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Record W4404038015 · doi:10.1101/2024.10.30.24315828

Clinical syndromes linked to biallelic germline variants in <i>MCM8</i> and <i>MCM9</i>

2024· preprint· en· W4404038015 on OpenAlexaff
Noah C. Helderman, Ting Yang, Claire Palles, Diantha Terlouw, Hailiang Mei, Ruben H.P. Vorderman, Davy Cats, Marjolijn C.J. Jongmans, Ashwin Ramdien, Mariano Golubicki, Marina Antelo, Laia Bonjoch, Mariona Terradas, Laura Valle, Ludmil B. Alexandrov, Hans Morreau, Tom van Wezel, Sergi Castellvı́-Bel, Yael Goldberg, Maartje Nielsen, Irma van de Beek, Thomas F. Eleveld, Andrew Green, Frederik J. Hes, Marry M. van den Heuvel‐Eibrink, Annelore Van Der Kelen, Sabine Kliesch, Roland P. Kuiper, Inge M. M. Lakeman, Lisa E.E.L.O. Lashley, Leendert H. J. Looijenga, Manon S. Oud, Johanna Steingröver, Yardena Tenenbaum‐Rakover, Carli M.J. Tops, Frank Tüttelmann, Richarda M. de Voer, Dineke Westra, Margot J. Wyrwoll

Bibliographic record

VenuemedRxiv · 2024
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenomics and Rare Diseases
Canadian institutionsInstitute of Cancer Research
Fundersnot available
KeywordsGermlineGeneticsBiologyGene

Abstract

fetched live from OpenAlex

Abstract Background MCM8 and MCM9 are newly proposed cancer predisposition genes, linked to polyposis and early-onset cancer, in addition to their association with hypogonadism. Given the uncertain range of phenotypic manifestations and unclear cancer risk estimates, this study aimed to delineate the molecular and clinical characteristics of individuals with biallelic germline MCM8 / MCM9 variants. Methods Population allele frequencies and biallelic variant carrier frequencies were calculated using data from gnomAD, and a variant enrichment analysis was conducted across multiple cancer and non-cancer phenotypes using data from the 100K Genomes Project and the 200K exome release of the UK Biobank. A case series was conducted, including previously reported variant carriers with and without updated clinical data and newly identified carriers through the European Reference Network (ERN) initiative for rare genetic tumor risk syndromes (GENTURIS). Additionally, mutational signature analysis was performed on tumor data from our case series and publicly available datasets from the Hartwig Medical Foundation and TCGA Pan-Cancer Atlas to identify mutational signatures potentially associated with MCM8/MCM9 deficiency. Results Predicted loss of function and missense variants in MCM8 (1.4 per 100,000 individuals) and MCM9 (2.5 per 100,000 individuals) were found to be rare in gnomAD. However, biallelic MCM9 variants showed significant enrichment in cases from the 100K Genomes Project compared to controls for colonic polyps (odds ratio (OR) 6.51, 95% confidence interval (CI) 1.24–34.11; P = 0.03), rectal polyps (OR 8.40, 95% CI 1.28–55.35; P = 0.03), and gastric cancer (OR 27.03, 95% CI 2.93– 248.5; P = 0.004). No significant enrichment was found for biallelic MCM8 variant carriers or in the 200K UK Biobank. In our case series, which included 26 biallelic MCM8 and 28 biallelic MCM9 variant carriers, we documented polyposis, gastric cancer, and early-onset colorectal cancer in 6, 1, and 6 biallelic MCM9 variant carriers, respectively, while these phenotypes were not observed in biallelic MCM8 variant carriers. Additionally, our case series indicates that, beyond hypogonadism—which was present in 23 and 26 of the carriers, respectively—biallelic MCM8 and MCM9 variants are associated with early-onset germ cell tumors (occurring before age 15) in 2 MCM8 and 1 MCM9 variant carriers. Tumors from MCM8 / MCM9 variant carriers with available germline sequencing data predominantly displayed clock-like mutational processes (single base substitution signatures 1 and 5), with no evidence of signatures associated with DNA repair deficiencies. Discussion Our data indicates that biallelic MCM9 variants are associated with polyposis, gastric cancer, and early-onset CRC, while both biallelic MCM8 and MCM9 variants are linked to hypogonadism and the early development of germ cell tumors. These findings underscore the importance of including MCM8 / MCM9 in diagnostic gene panels for certain clinical contexts and suggest that biallelic carriers may benefit from cancer surveillance.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0040.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.026
GPT teacher head0.317
Teacher spread0.292 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations1
Published2024
Admission routes1
Has abstractyes

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