Holland, A. J. & Cleveland, D. W. Boveri revisited: chromosomal instability, aneuploidy and tumorigenesis. Nat. Rev. Mol. Cell Biol. 10, 478–487 (2009).
Google Scholar
Beroukhim, R. et al. The landscape of somatic copy-number alteration across human cancers. Nature 463, 899–905 (2010).
Google Scholar
Ben-David, U. & Amon, A. Context is everything: aneuploidy in cancer. Nat. Rev. Genet. 21, 44–62 (2020).
Google Scholar
Watkins, T. B. K. et al. Pervasive chromosomal instability and karyotype order in tumour evolution. Nature 587, 126–132 (2020).
Google Scholar
Curtis, C. et al. The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups. Nature 486, 346–352 (2012).
Google Scholar
Perou, C. M. et al. Molecular portraits of human breast tumours. Nature 406, 747–752 (2000).
Google Scholar
Gao, R. et al. Punctuated copy number evolution and clonal stasis in triple-negative breast cancer. Nat. Genet. 48, 1119–1130 (2016).
Google Scholar
Pereira, B. et al. The somatic mutation profiles of 2,433 breast cancers refines their genomic and transcriptomic landscapes. Nat. Commun. 7, 11479 (2016).
Google Scholar
Steele, C. D. et al. Signatures of copy number alterations in human cancer. Nature 606, 984–991 (2022).
Google Scholar
Shih, J. et al. Cancer aneuploidies are shaped primarily by effects on tumour fitness. Nature 619, 793–800 (2023).
Google Scholar
Sheltzer, J. M. & Amon, A. The aneuploidy paradox: costs and benefits of an incorrect karyotype. Trends Genet. 27, 446–453 (2011).
Google Scholar
Girish, V. et al. Oncogene-like addiction to aneuploidy in human cancers. Science 381, eadg4521 (2023).
Google Scholar
Gerstung, M. et al. The evolutionary history of 2,658 cancers. Nature 578, 122–128 (2020).
Google Scholar
Schachter, N. F. et al. Single allele loss-of-function mutations select and sculpt conditional cooperative networks in breast cancer. Nat. Commun. 12, 5238 (2021).
Google Scholar
Kohler, B. A. et al. Annual Report to the Nation on the Status of Cancer, 1975-2011, featuring incidence of breast cancer subtypes by race/ethnicity, poverty, and state. J. Natl Cancer Inst. 107, djv048 (2015).
Google Scholar
Wu, S. Z. et al. A single-cell and spatially resolved atlas of human breast cancers. Nat. Genet. 53, 1334–1347 (2021).
Google Scholar
Pal, B. et al. A single-cell RNA expression atlas of normal, preneoplastic and tumorigenic states in the human breast. EMBO J. 40, e107333 (2021).
Google Scholar
Bassez, A. et al. A single-cell map of intratumoral changes during anti-PD1 treatment of patients with breast cancer. Nat. Med. 27, 820–832 (2021).
Google Scholar
Qian, J. et al. A pan-cancer blueprint of the heterogeneous tumor microenvironment revealed by single-cell profiling. Cell Res. 30, 745–762 (2020).
Google Scholar
Loganathan, S. K. et al. Rare driver mutations in head and neck squamous cell carcinomas converge on NOTCH signaling. Science 367, 1264–1269 (2020).
Google Scholar
Langille, E. et al. Loss of epigenetic regulation disrupts lineage integrity, induces aberrant alveogenesis, and promotes breast cancer. Cancer Discov. 12, 2930–2953 (2022).
Google Scholar
Liu, J. C. et al. Combined deletion of Pten and p53 in mammary epithelium accelerates triple-negative breast cancer with dependency on eEF2K. EMBO Mol. Med. 6, 1542–1560 (2014).
Google Scholar
Jiang, Z. et al. RB1 and p53 at the crossroad of EMT and triple-negative breast cancer. Cell Cycle 10, 1563–1570 (2011).
Google Scholar
Botti, G. et al. Morphological and pathological features of basal-like breast cancer. Transl. Cancer Res. 8, S503–S509 (2019).
Google Scholar
Liao, H. K. et al. In vivo target gene activation via CRISPR/Cas9-mediated trans-epigenetic modulation. Cell 171, 1495–1507 (2017).
Google Scholar
Dahlman, J. E. et al. Orthogonal gene knockout and activation with a catalytically active Cas9 nuclease. Nat. Biotechnol. 33, 1159–1161 (2015).
Google Scholar
Kiani, S. et al. Cas9 gRNA engineering for genome editing, activation and repression. Nat. Methods 12, 1051–1054 (2015).
Google Scholar
Shechner, D. M., Hacisuleyman, E., Younger, S. T. & Rinn, J. L. Multiplexable, locus-specific targeting of long RNAs with CRISPR-Display. Nat. Methods 12, 664–670 (2015).
Google Scholar
Sanson, K. R. et al. Optimized libraries for CRISPR-Cas9 genetic screens with multiple modalities. Nat. Commun. 9, 5416 (2018).
Google Scholar
Suehnholz, S. P. et al. Quantifying the expanding landscape of clinical actionability for patients with cancer. Cancer Discov. 14, 49–65 (2024).
Google Scholar
Chakravarty, D. et al. OncoKB: a precision oncology knowledge base. JCO Precis. Oncol. https://doi.org/10.1200/PO.17.00011 (2017).
Hollern, D. P. et al. A mouse model featuring tissue-specific deletion of p53 and Brca1 gives rise to mammary tumors with genomic and transcriptomic similarities to human basal-like breast cancer. Breast Cancer Res. Treat. 174, 143–155 (2019).
Google Scholar
Annunziato, S. et al. Comparative oncogenomics identifies combinations of driver genes and drug targets in BRCA1-mutated breast cancer. Nat. Commun. 10, 397 (2019).
Google Scholar
Andronicos, N. M. et al. Proteomics-based discovery of a novel, structurally unique, and developmentally regulated plasminogen receptor, Plg-RKT, a major regulator of cell surface plasminogen activation. Blood 115, 1319–1330 (2010).
Google Scholar
Miles, L. A. et al. Deficiency of plasminogen receptor, Plg-RKT, causes defects in plasminogen binding and inflammatory macrophage recruitment in vivo. J. Thromb. Haemost. 15, 155–162 (2017).
Google Scholar
Miles, L. A. et al. The plasminogen receptor, Plg-RKT, is essential for mammary lobuloalveolar development and lactation. J. Thromb. Haemost. 16, 919–932 (2018).
Google Scholar
Ying, Z. & Beronja, S. Embryonic barcoding of equipotent mammary progenitors functionally identifies breast cancer drivers. Cell Stem Cell 26, 403–419(2020).
Google Scholar
Faubert, B., Solmonson, A. & DeBerardinis, R. J. Metabolic reprogramming and cancer progression. Science https://doi.org/10.1126/science.aaw5473 (2020).
Harada, H., Moriya, K., Kobuchi, H., Ishihara, N. & Utsumi, T. Protein N-myristoylation plays a critical role in the mitochondrial localization of human mitochondrial complex I accessory subunit NDUFB7. Sci. Rep. 13, 22991 (2023).
Google Scholar
Lee, S. Y. et al. APEX fingerprinting reveals the subcellular localization of proteins of interest. Cell Rep. 15, 1837–1847 (2016).
Google Scholar
Antonicka, H. et al.A high-density human mitochondrial proximity interaction network. Cell Metab. 32, 479–497(2020).
Google Scholar
Lhuissier, C. et al. Mitochondrial F0F1-ATP synthase governs the induction of mitochondrial fission. iScience 27, 109808 (2024).
Google Scholar
Laine, R. F. et al. High-fidelity 3D live-cell nanoscopy through data-driven enhanced super-resolution radial fluctuation. Nat. Methods 20, 1949–1956 (2023).
Google Scholar
Zhang, J. et al. Systematic identification of anticancer drug targets reveals a nucleus-to-mitochondria ROS-sensing pathway. Cell 186, 2361–2379 (2023).
Google Scholar
Davoli, T. et al. Cumulative haploinsufficiency and triplosensitivity drive aneuploidy patterns and shape the cancer genome. Cell 155, 948–962 (2013).
Google Scholar
Davoli, T., Uno, H., Wooten, E. C. & Elledge, S. J. Tumor aneuploidy correlates with markers of immune evasion and with reduced response to immunotherapy. Science 355, eaaf8399 (2017).
Google Scholar
Hart, T. et al. High-resolution CRISPR screens reveal fitness genes and genotype-specific cancer liabilities. Cell 163, 1515–1526 (2015).
Google Scholar
Al-Ahmadie, H. et al. Synthetic lethality in ATM-deficient RAD50-mutant tumors underlies outlier response to cancer therapy. Cancer Discov. 4, 1014–1021 (2014).
Google Scholar
Schneider, R. K. et al. Role of casein kinase 1A1 in the biology and targeted therapy of del(5q) MDS. Cancer Cell 26, 509–520 (2014).
Google Scholar
Trakala, M. et al. Clonal selection of stable aneuploidies in progenitor cells drives high-prevalence tumorigenesis. Genes Dev. 35, 1079–1092 (2021).
Google Scholar
Mao, J. H. et al. Fbxw7/Cdc4 is a p53-dependent, haploinsufficient tumour suppressor gene. Nature 432, 775–779 (2004).
Google Scholar
McFadden, D. G. et al. Genetic and clonal dissection of murine small cell lung carcinoma progression by genome sequencing. Cell 156, 1298–1311 (2014).
Google Scholar
Baslan, T. et al. Ordered and deterministic cancer genome evolution after p53 loss. Nature 608, 795–802 (2022).
Google Scholar
Sack, L. M. et al. Profound tissue specificity in proliferation control underlies cancer drivers and aneuploidy patterns. Cell 173, 499–514 (2018).
Google Scholar
Watson, E. V. et al. Chromosome evolution screens recapitulate tissue-specific tumor aneuploidy patterns. Nat. Genet. 56, 900–912 (2024).
Google Scholar
Weigman, V. J. et al. Basal-like Breast cancer DNA copy number losses identify genes involved in genomic instability, response to therapy, and patient survival. Breast Cancer Res. Treat. 133, 865–880 (2012).
Google Scholar
Cai, Y. et al. Loss of chromosome 8p governs tumor progression and drug response by altering lipid metabolism. Cancer Cell 29, 751–766 (2016).
Google Scholar
Kremitzki, C. et al. Pathogenic morphological signatures of perturbations in mitochondrial-related genes revealed by pooled imaging assay. npj Imaging 3, 35 (2025).
Google Scholar
Zhou, H. et al. The plasminogen receptor directs maintenance of spermatogonial stem cells by targeting BMI1. Mol. Biol. Rep. 49, 4469–4478 (2022).
Google Scholar
Solimini, N. L. et al. Recurrent hemizygous deletions in cancers may optimize proliferative potential. Science 337, 104–109 (2012).
Google Scholar
Yoon, K. J. et al. Temporal control of mammalian cortical neurogenesis by m6A methylation. Cell 171, 877–889 (2017).
Google Scholar
Adams, J. R. et al. Cooperation between Pik3ca and p53 mutations in mouse mammary tumor formation. Cancer Res. 71, 2706–2717 (2011).
Google Scholar
Loganathan, S. K., Malik, A., Langille, E., Luxenburg, C. & Schramek, D. In Vivo CRISPR/Cas9 screening to simultaneously evaluate gene function in mouse skin and oral cavity. J. Vis. Exp. https://doi.org/10.3791/61693 (2020).
Google Scholar
Lourenco, C. et al. Modelling the MYC-driven normal-to-tumour switch in breast cancer. Dis. Model. Mech. https://doi.org/10.1242/dmm.038083 (2019).
Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).
Google Scholar
Langmead, B., Trapnell, C., Pop, M. & Salzberg, S. L. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 10, R25 (2009).
Google Scholar
Li, W. et al. MAGeCK enables robust identification of essential genes from genome-scale CRISPR/Cas9 knockout screens. Genome Biol. 15, 554 (2014).
Google Scholar
Tsherniak, A. et al. Defining a cancer dependency map. Cell 170, 564–576 (2017).
Google Scholar
Arafeh, R., Shibue, T., Dempster, J. M., Hahn, W. C. & Vazquez, F. The present and future of the Cancer Dependency Map. Nat. Rev. Cancer 25, 59–73 (2025).
Google Scholar
Schramek, D. et al. Direct in vivo RNAi screen unveils myosin IIa as a tumor suppressor of squamous cell carcinomas. Science 343, 309–313 (2014).
Google Scholar
Beronja, S. et al. RNAi screens in mice identify physiological regulators of oncogenic growth. Nature 501, 185–190 (2013).
Google Scholar
Beronja, S., Livshits, G., Williams, S. & Fuchs, E. Rapid functional dissection of genetic networks via tissue-specific transduction and RNAi in mouse embryos. Nat. Med. 16, 821–827 (2010).
Google Scholar
Dawson, S. J., Rueda, O. M., Aparicio, S. & Caldas, C. A new genome-driven integrated classification of breast cancer and its implications. EMBO J. 32, 617–628 (2013).
Google Scholar
Cerami, E. et al. The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data. Cancer Discov. 2, 401–404 (2012).
Google Scholar
Goldman, M. J. et al. Visualizing and interpreting cancer genomics data via the Xena platform. Nat. Biotechnol. 38, 675–678 (2020).
Google Scholar
Gayoso, A. et al. A Python library for probabilistic analysis of single-cell omics data. Nat. Biotechnol. 40, 163–166 (2022).
Google Scholar
Zhang, A. W. et al. Probabilistic cell-type assignment of single-cell RNA-seq for tumor microenvironment profiling. Nat Methods 16, 1007–1015 (2019).
Google Scholar
Parker, J. S. et al. Supervised risk predictor of breast cancer based on intrinsic subtypes. J. Clin. Oncol. 27, 1160–1167 (2009).
Google Scholar
The Cancer Genome Atlas Research Network et al. The Cancer Genome Atlas Pan-Cancer analysis project. Nat. Genet. 45, 1113–1120 (2013).
Google Scholar
Colaprico, A. et al. TCGAbiolinks: an R/Bioconductor package for integrative analysis of TCGA data. Nucleic Acids Res. 44, e71 (2016).
Google Scholar
Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010).
Google Scholar
Pfefferle, A. D. et al. Transcriptomic classification of genetically engineered mouse models of breast cancer identifies human subtype counterparts. Genome Biol. 14, R125 (2013).
Google Scholar
Haghverdi, L., Lun, A. T. L., Morgan, M. D. & Marioni, J. C. Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors. Nat. Biotechnol. 36, 421–427 (2018).
Google Scholar
Haider, S. et al. BioMart Central Portal—unified access to biological data. Nucleic Acids Res. 37, W23–W27 (2009).
Google Scholar
Tarashansky, A. J. et al. Mapping single-cell atlases throughout Metazoa unravels cell type evolution. eLife 10, e66747 (2021).
Google Scholar
Baldarelli, R. M. et al. Mouse Genome Informatics: an integrated knowledgebase system for the laboratory mouse. Genetics 227, iyae031 (2024).
Google Scholar
Paczkowska, M. et al. Integrative pathway enrichment analysis of multivariate omics data. Nat. Commun. 11, 735 (2020).
Google Scholar
Reimand, J., Kull, M., Peterson, H., Hansen, J. & Vilo, J. g:Profiler—a web-based toolset for functional profiling of gene lists from large-scale experiments. Nucleic Acids Res. 35, W193–W200 (2007).
Google Scholar
Reimand, J. et al. Pathway enrichment analysis and visualization of omics data using g:Profiler, GSEA, Cytoscape and EnrichmentMap. Nat. Protoc. 14, 482–517 (2019).
Google Scholar
Lange, S. et al. Analysis pipelines for cancer genome sequencing in mice. Nat. Protoc. 15, 266–315 (2020).
Google Scholar
Debnath, J., Muthuswamy, S. K. & Brugge, J. S. Morphogenesis and oncogenesis of MCF-10A mammary epithelial acini grown in three-dimensional basement membrane cultures. Methods 30, 256–268 (2003).
Google Scholar
Bray, N. L., Pimentel, H., Melsted, P. & Pachter, L. Near-optimal probabilistic RNA-seq quantification. Nat. Biotechnol. 34, 525–527 (2016).
Google Scholar
Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).
Google Scholar
Zhou, Y. et al. Metascape provides a biologist-oriented resource for the analysis of systems-level datasets. Nat. Commun. 10, 1523 (2019).
Google Scholar
Korotkevich, G. et al. Fast gene set enrichment analysis. Preprint at bioRxiv https://doi.org/10.1101/060012 (2021).
Liberzon, A. et al. The Molecular Signatures Database (MSigDB) Hallmark gene set collection. Cell Syst. 1, 417–425 (2015).
Google Scholar
Zorkau, M., Albus, C. A., Berlinguer-Palmini, R., Chrzanowska-Lightowlers, Z. M. A. & Lightowlers, R. N. High-resolution imaging reveals compartmentalization of mitochondrial protein synthesis in cultured human cells. Proc. Natl Acad. Sci. USA 118, e2008778118 (2021).
Google Scholar
Lefebvre, A., Ma, D., Kessenbrock, K., Lawson, D. A. & Digman, M. A. Automated segmentation and tracking of mitochondria in live-cell time-lapse images. Nat. Methods 18, 1091–1102 (2021).
Google Scholar
Al-Zahrani, K. N. et al. Data for ‘Aneuploidy selects for the acquisition of driver genes in breast cancer’. Figshare https://doi.org/10.6084/m9.figshare.32144932 (2026).
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