A randomized phase 2 trial of idiotype vaccination and adoptive autologous T-cell transfer in patients with multiple myeloma.

We hypothesized that combining adoptively transferred autologous T cells with a cancer vaccine strategy would enhance therapeutic efficacy by adding antimyeloma idiotype (Id)-keyhole limpet hemocyanin (KLH) vaccine to vaccine-specific costimulated T cells. In this randomized phase 2 trial, patients received either control (KLH only) or Id-KLH vaccine, autologous transplantation, vaccine-specific costimulated T cells expanded ex vivo, and 2 booster doses of assigned vaccine. In 36 patients (KLH, n = 20; Id-KLH, n = 16), no dose-limiting toxicity was seen. At last evaluation, 6 (30%) and 8 patients (50%) had achieved complete remission in KLH-only and Id-KLH arms, respectively (P = .22), and no difference in 3-year progression-free survival was observed (59% and 56%, respectively; P = .32). In a 594 Nanostring nCounter gene panel analyzed for immune reconstitution (IR), compared with patients receiving KLH only, there was a greater change in IR genes in T cells in those receiving Id-KLH relative to baseline. Specifically, upregulation of genes associated with activation, effector function induction, and memory CD8+ T-cell generation after Id-KLH but not after KLH control vaccination was observed. Similarly, in responding patients across both arms, upregulation of genes associated with T-cell activation was seen. At baseline, all patients had greater expression of CD8+ T-cell exhaustion markers. These changes were associated with functional Id-specific immune responses in a subset of patients receiving Id-KLH. In conclusion, in this combination immunotherapy approach, we observed significantly more robust IR in CD4+ and CD8+ T cells in the Id-KLH arm, supporting further investigation of vaccine and adoptive immunotherapy strategies. This trial was registered at www.clinicaltrials.gov as #NCT01426828.

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.

Integrative Bayesian models using Post-selective inference: A case study in radiogenomics.

Integrative analyses based on statistically relevant associations between genomics and a wealth of intermediary phenotypes (such as imaging) provide vital insights into their clinical relevance in terms of the disease mechanisms. Estimates for uncertainty in the resulting integrative models are however unreliable unless inference accounts for the selection of these associations with accuracy. In this paper, we develop selection-aware Bayesian methods, which (1) counteract the impact of model selection bias through a "selection-aware posterior" in a flexible class of integrative Bayesian models post a selection of promising variables via ℓ1 -regularized algorithms; (2) strike an inevitable trade-off between the quality of model selection and inferential power when the same data set is used for both selection and uncertainty estimation. Central to our methodological development, a carefully constructed conditional likelihood function deployed with a reparameterization mapping provides tractable updates when gradient-based Markov chain Monte Carlo (MCMC) sampling is used for estimating uncertainties from the selection-aware posterior. Applying our methods to a radiogenomic analysis, we successfully recover several important gene pathways and estimate uncertainties for their associations with patient survival times.

Bayesian hierarchical quantile regression with application to characterizing the immune architecture of lung cancer.

The successful development and implementation of precision immuno-oncology therapies requires a deeper understanding of the immune architecture at a patient level. T-cell receptor (TCR) repertoire sequencing is a relatively new technology that enables monitoring of T-cells, a subset of immune cells that play a central role in modulating immune response. These immunologic relationships are complex and are governed by various distributional aspects of an individual patient's tumor profile. We propose Bayesian QUANTIle regression for hierarchical COvariates (QUANTICO) that allows simultaneous modeling of hierarchical relationships between multilevel covariates, conducts explicit variable selection, estimates quantile and patient-specific coefficient effects, to induce individualized inference. We show QUANTICO outperforms existing approaches in multiple simulation scenarios. We demonstrate the utility of QUANTICO to investigate the effect of TCR variables on immune response in a cohort of lung cancer patients. At population level, our analyses reveal the mechanistic role of T-cell proportion on the immune cell abundance, with tumor mutation burden as an important factor modulating this relationship. At a patient level, we find several outlier patients based on their quantile-specific coefficient functions, who have higher mutational rates and different smoking history.

NetCellMatch: Multiscale Network-Based Matching of Cancer Cell Lines to Patients Using Graphical Wavelets.

Cancer cell lines serve as model in vitro systems for investigating therapeutic interventions. Recent advances in high-throughput genomic profiling have enabled the systematic comparison between cell lines and patient tumor samples. The highly interconnected nature of biological data, however, presents a challenge when mapping patient tumors to cell lines. Standard clustering methods can be particularly susceptible to the high level of noise present in these datasets and only output clusters at one unknown scale of the data. In light of these challenges, we present NetCellMatch, a robust framework for network-based matching of cell lines to patient tumors. NetCellMatch first constructs a global network across all cell line-patient samples using their genomic similarity. Then, a multi-scale community detection algorithm integrates information across topologically meaningful (clustering) scales to obtain Network-Based Matching Scores (NBMS). NBMS are measures of cluster robustness which map patient tumors to cell lines. We use NBMS to determine representative "avatar" cell lines for subgroups of patients. We apply NetCellMatch to reverse-phase protein array data obtained from The Cancer Genome Atlas for patients and the MD Anderson Cell Line Project for cell lines. Along with avatar cell line identification, we evaluate connectivity patterns for breast, lung, and colon cancer and explore the proteomic profiles of avatars and their corresponding top matching patients. Our results demonstrate our framework's ability to identify both patient-cell line matches and potential proteomic drivers of similarity. Our methods are general and can be easily adapted to other'omic datasets.

Bayesian graphical models for modern biological applications.

Graphical models are powerful tools that are regularly used to investigate complex dependence structures in high-throughput biomedical datasets. They allow for holistic, systems-level view of the various biological processes, for intuitive and rigorous understanding and interpretations. In the context of large networks, Bayesian approaches are particularly suitable because it encourages sparsity of the graphs, incorporate prior information, and most importantly account for uncertainty in the graph structure. These features are particularly important in applications with limited sample size, including genomics and imaging studies. In this paper, we review several recently developed techniques for the analysis of large networks under non-standard settings, including but not limited to, multiple graphs for data observed from multiple related subgroups, graphical regression approaches used for the analysis of networks that change with covariates, and other complex sampling and structural settings. We also illustrate the practical utility of some of these methods using examples in cancer genomics and neuroimaging.

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.

Bayesian data integration and variable selection for pan-cancer survival prediction using protein expression data.

Accurate prognostic prediction using molecular information is a challenging area of research, which is essential to develop precision medicine. In this paper, we develop translational models to identify major actionable proteins that are associated with clinical outcomes, like the survival time of patients. There are considerable statistical and computational challenges due to the large dimension of the problems. Furthermore, data are available for different tumor types; hence data integration for various tumors is desirable. Having censored survival outcomes escalates one more level of complexity in the inferential procedure. We develop Bayesian hierarchical survival models, which accommodate all the challenges mentioned here. We use the hierarchical Bayesian accelerated failure time model for survival regression. Furthermore, we assume sparse horseshoe prior distribution for the regression coefficients to identify the major proteomic drivers. We borrow strength across tumor groups by introducing a correlation structure among the prior distributions. The proposed methods have been used to analyze data from the recently curated "The Cancer Proteome Atlas" (TCPA), which contains reverse-phase protein arrays-based high-quality protein expression data as well as detailed clinical annotation, including survival times. Our simulation and the TCPA data analysis illustrate the efficacy of the proposed integrative model, which links different tumors with the correlated prior structures.

iDINGO-integrative differential network analysis in genomics with Shiny application.

Motivation: Differential network analysis is an important way to understand network rewiring involved in disease progression and development. Building differential networks from multiple 'omics data provides insight into the holistic differences of the interactive system under different patient-specific groups. DINGO was developed to infer group-specific dependencies and build differential networks. However, DINGO and other existing tools are limited to analyze data arising from a single platform, and modeling each of the multiple 'omics data independently does not account for the hierarchical structure of the data. Results: We developed the iDINGO R package to estimate group-specific dependencies and make inferences on the integrative differential networks, considering the biological hierarchy among the platforms. A Shiny application has also been developed to facilitate easier analysis and visualization of results, including integrative differential networks and hub gene identification across platforms. Availability and implementation: R package is available on CRAN (https://cran.r-project.org/web/packages/iDINGO) and Shiny application at https://github.com/MinJinHa/iDINGO. Contact: mjha@mdanderson.org. Supplementary information: Supplementary data are available at Bioinformatics online.

Spectral Clustering via sparse graph structure learning with application to Proteomic Signaling Networks in Cancer.

Clustering methods for multivariate data exploiting the underlying geometry of the graphical structure between variables are presented. As opposed to standard approaches for graph clustering that assume known graph structures, the edge structure of the unknown graph is first estimated using sparse regression based approaches for sparse graph structure learning. Subsequently, graph clustering on the lower dimensional projections of the graph is performed based on Laplacian embeddings using a penalized k-means approach, motivated by Dirichlet process mixture models in Bayesian nonparametrics. In contrast to standard algorithmic approaches for known graphs, the proposed method allows estimation and inference for both graph structure learning and clustering. More importantly, the arguments for Laplacian embeddings as suitable projections for graph clustering are formalized by providing theoretical support for the consistency of the eigenspace of the estimated graph Laplacians. Fast computational algorithms are proposed to scale the method to large number of nodes. Extensive simulations are presented to compare the clustering performance with standard methods. The methods are applied to a novel pan-cancer proteomic data set, and protein networks and clusters are evaluated across multiple different cancer types.

Inferring network structure in non-normal and mixed discrete-continuous genomic data.

Inferring dependence structure through undirected graphs is crucial for uncovering the major modes of multivariate interaction among high-dimensional genomic markers that are potentially associated with cancer. Traditionally, conditional independence has been studied using sparse Gaussian graphical models for continuous data and sparse Ising models for discrete data. However, there are two clear situations when these approaches are inadequate. The first occurs when the data are continuous but display non-normal marginal behavior such as heavy tails or skewness, rendering an assumption of normality inappropriate. The second occurs when a part of the data is ordinal or discrete (e.g., presence or absence of a mutation) and the other part is continuous (e.g., expression levels of genes or proteins). In this case, the existing Bayesian approaches typically employ a latent variable framework for the discrete part that precludes inferring conditional independence among the data that are actually observed. The current article overcomes these two challenges in a unified framework using Gaussian scale mixtures. Our framework is able to handle continuous data that are not normal and data that are of mixed continuous and discrete nature, while still being able to infer a sparse conditional sign independence structure among the observed data. Extensive performance comparison in simulations with alternative techniques and an analysis of a real cancer genomics data set demonstrate the effectiveness of the proposed approach.

Functional interaction-based nonlinear models with application to multiplatform genomics data.

Functional regression allows for a scalar response to be dependent on a functional predictor; however, not much work has been done when a scalar exposure that interacts with the functional covariate is introduced. In this paper, we present 2 functional regression models that account for this interaction and propose 2 novel estimation procedures for the parameters in these models. These estimation methods allow for a noisy and/or sparsely observed functional covariate and are easily extended to generalized exponential family responses. We compute standard errors of our estimators, which allows for further statistical inference and hypothesis testing. We compare the performance of the proposed estimators to each other and to one found in the literature via simulation and demonstrate our methods using a real data example.

Nuclear expression of Gli-1 is predictive of pathologic complete response to chemoradiation in trimodality treated oesophageal cancer patients.

BACKGROUND: Predictive biomarkers or signature(s) for oesophageal cancer (OC) patients undergoing preoperative therapy could help administration of effective therapy, avoidance of ineffective ones, and establishment new strategies. Since the hedgehog pathway is often upregulated in OC, we examined its transcriptional factor, Gli-1, which confers therapy resistance, we wanted to assess Gli-1 as a predictive biomarker for chemoradiation response and validate it. METHODS: Untreated OC tissues from patients who underwent chemoradiation and surgery were assessed for nuclear Gli-1 by immunohistochemistry and labelling indices (LIs) were correlated with pathologic complete response (pathCR) or

RNA Polymerase I Inhibition with CX-5461 as a Novel Therapeutic Strategy to Target MYC in Multiple Myeloma.

Dysregulation of MYC is frequently implicated in both early and late myeloma progression events, yet its therapeutic targeting has remained a challenge. Among key MYC downstream targets is ribosomal biogenesis, enabling increases in protein translational capacity necessary to support the growth and self-renewal programmes of malignant cells. We therefore explored the selective targeting of ribosomal biogenesis with the small molecule RNA polymerase (pol) I inhibitor CX-5461 in myeloma. CX-5461 induced significant growth inhibition in wild-type (WT) and mutant TP53 myeloma cell lines and primary samples, in association with increases in downstream markers of apoptosis. Moreover, Pol I inhibition overcame adhesion-mediated drug resistance and resistance to conventional and novel agents. To probe the TP53-independent mechanisms of CX-5461, gene expression profiling was performed on isogenic TP53 WT and knockout cell lines and revealed reduction of MYC downstream targets. Mechanistic studies confirmed that CX-5461 rapidly suppressed both MYC protein and MYC mRNA levels. The latter was associated with an increased binding of the RNA-induced silencing complex (RISC) subunits TARBP2 and AGO2, the ribosomal protein RPL5, and MYC mRNA, resulting in increased MYC transcript degradation. Collectively, these studies provide a rationale for the clinical translation of CX-5461 as a novel therapeutic approach to target MYC in myeloma.

Phase II study of the c-MET inhibitor tivantinib (ARQ 197) in patients with relapsed or relapsed/refractory multiple myeloma.

The hepatocyte growth factor/c-MET pathway has been implicated in the pathobiology of multiple myeloma, and c-MET inhibitors induce myeloma cell apoptosis, suggesting that they could be useful clinically. We conducted a phase II study with the c-MET inhibitor tivantinib in patients with relapsed, or relapsed and refractory myeloma whose disease had progressed after one to four prior therapies. Tivantinib, 360 mg orally per dose, was administered twice daily continuously over a 4-week treatment cycle without a cap on the number of allowed cycles, barring undue toxicities or disease progression. Primary objectives were to determine the overall response rate and the toxicities of tivantinib in this patient population. Sixteen patients were enrolled in a two-stage design. Notable grade 3 and 4 hematological adverse events were limited to neutropenia in five and four patients, respectively. Nonhematological adverse events of grade 3 or higher included hypertension (in four patients); syncope, infection, and pain (two each); and fatigue, cough, and pulmonary embolism (one each). Four of 11 evaluable patients (36%) had stable disease as their best response, while the remainder showed disease progression. Overall, tivantinib as a single agent did not show promise for unselected relapsed/refractory myeloma patients. However, the ability to achieve stable disease does suggest that combination regimens incorporating targeted inhibitors in patients with c-MET pathway activation could be of interest.

Statistical Contributions to Bioinformatics: Design, Modeling, Structure Learning, and Integration.

The advent of high-throughput multi-platform genomics technologies providing whole-genome molecular summaries of biological samples has revolutionalized biomedical research. These technologiees yield highly structured big data, whose analysis poses significant quantitative challenges. The field of Bioinformatics has emerged to deal with these challenges, and is comprised of many quantitative and biological scientists working together to effectively process these data and extract the treasure trove of information they contain. Statisticians, with their deep understanding of variability and uncertainty quantification, play a key role in these efforts. In this article, we attempt to summarize some of the key contributions of statisticians to bioinformatics, focusing on four areas: (1) experimental design and reproducibility, (2) preprocessing and feature extraction, (3) unified modeling, and (4) structure learning and integration. In each of these areas, we highlight some key contributions and try to elucidate the key statistical principles underlying these methods and approaches. Our goals are to demonstrate major ways in which statisticians have contributed to bioinformatics, encourage statisticians to get involved early in methods development as new technologies emerge, and to stimulate future methodological work based on the statistical principles elucidated in this article and utilizing all availble information to uncover new biological insights.

Rejoinder to statistical contributions to bioinformatics: Design, modelling, structure learning and Integration.

We thank the discussants for their kind comments and their insightful analysis and discussion that has substantially added to the contribution of this issue. Overall, it seems the discussants have affirmed many of our primary points, and have also raised a number of other relevant and important issues that we did not emphasize in the paper. Several common threads emerged from these discussions, including the importance of software development, appropriate dissemination, and close collaboration with biomedical scientists and technology experts in order to ensure our work is relevant and impactful. Each discussant also mentioned other areas of bioinformatics that have been impacted by statistical researchers that we did not highlight in the original paper. In response, we will first summarize discuss these general themes, and then respond to specific comments of each discussant, and finally talk about the additional areas of bioinformatics impacted by statisticians that were mentioned by the reviewers.

Mir-21-Sox2 Axis Delineates Glioblastoma Subtypes with Prognostic Impact.

Glioblastoma (GBM) is the most aggressive human brain tumor. Although several molecular subtypes of GBM are recognized, a robust molecular prognostic marker has yet to be identified. Here, we report that the stemness regulator Sox2 is a new, clinically important target of microRNA-21 (miR-21) in GBM, with implications for prognosis. Using the MiR-21-Sox2 regulatory axis, approximately half of all GBM tumors present in the Cancer Genome Atlas (TCGA) and in-house patient databases can be mathematically classified into high miR-21/low Sox2 (Class A) or low miR-21/high Sox2 (Class B) subtypes. This classification reflects phenotypically and molecularly distinct characteristics and is not captured by existing classifications. Supporting the distinct nature of the subtypes, gene set enrichment analysis of the TCGA dataset predicted that Class A and Class B tumors were significantly involved in immune/inflammatory response and in chromosome organization and nervous system development, respectively. Patients with Class B tumors had longer overall survival than those with Class A tumors. Analysis of both databases indicated that the Class A/Class B classification is a better predictor of patient survival than currently used parameters. Further, manipulation of MiR-21-Sox2 levels in orthotopic mouse models supported the longer survival of the Class B subtype. The MiR-21-Sox2 association was also found in mouse neural stem cells and in the mouse brain at different developmental stages, suggesting a role in normal development. Therefore, this mechanism-based classification suggests the presence of two distinct populations of GBM patients with distinguishable phenotypic characteristics and clinical outcomes. SIGNIFICANCE STATEMENT: Molecular profiling-based classification of glioblastoma (GBM) into four subtypes has substantially increased our understanding of the biology of the disease and has pointed to the heterogeneous nature of GBM. However, this classification is not mechanism based and its prognostic value is limited. Here, we identify a new mechanism in GBM (the miR-21-Sox2 axis) that can classify ∼50% of patients into two subtypes with distinct molecular, radiological, and pathological characteristics. Importantly, this classification can predict patient survival better than the currently used parameters. Further, analysis of the miR-21-Sox2 relationship in mouse neural stem cells and in the mouse brain at different developmental stages indicates that miR-21 and Sox2 are predominantly expressed in mutually exclusive patterns, suggesting a role in normal neural development.

Integrative Bayesian analysis of neuroimaging-genetic data with application to cocaine dependence.

Neuroimaging and genetic studies provide distinct and complementary information about the structural and biological aspects of a disease. Integrating the two sources of data facilitates the investigation of the links between genetic variability and brain mechanisms among different individuals for various medical disorders. This article presents a general statistical framework for integrative Bayesian analysis of neuroimaging-genetic (iBANG) data, which is motivated by a neuroimaging-genetic study in cocaine dependence. Statistical inference necessitated the integration of spatially dependent voxel-level measurements with various patient-level genetic and demographic characteristics under an appropriate probability model to account for the multiple inherent sources of variation. Our framework uses Bayesian model averaging to integrate genetic information into the analysis of voxel-wise neuroimaging data, accounting for spatial correlations in the voxels. Using multiplicity controls based on the false discovery rate, we delineate voxels associated with genetic and demographic features that may impact diffusion as measured by fractional anisotropy (FA) obtained from DTI images. We demonstrate the benefits of accounting for model uncertainties in both model fit and prediction. Our results suggest that cocaine consumption is associated with FA reduction in most white matter regions of interest in the brain. Additionally, gene polymorphisms associated with GABAergic, serotonergic and dopaminergic neurotransmitters and receptors were associated with FA.

Outcome of Patients with Multiple Myeloma and CKS1B Gene Amplification after Autologous Hematopoietic Stem Cell Transplantation.

The gain/amplification of the CKS1B gene on chromosome 1q21 region is associated with a poor outcome in patients with multiple myeloma (MM). However, there are limited data on the outcome of patients with CKS1B amplification after a single high-dose chemotherapy and autologous hematopoietic stem cell transplantation (auto-HCT). We retrospectively evaluated the outcome of patients with CKS1B amplification who received an auto-HCT between June 2012 and July 2014 at our institution. We identified 58 patients with MM and CKS1B gene amplification detected by fluorescent in situ hybridization (FISH). We compared their outcomes with a propensity score-matched control group of 58 patients without CKS1B amplification who were treated at approximately the same time. The primary objective was to compare the progression-free (PFS) and overall survival (OS) between the CKS1B and the control groups. Stratified log-rank test with the matched pairs as strata and double robust estimation under the Cox model were used to assess the effect of CKS1B gene amplification on PFS or OS in the matched cohort. Patients in the CKS1B and control groups were well matched for age, gender, disease status, year of auto-HCT, response to pretransplantation therapy, and baseline hemoglobin level. In both groups, 57% patients were in first remission and 43% had relapsed disease at auto-HCT. Twenty-seven (47%) patients with CKS1B amplification had concurrent monosomy 13 or 13q deletion; 6 (10%) by conventional cytogenetics only, 16 (28%) by FISH only, and 5 (9%) by both. Median follow-up after auto-HCT was 25.4 months. The median PFS of the CKS1B and the control groups were 15.0 months and 33.0 months (P = .002), respectively. The median OS have not been reached yet. The 2-year OS rates in the CKS1B and the control groups were 62% and 91% (P = .02), respectively. In conclusion, Patients with CKS1B amplification are more likely to have additional high-risk cytogenetic abnormalities and a shorter PFS and OS after an auto-HCT.