A global functional analysis of missense mutations reveals two major hotspots in the PALB2 tumor suppressor
Bibliographic record
Abstract
While biallelic mutations in the PALB2 tumor suppressor cause Fanconi anemia subtype FA-N, monoallelic mutations predispose to breast and familial pancreatic cancer. Although hundreds of missense variants in PALB2 have been identified in patients to date, only a few have clear functional and clinical relevance. Herein, we investigate the effects of 44 PALB2 variants of uncertain significance found in breast cancer patients and provide detailed analysis by systematic functional assays. Our comprehensive functional analysis reveals two hotspots for potentially deleterious variations within PALB2, one at each terminus. PALB2 N-terminus variants p.P8L [c.23C>T], p.Y28C [c.83A>G], and p.R37H [c.110G>A] compromised PALB2-mediated homologous recombination. At the C-terminus, PALB2 variants p.L947F [c.2841G>T], p.L947S [c.2840T>C], and most strikingly p.T1030I [c.3089C>T] and p.W1140G [c.3418T>C], stood out with pronounced PARP inhibitor sensitivity and cytoplasmic accumulation in addition to marked defects in recruitment to DNA damage sites, interaction with BRCA2 and homologous recombination. Altogether, our findings show that a combination of functional assays is necessary to assess the impact of germline missense variants on PALB2 function, in order to guide proper classification of their deleteriousness.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".