Abstract 959: FAS mutations induce therapeutic resistance in non-Hodgkin lymphomas
Bibliographic record
Abstract
Abstract Lymphomas are common (1/30 Canadians), but responses to chemotherapy depend on the tumour biology. We sequenced > 300 lymphomas at diagnosis and at relapse, identified FAS as a possible tumour suppressor gene and found that mutant FAS is associated with therapeutic resistance. FAS is the death receptor that initiates the extrinsic apoptotic pathway once activated by FAS ligand (FASL). Our studies indicated that the most common FAS mutation is a dominant negative allele whose product inhibits FAS-mediated apoptosis. This allele induces resistance to doxorubicin in murine lymphoma cells transplanted into syngeneic immune competent C57BL/6 mice but, unexpectedly, the allele has no effect on chemosensitivity in cultured lymphoma lines in vitro. Because FAS signaling is initiated by FASL from neighboring cells, we asked whether chemotherapy induces FAS or FASL expression in immune cells, thereby activating apoptosis in vivo but not in vitro. Indeed, exposure to chemotherapy induced FAS in primary and malignant B cells and increased FASL in benign T cells. Thus, we hypothesize that chemotherapy may induce an anti-tumour immune response mediated by FAS-FASL interactions, and that FAS mutations cause therapeutic resistance by blocking the chemotherapy-induced immune response. We will extend this work by evaluating the role of the immune response in chemotherapy-induced lymphoma apoptosis and by identifying therapies that will target FAS mutant lymphomas. Citation Format: Nathalie Johnson, Denis Gaucher, Ryan Morin, Randy Gascoyne, Joseph Connors, Marco Marra, Jerry Pelletier, Hawley Rigsby, Koren Mann. FAS mutations induce therapeutic resistance in non-Hodgkin lymphomas. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 959. doi:10.1158/1538-7445.AM2014-959
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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) | 0.006 | 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".