Evidence map›Paper›PMID 40851437›Full record

ReviewEpigenomics2025

Epigenetic synthetic lethality as a cancer therapeutic strategy: synergy of experimental and computational approaches.

Maria Farina-Morillas, Laia Ollé-Monràs, Silvana Ce Maas, Isabel de Rojas-P, Miguel F Segura, Jose A Seoane

Abstract readReview
In one paragraph

Review in Epigenomics, 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
–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

1 citing paper in PubMed.

  1. 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

6 authors.

Maria Farina-MorillasCancer Computational Biology Group, Vall d'Hebron Institute of Oncology (VHIO), Barcelona, Spain.ORCID 0000-0002-4378-7080
Laia Ollé-MonràsCancer Computational Biology Group, Vall d'Hebron Institute of Oncology (VHIO), Barcelona, Spain.ORCID 0000-0001-6058-0869
Silvana Ce MaasCancer Computational Biology Group, Vall d'Hebron Institute of Oncology (VHIO), Barcelona, Spain.ORCID 0000-0002-2087-4122
Isabel de Rojas-PChildhood Cancer and Blood Disorders Group, Vall d'Hebron Institut de Recerca (VHIR), Universitat Autònoma de Barcelona, Barcelona, Spain.ORCID 0009-0009-3690-139X
Miguel F SeguraChildhood Cancer and Blood Disorders Group, Vall d'Hebron Institut de Recerca (VHIR), Universitat Autònoma de Barcelona, Barcelona, Spain.ORCID 0000-0003-0916-3618
Jose A SeoaneCancer Computational Biology Group, Vall d'Hebron Institute of Oncology (VHIO), Barcelona, Spain.ORCID 0000-0002-3856-9177

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Cancer treatment is an ongoing challenge, as directly targeting oncogenic drivers is often unfeasible in many patients due to the lack of druggable targets. This has led to the exploration of alternative strategies, such as exploiting synthetic lethality (SL) relationships between genes. SL facilitates the indirect targeting of oncogenic drivers, as exemplified by the clinical success of PARP inhibitors against BRCA-mutated tumors. Advances in high-throughput perturbation screens and multi-omics technologies have deepened our understanding of SL relationships, while computational models enhance SL predictions to better reflect biological complexity. However, while numerous experimental and computational methods have been developed to identify SL interactions, difficulties remain in translating these findings into clinical applications.This review combines recent progress on SL relationships in cancer with emerging insights into epigenetic regulation, highlighting how epigenetic drugs (epidrugs) can provide new opportunities for targeted interventions, offering a way to minimize off-target effects and enhance therapeutic precision. To advance SL-based therapies, efforts must focus not only on identifying new SL interactions but also on consolidating existing knowledge and integrating experimental and computational approaches to characterize the vulnerabilities of cancer cells. Strengthening this foundation will be critical for the effective development of SL-based cancer treatments.

Indexed as

Antineoplastic AgentsEpigenesis, GeneticNeoplasmsSynthetic Lethal MutationsComputational BiologyHumansAntineoplastic Agentschromatin remodelingepidrugsepigeneticsmachine learningmethylationSynthetic lethality

Identifiers

PMID40851437
PMCPMC12520091

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
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.