Evaluation of T-Cell Compartment By Complex Multiparameter Flow Cytometry Reveals Distinct Patterns of T-Cell Exhaustion in DLBCL, FL and HL Patients
Bibliographic record
Abstract
Background: Immune checkpoint (IC) blockade has revolutionized the treatment of chemo-refractory solid tumors and has demonstrated promising results for the treatment of resistant blood cancers, including lymphoma. However, clinical response to PD1 blockade depends on the subtype of lymphoma, with durable responses observed in 70 % of Hodgkin lymphoma (HL) patients versus only 36 to 40% of Diffuse Large B-Cell Lymphoma (DLBCL) and Follicular Lymphoma (FL) patients(1-3). The biological aspects that hinder the efficacy of immunotherapy in DLBCL and FL patient are not well-known. Primary tumor resistance to PD1 blockade could be driven by inherent genomic alterations or by ineffective anti-tumor T cell responses. ICs expressed at the surface of tumor-infiltrating-lymphocytes (TILs) use distinct suppressive mechanisms to enforce T-cell dysfunction. The collective expression of ICs defines multiple subsets of exhausted T cells during cancer(4). Since multiple of these ICs collaborate to repress anti-tumor T cell functions, we hypothesized that TILs from DLBCL and FL patients express high levels of ICs and present a more advanced exhausted phenotype compared to TILs from HL patients. Methods: We studied lymphoma samples collected at diagnosis and frozen as a viable cell suspension from 57 patients with HL, FL or DLBCL. We performed an extended immunophenotype of TILs through flow cytometry using maturation markers, exhaustion markers, homing and chemokine receptors. The results were analyzed with FlowJo. Boolean gating was used to assess IC co-expression. Statistical analyses were performed using Prism. Results: Our data indicated that CD8 TILs from DLBCL patients express higher levels of classical ICs such as PD1 (1059 vs 1015 and 648), Tim3 (1747 vs 868 and 918,) and Lag3 (2603 vs 1013 and 1031) compared to FL and HL patients (p-value < 0.01), while the expression of 2B4, BTLA and CD160 were similar. Boolean analysis revealed that DLBCL and FL had a higher frequency of CD8T cells that co-expressed 6-7 ICs, compared to HL, suggesting a more advanced state of exhaustion (19% and 26% vs 14%, p-value < 0.05). This correlated with an increased frequency of PD-1highCD39high T cells in DLBCL and FL patients compared to HL (32% and 24% vs 14%, p-value < 0.005), which correlated with increased frequency of EOMEShigh cells in both DLBCL and FL (84% and 76% vs 55%, p-value < 0.005). These markers identify populations of cells with phenotypic and transcriptomic profiles of terminal exhaustion with reduced cytokine production, proliferative capacity and persistence (5, 6). In contrast, stem-like exhausted CD8 T cells are defined by intermediate expression of PD-1 and expression of the chemokine receptor CXCR5, and are driven by the transcription factor TCF-1(7, 8). We observed that DLBCL exhibited significantly less progenitor Tim3-/CXCR5+ cells compared to FL and HL (27% vs 52% and 40%, p-value < 0.01). Further, the frequency and mean fluorescent intensity of TCF-1 was significantly reduced in DLBCL compared to FL and HL (43% / 495 ± 64 vs 70% / 752 ± 42 and 61% / 670 ± 74, p-value < 0.01). This data correlated with lower frequency of CD27+ CD8 T cells in DLBCL vs FL and HL (82% vs 96% and 96%, p-value < 0.01). Conclusion: Collectively, our data indicate that TILs from DLBCL and FL patients express a whole array of ICs that have non-redundant suppressive functions. Further, these patients present subsets of cells with increased functional exhaustion, subsets demonstrated to be unresponsive to PD-1 blockade(7). Further, DLBCL patients lack protective stem-like T-cell subsets which were recently demonstrated to correlate with clinical outcome(9). These results enlighten our knowledge of the cellular defects precluding potent anti-tumor T cell functions in DLBCL and FL and propose novel targets for immunotherapies in PD-1-unresponsive lymphomas. Research funding: This work is supported by the Canadian Institutes of Health Research (PJT-155996), Canadian Cancer Society and Cole Foundation (grant #705478). References: 1. S. M. Ansell et al., in N Engl J Med 372, 311 (2015). 2. R. Chen et al., J Clin Oncol35, 2125 (2017). 3. A. M. Lesokhin et al., J Clin Oncol34, 2698 (2016). 4. B. Bengsch et al., Immunity48, 1029 (2018). 5. M. A. Paley et al., Science338, 1220 (2012). 6. P. K. Gupta et al., PLoS pathogens11, e1005177 (2015). 7. S. J. Im et al., Nature537, 417 (2016). 8. D. T. Utzschneider et al., Immunity45, 415 (2016). 9. M. Sade-Feldman et al., Cell176, 404 (2019). Disclosures Johnson: Roche: Consultancy, Employment, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Travel fees, gifts, and others, Research Funding; Abbvie: Consultancy, Employment, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Merck: Consultancy, Honoraria; BMS: Consultancy, Honoraria; BD Biosciences: Other: Provided a significant proportion of the antibodies used in this project free of cost.; Seattle Genetics: Honoraria; Lundbeck: Employment, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Travel fees, gifts, and others, Research Funding.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".