![]() Using sophisticated molecular analyses and ‘omic’ technologies (i.e., genomics, transcriptomics, proteomics, or metabolomics) clinicians can now identify precise targets and assign specific treatments 2, matching the right drug to every patient 3. With the development of novel anticancer agents and biomarkers, precision medicine was introduced to treat cancer patients. Based on available biomarkers, such as age, type of tumor, stage of development and clinical history, and other information, they stratify patients and assign therapy according to the approved protocols 1. When treating cancer patients, medical oncologists aim to identify the optimal treatment. In summary, this new technology could represent an important advance for precision medicine by providing a fast, easy-to-use and scalable microfluidic device to perform DBP in situ as a routine assay to identify the best treatment for cancer patients. We then validated ♝BP on a refractory GIST patient sample and identified that the combination of dactolisib and venetoclax increased apoptotic priming. We first examined this new technology’s predictive capacity using gastrointestinal stromal tumor (GIST) cell lines, by comparing imatinib sensitive and resistant cells, and we could detect differences in apoptotic priming and anticipate cytotoxicity. We used microfluidic chips to generate a gradient of BIM BH3 peptide, compared it with the standard flow cytometry based DBP, and tested different anticancer treatments. To solve this problem, we developed an innovative microfluidic-based DBP (♝BP) device that overcomes tissue limitations on primary samples. This predictive functional assay presents multiple advantages but a critical limitation: the cell number requirement, that limits drug screening on patient samples, especially in solid tumors. DBP uses synthetic BH3 peptides to measure early apoptotic events (‘priming’) and anticipate therapy-induced cell death leading to tumor elimination. Among these new technologies, dynamic BH3 profiling (DBP) has emerged and extensively been used to predict treatment efficacy in different types of cancer. Note: All fields described in the sections below (after the properties file example) are required unless explicitly described as “optional.” In your own properties file, you would replace values surrounded with with the relevant information.Precision medicine is starting to incorporate functional assays to evaluate anticancer agents on patient-isolated tissues or cells to select for the most effective. Contact us on the CellProfiler forums if you need help with this. Note: CPA 2.0+ is not compatible with properties files from CellProfiler Analyst version 1.0, but the two formats may be easily converted by hand. We suggest using Notepad on Windows, TextEdit on Mac OS, and Emacs on Linux. Note: When editing the properties file, it is important to use an editor that is capable of saving plain text. Settings that require a file path may be specified either as absolute or relative to the directory that the properties file is found in. Lines that begin with a # are ignored by CPA and may be used for comments. ![]() Otherwise, you can create one manually, referring to the Properties_README or the example provided below as a template.Įach setting in the properties file is stored on a separate line in the form field = value(s), and the order of the settings is not important. If you use CellProfiler to produce the data to be analyzed in CPA, you can automatically generate a nearly complete properties file with, using the ExportToDatabase module. It is selected and loaded upon startup of CPA. ![]() This file can be stored anywhere on your computer. The properties file is a plain text file that contains the configuration information necessary for CPA to access your data and images.
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