Value of a molecular screening program to support clinical trial enrollment in Asian cancer patients: The Integrated Molecular Analysis of Cancer (IMAC) Study.

The value of precision oncology initiatives in Asian contexts remains unresolved. Here, we review the institutional implementation of prospective molecular screening to facilitate accrual of patients into biomarker-driven clinical trials, and to explore the mutational landscape of advanced tumors occurring in a prospective cohort of Asian patients (n = 396) with diverse cancer types. Next-generation sequencing (NGS) and routine clinicopathological assays, such as immunohistochemistry, copy number analysis and in situ hybridization tests, were performed on tumor samples. Actionable biomarker results were used to identify eligibility for early-phase, biomarker-driven clinical trials. Overall, NGS was successful in 365 of 396 patients (92%), achieving a mean depth of 1,943× and coverage uniformity of 96%. The median turnaround time from sample receipt to return of genomic results was 26.0 days (IQR, 19.0-39.0 days). Reportable mutations were found in 300 of 365 patients (82%). Ninety-one percent of patients at study enrollment indicated consent to receive incidental findings and willingness to undergo genetic counseling if required. The most commonly mutated oncogenes included KRAS (19%), PIK3CA (16%), EGFR (5%), BRAF (3%) and KIT (3%); while the most frequently mutated tumor suppressor genes included TP53 (40%), SMARCB1 (12%), APC (8%), PTEN (6%) and SMAD4 (5%). Among 23 patients enrolled in genotype-matched trials, median progression-free survival was 2.9 months (IQR, 1.5-4.0 months). Nine of 20 evaluable patients (45%; 95% CI, 23.1-68.5%) derived clinical benefit, including 3 partial responses and 6 with stable disease lasting ≥ 8 weeks.

Statistical Process Control Charts for Monitoring Next-Generation Sequencing and Bioinformatics Turnaround in Precision Medicine Initiatives.

PURPOSE: Precision 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 METHODS: Trends 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. RESULTS: A 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). CONCLUSION: As 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.