PathwayPCA: an R/Bioconductor Package for Pathway Based Integrative Analysis of Multi-Omics Data.

The authors present pathwayPCA, an R/Bioconductor package for integrative pathway analysis that utilizes modern statistical methodology, including supervised and adaptive, elastic-net, sparse principal component analysis. pathwayPCA can be applied to continuous, binary, and survival outcomes in studies with multiple covariates and/or interaction effects. It outperforms several alternative methods at identifying disease-associated pathways in integrative analysis using both simulated and real datasets. In addition, several case studies are provided to illustrate pathwayPCA analysis with gene selection, estimating, and visualizing sample-specific pathway activities, identifying sex-specific pathway effects in kidney cancer, and building integrative models for predicting patient prognosis. pathwayPCA is an open-source R package, freely available through the Bioconductor repository. pathwayPCA is expected to be a useful tool for empowering the wider scientific community to analyze and interpret the wealth of available proteomics data, along with other types of molecular data recently made available by Clinical Proteomic Tumor Analysis Consortium and other large consortiums.

Expansion and CD2/CD3/CD28 stimulation enhance Th2 cytokine secretion of human invariant NKT cells with retained anti-tumor cytotoxicity.

BACKGROUND AIMS: Key obstacles in human iNKT cell translational research and immunotherapy include the lack of robust protocols for dependable expansion of human iNKT cells and the paucity of data on phenotypes in post-expanded cells. METHODS: We delineate expansion methods using interleukin (IL)-2, IL-7 and allogeneic feeder cells and anti-CD2/CD3/CD28 stimulation by which to dependably augment Th2 polarization and direct cytotoxicity of human peripheral blood CD3+Vα24+Vß11+ iNKT cells. RESULTS: Gene and protein expression profiling demonstrated augmented Th2 cytokine secretion (IL-4, IL-5, IL-13) in expanded iNKT cells stimulated with anti-CD2/CD3/CD28 antibodies. Cytotoxic effector molecules including granzyme B were increased in expanded iNKT cells after CD2/CD3/CD28 stimulation. Direct cytotoxicity assays using unstimulated expanded iNKT cell effectors revealed α-galactosyl ceramide (α-GalCer)-dependent killing of the T-ALL cell line Jurkat. Moreover, CD2/CD3/CD28 stimulation of expanded iNKT cells augmented their (α-GalCer-independent) killing of Jurkat cells. Co-culture of expanded iNKT cells with stimulated responder cells confirmed contact-dependent inhibition of activated CD4+ and CD8+ responder T cells. DISCUSSION: These data establish a robust protocol to expand and novel pathways to enhance Th2 cytokine secretion and direct cytotoxicity in human iNKT cells, findings with direct implications for autoimmunity, vaccine augmentation and anti-infective immunity, cancer immunotherapy and transplantation.

Transcriptome-wide analysis of changes in the fetal placenta associated with prenatal arsenic exposure in the New Hampshire Birth Cohort Study.

BACKGROUND: Increasing evidence suggests that prenatal exposure to arsenic, even at common environmental levels, adversely affects child health. These adverse effects include impaired fetal growth, which can carry serious health implications lifelong. However, the mechanisms by which arsenic affects fetal health and development remain unclear. METHODS: We addressed this question using a group of 46 pregnant women selected from the New Hampshire Birth Cohort Study (NHBCS), a US cohort exposed to low-to-moderate arsenic levels in drinking water through the use of unregulated private wells. Prenatal arsenic exposure was assessed using maternal urine samples taken at mid-gestation. Samples of the fetal portion of the placenta were taken from the base of the umbilical cord insertion at the time of delivery, stored in RNAlater and frozen. We used RNA sequencing to analyze changes in global gene expression in the fetal placenta associated with in utero arsenic exposure, adjusting for maternal age. Gene set enrichment analysis and enrichment mapping were then used to identify biological processes represented by the differentially expressed genes. Since our previous analyses have identified considerable sex differences in placental gene expression associated with arsenic exposure, we analyzed male and female samples separately. RESULTS: At FDR < 0.05, no genes were differentially expressed in female placenta, while 606 genes were differentially expressed in males. Genes showing the most significant associations with arsenic exposure in females were LEMD1 and UPK3B (fold changes 2.51 and 2.48), and in males, FIBIN and RANBP3L (fold changes 0.14 and 0.15). In gene set enrichment analyses, at FDR < 0.05, a total of 211 gene sets were enriched with differentially expressed genes in female placenta, and 154 in male placenta. In female but not male placenta, 103 of these gene sets were also associated with reduced birth weight. CONCLUSIONS: Our results reveal multiple biological functions in the fetal placenta that are potentially affected by increased arsenic exposure, a subset of which is sex-dependent. Further, our data suggest that in female infants, the mechanisms underlying the arsenic-induced reduction of birth weight may involve activation of stress response pathways.

Targeting SMAD3 Improves Response to Oxaliplatin in Esophageal Adenocarcinoma Models by Impeding DNA Repair.

PURPOSE: TGFß signaling is implicated in the progression of most cancers, including esophageal adenocarcinoma (EAC). Emerging evidence indicates that TGFß signaling is a key factor in the development of resistance toward cancer therapy. EXPERIMENTAL DESIGN: In this study, we developed patient-derived organoids and patient-derived xenograft models of EAC and performed bioinformatics analysis combined with functional genetics to investigate the role of SMAD family member 3 (SMAD3) in EAC resistance to oxaliplatin. RESULTS: Chemotherapy nonresponding patients showed enrichment of SMAD3 gene expression when compared with responders. In a randomized patient-derived xenograft experiment, SMAD3 inhibition in combination with oxaliplatin effectively diminished tumor burden by impeding DNA repair. SMAD3 interacted directly with protein phosphatase 2A (PP2A), a key regulator of the DNA damage repair protein ataxia telangiectasia mutated (ATM). SMAD3 inhibition diminished ATM phosphorylation by enhancing the binding of PP2A to ATM, causing excessive levels of DNA damage. CONCLUSIONS: Our results identify SMAD3 as a promising therapeutic target for future combination strategies for the treatment of patients with EAC.

Tumor Cell-Intrinsic p38 MAPK Signaling Promotes IL1α-Mediated Stromal Inflammation and Therapeutic Resistance in Pancreatic Cancer.

Pancreatic ductal adenocarcinoma (PDAC) is characterized by a KRAS-driven inflammatory program and a desmoplastic stroma, which contribute to the profoundly chemoresistant phenotype. The tumor stroma contains an abundance of cancer-associated fibroblasts (CAF), which engage in extensive paracrine cross-talk with tumor cells to perpetuate protumorigenic inflammation. IL1α, a pleiotropic, tumor cell-derived cytokine, plays a critical role in shaping the stromal landscape. To provide insights into the molecular mechanisms regulating IL1A expression in PDAC, we performed transcriptional profiling of The Cancer Genome Atlas datasets and pharmacologic screening in PDAC cells and identified p38α MAPK as a key positive regulator of IL1A expression. Both genetic and pharmacologic inhibition of p38 MAPK significantly diminished IL1α production in vitro. Chromatin- and coimmunoprecipitation analyses revealed that p38 MAPK coordinates the transcription factors Sp1 and the p65 subunit of NFκB to drive IL1A overexpression. Single-cell RNA sequencing of a highly desmoplastic murine PDAC model, Ptf1aCre/+; LSL-KrasG12D/+; Tgfbr2flox/flox (PKT), confirmed that p38 MAPK inhibition significantly decreases tumor cell-derived Il1a and attenuates the inflammatory CAF phenotype in a paracrine IL1α-dependent manner. Furthermore, p38 MAPK inhibition favorably modulated intratumoral immunosuppressive myeloid populations and augmented chemotherapeutic efficacy to substantially reduce tumor burden and improve overall survival in PKT mice. These findings illustrate a cellular mechanism of tumor cell-intrinsic p38-p65/Sp1-IL1α signaling that is responsible for sustaining stromal inflammation and CAF activation, offering an attractive therapeutic approach to enhance chemosensitivity in PDAC. SIGNIFICANCE: Inhibition of p38 MAPK suppresses tumor cell-derived IL1α and attenuates the inflammatory stroma and immunosuppressive tumor microenvironment to overcome chemotherapeutic resistance in pancreatic cancer.

A Druggable UHRF1/DNMT1/GLI Complex Regulates Sonic Hedgehog-Dependent Tumor Growth.

Dysregulation of Sonic hedgehog (SHH) signaling drives the growth of distinct cancer subtypes, including medulloblastoma (MB). Such cancers have been treated in the clinic with a number of clinically relevant SHH inhibitors, the majority of which target the upstream SHH regulator, Smoothened (SMO). Despite considerable efficacy, many of these patients develop resistance to these drugs, primarily due to mutations in SMO. Therefore, it is essential to identify druggable, signaling components downstream of SMO to target in SMO inhibitor resistant cancers. We utilized an integrated functional genomics approach to identify epigenetic regulators of SHH signaling and identified a novel complex of Ubiquitin-like with PHD and RING finger domains 1 (UHRF1), DNA methyltransferase 1 (DNMT1), and GLI proteins. We show that this complex is distinct from previously described UHRF1/DNMT1 complexes, suggesting that it works in concert to regulate GLI activity in SHH driven tumors. Importantly, we show that UHRF1/DNMT1/GLI complex stability is targeted by a repurposed FDA-approved therapy, with a subsequent reduction in the growth of SHH-dependent MB ex vivo and in vivo. IMPLICATIONS: This work describes a novel, druggable UHRF1/DNMT1/GLI complex that regulates SHH-dependent tumor growth, and highlights an FDA-approved drug capable of disrupting this complex to attenuate tumor growth.

Distinct mechanisms of innate and adaptive immune regulation underlie poor oncologic outcomes associated with KRAS-TP53 co-alteration in pancreatic cancer.

Co-occurrent KRAS and TP53 mutations define a majority of patients with pancreatic ductal adenocarcinoma (PDAC) and define its pro-metastatic proclivity. Here, we demonstrate that KRAS-TP53 co-alteration is associated with worse survival compared with either KRAS-alone or TP53-alone altered PDAC in 245 patients with metastatic disease treated at a tertiary referral cancer center, and validate this observation in two independent molecularly annotated datasets. Compared with non-TP53 mutated KRAS-altered tumors, KRAS-TP53 co-alteration engenders disproportionately innate immune-enriched and CD8+ T-cell-excluded immune signatures. Leveraging in silico, in vitro, and in vivo models of human and murine PDAC, we discover a novel intersection between KRAS-TP53 co-altered transcriptomes, TP63-defined squamous trans-differentiation, and myeloid-cell migration into the tumor microenvironment. Comparison of single-cell transcriptomes between KRAS-TP53 co-altered and KRAS-altered/TP53WT tumors revealed cancer cell-autonomous transcriptional programs that orchestrate innate immune trafficking and function. Moreover, we uncover granulocyte-derived inflammasome activation and TNF signaling as putative paracrine mediators of innate immunoregulatory transcriptional programs in KRAS-TP53 co-altered PDAC. Immune subtyping of KRAS-TP53 co-altered PDAC reveals conflation of intratumor heterogeneity with progenitor-like stemness properties. Coalescing these distinct molecular characteristics into a KRAS-TP53 co-altered "immunoregulatory program" predicts chemoresistance in metastatic PDAC patients enrolled in the COMPASS trial, as well as worse overall survival.

LR Hunting: A Random Forest Based Cell-Cell Interaction Discovery Method for Single-Cell Gene Expression Data.

Cell-cell interactions (CCIs) and cell-cell communication (CCC) are critical for maintaining complex biological systems. The availability of single-cell RNA sequencing (scRNA-seq) data opens new avenues for deciphering CCIs and CCCs through identifying ligand-receptor (LR) gene interactions between cells. However, most methods were developed to examine the LR interactions of individual pairs of genes. Here, we propose a novel approach named LR hunting which first uses random forests (RFs)-based data imputation technique to link the data between different cell types. To guarantee the robustness of the data imputation procedure, we repeat the computation procedures multiple times to generate aggregated imputed minimal depth index (IMDI). Next, we identify significant LR interactions among all combinations of LR pairs simultaneously using unsupervised RFs. We demonstrated LR hunting can recover biological meaningful CCIs using a mouse cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) dataset and a triple-negative breast cancer scRNA-seq dataset.

ETV4 and ETV5 drive synovial sarcoma through cell cycle and DUX4 embryonic pathway control.

Synovial sarcoma is an aggressive malignancy with no effective treatments for patients with metastasis. The synovial sarcoma fusion SS18-SSX, which recruits the SWI/SNF-BAF chromatin remodeling and polycomb repressive complexes, results in epigenetic activation of FGF receptor (FGFR) signaling. In genetic FGFR-knockout models, culture, and xenograft synovial sarcoma models treated with the FGFR inhibitor BGJ398, we show that FGFR1, FGFR2, and FGFR3 were crucial for tumor growth. Transcriptome analyses of BGJ398-treated cells and histological and expression analyses of mouse and human synovial sarcoma tumors revealed prevalent expression of two ETS factors and FGFR targets, ETV4 and ETV5. We further demonstrate that ETV4 and ETV5 acted as drivers of synovial sarcoma growth, most likely through control of the cell cycle. Upon ETV4 and ETV5 knockdown, we observed a striking upregulation of DUX4 and its transcriptional targets that activate the zygotic genome and drive the atrophy program in facioscapulohumeral dystrophy patients. In addition to demonstrating the importance of inhibiting all three FGFRs, the current findings reveal potential nodes of attack for the cancer with the discovery of ETV4 and ETV5 as appropriate biomarkers and molecular targets, and activation of the embryonic DUX4 pathway as a promising approach to block synovial sarcoma tumors.

Mutant IDH1 Depletion Downregulates Integrins and Impairs Chondrosarcoma Growth.

Chondrosarcomas are a heterogeneous group of malignant bone tumors that produce hyaline cartilaginous matrix. Mutations in isocitrate dehydrogenase enzymes (IDH1/2) were recently described in several cancers, including conventional and dedifferentiated chondrosarcomas. These mutations lead to the inability of IDH to convert isocitrate into α-ketoglutarate (α-KG). Instead, α-KG is reduced into D-2-hydroxyglutarate (D-2HG), an oncometabolite. IDH mutations and D-2HG are thought to contribute to tumorigenesis due to the role of D-2HG as a competitive inhibitor of α-KG-dependent dioxygenases. However, the function of IDH mutations in chondrosarcomas has not been clearly defined. In this study, we knocked out mutant IDH1 (IDH1mut) in two chondrosarcoma cell lines using the CRISPR/Cas9 system. We observed that D-2HG production, anchorage-independent growth, and cell migration were significantly suppressed in the IDH1mut knockout cells. Loss of IDH1mut also led to a marked attenuation of chondrosarcoma formation and D-2HG production in a xenograft model. In addition, RNA-Seq analysis of IDH1mut knockout cells revealed downregulation of several integrin genes, including those of integrin alpha 5 (ITGA5) and integrin beta 5 (ITGB5). We further demonstrated that deregulation of integrin-mediated processes contributed to the tumorigenicity of IDH1-mutant chondrosarcoma cells. Our findings showed that IDH1mut knockout abrogates chondrosarcoma genesis through modulation of integrins. This suggests that integrin molecules are appealing candidates for combinatorial regimens with IDH1mut inhibitors for chondrosarcomas that harbor this mutation.

Model reduction and parameter estimation of non-linear dynamical biochemical reaction networks.

Parameter estimation for high dimension complex dynamic system is a hot topic. However, the current statistical model and inference approach is known as a large p small n problem. How to reduce the dimension of the dynamic model and improve the accuracy of estimation is more important. To address this question, the authors take some known parameters and structure of system as priori knowledge and incorporate it into dynamic model. At the same time, they decompose the whole dynamic model into subset network modules, based on different modules, and then they apply different estimation approaches. This technique is called Rao-Blackwellised particle filters decomposition methods. To evaluate the performance of this method, the authors apply it to synthetic data generated from repressilator model and experimental data of the JAK-STAT pathway, but this method can be easily extended to large-scale cases.

Controllability and stability analysis of large transcriptomic dynamic systems for host response to influenza infection in human.

BACKGROUND: Gene regulatory networks are complex dynamic systems and the reverse-engineering of such networks from high-dimensional time course transcriptomic data have attracted researchers from various fields. It is also interesting and important to study the behavior of the reconstructed networks on the basis of dynamic models and the biological mechanisms. We focus on the gene regulatory networks reconstructed using the ordinary differential equation (ODE) modelling approach and investigate the properties of these networks. RESULTS: Controllability and stability analyses are conducted for the reconstructed gene response networks of 17 influenza infected subjects based on ODE models. Symptomatic subjects tend to have larger numbers of driver nodes, higher proportions of critical links and lower proportions of redundant links than asymptomatic subjects. We also show that the degree distribution, rather than the structure of networks, plays an important role in controlling the network in response to influenza infection. In addition, we find that the stability of high-dimensional networks is very sensitive to randomness in the reconstructed systems brought by errors in measurements and parameter estimation. CONCLUSIONS: The gene response networks of asymptomatic subjects are easier to be controlled than those of symptomatic subjects. This may indicate that the regulatory systems of asymptomatic subjects are easier to recover from disease stimulations, so these subjects are less likely to develop symptoms. Our results also suggest that stability constraint should be considered in the modelling of high-dimensional networks and the estimation of network parameters.

Extended kalman filter for estimation of parameters in nonlinear state-space models of biochemical networks.

It is system dynamics that determines the function of cells, tissues and organisms. To develop mathematical models and estimate their parameters are an essential issue for studying dynamic behaviors of biological systems which include metabolic networks, genetic regulatory networks and signal transduction pathways, under perturbation of external stimuli. In general, biological dynamic systems are partially observed. Therefore, a natural way to model dynamic biological systems is to employ nonlinear state-space equations. Although statistical methods for parameter estimation of linear models in biological dynamic systems have been developed intensively in the recent years, the estimation of both states and parameters of nonlinear dynamic systems remains a challenging task. In this report, we apply extended Kalman Filter (EKF) to the estimation of both states and parameters of nonlinear state-space models. To evaluate the performance of the EKF for parameter estimation, we apply the EKF to a simulation dataset and two real datasets: JAK-STAT signal transduction pathway and Ras/Raf/MEK/ERK signaling transduction pathways datasets. The preliminary results show that EKF can accurately estimate the parameters and predict states in nonlinear state-space equations for modeling dynamic biochemical networks.