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Record W4415285495 · doi:10.47248/chp2502040018

Advancing Cancer Biology: Highlights from the 2025 FASEB SRC on Cellular Plasticity in Cancer

2025· article· en· W4415285495 on OpenAlexaff
Hans Clevers, Ankur Sharma, Sendurai A. Mani, Anna Golebiewska, Axel Behrens, Wai Leong Tam, Loic P. Deleyrolle, Huiping Liu, Karuna Ganesh, Walid T. Khaled, Shaheen S. Sikandar, Terence K. Lee, Tyler E. Miller, Dean G. Tang, Sheila K. Singh, Vivian Li, Justin D. Lathia, Stephanie Ma

Bibliographic record

VenueCancer Heterogeneity and Plasticity · 2025
Typearticle
Languageen
FieldMedicine
TopicCancer Cells and Metastasis
Canadian institutionsMcMaster UniversityMcMaster University Medical CentreJuravinski Cancer Centre
FundersNational Institute of Neurological Disorders and StrokeNational Cancer InstituteCroucher Foundation
KeywordsReprogrammingCancerCancer cellCancer stem cellDiseaseCellStem cell

Abstract

fetched live from OpenAlex

The inaugural FASEB Science Research Conference (SRC) on Cellular Plasticity in Cancer was held in May 2025 in Hong Kong SAR, China. This event brought together leading experts to discuss cutting-edge research centered on cancer cell plasticity. The conference featured comprehensive presentations covering a broad spectrum of topics, including oncofetal reprogramming in tumor development and progression, mechanisms regulating cancer cell plasticity, metabolic reprogramming and its role in tumor progression, cancer cell plasticity during metastasis, cancer stem cell programs within the tumor microenvironment, tumor plasticity and immune evasion, as well as innovative therapeutic strategies aimed at targeting stem cell-like states, modulating cancer cell states, and effectively controlling disease progression. It is anticipated that the insights gained from this meeting will catalyze further advancements in cancer biology and therapy.

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)0.006
metaresearch head score (Gemma)0.004
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: Not applicable
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0050.003
Open science0.0010.003
Research integrity0.0050.007
Insufficient payload (model declined to judge)0.0080.003

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 head0.031
GPT teacher head0.319
Teacher spread0.287 · 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
GenreCommentary

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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