Charis Eng: an appreciation
Bibliographic record
Abstract
was a distinguished clinician-scientist whose contributions in research, clinical care and medical education were wide-ranging and highly influential.She held editorial roles, including North American Editor of the Journal of Medical Genetics and Editor-in-Chief of Endocrine-Related Cancer and Human Molecular Genetics.She coauthored many scientific papers and monographs including the third and fourth editions of A Practical Guide to Human Cancer Genetics. 1 Her contribution to cancer genetics has been described in detail elsewhere, 2 3 but in this appreciation, as former coeditors on JMG, coauthors 1 and colleagues we reflect on her legacy.Born in Singapore and later moving to the USA via England, Charis was academically gifted, and she was admitted to the University of Chicago aged just 16 years.She attributed her interest in genetics to the influence of her high school biology teacher.After completing her residency in internal medicine at Beth Israel Hospital in Boston and a fellowship in medical oncology at Harvard's Dana-Farber Cancer Institute, she returned to England to undertake a Fellowship in cancer genetics at the University of Cambridge under the mentorship of Professor Sir Bruce Ponder.There, she helped identify germline mutations in RET in multiple endocrine neoplasia type 2 and made key contributions to understanding genotype-phenotype correlations in the disorder.[4][5][6] She maintained a lifelong interest in the clinical and molecular features of inherited predisposition to endocrine tumours, particularly phaeochromocytoma/paraganglioma, however, her greatest impact was in the field of PTEN hamartoma tumour syndrome (PHTS).After coleading the research that linked germline PTEN pathogenic variants to Cowden syndrome, 7 she proceeded to demonstrate that several related conditions such as Bannayan-Riley-Ruvalcaba syndrome, a Proteus-like overgrowth disorder and most notably, a subset of individuals with autism spectrum disorders and macrocephaly, were allelic. [8][9]][10] She also defined the role of somatic inactivation of PTEN in sporadic tumours including thyroid 11 and explored the effects of PTEN inactivation in dysregulating the phosphoinositol-3-kinase/Akt and other signalling pathways.Charis' work advanced both laboratory research and clinical management of PHTS, leading to improved diagnostic criteria, tumour surveillance guidelines and the development of the Cleveland Clinic PTEN Risk Calculator 12 ; (https://www.lerner.ccf.org/genomic-medicine/ccscore/).For the worldwide cancer genetics community, she was the go-to person for difficult cases.After Cambridge, Charis held positions at the Dana-Farber Cancer Institute and Ohio State University before she was invited to become the founding director of the Genomic Medicine Institute and the Center for Personalized Genetic Healthcare at the Cleveland Clinic, USA.Her bench to bedside translational research, commitment to precision medicine and her vision for transforming clinical care in inherited cancer predisposition syndromes earned her numerous honours, including election to the National Academy of Medicine (2010) and the American Cancer Society's Medal of Honor for Clinical Research (2018).Renowned for her mentorship, Charis supported many trainees and mentees, encouraging them to mentor both upwards and among their peers.She fostered a collaborative multidisciplinary environment, and recognising the need to build capacity and
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.304 | 0.126 |
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".