap incorporating tissue-based profiling to guide future trials testing adoptive cell therapy approaches and assess novel immunotherapy combinations while promoting collaborative research.
RESUMEN
Approaches to analyze and cluster T-cell receptor (TCR) repertoires to reflect antigen specificity are critical for the diagnosis and prognosis of immune-related diseases and the development of personalized therapies. Sequence-based approaches showed success but remain restrictive, especially when the amount of experimental data used for the training is scarce. Structure-based approaches which represent powerful alternatives, notably to optimize TCRs affinity toward specific epitopes, show limitations for large-scale predictions. To handle these challenges, TCRpcDist is presented, a 3D-based approach that calculates similarities between TCRs using a metric related to the physico-chemical properties of the loop residues predicted to interact with the epitope. By exploiting private and public datasets and comparing TCRpcDist with competing approaches, it is demonstrated that TCRpcDist can accurately identify groups of TCRs that are likely to bind the same epitopes. Importantly, the ability of TCRpcDist is experimentally validated to determine antigen specificities (neoantigens and tumor-associated antigens) of orphan tumor-infiltrating lymphocytes (TILs) in cancer patients. TCRpcDist is thus a promising approach to support TCR repertoire analysis and TCR deorphanization for individualized treatments including cancer immunotherapies.
RESUMEN
Adoptive cell therapy (ACT) using ex vivo-expanded tumor-infiltrating lymphocytes (TILs) can eliminate or shrink metastatic melanoma, but its long-term efficacy remains limited to a fraction of patients. Using longitudinal samples from 13 patients with metastatic melanoma treated with TIL-ACT in a phase 1 clinical study, we interrogated cellular states within the tumor microenvironment (TME) and their interactions. We performed bulk and single-cell RNA sequencing, whole-exome sequencing, and spatial proteomic analyses in pre- and post-ACT tumor tissues, finding that ACT responders exhibited higher basal tumor cell-intrinsic immunogenicity and mutational burden. Compared with nonresponders, CD8+ TILs exhibited increased cytotoxicity, exhaustion, and costimulation, whereas myeloid cells had increased type I interferon signaling in responders. Cell-cell interaction prediction analyses corroborated by spatial neighborhood analyses revealed that responders had rich baseline intratumoral and stromal tumor-reactive T cell networks with activated myeloid populations. Successful TIL-ACT therapy further reprogrammed the myeloid compartment and increased TIL-myeloid networks. Our systematic target discovery study identifies potential T-myeloid cell network-based biomarkers that could improve patient selection and guide the design of ACT clinical trials.