Evidence map›Paper›PMID 38429380›Full record

ArticleNPJ precision oncology2024

Germline determinants of aberrant signaling pathways in cancer.

Davide Dalfovo, Riccardo Scandino, Marta Paoli, Samuel Valentini, Alessandro Romanel

Open access · goldAbstract read
In one paragraph

Article in NPJ precision oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
2.2field-weighted citation impact, top 13% 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

2 citing papers in PubMed, 4 citations in OpenAlex.

  1. Review
  2. 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

5 authors at 1 institution in 1 country.

Davide DalfovoDepartment of Cellular, Computational and Integrative Biology (CIBIO), University of Trento, 38123, Trento, (TN), Italy.ORCID http://orcid.org/0000-0002-9760-7655
Riccardo ScandinoDepartment of Cellular, Computational and Integrative Biology (CIBIO), University of Trento, 38123, Trento, (TN), Italy.ORCID http://orcid.org/0000-0002-1640-5307
Marta PaoliDepartment of Cellular, Computational and Integrative Biology (CIBIO), University of Trento, 38123, Trento, (TN), Italy.
Samuel ValentiniDepartment of Cellular, Computational and Integrative Biology (CIBIO), University of Trento, 38123, Trento, (TN), Italy.
Alessandro RomanelDepartment of Cellular, Computational and Integrative Biology (CIBIO), University of Trento, 38123, Trento, (TN), Italy. alessandro.romanel@unitn.it.ORCID http://orcid.org/0000-0003-4855-8620
University of Trento · IT

Funding

Fondazione Italiana per la Ricerca sul Cancro (Italian Foundation for Cancer Research) MFAG 20621
6 · The paper itself

Abstract

Cancer is a complex disease influenced by a heterogeneous landscape of both germline genetic variants and somatic aberrations. While there is growing evidence suggesting an interplay between germline and somatic variants, and a substantial number of somatic aberrations in specific pathways are now recognized as hallmarks in many well-known forms of cancer, the interaction landscape between germline variants and the aberration of those pathways in cancer remains largely unexplored. Utilizing over 8500 human samples across 33 cancer types characterized by TCGA and considering binary traits defined using a large collection of somatic aberration profiles across ten well-known oncogenic signaling pathways, we conducted a series of GWAS and identified genome-wide and suggestive associations involving 276 SNPs. Among these, 94 SNPs revealed cis-eQTL links with cancer-related genes or with genes functionally correlated with the corresponding traits' oncogenic pathways. GWAS summary statistics for all tested traits were then used to construct a set of polygenic scores employing a customized computational strategy. Polygenic scores for 24 traits demonstrated significant performance and were validated using data from PCAWG and CCLE datasets. These scores showed prognostic value for clinical variables and exhibited significant effectiveness in classifying patients into specific cancer subtypes or stratifying patients with cancer-specific aggressive phenotypes. Overall, we demonstrate that germline genetics can describe patients' genetic liability to develop specific cancer molecular and clinical profiles.

Identifiers

PMID38429380
PMCPMC10907629
OpenAlexW4392359753

What OpenQuestion 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 OpenQuestion graph.