Abstract IA004: Risk of therapy-related subsequent cancers in individuals with germline <i>TP53</i> variants
Bibliographic record
Abstract
Abstract Background. Based primarily on in vitro and animal studies, germline TP53 variants are considered to confer sensitivity to ionizing radiation-induced DNA damage, but data in humans are sparse. We sought to quantify risk of developing radiotherapy and chemotherapy-related subsequent neoplasms by pooling individual-level data from nine cohorts of individuals with confirmed pathogenic/likely pathogenic germline TP53 variants. Methods. 891 individuals were followed from six months after first cancer until diagnosis of a subsequent neoplasm, death or date of last follow-up. Radiotherapy and chemotherapy for each cancer were considered as time-dependent covariates in Cox regression models with age as the time-scale, adjusted for sex and occurrence of prior malignant or benign tumors. Subsequent cancers were classified as in- or out-of-field based on the body region of previous cancers. Results. Among 891 individuals, 643 (72.2%) were female and 297 (33.3%) were diagnosed with a first cancer before age 20 years. Breast cancer was the most commonly diagnosed first cancer (N=371), followed by soft tissue sarcoma (N=131) and central nervous system cancers (N=84). First cancer treatment included radiotherapy for 237 (26.6%) and/or chemotherapy for 421 (47.3%) individuals. Analyses are underway to define the characteristics of subsequent neoplasms (including age at diagnosis, latency, and distribution of diagnosis type) and to calculate relative and absolute risk estimates for radiotherapy and chemotherapy overall and by subsequent neoplasm type. Conclusions. This study will quantify the contribution of radiotherapy and chemotherapy to the risk of subsequent cancers among individuals with Li-Fraumeni syndrome. Understanding the magnitude of this risk will support health care providers and patients in making informed decisions about cancer management. Citation Format: Anita Villani, Payal Khincha, Jessica Hatton, Sara Schonfeld, Catherine Goudie, Leatrisse Oba, Kishan Pithadia, Birte Sänger, Christian Kratz, Anh N. Le, Caitlin Orr, Kara N. Maxwell, Anne Naumer, Jennie Vagher, Wendy Kohlmann, Joshua Schiffman, Elizabeth Santana dos Santos, Orli Michaeli, Jacob J. Carson, Derek S. Tsang, Raymond Kim, Jamie L. Maciaszek, Kim E. Nichols, Victoria E. Goodman, Suzanne P. MacFarland, Lara Reichman, Maria Isabel Achatz, Sharon Savage, David Malkin, Lindsay M. Morton. Risk of therapy-related subsequent cancers in individuals with germline TP53 variants [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pediatric Cancer Research; 2024 Sep 5-8; Toronto, Ontario, Canada. Philadelphia (PA): AACR; Cancer Res 2024;84(17 Suppl):Abstract nr IA004.
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 | 0.001 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".