Evidence map›Paper›PMID 39590338›Full record

ReviewCurrent issues in molecular biology2024

From Genomic Exploration to Personalized Treatment: Next-Generation Sequencing in Oncology.

Vishakha Vashisht, Ashutosh Vashisht, Ashis K Mondal, Jana Woodall, Ravindra Kolhe

Abstract readReview
In one paragraph

Review in Current issues in molecular biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing 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

15 citing papers in PubMed.

  1. Review
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  4. Special Issue "Molecular Progression in Genome-Related Diseases".International journal of molecular sciences · 2026
    Article
  5. Article
  6. Review
  7. Article
  8. Review
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  12. Review
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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

5 authors.

Vishakha VashishtDepartment of Pathology, Medical College of Georgia, Augusta University, Augusta, GA 30912, USA.
Ashutosh VashishtDepartment of Pathology, Medical College of Georgia, Augusta University, Augusta, GA 30912, USA.
Ashis K MondalDepartment of Pathology, Medical College of Georgia, Augusta University, Augusta, GA 30912, USA.ORCID 0000-0003-3826-9489
Jana WoodallDepartment of Pathology, Medical College of Georgia, Augusta University, Augusta, GA 30912, USA.
Ravindra KolheDepartment of Pathology, Medical College of Georgia, Augusta University, Augusta, GA 30912, USA.ORCID 0000-0002-8283-2403

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Next-generation sequencing (NGS) has revolutionized personalized oncology care by providing exceptional insights into the complex genomic landscape. NGS offers comprehensive cancer profiling, which enables clinicians and researchers to better understand the molecular basis of cancer and to tailor treatment strategies accordingly. Targeted therapies based on genomic alterations identified through NGS have shown promise in improving patient outcomes across various cancer types, circumventing resistance mechanisms and enhancing treatment efficacy. Moreover, NGS facilitates the identification of predictive biomarkers and prognostic indicators, aiding in patient stratification and personalized treatment approaches. By uncovering driver mutations and actionable alterations, NGS empowers clinicians to make informed decisions regarding treatment selection and patient management. However, the full potential of NGS in personalized oncology can only be realized through bioinformatics analyses. Bioinformatics plays a crucial role in processing raw sequencing data, identifying clinically relevant variants, and interpreting complex genomic landscapes. This comprehensive review investigates the diverse NGS techniques, including whole-genome sequencing (WGS), whole-exome sequencing (WES), and single-cell RNA sequencing (sc-RNA-Seq), elucidating their roles in understanding the complex genomic/transcriptomic landscape of cancer. Furthermore, the review explores the integration of NGS data with bioinformatics tools to facilitate personalized oncology approaches, from understanding tumor heterogeneity to identifying driver mutations and predicting therapeutic responses. Challenges and future directions in NGS-based cancer research are also discussed, underscoring the transformative impact of these technologies on cancer diagnosis, management, and treatment strategies.

Indexed as

next-generation sequencingpersonalized treatmentRNA sequencingwhole-exome sequencingwhole-genome sequencing

Identifiers

PMID39590338
PMCPMC11592618

What Socratic 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 Socratic graph.