Evidence map›Paper›PMID 39109701›Full record

ReviewMolecular oncology2024

Precision Cancer Medicine 2.0-Oncology in the postgenomic era.

Riccardo Masina, Carlos Caldas

Abstract readReview
In one paragraph

Review in Molecular oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

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

Riccardo MasinaCancer Research UK Cambridge Institute and Department of Oncology, Li Ka Shing Centre, University of Cambridge, UK.
Carlos CaldasSchool of Clinical Medicine, University of Cambridge, UK.ORCID 0000-0003-3547-1489

Funding

CRUK RCCPDB-May23/100003European Research Council
6 · The paper itself

Abstract

Genomic medicine has transformed the lives of patients with cancer by enabling individualised and evidence-based clinical decision-making. Despite this progress, the implementation of precision cancer medicine is limited by its dependence on isolated biomarkers. The development of bulk and single-cell multiomic technologies has revealed the enormous complexity of the cancer ecosystem. Beyond the cancer cell, the tumour microenvironment, macroenvironment and host factors, including the microbiome, profoundly influence the cancer phenotype, and accounting for these enhances the resolution of precision medicine. The advent of robust multiomic profiling and interpretable machine learning algorithms mark the dawn of a new postgenomic era of personalised cancer medicine. In Precision Cancer Medicine 2.0, high-resolution personalised clinical decision-making is informed by the comprehensive multiomic profiling of tumour and host, integrated using artificial intelligence.

Indexed as

GenomicsNeoplasmsPrecision MedicineHumansMachine LearningMedical OncologyTumor Microenvironmentcancerdata integrationmachine learningprecision cancer medicinetranslational researchtumour biomarkers

Identifiers

PMID39109701
PMCPMC11467786

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.