资源环境科技发展态势分析平台

KRAS GTPases are activated in one-third of cancers, and KRAS(G12C) is one of the most common activating alterations in lung adenocarcinoma(1,2). KRAS(G12C) inhibitors(3,4) are in phase-I clinical trials and early data show partial responses in nearly half of patients with lung cancer. How cancer cells bypass inhibition to prevent maximal response to therapy is not understood. Because KRAS(G12C) cycles between an active and inactive conformation(4-6), and the inhibitors bind only to the latter, we tested whether isogenic cell populations respond in a non-uniform manner by studying the effect of treatment at a single-cell resolution. Here we report that, shortly after treatment, some cancer cells are sequestered in a quiescent state with low KRAS activity, whereas others bypass this effect to resume proliferation. This rapid divergent response occurs because some quiescent cells produce new KRAS(G12C) in response to suppressed mitogen-activated protein kinase output. New KRAS(G12C) is maintained in its active, drug-insensitive state by epidermal growth factor receptor and aurora kinase signalling. Cells without these adaptive changes-or cells in which these changes are pharmacologically inhibited-remain sensitive to drug treatment, because new KRAS(G12C) is either not available or exists in its inactive, drug-sensitive state. The direct targeting of KRAS oncoproteins has been a longstanding objective in precision oncology. Our study uncovers a flexible non-uniform fitness mechanism that enables groups of cells within a population to rapidly bypass the effect of treatment. This adaptive process must be overcome if we are to achieve complete and durable responses in the clinic.

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.