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Abstract B008: Utilizing pathway incompatibility for synthetic lethality: A therapeutic strategy for B-cell lymphoma

2024· article· en· W4399505349 on OpenAlexaboutno aff
Mia Knupke, Lai N. Chan

Bibliographic record

VenueMolecular Cancer Therapeutics · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProtein Degradation and Inhibitors
Canadian institutionsnot available
Fundersnot available
KeywordsSynthetic lethalityMAPK/ERK pathwayCancer researchLymphomaDiffuse large B-cell lymphomaBiologySTAT5CancerSignal transductionGeneticsMedicineImmunologyGeneDNA repair

Abstract

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Abstract Previous pan-cancer analyses revealed co-occurring mutations exploitable for combination therapies and mutually exclusive mutations assumed to reflect functional redundancy (Mina M et al. 2017; Sanchez-Vega F et al. 2018). However, we recently highlighted an alternative scenario when mutual exclusivity reflects the incapability of two pathways to function together, a phenomenon we term “pathway incompatibility” (Chan LN et al. 2020). We found that STAT5-and ERK-activating genetic lesions were not only mutually exclusive in B-ALL cases, but also reciprocally inhibited each other at the signal transduction level, inducing cell death instead of promoting leukemogenesis. Notably, we demonstrated, for the first time, that pharmacological activation of STAT5 in ERK-driven B-ALL cells and ERK in STAT5-driven B-ALL cells represents a tractable synthetic lethality (Chan LN et al. 2020). We now take the important next step of identifying potential candidates for synthetic lethal genetic interactions resulting from pathway incompatibility in B-cell malignancies more broadly. Diffuse large B-cell lymphoma (DLBCL) is a rapidly growing blood cancer affecting older adults and constituting about 25% of all lymphoma cases in the US. Around 40% of patients develop relapsed or refractory DLBCL (Pfreundschuh M et al. 2006; Ngu H et al. 2022). Thus, there is an unmet need to develop new therapeutic strategies to improve clinical outcomes. Studying 1684 DLBCL cases from published datasets, we found that about 30% of DLBCL cases carry loss-of-function (LOF) CBP or gain-of-function (GOF) MYD88 genetic lesions. CBP encodes a histone acetyltransferase and, together with p300, functions as a transcriptional coactivator and tumor suppressor. Conversely, GOF MYD88 mutations drive lymphomagenesis by regulating kinases and transcription factors. Our analysis revealed that LOF CBP and GOF MYD88 genetic lesions co-occurred less frequently than expected by chance (odds ratio: 0.57, P=0.0), indicating their mutual exclusivity. Experiments with human DLBCL cell lines expressing wild-type MYD88 and CBP showed that CBP/p300 inhibitors did not impact cell viability. However, cells expressing a GOF MYD88 mutant or stimulated with a toll-like receptor (TLR) agonist to activate MYD88 became sensitized to CBP/p300 inhibitors, resulting in cell death. This suggests that LOF CBP and GOF MYD88 are incompatible for cell survival, with their co-occurrence resulting in synthetic lethality. We then tested a synthetic lethality-based therapeutic concept based on CBP-MYD88 incompatibility in DLBCL. We found that MYD88-mutated DLBCL cell lines were more sensitive to pharmacological inhibition of CBP/p300, while CBP-deficient DLBCL cell lines showed increased sensitivity to TLR agonist treatment. In summary, we identified mutual exclusivity between LOF CBP and GOF MYD88 lesions in DLBCL, where their co-occurrence induces cell death. Moreover, we demonstrated the therapeutic potential of synthetic lethality resulting from co-introduction of LOF CBP and GOF MYD88 in DLBCL. Citation Format: Mia Knupke, Lai Chan. Utilizing pathway incompatibility for synthetic lethality: A therapeutic strategy for B-cell lymphoma [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Expanding and Translating Cancer Synthetic Vulnerabilities; 2024 Jun 10-13; Montreal, Quebec, Canada. Philadelphia (PA): AACR; Mol Cancer Ther 2024;23(6 Suppl):Abstract nr B008.

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How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.002

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.

Opus teacher head0.045
GPT teacher head0.318
Teacher spread0.273 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

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Citations0
Published2024
Admission routes1
Has abstractyes

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