| 2. |
- Botvinik-Nezer, Rotem, et al.
(författare)
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Variability in the analysis of a single neuroimaging dataset by many teams
- 2020
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Ingår i: Nature. - : Springer Science and Business Media LLC. - 0028-0836 .- 1476-4687. ; 582, s. 84-88
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Tidskriftsartikel (refereegranskat)abstract
- Data analysis workflows in many scientific domains have become increasingly complex and flexible. Here we assess the effect of this flexibility on the results of functional magnetic resonance imaging by asking 70 independent teams to analyse the same dataset, testing the same 9 ex-ante hypotheses(1). The flexibility of analytical approaches is exemplified by the fact that no two teams chose identical workflows to analyse the data. This flexibility resulted in sizeable variation in the results of hypothesis tests, even for teams whose statistical maps were highly correlated at intermediate stages of the analysis pipeline. Variation in reported results was related to several aspects of analysis methodology. Notably, a meta-analytical approach that aggregated information across teams yielded a significant consensus in activated regions. Furthermore, prediction markets of researchers in the field revealed an overestimation of the likelihood of significant findings, even by researchers with direct knowledge of the dataset(2-5). Our findings show that analytical flexibility can have substantial effects on scientific conclusions, and identify factors that may be related to variability in the analysis of functional magnetic resonance imaging. The results emphasize the importance of validating and sharing complex analysis workflows, and demonstrate the need for performing and reporting multiple analyses of the same data. Potential approaches that could be used to mitigate issues related to analytical variability are discussed. The results obtained by seventy different teams analysing the same functional magnetic resonance imaging dataset show substantial variation, highlighting the influence of analytical choices and the importance of sharing workflows publicly and performing multiple analyses.
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| 3. |
- Castell, Alina, et al.
(författare)
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MYCMI-7 : A Small MYC-Binding Compound that Inhibits MYC: MAX Interaction and Tumor Growth in a MYC-Dependent Manner
- 2022
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Ingår i: Cancer Research Communications. - : American Association For Cancer Research (AACR). - 2767-9764. ; 2:3, s. 182-201
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Tidskriftsartikel (refereegranskat)abstract
- Deregulated expression of MYC family oncogenes occurs frequently in human cancer and is often associated with aggressive disease and poor prognosis. While MYC is a highly warranted target, it has been considered "undruggable," and no specific anti-MYC drugs are available in the clinic. We recently identified molecules named MYCMIs that inhibit the interaction between MYC and its essential partner MAX. Here we show that one of these molecules, MYCMI-7, efficiently and selectively inhibits MYC:MAX and MYCN:MAX interactions in cells, binds directly to recombinant MYC, and reduces MYC-driven transcription. In addition, MYCMI-7 induces degradation of MYC and MYCN proteins. MYCMI-7 potently induces growth arrest/apoptosis in tumor cells in a MYC/MYCN-dependent manner and downregulates the MYC pathway on a global level as determined by RNA sequencing. Sensitivity to MYCMI-7 correlates with MYC expression in a panel of 60 tumor cell lines and MYCMI-7 shows high efficacy toward a collection of patient-derived primary glioblastoma and acute myeloid leukemia (AML) ex vivo cultures. Importantly, a variety of normal cells be- come G1 arrested without signs of apoptosis upon MYCMI-7 treatment. Finally, in mouse tumor models of MYC-driven AML, breast cancer, and MYCN-amplified neuroblastoma, treatment with MYCMI-7 downregu- lates MYC/MYCN, inhibits tumor growth, and prolongs survival through apoptosis with few side effects. In conclusion, MYCMI-7 is a potent and selective MYC inhibitor that is highly relevant for the development into clinically useful drugs for the treatment of MYC-driven cancer.Significance: Our findings demonstrate that the small-molecule MYCMI-7 binds MYC and inhibits interaction between MYC and MAX, thereby ham- pering MYC-driven tumor cell growth in culture and in vivo while sparing normal cells.
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