by Keyword: cell-line
Alcon, C, Zañudo, JGT, Albert, R, Wagle, N, Scaltriti, M, Letai, A, Samitier, J, Montero, J, (2021). ER+ Breast Cancer Strongly Depends on MCL-1 and BCL-xL Anti-Apoptotic Proteins Cells 10, 1659 
Breast cancer is the most frequent type of cancer and the major cause of mortality in women. The rapid development of various therapeutic options has led to the improvement of treatment outcomes; nevertheless, one-third of estrogen receptor (ER)-positive patients relapse due to cancer cell acquired resistance. Here, we use dynamic BH3 profiling (DBP), a functional predictive assay that measures net changes in apoptotic priming, to find new effective treatments for ER+ breast cancer. We observed anti-apoptotic adaptations upon treatment that pointed to metronomic therapeutic combinations to enhance cytotoxicity and avoid resistance. Indeed, we found that the anti-apoptotic proteins BCL-xL and MCL-1 are crucial for ER+ breast cancer cells resistance to therapy, as they exert a dual inhibition of the pro-apoptotic protein BIM and compensate for each other. In addition, we identified the AKT inhibitor ipatasertib and two BH3 mimetics targeting these anti-apoptotic proteins, S63845 and A-1331852, as new potential therapies for this type of cancer. Therefore, we postulate the sequential inhibition of both proteins using BH3 mimetics as a new treatment option for refractory and relapsed ER+ breast cancer tumors.
JTD Keywords: apoptosis, bh3 mimetics, cell-line, chemotherapy, classification, dbp, death, er+ breast cancer, fulvestrant, her2, inhibitor, kinase, pik3ca, priming, resistance, targeted therapies, Apoptosis, Bh3 mimetics, Dbp, Endocrine therapy, Er plus breast cancer, Er+ breast cancer, Priming, Resistance, Targeted therapies
Llorens, Franc, Hummel, Manuela, Pastor, Xavier, Ferrer, Anna, Pluvinet, Raquel, Vivancos, Ana, Castillo, Ester, Iraola, Susana, Mosquera, Ana M., Gonzalez, Eva, Lozano, Juanjo, Ingham, Matthew, Dohm, Juliane C., Noguera, Marc, Kofler, Robert, Antonio del Rio, Jose, Bayes, Monica, Himmelbauer, Heinz, Sumoy, Lauro, (2011). Multiple platform assessment of the EGF dependent transcriptome by microarray and deep tag sequencing analysis BMC Genomics 12, 326
Background: Epidermal Growth Factor (EGF) is a key regulatory growth factor activating many processes relevant to normal development and disease, affecting cell proliferation and survival. Here we use a combined approach to study the EGF dependent transcriptome of HeLa cells by using multiple long oligonucleotide based microarray platforms (from Agilent, Operon, and Illumina) in combination with digital gene expression profiling (DGE) with the Illumina Genome Analyzer.
Results: By applying a procedure for cross-platform data meta-analysis based on RankProd and GlobalAncova tests, we establish a well validated gene set with transcript levels altered after EGF treatment. We use this robust gene list to build higher order networks of gene interaction by interconnecting associated networks, supporting and extending the important role of the EGF signaling pathway in cancer. In addition, we find an entirely new set of genes previously unrelated to the currently accepted EGF associated cellular functions.
Conclusions: We propose that the use of global genomic cross-validation derived from high content technologies (microarrays or deep sequencing) can be used to generate more reliable datasets. This approach should help to improve the confidence of downstream in silico functional inference analyses based on high content data.
JTD Keywords: Gene-expression measurements, Quality-control maqc, Cancer-cell-lines, Real-time pcr, Oligonucleotide microarrays, Phosphorylation dynamics, In-vivo, Networks, Signal, Technologies
