Systematic analysis of telomere length and somatic alterations in 31 cancer types

Abstract

Cancer cells survive cellular crisis through telomere maintenance mechanisms. We report telomere lengths in 18,430 samples, including tumors and non-neoplastic samples, across 31 cancer types. Telomeres were shorter in tumors than in normal tissues and longer in sarcomas and gliomas than in other cancers. Among 6,835 cancers, 73% expressed telomerase reverse transcriptase (TERT), which was associated with TERT point mutations, rearrangements, DNA amplifications and transcript fusions and predictive of telomerase activity. TERT promoter methylation provided an additional deregulatory TERT expression mechanism. Five percent of cases, characterized by undetectable TERT expression and alterations in ATRX or DAXX, demonstrated elongated telomeres and increased telomeric repeat–containing RNA (TERRA). The remaining 22% of tumors neither expressed TERT nor harbored alterations in ATRX or DAXX. In this group, telomere length positively correlated with TP53 and RB1 mutations. Our analysis integrates TERT abnormalities, telomerase activity and genomic alterations with telomere length in cancer.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Figure 1: Telomere length in human cancer.
Figure 2: Multiple modalities associated with TERT overexpression.
Figure 3: Multivariable genomic determinants of TL.
Figure 4: ATRX-altered tumors show profound telomere elongation.
Figure 5: A substantial fraction of cancer samples lacks detectable TERT expression and mechanisms of ATRX deactivation.
Figure 6: TPE.

References

  1. 1

    O'Sullivan, R.J. & Karlseder, J. Telomeres: protecting chromosomes against genome instability. Nat. Rev. Mol. Cell Biol. 11, 171–181 (2010).

  2. 2

    de Lange, T. How telomeres solve the end-protection problem. Science 326, 948–952 (2009).

  3. 3

    Olovnikov, A.M. A theory of marginotomy. The incomplete copying of template margin in enzymic synthesis of polynucleotides and biological significance of the phenomenon. J. Theor. Biol. 41, 181–190 (1973).

  4. 4

    Shay, J.W., Pereira-Smith, O.M. & Wright, W.E. A role for both RB and p53 in the regulation of human cellular senescence. Exp. Cell Res. 196, 33–39 (1991).

  5. 5

    Stewart, S.A. & Weinberg, R.A. Telomeres: cancer to human aging. Annu. Rev. Cell Dev. Biol. 22, 531–557 (2006).

  6. 6

    Maser, R.S. & DePinho, R.A. Connecting chromosomes, crisis, and cancer. Science 297, 565–569 (2002).

  7. 7

    Sahin, E. & DePinho, R.A. Axis of ageing: telomeres, p53 and mitochondria. Nat. Rev. Mol. Cell Biol. 13, 397–404 (2012).

  8. 8

    Hackett, J.A. & Greider, C.W. Balancing instability: dual roles for telomerase and telomere dysfunction in tumorigenesis. Oncogene 21, 619–626 (2002).

  9. 9

    Greider, C.W. & Blackburn, E.H. Identification of a specific telomere terminal transferase activity in Tetrahymena extracts. Cell 43, 405–413 (1985).

  10. 10

    Morales, C.P. et al. Absence of cancer-associated changes in human fibroblasts immortalized with telomerase. Nat. Genet. 21, 115–118 (1999).

  11. 11

    Shay, J.W. & Bacchetti, S. A survey of telomerase activity in human cancer. Eur. J. Cancer 33, 787–791 (1997).

  12. 12

    Huang, F.W. et al. Highly recurrent TERT promoter mutations in human melanoma. Science 339, 957–959 (2013).

  13. 13

    Horn, S. et al. TERT promoter mutations in familial and sporadic melanoma. Science 339, 959–961 (2013).

  14. 14

    Zhang, A. et al. Frequent amplification of the telomerase reverse transcriptase gene in human tumors. Cancer Res. 60, 6230–6235 (2000).

  15. 15

    Peifer, M. et al. Telomerase activation by genomic rearrangements in high-risk neuroblastoma. Nature 526, 700–704 (2015).

  16. 16

    Bryan, T.M., Englezou, A., Dalla-Pozza, L., Dunham, M.A. & Reddel, R.R. Evidence for an alternative mechanism for maintaining telomere length in human tumors and tumor-derived cell lines. Nat. Med. 3, 1271–1274 (1997).

  17. 17

    Dilley, R.L. & Greenberg, R.A. ALTernative telomere maintenance and cancer. Trends Cancer 1, 145–156 (2015).

  18. 18

    Jiao, Y. et al. DAXX/ATRX, MEN1, and mTOR pathway genes are frequently altered in pancreatic neuroendocrine tumors. Science 331, 1199–1203 (2011).

  19. 19

    Heaphy, C.M. et al. Altered telomeres in tumors with ATRX and DAXX mutations. Science 333, 425 (2011).

  20. 20

    Ramamoorthy, M. & Smith, S. Loss of ATRX suppresses resolution of telomere cohesion to control recombination in ALT cancer cells. Cancer Cell 28, 357–369 (2015).

  21. 21

    Ding, Z., Mangino, M., Aviv, A., Spector, T. & Durbin, R. Estimating telomere length from whole genome sequence data. Nucleic Acids Res. 42, e75 (2014).

  22. 22

    Dlouha, D., Maluskova, J., Kralova Lesna, I., Lanska, V. & Hubacek, J.A. Comparison of the relative telomere length measured in leukocytes and eleven different human tissues. Physiol. Res. 63 (Suppl. 3), S343–S350 (2014).

  23. 23

    Albanell, J. et al. Telomerase activity in germ cell cancers and mature teratomas. J. Natl. Cancer Inst. 91, 1321–1326 (1999).

  24. 24

    Killela, P.J. et al. TERT promoter mutations occur frequently in gliomas and a subset of tumors derived from cells with low rates of self-renewal. Proc. Natl. Acad. Sci. USA 110, 6021–6026 (2013).

  25. 25

    Vinagre, J. et al. Frequency of TERT promoter mutations in human cancers. Nat. Commun. 4, 2185 (2013).

  26. 26

    Khan, A. & Zhang, X. dbSUPER: a database of super-enhancers in mouse and human genome. Nucleic Acids Res. 44, D164–D171 (2016).

  27. 27

    Calo, E. & Wysocka, J. Modification of enhancer chromatin: what, how, and why? Mol. Cell 49, 825–837 (2013).

  28. 28

    Kundaje, A. et al.; Roadmap Epigenomics Consortium et al. Integrative analysis of 111 reference human epigenomes. Nature 518, 317–330 (2015).

  29. 29

    Torres-García, W. et al. PRADA: pipeline for RNA sequencing data analysis. Bioinformatics 30, 2224–2226 (2014).

  30. 30

    Yoshihara, K. et al. The landscape and therapeutic relevance of cancer-associated transcript fusions. Oncogene 34, 4845–4854 (2015).

  31. 31

    Yates, A. et al. Ensembl 2016. Nucleic Acids Res. 44, D710–D716 (2016).

  32. 32

    Hrdlicková, R., Nehyba, J. & Bose, H.R. Jr. Alternatively spliced telomerase reverse transcriptase variants lacking telomerase activity stimulate cell proliferation. Mol. Cell. Biol. 32, 4283–4296 (2012).

  33. 33

    Castelo-Branco, P. et al. Methylation of the TERT promoter and risk stratification of childhood brain tumours: an integrative genomic and molecular study. Lancet Oncol. 14, 534–542 (2013).

  34. 34

    Blasco, M.A. Telomeres and human disease: ageing, cancer and beyond. Nat. Rev. Genet. 6, 611–622 (2005).

  35. 35

    Borah, S. et al. TERT promoter mutations and telomerase reactivation in urothelial cancer. Science 347, 1006–1010 (2015).

  36. 36

    Counter, C.M. et al. Telomerase activity is restored in human cells by ectopic expression of hTERT (hEST2), the catalytic subunit of telomerase. Oncogene 16, 1217–1222 (1998).

  37. 37

    Rohde, V. et al. Expression of the human telomerase reverse transcriptase is not related to telomerase activity in normal and malignant renal tissue. Clin. Cancer Res. 6, 4803–4809 (2000).

  38. 38

    Kilian, A. et al. Isolation of a candidate human telomerase catalytic subunit gene, which reveals complex splicing patterns in different cell types. Hum. Mol. Genet. 6, 2011–2019 (1997).

  39. 39

    Wong, M.S., Wright, W.E. & Shay, J.W. Alternative splicing regulation of telomerase: a new paradigm? Trends Genet. 30, 430–438 (2014).

  40. 40

    Katz, Y., Wang, E.T., Airoldi, E.M. & Burge, C.B. Analysis and design of RNA sequencing experiments for identifying isoform regulation. Nat. Methods 7, 1009–1015 (2010).

  41. 41

    Schwartzentruber, J. et al. Driver mutations in histone H3.3 and chromatin remodelling genes in paediatric glioblastoma. Nature 482, 226–231 (2012).

  42. 42

    Flynn, R.L. et al. Alternative lengthening of telomeres renders cancer cells hypersensitive to ATR inhibitors. Science 347, 273–277 (2015).

  43. 43

    Reddel, R.R. The role of senescence and immortalization in carcinogenesis. Carcinogenesis 21, 477–484 (2000).

  44. 44

    Heaphy, C.M. et al. Prevalence of the alternative lengthening of telomeres telomere maintenance mechanism in human cancer subtypes. Am. J. Pathol. 179, 1608–1615 (2011).

  45. 45

    Gonzalo, S. et al. Role of the RB1 family in stabilizing histone methylation at constitutive heterochromatin. Nat. Cell Biol. 7, 420–428 (2005).

  46. 46

    García-Cao, M., Gonzalo, S., Dean, D. & Blasco, M.A. A role for the Rb family of proteins in controlling telomere length. Nat. Genet. 32, 415–419 (2002).

  47. 47

    Robin, J.D. et al. Telomere position effect: regulation of gene expression with progressive telomere shortening over long distances. Genes Dev. 28, 2464–2476 (2014).

  48. 48

    Martínez, E. et al. Comparison of gene expression patterns across 12 tumor types identifies a cancer supercluster characterized by TP53 mutations and cell cycle defects. Oncogene 34, 2732–2740 (2015).

  49. 49

    Renaud, S. et al. Dual role of DNA methylation inside and outside of CTCF-binding regions in the transcriptional regulation of the telomerase hTERT gene. Nucleic Acids Res. 35, 1245–1256 (2007).

  50. 50

    Valentijn, L.J. et al. TERT rearrangements are frequent in neuroblastoma and identify aggressive tumors. Nat. Genet. 47, 1411–1414 (2015).

  51. 51

    Davis, C.F. et al. The somatic genomic landscape of chromophobe renal cell carcinoma. Cancer Cell 26, 319–330 (2014).

  52. 52

    Khoury, J.D. et al. Landscape of DNA virus associations across human malignant cancers: analysis of 3,775 cases using RNA-Seq. J. Virol. 87, 8916–8926 (2013).

  53. 53

    Xi, L. & Cech, T.R. Inventory of telomerase components in human cells reveals multiple subpopulations of hTR and hTERT. Nucleic Acids Res. 42, 8565–8577 (2014).

  54. 54

    Chapon, C., Cech, T.R. & Zaug, A.J. Polyadenylation of telomerase RNA in budding yeast. RNA 3, 1337–1351 (1997).

  55. 55

    Porro, A., Feuerhahn, S., Reichenbach, P. & Lingner, J. Molecular dissection of telomeric repeat-containing RNA biogenesis unveils the presence of distinct and multiple regulatory pathways. Mol. Cell. Biol. 30, 4808–4817 (2010).

  56. 56

    Feuerhahn, S., Iglesias, N., Panza, A., Porro, A. & Lingner, J. TERRA biogenesis, turnover and implications for function. FEBS Lett. 584, 3812–3818 (2010).

  57. 57

    Clynes, D. et al. Suppression of the alternative lengthening of telomere pathway by the chromatin remodelling factor ATRX. Nat. Commun. 6, 7538 (2015).

  58. 58

    Przybycin, C.G. et al. Chromophobe renal cell carcinoma: a clinicopathologic study of 203 tumors in 200 patients with primary resection at a single institution. Am. J. Surg. Pathol. 35, 962–970 (2011).

  59. 59

    Guo, Z. & Lloyd, R.V. Pheochromocytomas and paragangliomas: an update on recent molecular genetic advances and criteria for malignancy. Adv. Anat. Pathol. 22, 283–293 (2015).

  60. 60

    Davies, L. & Welch, H.G. Increasing incidence of thyroid cancer in the United States, 1973-2002. J. Am. Med. Assoc. 295, 2164–2167 (2006).

  61. 61

    Lenders, J.W.M., Eisenhofer, G., Mannelli, M. & Pacak, K. Phaeochromocytoma. Lancet 366, 665–675 (2005).

  62. 62

    Wilks, C. et al. The Cancer Genomics Hub (CGHub): overcoming cancer through the power of torrential data. Database (Oxford) https://dx.doi.org/10.1093/database/bau093 (2014).

  63. 63

    Seth, S. et al. Flowr: robust and efficient pipelines using a simple language-agnostic approach. Preprint at http://biorxiv.org/content/early/2015/10/22/029710 (2015).

  64. 64

    Mermel, C.H. et al. GISTIC2.0 facilitates sensitive and confident localization of the targets of focal somatic copy-number alteration in human cancers. Genome Biol. 12, R41 (2011).

  65. 65

    McKenna, A. et al. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 20, 1297–1303 (2010).

  66. 66

    Chiang, C. et al. SpeedSeq: ultra-fast personal genome analysis and interpretation. Nat. Methods 12, 966–968 (2015).

  67. 67

    Li, H. Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. Preprint https://arxiv.org/abs/1303.3997 (2013).

  68. 68

    Kent, W.J. et al. The human genome browser at UCSC. Genome Res. 12, 996–1006 (2002).

  69. 69

    Ben-Porath, I. et al. An embryonic stem cell-like gene expression signature in poorly differentiated aggressive human tumors. Nat. Genet. 40, 499–507 (2008).

  70. 70

    Lawrence, M.S. et al. Mutational heterogeneity in cancer and the search for new cancer-associated genes. Nature 499, 214–218 (2013).

Download references

Acknowledgements

We thank all patients who consented to have their samples analyzed by The Cancer Genome Atlas Network. We thank D. Jiao and D. Jackson at the High Performance Computing facility at MD Anderson Cancer Center for assistance with data downloading and storage. We thank I.R. Watson (McGill University) and T. Wu (MD Anderson Cancer Center) for insights on TCGA melanoma project, and D. Wheeler (Baylor College of Medicine) for insights on TCGA liver cancer project. We thank S. Borah and T.R. Cech (University of Colorado) for sharing telomerase enzymatic activity data of the 23 urothelial cancer cell lines. We thank D. Broccoli (Mercer University) for sharing the 14 gene signature from GSE20559. We thank X. Wang (Baylor College of Medicine) for help with CPTAC data and A. Barbo (MD Anderson Cancer Center) for statistical advice. We thank B. Murray (Broad Institute) for suggestions regarding the analysis of copy number data. This project is supported by grants from the National Institutes of Health (R01CA190121 and P01CA085878 to R.G.W.V., P50CA127001 to Z.S. and R.G.W.V. and U24CA143883) and the Cancer Prevention and Research Institute of Texas (CPRIT) (R140606) to R.G.W.V. This work was supported by Cancer Center Support Grants P30CA16672 and P30CA034196.

Author information

F.P.B. was involved in all aspects of data analysis. W.W. performed linear mixed modeling. M.T. and S.B.A. analyzed whole-genome sequencing data. E.M.-L. analyzed gene expression data. X.H. and Q.W. performed gene fusion analysis. K.C.A. was involved in methylation and epigenetics analysis. S.S., X.S. and J.Z. collected and analyzed low-pass sequencing data. T.L. collected clinical data for the solid tissue samples. J.H. provided critical insights into ALT and telomerase biology. S.Z. designed the telomerase signature score. S.Z., F.P.B. and R.G.W.V. conceived the study and wrote the paper. S.Z. and R.G.W.V. supervised the study.

Correspondence to Siyuan Zheng or Roel G W Verhaak.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 Sample selection procedure and TL ratio across cancer.

(a) Flowchart of sample selection. After exclusion of unsuitable samples, 18,430 samples remained. Tumor and normal samples were subsequently paired, and extra pairs per patient were dropped for a paired set consisting of n = 8,953 pairs. Further sample selection based on available data is presented in Figure 1a. The sample selection, tumor/normal pairing procedure and replicate analysis are explained in-depth in the supplementary methods. (b) Boxplot of log T/N TL ratio ‘log(Tumor TL/Control TL)’ across cancer types for all n = 8,953 samples in the paired set. Boxes colored in blue indicate that the median TL ratio for that cancer type is greater than 1 (log ratio greater than 0), and thus more than 50% of samples show TL elongation. Numbers and percentages at the top and bottom whiskers represent cancer cases with TL longer and shorter than matched normal, respectively.

Supplementary Figure 2 Benchmark of TL and TL ratio across centers and sequencing methods.

(a) Boxplots showing log(TL) across centers and sequencing methods. Each point indicates the median for a single cancer type. BI, Broad Institute sequencing center; BCM, Baylor College of Medicine sequencing center; WUGSC, Washington University sequencing center; HMS-RK, low pass sequencing. (b) Boxplots showing the log(Tumor TL/Normal TL) ratio across centers and sequencing methods. Each point indicates the median for a single cancer type. (c) Comparison of n = 3,883 log(TL) replicates. Sequencing center was kept constant for comparison across sequencing method and was variable for comparison within sequencing method. Replicate pairs that did not follow these criteria were dropped. (c) Boxplots showing the log(Tumor TL/Normal TL) ratio across centers and sequencing methods. Each point indicates the median for a single cancer type. (d) Comparison of n = 1,861 T/N TL ratio replicates. Sequencing center was kept constant for comparison across sequencing method and was variable for comparison within sequencing method. Replicate pairs that did not follow these criteria were dropped.

Supplementary Figure 3 Telomere length in matching normal tissue.

(a) Linear mixed model mean TL estimates by for each tissue type. Error bars indicate 95% confidence interval. Estimates were adjusted for age, gender and sequencing center. (b) Pairwise comparison of normal TL between tissue types. Tukey-Kramer adjustment was used for all pairwise comparisons. (c) Scatter plots for age and normal TL across each normal tissue type (n > 10).

Supplementary Figure 4 TERT alterations (promoter mutations, amplifications and structural variants) across cancer types.

(a) Barplot of TERTp mutations by tumor type in samples from the extended set with TERTp mutation status known (n = 1,581). (b) Barplot of TERT amplifications by tumor type in all samples from the extended set (n = 6,835). (c) Barplot of TERT and TERTp structural variants by tumor type in samples from the extended set with structural variant calls (n = 792). (d) Boxplot of H3K27ac, H3K27me3 and H3K27me1 levels from the Roadmap Epigenomics Consortium at the locations of TERTp structural variant proximal and distal breakpoints (n = 17). Each plot represents a comparison of the distal and proximal breakpoint in a single sample.

Supplementary Figure 5 Further characterization of TERT alterations in cancer.

(a) Circos plot of TERT fusion partners. Only segments of each chromosome are shown. Segment coordinates in kb are indicated in purple. The 5’ fusion partner is shown in blue and the 3’ fusion partner is shown in orange. (b) Pairwise comparison of TERT promoter beta value (cg11625005) in tumor and normal. P-values were calculated using a two sided Mann–Whitney U test. (c) Boxplot of TL length ratio in groups of TERT alterations. TL length ratio in TERT altered groups was compared to the TERT wt group using a two-sided t-test. TERTp meth, promoter methylation; TERTp mut, promoter hotspot mutation; TERT amp, copy number amplification; TERTp sv, promoter structural variation; TERT sv, gene body structural variation; TERT wt, cases without detectable evidences in all aforementioned groups. (d) Boxplot of TERC expression in groups of TERC alterations. TERC expression in TERC altered groups was compared to the TERC wt group using a two-sided t-test. ***P < 0.0001; **P < 0.001; *P < 0.05; N.S. not significant.

Supplementary Figure 6 Abundance of the minus beta splice variant.

(a) Example of TERT isoform abundance. The figure shows three samples with abundant full-length transcripts (in red), three samples with mixed full length and minus-beta transcripts (orange) and three samples with predominantly minus beta transcripts (green). Full-length and minus beta exon models are shown for reference. The MISO ψ (psi, percent spliced in) is shown for each sample and indicates the percentage of full-length transcripts relative to minus beta transcripts. (b) Histogram of the percentage of full-length transcripts relative to minus beta transcripts in n = 1,201 samples. Samples with less than 25% full length transcripts (more than 75% minus beta transcripts) are shown in green, samples with between 25% and 75% full-length transcripts in orange, and samples with more than 75% full-length transcripts in red. There is a significant enrichment of full-length transcripts relative to minus beta transcripts (one-sample t-test P < 0.0001; Mu = 50%).

Supplementary Figure 7 Inferring telomerase activity using a gene expression signature.

(a) The predicted telomerase activity is correlated with experimentally determined telomerase enzymatic activity in 11 urothelial cancer cell lines with a borderline significance (P = 0.07, Spearman correlation). (b) Telomerase signature score in tumor and normal samples in TCGA cohorts. Across all cancer types except KICH and THCA, tumor scores are significantly higher than that of normal samples (P < 0.001, t-test). (c) Distribution of telomerase activity score across TERT alteration categories. All TERT aberrant groups are significantly higher than the TERT wt group (P < 0.01). (d) Telomerase activity score in 31 cancer types. x-axis represents mean TERT expression measured by TPM. y-axis represents median telomerase score. The size of each dot is proportional to the percentage of TERT expressing samples in the corresponding cancer type. x-axis and y-axis are in log2 scale for better visualization.

Supplementary Figure 8 TL genomic associations, ATRX and TERRA.

(a) Scatterplot showing gene to TL ratio associations using the extended set (n = 6,835). Results are grouped by disease. P-values were calculated using a two-sided t-test and adjusted for multiple testing using FDR. Up to five negatively (left) or positively (right) associated genes with an FDR < 0.25 are listed in each plot. (b) Histogram of DNA breakpoints in ATRX in the core set (n = 473 samples). Bars are colored according to breakpoint detection method. (c) Circos plot of ATRX fusion partners. Only segments of each chromosome are shown. Segment coordinates in kb are indicated. The 5’ fusion partner is shown in blue and the 3’ fusion partner is shown in orange.

Supplementary Figure 9 Classification of tumors on the basis of TERT expression and ATRX or DAXX genomic alterations.

(a) Comparison of ATRX variant classification between samples classified as TERTexpr and samples classified as ATRX/DAXXalt. Silent mutations, DAXX alterations, deletions and structural variants were omitted. Truncating variants consist of frameshift, nonsense and splice site variants. Non-truncating mutations consist of missense mutations and in-frame indels. P-value was calculated using a Fisher’s exact test. (b) Distribution of telomerase signature score across TERT/ATRX/DAXX groups in the core set. A small group, TERTalt-TERTexpr-ATRX/DAXXalt, has only 2 cases thus were excluded from the comparison. All P values are derived from comparisons with the ATRX/DAXXalt group (blue). Red are TERT expressing groups, and purple is the double wild-type group. The group in black is TERTalt but without expression. (c) Number of copy number segments by TERTexpr-ATRX/DAXXalt group. Number of segments between groups was compared using two-sided t-tests. (d) Mutational burden by TERTexpr-ATRX/DAXXalt groups. Mutational burden between groups was compared using two-sided t-tests. (e) Survival differences between TERTexpr-ATRX/DAXXalt groups by cancer type. Number of patients included, number of events (deaths) and univariable log-rank P-values are indicated in the bottom left corner for each tumor type. Hazard ratios and 95% confidence intervals comparing survival in the double wild-type group (green) and ATRX/DAXXalt group (blue) relative to the TERTexpr group are shown in the top right corner of each tumor type. Groups with less than six samples were omitted. ***P < 0.0001; **P < 0.001; *P < 0.05; N.S. not significant.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–9 and Supplementary Note (PDF 2469 kb)

Supplementary Table 1

Sample information and telomere length estimate. (XLSX 10481 kb)

Supplementary Table 2

Output of linear mixed model. (XLSX 66 kb)

Supplementary Table 3

Structural variant calls and fusion calls. (XLSX 41 kb)

Supplementary Table 4

TERT isoform models and the telomere activity gene signature. (XLSX 10 kb)

Supplementary Table 5

Unsupervised analysis of genomic correlates with telomere length. (XLSX 400 kb)

Supplementary Table 6

Comparison of ATRX or DAXX alteration prevalence with published prevalence of ALT in 26 cancer types. (XLSX 23 kb)

Supplementary Table 7

Expression analysis. (XLSX 882 kb)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Barthel, F., Wei, W., Tang, M. et al. Systematic analysis of telomere length and somatic alterations in 31 cancer types. Nat Genet 49, 349–357 (2017). https://doi.org/10.1038/ng.3781

Download citation

Further reading