Vasan, N., Baselga, J. & Hyman, D. M. A view on drug resistance in most cancers. Nature 575, 299–309 (2019).
McGranahan, N. & Swanton, C. Clonal heterogeneity and tumor evolution: previous, current, and the long run. Cell 168, 613–628 (2017).
Sharma, S. V. et al. A chromatin-mediated reversible drug-tolerant state in most cancers cell subpopulations. Cell 141, 69–80 (2010).
Hugo, W. et al. Non-genomic and immune evolution of melanoma buying MAPKi resistance. Cell 162, 1271–1285 (2015).
Marine, J.-C., Dawson, S.-J. & Dawson, M. A. Non-genetic mechanisms of therapeutic resistance in most cancers. Nat. Rev. Most cancers 20, 743–756 (2020).
Shaffer, S. M. et al. Uncommon cell variability and drug-induced reprogramming as a mode of most cancers drug resistance. Nature 546, 431–435 (2017).
Oren, Y. et al. Biking most cancers persister cells come up from lineages with distinct packages. Nature 596, 576–582 (2021).
Brock, A., Chang, H. & Huang, S. Non-genetic heterogeneity–a mutation-independent driving power for the somatic evolution of tumours. Nat. Rev. Genet. 10, 336–342 (2009).
Pisco, A. O. et al. Non-Darwinian dynamics in therapy-induced most cancers drug resistance. Nat. Commun. 4, 2467 (2013).
Tsoi, J. et al. Multi-stage differentiation defines melanoma subtypes with differential vulnerability to drug-induced iron-dependent oxidative stress. Most cancers Cell 33, 890–904.e5 (2018).
Vander Velde, R. et al. Resistance to focused therapies as a multifactorial, gradual adaptation to inhibitor particular selective pressures. Nat. Commun. 11, 2393 (2020).
Domcke, S., Sinha, R., Levine, D. A., Sander, C. & Schultz, N. Evaluating cell strains as tumour fashions by comparability of genomic profiles. Nat. Commun. 4, 2126 (2013).
Lord, C. J. & Ashworth, A. PARP inhibitors: artificial lethality within the clinic. Science 355, 1152–1158 (2017).
Goyal, Y. et al. Numerous clonal fates emerge upon drug remedy of homogeneous most cancers cells. Nature 620, 651–659 (2023).
Shen, S., Vagner, S. & Robert, C. Persistent most cancers cells: the lethal survivors. Cell 183, 860–874 (2020).
Lin, L. et al. SOX17 and PAX8 represent an actionable lineage-survival transcriptional complicated in ovarian most cancers. Oncogene 41, 1767–1779 (2022).
Kim, C., Wang, X.-D. & Yu, Y. PARP1 inhibitors set off innate immunity through PARP1 trapping-induced DNA harm response. eLife 9, e60637 (2020).
Kreß, J. Ok. C. et al. The built-in stress response effector ATF4 is an compulsory metabolic activator of NRF2. Cell Rep. 42, 112724 (2023).
Shibue, T. & Weinberg, R. A. EMT, CSCs, and drug resistance: the mechanistic hyperlink and medical implications. Nat. Rev. Clin. Oncol. 14, 611–629 (2017).
Guo, W. et al. Slug and Sox9 cooperatively decide the mammary stem cell state. Cell 148, 1015–1028 (2012).
Arlt, M. F., Wilson, T. E. & Glover, T. W. Replication stress and mechanisms of CNV formation. Curr. Opin. Genet. Dev. 22, 204–210 (2012).
Quintanal-Villalonga, Á. et al. Lineage plasticity in most cancers: a shared pathway of therapeutic resistance. Nat. Rev. Clin. Oncol. 17, 360–371 (2020).
Schmitt, C. A. et al. A senescence program managed by p53 and p16INK4a contributes to the result of most cancers remedy. Cell 109, 335–346 (2002).
Birsoy, Ok. et al. A vital position of the mitochondrial electron transport chain in cell proliferation is to allow aspartate synthesis. Cell 162, 540–551 (2015).
Sayin, V. I. et al. Activation of the NRF2 antioxidant program generates an imbalance in central carbon metabolism in most cancers. eLife 6, e28083 (2017).
Shen, Y.-A. et al. Inhibition of the MYC-regulated glutaminase metabolic axis is an efficient artificial deadly strategy for treating chemoresistant ovarian cancers. Most cancers Res. 80, 4514–4526 (2020).
Debaugnies, M. et al. RHOJ controls EMT-associated resistance to chemotherapy. Nature 616, 168–175 (2023).
Search engine marketing, J. et al. AP-1 subunits converge promiscuously at enhancers to potentiate transcription. Genome Res. 31, 538–550 (2021).
Martínez-Zamudio, R. I. et al. AP-1 imprints a reversible transcriptional programme of senescent cells. Nat. Cell Biol. 22, 842–855 (2020).
Larsen, S. B. et al. Institution, upkeep, and recall of inflammatory reminiscence. Cell Stem Cell 28, 1758–1774.e8 (2021).
Freddolino, P. L., Yang, J., Momen-Roknabadi, A. & Tavazoie, S. Stochastic tuning of gene expression permits mobile adaptation within the absence of pre-existing regulatory circuitry. eLife 7, e31867 (2018).
Gallaher, J. A., Enriquez-Navas, P. M., Luddy, Ok. A., Gatenby, R. A. & Anderson, A. R. A. Spatial heterogeneity and evolutionary dynamics modulate time to recurrence in steady and adaptive most cancers therapies. Most cancers Res. 78, 2127–2139 (2018).
Rehman, S. Ok. et al. Colorectal most cancers cells enter a diapause-like DTP state to outlive chemotherapy. Cell 184, 226–242.e21 (2021).
Marsolier, J. et al. H3K27me3 circumstances chemotolerance in triple-negative breast most cancers. Nat. Genet. 54, 459–468 (2022).
Hata, A. N. et al. Tumor cells can observe distinct evolutionary paths to turn out to be immune to epidermal progress issue receptor inhibition. Nat. Med. 22, 262–269 (2016).
Blount, Z. D., Lenski, R. E. & Losos, J. B. Contingency and determinism in evolution: replaying life’s tape. Science 362, eaam5979 (2018).
Huang, S. Reconciling non-genetic plasticity with somatic evolution in most cancers. Traits Most cancers Res. 7, 309–322 (2021).
Zhang, Ok. et al. Longitudinal single-cell RNA-seq evaluation reveals stress-promoted chemoresistance in metastatic ovarian most cancers. Sci. Adv. 8, eabm1831 (2022).
Rukhlenko, O. S. et al. Management of cell state transitions. Nature 609, 975–985 (2022).
Boumahdi, S. & de Sauvage, F. J. The good escape: tumour cell plasticity in resistance to focused remedy. Nat. Rev. Drug Discov. 19, 39–56 (2020).
Zhao, W. et al. A brand new bliss independence mannequin to investigate drug mixture information. J. Biomol. Display. 19, 817–821 (2014).
Hangauer, M. J. et al. Drug-tolerant persister most cancers cells are weak to GPX4 inhibition. Nature 551, 247–250 (2017).
Cybulska, P. et al. A genomically characterised assortment of high-grade serous ovarian most cancers xenografts for preclinical testing. Am. J. Pathol. 188, 1120–1131 (2018).
Shen, Y. et al. BMN 673, a novel and extremely potent PARP1/2 inhibitor for the remedy of human cancers with DNA restore deficiency. Clin. Most cancers Res. 19, 5003–5015 (2013).
Klein, A. M. et al. Droplet barcoding for single-cell transcriptomics utilized to embryonic stem cells. Cell 161, 1187–1201 (2015).
Baron, M. et al. The stress-like most cancers cell state is a constant element of tumorigenesis. Cell Syst. 11, 536–546.e7 (2020).
Stoeckius, M. et al. Cell hashing with barcoded antibodies permits multiplexing and doublet detection for single cell genomics. Genome Biol. 19, 224 (2018).
Butler, A., Hoffman, P., Smibert, P., Papalexi, E. & Satija, R. Integrating single-cell transcriptomic information throughout totally different circumstances, applied sciences, and species. Nat. Biotechnol. 36, 411–420 (2018).
Corces, M. R. et al. An improved ATAC-seq protocol reduces background and permits interrogation of frozen tissues. Nat. Strategies 14, 959–962 (2017).
Gong, W., Kwak, I.-Y., Pota, P., Koyano-Nakagawa, N. & Garry, D. J. DrImpute: imputing dropout occasions in single cell RNA sequencing information. BMC Bioinformatics 19, 220 (2018).
Haghverdi, L., Lun, A. T. L., Morgan, M. D. & Marioni, J. C. Batch results in single-cell RNA-sequencing information are corrected by matching mutual nearest neighbors. Nat. Biotechnol. 36, 421–427 (2018).
Neftel, C. et al. An integrative mannequin of mobile states, plasticity, and genetics for glioblastoma. Cell 178, 835–849.e21 (2019).
Gel, B. et al. regioneR: an R/Bioconductor package deal for the affiliation evaluation of genomic areas based mostly on permutation exams. Bioinformatics 32, 289–291 (2016).
Kumar, L. & E Futschik, M. Mfuzz: a software program package deal for tender clustering of microarray information. Bioinformation 2, 5–7 (2007).
Langmead, B. & Salzberg, S. L. Quick gapped-read alignment with Bowtie 2. Nat. Strategies 9, 357–359 (2012).
Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).
Zhang, Y. et al. Mannequin-based evaluation of ChIP-seq (MACS). Genome Biol. 9, R137 (2008).
Amemiya, H. M., Kundaje, A. & Boyle, A. P. The ENCODE Blacklist: Identification of problematic areas of the genome. Sci. Rep. 9, 9354 (2019).
Heinz, S. et al. Easy combos of lineage-determining transcription elements prime cis-regulatory parts required for macrophage and B cell identities. Mol. Cell 38, 576–589 (2010).
Ramírez, F. et al. deepTools2: a subsequent technology net server for deep-sequencing information evaluation. Nucleic Acids Res. 44, W160–W165 (2016).
Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq information with DESeq2. Genome Biol. 15, 550 (2014).
Levin, M. et al. The mid-developmental transition and the evolution of animal physique plans. Nature 531, 637–641 (2016).
Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a versatile trimmer for Illumina sequence information. Bioinformatics 30, 2114–2120 (2014).
Li, H. & Durbin, R. Quick and correct quick learn alignment with Burrows–Wheeler remodel. Bioinformatics 25, 1754–1760 (2009).
Tarasov, A., Vilella, A. J., Cuppen, E., Nijman, I. J. & Prins, P. Sambamba: quick processing of NGS alignment codecs. Bioinformatics 31, 2032–2034 (2015).
McKenna, A. et al. The Genome Evaluation Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing information. Genome Res. 20, 1297–1303 (2010).
Boeva, V. et al. Management-FREEC: a software for assessing copy quantity and allelic content material utilizing next-generation sequencing information. Bioinformatics 28, 423–425 (2012).
Wang, Ok., Li, M. & Hakonarson, H. ANNOVAR: practical annotation of genetic variants from high-throughput sequencing information. Nucleic Acids Res. 38, e164 (2010).
Li, W. et al. MAGeCK permits sturdy identification of important genes from genome-scale CRISPR/Cas9 knockout screens. Genome Biol. 15, 554 (2014).
Pacold, M. E. et al. A PHGDH inhibitor reveals coordination of serine synthesis and one-carbon unit destiny. Nat. Chem. Biol. 12, 452–458 (2016).
Simón-Manso, Y. et al. Metabolite profiling of a NIST Normal Reference Materials for human plasma (SRM 1950): GC–MS, LC–MS, NMR, and medical laboratory analyses, libraries, and web-based sources. Anal. Chem. 85, 11725–11731 (2013).
Chen, W. W., Freinkman, E., Wang, T., Birsoy, Ok. & Sabatini, D. M. Absolute quantification of matrix metabolites reveals the dynamics of mitochondrial metabolism. Cell 166, 1324–1337.e11 (2016).
Smith, C. A. et al. METLIN: a metabolite mass spectral database. Ther. Drug Monit. 27, 747–751 (2005).

