Whole-exome sequencing of pancreatic cancer defines genetic diversity and therapeutic targets

Witkiewicz, Agnieszka K.; McMillan, Elizabeth A.; Balaji, Uthra; Baek, GuemHee; Lin, Wei‐Xia; Mansour, John C.; Mollaee, Mehri; Wagner, Kay Uwe; Koduru, Prasad; Yopp, Adam C.; Choti, Michael A.; Yeo, Charles J.; McCue, Peter A.; White, Michael A.; Knudsen, Erik S.

doi:10.1038/ncomms7744

Cited by 904 publications

(945 citation statements)

References 41 publications

(58 reference statements)

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“…Mutations of PTEN were found in 7.3% of PanNET and more recently mutations in TSC2, 8.8%; PIK3CA, 1.4% were also described (Perren et al 2000. TP53 mutations are rare in PanNET Jiao et al 2011, Cao et al 2013, Francis et al 2013, Cancer Genome Atlas Research 2014, Fernandez-Cuesta et al 2014, Cromer et al 2015, Lichtenauer et al 2015, Witkiewicz et al 2015, Scarpa et al 2017.…”

Section: Cellmentioning

confidence: 99%

Genetic and epigenetic drivers of neuroendocrine tumours (NET)

Domenico

Wiedmer

Perren

2017

Endocrine-Related Cancer

102

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Neuroendocrine tumours (NET) of the gastrointestinal tract and the lung are a rare and heterogeneous group of tumours. The molecular characterization and the clinical classification of these tumours have been evolving slowly and show differences according to organs of origin. Novel technologies such as next-generation sequencing revealed new molecular aspects of NET over the last years. Notably, whole-exome/genome sequencing (WES/WGS) approaches underlined the very low mutation rate of well-differentiated NET of all organs compared to other malignancies, while the engagement of epigenetic changes in driving NET evolution is emerging. Indeed, mutations in genes encoding for proteins directly involved in chromatin remodelling, such as DAXX and ATRX are a frequent event in NET. Epigenetic changes are reversible and targetable; therefore, an attractive target for treatment. The discovery of the mechanisms underlying the epigenetic changes and the implication on gene and miRNA expression in the different subgroups of NET may represent a crucial change in the diagnosis of this disease, reveal new therapy targets and identify predictive markers. Molecular profiles derived from omics data including DNA mutation, methylation, gene and miRNA expression have already shown promising results in distinguishing clinically and molecularly different subtypes of NET. In this review, we recapitulate the major genetic and epigenetic characteristics of pancreatic, lung and small intestinal NET and the affected pathways. We also discuss potential epigenetic mechanisms leading to NET development.

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Section: Cellmentioning

confidence: 99%

Genetic and epigenetic drivers of neuroendocrine tumours (NET)

Domenico

Wiedmer

Perren

2017

Endocrine-Related Cancer

102

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“…However, proper selection of offlabel drugs in these cancer types suffers from the lack of knowledge base and computational search strategies [12]. With the availablity of clinical tumor samples from TCGA data [13] and other large-scale cancer cell line drug screening data, such as the Cancer Cell Line Encyclopedia (CCLE) [14] and the Cancer Therapeutics Response Portal (CTRP) [15], we anticipate that bioinformatics data integration and analysis will make up this knowledge gap. Advances in high-throughput nextgeneration sequencing (NGS) technologies now allow the identification of actionable molecular alterations (e.g., clinically relevant driver mutations) in an individual patient in clinical practice [16].…”

Section: Introductionmentioning

confidence: 99%

PMTDS: a computational method based on genetic interaction networks for Precision Medicine Target‐Drug Selection in cancer

et al. 2017

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Background: Precision medicine attempts to tailor the right therapy for the right patient. Recent progress in largescale collection of patents' tumor molecular profiles in The Cancer Genome Atlas (TCGA) provides a foundation for systematic discovery of potential drug targets specific to different types of cancer. However, we still lack powerful computational methods to effectively integrate multiple omics data and protein-protein interaction network technology for an optimum target and drug recommendation for an individual patient. Methods: In this study, a computation method, Precision Medicine Target-Drug Selection (PMTDS) based on genetic interaction networks is developed to select the optimum targets and associated drugs for precision medicine style treatment of cancer. The PMTDS system includes three parts: a personalized medicine knowledgebase for each cancer type, a genetic interaction network-based algorithm and a single patient molecular profiles. The knowledgebase integrates cancer drugs, drug-target databases and gene biological pathway networks. The molecular profiles of each tumor consists of DNA copy number alteration, gene mutation, and tumor gene expression variation compared to its adjacent normal tissue. Results: The novel integrated PMTDS system is applied to select candidate target-drug pairs for 178 TCGA pancreatic adenocarcinoma (PDAC) tumors. The experiment results show known drug targets (EGFR, IGF1R, ERBB2, NR1I2 and AKR1B1) of PDAC treatment are identified, which provides important evidence of the PMTDS algorithm's accuracy. Other potential targets PTK6, ATF, SYK are, also, recommended for PDAC. Further validation is provided by comparison of selected targets with, both, cell line molecular profiles from the Cancer Cell Line Encyclopedia (CCLE) and drug response data from the Cancer Therapeutics Response Portal (CTRP). Results from experimental analysis of forty six individual pancreatic cancer samples show that drugs selected by PMTDS have more sample-specific efficacy than the current clinical PDAC therapies. Conclusions: A novelty target and drug priority algorithm PMTDS is developed to identify optimum target-drug pairs by integrating the knowledgebase base with a single patient's genomics. The PMTDS system provides an accurate and reliable source for target and off-label drug selection for precision cancer medicine.

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“…6). MYC alone is sufficient to initiate tumors and to drive tumor progression in the pancreas in vivo (5,(7)(8)(9)(10). Furthermore, compelling evidence exists that MYC is an essential downstream effector of oncogenic KRAS in the pancreas (11)(12)(13)(14).…”

mentioning

confidence: 99%

Concepts to Target MYC in Pancreatic Cancer

Wirth

Mahboobi

Krämer

et al. 2016

Molecular Cancer Therapeutics

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Current data suggest that MYC is an important signaling hub and driver in pancreatic ductal adenocarcinoma (PDAC), a tumor entity with a strikingly poor prognosis. No targeted therapies with a meaningful clinical impact were successfully developed against PDAC so far. This points to the need to establish novel concepts targeting the relevant drivers of PDAC, like KRAS or MYC. Here, we discuss recent developments of direct or indirect MYC inhibitors and their potential mode of action in PDAC.

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Whole-exome sequencing of pancreatic cancer defines genetic diversity and therapeutic targets

Cited by 904 publications

References 41 publications

Genetic and epigenetic drivers of neuroendocrine tumours (NET)

Genetic and epigenetic drivers of neuroendocrine tumours (NET)

PMTDS: a computational method based on genetic interaction networks for Precision Medicine Target‐Drug Selection in cancer

Concepts to Target MYC in Pancreatic Cancer

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