Abstract B41: Genomic landscape of somatic mutations in osteosarcomas
Bibliographic record
Abstract
Abstract Pediatric cancers are among the leading causes of childhood mortality, with osteosarcomas being the most prevalent bone tumors, characterized by an aggressive clinical course. However, knowledge about the molecular basis of osteosarcomas is still limited, hampering advances in diagnosis and treatment. In this context, we characterized the genomic landscape of somatic mutations in 28 osteosarcomas (24 pediatric patients and 4 adults) aiming to delineate the panel of prevalent mutations in Brazilian patients. Genomic libraries of these osteosarcoma samples were constructed using the TruSight One panel (Illumina), covering 4,813 clinically relevant genes, including known cancer genes. Generated data at high coverage (median >100x depth coverage) were annotated based on public databases containing population variant frequencies (including the Brazilian ABRAOM), as well as clinical information. For further analysis, we filtered only coding nonsynonymous variants absent from both population databases and an additional pool of 20 nonrelated germline samples. In total, 728 variants were identified mapping in 606 genes, with an average rate of 41 mutations per tumor. The panel of detected variants was composed by 93 loss-of-function mutations (38 frameshift, 25 splicing, and 30 premature stop codon) and 635 missense (43 inframe insertions/deletions and 592 single-nucleotide variations), some of them already reported in cancer databases (27 in COSMIC and 39 in ICGC). From 606 genes, 25 were previously associated with osteosarcomas, and they are related to apoptosis, cell growth and differentiation, DNA repair, transcriptional regulation, and tumor-suppression mechanisms. The most frequent mutations were detected in RB1 and TP53; other recurrently mutated genes were mostly linked to biologic pathways potentially related to the disease onset, such as hormonal response (AR), transcriptional regulation and muscle differentiation (FRG1), DNA repair (HERC2), cell signaling (KIR2DL4), and cell proliferation/differentiation (PTPRQ). Additionally, the tumors presented a highly complex pattern of copy number alterations, some of them resembling chromothripsis. This is the first assessment of a Brazilian cohort of osteosarcomas, and functional studies will be further applied to better understand the role of the disclosed mutations for tumor biology. Citation Format: Sara F. Pires, Silvia S. Costa, Daniel O. Vidal, André van Helvoort Lengert, Érica Boldrini, Sandra R.M. da Silva, Carla Rosenberg, Luiz F. Lopes, Mariana Maschietto, Ana Krepischi. Genomic landscape of somatic mutations in osteosarcomas [abstract]. In: Proceedings of the AACR Special Conference on the Advances in Pediatric Cancer Research; 2019 Sep 17-20; Montreal, QC, Canada. Philadelphia (PA): AACR; Cancer Res 2020;80(14 Suppl):Abstract nr B41.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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 | 0.001 |
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".