Genetic Mutations of Pancreatic Cancer and Genetically Engineered Mouse Models

Saiki, Yuriko; Jiang, Can; Ohmuraya, Masaki; Furukawa, Toru

doi:10.3390/cancers14010071

Cited by 23 publications

(14 citation statements)

References 113 publications

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“…Genetic alterations play an important role in the development of human malignant tumors [ 20 ]. In this case, we analyzed the genetic variation of PDHB in malignant tumors by cBioPortal database.…”

Section: Resultsmentioning

confidence: 99%

A combined analysis of bulk and single-cell sequencing data reveals metabolic enzyme, pyruvate dehydrogenase E1 subunit beta (PDHB), as a prediction biomarker for the tumor immune response and immunotherapy

Zhang¹,

Yan²,

Liang³

et al. 2023

Heliyon

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Section: Resultsmentioning

confidence: 99%

A combined analysis of bulk and single-cell sequencing data reveals metabolic enzyme, pyruvate dehydrogenase E1 subunit beta (PDHB), as a prediction biomarker for the tumor immune response and immunotherapy

Zhang¹,

Yan²,

Liang³

et al. 2023

Heliyon

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“…Previous studies have shown that select gene alterations can influence PDAC disease status in both classical and basal patients (Saiki et al 2021). We tested for differences in mutation rates, gene expression, and amino acid changes using Fisher’s tests in KRAS, TP53, CDKN2A , and SMAD4 , four genes previously associated with PDAC (Cicenas et al 2017), as well as in 10 additional genes with high mutation prevalence in the validation cohort (Figure 3B).…”

Section: Resultsmentioning

confidence: 99%

Real-world data validation of the PurIST pancreatic ductal adenocarcinoma gene expression classifier and its prognostic implications

Wenric

Davison²,

Guittar

et al. 2023

Preprint

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Background: Pancreatic ductal adenocarcinoma (PDAC) is amongst the deadliest cancers, with few modern tools to inform patient prognosis and help guide treatment options. Transcriptome-based molecular subtyping is one emerging technology that has been employed to help patients optimize available therapeutic approaches. Here we retrospectively demonstrate the clinical validity of PurIST (Purity Independent Subtyping of Tumors), an RNA-based classifier that divides PDAC patients into two subtypes with differential prognoses, as a validated laboratory-developed test (LDT) on the Tempus Labs sequencing platform. Methods: A cohort comprising 258 late-stage PDAC patients with available transcriptomic and outcomes data was drawn from the Tempus clinicogenomic database and classified using PurIST into one of two subtypes ("Basal" or "Classical"). Differences in patient survival from the date of diagnosis were compared between subtypes, and between two common first-line treatment regimens, FOLFIRINOX, and gemcitabine + nab-paclitaxel. Results: Of the 258 PDAC patients in the validation cohort, PurIST classified 173 as classical subtype, 59 as basal subtype, and 26 as no-calls. Reinforcing previous findings, patients of the basal subtype had significantly lower overall survival than those of the classical subtype. Notably, differential survival by subtype was significant among the subset of patients on FOLFIRINOX, but not those on gemcitabine + nab-paclitaxel. Conclusions: The implementation of PurIST on a high-throughput clinical laboratory RNA-Seq platform and the demonstration of the model's clinical utility in a real-world cohort together show that PurIST can be used at scale to refine PDAC prognosis and thereby inform treatment selection to improve outcomes for advanced-stage PDAC patients.

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“…The simultaneous loss of this biomarker and PTEN in mouse pancreatic ductal cells leads to intraductal tubulopapillary neoplasms, which can progress to PDAC (50). CDKN2A, clinically identified as a common PDAC biomarker (51,52,53), is a negative regulator of normal cell proliferation. This gene strongly interacts with CDK4 and CDK6, the binding partners of cyclones in the hyper-phosphorylation of retinoblastoma within the cell cycle.…”

Section: 2) Ti-mutanet Suggests Clinically Relevant Biomarkers For St...mentioning

confidence: 99%

Text-based Integration of Mutational Profiles for Classification and Biomarker Identification of Intraductal Papillary Mucinous Neoplasms of the Pancreas

Nguyen¹,

Teer²,

Park³

et al. 2023

Preprint

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Motivation: Intraductal Papillary Mucinous Neoplasms (IPMNs) are a common cystic precursor for pancreatic ductal adenocarcinoma (PDAC). Detecting these pre-malignant lesions poses a challenge for diagnostic tools due to their relatively low occurrence rate. However, a better understanding of the lesions composition could enable effective decision-making, risk assessment, treatment selection, and, most importantly, prevention. Methods: In this work, we introduce a new framework for integrating information from mutational profiles using transformer-based models for stratification and biomarker identification in IPMNs vs. PDAC. We show that the numerical descriptor vectors can be used to construct highly predictive Artificial Neural Networks for disease classification. The derived mutational representations can be supported by other data types (here, mRNA) and further improve the accuracy of the classifiers. Besides the AI-driven methodology for biomarker discovery in cancer research, we also propose methods to maximize AI utility by recycling its knowledge to facilitate our limited understanding of the disease. We propose Natural Adversary Analysis, an AI-driven inference to detect IPMNs with a high probability of progression to malignancy. Results: The proposed model supports 12 clinically relevant genetic biomarkers with high mutation rates (such as KRAS, GNAS, ARID1A, and CDKN2A) and suggests biomarkers not yet recognized (such as RADIL, TTN, and ZNF287). We broaden the study scope by investigating rarely mutated genes and reveal 14 biomarkers with potential clinical importance. Several genes with low mutation rates, including TMPRSS1, CDH22, CCND2, CYFIP2, CBLL1, and OPCML, are also addressed as potential biomarkers by our framework. Finally, the predictive robustness of the identified biomarker set is validated externally on the patient data from the Moffitt Cancer Center study, including six pairs of matched tumor and normal IPMN samples. We show that the presented mutational profile (MP-derived) gene panel has equivalent predictive power to clinically driven panels. Conclusions: Here, we show the proof-of-concept that AI can serve the clinic and discover biomarkers beyond clinically known regimes. In line with that, we propose a translational AI-based approach for 1) disease stratification (IPMNs vs. PDAC), 2) biomarker identification, and 3) transferring the model knowledge to predict cysts risk of progression.

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Genetic Mutations of Pancreatic Cancer and Genetically Engineered Mouse Models

Cited by 23 publications

References 113 publications

A combined analysis of bulk and single-cell sequencing data reveals metabolic enzyme, pyruvate dehydrogenase E1 subunit beta (PDHB), as a prediction biomarker for the tumor immune response and immunotherapy

A combined analysis of bulk and single-cell sequencing data reveals metabolic enzyme, pyruvate dehydrogenase E1 subunit beta (PDHB), as a prediction biomarker for the tumor immune response and immunotherapy

Real-world data validation of the PurIST pancreatic ductal adenocarcinoma gene expression classifier and its prognostic implications

Text-based Integration of Mutational Profiles for Classification and Biomarker Identification of Intraductal Papillary Mucinous Neoplasms of the Pancreas

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