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Record W4409625604 · doi:10.1158/1538-7445.am2025-3801

Abstract 3801: Serine starvation inhibits SRSF protein expression and modulates RNA splicing in breast cancer cells

2025· article· en· W4409625604 on OpenAlexaff
Philippa Burns, Negar Tabatabaei, Laura M. Selfors, Aanshi Vashi, Debolanle O. Dahunsi, Patrick J. Murphy, Soroush Tahmasebi, Jonathan L. Coloff

Bibliographic record

VenueCancer Research · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA modifications and cancer
Canadian institutionsUniversity of Prince Edward Island
Fundersnot available
KeywordsRNA splicingBreast cancerSerineProtein expressionCancer researchRNA-binding proteinCancerRNAChemistryCell biologyBiologyMolecular biologyBiochemistryPhosphorylationGeneticsGene

Abstract

fetched live from OpenAlex

Breast cancer is the most common cancer in women worldwide, and luminal/estrogen receptor positive (ER+) breast cancer accounts for ∼50% of breast cancer deaths. Our lab has identified serine auxotrophy as a metabolic vulnerability of luminal/ER+ breast tumors due to low levels of the serine synthesis enzyme phosphoserine aminotransferase 1 (PSAT1). Serine is critically important for cancer cell growth due to its multiple downstream functions in protein, nucleotide, and lipid biosynthesis. There is considerable interest in starving serine auxotrophic tumors of serine for therapy, including an ongoing clinical trial in pancreatic cancer. While the metabolic effects of serine starvation have been extensively investigated, our goal is to better understand how serine starvation affects protein synthesis and expression. We found that serine starvation inhibits global translation, while inducing adaptive translation of ATF4, and causes ribosome stalling and fall-off at serine TCC codons. Analysis of changes in the proteome upon serine starvation in auxotrophic luminal breast cancer cells shows that serine-rich proteins are particularly sensitive to serine starvation; this includes the serine/arginine-rich splicing factor (SRSF) family that are important regulators of mRNA splicing. Accordingly, replicate multivariate analysis of transcript splicing (rMATS) on RNA-sequencing data suggests that serine starvation dramatically changes alternative mRNA splicing in multiple breast cancer cell lines, with many differential splicing events being conserved across cell lines. These results have led to our current hypothesis that serine starvation induces splicing changes due to reduced translation of serine-rich members of the SRSF family (such as SRSF6), which ultimately impacts DNA damage and cell survival. We believe that SRSF6 depletion and subsequent alteration of RNA splicing upon serine starvation may sensitize to DNA damaging agents (e.g., chemotherapy). Citation Format: Philippa Burns, Negar Tabatabaei, Laura Selfors, Aanshi Vashi, Debolanle O. Dahunsi, Patrick Murphy, Soroush Tahmasebi, Jonathan Coloff. Serine starvation inhibits SRSF protein expression and modulates RNA splicing in breast cancer cells [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 3801.

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 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: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.011

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.027
GPT teacher head0.357
Teacher spread0.330 · 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 designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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