Recessive FANCM cancer syndrome with high cancer risks, chemotherapy toxicity, chromosome fragility, and gonadal failure
Bibliographic record
Abstract
PURPOSE: Heterozygous FANCM variants have been associated with breast cancer. Only a few studies have examined other cancer types. Biallelic truncating variants have been linked to a Fanconi anemia (FA)-like cancer prone syndrome in case reports; however, the range of cancers and the risk estimates are lacking. METHODS: We studied the association of Finnish-enriched variants c.5101C>T p.(Gln1701Ter) and c.5791C>T p.(Arg1931Ter) with risk of any cancer and FA-related conditions in the FinnGen data with 500,348 individuals. RESULTS: ), suggesting a risk effect wider than previously described. Homozygous c.5101C>T (N = 76) was associated with a high risk of breast, head and neck, gastrointestinal, gynecological, hematologic, skin, and lung cancer, whereas c.5791C>T was rare. Additionally, high recessive risks of ovarian dysfunction and hematologic side effects after cancer treatment were detected, but no risks of bone marrow failure or physical features of FA. CONCLUSION: Based on the pattern of risks associated with biallelic variants, we suggest a novel FANCM cancer syndrome that is separate from FA and other characterized cancer susceptibility syndromes.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".