Evidence map›Paper›PMID 37151885›Full record

ArticleInternational journal of biological sciences2023

Vistas in Non-Small Cell Lung Cancer (NSCLC) Treatment: of Kinome and Signaling Networks.

Dimitrios J Stravopodis, Kostas A Papavassiliou, Athanasios G Papavassiliou

Abstract readEditorial
In one paragraph

Article in International journal of biological sciences, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing 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

14 citing papers in PubMed.

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  13. An advanced NSCLC patient withTranslational lung cancer research · 2023
    Article
  14. 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

3 authors.

Dimitrios J StravopodisSection of Cell Biology and Biophysics, Department of Biology, National and Kapodistrian University of Athens, 15701 Athens, Greece.
Kostas A PapavassiliouFirst University Department of Respiratory Medicine, 'Sotiria' Hospital, Medical School, National and Kapodistrian University of Athens, 11527 Athens, Greece.
Athanasios G PapavassiliouDepartment of Biological Chemistry, Medical School, National and Kapodistrian University of Athens, Athens 11527, Greece.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Non-small cell lung cancer (NSCLC) is the prevailing lung cancer type, accounting for ~85% of all lung cancer cases. Despite their initial promise, current chemotherapeutic protocols are reaching their limits. This necessitates the prompt discovery of new molecular drivers and the development of novel regimens for advanced NSCLC. Herein, we pose that there is a need to systematically profile the human kinome activity of NSCLC. Using available state-of-the-art technologies, a wide gamut of kinase activities can be simultaneously mapped and quantified specifically in the primary or metastatic cancer states, with oncogenic kinase functions being likely linked to mutation signatures and malignant features of NSCLC. New chemical compound libraries can then be screened for kinase inhibitory properties in preclinical model systems, with presumptive induction of programmed cell-death subroutines and signaling-disintegration routes serving as major outputs of novel inhibitor tumor-suppressor potentials.

Indexed as

Carcinoma, Non-Small-Cell LungLung NeoplasmsCell Line, TumorGenes, Tumor SuppressorHumansPhosphorylationSignal TransductionHuman kinome activityNon-small cell lung cancer (NSCLC)Serine/threonine protein kinasesSignal transduction networksTherapeutic strategiesTyrosine protein kinases

Identifiers

PMID37151885
PMCPMC10158021

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.