The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
Molecular profiling of cancer patients enables personalized combination therapy: the I-PREDICT study
Open accessAbstract
Cancer treatments have evolved from indiscriminate cytotoxic agents to selective genome- and immune-targeted drugs that have transformed the outcomes of some malignancies1. Tumor complexity and heterogeneity suggest that the ‘precision medicine’ paradigm of cancer therapy requires treatment to be personalized to the individual patient2–6. To date, precision oncology trials have been based on molecular matching with predetermined monotherapies7–14. Several of these trials have been hindered by very low matching rates, often in the 5–10% range15, and low response rates. Low matching rates may be due to the use of limited gene panels, restrictive molecular matching algorithms, lack of drug availability, or the deterioration and death of end-stage patients before therapy can be implemented. We hypothesized that personalized treatment with combination therapies would improve outcomes in patients with refractory malignancies. As a first test of this concept, we implemented a cross-institutional prospective study (I-PREDICT, NCT02534675) that used tumor DNA sequencing and timely recommendations for individualized treatment with combination therapies. We found that administration of customized multidrug regimens was feasible, with 49% of consented patients receiving personalized treatment. Targeting of a larger fraction of identified molecular alterations, yielding a higher ‘matching score’, was correlated with significantly improved disease control rates, as well as longer progression-free and overall survival rates, compared to targeting of fewer somatic alterations. Our findings suggest that the current clinical trial paradigm for precision oncology, which pairs one driver mutation with one drug, may be optimized by treating molecularly complex and heterogeneous cancers with combinations of customized agents. A prospective clinical study of cancer patients (I-PREDICT) demonstrated the feasibility of matching genomic alterations found in tumors to combined drug treatments.
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Cited by
Ooft, S. N., Weeber, F., Dijkstra, K. K., McLean, C. M., Kaing, S., van Werkhoven, E., Schipper, L., Hoes, L., Vis, D. J., van de Haar, J., et al. (2019) · Science Translational Medicine · doi:10.1126/scitranslmed.aay2574 · Cited by 813
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Overcoming resistance to tumor-targeted and immune-targeted therapies
Aldea, M., Andre, F., Marabelle, A., Dogan, S., Barlesi, F., & Soria, J.-C. (2021) · Cancer Discovery · doi:10.1158/2159-8290.cd-20-1638 · Cited by 311
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Kornauth, C., Pemovska, T., Vladimer, G. I., Bayer, G., Bergmann, M., Eder, S., Eichner, R., Erl, M., Esterbauer, H., Exner, R., et al. (2022) · Cancer Discovery · doi:10.1158/2159-8290.cd-21-0538 · Cited by 206
“329 This strategy could raise the limited matching numbers, but has not yet been validated in a 330 prospective fashion (28).”
Background
Metadata
- Authors
- Sicklick, J. K., Kato, S., Okamura, R., Schwaederle, M., Hahn, M. E., Williams, C. B., De, P., Krie, A., Piccioni, D. E., Miller, V. A., Ross, J. S., Benson, A., Webster, J., Stephens, P. J., Lee, J. J., Fanta, P. T., Lippman, S. M., Leyland-Jones, B., & Kurzrock, R.
- Venue
- Nature Medicine
- Published
- 2019-04-22
- Volume
- 25
- Issue
- 5
- Pages
- 744-750
- ISSN
- 1078-8956 (ISSN-L)
- 1546-170X
- OpenAlex
- W2938206352
- Cited by
- 707
- References
- 8
- FWCI
- 24.33
- Type
- journal article
- Language
- en
- Open access
- green
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- Sicklick, J. K., Kato, S., Okamura, R., Schwaederle, M., Hahn, M. E., Williams, C. B., De, P., Krie, A., Piccioni, D. E., Miller, V. A., Ross, J. S., Benson, A., Webster, J., Stephens, P. J., Lee, J. J., Fanta, P. T., Lippman, S. M., Leyland-Jones, B., & Kurzrock, R. (2019). Molecular profiling of cancer patients enables personalized combination therapy: the I-PREDICT study. Nature Medicine. https://doi.org/10.1038/s41591-019-0407-5
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
