Patient-derived Models of Acquired Resistance Can Identify Effective Drug Combinations for Cancer
By A. Crystal, A. Shaw, L. Sequist, L. Friboulet, M. Niederst, Elizabeth Lockerman, R. Frias, J. Gainor, A. Amzallag, Patricia Greninger and 16 more. Science, 2014
708 citations recorded by the source corpus.
Targeted cancer therapies have produced substantial clinical responses, but most tumors develop resistance to these drugs. Here, we describe a pharmacogenomic platform that facilitates rapid discovery of drug combinations that can overcome resistance. We established cell culture models derived from biopsy samples of lung cancer patients whose disease had progressed while on treatment with epidermal growth factor receptor (EGFR) or anaplastic lymphoma kinase (ALK) tyrosine kinase inhibitors and then subjected these cells to genetic analyses and a pharmacological screen. Multiple effective drug combinations were identified. For example, the combination of ALK and MAPK kinase (MEK) inhibitors was active in an ALK-positive resistant tumor that had developed a MAP2K1 activating mutation, and the combination of EGFR and fibroblast growth factor receptor (FGFR) inhibitors was active in an EGFR mutant resistant cancer with a mutation in FGFR3. Combined ALK and SRC (pp60c-src) inhibition was effective in several ALK-driven patient-derived models, a result not predicted by genetic analysis alone. With further refinements, this strategy could help direct therapeutic choices for individual patients. Secondary chemotherapies can be developed by screening drug-resistant cells from individual cancer patients. Drug resistance, up close and personal Cancer therapies that target specific genetic mutations driving tumor growth have shown promising results in patients; however, the response is often short-lived because the tumors acquire new mutations that render them resistant to these therapies. Complicating matters, the mechanism of resistance can vary from patient to patient. To identify drugs most likely to be effective against resistant tumors, Crystal et al. established cell lines from the tumors of individual patients after resistance occurred and performed a drug screen and genetic analysis on the cultured cells. This strategy successfully identified drug combinations that halted the growth of resistant tumor cells both in culture and in mice. In the future, pharmacological profiling of patient-derived cells could be an efficient way to direct therapeutic choices for individual cancer patients. Science, this issue p. 1480
Subfields: Cancer Research, Oncology, Pulmonary and Respiratory Medicine
View Paper: https://www.semanticscholar.org/paper/e8a1335793f6a20a31f8c5fd236883e3437f463a
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Citations in the corpus
- Molecular Mechanisms of Resistance to First- and Second-Generation ALK Inhibitors in ALK-Rearranged Lung Cancer (2016, positive): “In addition, for MGH049-1A, we previously observed that agents targeting SRC, EGFR and PI3K re-sensitized cells to ALK inhibition, implicating these signaling pathways as mediators of resistance in this model (10).”
- Patient-derived organoids can predict response to chemotherapy in metastatic colorectal cancer patients (2019, background): “Previous attempts to use patient material to determine treatment responsiveness have had limited success due to the long turnaround times, poor scalability, or low success rates of establishing patient cell lines or xenografts (15, 16).”
- Tumor cells can follow distinct evolutionary paths to become resistant to epidermal growth factor receptor inhibition (2016, neutral): “The gene targets covered by this assay are as follows (exons): AKT1 (3), ALK (22, 23, 25), APC (16), BRAF (11, 15), CDH1 (1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11,12, 13, 14, 15, 16), CDKN2A (1, 2, 3), CTNNB1 (3), DDR2 (12, 13, 14, 15, 16, 17, 18), EGFR (7, 15, 18, 19, 20, 21), ERBB2 (10, 20), ESR1 (8), FB”
- Semi-Supervised Learning of the Electronic Health Record for Phenotype Stratification (2016, background): “Higher-level subtypes have been found to differ in cell size, cell shape, tumor origin site and chemical properties and it is becoming clear that genome-phenotype associations to these subtypes are a many-to-one relationship [11].”
- Harnessing Connectivity in a Large-Scale Small-Molecule Sensitivity Dataset (2015, positive): “Furthermore, recent efforts have demonstrated that CCLs derived from patients who developed resistance to targeted therapies can be used in vitro to suggest combinations for overcoming resistance (11).”
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