A robust qualitative transcriptional signature for the correct pathological diagnosis of gastric cancer.

BACKGROUND: Currently, pathological examination of gastroscopy biopsy specimens is the gold standard for gastric cancer (GC) diagnosis. However, it has a false-negative rate of 10-20% due to inaccurate sampling locations and/or insufficient sampling amount. A signature should be developed to aid the early diagnosis of GC using biopsy specimens even when they are sampled from inaccurate locations. METHODS: We extracted a robust qualitative transcriptional signature, based on the within-sample relative expression orderings (REOs) of gene pairs, to discriminate both GC tissues and adjacent-normal tissues from non-GC gastritis, intestinal metaplasia and normal gastric tissues. RESULTS: A signature consisting of two gene pairs for GC diagnosis was identified and validated in data of both biopsy specimens and surgical resection specimens pooled from publicly available datasets measured by different laboratories with different platforms. For gastroscopy biopsy specimens, 96.20% of 79 non-GC tissues were correctly identified as non-GC, and 96.84% of 158 GC tissues and six of seven adjacent-normal tissues were correctly identified as GC. For surgical resection specimens, 98.37% of 2560 GC tissues and 97.28% of 221 adjacent-normal tissues were correctly identified as GC. Especially, 97.67% of the 257 GC patients at stage I were exactly diagnosed as GC. We additionally measured 21 GC tissues from seven different GC patients, each with three specimens sampled from three tumor locations with different proportions of the tumor epithelial cell. All these GC tissues were correctly identified as GC, even when the proportion of the tumor epithelial cell was as low as 14%. CONCLUSIONS: The qualitative transcriptional signature can distinguish both GC and adjacent-normal tissues from normal, gastritis and intestinal metaplasia tissues of non-GC patients even using inaccurately sampled biopsy specimens, which can be applied robustly at the individual level to aid the early GC diagnosis.

Inference of patient-specific subpathway activities reveals a functional signature associated with the prognosis of patients with breast cancer.

Breast cancer is one of the most deadly forms of cancer in women worldwide. Better prediction of breast cancer prognosis is essential for more personalized treatment. In this study, we aimed to infer patient-specific subpathway activities to reveal a functional signature associated with the prognosis of patients with breast cancer. We integrated pathway structure with gene expression data to construct patient-specific subpathway activity profiles using a greedy search algorithm. A four-subpathway prognostic signature was developed in the training set using a random forest supervised classification algorithm and a prognostic score model with the activity profiles. According to the signature, patients were classified into high-risk and low-risk groups with significantly different overall survival in the training set (median survival of 65 vs 106 months, P = 1.82e-13) and test set (median survival of 75 vs 101 months, P = 4.17e-5). Our signature was then applied to five independent breast cancer data sets and showed similar prognostic values, confirming the accuracy and robustness of the subpathway signature. Stratified analysis suggested that the four-subpathway signature had prognostic value within subtypes of breast cancer. Our results suggest that the four-subpathway signature may be a useful biomarker for breast cancer prognosis.

Quantitative or qualitative transcriptional diagnostic signatures? A case study for colorectal cancer.

BACKGROUND: Due to experimental batch effects, the application of a quantitative transcriptional signature for disease diagnoses commonly requires inter-sample data normalization, which would be hardly applicable under common clinical settings. Many cancers might have qualitative differences with the non-cancer states in the gene expression pattern. Therefore, it is reasonable to explore the power of qualitative diagnostic signatures which are robust against experimental batch effects and other random factors. RESULTS: Firstly, using data of technical replicate samples from the MicroArray Quality Control (MAQC) project, we demonstrated that the low-throughput PCR-based technologies also exist large measurement variations for gene expression even when the samples were measured in the same test site. Then, we demonstrated the critical limitation of low stability for classifiers based on quantitative transcriptional signatures in applications to individual samples through a case study using a support vector machine and a naïve Bayesian classifier to discriminate colorectal cancer tissues from normal tissues. To address this problem, we identified a signature consisting of three gene pairs for discriminating colorectal cancer tissues from non-cancer (normal and inflammatory bowel disease) tissues based on within-sample relative expression orderings (REOs) of these gene pairs. The signature was well verified using 22 independent datasets measured by different microarray and RNA_seq platforms, obviating the need of inter-sample data normalization. CONCLUSIONS: Subtle quantitative information of gene expression measurements tends to be unstable under current technical conditions, which will introduce uncertainty to clinical applications of the quantitative transcriptional diagnostic signatures. For diagnosis of disease states with qualitative transcriptional characteristics, the qualitative REO-based signatures could be robustly applied to individual samples measured by different platforms.

[A signature based on relative gene expression orderings for lung cancer diagnosis].

Traditional classifiers, such as support vector machine and Bayesian classifier, require data normalization for removing experimental batch effects, which limit their applications at the individual level. In this paper,we aim to build a classifier to distinguish lung cancer and non-cancer lung tissues(pneumonia and normal lung tissues).We identified gene pairs as signatures to build a classifier based on the within-sample relative expression orderings of gene pairs in a particular type of tissues(cancer or non-cancer). Using multiple independent datasets as the training data,including a total of 197 lung cancer cases and 189 non-cancer cases, we identified three gene pairs. Classifying a sample by the majority voting rule, the average accuracy reached 95.34% in the training data. Using multiple independent validation datasets, including a total of 251 lung cancer samples and 141 non-cancer samples without data normalization, the average accuracy was as high as 96.78%. The rank-based signature is robust against experimental batch effects and can be used to diagnose lung cancer using samples measured by different laboratories at the individual level.

Identification of Somatic Mutation-Driven Immune Cells by Integrating Genomic and Transcriptome Data.

Tumor somatic mutations in protein-coding regions may generate neoantigens which may trigger antitumor immune cell response. Increasing evidence supports that immune cell response may profoundly influence tumor progression. However, there are no calculated tools to systematically identify immune cells driven by specific somatic mutations. It is urgent to develop a calculated method to comprehensively detect tumor-infiltrating immune cells driven by the specific somatic mutations in cancer. We developed a novel software package (SMDIC) that enables the automated identification of somatic mutation-driven immune cell. SMDIC provides a novel pipeline to discover mutation-specific immune cells by integrating genomic and transcriptome data. The operation modes include inference of the relative abundance matrix of tumor-infiltrating immune cells, detection of differential abundance immune cells with respect to the gene mutation status, conversion of the abundance matrix of significantly dysregulated cells into two binary matrices (one for upregulated and one for downregulated cells), identification of somatic mutation-driven immune cells by comparing the gene mutation status with each immune cell in the binary matrices across all samples, and visualization of immune cell abundance of samples in different mutation status for each gene. SMDIC provides a user-friendly tool to identify somatic mutation-specific immune cell response. SMDIC may contribute to understand the mechanisms underlying anticancer immune response and find targets for cancer immunotherapy. The SMDIC was implemented as an R-based tool which was freely available from the CRAN website https://CRAN.R-project.org/package=SMDIC.

Identification of Cancer Dysfunctional Subpathways by Integrating DNA Methylation, Copy Number Variation, and Gene-Expression Data.

A subpathway is defined as the local region of a biological pathway with specific biological functions. With the generation of large-scale sequencing data, there are more opportunities to study the molecular mechanisms of cancer development. It is necessary to investigate the potential impact of DNA methylation, copy number variation (CNV), and gene-expression changes in the molecular states of oncogenic dysfunctional subpathways. We propose a novel method, Identification of Cancer Dysfunctional Subpathways (ICDS), by integrating multi-omics data and pathway topological information to identify dysfunctional subpathways. We first calculated gene-risk scores by integrating the three following types of data: DNA methylation, CNV, and gene expression. Second, we performed a greedy search algorithm to identify the key dysfunctional subpathways within pathways for which the discriminative scores were locally maximal. Finally, a permutation test was used to calculate the statistical significance level for these key dysfunctional subpathways. We validated the effectiveness of ICDS in identifying dysregulated subpathways using datasets from liver hepatocellular carcinoma (LIHC), head-neck squamous cell carcinoma (HNSC), cervical squamous cell carcinoma, and endocervical adenocarcinoma. We further compared ICDS with methods that performed the same subpathway identification algorithm but only considered DNA methylation, CNV, or gene expression (defined as ICDS_M, ICDS_CNV, or ICDS_G, respectively). With these analyses, we confirmed that ICDS better identified cancer-associated subpathways than the three other methods, which only considered one type of data. Our ICDS method has been implemented as a freely available R-based tool (https://cran.r-project.org/web/packages/ICDS).

Prioritization of candidate cancer drugs based on a drug functional similarity network constructed by integrating pathway activities and drug activities.

Due to the speed, efficiency, relative risk, and lower costs compared to traditional drug discovery, the prioritization of candidate drugs for repurposing against cancers of interest has attracted the attention of experts in recent years. Herein, we present a powerful computational approach, termed prioritization of candidate drugs (PriorCD), for the prioritization of candidate cancer drugs based on a global network propagation algorithm and a drug-drug functional similarity network constructed by integrating pathway activity profiles and drug activity profiles. This provides a new approach to drug repurposing by first considering the drug functional similarities at the pathway level. The performance of PriorCD in drug repurposing was evaluated by using drug datasets of breast cancer and ovarian cancer. Cross-validation tests on the drugs approved for the treatment of these cancers indicated that our approach can achieve area under receiver-operating characteristic curve (AUROC) values greater than 0.82. Furthermore, literature searches validated our results, and comparison with other classical gene-based repurposing methods indicated that our pathway-level PriorCD is comparatively more effective at prioritizing candidate drugs with similar therapeutic effects. We hope that our study will be of benefit to the field of drug discovery. In order to expand the usage of PriorCD, a freely available R-based package, PriorCD, has been developed to prioritize candidate anticancer drugs for drug repurposing.