Abstract
Aneuploidies—whole-chromosome or whole-arm imbalances—are the most prevalent alteration in cancer genomes1,2. However, it is still debated whether their prevalence is due to selection or ease of generation as passenger events1,2. Here we developed a method, BISCUT, that identifies loci subject to fitness advantages or disadvantages by interrogating length distributions of telomere- or centromere-bounded copy-number events. These loci were significantly enriched for known cancer driver genes, including genes not detected through analysis of focal copy-number events, and were often lineage specific. BISCUT identified the helicase-encoding gene WRN as a haploinsufficient tumour-suppressor gene on chromosome 8p, which is supported by several lines of evidence. We also formally quantified the role of selection and mechanical biases in driving aneuploidy, finding that rates of arm-level copy-number alterations are most highly correlated with their effects on cellular fitness1,2. These results provide insight into the driving forces behind aneuploidy and its contribution to tumorigenesis.
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Data availability
All SNP array data used for analysis are publicly available from TCGA’s Genomic Data Commons Data Portal at https://portal.gdc.cancer.gov/. All DNA and RNA sequencing data generated in this study are available at the NIH Sequence Read Archive (accession code PRJNA967303).
Code availability
The codes used to merge copy-number segments, call partial-SCNAs, detect loci under selection and determine RF values and mechanical coefficients are freely available for download at https://github.com/beroukhim-lab/BISCUT-py3 and https://doi.org/10.5281/zenodo.7896522.
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Acknowledgements
We thank the members of the laboratories of R.B., A.M.T. and M.M. for their support and helpful discussions, as well as C. Stewart, C.-Z. Zhang and S. Schumacher. We also thank G. Macintyre for suggesting the stick-breaking model to explain partial-SCNA length distributions, I. Martincorena for personal communications regarding negative selection in cancer, E. Chan for the WRN overexpression construct and M. Huang for the name of the main method. For flow cytometry analyses, we acknowledge National Institutes of Health P30CA013696 (HICCC flow cytometry core), and for RNA sequencing of cell lines we acknowledge the Sulzberger Genome Center. We also acknowledge the following sources of financial support: the National Institutes of Health and National Cancer Institute (R.B. and A.M.T.), National Institute for General Medical Sciences (A.M.T.), Fund for Innovative Cancer Informatics (R.B.), Gray Matters Brain Cancer Foundation (R.B.), Pediatric Brain Tumor Foundation (R.B.), Brain Tumour Charity (R.B.) and St. Baldrick’s Foundation (R.B.).
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Contributions
J.S., A.D.C., A.M.T. and R.B. conceived of this study. J.S., G.F.G., L.F.S., A.C.B., A.D.C. and R.B. developed analytic methods. J.S. and S.S. implemented analytic methods. J.S., S.S., S.Z., S.H.H., N.Z.-K., Y.G., H.S. and A.M.T. carried out computational analyses. N.Z.-K., Y.G., M.S.C. and A.M.T. developed and carried out in vitro experiments. G.H., V.R. and M.M. provided feedback and advice on analyses. A.D.C., A.M.T. and R.B. supervised the work. J.S., N.Z.-K., S.S., S.Z., A.M.T. and R.B. wrote the manuscript with input from all coauthors.
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Competing interests
G.F.G., A.C.B., A.D.C. and M.M. receive or received research support from Bayer AG. M.M. and A.M.T. received research support from Ono Pharmaceutical. M.M. is an equity holder of, consultant for, and Scientific Advisory Board chair for OrigiMed. M.M. additionally receives research support from Novo Nordisk and Janssen Pharmaceuticals, consults for Interline Therapeutics, and is an inventor of a patent for EGFR mutation diagnosis in lung cancer, licensed to Labcorp. R.B. consults for and owns equity in Scorpion Therapeutics and receives research support from Novartis. J.S., S.S., S.Z., N.Z.-K., Y.G., S.H.H., M.S.C., L.F.S., G.H., V.R. and H.S. declare no competing interests.
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Extended data figures and tables
Extended Data Fig. 1 Additional information on different types of SCNA and the BISCUT method.
(a) Empirical examples of low centromeric mechanical bias (1q telomere-bounded deletions, for which the ratio of breakpoints occurring in the centromere over those occurring in the arm is less than 1), and high centromeric mechanical bias (5p telomere-bounded amplifications, for which the centromere/arm breakpoint ratio is much greater than 1). Within the chromosome arm, bins are 1 Mb large. (b) Mean amplification and deletion breakpoint density within chromosome arms, aggregated across all tumors and all chromosome arms (n = 67; binned by Mb), versus breakpoint density within all centromeres (values in breakpoints per megabase). Error bars represent the 95% confidence interval for the mean. C/A Ratio represents centromeric breaks over arm breaks. (c) Comparison of length distributions of telomere-bounded, centromere-bounded, and interstitial amplifications and deletions, aggregated across all chromosome arms. (d) Example depicting BISCUT’s recursion steps. From top to bottom: BISCUT detects peaks iteratively, walking both left and right if a significant peak is detected, with the new boundaries including the detected peak. If a peak is not detected, overlaps with a previous peak, or there are fewer than 4 samples, the analysis is stopped. See Fig. 2c and Methods for details.
Extended Data Fig. 2 Pan-cancer BISCUT analysis.
(a) Summary statistics of the four types of BISCUT peaks in pan-cancer. (b) Sizes of peaks (in bases) from the pan-cancer BISCUT analysis. From left to right, peaks are categorized by direction of selection (n = 90 and 103 for positive and negative selection respectively), direction of copy number imbalance (n = 80 and 113 for amplifications and deletions respectively), and origin of partial-SCNA (n = 163 and 30 for telomere-bounded and centromere-bounded respectively). Two-tailed p-value was calculated using a Mann-Whitney U test. (c) Overlap between genes in BISCUT peaks and Tier 1 COSMIC cancer genes. The numbers of peaks containing these genes are depicted in green. A one-tailed p-value was calculated using a permutation test as outlined in the Methods. (d) Negative selection peaks from the pan-cancer BISCUT analysis, sorted from highest to lowest by fraction of samples subject to these fitness effects that also possessed overlapping focal SCNAs in the opposite direction. Peaks that overlap with GISTIC 2.0 peaks are denoted in dark red and dark blue. (e) BISCUT analysis detecting two positive selection peaks (top: 9p telomere-bounded deletions, overlapping with CDKN2A focal deletions; bottom: 8q telomere-bounded amplifications, overlapping with MYC focal amplifications) with focal SCNAs removed (left) and with focal SCNAs included (right). (f) BISCUT analysis detecting two negative selection peaks (top: 8q telomere-bounded deletions, overlapping with MYC focal amplifications; bottom: 11q telomere-bounded deletions, overlapping with YAP1/BIRC3 focal amplifications) with focal SCNAs removed (left) and with focal SCNAs included (right).
Extended Data Fig. 3 Lineage-specific divergence of breakpoint distributions from the background distribution.
Heatmaps of lineage divergence scores for each tumor type (x-axis) and chromosome arm (y-axis). Amplifications are on top (in red) and deletions are on the bottom (in blue). Darker color represents a higher divergence score.
Extended Data Fig. 4 Patterns of chromosome 3p deletions are highly lineage-specific.
(a) Lineage-specificity scores across chromosomes. The left chromatid is shaded in red and represents amplifications, whereas the right chromatid is shaded in blue and represents deletions. Darker colors indicate greater lineage-specificity. (b) BISCUT analysis of telomere-bounded deletions on chromosome 3p in three different cohorts. The top panels display telomere-bounded deletions, sorted by length. The bottom panels show the vertical distance of each tel-SCNA from the background distribution; the maximum deviation is denoted by the solid vertical line. The dashed lines represent the peak regions determined to be under significant positive selection (i.e. conferring survival advantage in this cohort). (c) Genomic locations and corresponding significance score of positive selection deletion BISCUT peaks on chromosome 3p across lineages. See Supplementary Table 2a for tumor type abbreviations.
Extended Data Fig. 5 Hierarchical clustering of BISCUT peaks across lineages.
Matrix of significantly recurring BISCUT peaks across 33 independent tumor types. Peaks are sorted by genomic location (vertical axis), with four distinct classes of peaks in dark red (positive selection in amplifications), light red (negative selection in deletions), light blue (negative selection in amplifications), and dark blue (positive selection in deletions). Tumor types are sorted and color-coded (k = 5) according to hierarchical clustering by Ward’s method (horizontal axis).
Extended Data Fig. 6 Cells engineered with chr8p deletion for validation of genes in BISCUT selection peaks.
(a) Schematic for 8p deletion approach. Cells were transfected with a CRISPR targeting 8p just outside the centromere and with a linearized plasmid containing an artificial telomere, puromycin selection cassette, and 1 kilobase of sequence homologous to the 8p pericentromeric sequence. Puromycin selection was used to isolate cells with 8p replaced by the artificial telomere. (b) ichorCNA output of ultra-low-pass whole genome sequencing data from five AALE cell clones with 8p disomy or 8p monosomy. Horizontal axis is chromosome number, vertical axis is log copy number ratio. Green denotes copy number loss, red denotes copy number gain. (c) Caspase-glo for cells with 8p deletion compared to cells with 8p disomy (n = 3 for both). Each point represents one biological replicate from a representative experiment. One-tailed p-values from two different experiments were combined using Fisher’s method. (d) Flow cytometry analysis of cells with 8p deletion compared to cells with 8p disomy. Bar graphs represent the percentage of apoptotic cells dually stained for Annexin V and PI. One representative experiment is shown. One-tailed p-values from three independent experiments were combined using Fisher’s method. (e) Vertical axis represents normalized read counts from RNA sequencing of cells with 8p disomy or 8p deletion. Each point is an individual clone (n = 8 for all columns). Two-tailed p-values are reported. (f) Relative COSMIC SBS39 mutational signature activity (vertical axis) of engineered cells with 8p disomy versus 8p monosomy. Two-tailed p-values are calculated from a Mann-Whitney U test. (g) WRN qPCR for cell clones with 8p disomy after siRNA treatment. Cells were treated with either control siRNA or siRNA against WRN for 3 days prior to qPCR (n = 2 for each condition). Each point represents the average value across technical replicates in an individual biological replicate. h) Percentages of apoptotic cells detected by flow cytometry for Annexin V and propidium iodide (PI) across three 8p wild-type cell lines, on day three after transfection with WRN versus control siRNAs. This is representative data from one of four experiments. A ratio paired t-test was used to calculate one-sided p-values for all four experiments, which were combined using Fisher’s method. (i) Log-fold changes in apoptotic cells detected by trypan blue across these three 8p wild-type cell lines (n = 3 for all cell lines), on day three after transfection with WRN vs control siRNAs. Each point represents a different experiment. One-tailed p-values from all experiments were combined using Fisher’s method. (j) WRN qPCR in 8p disomic cell clones with overexpression of WRN or GFP (n = 3 for both). A two-tailed p-value is reported. (k) Cell viability is significantly lower when genes in del-neg peaks are knocked down by RNAi (left, DEMETER2 score) or knocked out by CRISPR (right, Chronos score) in Dependency Map screens31,32, compared to all other genes. The reported p-value is two-tailed. Box plots center on median values and extend to the first and third quartiles; the whiskers extend to 1.5 times the interquartile range. (l) KAT6A qPCR for cell clones three days after siRNA-mediated knockdown (n = 3 for both conditions). A two-tailed p-value is reported. (m) EPN2 qPCR for cell clones three days after siRNA-mediated knockdown (n = 3 for both conditions). The reported p-value is two-tailed. All p-values in this figure were calculated using Student’s t-test except as otherwise noted; no adjustments were made for multiple comparisons.
Extended Data Fig. 7 Quantitative assessment of selective and mechanical pressures driving aneuploidy.
(a) Calculation of peak-specific relative fitness (RF), arm-level RF, telomeric mechanical coefficients, and chromosome-level centromeric mechanical coefficients. See Methods for further details. (b) Centromeric mechanical coefficients (log) plotted against centromere length (in bases). (c) Centromeric mechanical coefficients (log) plotted against total frequency of arm-SCNAs affecting a specific chromosome (i.e. amplifications and deletions of the p and q arms in aggregate). Acrocentric chromosomes are excluded from analysis. (d) From the original BISCUT analysis: telomeric mechanical coefficients (log) plotted against telomere length, in RTLU, for amplifications (left; in red) and deletions (right; in blue). (e) From the original BISCUT analysis: telomeric mechanical coefficients (log) plotted against frequency of arm-level amplifications (left; in red) and deletions (right; in blue). For all panels, two-tailed p-values and rho correlation coefficients were calculated using Spearman’s rank correlation. No adjustments were made for multiple comparisons.
Extended Data Fig. 8 Telomeric mechanical pressures are better reflected when using baseline ploidies of 2 or 4.
(a) Relative fitness (log) plotted against frequency of arm-SCNAs. From left to right: positive selection in amplifications (dark red), negative selection in amplifications (light blue), positive selection in deletions (dark blue), and negative selection in deletions (light red). (b) Missegregation probability in percentage (determined by single-cell sequencing of RPE1-hTERT non-transformed cells42) plotted against frequency of all arm-SCNAs affecting each chromosome, averaged across arms. The horizontal black line at 4.3% reflects the expected random chance of missegregation of each chromosome. (c) Relative fitness (log) plotted against arm length. (d) Chromosome arm length plotted against frequency of arm-SCNAs. (e) Strength of correlation (β; vertical axis) between various coefficients (horizontal axis) and arm-SCNA rates from a multivariate Generalized Linear Model (GLM), with p-values above each predictor (significant values in bold). Amplifications are in red, and deletions are in blue. All p-values in this figure were calculated using Spearman’s correlation except as otherwise noted; no adjustments were made for multiple comparisons.
Supplementary information
Supplementary Information
This file contains Supplementary Figs. 1–4, Notes 1–5 and References that extend and support the data and discussion presented in the main text.
Supplementary Table 1
Most common somatic alterations in cancer.
Supplementary Table 2
Cohort information and statistics of different types of SCNA.
Supplementary Table 3
Pan-cancer regions of selection.
Supplementary Table 4
Cohort-specific regions of selection and lineage-specificity analysis.
Supplementary Table 5
Validation of peak regions.
Supplementary Table 6
RF values and mechanical coefficients.
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Shih, J., Sarmashghi, S., Zhakula-Kostadinova, N. et al. Cancer aneuploidies are shaped primarily by effects on tumour fitness. Nature 619, 793–800 (2023). https://doi.org/10.1038/s41586-023-06266-3
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DOI: https://doi.org/10.1038/s41586-023-06266-3
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