AR-negative prostate cancer is vulnerable to loss of JMJD1C demethylase.

Prostate cancer is a leading cause of cancer-related mortality in men. The widespread use of androgen receptor (AR) inhibitors has generated an increased incidence of AR-negative prostate cancer, triggering the need for effective therapies for such patients. Here, analysis of public genome-wide CRISPR screens in human prostate cancer cell lines identified histone demethylase JMJD1C (KDM3C) as an AR-negative context-specific vulnerability. Secondary validation studies in multiple cell lines and organoids, including isogenic models, confirmed that small hairpin RNA (shRNA)-mediated depletion of JMJD1C potently inhibited growth specifically in AR-negative prostate cancer cells. To explore the cooperative interactions of AR and JMJD1C, we performed comparative transcriptomics of 1) isogenic AR-positive versus AR-negative prostate cancer cells, 2) AR-positive versus AR-negative prostate cancer tumors, and 3) isogenic JMJD1C-expressing versus JMJD1C-depleted AR-negative prostate cancer cells. Loss of AR or JMJD1C generates a modest tumor necrosis factor alpha (TNFα) signature, whereas combined loss of AR and JMJD1C strongly up-regulates the TNFα signature in human prostate cancer, suggesting TNFα signaling as a point of convergence for the combined actions of AR and JMJD1C. Correspondingly, AR-negative prostate cancer cells showed exquisite sensitivity to TNFα treatment and, conversely, TNFα pathway inhibition via inhibition of its downstream effector MAP4K4 partially reversed the growth defect of JMJD1C-depleted AR-negative prostate cancer cells. Given the deleterious systemic side effects of TNFα therapy in humans and the viability of JMJD1C-knockout mice, the identification of JMJD1C inhibition as a specific vulnerability in AR-negative prostate cancer may provide an alternative drug target for prostate cancer patients progressing on AR inhibitor therapy.

Dynamic rewiring of biological activity across genotype and lineage revealed by context-dependent functional interactions.

BACKGROUND: Coessentiality networks derived from CRISPR screens in cell lines provide a powerful framework for identifying functional modules in the cell and for inferring the roles of uncharacterized genes. However, these networks integrate signal across all underlying data and can mask strong interactions that occur in only a subset of the cell lines analyzed. RESULTS: Here, we decipher dynamic functional interactions by identifying significant cellular contexts, primarily by oncogenic mutation, lineage, and tumor type, and discovering coessentiality relationships that depend on these contexts. We recapitulate well-known gene-context interactions such as oncogene-mutation, paralog buffering, and tissue-specific essential genes, show how mutation rewires known signal transduction pathways, including RAS/RAF and IGF1R-PIK3CA, and illustrate the implications for drug targeting. We further demonstrate how context-dependent functional interactions can elucidate lineage-specific gene function, as illustrated by the maturation of proreceptors IGF1R and MET by proteases FURIN and CPD. CONCLUSIONS: This approach advances our understanding of context-dependent interactions and how they can be gleaned from these data. We provide an online resource to explore these context-dependent interactions at diffnet.hart-lab.org.

Discovery of putative tumor suppressors from CRISPR screens reveals rewired lipid metabolism in acute myeloid leukemia cells.

CRISPR knockout fitness screens in cancer cell lines reveal many genes whose loss of function causes cell death or loss of fitness or, more rarely, the opposite phenotype of faster proliferation. Here we demonstrate a systematic approach to identify these proliferation suppressors, which are highly enriched for tumor suppressor genes, and define a network of 145 such genes in 22 modules. One module contains several elements of the glycerolipid biosynthesis pathway and operates exclusively in a subset of acute myeloid leukemia cell lines. The proliferation suppressor activity of genes involved in the synthesis of saturated fatty acids, coupled with a more severe loss of fitness phenotype for genes in the desaturation pathway, suggests that these cells operate at the limit of their carrying capacity for saturated fatty acids, which we confirm biochemically. Overexpression of this module is associated with a survival advantage in juvenile leukemias, suggesting a clinically relevant subtype.

Differential Detection of Encapsidated versus Unencapsidated Enterovirus RNA in Samples Containing Pancreatic Enzymes-Relevance for Diabetes Studies.

Using immunohistochemistry, enterovirus capsid proteins were demonstrated in pancreatic islets of patients with type 1 diabetes. Virus proteins are mainly located in beta cells, supporting the hypothesis that enterovirus infections may contribute to the pathogenesis of type 1 diabetes. In samples of pancreatic tissue, enterovirus RNA was also detected, but in extremely small quantities and in a smaller proportion of cases compared to the enteroviral protein. Difficulties in detecting viral RNA could be due to the very small number of infected cells, the possible activity of PCR inhibitors, and the presence-during persistent infection-of the viral genome in unencapsidated forms. The aim of this study was twofold: (a) to examine if enzymes or other compounds in pancreatic tissue could affect the molecular detection of encapsidated vs. unencapsidated enterovirus forms, and (b) to compare the sensitivity of RT-PCR methods used in different laboratories. Dilutions of encapsidated and unencapsidated virus were spiked into human pancreas homogenate and analyzed by RT-PCR. Incubation of pancreatic homogenate on wet ice for 20 h did not influence the detection of encapsidated virus. In contrast, a 15-min incubation on wet ice dramatically reduced detection of unencapsidated forms of virus. PCR inhibitors could not be found in pancreatic extract. The results show that components in the pancreas homogenate may selectively affect the detection of unencapsidated forms of enterovirus. This may lead to difficulties in diagnosing persisting enterovirus infection in the pancreas of patients with type 1 diabetes.

Identifying chemogenetic interactions from CRISPR screens with drugZ.

BACKGROUND: Chemogenetic profiling enables the identification of gene mutations that enhance or suppress the activity of chemical compounds. This knowledge provides insights into drug mechanism of action, genetic vulnerabilities, and resistance mechanisms, all of which may help stratify patient populations and improve drug efficacy. CRISPR-based screening enables sensitive detection of drug-gene interactions directly in human cells, but until recently has primarily been used to screen only for resistance mechanisms. RESULTS: We present drugZ, an algorithm for identifying both synergistic and suppressor chemogenetic interactions from CRISPR screens. DrugZ identifies synthetic lethal interactions between PARP inhibitors and both known and novel members of the DNA damage repair pathway, confirms KEAP1 loss as a resistance factor for ERK inhibitors in oncogenic KRAS backgrounds, and defines the genetic context for temozolomide activity. CONCLUSIONS: DrugZ is an open-source Python software for the analysis of genome-scale drug modifier screens. The software accurately identifies genetic perturbations that enhance or suppress drug activity. Interestingly, analysis of new and previously published data reveals tumor suppressor genes are drug-agnostic resistance genes in drug modifier screens. The software is available at github.com/hart-lab/drugz .