Abstract
Most patients with advanced malignancies are treated with severely toxic, first-line chemotherapies. Personalized treatment strategies have led to improved patient outcomes and could replace one-size-fits-all therapies, yet they need to be tailored by testing of a range of targeted drugs in primary patient cells. Most functional precision medicine studies use simple drug-response metrics, which cannot quantify the selective effects of drugs (i.e., the differential responses of cancer cells and normal cells). We developed a computational method for selective drug-sensitivity scoring (DSS), which enables normalization of the individual patient’s responses against normal cell responses. The selective response scoring uses the inhibition of noncancerous cells as a proxy for potential drug toxicity, which can in turn be used to identify effective and safer treatment options. Here, we explain how to apply the selective DSS calculation for guiding precision medicine in patients with leukemia treated across three cancer centers in Europe and the USA; the generic methods are also widely applicable to other malignancies that are amenable to drug testing. The open-source and extendable R-codes provide a robust means to tailor personalized treatment strategies on the basis of increasingly available ex vivo drug-testing data from patients in real-world and clinical trial settings. We also make available drug-response profiles to 527 anticancer compounds tested in 10 healthy bone marrow samples as reference data for selective scoring and de-prioritization of drugs that show broadly toxic effects. The procedure takes <60 min and requires basic skills in R.
Key points
-
The selective scoring of ex vivo drug responses facilitates the identification of targeted, nontoxic compounds, enabling safer individualized treatment options for guiding clinical decision-making.
-
Drug-sensitivity scoring requires calculations in R v.3.5.1 and RStudio and is tested here with up to 527 compounds on the FIMM-AML (n = 164) and BeatAML (n = 631) cohorts of patients with acute myeloid leukemia.
This is a preview of subscription content, access via your institution
Access options
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 / 30 days
cancel any time
Subscribe to this journal
Receive 12 print issues and online access
$259.00 per year
only $21.58 per issue
Buy this article
- Purchase on Springer Link
- Instant access to full article PDF
Prices may be subject to local taxes which are calculated during checkout
Similar content being viewed by others
Data availability
The original clinical summary and drug-response data from the FIMM-AML and BeatAML cohorts are available at https://zenodo.org/record/7274740 and https://biodev.github.io/BeatAML2/, respectively. The drug-response data file consisting of 10 healthy control responses and data on patients with AML for Procedures 1 and 2 are available at https://github.com/yingjchen/DSS-v2.0/tree/main/controls.
Code availability
The R-codes can be used by anyone with basic skills in R and with basic knowledge of dose–response testing data. The codes come with readme files, user instructions and example data from FIMM-AML patients and healthy controls, along with the expected visualization outcomes (a zip file from GitHub, 1.7 MB), freely available at https://github.com/yingjchen/DSS-v2.0.
References
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).
Malani, D. et al. Implementing a functional precision medicine tumor board for acute myeloid leukemia. Cancer Discov. 12, 388–401 (2022).
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).
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).
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).
Pemovska, T. et al. Axitinib effectively inhibits BCR-ABL1(T315I) with a distinct binding conformation. Nature 519, 102–105 (2015).
Hatzis, C. et al. Enhancing reproducibility in cancer drug screening: how do we move forward? Cancer Res. 74, 4016–4023 (2014).
Haibe-Kains, B. et al. Inconsistency in large pharmacogenomic studies. Nature 504, 389–393 (2013).
Yadav, B. et al. Quantitative scoring of differential drug sensitivity for individually optimized anticancer therapies. Sci. Rep. 4, 5193 (2014).
Barretina, J. et al. The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature 483, 603–607 (2012).
Garnett, M. J. et al. Systematic identification of genomic markers of drug sensitivity in cancer cells. Nature 483, 570–575 (2012).
Mpindi, J. P. et al. Consistency in drug response profiling. Nature 540, E5–E6 (2016).
Pemovska, T. et al. Individualized systems medicine strategy to tailor treatments for patients with chemorefractory acute myeloid leukemia. Cancer Discov. 3, 1416–1429 (2013).
Yin, Y. et al. Functional testing to characterize and stratify PI3K inhibitor responses in chronic lymphocytic leukemia. Clin. Cancer Res. 28, 4444–4455 (2022).
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).
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).
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).
Yamada, S. et al. Clinical relevance of in vitro chemoresistance in childhood acute myeloid leukemia. Leukemia 15, 1892–1897 (2001).
Volm, M. & Efferth, T. Prediction of cancer drug resistance and implications for personalized medicine. Front. Oncol. 5, 282 (2015).
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).
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).
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).
Heinemann, T. et al. Deep morphology learning enhances ex vivo drug profiling-based precision medicine. Blood Cancer Discov. 3, 502–515 (2022).
Kropivsek, K. et al. Ex vivo drug response heterogeneity reveals personalized therapeutic strategies for patients with multiple myeloma. Nat. Cancer 4, 734–753 (2023).
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).
Ianevski, A. et al. Patient-tailored design for selective co-inhibition of leukemic cell subpopulations. Sci. Adv. 7, eabe4038 (2021).
Goh, J. et al. An ex vivo platform to guide drug combination treatment in relapsed/refractory lymphoma. Sci. Transl. Med. 14, eabn7824 (2022).
He, L. et al. Patient-customized drug combination prediction and testing for T-cell prolymphocytic leukemia patients. Cancer Res. 78, 2407–2418 (2018).
He, L. et al. Network-guided identification of cancer-selective combinatorial therapies in ovarian cancer. Brief. Bioinform. 22, bbab272 (2021).
Hanes, R. et al. screenwerk: a modular tool for the design and analysis of drug combination screens. Bioinformatics 39, btac840 (2023).
Ritz, C., Baty, F., Streibig, J. C. & Gerhard, D. Dose-response analysis using R. PLoS One 10, e0146021 (2015).
Tipping, M. E. & Bishop, C. M. Probabilistic principal component analysis. J. R. Stat. Soc. Ser. B Stat. Methodol. 61, 611–622 (1999).
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).
Kuusanmäki, H. et al. Ex vivo venetoclax sensitivity testing predicts treatment response in acute myeloid leukemia. Haematologica 108, 1768–1781 (2023).
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).
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).
Acknowledgements
We are most grateful to the patients and their families for participating in the studies. The authors thank K. Porkka (Helsinki University Hospital, Finland), O. Kallioniemi (Science for Life Laboratory, Sweden) and J. Tyner (Oregon Health & Science University, USA) for making the AML drug testing data openly available. We thank V. Davidsson (University of Helsinki, Finland) for testing the R-codes and procedures; M. Zucknick (University of Oslo, Norway) for discussions about statistical aspects of drug-sensitivity scoring; and S. Skånland (Oslo University Hospital) for discussions about patient drug testing. We thank R. Hanes for bioinformatics assistance and QC analysis, and A. Andersen and A. Brodersen for theoretical input. CSC (IT Center for Science, Finland) is thanked for the computational resources, and the FIMM High-Throughput Biology unit and the NCMM Chemical Biology Platform are thanked for the drug screening. Y.C. received a personal grant from CSC (China) and EDUFI (Finland). L.H. received funding from the Nordic EMBL Partnership Hub for Molecular Medicine (NordForsk grant #96782) and the Cancer Foundation Finland (travel grant). S.M. received funding from the Academy of Finland (grants 336666, 326588, 312413 and 353177) and the Competitive State Research Financing of the Expert Responsibility area of Tampere University Hospital. C.A.H. received funding from the Academy of Finland (grants 1320185 and 334781), the Cancer Foundation Finland and the Sigrid Jusélius Foundation. J.M.E. received funding from the Norwegian Health Authority South-East (HSØ grants 2017064, 2018012 and 2019096), the Norwegian Cancer Society (grants 182524 and 208012) and the Research Council of Norway through its Centers of Excellence funding scheme (grant 262652) and through research grants 261936, 294916 and 314811, and from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 847912 (RESCUER). T.A. received funding from the European Union’s Horizon Europe Research & Innovation programme (REMEDi4ALL project, grant agreement no. 101057442), the European Union’s Horizon 2020 Research and Innovation Programme (ERA PerMed JAKSTAT-TARGET and CLL-CLUE projects), the Academy of Finland (grants 310507, 313267, 326238, 340141, 344698 and 345803), the Norwegian Health Authority South-East (grant 2020026), the Cancer Society of Finland, the Norwegian Cancer Society and the Sigrid Jusélius Foundation. For S.P. and J.S., the FIMM-HTB unit is supported by the University of Helsinki, HiLIFE and the Biocenter Finland.
Author information
Authors and Affiliations
Contributions
Y.C. implemented the new DSSs, analyzed the data, drafted the figures and wrote the protocols and troubleshooting advice. L.H. designed the experiments, supervised the code writing and tested the codes. A.I. made the final figures, reviewed the text and tested the codes. P.A.-D. screened the Oslo patient samples and wrote the Oslo materials section. S.P. contributed to the drug-response analysis codes and tested the Procedures. J.S. supervised the drug-response data analyses, reviewed the text and completed Table 1 for the FIMM cohort. J.J.M. wrote the FIMM materials section and reviewed the text. S.K., S.M. and M.M. obtained and prepared the healthy donor samples for screening. C.A.H. supervised the FIMM drug screening and reviewed and edited the text. J.M.E. supervised the Oslo drug screening and reviewed and edited the text. K.W. designed the DSSs and reviewed and edited the text. T.A. designed the DSSs and analyses, supervised the work and wrote the first version of the manuscript.
Corresponding author
Ethics declarations
Competing interests
S.M. receives research funding from Oncopeptides for work unrelated to the present study. C.A.H. receives research funding from Kronos Bio, Novartis, Oncopeptides, WNTResearch and Zentalis Pharmaceuticals for work unrelated to the present study and honoraria from Amgen. T.A. receives research funding from the European Union’s Horizon Europe Research & Innovation programme under grant agreement no. 101057442. The views and opinions expressed in this document are those of the authors only. They do not necessarily reflect those of the European Union who cannot be held responsible for the information it contains. All the other authors declare no competing interests.
Peer review
Peer review information
Nature Protocols thanks Miguel Rocha and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
Additional information
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Related links
Key references using this protocol
Pemovska, T. et al. Cancer Discov. 3, 1416–1429 (2013): https://doi.org/10.1158/2159-8290.CD-13-0350
Yadav, B. et al. Sci. Rep. 4, 5193 (2014): https://doi.org/10.1038/srep05193
Mpindi, J. P. et al. Nature 540, E5–E6 (2016): https://doi.org/10.1038/nature20171
Malani, D. et al. Cancer Discov. 12, 388–401 (2022): https://doi.org/10.1158/2159-8290.CD-21-0410
Murumagi, A. et al. Br. J. Cancer 128, 678–690 (2023): https://doi.org/10.1038/s41416-022-02067-z
Supplementary information
Supplementary Information
Supplementary Figs. 1–4
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Chen, Y., He, L., Ianevski, A. et al. Robust scoring of selective drug responses for patient-tailored therapy selection. Nat Protoc 19, 60–82 (2024). https://doi.org/10.1038/s41596-023-00903-x
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1038/s41596-023-00903-x
Comments
By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.
