Breast cancer patient-derived whole-tumor cell culture model for efficient drug profiling and treatment response prediction
Breast cancer (BC) is a complex disease comprising multiple distinct subtypes with different genetic features and pathological characteristics. Although a large number of antineoplastic compounds have been approved for clinical use, patient-to-patient variability in drug response is frequently obser...
| Main Authors: | , , , , , , , , , , , , , , , |
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| Format: | Journal Article |
| Language: | English |
| Published: |
2023
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| Online Access: | https://hdl.handle.net/10356/171813 |
| _version_ | 1824453400759107584 |
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| author | Chen, Xinsong Sifakis, Emmanouil G. Robertson, Stephanie Neo, Shi Yong Jun, Seong-Hwan Tong, Le Tay, Apple Hui Min Lövrot, John Hellgren, Roxanna Margolin, Sara Bergh, Jonas Foukakis, Theodoros Lagergren, Jens Lundqvist, Andreas Ma, Ran Hartman, Johan |
| author2 | School of Biological Sciences |
| author_facet | School of Biological Sciences Chen, Xinsong Sifakis, Emmanouil G. Robertson, Stephanie Neo, Shi Yong Jun, Seong-Hwan Tong, Le Tay, Apple Hui Min Lövrot, John Hellgren, Roxanna Margolin, Sara Bergh, Jonas Foukakis, Theodoros Lagergren, Jens Lundqvist, Andreas Ma, Ran Hartman, Johan |
| author_sort | Chen, Xinsong |
| collection | NTU |
| description | Breast cancer (BC) is a complex disease comprising multiple distinct subtypes with different genetic features and pathological characteristics. Although a large number of antineoplastic compounds have been approved for clinical use, patient-to-patient variability in drug response is frequently observed, highlighting the need for efficient treatment prediction for individualized therapy. Several patient-derived models have been established lately for the prediction of drug response. However, each of these models has its limitations that impede their clinical application. Here, we report that the whole-tumor cell culture (WTC) ex vivo model could be stably established from all breast tumors with a high success rate (98 out of 116), and it could reassemble the parental tumors with the endogenous microenvironment. We observed strong clinical associations and predictive values from the investigation of a broad range of BC therapies with WTCs derived from a patient cohort. The accuracy was further supported by the correlation between WTC-based test results and patients' clinical responses in a separate validation study, where the neoadjuvant treatment regimens of 15 BC patients were mimicked. Collectively, the WTC model allows us to accomplish personalized drug testing within 10 d, even for small-sized tumors, highlighting its potential for individualized BC therapy. Furthermore, coupled with genomic and transcriptomic analyses, WTC-based testing can also help to stratify specific patient groups for assignment into appropriate clinical trials, as well as validate potential biomarkers during drug development. |
| first_indexed | 2024-10-01T07:59:45Z |
| format | Journal Article |
| id | ntu-10356/171813 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T07:59:45Z |
| publishDate | 2023 |
| record_format | dspace |
| spelling | ntu-10356/1718132023-11-13T15:32:18Z Breast cancer patient-derived whole-tumor cell culture model for efficient drug profiling and treatment response prediction Chen, Xinsong Sifakis, Emmanouil G. Robertson, Stephanie Neo, Shi Yong Jun, Seong-Hwan Tong, Le Tay, Apple Hui Min Lövrot, John Hellgren, Roxanna Margolin, Sara Bergh, Jonas Foukakis, Theodoros Lagergren, Jens Lundqvist, Andreas Ma, Ran Hartman, Johan School of Biological Sciences Science::Biological sciences Breast Cancer Drug Profiling Breast cancer (BC) is a complex disease comprising multiple distinct subtypes with different genetic features and pathological characteristics. Although a large number of antineoplastic compounds have been approved for clinical use, patient-to-patient variability in drug response is frequently observed, highlighting the need for efficient treatment prediction for individualized therapy. Several patient-derived models have been established lately for the prediction of drug response. However, each of these models has its limitations that impede their clinical application. Here, we report that the whole-tumor cell culture (WTC) ex vivo model could be stably established from all breast tumors with a high success rate (98 out of 116), and it could reassemble the parental tumors with the endogenous microenvironment. We observed strong clinical associations and predictive values from the investigation of a broad range of BC therapies with WTCs derived from a patient cohort. The accuracy was further supported by the correlation between WTC-based test results and patients' clinical responses in a separate validation study, where the neoadjuvant treatment regimens of 15 BC patients were mimicked. Collectively, the WTC model allows us to accomplish personalized drug testing within 10 d, even for small-sized tumors, highlighting its potential for individualized BC therapy. Furthermore, coupled with genomic and transcriptomic analyses, WTC-based testing can also help to stratify specific patient groups for assignment into appropriate clinical trials, as well as validate potential biomarkers during drug development. Published version The computations and data handling were enabled by resources provided by the Swedish National Infrastructure for Computing (SNIC) at Uppmax, partially funded by the Swedish Research Council through grant agreement no. 2018-05973. This work was supported by grants from the Swedish Society of Medicine, Swedish Society for Medical Research (SSMF), Swedish Cancer Society, Swedish Research Council (no. 2018-06217), Cancer Society in Stockholm, Region Stockholm, MedTechLabs, and Swedish Breast Cancer Association. 2023-11-08T08:02:49Z 2023-11-08T08:02:49Z 2023 Journal Article Chen, X., Sifakis, E. G., Robertson, S., Neo, S. Y., Jun, S., Tong, L., Tay, A. H. M., Lövrot, J., Hellgren, R., Margolin, S., Bergh, J., Foukakis, T., Lagergren, J., Lundqvist, A., Ma, R. & Hartman, J. (2023). Breast cancer patient-derived whole-tumor cell culture model for efficient drug profiling and treatment response prediction. Proceedings of the National Academy of Sciences, 120(1), e2209856120-. https://dx.doi.org/10.1073/pnas.2209856120 0027-8424 https://hdl.handle.net/10356/171813 10.1073/pnas.2209856120 36574653 2-s2.0-85144787419 1 120 e2209856120 en Proceedings of the National Academy of Sciences © 2022 the Author(s). Published by PNAS. This open access article is distributed under Creative Commons Attribution License 4.0 (CC BY). application/pdf |
| spellingShingle | Science::Biological sciences Breast Cancer Drug Profiling Chen, Xinsong Sifakis, Emmanouil G. Robertson, Stephanie Neo, Shi Yong Jun, Seong-Hwan Tong, Le Tay, Apple Hui Min Lövrot, John Hellgren, Roxanna Margolin, Sara Bergh, Jonas Foukakis, Theodoros Lagergren, Jens Lundqvist, Andreas Ma, Ran Hartman, Johan Breast cancer patient-derived whole-tumor cell culture model for efficient drug profiling and treatment response prediction |
| title | Breast cancer patient-derived whole-tumor cell culture model for efficient drug profiling and treatment response prediction |
| title_full | Breast cancer patient-derived whole-tumor cell culture model for efficient drug profiling and treatment response prediction |
| title_fullStr | Breast cancer patient-derived whole-tumor cell culture model for efficient drug profiling and treatment response prediction |
| title_full_unstemmed | Breast cancer patient-derived whole-tumor cell culture model for efficient drug profiling and treatment response prediction |
| title_short | Breast cancer patient-derived whole-tumor cell culture model for efficient drug profiling and treatment response prediction |
| title_sort | breast cancer patient derived whole tumor cell culture model for efficient drug profiling and treatment response prediction |
| topic | Science::Biological sciences Breast Cancer Drug Profiling |
| url | https://hdl.handle.net/10356/171813 |
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