Life Sciences Discovery and Technology Highlights

摘要

Liver cancer is the fourth leading cause of cancer-related mortality and is distinguished by a relative paucity of chemotherapy options. It has been hypothesized that intratumor genetic heterogeneity may contribute to the high failure rate of chemotherapy. Here, Li et al. evaluated functional heterogeneity in a cohort of primary human liver cancer organoid lines. Each primary human liver cancer surgical specimen was used to generate multiple cancer organoid lines, obtained from distinct regions of the tumor. A total of 27 liver cancer lines were established and tested with 129 cancer drugs, generating 3483 cell survival data points. The authors found a rich intratumor, functional (drug response) heterogeneity in their liver cancer patients. Furthermore, the authors established that most drugs were either ineffective or effective only in select organoid lines. In contrast, Li et al. found that a subset of drugs appeared pan-effective, displaying at least moderate activity in the majority of these cancer organoid lines. These drugs, which are FDA approved for indications other than liver cancers, deserve further consideration as either systemic or local therapeutics. Of note, molecular profiles, obtained for a reduced sample set, did not correlate with the drug response heterogeneity of liver cancer organoid lines. Taken together, these findings lay the foundation for in-depth studies of pan-effective drugs, as well as for functional personalized oncology approaches. Lastly, these functional studies demonstrate the utility of cancer organoid drug testing as part of a drug discovery pipeline. (Li, L.; et al. JCI Insight 2019, 4(2).) High-throughput studies constitute an essential and valued source of information for researchers. However, high-throughput experimental workflows are often complex, with multiple data sets that may contain large numbers of false positives. The representation of high-throughput data in the Gene Ontology (GO) therefore presents a challenging annotation problem, when the overarching goal of GO curation is to provide the most precise view of a gene’s role in biology. To address this, representatives from annotation teams within the GO Consortium reviewed high-throughput data annotation practices. Attrill et al. present an annotation framework for high-throughput studies that will facilitate good standards in GO curation and, using new high-throughput evidence codes, increase the visibility of these annotations to the research community. (Attrill, H.; et al. Database (Oxford) 2019, DOI: 10.1093/database/baz007.) Natural products and their derivatives continue to be wellsprings of nascent therapeutic potential. However, many laboratories have limited resources for biological evaluation, leaving their previously isolated or synthesized compounds largely or completely untested. To address this issue, the Canvass library of natural products was assembled, in collaboration with academic and industry researchers, for quantitative high-throughput screening (qHTS) across a diverse set of cell-based and biochemical assays. Characterization of the library in terms of physicochemical properties, structural diversity, and similarity to compounds in publicly available libraries indicates that the Canvass library contains many structural elements in common with approved drugs. The assay data generated were analyzed using a variety of quality control metrics, and the resultant assay profiles were explored using statistical methods, such as clustering and compound promiscuity analyses. Individual compounds were then sorted by structural class and activity profiles. Differential behavior based on these classifications, as well as noteworthy activities, is outlined herein. One such highlight is the activity of (–)-2(S)-cathafoline, which was found to stabilize calcium levels in the endoplasmic reticulum. The workflow described here illustrates a pilot effort to broadly survey the biological potential of natural products by