Abstract 260: Application of integrated analysis of whole genome sequencing and RNA sequencing to personalized therapy decision making in pediatric and young adult cancer
Bibliographic record
Abstract
Abstract The use of comprehensive personal genomic data to guide cancer treatment decisions has demonstrated success in recent years. However, its application to pediatric cancer remains challenging. Most pediatric cancer genomes have relatively few somatic aberrations and the recognized oncogenic drivers are often not targetable. Here we describe a whole genome and transcriptome analysis approach to elucidate the impact of currently un-targetable disease drivers on pathways and regulatory networks within cancer cells, in an effort to identify aberrations that can be targeted therapeutically. In the pediatric personalized oncogenomics (PedsPOG) project, we have analyzed 81 pediatric and young adult cases representing diverse types of relapsed, refractory or very poor prognosis cancers (17 primary central nervous system (CNS), 5 hematopoietic and lymphoid malignancies, and 59 from a variety of non-CNS solid cancers). Patients' ages ranged from 0.4-20.7 years at enrollment, with a median of 11.5 years. For each patient, genomic sequencing data from tumour and normal samples were analysed to detect germline and somatic variants including simple mutations to more complex alterations such as gene fusions, mutation burden and mutation signatures. Tumour transcriptome data was analyzed to identify aberrant gene expression and calculate immune-related signatures such as presence and composition of T cells in the tumour sample. Combined interpretation of germline and somatic variants was used to understand the underlying tumour biology and provide rationale for treatment options. Using the integrated analysis of genomic and transcriptomic data, actionable targets were identified in 80 out of 81 patients (98%), while therapeutic targets would have been identified in only 32-50% of the patients if individual genomic variants were used separately (such as mutation, fusion, and copy number changes). RNA expression data appears to be highly informative, from which targets were detected in 90% of the patients, albeit at lower levels of evidence. In particular, prediction of outlier T-cell presence based on expression data was identified as a potential biomarker which led to favorable response to immune checkpoint inhibition. Twenty-four cases received PedsPOG-informed therapy and disease response was evaluated by RECIST or RANO criteria. Complete response was observed in four patients, and thirteen patients demonstrated partial response or stable disease. In conclusion, the use of whole genome sequencing and transcriptome sequencing has yielded novel findings and therapeutic options in children and adolescents with poor prognosis cancer. Citation Format: Yaoqing Shen, Melika Bonakdar, Laura Williamson, Erin Pleasance, Karen Mungall, Richard A. Moore, Andrew J. Mungall, Stephen Yip, Anna F. Lee, Christopher Dunham, Janessa Laskin, Marco M. Marra, Steven J. Jones, Shahrad R. Rassekh, Rebecca J. Deyell. Application of integrated analysis of whole genome sequencing and RNA sequencing to personalized therapy decision making in pediatric and young adult cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 260.
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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 | 0.000 |
| 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".