MP01-06 miRNA AS A LIQUID BIOMARKER TO DETECT MALIGNANCY IN SMALL TESTICULAR MASSES
Bibliographic record
Abstract
You have accessJournal of UrologyPenile & Testicular Cancer I (MP01)1 May 2024MP01-06 miRNA AS A LIQUID BIOMARKER TO DETECT MALIGNANCY IN SMALL TESTICULAR MASSES Julian Chavarriaga, Carley Langleben, João Lobo, Lucia Nappi, George M. Yousef, Sajjad Janfaza, Nuno Tiago Tavares, Qiang Ding, Adam Bobrowski, Susan Prendeville, Lynn Anson-Cartwright, Carmen Jeronimo, Heidi Wagner, Keith Jarvi, Martin O'Malley, Ricardo Leão, Katherine Lajkopsz, and Robert J. Hamilton Julian ChavarriagaJulian Chavarriaga , Carley LanglebenCarley Langleben , João LoboJoão Lobo , Lucia NappiLucia Nappi , George M. YousefGeorge M. Yousef , Sajjad JanfazaSajjad Janfaza , Nuno Tiago TavaresNuno Tiago Tavares , Qiang DingQiang Ding , Adam BobrowskiAdam Bobrowski , Susan PrendevilleSusan Prendeville , Lynn Anson-CartwrightLynn Anson-Cartwright , Carmen JeronimoCarmen Jeronimo , Heidi WagnerHeidi Wagner , Keith JarviKeith Jarvi , Martin O'MalleyMartin O'Malley , Ricardo LeãoRicardo Leão , Katherine LajkopszKatherine Lajkopsz , and Robert J. HamiltonRobert J. Hamilton View All Author Informationhttps://doi.org/10.1097/01.JU.0001008660.87408.90.06AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Approximately 1-4% of individuals undergoing scrotal ultrasounds are found with incidental small (≤ 2cm) testicular mases (STMs), with the vast majority being benign (∼13-21% malignant). Distinguishing between malignant and benign STMs remains a challenge, as neither serum tumor markers nor imaging methods offer reliable predictive capabilities. This study explores the potential of miRNAs, as liquid biomarkers for predicting germ cell tumours (GCTs) in STMs. METHODS: Pre-orchiectomy serum/plasma samples, drawn between 1 day and<6 months before surgery, were analyzed using different miRNA extraction methods (qRT-PCR, DdPCR) and platforms across three research laboratory facilities in three different centres (Portugal, Vancouver, Toronto). The primary endpoint of our study was the association between miRNA (miR-371a-3p) and the presence of GCTs. Additionally, we analyzed miRNAs 372, 373 and 367 aiming to improve the diagnostic performance of miRNA to predict GCTs in STMs. Our research laboratories used quantitative (Ct value <40) and qualitative analysis. We used the area under the receiver operating characteristic curve (AUROC) calculations to establish optimal thresholds for miRNAs. Comparison of pre-orchiectomy miRNA and surgical pathology was done using the Wilcoxon rank-sum test. RESULTS: From 2009 to 2023 we identified 65 patients with STMs who had banked serum/plasma prior to orchiectomy. Our cohort included 41 patients with confirmed GCTs, 20 with benign histology, and four patients who had been on surveillance for >12 months and were deemed to have benign STMs. The median age was 38 years, median tumour size was 13.5 mm (9-19), 77% and 12% underwent radical and partial orchiectomy, respectively. Of the patients with GCTs 27 (67.5%) had seminoma and 13 (32.5%) Nonseminomatous GCTs. Of the benign tumours 31% were sex cord-stromal (6 Leydig and 2 Sertoli cell tumours). Our first lab used magnetic beads-based for extraction on serum. miR371a-3p showed a sensitivity, specificity, PPV and NPV of 67.5%, 100%. 100%, and 62.5%, respectively. The AUROC was 0.774. We examined plasma with a qRT-PCR extraction kit in our second research laboratory. With a Ct mean threshold of >28, miR371a-3p showed a sensitivity and specificity of 92.3% and 85%, the AUROC was 0.912 (95% CI 0.826-0.998; p<0.0001). Other miRNAs were not informative. CONCLUSIONS: This is the largest series of STMs with banked blood/serum to date, our unique interlaboratory comparison represents a meaningful contribution to the field. miR-371a-3p appears sensitive to detect the presence of GCTs in STMs. Further research in this area is needed and could revolutionize the approach to managing these incidental STMs. Source of Funding: Agnico-Eagle Grand Challenge © 2024 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 211Issue 5SMay 2024Page: e3 Advertisement Copyright & Permissions© 2024 by American Urological Association Education and Research, Inc.Metrics Author Information Julian Chavarriaga More articles by this author Carley Langleben More articles by this author João Lobo More articles by this author Lucia Nappi More articles by this author George M. Yousef More articles by this author Sajjad Janfaza More articles by this author Nuno Tiago Tavares More articles by this author Qiang Ding More articles by this author Adam Bobrowski More articles by this author Susan Prendeville More articles by this author Lynn Anson-Cartwright More articles by this author Carmen Jeronimo More articles by this author Heidi Wagner More articles by this author Keith Jarvi More articles by this author Martin O'Malley More articles by this author Ricardo Leão More articles by this author Katherine Lajkopsz More articles by this author Robert J. Hamilton More articles by this author Expand All Advertisement PDF downloadLoading ...
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 0.011 |
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".