Editorial: Functional screening for cancer drug discovery: from experimental approaches to data integration

Abstract

Editorial on the Research Topic Functional screening for cancer drug discovery: from experimental approaches to data integration Cancer is still one of the leading causes of death worldwide, despite that tremendous resources are being invested in drug discovery (Siegel et al., 2022). Recent developments in uncovering the molecular biology of oncogenesis and tumor development (Zhou et al., 2018; Liu M. et al., 2022;Yan et al., 2023), and the studies on cancer polypharmacology (Pushpakom et al., 2019;Cohen et al., 2021) have challenged researchers to come up with new strategies for integrating ever-growing data at the molecular levels.The majority of cancer medications consist of small molecule inhibitors. These inhibitors are engineered to enhance their efficacy by binding to specific cellular protein targets, which in turn initiate a series of downstream changes in cancer signaling and metabolic pathways (Yang et al., 2019;Goel et al., 2020). High-throughput phenotype-based drug screening has proven successful in identifying compounds that demonstrate promising cytotoxic effects (Letai et al., 2022). However, it is essential to further investigate the processes occurring between drug administration and the ultimate phenotypic response. Additionally, determining the genetic factors that influence variations in drug sensitivity or resistance is crucial for advancing cancer treatment strategies (Kuusanmäki et al., 2020).Depending on the study's focus, high-throughput genetic perturbation experiments can yield results related to either 1) proliferation-associated phenotypes, or 2) intermediate phenotypes, such as transcriptomic alterations. Numerous well-established data portals, including The Cancer Genome Atlas (TCGA), DepMap Portal (Gonçalves et al., 2020), and The cBioPortal for Cancer Genomics (Cerami et al., 2012), have curated and standardized experimental data from tissues and cell lines, facilitating an easy access for subsequent systems medicine approaches (Wang et al., 2019). Moreover, cutting-edge studies are increasingly generating new data types, such as those derived from 3D organoids (Weeber et al., 2017; Hahn et al., 2021) and in vivo patient-derived xenograft (PDX) samples (Gao et al., 2015;Zanella et al., 2022). These innovative techniques hold the potential to bring researchers closer to a more biologically relevant foundation, moving beyond flat biological platforms. To fully utilize data from these advanced methodologies in identifying genetic signatures and molecular mechanisms for cancer drug discovery, versatile and robust

The Causal Relationship between PCSK9 Inhibitors and Malignant Tumors: A Mendelian Randomization Study Based on Drug Targeting

“…Advanced methods such as 3D organ-like models and patient-derived xenotransplantation offer biologically relevant platforms. These approaches can provide new evidence for the application of PCSK9 inhibitors in treating malignant tumors [41].…”