Proteogenomic Characterization of Human Early-Onset Gastric Cancer

Mun, Dong Gi; Bhin, Jinhyuk; Kim, Sangok; Kim, Hyun Woo; Jung, Jae Hun; Jung, Yeonjoo; Jang, Ye Eun; Park, Jong; Kim, Hokeun; Lee, Hangyeore; Bae, Jingi; Back, Seunghoon; Kim, Su Jin; Kim, Jieun; Park, Heejin; Li, Honglan; Hwang, Kyu Baek; Park, Young Soo; Yook, Jeong Hwan; Kim, Byung Sik; Kwon, Sun Young; Ryu, Seung Wan; Park, Do Youn; Jeon, Tae Yong; Kim, Dae Hwan; Lee, Jae Hyuck; Han, Sang Uk; Song, Kyu Sang; Park, Dongmin; Park, Jung Won; Rodriguez, Henry; Kim, Jaesang; Lee, Hookeun; Kim, Kwang Pyo; Yang, Eun Gyeong; Kim, Hark Kyun; Paek, Eunok; Lee, Sanghyuk; Lee, Sang Won; Hwang, Daehee

doi:10.1016/j.ccell.2018.12.003

Cited by 219 publications

(212 citation statements)

References 81 publications

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“…This is achieved by simultaneously performing whole-exome sequencing (WES), RNA sequencing (RNA-Seq), and tandem mass spectrometry (MS/MS)-based shotgun proteomics analysis on matched samples, producing customized, sample-specific protein databases from DNA, and/or RNA sequencing data, and then searching MS/MS data against the customized protein databases. In contrast to proteomics data analysis that relies on reference protein databases alone, this approach allows the identification of peptides not included in reference protein databases, providing new opportunities to improve protein-coding genome annotation 4,5 and to identify disease-specific protein sequences [6][7][8][9][10][11][12][13] .…”

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confidence: 99%

Cancer neoantigen prioritization through sensitive and reliable proteogenomics analysis

et al. 2020

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Genomics-based neoantigen discovery can be enhanced by proteomic evidence, but there remains a lack of consensus on the performance of different quality control methods for variant peptide identification in proteogenomics. We propose to use the difference between accurately predicted and observed retention times for each peptide as a metric to evaluate different quality control methods. To this end, we develop AutoRT, a deep learning algorithm with high accuracy in retention time prediction. Analysis of three cancer data sets with a total of 287 tumor samples using different quality control strategies results in substantially different numbers of identified variant peptides and putative neoantigens. Our systematic evaluation, using the proposed retention time metric, provides insights and practical guidance on the selection of quality control strategies. We implement the recommended strategy in a computational workflow named NeoFlow to support proteogenomics-based neoantigen prioritization, enabling more sensitive discovery of putative neoantigens.

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confidence: 99%

Cancer neoantigen prioritization through sensitive and reliable proteogenomics analysis

et al. 2020

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“…In terms of the GC proteomic landscape, our understanding is still rudimentary. Of note are two recent large‐scale proteomic studies focused on DGC, which independently delineated subtypes of potential prognostic and therapeutic value . Nonetheless, these studies remain to be rigorously validated owing to the general lack of comprehensive proteomic datasets.…”

Section: Multifaceted Molecular Classification Of Gcmentioning

confidence: 99%

Dissection of gastric cancer heterogeneity for precision oncology

Tan

2019

Cancer Science

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Gastric cancer (GC) remains the fifth most prevalent cancer worldwide and the third leading cause of global cancer mortality. Comprehensive ‐omic studies have unveiled a heterogeneous GC landscape, with considerable molecular diversity both between and within tumors. Given the complex nature of GC, a long‐sought goal includes effective identification of distinct patient subsets with prognostic and/or predictive outcomes to enable tailoring of specific treatments (“precision oncology”). In this review, we highlight various approaches to molecular classification in GC, covering recent genomic, transcriptomic, proteomic and epigenomic features. We pay special attention to the translational significance of classifier systems and examine potential confounding factors which deserve further investigation. In particular, we discuss recent advancements in our knowledge of intra‐subtype, intra‐patient and intra‐tumor heterogeneity, and the pivotal role of the tumor stromal microenvironment.

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“…Recently, massive efforts have been put forth to prioritize the functional importance of phosphorylation sites (54,55) as well as decipher correlation between mutation and phosphorylation in cancer (75). For this reason, we compiled a curated list of susceptible nullomer-making phosphosites retrieved from PhosphoSite Plus database (36).…”

Section: Peptide Nullomers and Nullomer-making Mutationsmentioning

confidence: 99%

Significant non-existence of sequences in genomes and proteomes

Koulouras

Frith

2020

Preprint

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Nullomers are minimal-length oligomers absent from a genome or proteome. Although research has shown that artificially synthesized nullomers have deleterious effects, there is still a lack of a strategy for the prioritisation and classification of non-occurring sequences as potentially malicious or benign. In this work, by using Markovian models with multiple-testing correction, we reveal significant absent oligomers which are statistically expected to exist. This strongly suggests that their absence is due to negative selection. We survey genomes and proteomes covering the diversity of life, and find thousands of significant absent sequences. Common significant nullomers are often mono-or dinucleotide tracts, or palindromic. Significant viral nullomers are often restriction sites, and may indicate unknown restriction motifs. Surprisingly, significant mammal genome nullomers are often present, but rare, in other mammals, suggesting that they are suppressed but not completely forbidden. Significant human nullomers are rarely present in human viruses, indicating viral mimicry of the host. More than 1/4 of human proteins are one substitution away from containing a significant nullomer. We provide a webbased, interactive database of significant nullomers across genomes and proteomes.In this study, we introduce a robust probabilistic method named Nullomers Assessor (https://github.com/gkoulouras/nullomers-assessor) for the evaluation of globally missing sets of oligomers in any species, considering the fact that biological sequences of living organisms are driven by mutational biases and natural selection, and consequently are not entirely random (10). Naturally occurring sequences present patterns and combinatoric properties which can be signatures for the identification of functional elements as such promoters, tandem repeat expansions, introns, exons, and regulatory elements (18). In addition, evolutionarily well-separated species are known to possess distinct statistical characteristics in their DNA or peptide sequence chains (19). All these distinctive properties of biological sequences have frequently been studied using probabilistic models. Markov chain models have been widely and successfully employed in various biological problems including sequence analysis in the past (26-29). Taking advantage of these particular properties of biological sequences, we developed a method which approximates the likelihood of an absent sequence to occur exactly zero times, in order to address the following three questions. First, are there statistically significant minimal absent sequences in biological species; in simple words, what is the expected probability for an actual nullomer to be indeed absent, based on the compositional pattern in the full genome or proteome of a species? Second, are there significant MAWs in common across evolutionarily diverse living organisms? And finally, does the creation of a previously absent sequence perturb a molecule; more precisely, are there mutations with functional or stability impact ...

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Proteogenomic Characterization of Human Early-Onset Gastric Cancer

Cited by 219 publications

References 81 publications

Cancer neoantigen prioritization through sensitive and reliable proteogenomics analysis

Cancer neoantigen prioritization through sensitive and reliable proteogenomics analysis

Dissection of gastric cancer heterogeneity for precision oncology

Significant non-existence of sequences in genomes and proteomes

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