The DOI matches its Crossref record; 126 of the 126 references it cites were found in a registry or an index.
Review of precision cancer medicine: Evolution of the treatment paradigm
Open accessAbstract
In recent years, biotechnological breakthroughs have led to identification of complex and unique biologic features associated with carcinogenesis. Tumor and cell-free DNA profiling, immune markers, and proteomic and RNA analyses are used to identify these characteristics for optimization of anticancer therapy in individual patients. Consequently, clinical trials have evolved, shifting from tumor type-centered to gene-directed, histology-agnostic, with innovative adaptive design tailored to biomarker profiling with the goal to improve treatment outcomes. A plethora of precision medicine trials have been conducted. The majority of these trials demonstrated that matched therapy is associated with superior outcomes compared to non-matched therapy across tumor types and in specific cancers. To improve the implementation of precision medicine, this approach should be used early in the course of the disease, and patients should have complete tumor profiling and access to effective matched therapy. To overcome the complexity of tumor biology, clinical trials with combinations of gene-targeted therapy with immune-targeted approaches (e.g., checkpoint blockade, personalized vaccines and/or chimeric antigen receptor T-cells), hormonal therapy, chemotherapy and/or novel agents should be considered. These studies should target dynamic changes in tumor biologic abnormalities, eliminating minimal residual disease, and eradicating significant subclones that confer resistance to treatment. Mining and expansion of real-world data, facilitated by the use of advanced computer data processing capabilities, may contribute to validation of information to predict new applications for medicines. In this review, we summarize the clinical trials and discuss challenges and opportunities to accelerate the implementation of precision oncology.
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Cites
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Cited by
Yang, Y., Li, S., Wang, Y., Zhao, Y., & Li, Q. (2022) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-022-01168-8 · Cited by 294
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
“In contrast, a major 315 setback uniformly described in other personalized medicine trials is patient deterioration or 316 death during time of analysis (4,33).”
BackgroundPersonalizing neoadjuvant immune-checkpoint inhibition in patients with melanoma
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Learning chemical sensitivity reveals mechanisms of cellular response
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Metadata
- Authors
- Tsimberidou, A. M., Fountzilas, E., Nikanjam, M., & Kurzrock, R.
- Venue
- Cancer Treatment Reviews
- Publisher
- Elsevier BV
- Published
- 2020-06
- Volume
- 86
- Pages
- 102019
- ISSN
- 0305-7372 (ISSN-L)
- OpenAlex
- W3014115276
- Cited by
- 756
- References
- 126
- FWCI
- 23.59
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- Tsimberidou, A. M., Fountzilas, E., Nikanjam, M., & Kurzrock, R. (2020). Review of precision cancer medicine: Evolution of the treatment paradigm. Cancer Treatment Reviews. https://doi.org/10.1016/j.ctrv.2020.102019
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
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.
