In Memoriam: Pierre Hainaut (1958-2025)
Bibliographic record
Abstract
On behalf of the Li-Fraumeni Syndrome Consortium, it is with immense sorrow that we announce the passing of Professor Pierre Hainaut, an internationally respected scientist, who died while hiking in the Italian Alps on July 31, 2025, at the age of 67 years. His sudden departure has left a profound void in the scientific community and in the hearts of those who knew him—as a brilliant scientist, a generous mentor, and a deeply compassionate human being. Born in Liège, Belgium, Pierre Hainaut was a pioneer in cancer biology, whose career was defined by groundbreaking work on the TP53 tumor suppressor gene. His research transformed our understanding of the molecular mechanisms underlying tumor development, particularly through his landmark contributions to the classification and interpretation of TP53 mutations. In this regard, he played a central role in developing the TP53 mutation database, initially housed at the International Agency for Research on Cancer and now at the National Cancer Institute, which has become an essential resource for researchers worldwide. His keen interest and expertise in the field of genetic epidemiology enabled him to explore patterns of tumor presentation in the context of genetic predisposition across different population demographics. This work underpinned and highlighted the vast inequities around the globe in access to genetic testing and care for individuals with cancer predisposition. Pierre dedicated his time and efforts to help colleagues address and identify solutions to these challenges. In recent years, he was immersed in efforts to unravel ways to use functional assays to stratify the cancer risks associated with TP53 variants and the complex relationship between TP53 genotype and immune profiling. It will be incumbent on his colleagues to continue this work.
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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 | 0.002 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.015 |
| Insufficient payload (model declined to judge) | 0.032 | 0.036 |
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".