Cell type identification in spatial transcriptomics data can be improved by leveraging cell-type-informative paired tissue images using a Bayesian probabilistic model.

Spatial transcriptomics technologies have recently emerged as a powerful tool for measuring spatially resolved gene expression directly in tissues sections, revealing cell types and their dysfunction in unprecedented detail. However, spatial transcriptomics technologies are limited in their ability to separate transcriptionally similar cell types and can suffer further difficulties identifying cell types in slide regions where transcript capture is low. Here, we describe a conceptually novel methodology that can computationally integrate spatial transcriptomics data with cell-type-informative paired tissue images, obtained from, for example, the reverse side of the same tissue section, to improve inferences of tissue cell type composition in spatial transcriptomics data. The underlying statistical approach is generalizable to any spatial transcriptomics protocol where informative paired tissue images can be obtained. We demonstrate a use case leveraging cell-type-specific immunofluorescence markers obtained on mouse brain tissue sections and a use case for leveraging the output of AI annotated H&E tissue images, which we used to markedly improve the identification of clinically relevant immune cell infiltration in breast cancer tissue. Thus, combining spatial transcriptomics data with paired tissue images has the potential to improve the identification of cell types and hence to improve the applications of spatial transcriptomics that rely on accurate cell type identification.

Random-effects meta-analysis of effect sizes as a unified framework for gene set analysis.

Gene set analysis (GSA) remains a common step in genome-scale studies because it can reveal insights that are not apparent from results obtained for individual genes. Many different computational tools are applied for GSA, which may be sensitive to different types of signals; however, most methods implicitly test whether there are differences in the distribution of the effect of some experimental condition between genes in gene sets of interest. We have developed a unifying framework for GSA that first fits effect size distributions, and then tests for differences in these distributions between gene sets. These differences can be in the proportions of genes that are perturbed or in the sign or size of the effects. Inspired by statistical meta-analysis, we take into account the uncertainty in effect size estimates by reducing the influence of genes with greater uncertainty on the estimation of distribution parameters. We demonstrate, using simulation and by application to real data, that this approach provides significant gains in performance over existing methods. Furthermore, the statistical tests carried out are defined in terms of effect sizes, rather than the results of prior statistical tests measuring these changes, which leads to improved interpretability and greater robustness to variation in sample sizes.

Long non-coding RNA transcriptome of uncharacterized samples can be accurately imputed using protein-coding genes.

Long non-coding RNAs (lncRNAs) play an important role in gene regulation and are increasingly being recognized as crucial mediators of disease pathogenesis. However, the vast majority of published transcriptome datasets lack high-quality lncRNA profiles compared to protein-coding genes (PCGs). Here we propose a framework to harnesses the correlative expression patterns between lncRNA and PCGs to impute unknown lncRNA profiles. The lncRNA expression imputation (LEXI) framework enables characterization of lncRNA transcriptome of samples lacking any lncRNA data using only their PCG profiles. We compare various machine learning and missing value imputation algorithms to implement LEXI and demonstrate the feasibility of this approach to impute lncRNA transcriptome of normal and cancer tissues. Additionally, we determine the factors that influence imputation accuracy and provide guidelines for implementing this approach.

Discovering long noncoding RNA predictors of anticancer drug sensitivity beyond protein-coding genes.

Large-scale cancer cell line screens have identified thousands of protein-coding genes (PCGs) as biomarkers of anticancer drug response. However, systematic evaluation of long noncoding RNAs (lncRNAs) as pharmacogenomic biomarkers has so far proven challenging. Here, we study the contribution of lncRNAs as drug response predictors beyond spurious associations driven by correlations with proximal PCGs, tissue lineage, or established biomarkers. We show that, as a whole, the lncRNA transcriptome is equally potent as the PCG transcriptome at predicting response to hundreds of anticancer drugs. Analysis of individual lncRNAs transcripts associated with drug response reveals nearly half of the significant associations are in fact attributable to proximal cis-PCGs. However, adjusting for effects of cis-PCGs revealed significant lncRNAs that augment drug response predictions for most drugs, including those with well-established clinical biomarkers. In addition, we identify lncRNA-specific somatic alterations associated with drug response by adopting a statistical approach to determine lncRNAs carrying somatic mutations that undergo positive selection in cancer cells. Lastly, we experimentally demonstrate that 2 lncRNAs, EGFR-AS1 and MIR205HG, are functionally relevant predictors of anti-epidermal growth factor receptor (EGFR) drug response.

Discovering novel pharmacogenomic biomarkers by imputing drug response in cancer patients from large genomics studies.

Obtaining accurate drug response data in large cohorts of cancer patients is very challenging; thus, most cancer pharmacogenomics discovery is conducted in preclinical studies, typically using cell lines and mouse models. However, these platforms suffer from serious limitations, including small sample sizes. Here, we have developed a novel computational method that allows us to impute drug response in very large clinical cancer genomics data sets, such as The Cancer Genome Atlas (TCGA). The approach works by creating statistical models relating gene expression to drug response in large panels of cancer cell lines and applying these models to tumor gene expression data in the clinical data sets (e.g., TCGA). This yields an imputed drug response for every drug in each patient. These imputed drug response data are then associated with somatic genetic variants measured in the clinical cohort, such as copy number changes or mutations in protein coding genes. These analyses recapitulated drug associations for known clinically actionable somatic genetic alterations and identified new predictive biomarkers for existing drugs.

Seq-ing improved gene expression estimates from microarrays using machine learning.

BACKGROUND: Quantifying gene expression by RNA-Seq has several advantages over microarrays, including greater dynamic range and gene expression estimates on an absolute, rather than a relative scale. Nevertheless, microarrays remain in widespread use, demonstrated by the ever-growing numbers of samples deposited in public repositories. RESULTS: We propose a novel approach to microarray analysis that attains many of the advantages of RNA-Seq. This method, called Machine Learning of Transcript Expression (MaLTE), leverages samples for which both microarray and RNA-Seq data are available, using a Random Forest to learn the relationship between the fluorescence intensity of sets of microarray probes and RNA-Seq transcript expression estimates. We trained MaLTE on data from the Genotype-Tissue Expression (GTEx) project, consisting of Affymetrix gene arrays and RNA-Seq from over 700 samples across a broad range of human tissues. CONCLUSION: This approach can be used to accurately estimate absolute expression levels from microarray data, at both gene and transcript level, which has not previously been possible. This methodology will facilitate re-analysis of archived microarray data and broaden the utility of the vast quantities of data still being generated.

Genome-wide interrogation of longitudinal FEV1 in children with asthma.

RATIONALE: Most genomic studies of lung function have used phenotypic data derived from a single time-point (e.g., presence/absence of disease) without considering the dynamic progression of a chronic disease. OBJECTIVES: To characterize lung function change over time in subjects with asthma and identify genetic contributors to a longitudinal phenotype. METHODS: We present a method that models longitudinal FEV1 data, collected from 1,041 children with asthma who participated in the Childhood Asthma Management Program. This longitudinal progression model was built using population-based nonlinear mixed-effects modeling with an exponential structure and the determinants of age and height. MEASUREMENTS AND MAIN RESULTS: We found ethnicity was a key covariate for FEV1 level. Budesonide-treated children with asthma had a slight but significant effect on FEV1 when compared with those treated with placebo or nedocromil (P < 0.001). A genome-wide association study identified seven single-nucleotide polymorphisms nominally associated with longitudinal lung function phenotypes in 581 white Childhood Asthma Management Program subjects (P < 10(-4) in the placebo ["discovery"] and P < 0.05 in the nedocromil treatment ["replication"] group). Using ChIP-seq and RNA-seq data, we found that some of the associated variants were in strong enhancer regions in human lung fibroblasts and may affect gene expression in human lung tissue. Genetic mapping restricted to genome-wide enhancer single-nucleotide polymorphisms in lung fibroblasts revealed a highly significant variant (rs6763931; P = 4 × 10(-6); false discovery rate < 0.05). CONCLUSIONS: This study offers a strategy to explore the genetic determinants of longitudinal phenotypes, provide a comprehensive picture of disease pathophysiology, and suggest potential treatment targets.

Integrative analyses of genetic variation, epigenetic regulation, and the transcriptome to elucidate the biology of platinum sensitivity.

BACKGROUND: Using genome-wide genetic, gene expression, and microRNA expression (miRNA) data, we developed an integrative approach to investigate the genetic and epigenetic basis of chemotherapeutic sensitivity. RESULTS: Through a sequential multi-stage framework, we identified genes and miRNAs whose expression correlated with platinum sensitivity, mapped these to genomic loci as quantitative trait loci (QTLs), and evaluated the associations between these QTLs and platinum sensitivity. A permutation analysis showed that top findings from our approach have a much lower false discovery rate compared to those from a traditional GWAS of drug sensitivity. Our approach identified five SNPs associated with 10 miRNAs and the expression level of 15 genes, all of which were associated with carboplatin sensitivity. Of particular interest was one SNP (rs11138019), which was associated with the expression of both miR-30d and the gene ABCD2, which were themselves correlated with both carboplatin and cisplatin drug-specific phenotype in the HapMap samples. Functional study found that knocking down ABCD2 in vitro led to increased apoptosis in ovarian cancer cell line SKOV3 after cisplatin treatment. Over-expression of miR-30d in vitro caused a decrease in ABCD2 expression, suggesting a functional relationship between the two. CONCLUSIONS: We developed an integrative approach to the investigation of the genetic and epigenetic basis of human complex traits. Our approach outperformed standard GWAS and provided hints at potential biological function. The relationships between ABCD2 and miR-30d, and ABCD2 and platin sensitivity were experimentally validated, suggesting a functional role of ABCD2 and miR-30d in sensitivity to platinating agents.

The impact of microRNA expression on cellular proliferation.

As an important class of non-coding regulatory RNAs, microRNAs (miRNAs) play a key role in a range of biological processes. These molecules serve as post-transcriptional regulators of gene expression and their regulatory activity has been implicated in disease pathophysiology and pharmacological traits. We sought to investigate the impact of miRNAs on cellular proliferation to gain insight into the molecular basis of complex traits that depend on cellular growth, including, most prominently, cancer. We examined the relationship between miRNA expression and intrinsic cellular growth (iGrowth) in the HapMap lymphoblastoid cell lines derived from individuals of different ethnic backgrounds. We found a substantial enrichment for miRNAs (53 miRNAs, FDR < 0.05) correlated with cellular proliferation in pooled CEU (Caucasian of northern and western European descent) and YRI (individuals from Ibadan, Nigeria) samples. Specifically, 119 miRNAs (59 %) were significantly correlated with iGrowth in YRI; of these miRNAs, 18 were correlated with iGrowth in CEU. To gain further insight into the effect of miRNAs on cellular proliferation in cancer, we showed that over-expression of miR-22, one of the top iGrowth-associated miRNAs, leads to growth inhibition in an ovarian cancer cell line (SKOV3). Furthermore, over-expression of miR-22 down-regulates the expression of its target genes (MXI1 and SLC25A37) in this ovarian cancer cell line, highlighting an miRNA-mediated regulatory network potentially important for cellular proliferation. Importantly, our study identified miRNAs that can be used as molecular targets in cancer therapy.

Gene-set analysis is severely biased when applied to genome-wide methylation data.

MOTIVATION: DNA methylation is an epigenetic mark that can stably repress gene expression. Because of its biological and clinical significance, several methods have been developed to compare genome-wide patterns of methylation between groups of samples. The application of gene set analysis to identify relevant groups of genes that are enriched for differentially methylated genes is often a major component of the analysis of these data. This can be used, for example, to identify processes or pathways that are perturbed in disease development. We show that gene-set analysis, as it is typically applied to genome-wide methylation assays, is severely biased as a result of differences in the numbers of CpG sites associated with different classes of genes and gene promoters. RESULTS: We demonstrate this bias using published data from a study of differential CpG island methylation in lung cancer and a dataset we generated to study methylation changes in patients with long-standing ulcerative colitis. We show that several of the gene sets that seem enriched would also be identified with randomized data. We suggest two existing approaches that can be adapted to correct the bias. Accounting for the bias in the lung cancer and ulcerative colitis datasets provides novel biological insights into the role of methylation in cancer development and chronic inflammation, respectively. Our results have significant implications for many previous genome-wide methylation studies that have drawn conclusions on the basis of such strongly biased analysis. CONTACT: cathal.seoighe@nuigalway.ie SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Bone morphogenetic protein (BMP) signaling determines neuroblastoma cell fate and sensitivity to retinoic acid.

Retinoic acid (RA) is a standard-of-care neuroblastoma drug thought to be effective by inducing differentiation. Curiously, RA has little effect on primary human tumors during upfront treatment but can eliminate neuroblastoma cells from the bone marrow during post-chemo consolidation therapy-a discrepancy that has never been explained. To investigate this, we treated a large cohort of neuroblastoma cell lines with RA and observed that the most RA-sensitive cells predominantly undergo apoptosis or senescence, rather than differentiation. We conducted genome-wide CRISPR knockout screens under RA treatment, which identified BMP signaling as controlling the apoptosis/senescence vs differentiation cell fate decision and determining RA's overall potency. We then discovered that BMP signaling activity is markedly higher in neuroblastoma patient samples at bone marrow metastatic sites, providing a plausible explanation for RA's ability to clear neuroblastoma cells specifically from the bone marrow, seemingly mimicking interactions between BMP and RA during normal development.

An integrated single-cell RNA-seq map of human neuroblastoma tumors and preclinical models uncovers divergent mesenchymal-like gene expression programs.

BACKGROUND: Neuroblastoma is a common pediatric cancer, where preclinical studies suggest that a mesenchymal-like gene expression program contributes to chemotherapy resistance. However, clinical outcomes remain poor, implying we need a better understanding of the relationship between patient tumor heterogeneity and preclinical models. RESULTS: Here, we generate single-cell RNA-seq maps of neuroblastoma cell lines, patient-derived xenograft models (PDX), and a genetically engineered mouse model (GEMM). We develop an unsupervised machine learning approach ("automatic consensus nonnegative matrix factorization" (acNMF)) to compare the gene expression programs found in preclinical models to a large cohort of patient tumors. We confirm a weakly expressed, mesenchymal-like program in otherwise adrenergic cancer cells in some pre-treated high-risk patient tumors, but this appears distinct from the presumptive drug-resistance mesenchymal programs evident in cell lines. Surprisingly, however, this weak-mesenchymal-like program is maintained in PDX and could be chemotherapy-induced in our GEMM after only 24 h, suggesting an uncharacterized therapy-escape mechanism. CONCLUSIONS: Collectively, our findings improve the understanding of how neuroblastoma patient tumor heterogeneity is reflected in preclinical models, provides a comprehensive integrated resource, and a generalizable set of computational methodologies for the joint analysis of clinical and pre-clinical single-cell RNA-seq datasets.

An integrated single-cell RNA-seq map of human neuroblastoma tumors and preclinical models uncovers divergent mesenchymal-like gene expression programs.

Neuroblastoma is a common pediatric cancer, where preclinical studies suggest that a mesenchymal-like gene expression program contributes to chemotherapy resistance. However, clinical outcomes remain poor, implying we need a better understanding of the relationship between patient tumor heterogeneity and preclinical models. Here, we generated single-cell RNA-seq maps of neuroblastoma cell lines, patient-derived xenograft models (PDX), and a genetically engineered mouse model (GEMM). We developed an unsupervised machine learning approach ('automatic consensus nonnegative matrix factorization' (acNMF)) to compare the gene expression programs found in preclinical models to a large cohort of patient tumors. We confirmed a weakly expressed, mesenchymal-like program in otherwise adrenergic cancer cells in some pre-treated high-risk patient tumors, but this appears distinct from the presumptive drug-resistance mesenchymal programs evident in cell lines. Surprisingly however, this weak-mesenchymal-like program was maintained in PDX and could be chemotherapy-induced in our GEMM after only 24 hours, suggesting an uncharacterized therapy-escape mechanism. Collectively, our findings improve the understanding of how neuroblastoma patient tumor heterogeneity is reflected in preclinical models, provides a comprehensive integrated resource, and a generalizable set of computational methodologies for the joint analysis of clinical and pre-clinical single-cell RNA-seq datasets.

A spatial cell atlas of neuroblastoma reveals developmental, epigenetic and spatial axis of tumor heterogeneity.

Neuroblastoma is a pediatric cancer arising from the developing sympathoadrenal lineage with complex inter- and intra-tumoral heterogeneity. To chart this complexity, we generated a comprehensive cell atlas of 55 neuroblastoma patient tumors, collected from two pediatric cancer institutions, spanning a range of clinical, genetic, and histologic features. Our atlas combines single-cell/nucleus RNA-seq (sc/scRNA-seq), bulk RNA-seq, whole exome sequencing, DNA methylation profiling, spatial transcriptomics, and two spatial proteomic methods. Sc/snRNA-seq revealed three malignant cell states with features of sympathoadrenal lineage development. All of the neuroblastomas had malignant cells that resembled sympathoblasts and the more differentiated adrenergic cells. A subset of tumors had malignant cells in a mesenchymal cell state with molecular features of Schwann cell precursors. DNA methylation profiles defined four groupings of patients, which differ in the degree of malignant cell heterogeneity and clinical outcomes. Using spatial proteomics, we found that neuroblastomas are spatially compartmentalized, with malignant tumor cells sequestered away from immune cells. Finally, we identify spatially restricted signaling patterns in immune cells from spatial transcriptomics. To facilitate the visualization and analysis of our atlas as a resource for further research in neuroblastoma, single cell, and spatial-omics, all data are shared through the Human Tumor Atlas Network Data Commons at www.humantumoratlas.org.

A CRISPR-drug perturbational map for identifying compounds to combine with commonly used chemotherapeutics.

Combination chemotherapy is crucial for successfully treating cancer. However, the enormous number of possible drug combinations means discovering safe and effective combinations remains a significant challenge. To improve this process, we conduct large-scale targeted CRISPR knockout screens in drug-treated cells, creating a genetic map of druggable genes that sensitize cells to commonly used chemotherapeutics. We prioritize neuroblastoma, the most common extracranial pediatric solid tumor, where ~50% of high-risk patients do not survive. Our screen examines all druggable gene knockouts in 18 cell lines (10 neuroblastoma, 8 others) treated with 8 widely used drugs, resulting in 94,320 unique combination-cell line perturbations, which is comparable to the largest existing drug combination screens. Using dense drug-drug rescreening, we find that the top CRISPR-nominated drug combinations are more synergistic than standard-of-care combinations, suggesting existing combinations could be improved. As proof of principle, we discover that inhibition of PRKDC, a component of the non-homologous end-joining pathway, sensitizes high-risk neuroblastoma cells to the standard-of-care drug doxorubicin in vitro and in vivo using patient-derived xenograft (PDX) models. Our findings provide a valuable resource and demonstrate the feasibility of using targeted CRISPR knockout to discover combinations with common chemotherapeutics, a methodology with application across all cancers.

The regulatory effect of miRNAs is a heritable genetic trait in humans.

BACKGROUND: microRNAs (miRNAs) have been shown to regulate the expression of a large number of genes and play key roles in many biological processes. Several previous studies have quantified the inhibitory effect of a miRNA indirectly by considering the expression levels of genes that are predicted to be targeted by the miRNA and this approach has been shown to be robust to the choice of prediction algorithm. Given a gene expression dataset, Cheng et al. defined the regulatory effect score (RE-score) of a miRNA as the difference in the gene expression rank of targets of the miRNA compared to non-targeted genes. RESULTS: Using microarray data from parent-offspring trios from the International HapMap project, we show that the RE-score of most miRNAs is correlated between parents and offspring and, thus, inter-individual variation in RE-score has a genetic component in humans. Indeed, the mean RE-score across miRNAs is correlated between parents and offspring, suggesting genetic differences in the overall efficiency of the miRNA biogenesis pathway between individuals. To explore the genetics of this quantitative trait further, we carried out a genome-wide association study of the mean RE-score separately in two HapMap populations (CEU and YRI). No genome-wide significant associations were discovered; however, a SNP rs17409624, in an intron of DROSHA, was significantly associated with mean RE-score in the CEU population following permutation-based control for multiple testing based on all SNPs mapped to the canonical miRNA biogenesis pathway; of 244 individual miRNA RE-scores assessed in the CEU, 214 were associated (p < 0.05) with rs17409624. The SNP was also nominally significantly associated (p = 0.04) with mean RE-score in the YRI population. Interestingly, the same SNP was associated with 17 (8.5% of all expressed) miRNA expression levels in the CEU. We also show here that the expression of the targets of most miRNAs is more highly correlated with global changes in miRNA regulatory effect than with the expression of the miRNA itself. CONCLUSIONS: We present evidence that miRNA regulatory effect is a heritable trait in humans and that a polymorphism of the DROSHA gene contributes to the observed inter-individual differences.

Facilitating Drug Discovery in Breast Cancer by Virtually Screening Patients Using In Vitro Drug Response Modeling.

(1) Background: Drug imputation methods often aim to translate in vitro drug response to in vivo drug efficacy predictions. While commonly used in retrospective analyses, our aim is to investigate the use of drug prediction methods for the generation of novel drug discovery hypotheses. Triple-negative breast cancer (TNBC) is a severe clinical challenge in need of new therapies. (2) Methods: We used an established machine learning approach to build models of drug response based on cell line transcriptome data, which we then applied to patient tumor data to obtain predicted sensitivity scores for hundreds of drugs in over 1000 breast cancer patients. We then examined the relationships between predicted drug response and patient clinical features. (3) Results: Our analysis recapitulated several suspected vulnerabilities in TNBC and identified a number of compounds-of-interest. AZD-1775, a Wee1 inhibitor, was predicted to have preferential activity in TNBC (p < 2.2 × 10-16) and its efficacy was highly associated with TP53 mutations (p = 1.2 × 10-46). We validated these findings using independent cell line screening data and pathway analysis. Additionally, co-administration of AZD-1775 with standard-of-care paclitaxel was able to inhibit tumor growth (p < 0.05) and increase survival (p < 0.01) in a xenograft mouse model of TNBC. (4) Conclusions: Overall, this study provides a framework to turn any cancer transcriptomic dataset into a dataset for drug discovery. Using this framework, one can quickly generate meaningful drug discovery hypotheses for a cancer population of interest.

The chemotherapeutic CX-5461 primarily targets TOP2B and exhibits selective activity in high-risk neuroblastoma.

Survival in high-risk pediatric neuroblastoma has remained around 50% for the last 20 years, with immunotherapies and targeted therapies having had minimal impact. Here, we identify the small molecule CX-5461 as selectively cytotoxic to high-risk neuroblastoma and synergistic with low picomolar concentrations of topoisomerase I inhibitors in improving survival in vivo in orthotopic patient-derived xenograft neuroblastoma mouse models. CX-5461 recently progressed through phase I clinical trial as a first-in-human inhibitor of RNA-POL I. However, we also use a comprehensive panel of in vitro and in vivo assays to demonstrate that CX-5461 has been mischaracterized and that its primary target at pharmacologically relevant concentrations, is in fact topoisomerase II beta (TOP2B), not RNA-POL I. This is important because existing clinically approved chemotherapeutics have well-documented off-target interactions with TOP2B, which have previously been shown to cause both therapy-induced leukemia and cardiotoxicity-often-fatal adverse events, which can emerge several years after treatment. Thus, while we show that combination therapies involving CX-5461 have promising anti-tumor activity in vivo in neuroblastoma, our identification of TOP2B as the primary target of CX-5461 indicates unexpected safety concerns that should be examined in ongoing phase II clinical trials in adult patients before pursuing clinical studies in children.

BioconductorBuntu: a Linux distribution that implements a web-based DNA microarray analysis server.

SUMMARY: BioconductorBuntu is a custom distribution of Ubuntu Linux that automatically installs a server-side microarray processing environment, providing a user-friendly web-based GUI to many of the tools developed by the Bioconductor Project, accessible locally or across a network. System installation is via booting off a CD image or by using a Debian package provided to upgrade an existing Ubuntu installation. In its current version, several microarray analysis pipelines are supported including oligonucleotide, dual-or single-dye experiments, including post-processing with Gene Set Enrichment Analysis. BioconductorBuntu is designed to be extensible, by server-side integration of further relevant Bioconductor modules as required, facilitated by its straightforward underlying Python-based infrastructure. BioconductorBuntu offers an ideal environment for the development of processing procedures to facilitate the analysis of next-generation sequencing datasets. AVAILABILITY: BioconductorBuntu is available for download under a creative commons license along with additional documentation and a tutorial from (http://bioinf.nuigalway.ie).