Evidence map›Paper›PMID 38445692›Full record

ReviewCurrent cancer drug targets2025

A Review on Patient-derived 3D Micro Cancer Approach for Drug Screen in Personalized Cancer Medicine.

Zulal Atlı Sekeroglu, Vedat Sekeroglu

Abstract readReview
PubMed Publisher
In one paragraph

Review in Current cancer drug targets, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.7field-weighted citation impact, top 35% 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

1 citing paper in PubMed, 4 citations in OpenAlex.

  1. 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 at 1 institution in 1 country.

Zulal Atlı SekerogluDepartment of Molecular Biology and Genetics, Faculty of Science and Letters, Ordu University, Ordu, Turkey.ORCID 0000-0002-3552-3819
Vedat SekerogluDepartment of Molecular Biology and Genetics, Faculty of Science and Letters, Ordu University, Ordu, Turkey.ORCID 0000-0002-8547-6571
Ordu University · TR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Precision medicine in oncology aims to identify an individualized treatment plan based on genomic alterations in a patient's tumor. It helps to select the most beneficial therapy for an individual patient. As it is now known that no patient's cancer is the same, and therefore, different patients may respond differently to conventional treatments, precision medicine, which replaces the one-size-fits-all approach, supports the development of tailored treatments for specific cancers of different patients. Patient-specific organoid or spheroid models as 3D cell culture models are very promising for predicting resistance to anti-cancer drugs and for identifying the most effective cancer therapy for high-throughput drug screening combined with genomic analysis in personalized medicine. Because tumor spheroids incorporate many features of solid tumors and reflect resistance to drugs and radiation, as in human cancers, they are widely used in drug screening studies. Testing patient-derived 3D cancer spheroids with some anticancer drugs based on information from molecular profiling can reveal the sensitivity of tumor cells to drugs and provide the right compounds to be effective against resistant cells. Given that many patients do not respond to standard treatments, patient-specific treatments will be more effective, less toxic. They will affect survival better compared to the standard approach used for all patients.

Indexed as

Antineoplastic AgentsNeoplasmsPrecision MedicineSpheroids, CellularDrug Screening Assays, AntitumorHumansAntineoplastic Agents3D cultureCancerchemotherapy.drug screeningprecision medicinespheroid

Identifiers

PMID38445692
OpenAlexW4392512844

What Socratic 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 Socratic graph.