Prevalence and outcomes of cancer and treatment-associated toxicities for patients with ataxia telangiectasia
Bibliographic record
Abstract
BACKGROUND: Ataxia telangiectasia (A-T) is a DNA repair disorder with cancer predisposition. OBJECTIVE: We sought to characterize the prevalence and outcomes of hematologic and solid cancers and treatment-associated toxicities in individuals with A-T. METHODS: Data were retrospectively analyzed from the Johns Hopkins Ataxia Telangiectasia Clinical Center cohort. Cumulative incidence and standardized incidence ratios of cancer, survival probability after cancer diagnosis, and standardized mortality ratios were calculated. Cox regression estimated risk of death on the basis of chemotherapy (standard vs reduced) dosing, and multivariable logistic regression evaluated cancer risk associations with ataxia telangiectasia mutated (ATM) exons and variants. RESULTS: Eighty-four (16.5%) of 508 individuals were diagnosed with a primary cancer, of whom 62 (74%) were hematologic in origin and 22 (26%) were solid-organ cancers. The cumulative incidence of cancer was 29% by age 35 years. Non-Hodgkin lymphoma occurred most frequently (n = 39), whereas solid cancers disproportionately affected those 18 years and older (n = 22). The standardized mortality ratio was 24.6 (95% CI, 21.1-28.4) overall and 232.9 (95% CI,178.1-299.2) among individuals with cancer. Risk of death was higher when treated with standard/unknown versus modified chemotherapy (hazard ratio, 2.2; 95% CI, 1.1-4.4; P = .024). Chemotherapy-associated toxicities developed in 58% of individuals, predominantly neurologic (n = 14) and gastrointestinal (n = 10) systems. Three exons were enriched for cancer-associated variants. CONCLUSIONS: Individuals with A-T experience a wide array of blood and solid-organ malignancies, high mortality rates, and treatment-related toxicities, highlighting need for targeted therapies to mitigate toxicity and optimize survival.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".