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In eukaryotes, capped RNAs include long transcripts such as messenger RNAs and long noncoding RNAs, as well as shorter transcripts such as spliceosomal RNAs, small nucleolar RNAs, and enhancer RNAs. Long capped transcripts can be profiled using cap analysis gene expression (CAGE) sequencing and other methods. Here, we describe a sequencing library preparation protocol for short capped RNAs, apply it to a differentiation time course of the human cell line THP-1, and systematically compare the landscape of short capped RNAs to that of long capped RNAs. Transcription initiation peaks associated with genes in the sense direction have a strong preference to produce either long or short capped RNAs, with one out of six peaks detected in the short capped RNA libraries only. Gene-associated short capped RNAs have highly specific 3′ ends, typically overlapping splice sites. Enhancers also preferentially generate either short or long capped RNAs, with 10% of enhancers observed in the short capped RNA libraries only. Enhancers producing either short or long capped RNAs show enrichment for GWAS-associated disease SNPs. We conclude that deep sequencing of short capped RNAs reveals new families of noncoding RNAs and elucidates the diversity of transcripts generated at known and novel promoters and enhancers.

Full listing of supplementary files and their legends

Cancer precision medicine aims to predict the drug likely to yield the best response for a patient. Genomic sequencing of tumors is currently being used to better inform treatment options; however, this approach has had a limited clinical impact due to the paucity of actionable mutations. An alternative to mutation status is the use of gene expression signatures to predict response. Using data from two large-scale studies, The Genomics of Drug Sensitivity of Cancer (GDSC) and The Cancer Therapeutics Response Portal (CTRP), we investigated the relationship between the sensitivity of hundreds of cell lines to hundreds of drugs, and the relative expression levels of the targets these drugs are directed against. For approximately one third of the drugs considered (73/222 in GDSC and 131/360 in CTRP), sensitivity was significantly correlated with the expression of at least one of the known targets. Surprisingly, for 8% of the annotated targets, there was a significant anticorrelation between target expression and sensitivity. For several cases, this corresponded to drugs targeting multiple genes in the same family, with the expression of one target significantly correlated with sensitivity and another significantly anticorrelated suggesting a possible role in resistance. Furthermore, we identified nontarget genes that are significantly correlated or anticorrelated with drug sensitivity, and find literature linking several to sensitization and resistance. Our analyses provide novel and important insights into both potential mechanisms of resistance and relative efficacy of drugs against the same target.

Table S1. Cell samples used for CAGE profiling. Table S2. Public datasets used in this study. Table S3. Differentially expressed promoters in FANTOM5 NSCLC cell lines. Table S4. Up-regulated/hypomethylated promoters in NSCLC cell lines. Table S5. Epi-markers in NSCLC. Table S6. Extended information for Table 2. Table S7. Univariate and multivariate Cox regression models of TCGA LUAD and LUSC datasets. Table S8. Gene expression profiling of SAECs treated with 5-aza-dC and TSA. Table S9. Up-regulated/hypomethylated genes in NSCLC cell lines that show up-regulation by 5-aza-dC and TSA in SAECs. Table S10. Promoters and methylation array probes that overlap nine families of repetitive elements. Table S11. Promoters and methylation array probes that overlap REP522 repetitive elements. Table S12. Transcripts commonly down-regulated by two different MYEOV siRNAs in A549 cells. Table S13. The relationship between expression of 22 epi-markers and EGFR/KRAS mutation in TCGA LUAD dataset.

Supplementary table 2 shows Spearman correlation values between drug sensitivity and gene expression for 481 drugs studied in the CTRP dataset.

Supplementary Table 1 shows Spearman correlation values between drug sensitivity and gene expression for 250 drugs studied in the GDSC dataset.

Supplementary Table 6a: Spearman correlation values between drug sensitivity and target expression for 86 drugs common between GDSC and CTRP. Supplementary Table 6b: Correlation matrix comparing gene expression values. There are 558 cell lines and 15,934 genes common between both the datasets. Calculated 558x 558 - Correlation matrix comparing gene expression values for 15,934 genes in one cell line from GDSC compared to 15,934 genes in CTRP. Correlation method used was Spearman. Supplementary Table 6c: Spearman correlation values comparing Area under the curve (AUC) values across common cell lines in the GDSC and CTRP datasets for the 86 drugs common between them. Supplementary Table 6d: List of the cell lines where a better gene expression correlation was observed with a different cell line.

Lung cancer is the leading cause of cancer-related deaths worldwide. The majority of cancer driver mutations have been identified; however, relevant epigenetic regulation involved in tumorigenesis has only been fragmentarily analyzed. Epigenetically regulated genes have a great theranostic potential, especially in tumors with no apparent driver mutations. Here, epigenetically regulated genes were identified in lung cancer by an integrative analysis of promoter-level expression profiles from Cap Analysis of Gene Expression (CAGE) of 16 non–small cell lung cancer (NSCLC) cell lines and 16 normal lung primary cell specimens with DNA methylation data of 69 NSCLC cell lines and 6 normal lung epithelial cells. A core set of 49 coding genes and 10 long noncoding RNAs (lncRNA), which are upregulated in NSCLC cell lines due to promoter hypomethylation, was uncovered. Twenty-two epigenetically regulated genes were validated (upregulated genes with hypomethylated promoters) in the adenocarcinoma and squamous cell cancer subtypes of lung cancer using The Cancer Genome Atlas data. Furthermore, it was demonstrated that multiple copies of the REP522 DNA repeat family are prominently upregulated due to hypomethylation in NSCLC cell lines, which leads to cancer-specific expression of lncRNAs, such as RP1-90G24.10, AL022344.4, and PCAT7. Finally, Myeloma Overexpressed (MYEOV) was identified as the most promising candidate. Functional studies demonstrated that MYEOV promotes cell proliferation, survival, and invasion. Moreover, high MYEOV expression levels were associated with poor prognosis.Implications: This report identifies a robust list of 22 candidate driver genes that are epigenetically regulated in lung cancer; such genes may complement the known mutational drivers.Visual Overview: http://mcr.aacrjournals.org/content/molcanres/15/10/1354/F1.large.jpg. Mol Cancer Res; 15(10); 1354–65. ©2017 AACR.

Figure S1. Methylation and expression levels of genes in lung cancer and normal lung tissues. Figure S2. Expression levels of epi-markers in TCGA LUAD dataset. Figure S3. Expression levels of epi-markers in TCGA LUSC dataset. Figure S4. The signature of 22 epigenetically regulated genes in non-small cell lung cancer. Figure S5. ZENBU genome browser view of RP1-90G24.10 and AL022344.4. Figure S6. ZENBU genome browser view of MYEOV. Figure S7. Higher MYEOV expression is associated with poor prognosis in NSCLC.

Supplementary Table 3a: Molecular Targets of the Anticancer Drugs from GDSC, CTRP and DrugBank. Spearman correlation and permutation testing results are provided for both the GDSC and CTRP datasets. Supplementary Table 4: List of drugs where we observe both a correlated and an anti-correlated target in GDSC and CRTP datasets. Supplementary Table 5: Fractions of single cells in each cell line expressing both targets from Figure 3. Supplementary Table 7: Transcript isoform aware reanalysis of CTRP-GDSC concordance. Spearman correlation values between drug sensitivity and drug-target transcript isoforms expression for 74 drugs common between GDSC and CTRP. The 541 cell lines common in GDSC and CTRP where drug-target transcript isoform expression and AUC values could be obtained were considered. As GDSC and CTRP provide gene-level expression measurements from different microarray versions (Affymetrix GeneChip HG-U133A in GDSC and HG-U133PLUS2 in CTRP), which potentially detect different isoforms of each gene, we repeated our analysis at the transcript level. Supplementary Table 8: The top 20 strongest correlations and top 20 strongest anti-correlations between drug sensitivity and gene expression for the GDSC and CTRP datasets. Supplementary Table 9: Drug-target pairs that are significantly correlated in cell lines derived from a cancer subtype but not significantly correlated in the pan-cancer analysis.

Supplementary Figure 1: Comparison of correlations for ALK and MET. Blue, red and grey cells denote significant correlations, significant anti-correlations and non-significant correlations respectively. White cells correspond to combinations that were not tested or are not valid drug-target pairs. Supplementary Figure 2: Comparison of correlations for HDAC1, HDAC2, HDAC3, HDAC6 and HDAC8. Blue, red and grey cells denote significant correlations, significant anti-correlations and non-significant correlations respectively. White cells correspond to combinations that were not tested or are not valid drug-target pairs. Supplementary Figure 3: Summary of the degree of overlap between the GDSC and CTRP analyses at the transcript level. As GDSC and CTRP provide gene-level expression measurements from different microarray versions (Affymetrix GeneChip HG-U133A in GDSC and HG-U133PLUS2 in CTRP) which potentially detect different isoforms of each gene, we repeated our concordance analysis at the transcript level. The correlation coefficient was calculated for 74 common drugs (targeting drug-target transcript isoforms). The 541 cell lines common in GDSC and CTRP where drug-target transcript isoform expression and AUC values could be obtained were considered.

The list of genes differentially expressed in TCGAs primary tumors vs. normal tissues.

The expression changes of cancer related genes that were differentially expressed in FANTOM5 cancer cell lines (S1); Heatmaps showing the spectral count data corresponding to protein levels of selected genes in 90 colorectal cancers and 30 normal samples generated by Zhang et al (S2); Pan-cancer long non-coding RNAs and neighboring cancer associated genes (S3); ChIA-pet data show pan-cancer enhancers are physically associated with genes implicated in cancer (S4); The correlation between expression fold changes and the frequency of overlapping repetitive element (S5).

Description of additional methods and procedures used in the study.

Current approaches to stage chronic liver diseases have limited utility to predict liver cancer risk. Here, we employed single nucleus RNA sequencing (snRNA-seq) to characterize the cellular microenvironment of healthy and pre-malignant livers using two distinct mouse models. Downstream analyses unraveled a previously uncharacterized disease-associated hepatocyte transcriptional state (daHep). These cells were absent in healthy livers but increasingly prevalent as chronic liver disease progressed. Copy number variation (CNV) analysis of microdissected tissue demonstrated that daHep enriched regions are riddled with structural variants, suggesting these cells represent a pre-malignant intermediary. Integrated analysis of three recent human snRNA-seq datasets confirmed the presence of a similar phenotype in human chronic liver disease and further supported its enhanced mutational burden. Importantly, we show that high daHep levels precede carcinogenesis and predict a higher risk of hepatocellular carcinoma development. These findings may change the way chronic liver disease patients are staged, surveilled and risk-stratified.

Human brain development is underpinned by cellular and molecular reconfigurations continuing into the third decade of life. To reveal cell dynamics orchestrating neural maturation, we profiled human prefrontal cortex gene expression and chromatin accessibility at single-cell resolution from gestation to adulthood. Integrative analyses define the dynamic trajectories of each cell type, revealing major gene expression reconfiguration at the prenatal-to-postnatal transition in all cell types followed by continuous reconfiguration into adulthood and identifying regulatory networks guiding cellular developmental programs, states, and functions. We uncover links between expression dynamics and developmental milestones, characterize the diverse timing of when cells acquire adult-like states, and identify molecular convergence from distinct developmental origins. We further reveal cellular dynamics and their regulators implicated in neurological disorders. Finally, using this reference, we benchmark cell identities and maturation states in organoid models. Together, this captures the dynamic regulatory landscape of human cortical development. This work was supported by the following grants: R.L.—National Health and Medical Research Council (NHMRC) Project Grant 1130168, NHMRC Investigator Grant 1178460, Silvia and Charles Viertel Senior Medical Research Fellowship, Howard Hughes Medical Institute International Research Scholarship, and Australian Research Council (ARC) LE170100225; S.F.—NHMRC Ideas Grant 1184421; I.V.—ARC Future Fellowship FT170100359, UNSW Scientia Fellowship, and NHMRC Project Grant RG170137; S.B.—NHMRC-ARC Dementia Research Development Fellowship 1111206; C.P.—Raine Foundation Priming Grant RPG66-21; J.M.P.—Silvia and Charles Viertel Senior Medical Research Fellowship, ARC Future Fellowship FT180100674. This work was supported by a Cancer Research Trust grant ‘‘Enabling advanced single cell cancer genomics in WA’’ and Cancer Council WA enabling grant. Genomic data were generated at the ACRF Centre for Advanced Cancer Genomics and Genomics WA. Human brain tissue was received from the UMB Brain and Tissue Bank at the University of Maryland, part of the NIH NeuroBioBank. The glioblastoma sample was procured and provided by the AGOG biobank, funded by CINSW grant SRP-08-10. L.M. was a fellow of The Lorenzo and Pamela Galli Medical Research Trust. We thank Ankur Sharma and Greg Neely for valuable feedback. The graphical abstract and elements of Figure 1A were created with BioRender. Sí