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Record W2739823755 · doi:10.1158/1538-7445.am2017-3001

Abstract 3001: Germline mutations in cancer predisposition genes and risk for subsequent neoplasms among long-term survivors of childhood cancer in the St. Jude Lifetime Cohort

2017· article· en· W2739823755 on OpenAlexaff
Zhaoming Wang, Carmen L. Wilson, John Easton, Dale J. Hedges, Qi Liu, Gang Wu, Michael Rusch, Michael N. Edmonson, Shawn Levy, Jennifer Q. Lanctot, Eric Caron, Kyla Shelton, Kelsey Currie, Matthew Lear, Heather L. Mulder, Donald Yergeau, Celeste Rosencrance, Bhavin Vadodaria, Yadav Sapkota, Russell J. Brooke, Wonjong Moon, Evadnie Rampersaud, Xiaotu Ma, Shuoguo Wang, Ti‐Cheng Chang, Stephen V. Rice, Andrew Thrasher, Aman Patel, Cynthia Pepper, Xin Zhou, Xiang Chen, Wenan Chen, Angela Jones, Braden Boone, Deo Kumar Srivastava, Chimene Kesserwan, Kim E. Nichols, James R. Downing, Melissa M. Hudson, Yutaka Yasui, Leslie L. Robison, Jinghui Zhang

Bibliographic record

VenueCancer Research · 2017
Typearticle
Languageen
FieldMedicine
TopicChildhood Cancer Survivors' Quality of Life
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMedicineCancerOncologyInternal medicineGermlineGermline mutationBreast cancerCohortExomeExome sequencingMutationGeneticsBiologyGene

Abstract

fetched live from OpenAlex

Abstract Childhood cancer survivors are at increased risk of subsequent neoplasms (SN), largely considered to be therapy-related. Studies of cancer predisposition genes (CPGs) and risk of SN among long-term survivors are lacking. We characterized germline mutations in CPGs in childhood cancer survivors to determine their contribution to SN risk. Whole genome (30x) and exome (100x) sequencing was performed for 2988 5+ year survivors of childhood cancer (1629 leukemia/lymphoma, 332 CNS, 1027 other solid tumors, 53% male, median follow-up 28 [range 6-55] years). Survivors underwent a comprehensive clinical assessment, treatment exposures were abstracted from medical records, and SN were validated by pathology reports. Germline mutations in 63 CPGs were classified using the American College of Medical Genetics and Genomics guidelines as previously described (Zhang et al. NEJM 2015). Logistic regression, adjusting for age, sex and race, was used to evaluate associations between mutation status, cancer therapy and the SN risk. 1062 SNs were diagnosed in 437 survivors, of whom 98 developed ≥2 histologically distinct SNs. Median age at SN and time to first SN was 38.2 (range 3.3-67.4) and 29.2 (0.9-48.4) years, respectively. Common SNs were basal cell carcinoma (542 in 153 survivors), meningioma (201 in 100), thyroid (64 in 64), and breast cancer (58 in 50). Cumulative incidence of SN at age 45 was 25.5% (95% CI: 22.9-27.9). 169 survivors (5.7%) had a pathogenic/likely pathogenic (P/LP) mutation in a CPG, consisting of 97 single nucleotide variations, 63 insertion/deletions and 9 copy number alterations (49% of mutations not in ClinVar). Frequently mutated genes were: RB1 (n=41), NF1 (n=22), BRCA2 (n=13), BRCA1 (n=12) and TP53 (n=10). Our data confirmed known associations between CPG mutations and specific primary diagnoses including RB1 mutations in 32 of 41 (78%) of bilateral and 7 of 57 (12%) of unilateral retinoblastoma survivors, 22 NF1 (20 of 332 CNS survivors), 4 SUFU (all in medulloblastoma survivors) and 5 WT1 mutations (all in Wilms’ tumor survivors). Analyses revealed novel associations between CPG mutations and SN risk. Among 1326 survivors not exposed to radiation therapy (non-RT), 62 SNs developed in 54 survivors, of which 15 (24.2%) occurred in P/LP mutation carriers. Non-RT exposed survivors with a P/LP mutation had an increased risk of SN (OR=5.6, 95% CI=2.6-12.0, P<0.001) and the odds of developing ≥2 distinct histologic types of SNs was increased by 23.6-fold (95% CI=5.4-102.7, P<0.001). In 1662 RT exposed survivors, P/LP-mutation carriers had an odds ratio of 2.3 (95% CI=0.9-6.0, P=0.08) for developing ≥2 distinct histologic types of SNs. Our findings indicate that a substantial proportion of non-RT exposed childhood cancer survivors who develop one or more SN carry a CPG mutation, and should be referred to genetic testing and counseling services. Citation Format: Zhaoming Wang, Carmen L. Wilson, John Easton, Dale Hedges, Qi Liu, Gang Wu, Michael Rusch, Michael Edmonson, Shawn Levy, Jennifer Q. Lanctot, Eric Caron, Kyla Shelton, Kelsey Currie, Matthew Lear, Heather L. Mulder, Donald Yergeau, Celeste Rosencrance, Bhavin Vadodaria, Yadav Sapkota, Russell J. Brooke, Wonjong Moon, Evadnie Rampersaud, Xiaotu Ma, Shuoguo Wang, Ti-Cheng Chang, Stephen Rice, Andrew Thrasher, Aman Patel, Cynthia Pepper, Xin Zhou, Xiang Chen, Wenan Chen, Angela Jones, Braden Boone, Deo Kumar Srivastava, Chimene A. Kesserwan, Kim E. Nichols, James R. Downing, Melissa M. Hudson, Yutaka Yasui, Leslie L. Robison, Jinghui Zhang. Germline mutations in cancer predisposition genes and risk for subsequent neoplasms among long-term survivors of childhood cancer in the St. Jude Lifetime Cohort [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 3001. doi:10.1158/1538-7445.AM2017-3001

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.060
GPT teacher head0.418
Teacher spread0.358 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2017
Admission routes1
Has abstractyes

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