Abstract PR005: A toolbox for the use of cfDNA in pediatric cancer patients
Bibliographic record
Abstract
Abstract Testing of circulating tumor derived cell-free DNA (ctDNA) in liquid biopsies has many promising applications in adult cancers. Pediatric cancers are characterized by heterogeneous genetic aberrations that involve chromosomal copy number alterations (CNA), chromoplexy and chromothripsis and few recurrent tumor driving mutations. We therefore developed a toolbox for cfDNA testing for different pediatric tumors, designed for low volume samples, targeting profuse genetic aberrations and different purposes, such establishing diagnosis and prognosis, response monitoring and relapse surveillance. Method: We used reduced representation bisulfite sequencing (cfRRBS) to establish a diagnosis based on DNA methylation of the cfDNA. We established a whole-exome sequencing (WES, 250x coverage) pipeline for cfDNA, to identify mutations and copy number alterations (CNA). Droplet digital PCR (ddPCR) for sensitive detection of mutations (ALK), amplification of MYCN, the methylation status of the tumor suppressor gene RASSF1A (RASSF1A-M) and patient-specific ddPCR, designed for tumor-specific breakpoints, were used for prognostication, response, and early recurrence detection. Liquid biopsies from patients with pediatric renal tumors (n= 50), Neuroblastoma (n> 100), Rhabdomyosarcoma (RMS)(n= 57) and testicular germ cell tumors (TGCT) (n= 97) were tested. Results: In 92% (24/26) the diagnosis RMS was correctly established by cfRBBS, and in 5/10 renal tumor samples. It failed in patients with diffuse nephroblastomatosis, benign cystic lesion and mixed type Wilms tumor. In 20/31 diagnostic renal cfDNA samples and 12/22 paired tumor samples, CNAs (1q gain, loss of 1p, 16q, 11q, 17p and others) were detected. Thirteen CNAs were detected only in cfDNA and 6 in tumor only. Detected mutations in cfDNA consisted of WT1, DROSHA, CTNNB1, SIX1, TRIM28, TP53, DGCR8, MDM2 and DICER1. For neuroblastoma, in 28% (21/74) CNAs were detected only in cfDNA, in 68% (51/74) both in tumor and cfDNA and in 3% (2/74) in tumor only. Aberrations in ALK, CDKN2A, NF1, BRAF, ATM, CCND1, SMARCA4, CREBBP, CDK4, HRAS, MDM4 and NRAS were detected in cfDNA from plasma and/or BM plasma. RASSF1A-M ddPCR was positive in 21/57 patients with RMS and correlated with a 5-year EFS of 46.2% vs 100% (p = 0.001) for RASSF1A-M negative patients, and 5Y OS of 55.7% and 100% (p< 0.001), respectively. In all high-risk neuroblastoma patients RASSF1A-M was detected at diagnosis, high-levels of RASSF1A-M correlated with poor survival (p = .0007) and RASSF1A-M ddPCR further allowed for sensitive disease monitoring. In TGCT, combined miR371a-3p (RT-qPCR) and RASSF1A-M ddPCR correctly detected TGCT patients at diagnosis. Design of tumor-specific ddPCR allowed for sensitive disease monitoring in n= 25 patients, with currently n= 50 PCRs being designed and validated. Conclusion: in pediatric cancer, our cfDNA toolbox can be used to assist establishing a diagnosis, prognosis, sensitive response monitoring but also identification of targetable alterations and tumor heterogeneity. Citation Format: Godelieve Tytgat, Lieke van Zogchel, Nathalie Lak, Nina Gelineau, Julia Sprokkerieft, Alexandra Letunovska, Marry van den Heuvel, Hans Merks, Leendert Looijenga, Maaike Bos, Zeinab Van Gestel-Fadaie, C. Ellen van der Schoot. A toolbox for the use of cfDNA in pediatric cancer patients [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pediatric Cancer Research; 2024 Sep 5-8; Toronto, Ontario, Canada. Philadelphia (PA): AACR; Cancer Res 2024;84(17 Suppl):Abstract nr PR005.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.007 |
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".