Primary Patient-Derived Cancer Cells and Their Potential for Personalized Cancer Patient Care.

Primary Patient-Derived Cancer Cells and Their Potential for Personalized Cancer Patient Care.
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DOI:
10.1016/j.celrep.2017.11.051
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发表时间:
2017-12-12
期刊:
影响因子:
8.8
通讯作者:
Benes CH
Benes CH
中科院分区:
生物学1区
文献类型:
--
作者:
Kodack DP;Farago AF;Dastur A;Held MA;Dardaei L;Friboulet L;von Flotow F;Damon LJ;Lee D;Parks M;Dicecca R;Greenberg M;Kattermann KE;Riley AK;Fintelmann FJ;Rizzo C;Piotrowska Z;Shaw AT;Gainor JF;Sequist LV;Niederst MJ;Engelman JA;Benes CH

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Personalized cancer therapy is based on a patient’s tumor lineage, histopathology, expression analyses, and/or tumor DNA or RNA analysis. Here, we aim to develop an in vitro functional assay of a patient’s living cancer cells that could complement these approaches. We present methods for developing cell cultures from tumor biopsies and identify the types of samples and culture conditions associated with higher efficiency of model establishment. Toward the application of patient-derived cell cultures for personalized care, we established an immunofluorescence-based functional assay that quantifies cancer cell responses to targeted therapy in mixed cell cultures. Assaying patient-derived lung cancer cultures with this method showed promise in modeling patient response for diagnostic use. This platform should allow for the development of co-clinical trial studies to prospectively test the value of drug profiling on tumor-biopsy-derived cultures to direct patient care. Successful culture of cancer cells from tumor biopsies An immunofluorescence-based assay to quantify drug sensitivity in mixed cell cultures NSCLC patients’ biopsy culture sensitivities reflect clinical response Kodack et al. report on the development of cancer models from tumor biopsies and technologies toward a functional approach for personalized medicine. They describe the ability to reliably test drug response in patient-derived samples of mixed cell populations. In doing so, they show that patient biopsy cultures may predict patient clinical responses.
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