Nature Genetics: doi: /ng Supplementary Figure 1. Sample selection procedure and TL ratio across cancer.

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1 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.

2 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.

3 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).

4 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.

5 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 (cg ) 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 twosided 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 < ; **P < 0.001; *P < 0.05; N.S. not significant.

6 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). Fulllength 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 < ; Mu = 50%).

7 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 log 2 scale for better visualization.

8 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.

9 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 TERT expr and samples classified as ATRX/DAXX alt. 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, TERT alt -TERT expr -ATRX/DAXX alt, has only 2 cases thus were excluded from the comparison. All P values are derived from comparisons with the ATRX/DAXX alt group (blue). Red are TERT expressing groups, and purple is the double wild-type group. The group in black is TERT alt but without expression. (c) Number of copy number segments by TERT expr -ATRX/DAXX alt group. Number of segments between groups was compared using two-sided t-tests. (d) Mutational burden by TERT expr -ATRX/DAXX alt groups. Mutational burden between groups was compared using two-sided t-tests. (e) Survival differences between TERT expr -ATRX/DAXX alt 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/DAXX alt group (blue) relative to the TERT expr group are shown in the top right corner of each tumor type. Groups with less than six samples were omitted. ***P < ; **P < 0.001; *P < 0.05; N.S. not significant.

10 Data Generation Raw sequencing data collection Pre-aligned whole-genome (WGS), low-pass whole-genome (LPS) and exome (WXS) BAM files (n=24,049 BAM files; Table S1B; ~720 TB) were downloaded from CGHub for analysis. BAM files were processed through a pipeline for telomere length calling. WGS and LPS BAM files were additionally processed for structural variant calling and TERT promoter pileup. Mutation calling was not performed and mutation calls based on WXS were downloaded and compiled as described elsewhere in this document. Pre-aligned RNA sequencing BAM files (n=12,160 BAM files; ~83 TB) were downloaded from CGHub for analysis. BAM files were processed through a pipeline for TERRA expression estimation. Unaligned RNA sequencing BAM files were downloaded from CGHub and processed through a pipeline of RNA-seq Data Analysis (PRADA) as previously described 1,2. Processed data was used for fusion transcript detection and TERT isoform detection. Clinical data collection Clinical data (~237 MB) was downloaded from Firehose (stdata 2016_01_28) using the firehose_get tool. Clinical data elements comprise histology, grade, gender, age at diagnosis, race/ethnicity, smoking history, vital status and overall survival. Overall survival was defined as the time from surgical diagnosis until death. Cases that were still alive at the time of this study have overall survival time censored at the time of last follow-up. Mutation data collection Mutation annotation files (MAF) for the different cancer types were obtained from the DCC, the publication supplements or the active working group (~ 3 GB). Because MAF files were obtained from different sources and initial MAF files were generated by different sequencing centers, the files had slightly different structures. Column names were therefore renamed to reflect similar data in each file, and the resulting files were combined into a single pan-cancer MAF file. No attempts were made to liftover older hg18 calls to hg19 or to re-annotate the MAF files using uniform gene annotations. Replicate samples and samples based on whole genome amplified DNA were excluded. Intronic variants and blacklisted variants were subsequently dropped. Variants with a variant allele fraction < 0.1 were dropped when this information was available. Samples were annotated as being hypermutated if the mutation frequency for that sample exceeded the median mutation frequency for the respective tumor type, plus three times the median absolute deviation. For each gene we calculated the mutation frequency, the proportion of hotspot and the proportion of truncating mutations. To avoid incorrect frequencies of hotspot mutations, only hg19 aligned mutation calls were included in this analysis. Genes with greater than 20% hotspot mutations were annotated as putative oncogene, whereas genes with greater than 20% truncating mutations were annotated as putative tumor suppressor gene 3. mrna expression data collection 1

11 Illumina HiSeq UNC aligned (RNAseq V2) gene-level, isoform-level and exon-level expression data (~85 GB) was downloaded from Broad Firehose (stdata 2016_01_28) using the firehose get_tool. Aliquots from all tumor types were combined, matched against TCGA annotation and aliquots with redactions were removed. A gene expression matrix was created for TPM data and raw RSEM read counts. TPM data was mediancentered by cancer type to alleviate tissue-specific expression effects. Prior to median-centering, genes with variability below 9 or with a TPM-value of 2 or less in 95% of samples or more were filtered. DNA methylation data collection Illumina Infinium Human Methylation 450k DNA methylation data (~177 GB) was downloaded from Broad Firehose (stdata 2016_01_28) using the firehose get tool. Aliquots from all tumor types were combined, matched against TCGA annotation and aliquots with relevant redactions were removed. Probes with any NA values were removed. DNA copy number data collection SNP6 segmentation files (~860 MB) were downloaded from Broad Firehose (stdata 2016_01_28) using the firehose_get tool. Aliquots from all tumor types were merged, matched against TCGA annotation, and aliquots with relevant redactions were removed. The tool GISTIC 2.0 was then used to identify significantly reoccurring focal and broad copy number changes 4 on the combined segmentation file. Events with a Q-value < 0.10 were considered significant. For each statistically significant peak, GISTIC 2.0 indicates a narrow focal peak and a wider surrounding peak. Breakpoints in ATRX were manually curated to evaluate suspected structural variants. The ATRX region of interest (ROI) was defined as the ATRX gene body according to UCSC (chrx: ). ATRX was deemed to be broken when there were one or more copy number segment(s) partially overlapping the ATRX ROI or copy number segment(s) within the ATRX. When there were two or more copy number segments, we required a difference in segment mean of at least 0.3, whereas if there was only one segment involved, we required minimum absolute amplitude of at least 0.3. Sample Selection We included all purified DNA hg19-aligned exome, whole genome and low-pass sequencing samples available on CGHub 5 from the following 32 TCGA cohorts: ACC, BLCA, BRCA, CESC, CHOL, COAD, DLBC, ESCA, GBM, HNSC, KICH, KIRC, KIRP, LAML, LGG, LIHC, LUAD, LUSC, OV, PAAD, PCPG, PRAD, READ, SARC, SKCM, STAD, TGCT, THCA, THYM, UCEC, UCS and UVM. An exception was made for LAML, which did not have purified DNA exome sequencing samples available, and amplified DNA samples were included instead. A full list of all N=24,049 samples can be found in Supplementary Table 1. A flowchart of the sample selection procedure can be found in Supplementary Figure 1a. 2

12 All N=24,049 downloaded samples were then annotated based on TCGA sample annotation downloaded on May 12, 2016 from the TCGA portal ( Samples that were annotated as having a bad DNA quality or failing a QC step were excluded. We also dropped samples with less than 20 million reads and samples where we failed to compute a telomere length. We next removed technical replicates (N=4,973), although we kept all replicates for replicate analysis described elsewhere in this document. We then dropped samples from patients who were later marked as having received systematic treatment prior to sample collection. Similarly, we dropped samples from patients who after revision of the pathological diagnosis, no longer met inclusion criteria for the intended study. In order to keep only one tumor sample per patient, metastatic and recurrent samples were dropped. An exception is melanoma, where the only sample was often a metastatic tumor. In this case the metastatic tumor was kept. Rarely patients had additional primary tumor samples, buccal cell or bone marrow normal controls. Because these samples were very rare, they were dropped from our analysis. Some patients had a tumor-normal pair profiled on one platform/center, but an unpaired sample of a different type profiled on another platform. In order to limit each patient to a single center and platform, the unpaired partner in these instances was also dropped (N=114). After exclusion, an unpaired set of N=18,430 remained. In order to match tumor and normal samples from the same patient together we first removed all samples without a possible paired partner (n=222). Only pairs where both tumor and normal were profiled by the same sequencing center and method were considered. Next, all possible tumor-normal pairs were constructed (N=9,275), and these consisted of solid tumor vs. blood normal, solid tumor vs. solid normal, metastatic tumor vs. blood normal, metastatic tumor vs. solid normal and blood tumor vs. solid control. Several patients had more than one tumor-normal pairs and we therefore dropped N=322 second pairs, preferring blood normal over solid normal when both were available. The final paired set consisted of N=8,953 tumor/normal pairs. We then took the set of 473 T/N pairs with available whole genome sequencing-based TL, RNA sequencing (gene expression, gene fusion and TERRA expression), DNA methylation profiles, DNA copy number profiling, exome sequencing-based mutation calls, whole-genome or targeted sequencing based TERT promoter mutation calls and whole genome sequencing based structural variant calls and labeled this as the core cohort. To maximize our power, we constructed an extended dataset (n=6,835) including all core set samples but additionally containing cases with overlapping low-pass or exome sequencingbased TL, exome sequencing-based mutation calls, DNA copy number profiles and RNA sequencing (gene expression, gene fusion and TERRA expression). Each sample in the extended set was annotated based on TERT expression and somatic alterations in DAXX and/or ATRX. Samples were classified as TERT expressed when we detected 2 or more raw aligned RSEM quantified reads. The remaining samples were classified as double wild-type or ATRX/DAXX altered if we detected ATRX or DAXX mutations, deletions or structural variants. 3

13 In 3% of tumors we found co-occurring ATRX/DAXX alterations and TERT expression (n=210/6,835, 3%). However, these co-occurring ATRX mutations tended to be non-truncating events (missensense, inframe indel), whereas non co-occurring ATRX mutations were more often loss-of-function truncating mutations (eg. frame shift, nonsense; Fisher s Exact OR 14.15, 95% CI , P < ). Moreover, co-occurring ATRX mutations demonstrated a lower variant allele fraction (0.29±0.15) compared to non co-occurring ATRX mutations (0.36±0.12, two-sided t-test P<0.0001). Lastly, these samples with cooccurring ATRX/DAXX mutations and TERT expression were more often found to show a hypermutator phenotype (Fisher s Exact OR 0.12, 95% CI , P<0.0001). Telomere length technical replicate analysis During sample selection we identified 4,973 technical replicates that were subsequently dropped. Because each replicate was linked to at least one other aliquot for the same sample that was not dropped, these were pulled back in, resulting in 9,058 samples. Most samples had one replicate (N=3,373 samples), some had two (N=592) all the way up to eight replicates in two samples. We then constructed all the possible unique combination between replicates from a single sample, for example a sample with only one replicate could form only one possible combination (A versus B), whereas a sample with two replicates could form two possible combinations (A vs. B, A vs. C and B vs. C), also all the way up to 36 different combinations for the two samples with eight replicates. This resulted in N=6,152 possible combinations for 9,058 samples. For the identification of replicate tumor-normal pairs we had to re-do the sample selection procedure but omitting to drop replicate samples prior to matching tumors to controls. After pairing we identified n=3,554 tumor/normal pairs with replicates. These tumor/normal pairs were then combined much like was done with the replicate samples, except that pairs mostly had one (N=1,555 pairs) and at most two (N=148 pairs) replicate partners. This then resulted in N=1,999 possible combinations for N=3,554 tumor/normal pairs. In order to simplify the interpretation of the replicate analysis we then applied the following filters to both samples and pairs. 1. Only replicates originating from a single vial of DNA were considered (replicate samples only) 2. Replicates from the same center and same sequencing method were excluded 3. Replicates from different centers and different sequencing method were also excluded 4. Multiple replicates from the same sample were excluded This filtering resulted in a total of N=3,883 unpaired replicates and N=1,861 replicate tumor normal pairs. Inter and intra sequencing method replicate correlation varied, with the highest correlations occurring for whole-genome sequencing based replicates between different centers and between replicates from comparing low-pass and whole-genome sequencing. Replicates between whole-genome and wholeexome and between low-pass and whole-exome sequencing showed significant but poor correlations, suggesting whole-genome and low-pass based TL estimates are more robust compared to whole-exome 4

14 based estimates. Previously published comparisons between whole-genome and whole-exome based TL measurements 6,7 have demonstrated better correlations than found in our dataset, possibly due to better controlled and selected datasets. In general, tumor/normal pair replicates showed better correlations than individual samples. Telomere length expression associations This analysis was performed using median-centered gene expression and median-centered absolute TL in normal and tumor samples separately, as well as using median-centered gene expression and TL ratio. A spearman correlation and P-value was calculated for each gene across all samples and within diseases. Genes were annotated for chromosome, arm, and distance to telomere in bp. In the disease specific analysis using the TL ratio, we took the sum of all the disease-specific negative log10 P-values multiplied by the effect direction for each gene in order to rank genes. The top and bottom 500 genes in this list were used to obtain pathway associations in MSigDB 8 using the Reactome 9 database for genes associated with longer and shorter telomeres, respectively. In the overall analysis using absolute median-centered TL in normal and tumor samples we computed the Benjamini-Hochberg false discovery rate to adjust P-values for multiple testing. Genes with an FDR < 0.10 were dropped. This analysis was limited to genes on chromosome arms longer than or equal to 1e8 bp in length, namely: 1p, 1q, 2q, 3q, 4q, 5q, 6q and 8q. Loess regression was used to model the correlation Rho to distance to telomere in tumor samples, and the median expression in tumor and normal samples. Telomerase activity signature We propose to define a gene signature to predict telomerase activity from gene expression data. In principle the aggregated expression of the signature should be correlated with telomerase activity and distinguishes high from low telomerase cancers. To define such a signature, we first tested the NCI60 cell lines hoping to find telomerase positive and negative lines. We searched the literature for each line and found all but one line are telomerase positive. The only exception is the breast cancer line Hs578T (Irving et al., 2004). Lack of telomerase negative lines thus precludes NCI60 for this analysis purpose. We then queried the public Gene Expression Ominibus (GEO, 10 and identified two published datasets, GSE14533 and GSE ,12. The GSE14533 dataset, also referred to as LWK dataset, encompasses 18 ALT samples (8 cell lines and 10 cell lines) and 16 telomerase samples (8 cell lines and 8 tumors). All tumors are liposarcoma with unknown histology. The GSE20559 dataset, referred to as Doyle dataset, encompasses 4 telomerase positive and 4 ALT samples. All 8 samples in Doyle set are dedifferentiated liposarcoma thus the comparison eludes the potential confounding effect of tumor histology. We downloaded both datasets and re-processed them. For Doyle set we performed signal extraction/normalization using the aroma package ( For LWK we normalized its two subseries by z-score transformation to reduce potential batch effects. We compared ALT and telomerase active samples with Significance Analysis of Microarrays implemented in the R package siggenes ( Using a cutoff of FDR 0.05 and Fold Change (FC) 1.5 we identified 666 up regulated genes in the 5

15 telomerase positive samples in the LWK dataset (named as LWK_TELUP signature). Similarly using a cutoff of p value 0.05 and FC 1.5 we found 225 genes up regulated in the four telomerase positive tumors (named as Doyle_DD_TELUP). We note that we did not use FDR to filter the Doyle dataset because no gene passed FDR 0.1, likely because of a very small sample size (n=8). This is consistent with the original report by the group (Doyle et al., 2012). In addition, we obtained a signature of 14 genes from the Doyle et al., which was reported to differentiate telomerase positive tumors from ALT tumors 12. We named this signature as Doyle_DD_G14. We reasoned that despite the defined phenotypes of the samples (ALT vs telomerase), the resulting signatures might contain signals irrelevant to telomerase activity. Embryonic stem cell (ESC) is another cell lineage that has positive telomerase activity, so by overlapping genes up regulated in embryonic stem cells presumably reduce irrelevant signals. We intersected the aforementioned gene signatures with a list of 420 genes up regulated in ESCs curated by an independent study 13 and found only a few genes left. We thus further loosened the criteria using FC 1.5 for both LWK and Doyle datasets. The intersection led to a gene signature of 43 genes for Doyle data set (named as Doyle_DD_TELUP_ESC) and a gene signature of 41 genes for LWK dataset (named as LWK_TELUP_ESC). In parallel with the signatures from unsupervised approaches, we searched the MSigDB resource ( and included telomerase related gene sets icluding BIOCARTA_TEL_PATHWAY from Biocarta, REACTOME_TELOMERE_MAINTENANCE from Reactome, and positive regulation of telomere maintenance via telomerase (GO: ) from Gene Ontology. In total we have 8 gene signatures for testing. To test these signatures, we applied them to a set of urothelial cancer cell lines using single sample gene set enrichment analysis 14. This method has been used in our previous publications to characterize parenchymal cells of the brain in the four expression subtypes of glioblastoma 14 and steroidogenic enzymes in adrenocortical carcinoma 6. Telomerase enzymatic activity of 23 urothelial cancer cell lines has been recently measured and correlated to TERT expression (by RT-PCR) 15. This is a very useful data because telomerase activity was quantified as continuous values instead of dichotomous positive or negative. Of the 23 cell lines 11 are included in Cancer Cell Line Encyclopedia (CCLE) 16 and have RNAseq data available. We applied the 8 signatures to these 11 lines and correlated the result signature scores to the experimentally determined enzymatic activity of telomerase kindly provided by the authors. Interestingly the Doyle_DD_TELUP_ESC signature outperformed other signatures by showing an impressive correlation coefficient r=0.58 (p=0.07, Spearman correlation) in spite of a small sample size (n=11) (Supplementary Figure 8a), suggesting it provides a reasonable accuracy in predicting telomerase enzymatic activity. None of TERT, TERC, DKC1, TERF1 or TERF2 is a member of this signature. It is worth noting that the Doyle dataset is based on Affymetrix HG-U133plus2.0 platform whereas the CCLE expression data is RNA sequencing based. In the same array based NCI60 dataset, the telomerase negative Hs578T line demonstrated the lowest score (1703 vs 2993 by average). Supplementary References 6

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