Challenges in cancer genomics
The primary study goals of cancer genomics are not simple, which include the studies to get either clinical or mechanistic insights from the cancer genome. To clarify the complicated study designs and strategies of cancer genomics, we have categorized the gene signatures obtained from cancer genome data into four classes prediction, phenotype, function, and molecular targets based on the study goals [25]. The majority of the previous studies have suggested the translational or clinical utility of the genomics data by addressing the candidate biomarkers or the prediction signatures for predicting patients clinical outcomes, such as recurrence, survival, metastasis, or response to therapies. Notwithstanding the overwhelming identification of candidate biomarkers from the cancer genome, only a handful of candidate biomarkers that have been discovered from genomic analyses can succeed in the validation of the clinical utility [18]. There are several challenges in cancer genomics that preclude clinical utility. One of them would be data reproducibility. They might be due in part to the experimental biases as well as sample cohort issues. The use of different platforms measuring gene expressions and different data processing methods could produce biased observation in each study. Increasing sample size will be one of the solutions to find proper biomarkers, overcoming the reproducibility problem. Undoubtedly, large-scale sample collection provides increased statistical power. However, previous studies, even with large sample sizes, have often failed to reproduce their findings in independent studies [26]. This might be due mostly to the use of different protocols and analysis methods. Moreover, biased sample collection may also affect the performance of prognostic biomarkers, leading to subsequent failure to validate the biomarker in another patient population [27]. For example, diagnostic biomarkers must be discovered in early-stage tumors; however, the sample collection of early tumors with enough of a sample size might be difficult in the clinical setting [18, 28]. In addition, the cost-effectiveness of the sample size enrolled in a study should be considered. Simply increasing the sample size might not be the best solution.
The sample sources and qualities are also important factors to be considered in the study design. For example, circulating DNAs or microRNAs in the plasma or urine can be used to develop "noninvasive" biomarkers in cancer patients, which might bring the technology much closer to the clinic [29, 30]. Attempts to use formalin-fixed paraffin-embedded (FFPE) tissues might also be more applicable to the clinic [31], although the quality and the quantity of the DNAs or RNA extracts from FFPE or plasma are still problematic for genomics studies requesting further elaboration.