Evidence map›Paper›PMID 39272814›Full record

ReviewCancers2024

Mutational Signatures in Colorectal Cancer: Translational Insights, Clinical Applications, and Limitations.

Giovanni Crisafulli

Abstract readReview
In one paragraph

Review in Cancers, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Review
  6. Review
  7. 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

1 author.

Giovanni CrisafulliIFOM ETS-The AIRC Institute of Molecular Oncology, 20139 Milano, Italy.ORCID 0000-0002-5511-1555

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

A multitude of exogenous and endogenous processes have the potential to result in DNA damage. While the repair mechanisms are typically capable of correcting this damage, errors in the repair process can result in mutations. The findings of research conducted in 2012 indicate that mutations do not occur randomly but rather follow specific patterns that can be attributed to known or inferred mutational processes. The process of mutational signature analysis allows for the inference of the predominant mutational process for a given cancer sample, with significant potential for clinical applications. A deeper comprehension of these mutational signatures in CRC could facilitate enhanced prevention strategies, facilitate the comprehension of genotoxic drug activity, predict responses to personalized treatments, and, in the future, inform the development of targeted therapies in the context of precision oncology. The efforts of numerous researchers have led to the identification of several mutational signatures, which can be categorized into different mutational signature references. In CRC, distinct mutational signatures are identified as correlating with mismatch repair deficiency, polymerase mutations, and chemotherapy treatment. In this context, a mutational signature analysis offers considerable potential for enhancing minimal residual disease (MRD) tests in stage II (high-risk) and stage III CRC post-surgery, stratifying CRC based on the impacts of genetic and epigenetic alterations for precision oncology, identifying potential therapeutic vulnerabilities, and evaluating drug efficacy and guiding therapy, as illustrated in a proof-of-concept clinical trial.

Indexed as

bioinformaticsclinical applicationclinical trialcolorectal cancerscomputational biologygeneticsgenomicsmutational signaturesoncologyprecision oncology

Identifiers

PMID39272814
PMCPMC11393898

What OpenQuestion holds

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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.