MétaCan
Menu
← Back to cohort

Abstract IA06: New strategies for multidimensional molecular characterization and follow-up of high-risk pediatric cancers

2020· article· en· W3048770784 on OpenAlexaboutno aff
Gudrun Schleiermacher

Bibliographic record

VenueCancer Research · 2020
Typearticle
Languageen
FieldMedicine
TopicGlioma Diagnosis and Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineOncologyPediatric cancerCancerTumor progressionNeuroblastomaInternal medicineExome sequencingRhabdomyosarcomaPrimary tumorCirculating tumor cellGermlineDiseaseSarcomaBioinformaticsPathologyBiologyMutationGeneticsGeneMetastasis

Abstract

fetched live from OpenAlex

Abstract Although 80% of all pediatric patients with cancer can be cured based on modern multimodal therapeutic strategies, at least 20% of these patients will undergo tumor progression or relapse. Understanding the molecular mechanisms underlying tumor progression and resistance to treatment is crucial to the development of new treatment approaches. In high-risk pediatric cancers, somatic DNA alterations such as genomic amplifications, copy number alterations, translocations, or mutations play an important role as molecular diagnostic, prognostic, and predictive biomarkers, which might further define targeted treatment approaches. However, it is now known that in many high-risk pediatric cancers, genetic alterations in a given tumor are not static but undergo modifications, with clonal evolution most likely playing a major role in high-risk tumor progression. Circulating tumor DNA, a fraction of cell-free DNA, can be readily isolated from plasma and now provides an important tool and surrogate for tumor molecular analyses, both at diagnosis and during treatment and follow-up. At diagnosis, a prospective trial, NGSKids (NCT02546453), subsequently enrolled 28 patients with high-risk pediatric cancer (neuroblastoma, rhabdomyosarcoma, Ewing sarcoma, other high-risk cancers) at diagnosis. Whole-exome sequencing (WES) was performed on tumor and germline DNA as well as on cfDNA extracted from plasma at diagnosis, during treatment, and follow-up (2-9 sequential cfDNA samples per patient). Whereas all cfDNA samples obtained at follow-up in patients without evidence of disease revealed no or few tumor cell-specific SNVs, interestingly, cfDNA samples obtained at relapse harbored additional, new relapse-specific SNVs (mean: 10; range 2-42) in all cases, targeting genes such as MAPK and MLL4. Deep sequencing capture techniques enable to develop models of clonal evolution. In nonmetastatic brain tumors, ctDNA isolated from CSF resulted in a higher sensitivity and specificity of detection to tumor cell-specific genetic alterations. A French national program, MICCHADO (NCT03496402), now aims to study clonal evolution based on sequential ctDNA analysis from the time of diagnosis in all high-risk pediatric cancers. At relapse, ctDNA studies can provide complementary information to molecular analyses of tumor samples performed within programs such as MAPPYACTS (NCT02613962). ctDNA also enables to infer expression profiles. Indeed, gene expression levels are reflected by nucleosome positioning, and differences in nucleosome organization at transcription start sites (TSS) lead to differential clipping of fragments upon ctDNA release and distinct nucleosome footprints depending on the expression of a given gene in the originating cells. New technologies are also being explored to analyze epigenetic features. Altogether, the presence of tumor genetic and epigenetic abnormalities in ctDNA can be documented in most patients with high-risk pediatric cancer and frequently suggests spatial and temporal heterogeneity. Sequential studies will further elucidate mechanisms of clonal evolution, tumor progression, and therapy resistance. Thus, sequential studies of ctDNA should now be integrated into the development and optimization of targeted treatment strategies. Citation Format: Gudrun Schleiermacher. New strategies for multidimensional molecular characterization and follow-up of high-risk pediatric cancers [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 IA06.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.061
GPT teacher head0.365
Teacher spread0.305 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueCancer Research→Same topicGlioma Diagnosis and Treatment→French-language works237,207→