Evidence mapPaperPMID 40869045Full record

ReviewInternational journal of molecular sciences2025

Towards Post-Genomic Oncology: Embracing Cancer Complexity via Artificial Intelligence, Multi-Targeted Therapeutics, Drug Repurposing, and Innovative Study Designs.

Annabella Di Mauro, Massimiliano Berretta, Mariachiara Santorsola, Gerardo Ferrara, Carmine Picone, Giovanni Savarese, Alessandro Ottaiano

Abstract readReview
In one paragraph

Review in International journal of molecular sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

9 citing papers in PubMed.

  1. Article
  2. Review
  3. Further Promise and Potential for Precision Medicine in Oncology.Journal of medical Internet research · 2026
    Article
  4. Review
  5. Article
  6. Article
  7. Targeting tumor transition windows.Exploration of targeted anti-tumor therapy · 2026
    Review
  8. Review
  9. 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

7 authors.

Annabella Di MauroIstituto Nazionale Tumori di Napoli, IRCCS "G. Pascale", Via M. Semmola, 80131 Naples, Italy.ORCID 0000-0002-9128-3186
Massimiliano BerrettaDepartment of Clinical and Experimental Medicine, University of Messina, 98125 Messina, Italy.ORCID 0000-0002-9837-9148
Mariachiara SantorsolaIstituto Nazionale Tumori di Napoli, IRCCS "G. Pascale", Via M. Semmola, 80131 Naples, Italy.
Gerardo FerraraIstituto Nazionale Tumori di Napoli, IRCCS "G. Pascale", Via M. Semmola, 80131 Naples, Italy.ORCID 0000-0003-0727-4015
Carmine PiconeIstituto Nazionale Tumori di Napoli, IRCCS "G. Pascale", Via M. Semmola, 80131 Naples, Italy.
Giovanni SavareseDepartment of Genetics, AMES, Centro Polidiagnostico Strumentale srl, Via Padre Carmine Fico 24, 80013 Casalnuovo Di Napoli, Italy.ORCID 0009-0004-5274-3989
Alessandro OttaianoIstituto Nazionale Tumori di Napoli, IRCCS "G. Pascale", Via M. Semmola, 80131 Naples, Italy.ORCID 0000-0002-2901-3855

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Recent advances in precision oncology have led to significant breakthroughs through the targeting of defined oncogenic drivers. However, the clinical efficacy of single-target therapies is increasingly constrained by the intrinsic complexity and adaptability of cancer. Solid tumors frequently arise from multifactorial oncogenic processes and adapt via diverse resistance mechanisms, ultimately limiting the durability of monotherapies. This review advocates for a paradigm shift toward multi-targeted, AI-enhanced strategies that harness high-throughput multi-omic data to inform the rational design of combination therapies. By leveraging artificial intelligence for drug discovery and repurposing, response prediction, and clinical trial optimization, the field of oncology is poised to transcend reductionist approaches and more fully address the biological intricacy of cancer.

Indexed as

Artificial IntelligenceDrug RepositioningMedical OncologyMolecular Targeted TherapyNeoplasmsAntineoplastic AgentsDrug DiscoveryGenomicsHumansPrecision MedicineAntineoplastic Agentsartificial intelligencedrug repurposingnext-generation sequencingprecision oncologytarget therapytumor heterogeneity

Identifiers

PMID40869045
PMCPMC12387099

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.