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Das vorliegende Arbeitspapier untersucht Ungleichheit aus verschiedenen interdisziplinären Perspektiven auf Ursachen und Implikationen.
Inhaltsverzeichnis:
Philipp Harms, Claudia Landwehr, Mario Scharfbillig, Daniel Schunk: Ungleichheit: Interdisziplinäre Perspektiven auf Ursachen und Implikationen - Einleitung
Konstantin M. Wacker: Warum wir Ungleichheit verringern müssen, um globale Armut bis 2030 zu beenden
Joachim Klose: Heimatverlust als Indikator zunehmender Ungleichheit
Gunnar Otte: Bildungsforschung und Bildungsreformen
Sibylle Kalmbach: Bildungsgerechtigkeit und Ungleichheit im Hochschulbereich – am Beispiel von Stipendien
Claudia Landwehr und Oliver Tüscher: Ursachen ungleicher politischer Beteiligung
Michael Edinger: Gleicher Zugang zur Macht? Über soziale Schließungsprozesse in der Politik
Sascha Huber: Wählermobilisierung und Ungleichheit in Deutschland: Ein Feldexperiment zur Steigerung der Wahlbeteiligung bei der Landtagswahl in Baden-Württemberg 2016
Tonio Rieger: Der ganzheitliche Ansatz zur Bekämpfung von Langzeitarbeitslosigkeit
As new generations of targeted therapies emerge and tumor genome sequencing discovers increasingly comprehensive mutation repertoires, the functional relationships of mutations to tumor phenotypes remain largely unknown. Here, we measured ex vivo sensitivity of 246 blood cancers to 63 drugs alongside genome, transcriptome, and DNA methylome analysis to understand determinants of drug response. We assembled a primary blood cancer cell encyclopedia data set that revealed disease-specific sensitivities for each cancer. Within chronic lymphocytic leukemia (CLL), responses to 62% of drugs were associated with 2 or more mutations, and linked the B cell receptor (BCR) pathway to trisomy 12, an important driver of CLL. Based on drug responses, the disease could be organized into phenotypic subgroups characterized by exploitable dependencies on BCR, mTOR, or MEK signaling and associated with mutations, gene expression, and DNA methylation. Fourteen percent of CLLs were driven by mTOR signaling in a non–BCR-dependent manner. Multivariate modeling revealed immunoglobulin heavy chain variable gene (IGHV) mutation status and trisomy 12 as the most important modulators of response to kinase inhibitors in CLL. Ex vivo drug responses were associated with outcome. This study overcomes the perception that most mutations do not influence drug response of cancer, and points to an updated approach to understanding tumor biology, with implications for biomarker discovery and cancer care.
Non-standard errors
(2021)
In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in sample estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence-generating process (EGP). We claim that EGP variation across researchers adds uncertainty: non-standard errors. To study them, we let 164 teams test six hypotheses on the same sample. We find that non-standard errors are sizeable, on par with standard errors. Their size (i) co-varies only weakly with team merits, reproducibility, or peer rating, (ii) declines significantly after peer-feedback, and (iii) is underestimated by participants.
