Increased APC mutations in MRI risk‐stratified colorectal liver metastases
Notice bibliographique
Résumé
Surgery is the treatment of choice of patients with colorectal liver metastases (CRCLM), often used in conjunction with chemotherapy. Individual tumor genomics play a key role in determining patient prognosis, response to chemotherapy and in guiding therapy. A recent study demonstrated that harboring two or more APC mutations is significantly associated with lower overall survival compared to none or one mutation and another study showed that the double mutation of APC and PIK3CA negatively affected survival after hepatectomy. Earlier work by our group demonstrated that target tumour enhancement (TTE), defined as the mean contrast‐to‐noise ratio of target lesions on late‐phase gadobutrol‐enhanced MRI, was associated with overall survival following liver surgery. Strong TTE was associated with improved survival compared to weak TTE (3‐year survival: 88.4% vs. 58.8%, p = 0.003) with a hazard ratio of 0.32 (95% CI: 0.14–0.75, p = 0.008), after taking into account known prognostic variables. Inter‐rater reliability was very good with a relative intraclass correlation of 0.84 (95% CI: 0.77–0.89). Here we show results of next generation deep sequencing in 20 patients from the cohort, clustered by MRI TTE, using an Ion AmpliSeq Custom Next‐Generation Sequencing Colorectal Cancer Panel. Matched tumor and adjacent normal tissue samples from 10 patients with weak TTE (CNR: −111.2 to −10.8) and 10 patients with strong TTE (CNR: 15.9 to 74.0) were analyzed to detect somatic mutations. We detected a total of 41 non‐synonymous somatic mutations from 10 patients with weak TTE (mean of 4.1 mutations) and a total of 25 non‐synonymous somatic mutations from 10 patients with strong TTE (mean of 2.5 mutations) when compared with the respective normal tissue samples. The mutation burden between weak TTE and strong TTE tumors was statistically different using the t‐test (p=0.0149). APC was the most commonly altered gene, 30 mutations in 17 patients (85.0%) followed by 11 KRAS mutations in 11 patients (55%) and 10 TP53 mutations in 10 patients (50.0%). Among total of 30 mutations detected in APC, 26 mutations (86.7%) were either nonsense or frameshift mutations which are predicted to cause protein truncation or nonsense‐mediated decay. Of 26 truncation mutations, 18 mutations (69.2%) are found in weak TTE and 8 mutations (30.8%) are in strong TTE group. There was also a significant difference in the number of patients with double truncation mutation in APC between the weak TTE group (8/10=80%) and the strong TTE group (2/10=20%) using the Fisher's exact test (p=0.01). In contrast, 80% of patients (8/10) with strong TTE tumors had TP53 mutations compared to 20% of patients (2/10) with weak TTE tumors. TP53 mutations in strong TTE were also strongly associated with APC mutations (87.5%). These data suggest that a tumor with the MRI phenotype associated with the worst prognosis harbored mutations known to be associated with a poorer outcome. This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .
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 | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 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 ».