Abstract 1757: Characterizing novel immunomodulating interactions for potentiating HDAC3 inhibition in non-Hodgkin lymphoma
Bibliographic record
Abstract
Abstract Diffuse large B-cell lymphoma (DLBCL) is the most common subtype of non-Hodgkin lymphoma and represents a highly heterogeneous and aggressive disease. Up to 40% of all DLBCL patients experience treatment failure after the standard immunochemotherapy R-CHOP. Defining novel therapeutic strategies is therefore a pressing need. Reduced acetylation at histone 3 lysine 27 (H3K27ac) represents a pathogenetic hallmark of DLBCL and selective histone deacetylase 3 inhibition (HDAC3i) has recently emerged as a viable strategy to reverse abnormal epigenetic signatures and induce strong anti-tumor effects by increasing H3K27ac. Despite this compelling rationale, the overall response remains suboptimal, suggesting unrecognized mechanisms of resistance. To unbiasedly uncover sensitizers, we performed a genome-wide CRISPR screen and identified knockout (KO) of the top candidate GNAS (encoding G-protein α subunit, Gαs) for sensitizing resistant DLBCL cells to HDAC3i. The sensitizing effects on reduced viable cell number were only observed in GNAS KO cells treated with HDAC3i, but not in the control cells (“synthetic lethality”). We have validated this sensitizing phenotype in multiple resistant cell lines including three human DLBCL cell lines and one mouse B-cell lymphoma cell line. Interestingly, we showed that the sensitization was not related to the canonical G-protein functions but to novel regulatory mechanisms in viral mimicry activation and chromatin modification. Compared with the control conditions, the interaction between GNAS KO and HDAC3i has been linked with differential chromatin accessibility and endogenous retrovirus expression, upregulated double-stranded RNA expression, and increased phosphorylation of TBK1 whose activation plays a vital role in antiviral immune responses. Strikingly, a selective TBK1 degrader completely and specifically rescued the sensitizing phenotype in GNAS KO cells treated with HDAC3i, confirming the critical role of TBK1 in inducing sensitization. Furthermore, we identified potential immune interactions by exploring the clinical data from tumor samples of 309 DLBCL patients that low GNAS expression was correlated with cytokine and inflammatory signaling as well as stromal cell interactions in the tumor microenvironment, altogether associated with longer overall survival. These observations were aligned with our results in the mouse xenograft and allograft tumor experiments that GNAS KO with/without HDAC3i led to differential phenotypes. Overall, our research uncovers novel immunomodulating activities for potentiating the efficacy of epigenetic agents in non-Hodgkin lymphoma. This expands our understanding of novel interactions between immune and epigenetic regulation in lymphoma biology and provides new therapeutic opportunities for patients with resistant lymphoma who have poor prognoses and limited treatment options. Citation Format: Michael Y. He, Kit I. Tong, Mehran Bakhtiari, Ryder Whittaker Hawkins, Ting Liu, Yong Zeng, Noorhan Ghanem, Housheng Hansen He, Robert Kridel. Characterizing novel immunomodulating interactions for potentiating HDAC3 inhibition in non-Hodgkin lymphoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 1757.
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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 | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".