Stefan Dentro

Supplementary Figures S1 to S52 S1 WGD frequencies across cancer types and stage. S2 Effect of WGD constraint on timing accuracy. S3 Measuring timing accuracy on simulated data. S4-7 Measuring timing accuracy on simulated data by copy number state. S8-11 Measuring inferred route probabilities on simulated data. S12 Timing of gains in multi-region tumors. S13-14 Difference in timing between different gain routes. S15 Non-parsimony in copy number gain evolution. S16 Non-parsimony by copy number state. S17-18 Calibrating a penalty on non-parsimony. S19 Clear-cell sample gain timing. S20 Gain route agreement within chromosomes. S21 Probability of pre-WGD gains in different chromosomes and copy number states. S22 Distribution of gain timing by major copy number. S23 Single-cell copy number profiles of an undifferentiated sarcoma. S24 Distribution of gain rates relative to WGD by cancer type. S25 Distribution of gain rates relative to WGD compared to simulations. S26 Example sample gain timing posterior. S27 Combined distribution over gain timing by WGD status. S28 The timing of gains relative to WGD. S29 The timing of gains relative to WGD by cancer type. S30 Proportion of copy number events post-WGD. S31 The relationship between genome gained post-WGD and WGD timing by cancer type. S32 The relationship between genome gained pre-WGD and WGD timing by cancer type. S33 The relationship between fraction of genome lost pre and post-WGD and WGD timing by cancer type. S34 Punctuated gains in WGD tumors. S35 Association between chromothripsis and punctuated gains. S36 Genomic features of punctuated gains. S37 Frequency of arm gains pre and post-WGD and in non-WGD tumors. S38 Frequency of arm gains pre and post-WGD and in non-WGD tumors by cancer type. S39 Frequency of arm losses pre and post-WGD and in non-WGD tumors by cancer type. S40 Effect of oncogene and tumor suppressor gene density on arm gain rates. S41 Effect of oncogene and tumor suppressor gene density on arm loss rates. S42-46 Pan-genome frequencies of pre and post-WGD gains by cancer type. S47-49 Pan-genome frequencies of pre and post-WGD losses by cancer type. S50 The effect of NRPCC and mutation count on gain timing inference. S51 WGD status calling in GRITIC. S52 The effect of the non-parsimony penalty on event timing.

Table S1 shows the number of possible routes for different copy numbers states.

Supplementary methods, including full description of the GRITIC method

Tumors frequently display high chromosomal instability and contain multiple copies of genomic regions. Here, we describe Gain Route Identification and Timing In Cancer (GRITIC), a generic method for timing genomic gains leading to complex copy number states, using single-sample bulk whole-genome sequencing data. By applying GRITIC to 6,091 tumors, we found that non-parsimonious evolution is frequent in the formation of complex copy number states in genome-doubled tumors. We measured chromosomal instability before and after genome duplication in human tumors and found that late genome doubling was followed by an increase in the rate of copy number gain. Copy number gains often accumulate as punctuated bursts, commonly after genome doubling. We infer that genome duplications typically affect the landscape of copy number losses, while only minimally impacting copy number gains. In summary, GRITIC is a novel copy number gain timing framework that permits the analysis of copy number evolution in chromosomally unstable tumors.Significance:Complex genomic gains are associated with whole-genome duplications, which are frequent across tumors, span a large fraction of their genomes, and are linked to poorer outcomes. GRITIC infers when these gains occur during tumor development, which will help to identify the genetic events that drive tumor evolution.See related commentary by Taylor, p. 1766

Table S1 shows the number of possible routes for different copy numbers states.

Supplementary Figures S1 to S52 S1 WGD frequencies across cancer types and stage. S2 Effect of WGD constraint on timing accuracy. S3 Measuring timing accuracy on simulated data. S4-7 Measuring timing accuracy on simulated data by copy number state. S8-11 Measuring inferred route probabilities on simulated data. S12 Timing of gains in multi-region tumors. S13-14 Difference in timing between different gain routes. S15 Non-parsimony in copy number gain evolution. S16 Non-parsimony by copy number state. S17-18 Calibrating a penalty on non-parsimony. S19 Clear-cell sample gain timing. S20 Gain route agreement within chromosomes. S21 Probability of pre-WGD gains in different chromosomes and copy number states. S22 Distribution of gain timing by major copy number. S23 Single-cell copy number profiles of an undifferentiated sarcoma. S24 Distribution of gain rates relative to WGD by cancer type. S25 Distribution of gain rates relative to WGD compared to simulations. S26 Example sample gain timing posterior. S27 Combined distribution over gain timing by WGD status. S28 The timing of gains relative to WGD. S29 The timing of gains relative to WGD by cancer type. S30 Proportion of copy number events post-WGD. S31 The relationship between genome gained post-WGD and WGD timing by cancer type. S32 The relationship between genome gained pre-WGD and WGD timing by cancer type. S33 The relationship between fraction of genome lost pre and post-WGD and WGD timing by cancer type. S34 Punctuated gains in WGD tumors. S35 Association between chromothripsis and punctuated gains. S36 Genomic features of punctuated gains. S37 Frequency of arm gains pre and post-WGD and in non-WGD tumors. S38 Frequency of arm gains pre and post-WGD and in non-WGD tumors by cancer type. S39 Frequency of arm losses pre and post-WGD and in non-WGD tumors by cancer type. S40 Effect of oncogene and tumor suppressor gene density on arm gain rates. S41 Effect of oncogene and tumor suppressor gene density on arm loss rates. S42-46 Pan-genome frequencies of pre and post-WGD gains by cancer type. S47-49 Pan-genome frequencies of pre and post-WGD losses by cancer type. S50 The effect of NRPCC and mutation count on gain timing inference. S51 WGD status calling in GRITIC. S52 The effect of the non-parsimony penalty on event timing.

Supplementary methods, including full description of the GRITIC method

Tumors frequently display high chromosomal instability and contain multiple copies of genomic regions. Here, we describe Gain Route Identification and Timing In Cancer (GRITIC), a generic method for timing genomic gains leading to complex copy number states, using single-sample bulk whole-genome sequencing data. By applying GRITIC to 6,091 tumors, we found that non-parsimonious evolution is frequent in the formation of complex copy number states in genome-doubled tumors. We measured chromosomal instability before and after genome duplication in human tumors and found that late genome doubling was followed by an increase in the rate of copy number gain. Copy number gains often accumulate as punctuated bursts, commonly after genome doubling. We infer that genome duplications typically affect the landscape of copy number losses, while only minimally impacting copy number gains. In summary, GRITIC is a novel copy number gain timing framework that permits the analysis of copy number evolution in chromosomally unstable tumors. Significance: Complex genomic gains are associated with whole-genome duplications, which are frequent across tumors, span a large fraction of their genomes, and are linked to poorer outcomes. GRITIC infers when these gains occur during tumor development, which will help to identify the genetic events that drive tumor evolution. See related commentary by Taylor, p. 1766

Tumors frequently display high chromosomal instability (CIN) and contain multiple copies of genomic regions. Here, we describe GRITIC, a generic method for timing genomic gains leading to complex copy number states, using single-sample bulk whole-genome sequencing data. By applying GRITIC to 5,656 tumors, we found that non-parsimonious evolution is frequent in the formation of complex copy number states in genome-duplicated tumors. We measured CIN before and after genome duplication in human tumors and found that late genome doubling was followed by an increase in the rate of copy number gain. Copy number gains often accumulate as punctuated bursts, commonly after genome duplication. We infer that genome duplications typically affect the selection landscape of copy number losses, while only minimally impacting copy number gains. In summary, GRITIC is a novel copy number gain timing framework that permits the analysis of copy number evolution in chromosomally unstable tumors. Statement of significance: Complex genomic gains are associated with whole-genome duplications, which are frequent across tumors, span a large fraction of their genomes, and are linked to poorer outcomes. GRITIC infers when these gains occur during tumor development, which will help to identify the genetic events that drive tumor evolution.

NOTCH1 mutant clones occupy the majority of normal human esophagus by middle age but are comparatively rare in esophageal cancers, suggesting NOTCH1 mutations drive clonal expansion but impede carcinogenesis. Here we test this hypothesis. Sequencing NOTCH1 mutant clones in aging human esophagus reveals frequent biallelic mutations that block NOTCH1 signaling. In mouse esophagus, heterozygous Notch1 mutation confers a competitive advantage over wild-type cells, an effect enhanced by loss of the second allele. Widespread Notch1 loss alters transcription but has minimal effects on the epithelial structure and cell dynamics. In a carcinogenesis model, Notch1 mutations were less prevalent in tumors than normal epithelium. Deletion of Notch1 reduced tumor growth, an effect recapitulated by anti-NOTCH1 antibody treatment. Notch1 null tumors showed reduced proliferation. We conclude that Notch1 mutations in normal epithelium are beneficial as wild-type Notch1 favors tumor expansion. NOTCH1 blockade may have therapeutic potential in preventing esophageal squamous cancer.