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SummaryTranscription factors (TFs) orchestrate the gene expression programs that define each cell’s identity. The canonical TF accomplishes this with two domains, one that binds specific DNA sequences and the other that binds protein coactivators or corepressors. We find that at least half of TFs also bind RNA, doing so through a previously unrecognized domain with sequence and functional features analogous to the arginine-rich motif of the HIV transcriptional activator Tat. RNA-binding contributes to TF function by promoting the dynamic association between DNA, RNA and TF on chromatin. TF-RNA interactions are a conserved feature essential for vertebrate development and disrupted in disease. We propose that the ability to bind DNA, RNA and protein is a general property of many TFs and is fundamental to their gene regulatory function.

Long noncoding RNAs (lncRNAs) can act as tumour suppressors or oncogenes to repress/promote tumour cell proliferation via RNA-dependent mechanisms. Recently, genome sequencing has identified elevated densities of tumour somatic single nucleotide variants (SNVs) in lncRNA genes. However, this has been attributed to phenotypically-neutral “passenger” processes, and the existence of positively-selected fitness-altering “driver” SNVs acting via lncRNAs has not been addressed. We developed and used ExInAtor2, an improved driver-discovery pipeline, to map pancancer and cancer-specific mutated lncRNAs across an extensive cohort of 2583 primary and 3527 metastatic tumours. The 54 resulting lncRNAs are mostly linked to cancer for the first time. Their significance is supported by a range of clinical and genomic evidence, and display oncogenic potential when experimentally expressed in matched tumour models. Our results revealed a striking SNV hotspot in the iconic NEAT1 oncogene, which was ascribed by previous studies to passenger processes. To directly evaluate the functional significance of NEAT1 SNVs, we used in cellulo mutagenesis to introduce tumour-like mutations in the gene and observed a consequent increase in cell proliferation in both transformed and normal backgrounds. Mechanistic analyses revealed that SNVs alter NEAT1 ribonucleoprotein assembly and boost subnuclear paraspeckles. This is the first experimental evidence that mutated lncRNAs can contribute to the pathological fitness of tumour cells.

Long noncoding RNAs (lncRNAs) are widely dysregulated in cancer, yet their functional roles in cellular disease hallmarks remain unclear. Here we employ pooled CRISPR deletion to perturb all 831 lncRNAs in KRAS-mutant non-small cell lung cancer (NSCLC), and measure their contribution to proliferation, chemoresistance and migration across two cell backgrounds. Integrative analysis of this data outperforms conventional “dropout” screens in identifying cancer genes, while prioritising disease-relevant lncRNAs with pleiotropic and background-independent roles. Altogether 60 high-confidence oncogenic lncRNAs are active in NSCLC, the majority identified here for the first time, and which tend to be amplified and overexpressed in tumours. A follow-up antisense oligonucleotide (ASO) screen shortlisted two candidates, Cancer Hallmarks in Lung LncRNA (CHiLL 1&2), whose knockdown consistently suppressed cancer hallmarks in a variety of 2D and 3D tumour models. Molecular phenotyping reveals that CHiLL 1&2 control cellular-level phenotypes via distinct transcriptional networks converging on common oncogenic pathways. In summary, this work reveals a multi-dimensional functional lncRNA landscape underlying NSCLC that contains potential therapeutic vulnerabilities.

Long non-coding RNAs (lncRNAs) play key roles in cancer and are at the vanguard of precision therapeutic development. These efforts depend on large and high-confidence collections of cancer lncRNAs. Here, we present the Cancer LncRNA Census 2 (CLC2). With 492 cancer lncRNAs, CLC2 is 4-fold greater in size than its predecessor, without compromising on strict criteria of confident functional/genetic roles and inclusion in the GENCODE annotation scheme. This increase was enabled by leveraging high-throughput transposon insertional mutagenesis screening data, yielding 92 novel cancer lncRNAs. CLC2 makes a valuable addition to existing collections: it is amongst the largest, contains numerous unique genes (not found in other databases) and carries functional labels (oncogene/tumour suppressor). Analysis of this dataset reveals that cancer lncRNAs are impacted by germline variants, somatic mutations and changes in expression consistent with inferred disease functions. Furthermore, we show how clinical/genomic features can be used to vet prospective gene sets from high-throughput sources. The combination of size and quality makes CLC2 a foundation for precision medicine, demonstrating cancer lncRNAs’ evolutionary and clinical significance., Graphical Abstract Graphical AbstractCancer LncRNA Census 3 integrates manual and automated curation to identify high confidence functional cancer lncRNAs, whose disease roles are supported by a range of clinical features.

BackgroundIn multiple sclerosis (MS), clinical assessment, MRI and cerebrospinal fluid are important in the diagnostic process. However, no blood biomarker has been confirmed as a useful tool in the diagnostic work-up.ObjectivesBlood platelets contain a rich spliced mRNA repertoire that can alter during megakaryocyte development but also during platelet formation and platelet circulation. In this proof of concept study, we evaluate the diagnostic potential of spliced blood platelet RNA for the detection of MS.MethodsWe isolated and sequenced platelet RNA of blood samples obtained from 57 MS patients and 66 age- and gender-matched healthy controls (HCs). 60% was used to develop a particle swarm-optimized (PSO) support vector machine classification algorithm. The remaining 40% served as an independent validation series.ResultsIn total, 1249 RNAs with differential spliced junction expression levels were identified between platelets of MS patients as compared to HCs, including EPSTI1, IFI6, and RPS6KA3, in line with reported inflammatory signatures in the blood of MS patients. The RNAs were subsequently used as input for a MS classifier, capable of detecting MS with 80% accuracy in the independent validation series.ConclusionsSpliced platelet RNA may enable the blood-based diagnosis of MS, warranting large-scale validation.

Platelets are multifunctional cell fragments, circulating in blood in high abundance. Platelets assist in thrombus formation, sensing of pathogens entering the blood stream, signaling to immune cells, releasing vascular remodeling factors, and, negatively, enhancing cancer metastasis. Platelets are ‘educated’ by their environment, including in patients with cancer. Cancer cells appear to initiate intraplatelet signaling, resulting in splicing of platelet pre-mRNAs, and enhance secretion of cytokines. Platelets can induce leukocyte and endothelial cell modeling factors, for example, through adenine nucleotides (ATP), thereby facilitating extravasation of cancer cells. Besides releasing factors, platelets can also sequester RNAs and proteins released by cancer cells. Thus, platelets actively respond to queues from local and systemic conditions, thereby altering their transcriptome and molecular content. Platelets contain a rich repertoire of RNA species, including mRNAs, small non-coding RNAs and circular RNAs; although studies regarding the functionality of the various platelet RNA species require more attention. Recent advances in high-throughput characterization of platelet mRNAs revealed 10 to > 1000 altered mRNAs in platelets in the presence of disease. Hence, platelet RNA appears to be dynamically affected by pathological conditions, thus possibly providing opportunities to use platelet RNA as diagnostic, prognostic, predictive, or monitoring biomarkers. In this review, we cover the literature regarding the platelet RNA families, processing of platelet RNAs, and the potential application of platelet RNA as disease biomarkers.

Introduction Detection of early-stage breast cancer from blood is a highly desirable screening application of liquid biopsies. Tumor-educated platelets (TEP) contain distinct spliced RNA repertoires and can function as a blood-based biosource for the detection of cancer (Best et al. Cancer Cell 2015). Here, we investigated the potential of TEP RNA profiles as an early breast cancer diagnostics algorithm. Material and methods We sequenced platelets of women with stage I-III breast cancer (n=134) and asymptomatic non-cancer females (n=121). Samples were divided over a training, evaluation and validation set. We developed a TEP RNA-based classification algorithm, which was tailored towards two clinically relevant diagnostic applications. The first application is to rule out breast cancer in women with an abnormal mammography. The second application is to rule in breast cancer in asymptomatic women carrying an elevated baseline breast cancer risk. We examined if classifications depended on patient age, tumour stage, subtype, BRCA mutation status, and breast density. In addition, we tested if the algorithm was breast cancer specific by testing our algorithm in a pan-cancer cohort (n=192). Results and discussions In the evaluation cohort (n=48 of which n=24 early breast cancer cases) the TEP RNA-based breast cancer classification had an accuracy of 90% with an AUC of 0.96 (95% confidence interval (CI) 0.92–1.00; p Conclusion We show that platelet RNA signatures may enable blood-based screening for early breast cancer, and warrant validation in a confirmatory and screening setting.

Summary Blood-based liquid biopsies, including tumor-educated blood platelets (TEPs), have emerged as promising biomarker sources for non-invasive detection of cancer. Here we demonstrate that particle-swarm optimization (PSO)-enhanced algorithms enable efficient selection of RNA biomarker panels from platelet RNA-sequencing libraries (n = 779). This resulted in accurate TEP-based detection of early- and late-stage non-small-cell lung cancer (n = 518 late-stage validation cohort, accuracy, 88%; AUC, 0.94; 95% CI, 0.92–0.96; p < 0.001; n = 106 early-stage validation cohort, accuracy, 81%; AUC, 0.89; 95% CI, 0.83–0.95; p, Graphical Abstract, Highlights • Tumor-educated platelet (TEP) RNA profiles allow for blood-based cancer diagnostics • Inflammatory conditions only minimally confound TEP-based cancer detection • Swarm intelligence algorithms enable efficient selection of biomarker gene panels • TEP gene panels enable support vector machine-based classification of lung cancer, Best et al. use particle-swarm optimization algorithms and RNA-seq of tumor-educated platelets from patients to generate RNA sets capable of identifying patients with non-small-cell lung cancer, including those having early stage, from individuals without cancer, including those having inflammatory conditions.

I: Studies have shown the activity of anti-PD(L)-1 therapies in patients with advanced non-small-cell lung cancer (NSCLC), however response rates are rather low (~20%). Therefore, there is an urgent need to identify biomarkers that predict patient outcome to immunotherapy (IT). Previously we have shown that RNA signatures of tumor-educated platelets (TEPs) may have predictive value for tumor type-specific diagnostics. Platelets are intimately involved in immune responses. We hypothesized that TEP RNA profiles have predictive value for IT response. M: We collected baseline platelet pellets, isolated from whole blood by differential centrifugation, from 389 stage IV NSCLC patients treated with Nivolumab (table 1). Tumor response was evaluated at 3 and 6 months and reported according to RECIST 1.1. Platelet pellets were subjected to total RNA isolation, SMARTer cDNA amplification, and libraries were sequenced on the HiSeq platform. Raw data (~20 M reads per sample) was mapped to the human genome, intron-spanning spliced RNA reads were selected for analysis. Gene panels were calculated by ANOVA statistics. R: Until now 64 samples were sequenced, 30 of patients with clinical benefit (PR or SD at six months; CB) and 34 of patients with no clinical benefit (PD; no CB). 40 randomly selected samples (20 CB, 20 no CB) were used for training of the support vector machine (SVM)-based therapy response classification algorithm. 24 samples were used for independent evaluation of the classifier. Hierarchical clustering of genes with p Table 1.Patient CharacteristicsTotal CohortNumber of patients389Start of treatmentAugust 2015-november 2016Date lock1th of November, 2016Gender (Male)56%Age (med, range)64.5 years (29-83)Treatment lines1st line1%2nd line72%>2nd line27%DiagnosisNon-Squamous68%Squamous26%Other6%ResponseClinical benefit27%Progressive54%Unknown19%PD-L1 expressionResults expected in april Citation Format: Mirte Muller, Myron Best, Nik Sol, Anna-Larissa N. Niemeijer, Adrienne Vancura, Robert D. Schouten, Jeroen J.N. Hiltermann, Sjors G.J.G. In 't Veld, Daan van den Broek, Vincent van der Noort, Adrianus J. de Langen, Ed M.D. Schuuring, Thomas Wurdinger, Michel M. van den Heuvel. RNA-sequencing of tumor-educated platelets enables nivolumab immunotherapy response prediction [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr LB-248. doi:10.1158/1538-7445.AM2017-LB-248