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Record W4393093034 · doi:10.1158/1538-7445.am2024-775

Abstract 775: Underlying germline genetic architecture of pediatric sarcomas: Evaluating the role of common and rare variants in 4,160 patients

2024· article· en· W4393093034 on OpenAlexaff
Laura E. Egolf, Bin Zhu, D. Matthew Gianferante, Kevin Wang, Shengchao Alfred Li, Mitchell J. Machiela, Logan G. Spector, Joshua D. Schiffman, Aniko Sabo, Alexander Renwick, Bailey A. Martin‐Giacalone, Michael E. Scheurer, Sharon E. Plon, Douglas S. Hawkins, Rajkumar Venkatramani, Douglas R. Stewart, Lindsay M. Morton, Melissa M. Hudson, Gregory T. Armstrong, Smita Bhatia, Michael Dean, Katherine A. Janeway, Ana Patiño‐García, Fernando Lecanda, Massimo Serra, Claudia Maria Hattinger, Katia Scotlandi, Adrienne M. Flanagan, Fernanda Amary, Irene L. Andrulis, Jay S. Wunder, Mandy L. Ballinger, David M. Thomas, Olivier Delattre, Aubrey K. Hubbard, Jia Liu, Wen Luo, Belynda Hicks, Meredith Yeager, Maryam Rafati, Wen‐Yi Huang, Maria Teresa Landi, Adriana Lori, W. Ryan Diver, Sharon A. Savage, Stephen J. Chanock, Philip J. Lupo

Bibliographic record

VenueCancer Research · 2024
Typearticle
Languageen
FieldMedicine
TopicSarcoma Diagnosis and Treatment
Canadian institutionsSinai Health SystemLunenfeld-Tanenbaum Research InstituteUniversity of Toronto
Fundersnot available
KeywordsGermlineSarcomaGenetic architectureGermline mutationMedicineCancerBiologyGeneticsPathologyMutationGenePhenotype

Abstract

fetched live from OpenAlex

Abstract Some evidence suggests that pediatric sarcomas have both shared and distinct genetic profiles; however, large-scale efforts to characterize germline genetic susceptibility across these malignancies are limited by their rarity. We evaluated the role of common and rare variants in the genetic etiology of the more frequent pediatric sarcomas: osteosarcoma (OS); Ewing sarcoma (ES); and rhabdomyosarcoma (RMS), subcategorized into embryonal (ERMS) and alveolar (ARMS). METHODS: We evaluated 4,161 European-ancestry cases with genotype data (1,843 OS, 733 ES, 1,585 RMS, and ~61,000 cancer-free adult controls) and 2,474 cases with exome or genome sequencing (1,002 OS, 579 ES, 893 RMS, and 1,057 controls; jointly called with the same QC). Analyses included: 1) estimating disease heritability for both common SNPs (MAF>3%; genome-wide) and rare loss-of-function variants (MAF<1%; exome-wide); and 2) determining the frequency of rare predicted pathogenic (P) or likely pathogenic (LP; ACMG-AMP) variants in cancer susceptibility genes (CSG). RESULTS: For OS, we conducted a new GWAS and determined that common variants explained 2.7% (SE 1.1%) of disease heritability, while rare LOF variants explained 12.7% (SE 1.5%; compared to 0.4% for synonymous variants). A similar pattern was observed for ERMS, where rare LOF variants explained a greater proportion of disease heritability (RMS 8.6%, SE 1.2%; ERMS 12.6%, SE 1.7%; ARMS n/a due to small sample size) compared to common variants from our new GWAS (0.9%, SE 1.6%). Conversely, common variants explained a greater proportion of ES heritability (5.4%, SE 0.5%) and of ARMS heritability (15.5%, SE 6.4%). For 113 established CSGs, and for the 60 moderate-to-high penetrant autosomal dominant (AD) genes, OS and ERMS had significantly (Pexact<0.01) more rare P/LP variants overall compared to controls; whereas ARMS and ES had significantly fewer P/LP variants than ERMS and OS, similar to the controls. We confirmed previously reported AD genes, and identified new genes, with an enrichment of P/LP variants in ERMS and OS compared to controls. For both ARMS and ES, the only AD gene significantly enriched for P/LP variants was CHEK2. For all cases, patients with a P/LP variant were significantly younger and had significantly more poor outcomes (ie, metastasis, stage 4 disease, and/or death) than those without. For 49 autosomal recessive CSGs, heterozygous P/LP carrier frequencies were similar among all sarcomas (8-11%), and several specific genes had similar P/LP variant enrichment across sarcomas, and for ES and ARMS only, compared to controls. CONCLUSION: In the largest set of pediatric sarcoma cases assembled to date, genetic susceptibility was largely driven by rare P/LP AD gene variants in tumor types not characterized by canonical somatic fusions (OS and ERMS). In contrast, fusion-driven tumor types (ES and ARMS) were driven more by common variants. Citation Format: Lisa Mirabello, Laura E. Egolf, Bin Zhu, D. Matthew Gianferante, Kevin Wang, Shengchao Alfred Li, Mitchell J. Machiela, Logan G. Spector, Joshua D. Schiffman, Aniko Sabo, Alexander Renwick, Bailey Martin-Giacalone, Michael E. Scheurer, Sharon Plon, Douglas Hawkins, Rajkumar Venkatramani, Douglas Stewart, Lindsay M. Morton, Melissa M. Hudson, Gregory T. Armstrong, Smita Bhatia, Michael Dean, Katherine A. Janeway, Ana Patiño-Garcia, Fernando Lecanda, Massimo Serra, Claudia Hattinger, Katia Scotlandi, Adrienne M. Flanagan, Fernanda Amary, Irene L. Andrulis, Jay S. Wunder, Mandy L. Ballinger, David M. Thomas, Olivier Delattre, Aubrey K. Hubbard, Jia Liu, Wen Luo, Belynda D. Hicks, Meredith Yeager, Maryam Rafati, Wen-Yi Huang, Maria T. Landi, Adriana Lori, Ryan Diver, Sharon A. Savage, Stephen J. Chanock, Philip J. Lupo. Underlying germline genetic architecture of pediatric sarcomas: Evaluating the role of common and rare variants in 4,160 patients [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 775.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.115
GPT teacher head0.447
Teacher spread0.332 · 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".

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Citations0
Published2024
Admission routes1
Has abstractyes

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