234 Bead-bound antibody-activation of T cells provides sub-optimal metabolic programming and tumour control compared to dendritic cell-activated T cells
Bibliographic record
Abstract
<h3>Background</h3> Adoptive cell therapies (ACT), including chimeric antigen receptor (CAR) or T cell receptor (TCR) transgenic T cells, have demonstrated impressive efficacy for the treatment of cancer. Unfortunately, ACT still does not result in durable responses for many patients.<sup>1</sup> Much investigation has centered around defining the characteristics of T cells that drive the clinical efficacy of ACT. Metabolic programming, and particularly oxidative metabolism, has emerged as a hallmark of T cells associated with superior performance in ACT due to important associations with <i>in vivo</i> persistence and metabolic resiliency in nutrient limiting environments.<sup>2–7</sup> However, further investigation is required to define the optimal <i>ex vivo</i> activation conditions to impart optimal metabolic programming on the T cells used for ACT. We therefore interrogated the difference in metabolic programming resulting from activation with antibody-coated beads versus peptide-pulsed dendritic cells (DCs). <h3>Methods</h3> CD8+ T cells were isolated from P14 TCR transgenic mice (recognizing H2-D<sup>b</sup> gp33 peptide from lymphocytic choriomeningitis virus). T cells were activated with either bone-marrow derived DCs pulsed with gp33 peptide (1:10 DC:T cells) or with bead-bound anti-CD3/anti-CD28 antibodies (1:1 beads:T cells). Oxidative and glycolytic metabolism were measured by Seahorse Extracellular Flux analyzer. These data were used to calculate ATP production rate. <i>In vivo</i>, we examined the performance of these differentially activated P14 T cells to control the growth of subcutaneously implanted B16-gp33 melanoma tumours. <h3>Results</h3> DC-activated T cells showed increased oxidative and glycolytic metabolism compared to bead-bound antibody-activated T cells. This resulted in an enhanced rate of ATP production in the DC-activated T cells. These metabolic data were associated with efficacy in the B16-gp33 model of ACT. Mice treated with DC-activated T cells had significantly diminished tumour growth and improved survival when compared to the bead-bound antibody-activation treatment condition. The latter treatment provided little advantage over the control (no treatment) group. <h3>Conclusions</h3> Bead-bound antibody activation of T cells at a ratio of 1:1 (beads:T cells) provides sub-optimal metabolic priming which is associated with decreased performance in ACT, particularly when compared to DC-activated T cells. Further investigation into the metabolic programming of T cells by different activation conditions may reveal metabolic or signaling modules that can be modified in these conditions to improve therapy. This is relevant to the efficacy of CAR T cell therapy which often uses bead-activation of T cells for clinical protocols. <h3>References</h3> C. H. June, R. S. O’Connor, O. U. Kawalekar, S. Ghassemi, and M. C. Milone, “CAR T cell immunotherapy for human cancer,” <i>Science</i>, vol. <b>359</b>, no. 6382, pp. 1361–1365, Mar. 2018; doi: 10.1126/science.aar6711. S. D. Saibil <i>et al</i>., “Activation of Peroxisome Proliferator-Activated Receptors a and d Synergizes with Inflammatory Signals to Enhance Adoptive Cell Therapy,” <i>Cancer Res</i>, vol. <b>79</b>, no. 3, pp. 445–451, Feb. 2019; doi: 10.1158/0008-5472.CAN-17-3053. M. St Paul <i>et al</i>., “Coenzyme A fuels T cell anti-tumor immunity,” <i>Cell Metab</i>, vol. <b>33</b>, no. 12, pp. 2415-2427.e6, Dec. 2021; doi: 10.1016/j.cmet.2021.11.010. G. J. W. van der Windt <i>et al</i>., “Mitochondrial Respiratory Capacity Is a Critical Regulator of CD8+ T Cell Memory Development,” <i>Immunity</i>, vol. <b>36</b>, no. 1, pp. 68–78, Jan. 2012; doi: 10.1016/j.immuni.2011.12.007. Y. Zhang <i>et al</i>., “Enhancing CD8+ T Cell Fatty Acid Catabolism within a Metabolically Challenging Tumor Microenvironment Increases the Efficacy of Melanoma Immunotherapy,” <i>Cancer Cell</i>, vol. <b>32</b>, no. 3, pp. 377-391.e9, Sep. 2017; doi: 10.1016/j.ccell.2017.08.004. Y. Sun <i>et al</i>., “Zbtb20 Restrains CD8 T Cell Immunometabolism and Restricts Memory Differentiation and Antitumor Immunity,” <i>The Journal of Immunology</i>, vol. <b>205</b>, no. 10, pp. 2649–2666, Nov. 2020; doi: 10.4049/jimmunol.2000459. M. D. Buck <i>et al</i>., “Mitochondrial Dynamics Controls T Cell Fate through Metabolic Programming,” <i>Cell</i>, vol. <b>166</b>, no. 1, pp. 63–76, Jun. 2016; doi: 10.1016/j.cell.2016.05.035. <h3>Ethics Approval</h3> This study was approved by The University Health Network Animal Care Committee; approval number 929.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".