Evidence map›Paper›PMID 38656686›Full record

ReviewCurrent treatment options in oncology2024

Molecular Heterogeneity in Leiomyosarcoma and Implications for Personalised Medicine.

Sara Arfan, Khin Thway, Robin L Jones, Paul H Huang

Abstract readReview
PubMed Publisher
In one paragraph

Review in Current treatment options in oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.4field-weighted citation impact, top 38% 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

2 citing papers in PubMed, 1 citations in OpenAlex.

  1. Article
  2. 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

4 authors at 2 institutions in 1 country.

Sara ArfanDivision of Molecular Pathology, The Institute of Cancer Research, 15 Cotswold Road, Sutton, SM2 5NG, UK.
Khin ThwayDivision of Molecular Pathology, The Institute of Cancer Research, 15 Cotswold Road, Sutton, SM2 5NG, UK.
Robin L JonesThe Royal Marsden NHS Foundation Trust, Fulham Road, London, SW3 6JJ, UK.
Paul H HuangDivision of Molecular Pathology, The Institute of Cancer Research, 15 Cotswold Road, Sutton, SM2 5NG, UK. paul.huang@icr.ac.uk.
Institute of Cancer Research · GBRoyal Marsden NHS Foundation Trust · GB

Funding

Cancer Research UK C56167/A29363
6 · The paper itself

Abstract

opinion statementLeiomyosarcoma (LMS) is one of the more common subtypes of soft tissue sarcomas (STS), accounting for about 20% of cases. Differences in anatomical location, risk of recurrence and histomorphological variants contribute to the substantial clinical heterogeneity in survival outcomes and therapy responses observed in patients. There is therefore a need to move away from the current one-size-fits-all treatment approach towards a personalised strategy tailored for individual patients. Over the past decade, tissue profiling studies have revealed key genomic features and an additional layer of molecular heterogeneity among patients, with potential utility for optimal risk stratification and biomarker-matched therapies. Furthermore, recent studies investigating intratumour heterogeneity and tumour evolution patterns in LMS suggest some key features that may need to be taken into consideration when designing treatment strategies and clinical trials. Moving forward, national and international collaborative efforts to aggregate expertise, data, resources and tools are needed to achieve a step change in improving patient survival outcomes in this disease of unmet need.

Indexed as

Biomarkers, TumorGenetic HeterogeneityLeiomyosarcomaPrecision MedicineDisease ManagementDisease SusceptibilityHumansMolecular Targeted TherapyPrognosisBiomarkers, TumorBiomarkersLeiomyosarcomaPersonalised medicineTumour heterogeneity

Identifiers

PMID38656686
OpenAlexW4395075481

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.