Cellular Fitness Phenotypes of Cancer Target Genes from Oncobiology to Cancer Therapeutics

Abstract

To define the growing significance of cellular targets and/or effectors of cancer drugs, we examined the fitness dependency of cellular targets and effectors of cancer drug targets across human cancer cells from 19 cancer types. We observed that the deletion of 35 out of 47 cellular effectors and/or targets of oncology drugs did not result in the expected loss of cell fitness in appropriate cancer types for which drugs targeting or utilizing these molecules for their actions were approved. Additionally, our analysis recognized 43 cellular molecules as fitness genes in several cancer types in which these drugs were not approved, and thus, providing clues for repurposing certain approved oncology drugs in such cancer types. For example, we found a widespread upregulation and fitness dependency of several components of the mevalonate and purine biosynthesis pathways (currently targeted by bisphosphonates, statins, and pemetrexed in certain cancers) and an association between the overexpression of these molecules and reduction in the overall survival duration of patients with breast and other hard-to-treat cancers, for which such drugs are not approved. In brief, the present analysis raised cautions about off-target and undesirable effects of certain oncology drugs in a subset of cancers where the intended cellular effectors of drug might not be good fitness genes and that this study offers a potential rationale for repurposing certain approved oncology drugs for targeted therapeutics in additional cancer types.

The Revelation of Continuously Organized, Co-Overexpressed Protein-Coding Genes with Roles in Cellular Communications in Breast Cancer

“…For this purpose, we used a cell dependency dataset representing a comprehensive CRISPR-Cas9 fitness screen, wherein 7470 genes were individually knocked down in 324 cancer cell lines from 30 different types of cancers [ 52 ]. The outcome for each gene is presented either as a fitness gene (i.e., knockdown of the test gene leads to the loss of cell viability) or a poor fitness gene for a given cell line [ 52 , 53 ]. We noticed that the cell dependency CRISPR-Cas9 screen dataset contained results pertaining to only 12 of the CanCord34 genes ( Figure 4 B).…”

“…We used the Genome RNAi database [ 50 ] to build the broad aspect of the gene’s function by annotating the gene functionally. To assess the role of the CanCord34 genes in cell viability, we used publicly available cell dependency datasets [ 51 , 52 ] and the recent approach used to analyze a given gene’s cellular fitness [ 53 ]. We downloaded and curated the data on genes for which fitness scores were available.…”

Identification of the pyroptosis-related gene signature and risk score model for esophageal squamous cell carcinoma

“…FCGR3A usually shows upregulated in pan-cancer [ 34 ]. In survival analysis, FCGR3A has been found to be a major risk factor for many tumors [ 35 ]. Besides, FCGR3A expression is associated with infiltrating degrees of certain immune cells [ 36 ], levels of DNA mismatch repair genes, and numerous immune checkpoint genes.…”

Delineation of Pathogenomic Insights of Breast Cancer in Young Women

“…Fitness scores for breast carcinoma cell lines were collected for each gene from the cancer dependency map [ 45 , 46 ] and the boxplots were generated using the ggplot2 library in the R programming language.…”