Fas Mutations in Non-Hodgkin's Lymphoma (NHL): Implications for Disease Progression and Therapeutic Resistance
Bibliographic record
Abstract
Introduction: Diffuse large B cell lymphoma (DLBCL) is the most common non-Hodgkin lymphoma and standard frontline treatment is carried out with R-CHOP chemotherapy. However, DLBCL remains an extremely heterogenous disease and refractory/relapse events are common. Recent sequencing experiments have found 18% of relapsed DLBCL contains Fas mutations, an increase from mutations seen at initial diagnosis. Fas receptor (FasR) is a transmembrane protein encoded by the Fas gene that is critical for the induction of extrinsic apoptosis. Once FasR is bound by Fas Ligand expressed on cytotoxic T cells, it initiates the formation of the death-inducing signaling complex and subsequent programmed cell death. Downregulation of FasR expression on B cells has also been a proposed method by which B cell lymphomas are able to evade immune surveillance. Therefore, we hypothesized that dysfunction in Fas signaling contributes to chemotherapy resistance and disease relapse in DLBCL. We used an immune-competent mouse model that can generate aggressive B cell lymphomas (Eμ-Myc) to investigate the role of Fas mutations in lymphomagenesis and response to chemotherapy. Methods: We designed a breeding program, in which we crossed heterozygous Lpr mice, which harbor a germline mutation in Fas, with Eμ-Myc mice. This led to the development of mice that grow spontaneous Eμ-Myc lymphomas with either FasWT or Fasmut gene alterations, hereafter designated WT and MUT. These mice were analyzed for disease progression and their lymphoma cells were intravenously injected into C57BL/6 mice to create 2nd generation lymphomas. A 3rd generation cohort was developed similarly from injecting 2nd generation lymphoma cells into another group of C57BL/6 mice. 3rd generation mice were treated with components of R-CHOP chemotherapy, namely doxorubicin, vincristine, and cyclophosphamide. Overall and disease-related survival were monitored for all cohorts, and lymph node and spleen tissues were preserved from all 3 generations as formalin-fixed paraffin-embedded (FFPE) blocks. A tissue microarray was created to analyze the tumor microenvironment and elements of extrinsic apoptosis/immune response. Immunohistochemistry staining of the microarray using T cell and lymphoma-related markers is currently ongoing (CD3, CD4, CD8, CD25, FoxP3, TP53, Nfkb, Bcl2). Results: In the 1st generation, 21/37 WT and 11/18 MUT mice developed lymphoma, with the time to lymphoma death being similar in both groups (170 days versus 140 days, respectively, p =0.32). Of the 32 primary NHLs generated, 3 didn't have sufficient viable cells to perform subsequent experiments. The remaining 29 NHLs were injected into at least two different C57BL/6 mice, with the exception of one who only had enough cells to inject into one mouse. Thus, the second generation included 57 mice transplanted with 29 primary NHLs. Lymphoma development was higher in the MUT cohort (12/37 WT and 14/20 MUT, p=0.011). The all-cause overall survival was not different between both genotypes (p=0.152), but lymphoma specific survival was significantly shorter in the MUT mice (67 days for MUT and 136 days for WT, p=0.026). In the 3rd generation, 93 mice developed lymphoma (54 WT and 39 MUT), of which 15 were used as untreated controls and 78 were treated with components of R-CHOP. Overall, WT mice appeared to have durable responses to therapy, as shown by increased survival when compared to controls across doxorubicin, vincristine, and cyclophosphamide groups (p=0.0147, 0.0406, 0.0321 respectively). However, no difference in survival was seen in the MUT cohort between treated and untreated controls. Conclusion: Fas mutations may provide survival advantages to lymphoma cells implanted into immune-competent mice. They may also promote resistance to R-CHOP, particularly vincristine, doxorubicin, and cyclophosphamide, but the exact mechanism by which this occurs is unclear. The immune-tumor cell interactions are being investigated by IHC and will be presented. 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.
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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) | 0.001 | 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".