Evidence map›Paper›PMID 32100151›Full record

ReviewClinical and experimental medicine2020

Patient-derived xenograft models for personalized medicine in colorectal cancer.

Jun Xie, Yan Lin

Abstract readReview
PubMed Publisher
In one paragraph

Review in Clinical and experimental medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.5field-weighted citation impact, top 34% 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

3 citing papers in PubMed, 9 citations in OpenAlex.

  1. Review
  2. Review
  3. Review
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

2 authors at 2 institutions in 1 country.

Jun XieGeneral Surgery, Affiliated Hospital of Shaoxing University, Shaoxing, 312000, Zhejiang, China.
Yan LinDepartment of Gastroenterology, Zhejiang Provincial People's Hospital (People's Hospital of Hangzhou Medical College), No. 158 Shangtang Road, Hangzhou, 310014, Zhejiang, China. linyan@hmc.edu.cn.ORCID http://orcid.org/0000-0002-1235-9147
Shaoxing University · CNZhejiang Provincial People's Hospital · CN

Funding

Zhejiang Provincial Science and Technology Projects 2017C37159
6 · The paper itself

Abstract

Establishing superior preclinical models is critical for translational cancer research owing to the high failure rates of novel therapeutics in clinical studies. Even though cell line-derived xenograft models are easy to create, they have numerous limitations since these models do not represent the distinctive features of each cancer patient adequately. To circumvent the discrepancies between xenograft models and tumors, patient-derived xenograft (PDX) models have been developed. These models are established through the engraftment of tissue from a patient's tumor into an immune-deficient mouse, which preserves cell-cell interactions and tumor microenvironment. Since PDXs precisely replicate intratumoral heterogeneity, a range of chemotherapeutic agents can be tested on individual tumors. Colorectal cancer represents a unique case to demonstrate clinical perspectives revealed by PDX models since they surmount limitations of conventional ex vivo models. Even though PDX models have been associated with drawbacks with respect to prediction of clinical outcomes, they are currently the model of choice for preclinical investigations in colorectal cancer. In the current review, we provide an overview of the methodology and applications of PDX for colorectal cancer and discuss critical issues for the advancement of these models for preclinical research.

Indexed as

AnimalsColorectal NeoplasmsHumansMiceMolecular Targeted TherapyPrecision MedicineXenograft Model Antitumor AssaysColorectal cancerImmune-deficient mousePatient-derived xenograftsPersonalized medicinePreclinical model

Identifiers

PMID32100151
OpenAlexW3008076936

What OpenQuestion holds

Textmetadata
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.