Abstract 1224: Deep targeted tumor sequencing of colorectal cancer cases to study associations of molecular subtypes with clinical, genetic, and lifestyle risk factors
Notice bibliographique
Résumé
Abstract Colorectal cancer (CRC), a common malignancy, is a biologically heterogeneous disease. Next-generation sequencing (NGS) has enabled CRC characterization by identifying somatically mutated genes which now allow us to better define colorectal tumor subtypes (e.g. by mutated pathways). However, the relationship of such CRC subtypes to patient survival and genetic and lifestyle risk factors has not been comprehensively studied. To identify somatic mutations in CRC cases, we designed a targeted AmpliSeq panel of CRC related genes and genomic regions informed by whole exome sequencing data from ~1,200 CRC cases. The sequencing was conducted on Illumina HiSeq 2500 with a mean coverage of 740x and 240x for DNA extracted from FFPE tumor tissues and matched normal samples, respectively. Strelka, MuTect, VarDict, and Varscan2 were used to identify somatic single nucleotide variants and indels. Sanger sequencing was performed to validate a subset of variants. To date, we have sequenced ~2,400 CRC tumors and matched control tissues from four studies participating in the Colon Cancer Family Registry (CCFR) and Genetics and Epidemiology of Colorectal Cancer Consortium (GECCO). In most tumors, we identified non-silent mutations in genes belonging to the WNT (77%), p53 (44%), IGF2/PI3K (22%), RTK-RAS (47%), and TGF-beta (26%) signaling pathways. Among the 15% of tumors that could be classified as hypermutated, based on the number of mutations, 39% exhibited non-silent mutations in MLH1, MLH3, MSH2, MSH6, and PMS2 and 41% exhibited non-silent mutations in POLE and POLD1. In a subset of studies with available survival data, we used Cox regression to assess the association of hypermutation status and the presence of non-silencing mutations in key signaling pathways with overall (OS) and disease-specific (DSS) survival. OS and DSS were significantly more favorable in cases with hypermutated vs. non-hypermutated CRC (HR=0.77, 95% CI: 0.60-0.98, p=0.04 and HR=0.35, 95% CI: 0.22-0.57, p=2x10-5, respectively); these associations were most pronounced for POLE/POLD1 mutated hypermutated CRC (HR=0.69, 95% CI: 0.46-1.02, p=0.06, HR=0.21, 95% CI: 0.08-0.56, p=2x10-3, respectively). There was no significant association of mutations in WNT, p53, IGF2/PI3K, RTK-RAS, or TGF-beta pathways with survival (p>0.05). The comprehensive molecular characterization of this large panel of CRC cases will support further studies of molecular subtypes of CRC with clinical, lifestyle, and environmental factors. A better understanding of molecular mechanisms of CRC will be valuable in developing strategies for prevention, diagnosis, and treatment of this life-threatening disease. Citation Format: Syed Zaidi, Amanda Phipps, Tabitha Harrison, Catherine Grasso, Robert Steinfelder, Quang Trinh, Charles Connolly, Barbara Banbury, Adilya Rafikova, Philipp Hofer, Stefanie Brezina, Marios Giannakis, Xinmeng Jasmine Mu, Michael Quist, Charles Fuchs, Levi Garraway, Li Hsu, Lincoln Stein, Andrea Gsur, Shuji Ogino, Steven Gallinger, Polly Newcomb, Peter Campbell, Wei Sun, Thomas Hudson, Ulrike Peters. Deep targeted tumor sequencing of colorectal cancer cases to study associations of molecular subtypes with clinical, genetic, and lifestyle risk factors [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 1224.
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 | 0,002 | 0,001 |
| 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) | 0,004 | 0,001 |
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 ».