Abstract A01: Novel childhood genitourinary manifestations of <i>DICER1</i> syndrome
Bibliographic record
Abstract
Abstract Pathogenic germline variants in DICER1, a gene encoding an RNase key in microRNA-mediated silencing, cause DICER1 syndrome. This genetic disorder predisposes to the development of a wide array of mainly childhood-onset conditions including genitourinary tumors such as cystic nephroma (CN), anaplastic sarcoma of the kidney, Wilms’ tumor (WT), Sertoli-Leydig cell tumor (SLCT), and cervical embryonal rhabdomyosarcoma (ERMS). As the spectrum of clinical manifestations is not yet fully defined, we sought to explore the involvement of DICER1 mutations in pediatric genitourinary lesions not previously well studied. A series of 31 formalin-fixed, paraffin-embedded tumor samples including 15 paratesticular ERMS (ptERMS), one unclassifiable ovarian sex cord-stromal tumor, one fallopian tube ERMS (ftERMS), six cystic partially differentiated nephroblastomas (CPDN), two cystic WT, two CN, and four rare renal lesions were collected. Median age at diagnosis was 4 years. Tumor DNA was screened for DICER1 variants using Sanger sequencing or a Fluidigm array. Sanger sequencing on tumor DNA was used to validate the variants, and on normal DNA, to determine the germline status. Seven samples harbored biallelic DICER1 mutations and in 6/7 cases, the loss-of-function variant was confirmed to be of germline origin. The paratesticular tumors were initially diagnosed as ERMS, but pathology review reclassified one as an ectomesenchymoma and another as an undifferentiated low-grade myxoid sarcoma. No DICER1 mutation was identified in any of the ptERMS, but the myxoid sarcoma had DICER1 mutations. Interestingly, the patient with the paratesticular myxoid sarcoma also developed a CN. We identified DICER1 mutations in a tumor originally classified as an ovarian WT. Subsequent pathologic evaluation of the lesion led to its reclassification as a retiform SLCT with rhabdomyosarcomatous elements. A typical hotspot and a potential splicing mutation were detected in the ftERMS. Also, two atypical cystic kidney lesions harbored DICER1 mutations: one composed of blastema-type cells expressing nuclear WT1 diagnosed as a CPDN and another unusual multicystic renal lesion considered to be a CN that had moved into a proliferative phase but lacked blastema and anaplastic foci. The patient with the latter renal lesion had previously developed a unilateral CN with a distinct hotspot mutation. As expected, the two classical CN had DICER1 mutations. Based on these results, patients diagnosed with rare genitourinary tumors (except for ptERMS), may have DICER1 syndrome, especially if these individuals have a personal or family history of DICER1-associated lesions. In these cases, surveillance for DICER1-related conditions is advised given that the risk of tumor development is highest in early childhood. In summary, specialist pathology review and DICER1 testing in this setting will lead to improved diagnosis with potential implications for clinical care. Citation Format: Maria V. Apellaniz-Ruiz, Catherine Goudie, Noelle Cullinan, Elvis T. Valera, Krisztina Z. Hanley, Luiz G. Tone, Paula Marrano, Ronald Grant, W. Glenn McCluggage, Gordan M. Vujanic, Paul S. Thorner, William D. Foulkes. Novel childhood genitourinary manifestations of DICER1 syndrome [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 A01.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".