Abstract A011 Molecular analysis improves the diagnosis of young people with renal tumors
Bibliographic record
Abstract
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.
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 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 | 0.007 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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.
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".