Abstract A043: Mutational profiling and machine learning for risk stratification and biomarker identification in intraductal papillary mucinous neoplasms progressing to pancreatic cancer
Notice bibliographique
Résumé
Abstract Intraductal papillary mucinous neoplasms (IPMNs) are common precursors to invasive pancreatic ductal adenocarcinoma (PDAC), with the risk of progression varying by IPMN type and anatomic location. Despite some IPMNs having a high risk of progression, there are limited non-surgical options for precision prevention in patients with IPMNs. One of the main challenges is that existing radiologic and molecular markers are insufficient for reliably assessing the risk of progression, mostly due to a lack of validated intervention targets. Currently, cancer prevention for patients with IPMN is centered on surgery or surveillance using a risk-based strategy; the clinical ability to stratify risk of cancer progression of individual IPMN tumors is poor and essentially no effective non-surgical interventions exist. Thus, the objective of this work is to apply statistical features extraction and use latent features derived from sequencing data as input to machine learning (ML) models to identify unexplored markers driving the progression of IPMNs to invasive PDAC. Data consisting of formalin-fixed, paraffin-embedded tissue cores sampled from 34 unique patients with the following pathological diagnoses: 8 non-IPMN-derived PDAC, 7 IPMN-derived PDAC, 7 high-grade IPMNs, and 12 low-grade IPMNs, were analyzed using the Moffitt STAR 2.0 Cancer Mutation and Molecular Biomarker Profiling panel. This STAR 2.0 next generation sequencing method performed using the TruSight Oncology 500 panel from Illumina, Inc., is designed to interpret sequence information for over 500 somatically altered genes. The analyses of sequencing data incorporated statistical and ML analyses of the mutational profiles of patients’ genomes followed by integration of trinucleotide sequence-derived mutational features. By applying non-negative matrix factorization to DNA trinucleotide motif mutational data, we identified 4 distinct mutational signatures. These signatures exhibited varying degrees of similarity to the Single Base Substitution Signatures from COSMIC (Sanger Institute). However, the contribution of these signatures to the mutational profile in each sample is more complex, as samples may carry a combination of signatures rather than a single, defining signature. Preliminary results from the discriminative ML models showed high performance in predicting type of malignancy (multiclass area under the curve > 0.8), using the mutational counts and each sample-to-signature contribution. We demonstrate that insights extracted from mutational profiles have the potential to enhance the interpretation of mutational patterns and improve the stratification of IPMNs. Further analyses are needed to fully understand the complex interplay of mutational processes across the samples, beyond the initial identification of signatures. We aim to uncover key gene patterns, focusing on codon mutations and their mapping to protein alterations, to better understand the molecular mechanisms driving the progression of IPMNs and identify potential therapeutic targets for early intervention. Citation Format: Aleksandra Karolak, Evan W. Davis, Mouktik Isukapalli, Rohit Veligeti, Margaret A. Park, Jamie K. Teer, Daniel K. Jeong, Kun Jiang, Dung-Tsa Chen, Jennifer B. Permuth, Ghulam Rasool. Mutational profiling and machine learning for risk stratification and biomarker identification in intraductal papillary mucinous neoplasms progressing to pancreatic cancer [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Artificial Intelligence and Machine Learning; 2025 Jul 10-12; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(13_Suppl):Abstract nr A043.
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 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| 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 ».