Abstract
The era of personalized medicine for cancer therapeutics has taken an important step forward in making accurate prognoses for individual patients with the adoption of high-throughput microarray technology. However, microarray technology in cancer diagnosis or prognosis has been primarily used for the statistical evaluation of patient populations, and thus excludes inter-individual variability and patient-specific predictions. Here we propose a metric called clinical confidence that serves as a measure of prognostic reliability to facilitate the shift from population-wide to personalized cancer prognosis using microarray-based predictive models. The performance of sample-based models predicted with different clinical confidences was evaluated and compared systematically using three large clinical datasets studying the following cancers: breast cancer, multiple myeloma, and neuroblastoma. Survival curves for patients, with different confidences, were also delineated. The results show that the clinical confidence metric separates patients with different prediction accuracies and survival times. Samples with high clinical confidence were likely to have accurate prognoses from predictive models. Moreover, patients with high clinical confidence would be expected to live for a notably longer or shorter time if their prognosis was good or grim based on the models, respectively. We conclude that clinical confidence could serve as a beneficial metric for personalized cancer prognosis prediction utilizing microarrays. Ascribing a confidence level to prognosis with the clinical confidence metric provides the clinician an objective, personalized basis for decisions, such as choosing the severity of the treatment.
Highlights
IntroductionThe development of personalized therapeutic regimens optimized for individual patients represents a major goal of 21st-century medicine [1]
Not all individuals respond to drug treatment in the same way
We demonstrated that clinical confidence is both capable of separating patients that can be more reliably predicted from those that are less accurately predicted, and predictive of the survival rate for the patients after they are grouped into different prognostic groups
Summary
The development of personalized therapeutic regimens optimized for individual patients represents a major goal of 21st-century medicine [1]. Modern tools are being utilized to assist physicians in effectively treating patients as individuals and providing personalized drug intervention. Inter-individual variation in response to drug treatment is strongly influenced by a patient’s physiological state at the time of treatment. This state can be characterized by gene expression profiles [2]. Microarray technology can guide the selection of drugs or therapeutic regimens and be employed to assess the susceptibility of a patient to certain diseases, enabling a personalized plan for prevention monitoring and treatment [3]. Microarray-based predictive models (or genomic signatures) have shown utility in associating different subgroups of breast cancer with distinct clinical outcomes [8,9,10,11,12,13], such as Mamma-
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