Abstract
PurposePrecision oncology, such as next generation sequencing (NGS) molecular analysis and bioinformatics are used to guide targeted therapies. The laboratory turnaround time (TAT) is a key performance indicator of laboratory performance. This study aims to formally apply statistical process control (SPC) methods such as CUSUM and EWMA to a precision medicine programme to analyze the learning curves of NGS and bioinformatics processes.Patients and MethodsTrends in NGS and bioinformatics TAT were analyzed using simple regression models with TAT as the dependent variable and chronologically-ordered case number as the independent variable. The M-estimator “robust” regression and negative binomial regression were chosen to serve as sensitivity analyses to each other. Next, two popular statistical process control (SPC) approaches which are CUSUM and EWMA were utilized and the CUSUM log-likelihood ratio (LLR) charts were also generated. All statistical analyses were done in Stata version 16.0 (StataCorp), and nominal P < 0.05 was considered to be statistically significant.ResultsA total of 365 patients underwent successful molecular profiling. Both the robust linear model and negative binomial model showed statistically significant reductions in TAT with accumulating experience. The EWMA and CUSUM charts of overall TAT largely corresponded except that the EWMA chart consistently decreased while the CUSUM analyses indicated improvement only after a nadir at the 82nd case. CUSUM analysis found that the bioinformatics team took a lower number of cases (54 cases) to overcome the learning curve compared to the NGS team (85 cases).ConclusionAs NGS and bioinformatics lead precision oncology into the forefront of cancer management, characterizing the TAT of NGS and bioinformatics processes improves the timeliness of data output by potentially spotlighting problems early for rectification, thereby improving care delivery.
Highlights
Precision oncology refers to the use of therapeutics expected to benefit patients who harbor specific molecular or histopathological biomarkers [1]
Patients referred to the Developmental Therapeutics Unit (DTU) at the National University Cancer Institute, Singapore (NCIS) were offered participation in the Integrated Molecular Analysis of Cancers (IMAC) programme if they were above 21 years of age, and had a histologically confirmed diagnosis of a solid malignancy or lymphoma, and had adequate tumor tissue for genome characterization
The deteriorating phase corresponds largely with the findings in the exponentially weighted moving average (EWMA) chart which showed an increase in turnaround time (TAT) from 250 to 300 cases (Figures 1C, D)
Summary
Precision oncology refers to the use of therapeutics expected to benefit patients who harbor specific molecular or histopathological biomarkers [1]. Due to the known complexity of cancers and the expanding body of knowledge of oncogenesis, molecular profiling, such as generation sequencing (NGS) molecular analysis and bioinformatics, is being used to identify genetic mutations [2, 3], which may guide the deployment of targeted therapies [4, 5]. The Integrated Molecular Analysis of Cancers (IMAC) study was conducted to establish the prevalence and range of mutations in Asian patients with advanced cancers [9] to further develop and understand the value of targeted molecular profiling and gene sequencing in clinical decision making. NGS labs are often hampered by various logistical and technical difficulties [12] and bioinformatics challenges [13]
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