Genomic alterations in intraductal prostate cancer: Insights from the Genomic Umbrella Neoadjuvant study (GUNS) in high-risk localized disease.
Notice bibliographique
Résumé
417 Background: Intraductal carcinoma of the prostate (IDC) is an aggressive histological variant of prostate cancer, characterized by the presence of malignant cells within the prostatic ducts. Retrospective studies have shown IDC linked to higher tumor grades and odds of lymphatic metastasis and worse oncological outcomes. In patient derived xerograph models, IDC can persist after castration, with a subpopulation of castrate tolerant cells able to regenerate upon testosterone restoration. This suggests that IDC contributes to therapy resistance and highlights the need to understand molecular alterations of IDC. This study aims to evaluate genomic profiles of these tumors in the GUNS trial. Methods: From 9/2021 to 8/2024, GUNS enrolled 95 patients in Canada. Diagnostic biopsies underwent Tempus’ CLIA-certified 648-gene panel DNA sequencing. A total of 93 patients were evaluable for genomic alterations. Of those, 81 additionally had immunohistochemistry (IHC) staining for PTEN. All biopsy specimens were centrally reviewed by TvK. Associations between IDC and genomic alterations or PTEN IHC staining (positive vs. negative/heterogeneous) were assessed by Fisher’s exact test, and the false discovery rate (FDR) was controlled using the Benjamini-Hochberg method. Only genomic alterations annotated as biologically significant by Tempus were considered for analysis. Results: Of the 93 evaluable patients, 36 (39%) had IDC on biopsy. PTEN IHC status showed a significant association with IDC status, with negative or heterogeneous PTEN IHC staining being more prevalent among IDC-positive cases (p = 0.002; FDR q = 0.05). Specifically, PTEN IHC staining was negative/heterogeneous in 15/32 (47%) IDC-positive cases but only 7/49 (14%) IDC-negative cases. Genomic PTEN (22% vs. 7%) and TP53 (19% vs. 9%) alterations were also more common in IDC-positive cases than IDC-negative cases, although these trends were not statistically significant. Conversely, CDKN1B was exclusively altered in IDC-negative cases (0% vs. 9%), and BRCA2 alterations (germline or somatic) were also more frequent in IDC-negative cases (3% vs 11%), but these observations were also not statistically significant. Relatively low frequencies of genomic alterations were likely impediments to statistical significance, warranting larger sample sizes to assess these trends. Conclusions: Negative or heterogeneous PTEN IHC staining was more common in IDC-positive cases, consistent with the known association between PTEN loss and aggressive disease. PTEN IHC status, along with other genetic markers, may help to define subgroups within prostate cancer that differ in their underlying biology and response to neoadjuvant treatment. This highlights the importance of integrating molecular and histological data to better understand prostate cancer progression and to tailor therapeutic approaches.
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 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
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 ».