Deciphering Repertoire of B16 Melanoma Reactive TCRs by Immunization, In Vitro Restimulation and Sequencing of IFNγ-Secreting T Cells.
Anna V IzosimovaDiana V YuzhakovaValeria D SkatovaLilia N VolchkovaElena V ZagainovaDmitry M ChudakovGeorge V SharonovPublished in: International journal of molecular sciences (2021)
Recent advances in cancer immunotherapy have great promise for the treatment of solid tumors. One of the key limiting factors that hamper the decoding of physiological responses to these therapies is the inability to distinguish between specific and nonspecific responses. The identification of tumor-specific lymphocytes is also the most challenging step in cancer cell therapies such as adoptive cell transfer and T cell receptor (TCR) cloning. Here, we have elaborated a protocol for the identification of tumor-specific T lymphocytes and the deciphering of their repertoires. B16 melanoma engraftment following anti-PD1 checkpoint therapy provides better antitumor immunity compared to repetitive immunization with heat-shocked tumor cells. We have also revealed that the most error-prone part of dendritic cell (DC) generation, i.e., their maturation step, can be omitted if DCs are cultured at a sufficiently high density. Using this optimized protocol, we have achieved a robust IFNγ response to B16F0 antigens, but only within CD4+ T helper cells. A comparison of the repertoires of IFNγ-positive and -negative cells shows a prominent enrichment of certain clones with putative tumor specificity among the IFNγ+ fraction. In summary, our optimized protocol and the data provided here will aid in the acquisition of broad statistical data and the creation of a meaningful database of B16-specific TCRs.
Keyphrases
- dendritic cells
- regulatory t cells
- immune response
- induced apoptosis
- high density
- randomized controlled trial
- single cell
- cell cycle arrest
- cell therapy
- dna damage
- big data
- electronic health record
- emergency department
- cell death
- oxidative stress
- peripheral blood
- endoplasmic reticulum stress
- deep learning
- binding protein