Multi-biomarker panel prediction model for diagnosis of pancreatic cancer.

BACKGROUND/PURPOSE: The current study aimed to develop a prediction model using a multi-marker panel as a diagnostic screening tool for pancreatic ductal adenocarcinoma. METHODS: Multi-center cohort of 1991 blood samples were collected from January 2011 to September 2019, of which 609 were normal, 145 were other cancer (colorectal, thyroid, and breast cancer), 314 were pancreatic benign disease, and 923 were pancreatic ductal adenocarcinoma. The automated multi-biomarker Enzyme-Linked Immunosorbent Assay kit was developed using three potential biomarkers: LRG1, TTR, and CA 19-9. Using a logistic regression model on a training data set, the predicted values for pancreatic ductal adenocarcinoma were obtained, and the result was classification into one of the three risk groups: low, intermediate, and high. The five covariates used to create the model were sex, age, and three biomarkers. RESULTS: Participants were categorized into four groups as normal (n = 609), other cancer (n = 145), pancreatic benign disease (n = 314), and pancreatic ductal adenocarcinoma (n = 923). The normal, other cancer, and pancreatic benign disease groups were clubbed into the non-pancreatic ductal adenocarcinoma group (n = 1068). The positive and negative predictive value, sensitivity, and specificity were 94.12, 90.40, 93.81, and 90.86, respectively. CONCLUSIONS: This study demonstrates a significant diagnostic performance of the multi-marker panel in distinguishing pancreatic ductal adenocarcinoma from normal and benign pancreatic disease states, as well as patients with other cancers.

CORRIGENDUM: Addition of data source: Diagnostic model for pancreatic cancer using a multi-biomarker panel.

[This corrects the article on p. 144 in vol. 100, PMID: 33748028.].

Diagnostic model for pancreatic cancer using a multi-biomarker panel.

PURPOSE: Diagnostic biomarkers of pancreatic ductal adenocarcinoma (PDAC) have been used for early detection to reduce its dismal survival rate. However, clinically feasible biomarkers are still rare. Therefore, in this study, we developed an automated multi-marker enzyme-linked immunosorbent assay (ELISA) kit using 3 biomarkers (leucine-rich alpha-2-glycoprotein [LRG1], transthyretin [TTR], and CA 19-9) that were previously discovered and proposed a diagnostic model for PDAC based on this kit for clinical usage. METHODS: Individual LRG1, TTR, and CA 19-9 panels were combined into a single automated ELISA panel and tested on 728 plasma samples, including PDAC (n = 381) and normal samples (n = 347). The consistency between individual panels of 3 biomarkers and the automated multi-panel ELISA kit were accessed by correlation. The diagnostic model was developed using logistic regression according to the automated ELISA kit to predict the risk of pancreatic cancer (high-, intermediate-, and low-risk groups). RESULTS: The Pearson correlation coefficient of predicted values between the triple-marker automated ELISA panel and the former individual ELISA was 0.865. The proposed model provided reliable prediction results with a positive predictive value of 92.05%, negative predictive value of 90.69%, specificity of 90.69%, and sensitivity of 92.05%, which all simultaneously exceed 90% cutoff value. CONCLUSION: This diagnostic model based on the triple ELISA kit showed better diagnostic performance than previous markers for PDAC. In the future, it needs external validation to be used in the clinic.

Optimization of a microarray for fission yeast.

Bar-code (tag) microarrays of yeast gene-deletion collections facilitate the systematic identification of genes required for growth in any condition of interest. Anti-sense strands of amplified bar-codes hybridize with ~10,000 (5,000 each for up- and down-tags) different kinds of sense-strand probes on an array. In this study, we optimized the hybridization processes of an array for fission yeast. Compared to the first version of the array (11 µm, 100K) consisting of three sectors with probe pairs (perfect match and mismatch), the second version (11 µm, 48K) could represent ~10,000 up-/down-tags in quadruplicate along with 1,508 negative controls in quadruplicate and a single set of 1,000 unique negative controls at random dispersed positions without mismatch pairs. For PCR, the optimal annealing temperature (maximizing yield and minimizing extra bands) was 58°C for both tags. Intriguingly, up-tags required 3 higher amounts of blocking oligonucleotides than down-tags. A 1:1 mix ratio between up- and down-tags was satisfactory. A lower temperature (25°C) was optimal for cultivation instead of a normal temperature (30°C) because of extra temperature-sensitive mutants in a subset of the deletion library. Activation of frozen pooled cells for >1 day showed better resolution of intensity than no activation. A tag intensity analysis showed that tag(s) of 4,316 of the 4,526 strains tested were represented at least once; 3,706 strains were represented by both tags, 4,072 strains by up-tags only, and 3,950 strains by down-tags only. The results indicate that this microarray will be a powerful analytical platform for elucidating currently unknown gene functions.

Mutation Analysis of Synthetic DNA Barcodes in a Fission Yeast Gene Deletion Library by Sanger Sequencing.

Incorporation of unique barcodes into fission yeast gene deletion collections has enabled the identification of gene functions by growth fitness analysis. For fine tuning, it is important to examine barcode sequences, because mutations arise during strain construction. Out of 8,708 barcodes (4,354 strains) covering 88.5% of all 4,919 open reading frames, 7,734 barcodes (88.8%) were validated as high-fidelity to be inserted at the correct positions by Sanger sequencing. Sequence examination of the 7,734 high-fidelity barcodes revealed that 1,039 barcodes (13.4%) deviated from the original design. In total, 1,284 mutations (mutation rate of 16.6%) exist within the 1,039 mutated barcodes, which is comparable to budding yeast (18%). When the type of mutation was considered, substitutions accounted for 845 mutations (10.9%), deletions accounted for 319 mutations (4.1%), and insertions accounted for 121 mutations (1.6%). Peculiarly, the frequency of substitutions (67.6%) was unexpectedly higher than in budding yeast (∼28%) and well above the predicted error of Sanger sequencing (∼2%), which might have arisen during the solid-phase oligonucleotide synthesis and PCR amplification of the barcodes during strain construction. When the mutation rate was analyzed by position within 20-mer barcodes using the 1,284 mutations from the 7,734 sequenced barcodes, there was no significant difference between up-tags and down-tags at a given position. The mutation frequency at a given position was similar at most positions, ranging from 0.4% (32/7,734) to 1.1% (82/7,734), except at position 1, which was highest (3.1%), as in budding yeast. Together, well-defined barcode sequences, combined with the next-generation sequencing platform, promise to make the fission yeast gene deletion library a powerful tool for understanding gene function.

Correction: Diagnostic performance enhancement of pancreatic cancer using proteomic multimarker panel.

[This corrects the article DOI: 10.18632/oncotarget.21861.].

Diagnostic performance enhancement of pancreatic cancer using proteomic multimarker panel.

Due to its high mortality rate and asymptomatic nature, early detection rates of pancreatic ductal adenocarcinoma (PDAC) remain poor. We measured 1000 biomarker candidates in 134 clinical plasma samples by multiple reaction monitoring-mass spectrometry (MRM-MS). Differentially abundant proteins were assembled into a multimarker panel from a training set (n=684) and validated in independent set (n=318) from five centers. The level of panel proteins was also confirmed by immunoassays. The panel including leucine-rich alpha-2 glycoprotein (LRG1), transthyretin (TTR), and CA19-9 had a sensitivity of 82.5% and a specificity of 92.1%. The triple-marker panel exceeded the diagnostic performance of CA19-9 by more than 10% (AUCCA19-9 = 0.826, AUCpanel= 0.931, P < 0.01) in all PDAC samples and by more than 30% (AUCCA19-9 = 0.520, AUCpanel = 0.830, P < 0.001) in patients with normal range of CA19-9 (<37U/mL). Further, it differentiated PDAC from benign pancreatic disease (AUCCA19-9 = 0.812, AUCpanel = 0.892, P < 0.01) and other cancers (AUCCA19-9 = 0.796, AUCpanel = 0.899, P < 0.001). Overall, the multimarker panel that we have developed and validated in large-scale samples by MRM-MS and immunoassay has clinical applicability in the early detection of PDAC.

Molecular subtypes of pancreatic cancer based on miRNA expression profiles have independent prognostic value.

BACKGROUND AND AIM: Altered microRNAs (miRNA) expression, a typical feature of many cancers, is reportedly associated with prognosis according to several studies. Although numerous studies on miRNAs in pancreatic ductal adenocarcinoma have also attempted to identify prognostic biomarkers, more large-scale clinical studies are needed to establish the clinical significance of the results. Present study aimed to identify prognosis-related molecular subtypes of primary pancreas tumors using miRNA expression profiling. METHODS: Expression profiles of 1733 miRNAs were obtained by using microarray analysis of 104 pancreatic tumors of Korean patients. To detect subgroups informative in predicting the patient's prognosis, we applied unsupervised clustering methods and then analyzed the association of the molecular subgroups with survival time. Then, we constructed a classifier to predict the subgroup using penalized regression models. RESULTS: We have determined three pancreatic ductal adenocarcinoma tumor subtypes associated with prognosis based on miRNA expression profiles. These subtypes showed significantly different survival time for patients with the same clinical conditions. This demonstrates that our prognostic molecular subgroup has independent prognostic utility. The molecular subtypes can be predicted with a classifier of 19 miRNAs. Of the 19 signature miRNAs, miR-106b-star, miR-324-3p, and miR-615 were related to a p53 canonical pathway, and miR-324, miR-145-5p, miR-26b-5p, and miR-574-3p were related to a Cox-2 centered pathway. CONCLUSIONS: Our prognostic molecular subtypes demonstrated that miRNA profiles could be used as prognostic markers. Additionally, we have constructed a classifier that may be used to determine the molecular subgroup of new patient sample data. Further studies are needed for validation.

Microbial and biochemical basis of a Fusarium wilt-suppressive soil.

Crops lack genetic resistance to most necrotrophic pathogens. To compensate for this disadvantage, plants recruit antagonistic members of the soil microbiome to defend their roots against pathogens and other pests. The best examples of this microbially based defense of roots are observed in disease-suppressive soils in which suppressiveness is induced by continuously growing crops that are susceptible to a pathogen, but the molecular basis of most is poorly understood. Here we report the microbial characterization of a Korean soil with specific suppressiveness to Fusarium wilt of strawberry. In this soil, an attack on strawberry roots by Fusarium oxysporum results in a response by microbial defenders, of which members of the Actinobacteria appear to have a key role. We also identify Streptomyces genes responsible for the ribosomal synthesis of a novel heat-stable antifungal thiopeptide antibiotic inhibitory to F. oxysporum and the antibiotic's mode of action against fungal cell wall biosynthesis. Both classical- and community-oriented approaches were required to dissect this suppressive soil from the field to the molecular level, and the results highlight the role of natural antibiotics as weapons in the microbial warfare in the rhizosphere that is integral to plant health, vigor and development.

Integrative analysis of multi-omics data for identifying multi-markers for diagnosing pancreatic cancer.

BACKGROUND: microRNA (miRNA) expression plays an influential role in cancer classification and malignancy, and miRNAs are feasible as alternative diagnostic markers for pancreatic cancer, a highly aggressive neoplasm with silent early symptoms, high metastatic potential, and resistance to conventional therapies. METHODS: In this study, we evaluated the benefits of multi-omics data analysis by integrating miRNA and mRNA expression data in pancreatic cancer. Using support vector machine (SVM) modelling and leave-one-out cross validation (LOOCV), we evaluated the diagnostic performance of single- or multi-markers based on miRNA and mRNA expression profiles from 104 PDAC tissues and 17 benign pancreatic tissues. For selecting even more reliable and robust markers, we performed validation by independent datasets from the Gene Expression Omnibus (GEO) data depository. For validation, miRNA activity was estimated by miRNA-target gene interaction and mRNA expression datasets in pancreatic cancer. RESULTS: Using a comprehensive identification approach, we successfully identified 705 multi-markers having powerful diagnostic performance for PDAC. In addition, these marker candidates annotated with cancer pathways using gene ontology analysis. CONCLUSIONS: Our prediction models have strong potential for the diagnosis of pancreatic cancer.

Oak tree canker disease supports arthropod diversity in a natural ecosystem.

Microorganisms have many roles in nature. They may act as decomposers that obtain nutrients from dead materials, while some are pathogens that cause diseases in animals, insects, and plants. Some are symbionts that enhance plant growth, such as arbuscular mycorrhizae and nitrogen fixation bacteria. However, roles of plant pathogens and diseases in natural ecosystems are still poorly understood. Thus, the current study addressed this deficiency by investigating possible roles of plant diseases in natural ecosystems, particularly, their positive effects on arthropod diversity. In this study, the model system was the oak tree (Quercus spp.) and the canker disease caused by Annulohypoxylon truncatum, and its effects on arthropod diversity. The oak tree site contained 44 oak trees; 31 had canker disease symptoms while 13 were disease-free. A total of 370 individual arthropods were detected at the site during the survey period. The arthropods belonged to 25 species, 17 families, and seven orders. Interestingly, the cankered trees had significantly higher biodiversity and richness compared with the canker-free trees. This study clearly demonstrated that arthropod diversity was supported by the oak tree canker disease.

Systematic targeted gene deletion using the gene-synthesis method in fission yeast.

Genome-wide targeted gene deletion, a systematic method to study gene function by replacing target genes with deletion cassettes, using serial-PCR or block-PCR requires elaborate skill. We developed a novel gene-synthesis method to systematically prepare deletion cassettes on a 96-well basis in fission yeast. We designed the 2129-bp deletion cassette as three modules: a central 1397-bp KanMX4 selection marker module and two flanking 366-bp gene-specific artificial linker modules. The central KanMX4 module can be used in multiple deletion cassettes in combination with different sets of flanking modules. The deletion cassettes consisted of 147 oligonucleotides (93 for the central module+25 for each of the flanking modules+4 for the joints) and the oligonucleotides were designed as ~29mers using an in-house program. Oligonucleotides were synthesized on a 96-well basis and ligated into deletion cassettes without gaps by ligase chain reaction, which was followed by two rounds of nested PCR to amplify trace amounts of the ligated cassettes. After the artificial linkers were removed from the deletion cassettes, the cassettes were transformed into wild-type diploid fission yeast strain SP286. We validated the transformed colonies via check PCR and subjected them to tetrad analysis to confirm functional integrity. Using this method, we systematically deleted 563 genes in the fission yeast Schizosaccharomyces pombe with a >90% success rate and a point-mutation rate of ~0.4 mutations per kb. Our method can be used to create systematic gene deletions in a variety of yeasts especially when it included a bar-code system for parallel analyses.

Identifying molecular subtypes related to clinicopathologic factors in pancreatic cancer.

BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal tumors and usually presented with locally advanced and distant metastasis disease, which prevent curative resection or treatments. In this regard, we considered identifying molecular subtypes associated with clinicopathological factor as prognosis factors to stratify PDAC for appropriate treatment of patients. RESULTS: In this study, we identified three molecular subtypes which were significant on survival time and metastasis. We also identified significant genes and enriched pathways represented for each molecular subtype. Considering R0 resection patients included in each subtype, metastasis and survival times are significantly associated with subtype 1 and subtype 2. CONCLUSIONS: We observed three PDAC molecular subtypes and demonstrated that those subtypes were significantly related with metastasis and survival time. The study may have utility in stratifying patients for cancer treatment.

Construction of the first compendium of chemical-genetic profiles in the fission yeast Schizosaccharomyces pombe and comparative compendium approach.

Genome-wide chemical genetic profiles in Saccharomyces cerevisiae since the budding yeast deletion library construction have been successfully used to reveal unknown mode-of-actions of drugs. Here, we introduce comparative approach to infer drug target proteins more accurately using two compendiums of chemical-genetic profiles from the budding yeast S. cerevisiae and the fission yeast Schizosaccharomyces pombe. For the first time, we established DNA-chip based growth defect measurement of genome-wide deletion strains of S. pombe, and then applied 47 drugs to the pooled heterozygous deletion strains to generate chemical-genetic profiles in S. pombe. In our approach, putative drug targets were inferred from strains hypersensitive to given drugs by analyzing S. pombe and S. cerevisiae compendiums. Notably, many evidences in the literature revealed that the inferred target genes of fungicide and bactericide identified by such comparative approach are in fact the direct targets. Furthermore, by filtering out the genes with no essentiality, the multi-drug sensitivity genes, and the genes with less eukaryotic conservation, we created a set of drug target gene candidates that are expected to be directly affected by a given drug in human cells. Our study demonstrated that it is highly beneficial to construct the multiple compendiums of chemical genetic profiles using many different species. The fission yeast chemical-genetic compendium is available at http://pombe.kaist.ac.kr/compendium.

FitSearch: a robust way to interpret a yeast fitness profile in terms of drug's mode-of-action.

BACKGROUND: Yeast deletion-mutant collections have been successfully used to infer the mode-of-action of drugs especially by profiling chemical-genetic and genetic-genetic interactions on a genome-wide scale. Although tens of thousands of those profiles are publicly available, a lack of an accurate method for mining such data has been a major bottleneck for more widespread use of these useful resources. RESULTS: For general usage of those public resources, we designed FitRankDB as a general repository of fitness profiles, and developed a new search algorithm, FitSearch, for identifying the profiles that have a high similarity score with statistical significance for a given fitness profile. We demonstrated that our new repository and algorithm are highly beneficial to researchers who attempting to make hypotheses based on unknown modes-of-action of bioactive compounds, regardless of the types of experiments that have been performed using yeast deletion-mutant collection in various types of different measurement platforms, especially non-chip-based platforms. CONCLUSIONS: We showed that our new database and algorithm are useful when attempting to construct a hypothesis regarding the unknown function of a bioactive compound through small-scale experiments with a yeast deletion collection in a platform independent manner. The FitRankDB and FitSearch enhance the ease of searching public yeast fitness profiles and obtaining insights into unknown mechanisms of action of drugs. FitSearch is freely available at http://fitsearch.kaist.ac.kr.

An omics perspective of protein disorder.

Disordered regions within proteins have increasingly been associated with various cellular functions. Identifying the specific roles played by disorder in these functions has proved difficult. However, the development of reliable prediction algorithms has expanded the study of disorder from a few anecdotal examples to a proteome-wide scale. Moreover, the recent omics revolution has provided the sequences of numerous organisms as well as thousands of genome-wide data sets including several types of interactomes. Here, we review the literature regarding genome-wide studies of disorder and examine how these studies give rise to new characterizations and categories of this elusive phenomenon.

Putting genetic interactions in context through a global modular decomposition.

Genetic interactions provide a powerful perspective into gene function, but our knowledge of the specific mechanisms that give rise to these interactions is still relatively limited. The availability of a global genetic interaction map in Saccharomyces cerevisiae, covering ∼30% of all possible double mutant combinations, provides an unprecedented opportunity for an unbiased assessment of the native structure within genetic interaction networks and how it relates to gene function and modular organization. Toward this end, we developed a data mining approach to exhaustively discover all block structures within this network, which allowed for its complete modular decomposition. The resulting modular structures revealed the importance of the context of individual genetic interactions in their interpretation and revealed distinct trends among genetic interaction hubs as well as insights into the evolution of duplicate genes. Block membership also revealed a surprising degree of multifunctionality across the yeast genome and enabled a novel association of VIP1 and IPK1 with DNA replication and repair, which is supported by experimental evidence. Our modular decomposition also provided a basis for testing the between-pathway model of negative genetic interactions and within-pathway model of positive genetic interactions. While we find that most modular structures involving negative genetic interactions fit the between-pathway model, we found that current models for positive genetic interactions fail to explain 80% of the modular structures detected. We also find differences between the modular structures of essential and nonessential genes.

Bringing order to protein disorder through comparative genomics and genetic interactions.

BACKGROUND: Intrinsically disordered regions are widespread, especially in proteomes of higher eukaryotes. Recently, protein disorder has been associated with a wide variety of cellular processes and has been implicated in several human diseases. Despite its apparent functional importance, the sheer range of different roles played by protein disorder often makes its exact contribution difficult to interpret. RESULTS: We attempt to better understand the different roles of disorder using a novel analysis that leverages both comparative genomics and genetic interactions. Strikingly, we find that disorder can be partitioned into three biologically distinct phenomena: regions where disorder is conserved but with quickly evolving amino acid sequences (flexible disorder); regions of conserved disorder with also highly conserved amino acid sequences (constrained disorder); and, lastly, non-conserved disorder. Flexible disorder bears many of the characteristics commonly attributed to disorder and is associated with signaling pathways and multi-functionality. Conversely, constrained disorder has markedly different functional attributes and is involved in RNA binding and protein chaperones. Finally, non-conserved disorder lacks clear functional hallmarks based on our analysis. CONCLUSIONS: Our new perspective on protein disorder clarifies a variety of previous results by putting them into a systematic framework. Moreover, the clear and distinct functional association of flexible and constrained disorder will allow for new approaches and more specific algorithms for disorder detection in a functional context. Finally, in flexible disordered regions, we demonstrate clear evolutionary selection of protein disorder with little selection on primary structure, which has important implications for sequence-based studies of protein structure and evolution.

Saccharomyces cerevisiae genome-wide mutant screen for sensitivity to 2,4-diacetylphloroglucinol, an antibiotic produced by Pseudomonas fluorescens.

2,4-Diacetylphloroglucinol (2,4-DAPG), an antibiotic produced by Pseudomonas fluorescens, has broad-spectrum antibiotic activity, inhibiting organisms ranging from viruses, bacteria, and fungi to higher plants and mammalian cells. The biosynthesis and regulation of 2,4-DAPG in P. fluorescens are well described, but the mode of action against target organisms is poorly understood. As a first step to elucidate the mechanism, we screened a deletion library of Saccharomyces cerevisiae in broth and agar medium supplemented with 2,4-DAPG. We identified 231 mutants that showed increased sensitivity to 2,4-DAPG under both conditions, including 22 multidrug resistance-related mutants. Three major physiological functions correlated with an increase in sensitivity to 2,4-DAPG: membrane function, reactive oxygen regulation, and cell homeostasis. Physiological studies with wild-type yeast validated the results of the mutant screens. The chemical-genetic fitness profile of 2,4-DAPG resembled those of menthol, sodium azide, and hydrogen peroxide determined in previous high-throughput screening studies. Collectively, these findings indicate that 2,4-DAPG acts on multiple basic cellular processes.