Identifying Reproducible Molecular Biomarkers for Gastric Cancer Metastasis with the Aid of Recurrence Information

Analyzing biomarker discovery: Estimating the reproducibility of biomarker sets

Many researchers try to understand a biological condition by identifying biomarkers. This is typically done using univariate hypothesis testing over a labeled dataset, declaring a feature to be a biomarker if there is a significant statistical difference between its values for the subjects with different outcomes. However, such sets of proposed biomarkers are often not reproducible – subsequent studies often fail to identify the same sets. Indeed, there is often only a very small overlap between the biomarkers proposed in pairs of related studies that explore the same phenotypes over the same distribution of subjects. This paper first defines the Reproducibility Score for a labeled dataset as a measure (taking values between 0 and 1) of the reproducibility of the results produced by a specified fixed biomarker discovery process for a given distribution of subjects. We then provide ways to reliably estimate this score by defining algorithms that produce an over-bound and an under-bound for this score for a given dataset and biomarker discovery process, for the case of univariate hypothesis testing on dichotomous groups. We confirm that these approximations are meaningful by providing empirical results on a large number of datasets and show that these predictions match known reproducibility results. To encourage others to apply this technique to analyze their biomarker sets, we have also created a publicly available website, https://biomarker.shinyapps.io/BiomarkerReprod/, that produces these Reproducibility Score approximations for any given dataset (with continuous or discrete features and binary class labels).

Tissue-derived extracellular vesicles in cancers and non-cancer diseases: Present and future

Extracellular vesicles (EVs) are lipid‐bilayer membrane structures secreted by most cell types. EVs act as messengers via the horizontal transfer of lipids, proteins, and nucleic acids, and influence various pathophysiological processes in both parent and recipient cells. Compared to EVs obtained from body fluids or cell culture supernatants, EVs isolated directly from tissues possess a number of advantages, including tissue specificity, accurate reflection of tissue microenvironment, etc., thus, attention should be paid to tissue‐derived EVs (Ti‐EVs). Ti‐EVs are present in the interstitium of tissues and play pivotal roles in intercellular communication. Moreover, Ti‐EVs provide an excellent snapshot of interactions among various cell types with a common histological background. Thus, Ti‐EVs may be used to gain insights into the development and progression of diseases. To date, extensive investigations have focused on the role of body fluid‐derived EVs or cell culture‐derived EVs; however, the number of studies on Ti‐EVs remains insufficient. Herein, we summarize the latest advances in Ti‐EVs for cancers and non‐cancer diseases. We propose the future application of Ti‐EVs in basic research and clinical practice. Workflows for Ti‐EV isolation and characterization between cancers and non‐cancer diseases are reviewed and compared. Moreover, we discuss current issues associated with Ti‐EVs and provide potential directions.

A qualitative transcriptional signature of recurrence risk for stages II-III gastric cancer patients after surgical resection

BACKGROUND AND AIM

Metastasis is the leading cause of recurrence in gastric cancer. However, the imaging techniques and pathological examinations for tumor metastasis have a high false-positive rate or a high false-negative rate, and many proposed that metastasis-related molecular biomarkers can hardly be validated in independent datasets.

METHODS

We propose to use significantly stable gene pairs with reversal relative expression orderings (REOs) between non-metastasis and metastasis gastric cancer samples as the metastasis-related gene pairs. Based on the REOs of these gene pairs, we developed a qualitative transcriptional signature for predicting the recurrence risk of stages II-III gastric cancer patients after surgical resection.

RESULTS

A REOs-based signature, consisting of 19 gene pairs (19-GPS), was selected from 77 stages II-III gastric cancer patients and validated in two independent datasets. Samples in the high-risk group had shorter disease-free survival time and overall survival time than those in the low-risk group. Differentially expressed genes (DEGs) between the high- and low-risk groups classified by 19-GPS were highly reproducible comparing with those between lymph node metastasis and lymph node non-metastasis groups. Functional enrichment analysis showed that these DEGs were significantly enriched in metastasis-related pathways, such as PI3K-Akt and Rap1 signaling pathways. The multi-omics analyses suggested that the epigenetic and genomic features might cause transcriptional differences between two subgroups, which help to characterize the mechanism of gastric cancer metastasis.

CONCLUSIONS

The signature could robustly identify patients at high recurrence risk after resection surgery, and the multi-omics analyses might aid in revealing the metastasis-related characteristics of gastric cancer.

Polymyxin-Induced Metabolic Perturbations in Human Lung Epithelial Cells

Inhaled polymyxins are associated with toxicity in human lung epithelial cells that involves multiple apoptotic pathways. However, the mechanism of polymyxin-induced pulmonary toxicity remains unclear. This study aims to investigate polymyxin-induced metabolomic perturbations in human lung epithelial A549 cells. A549 cells were treated with 0.5 or 1.0 mM polymyxin B or colistin for 1, 4, and 24 h. Cellular metabolites were analyzed using liquid chromatography-tandem mass spectrometry (LC-MS/MS), and significantly perturbed metabolites (log₂ fold change [log₂FC] ≥ 1; false-discovery rate [FDR] ≤ 0.2) and key pathways were identified relative to untreated control samples. At 1 and 4 h, very few significant changes in metabolites were observed relative to the untreated control cells. At 24 h, taurine (log₂FC = -1.34 ± 0.64) and hypotaurine (log₂FC = -1.20 ± 0.27) were significantly decreased by 1.0 mM polymyxin B. The reduced form of glutathione (GSH) was significantly depleted by 1.0 mM polymyxin B at 24 h (log₂FC = -1.80 ± 0.42). Conversely, oxidized glutathione (GSSG) was significantly increased by 1.0 mM both polymyxin B (log₂FC = 1.38 ± 0.13 at 4 h and 2.09 ± 0.20 at 24 h) and colistin (log₂FC = 1.33 ± 0.24 at 24 h). l-Carnitine was significantly decreased by 1.0 mM of both polymyxins at 24 h, as were several key metabolites involved in biosynthesis and degradation of choline and ethanolamine (log₂FC ≤ -1); several phosphatidylserines were also increased (log₂FC ≥ 1). Polymyxins perturbed key metabolic pathways that maintain cellular redox balance, mitochondrial β-oxidation, and membrane lipid biogenesis. These mechanistic findings may assist in developing new pharmacokinetic/pharmacodynamic strategies to attenuate the pulmonary toxicities of inhaled polymyxins and in the discovery of new-generation polymyxins.

A Five-Gene-Pair-Based Prognostic Signature for Predicting the Relapse Risk of Early Stage ER+ Breast Cancer

About 20-30% of early-stage breast cancer patients suffer relapses after surgery. To identify such high-risk patients, many signatures have been reported, but they lack robustness in data measured on different platforms. Here, we developed a signature which is robust across multiple profiling platforms, and identified reproducible omics features characterizing metastasis of estrogen receptor (ER)-positive breast cancer from the Gene Expression Omnibus database with the aid of the signature. Based on the stable within-sample relative expression orderings (REOs), we constructed a signature consisting of five gene pairs, named 5-GPS, whose REOs were significantly correlated with relapse-free survival using the univariate Cox regression model. Using 5-GPS, patients were classified into the low-risk and high-risk groups. Patients in the high-risk group have worse survival compared to those in the low-risk group using Kaplan-Meier curve analysis with the log-rank test. Applying 5-GPS to the RNA-sequencing data of stage I-IV breast cancer samples archived in The Cancer Genome Atlas (TCGA), we found that the proportion of the high-risk patients increases with the stage. The proposed REO-based signature shows potential in identifying early-stage ER+ breast cancer patients with high risk of relapse after surgery.

Predicting Cancer Tissue-of-Origin by a Machine Learning Method Using DNA Somatic Mutation Data

Patients with carcinoma of unknown primary (CUP) account for 3-5% of all cancer cases. A large number of metastatic cancers require further diagnosis to determine their tissue of origin. However, diagnosis of CUP and identification of its primary site are challenging. Previous studies have suggested that molecular profiling of tissue-specific genes could be useful in inferring the primary tissue of a tumor. The purpose of this study was to evaluate the performance somatic mutations detected in a tumor to identify the cancer tissue of origin. We downloaded the somatic mutation datasets from the International Cancer Genome Consortium project. The random forest algorithm was used to extract features, and a classifier was established based on the logistic regression. Specifically, the somatic mutations of 300 genes were extracted, which are significantly enriched in functions, such as cell-to-cell adhesion. In addition, the prediction accuracy on tissue-of-origin inference for 3,374 cancer samples across 13 cancer types reached 81% in a 10-fold cross-validation. Our method could be useful in the identification of cancer tissue of origin, as well as the diagnosis and treatment of cancers.

Pan-transcriptomic analysis identified common differentially expressed genes of Acinetobacter baumannii in response to polymyxin treatments

Multidrug-resistant Acinetobacter baumannii is a top-priority Gram-negative pathogen and polymyxins are a last-line therapeutic option. Previous systems pharmacological studies examining polymyxin killing and resistance usually focused on individual strains, and the derived knowledge could be limited by strain-specific genomic context. In this study, we examined the gene expression of five A. baumannii strains (34654, 1207552, 1428368, 1457504 and ATCC 19606) to determine the common differentially expressed genes in response to polymyxin treatments. A pan-genome containing 6061 genes was identified for 89 A. baumannii genomes from RefSeq database which included the five strains examined in this study; 2822 of the 6061 genes constituted the core genome. After 2 mg L^-1 or 0.75 × MIC polymyxin treatments for 15 min, 41 genes were commonly up-regulated, including those involved in membrane biogenesis and homeostasis, lipoprotein and phospholipid trafficking, efflux pump and poly-N-acetylglucosamine biosynthesis; six genes were commonly down-regulated, three of which were related to fatty acid biosynthesis. Additionally, comparison of the gene expression at 15 and 60 min in ATCC 19606 revealed that polymyxin treatment resulted in a rapid change in amino acid metabolism at 15 min and perturbations on envelope biogenesis at both time points. This is the first pan-transcriptomic study for polymyxin-treated A. baumannii and our results identified that the remodelled outer membrane, up-regulated efflux pumps and down-regulated fatty acid biosynthesis might be essential for early responses to polymyxins in A. baumannii. Our findings provide important mechanistic insights into bacterial responses to polymyxin killing and may facilitate the optimisation of polymyxin therapy against this problematic 'superbug'.

A qualitative transcriptional prognostic signature for patients with stage I-II pancreatic ductal adenocarcinoma

Accurately prognostic evaluation of patients with stage I-II pancreatic ductal adenocarcinoma (PDAC) is of importance to treatment decision and patient management. Most previously reported prognostic signatures were based on risk scores summarized from quantitative expression measurements of signature genes, which are susceptible to experimental batch effects and impractical for clinical applications. Based on the within-sample relative expression orderings of genes, we developed a robust qualitative transcriptional prognostic signature, consisting of 64 gene pairs (64-GPS), to predict the overall survival (OS) of 161 stage I-II PDAC patients in the training dataset who were treated with surgery only. Samples were classified into the high-risk group when at least 25 of 64 gene pairs suggested it was at high risk. The signature was successfully validated in 324 samples from 6 independent datasets produced by different laboratories. All samples in the low-risk group had significantly better OS than samples in the high-risk group. Multivariate Cox regression analyses showed that the 64-GPS remained significantly associated with the OS of patients after adjusting available clinical factors. Transcriptomic analysis of the 2 prognostic subgroups showed that the differential expression signals were highly reproducible in all datasets, whereas the differences between samples grouped by the TNM staging system were weak and irreproducible. The epigenomic analysis showed that the epigenetic alternations may cause consistently transcriptional changes between the 2 different prognostic groups. The genomic analysis revealed that mutation‑induced disturbances in several key genes, such as LRMDA, MAPK10, and CREBBP, might lead to poor prognosis for PDAC patients. Conclusively, the 64-GPS can robustly predict the prognosis of patients with stage I-II PDAC, which provides theoretical basis for clinical individualized treatment.

A Qualitative Transcriptional Signature for Predicting Recurrence Risk of Stage I-III Bladder Cancer Patients After Surgical Resection

Background: Previously reported transcriptional signatures for predicting the prognosis of stage I-III bladder cancer (BLCA) patients after surgical resection are commonly based on risk scores summarized from quantitative measurements of gene expression levels, which are highly sensitive to the measurement variation and sample quality and thus hardly applicable under clinical settings. It is necessary to develop a signature which can robustly predict recurrence risk of BLCA patients after surgical resection.

Methods: The signature is developed based on the within-sample relative expression orderings (REOs) of genes, which are qualitative transcriptional characteristics of the samples.

Results: A signature consisting of 12 gene pairs (12-GPS) was identified in training data with 158 samples. In the first validation dataset with 114 samples, the low-risk group of 54 patients had a significantly better overall survival than the high-risk group of 60 patients (HR = 3.59, 95% CI: 1.34~9.62, p = 6.61 × 10⁻⁰³). The signature was also validated in the second validation dataset with 57 samples (HR = 2.75 × 10⁰⁸, 95% CI: 0~Inf, p = 0.05). Comparison analysis showed that the transcriptional differences between the low- and high-risk groups were highly reproducible and significantly concordant with DNA methylation differences between the two groups.

Conclusions: The 12-GPS signature can robustly predict the recurrence risk of stage I-III BLCA patients after surgical resection. It can also aid the identification of reproducible transcriptional and epigenomic features characterizing BLCA metastasis.

Comprehensive functional enrichment analysis of male infertility

Spermatogenesis is a multifactorial process that forms differentiated sperm cells in a complex microenvironment. This process involves the genome, epigenome, transcriptome, and proteome to ensure the stability of the spermatogonia and supporting cells. The identification of signaling pathways linked to infertility has been hampered by the inherent complexity and multifactorial aspects of spermatogenesis. Systems biology is a promising approach to unveil underlying signaling pathways and genes and identify putative biomarkers. In this study, we analyzed thirteen microarray libraries of infertile humans and mice, and different classes of male infertility were compared using differentially expressed genes and functional enrichment analysis. We found regulatory processes, immune response, glutathione transferase and muscle tissue development to be among the most common biological processes in up-regulated genes, and genes involved in spermatogenesis were down-regulated in maturation arrest (MArrest) and oligospermia cases. We also observed the overexpression of genes involved in steroid metabolism in post-meiotic and meiotic arrest. Furthermore, we found that the infertile mouse model most similar to human MArrest was the Dazap1 mutant mouse. The results of this study could help elucidate features of infertility etiology and provide the basis for diagnostic markers.

Evaluating hepatocellular carcinoma cell lines for tumour samples using within-sample relative expression orderings of genes

BACKGROUND & AIMS

Concerns are raised about the representativeness of cell lines for tumours due to the culture environment and misidentification. Liver is a major metastatic destination of many cancers, which might further confuse the origin of hepatocellular carcinoma cell lines. Therefore, it is of crucial importance to understand how well they can represent hepatocellular carcinoma.

METHODS

The HCC-specific gene pairs with highly stable relative expression orderings in more than 99% of hepatocellular carcinoma but with reversed relative expression orderings in at least 99% of one of the six types of cancer, colorectal carcinoma, breast carcinoma, non-small-cell lung cancer, gastric carcinoma, pancreatic carcinoma and ovarian carcinoma, were identified.

RESULTS

With the simple majority rule, the HCC-specific relative expression orderings from comparisons with colorectal carcinoma and breast carcinoma could exactly discriminate primary hepatocellular carcinoma samples from both primary colorectal carcinoma and breast carcinoma samples. Especially, they correctly classified more than 90% of liver metastatic samples from colorectal carcinoma and breast carcinoma to their original tumours. Finally, using these HCC-specific relative expression orderings from comparisons with six cancer types, we identified eight of 24 hepatocellular carcinoma cell lines in the Cancer Cell Line Encyclopedia (Huh-7, Huh-1, HepG2, Hep3B, JHH-5, JHH-7, C3A and Alexander cells) that are highly representative of hepatocellular carcinoma. Evaluated with a REOs-based prognostic signature for hepatocellular carcinoma, all these eight cell lines showed the same metastatic properties of the high-risk metastatic hepatocellular carcinoma tissues.

CONCLUSIONS

Caution should be taken for using hepatocellular carcinoma cell lines. Our results should be helpful to select proper hepatocellular carcinoma cell lines for biological experiments.

An individualized gene expression signature for prediction of lung adenocarcinoma metastases

Our laboratory previously reported an individual‐level signature consisting of nine gene pairs, named 9‐GPS. This signature was developed by training on microarray expression data and validated using three independent integrated microarray data sets, with samples of stage I non‐small‐cell lung cancer after complete surgical resection. In this study, we first validated the cross‐platform robustness of 9‐GPS by demonstrating that 9‐GPS could significantly stratify the overall survival of 213 stage I lung adenocarcinoma (LUAD) patients detected with RNA‐sequencing platform in The Cancer Genome Atlas (TCGA; log‐rank P = 0.0318, C‐index = 0.55). Applying 9‐GPS to all the 423 stage I‐IV LUAD samples in TCGA, the predicted high‐risk samples were significantly enriched with clinically diagnosed metastatic samples (Fisher's exact test, P = 0.0015). We further modified the voting rule of 9‐GPS and found that the modified 9‐GPS had a better performance in predicting metastasis states (Fisher's exact test, P < 0.0001). With the aid of the modified 9‐GPS for reclassifying the metastasis states of patients with LUAD, the reclassified metastatic samples presented clearer transcriptional and genomic characteristics compared to the reclassified nonmetastatic samples. Finally, regulator network analysis identified TP53 and IRF1 with frequent genomic aberrations in the reclassified metastatic samples, indicating their key roles in driving tumor metastasis. In conclusion, 9‐GPS is a robust signature for identifying early‐stage LUAD patients with potential occult metastasis. This occult metastasis prediction was associated with clear transcriptional and genomic characteristics as well as the clinical diagnoses.

Mitochondrial Genomic Backgrounds Affect Nuclear DNA Methylation and Gene Expression

Mitochondrial DNA (mtDNA) mutations and polymorphisms contribute to many complex diseases, including cancer. Using a unique mouse model that contains nDNA from one mouse strain and homoplasmic mitochondrial haplotypes from different mouse strain(s)-designated Mitochondrial Nuclear Exchange (MNX)-we showed that mtDNA could alter mammary tumor metastasis. Because retrograde and anterograde communication exists between the nuclear and mitochondrial genomes, we hypothesized that there are differential mtDNA-driven changes in nuclear (n)DNA expression and DNA methylation. Genome-wide nDNA methylation and gene expression were measured in harvested brain tissue from paired wild-type and MNX mice. Selective differential DNA methylation and gene expression were observed between strains having identical nDNA, but different mtDNA. These observations provide insights into how mtDNA could be altering epigenetic regulation and thereby contribute to the pathogenesis of metastasis. Cancer Res; 77(22); 6202-14. ©2017 AACR.