Exciting therapeutic targets are emerging from CRISPR-based screens of high mutational burden adult cancers. A key question, however, is whether functional genomic approaches will yield new targets in pediatric cancers, known for remarkably few mutations which often encode proteins considered challenging drug targets. To address this, we created a first-generation Pediatric Cancer Dependency Map representing 13 pediatric solid and brain tumor types. Eighty-two pediatric cancer cell lines were subjected to genome-scale CRISPR-Cas9 loss-of-function screening to identify genes required for cell survival. In contrast to the finding that pediatric cancers harbor fewer somatic mutations, we found a similar complexity of genetic dependencies in pediatric cancer cell lines compared to adult models. Findings from the Pediatric Cancer Dependency Map provide pre-clinical support for ongoing precision medicine clinical trials. The vulnerabilities seen in pediatric cancers were often distinct from adult, indicating that repurposing adult oncology drugs will be insufficient to address childhood cancers.

PRMT5 is an arginine methyltransferase that accounts for the vast majority of the symmetric methylation in cells. PRMT5 exerts its function when complexed with MEP50/WDR77. This activity is often elevated in cancer cells and correlates with poor prognosis, making PRMT5 a therapeutic target. To investigate the PRMT5 signaling pathway and to identify genes whose loss-of-function sensitizes cancer cells to PRMT5 inhibition, we performed a CRISPR/Cas9 genetic screen in the presence of a PRMT5 inhibitor. We identified known components of the PRMT5 writer/reader pathway including PRMT5 itself, MEP50/WDR77, PPP4C, SMNDC1 and SRSF3. Interestingly, loss of PRMT1, the major asymmetric arginine methyltransferase, also sensitizes cells to PRMT5 inhibition. We investigated the interplay between PRMT5 and PRMT1, and found that combinatorial inhibitor treatment of small cell lung cancer and pancreatic cancer cell models have a synergistic effect. Furthermore, MTAP -deleted cells, which harbor an attenuated PRMT5–MEP50 signaling pathway, are generally more sensitive to PRMT1 inhibition. Together, these findings demonstrate that there is a degree of redundancy between the PRMT5 and PRMT1 pathways, even though these two enzymes deposit different types of arginine methylation marks. Targeting this redundancy provides a vulnerability for tumors carrying a co-deletion of MTAP and the adjacent CDKN2A tumor suppressor gene.

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Although we have seen a proliferation of algorithms for recommending visualizations, these algorithms are rarely compared with one another, making it difficult to ascertain which algorithm is best for a given visual analysis scenario. Though several formal frameworks have been proposed in response, we believe this issue persists because visualization recommendation algorithms are inadequately specified from an evaluation perspective. In this paper, we propose an evaluation-focused framework to contextualize and compare a broad range of visualization recommendation algorithms. We present the structure of our framework, where algorithms are specified using three components: (1) a graph representing the full space of possible visualization designs, (2) the method used to traverse the graph for potential candidates for recommendation, and (3) an oracle used to rank candidate designs. To demonstrate how our framework guides the formal comparison of algorithmic performance, we not only theoretically compare five existing representative recommendation algorithms, but also empirically compare four new algorithms generated based on our findings from the theoretical comparison. Our results show that these algorithms behave similarly in terms of user performance, highlighting the need for more rigorous formal comparisons of recommendation algorithms to further clarify their benefits in various analysis scenarios.