Multidimensional Genomic Data Research Articles

C-ziptf: stable tensor factorization for zero-inflated multi-dimensional genomics data.

In the past two decades, genomics has advanced significantly, with single-cell RNA-sequencing (scRNA-seq) marking a pivotal milestone. ScRNA-seq provides unparalleled insights into cellular diversity and has spurred diverse studies across multiple conditions and samples, resulting in an influx of complex multidimensional genomics data. This highlights the need for robust methodologies capable of handling the complexity and multidimensionality of such genomics data. Furthermore, single-cell data grapples with sparsity due to issues like low capture efficiency and dropout effects. Tensor factorizations (TF) have emerged as powerful tools to unravel the complex patterns from multi-dimensional genomics data. Classic TF methods, based on maximum likelihood estimation, struggle with zero-inflated count data, while the inherent stochasticity in TFs further complicates result interpretation and reproducibility. Our paper introduces Zero Inflated Poisson Tensor Factorization (ZIPTF), a novel method for high-dimensional zero-inflated count data factorization. We also present Consensus-ZIPTF (C-ZIPTF), merging ZIPTF with a consensus-based approach to address stochasticity. We evaluate our proposed methods on synthetic zero-inflated count data, simulated scRNA-seq data, and real multi-sample multi-condition scRNA-seq datasets. ZIPTF consistently outperforms baseline matrix and tensor factorization methods, displaying enhanced reconstruction accuracy for zero-inflated data. When dealing with high probabilities of excess zeros, ZIPTF achieves up to better accuracy. Moreover, C-ZIPTF notably enhances the factorization's consistency. When tested on synthetic and real scRNA-seq data, ZIPTF and C-ZIPTF consistently uncover known and biologically meaningful gene expression programs. Access our data and code at: https://github.com/klarman-cell-observatory/scBTF and https://github.com/klarman-cell-observatory/scbtf_experiments .

Classification of multiple primary lung cancer in patients with multifocal lung cancer: assessment of a machine learning approach using multidimensional genomic data.

Multiple primary lung cancer (MPLC) is an increasingly well-known clinical phenomenon. However, its molecular characterizations are poorly understood, and still lacks of effective method to distinguish it from intrapulmonary metastasis (IM). Herein, we propose an identification model based on molecular multidimensional analysis in order to accurately optimize treatment. A total of 112 Chinese lung cancers harboring at least two tumors (n = 270) were enrolled. We retrospectively selected 74 patients with 121 tumor pairs and randomly divided the tumor pairs into a training cohort and a test cohort in a 7:3 ratio. A novel model was established in training cohort, optimized for MPLC identification using comprehensive genomic profiling analyzed by a broad panel with 808 cancer-related genes, and evaluated in the test cohort and a prospective validation cohort of 38 patients with 112 tumors. We found differences in molecular characterizations between the two diseases and rigorously selected the characterizations to build an identification model. We evaluated the performance of the classifier using the test cohort data and observed an 89.5% percent agreement (PA) for MPLC and a 100.0% percent agreement for IM. The model showed an excellent area under the curve (AUC) of 0.947 and a 91.3% overall accuracy. Similarly, the assay achieved a considerable performance in the independent validation set with an AUC of 0.938 and an MPLC predictive value of 100%. More importantly, the MPLC predictive value of the classification achieved 100% in both the test set and validation cohort. Compared to our previous mutation-based method, the classifier showed better κ consistencies with clinical classification among all 112 patients (0.84 vs. 0.65, p <.01). These data provide novel evidence of MPLC-specific genomic characteristics and demonstrate that our one-step molecular classifier can accurately classify multifocal lung tumors as MPLC or IM, which suggested that broad panel NGS may be a useful tool for assisting with differential diagnoses.

SETD2 variation correlates with tumor mutational burden and MSI along with improved response to immunotherapy

BackgroundSETD2 protects against genomic instability via maintenance of homologous recombination repair (HRR) and mismatch repair (MMR) in neoplastic cells. However, it remains unclear whether SETD2 dysfunction is a complementary or independent factor to microsatellite instability-high (MSI-H) and tumor mutational burden-high (TMB-H) for immunocheckpoint inhibitor (ICI) treatment, and little is known regarding whether this type of dysfunction acts differently in various types of cancer.MethodsThis cohort study used multidimensional genomic data of 6726 sequencing samples from our cooperative and non-public GenePlus institute from April 1 through April 10, 2020. MSIsensor score, HRD score, RNAseq, mutational data, and corresponding clinical data were obtained from the TCGA and MSKCC cohort for seven solid tumor types.ResultsA total of 1021 genes underwent target panel sequencing reveal that SETD2 mutations were associated with a higher TMB. SETD2 deleterious mutation dysfunction affected ICI treatment prognosis independently of TMB-H (p < 0.01) and had a lower death hazard than TMB-H in pancancer patients (0.511 vs 0.757). Significantly higher MSI and lower homologous recombination deficiency were observed in the SETD2 deleterious mutation group. Improved survival rate was found in the MSKCC-IO cohort (P < 0.0001) and was further confirmed in our Chinese cohort.ConclusionWe found that SETD2 dysfunction affects ICI treatment prognosis independently of TMB-H and has a lower death hazard than TMB-H in pancancer patients. Therefore, SETD2 has the potential to serve as a candidate biomarker for ICI treatment. Additionally, SETD2 should be considered when dMMR is detected by immunohistochemistry.

Loss of Human Leukocyte Antigen and Immune Escape in Head and Neck Cancer.

Cancer cells evade recognition by the immune system to survive. Head and neck squamous cell carcinoma (HNSCC) is characterized by high levels of immune infiltration and mutation-associated neoantigens; therefore, immune evasion is likely to be an important mechanism in HNSCC tumorigenesis and progression. A commonly employed mechanism of immune evasion is downregulation of human leukocyte antigen (HLA) or loss of heterozygosity (LOH) in tumor cells. The objective of this study was to integrate multi-dimensional genomic and transcriptomic data from HNSCC tumors to better understand the clinical and immunologic implications of HLA LOH. Cross-sectional integrated clinical and genomic analysis. Whole-exome sequencing and RNA-sequencing data from 522 tumors profiled in The Cancer Genome Atlas HNSCC cohort were analyzed and integrated with secondary analyses including immune cell deconvolution data. Associations were analyzed with categorical hypothesis testing and multivariable logistic and Cox regression. HLA LOH was a prevalent event that was identified in 53% of HNSCC tumors; in many cases, more than one class I HLA gene was targeted for LOH. HLA LOH was more common in advanced-stage tumors. Tumors with somatic HLA LOH had tumor microenvironments defined by decreased lymphocyte and T cell infiltration. HLA LOH is one of the most prevalent genetic alterations in HNSCC, and is associated with a cold immune microenvironment, suggesting that HLA LOH is a means of immune evasion. It may have value as a predictive biomarker or potential as a cancer cell-specific therapeutic target. 3 Laryngoscope, 134:160-165, 2024.

A network-based matrix factorization framework for ceRNA co-modules recognition of cancer genomic data.

With the development of high-throughput technologies, the accumulation of large amounts of multidimensional genomic data provides an excellent opportunity to study the multilevel biological regulatory relationships in cancer. Based on the hypothesis of competitive endogenous ribonucleic acid (RNA) (ceRNA) network, lncRNAs can eliminate the inhibition of microRNAs (miRNAs) on their target genes by binding to intracellular miRNA sites so as to improve the expression level of these target genes. However, previous studies on cancer expression mechanism are mostly based on individual or two-dimensional data, and lack of integration and analysis of various RNA-seq data, making it difficult to verify the complex biological relationships involved. To explore RNA expression patterns and potential molecular mechanisms of cancer, a network-regularized sparse orthogonal-regularized joint non-negative matrix factorization (NSOJNMF) algorithm is proposed, which combines the interaction relations among RNA-seq data in the way of network regularization and effectively prevents multicollinearity through sparse constraints and orthogonal regularization constraints to generate good modular sparse solutions. NSOJNMF algorithm is performed on the datasets of liver cancer and colon cancer, then ceRNA co-modules of them are recognized. The enrichment analysis of these modules shows that >90% of them are closely related to the occurrence and development of cancer. In addition, the ceRNA networks constructed by the ceRNA co-modules not only accurately mine the known correlations of the three RNA molecules but also further discover their potential biological associations, which may contribute to the exploration of the competitive relationships among multiple RNAs and the molecular mechanisms affecting tumor development.

Driver gene detection through Bayesian network integration of mutation and expression profiles.

The identification of mutated driver genes and the corresponding pathways is one of the primary goals in understanding tumorigenesis at the patient level. Integration of multi-dimensional genomic data from existing repositories, e.g., The Cancer Genome Atlas (TCGA), offers an effective way to tackle this issue. In this study, we aimed to leverage the complementary genomic information of individuals and create an integrative framework to identify cancer-related driver genes. Specifically, based on pinpointed differentially expressed genes, variants in somatic mutations and a gene interaction network, we proposed an unsupervised Bayesian network integration (BNI) method to detect driver genes and estimate the disease propagation at the patient and/or cohort levels. This new method first captures inherent structural information to construct a functional gene mutation network and then extracts the driver genes and their controlled downstream modules using the minimum cover subset method. Using other credible sources (e.g. Cancer Gene Census and Network of Cancer Genes), we validated the driver genes predicted by the BNI method in three TCGA pan-cancer cohorts. The proposed method provides an effective approach to address tumor heterogeneity faced by personalized medicine. The pinpointed drivers warrant further wet laboratory validation. The supplementary tables and source code can be obtained from https://xavieruniversityoflouisiana.sharefile.com/d-se6df2c8d0ebe4800a3030311efddafe5. Supplementary data are available at Bioinformatics online.

Review of Innovative Immersive Technologies for Healthcare Applications

ABSTRACTImmersive technologies, including virtual reality (VR), augmented reality (AR), and mixed reality (MR), can connect people using enhanced data visualizations to better involve stakeholders as integral members of the process. Immersive technologies have started to change the research on multidimensional genomic data analysis for disease diagnostics and treatments. Immersive technologies are highlighted in some research for health and clinical needs, especially for precision medicine innovation. The use of immersive technology for genomic data analysis has recently received attention from the research community. Genomic data analytics research seeks to integrate immersive technologies to build more natural human-computer interactions that allow better perception engagements. Immersive technologies, especially VR, help humans perceive the digital world as real and give learning output with lower performance errors and higher accuracy. However, there are limited reviews about immersive technologies used in healthcare and genomic data analysis with specific digital health applications. This paper contributes a comprehensive review of using immersive technologies for digital health applications, including patient-centric applications, medical domain education, and data analysis, especially genomic data visual analytics. We highlight the evolution of a visual analysis using VR as a case study for how immersive technologies step, can by step, move into the genomic data analysis domain. The discussion and conclusion summarize the current immersive technology applications' usability, innovation, and future work in the healthcare domain, and digital health data visual analytics.

Development and Validation of Epigenetic Signature Predict Survival for Patients with Laryngeal Squamous Cell Carcinoma.

Establishing epigenetic signature to improve the accuracy of survival prediction and optimize therapeutic strategies for laryngeal squamous cell carcinoma (LSCC) by a genome-wide integrated analysis of methylation and the transcriptome. LSCC DNA methylation datasets and RNA sequencing datasets were acquired from the Cancer Genome Atlas (TCGA). MethylMix was applied to detect DNA methylation-driven genes (MDGs), which developed an epigenetic signature. The predictive accuracy and clinical value of the epigenetic signature were evaluated by receiver operating characteristic and decision curve analysis, and compared with tumor-node-metastasis (TNM) stage system. In addition, prognostic value of the epigenetic signature was validated by external Gene Expression Omnibus (GEO) database. According to five MDGs of epigenetic signature, the candidate small molecules for LSCC were screen out by the CMap database. A total of 88 DNA MDGs were identified, five of which (MAGEB2, SUSD1, ZNF382, ZNF418, and ZNF732) were chosen to construct an epigenetic signature. The epigenetic signature can effectively divide patients into high-risk and low-risk group, with the area under curve (AUC) of 0.8 (5-year overall survival [OS]) and AUC of 0.745 (3-year OS). Stratification analysis affirmed that the epigenetic signature was still a significant statistical prognostic model in subsets of patients with different clinical variables. Multivariate Cox regression analysis indicated that the efficacy of epigenetic signature appears independent of other clinicopathological characteristics. In terms of predictive capacity and clinical usefulness, the epigenetic signature was superior to traditional TNM stage. In addition, the epigenetic signature was confirmed in external LSCC cohorts from GEO. Finally, CMap matched the 10 most significant small molecules as promising therapeutic drugs to reverse the LSCC gene expression. An epigenetic signature, with five DNA MDGs, was identified and validated in LSCC patients by integrating multidimensional genomic data, which may offer novel research directions and prospects for individualized treatment of patients with LSCC.

Association of MUC16 Mutation With Response to Immune Checkpoint Inhibitors in Solid Tumors

As the third most frequently mutated gene in cancers, the association between MUC16 mutation and response to immune checkpoint inhibitors (ICIs) in solid tumors remains unclear. To examine whether MUC16 mutation is associated with genomic factors in ICI response in solid tumors and with outcomes in ICI-treated patients. This cohort study used multidimensional genomic data of 10 195 patients from The Cancer Genome Atlas (TCGA) across 30 solid tumor types, 56 patients from a non-small cell lung cancer (NSCLC) cohort, and 145 patients from a melanoma cohort. Genomic factors associated with ICI response covered tumor mutational burden, neoantigens, immune-related gene signatures, and tumor immune microenvironment. Both NSCLC and melanoma cohorts included ICI-treated patients. The TCGA cohort was used to examine the association of MUC16 mutation with genomic factors. Two ICI-treated cohorts were used to explore the significance of outcomes associated with MUC16 mutation, using Kaplan-Meier curves and Cox models with adjusting for potential confounders. Gene set enrichment analysis was used to identify MUC16 mutation-associated biological processes. Data were obtained from October 1 through October 10, 2019, and were analyzed from October 11 through December 31, 2019. Genomic factors associated with ICI response, overall survival, and clinical response. Of the 10 195 patients, 4821 (47.6%) were men (median [interquartile range {IQR}] age, 60 [50-70] years). MUC16 was mutated in 2006 of 10 195 patients (19.68%). In this pan-cancer data set, patients with MUC16 mutation had higher tumor mutational burden (median [IQR], 230 [93-595] mutations vs 48 [25-92] mutations; difference, 182 mutations; 95% CI, 164-199 mutations; P < .001) and neoantigen load (median [IQR], 179 [74-394.5] neoantigens vs 48 [24-89] neoantigens; difference, 131 antigens; 95% CI, 116.5-145 neoantigens; P < .001) than those without mutations. The tumor immune microenvironment with dual-positive CD8A and PD-L1 was overrepresented in MUC16-mutated tumors compared with wild-type ones (43.8% vs 32.4%; odds ratio, 1.63; 95% CI, 1.46-1.80; P < .001). Of the 40 immune-related genes, 37 (92.5%) exhibited differential expression between 2 states. MUC16 mutation was associated with improved overall survival in both the NSCLC (hazard ratio, 0.34; 95% CI, 0.12-0.99; P = .04) and melanoma (hazard ratio, 0.57; 95% CI, 0.36-0.90; P = .02) cohorts. The improvement persisted after adjusting for age, sex, and dominant mutational signatures in the melanoma cohort (hazard ratio, 0.57; 95% CI, 0.33-0.96; P = .04). MUC16 mutation was associated with greater response rates in the NSCLC cohort (odds ratio, 4.03; 95% CI, 1.06-16.43; P = .03) and the melanoma cohort (odds ratio, 3.38; 95% CI, 1.07-14.25; P = .03). Gene set enrichment analysis revealed that gene sets regarding cell proliferation and immune response were enriched in MUC16-mutated tumors (false discovery rate, <.001). MUC16 mutation appears to be associated with reported genomic factors associated with response to and improved outcomes for ICI treatment in solid tumors. It may hold promise as a marker for guiding immunotherapeutic responsiveness.

Hyper-graph based sparse canonical correlation analysis for the diagnosis of Alzheimer’s disease from multi-dimensional genomic data

The effective and accurate diagnosis of Alzheimer's disease (AD), especially in the early stage (i.e., mild cognitive impairment (MCI)) remains a big challenge in AD research. So far, multiple biomarkers have been associated with AD diagnosis and progression. However, most of the existing research only utilized single modality data for diagnostic biomarker identification, which did not take the advantages of multi-modal data that provide comprehensive and complementary information at multiple levels into consideration. In this paper, we integrate multi-modal genomic data from postmortem AD brains (i.e., mRNA, miRNA and epigenomic data) and propose a hyper-graph based sparse canonical correlation analysis (HGSCCA) method to extract the most correlated multi-modal biomarkers associated with AD and MCI. Specifically, our model utilizes the sparse canonical correlation analysis framework (SCCA), which aims at finding the best linear projections for each input modality so that the strongest correlation within the selected features of multi-dimensional genomic data can be captured. In addition, with the consideration of high-order relationships among different subjects, we also introduce a hyper-graph-based regularization term that will lead to the selection of more discriminative biomarkers. To evaluate the effectiveness of the proposed method, we conduct the experiments on the well-known AD cohort study, The Religious Orders Study and Memory and Aging Project (ROSMAP) dataset, and the results show that our method can not only identify meaningful biomarkers for the diagnosis AD disease, but also achieve superior classification performance than the comparing methods.

Identification of Multidimensional Regulatory Modules Through Multi-Graph Matching With Network Constraints.

The accumulation of large amounts of multidimensional genomic data provides new opportunities to study multilevel biological regulatory associations. Identifying multidimensional regulatory modules (md-modules) from omics data is crucial to provide a comprehensive understanding of the regulatory mechanisms of biological systems. We develop a multi-graph matching with multiple network constraints (MGMMNC) model to identify the md-modules. The MGMMNC model aims to accurately capture highly relevant md-modules by considering the relationships intra- and inter-multidimensional omics data, including interactions within a network and cycle consistency information. The proposed technique adopts a novel graph-smoothing similarity measurement for the highly contaminated genetic data. The superiority and effectiveness of MGMMNC have been demonstrated by comparative experiments with three state-of-the-art techniques using simulated and cervical cancer data. MGMMNC can accurately and efficiently identify the md-modules that are significantly enriched in gene ontology biological processes and in Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Many different level molecules in the same md-module collaboratively regulate the same pathway. Moreover, the md-modules are capable of stratifying patients into subtypes with significant survival differences. The problem of identifying multidimensional regulatory modules from omics data is formulated as a multi-graph matching problem, and multiple network constraints and cycle consistency information are seamlessly integrated into the matching model.

Integrative Analysis of Pathological Images and Multi-Dimensional Genomic Data for Early-Stage Cancer Prognosis.

The integrative analysis of histopathological images and genomic data has received increasing attention for studying the complex mechanisms of driving cancers. However, most image-genomic studies have been restricted to combining histopathological images with the single modality of genomic data (e.g., mRNA transcription or genetic mutation), and thus neglect the fact that the molecular architecture of cancer is manifested at multiple levels, including genetic, epigenetic, transcriptional, and post-transcriptional events. To address this issue, we propose a novel ordinal multi-modal feature selection (OMMFS) framework that can simultaneously identify important features from both pathological images and multi-modal genomic data (i.e., mRNA transcription, copy number variation, and DNA methylation data) for the prognosis of cancer patients. Our model is based on a generalized sparse canonical correlation analysis framework, by which we also take advantage of the ordinal survival information among different patients for survival outcome prediction. We evaluate our method on three early-stage cancer datasets derived from The Cancer Genome Atlas (TCGA) project, and the experimental results demonstrated that both the selected image and multi-modal genomic markers are strongly correlated with survival enabling effective stratification of patients with distinct survival than the comparing methods, which is often difficult for early-stage cancer patients.

Immune response characterization of endometrial cancer.

BackgroundThe comprehensive characterization and prognostic relevance of immune activation in endometrial cancer remain largely unknown.ResultsWe systematically reported a subset of endometrioid-type endometrial cancer characterized by multifaceted immune features such as low tumor purity, high leukocyte percentage, and striking CD8 lymphocytic infiltration with anti-tumor efficacy along with marked upregulation of immunosuppressive gene markers. We also showed that genes whose expression was significantly correlated with better survival were significantly enriched in the immune-related signaling pathways, suggesting that tumor-infiltrating lymphocytes give rise to a favorable prognosis in endometrial cancer. Furthermore, we showed that immune cell recruitment in this subset of tumors is likely due to the transcriptional activation of the STAT1 signaling network.MethodsWe obtained the multi-dimensional genomic data from publicly available databases and correlated them with the four gene expression-based subtypes we recently identified in endometrial cancer. Upstream regulator analysis was used to identify the most significantly enriched transcription regulators and Ingenuity pathway analysis was applied to determine enrichment of signaling pathways in survival-associated genes. Gene set enrichment analysis was performed on the 200-gene T-cell tumor infiltration gene signature comparing Cluster IV with the other three clusters combined. All statistical tests were two-sided, and a P value of less than 0.05 is considered significant across all analyses performed.ConclusionThis study helps to identify patients with immune activation who are likely to benefit from emerging immune checkpoint inhibitors.

DNA methylation-driven genes for constructing diagnostic, prognostic, and recurrence models for hepatocellular carcinoma.

In this study, we performed a comprehensively analysis of gene expression and DNA methylation data to establish diagnostic, prognostic, and recurrence models for hepatocellular carcinoma (HCC).Methods: We collected gene expression and DNA methylation datasets for over 1,200 clinical samples. Integrated analyses of RNA-sequencing and DNA methylation data were performed to identify DNA methylation-driven genes. These genes were utilized in univariate, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analyses to build a prognostic model. Recurrence and diagnostic models for HCC were also constructed using the same genes.Results: A total of 123 DNA methylation-driven genes were identified. Two of these genes (SPP1 and LCAT) were chosen to construct the prognostic model. The high-risk group showed a markedly unfavorable prognosis compared to the low-risk group in both training (HR = 2.81; P < 0.001) and validation (HR = 3.06; P < 0.001) datasets. Multivariate Cox regression analysis indicated the prognostic model to be an independent predictor of prognosis (P < 0.05). Also, the recurrence model successfully distinguished the HCC recurrence rate between the high-risk and low-risk groups in both training (HR = 2.22; P < 0.001) and validation (HR = 2; P < 0.01) datasets. The two diagnostic models provided high accuracy for distinguishing HCC from normal samples and dysplastic nodules in the training and validation datasets, respectively.Conclusions: We identified and validated prognostic, recurrence, and diagnostic models that were constructed using two DNA methylation-driven genes in HCC. The results obtained by integrating multidimensional genomic data offer novel research directions for HCC biomarkers and new possibilities for individualized treatment of patients with HCC.

Multi-omic tumor data reveal diversity of molecular mechanisms that correlate with survival

Outcomes for cancer patients vary greatly even within the same tumor type, and characterization of molecular subtypes of cancer holds important promise for improving prognosis and personalized treatment. This promise has motivated recent efforts to produce large amounts of multidimensional genomic (multi-omic) data, but current algorithms still face challenges in the integrated analysis of such data. Here we present Cancer Integration via Multikernel Learning (CIMLR), a new cancer subtyping method that integrates multi-omic data to reveal molecular subtypes of cancer. We apply CIMLR to multi-omic data from 36 cancer types and show significant improvements in both computational efficiency and ability to extract biologically meaningful cancer subtypes. The discovered subtypes exhibit significant differences in patient survival for 27 of 36 cancer types. Our analysis reveals integrated patterns of gene expression, methylation, point mutations, and copy number changes in multiple cancers and highlights patterns specifically associated with poor patient outcomes.

Finding Correlated Patterns via High-Order Matching for Multiple Sourced Biological Data.

The emergence of multidimensional genomic data poses new challenges in data analysis. Finding correlated patterns within multiple-sourced biological data is useful in understanding potential interactions between the multimodal genomic data. Multidimensional genomic data contain multiple genomic data types, and different types of genomic data have different scales and units. These data cannot simply be aggregated for analysis. To address this issue, a correlated pattern discovery model incorporating prior knowledge is proposed. Tensor similarity is used to measure the correlation between common patterns. The model is combined with prior knowledge, the expression of which is transformed into constraints. Efficient numerical solutions are designed and analyzed. The proposed method is shown to perform robustly and effectively with both simulated data and real biological data. We conduct experiments on five real cancer data sets to reveal various cancer subtypes. A survival analysis of these subtypes confirms the effectiveness of the model. We introduce a correlated pattern discovery model incorporating prior knowledge. This model is meaningful for the realization of personalized diagnoses by doctors in the treatment of cancer and other diseases. The problem of finding correlated patterns from multiple-sourced biological data was formulated as a high-order graph matching problem, and the prior knowledge data were seamlessly incorporated into the matching model.

Systematic identification of non-coding pharmacogenomic landscape in cancer

Emerging evidence has shown long non-coding RNAs (lncRNAs) play important roles in cancer drug response. Here we report a lncRNA pharmacogenomic landscape by integrating multi-dimensional genomic data of 1005 cancer cell lines and drug response data of 265 anti-cancer compounds. Using Elastic Net (EN) regression, our analysis identifies 27,341 lncRNA-drug predictive pairs. We validate the robustness of the lncRNA EN-models using two independent cancer pharmacogenomic datasets. By applying lncRNA EN-models of 49 FDA approved drugs to the 5605 tumor samples from 21 cancer types, we show that cancer cell line based lncRNA EN-models can predict therapeutic outcome in cancer patients. Further lncRNA-pathway co-expression analysis suggests lncRNAs may regulate drug response through drug-metabolism or drug-target pathways. Finally, we experimentally validate that EPIC1, the top predictive lncRNA for the Bromodomain and Extra-Terminal motif (BET) inhibitors, strongly promotes iBET762 and JQ-1 resistance through activating MYC transcriptional activity.

65 YEARS OF THE DOUBLE HELIX: Classification of endocrine tumors in the age of integrated genomics.

The classification of human cancers represents one of the cornerstones of modern pathology. Over the last century, surgical pathologists established the current taxonomy of neoplasia using traditional histopathological parameters, which include tumor architecture, cytological features and cellular proliferation. This morphological classification is efficient and robust with high reproducibility and has served patients and health care providers well. The most recent decade has witnessed an explosion of genome-wide molecular genetic and epigenetic data for most cancers, including tumors of endocrine organs. The availability of this expansive multi-dimensional genomic data, collectively termed the cancer genome, has catalyzed a re-examination of the classification of endocrine tumors. Here, recent cancer genome studies of various endocrine tumors, including those of the thyroid, pituitary and adrenal glands, pancreas, small bowel, lung and skin, are presented with special emphasis on how genomic insights are impacting endocrine tumor classification.

Discovery of two-level modular organization from matched genomic data via joint matrix tri-factorization.

With the rapid development of biotechnology, multi-dimensional genomic data are available for us to study the regulatory associations among multiple levels. Thus, it is essential to develop a tool to identify not only the modular patterns from multiple levels, but also the relationships among these modules. In this study, we adopt a novel non-negative matrix factorization framework (NetNMF) to integrate pairwise genomic data in a network manner. NetNMF could reveal the modules of each dimension and the connections within and between both types of modules. We first demonstrated the effectiveness of NetNMF using a set of simulated data and compared it with two typical NMF methods. Further, we applied it to two different types of pairwise genomic datasets including microRNA (miRNA) and gene expression data from The Cancer Genome Atlas and gene expression and pharmacological data from the Cancer Genome Project. We respectively identified a two-level miRNA–gene module network and a two-level gene–drug module network. Not only have the majority of identified modules significantly functional implications, but also the three types of module pairs have closely biological associations. This module discovery tool provides us comprehensive insights into the mechanisms of how the two levels of molecules cooperate with each other.

Matrix Integrative Analysis (MIA) of Multiple Genomic Data for Modular Patterns.

The increasing availability of high-throughput biological data, especially multi-dimensional genomic data across the same samples, has created an urgent need for modular and integrative analysis tools that can reveal the relationships among different layers of cellular activities. To this end, we present a MATLAB package, Matrix Integration Analysis (MIA), implementing and extending four published methods, designed based on two classical techniques, non-negative matrix factorization (NMF), and partial least squares (PLS). This package can integrate diverse types of genomic data (e.g., copy number variation, DNA methylation, gene expression, microRNA expression profiles, and/or gene network data) to identify the underlying modular patterns by each method. Particularly, we demonstrate the differences between these two classes of methods, which give users some suggestions about how to select a suitable method in the MIA package. MIA is a flexible tool which could handle a wide range of biological problems and data types. Besides, we also provide an executable version for users without a MATLAB license.