Abstract B20: Integrative analysis of whole-genome and RNA sequencing in high-risk pediatric malignancies
Bibliographic record
Abstract
Abstract Targeted gene panel sequencing has become increasingly common in the management of pediatric cancer patients. For some patients, these cancer gene panel tests have identified clinically actionable findings, but for many pediatric patients, no actionable alterations are identified. This is in part due to the low mutational burden of pediatric malignancies; thus, an unbiased approach may shed light on potentially actionable findings. To accomplish this, we examined the feasibility and utility of whole-genome sequencing (WGS) and RNA sequencing (RNAseq) in the management of high-risk pediatric oncology patients. We describe our experience with a cohort of over 100 high-risk pediatric oncology patients, with a combination of solid tumors, brain tumors, and hematologic malignancies. The majority of patients were deemed high-risk due to relapsed/refractory disease. A second group of patients was defined as high-risk at time of initial diagnosis due to the presence of metastatic disease, an estimated overall survival of less than 50%, a rare tumor, an undifferentiated tumor, or prior history of another malignancy. When possible, multiple samples from an individual patient were collected (i.e., specimens at biopsy, resection, relapse, and/or from metastatic sites) to allow for evaluation of inter- and intratumoral heterogeneity. Close to 200 tumor samples were available for analysis using WGS and/or RNAseq analysis. Somatic DNA samples were sequenced to an average depth of 60X and germline samples to 30X. WGS samples were analyzed for SNVs, structural rearrangements (SVs), copy-number alterations (CNAs), and mutational signatures. RNAseq was performed to a depth of at least 20 million paired-end reads for each sample. These samples were analyzed to identify known and novel gene-fusions, measure allele specific expression of SNVs, and perform gene-expression outlier analysis. Expression of variants (SNV/SV) identified using WGS were confirmed using RNAseq. For gene expression outliers detected using RNAseq, the WGS data were used to predict possible mechanisms for the aberrant expression (such as CNA, gene fusions, or promoter hijacking). This analysis suggests that WGS and RNAseq analysis is feasible in a clinical setting and can reliably identify variants reported on gene panel tests. Furthermore, the use of WGS/RNAseq results in additional clinically informative findings while also enabling novel research to further advance our understanding of these rare and highly aggressive pediatric malignancies. Citation Format: Avanthi T. Shah, Marcus R. Breese, Alex G. Lee, Henry J. Martell, Bogdan Tanasa, Stanley G. Leung, Aviv Spillingeer, Heng-Yi Liu, Inge Behroozfard, Phuong Dinh, Florette K. Hazard, Soo-Jin Cho, Arun Rangaswami, Norman J. Lacayo, Sheri L. Spunt, Tabitha Cooney, Jennifer G. Michlitsch, Anurag K. Agarwaal, Alejandro Sweet-Cordero. Integrative analysis of whole-genome and RNA sequencing in high-risk pediatric malignancies [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 B20.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.001 | 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".