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Chung, Mirra

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Chung

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Mirra

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Chung, Mirra
Author's Publications: search.filters.isAuthorOfPublication.db432f1a-6845-45bc-987d-b7e4fd4b6ccd

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    Growth rate inhibition metrics correct for confounders in measuring sensitivity to cancer drugs
    (2016) Hafner, Marc; Niepel, Mario; Chung, Mirra; Sorger, Peter
    Drug sensitivity and resistance are conventionally quantified by IC50 or Emax values, but these metrics are highly sensitive to the number of divisions taking place over the course of a response assay. The dependency of IC50 and Emax on division rate creates artefactual correlations between genotype and drug sensitivity while obscuring valuable biological insights and interfering with biomarker discovery. We derive alternative drug response metrics that are insensitive to division number. These are based on estimating the magnitude of drug-induced growth rate inhibition (GR) using endpoint or time-course assays. We show that GR50 and GRmax are superior to conventional metrics for assessing the effects of drugs in dividing cells. Moreover, adopting GR metrics requires only modest changes in experimental protocols. We expect GR metrics to improve the study of cell signaling and growth using drugs, discovery of drug response biomarkers, and identification of drugs effective on specific patient-derived tumor cells.
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    Analysis of growth factor signaling in genetically diverse breast cancer lines
    (BioMed Central, 2014) Niepel, Mario; Hafner, Marc; Pace, Emily A; Chung, Mirra; Chai, Diana H; Zhou, Lili; Muhlich, Jeremy; Schoeberl, Birgit; Sorger, Peter
    Background: Soluble growth factors present in the microenvironment play a major role in tumor development, invasion, metastasis, and responsiveness to targeted therapies. While the biochemistry of growth factor-dependent signal transduction has been studied extensively in individual cell types, relatively little systematic data are available across genetically diverse cell lines. Results: We describe a quantitative and comparative dataset focused on immediate-early signaling that regulates the AKT (AKT1/2/3) and ERK (MAPK1/3) pathways in a canonical panel of well-characterized breast cancer lines. We also provide interactive web-based tools to facilitate follow-on analysis of the data. Our findings show that breast cancers are diverse with respect to ligand sensitivity and signaling biochemistry. Surprisingly, triple negative breast cancers (TNBCs; which express low levels of ErbB2, progesterone and estrogen receptors) are the most broadly responsive to growth factors and HER2amp cancers (which overexpress ErbB2) the least. The ratio of ERK to AKT activation varies with ligand and subtype, with a systematic bias in favor of ERK in hormone receptor positive (HR+) cells. The factors that correlate with growth factor responsiveness depend on whether fold-change or absolute activity is considered the key biological variable, and they differ between ERK and AKT pathways. Conclusions: Responses to growth factors are highly diverse across breast cancer cell lines, even within the same subtype. A simple four-part heuristic suggests that diversity arises from variation in receptor abundance, an ERK/AKT bias that depends on ligand identity, a set of factors common to all receptors that varies in abundance or activity with cell line, and an “indirect negative regulation” by ErbB2. This analysis sets the stage for the development of a mechanistic and predictive model of growth factor signaling in diverse cancer lines. Interactive tools for looking up these results and downloading raw data are available at http://lincs.hms.harvard.edu/niepel-bmcbiol-2014/.
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    Common and cell-type specific responses to anti-cancer drugs revealed by high throughput transcript profiling
    (Nature Publishing Group UK, 2017) Niepel, Mario; Hafner, Marc; Duan, Qiaonan; Wang, Zichen; Paull, Evan O.; Chung, Mirra; Lu, Xiaodong; Stuart, Joshua M.; Golub, Todd; Subramanian, Aravind; Ma’ayan, Avi; Sorger, Peter
    More effective use of targeted anti-cancer drugs depends on elucidating the connection between the molecular states induced by drug treatment and the cellular phenotypes controlled by these states, such as cytostasis and death. This is particularly true when mutation of a single gene is inadequate as a predictor of drug response. The current paper describes a data set of ~600 drug cell line pairs collected as part of the NIH LINCS Program (http://www.lincsproject.org/) in which molecular data (reduced dimensionality transcript L1000 profiles) were recorded across dose and time in parallel with phenotypic data on cellular cytostasis and cytotoxicity. We report that transcriptional and phenotypic responses correlate with each other in general, but whereas inhibitors of chaperones and cell cycle kinases induce similar transcriptional changes across cell lines, changes induced by drugs that inhibit intra-cellular signaling kinases are cell-type specific. In some drug/cell line pairs significant changes in transcription are observed without a change in cell growth or survival; analysis of such pairs identifies drug equivalence classes and, in one case, synergistic drug interactions. In this case, synergy involves cell-type specific suppression of an adaptive drug response.