Identification of responders/non-responders to 5-fluorouracil based on individual 50% inhibitory area under the concentration curve of 5-fluorouracil obtained with collagen gel droplet-embedded culture-drug sensitivity test in colorectal cancer

Abstract

Abstract. We previously reported the 5-fluorouracil (5-FU) sensitivity of cancer cells obtained from colorectal cancer (CRC) patients using the collagen gel droplet-embedded culturedrug sensitivity test (CD-DST). Multiple drug concentrations and contact durations, and the area under the concentration curve (AUC) and growth inhibition rate (IR) were combined, resulting in the AUC-IR curve, which was approximated to the logarithmic curve. Moreover, the individualized AUC IR50 , the AUC value which gives 50% growth inhibition, was calculated using the AUC-IR curve. This study aimed to identify responders/non-responders to 5-FU based on the individual AUC IR50 obtained with CD-DST in order to establish individualized chemotherapy for CRC patients. The individual AUC IR50 was calculated from each AUC-inhibition rate regression curve in all patients using the CD-DST. The cumulative distribution of the individual AUC IR50 in CRC patients was evaluated. The cumulative distribution of the individual AUC IR50 was regressed over the sigmoid curve (logarithmic scale). The approximate expression was almost exactly y=ab^exp(-cx) (a=0.9739, b=1.7096E-21, c=0.8990, the sum of square residuals, 0.0279). In the 80 cases examined, no notable change was observed in the regression curve when the number of patients increased. A standard curve was obtained describing responders to 5-FU among all CRC patients. From this standard curve, we ascertained that non-responders accounted for approximately 5% of all patients. Moreover, we were able to classify responders into good or intermediate responders to 5-FU. The standard curve describing response to 5-FU in CRC patients offers a useful tool in the establishment of individualized chemotherapy.

A modifiable microarray-based universal sensor: providing sample-to-results automation

“…Molecular diagnostics has become a valuable tool in healthcare by providing a means to identify infectious agents [1], characterize malignant tumors [2], predict sensitivity to pharmacologic agents [3], and stratify oncology patients into responders and non-responders [4], thus allowing physicians to make well-informed decisions about medical treatment. Bioinformatics information gleaned from the human genome project can also provide valuable information about possible health risk factors [5].…”