Evaluation of serum-based cancer biomarkers: A brief review from a clinical and computational viewpoint

Abstract

Biomarkers are vital to detect diseases in various clinical stages. A variety of cancer serum biomarkers are already known, while for more accurate cancer-type detection, there required more rigorous evaluation manners, especially computational evaluation measures, for biomarkers. In this review, we first show three typical pitfalls in finding biomarkers and their examples, after briefly presenting standard five clinical biomarker screening phases by National Cancer Institute. We then introduce current computational biomarker evaluation measures, including current, standard methods with their intrinsic features. We further show an up-to-date list of existing cancer serum biomarkers, pointing out several issues, being caused by the limitations of current biomarker evaluation approaches. Finally we discuss the current attempts to develop new, statistically robust, computational serum-based biomarker measures in terms of specificity to each of various cancer types.

Introduction

Biomarkers are believed to increase the accuracy of diagnosis to precisely characterize the disease in a diagnostic or prognostic level. Biomarkers predict the response of the patient, helping to guide a more tailored treatment for the patient. Serum biomarkers are more appealing due to their simplicity of obtaining the blood samples. There are several serum cancer biomarkers, which are routinely used in clinical oncology, e.g. prostate specific antigen (PSA) for prostate cancer and cancer antigen (CA)-125 for ovarian cancer. However, their applications have significant limitations, because of low specificity, i.e. small probability of samples with no biomarkers in all non-diseased samples. In fact, the issue of specificity has become much more acute, since more than 30% or higher circulatory PSA level patients have to go for extensive testing and treatment, indicating its lack of specificity of prostate tumor detection [1]. In summary, lack of specific serum biomarkers has impeded the change in morbidity and mortality in cancer patients.

The traditional “a priori” approach for biomarker development needs a well-established biological procedure, being subjected to two-step clinical validation: (1) simple test with a high level of quality control, and (2) planned statistical prospective evaluations within the validation pilot studies to prove an established clinical impact [2]. Contrarily, more recently “a posteriori” approaches evaluate the clinical rationale of a “biological indicator” through a systematic discovery of various screening tools (e.g. microarray, bioinformatics, High-throughput DNA sequencing). These biological instruments are “black boxes”, meaning that a clinical usage can be discovered through research pilot studies. Computational approaches give possible candidates for detecting certain diseases, by “sensitivity” and “specificity”, within a patient population, but the proper quantification of a single biomarker in serum is limited to the evaluation technicalities. Therefore this review will focus on the latter approach since the recent technologies provide a plethora of potential candidates which are in proper need of evaluation.

Over the past twenty years, biomarkers have shown significant promise in the mechanism of how it will transform a patient's treatment. Therefore, biomarker research has been aimed towards the development of personalized targeted therapy. Despite the recent technological advancements, there are still relatively few biomarkers that are in routine clinical use today [3]. With a growing number of complex genomic tests for biomarker signatures becoming commercially available, the promise of personalized medicine is fast becoming a reality. Much attention has to be placed on the reason why the promising biomarkers and the biomarkers signatures entering the clinic is a long road ahead [4].