Noncoding Mutations In Cancer Research Articles

Determinants of gastric cancer immune escape identified from non-coding immune-landscape quantitative trait loci

The landscape of non-coding mutations in cancer progression and immune evasion is largely unexplored. Here, we identify transcrptome-wide somatic and germline 3′ untranslated region (3′-UTR) variants from 375 gastric cancer patients from The Cancer Genome Atlas. By performing gene expression quantitative trait loci (eQTL) and immune landscape QTL (ilQTL) analysis, we discover 3′-UTR variants with cis effects on expression and immune landscape phenotypes, such as immune cell infiltration and T cell receptor diversity. Using a massively parallel reporter assay, we distinguish between causal and correlative effects of 3′-UTR eQTLs in immune-related genes. Our approach identifies numerous 3′-UTR eQTLs and ilQTLs, providing a unique resource for the identification of immunotherapeutic targets and biomarkers. A prioritized ilQTL variant signature predicts response to immunotherapy better than standard-of-care PD-L1 expression in independent patient cohorts, showcasing the untapped potential of non-coding mutations in cancer.

Landscape analysis of urothelial carcinoma (UC) by telomerase reverse transcriptase (TERT) alterations.

4524 Background: TERT is a catalytic subunit of telomerase, the unique enzyme that confers immortality to cells and is expressed in >90% of cancer cells. Mutations in the TERT promoter region (pTERTmut) are the most prevalent noncoding mutations in cancer. Recent data suggest pTERTmut are associated with improved outcomes in patients with UC treated with immune checkpoint inhibitors. We evaluated the molecular and immune landscape of UC with and without pTERTmut. Methods: UC tissue samples were analyzed for DNA alterations (NextSeq, 592 Genes; NovaSeq, WES) and mRNA expression (NovaSeq, WTS). Immune cell fraction was calculated by QuantiSeq (Finotello 2019, Genome Medicine). PD-L1 expression was assessed by immunohistochemistry (IHC) (Caris Life Sciences, Phoenix, AZ). MSI/MMR was tested by fragment analysis, IHC and NGS. TMB-H was based on a cut-off of > 10 mut/MB. We compared alterations between samples with and without detected pathogenic pTERTmut. Significance was determined by Mann–Whitney U, X2, and Fischer-Exact and p adjusted for multiple comparisons (q) was < 0.05 using Benjamini-Hochberg. Results: Overall, 1686 UC samples were analyzed, 1166 from primary lesions and 499 from a lymph node or metastatic site. pTERTmut was present in 68% of primary and 61% of metastatic tumors, and correlated with modest increase of TERT expression (1.18 fold, p=0.015). pTERTmut was associated with less frequent alterations in TP53, KMT2D, CCND1, MYC, KEAP1 and less MSI/dMMR. By contrast, pTERTmut was associated with more frequent alterations in ARID1A, TSC1, PIK3CA and TMB-H (all q<0.05). Over 41% of pTERTmut were TMB-H. TERTp mutations were not associated with FGFR alterations. The frequency of co-occurring mutations was similar by specimen site. In evaluating the immune landscape (Table), pTERTmut was associated with higher expression of PD-L1 (IHC, mRNA), PD-L2 (mRNA) and TIM3 (mRNA) in tumors from primary sites (all p and q<0.05), but not in metastatic sites. Investigation of tumor-associated immune cells demonstrated that pTERTmut correlated with higher percentage of M1-macrophages and CD8+ T cells in primary tumors, and was inversely-correlated with NK cells in metastatic sites (all q<0.05). Conclusions: This is the largest analysis looking at the molecular and immune landscape of pTERTmut UC tumors. We observed differential patterns of DNA alterations and tumor immune microenvironment based on pTERTmut status. Further work is needed to understand differences in these molecular cohorts and the association of these data with clinical outcomes. [Table: see text]

PanCancer analysis of somatic mutations in repetitive regions reveals recurrent mutations in snRNA U2

Current somatic mutation callers are biased against repetitive regions, preventing the identification of potential driver alterations in these loci. We developed a mutation caller for repetitive regions, and applied it to study repetitive non protein-coding genes in more than 2200 whole-genome cases. We identified a recurrent mutation at position c.28 in the gene encoding the snRNA U2. This mutation is present in B-cell derived tumors, as well as in prostate and pancreatic cancer, suggesting U2 c.28 constitutes a driver candidate associated with worse prognosis. We showed that the GRCh37 reference genome is incomplete, lacking the U2 cluster in chromosome 17, preventing the identification of mutations in this gene. Furthermore, the 5′-flanking region of WDR74, previously described as frequently mutated in cancer, constitutes a functional copy of U2. These data reinforce the relevance of non-coding mutations in cancer, and highlight current challenges of cancer genomic research in characterizing mutations affecting repetitive genes.

Functional annotation of noncoding mutations in cancer.

In a cancer genome, the noncoding sequence contains the vast majority of somatic mutations. While very few are expected to be cancer drivers, those affecting regulatory elements have the potential to have downstream effects on gene regulation that may contribute to cancer progression. To prioritize regulatory mutations, we screened somatic mutations in the Pan-Cancer Analysis of Whole Genomes cohort of 2,515 cancer genomes on individual bases to assess their potential regulatory roles in their respective cancer types. We found a highly significant enrichment of regulatory mutations associated with the deamination signature overlapping a CpG site in the CCAAT/Enhancer Binding Protein β recognition sites in many cancer types. Overall, 5,749 mutated regulatory elements were identified in 1,844 tumor samples from 39 cohorts containing 11,962 candidate regulatory mutations. Our analysis indicated 20 or more regulatory mutations in 5.5% of the samples, and an overall average of six per tumor. Several recurrent elements were identified, and major cancer-related pathways were significantly enriched for genes nearby the mutated regulatory elements. Our results provide a detailed view of the role of regulatory elements in cancer genomes.

Crossroads of telomere biology and anticancer drug discovery.

The telomere is the specialized nucleoprotein complex at the end of the chromosome. Its highly conserved 5′‐TTAGGG‐3′ repeats and shelterin protein complexes form a protective loop structure to maintain the integrity and stability of linear chromosomes. Although human somatic cells gradually shorten telomeres to undergo senescence or crisis, cancer cells activate telomerase, or the recombination‐based mechanism to maintain telomeres and exhibit immortality. As the most frequent non‐coding mutations in cancer, gain‐of‐function mutations in the promoter region of the telomerase catalytic subunit, TERT, trigger telomerase activation. Promoter methylation and copy number gain are also associated with the enhanced TERT expression. Although telomerase inhibitors were pioneered from telomere‐directed therapeutics, their efficacies are limited to cancer with short telomeres and some hematological malignancies. Other therapeutic approaches include a nucleoside analog incorporated to telomeres and TERT promoter‐driven oncolytic adenoviruses. Tankyrase poly(ADP‐ribose) polymerase, a positive regulator of telomerase, has been rediscovered as a target for Wnt‐driven cancer. Meanwhile, telomeric nucleic acids form a higher‐order structure called a G‐quadruplex (G4). G4s are formed genome‐wide and their dynamics affect various events, including replication, transcription, and translation. G4‐stabilizing compounds (G4 ligands) exert anticancer effects and are in clinical investigations. Collectively, telomere biology has provided clues for deeper understanding of cancer, which expands opportunities to discover innovative anticancer drugs.

BayesPI-BAR2: A New Python Package for Predicting Functional Non-coding Mutations in Cancer Patient Cohorts.

Most of somatic mutations in cancer occur outside of gene coding regions. These mutations may disrupt the gene regulation by affecting protein-DNA interaction. A study of these disruptions is important in understanding tumorigenesis. However, current computational tools process DNA sequence variants individually, when predicting the effect on protein-DNA binding. Thus, it is a daunting task to identify functional regulatory disturbances among thousands of mutations in a patient. Previously, we have reported and validated a pipeline for identifying functional non-coding somatic mutations in cancer patient cohorts, by integrating diverse information such as gene expression, spatial distribution of the mutations, and a biophysical model for estimating protein binding affinity. Here, we present a new user-friendly Python package BayesPI-BAR2 based on the proposed pipeline for integrative whole-genome sequence analysis. This may be the first prediction package that considers information from both multiple mutations and multiple patients. It is evaluated in follicular lymphoma and skin cancer patients, by focusing on sequence variants in gene promoter regions. BayesPI-BAR2 is a useful tool for predicting functional non-coding mutations in whole genome sequencing data: it allows identification of novel transcription factors (TFs) whose binding is altered by non-coding mutations in cancer. BayesPI-BAR2 program can analyze multiple datasets of genome-wide mutations at once and generate concise, easily interpretable reports for potentially affected gene regulatory sites. The package is freely available at http://folk.uio.no/junbaiw/BayesPI-BAR2/.

The Identification and Interpretation of cis-Regulatory Noncoding Mutations in Cancer.

In the need to characterise the genomic landscape of cancers and to establish novel biomarkers and therapeutic targets, studies have largely focused on the identification of driver mutations within the protein-coding gene regions, where the most pathogenic alterations are known to occur. However, the noncoding genome is significantly larger than its protein-coding counterpart, and evidence reveals that regulatory sequences also harbour functional mutations that significantly affect the regulation of genes and pathways implicated in cancer. Due to the sheer number of noncoding mutations (NCMs) and the limited knowledge of regulatory element functionality in cancer genomes, differentiating pathogenic mutations from background passenger noise is particularly challenging technically and computationally. Here we review various up-to-date high-throughput sequencing data/studies and in silico methods that can be employed to interrogate the noncoding genome. We aim to provide an overview of available data resources as well as computational and molecular techniques that can help and guide the search for functional NCMs in cancer genomes.

Abstract 2353: Analysis of noncoding mutations in cancer revealed novel potentially pathogenic recurrent mutations

Abstract Introduction: 98% of mutations are non-coding mutations. Thus far, most non-coding mutations are ignored or merely classified as variant of unknown significance with a few exceptions, despite the fact that non-coding region plays significant role in regulations and other important biological processes. Although they would not be directly druggable, these non-coding mutations might be important for cancer etiology, response to treatments and prognosis assessments. Method: Mutations in non-coding genes, intron, upstream and downstream, and intergenic regions are collected from COSMIC and TCGA as defined in Ensemble GRCh37.75 gene definition and hg19 RefSeq. Over 15 million non-coding mutations are analyzed for recurrence over 10 samples. Mutations in neighborhood of IG genes and dbSNP150 polymorphisms are removed from further investigation. We analyzed remaining mutations for possible super enhancer and transcription factor binding. Result: 911 distinct recurrent mutations are identified. Promoter mutations in protein coding genes like MPRS31, NUDFB9, SZT2, WASF3 in addition to TERT are discovered. Intronic mutations of well-known cancer genes such as HIF1A, NF1, KMT2C, and ATR are discovered with their associated cancer type specificity. As for non-coding RNA's, lincRNA CROCCP2, LINC00969 and miRNA MIR4477 appear frequently. Only 182 mutations are reluctantly classified as intergenic; intergenic loci RP11-96F8.1-KSR1P1, MLLT10P1-DEFB115, and bP-21201H5.1-IGHV1OR21-1 contain unusually high recurrent mutations. Conclusion: Many non-coding mutations worth further investigation are found. Still, it is quite possible that many of these mutations might be artifacts resulting from paralogous alignments, gene definition compatibility and other technical issues. Importantly, we discovered novel intronic mutations in genes already associated with treatments. Mutations in intergenic regions might be involved in regulatory mechanisms and chromosomal structures. Understanding these non-coding mutations will further refine our understanding of biology as well as cancer. Citation Format: Takahiko Koyama, Laxmi Parida. Analysis of noncoding mutations in cancer revealed novel potentially pathogenic recurrent mutations [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 2353.

A global transcriptional network connecting noncoding mutations to changes in tumor gene expression.

Although cancer genomes are replete with noncoding mutations, the effects of these mutations remain poorly characterized. Here we perform an integrative analysis of 930 tumor whole genomes and matched transcriptomes, identifying a network of 193 noncoding loci in which mutations disrupt target gene expression. These “somatic eQTLs” (expression Quantitative Trait Loci) are frequently mutated in specific cancer tissues, and the majority can be validated in an independent cohort of 3,382 tumors. Among these, we find that the effects of noncoding mutations on DAAM1, MTG2 and HYI transcription are recapitulated in multiple cancer cell lines, and that increasing DAAM1 expression leads to invasive cell migration. Collectively the noncoding loci converge on a set of core pathways, permitting a classification of tumors into pathway-based subtypes. The somatic eQTL network is disrupted in 88% of tumors, suggesting widespread impact of noncoding mutations in cancer.

Abstract 1574: Computational approach for discovery of regulatory noncoding variants in cancer

Abstract Noncoding mutations in the cancer genome have historically been poorly understood. Recent studies have shown however that mutations in intergenic regions may play an important role in cancer by introducing de novo regulatory elements and thus cis-regulating target gene expression. While mono-allelic expression of the dysregulated gene is an important signature of regulatory noncoding mutations, recognition of this feature has not been implemented by any methods evaluating noncoding mutations to date. To perform genome-wide detection of noncoding mutations with a potential regulatory function, we developed a novel computational approach implementing this allelic specific expression signature (ASE) as a core component, using WGS and RNAseq data as input. We first searched for genes showing ASE across the tumor genome by comparing the minor allele frequency of heterozygous markers between WGS and RNAseq data. We excluded genes known to be in an imprinted locus, as well as sites matching a precomputed set of 5,576,857 problematic polymorphism markers (estimated by analyzing WGS data from 625 normal genomes). Next, the somatic mutations were filtered for regulatory candidates that could introduce a transcription factor (TF) binding motif absent from the reference sequence. We included 614 TF binding motifs from the HOCOMOCO database and performed motif analysis with the FIMO package. Finally, the somatic mutations were assigned to dysregulated genes in the same topologically associating domains in human defined by public Hi-C data and prioritized by open chromatin state defined by DNaseI hypersensitivity regions (ENCODE project). We applied our approach to 22 T-ALL tumor samples collected at Shanghai Children’s Medical Center in a pilot study consisting of 13 diagnosis and 9 relapse samples (9 of these were trios). WGS was performed on both tumor and normal samples and RNAseq was performed for the tumor sample only. We predicted 213 regulatory noncoding mutations (157 unique variants) in total from this cohort, potentially targeting 119 genes. Among the dysregulated targets we found TAL1 and LMO2, which are known TFs playing a key role in T-ALL. TAL1 mutations were identified in a total of 6 tumors (3 trios). Besides the known oncogenic insertion at ~7kb upstream of TAL1 (1 trio), we also discovered a novel mutation in the first intron of TAL1 (2 trios) that may potentially drive ectopic TAL1 expression. Novel LMO2 mutations were found in 6 tumors (3 trios) at 3 different sites. This approach also identified novel targets such as GPSM1 and PTPRN2, shedding light on heretofore unknown players in T-ALL targeted by noncoding mutations. By integrating WGS and RNAseq data, our novel computational framework can identify somatic noncoding mutations in cancer that may introduce de novo regulatory elements, thus driving target gene expression. Wider application of this approach will expand our understanding of the role of noncoding mutations in the cancer genome. Citation Format: Yu Liu, Michael Edmonson, Xiaotu Ma, Michael Rusch, Yongjin Li, Benshang Li, Shuhong Shen, A. Thomas Look, Jinghui Zhang. Computational approach for discovery of regulatory noncoding variants in cancer [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 1574. doi:10.1158/1538-7445.AM2017-1574

Abstract 3005: Whole-genome sequencing identified novel non-coding mutations causal of oncogene activation in T-cell acute lymphoblastic leukemia

Abstract There is growing evidence that non-coding sequences in human genome often function as transcriptional regulatory elements of protein-coding genes. In fact, germline polymorphisms and somatically acquired mutations within regulatory DNA can profoundly alter chromatin structure and modify gene transcription, directly contributing to tumorigenesis. However, there is a paucity of unbiased genome-wide characterization of somatic non-coding mutations in cancer. Using T-cell acute lymphoblastic leukemia (T-ALL) as a model disease, we herein report a systematic interrogation of driver non-coding genomic alterations by paired whole-genome and transcriptome sequencing of 31 children with T-ALL. To identify non-coding mutations with potential regulatory impact in a genome-wide fashion, our analytical pipeline consisted of 3 approaches: 1) the “hotspot analysis” for recurring mutations at the nearby positions, 2) the “regional recurrence analysis” for predefined regulatory regions with significant enrichment of non-coding mutations, 3) the “transcriptional factor analysis” for mutations that potentially result in gain/loss of transcription factor binding sites and alter expression of adjacent genes. Remarkably, T-ALL oncogenes LMO1 and TAL1 emerged as loci with the most significant recurring non-coding mutations. At the LMO1 locus, 3 patients (9.7%) showed an identical single-nucleotide mutation proximal to the transcription start site of the long isoform of LMO1. This recurring mutation resulted in the gain of a canonical Myb binding site (AACGG) and ~120-fold increase in LMO1 transcription compared to patients with wildtype genotype. TAL1 overexpression was observed in 15 patients, of whom 11 had intrachromosomal rearrangement (STIL-TAL1 fusion). The remaining 4 patients had somatic insertion that created a MYB-mediated super enhancer, consistent with recent reports. LMO1 enhancer mutation was further confirmed in an independent validation cohort (N=26), in which we additionally identified a novel intrachromosomal rearrangement between MED17 and LMO1 resulting in transcriptional activation of the latter. In a panel of T-ALL cell lines, LMO1 enhancer mutation was again associated with dramatic overexpression of LMO1, an active enhancer histone mark (H3K27ac), Dnase hypersensitivity, and allele-specific binding of MYB. Interestingly, we also observed robust binding of TAL1, CREBBP, RUNX1, ETS1, ELF1 and RNA Polymerase II at this site. Reporter gene assay confirmed the MYB-mediated transcription activation effects of this LMO1 enhancer mutation in vitro. In this genome-wide investigation of non-coding mutations in T-ALL, we identified novel enhancer mutations with drastic effects on oncogene activation. Our findings expand the understanding of how genomic alterations in regulatory DNA contribute to cancer pathogenesis. Citation Format: Maoxiang Qian, Shaoyan Hu, Hui Zhang, Yu Guo, Jin Yang, Xujie Zhao, Lin Wan, Jun Lu, Jian Pan, Meimei Chang, Shirley K. Kham, Yong Cheng, Chunliang Li, Allen E. Yeoh, Anders Skanderup, Jun J. Yang. Whole-genome sequencing identified novel non-coding mutations causal of oncogene activation in T-cell acute lymphoblastic leukemia [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 3005. doi:10.1158/1538-7445.AM2017-3005

Abstract A14: Convergence analysis of regulatory mutations into immuno-modulatory pathways across 14 tumor types

Abstract Evasion of immune system surveillance is a hallmark of cancer. Cancer cells may secrete cytokines like TGF-β which hampers cytotoxic T-cells and natural killer cells in addition to recruiting tumor-infiltrating regulatory T-cells endowed with immunosuppressive potential. Little is known about the genomic basis for immune evasion especially in the context of dysregulation and rewiring of the immune-related circuitry of tumor cells. A vast majority of mutations in cancer frequently occur in non-coding regions. The functional impact of these mutations in mediating interactions with the tumor microenvironment have largely been unexplored. Given that recent efforts have implicated non-coding elements in various disease association studies, it can be expected that a significant number of recurrent non-coding mutations in cancer have a regulatory effect. Pan-Cancer analysis has improved the discovery and analysis of these regulatory mutations while avoiding the type I and type II errors made in several tissue-specific cancer projects. By leveraging recent pan-cancer whole-genome sequencing efforts, we have been able to characterize the non-coding mutational profiles of 505 samples, spread across 14 tumor types. These methods amplify the power to detect heterogeneous signals of positive selection thereby enhancing our ability to distinguish ‘driver’ from ‘passenger’ alterations. This study aims at applying a pan-cancer genome-wide approach towards identifying regulatory mutations that potentially impact immune modulation and evasion. We first infer the genome-wide position- and sample-specific probabilities of mutation from somatic mutations calls. A multinomial logistic regression model describes the relationship between the mutation rate and a set of explanatory variables such as the sample ID, replication timing, the genomic context and the local mutation rate. The predicted site-specific probabilities, when overlaid with tissue-specific annotations from the Roadmap Epigenomics consortium and GENCODE, allow us to derive a test statistic for each cis non-coding region that yields information regarding the likelihood that the region of interest is a “driver region”. By utilizing published databases on enhancer-gene links, we extend this framework to comprehensively characterize distal enhancer regions mapped with their target genes. We then systematically identify key immunomodulatory networks enriched for non-coding mutations and evaluate these in a pan-cancer context. A number of immune-related genes are found to harbor an excess of non-coding mutations suggesting higher-order regulatory convergence. We assign a significance score to this convergence by factoring the proximity to the target gene as well as the genomic instability modeled through local copy number changes. Therefore, we integrate individual low-frequency alterations into high-frequency recurrent events across different tumor types. We believe that the proposed model presents an unbiased method towards characterizing the impact of regulatory mutations towards immunomodulation, immune suppression and evasion. We anticipate that this approach will serve as a template for future functional non-coding mutational dissections in tumor-related studies. Citation Format: Karthik Murugadoss, Malene Rasmussen, Alvin Shi, Manolis Kellis. Convergence analysis of regulatory mutations into immuno-modulatory pathways across 14 tumor types. [abstract]. In: Proceedings of the AACR Special Conference on Tumor Immunology and Immunotherapy; 2016 Oct 20-23; Boston, MA. Philadelphia (PA): AACR; Cancer Immunol Res 2017;5(3 Suppl):Abstract nr A14.

Chromatin structure-based prediction of recurrent noncoding mutations in cancer.

Recurrence is a hallmark of cancer-driving mutations. Recurrent mutations can arise at the same site or affect the same gene at different sites. Here we identified a set of mutations arising in individual samples and altering different cis-regulatory elements that converge on a common gene via chromatin interactions. The mutations and genes identified in this fashion showed strong relevance to cancer, in contrast to noncoding mutations with site-specific recurrence only. We developed a prediction method that identifies potentially recurrent mutations on the basis of the features shared by mutations whose recurrence is observed in a given cohort. Our method was capable of accurately predicting recurrent mutations at the level of target genes but not mutations recurring at the same site. We experimentally validated predicted mutations in distal regulatory regions of the TERT gene. In conclusion, we propose a novel approach to discovering potential cancer-driving mutations in noncoding regions.

TERT promoter mutations and monoallelic activation of TERT in cancer.

Here we report that promoter mutations in telomerase (TERT), the most common noncoding mutations in cancer, give rise to monoallelic expression of TERT. Through deep RNA sequencing, we find that TERT activation in human cancer cell lines can occur in either mono- or biallelic manner. Without exception, hotspot TERT promoter mutations lead to the re-expression of only one allele, accounting for approximately half of the observed cases of monoallelic TERT expression. Furthermore, we show that monoallelic TERT expression is highly prevalent in certain tumor types and widespread across a broad spectrum of cancers. Taken together, these observations provide insights into the mechanisms of TERT activation and the ramifications of noncoding mutations in cancer.

Mutation of the TERT promoter, switch to active chromatin, and monoallelic TERT expression in multiple cancers.

Somatic mutations in the promoter of the gene for telomerase reverse transcriptase (TERT) are the most common noncoding mutations in cancer. They are thought to activate telomerase, contributing to proliferative immortality, but the molecular events driving TERT activation are largely unknown. We observed in multiple cancer cell lines that mutant TERT promoters exhibit the H3K4me2/3 mark of active chromatin and recruit the GABPA/B1 transcription factor, while the wild-type allele retains the H3K27me3 mark of epigenetic silencing; only the mutant promoters are transcriptionally active. These results suggest how a single-base-pair mutation can cause a dramatic epigenetic switch and monoallelic expression.

Cancer-associated TERT promoter mutations abrogate telomerase silencing.

Mutations in the human telomerase reverse transcriptase (TERT) promoter are the most frequent non-coding mutations in cancer, but their molecular mechanism in tumorigenesis has not been established. We used genome editing of human pluripotent stem cells with physiological telomerase expression to elucidate the mechanism by which these mutations contribute to human disease. Surprisingly, telomerase-expressing embryonic stem cells engineered to carry any of the three most frequent TERT promoter mutations showed only a modest increase in TERT transcription with no impact on telomerase activity. However, upon differentiation into somatic cells, which normally silence telomerase, cells with TERT promoter mutations failed to silence TERT expression, resulting in increased telomerase activity and aberrantly long telomeres. Thus, TERT promoter mutations are sufficient to overcome the proliferative barrier imposed by telomere shortening without additional tumor-selected mutations. These data establish that TERT promoter mutations can promote immortalization and tumorigenesis of incipient cancer cells.