Evidence map›Paper›PMID 39484404›Full record

ArticlebioRxiv : the preprint server for biology2024

Expression-Driven Genetic Dependency Reveals Targets for Precision Medicine.

Abdulkadir Elmas, Hillary M Layden, Jacob D Ellis, Luke N Bartlett, Xian Zhao, Reika Kawabata-Iwakawa, Hideru Obinata, Scott W Hiebert, Kuan-Lin Huang

Abstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in bioRxiv : the preprint server for biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

9 authors.

Abdulkadir ElmasDepartment of Genetics and Genomic Sciences, Department of Artificial Intelligence and Human Health, Center for Transformative Disease Modeling, Tisch Cancer Institute, Icahn Genomics Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.
Hillary M LaydenDepartment of Biochemistry, Vanderbilt University School of Medicine, Nashville, Tennessee 37232, USA.
Jacob D EllisDepartment of Biochemistry, Vanderbilt University School of Medicine, Nashville, Tennessee 37232, USA.
Luke N BartlettDepartment of Biochemistry, Vanderbilt University School of Medicine, Nashville, Tennessee 37232, USA.
Xian ZhaoDepartment of Biochemistry, Gunma University Graduate School of Medicine, Maebashi, Gunma 371-8511, Japan. Current affiliation: Department of Pharmacy, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, 321 Zhongshan Road, Nanjing, Jiangsu, 210008, China.
Reika Kawabata-IwakawaDivision of Integrated Oncology Research, Gunma University Initiative for Advanced Research, Gunma University, Maebashi, Gunma 371-8511, Japan.
Hideru ObinataEducation and Research Support Center, Gunma University Graduate School of Medicine, Maebashi, Gunma 371-8511, Japan.
Scott W HiebertDepartment of Biochemistry, Vanderbilt University School of Medicine, Nashville, Tennessee 37232, USA.
Kuan-Lin HuangDepartment of Genetics and Genomic Sciences, Department of Artificial Intelligence and Human Health, Center for Transformative Disease Modeling, Tisch Cancer Institute, Icahn Genomics Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.

Funding

Integrative Approaches for Identifying Causal Gene-Cell Type Pairs of Complex DiseaseR35GM138113 · NIGMS · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI Kuan-lin Huang · 2020 to 2026
$3.0M
HSRD VA IVI 22-115NIGMS NIH HHS R35 GM138113
6 · The paper itself

Abstract

Cancer cells are heterogeneous, each harboring distinct molecular aberrations and are dependent on different genes for their survival and proliferation. While successful targeted therapies have been developed based on driver DNA mutations, many patient tumors lack druggable mutations and have limited treatment options. Here, we hypothesize that new precision oncology targets may be identified through "expression-driven dependency", whereby cancer cells with high expression of a targeted gene are more vulnerable to the knockout of that gene. We introduce a Bayesian approach, BEACON, to identify such targets by jointly analyzing global transcriptomic and proteomic profiles with genetic dependency data of cancer cell lines across 17 tissue lineages. BEACON identifies known druggable genes, e.g.,

Identifiers

PMID39484404
PMCPMC11527036

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.