Evidence map›Paper›PMID 38143269›Full record

ArticleGenome medicine2023

Systematic analysis of Mendelian disease-associated gene variants reveals new classes of cancer-predisposing genes.

Seulki Song, Youngil Koh, Seokhyeon Kim, Sang Mi Lee, Hyun Uk Kim, Jung Min Ko, Se-Hoon Lee, Sung-Soo Yoon, Solip Park

Open access · goldAbstract read
In one paragraph

Article in Genome medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
1.3field-weighted citation impact, top 16% 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

3 citing papers in PubMed, 4 citations in OpenAlex.

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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

9 authors at 5 institutions in 4 countries.

Seulki Song *Cancer Research Institute, Seoul National University College of Medicine, Seoul, 03080, Republic of Korea.
Youngil Koh *Cancer Research Institute, Seoul National University College of Medicine, Seoul, 03080, Republic of Korea.
Seokhyeon Kim *Cancer Research Institute, Seoul National University College of Medicine, Seoul, 03080, Republic of Korea.
Sang Mi LeeDepartment of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea.
Hyun Uk KimDepartment of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea. ehukim@kaist.ac.kr.
Jung Min KoDepartment of Pediatrics, Seoul National University Children's Hospital, Seoul National University College of Medicine, Seoul, 03080, Republic of Korea. jmko@snu.ac.kr.
Se-Hoon LeeDivision of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, 06351, Republic of Korea. sehoon.lee119@gmail.com.
Sung-Soo YoonCancer Research Institute, Seoul National University College of Medicine, Seoul, 03080, Republic of Korea. ssysmc@snu.ac.kr.
Solip ParkStructural Biology Program, Centro Nacional de Investigaciones Oncológicas (CNIO), Calle de Melchor Fernández Almagro, 3, Madrid, 28029, Spain. solippark@cnio.es.ORCID 0000-0002-5988-5339
Korea Advanced Institute of Science and Technology · KRSeoul National University · KRCancer Research Institute · USSeoul National University Children's Hospital · KRSpanish National Cancer Research Centre · ES

Funding

Agencia Estatal de Investigación PID2019-109571RA-I00College of Medicine, Seoul National University 1120190020Korea Health Industry Development Institute HI18C1876National Research Foundation of Korea 2021R1A2C3005360Seoul National University Hospital 03-2020-0380
6 · The paper itself

Abstract

backgroundDespite the acceleration of somatic driver gene discovery facilitated by recent large-scale tumor sequencing data, the contribution of inherited variants remains largely unexplored, primarily focusing on previously known cancer predisposition genes (CPGs) due to the low statistical power associated with detecting rare pathogenic variant-phenotype associations.

methodsHere, we introduce a generalized log-regression model to measure the excess of pathogenic variants within genes in cancer patients compared to control samples. It aims to measure gene-level cancer risk enrichment by collapsing rare pathogenic variants after controlling the population differences across samples.

resultsIn this study, we investigate whether pathogenic variants in Mendelian disease-associated genes (OMIM genes) are enriched in cancer patients compared to controls. Utilizing data from PCAWG and the 1,000 Genomes Project, we identify 103 OMIM genes demonstrating significant enrichment of pathogenic variants in cancer samples (FDR 20%). Through an integrative approach considering three distinct properties, we classify these CPG-like OMIM genes into four clusters, indicating potential diverse mechanisms underlying tumor progression. Further, we explore the function of PAH (a key metabolic enzyme associated with Phenylketonuria), the gene exhibiting the highest prevalence of pathogenic variants in a pan-cancer (1.8%) compared to controls (0.6%).

conclusionsOur findings suggest a possible cancer progression mechanism through metabolic profile alterations. Overall, our data indicates that pathogenic OMIM gene variants contribute to cancer progression and introduces new CPG classifications potentially underpinning diverse tumorigenesis mechanisms.

Indexed as

Genetic Predisposition to DiseaseNeoplasmsHumansPhenotypeRiskCancer genomicsCancer predisposition geneMendelian disease-associated genePathogenic variant

Identifiers

PMID38143269
PMCPMC10749499
OpenAlexW4390176032

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LicenceCC BY
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Registered trials

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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.