Evidence map›Paper›PMID 37315874›Full record

ArticleGenomics2023

Genomic screening methodology not requiring barcoding: Single nucleotide polymorphism-based, mixed-cell screening (SMICS).

Zhuwei Zhang, Xi Chen, Wen Zhang, Jinpeng Liu, Yanqi Xie, Shulin Zhang, Arnold J Stromberg, David S Watt, Xifu Liu, Chi Wang and 1 more

Abstract read
In one paragraph

Article in Genomics, 2023. 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

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Zhuwei ZhangDepartment of Statistics, College of Arts and Sciences, University of Kentucky, Lexington, KY 40506, United States of America.
Xi ChenLucille Parker Markey Cancer Center, University of Kentucky, Lexington, KY 40536, United States of America; Center for Drug Innovation and Discovery, Hebei Normal University, Shijiazhuang, Hebei 050024, People's Republic of China.
Wen ZhangLucille Parker Markey Cancer Center, University of Kentucky, Lexington, KY 40536, United States of America; Department of Molecular and Cellular Biochemistry, College of Medicine, University of Kentucky, Lexington, KY 40536, United States of America.
Jinpeng LiuLucille Parker Markey Cancer Center, University of Kentucky, Lexington, KY 40536, United States of America.
Yanqi XieLucille Parker Markey Cancer Center, University of Kentucky, Lexington, KY 40536, United States of America; Department of Molecular and Cellular Biochemistry, College of Medicine, University of Kentucky, Lexington, KY 40536, United States of America.
Shulin ZhangDepartment of Pathology and Laboratory Medicine, College of Medicine, University of Kentucky, Lexington, KY 40536, United States of America.
Arnold J StrombergDepartment of Statistics, College of Arts and Sciences, University of Kentucky, Lexington, KY 40506, United States of America.
David S WattLucille Parker Markey Cancer Center, University of Kentucky, Lexington, KY 40536, United States of America; Department of Molecular and Cellular Biochemistry, College of Medicine, University of Kentucky, Lexington, KY 40536, United States of America.
Xifu LiuCenter for Drug Innovation and Discovery, Hebei Normal University, Shijiazhuang, Hebei 050024, People's Republic of China. Electronic address: xfliu@hebtu.edu.cn.
Chi WangLucille Parker Markey Cancer Center, University of Kentucky, Lexington, KY 40536, United States of America; Department of Internal Medicine, College of Medicine, University of Kentucky, Lexington, KY 40506, United States of America. Electronic address: chi.wang@uky.edu.
Chunming LiuLucille Parker Markey Cancer Center, University of Kentucky, Lexington, KY 40536, United States of America; Department of Molecular and Cellular Biochemistry, College of Medicine, University of Kentucky, Lexington, KY 40536, United States of America. Electronic address: chunming.liu@uky.edu.

Funding

University of Kentucky Markey Cancer Center Support Grant ECIA SupplementP30CA177558 · NCI · UNIVERSITY OF KENTUCKY · PI Bernard Mark Evers · 2013 to 2026
$38.3M
Kentucky Center for Clinical and Translational ScienceUL1TR001998 · NCATS · UNIVERSITY OF KENTUCKY · PI HARTMANN, KATHERINE E, KERN, PHILIP A · 2016 to 2025
$34.2M
NCATS NIH HHS UL1 TR001998NCI NIH HHS P30 CA177558
6 · The paper itself

Abstract

Although high-throughput, cancer cell-line screening is a time-honored, important tool for anti-cancer drug development, this process involves the testing of each, individual drug in each, individual cell-line. Despite the availability of robotic liquid handling systems, this process remains a time-consuming and costly investment. The Broad Institute developed a new method called Profiling Relative Inhibition Simultaneously in Mixtures (PRISM) to screen a mixture of barcoded, tumor cell-lines. Although this methodology significantly improved the efficiency of screening large numbers of cell-lines, the barcoding process itself was tedious that requires gene transfection and subsequent selection of stable cell-lines. In this study, we developed a new, genomic approach for screening multiple cancer cell-lines using endogenous "tags" that did not require prior barcoding: single nucleotide polymorphism-based, mixed-cell screening (SMICS). The code for SMICS is available at https://github.com/MarkeyBBSRF/SMICS.

Indexed as

Antineoplastic AgentsPolymorphism, Single NucleotideCell Line, TumorGenomicsHigh-Throughput Nucleotide SequencingAntineoplastic AgentsCell-line screeningDrug screeningSingle nucleotide polymorphismsWhole exome sequencing

Identifiers

PMID37315874
PMCPMC10551848

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.