Abstract 5081: Systematic identification of germline mutations in rhabdomyosarcoma and neuroblastoma using massively paralleled sequencing
Bibliographic record
Abstract
Abstract Despite improvement of survival using multimodal chemo- and immunotherapy, high mortality and morbidity is still substantial for pediatric patients with metastatic cancers. Recent large-scale sequencing studies of pediatric tumors including rhabdomyosarcoma (RMS) and neuroblastoma (NB) have been focusing on somatic mutations, and revealed a low somatic mutation rate and surprisingly few recurrently somatic mutated genes in these childhood tumors. Currently, only a small portion of pediatric cancer cases can be explained by somatic driver events; whereas the cause for the majority of these diseases remains unknown. Because both these two types of tumors are uncommon, here we hypothesize that infrequent germline mutations (frequency<0.05 in control populations) may play a role in the initiation of sporadically occurring tumor. To test this hypothesis, we utilized sequencing data from two cancer patient cohorts consisting of RMS (n=133) and NB (n=222) patients, of which latter is a part of the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) initiative for pediatric cancers. First, high-quality protein-coding changing single nucleotide variants (SNVs) were called in both paired germline and tumor genomic DNAs. Then we excluded common variants with frequency >5% in a normal human population using the 1000 Genomes data. Due to our interest in the enriched variants, we further required the frequencies of variants in our rhabdomyosarcoma and neuroblastoma patient cohorts are higher than those in the ESP dataset, a non-cancer control population comprising 6503 individuals. There are 63247 SNVs fulfilled these selection criteria. Among them, 1589 have been reported in these pediatric cancers or in other malignancies in the Cancer Genome Atlas (TCGA) project; and 1178 variants are present in the Human Gene Mutation Database (HGMD). Of these HGMD variants, 49 have been reported in human diseases and 34 of them are known disease-causing mutations for human cancers and genetic disorders including TP53, ALK, CHEK2, and PINK1. Interestingly, the most frequent germline mutations in these pediatric tumors were rarely found in the TCGA project which mostly consists of adult cancers. This observation suggests a very different genetic background of pediatric cancer patients from that of the adult cancers, and warrants a careful examination of germline mutations in these cancers. Furthermore, previous studies have highlighted the importance of expression of variant genes (including tumor suppressor genes) for identification of driver mutations in cancers. Therefore we will use 178 transcriptome sequencing experiments available for these tumors (RMS=84; NB=93) to identify expressed variants in tumor. Statistical and pathway analyses are currently underway to determine potential pathological or casual germline mutations associated with neuroblastoma and rhabdomyosarcoma. Citation Format: Jun S. Wei, Rajesh Patidar, John Shern, Shile Zhang, Trevor Pugh, Sharon J. Diskin, Sivasish Sindiri, Young K. Song, Hongling Liao, Xinyu Wen, Jianjun Wang, Stephen X. Skapek, James R. Anderson, Frederic G. Barr, Robert C. Seeger, John M. Maris, Douglas S. Hawkins, Javed Khan. Systematic identification of germline mutations in rhabdomyosarcoma and neuroblastoma using massively paralleled sequencing. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 5081. doi:10.1158/1538-7445.AM2014-5081
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".