Allele-specific transcriptional effects of subclonal copy number alterations enable genotype-phenotype mapping in cancer cells.
Journal
Nature communications
ISSN: 2041-1723
Titre abrégé: Nat Commun
Pays: England
ID NLM: 101528555
Informations de publication
Date de publication:
20 Mar 2024
20 Mar 2024
Historique:
received:
18
01
2023
accepted:
01
03
2024
medline:
21
3
2024
pubmed:
21
3
2024
entrez:
21
3
2024
Statut:
epublish
Résumé
Subclonal copy number alterations are a prevalent feature in tumors with high chromosomal instability and result in heterogeneous cancer cell populations with distinct phenotypes. However, the extent to which subclonal copy number alterations contribute to clone-specific phenotypes remains poorly understood. We develop TreeAlign, which computationally integrates independently sampled single-cell DNA and RNA sequencing data from the same cell population. TreeAlign accurately encodes dosage effects from subclonal copy number alterations, the impact of allelic imbalance on allele-specific transcription, and obviates the need to define genotypic clones from a phylogeny a priori, leading to highly granular definitions of clones with distinct expression programs. These improvements enable clone-clone gene expression comparisons with higher resolution and identification of expression programs that are genomically independent. Our approach sets the stage for dissecting the relative contribution of fixed genomic alterations and dynamic epigenetic processes on gene expression programs in cancer.
Identifiants
pubmed: 38509111
doi: 10.1038/s41467-024-46710-0
pii: 10.1038/s41467-024-46710-0
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
2482Subventions
Organisme : Cancer Research UK (CRUK)
ID : C42358/A27460
Organisme : U.S. Department of Health & Human Services | National Institutes of Health (NIH)
ID : RM1-HG011014
Organisme : U.S. Department of Health & Human Services | NIH | National Cancer Institute (NCI)
ID : P30-CA008748
Informations de copyright
© 2024. The Author(s).
Références
Hanahan, D. & Weinberg, R. A. Hallmarks of cancer: the next generation. Cell 144, 646–674 (2011).
doi: 10.1016/j.cell.2011.02.013
pubmed: 21376230
Drews, R. M. et al. A pan-cancer compendium of chromosomal instability. Nature 606, 976–983 (2022).
doi: 10.1038/s41586-022-04789-9
pubmed: 35705807
pmcid: 7613102
Funnell, T. et al. Single-cell genomic variation induced by mutational processes in cancer. Nature 612, 106–115 (2022).
doi: 10.1038/s41586-022-05249-0
pubmed: 36289342
pmcid: 9712114
Black, J. R. M. & McGranahan, N. Genetic and non-genetic clonal diversity in cancer evolution. Nat. Rev. Cancer 21, 379–392 (2021).
doi: 10.1038/s41568-021-00336-2
pubmed: 33727690
Tang, Y.-C. & Amon, A. Gene copy-number alterations: a cost-benefit analysis. Cell 152, 394–405 (2013).
doi: 10.1016/j.cell.2012.11.043
pubmed: 23374337
pmcid: 3641674
Salehi, S. et al. Clonal fitness inferred from time-series modelling of single-cell cancer genomes. Nature 595, 585–590 (2021).
doi: 10.1038/s41586-021-03648-3
pubmed: 34163070
pmcid: 8396073
Vázquez-García, I. et al. Ovarian cancer mutational processes drive site-specific immune evasion. Nature 612, 778–786 (2022).
doi: 10.1038/s41586-022-05496-1
pubmed: 36517593
pmcid: 9771812
Bhattacharya, A. et al. Transcriptional effects of copy number alterations in a large set of human cancers. Nat. Commun. 11, 715 (2020).
doi: 10.1038/s41467-020-14605-5
pubmed: 32024838
pmcid: 7002723
Ding, J. et al. Systematic analysis of somatic mutations impacting gene expression in 12 tumour types. Nat. Commun. 6, 8554 (2015).
doi: 10.1038/ncomms9554
pubmed: 26436532
Jörnsten, R. et al. Network modeling of the transcriptional effects of copy number aberrations in glioblastoma. Mol. Syst. Biol. 7, 486 (2011).
doi: 10.1038/msb.2011.17
pubmed: 21525872
pmcid: 3101951
Pollack, J. R. et al. Microarray analysis reveals a major direct role of DNA copy number alteration in the transcriptional program of human breast tumors. Proc. Natl. Acad. Sci. USA 99, 12963–12968 (2002).
doi: 10.1073/pnas.162471999
pubmed: 12297621
pmcid: 130569
Henrichsen, C. N. et al. Segmental copy number variation shapes tissue transcriptomes. Nat. Genet. 41, 424–429 (2009).
doi: 10.1038/ng.345
pubmed: 19270705
Sztal, T. E. & Stainier, D. Y. R. Transcriptional adaptation: A mechanism underlying genetic robustness. Development 147, dev186452 (2020).
doi: 10.1242/dev.186452
pubmed: 32816903
El-Brolosy, M. A. & Stainier, D. Y. R. Genetic compensation: A phenomenon in search of mechanisms. PLoS Genet. 13, e1006780 (2017).
doi: 10.1371/journal.pgen.1006780
pubmed: 28704371
pmcid: 5509088
Fehrmann, R. S. N. et al. Gene expression analysis identifies global gene dosage sensitivity in cancer. Nat. Genet. 47, 115–125 (2015).
doi: 10.1038/ng.3173
pubmed: 25581432
Veitia, R. A., Bottani, S. & Birchler, J. A. Gene dosage effects: Nonlinearities, genetic interactions, and dosage compensation. Trends Genet. 29, 385–393 (2013).
doi: 10.1016/j.tig.2013.04.004
pubmed: 23684842
Macaulay, I. C. et al. G&T-seq: parallel sequencing of single-cell genomes and transcriptomes. Nat. Methods 12, 519–522 (2015).
doi: 10.1038/nmeth.3370
pubmed: 25915121
Dey, S. S., Kester, L., Spanjaard, B., Bienko, M. & van Oudenaarden, A. Integrated genome and transcriptome sequencing of the same cell. Nat. Biotechnol. 33, 285–289 (2015).
doi: 10.1038/nbt.3129
pubmed: 25599178
pmcid: 4374170
Campbell, K. R. et al. clonealign: statistical integration of independent single-cell RNA and DNA sequencing data from human cancers. Genome Biol. 20, 54 (2019).
doi: 10.1186/s13059-019-1645-z
pubmed: 30866997
pmcid: 6417140
Ferreira, P. F., Kuipers, J. & Beerenwinkel, N. Mapping single-cell transcriptomes to copy number evolutionary trees. bioRxiv 2021.11.04.467244 (2021).
Bai, X., Duren, Z., Wan, L. & Xia, L. C. Joint inference of clonal structure using single-cell genome and transcriptome sequencing data. bioRxiv 2020.02.04.934455 (2020).
Mu, P. et al. SOX2 promotes lineage plasticity and antiandrogen resistance in TP53- and RB1-deficient prostate cancer. Science 355, 84–88 (2017).
doi: 10.1126/science.aah4307
pubmed: 28059768
pmcid: 5247742
Chan, J. M. et al. Lineage plasticity in prostate cancer depends on JAK/STAT inflammatory signaling. Science 377, 1180–1191 (2022).
doi: 10.1126/science.abn0478
pubmed: 35981096
pmcid: 9653178
Johnson, K. C. et al. Single-cell multimodal glioma analyses identify epigenetic regulators of cellular plasticity and environmental stress response. Nat. Genetics 53, 1456–1468 (2021).
doi: 10.1038/s41588-021-00926-8
pubmed: 34594038
Gao, T. et al. Haplotype-aware analysis of somatic copy number variations from single-cell transcriptomes. Nat. Biotechnol. 41, 417–426 (2023).
doi: 10.1038/s41587-022-01468-y
pubmed: 36163550
Zaccaria, S. & Raphael, B. J. Characterizing allele- and haplotype-specific copy numbers in single cells with CHISEL. Nat. Biotechnol. 39, 207–214 (2021).
doi: 10.1038/s41587-020-0661-6
pubmed: 32879467
Bingham, E. et al. Pyro: Deep universal probabilistic programming. CoRR abs/1810.09538. http://arxiv.org/abs/1810.09538 (2018).
Tickle, T., Georgescu, C., Brown, M. & Haas, B. inferCNV of the trinity CTAT project. Klarman Cell Observatory, Broad Institute of MIT (2019).
Gonzalo Parra, R. et al. Single cell multi-omics analysis of chromothriptic medulloblastoma highlights genomic and transcriptomic consequences of genome instability. bioRxiv 2021.06.25.449944 (2021).
Laks, E. et al. Clonal decomposition and DNA replication states defined by scaled Single-Cell genome sequencing. Cell 179, 1207–1221.e22 (2019).
doi: 10.1016/j.cell.2019.10.026
pubmed: 31730858
pmcid: 6912164
Zheng, G. X. Y. et al. Massively parallel digital transcriptional profiling of single cells. Nat. Commun. 8, 14049 (2017).
doi: 10.1038/ncomms14049
pubmed: 28091601
pmcid: 5241818
Andor, N. et al. Joint single cell DNA-seq and RNA-seq of gastric cancer cell lines reveals rules of in vitro evolution. NAR Genom. Bioinform. 2, lqaa016 (2020).
doi: 10.1093/nargab/lqaa016
pubmed: 32215369
pmcid: 7079336
Jamal-Hanjani, M. et al. Tracking the evolution of Non-Small-Cell lung cancer. N. Engl. J. Med. 376, 2109–2121 (2017).
doi: 10.1056/NEJMoa1616288
pubmed: 28445112
Martins, F. C. et al. Clonal somatic copy number altered driver events inform drug sensitivity in high-grade serous ovarian cancer. Nat. Commun. 13, 6360 (2022).
doi: 10.1038/s41467-022-33870-0
pubmed: 36289203
pmcid: 9606297
Sondka, Z. et al. The COSMIC cancer gene census: Describing genetic dysfunction across all human cancers. Nat. Rev. Cancer 18, 696–705 (2018).
doi: 10.1038/s41568-018-0060-1
pubmed: 30293088
pmcid: 6450507
Liberzon, A. et al. The molecular signatures database (MSigDB) hallmark gene set collection. Cell Syst. 1, 417–425 (2015).
doi: 10.1016/j.cels.2015.12.004
pubmed: 26771021
pmcid: 4707969
Finak, G. et al. MAST: a flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data. Genome Biol. 16, 278 (2015).
doi: 10.1186/s13059-015-0844-5
pubmed: 26653891
pmcid: 4676162
Benci, J. L. et al. Tumor interferon signaling regulates a multigenic resistance program to immune checkpoint blockade. Cell 167, 1540–1554.e12 (2016).
doi: 10.1016/j.cell.2016.11.022
pubmed: 27912061
pmcid: 5385895
Stuart, T. et al. Comprehensive integration of Single-Cell data. Cell 177, 1888–1902.e21 (2019).
doi: 10.1016/j.cell.2019.05.031
pubmed: 31178118
pmcid: 6687398
Huang, X. & Huang, Y. Cellsnp-lite: an efficient tool for genotyping single cells. Bioinformatics 37, 4569–4571 (2021).
doi: 10.1093/bioinformatics/btab358
pubmed: 33963851
Wang, C. & Blei, D. M. Variational inference in nonconjugate models. J. Mach. Learn. Res., 14, 1005–1031 (2013).
Medina-Martínez, J. S. et al. Isabl platform, a digital biobank for processing multimodal patient data. BMC Bioinformatics 21, 549 (2020).
doi: 10.1186/s12859-020-03879-7
pubmed: 33256603
pmcid: 7708092
Lai, D., Ha, G. & Shah, S. HMMcopy: Copy number prediction with correction for GC and mappability bias for HTS data. HMMcopy, R package version 1.44.0 (2023).
Zhang, A. W. et al. Probabilistic cell-type assignment of single-cell RNA-seq for tumor microenvironment profiling. Nat. Methods 16, 1007–1015 (2019).
doi: 10.1038/s41592-019-0529-1
pubmed: 31501550
pmcid: 7485597
Hao, Y. et al. Integrated analysis of multimodal single-cell data. Cell 184, 3573–3587 (2021).
doi: 10.1016/j.cell.2021.04.048
pubmed: 34062119
pmcid: 8238499
Korotkevich, G. et al. Fast gene set enrichment analysis. bioRxiv 060012 (2021).
Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl. Acad. Sci. USA 102, 15545–15550 (2005).
doi: 10.1073/pnas.0506580102
pubmed: 16199517
pmcid: 1239896