AACR Project GENIE: Powering Precision Medicine through an International Consortium

André, Fabrice; Arnedos, Mónica; Baras, Alexander S.; Baselga, J.; Bédard, Philippe L.; Berger, Michael F.; Bierkens, Mariska; Calvo, Fabien; Cerami, Ethan; Chakravarty, Debyani; Dang, Kristen K.; Davidson, Nancy E.; Fitz, Catherine Del Vecchio; Doğan, Semih; DuBois, Raymond N.; Ducar, Matthew D.; Futreal, P. Andrew; Gao, Jianjiong; Garcia, Francisco Moacir Pinheiro; Gardos, Stu; Gocke, Christopher D.; Groß, Benjamin; Guinney, Justin; Heins, Zachary J.; Hintzen, Stephanie; Horlings, Hugo M.; Hudeček, Jan; Hyman, David M.; Kamel‐Reid, Suzanne; Kandoth, Cyriac; Kinyua, Walter; Kumari, Priti; Kundra, Ritika; Ladanyi, Marc; Lefèbvre, Céline; Lenoue-Newton, Michele L.; Lepisto, Eva M.; Levy, Mia A.; Lindeman, Neal I.; Lindsay, James; Liu, David; Lu, Zhibin; MacConaill, Laura E.; Maurer, Ian; Maxwell, David S.; Meijer, Gerrit A.; Meric‐Bernstam, Funda; Micheel, Christine M.; Miller, Clinton; Mills, Gordon B.; Moore, Nathanael D.; Nederlof, Petra M.; Omberg, Larsson; Orechia, John A.; Park, Ben Ho; Pugh, Trevor J.; Reardon, Brendan; Rollins, Barrett J.; Routbort, Mark J.; Sawyers, Charles L.; Schrag, Deborah; Schultz, Nikolaus; Shaw, Kenna; Shivdasani, Priyanka; Siu, Lillian L.; Solit, David B.; Sonke, Gabe S.; Soria, Jean Charles; Sripakdeevong, Parin; Stickle, Natalie; Stricker, Thomas; Sweeney, Shawn M.; Taylor, Barry S.; Hoeve, Jelle J. ten; Thomas, Stacy B.; Allen, Eliezer M. Van; Veer, Laura J. van ’t; Velde, Tony van de; Tinteren, Harm van; Velculescu, Victor E.; Virtanen, Carl; Voest, Emile E.; Wang, Lucy Lu; Wathoo, Chetna; Watt, Stuart; Yu, Celeste; Yu, Thomas; Yu, Emily; Zehir, Ahmet; Zhang, Hongxin

doi:10.1158/2159-8290.cd-17-0151

Cited by 1,445 publications

(847 citation statements)

References 27 publications

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“…31 Data from GENIE were obtained as previously described. 31 The 10 most common diagnoses that harbor MDM2 amplification from the current report were selected for the comparison. MDM2 amplifications were seen in 3.5% (3,650 of 102,878) of patients in the current report versus 5.5% (744 of 13,473) from GENIE.…”

Section: Figmentioning

confidence: 99%

Analysis ofMDM2Amplification: Next-Generation Sequencing of Patients With Diverse Malignancies

Kato

Ross

Gay

et al. 2018

JCO Precision Oncology

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Purpose MDM2 amplification can promote tumorigenesis directly or indirectly through p53 inhibition. MDM2 has increasing clinical relevance because inhibitors are under evaluation in clinical trials, and MDM2 amplification is a possible genomic correlate of accelerated progression, known as hyperprogression, after anti–PD-1/PD-L1 immunotherapy. We used next-generation sequencing (NGS) to ascertain MDM2 amplification status across a large number of diverse cancers. Methods We interrogated the molecular profiles of 102,878 patients with diverse malignancies for MDM2 amplification and co-altered genes using clinical-grade NGS (182 to 465 genes). Results MDM2 amplification occurred in 3.5% of patients (3,650 of 102,878). The majority of tumor types had a small subset of patients with MDM2 amplification. Most of these patients (99.0% [3,613/3,650]) had co-alterations that accompanied MDM2 amplification. Various pathways, including those related to tyrosine kinase (37.9% [1,385 of 3,650]), PI3K signaling (25.4% [926 of 3,650]), TP53 (24.9% [910 of 3,650]), and MAPK signaling (23.6% [863 of 3,650]), were involved. Although infrequent, mismatch repair genes and PD-L1 amplification also were co-altered (2.2% [79 of 3,650]). Most patients (97.6% [3,563 of 3,650]) had one or more co-alterations potentially targetable with either a Food and Drug Administration–approved or investigational agent. MDM2 amplifications were less frequently associated with high tumor mutation burden compared with the MDM2 wild-type population (2.9% v 6.5%; P < .001). An illustrative patient who harbored MDM2 amplification and experienced hyperprogression with an immune checkpoint inhibitor is presented. Conclusion MDM2 amplification was found in 3.5% of 102,878 patients, 97.6% of whom harbored genomic co-alterations that were potentially targetable. This study suggests that a small subset of most tumor types have MDM2 amplification as well as pharmacologically tractable co-alterations.

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Section: Figmentioning

confidence: 99%

Analysis ofMDM2Amplification: Next-Generation Sequencing of Patients With Diverse Malignancies

Kato

Ross

Gay

et al. 2018

JCO Precision Oncology

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“…1h and Supplementary Fig. 4e,f) 1,4 . Notably, despite a ~5-fold enrichment in the co-occurrence of these two alterations, this interaction would be statistically insignificant in a naive survey of all potential pairwise driver interactions after correcting for multiple-hypothesis testing, thus illustrating the need to study genetic interactions beyond co-occurrence patterns ( P = 0.32 after Bonferroni correction for 10 pairwise interactions) 13 .…”

mentioning

confidence: 97%

“…Cancer genome sequencing has catalogued many of these alterations; however, the combinatorial effects of these alterations on tumor growth is largely unknown 1,4 . Most putative drivers are altered in less than 10% of tumors 2 , suggesting that these alterations may be inert, weakly beneficial, or beneficial only in certain genomic contexts.…”

mentioning

confidence: 99%

“…The tumor suppressor TP53 is inactivated in more than half of human lung adenocarcinomas 1, 4 . To determine the effect of Trp53 ( p53 ) deletion on the growth suppressive effects of ten other putative tumor suppressors, we initiated tumors in Kras LSL-G12D ; Rosa26 LSL-tdTomato ; H11 LSL-Cas9 ( KT;Cas9 ) and KT; Trp53 flox/flox ; Cas9 ( KPT;Cas9 ) mice using a pool of barcoded Lenti-sgRNA/Cre vectors targeting many common tumor suppressor genes and four barcoded Lenti-sgInert/Cre vectors (Lenti-sgTS-Pool/Cre; Fig.…”

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confidence: 99%

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Mapping the in vivo fitness landscape of lung adenocarcinoma tumor suppression in mice

et al. 2018

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The functional impact of most genomic alterations found in cancer, alone or in combination, remains largely unknown. Here we integrate tumor barcoding, CRISPR/Cas9-mediated genome editing and ultra-deep barcode sequencing to interrogate pairwise combinations of tumor suppressor alterations in autochthonous mouse models of human lung adenocarcinoma. We map the tumor suppressive effects of 31 common lung adenocarcinoma genotypes and identify a landscape of context dependence and differential effect strengths.

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“…There is no gold standard knowledgebase for genomic data interpretation. Therefore, the efforts in quality control [30] and genotype-phenotype knowledgebase construction [31] should be followed.…”

Section: Genomic Datamentioning

confidence: 99%

Status and Direction of Healthcare Data in Korea for Artificial Intelligence

Park

Shin

2017

Hanyang Med Rev

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Predicting temporal patterns across various domains poses significant challenges due to their nuanced and often nonlinear trajectories. To address this challenge, prediction frameworks have been continuously refined, employing data-driven statistical methods, mathematical models, and machine learning. Recently, as one of the challenging systems, shared transport systems such as public bicycles have gained prominence due to urban constraints and environmental concerns. Predicting rental and return patterns at bicycle stations remains a formidable task due to the system's openness and imbalanced usage patterns across stations. In this study, we propose a deep learning framework to predict rental and return patterns by leveraging cartogram approaches. The cartogram approach facilitates the prediction of demand for newly installed stations with no training data as well as long-period prediction, which has not been achieved before. We apply this method to public bicycle rentaland-return data in Seoul, South Korea, employing a spatial-temporal convolutional graph attention network. Our improved architecture incorporates batch attention and modified node feature updates for better prediction accuracy across different time scales. We demonstrate the effectiveness of our framework in predicting temporal patterns and its potential applications.

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AACR Project GENIE: Powering Precision Medicine through an International Consortium

Cited by 1,445 publications

References 27 publications

Analysis ofMDM2Amplification: Next-Generation Sequencing of Patients With Diverse Malignancies

Analysis ofMDM2Amplification: Next-Generation Sequencing of Patients With Diverse Malignancies

Mapping the in vivo fitness landscape of lung adenocarcinoma tumor suppression in mice

Status and Direction of Healthcare Data in Korea for Artificial Intelligence

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