Columbia University

Computational Human High-grade Glioblastoma Multiform (GBM) Interactome - miRNA (Post-transcriptional) Layer

The Human High-Grade Glioma Interactome (HGi) contains a genome-wide complement of molecular interactions that are Glioblastoma Multiforme (GBM)-specific. HGi v3 contains the post-transcriptional layer of the HGi, which includes the miRNA-target (RNA-RNA) layer of the interactome.

Experimental Approaches

microRNA target predictions were obtained using a two-step machine learning approach. First, sites predicted using miRanda, PITA and TargetScan were scored by classifying sites against a gold standard of validated interactions using a Support Vector Machine (SVM). The SVM is trained on features including the normalized score from the predicting algorithm, conservation across mammalian genomes, and site location relative to the start and end positions of the 3’ UTR. Then co-expression, site scores, and modular site grammar were used to predict interactions with SVM. Features and parameters were selected using cross validation and produced high confidence predictions after retraining the SVM on the complete dataset.

Direct Reversal of Glucocorticoid Resistance by AKT Inhibition in Acute Lymphoblastic Leukemia (T-ALL)

The goal of this project is to identify key druggable regulators of glucocorticoid resistance in T-ALL. To this end, a reverse-engineered T-ALL context-specific regulatory interaction network was created from a phenotypically diverse T-ALL gene expression dataset, and then this network was interrogated using master regulator analysis to find drivers of glucocorticoid resistance. The T-ALL gene expression dataset represented many different biological conditions, genotypes, signaling and transcriptional states, thus providing significant variation in which to detect gene expression correlations.

The expression level of transcription factors is often a poor predictor of their activity and biological relevance. However, their activity at the protein level can be inferred by measuring changes in the gene expression of their targets between two phenotypes, for example between tumor and normal tissue. This approach, called master regulator analysis, has been used successfully to identify functional drivers of cancer in a number of studies. In this study, master regulator analysis was used to identify regulatory genes whose network targets were enriched in the signal transduction cascade (as reflected in a differential gene expression signature) associated with glucocorticoid resistance. 

Microarray gene expression data used in network generation and master regulator analysis is available in Gene Expression Omnibus under accession number GSE32215.

Experimental Approaches

Reverse-Engineering of T-ALL Transcriptional Network (ARACNe)

For each gene in a list of regulatory genes (hubs), the ARACNe algorithm^1,2 is used to measure the mutual information between that gene and all remaining genes in the dataset. First, a preprocessing run is performed in which a curve relating mutual information to significance is generated. Next, ARACNe is run using the adaptive partitioning algorithm, repeated 100 times with bootstrapping³. A key step after each run of ARACNe is the application of the Data Processing Inequality to remove indirect interactions, typically with a zero threshold. A final consensus network is reconstructed from the bootstrapped networks based on the support of each edge, using a null distribution obtained via permutations.

Gene expression data from 223 T-ALLs (Human U133 Plus2.0 Affymetrix microarray platform) was subjected to GC Robust Multi-Aarray normalization and non-specific filtering (removing probes with no Entrez id, Affymetrix control probes, and non-informative probes by IQR variance filtering with a cutoff of 0.5). A set of hub genes was defined including genes with annotated functions in signaling transduction (GO:0007165) such as kinases, phosphatases, ubiquitin ligases, etc. to establish a signaling factor-centered interactome at the transcriptional level. ARACNe was used to identify targets of these hub genes (that is, genes with significant mutual information with the hub genes). It was run using the adaptive partitioning algorithm with a p-value threshold of 1e-7, DPI tolerance of 0, and 100 rounds of bootstrapping.

Master Regulator Analysis (MARINa)

For master regulator analysis, a group of 22 glucocorticoid resistant and 10 glucocorticoid sensitive T-ALLs was selected from the larger dataset used in network generation. Genes were ranked by their differential expression between these two conditions. The MARINa algorithm uses Gene Set Enrichment Analysis (GSEA)⁴ to test the differential enrichment of the regulons of hub genes (network first-degree neighbors) in the rank of genes differentially expressed between glucocorticoid sensitive and glucocorticoid resistant samples⁵. For GSEA method the ‘maxmean’ statistic⁶ was applied to score the enrichment of the gene set in the glucocorticoid resistant vs. glucocorticoid sensitive leukemias and sample permutation was used to build the null distribution for statistical significance.

References

1. Basso K, et al. (2005). Reverse engineering of regulatory networks in human B cells. Nature Genet. 37(4):382-390 (PMID: 15778709)
2. Margolin AA, et al. (2006). ARACNE: An Algorithm for the Reconstruction of Gene Regulatory Networks in a Mammalian Cellular Context. BMC Bioinformatics. 7(Suppl.1):S7 (PMID: 16723010)
3. Margolin A, et al. (2006). Reverse Engineering Cellular Networks. Nature Protocols 1(2):663-72 (PMID: 17406294)
4. Subramanian A, et al. (2005). Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci USA. 102(43):15545-50 (PMID: 16199517)
5. Carro MS, et al. (2010). The transcriptional network for mesenchymal transformation of brain tumors. Nature. 463(7279):318-25 (PMID: 20032975)
6. Efron B and Tibshirani R. (2007). On testing the significance of sets of genes. The Annals of Applied Statistics. 1, 107-129.

Expression Profile of Neuroendocrine Tumor Cell-line Perturbed with Small Molecules

We have developed a new precision oncology framework for the systematic prioritization of drugs targeting mechanistic tumor dependencies in individual patients. As a component of this project, we used drug perturbation assays to scan a library of compounds against the H-STS neuroendocrine tumor cell line. We evaluated each compound’s ability to invert the concerted activity of master regulator proteins that mechanistically regulate tumor cell state.

Experimental Approaches

H-STS cells were perturbed with a library of 107 small-molecule compounds at their corresponding ED₂₀ concentration and one-tenth of it. Cells were lysed at 6 h and 24 h after small-molecule compound perturbation and total RNA was isolated for RNA-Seq analysis. Libraries for RNA-seq were generated with the TruSeq protocol (Illumina) and sequenced in a Hi-Seq 2500 instrument (Illumina). Summarized expression data resulting from these analyses are available from the Gene Expression Omnibus database (GSE96760).

PLATE-seq for Genome-wide Regulatory Network Analysis of High-throughput Screens

Pooled Library Amplification for Transcriptome Expression (PLATE-Seq) is a new, highly scalable and multiplexed RNA-Seq protocol for barcoding and pooling cDNA libraries to substantially reduce the cost and complexity of multi-sample analysis. Here we describe its application to small molecule perturbation experiments using BT20 breast cancer cells. PLATE-Seq is part of a larger analysis pipeline that uses reverse-engineered gene regulatory networks, greatly reducing the sample sizes required to infer regulatory protein activity.

Experimental Approaches

We use automated liquid-handling to introduce lysis buffer, capture polyadenylated mRNA with an oligo(dT)-grafted plate, and deliver well-specific, barcoded oligo(dT) primers to every sample in a multi-well plate. After reverse transcription, the cDNA in each well contains a specific barcode sequence on its 5’-end and a common adapter, such that all samples can be combined into a single pool for purification and concentration. We then use Klenow large fragment for pooled second-strand synthesis from adapter-linked random primers. Because this polymerase lacks strand-displacement and 5’-to-3’ exonuclease activities, each cDNA molecule produces at most, one second-strand synthesis product containing the sample barcode. Finally, the pooled library is enriched in a single PCR prior to sequencing. The resulting libraries represent the 3’-ends of mRNAs and are sequenced to a depth of 0.5-2 million raw reads per sample. 

To characterize the performance of PLATE-Seq, we conducted a fully automated, 96-well screen to profile BT20 breast cancer cells following treatment with seven well-characterized small-molecule perturbagens (plus DMSO controls) and 12 replicates per condition.

Pharmacological Targeting of Mechanistic Dependencies in Neuroendocrine Tumors

We have developed a new precision oncology framework for the systematic prioritization of drugs targeting mechanistic tumor dependencies in individual patients.

In the course of validating the approach, we reverse-engineered a gene regulatory network using gene expression profiles from a cohort of 212 gastroenteropancreatic neuroendocrine tumors (GEP-NETs), a rare malignancy originating in the pancreas and gastrointestinal tract.

Experimental Approaches

Expression profiles were obtained for the samples by RNA-Seq. Expression data were normalized by equi-variance transformation, based on the negative binomial distribution with the DESeq R-system package (Bioconductor). The regulatory network was reverse-engineered using the ARACNe algorithm^1,2. ARACNe was run with 100 bootstrap iterations using a set of 1,813 annotated transcription factors. Parameters were set to 0 DPI (Data Processing Inequality) tolerance and MI (Mutual Information) P value threshold of 10⁻⁸. The gene expression profiles are available on GEO as GSE98894. The resulting ARACNe regulatory network is included in this submission.

References

1. Basso K, et al. (2005). Reverse engineering of regulatory networks in human B cells. Nat Genet. 37(4):382-90. (PMID: 15778709)
2. Margolin AA, et al. (2006). ARACNE: an algorithm for the reconstruction of gene regulatory networks in a mammalian cellular context. BMC Bioinformatics. Suppl 1:S7. (PMID: 16723010)

Core Regulatory Elements of High-risk Neuroblastoma

This project provides a framework to determine the downstream effectors of the genetic alterations sustaining neuroblastoma subtypes.

The results show the critical effect of disrupting a 10-protein module centered around a YAP/TAZ-independent TEAD4-MYCN positive-feedback loop in MYCN^Amp neuroblastomas, nominating TEAD4 as a novel candidate for therapeutic intervention.

Experimental Approaches

The subtype-specific candidate master regulator (MR) proteins were inferred by independent analysis of the National Cancer Institute’s Therapeutically Applicable Research to Generate Effective Treatments (TARGET) and the European Neuroblastoma Research Consortium (NRC) datasets. Algorithm for the Reconstruction of Accurate Cellular Networks based on an Adaptive Partitioning strategy (ARACNe-AP) was used to assemble cohort specific interactomes from the gene-expression profiles of neuroblastoma samples from TARGET and NRC datasets. Candidate MR proteins for each of the high-risk subtypes were then prioritized based on the enrichment of their transcriptional target genes in the subtype-specific signature using the Virtual Inference of Protein activity by Enriched Regulon (VIPER) algorithm.

Proteome-wide Signaling-network Analysis in Lung Adenocarcinoma

Phospho- Algorithm for the Reconstruction of Accurate Cellular Networks (pARACNe) is a novel algorithm for the systematic inference of protein kinase pathways.

In this study, pARACNe was applied to analyze published mass spectrometry-based phosphotyrosine profile data from 250 lung adenocarcinoma (LUAD) samples. The resulting network includes 43 Tyrosine Kinases (TKs) and 415 inferred, LUAD-specific substrates. The predictions were validated at >60% accuracy by Stable Isotope Labeling with Amino acids in Cell culture (SILAC) assays, including “novel” substrates of the EGFR and c-MET TKs, which play a critical oncogenic role in lung cancer.

Experimental Approaches

The Califano lab developed a new algorithm, pARACNe, for inferring signaling networks from phosphoproteomics data. This method reports the abundance of phospho-proteins as measured by high-throughput mass spectroscopy (MS) based assay, to reveal how kinases interact with their substrates. Inferring transcriptional regulatory networks with ARACNe relies on the gene-expression data that are usually continuous and non-sparse. Data obtained from methods, such as liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS) via spectral counting, are typically discrete and very sparse. To handle these discrete abundances, the mutual information computation approach was modified from a kernel density estimation-based method to a histogram-based Naïve-Bayes approach.

CTD² Pancancer Drug Activity Challenge

The goal of the CTD² Pancancer Drug Activity DREAM Challenge is to foster the development and benchmarking of algorithms to predict targets of chemotherapeutic compounds from post-treatment transcriptional data. The drug perturbational profiles on 11 cell lines and their dose-response curves for 32 chosen compounds with well-established targets will be provided to challenge participants, without revealing the identity of the drugs. These profiles will be removed from any public dataset and added back only after the challenge is completed. Transcriptional profiles for all the cell lines in which the compounds have been profiled have been provided to challenge participants, including the specific concentration at which the compound was titrated.

The package contains 2 metadata files, 22 data files, a README file that describes the data, and a COLUMNS file of descriptions of column headers shared by the 24 data files.

Experimental Approaches

Methods overview
This dataset was developed in collaboration between Columbia University Irving Medical Centers (CUIMC)’s High Throughput Screening Center (HTS), Sulzberger Genome Center and the Califano Laboratory in the Department of Systems Biology. Briefly, HTS handled cell-culture, cell-perturbation experiments and RNA extraction; the Genome Center performed RNA sequencing and the Califano laboratory performed data normalization, quality control, benchmarking and scientific and statistical analysis.

Compound titration curves
To determine the 48h ED20 of each drug, cell lines were plated into 96-well tissue culture plates, in 100 μL total volume, and incubated at 37°C. After 16 hours the plates were removed from the incubator and compounds were transferred into assay wells (1 μL) in triplicate. Plates were then returned to the incubator. After 48 hours the assay plates were removed from the incubator and allowed to cool to room temperature prior to the addition of 100 μL of CellTiter-Glo (Promega Inc.) per well. The plates were then mechanically shaken for 5 minutes prior to readout on the EnVision Multi-Label Reader (Perkin Elmer Inc.) using the enhanced luminescence module. Relative cell viability was computed using matched DMSO control wells as reference. ED20 was estimated by fitting a four-parameter sigmoid model to the titration results.

Perturbational profile generation
Using the previously described plating and perturbation procedure we perturbed each cell-line with each drug at its 48h ED20 value (measured above) or its CMax concentration. In order to optimize the clinical translation potential of the perturbation databases, we used the CMax, defined as the maximum plasma concentration after the administration of the drug at the maximum tolerated dose in patients, (whenever available from published pharmacokinetic studies), as an upper bound for the perturbation studies (Table S1). The mRNA from these cells was isolated and profiled by PLATESeq (Nat. Commun. 2017, 8, 105) at 24h after each perturbation.

Profile normalization
RNASeq reads were mapped for each well to the human reference genome assembly 38 using the STAR aligner,57 version 2.5.2b. Individual plates counts files were then combined, normalized and corrected for batch effects. First, individual counts files were combined across genes and ERCC2 spike-in counts removed, yielding the raw counts file for each cell-line experiment. Second, raw counts were quantile normalized and variance stabilized based on the negative binomial distribution with the DESeq R system package.59 To account for plate-based batch effects (which are common with drug-perturbed transcriptomic data) normalized expression was batch corrected using ComBat.60