Kornauth, C. et al. Functional precision medicine provides clinical benefit in advanced aggressive hematologic cancers and identifies exceptional responders. Cancer Discov. 12, 372–387 (2022).

Article  PubMed  Google Scholar 

Malani, D. et al. Implementing a functional precision medicine tumor board for acute myeloid leukemia. Cancer Discov. 12, 388–401 (2022).

Article  CAS  PubMed  Google Scholar 

Letai, A., Bhola, P. & Welm, A. L. Functional precision oncology: testing tumors with drugs to identify vulnerabilities and novel combinations. Cancer Cell 40, 26–35 (2022).

Article  CAS  PubMed  Google Scholar 

Tognon, C. E., Sears, R. C., Mills, G. B., Gray, J. W. & Tyner, J. W. Ex vivo analysis of primary tumor specimens for evaluation of cancer therapeutics. Annu. Rev. Cancer Biol. 5, 39–57 (2021).

Article  PubMed  Google Scholar 

Flobak, Å., Skånland, S. S., Hovig, E., Taskén, K. & Russnes, H. G. Functional precision cancer medicine: drug sensitivity screening enabled by cell culture models. Trends Pharmacol. Sci. 43, 973–985 (2022).

Article  CAS  PubMed  Google Scholar 

Pemovska, T. et al. Axitinib effectively inhibits BCR-ABL1(T315I) with a distinct binding conformation. Nature 519, 102–105 (2015).

Article  CAS  PubMed  Google Scholar 

Hatzis, C. et al. Enhancing reproducibility in cancer drug screening: how do we move forward? Cancer Res. 74, 4016–4023 (2014).

Article  CAS  PubMed  PubMed Central  Google Scholar 

Haibe-Kains, B. et al. Inconsistency in large pharmacogenomic studies. Nature 504, 389–393 (2013).

Article  CAS  PubMed  PubMed Central  Google Scholar 

Yadav, B. et al. Quantitative scoring of differential drug sensitivity for individually optimized anticancer therapies. Sci. Rep. 4, 5193 (2014).

Article  CAS  PubMed  PubMed Central  Google Scholar 

Barretina, J. et al. The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature 483, 603–607 (2012).

Article  CAS  PubMed  PubMed Central  Google Scholar 

Garnett, M. J. et al. Systematic identification of genomic markers of drug sensitivity in cancer cells. Nature 483, 570–575 (2012).

Article  CAS  PubMed  PubMed Central  Google Scholar 

Mpindi, J. P. et al. Consistency in drug response profiling. Nature 540, E5–E6 (2016).

Article  CAS  PubMed  Google Scholar 

Pemovska, T. et al. Individualized systems medicine strategy to tailor treatments for patients with chemorefractory acute myeloid leukemia. Cancer Discov. 3, 1416–1429 (2013).

Article  CAS  PubMed  Google Scholar 

Yin, Y. et al. Functional testing to characterize and stratify PI3K inhibitor responses in chronic lymphocytic leukemia. Clin. Cancer Res. 28, 4444–4455 (2022).

Article  CAS  PubMed  PubMed Central  Google Scholar 

Andersen, A. N. et al. Clinical forecasting using ex vivo drug sensitivity profiling of acute myeloid leukemia. Preprint at https://www.biorxiv.org/content/10.1101/2022.10.11.509866v2 (2023).

Bottomly, D. et al. Integrative analysis of drug response and clinical outcome in acute myeloid leukemia. Cancer Cell 40, 850–864.e9 (2022).

Article  CAS  PubMed  PubMed Central  Google Scholar 

Potdar, S. et al. Breeze 2.0: an interactive web-tool for visual analysis and comparison of drug response data. Nucleic Acids Res. 51, W57–W61 (2023).

Article  PubMed  PubMed Central  Google Scholar 

Leek, J. T., Johnson, W. E., Parker, H. S., Jaffe, A. E. & Storey, J. D. The sva package for removing batch effects and other unwanted variation in high-throughput experiments. Bioinformatics 28, 882–883 (2012).

Article  CAS  PubMed  PubMed Central  Google Scholar 

Yamada, S. et al. Clinical relevance of in vitro chemoresistance in childhood acute myeloid leukemia. Leukemia 15, 1892–1897 (2001).

Article  CAS  PubMed  Google Scholar 

Volm, M. & Efferth, T. Prediction of cancer drug resistance and implications for personalized medicine. Front. Oncol. 5, 282 (2015).

Article  PubMed  PubMed Central  Google Scholar 

Gupta, A., Gautam, P., Wennerberg, K. & Aittokallio, T. A normalized drug response metric improves accuracy and consistency of anticancer drug sensitivity quantification in cell-based screening. Commun. Biol. 3, 42 (2020).

Article  PubMed  PubMed Central  Google Scholar 

Hafner, M., Niepel, M., Chung, M. & Sorger, P. K. Growth rate inhibition metrics correct for confounders in measuring sensitivity to cancer drugs. Nat. Methods 13, 521–527 (2016).

Article  CAS  PubMed  PubMed Central  Google Scholar 

Murumagi, A. et al. Drug response profiles in patient-derived cancer cells across histological subtypes of ovarian cancer: real-time therapy tailoring for a patient with low-grade serous carcinoma. Br. J. Cancer 128, 678–690 (2023).

Article  CAS  PubMed  Google Scholar 

Heinemann, T. et al. Deep morphology learning enhances ex vivo drug profiling-based precision medicine. Blood Cancer Discov. 3, 502–515 (2022).

Article  PubMed  PubMed Central  Google Scholar 

Kropivsek, K. et al. Ex vivo drug response heterogeneity reveals personalized therapeutic strategies for patients with multiple myeloma. Nat. Cancer 4, 734–753 (2023).

Article  CAS  PubMed  PubMed Central  Google Scholar 

Kuusanmäki, H. et al. Phenotype-based drug screening reveals association between venetoclax response and differentiation stage in acute myeloid leukemia. Haematologica 105, 708–720 (2020).

Article  PubMed  PubMed Central  Google Scholar 

Ianevski, A. et al. Patient-tailored design for selective co-inhibition of leukemic cell subpopulations. Sci. Adv. 7, eabe4038 (2021).

Article  CAS  PubMed  PubMed Central  Google Scholar 

Goh, J. et al. An ex vivo platform to guide drug combination treatment in relapsed/refractory lymphoma. Sci. Transl. Med. 14, eabn7824 (2022).

Article  CAS  PubMed  Google Scholar 

He, L. et al. Patient-customized drug combination prediction and testing for T-cell prolymphocytic leukemia patients. Cancer Res. 78, 2407–2418 (2018).

Article  CAS  PubMed  Google Scholar 

He, L. et al. Network-guided identification of cancer-selective combinatorial therapies in ovarian cancer. Brief. Bioinform. 22, bbab272 (2021).

Article  PubMed  PubMed Central  Google Scholar 

Hanes, R. et al. screenwerk: a modular tool for the design and analysis of drug combination screens. Bioinformatics 39, btac840 (2023).

Article  CAS  PubMed  Google Scholar 

Ritz, C., Baty, F., Streibig, J. C. & Gerhard, D. Dose-response analysis using R. PLoS One 10, e0146021 (2015).

Article  PubMed  PubMed Central  Google Scholar 

Tipping, M. E. & Bishop, C. M. Probabilistic principal component analysis. J. R. Stat. Soc. Ser. B Stat. Methodol. 61, 611–622 (1999).

Article  Google Scholar 

Lee, S. H. R. et al. Pharmacotypes across the genomic landscape of pediatric acute lymphoblastic leukemia and impact on treatment response. Nat. Med. 29, 170–179 (2023).

Article  CAS  PubMed  PubMed Central  Google Scholar 

Kuusanmäki, H. et al. Ex vivo venetoclax sensitivity testing predicts treatment response in acute myeloid leukemia. Haematologica 108, 1768–1781 (2023).

Article  PubMed  Google Scholar 

Majumder, M. M. et al. Identification of precision treatment strategies for relapsed/refractory multiple myeloma by functional drug sensitivity testing. Oncotarget 8, 56338–56350 (2017).

Article  PubMed  PubMed Central  Google Scholar 

Pearson, K. LIII. On lines and planes of closest fit to systems of points in space. The Lond., Edinb. Dublin Philos. Mag. J. Sci. 2, 559–572 (1901).

Article  Google Scholar