Evidence map›Paper›PMID 35906256›Full record

ArticleScientific reports2022

CombPDX: a unified statistical framework for evaluating drug synergism in patient-derived xenografts.

Licai Huang, Jing Wang, Bingliang Fang, Funda Meric-Bernstam, Jack A Roth, Min Jin Ha

Open access · goldAbstract read
In one paragraph

Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed, 1 pooled it
1.6field-weighted citation impact, top 16% of its field
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

14 citing papers in PubMed, 1 synthesis or guideline pooled it, 20 citations in OpenAlex.

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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

6 authors at 3 institutions in 2 countries.

Licai HuangDepartment of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA.
Jing WangDepartments of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA.
Bingliang FangDepartment of Thoracic and Cardiovascular Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA.
Funda Meric-BernstamDepartment of Investigational Cancer Therapeutics, Division of Cancer Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA.
Jack A RothDepartment of Thoracic and Cardiovascular Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA.
Min Jin HaDepartment of Biostatistics, Graduate School of Public Health, Yonsei University, Seoul, South Korea. mjha@yuhs.ac.
The University of Texas MD Anderson Cancer Center · USThe University of Texas Health Science Center at Houston · USYonsei University · KR

Funding

UNIVERSITY OF TEXAS--SPORE IN LUNG CANCERP50CA070907 · NCI · UT SOUTHWESTERN MEDICAL CENTER · PI HEYMACH, JOHN V., MINNA, JOHN D. · 1996 to 2024
$57.4M
UCSF Project 1U54CA224081 · NCI · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI Trever G Bivona, Jack Roth · 2017 to 2026
$12.9M
University of Texas PDX Development and Trial CenterU54CA224065 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI WANG, JING · 2017 to 2022
$7.7M
Strategies for Cell Death Manipulation in CancerP01CA078778 · NCI · UNIVERSITY OF TEXAS MD ANDERSON CAN CTR · PI ROTH, JACK · 1999 to 2003
$4.4M
Bayesian Network-Based Integrative Genomics Methods for Precision MedicineR01CA244845 · NCI · UNIVERSITY OF PENNSYLVANIA · PI BALADANDAYUTHAPANI, VEERABHADRAN, MORRIS, JEFFREY S · 2021 to 2024
$1.8M
NCI NIH HHS P01 CA078778NCI NIH HHS P50 CA070907NCI NIH HHS R01 CA244845NCI NIH HHS U54 CA224065NCI NIH HHS U54 CA224081NIH HHS 2P50CA070907-21A1NIH HHS 5U54CA224065-04
6 · The paper itself

Abstract

Anticancer combination therapy has been developed to increase efficacy by enhancing synergy. Patient-derived xenografts (PDXs) have emerged as reliable preclinical models to develop effective treatments in translational cancer research. However, most PDX combination study designs focus on single dose levels, and dose-response surface models are not appropriate for testing synergism. We propose a comprehensive statistical framework to assess joint action of drug combinations from PDX tumor growth curve data. We provide various metrics and robust statistical inference procedures that locally (at a fixed time) and globally (across time) access combination effects under classical drug interaction models. Integrating genomic and pharmacological profiles in non-small-cell lung cancer (NSCLC), we have shown the utilities of combPDX in discovering effective therapeutic combinations and relevant biological mechanisms. We provide an interactive web server, combPDX ( https://licaih.shinyapps.io/CombPDX/ ), to analyze PDX tumor growth curve data and perform power analyses.

Indexed as

Carcinoma, Non-Small-Cell LungLung NeoplasmsAnimalsDisease Models, AnimalDrug SynergismHeterograftsHumansXenograft Model Antitumor Assays

Identifiers

PMID35906256
PMCPMC9338066
OpenAlexW4288694721

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.