Abstract A011 Molecular analysis improves the diagnosis of young people with renal tumors
Notice bibliographique
Résumé
Abstract Background Accurate oncological diagnoses are essential to provide personalized and optimum care for patients. In children, renal tumors account for approximately one in 20 malignancies. Diagnostic workup in pediatric renal tumors is current focused on epidemiology, radiology, and histology, with a very limited role for molecular analysis outside of suspected cancer predisposition. Methods Retrospective clinical record review of seven pediatric and young adult renal tumor patients presenting to a single principal treatment center in the East of England, UK. Data collection was focused on their clinical presentation, radiology, histopathology, and molecular investigations including whole genome sequencing (WGS), treatment and outcomes. We analyzed the impact of molecular analysis on the care of these patients. Results Four patients presented with histologically difficult to classify renal tumors. Subsequent nephrectomy provided no additional information over biopsy in the two cases where a biopsy was performed first. Two young patients with histological concerns over renal cell carcinoma (RCC) had somatic mutations reported in Wilms tumor (WT) and responded well to WT treatment. One patient had histologically mixed features of papillary RCC and epithelial WT. WGS revealed a copy number profile consistent with papillary RCC as well as somatic WT changes and responded well and durably to chemo/radiotherapy, not expected for RCC alone. A young adult with an atypical RCC was shown to harbor a ERC1::CCNY fusion likely defining a novel entity. A further three cases, who did not have classical features of WT/cancer predisposition, were found to harbor mosaic/germline predisposition variants; allowing for appropriate treatment as per syndromic WT protocols. Discussion Diagnostic uncertainty is a challenge for oncologists, their patients, and families. Here we provide evidence that agnostic molecular analysis, including whole genomic sequencing (WGS), can be helpful in delineating such cases. In two children the molecular analysis helped to define the diagnosis as WT despite histological concerns for RCC. A novel tumor with mixed WT/RCC phenotype and genotype was responsive to chemo/radiotherapy. Molecular analysis thus improves the accuracy of diagnosis and helps to define novel entities. WT is well recognized to occur in the context of cancer predisposition syndromes. At present, genetics referrals and investigations are limited to those with suggestive family history or clinical features. Routinely undertaking molecular analysis will increase the rate of detection of underlying predisposition. We propose rapid turn-around molecular analysis for those undergoing pre-operative chemotherapy (as practiced in Europe) to identify patients and to plan for nephron-sparing surgery to reduce the risk of long-term renal replacement therapy. The molecular multidisciplinary team is crucial for the interpretation of routinely performed agnostic molecular analysis in children and young people with renal tumors given the evolving complexity of such cases. Citation Format: Sarah M. Leiter, Aisosa Guobadia, Ben Fleming, Thankamma Ajithkumar, James Armitage, Ruth Armstrong, GA Amos Burke, Charlotte Burns, Tanzina Chowdhury, Nicholas Coleman, Helen Hatcher, Gail Horan, Lisa Howell, Anna-May Long, Sarah McDonald, Thomas J. Mitchell, James C. Nicholson, Thomas Roberts, Grant D. Stewart, John A. Tadross, Patrick Tarpey, Claire Trayers, Jamie Trotman, James Watkins, Anne Y. Warren, Godran Vujanic, C. Elizabeth Hook, Sam Behjati, Matthew J. Murray. Molecular analysis improves the diagnosis of young people with renal tumors [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 A011.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,007 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».