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Record W4220922822 · doi:10.1126/sciadv.abj6526

HSF2 cooperates with HSF1 to drive a transcriptional program critical for the malignant state

2022· article· en· W4220922822 on OpenAlexaff
Roger S. Smith, Seesha Takagishi, David R. Amici, Kyle Metz, Sitaram Gayatri, Milad J. Alasady, Yaqi Wu, Sonia Brockway, Stephanie L. Taiberg, Natalia Khalatyan, Mikko Taipale, Sandro Santagata, Luke Whitesell, Susan Lindquist, Jeffrey N. Savas, Marc L. Mendillo

Bibliographic record

VenueScience Advances · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHeat shock proteins research
Canadian institutionsUniversity of TorontoCanadian Institute for Advanced Research
FundersNational Institute of General Medical SciencesNational Cancer InstituteU.S. Department of Veterans Affairs
KeywordsHSF1Heat shock factorHeat shockHeat shock proteinBiologyChromatinCarcinogenesisCancer researchTranscriptomeMalignancyGeneCell biologyGeneticsGene expressionHsp70

Abstract

fetched live from OpenAlex

Heat shock factor 1 (HSF1) is well known for its role in the heat shock response (HSR), where it drives a transcriptional program comprising heat shock protein (HSP) genes, and in tumorigenesis, where it drives a program comprising HSPs and many noncanonical target genes that support malignancy. Here, we find that HSF2, an HSF1 paralog with no substantial role in the HSR, physically and functionally interacts with HSF1 across diverse types of cancer. HSF1 and HSF2 have notably similar chromatin occupancy and regulate a common set of genes that include both HSPs and noncanonical transcriptional targets with roles critical in supporting malignancy. Loss of either HSF1 or HSF2 results in a dysregulated response to nutrient stresses in vitro and reduced tumor progression in cancer cell line xenografts. Together, these findings establish HSF2 as a critical cofactor of HSF1 in driving a cancer cell transcriptional program to support the anabolic malignant state.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.381
Threshold uncertainty score0.708

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.017
GPT teacher head0.353
Teacher spread0.336 · 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 teacher head, 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".

Quick stats

Citations35
Published2022
Admission routes1
Has abstractyes

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