Evidence map›Paper›PMID 39338198›Full record

ReviewJournal of personalized medicine2024

Mass Spectrometry-Based Proteomics for Classification and Treatment Optimisation of Triple Negative Breast Cancer.

Essraa Metwali, Stephen Pennington

Abstract readReview
In one paragraph

Review in Journal of personalized medicine, 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
–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

2 citing papers in PubMed.

  1. Review
  2. 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

2 authors.

Essraa MetwaliSchool of Medicine, UCD Conway Institute for Biomolecular Research, University College Dublin, D04 C1P1 Dublin, Ireland.
Stephen PenningtonSchool of Medicine, UCD Conway Institute for Biomolecular Research, University College Dublin, D04 C1P1 Dublin, Ireland.ORCID 0000-0001-7529-1015

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Triple-negative breast cancer (TNBC) presents a significant medical challenge due to its highly invasive nature, high rate of metastasis, and lack of drug-targetable receptors, which together lead to poor prognosis and limited treatment options. The traditional treatment guidelines for early TNBC are based on a multimodal approach integrating chemotherapy, surgery, and radiation and are associated with low overall survival and high relapse rates. Therefore, the approach to treating early TNBC has shifted towards neoadjuvant treatment (NAC), given to the patient before surgery and which aims to reduce tumour size, reduce the risk of recurrence, and improve the pathological complete response (pCR) rate. However, recent studies have shown that NAC is associated with only 30% of patients achieving pCR. Thus, novel predictive biomarkers are essential if treatment decisions are to be optimised and chemotherapy toxicities minimised. Given the heterogeneity of TNBC, mass spectrometry-based proteomics technologies offer valuable tools for the discovery of targetable biomarkers for prognosis and prediction of toxicity. These biomarkers can serve as critical targets for therapeutic intervention. This review aims to provide a comprehensive overview of TNBC diagnosis and treatment, highlighting the need for a new approach. Specifically, it highlights how mass spectrometry-based can address key unmet clinical needs by identifying novel protein biomarkers to distinguish and early prognostication between TNBC patient groups who are being treated with NAC. By integrating proteomic insights, we anticipate enhanced treatment personalisation, improved clinical outcomes, and ultimately, increased survival rates for TNBC patients.

Indexed as

mass spectrometry (MS)multiple reaction monitoring (MRM)neoadjuvant chemotherapy (NAC)pathological complete response (pCR)triple-negative breast cancer (TNBC)

Identifiers

PMID39338198
PMCPMC11432759

What Socratic holds

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Registered trials

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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 Socratic graph.