Investigating genetic aberrations in metastatic gastric cancer and in primary renal cell carcinoma
Notice bibliographique
Résumé
The advent of next-generation sequencing (NGS) has substantially contributed to our understanding of underlying factors of different cancers. In the first chapter of this thesis, I used NGS (whole-exome sequencing (WES)) to investigate genetic abnormalities associated with metastatic gastric cancer. In the second chapter of this thesis, I used NGS data (targeted sequencing) to investigate association between mutational status of PBRM1 (the second most commonly mutated gene in renal cancer) and clinical variables of affected patients. I have also extended my work to analyze function of PBRM1 in an in vitro model of renal cancer.Chapter-1: Gastric cancer is one of the leading causes of cancer related death worldwide accounting for over 2000 deaths/year in Canada. Peritoneal metastasis is the most common site of gastric cancer progression after curative intent surgery and is the leading cause of death. Metastasis to this site represents a significant challenge in patient management and there is limited knowledge about the underlying factors. We have performed WES of patient-matched trio samples including normal DNA, primary gastric cancer, and peritoneal metastasis from five unrelated patients in order to identify somatic mutations associated with the metastasis. We found several genes recurrently mutated in these tumors including known cancer-related genes such as KRAS, TP53 and CDH1. Moreover, our analysis revealed additional potentially interesting genes including DSG1, SPTA1 and HMCN1. Our findings highlight a heterogeneous mutational pattern in gastric cancer peritoneal metastasis, and provide the first catalogue of somatic mutations in this entity.Chapter-2: Clear cell renal cell carcinoma (ccRCC) is the most prevalent type of renal cell carcinoma. PBRM1 (polybromo-1) is the second most commonly mutated gene affected in about 40% of ccRCC cases after VHL (Von Hippel-Lindau, mutated in about 70% of patients). Molecular mechanisms by which deficiency of PBRM1 contribute to ccRCC are not fully understood despite the high prevalence of PBRM1 mutations in ccRCC. Our lab has previously shown that tumors with mutated PBRM1 harbor larger number of somatic mutations compared to those without PBRM1 mutations. We have also shown that tumors exhibiting a mutational signature compatible to the exposure of Aristolochic Acid (AA) (which induces A>T mutations) showed higher rate of truncating mutations in PBRM1. However, these PBRM1 mutations were not due to AA exposure. These findings suggest that PBRM1 deficiency may at least partly contribute to the dysregulation of response to DNA damage and apoptosis. I examined this hypothesis in 786-O cell line model of ccRCC by analyzing cell proliferation and apoptosis following modulation of PBRM1 expression in combination with treatment with AA. Our results indicate that PBRM1 deficiency impairs induction of AA-related apoptosis in 786-O cell line. This suggests that PBRM1 may be involved in response to environmental stress such as toxic agents including DNA-damage inducers.
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 | 0,001 |
| 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) | 0,002 | 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 ».