Personalized Integrated Network Modeling of the Cancer Proteome Atlas

A prognostic matrix gene expression signature defines functional glioblastoma phenotypes and niches

Background

Interactions among tumor, immune, and vascular niches play major roles in driving glioblastoma (GBM) malignancy and treatment responses. The composition, heterogeneity, and localization of extracellular core matrix proteins (CMPs) that mediate such interactions, however, are not well understood.

Methods

Here, through computational genomics and proteomics approaches, we analyzed the functional and clinical relevance of CMP expression in GBM at bulk, single cell, and spatial anatomical resolution.

Results

We identified genes encoding CMPs whose expression levels categorize GBM tumors into CMP expression-high (M-H) and CMP expression-low (M-L) groups. CMP enrichment is associated with worse patient survival, specific driver oncogenic alterations, mesenchymal state, infiltration of pro-tumor immune cells, and immune checkpoint gene expression. Anatomical and single-cell transcriptome analyses indicate that matrisome gene expression is enriched in vascular and leading edge/infiltrative niches that are known to harbor glioma stem cells driving GBM progression. Finally, we identified a 17-gene CMP expression signature, termed Matrisome 17 (M17) signature that further refines the prognostic value of CMP genes. The M17 signature is a significantly stronger prognostic factor compared to MGMT promoter methylation status as well as canonical subtypes, and importantly, potentially predicts responses to PD1 blockade.

Conclusion

The matrisome gene expression signature provides a robust stratification of GBM patients by survival and potential biomarkers of functionally relevant GBM niches that can mediate mesenchymal-immune cross talk. Patient stratification based on matrisome profiles can contribute to selection and optimization of treatment strategies.

A prognostic matrix code defines functional glioblastoma phenotypes and niches

Interactions among tumor, immune and vascular niches play major roles in driving glioblastoma (GBM) malignancy and treatment responses. The composition, heterogeneity, and localization of extracellular core matrix proteins (CMPs) that mediate such interactions, however, are not well understood. Here, we characterize functional and clinical relevance of genes encoding CMPs in GBM at bulk, single cell, and spatial anatomical resolution. We identify a "matrix code" for genes encoding CMPs whose expression levels categorize GBM tumors into matrisome-high and matrisome-low groups that correlate with worse and better patient survival, respectively. The matrisome enrichment is associated with specific driver oncogenic alterations, mesenchymal state, infiltration of pro-tumor immune cells and immune checkpoint gene expression. Anatomical and single cell transcriptome analyses indicate that matrisome gene expression is enriched in vascular and leading edge/infiltrative anatomic structures that are known to harbor glioma stem cells driving GBM progression. Finally, we identified a 17-gene matrisome signature that retains and further refines the prognostic value of genes encoding CMPs and, importantly, potentially predicts responses to PD1 blockade in clinical trials for GBM. The matrisome gene expression profiles provide potential biomarkers of functionally relevant GBM niches that contribute to mesenchymal-immune cross talk and patient stratification which could be applied to optimize treatment responses.

A prognostic matrix code defines functional glioblastoma phenotypes and niches

Interactions among tumor, immune and vascular niches play major roles in driving glioblastoma (GBM) malignancy and treatment responses. The composition, heterogeneity, and localization of extracellular core matrix proteins (CMPs) that mediate such interactions, however, are not well understood. Here, we characterize functional and clinical relevance of genes encoding CMPs in GBM at bulk, single cell, and spatial anatomical resolution. We identify a "matrix code" for genes encoding CMPs whose expression levels categorize GBM tumors into matrisome-high and matrisome-low groups that correlate with worse and better survival, respectively, of patients. The matrisome enrichment is associated with specific driver oncogenic alterations, mesenchymal state, infiltration of pro-tumor immune cells and immune checkpoint gene expression. Anatomical and single cell transcriptome analyses indicate that matrisome gene expression is enriched in vascular and leading edge/infiltrative anatomic structures that are known to harbor glioma stem cells driving GBM progression. Finally, we identified a 17-gene matrisome signature that retains and further refines the prognostic value of genes encoding CMPs and, importantly, potentially predicts responses to PD1 blockade in clinical trials for GBM. The matrisome gene expression profiles may provide biomarkers of functionally relevant GBM niches that contribute to mesenchymal-immune cross talk and patient stratification to optimize treatment responses.

Edge and modular significance assessment in individual-specific networks

Individual-specific networks, defined as networks of nodes and connecting edges that are specific to an individual, are promising tools for precision medicine. When such networks are biological, interpretation of functional modules at an individual level becomes possible. An under-investigated problem is relevance or "significance" assessment of each individual-specific network. This paper proposes novel edge and module significance assessment procedures for weighted and unweighted individual-specific networks. Specifically, we propose a modular Cook's distance using a method that involves iterative modeling of one edge versus all the others within a module. Two procedures assessing changes between using all individuals and using all individuals but leaving one individual out (LOO) are proposed as well (LOO-ISN, MultiLOO-ISN), relying on empirically derived edges. We compare our proposals to competitors, including adaptions of OPTICS, kNN, and Spoutlier methods, by an extensive simulation study, templated on real-life scenarios for gene co-expression and microbial interaction networks. Results show the advantages of performing modular versus edge-wise significance assessments for individual-specific networks. Furthermore, modular Cook's distance is among the top performers across all considered simulation settings. Finally, the identification of outlying individuals regarding their individual-specific networks, is meaningful for precision medicine purposes, as confirmed by network analysis of microbiome abundance profiles.

A unified mediation analysis framework for integrative cancer proteogenomics with clinical outcomes

MOTIVATION

Multilevel molecular profiling of tumors and the integrative analysis with clinical outcomes have enabled a deeper characterization of cancer treatment. Mediation analysis has emerged as a promising statistical tool to identify and quantify the intermediate mechanisms by which a gene affects an outcome. However, existing methods lack a unified approach to handle various types of outcome variables, making them unsuitable for high-throughput molecular profiling data with highly interconnected variables.

RESULTS

We develop a general mediation analysis framework for proteogenomic data that include multiple exposures, multivariate mediators on various scales of effects as appropriate for continuous, binary and survival outcomes. Our estimation method avoids imposing constraints on model parameters such as the rare disease assumption, while accommodating multiple exposures and high-dimensional mediators. We compare our approach to other methods in extensive simulation studies at a range of sample sizes, disease prevalence and number of false mediators. Using kidney renal clear cell carcinoma proteogenomic data, we identify genes that are mediated by proteins and the underlying mechanisms on various survival outcomes that capture short- and long-term disease-specific clinical characteristics.

AVAILABILITY AND IMPLEMENTATION

Software is made available in an R package (https://github.com/longjp/mediateR).

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

SpaceX: gene co-expression network estimation for spatial transcriptomics

MOTIVATION

The analysis of spatially resolved transcriptome enables the understanding of the spatial interactions between the cellular environment and transcriptional regulation. In particular, the characterization of the gene-gene co-expression at distinct spatial locations or cell types in the tissue enables delineation of spatial co-regulatory patterns as opposed to standard differential single gene analyses. To enhance the ability and potential of spatial transcriptomics technologies to drive biological discovery, we develop a statistical framework to detect gene co-expression patterns in a spatially structured tissue consisting of different clusters in the form of cell classes or tissue domains.

RESULTS

We develop SpaceX (spatially dependent gene co-expression network), a Bayesian methodology to identify both shared and cluster-specific co-expression network across genes. SpaceX uses an over-dispersed spatial Poisson model coupled with a high-dimensional factor model which is based on a dimension reduction technique for computational efficiency. We show via simulations, accuracy gains in co-expression network estimation and structure by accounting for (increasing) spatial correlation and appropriate noise distributions. In-depth analysis of two spatial transcriptomics datasets in mouse hypothalamus and human breast cancer using SpaceX, detected multiple hub genes which are related to cognitive abilities for the hypothalamus data and multiple cancer genes (e.g. collagen family) from the tumor region for the breast cancer data.

AVAILABILITY AND IMPLEMENTATION

The SpaceX R-package is available at github.com/bayesrx/SpaceX.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Lung Cancer Computational Biology and Resources

Comprehensive clinical, pathological, and molecular data, when appropriately integrated with advanced computational approaches, are transforming the way we characterize and study lung cancer. Clinically, cancer registry and publicly available historical clinical trial data enable retrospective analyses to examine how socioeconomic factors, patient demographics, and cancer characteristics affect treatment and outcome. Pathologically, digital pathology and artificial intelligence are revolutionizing histopathological image analyses, not only with improved efficiency and accuracy, but also by extracting additional information for prognostication and tumor microenvironment characterization. Genetically and molecularly, individual patient tumors and preclinical models of lung cancer are profiled by various high-throughput platforms to characterize the molecular properties and functional liabilities. The resulting multi-omics data sets and their interrogation facilitate both basic research mechanistic studies and translation of the findings into the clinic. In this review, we provide a list of resources and tools potentially valuable for lung cancer basic and translational research. Importantly, we point out pitfalls and caveats when performing computational analyses of these data sets and provide a vision of future computational biology developments that will aid lung cancer translational research.

GeneCoNet: A web application server for constructing cancer patient-specific gene correlation networks with prognostic gene pairs

BACKGROUND AND OBJECTIVE

Most prognostic gene signatures that have been known for cancer are either individual genes or combination of genes. Both individual genes and combination of genes do not provide information on gene-gene relations, and often have less prognostic significance than random genes associated with cell proliferation. Several methods for generating sample-specific gene networks have been proposed, but programs implementing the methods are not publicly available.

METHODS

We have developed a method that builds gene correlation networks specific to individual cancer patients and derives prognostic gene correlations from the networks. A gene correlation network specific to a patient is constructed by identifying gene-gene relations that are significantly different from normal samples. Prognostic gene pairs are obtained by carrying out the Cox proportional hazards regression and the log-rank test for every gene pair.

RESULTS

We built a web application server called GeneCoNet with thousands of tumor samples in TCGA. Given a tumor sample ID of TCGA, GeneCoNet dynamically constructs a gene correlation network specific to the sample as output. As an additional output, it provides information on prognostic gene correlations in the network. GeneCoNet found several prognostic gene correlations for six types of cancer, but there were no prognostic gene pairs common to multiple cancer types.

CONCLUSION

Extensive analysis of patient-specific gene correlation networks suggests that patients with a larger subnetwork of prognostic gene pairs have shorter survival time than the others and that patients with a subnetwork that contains more genes participating in prognostic gene pairs have shorter survival time than the others. GeneCoNet can be used as a valuable resource for generating gene correlation networks specific to individual patients and for identifying prognostic gene correlations. It is freely accessible at http://geneconet.inha.ac.kr.

Recent Advances in Integrative Multi-Omics Research in Breast and Ovarian Cancer

The underlying molecular heterogeneity of cancer is responsible for the dynamic clinical landscape of this disease. The combination of genomic and proteomic alterations, including both inherited and acquired mutations, promotes tumor diversity and accounts for variable disease progression, therapeutic response, and clinical outcome. Recent advances in high-throughput proteogenomic profiling of tumor samples have resulted in the identification of novel oncogenic drivers, tumor suppressors, and signaling networks; biomarkers for the prediction of drug sensitivity and disease progression; and have contributed to the development of novel and more effective treatment strategies. In this review, we will focus on the impact of historical and recent advances in single platform and integrative proteogenomic studies in breast and ovarian cancer, which constitute two of the most lethal forms of cancer for women, and discuss the molecular similarities of these diseases, the impact of these findings on our understanding of tumor biology as well as the clinical applicability of these discoveries.

Personalized Network Modeling of the Pan-Cancer Patient and Cell Line Interactome

PURPOSE

Personalized network inference on diverse clinical and in vitro model systems across cancer types can be used to delineate specific regulatory mechanisms, uncover drug targets and pathways, and develop individualized predictive models in cancer.

METHODS

We developed TransPRECISE (personalized cancer-specific integrated network estimation model), a multiscale Bayesian network modeling framework, to analyze the pan-cancer patient and cell line interactome to identify differential and conserved intrapathway activities, to globally assess cell lines as representative models for patients, and to develop drug sensitivity prediction models. We assessed pan-cancer pathway activities for a large cohort of patient samples (> 7,700) from the Cancer Proteome Atlas across ≥ 30 tumor types, a set of 640 cancer cell lines from the MD Anderson Cell Lines Project spanning 16 lineages, and ≥ 250 cell lines' response to > 400 drugs.

RESULTS

TransPRECISE captured differential and conserved proteomic network topologies and pathway circuitry between multiple patient and cell line lineages: ovarian and kidney cancers shared high levels of connectivity in the hormone receptor and receptor tyrosine kinase pathways, respectively, between the two model systems. Our tumor stratification approach found distinct clinical subtypes of the patients represented by different sets of cell lines: patients with head and neck tumors were classified into two different subtypes that are represented by head and neck and esophagus cell lines and had different prognostic patterns (456 v 654 days of median overall survival; P = .02). High predictive accuracy was observed for drug sensitivities in cell lines across multiple drugs (median area under the receiver operating characteristic curve > 0.8) using Bayesian additive regression tree models with TransPRECISE pathway scores.

CONCLUSION

Our study provides a generalizable analytic framework to assess the translational potential of preclinical model systems and to guide pathway-based personalized medical decision making, integrating genomic and molecular data across model systems.

NExUS: Bayesian simultaneous network estimation across unequal sample sizes

MOTIVATION

Network-based analyses of high-throughput genomics data provide a holistic, systems-level understanding of various biological mechanisms for a common population. However, when estimating multiple networks across heterogeneous sub-populations, varying sample sizes pose a challenge in the estimation and inference, as network differences may be driven by differences in power. We are particularly interested in addressing this challenge in the context of proteomic networks for related cancers, as the number of subjects available for rare cancer (sub-)types is often limited.

RESULTS

We develop NExUS (Network Estimation across Unequal Sample sizes), a Bayesian method that enables joint learning of multiple networks while avoiding artefactual relationship between sample size and network sparsity. We demonstrate through simulations that NExUS outperforms existing network estimation methods in this context, and apply it to learn network similarity and shared pathway activity for groups of cancers with related origins represented in The Cancer Genome Atlas (TCGA) proteomic data.

AVAILABILITY AND IMPLEMENTATION

The NExUS source code is freely available for download at https://github.com/priyamdas2/NExUS.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Large-scale datasets uncovering cell signalling networks in cancer: context matters

Cell signaling pathways control the responses of cells to external perturbations. Depending on the cell's internal state, genetic background and environmental context, signaling pathways rewire to elicit the appropriate response. Such rewiring also can lead to cancer development and progression or cause resistance to therapies. While there exist static maps of annotated pathways, they do not capture these rewired networks. As large-scale datasets across multiple contexts and patients are becoming available the doors to infer and study context-specific signaling network have also opened. In this review, we will highlight the most recent approaches to study context-specific signaling networks using large-scale omics and genetic perturbation datasets, with a focus on studies of cancer and cancer-related pathways.