Next Article in Journal
Perspective: Cellular and Molecular Profiling Technologies in Personalized Oncology
Next Article in Special Issue
Impacts of Sex Differences in Pulse Pressure among Patients with Chronic Kidney Disease
Previous Article in Journal
Evidence to Support Inclusion of Pharmacogenetic Biomarkers in Randomised Controlled Trials
Previous Article in Special Issue
Blockchains for Secure Digitized Medicine
Open AccessReview

Precision Oncology—The Quest for Evidence

Molecular Health GmbH, 69115 Heidelberg, Germany
*
Author to whom correspondence should be addressed.
J. Pers. Med. 2019, 9(3), 43; https://doi.org/10.3390/jpm9030043
Received: 30 June 2019 / Revised: 7 August 2019 / Accepted: 22 August 2019 / Published: 5 September 2019
The molecular characterization of patient tumors provides a rational and highly promising approach for guiding oncologists in treatment decision-making. Notwithstanding, genomic medicine still remains in its infancy, with innovators and early adopters continuing to carry a significant portion of the clinical and financial risk. Numerous innovative precision oncology trials have emerged globally to address the associated need for evidence of clinical utility. These studies seek to capitalize on the power of predictive biomarkers and/or treatment decision support analytics, to expeditiously and cost-effectively demonstrate the positive impact of these technologies on drug resistance/response, patient survival, and/or quality of life. Here, we discuss the molecular foundations of these approaches and highlight the diversity of innovative trial strategies that are capitalizing on this emergent knowledge. We conclude that, as increasing volumes of clinico-molecular outcomes data become available, in future, we will begin to transition away from expert systems for treatment decision support (TDS), towards the power of AI-assisted TDS—an evolution that may truly revolutionize the nature and success of cancer patient care. View Full-Text
Keywords: clinical trial design; personalized cancer medicine; genome-based diagnostics; clinical utility; treatment decision support clinical trial design; personalized cancer medicine; genome-based diagnostics; clinical utility; treatment decision support
Show Figures

Figure 1

MDPI and ACS Style

Soldatos, T.G.; Kaduthanam, S.; Jackson, D.B. Precision Oncology—The Quest for Evidence. J. Pers. Med. 2019, 9, 43.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop