MétaCan
Menu
Back to cohort

Abstract A007: Assessment of metabolic vulnerabilities of breast cancer brain metastasis

2024· article· en· W4399505326 on OpenAlexaboutno aff
Keene L. Abbott, Sonu Subudhi, Raphaël Ferreira, Yetiş Gültekin, Sophie Charlotte Steinbuch, Sophie Honeder, Ashwin S. Kumar, Michelle Xiao Wu, Diya Ramesh, Jacob Hansen, Lisa Maria Riedmayr, Mark Duquette, Ahmed Ali, Nicole Henning, Sharanya Sivanand, Tenzin Kunchok, Millenia Waite, T. Brian, Virginia Spanoudaki, Francisco J. Sánchez‐Rivera, George M. Church, Matthew G. Vander Heiden

Bibliographic record

VenueMolecular Cancer Therapeutics · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA modifications and cancer
Canadian institutionsnot available
Fundersnot available
KeywordsBreast cancerCancerBrain metastasisMetastasisMedicineOncologyInternal medicine

Abstract

fetched live from OpenAlex

Abstract The metastasis of cancer to the brain is a major contributor to patient morbidity and mortality. Prior work suggests that there is therapeutic potential in targeting metabolic processes within brain metastatic cancer, however how best to identify novel targets and select patient populations remains unclear. In this study, we determine what nutrients are available to breast cancer cells in the brain and how those nutrients are used. We also engineer breast cancer cells that are auxotrophic for specific nutrients and assess how this impacts tumor growth in the brain using various approaches. These methodologies include direct implantation into the brain and introduction into the circulation to evaluate tumor formation as brain metastases. Unexpectedly, we find that no single approach, including assessment of brain nutrient availability, tumor biosynthetic activity, and evaluation of genetic dependencies using in vivo CRISPR screens reliably predicts metabolic dependencies that broadly extend across models of breast cancer brain metastasis. Our findings underscore the necessity of a holistic approach in considering how best to identify and prioritize new targets for treating metastatic cancer. Citation Format: Keene L. Abbott, Sonu Subudhi, Raphael Ferreira, Yetiş Gültekin, Sophie C. Steinbuch, Sophie E. Honeder, Ashwin S Kumar, Michelle Wu, Diya Ramesh, Jacob Hansen, Lisa M. Riedmayr, Mark Duquette, Ahmed Ali, Nicole Henning, Sharanya Sivanand, Tenzin Kunchok, Millenia Waite, Brian T. Do, Virginia Spanoudaki, Francisco J. Sánchez-Rivera, George M. Church, Rakesh K Jain, Matthew G. Vander Heiden. Assessment of metabolic vulnerabilities of breast cancer brain metastasis [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 A007.

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 score0.007
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.001

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 head0.023
GPT teacher head0.342
Teacher spread0.319 · 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
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueMolecular Cancer TherapeuticsSame topicRNA modifications and cancerFrench-language works237,207