ReviewCancers2025
Advances in Precision Oncology: From Molecular Profiling to Regulatory-Approved Targeted Therapies.
Review in Cancers, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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.
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.
Who cites it
7 citing papers in PubMed.
- Artificial intelligence in medicine: from molecular data analysis to clinical decision-making.Croatian medical journal · 2026Article
- Integrating multi-omics data for next-generation cancer research and precision medicine.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026Review
- Review
- Further Promise and Potential for Precision Medicine in Oncology.Journal of medical Internet research · 2026Article
- HER2/neu Expression in Low-Grade Serous Ovarian Carcinoma: A Pilot Two-Center Retrospective Study.Journal of clinical medicine · 2026Article
- A Review of 3D-Printed Medical Devices for Cancer Radiation Therapy.Bioengineering (Basel, Switzerland) · 2026Review
- Artificial intelligence in peptide cancer vaccine design: from neoantigen discovery to immunogenicity prediction.Frontiers in genetics · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The rapid evolution of sequencing technologies has profoundly advanced precision oncology. Whole-exome sequencing (WES), whole-genome sequencing (WGS), and whole-transcriptome sequencing (RNA-Seq) enable comprehensive characterization of tumor biology by detecting actionable mutations, gene fusions, splice variants, copy number alterations, and pathway dysregulation. These approaches also provide critical insights into biomarkers such as homologous recombination deficiency (HRD), tumor mutational burden (TMB), and microsatellite instability (MSI), which are increasingly essential for guiding therapeutic decisions. Importantly, comprehensive genomic profiling not only refines patient stratification for targeted therapies but also sheds light on tumor-immune interactions and the tumor microenvironment, paving the way for more effective immunotherapeutic combinations. WGS is considered the gold standard for detecting germline mutations and complex structural variants, while WES remains central for detecting somatic driver mutations that guide targeted therapies. RNA-Seq complements these methods by capturing gene expression dynamics, identifying clinically relevant fusions, and revealing mechanisms of resistance. Together with advances in bioinformatics and artificial intelligence, these tools translate molecular data into actionable strategies for patient care. This review integrates insights from WGS, WES, and RNA-Seq with an overview of FDA- and EMA-approved targeted therapies, organized by tumor type, and highlights the molecular signaling pathways that drive cancer development and treatment. By bridging genomic profiling with regulatory-approved therapies, we outline current advances and future perspectives in delivering personalized cancer care.
Indexed as
Identifiers
What Socratic holds
Registered trials
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.