Evidence map›Paper›PMID 34249240›Full record

ArticleOncology reviews2021

Pre-clinical modelling of rectal cancer to develop novel radiotherapy-based treatment strategies.

Michael A Gillespie, Colin W Steele, Tamsin R M Lannagan, Owen J Sansom, Campbell S D Roxburgh

Open access · goldAbstract read
In one paragraph

Article in Oncology reviews, 2021. 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
0.4field-weighted citation impact, top 35% 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, 5 citations in OpenAlex.

  1. Review
  2. Review
  3. Article
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

5 authors at 2 institutions in 1 country.

Michael A GillespieCancer Research UK Beatson Institute, Glasgow, UK.
Colin W Steele
Tamsin R M LannaganCancer Research UK Beatson Institute, Glasgow, UK.
Owen J SansomCancer Research UK Beatson Institute, Glasgow, UK.
Campbell S D RoxburghInstitute of Cancer Sciences, University of Glasgow, Glasgow, UK.
Cancer Research UK Scotland Institute · GBUniversity of Glasgow · GB

Funding

Cancer Research UK 21139Chief Scientist Office SCAF/19/01
6 · The paper itself

Abstract

Pre-operative chemoradiotherapy reduces local recurrence rates in locally advanced rectal cancer. 10-20% of patients undergo complete response to chemoradiotherapy, however, many patients show no response. The mechanisms underlying this are poorly understood; identifying molecular and immunological factors underpinning heterogeneous responses to chemoradiotherapy, will promote development of treatment strategies to improve responses and overcome resistance mechanisms. This review describes the advances made in pre-clinical modelling of colorectal cancer, including genetically engineered mouse models, transplantation models, patient derived organoids and radiotherapy platforms to study responses to chemoradiotherapy. Relevant literature was identified through the PubMed and MEDLINE databases, using the following keywords: rectal cancer; mouse models; organoids; neo-adjuvant treatment; radiotherapy; chemotherapy. By delineating the advantages and disadvantages of available models, we discuss how modelling techniques can be utilized to address current research priorities in locally advanced rectal cancer. We provide unique insight into the potential application of pre-clinical models in the development of novel neo-adjuvant treatment strategies, which will hopefully guide future clinical trials.

Indexed as

chemotherapymouse modelsneo-adjuvant treatmentorganoidsradiotherapyRectal cancer

Identifiers

PMID34249240
PMCPMC8237517
OpenAlexW3174717488

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.