MétaCan
Menu
Back to cohort

Abstract A054 A genome-derived cell state reporter permits dissection and control of intratumoral heterogeneity and chemoresistance

2024· article· en· W4402266536 on OpenAlexaboutno aff
Noha AM Shendy, Yang Zhang, Hawon Lee, Stephanie Nance, Yousef Khashana, Mohammad AM Nezhady, Elaine Ritter, Shivendra V. Singh, Qi Liu, Yiping Fan, Jun J. Yang, Anand G. Patel, Jun Qi, Taosheng Chen, Brian J. Abraham, Adam D. Durbin

Bibliographic record

VenueCancer Research · 2024
Typearticle
Languageen
FieldEngineering
TopicBiomedical and Engineering Education
Canadian institutionsnot available
Fundersnot available
KeywordsGenomeDissection (medical)CellCancer researchBiologyTumour heterogeneityComputational biologyGeneticsGeneCancerAnatomy

Abstract

fetched live from OpenAlex

Abstract Sequential changes in epigenomically specified transcriptional cell states are essential for normal developmental progression. These normal processes are co-opted in cancers, allowing genetically identical cells in different states to cooperatively form heterogeneous tumors. Specific cell states may display distinct malignant properties, including variability in sensitivity to conventional therapies, resulting in heterogeneous responses to therapy and a potential mechanism of patient relapse. As a result, deriving mechanistic approaches to control cell state transitions for therapeutic benefit has been limited by the lack of tractable live-cell tools able to reflect complex and multifaceted cell states. To address this problem, here we demonstrate the design, conception, synthesis and interrogation of a new method termed “Transcriptional Reporter Elements of Cell State” (TRECS). TRECS integrates epigenomics and transcriptomics to identify endogenous genomic elements that can label and separate cell states within heterogenous populations. Using integrative three-dimentional chromatin conformation data and CRISPR deletion studies, we link the mechanism of TRECS reporter activity to control by master transcriptional regulators of distinct cell states. Implementing this system in the high-risk pediatric solid tumor neuroblastoma, we quantitate intratumoral transcriptional and epigenetic heterogeneity and observe real-time cell state plasticity. We demonstrate that neuroblastoma cell line models generally contain plastic cell populations, including one cell state with intrinsic broad chemoresistance. Capitalizing on this intrinsic cell state plasticity, we perform a high-throughput imaging-based small molecule screen to identify chemical controllers of cell state. We identify the coactivator proteins EP300/CBP as primary regulators of the neuroblastoma chemoresistant cell state. Transient disruption of EP300/CBP activity induces sustained epigenetic and transcriptional reprogramming, which fundamentally alters the chemoresistant neuroblastoma cell state, resulting in enhanced chemosensitivity. These findings demonstrate a new, scalable approach to visualize and dissect high-risk cell states with distinct malignant properties in heterogenous tumors. This platform also guides chemical-genetic strategies to control especially challenging cell states, for example, by targeting EP300/CBP maintenance of chemoresistance. Citation Format: Noha AM Shendy, Yang Zhang, Ha-Won Lee, Stephanie Nance, Yousef Khashana, Mohammad AM Nezhady, Elaine Ritter, Shivendra Singh, Qi Liu, Yiping Fan, Jun Yang, Anand G. Patel, Jun Qi, Taosheng Chen, Brian J. Abraham, Adam D. Durbin. A genome-derived cell state reporter permits dissection and control of intratumoral heterogeneity and chemoresistance [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pediatric Cancer Research; 2024 Sep 5-8; Toronto, Ontario, Canada. Philadelphia (PA): AACR; Cancer Res 2024;84(17 Suppl):Abstract nr A054.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.549
Threshold uncertainty score0.264

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.022
GPT teacher head0.308
Teacher spread0.286 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueCancer ResearchSame topicBiomedical and Engineering EducationFrench-language works237,207