Strengths and limitations of microarray-based phenotype prediction: lessons learned from the IMPROVER Diagnostic Signature Challenge

Phenylacetylglutamine as a novel biomarker of type 2 diabetes with distal symmetric polyneuropathy by metabolomics

PURPOSE

Type 2 diabetes mellitus (T2DM) with distal symmetric polyneuropathy (DSPN) is a disease involving the nervous system caused by metabolic disorder, while the metabolic spectrum and key metabolites remain poorly defined.

METHODS

Plasma samples of 30 healthy controls, 30 T2DM patients, and 60 DSPN patients were subjected to nontargeted metabolomics. Potential biomarkers of DSPN were screened based on univariate and multivariate statistical analyses, ROC curve analysis, and logistic regression. Finally, another 22 patients with T2DM who developed DSPN after follow-up were selected for validation of the new biomarker based on target metabolomics.

RESULTS

Compared with the control group and the T2DM group, 6 metabolites showed differences in the DSPN group (P < 0.05; FDR < 0.1; VIP > 1) and a rising step trend was observed. Among them, phenylacetylglutamine (PAG) and sorbitol displayed an excellent discriminatory ability and associated with disease severity. The verification results demonstrated that when T2DM progressed to DSPN, the phenylacetylglutamine content increased significantly (P = 0.004).

CONCLUSION

The discovered and verified endogenous metabolite PAG may be a novel potential biomarker of DSPN and involved in the disease pathogenesis.

Crowdsourced benchmarking of taxonomic metagenome profilers: lessons learned from the sbv IMPROVER Microbiomics challenge

Background

Selection of optimal computational strategies for analyzing metagenomics data is a decisive step in determining the microbial composition of a sample, and this procedure is complex because of the numerous tools currently available. The aim of this research was to summarize the results of crowdsourced sbv IMPROVER Microbiomics Challenge designed to evaluate the performance of off-the-shelf metagenomics software as well as to investigate the robustness of these results by the extended post-challenge analysis. In total 21 off-the-shelf taxonomic metagenome profiling pipelines were benchmarked for their capacity to identify the microbiome composition at various taxon levels across 104 shotgun metagenomics datasets of bacterial genomes (representative of various microbiome samples) from public databases. Performance was determined by comparing predicted taxonomy profiles with the gold standard.

Results

Most taxonomic profilers performed homogeneously well at the phylum level but generated intermediate and heterogeneous scores at the genus and species levels, respectively. kmer-based pipelines using Kraken with and without Bracken or using CLARK-S performed best overall, but they exhibited lower precision than the two marker-gene-based methods MetaPhlAn and mOTU. Filtering out the 1% least abundance species—which were not reliably predicted—helped increase the performance of most profilers by increasing precision but at the cost of recall. However, the use of adaptive filtering thresholds determined from the sample’s Shannon index increased the performance of most kmer-based profilers while mitigating the tradeoff between precision and recall.

Conclusions

kmer-based metagenomic pipelines using Kraken/Bracken or CLARK-S performed most robustly across a large variety of microbiome datasets. Removing non-reliably predicted low-abundance species by using diversity-dependent adaptive filtering thresholds further enhanced the performance of these tools. This work demonstrates the applicability of computational pipelines for accurately determining taxonomic profiles in clinical and environmental contexts and exemplifies the power of crowdsourcing for unbiased evaluation.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-022-08803-2.

The Multi-Dimensional Biomarker Landscape in Cancer Immunotherapy

The field of immuno-oncology is now at the forefront of cancer care and is rapidly evolving. The immune checkpoint blockade has been demonstrated to restore antitumor responses in several cancer types. However, durable responses can be observed only in a subset of patients, highlighting the importance of investigating the tumor microenvironment (TME) and cellular heterogeneity to define the phenotypes that contribute to resistance as opposed to those that confer susceptibility to immune surveillance and immunotherapy. In this review, we summarize how some of the most widely used conventional technologies and biomarkers may be useful for the purpose of predicting immunotherapy outcomes in patients, and discuss their shortcomings. We also provide an overview of how emerging single-cell spatial omics may be applied to further advance our understanding of the interactions within the TME, and how these technologies help to deliver important new insights into biomarker discovery to improve the prediction of patient response.

The amniotic fluid proteome predicts imminent preterm delivery in asymptomatic women with a short cervix

Preterm birth, the leading cause of perinatal morbidity and mortality, is associated with increased risk of short- and long-term adverse outcomes. For women identified as at risk for preterm birth attributable to a sonographic short cervix, the determination of imminent delivery is crucial for patient management. The current study aimed to identify amniotic fluid (AF) proteins that could predict imminent delivery in asymptomatic patients with a short cervix. This retrospective cohort study included women enrolled between May 2002 and September 2015 who were diagnosed with a sonographic short cervix (< 25 mm) at 16–32 weeks of gestation. Amniocenteses were performed to exclude intra-amniotic infection; none of the women included had clinical signs of infection or labor at the time of amniocentesis. An aptamer-based multiplex platform was used to profile 1310 AF proteins, and the differential protein abundance between women who delivered within two weeks from amniocentesis, and those who did not, was determined. The analysis included adjustment for quantitative cervical length and control of the false-positive rate at 10%. The area under the receiver operating characteristic curve was calculated to determine whether protein abundance in combination with cervical length improved the prediction of imminent preterm delivery as compared to cervical length alone. Of the 1,310 proteins profiled in AF, 17 were differentially abundant in women destined to deliver within two weeks of amniocentesis independently of the cervical length (adjusted p-value < 0.10). The decreased abundance of SNAP25 and the increased abundance of GPI, PTPN11, OLR1, ENO1, GAPDH, CHI3L1, RETN, CSF3, LCN2, CXCL1, CXCL8, PGLYRP1, LDHB, IL6, MMP8, and PRTN3 were associated with an increased risk of imminent delivery (odds ratio > 1.5 for each). The sensitivity at a 10% false-positive rate for the prediction of imminent delivery by a quantitative cervical length alone was 38%, yet it increased to 79% when combined with the abundance of four AF proteins (CXCL8, SNAP25, PTPN11, and MMP8). Neutrophil-mediated immunity, neutrophil activation, granulocyte activation, myeloid leukocyte activation, and myeloid leukocyte-mediated immunity were biological processes impacted by protein dysregulation in women destined to deliver within two weeks of diagnosis. The combination of AF protein abundance and quantitative cervical length improves prediction of the timing of delivery compared to cervical length alone, among women with a sonographic short cervix.

A novel HIF1α-STIL-FOXM1 axis regulates tumor metastasis

BACKGROUND

Metastasis is the major cause of morbidity and mortality in cancer that involves in multiple steps including epithelial-mesenchymal transition (EMT) process. Centrosome is an organelle that functions as the major microtubule organizing center (MTOC), and centrosome abnormalities are commonly correlated with tumor aggressiveness. However, the conclusive mechanisms indicating specific centrosomal proteins participated in tumor progression and metastasis remain largely unknown.

METHODS

The expression levels of centriolar/centrosomal genes in various types of cancers were first examined by in silico analysis of the data derived from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), and European Bioinformatics Institute (EBI) datasets. The expression of STIL (SCL/TAL1-interrupting locus) protein in clinical specimens was further assessed by Immunohistochemistry (IHC) analysis and the oncogenic roles of STIL in tumorigenesis were analyzed using in vitro and in vivo assays, including cell migration, invasion, xenograft tumor formation, and metastasis assays. The transcriptome differences between low- and high-STIL expression cells were analyzed by RNA-seq to uncover candidate genes involved in oncogenic pathways. The quantitative polymerase chain reaction (qPCR) and reporter assays were performed to confirm the results. The chromatin immunoprecipitation (ChIP)-qPCR assay was applied to demonstrate the binding of transcriptional factors to the promoter.

RESULTS

The expression of STIL shows the most significant increase in lung and various other types of cancers, and is highly associated with patients' survival rate. Depletion of STIL inhibits tumor growth and metastasis. Interestingly, excess STIL activates the EMT pathway, and subsequently enhances cancer cell migration and invasion. Importantly, we reveal an unexpected role of STIL in tumor metastasis. A subset of STIL translocate into nucleus and associate with FOXM1 (Forkhead box protein M1) to promote tumor metastasis and stemness via FOXM1-mediated downstream target genes. Furthermore, we demonstrate that hypoxia-inducible factor 1α (HIF1α) directly binds to the STIL promoter and upregulates STIL expression under hypoxic condition.

CONCLUSIONS

Our findings indicate that STIL promotes tumor metastasis through the HIF1α-STIL-FOXM1 axis, and highlight the importance of STIL as a promising therapeutic target for lung cancer treatment.

PKP1 and MYC create a feedforward loop linking transcription and translation in squamous cell lung cancer

PURPOSE

Plakophilin 1 (PKP1) is well-known as an important component of the desmosome, a cell structure specialized in spot-like cell-to-cell adhesion. Although desmosomes have generally been associated with tumor suppressor functions, we recently found that PKP1 is recurrently overexpressed in squamous cell lung cancer (SqCLC) to exert an oncogenic role by enhancing the translation of MYC (c-Myc), a major oncogene. In this study, we aim to further characterize the functional relationship between PKP1 and MYC.

METHODS

To determine the functional relationship between PKP1 and MYC, we performed correlation analyses between PKP1 and MYC mRNA expression levels, gain/loss of function models, chromatin immunoprecipitation (ChIP) and promoter mutagenesis followed by luciferase assays.

RESULTS

We found a significant correlation between the mRNA levels of MYC and PKP1 in SqCLC primary tumor samples. In addition, we found that MYC is a direct transcription factor of PKP1 and binds to specific sequences within its promoter. In agreement with this, we found that MYC knockdown reduced PKP1 protein expression in different SqCLC models, which may explain the PKP1-MYC correlation that we found. Conversely, we found that PKP1 knockdown reduced MYC protein expression, while PKP1 overexpression enhanced MYC expression in these models.

CONCLUSIONS

Based on these results, we propose a feedforward functional relationship in which PKP1 enhances MYC translation in conjunction with the translation initiation complex by binding to the 5'-UTR of MYC mRNA, whereas MYC promotes PKP1 transcription by binding to its promoter. These results suggest that PKP1 may serve as a therapeutic target for SqCLC.

Computational Analyses of YY1 and Its Target RKIP Reveal Their Diagnostic and Prognostic Roles in Lung Cancer

Simple Summary

Lung cancer (LC) is the tumor with the highest global mortality rate. Novel personalized therapies are currently being tested (e.g., targeted inhibitors, the immune-checkpoint inhibitors), but they cannot yet prevent the very frequent relapse and generalized metastases observed in a large population of LC patients. Currently, there is an urgent need for novel reliable biomarkers for the prognosis and diagnosis of LC. Through the systematic analysis of multiple deposited expression datasets, this report aims to explore the role of the Yin-Yang 1 (YY1) transcription factor and its target the Raf Kinase Inhibitory Protein (RKIP) in LC. The computational analysis suggested the predictive diagnostic and prognostic roles for both YY1 and RKIP stimulating further studies for proving their implication as novel biomarkers, as well as therapeutically druggable targets in LC.

Abstract

Lung cancer (LC) represents a global threat, being the tumor with the highest mortality rate. Despite the introduction of novel therapies (e.g., targeted inhibitors, immune-checkpoint inhibitors), relapses are still very frequent. Accordingly, there is an urgent need for reliable predictive biomarkers and therapeutically druggable targets. Yin-Yang 1 (YY1) is a transcription factor that may work either as an oncogene or a tumor suppressor, depending on the genotype and the phenotype of the tumor. The Raf Kinase Inhibitory Protein (RKIP), is a tumor suppressor and immune enhancer often found downregulated in the majority of the examined cancers. In the present report, the role of both YY1 and RKIP in LC is thoroughly explored through the analysis of several deposited RNA and protein expression datasets. The computational analyses revealed that YY1 negatively regulates RKIP expression in LC, as corroborated by the deposited YY1-ChIP-Seq experiments and validated by their robust negative correlation. Additionally, YY1 expression is significantly higher in LC samples compared to normal matching ones, whereas RKIP expression is lower in LC and high in normal matching tissues. These observed differences, unlike many current biomarkers, bear a diagnostic significance, as proven by the ROC analyses. Finally, the survival data support the notion that both YY1 and RKIP might represent strong prognostic biomarkers. Overall, the reported findings indicate that YY1 and RKIP expression levels may play a role in LC as potential biomarkers and therapeutic targets. However, further studies will be necessary to validate the in silico results.

Robust meta-analysis for large-scale genomic experiments based on an empirical approach

Background

Recent high-throughput technologies have opened avenues for simultaneous analyses of thousands of genes. With the availability of a multitude of public databases, one can easily access multiple genomic study results where each study comprises of significance testing results of thousands of genes. Researchers currently tend to combine this genomic information from these multiple studies in the form of a meta-analysis. As the number of genes involved is very large, the classical meta-analysis approaches need to be updated to acknowledge this large-scale aspect of the data.

Methods

In this article, we discuss how application of standard theoretical null distributional assumptions of the classical meta-analysis methods, such as Fisher’s p-value combination and Stouffer’s Z, can lead to incorrect significant testing results, and we propose a robust meta-analysis method that empirically modifies the individual test statistics and p-values before combining them.

Results

Our proposed meta-analysis method performs best in significance testing among several meta-analysis approaches, especially in presence of hidden confounders, as shown through a wide variety of simulation studies and real genomic data analysis.

Conclusion

The proposed meta-analysis method produces superior meta-analysis results compared to the standard p-value combination approaches for large-scale simultaneous testing in genomic experiments. This is particularly useful in studies with large number of genes where the standard meta-analysis approaches can result in gross false discoveries due to the presence of unobserved confounding variables.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12874-022-01530-y.

The amniotic fluid proteome changes with gestational age in normal pregnancy: a cross-sectional study

The cell-free transcriptome in amniotic fluid (AF) has been shown to be informative of physiologic and pathologic processes in pregnancy; however, the change in AF proteome with gestational age has mostly been studied by targeted approaches. The objective of this study was to describe the gestational age-dependent changes in the AF proteome during normal pregnancy by using an omics platform. The abundance of 1310 proteins was measured on a high-throughput aptamer-based proteomics platform in AF samples collected from women during midtrimester (16-24 weeks of gestation, n = 15) and at term without labor (37-42 weeks of gestation, n = 13). Only pregnancies without obstetrical complications were included in the study. Almost 25% (320) of AF proteins significantly changed in abundance between the midtrimester and term gestation. Of these, 154 (48.1%) proteins increased, and 166 (51.9%) decreased in abundance at term compared to midtrimester. Tissue-specific signatures of the trachea, salivary glands, brain regions, and immune system were increased while those of the gestational tissues (uterus, placenta, and ovary), cardiac myocytes, and fetal liver were decreased at term compared to midtrimester. The changes in AF protein abundance were correlated with those previously reported in the cell-free AF transcriptome. Intersecting gestational age-modulated AF proteins and their corresponding mRNAs previously reported in the maternal blood identified neutrophil-related protein/mRNA pairs that were modulated in the same direction. The first study to utilize an aptamer-based assay to profile the AF proteome modulation with gestational age, it reveals that almost one-quarter of the proteins are modulated as gestation advances, which is more than twice the fraction of altered plasma proteins (~ 10%). The results reported herein have implications for future studies focused on discovering biomarkers to predict, monitor, and diagnose obstetrical diseases.

Maternal whole blood mRNA signatures identify women at risk of early preeclampsia: a longitudinal study

PURPOSE

To determine whether previously established mRNA signatures are predictive of early preeclampsia when evaluated by maternal cellular transcriptome analysis in samples collected before clinical manifestation.

MATERIALS AND METHODS

We profiled gene expression at exon-level resolution in whole blood samples collected longitudinally from 49 women with normal pregnancy (controls) and 13 with early preeclampsia (delivery <34 weeks of gestation). After preprocessing and removal of gestational age-related trends in gene expression, data were converted into Z-scores based on the mean and standard deviation among controls for six gestational-age intervals. The average Z-scores of mRNAs in each previously established signature considered herein were compared between cases and controls at 9-11, 11-17, 17-22, 22-28, 28-32, and 32-34 weeks of gestation.Results: (1) Average expression of the 16-gene untargeted cellular mRNA signature was higher in women diagnosed with early preeclampsia at 32-34 weeks of gestation, yet more importantly, also prior to diagnosis at 28-32 weeks and 22-28 weeks of gestation, compared to controls (all, p < .05). (2) A combination of four genes from this signature, including a long non-protein coding RNA [H19 imprinted maternally expressed transcript (H19)], fibronectin 1 (FN1), tubulin beta-6 class V (TUBB6), and formyl peptide receptor 3 (FPR3) had a sensitivity of 0.85 (0.55-0.98) and a specificity of 0.92 (0.8-0.98) for prediction of early preeclampsia at 22-28 weeks of gestation. (3) H19, FN1, and TUBB6 were increased in women with early preeclampsia as early as 11-17 weeks of gestation (all, p < .05). (4) After diagnosis at 32-34 weeks, but also prior to diagnosis at 11-17 weeks, women destined to have early preeclampsia showed a coordinated increase in whole blood expression of several single-cell placental signatures, including the 20-gene signature of extravillous trophoblast (all, p < .05). (5) A combination of three mRNAs from the extravillous trophoblast signature (MMP11, SLC6A2, and IL18BP) predicted early preeclampsia at 11-17 weeks of gestation with a sensitivity of 0.83 (0.52-0.98) and specificity of 0.94 (0.79-0.99).

CONCLUSIONS

Circulating early transcriptomic markers for preeclampsia can be found either by untargeted profiling of the cellular transcriptome or by focusing on placental cell-specific mRNAs. The untargeted cellular mRNA signature was consistently increased in early preeclampsia after 22 weeks of gestation, and individual mRNAs of this signature were significantly increased as early as 11-17 weeks of gestation. Several single-cell placental signatures predicted future development of the disease at 11-17 weeks and were also increased in women already diagnosed at 32-34 weeks of gestation.

Performance of gene expression-based single sample predictors for assessment of clinicopathological subgroups and molecular subtypes in cancers: a case comparison study in non-small cell lung cancer

The development of multigene classifiers for cancer prognosis, treatment prediction, molecular subtypes or clinicopathological groups has been a cornerstone in transcriptomic analyses of human malignancies for nearly two decades. However, many reported classifiers are critically limited by different preprocessing needs like normalization and data centering. In response, a new breed of classifiers, single sample predictors (SSPs), has emerged. SSPs classify samples in an N-of-1 fashion, relying on, e.g. gene rules comparing expression values within a sample. To date, several methods have been reported, but there is a lack of head-to-head performance comparison for typical cancer classification problems, representing an unmet methodological need in cancer bioinformatics. To resolve this need, we performed an evaluation of two SSPs [k-top-scoring pair classifier (kTSP) and absolute intrinsic molecular subtyping (AIMS)] for two case examples of different magnitude of difficulty in non-small cell lung cancer: gene expression–based classification of (i) tumor histology and (ii) molecular subtype. Through the analysis of ~2000 lung cancer samples for each case example (n = 1918 and n = 2106, respectively), we compared the performance of the methods for different sample compositions, training data set sizes, gene expression platforms and gene rule selections. Three main conclusions are drawn from the comparisons: both methods are platform independent, they select largely overlapping gene rules associated with actual underlying tumor biology and, for large training data sets, they behave interchangeably performance-wise. While SSPs like AIMS and kTSP offer new possibilities to move gene expression signatures/predictors closer to a clinical context, they are still importantly limited by the difficultness of the classification problem at hand.

MOUSSE: Multi-Omics Using Subject-Specific SignaturEs

Simple Summary

Modern profiling technologies have led to relevant progress toward precision medicine and disease management. A new trend in patient classification is to integrate multiple data types for the same subjects to increase the chance of identifying meaningful phenotype groups. However, these methodologies are still in their infancy, with their performance varying widely depending on the biological conditions analyzed. We developed MOUSSE, a new unsupervised and normalization-free tool for multi-omics integration able to maintain good clustering performance across a wide range of omics data. We verified its efficiency in clustering patients based on survival for ten different cancer types. The results we obtained show a higher average score in classification performance than ten other state-of-the-art algorithms. We have further validated the method by identifying a list of biological features potentially involved in patient survival, finding a high degree of concordance with the literature.

Abstract

High-throughput technologies make it possible to produce a large amount of data representing different biological layers, examples of which are genomics, proteomics, metabolomics and transcriptomics. Omics data have been individually investigated to understand the molecular bases of various diseases, but this may not be sufficient to fully capture the molecular mechanisms and the multilayer regulatory processes underlying complex diseases, especially cancer. To overcome this problem, several multi-omics integration methods have been introduced but a commonly agreed standard of analysis is still lacking. In this paper, we present MOUSSE, a novel normalization-free pipeline for unsupervised multi-omics integration. The main innovations are the use of rank-based subject-specific signatures and the use of such signatures to derive subject similarity networks. A separate similarity network was derived for each omics, and the resulting networks were then carefully merged in a way that considered their informative content. We applied it to analyze survival in ten different types of cancer. We produced a meaningful clusterization of the subjects and obtained a higher average classification score than ten state-of-the-art algorithms tested on the same data. As further validation, we extracted from the subject-specific signatures a list of relevant features used for the clusterization and investigated their biological role in survival. We were able to verify that, according to the literature, these features are highly involved in cancer progression and differential survival.

Crowdsourcing assessment of maternal blood multi-omics for predicting gestational age and preterm birth

Identification of pregnancies at risk of preterm birth (PTB), the leading cause of newborn deaths, remains challenging given the syndromic nature of the disease. We report a longitudinal multi-omics study coupled with a DREAM challenge to develop predictive models of PTB. The findings indicate that whole-blood gene expression predicts ultrasound-based gestational ages in normal and complicated pregnancies (r = 0.83) and, using data collected before 37 weeks of gestation, also predicts the delivery date in both normal pregnancies (r = 0.86) and those with spontaneous preterm birth (r = 0.75). Based on samples collected before 33 weeks in asymptomatic women, our analysis suggests that expression changes preceding preterm prelabor rupture of the membranes are consistent across time points and cohorts and involve leukocyte-mediated immunity. Models built from plasma proteomic data predict spontaneous preterm delivery with intact membranes with higher accuracy and earlier in pregnancy than transcriptomic models (AUROC = 0.76 versus AUROC = 0.6 at 27-33 weeks of gestation).

Viral fibrotic scoring and drug screen based on MAPK activity uncovers EGFR as a key regulator of COVID-19 fibrosis

Understanding the molecular basis of fibrosis, the lethal complication of COVID-19, is urgent. By the analysis of RNA-sequencing data of SARS-CoV-2-infected cells combined with data mining we identified genes involved in COVID-19 progression. To characterize their implication in the fibrosis development we established a correlation matrix based on the transcriptomic data of patients with idiopathic pulmonary fibrosis. With this method, we have identified a cluster of genes responsible for SARS-CoV-2-fibrosis including its entry receptor ACE2 and epidermal growth factor EGF. Then, we developed Vi-Fi scoring—a novel drug repurposing approach and simultaneously quantified antiviral and antifibrotic activities of the drugs based on their transcriptomic signatures. We revealed the strong dual antifibrotic and antiviral activity of EGFR/ErbB inhibitors. Before the in vitro validation, we have clustered 277 cell lines and revealed distinct COVID-19 transcriptomic signatures of the cells with similar phenotypes that defines their suitability for COVID-19 research. By ERK activity monitoring in living lung cells, we show that the drugs with predicted antifibrotic activity downregulate ERK in the host lung cells. Overall, our study provides novel insights on SARS-CoV-2 dependence on EGFR/ERK signaling and demonstrates the utility of EGFR/ErbB inhibitors for COVID-19 treatment.

Glypican-1 is a novel immunohistochemical marker to differentiate poorly differentiated squamous cell carcinoma from solid predominant adenocarcinoma of the lung

Background

The histological classification of non-small cell lung cancer (NSCLC) is essential in determining new cancer-specific targeted therapies. However, the accurate typing of poorly differentiated is difficult, particularly for poorly differentiated squamous cell carcinoma and adenocarcinoma of the lung with limited immunohistochemical markers. Thus, novel immunohistochemical markers are required. We assumed the possibility of the immunohistochemical expression of glypican-1 in lung squamous cell carcinoma.

Methods

The microarray dataset GSE43580 from Gene Expression Omnibus database were analyzed for confirming the gene expression of glypican-1 in lung squamous cell carcinoma. We immunohistochemically investigated the use of glypican-1 as a novel positive diagnostic marker for lung squamous cell carcinoma. Glypican-1 expression in 63 cases of poorly differentiated lung squamous cell carcinoma and 60 cases of solid predominant lung adenocarcinoma was investigated by immunohistochemistry. Additionally, we compared glypican-1 expression with the expressions of p40, cytokeratin 5/6, thyroid transcription factor-1 (TTF-1), and napsin A.

Results

All 63 cases of lung squamous cell carcinoma showed glypican-1 expression. In contrast, only 2 cases of lung adenocarcinoma showed glypican-1 expression. The sensitivity, specificity, and diagnostic accuracy of glypican-1 expression for differentiating lung squamous cell carcinoma from lung adenocarcinoma were 100%, 96.7%, and 98.4%, respectively. These were similar to those of p40 and significantly better than those of CK 5/6.

Conclusions

We recommend the use of glypican-1 as an additional positive marker of lung squamous cell carcinoma.

Cigarette smoke-induced LKB1/AMPK pathway deficiency reduces EGFR TKI sensitivity in NSCLC

Smoker patients with non-small cell lung cancer (NSCLC) have poorer prognosis and survival than those without smoking history. However, the mechanisms underlying the low response rate of those patients to EGFR tyrosine kinase inhibitors (TKIs) are not well understood. Here we report that exposure to cigarette smoke extract enhances glycolysis and attenuates AMP-activated protein kinase (AMPK)-dependent inhibition of mTOR; this in turn reduces the sensitivity of NSCLC cells with wild-type EGFR (EGFR^WT) to EGFR TKI by repressing expression of liver kinase B1 (LKB1), a master kinase of the AMPK subfamily, via CpG island methylation. In addition, LKB1 expression is correlated positively with sensitivity to TKI in patients with NSCLC. Moreover, combined treatment of EGFR TKI with AMPK activators synergistically increases EGFR TKI sensitivity. Collectively, the current study suggests that LKB1 may serve as a marker to predict EGFR TKI sensitivity in smokers with NSCLC carrying EGFR^WT and that the combination of EGFR TKI and AMPK activator may be a potentially effective therapeutic strategy against NSCLC with EGFR^WT.

Role of FUT8 expression in clinicopathology and patient survival for various malignant tumor types: a systematic review and meta-analysis

Dysregulation of α(1,6)-fucosyltransferase (FUT8) plays significant roles in development of a variety of malignant tumor types. We collected as many relevant articles and microarray datasets as possible to assess the prognostic value of FUT8 expression in malignant tumors. For this purpose, we systematically searched PubMed, Embase, Web of Science, Springer, Chinese National Knowledge Infrastructure (CNKI), and Wan Fang, and eventually identified 7 articles and 35 microarray datasets (involving 6124 patients and 10 tumor types) for inclusion in meta-analysis. In each tumor type, FUT8 expression showed significant (p< 0.05) correlation with one or more clinicopathological parameters; these included patient gender, molecular subgroup, histological grade, TNM stage, estrogen receptor, progesterone receptor, and recurrence status. In regard to survival prognosis, FUT8 expression level was associated with overall survival in non-small cell lung cancer (NSCLC), breast cancer, diffuse large B cell lymphoma, gastric cancer, and glioma. FUT8 expression was also correlated with disease-free survival in NSCLC, breast cancer, and colorectal cancer, and with relapse-free survival in pancreatic ductal adenocarcinoma. For most tumor types, survival prognosis of patients with high FUT8 expression was related primarily to clinical features such as gender, tumor stage, age, and pathological category. Our systematic review and meta-analysis confirmed the association of FUT8 with clinicopathological features and patient survival rates for numerous malignant tumor types. Verification of prognostic value of FUT8 in these tumor types will require a large-scale study using standardized methods of detection and analysis.

TRIM28 is a distinct prognostic biomarker that worsens the tumor immune microenvironment in lung adenocarcinoma

The tumor immune microenvironment (TIME) is an important determinant of cancer prognosis and treatment efficacy. To identify immune-related prognostic biomarkers of lung adenocarcinoma, we used the ESTIMATE algorithm to calculate the immune and stromal scores of 517 lung adenocarcinoma patients from The Cancer Genome Atlas (TCGA). We detected 985 differentially expressed genes (DEGs) between patients with high and low immune and stromal scores, and we analyzed their functions and protein-protein interactions. TRIM28 was upregulated in lung adenocarcinoma patients with low immune and stromal scores, and was associated with a poor prognosis. The TISIDB and TIMER databases indicated that TRIM28 expression correlated negatively with immune infiltration. We then explored genes that were co-expressed with TRIM28 in TCGA, and investigated DEGs based on TRIM28 expression in GSE43580 and GSE7670. The 429 common DEGs from these analyses were functionally analyzed. We also performed a Gene Set Enrichment Analysis using TCGA data, and predicted substrates of TRIM28 using UbiBrowser. The results indicated that TRIM28 may negatively regulate the TIME by increasing the SUMOylation of IRF5 and IRF8. Correlation analyses and validations in two lung adenocarcinoma cell lines (PC9 and H1299) confirmed these findings. Thus, TRIM28 may worsen the TIME and prognosis of lung adenocarcinoma.

Schlafen 12 Is Prognostically Favorable and Reduces C-Myc and Proliferation in Lung Adenocarcinoma but Not in Lung Squamous Cell Carcinoma

Schlafen 12 (SLFN12) is an intermediate human Schlafen that induces differentiation in enterocytes, prostate, and breast cancer. We hypothesized that SLFN12 influences lung cancer biology. We investigated survival differences in high versus low SLFN12-expressing tumors in two databases. We then adenovirally overexpressed SLFN12 (AdSLFN12) in HCC827, H23, and H1975 cells to model lung adenocarcinoma (LUAD), and in H2170 and HTB-182 cells representing lung squamous cell carcinoma (LUSC). We analyzed proliferation using a colorimetric assay, mRNA expression by RT-qPCR, and protein by Western blot. To further explore the functional relevance of SLFN12, we correlated SLFN12 with seventeen functional oncogenic gene signatures in human tumors. Low tumoral SLFN12 expression predicted worse survival in LUAD patients, but not in LUSC. AdSLFN12 modulated expression of SCGB1A1, SFTPC, HOPX, CK-5, CDH1, and P63 in a complex fashion in these cells. AdSLFN12 reduced proliferation in all LUAD cell lines, but not in LUSC cells. SLFN12 expression inversely correlated with expression of a myc-associated gene signature in LUAD, but not LUSC tumors. SLFN12 overexpression reduced c-myc protein in LUAD cell lines but not in LUSC, by inhibiting c-myc translation. Our results suggest SLFN12 improves prognosis in LUAD in part via a c-myc-dependent slowing of proliferation.

Comprehensive Analysis of the Expression, Relationship to Immune Infiltration and Prognosis of TIM-1 in Cancer

TIM-1 is a critical gene that regulates T-helper cell development. However, little research has revealed the distribution, prognosis, and immune infiltration of TIM-1 in cancers. TCGA, GEO, Oncomine, TIMER, Kaplan-Meier, PrognoScan, GEPIA, TISIDB, and HPA databases were used to analyze TIM-1 in cancers. High TIM-1 expression was observed in bladder, cholangio, head and neck, colorectal, gastric, kidney, liver, lung adenocarcinoma, skin, uterine corpus endometrial, and pancreatic cancers compared to the normal tissues, and immunofluorescence shows that TIM-1 is mainly localized in vesicles. Simultaneously, high TIM-1 expression was closely related with poorer overall survival in gastric, lung adenocarcinoma, and poorer disease-specific survival in gastric cancer in the TCGA cohort, and was validated in the GEO cohort. Moreover, high expression of TIM-1, correlated with clinical relevance of gastric cancer and lung adenocarcinoma, was associated with tumor-infiltrating lymphocytes in lung adenocarcinoma and gastric cancer. Finally, immunohistochemistry showed TIM-1 expression was higher in lung adenocarcinoma and gastric cancer compared to the normal tissues. In summary, we applied integrated bioinformatics approaches to suggest that TIM-1 can be used as a prognostic biomarker in gastric and lung adenocarcinoma, which might provide a novel direction to explore the pathogenesis of gastric and lung adenocarcinoma.

Analyses of multi-omics differences between patients with high and low PD1/PDL1 expression in lung squamous cell carcinoma

BACKGROUND

Immunotherapy has achieved excellent results in patients with lung squamous cell carcinoma. However, in which population it can exert the greatest effect is still unknown. Some studies have suggested that its effect is related to the expression level of PD1. Analyzing the relationship between PD1 expression level and genetic differences in lung squamous cell carcinoma patients will be helpful in understanding the underlying causes of this immunotherapy effect and provide a reference for clinical practice.

METHODS

In this study, we used RNA-seq, miRNA-seq, methylation array, mutation profiles, and copy number variation data from the TCGA database and RNA-seq data from the GEO database to analyze the distinctive genomic patterns associated with PD1 and PDL1 expression. RNA-seq data from 44 LUSC patients who underwent surgery at Zhongshan Hospital were also included in the study.

RESULTS

After grouping LUSC patients according to the expression levels of PD1 and PDL1, we found no significant difference in survival between the two groups. However, 178 genes, including IL-21, KLRC3, and KLRC4, were significantly upregulated in both the TCGA and GEO databases in the high expression group, and there was a precise correlation between gene expression and epigenetic changes in the two groups. At the same time, the overall level of somatic mutations was not significantly different between the two groups. It is worth noting that the gene enrichment results showed that the differential pathways were mainly enriched in immune regulation, especially T cell-related immune activities.

CONCLUSION

We found that the differences in gene expression between the two groups were related to immunity, which may affect the effectiveness of immunotherapy. We hope our results can provide a reference for further research and help in finding other targets to improve the efficacy of immunotherapy.

A gene expression-based single sample predictor of lung adenocarcinoma molecular subtype and prognosis

Disease recurrence in surgically treated lung adenocarcinoma (AC) remains high. New approaches for risk stratification beyond tumor stage are needed. Gene expression-based AC subtypes such as the Cancer Genome Atlas Network (TCGA) terminal-respiratory unit (TRU), proximal-inflammatory (PI) and proximal-proliferative (PP) subtypes have been associated with prognosis, but show methodological limitations for robust clinical use. We aimed to derive a platform independent single sample predictor (SSP) for molecular subtype assignment and risk stratification that could function in a clinical setting. Two-class (TRU/nonTRU=SSP2) and three-class (TRU/PP/PI=SSP3) SSPs using the AIMS algorithm were trained in 1655 ACs (n = 9659 genes) from public repositories vs TCGA centroid subtypes. Validation and survival analysis were performed in 977 patients using overall survival (OS) and distant metastasis-free survival (DMFS) as endpoints. In the validation cohort, SSP2 and SSP3 showed accuracies of 0.85 and 0.81, respectively. SSPs captured relevant biology previously associated with the TCGA subtypes and were associated with prognosis. In survival analysis, OS and DMFS for cases discordantly classified between TCGA and SSP2 favored the SSP2 classification. In resected Stage I patients, SSP2 identified TRU-cases with better OS (hazard ratio [HR] = 0.30; 95% confidence interval [CI] = 0.18-0.49) and DMFS (TRU HR = 0.52; 95% CI = 0.33-0.83) independent of age, Stage IA/IB and gender. SSP2 was transformed into a NanoString nCounter assay and tested in 44 Stage I patients using RNA from formalin-fixed tissue, providing prognostic stratification (relapse-free interval, HR = 3.2; 95% CI = 1.2-8.8). In conclusion, gene expression-based SSPs can provide molecular subtype and independent prognostic information in early-stage lung ACs. SSPs may overcome critical limitations in the applicability of gene signatures in lung cancer.

Identification of 17 mRNAs and a miRNA as an integrated prognostic signature for lung squamous cell carcinoma

BACKGROUND

Gene signatures for predicting the outcome of lung squamous cell carcinoma (LUSC) have been employed for many years. However, various signatures have been applied in clinical practice. Therefore, in the present study, we aimed to filter out an effective LUSC prognostic gene signature by simultaneously integrating mRNA and microRNA (miRNA).

METHODS

First, based on data from the Cancer Genome Atlas (TCGA) (https://www.cancer.gov/tcga), mRNAs and miRNAs that were related to overall survival of LUSC were obtained by the least absolute shrinkage and selection operator method. Subsequently, the predicting effect was tested by time-dependent receiver operating characteristic curve analysis and Kaplan-Meier survival analysis. Next, related clinical indices were added to evaluate the efficiency of the selected gene signatures. Finally, validation and comparison using three independent gene signatures were performed using data from the Gene Expression Omnibus database (https://www.ncbi.nlm.nih.gov/geo).

RESULTS

Our data showed that the prognostic index (PI) contained 17 mRNAs and one miRNA. According to the best normalized cut-off of PI (0.0247), the hazard ratio of the PI was 3.40 (95% confidence interval = 2.33-4.96). Moreover, when clinical factors were introduced, the PI was still the most significant index. In addition, only two Gene Ontology terms with p < 0.05 were reported. Furthermore, validation implied that, using our 18-gene signature, only hazard ratio = 1.36 (95% confidence interval = 1.01-1.83) was significant compared to the other three groups of gene biomarkers.

CONCLUSIONS

The 18-gene signature selected based on data from the TCGA database had an effective prognostic value for LUSC patients.

PDXGEM: patient-derived tumor xenograft-based gene expression model for predicting clinical response to anticancer therapy in cancer patients

Background

Cancer is a highly heterogeneous disease with varying responses to anti-cancer drugs. Although several attempts have been made to predict the anti-cancer therapeutic responses, there remains a great need to develop highly accurate prediction models of response to the anti-cancer drugs for clinical applications toward a personalized medicine. Patient derived xenografts (PDXs) are preclinical cancer models in which the tissue or cells from a patient’s tumor are implanted into an immunodeficient or humanized mouse. In the present study, we develop a bioinformatics analysis pipeline to build a predictive gene expression model (GEM) for cancer patients’ drug responses based on gene expression and drug activity data from PDX models.

Results

Drug sensitivity biomarkers were identified by performing an association analysis between gene expression levels and post-treatment tumor volume changes in PDX models. We built a drug response prediction model (called PDXGEM) in a random-forest algorithm by using a subset of the drug sensitvity biomarkers with concordant co-expression patterns between the PDXs and pretreatment cancer patient tumors. We applied the PDXGEM to several cytotoxic chemotherapies as well as targeted therapy agents that are used to treat breast cancer, pancreatic cancer, colorectal cancer, or non-small cell lung cancer. Significantly accurate predictions of PDXGEM for pathological response or survival outcomes were observed in extensive independent validations on multiple cancer patient datasets obtained from retrospective observational studies and prospective clinical trials.

Conclusion

Our results demonstrated the strong potential of using molecular profiles and drug activity data of PDX tumors in developing a clinically translatable predictive cancer biomarkers for cancer patients. The PDXGEM web application is publicly available at http://pdxgem.moffitt.org.

rScudo: an R package for classification of molecular profiles using rank-based signatures

SUMMARY

The classification of biological samples by means of their respective molecular profiles is a topic of great interest for its potential diagnostic, prognostic and investigational applications. rScudo is an R package for the classification of molecular profiles based on a radically new approach consisting in the analysis of the similarity of rank-based sample-specific signatures. The validity of rScudo unconventional approach has been validated through direct comparison with current methods in the international SBV IMPROVER Diagnostic Signature Challenge. Due to its novelty, there is ample room for conceptual improvements and for exploring additional applications. The rScudo package has been specifically designed to facilitate experimenting with the rank-based signature approach, to test its application to different types of molecular profiles and to simplify direct comparison with existing methods.

AVAILABILITY AND IMPLEMENTATION

The package is available as part of the Bioconductor suite at https://bioconductor.org/packages/rScudo.

Analysis of gene expression profiles of lung cancer subtypes with machine learning algorithms

Lung cancer is one of the most common cancer types worldwide and causes more than one million deaths annually. Lung adenocarcinoma (AC) and lung squamous cell cancer (SCC) are two major lung cancer subtypes and have different characteristics in several aspects. Identifying their differentially expressed genes and different gene expression patterns can deepen our understanding of these two subtypes at the transcriptomic level. In this work, we used several machine learning algorithms to investigate the gene expression profiles of lung AC and lung SCC samples retrieved from Gene Expression Omnibus. First, the profiles were analyzed by using a powerful feature selection method, namely, Monte Carlo feature selection. A feature list, ranking all features according to their importance, and some informative features were obtained. Then, the feature list was used in the incremental feature selection method to extract optimal features, which can allow the support vector machine (SVM) to yield the best performance for classifying lung AC and lung SCC samples. Some top genes (CSTA, TP63, SERPINB13, CLCA2, BICD2, PERP, FAT2, BNC1, ATP11B, FAM83B, KRT5, PARD6G, PKP1) were extensively analyzed to prove that they can be differentially expressed genes between lung AC and lung SCC. Meanwhile, a rule learning procedure was applied on informative features to construct the classification rules. These rules provide a clear procedure of classification and show some different gene expression patterns between lung AC and lung SCC.

Hexokinase 2 Depletion Confers Sensitization to Metformin and Inhibits Glycolysis in Lung Squamous Cell Carcinoma

Lung squamous cell carcinomas (SCCs) are highly aggressive tumors, and there is currently no effective targeted therapy owing to the lack of specific mutation targets. Compared with lung adenocarcinoma (ADCs), lung SCCs reportedly utilized higher levels of glucose metabolism to meet the anabolic and catabolic needs required to sustain rapid tumor growth. Hexokinase 2 (HK2) is an enzyme that catalyzes the rate-limit and first committed step in glucose metabolism. Here, we investigated the expression and effect of HK2 in lung SCCs. We found a significantly higher HK2 expression in lung SCCs, but not lung ADC or normal tissues. HK2 depletion or inhibition decreased the glycolysis and tumor growth via activating AMPK signaling pathway, which downregulated mTORC1 activity. Furthermore, we found an increased oxygen respiration rate compensating for HK2 depletion. Thus, metformin treatment showed combinatorial therapeutic value, which resulted in greater induction of lung SCC apoptosis in vitro and in vivo. Our study suggests that HK2 depletion in combination with metformin might be a novel effective strategy for lung SCCs therapy.

CXCL1 as an Unfavorable Prognosis Factor Negatively Regulated by DACH1 in Non-small Cell Lung Cancer

Background: Interaction between cancer cells with microenvironment is essential for cancer progression, therapeutic resistance and prognosis. Chemokine CXCL1 shows variable roles in the development of cancers. DACH1 has been considered as a tumor suppressor and represses the expressions of several chemokines. The relationship between CXCL1 and DACH1 in non-small cell lung cancer (SCLC) deserves further investigation.

Methods: Immunohistochemistry staining was performed on tumor tissue microarrays from lung cancer patients to detect CXCL1 protein. The CXCL1 concentration in the serum of adenocarcinoma patients was measured by ELISA. The CXCL1 protein secreted by cancer cell lines was detected by SearchLight proteome array and human cytokine antibody array. The meta-analysis of CXCL1 expression form public databases was performed and correlation between CXCL1 and DACH1 was analyzed. Moreover, the association between clinicopathological features and prognosis with CXCL1 and DACH1 was analyzed by tissue array and KM-plotter from public database.

Results: The protein abundance of CXCL1 in lung cancer tissues was significantly higher than that in adjacent normal tissues. CXCL1 was closely related to TNM stage, tumor size, and lymph node metastasis and predicted worse overall survival in adenocarcinoma. The level of CXCL1 in the peripheral blood of adenocarcinoma patients also significantly elevated and positively related with clinical stage. The meta-analysis demonstrated that CXCL1 mRNA level was increased in lung cancer tissues and high level of CXCL1 indicated tumor progression in lung adenocarcinoma. In addition, public database analyses showed that CXCL1 negatively correlated with DACH1. Stable overexpressing DACH1 in cultured lung cancer cells remarkably decreased CXCL1 protein. Moreover, ectopic expression of DACH1 significantly inhibited the expression of CXCL1, Ki67, and cyclin D1 in tumor tissues compared with A549 cells with empty vector. Survival analysis showed that high CXCL1 and low DACH1 indicated poor overall survival and progression-free survival.

Conclusion: CXCL1 is closely associated with tumor progression and poor survival. DACH1 significantly inhibits the expression of CXCL1 and indicates good prognosis. Therefore, combined detection of CXCL1 and DACH1 could more precisely predict prognosis of lung adenocarcinoma.

Distinct Roles of VEGFA and ANGPT2 in Lung Adenocarcinoma and Squamous Cell Carcinoma

Background: Vascular endothelial growth factor A (VEGFA) and angiopoietin 2 (ANGPT2) are key mediators in angiogenesis. The expression and clinical significance of VEGFA and ANGPT2 have been investigated in lung cancer, but the results are controversial. The specific roles of VEGFA and ANGPT2 in adenocarcinoma (ADC) and squamous cell carcinoma (SQC) are still not fully understood. To characterize it, we conducted the current study.

Materials and Methods: The relationships between clinic-pathological characteristics and the protein expressions of VEGFA and ANGPT2 were analyzed on tissue microarrays by immunohistochemistry (IHC) staining. Then public databases were used to evaluate the association of VEGFA and ANGPT2 mRNA expressions with clinic-pathological parameters and prognosis. Cobalt chloride (CoCl₂) was adopted to mimic a hypoxic microenvironment and western blot was used to detect the expression of hypoxia inducible factor-1α (HIF-1α), VEGFA and ANGPT2 in lung cancer cell lines.

Results: IHC staining revealed that the expressions of VEGFA and ANGPT2 were enriched in lung cancer tissues compared with normal tissues. Additionally, both VEGFA and ANGPT2 protein levels were significantly associated with the tumor size and lymph node metastasis only in ADC, not SQC. More importantly, increased VEGFA and ANGPT2 protein levels were negatively correlated with overall survival (OS) of ADC individuals. Meta-analyses of 22 gene expression omnibus (GEO) databases of lung cancer implicated that patients with higher VEGFA and ANGPT2 mRNA expressions tended to have advanced stage in ADC rather than SQC. Kaplan-Meier plot analyses further verified that high levels of VEGFA and ANGPT2 mRNA were associated with poor survival only in ADC. Moreover, the combination of VEGFA and ANGPT2 could more precisely predict prognosis in ADC. In hypoxia-mimicking conditions, induced expression of HIF-1α unregulated VEGFA and ANGPT2 proteins abundance.

Conclusion: Our results showed hypoxia upregulated the protein levels of VEGFA and ANGPT2 in lung cancer cell lines, and the roles of VEGFA and ANGPT2 were distinct in ADC and SQC. Combined detections of VEGFA and ANGPT2 may be valuable prognostic biomarkers for ADC and double block of VEGFA and ANGPT2 may improve therapeutic outcome.

A qualitative transcriptional signature for the histological reclassification of lung squamous cell carcinomas and adenocarcinomas

Background

Targeted therapy for non-small cell lung cancer is histology dependent. However, histological classification by routine pathological assessment with hematoxylin-eosin staining and immunostaining for poorly differentiated tumors, particularly those from small biopsies, is still challenging. Additionally, the effectiveness of immunomarkers is limited by technical inconsistencies of immunostaining and lack of standardization for staining interpretation.

Results

Using gene expression profiles of pathologically-determined lung adenocarcinomas and squamous cell carcinomas, denoted as pADC and pSCC respectively, we developed a qualitative transcriptional signature, based on the within-sample relative gene expression orderings (REOs) of gene pairs, to distinguish ADC from SCC. The signature consists of two genes, KRT5 and AGR2, which has the stable REO pattern of KRT5 > AGR2 in pSCC and KRT5 < AGR2 in pADC. In the two test datasets with relative unambiguous NSCLC types, the apparent accuracy of the signature were 94.44 and 98.41%, respectively. In the other integrated dataset for frozen tissues, the signature reclassified 4.22% of the 805 pADC patients as SCC and 12% of the 125 pSCC patients as ADC. Similar results were observed in the clinical challenging cases, including FFPE specimens, mixed tumors, small biopsy specimens and poorly differentiated specimens. The survival analyses showed that the pADC patients reclassified as SCC had significantly shorter overall survival than the signature-confirmed pADC patients (log-rank p = 0.0123, HR = 1.89), consisting with the knowledge that SCC patients suffer poor prognoses than ADC patients. The proliferative activity, subtype-specific marker genes and consensus clustering analyses also supported the correctness of our signature.

Conclusions

The non-subjective qualitative REOs signature could effectively distinguish ADC from SCC, which would be an auxiliary test for the pathological assessment of the ambiguous cases.

The plasma metabolome of women in early pregnancy differs from that of non-pregnant women

BACKGROUND

In comparison to the non-pregnant state, the first trimester of pregnancy is characterized by systemic adaptation of the mother. The extent to which these adaptive processes are reflected in the maternal blood metabolome is not well characterized.

OBJECTIVE

To determine the differences between the plasma metabolome of non-pregnant and pregnant women before 16 weeks gestation.

STUDY DESIGN

This study included plasma samples from 21 non-pregnant women and 50 women with a normal pregnancy (8-16 weeks of gestation). Combined measurements by ultrahigh performance liquid chromatography/tandem mass spectrometry and by gas chromatography/mass spectrometry generated molecular abundance measurements for each sample. Molecular species detected in at least 10 samples were included in the analysis. Differential abundance was inferred based on false discovery adjusted p-values (FDR) from Mann-Whitney-Wilcoxon U tests <0.1 and a minimum median abundance ratio (fold change) of 1.5. Alternatively, metabolic data were quantile normalized to remove sample-to-sample differences in the overall metabolite abundance (adjusted analysis).

RESULTS

Overall, 637 small molecules met the inclusion criteria and were tested for association with pregnancy; 44% (281/637) of small molecules had significantly different abundance, of which 81% (229/281) were less abundant in pregnant than in non-pregnant women. Eight percent (14/169) of the metabolites that remained significant in the adjusted analysis also changed as a function of gestational age. A pathway analysis revealed enrichment in steroid metabolites related to sex hormones, caffeine metabolites, lysolipids, dipeptides, and polypeptide bradykinin derivatives (all, FDR < 0.1).

CONCLUSIONS

This high-throughput mass spectrometry study identified: 1) differences between pregnant vs. non-pregnant women in the abundance of 44% of the profiled plasma metabolites, including known and novel molecules and pathways; and 2) specific metabolites that changed with gestational age.

Construction of subtype-specific prognostic gene signatures for early-stage non-small cell lung cancer using meta feature selection methods

Feature selection in the framework of meta-analyses (meta feature selection), combines meta-analysis with a feature selection process and thus allows meta-analysis feature selection across multiple datasets. In the present study, a meta feature selection procedure that fitted a multiple Cox regression model to estimate the effect size of a gene in individual studies and to identify the overall effect of the gene using a meta-analysis model was proposed. The method was used to identify prognostic gene signatures for lung adenocarcinoma and lung squamous cell carcinoma. Furthermore, redundant gene elimination (RGE) is of crucial importance during feature selection, and is also essential for a meta feature selection process. The current study demonstrated that the proposed meta feature selection procedure with RGE outperforms that without RGE in terms of predictive ability, model parsimony and biological interpretation.

The prediction of early preeclampsia: Results from a longitudinal proteomics study

OBJECTIVES

To identify maternal plasma protein markers for early preeclampsia (delivery <34 weeks of gestation) and to determine whether the prediction performance is affected by disease severity and presence of placental lesions consistent with maternal vascular malperfusion (MVM) among cases.

STUDY DESIGN

This longitudinal case-control study included 90 patients with a normal pregnancy and 33 patients with early preeclampsia. Two to six maternal plasma samples were collected throughout gestation from each woman. The abundance of 1,125 proteins was measured using high-affinity aptamer-based proteomic assays, and data were modeled using linear mixed-effects models. After data transformation into multiples of the mean values for gestational age, parsimonious linear discriminant analysis risk models were fit for each gestational-age interval (8-16, 16.1-22, 22.1-28, 28.1-32 weeks). Proteomic profiles of early preeclampsia cases were also compared to those of a combined set of controls and late preeclampsia cases (n = 76) reported previously. Prediction performance was estimated via bootstrap.

RESULTS

We found that 1) multi-protein models at 16.1-22 weeks of gestation predicted early preeclampsia with a sensitivity of 71% at a false-positive rate (FPR) of 10%. High abundance of matrix metalloproteinase-7 and glycoprotein IIbIIIa complex were the most reliable predictors at this gestational age; 2) at 22.1-28 weeks of gestation, lower abundance of placental growth factor (PlGF) and vascular endothelial growth factor A, isoform 121 (VEGF-121), as well as elevated sialic acid binding immunoglobulin-like lectin 6 (siglec-6) and activin-A, were the best predictors of the subsequent development of early preeclampsia (81% sensitivity, FPR = 10%); 3) at 28.1-32 weeks of gestation, the sensitivity of multi-protein models was 85% (FPR = 10%) with the best predictors being activated leukocyte cell adhesion molecule, siglec-6, and VEGF-121; 4) the increase in siglec-6, activin-A, and VEGF-121 at 22.1-28 weeks of gestation differentiated women who subsequently developed early preeclampsia from those who had a normal pregnancy or developed late preeclampsia (sensitivity 77%, FPR = 10%); 5) the sensitivity of risk models was higher for early preeclampsia with placental MVM lesions than for the entire early preeclampsia group (90% versus 71% at 16.1-22 weeks; 87% versus 81% at 22.1-28 weeks; and 90% versus 85% at 28.1-32 weeks, all FPR = 10%); and 6) the sensitivity of prediction models was higher for severe early preeclampsia than for the entire early preeclampsia group (84% versus 71% at 16.1-22 weeks).

CONCLUSION

We have presented herein a catalogue of proteome changes in maternal plasma proteome that precede the diagnosis of preeclampsia and can distinguish among early and late phenotypes. The sensitivity of maternal plasma protein models for early preeclampsia is higher in women with underlying vascular placental disease and in those with a severe phenotype.

Identification of monotonically differentially expressed genes for non-small cell lung cancer

Background

Monotonically expressed genes (MEGs) are genes whose expression values increase or decrease monotonically as a disease advances or time proceeds. Non-small cell lung cancer (NSCLC) is a multistage progression process resulting from genetic sequences mutations, the identification of MEGs for NSCLC is important.

Results

With the aid of a feature selection algorithm capable of identifying MEGs – the MFSelector method – two sets of potential MEGs were selected in this study: the MEGs across the different pathologic stages and the MEGs across the risk levels of death for the NSCLC patients at early stages. For the lung adenocarcinoma (AC) subtypes no statistically significant MEGs were identified across pathologic stages, however dozens of MEGs were identified across the risk levels of death. By contrast, for the squamous cell lung carcinoma (SCC) there were no statistically significant MEGs as either stage or risk level advanced.

Conclusions

The pathologic stage of non-small cell lung cancer patients at early stages has no prognostic value, making the identification of prognostic gene signatures for them more meaningful and highly desirable.

Multi-Level Model to Predict Antibody Response to Influenza Vaccine Using Gene Expression Interaction Network Feature Selection

Vaccination is an effective prevention of influenza infection. However, certain individuals develop a lower antibody response after vaccination, which may lead to susceptibility to subsequent infection. An important challenge in human health is to find baseline gene signatures to help identify individuals who are at higher risk for infection despite influenza vaccination. We developed a multi-level machine learning strategy to build a predictive model of vaccine response using pre−vaccination antibody titers and network interactions between pre−vaccination gene expression levels. The first-level baseline−antibody model explains a significant amount of variation in post-vaccination response, especially for subjects with large pre−existing antibody titers. In the second level, we clustered individuals based on pre−vaccination antibody titers to focus gene−based modeling on individuals with lower baseline HAI where additional response variation may be predicted by baseline gene expression levels. In the third level, we used a gene−association interaction network (GAIN) feature selection algorithm to find the best pairs of genes that interact to influence antibody response within each baseline titer cluster. We used ratios of the top interacting genes as predictors to stabilize machine learning model generalizability. We trained and tested the multi-level approach on data with young and older individuals immunized against influenza vaccine in multiple cohorts. Our results indicate that the GAIN feature selection approach improves model generalizability and identifies genes enriched for immunologically relevant pathways, including B Cell Receptor signaling and antigen processing. Using a multi-level approach, starting with a baseline HAI model and stratifying on baseline HAI, allows for more targeted gene−based modeling. We provide an interactive tool that may be extended to other vaccine studies.

Targeted expression profiling by RNA-Seq improves detection of cellular dynamics during pregnancy and identifies a role for T cells in term parturition

Development of maternal blood transcriptomic markers to monitor placental function and risk of obstetrical complications throughout pregnancy requires accurate quantification of gene expression. Herein, we benchmark three state-of-the-art expression profiling techniques to assess in maternal circulation the expression of cell type-specific gene sets previously discovered by single-cell genomics studies of the placenta. We compared Affymetrix Human Transcriptome Arrays, Illumina RNA-Seq, and sequencing-based targeted expression profiling (DriverMap^TM) to assess transcriptomic changes with gestational age and labor status at term, and tested 86 candidate genes by qRT-PCR. DriverMap identified twice as many significant genes (q < 0.1) than RNA-Seq and five times more than microarrays. The gap in the number of significant genes remained when testing only protein-coding genes detected by all platforms. qRT-PCR validation statistics (PPV and AUC) were high and similar among platforms, yet dynamic ranges were higher for sequencing based platforms than microarrays. DriverMap provided the strongest evidence for the association of B-cell and T-cell gene signatures with gestational age, while the T-cell expression was increased with spontaneous labor at term according to all three platforms. We concluded that sequencing-based techniques are more suitable to quantify whole-blood gene expression compared to microarrays, as they have an expanded dynamic range and identify more true positives. Targeted expression profiling achieved higher coverage of protein-coding genes with fewer total sequenced reads, and it is especially suited to track cell type-specific signatures discovered in the placenta. The T-cell gene expression signature was increased in women who underwent spontaneous labor at term, mimicking immunological processes at the maternal-fetal interface and placenta.

Embracing Transparency Through Data Sharing

Low rates of reproducibility and translatability of data from nonclinical research have been reported. Major causes of irreproducibility include oversights in study design, failure to characterize reagents and protocols, a lack of access to detailed methods and data, and an absence of universally accepted and applied standards and guidelines. Specific areas of concern include uncharacterized antibodies and cell lines, the use of inappropriate sampling and testing protocols, a lack of transparency and access to raw data, and deficiencies in the translatability of findings to the clinic from studies using animal models of disease. All stakeholders—academia, industry, funding agencies, regulators, nonprofit entities, and publishers—are encouraged to play active roles in addressing these challenges by formulating and promoting access to best practices and standard operating procedures and validating data collaboratively at each step of the biomedical research life cycle.

Progression and prognostic value of ECT2 in non-small-cell lung cancer and its correlation with PCNA

Purpose

Epithelial cell transforming sequence 2 (ECT2) is a guanine nucleotide exchange factor, which is involved in cell division regulation and cell cycle modulation. Recent evidence indicates that ECT2 is overexpressed in many human cancers. However, the exact prognostic value of ECT2 in lung cancer has not been elucidated.

Patients and methods

In the current study, we performed correlation and prognosis analyses using public databases and conducted immunohistochemical staining in tissue microarrays, using samples from 204 lung cancer patients with survival data.

Results

We found that the expression of ECT2 was markedly increased in lung cancer tissues compared with normal tissues. Moreover, we demonstrated that the expression of ECT2 was related to tumor cell differentiation degree, TNM stage, lymph node metastasis, and prognosis in non-small-cell lung cancer (NSCLC). A correlation analysis indicated that ECT2 levels were significantly correlated with proliferating cell nuclear antigen (PCNA) levels in NSCLC. Furthermore, Kaplan–Meier analyses revealed that high ECT2 expression was associated with unfavorable overall survival (OS) and progression-free survival (PFS) in NSCLC patients.

Conclusion

Taken together, these results indicate that the overexpression of ECT2 contributes to tumor invasion and progression, suggesting that ECT2 is a potential prognostic marker for NSCLC patients.

Inhibiting proliferation and migration of lung cancer using small interfering RNA targeting on Aldo-keto reductase family 1 member B10

Lung cancer is the leading cause of global cancer‑associated mortality. Genomic alterations in lung cancers have not been widely characterized, however, the molecular mechanism of tumor initiation and progression remain unknown, and no molecularly targeted have been specifically developed for its treatment and diagnosis. The present study observed the upregulation of Aldo‑keto reductase family 1 member Bio10 (AKR1B10) lung cancer tissues by analyzing two public lung cancer gene expression datasets. Further experiments in silencing AKR1B10 demonstrated that the expression of AKR1B10 was associated with cell proliferation, cell cycle, adhesion and invasion, as well as extracellular‑signal‑regulated kinase/mitogen activated protein kinase signal pathway. The overexpression of AKR1B10 in lung cancer indicates the important role of AKR1B10 in tumorigenesis. These findings suggest that AKR1B10 could be a potential diagnosis and treatment mark of lung cancer.

Platform-independent gene expression signature differentiates sessile serrated adenomas/polyps and hyperplastic polyps of the colon

Background

Sessile serrated adenomas/polyps are distinguished from hyperplastic colonic polyps subjectively by their endoscopic appearance and histological morphology. However, hyperplastic and sessile serrated polyps can have overlapping morphological features resulting in sessile serrated polyps diagnosed as hyperplastic. While sessile serrated polyps can progress into colon cancer, hyperplastic polyps have virtually no risk for colon cancer. Objective measures, differentiating these types of polyps would improve cancer prevention and treatment outcome.

Methods

RNA-seq training data set and Affimetrix, Illumina testing data sets were obtained from Gene Expression Omnibus (GEO). RNA-seq single-end reads were filtered with FastX toolkit. Read mapping to the human genome, gene abundance estimation, and differential expression analysis were performed with Tophat-Cufflinks pipeline. Background correction, normalization, and probe summarization steps for Affimetrix arrays were performed using the robust multi-array method (RMA). For Illumina arrays, log₂-scale expression data was obtained from GEO. Pathway analysis was implemented using Bioconductor package GSAR. To build a platform-independent molecular classifier that accurately differentiates sessile serrated and hyperplastic polyps we developed a new feature selection step. We also developed a simple procedure to classify new samples as either sessile serrated or hyperplastic with a class probability assigned to the decision, estimated using Cantelli’s inequality.

Results

The classifier trained on RNA-seq data and tested on two independent microarray data sets resulted in zero and three errors. The classifier was further tested using quantitative real-time PCR expression levels of 45 blinded independent formalin-fixed paraffin-embedded specimens and was highly accurate. Pathway analyses have shown that sessile serrated polyps are distinguished from hyperplastic polyps and normal controls by: up-regulation of pathways implicated in proliferation, inflammation, cell-cell adhesion and down-regulation of serine threonine kinase signaling pathway; differential co-expression of pathways regulating cell division, protein trafficking and kinase activities.

Conclusions

Most of the differentially expressed pathways are known as hallmarks of cancer and likely to explain why sessile serrated polyps are more prone to neoplastic transformation than hyperplastic. The new molecular classifier includes 13 genes and may facilitate objective differentiation between two polyps.

Electronic supplementary material

The online version of this article (10.1186/s12920-017-0317-7) contains supplementary material, which is available to authorized users.

Classification of early-stage non-small cell lung cancer by weighing gene expression profiles with connectivity information

Pathway-based feature selection algorithms, which utilize biological information contained in pathways to guide which features/genes should be selected, have evolved quickly and become widespread in the field of bioinformatics. Based on how the pathway information is incorporated, we classify pathway-based feature selection algorithms into three major categories-penalty, stepwise forward, and weighting. Compared to the first two categories, the weighting methods have been underutilized even though they are usually the simplest ones. In this article, we constructed three different genes' connectivity information-based weights for each gene and then conducted feature selection upon the resulting weighted gene expression profiles. Using both simulations and a real-world application, we have demonstrated that when the data-driven connectivity information constructed from the data of specific disease under study is considered, the resulting weighted gene expression profiles slightly outperform the original expression profiles. In summary, a big challenge faced by the weighting method is how to estimate pathway knowledge-based weights more accurately and precisely. Only until the issue is conquered successfully will wide utilization of the weighting methods be impossible.

The clinical significance of CXCL5 in non-small cell lung cancer

As a CXC-type chemokine, ENA78/CXCL5 is an important attractant for granulocytes by binding to its receptor CXCR2. Recent studies proved that CXCL5/CXCR2 axis plays an oncogenic role in many human cancers. However, the exact clinical significance of CXCL5 in lung cancer has not been well defined. Here, we found that the serum protein expression of CXCL5 was significantly increased in non-small cell lung cancer (NSCLC) compared with that in healthy volunteers. Immunohistochemistry staining revealed that CXCL5 protein was higher in various lung cancer tissues compared with normal tissues. Moreover, CXCL5 expression correlated with histological grade, tumor size, and TNM stage in NSCLC. Elevated CXCL5 protein abundance predicted poor overall survival in adenocarcinoma patients. Further meta-analysis demonstrated that CXCL5 mRNA expression was also positively associated with tumor stage, lymph node metastasis, and worse survival. Kaplan–Meier plot analyses indicated high CXCL5 was associated with short overall survival and progression-free survival. Together, these results indicated that CXCL5 may be a potential biomarker for NSCLC.

EAMA: Empirically adjusted meta-analysis for large-scale simultaneous hypothesis testing in genomic experiments

Recent developments in high throughput genomic assays have opened up the possibility of testing hundreds and thousands of genes simultaneously. However, adhering to the regular statistical assumptions regarding the null distributions of test statistics in such large-scale multiple testing frameworks has the potential of leading to incorrect significance testing results and biased inference. This problem gets worse when one combines results from different independent genomic experiments with a possibility of ending up with gross false discoveries of significant genes. In this article, we develop a meta-analysis method of combining p-values from different independent experiments involving large-scale multiple testing frameworks, through empirical adjustments of the individual test statistics and p-values. Even though, it is based on various existing ideas, this specific combination is novel and potentially useful. Through simulation studies and real genomic datasets we show that our method outperforms the standard meta-analysis approach of significance testing in terms of accurately identifying the truly significant set of genes.

A novel approach for data integration and disease subtyping

Advances in high-throughput technologies allow for measurements of many types of omics data, yet the meaningful integration of several different data types remains a significant challenge. Another important and difficult problem is the discovery of molecular disease subtypes characterized by relevant clinical differences, such as survival. Here we present a novel approach, called perturbation clustering for data integration and disease subtyping (PINS), which is able to address both challenges. The framework has been validated on thousands of cancer samples, using gene expression, DNA methylation, noncoding microRNA, and copy number variation data available from the Gene Expression Omnibus, the Broad Institute, The Cancer Genome Atlas (TCGA), and the European Genome-Phenome Archive. This simultaneous subtyping approach accurately identifies known cancer subtypes and novel subgroups of patients with significantly different survival profiles. The results were obtained from genome-scale molecular data without any other type of prior knowledge. The approach is sufficiently general to replace existing unsupervised clustering approaches outside the scope of bio-medical research, with the additional ability to integrate multiple types of data.

Systems toxicology meta-analysis of in vitro assessment studies: biological impact of a candidate modified-risk tobacco product aerosol compared with cigarette smoke on human organotypic cultures of the aerodigestive tract

Systems biology combines comprehensive molecular analyses with quantitative modeling to understand the characteristics of a biological system as a whole. Leveraging a similar approach, systems toxicology aims to decipher complex biological responses following exposures. This work reports a systems toxicology meta-analysis in the context of in vitro assessment of a candidate modified-risk tobacco product (MRTP) using three human organotypic cultures of the aerodigestive tract (buccal, bronchial, and nasal epithelia). Complementing a series of functional measures, a causal network enrichment analysis of transcriptomic data was used to compare quantitatively the biological impact of aerosol from the Tobacco Heating System (THS) 2.2, a candidate MRTP, with 3R4F cigarette smoke (CS) at similar nicotine concentrations. Lower toxicity was observed in all cultures following exposure to THS2.2 aerosol compared with 3R4F CS. Because of their morphological differences, a smaller exposure impact was observed in the buccal (stratified epithelium) compared with the bronchial and nasal (pseudostratified epithelium). However, the causal network enrichment approach supported a similar mechanistic impact of CS across the three cultures, including the impact on xenobiotic, oxidative stress, and inflammatory responses. At comparable nicotine concentrations, THS2.2 aerosol elicited reduced and more transient effects on these processes. To demonstrate the benefits of additional data modalities, we employed a newly established targeted mass-spectrometry marker panel to further confirm the reduced cellular stress responses elicited by THS2.2 aerosol compared with 3R4F CS in the nasal culture. Overall, this work demonstrates the applicability and robustness of the systems toxicology approach for in vitro inhalation toxicity assessment.

The prediction of late-onset preeclampsia: Results from a longitudinal proteomics study

BACKGROUND

Late-onset preeclampsia is the most prevalent phenotype of this syndrome; nevertheless, only a few biomarkers for its early diagnosis have been reported. We sought to correct this deficiency using a high through-put proteomic platform.

METHODS

A case-control longitudinal study was conducted, including 90 patients with normal pregnancies and 76 patients with late-onset preeclampsia (diagnosed at ≥34 weeks of gestation). Maternal plasma samples were collected throughout gestation (normal pregnancy: 2-6 samples per patient, median of 2; late-onset preeclampsia: 2-6, median of 5). The abundance of 1,125 proteins was measured using an aptamers-based proteomics technique. Protein abundance in normal pregnancies was modeled using linear mixed-effects models to estimate mean abundance as a function of gestational age. Data was then expressed as multiples of-the-mean (MoM) values in normal pregnancies. Multi-marker prediction models were built using data from one of five gestational age intervals (8-16, 16.1-22, 22.1-28, 28.1-32, 32.1-36 weeks of gestation). The predictive performance of the best combination of proteins was compared to placental growth factor (PIGF) using bootstrap.

RESULTS

1) At 8-16 weeks of gestation, the best prediction model included only one protein, matrix metalloproteinase 7 (MMP-7), that had a sensitivity of 69% at a false positive rate (FPR) of 20% (AUC = 0.76); 2) at 16.1-22 weeks of gestation, MMP-7 was the single best predictor of late-onset preeclampsia with a sensitivity of 70% at a FPR of 20% (AUC = 0.82); 3) after 22 weeks of gestation, PlGF was the best predictor of late-onset preeclampsia, identifying 1/3 to 1/2 of the patients destined to develop this syndrome (FPR = 20%); 4) 36 proteins were associated with late-onset preeclampsia in at least one interval of gestation (after adjustment for covariates); 5) several biological processes, such as positive regulation of vascular endothelial growth factor receptor signaling pathway, were perturbed; and 6) from 22.1 weeks of gestation onward, the set of proteins most predictive of severe preeclampsia was different from the set most predictive of the mild form of this syndrome.

CONCLUSIONS

Elevated MMP-7 early in gestation (8-22 weeks) and low PlGF later in gestation (after 22 weeks) are the strongest predictors for the subsequent development of late-onset preeclampsia, suggesting that the optimal identification of patients at risk may involve a two-step diagnostic process.

Human blood gene signature as a marker for smoking exposure: computational approaches of the top ranked teams in the sbv IMPROVER Systems Toxicology challenge

Crowdsourcing has emerged as a framework to address methodological challenges in omics data analysis and assess the extent to which omics data are predictive of phenotypes of interest. The sbv IMPROVER Systems Toxicology Challenge was designed to leverage crowdsourcing to determine whether human blood gene expression levels are informative of current and past smoking. Participating teams were invited to use a training gene expression dataset to derive parsimonious models (up to 40 genes) that can accurately classify subjects into exposure groups: smokers, former smokers that quit for at least one year, and never-smokers. Teams were ranked based on two classification performance metrics evaluated on a blinded test dataset. The analytical approaches of the first- and third-ranked teams, that are presented in detail in this article, involved feature selection by moderated t-test or LASSO regression and linear discriminant analysis (LDA) and logistic regression classifiers, respectively. While the 12-gene signature of the top team allowed the classification of current smokers with 100% sensitivity at 93% specificity, discriminating former smokers from never-smokers was much more challenging (65% sensitivity at 57% specificity). Gene ontology molecular functions and KEGG pathways associated with current smoking included G protein-coupled receptor activity, signaling receptor activity, calcium ion binding, and the Neuroactive ligand-receptor interaction pathway. Selection of marker genes by either moderated t-test or multivariate LASSO regression followed by LDA or logistic regression, are robust approaches to classification with omics data, confirming in part findings of previous sbv IMPROVER challenges. While current smoking is accurately identified based on blood mRNA levels, smoking cessation for more than one year is accompanied by a "normalization" of the expression of certain mRNAs, making it difficult to distinguish former smokers from never-smokers.

The sbv IMPROVER Systems Toxicology Computational Challenge: Identification of Human and Species-Independent Blood Response Markers as Predictors of Smoking Exposure and Cessation Status

Cigarette smoking entails chronic exposure to a mixture of harmful chemicals that trigger molecular changes over time, and is known to increase the risk of developing diseases. Risk assessment in the context of 21^st century toxicology relies on the elucidation of mechanisms of toxicity and the identification of exposure response markers, usually from high-throughput data, using advanced computational methodologies. The sbv IMPROVER Systems Toxicology computational challenge (Fall 2015-Spring 2016) aimed to evaluate whether robust and sparse (≤40 genes) human (sub-challenge 1, SC1) and species-independent (sub-challenge 2, SC2) exposure response markers (so called gene signatures) could be extracted from human and mouse blood transcriptomics data of current (S), former (FS) and never (NS) smoke-exposed subjects as predictors of smoking and cessation status. Best-performing computational methods were identified by scoring anonymized participants' predictions. Worldwide participation resulted in 12 (SC1) and six (SC2) final submissions qualified for scoring. The results showed that blood gene expression data were informative to predict smoking exposure (i.e. discriminating smoker versus never or former smokers) status in human and across species with a high level of accuracy. By contrast, the prediction of cessation status (i.e. distinguishing FS from NS) remained challenging, as reflected by lower classification performances. Participants successfully developed inductive predictive models and extracted human and species-independent gene signatures, including genes with high consensus across teams. Post-challenge analyses highlighted "feature selection" as a key step in the process of building a classifier and confirmed the importance of testing a gene signature in independent cohorts to ensure the generalized applicability of a predictive model at a population-based level. In conclusion, the Systems Toxicology challenge demonstrated the feasibility of extracting a consistent blood-based smoke exposure response gene signature and further stressed the importance of independent and unbiased data and method evaluations to provide confidence in systems toxicology-based scientific conclusions.

The maternal plasma proteome changes as a function of gestational age in normal pregnancy: a longitudinal study

OBJECTIVE

Pregnancy is accompanied by dramatic physiological changes in maternal plasma proteins. Characterization of the maternal plasma proteome in normal pregnancy is an essential step for understanding changes to predict pregnancy outcome. The objective of this study was to describe maternal plasma proteins that change in abundance with advancing gestational age and determine biological processes that are perturbed in normal pregnancy.

STUDY DESIGN

A longitudinal study included 43 normal pregnancies that had a term delivery of an infant who was appropriate for gestational age without maternal or neonatal complications. For each pregnancy, 3 to 6 maternal plasma samples (median, 5) were profiled to measure the abundance of 1125 proteins using multiplex assays. Linear mixed-effects models with polynomial splines were used to model protein abundance as a function of gestational age, and the significance of the association was inferred via likelihood ratio tests. Proteins considered to be significantly changed were defined as having the following: (1) >1.5-fold change between 8 and 40 weeks of gestation; and (2) a false discovery rate-adjusted value of P < .1. Gene ontology enrichment analysis was used to identify biological processes overrepresented among the proteins that changed with advancing gestation.

RESULTS

The following results were found: (1) Ten percent (112 of 1125) of the profiled proteins changed in abundance as a function of gestational age; (2) of the 1125 proteins analyzed, glypican-3, sialic acid-binding immunoglobulin-type lectin-6, placental growth factor, C-C motif-28, carbonic anhydrase 6, prolactin, interleukin-1 receptor 4, dual-specificity mitogen-activated protein kinase 4, and pregnancy-associated plasma protein-A had more than a 5-fold change in abundance across gestation (these 9 proteins are known to be involved in a wide range of both physiological and pathological processes, such as growth regulation, embryogenesis, angiogenesis immunoregulation, inflammation etc); and (3) biological processes associated with protein changes in normal pregnancy included defense response, defense response to bacteria, proteolysis, and leukocyte migration (false discovery rate, 10%).

CONCLUSION

The plasma proteome of normal pregnancy demonstrates dramatic changes in both the magnitude of changes and the fraction of the proteins involved. Such information is important to understand the physiology of pregnancy and the development of biomarkers to differentiate normal vs abnormal pregnancy and determine the response to interventions.